diff --git a/README.md b/README.md
index a94f7f0..8411961 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,18 @@
-
+<a name=top></a>
-# Examples using the NAG Library for Python
+# Content<a name=content></a>
-This repository contains examples and demonstrations using the [NAG Library for Python](https://www.nag.com/nag-library-python). The NAG Library for Python contains 1900+ functions spanning many areas of numerical computing and data science.
+* [Examples using the *n*AG Library for Python](#examples)
+* [How to install the *n*AG Library for Python](#install)
+* [How to run the Jupyter notebook examples](#jupyter)
+* [List of Chapters in the *n*AG Library for Python](#chapters)
+* [Useful links](#links)
-Designed to work alongside the open source Python packages, [Numpy](http://www.numpy.org/) and [Scipy](https://www.scipy.org/), The NAG Library for Python can augment your computational workflow in many areas.
+# Examples using the *n*AG Library for Python <a name=examples></a>
+
+This repository contains examples and demonstrations using the [*n*AG Library for Python](https://nag.com/nag-library/). The *n*AG Library for Python contains 1900+ functions spanning many areas of numerical computing and data science.
+
+Designed to work alongside the open source Python packages, [Numpy](http://www.numpy.org/) and [Scipy](https://www.scipy.org/), The *n*AG Library for Python can augment your computational workflow in many areas.
## Directory of GitHub examples
@@ -23,40 +31,187 @@ Designed to work alongside the open source Python packages, [Numpy](http://www.n
## Examples that ship with the product
-In addition to those presented here, The NAG Library for Python ships with a set of usage examples. To see them all, run the following command
+In addition to those presented here, The *n*AG Library for Python ships with a set of usage examples. To see them all, run the following command
```
python -m naginterfaces.library.examples --locate
```
-## NAG Library for Python installation
+# How to install the *n*AG Library for Python<a name=install></a>
-The NAG Library for Python comes in two versions. One is linked to the Intel MKL for improved linear algebra performance on x86 architectures and the other is linked to NAG's self contained linear algebra libraries.
+In this section we illustrate how to install the *n*AG Library for Python, request a Trial Licence and make sure the Library is working. Details and further information regarding the installation can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).
-When using Intel or AMD CPUs we recommend the use of the Intel MKL version of the NAG Library for Python.
+**Note** Before starting make sure you have access to a host that has Python 3 (3.4 or more recent).
-Install using the following command
+### Step 1. Downloading and installing
+Installing the *n*AG Library is done using the `pip` package manager, fire-up a terminal and create a Python 3 virtual environment where to install and test the *n*AG Library
+```{bash}
+guest@nag-37:~$ python3 -m venv nag3
+guest@nag-37:~$ . nag3/bin/activate
+(nag3) guest@nag-37:~$
+```
+Now use `pip` to install the *n*AG Library for Python
+```{bash}
+(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_nag naginterfaces
+```
+or if you prefer the version of the package that relies on Intel MKL for optimized linear algebra routines, then use
+```{bash}
+(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_mkl naginterfaces
+```
+The output should be similar to
+```{bash}
+Collecting naginterfaces
+ Downloading https://www.nag.com/downloads/py/naginterfaces_nag/naginterfaces/naginterfaces-27.1.0.0-py2.py3-none-linux_x86_64.whl (55.8MB)
+ 100% |████████████████████████████████| 55.8MB 21kB/s
+Collecting numpy>=1.15 (from naginterfaces)
+ Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)
+ 100% |████████████████████████████████| 13.4MB 70kB/s
+Installing collected packages: numpy, naginterfaces
+Successfully installed naginterfaces-27.1.0.0 numpy-1.19.5
```
-python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_mkl naginterfaces
+The output indicates that the installation was successful.
+
+### Step 2. Getting a trial licence
+The next step is to get the licensing info (**product code** and **KUSARI ID**) and use it to request a licence. From the same virtual terminal, try
+```{bash}
+(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
+```
+The output should be similar to
```
+The *n*AG Library for Python on this platform uses
+underlying Library NLL6I271VL.
+This Library has been installed as part of the package
+and it requires a valid licence key.
+No such key could be validated:
+the key may not have been installed correctly or
+it may have expired.
+The Kusari licence-check utility reports the following:
+User: guest
+Directory: /home/guest
+NAG_KUSARI_FILE=""
+File /home/guest/nag.key does not exist
+-------------------------------------------------------------------------------
+Error: Licence not found; this product requires a key for NLL6I271VL
+The above information has been generated on machine nag-37
+For information on how to obtain a licence, please see
+https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html
+KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"
+```
+The **two** important bits are the
-**Obtaining a license**
+ 1. **product code** shown as **`underlying Library NLL6I271VL.`** which identifies the licence to request, and
+
+ 2. **KUSARI ID** shown as **`KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"`** which identifies the host you are running the library on.
+
+ **Note** that the **product code** and **KUSARI ID** can be different from the previous example.
+
+ With these, you are set to [contact *n*AG and request a trial licence](https://nag.com/contact-us/).
+
+ The trial licence is a plain text chunk similar to
+ ```
+ NLL6I271V TRIAL 2021/01/27 "RverXn0Pc-Ib?ctdgF=Wpis2j7I"
+ ```
+ Save or copy the text into the file `/home/guest/nag.key`.
+
+ The final step is to make sure the licence is valid and the library is working as expected.
+
+### Step 3. Testing the *n*AG Library
+The last step is to make sure the licence was correctly stored and that the *n*AG Library is working correctly. From the same virtual terminal re-run the Kusari licence module
+```{bash}
+(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
+```
+This time the output should be similar to
+```
+Licence available; the required NLL6I271VL licence key for this product is valid
+TRIAL licence, 27 days remaining (licence from file)
+```
+Now let's try a more interesting example ([list of optimization examples](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#examples))
-Before you can use the NAG Library for Python, you'll need a license. Free trial licenses are available!
+This command runs the example for the [FOAS (First-Order Active set method) solver and minimizes the Rosenbrock 2D function](./FOAS).
+```
+(nag3) guest@nag-37:~$ python -m naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex
+```
+Should generate an outputsimilar to
+```{bash}
+Trying:
+ main()
+Expecting:
+ naginterfaces.library.opt.handle_solve_bounds_foas Python Example Results.
+ Minimizing a bound-constrained Rosenbrock problem.
+ E04KF, First order method for bound-constrained problems
+...
+ Status: converged, an optimal solution was found
+ Value of the objective 4.00000E-02
+ ...
+ok
+```
+indicating that the example was successfully executed. The source code can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/_modules/naginterfaces/library/examples/opt/handle_solve_bounds_foas_ex.html#main).
-Run the following command to begin the licensing process and email [support@nag.com](mailto:support@nag.com) if you have any problems.
+### Running more examples
+To display the full list of example source files on disk, but not run them, execute
```
-# This will launch a license request GUI on windows
-# On Mac and Linux, it gives the information you need to send to support@nag.com to request a trial license
-python -m naginterfaces.kusari
+python -m naginterfaces.library.examples --locate
```
+All examples may be executed sequentially by running
+```
+python -m naginterfaces.library.examples
+```
+Run `python -m naginterfaces.library.examples --help` to see any additional usage.
+
+
-* More detailed installation instructions are [available in the official documentation](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).
-* More detailed license management instructions are available at [https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari)
+# How to run the Jupyter notebook examples<a name=jupyter></a>
-**Official documentation links**
+This section briefly illustrates how to setup a host in order to open and run the [Jupyter notebooks](https://jupyter.org/) provided in this repository.
+Before running the notebooks make sure the [*n*AG Library is installed and working](#install). Before starting, it is advised to read [Jupyter's installation page](https://jupyter.org/install.html).
+
+<!-- You can [view a static render of the notebooks using Jupyter's nbviewer here](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/)
+[](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/)
+or alternatively use [Binder](https://mybinder.org/) to [view them here](https://mybinder.org/v2/gh/numericalalgorithmsgroup/NAGPythonExamples/HEAD)
+[](https://mybinder.org/v2/gh/numericalalgorithmsgroup/NAGPythonExamples/HEAD). -->
+
+
+### Installing Jupyter notebook
+To install Jupyter, launch a terminal and activate the virtual environment used to install the *n*AG Library for Python
+```{bash}
+guest@nag-37:~$ . nag3/bin/activate
+(nag3) guest@nag-37:~$ pip install notebook matplotlib
+Collecting notebook
+ Downloading https://files.pythonhosted.org/packages/74/19/50cd38acf22e33370d01fef764355f1e3517f6e12b4fceb8d434ece4f8fd/notebook-6.2.0-py3-none-any.whl (9.5MB)
+ 100% |████████████████████████████████| 9.5MB 115kB/s
+Collecting argon2-cffi (from notebook)
+...
+Successfully installed jupyter-client-6.1.11 jupyterlab-pygments-0.1.2 ... wcwidth-0.2.5
+```
+This indicates that Jupyter and matplotlib were successfully installed. The next section shows how to start the notebok interface and open an example.
+
+### Running the notebook examples
+To run an example, grab a copy of the notebook of interest and start up the notebook interface.
+For example, download the [Rosenbrock 2D optimization example](./FOAS/rosenbrock2d.ipynb) notebook `rosenbrock2d.ipynb` into the current directory
+```{bash}
+(nag3) guest@nag-37:~$ curl -O https://raw.githubusercontent.com/numericalalgorithmsgroup/NAGPythonExamples/master/local_optimization/FOAS/rosenbrock2d.ipynb
+ % Total % Received % Xferd Average Speed Time Time Time Current
+ Dload Upload Total Spent Left Speed
+100 61961 100 61961 0 0 382k 0 --:--:-- --:--:-- --:--:-- 382k
+```
+and now open it using `jupyter-notebook`
+```{bash}
+(nag3) guest@nag-37:~$ jupyter-notebook rosenbrock2d.ipynb
+[I 12:24:07.336 NotebookApp] Serving notebooks from local directory: /home/guest
+[I 12:24:07.336 NotebookApp] Jupyter Notebook 6.2.0 is running at:
+[I 12:24:07.336 NotebookApp] http://localhost:8888/?token=f1836a06799a92f25ef9966439bf3491b2f0960dcb51806d
+...
+```
+This command will fire-up your web browser and open the `rosenbrock2d.ipynb` notebook, the window should be similar to
+
+
+
+
+
+
+# List of Chapters in the *n*AG Library for Python<a name=chapters></a>
The following links take you to the relevant section in the official documentation
@@ -108,3 +263,12 @@ The following links take you to the relevant section in the official documentati
* [library.univar](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.univar.html) - Univariate Estimation
* [library.wav](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.wav.html) - Wavelet Transforms
* [library.zeros](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.zeros.html) - Zeros of Polynomials
+
+
+# Useful links<a name=links></a>
+
+* [*n*AG Library for Python Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
+* [Kusari licence module Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari)
+
+
+**[Back to Top](#top)**
diff --git a/correlation_and_regression_analysis/quantile_linreg_easy.ipynb b/correlation_and_regression_analysis/quantile_linreg_easy.ipynb
index 04dfe8d..0671543 100644
--- a/correlation_and_regression_analysis/quantile_linreg_easy.ipynb
+++ b/correlation_and_regression_analysis/quantile_linreg_easy.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Quantile Regression\n",
"\n",
- "The NAG function [`correg.quantile_linreg_easy`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.correg.html#naginterfaces.library.correg.quantile_linreg_easy) can be used to model the conditional $\\tau$-th quantile of a dependent variable against one or more independent or explanatory variables.\n",
+ "The NAG function [`correg.quantile_linreg_easy`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.correg.quantile_linreg_easy.html) can be used to model the conditional $\\tau$-th quantile of a dependent variable against one or more independent or explanatory variables.\n",
"\n",
"Whereas the method of least squares results in estimates of the conditional <em>mean</em> of the response (dependent) variable, quantile regression gives estimates of the conditional <em>median</em> (or any other quantile) of the response variable.\n",
"\n",
@@ -188,36 +188,38 @@
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
+ "/* global mpl */\n",
"window.mpl = {};\n",
"\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
+ "mpl.get_websocket_type = function () {\n",
+ " if (typeof WebSocket !== 'undefined') {\n",
" return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
- " alert('Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.'\n",
+ " );\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ " this.supports_binary = this.ws.binaryType !== undefined;\n",
"\n",
" if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " var warnings = document.getElementById('mpl-warnings');\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
+ " warnings.textContent =\n",
+ " 'This browser does not support binary websocket messages. ' +\n",
+ " 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
@@ -232,11 +234,11 @@
"\n",
" this.image_mode = 'full';\n",
"\n",
- " this.root = $('<div/>');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
+ " this.root = document.createElement('div');\n",
+ " this.root.setAttribute('style', 'display: inline-block');\n",
+ " this._root_extra_style(this.root);\n",
"\n",
- " $(parent_element).append(this.root);\n",
+ " parent_element.appendChild(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
@@ -246,285 +248,366 @@
"\n",
" this.waiting = false;\n",
"\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+ " fig.send_message('send_image_mode', {});\n",
+ " if (fig.ratio !== 1) {\n",
+ " fig.send_message('set_device_pixel_ratio', {\n",
+ " device_pixel_ratio: fig.ratio,\n",
+ " });\n",
" }\n",
+ " fig.send_message('refresh', {});\n",
+ " };\n",
"\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
+ " this.imageObj.onload = function () {\n",
+ " if (fig.image_mode === 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
"\n",
- " this.imageObj.onunload = function() {\n",
+ " this.imageObj.onunload = function () {\n",
" fig.ws.close();\n",
- " }\n",
+ " };\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype._init_header = function () {\n",
+ " var titlebar = document.createElement('div');\n",
+ " titlebar.classList =\n",
+ " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+ " var titletext = document.createElement('div');\n",
+ " titletext.classList = 'ui-dialog-title';\n",
+ " titletext.setAttribute(\n",
+ " 'style',\n",
+ " 'width: 100%; text-align: center; padding: 3px;'\n",
+ " );\n",
+ " titlebar.appendChild(titletext);\n",
+ " this.root.appendChild(titlebar);\n",
+ " this.header = titletext;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
- "}\n",
+ "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
+ "mpl.figure.prototype._init_canvas = function () {\n",
" var fig = this;\n",
"\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+ " canvas_div.setAttribute(\n",
+ " 'style',\n",
+ " 'border: 1px solid #ddd;' +\n",
+ " 'box-sizing: content-box;' +\n",
+ " 'clear: both;' +\n",
+ " 'min-height: 1px;' +\n",
+ " 'min-width: 1px;' +\n",
+ " 'outline: 0;' +\n",
+ " 'overflow: hidden;' +\n",
+ " 'position: relative;' +\n",
+ " 'resize: both;'\n",
+ " );\n",
"\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
+ " function on_keyboard_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.key_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
+ " canvas_div.addEventListener(\n",
+ " 'keydown',\n",
+ " on_keyboard_event_closure('key_press')\n",
+ " );\n",
+ " canvas_div.addEventListener(\n",
+ " 'keyup',\n",
+ " on_keyboard_event_closure('key_release')\n",
+ " );\n",
+ "\n",
+ " this._canvas_extra_style(canvas_div);\n",
+ " this.root.appendChild(canvas_div);\n",
"\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ " var canvas = (this.canvas = document.createElement('canvas'));\n",
+ " canvas.classList.add('mpl-canvas');\n",
+ " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
+ " this.context = canvas.getContext('2d');\n",
"\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
+ " var backingStore =\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " this.context.webkitBackingStorePixelRatio ||\n",
+ " this.context.mozBackingStorePixelRatio ||\n",
+ " this.context.msBackingStorePixelRatio ||\n",
+ " this.context.oBackingStorePixelRatio ||\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " 1;\n",
"\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+ " 'canvas'\n",
+ " ));\n",
+ " rubberband_canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+ " );\n",
"\n",
- " var pass_mouse_events = true;\n",
+ " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+ " if (this.ResizeObserver === undefined) {\n",
+ " if (window.ResizeObserver !== undefined) {\n",
+ " this.ResizeObserver = window.ResizeObserver;\n",
+ " } else {\n",
+ " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+ " this.ResizeObserver = obs.ResizeObserver;\n",
+ " }\n",
+ " }\n",
"\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
+ " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+ " var nentries = entries.length;\n",
+ " for (var i = 0; i < nentries; i++) {\n",
+ " var entry = entries[i];\n",
+ " var width, height;\n",
+ " if (entry.contentBoxSize) {\n",
+ " if (entry.contentBoxSize instanceof Array) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " width = entry.contentBoxSize[0].inlineSize;\n",
+ " height = entry.contentBoxSize[0].blockSize;\n",
+ " } else {\n",
+ " // Firefox implements old version of spec.\n",
+ " width = entry.contentBoxSize.inlineSize;\n",
+ " height = entry.contentBoxSize.blockSize;\n",
+ " }\n",
+ " } else {\n",
+ " // Chrome <84 implements even older version of spec.\n",
+ " width = entry.contentRect.width;\n",
+ " height = entry.contentRect.height;\n",
+ " }\n",
+ "\n",
+ " // Keep the size of the canvas and rubber band canvas in sync with\n",
+ " // the canvas container.\n",
+ " if (entry.devicePixelContentBoxSize) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " canvas.setAttribute(\n",
+ " 'width',\n",
+ " entry.devicePixelContentBoxSize[0].inlineSize\n",
+ " );\n",
+ " canvas.setAttribute(\n",
+ " 'height',\n",
+ " entry.devicePixelContentBoxSize[0].blockSize\n",
+ " );\n",
+ " } else {\n",
+ " canvas.setAttribute('width', width * fig.ratio);\n",
+ " canvas.setAttribute('height', height * fig.ratio);\n",
+ " }\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
" });\n",
+ " this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'dblclick',\n",
+ " on_mouse_event_closure('dblclick')\n",
+ " );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
"\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
- " mouse_event_fn(event);\n",
+ " on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
"\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+ " event.preventDefault();\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
- " // put a spacer in here.\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
"\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
"\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(option);\n",
" }\n",
"\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ "mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
+ "mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+ " var x0 = msg['x0'] / fig.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+ " var x1 = msg['x1'] / fig.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
@@ -535,78 +618,96 @@
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / fig.ratio,\n",
+ " fig.canvas.height / fig.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
+ "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+ " fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
+ " this.send_message('ack', {});\n",
+ "};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
+ " var img = evt.data;\n",
+ " if (img.type !== 'image/png') {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " img.type = 'image/png';\n",
+ " }\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " img\n",
+ " );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -619,9 +720,12 @@
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -630,62 +734,74 @@
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
+ "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
- " if (!e)\n",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\n",
"\n",
- " // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
+ " * https://stackoverflow.com/a/24161582/3208463\n",
" */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
+ " var x = canvas_pos.x * this.ratio;\n",
+ " var y = canvas_pos.y * this.ratio;\n",
"\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -693,265 +809,337 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.key === this._key) {\n",
" return;\n",
- " else\n",
- " this._key = event.which;\n",
+ " } else {\n",
+ " this._key = event.key;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\n",
"\n",
" var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
+ " if (event.ctrlKey && event.key !== 'Control') {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " else if (event.altKey && event.key !== 'Alt') {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " else if (event.shiftKey && event.key !== 'Shift') {\n",
+ " value += 'shift+';\n",
+ " }\n",
"\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
+ " value += 'k' + event.key;\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+ " if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+ "// prettier-ignore\n",
+ "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
- " ws.close = function() {\n",
- " comm.close()\n",
+ " ws.binaryType = comm.kernel.ws.binaryType;\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " function updateReadyState(_event) {\n",
+ " if (comm.kernel.ws) {\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " } else {\n",
+ " ws.readyState = 3; // Closed state.\n",
+ " }\n",
+ " }\n",
+ " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+ "\n",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\n",
+ " ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
+ " comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
+ " var data = msg['content']['data'];\n",
+ " if (data['blob'] !== undefined) {\n",
+ " data = {\n",
+ " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+ " };\n",
+ " }\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
+ " ws.onmessage(data);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ "mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
- " fig.parent_element = element.get(0);\n",
+ " fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
+ " fig.cell_info[0].output_area.element.on(\n",
+ " 'cleared',\n",
+ " { fig: fig },\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
"};\n",
"\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / fig.ratio;\n",
+ " fig.cell_info[0].output_area.element.off(\n",
+ " 'cleared',\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
+ " var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
+ " this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
- " if (!name) { continue; };\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
"\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+ " var fig = event.data.fig;\n",
+ " if (event.target !== this) {\n",
+ " // Ignore bubbled events from children.\n",
+ " return;\n",
+ " }\n",
+ " fig.close_ws(fig, {});\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
+ " if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\n",
+ "mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
+ " for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " if (cell.cell_type === 'code') {\n",
+ " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
- " if (data['text/html'] == html_output) {\n",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -964,7 +1152,7 @@
{
"data": {
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\" 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -992,13 +1180,13 @@
" 'Quantile Regression\\n'\n",
" 'Engels\\' 1857 Study of Household Expenditure on Food'\n",
")\n",
- "plt.show();"
+ "plt.show()"
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1012,7 +1200,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/curve_and_surface_fitting/dim2_spline_ts_sctr.ipynb b/curve_and_surface_fitting/dim2_spline_ts_sctr.ipynb
index 2f38f43..1defa4a 100644
--- a/curve_and_surface_fitting/dim2_spline_ts_sctr.ipynb
+++ b/curve_and_surface_fitting/dim2_spline_ts_sctr.ipynb
@@ -112,36 +112,38 @@
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
+ "/* global mpl */\n",
"window.mpl = {};\n",
"\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
+ "mpl.get_websocket_type = function () {\n",
+ " if (typeof WebSocket !== 'undefined') {\n",
" return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
- " alert('Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.'\n",
+ " );\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ " this.supports_binary = this.ws.binaryType !== undefined;\n",
"\n",
" if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " var warnings = document.getElementById('mpl-warnings');\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
+ " warnings.textContent =\n",
+ " 'This browser does not support binary websocket messages. ' +\n",
+ " 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
@@ -156,11 +158,11 @@
"\n",
" this.image_mode = 'full';\n",
"\n",
- " this.root = $('<div/>');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
+ " this.root = document.createElement('div');\n",
+ " this.root.setAttribute('style', 'display: inline-block');\n",
+ " this._root_extra_style(this.root);\n",
"\n",
- " $(parent_element).append(this.root);\n",
+ " parent_element.appendChild(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
@@ -170,285 +172,366 @@
"\n",
" this.waiting = false;\n",
"\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+ " fig.send_message('send_image_mode', {});\n",
+ " if (fig.ratio !== 1) {\n",
+ " fig.send_message('set_device_pixel_ratio', {\n",
+ " device_pixel_ratio: fig.ratio,\n",
+ " });\n",
" }\n",
+ " fig.send_message('refresh', {});\n",
+ " };\n",
"\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
+ " this.imageObj.onload = function () {\n",
+ " if (fig.image_mode === 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
"\n",
- " this.imageObj.onunload = function() {\n",
+ " this.imageObj.onunload = function () {\n",
" fig.ws.close();\n",
- " }\n",
+ " };\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype._init_header = function () {\n",
+ " var titlebar = document.createElement('div');\n",
+ " titlebar.classList =\n",
+ " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+ " var titletext = document.createElement('div');\n",
+ " titletext.classList = 'ui-dialog-title';\n",
+ " titletext.setAttribute(\n",
+ " 'style',\n",
+ " 'width: 100%; text-align: center; padding: 3px;'\n",
+ " );\n",
+ " titlebar.appendChild(titletext);\n",
+ " this.root.appendChild(titlebar);\n",
+ " this.header = titletext;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
- "}\n",
+ "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
+ "mpl.figure.prototype._init_canvas = function () {\n",
" var fig = this;\n",
"\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+ " canvas_div.setAttribute(\n",
+ " 'style',\n",
+ " 'border: 1px solid #ddd;' +\n",
+ " 'box-sizing: content-box;' +\n",
+ " 'clear: both;' +\n",
+ " 'min-height: 1px;' +\n",
+ " 'min-width: 1px;' +\n",
+ " 'outline: 0;' +\n",
+ " 'overflow: hidden;' +\n",
+ " 'position: relative;' +\n",
+ " 'resize: both;'\n",
+ " );\n",
"\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
+ " function on_keyboard_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.key_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
+ " canvas_div.addEventListener(\n",
+ " 'keydown',\n",
+ " on_keyboard_event_closure('key_press')\n",
+ " );\n",
+ " canvas_div.addEventListener(\n",
+ " 'keyup',\n",
+ " on_keyboard_event_closure('key_release')\n",
+ " );\n",
+ "\n",
+ " this._canvas_extra_style(canvas_div);\n",
+ " this.root.appendChild(canvas_div);\n",
"\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ " var canvas = (this.canvas = document.createElement('canvas'));\n",
+ " canvas.classList.add('mpl-canvas');\n",
+ " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
+ " this.context = canvas.getContext('2d');\n",
"\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
+ " var backingStore =\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " this.context.webkitBackingStorePixelRatio ||\n",
+ " this.context.mozBackingStorePixelRatio ||\n",
+ " this.context.msBackingStorePixelRatio ||\n",
+ " this.context.oBackingStorePixelRatio ||\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " 1;\n",
"\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+ " 'canvas'\n",
+ " ));\n",
+ " rubberband_canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+ " );\n",
"\n",
- " var pass_mouse_events = true;\n",
+ " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+ " if (this.ResizeObserver === undefined) {\n",
+ " if (window.ResizeObserver !== undefined) {\n",
+ " this.ResizeObserver = window.ResizeObserver;\n",
+ " } else {\n",
+ " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+ " this.ResizeObserver = obs.ResizeObserver;\n",
+ " }\n",
+ " }\n",
"\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
+ " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+ " var nentries = entries.length;\n",
+ " for (var i = 0; i < nentries; i++) {\n",
+ " var entry = entries[i];\n",
+ " var width, height;\n",
+ " if (entry.contentBoxSize) {\n",
+ " if (entry.contentBoxSize instanceof Array) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " width = entry.contentBoxSize[0].inlineSize;\n",
+ " height = entry.contentBoxSize[0].blockSize;\n",
+ " } else {\n",
+ " // Firefox implements old version of spec.\n",
+ " width = entry.contentBoxSize.inlineSize;\n",
+ " height = entry.contentBoxSize.blockSize;\n",
+ " }\n",
+ " } else {\n",
+ " // Chrome <84 implements even older version of spec.\n",
+ " width = entry.contentRect.width;\n",
+ " height = entry.contentRect.height;\n",
+ " }\n",
+ "\n",
+ " // Keep the size of the canvas and rubber band canvas in sync with\n",
+ " // the canvas container.\n",
+ " if (entry.devicePixelContentBoxSize) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " canvas.setAttribute(\n",
+ " 'width',\n",
+ " entry.devicePixelContentBoxSize[0].inlineSize\n",
+ " );\n",
+ " canvas.setAttribute(\n",
+ " 'height',\n",
+ " entry.devicePixelContentBoxSize[0].blockSize\n",
+ " );\n",
+ " } else {\n",
+ " canvas.setAttribute('width', width * fig.ratio);\n",
+ " canvas.setAttribute('height', height * fig.ratio);\n",
+ " }\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
" });\n",
+ " this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'dblclick',\n",
+ " on_mouse_event_closure('dblclick')\n",
+ " );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
"\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
- " mouse_event_fn(event);\n",
+ " on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
"\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+ " event.preventDefault();\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
- " // put a spacer in here.\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
"\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
"\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(option);\n",
" }\n",
"\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ "mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
+ "mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+ " var x0 = msg['x0'] / fig.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+ " var x1 = msg['x1'] / fig.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
@@ -459,78 +542,96 @@
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / fig.ratio,\n",
+ " fig.canvas.height / fig.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
+ "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+ " fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
+ " this.send_message('ack', {});\n",
+ "};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
+ " var img = evt.data;\n",
+ " if (img.type !== 'image/png') {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " img.type = 'image/png';\n",
+ " }\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " img\n",
+ " );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -543,9 +644,12 @@
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -554,62 +658,74 @@
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
+ "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
- " if (!e)\n",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\n",
"\n",
- " // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
+ " * https://stackoverflow.com/a/24161582/3208463\n",
" */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
+ " var x = canvas_pos.x * this.ratio;\n",
+ " var y = canvas_pos.y * this.ratio;\n",
"\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -617,265 +733,337 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.key === this._key) {\n",
" return;\n",
- " else\n",
- " this._key = event.which;\n",
+ " } else {\n",
+ " this._key = event.key;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\n",
"\n",
" var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
+ " if (event.ctrlKey && event.key !== 'Control') {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " else if (event.altKey && event.key !== 'Alt') {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " else if (event.shiftKey && event.key !== 'Shift') {\n",
+ " value += 'shift+';\n",
+ " }\n",
"\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
+ " value += 'k' + event.key;\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+ " if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+ "// prettier-ignore\n",
+ "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
- " ws.close = function() {\n",
- " comm.close()\n",
+ " ws.binaryType = comm.kernel.ws.binaryType;\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " function updateReadyState(_event) {\n",
+ " if (comm.kernel.ws) {\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " } else {\n",
+ " ws.readyState = 3; // Closed state.\n",
+ " }\n",
+ " }\n",
+ " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+ "\n",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\n",
+ " ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
+ " comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
+ " var data = msg['content']['data'];\n",
+ " if (data['blob'] !== undefined) {\n",
+ " data = {\n",
+ " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+ " };\n",
+ " }\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
+ " ws.onmessage(data);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ "mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
- " fig.parent_element = element.get(0);\n",
+ " fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
+ " fig.cell_info[0].output_area.element.on(\n",
+ " 'cleared',\n",
+ " { fig: fig },\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
"};\n",
"\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / fig.ratio;\n",
+ " fig.cell_info[0].output_area.element.off(\n",
+ " 'cleared',\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
+ " var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
+ " this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
- " if (!name) { continue; };\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
"\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+ " var fig = event.data.fig;\n",
+ " if (event.target !== this) {\n",
+ " // Ignore bubbled events from children.\n",
+ " return;\n",
+ " }\n",
+ " fig.close_ws(fig, {});\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
+ " if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\n",
+ "mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
+ " for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " if (cell.cell_type === 'code') {\n",
+ " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
- " if (data['text/html'] == html_output) {\n",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -888,7 +1076,7 @@
{
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\" 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ASaAqkPKEzIC2iAwWL4Ff8dgsWZt8LbqrJ56+wafr7KxnbaWNtCY5P6cv73lTVRS10bTc2IpITLUL0ikF9CIggzuD+yX6fhlmyLABNCmhuVj2QcBFmu2jy21PImvYs2ue2ru6KKo0OAB23T3NV/PgWIQ5ByyJIyvCPJ7jIB6CDABVA9LnokRUA0Bd/ItMlmf21qpBrPlJ3IX4oVH2BuxZj1B4P7AeqLNazECwyPABJBvCCNgEgTcybewWLNJjGOybYBIyVAvtuZpazY8293TSzUtHZQSHaacqqWjm7p6eig2XNtuHUb3B2ZJGJNdZN6OoQgwATQUfl6cEejTKUOCPMJjqOQF6eOCDr4ZgxEYytuHNmue9tnt6e2lTYfrqbq5g07IQGFHBIH8fXWoThDD2bnxFBM+MASstiW4GERtRHk+RsA3BJgA+oYbv8UI+IWA9PZJ0sfyLX7BaduX3RV0yFQAX4t/dpU10uFj1b2jk6LoaH0btXV1U2RIEM3Ji6ew4CBN8TRSEkYWg4A0Iw+RByPgZASYADrZ+nx23RFgsWbdIbfkgmp4+4Y7uCsJxHN6kT+5J+4PbMlryZu2GQJMAG1mUD6O+RBwFWuGx082qPfVg2O+E/KO1EDAV/kWX9ZG2PfzA9XKq9D9y0+O8mUqn94xuj8w1occDf7wYAScigATQKdans+tOQIs36I5xLZYYCix5vZuouhBRRlqSLLInD+EfV2HzAnUC1Sj+wMjlI7WdFxVr5fFeR2zIcAE0GwW4f1YGoHBYs3S2wdZDv5FY2nTqrr5kbx9De19hRmZceE0KSNGrH2opoV2lzXR1KxYyojzrSsHikBWH6ih1s6+nD8UfRysblFyAmeNiqekKH1y47g/sKpXiidjBLxGgAmg15DxC4zAQARkYjk8GjLEy/ItfEvcIeCpWDO6b2wrbRBTjEqIoKiwINpV1iT+np8USQVp0T4DXNbQRoWVLTQrN04p+EBOIFq/TcmKpcCAAJ/n9uZFI4tBsE/ZHxhhYP5w5o3l+Fm7IMAE0C6W5HPojoA7sWaWb9HdDKZf0Fex5pK6VtpW2jjgfKOTImm8H+RPTgZP4GCih/usNxFiSRjTX1/eoI0RYAJoY+Py0dRHwJ1YM1YB8cMvT71/gap/Qp5RLQT8EWuWe1hTWEMNbV3Kls6YmGKrO2akFxDfy/gDSRguBlHr1vM8VkKACaCVrMV7NQwBlm8xDHpLLaymfAty/mTYV4KAcLDMCTQbMOgk0tNLFBoUqGwNuYbhwX0fjoYashgEJMxTQWu1zi5zdLkYRC1EeR4rIcAE0ErW4r3qioCrfAt0y1isWVf4LbOYLOiQ/5WeYBT++Cr1A6HmHUf7wr8I+0aHBSnh4NzECJqY3lcYYpYB8rexuJ66jnUTAQlsau+i9UV1lBoTRpMyoockgWboDwzyCU8gD0bASQgwAXSStfmsHiEgvX3t7e2C9HFrNo9gc9xDanr7BoPX0NZJ6w/VUXZ8hJLzh5xAkMLp2XGCVJlpNLd30ZdFddTR3SNayU1Mj6bNhxuUv8/JjacQF8+g6965P7CZLMl7cRICTACdZG0+65AIDJZvwd8xfPXgMNT2RGCo1mzwHsHzp2YIsw3h05CBbdncfc0sSEuPH0igHCCDkJpxDQu7268ZikHQGg5eWx6MgFMQYALoFEvzOd0iwGLNfDE8QWAosWYZ5vVkDqs9U1TdIiqFRyVGiK2jcnhHaSPlJEZQfIT7DhqQmNlypE++BuPEsYkUFTpyaNXIYhDsU0rCcH9gq91S3q8/CDAB9Ac9fteSCEj5FuT14Q+LNVvSjJpveiSxZjW9fZofxssFals66IuiOvHWpPQYyk4IF8SuvLFdePOWjEuk4MD+Yg88548HEO8b3R8Ye0AxiJ3t6uU14MdtjgATQJsbmI/Xh4A7+RYWa+bb4YoAPFyoYg2kXiX3s7O7h0KDg0Ro0M7ePnc3YU95k+gS4joCA4hm5MRRSvTAHER/cgDl/NILaERBhhRz5/7A/DPBSQgwAXSStR14VvlLBVIT8DBwQYcDL4EHR+7q7mu91tnVQ7NGxYqChfauXvqyuIGyEyJp4rF2bB5MZatH0CHkUE2rcqZZo44nf/hHVAFvOFRP3b29Ss6fp1XAroDh+xS5lOHhvrW68wd8loTxBz1+14oIMAG0otV4z8MiIL19kvSxfAtfmKEQkK3ZGlo7aW1RvfAAJkeH0bScOFpfXE8tHd0UGRpES8YmDVnFald04RGVYV95RoSDZU7g4HP7qgPoOg/3B7brbeJzmREBJoBmtArvyScEWKzZJ9gc99JQ8i0N7T20vrhBeLPkAPlbmJ9IEYOqce0Omiv5Q9gXhRyN7X0dSYYjgf7iYnQxCNZHDiCKQbirj7/W5PfNjgATQLNbiPc3orcPXgOEd+Hxk2Ecbs3GF8cVgcFizfg3KfHjmttXUtdGGw/3FT9gnFyQTNFhI1ex2g3tupZO+vJQrTiWzPmTOYEgw4vGJBxXBKIWBmaQhOFiELWsyfOYGQEmgGa2Du9tSARYvoUvhycIeCPWjHDv2oM1IuwrR1JUKM3Ni9eM7HhyBqOeqWhsJ3Rwcy34KKxqpvTYcBEW12oY6QWU/YHxoYAlYbSyMM9rFgSYAJrFEryPEREYLNbM8i0jQubIB4YSax6uNZsr+QO5Qas15L8hHOxkEmjUBeL+wEYhz+s6CQEmgE6ytgXPKuUZ8AtBhnhZvsWChtRhy/6INXd09dDawhrq6u1Vcv6ghbfuYB2lRIfSzFFxQhSZhz4IGN0fGHcJHkDuD6yPvXkVYxBgAmgM7rzqCAhIsWaQPlkZKHO2ODmbr49EQE2xZpBAyJi4Fnw0tnVRVFgQkz+VrhwwrmnpEGFkOZBvCE3p2PD+7iKyOhvf68jH03uwJIzeiPN6RiDABNAI1HlNtwi4E2vGg1zQwRdmMAKSIMiezSAKThRrttLNgKj2uoO1omPIlMxYyk6IIFlsAu/q/NEJAwpujC4GwR0D+eT+wFa6ZbxXbxBgAugNWvysJgiwfIsmsNpuUjW9fbYDxyIHQi/h4tq+7iI5CRFUWt9G3T29lBgZSrNy4wYU2xhZDIL9cX9gi1wq3qbPCDAB9Bk6ftEfBGRBh+zHy2LN/qBp73cHe/vcybfYGwF7nc6VBOJk7sifPLGR/YFlRTBLwtjr/vFp+hFgAsi3QVcEpLevvb1d6bfKuX26msASiw0l3yLDvJY4BG/SLQJ9xTV9GoMYkzJiKDcx0u2z3B+YLxEjoB0CTAC1w5ZnPobAYPkWmbcliR8DxQhIBOAJlqFefI29ffa6GzLnD2Ff1yFzAt2dlvsD2+sO8GnMgwATQPPYwnY7YbFm25lUkwN5I9asyQZ4Ul0QQBHIyn3VhP/KsO+esmYlJxAt9+Ii+iuB5aakCgBkWfQuyJBh4JCQEJaE0eWW8CJ6IsAEUE+0HbCWlG+RuX0s1uwAo/twRF/Emn1Yhl8xGQIo+iipbaUZo/oLPpATGBIUQAVp0W53K8PARnXn4P7AJrtEvB3VEGACqBqUzp3InXwLizU79z4Md3J/xJoZUeciYLQkDH6eGeGBdK7F+eR6IMAEUA+UbbqG/GSOHB14/PBDkgs6bGpsP47F8i1+gMevCgRYEoYvAiOgPgJMANXH1NYzSm+fJH0s32Jrc/t1OF+8fcgP++JgrQgHpsb0dYBoaOukTcX1NCcvgdCnl4czEcCHTAh+Ix8PHzT1HLjLGCwJoyfqvJbWCDAB1Bphm8zPYs02MaTGx/DX24d8sN3lTRQUSKInb3hIIK3aV0PtXT2UFR8uukXwcCYC0gtoRChW9iQH+cQfHoyAHRBgAmgHK2p0BlexZnj8ZEEHt2bTCHALT6uWWDPkQeABPNrQPgCN+MhgWjwmiUKD9fX8WNgktts69we2nUn5QAYjwATQYAOYcXmWbzGjVcy3J63EmkECP95dSc3t3cqhz52SxuTPxyvQ2NZFGw/X0+xRcRQVFixmKalrpeKaVpqTFz+g/ZqPS+j2GheD6AY1L+QABJgAOsDInhxxsFgzy7d4gpozn9FarBk5fyv2Qi+uXyx48dhEJSfQmaj7fuo1hTVU1dRBESFBtDA/gepaOwUh7O0lmpwZQ/nJUb5PrvObZigGkXI0yEfkwQhYGQEmgFa2np97l3ktCO/KEC/Lt/gJqk1f10usGeRP5vzFhgcLfbjq5k4lJ1AWhtgUZk2O1d7VTWsO1FJje9eA+XMTI2hqVqworLDS4P7AVrIW79XMCDABVNk6q1atogcffJA2bNhAR48epTfeeIPOP//8YVdZsWIF3XbbbbRjxw7Kycmhu+++m66++mqVd9Y/nRRrBumTKvss36IZ3Jad2Aix5v2VzbTlSAPJnL+gwAAlJ3BMciRNz4mzLJ5Gbhwk8MOdlcoWkqNCaUF+guXIHw4gC9JQDBIc3BfS1mvIziBGiVLrdU5exxkIMAFU2c7vv/8+rV69mmbNmkUXXnjhiATw4MGDNHnyZLrhhhvo+uuvp2XLltGPf/xjevfdd+mMM85QbXfuxJoxORd0qAaxbSbSy9s3FGBF1S2UGReu5PwhJxBfy0+OtCRhMcPFQM7fhuJ6ZSuorl6Un6jkBJphj97sgfsDe4MWP8sIuEeACaCGNwOhlZE8gHfeeacge9u3b1d2ctlll1FdXR198MEHfu+O5Vv8htARE/gr3+IIkCx6SJA/mfOXHhsmimsQDpY5gbIwpKa5g6qbO2hcan9LtsKqZooOCzZd/iX3B7boZeRtmwoBJoAamsMTArhkyRKaOXMmPfzww8pOnnnmGeEFrK/v/8TuzTZd5VuQL8Nizd6gp8+z9a2dAxrfQwC5o6tHd4+ML2LN+iDEq6iFwKbD9XS4tpVkzl9Hd4+SEzhrVLzQV2zr7Kble6qoq6eXClKjaUJ6NIH8bS9tpMCAADq5IEn3uznc+c3QHxg/3yEMbbUcSrXuFc9jfQSYAGpoQ08IYEFBAV1zzTX005/+VNnJe++9R2effTa1tLRQRESEVzvED8alS5cKQpmXl8et2bxCT5+HNx+ppz3lzXTimETxyxfkb+W+Gmru6KJTxycLj4uWg719WqJrvrnxgfBwbRvlJIQrZAU5gTXNnZQRF65sGPmXO482ir+j+EZWYY9LjaKJ6THHHQwfWKqaO0S4Xo66lk6iAKL4CO3FklkSxnx3jXdkLQSYAGpoLyMIII5zwQUX0Pjx4+nnP/+5hqfjqX1BAL+MVxfWCg02FDgsGJ1AeyuaqaKxnUKCAumU8UmUFBXqy9QjvqOWWPOIC5n4gQOVzRQeEiSIN0ZPby9tPdJAY1OjNCfeJobFLQnEF4cif109PbR6fw3Vt3XRtOxYyk2MJJC/tQdrxFyLxiRSbLi2JNAMkjAoQkExCg9GwIoIMAHU0GqeEEAtQsD/+9//6NZbb6XNmzfr3jNTQzhtMzVIx5pjJFAeCuQPYbbkaHV/mWgl1mxFY5Q1tNHqAzXCCzYvL4Ey4sLoy6I6IYocGRpMZ0xKEeFOJw8Z9pUYyHCwO0y2lTTQweoW8U8IL5fWtwmvYWJkCM0bnSA+0Gg9WBJGa4R5fjsjwARQQ+t6QgBRBIKQ77Zt25SdXH755VRTU+NzEQgq5DIzM+nf//43LVy4UMMT8tS+IoDw2WubjiqvQ5AXmmxqDa3FmtXap57zgHivL6qjI3WtYlmQPXwN36foMewaytRzX2ZZy5X8SWywN09JIJ7Vk/xhPe4PbJbbw/uwIgJMAFW2WlNTE+3fv1/MOmPGDHrooYfo5JNPpsTERBo1apTI9SspKaHnnntOPCNlYH74wx/StddeS8uXL6dbbrnFbxmYm266ibAXhIFffPFFmj59Op1yyikqn5an8wUBmfOHsK8cCAfLnEBf5pS/DKXHD38HsYHMD8JU+C+PvpCv9PpJPBbkJzqe/KEIZNmeKoLkjgz7ypxAOEVPKUh2WwSCsO+q/dXK1TohI4bGpOjXWUQSQNxvFGToPWTHJC4G0Rt5Xk/MGLbuAAAgAElEQVQNBJgAqoGiyxwQdQbhGzyuuuoqevbZZ4XAc1FREeE5OfD/CNnu3LmTsrOz6Re/+IVfQtBIjv7rX/9K8C6CBCxevJj+7//+jxYtWqTyaXk6bxEAAfl0TzWVH8v5WzoukfZUNCs5gSeNSyJIdXg6ZEGH/C/sjT8QqpXi3p7O5YTngP8XB2tFuFKO+aP7inGcPiABg5Zx49P6ZWCQM4mipDQ3d1Lm/Lm27AOGMidQLzzNUAwSEhKiuyi1XvjyOvZFgAmgDrZ95JFHRHeQsrIymjZtGv3tb3+juXPnul0ZJBFVwa4Dny7b2vp/YQ215cLCQnr66acJMjL45Y937rjjDiEyzcM8COwpb6KtJY1Kzp/MCWxs66JTxidTWPDI3jqjxZrNg6bnOxns/YPXFR4vmRPIJNBzLOHFhscQqQwy7Lu7rEnJCUTv5oRIdfNZh9qd0cUgWB8/b1EMwpIwnt8hftJ4BJgAamyDl19+mb7zne/Q448/TvPmzRPyLK+++irt2bOHUlNTj1sdBPBHP/qR+Hc58EMlLS1t2J1eccUVYt5zzjlHdBT52te+Rg888AB9+umn9N///lfjU/L03iKAkBuqUeUAOenq7lW6X7ibb6jWbAjxynCvt/vw9nnIh1Q0dlBOQr88ETxHKJ1IVLl6ubWzm9YU1tCM7DhlboTNd5Q20sIxCRQW3I+fJ+cob2inzw9UKzl/8LTKnEAuAvEEwYHPwIt6sKqF5ubFKwUfKAxB/uAJmcfLxni/gudvmMELiA/qnGrhuc34SeMRYAKosQ1A+ubMmUN///vfxUr4JY5+vzfffDPdddddbgkgRKDRCcSbgR7EkH5xJYpHjhyh/Px80WM4PT3dm+n4WRMhYBaxZnh9Pt1bRfWtXTQzJ07keoH8fXYsB+zkguQB4tb+QvjVoTo6VNNCwYGBBI8SRIpRxQuyPDYliqZle98XGIUOIN6y4ANzgbTgLFrrL/qLB78/NALSC2hUKBZFVywJwzfUaggwAdTQYqjGjYyMpNdee43OP/98ZSXkA4Lgvfnmm24JIDx4WVlZgiyiS8h9991HJ5xwgk87Pe2000ROIgpLeFgHAbOKNW85Ui90CzHyEiOo5Jj0R0p0KJ04NlGQNbWG1JqD2LDrSI8NF1W7COHyYAQkArI/MEKxenvi8P2KER7eL7bNlmEEzI4AE0ANLVRaWiqI3Jo1a2jBggXKSsjLW7lyJX3xxRfHrb527Vrat28fTZ06VbSC++Mf/0jw7sGLhwIRb8fzzz9Pf/jDHwjzcn6Kt+jp//xgsWZZ0CGLOvTf0fErupJA/KsW5E+uChL45pYyZROhwYF01glpTP7McBFMtgd44fAHBBDfL3oOCLzjexceSPzhwQhYAQEmgBpayRcCOHg7EDqdOHEifetb36Lf/va3Xu+2ublZhIU//PBDQSp5mA8Bq4k1I+yLvrFyTMuKpQKXylE1EUbOnwwxY17kl6FSWu18QzX3zHMZgwD3BzYGd17VuggwAdTQdr6EgN1t55JLLhH5JS+99JJPu4X0TFRUlPAE8jAPAlZszSZz/gZLf8icQDXRBfmTOX/J0WHU09NLNS0dSk4gk0A10bbHXGYoBjHCA2kP6/Ep9EaACaDGiKMIBJIvkH7BwC99CEJDqNldEcjg7SCkgfy/s846S4hK+zIgLg0P4q5duzg84QuAKr5jNW+f69FRBPL+jgpq7+oRYV/0e915tFHJCYSEjZp9jDH3rrJGkjl/vdQr+s8iJxBdU8al9uvVqWginsrCCBgtCSOLQRAG5pQbC18kh2ydCaDGhoYMDIo+nnjiCUEEIQPzyiuv0O7du0VoFhIxyBP8/e9/L3Zyzz330Pz582ns2LGiUAT6gejtu2HDBpo0aZJPu8UPxby8PDFXcnIyYU+/+93vRMIyD+0RGEqsGYnqZsrt8wQJ9M3dX9lCi8YkKAUfyAlEaHaKiq3s5F6KqluE5Iws+EBOYGldO41K7Jeh8WTf/IxzEOD+wM6xNZ/UPwSYAPqHn0dvQwJGCkGjJRu6dMAziLF06VJBzqD/h4GOIK+//roQjU5ISKBZs2bRvffeK9rK+Tqqqqrosssuoy+//FJ8KkVI+e6776akpCRfp+T3PECAxZo9AIkfYQRURsDo/sAoCEHKDheDqGxYnk51BJgAqg7pyBN60xkEs0HgGe3h0EJu3LhxdP/994uQ8HADPwQR+n3qqaeEB3HKlCm0efNm2r59O2VmZo68SX7CJwSGEmvm1mw+wckvMQI+IYD8a+4P7BN0/JKDEGACqLOxve0MAgmZJUuWiBAxuny8+OKLggBu3LiRJk+e7Hb3TU1NouVcY2Oj6Cl87bXX0oQJE4TX8fLLLxd/56EuAmYRa1b3VDwbI2BNBFAMgu9JI7pzwAOIP0aJUlvTYrxrIxBgAqgz6t52Brn00ksJUi7vvPOOslPkCCKUjPZyQ40VK1bQwoULhSaWHI8++ihBF/Cjjz7S+dT2XM6sYs32RJtPpRcC6I88WGTb3df02o8v6xhdDML9gX2xGr+jNwJMAHVE3BdZGFQM33bbbYT2cHL86le/EmHdLVu2eLX7mpoaysjIoHXr1okiEx6+IWAFsWbfTsZvOR2B5o4uWrWvmiZlxFBuYqSA42h9G20orhft+OIirCNybAYvIEvCOP07ytznZwKoo318EYbGD5B//etfQsbF1ZP3m9/8hsrLy73e/YUXXijyCFEEwsNzBNjb5zlW/KR1EdhR2kg7yxrFAebmxVNoUCCtKawV/ZdHJ0XS7Nx4yxyO+wNbxlS8UYMQYAKoI/BmIIBvvfUW3XzzzbR161bd+2XqCLVqS1lRrFm1w/NEjkMAuWvw9h2sbhlw9qy4cJqfnyDkfqw0ZDEI8vH07g8scwGNyEO0ko14r8YhwARQR+yNDgHjqNgDdAefe+45OvHEE3U8vXWWsrJYs3VQ5p2aFQEQl/d2VFBLR7eyxYtmZFiO/GHz3B/YrLeM92UGBJgA6mwFbzuDoAikpaWF3n77bWWnKO5AX9/hikCGOxY8gBCZRlEIBnJloFvl9IFfFjLUCyzgMfBXrLmts5vCQwY2pnf3Nadjz+c3DwLI+VtTWEM9vf17QjhY5gSaZ6cj74T7A4+MET/hXASYAOpse287g0AG5qSTThJ9fM8++2z6z3/+Q/fdd9+wMjAjHQldReScmA85gRCnduLQUqy5rKGNPt5VRfNHJ9C41CgB77aSBtpW2kBnnpBKCZH9FdpOxJ7PbD4E+shfX85fZlw4hQYFUFFNq9ioVUkg9wc23z3jHZkDASaABthhpM4gqNSF5wleP/x35syZdPjwYSouLhZk7YEHHjhOCBodRVauXDngNN///veP8xJu2rSJ/vGPf4jWdOnp6SIfEF1CnNQVRC+x5i8O1tKOo30J9YvHJhE8f+sP1Ym/I5ke/Wx5MAJmQqC4ppW+LKoV5A85f8j4kzmB07JiqSDNev2XWRLGTDeM92ImBJgAmskax/Zy5pln0tGjRwVJQ1/La665hubMmSNEoIcaIIAFBQWil7AckZGRFBsbS/X19fTSSy8J4rdnzx5B+JCYjH7EkJNxytBbrBm5VGsLa2l3edMAiGfkxBH+8GAE1ECgqb2LwkMCld7MmLOupZPiI32TbKlobKfk6FAl5w/3uKKxg1JjQkUrSTnwdde/q3EWrebg/sBaIcvzWhkBJoAms96uXbto0qRJtH79epo9e7bY3QcffCA8fkeOHBmyjRsIIMShH3744eNOhLDzn//8Z7r++usJOYUxMTFUUlIiehDbvTWc0fIt+CX5zNrDik1iwoPpkpncis9k33aW3U5DWyct210l9PmWjEsUJPBQTYv44AEtP7W8zPsqmqikro1OHNu3BkLE6wprKTYimCZnmt+TbXR/YKyPPGtXYX7LXjreuG0QYAJoMlM+/fTTdPvtt1Ntba2yM+SwhIeHi57AF1xwgdsdgwDu2LFDtCBCaPfcc88V/YPhBRzqk/rpp58u2sy5ikybDA6ft6O3t2+ojSLnT4Z95TMIB8ucQJ8PyC8yAkRU1dRBK/ZWUVdPL6XGhFFeUgR9WdSXZgDdPuTt+eqlQ/eP3WVNlJsUQR/urKDO7l6KjwimUYkRVNfSRcW1rcJLiHxWfLAx++D+wGa3EO9PbwSYAOqN+AjrocADws8I1bqO1NRUgvjzjTfe6HaGJ598knJzc4WHEBp/d955J82dO5def/31IVf897//Tb/73e9EZxBff0mYCT6tvX2Ha1spJyFCOXJHdw/VNHdQemy4Wxi2lzYov4ynZ8dSW2ePEg5eMi6Jxqb0FYbwYAT8QaCyqZ1W7q0WJFAOf8kf5llzoEaQvPTYMJqQHi08jTuPNlJ7V48ggbj3i8YkUla8+/vvz5m0eNfoziD4+QQPICsuaGFdntMXBJgA+oKaD+/cdddddP/99w/7JsK/IGy+EMDBEy9fvpxOPfVU2r9/P40ZM8btuugxDG/he++9J8LHVh16iDWjoAPVu9Oz40QBB8gfvCKVjR10+sSUAcRQ4lhY1Uwr91WLd5DzJ3MC91c206kTkikrvp9MWhV73rc5ENhYXEd7K5qVzUC3LyQo0K/NIRcQ9xeeQHj4qps6aGtJA4UFB9L4tGg6eXyy23vv16IavmyGYhB80Eb+tR0+cGtoKp5aJwSYAOoEdGVlJVVXVw+7Wn5+Pr3wwgs+hYAHTwxyFx0dLfIHzzjjjCHXRYEJwsuoLLbS0FusGaHcL4r6wvLIrapu7qDyhnbRKgshsJSYMLfw1bZ0DJB7AQmsa+1kCRgrXTaT71Xm/LluE+FgmRPoz/ZBApfvqRI5fwerWqipvZsmpkdTaHCg8AzKnEB/1tDzXZaE0RNtXsvsCDABNJmFZBHIV199RbNmzRK7++ijj+jrX//6sEUgg4+xevVq0eljy5YtQjR6qLFixQq65JJLRMgZ7ZLMPrQQa/b0zK4kEO+MRP48nZefYwR8RQCFGZ/t7/tgibBvfnKk8NohHJwWE0ZLC5L88jY1t3fRm1vLBPmrbemkyNAgumBaOh2oahFrWI0EGu0FxM8vhIDxs5a9gL7een5PLQSYAKqFpIrzQAamvLxcaPhJGRhUBEsZGFTwIryLdm7I8ztw4ID4N1QKQ88POYC33norZWdnH6cNOHib+IEIz+Pvf/97EZp45513RMWw3n0zh4NPS7Fmb8yGsO9z6/orehHChfePByNgFALQlvx0bxUlRoYqBR/ICVy1r1qkHeQn+55nCvK3fG+VkJTZW95EbV09NCY5SuQD4s/n+2uEJxDpDFGh5i8CkTbi/sBG3VZe12wIMAE0m0WIqKamhm666SZFCPqiiy4SnToQ0sUoKiqi0aNH06effkqo/oVI9BVXXCEkXRD6zcnJEdXCd999t9ABHG4cOnSIrrzySoLHMS4uTszzk5/8RFQPGzn0Emv29Iwy5w9hX9chcwI9nYefYwTURqCjq4dCggIGeJTau7opLHhgC0Jv1125t4qONrRTVFgQLcpPpNL6NtpV1iRyAiEvkxITSpEhQRQVpj/56+zuoaDAgAH9iYEDCOlIg/sDj4QQ/7tTEGACaAFLo1L33Xffpc2bN4sqMvTxHWkg1+xXv/qVEH/G84sWLaLHHntMdBLBJ+C33nqLnnrqKUKxCELFn332mRCGRrWxkcMs3r7BGHyws4KO1LYqYd+yhnYlJ3BhfqLIC+TBCNgJgdbOblHFPjs3TvHwIScQRUxobwgJGCMGyB/C3EjBQA4i9lHf2ilyFeGZnJg+/Pci9wc2wmq8phkRYAJoRqsM2hOIXHx8vMgB/Oc//+kRAUTFMcK6qCiGtxCagNu2bRNagegq0tLSIoShr7rqKsrKyqL58+cLkWh8Te+htXyLGueB5w+htlPHJysFH8gJxC/Dsyan+u1tUWOPPAcj4AQEoH0oC1PQsg7eSHxvQp4Ggthfm5givIPDDTMUgyAPkCVhnHBjzXtGJoDmtc1xO3v22WeFaPNIHkB4/6AHCEFphHMx0A4uLS2NMAcIIEiha54f8g2feeYZ+uSTT3RDxCxizZ4eGKGvwb9Y3H3N0/n4OUaAEfANgaP1KH6pEdXJcoD8nTI+yaMPY0YXg2B9/PxFRIeLQXy7A/yW/wgwAfQfQ91m8JQAFhYWCu2/TZs2DdD3O+mkk8Tf//KXvxy3Z+QdgjSuWbNGhIm1Glbw9ml1dp6XEbASAsh7RZhVDoRegwMH5hoaeR4Upmw8XK9s4byp6aJK2dNhhv7AIIBBQZ7v2dOz8XOMgCcIMAH0BCWTPOMpAQSJQ85faWkpZWRkKLv/5je/KT5tojewu3HxxRcLz+Avf/lL1U+sh1iz6pvmCRkBhyIA4fPKJoicJwuPGgoslu2pFCHWBaMTDPdayZw/hH3lQDhY5gR6YjbpBTQqFCslYbg/sCfW4me0QIAJoBaoejCnp51BJkyYoMymNQF8++236Yc//KHIFVRDBkZvsWYPYOdHGAFGYAQEmju66L3tFQSJmYTIUDppXBJ9fqBa9B1Gle3Zk9Mo2oDKX7nthrZO0ZZO5vxBmBrFKggHe0sCURCHD8UQw9d74OcjBuS31Ph5q/f+eT3rI8AE0CAbetoZxPXToacE0JcQMGBASAQFIcgFXLJkic/IGCnW7POm+UVGgBFQEEC3mo93VQoSKAfIHwosQAqNHCCoKAIJDgxUcv5kTmBuYoSih+jJHs3QHxgeSCuI8HuCJz9jLQSYAFrIXp4SQFkEggIQFIJgNDQ0CIkXzHHZZZcNeeof/ehHVFVVRfgvKoivu+46j3ICzSrfYiHz8lY1RGB3WRN19fTQ5Mw+XUx4i9YV1lJ+SiSlx+rv/dHwqKpNXdnYTpA/kgM9r82CFUgg8hFdtQ4hWB0XEexVeNoMxSDcH1i1K8sTeYkAE0AvATPi8eLiYiEODe2+Bx98UGj2YYwdO1YRh0aoGLIvEIDGgAzMH/7whwEyMOgQsnPnziHDHU1NTWL+e++9V1SnfeMb36A77rhjSAJoNrFmI2yj5ZrwwiDXKTexX5S7qLqFEiJDRC4WD88QQN/mj3ZWioenZMUIzcbVB2roSG0bhQYH0LlT0wcUO3g2q72fkjl/CPvKAc+fzAm00+nNIAnDxSB2ulHWOQsTQAvY6uqrrxZEbvCQnUDwdXyKROgWz2JIIegnn3xSyMZA7PnRRx+lgoKCAdPgOXQBgWD0Sy+9JKqHKyoqBPGD98/dYG+f9pemCT1Yt5SJENypE1IoLylS9GNFIj4qHb8xNd2QDgzan1ybFXYebaQtRxoGTB4YSLR4bJLIG+PRj4Ar+UPYd15eAq0/VKfkBNqNBBrtBUTKDCqBWRKGvwv1RoAJoN6Im2y9hx56iH7961/Tt7/9bSECPXPmTPrjH/9I77//Pr355pvKbofy9kHIFOSTk5jVNSxClCv2VtOBymaCpu2YlCghOg3Zs3GpUSIxn/XDvMN8e2kDbStpVF46qYDJnzsEIfeybE+V8D7LnD+ZExgdFkSnjk/xqOWad9Yx9mnkP+P7Cbl4ev8sw4dw/OFiEGPvgBNXZwLoRKu7nLm2tlZ88oyK6m8aD/mY3NxcUQ0MGRnp8ZOeRnxaxR+9f1A6zVSuJFCencmfb7cAWMqwr5wB4WCZE+jbrPZ9CySwuaOb4l1SDUAII0KCbEf+YEWj+wODAEovoH1vFZ/MbAgwATSbRUyyn8WLFwtNwD//+c+Khw8/oNjbp6+B4AFExaMcp01IodHJ/TmB+u7Gmqu5kj+EfZOjQ6mioS+3jUmgNW2q9q6lTqksyFB7/pHmw/pcDDISSvzvaiPABFBtRG0yH1rOIe/w4MGDol8le/uONyyqDkEoYsP7CzJK6tooLTZUSFT4O2TOn0u3KxEOljmB/s7vlPdrWzro411V1Eu9Ss6fzAmMCguir5+QykUgTrkMw5zTDMUgRolSs/mdiQATQGfafcRTt7W1UXZ2Nr3wwgt+aQKOuJBFHwD5e3NrGQUGBNA3pqUJEogcvU92VVJ2QgSdeULqiA3phzt6Y1sXvbKhhHqO5fyhWGHlvmM5gYEBdOmsTEPFeK1mtorGdurq6RMKlmNfRTNlxoVxMY3VjKnRfo0uBmEvoEaG5WmHRIAJIF+OIRG49dZbRTs5VBfzGIgAdMje2lJOSI5HV4TJmTGE9lloTT8+LZpOLvC/SANeKhAXWfCBUObKvdWUERdOE9Kj2SSMACOgMgLcH1hlQHk6UyPABNDU5jF2c7t27aIZM2bQvn37KCkpydjNmHB1SQJL6lpFcnxgYIBC/soa2gVR48EIMALWQQBeOJBAFMYh9UXvwf2B9Ubc2esxAXS2/Uc8PYpBzj33XLrppptGfNaJD3yyu5JeXF9CseHBNC4liq6Yly1CwfAGzs1LoNm58U6Ehc/MCFgWASP7A7MkjGWvjSU3zgTQkmbTb9PPP/883XfffUIsmnXnBuIOovf6plLaU94sWouhO0dKdKgQag4LDmQCqN815ZUYAdUQkP2BjejOAQIILyT3B1bNnDzRMAgwAeTrMSwCra2tlJmZSa+99hotWLCA0TqGAJrPo1MHcv5A+hDy3VHaSG1d3UIn7dqFOTQvL5HxYgQYAYshIItBjNLl42IQi10YC2+XCaCFjafX1m+++WaCYDTayvHoQwAeP4R/IfeCgo93t1fQ/som4Q1Mjw2jn585ThUpGMabEWAE9EeAJWH0x5xX1B8BJoD6Y265FdERZN68eaIYJCEhwXL712rDIIEBRLTxcL3I+esjhiS0+nIS+6Rg1NAD1Gr/PC8jwAi4R8AMkjDQXuX+wHxDtUSACaCW6NpoboR/L7nkErrhhhtsdCr/j4Kw78p9fZ06UPSByt93t5dTV3cPjU6OEiSQByPACFgPATNIwnB/YOvdGyvtmAmglaxl4F6ffvpp0RZu3bp1XAziYgdIwSAXsCA1Wqn4RTeQD3dW0NcmpghRaB6MACNgHgRQaPHVoTqamBGjiKm3dnbTliMNNCc3XhFwl15AI4pBgBZLwpjnzth1J0wA7WpZlc/V3NxMGRkZ9Pbbb9OcOXNUnt3a03V291BI0MDWb+6+Zu1Ter/77p7e47qhuPua9zPzG4xAHwL4ABYV2q/XB3LX2tkjKvGHGuuLamlrSQNFhwfT2ZPTxB19b3sF1bV0UEFatGgXiGGG/sDYR3h4OH/o5guvCQJMADWB1Z6TIvyLFnGPPvqoPQ/Ip1INARC9VfuqKTosiObk9eWNNrV30fI9VTQ1K5bykiJVW4snciYCe8ubaMW+auFpx30C+cOdK65to/OmpglZJnejub1LFG01tnUqH9zwgS0yNJjOnJxK8S7vGV0MwpIwzrzbep2aCaBeSNtgnY0bNxKEoQ8cOECxsbE2OBEfQSsESuvb6NM9fbmRBalRItyGqunm9m7xi/msyamijzIPRsBXBHCf0M8Z1+jrk1LpUHUL7SxrEoVZp09MoTEpUUNODRL4301HCcRPjotmZg4gf9ILCBKIriDQ5tN7sCSM3og7az0mgM6yt9+nnT17Nl111VV03XXX+T0XT2BvBA5UNtO6Y9XR8qQx4cF02oSUYUN09kaFT6cWAlKK6UBlizIlyN8p45NFKHe4gZy/1zaWUkdXPwG8dHaWkhPo+q4ZikGMykNUy1Y8jzkRYAJoTruYdldPPPGE0AP87LPPCCLRlZWVNGrUKNPulzdmLALItdpW0qBs4oLpGUz+jDWJrVZHqsGTnx9SzjQ2JUp4/0YifzLnz/U5mRMYHTawB7BZ+gPDA8ndmGx1fQ0/DBNAw01grQ00NDRQWloanX322bRs2TI677zz6LHHHrPWIXi3uiCAnD8Z9pULIhwscwJ12QQvYlsEZM4fwr5yyHDwcDmm0OzcXtqg5PyFBAYoOYFjU6PppHF9RSCug/sD2/YaOfpgTAAdbX7PD9/Y2EgvvfQSPfXUU4RcwPz8fEH85s+fz59KPYfRMU+6kj+EfcckR9HmI/Xi/HqQwK6engEi3PAUQaCbPSj2uYIr91YpOX9LC5KpuLaFEA4GCYT+Zm6i+0Ij3IW1hTU0OStWyflDTiCkYRaOSRxQ0Q+SiTsDSRb8MSIUiz3gj1F5iPa5MXySwQgwAeQ7MSwCdXV1dPvtt9PLL79MEyZMoOuvv54KCgro/PPPp/3791N09PC5NgyvMxGoa+kU3j/0RZY5fzIncFRiBC0ak6hZEciWI/V0sLpFkICw4CDCL/xleyopIiSIThyT6DEJbGjrpPKGdhqX2n/HD9W0CNmR5OhQZxrWRKeGF+/z/TVKzp/MCYQO53lT0ykpynMb4d1lu6soMSqEZo2KF6eExMz72yto/ugEyowLIxSDcH9gE10A3orfCDAB9BtCe0+AT70/+9nP6LLLLqMZM2aIw+LT6PTp00VXEBSE8GAE3CEAEggC6KrJVtbQRqkxYZqRv7bOblHdif8mRYeKfLDVB2rocE2r0Hv7xrR0SogcmRigSOD1TUeFdM1J45JpQno0FVW30Pvby8WZXCtG8czgvDG+EfogUNfaOaByF0QO9ogN965iF7aFeDvG7Nx4Ye+3t5ZTPeaPDKFLZmZST3e3+NkHLyDatOk5pBcQeYDwBPJgBNRAgAmgGig6cI5HHnmE/vWvf9GKFSsceHo+spkRqG3pEMK+IIFygPydPjGVsuLDPd76mgM1tK20r4AFOWWFlc0i5JgZH063nZovvIsVje30zrZyOiEjhuaN5j7ZHoNrwgc3o6d3UV9PbzmQvnDulHTCf7k/sAmNxlvyCwEmgH7B59yXERrOzMykTz/9lKZMmeJcICx8cng3BovluvuaFY9Y1dRBb245qmz9lPEpNDrZe/FpVxKIPLHKpg4alRgpiCQ8RR/sqKCO7h7KiA2nc6b0dZXgYV0EXO2NU1w+J1uQPzlYEh3ZNhEAACAASURBVMa6tuWdH48AE0C+FT4jcPXVV1NERAT96U9/8nkOftEYBJCP9/HuSlowOoGmZceJTXy+v5p2lzcJIpMe67mnzJgTDL2qzPlD2FcOhINlTqA3+3UNDeK9SRkxtLeieYCAMMgfOkiEDmoH6M06RjyLThpVzR20MD9RLC+ranOTIh3ZqQU5fzLsK+0Bki9zAvE17g9sxE3lNbVCgAmgVsg6YN7Vq1fTOeecQ/v27aPISO+9Kw6AiNARIy0mTPEM4ZcsvpYVH2Ho8dEP9avivqrchfkJ1NjWRdtKG8XflxYk0cT0GEP35+viruQP3ri5eQm06XC9khPoDQkE+ft4VyUhr8x1ZMaFU0l9m/Kl78zLsZy2IcLkT685LM62KD+RFuQn0PK9VbSxuJ6CAwPoeyfmUtQgPTxfbWKF91zJHzx+OQkRtPNo3/eDKwmUBBA5gGFhYbofTeYCYm298xB1PywvqDkCTAA1h9i+C+CH0eTJk+nHP/4xffvb37bvQX08WWFVs9AXy0uKoLNOSBMyJChI2FBcTyeOTRzgWfBxCb9e+7KoVuzFdUADDV4uqw6EY5HMX93UoeT8yZxAFG+gBR2qeEcaLR3d9NJXJdTV3SNaiqG7xLrCWlp7sIb2lDfTpIxoCg8JEtOAEIJYhljMAwj7r9xXfRwUZ0xKFf2anTRQnITv1YiQQCXnT+YEomodmMjWhdwf2Ek3w95nZQJob/tqfrqHH36YXn31Vfr44481X8tqC8CDhAIBeKXyUyIpNiyYNh/pKyqAl02GXo0812OripTlEyJD6LLZWUZuR5W1QQJRgYxqYznw95DgAI/In3wHBL6oulXYCr/8K5va6e+fHhTeXNhu1qg4+nBXpQgHgwSePdl6OYCDSaATyZ+099H6NlHN7Zrzh1SJ3KSIAZqSZigGgTYhvICsa6nKjwzHTsIE0LGmV+fg1dXVlJWVRWvWrBE6gVYYIAhI6HeVA4GcRGRIkJD4UHO4kkA5rx7kD6E96bGQ6w7+GnL+ZNhXPoNwsBmIqZo2UGsuSMOA0CPXT+b8QSfwne3lNCk9mhYcy6VTaz2t54EHX4Z95VoIB0MMmcfwCBjtBZRyNNAl5MEI+IoAE0BfkeP3FAQQ/k1MTKT777/f9KiA/L21pUwkv188I1MI+iJE+OrGoxQbHkwXTs9QlQTiB/WL60sIValy3LR0tKbVou1d3fTW1nLRcUOSOaz/3vZy+tqkFFHgsaawhrYc80Yi7AvtNBkOXjw2kSZnOisE6OnFBQmEB9C14AP5k65eI0/nMvK5weQvMTKEalo6xZaYBI5sGekFNEqXD+sjBxCahOwFHNle/IR7BJgA2uxmrFq1ih588EHasGEDHT16lN544w3RtWO4AS2/2267jXbs2EE5OTl09913Eyp8PR14/+KLL6a9e/dSeLi5q0elUDD02yJCg+jU8Sn06d4q4RFMjg6ji2ZkqJbQj1+yMufPFUuEg5ETqJVkyNaSBkKbLIwl45JEwckbm/vEkXMSI+j8aRlUXNNKH+ysEJ0xZM4fwoHwCJ43JY1SXMKnnt4Dfs46COBDz7/WHRHhaxn2leHg8OBAunbhKEcVgfhiOdkf2EhhaC4G8cVy/I5EgAmgze7C+++/T6jOnTVrFl144YUjEsCDBw+KQg509UCbt2XLlomijnfffZfOOOMMj9AB0Rk/frzoGPLNb37To3eMfMiVBMp9qE3+MC96i4IAYiDsC809mRM4MSOavjYxVRMYYI81hbW0sbhuwPypsWF0/rR0IWCMAdI7uNLT3dc02aQFJt1QXEdNbV2CRIt+sD29osBkXGrUgPZwFjiK2y0erm0VuZJTXAo+cObs+AhKi9W/wtVqOBrdHxheQHQFAQHlwQj4ggATQF9Qs8g7+KU1kgfwzjvvFGRv+/btyqnQ9g1Czx988IHHJ4XXEfO89957Hr9j5INI+P7PVyXKFi6emSmkH9Qc6CWLdmIzcuKUUKyUFjl3qrZaeyCBb28rp0PVLcqRvrc4VyF/ap7TjnNVN3co92NyRgwtGptIH+yoJPQCRrXvlfOyRW9hHs5FQIaBuT+wc++A1U/OBNDqFhxm/54QwCVLltDMmTMJ1bxyPPPMM8ILWF8/UCJkOKgqKipE+Hj9+vU0duxYU6Mqc/7g7ZID4WCZE6jm5hFiGywP4u5raq6JuZDzJ8O+cm54srjAw3Ok95Q30Se7Kwe8gLD9OZPTKFvlDwue74qfNBMCRheDgITCA8j9gc10K6yzFyaA1rGV1zv1hAAWFBTQNddcQz/96U+V+eHFO/vss6mlpUV0+vB0IPybnZ1N9957r6ev6P4cih1QlCFz/s6Zkir6xsqcwMvnZHndSF73Q4ywoCv5Q9g3JTqMdhzracsk0DtrQQwYOaJyfGNqOpM/7yC09dMsCWNr89r+cEwAbWxivQngJ598QpdffrkoBjFrXgpCo8v2VNHR+nal4EPmBKJd2Ncmphwnn2K1K4Iw87vby0WFM3L+ULEqcwKR77W0INlqRzJkv8j5e39HhQj7yoFwsMwJNGRTvKjpEOD+wKYzCW/IQwSYAHoIlBUf84QAqhUCBj74NIzw729/+1u64IILTAsZSCDkYGQxBDba0dVDwUEBlid/EnQk+KfGhCpnxJkLq1ooPzmSZSM8uJmu5A9hXxR+7C5rEm8yCfQAQAc9Ypb+wJCkYUkYB108FY7KBFAFEM06hScEEEUgCPlu27ZNOQa8eDU1NV4VgciX77vvPlq+fDm99dZbZoWF98UIjIgAhMH/u+mokEmROX8yJxDV3BfPyFBawY04GT9gewQgCcP9gW1vZtsdkAmgzUza1NRE+/fvF6eaMWMGPfTQQ3TyyScLoeZRo0aJXL+SkhJ67rnnxDNSBuaHP/whXXvttYK83XLLLV7JwLhCCO3B3Nxc2rx5M+Xl5dkMXT6OkxBAJTDSA6CjKAdag6XHhrFGnpMuggdnRTEIPIFG6PLBu4+14QHEHx6MgKcIMAH0FCmLPAdRZhC+weOqq66iZ599Vgg8FxUVEZ6TA/9/66230s6dO0URxy9+8QuvhKAHrwX9QYSCf/3rX1sENd4mI6AtAl3QbAsc2GbQ3de03QXPrhUCXAyiFbI8r5YIMAHUEl2Hzg0x6uuuu452797N8gQOvQN87H4EILb82qZSQsu9canR4h/2VzbTir1VohDJtSc142ZdBIz2AsITaFRrOutazdk7ZwLobPtrcnoo5Ofn54uWdOecc44ma/CkjIBVEADRQ5/lgACic6ekiUT9t7eWUU8vCZHwU8ZzVbZZbemNjif3BzarFXlfQyHABJDvhiYI/OY3v6G1a9fS66+/rsn8PCkjYBUEenp76YMdFbTrWBWx3Pf4tGg6a3KqbSrPrWIPT/eJTj6vbTxKc/PiaXJmrHgNEksf7aoU8kqpbvply2IQeOJQFKLngAcQfyDBhe4kPBiBkRBgAjgSQvzvPiFw5MgR4QVEdTE6hPBgBJyMAEjgIyuLhNwQRmAA0Y9OyWfyZ+JLse5gLa0prKEAIjp9YgpFhwXTW1vLqKunl07IiKEzJh3fy9vI/sCAEutzf2ATXyqTbY0JoMkMYqftIPw7bdo0+vnPf26nY/FZGAGvEUDO31tbyqjX5c3zpqYpOYFeT8gvaI4AvGkr9lbTpiMDW2KOTYmisyenEfQhBw8z9AfGnoyoRtbcILyA6ggwAVQdUp5QIgAtwB/84Ae0a9cuDknwtXAsAiB/MucPgtKBAQEETUGZEygLQxwLkIkPDhL47LrDVNvSqezyRyfnuyV/8gEz9AdmSRgTXyoTbY0JoImMYbet4AchNAH/9re/0de//nW7HY/Pwwh4hMDWkgb6eFclyZw/vCRzAk+bkEzTsuM8mocf0h8B5Py9uaWMunv7fbdoFylzAt3tiCVh9LcTr+gbAkwAfcON3/IQgbvvvpu2bNlCL7/8sodv8GOMgP0QAJEYlRih5PwhJ7C4ppXykiLtd1ibnAg2kzl/CPtGhgYRyLzMCRyOBHJ/YJtcApsfgwmgzQ1s9PEgOl1QUCDCwBkZGUZvh9dnBBgBEyKAUOuawlqaOSqOIkL6Kljbu7rpi4N1tHhsoiE9bvdVNNG72ytE/2zk/CHlT+YEQtNx1qj4IZE0uj8w1kcVMiqCuT+wCS+8SbbEBNAkhrDzNs444wxasGAB3XHHHXY+Jp+NEWAEfEQAIfKPdlVQdnwEfW9xriBb/1xdTAerW+ikccl0zpQ0H2f277WSulZKjw1Xcv5AVI/UtVFOQn97wKFW4P7A/mHPb2uPABNA7TF2/ArQArztttto+/btumtjOR58BoARsAACpfVt9MRnh6ilo0vo66FQpqyhjcJDgui7i3JF+NxqwwydQaAHCC8gD0bAHQJMAPleaI4A8mGgBfjkk0/Saaedpvl6Vl+gu6f3uCpDd1+z+jl5/4yAKwIggX/79CChR7IcNy/NtyT5w/65GITvt9kRYAJodgvZZH933XUX7d27l1544QWbnEibYzS1d9F/viqhhfmJNCkjRixyqKaF3t9eQRfPzKTkaP40rw3yPKvRCCDn797391FbZ7eylXvOnaDkBBq9P1/WN4MkDDyAEIfmwQgMRoAJIN8JXRA4cOAATZo0iXbv3k1pacbk8+hyUD8XWbWvWuk+cO7UdIoKCxLtqNCTFFWHRuVC+Xksfp0RGBYBkD+Z8+f6oMwJlIUhVoORvYBWs5iz9ssE0Fn2NvS0p556Kp188skiH5CHewSQZP7ejgraVtIw4IHRyZF00YwMCta5vyjbiREYDoGSujZKjw0bkLJwuLbVoyIJ13mhtff5gWol5y84KEDJCZydG0+XzsqyrCGM7g8MEorOINwf2LJXSLONMwHUDFqeeDAC0AJEWzjoArI0wdD3AyQQCfF1rf3dB35y+hgmf/wtZSoE9lU005OfF4lUhe/MyxEkENW8724vFx9WFo9N8ni/rZ3d9O8vj9DXJqYqOX/ICUQHlSvmZVNUqHVDmNwf2ONrwA/qjAATQJ0Bd/Jy7e3tlJWVRc8//zyddNJJToZi2LMj5++VDaWEwg85zpuaruQEMnCMwEgINLR1UlF1K03NilUePVjVQmEhgZQZFz7S6x79+47SRvrnmmKCqPW07Fgx7/s7KsS70M07fWKKR/PY/SEZBoYuHzxxeg98oMQf7g+sN/LmX48JoPltZKsd3n777XT48GF69tlnbXUutQ4D8idz/tAlAt0Hdh5tFN0HkBMoC0PUWg+/GPZXthB61MqBJPyKxg7LVl+qhY1V54E37dGVRVTV1CE8cQihgvw9vbaYggMD6AdL8iglRh0i4koCJV5nTU4Vnjwe/QiYoRiE+wPzjRyMABNAvhO6IoAikOnTp4uK4OTkZF3XtsJi20sb6N1t5ZR3LOcvKCBAyQlcMi5JVAerNUD+PtxVSRuK62jxmCTC/CB/L64vocrGdrpkViblJ/cTQ7XW5Xm0RQB2fXtb+bFiogCakRNH20obRCHRuNRo+s68bAoJClRtE39ZXigEm+X400UnHCdjpNpiFp2Ii0Esajibb5sJoM0NbMbjIfx75pln0i233GLG7Rm+p8KqZuF9kwUf7rx0am1y3cFaWranUkw3NzeBkMB/tKFNSG9cMTdbiPLysB4CriRQ7l4L8idz/lwRQjhY5gRaDzntdmyG/sDwArIkjHY2ttrMTACtZjEb7BdagPfeey9t2LCBi0FMYE9XEojtMPkzgVFU2AI+SDz5+SFlpm9MTacFKnqQP9tfTf/ddFTMj7BvVlyEkhM4Jzeevj03W4VT2GcK6QU0ioRxf2D73CW1TsIEUC0keR6PEWhtbRXFIKgKXrRokcfvefsgvFmoTHRNet9a0kBjU6JEbh2PPgQQ9v3TsgMKHONTo4XoNA/rIiBz/hD2lSOAApScQDVOhhzDv688SAvzE5ScP+QEvvDlEbpmQQ4VpEWrsYyt5oAkDBQQwsPVKcTxBhxZDAJhaJaE8QY5+z7LBNC+tjX1yRD+ra6upn/84x+a7PNofZv4RYSeot+akyVI4BcHa+mTPZWUHhNGV87PoVAV86A0OYQOk8qcP4R9XYfMCdRhC7yEygi0dHTTAx/vF8QeYd8r52XTBzsqlJzAm08erVolMNYa/GHK3ddUPqJlpzOyPzBAgyQNQsDcH9iyV0jVjTMBVBVOnsxTBLZv305z5syh/fv3U0JCgqevefxcR1ePaKl2uK6VwoODaExKJO042ijePxEFD2MTHR9+hkcABR9FNS1K2LewqkXJCTz7hDSanhPnMeb8oHkQgKd7Y3GdCMOi4EPmBIKsnTaB5VmMspQZikFwdpaEMeoGmGtdJoDmsoejdrNw4UK66KKL6MYbb6Ti4mJKSUmhiIgI1TBwJYFyUiZ/A+FFmBxdGL45K1Mp+EBO4K6yRvrW7CzRmcHoIYtgxqZEKqS9o7uHSuvaCFI5PNwjANxcBdcH/91o3L46VCe8h1LaSHbBWTA6gRKj7NvzmiVhjL55vL5EgAkg3wXDEHjqqafonnvuofHjx9PKlSvpf//7Hy1dulTV/SBRfdX+amXOaxaMUi38pepGDZwMgtPIlXQd7r5m1BaX76mi1QdqaP7oBDptQjJ19vTSf9aXUHFtK10yM5PGc66ZUabxed19FU30108Pinv3vRNzaWJ6NL28oZQ+P1BDKdGh9LOvj1NVqsbnjWrwohm8gPhgAC8gd2TSwMAWmpIJoIWMZZet7tmzh/75z3/SM888I/IAr7vuOrrrrrsoIyND1SPKnD/XSREOljmBqi7Gk2mGADxFssPErFFxQuD4UE0rhQYH0rfnZFF2gnpeY80OwRMPQKCrp4eeXnOYEKqGOHVcRDBVN3cKwXPoFM7JUz8txEwmgCQMyBcqgtEhRM/BxSB6om3utZgAmts+ttodvHy//OUvad26dXTBBRfQd7/7XXr11VcJVcGPPfaYqmctrmml5788LOZE2BdhJZkTGB0aTDeelMdFIKoiru1kriQQKzH50xZvPWZ3JYFyvascQP5wVukFNKoiVxaDgICyF1CP227ONZgAmtMuttzVZ599RuvXr6fvfOc7SheQzZs3CymYAwcOUGxsf99SfwHAp9xle6pEGEkWfCAn8LVNpTQvL4HGpHCHC38x1vN95Pzd/+F+ZcnshHC6en4O//LS0wgqr4Xv0X+vLyHknMqBNnVqtztUeduqTAcCCBImQ7GqTOrFJNwf2AuwbPwoE0AbG9cqR0M18JVXXknXX3+9qlvGDzkMMyfCq3pgm04G8oecP4R9XYfMCWQPhvUMj+9NmfPnunuEg5ET6AQSaHQxCGwAPUCWhLHe949aO2YCqBaSPI/PCEAL8NFHH6XVq1f7PAe/aF8EXtlQSnvKm5Swb1lDu5ITiKIQNbtb2BdFc51sd1mTEJGWOX8zRsUpOYEJkSH0y7MKbFsEIi3BxSDmupNO3A0TQCda3WRnbmxsFAUgH374Ic2YMcNkuzt+O5VN7ZQYGTqgcra8oZ3SYrlvrhbGK61vo9c2ltKF0zOUgg/kBG4orqMr5mVTVGiwFsvynBojgOrumLAgpeADOYEvf1VKJ49P1q1Sv7GtS+hennVCmviAgYEuKgerW+jkgiSPUgyaO7qosLKFpmT1p7CU1LUSAhAjFShxf2CNLxlPPywCTAD5gpgCARSE4BPx3/72N1PsZ6hNlNS10fNfHBb6c5AggYwFyMg728vpjIkp7I3SyHogB8GDqiXdfU2j5XlaGyKAEOgTnx2ikvo2Gp0USVfMzSbx/f3lYers7hUfOGaMIISO9ITnvzgiKtNPKUgSZBbkD15rjMvnZA/7wRA/80ACEYZFhw69B/cH1htxc63HBNBc9nDsblAccsopp4hikOho8/YQ3VfRTC9vKKGunl6akBYtfnG8v7NC2A2VxmdMSnWsDfngjIDVEIBawHNfHKb2rh4Rju7LGibRL/zyOVkjhqFBIqFRubqwr5AFPxMKq5qpo7uXchMj6MIZGSOqDXB/YKvdGvvslwmgfWxp6ZPgB+nMmTOFNMzVV19t6rO4kkC5UZC/r01M8ShkZOrDWXxz7rpf4EhOLxQBKRmVGKF4UWV3lXGpXA0PEviP1YeUmw/idtX8nBHJn3xhMAnE1z0lf3hW9gc2WhKGi0Es/sPPh+0zAfQBNH5FGwRQCAJxaOgFmn1AU3B3eZOyzbvPHHdciNLsZ7Db/raXNtCXRXUilId8Lvxifnd7BQUHBYjwvFNJ4JYj9fTcF0dEZe1V87MpKCCA3thSRuiSc96UdJFz5+SBnL+n1xYrEOQkRAiJIZkT6Ak2CPu+8GWJ8ujiMYm0cEzigFd7enspMKC/445UKcB/QQKNqshlSRhPLGzPZ5gA2tOuljxVfX29KAZZvnw5TZ061bRnkDl/rhtE6EfmBJp24zbeWFtnNz20rJBaOropLymCrpyXTR/vqhIac/id+/0Tc0dMyLcrPKi4/eeaYkLO5OTMWIoLD6bVhTXiuJfOyhIt9pw6QP5kzl9YcKDI/QNRkzmBnpBAmfOHsK/rkDmB+NrOo4208XC9yCtE/2OQro93V4pCEUQOoAmIr8ELZ0RnEOQCQhQaf3g4BwEmgM6xtU8nfeSRR+jBBx+ksrIymjZtmijSmDt37pBzPfzww6KrR3FxsRB7vvjii+n3v/89hYeHe7T+NddcI3pUPvTQQx49r/dD8KbAe4KBsG9+cqTQM0NO4OSMGLp4ZqbeW+L1jiFwpLaVnl13mNo6exRMQP7On5ZOs0bFOxqnPhJ4SNxTOZxO/kC4nvz8EB2pa1Ny/o7Wtys5gRdNz6DpHhSBoJAEHzxk2PfLg7VKTiBaFabGhtGzaw9TU3sXpUSH0UUzMujzA9W0vbRR5B1eOjuL0mNChRcQhSBGkDAQQO4P7LwfEUwAnWdzj0/88ssvi64djz/+OM2bN49A7tC6Db18U1OPL3Z48cUX6dprr6Wnn36aFi5cSHv37hX5fJdddpnHhG7t2rV05pln0v79+ykyMtLjver1YF1rp/hhjub1MucPOYGvbiylC6an08T0GL22wuu4QQAk8PHP+vO5zpqcSgvzB4binAgcyM79H+2n8sZ25fgPXjjJ8WkLze1dtGJftfheRtcgDOQEHqppocVjkzy6KvAiflVcR9+Yli4KPmROIByCsgtRTXMHvbKxVBBFOUD+zjwhjSak9xW9mUESxqg8RI+A5odUR4AJoOqQ2mdCkD506fj73/8uDoVPiTk5OXTzzTfTXXfdddxBb7rpJtq1axctW7ZM+bfbb7+dvvjiC/r88889AgY/PKdMmUK33HILXXHFFR69o/dD+CEeERI4IKcMX0Noh4dxCMicP9fWYggHf2eed/lcxp1Am5WBi8z5c10B4WDkBA6W19FmF/ae1ZPio+rmDvrXur7+5BinFCQP8DAa3R+YJWHsfUfdnY4JoPNs7tGJIU0AD9xrr71G559/vvLOVVddRXV1dfTmm28eNw88gD/4wQ/oo48+EmHiwsJCOvvss0Wbt5/97GcerYuH/vrXv9J//vMf+uSTTzx+hx90NgKu5A9h39mj4mlbaYMIBzudBH60q0LpnIKwb3xEiJITODc3gb41J8vZl0eH08ucP4R95ZDhYPnBkfsD62AIXmIAAkwA+UK4RaC0tJSysrJozZo1tGDBAuWZO+64Q1TpwqvnboC8/eQnPxFhEOS03HDDDSIn0JtRU1Mj1v7ss89o0qRJ3rzKzzoUgfaubvrH58UixClz/mROIAjhdxflUmqMMzu11LZ00CMri+i0CSlKwQdyAl/6qoS+u2iUbsUx6PwxJStG5MFhQET5o52V9LVJKSNq5Vn5WruSP4R9541OoK0lDSIcPJgEGt0fGCQUeYgsCWPlG+f53pkAeo6Vo570hQCuWLFC5Pvde++9ImcQeXw/+tGPhLbfL37xC6/wQ/g3Pj6eHnjgAa/e44ediwBachVVtdIJmf15mCCBgYEBurUWMyv6IFvIT3Md7r6m1f5X7q2iVzYepcSoELrl5NEUFxFCT6wqot3lzTQjJ5auX5Sr1dKGz9vZ3UNvbC4THUJkzp/MCcTmvjkzkxKjQsU+uT+w4eZy1AaYADrK3J4f1pcQ8OLFi2n+/PmialiOF154gb73ve9RU1OTV/IGq1atogsuuID27dvncQWx56dz9pP4hSQT3iUS7r7mbJT49GoiUN/aSX/5tJDKGzooJjyIwoKDRPs0SK/84KQ8UYVr5wGyXVrXJlpIygESiJrspGPkT/le7OwU/8uSMHa+EeY4GxNAc9jBlLuAFw+5fLI/Lz6djho1ilDs4a4IZNasWXTaaafR/fffr5znpZdeouuuu44aGxuF0KmnA2GTiRMn0p133kmXXnqpp6/xcyMggCrmR1YeFAnoC45Vx+6raBIitnqGA9lQzkMAJPCPnxygmuY+goNx66n5tid/3lraDP2BWRLGW6tZ83kmgNa0my67hgwMij6eeOIJQQQhA/PKK6/Q7t27KS0tTUjEIFcPOn8Yv/71r4Xcy5NPPqmEgG+88UYCMcRc3o4//elPotjkgw8+8PZVfn4IBD7eVUnvbi8X/3rprExKjg4VWmgQwJ01Ko6unJfD2PmJQGVTu5LnhqnwYaaquWPA1/xcwpKvwwv24Mf7qbSuX4rm1+cUOB4Xd8bk/sCWvOKW2zQTQMuZTN8NQwJGCkFPnz5dVOjCM4ixdOlSysvLo2effVb8HQnMv/vd7+j555+nkpISSklJoXPPPVd8Dfl83o7KykrKzs6mL7/8ksaNG+ft6/y8GwRARl7f3NcGzHVA1/DahaM87n8q38U8SGY/Y1KfLmR3T68oLpibG08FaX36Zk4a20oaxPmhK7e0IFmQv3e2l4sWdWgvNsbmoc6hbA3yJ3P+XJ+ROYGyMMRJd2W4MKf6uAAAIABJREFUs6IzCP4YpcuHtaUotVNbKDrhLjIBdIKVLXxGFJWgPRxIJI/jEdh0uJ4O17bSuVPShC4hCMf/tpQJojE1K9YtZHjmgY/209GGgaLAg/MCR8Ib6/7hw33isXOnptPpE1JEFwW0ygsPCaLfnjvBcdqIn+6pog93VQhMvj4plRrbu2j1gb62axfPyKTZud5/EBrJDlb4d0jRvLmlXMn5S4kOVXICJ2VE0w9PGm2FY+i2R1kMwv2BdYPckQsxAXSk2a1zaIhKf+tb3xJdRewmTQBvGXrYRoUFKwZBZwKQp6DA/qbxQ1kLwrK/e3+f6F26tCCJvjE1nf676Sh9fqBGvH/3meMoIbKvutB1IOdPhn3l1xEOljmB3tyOD3ZW0Ntb+1rjyYGG99cvGkXTsuO8mco2zy7bU0kItbsO9ICdm+fcnru46y+uL6EF+QlKzh9yAtFG8bLZmRQbzj1oB38DmEEShvsD2+bHktuDMAG0t30tfzp8Ei4oKBD5hRdeeKHlzyMPIEOlJXVtdOOSXPELsKGtkx5fdYgy4sLo8jnZHpHANQdqRIupweNbs7OE3thw5A8tqBIiQ2htYa14zFcS+P6OcnpnW19eIcb3Tsx1LPnD+eFh/embuxQ8UOX5f6ePtc3d5YPogwBLwuiDs5NXYQLoZOtb5OwoMkFXkLffftsiOx55m/B+/G3FQapr6aSUmDC6Ym42/fvLI1TR2C400m4+ebTo2ODJQIgRvYjlGIr84d+3HKmnZ9cdpglpfTl/wYEBSk7g2ZPT6PSJKZ4sqTwDIivDvvKLCAcj/OnEIXP+ZNhXYgA8kBPIgxHwBgEz9AeGFxD5gDzshwATQPvZ1HYnKisro9zcXNq4cSONHm2fXCGEcB9bVSRIoBwgfzcuyRPVuZ4MEA4Z9pXPy3DwUMnb+yubKTcxQin4wBx7ypuVpvSerItnXMkfwr45CRF0qKZFvO5UEghv6Mp9fQU2CPsiB1CGg8+ZnEYnjk3yFF5TPQeR7fDggakJjW1dFBPOxEBLQ3F/YC3R5bmZAPIdsAQCF110EeXn59NvfvMbS+zX000WVbfQ31ccVB5HMvzo5H6x2OHmGUz+QOoO1bSKV0YigZ7ub7jn0NngwY8PCCIoc/5kTiCI7N1nFjiuCGRfRbPwiKIoR+b8ISfw8/01AqOs+Ag1oNd1jqb2LuGtTo0OpasXjBKpCbi3aC93zuRUOok9m5rZA9/j8AIGBgZSWJj+rQyxPv4YVY2sGbA8sUCACSBfBEsg8OGHH9LVV18tNAgRkrDDkDl/CPvKgXCwzAkc6YzwIP7x4wPU2tlNMuwrcwLRYP6O08dSfKS2WO0tbxLruxZ8rNhbRSdkxIjQthOHO8+Ylb1lsPGjq4oE0Z+eHSsI3xOfFVFbZw8VpEbRD04a7VG+qhPvghpnRjEIPIEggCCCeg8pCWO3Ijy9cTTjekwAzWgV3tNxCOAHIDyAf/jDH+i8886zPELwqjy6skjJ+UMBBoo5ZE7gD5bkeRReK65ppfKGNprjUmG67mAt5SSEW9LbZHnDengAkOaIkL7OODuPNgqdwEtnZ1BESLDwuLy3o4Kiw4LppHHmCBnvKG2kf6w+JEigHONSo+j7i3NFWzce2iFghmIQnM4oAqodsjwzE0C+A5ZB4J577qHVq1fTG2+8YZk9D7VR9N59Zu1hKm9oV3L+ZE4gNNKuWTiKQoP0/7RveWAtcACEhFF5fcPiPIoIDRRSPrvLGik1Joz+fMkJtHxPNcGLivHjU8ZQVny4KU6FDjIf7OjTOMR44MJJColVc4OQRmpq71byYCsb28XfgUNocN/3BELQoxIjCLmnThgsCeMEK+t/RiaA+mPOK/qIALqLoAhk69atoiex1QdIIDyBrlp9tS0dQheQyZ/Vret+/x1dPfTn5YUEUoPw/HcX5dLTa4tp+e4qogASIVWE7zHOn5ZBi8YkmgKIvpy/gyLsKwfCwTInUK1NgvxBoxIfhqQ4NPJMEYaGN/SmpaNpV1kjPffFEZqRE0eXz8lyBAmUXkCjKnKxPvcHVuuWm2ceJoDmsQXvxAME0FpuypQpdPfdd3vwND/CCJgPAeR+PrbqkCCBcrR0dlNPT68S9j93SjotMUn415X8Iey7eGwS/WvdYSUnEN5qtTxx+ECE4pKj9W0UFxFMp45Ppvs/OkAtHV2UHB1GNyzOFeFxiJ/PyY2ny2Y7gwDinsj+wMjF0zsXkItBzPdzRI0dMQFUA0WeQzcE3nnnHbrhhhto586drE2lG+q8kNoIwNOL0C8GsuoQzjxS21fBjYEimivmZVOwAUn/g88Kb9xfPy0kCFrLnD+ZE3jK+GSlDaFaGLmSQMzZ3tUjCCHyWoOPpUU4jfwBB6P7A8MLCOIJAsr9gdW67cbOwwTQWPx5dS8RQC5MXl4e/eUvf6EzzzzTy7f5cUbAeAQQ+ocHbXdZkyB/IDe1LZ0E7xrCmnvLm6mrp8dUJBCEFaFp14IP5K+mxmhDBqBV6SqPNDcvntYfqlOM95tzxjuufRz3Bzb+e9duO2ACaDeLOuA8v/zlL4Uo9CuvvOKA0zr3iOiWglZ5kzJiFBCgs4f2dZ4KZZsNPVfyFxIUSOdOTaM/fXKAcNbxadF0z7kTqKqpg55de1hIq9y8dDSlxTpLTqesoU2EgSGdgwFsSuvbKD85UvTJxkiPDSdPK+XNdgf82Y/RxSAIBQcFBdmuL7s/NrHyu0wArWw9h+790KFDNG7cOBEGzszMdCgK9j42fvnf895eIXHzk9PH0tSsWFEI8PsP94lWeb88q4ASozzrlmImpCCj8uL6EiH9glZ88PoV17TQw8sLRRXwDUvyRAvAPeVNosIWoWEnDXgaH1pWKMgfqn4Xj0mke97fS60d3ZQZH06/Pns8vfBlieibnRkXTreemm+KMLleNjKDJAwXg+hlbe3XYQKoPca8ggYIIPw7d+5cuvPOOzWYnac0GgF4yv766UHaUFwn+hV/Y1o6vbOtXOSDwSP4f6ePsaz+HEhgWUP7AHkXEBpI7Hna/9lf+0A/8r0d5XTV/BxFygUi4mWN7XTBtHTDcrxQ3AGCjLA4PHzwkqL6eGtJg+hTDcFzeEghTI3+yvNGJ/gLheXeN7o/MEgo8gC5P7Dlrs5xG2YCaH0bOvIE0AL88Y9/TDt27NC9Is6RgBtwaFcSKJe3OvkzAMbjlgQBhScVRArtA+F13FRcL4TIMeCZhMfVqAESCKIvhbI7unsI8jlRoUEKMcXfpSagUfs0al0z9AdmL6BR1ld3XSaA6uLJs+mEAD4FQwvw8ccfp9NPP12nVXkZvRFAqPTe9/cqy9526hianRuv9zZstx76OCPPrqWje8DZ9OghbTswDTgQJGGM7A8sW9MhH5CHdRFgAmhd2zl+5z/96U9p165d9OKLLzoeCzsCIHP+4A2SA+FgmRNoxzPreSaQQIgsyzF/dAKhJSFLfOhpBd/WMkt/YAhT833xzYZmeIsJoBmswHvwCYHCwkKaOHGiIIHp6ek+zcEvmRMB9ES+7b87CJ0hEPZFsv/jqw4pOYFoQ4ZKUB6+I4CcPxn2xSwyHCxDr77PzG9qjYDRxSBSGJr7A2ttaW3nZwKoLb48u8YInHbaabRkyRL6yU9+ovFKPL3eCKD37KbD9UrBh8wJzEmIoEtmZrDnwQ+D/H97ZwJe0/W18WUmppgSRCSCoOYh5qJotWhpP62pKFod0KKtKkpHWvxbpabqpNVWzUpHtGY1tWZijClCiBAhiUS/5916rptI5M73nHPf/TyeVnLOPmv/9pG8d+01WIs/ZCKj3A6OgykCnYDq4Vu96QWEAIQIhQcQfziMSYAC0Jj7Rqv/I4BagDgK3rVrF5NBTPhWIGEB9fC0gb/jr2Y4doLg0vr+Yn0o/oxMYHf3gQbDj/88JsgE1mL+UGcPMYE4bn+2eYiEBxYy4dtkriV52wvI/sDGf58oAI2/hz69AgRDlytXTr788ku57777fJoFF28cAqsjY2Xd4YsyqGUFKVU4nxJ/X246JfByPt0sxO0ZrhCf6KzRolJxi5hGTCA6ktQo670MYOPsoD4s1ZJB4IXzVn9gPJslYfTxPthrBQWgvcR4ve4I4PgXxaHnzJmjO9toEAlkJICyJuj+gVZqRQvklufuDVU1DvedTVA1Dwe1qiChJfwIjgSyJcD+wNki4gV3IUAByNfD8AQOHToktWrVksjISClVqpQgPsUMR4SG3xguIEsCKPyMI1eIQG1A/MH7V7U0j1/56thGQC/9gVEYmiVhbNszPV1FAain3aAtDhNo1aqVREREKOGHItEbNmyQwoVv95B1eGLeSAJuIoC2Z2/9dLvGYZe6ZaR5pRIOPQ0felbsPSfF/fJKs4rF1RzJqWkyd8tpub9agM+1lHMIokFv8mZ/YCCDFxJHwBCBHMYiQAForP2itRkIIAZm2bJl8vbbb8vevXvl4Ycflr59+wqyg/mJlK+LXgloMX849tUGjoO1mEB77UbB7M82nlS3/V/dMtIgpKh8uuGkHL9wTYoXzCMj2lXyqZ659vIz8vV6SAYBP5aEMd5bRAFovD2jxSLquPezzz5TcX/w9D311FPy4Ycfyg8//CDNmzcnIxLQLQFr8Ydj324NgmTlwVhLTCBiAEsVymeX/fAALt9zTtYcupjuvvx5cqoYw/LFC9g1n14vRpu4nDluZ4XDzsy+plf73WWXHvoDsySMu3bXffNSALqPLWd2I4GePXuqo4dnnnlGZf8iAw69gc+fP6+EIQcJ6JUAyrB8t+207Dp9xRLzp8UE4nsDW4ZKMT/7j9MgAhf9c1Y2HbtkWfqQ1mGmEX/x12/Il5tOSseagVI54Fac5OHzibJ8T4z0a1Je/P18tx4d+wPr9V+7vu2iANT3/tC6LAhkluixf/9+qV+/vhw+fFiKF78VB8VBAnokAKEXcyVJgvxve+YgAvF1R8Qf1oiYP+3YV1szjoO1mEBPcLh8/YZEXbwmtcsVtTzuSGyioLtIkL9znVt+3B0jm47FqbqQTzUOFpEcMuevU6qEDtrYda5d5q5LRPb1qbjrUrFUQct1F66mqKQxlOIx+kA4DGKg8+d3jrMjHLTOIEwGcYSe9+6hAPQeez7ZDQRw/Nu5c2d54YUX3DA7pyQB7xG42/GntfjDsS/E1tHYa8pYT4nAxJRUmfrncbmYmCKP1ysrDUOLKQ/dF5tOSp5ct8rbBDghtCD0vtt6RvbH3I6bxPqqlS4sPRsG3TXGEfUV5249LVEXrwuSbWoGFRGIv682n1TFt/s2Dbb72N17b0LmT/ZmZxBYhBMZxF1DBLIKg97ejsztoQA0xj7RShsJICZwwoQJsnXrVv4QspEZL9M/gbjEFJm94YQ8VresoHUbxoGYBFm++5w8e2+IxF5NkVnroyRPrlsxf8HF8ltiAiEGX2pdwe1JIPACLd4ZI5uPxQnC9OqX91fH3BBfKG3Tp3Gwss+ZARH49k+HBN48DHgD3+pYJdu1wbZlu2NUa0F4DluHl1CFsBOSU5UohUexYL7czpjm9Xu9nQzC/sBefwXsNoAC0G5kvEHPBK5duyZly5ZVpWAaNWqkZ1N9yjZkqZYslDedBwj9aBuE+ru99ZkZQP+w44xsPnZJCagBzUPUsSc8azfS/lXt3HD8uS86QQrnz22J+cMv5HVH4qRB+aIeEzfWIlDj7irxh/ngUfxSee3+VdPnkBzSr2mwJSbwbnudXgTeutIs4k9btzdLwmgCUPMCmuHfndnXQAFo9h32wfUNHDhQrly5IrNmzfLB1etvyRB/Ly3YK8X88sgnXWuqX7oL/46WD1cfk5aVS8g7j2TvwdHfqm5ZhH66gUXySr7cuSwmQqRUKuXnUg80vGgQfAdirqZDgaNMeK+s+yV7mxXWD2+kNh6rW0aahjkfk4t5tZg/JIEgF/jQ+auWmEAtMeRu64+9miyfrDluueSh6oEqftAsw9teQPYHNtabRAForP2itTYQ2LVrlzRt2lSOHDkiRYveDka34VZe4gYCZy8nyeD5eyQ6PlnKFcsvrSqXkLlbz6gn9WpUTp67N8SlYinjEhDrBfFpLZJiE5KdDvw/GpuoWrqhxMrQNmFKBKLHL4ovt68RqGLNXBkLBRE4fMl++c/5JX55c8k7D1fVnfi75Zm8dUSLgeNgLSbQmddrw5GLqti1FvOHubSYwPbVA6VF5bsX0dZi/nDse3vksMQEOmObnu5lf2A97Ya+baEA1Pf+0DoHCeD4t0ePHqpMDIf3CViLQM0aT4i/M/FJ8vrS/SorFSVRIAJx9Dxh5RF5vkWotLsnwGE4mgC8fiNNqgQWktrlisj8HdFqPncIQMT8zVp/wmIv1oJ4Py0m0OGFuOhGJIGM+/WwJN+4FfPXu3GwilHUYgKHtqkoZYs6l6GKY+4qpQtaYv5wFH4w5qrUKFvkrqtISb0pU9ccF2RawwONeMRVB2MtMYH9m5Y3TbkcPfQHxgcfFIZ25QcgF72mnMb648+/OLjnIAGTEUAtwE8++UQ2btzIH0I62VscvX237ZbnD2PpcxFOZYXasqy/jl9SogQxY/eFl5RGof4yYeVRy9+HtQlz6v1AiZMPVx0ViEBtuEv8aTF/95QprMrFRJ67aokJ9LYIRBYyPKA7T12WHacuS+9G5RTjvLlyqsQQdDlpW7WULVvmtmtgG8rIQPwh4UOLCYSz8tE6pe8oMO02Q9w8sXYMjNqoEGGeHiwJ42nijj+PHkDH2fFOOwhMmzZNJk6cKDExMVK7dm2ZOnWqNGzYMMsZ4uPjZdSoUbJ48WKJi4uTkJAQmTx5srRv396mp169elXKlCkjv/zyi9SrV8+me3iRcwTgbdtxMl4eqVXaMtHGo3EqMeHQuasq5s964DhYiwl07sl3vxs2fPD7EUviAK7GMTQ8Uq6Inft0wwnl5dLGjO61JH+e2zGBrlgbvFUr9pxT5UsQ8wdxpcUEPlanTLbHn66wIas5kKE8c/0JVW8QMZ0QAIhV/GFHtLI1tEQBp0S2K22HcLbecyVWREwj/jRW3kwGgQ3sD+zKt9Z9c1EAuo8tZ/6PANqz9e7dW2bOnKkycyHkFixYoNq5BQTceQSHGJZmzZqp740cOVKCgoLkxIkT4u/vr8SjrWPAgAGCH4TwBHK4lwCO1np++beK83v1/oqqvRmE18uL9qkH45wBv3hx7Nu5dmlLTGBYST/5qk+dbMt4OGv9pJVHZM3h223SljwbkWVJkqQbaep71kLhWkqairnLOLSYP+uv4zhYiwl01m7r+3edvqyOOjW7EGcHoVUr6O7Hn660IbO51h+5KD/uPqe+hS4dpQrllW+2nJbUm/+qnsRd6we52wTOn4GAt5NBWBLGGK8kBaAx9snQVkL0RUREWIQYfjgFBwfL4MGDZcSIEXesDUIR3sKDBw8K+ks6Onbs2CGtWrVSySDoF8zhPgL4gT9tbZR8ufmUekizisVka1S8KlPSMryE5JB/pVKpQpaED8QEDlu4TxUHdnenCsT8vZ/BA4jjYC0m0JoKjnLH/3pYJY28eN+tmMEz8dflzRWR0qVe2XQxg/D6wfuHgWPfusFFLcfB1csUlpfbVtSN58t9O39r5t8PnJeVBy6ke0ytoMLSI6KcS7ys7rbfjPOzP7AZd9W1a6IAdC1PzpaBALx5fn5+snDhQtWhQxt9+vQRHPMuW7bsDmY45kUrN9yH75cqVUoldLz22muq0rw9A8e//fv3l759+9pzG691gEBGEYgpWlcpIeM6VVOZoLly5EgniBDAnzunc4WBszNz5roombf9jBTKn1sd+0aE+suoZQelQJ6cghIg8NRZB6ojyeDtnyMl7d9/pVlYcfm/umXlrZ8i5XLSDalQwk+tRStmnJCUqpJJ4IHTMn4REzjlz2OCpALrdmjZ2enO719NTpVCVkWOcQyalJomBfO6tvAx1n3qUpJlKe93rkbx586NzWZuzQuID9G5c7t2r21ZFkvC2ELJu9dQAHqXv+mfHh0drY5wN23aJE2aNLGsd/jw4bJ27VrZsmXLHQyqVq0qUVFR0rNnT9XSDR48/PfFF1+UsWPH2sUM3kQkhKxbt86u+3ixYwRw7Pvi/L2WmyGwnmxYzrHJnLwLPWmfnrtTtfyKCPGX6d1ryYx1UbLwn7Ny5foNGdOhiiB+LuPYGnVJlXeBCNRGaHE/GdMhXIrkT++RxnFxvtw504lIfM3VMYCOooD3E6VTnmkWIhVK+qnkESTiwAP7QsvQdMIws2dA1F9ITEnXJg1dOK6npEnRArdZoNajduyrzYPjYMQEcniPgB76A3tLgHqPunGeTAFonL0ypKWOCMDw8HBJSkqS48ePWzx+H374oToWPnv2rF0cUBAaySCrVq2yK37QrofwYkVAi/nDsa/10GICvYHpmy2n5Ku/TqkgfxQOxoA3EoIIR7pZjSU7z8q3205bvv3Zk3XE30rweGMt9j4T4m3WhhOqewZEKrySm45dkp2nLyseTzcrr8rXZDVw/28HYmXd4YvSu1GwKu0C8fflppMSd+2GPH9vqPj75RFr8Ydj35KF8skfkbeOgykC7d01117v7f7A8AIiG5n9gV27r66ajQLQVSQ5T6YEHDkCbtmypYr9g2jTBrJ5cTScnJysfpjYM/r166fm++ijj+y5jdfaQeDy9Rvy8IytkpicZjn2Rc06LSbw+371JPwuYsOOR9l9KbpHfLv1tph79f5Kcn+1rEuSIOZvzPJbx77awHGwFhNotwFevAH175AtfDg20WIFxF+fxuWyrZ2HTGN49fZGJ0ieXDmkR8StxJ4jsdeUoISADC3hpzK8IbKrlS5kifnTYgI71QqU5pXoBfTWK6AdA3urPRuTQby187Y9lwLQNk68ygkCSAJByReUfsHAD6Xy5cvLoEGDMk0CQebvd999J8eOHVOfHjE+/vhj+eCDDwQeRXvHX3/9Je3atVNHyQULFrT3dl5vIwFkg/6y77y81bGKipPTYgJLFMwr3SO8kwkKEYP6gyihog300s3K+2ct/nDs+1CNAPl0/QlLTKARRSCOpEf9eNCyfiTAwDNny0Cc5rdbzygRqA1r8ad97fSl61KmaP50MX8n4q5JSHE/Wx7Da9xIwJslYfAzADG29AC6cYOdmJoC0Al4vNU2AigDg6QP9OaFEEQZmPnz56ss38DAQFUiBnGC48ePVxOeOnVKqlevru5BpvDhw4cFXjzEAKI2oL0DP4RQPgY9gnv16mXv7bzeoASsxR+OfQML55OYK8lqNVmJwJgrSTJ2RaQUzpfbEvOnxQSi1Ri6h8CDZpShxfzh2NdawGkxgbasA5nR8Ihqo23Vkk51ULHlmbzGdQS8XRIGpy/aB3nXrYozuYIABaArKHKObAmgFp9WCLpOnToyZcoUVRMQA6VaQkND5auvvrLMs3nzZhk6dKjs3LlTiUNk8jqSBaxNCO8jvIqrV6/O1lZeYA4CSAIZOG+PwIv1Sttbx76ICcSxZpECueXzJ+ukS2TQVn3uSrIUyJszXcIH2r4hicJI4g8ffNBzWYv5wxHuluOX1HEwvHgQwTjCvdvQYv5w7KsNHAdrMYHmeFPMvwqUhIEnztNiDMLPmVJe5t8Z766QAtC7/Pl0DxG4dOmSlC1bVtavXy/33HOPh57Kx3ibwPYT8RJ//Ua6NmQ/7DijsoLDSpo7HAAC8Nf9sSohQ4v502ICz19NlhdaVJCShW7H0+KoGAWvixe89TV4UCevPipnLidLgTy5pG+TYNlwNM4SE9ivaXmpVMrcDL39/rrq+ZoXEEex9pbScsYGHv06Q8/991IAup8xn6ATAjhqRkFoeCI5SMAXCEAExl5NSddzGSIwMSVVivmlF3+zN54UJPPgmBtxmygVg+PwS9duyPhOVaVSQCHlTUVMIErrPHtvSLZlZHyBsRHWCAGI9mzwAnqqPzCEpjfqDxphP/RiIwWgXnaCdridALx/nTp1Uskg+fPnd/vz+AASMAoBtPKbue6ExF5NVsfij9YuI/P/jpZrKanKS4jkF3gBMSACk1NvuryQtFFYGdVOTyeD0Pun/zeFAlD/e0QLXUQA3hAc/7766qvSrVs3F83KaUjAHASsRaC2oiD/AipWMLM+yOZYte+swpPJIPT+GeO9ogA0xj7RShcRQEHpJUuWyG+//eaiGTmNWQggYxa9f61HZl8zy3ozWwdq+s3eeKu/McZL94VJuWIFzLxkn1qbJ/oDa8km1i0WfQqygRZLAWigzaKpzhO4cOGClCtXTlAbEB1HOEgABNAv943lB1V9vDZVbhWJRl/fCb8fkZEPVs42W9YMFBHzN3P9CXXsqw0cB2sxgWZYo6+vAV5AiEAcz7orPg/zejLRxNf31Jn1UwA6Q4/3GpJA9+7dJSAgwFJ30JCLoNEuJbDw72j5YvNJQcO4YW3DVAHjUcsOSEJyqjQKLSZjO1Rx6fO8ORm8msjwRbFubcRcTpIZ/4k/HPt2bxAk6KCixQTCE1g4f25vms1nu4iAO/sD0/vnok3y0DQUgB4Czcfoh8Cff/4pTzzxhBw6dMhjGXH6WT0tyYwABNH0tVHy877bHUNwXdXAwvLOw1WkYD5ziB+Iv++3n5HrKWnyVJNgJQIR+zdjXZQkJKWphA8t5k+LCaxYyk8eq1NGZZBy2E8A5XVQgNy65iJqTebNnSNdJrb9Mzt2h9Yf2B0lYTxdZ9AxArxLI0AByHfB5wggGQTHv2+88YZ06dLF59bPBWdOACLw5UX7JPLcVcsFC55uYBrxh0XhmHfqmuOCUjBVAgtJl3plZPaGk3I+IVl5+AbfV0GKW5WHQV3AAnlyUvw5+I8mOTVNftgeLecSkqVTrdKqHzbE37ztZ/7rr1xO/P3yODi7Y7e5qz8wij7j+JcfFBzbF2/cRQHoDep8ptcJoK8wEkFWrFjhdVsEQzKaAAAgAElEQVRogD4IIOZv5LIDKh5QGy+3rWiJCdSHlc5bgXV+semkEoHa0GL9rAtDO/8kzoAPFT/tPSf7z14V5Bc1rlBM/j55WZJSb0rZovnlifplJF/uW+V1PDncURKG3j9P7qBrnkUB6BqOnMVgBM6dOyfly5eXHTt2SFhYmMGsp7muJgBRpMX8hQcUUr+c1xy+YIkJ1BJDXP1cb82389Rlmbv1tOXxr9xfUUoXYW1Md+yHtQjU5vem+IMNri4Jw5Zv7nhz3D8nBaD7GfMJOiXw+OOPS0hIiLz99ts6tZBmeYrAusMXZcLKIwLxh5i/AnlzWWICcXT37L2hnjLF7c/RYvtw7KsNHAdrMYFuN8AHH4Cj96+33Bbc7asHSM2gIl4l4cqSMCz67NWtdPjhFIAOo+ONRifw+++/C9rDRUZG+kzD8k3H4mTT0TjB0aYWqzNrfZQKUG93T4DRt9Qp+9E3uFrpQpaYP3huNh6Nk+YVi5smrsla/OHYt909pWTprhhLTCBFoFOvUKY3azF/OPbVBo6DtZhA1z/Rthld1R+YRZ9t463HqygA9bgrtMkjBPADsGLFijJu3DjVIs5bA0kpGQOnM/uas/ahf+vDM7ZI0o2b8kT9sjLigUoya/0JmbXhhOTMkUMWDWjg9Xp3GQsv+1ohZmf3OLv7Vcbv2ihJSftX1fdDzJ8WExhSvID0bVpe8lqVh8luPn7/7gSQATx7wwm5duNWzN/j9crIyoOxlpjAPo2D0/Vp9iRPTQDi+NaZ/sD0/nly11z7LApA1/LkbAYj8O6778q6detk6dKlXrEcv3w/33hSXn+wkhTJfysb8Jd95wUdGZCRCWHmyvHj7hh5c0Wk/Jth0mFtwqRXo2BXPsruuaavPS77Y67Kh12qKxGCDMrBP+yVJmHFpG+T8nbPxxsyJwARmJL6rxJ/2jh96boEFMlH8eeGl2bX6cuyJzpBiT8kfGgxgeit3KZKSa96l51NBqH3zw0vjAenpAD0IGw+Sn8EoqOjJTQ0VHbt2qXiAT054N0aOG+3nLqUJBVK+sm4TlVl49FL8sma48qM4fdXlJbhJV1uEkTg2BWRlnmHtg6T3o29K/5OXbouD0/fKilpN6V1lZLyfudqMmzhPtlwNE7wi/LngY0ksEg+l7PghCTgCQIQfdYf5vB3fLTzdskUZ5JBWPTZE2+Oe59BAehevpzdAAQ6d+4s1apVU3UBPT1Oxl2X15ful/jrt0uPwIZOtUvLM83Ku+UXxMx1UerYVxvacbC3fxkh3m7gvD1KBGoDNehm9agtDUL8Pb01fB4J+AQBR5NB2PLN+K8HBaDx99AQK0BMG4a3RUZmsH766ScZMGCAHDhwwG39Me+2SRCBz3+/23IJPGA4knUHK2vxVzmgoBw5n6iOg/UiAv+IvCCDfthjYfHZk7WlaVhxQ7zjNJIEjEjAkf7A9P4ZcafvtJkC0Bz7aJhVHD9+XCpUqKAre9PS0tQx8EcffSTt27f3uG2I+dOOffFw7ThYiwl0lUFIAukye5tcvp6qBCZi/rSYwPx5csn3/eupHrjeGlrMH459tQExrMUEessuPpcEzE7A3v7A9P6Z442gADTHPupyFdZev8OHD8v06dNl7ty5cuzYMSlcuLCubB47dqxs27ZNFi5c6FG7rMVfvfJF5VhsojoOdpcIRJuzf05dlm4NgizrXL4nRsr5F5C6wUU9unbrh1mLPxz79m4ULF9uPmWJCXSFCMyYWe2OTGuvAeSDScAJAvgQjD+29Aem988J0Dq7lQJQZxtiVHNwjIByApn9Ut27d6+89957cubMGenYsaM8++yzUrSo98RGZoxPnjwplSpVkn379klQ0G1x5M79QBIIWo/tjU6wxPwhIQQxgVeSU+XNDlWkfnnfiH1DUeJeX/0t8FJqMX9aTGC5Yvnl6z51pXjB21mr9u4L2nFtOxEvox+qLLlz5hSw1wo//1/dMvZOx+tJwFQE7OkPzJZv5tl6CkDz7KVXV/LOO+9Ijx49VF09baDdGnrufvzxxxIREaFEYJs2bbxq590ejuPf+vXry+uvv+4xG6/fSJNVB2OlY41AS8wfYgKjLyepvqG+NLDmmMtJUs9K9G6NuiQVSvhJqcKOZwDHJiRL1893yI3/MoxHPxQuE1celV/2nZNcOXPIt33rS5A/26D50rvGtd5JwJaSMGz5Zq43hwLQXPvp8dVonr9HHnlEzp8/r8ReyZIlJSYmRiZOnCiILXnqqafkiSee8Lht9j5w2bJlMnjwYOUFRH0rDvMQgDdx9I8HJfWmdTeGHDKmQ7iYrc+veXaNK/EkAVtKwtD758kdcf+zKADdz9jUT9AE4KlTp5T4mzNnjpQvX17g/UONvVq1aknr1q3lwQcfFHzCbNasmTom9vfX39Em7IPtiFV84IEHTL1vZlwc3qtdp69IHatYxsSUVDlzKUnCAwvJ+iMX1ZG7Nsa0ryL3VytlRhRcEwk4ROBuJWHo/XMIqa5vogDU9fYYz7i4uDhBpi/66yYkJMjmzZuV4FuzZo0UL15czp49K2XKlFFeQiRc6C0WcNSoUYKYxe+//9548H3YYrxjk/84Jt9vPyOv3l9JHq9XViD+Xpq/V47GXpOPH68uK/aeV8e+2kCG8Rvtw1VMIAcJkIDI3foDs+Wb+d4QCkDz7anuVgTPGsoGIAkER8NXrlyR+fPny/Dhw3VXEgbitUqVKqomYLFixeSff/6RJk2a6I4pDUpPAALwk7VR8s2WU+obA1tWUB6/3WeuSKF8uaVecFHZEnVJdWOA12/1wQvqOJgikG8SCaQngLCdjP2B2fLNnG8JBaA599Xrq9KygfVcADorSBB8sPvQoUPqSHj9+vWMCfT6G5W9ARlFIO6A+HvvkaoycdVRuXg1xRLzp8UEFimQW2b1qCWlizAJJHvCvMIXCOADOzyB+fLlU0IQg94/c+48BaA591X3q9JbDbbExETllZw9e7Zs375d/fBbtGiRill0R0cO3W+QQQ3Ese99H22yWP9wzdLqmPdMfJIcjU2UFpVLWL636ViclC2aX0JLeLb4NfrA4o/10TMylPPk4lG0QV87U5mdMRmERZ9Ntb3pFkMBaN695cpsIAAhOnToUPnyyy8lJCREnnnmGZWxXLNmTfniiy9UAguHMQhoMX849rUeWkygHlYB4ffZxpOSnHpTnm8RokTglaQbMuH3o9Kycgkmpehhk2iDStjDz0Z8EMYffgg250tBAWjOfeWq7CAwY8YMqVu3rjRq1Mjygw7xiehY8vXXX9sxEy/1FgH8snph3h7ZcTJeHftO61ZTVh28YIkJfPeRqvJAtQBvmWd5btTFa/LOz4ck9ea/0iDEX3o1LCcTVx2R05eSpJhfHhnXqZr45WUJIq9vlI8bAC/gnj17xM/PT2rXru3jNMy7fApA8+4tV+YEAbSuq1GjhooDLFWKpUKcQOmxW5H08d6vh+WjLtWlWunCyoOBxBAUk/6ka00pWiCPx2y524N2nb4sU/48rkSgNvwL5JER7SpJmaKMRdTFJvmoEdeuXVOhL1999ZXs3LlTJkyYoGqjcpiTAAWgOfeVq3IBARz/tm3bVoYMGeKC2cw1xbWUNDkRd00JLW3Au1Ukf26nWrY5SwmdVQrkue1BgwhMSr2Z7mvOPsMV90Os4ihYG+M6VZUg/wKumJpzkIBdBPBvZP/+/SrkZd68eVK6dGl5+umnpU+fPqpcF4d5CVAAmndvuTInCaAW4NixY1UpGMbA3IYJ8Tdw3m7Vw3h2z9qq8DLEX585/4i/Xx75slcdr4pAJ7dd0Je4ZKG8qmSMNmKuJLksUxgxfx/8fuvYVxs4DtZiAp21n/eTgC0EMnr7HnvsMRkwYIDce++9luxfW+bhNcYlQAFo3L2j5W4mkJSUJOXKlZNvv/1W/VDkuEVAE4BbouKlYN5cMuqhyvLR6mMSezVFKgcUNLQAhJAdvmS/6sM8pHWYEoGrI2Plf6uOyrA2FaVtVefCAazFH459H6oRIAt2RFtiAikC+a/MnQTo7XMnXePNTQFovD2jxR4kgAxhFK/+/PPPPfhU/T/KWgRq1hpd/GEd6w5flPG/HRaE57W7p5TUCioik1YdlX//FelYM1AGt6rg1ObEXk2W8b8ekbSb/1pi/rSYwEoBBWVYmzDJl5tJIE5B9uGb4dVDS86XX35Z1e7TBr6+ePFiVe0AsX309vnwS2K1dApAvgckcBcC6AiCDOEjR46oVnYctwkciEmQ//t0u+ULSLRAZw2jjzWHLqgjWqscDelQI0AGtaqQ7ljY0XVCBKam/Zsu4ePQuasSUqIAxZ+jUHmfIoDsXWTtvvnmm0rkWcf2BQYGqjJXjO3jy6IRoADku0AC2RDA8e8jjzwiAwcOJKv/CGgxfzj21QaOg7WYQKODGrP8oOCIWxs/D2wkuXLejgk0+vpov3kJTJo0SebOnSslSpSwePsg/Fq0aMHYPvNuu0MrowB0CBtv8iaBadOmycSJE9XRLD7tTp06VRo2bJitSchw6969u3Tq1EmWLl2a7fXaBd98842MHz9etm3bxmQQEYlLTJFHZ22zxPxN71ZLRv94QAkmiMD5TzeQCiU9213D5s204ULE/E1ceevYVxs4DtZiAm2YgpeQgEcJILYPpxXI5EXMckJCggwbNkxGjBjBTF6P7oSxHkYBaKz98nlrf/jhB+ndu7fMnDlTFW6ePHmyLFiwQCIjIyUgIOtCv1FRUdK8eXMJCwtTR7n2CMDr169L2bJlVX2sxo0b+/we4JfNx38elz8PXbAkfGgxgYFF8sl7j1QzrLds7aELMv73I0r84di3RtkiMnHlrePg9tUD5KXWYT6//wSgHwJabB/q9qFawaOPPqoyeefMmSP58+cXFLnnIIGsCFAA8t0wFAGIvoiICPnkk0+U3Yh5CQ4OVsVK8Wk3s5GWlqaOP/r16yfr16+X+Ph4uwQg5hw0aJBcvnxZZs2aZShe7jIWIvDajTQpmDe35RFJN9JUP1sjH5WituFrSw5I07Bilpg/xAQiEQRZwLbEOJ66dF1+2Xde+jUNtvT73XDkolxJSpX2NQLdtSWc10cIWHv7UKoqs9i+HTt2SMuWLSU6OlqKFCniI2S4THsJUADaS4zXe41ASkqKak20cOFC6dy5s8UOBDVD1C1btixT21DLb/fu3bJkyRJ56qmnHBKAaIsE8YlkEH9/f68x4IPdTwBJGiUKpq8DGJuQLKUK58v24SlpN2XI/L1yMTFFGoYWk5daV5C/jl1SHUnwi3vUQ+Eqs5iDBOwlkJW3L6vYPvy86tWrl/rwykECmRGgAOR7YRgC+DQbFBQkmzZtkiZNmljsRt/etWvXypYtW+5Yy4YNG6Rbt24qGBpV7R0VgJgYx79du3aVZ5991jDMaKjnCfxz6rJMWnkkXas3WHFflZIyoHmISzKJPb8qPtEbBDLz9qFLB36OZdelA33MP/jgA9m7dy9jl72xeQZ4JgWgATaJJt4iYK8ARCB0rVq1ZPr06fLQQw+pOZwRgAiwRszh5s2b+QOVL+VdCUAEvv/bYcs1LSqXkOdbhFL88b2xiYC93r7MJkUhe8Q+V61a1aZn8iLfI0AB6Ht7btgV23sEDK8favjlynW7sC5iBjFy5sypEkcqVqxoM4/ExEQpU6aMrFixQho0aGDzfbzQ9wgg5m/qmuOWhUeE+MuQNmGWmEDfI8IVZ0fAGW9fdnPz+ySQGQEKQL4XhiKAuBaUfEHpFwwIuvLly6s4l4xJIPgEjJg96zF69GhVIgHV8sPDw9NVy7cFxHPPPSfJycmCUjQcJJAZAYg/LeavmF8elfyBzh9aTGDunDkJjgQsBODtQ3wyunT8/fffli4drNvHl8TdBCgA3U2Y87uUAMrAIOkD2bgQgjiSnT9/vhw8eFBlw6FEDOIEUbcvs+HMETDmww9o/GA+evSoFC5c2KVr42TGJ4AkkFcW7ZNzV5ItMX+7Tl+xxASOaV9Fqpfle2P8nXZuBZq3D6Lvu+++UyWstC4dpUo512/aOct4ty8RoAD0pd02yVpRAkYrBF2nTh2ZMmWKytDFaNWqlYSGhgrqYrlDAGJOHP9CSKKsDAcJZCSAjOHVkRfkifplLTF/iAlMSEoVxAJy+C4Bevt8d+/1uHIKQD3uCm3SNQF4Hz/99FNVU5CDBEiABO5GgN4+vh96JUABqNedoV26JXDlyhXVGeT3338XeCA5SIAESCAjAXQQWrx4sTqNQGFmdOlACSnG9vFd0QsBCkC97ATtMBSB/v37q+xixCBykAAJkAAIZObt0+r2MbaP74jeCFAA6m1HaI8hCGzdulXatm2rkkEKFixoCJtpJAmQgHsI0NvnHq6c1b0EKADdy5ezm5QAPunj+Pf5559XmcccJEACvkXA2tuHnrzw8NHb51vvgNFXSwFo9B2k/V4jgGzkb775Rv7880+v2cAHkwAJeJYAvH1a3T4ttm/AgAHSsmVLVWCegwSMQoAC0Cg7RTt1RyA+Pl4lg6xZs0Zq1KihO/toEAmQgH0EUlNTVV/xNm3apLuR3j77OPJqYxCgADTGPtFKnRJAUWrEAE6aNEmnFtIsEiABWwmgS1ClSpXk559/lvr164vm7UMm7/bt21UmL719ttLkdXonQAGo9x2ifbomsHHjRunYsaNqOVegQAFd20rjSIAEsifw0ksvyblz51SLSXTpKFmypOrSgeLvzOTNnh+vMA4BCkDj7BUt1SEBHA1Vr15dhg0bJj169NChhTSJBEjAFgKatw99vnfu3CmdO3eWF198kbF9tsDjNYYkQAFoyG2j0XoigFqACxcuVIWhOUiABIxDAB/g0Edc68mrefsWLVokXbt2laFDhxpnMbSUBOwkQAFoJzBeTgIZCVy8eFGCgoJk8+bNUqVKFQIiARLQOYGMsX3w9qFLh5bJi7IuY8eOVeLQKJm969atUz3SkZl89uxZlamMdd1tIIENpxf79u2T4OBgGT16tDrq5vANAhSAvrHPXKWbCfTs2VNKlCgh77//vpufxOlJgAQcIZCZt0+r2xcQEJBuyuTkZBUDOHfuXLn//vsdeZzH7/nll18EMclIXnnssceyFYDHjx9X1Quee+45Vb9w9erVMmTIEPnpp5+kXbt2HrefD/Q8AQpAzzPnE01IAJ+ku3TpIocPH5Z8+fKZcIVcEgkYk0Bm3j5k8rZq1equ3r2RI0cqDyD6+Rpt5MiRI1sB+Nprrymxt3fvXsvyunXrJihv9euvvxptybTXAQIUgA5A4y0kkJEAvAs4/h01apQ8/vjjBEQCJOBFApq3D+VbkMkL73xW3r6szDx58qSsX79e4N032rBFALZo0ULq1auXrp85YiHhBbx8+bLRlkx7HSBAAegANN5CApkRQPwN6ofhUzUHCZCA5wnA27d06VKV1IG6fYiBs8Xb53lL3ftEWwRgeHi49O3bV15//XWLMfj51aFDB7l27RrLWrl3i3QxOwWgLraBRpiBwPnz51UgNX7xVKxY0QxL4hpIQPcEXOHt0/0i7TSQAtBOYD56OQWgj248l+0eAk888YQSge+88457HsBZSYAEFAHN24dj3m3btvmsty+z18EWAcgjYP5DogDkO0ACLiSwcuVKefLJJyUyMlLy5s3rwpk5FQmQAL19tr0DtghAJIHgyHfPnj2WSVHMPi4ujkkgtmE2/FUUgIbfQi5ATwRu3rypeom+++672dbg0pPdtIUE9Ewgo7evU6dOqm5fdpm8el6Tq227evWqakmJUbduXfnwww/lvvvuk+LFi6uSNoj1O3PmjHz99dfqGq0MzMCBA6Vfv37yxx9/qM4nLAPj6p3R73wUgPrdG1pmUALvvfeeoCzMsmXLDLoCmk0C3icAbx886VqXDmTy9u/fXyUuZKzb531rvW8BfuZA8GUcffr0ERyTo8BzVFSU+tmkDfw/up3s379fypUrJ2+88QYLQXt/Kz1mAQWgx1DzQb5CAFX4Q0JCZNeuXeq/3h4paTflh+3R0rVBWcmbK6cy5+zlJNkaFS+dapf2tnl8PgmkI5CUlGTJ5EVsH719fEFIwD0EKADdw5Wz+jiBRx99VNUFHDNmjNdJjF1xUNYevihNKhSXtx6uIhevpsjQhfsk5kqSDGtTUR6pRRHo9U3ycQPo7fPxF4DL9woBCkCvYOdDzU4AbZlQePbAgQOSO3dury53x8l4eX3pAYEnsEIJP7mclCpxiSlSzr+AfPR4dSlViJ1LvLpBPvzwzLx9qNuHo0yj9OD14e3j0g1OgALQ4BtI8/VJIC0tTcLCwmTSpEmqsKq3B0Tgy4v2WcwILJxPPulWk+LP2xvjg8+nt88HN51L1iUBCkBdbguNMgOBt956S/766y9ZtGiR15eDmL/uX+yw2BEeUEgJQC0m0OsG0gDTEEhMTJSCBQvesR7N24eEhK1bt6rYPnr7TLPtXIgBCVAAGnDTaLIxCJw+fVp5AdFsPX/+/EoMduzY0ePGQ/xpMX/WD9diAikCPb4lpn3gP//8I126dFGhD6iDmZW3DxmpgYGBpuXAhZGAEQhQABphl2ijIQngl1+TJk0kISFBjh49Ks2aNVPZjbly5fLoel5ZtE+2n4y3xPydjLtuiQl87t5Q6dYgyKP28GHmJYA6mLVr15ZRo0apRWrevkceeUTV7WNsn3n3niszHgEKQOPtGS3WOQH0BJ4zZ47Mnj1bzp07J/iluHbtWqlatapXLI9NSJZJq47KK/dXtMT8ISbw1/3n5bUHKknunLdKw3CQgDME8IHn0KFDMmjQIHXEW6FCBVW3j94+Z6jyXhJwHwEKQPex5cw+RgC/AHv16iULFiyQ5s2bq/gmHPmGh4fL1KlT5cEHH/QxIlyuLxCwju3bsmWLes+RBY/j4OrVq/sCAq6RBAxJgALQkNtGo/VK4Ntvv5WGDRtK5cqVLSaOHj1adu/eLfPmzdOr2bSLBOwioHn7vvjiC/nuu+9UuzGtSwdi+/BBCJ07Jk+ebNe8erh42rRpMnHiRImJiVHH2fjwhn/TWQ2sccaMGXLy5EkpWbKkioEcP368ivvlIAE9E6AA1PPu0DZTEED7JXgBERhfpkwZU6yJi/BNAvD2ocUh2rPB26dl8rZu3Tpd3b6NGzcq7zd6z/r5+RkG1g8//CC9e/eWmTNnSqNGjZSAhUcfLekyaz8H8Ys+uhDCTZs2VUfgOPLu1q2b6sXLQQJ6JkABqOfdoW2mIdCuXTv1C+LVV181zZq4EN8goHn7tJ68xYoVS+fty4wC7oH3bMiQIUogGWVA9EVERMgnn3yiTEb8bnBwsAwePFhGjBhxxzIQ74gPdqtXr7Z87+WXX1bieMOGDUZZNu30UQIUgD668Vy2ZwmgFuArr7wie/bsYYcDz6Ln0xwkYKu3L6vpkQgVFxcnQ4cOddACz96WkpKivJULFy6Uzp07Wx7ep08fiY+PV57PjAMewBdeeEF+//13dUx87NgxVfgdR+AjR4707AL4NBKwkwAFoJ3AeDkJOELgxo0bypOAzOA2bdo4MgXvIQG3E7ibtw9HoDly5HC7Dd56QHR0tAQFBcmmTZtU+SZtDB8+XGXxw6uX2ZgyZYr6cAd2qamp8txzz6mYQA4S0DsBCkC97xDtMw2B1157TY4cOSLffPONadbEhZiDgLW3DyVcULcPWewZY/vMsdrMV+GIAFyzZo2K93v33XdVzCD+fb/00kvyzDPPyBtvvGFmXFybCQhQAJpgE7kEYxDALweUxcgqoNwYq6CVZiHgy96+zPbQkSPge++9Vxo3bqyyhrUxd+5cJZ6vXr3KcA+z/GMx6TooAE26sVyWPgng+BdeFaPERemTIq1yhkDG2D6tS4cvefuy4gcvHmL5UPoFA0kg5cuXV8WtM0sCqV+/vrRt21Y++OADy5Tff/+9SpJBByBPd/1x5r3gvb5HgALQ9/acK7YiYE/NL8Tvff3116q3LwZ++I8bN+6uNcIywkaZCdQF3Llzp6njqfiS6YsAvH2HDx9W5UqQuODv758uk9fMsX327AT+fSLpY9asWerfNcrAzJ8/Xw4ePKh6F6NEDOIEUecP480331TlXj799FPLEfDzzz+vfjZgLg4S0DMBCkA97w5tcysBe2t+9ezZU/XzRTkXFHnFp/4lS5bIvn371C8FW0ZycrK6FsdELVq0sOUWXkMCDhOAt+/HH39Uwg9JDPT2ZY8SJWC0QtB16tQRJHnAM4jRqlUrCQ0NVT2OMZD08d5776m4XtQ8LFWqlDz88MPqaxDZHCSgZwIUgHreHdrmVgL21vzKaExaWpqgJhp+YcAzYOtAnbDTp0+rYrocJOBqAvT2uZoo5yMBcxKgADTnvnJV2RBwJOA745SI8UFpDHQKQNcDWweOk+BZwJEc2mVxkIArCGjePnyw+Ouvv9Jl8jIWzRWEOQcJmIsABaC59pOrsZGAIyUfMk6NArC//fabOgK2t+9ny5YtpX379qrDAAcJOEpA8/ZB9KEPtRbbh3ZkpUuXZpypo2B5Hwn4AAEKQB/YZC7xTgLOCsD3339fJkyYIKgDVqtWLbsRIwYQcULbt2/nL2m76fEGevv4DpAACThLgALQWYK835AEnDkCnjRpkir8umrVKmnQoIFD679+/bpKBkGGIZJKOEggOwL09mVHiN8nARKwhwAFoD20eK2pCNhb8wuLh9cPnjsc/aIArDPjxRdfVL1SUULClSMxOVUOxFyVBiG3sxCPXUiU3DlzSvniBVz5KM7lYgKIJ42NjVX9ZbWBzHH0odVi+5Bl+uyzz6p6koztc/EGcDoS8CECFIA+tNlcanoC9tb8QtmXMWPGqDpqKAejjUKFCgn+2DtQTxC1xpAMgmxiV4zrN9LktSX7JfJcooxsV0lahpcUiL9XF++X3DlzyIddqkuQP0WgK1i7Y45ff/1VFSe/g70AABQuSURBVB0+cOCAHD9+XIk+vG9FixZVdfsY2+cO6pyTBHyTAAWgb+47V/0fAXtqfqH+14kTJ+5gN3bsWFUQ1pGB498uXbqoBvKuGGk3/5UPVx+V3w/ESs4cOaRbg7KyYs85uZKUKuEBBeWDR++RQvlyu+JRnMMNBK5duyZVq1ZVRYfROpDePjdA5pQkQAKKAAUgXwQS8CIBeHjQSQBlO1zVjcFaBGpLo/jz4iZn82jE9kHsaZm8qC9ZsmRJWbt2LTN59btttIwEDE+AAtDwW8gFGJlAYmKilC1bVnVriIiIcNlSjsQmyvPf77bMN6R1mHSoEeiy+TmR8wQQ26d16cAHAHj7BgwYIPfcc49UrFhRHQOHhYU5/yAPz2BPe0WYFh8fL6NGjZLFixermNiQkBDVgg1lkjhIgATcR4AC0H1sOTMJ2EQAvUORFTx9+nSbrs/uIi3mD8e+2sBxsBYTmN39/L77CGT09hUpUsTSk9e6bh/CAipXrmzpOes+i1w7s73tFZGNj3haFFQfOXKkyoxHmAXqGdauXdu1xnE2EiCBdAQoAPlCkICXCezcuVP9Ejx69KhAEDgzElNS5ak5OyX++g0V8ze+czWZtf6EJSZw6hM1JDzQ/oQVZ2zivSKatw/HvJs3b7Z4+9q0aZNpJi9KDPXo0UO1DMybN69hENrbXnHmzJmq7y664+TJk8cw66ShJGAGAhSAZthFrsHwBHD8i37CyPR0dvy6/7z8tOecEn9I+NBiAvPlzimDWlVQySEc7idgq7cvM0tu3rwpVapUkY8//tgwR6GO1NbEMW/x4sXFz89PlbopVaqUEr6vvfYaS9y4/xXlE3ycAAWgj78AXL4+CMyePVtmzJghGzZscIlBEH25ct4Wevg7dB/Fn0vw3nUSe719WU128eJFQ/WKdqS7DjKeo6KipGfPnqr2IZJh8F/UyER2PQcJkID7CFAAuo8tZyYBmwkkJCRImTJlVIHpunXr2nwfL9QHAWe8ffpYgfNWOCIAw8PDBW3tUPNQK2qNrHgcC589e9Z5ozgDCZBAlgQoAPlykIBOCDzzzDMCITFlyhSdWEQzsiMAb9/y5cvliy++ULF9HTt2VF06sorty24+I3/fkSPgli1bqtg/xDxq45dfflHH3mBrpPhHI+8dbfdNAhSAvrnvXLUOCWzbtk0JBxyDOdJZRIdLMqVJmXn7+vXrJ3379lVeXFfVczQiPHvbKyLzF51Ojh07Jjlz5lRLRtwjuu7Ao8hBAiTgPgIUgO5jy5lJwC4CEBb16tVTteD69Olj17282P0E6O3LnrG97RVPnTol1atXV+/74MGDVVtEiGnEAKI2IAcJkID7CFAAuo8tZyYBuwmgFuBXX30la9assfte3uB6Apq3D3vy7bffKs8sMrXp7cuatT3tFTELjs6HDh0qKIeEOoDgyyxg17/LnJEEMhKgAOQ7QQI6InD58mV1jPjnn39KzZo1dWSZb5mieftQt2/Tpk0qtg+e2bZt27I8iW+9ClwtCZiWAAWgabeWCzMqAXiX8ufPL//73/+MugRD2k1vnyG3jUaTAAk4SIAC0EFwvI0E3EUAHqcOHTqoeCgUyOVwLwF6+9zLl7OTAAnokwAFoD73hVb5MAF4onD8+9JLL6kCuRyuJwDGaL2HI17G9rmeL2ckARLQPwEKQP3vES30QQIohTF//nxZuXKlD67efUvO6O2DpxWxfffffz9j+9yHnTOTAAnokAAFoA43hSaRQFxcnMqIRGu4atWqEYgNBNA/F51UWrduLfny5bPckZm3D6VG8MfX6/bZgJWXkAAJmJQABaBJN5bLMj6BJ598UooVK6aK4nJkTwACsE6dOjJmzBjp0qWL6iSxYsUK1aVDi6tEt5UHHniA3r7scfIKEiABkxOgADT5BnN5xiWwbt06efTRR1UyCLKCObIn8NFHH8myZcukefPmMnfuXFW3j96+7LnxChIgAd8jQAHoe3vOFRuEAI4ucfw7YsQIeeKJJwxitXfM1Lx9M2fOVN4+1OsbNmwYvX3e2Q4+lQRIwAAEKAANsEk00XcJoBbg8uXL5eeff/ZdCFmsXIvtQ5cOa28feipXqlTJsHUUp02bJhMnTpSYmBipXbu2TJ06VRo2bJjt/s+bN0+6d+8unTp1kqVLl2Z7PS8gARLwbQIUgL69/1y9zgnExsZKuXLlRBM1OjfXI+alpKQoUYwSLhs3blQ1E61j+9BF5fHHH5czZ86kSwbxiHFOPgS9dHv37i3wZDZq1EgmT54sCxYskMjISAkICMhy9qioKHXsHRYWJsWLF6cAdHIfeDsJ+AIBCkBf2GWu0dAEunXrJmXLlpV3333X0Otwxnhrbx/q9hUsWFDF9qFrCtjkyJHDMj2urVq1qrz55pvKI2akAdEXEREh6KeLgcSW4OBgGTx4sAoFyGykpaVJixYtFI/169dLfHw8BaCRNp22koCXCFAAegk8H0sCthJYvXq1EjKHDh2SvHnz2nqbKa7L6O1r3769qtuXXSavdnS+Zs0aw3DAWtH5ZeHChdK5c2eL3X369FGiDsktmY2xY8fK7t27ZcmSJfLUU09RABpmx2koCXiXAAWgd/nz6SSQLQF4gSpXrixvv/22ygo2+4AH79ixY5YuHRBFWiZvRm9fViwuXLigjk6fe+65dN5BPbOLjo5WtR+RxNKkSROLqcOHD5e1a9fKli1b7jAfdSLhId65c6eULFmSAlDPG0zbSEBnBCgAdbYhNIcEMiMwfvx4gSfwxx9/NC0geMC0un2I7bPV22cWIPYKwISEBKlVq5ZMnz5dHnroIYWBHkCzvA1cBwm4nwAFoPsZ8wkk4DQBZISGhITIP//8I6GhoXLixAn1d6MPV3j7jM5As9/eI2B4/erWrZuuqDW8xRg5c+ZUiSMVK1Z0GR7sFWIt4XVEKIItmckuezgnIgEScDkBCkCXI+WEJOAeAijvAZFw8eJFOXDggIoJLFq0qHse5uZZNW8fMnkhKHzN25cVXiSBQFih9AsGBF358uVl0KBBdySBJCUlyZEjR9JNNXr0aIFnEL2kw8PDXR4zum/fPuVtRJYy9oyDBEjAuAQoAI27d7TcRwggwH/WrFmCencQTjgO7tGjh/j7+xuKgObt0+r2ORLbZ6gFO2AsysAg6QP7DSGIMjDz58+XgwcPSmBgoCoRgzhBvAOZDVcfAe/du1cWLVokSDTBwHEzbNu1a5cDq+MtJEACeiJAAain3aAtJGBFAMWNUQ4EAhCB/v3791fCb8KECfLwww8bhlVm3j7U7WvXrh178mayi9hzrRA0ehtPmTJF1QTEaNWqlQoBgIh2twCEYF+8eLGqqdirVy8ZNWqUPP3006rP8osvvijakbBhXkQaSgIkkI4ABSBfCBLQKYE33nhDSpcuLT179rR4+5AJjCxR/GLW86C3T8+7Y59tf/zxh7z11lty9uxZ5X38+uuvVXFy69qL9s3Iq0mABPRAgAJQD7tAG0jARgLoblGhQgXZs2ePKhCst2Ht7UNRYi22j94+ve1U9vZYe/jQkQalaVCeB8knNWvWVAKQXsDsOfIKEtArAQpAve4M7SKBLAjg+BflP3Akp4cBEXD8+HFVtw/H1lpsH7p0wGNET5EedslxG7C/6DaC4+fTp08r7x9CEpCYgkER6Dhb3kkC3iRAAehN+nw2CThAALXyUOAYmcC5cuVyYAbX3EJvn2s4GmEWJKeMGzdO1aHE/48cOVJeeeUV1Z4wd+7cRlgCbSQBEshAgAKQrwQJGIxAamqqSgRAcsCDDz7oUes1b5+WyZs/f35Llw56+zy6FR59GDx+KEeDBCQMiMHk5GR59dVXpVChQh61hQ8jARJwDQEKQNdw5Cwk4FECY8aMUUWh4Y1x5UDdORQRzjjg7fvpp5/kiy++EC22D5m8EKDe9EK6cu2cK3MC586dUyVpPv/8c2nbti0xkQAJmIQABaBJNpLL8C0C6ASC/sA4Bi5TpoxLFo9f8Nu3b5cZM2ao+ejtcwlWw0+CjHMU637nnXdUfKcW04kPC/h/xngafou5AB8lQAHooxvPZRufALxvjRs3luHDh7tkMSj6i0D//fv3C3rx0tvnEqychARIgAR0SYACUJfbQqOMSGDatGmWAr61a9dW7bzu1i91wYIFglp/UVFRypv3wQcf2NVea8mSJTJ06FCBcMvs2NYehpq374EHHpCrV69K8eLFGdtnD0CTX4v3A3+cfc9MjonLIwFDEaAANNR20Vi9EkAsHtp0oUcqujaghRcEXmRkpAQEBNxhNoo5t2jRQrX06tixo3z33XdKAP79999So0YNm5Z548YNVQvw008/dTg2S4vtQwkXxPZVr15dLl26JIcPH2Z2p027wItIgARIwJgEKACNuW+0WmcEIPoiIiJU6zYMxEdBnA0ePFhGjBhxh7Vdu3aVxMREQUkXbeA4F62/ICJtHa+//rrqE/vtt9/aeovy5MDrqNXts87kLVasmKrd9+uvv6rjZSMOezyxs2fPVp0t4EXFqF+/vspwvZvn1ohMaDMJkAAJZCRAAch3ggScJAAvGoLjFy5cKJ07d7bM1qdPH4mPj5dly5bd8QSU1Bg2bJgMGTLE8r2xY8fK0qVLZdeuXTZbdPToUbnnnnuUCAwMDLzrfbDz559/VrF969atk4ceekgGDBhwRyYvCvxev35dZX0abdjriUWbvWbNmknTpk0FQhheWByt79u3TwlhDhIgARIwKwEKQLPuLNflMQLR0dFKLOBYF+2ytIHkjLVr18qWLVvusCVv3rwyZ84c6d69u+V706dPVz1XUXbDnoHSHC1btpSXX375jtsyevvy5cunYvv69++fZZcOCFAIIqyraNGi9pji9Wvt9cRmNBgdL+AFhScXR/ocJEACJGBWAhSAZt1ZrstjBLwtAOfPn686M0C4aSU5bPX2ZQUJ2cBIUGnTpo3HODr7IEc8sRmfmZCQoGI2Eb+J2EwOEiABEjArAQpAs+4s1+UxAo4ID1cdAWOReD76s6I7R0hIiPovevLCy5idty8rSEbs7+qIEM+4/hdeeEF+++03dQSMI2EOEiABEjArAQpAs+4s1+VRAjh6ROIASr9gIAkEIg/xdFklgVy7dk2WL19usRPHrrVq1bIrCUS7GfGGEC5xcXEqtk/r0uFLfVqdFYDvv/++anW2Zs0atQ8cJEACJGBmAhSAZt5drs1jBJB8ABE2a9YsJQRRBgZHs1pyBuLJECeIsi8YiBdE3B5ER4cOHWTevHkq+9SeMjDWi1u5cqVMmjRJPvvsM+UN9MXuDI54YjWGYPfuu+/KqlWrpEGDBh57b/ggEiABEvAWAQpAb5Hnc01HAIkDEydOlJiYGFXOZcqUKaomIAZi6kJDQ9XxrDYQZzZ69GhLIWh4n9q3b286Lp5ckL2eWNgG7u+9957yoBq19I0nGfNZJEAC5iBAAWiOfeQqSIAERMReTyzKvowZM0YV4kY5GG0UKlRI8IeDBEiABMxKgALQrDvLdZGAjxKwxxMLr+yJEyfuIIWajG+++aaPEuSySYAEfIEABaAv7DLXSAIkQAIkQAIkQAJWBCgA+TqQAAmQAAmQAAmQgI8RoAD0sQ3nckmABEiABEiABEiAApDvAAmQAAmQAAmQAAn4GAEKQB/bcC6XBEiABEiABEiABCgA+Q6QAAmQAAmQAAmQgI8RoAD0sQ3nckmABEiABEiABEiAApDvAAmQAAmQAAmQAAn4GAEKQB/bcC6XBEiABEiABEiABCgA+Q6QAAlYCEybNs3Sz7h27doydepUadiwYZaE0M/4jTfesPQzRms19jPmC0UCJEAC+idAAaj/PaKFJOARAuij27t3b5k5c6Y0atRIJk+eLBB4kZGREhAQcIcNmzZtkhYtWsj48eOlY8eOqp8uBODff/8tNWrU8IjNfAgJkAAJkIBjBCgAHePGu0jAdAQg+iIiIgS9dDFu3rwpwcHBMnjwYBkxYsQd6+3ataskJibKihUrLN9r3Lix1KlTR4lIDhIgARIgAf0SoADU797QMhLwGIGUlBTx8/OThQsXSufOnS3P7dOnj8THx8uyZcvusKV8+fIybNgwGTJkiOV7Y8eOlaVLl8quXbs8ZjsfRAIkQAIkYD8BCkD7mfEOEjAdgejoaAkKChIc6zZp0sSyvuHDh8vatWtly5Ytd6w5b968MmfOHOnevbvle9OnT5e33npLzp07ZzpGXBAJkAAJmIkABaCZdpNrIQEHCVAAOgiOt5EACZCAQQlQABp042g2CbiSAI+AXUmTc5EACZCA/glQAOp/j2ghCXiEAJJAUPIFpV8wkASCOL9BgwZlmQRy7do1Wb58ucW+pk2bSq1atZgE4pEd40NIgARIwHECFICOs+OdJGAqAigDg6SPWbNmKSGIMjDz58+XgwcPSmBgoCoRgzhBlH3BQLxgy5Yt5f3335cOHTrIvHnzZNy4cSwDY6q3goshARIwKwEKQLPuLNdFAg4QQAmYiRMnSkxMjCrnMmXKFFUTEKNVq1YSGhoqX331lWVm1AkcPXq0pRD0hAkTWAjaAe68hQRIgAQ8TYAC0NPE+TwSIAESIAESIAES8DIBCkAvbwAfTwIkQAIkQAIkQAKeJkAB6GnifB4JkAAJkAAJkAAJeJkABaCXN4CPJwESIAESIAESIAFPE6AA9DRxPo8ESIAESIAESIAEvEyAAtDLG8DHkwAJkAAJkAAJkICnCVAAepo4n0cCJEACJEACJEACXiZAAejlDeDjSYAESIAESIAESMDTBCgAPU2czyMBEiABEiABEiABLxOgAPTyBvDxJEACJEACJEACJOBpAhSAnibO55EACZAACZAACZCAlwlQAHp5A/h4EiABEiABEiABEvA0gf8Hbze0tmdaWkAAAAAASUVORK5CYII=\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -905,7 +1093,7 @@
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"fig = plt.figure()\n",
- "ax = Axes3D(fig)\n",
+ "ax = Axes3D(fig, auto_add_to_figure=False)\n",
"ax.grid(False)\n",
"ax.set_title('Scattered data points from the Franke Function')\n",
"ax.set_xlabel(r'$\\mathit{x}$')\n",
@@ -914,7 +1102,8 @@
"ax.azim = -20\n",
"ax.elev = 20\n",
"res = ax.scatter(x, y, f, marker='x')\n",
- "plt.show();"
+ "fig.add_axes(ax)\n",
+ "plt.show()"
]
},
{
@@ -923,7 +1112,7 @@
"source": [
"Now, how do we go about fitting a nice, smooth surface through those scattered points?\n",
"\n",
- "The NAG Library supplies a very configurable function (based on the TSFIT package of Davydov and Zeilfelder) for fitting a continuously differentiable ($C^1$ or $C^2$) surface to scattered data: [`fit.dim2_spline_ts_sctr`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.fit.html#naginterfaces.library.fit.dim2_spline_ts_sctr).\n",
+ "The NAG Library supplies a very configurable function (based on the TSFIT package of Davydov and Zeilfelder) for fitting a continuously differentiable ($C^1$ or $C^2$) surface to scattered data: [`fit.dim2_spline_ts_sctr`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.fit.dim2_spline_ts_sctr.html).\n",
"\n",
"We need to choose a granularity for the triangulation that the function will use"
]
@@ -1077,36 +1266,38 @@
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
+ "/* global mpl */\n",
"window.mpl = {};\n",
"\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
+ "mpl.get_websocket_type = function () {\n",
+ " if (typeof WebSocket !== 'undefined') {\n",
" return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
- " alert('Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.'\n",
+ " );\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ " this.supports_binary = this.ws.binaryType !== undefined;\n",
"\n",
" if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " var warnings = document.getElementById('mpl-warnings');\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
+ " warnings.textContent =\n",
+ " 'This browser does not support binary websocket messages. ' +\n",
+ " 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
@@ -1121,11 +1312,11 @@
"\n",
" this.image_mode = 'full';\n",
"\n",
- " this.root = $('<div/>');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
+ " this.root = document.createElement('div');\n",
+ " this.root.setAttribute('style', 'display: inline-block');\n",
+ " this._root_extra_style(this.root);\n",
"\n",
- " $(parent_element).append(this.root);\n",
+ " parent_element.appendChild(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
@@ -1135,285 +1326,366 @@
"\n",
" this.waiting = false;\n",
"\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+ " fig.send_message('send_image_mode', {});\n",
+ " if (fig.ratio !== 1) {\n",
+ " fig.send_message('set_device_pixel_ratio', {\n",
+ " device_pixel_ratio: fig.ratio,\n",
+ " });\n",
" }\n",
+ " fig.send_message('refresh', {});\n",
+ " };\n",
"\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
+ " this.imageObj.onload = function () {\n",
+ " if (fig.image_mode === 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
"\n",
- " this.imageObj.onunload = function() {\n",
+ " this.imageObj.onunload = function () {\n",
" fig.ws.close();\n",
- " }\n",
+ " };\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype._init_header = function () {\n",
+ " var titlebar = document.createElement('div');\n",
+ " titlebar.classList =\n",
+ " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+ " var titletext = document.createElement('div');\n",
+ " titletext.classList = 'ui-dialog-title';\n",
+ " titletext.setAttribute(\n",
+ " 'style',\n",
+ " 'width: 100%; text-align: center; padding: 3px;'\n",
+ " );\n",
+ " titlebar.appendChild(titletext);\n",
+ " this.root.appendChild(titlebar);\n",
+ " this.header = titletext;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
- "}\n",
+ "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
+ "mpl.figure.prototype._init_canvas = function () {\n",
" var fig = this;\n",
"\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+ " canvas_div.setAttribute(\n",
+ " 'style',\n",
+ " 'border: 1px solid #ddd;' +\n",
+ " 'box-sizing: content-box;' +\n",
+ " 'clear: both;' +\n",
+ " 'min-height: 1px;' +\n",
+ " 'min-width: 1px;' +\n",
+ " 'outline: 0;' +\n",
+ " 'overflow: hidden;' +\n",
+ " 'position: relative;' +\n",
+ " 'resize: both;'\n",
+ " );\n",
"\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
+ " function on_keyboard_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.key_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
+ " canvas_div.addEventListener(\n",
+ " 'keydown',\n",
+ " on_keyboard_event_closure('key_press')\n",
+ " );\n",
+ " canvas_div.addEventListener(\n",
+ " 'keyup',\n",
+ " on_keyboard_event_closure('key_release')\n",
+ " );\n",
+ "\n",
+ " this._canvas_extra_style(canvas_div);\n",
+ " this.root.appendChild(canvas_div);\n",
"\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ " var canvas = (this.canvas = document.createElement('canvas'));\n",
+ " canvas.classList.add('mpl-canvas');\n",
+ " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
+ " this.context = canvas.getContext('2d');\n",
"\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
+ " var backingStore =\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " this.context.webkitBackingStorePixelRatio ||\n",
+ " this.context.mozBackingStorePixelRatio ||\n",
+ " this.context.msBackingStorePixelRatio ||\n",
+ " this.context.oBackingStorePixelRatio ||\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " 1;\n",
"\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+ " 'canvas'\n",
+ " ));\n",
+ " rubberband_canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+ " );\n",
"\n",
- " var pass_mouse_events = true;\n",
+ " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+ " if (this.ResizeObserver === undefined) {\n",
+ " if (window.ResizeObserver !== undefined) {\n",
+ " this.ResizeObserver = window.ResizeObserver;\n",
+ " } else {\n",
+ " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+ " this.ResizeObserver = obs.ResizeObserver;\n",
+ " }\n",
+ " }\n",
"\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
+ " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+ " var nentries = entries.length;\n",
+ " for (var i = 0; i < nentries; i++) {\n",
+ " var entry = entries[i];\n",
+ " var width, height;\n",
+ " if (entry.contentBoxSize) {\n",
+ " if (entry.contentBoxSize instanceof Array) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " width = entry.contentBoxSize[0].inlineSize;\n",
+ " height = entry.contentBoxSize[0].blockSize;\n",
+ " } else {\n",
+ " // Firefox implements old version of spec.\n",
+ " width = entry.contentBoxSize.inlineSize;\n",
+ " height = entry.contentBoxSize.blockSize;\n",
+ " }\n",
+ " } else {\n",
+ " // Chrome <84 implements even older version of spec.\n",
+ " width = entry.contentRect.width;\n",
+ " height = entry.contentRect.height;\n",
+ " }\n",
+ "\n",
+ " // Keep the size of the canvas and rubber band canvas in sync with\n",
+ " // the canvas container.\n",
+ " if (entry.devicePixelContentBoxSize) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " canvas.setAttribute(\n",
+ " 'width',\n",
+ " entry.devicePixelContentBoxSize[0].inlineSize\n",
+ " );\n",
+ " canvas.setAttribute(\n",
+ " 'height',\n",
+ " entry.devicePixelContentBoxSize[0].blockSize\n",
+ " );\n",
+ " } else {\n",
+ " canvas.setAttribute('width', width * fig.ratio);\n",
+ " canvas.setAttribute('height', height * fig.ratio);\n",
+ " }\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
" });\n",
+ " this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'dblclick',\n",
+ " on_mouse_event_closure('dblclick')\n",
+ " );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
"\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
- " mouse_event_fn(event);\n",
+ " on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
"\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+ " event.preventDefault();\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
- " // put a spacer in here.\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
"\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
"\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(option);\n",
" }\n",
"\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ "mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
+ "mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+ " var x0 = msg['x0'] / fig.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+ " var x1 = msg['x1'] / fig.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
@@ -1424,78 +1696,96 @@
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / fig.ratio,\n",
+ " fig.canvas.height / fig.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
+ "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+ " fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
+ " this.send_message('ack', {});\n",
+ "};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
+ " var img = evt.data;\n",
+ " if (img.type !== 'image/png') {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " img.type = 'image/png';\n",
+ " }\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " img\n",
+ " );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -1508,9 +1798,12 @@
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -1519,62 +1812,74 @@
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
+ "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
- " if (!e)\n",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\n",
"\n",
- " // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
+ " * https://stackoverflow.com/a/24161582/3208463\n",
" */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
+ " var x = canvas_pos.x * this.ratio;\n",
+ " var y = canvas_pos.y * this.ratio;\n",
"\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -1582,265 +1887,337 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.key === this._key) {\n",
" return;\n",
- " else\n",
- " this._key = event.which;\n",
+ " } else {\n",
+ " this._key = event.key;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\n",
"\n",
" var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
+ " if (event.ctrlKey && event.key !== 'Control') {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " else if (event.altKey && event.key !== 'Alt') {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " else if (event.shiftKey && event.key !== 'Shift') {\n",
+ " value += 'shift+';\n",
+ " }\n",
"\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
+ " value += 'k' + event.key;\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+ " if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+ "// prettier-ignore\n",
+ "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
- " ws.close = function() {\n",
- " comm.close()\n",
+ " ws.binaryType = comm.kernel.ws.binaryType;\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " function updateReadyState(_event) {\n",
+ " if (comm.kernel.ws) {\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " } else {\n",
+ " ws.readyState = 3; // Closed state.\n",
+ " }\n",
+ " }\n",
+ " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+ "\n",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\n",
+ " ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
+ " comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
+ " var data = msg['content']['data'];\n",
+ " if (data['blob'] !== undefined) {\n",
+ " data = {\n",
+ " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+ " };\n",
+ " }\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
+ " ws.onmessage(data);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ "mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
- " fig.parent_element = element.get(0);\n",
+ " fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
+ " fig.cell_info[0].output_area.element.on(\n",
+ " 'cleared',\n",
+ " { fig: fig },\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
"};\n",
"\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / fig.ratio;\n",
+ " fig.cell_info[0].output_area.element.off(\n",
+ " 'cleared',\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
+ " var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
+ " this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
- " if (!name) { continue; };\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
"\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+ " var fig = event.data.fig;\n",
+ " if (event.target !== this) {\n",
+ " // Ignore bubbled events from children.\n",
+ " return;\n",
+ " }\n",
+ " fig.close_ws(fig, {});\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
+ " if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\n",
+ "mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
+ " for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " if (cell.cell_type === 'code') {\n",
+ " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
- " if (data['text/html'] == html_output) {\n",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -1853,7 +2230,7 @@
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\" 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -1865,7 +2242,7 @@
],
"source": [
"fig = plt.figure()\n",
- "ax = Axes3D(fig)\n",
+ "ax = Axes3D(fig, auto_add_to_figure=False)\n",
"ax.grid(False)\n",
"ax.plot_wireframe(X, Y, z_m, color='red', linewidths=0.4)\n",
"ax.contour(X, Y, z_m, 15, offset=-1.2, cmap=cm.jet)\n",
@@ -1876,13 +2253,14 @@
"ax.azim = -20\n",
"ax.elev = 20\n",
"res = ax.scatter(x, y, f, marker='x')\n",
- "plt.show();"
+ "fig.add_axes(ax)\n",
+ "plt.show()"
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1896,7 +2274,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/dimension_reduction/web_site_classification_using_nnmf.ipynb b/dimension_reduction/web_site_classification_using_nnmf.ipynb
index 2eef4ec..34c584d 100644
--- a/dimension_reduction/web_site_classification_using_nnmf.ipynb
+++ b/dimension_reduction/web_site_classification_using_nnmf.ipynb
@@ -80,17 +80,17 @@
"['Kirstjen Nielsen: Walking a tightrope working for Trump - BBC News']\n",
"['Trump homes plan at Menie being recommended for approval - BBC News']\n",
"['Theresa May at her worst during Brexit speech - Mark Drakeford - BBC News']\n",
- "[\"President Trump shows map of 'IS defeat' - BBC News\"]\n",
+ "['President Trump shows map of 'IS defeat' - BBC News']\n",
"['Brexit: What happens now? - BBC News']\n",
"['Trump spooks markets with China trade tariffs warning - BBC News']\n",
"['A tale of two Trumps: Jair Bolsonaro goes to Washington - BBC News']\n",
"['Brexit: Theresa May to formally ask for delay - BBC News']\n",
"['Corbyn calls for compromise to avoid no-deal Brexit - BBC News']\n",
- "[\"Trump: I didn't get a thank you for McCain funeral - BBC News\"]\n",
+ "['Trump: I didn't get a thank you for McCain funeral - BBC News']\n",
"['Brexit: EU leaders agree Article 50 delay plan - BBC News']\n",
"['Trump: Time to recognise Golan Heights as Israeli territory - BBC News']\n",
"['Brexit: MPs urged not to travel home alone as tensions rise - BBC News']\n",
- "[\"'Cancel Brexit' petition passes 2m signatures on Parliament site - BBC News\"]\n"
+ "[''Cancel Brexit' petition passes 2m signatures on Parliament site - BBC News']\n"
]
}
],
@@ -98,10 +98,8 @@
"from naginterfaces.library.matop import real_nmf\n",
"from collections import Counter\n",
"import string\n",
- "from string import punctuation\n",
"import urllib.request\n",
"import re\n",
- "import scipy as sp\n",
"from scipy.linalg import norm\n",
"import numpy as np\n",
"\n",
@@ -122,7 +120,7 @@
" pagewords = []\n",
" paras = re.findall(r'<p>(.*?)</p>', f1.read().decode().lower())\n",
" f2 = urllib.request.urlopen(link)\n",
- " title = re.findall(r'<title>(.*?)</title>', f2.read().decode())\n",
+ " title = re.findall(r'<title data-rh=\"true\">(.*?)</title>', f2.read().decode())\n",
" print(title)\n",
" titles.append(title)\n",
" for para in paras:\n",
@@ -149,26 +147,26 @@
{
"data": {
"text/plain": [
- "[('a', 32),\n",
- " ('the', 29),\n",
- " ('to', 16),\n",
- " ('uk', 15),\n",
- " ('of', 13),\n",
+ "[('the', 37),\n",
+ " ('a', 37),\n",
+ " ('to', 21),\n",
+ " ('uk', 17),\n",
+ " ('of', 15),\n",
+ " ('that', 10),\n",
" ('in', 10),\n",
- " ('that', 9),\n",
+ " ('class', 9),\n",
+ " ('ssrcss', 9),\n",
+ " ('eu', 9),\n",
" ('it', 9),\n",
" ('on', 9),\n",
- " ('eu', 8),\n",
+ " ('bbc', 9),\n",
+ " ('href', 8),\n",
" ('would', 8),\n",
+ " ('an', 7),\n",
" ('extension', 7),\n",
- " ('href', 7),\n",
- " ('class', 7),\n",
- " ('story', 7),\n",
- " ('body', 7),\n",
- " ('link', 7),\n",
- " ('an', 6),\n",
- " ('article', 6),\n",
- " ('50', 6)]"
+ " ('and', 7),\n",
+ " ('www', 7),\n",
+ " ('co', 7)]"
]
},
"execution_count": 3,
@@ -195,7 +193,7 @@
{
"data": {
"text/plain": [
- "2186"
+ "2268"
]
},
"execution_count": 4,
@@ -218,7 +216,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "This list of 2279 words contains many common words such as 'the', 'that' and 'with' which we want to ignore. \n",
+ "This list of 2268 words contains many common words such as 'the', 'that' and 'with' which we want to ignore. \n",
"These unwanted words are commonly referred to as [stopwords](https://en.wikipedia.org/wiki/Stop_words). \n",
"The explicit list of stopwords we are going to use in this analysis are defined in the next cell"
]
@@ -287,7 +285,7 @@
{
"data": {
"text/plain": [
- "1795"
+ "1873"
]
},
"execution_count": 8,
@@ -334,7 +332,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1753 distinct words will be used to form the data matrix\n"
+ "1825 distinct words will be used to form the data matrix\n"
]
}
],
@@ -366,7 +364,7 @@
"output_type": "stream",
"text": [
"Creating data matrix\n",
- "Final data matrix has size:(1753, 15)\n"
+ "Final data matrix has size:(1825, 15)\n"
]
}
],
@@ -396,12 +394,12 @@
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 1., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 1., 0.],\n",
" ...,\n",
" [1., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 1., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 1., ..., 0., 0., 0.]])"
+ " [1., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 12,
@@ -464,8 +462,8 @@
"\n",
"print(st, file=f2)\n",
"\n",
- "for i in range(len(words)):\n",
- " st = words[i].ljust(14,' ')\n",
+ "for i, v in enumerate(words):\n",
+ " st = v.ljust(14,' ')\n",
" for j in range(n_links):\n",
" st += str(int(a[i,j])).center(8, ' ')\n",
" print(st, file=f2)\n",
@@ -502,12 +500,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "<ipython-input-14-fb37620fc264>:8: NagAlgorithmicWarning: (NAG Python function naginterfaces.base.matop.real_nmf, code 7:7,99992)\n",
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: NagAlgorithmicWarning: (NAG Python function naginterfaces.base.matop.real_nmf, code 7:7,99992)\n",
"** The function has failed to converge after 500 iterations.\n",
"** The factorization given by w and h may still be a good enough approximation\n",
"** to be useful. Alternatively an improved factorization may be obtained by\n",
"** increasing maxit or using different initial choices of w and h.\n",
- " w, h = real_nmf(a, k=2, seed=seed, errtol=errtol, maxit=maxit)\n"
+ " \n"
]
}
],
@@ -530,7 +528,7 @@
{
"data": {
"text/plain": [
- "(1753, 2)"
+ "(1825, 2)"
]
},
"execution_count": 15,
@@ -579,7 +577,7 @@
"output_type": "stream",
"text": [
"norm of residual:\n",
- "0.7313733530687229\n"
+ "0.4996361512168467\n"
]
}
],
@@ -611,16 +609,16 @@
"text": [
"\n",
"The most important words in column 1 of w are:\n",
- "['deal', 'brexit', 'parliament', 'delay', 'body', 'story', 'class', 'minister', 'prime', 'vote']\n",
+ "['quot', 'deal', 'brexit', 'ssrcss', 'class', 'delay', 'parliament', 'minister', 'prime', 'e1no5rhv0']\n",
"\n",
"The most important words in column 2 of w are:\n",
- "['trump', 'president', 'women', 'nielsen', 'border', 'mccain', 'security', 'senator', 'secretary', 'administration']\n"
+ "['quot', 'trump', 'president', 'women', 'mccain', 'class', 'ssrcss', 'nielsen', 'border', 'security']\n"
]
}
],
"source": [
"for i in range(k):\n",
- " tmp = sorted(words, key=lambda x: w[words.index(x),i], reverse=True)\n",
+ " tmp = sorted(words, key=lambda x, ind=i: w[words.index(x),ind], reverse=True)\n",
" st = \"\\nThe most important words in column \" + str(i+1) + \" of w are:\"\n",
" print(st)\n",
" print(tmp[:10])"
@@ -630,7 +628,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Looking at these lists, you'll hopefully agree that the first column corresponds to Brexit and the second column Trump. It seems that our non-negative matrix factorization has successfully detected the two categories of web page. Let's denote these using the numbers 0 and 1. Can we now use the NMF to accurately categorise the individual pages? To do this we need to look at the coefficients matrix H.\n",
+ "Looking at these lists, you'll hopefully agree that the first column corresponds to Trump and the second column Brexit. It seems that our non-negative matrix factorization has successfully detected the two categories of web page. Let's denote these using the numbers 0 and 1. Can we now use the NMF to accurately categorise the individual pages? To do this we need to look at the coefficients matrix H.\n",
"\n",
"We convert h to a pandas dataframe for display purposes"
]
@@ -681,52 +679,52 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>12.072</td>\n",
- " <td>7.953e-16</td>\n",
- " <td>2.419</td>\n",
- " <td>19.137</td>\n",
- " <td>7.953e-16</td>\n",
- " <td>4.437</td>\n",
- " <td>1.823</td>\n",
- " <td>0.044</td>\n",
- " <td>3.159e+01</td>\n",
- " <td>7.113e+00</td>\n",
- " <td>7.953e-16</td>\n",
- " <td>15.560</td>\n",
- " <td>4.042</td>\n",
- " <td>1.429</td>\n",
- " <td>2.800e+01</td>\n",
+ " <td>1.226e+01</td>\n",
+ " <td>1.195e-15</td>\n",
+ " <td>5.253</td>\n",
+ " <td>50.795</td>\n",
+ " <td>1.353</td>\n",
+ " <td>6.003e+00</td>\n",
+ " <td>5.638</td>\n",
+ " <td>10.739</td>\n",
+ " <td>76.032</td>\n",
+ " <td>1.913e+01</td>\n",
+ " <td>1.195e-15</td>\n",
+ " <td>34.929</td>\n",
+ " <td>1.195e-15</td>\n",
+ " <td>4.433</td>\n",
+ " <td>5.544e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>1.545</td>\n",
- " <td>3.538e+01</td>\n",
- " <td>2.133</td>\n",
- " <td>1.220</td>\n",
- " <td>1.578e+00</td>\n",
- " <td>0.140</td>\n",
- " <td>2.411</td>\n",
- " <td>11.886</td>\n",
- " <td>7.621e-16</td>\n",
- " <td>7.621e-16</td>\n",
- " <td>2.282e+01</td>\n",
- " <td>0.208</td>\n",
- " <td>17.007</td>\n",
- " <td>0.203</td>\n",
- " <td>7.621e-16</td>\n",
+ " <td>9.842e-16</td>\n",
+ " <td>5.373e+01</td>\n",
+ " <td>5.094</td>\n",
+ " <td>3.985</td>\n",
+ " <td>3.680</td>\n",
+ " <td>9.842e-16</td>\n",
+ " <td>7.298</td>\n",
+ " <td>24.121</td>\n",
+ " <td>0.117</td>\n",
+ " <td>9.842e-16</td>\n",
+ " <td>4.388e+01</td>\n",
+ " <td>0.353</td>\n",
+ " <td>2.589e+01</td>\n",
+ " <td>1.619</td>\n",
+ " <td>9.842e-16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " link1 link2 link3 link4 link5 link6 link7 link8 \\\n",
- "0 12.072 7.953e-16 2.419 19.137 7.953e-16 4.437 1.823 0.044 \n",
- "1 1.545 3.538e+01 2.133 1.220 1.578e+00 0.140 2.411 11.886 \n",
+ " link1 link2 link3 link4 link5 link6 link7 link8 \\\n",
+ "0 1.226e+01 1.195e-15 5.253 50.795 1.353 6.003e+00 5.638 10.739 \n",
+ "1 9.842e-16 5.373e+01 5.094 3.985 3.680 9.842e-16 7.298 24.121 \n",
"\n",
- " link9 link10 link11 link12 link13 link14 link15 \n",
- "0 3.159e+01 7.113e+00 7.953e-16 15.560 4.042 1.429 2.800e+01 \n",
- "1 7.621e-16 7.621e-16 2.282e+01 0.208 17.007 0.203 7.621e-16 "
+ " link9 link10 link11 link12 link13 link14 link15 \n",
+ "0 76.032 1.913e+01 1.195e-15 34.929 1.195e-15 4.433 5.544e+01 \n",
+ "1 0.117 9.842e-16 4.388e+01 0.353 2.589e+01 1.619 9.842e-16 "
]
},
"execution_count": 19,
@@ -764,21 +762,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Article \"Brexit delay: How is Article 50 extended? - BBC News\" is in category 0 (Trump)\n",
- "Article \"Kirstjen Nielsen: Walking a tightrope working for Trump - BBC News\" is in category 1 (Brexit)\n",
- "Article \"Trump homes plan at Menie being recommended for approval - BBC News\" is in category 0 (Trump)\n",
- "Article \"Theresa May at her worst during Brexit speech - Mark Drakeford - BBC News\" is in category 0 (Trump)\n",
- "Article \"President Trump shows map of 'IS defeat' - BBC News\" is in category 1 (Brexit)\n",
- "Article \"Brexit: What happens now? - BBC News\" is in category 0 (Trump)\n",
- "Article \"Trump spooks markets with China trade tariffs warning - BBC News\" is in category 1 (Brexit)\n",
- "Article \"A tale of two Trumps: Jair Bolsonaro goes to Washington - BBC News\" is in category 1 (Brexit)\n",
- "Article \"Brexit: Theresa May to formally ask for delay - BBC News\" is in category 0 (Trump)\n",
- "Article \"Corbyn calls for compromise to avoid no-deal Brexit - BBC News\" is in category 0 (Trump)\n",
- "Article \"Trump: I didn't get a thank you for McCain funeral - BBC News\" is in category 1 (Brexit)\n",
- "Article \"Brexit: EU leaders agree Article 50 delay plan - BBC News\" is in category 0 (Trump)\n",
- "Article \"Trump: Time to recognise Golan Heights as Israeli territory - BBC News\" is in category 1 (Brexit)\n",
- "Article \"Brexit: MPs urged not to travel home alone as tensions rise - BBC News\" is in category 0 (Trump)\n",
- "Article \"'Cancel Brexit' petition passes 2m signatures on Parliament site - BBC News\" is in category 0 (Trump)\n"
+ "Article \"Brexit delay: How is Article 50 extended? - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Kirstjen Nielsen: Walking a tightrope working for Trump - BBC News\" is in category 1 (Trump)\n",
+ "Article \"Trump homes plan at Menie being recommended for approval - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Theresa May at her worst during Brexit speech - Mark Drakeford - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"President Trump shows map of 'IS defeat' - BBC News\" is in category 1 (Trump)\n",
+ "Article \"Brexit: What happens now? - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Trump spooks markets with China trade tariffs warning - BBC News\" is in category 1 (Trump)\n",
+ "Article \"A tale of two Trumps: Jair Bolsonaro goes to Washington - BBC News\" is in category 1 (Trump)\n",
+ "Article \"Brexit: Theresa May to formally ask for delay - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Corbyn calls for compromise to avoid no-deal Brexit - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Trump: I didn't get a thank you for McCain funeral - BBC News\" is in category 1 (Trump)\n",
+ "Article \"Brexit: EU leaders agree Article 50 delay plan - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"Trump: Time to recognise Golan Heights as Israeli territory - BBC News\" is in category 1 (Trump)\n",
+ "Article \"Brexit: MPs urged not to travel home alone as tensions rise - BBC News\" is in category 0 (Brexit)\n",
+ "Article \"'Cancel Brexit' petition passes 2m signatures on Parliament site - BBC News\" is in category 0 (Brexit)\n"
]
}
],
@@ -786,7 +784,7 @@
"for i, link in enumerate(links):\n",
" category = 0 if h[0,i] > h[1,i] else 1\n",
" title = '\"' + (titles[i][0]) + '\"'\n",
- " st = 'Article ' + title.ljust(78,' ') + ' is in category ' + str(category) + [\" (Trump)\",\" (Brexit)\"][category]\n",
+ " st = 'Article ' + title.ljust(78,' ') + ' is in category ' + str(category) + [\" (Brexit)\",\" (Trump)\"][category]\n",
" print(st)"
]
},
@@ -799,6 +797,13 @@
"* [Presentation on Non-Negative Matrix Factorization](https://www.nag.com/market/non-negative-matrix-factorization.pdf) by the author of the NAG routines **real_nmf** and **real_nmf_rcomm**\n",
"* [NAG Blog post on which this notebook is based](https://www.nag.com/content/classifying-web-pages-using-non-negative-matrix-factorization)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -817,7 +822,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.7.3"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/global_optimization/PSO_demo.ipynb b/global_optimization/PSO_demo.ipynb
index 4067e5c..baadbbf 100644
--- a/global_optimization/PSO_demo.ipynb
+++ b/global_optimization/PSO_demo.ipynb
@@ -60,7 +60,7 @@
"Z = ackley(X, Y)\n",
"\n",
"fig = plt.figure(figsize=(20,10))\n",
- "ax = Axes3D(fig)\n",
+ "ax = Axes3D(fig, auto_add_to_figure=False)\n",
"ax.set_xlim3d(-5, 5)\n",
"ax.set_ylim3d(-5, 5)\n",
"ax.set_zlim3d(0, 15)\n",
@@ -69,6 +69,7 @@
"ax.plot_wireframe(X, Y, Z, color='black', lw=0.5, rstride=10, cstride=20)\n",
"lev = [0.1, 1, 2, 3, 4, 5, 6, 7, 8, 10]\n",
"ax.contour(X, Y, Z, levels=lev, colors='black', linestyles=\"solid\", offset=-1)\n",
+ "fig.add_axes(ax)\n",
"plt.show()"
]
},
@@ -101,7 +102,7 @@
" \n",
" return objf, vecout\n",
"\n",
- "We will explore the meaning of all of these arguments later (or you can skip to the [full documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.html#naginterfaces.library.glopt.bnd_pso) for details) but for now, all we need to worry about is the input vector `in` and the input/output vectors `objf` and `vecout`\n",
+ "We will explore the meaning of all of these arguments later (or you can skip to the [full documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.bnd_pso.html) for details) but for now, all we need to worry about is the input vector `in` and the input/output vectors `objf` and `vecout`\n",
"\n",
"* `x` contains a vector of co-ordinates of the N-dimensional point where we wish to evaluate the objective function \n",
"* `objf` contains the value of the objective function at the point `x`\n",
@@ -118,7 +119,7 @@
"source": [
"# Objective function in the form that the PSO solver wants\n",
"# We don't need to use all of the input arguments, they just need to be included in the function definition\n",
- "def objfun(mode, x, objf, vecout, nstate):\n",
+ "def objfun(_mode, x, objf, vecout, _nstate):\n",
" # Evaluate the objective function using the ackley function defined earlier\n",
" objf = ackley(x[0], x[1])\n",
" return objf, vecout"
@@ -312,7 +313,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Switching the warning off doesn't mean we shouldn't worry about what's going on and you should refer to [The documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.html#naginterfaces.library.glopt.bnd_pso) for the meaning of the various `inform` values. \n",
+ "Switching the warning off doesn't mean we shouldn't worry about what's going on and you should refer to [The documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.bnd_pso.html) for the meaning of the various `inform` values. \n",
"\n",
"In the example above, `inform=2` which means that the standard deviation of the location of all the particles in the swarm is below the set threshold (‘Swarm Standard Deviation’). We may or may not choose to worry about this. Since we know we have found the global minimum in this example, we will ignore it"
]
@@ -323,7 +324,7 @@
"metadata": {},
"outputs": [],
"source": [
- "def ackley_ndim(mode, x, objf, vecout, nstate):\n",
+ "def ackley_ndim(_mode, x, objf, vecout, _nstate):\n",
" n = len(x)\n",
" sum1 = np.sum(x**2)\n",
" sum2 = np.sum(np.cos(2.0*np.pi*x))\n",
@@ -393,7 +394,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/global_optimization/SQP_multistart.ipynb b/global_optimization/SQP_multistart.ipynb
index cec8b40..54ea6b9 100644
--- a/global_optimization/SQP_multistart.ipynb
+++ b/global_optimization/SQP_multistart.ipynb
@@ -23,7 +23,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -93,7 +93,7 @@
" self.npts = 0\n",
" self.pts = []\n",
" \n",
- "def objfun(mode, x, objgrd, nstate, data=None):\n",
+ "def objfun(mode, x, objgrd, _nstate, data=None):\n",
" # Himmelblau's function\n",
" objf = (x[0]**2 + x[1] - 11)**2 + (x[0] + x[1]**2 - 7)**2\n",
" if mode > 0:\n",
@@ -116,13 +116,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "4 solutions were found\n",
+ "3 solutions were found\n",
"\n",
- "The 4 computed solutions are:\n",
- "points coordinate: -2.805, 3.131, objective value: 0.000\n",
+ "The 3 computed solutions are:\n",
+ "points coordinate: 3.000, 2.000, objective value: 0.000\n",
"points coordinate: -3.779, -3.283, objective value: 0.000\n",
- "points coordinate: 3.584, -1.848, objective value: 0.000\n",
- "points coordinate: 3.000, 2.000, objective value: 0.000\n"
+ "points coordinate: 3.584, -1.848, objective value: 0.000\n"
]
}
],
@@ -173,7 +172,7 @@
"outputs": [
{
"data": {
- "image/png": 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IYMO3BDavI7h9C3pLa/6yyYQpLRNTeiZqajpqYrIxsnbGoTicCKsNYW7dEUgIY1SuGemHMhBAD/iRPi96SzN6SzNaUyN6Yz1aYz1afS16k3tfJ4RATUnDlJWDuUce5tx8zHm9UBOP3G2p1DVjErV0iyHupVvR68uMFxUVJaMXppwBhrD3GIBISIuN2mO0CamHkVUbkKXL0YuXQsNO44WUvigFJ6D0nmZ46ncxpHc3rL3GiJsPeQyRMrnddR5uIf8v8CAQB9x6MCEXQlwNXA2Ql5c3uqioKOJ2wgsfRG7/HNPZ/0Sk9ouqr3pzHd7X7kWvKcZ60lVYxkZuMSmDAXyrl+Nbvojgjq0gJUpiMtZ+g7D06Y85rwBTZraRK36Y0AN+tNpqwtUVhCvLCVeUESovQauu2JumpiQkYe7ZG0tBHyy9+2POzT+sffpeH71uY7Re0iru5dsgFABAuJJRc/qj9hgQi7XHiAjZXI6+6yvkrgXImk0AiKyRKP1PR+RP7VI2HDJYa4i5pxAGP4pIO6Fd9R02IRdCnAGcJqW8VghxHD8g5PsTzYhcr96I9v6vUYbNRB13TVR91Zvq8Lx8B9LTiOOCP2AqGBFZ+ZZmPAs+xbPoC6TXg5qeiX30BGwjxmLKyukSI0wZDBAqKyZUtJNg0U5Cu7ej1RrxbGGxYundD0u/wVgHDMGUnYs4gi57UtfQq3YZ4l66hXDpFmRjpfGioqKk56P26GdMpGb3Q0npcdTnIsdoH7KpHH375+iFn0JzOVgTUPqfhjLovC4zSpfhZth8B/T6LcIV3QB0D4dTyB8ELgPCgA2IB96WUl76Q2UiFXIpJdpH1yPdpZhmvGo470WI9LXgefF36E21OGbejymn7ZulylAIz5ef0DLvA2QwgG3YaBzHnYyld8fs/3e40ZoaCe7YRrBwM8HCzYQrjZCHEpeAddAwrINHYB04FMVmP+J9M2LtW1sf29AqCiHgNV602FEze6Nm9UHN6o2S2RslOTsm7jG+h5Q6snw1+ub3kEWLAIEoOB51+CWI5O87dHZXjkj64eEakevlq9A+vinqVEOpa3hfvw9t93pDxPPb7i8c3L2Dxlf+hVZVjnXoKOLOnIE5q+tOsrQFzd1AYMsGApvWEdiyHun1gMmEtd9gbCPGYBs2BsXZOdkBUurotaVGdkzFdvSK7WiVO42FKwBmG2p6TyNTJj0fJa0nanpPhD22Q9EepK5B0LB4kCE/hIwMKUIBYxVq2FiBKnXNWBgk91thCq3L+4VhN6CqhvWAyYIwW43wl9WOsDoM2wmTtcsNZmRzJfrG/6Jv/QBCPkT+FNTRVyGSenV219pNtxby8Ke3IesKMV34RlTxr8CSNwnMfwnbaddhGXVym8pIKfEu/Jymd15DSUgk4aIrsQ06+uxFpaYR3FVIYN1q/GtXGBk2iop1wBDsYyZgHTa6U1MfjT6G0WtLjNz2yp1oVTvRqneD37P3HOFMNLxkUnNQknugJGcZfjOJGd0u9i6lNMTX19zqsdOM9O939LcYz+/x4Al4IeBBBnzGz9/13jmcqGbDS8iZYHwGcSn7TNwSM1CSMhHxqZ1yFyX9TYagb/iPIej9TkUd/YtuvRdBt10QJJsrCL9xEcrIK1BHR75Bs1ZTjOfZGzANGI/93N+1afQgdZ2mt17B+9U8rENHkXjp1SiOw+uh0BWQUhIu2Y1v9TJ8q5ejN9QhrDZsI8fhmDAVc6++XWb0JaVENtftc56sLUGvK0WrLQX/gUvGhSMBkZDW6hSZYjhHOhONhz3esAi2uQy/GZOl/TurS90Y/Qb9yKAPAt5WkfUgW03Q8Hv2CbL/O2Lta/lx50zFZPTZ6jT8fVpHx8LiMN6DxY6w2hFmG8JiB4vVGACZLAiTBUxGtpRQTMbORPubeEGrN4sOumbkeYdDxtL9UBAZ8kHrBWPvRcXjRnrdhplbSz2ypZEDPGJUE0pytuFPlNZ6N5VZgIg/MplL0u9G//Zl9E3vgGIyrK2HzuiWq0i7rZBra15EXzUX00X/iXjyQkqJ9993oVXuwPXrZ1Cch/ZHkVLS9OaLeBd9gfP4U4k756IjOiHYVZC6TnDHVnzfLMa/ejkyGMCUlYPj2BOxj5vUKfH0tqJ7m9AbKpANFeiN1YaZmLsG2VSD3lJ/wEj+eygqmG3Gil6T2fCHV00HCh3S0CndMMaSWtgI/YRDyFDA8C1pC/t7zdviWj3n477/+wE/u8Dc9cIZ+yO1ELKpzvi711cYBm61JWi1JciGSvZaQDgT9601yBuMmtXnsGZVyaYytGVPIYuXQFIv1Mm/R0kfdNjaOxx0WyEPvXUFwhqH6YynIm4nvGst3n/fiXX6L7GOO6tNZVq++Jjmd1/DeeJpxJ19UZf+whwp9IAf/6pleBd/SahkF8JmxzHpeJxTT0ZN6j5LqPcgw0FkSyPS6zasgX3NrSNl797QhCHIIcNHRNf2jVJhn92sohqbgSgmY5S7Z8RrthobgphbNwexOvZ7OMHmNEbQP8FJWxn0G3dRlTv2LijT68uNFy12TD2HYuozGlOfsSgJaYelD3rRYrQlj4KvDmXYJSijZ3Wb0Xm3FHLZXEn4jRkox/wGdeiFEbfjefVu9OoiXNc9a3zBDkFw13bqHvsjtmGjSbzy+g5fpu8rqqVpQwmewkp8JXUEa5sJub1o/iBS0xGqgmq3YI53YE5xYctKxJ6XirN3BnEDemCK6+RYtZSEdu/As+Az/N9+Awjs4ybhmn4WprSukeoVo/uhexrRijYQ3r2W8M41yMYqAJSsPpgHTsI8aApKYnqHtimDLcbofNvHiLSBqCfcY+wb28XplkKub/0QbdHDmM5/CZGUH1Eben0FLX+/GuvUS7BOvuiQ50stTO1DdyIDflLveBDF3jGhA19JHZUfrKJ2/iYCVcZqTNVhwZ6XijU9AXOiA8VmRqgKUtPRfEHCbh/Bumb85Q2EGlrDAAKcvTNIHNublGMHEDc4B6F03t1CuK4Gz5ef4F26ADQN+/gpxJ167hFdTRrj6ENKiV5XSnjbckJblqKXbwNAzRuCecRJmAcd26ZBWVvRdy1AW/Sw0cbxd6Pkju+wug8H3VLIwwsfRJZ8jemS9yMeHQcWvUFg4Su4rp/bpls075Ivcb/+PEm/vBHbsMh2yz4Y/vIGdv/zf9TO34RQFRKP6UPKpH4kjMjH1iMZoQhCTQG8Fc2E3H70oIYwKZhcFmxpTmxpTuOcRi+e7ZU0bSzBvXo3TeuLkWEda0YCGaeNIPOsMVhSOs9ISHM30jLvfbyLv0SoKs4TT8c17QyE5cjukRrj6ERvqCS0YSGhdV+gN1Qg7HGYR56MZcwZKPEpHdKGbCon/L87oX4HyjHXogyZ0WVDqt1SyENvXYFwZWA6+eGI22iZcxNCUXHO+tshz5W6Ts0fb0NxxZFy8z3t/hArP1zNzsc/AUXQ44LxZJ0/Dkuyi7AnSMUXO6lauIu6NRX4Kw9iyN+KajMR1zeF5JFZpE/II3VCDqrFRNgToH7JVqo/W0fjih0Ii0rmGaPI+/lUzImdl1kTrq2m+f038K/5BjU1nYSLrsTaP7LdxmPE+CGk1NF2rye46iPCW5eDUDAPOwHrpJ+hJGW2v/6Qz7DG3r0QZdD5KBOu75L7D3Q7IZdaiPAL01GGXYw69uqI6pf+Fpr/NhPrlIuxTrn4kOcHtm6k/qmHSPz5tdhHT4iore9SNGc+JS9+ReKYAvrefjbW9HjC3hCFc1ax8+VvCbcEsaY5SR3Xg4QBaTh6xGNJtKHagzhybqXhiRPxb6yhZfSJuD9cRkOzDT0M5ngrOWf0p/flI3DmGQZfvpI6Sl9bQtUn36LarRRcfzLppwzv1NFEYOtG3G+8gFZTiWPqdOLPvhBhjo3OuzJS19E9zejNTYZJm9eD9PuMRzBoGLlp2j5PesXYA1SYLYblst2OYneguOJQ4hJQ4xMP6x2Z3lBJYNk7hL6dB1LHPPJkrFMujtr4bg9S6ujL/46+4T+Gu+rUP3S5SdAfEvKu1cv9aa4AqSES8yIuqpUXAhI1Z2CbzvevXYGwWLENbV9IpfLD1ZS8+BUZp4+kz61nIFSFpsI6vrn+QzzFbrKn96HgshEkj8yipczLrs/K2P1+DU3FHpL7fcPxj7zLrtJctA9ryXzvE/qFN2D67a+pPe96Sj/aStGbG9j9xnp6XTyMgTdMwJ6bQt/fnUWPGRPYPvsjCh98D/faIvrcegaKqXMyIqz9B5N2+59pev8NvAs/J7SrkKRf3hiLnXcyesBvmKtVV6BVVxKurUKrr0NrqEN3NxqZOT+G8t088x8fACquONSUNNTUDEwZWXudOdXU9HYPNJSkTOyn/hrrpBkElrxBaPWnhNbPxzplJpaxZ0SdwiiEgjr+OrAloq/8F5quoR5/V5cT84PRZXsoW1rNlOKyIi6rVRvOikpm25bkBrZuwtJ3YLtGEcG6FnY++SkJo3vtFfGW3Y0sueIthFll0ovnkTo2h6biFj6+YhE7PigGCdZEC6Z4C31OX4qU4DimlCVvnskacRqqDNIvMIBxPVMZ/XA+g287lq1//4ad/15L9ZJixv/jTJx5iTjy0xj62OUUv7CQkhe/QvcF6X/PBZ02GSosFhIuuAxr34E0vvxPamffR8p1t2PKiPyzjBE5utdDqGiHYZpWUkS4rAitbr+t1oRATUpBTUnD2ncgSkISakJSq82yC8XpQtgdKFab4XNvMn1vLYXUdWNjlGAA6fejez3oLU3ozU1o7ga0+lq0uhpCuwrxr162V/iF3YElvzfmgn5Y+w7EnN87auFV4lOwn3otlnFnEZg3h8D/5hBa/yX2M36LmtUn6r+fOuJSUFT0b/6BZrKiTrm9S4ZZ9qfLCjneWgCEI/JcUr2hAmzONm2QrAf8aNUV2MdOjLid/Sl/azl6IEyfm083MlCkZPXtnwEw+eXzceYlUr2mjnfO+QI9pJN1XDaF8yspL9GBAKcfuw4hIHPCSkpaZpCu1JBlLqPwPyYK3y3ihCfGM2BGL4bfczw9Tu3Lihs/Zsmsd5j6nwuxpjgQqkLPq45HdVjZ/Y95uAYuJeei9r2n9mIbPoaUlDTq//4wdU8+SOot96AmdcwEVYx96J4WAts2Edy2ieCOrYQrSve+pqZlYs4rwD5+CuasHNSMLEwp6YYXfjsQioKw2sBqg7gf/57JYIBQRRnh0iKCxTsJ7dpO4OO3aZESYbNh7T8E69BR2IaOimoFtZqSg/3CuwlvXYb/02fwPH8r1skXYZn0s6hz9dVhF0M4gL56Lro9CXXcr6Oq50jRZYVc+ls3TrBFvlu9bKlHiWubYOwZqZjS2jdhUvfVFhJHF2DPNdqtW1lGw7oqRtx/As68RMIBjY8v/wpLvJlTXnmUrOGLDigf8hsfRUrfCh7SD4zrly8YyX/PuRlHqo28E7JIHZfDhOfOYdHMN9n4t8WMenD63nN7XDSBprVFlLywkMwzRmFydW7uuTmnJ8nX3U7do3+k4bknSLn5riPqiX60Eq6uwP/tSvwb1hDavR2k3GtTbBt1DJZefTHn9UKxd/5u9cJixdKzAEvPAhyTjgdaLz6FmwluWY9/47f4167ErapYBw3HccxkrENGRPR/IoTAPGACpp5D8X36DIGF/ya8ex32c2+LesNwZeQV4K1DX/caIj4XZcCPWkh1Kl33GxXyAgKitKwVtra54enNTQAo8ZFfMPbWEQzjK64l9YTWLI25c6l9twSUZHp89Tw0DaYkezpNxR6mPTOR1y87m6s+24otsQGL3XD1M9vCBxwBQl4TLdWJhN87hsSCOJY9sJa8E4zwROKgdPJnDGHX6+sZdudxmJxGWEgIQc5lk6n/ehsNywpJm9Z2p8fDhTk7l4SZV9E49ynD+uC4thmXxTgQrcmNb8USfCu/JlxqhA/Nub1wnXw21oFDMfcs6DYXScXpwj5iLPYRY4mXklDRTvyrl+Nb9TUN61ejxCfimHwizmNPRHG13dlS2F04zr2VYMFI/J/8Hc+cm3DMuDOqUIsQAmXiDciWCrSvH4GkfJSMIRHXcyTouoGfcADUKA2MtBC0MVNChgwhbU9mhR4w6jC5Wp0ZN2wg9M1aTLOnnU8AACAASURBVCEfpicfgQ0b8FQa+1kqTjNVG3J4/ufPse6jEfg8B2830GJh+3t9eWTI3yjOv5GeJ2VTt6nxgHOSR2YhwzresqYDno/rb6xQ8xbXRf2eOhrbiHFY+gzAM/9TOiNTqjsT3L2dhuefovquG2h+9zWEqhJ/3iWk3/8Yqb+7n7jTz8dS0K/biPh3EUJgye9N/HkzSb//cZKuvglzj1xaPnqL6ntupOmdV9Gamw5d0X5Yhp9opB4rKp4Xf09o2zfR9U0xoR5/DzjT0b64B+lvPHShTqDrCrnUDC+Lw4xQW9s41Kz9j6A6rAiTQrCuNS989mzsoUbCqg2fKR5mzyZlkJEa5S1pwRpvJlCs8viMG3n7/nPwew8U84DXzH/vuYAvZl5AyGujx5J/U7qoisTeB45MvOXG/qCWpANXoWp+w7RJtXadL7YQxnJ+rb4Wraqis7vTLQju3k7dkw9RN/s+ApvX45wyjdT/e4jUW+/DefwpR+V8g1BVbENHkXzt70j9w4PYho/FM/9Tau67hebP3jN81duImtEL56y/oaTl4XvzzwTXz4+uT9Y4TCfeB/5GtEUPd8mBSNcVcgQHWGFGgmpuswOdaJ1c0T0/vDjnkHWoCs4+mTStKzaeuOUWMlqMvQSLE8fDLbeQOTaV7InpLH9gLSfc1IeGbc30TraT26sBVdHRdQh4LOg6KIqkX98KSsVgJgyrYPMKC3UbGxl7674wScgTZNer60ganokt7cAJovqlhQC4BuVE/Z4OB2qq4ceiuRs6uSddG72lmcaXnqFu9n2EK0qIO+di0u9/jPjzL8Wc2b03NYkEc1YOiZdfQ9ofHsLSfzAtH/6XmgfuILBlQ5vrUFxJOC97ADVvCP73Ho1ezFP7o4y9Glm0GFn4SVR1HE66rpArJmP3kigQNifyx6xK92NPfrNW374wRMqUgTRvLMWzsxqGDCHuNzPJmlZAYcapNGUMRAjBKXOPxZlhZ93j65lwYSY5wzxMuvJLhAB3cSpvXHo97pJUhIBRV37FtB4fUbdDsL0uj2PHldLnbCOnXguEWXXLp/iqWhh827EH9EPzhyh+fgH2nqkkDO/ZrvfU0eydj/gJeLtHS2DbJmoeuAPf6mU4p59F2j2zcZ14Wpe2DT7cmDKzSf7ljSRfdzsIhfqn/4L7jeeRwUCbyguLHcdFd6HmD8X//mOEti6Lqh/KkJ8hMocbZlutWXVdha4r5CYb6CHD2D5ChCMB6WlbLEuJT0DY7Hv3sYyWzDNHoTos7P7HPOSsWfDIIwy763jMqXF8/ZGgcVM1riwHF355KoMv603Rp6WMm/k8qkWjdPFEPv31cyj10/jsV89SsvAYVItG2t3lxOl1/Mz7J0Z9/nsAWnY3sPjyt6j6ajfD7z6elFH7HNukplP44Hv4y+rpfeNpnWqqdTD861Yh7A5M2V3rTqGr4Fu1lPqnH0Y4nKTedj/xZ/6s03dn6krsWWzmPPE0vEvmU/vXewhXlreprDDbcMy4EyWrD753/kq4bGvE7QuhoE7+HWhBtGVPR1z+cNJ1hdzSOmoLeiMuqiSkIT2Nhqf0IRBCYM7NJ1S0M+J29sec4KDnL0+gYfl2yt9cDoAtzcnEOeciTAqLZr7Jtn+twGRTOOHx8fx8/TlkT4xjzT9uY+EtN+He6Mezuwl/Oaz9x33seOgC+pmX8TPvH8nSCvH95nds+Msi5p/9bzxFjYx74nTyZ+ybQdcCIbbe/za18zeSf81JJI7uWvsThkqL8K9ehmPC1G47KXc4CWzdSONLz2Ap6EvqLfdi7hH5iuafAsJiIf6ci0m+9jb0liZqZ9+Lf9O6Npa147jwboQrGd+bf0ZvjvwuXCTkogybidz5BXrFtxGXP1x02W+UsMYbPwTcYIuPqKySZKTo6Q2VqOmHDi9Y+gyg5dN30T0t7dp0OOvccbhX72bX059hirORceoI4nonc9ybF7H2vvlsfmwpO19eS955g8ie3oeUge+QOkgw8mBrDeYW4VueT/Fbs6h4/H2qFjqQ4ltyzxrAoJsmHhAX9+yoYuuf3sG7s4r8X59EzsWduxDou+g+Lw0v/B0lLh7X9LZt8PFTQg/4aXz5n5jSs0j61c0/6TBKW7EOGErqbfdT/+xjNPxzNgkzf4HjmMmHLKc4E3DMuBPP87fie/thHJc9EPGiIWX4TPRtH6Evfxpx9r+6hFNilxVy7EYSv/Q1IBJyIyqqpBrn6zVFbRJy68BhtHzyDv5Na3GMnRR5X1sRiqDf3eex+Y7XKXzwPXyldfScdTzWFAfjnjid2hWlbH9+NYVzVlH47EpMLgtxfZKxZ7gwx1sBQdgbxF/toWWnTqAuFxb+D5vNQu+fjyJ/x/s4+zihVcRDbi8lLy+i4q1vMMXZGPSXmSSP7xt1/w8HesBPw78eQaupIvm637frQnm04lu6EN3dQNJV18dEPALU5FRSbvg/Gp57HPcr/0KGQjiPPeHQ5dJ7Yj/9N/jenU1g0evYpl4SUbvCZEMd/Qu0rx5E7l6I6HVclO+g4+iyQi4cralVnpofP/EgKGm5oJrQKndgHjzlkOebexagJCbjX7m0XUIOoFrNDHroYnY8+jGlLy+mYdl2Cn57CgnDe5I6NofUsTkE6r1ULy6ifm0lLTvqadpWR6g5ABJMDjPWNAfpk3uSMDCN1IWvEz/nYcSa1gwex034yxuoeHcFle+tQvMHyThtJPm/moY5sfNX8e2P1uym4V+PEiraSeIVv8bat20mZj81/OtWYeqRh6VX17oIHwqpS/xl9Xh2VROobCTk9qL7jTUVitWMKc6ONS0OW04Kjl5pqNb22QIcDMVmJ/lXt9Aw5wma3ngeYbW26TtsHnIc4R2rCS7+D+Z+x0S8YEj0mQ7rXkVb/Twif0qne7F0WSGndaNl6amOuKhQzaiZvdFK2zahIRQF+9hJeP73IVpDfbv3oVQsJvr+/iySxvdl5xOfsP76F4gf0ZOss8eQPKk/1mQHuWcNJPesNgjbpcNhzl8IWOKpT+5HrTYc98VPgCJIO34wOZdNxtmrY7fB6giCxTtpnPMkWrObpKt+i23495w3Y7QSrqnE2r9rrhj8LnpYo37JNmrnb6Rx5U7CTb59L6oC1WIGAXogjNT0A15z9s4kcVQ+yZP6Ez80r8Mm44XZTNJV11P/zGzcrzyLGp/YJi9828lXE969Ft+HT+C86tGIQixCUVFHXI624I/IosWI/EMPGA8nXVbIhcUFFpdhZxsFau5Agis+RIYCxia4h8Ax6Xg8//sQz1fziD878v1BD0bq1IEkHdOHyvdWUv7Wcrbe9xaK1UTCiHziBufg6JWOLSsRc6IT1W6MVjRfiFCTl2B1E76SOjyvf0jT6Ovxt96h2DftIG/WiWScOgJrRvS2AocLqet45n9K8wf/QYlPJOXGO7HkFXR2t7o4nR9jPRRSl1R9vIbi5xcSrGnCnOxsFeRcnL0zsGUnYYq3740XSynRPAEC1U34imvxFFbiXl9M+X+XU/b6Uixp8WScNoKss8dgSW37EvwfQpgtJP3iBuoeuZ+GuU+Rett9mFJ/fIAjbC5s06/G99ZDBFd+1OYN2veWLzgeVs1BX/caSkzIfxgRn4NsKj30iQdBzR8Oy95FK9mEqWDkIc83paRhGzEW7+L/4Zp2eofFclWbmR4XTiD7gmNwf1tE3eItuFftouGb7W1a72S224jLFmTOPImkhW/jGDMQ8fOpHdK3jiZcXYn71ecI7tiKddhoEmdeheJs/5f0aMeUmk64on3pr4eTUKOHLff8F/ea3cQNzqHPLaeTdEwfhPrD4QQhBCaXDZPLhrMgndTjBgEQ9gZoWFpI9adrKXnpK0pfXULmGaPI/flULEntW1+g2B0kXX0TtX+9m8bnn26TQZtpwETUXiMIfPUalqEnIOxt/94LxYQy+AL0ZU+g12xGSeu80GGXFnIScpGVa6MqasobAqqZ8PaVbRJyANcp5+D/dgUt//uQ+LMPvWFzJAhVIXF0r71pgWFvAH9JHf5KN2G3Fy0QAmkIvynejiU1DntO8oHbt3WyLe0PIUNBWv73ES2ff4Awm0m45JfYj5ncJWbzuwOWfoNo+eSdDgnrdTQht5d1179AoKKRPr8/i4zTRhz0c9X8YWq/KaVxQxWeEjehpgBSSkxOC/YMF3F9UkganokrP5G0E4eQduIQfKX1lL62hIr3V1L9+Tryr5lG5lmj2/V/Y0rLIGHmL2ic8wQtn7xL3BkX/Oj5Qghs067E8+wNBJa+he2EKyJqT+l3KvqqZ9E3vYsyNSbkB0Uk5SN3zEMGvQhLZBN5wmLDlD+M0LZvsJ70izb9c5izc7GPmYhnwec4Jh6PKS0j2q4fEpPDiqt/Nq7+2Yc+uYsipSSwbhVN776GVluNbeQxxJ9/KWpC+7bc+qlhHzuJlk/ewbNoHvFndUxYr6MofOh9/OUNDJl9KQkj8r/3ur/Wy7ZnvqH43c1o3hAIsGW4sCbaQAhCLfVUVLagh4x4uT07jh6n9qPnBYNx9Uym721n0mPGBHY8+jE7Zn9E/eKt9LvzXMwJ0U/c20eMJTDuWFrmfYBtxFjMOT+euaZm9MI0eDLBFR9iGX9Om/Yx2IOwOFF6T0Mv/Bw54XojJNwJdHEhN0avsmEnIgr7SNOAiYQ/ehK9ckebZ6Xjzr4Q/7pVuP/zIsnX3hYbVf4AweKdNL/7OsHCzZgye5D8m99jHdA9Juy6GqbUdGwjx+FdOA/n1OmoCdH5Z3c07nXF1C/ZSv410w4q4tVfF7Pylk8Je4LknN6PnDP6kzwyG5PjwOwUqem07G6kbmUZlQt2sePFNWyfu4rs6X0YeMNEXPmpDHn0MireWcGupz9n7a+eY/BfL9nr7R8N8eddQmDTWtxvvkTKjXce8ntsnXwR4Y1fEVzxYeTpiP1Ohy0fIHcuQHSSZ3nXXdkJiOTeAMi67VGVN/UfD4qJ0MaFbS6jJiQRd9YMglvW4/t6QVTtHs2EK8tpmPskdX+9h3BFKfE/u5zU2/8cE/F2EnfGz5C6TtNbr3R2V/ZS8/k6VLuFrPPGfe+1hvVVLL/2A+wZTo5/dyajHpxO+qSe3xNxXdNBCOJ6J5N/4VDG/+Mspn8xi76/HEPVoiLmn/0KW59ZAbok+7xxDH3i52jeAOuuex7Prsgz1vagOF3EnTmD0M5t+L9dccjz1dRcTP2OIbTyozatCN8fkTYQEvLQt38ebXfbTZcWclyZYI1D1hVGVVxxxGPqM5rQhoXICGxqHceeiKXfYJrefoVQF56EOpKEa6pofPmf1DxwO4GNa3Gdcg5pd8/GOeWkfVbAMaLGlJZB3Cnn4F/zDb5VSzu7OwC0bKsgbnAOqu07I2wpWXvvl1iT7Ux8/jziCpIPeG3XZ6V8OHMBz/Z+k6eSX+XJ5H8zZ+DbfDhzARteKERYzAy6cSLTPrmcrGm92fLEUr7+5XsE3X7iB+cw9KlZCEWw8ZZX8Fe5o+6/ffwUTJk9aP7oLaSuH/J8y9gzkb5mQpsWR9SOEAKl4ARk5Vqkp3PMtLq0kAshECn9kLWRG9zswTz8RGRLA+Htq9rerqKQePmvEFYbDc89ju6L3O/laCFcWU7jS89Q88fb8K1ZjvO4U0i7dzZxp5+PYo+tQuxInNNOx9yrD+7X5xKuruzs7hBu8mE+SCaJe1MN7s019L92HNb9vPC1kM7nv1zCBzMWULWmnl6n5HDMyWFGD60g59h0ar/awZc3LGfuoLdZ9ue1qE4rY2afyog/TaN+VRmLL3+LQJ0XR14qg2dfhuYLsvn/3kAPROmCqii4TjsXraoc/9o2jMrzh6Gk9CC05rOI21IKTgAketGiQ557OOjSQg6tty31O5DhyG539mDqMxbhSia06uOIyqkJSSTOug6ttpqGuU8io7TU7a4Ei3fSMOcJah64Hf/aFTiPO4X0ex8h/ryZqIfYbDdGdAhVJennvwHFRMNzj6H7fYcudBhR7BY0z/e/d40bjZBH2oQDjb1W/G09W9/czfg7h3PxotPwma3M+1jlgyV5LHiuBHdDNef8+zFyjkvgm4fX8+qkj6hZ30DP8wYx/p9n4y1xs/RX7xH2hnAWpNPvznPxbKtg97/+F/V7sA0fi5qWieeLQ3//hRCYh5+EVroZrb5trop7SexpZNnFhPzgiPSBIDVk7bboyqsmzCOnE96xOuIPx9p3IAkXX0lwywYaX/5Xm27PujNS1/Fv/Ja6Jx6g7q/3ENi6Edf0M0m791FDwON/IBtl1iwYPRp03TjOmnVkO34UoSankjTrN4SrKmh88e+d+j9nz00x/PW/Q9hrbNpijtu30E4P66z9x1Z6n5XHyOsH8exJC1n69Hayj+tJrlxLErtIn1BM3hmrqK79mtxzCgj7wvz3lM8oW1JF2vhcxj56Gu4ttay990sAUib1J+ucMZT/dznNm6MLcQpFwTl1OqGinQSLD+1wah5yHCAIb2j7vBq0hlfyJiIr1iJDR/4OvusLeZqx1FZWt31XkO9iGXUqKCrBb96PuKxj/BTizpyBf9VSw5hHi35LuK6KHvDjWfwFNQ/cQcMzswnXVBq70tz3GHFn/Aw17hDuk+vWwerVoKrGcV3bbEVjHBzrgCHEn38pgQ3f0vT2K522tVjCsDwClY34Sg60e7UmG6mBvup9u2o1l3oJuIPkT89mzSu7qVjbyIkPDuXreUUsYxDbScU1rQQpod9Zm1j+cgl6dhLOLAcfXLSA+q1uMqbmM+DacZR+uJXyeUaCQ89fTcOc5GLX059H/Xewj5sEZgu+rw8tzkp8CmreIEKbI4uTA4jc8cYeCuVroulmu2i3kAshcoUQ84UQm4QQG4UQN3REx/bW70iGuGxkVfRCrsQlYx4yldC389C9kU+euKafiev08/GtWGKEWdq4M0lXJ1xTRdPbr1J91w00vfECisVK4uXXkH7vI8auNG2Nga9Y8eO/x4gY55STcB5/Kt6F8/B88VGn9CFl8gAQUD3vwAtz4hBj6Xv9qn13uIraujQ/LClf04gt0cyahZXYnAqu3FpWm3UGnr4GISB/6gqCSSZ2LKxByU1CtSh8etVitJBO36vHEt8/lQ1/WYQWDGNyWsn7+RSa1hXjXr0rqveh2B3Yho/Bt2Y5MnzoEKlpwET0mmL0CO/gRcZQMNmQZUf+/78j8sjDwC1SytVCiDhglRBinpRyUwfUDYDIHIosWY6UMuq8bsvE8wmt+4LgNx9gO+7SiMvHnXIOit1B01uvUPf4n0n6xY1dbhVeW5BaGP/6NXiXzCe4ZT0oKrbhY3BOPQlzQb/o/r5jx37/91Vtn1yOcXDizrkIzd1A83tvoLjicYw/sn4e1owEksb1ofL9VeReMhmldTNvV68knHkJlH68jfwLjX1kXT0cWBLMVKyowRrvZMaLDzLozJUH1BcIGNlNGX0reKTuwBWXOz4axaaX+zL0yn4MvmUSS69+j9IPttLz/MFknDaS4hcWUvbmchJHR+fbYx89Af/Krwls3YBt8IgfPdfcdxyBz58lVLgC6zFnt7kNoVoQGUPRy1dzpPO42j0il1JWSClXt/7cDGwGOnSHWCVjOPgbwV0cdR1qai6mARMJrvgA6Ytuo2Xn1Okk/fJGwlUV1D58J/6NXWeHkEMRqiij6d3XqL7rRhrnPEG4sgzXaeeRfv9jJF15HZbe/aNf/DRsGIwaBZpmHIcN69jO/0QRikLiZb/CMmAo7lefw9eGfOiOpsfMSYTqPVS8t0+UhRD0vGAwdSvK9k58CkXQ+/Rctr9fQu64ZD6940LclekEfftSF61WIyxpse4bFYd8FprK09n21i9Z89RmpJSkTcojvn8qu15fDxhuohmnjaRhWSHBuui+u9b+gxE2G/51hx5gKEmZKCk9CO9cHXE7InskNO5G+tq21WRH0aExciFEPjASWH6Q164WQqwUQqysqamJaFJMZA0HiNp3ZQ/WyRdBwEtg2TtR12EbOorUW+9DiU+k4ZnZNL42B93bto2ejzRakxvPgs+o/es91D5wO575n2HO703Sr24m/b5HiTv13I5ZTv/888YIXFGM4/PPt7/OGAAIk4mkX/wWc35vGl94msDmIzv/kDgyn8QxBZS89BWh/Sxr8y8cijneyqbHvt4bux5+zQBCzSHc62vRw4N49vinWP3+KPzeg7uPBjxWChdN4K/9HiZlyGQadzRTu74BIQS5Zw/AvbEaT7EhiGknDQVdUrdoc1TvQ5jNWAcMJbBpXZti7WqvkWjFG5FaKLJ2Mlq1qmp9VP2Mlg4TciGEC3gLuFFK2fTd16WU/5JSjpFSjkkrLo5sUiw+B+zJ7d4jT83ohWnQZILfvI/e0hB1PabMbFJvvRfniafhW7qQmj/9Du+S+V1iIlRrduNdMp+6px6i+s7raXrrFaQWJu7cmaT/8XGSr74J25CRCKXLz3PHaEWx2ki+5lZMWTnUP/sYgcLoxCxaev1mOmGPn93/mLf3OXOclf7XjqNmSTEV83YAkD48mQEX9WLNk5uZekMfarfqfHnvXbx11/n4vQcuKvJ5zcybfS5v330HIa8NU7KxyXT5cmMjmczjjBBKzdISABz5adiyk2hYviPq92EdMAS9sR6t6tDW2Kb8oRAKoJVHthhRpPYDxdyuOb1o6JBvsxDCjCHi/5ZSvt3mgm2cFBNCILJGICu+bfcMvnXqJRAOElj0ervqEWZjE9jU2+5HTcvE/fpcav78e7xfL0CGgu2qOxKklIQqy2j54iPqHvsT1f93Pe7X56LV1+I86UxS//Agabf/GdcJp6LGx/K/uyuKw0nytb/DlJJGwz8fIbg7OtuKaHD2zqDHjAlUfbSGhhX7hLTXzOEkDEpj3R/n4681Uu4mPzgGe6qVTf/cxKTr+tC0tYV+IxtQFYmug99jRtfBrGg40mpIzTMWHJnjLKhWheZi4+7W2TMBS6JtX+hGCBJG9KRpfXHUGmDpZ2TABbYf+kKo5hrnaiWRTfUJkxWR0hdZszHyDraDdk92CiOwOgfYLKV8JKLCEUyKiayRyJ1fQlMpRLiH5/6oKT0wjzqF0OpPsYw9AzU1+roAzLn5pNx4J4H1q2n+5B3cr82h6f03cIydhG30BMw9CzrUeEtKidZQR3DHVoLbNhHYuhG9wUgPM2Xn4jr5bGzDx2DqkXdUGn7JoB+9qQbZ0oD0NCJ9zUhfCzLkh3DQWLglJQhheFGbrQirA2GLQzgTUOKSEfFpCGdit/v7qHHxJF93O3WP/Yn6v/+VlOvvwJybf0TazrvyOOqXbKPwofcYOfcazAkOFJPCqAens3DG66y+/TMm/PNs7MlWTntxCm+dMQ/7phomXudk1IzPkQjqilN55abLuOLRF4jLamLKVQsQnhC7/wsZgxJQzIrhzYIh3K6CZFqK9sWaXQN7UPXxtwSq3NgyIw8JqqnpKPEJhHYWwrEn/ui5ijMBJTkbrXRLxO2ItIHo2z5G6lrEGztHS0dkrUwCLgPWCyH2xD7+IKX84aVUo0cbX7YIJsWUrJHogKxYE/FmzN/FOmUmoQ0L8M97DufF97WrLmj1NB42GuvQUQS3bcK7+As8i7/As+AzlIQkrAOGYOndD3NuL0wZ2Qhz2/YulKEQ4bpqtKoKQmXFhEqLCBXtRG8y/rmFw4m170As08/CNng4alL0bnFdCSklsrkOrWoXek0Rem0Jen05en0F8ofSR5VW0VZVQIDUDVEPBUAeZFGN2YaS0gM1NRclswA1qw9qVl+ExXZY31t7UROSSL7+Duof+xN1T/+FlBv+D3NWzuFv12qm/13nsfbXz7HtwfcY9MBFCEUQ3zeFYf83lW/v/pJNjy1l8C2TyDomjVPmHMsnP1/Eqb99DdWisfaNCXxy3ZUk127h9c8v4bS575N/YTHOxIfJm3ALcRkWQi1hnOn7Ul6tqXaat9fv/d3RMw0AX3FtVEIuhMCc34fg7raFZ9Qe/QnvinxeTqT1h01vgbsEkvIjLh8N7RZyKeViotmrKtL0tIRccKSil69GGRDZlkzfRXEmYJ1yMYF5cwhtW4653zHtqm8PQgis/Qdj7T8Y3evBv341gQ1r8G9Yg2/5oj0noSYmoyQmoThcCKsNYTKBlMhQEN3vR/c0o7sb0ZvdxgVvT7m0DCz9BmHp1QdLQT9M2blHRaxbBrxoZVvRSreglW9DKy88QLCFKxklJQdTv2NQkjIR8akormSEKxHhSEBYnQjTwS+OUkoIBZC+JqTHjd5ch+6uRjZUotWVEi5ah9ywoLUhBSWzAFP+cEy9RqDmDf7BejsTU3IqydffYYzMn3yIlBvvxJSeedjbdfXPotdvprPz8U8pfXUxuZdOBqDnBUNo3FjD9jmrcOYlkP+zIfQ5K49TX5hMyPtPFt/9GxL630xSyjJ21o0FHzxz8bGM/mw+A8/YzkX/z955h0dVpX/8c+6dPumNVCAkBKT3oohIEwTBtfeCvXfXXXX3p7uuuiu2dV11FftasGGniIL0XkKVXkIgvU2/9/z+mISikMwkMynsfJ7HJ87k3Pcc8sy899z3vO/3fW8IO7/zV26mn3qkPZtqNCC9R27C5jS/83Y3QUjL2D4b97qV6E5ng3USSloucv2P6FUlKNGBb5JEYh4Asngroq048ubicJy8YFWT8snrMA2YiHf1LFwzX8OQ3RthDO1OTLHZsQ0+Hdvg05G6jlZ0EO/eXfgOFqCVFKFVlKNVlCHdLn/anhAIoxFhtqBGx2DMaI8an4ia3A5DShqG1HQUc+veLQaK9LrR9mzA99V7+Mr3oBu9h3fNSnJ7DLkDUNNyUNp1Qk3pgLA0XqxfCAEmi3+nHZuCym871es15WgHtqHt3YS2ZwOepTPwLP4UTFYMOf0wdj0VQ+dBrWq3bkhuR8IdD1H6whOUvPQUiXc9jCExOezzpp03i0DQFwAAIABJREFUiMr8fez+z1zsOakkDPX/PXv+cTiOgkrWPvYjplgL6WNzyZ3UngPLP2XRYz/jPLSYfnd1Z+LZWRxYX0FlgYukvFPpcnY63movS59cR2L3OFIHJh2ey+f0olqPuChTbbcsb0XjS+CNGf4mE76CPZhyutQ7Vk31y2hrhTuCcuTEtQfVhCzdBoxt7FKDos04cgAlrR/a9jlQvrvJjyxCNWAZfwuOd/+I++ePgm7xFNRcioKhXRqGdmlhm6O1o5cewLttOb5tK9B254NW29puXwmmXaUYdpeiTroU8cgLzb42xR6HkjsAY+4AwB+H9+1ah++XZf7/Ni0Eoxljl6EY+4xB7dADIVr+SchY29Cj5MW/UfrSUyTe/WjYuzMJIej84Dk49xSz5fFP6fXyFOzZKShGlYHPnc3iG75gxf3fM+DZ8aSPziFtYDKXLZzAz39cyYqp+ax7fQu5k9rTcUwGlkQTm97bzsoXNuAocjH+7WPbAzoLq7CkHFFfFCYVFIHmbHwygSHdH4byHtjfsCNP6QiAfmgXdB5Y79ijEYoB4jogSxufYRMsbcqRi3R/7039wGrUEDyyGDr0xNhrFJ4ln2PsPhy1XXaTbUbwI6VEL9yOd/MifFuWoBf708iUxExM/cdjyOmHmtkNYTmqpdczz7fQao9FmCwY8wZhzBuE1DW0vRvx5s/Du3EB3vyfUBLSMfY/G1Pv0QhL0xoGNxVjZgcSbnmA0peeovRf/p15uBteq1YT3f52CWtuep2Nv/8vvf99PabEKAw2I0NemcTiG2ew4p5v6fvEGLImdcWaYGbsK6fS56YurHppE9tm7GHju0ecXFLPeMZNG0Zq/yO7cd2jUbWtlKRLj5yjCSEQigC98ZlralwCwmTCd7Dh8nthsSNiktAO7Q56HhHfCVnQfNXNbcqRE50O9mTkgTXQ7XchMWkePQXf9pU4v34R+7XPNNsp88nIYee98We8GxcgKw6BUFDb98DcbxzGzoNQ4o+K5d5777EG7rsPng0u8SncCEXF0KEnhg49sYy9Ae/mRXhXfot79uu4572Pqd84TIPPRYluObkGU3Yu8TfdS+m/n6H05WdIuOMhFEt4teLN7WLp9tSlrL/zLTb8/r/0fPFqDDYzxmgzQ18/l2W3f82qh2bhPFBF5xsHIIQgpW8i494Yhs+lUbqpHFe5h+gsO3E50b8JlZasKkD3aCT2P9LTVkqJ9OkIQ+O/o0JRUJNT0QLUe1eS2x/ehAQ1T3w2cttMpLsKYQ7vjRXagPrh0fjj5H1Dkk9eh2KLwTLuZvQD2/As+jQkNv/X0MsKcf/8ITX/voWaN+7Bs3QGalIWlol3EnXPu9ivfALzoEnHOnGAHj3gnnv88rf33ON/3YoRRjOmnmdiv+Yf2Kc8iyF3IJ6lM6h+6XpcM19tUpFZUzHndSN+yu149+2i7D8vIL3BVSQ2huiu6XR97AJqthey+ZGP0T3+0nuj3cSQVyeROaELm15YzMoHZuKrORIOMVhUUvom0v7MNOJzY4573rXn842oNiPJpx7RPK8Lqag2U5PWbUhuh684sDZyalIWesk+5PEyn+qh7pBTlu8KcnWNo23tyAEltQ/atll+3ZW4+rtjB4rxlNPwdhuGe/4HGDoPjIRYAkC6Hf5Qw7q5aHs3AAK1Qw8sQ36HoetQFFsD0rcAU6Yc+f9WthNvCDW9M7bzHkAvvQL3oul4VnyLZ/UsTIMnYz71AoS58V3gG4ulZz9iL7uBivdepfzdV4i75rawZzUlDM2j84OT+OXJGWz56+d0/dP5CIOCajLQ7+9jie6cwKYXl1C+8RD9nz6L+J7tGrRZua2E/d9uJfvSXsf0APWW+ouFjHFNC2epicm48tcgdb3Bv4+SmOmvT6goRsSl1Dv2aERs7Q2ofA+069mU5QZEm3Pkdbor+oE1qCFy5ACWcTdTs2cDzi+mYr/uWYShaXf9kxEpJdr+LXhXz/T3NfS6UBIzMJ95FcYeI1Biw5810dpQEtKwTrwT86kX4PrpfTwLp+NdMxvzmVdj7D2y2Q9FbYOHoVdXUvXFB1TGxBJz/pVhL3xqN74PvionO1+axS9WE51/PwmhCIQQ5N04kIQ+aax8aBbzL/2YTpf1osutgzHFHT8DyFvlZuUDMzFEmcm7+dgDRvdBf/2EuV3TKpTV+CTwedGrK0/cLKUWJcEf2tFLC1CCcOREp/pL9Zsg9BcMbc6R1+muyIPr4ZTAJSYbQrHFYpl4J84PH8M9920sY28Ime22jnTV4M3/Cc/K79CLdoPJirH76Rj7jEXNaIJq4kmEkpCO7bwH0IZMxjXrP7i+fgHvmplYzr7tcPZDcxE16mz0inJqfvwONS6RqNETwj5nxkVD0Wrc7HlzHopRJefeCYi33oT8fJKmTmVk9w1sbJfDjvfXsufzjXS4sAdZk7oS0yUJIQRSSkpW7GfdX36ielc5Q14+53ADizocu/yNja3tk463hIBRE/yphFpZacOOPN6faaaXHQDql789GqEYICYDWRF8fL0xtDlHLoRAtOuJLAy9CpwxdwDawIl4ln2Jmt0HYxApRycj2qFdeFZ8g3f9T/7dd1oulrNvx9j99BYJHbQF1PQ8bFf/He+6ubh/mEbN63djOvUCzMMubtbiIr+WeSlVMz5ETUjC2i80RW/1kXXNGehejX3vLQBFkLM3H/H8c/DccxiB3vfcQ/bjD7P1lWXseG8t299ajarqWDsk4tlbjMerYk62M+SVSaSc2v439mu2FWKItWJKbHxdAfirYwH08lLoUL++uYhJBNVY68iDQ8RmIiv2NWqNwdLmHDmAaNcDuWse0lGMsDXt7vxrzKOuxbdnA64vn0O9/oX/uXCB1DV8W5fhWf4V2u71YDBh7D4cU//xqOl5Lb28NoEQAlPvURg6D8Q9+3U8Cz7Ct3Up1nPva7bduVAU4q64kZLyMsrffRU1PhFTdm545xSCDjeMRGo6+z9YBOecSQ7PI6hNTJg6lRghGDB1PO4SB4W3PEXl/HxcZbEYNQcJw/NIH2nH8MnzMHSqP4upR4/DZylVG/YR3TWjyU+ASm2uvVbZsGa4EApKXDv08sCyXI65NiYTuW8ZUuphD7G1qayVOkQ7f3aDPBSyJkRHbBtMWM/7PVLz4fz870HrEbdVpNuBe+kMql++Cecnf0MvK8Q88hqi7nwT6zl3RZx4I1BsMVgn34v1okeRNeXUvHEvnuVfN1sPTmE0kXDD3ahx8ZT95zm00uLwzykEHW8eTeaVwyj8ahVb885F1rmZ++47PM6caKPDR4/R8+DnDNz/Fn0KP6b9h49j2LIBnnvOr23/3HOQ75eDdRdX4dhVRGzfjk1eoxIVA0KgVwZW6u935AeDnygmAzQP1BQFf22QtE1HnpALiiEsjhz8ConWiXeg7duMe8604A1Mm+bPkZbS/3NaI2w0E3plMa4506h68Vrcs19HiU7Eev5DRN3+H8ynnh9Y9kmEejHmDcJ+4z8xZPfGNfNVnJ8+hXQ3T6d1JSqa+JvuRXq9lP7n+WbpNyuEoMP1I+nQN5aidr3Z9If30e86TnrpUY798OupU499r/Z12aKtAMQPbvpThVBVlKhov5ZRIOPjUtDLA0tXPOa6GH8VqawMf3ilbTpygxniOyGLt4RtDmO30zENnoxn+dd41s0N7uL8/OPuKloT2qHdOGc8S/VL1+NZOgNDTn/sU6Ziv/ppjKecFimMCjGKPQ7rxX/yh+62LKFm2r1ojSg0aQzG1AzirrkV3/49lP/3jWZ5IhBCkPXC3XS6azylC7aQr/XCd+Glxw46Xh3B8Zw7cGj2eqxZidg6BZE5Ug9KdCxaoDvy2Hbgqg765itiaouZKoNr4twY2qQjB1CSuyBLtob1Q2keeQ1qh164vnkJ3/4gbhon2FW0Bnx7N+L48DFqXrsd7+bFmPqfTdRtr2E778FI+CTMCCEwDz0P2xV/RTqrqXnzfnzbg+8L2Rgs3fsQPeECXCsX45g3q1nmBEg/fxBd/nw+VRv2svbWabgKjiqamjLFXz8ghP/nlCnHde6OXUVUrt1NyrjeIcuQUqJi0Kt+08js+GNr0w71iiB35fYUECqyKuLIT0xCZ3BXQU3wjzyBIlQD1vMfREQn4Jz+BHpFgLGuE+wqWgopJd5tK6h5+yEcb/8erWAr5jMuJ/rOaVjOuhElruEijQihw9ChJ/Ypz6LEpuD48DE8q5vHsdrHTMTcsx+Vn3+AZ2fzdRhKHtWD7lOvxFtazZqbXqd89a4TDz6Oc9/3/kIUs4HUc/qFbE1KTAx6dWCOXMTWOvIgwytCUSE6FVlZEPZwa5t15CLBLzEZboUxxRaL7eJHkV43jo8eD+zxqpWUnkup4928iJo37sH54WPo5Qcxj72BqDvewHz6JQhr+DUgIhwfJS4F+9VPo2b3wfXNP3Ev+CjsIY+6TBY1PoHyN19q1qbhcX070vuV6zHG2si/9x32vb8AGYD4VdXmAg7NWkvauQObXNF5NGowO/JaRy4rgz+0FNHpUHUg7OHWtuvI67QMynaFfS41uQO28x9CL96LY/rfkL4GMlmO98gYCCG6a0tdw5s/j5rX7sD5yZPgcfp1T25/DfOgSSHXXo/QOITZhu3iRzH2GIH7p/dwz3077M5csdmJu+Y2tIpyKj5onnh5HdasRHq/ej2Jp5/Crld/IP/ed48NtfwKX7WLrX/9HFNiNFlXDQ/pWpSoGKTbhfQ0LIkr7LH+XPJgQyv4Hbms2h/2cGvbdeTmaH+FZ3OVwHbqi2XCHWi71uL88jmkroV+kibetaWu4Vk3l5pXbsP5xTMgJdZz78N+88uY+oxBqK2v283/OkI1YJl8D8b+4/Es/hT3nGlhd66mjjlET7wA15rlOJctCOtcv8ZgN9P1sQvIffAcqjftZ9VVL7PrlTl4SquPGec+WEH+fe/h2l9K3qPnYYgOceOXaH82lhZAeEUIBSUmqVGOnJh0cFchH7jr2PdDHG5tkwVBdYjYTH8z5mbC1HsU0lGB+4c3cZmsWCbcHtry9KlT/Q786NcBIDUf3vU/4V74MbLsAEq7bKznP4Sh69BW0QAhQv0IoWAZdwsIFc/SL8BgwnLmlWGd0z7qbNwb1lD5ybuY87o1a79XIQSpE/sRPyiXXa/OYd8HC9n/0WJierXHnBKLz+GmfOk2UARdH7+QuBDkjv+aOkeuV1ZAQsNFhSI2BRnoGdnR10XXNpM5JcsfZp16VKFTCGnTjpzoNGRB85z612Eeeh7SVYNn4cegGrCMuzl0zvx4h6T1qAJKzYt37Vy/A684hJKag+XChzHkDY7on7QxhBBYzroRNC+ehR8jrNGYh5wbvvkUhdgrbqT4yT9Q8eE04m++v9k/M+aUGLo8eh5ZVw/n4DerKV+5E9fqnQijgZSz+5B5yalY0uPDMrcS7RfeCjSXXIlLwffL8qDnqXPkcuRAxHUj/G+GQemzTTtyEZWKdJQgdZ9fpKaZMI+4AnQfnsWfga5hGX9LaPKu6w5JG7hrS58H75rZuBd9gqwsRknPwzLuJgy5AyMOvA0jhMAy/hakqxr3nDdQYhIxdjs9bPMZklKIPuciKj99D9fKJVgHDA3bXPVha59E9i1jmnXOupZ4egBl+uA/8JQ15UifJzhl1Gh/LrmsCl6rJRjatCPHluRv2uss9edsNhNCCMwjrwFFxbNwOtJVjXXyvU2Xvm1An1t6XHhWz8Sz5DNkVSlq5imYJ9yO2qlfxIGfJAhFxTr5XhzVZThnPIeITcGQUX9vyaZgGz4G5/KFVH72PubuvVGs/xtiaHU7cq0iMEd+OAWx4hBqYmbA8whzNJhjwl4U1KYDqMJa217L2fydWYQQWM68yt8qbtNCHO8/il4T2GNasEhnNe6fP6L6pev8ZfQJGdgu/yu2q5/GkNM/4sRPMoTBhPXCP/rrFz75W1g7DwlFIebia9CrK6n+/ouwzdPaEAYDSnQMWkVgf9u6WgvZCM2Vw5krYaRNO3Is/gML6a5qsSWYh/wO6+8eRDuwjZo37sa3b3PIbOvlB3HN+g9V/5yCe957tRKpT2O/8m8YskNX5Rah9aHYYrFd+DDSWY3z83+EJ0uqFlP7TliHDKdm3ix8RY0Qh2qjKLHx6OWBOnJ/m8JGiWdFp4U9tNKmHbkw1uoSe6rrHxhmjN1Px3710yAUHG//HtdP7yF9DeenHg8pdXw71+KY/jeq/3UjnhXfYMwbjP2GF7Fd8mcMWd1CvPoIrRW1XTaW8bei7V6PZ8HHYZ0resIFCFWl6uvpYZ2nNaHGJaCVlwY0VkTH1+qSN0bONgOqCpG6L+hrA6Vtx8gNZv9PX/gV3RpCTcsl6oYXcc18Fc+Cj/Dm/4R5+GUYuw9HqA3/mbWSffg2/Ixn3VxkeSHCGo1p6HmYBkxAiQmt5nqEtoOp9yi0nWtw//whak6/sMXL1dg47GeOo3rml3jHTsKY8dvGDicbanwinu2BaSgJoaDEpzbSkaeD1KD6kD+vPAy0bUdeV+Citw7NcGGxY518L8aeI3H9MA3Xl8/hnvsWxlNOQ+3QEyUpC2GJAl1DVpehFe9FK9iKtnMNesl+/A2Me2I84zK/AmGkb2gE/P1kfXvycX35PPYbXgjb58I+8mxq5s2m+vsviL/uzrDM0ZpQE5OQTge6owbF1nD5v9+RNyJEcpScrYg48uNQFyNuxjLjQDB06oM9+3l821biXTPLL4q0/OvjDzZaUNt3wzJgAoYuQyO77wi/QVjsWCfcgeODP+Ne+AmWMy4LyzyKzY7t9NHUzPka36FCDCmpYZmntaAm+jNRtJKiAB15Gr5d65BSBnU+JWIyAPziWWGibTtyXff/bIXVi0IoGDsPxNh5INLnQTu4E1l2wC+6JRSELRYlMR0lMTOi/R2hQQw5/TB0H45n0XRMPc9ESUgLyzz2EWdR8+N31Pz0PbEXXROWOVoLhiS/I/cVHcSY1bHB8UpCOnjdyKpSfy/PQLElgmoOaxV66/OAwaDVHiiqrTsEIQwmDBldMPYYgan/2Zj6jcPYdShqcoeIE48QMJbRU0Ax4Jr7ZtjmUGNisfYbgnPZQnSXM2zztAbUZH9KoRZgpo5Smz+ulwaXSiiEAjEZYW3E3LYdua/2g2a0tuw6IkRoBpToRMynno9v8+KQprn+GtuwUUi3C9fKJWGbozWgmC0ocQn4DgYW8lAS/PFtvSR4hyxiM8Pa8q1NO3LpqlUuM0d0tSP8b2AaPBlhi8U97/2wzWHsmIMhNQNHMysjtgSG1HR8hYE5chGTBEYLenHwDlkddj+G88LXuzckjlwIMU4IsUUIsU0I8VAobAZEbUWnsIRHWCdChNaGMFkxDT0PbecatP1bwzOHEFgHnIp3x1a00uKwzNFaMKRm4Du4H1l33lYPQgiUpMxG9VoVljhEGEPATXbkQggV+BcwHugGXCqEaJaqFemo1Qe2RzI9IvzvYOo3Dix23Es+C9scln6DAXCtWxm2OVoDxvRMpMeDVhKYRK2a1B69uHl6IARDKHbkg4BtUsodUkoP8CEwOQR2G6bqAJhjEcb/DaGfCBHA31nI1PcsfJsXo1eGZ8dsSG6HITUD1/rVYbHfWjDUFj559+0OaLyS3B5ZVYp0tWw1+a8JhSPPAI5+1thX+94xCCFuFEKsEEKsKCoKXqD9eMiKff7mEhEi/I9h6jcepI53zeywzWHu1gvPji1IT8tXTocLY1omKErgjjylAwDaocDGNxfNdtgppXxNSjlASjkgOTk5FPaQZTshrkMIVhchQgCEuRN6MCjxqagde/klHcJUEGfK6w4+H56d28JivzUgjCYMaZl49+wMaLya7Pc3+qFdYVxV8ITCke8Hso56nVn7XnhxlICrHJGQG/apIkQAwt4JPViMPc5ElheiF/wSFvumTp1BCDw7wnOo2lowtu+Ed8+OgG6IIiYJLFFoBwNz/M1FKBz5cqCzECJbCGECLgG+DIHdepFFmwAQyeET3Y8Q4RjC3Ak9WIxdhoCi4t2yKCz2FasNQ7s0vHt2hMV+a8HUMQfpqEEralgQSwiBmtLx5NuRSyl9wO3ATGAT8LGUckNT7TY478F8UAyIxLxwTxUhgp/j9VRtQYQ1CrV9D3xbl4VtDkNmR7z7Wl+WRigxdswBCDiEpKZ28ktuhFEjPlhCEiOXUn4rpcyTUuZIKZ8Ihc0G5yxcjUg+BVEnZRshQrip66mq6/6fIe6E3hgMuf3Ri/eGLXvFmJaJXl6K7jx5y/UNqRkIqy3gEJKSmgM+T61iaeugTVZ2SncVsngrIr1/aAxeey307+//gvbv738dIcKvmTLF30tVCP/Po3usthCGjr0A8O1eHxb7aq0ColZ88nYOEoqCKbsznu2BOXI11b+D1wq3h3NZQdE2Hfm+ZSB1RObA0Bhctw5WrQJV9f9cty40diNECDNKSkcw29D2bQqLfUOiP8PMF2DBTFvFlNMF7WABWlXDfXeVpEwwmNAPtJ5snjbpyPU9C8ESi0gOUQHp8uX1v27rtKK0uQihRSgqalpntDBlrihx/gbneoAt0doqps6nAODZ1rAYmVBUf5w84sgbj9Q8yD2LEe1PC50E7MCB9b9u67SytLkIoUVN7YR+aFdYDt8UexQoClpVZchttyaM7TsiTGY8vwT2ZKOm5aIVbm81B55trrGE3LcMvDUo2SNCZ7SXP87I8uV+J173Oti1SYksK0Q78At6WaG/iYRqQIlOREnthJqW2zL641On+h340a8jnDQoKR1B86GXFqAmZTU4PhiEoqDY7EhH6ypJDzVCNWDK7YJ7y8aAxitpnWH51+jF+1BTWr4osc05cn3bbH9YJWNA6Iy+eZRQ/8rgRYK04r14187Bu+x7pOY46jcKCED6ldWENRpDt2GYBp4T8i9cvRwvbe7ZZ5tv/ghhRa1reFCyPyyfK2GxnvRNJsBfyere+AFaeSlqbUjpRKgZ/rRnrWBrxJEHi3RXIfcsROlyDkJp+aX7dq/HvXA62o7VoKgYXGYMM9ej7ilDKa5G3HEXcuozyMpitP1b8G5ZinftD3hXfo+xxxmYz7wKJbbpcgUNUpc2N3Wq34m3grS5CKFDxPszS/Ty4Du8B2TfZEa6W05vpWb7QSrW7EZ3ebC2TyJuYA6qxRjyecxdulMFuLdswDb49HrHKgnpYLajFWyFPmNCvpZgaXlvGAT6tlmgeVDyxrfoOrTC7bh+eAtt5xqEPQ7ziCsw9j0LxRYLfznq2GHqVIQQiNgUlNgUjN1OR6+pwLP0czzLvsK7ZQnmM6/ENHCivx1UuDg6TS6yEz/pENZoMFqQFeHJLBEGA2i+sNiuD3dxFdue+ZqyRcemBapRFjIvO5WMi09FMYYuVGlIz0KJjsG9Ob9BRy6Egpre2e/IWwFtxpFLKdE3f4lI6oJIaplqTt1RifvHd/CunoWwRmEecx2mfuMRxtqipHvvPfaC44QwFHsslpHXYOo3Htd3/8Y96z/4ti7DOvlelOj6H+ciRDgeQgiU6AT06jBllihqQI0XQoljTzH597yDr8pFhxtHkTK2F4YoC1Wb9lMwfQm7X5tLyfzNdP3LRVjaxYZkTqEomLp0x7MlH6nrCKX+zZWanodn0SdIrwthtIRkDY2lzWStyMK1ULYT5ZTmkTo/Zm4p8eb/RM0rt+BdMxvT4ElE3fYa5sHnHnHiEFTlnxLXDuslf8Yy4Xa0fZuoef0ufLsj2SQRGoewxyFrGs6Bbgt4SqvZcN97SJ9O739fR9YVwzCnxKDaTMT1z6bbU5fS9a8X4dxbwtqbXqdme+iKlcxde6JXVeIraLgLkJqRB1JHO9DyhUEt58iDzGfW86eDOQaR07zxKL26DOf0J3B+MRUlPg37DS9gGXM9whL128FBVv4JITD1PQv7dc8izHYc7z+CZ+V3YfqXRDiZEdZopLMqPMZ1Pbyhv6OQumTrXz/HW15D979fjj2n3XHHJQ0/hV4vT0GogvV3v03NzkMhmd/c1b/5cm9uuFJWTT9y4NnStIwjX7kyqHxmWVngP+Tsek6zaqt4tyyh5rXb8W1fhXn0FGxXP42a0jHk86jJHbBPmYrBmITru5dxzX4dee89kcKdCAEjzHakqyYstqXm81c9NwOFX66gfMUOOt0xjqguafWOtWen0POFaxCqyob738dd3PQbmRobjyEtE/emhh25EhWPiE1B27+lyfM2lZYNrQSYz6znfwRCQel2fpgX5Ef6PDi/fwXn9CcQMUnYr38e85DfhTUHXFjsWA9EY1yyE8/SGTj3z0fmR6QCIgSI2Yr0hCdFUPp8CGPos0R+jbfcwa7X5hLbP5t25/QL6BprZgLd/3E5WrWLTX/8EN3d9ENZ8yk98ezYGlBnJDWjS9iaYAdDyzryAGRApbMMfcs3iNyxiGZosqyXFlDz5gN4V3yDafC52K95BjW5/YkvCGH5u5j6LJZvNmD+fiO+nuk4+keF7csZ4eRCGM3g84THuMeNMIavA3wde9+Zj+Z0k3PXeIQQAV8X1TmVvEd+R/XmAnb88/smr8PctSf4fLh/abhcX03PQ1YWoVeXNXneptAyjrx//4BlQPX8j0Hzova6LOzL8m5dRvUb96JXHMJ68aNYxlyHMDSwEwll+ft99yEA88IdWD5dg7ZjLY73H0U6T+6qugghQDWCzxuWtm/S60GYwhvSdBdVcmDGCtqN64OtY/C1FYmndyXj0lMp/HIlxfObJiBmyukCRiOeLQ1/lw8XBrVweKXlduQBHAZKVyX6xs8R2SMQcfXsipuIlDquee/j/PgvKAlpRF3/PMbOgwK7OJRdY47KejGdeSHWhMFoB7ZT8/4j6I6TW+siQtMQqgGQh6uIQ4nuciEs4U2vK/h4CVLXybqy/vzt+uhw/UjseWlsf+ZrvOWNPy8QJhOmTnm4NwfgyFNeMwM/AAAgAElEQVQ7gVDCJloWKK06/VDP/xi8DtS+V4VtDulx4vzkSTw/f4ix10jsVz+NEnf8k/LjEsquMb/KejHe9gjWix5GL9qD4/1H0B0nR3pZhDBQl1US4nxv6fOBz4swh8+Raw4PhV+vImlEdyzp8Scc5yp2ULKqgLL8g/ic3t/8XjGq5P3xXHzVLnb8c2aT1mTu0gPfgX1oFeX1jhNGC0pKxxbPXGm1jly6ytE3TEdkn4lIyAnLHHrFIWreehDf1mWYx96A5Zy7EYYgY4Fh7hpjzB2A7aJH0Ev214ZZwpRiFqFtUxdTDvGOXLr9ZzSKxRpSu0dTNGc9Wo2b9POP/xRcuvoAC67+lJnDX2fBFZ8w/6KP+G7oa6x8cCbVu46NTds7pZB5+TCKZq+nfPWuRq/J3KU7AJ5fGhbRUtM7ox3YFpawVqC0Wkeur/sAvC7UfteExb5W8As1b96PXnEI2yV/xjxoUlAHLIdphq4xhpx+2C58GL14HzX//VPY0switGEOf3ZD60zqWryJMDryg9+twZadTHT3zN/8btu0Vfx8xXRq9pTT9c6hDHl1EgNfnECHC7pzYO4Ofpz8PtvfXXOME828Yhjm1Dh2vPg9Umvcjc2Q2QFhteHe0nD7YTUtF1zVyPKW66LUKh25dBSjb/gMkTMaEZ8dcvvercuoefcPoBqxX/MPDDmBpTq1JIacflgveAj94E4cHz6G9LhaekkRWhWN2IQEgHSF15G7Csqo2rCPlHG9f7OR2vbWKjY8s4D0szoz6usr6XLzQNqd3pH00Tn0emQEo7+7ipRhHch/cj7r/voTUvc7c9VspONNo3BsP8ihWY1L4RWKgim3a0CNJtS0XIAWbTTRKh25vvod0H2o/UO/u/Wsmolz+hMoSVnYr20gtbCVYew8COu596Pt34LjkyeR2m/jhBEihBLp8m8YlDAddhbP84cukkZ0P/b95fvY8I8FpI/NZcA/zsJg/23I05JsZ9A/J5I7pR+7PljPhn/8fPh3SWd2x56Xxp635qH7Gtf8wZTbFa34EFpZ/Ro2SnIHUAwt2sOz1TlyWbEPffNX/irOmIzQ2ZUS988f4fr2JQyd+mK/8kmUqBMfrLRWjN2GYTn7NrQdq3B++TwyDFkKESLUobv9jlyYw7MjL130C/bOqVjS4g6/p3l8rHnkB+yZsfR9YjRCrXVTx6nZEIqg232nkX15b7a/vYbdn/pDIUIRtL/2DNwHyima07iUYHNuVwA82+tPLRQGI0pyFlrhjkbNEwpanSPXVr4OqhGl79Uhsymljnv267jnvYex55lYL3oEYWpZtbKmYOo7FvPIq/FtmI97TqSMP8JRhPi8rS6EJ8yhzyP3OdxU5e8lftCxyQy7PsqnZm8Fvf404tid+AlqNoQQ9HzodJKGZLH+iXmHD0ATTs3Dlp3C/g8XNeog0pDR3t/+bUfDGSlqSjb6oZ1BzxEqWpUj14s2IXfMRelxEcKWGBKbUtdwff1PPMu+xDRoEpZJd9fm3LZtTEPPxzRwIp6lM3Av+7KllxOhpQlX1orHH74LR2VnVf4+pKYT2/fIOZju09n+5ioSB2aQctqRzjtSSraPuJsvrffyjv3vfGT/PxbHX4XjUG0MX1Xo//RYFJPKmj/9gJQSIQTpFw3BseMQlWt3B70+oaoYO3TCu6vhkInSriOyuqzFUoRbjSOXUqIvewUscSi9Lg2NTc2H84tn8a6dg+n0SzGPub7ZVNzCjRAC85jrMXQZinvW63i3Lm3pJUVoSeo+16FOgfPVOvKGKpwbQdWm/QBEdzsSQj20YDfOwmpyruxz+D3NozHz+oV8c+V8StRMkvXdGKSHFVPX886AL9n25R7AHzPvdu9plKwo4MAs/8Fj8qgeqFFmCr9a1ag1Gjvm4N2/B+mtX/5ATfbfdPRDexo1T1NpNV5N7l2MPLAapd81CJO96fY0H87P/45v43zMo67FcsZljUsvbMUIRcV67r0o6bk4P38G7WDLPdpFaGFqmyCEuqu71GrthUH9sGb7QSwZ8RiijoQ593//C8ZYC+3O6Hj4vXkPLGfrJ7sYeraPq6/by8jCZ5lwjYcrHvIS3zmWb6+az+aP/PHpDud3IzongU3/XILUJarFSPKoHpT8vBnNEbwWjbF9J9A1vPvr1ydXapMm9OL/YUcudR/asn9DTCZK10lNt6f5cH72d3ybF2MeewPmoeeFYJWtE2G0YLvwEYTFjuOjv6CfJM0FIgSHUGt3zCHPZKrd4YdhD+TcU4ytwxFdFSklRQt30+70DodbuO2dX0j+W9vof3d3Mh6azGurJvDnuM/5v38P4L1PMul1dw8yT09lzm1LOLiqBKEq5N08kOodZRycvwvw78p1l5fSJcGX0Rsz/Ttt7776QzMiOhFMVvSSfUHPEQpahSPXt3wD5btRB93c5KbKUtdwfvEMvi21TnxQ028MrR0lOgHbhQ8jHRU4P/t7yHdlEdoAdZ2qGggBBM3h2HvoqxbdByswH5Wt4thfibvESWL/9MPvrXgmH3u6jfYTsnh1+FxK1h9icO8DDLw1lVG//zP/vWQuiaelY0uxMOf2xeiaTvrYXMzJdnZP9x+GxvRsjyHWSuni4Mvo1cRkhMXSYMcgIQRKQjpaSUHQc4SCFnfk0utAX/Umol0vRIfGC+ZArROf8Ry+TQsxj7nuf8KJ16Gmd/anJe5eh/vHd1p6ORGambosrFDLHh9ODPCFtvmy7vGh1bgxxh0Jo1Zt8+drx3Txy1XXFDrYO6+QHlfnMuP2VZjsBkyx5Xy6NoWtW2bS7cIl9B6/ju8fXk/upTmUbChn6ye7UIwqWRO7cPDn3XgqXAhVIW5ADuXLdwSdvSKEwJCaga9wf4NjlYQ09LIDQdkPFS3uyPV1H4CzFGXwrU2KYUspcX33b3wb5mE+8yrMg88N4SrbBqZeIzH2G49n8Wd4ty5r6eVEaEaE2e8QQy3fUCdfKz2h3elrLn8ISLUdyYZxFvp1hKzp0QAULC4CwJJmZ9+KMuxdoli3N5Zs1tB/9I9ICeb+W7GnWlj+/j7i82JY+5p/1502Ogfp0yla7N9Jx/bpgLe0GveB+kWwjochJQ1fUWGD45S4VGRFUYs8EbeoI5c1RejrPkR0GomS0q1Jttw/vo139UxMp12I+bQLQ7TCtodl7PUoqZ1wffU8emVJSy8nQjMhbDEASGdo5Y6F1V8IpDsdIbVLbTm9UI5s3nzV/puFMcp/8yjf5v+3FO/2P2WsW1xEXEIV3xk6kT1hN0JA33NWUlxeQ9HWapIHpHBwRTFV+2uI69kO1WqkZKU/1BHd1R+uqd4a/I5ZTUpBLy9rMHNFiU0G3YesDv5m0VRaNKFaW/kGSB11wI1NsuNe8gWeRZ9i7Dce84grQ7S64yOlBK/HL+8pFITZjFBa/MHmMMJgwvq7B6h5/W6cXz2P7bLHTpqUywgnRtRWKcvq+svJg0Wx+3fHenVobxCKye96jmnN9qt4vKfah2JSOGXybYz60/xjrve4/Z/plM6HeL7m8sPvj/s3VBeMRjHMJvaUJCo3+3f11tpDVcfu4qDXqib4Qz1aWSmGlNQTjhPR/toXWVUCMaGpgwmUFnPksnQ78pfvUbpfgIhJb/iCE+DN/wn3nDcwnHIalnE3hSzFUEqJVnQQz/YtePfsxFe4D19xEXplBfzq0UmJikZNSMKQmoGxfTamnC4Y0rNazMGriZlYRl+H67uX8a78DtOACS2yjgjNh7DFgmpErwhNN/k61Fj/DUKrCG0rM8VqRDEZ8JYdCQWZYv07cU+5C4PdhDHKgO7R2fTVrVjjN2JPrsBk9YdkTGa99ueRG4HPZcJxKIbdc26l5xSwZ8VSvNSfRaJajBjj7bgPBZ/VdfhvUFleryNXohIA0GvKaJ5W1UdokiMXQvwDOAfwANuBa6WUAT1XaMteAaMNpU/jm0b4dq/H+eULqO27Y518b5ObI0sp8e78BeeqpbjXr0Ir9d+9hcWKIS0Tc143lJg4FJsdYTAgdR3pcqJXVeIrOYR783qcyxYAoMTGY+k9AOugYRjbZzd7Drux3zi8Wxbj+uEtDJ0HosSmNOv8EZoXIQRKfCp6aWgP25TYODAY0IpDe4MQQmBOi8O5/8gThC0rFoDqXWXYMmKIy/GHi3B14dluzzBx2iv0nrAai/23TZE9DjO7Fw3lhylX0nNKRwCMsRa8VUfGGuNs+CqCPwxWouqeSurvBSBs/vXLFkgBbuqOfDbwBymlTwjxNPAH4PcNXuV1IPctRRl0C8IS06iJtZJ9OKb/DSUhzZ9HHWxDiKPQHTU4Fs/DsXAuWtFBMBoxd+mBffQETJ1PwZCSFvDuWistxr11I+781TgW/4Rj/myMWdnYR52Npe+gZtulCyGwnn0b1a/ehuv7V7Fd/GizzBuh5VCSstBDXBQmFAVDciq+wtCn1dlz2lGVfyStL+6UFFAEpasOkHJaBzJOSwEBqteHz2Vl9qMPs2Plh/zuzx9jsR3Jl3c7TPz05MWUbLsJT8UOzDG1OfW/SlARRgO6N/jsG8VWe5DsrP8gWVij/ONczd9jt0mOXEo566iXS4ALArqupgjs3VC6Na5QR3dU4vzwcYSiYrv4T4f/gMGiVVVSM/dbHD//gHS7MHbKI+qsyVh6D2h0RxQ1IQnbkOHYhgxHdzpwLl9EzbxZlL/1LwwzvyB68qVYuvdulO1gUeLaYR5+Ge4f3sS7bQXG3AHNMm+ElkFt1wnf5kVIVw3C0vTq6DoMGe3xbG24U06wxPRsT/HcDTj3lWLNTMAYYyahdyoFc7bT5fbB2NtZ6Tgmna3Td9L/ivasfH8PWV2LURWJroPPacRg9aIqGjGpBzCrdoq/haQe/lCIp8yJMfZI1aj0+lAMwT+11wmGSfdvnwSOwWQFBNId4oPhAAjl9nAK8N2JfimEuFEIsUIIsQKfC7X/dQhD8IpqUtdwfvY0emUR1gsfRok/cczqhDY8bqq+/4Kix+6j5odvMffoS9KDfyHpnkexDT49ZG2tFKsN+/DRJD/8FHHX3o70aZS98gyl/3mhwV6AocI06ByUhAzcs9+IFAqd5ISro7upYy56ZTm+kqKQ2k041b/e4p+OdOHJmnwKVb+UULLcn7c95I+9cZd5sPjcdBrhY+jlcxACyvcl8tYt91BVkABCMOjGuXgObMcUayRjmL/nbsWWYqI7JRy27S2rwRBnC36htSFb2UA/VCEEGE3ga8Dhh4EGHbkQYo4QIv84/00+aszDgA94/0R2pJSvSSkHSCkHYIpC5I5t1ILdc6ah7VqH5ezbMGSdEvT1rvWrKHriIaq/+RRz1x4k//Ep4q+5FWNWx0atJxCEomDtN5jkPz5J9KSLcW9aS9Hf/oBrfeOEfIKaWzViHnk1esk+vOvmhn2+CC2HmtkVFAO+XWtDateU5/+eeQLoKh8MlrQ4Ynq1p/CrVUif30lmntMFc7KdDVMXIjWdlL6JDH20Nzu+2svYhz5GNWlsmDGI57pNZcd7/Xim67PsWTIM1ajRbsDL9L+rO0abAUdBFVW/lJA40C/I5aty4i13YM1IqG9Jx6c2iyagcy7VAFpoi6cCocHQipRydH2/F0JcA0wERskAy6ZETEajDia9+T/55WgHnoOpd73L+g16dRUVH7+Na/VSDGmZJNz18GHh+OZCGAxEjZmIpVd/yt5+mbLXnsM+dhLRE84Pa+zc0GUISlou7gUfY+w1ssmHwhFaJ8JkRc3qhu+X5TDq2pDZNaRmoMYn4spfhe20MxtnZNo0v3741Klw333+JuVTppB+0RA2P/Ixh2avo934PhisRno8MIyVD85ky7+X0fX2IfS/pzu6T6em8DXm//FWFNP1jHnIgDAo2GJUNr7age0f5dL14r1knOavR9n537UgIGN8ZwAqa2PxUV2Cz5CT3tp4vDEQBUgRDjWDBmlq1so44EHgDCllWANDWtFunN+8hJrVHfPo4FrAuTetp/zdV9Ed1URNvICo0RNaVJPc0C6NpHv+RMX0d6iZ9SVa8UHirrwZYQjPmoQQmIddhHP63/BtXoSxW9OkECK0Xoxdh+Ka+Spa0Z6QtTEUQmDpM4ia+bPQa6oO55YHRV1TiOee87++5x4AEod1JaprOrtenUPCaV0wxljJmJDHoYW72fLyMsxJdrIv6cmgB3tRsOQTNn2wloKFGw/35wQwx5rod+ddZJzWDcWgULGlmB3vriHznK7YM/2ZJCXzNqFaTcT0yAp66XrtIadiCSAso2stknbc1BlfAqKB2UKINUKIV0Kwpt8gPU6cnzyFMFmxnvdgwE5Y6jpVX39C6ct/R4mKIumBx4k+a3KraCwhjEZiL51C9OSLca1aStkbL/iLjMKEIW8wIj4Nz/JvwjZHhJbHcMppIBS8634IqV3r4GGgaTiW/Nzw4OMxdepxXwtFkHv/RHwVTn55agZS9zeE6P3YSNqd0ZF1j//I+qfmo7l8pA9J4fyvx3DdlvM45+MRjJs2jAu+H8t1W89n4P09UAwK1bvKWHrLlxjjrPR4YBgA3nIHRXM3kHRmNxRz8N99vdKfTqhE159hJ6UEnwcacfbXVJrkyKWUuVLKLClln9r/bg7Vwo7GNfM19JL9WM+9HyU6sBiX7nRQ9upUqmfOwDpkOEn3P4Yxo3U1WhZCEDV6IjEXXY07fw3l777S4IFK4+dSMPU9C23vBrTSllFoixB+lKh4DF0G410zG+l1hcyuMaM9ppwuOObNQjYmBnzffSd8HZWXRsdbxlC6YAs7X56FlBLVZGDQixPIvrw3O95Zw9xJ77H70w34HF5sKVayz8ok7/yOpA9NwWBR8Tm8bH9nNfMu/BDNozH01UmYE/076D3TfkT3+Mi45NRG/du12kPeugrPE+Jz+3fkYepvWh8tvzVtAO/Gn/0dfk67CEN2YGl7vtJiyv79DL5DhcRcfC32YSPDvMqmYT99NNLtomrGR1QntyN6Yni0Yow9RuCe+za+DfNRT78kLHNEaHlMgybj2LwY75rZmAaeEzK79jETKXtlKs4l87GdFuR3qkcPfzjl6Bj5UaRfOBjXgTIKPl6C9PjodOd4FKNKr4fPIG1UJzY8s4A1j/7A+ifmkdA3jajseEzxVjSnl6odpRQv24/m8JJ8ahZ9Hh+NrVZ4q2TBFg58sYL0CwZj65h8vJU1iK9wPxiMDTpy6fDLGNTp3jQnrdqR65UlOL99GSU9D/PwwNq/eQv2UvqvvyO9HhJufQBzl+5hXmVosI+agO9QIdUzv8TYsTOWHn0avihIlJhE1Iw8vL8swxxx5CctalY31KzuuBdOx9h7TMgajZu79caY3Zmqbz7D0m8oijWIneeUo861nn32N78WQtDpznEoJgP7P1hEzY5D5P3xXCzp8SQPyeKM6ZdQurKA/TO3UbpyP3vzD+Kr8qCYVGyZMWRO6ELWpK4k9Es7nF1StnQbWx77hKiu6XS4aVSj/93evbswpmciGuiSpFf5q1RFVCMyY5pIq3XkUkqc37wImhfrufcFFNf27tlJyb/+jjAaSbz7EYzpwR9stBRCCGIvvArv7h1U/Pd1TA8/2bhDpQZQO/XD8/OHIS8aidB6EEJgHnk1jrcfxL3oEywjrgiZ3ZgLrqDkmf+j6suPiL34mpDYPdp+9i1jsOe0Y/uz37Dq6pfJuGgo6RcPxRhjJXFABokDjvT3lLo8Rj2xDs3tZd+7P7P3vQXYO6XQ7enLUM2N6zkqvR48u7YF9AQiy/1St0pc88thtFpZPO/aH9C2r8Iy8hrUhIZThrx7d1Hy0lMoFkubc+J1CKOJuCtvQq+pouqr6WGZw5DZFZBoB7aFxX6E1oEh6xSMPUbgWfwpWlHo+kia2nfCPuIsHAt+wLUhtPnqdaSM7UW/d24j4bQu7H33Z5af/yxbn/iCkoVb8NUcKbY52olLKXHsKWbP2/NYecmL7H3nZ1LG9KLXS1MwxTd+w+Leugm8XsxdezQ4ViveB0JBiU9r9HyNpVXuyPXqMlxzXkdt3x3jgLMbHO8t3E/Jv55GsVhJuOthDA0dSrRijJkdsA0fg2PeLGwjzsKYmtHwRUGgtMsG/OmcgZ45RGibmMdch2/HKpwznsV+7T+O9PVsItETL8S9ZQPl775C0v2PYUgK/Q7UnBJD1/+7gJqrhlPw6VKK527g0Ex/brglPR5LWjxqlAV0ibe8BufeksNKinEDOtHlzxcQ26dDk9fhWrUEYbFizmu4X4J+cCdKYkaTdJ8aS6vckbtmvw5eN5YJtzeopa2Vl1L68j8QikrCHX9o0068jqixkxBGIzWzvw65bWGPA6MZGWK50witD8Ueh2XC7eiF23HPmRYyu8JkIv66O0HXKXv1WfSa8IlE2Tul0PmBcxg84356PHcl7a85g6i8NHw1bhw7D+HcWwxCED+0Mzn3TWDA9Lvp8eyVIXHiek01ztXLsPYfgjDW75yllGj7t6CmdW7yvI2h1e3IfbvW4dswH9Ppl6ImZtY7Vne7KH3tOaSjhsS7HsaQ3K6ZVhle1OgYrINPx7F4PjHnXY5ib5wo2PEQQiCsMUhH/ZKcERrgBJWKrQ1jl6FogyfjWToDJSUbU9/GSWP8GkNKKvHX30Xpv/9B6SvPkHDr74M7/AwSxWQgrn8n4vp3Ctscv6Zm/mzwerANH9PgWL14D9JRgdq+ZZIrWtWOXOoarpmvIeLaYT71/PrHSknFf1/Ht283cdfcFlatlJbANuQM8HlxrVsRctvCYGxcLnCEI9RVKiqK/2d+aHVIQol51LWonfr6G41sC93nyZzXjfhrbsO7ZxelLz2JVhXaLkItiVZVQc3c7zD37BfQeZtv20oADJ1Cn20WCK3KkXtXz0Iv2o1l9BSEsf7qKMe8WbhWLSV64oUhS9VzF1VS9EM+u177gS2Pf8aGB95nwwPvs/mxT9n12g8Uz9uEryq0XcpPhCGrI0pcAu6N60JuW/q8YZMD+J/hBJWKrRGhqNjOfwglpSPOT57EtzN0h5SW3gOIv+EuvAf2UfLsY2HRLW8JKj/7L9LrIWbSxQGN925agJKa02INXFrNt1l6XLjn/xc1qxuGLkPrHevdu4vKLz7A3LMf9jETmzSv9+X/cHDBdoos2dRs86cPCVXB3C4WQ7QFhMC5t4SSnzYiNR2hKiQM60Lm5cMON3QNB0IITDld8OzYGlK7UkqkowJhbf6ihZOK41UqHic/urUgzDZslz6G472HcXz0ONbzH8LYeWBIbFt69CXxjj9Q9p/nKZ76Z2Ivux5r38Ehsd0SOFcswrViEVHjf4chteHvuFa0B73gl6A1oEJJq3HknuVfIWvKMV/wx3rlIqXXQ/k7r6BExRB3+fWNbqHmrXCw992fKfxkP7puJbpyOR2LNxE3YSi2fz39GwF63eOjeksBxfM2cei7NZTM20Tq5P5k335Wo3NUG8KYlolr5WKkx40whUa/QVaXgs+DEndynCe0GA1UKrZGFHsstiufwPHB/+H8+K/Is2/F1PeskNg2ZXcm6YHHKZv2T8qnvYR7yDr/+Y61EfrfwXLttbBuHSxfDgMHQq9e8OabjTLl2b2D8v++cbjJTEDXrPgGVAPGni1XQd4qHLn0OPEs+RxDTv8GNcarZ87AV7if+FseaFTBjJSSQ9+tYee/ZuGrcZMytg8ZT9yE3eHP4tBenIdjfxXeKjeKQcGaGoUpzopiMhDTsz0xPdvT/toR7Ln/JQpmrKTml0K6mzZi6BP6wy4lNs6/psqKkKV4aQe2+22ndAyJvf9ZGqhUbK0otljsVzyB49OncX3zEnrRHsyjrg2JkJwan0ji3Y9Q9e3n1Mz+Cvem9cScd7m/xWE4e9auWwerVkEDlZcN4d27i9KX/4EaE0v8dXc2WMkJ/lRp79o5GHuMQLHHNmn+ptAqHLln1fdIZxWmBsrGfYUFVM/+BuugYVi69Qp6Hl+Nm1+emkHJvE3E9GpPzr0TsL/0FDVejc1J4zgY1Y2Kfv9CymM/dPasWNLG5tLp8l5YU6Mx2M10su4nZuP3bNEvYEv5Ibqp+YT6o3o4jq2FrrOPtnsdqEbU9JZJk4rQ8gizDdslf8I9+w08y75EO7Ad6+/uR4lpeuquUA3EnHMhll79qfhwGuVvvoTp5y5ET7oEU3ZuCFZ/HJYvP9aJL18etAn3pvWUTfsnwmoj4bbfo8YE5pTdCz4CzYfptPDoIwVKiztyqfnwLP0StX2P2qrDE1P5+fsIs5noc4PXCXEXV7Hh/vdw7C6i4y2jybj4VGr2VrB8Zy4FuY+CECTEusjNMxH1uxGYYszoHo2afZWULN/P9rdWsfP9tXR/8HSyL+kJU6eS9NxzeHd8z/bcCRwaO5mggxUNpLDV9QgUptAUGEip4928GEPHXg0eJp8sSM0D1YeQjmJwV4KnBulzgfSrTArVBAYLmKPBEouwJYE14aRvviEUFctZN6Jm5OH85l9Uv3YH1rNvw9htWEjsmzp0IumBx3Es+onqbz+l5NnHMHfrRdTYSRg75YV2hz5w4G9fr1wZ0KVS81H9/QyqZ87wN5y5+T7U+MSArtUO7sS78juM/cYFVH0eTlrckfu2LEFWFWMaX78CrvuXTbg3riP63EtQo4N7hPGUVLP+jrfwllbT/e+XE9e/E9vfWs2mFxcjDAqdbxhI9qU9McZLfM7rKM4/C2GNIalLLOlRRjpf1x/H/krWPvYj6x7/Ed3tI2ftuwCkHlhBYWo/9j/3GSnjegf3AT2B2H4dWlkJCIES5L/3RGi7NyArDmE44/KQ2GttSEcJ8mA+sngLsuQXZPluqD7Ib9qpN4RigKhURGwWIq4jIiEHkdQZYtufdA7e2GMESlpnnDOm4vzsabybFmA56yaUqPgm2xaKgn3YSKwDT8UxbxbVc7+j5Pm/YmzfCdvw0Vj7DgrN2U+v2qfzo2PkAQDAkAUAACAASURBVODeupHKT9/DV7AX66BhxFx0NYo5MIExqXlxfvU8whYTMi2bpiAC7M4WUgYMGCBXDB8OPXpQY9qOXlZI1G2v1fslKXnhCXxFB0n50zNB7VA1l5d1t7+Jc28xPaZeiT0vjVUPzabg+19IG51Dr0dHULylmrWvbEZzz2TS9Cf5bNJD7JvXHcWokDspi9Me70d0ph2p6Sy/9zsKf9zJmVdZiC7YCFOnUnDlH9ixx8qAD+/Ekh7EF0BKfx5yHboOR90ISl+diq/oECmPPB24zXpwfPxXtL0bibrzzZNiRy59LuT+lch9S9ELVv5/e+cdX1d15fvvPudW9WpLsizJltwl925MMxgDDr2XFyAhPSEDM5kE3rzMY5KXTIYJw4SUSQgwCYwJLZQQigGDbWyMe++2LMnqVi+3nbPeH1tuxJYtWdKVrPP9fPbnSveee+4+t6yzztpr/RY06nZeKBOS81DJI1AJ2aj4TIhJQ/kSwROnhf8NU9t3KwjhdiTUAoF6pPUItFQizeVIQ4nep93R6svlQ6WNQQ0tRA2diMooQnl6rlgrmogVIbT6VYIrloDLi++Su3FPXdSjJy47GKB9zUpaly/FqipH+Xz4Js/EP3UWnlHj+yQlVkQI7d5Gy9K3CO3ZjpmcSsJNd+ObOK1L+wm89ztCn72B/+ZHcI+Z3Uuz/VuUUutFZPrn74+OR75+Paxfj/3Qt7CSivFedFenX5hQ8X5C+3YRf/0dXQ4zHPzFO7TurWD8T24nftwwPnvgr1R+eIDxD80j+7oJfPDdz9j/RgmeBDdzHt2DCAxfvJfm9gWkZno58HYZZSuruOX9RSTkxDHp/1xC1UcHOWSNofDnXwYg7vvfhG88Tduh2q4Z8k5S2MSKENq3G18PpXFZFfuI7FmD98I7BrQRl1AbUrIS++BHSNlabYhdflTmJNSYxaihRajUUagudmk53XWU2BFoLEVq9yA1u5Ca7dhbXgB5HpSBSh2NypqCGjZDv3YUusP0BMp04b3gFlzj5hJ4+9cE3vkNoQ3v4FtwL678qT3yGobXR+yFlxEzfwGhfbtoX7OCwKa1tH+6XOuZjC3EO7YQz6hxmOkZPRZ+EREih0sIbF5H+7pVWLXVGPGJxF93O7HzL+uyTQltWnqsd3BfGvHOiGpoJXzdfPioGHdR5w1d25YvRfl8xMy9uEv7b9hwkMo3NzDs9rmkzB3NzidWU/nhAYoevoghC0bx0sJ3aSpuwYhTFB8WrpvxGUrB8HlreOPvb2dXa4SrfjKRbU9sZfWjm7jiqQvwpsaQPDGD+q1Vx17HcOuTkES6uCjZSQpbcOdWJNCOr2hK1/Z5CkSEwHtPoWIS8My85pz319eIbSHl67H3voMUr9DGOyYdY8zVqNwLUBkTday7F1CGC5JHoJJHwCidqieRAFK9HanYjFRswN72EmxZAqYXlTkZNXw2Rs4cVHx046bdwUzNJubOHxHZtYrAB8/QtuSHmHmT8F581xnXsM4WpRTeUePwjhqH3HoPwV3bCGzdSHDnZgKb9EKlio3DkzMC17AcXEOzcA3JwExOw0hI7DSbRMIhrPo6IjWVRCrKCJccJLR/t27XphSegrHEXXUD/skzUWfVTPlkwrtWEXjrScyRU6KaN/55omvI338RM3dMp/q9dqCd9k1riZl1AYbv7LUcRITi37yPNyORnPsupmlPLXt+t46c68eTd1sRFWvm8r/Wrzl5PgH9dqTmV/IvTTcdu3/+96B46XRAf8kMr0m4OXTs8UB5PaAV27pEJylsbSs+wIhPxNuN7JzPE960FKt0O76rvjWgNMiltRp791vYu9+C1mrwxmOMWoQquFyHN84gqNZbKJcPlTUNsqYB9yHhNm3Uyz7DLvsUWf0E9uonICkXI2cuKucC1JDxAya+rpTCPW4erlEzCa3/K6FPXqLt2X/QxmvezZg5hT3mLSu3B1/RVHxFU7XwVHUloX27CBXvI1xaTHDPOydnbSmF8sdoW+D2oAylGzGHgtjtbUjg5MprMyUNz+jxeMcU4h0/6ayzUU5FaOsyAm/8B+aw0cTc9IN+0fv3KNGZybRp2BfNwXYV4z1DFWdw20YIh/DPmNell2jeVkbLrnIK/n4xptfNnv9aiyvGzYR/uIAdz+9n06+u49rXDuBLasDt1zFQty9y0i1AqM1NpC2F9U/cSd7lYEdsGnfWMPTCvGPb1H26DzPWS0x+zxTZhEsOEtyxmbirbzznL4tdV0Fg6VOYuUW4p5xZ/CfaiAhSsQl7xyvIoU9AbNSw6RizvonKnddrnve5oNwxqJw5kDMHkweQxjLs0lVIyWrsrS9qb92XhMqZi5F7gQ7DDIAQjHK58c66Fs+UhYTW/ZXQmtdo++PDGFmj8M66DtfYuT1qzJRSuIZm4hqaScw8fZUuVgTrSA2Rmmqs+lrspkbs1hakvQ2JhPW6kmGg3B5t4OPiMZNTcaUNwZUxrEcE50RsgsuXEFrxAmbuRGJueQTl6fu+nJ0RtVNK5O5r4S9P4CrofJEhsH0TRnwC7ryu5aDWLtuO4XGRdlkhVjBCxYcHyB1v4Hn0YXbvvhKpSOVno37GjX/8LWMXrscXG/qbfYRavRxcPpPl376PvIWjATj89h5C9QEyL9fzCdY2U/vBNoZcMfFvqkG7g9g2ja88hxEXT+xF56ZUJ+Egba/+FAwD/xe+GzUP9myQSBDZ/z7Wtpeg/gB4EzGKbsEYey0qYWCFKFRiNmbiLVB4CxJqQUrXYJd8ghz8GGvPX/WiafYsjLz5qOFzUN6e7wTVkyiPH+/cG/HMWEx4yweE1rxO+5//DRWXgnvy5XgmL+y1rjjKdOEakolrSN83awCwm2ppf+M/sIo34564AN9V30S5eqeS+1yImiG3Srah/PEY6afXDRYRQnt34Rk9AWV0zQg1bS8jfkI2rhgvjTtrsIMWqVIBjz9OS9wwUqwywoHZvHz/tyi673Wu/efX8MUcN+ahNg/v//BGaj++jXBLCzMeKiRQ08r2n60kcXw6GRePQEQ4+It3EBGy7+yZ/Nu2Tz4kfGAPiXd86ZzKm0Vs2t98ArvyIP5b/3dU2k+dDRJoxN75Gvb2VyFQDyn5mPO/h8q/fEB4rWdCeeJQ+Qsw8hcgVhip2IgUr8AuWYlV/DEYLlTWVIy8C3UIJqbv+z2eLcrtxTPtKtxTFxHZv4HwurcIrXyR0MoXMfMm4p54Ke4xs1HePijL72XEtghveIfAsj+AbeG7+lu4Jy/s3QrVcyB6hrx8D2b22E7fGLupEbuxHk9efpf3H6ppInmmfp4V0jE28/77YMmPSbSrqDVzyZ2bQtmaWobnVWMaNrYNkXYPLn8Iw7DILaziwDNNXPnsfPzJLlZ96TUibSGm/uR6lKEof3kNtct2kPuVS7uWrXIawodLaPrzEjxji/DPvqjb+xERgu8/Q2THCryX3oN71MxznltPI80V2Fv/hL3nrxAJaA+16DZU1tR++2M5V5TpRmXPhOyZGPJ3SPUObdQPLcda+Rion+vYf96FGHkXoeL6px6OUgbugum4C6ZjN1RrL33LBwTeeJyAy4OrYBrusfNwFUwfUGsyoB2gyO5PCX70HHZtKeaIyfiv/AZGyrldEdj73uvVq6/oGHIR7COHcY2d2+lmkSotienqTrsz1bEIAsTlJoGC+n9/jgxgevBN/hzzA5JLiym8KpZZX16BiKKxJI1l/3QPl//0WfxpDYy98yNSxj+OL8bL8tteJFDTyqxfLCZhVCqVf9nAgV+8o5UQ7zh3b9xqaqT+t49j+GNIuvur3TZmIkLw4+cJrXkNz4zFeObccM5z60mkbj/W5v9BDnwIgCpYiDnxNp0VMohQykANLYShhRgzvwZ1+7GLl2MXf4x8+iT2p0/qnPW8CzFy56OS86I95VNiJA3Be+HteObfhlW2k/D2FUR2fUJk12owXJg5E3AVTMeVPwUjLaffnqQl2EZ428eEPnsD+0gZRuow/Dc9jGvM7HOes31gGdZHP8KYfDfm9Pt7aMYnEx1DboVBbIzUzg201aizQc62ZPZE/Nkpx2RpPUk+Mi4ewf6VBxl2//fI/q+fcvVNj7Ji00gmfuH3mB6L3S/P4oNvf5lIm4839z/JomefIWX0u7iNf2LF7Qvwpscy79kbSS4cQvHvPqDsjytJmpnPmB/eeLwJbDe7xtjtbdT95jGs5iZSH3ik2yvrIkJw2X8TWvUK7kmX4114f7/54dhVW7E3PYeUrgaXH2PCjRhFt6Bi+2fIpy9RSkFqAWZqAea0+5DGUuzi5dpbX/c77HW/g8QcHVPPnY9KH9vv1juUUriGj8c1fDxyxf1YZbuI7FlDZO9agu//nuD7oOKSMXOKcOVOwMweh5Ee3UpZCQeIHNhEZOcnhHevhnAQIyMf/3UP4Ro/v0fmZlduxfr4x6ghEzCmfLEHZn1qomLIj3anMRLSO98uGABA+c6ubPZEUuaN4eCT79K0vYyECdkUPXwRy2+rYuWWkYz701Zy/vgwd3tMAlv+i7LHryfg/39cNutFUmcMIzx/HsXPJHPInUbq1L3k3lTI+L+bS7iuiS3ffpbmbaUMXTyF/L+7+lgOOXDGkvtTYbe1UvernxEpLyX5/u/iye1eKyuxIroDzKaluKcuwnfl16P+YxcR5PBa7E1/RCo36wXMaV/CGHc9yufooZ8OlTgcc9KdMOlOpLUG+9AKbdS3vACbn4eYNIyceTqHPmtKv8vkUco4ZtRZcC92QzWRgxuJFG/BOrSNyI7lekO3DzMzHzMjH2PoCMz0XN28uJdi7BJoxarcr08yh7ZilWzXTqUvDnfhxbgnXYY5bEyPOT92zS6s9/4R4oZiXv6TXv2colKiP23CWFl2+yhiv/pLzPSc027X9skyGl94miGPPoGZ3LVFoEhbkA13/RLT72HiL+/FnRRLa1kjG3+wlCPry1GmInlSBjFZCRhuk3BLkLayJpr3HcEO27jiPGRfPYb8L07GHWtS9txKKt9YjxnjYeQDVzJk4Snyu89Qcv95rPoj1P3634jUVJF837fxFXWvgk7aW2h79V+xDm7Cc8GteC+6M6qeuNgWcmgF1qbn4MgeiE3HKLoVY8wXUO7+lbY1kJBAE1K6Whv2srUQaQe3X68v5MxDDZ+tZQj6MSKC1FcSObwL6/Ae7Iq9WFUHIXI80UDFp2AkZWIkpqMS0jHiU1CxSSh/vI65e2NQphtMFygDELAsJBKEYBsSaEVaG7Bb6pDGauy6Cqza0pMajhvpubhGTMJVMAMzt7DHc8Ltik1Y7/0AfAm4rv7PHlvvOF2JfpQM+WhZdvsY4r75O4zkjNNu175pLQ2//0/S/vFHuLO73hW7cUsJ2x/8I560eEZ9/xoSJ+chItStL6dy2UHqNlcSqG7Bjti4YtzEZCWQMCaNtBnDSJ2eRcvjT1G19jC1TT4kYpExOo7cx76OO+k0CzgPPnjcGwftkZ9Gqzp0YA/1v/9PJBQi+csP4B3TvaatVvUh2l/6MXZjDb6rvoFncvRyxcWOIPuWYm1+HhpLICEbc9IdqIIr9A/PoceQSBAp34B9aCVS8gm012nJgCGFqJzZGMPn6orUfhJa6wyxLez6CuyaEuzaUuy6Cuz6SuzGaqT5yDGlym7h8WMkZ2KkDsMckouRkY9r2BiUv/dSPu2972Gt+FeIz8R15b/36KJ1/9JaOapscYYP6GgzhUhlebcMeeLEHAqf+CK7/+/LbP3Of5MwMYe0SyaQMDGHsd+Zdayzj4gQaQ4QKDtCy95KGj7eQPFjLxFuaMOM2GRUrWbY4dX4pt8LpzPicFZdY8S2af3oXZpf/xNmSiop3/o+7szsLh+biBDe/AGBd36N8sUSc/eP9aVsFJBIQFdgblmiKzBTCjAv/WdU3kUDpppxoKFcXlTOHIycOYg8pBUfS1Zhl6xC1v4We+1vIW4oRvYs1PBZqMxpKE//TAtUhomZmo2Z+re/A7EtpK0JaWtE2pqRYCuEAtr7tiL6KlgpLYLm8qC8MShfLCo2SSs4emP77GQmVgh7za+xd7yCypyCueBf+iyEGB2PvHCcLLutgNgvPY6ZefpCH7EiVH3va/hnXkDirfd0+/WsQJjK19dR+eYG2ktqj91v+Nwo08AOhpHI8ZOKOzmWpGkjSb1gDMmXT8a0Oyo9zxAqOeM86uto+J+nCO3airdoKkl3fQUjpuvpWdLeQvvbvyKyYwVm7kTdFKAHZEe7PI9gM/aOP2NvfxkCDaihRRiT70Jln/tKv0P3kdYaXYRUuhopXwfhdp2vPrQIlT0DY9gMSB0V9TWU8wmp3UNk+U+gbj9G4c0YM7+udXp6mP7lkXeI3tjNdZidpGcq04VnbBGBLetIuOmubsexTJ+bYbfOIeuW2QTK62nZVU6gooFwQyvYguF14U6OxZeZTGz+ULyZSdoQPfgg2MfL9bvbYFdsm7aVH9L85p/Atkm45YvEXLCgW8YuvHctgb8+ibQ24r34bjxzb+xzr1daa7G3vYS963UIt+kY7eS7MDIm9ek8HE6Nik1HjV2MMXaxLkKq2qqlfss+O+6t+xJRWdMwsqahsqZCfJZz8u0GEmrF3vgs9raXwZeIufBfMXI6lx3pDaJiyI+u3tq1JTC682KVmDkXUb9lHe3rPiVm1rnlayul8A9LwT/sLBdOe6DBbmjfbhpffY5IaTGeMRNIvO2+bvXftFvqCSx9isj25RjpucTc8k+dXs30BtJQgrVlCbLvPRALNeISHQNPddrG9VeU6daGOmsq5syv6+Ybh9dhH16HlK/D6sjnJy4DlTkZI3MKKnMSxGU6hr0TxAph73oTe+MfIFCPGrMYc8bXopaN1SOhFaXUQ8BjQLqI1J5p++nTp8uye6ZgDs0j5uZHOt1WbJvax36I3dxE+sM/xfAPjKyHcHkpzX95meDWDRhJySRcdzu+qV0POYhtEV7/NoGPnoNIEO+8W/DMu6lPFw/tqq3YW5ZoESvTjTH6Kl2FOcA0UBxORkSg4RB2xQakfANSsRmCjfrB2HRUxiQdLhtapBdOnfUO7YHvfgt765+grQaVMRlj1tcx0jtvGt9T9FpoRSk1HFgIlHTlea4RkwhvX45EQijX6fMrlWGQeMsXOfLzR2lc8hRJ93yzy7orfUmo5ACtS/9CYNNalM9P3OKbiLtkUbdaWkUObCSw9PfYNYcwR0zGt+irp1wQ6g10CuFK7K0vINXbwZuAMflujAk3ovx9H4936HmUUpCch5mcB+NvQMSG+oPYFZuQyi1aF2b/+9gA7hhU+njUkHH6Nn0sKqbrhXoDERGBI3u0Ad/3ng4nZkzCuPD7qGHT+8WVS0+EVh4Hvge83pUnucfOJbzxXSJ71uAeP7/TbT15BcRfcyvNr79AU1wCCTfd3a+MuVgRAls30vbxu4T27Ub5Y4i74lpiL1nULRlNq2IfgWV/wDqwEZU0FP9NP8A1Zk6ffGEk3Ia9523dLKG5HOKzMOY8gDH6KicH/DxHKUOLlqXkw4QbtQFrrtAx9urt2NXbkc3/A9KhDx6bjkobi0obrbsypRZATHq/MGzniogcl0048KFOpzU9qBGXYEy4oc888LPlnAy5Uupa4LCIbD7Th6eU+grwFYCcnBzMEZNQSUMJrXkd17gLzvjhxy64Cru5kdYP38ZqbCDpzi93K+OjJ4lUltP22Ura16zAbmrATE4l/rrbiZl7SbdCQFZ1McHlS4jsWoXyx+O97Et4pl/dJ7KZ0lKFvf1V7N1vQqhFlxTP/JouCXcuqQclSilIyNIhtFFXYAISbkeO7EFqdiO1u3Ta46GVHGtw7U1AJY/U2jDJI1BJuaikXPCn9GsDLyK6V2vlVh1qOrwWWmsApUNMhTdjjLy030oOnzFGrpR6HzhV1c4jwMPAQhFpVEoVA9PPNka+bt06QuvfJvD2r/Df/DDuMzSYAP1mt330Lk2vLcGITyTh+tvxTZnVZ965iGBVVRDYsp72jWuIlB0CpfBOmETM3EvwTpjcrblYh/cQXPUSkd2fgjcGz8xr8M66rteV40QEqdqCvf0V3UINUHkXah3wId0rUOqX3HsvbNlycpf1Z56J9qzOGyTUhtTt0+PIfqg/gNQXQ7j1+EbuWFRiNiQMQ8UP0w2x44ai4jO0Z+/qugxHt+cbCUJTGVJfjNQfRI7sQ2p36aIqAG+8zujJnqUVC/uRtHCPV3YqpYqAD4C2jruygXJgpohUdvbco4ZcrAitTz2ABNqI+8ovUP6zC0OESg7QuORpImWHcGVmE3vRQnxTZ/fKQqjd2kxo326Cu7cT3LkFq1aX+bpzR+KbOhv/tDmYiUld3q+ITWTvOkJrXsM6tBV8sXhmfAHvzGt6teoMOnpO7n8fa8ef4che3UJt9GKMCTf0W+nUc2LaNNiw4fj/U6fqBuAOvYaIQGs10nAIaSyFxhKksQxpOgwtVcfDM0fxxEFMKsqfAv5kLTXgTQRvHMoTB+4YcHnB9OnCH+Noeb4CsXV83w7rUv9IO4TbkFALBJog0IC0HYG2WqSlEtqOHH9dZULicK00OWScXthNye+3Ofa9XqLfHY8ctC5567Pfw5U/Ff/Nj5z1ZbzYNoH1q2l5/y0i5aXgduMdPQHv2ELcI0bhzspGuc9epEZsG7uxgUhVOeHyUiJlhwgdOoBVXaGPz+PBUzAO74TJ+IqmdEuREXQxT2jLB4TWvYXUV6AS0vDMvAbPlCt6XZBfGst0E4e9b0OwGZJHYk64AVWwsE89oj7Hto/VLgC6B2Q/WmMZbIgdgdYOo9pShbTWQFtNh7GtQwINOnsm2MKxkM254InToZ3Y9I6rgExUQjYqKQcScwZUA5N+a8gBQuveIvDOb3BPuQLfVd/o0tlQRAgX76N9/acEt2865jGjFGZyKmZKKkZcAsofi/K49VnctpFQEAm0Y7c0YTU2YjUcgXD42H6NhCTcOSPwjCjAkz8Gd24+ytW9JQURwSrdQXjje4R3roRICDN7LJ4Z1+AaO6dXm7iKHUEOfYK963Xk8DpQJirvQozx1+vYXz+OW/YYjkc+IBHbglCrDtGE25BIACJBsEJgWyd49Ur/rk03mB7dSs8dow24L6FXKiyjRa9XdopIXnef65l+NXZzHaFPXkSC7fi/8B2U++zOkkopPCNG4RkxCm66G6v+CKHi/UTKS4nUVmHX1xGuOIy0tyLh8DGFQuXxYPj8GLHxuLNz8U2cipmarvsDZmVjxp+7ipxdV05428eEty7Drq8Ajx/3xEvxTF2EmdH1rkddQZrKdbrUnrd07C82XUvIjrkaFZPWq6/d75jYoVR5Yozcod+jDBN8CXpwTKHJ4RRERWvl8x45dPTnXPUywWV/wBg6Ev91D3UqcdtfsesrCe9aRXjHCuyKfQC6g/3EBbjHzUN5ei+EIZEgcmgF9u63kPL1Wg0vezbG2C9oiVMn+8TBYUDTv7RWToFSCu+8mzGG5BF443Fan3oAz+wb8M69sV83cxXbwirfS2TvWiJ7P8OuLgbAyCzAu+Be3OPnYyR23kDjnF5fBKnZiex5G/vABxBqgbgM7X2PvtLpwOPgMAjoNx75iWhdkd8T2f4xyh+PZ+Y1uKcuwojtenZITyMi2EfKdKeT4s1EDm6GQAsoA3P4OFyjZ+MeM7tTnfUemUdzJfb+pdh73+0oVvCi8uZjjL5ad43pp6vuDg4O3adfNZY4kyE/inV4D8EVS4jsWweGC9eoGbjHX4ArfyrK1/WKye4ggRasiv1YFXuxDu/GKt2JtGk9ChWfpruM5E/DNXJy76cNBhqwD36M7F+KVG7Rc8iYiFGwCDXyYp2m5eDgcN4yIA35UazaUsIb39XaLC312vvNGoU5fBxm5iiMIbkYKVndFpIS20Ja6rEbqnR3kroy7JpSrOrik9tDJWdiZo/DzBmPmVuEkdz7CnESbNaaJwc+1FknYkFSLkb+5RgFl6HiHeEqB4fBwoA25EcR28I6vJvIvvVYh7ZiVezTzVNBL+zFpWAkpKJiEnVVpMevy9uP9fWLIJEwEmxHAi1IexPSUo+0Nuh0pqMYLozULIz0XMyhI3Rz2MwCjJi+kaiUQCNS8on2vg+v1ZrocRkYIy/ByL8MUgoGR9qgg4PDSfT7xc6zQRnm8e7cgFhh7TnXHMKuK0caqrFb6rCbapDqYgi1I1ZEG2mldJcUtwfl8YMvFiMmEZWeq5u9JqRjJA3RXnbS0L5v1tBcqfsvHlqhwyZiaeM94UbUiEtQ6eMc4+3g4HBKBpQh/zzKdGNmjMTMGBntqXQZsS2kZgdS8il26Sqo268fSMrFmHgHKm++Lht2jLeDg8MZGNCGfKAhrTW6O0vZZzpkEmzSlZZDi1Azv4GROw+VODza03T4HDr8aJ+iWbjSn59zsnU4A9JeCr7sXvuuRMeQtx1BWqvP+xxnCTQilZuR8g3Y5euh4ZB+wJ+Cypmr1dWyZ/ZbacyBitgRCNd3jDoIN0KkEcJNEGnWw2qBSCtYbXrYQbACICH9tx0CiXT0bP28Af/c62Fo8SXDBcoDhhsM7/Fh+sDwgxnTMfzgigUzXt+64sEVB64EPdyJ4EpEmeex/s0gQEK1UP0eVL0FTZtg2guQUNQrrxUVQy5tR4i8eIduGVZ4c896oU8/Ddu2ndxn8777em7/nSAtVUjVNi0NW7nleLjE5UNlTESNvkp3ME/Jd7y4biAi2iAHyiFQCcFKCFZBqBqCNRCqgVAthBtOvxPl0obTjANXh2F1J3YYWl+H8XV3GGSX3l65OKbncaxQXPSQDs2Po0Zfwh0ngTBYQbAD+gRhter5HT1xRFr0Np0dr+EFdxK4EsGdDJ5kfXt0eFLBnQKeFP23K8GpH4gGHTZHHnsMfvh1mGTCyFZo3ADYEFsAIx8E37Bem0J0mi+njEAVXKG1QHa+hsqajjF6ESrnoeyqogAABitJREFUApTnHKs4t22Dxx/XA3Tz5F5AQi1I7V4trl+zU7dDa63RD7r8qCETUNO+hMqcohcq+7DH5kBG7BC0l0J7ScdtGQTK9G2wHKz2k5+gXOBJB286+HMhcZo2ap4UbeTcSceHKwEMX785iYod0gb96FVCpOOK4dgVRKM+KUUaIFQPzbv0FUak6dQ7VC7Ekwru1I73IK1jnPC3N02/X2Zsv3kfBjISrIL690AthxfehEs7FFcjoyDvq5C+EBU3utfnEdX0Q2k7gr37L9i734KWSt1KKWsaavgsjKzpWie4q1+2DlGsY9i2zljpJmJHdLur+oNI3QGkbj9Stw+aDh/fKD6zo5/hBNTQQlRqwXmluNbTiAiEj0DrAWg7AG0HO8Yh7W2fGMowY8GfDb5s8GVqr8aXCd5M8A4FT+qg80KPh47qIFSn38tQHYSOdIza47fhOn218HkM3wmG/qiBTzul8VfG2ctBn89IuBFadkPzDmjeBk2bO76vQIsFm1phXSss2YKK6Z3euv0vj/zCC4+FPURs3Rfw4EfYJaugWWuA40vS3mzaaN0+KilHdxjpTDv7wQePe+OgPfKf//y0m4uI1idprUFaq5DmSmguR5oOI41l2mDbRy+BO1pfpRToHoVpY/Twd0E6IIqhn2gg4QZo2Qute6F13/ERaTy+kekHfx7EdAx/LvhzwD8c3MmO53gOiNjasw/VnmLUQPCE/0/8TE7EldARxkk9HsZxH71NPiHsk6LDOwPYiRE7okN2gTJoK+lwMA7o72zwhH453gxImASJk+Hpj+HRp1FH/Y9T2Zwe+t33rzzy9ev16Ah7KGWgMiZBxiSM2d+BpsNIxUbsqq1IzS6kbM1JGQOuW17QfQRPRWGh3u+Jb1gn2Gue1I2GT8T0QHwWKjEblTMHlZQHSbmo5BHn3oC4j0I//QFpL4VPFx2/w5UAsfkwZCHE5EPsSIgZCd4Mx1j3EkoZ2sh6koFRnW4rdugEj772BM/+hNG6F+o/PX14B4XMeAUVN6bHj6VP+GyxDukdxfBp5yJpGsSOgrixED8e5TmhsczwCDyQ2LnN6eXffVQ88jSlJA9YD9FS908DztgAo7eYBtOO/h2l9yCqxx9lBvOxg3P8UTv+Hvrd54rI38ipRsWQRxul1LpTXZ4MFgbz8Q/mYwfn+M/X4x9cq0QODg4O5yGOIXdwcHAY4AxWQ/7baE8gygzm4x/Mxw7O8Z+Xxz8oY+QODg4O5xOD1SN3cHBwOG9wDLmDg4PDAGfQG3Kl1ENKKVFKpUV7Ln2FUurflFK7lFJblFJ/VkpFv6t1H6CUWqSU2q2U2qeU+n6059OXKKWGK6WWKaV2KKW2K6UeiPac+hqllKmU2qiU+ku059LTDGpDrpQaDiwESqI9lz5mKVAoIhOBPcAPojyfXkcpZQK/BK4ExgO3K6XGR3dWfUoEeEhExgOzgW8OsuMHeADYGe1J9AaD2pADjwPfQ2uSDhpE5D2RY0pKnwK9o/DTv5gJ7BORAyISAl4Aro3ynPoMEakQkQ0dfzejDVrv6ar2M5RS2cDVwFPRnktvMGgNuVLqWuCwiGyO9lyizH3A29GeRB8wDDhBRIMyBpEhOxGlVB4wBVgT3Zn0Kf+Bdto67xIyQBm4MmVngVLqfSDjFA89AjyMDqucl3R27CLyesc2j6AvuZ/vy7k5RA+lVBzwCvBdETmd8tV5hVJqMVAtIuuVUhdHez69wXltyEXkslPdr5QqAkYAmztU97KBDUqpmSJSearnDDROd+xHUUrdAywGFsjgKCY4DJzYiiq7475Bg1LKjTbiz4vIq9GeTx8yD7hGKXUV4AMSlFLPichdUZ5Xj+EUBAFKqWJguogMClU4pdQi4OfARSJSE+359AVKKRd6YXcB2oCvBe4Qke1RnVgfobTH8t9AnYh8N9rziRYdHvnfi8jiaM+lJxm0MfJBzpNAPLBUKbVJKfWbaE+ot+lY3P0W8C56oe/FwWLEO5gH3A1c2vGZb+rwUB3OAxyP3MHBwWGA43jkDg4ODgMcx5A7ODg4DHAcQ+7g4OAwwHEMuYODg8MAxzHkDg4ODgMcx5A7ODg4DHAcQ+7g4OAwwPn/+MrbZoDjOkEAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -197,7 +196,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -211,7 +210,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/global_optimization/bnd_mcs_solve.ipynb b/global_optimization/bnd_mcs_solve.ipynb
index 1623059..6dfaae2 100644
--- a/global_optimization/bnd_mcs_solve.ipynb
+++ b/global_optimization/bnd_mcs_solve.ipynb
@@ -5,7 +5,7 @@
"metadata": {},
"source": [
"# Global Optimization\n",
- "Finding the absolute maximum or minimum value of a function can be hard. For problems with a few tens of variables and only bound constraints the NAG solver [`glopt.bnd_mcs_solve`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.html#naginterfaces.library.glopt.bnd_mcs_solve) for multi-level coordinate search (from Huyer and Neumaier) is an effective routine.\n",
+ "Finding the absolute maximum or minimum value of a function can be hard. For problems with a few tens of variables and only bound constraints the NAG solver [`glopt.bnd_mcs_solve`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.bnd_mcs_solve.html) for multi-level coordinate search (from Huyer and Neumaier) is an effective routine.\n",
"\n",
"For a quick demonstration of `glopt.bnd_mcs_solve` we find the global minimum of the two-dimensional 'peaks' function, which (in a suitable form for the solver's `objfun` argument) is"
]
@@ -93,7 +93,18 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_709387/730370878.py:2: NagDeprecatedWarning: (NAG Python function naginterfaces.library.glopt.bnd_mcs_init)\n",
+ "This function is deprecated.\n",
+ "There is no replacement for this routine.\n",
+ " comm = glopt.bnd_mcs_init()\n"
+ ]
+ }
+ ],
"source": [
"from naginterfaces.library import glopt\n",
"comm = glopt.bnd_mcs_init()"
@@ -110,9 +121,22 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_709387/1372224297.py:1: NagDeprecatedWarning: (NAG Python function naginterfaces.library.glopt.bnd_mcs_solve)\n",
+ "This function is deprecated.\n",
+ "The following advice is given for making a replacement:\n",
+ "Please use handle_solve_mcs instead.\n",
+ "See also https://www.nag.com/numeric/py/nagdoc_latest/replace.html\n",
+ " _ = glopt.bnd_mcs_solve(objfun, ibound, bl, bu, comm, monit=monit)\n"
+ ]
+ }
+ ],
"source": [
- "glopt.bnd_mcs_solve(objfun, ibound, bl, bu, comm, monit=monit);"
+ "_ = glopt.bnd_mcs_solve(objfun, ibound, bl, bu, comm, monit=monit)"
]
},
{
@@ -221,7 +245,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -253,7 +277,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -267,7 +291,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/linear_algebra/real_toeplitz_solve.ipynb b/linear_algebra/real_toeplitz_solve.ipynb
index 948fa4b..c10f2ad 100644
--- a/linear_algebra/real_toeplitz_solve.ipynb
+++ b/linear_algebra/real_toeplitz_solve.ipynb
@@ -49,8 +49,8 @@
"\n",
"# Construct a real, symmetric, positive definite toeplitz matrix \n",
"matrix_size = 5000\n",
- "t = np.arange(0, matrix_size);\n",
- "a = np.exp(-np.abs(t)/10);\n",
+ "t = np.arange(0, matrix_size)\n",
+ "a = np.exp(-np.abs(t)/10)\n",
"# The toeplitz matrix is defined by its diagonals. We can construct the full matrix from the diagonals using scipy\n",
"A = scipy.linalg.toeplitz(a, a)\n",
"# Construct and a random Righ hand side\n",
@@ -202,39 +202,39 @@
"\n",
"### Real matrices\n",
"\n",
- "* [library.linsys.real_toeplitz_solve](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.linsys.html#naginterfaces.library.linsys.real_toeplitz_solve) Solution of **real symmetric positive definite Toeplitz** system of linear equations.\n",
- "* [library.lapacklin.dgbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dgbsv) Computes the solution to a **real banded** system of linear equations.\n",
- "* [library.lapacklin.dsgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dsgesv) Computes the solution to a **real** system of linear equations using **mixed precision arithmetic**.\n",
- "* [library.lapacklin.dsysv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dsysv) Computes the solution to a **real symmetric** system of linear equations.\n",
- "* [library.lapacklin.dspsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dspsv) Computes the solution to a **real symmetric** system of linear equations, **packed storage**.\n",
- "* [library.lapacklin.dpbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dpbsv) Computes the solution to a **real symmetric positive definite banded** system of linear equations.\n",
- "* [library.lapacklin.dposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dposv) Computes the solution to a **real symmetric positive definite** system of linear equations.\n",
- "* [library.lapacklin.dppsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dppsv) Computes the solution to a **real symmetric** positive definite system of linear equations, **packed storage**.\n",
- "* [library.lapacklin.dsposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dsposv) Computes the solution to a **real symmetric positive definite** system of linear equations using **mixed precision arithmetic**.\n",
- "* [library.lapacklin.dptsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dptsv) Computes the solution to a **real symmetric positive definite tridiagonal** system of linear equations.\n",
- "* [library.lapacklin.dtbtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dtbtrs) Solution of **real band triangular** system of linear equations, **multiple right-hand sides**.\n",
- "* [library.lapacklin.dtrtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dtrtrs) Solution of **real triangular** system of **linear equations, multiple right-hand sides**.\n",
- "* [library.lapacklin.dtptrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dtptrs) Solution of **real triangular** system of linear equations, **multiple right-hand sides, packed storage**.\n",
- "* [library.lapacklin.dgtsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.dgtsv) Computes the solution to a **real tridiagonal** system of linear equations.\n",
+ "* [library.linsys.real_toeplitz_solve](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.linsys.real_toeplitz_solve.html) Solution of **real symmetric positive definite Toeplitz** system of linear equations.\n",
+ "* [library.lapacklin.dgbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dgbsv.html) Computes the solution to a **real banded** system of linear equations.\n",
+ "* [library.lapacklin.dsgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dsgesv.html) Computes the solution to a **real** system of linear equations using **mixed precision arithmetic**.\n",
+ "* [library.lapacklin.dsysv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dsysv.html) Computes the solution to a **real symmetric** system of linear equations.\n",
+ "* [library.lapacklin.dspsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dspsv.html) Computes the solution to a **real symmetric** system of linear equations, **packed storage**.\n",
+ "* [library.lapacklin.dpbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dpbsv.html) Computes the solution to a **real symmetric positive definite banded** system of linear equations.\n",
+ "* [library.lapacklin.dposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dposv.html) Computes the solution to a **real symmetric positive definite** system of linear equations.\n",
+ "* [library.lapacklin.dppsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dppsv.html) Computes the solution to a **real symmetric** positive definite system of linear equations, **packed storage**.\n",
+ "* [library.lapacklin.dsposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dsposv.html) Computes the solution to a **real symmetric positive definite** system of linear equations using **mixed precision arithmetic**.\n",
+ "* [library.lapacklin.dptsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dptsv.html) Computes the solution to a **real symmetric positive definite tridiagonal** system of linear equations.\n",
+ "* [library.lapacklin.dtbtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dtbtrs.html) Solution of **real band triangular** system of linear equations, **multiple right-hand sides**.\n",
+ "* [library.lapacklin.dtrtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dtrtrs.html) Solution of **real triangular** system of **linear equations, multiple right-hand sides**.\n",
+ "* [library.lapacklin.dtptrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dtptrs.html) Solution of **real triangular** system of linear equations, **multiple right-hand sides, packed storage**.\n",
+ "* [library.lapacklin.dgtsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.dgtsv.html) Computes the solution to a **real tridiagonal** system of linear equations.\n",
"\n",
"### Complex matrices\n",
"\n",
- "* [library.lapacklin.zgbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zgbsv) Computes the solution to a **complex banded** system of linear equations.\n",
- "* [library.lapacklin.zhesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zhesv) Computes the solution to a **complex Hermitian** system of linear equations.\n",
- "* [library.lapacklin.zhpsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zhpsv) Computes the solution to **complex Hermitian** of linear equations, **packed storage.**\n",
- "* [library.lapacklin.zpbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zpbsv) Computes the solution to a **complex Hermitian positive definite banded** system of linear equations.\n",
- "* [library.lapacklin.zposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zposv) Computes the solution to a **complex Hermitian positive definite** system of linear equations.\n",
- "* [library.lapacklin.zppsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zppsv) Computes the solution to a **complex Hermitian positive definite** system of linear equations, **packed storage**.\n",
- "* [library.lapacklin.zcposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zcposv) Computes the solution to a **complex Hermitian positive definite** system of linear equations using **mixed precision** arithmetic.\n",
- "* [library.lapacklin.zptsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zptsv) Computes the solution to a **complex Hermitian positive definite tridiagonal** system of linear equations.\n",
- "* [library.lapacklin.zgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zgesv) Computes the solution to a **complex** system of linear equations.\n",
- "* [library.lapacklin.zcgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zcgesv) Computes the solution to a **complex** system of linear equations using **mixed precision** arithmetic.\n",
- "* [library.lapacklin.zsysv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zsysv) Computes the solution to a **complex symmetric** system of linear equations. \n",
- "* [library.lapacklin.zspsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zspsv) Computes the solution to a **complex symmetric** system of linear equations, **packed storage**.\n",
- "* [ibrary.lapacklin.ztbtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.ztbtrs) Solution of **complex band triangular** system of linear equations, **multiple right-hand sides**.\n",
- "* [library.lapacklin.ztrtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.ztrtrs) Solution of **complex triangular** system of linear equations, **multiple right-hand sides**.\n",
- "* [library.lapacklin.ztptrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.ztptrs) Solution of **complex triangular** system of linear equations, **multiple right-hand sides**, **packed storage**.\n",
- "* [library.lapacklin.zgtsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html#naginterfaces.library.lapacklin.zgtsv) Computes the solution to a **complex tridiagonal** system of linear equations.\n"
+ "* [library.lapacklin.zgbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zgbsv.html) Computes the solution to a **complex banded** system of linear equations.\n",
+ "* [library.lapacklin.zhesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zhesv.html) Computes the solution to a **complex Hermitian** system of linear equations.\n",
+ "* [library.lapacklin.zhpsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zhpsv.html) Computes the solution to **complex Hermitian** of linear equations, **packed storage.**\n",
+ "* [library.lapacklin.zpbsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zpbsv.html) Computes the solution to a **complex Hermitian positive definite banded** system of linear equations.\n",
+ "* [library.lapacklin.zposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zposv.html) Computes the solution to a **complex Hermitian positive definite** system of linear equations.\n",
+ "* [library.lapacklin.zppsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zppsv.html) Computes the solution to a **complex Hermitian positive definite** system of linear equations, **packed storage**.\n",
+ "* [library.lapacklin.zcposv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zcposv.html) Computes the solution to a **complex Hermitian positive definite** system of linear equations using **mixed precision** arithmetic.\n",
+ "* [library.lapacklin.zptsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zptsv.html) Computes the solution to a **complex Hermitian positive definite tridiagonal** system of linear equations.\n",
+ "* [library.lapacklin.zgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zgesv.html) Computes the solution to a **complex** system of linear equations.\n",
+ "* [library.lapacklin.zcgesv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zcgesv.html) Computes the solution to a **complex** system of linear equations using **mixed precision** arithmetic.\n",
+ "* [library.lapacklin.zsysv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zsysv.html) Computes the solution to a **complex symmetric** system of linear equations. \n",
+ "* [library.lapacklin.zspsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zspsv.html) Computes the solution to a **complex symmetric** system of linear equations, **packed storage**.\n",
+ "* [ibrary.lapacklin.ztbtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.ztbtrs.html) Solution of **complex band triangular** system of linear equations, **multiple right-hand sides**.\n",
+ "* [library.lapacklin.ztrtrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.ztrtrs.html) Solution of **complex triangular** system of linear equations, **multiple right-hand sides**.\n",
+ "* [library.lapacklin.ztptrs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.ztptrs.html) Solution of **complex triangular** system of linear equations, **multiple right-hand sides**, **packed storage**.\n",
+ "* [library.lapacklin.zgtsv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.zgtsv.html) Computes the solution to a **complex tridiagonal** system of linear equations.\n"
]
}
],
@@ -254,7 +254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/linear_algebra/triangular_matrix_multiply.ipynb b/linear_algebra/triangular_matrix_multiply.ipynb
index b4c4952..6299d16 100644
--- a/linear_algebra/triangular_matrix_multiply.ipynb
+++ b/linear_algebra/triangular_matrix_multiply.ipynb
@@ -150,7 +150,8 @@
" [0.0,0.0,0.0,7.4]]\n",
")\n",
"\n",
- "alpha*(np.transpose(A) @ B)"
+ "product = alpha*(np.transpose(A) @ B)\n",
+ "product"
]
},
{
@@ -234,7 +235,8 @@
"metadata": {},
"outputs": [],
"source": [
- "from naginterfaces.library.blas import dtrmm"
+ "# The dtrmm import below is used in a timeit block:\n",
+ "from naginterfaces.library.blas import dtrmm # pylint: disable=unused-import "
]
},
{
diff --git a/local_optimization/BXNL/Readme.md b/local_optimization/BXNL/Readme.md
index 8b13789..1a3e2cb 100644
--- a/local_optimization/BXNL/Readme.md
+++ b/local_optimization/BXNL/Readme.md
@@ -1 +1,108 @@
+[](https://www.nag.com)
+# Nonlinear Least-Squares Trust-Region Method (BXNL)
+
+[[`handle_solve_bxnl`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bxnl) | [`e04ggf`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html) |
+[`e04ggc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ggc.html) ]
+
+Data fitting and calibrating parameters of complex numerical models is one of the most common
+problems found in numerous industries such as physics, space exploration, simulations, engineering, amongs many others.
+[NAG](https://www.nag.co.uk/) introduces to the [NAG Library at Mark 27.1](https://www.nag.co.uk/content/nag-library) a novel [nonlinear least-square](https://en.wikipedia.org/wiki/Non-linear_least_squares) [trust-region solver](https://en.wikipedia.org/wiki/Trust_region) for unconstrained or bound-constrained fitting problems, [`handle_solve_bxnl`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bxnl) ([`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html)). It offers a significant variety of algorithms and regularisation techniques.
+
+The solver [`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html) is aimed at small to medium sized fitting problems (up to 1000s of parameters) bound-constrained nonlinear least-squares problems
+and is also part of the [NAG Optimization Modelling Suite](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#optsuite) common handle interface. It offers clarity and consistency of the interface of the solvers within the suite, making it trivial to switch among compatible solvers.
+
+Figure 1 shows an illustrative simple problem of data fitting ([Jupyter Notebook](./notebooks/orbit_ex.ipynb)). The task is to find the optimal orbit path given a variety of measurements for which the orbit has to approximatly pass-by.
+
+<table>
+ <tr>
+ <td width=50%><img src="./images/est_orbit.png" width="100%" alt="Optimal orbit from data orbit measurements."/>
+ <td width=50%><img src="./images/estw_orbit.png" width="100%" alt="Weighted optimal orbit from data orbit measurements."/></td>
+</tr>
+</table>
+
+**Figure 1.** Example of a NLLS orbital data fitting.
+ Given a set of 7 orbital data points the task is to estimate an optimal orbit path that minimizes the error between the path and the fixed data points. For this example assume that expert knowledge provides insight on the reliability of each measument and that for this satellite configuration operational orbit height should around 250 +/-3 units. Center plot shows a simple fit where each measurement (data point) contributes the same amount and provides an optimal orbit height of 238.76 units. The fit is quite poor in the sense that it does not satisfy expert advice. Evidently data point 0 (yellow cross closest to earch surface) unreliablity should be taken into account while doing the fitting. Weights for the residuals should be proportional to the inverse of their variability. For this example suppose we are provided with the accuracy for each of the data measurements, this can be factored using weighted nonlinear least-squares. The rightmost plot shows the weighted optimal solution with orbit height of 254.90 units wich is withing the suggested tolerance. Image credit: [Image of Earth](https://pics.eumetsat.int/viewer/index.html) was taken from [EUMETSAT, Copyright 2020](https://pics.eumetsat.int/viewer/index.html#help).
+
+
+# More Info
+ 1. [BXNL information leaflet](https://www.nag.com/content/faster-data-fitting-solver)
+ 2. [BXNL in the NAG Library for Python](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bxnl)
+ 3. Examples [[Python example](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_disable_ex.main), [C example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ggc.html#example), [Fortran example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html#example)]
+
+ # Unfolding Nuclear Track Data
+
+ [[Python Jupyter notebook for this example](./notebooks/simple_BXNL.ipynb)]
+
+This example illustrates the usage of [`handle_solve_bxnl`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ggc.html) ([`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html)) to fit PADC
+etched nuclear track data of alpha particles to a convoluted distribution. A target
+sheet is scanned and track diameters are recorded (red wedges,
+left in Figure 2) into a histogram (Blue bars right plot of Figure 2)
+and a mixed Normal and log-Normal model is to be fitted
+to the obtained experimental histogram.
+
+The [Jupyter notebook](./notebooks/simple_BXNL.ipynb) uses `e04gg` to fit the
+six parameter model φ(t, x = (a,b,Al,μ,σ,Ag)) = Al log-Normal(a, b) + Ag Normal(μ, σ) with 0 ≤ x,
+using as data the histogram heights. The NLLS solution provides the unfolded
+parameters for the two distributions (red and blue curves in right plot in Figure 2).
+Adding these together produces the green curve which is the one used to perform the fitting with.
+
+<table>
+<tr>
+<td valign="top" width=50% ><img src="./images/tracks.png" width="100%" alt="PADC etch track diameter histogram unfolding"/></td>
+<td width=50%><img src="./images/fig-unfolding.png" width="100%" alt="Experimental histogram of track diameter"/></td>
+</tr>
+</table>
+
+**Figure 2.** Left: example of a PADC target with alpha
+particle etched tracks, wedges in red show the track diameter.
+Right: experimental data histogram of track diameters (blue bars),
+aggregated model used in the fitting (green curve) and unfolded models (blue and red curves).
+Optimal parameter values are reported in the legend.
+
+
+# Modern Replacement Alternative
+Solver [`handle_solve_bxnl`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ggc.html) ([`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html)) is a modern and attractive replacement for the unconstrained nonlinear least-squares solver [`lsq_uncon_quasi_deriv_comp`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04gbc.html) ([`e04gb`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04gbf.html)).
+
+More recent and modern methods have been incorporated into [`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html) making it much faster than [`e04gb`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04gbf.html). Our benchmarks comparing [`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html) to [`e04gb`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04gbf.html) using 68 unconstrained nonlinear least-squares CUTEst problems is reported in Figure 3 using performance profiles.
+
+Contrasting the three plots, it can be seen that the new solver is more efficient in time: solves 60%
+of the problems faster (left plot). In general terms it is more robust (solves 25% more problems) and less expensive in terms of user call-backs: 55% of problems
+require less function calls (center plot) and 65% of the problems require less gradient evaluations (right plot).
+
+
+[`e04gg`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04ggf.html)
+should present significant improvement for unconstrained or bound-constrained nonlinear
+least-squares solvers in the NAG Library and current users of [`e04gb`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04gbf.html)
+are highly encourage to try out the new solver.
+
+<table>
+ <tr>
+ <td width=30%><img src="./images/b-ral_sif-e04gg-e04gb-NT.png" width="100%" alt="Performance Profile (time:seconds)"/>
+ <td width=30%><img src="./images/b-ral_sif-e04gg-e04gb-NF.png" width="100%" alt="Performance Profile (number of function calls)"/>
+ <td width=30%><img src="./images/b-ral_sif-e04gg-e04gb-NG.png" width="100%" alt="Performance Profile (number of gradient calls)"/>
+</tr>
+</table>
+
+**Figure 3.** Performance profiles comparing solvers e04gg and e04gb over 68 CUTEst unconstrained nonlinear least-squares problems.
+Performance measure are: time in seconds (left), number of function calls (center) and number of gradient calls
+(right). For the time plot (left), higher line indicates faster solver. For the center and right plots, higher line
+represent less functions and gradients calls.
+
+
+
+# References
+
+ * Gould N I M, Rees T, and Scott J A (2017) _A higher order method for solving nonlinear least-squares problems_. Technical report, RAL-P-1027-010 RAL Library. STFC Rutherford Appleton Laboratory http://www.numerical.rl.ac.uk/people/rees/pdf/RAL-P-2017-010.pdf
+ * Kanzow C, Yamashita N, and Fukushima M (2004) _Levenberg-Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints_. Journal of Computational and Applied Mathematics 174 375–397
+ * Nocedal J and Wright S J (2006) _Numerical Optimization_. (2nd Edition) Springer Series in Operations Research, Springer, New York
+ * Adachi S, Iwata S, Nakatsukasa Y, and Takeda A (2015) _Solving the trust region subproblem by a generalized eigenvalue problem_. Technical report, METR 2015-14. Mathematical Engineering, The University of Tokyo https://www.keisu.t.u-tokyo.ac.jp/data/2015/METR15-14.pdf
+ * Conn A R, Gould N I M and Toint Ph L (2000) _Trust Region Methods_. SIAM, Philadephia
+
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
diff --git a/local_optimization/BXNL/images/b-ral_sif-e04gg-e04gb-NF.png b/local_optimization/BXNL/images/b-ral_sif-e04gg-e04gb-NF.png
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similarity index 100%
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rename to local_optimization/BXNL/images/fig-unfolding.png
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diff --git a/local_optimization/BXNL/notebooks/ltx_optprb.png b/local_optimization/BXNL/notebooks/ltx_optprb.png
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diff --git a/local_optimization/BXNL/notebooks/orbit_ex.ipynb b/local_optimization/BXNL/notebooks/orbit_ex.ipynb
new file mode 100644
index 0000000..5bd303b
--- /dev/null
+++ b/local_optimization/BXNL/notebooks/orbit_ex.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Orbital Data Fitting\n",
+ "\n",
+ "Example of a nonlinear least-square orbital data fitting. Given a set of orbital data points the task is to estimate an optimal orbit path that minimizes the error between the path and the fixed data points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.patches as pch\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "im = plt.imread(\"earth.png\")\n",
+ "implot = plt.imshow(im)\n",
+ "tx = np.array([441.23, 484.31, 265.15, 98.25, 180.66, 439.13, 596.54])\n",
+ "ty = np.array([333.92, 563.46, 577.40, 379.23, 148.62, 100.28, 285.99])\n",
+ "cc = np.array([355.00, 347.00])\n",
+ "tr = (tx-cc[0])**2 + (ty-cc[1])**2\n",
+ "plt.plot(tx,ty,'y+')\n",
+ "plt.axis('off')\n",
+ "plt.savefig('dat_orbit.png', bbox_inches='tight', dpi=150)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Image credit: [Image of Earth](http://pics.eumetsat.int/viewer/index.html) was taken from [EUMETSAT, Copyright 2020](http://pics.eumetsat.int/viewer/index.html#help).\n",
+ "\n",
+ "\n",
+ "The previous image shows the orbit measurements to which an optimal orbit, `r`, must be estimated. \n",
+ "The simple univariate problem to solve is:\n",
+ "\n",
+ "![LaTeX equation: min f(x) = sum i=1 to nres of (tr[i]^2 - r^2)^2](ltx_optprb.png)\n",
+ "\n",
+ "Here `tr[i]` contains the squared norm for the measurement point `i`, given by the coordinate pair `(tx[i], ty[i])`. Note that the coordinates for the center of the planet are provided by the vector `cc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# problem data\n",
+ "# number of observations\n",
+ "nres = len(tx)\n",
+ "# observations\n",
+ "# Define the data structure to be passed to the callback functions\n",
+ "data = {'tr': tr}\n",
+ "# number of parameter to fit\n",
+ "nvar = 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the least-square function and add first derivatives.\n",
+ "def lsqfun(x, nres, inform, data):\n",
+ " \"\"\"\n",
+ " Objective function call back passed to the least squares solver.\n",
+ " Return the difference between the current estimated radius squared, r^2=x^2 and \n",
+ " the squared norm of the data point stored in tr[i] for i = 1 to nres:\n",
+ " rx[i] = r^2 - tr[i], i = 1, ..., nres.\n",
+ " \"\"\"\n",
+ " rx = x**2 - data['tr']\n",
+ " return rx, inform\n",
+ "\n",
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " \"\"\"\n",
+ " Computes the Jacobian of the least square residuals.\n",
+ " Simply return rdx[i] = 2r, i = 1, ..., nres.\n",
+ " \"\"\"\n",
+ " rdx[:] = 2.0*np.ones(nres)*x[:]\n",
+ " return inform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize the model handle\n",
+ "handle = opt.handle_init(nvar)\n",
+ "\n",
+ "# Define a dense nonlinear least-squares objective function\n",
+ "opt.handle_set_nlnls(handle, nres)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Restrict parameter space (0 <= x)\n",
+ "opt.handle_set_simplebounds(handle, np.zeros(nvar), 1000.0*np.ones(nvar))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set some optional parameters to control the output of the solver\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Solution = X',\n",
+ " 'Bxnl Iteration Limit = 100'\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define initial guess (starting point) Away from zero which is problematic.\n",
+ "x = np.ones(nvar)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Call the solver"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GG, Nonlinear least squares method for bound-constrained problems\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 1.45373E+09\n",
+ " Norm of projected gradient 2.23690E-01\n",
+ " Norm of scaled projected gradient 4.14848E-06\n",
+ " Norm of step 3.75533E-06\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 2.38765E+02 1.00000E+03\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Call the solver\n",
+ "slv = opt.handle_solve_bxnl(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimal Orbit Height: 238.76\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Optimal parameter values\n",
+ "rstar = slv.x\n",
+ "print('Optimal Orbit Height: %3.2f' % rstar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figure, axes = plt.figure(), plt.gca()\n",
+ "implot = plt.imshow(im)\n",
+ "orbit = plt.Circle(cc, radius=rstar, color='w', fill=False)\n",
+ "axes.add_patch(orbit)\n",
+ "plt.plot(tx, ty, 'y+')\n",
+ "plt.axis('off')\n",
+ "plt.savefig('est_orbit.png', bbox_inches='tight', dpi=150)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Suppose expert knowledge provides insight on the reliability of each measurement and that for this satellite configuration, operational orbit height should be around 250 +/-6 units. The previous image shows a fit where each measurement (data point) contributes the same amount and provides an optimal orbit height of 238.76 units. The fit is quite poor in the sense that it does not satisfy expert advice. Evidently data point 0 (yellow cross closest to Earth surface) is unreliable. Unreliability should be taken into account while doing the fitting. For this end, weights for each residuals are introduced (weights should be set to be proportional to the inverse of their variability). For this example, suppose we are provided with the accuracy for each of the data measurements. \n",
+ "\n",
+ "With this new information, the problem is solved again using weighted nonlinear least-squares. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GG, Nonlinear least squares method for bound-constrained problems\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 1.25035E+06\n",
+ " Norm of projected gradient 6.26959E-03\n",
+ " Norm of scaled projected gradient 3.96468E-06\n",
+ " Norm of step 8.72328E-03\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 2.54896E+02 1.00000E+03\n",
+ "Optimal Orbit Height: 254.90\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Add weights for each residual\n",
+ "weights = np.array([0.10, 0.98, 1.01, 1.00, 0.92, 0.97, 1.02])\n",
+ "weights /= weights.sum()\n",
+ "\n",
+ "# Define the reliability of the measurements (weights)\n",
+ "opt.handle_set_get_real(handle, 'rw', rarr=weights)\n",
+ "\n",
+ "# Indicate to the solver that weights are to be used\n",
+ "for option in [\n",
+ " 'Bxnl Use weights = Yes',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Solve again\n",
+ "slv = opt.handle_solve_bxnl(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom) # monit=monit,\n",
+ "\n",
+ "# Objective and solution\n",
+ "rstar = slv.x\n",
+ "print('Optimal Orbit Height: %3.2f' % rstar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimal Orbit Height: 254.90 (should be between 244 and 256)\n"
+ ]
+ }
+ ],
+ "source": [
+ "figure, axes = plt.figure(), plt.gca()\n",
+ "implot = plt.imshow(im)\n",
+ "orbit = plt.Circle(cc, radius=rstar, color='w', fill=False)\n",
+ "axes.add_patch(orbit)\n",
+ "plt.plot(tx, ty,'y+')\n",
+ "plt.axis('off')\n",
+ "plt.savefig('estw_orbit.png', bbox_inches='tight', dpi=150)\n",
+ "plt.show()\n",
+ "print('Optimal Orbit Height: %3.2f (should be between 244 and 256)' % rstar)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For this weighted model the orbital solution provided concurs with expert advice."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/local_optimization/BXNL/notebooks/simple_BXNL.ipynb b/local_optimization/BXNL/notebooks/simple_BXNL.ipynb
new file mode 100644
index 0000000..708f958
--- /dev/null
+++ b/local_optimization/BXNL/notebooks/simple_BXNL.ipynb
@@ -0,0 +1,563 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Simple Nonlinear Least-Squares Fitting Example\n",
+ "\n",
+ "This example demontrates how to fit data to a model using weighted nonlinear least-squares. \n",
+ "\n",
+ "**handle_solve_bxnl** (`e04gg`) is a bound-constrained nonlinear least squares trust region solver (BXNL) from the NAG optimization modelling suite aimed for small to medium-scale problems. It solves the problem:\n",
+ "\n",
+ "$$\n",
+ "\\begin{array}{ll}\n",
+ "{\\underset{x \\in \\mathbb{R}^{n_{\\text{var}}}}{minimize}\\ } & \n",
+ "\\frac{1}{2} \\sum_{i=1}^{n_{\\text{res}}} w_i r_i(x)^2 + \\frac{\\sigma}{p}\\|x\\|^p_2\\\\\n",
+ "\\text{subject to} & l_{x} \\leq x \\leq u_{x}\n",
+ "\\end{array}\n",
+ "$$\n",
+ "\n",
+ "\n",
+ "where $r_i(x),i=1,\\dots,n_{\\text{res}}$, are smooth nonlinear functions called residuals, $w_i ,i=1,\\dots,n_{\\text{res}}$ are weights (by default they are all defined to 1, and the rightmost element represents the regularization term with parameter $\\sigma\\geq0$ and power $p>0$. The constraint elements $l_x$ and $u_x$ are $n_{\\text{var}}$-dimensional vectors defining the bounds on the variables.\n",
+ "\n",
+ "Typically in a calibration or data fitting context, the residuals will be defined as the difference between the observed values $y_i$ at $t_i$ and the values provided by a nonlinear model $\\phi(t;x)$, i.e., $$r_i(x)≔y_i-\\phi(t_i;x).$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following example illustrates the usage of `e04gg` to fit PADC target with $\\alpha$ particle\n",
+ "etched nuclear track data to a convoluted distribution. A target\n",
+ "sheet is scanned and track diameters (red wedges in\n",
+ "the following Figure 1) are recorded into a histogram and a mixed Normal and log-Normal model is to be fitted to the experimental histogram (see Figure 2).\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Figure 1**: PADC with etched $\\alpha$ particle tracks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`e04gg` is used to fit the six parameter model\n",
+ "$$\n",
+ "\\begin{array}{ll}\n",
+ "\\phi\\big(t, x = (a, b, A_{\\ell}, \\mu, \\sigma, A_g)\\big) = \\text{log-Normal}(a, b, A_l) + \\text{Normal}(\\mu, \\sigma^2, A_g)\\\\\n",
+ "\\text{subject to } 0 \\leq x,\n",
+ "\\end{array}$$\n",
+ "using the histogram heights reported in Figure 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt\n",
+ "\n",
+ "# problem data\n",
+ "# number of observations\n",
+ "nres = 64\n",
+ "# observations\n",
+ "diameter = range(1, nres+1)\n",
+ "density = [\n",
+ " 0.0722713864, 0.0575221239, 0.0604719764, 0.0405604720, 0.0317109145, \n",
+ " 0.0309734513, 0.0258112094, 0.0228613569, 0.0213864307, 0.0213864307,\n",
+ " 0.0147492625, 0.0213864307, 0.0243362832, 0.0169616519, 0.0095870206,\n",
+ " 0.0147492625, 0.0140117994, 0.0132743363, 0.0147492625, 0.0140117994,\n",
+ " 0.0140117994, 0.0132743363, 0.0117994100, 0.0132743363, 0.0110619469,\n",
+ " 0.0103244838, 0.0117994100, 0.0117994100, 0.0147492625, 0.0110619469,\n",
+ " 0.0132743363, 0.0206489676, 0.0169616519, 0.0169616519, 0.0280235988,\n",
+ " 0.0221238938, 0.0235988201, 0.0221238938, 0.0206489676, 0.0228613569,\n",
+ " 0.0184365782, 0.0176991150, 0.0132743363, 0.0132743363, 0.0088495575,\n",
+ " 0.0095870206, 0.0073746313, 0.0110619469, 0.0036873156, 0.0051622419,\n",
+ " 0.0058997050, 0.0014749263, 0.0022123894, 0.0029498525, 0.0014749263,\n",
+ " 0.0007374631, 0.0014749263, 0.0014749263, 0.0007374631, 0.0000000000,\n",
+ " 0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000\n",
+ " ]\n",
+ "\n",
+ "# Define the data structure to be passed to the callback functions\n",
+ "data = {'d': diameter, 'y': density}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot histogram of PADC etch track diameter count (densities)\n",
+ "dh = np.arange(1, 10*nres+9)/10.0\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "ax.set_title('PADC etch track diameter histogram', fontsize=16)\n",
+ "ax.set_xlabel('Diameter (nm)')\n",
+ "ax.set_ylabel('Density')\n",
+ "ax.set_xlim(xmin=1, xmax=65)\n",
+ "ax.bar(diameter, data['y'], color='lightsteelblue')\n",
+ "ax.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Figure 2**: Histogram of etched track diameter of $\\alpha$ particles. Bar heights are the data that will be fitted unsing the aggregated model $\\phi(x, t)$. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define Normal and log-Normal distributions\n",
+ "def lognormal(d, a, b, Al):\n",
+ " return Al/(d*b*np.sqrt(2*math.pi))*np.exp(-((np.log(d)-a)**2)/(2*b**2))\n",
+ "\n",
+ "def gaussian(d, mu, sigma, Ag):\n",
+ " return Ag*np.exp(-0.5*((d-mu)/sigma)**2)/(sigma*np.sqrt(2*math.pi))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In terms of solving this problem, the function to minimize is the sum of residuals using the model $\\phi(x;t)$\n",
+ "and the data pair (`diameter`, `density`). The parameter vector is $x = (a, b, A_l, \\mu, \\sigma, A_g)$. The next step is to define a function to return the residual vector \n",
+ "$\\text{lsqfun}(x) := \\big[r_1(x), r_2(x), \\dots, r_{n_{\\text{res}}}(x)\\big]$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the least-square function as a mixture of Normal and log-Normal \n",
+ "# functions. Also add its first derivatives\n",
+ "def lsqfun(x, nres, inform, data):\n",
+ " \"\"\"\n",
+ " Objective function callback passed to the least squares solver.\n",
+ " x = (a, b, Al, mu, sigma, Ag)\n",
+ " \"\"\"\n",
+ " rx = np.zeros(nres)\n",
+ " d = data['d']\n",
+ " y = data['y']\n",
+ " a = x[0]\n",
+ " b = x[1]\n",
+ " Al = x[2]\n",
+ " mu = x[3]\n",
+ " sigma = x[4]\n",
+ " Ag = x[5]\n",
+ " for i in range(nres):\n",
+ " rx[i] = lognormal(d[i], a, b, Al) + gaussian(d[i], mu, sigma, Ag) - y[i]\n",
+ " return rx, inform\n",
+ "\n",
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " \"\"\"\n",
+ " Computes the Jacobian of the least square residuals.\n",
+ " x = (a, b, Al, mu, sigma, Ag)\n",
+ " \"\"\"\n",
+ " n = len(x)\n",
+ " d = data['d']\n",
+ " a = x[0]\n",
+ " b = x[1]\n",
+ " Al = x[2]\n",
+ " mu = x[3]\n",
+ " sigma = x[4]\n",
+ " Ag = x[5]\n",
+ " for i in range(nres):\n",
+ " # log-Normal derivatives\n",
+ " l = lognormal(d[i], a, b, Al)\n",
+ " # dl/da\n",
+ " rdx[i*n+0] = (np.log(d[i])-a)/(b**2) * l\n",
+ " # dl/db\n",
+ " rdx[i*n+1] = ((np.log(d[i])-a)**2 - b**2)/b**3 * l\n",
+ " # dl/dAl\n",
+ " rdx[i*n+2] = lognormal(d[i], a, b, 1.0)\n",
+ " # Gaussian derivatives\n",
+ " g = gaussian(d[i], mu, sigma, Ag)\n",
+ " # dg/dmu\n",
+ " rdx[i*n+3] = (d[i] - mu) / sigma**2 * g\n",
+ " # dg/dsigma\n",
+ " rdx[i*n+4] = ((d[i] - mu)**2 - sigma**2)/sigma**3 * g\n",
+ " # dg/dAg\n",
+ " rdx[i*n+5] = gaussian(d[i], mu, sigma, 1.0)\n",
+ " return inform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# parameter vector: x = (a, b, Al, mu, sigma, Ag)\n",
+ "nvar = 6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize the model handle\n",
+ "handle = opt.handle_init(nvar)\n",
+ "\n",
+ "# Define a dense nonlinear least-squares objective function\n",
+ "opt.handle_set_nlnls(handle, nres)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "HandleSetGetRealReturnData(lrarr=64, rarr=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Add weights for each residual\n",
+ "weights = np.ones(nres)\n",
+ "weights[55:63] = 5.0\n",
+ "weights /= weights.sum()\n",
+ "\n",
+ "# Define the reliability of the measurements (weights)\n",
+ "opt.handle_set_get_real(handle, 'rw', rarr=weights)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Restrict parameter space (0 <= x)\n",
+ "opt.handle_set_simplebounds(handle, np.zeros(nvar), 100.0*np.ones(nvar))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set some optional parameters to control the output of the solver\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Solution = X',\n",
+ " 'Bxnl Iteration Limit = 100',\n",
+ " 'Bxnl Use weights = YES',\n",
+ " # Add cubic regularization term (avoid overfitting)\n",
+ " 'Bxnl Reg Order = 3',\n",
+ " 'Bxnl Glob Method = REG',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define initial guess (starting point)\n",
+ "x = np.array([1.63, 0.88, 1.0, 30, 1.52, 0.24], dtype=float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Call the solver"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GG, Nonlinear least squares method for bound-constrained problems\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 4.44211E-08\n",
+ " Norm of projected gradient 1.18757E-09\n",
+ " Norm of scaled projected gradient 3.98428E-06\n",
+ " Norm of step 1.66812E-01\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 2.02043E+00 1.00000E+02\n",
+ " 2 0.00000E+00 1.39726E+00 1.00000E+02\n",
+ " 3 0.00000E+00 6.93255E-01 1.00000E+02\n",
+ " 4 0.00000E+00 3.65929E+01 1.00000E+02\n",
+ " 5 0.00000E+00 7.01808E+00 1.00000E+02\n",
+ " 6 0.00000E+00 3.36877E-01 1.00000E+02\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Call the solver\n",
+ "slv = opt.handle_solve_bxnl(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The optimal solution $x$ provides the unfolded parameters for the two distributions, Normal and log-Normal (blue and red curves in Figure 4). Adding these together produces the aggragated curve (shown in color green of Figure 3 and 4) this last one is the one used to perform the fitting with. The optimal solution is"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Optimal parameter values\n",
+ "# Al * log-Normal(a, b):\n",
+ "aopt = slv.x[0]\n",
+ "bopt = slv.x[1]\n",
+ "Alopt = slv.x[2]\n",
+ "\n",
+ "# Ag * gaussian(mu, sigma):\n",
+ "muopt = slv.x[3]\n",
+ "sigmaopt = slv.x[4]\n",
+ "Agopt = slv.x[5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "and the objective function value is "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4.4421102582032486e-08\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(slv.rinfo[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next plot in Figure 3 illustrates the mixed-distribution fit over the histogram:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lopt = lognormal(dh, aopt, bopt, Alopt)\n",
+ "gopt = gaussian(dh, muopt, sigmaopt, Agopt)\n",
+ "w = lopt + gopt\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "ax.set_title('PADC etch track diameter histogram and fit', fontsize=16)\n",
+ "ax.set_xlabel('Diameter (nm)')\n",
+ "ax.set_ylabel('Density')\n",
+ "ax.set_xlim(xmin=1, xmax=65)\n",
+ "ax.bar(diameter, data['y'], color='lightsteelblue')\n",
+ "ax.plot(dh, w, '-', linewidth=3, color='tab:green')\n",
+ "ax.grid()\n",
+ "ax.legend(['Aggregated', 'Measured track diameter density'])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Figure 3**: Histogram with aggregated fit.\n",
+ "\n",
+ "The plot below in Figure 4 shows the unfolded fit, in red the log-Normal distribution and blue the Normal one:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "ax.set_title('PADC etch track diameter histogram unfolding', fontsize=16)\n",
+ "ax.set_xlabel('Diameter (nm)')\n",
+ "ax.set_ylabel('Density')\n",
+ "ax.set_xlim(xmin=1, xmax=65)\n",
+ "ax.bar(diameter, data['y'], color='lightsteelblue')\n",
+ "ax.plot(dh, lopt, '-', linewidth=4, color='tab:red')\n",
+ "ax.plot(dh, gopt, '-', linewidth=4, color='tab:blue')\n",
+ "ax.plot(dh, w, '-', linewidth=3, color='tab:green')\n",
+ "ax.grid()\n",
+ "glab = 'Unfolded Normal($\\\\mu=%1.2f$, $\\\\sigma=%1.2f, A=%1.2f$)' % (muopt, sigmaopt, Agopt)\n",
+ "llab = 'Unfolded log-Normal($a=%1.2f$, $b=%1.2f, A=%1.2f$)' % (aopt, bopt, Alopt)\n",
+ "ax.legend([llab, glab, 'Aggregated', 'Measured track diameter density'])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Figure 4**: Aggregated model used for the fitting (green curve) and unfolded models (blue and red curves).\n",
+ "Optimal parameter values are ported in the legend."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, clean up and destroy the handle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Destroy the handle:\n",
+ "opt.handle_free(handle)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/local_optimization/DFO_noisy.ipynb b/local_optimization/DFO/DFO_noisy.ipynb
similarity index 88%
rename from local_optimization/DFO_noisy.ipynb
rename to local_optimization/DFO/DFO_noisy.ipynb
index b3df82f..83f36a2 100644
--- a/local_optimization/DFO_noisy.ipynb
+++ b/local_optimization/DFO/DFO_noisy.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -285,7 +296,7 @@
" data.noiselev = noiselevel\n",
" print(data.fun)\n",
" try:\n",
- " sln = opt.bounds_quasi_func_easy(ibound, objfun_bob, xl, xu, xstart, data=data)\n",
+ " _ = opt.bounds_quasi_func_easy(ibound, objfun_bob, xl, xu, xstart, data=data)\n",
" except utils.NagValueError as e:\n",
" if e.errno == 2 and not data.ok:\n",
" print('Maximum number of evaluations exceeded:', 400*n)\n",
@@ -334,8 +345,8 @@
" opt.handle_opt_set(handle, optstr)\n",
" # Call the solver\n",
" try:\n",
- " x, rinfo, stats = opt.handle_solve_dfno(handle, objfun, xstart, data=data, io_manager=iom)\n",
- " except utils.NagCallbackTerminateWarning as e:\n",
+ " _ = opt.handle_solve_dfno(handle, xstart, objfun=objfun, data=data, io_manager=iom)\n",
+ " except utils.NagCallbackTerminateWarning as _e:\n",
" pass\n",
" # print results\n",
" print(pname)\n",
@@ -548,7 +559,7 @@
"text": [
"Help on function handle_solve_dfno in module naginterfaces.library.opt:\n",
"\n",
- "handle_solve_dfno(handle, objfun, x, monit=None, data=None, io_manager=None)\n",
+ "handle_solve_dfno(handle, x, objfun=None, monit=None, data=None, io_manager=None)\n",
" Direct communication derivative-free (DFO) solver for a nonlinear\n",
" objective function with bounded variables.\n",
" \n",
@@ -563,19 +574,32 @@
" For full information please refer to the NAG Library document for\n",
" e04jd\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04jdf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04jdf.html\n",
" \n",
" Parameters\n",
" ----------\n",
" handle : Handle\n",
- " The handle to the problem. It needs to be initialized by\n",
- " ``handle_init`` and **must not** be changed before the call to\n",
- " ``handle_solve_dfno``.\n",
+ " The handle to the problem. It needs to be initialized (e.g., by\n",
+ " ``handle_init``) and to hold a problem formulation compatible\n",
+ " with ``handle_solve_dfno``. It **must not** be changed between\n",
+ " calls to the NAG optimization modelling suite.\n",
+ " \n",
+ " x : float, array-like, shape (nvar)\n",
+ " x_0, the initial estimates of the variables, x.\n",
+ " \n",
+ " objfun : None or callable (fx, inform) = objfun(x, inform,\n",
+ " data=None), optional\n",
+ " Note: if this argument is None then a NAG-supplied facility will\n",
+ " be used.\n",
" \n",
- " objfun : callable (fx, inform) = objfun(x, inform, data=None)\n",
" `objfun` calculates the value of the objective function f(x) at\n",
" a specified point x.\n",
" \n",
+ " If there is no nonlinear objective (e.g., ``handle_set_linobj``\n",
+ " or ``handle_set_quadobj`` was called to define a linear or\n",
+ " quadratic objective function), `objfun` will never be called by\n",
+ " ``handle_solve_dfno`` and may be None.\n",
+ " \n",
" Parameters\n",
" ~~~~~~~~~~\n",
" x : float, ndarray, shape (nvar)\n",
@@ -610,9 +634,6 @@
" return the best available point as well as the solve\n",
" statistics.\n",
" \n",
- " x : float, array-like, shape (nvar)\n",
- " x_0, the initial estimates of the variables x.\n",
- " \n",
" monit : None or callable monit(x, rinfo, stats, data=None), optional\n",
" Note: if this argument is None then a NAG-supplied facility will\n",
" be used.\n",
@@ -653,7 +674,7 @@
" Returns\n",
" -------\n",
" x : float, ndarray, shape (nvar)\n",
- " The final values of the variables x.\n",
+ " The final values of the variables, x.\n",
" \n",
" rinfo : float, ndarray, shape (100)\n",
" Optimal objective value and various indicators at monitoring\n",
@@ -997,11 +1018,10 @@
" The problem is already being solved.\n",
" \n",
" (`errno` 4)\n",
- " The information supplied does not match with that previously\n",
- " stored.\n",
+ " On entry, nvar = *<value>*, expected value = *<value>*.\n",
" \n",
- " On entry, nvar = *<value>* must match that given during\n",
- " initialization of the `handle`, i.e., *<value>*.\n",
+ " Constraint: nvar must match the current number of variables\n",
+ " of the model in the `handle`.\n",
" \n",
" (`errno` 5)\n",
" Inconsistent options 'DFO Trust Region Tolerance' rho_end\n",
@@ -1043,6 +1063,9 @@
" Growing the interpolation set is not supported for this\n",
" solver.\n",
" \n",
+ " (`errno` 7)\n",
+ " Please provide a proper `objfun` function.\n",
+ " \n",
" Warns\n",
" -----\n",
" NagAlgorithmicWarning\n",
@@ -1106,6 +1129,74 @@
" (`errno` 20)\n",
" User requested termination during a monitoring step.\n",
" \n",
+ " Notes\n",
+ " -----\n",
+ " ``handle_solve_dfno`` is aimed at minimizing a nonlinear objective\n",
+ " function subject to bound constraints:\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " Here f is a smooth nonlinear function and l_x and u_x are\n",
+ " n-dimensional vectors defining bounds on the variables.\n",
+ " \n",
+ " ``handle_solve_dfno`` serves as a solver for compatible problems\n",
+ " stored as a handle.\n",
+ " The handle points to an internal data structure which defines the\n",
+ " problem and serves as a means of communication for functions in the\n",
+ " NAG optimization modelling suite.\n",
+ " To define a compatible problem handle, you must call ``handle_init``\n",
+ " followed by ``handle_set_nlnobj`` to initialize it and optionally\n",
+ " call ``handle_set_simplebounds`` to define bounds on the variables.\n",
+ " If ``handle_set_simplebounds`` is not called, all the variables will\n",
+ " be considered free by the solver.\n",
+ " It should be noted that ``handle_solve_dfno`` always assumes that\n",
+ " the gradient of the objective is dense, therefore, defining a sparse\n",
+ " structure for the residuals in the call to ``handle_set_nlnobj``\n",
+ " will have no effect.\n",
+ " See [the E04 Introduction] for more details about the NAG\n",
+ " optimization modelling suite.\n",
+ " \n",
+ " The solver allows fixing variables with the definition of the\n",
+ " bounds.\n",
+ " However, the following constraint must be met in order to be able to\n",
+ " call the solver:\n",
+ " \n",
+ " for all non-fixed variable x_i, the value of u_x(i)-l_x(i) must\n",
+ " be at least twice the starting trust region radius (see the\n",
+ " consistency constraint of the option 'DFO Starting Trust\n",
+ " Region').\n",
+ " \n",
+ " The solver is based on a derivative-free trust region framework.\n",
+ " This type of method is well suited for small to medium-scale\n",
+ " problems (around 100 variables) for which the derivatives are\n",
+ " unavailable or not easy to compute, and/or for which the function\n",
+ " evaluations are expensive or noisy.\n",
+ " For a detailed description of the algorithm see [Algorithmic\n",
+ " Details].\n",
+ " \n",
+ " The algorithm behaviour and solver strategy can be modified by\n",
+ " various options (see [Other Parameters]) which can be set by\n",
+ " ``handle_opt_set`` and ``handle_opt_set_file`` at any time between\n",
+ " the initialization of the handle by ``handle_init`` and a call to\n",
+ " the solver.\n",
+ " The options' names specific for this solver start either with the\n",
+ " prefix DFO (Derivative-free Optimization) or DFNO (Derivative-free\n",
+ " Nonlinear Optimization).\n",
+ " The default values for these options are chosen to work well in the\n",
+ " general case, but it is recommended you tune them to your particular\n",
+ " problem.\n",
+ " In particular, if the objective function is known to be noisy, it is\n",
+ " highly recommended to set the option 'DFO Noisy Problem' to 'YES'.\n",
+ " Once the solver has finished, options may be modified for the next\n",
+ " solve.\n",
+ " The solver may be called repeatedly with various starting points\n",
+ " and/or options.\n",
+ " \n",
+ " The underlying algorithm implemented for ``handle_solve_dfno`` is\n",
+ " the same as the one used by ``handle_solve_dfno_rcomm``.\n",
+ " ``handle_solve_dfno`` serves as a forward communication interface to\n",
+ " the derivative-free solver for nonlinear objective functions.\n",
+ " \n",
" References\n",
" ----------\n",
" Cartis, C, Fiala, J, Marteau, B and Roberts, L, 2018, `Improving the\n",
@@ -1159,7 +1250,7 @@
" For full information please refer to the NAG Library document for\n",
" e04jy\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04jyf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04jyf.html\n",
" \n",
" Parameters\n",
" ----------\n",
@@ -1358,6 +1449,72 @@
" No equivalent traditional C interface for this routine exists in the\n",
" NAG Library.\n",
" \n",
+ " ``bounds_quasi_func_easy`` is applicable to problems of the form:\n",
+ " \n",
+ " MinimizeF(x_1,x_2,...,x_n) subject to l_j <= x_j <= u_j, j =\n",
+ " 1,2,...,n\n",
+ " \n",
+ " when derivatives of F(x) are unavailable.\n",
+ " \n",
+ " Special provision is made for problems which actually have no bounds\n",
+ " on the x_j, problems which have only non-negativity bounds and\n",
+ " problems in which l_1 = l_2 = ... = l_n and u_1 = u_2 = ... = u_n.\n",
+ " You must supply a function to calculate the value of F(x) at any\n",
+ " point x.\n",
+ " \n",
+ " From a starting point you supplied there is generated, on the basis\n",
+ " of estimates of the gradient and the curvature of F(x), a sequence\n",
+ " of feasible points which is intended to converge to a local minimum\n",
+ " of the constrained function.\n",
+ " An attempt is made to verify that the final point is a minimum.\n",
+ " \n",
+ " A typical iteration starts at the current point x where n_z (say)\n",
+ " variables are free from both their bounds.\n",
+ " The projected gradient vector g_z, whose elements are finite\n",
+ " difference approximations to the derivatives of F(x) with respect to\n",
+ " the free variables, is known.\n",
+ " A unit lower triangular matrix L and a diagonal matrix D (both of\n",
+ " dimension n_z), such that LDL^T is a positive definite approximation\n",
+ " of the matrix of second derivatives with respect to the free\n",
+ " variables (i.e., the projected Hessian) are also held.\n",
+ " The equations\n",
+ " \n",
+ " LDL^Tp_z = -g_z\n",
+ " \n",
+ " are solved to give a search direction p_z, which is expanded to an\n",
+ " n-vector p by an insertion of appropriate zero elements.\n",
+ " Then alpha is found such that F(x+alpha p) is approximately a\n",
+ " minimum (subject to the fixed bounds) with respect to alpha; x is\n",
+ " replaced by x+alpha p, and the matrices L and D are updated so as to\n",
+ " be consistent with the change produced in the estimated gradient by\n",
+ " the step alpha p.\n",
+ " If any variable actually reaches a bound during the search along p,\n",
+ " it is fixed and n_z is reduced for the next iteration.\n",
+ " Most iterations calculate g_z using forward differences, but central\n",
+ " differences are used when they seem necessary.\n",
+ " \n",
+ " There are two sets of convergence criteria -- a weaker and a\n",
+ " stronger.\n",
+ " Whenever the weaker criteria are satisfied, the Lagrange multipliers\n",
+ " are estimated for all the active constraints.\n",
+ " If any Lagrange multiplier estimate is significantly negative, then\n",
+ " one of the variables associated with a negative Lagrange multiplier\n",
+ " estimate is released from its bound and the next search direction is\n",
+ " computed in the extended subspace (i.e., n_z is increased).\n",
+ " Otherwise minimization continues in the current subspace provided\n",
+ " that this is practicable.\n",
+ " When it is not, or when the stronger convergence criteria are\n",
+ " already satisfied, then, if one or more Lagrange multiplier\n",
+ " estimates are close to zero, a slight perturbation is made in the\n",
+ " values of the corresponding variables in turn until a lower function\n",
+ " value is obtained.\n",
+ " The normal algorithm is then resumed from the perturbed point.\n",
+ " \n",
+ " If a saddle point is suspected, a local search is carried out with a\n",
+ " view to moving away from the saddle point.\n",
+ " A local search is also performed when a point is found which is\n",
+ " thought to be a constrained minimum.\n",
+ " \n",
" References\n",
" ----------\n",
" Gill, P E and Murray, W, 1976, `Minimization subject to bounds on\n",
@@ -1373,7 +1530,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1387,7 +1544,25 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
}
},
"nbformat": 4,
diff --git a/local_optimization/DFO/Readme.md b/local_optimization/DFO/Readme.md
new file mode 100644
index 0000000..9460b19
--- /dev/null
+++ b/local_optimization/DFO/Readme.md
@@ -0,0 +1,89 @@
+[](https://www.nag.com)
+
+# Derivative-Free Optimization ([DFO](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#derivatives))
+
+[DFO](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#derivatives) solvers are aimed at optimizing _black box_ models and can handle either [calibration (nonlinear least squares)](https://en.wikipedia.org/wiki/Non-linear_least_squares) problems (DFLS)
+or [problems with a generic objective function](https://en.wikipedia.org/wiki/Nonlinear_programming) (DFNO).
+
+* Calibration: DFLS (Derivative Nonlinear least squares)
+[[`handle_solve_dfls`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_dfls) |
+[`e04fff`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04fff.html) |
+[`e04ffc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ffc.html) ]
+
+ * DFNO (Derivative-Free Nonlinear Optimization)
+ [[`handle_solve_dfno`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_dfno) |
+ [`e04jdf`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04jdf.html) |
+[`e04jdc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04jdc.html) ]
+
+
+Optimizing complex numerical models is one of the most common problems found in the industry (finance, multi-physics simulations,
+engineering, etc.). To solve these optimization problems with a standard optimization algorithm such as
+[Gauss–Newton](https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm) (for
+problems with a nonlinear least squares structure) or
+[CG](https://en.wikipedia.org/wiki/Conjugate_gradient_method) (for unstructured nonlinear objective) requires good estimates
+of the model's derivatives.
+If exact derivatives are easy to compute then using derivative-based methods is preferable. However, explicitly writing the derivatives
+or applying [AD methods](https://www.nag.com/content/algorithmic-differentiation-software) might be impossible if the model is a black box.
+The alternative, estimating derivatives via [finite differences](https://en.wikipedia.org/wiki/Finite_difference#Relation_with_derivatives),
+can quickly become impractical or too computationally expensive. Under these circumstances, an attractive optimization solver that does not
+require the user to provide any derivatives is the model-based DFO solver.
+
+NAG's model-based [DFO](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#derivatives) [solvers for DFLS and DFNO]() present a number of attractive features:
+
+ * Proved resilient to noise,
+ * The least-square solver is able to start making progress with as few as two objective evaluations,
+ * Integrated to the [NAG Optimization Modeling Suite (NOMS)](https://www.nag.com/numeric/nl/nagdoc_latest/clhtml/e04/e04intro.html#optsuite) with simple interfaces for the solvers and related routines,
+ * Optional reverse communication interface.
+
+
+
+**Figure 1.** Animation showing 2 iterations of a model-based DFO algorithm [`handle_solve_dfls`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_dfls).
+
+
+## Poster
+<table><tr>
+<td valign="top">A 2019 poster discussing NAG's DFO/DFLS functionality
+<a href="https://www.nag.com/market/posters/derivative_free_optimization_solver_calibration_problems.pdf">is available on the NAG website</a>.</td>
+<!--- td> </td --->
+<td><a href="https://www.nag.com/market/posters/derivative_free_optimization_solver_calibration_problems.pdf">
+<img src="https://www.nag.com/sites/default/files/styles/paragraph_image_/public/2020-01/dfo-solver_1.png?itok=6_PijS2l"
+width="500px" alt="DFO Poster thumbnail"/></a></td>
+</tr></table>
+
+## Example
+
+The Jupyter notebook showcases the optimization of noisy problems where the objective function is not deterministic.
+The example discuses and illustrates the advantages of using a DFO solver instead of a derivative-based solver using
+finite difference estimations for the gradient.
+
+ * [Noisy problem notebook.](DFO_noisy.ipynb)
+
+## More information
+
+ * [Informative Leaflet](https://www.nag.com/content/derivative-free-optimization-dfo)
+
+ * Blog post from the OptCorner [The price of derivatives - Derivative-free Optimization](https://www.nag.com/blog/optcorner-price-derivatives-derivative-free-optimization)
+
+ * [DFO/DFLS in the NAG Library for Python](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_dfls)
+
+ * Examples [ [Python example](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_dfls_ex.main) | [C example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04ffc.html#example) | [Fortran 90 example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04fff.html#example) ]
+
+ * [DFO/DFNO in the NAG Library for Python](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_dfno)
+
+ * Examples [ [Python example](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_dfno_ex.main) | [C example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04jdc.html#example) | [Fortran 90 example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04jdf.html#example) ]
+
+
+## References
+
+* C. Cartis, J. Fiala, B. Marteau, and L. Roberts (2019) _Improving the Flexibility and robustness of
+ model-based derivative-free optimization solvers_. ACM Transactions On Numerical Software.
+* C. Cartis and L. Roberts (2017) _A derivative-free Gauss–Newton method_. Mathematical Programming Computation.
+* Powell M. J. D. (2009) _The BOBYQA algorithm for bound constrained optimization without derivatives_. Report DAMTP 2009/NA06 University of Cambridge.
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
+
diff --git a/local_optimization/DFO/animation.gif b/local_optimization/DFO/animation.gif
new file mode 100644
index 0000000..80c75da
Binary files /dev/null and b/local_optimization/DFO/animation.gif differ
diff --git a/local_optimization/animation.mp4 b/local_optimization/DFO/animation.mp4
similarity index 100%
rename from local_optimization/animation.mp4
rename to local_optimization/DFO/animation.mp4
diff --git a/local_optimization/FOAS/README.md b/local_optimization/FOAS/README.md
index ed229aa..fbceeba 100644
--- a/local_optimization/FOAS/README.md
+++ b/local_optimization/FOAS/README.md
@@ -1 +1,74 @@
-# First-order active-set method
+[](https://www.nag.com)
+
+# First-order active-set method (FOAS)
+[[`handle_solve_bounds_foas`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bounds_foas) | [`e04kff`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04kff.html) |
+[`e04kfc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04kfc.html) ]
+
+Implementations of first-order methods not only are ubiquitous and have a widespread use, they have also demonstrated to endure the challenges of ever-growing problems sizes imposed by the industry. Most notable are applications in statistics, e.g. parameter calibration for log-linear models, conditional random fields (L2-regularisation) or logistic multi-class regression, amongs many other. First-order methods and the Conjugate Gradient method inparticular have been a research subject for well over 50 years and continue to be improved.
+
+FOAS is a [first-order nonlinear conjugate method](https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method) for large-scale bound-constrained nonlinear optimization. The solver is ideal for very large problems (tens of thousands or more variables) where the first-order derivatives are available or are relatively _cheap_ to estimate.
+
+e04kf is also part of the [NAG Optimization Modelling Suite](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#optsuite) common handle interface. It offers clarity and consistency of the interface of the solvers within the suite, making it trivial to switch among compatible solvers.
+
+The following example illustrates the simple usage of FOAS to solve the bound-constrained 2D version of the [Rosenbrock function](https://en.wikipedia.org/wiki/Rosenbrock_function) which is a classical test function to measure and profile performance of solvers. Source of this example is avaible in [rosenbrock2d.ipynb](rosenbrock2d.ipynb).
+
+<table><tr>
+<td><img src="./images/Rosenbrock2dw.png" width="412px" alt="2D Rosenbrock example"/></td>
+ <td><img src="./images/handle_solve_bounds_foas_ex.png" width="412px" alt="2D Rosenbrock with bounds"/></td>
+</tr></table>
+
+**Figure 1.** 2D Rosenbrock function, (left) the minimum is shown as a yellow dot at x=(1,1). On the right a bound constrained version showing with a purple dotted line a path towards the constrained solution point on the border.
+
+## More information
+ 1. [FOAS information page](https://www.nag.com/content/limited-memory-nonlinear-conjugate-gradient-solver)
+ 2. [FOAS in the NAG Library for Python](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bounds_foas)
+ 3. [FOAS documentation page](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04kfc.html) [ [C](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04kfc.html) | [Fortran](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04kff.html) ]
+ 4. Examples [ [Python example](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex.main) | [C example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04kfc.html#example) | [Fortran example](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04kff.html#example) ]
+
+## A modern replacement for NAG solver [`uncon_conjgrd_comp` (`e04dg`)](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04dgf.html)
+One of the main design objectives for `handle_solve_bounds_foas` (`e04kf`) was to provide a modern and attractive replacement for the CG solver `e04dg` introduced in Mark 12. While this solver was targeted for unconstrained NLPs, `e04kf` has been extended with an active-set method in order to solve bound-constrained NLPs.
+
+More recent and modern methods have been incorporated into `e04kf` making it much faster than `e04dg`. The following Figure 2 reports performance profiles over 114 unconstrained NLP CUTEst problems for both solvers `e04kf` and `e04dg`. Contrasting the three plots, it is evident that the new solver is more efficient in time (40% faster) and in general terms is less expensive: requires less function and gradient evaluations.
+
+<table><tr>
+<td><img src="./images/KF_DG_unconst_tokyo_notriv-NT.png" width="275px" alt="Perf profile e04kf/e04dg time (s)"/></td>
+<td><img src="./images/KF_DG_unconst_tokyo_notriv-NF.png" width="275px" alt="Perf profile e04kf/e04dg function evaluations"/></td>
+<td><img src="./images/KF_DG_unconst_tokyo_notriv-NG.png" width="275px" alt="Perf profile e04kf/e04dg gradient evaluations"/></td>
+</tr></table>
+
+**Figure 2.** Performance profiles comparing solvers `e04kf` and `e04dg`. In the time plot on the left, higher line indicates faster solver. For the center and right plots higher line represent less functions (NF) or gradients (NG) calls. For all three plots it can be seen that `e04kf` is 40% faster in time and requires less function and gradient calls.
+
+## Migrating from Marks 25 and 26 to Mark 27
+
+Notes and comments on migrating your code from [`uncon_conjgrd_comp (e04dg)`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04dgf.html) to the new FOAS solver [`handle_solve_bounds_foas`](https://www.nag.co.uk/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.handle_solve_bounds_foas) ([`e04kff`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04kff.html),
+[`e04kfc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04kfc.html)):
+
+ * [Python](migration/migration_e04dg_e04kf.ipynb)
+ * [Fortran 90](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/genint/replace.html#e04dgf)
+ * [C](https://www.nag.com/numeric/nl/nagdoc_latest/clhtml/genint/replace.html#e04dgc)
+
+## Beale's function
+This example compares the steps taken by FOAS and L-BFGS-B 3.0 to find the solution point to [Beale's function](https://en.wikipedia.org/wiki/Test_functions_for_optimization). It is a classic nonconvex test function used to benchmark nonlinear optimization solvers.
+
+Both solvers are used to find a minimum to the function and are started at the same initial point (2, 2). The following figure shows an animation of the steps taken by each solver to find a minimum to the function.
+It illustrates the agressive steps taken by the [Conjugate Gradient method](https://en.wikipedia.org/wiki/Conjugate_gradient_method) compared to the more conservative steps of BFGS.
+
+<img src="./images/animated.gif" width="400px" alt="Beale function solved using e04kf and L-BFGS-B"/>
+
+**Figure 3.** Contour plots for Beale's function (thin blue lines), and the steps taken by FOAS (red) and L-BFGS-B 3.0 (blue) to find the minimum of Beale's funtion at (3, 0.5) marked with a magenta star. It can be seen that FOAS by the 8th step provides a reasonable approximation to the solution point while L-BFGS-B is still relatively far from it.
+
+## References
+
+ * Hager W W and Zhang H (2005) _A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search_. SIAM J. Optim. 16(1) 170–192
+ * Hager W W and Zhang H (2006a) _Algorithm 851: CG DESCENT, a Conjugate Gradient Method with Guaranteed Descent_. ACM Trans. Math. Software 32(1) 113–137
+ * Hager W W and Zhang H (2006b) _A New Active Set Algorithm for Box Constrained Optimization_. SIAM J. Optim. 17(2) 525–557
+ * Hager W W and Zhang H (2013) _The Limited Memory Conjugate Gradient Method_. SIAM J. Optim. 23(4) 2150–2168
+ * Nocedal J and Wright S J (2006) _Numerical Optimization_. (2nd Edition) Springer Series in Operations Research, Springer, New York
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
+
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diff --git a/local_optimization/FOAS/migration/migration_e04dg_e04kf.ipynb b/local_optimization/FOAS/migration/migration_e04dg_e04kf.ipynb
new file mode 100644
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+++ b/local_optimization/FOAS/migration/migration_e04dg_e04kf.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Migrating from `uncon_conjgrd_comp` to `handle_solve_bounds_foas`\n",
+ "\n",
+ "This notebook illustrates the steps required to upgrade from the solver `uncon_conjgrd_comp` (`E04DG`) to `handle_solve_bounds_foas` (`E04KF`) introduced at Mark 27 of the NAG Library.\n",
+ "\n",
+ "From the usage perspective, the main difference between the solvers is the user call-backs,\n",
+ "`uncon_conjgrd_comp` has a single user call-back that can return *objective \n",
+ "function* and *gradient* evaluations, while `handle_solve_bounds_foas` has two separate user call-backs, \n",
+ "one for the *objective funtion* and one for the *objective gradient*.\n",
+ "\n",
+ "In this notebook the 2d Rosenbrock problem is solved with both solvers and illustrates the changes necessary for the migration to `handle_solve_bounds_foas`. The solution to the problem is (1, 1)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NAG Copyright 2020.\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define E04DG user call-back\n",
+ "def objfun_e04dg(mode, x, _nstate, _data=None):\n",
+ " objf = (1. - x[0])**2 + 100.*(x[1] - x[0]**2)**2\n",
+ " if mode == 2:\n",
+ " fdx = [\n",
+ " 2.*x[0] - 400.*x[0]*(x[1]-x[0]**2) - 2.,\n",
+ " 200.*(x[1]-x[0]**2),\n",
+ " ]\n",
+ " return objf, fdx\n",
+ " return objf, np.zeros(len(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define user call-backs for E04KF\n",
+ "def objfun_e04kf(x, inform, _data=None): \n",
+ " \"\"\"Return the objective function value\"\"\"\n",
+ " objf = (1. - x[0])**2 + 100.*(x[1] - x[0]**2)**2\n",
+ " return objf, inform\n",
+ "\n",
+ "def objgrd_e04kf(x, fdx, inform, _data=None):\n",
+ " \"\"\"The objective's gradient. Note that fdx has to be updated IN-PLACE\"\"\"\n",
+ " fdx[:] = [\n",
+ " 2.*x[0] - 400.*x[0]*(x[1]-x[0]**2) - 2.,\n",
+ " 200.*(x[1]-x[0]**2),\n",
+ " ]\n",
+ " return inform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The initial guess\n",
+ "x = [-1.5, 1.9]\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output\n",
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Solve the problem with `uncon_conjgrd_comp`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-5-a8bdc418615c>:2: NagDeprecatedWarning: (NAG Python function naginterfaces.library.opt.uncon_conjgrd_comp)\n",
+ "This function is deprecated.\n",
+ "The following advice is given for making a replacement:\n",
+ "Please use handle_solve_bounds_foas instead.\n",
+ "See also https://www.nag.com/numeric/py/nagdoc_latest/replace.html\n",
+ " slv = opt.uncon_conjgrd_comp(objfun_e04dg, x, comm, io_manager=iom)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Solution: \n",
+ " [1.00000676 1.00001354]\n"
+ ]
+ }
+ ],
+ "source": [
+ "comm = opt.nlp1_init('uncon_conjgrd_comp')\n",
+ "slv = opt.uncon_conjgrd_comp(objfun_e04dg, x, comm, io_manager=iom)\n",
+ "print('Solution: \\n', slv.x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now solve with the new solver `handle_solve_bounds_foas`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04KF, First order method for bound-constrained problems\n",
+ "\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 2.12807E-15\n",
+ " Norm of gradient 3.67342E-08\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf 1.00000E+00 inf\n",
+ " 2 -inf 1.00000E+00 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create an empty handle for the problem\n",
+ "nvar = len(x)\n",
+ "handle = opt.handle_init(nvar)\n",
+ "\n",
+ "# Define the nonlinear objective in the handle\n",
+ "# Setup a gradient vector of length nvar\n",
+ "opt.handle_set_nlnobj(handle, idxfd=list(range(1, nvar+1)))\n",
+ "\n",
+ "# Set some algorithmic options\n",
+ "for option in [\n",
+ " 'Print Options = No', # print Options?\n",
+ " 'Print Solution = Yes', # print on the screen the solution point X\n",
+ " 'Print Level = 1', # print details of each iteration (screen)\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ " \n",
+ "# Solve the problem and print the solution\n",
+ "opt.handle_solve_bounds_foas(handle, x, objfun=objfun_e04kf, objgrd=objgrd_e04kf,\n",
+ " io_manager=iom)\n",
+ "\n",
+ "# Destroy the handle and free allocated memory\n",
+ "opt.handle_free(handle)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/local_optimization/FOAS/rosenbrock2d.ipynb b/local_optimization/FOAS/rosenbrock2d.ipynb
new file mode 100644
index 0000000..ff28b88
--- /dev/null
+++ b/local_optimization/FOAS/rosenbrock2d.ipynb
@@ -0,0 +1,1437 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Rosenbrock function: Bound constrained optimization\n",
+ "First order active set bound-constrained nonlinear programming\n",
+ "\n",
+ "2d Rosenbrock example: This notebook illustrates the usage of FOAS to solve the bound-constrained 2d Rosenbrock function. It produces a plot showing the steps taken by the solver to find the solution point."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Add objective function, gradient and monitoring callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "objfun = lambda x, inform: ((1. - x[0])**2 + 100.*(x[1] - x[0]**2)**2, inform)\n",
+ "\n",
+ "def objgrd(x, fdx, inform):\n",
+ " \"\"\"The objective's gradient.\"\"\"\n",
+ " fdx[:] = [\n",
+ " 2.*x[0] - 400.*x[0]*(x[1]-x[0]**2) - 2.,\n",
+ " 200.*(x[1]-x[0]**2),\n",
+ " ]\n",
+ " return inform\n",
+ "\n",
+ "\n",
+ "steps = []\n",
+ "def monit(x, rinfo, _stats, _data=None):\n",
+ " \"\"\"The monitor function.\"\"\"\n",
+ " steps.append([x[0], x[1], rinfo[0]])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Specify initial guess"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = [-1., -1.5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define the nonlinear objective (add to handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nvar = len(x)\n",
+ "handle = opt.handle_init(nvar)\n",
+ "opt.handle_set_nlnobj(handle, idxfd=list(range(1, nvar+1)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Add the box bounds on the variable x to the handle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bl = [-1., -2.]\n",
+ "bu = [0.8, 2.]\n",
+ "opt.handle_set_simplebounds(\n",
+ " handle,\n",
+ " bl=bl,\n",
+ " bu=bu,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set some algorithmic options"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for option in [\n",
+ " 'FOAS Print Frequency = 1',\n",
+ " 'Print Solution = yes',\n",
+ " 'FOAS Monitor Frequency = 1',\n",
+ " 'Print Level = 2',\n",
+ " 'Monitoring Level = 1',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use an explicit I/O manager for abbreviated iteration output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Solve the problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " ----------------------------------------------------------\n",
+ " E04KF, First order method for bound-constrained problems\n",
+ " ----------------------------------------------------------\n",
+ "\n",
+ " Begin of Options\n",
+ " Print File = 9 * d\n",
+ " Print Level = 2 * U\n",
+ " Print Options = Yes * d\n",
+ " Print Solution = All * U\n",
+ " Monitoring File = -1 * d\n",
+ " Monitoring Level = 1 * U\n",
+ " Foas Monitor Frequency = 1 * U\n",
+ " Foas Print Frequency = 1 * U\n",
+ "\n",
+ " Infinite Bound Size = 1.00000E+20 * d\n",
+ " Task = Minimize * d\n",
+ " Stats Time = No * d\n",
+ " Time Limit = 1.00000E+06 * d\n",
+ " Verify Derivatives = No * d\n",
+ "\n",
+ " Foas Estimate Derivatives = No * d\n",
+ " Foas Finite Diff Interval = 1.05367E-08 * d\n",
+ " Foas Iteration Limit = 10000000 * d\n",
+ " Foas Memory = 11 * d\n",
+ " Foas Progress Tolerance = 1.08158E-12 * d\n",
+ " Foas Rel Stop Tolerance = 1.08158E-12 * d\n",
+ " Foas Restart Factor = 6.00000E+00 * d\n",
+ " Foas Slow Tolerance = 1.01316E-02 * d\n",
+ " Foas Stop Tolerance = 1.00000E-06 * d\n",
+ " Foas Tolerance Norm = Infinity * d\n",
+ " End of Options\n",
+ "\n",
+ " Problem Statistics\n",
+ " No of variables 2\n",
+ " free (unconstrained) 0\n",
+ " bounded 2\n",
+ " Objective function Nonlinear\n",
+ "\n",
+ "\n",
+ " -------------------------------------------------------------------------------\n",
+ " iters | objective | optim | dir\n",
+ " -------------------------------------------------------------------------------\n",
+ " 0 6.29000E+02 5.00E+02 3.50E+00\n",
+ " 1 6.29000E+02 5.00E+02 3.50E+00\n",
+ " 2 4.00000E+00 0.00E+00 1.80E+00\n",
+ " 3 4.00000E+00 0.00E+00 1.80E+00\n",
+ " 4 3.99156E+00 2.80E+00 2.80E+00\n",
+ " 5 3.99156E+00 2.80E+00 2.80E+00\n",
+ " 6 3.98433E+00 1.44E+00 1.44E+00\n",
+ " 7 3.97076E+00 5.76E+00 1.79E+00\n",
+ " 8 3.41157E+00 1.66E+01 1.60E+00\n",
+ " 9 3.15876E+00 2.07E+01 1.65E+00\n",
+ " 10 2.34744E+00 2.55E+00 2.29E+00\n",
+ " 11 2.06122E+00 5.09E+00 1.83E+00\n",
+ " 12 1.97065E+00 6.49E+00 1.88E+00\n",
+ " 13 1.77751E+00 9.58E+00 1.99E+00\n",
+ " 14 1.19453E+00 2.20E+00 8.93E-01\n",
+ " 15 1.12429E+00 2.33E+00 2.01E+00\n",
+ " 16 1.01998E+00 5.04E+00 2.02E+00\n",
+ " 17 8.94996E-01 8.97E+00 2.02E+00\n",
+ " 18 7.06235E-01 1.32E+00 1.11E+00\n",
+ " 19 5.06072E-01 5.09E+00 1.91E+00\n",
+ " -------------------------------------------------------------------------------\n",
+ " iters | objective | optim | dir\n",
+ " -------------------------------------------------------------------------------\n",
+ " 20 3.18869E-01 9.51E-01 3.65E-01\n",
+ " 21 2.98131E-01 1.03E+00 1.03E+00\n",
+ " 22 2.48807E-01 2.90E+00 1.74E+00\n",
+ " 23 2.10033E-01 5.38E+00 1.65E+00\n",
+ " 24 1.19320E-01 1.40E+00 5.40E-01\n",
+ " 25 8.38051E-02 4.97E+00 1.77E+00\n",
+ " 26 6.45222E-02 8.65E-01 8.65E-01\n",
+ " 27 5.31881E-02 6.17E-01 6.17E-01\n",
+ " 28 4.20831E-02 7.71E-01 7.71E-01\n",
+ " 29 4.04842E-02 4.40E-01 4.40E-01\n",
+ " 30 4.04842E-02 4.40E-01 4.40E-01\n",
+ " 31 4.01532E-02 2.48E-01 2.48E-01\n",
+ " 32 4.01532E-02 2.48E-01 2.48E-01\n",
+ " 33 4.00000E-02 0.00E+00 0.00E+00\n",
+ " -------------------------------------------------------------------------------\n",
+ " Status: converged, an optimal solution was found\n",
+ " -------------------------------------------------------------------------------\n",
+ " Value of the objective 4.00000E-02\n",
+ " Norm of inactive gradient 0.00000E+00\n",
+ " Norm of projected direction 0.00000E+00\n",
+ " Iterations 33\n",
+ " Function evaluations 80\n",
+ " FD func. evaluations 0\n",
+ " Gradient evaluations 40\n",
+ " NPG function calls 18\n",
+ " NPG gradient calls 3\n",
+ " CG function calls 9\n",
+ " CG gradient calls 5\n",
+ " LCG function calls 53\n",
+ " LCG gradient calls 32\n",
+ " -------------------------------------------------------------------------------\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -1.00000E+00 8.00000E-01 8.00000E-01\n",
+ " 2 -2.00000E+00 6.40000E-01 2.00000E+00\n",
+ "\n",
+ " Box bounds dual variables:\n",
+ " idx Lower bound Value Upper bound Value\n",
+ " 1 -1.00000E+00 0.00000E+00 8.00000E-01 4.00000E-01\n",
+ " 2 -2.00000E+00 0.00000E+00 2.00000E+00 0.00000E+00\n"
+ ]
+ }
+ ],
+ "source": [
+ "ret = opt.handle_solve_bounds_foas(handle, x, objfun=objfun, objgrd=objgrd, monit=monit, io_manager=iom)\n",
+ "steps.append([ret.x[0], ret.x[1], ret.rinfo[0]]) # Add last step"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Retrieve Lagrange multipliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Lagrange multipliers: [-0.4 0. ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from naginterfaces.base.opt import handle_set_get_real\n",
+ "mult = np.empty(2*nvar)\n",
+ "mult.fill(0.)\n",
+ "ret = handle_set_get_real(handle, \"Dual Variables\", 1, 2*nvar, mult)\n",
+ "print(\"Lagrange multipliers: \", mult[0:-1:2]-mult[1:ret+1:2])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Destroy the handle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Evaluate the funtion over the domain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_m = np.linspace(bl[0]-0.5, bu[0]+0.5, 101)\n",
+ "y_m = np.linspace(bl[1]-0.5, bu[1]+0.5, 101)\n",
+ "z_m = np.empty((101, 101))\n",
+ "j = y_m[0]\n",
+ "for i in range(0, 101):\n",
+ " for j in range(0, 101):\n",
+ " z_m[i, j], _inform = objfun([x_m[i], y_m[j]], 1)\n",
+ "nb = 25\n",
+ "x_box = np.linspace(bl[0], bu[0], nb)\n",
+ "y_box = np.linspace(bl[1], bu[1], nb)\n",
+ "box = np.array([np.concatenate([x_box, bu[0]*np.ones(nb), x_box[::-1], bl[0]*np.ones(nb)]),\n",
+ " np.concatenate([bl[1]*np.ones(nb), y_box, bu[1]*np.ones(nb), y_box[::-1]])])\n",
+ "z_box = np.empty(box[0].shape)\n",
+ "for i in range(0, (box[0].size)):\n",
+ " z_box[i], _inform = objfun([box[0][i], box[1][i]], 1)\n",
+ "\n",
+ "X, Y = np.meshgrid(x_m, y_m, indexing='ij')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot function and steps taken"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select the display backend for Jupyter:\n",
+ "%matplotlib nbagg\n",
+ "steps = np.column_stack(steps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "/* global mpl */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "mpl.get_websocket_type = function () {\n",
+ " if (typeof WebSocket !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof MozWebSocket !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.'\n",
+ " );\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = this.ws.binaryType !== undefined;\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById('mpl-warnings');\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent =\n",
+ " 'This browser does not support binary websocket messages. ' +\n",
+ " 'Performance may be slow.';\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = document.createElement('div');\n",
+ " this.root.setAttribute('style', 'display: inline-block');\n",
+ " this._root_extra_style(this.root);\n",
+ "\n",
+ " parent_element.appendChild(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+ " fig.send_message('send_image_mode', {});\n",
+ " if (fig.ratio !== 1) {\n",
+ " fig.send_message('set_device_pixel_ratio', {\n",
+ " device_pixel_ratio: fig.ratio,\n",
+ " });\n",
+ " }\n",
+ " fig.send_message('refresh', {});\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onload = function () {\n",
+ " if (fig.image_mode === 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function () {\n",
+ " fig.ws.close();\n",
+ " };\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function () {\n",
+ " var titlebar = document.createElement('div');\n",
+ " titlebar.classList =\n",
+ " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+ " var titletext = document.createElement('div');\n",
+ " titletext.classList = 'ui-dialog-title';\n",
+ " titletext.setAttribute(\n",
+ " 'style',\n",
+ " 'width: 100%; text-align: center; padding: 3px;'\n",
+ " );\n",
+ " titlebar.appendChild(titletext);\n",
+ " this.root.appendChild(titlebar);\n",
+ " this.header = titletext;\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function () {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+ " canvas_div.setAttribute(\n",
+ " 'style',\n",
+ " 'border: 1px solid #ddd;' +\n",
+ " 'box-sizing: content-box;' +\n",
+ " 'clear: both;' +\n",
+ " 'min-height: 1px;' +\n",
+ " 'min-width: 1px;' +\n",
+ " 'outline: 0;' +\n",
+ " 'overflow: hidden;' +\n",
+ " 'position: relative;' +\n",
+ " 'resize: both;'\n",
+ " );\n",
+ "\n",
+ " function on_keyboard_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.key_event(event, name);\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " canvas_div.addEventListener(\n",
+ " 'keydown',\n",
+ " on_keyboard_event_closure('key_press')\n",
+ " );\n",
+ " canvas_div.addEventListener(\n",
+ " 'keyup',\n",
+ " on_keyboard_event_closure('key_release')\n",
+ " );\n",
+ "\n",
+ " this._canvas_extra_style(canvas_div);\n",
+ " this.root.appendChild(canvas_div);\n",
+ "\n",
+ " var canvas = (this.canvas = document.createElement('canvas'));\n",
+ " canvas.classList.add('mpl-canvas');\n",
+ " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+ "\n",
+ " this.context = canvas.getContext('2d');\n",
+ "\n",
+ " var backingStore =\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " this.context.webkitBackingStorePixelRatio ||\n",
+ " this.context.mozBackingStorePixelRatio ||\n",
+ " this.context.msBackingStorePixelRatio ||\n",
+ " this.context.oBackingStorePixelRatio ||\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " 1;\n",
+ "\n",
+ " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+ " 'canvas'\n",
+ " ));\n",
+ " rubberband_canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+ " );\n",
+ "\n",
+ " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+ " if (this.ResizeObserver === undefined) {\n",
+ " if (window.ResizeObserver !== undefined) {\n",
+ " this.ResizeObserver = window.ResizeObserver;\n",
+ " } else {\n",
+ " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+ " this.ResizeObserver = obs.ResizeObserver;\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+ " var nentries = entries.length;\n",
+ " for (var i = 0; i < nentries; i++) {\n",
+ " var entry = entries[i];\n",
+ " var width, height;\n",
+ " if (entry.contentBoxSize) {\n",
+ " if (entry.contentBoxSize instanceof Array) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " width = entry.contentBoxSize[0].inlineSize;\n",
+ " height = entry.contentBoxSize[0].blockSize;\n",
+ " } else {\n",
+ " // Firefox implements old version of spec.\n",
+ " width = entry.contentBoxSize.inlineSize;\n",
+ " height = entry.contentBoxSize.blockSize;\n",
+ " }\n",
+ " } else {\n",
+ " // Chrome <84 implements even older version of spec.\n",
+ " width = entry.contentRect.width;\n",
+ " height = entry.contentRect.height;\n",
+ " }\n",
+ "\n",
+ " // Keep the size of the canvas and rubber band canvas in sync with\n",
+ " // the canvas container.\n",
+ " if (entry.devicePixelContentBoxSize) {\n",
+ " // Chrome 84 implements new version of spec.\n",
+ " canvas.setAttribute(\n",
+ " 'width',\n",
+ " entry.devicePixelContentBoxSize[0].inlineSize\n",
+ " );\n",
+ " canvas.setAttribute(\n",
+ " 'height',\n",
+ " entry.devicePixelContentBoxSize[0].blockSize\n",
+ " );\n",
+ " } else {\n",
+ " canvas.setAttribute('width', width * fig.ratio);\n",
+ " canvas.setAttribute('height', height * fig.ratio);\n",
+ " }\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " this.resizeObserverInstance.observe(canvas_div);\n",
+ "\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'dblclick',\n",
+ " on_mouse_event_closure('dblclick')\n",
+ " );\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
+ "\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " on_mouse_event_closure('scroll')(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
+ "\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\n",
+ "\n",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus() {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
+ " var fig = this;\n",
+ "\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
+ "\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " continue;\n",
+ " }\n",
+ "\n",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ "\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
+ "\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(option);\n",
+ " }\n",
+ "\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function (type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function () {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+ " var x0 = msg['x0'] / fig.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+ " var x1 = msg['x1'] / fig.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / fig.ratio,\n",
+ " fig.canvas.height / fig.ratio\n",
+ " );\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+ " fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message('ack', {});\n",
+ "};\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " var img = evt.data;\n",
+ " if (img.type !== 'image/png') {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " img.type = 'image/png';\n",
+ " }\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src\n",
+ " );\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " img\n",
+ " );\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig['handle_' + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "};\n",
+ "\n",
+ "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function (e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e) {\n",
+ " e = window.event;\n",
+ " }\n",
+ " if (e.target) {\n",
+ " targ = e.target;\n",
+ " } else if (e.srcElement) {\n",
+ " targ = e.srcElement;\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ " }\n",
+ "\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+ "\n",
+ " return { x: x, y: y };\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * https://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
+ "\n",
+ " if (name === 'button_press') {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * this.ratio;\n",
+ " var y = canvas_pos.y * this.ratio;\n",
+ "\n",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
+ " // Prevent repeat events\n",
+ " if (name === 'key_press') {\n",
+ " if (event.key === this._key) {\n",
+ " return;\n",
+ " } else {\n",
+ " this._key = event.key;\n",
+ " }\n",
+ " }\n",
+ " if (name === 'key_release') {\n",
+ " this._key = null;\n",
+ " }\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.key !== 'Control') {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " else if (event.altKey && event.key !== 'Alt') {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " else if (event.shiftKey && event.key !== 'Shift') {\n",
+ " value += 'shift+';\n",
+ " }\n",
+ "\n",
+ " value += 'k' + event.key;\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+ " return false;\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+ " if (name === 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message('toolbar_button', { name: name });\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "\n",
+ "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+ "// prettier-ignore\n",
+ "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "var comm_websocket_adapter = function (comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.binaryType = comm.kernel.ws.binaryType;\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " function updateReadyState(_event) {\n",
+ " if (comm.kernel.ws) {\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " } else {\n",
+ " ws.readyState = 3; // Closed state.\n",
+ " }\n",
+ " }\n",
+ " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+ "\n",
+ " ws.close = function () {\n",
+ " comm.close();\n",
+ " };\n",
+ " ws.send = function (m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function (msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " var data = msg['content']['data'];\n",
+ " if (data['blob'] !== undefined) {\n",
+ " data = {\n",
+ " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+ " };\n",
+ " }\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(data);\n",
+ " });\n",
+ " return ws;\n",
+ "};\n",
+ "\n",
+ "mpl.mpl_figure_comm = function (comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
+ "\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element;\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error('Failed to find cell for figure', id, fig);\n",
+ " return;\n",
+ " }\n",
+ " fig.cell_info[0].output_area.element.on(\n",
+ " 'cleared',\n",
+ " { fig: fig },\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / fig.ratio;\n",
+ " fig.cell_info[0].output_area.element.off(\n",
+ " 'cleared',\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ " fig.close_ws(fig, msg);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width / this.ratio;\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message('ack', {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
+ " var fig = this;\n",
+ "\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
+ "\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
+ "\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
+ " });\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+ " var fig = event.data.fig;\n",
+ " if (event.target !== this) {\n",
+ " // Ignore bubbled events from children.\n",
+ " return;\n",
+ " }\n",
+ " fig.close_ws(fig, {});\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+ " // this is important to make the div 'focusable\n",
+ " el.setAttribute('tabindex', 0);\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " } else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which === 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "};\n",
+ "\n",
+ "mpl.find_output_cell = function (html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i = 0; i < ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code') {\n",
+ " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] === html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib import cm\n",
+ "ax = plt.figure().add_subplot(projection='3d')\n",
+ "ax.grid(False)\n",
+ "ax.plot(box[0], box[1], z_box, 'k-', linewidth=1.5)\n",
+ "ax.plot([bl[0], bu[0], bu[0], bl[0], bl[0]], [bl[1], bl[1], bu[1], bu[1], bl[1]], -1.2*np.ones(5), 'k-')\n",
+ "ax.contour(X, Y, z_m, 15, offset=-1.2, cmap=cm.jet)\n",
+ "ax.plot_surface(X, Y, z_m, cmap=cm.jet, alpha=0.5)\n",
+ "ax.set_title('Rosenbrock Function')\n",
+ "ax.set_xlabel(r'$\\mathit{x}$')\n",
+ "ax.set_ylabel(r'$\\mathit{y}$')\n",
+ "ax.plot(steps[0], steps[1], steps[2], 'o-', color='red', markersize=3, linewidth=2)\n",
+ "ax.azim = 160\n",
+ "ax.elev = 35\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Obtaining the NAG Library for Python\n",
+ "\n",
+ "The [NAG Library for Python](https://www.nag.com/content/nag-library-python) is commercially licensed software but this notebook is licensed under the [BSD 3-Clause License](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/LICENSE)\n",
+ "* [Click here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples#nag-library-for-python-installation) for NAG Library for Python installation details\n",
+ "* [Click here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples#obtaining-a-license) for details on how to obtain a license for the NAG Library for Python"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/local_optimization/MILP/BESS_MILP.ipynb b/local_optimization/MILP/BESS_MILP.ipynb
new file mode 100644
index 0000000..ee528d4
--- /dev/null
+++ b/local_optimization/MILP/BESS_MILP.ipynb
@@ -0,0 +1,668 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installing the NAG Library and running this notebook\n",
+ "The model in this notebook is solved by the NAG MILP solver featured in the NAG Optimization Modelling Suite. To run this notebook, you will need to install the NAG Library for Python (Mark 29.3 or newer) and a license key. You can find the software and obtain a license key (trials are available) from [Getting Started with the NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linuxto).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Battery sizing problem in a Grid-connected Microgrid using MILP in the NAG Library**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Energy storage systems (ESS) are increasingly recognized as vital components of traditional power grids combined with renewable energy sources for several reasons. Firstly, the stability and reliability of the power grids can be enhanced by ESS, which provides fast-response ancillary services such as frequency regulation, voltage control, and grid balancing. These services help mitigate the impact of sudden fluctuations in electricity demand or supply, reducing the risk of blackouts and ensuring a more resilient grid infrastructure. Secondly, ESS allows utilities to shift electricity generation from times of low demand to times of peak demand, helping to manage load profiles more effectively. Moreover, ESS plays an important role in helping smooth out the variability and intermittency associated with renewable resources such as solar and wind power. This enables greater penetration of renewable energy into the grid without compromising reliability or stability.\n",
+ "\n",
+ "At Mark 29.3 the NAG Library features a new Mixed Integer Linear Programming (MILP) solver, which is a powerful tool for the design and operation of energy storage systems, including optimal sizing and configuration, energy management and dispatch, and risk management. In order to illustrate the usage of the MILP solver, we build the model and solve an optimal battery sizing problem for a microgrid that is connected to an external grid and consists of a battery energy storage system (BESS), several generators and loads. The model also provides optimal operation of the system, including generator scheduling and power dispatch, battery charging and discharging schedule and optimal grid power import. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Microgrid Modelling**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The microgrid of interest in this notebook is depicted in Figure 1. Loads demand can be fulfilled by generators, batteries and the external grid. Generators are assumed to be owned by a utility, therefore the energy management system will find an optimal schedule for all components to minimize the operation cost of generators and the battery. The planning horizon $N$ is set to 24 hours.\n",
+ "\n",
+ "<img src=\"Grid_Model.png\" alt=\"GRID\" width=\"500\" height=\"200\">\n",
+ "\n",
+ "Figure 1: Grid-connected microgrid model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Decision variables**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The decision variables controlling various components in the microgrid are listed below.\n",
+ "- Generators\n",
+ " - $p_{ij}^g$: dispatched power from the $i$-th generator at the $j$-th hour.\n",
+ " - $s_{ij}$: binary switch that is equal to 1 if the $i$-th generator is online and 0 otherwise at the $j$-th hour.\n",
+ "- BESS\n",
+ " - $p^b_j$: power discharged or charged to the battery energy storage system at the $j$-th hour.\n",
+ " - $c^b$: power rate of the battery energy system.\n",
+ " - $e^b$: energy rate of the battery energy system.\n",
+ "- External grid\n",
+ " - $p_j^{im}$: imported power at the $j$-th hour."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Model parameters and coefficients**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model parameters are defined directly in the code cell below for building the model later. See the comments for the description of each parameter. Fuel cost of the generators are usually calculated using a quadratic function. Based on the property of the quadratic function, in practice it is reasonable to linearize it using piecewise functions and then make use of the powerful MILP solver. In this notebook, we'll simply adopt a linear approximation to the quadratic function for illustration purposes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " a b pl pu ut dt\n",
+ "Gen1 0.010694 142.7348 30.0 70.0 8.0 6.0\n",
+ "Gen2 0.018761 168.9075 50.0 100.0 8.0 6.0\n",
+ "Gen3 0.007612 313.9102 30.0 120.0 8.0 6.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Generator parameters\n",
+ "# Number of generators\n",
+ "n_gen = 3\n",
+ "# Data frame for the specifications of the generators\n",
+ "df = pd.DataFrame(\n",
+ " {\n",
+ " 'Gen1':[0.010694, 142.7348, 30, 70, 8, 6],\n",
+ " 'Gen2':[0.018761, 168.9075, 50, 100, 8, 6],\n",
+ " 'Gen3':[0.0076121, 313.9102, 30, 120, 8, 6],\n",
+ " }\n",
+ ")\n",
+ "df.index=['a','b','pl','pu','ut','dt']\n",
+ "print(df.T.to_string())\n",
+ "spec_gen = df.T.to_numpy() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The above DataFrame contains all the specification of the generators. The fuel cost is defined as the linear function\n",
+ "$$\n",
+ "cost_i(p_{ij}^g) = a_i * p_{ij}^g + b_i\n",
+ "$$\n",
+ "for generator $i$ at the $j$-th hour. $pl$ and $pu$ are the minimum and maximum power output of the $i$-th generator respectively. The rest of the parameters are\n",
+ "- $ut$: minimum up time of the generator\n",
+ "- $dt$: minimum down time of the generator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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g0WiIjIzE3t5eHvQXQgghRKWlKAr37t3Dy8sLE5Oi2yKlqCxAZGQkNWrUMHYaQgghhBBlwo0bN6hevXqR60hRWYCcKblu3LiBg4ODwbaTmZnJb7/9RmBgIObm5hJbYktsiS2xJbbElthGj/2wpKQkatSoodV0pVJUFiDnlreDg4PBi0obGxscHBwMcrBJbIktsSW2xJbYElti64M2jwNKRx0hhBBCCFFqJWqpvH79OteuXSM1NRU3NzcaNmyIpaWlvnMTQgghKhW1RuFIRDwhcSqqRMTT1s8dUxPpMCrKB61bKq9evcrMmTPx9vbG19eXzp0706dPH1q2bImjoyM9e/Zky5YtaDQanRJYvnw5Pj4+WFlZ0aZNG44ePVrouqdPn2bQoEH4+PigUqlYunRpkbEXLlyISqVi2rRpOuUkhBBCPG67w6PosGgfo9YcY91FU0atOUaHRfvYHR5l7NSE0IpWReWUKVNo2rQpERERvP/++5w5c4bExEQyMjKIjo7m559/pkOHDsyZM4cmTZrw77//arXxzZs3M336dObOnUtoaChNmzalV69exMbGFrh+amoqtWrVYuHChXh4eBQZ+99//2XVqlU0adJEq1yEEEIIY9kdHsWk4FCiEtPyLI9OTGNScKgUlqJc0KqotLW15cqVK3z33XeMHj2aevXqYW9vj5mZGe7u7nTr1o25c+dy9uxZFi9ezI0bN7Ta+JIlS5gwYQLPPfcc/v7+rFy5EhsbG9asWVPg+q1ateLjjz9m2LBhRd5uT05OZuTIkXz55Zc4OztrlYsQQghhDGqNwvydZyhozuScZfN3nkGtkVmVRdmm1TOVCxYs0Dpg7969tVovIyODkJAQZs2albvMxMSEHj16cPjwYa23V5DJkyfTt29fevTowfvvv1/s+unp6aSnp+f+npSUBGT3rMrMzCxVLkXJiW2IbUhsiS2xJbbELh+xj0TE52uhfJgCRCWmcfhSLG18XUq1rfKyTyS2cWMXtB1tqBRFMcpXn8jISKpVq8bff/9N27Ztc5e/8cYbHDx4kCNHjhT5fh8fH6ZNm5bveclNmzbxwQcf8O+//2JlZUWXLl1o1qxZkc9fzps3j/nz5+dbvmHDBmxsbHT6XEIIIYQuQuJUrLtoWux6Y+qoCXCV1krxeKWmpjJixAgSExOLHWZR597fMTExvP766+zdu5fY2FgerUnVarWuIfXmxo0bTJ06lT179mBlZaX1+2bNmsX06dNzf88Z6DMwMNDg41Tu2bOHnj17GmT8KoktsSW2xJbYZT92lYh41l08Vux6gR3b6KWlsjzsE4lt3NgPy7l7qw2di8px48Zx/fp1Zs+ejaenZ4nnxnZ1dcXU1JSYmJg8y2NiYorthFOYkJAQYmNjadGiRe4ytVrNoUOH+Pzzz0lPT8fUNP+3QUtLywKf0TQ3N38sA4oacjsSW2JLbIktsct27LZ+7jjZmJOQWvhtRk9HK70OL1TW94nELhuxc+JrS+ei8s8//+SPP/6gWbNmur41DwsLCwICAti7dy8DBgwAQKPRsHfvXoKCgkoUs3v37pw6dSrPsueee4769eszc+bMAgtKIYQQwpgi4lJIyyj6Lt+EjrVkvEpR5ulcVNaoUSPfLe+Smj59OmPHjqVly5a0bt2apUuXkpKSwnPPPQfAmDFjqFatWm5HoYyMDM6cOZP7/7du3SIsLAw7Ozv8/Pywt7enUaNGebZha2tLlSpV8i0XQgghjC05PYuJwSGkZWnwc7cjOS2L6KT/Ou1YmJqQodYQ/M81nm1ZHQcrw989E6KkdC4qly5dyptvvsmqVavw8fEp1caHDh3K7du3mTNnDtHR0TRr1ozdu3dTtWpVIHvmHhOT/0Y9ioyMpHnz5rm/L168mMWLF9O5c2cOHDhQqlyEEEKIx0lRFGZsOcGl2GSqOliyccITuNhacPhSLL/9cYTAjm2o6+HIgOV/cSUuhde+O8GqUQGYSIulKKO0KiqdnZ3zPDuZkpJC7dq1sbGxyXevPT4+XqcEgoKCCr3d/Wih6OPjo3MrqRSbQgghyqJVh67wS3g05qYqvhgZgJt99rP9bXxduHNWoY2vC+bm5qwYFcDglYfZcyaGFQcvM7mrn5EzF6JgWhWVxU2HKIQQQgjt/XUpjo92nwNgbr+GBHgXPlFH0xpOvDegITO/P8Xi387TqJojneu6Pa5UhdCaVkXl2LFjDZ2HEEIIUSncvJtK0IZQNAo8G1CdkW1qFvueoa1qEnYjgY1HbzB103F2BnWghouMoyzKFq2maQTo3Lkz7777Ln/88YfBR28XQgghKqK0TDWTgkO5m5pJo2oOvD+gkdZD8817uiFNaziRkJrJS9+GcL+YHuNCPG5aF5W+vr6sXbuWzp074+TkRI8ePfjggw84fPiwUQc8F0IIIcoDRVGY82M4p24l4mxjzspRAViZaz/UnaWZKStGtqCKrQVnopJ4+4dTehuNRQh90Lqo/Prrr4mIiODKlSssW7aMatWqsXr1atq3b4+zszN9+vTh448/NmSuQgghRLm18egNvjt2ExMVfDa8OdWddb997eVkzbIRzTFRwbbQWwT/c80AmQpRMloXlTl8fHwYP34833zzDdeuXePSpUtMmTKFv//+mzfffNMQOQohhBDl2vHrd5m34zQAr/eqR8c6Je9o0662K7P6NABg/s4zhFzTbdQVIQxF53EqAa5du8aBAwdyf2JjY3niiSfo3LmzvvMTQgghyrXb99KZFBxKhlpDr4ZVmdS5dqljvtDRl7CbCew6GcWk4FB+mtIBd3srPWQrRMlpXVSuW7cut4iMi4ujXbt2dO7cmQkTJtCqVavHMke2EEIIUZ5kqTW8sjGU6KQ0arvZsnhwU6075hRFpVLx0aAmXIy5x4WYZCavD2XDhCcwN9X5BqQQeqN1UTlu3Dhq1qzJm2++yfPPPy9FpBBCCFGMRbvP8c+VeGwtTFk1OgB7PU6zaGtpxspRAfT//C/+vXqXD3adZd7TDfUWXwhdaf2V5osvvuCJJ55g/vz5uLu7069fPz755BOOHTsmvc+EEEKIR+w8EcmXf0QAsHhwU/zc7fW+jVpudiwZ2gyAr/++yvbjN/W+DSG0pXVROXHiRDZt2kRUVBR//fUXTz75JEePHqVv3744OzvTt29fFi9ebMhchRBCiHLhfPQ93th6EoCJnWvTp7GnwbbV078qr3TLnrpx1rZTnIlMMti2hChKiR6+8Pf3Z9KkSWzevJnjx48TFBTEn3/+ycyZM/WdnxBCCFGuJN7PZGJwCPcz1XTwc+X1wLoG3+a0HnXpXNeNtEwNLwUfIyE1w+DbFOJROvf+jo2NZf/+/bmddi5cuIC5uTlPPPEEXbt2NUSOQgghRLmg0Si89l0YEXEpVHOy5rPhzTF7DJ1nTE1UfDqsGf0+/5Mb8feZtjmMNWNbYWJS+k5BQmhL66Ly5Zdf5sCBA5w/fx4zMzNat27Ns88+S9euXWnXrh1WVjKUgRBCiMpt+f5L/H42FgszE1aMaoGLrcVj27aTjQUrRwUw8Iu/OXD+Nkv3XmR6T8O3kgqRQ+ui8vjx4wwYMICuXbvSvn17bGxkInshhBAix/7zsSz5/QIA7w9oRJPqTo89h4Zejiwc1JhXN5/gs70XaVLNkR7+VR97HqJy0rqoPHz4MABJSUmFFpSXLl3Cz89PP5kJIYQQ5cT1O6lM3XgcRYERbWoypGUNo+XyTPPqhF1P4JvD13h1cxg7XumAr6ut0fIRlYfOD3r07duXtLS0fMvPnz9Ply5d9JGTEEIIUW7cz1DzUnAISWlZNKvhxNx+/sZOibf7+tPS25l76VlM/DaElPQsY6ckKgGdi0o7OzsGDhxIVtZ/B+jZs2fp0qULgwYN0mtyQojySa1ROBIRT0iciiMR8ag1MpatqDgePr7/uXKHN78/wdmoJFztLFgxqgWWZqbGThELMxO+GNkCN3tLzsfcY+b3J8lSa+S8FAalc+/vbdu20aNHD0aOHMmmTZs4ffo03bt3Z+TIkSxZssQQOQohypHd4VHM33mGqMQ0wJR1F4/h6WjF3H7+9G5kuLH6hHgc8h/fIQCYqODzES3wdLQ2boIPcXewYsXIFgxb/Q8/nYzi4IXb3EvLQs5LYSg6t1RaW1uza9cuzp8/z5AhQ+jevTtjxoyRglIIwe7wKCYFhz644P4nOjGNScGh7A6PMlJmQpReYcc3gEahTI4N2dLHhUEtqgM8KCj/I+el0DetisqkpKQ8PyYmJmzevJkjR44waNAgZs+enfuaEKJyUmsU5u88Q0E31HKWzd95Rm65iXKpqOMbQEXZPL7VGoWDF28X+Jqcl0LftCoqnZyccHZ2zvPj7+/PzZs3WblyJc7OzrnrCCEqp6MR8QW24ORQgKjENI5GxD++pITQk/J6fB+NiCe6HOYtyietnqncv3+/ofMQQpRzsfcKv3CVZD0hypLyenyX17xF+aRVUdm5c2fWrFlDv379cHNzM3ROQohyyN1eu1m1tF1PiLKkvB7f5TVvUT5p3VEnODiYGjVq0K5dOxYtWsTZs2cNmZcQopxp7euCp6MVRc007OloRWtfl8eWkxD60trXBVe7wqdcVFE2j285L8XjpHVRuW/fPqKionj55ZcJCQmhTZs21KlTh9dee41Dhw6h0WgMmacQoowzNVExt59/oR0ZAEa0rompSVGXNyHKpntpmSiFHNw5R/Tcfv5l7vjOOS+BQgvL6T3rlrm8Rfmk05BCzs7OjBo1iu+++464uDiWLVvG/fv3GTlyJO7u7owZM4atW7eSkpJiqHyFEGVY70aedPBzzbfcyjz7T803h68RkyTPbonyRa1RmLIpjDspGbjaWVDVwTLP6x6OVqwY1aLMjvfYu5EnK0a1wMMx7y3unELyh7BbZKmlYUiUns7jVOawsLCgd+/efPHFF9y4cYPdu3fj4+PDe++9J2NWClFJpaRncfz6XQDe6lOXMXXUBI9vyb9v96C+hz1xyem8vD6UjCy5gInyY+nvFzh04TZW5iasG9+Gv9/sTvD4lrnH958zu5XZgjJH70ae/DmzW568dwZ1wMbClL8u3WHxbxeMnaKoAEpcVD6qZcuWvPvuu5w4cYI333xTX2GFEOXITycjSclQ4+tqy7i23gS4KrTxdcHeypyVowKwtzIj5Npd3t91xtipCqGV305Hs2zfJQAWDmyCv5cDpiYq2vi65B7f5eXW8aN5+3s58NGzTQBYefAyv5ySQdBF6ehUVKakpDBnzhwaNWqEnZ0d9vb2NGnShHfffZfU1NTc9czNzfWeqBCi7Nv07w0AhraqgUqV90Lr42rLp8OaAbDu8DW+D7n5uNMTQidXbifz2ncnABjXzocBzasZOSP9e6qJFxM6+gLw+pYTXIq9Z+SMRHmmdVGZkZFB586d+eijj6hTpw6vvPIKkydPxtfXlw8++IDu3buTmZlpyFyFEGXY+eh7HL+egJmJKndauEd1q1+Vqd3rAPDW9lOE30p8nCkKobWU9Cxe+jaEe+lZtPZx4e2+DYydksHM7F2fJ2q5kJKh5sVvQ7iXJtdyUTJaF5UrVqzg5s2bnDhxgu3bt7NgwQIWLlzIjz/+yIkTJ4iIiGDlypWGzFUIUYZtPHodgB4NquJmb1noelO716FrPTfSszRMDA7hbkrZmy9ZVG6KovDG1pNcjE3G3d6Sz0c2x9xUb0+LlTlmpiZ8PqIFno5WXLmdwmvfnUAj0zaKEtD6LNm2bRuzZ8+mXr16+V6rX78+b7/9Nlu3btVrckKI8iEtU83247cAGNa6RpHrmpioWDq0Od5VbLh59z5TNh2XeYdFmfLlH1fYdSoKc1MVK0a1qBQDg7vaWbJiVAAWpib8diaGFQcvGzslUQ5pXVSeOXOGLl26FPp6165dOXNGHr4XojLaHR5N4v1MqjlZ07FO8bNuOdpkd9yxMjfhj4tx/G+P9DwVZcPfl+JY+Ms5AOY85U+Ad+UZFLxZDSfm928IwCe/nefQhdtGzkiUN1oXlQkJCVSpUqXQ16tUqUJiojwfJURllHPre0jLGlr3hG3g6cCiQdk9Tz/ff4lfT0cbLD8htBGZcJ+gjcfRKDCoRXVGPeFt7JQeu+GtazKsVQ00CkzZdJwb8anFv0mIB7QuKjUaDaampoUHMjFBrVbrJSkhRPlx5XYyRyLiMVHB4JYFd9ApTP9m1XiuvQ8Ar313gsu3kw2QoRDFS8tUMyk4hPiUDBp6OfDBM43yjWBQWcx7uiFNqjuSkJrJxOAQ0jLl2i60Y6btioqi0L17d8zMCn5LVlaW3pISQpQfm49lDyPUua4bXk7WOr//rScbcPpWEkevxjPx2xC2T26PnaXWf5qE0Iv5O09z4mYiTrmPZhTeiFLRWZmbsmJUAP2W/cnpyCTe3h7O4sFNKm2RLbSn9V/uuXPnFrvOoEGDSpWMEKJ8ycjS5I43Oax1zRLFMDc14fORzem37E8uxibzxtYTLB/RQi5g4rHZePQ6G4/eQKWCz4Y1p4aLjbFTMrpqTtZ8Prw5o746wvehN2lW04nRlfBxAKEbvRaVQojKZe/ZGOKSM3Czt6RbffcSx3G3t+KLkQEMW32Yn09Fs/rQFV7qXFuPmQpRsLAbCcz98TQArwfWo1Pd4juaVRbt/FyZ2bs+C345x7s7T+Pv6UCAt7Ox0xJlWKkH3jp48CA///wzd+/e1Uc+QohyZOODGXQGB1Qv9Th+Ad7OzOmX3fN00e5z/H0prtT5CVGUuOR0JgWHkKHWEOhflUnyRSafFzvV4snGHmSqFV5eH0LsvTRjpyTKMK2vAosWLWL27Nm5vyuKQu/evenatStPPfUUDRo04PTp0wZJUghR9ty8m8ofF7OHHBnaquixKbU1qk1Nng2ojkaBoI3HuZVwXy9xhXhUllrDKxuOE5WYRi1XWz4Z0hSTcjKH9+OkUqn46Nmm1HG3IyYpnaD1x8lUa4ydliijtC4qN2/eTKNGjXJ/37p1K4cOHeKPP/4gLi6Oli1bMn/+fIMkKYQoe747dhNFgXa1q+BdxVYvMVUqFe8PaESjag7Ep2QwSXqeCgP56NfzHL5yBxsLU1aNDsDeytzYKZVZdpZmrBwdgJ2lGUevxvPhz2eNnZIoo7QuKiMiImjSpEnu7z///DPPPvss7du3x8XFhXfeeYfDhw8bJEkhRNmi1ihsedDru6QddApjZW7KipEBONmYc/JmIvN2yB0QoV+7Tkax+tAVABYPbkqdqvZGzqjsq+1mxydDmgKw9q+r/Bh2y8gZibJI66IyKysLS8v/5vM9fPgw7dq1y/3dy8uLuDh5BkqIyuDghViiEtNwtjGnV8Oqeo9fw8WGZcObY6KCTf/eyB1cXYjSuhBzjxlbTwDwUudaPNnY08gZlR+9GnoQ1NUPgJnfn+RsVJKRMxJljdZFZe3atTl06BAA169f58KFC3Tq1Cn39Zs3bxY5444QouLYeDS7lXJgi+pYmhlmPL+Oddx4LbAeAHN/PE3YjQSDbEdUHklpmUz8NoTUDDXtaldhxoPjS2jv1Z516VTXjbRMDRODQ0hMzTR2SqIM0bqonDx5MkFBQTz//PP06dOHtm3b4u/vn/v6vn37aN68uUGSFEKUHbFJaew7FwvA8Nb66aBTmJe71KZXw6pkqDVMCg4hLjndoNsTFZdGo/Dadye4EpeCl6MVy4Y3x6yUIxZURqYmKj4d2ozqztZcu5PKtM3H0WgUY6clygitz6gJEybw2WefER8fT6dOnfj+++/zvB4ZGcn48eP1nqAQomzZEnITtUahpbczfu6GfRZNpVKxeHBTarnZEpWYRtCGUNIz1RyJiCckTsWRiHjUckETBVBrlDzHyef7L7LnTAwWpiasGBVAFTvL4oOIAjnbWrByVACWZibsP3+bT/dezLe/5bysnHT6mjZ+/Hi2b9/OihUr8PDwyPPaF198wTPPPKNzAsuXL8fHxwcrKyvatGnD0aNHC1339OnTDBo0CB8fH1QqFUuXLs23zoIFC2jVqhX29va4u7szYMAAzp8/r3NeQoj8NBqFzQ/GptTXMELFsbcyZ/XoAGwtTPnnSjwt3tvDqDXHWHfRlFFrjtFh0T52h0c9llxE+bA7PIoOi/blOU6W7LkIwHsDGtK0hpNxE6wAGlVz5INnGgPw6d6LtHxfzkuhQ1F58uTJAn+uXbuGopTsG8nmzZuZPn06c+fOJTQ0lKZNm9KrVy9iY2MLXD81NZVatWqxcOHCfEVtjoMHDzJ58mT++ecf9uzZQ2ZmJoGBgaSkpJQoRyHEfw5fucP1+FTsLc3o2+TxdXDwc7dnZJvsXuYpGXmHGIpOTGNScKhcwASQXVBOCg4lKrHgQbodrWXoIH15NqA6nR/MQHT3kWcr5bysnLSeprFZs2aoVKp8BaRKpcLKyopp06bx7rvvYmqq/UP7S5YsYcKECTz33HMArFy5kl27drFmzRrefPPNfOu3atWKVq1aART4OsDu3bvz/P7111/j7u5OSEhIno5FQgjd5fTC7t/cCxsLrf98lJpao7DzZMEXJwVQAfN3nqGnvwemMoB1paXWKMzfeYbCmjnkONEvtUbhfPS9Al+T87Jy0vqqEBERUeDyhIQEQkJCmD17Ns7Ozrz++utaxcvIyCAkJIRZs2blLjMxMaFHjx56He8yMTERABcXl0LXSU9PJz39vw4ASUnZwyRkZmaSmWm4nm05sQ2xDYktsfUdOz4lg19PRwPwbHOvYt+jz7yPRMQX2vIE2RewqMQ0Dl+KpY1v4ee6NsrK/pbYupPj5PHGPhIRT3SS7O+KGLug7WhDpZT03vUjtm7dyvz58zl16pRW60dGRlKtWjX+/vtv2rZtm7v8jTfe4ODBgxw5cqTI9/v4+DBt2jSmTZtW6DoajYann36ahIQE/vzzz0LXmzdvXoGzAW3YsAEbG5viP4wQlcD+SBU/XDOlhq3C600e7yw3IXEq1l0s/i7ImDpqAlylg0BlJcfJ4yX7u3JITU1lxIgRJCYm4uDgUOS6ert/FRAQUGhrprFMnjyZ8PDwIgtKgFmzZjF9+vTc35OSkqhRowaBgYHF7sDSyMzMZM+ePfTs2RNzc/0+5yOxJbY+YyuKwmfL/gZSeKGbP09qMZSQPvOuEhHPuovHil0vsGMbvbSIGHt/S+ySkePk8caW/V1xYz8s5+6tNvRWVEZHR+Pm5qb1+q6urpiamhITE5NneUxMTKGdcHQRFBTETz/9xKFDh6hevXqR61paWuaZLSiHubm5Qf+hHsd2JLbE1kfsY1fjuXw7BWtzUwYG1NApD33k3dbPHU9HK6IT0wp9Xs7T0Yq2fu56e3arov5bVuTYbf3ccbe3JPZeweOZqgAPOU70FlvOy4ofOye+tvQy8uvt27eZPXs2Xbt21fo9FhYWBAQEsHfv3txlGo2GvXv35rkdritFUQgKCmL79u3s27cPX1/fEscSQmTLmUHnqSae2Fs9/t6zpiYq5vbLnmyhsEvTy138pDNAJafWKNhbFdxWknNkzO3nL8eJnmhzXr71ZH3Z35WI1i2VzZs3R6XKf2AkJiZy8+ZN6tWrR3BwsE4bnz59OmPHjqVly5a0bt2apUuXkpKSktsbfMyYMVSrVo0FCxYA2Z17zpw5k/v/t27dIiwsDDs7O/z8sucjnTx5Mhs2bODHH3/E3t6e6OjsjgWOjo5YW1vrlJ8QAhLvZ7LrVCQAw1rXNFoevRt5smJUC+bvPJOnM4a5qYpMtcKGo9d5NqA61haGmTZSlH0f/nyWy7dTsDIzwd7KnNsPzcDk4WjF3H7+9G4kc33rU2HnpYrsjjr/XImnX9NqRstPPF5aF5UDBgwocLmDgwP16tWjV69eOg0nBDB06FBu377NnDlziI6OplmzZuzevZuqVasC2XOMm5j815gaGRmZZyrIxYsXs3jxYjp37syBAwcAWLFiBQBdunTJs621a9cybtw4nfITQsCOsFukZWqoW9WOFjWdjJpL70ae9PT34PClWH774wiBHdvg42ZP/8//4mxUEm9vP8UnQ5oW+AVYVGzbj9/k67+vArBsRAu61XfPc5zo8xasyKug8zJdDS+sO8b6I9dpWsOJIS0fz2QJwri0Lirnzp1rkASCgoIICgoq8LWcQjGHj49PsQOt66kzuxDigU0PZtAZ1qpmmSjWTE1UtPF14c5ZhTa+Lpibm/P5iBaM+uoI247fomkNJ8a28zF2muIxOh2ZyKxt2SOPTOnmR0//7IaJh48TKSgNq6Dz8tUedVmy5wLv/BBOAw8HGld3NHaawsC0eqZSCjUhKqdTNxM5HZmEhakJzzQvu7ew2tauwqw+9QF476czHLsab+SMxOOSkJrBxOAQ0jI1dK7rxtQedY2dknggqKsfPRq4k5GlYWJwCPEpGcZOSRiYVkVlw4YN2bRpExkZRR8QFy9eZNKkSSxcuFAvyQkhjGvjv9kz6PRu5IGzrYWRsyna8x18eaqJJ1kahUnrQ4ktYlBmUTGoNQpTN4VxI/4+NVys+XRYM2mRLENMTFR8MqQZPlVsuJVwnykbj6PWSCNVRaZVUbls2TIWL16Mh4cHQ4cO5eOPP2b9+vV8//33/N///R/Tp0+ndevWNGvWDAcHByZNmmTovIUQBpaSnsWOsJwOOmX/eSiVSsWiQU2oW9WO2/fSeXl9KBlZGmOnJQzo098vcPDCbazMTVg1qiVONmX7i09l5GhtzqrRLbE2N+XPS3Es/u28sVMSBqRVUdm9e3eOHTvGjh07cHd3Z/369QQFBTFy5EjmzZvHxYsXGTNmDDdv3mTRokU4OspzE0KUd7tORpGcnoVPFRva1qpi7HS0YmtpxqrRLbG3NOPYtbt8+PNZY6ckDGTPmRg+23cJgAUDG+PvZbiJKkTp1POw56NnmwCw4sBldodHGTkjYSg6DX7eoUMHOnToYKhchBBlSM6t76FlpIOOtnxdbfnf0Ga8sO4YX/99lSbVHRnYougJEET5EhGXwvTNYQCMa+fDM83l37es69fUixM3Evi/PyN47bsT+Lnb4+duZ+y0hJ7pZfBzIUTFcj76HsevJ2BmomJQQNntoFOYHv5VmdIte+zaWdtOcToy0cgZCX1JSc/ipW+PcS89i1Y+zrz1ZANjpyS09Gaf+jxRy4WUDHX2v2FaprFTEnomRaUQIp9ND1opuzdwx93eysjZlMzUHnXpUs+N9Ac9TxNSpedpeacoCm98f5ILMcm42VuyfEQLLMzkMlZemJma8PmIFng4WHH5dgoztpyU0WUqGDkbhRB5pGWq2X78FmDcGXRKy9RExdKhzajhYs2N+PtM3RQmPU/Lua/+jGDXySjMTFSsGNkCd4fy+YWnMnO1s2TFqBZYmJqw+3Q0Kw9eMXZKQo+kqBRC5PHr6WgSUjOp5mRNpzpuxk6nVJxsLFg1qiVW5iYcvHCbT3+/YOyURAn9fTmOBb+cA2BOP39a+rgYOSNRUs1rOjPv6YYAfPzrOf64eNvIGQl9kaJSCJHHxqPZt74Ht6xeIcb88/dyYMHAxgB8tu8Se87EGDkjoavIhPu8siF7jMOBzasx+glvY6ckSml46xoMaVkdjQJTNh7n5t1UY6ck9KBEReXly5d55513GD58OLGxsQD88ssvnD59Wq/JCSEer4i4FP65Eo9KBYMr0Fy9zzSvzrgHUzdO3xzGldvJxk1IaC09S82k9aHcScnA39OBD55pXK5GIxAFU6lUvNu/EU2qO3I3NfPBrEhqY6clSknnovLgwYM0btyYI0eOsG3bNpKTs/84nzhxwmDzgwshHo/ND+b57lzXjWpO1kbORr/eerIBrXycuZeexcTgEFLSs4ydktDCvB1nOHEj4cEg2gFYW5gaOyWhJ1bmpqwYFYCLrQXht5J454dw6bhTzulcVL755pu8//777NmzBwuL/2Yv6NatG//8849ekxNCPD6Zag1bQ24CMKxV+e2gUxgLMxOWj2iBu70lF2KSeeN76Xla1m3+9zobj15HpYLPhjenhouNsVMSelbNyZplw5tjooKtITdZf+S6sVMSpaBzUXnq1CmeeeaZfMvd3d2Ji4vTS1JCiMdv79kY4pLTcbWzpHsDd2OnYxDuDlasGNUCMxMVu05G8X9/RBg7JVGIEzcSmP1j9iNVr/WsS+e65bvTmChcez9X3uhdH4D5O08Tev2ukTMSJaVzUenk5ERUVP4plo4fP061auVvkGQhRLaNR7NvfQ9uWR1z04rbhy/A24U5/fwBWLj7HH9fli/DZc2d5HQmBYeQkaWhp39VXu7iZ+yUhIG91KkWfRp5kKlWmBQcwu176cZOSZSAzleOYcOGMXPmTKKjo1GpVGg0Gv766y9ef/11xowZY4gchRAGdivhPoceDOsxtAJ10CnM6Ce8Gdi8GmqNwisbjhOZcN/YKYkHstQaXtl4nMjENGq52vLJkKaYVIBRCETRVCoVHw9uip+7HTFJ6UzeEEqmWmPstISOdC4qP/zwQ+rXr0+NGjVITk7G39+fTp060a5dO9555x1D5CiEMLDvQ2+hKNC2VhV8XG2NnY7BqVQqPnimMf6eDtxJyWDS+lDSs6TnaVnw8W/n+fvyHWwsTFk5OgAHK3NjpyQeEztLM1aOCsDO0oyjEfEsfDAuqSg/dC4qLSws+PLLL7ly5Qo//fQTwcHBnDt3jm+//RZTU+mVJ0R5odYoHImI59htFesf3Poe1rrit1LmsLYwZdXoABytzTlxI4F5O87k7pOQOBVHIuJlBh4De3R/7wyLZNWDGVY+frYpdavaGzlD8bj5uduxeHBTIHsGpR/Dbsl5WY6YlfSNNWrUoEaNynMBEqIi2R0exfydZ4hKTANMgUxUKqhsNxlruNjw2fDmjFt7lI1Hr/PzqSgS72cCpqy7eAxPRyvm9vOndyNPY6da4Tx6DK67eCz3+HuxUy36NpF9Xln1buTBy11q88WBy8zYcpL3fjpDXHIGcl6WfTq3VA4aNIhFixblW/7RRx8xePBgvSQlhDCc3eFRTAoOfXAx/4+iwNRNYewOz98RryLrXNeNfg8KmOyC8j/RiWlMCg6tdPvE0Ao9Bh/8t2l1x8eflChTXgusRwNPezLUmgcF5X/kvCy7dC4qDx06xJNPPplveZ8+fTh06JBekhJCGIZaozB/5xmKunk0f+eZSnV7Sa1ROBpR8BAmOXuhsu0TQ9LmGHx/11nZ34L4R4rJHHJell06F5XJycl5Bj3PYW5uTlJSkl6SEkIYxtGI+HytQw9TgKjENI5GxD++pIzsaEQ80UmyTx6X4o5BkP0tso+TmCKGFZLzsmzSuahs3Lgxmzdvzrd806ZN+Pv76yUpIYRhxN4r+mKu63oVgeyTx0v2t9CGHCflk84ddWbPns3AgQO5fPky3bp1A2Dv3r1s3LiRLVu26D1BIYT+uNtb6XW9ikD2yeMl+1toQ46T8knnlsp+/frxww8/cOnSJV5++WVee+01bt68ye+//86AAQMMkKIQQl9a+7rg6WhVaC9vFeDpaEVrX5fHmZZRFbdPoPLtE0Nq7euCh4Nloa9XxmNQ5Cd/q8qnEs3F1rdvX/766y9SUlKIi4tj3759dO7cWd+5CSH0zNRExdx+BT+mkvPHe24/f0wr0QwmD++Twj71jF71KtU+MSQTFXhXKXiA/cp6DIr8ijsvFWDOU3KclDUVd4JfIUSBejfyZFCLavmWezhasWJUi0o59lvvRp6sGNUCD8e8t9Jyrle/hEejkV6mehH8zzWORMSjAlxs83b6rMzHoMivsPMyR3EdvsTjp9UzlS4uLly4cAFXV1ecnZ1RqQr/ZhAfLz2xhCjLFEXhxM1EAMa2rYkSF0Fgxza09XOv1N/6ezfypKe/B4cvxfLbH0cI7NgGG0sLhq3+hz1nYlhx8DKTu/oZO81yLeRaPO/+dAaAWU/W5/kOtfLs78p+DIr8Cjovz8Wk8P6us3z481kaejnQplYVY6cpHtCqqPzf//6HvX32dFlLly41ZD5CCAMLvX6Xi7HJWJubMrVbbf7Yd4U2vi5yMSf7llsbXxfunFVo4+uCubk57/ZvyJvbTrH4t/M0ruZIp7puxk6zXIq9lz1gdaZaoW9jTyZ0rIVKlXd/yzEoCvLoedm+jjunbiXyY1gkkzcc56dXOhTamikeL62KyrFjxwKQlZWFSqWiV69eVK1a1aCJCSEMY+ODeb77NvHE3srcyNmUfcNa1yTsRgKb/r3BlE3H2RnUgRouNsZOq1zJVGsIWn+c2Hvp1HG346NnmxR5x0uIoqhUKhYMbMz56Huci77HpPUhbH6xLRZm8kSfsen0L2BmZsbEiRNJS5PnGIQoj5LSMvnpZCQAw1vXMHI25ce8pxvStLojCamZvPRtCGmZamOnVK58+PNZjl6Nx97SjJWjA7C11Hk0OyHysLEwY9XoAByszDh+PYH3HjxWIYxL57K+devWHD9+3BC5CCEM7MewSNIyNdRxt6NFTWdjp1NuWJmbsmJUAFVsLTgTlcRb20+hKNJxRxs/HL/F2r+uAvDJkKbUdrMzbkKiwvCuYsvSYc0A+Pafa2wNuWnchITug58/PDZlQEAAtrZ5h4Zo0qSJ3pITQujX5n+vA9m3dOX2o268nKxZNrw5o746wrbQWzSv4cTotj7GTqtMOxOZxJvbTgIQ1NWPwIYeRs5IVDTd6ldlWo86LP39Im9vP0V9D3saVXM0dlqVls5F5bBhwwCYMmVK7jKVSoWiKKhUKtRquS0kRFkUfiuR8FtJWJia8Ezz/EMKieK183PlzT71+fDnc8zfeQZ/LwcCvGXw5YIkpmYyMTiEtEwNneq68WrPusZOSVRQU7rV4dTNRPaei+Wlb0P46ZUOOD8yXJV4PHS+/R0REZHv58qVK7n/FUKUTRuPZrdS9mrkkW98QKG9CR1r0bexJ1kahUnBoTL3cAE0GoVpm49zPT6V6s7WfDasmfTsFgZjYqJiydBmeFex4VbCfaZsOo5axpU1Cp2KyqSkJC5cuEB4eDg2NjZ4e3vn+xFClD2pGVn8GPagg04r6aBTGiqVio+ebUIddzti76UzeX0omWqNsdMqUz7de5H9529jaWbCylEBONnIlxhhWI7W5qwcFYC1uSl/XIxjyZ7zxk6pUtK6qAwLC6N+/fr07t2bfv364efnx6+//mrI3IQQevLTySiS07PwrmLDEzJQcKnZPujFbG9pxr9X7/LBrrPGTqnM2Hs2hk/3XgRgwcDG8nybeGwaeDqwcFBjAJbvv8zu8GgjZ1T5aF1Uzpw5E19fX/78809CQkLo3r07QUFBhsxNCKEnmx7c+h7aqgYmchtSL2q72fHJkKYAfP33VX44fsvIGRnf1bgUpm0OA2BMW28Gtqhu3IREpdO/WTXGt/cF4PUtJ7gUm2zkjCoXrYvKkJAQli1bRtu2bWnevDlr1qzh8uXLJCUlGTI/IUQpXYi5R+j1BExNVDwbIBd5fQps6EHQg6kb39x2kjORlffvYWpGFi99G8K9tCwCvJ15p6+/sVMSldSsJ+vT2teF5PQsJgaHkJyeZeyUKg2ti8r4+HiqV//vguTk5IStrS137twxSGJCCP3Y9GAGne713XG3l6nM9O3VnnXpVNeNtEwNE4NDSEzNNHZKj52iKMz8/hTnY+7hZm/JFyNbyOwmwmjMTU1YPqIFVR0suRSbzIwtJ2Rc2cdEp7P+zJkznDx5MvdHURTOnj2bZ5kQouxIy1Sz7Xj2gMDDW9c0cjYVk6mJis+GNaOGizXX41OZuvk4mkrW8/SrPyPYeSISMxMVX4xsQVUH+fIijMvN3pIVowIwN1XxS3g0qw/J6DSPg07jVHbv3j1ftf/UU0/JOJVClFG/no4mITUTL0crOtV1M3Y6FZaTjQUrRgYwaMXfHDh/m6V7LzK9kozL+M+VOyz45RwA7/RtQCsfGbdTlA0tajozt19D3vkhnEW7z9GomiPt/VyNnVaFpnVRGRERYcg8hBAGkHPre3DLGjJOoIE1qubIgoGNmf7dCT7be5Em1Rzp4V/V2GkZVFTifYI2hKLWKAxo5sXYdj7GTkmIPEa2qUnYjQS2htzklY3H2flKB6o5WRs7rQpL66JSxqAUony5GpfC4St3UKlgiIxN+VgMbFGdEzcS+ObwNV79LowdQR3wdbUt/o3lUHqWmknBocQlZ9DA04EFA5vI1J+izFGpVLw/oBHnopMIv5XEpOAQvnupLVbmpsZOrUKSJ6mFqKA2H8tupexc102+mT9Gb/f1J8DbmXtpWUz8NoSUCtrz9N2dZwi7kYCDlRmrRgVgbSEXaVE2WZmbsnJUAM425py8mcicH8Ol446BSFEpRAWUqdaw5Vh2B51h0kr5WFmYmfDFyBa42VtyPuYeM78/WeEuYN8du8H6I9dRqeDT4c2pWcXG2CkJUaTqzjZ8Nrw5Jir47thNNj54NEjolxSVQlRAe8/GEpecjqudJd0bVOzn+sqiqg5WfDGyBWYmKn46GcVXf1acZ9JP3UzknR/CAXi1R1261nM3ckZCaKdjHTde71UPgHk7TnP8+l0jZ1TxGL2oXL58OT4+PlhZWdGmTRuOHj1a6LqnT59m0KBB+Pj4oFKpWLp0aaljClERbfo3ewadZwOqY25q9NO8Umrl48I7fRsAsOCXc/x1MY4jEfGExKk4EhGPWo/DDqk1ymOJvedMDC99e4yMLA09GrjnDvwuRHkxqXNtejWsSoZaw6TgUGKS0gx27lRGOg0pBDB37lzGjx+vl447mzdvZvr06axcuZI2bdqwdOlSevXqxfnz53F3z//tNzU1lVq1ajF48GBeffVVvcQUoqK5lXCfgxduA3Lr29jGtvPhxM1Eth+/xeg1R8i+Xpmy7uIxPB2tmNvPn96NPEu1jd3hUczfeYaoxLTHEPsEAG52FnwypJlM+SnKHZVKxeLBTbkU+xeXb6fQcdE+MtQK+j53KiudmzB+/PFHateuTffu3dmwYQPp6ekl3viSJUuYMGECzz33HP7+/qxcuRIbGxvWrFlT4PqtWrXi448/ZtiwYVhaWuolphAVzZZjN1AUaFurCj4VtOdxeaFSqehSL3t80EcbQKIT05gUHMru8KgSx98dHsWk4NAHRd/jiQ1wOzmDw5fjShxbCGOytzJn1BPZDWPZBeV/9HHuVGY6t1SGhYVx/Phx1q5dy9SpU5k8eTLDhg1j/PjxtGrVSus4GRkZhISEMGvWrNxlJiYm9OjRg8OHD+ualsFiClGeqDUK3/2b/QD6sNbSSmlsao3CwgcDgz8q51I28/tT3E5Ox0TH4Xg0isLHu89T0M06Q8YGUAHzd56hp7+HjH8qyh21Ril0hh0FOb5LQ+eiEqB58+Y0b96cTz75hJ07d7J27Vrat29P/fr1ef755xk3bhyOjo5FxoiLi0OtVlO1at5OBFWrVuXcuYL/CBenpDHT09PztLgmJSUBkJmZSWam4ebxzYltiG1I7MoZ++CF20QmpuFkbU73ulWKfU9Zybuixj4SEV9gS9/DEu9nMvuH06Xe1uOMrQBRiWkcvhRLG9/SzaBTXv4tJXbFiV3ceSnHd8Hb0YZKKcVYFxkZGWzfvp01a9awb98+2rVrR2RkJDExMXz55ZcMHTq00PdGRkZSrVo1/v77b9q2bZu7/I033uDgwYMcOXKkyG37+Pgwbdo0pk2bVuqY8+bNY/78+fmWb9iwARsbGSpDlB9fnTfhZLwJnT00DPTVGDudSi8kTsW6i8WP31jDVoNzwU/0FOpuOtxIKf4JJkPGHlNHTYCrdGwQ5Yu256Uc39lSU1MZMWIEiYmJODg4FLluiVoqQ0JCWLt2LRs3bsTS0pIxY8awfPly/PyyewIuW7aMKVOmFFlUurq6YmpqSkxMTJ7lMTExeHh4lCStEsecNWsW06dPz/09KSmJGjVqEBgYWOwOLI3MzEz27NlDz549MTc3l9gSu1Sxb99L57UjhwCFmc92oE5VO73FLgmJDVUi4ll38Vix6y0Y2lrnFpEjEfGMWmPc2IEd2+ilJac8/FtK7IoTW9vzsjIf3w/LuXurDZ2LysaNG3Pu3DkCAwP56quv6NevH6ameSv+4cOHM3Xq1CLjWFhYEBAQwN69exkwYAAAGo2GvXv3EhQUpGtapYppaWlZYMcfc3Nzg/5DPY7tSOzKE/uHk9fI0ii0qOmEf3VnvcYujcocu62fO56OVkQnphX4fKIK8HC0oq2fu87PbpXX2IUp6/+WErvixC7u+AbwlOM7T3xt6dz7e8iQIVy9epVdu3YxYMCAfAUlZLcYajTF33qbPn06X375Jd988w1nz55l0qRJpKSk8NxzzwEwZsyYPJ1uMjIyCAsLIywsjIyMDG7dukVYWBiXLl3SOqYQFZGiKGzO7aBT08jZiBymJirm9vMHsguxh+X8Preff4kuXOU1thDGVtTxnWNgi2pyfJeAzkWloig4O+dvBbl//z7vvvuuTrGGDh3K4sWLmTNnDs2aNSMsLIzdu3fndrS5fv06UVH/deuPjIzM7SQUFRXF4sWLad68OS+88ILWMYWoiA5fucO1O6nYW5rxVBMZX60s6d3IkxWjWuDhaJVnuYejFStGtSjVeHjlNbYQxlbY8W3zYA774H+uc/1OqjFSK9d0vv09f/58Jk6cmK8DS2pqKvPnz2fOnDk6xQsKCir01vSBAwfy/O7j46PVHLpFxRSiItr0YB7bp5t5YWNRokelhQH1buRJT38PDl+K5bc/jhDYsY3ebq2V19hCGFtBx3cLnyqM+PIIYTcSeCk4hG2T2mFtUXynHpGtRC2VqgLGPDtx4gQuLqV7oFUIobu7KRnsDo8GYFgrufVdVpmaqGjj60KAq0IbXxe9FmblNbYQxvbo8W1jYcaKUS1wtbPgbFQSb20/pVVjlsimdZOGs7MzKpUKlUpF3bp18xSWarWa5ORkJk6caJAkhRCF23b8FhlqDQ29HGhcvejxYYUQQhTN09Gaz0e0YOT/HWH78Vs0q+HE2HY+xk6rXNC6qFy6dCmKojB+/Hjmz5+fZ3BzCwsLfHx88owNKYQwPEVR2HT0OiAddIQQQl+eqFWFWX3q8/6us7z30xn8vRxo5SN3Y4ujdVE5duxYAHx9fWnXrt1jGWpHCFG00OsJXIxNxtrclP7NvIydjhBCVBjPd/DlxM1Edp6I5OX1oex6pQPuDlbFv7ES0+qZyocHvmzevDn3798nKSmpwB8hxOOT00rZt4knDlbyRU8IIfRFpVKxaFBj6lW15/a9dF5eH0pGlsxUVhStikpnZ2diY2MBcHJywtnZOd9PznIhxONxLy2Tn05mD7k1vHUNI2cjhBAVj42FGatGB2BvZcaxa3f5YNcZY6dUpml1+3vfvn25Pbv37dtXYO9vIcTj9WNYJPcz1fi529GipnyhE0IIQ/BxtWXp0GY8/80xvjl8jaY1nBjYorqx0yqTtCoqO3funPv/Xbp0MVQuQggdbPr3QQedVjXki54QQhhQ9wZVmdq9Dp/uvcisbaeoW9WeRtVktI1H6TxO5dq1a9myZUu+5Vu2bOGbb77RS1JCiKKF30ok/FYSFqYm8o1ZCCEeg6nd69C1nhvpWRomrQ8hITXD2CmVOToXlQsWLMDV1TXfcnd3dz788EO9JCWEKFpOK2WvRh642FoYORshhKj4TExULB3anJouNtyIv8+UTWGoNTIw+sN0LiqvX7+Or69vvuXe3t5cv35dL0kJIQqXmpHFj8cjARjeSjroCCHE4+JoY86q0QFYmZtw6MJtlv5+wdgplSk6F5Xu7u6cPHky3/ITJ05QpUoVvSQlhCjcrpNR3EvPwruKDU/UknNOCCEepwaeDiwa1ASAZfsu8dvpaCNnVHboXFQOHz6cKVOmsH//ftRqNWq1mn379jF16lSGDRtmiByFEA/Z9O8NAIa0rIGJzMMshBCPXf9m1XiuvQ8Ar313giu3k42bUBmhc1H53nvv0aZNG7p37461tTXW1tYEBgbSrVs3eaZSCAO7EHOPkGt3MTVRMThAOugIIYSxvPVkA1r7uHAvPYuXvg0hJT3L2CkZnc5FpYWFBZs3b+bcuXOsX7+ebdu2cfnyZdasWYOFhXQYEMKQNj9opexe312mCxNCCCMyNzXh85HNqepgycXYZN7YehJFqdwdd7Se+/tRdevWpW7duvrMRQhRhPQsDdtCbwIwvHVNI2cjhBDC3d6KL0YGMGz1YXadiqLZH05M6FTL2GkZjVZF5fTp03nvvfewtbVl+vTpRa67ZMkSvSQmhMhrz5kY7qZm4uVoRae6bsZORwghBBDg7cycfg2Z/UM4C345S0MvB9r55R96sTLQqqg8fvw4mZmZAISGhhY6e4fM6iGE4XwXcguAwS1rYCoddIQQoswY1aYmYdcT+D70JkEbj/PTKx3wcrI2dlqPnVZF5f79+3P//8CBA4bKpVJRaxSORMQTEqeiSkQ8bf3cpVAQ+eQcJwciVRy+Fg/A4JbSQUcIIcoSlUrFB8804lx0Eqcjk5gUHMKGCU9w/JphrvNltYbQ6ZnKzMxMrK2tCQsLo1GjRobKqcLbHR7F/J1niEpMA0xZd/EYno5WzO3nT+9GnsZOT5QRjx4nABZmJoTfSqS6s41xkxNCCJGHlbkpK0cF0O/zPzlxM5GWH/zO/Qw1+r7Ol+UaQqfe3+bm5tSsWRO1Wm2ofCq83eFRTAoOfXAw/Cc6MY1JwaHsDo8yUmaiLCnsOMnI0shxIoQQZVQNFxvGtPUGeFBQ/kcf1/myXkPo3Pv77bff5q233uLbb7/FxcXFEDlVWGqNwvydZyhowAEFUAHzd56hp79HmWjGFsZR1HGSQ44TIYQoe9QahS3Hbhb4Ws7f9Le3h+NkY6Hz32+1RuHt7eFluobQuaj8/PPPuXTpEl5eXnh7e2Nra5vn9dDQUL0lV9EcjYjP9+3iYQoQlZjG0Yh42taW6fcqKzlOhBCifCru7zfAnZQMhq3+R+/bLgvXBp2Lyv79+0sv7xKKvVf0gabreqJikuNECCHKJ23/LrvbW2JnqVsJlpyeRey9dL3lYAg6F5Xz5s0zQBqVg7u9djOgaLueqJjkOBFCiPJJ27/Lnw5rrnNr4uHLdxj+ZfEtnMa8Nug8TWOtWrW4c+dOvuUJCQnUqlV5R5HXRmtfFzwdrSiqndfDwZLWvvKsamXW2tcFV7vCpzxVAZ6OVnKcCCFEGVPcdb40f78NGVtfdC4qr169WmDv7/T0dG7eLPjhVJHN1ETF3H7+AIUeFB6OVkjfi8ot6X4mGk3Br+UcGnP7+UsnHSGEKGOKus6X9u+3IWPri9a3v3fs2JH7/7/++iuOjo65v6vVavbu3Yuvr69+s6uAejfyZMWoFg+NMZXN1c6ChNRMwm4ksuLgZV7u4mfELIWxqDUKUzYdJz41Azc7C0xMVMQk/fcMjUcZGYtMCCFEwQq7zuvj77chY+uD1kXlgAEDgOxR48eOHZvnNXNzc3x8fPjkk0/0mlxF1buRJz39PTh8KZbf/jhCYMc2tPVz57tjN5i17RSLfz1PIy9Hmd+5Elqy5zx/XIzD2tyUdc+3oW5V+3zHibRQCiFE2VbYdV4ff78NGbu0tL79rdFo0Gg01KxZk9jY2NzfNRoN6enpnD9/nqeeesqQuVYopiYq2vi6EOCq0MbXBVMTFcNb12RoyxpoFJiy6Tg34lONnaZ4jH49Hc3y/ZcBWDioMQ08HQo8ToQQQpR9hvz7XVavDTo/UxkREYGrq6shchHA/P4NaVLdkYTUTCatDyEtU2Yvqgwu307mte9OADC+vS/9m1UzckZCCCGEbnQuKqdMmcJnn32Wb/nnn3/OtGnT9JFTpWZlbsqKUQG42FoQfispe/R8pai5VUR5l5yexUvfhpCcnkVrXxdmPVnf2CkJIYQQOtO5qPz+++9p3759vuXt2rVj69atekmqsqvmZM3nw5tjooLvQ28SfOS6sVMSBqIoCjO2nOBSbDJVHSxZPqIF5qY6n5ZCCCGE0el89bpz506ent85HBwciIuL00tSAtr5uTKzd3aL1bs7TxNy7a6RMxKGsPrQFX4Jj8bcVMWKUQG42VsaOyUhhBCiRHQuKv38/Ni9e3e+5b/88osMfq5nL3aqxZONPchUK7y8PkSm5atg/roUx6Ld5wCY268hLWo6GzkjIYQQouR0nqZx+vTpBAUFcfv2bbp16wbA3r17+eSTT1i6dKm+86vUVCoVHz3blAsxyVyKTSZo/XHWT2gjt0crgFsJ93ll43E0CjwbUJ2RbWoaOyUhhBCiVHSuTsaPH88nn3zCV199RdeuXenatSvBwcGsWLGCCRMmGCLHSs3O0oxVowOwszTj6NV4Fvx8ztgpiVJKy1QzKTiE+JQMGlVz4P0BjVCpysZwEEIIIURJlajJa9KkSdy8eZOYmBiSkpK4cuUKY8aM0Xdu4oHabnZ8MqQpAGv+iuDHsFtGzkiUlKIozPkxnJM3E3G2MWflqACszE2NnZYQQghRaiUqKrOysvj999/Ztm1b7nA3kZGRJCcn6zU58Z9eDT2Y3LU2ADO/P8nZqCQjZyRKYuPRG3x37CYmKvhseHOqO9sYOyUhhBBCL3QuKq9du0bjxo3p378/kydP5vbt2wAsWrSI119/Xe8Jiv9M71mPjnVcScvUMDE4hMTUTGOnJHRw/Ppd5u04DcDrverRsY5MwymEEKLi0LmonDp1Ki1btuTu3btYW1vnLn/mmWfYu3evXpMTeZmaqPhsWHOqO1tz7U4q0zYfR6ORgdHLg9v30pkUHEqGWkOvhlWZ1Lm2sVMSQggh9ErnovKPP/7gnXfewcLCIs9yHx8fbt2SZ/0MzdnWgpWjArA0M2H/+dt8uveisVMSxchSawjaEEp0Uhq13WxZPLipdMwRQghR4ehcVGo0GtTq/PNR37x5E3t7e70kJYrWqJojHzzTGIBP915k79kYI2ckirLwl3MciYjH1sKUVaMDsLcyN3ZKQgghhN7pXFQGBgbmGY9SpVKRnJzM3LlzefLJJ/WZmyjCswHVGdPWG4Bpm8O4Gpdi5IxEQXaeiOT//owA4JMhTfFzly9eQgghKiadi8pPPvmEv/76C39/f9LS0hgxYkTure9FixYZIkdRiHf6+hPg7cy9tCxe+jaE1IwsY6ckHnI++h5vbD0JwMTOtendyNPIGQkhhBCGo3NRWb16dU6cOMFbb73Fq6++SvPmzVm4cCHHjx/H3d3dEDmKQliYmfDFyBa42llyPuYeM78/lTvEkzCuxPuZvPTtMe5nqung58rrgXWNnZIQQghhUDpP0whgZmbGqFGj9J2LKIGqDlZ8MbIFI778h50nImlWw4nnO/gaO61KTaNReO27MK7eSaWakzWfDW+OmUytKYQQooLTqqjcsWOH1gGffvrpEicjSqa1rwtv923A/J1n+PDnszTwsEejURMSp6JKRDxt/dwxNdFPb2O1RuFIRLzELiL2seuJ/H42FgszE1aOCsDF1qL4IEIIIUQ5p1VROWDAAK2CqVSqAnuGF2X58uV8/PHHREdH07RpU5YtW0br1q0LXX/Lli3Mnj2bq1evUqdOHRYtWpSng1BycjJvvvkmP/zwA3fu3MHX15cpU6YwceJEnfIqb8a18+HEjQR+CItk1FdHyB6+0pR1F4/h6WjF3H7+pX6mb3d4FPN3niEqMU1iFxE7x/sDGtG4umOp4gshhBDlhVb35DQajVY/uhaUmzdvZvr06cydO5fQ0FCaNm1Kr169iI2NLXD9v//+m+HDh/P8889z/PhxBgwYwIABAwgPD89dZ/r06ezevZvg4GDOnj3LtGnTCAoK0qm1tTxSqVR0rZ/9TOuj46FHJ6YxKTiU3eFRJY6/OzyKScGhD4oniV1U7BwOViV6ukQIIYQol4x61VuyZAkTJkzgueeeA2DlypXs2rWLNWvW8Oabb+Zb/9NPP6V3797MmDEDgPfee489e/bw+eefs3LlSiC78Bw7dixdunQB4MUXX2TVqlUcPXq0Qt+aV2sUFv5yrsDXFEAFzN1xmlY+Ljrf9lVrFObuOE1BXYAkdsFUwPydZ+jp76G32+xCCCFEWaZ1Ufnkk0+yceNGHB2zb+ctXLiQiRMn4uTkBMCdO3fo2LEjZ86c0SpeRkYGISEhzJo1K3eZiYkJPXr04PDhwwW+5/Dhw0yfPj3Psl69evHDDz/k/t6uXTt27NjB+PHj8fLy4sCBA1y4cIH//e9/heaSnp5Oenp67u9JSUkAZGZmkplpuPm1c2LrYxtHIuILbTGD7CIqJimdgPd/L/W2JLZ2saMS0zh8KZY2vi6liqXP40RiS2yJLbEltsQuyXa0oVK0HIPG1NSUqKio3GGDHBwcCAsLo1atWgDExMTg5eWl9S3wyMhIqlWrxt9//03btm1zl7/xxhscPHiQI0eO5HuPhYUF33zzDcOHD89d9sUXXzB//nxiYrJnlUlPT+fFF19k3bp1mJmZYWJiwpdffsmYMWMKzWXevHnMnz8/3/INGzZgY2Oj1ecxtpA4Fesumho7DfGIMXXUBLjKME9CCCHKp9TUVEaMGEFiYiIODg5Frqt1S+WjtWdZHQ9x2bJl/PPPP+zYsQNvb28OHTrE5MmT8fLyokePHgW+Z9asWXlaQJOSkqhRowaBgYHF7sDSyMzMZM+ePfTs2RNz89JN3VclIj5PJ5HCfDO2Ba11bDk7GhHP2G9CJXYJYgd2bKOXlkp9HScSW2JLbIktsSW2LnLu3mrDaM9Uurq6YmpqmtvCmCMmJgYPD48C3+Ph4VHk+vfv3+ett95i+/bt9O3bF4AmTZoQFhbG4sWLCy0qLS0tsbS0zLfc3NzcoP9Q+txOWz93PB2tiE5MK/A5PxXg4WhFh3q6P+PXoZ6HxC5hbH0OXWTI41FiS2yJLbEltsQuLL62tB6RWaVSoVKp8i0rKQsLCwICAti7d2/uMo1Gw969e/PcDn9Y27Zt86wPsGfPntz1c56BNDHJ+7FMTU3RaDQlzrU8MDVRMbefP5Bd0Dws5/e5/fxLVOBI7McbWwghhCiPdLr9PW7cuNwWvbS0NCZOnIitrS1Ano4u2po+fTpjx46lZcuWtG7dmqVLl5KSkpLbG3zMmDFUq1aNBQsWADB16lQ6d+7MJ598Qt++fdm0aRPHjh1j9erVQPZznp07d2bGjBlYW1vj7e3NwYMHWbduHUuWLNE5v/KmdyNPVoxq8dC4idk89DAmo8R+vLGFEEKI8kbronLs2LF5fi9omsaiOsMUZOjQody+fZs5c+YQHR1Ns2bN2L17N1WrVgXg+vXreVod27Vrx4YNG3jnnXd46623qFOnDj/88AONGjXKXWfTpk3MmjWLkSNHEh8fj7e3Nx988EGFH/w8R+9GnvT09+DwpVh+++MIgR3b6O0WrMR+vLGFEEKI8kTronLt2rUGSSAoKIigoKACXztw4EC+ZYMHD2bw4MGFxvPw8DBYruWFqYmKNr4u3Dmr0MZX9zEYJXbZiS2EEEKUF1o/UymEEEIIIURhZB65AuQMl6RLN/qSyMzMJDU1laSkJIMMNSCxJbbEltgSW2JLbIldGjm1kDZDSUpRWYB79+4BUKNGDSNnIoQQQghhfPfu3cudVbEwWs+oU5loNBoiIyOxt7cv1bBJxckZZP3GjRt6H2RdYktsiS2xJbbEltgSu7QUReHevXt4eXnlG7LxUdJSWQATExOqV6/+2Lbn4OBgsANCYktsiS2xJbbEltgSuzSKa6HMIR11hBBCCCFEqUlRKYQQQgghSk2KSiOytLRk7ty5Bc47LrEltsSW2BJbYktsiW2M2CUlHXWEEEIIIUSpSUulEEIIIYQoNSkqhRBCCCFEqUlRKYQQQgghSk2KSiGEEEIIUWpSVBrR8uXL8fHxwcrKijZt2nD06FG9xD106BD9+vXDy8sLlUrFDz/8oJe4CxYsoFWrVtjb2+Pu7s6AAQM4f/68XmKvWLGCJk2a5A7i2rZtW3755Re9xH7UwoULUalUTJs2rdSx5s2bh0qlyvNTv3790if5wK1btxg1ahRVqlTB2tqaxo0bc+zYsVLH9fHxyZe3SqVi8uTJpY6tVquZPXs2vr6+WFtbU7t2bd577z2t5o3Vxr1795g2bRre3t5YW1vTrl07/v33X53jFHeeKIrCnDlz8PT0xNramh49enDx4kW9xN62bRuBgYFUqVIFlUpFWFiYXvLOzMxk5syZNG7cGFtbW7y8vBgzZgyRkZF6yXvevHnUr18fW1tbnJ2d6dGjB0eOHNFL7IdNnDgRlUrF0qVL9RJ73Lhx+Y713r176y3vs2fP8vTTT+Po6IitrS2tWrXi+vXrpY5d0DmqUqn4+OOPSx07OTmZoKAgqlevjrW1Nf7+/qxcubLYuNrEjomJYdy4cXh5eWFjY0Pv3r21One0ucakpaUxefJkqlSpgp2dHYMGDSImJkYvsVevXk2XLl1wcHBApVKRkJBQbFxtYsfHx/PKK69Qr149rK2tqVmzJlOmTCExMVEveb/00kvUrl0ba2tr3Nzc6N+/P+fOndMqd32TotJINm/ezPTp05k7dy6hoaE0bdqUXr16ERsbW+rYKSkpNG3alOXLl+sh0/8cPHiQyZMn888//7Bnzx4yMzMJDAwkJSWl1LGrV6/OwoULCQkJ4dixY3Tr1o3+/ftz+vRpPWT+n3///ZdVq1bRpEkTvcVs2LAhUVFRuT9//vmnXuLevXuX9u3bY25uzi+//MKZM2f45JNPcHZ2LnXsf//9N0/Oe/bsAWDw4MGljr1o0SJWrFjB559/ztmzZ1m0aBEfffQRy5YtK3VsgBdeeIE9e/bw7bffcurUKQIDA+nRowe3bt3SKU5x58lHH33EZ599xsqVKzly5Ai2trb06tWLtLS0UsdOSUmhQ4cOLFq0SKeci4udmppKaGgos2fPJjQ0lG3btnH+/HmefvrpUscGqFu3Lp9//jmnTp3izz//xMfHh8DAQG7fvl3q2Dm2b9/OP//8g5eXl1Y5axu7d+/eeY75jRs36iX25cuX6dChA/Xr1+fAgQOcPHmS2bNnY2VlVerYD+cbFRXFmjVrUKlUDBo0qNSxp0+fzu7duwkODubs2bNMmzaNoKAgduzYUarYiqIwYMAArly5wo8//sjx48fx9vamR48exV4rtLnGvPrqq+zcuZMtW7Zw8OBBIiMjGThwYLE5axM7NTWV3r1789ZbbxUbT5fYkZGRREZGsnjxYsLDw/n666/ZvXs3zz//vF7yDggIYO3atZw9e5Zff/0VRVEIDAxErVbr9Dn0QhFG0bp1a2Xy5Mm5v6vVasXLy0tZsGCBXrcDKNu3b9drzByxsbEKoBw8eNAg8Z2dnZX/+7//01u8e/fuKXXq1FH27NmjdO7cWZk6dWqpY86dO1dp2rRpqeMUZObMmUqHDh0MEvtRU6dOVWrXrq1oNJpSx+rbt68yfvz4PMsGDhyojBw5stSxU1NTFVNTU+Wnn37Ks7xFixbK22+/XeK4j54nGo1G8fDwUD7++OPcZQkJCYqlpaWycePGUsV+WEREhAIox48fL0HW2p3fR48eVQDl2rVreo+dmJioAMrvv/+ul9g3b95UqlWrpoSHhyve3t7K//73P53iFhZ77NixSv/+/XWOpU3soUOHKqNGjTJI7Ef1799f6datm15iN2zYUHn33XfzLCvJefRo7PPnzyuAEh4enrtMrVYrbm5uypdffqlT7EevMQkJCYq5ubmyZcuW3HXOnj2rAMrhw4dLFfth+/fvVwDl7t27OsXUJnaO7777TrGwsFAyMzP1HvvEiRMKoFy6dEmn2PogLZVGkJGRQUhICD169MhdZmJiQo8ePTh8+LARM9NNTtO9i4uLXuOq1Wo2bdpESkoKbdu21VvcyZMn07dv3zz7XR8uXryIl5cXtWrVYuTIkVrd9tLGjh07aNmyJYMHD8bd3Z3mzZvz5Zdf6iX2wzIyMggODmb8+PGoVKpSx2vXrh179+7lwoULAJw4cYI///yTPn36lDp2VlYWarU6XyuQtbW13lqIASIiIoiOjs5zrDg6OtKmTZtydY5C9nmqUqlwcnLSa9yMjAxWr16No6MjTZs2LXU8jUbD6NGjmTFjBg0bNtRDhnkdOHAAd3d36tWrx6RJk7hz506pY2o0Gnbt2kXdunXp1asX7u7utGnTRm+PHD0sJiaGXbt2adW6pY127dqxY8cObt26haIo7N+/nwsXLhAYGFiquOnp6QB5zlETExMsLS11PkcfvcaEhISQmZmZ57ysX78+NWvW1Pm8NNT1S9vYiYmJODg4YGZmptfYKSkprF27Fl9fX2rUqKFTbH2QotII4uLiUKvVVK1aNc/yqlWrEh0dbaSsdKPRaJg2bRrt27enUaNGeol56tQp7OzssLS0ZOLEiWzfvh1/f3+9xN60aROhoaEsWLBAL/FytGnTJvdWxooVK4iIiKBjx47cu3ev1LGvXLnCihUrqFOnDr/++iuTJk1iypQpfPPNN3rI/D8//PADCQkJjBs3Ti/x3nzzTYYNG0b9+vUxNzenefPmTJs2jZEjR5Y6tr29PW3btuW9994jMjIStVpNcHAwhw8fJioqSg/ZZ8s5D8vzOQrZz5/NnDmT4cOH4+DgoJeYP/30E3Z2dlhZWfG///2PPXv24OrqWuq4ixYtwszMjClTpughy7x69+7NunXr2Lt3L4sWLeLgwYP06dOn1LcHY2NjSU5OZuHChfTu3ZvffvuNZ555hoEDB3Lw4EE9ZZ/tm2++wd7eXqtbvdpYtmwZ/v7+VK9eHQsLC3r37s3y5cvp1KlTqeLmFHmzZs3i7t27ZGRksGjRIm7evKnTOVrQNSY6OhoLC4t8X5B0PS8Ncf3SJXZcXBzvvfceL774ot5if/HFF9jZ2WFnZ8cvv/zCnj17sLCwKPHnKCndSmQhHpg8eTLh4eF6bR2qV68eYWFhJCYmsnXrVsaOHcvBgwdLXVjeuHGDqVOnsmfPHq2ec9LFw61vTZo0oU2bNnh7e/Pdd9+VukVBo9HQsmVLPvzwQwCaN29OeHg4K1euZOzYsaWK/bCvvvqKPn366PQMW1G+++471q9fz4YNG2jYsCFhYWFMmzYNLy8vveT97bffMn78eKpVq4apqSktWrRg+PDhhISE6CH7iiMzM5MhQ4agKAorVqzQW9yuXbsSFhZGXFwcX375JUOGDOHIkSO4u7uXOGZISAiffvopoaGhemktf9SwYcNy/79x48Y0adKE2rVrc+DAAbp3717iuBqNBoD+/fvz6quvAtCsWTP+/vtvVq5cSefOnUuX+EPWrFnDyJEj9fY3bNmyZfzzzz/s2LEDb29vDh06xOTJk/Hy8irV3Rxzc3O2bdvG888/j4uLC6ampvTo0YM+ffro1FnPENeYshA7KSmJvn374u/vz7x58/QWe+TIkfTs2ZOoqCgWL17MkCFD+Ouvv/R+zSuOtFQagaurK6ampvl6rMXExODh4WGkrLQXFBTETz/9xP79+6levbre4lpYWODn50dAQAALFiygadOmfPrpp6WOGxISQmxsLC1atMDMzAwzMzMOHjzIZ599hpmZmV4fZnZycqJu3bpcunSp1LE8PT3zFdQNGjTQ2+11gGvXrvH777/zwgsv6C3mjBkzclsrGzduzOjRo3n11Vf11kpcu3ZtDh48SHJyMjdu3ODo0aNkZmZSq1YtvcQHcs/D8nqO5hSU165dY8+ePXprpQSwtbXFz8+PJ554gq+++gozMzO++uqrUsX8448/iI2NpWbNmrnn6LVr13jttdfw8fHRT+IPqVWrFq6urqU+T11dXTEzMzP4efrHH39w/vx5vZ2n9+/f56233mLJkiX069ePJk2aEBQUxNChQ1m8eHGp4wcEBBAWFkZCQgJRUVHs3r2bO3fuaH2OFnaN8fDwICMjI1+vbF3OS0Ndv7SJfe/ePXr37o29vT3bt2/H3Nxcb7EdHR2pU6cOnTp1YuvWrZw7d47t27eX6vOUhBSVRmBhYUFAQAB79+7NXabRaNi7d69enyHUN0VRCAoKYvv27ezbtw9fX1+Dbk+j0eQ+n1Ma3bt359SpU4SFheX+tGzZkpEjRxIWFoapqakess2WnJzM5cuX8fT0LHWs9u3b5xs64sKFC3h7e5c6do61a9fi7u5O37599RYzNTUVE5O8f1pMTU1zW3X0xdbWFk9PT+7evcuvv/5K//799Rbb19cXDw+PPOdoUlISR44cKdPnKPxXUF68eJHff/+dKlWqGHR7+jhPR48ezcmTJ/Oco15eXsyYMYNff/1VT5n+5+bNm9y5c6fU56mFhQWtWrUy+Hn61VdfERAQoJdnVyH7GMnMzDT4eero6IibmxsXL17k2LFjxZ6jxV1jAgICMDc3z3Nenj9/nuvXrxd7Xhry+qVN7KSkJAIDA7GwsGDHjh1atyCWJG9FUVAURS/XT13J7W8jmT59OmPHjqVly5a0bt2apUuXkpKSwnPPPVfq2MnJyXm+gUdERBAWFoaLiws1a9YscdzJkyezYcMGfvzxR+zt7XOfYXF0dMTa2rpUOc+aNYs+ffpQs2ZN7t27x4YNGzhw4IBeLij29vb5nj+xtbWlSpUqpX6e5vXXX6dfv354e3sTGRnJ3LlzMTU1Zfjw4aWKC9lDZ7Rr144PP/yQIUOGcPToUVavXs3q1atLHRuyi4G1a9cyduxYnR8WL0q/fv344IMPqFmzJg0bNuT48eMsWbKE8ePH6yV+zpAZ9erV49KlS8yYMYP69evrfO4Ud55MmzaN999/nzp16uDr68vs2bPx8vJiwIABpY4dHx/P9evXc8ePzClKPDw8im1xKSq2p6cnzz77LKGhofz000+o1erc89TFxaXYZ6yKil2lShU++OADnn76aTw9PYmLi2P58uXcunVLq6Goitsnjxa/5ubmeHh4UK9evVLFdnFxYf78+QwaNAgPDw8uX77MG2+8gZ+fH7169Sp13jNmzGDo0KF06tSJrl27snv3bnbu3MmBAwdKHRuyi5EtW7bwySefFBtPl9idO3dmxowZWFtb4+3tzcGDB1m3bh1LliwpdewtW7bg5uZGzZo1OXXqFFOnTmXAgAHFdgIq7hrj6OjI888/z/Tp03FxccHBwYFXXnmFtm3b8sQTT5QqNmQ/sxkdHZ372U6dOoW9vT01a9YsstNNcbFzCsrU1FSCg4NJSkoiKSkJADc3tyIbNoqLfeXKFTZv3kxgYCBubm7cvHmThQsXYm1tzZNPPlnkPjGIx97fXORatmyZUrNmTcXCwkJp3bq18s8//+glbs5wCI/+jB07tlRxC4oJKGvXri11zuPHj1e8vb0VCwsLxc3NTenevbvy22+/lTpuYfQ1pNDQoUMVT09PxcLCQqlWrZoydOhQvQ7jsHPnTqVRo0aKpaWlUr9+fWX16tV6i/3rr78qgHL+/Hm9xVQURUlKSlKmTp2q1KxZU7GyslJq1aqlvP3220p6erpe4m/evFmpVauWYmFhoXh4eCiTJ09WEhISdI5T3Hmi0WiU2bNnK1WrVlUsLS2V7t27a72viou9du3aAl+fO3duqWLnDFFU0M/+/ftLFfv+/fvKM888o3h5eSkWFhaKp6en8vTTTytHjx7Vyz55lC5DChUVOzU1VQkMDFTc3NwUc3NzxdvbW5kwYYISHR2tt7y/+uorxc/PT7GyslKaNm2q/PDDD3qLvWrVKsXa2lrnY7y42FFRUcq4ceMULy8vxcrKSqlXr57yySefaDWsWHGxP/30U6V69eqKubm5UrNmTeWdd97R6vzX5hpz//595eWXX1acnZ0VGxsb5ZlnnlGioqL0Envu3LklusYVF7uw/QUoERERpYp969YtpU+fPoq7u7tibm6uVK9eXRkxYoRy7ty5YveJIageJC2EEEIIIUSJyTOVQgghhBCi1KSoFEIIIYQQpSZFpRBCCCGEKDUpKoUQQgghRKlJUSmEEEIIIUpNikohhBBCCFFqUlQKIYQQQohSk6JSCCGEEEKUmhSVQghRiHHjxmk1LaOhjB49mg8//NBo2y/IypUr6devn7HTEEKUQTKjjhCiUlKpVEW+PnfuXF599VUURcHJyenxJPWQEydO0K1bN65du4adnR0AXbp0oVmzZixdujTPul9//TXTpk0jISHB4HllZGTg6+vLpk2b6Nixo8G3J4QoP8yMnYAQQhhDVFRU7v9v3ryZOXPmcP78+dxldnZ2ucWcMSxbtozBgwcbNYeHKYqCWq3GwsKCESNG8Nlnn0lRKYTIQ25/CyEqJQ8Pj9wfR0dHVCpVnmV2dnb5bn936dKFV155hWnTpuHs7EzVqlX58ssvSUlJ4bnnnsPe3h4/Pz9++eWXPNsKDw+nT58+2NnZUbVqVUaPHk1cXFyhuanVarZu3Vqq28wrVqygdu3aWFhYUK9ePb799tvc165evYpKpSIsLCx3WUJCAiqVigMHDgBw4MABVCoVv/zyCwEBAVhaWvLnn38C0K9fP3bs2MH9+/dLnJ8QouKRolIIIXTwzTff4OrqytGjR3nllVeYNGkSgwcPpl27doSGhhIYGMjo0aNJTU0Fsou1bt260bx5c44dO8bu3buJiYlhyJAhhW7j5MmTJCYm0rJlyxLluH37dqZOncprr71GeHg4L730Es899xz79+/XOdabb77JwoULOXv2LE2aNAGgZcuWZGVlceTIkRLlJ4SomKSoFEIIHTRt2pR33nmHOnXqMGvWLKysrHB1dWXChAnUqVOHOXPmcOfOHU6ePAnA559/TvPmzfnwww+pX78+zZs3Z82aNezfv58LFy4UuI1r165hamqKu7t7vte++OKL3FvzOT8TJ07Ms87ixYsZN24cL7/8MnXr1mX69OkMHDiQxYsX6/x53333XXr27Ent2rVxcXEBwMbGBkdHR65du6ZzPCFExSVFpRBC6CCntQ7A1NSUKlWq0Lhx49xlVatWBSA2NhbI7nCzf//+PEVg/fr1Abh8+XKB27h//z6WlpYFdiYaOXIkYWFheX7efffdPOucPXuW9u3b51nWvn17zp49q/PnLay11NraOrc1VgghQDrqCCGETszNzfP8rlKp8izLKQQ1Gg0AycnJ9OvXj0WLFuWL5enpWeA2XF1dSU1NJSMjAwsLizyvOTo64ufnl2dZQS2aRTExyW5PeHjwj8zMzALXtbW1LXB5fHw8bm5uOm1XCFGxSUulEEIYUIsWLTh9+jQ+Pj74+fnl+SmsYGvWrBkAZ86cKdE2GzRowF9//ZVn2V9//YW/vz9AbjH4cA/4hzvtFOfy5cukpaXRvHnzEuUnhKiYpKgUQggDmjx5MvHx8QwfPpx///2Xy5cv8+uvv/Lcc8+hVqsLfI+bmxstWrTI7W2tqxkzZvD111+zYsUKLl68yJIlS9i2bRuvv/46kH3r+oknnsjtgHPw4EHeeecdreP/8ccf1KpVi9q1a5coPyFExSRFpRBCGJCXlxd//fUXarWawMBAGjduzLRp03Bycsq9DV2QF154gfXr15domwMGDODTTz9l8eLFNGzYkFWrVrF27Vq6dOmSu86aNWvIysoiICCAadOm8f7772sdf+PGjUyYMKFEuQkhKi6ZUUcIIcqg+/fvU69ePTZv3kzbtm2NnU6u06dP061bNy5cuICjo6Ox0xFClCHSUimEEGWQtbU169atK3KQdGOIiopi3bp1UlAKIfKRlkohhBBCCFFq0lIphBBCCCFKTYpKIYQQQghRalJUCiGEEEKIUpOiUgghhBBClJoUlUIIIYQQotSkqBRCCCGEEKUmRaUQQgghhCg1KSqFEEIIIUSpSVEphBBCCCFK7f8B7DZYOghEiMgAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 750x250 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Battery parameters\n",
+ "cost_bat_power = 0.2\n",
+ "cost_bat_energy = 0.25\n",
+ "# External grid import price\n",
+ "cost_imp_energy = np.array([\n",
+ " 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, # 12:00 AM - 6:00 AM\n",
+ " 0.11, 0.13, 0.12, 0.11, 0.10, 0.10, 0.10, # 7:00 AM - 1:00 PM\n",
+ " 0.11, 0.12, 0.13, 0.14, 0.13, 0.12, 0.11, # 2:00 PM - 7:00 PM\n",
+ " 0.10, 0.09, 0.09 # 8:00 PM - 11:00 PM\n",
+ "])\n",
+ "\n",
+ "# Number of hours in the planning horizon\n",
+ "n_hours = 24\n",
+ "\n",
+ "# Plot import energy cost\n",
+ "plt.figure(figsize=(7.5, 2.5))\n",
+ "plt.plot(np.arange(n_hours), cost_imp_energy, marker='o')\n",
+ "plt.title('Hourly Electricity Prices')\n",
+ "plt.xlabel('Time (Hour)')\n",
+ "plt.ylabel('Electricity Price (USD/kWh)')\n",
+ "plt.xticks(np.arange(0, 24, step=1))\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 750x250 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load demand to be fulfilled by the energy management system\n",
+ "load_demand = np.array([\n",
+ " 200, 180, 170, 160, 150, 150, 170, 250, 320, 300, 280, 260, 270, 280, 290, \n",
+ " 300, 320, 350, 340, 330, 320, 280, 240, 220\n",
+ "])\n",
+ "\n",
+ "# Plot load demand\n",
+ "plt.figure(figsize=(7.5, 2.5))\n",
+ "plt.plot(np.arange(n_hours), load_demand, marker='o')\n",
+ "plt.title('Load Demand')\n",
+ "plt.xlabel('Time (Hour)')\n",
+ "plt.ylabel('Load Demand (kW)')\n",
+ "plt.xticks(np.arange(0, 24, step=1))\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Mixed Integer Linear Programming using the NAG Library**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the NAG Library for Python\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt, mip\n",
+ "\n",
+ "# Total number of variables in the model\n",
+ "# generators: 3 * 24 * 2\n",
+ "# battery: 24 + 2\n",
+ "# power import: 24\n",
+ "# pg + pim + cb + eb + pb + s \n",
+ "nvar = 3*24 + 24 + 1 + 1 + 24 + 3*24 \n",
+ "\n",
+ "# Create a problem handle to hold model data\n",
+ "handle = opt.handle_init(nvar=nvar)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The objective function which is the total operation cost of the utility is defined as\n",
+ "$$\n",
+ "\\min \\sum_{j=1}^{n\\_hours} (\\sum_{i=1}^{n\\_gen} cost_i(p_{ij}^g) + cost\\_imp\\_energy_j*p_{j}^{im}) + cost\\_bat\\_power * c^b + cost\\_bat\\_energy * e^b\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set objective coefficient\n",
+ "idxc = list(range(1, 3*24 + 24 + 1 + 1 + 1))\n",
+ "c = np.tile(spec_gen[:,0], 24)\n",
+ "c = np.concatenate((c, cost_imp_energy, [cost_bat_power, cost_bat_energy]))\n",
+ "\n",
+ "# Set model objective\n",
+ "opt.handle_set_quadobj(handle=handle, idxc=idxc, c=c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The load balance of every hour needs to be met, therefore we have the following power balance for $j$-th hour:\n",
+ "$$\n",
+ "\\sum_{i=1}^{n\\_gen} p^g_{ij} + p^b_j + p^{im}_j = load\\_demand_j.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add power balance constraint for each hour\n",
+ "for hour in range(n_hours):\n",
+ "\tirowa = np.full(5, 1, dtype=int)\n",
+ "\ticola = np.array([3*hour+1, 3*hour+2, 3*hour+3, 3*24+hour+1, 3*24+24+1+1+hour+1], dtype=int)\n",
+ "\ta = np.full(5, 1.0, dtype=float)\n",
+ "\tbl = np.full(1, load_demand[hour], dtype=float)\n",
+ "\tbu = np.full(1, load_demand[hour], dtype=float)\n",
+ "\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The generators' power output should be within the limits given as $pl$ and $pu$ in the generators specification, when they are online. Therefore the generators limits constraint for the $i$-th generator at the $j$-th hour is defined as\n",
+ "$$\n",
+ "pl_i*s_{ij} \\leq p^g_{ij} \\leq pu_i*s_{ij}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add generator limits constraint\n",
+ "for generator in range(n_gen):\n",
+ "\tfor hour in range(n_hours):\n",
+ "\t\tirowa = [1, 1, 2, 2]\n",
+ "\t\ticola = np.tile([3*24+24+1+1+24+hour*3+generator+1, hour*3+generator+1],2)\n",
+ "\t\ta = np.array([spec_gen[generator, 2], -1.0, spec_gen[generator, 3], -1.0], dtype=float)\n",
+ "\t\tbl = np.array([-1.e20, 0.])\n",
+ "\t\tbu = np.array([0., 1.e20])\n",
+ "\t\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each generator has a minimum up and down time constraint:\n",
+ "$$\n",
+ "\\sum_{t=j}^{j+ut-1} s_{it} \\geq ut*(s_{ij}-s_{ij-1}),\n",
+ "$$\n",
+ "$$\n",
+ "\\sum_{t=j}^{j+dt-1} (1 - s_{it}) \\geq dt*(s_{ij-1}-s_{ij}).\n",
+ "$$\n",
+ "Note we assume all generators have the same minimum up time and minimum down time for simplicity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add minimum up and down time constraints\n",
+ "ut = 8.0\n",
+ "dt = 6.0\n",
+ "s_start = 3*24 + 24 + 1 + 1 + 24\n",
+ "# minimum up time constraints\n",
+ "for generator in range(n_gen):\n",
+ "\tfor hour in range(1, n_hours):\n",
+ "\t\tirowa = np.array([],dtype=int)\n",
+ "\t\ticola = np.array([],dtype=int)\n",
+ "\t\ta = np.array([],dtype=float)\n",
+ "\t\tfor i in range(hour-1, min(hour+ 8, n_hours)):\n",
+ "\t\t\ticola = np.append(icola, s_start+i*3+generator+1)\n",
+ "\t\t\tirowa = np.append(irowa, 1)\n",
+ "\t\t\ta = np.append(a, 1.0)\n",
+ "\t\ta[0] = ut \n",
+ "\t\ta[1] = 1 - ut\n",
+ "\t\tbl = 0.0\n",
+ "\t\tbu = 1.e20\n",
+ "\t\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)\n",
+ "# minimum down time constraints\n",
+ "for generator in range(n_gen):\n",
+ "\tfor hour in range(1, n_hours):\n",
+ "\t\tirowa = np.array([],dtype=int)\n",
+ "\t\ticola = np.array([],dtype=int)\n",
+ "\t\ta = np.array([],dtype=float)\n",
+ "\t\tbu = -1.0\n",
+ "\t\tfor i in range(hour-1, min(hour+ 6, n_hours)):\n",
+ "\t\t\ticola = np.append(icola, s_start+i*3+generator+1)\n",
+ "\t\t\tirowa = np.append(irowa, 1)\n",
+ "\t\t\ta = np.append(a, 1.0)\n",
+ "\t\t\tbu += 1.0\n",
+ "\t\ta[0] = dt \n",
+ "\t\ta[1] = 1 - dt\n",
+ "\t\tbl = -1.e20\n",
+ "\t\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Battery power at any time cannot be off the limits. Therefore we define the following power rating limits constraint.\n",
+ "$$\n",
+ "-c^b \\leq p^b_j \\leq c^b.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add battery power limits constraints\n",
+ "for hour in range(n_hours):\n",
+ "\tirowa = [1, 1, 2, 2]\n",
+ "\ticola = [3*24+24+1+1+hour+1, 3*24+24+1, 3*24+24+1+1+hour+1, 3*24+24+1]\n",
+ "\ta = [1.0, -1.0, 1.0, 1.0]\n",
+ "\tbl = [-1.e20, 0.]\n",
+ "\tbu = [0., 1.e20]\n",
+ "\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Energy rating of the battery storage system follows\n",
+ "$$\n",
+ "-e^b \\leq \\sum_{j=1}^t p^b_j \\leq 0, for ~t = 1, \\ldots, n\\_hours.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add battery energy limits constraints\n",
+ "for hour in range(n_hours):\n",
+ "\t# rhs\n",
+ "\tirowa = np.array([],dtype=int)\n",
+ "\ticola = np.array([],dtype=int)\n",
+ "\ta = np.array([],dtype=float)\n",
+ "\tfor i in range(hour+1):\n",
+ "\t\ticola = np.append(icola, 3*24+24+1+1+i+1)\n",
+ "\t\tirowa = np.append(irowa, 1)\n",
+ "\t\ta = np.append(a, 1.0)\n",
+ "\tbl = -1.e20\n",
+ "\tbu = 0.\n",
+ "\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)\n",
+ "\t# lhs\n",
+ "\tirowa = np.array([1],dtype=int)\n",
+ "\ticola = np.array([3*24+24+1+1],dtype=int)\n",
+ "\ta = np.array([1.],dtype=float)\n",
+ "\tfor i in range(hour+1):\n",
+ "\t\ticola = np.append(icola, 3*24+24+1+1+i+1)\n",
+ "\t\tirowa = np.append(irowa, 1)\n",
+ "\t\ta = np.append(a, 1.0)\n",
+ "\tbl = 0.\n",
+ "\tbu = 1.e20\n",
+ "\topt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The imported power is limited to $15$ kW maximum.\n",
+ "$$\n",
+ "0 \\leq p^{im}_j \\leq 15.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add limits to the imported power\n",
+ "for i in range(n_hours):\n",
+ "\topt.handle_set_bound(handle=handle, comp='Var', idx=3*24+i+1, bli=0., bui=15.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Switches for the generators are binary\n",
+ "switches = np.arange(3*24+24+1+1+24+1, nvar+1)\n",
+ "opt.handle_set_property(handle=handle, ptype='Bin', idx=switches)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " H02BK, Solver for MILP problems\n",
+ " Status: converged, an optimal solution found\n",
+ " Final primal objective value 1.218345E+02\n",
+ " Final dual objective bound 1.218345E+02\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set options\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)\n",
+ "\n",
+ "# Solve the problem.\n",
+ "x, _, _ = mip.handle_solve_milp(handle, io_manager=iom)\n",
+ "\n",
+ "# Destroy the handle:\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can extract the optimal configuration from the solution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Power Rate Energy Rate\n",
+ "Battery 45.0 135.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generator schedule for 24 hours\n",
+ "switch1 = np.array([x for i, x in enumerate(x[-3*24:]) if i % 3 ==0])\n",
+ "switch2 = np.array([x for i, x in enumerate(x[-3*24:]) if i % 3 ==1])\n",
+ "switch3 = np.array([x for i, x in enumerate(x[-3*24:]) if i % 3 ==2])\n",
+ "# Generator energy for 24 hours\n",
+ "generator1 = np.array([x for i, x in enumerate(x[:3*24]) if i % 3 ==0])\n",
+ "generator2 = np.array([x for i, x in enumerate(x[:3*24]) if i % 3 ==1])\n",
+ "generator3 = np.array([x for i, x in enumerate(x[:3*24]) if i % 3 ==2])\n",
+ "# Power import\n",
+ "power_import = x[3*24 : 3*24+24]\n",
+ "battery_power_rate = x[3*24+24]\n",
+ "battery_energy_rate = x[3*24+24+1]\n",
+ "battery_power = x[3*24+24+1+1 : 3*24+24+1+1+24]\n",
+ "\n",
+ "df = pd.DataFrame(\n",
+ "\t{\n",
+ "\t'Battery':[battery_power_rate, battery_energy_rate],\n",
+ "\t}\n",
+ ")\n",
+ "df.index=['Power Rate','Energy Rate']\n",
+ "print(df.T.to_string())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model gives the optimal battery power rate as $45$ kW and optimal energy rate as $135$ kWh. We can plot the battery discharging schedule and energy import schedule as below. It's shown that the battery charges itself during off-peak time and dispatches during peak hours to mitigate the need of importing external energy. The imported energy is kept well under $15$ kW and only happens during the second peak."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 750x250 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot dispatching from battery energy storage system\n",
+ "plt.figure(figsize=(7.5, 2.5))\n",
+ "plt.plot(np.arange(1, n_hours+1), battery_power, marker='o', linestyle='-')\n",
+ "plt.title('Battery Dispatching Power for 24 Hours')\n",
+ "plt.xlabel('Time (hours)')\n",
+ "plt.ylabel('Power (kW)')\n",
+ "plt.xticks(np.arange(0, 25, step=1))\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 750x250 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot power import from external grid\n",
+ "plt.figure(figsize=(7.5, 2.5))\n",
+ "plt.plot(np.arange(1, n_hours+1), power_import, marker='o', linestyle='-')\n",
+ "plt.title('Import Power for 24 Hours')\n",
+ "plt.xlabel('Time (hours)')\n",
+ "plt.ylabel('Power (kW)')\n",
+ "plt.xticks(np.arange(0, 25, step=1))\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Conclusion**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, we illustrated how to use the MILP solver featured in the NAG Optimization Modelling Suite from the NAG Library to model a microgrid and design it's battery energy storage system capacity. The model is also able to give operational schedules for various components in the microgrid. The model can be easily extended with additional components, such as additional power source from solar panels or wind farms and more switchable loads. The [NAG MILP solver](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.mip.handle_solve_milp.html) serves as a powerful tool for modelling and managing energy systems. Learn more about it [here](https://nag.com/mixed-integer-linear-programming/). "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.17"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/local_optimization/MILP/Grid_Model.png b/local_optimization/MILP/Grid_Model.png
new file mode 100644
index 0000000..abe05d1
Binary files /dev/null and b/local_optimization/MILP/Grid_Model.png differ
diff --git a/local_optimization/MILP/portfolio_optimization_using_milp.ipynb b/local_optimization/MILP/portfolio_optimization_using_milp.ipynb
new file mode 100644
index 0000000..b2e596c
--- /dev/null
+++ b/local_optimization/MILP/portfolio_optimization_using_milp.ipynb
@@ -0,0 +1,580 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "cac8a62f-1a60-440e-a4db-616c55986648",
+ "metadata": {},
+ "source": [
+ "## Installing the NAG Library and running this notebook\n",
+ "To run this notebook, you will need to install the NAG Library for Python (Mark 29.3 or newer) and a license key. You can find the software and obtain a license key (trials are available) from [Getting Started with the NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linuxto).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f120907d-0410-4d07-aec3-b0163e539031",
+ "metadata": {},
+ "source": [
+ "# Portfolio optimization with MILP using the NAG Library\n",
+ "\n",
+ "A mixed integer linear programming (MILP) model is an extension to a linear programming model where some or all of the variables are constrained to be integer. The ability to handle integer variables makes this an extremely powerful tool with applications in a huge number of industries. Here, we will consider a MILP model of an optimal mean/Value-at-Risk (VaR) portfolio optimization problem by [Benati and Rizzi (2007)](#References). This is an extension to a classic Markowitz model in which the aim is to maximize returns while minimizing risk. However, in this instance, the variance risk measure has been replaced by VaR.\n",
+ "\n",
+ "VaR is a widely used risk measure in portfolio management: it quantifies the maximum potential loss within a specified confidence level over a defined time frame. Specifically, VaR is simply the $\\alpha$-quantile of the return distribution function. Unlike other risk measures such as standard deviation or expected shortfall, VaR provides a clear and intuitive assessment of downside risk, making it a preferred choice for investors concerned with the probability of experiencing significant losses. While the mathematical properties of VaR are slightly unappealing (it is a piece-wise linear function and not convex), simplicity in its interpretation makes VaR a popular tool nonetheless.\n",
+ "\n",
+ "In this instance, VaR is calculated using a non-parametric method - this means that no assumptions are made about the distribution of portfolio returns. Instead of fitting a parametric model (e.g. normal or student-t distribution) to historical data, which can be restrictive, we use historical simulation. This method reorganizes historical return data, putting it in order of worst to best. The $\\alpha$-quantile is then estimated by the position of the observation that has the $\\alpha$-percent of data on the left.\n",
+ "\n",
+ "In this notebook, we solve the \"Min Risk/Fixed Return\" implementation of this problem. This problem contains VaR constraints, cardinality constraints, semicontinuous constraints, a minimum return constraint, a budget (full investment) constraint, and long-only constraints."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6290f139-6198-4657-8407-0c134b3e7ff1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import modules\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt, mip\n",
+ "import numpy as np\n",
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "946f315c-92cb-4d53-a187-2123f45509b4",
+ "metadata": {},
+ "source": [
+ "For this model, we need to set some parameters. \n",
+ "\n",
+ "We have past observations $i \\in I=\\{1,\\dots,T\\}$ and assets $j \\in J=\\{1,\\dots,K\\}$.\n",
+ "\n",
+ "We set $r^*$ to be the minimum expected return that will be accepted and $r^{\\textrm{Min}}$ is the minimum return that can be observed in the market. We also choose $r^{\\textrm{VaR}}$: a parameter set by the decision maker to control risk. They will only accept portfolios for which the probability of a return less than $r^{\\textrm{VaR}}$ is less than or equal to $\\alpha^{\\textrm{VaR}}$. Further, we have probabilities $p_i$ associated with each observation $x_i$. These probabilities represent the occurrence of past realization $i$. For illustrative purposes, we set each observation to have equal probability and we synthetically generate returns data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4dc120e8-9e78-45a6-846c-12a90658f69c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set parameters\n",
+ "n_assets = 300 # K\n",
+ "n_periods = 20 # T\n",
+ "r_star = 0.05\n",
+ "r_min = -1\n",
+ "r_var = 0.05\n",
+ "prob = 1/n_periods\n",
+ "\n",
+ "# Synthetic data generation of expected returns for each asset\n",
+ "np.random.seed(0)\n",
+ "expected_returns = 0.25 * np.random.randn(n_periods, n_assets) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26a04836-73b7-4150-b510-3f6a1753afcc",
+ "metadata": {},
+ "source": [
+ "Next, we define the variables used in the model:\n",
+ "* Variables $\\lambda_j$ are the percentage of wealth that is allocated to asset $j$.\n",
+ "* Variables $x_i$ represent the portfolio observed return in time $i$.\n",
+ "* Variables $y_i$ are binary (associated with $x_i$) and used for modelling the VaR constraints.\n",
+ "* Variables $z_j$ are binary (associated with $\\lambda_j$) and used for modelling the cardinality and semicontinuous constraints.\n",
+ "* $\\alpha^{\\textrm{VaR}}$ is the probability associated with VaR."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4b81e7b1-aac9-49a0-8c06-bef34f503409",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# There are asset_weights[n_assets] + binary_y[n_periods] + observed_return[n_periods] + binary_z[n_assets] + aVaR[1]\n",
+ "n_vars = n_assets + n_periods + n_periods + n_assets + 1\n",
+ "\n",
+ "# Create index sets for each set of variables:\n",
+ "idx_asset_w = np.arange(1, n_assets+1, dtype=int)\n",
+ "idx_bin_y = np.arange(n_assets+1, n_assets+n_periods+1, dtype=int)\n",
+ "idx_obs_ret = np.arange(n_assets+n_periods+1, n_assets+2*n_periods+1, dtype=int)\n",
+ "idx_bin_z = np.arange(n_assets+2*n_periods+1, 2*n_assets+2*n_periods+1, dtype=int)\n",
+ "idx_avar = [n_vars]\n",
+ "\n",
+ "# Initialize the problem handle:\n",
+ "handle = opt.handle_init(nvar=n_vars)\n",
+ "\n",
+ "# Set binary variables:\n",
+ "opt.handle_set_property(handle=handle, ptype='Bin', idx=idx_bin_y)\n",
+ "opt.handle_set_property(handle=handle, ptype='Bin', idx=idx_bin_z)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10f71de2-da9b-4bf5-817d-576956196ead",
+ "metadata": {},
+ "source": [
+ "## Constraints:\n",
+ "Since this is a MILP model, we set all linear constraints using [***handle_set_linconstr()***](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_set_linconstr.html).\n",
+ "\n",
+ "The first constraint that we implement enforces that the optimal portfolio should be greater than the **minimum acceptable portfolio expected return**:\n",
+ "$$\\sum_{i=1}^{T} p_i x_i \\geq r^*.$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0cc5b36d-0ed7-4925-acff-a07cda854dcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ones_periods = np.ones(n_periods, dtype=int)\n",
+ "opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=r_star,\n",
+ " bu=1.e20,\n",
+ " irowb=ones_periods,\n",
+ " icolb=idx_obs_ret,\n",
+ " b=[prob]*n_periods\n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ea59af3-a465-4dae-a732-1878d401e892",
+ "metadata": {},
+ "source": [
+ "Next, we need to enforce that $x_i$ is the **result of the percentage of wealth allocated to each asset and the expected return of that asset** for each time period:\n",
+ "\n",
+ "$$\\sum_{j=1}^{K} \\lambda_j r_{ij} = x_i \\quad \\forall \\, i=1,\\dots,T.$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2a523e8-9a71-4e24-979d-dd07243a1fbf",
+ "metadata": {},
+ "source": [
+ "This requires slight rearrangement to be input into the model - we stack the variables $\\lambda$ and $x$ and express the rearrangement in matrix notation:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\begin{bmatrix}\n",
+ "r_{i1} &\n",
+ "\\dots &\n",
+ "r_{iK} &\n",
+ "-1\n",
+ "\\end{bmatrix}\n",
+ "\\begin{bmatrix}\n",
+ "\\lambda_1 \\\\\n",
+ "\\vdots \\\\\n",
+ "\\lambda_K \\\\\n",
+ "x_i\n",
+ "\\end{bmatrix}\n",
+ "= 0 \\quad \\forall \\, i = 1,\\dots,T.\n",
+ "\\end{equation*}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8dbfe45d-cd8d-4ac5-a923-2c62c4b0d296",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ones_assets_1 = np.ones(n_assets+1, dtype=int)\n",
+ "for i in range(n_periods):\n",
+ " i_obs_ret = n_assets + n_periods + 1 + i\n",
+ " returns = expected_returns[i, :]\n",
+ " returns_x = np.append(returns, -1.)\n",
+ " idx_col = np.append(idx_asset_w, i_obs_ret)\n",
+ " opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=0.,\n",
+ " bu=0.,\n",
+ " irowb=ones_assets_1,\n",
+ " icolb=idx_col,\n",
+ " b=returns_x\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09ef6f86-37f9-4c81-a4e9-9114d9560cee",
+ "metadata": {},
+ "source": [
+ "Next, we want to add constraints that **prevent the selection of portfolios with VaR below the threshold**. To do this, we introduce binary variables $y_i$ associated with each $x_i$. Then the VaR constraint can be modelled in the following way:\n",
+ "\n",
+ "$$r^{\\textrm{Min}} + (r^{\\textrm{VaR}} - r^{\\textrm{Min}})y_i \\leq x_i \\quad \\forall \\, i=1,\\dots,T,$$\n",
+ "$$\\sum_{i=1}^{T} p_i(1-y_i) \\leq \\alpha^{\\textrm{VaR}}.$$\n",
+ "\n",
+ "The first constraint enforces that $y_i$ is equal to $0$ for $x_i$ less than $r^{\\textrm{VaR}}$. In the second constraint, this corresponds to $1 - y_i = 1 - 0 = 1$, leading to the summation of probabilities of time periods $i$ with returns less than the VaR threshold. If this probability is greater than $\\alpha^{\\textrm{VaR}}$, it results in an infeasible portfolio.\n",
+ "\n",
+ "Rearranged into matrix notation, the first constraint becomes:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\begin{bmatrix}\n",
+ "(r^{\\textrm{Min}}-r^{\\textrm{VaR}}) &\n",
+ "1\n",
+ "\\end{bmatrix}\n",
+ "\\begin{bmatrix}\n",
+ "y_i \\\\\n",
+ "x_i\n",
+ "\\end{bmatrix}\n",
+ "\\geq r^{\\textrm{Min}} \\quad \\forall \\, i = 1,\\dots,T,\n",
+ "\\end{equation*}\n",
+ "\n",
+ "and the second constraint becomes:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\begin{bmatrix}\n",
+ "p_{1} &\n",
+ "\\dots &\n",
+ "p_{T} &\n",
+ "1\n",
+ "\\end{bmatrix}\n",
+ "\\begin{bmatrix}\n",
+ "y_1 \\\\\n",
+ "\\vdots \\\\\n",
+ "y_T \\\\\n",
+ "\\alpha^{\\textrm{VaR}}\n",
+ "\\end{bmatrix}\n",
+ "\\geq 1.\n",
+ "\\end{equation*}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "132124cd-faec-4f3a-9efa-3b8ec5e04b29",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_coef = r_min - r_var\n",
+ "for i in range(n_periods):\n",
+ " y_i = n_assets + 1 + i\n",
+ " x_i = n_assets + n_periods + 1 + i \n",
+ " opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=r_min,\n",
+ " bu=1.e20,\n",
+ " irowb=[1, 1],\n",
+ " icolb=[y_i, x_i],\n",
+ " b=[y_coef, 1.]\n",
+ " )\n",
+ "\n",
+ "ones_periods_1 = np.ones(n_periods+1, dtype=int)\n",
+ "idx_bin_avar = np.append(idx_bin_y, idx_avar)\n",
+ "opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=1.0,\n",
+ " bu=1.e20,\n",
+ " irowb=ones_periods_1,\n",
+ " icolb=idx_bin_avar,\n",
+ " b=np.append([prob]*n_periods, 1.)\n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e1a63193-f373-4d39-a32d-d371b832b27b",
+ "metadata": {},
+ "source": [
+ "**Full investment** constraint:\n",
+ "\n",
+ "$$\\sum_{j=1}^{K} \\lambda_j = 1.$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0efec5b9-1b3a-4d83-9791-65942f7fa985",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ones_assets_float = np.ones(n_assets, dtype=float)\n",
+ "ones_assets = np.ones(n_assets, dtype=int)\n",
+ "opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=1.0,\n",
+ " bu=1.0,\n",
+ " irowb=ones_assets,\n",
+ " icolb=idx_asset_w,\n",
+ " b=ones_assets_float\n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3577eb0-3492-4921-9c93-de327fb41b62",
+ "metadata": {},
+ "source": [
+ "Next, we want to add **semicontinuous constraints** to confine the optimal allocation of the assets to be between $5\\%$ and $70\\%$:\n",
+ "$$\\lambda_j \\in 0 \\cup [0.05, 0.7] \\quad \\forall \\, j = 1,\\dots, K.$$\n",
+ "\n",
+ "Using binary variables $z_j$, the semicontinuous constraint can be expressed as follows:\n",
+ "\n",
+ "$$0.05 \\cdot z_j \\leq \\lambda_j \\leq 0.7 \\cdot z_j \\quad \\forall \\, j=1,\\dots, K.$$\n",
+ "\n",
+ "These constraints enforce that if $z_j$ is equal to $1$, then $\\lambda_j$ can take any value in the interval between $0.05$ and $0.7$. When $z_j$ equals $0$, then $\\lambda_j$ must also equal $0$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cc09f5a8-b2da-443c-8026-2a88ad2c4ea8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for j in range(n_assets):\n",
+ " lambda_j = 1 + j\n",
+ " z_j = n_assets + 2*n_periods + 1 + j\n",
+ " opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=-1.e20,\n",
+ " bu=0.0,\n",
+ " irowb=[1, 1],\n",
+ " icolb=[lambda_j, z_j],\n",
+ " b=[1., -0.7]\n",
+ " )\n",
+ " \n",
+ " opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=0.0,\n",
+ " bu=1.e20,\n",
+ " irowb=[1, 1],\n",
+ " icolb=[lambda_j, z_j],\n",
+ " b=[1., -0.05]\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "934646d5-57e8-43f2-adb6-828f17473105",
+ "metadata": {},
+ "source": [
+ "Next, we want $10\\%$ of the assets to be included in the optimal portfolio, so we add a **cardinality constraint**:\n",
+ "$$||\\lambda||_0 \\leq 0.1 \\cdot K.$$\n",
+ "\n",
+ "This can be modelled by adding a binary variable $z_j$ for each $\\lambda_j$. When $z_j$ is equal to $0$, this indicates that asset $\\lambda_j$ is not allocated; when $z_j$ is equal to $1$, asset $\\lambda_j$ has been allocated. Then the cardinality constraint can be modelled by summing the binary variables:\n",
+ "\n",
+ "$$\\sum_{j=1}^K z_j \\leq 0.1 \\cdot K.$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "bea9b819-077e-4075-a954-2fcf3357a197",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt.handle_set_linconstr(\n",
+ " handle=handle,\n",
+ " bl=-1.e20,\n",
+ " bu=0.1*n_assets,\n",
+ " irowb=ones_assets,\n",
+ " icolb=idx_bin_z,\n",
+ " b=ones_assets_float\n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810bba10-a1aa-4cd4-95d5-9b0d968495be",
+ "metadata": {},
+ "source": [
+ "Bound constraints to **prevent short-selling**:\n",
+ "\n",
+ "$$\\lambda_j \\geq 0 \\quad \\forall \\, j=1,\\dots,K.$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "afb2b374-e6c6-4f1d-af3e-b69ace80ae57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set bound constraints:\n",
+ "zeros_asset_w = np.zeros(n_assets)\n",
+ "neg_lrg_bnd = np.full((n_periods + n_periods + n_assets + 1), -1.e20, dtype=float).ravel()\n",
+ "bl = np.hstack([zeros_asset_w, neg_lrg_bnd])\n",
+ "opt.handle_set_simplebounds(\n",
+ " handle,\n",
+ " bl=bl,\n",
+ " bu=[1.e20]*n_vars,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70f84e9d-b236-4342-9999-d3bf1b226488",
+ "metadata": {},
+ "source": [
+ "## Objective function:\n",
+ "We want to minimize risk:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\min_{\\alpha^{\\textrm{VaR}},\\lambda,x,y} \\alpha^{\\textrm{VaR}}\n",
+ "\\end{equation*}\n",
+ "\n",
+ "\n",
+ "Even though the objective function is linear, we use [***handle_set_quadobj()***](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_set_quadobj.html) because it is sparse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "07fa779f-a9f6-4aa0-a187-3d088cad238b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set the objective function:\n",
+ "opt.handle_set_quadobj(\n",
+ " handle,\n",
+ " idxc=idx_avar,\n",
+ " c=[1.0],\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bfe7a7c-ad79-491f-918d-3d23a30172d5",
+ "metadata": {},
+ "source": [
+ "## Optimal mean/Value-at-Risk model\n",
+ "We can now specify the full model and solve it using the [**MILP solver**](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.mip.handle_solve_milp.html).\n",
+ "\n",
+ "\\begin{equation*}\n",
+ " \\begin{aligned}\n",
+ " &\\min_{\\alpha^{\\textrm{VaR}},\\lambda,x,y,z} &&\\alpha^{\\textrm{VaR}}\n",
+ " \\\\\n",
+ " &\\textrm{subject to } &&\\sum_{i=1}^{T} p_i x_i \\geq r^*,\n",
+ " \\\\\n",
+ " & &&\\sum_{j=1}^{K} \\lambda_j r_{ij} = x_i \\quad \\forall \\, i=1,\\dots,T,\n",
+ " \\\\\n",
+ " & &&r^{\\textrm{Min}} + (r^{\\textrm{VaR}} - r^{\\textrm{Min}})y_i \\leq x_i \\quad \\forall \\, i=1,\\dots,T,\n",
+ " \\\\\n",
+ " & &&\\sum_{i=1}^{T} p_i(1-y_i) \\leq \\alpha^{\\textrm{VaR}},\n",
+ " \\\\\n",
+ " & &&\\sum_{j=1}^{K} \\lambda_j = 1,\n",
+ " \\\\\n",
+ " & &&\\sum_{j=1}^K z_j \\leq 0.1 \\cdot K,\n",
+ " \\\\\n",
+ " & && 0.05 \\cdot z_j \\leq \\lambda_j \\leq 0.7 \\cdot z_j \\quad \\forall \\, j = 1,\\dots, K,\n",
+ " \\\\\n",
+ " & &&y_i \\in \\{0,1\\} \\quad \\forall \\, i=1,\\dots,T,\n",
+ " \\\\\n",
+ " & &&z_j \\in \\{0,1\\} \\quad \\forall \\, j=1,\\dots,K,\n",
+ " \\\\\n",
+ " & &&\\lambda_j \\geq 0 \\quad \\forall \\, j=1,\\dots,K.\n",
+ " \\end{aligned}\n",
+ "\\end{equation*}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "2cbeef24-1946-4718-8d37-20c0adbecd51",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " H02BK, Solver for MILP problems\n",
+ " Begin of Options\n",
+ " Print File = 9 * d\n",
+ " Print Level = 1 * U\n",
+ " Print Options = Yes * d\n",
+ " Print Solution = No * d\n",
+ " Monitoring File = -1 * d\n",
+ " Monitoring Level = 4 * d\n",
+ "\n",
+ " Infinite Bound Size = 1.00000E+20 * d\n",
+ " Task = Minimize * d\n",
+ " Time Limit = 1.00000E+06 * d\n",
+ "\n",
+ " Milp Presolve = Yes * d\n",
+ " Milp Random Seed = 0 * d\n",
+ " Milp Feasibility Tol = 1.00000E-06 * d\n",
+ " Milp Rel Gap = 1.00000E-04 * d\n",
+ " Milp Abs Gap = 1.00000E-06 * d\n",
+ " Milp Small Matrix Value = 1.00000E-09 * d\n",
+ " Milp Detect Symmetry = Yes * d\n",
+ " Milp Max Nodes = 2147483647 * d\n",
+ " End of Options\n",
+ "\n",
+ " Status: converged, an optimal solution found\n",
+ " Final primal objective value -5.551115E-17\n",
+ " Final dual objective bound 0.000000E+00\n",
+ "\n",
+ " Computation time: 0.405 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set some algorithmic options:\n",
+ "for option in [\n",
+ " 'Print Level = 1',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)\n",
+ "\n",
+ "# Start timer:\n",
+ "start_solve = time.time()\n",
+ "\n",
+ "# Call the MILP solver:\n",
+ "mip.handle_solve_milp(handle, io_manager=iom)\n",
+ "\n",
+ "# Print computation time:\n",
+ "end = time.time()\n",
+ "print(f\"\\n Computation time: {end-start_solve:.3f} seconds\")\n",
+ "\n",
+ "# Destroy the handle:\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93760299-5a24-44e0-8c4c-3472b207c25e",
+ "metadata": {},
+ "source": [
+ "In this instance, we solved a model with $641$ variables ($320$ of which were binary) and $644$ constraints. This was efficiently solved in $0.41$ seconds.\n",
+ "\n",
+ "Using MILP allows the modelling of complex constraints, adding flexibility and sophistication to your problem. The MILP solver used in this portfolio optimization example is available in the NAG Library Optimization Modelling Suite. Learn more about it [here](https://nag.com/mixed-integer-linear-programming/). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "def14a4d-181f-4b62-a7c9-81a82f0dbc17",
+ "metadata": {},
+ "source": [
+ "## References\n",
+ "\n",
+ "Benati, S., & Rizzi, R. (2007). A mixed integer linear programming formulation of the optimal mean/value-at-risk portfolio problem. *European Journal of Operational Research*, *176*(1), 423-434."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/local_optimization/LP_demo.ipynb b/local_optimization/Modelling/LP_demo.ipynb
similarity index 77%
rename from local_optimization/LP_demo.ipynb
rename to local_optimization/Modelling/LP_demo.ipynb
index 7aec8b5..e0e6b4c 100644
--- a/local_optimization/LP_demo.ipynb
+++ b/local_optimization/Modelling/LP_demo.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -187,40 +198,39 @@
" Lpipm System Formulation = Auto * d\n",
" End of Options\n",
"\n",
- " Original Problem Statistics\n",
+ " Problem Statistics\n",
+ " No of variables 5\n",
+ " free (unconstrained) 0\n",
+ " bounded 5\n",
+ " No of lin. constraints 4\n",
+ " nonzeroes 12\n",
+ " Objective function Linear\n",
"\n",
- " Number of variables 5\n",
- " Number of constraints 4\n",
- " Free variables 0\n",
- " Number of nonzeros 12\n",
- "\n",
- "\n",
- " Presolved Problem Statistics\n",
- "\n",
- " Number of variables 9\n",
- " Number of constraints 4\n",
- " Free variables 0\n",
- " Number of nonzeros 16\n",
+ " Presolved Problem Measures\n",
+ " No of variables 9\n",
+ " free (unconstrained) 0\n",
+ " No of lin. constraints 4\n",
+ " nonzeroes 16\n",
"\n",
"\n",
" ------------------------------------------------------------------------------\n",
" it| pobj | dobj | optim | feas | compl | mu | mcc | I\n",
" ------------------------------------------------------------------------------\n",
- " 0 2.95739E-01 -2.29831E-17 2.38E+00 1.58E-01 2.38E-01 2.9E-01\n",
+ " 0 2.95739E-01 -1.53220E-17 2.38E+00 1.58E-01 2.38E-01 2.9E-01\n",
" 1 3.82786E-02 -2.79688E-01 3.71E-02 6.98E-17 1.91E-02 2.0E-02 0\n",
" 2 1.00015E-02 -5.05058E-03 1.88E-03 1.29E-14 9.30E-04 9.4E-04 0\n",
- " 3 1.78344E-05 -2.39954E-05 1.86E-16 1.48E-15 2.19E-06 2.2E-06 0\n",
- " 4 8.91734E-09 -1.19978E-08 8.80E-17 2.79E-16 1.09E-09 1.1E-09 0\n",
- " 5 4.45867E-12 -5.99890E-12 8.35E-17 4.48E-16 5.46E-13 5.5E-13 0\n",
+ " 3 1.78344E-05 -2.39954E-05 1.83E-16 1.48E-15 2.19E-06 2.2E-06 0\n",
+ " 4 8.91734E-09 -1.19978E-08 9.23E-17 2.78E-16 1.09E-09 1.1E-09 0\n",
+ " 5 4.45867E-12 -5.99890E-12 9.02E-17 5.83E-17 5.46E-13 5.5E-13 0\n",
" ------------------------------------------------------------------------------\n",
" Status: converged, an optimal solution found\n",
" ------------------------------------------------------------------------------\n",
" Final primal objective value 4.458673E-12\n",
" Final dual objective value -5.998898E-12\n",
- " Absolute primal infeasibility 2.355139E-16\n",
- " Relative primal infeasibility 8.352537E-17\n",
- " Absolute dual infeasibility 7.761642E-16\n",
- " Relative dual infeasibility 4.476207E-16\n",
+ " Absolute primal infeasibility 2.543841E-16\n",
+ " Relative primal infeasibility 5.826594E-17\n",
+ " Absolute dual infeasibility 1.010318E-16\n",
+ " Relative dual infeasibility 9.021771E-17\n",
" Absolute complementarity gap 2.464120E-12\n",
" Relative complementarity gap 5.464522E-13\n",
" Iterations 5\n"
@@ -254,7 +264,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -268,7 +278,25 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
}
},
"nbformat": 4,
diff --git a/local_optimization/Modelling/Readme.md b/local_optimization/Modelling/Readme.md
new file mode 100644
index 0000000..0f04ca4
--- /dev/null
+++ b/local_optimization/Modelling/Readme.md
@@ -0,0 +1,58 @@
+[](https://www.nag.com)
+
+# Dense Problem Examples
+Python examples for dense problems
+
+* [Linear Programming (LP)](dense_lp_solve_ex.py)
+* [Quadratic Programming (QP)](dense_qp_solve_ex.py)
+
+# Modelling Optimization Problems
+
+In this folder you will find notebook examples, tips and tricks related to modeling optimization problems.
+
+* **Christmas Special** [How to decorate your Christmas tree (Notebook)](christmas_demo.ipynb)
+ or view the [Blog-post](https://www.nag.com/blog/optcorner-christmas-edition).
+<table><tr>
+
+<td><a href="christmas_demo.ipynb">
+<img src="../images/xmas_tree.png"
+width="100" height="100px" alt="Xmas Tree Plot"/></a></td>
+
+<td valign="top">See how optimization can help to decorate a Christmas tree...</td>
+</tr></table>
+
+
+* **Demo** [Linear Programming (LP)](LP_demo.ipynb).
+
+ Tiny and Cute LP problem...
+* **Demo** [Nonlinear calibration (data fitting)](handle_disable_ex.ipynb).
+<table><tr>
+<td><a href="handle_disable_ex.ipynb">
+<img src="../images/nlls.png"
+width="100" height="80px" alt="Data fitting Plot"/></a></td>
+
+<td valign="top">This demo shows useful features of the <a href="https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#optsuite">NAG Optimization Modelling Suite (NOMS)</a> in a Nonlinear Least-squares problem.</td>
+
+</tr></table>
+
+
+* **Demo** [Production Planning](production_planning.ipynb).
+
+ Optimal production planning showcasing features of the [NAG Optimization Modelling Suite (NOMS)](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#optsuite).
+
+# Useful Links
+* [Background to Optimization Methods](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#algorithms)
+* [NAG Insights](https://nag.com/insights/)
+
+<!-- When ready add links?
+* Blog-post: Introducing the NAG Optimization Modelling Suite (NOMS)
+* The right tool for the job I – matching problem with optimizer
+* Blog-post: The right tool for the job II - dense vs. sparse
+--->
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
diff --git a/local_optimization/christmas_demo.ipynb b/local_optimization/Modelling/christmas_demo.ipynb
similarity index 98%
rename from local_optimization/christmas_demo.ipynb
rename to local_optimization/Modelling/christmas_demo.ipynb
index 7ca80a6..4ecae93 100644
--- a/local_optimization/christmas_demo.ipynb
+++ b/local_optimization/Modelling/christmas_demo.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -79,7 +90,7 @@
"metadata": {},
"outputs": [],
"source": [
- "def plot_domain(orig,lb,ub,cm, axis, ax):\n",
+ "def plot_domain(orig,lb,ub,_cm, axis, ax):\n",
" xmax = orig - lb\n",
" X, Y = np.meshgrid(np.arange(-xmax,xmax,0.01), np.arange(lb,orig,0.01))\n",
" Z = objf_plot(X,Y)\n",
@@ -218,12 +229,12 @@
"outputs": [],
"source": [
"# main callback function for the SQP solver\n",
- "def objfun_e04wd(mode, x, grad, ntsate, data):\n",
- " if mode == 0 or mode == 2:\n",
+ "def objfun_e04wd(mode, x, grad, _nstate, data):\n",
+ " if mode in (0, 2):\n",
" objf = x[0]**2 + x[0] + np.cos(2*np.pi*x[1])**2 + 0.5*x[1]\n",
" data.npts += 1\n",
" data.pts.append((x[0], x[1]))\n",
- " if mode == 1 or mode == 2:\n",
+ " if mode in (1, 2):\n",
" grad[0] = 2*x[0] + 1\n",
" grad[1] = -4*np.pi*np.sin(2*np.pi*x[1])*np.cos(2*np.pi*x[1]) + 0.5\n",
" return mode, objf, grad"
@@ -242,7 +253,7 @@
"ncnln = 0\n",
"a = [[1.,1.],[-1.,1.]]\n",
"bl = [-bigbnd,5,-bigbnd,-bigbnd]\n",
- "bu = [bigbnd,bigbnd,6,6];\n",
+ "bu = [bigbnd,bigbnd,6,6]\n",
"\n",
"# Initialize the rest of the solver variables: we don't need warm starting information or 2nd derivative information\n",
"istate = [0,0,0,0]\n",
@@ -367,9 +378,9 @@
"outputs": [],
"source": [
"# Main callback function for the MINLP solver\n",
- "def objfun_h02da(mode, varcon, x, objgrd, nstate, data):\n",
- " objmip = x[0]**2 + x[0] + np.cos(2*np.pi*x[1])**2 + 0.5*x[1];\n",
- " data.npts = data.npts + 1;\n",
+ "def objfun_h02da(mode, _varcon, x, objgrd, _nstate, data):\n",
+ " objmip = x[0]**2 + x[0] + np.cos(2*np.pi*x[1])**2 + 0.5*x[1]\n",
+ " data.npts = data.npts + 1\n",
" data.pts.append((x[0], x[1]))\n",
" if x[1] <= 1.8 and data.present:\n",
" objmip = (x[0]+0.625)**2 + (x[1]-1.55)**2\n",
@@ -405,7 +416,7 @@
"varcon = [0,0,1,1,4,4,4,4,4,4,4]\n",
"\n",
"# Choose a starting point and initialize the solver and user data\n",
- "xstart = [0.5,4.5,0.,0.];\n",
+ "xstart = [0.5,4.5,0.,0.]\n",
"x = xstart\n",
"comm = {}\n",
"data = usr_data()\n",
@@ -588,7 +599,25 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
}
},
"nbformat": 4,
diff --git a/local_optimization/Modelling/dense_lp_solve_ex.py b/local_optimization/Modelling/dense_lp_solve_ex.py
new file mode 100644
index 0000000..049546f
--- /dev/null
+++ b/local_optimization/Modelling/dense_lp_solve_ex.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+
+# Example to solve dense LP problem using (e04mf/lp_solve)
+
+# Solve the problem
+# min cvec^T * x
+# subject to bl <= A * x <= bu
+# and if present: blx <= x <= bux
+
+from naginterfaces.library import opt
+from naginterfaces.base import utils
+import numpy as np
+
+print('naginterfaces.library.opt.lp_solve Python Example Results.')
+print('Solve a small dense LP problem.')
+
+infty = 1.0E+25
+
+# Problem size
+n = 7
+
+# Number of linear constraints
+nclin = 7
+
+# Cost vector
+cvec = np.array([-0.02, -0.20, -0.20, -0.20, -0.20, 0.04, 0.04])
+
+# Block of linear constraints (Matrix A)
+a = np.array([
+[ 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,],
+[ 0.15, 0.04, 0.02, 0.04, 0.02, 0.01, 0.03,],
+[ 0.03, 0.05, 0.08, 0.02, 0.06, 0.01, 0.00,],
+[ 0.02, 0.04, 0.01, 0.02, 0.02, 0.00, 0.00,],
+[ 0.02, 0.03, 0.00, 0.00, 0.01, 0.00, 0.00,],
+[ 0.70, 0.75, 0.80, 0.75, 0.80, 0.97, 0.00,],
+[ 0.02, 0.06, 0.08, 0.12, 0.02, 0.01, 0.97 ] ])
+
+# Bounds on the linear constraints
+bl = np.array([-0.01, -0.10,-0.01,-0.04,-0.10, -0.01, -0.01, -0.13, -infty, -infty, -infty, -infty, -9.92E-2,-3.0E-3])
+bu = np.array([ 0.01, 0.15, 0.03, 0.02, 0.05, infty, infty, -0.13, -4.9E-3, -6.4E-3, -3.7E-3, -1.2E-3, infty, 2.0E-3])
+
+# Initial Guess
+x = np.array([-0.01, -0.03, 0.00, -0.01, -0.10, 0.02, 0.01])
+
+# Init solver
+comm = opt.nlp1_init('lp_solve')
+
+# Increase printing verbosity
+opt.lp_option_string('Print Level = 2', comm)
+
+# Defile io manager
+iom = utils.FileObjManager(locus_in_output=False)
+
+# Solve the problem
+ret = opt.lp_solve(bl, bu, x, comm, a=a, cvec=cvec, istate=None, io_manager=iom)
+
+# Report the solution and Lagrange multipliers if solved correctly
+print('id V Value Lagr Mult')
+for id in range(n):
+ print('{:5} {: 9.3e} {: 9.3e}'.format(id+1, ret.x[id], ret.clamda[id]))
+
+print('id LC Value Lagr Mult')
+for id in range(nclin):
+ print('{:5} {: 9.3e} {: 9.3e}'.format(n+id+1, ret.ax[id], ret.clamda[n+id]))
+
+print('Objective value at solution {:0.5f}'.format(ret.obj))
diff --git a/local_optimization/Modelling/dense_qp_solve_ex.py b/local_optimization/Modelling/dense_qp_solve_ex.py
new file mode 100644
index 0000000..6ec784f
--- /dev/null
+++ b/local_optimization/Modelling/dense_qp_solve_ex.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+# Example to solve dense QP problem using (e04nf/qp_solve)
+
+# Solve the dense QP problem defined in https://www.nag.com/numeric/nl/nagdoc_27.3/flhtml/e04/e04nff.html#example
+
+from naginterfaces.library import opt
+from naginterfaces.base import utils
+import numpy as np
+
+print('naginterfaces.library.opt.qp_dense_solve Python Example Results.')
+print('Solve a small dense QP problem.')
+
+infty = 1.0E+25
+
+# Problem size
+n = 7
+
+# Number of linear constraints
+nclin = 7
+
+# Cost vector
+cvec = np.array([-0.02, -0.20, -0.20, -0.20, -0.20, 0.04, 0.04])
+
+h = np.array([
+[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,],
+[0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0,],
+[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0,],
+[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0,],
+[0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0,],
+[0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0,],
+[0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0 ] ])
+
+# Block of linear constraints (Matrix A)
+a = np.array([
+[ 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,],
+[ 0.15, 0.04, 0.02, 0.04, 0.02, 0.01, 0.03,],
+[ 0.03, 0.05, 0.08, 0.02, 0.06, 0.01, 0.00,],
+[ 0.02, 0.04, 0.01, 0.02, 0.02, 0.00, 0.00,],
+[ 0.02, 0.03, 0.00, 0.00, 0.01, 0.00, 0.00,],
+[ 0.70, 0.75, 0.80, 0.75, 0.80, 0.97, 0.00,],
+[ 0.02, 0.06, 0.08, 0.12, 0.02, 0.01, 0.97 ] ])
+
+# Bounds on the linear constraints
+bl = np.array([-0.01, -0.10,-0.01,-0.04,-0.10, -0.01, -0.01, -0.13, -infty, -infty, -infty, -infty, -9.92E-2,-3.0E-3])
+bu = np.array([ 0.01, 0.15, 0.03, 0.02, 0.05, infty, infty, -0.13, -4.9E-3, -6.4E-3, -3.7E-3, -1.2E-3, infty, 2.0E-3])
+
+# Initial Guess
+x = np.array([-0.01, -0.03, 0.00, -0.01, -0.10, 0.02, 0.01])
+
+# Init solver
+comm = opt.nlp1_init('lp_solve')
+
+# Increase printing verbosity
+opt.lp_option_string('Print Level = 2', comm)
+
+# Defile io manager
+iom = utils.FileObjManager(locus_in_output=False)
+
+# Note for qphess:
+# Note: if this argument is None then a NAG-supplied facility will be used.
+# In general, you need not provide a version of qphess.
+# However, the algorithm of qp_dense_solve requires only the product of H
+# or HTH and a vector x; and in some cases you may obtain increased efficiency
+# by providing a version of qphess that avoids the need to define the elements
+# of the matrices H or HTH explicitly.
+
+# Solve the problem
+ret = opt.qp_dense_solve(bl, bu, h, x, comm, a=a, cvec=cvec, qphess=None, istate=None, data=None, io_manager=None)
+
+# Report the solution and Lagrange multipliers if solved correctly
+print('id V Value Lagr Mult')
+for id in range(n):
+ print('{:5} {: 9.3e} {: 9.3e}'.format(id+1, ret.x[id], ret.clamda[id]))
+
+print('id LC Value Lagr Mult')
+for id in range(nclin):
+ print('{:5} {: 9.3e} {: 9.3e}'.format(n+id+1, ret.ax[id], ret.clamda[n+id]))
+
+print('Objective value at solution {:0.5f}'.format(ret.obj))
+
+print('Exit from problem afte {:6} iterations.'.format(ret.itera))
diff --git a/local_optimization/Modelling/dynamic_pricing_lp.ipynb b/local_optimization/Modelling/dynamic_pricing_lp.ipynb
new file mode 100644
index 0000000..61aa817
--- /dev/null
+++ b/local_optimization/Modelling/dynamic_pricing_lp.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG Library and running this notebook\n",
+ "To run this notebook, you will need to install the NAG Library for Python (Mark 26.1 or newer) and a license key. You can find the software and have a license key (trials are available) from our website here: [Getting Started with NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linux)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Dynamic pricing in revenue management using the NAG Library"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Disciplined pricing tactics are the key in revenue management in order to sell the right product to the right customer at the right time. Commonly implemented pricing strategies include cost-based pricing, market-based pricing, dynamic pricing, price skimming, etc. It depends on various factors such as the nature of the business and the target market to decide the most suitable strategy. During the pricing process, optimization plays an important role in demand forecasting and revenue maximization. In this notebook, we show how to use the NAG Optimization Modelling Suite (delivered with the NAG Library) by implementing a dynamic pricing strategy which achieves increased revenue over random pricing and price matching strategy. \n",
+ "\n",
+ "The models considered here do not assume that the demand as a function of price is known in advance, but rather assume parametric family of demand functions that are learned over time. During the learning process, the Linear Programming (LP) solver [handle_solve_lp_ipm](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_lp_ipm.html) in the NAG Library is used to build and solve the models. Included in the NAG Optimization Modelling Suite, the LP solver is well suited for this task, due to the great flexibility provided on building and modifying the problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the optimization module from the NAG Library for Python, and necessary packages\n",
+ "from naginterfaces.library import opt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "We focus on a market with two companies competing for a single product in a dynamic environment, in which each company not only estimates its own demand, but also needs to predicts its competitor's demand and pricing strategy. For simplicity, we assume an uncapacitated setting, i.e., there is no limitation on the capacity that each company needs to allocate.\n",
+ "\n",
+ "Linear function is one of the most commonly used demand functions in theory and practice. Assuming the true demand parameters are time dependent, the demand function of company $k=1,2$ can be defined as\n",
+ "$$d_{k,t} = \\beta_{k,t}^0 + \\beta_{k,t}^1p_{1,t} + \\beta_{k,t}^2p_{2,t} + \\epsilon_{k,t},$$\n",
+ "where $p_{1,t}$ and $p_{2,t}$ are the prices of company 1 and 2 at time t, respectively. This model assumes that demand for each company $k = 1, 2$ depends on its own as well as its competitors current period prices $p_{1,t}$ and $p_{2,t}$. $\\beta_{k,t}^i$, $i = 0, 1, 2$ are unknown parameters and $\\epsilon_{k,t} \\sim N(0, \\sigma_{k,t}^2)$ is a random noise.\n",
+ "\n",
+ "In the beginning of each period t, company 1 knows the realizations of its own demand $d_{1,s}$, its own prices $p_{1,s}$ , as well as its competitor's prices $p_{2,s}$, for $s = 1, \\ldots, t-1$. It does not directly observe its competitor's demand. The objectives are to estimate its own demand, its competitor's reaction and finally, set its own prices dynamically to maximize the total expected revenue.\n",
+ "\n",
+ "In this exercise we define the true models of demand are as follows.\n",
+ "$$\n",
+ "d_{1,t} = 50 -0.05p_{1,t} + 0.03p_{2,t} + \\epsilon_{1,t},\n",
+ "$$\n",
+ "$$\n",
+ "d_{2,t} = 50 +0.02p_{1,t} - 0.06p_{2,t} + \\epsilon_{2,t},\n",
+ "$$\n",
+ "where the $\\epsilon_{1,t},~\\epsilon_{2,t}\\sim N(0, 16)$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def demand_true(company, p1, p2):\n",
+ " if company == 1:\n",
+ " return 50 - 0.05*p1 + 0.03*p2 + np.random.normal(scale=4.0)\n",
+ " elif company == 2:\n",
+ " return 50 + 0.02*p1 - 0.06*p2 + np.random.normal(scale=4.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Demand estimation\n",
+ "The following optimization problem is solved in order to estimate the demand of company 1.\n",
+ "$$\n",
+ "\\min_{\\hat{\\beta}_1}~~\\sum_{\\tau=1}^{t-1} |d_{1,\\tau} - (\\hat{\\beta}_{1,\\tau}^0+\\hat{\\beta}_{1,\\tau}^1p_{1,\\tau}+\\hat{\\beta}_{1,\\tau}^2p_{2,\\tau})|,\n",
+ "$$\n",
+ "$$\n",
+ "s.t. ~~ |\\hat{\\beta}_{1,\\tau}^i - \\hat{\\beta}_{1,\\tau+1}^i| \\leq \\delta_1(i), ~ i = 0, 1, 2, ~\\tau = 1,2,\\ldots, t-2,\n",
+ "$$\n",
+ "$$\n",
+ "~~ \\hat{\\beta}_{1,\\tau}^0\\geq0,~\\hat{\\beta}_{1,\\tau}^1 \\leq0,~\\hat{\\beta}_{1,\\tau}^2\\geq0,~\\tau = 1,2,\\ldots, t-1,\n",
+ "$$\n",
+ "where $\\hat{\\beta}_1$ is defined as an array of all the variables. Note the first set of constraints is to control the changes on the parameters.\n",
+ "\n",
+ "Once the optimal solution $(\\hat{\\beta}_{1, \\tau}^i)^*$, $i = 0, 1, 2$, $\\tau = 1, \\ldots, t-1$ is obtained, the new estimate is given by an average of the estimates of the N previous periods\n",
+ "$$\n",
+ "\\hat{\\beta}_{1, t}^i = \\frac{1}{N}\\sum_{l=t-N}^{t-1}(\\hat{\\beta}_{1, l}^i)^*,~ i = 0, 1, 2.\n",
+ "$$\n",
+ "\n",
+ "By introducing auxiliary variables $y_\\tau$, $\\tau = 1, \\ldots, t-1$, the above optimization problem can be reformulated as the following linear programming problem.\n",
+ "$$\n",
+ "\\min_{({\\hat{\\beta}_1}, y)}~~\\sum_{\\tau=1}^{t-1} y_{\\tau},\n",
+ "$$\n",
+ "$$\n",
+ "s.t. ~~-y_\\tau \\leq d_{1,\\tau} - (\\hat{\\beta}_{1,\\tau}^0+\\hat{\\beta}_{1,\\tau}^1p_{1,\\tau}+\\hat{\\beta}_{1,\\tau}^2p_{2,\\tau}) \\leq y_\\tau,~\\tau = 1,2,\\ldots, t-1,\n",
+ "$$\n",
+ "$$\n",
+ "~~ -\\delta_1(i)\\leq\\hat{\\beta}_{1,\\tau}^i - \\hat{\\beta}_{1,\\tau+1}^i \\leq \\delta_1(i), ~ i = 0, 1, 2, ~\\tau = 1,2,\\ldots, t-2,\n",
+ "$$\n",
+ "$$\n",
+ "~~ \\hat{\\beta}_{1,\\tau}^0\\geq0,~\\hat{\\beta}_{1,\\tau}^1 \\leq0,~\\hat{\\beta}_{1,\\tau}^2\\geq0,~\\tau = 1,2,\\ldots, t-1.\n",
+ "$$\n",
+ "Here we assume the prices for both companies range in the interval $[100, 900]$, the time horizon is $T = 150$. And the initial prices are $p_{1,1} = p_{2,1} = 500$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fix the random seed\n",
+ "np.random.seed(5)\n",
+ "\n",
+ "# Big bound to inform the solver on infinity\n",
+ "bigbnd = 1.e20\n",
+ "\n",
+ "# Optimization model parameters\n",
+ "delta = np.array([30, 0.05, 0.05])\n",
+ "T = 150\n",
+ "\n",
+ "# Initial prices\n",
+ "p1 = [500]\n",
+ "p2 = [500]\n",
+ "\n",
+ "# Array to store realised true demand of company 1\n",
+ "# For final revenue calculation\n",
+ "demand_1_true = []"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can go ahead and initialize an empty model. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize an empty problem\n",
+ "nvar = 0\n",
+ "handle = opt.handle_init(nvar)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Observe that for $t = 2, \\ldots, T$, the model is getting bigger with more variables and constraints added. At each time period t, a new set of 4 new variables $[\\hat{\\beta}_{1,t-1}^0, \\hat{\\beta}_{1,t-1}^1, \\hat{\\beta}_{1,t-1}^2, y_{t-1}]$ is added, as well as the contraints involving them."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pricing optimization\n",
+ "Company 1 can set its prices by maximizing its current period t revenues. That is to solve the following unconstrained quadratic programming problem\n",
+ "$$\n",
+ "\\max_{p_1,t}~~ p_{1,t} (\\hat{\\beta}_{1,t}^0+\\hat{\\beta}_{1,t}^1p_{1,t}+\\hat{\\beta}_{1,t}^2\\hat{p}_{2,t}),\n",
+ "$$\n",
+ "which has the optimal solution of the form\n",
+ "$$\n",
+ "p_{1,t}^* = \\frac{\\hat{\\beta}_{1,t}^0+\\hat{\\beta}_{1,t}^2\\hat{p}_{2,t}}{-2\\hat{\\beta}_{1,t}^1}.\n",
+ "$$\n",
+ "Note we've still got to estimate the price of company 2 at time t in order to use the pricing strategy above. For simplicity, we use an average of the prices company 2 sets in the N previous periods,\n",
+ "$$\n",
+ "\\hat{p}_{2,t} = \\frac{1}{N}\\sum_{l=t-N}^{t-1} p_{2, l}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Pricing of company 2, estimated by company 1\n",
+ "p2_est = lambda p2 : sum(p2)/len(p2)\n",
+ "\n",
+ "# Optimal pricing for company 1\n",
+ "def optimize_p1(t, p1, beta_t, p2_est):\n",
+ " \n",
+ " # When t = 2, the data is insufficient\n",
+ " # the linear programming problem has multiple optimal solutions\n",
+ " # Heuristic to make sure price 1 is well-defined\n",
+ " if (beta_t[1] == 0):\n",
+ " p1_opt = p1[t-2]\n",
+ " else:\n",
+ " p1_opt = (beta_t[0] + beta_t[2]*p2_est)/(-2.0*beta_t[1])\n",
+ "\n",
+ " # Project price if outside of interval\n",
+ " p1_opt = max(100, min(900, p1_opt))\n",
+ "\n",
+ " return p1_opt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With pricing functions prepared and empty model initialized, we are finally ready to build up the models and solve along the line for different time periods. With the NAG Optimization Modelling Suite, adding variables and constraints is very straight forward, which makes it very easy to learn the demand function coefficients with new data coming in after each time period, as demonstrated with the following code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for t in range(2, T+1):\n",
+ " \n",
+ " # In each demand estimation, 4 new variables will be added to the model\n",
+ " # [beta_0, beta_1, beta_2, y]\n",
+ " opt.handle_add_vars(handle, 4)\n",
+ " \n",
+ " # Only the auxiliary variable y will oppear in the objective\n",
+ " # Set y coefficient in the linear objective\n",
+ " opt.handle_set_linobj_coeff(handle, nvar+4, 1.0)\n",
+ "\n",
+ " # Set bound constraints for beta\n",
+ " # Beta_0\n",
+ " opt.handle_set_bound(handle, 'X', nvar+1, 0.0, bigbnd)\n",
+ " # Beta_1\n",
+ " opt.handle_set_bound(handle, 'X', nvar+2, -bigbnd, 0.0)\n",
+ " # Beta_2\n",
+ " opt.handle_set_bound(handle, 'X', nvar+3, 0.0, bigbnd)\n",
+ "\n",
+ " # Get realised demand for the previous period for company 1\n",
+ " d1_true = demand_true(1, p1[t-2], p2[t-2])\n",
+ " # Save true demand for previous period\n",
+ " demand_1_true.append(d1_true)\n",
+ "\n",
+ " # Add first set of contraints\n",
+ " opt.handle_set_linconstr(handle, -bigbnd, d1_true, [1, 1, 1, 1], \n",
+ " [nvar+1, nvar+2, nvar+3, nvar+4], [1, p1[t-2], p2[t-2], -1.0])\n",
+ " opt.handle_set_linconstr(handle, d1_true, bigbnd, [1, 1, 1, 1], \n",
+ " [nvar+1, nvar+2, nvar+3, nvar+4], [1, p1[t-2], p2[t-2], 1.0])\n",
+ "\n",
+ " # Add second set of constraints to control beta to vary slowly\n",
+ " if (t >= 3):\n",
+ " opt.handle_set_linconstr(handle, -delta, delta, [1, 1, 2, 2, 3, 3],\n",
+ " [nvar+1, nvar+1-4, nvar+2, nvar+2-4, nvar+3, nvar+3-4],\n",
+ " [1.0, -1.0, 1.0, -1.0, 1.0, -1.0])\n",
+ "\n",
+ " # Call the linear programming solver\n",
+ " # Choose the algorithm that will be used\n",
+ " opt.handle_opt_set(handle, 'LPIPM Algorithm = SD')\n",
+ " # Turn off printing of the log\n",
+ " opt.handle_opt_set(handle, 'Print File = -1')\n",
+ " sol = opt.handle_solve_lp_ipm(handle)\n",
+ "\n",
+ " # Calculate beta for period t based on the estimates for previous periods\n",
+ " beta_t = [0.0, 0.0, 0.0]\n",
+ " for i in range(0, nvar+4, 4):\n",
+ " beta_t+=sol.x[i:i+3]\n",
+ " beta_t /= t-1\n",
+ "\n",
+ " # Get optimal pricing for company 1 at time t\n",
+ " p1_opt = optimize_p1(t, p1, beta_t, p2_est(p2))\n",
+ "\n",
+ " # Save optimal pricing at time t\n",
+ " p1.append(p1_opt)\n",
+ "\n",
+ " # Assuming company 2 is implementing price match\n",
+ " p2.append(p1[t-2])\n",
+ "\n",
+ " # Update number of total variables in the model\n",
+ " nvar += 4\n",
+ "\n",
+ "# Solving process finished, deallocate problem model\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Results\n",
+ "After the pricing stage, we are ready to calculate the actual revenue for each strategy. For comparison, we implemented two other strategies: random and price matching. The random prices are generated under the uniform distribution from the price range. The price matching policy sets the price for the current period the company 1 set in the previous period."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 960x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Calculate revenue based on the pricing strategies\n",
+ "revenue_opt = 0.0\n",
+ "revenue_price_match= 0.0\n",
+ "revenue_random = 0.0\n",
+ "\n",
+ "# Get the true demand for period T for company 1\n",
+ "# since it was not used/generated during the pricing stage\n",
+ "demand_1_true.append(demand_true(1, p1[T-1], p2[T-1]))\n",
+ "\n",
+ "# Generate prices based on uniform distribution for comparison\n",
+ "p3 = np.random.uniform(100, 900, T)\n",
+ "\n",
+ "# Calculate revenues for each strategy\n",
+ "# revenue = price * demand\n",
+ "for i in range(T):\n",
+ " revenue_opt += p1[i]*demand_1_true[i]\n",
+ " revenue_price_match += p2[i]*demand_true(2, p1[i], p2[i])\n",
+ " revenue_random += p3[i]*demand_true(2, p1[i], p3[i])\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "fig = plt.figure(figsize=plt.figaspect(0.3))\n",
+ "ax = fig.add_subplot(1, 2, 1)\n",
+ "\n",
+ "ax.plot(np.arange(1, T+1), p1, 'b', label='Company 1')\n",
+ "ax.plot(np.arange(1, T+1), p2, 'g', label='Company 2')\n",
+ "ax.set_xlabel('Time period')\n",
+ "ax.set_ylabel('Price')\n",
+ "ax.legend()\n",
+ "\n",
+ "ax = fig.add_subplot(1, 2, 2)\n",
+ "strategies = ['Opt', 'Price matching', 'Random']\n",
+ "ax.set_xlabel('Strategy')\n",
+ "ax.set_ylabel('Revenue')\n",
+ "ax.bar(strategies, [revenue_opt, revenue_price_match, revenue_random])\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By the plot above on the price history over time, we can see the price of company 1, by employing the optimization learning approach, converges to a stable status as time goes. Company 2 is always one period behind as it uses price matching. From the figure on the right, for this single simulation, we can see using the optimization approach achieves much higher renenue than price matching. And random pricing is the worst in this scenario. \n",
+ "\n",
+ "In this notebook, we have shown how to use the NAG Optimization Modelling Suite for pricing strategies under complex market conditions and demand. The flexibility provided makes it very efficient to conduct simulations in search for the most suitable pricing strategies.\n",
+ "\n",
+ "Learn more about the [NAG Library](https://www.nag.com/content/nag-library) and the [NAG Optimization Modelling Suite](https://www.nag.com/mathematical-optimization/)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.17"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/local_optimization/Modelling/handle_disable_ex.ipynb b/local_optimization/Modelling/handle_disable_ex.ipynb
new file mode 100644
index 0000000..eab610d
--- /dev/null
+++ b/local_optimization/Modelling/handle_disable_ex.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG Library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG Library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the NAG Library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Removing outliers from a calibration problem with the NAG Optimization Modelling Suite\n",
+ "\n",
+ "## Correct Rendering of this notebook\n",
+ "\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "\n",
+ "## Calibration problem\n",
+ "\n",
+ "Consider a simple calibration problem where we want to fit the 5 parameters of a model $f$ to some given data points. The model is of the form:\n",
+ "$$\n",
+ "\\min_{x\\in\\mathbb{R}^5} \\sum_{i=1}^{30}\\left( f(t_i, x) - y_i \\right)^2\n",
+ "$$\n",
+ "where $x=[a,b,c,d,\\omega]$ are the parameters to fit and $f(t, x) = at^2 + bt+ c + d\\sin(\\omega t)$ is the model.\n",
+ "\n",
+ "### Data\n",
+ "\n",
+ "The initial data points $\\{(t_i, y_i)\\}$ were simulated using \n",
+ "$$(a=0.3,b=1.0,c=0.01,d=0.2,\\omega =5.0)$$\n",
+ "with $t_i$ uniformly spread in $\\left[-1.0,1.0\\right]$ and $y_i=f(t_i,x)+\\epsilon_i$ where $\\epsilon_i$ is random noise.\n",
+ "\n",
+ "Two of those points ($y_{10}$ and $y_{20}$) were modified to emulate the presence of outilers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pickle\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import math\n",
+ "\n",
+ "# load the simulated data containing 2 outliers\n",
+ "data = pickle.load(open('nlnlsq_data.pck', 'rb'))\n",
+ "nres = len(data['y'])\n",
+ "\n",
+ "# plot the data points and the true function used to generate them\n",
+ "f_true = lambda t : 0.3*t**2 + t + 0.01 + 0.2*math.sin(5.0*t) \n",
+ "plt.plot(data['t'], data['y'], '*')\n",
+ "t = np.linspace(-1.0,1.0, num=200)\n",
+ "y = [f_true(x) for x in t]\n",
+ "plt.plot(t, y)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Set up the calibration problem\n",
+ "\n",
+ "Start by defining the callbacks that will be used by the solver to determine the quality of the fit with any given set of parameters $x$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def lsqfun(x, nres, inform, data):\n",
+ " \"\"\"\n",
+ " Objective function callback passed to the least squares solver.\n",
+ " \"\"\"\n",
+ " rx = np.zeros(nres)\n",
+ " t = data['t']\n",
+ " y = data['y']\n",
+ " for i in range(nres):\n",
+ " rx[i] = (\n",
+ " x[0]*t[i]**2 + x[1]*t[i] + x[2] + x[3]*np.sin(x[4]*t[i]) - y[i]\n",
+ " )\n",
+ " return rx, inform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " \"\"\"\n",
+ " Computes the Jacobian of the least square residuals.\n",
+ " \"\"\"\n",
+ " n = len(x)\n",
+ " t = data['t']\n",
+ " for i in range(nres):\n",
+ " rdx[i*n+0] = t[i]**2\n",
+ " rdx[i*n+1] = t[i]\n",
+ " rdx[i*n+2] = 1.0\n",
+ " rdx[i*n+3] = np.sin(x[4]*t[i])\n",
+ " rdx[i*n+4] = x[3]*t[i]*np.cos(x[4]*t[i])\n",
+ " return inform"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Initialize the NAG 'handle' with the problem dimensions and some optional parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from naginterfaces.library import opt\n",
+ "from naginterfaces.base import utils\n",
+ "\n",
+ "nvar = 5\n",
+ "# Initialize the Optimization model handle with the number of variables\n",
+ "handle = opt.handle_init(nvar)\n",
+ "\n",
+ "# Define a dense nonlinear least-squares objective function\n",
+ "opt.handle_set_nlnls(handle, nres)\n",
+ "\n",
+ "# Set some optional parameters to control the output of the solver\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Solution = X',\n",
+ " 'Bxnl Iteration Limit = 100',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ " \n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Solve the problem with outliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GG, Nonlinear least squares method for bound-constrained problems\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 1.05037E+00\n",
+ " Norm of gradient 8.78014E-06\n",
+ " Norm of scaled gradient 6.05781E-06\n",
+ " Norm of step 1.47886E-01\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf 3.61301E-01 inf\n",
+ " 2 -inf 9.10227E-01 inf\n",
+ " 3 -inf 3.42138E-03 inf\n",
+ " 4 -inf -6.08965E+00 inf\n",
+ " 5 -inf 6.24881E-04 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Call the solver\n",
+ "x = np.ones(nvar, dtype=float)\n",
+ "res = opt.handle_solve_bxnl(handle, lsqfun, lsqgrd, x, nres, data=data,\n",
+ " io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can plot the model with the fitted parameters and see the influence of outliers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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SMMHdy8zsh8ATwFkNeQN3HwuMhaBqKP4hiqS/RjU4b8uq4I5iOz6Fy56A4y5KdUQSRTyqhjYAR0a87sSXjcIAuPsWd68aefIocFJ9txWRRuiTd+HRs6F0Bwx/SUkgzcUjEcwHephZNzNrCgwBpkUWMLPIrgEDgaXh8+nAeWbW0sxaAueFy0SksVo0BZ4cCAe3hutnwpF9Uh2R1CHmqiF3LzezGwlO4E2Ax919sZmNBorcfRrwMzMbCJQDW4ER4bZbzex3BMkEYLS7b401JhFJAXd483/g9THQ5dTg3sIHt0p1VFIPmmJCRGK3bw+8eCN8NAVOHAID7w8GjElaidZ9VLOPikhsdn4GE4fChn/C2XfAaTepZ1Ajo0QgIgdu4wcw4QrYsy2oCtKcQY2SEoGIHJgl02DqD+GgVsHN5dufmOqI5ABp0jkRaRj34E5ik68K5gz6wWwlgUZOVwQiUn/7SoNbSi56Fk4YDAP/DHlpPsWF1EmJQETqZ+emsFG4CM76NXznF2oUzhBKBCJStw0LYOKVUPo5DH4Kjh2Y6ogkjpQIRKR27z8Nf7sJmrVVo3CGUiIQkZpV7IPpo2DeI9DtdLh0HBxyeKqjkgRQIhCR/X2xGSYPh0/egpN/Ekwh3USni0ylT1ZEvurThTDpSviiBC5+BL4xJNURSYIpEYjIlz6cDNN+Gswceu3foUPvVEckSaBEICJQUQ4z74B3HwhmDr3sCWh2RKqjkiRRIhDJdrtK4Llr4eM3oc9I6P/f0CQv1VFJEikRiGSzdfOCRuE9W2HQg9D7ylRHJCmgRCCSjdxh3liY/l9waCe47jVo/41URyUpokQgkm3KdsFLP4OPnoOjz4eLH4KDWqY6KkkhJQKRbFKyDCZdBVtWwNm/gVNvghxNQpzt4vINMLMBZrbMzFaa2W01rL/ZzJaY2YdmNsvMukSsqzCzheFjWvVtRSROPnoexvaD3VvgqheCSeOUBIQ4XBGYWRPgQeBcYD0w38ymufuSiGLvA4XuvtvMbgD+AFwertvj7r1ijUNEoijfCzN+A3Mfgk59YPAT0KJDqqOSNBKPnwN9gJXuvtrd9wITgUGRBdz9dXffHb58D+gUh/2KSF22r4cnLgySQN8bYMTLSgKyn3i0EXQE1kW8Xg/0raX8dcCrEa8LzKwIKAfudvcXatrIzEYCIwE6d+4cS7wi2WHZq/DCDcHkcZc+DsdfkuqIJE0ltbHYzK4ECoEzIhZ3cfcNZtYdmG1mi9x9VfVt3X0sMBagsLDQkxKwSGNUvhdm3gnvPQjtToTLxsHhR6U6Kklj8UgEG4AjI153Cpd9hZmdA4wCznD3sqrl7r4h/LvazN4AegP7JQIRqYetH8OUa+HTfwajhM/9nW4lKXWKRyKYD/Qws24ECWAIMDSygJn1Bh4BBrh7ccTylsBudy8zs9bAqQQNySLSUItfCCaMw3QXMWmQmBOBu5eb2Y3AdKAJ8Li7Lzaz0UCRu08D7gWaAc9acI/Tte4+EOgJPGJmlQQN13dX620kInXZVxqMEC56DDqeFLQHtOya6qikETH3xlfdXlhY6EVFRakOQyT1Nq+EZ0fApkXw7Rvh7Dsgt2mqo5I0ZWYL3L2w+nKNJhFJE8U7Shn8yLsU7yytu7A7LHwGxp4BO9bDFZOg/11KAnJAlAhE0sT9s1Ywf81W7p+5ovaCe7bBlGuCrqHtvwE/ehu+PiA5QUpG0lxDIin29dtfpay88t+vx89dy/i5a8nPzWHZmPO/WnjNW/D8SNi1KagGOvU/IKdJkiOWTKMrApEUm3NLPwb26kBBXvDfsSAvh0G9OjDn1n5fFirfCzN/C+MuhNyCYNro79ysJCBxoSsCkRRr06KA5vm5lJVXkp+bQ1l5Jc3zc2nTPOz/v3klPH89fPo+fPNq6P97yG+W2qAloygRiKSBzbvKGNa3C0P7dOaZeWsp2VkaNAi//xS8eis0aQqDn4RjB9X9ZiINpO6jIulo91Z46T9g6TTodjpc9DAc2jHVUUkjF637qK4IRNLN8unBCOHdW+Hc0fDtn+q+AZJQSgQi6aJ0B0z/Fbw/HtocB8OmQPsTUx2VZAElApF08PGb8MKPYccGOO1mOPM2yM1PdVSSJZQIRFJp726Y9VuY+zC0OgqunQ5H9kl1VJJllAhEUmXdfHjhR7BlJfT5IZxzJzQ9ONVRSRZSIhBJtvK98I+74a37oEVHuHoadD+j7u1EEkRdEUQSaL+J5DYsCCaKm/O/0Gso3PC2koCknK4IRBKoaiK5h15bxB3NX4R3H4Rm7YLZQjVRnKQJJQKRBIicSK6vLeXqD26CnE1MrDyHIT95HAoOTXGEIl9S1ZBIAsy5pR+DTziU3zd9nEn5vyPH4M9H3sdZ//m0koCknbgkAjMbYGbLzGylmd1Ww/p8M5sUrp9rZl0j1v0qXL7MzPrHIx6RVGvz2Zv815prGGyzeLzyAgbsvZtNrb715URyImkk5qohM2sCPAicC6wH5pvZtGr3Hr4O2ObuXzOzIcA9wOVmdizBze6PAzoAM83saHeviDUukZTYvTWYJG7RZL7I68rEox/h9H7nc0nVRHIiaSgebQR9gJXuvhrAzCYCg4DIRDAIuDN8PgV4wIK72A8CJrp7GfCxma0M3+/dOMQlkjzu8MEEeO12KN0OZ9xKx+/8gh+Fo4PHXHR8igMUiS4eiaAjsC7i9Xqgb7Qy7l5uZtuBw8Pl71XbtsYpFs1sJDASoHPnznEIWyROSpbDyzfDmjnQqQ9874/Q9rhURyVSb42m15C7jwXGQjANdYrDEYF9pcF4gLf/CHkHwYV/hG8O10yh0ujEIxFsAI6MeN0pXFZTmfVmlgscCmyp57YiaWfboumUvvBz2ld8CidcBv3/G5q1SXVYIgckHj9d5gM9zKybmTUlaPydVq3MNGB4+PxSYLYHd8SZBgwJexV1A3oA8+IQk0hi7CqG535Ay+cGU7qvgr92vw8ueVRJQBq1mK8Iwjr/G4HpQBPgcXdfbGajgSJ3nwY8BjwVNgZvJUgWhOUmEzQslwM/UY8hSUuVFfDPJ9n+0igKKOPhiu/zl/KBlC1pym9ve5n83ByWjTk/1VGKHBDdqlKkLuvmwyu/hI0L2dvpFP6Q90PGr8yndF8lBXk59D+uHaMu6KkxApL2ot2qUq1aItHs3ARTb4DHzoFdm+CSx2h63SvsaXEUZeWV5OfmUFZeSfP8XCUBadQaTa8hkaSp2AdzH4E37obyUjjtJvjOLyG/GQCbd5UxrG8XhvbpzDMaKCYZQFVDIpFWvR6MDN68DL52Lpx/Dxx+VKqjEomLaFVDuiIQAdj2Cbw2Cpa+BC27whUT4egBYJbqyEQSTolAslvZzuBOYe8+CJYDZ90O3/4p5KnOX7KHEoFkp4pyeP8peP0u+KIkGBR2zp1waKdURyaSdEoEkn1WzoTpt0PJUjjy5OBuYZ1OSnVUIimjRCDZY9OSYHbQVbOgZTcY/CT0HKh2AMl6SgSS+XZuCqqA3n8K8pvDeXdBnx9AOEW0SLZTIpDMVbYzaAR+58/BeIA+P4QzboGDW6U6MpG0okQgmWdfKRQ9DnP+B3ZvgZ7fg7PvhNZfS3VkImlJiUAyR0U5fDgRXv897FgP3c6As+9QQ7BIHZQIpPFzDwaCzR4TjAju0BsGPQBH9Ut1ZCKNghKBNG6r34BZo2HDAmh9NAx+KqgKUk8gkXpTIpDGac1bwaRwa+ZAi04w6EE4cQg00VdapKH0v0Yal4/nwD/uCRJAs7bQ//dQeK2mhBCJgRKBNA4fzwmuAD55K0gAA+6Gk0YEN40XkZgoEUj6cg9++b9xT5gA2sGAe+Ck4UoAInEUUyIws1bAJKArsAYY7O7bqpXpBTwEtAAqgLvcfVK4bhxwBrA9LD7C3RfGEpNkAPegEfjNe+GTt5UARBIs1iuC24BZ7n63md0Wvr61WpndwNXuvsLMOgALzGy6u38erv9Pd58SYxySCSorgm6gb90HGxcGCeD8P8A3r1YCEEmgWBPBIODM8PkTwBtUSwTuvjzi+admVgwcAXwe474lU5SXwYeT4O0/wZaV0Ko7fO9PQS8gNQKLJFysiaCtu28Mn38GtK2tsJn1AZoCqyIW32VmvwFmAbe5e1mUbUcCIwE6d+4cY9iSFsp2woJxwXxAOzdC+2/AZeOCGUFzmqQ6OpGsUWciMLOZQLsaVo2KfOHubmZRb4BsZu2Bp4Dh7l4ZLv4VQQJpCowluJoYXdP27j42LENhYWHju9GyfGlXCcx7BOaNhdLt0O10uOgv0L1fQgaCFe8o5cYJ7/PA0N60aa4rDJHq6kwE7n5OtHVmtsnM2rv7xvBEXxylXAvgZWCUu78X8d5VVxNlZvZX4JcNil4al02L4b2H4MPJULEXel4Ip96U8LmA7p+1gvlrtnL/zBWMufiEhO5LpDGKtWpoGjAcuDv8+2L1AmbWFJgKPFm9UTgiiRhwEfBRjPFIuqmshBWvwXt/gY//AbkHQe9h0PcGOOLohO7667e/Sll55b9fj5+7lvFz15Kfm8OyMefvV15XDpKtcmLc/m7gXDNbAZwTvsbMCs3s0bDMYOB0YISZLQwfvcJ1T5vZImAR0BoYE2M8ki7KdsHcsfBAIUy4HDavCO4JfPMSuPC+hCcBgDm39GNgrw4U5AVf84K8HAb16sCcW2uejC7yykEkm8R0ReDuW4Cza1heBFwfPh8PjI+y/Vmx7F/S0Odrg7r/BU9C2XboeBJc8hgcOwia5CU1lDYtCmien0tZeSX5uTmUlVfSPD93v1/7Db1yEMk0GlkssausgJWzgpvBrJgOWHDiP/nHcOS3Uhra5l1lDOvbhaF9OvPMvLWU7Czdr8ycW/ox5pWlvLb4M0r3VVKQl0P/49ox6oKeKYhYJPmUCOTA7SoO7gO8YFxwJXBIGzjt5mAOoMOOTHV0ADxyVeG/n4+56Pgay9T3ykEkUykRSMO4B1NAFz0GS/8GlfuC7p/njoavXwC5TVMd4QGpz5WDSKYy98bXJb+wsNCLiopSHUZ2+WJLMPq36HHYsgIKDoNew6DwGmjdI9XRiUg9mNkCdy+svlxXBBJdRTmsnAELn4Zlfw9+/Xf6Flz0EBx3seb/EckQSgSyv+Kl8P74YODXF8VwcGvoMxJ6DYV2Ndezi0jjpUSQRGk9YGnPNlg0BRY+A5/+E3Jy4egBwcm/x3lJ7/opIsmjRBCjhpzc026qg317YPnfgwSwYgZUlEHb44PbP55wGTQ7ItURikgSKBHEqD4n97QasFSxD1bNDk7+y16BvbuCWz8WXgu9rghmABWRrKJeQweo+sm9Sk0n9+IdpVEHLCWliqiyIrjT10fPwZIXg2qggsPg2IFw/KXQ9TRN+yySBdRrKM4aMho1JQOWKsph7bvBHb+WvAi7PoO8Q+CY78Lxl8BRZzfaPv8iEl9KBAeooSf3pAxY2lca3Ov3Xy/Bv16BPVshtyA46Z9wSdD42/SQ+O9XRBo1JYIYNOTkXp+pDg7Ins9h1axglO+K14I6//wWcHR/6Pk9+No5GXnyT+seWI2Qjmd2UyKIQcJO7rVxh5JlweRuy18Lqn+8Ag45Ak64FI75XjDlQ4ZX+6RdD6xGTsczu6mxuDHYVxrM77NietDd8/O1wfK2xwd9/I/uH4z4zYIG34Y00kvddDyzixqLaUSXv5WVsGlRUN+/6vXgV395aXB3r+5nwKk/DxJAgmb4TOfjpCmj40vHUyDLEkFaX/5+vi448a9+HVb/A3ZvDpYf0RNOuga+dnbQzTMJ8/uk83HSlNHxpeMpEGMiMLNWwCSgK7AGGOzu22ooV0FwO0qAte4+MFzeDZgIHA4sAK5y972xxFSTtBrQBUE9/7Y1Qd/+T94J/m5bE6xr1jZo4O1+ZvBo0b7Wt4rnr/e0O05RaMro+NLxlJjaCMzsD8BWd7/bzG4DWrr7rTWU2+XuzWpYPhl43t0nmtnDwAfu/lBd+21oG0HqB3RVwuZl4Uk/fOz8NFh3UCvocgp0OTU48bfpCWb1fuvbpy7i6XlrGdanc8y/3lN+nEQkoRLVRjAIODN8/gTwBrBfIogSkAFnAUMjtr8TqDMRNFTSL3+/2AIbimD9/OCx4Z9QtiNY16wddD31y5N/669DTk6Dd5GIX++qJhDJTrEmgrbuvjF8/hnQNkq5AjMrAsqBu939BYLqoM/dvTwssx7oGGM8USXs8rd0B3y2CD77EDYsgPVFsO3jYJ01gbbHBd06OxZCl29Dy24N+sUfTaIa+VRNIJJ96kwEZjYTaFfDqlGRL9zdzSxaPVMXd99gZt2B2Wa2CNjekEDNbCQwEqBz584N2RSIU5//XcWw8UPYuDA48W/88MuTPkDz9tCpMLhrV8dC6NArYYO5Gvrrvb5tCQ05Tuncu0hE6q/ORODu50RbZ2abzKy9u280s/ZAcZT32BD+XW1mbwC9geeAw8wsN7wq6ARsqCWOscBYCNoI6oo7JrtKoORfwaN4aTCAq2Qp7N7yZZmWXaHdidD7ymDGznYnQvNoF0SJ0ZBf74noCZTOvYtEpP5ibSy+F9gS0Vjcyt1vqVamJbDb3cvMrDXwLjDI3ZeY2bPAcxGNxR+6+1/q2m9cBpSV7YStH8PW1eFjFWxZHTTqRpzwK5s2J6dNT2hzTNCVs/2J0O4EKDg0tv3XIpE9garE0pagQUgijVO0xuJYE8HhwGSgM/AJQffRrWZWCPzI3a83s1OAR4BKIAf4o7s/Fm7fnaD7aCvgfeBKdy+ra78HnAjeuCeYi3/r6uAWjJEOaQOtusMRR8MRxzBuRQEPL23KOd/6BmO+f2LD9xWDdO8JpN5FIo1TQnoNufsW4OwalhcB14fP3wFqPJu5+2qgTywxNMjuzeEtGPsHJ/1/P7pBfnOght4489Yxft66pPzabSw9gdS7SCSzZNXIYr57b51FUjnkvjH1BFLvIpHMkV2JoB5S+Ws3UftOxCypiZp5VT2RRJKv4SOZskDVr92pPz6VYX27ULKrzmaLjNh3OojsiSQiyaFpqCUtqCeSSOJFayzWFYGkhTm39GNgrw4U5AVfyYK8HAb16sCcW/ulODKRzKdEIGlBPZFEUkeNxZI21BNJJDXURiAikiXURiAiIjVSIhARyXJKBCIiWU6JQEQkyykRiIhkOSUCEZEs1yi7j5pZCcH9Dw5Ea2BzHMOJF8XVMIqrYRRXw2RqXF3c/YjqCxtlIoiFmRXV1I821RRXwyiuhlFcDZNtcalqSEQkyykRiIhkuWxMBGNTHUAUiqthFFfDKK6Gyaq4sq6NQEREviobrwhERCSCEoGISJbLyERgZpeZ2WIzqzSzqF2tzGyAmS0zs5VmdlvE8m5mNjdcPsnMmsYprlZmNsPMVoR/W9ZQpp+ZLYx4lJrZReG6cWb2ccS6XsmKKyxXEbHvaRHLU3m8epnZu+Hn/aGZXR6xLq7HK9r3JWJ9fvjvXxkej64R634VLl9mZv1jieMA4rrZzJaEx2eWmXWJWFfjZ5qkuEaYWUnE/q+PWDc8/NxXmNnwJMd1X0RMy83s84h1CTleZva4mRWb2UdR1puZ3R/G/KGZfTNiXezHyt0z7gH0BL4OvAEURinTBFgFdAeaAh8Ax4brJgNDwucPAzfEKa4/ALeFz28D7qmjfCtgK3Bw+HoccGkCjle94gJ2RVmesuMFHA30CJ93ADYCh8X7eNX2fYko82Pg4fD5EGBS+PzYsHw+0C18nyZJjKtfxHfohqq4avtMkxTXCOCBGrZtBawO/7YMn7dMVlzVyv8UeDwJx+t04JvAR1HWfxd4FTDgZGBuPI9VRl4RuPtSd19WR7E+wEp3X+3ue4GJwCAzM+AsYEpY7gngojiFNih8v/q+76XAq+6+O077j6ahcf1bqo+Xuy939xXh80+BYmC/kZNxUOP3pZZ4pwBnh8dnEDDR3cvc/WNgZfh+SYnL3V+P+A69B3SK075jiqsW/YEZ7r7V3bcBM4ABKYrrCmBCnPYdlbu/SfCjL5pBwJMeeA84zMzaE6djlZGJoJ46AusiXq8Plx0OfO7u5dWWx0Nbd98YPv8MaFtH+SHs/yW8K7w0vM/M8pMcV4GZFZnZe1XVVaTR8TKzPgS/8lZFLI7X8Yr2famxTHg8thMcn/psm8i4Il1H8MuySk2faTLjuiT8fKaY2ZEN3DaRcRFWoXUDZkcsTtTxqku0uONyrBrtPYvNbCbQroZVo9z9xWTHU6W2uCJfuLubWdS+u2G2PwGYHrH4VwQnxKYE/YlvBUYnMa4u7r7BzLoDs81sEcHJ7oDF+Xg9BQx398pw8QEfr0xkZlcChcAZEYv3+0zdfVXN7xB3LwET3L3MzH5IcDV1VpL2XR9DgCnuXhGxLJXHK2EabSJw93NifIsNwJERrzuFy7YQXHblhr/qqpbHHJeZbTKz9u6+MTxxFdfyVoOBqe6+L+K9q34dl5nZX4FfJjMud98Q/l1tZm8AvYHnSPHxMrMWwMsEPwLei3jvAz5eNYj2fampzHozywUOJfg+1WfbRMaFmZ1DkFzPcPeyquVRPtN4nNjqjMvdt0S8fJSgTahq2zOrbftGHGKqV1wRhgA/iVyQwONVl2hxx+VYZXPV0HyghwU9XpoSfOjTPGiBeZ2gfh5gOBCvK4xp4fvV5333q5sMT4ZV9fIXATX2MEhEXGbWsqpqxcxaA6cCS1J9vMLPbipB/emUauviebxq/L7UEu+lwOzw+EwDhljQq6gb0AOYF0MsDYrLzHoDjwAD3b04YnmNn2kS42of8XIgsDR8Ph04L4yvJXAeX70yTmhcYWzHEDS+vhuxLJHHqy7TgKvD3kMnA9vDHzrxOVaJaAFP9QO4mKCurAzYBEwPl3cAXoko911gOUFGHxWxvDvBf9SVwLNAfpziOhyYBawAZgKtwuWFwKMR5boSZPqcatvPBhYRnNDGA82SFRdwSrjvD8K/16XD8QKuBPYBCyMevRJxvGr6vhBUNQ0MnxeE//6V4fHoHrHtqHC7ZcD5cf6+1xXXzPD/QdXxmVbXZ5qkuH4PLA73/zpwTMS214bHcSVwTTLjCl/fCdxdbbuEHS+CH30bw+/yeoK2nB8BPwrXG/BgGPMiInpDxuNYaYoJEZEsl81VQyIighKBiEjWUyIQEclySgQiIllOiUBEJMspEYiIZDklAhGRLPf/AWtI5E1iDVBdAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the data points and the fitted function\n",
+ "x = res.x\n",
+ "f_out = lambda t : x[0]*t**2 + x[1]*t + x[2] + x[3]*math.sin(x[4]*t) \n",
+ "plt.plot(data['t'], data['y'], '*')\n",
+ "y = [f_out(z) for z in t]\n",
+ "plt.plot(t, y)\n",
+ "plt.savefig(\"outlier_fit.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Remove the outliers and solve again"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GG, Nonlinear least squares method for bound-constrained problems\n",
+ " Status: converged, an optimal solution was found\n",
+ " Value of the objective 5.96811E-02\n",
+ " Norm of gradient 1.19914E-06\n",
+ " Norm of scaled gradient 3.47087E-06\n",
+ " Norm of step 3.49256E-06\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf 3.53888E-01 inf\n",
+ " 2 -inf 1.06575E+00 inf\n",
+ " 3 -inf 1.91383E-03 inf\n",
+ " 4 -inf 2.17299E-01 inf\n",
+ " 5 -inf 5.17660E+00 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Disable the two outlier residuals\n",
+ "opt.handle_disable(handle, comp='NLS', idx=[10, 20])\n",
+ "\n",
+ "# Solve the problem again\n",
+ "x = np.ones(nvar, dtype=float)\n",
+ "res = opt.handle_solve_bxnl(handle, lsqfun, lsqgrd, x, nres, data=data,\n",
+ " io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fitted function is much closer to the data!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the data points and the fitted function\n",
+ "x = res.x\n",
+ "f_fit = lambda t : x[0]*t**2 + x[1]*t + x[2] + x[3]*math.sin(x[4]*t) \n",
+ "plt.plot(data['t'], data['y'], '*')\n",
+ "y = [f_fit(z) for z in t]\n",
+ "plt.plot(t, y)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The solver featured in this notebook is accessed via the Optimization Modelling Suite (delivered with the NAG Library). Learn more about the [Optimization Modelling Suite](https://www.nag.com/content/nag-optimization-modelling-suite) or complete the [form](https://www.nag.com/form/optimization-solutions-enquiry) and we’ll get back to you as soon as we can."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/local_optimization/Modelling/nlnlsq_data.pck b/local_optimization/Modelling/nlnlsq_data.pck
new file mode 100644
index 0000000..353830b
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG Library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG Library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the NAG Library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Edit and solve different variants of a production-planning problem\n",
+ "\n",
+ "## Correct Rendering of this notebook\n",
+ "\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "\n",
+ "## Problem description\n",
+ "We consider a situation where a factory can manufacture two different chemicals $A_1$ and $A_2$. The goal for the factory is to determine the quantities $x_1$ and $x_2$ of each chemical to maximize profit under the following circumstances:\n",
+ "- a unit of $A_1$ weighs 40kg and a unit of $A_2$ weighs 80kg;\n",
+ "- the total daily production cannot exceed 16000kg to match the transport capabilities;\n",
+ "- the factory generates \\\\$2 profit for each unit of $A_1$ and \\\\$4.5 profit for each unit of $A_2$;\n",
+ "- both products need to use the same machine as part of their respective processes; a unit of A1 requires 1.2 minutes of machine time while a unit of $A_2$ requires 3 minutes; the machine can only function for 1500 minutes daily;\n",
+ "- a unit of $A_1$ uses 6 square metres of packing material while a unit of $A_2$ uses 10 square metres; 6000 square metres of packing materials are available each day;\n",
+ "- production of $A_2$ is limited to 100 units per day.\n",
+ "\n",
+ "Note that since the chemicals are considered fluid, the quantities $x_1$ and $x_2$ are not limited to integer values.\n",
+ "\n",
+ "The problem can be formulated as a linear program:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\begin{array}{lll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{maximize}} & 2x_1+4.5x_2&\\\\[0.6ex]\n",
+ "\\mbox{subject to} & 1.2x_1 + 3x_2 \\leq 1500, &\\text{ (machine time constraint)} \\\\[0.6ex]\n",
+ " & 6x_1+10x_2 \\leq 6000, & \\text{ (packaging material constraint)} \\\\[0.6ex]\n",
+ " & 40x_1+80x_2 \\leq 16000, & \\text{ (transport constraint)} \\\\[0.6ex]\n",
+ " & 0 \\leq x_1, & \\text{ (capacity constraint)} \\\\[0.6ex]\n",
+ " & 0 \\leq x_2 \\leq 100 & \\text{ (capacity constraint)} \\\\[0.6ex]\n",
+ "\\end{array}\n",
+ "\\end{equation*}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04MT, Interior point method for LP problems\n",
+ " Status: converged, an optimal solution found\n",
+ " Final primal objective value 8.500000E+02\n",
+ " Final dual objective value 8.500000E+02\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 2.00000E+02 inf\n",
+ " 2 0.00000E+00 1.00000E+02 1.00000E+02\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define and solve the linear program with the NAG optimization modelling suite\n",
+ "\n",
+ "from naginterfaces.library import opt\n",
+ "from naginterfaces.base import utils\n",
+ "\n",
+ "infbnd = 1.0e20\n",
+ " \n",
+ "# Initialize the Optimization model handle with the number of variables\n",
+ "handle = opt.handle_init(2)\n",
+ "\n",
+ "# Define a linear objective function\n",
+ "opt.handle_set_linobj(handle, cvec=[2.0, 4.5])\n",
+ "\n",
+ "# Box constraints\n",
+ "opt.handle_set_simplebounds(\n",
+ " handle,\n",
+ " bl=[0.0, 0.0],\n",
+ " bu=[infbnd, 100])\n",
+ "\n",
+ "# Set the linear constraints\n",
+ "opt.handle_set_linconstr(\n",
+ " handle,\n",
+ " bl=[-infbnd, -infbnd, -infbnd],\n",
+ " bu=[1500.0, 6000.0, 16000.0],\n",
+ " irowb=[1, 1, 2, 2, 3, 3],\n",
+ " icolb=[1, 2, 1, 2, 1, 2],\n",
+ " b=[1.2, 3.0, 6.0, 10.0, 40.0, 80.0]\n",
+ ")\n",
+ "\n",
+ "# Set some alogorithmic options\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Task = Max',\n",
+ " 'Print Solution = X',\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)\n",
+ "\n",
+ "# Solve the problem\n",
+ "res = opt.handle_solve_lp_ipm(handle, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The optimal repartition is to produce 200 units of $A_1$ and 100 units of $A_2$ for a total profit of 850\\\\$.\n",
+ "\n",
+ "### Plant expansion: a new chemical $A_3$ can be produced \n",
+ "\n",
+ "The following data is available for $A_3$:\n",
+ "- a unit of $A_3$ takes 5 minutes on the common machine;\n",
+ "- a unit of $A_3$ takes 12 square metres of packaging material;\n",
+ "- a unit of $A_3$ weighs 120kg;\n",
+ "- a unit of $A_3$ generates \\\\$7 profit;\n",
+ "- production of $A_3$ is limited to 50 units per day.\n",
+ "\n",
+ "The problem becomes:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ "\\begin{array}{lll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{maximize}} & 2x_1+4.5x_2+7x_3&\\\\[0.6ex]\n",
+ "\\mbox{subject to} & 1.2x_1 + 3x_2 + 5x_3 \\leq 1500, &\\text{ (machine time constraint)} \\\\[0.6ex]\n",
+ " & 6x_1+10x_2+12x_3 \\leq 6000, & \\text{ (packaging material constraint)} \\\\[0.6ex]\n",
+ " & 40x_1+80x_2+120x_3 \\leq 16000, & \\text{ (transport constraint)} \\\\[0.6ex]\n",
+ " & 0 \\leq x_1, & \\text{ (capacity constraint)} \\\\[0.6ex]\n",
+ " & 0 \\leq x_1 \\leq 100 & \\text{ (capacity constraint)} \\\\[0.6ex]\n",
+ " & 0 \\leq x_3 \\leq 50 & \\text{ (capacity constraint)} \\\\[0.6ex]\n",
+ "\\end{array}\n",
+ "\\end{equation*}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04MT, Interior point method for LP problems\n",
+ " Status: converged, an optimal solution found\n",
+ " Final primal objective value 9.000000E+02\n",
+ " Final dual objective value 9.000000E+02\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 5.00000E+01 inf\n",
+ " 2 0.00000E+00 1.00000E+02 1.00000E+02\n",
+ " 3 0.00000E+00 5.00000E+01 5.00000E+01\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Edit the problem to account for the new plant capacity\n",
+ "# add a variable\n",
+ "opt.handle_add_vars(handle, nadd=1)\n",
+ "\n",
+ "# Box Constraint on the new variable\n",
+ "opt.handle_set_bound(handle, comp='X', idx=3, bli=0.0, bui=50.0)\n",
+ "\n",
+ "# Add the linear objective component\n",
+ "opt.handle_set_linobj_coeff(handle, idxci=3, ci=7.0)\n",
+ "\n",
+ "# Add linear constraints coefficients\n",
+ "opt.handle_set_linconstr_coeff(handle, idlc=1, icolbj=3, bij=5.0)\n",
+ "opt.handle_set_linconstr_coeff(handle, idlc=2, icolbj=3, bij=12.0)\n",
+ "opt.handle_set_linconstr_coeff(handle, idlc=3, icolbj=3, bij=120.0)\n",
+ "\n",
+ "# Solve the problem again\n",
+ "res = opt.handle_solve_lp_ipm(handle, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The new optimum is to produce 50 units of $A_1$, 100 units of $A_2$ and 50 units of $A_3$ for a total profit of 900\\\\$.\n",
+ "\n",
+ "### New regulation: additional constraint\n",
+ "\n",
+ "At a later date, regulation changes require that products $A_2$ and $A_3$ follow a rigorous quality assurance test before being sent to market. Now the factory is only able to process a total of 100 units per day which amounts to adding the following constraint to our linear program:\n",
+ "\\begin{equation*}\n",
+ "\\begin{array}{ll}\n",
+ " x_2+x_3 \\leq 100 & \\text{ (regulation constraint)} \\\\[0.6ex]\n",
+ "\\end{array}\n",
+ "\\end{equation*}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04MT, Interior point method for LP problems\n",
+ " Status: converged, an optimal solution found\n",
+ " Final primal objective value 8.750000E+02\n",
+ " Final dual objective value 8.750000E+02\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 0.00000E+00 1.50000E+02 inf\n",
+ " 2 0.00000E+00 5.00000E+01 1.00000E+02\n",
+ " 3 0.00000E+00 5.00000E+01 5.00000E+01\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Add a linear constraint\n",
+ "opt.handle_set_linconstr(\n",
+ " handle,\n",
+ " bl=[-infbnd],\n",
+ " bu=[100.0],\n",
+ " irowb=[1, 1],\n",
+ " icolb=[2, 3],\n",
+ " b=[1.0, 1.0]\n",
+ ")\n",
+ "\n",
+ "# Solve the problem again\n",
+ "res = opt.handle_solve_lp_ipm(handle, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With the new regulation, maximum profit of 875\\\\$ can be achived by producing 150 units of $A_1$, 50 units of $A_2$ and 50 units of $A_3$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The solver featured in this notebook is accessed via the Optimization Modelling Suite (delivered with the NAG Library). Learn more about the [Optimization Modelling Suite](https://www.nag.com/content/nag-optimization-modelling-suite) or complete the [form](https://www.nag.com/form/optimization-solutions-enquiry) and we’ll get back to you as soon as we can."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/local_optimization/NLDF/Readme.md b/local_optimization/NLDF/Readme.md
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--- /dev/null
+++ b/local_optimization/NLDF/Readme.md
@@ -0,0 +1,22 @@
+[](https://www.nag.com)
+
+# General Nonlinear Data-Fitting (NLDF)
+[[`handle_solve_nldf`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_nldf.html) | [`e04gnf`](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04gnf.html) | [`e04gnc`](https://www.nag.com/numeric/nl/nagdoc_latest/clhtml/e04/e04gnc.html) ]
+
+Data fitting/calibration is widespread. Commonly used in by those needing to fit a mathematical model to an experimental data set. Application use is found, but not limited to, econometrics and finance, image processing, civil engineering, mechanical engineering, and astronomy. Widely used methods, such as the [least squares (LSQ) method](../BXNL/Readme.md), don’t always capture the underlying data distribution. When the assumptions are unrealistic or the data set contains various level of outliers, the need for robustness appears.
+
+Figure 1 shows the shapes of different loss functions and their performance in dealing with a data set that has outliers. Clearly in this case, the Cauchy loss function provides the best fit, and the model using infinity norm is heavily influenced by the outliers.
+<table>
+ <tr>
+ <td width=45%><img src="./images/nldf_lossf.png" width="100%" alt="Optimal orbit from data orbit measurements."/>
+ <td width=55%><img src="./images/nldf_comp.png" width="100%" alt="Weighted optimal orbit from data orbit measurements."/></td>
+</tr>
+</table>
+
+**Figure 1.** Left: plot of various loss functions. Right: fitting results via various loss functions
+
+Within the [NAG<sup>®</sup> Library](https://www.nag.co.uk/content/nag-library) is a [nonlinear data fitting solver](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_nldf.html), which encapsulates a selection of calibration models (the loss function and regularization types), such as Least Absolute Value and Cauchy, making it a great starting point for the journey of exploring the nonlinear nature of your experimental data. In addition, the models can include general constraints such as bound, linear, quadratic, and nonlinear constraints. The switching between different types of models and regularizations is very easy.
+
+See the notebook below for the usage of [`handle_solve_nldf`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_nldf.html) and a further discussion on the performance of loss functions regarding robustness.
+
+* [Loss functions and robustness in data-fitting](data_fitting_robustness.ipynb)
diff --git a/local_optimization/NLDF/data_fitting_robustness.ipynb b/local_optimization/NLDF/data_fitting_robustness.ipynb
new file mode 100644
index 0000000..6302b19
--- /dev/null
+++ b/local_optimization/NLDF/data_fitting_robustness.ipynb
@@ -0,0 +1,599 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c17c4002",
+ "metadata": {},
+ "source": [
+ "# Loss Function and Robustness in Data-Fitting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2ef8975",
+ "metadata": {},
+ "source": [
+ "## Technical Setup\n",
+ "\n",
+ "### NAG Library install\n",
+ "To run this notebook, you will need to install the NAG Library for Python (Mark 28.5 or newer) and a license key. You can find the software and request a license key from our website here: [Getting Started with NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linux)\n",
+ "\n",
+ "### Correct rendering of this notebook\n",
+ "\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter, it may be because you have not installed this extension. See at [nbextensions - LATEX for Jupyter notebooks](https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html) for further information."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d7c29645",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "Fitting a non-linear model to data is typically modelled as a minimisation problem, where the objective function serves as a measurement of the quality of the model’s fit to data, depending on our parameters. A general model involves summing over our data points,\n",
+ "\n",
+ "$$\n",
+ "\\underset{x \\in \\mathbb{R}^{n_{\\text{var}}}}{\\text{minimize}} ~f(x) =\\sum_{i=1}^{n_{\\text{res}}} \\chi(r_i(x)),\n",
+ "$$\n",
+ "\n",
+ "where $x$ is a vector holding our model parameters, of which there are $n_\\text{var}$. We have $n_\\text{res}$ data points, and $r_i(x)= y_i - \\varphi(t_i;x), \\quad i = 1,...,n_\\text{res}$ is the $i^{th}$ residual, equal to the difference between the observed and predicted values of the independent variable at time $t_i$, denoted $y_i$ and $\\varphi(t_i;x)$ respectively. The loss function $\\chi$ has desirable properties such as being bounded from below, and increasing with $|r_i\\left(x\\right)|$. Summing over all data points then, the objective function will be small when the model fits the whole dataset well, which is what we want.\n",
+ "\n",
+ "There are plenty of choices for function $\\chi$, so how does our choice of loss function affect the fit we end up with? One important consideration is robustness. If some of the observed data points are far from the fitted model, how can we control the influence of those outliers? A robust loss function is one which doesn’t get thrown off easily by outliers in the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "475d193d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import all packages necessary for notebook\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edeb12b1",
+ "metadata": {},
+ "source": [
+ "## Single-outlier example\n",
+ "\n",
+ "To investigate the robustness aspect, here’s a toy dataset which is generated from $\\sin(t)$ and has an outlier at $t=1.5$, which is generated by $5\\sin(t)$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f4114a11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create data set\n",
+ "t = np.linspace(0.5, 2.5, num=21)\n",
+ "y = np.sin(t)\n",
+ "y[10] = 5*np.sin(t[10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1f2e8656",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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cPyfpU0ts03kfq1hX532sYl2t9bHGOmOFJ7pDxTelz0j6RLlsv6QPl7f/QtIT5ZN4QNI7e/b97fKFeVrSb3VZV3n/zyT95YL9rpJ0oqz3hKQbG67rTklzkn6oYk7rRkn7JO3r6TgHyrpPSJrqqL2Wq+t2Sd+VdLy8zJTLLynb6tHydf5Ex3X9Xk//mu49yPv1ga7qKre5QdJdC/Zru72uVjGf+1jPa7Vj1H2sYl2d97GKdbXWx/gJOQAkxy8TASA5ghoAkiOoASA5ghoAkiOoASA5ghoAkiOoASC5/wcTTGKQrpwqUQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig1 = plt.plot(t,y,'*b')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2fbc076c",
+ "metadata": {},
+ "source": [
+ "We will fit it with a model \n",
+ "\n",
+ "$$\n",
+ "\\varphi(t;x)\\ =x_1\\sin(x_2t)\n",
+ "$$\n",
+ "\n",
+ "using a variety of loss functions provided by NAG’s data-fitting solver **handle_solve_nldf** (`e04gn`), which constructs the appropriate objective function for us."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "693292f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a handle for the model\n",
+ "nvar = 2\n",
+ "handle = opt.handle_init(nvar=nvar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b7e8db17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# register residuals structure\n",
+ "nres = 21\n",
+ "opt.handle_set_nlnls(handle, nres)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "685b0186",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define the residual callback function and its gradient\n",
+ "def lsqfun(x, nres, inform, data):\n",
+ " rx = np.zeros(nres,dtype=float)\n",
+ " t = data[\"t\"]\n",
+ " y = data[\"y\"]\n",
+ " for i in range(nres):\n",
+ " rx[i] = (y[i] - x[0]*np.sin(x[1]*t[i]))\n",
+ " \n",
+ " return rx, inform\n",
+ "\n",
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " t = data[\"t\"]\n",
+ " nvar = len(x)\n",
+ " for i in range(nres):\n",
+ " rdx[i*nvar] = (-np.sin(x[1]*t[i]))\n",
+ " rdx[i*nvar + 1] = (-t[i]*x[0]*np.cos(x[1]*t[i]))\n",
+ "\n",
+ " return inform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "296aeb8e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create the data structure to be passed to the solver\n",
+ "data = {}\n",
+ "data[\"t\"] = t\n",
+ "data[\"y\"] = y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab256fc3",
+ "metadata": {},
+ "source": [
+ "### Start with $l_2$-norm loss function - Example 1\n",
+ "Starting with one of the most common loss functions, the $l_2$-norm, we form the problem\n",
+ "\n",
+ "$$\n",
+ "\\underset{x \\in \\mathbb{R}^{2}}{\\text{minimize}}~f(x) =\\sum_{i=1}^{21} r_i(x)^2\n",
+ "$$\n",
+ "\n",
+ "which is just least squares regression. $l_2$-norm loss has low robustness against outliers, so we should expect that the solution will be affected heavily by this one outlier. Let’s solve from a starting point at\n",
+ "\n",
+ "$$\n",
+ "x\\ =\\ (2.1,1.4)\n",
+ "$$\n",
+ "\n",
+ "to see what this outlier does to the minimum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "dca06b31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set loss function to l2-norm and printing options\n",
+ "for option in [\n",
+ " 'NLDF Loss Function Type = L2',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Options = No',\n",
+ " 'Print solution = Yes'\n",
+ "]:\n",
+ " opt.handle_opt_set (handle, option)\n",
+ "\n",
+ "# use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1dd02a32",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GN, Nonlinear Data-Fitting\n",
+ " Status: converged, an optimal solution found\n",
+ " Final objective value 1.470963E+01\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf 1.30111E+00 inf\n",
+ " 2 -inf 1.06956E+00 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set initial guess and solve\n",
+ "x = [2.1, 1.4]\n",
+ "soln1 = opt.handle_solve_nldf(\n",
+ " handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fbd3f796",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate fitted data using the optimal parameters\n",
+ "y_l2_fitted = soln1.x[0]*np.sin(soln1.x[1]*t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "fbef37c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the fitted curve\n",
+ "plt.title(\"Fitted with L2 Loss Function\")\n",
+ "plt.plot(t,y,'*b')\n",
+ "plt.plot(t,y_l2_fitted)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32da7f42",
+ "metadata": {},
+ "source": [
+ "The single outlier was able to disrupt the fit, since $l_2$-norm loss makes outliers contribute heavily to the objective function and search direction."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6241b78c",
+ "metadata": {},
+ "source": [
+ "### Try $l_1$-norm loss function - Example 2\n",
+ "Using $l_1$-norm loss gives us the problem\n",
+ "\n",
+ "$$\n",
+ "\\underset{x \\in \\mathbb{R}^{2}}{\\text{minimize}}~f(x) =\\sum_{i=1}^{21} |r_i(x)|,\n",
+ "$$\n",
+ "\n",
+ "which is more robust against outliers. This means if some large portion of the data is well-fitted by some solution $x^\\ast$, there is likely to be a local minimum very close to $x^\\ast$ which is relatively undisturbed by the remaining data that is outlying to the solution $x^\\ast$. Here’s the solution, again starting at $x=(2.1,1.4)$, using $l_1$ loss."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "07e8bf50",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GN, Nonlinear Data-Fitting\n",
+ " Status: converged, an optimal solution found\n",
+ " Final objective value 3.989980E+00\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf 1.00000E+00 inf\n",
+ " 2 -inf 1.00000E+00 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# change loss function to l1-norm and solve\n",
+ "opt.handle_opt_set(handle, 'NLDF Loss Function Type = L1')\n",
+ "soln2 = opt.handle_solve_nldf(\n",
+ " handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "93472886",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate fitted data using the optimal parameters\n",
+ "y_l1_fitted = soln2.x[0]*np.sin(soln2.x[1]*t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "a1af726a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the fitted curve\n",
+ "plt.title(\"Fitted with L1 Loss Function\")\n",
+ "plt.plot(t,y,'*b')\n",
+ "plt.plot(t,y_l1_fitted)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e02e4f9",
+ "metadata": {},
+ "source": [
+ "Clearly, this is a much better fit for most of the data, and the outlier hasn’t dragged the model off most of the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23ae855a",
+ "metadata": {},
+ "source": [
+ "## The trade-off of a loss function\n",
+ "\n",
+ "We can reuse the handle, the residual function (and gradient). Just changing the data and options, we can demonstrate more principles to consider regarding loss functions.\n",
+ "\n",
+ "There is a danger in choosing a very robust loss function. During an iterative optimization process, a loss function which is robust against outliers will usually prefer the data which is close to the current model. This means that if the algorithm finds local minima of the objective function, the search can fall into a local minimum when the model fits some subset of the data very well but fits the majority of the data very badly.\n",
+ "\n",
+ "To illustrate this, here’s a new dataset which we will try to fit with the same model, again starting at $x= (2.1,1.4)$. Most of the data was generated by $5\\sin(t)$, with the 3 data points at either end being generated by $\\sin(t)$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "76ff9fcb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create the data set\n",
+ "y_new = y\n",
+ "for i in range(3,18):\n",
+ " y_new[i] = 5*np.sin(t[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "a7b7c87e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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6KUnPSbpu0eMXSbq4cvsxSTs6rGvLwt9PxT/vf5TbrlYbaKuucvmPqxjHvqir7VX+7p+T9Jll1um8jdWsq/M2VrOu1tpYY42xxi+6U8We0uclfbp8bJ+kj5a3/1zS0+Uv8Yikd1ee+5vlH+Y5Sb/RZV3l/T+V9BeLnnedpONlvccl3d5wXfdJOiXpf1SMad0uaY+kPZWGs7+s+7ikyY6210p13SPp+5KOlZfZ8vGrym31ZPl3/nTHdf1epX3NVP/Je7WBruoq17lN0v2Lntf29rpexXjuU5W/1c5ht7GadXXexmrW1Vob4xByAEiOIxMBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBILn/A4RgDmxmXGPFAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(t,y_new,'*b')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9cc534b7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# recreate the data structure to be passed to the solver\n",
+ "data_new = {}\n",
+ "data_new[\"t\"] = t\n",
+ "data_new[\"y\"] = y_new"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e721f62",
+ "metadata": {},
+ "source": [
+ "We will fit this data set using 3 different loss functions with the same model $\\varphi(t;x)$ each time and discuss the results under the plots all at once below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0caf334e",
+ "metadata": {},
+ "source": [
+ "### Fit model with the $l_2$-norm, $l_1$-norm and Arctan loss function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "fa815c5c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "loss_functions = ['L2', 'L1', 'ATAN']\n",
+ "\n",
+ "# turn off printing of solver log\n",
+ "opt.handle_opt_set(handle, 'print file = -1')\n",
+ "\n",
+ "# solve using 3 different loss functions\n",
+ "for lfunc in loss_functions:\n",
+ " \n",
+ " # set option for loss function and solve\n",
+ " opt.handle_opt_set(handle, 'NLDF Loss Function Type =' + lfunc)\n",
+ " soln = opt.handle_solve_nldf(\n",
+ " handle, lsqfun, lsqgrd, x, nres, data=data_new, io_manager=iom)\n",
+ " # plot fitted curve\n",
+ " plt.plot(t, soln.x[0]*np.sin(soln.x[1]*t), label=lfunc)\n",
+ "\n",
+ "# plot data points \n",
+ "plt.plot(t,y_new,'*b')\n",
+ "plt.title(\"Fitted with Various Loss Functions\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eed845f1",
+ "metadata": {},
+ "source": [
+ "### Fitted Models and Contour Plots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49260d6b",
+ "metadata": {},
+ "source": [
+ "In the first row of plots, the data is fitted using $l_2$-norm loss, $l_1$-norm loss, and $\\arctan$ loss. Shown below each is the contour plot of the objective function value, where the black circles represent the parameters used to generate the data, the cyan circles represents the starting point for the solver, and the cyan wedges represent the optimized solution found by the solver.\n",
+ "\n",
+ "\n",
+ "\n",
+ "In the $l_2$-norm case in the left column, the outliers generated by $\\sin(t)$ have pulled the optimal solution away from $x = (5,1)$. The contour plot for $l_2$-norm loss indicates that we don’t have to worry too much about what starting point to use, since there are no local minima in the region displayed, other than global best solution.\n",
+ "\n",
+ "The behaviour of the solver is quite different when using an extremely robust loss function like $\\arctan$ loss, which looks like\n",
+ "\n",
+ "$$\n",
+ "\\underset{x \\in \\mathbb{R}^{2}}{\\text{minimize}} ~ f(x) =\\sum_{i=1}^{21} \\text{arctan}(r_i(x)^2)\n",
+ "$$\n",
+ "\n",
+ "The fitted model and corresponding contour plot for the $\\arctan$ case are in the middle. Here, there are eight local minima in the contour plot for $\\arctan$ loss, with seven of them being substantially worse solutions than the global minimum, and it is one of these we’ve converged to. Therefore, in this case the selection of initial estimation of the parameters is much more important.\n",
+ "\n",
+ "The model fitted with $l_1$-norm loss and the corresponding contour plot are in the right column. Looking at the contour plot, there are still a few local minima that do not correspond to the optimal solution, but the starting point of $x = (2.1,1.4)$ still converges to the global minimum, which lies at\n",
+ "$x = (5,1)$, meaning the part of the dataset generated from $\\sin(t)$ is effectively being ignoring. From the plots of the loss functions, we can see that $l_1$-norm loss is more robust than $l_2$-norm loss but less so than $\\arctan$ loss. \n",
+ "\n",
+ "So, what has happened in each case is: using $l_2$-norm loss, we move to the global minimum which is affected by the whole dataset. Using $l_1$-norm loss, we move to the global minimum which fits most of the data very well and ignores a small portion, treating them as outliers. Using $\\arctan$ loss we move to a local minimum which ignores a large portion of the data (treating them as outliers) and fits a small amount of data very well.\n",
+ "\n",
+ "## Conclusion\n",
+ "\n",
+ "The lesson here is that the same thing that makes a loss function robust – ignoring data that lies far from the current model to some degree – can populate the search space with local minima where the model predicts some of the data well and ignores most of it. In extreme cases like arctan loss, if the starting point fits some of the data very well, the model will likely just be optimized for that portion of the data, even if it is a small portion of the whole dataset. It is therefore important to try a variety of loss functions and stating points when setting up a data-fitting problem, since these will affect both the optimal solution, as well as how easily an optimal solution is found.\n",
+ "\n",
+ "[Learn more about the NAG Library](https://www.nag.com/content/nag-library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d00b4ddf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
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diff --git a/local_optimization/NLDF/dose_resp.dat b/local_optimization/NLDF/dose_resp.dat
new file mode 100644
index 0000000..44658fc
--- /dev/null
+++ b/local_optimization/NLDF/dose_resp.dat
@@ -0,0 +1,42 @@
+0.0005874,7.327
+0.0005874,-6.043
+0.0005874,-0.428
+0.002937,3.110
+0.002937,-2.340
+0.002937,3.103
+0.01468,-11.981
+0.01468,-4.885
+0.01468,-0.566
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+0.03283,-5.747
+0.03283,-1.660
+0.07341,-4.922
+0.07341,-4.183
+0.07341,-0.981
+0.1642,6.090
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+0.1642,-1.919
+0.3670,-11.767
+0.3670,-5.345
+0.3670,-7.134
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+0.8207,-2.099
+0.8207,-4.490
+1.835,-6.812
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+4.103,-0.600
+4.103,-5.707
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+9.175,4.313
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+45.87,-30.895
+45.87,-23.929
+91.74,-30.911
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\ No newline at end of file
diff --git a/local_optimization/NLDF/dose_resp.ipynb b/local_optimization/NLDF/dose_resp.ipynb
new file mode 100644
index 0000000..6bc07cb
--- /dev/null
+++ b/local_optimization/NLDF/dose_resp.ipynb
@@ -0,0 +1,371 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "bf21964c",
+ "metadata": {},
+ "source": [
+ "# Modelling dose–response relationships using data fitting\n",
+ "\n",
+ "The following is the Python code used to fit a nonlinear regression model to dose–response data of a chemical taken from the US National Toxicology Program library."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "793e1544",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import warnings\n",
+ "from naginterfaces.library import opt\n",
+ "from naginterfaces.base import utils"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bacb5e9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# read in the data\n",
+ "df = pd.read_csv('dose_resp.dat', header=None) \n",
+ "data = df.values\n",
+ "t, y = data[:, 0], data[:, 1]\n",
+ "\n",
+ "# plot input vs output\n",
+ "plt.scatter(t, y, marker='*', color='b')\n",
+ "plt.xlabel(r\"Compound concentration ($\\mu M$)\")\n",
+ "plt.ylabel(\"Percent activity\")\n",
+ "plt.title(\"Dose–response data\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "82510304",
+ "metadata": {},
+ "source": [
+ "## Fit the model with the good starting point:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "061e8555",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GN, Nonlinear Data-Fitting\n",
+ " Status: converged, an optimal solution found\n",
+ " Final objective value 1.219791E+03\n",
+ "\n",
+ " Primal variables:\n",
+ " idx Lower bound Value Upper bound\n",
+ " 1 -inf -3.81844E+01 inf\n",
+ " 2 -inf -3.36193E+00 inf\n",
+ " 3 -inf 3.25418E+01 inf\n",
+ " 4 -inf -3.39512E+00 inf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# create a handle for the model\n",
+ "nvar = 4\n",
+ "handle = opt.handle_init(nvar=nvar)\n",
+ "\n",
+ "# register residual structure\n",
+ "nres = len(t)\n",
+ "opt.handle_set_nlnls(handle, nres)\n",
+ "\n",
+ "# define the residual callback function\n",
+ "def lsqfun(x, nres, inform, data):\n",
+ " rx = np.zeros(nres, dtype=float)\n",
+ " t = data[\"t\"]\n",
+ " y = data[\"y\"]\n",
+ " \n",
+ " # fit the hill function to the data\n",
+ " for i in range(nres):\n",
+ " rx[i] = (y[i] - (x[0] + ((x[1] - x[0]) / (1 + (x[2] / t[i])**x[3]))))\n",
+ " \n",
+ " return rx, inform\n",
+ "\n",
+ "# define the residual gradient\n",
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " t = data[\"t\"]\n",
+ " nvar = len(x)\n",
+ "\n",
+ " for i in range(nres):\n",
+ " rdx[i*nvar] = -1 + (1 / (1 + (x[2] / t[i])**x[3]))\n",
+ " rdx[i*nvar + 1] = -(1 / (1 + (x[2] / t[i])**x[3]))\n",
+ " rdx[i*nvar + 2] = (x[1] - x[0]) * ((x[3] * x[2]**(x[3] - 1)) / (t[i]**x[3] * (1 + (x[2]**x[3] / t[i]**x[3]))**2))\n",
+ " rdx[i*nvar + 3] = (x[1] - x[0]) * np.log((x[2] / t[i])) * (x[2] / t[i])**x[3] * (1 / (1 + (x[2] / t[i])**x[3])**2)\n",
+ " \n",
+ " return inform\n",
+ "\n",
+ "# create the data structure to be passed to the solver\n",
+ "data = {}\n",
+ "data[\"t\"] = t\n",
+ "data[\"y\"] = y\n",
+ "\n",
+ "# set loss function to l2-norm and printing options\n",
+ "for option in [\n",
+ " 'NLDF Loss Function Type = L2',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Options = No',\n",
+ " 'Print solution = Yes',\n",
+ " ]:\n",
+ " opt.handle_opt_set (handle, option)\n",
+ "\n",
+ "# mute warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "# use an explicit I/O manager for abbreviated iteration output:\n",
+ "iom = utils.FileObjManager(locus_in_output=False)\n",
+ "\n",
+ "# set initial guess and solve\n",
+ "x = [-40., -5., 30., -5.]\n",
+ "\n",
+ "sol = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres,data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "92bc7d4d",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot result from NLS\n",
+ "plt.title(\"Dose–response curves\")\n",
+ "plt.plot(t, y, '*b')\n",
+ "dose = np.linspace(0.0005874, 91.74, 1000)\n",
+ "resp = sol.x[0] + ((sol.x[1] - sol.x[0]) / (1 + (sol.x[2] / dose)**sol.x[3]))\n",
+ "plt.plot(dose, resp, label='L2')\n",
+ "\n",
+ "# fit all loss functions and plot results\n",
+ "# loss function types\n",
+ "lossfuns = {'HUBER', 'L1', 'LINF', 'CAUCHY', 'ATAN', 'SMOOTHL1', 'QUANTILE'}\n",
+ "\n",
+ "# turn off log printing\n",
+ "opt.handle_opt_set(handle, 'Print File = -1')\n",
+ "\n",
+ "for lossfun in lossfuns:\n",
+ " \n",
+ " # set option for the loss function\n",
+ " opt.handle_opt_set(handle, 'NLDF Loss Function Type = ' + lossfun)\n",
+ " \n",
+ " # call the solver\n",
+ " sol = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)\n",
+ " \n",
+ " # calculate response using fitted parameters\n",
+ " resp = sol.x[0] + ((sol.x[1] - sol.x[0]) / (1 + (sol.x[2] / dose)**sol.x[3]))\n",
+ " \n",
+ " # plot curve\n",
+ " plt.plot(dose, resp, label=lossfun)\n",
+ "\n",
+ "# show the plot\n",
+ "plt.xlabel(r\"Compound concentration ($\\mu M$)\")\n",
+ "plt.ylabel(\"Percent activity\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "937d3904",
+ "metadata": {},
+ "source": [
+ "## Fit the model with the naive starting point and different regularisation functions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "839d5752",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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E57ZWQfB4nCkCH03rweKPOzP2lxs5+qJiRKR2O2cKHhFSU4TMtBT8aSn407z4071kpqXQITONvOx0urTz0cGfitTXl4cxScrn87Fp0yY6depkfxstQFXZtGkTPp+v8Y1DJG1yyMiA8vK+QN/aZTVVAtRQsvU75lfdC+zZHmLhFS+gVWm181/OzuXL2blIaiVDHvpFyE1PgrqtrmsCSiDspqjdH3gRSE/xuknES2Z6KtkZTkJxthR3O9lzPniM2smey30pPjr6OtLR15E+OX0YnDuYLv4uzXqvjImlgoICiouLaW5X/Cacz+ejoKCgSfskbXLY61WWwBl/mALkgIiznTg7DJh2PZ89ejbfzxlOTUU63vQKeo4pYuRFT5PRwWk0o3Vuj1V1ntUEAgRUqQko1QGlqibgPpTKmioqqmrYXh6o3d/rETLTvbTzpZKVnoLXE5Jb3OCD22rIuYLKqsvYUrGVndW7+2M5qNMgLhgwgaO7H2G/yFqzNv5/lwr07tY53mG0Ht40SElv8cMmbXJYuRIGDIDt24Pf/i718uiJ/8KXUs6uW/LC9ru8dChTKw/Bl7KLyso0jitdysPLp8cu8CbaJcK3aakU+dJ5pXIB12xazHGlO7lj42YyrFzXmNbvsGvgf25v8cMmZXJwipSCc3V/hSkTj1/J/Vd/Dh0fAN2zu951HxzNZad9zaRTlzL1lQMo2XgMHP/H8JOEffHW80XcyDYBVUq27mLp2u0sXrONHeXV5PhSGb1fJ4b1bE+Kp/H7CTKAg9zHeRrgsS0L+SufsyvvAP7UdRwpktz3JLQ+ltBNHd2ic/tsUiaHFSvgyivh5ZfD14kI2YV9yB/Xp959Z3zg3K101lkH8txzwZbUl9a77b7y4IyN2h34aU2A2UvXM+3TlTywaDNdv/dx7f/sz+nDCvB4IitmSAEuAdp9/Sx3fXYXj2f7uXjwxVGJ3RjTuiXlz8auXSEvvMQIcH7MP/JIw7er7uswoc2R4vVw3KB8nps0mqcuPoT8HB83vLiIs6bOYdm6HU061lkDzuLYnsfy9y/+zprSFhtgzxjThiRlcgBnLIb6eDywZk39CaClhgndFyLCYX07M+PyQ/nj6UP4dv0OTvjLxzz68Yom3ct846gbqdEaHl/8ePSCNca0WkmbHIqLoV+/8OUizpVFfQkgkYYJFRHOHNGDd687kqP6d+EPby7lkieL2FZWFdH++Zn5nNznZF5e/jJby7dGN1hjTKuTlMkhIwO6dYNly8LXjR3rJIDg1UBoF96JOExop6x0HvnlcG49eSAffruB06Z8yurNkQ0nOOGACVTUVDDr+1lRjtIY09okZXIIXgEEb/bx+ZyriOOPh5kznS/8YL9Ku3btmQCCw4TOmeNME6ELDRHhgsN686+LDmFjaSWnPvQpi4q3Nrpf/w796Z3Tm3+v/Hf0gzTGtCpJ3WX31KmQluaM4ZCfD/PnQ+/eobe57tZaOuL7bkMp5z82l607q3jiolEM69mhwe2nLJzClC+m8N6Z79E5wxoeGZNMrMvueoReAQwc6NyddMcdiVWv0Bz75Wbx3KQxdMxK49x/zmXe91sa3P7wgsNRlLklsRt+0BiT+JI2OcyYAdOmwdChsGSJs2zKFKcu4rnnEqteoam6tc/guUlj6JyVxoWPf97gra4HdDyAdmntmFMyJ4YRGmMSXdImB9j7VcLYsdGvV4h2t9/5OT7+ddEhpKV4OG/aXNZuq6esDPB6vBySf4glB2PMHpI6Oezt7qOZM+Ghh+Cgg5xpNEaGi0VDuh4d/Tx2/ki27ari/MfmsrOiut7thucNp2RnCevL1kcvGGNMq5LUyQFif/dRrBvSDeqew5RzhvPtuh3c8OKiehvKDeo8CIDFGxdHJwhjTKuT9MlhxozoXyWEikeF9xH753LjcQN488sSpn60Imx9/4798YrXkoMxplbSJ4dYi1dDuklH9OGEwfnc+9bX/Gf5xj3WZaRk0Ld9X0sOxphalhziIB4N6USE+04/iN6dM7n2+YVs2Vm5x/oDOx/I0s1LbexeYwwQQXIQkU6xCCTRRPNuolgXZQVlpqfw57MOZvPOSm6e8eUeiaBv+75srdjKpvJNsQnGGJPQIrlymCMiL4jICZJEY0vGo1vuWBjUPYfJY/vz1pK1vFBUXLt8v/b7AbBia3idhDEm+USSHPYHpgK/BJaJyN0isv++nFREzhCRJSISEJERddbdLCLLReQbERm3L+dpjkToljvaLjm8D2P6dOK215fw/aadgHPlALB86/J4hmaMSRCNJgd1vKOqZ+MMJHYeMFdEPhSRMc0872LgNOCj0IUiMhA4CzgQOA54WES8zTxHROoWH7X27jMi4fEI/3fmQXhFaouXcjNyaZfaju+2fhfv8IwxCSCiOgcRuVpEioDJwK+BzsD1wNPNOamqLlXVb+pZdQrwrKpWqOpKYDkwqjnniFTd4qNE7JY7Grq1z+CmEwbwn+828XzRakSE/drvZ1cOxhggsmKl/wLZwKmqeqKqzlDValUtAv7ewvF0B1aHzBe7y8KIyCQRKRKRog0bNjT5RA0VHyVit9zRcPbInozq3ZE/vLmU9dvL6VQ1iFevv67Nvl5jTOQiSQ6/U9U7VbW29lJEzgBQ1Xv3tpOIzBaRxfU8TmmBuFHVqao6QlVH5ObmNnn/hoqP4nU3Uax5PMI9pw2mojrA719dwoKnTmLb14P5/W31d7NhjEkeKRFscxPwfJ1lNwMvNLSTqh7bjHjWAD1C5gvcZS0uWYqPGtMnN4uV943jkcrdvxP+8YiHfzzSesawMMa0vL1eOYjI8SLyV6C7iPwl5PE4EK2flq8BZ4lIuoj0BvoBURtoIFh89PrrkJcHq1ZF60yJbfly6HLwOjypzn9ruq+mzVXCG2OapqFipR+BIqAcmBfyeA3Yp1tMRWS8iBQDY4A3ReRtAFVdgnOV8hXwFnClqtbsy7ka8tBDsHgxTJ/uJIrCwmidKbH16uFhdP9sAtVeJLWcygpPUl5FGWN2a3SYUBFJUdWELoRuzjChACkpUFNP6knG4pTTToNvtq2n4ohryFlwLb08I9tsXYsxxtGsYUJFJFjPsEBEFtV9RCXSGAneqVRfYkjW4pQZM+DtF7Pw5W8i87Q/WWIwJsk1VCF9tTs9KRaBxNKKFTB5MrzyCpSVOcu8XueW1mQuTino4KdPh158t3UZny7fyGF9O8c7JGNMnOz1ykFVS9ynPweqVPX70EdswouO0DuVPO47cNppcPnlbbdNQ6QOL+yPJ20Ld765hJqA9dBqTLKKpJ1DO+AdEflYRH4lInnRDioWgncqzZ8PV1wB1dVtu01DpHpmF4DU8M2GH3lpfnHjOxhj2qRG2zmo6u3A7SIyBPgF8KGIFDezHUPCCE0CDz0UvzgSTX6mU6Y2oKCG+9/+hhMHdyUzPZLmMMaYtqQpg/2sB9YCm4Au0QnHxFswOZwy3M/6HRX1DitqjGn7Iul47woR+QB4F+gEXKKqQ6IdmImPYHLw+0s5cXBXpn60grXbyuMclTEm1iK5cugBXKOqB6rqbar6VbSDMvGTnZZNRkoGJTtLuPG4AdQElPtn1deBrjGmLWuonUO2+/Q+4AcR6Rj6iE14JtZEhDx/HuvK1tGzk5/zDyvkpfnFLPlxW7xDM8bEUENXDsGxGubhdKMR2oVG05sjm1YjPzOftTude3qvPKov7TNSuevNpTTWmt4Y03Y01M7hJHfaW1X7uNPgo0/sQjSxFpoccjJSufqYfvznu028/836OEdmjImVSCqk341kmWk78jPz2bhrI1U1VQBMOKQXvTtncvfMr6muCcQ5OmNMLDRU5+Bz6xY6i0iHkPqGQvYyOptpG/L9+SjK+l3OlUJaiocbjxvA8vWlPFe0upG9jTFtQUNXDpfi1C8MYM/6hleBv0U/NBMvwdtZg0VLAOMOzGNkYQcefOdbSisSupNeY0wLaKjO4c+q2huYXKfO4SBVteTQhtWXHESE355wABtLK3nkw+/iFZoxJkYiaecQEJH2wRm3iOmK6IVk4q2+5ABwcM8OnHxQN/7x8QpKtiXZgBfGJJlIksMlqro1OKOqW4BLohaRibvM1EzapbULSw4AN4zrTyAA/zfr2zhEZoyJlUiSg1dEJDgjIl4gLXohmUSQ589jfVn4ras9Ovq5wBrGGdPmRZIc3gKeE5FjROQY4Bl3mWnD8jKdVtL1ueKovuRkpHL3TGsYZ0xbFUlyuBF4H7jcfbwL3BDNoEz85fvz95ocgg3jPl2+iQ++3RDjyIwxsdBoclDVgKpOUdXT3ccjqlrP6MumLeni78KmXZtqG8LVNfGQXhR28nP3m0utYZwxbVAkLaT7iciLIvKViKwIPmIRnImfPH8eirJhV/1XBmkpHm46fgDL1pfyfJGNGGdMWxNJsdJjwBSgGjgKeBKYHs2gTPzlZTqjwe6taAlg3IH5jOjVgQesYZwxbU4kySFDVd8FRFW/V9XbgBOjG5aJtzy/mxx27j05iAi3nHgAG0srmGoN44xpUyJJDhUi4gGWicivRGQ8kBXluEycRXLlAE7DuJOGdGXqxzZinDFtSSTJ4WrAD1wFDAfOAc6LZlAm/tqltiMjJaPR5ABw43ED3IZxNmKcMW1FJHcrfa6qpaparKoXqOrPVXVOLIIz8VM7IlwDxUpBPTr6Oe/QXrxoDeOMaTMiuXIwSaqhhnB1/eqofnTwp3Hrq0usYZwxbYAlB7NXwbGkI5HjT+Wm4wZQ9P0WZsxfE+XIjDHRFkk7h8MiWdYUInKfiHwtIotE5OU6vb7eLCLLReQbERm3L+cx+ybPn8eGsg3UBCJr83j68AKG9mjP//v3Urbtqr/xnDGmdYjkyuGvES5rineAQao6BPgWuBlARAYCZwEHAscBD7sd/Zk4yPPnUaM1bCrfFNH2Ho/wh1MHsWlnJQ++Y722GtOapexthYiMAQ4FckXkupBV2cA+fWGr6qyQ2TnA6e7zU4BnVbUCWCkiy4FRwH/35XymeWpvZ925ji7+LhHtM6h7Ducc0osn/7uKM0f0YGC37GiGaIyJkoauHNJw2jOkAO1CHtvZ/WXeEi4E/u0+7w6EDlJczF7GqxaRSSJSJCJFGzZY52/RUNsQLsJ6h6DJY/vT3p/G/766mEDAKqeNaY32euWgqh8CH4rI46r6fVMPLCKzgfx6Vt2iqq+629yC0y3HU009vqpOBaYCjBgxwr6BoiDShnB15fhTufn4AfzmxUU8NfcHfjm6VzTCM8ZE0V6TQ4h0EZkKFIZur6pHN7STqh7b0HoROR84CThGd9/7uAboEbJZgbvMxEGH9A6kelKbnBzAqZx+7YsfuWfmUo4e0IXu7TOiEKExJloiqZB+AVgA/A74Tcij2UTkOJwxIX6mqmUhq14DzhKRdBHpDfQD5u7LuUzzNaUhXH373j1+MArc8vKX1vbBmFYmkiuHalWd0sLn/RuQDrzjjkA6R1UvU9UlIvI88BVOcdOVNnZEfDWlIVxdPTr6+c24/tz++le8snAN4w8uaOHojDHREklyeF1ErgBeBiqCC1V1c3NPqqp9G1h3F3BXc49tWlaeP49FGxY1e/9zxxTy+hc/cvvrX/GTvrnktktvweiMMdESSbHSeTjFSP8B5rmPomgGZRJH8MqhucVCXo/wx9OHUFZZw00vLbLiJWNaiUg63utdz6NPLIIzsVVSAkceCWvX7l6W58+jKlDFlootzT5u3y7tuOm4Abz79Xqe+uyHFojUGBNtkXSf4ReR37l3LAWHDT0p+qGZWLvzTvjkE7jjjt3LIhn0JxLnH1rI4f0684c3v2L5+tJ9OpYxJvoiHSa0Eqe1NDi3lv4hahGZmMvIABGYMgUCAWcq4ixvbkO4ujwe4f/OOIiMVC/XPLeAyupAS4RujImSSJLDfqr6R6AKwL31VKIalYmpFStgwgTw+515vx8mToSVK/fsQmNfdcn2cc/Ph7B4zXb+37+X7vPxjDHRE0lyqBSRDEABRGQ/Qu5aMq1f166QnQ3l5eDzOdPsbMjPh06+TnjFu89XDkHjDszn/EMLeezTVbz2xY8tckxjTMuLJDncCrwF9BCRp4B3cRqwmTZk3Tq47DKYM8eZBiulvR4vuf7cFksOAL894QCG9+rATS8tYtm6HS123ERUXyW/Ma2BRHJroYh0AkbjFCfNUdWN0Q6sKUaMGKFFRXZ3bbScM/McfCk+Hh37aIsdc932ck78yydkZ6Tw6pWH0c6X2mLHTiRXXAGPPAKXXgoPPxzvaIzZk4jMU9UR9a2L5G6l8TitpN9U1TeAahE5tYVjNAmsuV1oNHjMbB9/m3Aw328q46pnFlBd07YqqBuq5DemNYioWElVa0eNV9WtOEVNJkl0zexKyc4SAtqyX+Cj+3Ti9p8dyPvfbOD2179qUw3kGqrkN6Y1iKT7jPoSSCT7mTaiR7seVNRUsKFsQ+3dSy3lnNG9WL25jEc+WkGvTn4uPrxttK9sqJLfmNYgkiuHIhF5QET2cx8P4HShYZJEj3ZOL+qrd6xuZMvmufG4AZwwOJ+7Zi7lzUUlUTlHPOytkt+Y1iCSK4BfA/8LPIdzO+s7wJXRDMokltDkMCK/3rqrfeLxCA+cOZT12z/j6mcXkJHm4egBLXuFEg8zZux+/tBD8YvDmOZo8MpBRLzAG6p6k6qOUNWRqvpbVd0Zo/hMAsjPyscr3qhdOQD4Ur1Mu2AkA7tlc9n0+XyyLKFuiDMm6TSYHNyxFAIikhOjeEwCSvWkkp+ZT/GO4qieJ9uXypMXjqJP50wufvJzSxDGxFEkdQ6lwJci8k8R+UvwEe3ATGLp0a4HxaXRTQ4A7f1pTL/4EAo7ZXLh45/z1uK2UwdhTGsSSXKYgVPn8BG7x3OwCukk06Ndj6gWK4XqnJXOc5PGMKh7Nlc8NZ/ni2JzXmPMbo1WSKvqE27fSj1V9ZsYxGQSUEG7ArZWbGV75Xay07Kjfr4cfyrTLz6ES/81jxteXMSPW3dx1dH98Hisz0djYiGSFtInAwtx+ldCRIaKyGtRjsskmMLsQgBWbVsVs3P601J49LwR/HxYAX+avYxfPTOfssrqmJ3fmGQWSbHSbcAoYCuAqi4E2kZLJROxvu2dYb+Xb10e0/Omp3i5/4wh3HLCAby1eC2nT/kvqzeXxTQGY5JRJMmhKrT7DFfb6gjHNKp7Vnd8Xl/MkwOAiHDJEX345/kjWb2ljBP+/LF1921MlEWSHJaIyATA6w4R+lfgP1GOyyQYr8dL75zefLf1u7jFcFT/Lsy86nD65WVx1TML+M0LX1BaYcVMxkRDJMnh18CBOAP8PA1sA66JYkwmQfVt3zcuVw6henT08/ylY/j10X15cX4xYx/4kPe+btkeY40xDSQHEfGJyDXAH4EfgDFuC+nfqWp5rAI0iaNvh76sL1vP9srtcY0jxevh+rH9efGyQ8nypXDh40X8+pkFrN9uH0tjWkpDVw5PACOAL4HjgftjEpFJWMFK6XgWLYUa3qsDb/z6cK77n/15e/Fafnr/B/xp9rd2R5MxLaCh5DBQVc9R1UeA04EjYhSTSVD7d9gfgKWblsY5kt3SUjxcdUw/3rnuCH7aP5c/zV7GT+/7gKc/+4HKartvwpjmaig5VAWfqKr9FDPk+fPonNGZLzd+Ge9QwvTqlMnDE4fz0uVj6N4hg9++/CVH3vc+j3+6kvKqmniHZ0yr01ByOEhEtruPHcCQ4HMRiW+hs4kLEWFw58EJmRyChvfqyIzLD+WJC0fRo4Of217/isPueY/73v6aNVt3xTs8Y1qNvXafoareWAZiWochuUN4f/X7bKvYRk56YnbWKyIcuX8uR+6fy9yVm5n60QqmfPAdUz74jqMH5DHhkB4c3i+XVG8kN+sZk5ziMtyniNwJnILTmG49cL6q/igiAvwZOAEoc5fPj0eMpn5DOg8BYOH6hRzZ48g4R9O4Ub07Mqp3R4q3lPHM3B94du5qZi9dRwd/KicO6copQ7szvGcH67PJmDokHoO6i0i2qm53n1+FU/l9mYicgNOu4gTgEODPqnpIY8cbMWKEFhUVRTVm46ioqeCwZw7jjP3P4MZRN8Y7nCarrA7w0bcbeGXhGmYvXUd5VYAu7dI5ekAXjhrQhZ/07Uxmug2RbpKDiMxT1XqHd4zLX0EwMbgycYYfBedq4kl1MtYcEWkvIl1V1Tr1TxDp3nSGdRnGnJI58Q6lWdJSPBw7MI9jB+ZRWlHNO1+t5Z2v1vHmohKe/Xw1aV4Po3p3ZHSfjhzSpxNDCnJIT7ESVpN84vYTSUTuAs7FaXF9lLu4OxDaeX+xuywsOYjIJGASQM+ePaMaq9nT6G6jeXDeg6zbuY68zNY71nNWegrjDy5g/MEFVFYHKPp+M+8tXc/HyzZy/6xvAUhP8XBwz/Yc3LMDg7vnMLh7DgUdMnBKQI1pu6JWrCQis4H8elbdoqqvhmx3M+BT1VtF5A3gHlX9xF33LnCjqjZYZmTFSrH13dbvOPXVU7lp1E1MPGBivMOJii07K5m7ajNzV27ms5Wb+LpkB9UB52+lvT+VQd1yOKBrO/p2yWK/3Cz6dsmivT8t7DglJXDWWfDcc5Bf31+DMftgXz9fcSlWUtVjI9z0KWAmcCuwBugRsq7AXWYSyH7t96Nv+77MWjWrzSaHDplpjDswn3EHOn9x5VU1fLN2B4t/3MbiNdv4cs02nvjv93s0tOuclUaf3CwKO/np3t5PQYcMnn6wMx9/ks5tt8Hf/25XG6Zl3XknfPIJ3HEHPPxwyx47XhXS/VR1mfv818CRqnq6iJwI/IrdFdJ/UdVRjR3Prhxib+qiqfx1wV+ZOX4mPbJ7NL5DG1QTUNZs2cXyDTtYvr6U79bvZPmGUlZvLqPo1qPQmvC6Ck9KgIunzSe3XTq5WT46t0sjNyud3HbpdM5KJ8efSlZait09ZRqUkQHl9XQl5vPBriY052noyiFeyeEloD/OrazfA5ep6hr3Vta/Acfh3Mp6QWNFSmDJIR7Wl61n3IvjOGvAWa3yrqVoW/lDDddcG+DtmSlUlAup6QH6jNjKwPErKfWUsrG0ks07K+vd1yOQk5HqPPxp5GSk0t6dz85IITM9hcy0FPxpXrLSU/Cnp5CZ5sWflkJmujPNSk/Bl+qxupE2qqQEJk+GV16BsjLw+2H8eLj//qYVLyXi3Uo/38tyBa6McTimGbr4uzC2cCwvL3+ZS4ZcQkdfx3iHlFB69/TSrYuXqkrn11xlpYejh3Tk4et3v09VNQE2lVayYUcFG0rL2VhayfZdVWwtq2Lbriq27nKm23ZV8cOmnbXPAxH+nhNxKtTTU7zONNV5nuYNPg9d505TPKSleEjzekjxCikeDykeIcXrIdUreIPP3amzLnQ7IbXOcq9H8Ijg8eBMRfAIIcud+brrRMTdpv51yaxrV8jOdq4efD5nmp3dsvVadkO3abZJQybx9qq3eXjhw/xu9O/iHU7CWbcOLrsMJk2CqVOdX3uhUr0e8nN85Of4gMham6sqFdUBdlZUU1ZZw87KanZW1LjzzvOyymp2VjrLKqsDVFQHqKiuoaIq5Hl1gIqqAFvLKqmoDuyxXXlVgKqaANUBpSbSTBQH9SUOjwg4/xARJPQ5TsKE0OUg7LkdweX1rHN3DztmcDvqLq9zDPbYJ+SYtbE527tPQieE5kNB+HjO/vQ5vJKLLlZW/7dr2OdrX8WlWKmlWbFS/Nz92d08+/Wz/GPsPzika6PtFU0ro6pUB5TqGqUqEKDGnVbXOIkjmESqagLuvFIdfB7Y/TygzrFq1HkeCCgBdY6hiru84XWquMdSd5v61wW/0lQVBVRBUXeKu96dr2ed4sxo2DF2zxO6X0PH38sxCD1X7T4h07rL9/hP2XObU4Z255zRvZr1/5twxUqm7bhm2DXMKZnD5A8n8+jYR+nfsX+8QzItSERI9QqpXsjAGgMmE+t5zOwTf6qfvx39N9K8aVw06yLe++G9eIdkjGkBlhzMPuuZ3ZPHxz1Ot8xuXP3+1Vwy6xLe/f5dyqtt2E5jWiurczAtpqqmiqe/fprHFj/GpvJNpHnS6N+xP/069CPfn09eZh5ZqVn4U/1kpmaSkZKBV7x4xYtHPM7U48GDB484j5a4K2V3lV8D20RwnkiOY0yspXvT8af6m7VvwrVzaGmWHBJLdaCaz9d+zn9+/A+LNy5m5baVbCrfFO+wjGmTLhx0IdcOv7ZZ+1qFtImpFE8KY7qNYUy3MbXLqmqq2LBrAzurdrKzaidl1WXsqt5FTaCGgAao0fBpYz9cIvlho7TQNm3gR5RpmwZ2GhiV41pyMDGR6k2lW1a3eIcRc9bxnmmtrELamCgK7RjNmNbEkoMxUZCR4bRonTIFAgFnKuIsN6Y1sORgTBSsWAETJjgdooEznTgRVq6Mb1zGRMqSgzFREIuO0YyJJksOxkRJsOO9OXOc6dq18Y7ImMjZ3UrGRMmMGbufP/RQ/OIwpjnsysEYY0wYSw7GGGPCWHIwxphWqqQEjjwyOvVZlhyMMaaVimYjS0sOxhjTysSikaUlB2OMaWVi0cjSkoMxxrQysWhkacnBGGNaoWg3srRGcMYY0wpFu5GlXTkYY4wJY8nBGGNMGEsOxhhjwlhyMMYYE8aSgzHGmDCWHIwxxoSJa3IQketFREWkszsvIvIXEVkuIotEZFg84zPGmGQVt+QgIj2AscAPIYuPB/q5j0nAlDiEZowxSS+eVw4PAjcAGrLsFOBJdcwB2otI17hEZ4wxSSwuyUFETgHWqOoXdVZ1B1aHzBe7y+o7xiQRKRKRog0bNkQpUmOMSU5R6z5DRGYD9XUDdQvwW5wipWZT1anAVIARI0ZoI5sbY4xpgqglB1U9tr7lIjIY6A18ISIABcB8ERkFrAF6hGxe4C4zxhgTQzEvVlLVL1W1i6oWqmohTtHRMFVdC7wGnOvetTQa2KaqJbGO0Rhjkl2i9co6EzgBWA6UARfENxxjjElOcU8O7tVD8LkCV8YvGmOMMWAtpI0xxtTDkoMxxpgwlhyMMcaEseRgjDEmjCUHY4wxYSw5GGOMCWPJwRhjTBhLDsYY00qVlMCRR8LatS1/bEsOxhjTSt15J3zyCdxxR8sf25KDMca0MhkZIAJTpkAg4ExFnOUtxZKDMca0MitWwIQJ4Pc7834/TJwIK1e23DksORhjTCvTtStkZ0N5Ofh8zjQ7G/LrG0GnmSw5GGNMK7RuHVx2GcyZ40xbulI67r2yGmOMaboZM3Y/f+ihlj++XTkYY4wJY8nBGGNMGEsOxhhjwlhyMMYYE8aSgzHGmDCWHIwxxoQRVY13DPtMRDYA3zdz987AxhYMpzWz98Jh74PD3ofd2up70UtVc+tb0SaSw74QkSJVHRHvOBKBvRcOex8c9j7slozvhRUrGWOMCWPJwRhjTBhLDjA13gEkEHsvHPY+OOx92C3p3oukr3MwxhgTzq4cjDHGhLHkYIwxJkxSJwcROU5EvhGR5SJyU7zjiRUR6SEi74vIVyKyRESudpd3FJF3RGSZO+0Q71hjQUS8IrJARN5w53uLyGfu5+I5EUmLd4yxICLtReRFEflaRJaKyJhk/EyIyLXu38ViEXlGRHzJ+JlI2uQgIl7gIeB4YCBwtogMjG9UMVMNXK+qA4HRwJXua78JeFdV+wHvuvPJ4Gpgacj8vcCDqtoX2AJcFJeoYu/PwFuqOgA4COc9SarPhIh0B64CRqjqIMALnEUSfiaSNjkAo4DlqrpCVSuBZ4FT4hxTTKhqiarOd5/vwPkS6I7z+p9wN3sCODUuAcaQiBQAJwKPuvMCHA286G6SLO9DDnAE8E8AVa1U1a0k4WcCZxC0DBFJAfxACUn4mUjm5NAdWB0yX+wuSyoiUggcDHwG5KlqibtqLZAXr7hi6E/ADUDAne8EbFXVanc+WT4XvYENwGNuEdujIpJJkn0mVHUNcD/wA05S2AbMIwk/E8mcHJKeiGQBLwHXqOr20HXq3OPcpu9zFpGTgPWqOi/esSSAFGAYMEVVDwZ2UqcIKUk+Ex1wrpZ6A92ATOC4uAYVJ8mcHNYAPULmC9xlSUFEUnESw1OqGhyNdp2IdHXXdwXWxyu+GDkM+JmIrMIpVjwap9y9vVukAMnzuSgGilX1M3f+RZxkkWyfiWOBlaq6QVWrgBk4n5Ok+0wkc3L4HOjn3oWQhlPp9FqcY4oJt1z9n8BSVX0gZNVrwHnu8/OAV2MdWyyp6s2qWqCqhTj//++p6kTgfeB0d7M2/z4AqOpaYLWI9HcXHQN8RZJ9JnCKk0aLiN/9Owm+D0n3mUjqFtIicgJOmbMXmKaqd8U3otgQkZ8AHwNfsrus/bc49Q7PAz1xukA/U1U3xyXIGBORnwKTVfUkEemDcyXREVgAnKOqFXEMLyZEZChOxXwasAK4AOcHZFJ9JkTkduAXOHf1LQAuxqljSKrPRFInB2OMMfVL5mIlY4wxe2HJwRhjTBhLDsYYY8JYcjDGGBPGkoMxxpgwlhyMMcaEseRgjDEmjCUH0ywiki8iz4rIdyIyT0Rmisj+8Y6rqUTkNhGZHO84GuOOtXBFS+wnIv9poZgyRORDt/v7xra9VETUbWwYXHalu2yciHwU0j2FSQCWHEyTud0KvAx8oKr7qepw4GbaeI+dcdYeqDc5iGNvf8th+6nqoS0U04XADFWtiWDbwcAXwAAAEfHjtDzeACzEGSviFy0Ul2kBlhxMcxwFVKnq34MLVPULVf1YRK5zR9BaLCLXBNeLSKE7wtjjIvKtiDwlIseKyKfuKGOjQrZ5yh2J7EX3SyR4jLBju/ssDtlmsns1UOge4x/uqF6zRCTD3eYWN4ZPgGBfQmFE5FwRWSQiX4jIvyKIY2/nCzuOiJwjInNFZKGIPBL89d3Ace4B9nO3v8/d7hsReRJYDPQQkVfcq7glIjLJDXeP/dxzlDb3tdQxkZA+hkTkAxEJfvl3Cv1/AYbgdD8xwJ2/CngBCKjqOuAV93gmUaiqPezRpAfOH/aD9SwfjtNfUyaQBSwBDnbXFeL0VTMY50fJPGAaIDhdJL/ibqPAYe4+03D6O9rrsd19FofEMBm4LeR8Q93lzwPnhBzHD2QDy4PnqPNaDgS+BTq78x0jiKO+84UdBzgAeB1IdZc9DJxb532qe5y6r7MQp1+s0SHLgjFm4CSMTnX3c9eXNue11DlGGrC2zrJiwOM+Pwp4JmTdBqAP8BbO1cwC4KfAbHe9F9gQ78+2PXY/7MrBtKSfAC+r6k5VLcXp7vjwkPUrVfVLVQ3gfBG9q843w5c4X0gAq1X1U/f5dPeYkRy7PitVdaH7fJ57jsPd45SpM4bF3nriPRp4QVU3AujuzuYaiqO+89V3nGNwvpg/F5GF7nyfRuKuz/eqOidk/ioR+QKYg9Mdfb+97BfU1NcSqjOwNTgjIr2ANe7/LThXCovcdT2ATaq6AugC/Ab4K7A/zv896hRNVYpIu0ZiNjFiFUCmOZawu/vipgjtxTIQMh9g92exbk+QjfUMWc2exaO+vZyvBucXdTRFej4BnlDVm/fxODtrD+hU9B4LjFHVMhH5gD3fi6ZqLIZddY5/EG4ycA0HnnOfD8ZNAsAOnMFzRuH0iDw/ZJ90oHwfYjYtyK4cTHO8B6SHlGsjIkNwKhZPFacv/ExgPE7X4E3RU0TGuM8nAJ+4zz/ey7HXAV3cMu504KRGjv+Re5wM91fqyQ28xjNEpJP7+jo2Esfe1Hecd4HTRaRLcJn7y7shO4CGflXnAFvcxDAAGB3Bfk19LbVUdQvgFZFgghiKmyxEpB9OUWEwIQwJeX4f8Cv3SqE2abjvz0Z1BtgxCcCSg2kytyhoPHCsOLeyLgH+H/Aj8DgwF2dsiEdVdUETD/8NcKWILAU6AFPcc86v79jul8kd7vJ3gK8biX0+zi/aL4B/4wz6VN92S4C7gA/dopoHGoqjgfOFHUdVvwJ+B8wSkUVu3F0biXsT8KlbcXxfPZu8BaS479s9OEVLDe7X1NdSj1nsLvY7CPC4r/H3OAPkBAcJGoxTB4KqvqGq/3WXD8S5CgWnjuLNJpzbRJmN52AShogUAm+o6qB4x2IaJyLDgGtV9ZcisgwYpqo7mnmsGcBNqvptiwZpms2uHIwxzeJeebwvIjnObLMTQxrwiiWGxGJXDsYYY8LYlYMxxpgwlhyMMcaEseRgjDEmjCUHY4wxYSw5GGOMCWPJwRhjTBhLDsYYY8L8fyhTTbfPFkuZAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# set loss function to huber\n",
+ "opt.handle_opt_set (handle, 'NLDF Loss Function Type = HUBER')\n",
+ "\n",
+ "# set initial guess and solve\n",
+ "x = [30., 30., 30., 30.]\n",
+ "\n",
+ "# regularisation types\n",
+ "regs = {'OFF', 'L1', 'L2'}\n",
+ "\n",
+ "# solve huber loss function with various regularisations\n",
+ "for reg in regs:\n",
+ " # set reg type\n",
+ " opt.handle_opt_set(handle, 'Reg Term Type =' + reg)\n",
+ " # call the solver\n",
+ " sol = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres,data=data, io_manager=iom)\n",
+ " # calculate response\n",
+ " resp = sol.x[0] + ((sol.x[1] - sol.x[0]) / (1 + (sol.x[2] / dose)**sol.x[3]))\n",
+ " # plot curve\n",
+ " plt.plot(dose, resp, label='reg = '+reg)\n",
+ "\n",
+ "# show huber curves\n",
+ "plt.title(\"Huber loss function\")\n",
+ "plt.plot(t, y, '*b')\n",
+ "plt.xlabel(r\"Compound concentration ($\\mu M$)\")\n",
+ "plt.ylabel(\"Percent activity\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f86a8a83",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# set loss function to NLS\n",
+ "opt.handle_opt_set (handle, 'NLDF Loss Function Type = L2')\n",
+ "\n",
+ "# solve NLS loss function with various regularisations\n",
+ "for reg in regs:\n",
+ " # set reg type\n",
+ " opt.handle_opt_set(handle, 'Reg Term Type =' + reg)\n",
+ " # call the solver\n",
+ " sol = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres,data=data, io_manager=iom)\n",
+ " # calculate response\n",
+ " resp = sol.x[0] + ((sol.x[1] - sol.x[0]) / (1 + (sol.x[2] / dose)**sol.x[3]))\n",
+ " # plot curve\n",
+ " plt.plot(dose, resp, label='reg = '+reg)\n",
+ "\n",
+ "# show NLS curves\n",
+ "plt.title(r\"$l_2$-norm loss function\")\n",
+ "plt.plot(t, y, '*b')\n",
+ "plt.xlabel(r\"Compound concentration ($\\mu M$)\")\n",
+ "plt.ylabel(\"Percent activity\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "d8984282",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# destroy the handle\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1258bec0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/local_optimization/NLDF/elastic_net.ipynb b/local_optimization/NLDF/elastic_net.ipynb
new file mode 100644
index 0000000..d52cbb3
--- /dev/null
+++ b/local_optimization/NLDF/elastic_net.ipynb
@@ -0,0 +1,536 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0edec5c6-4a7c-41d2-8241-03deed2b6acd",
+ "metadata": {},
+ "source": [
+ "# Using elastic net in a linear regression to predict a possum's length\n",
+ "\n",
+ "The routine **[handle_solve_nldf](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_nldf.html)** is a general nonlinear data-fitting solver in the [NAG® Library](https://nag.com/nag-library/) that supports a variety of different loss functions and regularization options - including elastic net.\n",
+ "\n",
+ "**Elastic net** regularization is a combination of L1 (lasso) and L2 (ridge) regularization that is ideally suited for high-dimensional and noisy data. In these cases, it can be used for **feature selection** by setting the coefficients of irrelevant features to zero. Further, it is particularly useful when dealing with **multicollinear** features - where two or more features are highly correlated. It achieves this by shrinking the coefficients of correlated features towards each other. It also helps to **reduce overfitting** by penalizing large coefficients, which can lead to better generalization performance.\n",
+ "\n",
+ "To demonstrate the use of elastic net regularization, we will build a linear regression model to predict a possum's total length based upon several features, such as, capture site, age, and head length.\n",
+ "\n",
+ "Note, the purpose of this notebook is to illustrate the use of handle_solve_nldf which is a general data-fitting framework that utilises nonlinear programming algorithms, such as sequential quadratic programming and interior point method. Therefore, it may not be as performant as one of our dedicated linear regression solvers.\n",
+ "\n",
+ "\n",
+ "**Reference:** \\\n",
+ "Lindenmayer, D. B., Viggers, K. L., Cunningham, R. B., and Donnelly, C. F. 1995. Morphological variation among columns of the mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala). Australian Journal of Zoology 43: 449-458. \\\n",
+ "Dataset source: https://www.kaggle.com/datasets/abrambeyer/openintro-possum"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fcd8009c-2402-4374-a125-87f7caf0ff01",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import packages\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from naginterfaces.library import opt\n",
+ "from naginterfaces.base import utils\n",
+ "\n",
+ "# Set a random seed\n",
+ "np.random.seed(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5411c4c6-2c14-40d1-86d0-c5e713f6a0ff",
+ "metadata": {},
+ "source": [
+ "## 1. Load and preprocess the data\n",
+ "This dataset has 13 features and 101 observations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5497ff22-6a92-4cd5-a112-ed4a3250435b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>site</th>\n",
+ " <th>Pop</th>\n",
+ " <th>sex</th>\n",
+ " <th>age</th>\n",
+ " <th>hdlngth</th>\n",
+ " <th>skullw</th>\n",
+ " <th>totlngth</th>\n",
+ " <th>taill</th>\n",
+ " <th>footlgth</th>\n",
+ " <th>earconch</th>\n",
+ " <th>eye</th>\n",
+ " <th>chest</th>\n",
+ " <th>belly</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>Vic</td>\n",
+ " <td>m</td>\n",
+ " <td>8.0</td>\n",
+ " <td>94.1</td>\n",
+ " <td>60.4</td>\n",
+ " <td>89.0</td>\n",
+ " <td>36.0</td>\n",
+ " <td>74.5</td>\n",
+ " <td>54.5</td>\n",
+ " <td>15.2</td>\n",
+ " <td>28.0</td>\n",
+ " <td>36.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>Vic</td>\n",
+ " <td>f</td>\n",
+ " <td>6.0</td>\n",
+ " <td>92.5</td>\n",
+ " <td>57.6</td>\n",
+ " <td>91.5</td>\n",
+ " <td>36.5</td>\n",
+ " <td>72.5</td>\n",
+ " <td>51.2</td>\n",
+ " <td>16.0</td>\n",
+ " <td>28.5</td>\n",
+ " <td>33.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>Vic</td>\n",
+ " <td>f</td>\n",
+ " <td>6.0</td>\n",
+ " <td>94.0</td>\n",
+ " <td>60.0</td>\n",
+ " <td>95.5</td>\n",
+ " <td>39.0</td>\n",
+ " <td>75.4</td>\n",
+ " <td>51.9</td>\n",
+ " <td>15.5</td>\n",
+ " <td>30.0</td>\n",
+ " <td>34.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1</td>\n",
+ " <td>Vic</td>\n",
+ " <td>f</td>\n",
+ " <td>6.0</td>\n",
+ " <td>93.2</td>\n",
+ " <td>57.1</td>\n",
+ " <td>92.0</td>\n",
+ " <td>38.0</td>\n",
+ " <td>76.1</td>\n",
+ " <td>52.2</td>\n",
+ " <td>15.2</td>\n",
+ " <td>28.0</td>\n",
+ " <td>34.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1</td>\n",
+ " <td>Vic</td>\n",
+ " <td>f</td>\n",
+ " <td>2.0</td>\n",
+ " <td>91.5</td>\n",
+ " <td>56.3</td>\n",
+ " <td>85.5</td>\n",
+ " <td>36.0</td>\n",
+ " <td>71.0</td>\n",
+ " <td>53.2</td>\n",
+ " <td>15.1</td>\n",
+ " <td>28.5</td>\n",
+ " <td>33.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " site Pop sex age hdlngth skullw totlngth taill footlgth earconch \\\n",
+ "0 1 Vic m 8.0 94.1 60.4 89.0 36.0 74.5 54.5 \n",
+ "1 1 Vic f 6.0 92.5 57.6 91.5 36.5 72.5 51.2 \n",
+ "2 1 Vic f 6.0 94.0 60.0 95.5 39.0 75.4 51.9 \n",
+ "3 1 Vic f 6.0 93.2 57.1 92.0 38.0 76.1 52.2 \n",
+ "4 1 Vic f 2.0 91.5 56.3 85.5 36.0 71.0 53.2 \n",
+ "\n",
+ " eye chest belly \n",
+ "0 15.2 28.0 36.0 \n",
+ "1 16.0 28.5 33.0 \n",
+ "2 15.5 30.0 34.0 \n",
+ "3 15.2 28.0 34.0 \n",
+ "4 15.1 28.5 33.0 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv('possum.csv', usecols=range(1,14))\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "06841ec7-443e-4831-b013-3aa79cc5333a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Remove NAN data\n",
+ "df.dropna(axis=0,inplace=True)\n",
+ "\n",
+ "# Categorical features (site, population, sex) need to be one-hot encoded\n",
+ "df_encoded = pd.get_dummies(df, columns=['site','Pop','sex'], dtype=float, drop_first=True)\n",
+ "\n",
+ "# Extract total length (y), which is the variable to be predicted\n",
+ "y = df_encoded[[\"totlngth\"]].values\n",
+ "X = df_encoded.drop(columns=[\"totlngth\"]).values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b654fd99-5544-42d4-8467-b959b109f3cd",
+ "metadata": {},
+ "source": [
+ "## 2. Split the data into training and testing sets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5c6e103d-4e2c-4640-b8d5-9f690c444192",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train_test(X, y, test_size=0.2):\n",
+ " \"\"\"\n",
+ " Split dataset into training and testing sets.\n",
+ "\n",
+ " Parameters:\n",
+ " X (numpy array): Features\n",
+ " y (numpy array): Observations\n",
+ " test_size (float, optional): Proportion of data to use for testing\n",
+ "\n",
+ " Returns:\n",
+ " X_train, y_train, X_test, y_test\n",
+ " \"\"\"\n",
+ " # Get total number of samples\n",
+ " num_samples = X.shape[0]\n",
+ "\n",
+ " # Calculate number of test samples\n",
+ " num_test_samples = int(num_samples * test_size)\n",
+ "\n",
+ " # Generate random indices for training set\n",
+ " train_indices = np.random.choice(num_samples, num_samples - num_test_samples, replace=False)\n",
+ "\n",
+ " # Create training sets\n",
+ " X_train = X[train_indices]\n",
+ " y_train = y[train_indices]\n",
+ "\n",
+ " # Create testing sets\n",
+ " test_indices = np.setdiff1d(np.arange(num_samples), train_indices)\n",
+ " X_test = X[test_indices]\n",
+ " y_test = y[test_indices]\n",
+ "\n",
+ " return X_train, y_train, X_test, y_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "86657422-9e33-45ff-899f-7f7840150914",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def scale_data(X_train, y_train, X_test, y_test):\n",
+ " \"\"\"\n",
+ " Scale the training and testing datasets.\n",
+ "\n",
+ " Returns:\n",
+ " X_train, y_train, X_test, y_test\n",
+ " \"\"\"\n",
+ " mu = X_train.mean(0)\n",
+ " sigma = X_train.std(0)\n",
+ " for j in range(X_train.shape[-1]):\n",
+ " xs = X_train[:,j]\n",
+ " is_categorical = np.logical_or(np.isclose(xs, 1.), np.isclose(xs, 0.)).all()\n",
+ " if not is_categorical:\n",
+ " X_train[:,j] = (X_train[:,j] - mu[j]) / sigma[j]\n",
+ " X_test[:,j] = (X_test[:,j] - mu[j]) / sigma[j]\n",
+ " \n",
+ " y_test = (y_test - y_train.mean()) / y_train.std()\n",
+ " y_train = (y_train - y_train.mean()) / y_train.std()\n",
+ "\n",
+ " return X_train, y_train, X_test, y_test\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "487c2e0f-f494-4ac3-8c34-1db320178693",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set shapes: (81, 17) (81, 1)\n",
+ "Testing set shapes: (20, 17) (20, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Split data into training and testing sets\n",
+ "X_train, y_train, X_test, y_test = train_test(X, y)\n",
+ "\n",
+ "# Scale the data\n",
+ "X_train, y_train, X_test, y_test = scale_data(X_train, y_train, X_test, y_test)\n",
+ "\n",
+ "print(\"Training set shapes:\", X_train.shape, y_train.shape)\n",
+ "print(\"Testing set shapes:\", X_test.shape, y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7acd46ae-881f-4d5d-b174-0ee5a66703b7",
+ "metadata": {},
+ "source": [
+ "## 3. Fit a linear regression with least squares loss and elastic net regularization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ad4796a5-724c-4a55-b485-4b18ce603328",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GN, Nonlinear Data-Fitting\n",
+ " Status: converged, an optimal solution found\n",
+ " Final objective value 1.652821E+01\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Number of variables = number of features + bias term\n",
+ "nvar = X_train.shape[1] + 1\n",
+ "\n",
+ "# Create a handle for the model\n",
+ "handle = opt.handle_init(nvar=nvar)\n",
+ "\n",
+ "# Register residual structure\n",
+ "nres = X_train.shape[0]\n",
+ "opt.handle_set_nlnls(handle, nres)\n",
+ "\n",
+ "# Create the data structure to be passed to the solver\n",
+ "data = {}\n",
+ "data[\"X_train\"] = X_train\n",
+ "data[\"y_train\"] = y_train\n",
+ "\n",
+ "# Define the residual callback function and its gradient\n",
+ "def lsqfun(x, nres, inform, data):\n",
+ " rx = np.zeros(nres, dtype=float)\n",
+ " X_train = data[\"X_train\"]\n",
+ " y_train = data[\"y_train\"].squeeze()\n",
+ " \n",
+ " # Fit a linear regression to the data\n",
+ " r_full = y_train - (x[0] + X_train @ x[1:]) \n",
+ " for i in range(nres):\n",
+ " rx[i] = r_full[i]\n",
+ " \n",
+ " return rx, inform\n",
+ " \n",
+ "def lsqgrd(x, nres, rdx, inform, data):\n",
+ " X_train = data[\"X_train\"]\n",
+ "\n",
+ " for i in range(nres):\n",
+ " for j in range(nvar):\n",
+ " if j==0:\n",
+ " rdx[i*nvar] = -1 \n",
+ " else:\n",
+ " rdx[i*nvar + j] = -X_train[i, j-1]\n",
+ " \n",
+ " return inform\n",
+ "\n",
+ "# Set loss function to l2-norm, elastic net regularization, and printing options\n",
+ "for option in [\n",
+ " 'NLDF Loss Function Type = L2',\n",
+ " 'Print Level = 1',\n",
+ " 'Print Options = No',\n",
+ " 'Reg Term Type = Elastic Net',\n",
+ " ]:\n",
+ " opt.handle_opt_set (handle, option)\n",
+ "\n",
+ "# Use an explicit I/O manager for abbreviated iteration output\n",
+ "iom = utils.FileObjManager(locus_in_output=False)\n",
+ "\n",
+ "# Set initial guess and solve\n",
+ "x = np.array([np.random.rand() for _ in range(nvar)])\n",
+ "\n",
+ "sol_en = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b84636fe-1a3c-4188-8dec-7449dbc059b2",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " E04GN, Nonlinear Data-Fitting\n",
+ " Status: converged, an optimal solution found\n",
+ " Final objective value 1.362383E+01\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Resolve the problem with no regularization\n",
+ "opt.handle_opt_set(handle, 'Reg Term Type = Off')\n",
+ "sol_noreg = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)\n",
+ "\n",
+ "# Destroy the handle\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ada90e01-6845-41b8-9f91-9944497a94ed",
+ "metadata": {},
+ "source": [
+ "## 4. Compute root mean square error (RMSE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "126cca3a-2b1e-461b-af09-df70c8c636b8",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def calculate_rmse(y_actual, y_pred):\n",
+ " \"\"\"\n",
+ " Calculate the Root Mean Square Error (RMSE) between two lists of numbers\n",
+ "\n",
+ " Args:\n",
+ " y_actual (list): The actual values\n",
+ " y_pred (list): The predicted values\n",
+ "\n",
+ " Returns:\n",
+ " float: The Root Mean Squared Error\n",
+ " \"\"\"\n",
+ " return np.sqrt(np.square(y_actual - y_pred).mean())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "207f4ff1-b1c3-4c54-81ff-4789fa3bbad0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using elastic net regularization decreased the RMSE by 0.034 compared to using no regularization.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Calculate predicted values for y with elastic net regularization and find RMSE\n",
+ "y_pred_en = sol_en.x[0] + X_test @ sol_en.x[1:]\n",
+ "rmse_elastic_net = calculate_rmse(y_test, y_pred_en)\n",
+ "\n",
+ "# Calculate predicted values for y with no regularization and find RMSE\n",
+ "y_pred_noreg = sol_noreg.x[0] + X_test @ sol_noreg.x[1:]\n",
+ "rmse_noreg = calculate_rmse(y_test, y_pred_noreg)\n",
+ "\n",
+ "# Report the difference in RMSE\n",
+ "print(f\"Using elastic net regularization decreased the RMSE by {round(rmse_noreg - rmse_elastic_net, 4)} compared to using no regularization.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4dfdf65a-c992-4c48-ba6a-0a8438328218",
+ "metadata": {},
+ "source": [
+ "For more information on the NAG® Library and our [Optimization Modelling Suite](https://nag.com/mathematical-optimization/) or to try it for yourself, visit [‘Getting Started with the NAG Library’](https://support.nag.com/content/getting-started-nag-library?_gl=1*xmlppm*_gcl_au*MTEwNDczODM2NS4xNzIyMDAyNzkz*_ga*MjA2NzgxMjY0NS4xNzIyMDAyNzk0*_ga_6MCQDQP46G*MTcyMzEzNDUxNi41LjAuMTcyMzEzNDUzNy4zOS4wLjA.), select your configuration and language, download the software, request a trial key and experiment for yourself."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/local_optimization/NLDF/possum.csv b/local_optimization/NLDF/possum.csv
new file mode 100644
index 0000000..0f7cc0f
--- /dev/null
+++ b/local_optimization/NLDF/possum.csv
@@ -0,0 +1,105 @@
+case,site,Pop,sex,age,hdlngth,skullw,totlngth,taill,footlgth,earconch,eye,chest,belly
+1,1,Vic,m,8,94.1,60.4,89,36,74.5,54.5,15.2,28,36
+2,1,Vic,f,6,92.5,57.6,91.5,36.5,72.5,51.2,16,28.5,33
+3,1,Vic,f,6,94,60,95.5,39,75.4,51.9,15.5,30,34
+4,1,Vic,f,6,93.2,57.1,92,38,76.1,52.2,15.2,28,34
+5,1,Vic,f,2,91.5,56.3,85.5,36,71,53.2,15.1,28.5,33
+6,1,Vic,f,1,93.1,54.8,90.5,35.5,73.2,53.6,14.2,30,32
+7,1,Vic,m,2,95.3,58.2,89.5,36,71.5,52,14.2,30,34.5
+8,1,Vic,f,6,94.8,57.6,91,37,72.7,53.9,14.5,29,34
+9,1,Vic,f,9,93.4,56.3,91.5,37,72.4,52.9,15.5,28,33
+10,1,Vic,f,6,91.8,58,89.5,37.5,70.9,53.4,14.4,27.5,32
+11,1,Vic,f,9,93.3,57.2,89.5,39,77.2,51.3,14.9,31,34
+12,1,Vic,f,5,94.9,55.6,92,35.5,71.7,51,15.3,28,33
+13,1,Vic,m,5,95.1,59.9,89.5,36,71,49.8,15.8,27,32
+14,1,Vic,m,3,95.4,57.6,91.5,36,74.3,53.7,15.1,28,31.5
+15,1,Vic,m,5,92.9,57.6,85.5,34,69.7,51.8,15.7,28,35
+16,1,Vic,m,4,91.6,56,86,34.5,73,51.4,14.4,28,32
+17,1,Vic,f,1,94.7,67.7,89.5,36.5,73.2,53.2,14.7,29,31
+18,1,Vic,m,2,93.5,55.7,90,36,73.7,55.4,15.3,28,32
+19,1,Vic,f,5,94.4,55.4,90.5,35,73.4,53.9,15.2,28,32
+20,1,Vic,f,4,94.8,56.3,89,38,73.8,52.4,15.5,27,36
+21,1,Vic,f,3,95.9,58.1,96.5,39.5,77.9,52.9,14.2,30,40
+22,1,Vic,m,3,96.3,58.5,91,39.5,73.5,52.1,16.2,28,36
+23,1,Vic,f,4,92.5,56.1,89,36,72.8,53.3,15.4,28,35
+24,1,Vic,m,2,94.4,54.9,84,34,75,53.5,16.2,27,32
+25,1,Vic,m,3,95.8,58.5,91.5,35.5,72.3,51.6,14.9,31,35
+26,1,Vic,m,7,96,59,90,36,73.6,56.2,15,29,38
+27,1,Vic,f,2,90.5,54.5,85,35,70.3,50.8,14.2,23,28
+28,1,Vic,m,4,93.8,56.8,87,34.5,73.2,53,15.3,27,30
+29,1,Vic,f,3,92.8,56,88,35,74.9,51.8,14,24,32
+30,1,Vic,f,2,92.1,54.4,84,33.5,70.6,50.8,14.5,24.5,33
+31,1,Vic,m,3,92.8,54.1,93,37,68,52.5,14.5,27,31
+32,1,Vic,f,4,94.3,56.7,94,39,74.8,52,14.9,28,34
+33,1,Vic,m,3,91.4,54.6,89,37,70.8,51.8,14.8,24,30
+34,2,Vic,m,2,90.6,55.7,85.5,36.5,73.1,53.1,14.4,26,28.5
+35,2,Vic,m,4,94.4,57.9,85,35.5,71.2,55.5,16.4,28,35.5
+36,2,Vic,m,7,93.3,59.3,88,35,74.3,52,14.9,25.5,36
+37,2,Vic,f,2,89.3,54.8,82.5,35,71.2,52,13.6,28,31.5
+38,2,Vic,m,7,92.4,56,80.5,35.5,68.4,49.5,15.9,27,30
+39,2,Vic,f,1,84.7,51.5,75,34,68.7,53.4,13,25,25
+40,2,Vic,f,3,91,55,84.5,36,72.8,51.4,13.6,27,30
+41,2,Vic,f,5,88.4,57,83,36.5,NA,40.3,15.9,27,30.5
+42,2,Vic,m,3,85.3,54.1,77,32,62.7,51.2,13.8,25.5,33
+43,2,Vic,f,2,90,55.5,81,32,72,49.4,13.4,29,31
+44,2,Vic,m,NA,85.1,51.5,76,35.5,70.3,52.6,14.4,23,27
+45,2,Vic,m,3,90.7,55.9,81,34,71.5,54,14.6,27,31.5
+46,2,Vic,m,NA,91.4,54.4,84,35,72.8,51.2,14.4,24.5,35
+47,3,other,m,2,90.1,54.8,89,37.5,66,45.5,15,25,33
+48,3,other,m,5,98.6,63.2,85,34,66.9,44.9,17,28,35
+49,3,other,m,4,95.4,59.2,85,37,69,45,15.9,29.5,35.5
+50,3,other,f,5,91.6,56.4,88,38,65,47.2,14.9,28,36
+51,3,other,f,5,95.6,59.6,85,36,64,43.9,17.4,28,38.5
+52,3,other,m,6,97.6,61,93.5,40,67.9,44.3,15.8,28.5,32.5
+53,3,other,f,3,93.1,58.1,91,38,67.4,46,16.5,26,33.5
+54,4,other,m,7,96.9,63,91.5,43,71.3,46,17.5,30,36.5
+55,4,other,m,2,103.1,63.2,92.5,38,72.5,44.9,16.4,30.5,36
+56,4,other,m,3,99.9,61.5,93.7,38,68.7,46.8,16.4,27.5,31.5
+57,4,other,f,4,95.1,59.4,93,41,67.2,45.3,14.5,31,39
+58,4,other,m,3,94.5,64.2,91,39,66.5,46.4,14.4,30.5,33
+59,4,other,m,2,102.5,62.8,96,40,73.2,44.5,14.7,32,36
+60,4,other,f,2,91.3,57.7,88,39,63.1,47,14.4,26,30
+61,5,other,m,7,95.7,59,86,38,63.1,44.9,15,26.5,31
+62,5,other,f,3,91.3,58,90.5,39,65.5,41.3,16,27,32
+63,5,other,f,6,92,56.4,88.5,38,64.1,46.3,15.2,25.5,28.5
+64,5,other,f,3,96.9,56.5,89.5,38.5,63,45.1,17.1,25.5,33
+65,5,other,f,5,93.5,57.4,88.5,38,68.2,41.7,14,29,38.5
+66,5,other,f,3,90.4,55.8,86,36.5,63.2,44.2,15.7,26.5,34
+67,5,other,m,4,93.3,57.6,85,36.5,64.7,44.1,16.5,27.5,29.5
+68,5,other,m,5,94.1,56,88.5,38,65.9,43.1,17.4,27,30
+69,5,other,m,5,98,55.6,88,37.5,65,45.6,15,28.5,34
+70,5,other,f,7,91.9,56.4,87,38,65.4,44.1,13,27,34
+71,5,other,m,6,92.8,57.6,90,40,65.7,42.8,15,27.5,34
+72,5,other,m,1,85.9,52.4,80.5,35,62,42.4,14.1,25.5,30
+73,5,other,m,1,82.5,52.3,82,36.5,65.7,44.7,16,23.5,28
+74,6,other,f,4,88.7,52,83,38,61.5,45.9,14.7,26,34
+75,6,other,m,6,93.8,58.1,89,38,66.2,45.6,16.9,26,33.5
+76,6,other,m,5,92.4,56.8,89,41,64.5,46.4,17.8,26,33
+77,6,other,m,6,93.6,56.2,84,36,62.8,42.9,16.2,25,35
+78,6,other,m,1,86.5,51,81,36.5,63,44.3,13.2,23,28
+79,6,other,m,1,85.8,50,81,36.5,62.8,43,14.8,22,28.5
+80,6,other,m,1,86.7,52.6,84,38,62.3,44.8,15,23.5,30.5
+81,6,other,m,3,90.6,56,85.5,38,65.6,41.7,17,27.5,35
+82,6,other,f,4,86,54,82,36.5,60.7,42.9,15.4,26,32
+83,6,other,f,3,90,53.8,81.5,36,62,43.3,14,25,29
+84,6,other,m,3,88.4,54.6,80.5,36,62.6,43.6,16.3,25,28.5
+85,6,other,m,3,89.5,56.2,92,40.5,65.6,43.5,14.5,27,31.5
+86,6,other,f,3,88.2,53.2,86.5,38.5,60.3,43.7,13.6,26,31
+87,7,other,m,2,98.5,60.7,93,41.5,71.7,46.8,15,26,36
+88,7,other,f,2,89.6,58,87.5,38,66.7,43.5,16,25.5,31.5
+89,7,other,m,6,97.7,58.4,84.5,35,64.4,46.2,14.4,29,30.5
+90,7,other,m,3,92.6,54.6,85,38.5,69.8,44.8,14.5,25.5,32.5
+91,7,other,m,3,97.8,59.6,89,38,65.5,48,15,26,32
+92,7,other,m,2,90.7,56.3,85,37,67.6,46.8,14.5,25.5,31
+93,7,other,m,3,89.2,54,82,38,63.8,44.9,12.8,24,31
+94,7,other,m,7,91.8,57.6,84,35.5,64.2,45.1,14.4,29,35
+95,7,other,m,4,91.6,56.6,88.5,37.5,64.5,45.4,14.9,27,31
+96,7,other,m,4,94.8,55.7,83,38,66.5,47.7,14,25,33
+97,7,other,m,3,91,53.1,86,38,63.8,46,14.5,25,31.5
+98,7,other,m,5,93.2,68.6,84,35,65.6,44.3,14.5,28.5,32
+99,7,other,f,3,93.3,56.2,86.5,38.5,64.8,43.8,14,28,35
+100,7,other,m,1,89.5,56,81.5,36.5,66,46.8,14.8,23,27
+101,7,other,m,1,88.6,54.7,82.5,39,64.4,48,14,25,33
+102,7,other,f,6,92.4,55,89,38,63.5,45.4,13,25,30
+103,7,other,m,4,91.5,55.2,82.5,36.5,62.9,45.9,15.4,25,29
+104,7,other,f,3,93.6,59.9,89,40,67.6,46,14.8,28.5,33.5
diff --git a/local_optimization/Notebooks/Readme.md b/local_optimization/Notebooks/Readme.md
new file mode 100644
index 0000000..9850f1e
--- /dev/null
+++ b/local_optimization/Notebooks/Readme.md
@@ -0,0 +1,15 @@
+[](https://www.nag.com)
+
+# Local Optimization Jupyter notebooks
+
+ * [Example on minimizing the generalized Rosenbrock function](bounds_quasi_func_easy.ipynb)
+ using [bounds_quasi_func_easy](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.bounds_quasi_func_easy.html).
+
+
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
diff --git a/local_optimization/bounds_quasi_func_easy.ipynb b/local_optimization/Notebooks/bounds_quasi_func_easy.ipynb
similarity index 97%
rename from local_optimization/bounds_quasi_func_easy.ipynb
rename to local_optimization/Notebooks/bounds_quasi_func_easy.ipynb
index 4431991..e5c5b26 100644
--- a/local_optimization/bounds_quasi_func_easy.ipynb
+++ b/local_optimization/Notebooks/bounds_quasi_func_easy.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -15,7 +26,7 @@
"One can see from the HTML documentation at https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.html that the relevant algorithmic submodule for (local) optimization is ``opt``.\n",
"\n",
"Studying the `opt` Functionality Index confirms that the relevant optimization solver to call is\n",
- "``bounds_quasi_func_easy``. The HTML documentation for this solver is at https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.opt.bounds_quasi_func_easy.\n",
+ "``bounds_quasi_func_easy``. The HTML documentation for this solver is at https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.bounds_quasi_func_easy.html.\n",
"\n",
"The optimization solver may be imported directly if desired"
]
@@ -64,7 +75,7 @@
" For full information please refer to the NAG Library document for\n",
" e04jy\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04jyf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04jyf.html\n",
" \n",
" Parameters\n",
" ----------\n",
@@ -263,6 +274,72 @@
" No equivalent traditional C interface for this routine exists in the\n",
" NAG Library.\n",
" \n",
+ " ``bounds_quasi_func_easy`` is applicable to problems of the form:\n",
+ " \n",
+ " MinimizeF(x_1,x_2,...,x_n) subject to l_j <= x_j <= u_j, j =\n",
+ " 1,2,...,n\n",
+ " \n",
+ " when derivatives of F(x) are unavailable.\n",
+ " \n",
+ " Special provision is made for problems which actually have no bounds\n",
+ " on the x_j, problems which have only non-negativity bounds and\n",
+ " problems in which l_1 = l_2 = ... = l_n and u_1 = u_2 = ... = u_n.\n",
+ " You must supply a function to calculate the value of F(x) at any\n",
+ " point x.\n",
+ " \n",
+ " From a starting point you supplied there is generated, on the basis\n",
+ " of estimates of the gradient and the curvature of F(x), a sequence\n",
+ " of feasible points which is intended to converge to a local minimum\n",
+ " of the constrained function.\n",
+ " An attempt is made to verify that the final point is a minimum.\n",
+ " \n",
+ " A typical iteration starts at the current point x where n_z (say)\n",
+ " variables are free from both their bounds.\n",
+ " The projected gradient vector g_z, whose elements are finite\n",
+ " difference approximations to the derivatives of F(x) with respect to\n",
+ " the free variables, is known.\n",
+ " A unit lower triangular matrix L and a diagonal matrix D (both of\n",
+ " dimension n_z), such that LDL^T is a positive definite approximation\n",
+ " of the matrix of second derivatives with respect to the free\n",
+ " variables (i.e., the projected Hessian) are also held.\n",
+ " The equations\n",
+ " \n",
+ " LDL^Tp_z = -g_z\n",
+ " \n",
+ " are solved to give a search direction p_z, which is expanded to an\n",
+ " n-vector p by an insertion of appropriate zero elements.\n",
+ " Then alpha is found such that F(x+alpha p) is approximately a\n",
+ " minimum (subject to the fixed bounds) with respect to alpha; x is\n",
+ " replaced by x+alpha p, and the matrices L and D are updated so as to\n",
+ " be consistent with the change produced in the estimated gradient by\n",
+ " the step alpha p.\n",
+ " If any variable actually reaches a bound during the search along p,\n",
+ " it is fixed and n_z is reduced for the next iteration.\n",
+ " Most iterations calculate g_z using forward differences, but central\n",
+ " differences are used when they seem necessary.\n",
+ " \n",
+ " There are two sets of convergence criteria -- a weaker and a\n",
+ " stronger.\n",
+ " Whenever the weaker criteria are satisfied, the Lagrange multipliers\n",
+ " are estimated for all the active constraints.\n",
+ " If any Lagrange multiplier estimate is significantly negative, then\n",
+ " one of the variables associated with a negative Lagrange multiplier\n",
+ " estimate is released from its bound and the next search direction is\n",
+ " computed in the extended subspace (i.e., n_z is increased).\n",
+ " Otherwise minimization continues in the current subspace provided\n",
+ " that this is practicable.\n",
+ " When it is not, or when the stronger convergence criteria are\n",
+ " already satisfied, then, if one or more Lagrange multiplier\n",
+ " estimates are close to zero, a slight perturbation is made in the\n",
+ " values of the corresponding variables in turn until a lower function\n",
+ " value is obtained.\n",
+ " The normal algorithm is then resumed from the perturbed point.\n",
+ " \n",
+ " If a saddle point is suspected, a local search is carried out with a\n",
+ " view to moving away from the saddle point.\n",
+ " A local search is also performed when a point is found which is\n",
+ " thought to be a constrained minimum.\n",
+ " \n",
" References\n",
" ----------\n",
" Gill, P E and Murray, W, 1976, `Minimization subject to bounds on\n",
@@ -1125,7 +1202,7 @@
" Z[j, i] = rosen(np.array([x_i, y_j]))\n",
"\n",
"fig = plt.figure()\n",
- "ax = Axes3D(fig)\n",
+ "ax = Axes3D(fig, auto_add_to_figure=False)\n",
"ax.grid(False)\n",
"ax.plot_wireframe(X, Y, Z, color='red', linewidths=0.4)\n",
"ax.contour(X, Y, Z, levels=[5, 25, 50, 100, 250, 500, 1000, 1500, 2000, 2500], offset=-3000.0, cmap=cm.jet)\n",
@@ -1135,7 +1212,8 @@
"ax.set_zlim3d(-1.0, 10000.0)\n",
"ax.azim = -20\n",
"ax.elev = 20\n",
- "plt.show();"
+ "fig.add_axes(ax)\n",
+ "plt.show()"
]
},
{
@@ -1280,7 +1358,25 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
}
},
"nbformat": 4,
diff --git a/local_optimization/Readme.md b/local_optimization/Readme.md
index d93803d..304ee0e 100644
--- a/local_optimization/Readme.md
+++ b/local_optimization/Readme.md
@@ -1,9 +1,228 @@
-# Local Optimization Examples
+[](https://www.nag.com)
+
+# Local Optimization<a name=top></a>
+
+Here you will find a variety of resources (mostly [Jupyter notebooks](https://jupyter.org/)) related to the use of our optimization routines and modelling suite. If you are new to NAG's optimization solvers we highly recomment to read the [E04 chapter](https://www.nag.com/numeric/nl/nagdoc_latest/clhtml/e04/e04intro.html) of the [NAG](https://www.nag.com) Library which is dedicated to local optimization. While if you are new to NAG Library for Python we encourage to review the [NAG Python documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html) and read the sections on [How to install the NAG Library for Python](#install) and [How to run the Jupyter notebook examples](#jupyter) of this `Readme`.
+
+If you are already familiar with NAG's optimization offering and just need to find the right solver to use for your problem, then we recommend reviewing the [Optimization Index](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/indexes/optimization.html) or the [Decision Tree for selecting the right Optimization solver](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#dtree).
+
+
+<table><tr>
+<td><img src="./images/dfo_calib.png" width="412px" alt="DFO Calibration example"/></td>
+ <td> </td>
+ <td><img src="./BXNL/images/fig-unfolding.png" width="412px" alt="Nonlinear least-squares callibration example (distribution unfolding)"/></td>
+</tr></table>
+
+**Figure 1.** Applied optimization examples. (left) DFO nonlinear least-square calibration for the Kowalik and Osborne function,
+red line shows the final solution. (right) Nonlinear least-squares fitting example, experimental data histogram (blue bars) is fitted with an
+aggregated model (green curve) while the unfolded models are the blue and red curves. Optimal parameter values are reported in the legend, for more details see [here](./BXNL/Readme.md#unfolding-nuclear-track-data).
+
+# Content<a name=content></a>
+
+* [How to install the NAG Library for Python](#install)
+* [How to run the Jupyter notebook examples](#jupyter)
+* [Useful links](#links)
+
+### Repository
* [Second Order Cone Programming (SOCP)](./SOCP/)
-* [First order active set CG (FOAS)](./FOAS)
+* [First order active set CG (FOAS)](./FOAS/)
* [Nonlinear Least-Squares (BXNL)](./BXNL)
* [Semi-Definite Programming (SDP)](./SDP/)
-* [Minimizing the generalized Rosenbrock function using bound constrained optimization](./bounds_quasi_func_easy.ipynb)
-* [Linear Programming Demo](./LP_demo.ipynb)
-* [Model-based derivative free optimization](./DFO_noisy.ipynb)
+* [Derivative-Free Optimization (DFO)](./DFO/)
+* [Tips and Tricks in modelling](./Modelling/)
+* [Assortment of example notebooks](./Notebooks) 
+
+
+# How to install the NAG Library for Python<a name=install></a>
+
+In this section we illustrate how to install the NAG Library for Python, request a Trial Licence and make sure the Library is working. Details and further information regarding the installation can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).
+
+**Note** Before starting make sure you have access to a host that has Python 3 (3.4 or more recent).
+
+### Step 1. Downloading and installing
+Installing the NAG Library is done using the `pip` package manager, fire-up a terminal with [Bash](https://www.gnu.org/software/bash/) and create a Python 3 virtual environment where to install and test the NAG Library
+```{bash}
+guest@nag-37:~$ python3 -m venv nag3
+guest@nag-37:~$ . nag3/bin/activate
+(nag3) guest@nag-37:~$
+```
+Now use `pip` to install the NAG Library for Python
+```{bash}
+(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_nag naginterfaces
+```
+or if you prefer the version of the package that relies on Intel MKL for optimized linear algebra routines, then use
+```{bash}
+(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_mkl naginterfaces
+```
+
+The output should be similar to
+```{bash}
+Collecting naginterfaces
+ Downloading https://www.nag.com/downloads/py/naginterfaces_nag/naginterfaces/naginterfaces-27.1.0.0-py2.py3-none-linux_x86_64.whl (55.8MB)
+ 100% |████████████████████████████████| 55.8MB 21kB/s
+Collecting numpy>=1.15 (from naginterfaces)
+ Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)
+ 100% |████████████████████████████████| 13.4MB 70kB/s
+Installing collected packages: numpy, naginterfaces
+Successfully installed naginterfaces-27.1.0.0 numpy-1.19.5
+```
+The output indicates that the installation was successful.
+
+### Step 2. Getting a trial licence
+The next step is to get the licensing info (**product code** and **KUSARI ID**) and use it to request a licence. From the same virtual terminal, try
+```{bash}
+(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
+```
+The output should be similar to
+```
+The NAG Library for Python on this platform uses
+underlying Library NLL6I271VL.
+This Library has been installed as part of the package
+and it requires a valid licence key.
+No such key could be validated:
+the key may not have been installed correctly or
+it may have expired.
+The Kusari licence-check utility reports the following:
+User: guest
+Directory: /home/guest
+NAG_KUSARI_FILE=""
+File /home/guest/nag.key does not exist
+-------------------------------------------------------------------------------
+Error: Licence not found; this product requires a key for NLL6I271VL
+The above information has been generated on machine nag-37
+For information on how to obtain a licence, please see
+https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html
+KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"
+```
+The **two** important bits are the
+
+ 1. **product code** shown as **`underlying Library NLL6I271VL.`** which identifies the licence to request, and
+
+ 2. **KUSARI ID** shown as **`KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"`** which identifies the host you are running the library on.
+
+ **Note** that the **product code** and **KUSARI ID** can be different from the previous example.
+
+ With these, you are set to [contact NAG and request a trial licence](https://www.nag.com/content/software-trials?product=NAG%20Library).
+
+ The trial licence is a plain text chunk similar to
+ ```
+ NLL6I271V TRIAL 2021/01/27 "RverXn0Pc-Ib?ctdgF=Wpis2j7I"
+ ```
+ Save or copy the text into the file `/home/guest/nag.key`.
+
+ The final step is to make sure the licence is valid and the library is working as expected.
+
+### Step 3. Testing the NAG Library
+The last step is to make sure the licence was correctly stored and that the NAG Library is working correctly. From the same virtual terminal re-run the Kusari licence module
+```{bash}
+(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
+```
+This time the output should be similar to
+```
+Licence available; the required NLL6I271VL licence key for this product is valid
+TRIAL licence, 27 days remaining (licence from file)
+```
+Now let's try a more interesting example ([list of optimization examples](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#examples))
+
+This command runs the example for the [FOAS (First-Order Active set method) solver and minimizes the Rosenbrock 2D function](./FOAS).
+```
+(nag3) guest@nag-37:~$ python -m naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex
+```
+Should generate an outputsimilar to
+```{bash}
+Trying:
+ main()
+Expecting:
+ naginterfaces.library.opt.handle_solve_bounds_foas Python Example Results.
+ Minimizing a bound-constrained Rosenbrock problem.
+ E04KF, First order method for bound-constrained problems
+...
+ Status: converged, an optimal solution was found
+ Value of the objective 4.00000E-02
+ ...
+ok
+```
+indicating that the example was successfully executed. The source code can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/_modules/naginterfaces/library/examples/opt/handle_solve_bounds_foas_ex.html#main).
+
+### Running more examples
+
+To display the full list of example source files on disk, but not run them, execute
+```
+python -m naginterfaces.library.examples --locate
+```
+All examples may be executed sequentially by running
+```
+python -m naginterfaces.library.examples
+```
+Run `python -m naginterfaces.library.examples --help` to see any additional usage.
+
+
+
+# How to run the Jupyter notebook examples<a name=jupyter></a>
+
+This section briefly illustrates how to setup a host in order to open and run the [Jupyter notebooks](https://jupyter.org/) provided in this repository.
+Before running the notebooks make sure the [NAG Library is installed and working](#install). Before starting, it is advised to read [Jupyter's installation page](https://jupyter.org/install.html).
+
+<!-- You can [view a static render of the notebooks using Jupyter's nbviewer here](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/)
+[](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/)
+or alternatively use [Binder](https://mybinder.org/) to [view them here](https://mybinder.org/v2/gh/numericalalgorithmsgroup/NAGPythonExamples/HEAD)
+[](https://mybinder.org/v2/gh/numericalalgorithmsgroup/NAGPythonExamples/HEAD). -->
+
+
+### Installing Jupyter notebook
+To install Jupyter, launch a terminal and activate the virtual environment used to install the NAG Library for Python
+```{bash}
+guest@nag-37:~$ . nag3/bin/activate
+(nag3) guest@nag-37:~$ pip install notebook matplotlib
+Collecting notebook
+ Downloading https://files.pythonhosted.org/packages/74/19/50cd38acf22e33370d01fef764355f1e3517f6e12b4fceb8d434ece4f8fd/notebook-6.2.0-py3-none-any.whl (9.5MB)
+ 100% |████████████████████████████████| 9.5MB 115kB/s
+Collecting argon2-cffi (from notebook)
+...
+Successfully installed jupyter-client-6.1.11 jupyterlab-pygments-0.1.2 ... wcwidth-0.2.5
+```
+This indicates that Jupyter and matplotlib were successfully installed. The next section shows how to start the notebok interface and open an example.
+
+### Running the notebook examples
+To run an example, grab a copy of the notebook of interest and start up the notebook interface.
+For example, download the [Rosenbrock 2D optimization example](./FOAS/rosenbrock2d.ipynb) notebook `rosenbrock2d.ipynb` into the current directory
+```{bash}
+(nag3) guest@nag-37:~$ curl -O https://raw.githubusercontent.com/numericalalgorithmsgroup/NAGPythonExamples/master/local_optimization/FOAS/rosenbrock2d.ipynb
+ % Total % Received % Xferd Average Speed Time Time Time Current
+ Dload Upload Total Spent Left Speed
+100 61961 100 61961 0 0 382k 0 --:--:-- --:--:-- --:--:-- 382k
+```
+and now open it using `jupyter-notebook`
+```{bash}
+(nag3) guest@nag-37:~$ jupyter-notebook rosenbrock2d.ipynb
+[I 12:24:07.336 NotebookApp] Serving notebooks from local directory: /home/guest
+[I 12:24:07.336 NotebookApp] Jupyter Notebook 6.2.0 is running at:
+[I 12:24:07.336 NotebookApp] http://localhost:8888/?token=f1836a06799a92f25ef9966439bf3491b2f0960dcb51806d
+...
+```
+This command will fire-up your web browser and open the `rosenbrock2d.ipynb` notebook, the window should be similar to
+
+
+
+
+
+
+
+
+
+
+# Useful links<a name=links></a>
+
+* [NAG Library for Python Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
+* [NAG Library Optimization (Chapter E04) Introduction](https://www.nag.com/numeric/nl/nagdoc_latest/clhtml/e04/e04intro.html)
+* [Optimization Index](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/indexes/optimization.html)
+* [Decision Tree for selecting the right optimization solver](https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/e04/e04intro.html#dtree)
+* [Request a trial licence](https://www.nag.com/content/software-trials?product=NAG%20Library)
+* [Kusari licence module Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari)
+
+
+**[Back to Top](#top)**
+<!-- # References
+* Kowalik J S and Osborne, M R (1968) _Methods for unconstrained optimization problems_. New York, American Elsevier Pub. Co
+--!>
diff --git a/local_optimization/SDP/NCM_SDP.ipynb b/local_optimization/SDP/NCM_SDP.ipynb
index f2532b9..875b386 100644
--- a/local_optimization/SDP/NCM_SDP.ipynb
+++ b/local_optimization/SDP/NCM_SDP.ipynb
@@ -18,15 +18,26 @@
"# Nearest correlation matrix using Semi-Definite Programming (SDP)\n",
"## Correct Rendering of this notebook\n",
"\n",
- "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installing the NAG library and running this notebook\n",
"\n",
- "## Introduction"
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Introduction\n",
+ "\n",
"We start with a matrix $G$ that is not quite a correlation matrix:\n",
"\\begin{bmatrix}\n",
"1 & -1 & 0 & 0\\\\\n",
@@ -220,14 +231,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " E04SV, NLP-SDP Solver (Pennon)\n",
- " ------------------------------\n",
- " Number of variables 3 [eliminated 0]\n",
- " simple linear nonlin\n",
- " (Standard) inequalities 0 0 0\n",
- " (Standard) equalities 0 0\n",
- " Matrix inequalities 1 0 [dense 1, sparse 0]\n",
- " [max dimension 4]\n",
+ "\n",
+ " --------------------------------\n",
+ " E04SV, NLP-SDP Solver (Pennon)\n",
+ " --------------------------------\n",
+ "\n",
+ " Problem Statistics\n",
+ " No of variables 3\n",
+ " bounds not defined\n",
+ " No of lin. constraints 0\n",
+ " nonzeroes 0\n",
+ " No of matrix inequal. 1\n",
+ " detected matrix inq. 1\n",
+ " linear 1\n",
+ " nonlinear 0\n",
+ " max. dimension 4\n",
+ " detected normal inq. 0\n",
+ " linear 0\n",
+ " nonlinear 0\n",
+ " Objective function Quadratic\n",
"\n",
" --------------------------------------------------------------\n",
" it| objective | optim | feas | compl | pen min |inner\n",
@@ -428,28 +450,29 @@
" ``handle_opt_set``, ``handle_opt_get``.\n",
" \n",
" ``handle_solve_pennon`` is a solver from the NAG optimization\n",
- " modelling suite for problems such as, quadratic programming (QP),\n",
- " linear semidefinite programming (SDP) and semidefinite programming\n",
+ " modelling suite for problems such as, Quadratic Programming (QP),\n",
+ " linear Semidefinite Programming (SDP) and semidefinite programming\n",
" with bilinear matrix inequalities (BMI-SDP).\n",
" \n",
" For full information please refer to the NAG Library document for\n",
" e04sv\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04svf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04svf.html\n",
" \n",
" Parameters\n",
" ----------\n",
" handle : Handle\n",
- " The handle to the problem. It needs to be initialized by\n",
- " ``handle_init`` and **must not** be changed before the call to\n",
- " ``handle_solve_pennon``.\n",
+ " The handle to the problem. It needs to be initialized (e.g., by\n",
+ " ``handle_init``) and to hold a problem formulation compatible\n",
+ " with ``handle_solve_pennon``. It **must not** be changed between\n",
+ " calls to the NAG optimization modelling suite.\n",
" \n",
" x : float, array-like, shape (nvar)\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
" > 0.\n",
" \n",
" If 'Initial X' = 'USER' (the default), x^0, the initial estimate\n",
- " of the variables x, otherwise `x` need not be set.\n",
+ " of the variables x; otherwise, `x` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -461,7 +484,7 @@
" \n",
" `On intermediate entry`: if set to 0, solving the current\n",
" problem is terminated and the function returns `errno` = 20;\n",
- " otherwise the function continues.\n",
+ " otherwise, the function continues.\n",
" \n",
" u : None or float, array-like, shape (nnzu), optional\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
@@ -477,7 +500,7 @@
" Note: if nnzu = 0, `u` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), u^0, the\n",
- " initial estimate of the Lagrangian multipliers u, otherwise `u`\n",
+ " initial estimate of the Lagrangian multipliers u; otherwise, `u`\n",
" need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
@@ -500,8 +523,8 @@
" If nnzua = 0, `ua` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), U^0, the\n",
- " initial estimate of the matrix Lagrangian multipliers U,\n",
- " otherwise `ua` need not be set.\n",
+ " initial estimate of the matrix Lagrangian multipliers U;\n",
+ " otherwise, `ua` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -722,7 +745,7 @@
" Constraint: 'Inner Stop Criteria' = 'HEURISTIC' or 'STRICT'.\n",
" \n",
" 'Inner Stop Tolerance' : float\n",
- " Default = 10^(-2)\n",
+ " Default = 10^-2\n",
" \n",
" This option sets the required precision alpha^0 for the first\n",
" inner problem solved by [Algorithm 2].\n",
@@ -836,7 +859,7 @@
" reaching the requested accuracy on the feasibility.\n",
" Under normal circumstances, the default value is recommended.\n",
" \n",
- " Constraint: epsilon <= 'P Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'P Min' <= 10^-2.\n",
" \n",
" 'Pmat Min' : float\n",
" Default = sqrt(epsilon)\n",
@@ -845,7 +868,7 @@
" option P_min.\n",
" The same advice applies.\n",
" \n",
- " Constraint: epsilon <= 'Pmat Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'Pmat Min' <= 10^-2.\n",
" \n",
" 'Preference' : str\n",
" Default = 'SPEED'\n",
@@ -951,7 +974,7 @@
" Constraint: 'Stop Criteria' = 'SOFT' or 'STRICT'.\n",
" \n",
" 'Stop Tolerance 1' : float\n",
- " Default = max(10^(-6), sqrt(epsilon))\n",
+ " Default = max(10^-6, sqrt(epsilon))\n",
" \n",
" This option defines epsilon_1 used as a tolerance for the\n",
" relative duality gap (0) and the relative precision (1), see\n",
@@ -960,7 +983,7 @@
" Constraint: 'Stop Tolerance 1' > epsilon.\n",
" \n",
" 'Stop Tolerance 2' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This option sets the value epsilon_2 which is used for\n",
" optimality (2) and complementarity (4) tests from KKT conditions\n",
@@ -971,7 +994,7 @@
" Constraint: 'Stop Tolerance 2' > epsilon.\n",
" \n",
" 'Stop Tolerance Feasibility' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This argument places an acceptance limit on the feasibility of\n",
" the solution (3), epsilon_feas.\n",
@@ -1053,17 +1076,14 @@
" This solver does not support the model defined in the\n",
" handle.\n",
" \n",
- " (`errno` 2)\n",
- " This solver does not support fixed variables.\n",
- " \n",
" (`errno` 3)\n",
- " A different solver from the suite has already been used.\n",
+ " The problem is already being solved.\n",
" \n",
" (`errno` 4)\n",
" On entry, nvar = *<value>*, expected value = *<value>*.\n",
" \n",
- " Constraint: nvar must match the value given during\n",
- " initialization of `handle`.\n",
+ " Constraint: nvar must match the current number of variables\n",
+ " of the model in the `handle`.\n",
" \n",
" (`errno` 5)\n",
" On entry, nnzu = *<value>*.\n",
@@ -1108,25 +1128,6 @@
" (`errno` 21)\n",
" The current starting point is unusable.\n",
" \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Inner iteration limit has been reached.\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Limited progress in the inner subproblem triggered a stop\n",
- " (heuristic inner stop criteria).\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Line search or another internal component failed.\n",
- " \n",
" (`errno` 51)\n",
" The problem was found to be infeasible during preprocessing.\n",
" \n",
@@ -1159,9 +1160,187 @@
" \n",
" The requested accuracy is not achieved.\n",
" \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Inner iteration limit has been reached.\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Limited progress in the inner subproblem triggered a stop\n",
+ " (heuristic inner stop criteria).\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Line search or another internal component failed.\n",
+ " \n",
" (`errno` 24)\n",
" Unable to make progress, the algorithm was stopped.\n",
" \n",
+ " Notes\n",
+ " -----\n",
+ " ``handle_solve_pennon`` serves as a solver for compatible problems\n",
+ " stored as a handle.\n",
+ " The handle points to an internal data structure which defines the\n",
+ " problem and serves as a means of communication for functions in the\n",
+ " NAG optimization modelling suite.\n",
+ " First, the problem handle is initialized by calling ``handle_init``.\n",
+ " Then some of the functions ``handle_set_linobj``,\n",
+ " ``handle_set_quadobj``, ``handle_set_simplebounds``,\n",
+ " ``handle_set_linconstr``, ``handle_set_linmatineq`` or\n",
+ " ``handle_set_quadmatineq`` may be used to formulate the objective\n",
+ " function, (standard) constraints and matrix constraints of the\n",
+ " problem.\n",
+ " Once the problem is fully set, the handle may be passed to the\n",
+ " solver.\n",
+ " When the handle is no longer needed, ``handle_free`` should be\n",
+ " called to destroy it and deallocate the memory held within.\n",
+ " See [the E04 Introduction] for more details about the NAG\n",
+ " optimization modelling suite.\n",
+ " \n",
+ " Problems which can be defined this way are, for example, (generally\n",
+ " nonconvex) Quadratic Programming (QP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " linear semidefinite programming problems (SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " or semidefinite programming problems with bilinear matrix\n",
+ " inequalities (BMI-SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " Here c, l_x and u_x are n-dimensional vectors, H is a symmetric n*n\n",
+ " matrix, l_B, u_B are m_B-dimensional vectors, B is a general m_B*n\n",
+ " rectangular matrix and A_i^k, Q_ij^k are symmetric matrices.\n",
+ " The expression S⪰0 stands for a constraint on eigenvalues of a\n",
+ " symmetric matrix S, namely, all the eigenvalues should be\n",
+ " non-negative, i.e., the matrix should be positive semidefinite.\n",
+ " See relevant functions in the suite for more details on the problem\n",
+ " formulation.\n",
+ " \n",
+ " The solver is based on a generalized Augmented Lagrangian method\n",
+ " with a suitable choice of standard and matrix penalty functions.\n",
+ " For a detailed description of the algorithm see [Algorithmic\n",
+ " Details].\n",
+ " Under standard assumptions on the problem (Slater constraint\n",
+ " qualification, boundedness of the objective function on the feasible\n",
+ " set, see Stingl (2006) for details) the algorithm converges to a\n",
+ " local solution.\n",
+ " In case of convex problems such as linear SDP or convex QP, this is\n",
+ " the global solution.\n",
+ " The solver is suitable for both small dense and large-scale sparse\n",
+ " problems.\n",
+ " \n",
+ " The algorithm behaviour and solver strategy can be modified by\n",
+ " various options (see [Other Parameters]) which can be set by\n",
+ " ``handle_opt_set`` and ``handle_opt_set_file`` anytime between the\n",
+ " initialization of the handle by ``handle_init`` and a call to the\n",
+ " solver.\n",
+ " Once the solver has finished, options may be modified for the next\n",
+ " solve.\n",
+ " The solver may be called repeatedly with various starting points\n",
+ " and/or options.\n",
+ " \n",
+ " There are several options with a multiple choice where the default\n",
+ " choice is 'AUTO' (for example, 'Hessian Density').\n",
+ " This value means that the decision over the option is left to the\n",
+ " solver based on the structure of the problem.\n",
+ " Option getter ``handle_opt_get`` can be called to retrieve the\n",
+ " choice of these options as well as on any other options.\n",
+ " \n",
+ " Option 'Task' may be used to switch the problem to maximization or\n",
+ " to ignore the objective function and find only a feasible point.\n",
+ " \n",
+ " Option 'Monitor Frequency' may be used to turn on the monitor mode\n",
+ " of the solver.\n",
+ " The solver invoked in this mode pauses regularly even before the\n",
+ " optimal point is found to allow monitoring the progress from the\n",
+ " calling program.\n",
+ " All the important error measures and statistics are available in the\n",
+ " calling program which may terminate the solver early if desired (see\n",
+ " argument `inform`).\n",
+ " \n",
+ " Structure of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " The algorithm works internally with estimates of both the decision\n",
+ " variables, denoted by x, and the Lagrangian multipliers (dual\n",
+ " variables) for standard and matrix constraints, denoted by u and U,\n",
+ " respectively.\n",
+ " You may provide initial estimates, request approximations during the\n",
+ " run (the monitor mode turned on) and obtain the final values.\n",
+ " The Lagrangian multipliers are split into two arrays, the\n",
+ " multipliers u for (standard) constraints are stored in array `u` and\n",
+ " multipliers U for matrix constraints in array `ua`.\n",
+ " Both arrays need to conform to the structure of the constraints.\n",
+ " \n",
+ " If the simple bounds were defined (``handle_set_simplebounds`` was\n",
+ " successfully called), the first 2n elements of `u` belong to the\n",
+ " corresponding Lagrangian multipliers, interleaving a multiplier for\n",
+ " the lower and for the upper bound for each x_i.\n",
+ " If any of the bounds were set to infinity, the corresponding\n",
+ " Lagrangian multipliers are set to 0 and may be ignored.\n",
+ " \n",
+ " Similarly, the following 2m_B elements of `u` belong to multipliers\n",
+ " for the linear constraints, if formulated by\n",
+ " ``handle_set_linconstr``.\n",
+ " The organization is the same, i.e., the multipliers for each\n",
+ " constraint for the lower and upper bounds are alternated and zeroes\n",
+ " are used for any missing (infinite bound) constraint.\n",
+ " \n",
+ " A Lagrangian multiplier for a matrix constraint (one block) of\n",
+ " dimension d*d is a dense symmetric matrix of the same dimension.\n",
+ " All multipliers U are stored next to each other in array `ua` in the\n",
+ " same order as the matrix constraints were defined by\n",
+ " ``handle_set_linmatineq`` and ``handle_set_quadmatineq``.\n",
+ " The lower triangle of each is stored in the packed column order (see\n",
+ " [the F07 Introduction]).\n",
+ " For example, if there are four matrix constraints of dimensions 7,\n",
+ " 3, 1, 1, the dimension of array `ua` should be 36.\n",
+ " The first 28 elements (d_1*(d_1+1)/2) belong to the packed lower\n",
+ " triangle of U_1, followed by six elements of U_2 and one element for\n",
+ " each U_3 and U_4.\n",
+ " \n",
+ " Approximation of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " By the nature of the algorithm, all inequality Lagrangian multiplier\n",
+ " u,U are always kept positive during the computational process.\n",
+ " This applies even to Lagrangian multipliers of inactive constraints\n",
+ " at the solution.\n",
+ " They will only be close to zero although they would normally be\n",
+ " equal to zero exactly.\n",
+ " This is one of the major differences between results from solvers\n",
+ " based on the active set method (such as ``qpconvex2_sparse_solve``)\n",
+ " and others, such as, ``handle_solve_pennon`` or interior point\n",
+ " methods.\n",
+ " As a consequence, the initial estimate of the multipliers (if\n",
+ " provided) might be adjusted by the solver to be sufficiently\n",
+ " positive, also the estimates returned during the intermediate exits\n",
+ " might only be a very crude approximation to their final values as\n",
+ " they do not satisfy all the Karush--Kuhn--Tucker (KKT) conditions.\n",
+ " \n",
+ " Another difference is that ``qpconvex2_sparse_solve`` merges\n",
+ " multipliers for both lower and upper inequality into one element\n",
+ " whose sign determines the inequality because there can be at most\n",
+ " one active constraint and multiplier for the inactive is exact zero.\n",
+ " Negative multipliers are associated with the upper bounds and\n",
+ " positive with the lower bounds.\n",
+ " On the other hand, ``handle_solve_pennon`` works with both\n",
+ " multipliers at the same time so they are returned in two elements,\n",
+ " one for the lower bound, the other for the upper bound (see\n",
+ " [Structure of the Lagrangian Multipliers]).\n",
+ " An equivalent result can be achieved by subtracting the upper bound\n",
+ " multiplier from the lower one.\n",
+ " This holds even when equalities are interpreted as two inequalities\n",
+ " (see option 'Transform Constraints').\n",
+ " \n",
" References\n",
" ----------\n",
" Ben--Tal, A and Zibulevsky, M, 1997, `Penalty/barrier multiplier\n",
@@ -1221,7 +1400,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SDP/Readme.md b/local_optimization/SDP/Readme.md
index 01a6c1e..f41b68e 100644
--- a/local_optimization/SDP/Readme.md
+++ b/local_optimization/SDP/Readme.md
@@ -1,7 +1,20 @@
+[](https://www.nag.com)
+
# Semi-Definite Programming (SDP)
+[[`handle_solve_pennon`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_pennon.html#naginterfaces.library.opt.handle_solve_pennon) | [`e04svf`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/flhtml/e04/e04svf.html) |
+[`e04svc`](https://www.nag.co.uk/numeric/nl/nagdoc_latest/clhtml/e04/e04svc.html) ]
+
+
Linear semidefinite programming can be viewed as a generalization of linear programming. While keeping many good properties of LP (such as the duality theory and solvability in polynomial time), SDP introduces a new highly nonlinear type of constraint – matrix inequality. It is an inequality on the eigenvalues of a matrix which depends on the decision variables. Typically, the matrix inequality is written in the form to request all eigenvalues of the matrix to be non-negative, thus the matrix is to be positive semidefinite
* [Matrix completion using Semi-Definite Programming (SDP)](./matrix_completion.ipynb)
* [Nearest correlation matrix using Semi-Definite Programming (SDP)](./NCM_SDP.ipynb)
* [Compute the Lovasz number of a graph using Semi-Definite Programming (SDP)](./theta_optimization.ipynb)
+
+<!-- foot banner for commercial material -->
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
diff --git a/local_optimization/SDP/matrix_completion.ipynb b/local_optimization/SDP/matrix_completion.ipynb
index 0f035e9..9069f98 100644
--- a/local_optimization/SDP/matrix_completion.ipynb
+++ b/local_optimization/SDP/matrix_completion.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -293,14 +304,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " E04SV, NLP-SDP Solver (Pennon)\n",
- " ------------------------------\n",
- " Number of variables 196 [eliminated 0]\n",
- " simple linear nonlin\n",
- " (Standard) inequalities 0 0 0\n",
- " (Standard) equalities 0 0\n",
- " Matrix inequalities 1 0 [dense 1, sparse 0]\n",
- " [max dimension 21]\n",
+ "\n",
+ " --------------------------------\n",
+ " E04SV, NLP-SDP Solver (Pennon)\n",
+ " --------------------------------\n",
+ "\n",
+ " Problem Statistics\n",
+ " No of variables 196\n",
+ " bounds not defined\n",
+ " No of lin. constraints 0\n",
+ " nonzeroes 0\n",
+ " No of matrix inequal. 1\n",
+ " detected matrix inq. 1\n",
+ " linear 1\n",
+ " nonlinear 0\n",
+ " max. dimension 21\n",
+ " detected normal inq. 0\n",
+ " linear 0\n",
+ " nonlinear 0\n",
+ " Objective function Linear\n",
"\n",
" --------------------------------------------------------------\n",
" it| objective | optim | feas | compl | pen min |inner\n",
@@ -441,28 +463,29 @@
" ``handle_opt_set``, ``handle_opt_get``.\n",
" \n",
" ``handle_solve_pennon`` is a solver from the NAG optimization\n",
- " modelling suite for problems such as, quadratic programming (QP),\n",
- " linear semidefinite programming (SDP) and semidefinite programming\n",
+ " modelling suite for problems such as, Quadratic Programming (QP),\n",
+ " linear Semidefinite Programming (SDP) and semidefinite programming\n",
" with bilinear matrix inequalities (BMI-SDP).\n",
" \n",
" For full information please refer to the NAG Library document for\n",
" e04sv\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04svf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04svf.html\n",
" \n",
" Parameters\n",
" ----------\n",
" handle : Handle\n",
- " The handle to the problem. It needs to be initialized by\n",
- " ``handle_init`` and **must not** be changed before the call to\n",
- " ``handle_solve_pennon``.\n",
+ " The handle to the problem. It needs to be initialized (e.g., by\n",
+ " ``handle_init``) and to hold a problem formulation compatible\n",
+ " with ``handle_solve_pennon``. It **must not** be changed between\n",
+ " calls to the NAG optimization modelling suite.\n",
" \n",
" x : float, array-like, shape (nvar)\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
" > 0.\n",
" \n",
" If 'Initial X' = 'USER' (the default), x^0, the initial estimate\n",
- " of the variables x, otherwise `x` need not be set.\n",
+ " of the variables x; otherwise, `x` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -474,7 +497,7 @@
" \n",
" `On intermediate entry`: if set to 0, solving the current\n",
" problem is terminated and the function returns `errno` = 20;\n",
- " otherwise the function continues.\n",
+ " otherwise, the function continues.\n",
" \n",
" u : None or float, array-like, shape (nnzu), optional\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
@@ -490,7 +513,7 @@
" Note: if nnzu = 0, `u` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), u^0, the\n",
- " initial estimate of the Lagrangian multipliers u, otherwise `u`\n",
+ " initial estimate of the Lagrangian multipliers u; otherwise, `u`\n",
" need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
@@ -513,8 +536,8 @@
" If nnzua = 0, `ua` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), U^0, the\n",
- " initial estimate of the matrix Lagrangian multipliers U,\n",
- " otherwise `ua` need not be set.\n",
+ " initial estimate of the matrix Lagrangian multipliers U;\n",
+ " otherwise, `ua` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -735,7 +758,7 @@
" Constraint: 'Inner Stop Criteria' = 'HEURISTIC' or 'STRICT'.\n",
" \n",
" 'Inner Stop Tolerance' : float\n",
- " Default = 10^(-2)\n",
+ " Default = 10^-2\n",
" \n",
" This option sets the required precision alpha^0 for the first\n",
" inner problem solved by [Algorithm 2].\n",
@@ -849,7 +872,7 @@
" reaching the requested accuracy on the feasibility.\n",
" Under normal circumstances, the default value is recommended.\n",
" \n",
- " Constraint: epsilon <= 'P Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'P Min' <= 10^-2.\n",
" \n",
" 'Pmat Min' : float\n",
" Default = sqrt(epsilon)\n",
@@ -858,7 +881,7 @@
" option P_min.\n",
" The same advice applies.\n",
" \n",
- " Constraint: epsilon <= 'Pmat Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'Pmat Min' <= 10^-2.\n",
" \n",
" 'Preference' : str\n",
" Default = 'SPEED'\n",
@@ -964,7 +987,7 @@
" Constraint: 'Stop Criteria' = 'SOFT' or 'STRICT'.\n",
" \n",
" 'Stop Tolerance 1' : float\n",
- " Default = max(10^(-6), sqrt(epsilon))\n",
+ " Default = max(10^-6, sqrt(epsilon))\n",
" \n",
" This option defines epsilon_1 used as a tolerance for the\n",
" relative duality gap (0) and the relative precision (1), see\n",
@@ -973,7 +996,7 @@
" Constraint: 'Stop Tolerance 1' > epsilon.\n",
" \n",
" 'Stop Tolerance 2' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This option sets the value epsilon_2 which is used for\n",
" optimality (2) and complementarity (4) tests from KKT conditions\n",
@@ -984,7 +1007,7 @@
" Constraint: 'Stop Tolerance 2' > epsilon.\n",
" \n",
" 'Stop Tolerance Feasibility' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This argument places an acceptance limit on the feasibility of\n",
" the solution (3), epsilon_feas.\n",
@@ -1066,17 +1089,14 @@
" This solver does not support the model defined in the\n",
" handle.\n",
" \n",
- " (`errno` 2)\n",
- " This solver does not support fixed variables.\n",
- " \n",
" (`errno` 3)\n",
- " A different solver from the suite has already been used.\n",
+ " The problem is already being solved.\n",
" \n",
" (`errno` 4)\n",
" On entry, nvar = *<value>*, expected value = *<value>*.\n",
" \n",
- " Constraint: nvar must match the value given during\n",
- " initialization of `handle`.\n",
+ " Constraint: nvar must match the current number of variables\n",
+ " of the model in the `handle`.\n",
" \n",
" (`errno` 5)\n",
" On entry, nnzu = *<value>*.\n",
@@ -1121,25 +1141,6 @@
" (`errno` 21)\n",
" The current starting point is unusable.\n",
" \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Inner iteration limit has been reached.\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Limited progress in the inner subproblem triggered a stop\n",
- " (heuristic inner stop criteria).\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Line search or another internal component failed.\n",
- " \n",
" (`errno` 51)\n",
" The problem was found to be infeasible during preprocessing.\n",
" \n",
@@ -1172,9 +1173,187 @@
" \n",
" The requested accuracy is not achieved.\n",
" \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Inner iteration limit has been reached.\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Limited progress in the inner subproblem triggered a stop\n",
+ " (heuristic inner stop criteria).\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Line search or another internal component failed.\n",
+ " \n",
" (`errno` 24)\n",
" Unable to make progress, the algorithm was stopped.\n",
" \n",
+ " Notes\n",
+ " -----\n",
+ " ``handle_solve_pennon`` serves as a solver for compatible problems\n",
+ " stored as a handle.\n",
+ " The handle points to an internal data structure which defines the\n",
+ " problem and serves as a means of communication for functions in the\n",
+ " NAG optimization modelling suite.\n",
+ " First, the problem handle is initialized by calling ``handle_init``.\n",
+ " Then some of the functions ``handle_set_linobj``,\n",
+ " ``handle_set_quadobj``, ``handle_set_simplebounds``,\n",
+ " ``handle_set_linconstr``, ``handle_set_linmatineq`` or\n",
+ " ``handle_set_quadmatineq`` may be used to formulate the objective\n",
+ " function, (standard) constraints and matrix constraints of the\n",
+ " problem.\n",
+ " Once the problem is fully set, the handle may be passed to the\n",
+ " solver.\n",
+ " When the handle is no longer needed, ``handle_free`` should be\n",
+ " called to destroy it and deallocate the memory held within.\n",
+ " See [the E04 Introduction] for more details about the NAG\n",
+ " optimization modelling suite.\n",
+ " \n",
+ " Problems which can be defined this way are, for example, (generally\n",
+ " nonconvex) Quadratic Programming (QP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " linear semidefinite programming problems (SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " or semidefinite programming problems with bilinear matrix\n",
+ " inequalities (BMI-SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " Here c, l_x and u_x are n-dimensional vectors, H is a symmetric n*n\n",
+ " matrix, l_B, u_B are m_B-dimensional vectors, B is a general m_B*n\n",
+ " rectangular matrix and A_i^k, Q_ij^k are symmetric matrices.\n",
+ " The expression S⪰0 stands for a constraint on eigenvalues of a\n",
+ " symmetric matrix S, namely, all the eigenvalues should be\n",
+ " non-negative, i.e., the matrix should be positive semidefinite.\n",
+ " See relevant functions in the suite for more details on the problem\n",
+ " formulation.\n",
+ " \n",
+ " The solver is based on a generalized Augmented Lagrangian method\n",
+ " with a suitable choice of standard and matrix penalty functions.\n",
+ " For a detailed description of the algorithm see [Algorithmic\n",
+ " Details].\n",
+ " Under standard assumptions on the problem (Slater constraint\n",
+ " qualification, boundedness of the objective function on the feasible\n",
+ " set, see Stingl (2006) for details) the algorithm converges to a\n",
+ " local solution.\n",
+ " In case of convex problems such as linear SDP or convex QP, this is\n",
+ " the global solution.\n",
+ " The solver is suitable for both small dense and large-scale sparse\n",
+ " problems.\n",
+ " \n",
+ " The algorithm behaviour and solver strategy can be modified by\n",
+ " various options (see [Other Parameters]) which can be set by\n",
+ " ``handle_opt_set`` and ``handle_opt_set_file`` anytime between the\n",
+ " initialization of the handle by ``handle_init`` and a call to the\n",
+ " solver.\n",
+ " Once the solver has finished, options may be modified for the next\n",
+ " solve.\n",
+ " The solver may be called repeatedly with various starting points\n",
+ " and/or options.\n",
+ " \n",
+ " There are several options with a multiple choice where the default\n",
+ " choice is 'AUTO' (for example, 'Hessian Density').\n",
+ " This value means that the decision over the option is left to the\n",
+ " solver based on the structure of the problem.\n",
+ " Option getter ``handle_opt_get`` can be called to retrieve the\n",
+ " choice of these options as well as on any other options.\n",
+ " \n",
+ " Option 'Task' may be used to switch the problem to maximization or\n",
+ " to ignore the objective function and find only a feasible point.\n",
+ " \n",
+ " Option 'Monitor Frequency' may be used to turn on the monitor mode\n",
+ " of the solver.\n",
+ " The solver invoked in this mode pauses regularly even before the\n",
+ " optimal point is found to allow monitoring the progress from the\n",
+ " calling program.\n",
+ " All the important error measures and statistics are available in the\n",
+ " calling program which may terminate the solver early if desired (see\n",
+ " argument `inform`).\n",
+ " \n",
+ " Structure of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " The algorithm works internally with estimates of both the decision\n",
+ " variables, denoted by x, and the Lagrangian multipliers (dual\n",
+ " variables) for standard and matrix constraints, denoted by u and U,\n",
+ " respectively.\n",
+ " You may provide initial estimates, request approximations during the\n",
+ " run (the monitor mode turned on) and obtain the final values.\n",
+ " The Lagrangian multipliers are split into two arrays, the\n",
+ " multipliers u for (standard) constraints are stored in array `u` and\n",
+ " multipliers U for matrix constraints in array `ua`.\n",
+ " Both arrays need to conform to the structure of the constraints.\n",
+ " \n",
+ " If the simple bounds were defined (``handle_set_simplebounds`` was\n",
+ " successfully called), the first 2n elements of `u` belong to the\n",
+ " corresponding Lagrangian multipliers, interleaving a multiplier for\n",
+ " the lower and for the upper bound for each x_i.\n",
+ " If any of the bounds were set to infinity, the corresponding\n",
+ " Lagrangian multipliers are set to 0 and may be ignored.\n",
+ " \n",
+ " Similarly, the following 2m_B elements of `u` belong to multipliers\n",
+ " for the linear constraints, if formulated by\n",
+ " ``handle_set_linconstr``.\n",
+ " The organization is the same, i.e., the multipliers for each\n",
+ " constraint for the lower and upper bounds are alternated and zeroes\n",
+ " are used for any missing (infinite bound) constraint.\n",
+ " \n",
+ " A Lagrangian multiplier for a matrix constraint (one block) of\n",
+ " dimension d*d is a dense symmetric matrix of the same dimension.\n",
+ " All multipliers U are stored next to each other in array `ua` in the\n",
+ " same order as the matrix constraints were defined by\n",
+ " ``handle_set_linmatineq`` and ``handle_set_quadmatineq``.\n",
+ " The lower triangle of each is stored in the packed column order (see\n",
+ " [the F07 Introduction]).\n",
+ " For example, if there are four matrix constraints of dimensions 7,\n",
+ " 3, 1, 1, the dimension of array `ua` should be 36.\n",
+ " The first 28 elements (d_1*(d_1+1)/2) belong to the packed lower\n",
+ " triangle of U_1, followed by six elements of U_2 and one element for\n",
+ " each U_3 and U_4.\n",
+ " \n",
+ " Approximation of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " By the nature of the algorithm, all inequality Lagrangian multiplier\n",
+ " u,U are always kept positive during the computational process.\n",
+ " This applies even to Lagrangian multipliers of inactive constraints\n",
+ " at the solution.\n",
+ " They will only be close to zero although they would normally be\n",
+ " equal to zero exactly.\n",
+ " This is one of the major differences between results from solvers\n",
+ " based on the active set method (such as ``qpconvex2_sparse_solve``)\n",
+ " and others, such as, ``handle_solve_pennon`` or interior point\n",
+ " methods.\n",
+ " As a consequence, the initial estimate of the multipliers (if\n",
+ " provided) might be adjusted by the solver to be sufficiently\n",
+ " positive, also the estimates returned during the intermediate exits\n",
+ " might only be a very crude approximation to their final values as\n",
+ " they do not satisfy all the Karush--Kuhn--Tucker (KKT) conditions.\n",
+ " \n",
+ " Another difference is that ``qpconvex2_sparse_solve`` merges\n",
+ " multipliers for both lower and upper inequality into one element\n",
+ " whose sign determines the inequality because there can be at most\n",
+ " one active constraint and multiplier for the inactive is exact zero.\n",
+ " Negative multipliers are associated with the upper bounds and\n",
+ " positive with the lower bounds.\n",
+ " On the other hand, ``handle_solve_pennon`` works with both\n",
+ " multipliers at the same time so they are returned in two elements,\n",
+ " one for the lower bound, the other for the upper bound (see\n",
+ " [Structure of the Lagrangian Multipliers]).\n",
+ " An equivalent result can be achieved by subtracting the upper bound\n",
+ " multiplier from the lower one.\n",
+ " This holds even when equalities are interpreted as two inequalities\n",
+ " (see option 'Transform Constraints').\n",
+ " \n",
" References\n",
" ----------\n",
" Ben--Tal, A and Zibulevsky, M, 1997, `Penalty/barrier multiplier\n",
@@ -1234,7 +1413,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SDP/theta_optimization.ipynb b/local_optimization/SDP/theta_optimization.ipynb
index b35d79d..e0efc2d 100644
--- a/local_optimization/SDP/theta_optimization.ipynb
+++ b/local_optimization/SDP/theta_optimization.ipynb
@@ -10,7 +10,6 @@
"from naginterfaces.base import utils\n",
"import numpy as np\n",
"import networkx as nx \n",
- "import matplotlib.pyplot as plt\n",
"import warnings"
]
},
@@ -22,15 +21,26 @@
"\n",
"## Correct Rendering of this notebook\n",
"\n",
- "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
"\n",
- "## Introduction"
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Introduction\n",
+ "\n",
"The Lovasz number allows to compute an upper bound on the Shannon capacity of a graph $G$ (the maximum size of independent sets of vertices in G). While the complexity of computing the Shannon capacity is not known, the Lovasz number can be efficiently computed in polynomial time via SDP. \n",
"\n",
"Start by defining a graph $G = (V, E)$"
@@ -148,10 +158,10 @@
"# A_2, A_3, ..., A_{ne+1} match the E_ij matrices\n",
"nnza[2:ne+2] = 1\n",
"for i in range(ne):\n",
- " irowa[idx] = ed[i][0]\n",
- " icola[idx] = ed[i][1]\n",
- " a[idx] = 1.0\n",
- " idx += 1\n",
+ " irowa[idx] = ed[i][0]\n",
+ " icola[idx] = ed[i][1]\n",
+ " a[idx] = 1.0\n",
+ " idx += 1\n",
"\n",
"idblk = opt.handle_set_linmatineq(handle, dima, nnza, irowa, icola, a, \n",
" blksizea=None, idblk=0)"
@@ -166,14 +176,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " E04SV, NLP-SDP Solver (Pennon)\n",
- " ------------------------------\n",
- " Number of variables 16 [eliminated 0]\n",
- " simple linear nonlin\n",
- " (Standard) inequalities 0 0 0\n",
- " (Standard) equalities 0 0\n",
- " Matrix inequalities 1 0 [dense 1, sparse 0]\n",
- " [max dimension 10]\n",
+ "\n",
+ " --------------------------------\n",
+ " E04SV, NLP-SDP Solver (Pennon)\n",
+ " --------------------------------\n",
+ "\n",
+ " Problem Statistics\n",
+ " No of variables 16\n",
+ " bounds not defined\n",
+ " No of lin. constraints 0\n",
+ " nonzeroes 0\n",
+ " No of matrix inequal. 1\n",
+ " detected matrix inq. 1\n",
+ " linear 1\n",
+ " nonlinear 0\n",
+ " max. dimension 10\n",
+ " detected normal inq. 0\n",
+ " linear 0\n",
+ " nonlinear 0\n",
+ " Objective function Linear\n",
"\n",
" --------------------------------------------------------------\n",
" it| objective | optim | feas | compl | pen min |inner\n",
@@ -283,28 +304,29 @@
" ``handle_opt_set``, ``handle_opt_get``.\n",
" \n",
" ``handle_solve_pennon`` is a solver from the NAG optimization\n",
- " modelling suite for problems such as, quadratic programming (QP),\n",
- " linear semidefinite programming (SDP) and semidefinite programming\n",
+ " modelling suite for problems such as, Quadratic Programming (QP),\n",
+ " linear Semidefinite Programming (SDP) and semidefinite programming\n",
" with bilinear matrix inequalities (BMI-SDP).\n",
" \n",
" For full information please refer to the NAG Library document for\n",
" e04sv\n",
" \n",
- " https://www.nag.com/numeric/nl/nagdoc_27/flhtml/e04/e04svf.html\n",
+ " https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/e04/e04svf.html\n",
" \n",
" Parameters\n",
" ----------\n",
" handle : Handle\n",
- " The handle to the problem. It needs to be initialized by\n",
- " ``handle_init`` and **must not** be changed before the call to\n",
- " ``handle_solve_pennon``.\n",
+ " The handle to the problem. It needs to be initialized (e.g., by\n",
+ " ``handle_init``) and to hold a problem formulation compatible\n",
+ " with ``handle_solve_pennon``. It **must not** be changed between\n",
+ " calls to the NAG optimization modelling suite.\n",
" \n",
" x : float, array-like, shape (nvar)\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
" > 0.\n",
" \n",
" If 'Initial X' = 'USER' (the default), x^0, the initial estimate\n",
- " of the variables x, otherwise `x` need not be set.\n",
+ " of the variables x; otherwise, `x` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -316,7 +338,7 @@
" \n",
" `On intermediate entry`: if set to 0, solving the current\n",
" problem is terminated and the function returns `errno` = 20;\n",
- " otherwise the function continues.\n",
+ " otherwise, the function continues.\n",
" \n",
" u : None or float, array-like, shape (nnzu), optional\n",
" Note: intermediate stops take place only if 'Monitor Frequency'\n",
@@ -332,7 +354,7 @@
" Note: if nnzu = 0, `u` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), u^0, the\n",
- " initial estimate of the Lagrangian multipliers u, otherwise `u`\n",
+ " initial estimate of the Lagrangian multipliers u; otherwise, `u`\n",
" need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
@@ -355,8 +377,8 @@
" If nnzua = 0, `ua` will not be referenced.\n",
" \n",
" If 'Initial U' = 'USER' (the default is 'AUTOMATIC'), U^0, the\n",
- " initial estimate of the matrix Lagrangian multipliers U,\n",
- " otherwise `ua` need not be set.\n",
+ " initial estimate of the matrix Lagrangian multipliers U;\n",
+ " otherwise, `ua` need not be set.\n",
" \n",
" `On intermediate entry`: the input is ignored.\n",
" \n",
@@ -577,7 +599,7 @@
" Constraint: 'Inner Stop Criteria' = 'HEURISTIC' or 'STRICT'.\n",
" \n",
" 'Inner Stop Tolerance' : float\n",
- " Default = 10^(-2)\n",
+ " Default = 10^-2\n",
" \n",
" This option sets the required precision alpha^0 for the first\n",
" inner problem solved by [Algorithm 2].\n",
@@ -691,7 +713,7 @@
" reaching the requested accuracy on the feasibility.\n",
" Under normal circumstances, the default value is recommended.\n",
" \n",
- " Constraint: epsilon <= 'P Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'P Min' <= 10^-2.\n",
" \n",
" 'Pmat Min' : float\n",
" Default = sqrt(epsilon)\n",
@@ -700,7 +722,7 @@
" option P_min.\n",
" The same advice applies.\n",
" \n",
- " Constraint: epsilon <= 'Pmat Min' <= 10^(-2).\n",
+ " Constraint: epsilon <= 'Pmat Min' <= 10^-2.\n",
" \n",
" 'Preference' : str\n",
" Default = 'SPEED'\n",
@@ -806,7 +828,7 @@
" Constraint: 'Stop Criteria' = 'SOFT' or 'STRICT'.\n",
" \n",
" 'Stop Tolerance 1' : float\n",
- " Default = max(10^(-6), sqrt(epsilon))\n",
+ " Default = max(10^-6, sqrt(epsilon))\n",
" \n",
" This option defines epsilon_1 used as a tolerance for the\n",
" relative duality gap (0) and the relative precision (1), see\n",
@@ -815,7 +837,7 @@
" Constraint: 'Stop Tolerance 1' > epsilon.\n",
" \n",
" 'Stop Tolerance 2' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This option sets the value epsilon_2 which is used for\n",
" optimality (2) and complementarity (4) tests from KKT conditions\n",
@@ -826,7 +848,7 @@
" Constraint: 'Stop Tolerance 2' > epsilon.\n",
" \n",
" 'Stop Tolerance Feasibility' : float\n",
- " Default = max(10^(-7), sqrt(epsilon))\n",
+ " Default = max(10^-7, sqrt(epsilon))\n",
" \n",
" This argument places an acceptance limit on the feasibility of\n",
" the solution (3), epsilon_feas.\n",
@@ -908,17 +930,14 @@
" This solver does not support the model defined in the\n",
" handle.\n",
" \n",
- " (`errno` 2)\n",
- " This solver does not support fixed variables.\n",
- " \n",
" (`errno` 3)\n",
- " A different solver from the suite has already been used.\n",
+ " The problem is already being solved.\n",
" \n",
" (`errno` 4)\n",
" On entry, nvar = *<value>*, expected value = *<value>*.\n",
" \n",
- " Constraint: nvar must match the value given during\n",
- " initialization of `handle`.\n",
+ " Constraint: nvar must match the current number of variables\n",
+ " of the model in the `handle`.\n",
" \n",
" (`errno` 5)\n",
" On entry, nnzu = *<value>*.\n",
@@ -963,25 +982,6 @@
" (`errno` 21)\n",
" The current starting point is unusable.\n",
" \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Inner iteration limit has been reached.\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Limited progress in the inner subproblem triggered a stop\n",
- " (heuristic inner stop criteria).\n",
- " \n",
- " (`errno` 23)\n",
- " The inner subproblem could not be solved to the required\n",
- " accuracy.\n",
- " \n",
- " Line search or another internal component failed.\n",
- " \n",
" (`errno` 51)\n",
" The problem was found to be infeasible during preprocessing.\n",
" \n",
@@ -1014,9 +1014,187 @@
" \n",
" The requested accuracy is not achieved.\n",
" \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Inner iteration limit has been reached.\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Limited progress in the inner subproblem triggered a stop\n",
+ " (heuristic inner stop criteria).\n",
+ " \n",
+ " (`errno` 23)\n",
+ " The inner subproblem could not be solved to the required\n",
+ " accuracy.\n",
+ " \n",
+ " Line search or another internal component failed.\n",
+ " \n",
" (`errno` 24)\n",
" Unable to make progress, the algorithm was stopped.\n",
" \n",
+ " Notes\n",
+ " -----\n",
+ " ``handle_solve_pennon`` serves as a solver for compatible problems\n",
+ " stored as a handle.\n",
+ " The handle points to an internal data structure which defines the\n",
+ " problem and serves as a means of communication for functions in the\n",
+ " NAG optimization modelling suite.\n",
+ " First, the problem handle is initialized by calling ``handle_init``.\n",
+ " Then some of the functions ``handle_set_linobj``,\n",
+ " ``handle_set_quadobj``, ``handle_set_simplebounds``,\n",
+ " ``handle_set_linconstr``, ``handle_set_linmatineq`` or\n",
+ " ``handle_set_quadmatineq`` may be used to formulate the objective\n",
+ " function, (standard) constraints and matrix constraints of the\n",
+ " problem.\n",
+ " Once the problem is fully set, the handle may be passed to the\n",
+ " solver.\n",
+ " When the handle is no longer needed, ``handle_free`` should be\n",
+ " called to destroy it and deallocate the memory held within.\n",
+ " See [the E04 Introduction] for more details about the NAG\n",
+ " optimization modelling suite.\n",
+ " \n",
+ " Problems which can be defined this way are, for example, (generally\n",
+ " nonconvex) Quadratic Programming (QP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " linear semidefinite programming problems (SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " or semidefinite programming problems with bilinear matrix\n",
+ " inequalities (BMI-SDP)\n",
+ " \n",
+ " [table omitted]\n",
+ " \n",
+ " Here c, l_x and u_x are n-dimensional vectors, H is a symmetric n*n\n",
+ " matrix, l_B, u_B are m_B-dimensional vectors, B is a general m_B*n\n",
+ " rectangular matrix and A_i^k, Q_ij^k are symmetric matrices.\n",
+ " The expression S⪰0 stands for a constraint on eigenvalues of a\n",
+ " symmetric matrix S, namely, all the eigenvalues should be\n",
+ " non-negative, i.e., the matrix should be positive semidefinite.\n",
+ " See relevant functions in the suite for more details on the problem\n",
+ " formulation.\n",
+ " \n",
+ " The solver is based on a generalized Augmented Lagrangian method\n",
+ " with a suitable choice of standard and matrix penalty functions.\n",
+ " For a detailed description of the algorithm see [Algorithmic\n",
+ " Details].\n",
+ " Under standard assumptions on the problem (Slater constraint\n",
+ " qualification, boundedness of the objective function on the feasible\n",
+ " set, see Stingl (2006) for details) the algorithm converges to a\n",
+ " local solution.\n",
+ " In case of convex problems such as linear SDP or convex QP, this is\n",
+ " the global solution.\n",
+ " The solver is suitable for both small dense and large-scale sparse\n",
+ " problems.\n",
+ " \n",
+ " The algorithm behaviour and solver strategy can be modified by\n",
+ " various options (see [Other Parameters]) which can be set by\n",
+ " ``handle_opt_set`` and ``handle_opt_set_file`` anytime between the\n",
+ " initialization of the handle by ``handle_init`` and a call to the\n",
+ " solver.\n",
+ " Once the solver has finished, options may be modified for the next\n",
+ " solve.\n",
+ " The solver may be called repeatedly with various starting points\n",
+ " and/or options.\n",
+ " \n",
+ " There are several options with a multiple choice where the default\n",
+ " choice is 'AUTO' (for example, 'Hessian Density').\n",
+ " This value means that the decision over the option is left to the\n",
+ " solver based on the structure of the problem.\n",
+ " Option getter ``handle_opt_get`` can be called to retrieve the\n",
+ " choice of these options as well as on any other options.\n",
+ " \n",
+ " Option 'Task' may be used to switch the problem to maximization or\n",
+ " to ignore the objective function and find only a feasible point.\n",
+ " \n",
+ " Option 'Monitor Frequency' may be used to turn on the monitor mode\n",
+ " of the solver.\n",
+ " The solver invoked in this mode pauses regularly even before the\n",
+ " optimal point is found to allow monitoring the progress from the\n",
+ " calling program.\n",
+ " All the important error measures and statistics are available in the\n",
+ " calling program which may terminate the solver early if desired (see\n",
+ " argument `inform`).\n",
+ " \n",
+ " Structure of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " The algorithm works internally with estimates of both the decision\n",
+ " variables, denoted by x, and the Lagrangian multipliers (dual\n",
+ " variables) for standard and matrix constraints, denoted by u and U,\n",
+ " respectively.\n",
+ " You may provide initial estimates, request approximations during the\n",
+ " run (the monitor mode turned on) and obtain the final values.\n",
+ " The Lagrangian multipliers are split into two arrays, the\n",
+ " multipliers u for (standard) constraints are stored in array `u` and\n",
+ " multipliers U for matrix constraints in array `ua`.\n",
+ " Both arrays need to conform to the structure of the constraints.\n",
+ " \n",
+ " If the simple bounds were defined (``handle_set_simplebounds`` was\n",
+ " successfully called), the first 2n elements of `u` belong to the\n",
+ " corresponding Lagrangian multipliers, interleaving a multiplier for\n",
+ " the lower and for the upper bound for each x_i.\n",
+ " If any of the bounds were set to infinity, the corresponding\n",
+ " Lagrangian multipliers are set to 0 and may be ignored.\n",
+ " \n",
+ " Similarly, the following 2m_B elements of `u` belong to multipliers\n",
+ " for the linear constraints, if formulated by\n",
+ " ``handle_set_linconstr``.\n",
+ " The organization is the same, i.e., the multipliers for each\n",
+ " constraint for the lower and upper bounds are alternated and zeroes\n",
+ " are used for any missing (infinite bound) constraint.\n",
+ " \n",
+ " A Lagrangian multiplier for a matrix constraint (one block) of\n",
+ " dimension d*d is a dense symmetric matrix of the same dimension.\n",
+ " All multipliers U are stored next to each other in array `ua` in the\n",
+ " same order as the matrix constraints were defined by\n",
+ " ``handle_set_linmatineq`` and ``handle_set_quadmatineq``.\n",
+ " The lower triangle of each is stored in the packed column order (see\n",
+ " [the F07 Introduction]).\n",
+ " For example, if there are four matrix constraints of dimensions 7,\n",
+ " 3, 1, 1, the dimension of array `ua` should be 36.\n",
+ " The first 28 elements (d_1*(d_1+1)/2) belong to the packed lower\n",
+ " triangle of U_1, followed by six elements of U_2 and one element for\n",
+ " each U_3 and U_4.\n",
+ " \n",
+ " Approximation of the Lagrangian Multipliers\n",
+ " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ " By the nature of the algorithm, all inequality Lagrangian multiplier\n",
+ " u,U are always kept positive during the computational process.\n",
+ " This applies even to Lagrangian multipliers of inactive constraints\n",
+ " at the solution.\n",
+ " They will only be close to zero although they would normally be\n",
+ " equal to zero exactly.\n",
+ " This is one of the major differences between results from solvers\n",
+ " based on the active set method (such as ``qpconvex2_sparse_solve``)\n",
+ " and others, such as, ``handle_solve_pennon`` or interior point\n",
+ " methods.\n",
+ " As a consequence, the initial estimate of the multipliers (if\n",
+ " provided) might be adjusted by the solver to be sufficiently\n",
+ " positive, also the estimates returned during the intermediate exits\n",
+ " might only be a very crude approximation to their final values as\n",
+ " they do not satisfy all the Karush--Kuhn--Tucker (KKT) conditions.\n",
+ " \n",
+ " Another difference is that ``qpconvex2_sparse_solve`` merges\n",
+ " multipliers for both lower and upper inequality into one element\n",
+ " whose sign determines the inequality because there can be at most\n",
+ " one active constraint and multiplier for the inactive is exact zero.\n",
+ " Negative multipliers are associated with the upper bounds and\n",
+ " positive with the lower bounds.\n",
+ " On the other hand, ``handle_solve_pennon`` works with both\n",
+ " multipliers at the same time so they are returned in two elements,\n",
+ " one for the lower bound, the other for the upper bound (see\n",
+ " [Structure of the Lagrangian Multipliers]).\n",
+ " An equivalent result can be achieved by subtracting the upper bound\n",
+ " multiplier from the lower one.\n",
+ " This holds even when equalities are interpreted as two inequalities\n",
+ " (see option 'Transform Constraints').\n",
+ " \n",
" References\n",
" ----------\n",
" Ben--Tal, A and Zibulevsky, M, 1997, `Penalty/barrier multiplier\n",
@@ -1076,7 +1254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SOCP/Readme.md b/local_optimization/SOCP/Readme.md
index f35f790..ad6cddd 100644
--- a/local_optimization/SOCP/Readme.md
+++ b/local_optimization/SOCP/Readme.md
@@ -1,3 +1,5 @@
+[](https://www.nag.com)
+
# Second Order Cone Programming
[Second Order Cone Programming (SOCP)](https://en.wikipedia.org/wiki/Second-order_cone_programming) is convex optimization which extends linear programming (LP) with second-order (Lorentz or the ice cream) cones. Search region of the solution is the intersection of an affine
@@ -23,7 +25,7 @@ This directory contains demonstrations using NAG's SOCP solver in Python.
* [A classification example using NAG's SOCP from CVXPY](./cvxpy_classification.ipynb)
-## Portfolio Optimization using Second Order Cone Programming (SOCP)
+## Portfolio Optimization as Quadratically Constrained Quadratic Programming (QCQP)
This demonstration is a walk-through of modelling techniques in portfolio optimization using second-order cone programming in the NAG Library. Models in portfolio optimization include
@@ -31,7 +33,13 @@ This demonstration is a walk-through of modelling techniques in portfolio optimi
* quadratically constrained quadratic programming (tev portfolio)
* optimization with objective of fraction of quadratic and linear (the Sharpe ratio).
-General functions are enclosed for users to get the principle idea on SOCP reformulation. They provide one of the ways to build and solve their problems using NAG's SOCP solver and could be copy and paste into a model and reuse repeatedly.
+NAG provides two functions for users to easily define quadratic objective and constraints. Then the second-order cone programming solver can be called directly to solve the problem without any extra effort on reformulation.
+
+* [portfolio_optimization_qcqp.ipynb](./portfolio_optimization_qcqp.ipynb) Jupyter notebook
+* [portfolio_optimization_qcqp.pdf](./static/portfolio_optimization_qcqp.pdf) Static pdf version
+* [portfolio_optimization_qcqp.html](./static/portfolio_optimization_qcqp.html) Static html version
+
+Users can also transform their QCQP problem into second-order cone programming model by hand. In the following notebook two general functions are enclosed for users to get the principle idea on SOCP reformulation.
* [portfolio_optimization_using_socp.ipynb](./portfolio_optimization_using_socp.ipynb) Jupyter notebook
* [portfolio_optimization_using_socp.pdf](./static/portfolio_optimization_using_socp.pdf) Static pdf version
@@ -44,11 +52,18 @@ A mean-variance model with probability constraint using randomly generated data.
* [robust_lp.ipynb](./robust_lp.ipynb) Jupyter Notebook
* [robust_lp.html](./static/robust_lp.html) Static html version
-# Data
+## Data
* [stock_price.pkl](./data/stock_price.pkl) - pickled data file contains daily prices of 30 stocks in DJIA from March 2018 to March 2019. It is used to estimate out-of-sample expected return and covariance matrix.
* [djia_close_price.csv](./data/djia_close_price.csv) - CSV version of daily prices of 30 stocks in DJIA from March 2018 to March 2019.
-# Poster
+## Poster
A 2019 poster discussing NAG's SOCP functionality is [available on the NAG website](https://www.nag.com/market/posters/socp.pdf)
+
+# Obtaining the NAG Library for Python
+
+ * Instructions on [how to install the NAG Library for Python](../Readme.md#install)
+ * Instructions on [how to run the Jupyter notebooks in the Repository](../Readme.md#jupyter)
+
+<!-- Instructions for how to download, install and license the NAG Library for Python can be found at https://github.com/numericalalgorithmsgroup/NAGPythonExamples#nag-library-for-python-installation-->
diff --git a/local_optimization/SOCP/cvxpy_classification.ipynb b/local_optimization/SOCP/cvxpy_classification.ipynb
index df06d26..0ae1273 100644
--- a/local_optimization/SOCP/cvxpy_classification.ipynb
+++ b/local_optimization/SOCP/cvxpy_classification.ipynb
@@ -13,15 +13,26 @@
"source": [
"## Correct Rendering of this notebook\n",
"\n",
- "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
"\n",
- "## Introduction"
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Introduction\n",
+ "\n",
"In this notebook, we demonstrate how NAG second-order conic programming (SOCP) solver can be used to find a quartic polynomial\n",
"\\vspace{0.1cm}\n",
"\\begin{equation}\\label{poly}\n",
@@ -227,20 +238,22 @@
" Socp Stop Tolerance 2 = 1.05367E-08 * d\n",
" Socp System Formulation = Auto * d\n",
" End of Options\n",
- " Original Problem Statistics\n",
- "\n",
- " Number of variables 32\n",
- " Number of linear constraints 176\n",
- " Number of nonzeros 2432\n",
- " Number of cones 1\n",
- "\n",
"\n",
- " Presolved Problem Statistics\n",
+ " Problem Statistics\n",
+ " No of variables 32\n",
+ " bounds not defined\n",
+ " No of lin. constraints 176\n",
+ " nonzeroes 2432\n",
+ " No of quad.constraints 0\n",
+ " No of cones 1\n",
+ " biggest cone size 16\n",
+ " Objective function Linear\n",
"\n",
- " Number of variables 191\n",
- " Number of linear constraints 175\n",
- " Number of nonzeros 2590\n",
- " Number of cones 1\n",
+ " Presolved Problem Measures\n",
+ " No of variables 191\n",
+ " No of lin. constraints 175\n",
+ " nonzeroes 2590\n",
+ " No of cones 1\n",
"\n",
"\n",
" ------------------------------------------------------------------------\n",
@@ -369,7 +382,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SOCP/data/stock_price.pkl b/local_optimization/SOCP/data/stock_price.pkl
index be4379c..6064972 100644
Binary files a/local_optimization/SOCP/data/stock_price.pkl and b/local_optimization/SOCP/data/stock_price.pkl differ
diff --git a/local_optimization/SOCP/portfolio_optimization_qcqp.ipynb b/local_optimization/SOCP/portfolio_optimization_qcqp.ipynb
new file mode 100644
index 0000000..f05f6ff
--- /dev/null
+++ b/local_optimization/SOCP/portfolio_optimization_qcqp.ipynb
@@ -0,0 +1,718 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quadratically constrained quadratic programming and its applications in portfolio optimization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Correct Rendering of this notebook\n",
+ "\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "\n",
+ "The notebook is also not rendered well by GitHub so if you are reading it from there, you may prefer the [pdf version instead](./static/portfolio_optimization_qcqp.pdf)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction\n",
+ "\n",
+ "Quadratically constrained quadratic programming (QCQP) is a type of optimization problem in which both the objective function and the constraints involve quadratic functions. A general QCQP problem has the following form\n",
+ "\\begin{equation}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{minimize}} & \\frac{1}{2}x^TP_0x+q_0^Tx+r_0\\\\[0.6ex]\n",
+ "\\mbox{subject to} & \\frac{1}{2}x^TP_ix+q_i^Tx+r_i\\leq0,\\quad i=1,\\ldots,p.\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "It appears in applications such as modern portfolio theory, machine learning, engineering and control. Convex QCQP is usually handled through conic optimization, or, more precisely, second-order cone programming (SOCP) due to its computational efficiency and ability to detect infeasibility. However, using SOCP to solve convex QCQP is nontrivial task which requires extra amount of effort to transform problem data and add auxiliary variables. In this notebook, we are going to demonstrate how to use the *NAG Optimization Modelling Suite* in the NAG Library to define and solve QCQP in portfolio optimization."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data Preparation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We consider daily prices for the 30 stocks in the DJIA from March 2018 to March 2019. In practice, the estimation of the mean return $r$ and covariance $V$ is often a nontrivial task. In this notebook, we estimate those entities using simple sample estimates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary libraries\n",
+ "import pickle as pkl\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load stock price data from stock_price.plk\n",
+ "# Stock_price: dict = ['close_price': [data], 'date_index': [data]]\n",
+ "stock_price = stock_price = pkl.load(open('./data/stock_price.pkl', 'rb'))\n",
+ "close_price = stock_price['close_price']\n",
+ "date_index = stock_price['date_index']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Size of data, m: number of observations, n: number of stocks\n",
+ "m = len(date_index)\n",
+ "n = len(close_price)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Extract stock closing prices to a numpy array\n",
+ "data = np.zeros(shape=(m, n))\n",
+ "i = 0\n",
+ "for stock in close_price:\n",
+ " data[:,i] = close_price[stock]\n",
+ " plt.plot(np.arange(m), data[:,i])\n",
+ " i += 1\n",
+ "# Plot closing prices\n",
+ "plt.xlabel('Time (days)')\n",
+ "plt.ylabel('Closing price ($)')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For each stock $i$, we first estimate the $j$th daily relative return as $$relative~return_{i,j} = \\frac{closing~price_{i,j+1}-closing~price_{i,j}}{closing~price_{i,j}}.$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Relative return\n",
+ "rel_rtn = np.zeros(shape=(m-1, n))\n",
+ "for j in range(m-1):\n",
+ " rel_rtn[j,:] = np.divide(data[j+1,:] - data[j,:], data[j,:])\n",
+ "# Plot relative return\n",
+ "for i in range(n):\n",
+ " plt.plot(np.arange(m-1),rel_rtn[:,i])\n",
+ "plt.xlabel('Time (days)')\n",
+ "plt.ylabel('Relative return')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Simply take arithmetic mean of each column of relative return to get mean return $r$ for each stock, followed by estimating covariance $V$ using numpy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mean return\n",
+ "r = np.zeros(n)\n",
+ "r = rel_rtn.sum(axis=0)\n",
+ "r = r / (m-1)\n",
+ "# Covariance matrix\n",
+ "V = np.cov(rel_rtn.T)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Classic Mean-Variance Model\n",
+ "## Efficient Frontier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the major goals of portfolio management is to achieve a certain level of return under a specific risk measurement. Here we demonstrate how to use NAG Library to build efficient frontier by solving classical Markowitz model with long-only constraint (meaning, buy to hold and short selling is not allowed):\n",
+ "\\begin{equation}\\label{MV_model}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{minimize}} & -r^Tx+\\mu x^TVx\\\\[0.6ex]\n",
+ "\\mbox{subject to} & e^Tx = 1,\\\\[0.6ex]\n",
+ " & x\\geq0,\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "where $e\\in\\Re^n$ is vector of all ones and $\\mu$ is a scalar controling trade-off between return and risk. Note one could build the efficient frontier by varying $\\mu$ from $0$ to a certain value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the NAG Library\n",
+ "from naginterfaces.base import utils\n",
+ "from naginterfaces.library import opt\n",
+ "# Import necessary math libraries\n",
+ "import math as mt\n",
+ "import warnings as wn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input for quadratic objective\n",
+ "# Sparsity pattern of upper triangular V\n",
+ "irowq, icolq = np.nonzero(np.triu(V))\n",
+ "v_val = V[irowq, icolq]\n",
+ "# Convert to 1-based\n",
+ "irowq = irowq + 1\n",
+ "icolq = icolq + 1\n",
+ "# Sparsity pattern of r, which is actually dense in this application\n",
+ "idxr = np.arange(1, n+1)\n",
+ "\n",
+ "# Input for linear constraint: e'x = 1\n",
+ "irowa = np.full(n, 1, dtype=int)\n",
+ "icola = np.arange(1, n+1)\n",
+ "a = np.full(n, 1.0, dtype=float)\n",
+ "bl = np.full(1, 1.0, dtype=float)\n",
+ "bu = np.full(1, 1.0, dtype=float)\n",
+ "\n",
+ "# Input for bound constraint: x >= 0\n",
+ "blx = np.zeros(n)\n",
+ "bux = np.full(n, 1.e20, float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The input data is ready, we can easily build the efficient frontier as follows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set step for mu\n",
+ "step = 2001\n",
+ "\n",
+ "# Initialize output data: absolute risk and return\n",
+ "ab_risk = np.empty(0, float)\n",
+ "ab_rtn = np.empty(0, float)\n",
+ "\n",
+ "for mu in np.linspace(0.0, 2000.0, step):\n",
+ " # Create problem handle\n",
+ " handle = opt.handle_init(n)\n",
+ " \n",
+ " # Set quadratic objective function\n",
+ " # In qcqp standard form q should be 2*mu*V\n",
+ " q = 2.0 * mu * v_val\n",
+ " idqc = -1\n",
+ " opt.handle_set_qconstr(handle, 0.0, idqc, idxr, -r, irowq, icolq, q)\n",
+ " \n",
+ " # Set linear constraint e'x = 1\n",
+ " opt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)\n",
+ " \n",
+ " # Set bound constraint\n",
+ " opt.handle_set_simplebounds(handle, blx, bux)\n",
+ " \n",
+ " # Set options\n",
+ " for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print File = -1',\n",
+ " 'SOCP Scaling = A'\n",
+ " ]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ " \n",
+ " # Call socp interior point solver\n",
+ " # Mute warnings and do not count results from warnings\n",
+ " wn.simplefilter('error', utils.NagAlgorithmicWarning)\n",
+ " try:\n",
+ " slt = opt.handle_solve_socp_ipm(handle)\n",
+ "\n",
+ " # Compute risk and return from the portfolio\n",
+ " ab_risk = np.append(ab_risk, mt.sqrt(slt.x[0:n].dot(V.dot(slt.x[0:n]))))\n",
+ " ab_rtn = np.append(ab_rtn, r.dot(slt.x[0:n]))\n",
+ " except utils.NagAlgorithmicWarning:\n",
+ " pass\n",
+ " \n",
+ " # Destroy the handle:\n",
+ " opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the result\n",
+ "plt.plot(ab_risk*100.0, ab_rtn*100.0)\n",
+ "plt.ylabel('Total Expected Return (%)')\n",
+ "plt.xlabel('Absolute Risk (%)')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Maximizing the Sharpe ratio"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Sharpe ratio is defined as the ratio of return of portfolio and standard deviation of the portfolio's excess return. It is usually used to measure the efficiency of a portfolio. Find the most efficient portfolio is equivalent to solve the following optimization problem.\n",
+ "\\begin{equation}\\label{sr_model}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{minimize}} & \\frac{\\sqrt{x^TVx}}{r^Tx}\\\\[0.6ex]\n",
+ "\\mbox{subject to} & e^Tx = 1,\\\\[0.6ex]\n",
+ " & x\\geq0.\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "By replacing $x$ with $\\frac{y}{\\lambda}, \\lambda\\gt0$, model (\\ref{sr_model}) is equivalent to\n",
+ "\\begin{equation}\\label{sr_model_eq}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{y\\in\\Re^n, \\lambda\\in\\Re}{\\mbox{minimize}} & y^TVy\\\\[0.6ex]\n",
+ "\\mbox{subject to} & e^Ty = \\lambda,\\\\[0.6ex]\n",
+ " & r^Ty=1, \\\\\n",
+ " & y\\geq0, \\\\\n",
+ " & \\lambda\\geq0.\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "Problem (\\ref{sr_model_eq}) is similar to problem (\\ref{MV_model}) in the sense that they both have a quadratic objective function and linear constraints."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input for linear constraint: e'y = lambda\n",
+ "irowa = np.full(n+1, 1, dtype=int)\n",
+ "icola = np.arange(1, n+2)\n",
+ "a = np.append(np.full(n, 1.0, dtype=float), -1.0)\n",
+ "bl = np.zeros(1)\n",
+ "bu = np.zeros(1)\n",
+ "\n",
+ "# Inpute for linear constraint: r'y = 1\n",
+ "irowa = np.append(irowa, np.full(n, 2, dtype=int))\n",
+ "icola = np.append(icola, np.arange(1, n+1))\n",
+ "a = np.append(a, r)\n",
+ "bl = np.append(bl, 1.0)\n",
+ "bu = np.append(bu, 1.0)\n",
+ "\n",
+ "# Input for bound constraint: x >= 0\n",
+ "blx = np.zeros(n+1)\n",
+ "bux = np.full(n+1, 1.e20, float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can call the NAG SOCP solver as follows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create problem handle\n",
+ "handle = opt.handle_init(n+1)\n",
+ "\n",
+ "# Set quadratic objective function\n",
+ "# In qcqp standard form q should be 2*V\n",
+ "q = 2.0 * v_val\n",
+ "idqc = -1\n",
+ "opt.handle_set_qconstr(handle, 0.0, idqc, irowq=irowq, icolq=icolq, q=q)\n",
+ "\n",
+ "# Set linear constraints\n",
+ "opt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)\n",
+ " \n",
+ "# Set bound constraint\n",
+ "opt.handle_set_simplebounds(handle, blx, bux)\n",
+ " \n",
+ "# Set options\n",
+ "for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print File = -1',\n",
+ " 'SOCP Scaling = A'\n",
+ "]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ " \n",
+ "# Call socp interior point solver\n",
+ "slt = opt.handle_solve_socp_ipm(handle)\n",
+ "\n",
+ "sr_risk = mt.sqrt(slt.x[0:n].dot(V.dot(slt.x[0:n])))/slt.x[n]\n",
+ "sr_rtn = r.dot(slt.x[0:n])/slt.x[n]\n",
+ "sr_x = slt.x[0:n]/slt.x[n]\n",
+ "\n",
+ "# Destroy the handle:\n",
+ "opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot result.\n",
+ "plt.plot(ab_risk*100.0, ab_rtn*100.0, label='Efficient frontier')\n",
+ "plt.plot([sr_risk*100], [sr_rtn*100], 'rs', label='Portfolio with maximum Sharpe ratio')\n",
+ "plt.plot([sr_risk*100, 0.0], [sr_rtn*100, 0.0], 'r-', label='Capital market line')\n",
+ "plt.axis([min(ab_risk*100), max(ab_risk*100), min(ab_rtn*100), max(ab_rtn*100)])\n",
+ "plt.ylabel('Total Expected Return (%)')\n",
+ "plt.xlabel('Absolute Risk (%)')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Portfolio optimization with tracking-error constraint"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To avoid taking unnecessary risk when beating a benchmark, the investors commonly impose a limit on the volatility of the deviation of the active portfolio from the benchmark, which is also known as tracking-error volatility (TEV) \\cite{J03}. The model to build efficient frontier in excess-return space is\n",
+ "\\begin{equation}\\label{er_tev}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{maximize}} & r^Tx\\\\\n",
+ "\\mbox{subject to} & e^Tx = 0,\\\\\n",
+ " & x^TVx\\leq tev,\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "where $tev$ is a limit on the track-error. Roll \\cite{R92} noted that problem (\\ref{er_tev}) is totally independent of the benchmark and leads to the unpalatable result that the active portfolio has systematically higher risk than the benchmark and is not optimal. Therefore, in this section we solve a more advanced model by taking absolute risk into account as follows.\n",
+ "\\begin{equation}\\label{tev_model}\n",
+ "\\begin{array}{ll}\n",
+ "\\underset{x\\in\\Re^n}{\\mbox{minimize}} & -r^Tx+\\mu (x+b)^TV(x+b)\\\\\n",
+ "\\mbox{subject to} & e^Tx = 0,\\\\\n",
+ " & x^TVx\\leq tev,\\\\\n",
+ " & x+b\\geq0,\n",
+ "\\end{array}\n",
+ "\\end{equation}\n",
+ "where $b$ is a benchmark portfolio. In this demonstration, it is generated synthetically. Note here we use the same covariance matrix $V$ for tev and absolute risk measurement for demonstration purpose. In practice one could use different covariance matrices from different markets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate a benchmark portfolio from efficient portfolio that maximiz the Sharpe ratio\n",
+ "# Perturb x\n",
+ "b = sr_x + 1.e-1\n",
+ "# Normalize b\n",
+ "b = b/sum(b)\n",
+ "\n",
+ "# Set limit on tracking-error\n",
+ "tev = 0.000002\n",
+ "\n",
+ "# Compute risk and return at the benchmark\n",
+ "b_risk = mt.sqrt(b.dot(V.dot(b)))\n",
+ "b_rtn = r.dot(b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input for linear constraint: e'x = 0\n",
+ "irowa = np.full(n, 1, dtype=int)\n",
+ "icola = np.arange(1, n+1)\n",
+ "a = np.full(n, 1.0, dtype=float)\n",
+ "bl = np.zeros(1)\n",
+ "bu = np.zeros(1)\n",
+ "\n",
+ "# Input for bound constraint: x >= -b\n",
+ "blx = -b\n",
+ "bux = np.full(n, 1.e20, float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize output data: TEV risk and return\n",
+ "tev_risk = np.empty(0, float)\n",
+ "tev_rtn = np.empty(0, float)\n",
+ "\n",
+ "for mu in np.linspace(0.0, 2000.0, step):\n",
+ " # Create problem handle\n",
+ " handle = opt.handle_init(n)\n",
+ " \n",
+ " # Set quadratic objective function\n",
+ " # In qcqp standard form q should be 2*mu*V\n",
+ " q = 2.0 * mu * v_val\n",
+ " r_mu = 2.0*mu*V.dot(b)-r\n",
+ " idqc = -1\n",
+ " opt.handle_set_qconstr(handle, 0.0, idqc, idxr, r_mu, irowq, icolq, q)\n",
+ " \n",
+ " # Set quadratic constraint\n",
+ " # In qcqp standard form q should be 2*V\n",
+ " q = 2.0 * v_val\n",
+ " idqc = 0\n",
+ " opt.handle_set_qconstr(handle, -tev, idqc, irowq=irowq, icolq=icolq, q=q)\n",
+ " \n",
+ " # Set linear constraint e'x = 1\n",
+ " opt.handle_set_linconstr(handle, bl, bu, irowa, icola, a)\n",
+ " \n",
+ " # Set bound constraint\n",
+ " opt.handle_set_simplebounds(handle, blx, bux)\n",
+ " \n",
+ " # Set options\n",
+ " for option in [\n",
+ " 'Print Options = NO',\n",
+ " 'Print Level = 1',\n",
+ " 'Print File = -1',\n",
+ " 'SOCP Scaling = A'\n",
+ " ]:\n",
+ " opt.handle_opt_set(handle, option)\n",
+ " \n",
+ " # Call socp interior point solver\n",
+ " # Mute warnings and do not count results from warnings\n",
+ " wn.simplefilter('error', utils.NagAlgorithmicWarning)\n",
+ " try:\n",
+ " slt = opt.handle_solve_socp_ipm(handle)\n",
+ "\n",
+ "# Compute risk and return from the portfolio\n",
+ " tev_risk = np.append(tev_risk, mt.sqrt((slt.x[0:n]+b).dot(V.dot(slt.x[0:n]+b))))\n",
+ " tev_rtn = np.append(tev_rtn, r.dot(slt.x[0:n]+b))\n",
+ " except utils.NagAlgorithmicWarning:\n",
+ " pass\n",
+ " \n",
+ " # Destroy the handle:\n",
+ " opt.handle_free(handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 540x396 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the result\n",
+ "plt.figure(figsize=(7.5, 5.5))\n",
+ "plt.plot(ab_risk*100.0, ab_rtn*100.0, label='Classic efficient frontier')\n",
+ "plt.plot([sr_risk*100], [sr_rtn*100], 'rs', label='Portfolio with maximum Sharpe ratio')\n",
+ "plt.plot([sr_risk*100, 0.0], [sr_rtn*100, 0.0], 'r-', label='Capital market line')\n",
+ "plt.plot(b_risk*100, b_rtn*100, 'r*', label='Benchmark portfolio')\n",
+ "plt.plot(tev_risk*100.0, tev_rtn*100.0, 'seagreen', label='Efficient frontier with tev constraint')\n",
+ "\n",
+ "plt.axis([min(ab_risk*100), max(ab_risk*100), min(tev_rtn*100), max(ab_rtn*100)])\n",
+ "plt.ylabel('Total Expected Return (%)')\n",
+ "plt.xlabel('Absolute Risk (%)')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Conclusion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, we demonstrated how to use NAG Library to solve various quadratic models in portfolio optimization. Conic optimization is usually a good choice to solve convex QCQP. It is worth pointing out that the versatility of SOCP is not just limited to the QCQP models mentioned here. It covers a lot more problems and constraints. For example, DeMiguel et al. \\cite{DGNU09} discussed portfolio optimization with norm constraint, which can be easily transformed into an SOCP problem. We refer readers to the NAG Library documentation \\cite{NAGDOC} on SOCP solver and \\cite{AG03, LVBL98} for more details."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# References\n",
+ "\n",
+ "[<a id=\"cit-J03\" href=\"#call-J03\">1</a>] Jorion Philippe, ``_Portfolio optimization with tracking-error constraints_'', Financial Analysts Journal, vol. 59, number 5, pp. 70--82, 2003.\n",
+ "\n",
+ "[<a id=\"cit-R92\" href=\"#call-R92\">2</a>] Roll Richard, ``_A mean/variance analysis of tracking error_'', The Journal of Portfolio Management, vol. 18, number 4, pp. 13--22, 1992.\n",
+ "\n",
+ "[<a id=\"cit-DGNU09\" href=\"#call-DGNU09\">3</a>] DeMiguel Victor, Garlappi Lorenzo, Nogales Francisco J <em>et al.</em>, ``_A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms_'', Management science, vol. 55, number 5, pp. 798--812, 2009.\n",
+ "\n",
+ "[<a id=\"cit-NAGDOC\" href=\"#call-NAGDOC\">4</a>] Numerical Algorithms Group, ``_NAG documentation_'', 2019. [online](https://www.nag.com/numeric/fl/nagdoc_latest/html/frontmatter/manconts.html)\n",
+ "\n",
+ "[<a id=\"cit-AG03\" href=\"#call-AG03\">5</a>] Alizadeh Farid and Goldfarb Donald, ``_Second-order cone programming_'', Mathematical programming, vol. 95, number 1, pp. 3--51, 2003.\n",
+ "\n",
+ "[<a id=\"cit-LVBL98\" href=\"#call-LVBL98\">6</a>] Lobo Miguel Sousa, Vandenberghe Lieven, Boyd Stephen <em>et al.</em>, ``_Applications of second-order cone programming_'', Linear algebra and its applications, vol. 284, number 1-3, pp. 193--228, 1998.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "number",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/local_optimization/SOCP/portfolio_optimization_using_cvxpy.ipynb b/local_optimization/SOCP/portfolio_optimization_using_cvxpy.ipynb
new file mode 100644
index 0000000..350540b
--- /dev/null
+++ b/local_optimization/SOCP/portfolio_optimization_using_cvxpy.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9a8d9072",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG Library and running this notebook\n",
+ "To run this notebook, you will need to install the NAG Library for Python (Mark 29.3 or newer) and a license key. You can find the software and have a license key (trials are available) from our website here: [Getting Started with the NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linux)\n",
+ "\n",
+ "We are solving a classic portfolio optimization problem using the NAG Library integration in CVXPY. It can be formulated in the following way:\n",
+ "\n",
+ "\\begin{equation*}\n",
+ " \\begin{aligned}\n",
+ " &\\min_{x \\in \\mathbb{R}^n} &&\\frac{1}{2} x^T Q_0 x + r^T_0 x\n",
+ " \\\\\n",
+ " &\\textrm{subject to } &&\\frac{1}{2} x^T Q_1 x + r^T_1 x \\leq 0,\n",
+ " \\\\\n",
+ " & &&e^Tx = 1,\n",
+ " \\\\\n",
+ " & && x \\geq 0,\n",
+ " \\end{aligned}\n",
+ "\\end{equation*}\n",
+ "where $e$ refers to the vector of all ones."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "dcbfd044",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import modules\n",
+ "from cvxpy import Minimize, Problem, Variable, quad_form, sum, atoms, OPTIMAL, NAG\n",
+ "import numpy as np\n",
+ "import naginterfaces"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4d4bec67-2af9-4d14-932c-ac616f223be8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-0.17415932352686342"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Define number of assets\n",
+ "n = 500 \n",
+ "\n",
+ "# Generate random data\n",
+ "np.random.seed(2)\n",
+ "r0 = np.matrix(np.random.randn(n, 1))\n",
+ "r1 = np.matrix(np.random.randn(n, 1))\n",
+ "q0 = np.matrix(np.random.randn(n, n))\n",
+ "q0 = q0.T * q0\n",
+ "q1 = np.matrix(np.random.randn(n, n))\n",
+ "q1 = q1.T * q1\n",
+ "\n",
+ "# Skip psd check due to issues with ARPACK\n",
+ "q0 = atoms.affine.wraps.psd_wrap(q0)\n",
+ "q1 = atoms.affine.wraps.psd_wrap(q1)\n",
+ "\n",
+ "# Create the cvxpy problem:\n",
+ "# Define the variables\n",
+ "x = Variable(len(r1))\n",
+ "\n",
+ "# Define the constraints\n",
+ "constraints = [\n",
+ " 0 <= x, \n",
+ " sum(x) == 1,\n",
+ " 0.5*quad_form(x, q1) + (r1.T @ x) <= 0]\n",
+ "\n",
+ "# Define the objective function\n",
+ "objective = Minimize(0.5*quad_form(x, q0) + r0.T @ x)\n",
+ "\n",
+ "# Set up dictionary for option setting\n",
+ "nag_params = {'SOCP System Formulation':'AS',\n",
+ " 'SOCP Factorization Method':'MA86'\n",
+ " }\n",
+ "\n",
+ "# Define the problem\n",
+ "prob = Problem(objective, constraints)\n",
+ "\n",
+ "# Solve the problem using NAG\n",
+ "prob.solve(solver=NAG, nag_params=nag_params)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/local_optimization/SOCP/portfolio_optimization_using_socp.ipynb b/local_optimization/SOCP/portfolio_optimization_using_socp.ipynb
index 7441bc6..504b1e1 100644
--- a/local_optimization/SOCP/portfolio_optimization_using_socp.ipynb
+++ b/local_optimization/SOCP/portfolio_optimization_using_socp.ipynb
@@ -15,8 +15,33 @@
"\n",
"This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
"\n",
- "The notebook is also not rendered well by GitHub so if you are reading it from there, you may prefer the [pdf version instead](./static/portfolio_optimization_using_socp.pdf).\n",
+ "The notebook is also not rendered well by GitHub so if you are reading it from there, you may prefer the [pdf version instead](./static/portfolio_optimization_using_socp.pdf)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Note for the users of the NAG Library Mark $27.1$ onwards\n",
"\n",
+ "At Mark $27.1$ of the NAG Library, NAG introduced two new additions to help users easily define a Quadratically Constrained Quadratic Programming (QCQP) problem. All the models in this notebook then can be solved in a much simpler way without the need of a reformulation or any extra effort. It's recommended that the users of the NAG Library Mark $27.1$ or newer should look at the [notebook on QCQP instead](./portfolio_optimization_qcqp.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"# Introduction"
]
},
@@ -225,7 +250,6 @@
"from naginterfaces.library import opt\n",
"from naginterfaces.library import lapackeig\n",
"# Import necessary math libraries\n",
- "from scipy.sparse import coo_matrix\n",
"import math as mt\n",
"import warnings as wn"
]
@@ -1169,7 +1193,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SOCP/robust_lp.ipynb b/local_optimization/SOCP/robust_lp.ipynb
index eda3c8c..c935cb1 100644
--- a/local_optimization/SOCP/robust_lp.ipynb
+++ b/local_optimization/SOCP/robust_lp.ipynb
@@ -13,15 +13,26 @@
"source": [
"# Correct Rendering of this notebook\n",
"\n",
- "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n",
+ "This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
"\n",
- "## Introduction"
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Introduction\n",
+ "\n",
"We consider a classic portfolio problem with loss risk constraints:\n",
"\\vspace{0.1cn}\n",
"\\begin{equation}\\label{prob}\n",
@@ -144,7 +155,6 @@
"# Import utility libraries and the NAG Library\n",
"import numpy as np\n",
"import math as mt\n",
- "from scipy.sparse import coo_matrix\n",
"from scipy.stats import norm\n",
"from naginterfaces.library import opt\n",
"from naginterfaces.library import lapackeig"
@@ -402,20 +412,22 @@
"naginterfaces.base.opt.handle_solve_socp_ipm: E04PT, Interior point method for SOCP problems\n",
"naginterfaces.base.opt.handle_solve_socp_ipm: ------------------------------------------------\n",
"naginterfaces.base.opt.handle_solve_socp_ipm:\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Original Problem Statistics\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm:\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of variables 17\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of linear constraints 10\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of nonzeros 89\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of cones 1\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm:\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm:\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Presolved Problem Statistics\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: Problem Statistics\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of variables 17\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: free (unconstrained) 9\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: bounded 8\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of lin. constraints 10\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: nonzeroes 89\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of quad.constraints 0\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of cones 1\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: biggest cone size 9\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: Objective function Linear\n",
"naginterfaces.base.opt.handle_solve_socp_ipm:\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of variables 17\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of linear constraints 10\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of nonzeros 89\n",
- "naginterfaces.base.opt.handle_solve_socp_ipm: Number of cones 1\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: Presolved Problem Measures\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of variables 17\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of lin. constraints 10\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: nonzeroes 89\n",
+ "naginterfaces.base.opt.handle_solve_socp_ipm: No of cones 1\n",
"naginterfaces.base.opt.handle_solve_socp_ipm:\n",
"naginterfaces.base.opt.handle_solve_socp_ipm:\n",
"naginterfaces.base.opt.handle_solve_socp_ipm: ------------------------------------------------------------------------\n",
@@ -560,9 +572,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 2",
+ "display_name": "Python 3",
"language": "python",
- "name": "python"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -574,7 +586,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SOCP/simple_SOCP.ipynb b/local_optimization/SOCP/simple_SOCP.ipynb
index fdd1354..d652874 100644
--- a/local_optimization/SOCP/simple_SOCP.ipynb
+++ b/local_optimization/SOCP/simple_SOCP.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installing the NAG library and running this notebook\n",
+ "\n",
+ "This notebook depends on the NAG library for Python to run. Please read the instructions in the [Readme.md](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#install) file to download, install and obtain a licence for the library.\n",
+ "\n",
+ "Instruction on how to run the notebook can be found [here](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Readme.md#jupyter)."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -120,14 +131,14 @@
"outputs": [],
"source": [
"# Set linear constraints\n",
- "opt.handle_set_linconstr(\n",
+ "_ = opt.handle_set_linconstr(\n",
" handle,\n",
" bl=[-1.e20, 1.0],\n",
" bu=[1.5, 1.e20],\n",
" irowb=[1, 1, 1, 2, 2, 2],\n",
" icolb=[1, 2, 3, 1, 2, 3],\n",
" b=[-0.1, -0.1, 1.0, -0.06, 1.0, 1.0]\n",
- " );"
+ " )"
]
},
{
@@ -146,12 +157,12 @@
"outputs": [],
"source": [
"# Set cone constraint\n",
- "opt.handle_set_group(\n",
+ "_ = opt.handle_set_group(\n",
" handle,\n",
" gtype='Q',\n",
" group=[ 3,1, 2],\n",
" idgroup=0\n",
- ");"
+ ")"
]
},
{
@@ -194,11 +205,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
- " ------------------------------------------------\n",
- " E04PT, Interior point method for SOCP problems\n",
- " ------------------------------------------------\n",
- "\n",
+ " E04PT, Interior point method for SOCP problems\n",
" Status: converged, an optimal solution found\n",
" Final primal objective value -1.951817E+01\n",
" Final dual objective value -1.951817E+01\n"
@@ -298,7 +305,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/local_optimization/SOCP/static/portfolio_optimization_qcqp.html b/local_optimization/SOCP/static/portfolio_optimization_qcqp.html
new file mode 100644
index 0000000..d0531e6
--- /dev/null
+++ b/local_optimization/SOCP/static/portfolio_optimization_qcqp.html
@@ -0,0 +1,13933 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8"/>
+
+<!-- javascript from CDN for conversion -->
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/marked/0.3.5/marked.js"></script>
+
+
+
+
+<script type="text/x-mathjax-config">
+// make sure that equations numbers are enabled
+MathJax.Hub.Config({ TeX: { equationNumbers: {
+ autoNumber: "AMS", // All AMS equations are numbered
+ useLabelIds: true, // labels as ids
+ // format the equation number - uses an offset eqNumInitial (default 0)
+ formatNumber: function (n) {return String(Number(n)+Number(1)-1)}
+ } }
+});
+</script>
+
+
+
+<meta charset="utf-8" />
+
+<title>portfolio_optimization_qcqp</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+
+
+<style type="text/css">
+ /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+ font-family: sans-serif;
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+}
+pre {
+ overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+ font-family: monospace, monospace;
+ font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+ color: inherit;
+ font: inherit;
+ margin: 0;
+}
+button {
+ overflow: visible;
+}
+button,
+select {
+ text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+ -webkit-appearance: button;
+ cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+ cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+ border: 0;
+ padding: 0;
+}
+input {
+ line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+ box-sizing: border-box;
+ padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+ height: auto;
+}
+input[type="search"] {
+ -webkit-appearance: textfield;
+ box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+ -webkit-appearance: none;
+}
+fieldset {
+ border: 1px solid #c0c0c0;
+ margin: 0 2px;
+ padding: 0.35em 0.625em 0.75em;
+}
+legend {
+ border: 0;
+ padding: 0;
+}
+textarea {
+ overflow: auto;
+}
+optgroup {
+ font-weight: bold;
+}
+table {
+ border-collapse: collapse;
+ border-spacing: 0;
+}
+td,
+th {
+ padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+ *,
+ *:before,
+ *:after {
+ background: transparent !important;
+ box-shadow: none !important;
+ text-shadow: none !important;
+ }
+ a,
+ a:visited {
+ text-decoration: underline;
+ }
+ a[href]:after {
+ content: " (" attr(href) ")";
+ }
+ abbr[title]:after {
+ content: " (" attr(title) ")";
+ }
+ a[href^="#"]:after,
+ a[href^="javascript:"]:after {
+ content: "";
+ }
+ pre,
+ blockquote {
+ border: 1px solid #999;
+ page-break-inside: avoid;
+ }
+ thead {
+ display: table-header-group;
+ }
+ tr,
+ img {
+ page-break-inside: avoid;
+ }
+ img {
+ max-width: 100% !important;
+ }
+ p,
+ h2,
+ h3 {
+ orphans: 3;
+ widows: 3;
+ }
+ h2,
+ h3 {
+ page-break-after: avoid;
+ }
+ .navbar {
+ display: none;
+ }
+ .btn > .caret,
+ .dropup > .btn > .caret {
+ border-top-color: #000 !important;
+ }
+ .label {
+ border: 1px solid #000;
+ }
+ .table {
+ border-collapse: collapse !important;
+ }
+ .table td,
+ .table th {
+ background-color: #fff !important;
+ }
+ .table-bordered th,
+ .table-bordered td {
+ border: 1px solid #ddd !important;
+ }
+}
+@font-face {
+ font-family: 'Glyphicons Halflings';
+ src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+ src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+ position: relative;
+ top: 1px;
+ display: inline-block;
+ font-family: 'Glyphicons Halflings';
+ font-style: normal;
+ font-weight: normal;
+ line-height: 1;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+ content: "\002a";
+}
+.glyphicon-plus:before {
+ content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+ content: "\20ac";
+}
+.glyphicon-minus:before {
+ content: "\2212";
+}
+.glyphicon-cloud:before {
+ content: "\2601";
+}
+.glyphicon-envelope:before {
+ content: "\2709";
+}
+.glyphicon-pencil:before {
+ content: "\270f";
+}
+.glyphicon-glass:before {
+ content: "\e001";
+}
+.glyphicon-music:before {
+ content: "\e002";
+}
+.glyphicon-search:before {
+ content: "\e003";
+}
+.glyphicon-heart:before {
+ content: "\e005";
+}
+.glyphicon-star:before {
+ content: "\e006";
+}
+.glyphicon-star-empty:before {
+ content: "\e007";
+}
+.glyphicon-user:before {
+ content: "\e008";
+}
+.glyphicon-film:before {
+ content: "\e009";
+}
+.glyphicon-th-large:before {
+ content: "\e010";
+}
+.glyphicon-th:before {
+ content: "\e011";
+}
+.glyphicon-th-list:before {
+ content: "\e012";
+}
+.glyphicon-ok:before {
+ content: "\e013";
+}
+.glyphicon-remove:before {
+ content: "\e014";
+}
+.glyphicon-zoom-in:before {
+ content: "\e015";
+}
+.glyphicon-zoom-out:before {
+ content: "\e016";
+}
+.glyphicon-off:before {
+ content: "\e017";
+}
+.glyphicon-signal:before {
+ content: "\e018";
+}
+.glyphicon-cog:before {
+ content: "\e019";
+}
+.glyphicon-trash:before {
+ content: "\e020";
+}
+.glyphicon-home:before {
+ content: "\e021";
+}
+.glyphicon-file:before {
+ content: "\e022";
+}
+.glyphicon-time:before {
+ content: "\e023";
+}
+.glyphicon-road:before {
+ content: "\e024";
+}
+.glyphicon-download-alt:before {
+ content: "\e025";
+}
+.glyphicon-download:before {
+ content: "\e026";
+}
+.glyphicon-upload:before {
+ content: "\e027";
+}
+.glyphicon-inbox:before {
+ content: "\e028";
+}
+.glyphicon-play-circle:before {
+ content: "\e029";
+}
+.glyphicon-repeat:before {
+ content: "\e030";
+}
+.glyphicon-refresh:before {
+ content: "\e031";
+}
+.glyphicon-list-alt:before {
+ content: "\e032";
+}
+.glyphicon-lock:before {
+ content: "\e033";
+}
+.glyphicon-flag:before {
+ content: "\e034";
+}
+.glyphicon-headphones:before {
+ content: "\e035";
+}
+.glyphicon-volume-off:before {
+ content: "\e036";
+}
+.glyphicon-volume-down:before {
+ content: "\e037";
+}
+.glyphicon-volume-up:before {
+ content: "\e038";
+}
+.glyphicon-qrcode:before {
+ content: "\e039";
+}
+.glyphicon-barcode:before {
+ content: "\e040";
+}
+.glyphicon-tag:before {
+ content: "\e041";
+}
+.glyphicon-tags:before {
+ content: "\e042";
+}
+.glyphicon-book:before {
+ content: "\e043";
+}
+.glyphicon-bookmark:before {
+ content: "\e044";
+}
+.glyphicon-print:before {
+ content: "\e045";
+}
+.glyphicon-camera:before {
+ content: "\e046";
+}
+.glyphicon-font:before {
+ content: "\e047";
+}
+.glyphicon-bold:before {
+ content: "\e048";
+}
+.glyphicon-italic:before {
+ content: "\e049";
+}
+.glyphicon-text-height:before {
+ content: "\e050";
+}
+.glyphicon-text-width:before {
+ content: "\e051";
+}
+.glyphicon-align-left:before {
+ content: "\e052";
+}
+.glyphicon-align-center:before {
+ content: "\e053";
+}
+.glyphicon-align-right:before {
+ content: "\e054";
+}
+.glyphicon-align-justify:before {
+ content: "\e055";
+}
+.glyphicon-list:before {
+ content: "\e056";
+}
+.glyphicon-indent-left:before {
+ content: "\e057";
+}
+.glyphicon-indent-right:before {
+ content: "\e058";
+}
+.glyphicon-facetime-video:before {
+ content: "\e059";
+}
+.glyphicon-picture:before {
+ content: "\e060";
+}
+.glyphicon-map-marker:before {
+ content: "\e062";
+}
+.glyphicon-adjust:before {
+ content: "\e063";
+}
+.glyphicon-tint:before {
+ content: "\e064";
+}
+.glyphicon-edit:before {
+ content: "\e065";
+}
+.glyphicon-share:before {
+ content: "\e066";
+}
+.glyphicon-check:before {
+ content: "\e067";
+}
+.glyphicon-move:before {
+ content: "\e068";
+}
+.glyphicon-step-backward:before {
+ content: "\e069";
+}
+.glyphicon-fast-backward:before {
+ content: "\e070";
+}
+.glyphicon-backward:before {
+ content: "\e071";
+}
+.glyphicon-play:before {
+ content: "\e072";
+}
+.glyphicon-pause:before {
+ content: "\e073";
+}
+.glyphicon-stop:before {
+ content: "\e074";
+}
+.glyphicon-forward:before {
+ content: "\e075";
+}
+.glyphicon-fast-forward:before {
+ content: "\e076";
+}
+.glyphicon-step-forward:before {
+ content: "\e077";
+}
+.glyphicon-eject:before {
+ content: "\e078";
+}
+.glyphicon-chevron-left:before {
+ content: "\e079";
+}
+.glyphicon-chevron-right:before {
+ content: "\e080";
+}
+.glyphicon-plus-sign:before {
+ content: "\e081";
+}
+.glyphicon-minus-sign:before {
+ content: "\e082";
+}
+.glyphicon-remove-sign:before {
+ content: "\e083";
+}
+.glyphicon-ok-sign:before {
+ content: "\e084";
+}
+.glyphicon-question-sign:before {
+ content: "\e085";
+}
+.glyphicon-info-sign:before {
+ content: "\e086";
+}
+.glyphicon-screenshot:before {
+ content: "\e087";
+}
+.glyphicon-remove-circle:before {
+ content: "\e088";
+}
+.glyphicon-ok-circle:before {
+ content: "\e089";
+}
+.glyphicon-ban-circle:before {
+ content: "\e090";
+}
+.glyphicon-arrow-left:before {
+ content: "\e091";
+}
+.glyphicon-arrow-right:before {
+ content: "\e092";
+}
+.glyphicon-arrow-up:before {
+ content: "\e093";
+}
+.glyphicon-arrow-down:before {
+ content: "\e094";
+}
+.glyphicon-share-alt:before {
+ content: "\e095";
+}
+.glyphicon-resize-full:before {
+ content: "\e096";
+}
+.glyphicon-resize-small:before {
+ content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+ content: "\e101";
+}
+.glyphicon-gift:before {
+ content: "\e102";
+}
+.glyphicon-leaf:before {
+ content: "\e103";
+}
+.glyphicon-fire:before {
+ content: "\e104";
+}
+.glyphicon-eye-open:before {
+ content: "\e105";
+}
+.glyphicon-eye-close:before {
+ content: "\e106";
+}
+.glyphicon-warning-sign:before {
+ content: "\e107";
+}
+.glyphicon-plane:before {
+ content: "\e108";
+}
+.glyphicon-calendar:before {
+ content: "\e109";
+}
+.glyphicon-random:before {
+ content: "\e110";
+}
+.glyphicon-comment:before {
+ content: "\e111";
+}
+.glyphicon-magnet:before {
+ content: "\e112";
+}
+.glyphicon-chevron-up:before {
+ content: "\e113";
+}
+.glyphicon-chevron-down:before {
+ content: "\e114";
+}
+.glyphicon-retweet:before {
+ content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+ content: "\e116";
+}
+.glyphicon-folder-close:before {
+ content: "\e117";
+}
+.glyphicon-folder-open:before {
+ content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+ content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+ content: "\e120";
+}
+.glyphicon-hdd:before {
+ content: "\e121";
+}
+.glyphicon-bullhorn:before {
+ content: "\e122";
+}
+.glyphicon-bell:before {
+ content: "\e123";
+}
+.glyphicon-certificate:before {
+ content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+ content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+ content: "\e126";
+}
+.glyphicon-hand-right:before {
+ content: "\e127";
+}
+.glyphicon-hand-left:before {
+ content: "\e128";
+}
+.glyphicon-hand-up:before {
+ content: "\e129";
+}
+.glyphicon-hand-down:before {
+ content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+ content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+ content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+ content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+ content: "\e134";
+}
+.glyphicon-globe:before {
+ content: "\e135";
+}
+.glyphicon-wrench:before {
+ content: "\e136";
+}
+.glyphicon-tasks:before {
+ content: "\e137";
+}
+.glyphicon-filter:before {
+ content: "\e138";
+}
+.glyphicon-briefcase:before {
+ content: "\e139";
+}
+.glyphicon-fullscreen:before {
+ content: "\e140";
+}
+.glyphicon-dashboard:before {
+ content: "\e141";
+}
+.glyphicon-paperclip:before {
+ content: "\e142";
+}
+.glyphicon-heart-empty:before {
+ content: "\e143";
+}
+.glyphicon-link:before {
+ content: "\e144";
+}
+.glyphicon-phone:before {
+ content: "\e145";
+}
+.glyphicon-pushpin:before {
+ content: "\e146";
+}
+.glyphicon-usd:before {
+ content: "\e148";
+}
+.glyphicon-gbp:before {
+ content: "\e149";
+}
+.glyphicon-sort:before {
+ content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+ content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+ content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+ content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+ content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+ content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+ content: "\e156";
+}
+.glyphicon-unchecked:before {
+ content: "\e157";
+}
+.glyphicon-expand:before {
+ content: "\e158";
+}
+.glyphicon-collapse-down:before {
+ content: "\e159";
+}
+.glyphicon-collapse-up:before {
+ content: "\e160";
+}
+.glyphicon-log-in:before {
+ content: "\e161";
+}
+.glyphicon-flash:before {
+ content: "\e162";
+}
+.glyphicon-log-out:before {
+ content: "\e163";
+}
+.glyphicon-new-window:before {
+ content: "\e164";
+}
+.glyphicon-record:before {
+ content: "\e165";
+}
+.glyphicon-save:before {
+ content: "\e166";
+}
+.glyphicon-open:before {
+ content: "\e167";
+}
+.glyphicon-saved:before {
+ content: "\e168";
+}
+.glyphicon-import:before {
+ content: "\e169";
+}
+.glyphicon-export:before {
+ content: "\e170";
+}
+.glyphicon-send:before {
+ content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+ content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+ content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+ content: "\e174";
+}
+.glyphicon-floppy-save:before {
+ content: "\e175";
+}
+.glyphicon-floppy-open:before {
+ content: "\e176";
+}
+.glyphicon-credit-card:before {
+ content: "\e177";
+}
+.glyphicon-transfer:before {
+ content: "\e178";
+}
+.glyphicon-cutlery:before {
+ content: "\e179";
+}
+.glyphicon-header:before {
+ content: "\e180";
+}
+.glyphicon-compressed:before {
+ content: "\e181";
+}
+.glyphicon-earphone:before {
+ content: "\e182";
+}
+.glyphicon-phone-alt:before {
+ content: "\e183";
+}
+.glyphicon-tower:before {
+ content: "\e184";
+}
+.glyphicon-stats:before {
+ content: "\e185";
+}
+.glyphicon-sd-video:before {
+ content: "\e186";
+}
+.glyphicon-hd-video:before {
+ content: "\e187";
+}
+.glyphicon-subtitles:before {
+ content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+ content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+ content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+ content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+ content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+ content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+ content: "\e194";
+}
+.glyphicon-registration-mark:before {
+ content: "\e195";
+}
+.glyphicon-cloud-download:before {
+ content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+ content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+ content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+ content: "\e200";
+}
+.glyphicon-cd:before {
+ content: "\e201";
+}
+.glyphicon-save-file:before {
+ content: "\e202";
+}
+.glyphicon-open-file:before {
+ content: "\e203";
+}
+.glyphicon-level-up:before {
+ content: "\e204";
+}
+.glyphicon-copy:before {
+ content: "\e205";
+}
+.glyphicon-paste:before {
+ content: "\e206";
+}
+.glyphicon-alert:before {
+ content: "\e209";
+}
+.glyphicon-equalizer:before {
+ content: "\e210";
+}
+.glyphicon-king:before {
+ content: "\e211";
+}
+.glyphicon-queen:before {
+ content: "\e212";
+}
+.glyphicon-pawn:before {
+ content: "\e213";
+}
+.glyphicon-bishop:before {
+ content: "\e214";
+}
+.glyphicon-knight:before {
+ content: "\e215";
+}
+.glyphicon-baby-formula:before {
+ content: "\e216";
+}
+.glyphicon-tent:before {
+ content: "\26fa";
+}
+.glyphicon-blackboard:before {
+ content: "\e218";
+}
+.glyphicon-bed:before {
+ content: "\e219";
+}
+.glyphicon-apple:before {
+ content: "\f8ff";
+}
+.glyphicon-erase:before {
+ content: "\e221";
+}
+.glyphicon-hourglass:before {
+ content: "\231b";
+}
+.glyphicon-lamp:before {
+ content: "\e223";
+}
+.glyphicon-duplicate:before {
+ content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+ content: "\e225";
+}
+.glyphicon-scissors:before {
+ content: "\e226";
+}
+.glyphicon-bitcoin:before {
+ content: "\e227";
+}
+.glyphicon-btc:before {
+ content: "\e227";
+}
+.glyphicon-xbt:before {
+ content: "\e227";
+}
+.glyphicon-yen:before {
+ content: "\00a5";
+}
+.glyphicon-jpy:before {
+ content: "\00a5";
+}
+.glyphicon-ruble:before {
+ content: "\20bd";
+}
+.glyphicon-rub:before {
+ content: "\20bd";
+}
+.glyphicon-scale:before {
+ content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+ content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+ content: "\e232";
+}
+.glyphicon-education:before {
+ content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+ content: "\e234";
+}
+.glyphicon-option-vertical:before {
+ content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+ content: "\e236";
+}
+.glyphicon-modal-window:before {
+ content: "\e237";
+}
+.glyphicon-oil:before {
+ content: "\e238";
+}
+.glyphicon-grain:before {
+ content: "\e239";
+}
+.glyphicon-sunglasses:before {
+ content: "\e240";
+}
+.glyphicon-text-size:before {
+ content: "\e241";
+}
+.glyphicon-text-color:before {
+ content: "\e242";
+}
+.glyphicon-text-background:before {
+ content: "\e243";
+}
+.glyphicon-object-align-top:before {
+ content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+ content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+ content: "\e246";
+}
+.glyphicon-object-align-left:before {
+ content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+ content: "\e248";
+}
+.glyphicon-object-align-right:before {
+ content: "\e249";
+}
+.glyphicon-triangle-right:before {
+ content: "\e250";
+}
+.glyphicon-triangle-left:before {
+ content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+ content: "\e252";
+}
+.glyphicon-triangle-top:before {
+ content: "\e253";
+}
+.glyphicon-console:before {
+ content: "\e254";
+}
+.glyphicon-superscript:before {
+ content: "\e255";
+}
+.glyphicon-subscript:before {
+ content: "\e256";
+}
+.glyphicon-menu-left:before {
+ content: "\e257";
+}
+.glyphicon-menu-right:before {
+ content: "\e258";
+}
+.glyphicon-menu-down:before {
+ content: "\e259";
+}
+.glyphicon-menu-up:before {
+ content: "\e260";
+}
+* {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+*:before,
+*:after {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+html {
+ font-size: 10px;
+ -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #000;
+ background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+ font-family: inherit;
+ font-size: inherit;
+ line-height: inherit;
+}
+a {
+ color: #337ab7;
+ text-decoration: none;
+}
+a:hover,
+a:focus {
+ color: #23527c;
+ text-decoration: underline;
+}
+a:focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+figure {
+ margin: 0;
+}
+img {
+ vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ display: block;
+ max-width: 100%;
+ height: auto;
+}
+.img-rounded {
+ border-radius: 3px;
+}
+.img-thumbnail {
+ padding: 4px;
+ line-height: 1.42857143;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ -webkit-transition: all 0.2s ease-in-out;
+ -o-transition: all 0.2s ease-in-out;
+ transition: all 0.2s ease-in-out;
+ display: inline-block;
+ max-width: 100%;
+ height: auto;
+}
+.img-circle {
+ border-radius: 50%;
+}
+hr {
+ margin-top: 18px;
+ margin-bottom: 18px;
+ border: 0;
+ border-top: 1px solid #eeeeee;
+}
+.sr-only {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ margin: -1px;
+ padding: 0;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+[role="button"] {
+ cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+ font-family: inherit;
+ font-weight: 500;
+ line-height: 1.1;
+ color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+ font-weight: normal;
+ line-height: 1;
+ color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+ margin-top: 18px;
+ margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+ font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+ margin-top: 9px;
+ margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+ font-size: 75%;
+}
+h1,
+.h1 {
+ font-size: 33px;
+}
+h2,
+.h2 {
+ font-size: 27px;
+}
+h3,
+.h3 {
+ font-size: 23px;
+}
+h4,
+.h4 {
+ font-size: 17px;
+}
+h5,
+.h5 {
+ font-size: 13px;
+}
+h6,
+.h6 {
+ font-size: 12px;
+}
+p {
+ margin: 0 0 9px;
+}
+.lead {
+ margin-bottom: 18px;
+ font-size: 14px;
+ font-weight: 300;
+ line-height: 1.4;
+}
+@media (min-width: 768px) {
+ .lead {
+ font-size: 19.5px;
+ }
+}
+small,
+.small {
+ font-size: 92%;
+}
+mark,
+.mark {
+ background-color: #fcf8e3;
+ padding: .2em;
+}
+.text-left {
+ text-align: left;
+}
+.text-right {
+ text-align: right;
+}
+.text-center {
+ text-align: center;
+}
+.text-justify {
+ text-align: justify;
+}
+.text-nowrap {
+ white-space: nowrap;
+}
+.text-lowercase {
+ text-transform: lowercase;
+}
+.text-uppercase {
+ text-transform: uppercase;
+}
+.text-capitalize {
+ text-transform: capitalize;
+}
+.text-muted {
+ color: #777777;
+}
+.text-primary {
+ color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+ color: #286090;
+}
+.text-success {
+ color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+ color: #2b542c;
+}
+.text-info {
+ color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+ color: #245269;
+}
+.text-warning {
+ color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+ color: #66512c;
+}
+.text-danger {
+ color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+ color: #843534;
+}
+.bg-primary {
+ color: #fff;
+ background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+ background-color: #286090;
+}
+.bg-success {
+ background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+ background-color: #c1e2b3;
+}
+.bg-info {
+ background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+ background-color: #afd9ee;
+}
+.bg-warning {
+ background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+ background-color: #f7ecb5;
+}
+.bg-danger {
+ background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+ background-color: #e4b9b9;
+}
+.page-header {
+ padding-bottom: 8px;
+ margin: 36px 0 18px;
+ border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+ margin-top: 0;
+ margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+ margin-bottom: 0;
+}
+.list-unstyled {
+ padding-left: 0;
+ list-style: none;
+}
+.list-inline {
+ padding-left: 0;
+ list-style: none;
+ margin-left: -5px;
+}
+.list-inline > li {
+ display: inline-block;
+ padding-left: 5px;
+ padding-right: 5px;
+}
+dl {
+ margin-top: 0;
+ margin-bottom: 18px;
+}
+dt,
+dd {
+ line-height: 1.42857143;
+}
+dt {
+ font-weight: bold;
+}
+dd {
+ margin-left: 0;
+}
+@media (min-width: 541px) {
+ .dl-horizontal dt {
+ float: left;
+ width: 160px;
+ clear: left;
+ text-align: right;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+ }
+ .dl-horizontal dd {
+ margin-left: 180px;
+ }
+}
+abbr[title],
+abbr[data-original-title] {
+ cursor: help;
+ border-bottom: 1px dotted #777777;
+}
+.initialism {
+ font-size: 90%;
+ text-transform: uppercase;
+}
+blockquote {
+ padding: 9px 18px;
+ margin: 0 0 18px;
+ font-size: inherit;
+ border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+ margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+ display: block;
+ font-size: 80%;
+ line-height: 1.42857143;
+ color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+ content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+ padding-right: 15px;
+ padding-left: 0;
+ border-right: 5px solid #eeeeee;
+ border-left: 0;
+ text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+ content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+ content: '\00A0 \2014';
+}
+address {
+ margin-bottom: 18px;
+ font-style: normal;
+ line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+ font-family: monospace;
+}
+code {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #c7254e;
+ background-color: #f9f2f4;
+ border-radius: 2px;
+}
+kbd {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #888;
+ background-color: transparent;
+ border-radius: 1px;
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+ padding: 0;
+ font-size: 100%;
+ font-weight: bold;
+ box-shadow: none;
+}
+pre {
+ display: block;
+ padding: 8.5px;
+ margin: 0 0 9px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ word-break: break-all;
+ word-wrap: break-word;
+ color: #333333;
+ background-color: #f5f5f5;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+}
+pre code {
+ padding: 0;
+ font-size: inherit;
+ color: inherit;
+ white-space: pre-wrap;
+ background-color: transparent;
+ border-radius: 0;
+}
+.pre-scrollable {
+ max-height: 340px;
+ overflow-y: scroll;
+}
+.container {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+@media (min-width: 768px) {
+ .container {
+ width: 768px;
+ }
+}
+@media (min-width: 992px) {
+ .container {
+ width: 940px;
+ }
+}
+@media (min-width: 1200px) {
+ .container {
+ width: 1140px;
+ }
+}
+.container-fluid {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+ position: relative;
+ min-height: 1px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+ float: left;
+}
+.col-xs-12 {
+ width: 100%;
+}
+.col-xs-11 {
+ width: 91.66666667%;
+}
+.col-xs-10 {
+ width: 83.33333333%;
+}
+.col-xs-9 {
+ width: 75%;
+}
+.col-xs-8 {
+ width: 66.66666667%;
+}
+.col-xs-7 {
+ width: 58.33333333%;
+}
+.col-xs-6 {
+ width: 50%;
+}
+.col-xs-5 {
+ width: 41.66666667%;
+}
+.col-xs-4 {
+ width: 33.33333333%;
+}
+.col-xs-3 {
+ width: 25%;
+}
+.col-xs-2 {
+ width: 16.66666667%;
+}
+.col-xs-1 {
+ width: 8.33333333%;
+}
+.col-xs-pull-12 {
+ right: 100%;
+}
+.col-xs-pull-11 {
+ right: 91.66666667%;
+}
+.col-xs-pull-10 {
+ right: 83.33333333%;
+}
+.col-xs-pull-9 {
+ right: 75%;
+}
+.col-xs-pull-8 {
+ right: 66.66666667%;
+}
+.col-xs-pull-7 {
+ right: 58.33333333%;
+}
+.col-xs-pull-6 {
+ right: 50%;
+}
+.col-xs-pull-5 {
+ right: 41.66666667%;
+}
+.col-xs-pull-4 {
+ right: 33.33333333%;
+}
+.col-xs-pull-3 {
+ right: 25%;
+}
+.col-xs-pull-2 {
+ right: 16.66666667%;
+}
+.col-xs-pull-1 {
+ right: 8.33333333%;
+}
+.col-xs-pull-0 {
+ right: auto;
+}
+.col-xs-push-12 {
+ left: 100%;
+}
+.col-xs-push-11 {
+ left: 91.66666667%;
+}
+.col-xs-push-10 {
+ left: 83.33333333%;
+}
+.col-xs-push-9 {
+ left: 75%;
+}
+.col-xs-push-8 {
+ left: 66.66666667%;
+}
+.col-xs-push-7 {
+ left: 58.33333333%;
+}
+.col-xs-push-6 {
+ left: 50%;
+}
+.col-xs-push-5 {
+ left: 41.66666667%;
+}
+.col-xs-push-4 {
+ left: 33.33333333%;
+}
+.col-xs-push-3 {
+ left: 25%;
+}
+.col-xs-push-2 {
+ left: 16.66666667%;
+}
+.col-xs-push-1 {
+ left: 8.33333333%;
+}
+.col-xs-push-0 {
+ left: auto;
+}
+.col-xs-offset-12 {
+ margin-left: 100%;
+}
+.col-xs-offset-11 {
+ margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+ margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+ margin-left: 75%;
+}
+.col-xs-offset-8 {
+ margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+ margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+ margin-left: 50%;
+}
+.col-xs-offset-5 {
+ margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+ margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+ margin-left: 25%;
+}
+.col-xs-offset-2 {
+ margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+ margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+ margin-left: 0%;
+}
+@media (min-width: 768px) {
+ .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+ float: left;
+ }
+ .col-sm-12 {
+ width: 100%;
+ }
+ .col-sm-11 {
+ width: 91.66666667%;
+ }
+ .col-sm-10 {
+ width: 83.33333333%;
+ }
+ .col-sm-9 {
+ width: 75%;
+ }
+ .col-sm-8 {
+ width: 66.66666667%;
+ }
+ .col-sm-7 {
+ width: 58.33333333%;
+ }
+ .col-sm-6 {
+ width: 50%;
+ }
+ .col-sm-5 {
+ width: 41.66666667%;
+ }
+ .col-sm-4 {
+ width: 33.33333333%;
+ }
+ .col-sm-3 {
+ width: 25%;
+ }
+ .col-sm-2 {
+ width: 16.66666667%;
+ }
+ .col-sm-1 {
+ width: 8.33333333%;
+ }
+ .col-sm-pull-12 {
+ right: 100%;
+ }
+ .col-sm-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-sm-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-sm-pull-9 {
+ right: 75%;
+ }
+ .col-sm-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-sm-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-sm-pull-6 {
+ right: 50%;
+ }
+ .col-sm-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-sm-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-sm-pull-3 {
+ right: 25%;
+ }
+ .col-sm-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-sm-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-sm-pull-0 {
+ right: auto;
+ }
+ .col-sm-push-12 {
+ left: 100%;
+ }
+ .col-sm-push-11 {
+ left: 91.66666667%;
+ }
+ .col-sm-push-10 {
+ left: 83.33333333%;
+ }
+ .col-sm-push-9 {
+ left: 75%;
+ }
+ .col-sm-push-8 {
+ left: 66.66666667%;
+ }
+ .col-sm-push-7 {
+ left: 58.33333333%;
+ }
+ .col-sm-push-6 {
+ left: 50%;
+ }
+ .col-sm-push-5 {
+ left: 41.66666667%;
+ }
+ .col-sm-push-4 {
+ left: 33.33333333%;
+ }
+ .col-sm-push-3 {
+ left: 25%;
+ }
+ .col-sm-push-2 {
+ left: 16.66666667%;
+ }
+ .col-sm-push-1 {
+ left: 8.33333333%;
+ }
+ .col-sm-push-0 {
+ left: auto;
+ }
+ .col-sm-offset-12 {
+ margin-left: 100%;
+ }
+ .col-sm-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-sm-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-sm-offset-9 {
+ margin-left: 75%;
+ }
+ .col-sm-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-sm-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-sm-offset-6 {
+ margin-left: 50%;
+ }
+ .col-sm-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-sm-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-sm-offset-3 {
+ margin-left: 25%;
+ }
+ .col-sm-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-sm-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-sm-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 992px) {
+ .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+ float: left;
+ }
+ .col-md-12 {
+ width: 100%;
+ }
+ .col-md-11 {
+ width: 91.66666667%;
+ }
+ .col-md-10 {
+ width: 83.33333333%;
+ }
+ .col-md-9 {
+ width: 75%;
+ }
+ .col-md-8 {
+ width: 66.66666667%;
+ }
+ .col-md-7 {
+ width: 58.33333333%;
+ }
+ .col-md-6 {
+ width: 50%;
+ }
+ .col-md-5 {
+ width: 41.66666667%;
+ }
+ .col-md-4 {
+ width: 33.33333333%;
+ }
+ .col-md-3 {
+ width: 25%;
+ }
+ .col-md-2 {
+ width: 16.66666667%;
+ }
+ .col-md-1 {
+ width: 8.33333333%;
+ }
+ .col-md-pull-12 {
+ right: 100%;
+ }
+ .col-md-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-md-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-md-pull-9 {
+ right: 75%;
+ }
+ .col-md-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-md-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-md-pull-6 {
+ right: 50%;
+ }
+ .col-md-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-md-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-md-pull-3 {
+ right: 25%;
+ }
+ .col-md-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-md-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-md-pull-0 {
+ right: auto;
+ }
+ .col-md-push-12 {
+ left: 100%;
+ }
+ .col-md-push-11 {
+ left: 91.66666667%;
+ }
+ .col-md-push-10 {
+ left: 83.33333333%;
+ }
+ .col-md-push-9 {
+ left: 75%;
+ }
+ .col-md-push-8 {
+ left: 66.66666667%;
+ }
+ .col-md-push-7 {
+ left: 58.33333333%;
+ }
+ .col-md-push-6 {
+ left: 50%;
+ }
+ .col-md-push-5 {
+ left: 41.66666667%;
+ }
+ .col-md-push-4 {
+ left: 33.33333333%;
+ }
+ .col-md-push-3 {
+ left: 25%;
+ }
+ .col-md-push-2 {
+ left: 16.66666667%;
+ }
+ .col-md-push-1 {
+ left: 8.33333333%;
+ }
+ .col-md-push-0 {
+ left: auto;
+ }
+ .col-md-offset-12 {
+ margin-left: 100%;
+ }
+ .col-md-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-md-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-md-offset-9 {
+ margin-left: 75%;
+ }
+ .col-md-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-md-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-md-offset-6 {
+ margin-left: 50%;
+ }
+ .col-md-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-md-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-md-offset-3 {
+ margin-left: 25%;
+ }
+ .col-md-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-md-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-md-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 1200px) {
+ .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+ float: left;
+ }
+ .col-lg-12 {
+ width: 100%;
+ }
+ .col-lg-11 {
+ width: 91.66666667%;
+ }
+ .col-lg-10 {
+ width: 83.33333333%;
+ }
+ .col-lg-9 {
+ width: 75%;
+ }
+ .col-lg-8 {
+ width: 66.66666667%;
+ }
+ .col-lg-7 {
+ width: 58.33333333%;
+ }
+ .col-lg-6 {
+ width: 50%;
+ }
+ .col-lg-5 {
+ width: 41.66666667%;
+ }
+ .col-lg-4 {
+ width: 33.33333333%;
+ }
+ .col-lg-3 {
+ width: 25%;
+ }
+ .col-lg-2 {
+ width: 16.66666667%;
+ }
+ .col-lg-1 {
+ width: 8.33333333%;
+ }
+ .col-lg-pull-12 {
+ right: 100%;
+ }
+ .col-lg-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-lg-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-lg-pull-9 {
+ right: 75%;
+ }
+ .col-lg-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-lg-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-lg-pull-6 {
+ right: 50%;
+ }
+ .col-lg-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-lg-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-lg-pull-3 {
+ right: 25%;
+ }
+ .col-lg-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-lg-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-lg-pull-0 {
+ right: auto;
+ }
+ .col-lg-push-12 {
+ left: 100%;
+ }
+ .col-lg-push-11 {
+ left: 91.66666667%;
+ }
+ .col-lg-push-10 {
+ left: 83.33333333%;
+ }
+ .col-lg-push-9 {
+ left: 75%;
+ }
+ .col-lg-push-8 {
+ left: 66.66666667%;
+ }
+ .col-lg-push-7 {
+ left: 58.33333333%;
+ }
+ .col-lg-push-6 {
+ left: 50%;
+ }
+ .col-lg-push-5 {
+ left: 41.66666667%;
+ }
+ .col-lg-push-4 {
+ left: 33.33333333%;
+ }
+ .col-lg-push-3 {
+ left: 25%;
+ }
+ .col-lg-push-2 {
+ left: 16.66666667%;
+ }
+ .col-lg-push-1 {
+ left: 8.33333333%;
+ }
+ .col-lg-push-0 {
+ left: auto;
+ }
+ .col-lg-offset-12 {
+ margin-left: 100%;
+ }
+ .col-lg-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-lg-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-lg-offset-9 {
+ margin-left: 75%;
+ }
+ .col-lg-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-lg-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-lg-offset-6 {
+ margin-left: 50%;
+ }
+ .col-lg-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-lg-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-lg-offset-3 {
+ margin-left: 25%;
+ }
+ .col-lg-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-lg-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-lg-offset-0 {
+ margin-left: 0%;
+ }
+}
+table {
+ background-color: transparent;
+}
+caption {
+ padding-top: 8px;
+ padding-bottom: 8px;
+ color: #777777;
+ text-align: left;
+}
+th {
+ text-align: left;
+}
+.table {
+ width: 100%;
+ max-width: 100%;
+ margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+ padding: 8px;
+ line-height: 1.42857143;
+ vertical-align: top;
+ border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+ vertical-align: bottom;
+ border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+ border-top: 0;
+}
+.table > tbody + tbody {
+ border-top: 2px solid #ddd;
+}
+.table .table {
+ background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+ padding: 5px;
+}
+.table-bordered {
+ border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+ border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+ border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+ background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+ background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+ background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+ background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+ background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+ background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+ background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+ background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+ background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+ background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+ background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+ background-color: #ebcccc;
+}
+.table-responsive {
+ overflow-x: auto;
+ min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+ .table-responsive {
+ width: 100%;
+ margin-bottom: 13.5px;
+ overflow-y: hidden;
+ -ms-overflow-style: -ms-autohiding-scrollbar;
+ border: 1px solid #ddd;
+ }
+ .table-responsive > .table {
+ margin-bottom: 0;
+ }
+ .table-responsive > .table > thead > tr > th,
+ .table-responsive > .table > tbody > tr > th,
+ .table-responsive > .table > tfoot > tr > th,
+ .table-responsive > .table > thead > tr > td,
+ .table-responsive > .table > tbody > tr > td,
+ .table-responsive > .table > tfoot > tr > td {
+ white-space: nowrap;
+ }
+ .table-responsive > .table-bordered {
+ border: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:first-child,
+ .table-responsive > .table-bordered > tbody > tr > th:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+ .table-responsive > .table-bordered > thead > tr > td:first-child,
+ .table-responsive > .table-bordered > tbody > tr > td:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:last-child,
+ .table-responsive > .table-bordered > tbody > tr > th:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+ .table-responsive > .table-bordered > thead > tr > td:last-child,
+ .table-responsive > .table-bordered > tbody > tr > td:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+ }
+ .table-responsive > .table-bordered > tbody > tr:last-child > th,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+ .table-responsive > .table-bordered > tbody > tr:last-child > td,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+ border-bottom: 0;
+ }
+}
+fieldset {
+ padding: 0;
+ margin: 0;
+ border: 0;
+ min-width: 0;
+}
+legend {
+ display: block;
+ width: 100%;
+ padding: 0;
+ margin-bottom: 18px;
+ font-size: 19.5px;
+ line-height: inherit;
+ color: #333333;
+ border: 0;
+ border-bottom: 1px solid #e5e5e5;
+}
+label {
+ display: inline-block;
+ max-width: 100%;
+ margin-bottom: 5px;
+ font-weight: bold;
+}
+input[type="search"] {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+ margin: 4px 0 0;
+ margin-top: 1px \9;
+ line-height: normal;
+}
+input[type="file"] {
+ display: block;
+}
+input[type="range"] {
+ display: block;
+ width: 100%;
+}
+select[multiple],
+select[size] {
+ height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+output {
+ display: block;
+ padding-top: 7px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+}
+.form-control {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #fff;
+ background-image: none;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+ color: #999;
+ opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+ color: #999;
+}
+.form-control::-webkit-input-placeholder {
+ color: #999;
+}
+.form-control::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+ cursor: not-allowed;
+}
+textarea.form-control {
+ height: auto;
+}
+input[type="search"] {
+ -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+ input[type="date"].form-control,
+ input[type="time"].form-control,
+ input[type="datetime-local"].form-control,
+ input[type="month"].form-control {
+ line-height: 32px;
+ }
+ input[type="date"].input-sm,
+ input[type="time"].input-sm,
+ input[type="datetime-local"].input-sm,
+ input[type="month"].input-sm,
+ .input-group-sm input[type="date"],
+ .input-group-sm input[type="time"],
+ .input-group-sm input[type="datetime-local"],
+ .input-group-sm input[type="month"] {
+ line-height: 30px;
+ }
+ input[type="date"].input-lg,
+ input[type="time"].input-lg,
+ input[type="datetime-local"].input-lg,
+ input[type="month"].input-lg,
+ .input-group-lg input[type="date"],
+ .input-group-lg input[type="time"],
+ .input-group-lg input[type="datetime-local"],
+ .input-group-lg input[type="month"] {
+ line-height: 45px;
+ }
+}
+.form-group {
+ margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+ position: relative;
+ display: block;
+ margin-top: 10px;
+ margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+ min-height: 18px;
+ padding-left: 20px;
+ margin-bottom: 0;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+ position: absolute;
+ margin-left: -20px;
+ margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+ margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+ position: relative;
+ display: inline-block;
+ padding-left: 20px;
+ margin-bottom: 0;
+ vertical-align: middle;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+ margin-top: 0;
+ margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+ cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+ cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+ cursor: not-allowed;
+}
+.form-control-static {
+ padding-top: 7px;
+ padding-bottom: 7px;
+ margin-bottom: 0;
+ min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-sm {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-sm {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+ height: auto;
+}
+.form-group-sm .form-control {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.form-group-sm select.form-control {
+ height: 30px;
+ line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+ height: auto;
+}
+.form-group-sm .form-control-static {
+ height: 30px;
+ min-height: 30px;
+ padding: 6px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.input-lg {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-lg {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+ height: auto;
+}
+.form-group-lg .form-control {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.form-group-lg select.form-control {
+ height: 45px;
+ line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+ height: auto;
+}
+.form-group-lg .form-control-static {
+ height: 45px;
+ min-height: 35px;
+ padding: 11px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.has-feedback {
+ position: relative;
+}
+.has-feedback .form-control {
+ padding-right: 40px;
+}
+.form-control-feedback {
+ position: absolute;
+ top: 0;
+ right: 0;
+ z-index: 2;
+ display: block;
+ width: 32px;
+ height: 32px;
+ line-height: 32px;
+ text-align: center;
+ pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+ width: 45px;
+ height: 45px;
+ line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+ width: 30px;
+ height: 30px;
+ line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+ color: #3c763d;
+}
+.has-success .form-control {
+ border-color: #3c763d;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+ border-color: #2b542c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+ color: #3c763d;
+ border-color: #3c763d;
+ background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+ color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+ color: #8a6d3b;
+}
+.has-warning .form-control {
+ border-color: #8a6d3b;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+ border-color: #66512c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+ color: #8a6d3b;
+ border-color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+ color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+ color: #a94442;
+}
+.has-error .form-control {
+ border-color: #a94442;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+ border-color: #843534;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+ color: #a94442;
+ border-color: #a94442;
+ background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+ color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+ top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+ top: 0;
+}
+.help-block {
+ display: block;
+ margin-top: 5px;
+ margin-bottom: 10px;
+ color: #404040;
+}
+@media (min-width: 768px) {
+ .form-inline .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .form-inline .form-control-static {
+ display: inline-block;
+ }
+ .form-inline .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .form-inline .input-group .input-group-addon,
+ .form-inline .input-group .input-group-btn,
+ .form-inline .input-group .form-control {
+ width: auto;
+ }
+ .form-inline .input-group > .form-control {
+ width: 100%;
+ }
+ .form-inline .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio,
+ .form-inline .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio label,
+ .form-inline .checkbox label {
+ padding-left: 0;
+ }
+ .form-inline .radio input[type="radio"],
+ .form-inline .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .form-inline .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+ margin-top: 0;
+ margin-bottom: 0;
+ padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+ min-height: 25px;
+}
+.form-horizontal .form-group {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .control-label {
+ text-align: right;
+ margin-bottom: 0;
+ padding-top: 7px;
+ }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+ right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-lg .control-label {
+ padding-top: 11px;
+ font-size: 17px;
+ }
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-sm .control-label {
+ padding-top: 6px;
+ font-size: 12px;
+ }
+}
+.btn {
+ display: inline-block;
+ margin-bottom: 0;
+ font-weight: normal;
+ text-align: center;
+ vertical-align: middle;
+ touch-action: manipulation;
+ cursor: pointer;
+ background-image: none;
+ border: 1px solid transparent;
+ white-space: nowrap;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ border-radius: 2px;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+ color: #333;
+ text-decoration: none;
+}
+.btn:active,
+.btn.active {
+ outline: 0;
+ background-image: none;
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+ cursor: not-allowed;
+ opacity: 0.65;
+ filter: alpha(opacity=65);
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+ pointer-events: none;
+}
+.btn-default {
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.btn-default:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+.btn-default .badge {
+ color: #fff;
+ background-color: #333;
+}
+.btn-primary {
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+ color: #fff;
+ background-color: #286090;
+ border-color: #122b40;
+}
+.btn-primary:hover {
+ color: #fff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ color: #fff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+ color: #fff;
+ background-color: #204d74;
+ border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.btn-success {
+ color: #fff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.btn-success:hover {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+ color: #fff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success .badge {
+ color: #5cb85c;
+ background-color: #fff;
+}
+.btn-info {
+ color: #fff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.btn-info:hover {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+ color: #fff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info .badge {
+ color: #5bc0de;
+ background-color: #fff;
+}
+.btn-warning {
+ color: #fff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.btn-warning:hover {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+ color: #fff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning .badge {
+ color: #f0ad4e;
+ background-color: #fff;
+}
+.btn-danger {
+ color: #fff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.btn-danger:hover {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+ color: #fff;
+ background-color: #ac2925;
+ border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger .badge {
+ color: #d9534f;
+ background-color: #fff;
+}
+.btn-link {
+ color: #337ab7;
+ font-weight: normal;
+ border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+ background-color: transparent;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+ border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+ color: #23527c;
+ text-decoration: underline;
+ background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+ color: #777777;
+ text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+ padding: 1px 5px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-block {
+ display: block;
+ width: 100%;
+}
+.btn-block + .btn-block {
+ margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+ width: 100%;
+}
+.fade {
+ opacity: 0;
+ -webkit-transition: opacity 0.15s linear;
+ -o-transition: opacity 0.15s linear;
+ transition: opacity 0.15s linear;
+}
+.fade.in {
+ opacity: 1;
+}
+.collapse {
+ display: none;
+}
+.collapse.in {
+ display: block;
+}
+tr.collapse.in {
+ display: table-row;
+}
+tbody.collapse.in {
+ display: table-row-group;
+}
+.collapsing {
+ position: relative;
+ height: 0;
+ overflow: hidden;
+ -webkit-transition-property: height, visibility;
+ transition-property: height, visibility;
+ -webkit-transition-duration: 0.35s;
+ transition-duration: 0.35s;
+ -webkit-transition-timing-function: ease;
+ transition-timing-function: ease;
+}
+.caret {
+ display: inline-block;
+ width: 0;
+ height: 0;
+ margin-left: 2px;
+ vertical-align: middle;
+ border-top: 4px dashed;
+ border-top: 4px solid \9;
+ border-right: 4px solid transparent;
+ border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+ position: relative;
+}
+.dropdown-toggle:focus {
+ outline: 0;
+}
+.dropdown-menu {
+ position: absolute;
+ top: 100%;
+ left: 0;
+ z-index: 1000;
+ display: none;
+ float: left;
+ min-width: 160px;
+ padding: 5px 0;
+ margin: 2px 0 0;
+ list-style: none;
+ font-size: 13px;
+ text-align: left;
+ background-color: #fff;
+ border: 1px solid #ccc;
+ border: 1px solid rgba(0, 0, 0, 0.15);
+ border-radius: 2px;
+ -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+ right: 0;
+ left: auto;
+}
+.dropdown-menu .divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+ display: block;
+ padding: 3px 20px;
+ clear: both;
+ font-weight: normal;
+ line-height: 1.42857143;
+ color: #333333;
+ white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+ text-decoration: none;
+ color: #262626;
+ background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+ color: #fff;
+ text-decoration: none;
+ outline: 0;
+ background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ text-decoration: none;
+ background-color: transparent;
+ background-image: none;
+ filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+ cursor: not-allowed;
+}
+.open > .dropdown-menu {
+ display: block;
+}
+.open > a {
+ outline: 0;
+}
+.dropdown-menu-right {
+ left: auto;
+ right: 0;
+}
+.dropdown-menu-left {
+ left: 0;
+ right: auto;
+}
+.dropdown-header {
+ display: block;
+ padding: 3px 20px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ color: #777777;
+ white-space: nowrap;
+}
+.dropdown-backdrop {
+ position: fixed;
+ left: 0;
+ right: 0;
+ bottom: 0;
+ top: 0;
+ z-index: 990;
+}
+.pull-right > .dropdown-menu {
+ right: 0;
+ left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+ border-top: 0;
+ border-bottom: 4px dashed;
+ border-bottom: 4px solid \9;
+ content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+ top: auto;
+ bottom: 100%;
+ margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+ .navbar-right .dropdown-menu {
+ left: auto;
+ right: 0;
+ }
+ .navbar-right .dropdown-menu-left {
+ left: 0;
+ right: auto;
+ }
+}
+.btn-group,
+.btn-group-vertical {
+ position: relative;
+ display: inline-block;
+ vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+ position: relative;
+ float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+ z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+ margin-left: -1px;
+}
+.btn-toolbar {
+ margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+ float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+ margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+ border-radius: 0;
+}
+.btn-group > .btn:first-child {
+ margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+ float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+ outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+ padding-left: 8px;
+ padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+ padding-left: 12px;
+ padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn .caret {
+ margin-left: 0;
+}
+.btn-lg .caret {
+ border-width: 5px 5px 0;
+ border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+ border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+ display: block;
+ float: none;
+ width: 100%;
+ max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+ float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+ margin-top: -1px;
+ margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group-justified {
+ display: table;
+ width: 100%;
+ table-layout: fixed;
+ border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+ float: none;
+ display: table-cell;
+ width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+ width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+ left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+ position: absolute;
+ clip: rect(0, 0, 0, 0);
+ pointer-events: none;
+}
+.input-group {
+ position: relative;
+ display: table;
+ border-collapse: separate;
+}
+.input-group[class*="col-"] {
+ float: none;
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-group .form-control {
+ position: relative;
+ z-index: 2;
+ float: left;
+ width: 100%;
+ margin-bottom: 0;
+}
+.input-group .form-control:focus {
+ z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+ display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+ width: 1%;
+ white-space: nowrap;
+ vertical-align: middle;
+}
+.input-group-addon {
+ padding: 6px 12px;
+ font-size: 13px;
+ font-weight: normal;
+ line-height: 1;
+ color: #555555;
+ text-align: center;
+ background-color: #eeeeee;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+}
+.input-group-addon.input-sm {
+ padding: 5px 10px;
+ font-size: 12px;
+ border-radius: 1px;
+}
+.input-group-addon.input-lg {
+ padding: 10px 16px;
+ font-size: 17px;
+ border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+ margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+ border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+ border-left: 0;
+}
+.input-group-btn {
+ position: relative;
+ font-size: 0;
+ white-space: nowrap;
+}
+.input-group-btn > .btn {
+ position: relative;
+}
+.input-group-btn > .btn + .btn {
+ margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+ z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+ margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+ z-index: 2;
+ margin-left: -1px;
+}
+.nav {
+ margin-bottom: 0;
+ padding-left: 0;
+ list-style: none;
+}
+.nav > li {
+ position: relative;
+ display: block;
+}
+.nav > li > a {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+ color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+ color: #777777;
+ text-decoration: none;
+ background-color: transparent;
+ cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+ background-color: #eeeeee;
+ border-color: #337ab7;
+}
+.nav .nav-divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.nav > li > a > img {
+ max-width: none;
+}
+.nav-tabs {
+ border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+ float: left;
+ margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+ margin-right: 2px;
+ line-height: 1.42857143;
+ border: 1px solid transparent;
+ border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+ border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+ color: #555555;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-bottom-color: transparent;
+ cursor: default;
+}
+.nav-tabs.nav-justified {
+ width: 100%;
+ border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+ float: none;
+}
+.nav-tabs.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-tabs.nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs.nav-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+ border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li > a {
+ border-bottom: 1px solid #ddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs.nav-justified > .active > a,
+ .nav-tabs.nav-justified > .active > a:hover,
+ .nav-tabs.nav-justified > .active > a:focus {
+ border-bottom-color: #fff;
+ }
+}
+.nav-pills > li {
+ float: left;
+}
+.nav-pills > li > a {
+ border-radius: 2px;
+}
+.nav-pills > li + li {
+ margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+ color: #fff;
+ background-color: #337ab7;
+}
+.nav-stacked > li {
+ float: none;
+}
+.nav-stacked > li + li {
+ margin-top: 2px;
+ margin-left: 0;
+}
+.nav-justified {
+ width: 100%;
+}
+.nav-justified > li {
+ float: none;
+}
+.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs-justified {
+ border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+ border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs-justified > li > a {
+ border-bottom: 1px solid #ddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs-justified > .active > a,
+ .nav-tabs-justified > .active > a:hover,
+ .nav-tabs-justified > .active > a:focus {
+ border-bottom-color: #fff;
+ }
+}
+.tab-content > .tab-pane {
+ display: none;
+}
+.tab-content > .active {
+ display: block;
+}
+.nav-tabs .dropdown-menu {
+ margin-top: -1px;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar {
+ position: relative;
+ min-height: 30px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+ .navbar {
+ border-radius: 2px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-header {
+ float: left;
+ }
+}
+.navbar-collapse {
+ overflow-x: visible;
+ padding-right: 0px;
+ padding-left: 0px;
+ border-top: 1px solid transparent;
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+ -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+ overflow-y: auto;
+}
+@media (min-width: 541px) {
+ .navbar-collapse {
+ width: auto;
+ border-top: 0;
+ box-shadow: none;
+ }
+ .navbar-collapse.collapse {
+ display: block !important;
+ height: auto !important;
+ padding-bottom: 0;
+ overflow: visible !important;
+ }
+ .navbar-collapse.in {
+ overflow-y: visible;
+ }
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-static-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ padding-left: 0;
+ padding-right: 0;
+ }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+ max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ max-height: 200px;
+ }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+ margin-right: 0px;
+ margin-left: 0px;
+}
+@media (min-width: 541px) {
+ .container > .navbar-header,
+ .container-fluid > .navbar-header,
+ .container > .navbar-collapse,
+ .container-fluid > .navbar-collapse {
+ margin-right: 0;
+ margin-left: 0;
+ }
+}
+.navbar-static-top {
+ z-index: 1000;
+ border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+ .navbar-static-top {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+ position: fixed;
+ right: 0;
+ left: 0;
+ z-index: 1030;
+}
+@media (min-width: 541px) {
+ .navbar-fixed-top,
+ .navbar-fixed-bottom {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top {
+ top: 0;
+ border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+ bottom: 0;
+ margin-bottom: 0;
+ border-width: 1px 0 0;
+}
+.navbar-brand {
+ float: left;
+ padding: 6px 0px;
+ font-size: 17px;
+ line-height: 18px;
+ height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+ text-decoration: none;
+}
+.navbar-brand > img {
+ display: block;
+}
+@media (min-width: 541px) {
+ .navbar > .container .navbar-brand,
+ .navbar > .container-fluid .navbar-brand {
+ margin-left: 0px;
+ }
+}
+.navbar-toggle {
+ position: relative;
+ float: right;
+ margin-right: 0px;
+ padding: 9px 10px;
+ margin-top: -2px;
+ margin-bottom: -2px;
+ background-color: transparent;
+ background-image: none;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.navbar-toggle:focus {
+ outline: 0;
+}
+.navbar-toggle .icon-bar {
+ display: block;
+ width: 22px;
+ height: 2px;
+ border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+ margin-top: 4px;
+}
+@media (min-width: 541px) {
+ .navbar-toggle {
+ display: none;
+ }
+}
+.navbar-nav {
+ margin: 3px 0px;
+}
+.navbar-nav > li > a {
+ padding-top: 10px;
+ padding-bottom: 10px;
+ line-height: 18px;
+}
+@media (max-width: 540px) {
+ .navbar-nav .open .dropdown-menu {
+ position: static;
+ float: none;
+ width: auto;
+ margin-top: 0;
+ background-color: transparent;
+ border: 0;
+ box-shadow: none;
+ }
+ .navbar-nav .open .dropdown-menu > li > a,
+ .navbar-nav .open .dropdown-menu .dropdown-header {
+ padding: 5px 15px 5px 25px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a {
+ line-height: 18px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-nav .open .dropdown-menu > li > a:focus {
+ background-image: none;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-nav {
+ float: left;
+ margin: 0;
+ }
+ .navbar-nav > li {
+ float: left;
+ }
+ .navbar-nav > li > a {
+ padding-top: 6px;
+ padding-bottom: 6px;
+ }
+}
+.navbar-form {
+ margin-left: 0px;
+ margin-right: 0px;
+ padding: 10px 0px;
+ border-top: 1px solid transparent;
+ border-bottom: 1px solid transparent;
+ -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+ .navbar-form .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control-static {
+ display: inline-block;
+ }
+ .navbar-form .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .navbar-form .input-group .input-group-addon,
+ .navbar-form .input-group .input-group-btn,
+ .navbar-form .input-group .form-control {
+ width: auto;
+ }
+ .navbar-form .input-group > .form-control {
+ width: 100%;
+ }
+ .navbar-form .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio,
+ .navbar-form .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio label,
+ .navbar-form .checkbox label {
+ padding-left: 0;
+ }
+ .navbar-form .radio input[type="radio"],
+ .navbar-form .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .navbar-form .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+@media (max-width: 540px) {
+ .navbar-form .form-group {
+ margin-bottom: 5px;
+ }
+ .navbar-form .form-group:last-child {
+ margin-bottom: 0;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-form {
+ width: auto;
+ border: 0;
+ margin-left: 0;
+ margin-right: 0;
+ padding-top: 0;
+ padding-bottom: 0;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+ }
+}
+.navbar-nav > li > .dropdown-menu {
+ margin-top: 0;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+ margin-bottom: 0;
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.navbar-btn {
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+ margin-top: 4px;
+ margin-bottom: 4px;
+}
+.navbar-text {
+ margin-top: 6px;
+ margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+ .navbar-text {
+ float: left;
+ margin-left: 0px;
+ margin-right: 0px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-left {
+ float: left !important;
+ float: left;
+ }
+ .navbar-right {
+ float: right !important;
+ float: right;
+ margin-right: 0px;
+ }
+ .navbar-right ~ .navbar-right {
+ margin-right: 0;
+ }
+}
+.navbar-default {
+ background-color: #f8f8f8;
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+ color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+ color: #5e5e5e;
+ background-color: transparent;
+}
+.navbar-default .navbar-text {
+ color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+ color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+ color: #333;
+ background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+ color: #555;
+ background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+ color: #ccc;
+ background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+ border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+ background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+ background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+ background-color: #e7e7e7;
+ color: #555;
+}
+@media (max-width: 540px) {
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+ color: #777;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #333;
+ background-color: transparent;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #555;
+ background-color: #e7e7e7;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #ccc;
+ background-color: transparent;
+ }
+}
+.navbar-default .navbar-link {
+ color: #777;
+}
+.navbar-default .navbar-link:hover {
+ color: #333;
+}
+.navbar-default .btn-link {
+ color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+ color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+ color: #ccc;
+}
+.navbar-inverse {
+ background-color: #222;
+ border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+ color: #fff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+ color: #fff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+ color: #fff;
+ background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+ color: #444;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+ border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+ background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+ background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+ border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+ background-color: #080808;
+ color: #fff;
+}
+@media (max-width: 540px) {
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+ border-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+ color: #9d9d9d;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #fff;
+ background-color: transparent;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #fff;
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #444;
+ background-color: transparent;
+ }
+}
+.navbar-inverse .navbar-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+ color: #fff;
+}
+.navbar-inverse .btn-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+ color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+ color: #444;
+}
+.breadcrumb {
+ padding: 8px 15px;
+ margin-bottom: 18px;
+ list-style: none;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+}
+.breadcrumb > li {
+ display: inline-block;
+}
+.breadcrumb > li + li:before {
+ content: "/\00a0";
+ padding: 0 5px;
+ color: #5e5e5e;
+}
+.breadcrumb > .active {
+ color: #777777;
+}
+.pagination {
+ display: inline-block;
+ padding-left: 0;
+ margin: 18px 0;
+ border-radius: 2px;
+}
+.pagination > li {
+ display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+ position: relative;
+ float: left;
+ padding: 6px 12px;
+ line-height: 1.42857143;
+ text-decoration: none;
+ color: #337ab7;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+ margin-left: 0;
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+ border-bottom-right-radius: 2px;
+ border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+ z-index: 2;
+ color: #23527c;
+ background-color: #eeeeee;
+ border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+ z-index: 3;
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+ cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+ color: #777777;
+ background-color: #fff;
+ border-color: #ddd;
+ cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+ border-bottom-left-radius: 3px;
+ border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+ border-bottom-right-radius: 3px;
+ border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+ border-bottom-left-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+ border-bottom-right-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.pager {
+ padding-left: 0;
+ margin: 18px 0;
+ list-style: none;
+ text-align: center;
+}
+.pager li {
+ display: inline;
+}
+.pager li > a,
+.pager li > span {
+ display: inline-block;
+ padding: 5px 14px;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+ float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+ float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+ color: #777777;
+ background-color: #fff;
+ cursor: not-allowed;
+}
+.label {
+ display: inline;
+ padding: .2em .6em .3em;
+ font-size: 75%;
+ font-weight: bold;
+ line-height: 1;
+ color: #fff;
+ text-align: center;
+ white-space: nowrap;
+ vertical-align: baseline;
+ border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+ color: #fff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.label:empty {
+ display: none;
+}
+.btn .label {
+ position: relative;
+ top: -1px;
+}
+.label-default {
+ background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+ background-color: #5e5e5e;
+}
+.label-primary {
+ background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+ background-color: #286090;
+}
+.label-success {
+ background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+ background-color: #449d44;
+}
+.label-info {
+ background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+ background-color: #31b0d5;
+}
+.label-warning {
+ background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+ background-color: #ec971f;
+}
+.label-danger {
+ background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+ background-color: #c9302c;
+}
+.badge {
+ display: inline-block;
+ min-width: 10px;
+ padding: 3px 7px;
+ font-size: 12px;
+ font-weight: bold;
+ color: #fff;
+ line-height: 1;
+ vertical-align: middle;
+ white-space: nowrap;
+ text-align: center;
+ background-color: #777777;
+ border-radius: 10px;
+}
+.badge:empty {
+ display: none;
+}
+.btn .badge {
+ position: relative;
+ top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+ top: 0;
+ padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+ color: #fff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.list-group-item > .badge {
+ float: right;
+}
+.list-group-item > .badge + .badge {
+ margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+ margin-left: 3px;
+}
+.jumbotron {
+ padding-top: 30px;
+ padding-bottom: 30px;
+ margin-bottom: 30px;
+ color: inherit;
+ background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+ color: inherit;
+}
+.jumbotron p {
+ margin-bottom: 15px;
+ font-size: 20px;
+ font-weight: 200;
+}
+.jumbotron > hr {
+ border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+ border-radius: 3px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.jumbotron .container {
+ max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+ .jumbotron {
+ padding-top: 48px;
+ padding-bottom: 48px;
+ }
+ .container .jumbotron,
+ .container-fluid .jumbotron {
+ padding-left: 60px;
+ padding-right: 60px;
+ }
+ .jumbotron h1,
+ .jumbotron .h1 {
+ font-size: 59px;
+ }
+}
+.thumbnail {
+ display: block;
+ padding: 4px;
+ margin-bottom: 18px;
+ line-height: 1.42857143;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ -webkit-transition: border 0.2s ease-in-out;
+ -o-transition: border 0.2s ease-in-out;
+ transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+ margin-left: auto;
+ margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+ border-color: #337ab7;
+}
+.thumbnail .caption {
+ padding: 9px;
+ color: #000;
+}
+.alert {
+ padding: 15px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.alert h4 {
+ margin-top: 0;
+ color: inherit;
+}
+.alert .alert-link {
+ font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+ margin-bottom: 0;
+}
+.alert > p + p {
+ margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+ padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+ position: relative;
+ top: -2px;
+ right: -21px;
+ color: inherit;
+}
+.alert-success {
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+ color: #3c763d;
+}
+.alert-success hr {
+ border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+ color: #2b542c;
+}
+.alert-info {
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+ color: #31708f;
+}
+.alert-info hr {
+ border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+ color: #245269;
+}
+.alert-warning {
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+ color: #8a6d3b;
+}
+.alert-warning hr {
+ border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+ color: #66512c;
+}
+.alert-danger {
+ background-color: #f2dede;
+ border-color: #ebccd1;
+ color: #a94442;
+}
+.alert-danger hr {
+ border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+ color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+@keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+.progress {
+ overflow: hidden;
+ height: 18px;
+ margin-bottom: 18px;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+ box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+ float: left;
+ width: 0%;
+ height: 100%;
+ font-size: 12px;
+ line-height: 18px;
+ color: #fff;
+ text-align: center;
+ background-color: #337ab7;
+ -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ -webkit-transition: width 0.6s ease;
+ -o-transition: width 0.6s ease;
+ transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+ -webkit-animation: progress-bar-stripes 2s linear infinite;
+ -o-animation: progress-bar-stripes 2s linear infinite;
+ animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+ background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+ background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+ background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+ background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+ margin-top: 15px;
+}
+.media:first-child {
+ margin-top: 0;
+}
+.media,
+.media-body {
+ zoom: 1;
+ overflow: hidden;
+}
+.media-body {
+ width: 10000px;
+}
+.media-object {
+ display: block;
+}
+.media-object.img-thumbnail {
+ max-width: none;
+}
+.media-right,
+.media > .pull-right {
+ padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+ padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+ display: table-cell;
+ vertical-align: top;
+}
+.media-middle {
+ vertical-align: middle;
+}
+.media-bottom {
+ vertical-align: bottom;
+}
+.media-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.media-list {
+ padding-left: 0;
+ list-style: none;
+}
+.list-group {
+ margin-bottom: 20px;
+ padding-left: 0;
+}
+.list-group-item {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+ margin-bottom: -1px;
+ background-color: #fff;
+ border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+ margin-bottom: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+ color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+ color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+ text-decoration: none;
+ color: #555;
+ background-color: #f5f5f5;
+}
+button.list-group-item {
+ width: 100%;
+ text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+ background-color: #eeeeee;
+ color: #777777;
+ cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+ color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+ color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+ z-index: 2;
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+ color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+ color: #c7ddef;
+}
+.list-group-item-success {
+ color: #3c763d;
+ background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+ color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+ color: #3c763d;
+ background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+ color: #fff;
+ background-color: #3c763d;
+ border-color: #3c763d;
+}
+.list-group-item-info {
+ color: #31708f;
+ background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+ color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+ color: #31708f;
+ background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+ color: #fff;
+ background-color: #31708f;
+ border-color: #31708f;
+}
+.list-group-item-warning {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+ color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+ color: #8a6d3b;
+ background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+ color: #fff;
+ background-color: #8a6d3b;
+ border-color: #8a6d3b;
+}
+.list-group-item-danger {
+ color: #a94442;
+ background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+ color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+ color: #a94442;
+ background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+ color: #fff;
+ background-color: #a94442;
+ border-color: #a94442;
+}
+.list-group-item-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.list-group-item-text {
+ margin-bottom: 0;
+ line-height: 1.3;
+}
+.panel {
+ margin-bottom: 18px;
+ background-color: #fff;
+ border: 1px solid transparent;
+ border-radius: 2px;
+ -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+ padding: 15px;
+}
+.panel-heading {
+ padding: 10px 15px;
+ border-bottom: 1px solid transparent;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+ color: inherit;
+}
+.panel-title {
+ margin-top: 0;
+ margin-bottom: 0;
+ font-size: 15px;
+ color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+ color: inherit;
+}
+.panel-footer {
+ padding: 10px 15px;
+ background-color: #f5f5f5;
+ border-top: 1px solid #ddd;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+ margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+ border-width: 1px 0;
+ border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+ border-top: 0;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+ border-bottom: 0;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+ border-top-width: 0;
+}
+.list-group + .panel-footer {
+ border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+ margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+ padding-left: 15px;
+ padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+ border-top-left-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+ border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+ border-bottom-left-radius: 1px;
+ border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+ border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+ border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+ border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+ border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+ border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+ border-bottom: 0;
+}
+.panel > .table-responsive {
+ border: 0;
+ margin-bottom: 0;
+}
+.panel-group {
+ margin-bottom: 18px;
+}
+.panel-group .panel {
+ margin-bottom: 0;
+ border-radius: 2px;
+}
+.panel-group .panel + .panel {
+ margin-top: 5px;
+}
+.panel-group .panel-heading {
+ border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+ border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+ border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+ border-bottom: 1px solid #ddd;
+}
+.panel-default {
+ border-color: #ddd;
+}
+.panel-default > .panel-heading {
+ color: #333333;
+ background-color: #f5f5f5;
+ border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+ color: #f5f5f5;
+ background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #ddd;
+}
+.panel-primary {
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #337ab7;
+}
+.panel-success {
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+ color: #3c763d;
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+ color: #dff0d8;
+ background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #d6e9c6;
+}
+.panel-info {
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+ color: #31708f;
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+ color: #d9edf7;
+ background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #bce8f1;
+}
+.panel-warning {
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+ color: #fcf8e3;
+ background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #faebcc;
+}
+.panel-danger {
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+ color: #a94442;
+ background-color: #f2dede;
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+ color: #f2dede;
+ background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+ position: relative;
+ display: block;
+ height: 0;
+ padding: 0;
+ overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ height: 100%;
+ width: 100%;
+ border: 0;
+}
+.embed-responsive-16by9 {
+ padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+ padding-bottom: 75%;
+}
+.well {
+ min-height: 20px;
+ padding: 19px;
+ margin-bottom: 20px;
+ background-color: #f5f5f5;
+ border: 1px solid #e3e3e3;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+ border-color: #ddd;
+ border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+ padding: 24px;
+ border-radius: 3px;
+}
+.well-sm {
+ padding: 9px;
+ border-radius: 1px;
+}
+.close {
+ float: right;
+ font-size: 19.5px;
+ font-weight: bold;
+ line-height: 1;
+ color: #000;
+ text-shadow: 0 1px 0 #fff;
+ opacity: 0.2;
+ filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+ color: #000;
+ text-decoration: none;
+ cursor: pointer;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+button.close {
+ padding: 0;
+ cursor: pointer;
+ background: transparent;
+ border: 0;
+ -webkit-appearance: none;
+}
+.modal-open {
+ overflow: hidden;
+}
+.modal {
+ display: none;
+ overflow: hidden;
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1050;
+ -webkit-overflow-scrolling: touch;
+ outline: 0;
+}
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, -25%);
+ -ms-transform: translate(0, -25%);
+ -o-transform: translate(0, -25%);
+ transform: translate(0, -25%);
+ -webkit-transition: -webkit-transform 0.3s ease-out;
+ -moz-transition: -moz-transform 0.3s ease-out;
+ -o-transition: -o-transform 0.3s ease-out;
+ transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+.modal-open .modal {
+ overflow-x: hidden;
+ overflow-y: auto;
+}
+.modal-dialog {
+ position: relative;
+ width: auto;
+ margin: 10px;
+}
+.modal-content {
+ position: relative;
+ background-color: #fff;
+ border: 1px solid #999;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ background-clip: padding-box;
+ outline: 0;
+}
+.modal-backdrop {
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1040;
+ background-color: #000;
+}
+.modal-backdrop.fade {
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+.modal-header {
+ padding: 15px;
+ border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+ margin-top: -2px;
+}
+.modal-title {
+ margin: 0;
+ line-height: 1.42857143;
+}
+.modal-body {
+ position: relative;
+ padding: 15px;
+}
+.modal-footer {
+ padding: 15px;
+ text-align: right;
+ border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+ margin-left: 5px;
+ margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+ margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+ margin-left: 0;
+}
+.modal-scrollbar-measure {
+ position: absolute;
+ top: -9999px;
+ width: 50px;
+ height: 50px;
+ overflow: scroll;
+}
+@media (min-width: 768px) {
+ .modal-dialog {
+ width: 600px;
+ margin: 30px auto;
+ }
+ .modal-content {
+ -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ }
+ .modal-sm {
+ width: 300px;
+ }
+}
+@media (min-width: 992px) {
+ .modal-lg {
+ width: 900px;
+ }
+}
+.tooltip {
+ position: absolute;
+ z-index: 1070;
+ display: block;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 12px;
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.tooltip.in {
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.tooltip.top {
+ margin-top: -3px;
+ padding: 5px 0;
+}
+.tooltip.right {
+ margin-left: 3px;
+ padding: 0 5px;
+}
+.tooltip.bottom {
+ margin-top: 3px;
+ padding: 5px 0;
+}
+.tooltip.left {
+ margin-left: -3px;
+ padding: 0 5px;
+}
+.tooltip-inner {
+ max-width: 200px;
+ padding: 3px 8px;
+ color: #fff;
+ text-align: center;
+ background-color: #000;
+ border-radius: 2px;
+}
+.tooltip-arrow {
+ position: absolute;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+ bottom: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+ bottom: 0;
+ right: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+ bottom: 0;
+ left: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+ top: 50%;
+ left: 0;
+ margin-top: -5px;
+ border-width: 5px 5px 5px 0;
+ border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+ top: 50%;
+ right: 0;
+ margin-top: -5px;
+ border-width: 5px 0 5px 5px;
+ border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+ top: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+ top: 0;
+ right: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+ top: 0;
+ left: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.popover {
+ position: absolute;
+ top: 0;
+ left: 0;
+ z-index: 1060;
+ display: none;
+ max-width: 276px;
+ padding: 1px;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 13px;
+ background-color: #fff;
+ background-clip: padding-box;
+ border: 1px solid #ccc;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+ box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+ margin-top: -10px;
+}
+.popover.right {
+ margin-left: 10px;
+}
+.popover.bottom {
+ margin-top: 10px;
+}
+.popover.left {
+ margin-left: -10px;
+}
+.popover-title {
+ margin: 0;
+ padding: 8px 14px;
+ font-size: 13px;
+ background-color: #f7f7f7;
+ border-bottom: 1px solid #ebebeb;
+ border-radius: 2px 2px 0 0;
+}
+.popover-content {
+ padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+ position: absolute;
+ display: block;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.popover > .arrow {
+ border-width: 11px;
+}
+.popover > .arrow:after {
+ border-width: 10px;
+ content: "";
+}
+.popover.top > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-bottom-width: 0;
+ border-top-color: #999999;
+ border-top-color: rgba(0, 0, 0, 0.25);
+ bottom: -11px;
+}
+.popover.top > .arrow:after {
+ content: " ";
+ bottom: 1px;
+ margin-left: -10px;
+ border-bottom-width: 0;
+ border-top-color: #fff;
+}
+.popover.right > .arrow {
+ top: 50%;
+ left: -11px;
+ margin-top: -11px;
+ border-left-width: 0;
+ border-right-color: #999999;
+ border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+ content: " ";
+ left: 1px;
+ bottom: -10px;
+ border-left-width: 0;
+ border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-top-width: 0;
+ border-bottom-color: #999999;
+ border-bottom-color: rgba(0, 0, 0, 0.25);
+ top: -11px;
+}
+.popover.bottom > .arrow:after {
+ content: " ";
+ top: 1px;
+ margin-left: -10px;
+ border-top-width: 0;
+ border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+ top: 50%;
+ right: -11px;
+ margin-top: -11px;
+ border-right-width: 0;
+ border-left-color: #999999;
+ border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+ content: " ";
+ right: 1px;
+ border-right-width: 0;
+ border-left-color: #fff;
+ bottom: -10px;
+}
+.carousel {
+ position: relative;
+}
+.carousel-inner {
+ position: relative;
+ overflow: hidden;
+ width: 100%;
+}
+.carousel-inner > .item {
+ display: none;
+ position: relative;
+ -webkit-transition: 0.6s ease-in-out left;
+ -o-transition: 0.6s ease-in-out left;
+ transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+ .carousel-inner > .item {
+ -webkit-transition: -webkit-transform 0.6s ease-in-out;
+ -moz-transition: -moz-transform 0.6s ease-in-out;
+ -o-transition: -o-transform 0.6s ease-in-out;
+ transition: transform 0.6s ease-in-out;
+ -webkit-backface-visibility: hidden;
+ -moz-backface-visibility: hidden;
+ backface-visibility: hidden;
+ -webkit-perspective: 1000px;
+ -moz-perspective: 1000px;
+ perspective: 1000px;
+ }
+ .carousel-inner > .item.next,
+ .carousel-inner > .item.active.right {
+ -webkit-transform: translate3d(100%, 0, 0);
+ transform: translate3d(100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.prev,
+ .carousel-inner > .item.active.left {
+ -webkit-transform: translate3d(-100%, 0, 0);
+ transform: translate3d(-100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.next.left,
+ .carousel-inner > .item.prev.right,
+ .carousel-inner > .item.active {
+ -webkit-transform: translate3d(0, 0, 0);
+ transform: translate3d(0, 0, 0);
+ left: 0;
+ }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ display: block;
+}
+.carousel-inner > .active {
+ left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ position: absolute;
+ top: 0;
+ width: 100%;
+}
+.carousel-inner > .next {
+ left: 100%;
+}
+.carousel-inner > .prev {
+ left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+ left: 0;
+}
+.carousel-inner > .active.left {
+ left: -100%;
+}
+.carousel-inner > .active.right {
+ left: 100%;
+}
+.carousel-control {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ width: 15%;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+ font-size: 20px;
+ color: #fff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+ left: auto;
+ right: 0;
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+ outline: 0;
+ color: #fff;
+ text-decoration: none;
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+ position: absolute;
+ top: 50%;
+ margin-top: -10px;
+ z-index: 5;
+ display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+ left: 50%;
+ margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+ right: 50%;
+ margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+ width: 20px;
+ height: 20px;
+ line-height: 1;
+ font-family: serif;
+}
+.carousel-control .icon-prev:before {
+ content: '\2039';
+}
+.carousel-control .icon-next:before {
+ content: '\203a';
+}
+.carousel-indicators {
+ position: absolute;
+ bottom: 10px;
+ left: 50%;
+ z-index: 15;
+ width: 60%;
+ margin-left: -30%;
+ padding-left: 0;
+ list-style: none;
+ text-align: center;
+}
+.carousel-indicators li {
+ display: inline-block;
+ width: 10px;
+ height: 10px;
+ margin: 1px;
+ text-indent: -999px;
+ border: 1px solid #fff;
+ border-radius: 10px;
+ cursor: pointer;
+ background-color: #000 \9;
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+ margin: 0;
+ width: 12px;
+ height: 12px;
+ background-color: #fff;
+}
+.carousel-caption {
+ position: absolute;
+ left: 15%;
+ right: 15%;
+ bottom: 20px;
+ z-index: 10;
+ padding-top: 20px;
+ padding-bottom: 20px;
+ color: #fff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+ text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-prev,
+ .carousel-control .icon-next {
+ width: 30px;
+ height: 30px;
+ margin-top: -10px;
+ font-size: 30px;
+ }
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .icon-prev {
+ margin-left: -10px;
+ }
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-next {
+ margin-right: -10px;
+ }
+ .carousel-caption {
+ left: 20%;
+ right: 20%;
+ padding-bottom: 30px;
+ }
+ .carousel-indicators {
+ bottom: 20px;
+ }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+ content: " ";
+ display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+ clear: both;
+}
+.center-block {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.pull-right {
+ float: right !important;
+}
+.pull-left {
+ float: left !important;
+}
+.hide {
+ display: none !important;
+}
+.show {
+ display: block !important;
+}
+.invisible {
+ visibility: hidden;
+}
+.text-hide {
+ font: 0/0 a;
+ color: transparent;
+ text-shadow: none;
+ background-color: transparent;
+ border: 0;
+}
+.hidden {
+ display: none !important;
+}
+.affix {
+ position: fixed;
+}
+@-ms-viewport {
+ width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+ display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+ display: none !important;
+}
+@media (max-width: 767px) {
+ .visible-xs {
+ display: block !important;
+ }
+ table.visible-xs {
+ display: table !important;
+ }
+ tr.visible-xs {
+ display: table-row !important;
+ }
+ th.visible-xs,
+ td.visible-xs {
+ display: table-cell !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-block {
+ display: block !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline {
+ display: inline !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm {
+ display: block !important;
+ }
+ table.visible-sm {
+ display: table !important;
+ }
+ tr.visible-sm {
+ display: table-row !important;
+ }
+ th.visible-sm,
+ td.visible-sm {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-block {
+ display: block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md {
+ display: block !important;
+ }
+ table.visible-md {
+ display: table !important;
+ }
+ tr.visible-md {
+ display: table-row !important;
+ }
+ th.visible-md,
+ td.visible-md {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-block {
+ display: block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg {
+ display: block !important;
+ }
+ table.visible-lg {
+ display: table !important;
+ }
+ tr.visible-lg {
+ display: table-row !important;
+ }
+ th.visible-lg,
+ td.visible-lg {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-block {
+ display: block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (max-width: 767px) {
+ .hidden-xs {
+ display: none !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .hidden-sm {
+ display: none !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .hidden-md {
+ display: none !important;
+ }
+}
+@media (min-width: 1200px) {
+ .hidden-lg {
+ display: none !important;
+ }
+}
+.visible-print {
+ display: none !important;
+}
+@media print {
+ .visible-print {
+ display: block !important;
+ }
+ table.visible-print {
+ display: table !important;
+ }
+ tr.visible-print {
+ display: table-row !important;
+ }
+ th.visible-print,
+ td.visible-print {
+ display: table-cell !important;
+ }
+}
+.visible-print-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-block {
+ display: block !important;
+ }
+}
+.visible-print-inline {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline {
+ display: inline !important;
+ }
+}
+.visible-print-inline-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline-block {
+ display: inline-block !important;
+ }
+}
+@media print {
+ .hidden-print {
+ display: none !important;
+ }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
+ * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+ font-family: 'FontAwesome';
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
+ font-weight: normal;
+ font-style: normal;
+}
+.fa {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+ font-size: 1.33333333em;
+ line-height: 0.75em;
+ vertical-align: -15%;
+}
+.fa-2x {
+ font-size: 2em;
+}
+.fa-3x {
+ font-size: 3em;
+}
+.fa-4x {
+ font-size: 4em;
+}
+.fa-5x {
+ font-size: 5em;
+}
+.fa-fw {
+ width: 1.28571429em;
+ text-align: center;
+}
+.fa-ul {
+ padding-left: 0;
+ margin-left: 2.14285714em;
+ list-style-type: none;
+}
+.fa-ul > li {
+ position: relative;
+}
+.fa-li {
+ position: absolute;
+ left: -2.14285714em;
+ width: 2.14285714em;
+ top: 0.14285714em;
+ text-align: center;
+}
+.fa-li.fa-lg {
+ left: -1.85714286em;
+}
+.fa-border {
+ padding: .2em .25em .15em;
+ border: solid 0.08em #eee;
+ border-radius: .1em;
+}
+.fa-pull-left {
+ float: left;
+}
+.fa-pull-right {
+ float: right;
+}
+.fa.fa-pull-left {
+ margin-right: .3em;
+}
+.fa.fa-pull-right {
+ margin-left: .3em;
+}
+/* Deprecated as of 4.4.0 */
+.pull-right {
+ float: right;
+}
+.pull-left {
+ float: left;
+}
+.fa.pull-left {
+ margin-right: .3em;
+}
+.fa.pull-right {
+ margin-left: .3em;
+}
+.fa-spin {
+ -webkit-animation: fa-spin 2s infinite linear;
+ animation: fa-spin 2s infinite linear;
+}
+.fa-pulse {
+ -webkit-animation: fa-spin 1s infinite steps(8);
+ animation: fa-spin 1s infinite steps(8);
+}
+@-webkit-keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+@keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+.fa-rotate-90 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
+ -webkit-transform: rotate(90deg);
+ -ms-transform: rotate(90deg);
+ transform: rotate(90deg);
+}
+.fa-rotate-180 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
+ -webkit-transform: rotate(180deg);
+ -ms-transform: rotate(180deg);
+ transform: rotate(180deg);
+}
+.fa-rotate-270 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
+ -webkit-transform: rotate(270deg);
+ -ms-transform: rotate(270deg);
+ transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
+ -webkit-transform: scale(-1, 1);
+ -ms-transform: scale(-1, 1);
+ transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
+ -webkit-transform: scale(1, -1);
+ -ms-transform: scale(1, -1);
+ transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+ filter: none;
+}
+.fa-stack {
+ position: relative;
+ display: inline-block;
+ width: 2em;
+ height: 2em;
+ line-height: 2em;
+ vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+ position: absolute;
+ left: 0;
+ width: 100%;
+ text-align: center;
+}
+.fa-stack-1x {
+ line-height: inherit;
+}
+.fa-stack-2x {
+ font-size: 2em;
+}
+.fa-inverse {
+ color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+ readers do not read off random characters that represent icons */
+.fa-glass:before {
+ content: "\f000";
+}
+.fa-music:before {
+ content: "\f001";
+}
+.fa-search:before {
+ content: "\f002";
+}
+.fa-envelope-o:before {
+ content: "\f003";
+}
+.fa-heart:before {
+ content: "\f004";
+}
+.fa-star:before {
+ content: "\f005";
+}
+.fa-star-o:before {
+ content: "\f006";
+}
+.fa-user:before {
+ content: "\f007";
+}
+.fa-film:before {
+ content: "\f008";
+}
+.fa-th-large:before {
+ content: "\f009";
+}
+.fa-th:before {
+ content: "\f00a";
+}
+.fa-th-list:before {
+ content: "\f00b";
+}
+.fa-check:before {
+ content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+ content: "\f00d";
+}
+.fa-search-plus:before {
+ content: "\f00e";
+}
+.fa-search-minus:before {
+ content: "\f010";
+}
+.fa-power-off:before {
+ content: "\f011";
+}
+.fa-signal:before {
+ content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+ content: "\f013";
+}
+.fa-trash-o:before {
+ content: "\f014";
+}
+.fa-home:before {
+ content: "\f015";
+}
+.fa-file-o:before {
+ content: "\f016";
+}
+.fa-clock-o:before {
+ content: "\f017";
+}
+.fa-road:before {
+ content: "\f018";
+}
+.fa-download:before {
+ content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+ content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+ content: "\f01b";
+}
+.fa-inbox:before {
+ content: "\f01c";
+}
+.fa-play-circle-o:before {
+ content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+ content: "\f01e";
+}
+.fa-refresh:before {
+ content: "\f021";
+}
+.fa-list-alt:before {
+ content: "\f022";
+}
+.fa-lock:before {
+ content: "\f023";
+}
+.fa-flag:before {
+ content: "\f024";
+}
+.fa-headphones:before {
+ content: "\f025";
+}
+.fa-volume-off:before {
+ content: "\f026";
+}
+.fa-volume-down:before {
+ content: "\f027";
+}
+.fa-volume-up:before {
+ content: "\f028";
+}
+.fa-qrcode:before {
+ content: "\f029";
+}
+.fa-barcode:before {
+ content: "\f02a";
+}
+.fa-tag:before {
+ content: "\f02b";
+}
+.fa-tags:before {
+ content: "\f02c";
+}
+.fa-book:before {
+ content: "\f02d";
+}
+.fa-bookmark:before {
+ content: "\f02e";
+}
+.fa-print:before {
+ content: "\f02f";
+}
+.fa-camera:before {
+ content: "\f030";
+}
+.fa-font:before {
+ content: "\f031";
+}
+.fa-bold:before {
+ content: "\f032";
+}
+.fa-italic:before {
+ content: "\f033";
+}
+.fa-text-height:before {
+ content: "\f034";
+}
+.fa-text-width:before {
+ content: "\f035";
+}
+.fa-align-left:before {
+ content: "\f036";
+}
+.fa-align-center:before {
+ content: "\f037";
+}
+.fa-align-right:before {
+ content: "\f038";
+}
+.fa-align-justify:before {
+ content: "\f039";
+}
+.fa-list:before {
+ content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+ content: "\f03b";
+}
+.fa-indent:before {
+ content: "\f03c";
+}
+.fa-video-camera:before {
+ content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+ content: "\f03e";
+}
+.fa-pencil:before {
+ content: "\f040";
+}
+.fa-map-marker:before {
+ content: "\f041";
+}
+.fa-adjust:before {
+ content: "\f042";
+}
+.fa-tint:before {
+ content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+ content: "\f044";
+}
+.fa-share-square-o:before {
+ content: "\f045";
+}
+.fa-check-square-o:before {
+ content: "\f046";
+}
+.fa-arrows:before {
+ content: "\f047";
+}
+.fa-step-backward:before {
+ content: "\f048";
+}
+.fa-fast-backward:before {
+ content: "\f049";
+}
+.fa-backward:before {
+ content: "\f04a";
+}
+.fa-play:before {
+ content: "\f04b";
+}
+.fa-pause:before {
+ content: "\f04c";
+}
+.fa-stop:before {
+ content: "\f04d";
+}
+.fa-forward:before {
+ content: "\f04e";
+}
+.fa-fast-forward:before {
+ content: "\f050";
+}
+.fa-step-forward:before {
+ content: "\f051";
+}
+.fa-eject:before {
+ content: "\f052";
+}
+.fa-chevron-left:before {
+ content: "\f053";
+}
+.fa-chevron-right:before {
+ content: "\f054";
+}
+.fa-plus-circle:before {
+ content: "\f055";
+}
+.fa-minus-circle:before {
+ content: "\f056";
+}
+.fa-times-circle:before {
+ content: "\f057";
+}
+.fa-check-circle:before {
+ content: "\f058";
+}
+.fa-question-circle:before {
+ content: "\f059";
+}
+.fa-info-circle:before {
+ content: "\f05a";
+}
+.fa-crosshairs:before {
+ content: "\f05b";
+}
+.fa-times-circle-o:before {
+ content: "\f05c";
+}
+.fa-check-circle-o:before {
+ content: "\f05d";
+}
+.fa-ban:before {
+ content: "\f05e";
+}
+.fa-arrow-left:before {
+ content: "\f060";
+}
+.fa-arrow-right:before {
+ content: "\f061";
+}
+.fa-arrow-up:before {
+ content: "\f062";
+}
+.fa-arrow-down:before {
+ content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+ content: "\f064";
+}
+.fa-expand:before {
+ content: "\f065";
+}
+.fa-compress:before {
+ content: "\f066";
+}
+.fa-plus:before {
+ content: "\f067";
+}
+.fa-minus:before {
+ content: "\f068";
+}
+.fa-asterisk:before {
+ content: "\f069";
+}
+.fa-exclamation-circle:before {
+ content: "\f06a";
+}
+.fa-gift:before {
+ content: "\f06b";
+}
+.fa-leaf:before {
+ content: "\f06c";
+}
+.fa-fire:before {
+ content: "\f06d";
+}
+.fa-eye:before {
+ content: "\f06e";
+}
+.fa-eye-slash:before {
+ content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+ content: "\f071";
+}
+.fa-plane:before {
+ content: "\f072";
+}
+.fa-calendar:before {
+ content: "\f073";
+}
+.fa-random:before {
+ content: "\f074";
+}
+.fa-comment:before {
+ content: "\f075";
+}
+.fa-magnet:before {
+ content: "\f076";
+}
+.fa-chevron-up:before {
+ content: "\f077";
+}
+.fa-chevron-down:before {
+ content: "\f078";
+}
+.fa-retweet:before {
+ content: "\f079";
+}
+.fa-shopping-cart:before {
+ content: "\f07a";
+}
+.fa-folder:before {
+ content: "\f07b";
+}
+.fa-folder-open:before {
+ content: "\f07c";
+}
+.fa-arrows-v:before {
+ content: "\f07d";
+}
+.fa-arrows-h:before {
+ content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+ content: "\f080";
+}
+.fa-twitter-square:before {
+ content: "\f081";
+}
+.fa-facebook-square:before {
+ content: "\f082";
+}
+.fa-camera-retro:before {
+ content: "\f083";
+}
+.fa-key:before {
+ content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+ content: "\f085";
+}
+.fa-comments:before {
+ content: "\f086";
+}
+.fa-thumbs-o-up:before {
+ content: "\f087";
+}
+.fa-thumbs-o-down:before {
+ content: "\f088";
+}
+.fa-star-half:before {
+ content: "\f089";
+}
+.fa-heart-o:before {
+ content: "\f08a";
+}
+.fa-sign-out:before {
+ content: "\f08b";
+}
+.fa-linkedin-square:before {
+ content: "\f08c";
+}
+.fa-thumb-tack:before {
+ content: "\f08d";
+}
+.fa-external-link:before {
+ content: "\f08e";
+}
+.fa-sign-in:before {
+ content: "\f090";
+}
+.fa-trophy:before {
+ content: "\f091";
+}
+.fa-github-square:before {
+ content: "\f092";
+}
+.fa-upload:before {
+ content: "\f093";
+}
+.fa-lemon-o:before {
+ content: "\f094";
+}
+.fa-phone:before {
+ content: "\f095";
+}
+.fa-square-o:before {
+ content: "\f096";
+}
+.fa-bookmark-o:before {
+ content: "\f097";
+}
+.fa-phone-square:before {
+ content: "\f098";
+}
+.fa-twitter:before {
+ content: "\f099";
+}
+.fa-facebook-f:before,
+.fa-facebook:before {
+ content: "\f09a";
+}
+.fa-github:before {
+ content: "\f09b";
+}
+.fa-unlock:before {
+ content: "\f09c";
+}
+.fa-credit-card:before {
+ content: "\f09d";
+}
+.fa-feed:before,
+.fa-rss:before {
+ content: "\f09e";
+}
+.fa-hdd-o:before {
+ content: "\f0a0";
+}
+.fa-bullhorn:before {
+ content: "\f0a1";
+}
+.fa-bell:before {
+ content: "\f0f3";
+}
+.fa-certificate:before {
+ content: "\f0a3";
+}
+.fa-hand-o-right:before {
+ content: "\f0a4";
+}
+.fa-hand-o-left:before {
+ content: "\f0a5";
+}
+.fa-hand-o-up:before {
+ content: "\f0a6";
+}
+.fa-hand-o-down:before {
+ content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+ content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+ content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+ content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+ content: "\f0ab";
+}
+.fa-globe:before {
+ content: "\f0ac";
+}
+.fa-wrench:before {
+ content: "\f0ad";
+}
+.fa-tasks:before {
+ content: "\f0ae";
+}
+.fa-filter:before {
+ content: "\f0b0";
+}
+.fa-briefcase:before {
+ content: "\f0b1";
+}
+.fa-arrows-alt:before {
+ content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+ content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+ content: "\f0c1";
+}
+.fa-cloud:before {
+ content: "\f0c2";
+}
+.fa-flask:before {
+ content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+ content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+ content: "\f0c5";
+}
+.fa-paperclip:before {
+ content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+ content: "\f0c7";
+}
+.fa-square:before {
+ content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+ content: "\f0c9";
+}
+.fa-list-ul:before {
+ content: "\f0ca";
+}
+.fa-list-ol:before {
+ content: "\f0cb";
+}
+.fa-strikethrough:before {
+ content: "\f0cc";
+}
+.fa-underline:before {
+ content: "\f0cd";
+}
+.fa-table:before {
+ content: "\f0ce";
+}
+.fa-magic:before {
+ content: "\f0d0";
+}
+.fa-truck:before {
+ content: "\f0d1";
+}
+.fa-pinterest:before {
+ content: "\f0d2";
+}
+.fa-pinterest-square:before {
+ content: "\f0d3";
+}
+.fa-google-plus-square:before {
+ content: "\f0d4";
+}
+.fa-google-plus:before {
+ content: "\f0d5";
+}
+.fa-money:before {
+ content: "\f0d6";
+}
+.fa-caret-down:before {
+ content: "\f0d7";
+}
+.fa-caret-up:before {
+ content: "\f0d8";
+}
+.fa-caret-left:before {
+ content: "\f0d9";
+}
+.fa-caret-right:before {
+ content: "\f0da";
+}
+.fa-columns:before {
+ content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+ content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+ content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+ content: "\f0de";
+}
+.fa-envelope:before {
+ content: "\f0e0";
+}
+.fa-linkedin:before {
+ content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+ content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+ content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+ content: "\f0e4";
+}
+.fa-comment-o:before {
+ content: "\f0e5";
+}
+.fa-comments-o:before {
+ content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+ content: "\f0e7";
+}
+.fa-sitemap:before {
+ content: "\f0e8";
+}
+.fa-umbrella:before {
+ content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+ content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+ content: "\f0eb";
+}
+.fa-exchange:before {
+ content: "\f0ec";
+}
+.fa-cloud-download:before {
+ content: "\f0ed";
+}
+.fa-cloud-upload:before {
+ content: "\f0ee";
+}
+.fa-user-md:before {
+ content: "\f0f0";
+}
+.fa-stethoscope:before {
+ content: "\f0f1";
+}
+.fa-suitcase:before {
+ content: "\f0f2";
+}
+.fa-bell-o:before {
+ content: "\f0a2";
+}
+.fa-coffee:before {
+ content: "\f0f4";
+}
+.fa-cutlery:before {
+ content: "\f0f5";
+}
+.fa-file-text-o:before {
+ content: "\f0f6";
+}
+.fa-building-o:before {
+ content: "\f0f7";
+}
+.fa-hospital-o:before {
+ content: "\f0f8";
+}
+.fa-ambulance:before {
+ content: "\f0f9";
+}
+.fa-medkit:before {
+ content: "\f0fa";
+}
+.fa-fighter-jet:before {
+ content: "\f0fb";
+}
+.fa-beer:before {
+ content: "\f0fc";
+}
+.fa-h-square:before {
+ content: "\f0fd";
+}
+.fa-plus-square:before {
+ content: "\f0fe";
+}
+.fa-angle-double-left:before {
+ content: "\f100";
+}
+.fa-angle-double-right:before {
+ content: "\f101";
+}
+.fa-angle-double-up:before {
+ content: "\f102";
+}
+.fa-angle-double-down:before {
+ content: "\f103";
+}
+.fa-angle-left:before {
+ content: "\f104";
+}
+.fa-angle-right:before {
+ content: "\f105";
+}
+.fa-angle-up:before {
+ content: "\f106";
+}
+.fa-angle-down:before {
+ content: "\f107";
+}
+.fa-desktop:before {
+ content: "\f108";
+}
+.fa-laptop:before {
+ content: "\f109";
+}
+.fa-tablet:before {
+ content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+ content: "\f10b";
+}
+.fa-circle-o:before {
+ content: "\f10c";
+}
+.fa-quote-left:before {
+ content: "\f10d";
+}
+.fa-quote-right:before {
+ content: "\f10e";
+}
+.fa-spinner:before {
+ content: "\f110";
+}
+.fa-circle:before {
+ content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+ content: "\f112";
+}
+.fa-github-alt:before {
+ content: "\f113";
+}
+.fa-folder-o:before {
+ content: "\f114";
+}
+.fa-folder-open-o:before {
+ content: "\f115";
+}
+.fa-smile-o:before {
+ content: "\f118";
+}
+.fa-frown-o:before {
+ content: "\f119";
+}
+.fa-meh-o:before {
+ content: "\f11a";
+}
+.fa-gamepad:before {
+ content: "\f11b";
+}
+.fa-keyboard-o:before {
+ content: "\f11c";
+}
+.fa-flag-o:before {
+ content: "\f11d";
+}
+.fa-flag-checkered:before {
+ content: "\f11e";
+}
+.fa-terminal:before {
+ content: "\f120";
+}
+.fa-code:before {
+ content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+ content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+ content: "\f123";
+}
+.fa-location-arrow:before {
+ content: "\f124";
+}
+.fa-crop:before {
+ content: "\f125";
+}
+.fa-code-fork:before {
+ content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+ content: "\f127";
+}
+.fa-question:before {
+ content: "\f128";
+}
+.fa-info:before {
+ content: "\f129";
+}
+.fa-exclamation:before {
+ content: "\f12a";
+}
+.fa-superscript:before {
+ content: "\f12b";
+}
+.fa-subscript:before {
+ content: "\f12c";
+}
+.fa-eraser:before {
+ content: "\f12d";
+}
+.fa-puzzle-piece:before {
+ content: "\f12e";
+}
+.fa-microphone:before {
+ content: "\f130";
+}
+.fa-microphone-slash:before {
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+ content: "\f284";
+}
+.fa-modx:before {
+ content: "\f285";
+}
+.fa-fort-awesome:before {
+ content: "\f286";
+}
+.fa-usb:before {
+ content: "\f287";
+}
+.fa-product-hunt:before {
+ content: "\f288";
+}
+.fa-mixcloud:before {
+ content: "\f289";
+}
+.fa-scribd:before {
+ content: "\f28a";
+}
+.fa-pause-circle:before {
+ content: "\f28b";
+}
+.fa-pause-circle-o:before {
+ content: "\f28c";
+}
+.fa-stop-circle:before {
+ content: "\f28d";
+}
+.fa-stop-circle-o:before {
+ content: "\f28e";
+}
+.fa-shopping-bag:before {
+ content: "\f290";
+}
+.fa-shopping-basket:before {
+ content: "\f291";
+}
+.fa-hashtag:before {
+ content: "\f292";
+}
+.fa-bluetooth:before {
+ content: "\f293";
+}
+.fa-bluetooth-b:before {
+ content: "\f294";
+}
+.fa-percent:before {
+ content: "\f295";
+}
+.fa-gitlab:before {
+ content: "\f296";
+}
+.fa-wpbeginner:before {
+ content: "\f297";
+}
+.fa-wpforms:before {
+ content: "\f298";
+}
+.fa-envira:before {
+ content: "\f299";
+}
+.fa-universal-access:before {
+ content: "\f29a";
+}
+.fa-wheelchair-alt:before {
+ content: "\f29b";
+}
+.fa-question-circle-o:before {
+ content: "\f29c";
+}
+.fa-blind:before {
+ content: "\f29d";
+}
+.fa-audio-description:before {
+ content: "\f29e";
+}
+.fa-volume-control-phone:before {
+ content: "\f2a0";
+}
+.fa-braille:before {
+ content: "\f2a1";
+}
+.fa-assistive-listening-systems:before {
+ content: "\f2a2";
+}
+.fa-asl-interpreting:before,
+.fa-american-sign-language-interpreting:before {
+ content: "\f2a3";
+}
+.fa-deafness:before,
+.fa-hard-of-hearing:before,
+.fa-deaf:before {
+ content: "\f2a4";
+}
+.fa-glide:before {
+ content: "\f2a5";
+}
+.fa-glide-g:before {
+ content: "\f2a6";
+}
+.fa-signing:before,
+.fa-sign-language:before {
+ content: "\f2a7";
+}
+.fa-low-vision:before {
+ content: "\f2a8";
+}
+.fa-viadeo:before {
+ content: "\f2a9";
+}
+.fa-viadeo-square:before {
+ content: "\f2aa";
+}
+.fa-snapchat:before {
+ content: "\f2ab";
+}
+.fa-snapchat-ghost:before {
+ content: "\f2ac";
+}
+.fa-snapchat-square:before {
+ content: "\f2ad";
+}
+.fa-pied-piper:before {
+ content: "\f2ae";
+}
+.fa-first-order:before {
+ content: "\f2b0";
+}
+.fa-yoast:before {
+ content: "\f2b1";
+}
+.fa-themeisle:before {
+ content: "\f2b2";
+}
+.fa-google-plus-circle:before,
+.fa-google-plus-official:before {
+ content: "\f2b3";
+}
+.fa-fa:before,
+.fa-font-awesome:before {
+ content: "\f2b4";
+}
+.fa-handshake-o:before {
+ content: "\f2b5";
+}
+.fa-envelope-open:before {
+ content: "\f2b6";
+}
+.fa-envelope-open-o:before {
+ content: "\f2b7";
+}
+.fa-linode:before {
+ content: "\f2b8";
+}
+.fa-address-book:before {
+ content: "\f2b9";
+}
+.fa-address-book-o:before {
+ content: "\f2ba";
+}
+.fa-vcard:before,
+.fa-address-card:before {
+ content: "\f2bb";
+}
+.fa-vcard-o:before,
+.fa-address-card-o:before {
+ content: "\f2bc";
+}
+.fa-user-circle:before {
+ content: "\f2bd";
+}
+.fa-user-circle-o:before {
+ content: "\f2be";
+}
+.fa-user-o:before {
+ content: "\f2c0";
+}
+.fa-id-badge:before {
+ content: "\f2c1";
+}
+.fa-drivers-license:before,
+.fa-id-card:before {
+ content: "\f2c2";
+}
+.fa-drivers-license-o:before,
+.fa-id-card-o:before {
+ content: "\f2c3";
+}
+.fa-quora:before {
+ content: "\f2c4";
+}
+.fa-free-code-camp:before {
+ content: "\f2c5";
+}
+.fa-telegram:before {
+ content: "\f2c6";
+}
+.fa-thermometer-4:before,
+.fa-thermometer:before,
+.fa-thermometer-full:before {
+ content: "\f2c7";
+}
+.fa-thermometer-3:before,
+.fa-thermometer-three-quarters:before {
+ content: "\f2c8";
+}
+.fa-thermometer-2:before,
+.fa-thermometer-half:before {
+ content: "\f2c9";
+}
+.fa-thermometer-1:before,
+.fa-thermometer-quarter:before {
+ content: "\f2ca";
+}
+.fa-thermometer-0:before,
+.fa-thermometer-empty:before {
+ content: "\f2cb";
+}
+.fa-shower:before {
+ content: "\f2cc";
+}
+.fa-bathtub:before,
+.fa-s15:before,
+.fa-bath:before {
+ content: "\f2cd";
+}
+.fa-podcast:before {
+ content: "\f2ce";
+}
+.fa-window-maximize:before {
+ content: "\f2d0";
+}
+.fa-window-minimize:before {
+ content: "\f2d1";
+}
+.fa-window-restore:before {
+ content: "\f2d2";
+}
+.fa-times-rectangle:before,
+.fa-window-close:before {
+ content: "\f2d3";
+}
+.fa-times-rectangle-o:before,
+.fa-window-close-o:before {
+ content: "\f2d4";
+}
+.fa-bandcamp:before {
+ content: "\f2d5";
+}
+.fa-grav:before {
+ content: "\f2d6";
+}
+.fa-etsy:before {
+ content: "\f2d7";
+}
+.fa-imdb:before {
+ content: "\f2d8";
+}
+.fa-ravelry:before {
+ content: "\f2d9";
+}
+.fa-eercast:before {
+ content: "\f2da";
+}
+.fa-microchip:before {
+ content: "\f2db";
+}
+.fa-snowflake-o:before {
+ content: "\f2dc";
+}
+.fa-superpowers:before {
+ content: "\f2dd";
+}
+.fa-wpexplorer:before {
+ content: "\f2de";
+}
+.fa-meetup:before {
+ content: "\f2e0";
+}
+.sr-only {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ padding: 0;
+ margin: -1px;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+code {
+ color: #000;
+}
+pre {
+ font-size: inherit;
+ line-height: inherit;
+}
+label {
+ font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+.corner-all {
+ border-radius: 2px;
+}
+.no-padding {
+ padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer. It allows the usage of flexible box
+model layouts accross multiple browsers, including older browsers. The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below). Browsers that are known to implement this
+new spec completely include:
+
+ Firefox 28.0+
+ Chrome 29.0+
+ Internet Explorer 11+
+ Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+.hbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.vbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+.vbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+ /* Old browsers */
+ -webkit-box-direction: reverse;
+ -moz-box-direction: reverse;
+ box-direction: reverse;
+ /* Modern browsers */
+ flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+ /* Old browsers */
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+ /* Old browsers */
+ -webkit-box-flex: 2;
+ -moz-box-flex: 2;
+ box-flex: 2;
+ /* Modern browsers */
+ flex: 2;
+}
+.box-group1 {
+ /* Deprecated */
+ -webkit-box-flex-group: 1;
+ -moz-box-flex-group: 1;
+ box-flex-group: 1;
+}
+.box-group2 {
+ /* Deprecated */
+ -webkit-box-flex-group: 2;
+ -moz-box-flex-group: 2;
+ box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+ /* Old browsers */
+ -webkit-box-pack: start;
+ -moz-box-pack: start;
+ box-pack: start;
+ /* Modern browsers */
+ justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+ /* Old browsers */
+ -webkit-box-pack: center;
+ -moz-box-pack: center;
+ box-pack: center;
+ /* Modern browsers */
+ justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+ /* Old browsers */
+ -webkit-box-pack: baseline;
+ -moz-box-pack: baseline;
+ box-pack: baseline;
+ /* Modern browsers */
+ justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+ /* Old browsers */
+ -webkit-box-pack: stretch;
+ -moz-box-pack: stretch;
+ box-pack: stretch;
+ /* Modern browsers */
+ justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+ /* Old browsers */
+ -webkit-box-align: start;
+ -moz-box-align: start;
+ box-align: start;
+ /* Modern browsers */
+ align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+ /* Old browsers */
+ -webkit-box-align: end;
+ -moz-box-align: end;
+ box-align: end;
+ /* Modern browsers */
+ align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+ /* Old browsers */
+ -webkit-box-align: center;
+ -moz-box-align: center;
+ box-align: center;
+ /* Modern browsers */
+ align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+ /* Old browsers */
+ -webkit-box-align: baseline;
+ -moz-box-align: baseline;
+ box-align: baseline;
+ /* Modern browsers */
+ align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+ /* Old browsers */
+ -webkit-box-align: stretch;
+ -moz-box-align: stretch;
+ box-align: stretch;
+ /* Modern browsers */
+ align-items: stretch;
+}
+div.error {
+ margin: 2em;
+ text-align: center;
+}
+div.error > h1 {
+ font-size: 500%;
+ line-height: normal;
+}
+div.error > p {
+ font-size: 200%;
+ line-height: normal;
+}
+div.traceback-wrapper {
+ text-align: left;
+ max-width: 800px;
+ margin: auto;
+}
+div.traceback-wrapper pre.traceback {
+ max-height: 600px;
+ overflow: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+ background-color: #fff;
+ /* This makes sure that the body covers the entire window and needs to
+ be in a different element than the display: box in wrapper below */
+ position: absolute;
+ left: 0px;
+ right: 0px;
+ top: 0px;
+ bottom: 0px;
+ overflow: visible;
+}
+body > #header {
+ /* Initially hidden to prevent FLOUC */
+ display: none;
+ background-color: #fff;
+ /* Display over codemirror */
+ position: relative;
+ z-index: 100;
+}
+body > #header #header-container {
+ display: flex;
+ flex-direction: row;
+ justify-content: space-between;
+ padding: 5px;
+ padding-bottom: 5px;
+ padding-top: 5px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+ width: 100%;
+ height: 1px;
+ background: #e7e7e7;
+ margin-bottom: -1px;
+}
+@media print {
+ body > #header {
+ display: none !important;
+ }
+}
+#header-spacer {
+ width: 100%;
+ visibility: hidden;
+}
+@media print {
+ #header-spacer {
+ display: none;
+ }
+}
+#ipython_notebook {
+ padding-left: 0px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+[dir="rtl"] #ipython_notebook {
+ margin-right: 10px;
+ margin-left: 0;
+}
+[dir="rtl"] #ipython_notebook.pull-left {
+ float: right !important;
+ float: right;
+}
+.flex-spacer {
+ flex: 1;
+}
+#noscript {
+ width: auto;
+ padding-top: 16px;
+ padding-bottom: 16px;
+ text-align: center;
+ font-size: 22px;
+ color: red;
+ font-weight: bold;
+}
+#ipython_notebook img {
+ height: 28px;
+}
+#site {
+ width: 100%;
+ display: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ overflow: auto;
+}
+@media print {
+ #site {
+ height: auto !important;
+ }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+ padding: 0.2em 0.8em;
+ font-size: 77%;
+}
+input.ui-button {
+ padding: 0.3em 0.9em;
+}
+span#kernel_logo_widget {
+ margin: 0 10px;
+}
+span#login_widget {
+ float: right;
+}
+[dir="rtl"] span#login_widget {
+ float: left;
+}
+span#login_widget > .button,
+#logout {
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+ color: #fff;
+ background-color: #333;
+}
+.nav-header {
+ text-transform: none;
+}
+#header > span {
+ margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+ max-height: calc(100vh - 200px);
+ overflow: auto;
+ flex: 1;
+}
+.modal-header {
+ cursor: move;
+}
+@media (min-width: 768px) {
+ .modal .modal-dialog {
+ width: 700px;
+ }
+}
+@media (min-width: 768px) {
+ select.form-control {
+ margin-left: 12px;
+ margin-right: 12px;
+ }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+ display: inline-block;
+ margin-bottom: -4px;
+}
+[dir="rtl"] .center-nav form.pull-left {
+ float: right !important;
+ float: right;
+}
+[dir="rtl"] .center-nav .navbar-text {
+ float: right;
+}
+[dir="rtl"] .navbar-inner {
+ text-align: right;
+}
+[dir="rtl"] div.text-left {
+ text-align: right;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+ background-color: none;
+ display: inline;
+}
+.alternate_upload.form {
+ padding: 0;
+ margin: 0;
+}
+.alternate_upload input.fileinput {
+ position: absolute;
+ display: block;
+ width: 100%;
+ height: 100%;
+ overflow: hidden;
+ cursor: pointer;
+ opacity: 0;
+ z-index: 2;
+}
+.alternate_upload .btn-xs > input.fileinput {
+ margin: -1px -5px;
+}
+.alternate_upload .btn-upload {
+ position: relative;
+ height: 22px;
+}
+::-webkit-file-upload-button {
+ cursor: pointer;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+ margin-bottom: 4px;
+}
+ul#tabs a {
+ padding-top: 6px;
+ padding-bottom: 4px;
+}
+[dir="rtl"] ul#tabs.nav-tabs > li {
+ float: right;
+}
+[dir="rtl"] ul#tabs.nav.nav-tabs {
+ padding-right: 0;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+ text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+ font-size: 16px;
+ margin-right: 4px;
+}
+ul.breadcrumb span {
+ color: #5e5e5e;
+}
+.list_toolbar {
+ padding: 4px 0 4px 0;
+ vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+ padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
+ float: left !important;
+ float: left;
+}
+[dir="rtl"] .list_toolbar .col-sm-4,
+[dir="rtl"] .list_toolbar .col-sm-8 {
+ float: right;
+}
+.dynamic-buttons {
+ padding-top: 3px;
+ display: inline-block;
+}
+.list_toolbar [class*="span"] {
+ min-height: 24px;
+}
+.list_header {
+ font-weight: bold;
+ background-color: #EEE;
+}
+.list_placeholder {
+ font-weight: bold;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+}
+.list_container {
+ margin-top: 4px;
+ margin-bottom: 20px;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+}
+.list_container > div {
+ border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+ background-color: red;
+}
+.list_container > div:last-child {
+ border: none;
+}
+.list_item:hover .list_item {
+ background-color: #ddd;
+}
+.list_item a {
+ text-decoration: none;
+}
+.list_item:hover {
+ background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+ margin-right: 7px;
+ margin-left: 14px;
+ vertical-align: text-bottom;
+ line-height: 22px;
+ position: relative;
+ top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+ margin-left: -1px;
+ vertical-align: baseline;
+ line-height: 22px;
+}
+[dir="rtl"] .list_item > div input {
+ margin-right: 0;
+}
+.new-file input[type=checkbox] {
+ visibility: hidden;
+}
+.item_name {
+ line-height: 22px;
+ height: 24px;
+}
+.item_icon {
+ font-size: 14px;
+ color: #5e5e5e;
+ margin-right: 7px;
+ margin-left: 7px;
+ line-height: 22px;
+ vertical-align: baseline;
+}
+.item_modified {
+ margin-right: 7px;
+ margin-left: 7px;
+}
+[dir="rtl"] .item_modified.pull-right {
+ float: left !important;
+ float: left;
+}
+.item_buttons {
+ line-height: 1em;
+ margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+ float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+ margin-left: 5px;
+}
+.item_buttons .btn {
+ min-width: 13ex;
+}
+.item_buttons .running-indicator {
+ padding-top: 4px;
+ color: #5cb85c;
+}
+.item_buttons .kernel-name {
+ padding-top: 4px;
+ color: #5bc0de;
+ margin-right: 7px;
+ float: left;
+}
+[dir="rtl"] .item_buttons.pull-right {
+ float: left !important;
+ float: left;
+}
+[dir="rtl"] .item_buttons .kernel-name {
+ margin-left: 7px;
+ float: right;
+}
+.toolbar_info {
+ height: 24px;
+ line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+ padding-top: 3px;
+ padding-bottom: 3px;
+ height: 22px;
+ line-height: 14px;
+ margin: 0px;
+}
+.highlight_text {
+ color: blue;
+}
+#project_name {
+ display: inline-block;
+ padding-left: 7px;
+ margin-left: -2px;
+}
+#project_name > .breadcrumb {
+ padding: 0px;
+ margin-bottom: 0px;
+ background-color: transparent;
+ font-weight: bold;
+}
+.sort_button {
+ display: inline-block;
+ padding-left: 7px;
+}
+[dir="rtl"] .sort_button.pull-right {
+ float: left !important;
+ float: left;
+}
+#tree-selector {
+ padding-right: 0px;
+}
+#button-select-all {
+ min-width: 50px;
+}
+[dir="rtl"] #button-select-all.btn {
+ float: right ;
+}
+#select-all {
+ margin-left: 7px;
+ margin-right: 2px;
+ margin-top: 2px;
+ height: 16px;
+}
+[dir="rtl"] #select-all.pull-left {
+ float: right !important;
+ float: right;
+}
+.menu_icon {
+ margin-right: 2px;
+}
+.tab-content .row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.folder_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f114";
+}
+.folder_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.folder_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.folder_icon:before.pull-left {
+ margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+}
+.notebook_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.notebook_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.running_notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+ color: #5cb85c;
+}
+.running_notebook_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.running_notebook_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.running_notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.file_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f016";
+ position: relative;
+ top: -2px;
+}
+.file_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.file_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.file_icon:before.pull-left {
+ margin-right: .3em;
+}
+.file_icon:before.pull-right {
+ margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+ padding-top: 0px;
+ margin-right: -1px;
+}
+ul#new-menu {
+ left: auto;
+ right: 0;
+}
+#new-menu .dropdown-header {
+ font-size: 10px;
+ border-bottom: 1px solid #e5e5e5;
+ padding: 0 0 3px;
+ margin: -3px 20px 0;
+}
+.kernel-menu-icon {
+ padding-right: 12px;
+ width: 24px;
+ content: "\f096";
+}
+.kernel-menu-icon:before {
+ content: "\f096";
+}
+.kernel-menu-icon-current:before {
+ content: "\f00c";
+}
+#tab_content {
+ padding-top: 20px;
+}
+#running .panel-group .panel {
+ margin-top: 3px;
+ margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+ background-color: #EEE;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+ text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+ padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+ margin-top: 0px;
+ margin-bottom: 0px;
+ border: 0px;
+ border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+ border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+ border-bottom: 0px;
+}
+.delete-button {
+ display: none;
+}
+.duplicate-button {
+ display: none;
+}
+.rename-button {
+ display: none;
+}
+.move-button {
+ display: none;
+}
+.download-button {
+ display: none;
+}
+.shutdown-button {
+ display: none;
+}
+.dynamic-instructions {
+ display: inline-block;
+ padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+ padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+ content: "\f00c";
+}
+#mode-menu {
+ overflow: auto;
+ max-height: 20em;
+}
+.edit_app #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+ /* Use a negative 1 bottom margin, so the border overlaps the border of the
+ header */
+ margin-bottom: -1px;
+}
+.dirty-indicator {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-dirty {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-dirty.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-dirty.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-dirty.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-clean.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f00c";
+}
+.dirty-indicator-clean:before.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean:before.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean:before.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+ margin-left: .3em;
+}
+#filename {
+ font-size: 16pt;
+ display: table;
+ padding: 0px 5px;
+}
+#current-mode {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+#texteditor-backdrop {
+ padding-top: 20px;
+ padding-bottom: 20px;
+}
+@media not print {
+ #texteditor-backdrop {
+ background-color: #EEE;
+ }
+}
+@media print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #fff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #fff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container {
+ padding: 0px;
+ background-color: #fff;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+.CodeMirror-dialog {
+ background-color: #fff;
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI escape sequences */
+/* The color values are a mix of
+ http://www.xcolors.net/dl/baskerville-ivorylight and
+ http://www.xcolors.net/dl/euphrasia */
+.ansi-black-fg {
+ color: #3E424D;
+}
+.ansi-black-bg {
+ background-color: #3E424D;
+}
+.ansi-black-intense-fg {
+ color: #282C36;
+}
+.ansi-black-intense-bg {
+ background-color: #282C36;
+}
+.ansi-red-fg {
+ color: #E75C58;
+}
+.ansi-red-bg {
+ background-color: #E75C58;
+}
+.ansi-red-intense-fg {
+ color: #B22B31;
+}
+.ansi-red-intense-bg {
+ background-color: #B22B31;
+}
+.ansi-green-fg {
+ color: #00A250;
+}
+.ansi-green-bg {
+ background-color: #00A250;
+}
+.ansi-green-intense-fg {
+ color: #007427;
+}
+.ansi-green-intense-bg {
+ background-color: #007427;
+}
+.ansi-yellow-fg {
+ color: #DDB62B;
+}
+.ansi-yellow-bg {
+ background-color: #DDB62B;
+}
+.ansi-yellow-intense-fg {
+ color: #B27D12;
+}
+.ansi-yellow-intense-bg {
+ background-color: #B27D12;
+}
+.ansi-blue-fg {
+ color: #208FFB;
+}
+.ansi-blue-bg {
+ background-color: #208FFB;
+}
+.ansi-blue-intense-fg {
+ color: #0065CA;
+}
+.ansi-blue-intense-bg {
+ background-color: #0065CA;
+}
+.ansi-magenta-fg {
+ color: #D160C4;
+}
+.ansi-magenta-bg {
+ background-color: #D160C4;
+}
+.ansi-magenta-intense-fg {
+ color: #A03196;
+}
+.ansi-magenta-intense-bg {
+ background-color: #A03196;
+}
+.ansi-cyan-fg {
+ color: #60C6C8;
+}
+.ansi-cyan-bg {
+ background-color: #60C6C8;
+}
+.ansi-cyan-intense-fg {
+ color: #258F8F;
+}
+.ansi-cyan-intense-bg {
+ background-color: #258F8F;
+}
+.ansi-white-fg {
+ color: #C5C1B4;
+}
+.ansi-white-bg {
+ background-color: #C5C1B4;
+}
+.ansi-white-intense-fg {
+ color: #A1A6B2;
+}
+.ansi-white-intense-bg {
+ background-color: #A1A6B2;
+}
+.ansi-default-inverse-fg {
+ color: #FFFFFF;
+}
+.ansi-default-inverse-bg {
+ background-color: #000000;
+}
+.ansi-bold {
+ font-weight: bold;
+}
+.ansi-underline {
+ text-decoration: underline;
+}
+/* The following styles are deprecated an will be removed in a future version */
+.ansibold {
+ font-weight: bold;
+}
+.ansi-inverse {
+ outline: 0.5px dotted;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+ color: black;
+}
+.ansired {
+ color: darkred;
+}
+.ansigreen {
+ color: darkgreen;
+}
+.ansiyellow {
+ color: #c4a000;
+}
+.ansiblue {
+ color: darkblue;
+}
+.ansipurple {
+ color: darkviolet;
+}
+.ansicyan {
+ color: steelblue;
+}
+.ansigray {
+ color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+ background-color: black;
+}
+.ansibgred {
+ background-color: red;
+}
+.ansibggreen {
+ background-color: green;
+}
+.ansibgyellow {
+ background-color: yellow;
+}
+.ansibgblue {
+ background-color: blue;
+}
+.ansibgpurple {
+ background-color: magenta;
+}
+.ansibgcyan {
+ background-color: cyan;
+}
+.ansibggray {
+ background-color: gray;
+}
+div.cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ border-radius: 2px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ border-width: 1px;
+ border-style: solid;
+ border-color: transparent;
+ width: 100%;
+ padding: 5px;
+ /* This acts as a spacer between cells, that is outside the border */
+ margin: 0px;
+ outline: none;
+ position: relative;
+ overflow: visible;
+}
+div.cell:before {
+ position: absolute;
+ display: block;
+ top: -1px;
+ left: -1px;
+ width: 5px;
+ height: calc(100% + 2px);
+ content: '';
+ background: transparent;
+}
+div.cell.jupyter-soft-selected {
+ border-left-color: #E3F2FD;
+ border-left-width: 1px;
+ padding-left: 5px;
+ border-right-color: #E3F2FD;
+ border-right-width: 1px;
+ background: #E3F2FD;
+}
+@media print {
+ div.cell.jupyter-soft-selected {
+ border-color: transparent;
+ }
+}
+div.cell.selected,
+div.cell.selected.jupyter-soft-selected {
+ border-color: #ababab;
+}
+div.cell.selected:before,
+div.cell.selected.jupyter-soft-selected:before {
+ position: absolute;
+ display: block;
+ top: -1px;
+ left: -1px;
+ width: 5px;
+ height: calc(100% + 2px);
+ content: '';
+ background: #42A5F5;
+}
+@media print {
+ div.cell.selected,
+ div.cell.selected.jupyter-soft-selected {
+ border-color: transparent;
+ }
+}
+.edit_mode div.cell.selected {
+ border-color: #66BB6A;
+}
+.edit_mode div.cell.selected:before {
+ position: absolute;
+ display: block;
+ top: -1px;
+ left: -1px;
+ width: 5px;
+ height: calc(100% + 2px);
+ content: '';
+ background: #66BB6A;
+}
+@media print {
+ .edit_mode div.cell.selected {
+ border-color: transparent;
+ }
+}
+.prompt {
+ /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+ min-width: 14ex;
+ /* This padding is tuned to match the padding on the CodeMirror editor. */
+ padding: 0.4em;
+ margin: 0px;
+ font-family: monospace;
+ text-align: right;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+ /* Don't highlight prompt number selection */
+ -webkit-touch-callout: none;
+ -webkit-user-select: none;
+ -khtml-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+ /* Use default cursor */
+ cursor: default;
+}
+@media (max-width: 540px) {
+ .prompt {
+ text-align: left;
+ }
+}
+div.inner_cell {
+ min-width: 0;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+ border: 1px solid #cfcfcf;
+ border-radius: 2px;
+ background: #f7f7f7;
+ line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+ is no content in the output_subarea and the prompt. The main purpose of this is
+ to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+ padding-top: 0;
+ padding-bottom: 0;
+}
+div.unrecognized_cell {
+ padding: 5px 5px 5px 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+ border-radius: 2px;
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+ border: 1px solid #cfcfcf;
+ background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+ color: inherit;
+ text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+@media (max-width: 540px) {
+ div.unrecognized_cell > div.prompt {
+ display: none;
+ }
+}
+div.code_cell {
+ /* avoid page breaking on code cells when printing */
+}
+@media print {
+ div.code_cell {
+ page-break-inside: avoid;
+ }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.input {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+ color: #303F9F;
+ border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+ margin: 0.4em;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+ margin: 0px;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height. See
+ * notebookmain.js. Such fonts will have keywords vertically offset with
+ * respect to the rest of the text. The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ * vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+ line-height: 1.21429em;
+ /* Changed from 1em to our global default */
+ font-size: 14px;
+ height: auto;
+ /* Changed to auto to autogrow */
+ background: none;
+ /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+ /* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+ /* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+ overflow-y: hidden;
+ overflow-x: auto;
+}
+.CodeMirror-lines {
+ /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+ /* we have set a different line-height and want this to scale with that. */
+ /* Note that this should set vertical padding only, since CodeMirror assumes
+ that horizontal padding will be set on CodeMirror pre */
+ padding: 0.4em 0;
+}
+.CodeMirror-linenumber {
+ padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+ /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
+ use .CodeMirror-lines for vertical */
+ padding: 0 0.4em;
+ border: 0;
+ border-radius: 0;
+}
+.CodeMirror-cursor {
+ border-left: 1.4px solid black;
+}
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+ .CodeMirror-cursor {
+ border-left: 2px solid black;
+ }
+}
+@media screen and (min-width: 4320px) {
+ .CodeMirror-cursor {
+ border-left: 4px solid black;
+ }
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+ color: #000;
+}
+.highlight-variable {
+ color: #000;
+}
+.highlight-variable-2 {
+ color: #1a1a1a;
+}
+.highlight-variable-3 {
+ color: #333333;
+}
+.highlight-string {
+ color: #BA2121;
+}
+.highlight-comment {
+ color: #408080;
+ font-style: italic;
+}
+.highlight-number {
+ color: #080;
+}
+.highlight-atom {
+ color: #88F;
+}
+.highlight-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.highlight-builtin {
+ color: #008000;
+}
+.highlight-error {
+ color: #f00;
+}
+.highlight-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.highlight-meta {
+ color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+ color: #00f;
+}
+.highlight-string-2 {
+ color: #f50;
+}
+.highlight-qualifier {
+ color: #555;
+}
+.highlight-bracket {
+ color: #997;
+}
+.highlight-tag {
+ color: #170;
+}
+.highlight-attribute {
+ color: #00c;
+}
+.highlight-header {
+ color: blue;
+}
+.highlight-quote {
+ color: #090;
+}
+.highlight-link {
+ color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+ color: #88F;
+}
+.cm-s-ipython span.cm-number {
+ color: #080;
+}
+.cm-s-ipython span.cm-def {
+ color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+ color: #000;
+}
+.cm-s-ipython span.cm-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+ color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+ color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+ color: #408080;
+ font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+ color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+ color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+ color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+ color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+ color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+ color: #997;
+}
+.cm-s-ipython span.cm-tag {
+ color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+ color: #00c;
+}
+.cm-s-ipython span.cm-header {
+ color: blue;
+}
+.cm-s-ipython span.cm-quote {
+ color: #090;
+}
+.cm-s-ipython span.cm-link {
+ color: #00c;
+}
+.cm-s-ipython span.cm-error {
+ color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+ background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+ background-position: right;
+ background-repeat: no-repeat;
+}
+div.output_wrapper {
+ /* this position must be relative to enable descendents to be absolute within it */
+ position: relative;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+ /* ideally, this would be max-height, but FF barfs all over that */
+ height: 24em;
+ /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+ width: 100%;
+ overflow: auto;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+ margin: 0px;
+ padding: 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+div.out_prompt_overlay {
+ height: 100%;
+ padding: 0px 0.4em;
+ position: absolute;
+ border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+ /* use inner shadow to get border that is computed the same on WebKit/FF */
+ -webkit-box-shadow: inset 0 0 1px #000;
+ box-shadow: inset 0 0 1px #000;
+ background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+ color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+ padding: 0px;
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+div.output_area .MathJax_Display {
+ text-align: left !important;
+}
+div.output_area .rendered_html table {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area .rendered_html img {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+ max-width: 100%;
+ height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+ max-width: none;
+}
+div.output_area .mglyph > img {
+ max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+ as that of bootstrap */
+.output {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.output_area {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ }
+}
+div.output_area pre {
+ margin: 0;
+ padding: 1px 0 1px 0;
+ border: 0;
+ vertical-align: baseline;
+ color: black;
+ background-color: transparent;
+ border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+ the prompt div. */
+div.output_subarea {
+ overflow-x: auto;
+ padding: 0.4em;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+ max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+ overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+ output types */
+/* all text output has this class: */
+div.output_text {
+ text-align: left;
+ color: #000;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+ background: #fdd;
+ /* very light red background for stderr */
+}
+div.output_latex {
+ text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+ padding: 0;
+}
+.js-error {
+ color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+ line-height: 1.21429em;
+ padding-top: 5px;
+}
+pre.raw_input_prompt {
+ /* nothing needed here. */
+}
+input.raw_input {
+ font-family: monospace;
+ font-size: inherit;
+ color: inherit;
+ width: auto;
+ /* make sure input baseline aligns with prompt */
+ vertical-align: baseline;
+ /* padding + margin = 0.5em between prompt and cursor */
+ padding: 0em 0.25em;
+ margin: 0em 0.25em;
+}
+input.raw_input:focus {
+ box-shadow: none;
+}
+p.p-space {
+ margin-bottom: 10px;
+}
+div.output_unrecognized {
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+}
+div.output_unrecognized a {
+ color: inherit;
+ text-decoration: none;
+}
+div.output_unrecognized a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+.rendered_html {
+ color: #000;
+ /* any extras will just be numbers: */
+}
+.rendered_html em {
+ font-style: italic;
+}
+.rendered_html strong {
+ font-weight: bold;
+}
+.rendered_html u {
+ text-decoration: underline;
+}
+.rendered_html :link {
+ text-decoration: underline;
+}
+.rendered_html :visited {
+ text-decoration: underline;
+}
+.rendered_html h1 {
+ font-size: 185.7%;
+ margin: 1.08em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h2 {
+ font-size: 157.1%;
+ margin: 1.27em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h3 {
+ font-size: 128.6%;
+ margin: 1.55em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h4 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h5 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h6 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h1:first-child {
+ margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+ margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+ margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+ margin-top: 1em;
+}
+.rendered_html h5:first-child {
+ margin-top: 1em;
+}
+.rendered_html h6:first-child {
+ margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+ padding-left: 2em;
+}
+.rendered_html ul {
+ list-style: disc;
+}
+.rendered_html ul ul {
+ list-style: square;
+ margin-top: 0;
+}
+.rendered_html ul ul ul {
+ list-style: circle;
+}
+.rendered_html ol {
+ list-style: decimal;
+}
+.rendered_html ol ol {
+ list-style: upper-alpha;
+ margin-top: 0;
+}
+.rendered_html ol ol ol {
+ list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+ list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+ list-style: decimal;
+}
+.rendered_html * + ul {
+ margin-top: 1em;
+}
+.rendered_html * + ol {
+ margin-top: 1em;
+}
+.rendered_html hr {
+ color: black;
+ background-color: black;
+}
+.rendered_html pre {
+ margin: 1em 2em;
+ padding: 0px;
+ background-color: #fff;
+}
+.rendered_html code {
+ background-color: #eff0f1;
+}
+.rendered_html p code {
+ padding: 1px 5px;
+}
+.rendered_html pre code {
+ background-color: #fff;
+}
+.rendered_html pre,
+.rendered_html code {
+ border: 0;
+ color: #000;
+ font-size: 100%;
+}
+.rendered_html blockquote {
+ margin: 1em 2em;
+}
+.rendered_html table {
+ margin-left: auto;
+ margin-right: auto;
+ border: none;
+ border-collapse: collapse;
+ border-spacing: 0;
+ color: black;
+ font-size: 12px;
+ table-layout: fixed;
+}
+.rendered_html thead {
+ border-bottom: 1px solid black;
+ vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+ text-align: right;
+ vertical-align: middle;
+ padding: 0.5em 0.5em;
+ line-height: normal;
+ white-space: normal;
+ max-width: none;
+ border: none;
+}
+.rendered_html th {
+ font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+ background: #f5f5f5;
+}
+.rendered_html tbody tr:hover {
+ background: rgba(66, 165, 245, 0.2);
+}
+.rendered_html * + table {
+ margin-top: 1em;
+}
+.rendered_html p {
+ text-align: left;
+}
+.rendered_html * + p {
+ margin-top: 1em;
+}
+.rendered_html img {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.rendered_html * + img {
+ margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+ max-width: 100%;
+ height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+ max-width: none;
+}
+.rendered_html .alert {
+ margin-bottom: initial;
+}
+.rendered_html * + .alert {
+ margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+ text-align: right;
+}
+div.text_cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.text_cell > div.prompt {
+ display: none;
+ }
+}
+div.text_cell_render {
+ /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+ outline: none;
+ resize: none;
+ width: inherit;
+ border-style: none;
+ padding: 0.5em 0.5em 0.5em 0.4em;
+ color: #000;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+ text-decoration: none;
+ padding: 0px 20px;
+ visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+ visibility: visible;
+}
+.text_cell.rendered .input_area {
+ display: none;
+}
+.text_cell.rendered .rendered_html {
+ overflow-x: auto;
+ overflow-y: hidden;
+}
+.text_cell.rendered .rendered_html tr,
+.text_cell.rendered .rendered_html th,
+.text_cell.rendered .rendered_html td {
+ max-width: none;
+}
+.text_cell.unrendered .text_cell_render {
+ display: none;
+}
+.text_cell .dropzone .input_area {
+ border: 2px dashed #bababa;
+ margin: -1px;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+ font-weight: bold;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+ font-size: 185.7%;
+}
+.cm-header-2 {
+ font-size: 157.1%;
+}
+.cm-header-3 {
+ font-size: 128.6%;
+}
+.cm-header-4 {
+ font-size: 110%;
+}
+.cm-header-5 {
+ font-size: 100%;
+ font-style: italic;
+}
+.cm-header-6 {
+ font-size: 100%;
+ font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+ .notebook_app {
+ padding-left: 0px;
+ padding-right: 0px;
+ }
+}
+#ipython-main-app {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook_panel {
+ margin: 0px;
+ padding: 0px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook {
+ font-size: 14px;
+ line-height: 20px;
+ overflow-y: hidden;
+ overflow-x: auto;
+ width: 100%;
+ /* This spaces the page away from the edge of the notebook area */
+ padding-top: 20px;
+ margin: 0px;
+ outline: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ min-height: 100%;
+}
+@media not print {
+ #notebook-container {
+ padding: 15px;
+ background-color: #fff;
+ min-height: 0;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+@media print {
+ #notebook-container {
+ width: 100%;
+ }
+}
+div.ui-widget-content {
+ border: 1px solid #ababab;
+ outline: none;
+}
+pre.dialog {
+ background-color: #f7f7f7;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ padding: 0.4em;
+ padding-left: 2em;
+}
+p.dialog {
+ padding: 0.2em;
+}
+/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems
+ to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+ white-space: pre-wrap;
+}
+#fonttest {
+ font-family: monospace;
+}
+p {
+ margin-bottom: 0;
+}
+.end_space {
+ min-height: 100px;
+ transition: height .2s ease;
+}
+.notebook_app > #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+ .notebook_app {
+ background-color: #EEE;
+ }
+}
+kbd {
+ border-style: solid;
+ border-width: 1px;
+ box-shadow: none;
+ margin: 2px;
+ padding-left: 2px;
+ padding-right: 2px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+.jupyter-keybindings {
+ padding: 1px;
+ line-height: 24px;
+ border-bottom: 1px solid gray;
+}
+.jupyter-keybindings input {
+ margin: 0;
+ padding: 0;
+ border: none;
+}
+.jupyter-keybindings i {
+ padding: 6px;
+}
+.well code {
+ background-color: #ffffff;
+ border-color: #ababab;
+ border-width: 1px;
+ border-style: solid;
+ padding: 2px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+ border: thin solid #CFCFCF;
+ border-bottom: none;
+ background: #EEE;
+ border-radius: 2px 2px 0px 0px;
+ width: 100%;
+ height: 29px;
+ padding-right: 4px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+ display: -webkit-flex;
+}
+@media print {
+ .celltoolbar {
+ display: none;
+ }
+}
+.ctb_hideshow {
+ display: none;
+ vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+ Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+ display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border-top-right-radius: 0px;
+ border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+ font-size: 87%;
+ padding-top: 3px;
+}
+.celltoolbar select {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #fff;
+ background-image: none;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+ width: inherit;
+ font-size: inherit;
+ height: 22px;
+ padding: 0px;
+ display: inline-block;
+}
+.celltoolbar select:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+ color: #999;
+ opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+ color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+ color: #999;
+}
+.celltoolbar select::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+ cursor: not-allowed;
+}
+textarea.celltoolbar select {
+ height: auto;
+}
+select.celltoolbar select {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+ height: auto;
+}
+.celltoolbar label {
+ margin-left: 5px;
+ margin-right: 5px;
+}
+.tags_button_container {
+ width: 100%;
+ display: flex;
+}
+.tag-container {
+ display: flex;
+ flex-direction: row;
+ flex-grow: 1;
+ overflow: hidden;
+ position: relative;
+}
+.tag-container > * {
+ margin: 0 4px;
+}
+.remove-tag-btn {
+ margin-left: 4px;
+}
+.tags-input {
+ display: flex;
+}
+.cell-tag:last-child:after {
+ content: "";
+ position: absolute;
+ right: 0;
+ width: 40px;
+ height: 100%;
+ /* Fade to background color of cell toolbar */
+ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
+}
+.tags-input > * {
+ margin-left: 4px;
+}
+.cell-tag,
+.tags-input input,
+.tags-input button {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #fff;
+ background-image: none;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+ box-shadow: none;
+ width: inherit;
+ font-size: inherit;
+ height: 22px;
+ line-height: 22px;
+ padding: 0px 4px;
+ display: inline-block;
+}
+.cell-tag:focus,
+.tags-input input:focus,
+.tags-input button:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.cell-tag::-moz-placeholder,
+.tags-input input::-moz-placeholder,
+.tags-input button::-moz-placeholder {
+ color: #999;
+ opacity: 1;
+}
+.cell-tag:-ms-input-placeholder,
+.tags-input input:-ms-input-placeholder,
+.tags-input button:-ms-input-placeholder {
+ color: #999;
+}
+.cell-tag::-webkit-input-placeholder,
+.tags-input input::-webkit-input-placeholder,
+.tags-input button::-webkit-input-placeholder {
+ color: #999;
+}
+.cell-tag::-ms-expand,
+.tags-input input::-ms-expand,
+.tags-input button::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.cell-tag[disabled],
+.tags-input input[disabled],
+.tags-input button[disabled],
+.cell-tag[readonly],
+.tags-input input[readonly],
+.tags-input button[readonly],
+fieldset[disabled] .cell-tag,
+fieldset[disabled] .tags-input input,
+fieldset[disabled] .tags-input button {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.cell-tag[disabled],
+.tags-input input[disabled],
+.tags-input button[disabled],
+fieldset[disabled] .cell-tag,
+fieldset[disabled] .tags-input input,
+fieldset[disabled] .tags-input button {
+ cursor: not-allowed;
+}
+textarea.cell-tag,
+textarea.tags-input input,
+textarea.tags-input button {
+ height: auto;
+}
+select.cell-tag,
+select.tags-input input,
+select.tags-input button {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.cell-tag,
+textarea.tags-input input,
+textarea.tags-input button,
+select[multiple].cell-tag,
+select[multiple].tags-input input,
+select[multiple].tags-input button {
+ height: auto;
+}
+.cell-tag,
+.tags-input button {
+ padding: 0px 4px;
+}
+.cell-tag {
+ background-color: #fff;
+ white-space: nowrap;
+}
+.tags-input input[type=text]:focus {
+ outline: none;
+ box-shadow: none;
+ border-color: #ccc;
+}
+.completions {
+ position: absolute;
+ z-index: 110;
+ overflow: hidden;
+ border: 1px solid #ababab;
+ border-radius: 2px;
+ -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+ box-shadow: 0px 6px 10px -1px #adadad;
+ line-height: 1;
+}
+.completions select {
+ background: white;
+ outline: none;
+ border: none;
+ padding: 0px;
+ margin: 0px;
+ overflow: auto;
+ font-family: monospace;
+ font-size: 110%;
+ color: #000;
+ width: auto;
+}
+.completions select option.context {
+ color: #286090;
+}
+#kernel_logo_widget .current_kernel_logo {
+ display: none;
+ margin-top: -1px;
+ margin-bottom: -1px;
+ width: 32px;
+ height: 32px;
+}
+[dir="rtl"] #kernel_logo_widget {
+ float: left !important;
+ float: left;
+}
+.modal .modal-body .move-path {
+ display: flex;
+ flex-direction: row;
+ justify-content: space;
+ align-items: center;
+}
+.modal .modal-body .move-path .server-root {
+ padding-right: 20px;
+}
+.modal .modal-body .move-path .path-input {
+ flex: 1;
+}
+#menubar {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ margin-top: 1px;
+}
+#menubar .navbar {
+ border-top: 1px;
+ border-radius: 0px 0px 2px 2px;
+ margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+ float: left;
+ padding-top: 7px;
+ padding-bottom: 7px;
+ border: none;
+}
+#menubar .navbar-collapse {
+ clear: left;
+}
+[dir="rtl"] #menubar .navbar-toggle {
+ float: right;
+}
+[dir="rtl"] #menubar .navbar-collapse {
+ clear: right;
+}
+[dir="rtl"] #menubar .navbar-nav {
+ float: right;
+}
+[dir="rtl"] #menubar .nav {
+ padding-right: 0px;
+}
+[dir="rtl"] #menubar .navbar-nav > li {
+ float: right;
+}
+[dir="rtl"] #menubar .navbar-right {
+ float: left !important;
+}
+[dir="rtl"] ul.dropdown-menu {
+ text-align: right;
+ left: auto;
+}
+[dir="rtl"] ul#new-menu.dropdown-menu {
+ right: auto;
+ left: 0;
+}
+.nav-wrapper {
+ border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+ padding-top: 4px;
+}
+[dir="rtl"] i.menu-icon.pull-right {
+ float: left !important;
+ float: left;
+}
+ul#help_menu li a {
+ overflow: hidden;
+ padding-right: 2.2em;
+}
+ul#help_menu li a i {
+ margin-right: -1.2em;
+}
+[dir="rtl"] ul#help_menu li a {
+ padding-left: 2.2em;
+}
+[dir="rtl"] ul#help_menu li a i {
+ margin-right: 0;
+ margin-left: -1.2em;
+}
+[dir="rtl"] ul#help_menu li a i.pull-right {
+ float: left !important;
+ float: left;
+}
+.dropdown-submenu {
+ position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+ top: 0;
+ left: 100%;
+ margin-top: -6px;
+ margin-left: -1px;
+}
+[dir="rtl"] .dropdown-submenu > .dropdown-menu {
+ right: 100%;
+ margin-right: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+ display: block;
+}
+.dropdown-submenu > a:after {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ display: block;
+ content: "\f0da";
+ float: right;
+ color: #333333;
+ margin-top: 2px;
+ margin-right: -10px;
+}
+.dropdown-submenu > a:after.fa-pull-left {
+ margin-right: .3em;
+}
+.dropdown-submenu > a:after.fa-pull-right {
+ margin-left: .3em;
+}
+.dropdown-submenu > a:after.pull-left {
+ margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+ margin-left: .3em;
+}
+[dir="rtl"] .dropdown-submenu > a:after {
+ float: left;
+ content: "\f0d9";
+ margin-right: 0;
+ margin-left: -10px;
+}
+.dropdown-submenu:hover > a:after {
+ color: #262626;
+}
+.dropdown-submenu.pull-left {
+ float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+ left: -100%;
+ margin-left: 10px;
+}
+#notification_area {
+ float: right !important;
+ float: right;
+ z-index: 10;
+}
+[dir="rtl"] #notification_area {
+ float: left !important;
+ float: left;
+}
+.indicator_area {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+[dir="rtl"] .indicator_area {
+ float: left !important;
+ float: left;
+}
+#kernel_indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+[dir="rtl"] #kernel_indicator {
+ float: left !important;
+ float: left;
+ border-left: 0;
+ border-right: 1px solid;
+}
+#modal_indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+[dir="rtl"] #modal_indicator {
+ float: left !important;
+ float: left;
+}
+#readonly-indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ margin-top: 2px;
+ margin-bottom: 0px;
+ margin-left: 0px;
+ margin-right: 0px;
+ display: none;
+}
+.modal_indicator:before {
+ width: 1.28571429em;
+ text-align: center;
+}
+.edit_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f040";
+}
+.edit_mode .modal_indicator:before.fa-pull-left {
+ margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.fa-pull-right {
+ margin-left: .3em;
+}
+.edit_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: ' ';
+}
+.command_mode .modal_indicator:before.fa-pull-left {
+ margin-right: .3em;
+}
+.command_mode .modal_indicator:before.fa-pull-right {
+ margin-left: .3em;
+}
+.command_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_idle_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f10c";
+}
+.kernel_idle_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_idle_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_idle_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_busy_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f111";
+}
+.kernel_busy_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_busy_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_busy_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_dead_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f1e2";
+}
+.kernel_dead_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_dead_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_dead_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f127";
+}
+.kernel_disconnected_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_disconnected_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_disconnected_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notification_widget {
+ color: #777;
+ z-index: 10;
+ background: rgba(240, 240, 240, 0.5);
+ margin-right: 4px;
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.notification_widget:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+.notification_widget .badge {
+ color: #fff;
+ background-color: #333;
+}
+.notification_widget.warning {
+ color: #fff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+ color: #fff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning .badge {
+ color: #f0ad4e;
+ background-color: #fff;
+}
+.notification_widget.success {
+ color: #fff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.notification_widget.success:hover {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+ color: #fff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+ color: #5cb85c;
+ background-color: #fff;
+}
+.notification_widget.info {
+ color: #fff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+ color: #fff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info .badge {
+ color: #5bc0de;
+ background-color: #fff;
+}
+.notification_widget.danger {
+ color: #fff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.notification_widget.danger:hover {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+ color: #fff;
+ background-color: #ac2925;
+ border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+ background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+ color: #d9534f;
+ background-color: #fff;
+}
+div#pager {
+ background-color: #fff;
+ font-size: 14px;
+ line-height: 20px;
+ overflow: hidden;
+ display: none;
+ position: fixed;
+ bottom: 0px;
+ width: 100%;
+ max-height: 50%;
+ padding-top: 8px;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ /* Display over codemirror */
+ z-index: 100;
+ /* Hack which prevents jquery ui resizable from changing top. */
+ top: auto !important;
+}
+div#pager pre {
+ line-height: 1.21429em;
+ color: #000;
+ background-color: #f7f7f7;
+ padding: 0.4em;
+}
+div#pager #pager-button-area {
+ position: absolute;
+ top: 8px;
+ right: 20px;
+}
+div#pager #pager-contents {
+ position: relative;
+ overflow: auto;
+ width: 100%;
+ height: 100%;
+}
+div#pager #pager-contents #pager-container {
+ position: relative;
+ padding: 15px 0px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+ top: 0px;
+ height: 8px;
+ background: #f7f7f7;
+ border-top: 1px solid #cfcfcf;
+ border-bottom: 1px solid #cfcfcf;
+ /* This injects handle bars (a short, wide = symbol) for
+ the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+ content: '';
+ top: 2px;
+ left: 50%;
+ height: 3px;
+ width: 30px;
+ margin-left: -15px;
+ position: absolute;
+ border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ line-height: 1.8em;
+}
+.shortcut_key {
+ display: inline-block;
+ width: 21ex;
+ text-align: right;
+ font-family: monospace;
+}
+.shortcut_descr {
+ display: inline-block;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+span.save_widget {
+ height: 30px;
+ margin-top: 4px;
+ display: flex;
+ justify-content: flex-start;
+ align-items: baseline;
+ width: 50%;
+ flex: 1;
+}
+span.save_widget span.filename {
+ height: 100%;
+ line-height: 1em;
+ margin-left: 16px;
+ border: none;
+ font-size: 146.5%;
+ text-overflow: ellipsis;
+ overflow: hidden;
+ white-space: nowrap;
+ border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+ background-color: #e6e6e6;
+}
+[dir="rtl"] span.save_widget.pull-left {
+ float: right !important;
+ float: right;
+}
+[dir="rtl"] span.save_widget span.filename {
+ margin-left: 0;
+ margin-right: 16px;
+}
+span.checkpoint_status,
+span.autosave_status {
+ font-size: small;
+ white-space: nowrap;
+ padding: 0 5px;
+}
+@media (max-width: 767px) {
+ span.save_widget {
+ font-size: small;
+ padding: 0 0 0 5px;
+ }
+ span.checkpoint_status,
+ span.autosave_status {
+ display: none;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ span.checkpoint_status {
+ display: none;
+ }
+ span.autosave_status {
+ font-size: x-small;
+ }
+}
+.toolbar {
+ padding: 0px;
+ margin-left: -5px;
+ margin-top: 2px;
+ margin-bottom: 5px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+ width: auto;
+ vertical-align: middle;
+ margin-right: 2px;
+ margin-bottom: 0px;
+ display: inline;
+ font-size: 92%;
+ margin-left: 0.3em;
+ margin-right: 0.3em;
+ padding: 0px;
+ padding-top: 3px;
+}
+.toolbar .btn {
+ padding: 2px 8px;
+}
+.toolbar .btn-group {
+ margin-top: 0px;
+ margin-left: 5px;
+}
+.toolbar-btn-label {
+ margin-left: 6px;
+}
+#maintoolbar {
+ margin-bottom: -3px;
+ margin-top: -8px;
+ border: 0px;
+ min-height: 27px;
+ margin-left: 0px;
+ padding-top: 11px;
+ padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+ float: none;
+ vertical-align: middle;
+ text-align: right;
+ margin-left: 5px;
+ margin-right: 0px;
+ margin-top: 0px;
+}
+.select-xs {
+ height: 24px;
+}
+[dir="rtl"] .btn-group > .btn,
+.btn-group-vertical > .btn {
+ float: right;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+ background-color: #F37626;
+ color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+ from {
+ opacity: 1;
+ }
+ to {
+ opacity: 0;
+ }
+}
+@-webkit-keyframes fadeOut {
+ from {
+ opacity: 1;
+ }
+ to {
+ opacity: 0;
+ }
+}
+@-moz-keyframes fadeIn {
+ from {
+ opacity: 0;
+ }
+ to {
+ opacity: 1;
+ }
+}
+@-webkit-keyframes fadeIn {
+ from {
+ opacity: 0;
+ }
+ to {
+ opacity: 1;
+ }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+ overflow: auto;
+ height: 200px;
+ -webkit-transition-property: height;
+ -webkit-transition-duration: 500ms;
+ -moz-transition-property: height;
+ -moz-transition-duration: 500ms;
+ transition-property: height;
+ transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+ -webkit-transition-property: height;
+ -webkit-transition-duration: 500ms;
+ -moz-transition-property: height;
+ -moz-transition-duration: 500ms;
+ transition-property: height;
+ transition-duration: 500ms;
+ text-overflow: ellipsis;
+ overflow: hidden;
+ height: 80px;
+}
+.tooltipbuttons {
+ position: absolute;
+ padding-right: 15px;
+ top: 0px;
+ right: 0px;
+}
+.tooltiptext {
+ /*avoid the button to overlap on some docstring*/
+ padding-right: 30px;
+}
+.ipython_tooltip {
+ max-width: 700px;
+ /*fade-in animation when inserted*/
+ -webkit-animation: fadeOut 400ms;
+ -moz-animation: fadeOut 400ms;
+ animation: fadeOut 400ms;
+ -webkit-animation: fadeIn 400ms;
+ -moz-animation: fadeIn 400ms;
+ animation: fadeIn 400ms;
+ vertical-align: middle;
+ background-color: #f7f7f7;
+ overflow: visible;
+ border: #ababab 1px solid;
+ outline: none;
+ padding: 3px;
+ margin: 0px;
+ padding-left: 7px;
+ font-family: monospace;
+ min-height: 50px;
+ -moz-box-shadow: 0px 6px 10px -1px #adadad;
+ -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+ box-shadow: 0px 6px 10px -1px #adadad;
+ border-radius: 2px;
+ position: absolute;
+ z-index: 1000;
+}
+.ipython_tooltip a {
+ float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+ border: 0;
+ border-radius: 0;
+ font-size: 100%;
+ background-color: #f7f7f7;
+}
+.pretooltiparrow {
+ left: 0px;
+ margin: 0px;
+ top: -16px;
+ width: 40px;
+ height: 16px;
+ overflow: hidden;
+ position: absolute;
+}
+.pretooltiparrow:before {
+ background-color: #f7f7f7;
+ border: 1px #ababab solid;
+ z-index: 11;
+ content: "";
+ position: absolute;
+ left: 15px;
+ top: 10px;
+ width: 25px;
+ height: 25px;
+ -webkit-transform: rotate(45deg);
+ -moz-transform: rotate(45deg);
+ -ms-transform: rotate(45deg);
+ -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+ margin-left: -10px;
+ width: 18px;
+}
+[dir="rtl"] ul.typeahead-list i {
+ margin-left: 0;
+ margin-right: -10px;
+}
+ul.typeahead-list {
+ max-height: 80vh;
+ overflow: auto;
+}
+ul.typeahead-list > li > a {
+ /** Firefox bug **/
+ /* see https://github.com/jupyter/notebook/issues/559 */
+ white-space: normal;
+}
+ul.typeahead-list > li > a.pull-right {
+ float: left !important;
+ float: left;
+}
+[dir="rtl"] .typeahead-list {
+ text-align: right;
+}
+.cmd-palette .modal-body {
+ padding: 7px;
+}
+.cmd-palette form {
+ background: white;
+}
+.cmd-palette input {
+ outline: none;
+}
+.no-shortcut {
+ min-width: 20px;
+ color: transparent;
+}
+[dir="rtl"] .no-shortcut.pull-right {
+ float: left !important;
+ float: left;
+}
+[dir="rtl"] .command-shortcut.pull-right {
+ float: left !important;
+ float: left;
+}
+.command-shortcut:before {
+ content: "(command mode)";
+ padding-right: 3px;
+ color: #777777;
+}
+.edit-shortcut:before {
+ content: "(edit)";
+ padding-right: 3px;
+ color: #777777;
+}
+[dir="rtl"] .edit-shortcut.pull-right {
+ float: left !important;
+ float: left;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+ background-color: #BBDEFB;
+ border-color: #90CAF9;
+ border-style: solid;
+ border-width: 1px;
+ border-radius: 0px;
+}
+[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
+ border-left: none;
+}
+[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
+ border-right: none;
+}
+#find-and-replace #replace-preview .replace .match {
+ background-color: #FFCDD2;
+ border-color: #EF9A9A;
+ border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+ background-color: #C8E6C9;
+ border-color: #A5D6A7;
+ border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+ max-height: 60vh;
+ overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+ padding: 5px 10px;
+}
+.terminal-app {
+ background: #EEE;
+}
+.terminal-app #header {
+ background: #fff;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+ width: 100%;
+ float: left;
+ font-family: monospace;
+ color: white;
+ background: black;
+ padding: 0.4em;
+ border-radius: 2px;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+ line-height: 1em;
+ font-size: 14px;
+}
+.terminal-app .terminal .xterm-rows {
+ padding: 10px;
+}
+.terminal-app .terminal-cursor {
+ color: black;
+ background: white;
+}
+.terminal-app #terminado-container {
+ margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+ </style>
+<style type="text/css">
+ .highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+ </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+ overflow: visible;
+ padding: 8px;
+}
+
+div#notebook {
+ overflow: visible;
+ border-top: none;
+}@media print {
+ div.cell {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output_wrapper {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output {
+ display: block;
+ page-break-inside: avoid;
+ }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
+ <!-- MathJax configuration -->
+ <script type="text/x-mathjax-config">
+ MathJax.Hub.Config({
+ tex2jax: {
+ inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+ displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+ processEscapes: true,
+ processEnvironments: true
+ },
+ // Center justify equations in code and markdown cells. Elsewhere
+ // we use CSS to left justify single line equations in code cells.
+ displayAlign: 'center',
+ "HTML-CSS": {
+ styles: {'.MathJax_Display': {"margin": 0}},
+ linebreaks: { automatic: true }
+ }
+ });
+ </script>
+ <!-- End of mathjax configuration -->
+
+
+
+<link rel="stylesheet" href="https://code.jquery.com/ui/1.11.4/themes/smoothness/jquery-ui.css">
+
+
+<!-- stylesheet from CDN -->
+<link rel="stylesheet" type="text/css" href="https://rawgit.com/jfbercher/jupyter_latex_envs/master/src/latex_envs/static/latex_envs.css">
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Load mathjax
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+
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+
+
+<body>
+ <div tabindex="-1" id="notebook" class="border-box-sizing">
+ <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Quadratically-constrained-quadratic-programming-and-its-applications-in-portfolio-optimization">Quadratically constrained quadratic programming and its applications in portfolio optimization<a class="anchor-link" href="#Quadratically-constrained-quadratic-programming-and-its-applications-in-portfolio-optimization">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Quadratically constrained quadratic programming (QCQP) is a type of optimization problem in which both the objective function and the constraints involve quadratic functions. A general QCQP problem has the following form</p>
+\begin{equation}
+\begin{array}{ll}
+\underset{x\in\Re^n}{\mbox{minimize}} & \frac{1}{2}x^TP_0x+q_0^Tx+r_0\\[0.6ex]
+\mbox{subject to} & \frac{1}{2}x^TP_ix+q_i^Tx+r_i\leq0,\quad i=1,\ldots,p.
+\end{array}
+\end{equation}<p>It appears in applications such as modern portfolio theory, machine learning, engineering and control. Convex QCQP is usually handled through conic optimization, or, more precisely, second-order cone programming (SOCP) due to its computational efficiency and ability to detect infeasibility. However, using SOCP to solve convex QCQP is nontrivial task which requires extra amount of effort to transform problem data and add auxiliary variables. In this notebook, we are going to demonstrate how to use the <em>NAG Optimization Modelling Suite</em> in the NAG Library to define and solve QCQP in portfolio optimization.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Data-Preparation">Data Preparation<a class="anchor-link" href="#Data-Preparation">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We consider daily prices for the 30 stocks in the DJIA from March 2018 to March 2019. In practice, the estimation of the mean return $r$ and covariance $V$ is often a nontrivial task. In this notebook, we estimate those entities using simple sample estimates.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [1]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import necessary libraries</span>
+<span class="kn">import</span> <span class="nn">pickle</span> <span class="k">as</span> <span class="nn">pkl</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [3]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load stock price data from stock_price.plk</span>
+<span class="c1"># Stock_price: dict = ['close_price': [data], 'date_index': [data]]</span>
+<span class="n">stock_price</span> <span class="o">=</span> <span class="n">stock_price</span> <span class="o">=</span> <span class="n">pkl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'./data/stock_price.pkl'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
+<span class="n">close_price</span> <span class="o">=</span> <span class="n">stock_price</span><span class="p">[</span><span class="s1">'close_price'</span><span class="p">]</span>
+<span class="n">date_index</span> <span class="o">=</span> <span class="n">stock_price</span><span class="p">[</span><span class="s1">'date_index'</span><span class="p">]</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [4]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Size of data, m: number of observations, n: number of stocks</span>
+<span class="n">m</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">date_index</span><span class="p">)</span>
+<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">close_price</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [5]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Extract stock closing prices to a numpy array</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">stock</span> <span class="ow">in</span> <span class="n">close_price</span><span class="p">:</span>
+ <span class="n">data</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">close_price</span><span class="p">[</span><span class="n">stock</span><span class="p">]</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="n">data</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span>
+ <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+<span class="c1"># Plot closing prices</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time (days)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Closing price ($)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For each stock $i$, we first estimate the $j$th daily relative return as $$relative~return_{i,j} = \frac{closing~price_{i,j+1}-closing~price_{i,j}}{closing~price_{i,j}}.$$</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [6]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Relative return</span>
+<span class="n">rel_rtn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">m</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+ <span class="n">rel_rtn</span><span class="p">[</span><span class="n">j</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">,:]</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="p">,:],</span> <span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="p">,:])</span>
+<span class="c1"># Plot relative return</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">m</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="n">rel_rtn</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time (days)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Relative return'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Simply take arithmetic mean of each column of relative return to get mean return $r$ for each stock, followed by estimating covariance $V$ using numpy.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [7]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Mean return</span>
+<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">r</span> <span class="o">=</span> <span class="n">rel_rtn</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">r</span> <span class="o">=</span> <span class="n">r</span> <span class="o">/</span> <span class="p">(</span><span class="n">m</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Covariance matrix</span>
+<span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cov</span><span class="p">(</span><span class="n">rel_rtn</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Classic-Mean-Variance-Model">Classic Mean-Variance Model<a class="anchor-link" href="#Classic-Mean-Variance-Model">¶</a></h1><h2 id="Efficient-Frontier">Efficient Frontier<a class="anchor-link" href="#Efficient-Frontier">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>One of the major goals of portfolio management is to achieve a certain level of return under a specific risk measurement. Here we demonstrate how to use NAG Library to build efficient frontier by solving classical Markowitz model with long-only constraint (meaning, buy to hold and short selling is not allowed):</p>
+\begin{equation}\label{MV_model}
+\begin{array}{ll}
+\underset{x\in\Re^n}{\mbox{minimize}} & -r^Tx+\mu x^TVx\\[0.6ex]
+\mbox{subject to} & e^Tx = 1,\\[0.6ex]
+ & x\geq0,
+\end{array}
+\end{equation}<p>where $e\in\Re^n$ is vector of all ones and $\mu$ is a scalar controling trade-off between return and risk. Note one could build the efficient frontier by varying $\mu$ from $0$ to a certain value.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [8]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import the NAG Library</span>
+<span class="kn">from</span> <span class="nn">naginterfaces.base</span> <span class="kn">import</span> <span class="n">utils</span>
+<span class="kn">from</span> <span class="nn">naginterfaces.library</span> <span class="kn">import</span> <span class="n">opt</span>
+<span class="kn">from</span> <span class="nn">naginterfaces.library</span> <span class="kn">import</span> <span class="n">lapackeig</span>
+<span class="c1"># Import necessary math libraries</span>
+<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">coo_matrix</span>
+<span class="kn">import</span> <span class="nn">math</span> <span class="k">as</span> <span class="nn">mt</span>
+<span class="kn">import</span> <span class="nn">warnings</span> <span class="k">as</span> <span class="nn">wn</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [9]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Input for quadratic objective</span>
+<span class="c1"># Sparsity pattern of upper triangular V</span>
+<span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">V</span><span class="p">))</span>
+<span class="n">v_val</span> <span class="o">=</span> <span class="n">V</span><span class="p">[</span><span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span><span class="p">]</span>
+<span class="c1"># Convert to 1-based</span>
+<span class="n">irowq</span> <span class="o">=</span> <span class="n">irowq</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">icolq</span> <span class="o">=</span> <span class="n">icolq</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="c1"># Sparsity pattern of r, which is actually dense in this application</span>
+<span class="n">idxr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Input for linear constraint: e'x = 1</span>
+<span class="n">irowa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">icola</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">bl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">bu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="c1"># Input for bound constraint: x >= 0</span>
+<span class="n">blx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">bux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mf">1.e20</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The input data is ready, we can easily build the efficient frontier as follows.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [10]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Set step for mu</span>
+<span class="n">step</span> <span class="o">=</span> <span class="mi">2001</span>
+
+<span class="c1"># Initialize output data: absolute risk and return</span>
+<span class="n">ab_risk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+<span class="n">ab_rtn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">mu</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2000.0</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
+ <span class="c1"># Create problem handle</span>
+ <span class="n">handle</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_init</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+ <span class="c1"># Set quadratic objective function</span>
+ <span class="c1"># In qcqp standard form q should be 2*mu*V</span>
+ <span class="n">q</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">v_val</span>
+ <span class="n">idqc</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_qconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">idqc</span><span class="p">,</span> <span class="n">idxr</span><span class="p">,</span> <span class="o">-</span><span class="n">r</span><span class="p">,</span> <span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
+
+ <span class="c1"># Set linear constraint e'x = 1</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_linconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">bl</span><span class="p">,</span> <span class="n">bu</span><span class="p">,</span> <span class="n">irowa</span><span class="p">,</span> <span class="n">icola</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+
+ <span class="c1"># Set bound constraint</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_simplebounds</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">blx</span><span class="p">,</span> <span class="n">bux</span><span class="p">)</span>
+
+ <span class="c1"># Set options</span>
+ <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="p">[</span>
+ <span class="s1">'Print Options = NO'</span><span class="p">,</span>
+ <span class="s1">'Print Level = 1'</span><span class="p">,</span>
+ <span class="s1">'Print File = -1'</span><span class="p">,</span>
+ <span class="s1">'SOCP Scaling = A'</span>
+ <span class="p">]:</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_opt_set</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">option</span><span class="p">)</span>
+
+ <span class="c1"># Call socp interior point solver</span>
+ <span class="c1"># Mute warnings and do not count results from warnings</span>
+ <span class="n">wn</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s1">'error'</span><span class="p">,</span> <span class="n">utils</span><span class="o">.</span><span class="n">NagAlgorithmicWarning</span><span class="p">)</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">slt</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_solve_socp_ipm</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+
+ <span class="c1"># Compute risk and return from the portfolio</span>
+ <span class="n">ab_risk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ab_risk</span><span class="p">,</span> <span class="n">mt</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]))))</span>
+ <span class="n">ab_rtn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ab_rtn</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]))</span>
+ <span class="k">except</span> <span class="n">utils</span><span class="o">.</span><span class="n">NagAlgorithmicWarning</span><span class="p">:</span>
+ <span class="k">pass</span>
+
+ <span class="c1"># Destroy the handle:</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_free</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [11]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plot the result</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">ab_rtn</span><span class="o">*</span><span class="mf">100.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Total Expected Return (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Absolute Risk (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Maximizing-the-Sharpe-ratio">Maximizing the Sharpe ratio<a class="anchor-link" href="#Maximizing-the-Sharpe-ratio">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The Sharpe ratio is defined as the ratio of return of portfolio and standard deviation of the portfolio's excess return. It is usually used to measure the efficiency of a portfolio. Find the most efficient portfolio is equivalent to solve the following optimization problem.</p>
+\begin{equation}\label{sr_model}
+\begin{array}{ll}
+\underset{x\in\Re^n}{\mbox{minimize}} & \frac{\sqrt{x^TVx}}{r^Tx}\\[0.6ex]
+\mbox{subject to} & e^Tx = 1,\\[0.6ex]
+ & x\geq0.
+\end{array}
+\end{equation}<p>By replacing $x$ with $\frac{y}{\lambda}, \lambda\gt0$, model (\ref{sr_model}) is equivalent to</p>
+\begin{equation}\label{sr_model_eq}
+\begin{array}{ll}
+\underset{y\in\Re^n, \lambda\in\Re}{\mbox{minimize}} & y^TVy\\[0.6ex]
+\mbox{subject to} & e^Ty = \lambda,\\[0.6ex]
+ & r^Ty=1, \\
+ & y\geq0, \\
+ & \lambda\geq0.
+\end{array}
+\end{equation}<p>Problem (\ref{sr_model_eq}) is similar to problem (\ref{MV_model}) in the sense that they both have a quadratic objective function and linear constraints.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [12]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Input for linear constraint: e'y = lambda</span>
+<span class="n">irowa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">icola</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">),</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="n">bl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">bu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Inpute for linear constraint: r'y = 1</span>
+<span class="n">irowa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">irowa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">))</span>
+<span class="n">icola</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">icola</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
+<span class="n">bl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bl</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="n">bu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bu</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># Input for bound constraint: x >= 0</span>
+<span class="n">blx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">bux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.e20</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now we can call the NAG SOCP solver as follows.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [13]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create problem handle</span>
+<span class="n">handle</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_init</span><span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Set quadratic objective function</span>
+<span class="c1"># In qcqp standard form q should be 2*V</span>
+<span class="n">q</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">v_val</span>
+<span class="n">idqc</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<span class="n">opt</span><span class="o">.</span><span class="n">handle_set_qconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">idqc</span><span class="p">,</span> <span class="n">irowq</span><span class="o">=</span><span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span><span class="o">=</span><span class="n">icolq</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+
+<span class="c1"># Set linear constraints</span>
+<span class="n">opt</span><span class="o">.</span><span class="n">handle_set_linconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">bl</span><span class="p">,</span> <span class="n">bu</span><span class="p">,</span> <span class="n">irowa</span><span class="p">,</span> <span class="n">icola</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+
+<span class="c1"># Set bound constraint</span>
+<span class="n">opt</span><span class="o">.</span><span class="n">handle_set_simplebounds</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">blx</span><span class="p">,</span> <span class="n">bux</span><span class="p">)</span>
+
+<span class="c1"># Set options</span>
+<span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="p">[</span>
+ <span class="s1">'Print Options = NO'</span><span class="p">,</span>
+ <span class="s1">'Print Level = 1'</span><span class="p">,</span>
+ <span class="s1">'Print File = -1'</span><span class="p">,</span>
+ <span class="s1">'SOCP Scaling = A'</span>
+<span class="p">]:</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_opt_set</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">option</span><span class="p">)</span>
+
+<span class="c1"># Call socp interior point solver</span>
+<span class="n">slt</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_solve_socp_ipm</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+
+<span class="n">sr_risk</span> <span class="o">=</span> <span class="n">mt</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">])))</span><span class="o">/</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
+<span class="n">sr_rtn</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">])</span><span class="o">/</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
+<span class="n">sr_x</span> <span class="o">=</span> <span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">/</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
+
+<span class="c1"># Destroy the handle:</span>
+<span class="n">opt</span><span class="o">.</span><span class="n">handle_free</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [14]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plot result.</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">ab_rtn</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Efficient frontier'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">sr_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">],</span> <span class="p">[</span><span class="n">sr_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">],</span> <span class="s1">'rs'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Portfolio with maximum Sharpe ratio'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">sr_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="n">sr_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="s1">'r-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Capital market line'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">ab_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ab_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">)])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Total Expected Return (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Absolute Risk (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Portfolio-optimization-with-tracking-error-constraint">Portfolio optimization with tracking-error constraint<a class="anchor-link" href="#Portfolio-optimization-with-tracking-error-constraint">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>To avoid taking unnecessary risk when beating a benchmark, the investors commonly impose a limit on the volatility of the deviation of the active portfolio from the benchmark, which is also known as tracking-error volatility (TEV) \cite{J03}. The model to build efficient frontier in excess-return space is</p>
+\begin{equation}\label{er_tev}
+\begin{array}{ll}
+\underset{x\in\Re^n}{\mbox{maximize}} & r^Tx\\
+\mbox{subject to} & e^Tx = 0,\\
+ & x^TVx\leq tev,
+\end{array}
+\end{equation}<p>where $tev$ is a limit on the track-error. Roll \cite{R92} noted that problem (\ref{er_tev}) is totally independent of the benchmark and leads to the unpalatable result that the active portfolio has systematically higher risk than the benchmark and is not optimal. Therefore, in this section we solve a more advanced model by taking absolute risk into account as follows.</p>
+\begin{equation}\label{tev_model}
+\begin{array}{ll}
+\underset{x\in\Re^n}{\mbox{minimize}} & -r^Tx+\mu (x+b)^TV(x+b)\\
+\mbox{subject to} & e^Tx = 0,\\
+ & x^TVx\leq tev,\\
+ & x+b\geq0,
+\end{array}
+\end{equation}<p>where $b$ is a benchmark portfolio. In this demonstration, it is generated synthetically. Note here we use the same covariance matrix $V$ for tev and absolute risk measurement for demonstration purpose. In practice one could use different covariance matrices from different markets.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [15]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Generate a benchmark portfolio from efficient portfolio that maximiz the Sharpe ratio</span>
+<span class="c1"># Perturb x</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">sr_x</span> <span class="o">+</span> <span class="mf">1.e-1</span>
+<span class="c1"># Normalize b</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
+
+<span class="c1"># Set limit on tracking-error</span>
+<span class="n">tev</span> <span class="o">=</span> <span class="mf">0.000002</span>
+
+<span class="c1"># Compute risk and return at the benchmark</span>
+<span class="n">b_risk</span> <span class="o">=</span> <span class="n">mt</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">)))</span>
+<span class="n">b_rtn</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [16]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Input for linear constraint: e'x = 0</span>
+<span class="n">irowa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">icola</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">bl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">bu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Input for bound constraint: x >= -b</span>
+<span class="n">blx</span> <span class="o">=</span> <span class="o">-</span><span class="n">b</span>
+<span class="n">bux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mf">1.e20</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [17]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Initialize output data: TEV risk and return</span>
+<span class="n">tev_risk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+<span class="n">tev_rtn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">mu</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2000.0</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
+ <span class="c1"># Create problem handle</span>
+ <span class="n">handle</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_init</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+ <span class="c1"># Set quadratic objective function</span>
+ <span class="c1"># In qcqp standard form q should be 2*mu*V</span>
+ <span class="n">q</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">v_val</span>
+ <span class="n">r_mu</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">*</span><span class="n">mu</span><span class="o">*</span><span class="n">V</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">-</span><span class="n">r</span>
+ <span class="n">idqc</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_qconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">idqc</span><span class="p">,</span> <span class="n">idxr</span><span class="p">,</span> <span class="n">r_mu</span><span class="p">,</span> <span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
+
+ <span class="c1"># Set quadratic constraint</span>
+ <span class="c1"># In qcqp standard form q should be 2*V</span>
+ <span class="n">q</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">v_val</span>
+ <span class="n">idqc</span> <span class="o">=</span> <span class="mi">0</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_qconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="o">-</span><span class="n">tev</span><span class="p">,</span> <span class="n">idqc</span><span class="p">,</span> <span class="n">irowq</span><span class="o">=</span><span class="n">irowq</span><span class="p">,</span> <span class="n">icolq</span><span class="o">=</span><span class="n">icolq</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+
+ <span class="c1"># Set linear constraint e'x = 1</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_linconstr</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">bl</span><span class="p">,</span> <span class="n">bu</span><span class="p">,</span> <span class="n">irowa</span><span class="p">,</span> <span class="n">icola</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+
+ <span class="c1"># Set bound constraint</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_set_simplebounds</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">blx</span><span class="p">,</span> <span class="n">bux</span><span class="p">)</span>
+
+ <span class="c1"># Set options</span>
+ <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="p">[</span>
+ <span class="s1">'Print Options = NO'</span><span class="p">,</span>
+ <span class="s1">'Print Level = 1'</span><span class="p">,</span>
+ <span class="s1">'Print File = -1'</span><span class="p">,</span>
+ <span class="s1">'SOCP Scaling = A'</span>
+ <span class="p">]:</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_opt_set</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">option</span><span class="p">)</span>
+
+ <span class="c1"># Call socp interior point solver</span>
+ <span class="c1"># Mute warnings and do not count results from warnings</span>
+ <span class="n">wn</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s1">'error'</span><span class="p">,</span> <span class="n">utils</span><span class="o">.</span><span class="n">NagAlgorithmicWarning</span><span class="p">)</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">slt</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">handle_solve_socp_ipm</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+
+<span class="c1"># Compute risk and return from the portfolio</span>
+ <span class="n">tev_risk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tev_risk</span><span class="p">,</span> <span class="n">mt</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">+</span><span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">+</span><span class="n">b</span><span class="p">))))</span>
+ <span class="n">tev_rtn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tev_rtn</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">slt</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span><span class="o">+</span><span class="n">b</span><span class="p">))</span>
+ <span class="k">except</span> <span class="n">utils</span><span class="o">.</span><span class="n">NagAlgorithmicWarning</span><span class="p">:</span>
+ <span class="k">pass</span>
+
+ <span class="c1"># Destroy the handle:</span>
+ <span class="n">opt</span><span class="o">.</span><span class="n">handle_free</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [18]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot the result</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">7.5</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">ab_rtn</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Classic efficient frontier'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">sr_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">],</span> <span class="p">[</span><span class="n">sr_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">],</span> <span class="s1">'rs'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Portfolio with maximum Sharpe ratio'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">sr_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="n">sr_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="s1">'r-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Capital market line'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">b_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">b_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="s1">'r*'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Benchmark portfolio'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">tev_risk</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">tev_rtn</span><span class="o">*</span><span class="mf">100.0</span><span class="p">,</span> <span class="s1">'seagreen'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Efficient frontier with tev constraint'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ab_risk</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">tev_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ab_rtn</span><span class="o">*</span><span class="mi">100</span><span class="p">)])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Total Expected Return (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Absolute Risk (%)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In this notebook, we demonstrated how to use NAG Library to solve various quadratic models in portfolio optimization. Conic optimization is usually a good choice to solve convex QCQP. It is worth pointing out that the versatility of SOCP is not just limited to the QCQP models mentioned here. It covers a lot more problems and constraints. For example, DeMiguel et al. \cite{DGNU09} discussed portfolio optimization with norm constraint, which can be easily transformed into an SOCP problem. We refer readers to the NAG Library documentation \cite{NAGDOC} on SOCP solver and \cite{AG03, LVBL98} for more details.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="References">References<a class="anchor-link" href="#References">¶</a></h1><p>[<a id="cit-J03" href="#call-J03">1</a>] Jorion Philippe, ``<em>Portfolio optimization with tracking-error constraints</em>'', Financial Analysts Journal, vol. 59, number 5, pp. 70--82, 2003.</p>
+<p>[<a id="cit-R92" href="#call-R92">2</a>] Roll Richard, ``<em>A mean/variance analysis of tracking error</em>'', The Journal of Portfolio Management, vol. 18, number 4, pp. 13--22, 1992.</p>
+<p>[<a id="cit-DGNU09" href="#call-DGNU09">3</a>] DeMiguel Victor, Garlappi Lorenzo, Nogales Francisco J <em>et al.</em>, ``<em>A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms</em>'', Management science, vol. 55, number 5, pp. 798--812, 2009.</p>
+<p>[<a id="cit-NAGDOC" href="#call-NAGDOC">4</a>] Numerical Algorithms Group, ``<em>NAG documentation</em>'', 2019. <a href="https://www.nag.com/numeric/fl/nagdoc_latest/html/frontmatter/manconts.html">online</a></p>
+<p>[<a id="cit-AG03" href="#call-AG03">5</a>] Alizadeh Farid and Goldfarb Donald, ``<em>Second-order cone programming</em>'', Mathematical programming, vol. 95, number 1, pp. 3--51, 2003.</p>
+<p>[<a id="cit-LVBL98" href="#call-LVBL98">6</a>] Lobo Miguel Sousa, Vandenberghe Lieven, Boyd Stephen <em>et al.</em>, ``<em>Applications of second-order cone programming</em>'', Linear algebra and its applications, vol. 284, number 1-3, pp. 193--228, 1998.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+ </div>
+ </div>
+</body>
+</html>
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<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Correct-Rendering-of-this-notebook">Correct Rendering of this notebook<a class="anchor-link" href="#Correct-Rendering-of-this-notebook">¶</a></h1><p>This notebook makes use of the <code>latex_envs</code> Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed this extension. Details at <a href="https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html">https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html</a></p>
<p>The notebook is also not rendered well by GitHub so if you are reading it from there, you may prefer the <a href="./static/portfolio_optimization_using_socp.pdf">pdf version instead</a>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Note-for-the-users-of-the-NAG-Library-Mark-$27.1$-onwards">Note for the users of the NAG Library Mark $27.1$ onwards<a class="anchor-link" href="#Note-for-the-users-of-the-NAG-Library-Mark-$27.1$-onwards">¶</a></h1><p>At Mark $27.1$ of the NAG Library, NAG introduced two new additions to help users easily define a Quadratically Constrained Quadratic Programming (QCQP) problem. All the models in this notebook then can be solved in a much simpler way without the need of a reformulation or any extra effort. It's recommended that the users of the NAG Library Mark $27.1$ or newer should look at the <a href="./portfolio_optimization_qcqp.ipynb">notebook on QCQP instead</a>.</p>
+
+</div>
+</div>
+</div>
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<h1 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">¶</a></h1>
</div>
</div>
@@ -13413,7 +13428,6 @@ <h1 id="Classic-Mean-Variance-Model">Classic Mean-Variance Model<a class="anchor
<span class="kn">from</span> <span class="nn">naginterfaces.library</span> <span class="kn">import</span> <span class="n">opt</span>
<span class="kn">from</span> <span class="nn">naginterfaces.library</span> <span class="kn">import</span> <span class="n">lapackeig</span>
<span class="c1"># Import necessary math libraries</span>
-<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">coo_matrix</span>
<span class="kn">import</span> <span class="nn">math</span> <span class="k">as</span> <span class="nn">mt</span>
<span class="kn">import</span> <span class="nn">warnings</span> <span class="k">as</span> <span class="nn">wn</span>
</pre></div>
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@@ -0,0 +1,114 @@
+[](https://www.nag.com)
+
+# Local Optimization<a name=top></a>
+
+See full offering of solvers at
+https://www.nag.com/numeric/nl/nagdoc_latest/flhtml/indexes/optimization.html </br>
+(https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
+NOMS solvers are identified with "handle_solve" prefix.
+
+**Abbreviations used**
+
+|Abbreviation | Description |
+|--------|--------------|
+| (FD) | Indicates the solver does not require the user to provide 1st order derivativas and in which case it will use a Finite-Difference method to estimate them |
+| (no-FD)| Indicates that the solver requires the user to provide 1st or 2nd order derivatives|
+| AS | Active-Set Method
+| DFNO | Derivative-Free Nonlinear Programming
+| IPM | Interior-Point Method
+| LP | Linear Programming
+| NLP | Nonlinear Programming
+| QCQP | Quadratically-constrained Quadratic Programming
+| QP | Quadratic Programming
+| SOCP | Second-Order Cone Programming
+| SQP | Sequential Quadratic Programming
+
+## LP
+
+### Sparse LP
+
+ * IPM: `e04mt (handle_solve_lp_ipm)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_lp_ipm.html#naginterfaces.library.opt.handle_solve_lp_ipm</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_lp_ipm_ex.main</br>
+ Demo: https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Modelling/LP_demo.ipynb</br>
+ Demo: https://github.com/numericalalgorithmsgroup/NAGPythonExamples/blob/master/local_optimization/Modelling/production_planning.ipynb </br>
+
+ * Primal-Simplex AS `e04nc (qpconvex2_sparse_solve)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.qpconvex2_sparse_solve.html#naginterfaces.library.opt.qpconvex2_sparse_solve</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.qpconvex2_sparse_solve_ex.main</br>
+
+### Dense LP
+
+ * AS `e04mf (lp_solve)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.lp_solve.html#naginterfaces.library.opt.lp_solve</br>
+
+ * AS `e04nc (lsq_lincon_solve)` (also solves convex QP)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.lsq_lincon_solve.html#naginterfaces.library.opt.lsq_lincon_solve</br>
+
+
+## QP
+
+### Sparse QP
+
+ * AS `e04nq (qpconvex2_sparse_solve)` (convex)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.qpconvex2_sparse_solve.html#naginterfaces.library.opt.qpconvex2_sparse_solve</br>
+
+ * IPM `e04st (handle_solve_ipopt)` (convex, possibly also nonconvex)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_ipopt.html#naginterfaces.library.opt.handle_solve_ipopt</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_ipopt_ex.main</br>
+
+### Convex QCQP (SOCP)
+
+ * Solver of choice: `e04pt (handle_solve_socp_ipm)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_socp_ipm.html#naginterfaces.library.opt.handle_solve_socp_ipm</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_socp_ipm_ex.main</br>
+ Demo: https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/SOCP</br>
+
+### Dense QP
+
+ * AS `e04nf (qp_dense_solve)` (convex, possibly also nonconvex)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.qp_dense_solve.html#naginterfaces.library.opt.qp_dense_solve</br>
+
+ * AS `e04nc (lsq_lincon_solve)` (convex)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.lsq_lincon_solve.html#naginterfaces.library.opt.lsq_lincon_solve</br>
+
+
+## NLP
+
+### Sparse
+
+ * SQP AS (FD) `e04sr (handle_solve_ssqp)` (Mark 28.3 / 28.4)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_ssqp.html#naginterfaces.library.opt.handle_solve_ssqp</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/_modules/naginterfaces/library/examples/opt/handle_solve_ssqp_ex.html#main</br>
+
+ * SQP AS (FD) `e04vh (nlp2_sparse_solve)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.nlp2_sparse_solve.html#naginterfaces.library.opt.nlp2_sparse_solve</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.nlp2_sparse_solve_ex.main</br>
+
+ * IPM (no-FD) `e04st (handle_solve_ipopt)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_ipopt.html#naginterfaces.library.opt.handle_solve_ipopt</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_ipopt_ex.main</br>
+
+ * Conjugate Gradient AS (FD) (only bound constraints) `e04kf (handle_solve_bounds_foas)` (also works on dense)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_bounds_foas.html#naginterfaces.library.opt.handle_solve_bounds_foas</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex.main</br>
+ Demo: https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/FOAS</br>
+
+ * DFNO Model-based `e04jd (handle_solve_dfno)` (no derivatives required, solver of choice is problem is noisy)</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_dfno.html#naginterfaces.library.opt.handle_solve_dfno</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.handle_solve_dfno_ex.main</br>
+ Demo: https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/DFO</br>
+
+ * SQP AS `e04ug (nlp1_sparse_solve)`
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.nlp1_sparse_solve.html#naginterfaces.library.opt.nlp1_sparse_solve</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.nlp1_sparse_solve_ex.main</br>
+
+### Dense
+
+ * SQP AS (FD) `e04uc (nlp1_solve)`</br>
+ Doc: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.nlp1_solve.html#naginterfaces.library.opt.nlp1_solve</br>
+ Example: https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#naginterfaces.library.examples.opt.nlp1_solve_ex.main</br>
+
+
+
+
diff --git a/multivariate_methods/k-means.ipynb b/multivariate_methods/k-means.ipynb
index 68b92e1..9c724b4 100644
--- a/multivariate_methods/k-means.ipynb
+++ b/multivariate_methods/k-means.ipynb
@@ -28,7 +28,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -58,7 +58,7 @@
"X = np.concatenate((X1, X2))\n",
"\n",
"# Plot the combined dataset\n",
- "plt.plot(X[:,0], X[:,1], 'x');"
+ "_ = plt.plot(X[:,0], X[:,1], 'x')"
]
},
{
@@ -80,7 +80,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -94,13 +94,13 @@
"source": [
"colours = ['orange', 'blue']\n",
"plt.scatter(X[:,0], X[:,1], marker='x', c=kmeans.inc, cmap=matplotlib.colors.ListedColormap(colours))\n",
- "plt.scatter(kmeans.cmeans[:,0], kmeans.cmeans[:,1], color='red', s=200);"
+ "_ = plt.scatter(kmeans.cmeans[:,0], kmeans.cmeans[:,1], color='red', s=200)"
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -114,7 +114,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/nag_logo.png b/nag_logo.png
index ee152a2..7954c3d 100644
Binary files a/nag_logo.png and b/nag_logo.png differ
diff --git a/nearest_correlation_matrices/ncm_nag.ipynb b/nearest_correlation_matrices/ncm_nag.ipynb
index 42cf310..9797e34 100644
--- a/nearest_correlation_matrices/ncm_nag.ipynb
+++ b/nearest_correlation_matrices/ncm_nag.ipynb
@@ -276,8 +276,8 @@
"source": [
"def cov_bar(P):\n",
" \"\"\"Returns an approximate sample covariance matrix\"\"\"\n",
- " # P.shape returns a tuple (m, n) that we unpack to m and n\n",
- " m, n = P.shape\n",
+ " # P.shape returns a tuple (m, n) that we unpack to _m and n\n",
+ " _m, n = P.shape # pylint: disable=unused-variable\n",
" # Initialize an n-by-n zero matrix\n",
" S = np.zeros((n, n))\n",
" for i in range(n): \n",
@@ -484,7 +484,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Sorted eigenvalues of X [-0.0000 -0.0000 0.0380 0.1731 0.6894 1.7117 1.9217 3.4661 ]\n"
+ "Sorted eigenvalues of X [-0.0000 0.0000 0.0380 0.1731 0.6894 1.7117 1.9217 3.4661 ]\n"
]
}
],
@@ -653,7 +653,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Sorted eigenvalues of X [0.0010-0.0000j 0.0010+0.0000j 0.0305+0.0000j 0.1646+0.0000j 0.6764+0.0000j 1.7716+0.0000j 1.8910+0.0000j 3.4639+0.0000j ]\n"
+ "Sorted eigenvalues of X [0.0010 0.0010 0.0305 0.1646 0.6764 1.7716 1.8910 3.4639 ]\n"
]
}
],
@@ -829,7 +829,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Sorted eigenvalues of X [0.0010 0.0010 0.0375 0.1734 0.6882 1.7106 1.9224 3.4660 ]\n"
+ "Sorted eigenvalues of X [0.0010-0.0000j 0.0010+0.0000j 0.0375+0.0000j 0.1734+0.0000j 0.6882+0.0000j 1.7106+0.0000j 1.9224+0.0000j 3.4660+0.0000j ]\n"
]
}
],
@@ -1138,7 +1138,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
},
"nbpresent": {
"slides": {
diff --git a/operations_research/nag_rugby_tsm.ipynb b/operations_research/nag_rugby_tsm.ipynb
index 4816b1c..508ce88 100644
--- a/operations_research/nag_rugby_tsm.ipynb
+++ b/operations_research/nag_rugby_tsm.ipynb
@@ -362,7 +362,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -376,7 +376,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/random_number_generation/rand_corr_mat.ipynb b/random_number_generation/rand_corr_mat.ipynb
index 2077a02..d3b0d86 100644
--- a/random_number_generation/rand_corr_mat.ipynb
+++ b/random_number_generation/rand_corr_mat.ipynb
@@ -120,9 +120,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "from naginterfaces.library import rand as nag_rand"
+ "import pandas as pd"
]
},
{
@@ -274,7 +272,7 @@
"outputs": [],
"source": [
"seed_pd = [1]\n",
- "statecomm_pd = nag_rand.init_repeat(1,seed_pd)"
+ "statecomm_pd = nirand.init_repeat(1,seed_pd)"
]
},
{
@@ -417,7 +415,7 @@
"metadata": {},
"outputs": [],
"source": [
- "c_df = nag_rand.matrix_corr(df[0],statecomm_pd)"
+ "c_df = nirand.matrix_corr(df[0],statecomm_pd)"
]
},
{
@@ -623,7 +621,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/roots/Anderson_Acceleration_Poisson.ipynb b/roots/Anderson_Acceleration_Poisson.ipynb
index b5867fc..1670c53 100644
--- a/roots/Anderson_Acceleration_Poisson.ipynb
+++ b/roots/Anderson_Acceleration_Poisson.ipynb
@@ -3,11 +3,19 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:38:59.077059Z",
+ "iopub.status.busy": "2024-08-14T18:38:59.076364Z",
+ "iopub.status.idle": "2024-08-14T18:39:01.881442Z",
+ "shell.execute_reply": "2024-08-14T18:39:01.880323Z"
+ }
+ },
"outputs": [],
"source": [
"import numpy as np\n",
- "from numba import jit, jitclass, float64, int64\n",
+ "from numba import jit, float64, int64\n",
+ "from numba.experimental import jitclass\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"np.set_printoptions(precision=16, suppress=True)\n",
@@ -52,12 +60,19 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:01.885397Z",
+ "iopub.status.busy": "2024-08-14T18:39:01.885034Z",
+ "iopub.status.idle": "2024-08-14T18:39:01.894826Z",
+ "shell.execute_reply": "2024-08-14T18:39:01.893627Z"
+ }
+ },
"outputs": [
{
"data": {
"text/plain": [
- "(88, 0.7390851332151603)"
+ "(88, np.float64(0.7390851332151603))"
]
},
"execution_count": 2,
@@ -99,7 +114,14 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:01.965655Z",
+ "iopub.status.busy": "2024-08-14T18:39:01.964974Z",
+ "iopub.status.idle": "2024-08-14T18:39:01.985335Z",
+ "shell.execute_reply": "2024-08-14T18:39:01.983931Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -135,7 +157,14 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:01.989441Z",
+ "iopub.status.busy": "2024-08-14T18:39:01.988966Z",
+ "iopub.status.idle": "2024-08-14T18:39:02.002859Z",
+ "shell.execute_reply": "2024-08-14T18:39:02.001056Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -163,7 +192,14 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:02.007575Z",
+ "iopub.status.busy": "2024-08-14T18:39:02.007166Z",
+ "iopub.status.idle": "2024-08-14T18:39:02.026844Z",
+ "shell.execute_reply": "2024-08-14T18:39:02.025595Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -230,18 +266,23 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:02.033981Z",
+ "iopub.status.busy": "2024-08-14T18:39:02.033537Z",
+ "iopub.status.idle": "2024-08-14T18:39:02.250758Z",
+ "shell.execute_reply": "2024-08-14T18:39:02.250109Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -268,7 +309,7 @@
"\n",
"x0 = init_problem(50)\n",
"plt.title('Initial conditions of our example problem on a 50 x 50 grid')\n",
- "plt.imshow(x0);"
+ "_ = plt.imshow(x0)"
]
},
{
@@ -304,7 +345,14 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:02.260006Z",
+ "iopub.status.busy": "2024-08-14T18:39:02.259729Z",
+ "iopub.status.idle": "2024-08-14T18:39:02.275710Z",
+ "shell.execute_reply": "2024-08-14T18:39:02.274429Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
@@ -339,7 +387,14 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:02.282956Z",
+ "iopub.status.busy": "2024-08-14T18:39:02.282614Z",
+ "iopub.status.idle": "2024-08-14T18:39:02.296215Z",
+ "shell.execute_reply": "2024-08-14T18:39:02.294561Z"
+ }
+ },
"outputs": [],
"source": [
"# This spec is required for Numba jit compilation\n",
@@ -352,7 +407,7 @@
"class solverinfo:\n",
" \"\"\"A class used to get information to/from a solver\n",
" \"\"\"\n",
- " def __init__(self, w=1):\n",
+ " def __init__(self, _w=1):\n",
" self.iterations = 0\n",
" self.w = 1 # Used for SOR and ignored for everything else\n",
"\n",
@@ -383,7 +438,14 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:02.304620Z",
+ "iopub.status.busy": "2024-08-14T18:39:02.304003Z",
+ "iopub.status.idle": "2024-08-14T18:39:07.089796Z",
+ "shell.execute_reply": "2024-08-14T18:39:07.088311Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -395,14 +457,12 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -414,7 +474,7 @@
"jacobi_sol = solve_poisson(u, tol, jacobi_info, jacobi)\n",
"print(f\"Gauss-Seidel on a {N} by {N} grid\")\n",
"print(f\"Solution found in {jacobi_info.iterations} iterations\")\n",
- "plt.imshow(jacobi_sol);"
+ "_ = plt.imshow(jacobi_sol)"
]
},
{
@@ -427,18 +487,23 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:07.097946Z",
+ "iopub.status.busy": "2024-08-14T18:39:07.097654Z",
+ "iopub.status.idle": "2024-08-14T18:39:07.272995Z",
+ "shell.execute_reply": "2024-08-14T18:39:07.270246Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -446,7 +511,7 @@
"Ex, Ey = np.gradient(jacobi_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"plt.imshow(E)\n",
- "plt.title('Electric Field');"
+ "_ = plt.title('Electric Field')"
]
},
{
@@ -469,7 +534,14 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:07.281558Z",
+ "iopub.status.busy": "2024-08-14T18:39:07.280831Z",
+ "iopub.status.idle": "2024-08-14T18:39:07.295668Z",
+ "shell.execute_reply": "2024-08-14T18:39:07.293469Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
@@ -487,7 +559,14 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:07.304350Z",
+ "iopub.status.busy": "2024-08-14T18:39:07.303619Z",
+ "iopub.status.idle": "2024-08-14T18:39:10.273861Z",
+ "shell.execute_reply": "2024-08-14T18:39:10.272963Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -499,14 +578,12 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 720x432 with 2 Axes>"
+ "<Figure size 1000x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -525,7 +602,7 @@
"Ex, Ey = np.gradient(gs_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"axes[1].imshow(E)\n",
- "axes[1].set_title('Electric Field');"
+ "_ = axes[1].set_title('Electric Field')"
]
},
{
@@ -558,7 +635,14 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:10.280808Z",
+ "iopub.status.busy": "2024-08-14T18:39:10.280585Z",
+ "iopub.status.idle": "2024-08-14T18:39:10.287165Z",
+ "shell.execute_reply": "2024-08-14T18:39:10.286130Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
@@ -585,7 +669,14 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:10.291933Z",
+ "iopub.status.busy": "2024-08-14T18:39:10.291674Z",
+ "iopub.status.idle": "2024-08-14T18:39:10.800879Z",
+ "shell.execute_reply": "2024-08-14T18:39:10.799940Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -597,14 +688,12 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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IIYQQQkjt4Y0NIYQQQgghpPbwxoYQQgghhBBSe3hjQwghhBBCCKk9fbuxueCCC+TAAw+UoaEhOeKII+SWW27p16IIIYSQCeF1iRBCdm36Evf8T//0T/Lnf/7n8ulPf1qOOOIIOe+88+TKK6+Uu+66S/bdd184b1mW8tvf/lbmzZsnRVH0etUIIYQAnHOyefNm2X///aXR2HUe6k/muiTCaxMhhEwXWdcl1wde8IIXuFWrVnWm2+2223///d3atWsnnPeBBx5wIsL/+B//43/8bxr/e+CBB/pxeZg2JnNdco7XJv7H//gf/5vu/1KuSy3pMdu3b5fbbrtNTj/99M7fGo2GLF++XG688cag/8jIiIyMjHSm3eMPkBaf+X5pDA31evUIIYQAym3b5IGzPiLz5s2b7lXpGbnXJZH4teloeYW0ZODxQZqd9mLAv5wWrXibNFuqn2lT80nT/Mtkc7zNqc9BX/Mvmq41Pu3sv3aqYZx5EuXs8nWz7dtQ0+AfVO0yqlIgsUmpF+j3K0o1bYYo2qU/redtm76l6ts2y2irznZM3Vb6bTLW7v5ZRJwepz3mt42p6dFR09bu3k/Eq18RkaKpa9a06ToNalbXs6lLXW+mzatFUM9hzRagzZ/06q0BatbWM6j1nmDr0sXbdJ3auvfqWURETRdt21Ymtdna82vdHAjt+HxevdmaHR1T/drRNil3tI3JqNwg3066LvX8xub3v/+9tNttWbhwoff3hQsXyi9+8Yug/9q1a+VDH/pQ8PfG0BBvbAghZJrYleRWudclkfi1qSUD0ioev7Ep1A/BwtzYqGnbJg3V1oi32RsU/UM068Ym+UfiLL+xKcp4X9PZ72vavLsge7MUb5OGamuYH4JO9S3N/i3UdPADXf2AtNu+MDcvifUsoGbDetZ1aW5smv24sUF1uQve2NgbFFWXRVDgaW3izI2NxNtE16WtWb1vQM3qGt0xrffL4/0eX92U61LPb2xyOf3002XNmjWd6U2bNsnixYuj/d1MvtbO5HXbFQDXsemmmMHrNpO3G4nAc8m0E702NZqdH4Tev3DbC66+qBfglz6i4g+qXt084GXk9E3sbLuBc5ceE97kTAfg++of0+Y+KnkMiP2hr2oveDoYzBv/oW9vCqaVIu0m2jIVx0VV9PHUq98T9vsmf/t+bCd7DlR1Gpw79ZPpzocy+HeAGD2/sdl7772l2WzKhg0bvL9v2LBBFi1aFPQfHByUwcHBXq8GIYQQIiL51yURXpsIIaSO9DzyZs6cOXLYYYfJunXrOn8ry1LWrVsny5Yt6/XiCCGEEAivS4QQMjvoixRtzZo1snLlSjn88MPlBS94gZx33nmyZcsWedOb3lRpvL7Iz3ox5jQ/1ZxJsrwpkWJlyBQqU3HMfjxG7s/363/RzGhZXkWm5Fgr4tr/9DF6sia7JL26LhUDrY7fwJNQNK1fIa7tF+CJQFgPjFlg8jiV6YtEJbGtV+cV/R2shM1uX93csL6HxJVD2yzwvIC+wCtSaD+KCSTw2syJDMongaTNrif0PVSt2Ypysyk5DjQ566Z3jV1PXYvBd4hfG+y1yd8XgYksPp9dVz2KJ5/MqWddT9ajptbFnjv1xOPLLpwTGZEk+nJj82d/9mfy0EMPyZlnninr16+X5zznOXL11VcHxk1CCCFkKuB1iRBCdn36Fh5w8skny8knn9yv4QkhhJAseF0ihJBdm13ntdKEEEIIIYSQWcu0xz33lKrSyorz9Ux3vwuYEia1LSrOXHmRaHNDoWnVBWaQuoyq22wm+3+qLn8qrAVVawYQ7MLUfYpWpg/rSXyKVnP8nR6JkaWBJwG8c8brm+PP0Nh/skR+BegFii8CLRPG6vbhOu1MY6H1/NYT4HkZgoGqYbcpOpaTfTT+oDrS2pn3wXgvZWzGX4JZ2BeC2lXT89pa0NP2nTOoZlPbMuKks2KbEz0+wS6r6vGB66I+J0YXW8IIZ1C0sC7j/pvKvrDgvVv6/Tfx82NwGHap2cKVyR4bPrEhhBBCCCGE1B7e2BBCCCGEEEJqT/2laKlPJLPekpw6ZsZz6ymWyU07OY/0K8bc+k9V4xsq2E1V5Wb9iB7NGSfnO/ZkeRl9AX0v4T7I4rKGzKmv/G47FpFY67uCrHWmo+Oe/ThTINHIke40QRR0I3F5Zj4H2yL9uuCVXo4cKGMZyUMCSZmWplmpjhfNL/Ft8fhCxj+C7QbbgghpHXMbl5sFsh5vDHOct9RPORsFXerr6wT6J13PzXjcM5RdBvOp79ujmo2OIeE+9MexbfFx0D/796KGCxsfriSTztRsgX58oIhyWyfefGZab4xgTNW5NGPq/W2WV6gahlemhJotJqpdPVxyT0IIIYQQQgiZofDGhhBCCCGEEFJ7eGNDCCGEEEIIqT3199ggUDwkkkginXofPD3V42KnV09fQK0/mDEnkjb5K8Y72ihQb3HIH9GvzduDSGe466u22WWkd62+raa6hHvlf0ms7159Pbg45L9B/rW6+vemm2ZLpNHFY2MokFcGeRK0Lh34FQJvQRN4EvQwQVvcg4CjXf0m7GUAy6gIPK/rYre7CHkZ7JB629jjR3sk7HHntZkV0P4B5KECFC3z063dVoszY1b12Fjfg+fvAnVp2hyKNm+k1ayz88FatzUbWU87jerZ0oMazoko13Wa5Quz+0JlTBfmCzsVBR54qLRPCvnC7PlK9wv+oMZsm7rUw+ys2QmiymOzE0IIIYQQQkgt4Y0NIYQQQgghpPbUT4oGJWTxpizpWcVlpD6ezJKQTXdMNHrJeer3mEACkyxpq6zrSZepeV8JyeIqS+YmIFV+VlFuBsuiR1HQfVFIZsWHT36cCSPfUS3oJnROqirJRGozysv6TtFqSdGRooENjqQ7QKbmwNvn9XyBPMd7izqQkkCZGFgXESMPQvG8dhmSBpJRWfTiTDfvvG43k8QlP3C9bWK3GtiZ18jra1qgDgVyncTTSoja9oWSpYmIkfVM8G/Z6C3yXnx5XD4J5XVQdgnms6vtSaNAHdq+qJ6D+UAbIvE3SxhDHj9+CrVLJ5JPelIxI/EqwHmgUNvRmQ0OL6lNF++HIruR9M1bwI51KShFI4QQQgghhMwmeGNDCCGEEEIIqT28sSGEEEIIIYTUnvp5bDJI9tXk+Gagj6eiuWEm6+J7sW4T+FEciqT1hulZTqga1Ohc1TKqRkGjxN0JO6P5En01lX00k/HNJEdYJ/abDFWXATT7qG9Yz2BdgOTYGyOn9qAAOudLkSRaTRX3jDw2aX6FINIZeBI8X431FsA2tLw0D0IwDvQk+E3eMnJO49Z3oRfnpRgb34H6HMRCazuI9TlYz6VusxJ/HasbeJPGF1IY/432L+TYAb2+wK9Q3dhn5g1ik4EXC0U6gzbPH5PThvwv1vvlHYd+V4f8N5qqnrHUc7OIuYabulTbIvSTmUWqYyE41r24ZztOD2rW7IuiDepS+9vKhGcs9NgQQgghhBBCZhO8sSGEEEIIIYTUnvpL0dDjb68fiHTOkIkly80mo5qqKmmbTnLkVjmyPC/KNv4MNpCppeZm9kpS1iOSl5GRUO71zZGb9Uu25o0zjQUN6mkycc8Vy80fw5ZzYtR40DRTzxd1ZqA1LkVDwJhZEI+LIp11JCtsQ1KhDKkO6JslNwPyqMo1mhq/byOjU18vMEGTH4lrYnWj/XyZT1asrt6GZTzGVxroopYhRUPR3yBSuWqkc9CGIspT61mkNxHlwXxSCai6L8EFyKtnM6aJIfcOC1P7vjStJjXbmb85cZ+dwyX3JIQQQgghhJAZCm9sCCGEEEIIIbWHNzaEEEIIIYSQ2lN/jw0CRTon+jwCX0dVH00/oqCnO77VxfWpfr8J8gljY040rjekjfvsgXB7ug0KwPOS7Kmx81WNkJ4w7jktlhWPkdhvMnhC4mpa+x3tKlLTtiX6b3K+rpYjwyjoHFJ9aMTDNZvimgl6b+BXQFHQntcAxAj3KtLZX54/G+qLfDRhW+TzJPCOA+RJQP6bYND05SNPQuVYXR0TjYx2bbvvXfd+lkl4bDxsfXl+Mlt7+nO1uOfANwP8a6FXR+J9U3009jisWMPgTRP44lCCggY/UYP4ZR0FPZNq1tKlTl2bHhtCCCGEEELILII3NoQQQgghhJDas2tL0VIJYv6qStgStULBmH2QqU0F8JG3+jxRBi16PovkBuD7a2laUVHeZp/x6rdSZ0nBpgIQ6VyAtmS5mdmGuO8E0xGmQlmJI+HVx4nkXkiGiWRq3bsF48D5wNP+oK/uF+jiplnKWldaTZFuUjQk3QmkLDoeF8hsMuJxvTFRTDOSsNl/6syKe46siwWdt63CCb1sHJ6DVGNpDxgwn42GTrxYhKcH9UXMmIU+P9jo3rZqs8enaoOSn4nkZqmgiG4kN8uQT6Jad0A+6UWbo1hqMTWVU88V5ZN6fQqwL4ImvQKoDO0x2gbLB+d4JDez8cuVa9Yl1qylW5vNtQbwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ7eGNDCCGEEEIIqT3189gEfhjdlhHNrOWMVSOds5aXmtVrxwEayWn22EApLzIzoPhnaIKw8wGfgzeX0YtOe4yz1uDatshnMV+xaqQz9N8AH82Ecc9g+bF1yWnrEfCYQTUEzh+hVhnsX61VzrAhTHfJknGicc/onwlt4SEfDYiyhbHNejrDr+BJ+8F8O9YttU3ibRkXrmAcPaTnKzGNJTpA9TFoznnAN2S9DN641gtk+8YorKko7pXxfA7BNRScPJFPCRHsw6L7Z7H+LjuO8oXZ3286NhrVsxkT1nPgPYuMKQJ/s3nfybYlPhIIfI16cXa/eNdb8FunjDeJ+J6X8LyjPqOaDZahrmnBcQD8XXocG++c6QtzDXpsCCGEEEIIIbMI3tgQQgghhBBCak89pGhVZRjwMWMPIp2DR7U9GFPM08OqUdC2a2K0q8vQvKRKjiZ8U7r3rDhDpoakCGDjeFHQWTmO6V17Mh8ap1eRzlVlcRl94XyxMfpFqtzMPsFH0cxxJVF43vEiw22cOFhPtE3B4TPtkfC7IG4gIkWzgAhcT24WyJ+AFC3xjeuVI2+B5GfH8vXyTF8k3UmUdwfHEkyEjZ+79PcIInfb8fUMpoGMy1NCB5I2tcw2OHlaeY6OhrbR00jy441hZkNveDdAmWDFevbkZoGkC8gnUa3raHEg5Qz6Zsgng76R+SxeXaD6DVSIWmpo6qn0OsYXKOJ/f1NDlWtW15uVT4Ka1V2hfDJBLumEUjRCCCGEEELILII3NoQQQgghhJDawxsbQgghhBBCSO2ph8cmlRw9eaLmN9DAIv8LGlP1DWSsYBzojUES0Ira+kD3X9ET4Ukyga9jxzC6A1rxDANB4pBZUdCexrlP5oXEVPCqfpiiNKP2wLczYV9AovWrZ6DFFaBmsqKZ0TK8fkbjrOa0x50/3wQrEFsZ+m16Q6sQaYb/JghPCUHEMvJ5AI8N8At4XhkU9xx4esCY1kcDxnHAg5HqsbHA0wO8xqjrrTnnad1/YX0sbbNyXjy7HSfuZfCic5Fvx56Pgf/Gv6bGvTnWAtEzi6de1Yx69mrK1gXwhfl+rv77wnBEuW1LK2Jcv+C3hz3/l7qfaQPxy3ZbeMdCTs3qdTP+G9fQx1q1mkX2mc7XyfhRyyc2hBBCCCGEkNrDGxtCCCGEEEJI7am/FC1RquWQbAyNiWRjwSP9RLlZkM6HpGhgHNsUbalOEX866pMRxRzIbICMyetrl+E99rSSDR0lGH80naNu8yKsM2JJITlSv8S+UPqXEwUNHn/3KtK57/HPaNeDqNegLHoV9xxPPvWlaUADGsyn9yFQGk6UEkrSKFsNKVsJ/yYIpBO+NCsuz0GynjBWt6KErQnaQP1mRedWlKJBwLlLS26CWF0t2wrkZbav+mwlP2qcQKamd6GZT0vYAikckqk5cAFS17vgzFH1PAp/I4E6AbHNsJ6D+br3E5G+yCdhPYNrRWVspDOSheuaBdLKHe3qM5KpoZq166blZ0imFsjNdJOV3ukx4kW6s6W0XxTAJzaEEEIIIYSQ2sMbG0IIIYQQQkjt4Y0NIYQQQgghpPbUzmMDdeGV2ypGOps4xgK2IY9NfH3ClEFkigBjVsQBaaXeGTCCFvlvzMyB/0Zrpe1SUNymZ1QC5oLAk6C2/TSYEJLLFPqUbMRj4nwlaJvIGwM9PhInrZx7hrfrkW56Ak018q4gr4HeF0FbbAF2MjTnxNtIz3HNhrgucc/onwmtnt3X76dr+5PjcXN8NI3unyfV16438p0l1mzVc471sehrSmHPebav2jfBboJeBrUqQeSuusYA/w3yWYSeBH0tlP6g933Veu6DjwbV6ETLQPUMj6cenGdDX1jcl5xVs9rDleUZU4u3y4C+MOT/ifvC/GvoxBd/R48NIYQQQgghZDbBGxtCCCGEEEJI7amdFC0HB+RfHratAeZDkc5em308qCdMm10dLUVDkrZgvmgTnA/htNwM5Eg6+5hRP4EskeZG/EeU9s213lui7aNMkHOb2gY1Rma2ItIvk+RdkRUFrR4V9yHSOXxsDtYHyuRAm6UPcc/J54Hg2E7v65WQ2U5+PcfbAjmhd6yBdUHbjDK1nlAOpMY9x5vgW9xBPG2/5WZhW7W+gazHkzGZttS6zJHR6vOalcd4bXGZWk7fvMjd8Y9QpmblqN65Gpx0TNxxisynG4GkzBvU9lUTOfWM5GbJbWZd4HER74slbH4bvFYgYKRz/BpevWbNOKoWK0ebBzWrfyPaSOf4bzv9HR08We6cnVI0QgghhBBCyCyCNzaEEEIIIYSQ2sMbG0IIIYQQQkjtqb/HpqrWEflvwJipkc6BflG3Bf4bME6wfDBODzw21isjQNuP/Deex8YIPV3glVHYyEc1UGAt0NpOsw1FLwNZbCxVfTRV56vYN9DgpvpYciKdkf+majQ0iCLNiolOJePYhm12XTzPXLwtrFnVZv9ZCexfT4Mc6JhVU5beWx8jU5C1vYvgWoW4Vrih8asIQFxsjo+mB74D7I2J+xMmXEail6Evcc+B70B9tpHKyH8T9I1H2TZ03+CEHN/e0Mugty/wYIR2V+DN6ZG5zq/ZGVzPQc3G+5a6b4ZnzPMfVfTYWO8T8sYIqNkGrFnTpmOb2yDaPKNmPd8o+B0Y+EahvzZsLDPeYcInNoQQQgghhJDawxsbQgghhBBCSO3hjQ0hhBBCCCGk9tTfY4NIfmeFFbOqj9a7kfiumrCtVG1mSOCxQW3BMvSYPdLMly6u6/U9Nv6XKr1sczOmXYjuYN95o7+V2Rb++0KQ5jeuf0Y+Dhd4mIAnIcvcAEDvnAF6VZSPnzpmoMdNfE/EhH0rvuOmHx4bb9cjjw16b4EBaegD3XZ0wnhl4qekYD6Htm/cmpOnDScdymYhZRePTYBXa7YQVRuoNTsffFcM8HWUqe+4sf4E4LEpm+nvCxHoV1ATwNsG36WFfAfmOzSUtyDw1Nhjua18B/bCpa/FGe+/0dsfehnQO7ACUyvw33j9UKMk/2aqXM+29sB8yb4wW4cZPhrvuADj9Oq9TN4+tZ6TdqSf+H4Y+P4z8WsR+m+C99GA5eu2DG8o+n3hvPkmvjiF/u84fGJDCCGEEEIIqT28sSGEEEIIIYTUnvpJ0YBszEqHUuVmYTxh/NlacqRzw3/upiVlSF5m221bE8Y9q2VkRD+jR3z6Kbrtp6dL+1hVPaou7WN68xhbt5dWx+RJ0+zjbxUFDaN744/Nw2enaTK16QCUJZaUld372TYU6QzHzFhGKL9yoC0+n98x3pRVF7rUrNLDHutgdbwUZduox7GP5sH4VhJEpo9yoJByIE+KZkFxsQ7IXGBsMpDOaNkYjGk2dQblZkC2Fo4TX7fkmHVwXrGSG6dlPUGbkuqMxSN3RYy8PIi51ZIfFN0bl/zY1xTovvAcnyHbDSR0iQT7yRvU9O1BPVeNIZ8o7hnKzVJrFkgyc+LLk+VeptYaY0W0zdol9LHQNtumAaSVMCa6jM/nR42DWrfXMFTPXSgztNN8YkMIIYQQQgipPVk3NmvXrpXnP//5Mm/ePNl3333l1a9+tdx1111en23btsmqVatkwYIFsscee8jxxx8vGzZs6OlKE0IIITvhtYkQQohI5o3N9ddfL6tWrZKbbrpJrrnmGhkdHZX/+T//p2zZsqXT59RTT5VvfOMbcuWVV8r1118vv/3tb+W4447r+YoTQgghIrw2EUII2UGWx+bqq6/2pi+99FLZd9995bbbbpP/8T/+hzzyyCPyuc99Ti6//HI55phjRETkkksukWc84xly0003yQtf+MJqa9mLWFLoozFN0H+j+8Ujna2PBnlsGsaPg3w0TeS/0cvvQ9xz20QxO6/NxG3qtsBTk34/7Xlu7Hye7hbEX8J9D2KT+xWHGy89HNsMSI1NDqxmiZHOWR4bpN3N8N/0wuMU7F4Qye4lTtoozIyIZR0vG6RYgvpCcc846ju+fWeYTaxvTOW1qWzlxz1b/IjjnAjc+Pjah1ACTwD2xphzte3b0n0l2teZXxalnq9l44/VBIp7tucV5TtwY7Zt/HMQeavarHejYcdBntq219FvA3HPMPIXeR6BlwGd46v682C6LvKVoIhy6AvLiCEHbdAXZuuyapuuYRBfjnyr2jcj4tdT4OdS6xLUaNt8X28cswLAM4Z8NMh/o78wvr7Hr0ZBPXfB+rgRk/LYPPLIIyIistdee4mIyG233Sajo6OyfPnyTp+DDz5YlixZIjfeeGPXMUZGRmTTpk3ef4QQQkhVeG0ihJDZSeUbm7IsZfXq1XLUUUfJIYccIiIi69evlzlz5siee+7p9V24cKGsX7++6zhr166V4eHhzn+LFy+uukqEEEJmObw2EULI7KVy3POqVavkzjvvlBtuuGFSK3D66afLmjVrOtObNm2qfgHJkQ7BKGj9+NnKzeJSMCw3021x6Zltb5pxtNwsmE9NNxvxjEcrU0OP+LT8zC6vrR9BFnGZWpg5aNct7f7aRkG70tMVmSHjMcICoikh/ZKmxQikCCCKOjU6sWKkc/gmZNsXRUXGl5/cZvBkW0Auatv8qFUznyf3snIZIxuIr1p6nQAVZNb+BaU+JdLKGUa/r02uWXQkW8nqCCjdibcheY6dL/kN6yg6F0jI7HQgARrQn83xo+Yr59g2FduP4o9HjWxsVI1p5DkN1deNmjZ92TDz2e1WgJhdfR21Kmn/LfLpMjVPKhucD9V3QvHOfdKfptYsjnsGcjMU99wj+aStZz2Ort8d02r/2uNA1bCVVqJIf11PDVuXWj45CraT+X62hvU4BTh/BK8d8WrWLAPGPat+zh6/ul/8ZJkU95whRat0Y3PyySfLN7/5Tfm3f/s3edKTntT5+6JFi2T79u2yceNG71/GNmzYIIsWLeo61uDgoAwODlZZDUIIIaQDr02EEDK7yZKiOefk5JNPlquuukquu+46Wbp0qdd+2GGHycDAgKxbt67zt7vuukvuv/9+WbZsWW/WmBBCCFHw2kQIIUQk84nNqlWr5PLLL5evfe1rMm/evI42eXh4WObOnSvDw8Pylre8RdasWSN77bWXzJ8/X0455RRZtmxZ9UQ0QgghBMBrEyGEEJHMG5uLLrpIRERe8pKXeH+/5JJL5KSTThIRkb//+7+XRqMhxx9/vIyMjMiKFSvkwgsv7MnKdgVGEoJcXS+22TR5Ppr44gL/jWpEkc4t67EBPhrbt+X5b/w2z2NTxNsQVseo9dhjRgSr/TdjZvwxEOlsNce+RDQ+nzPr5mmcgx0Vj3T29cBWyBydAP0mIEMbinZTaqQz8qoE2lkUp6rnA56aCZcBtbvxMZN9Q4Zkj0KGj8UZ7XCpCiBIqAUWLq/NbAutnYY+mhwPVWpbzZnKa5NrhZp7kdx43HiRJMc9I0+CbVM+hMCDkBh5K4J9NF7boGkbVMU+aK5/Q+MniEbTP9GU7fEVaG/zV6YcGf+SjRHj8YTeDeVzsPvMemX0brKXJjWzjdUtwHHun/NAbLOdD/hGi3hTdPyuJEeU2zZQz4lxz9PuCzPeLz1t69mpGm4M+b9gmq3xtvaY/6XKkfEVcCO2nse/Y9Osp/aFifHf2J8+AmrWm7a+Uf27F3lq0e8LEF8eALw5XbujsQxZNzb24t6NoaEhueCCC+SCCy7IGZoQQgipBK9NhBBCRCb5HhtCCCGEEEIImQlUjnueNjIe9/tt4NktkiOZtkLJv5DcDEU6W+lZyzx+130HTJuWog004m0tox3Ssi0U92ylaGPq2W3TyMtGi/E2G32tCdvsq5DH19umbzr1rNpub/30P3iLvKcLjEucgqIpYv1EnI4B71V2bk5sJyrZihI2KCFLfXu2TCA3A20NtRNxTLRZPox7VrGoZt8nyxsmeADQUBuylLg0ACWNV4+itm2oaPUChPSAsllI0Zp4Y1aOx9XTOXIzVc/oTe0w8ta2WVmPjsCd47e1lVzHGblZsdv4mX3u7iNe24I9tnY+77/HI17bbx8d7nz+f4/u5rU9tmU8sa5smBXVmpsMmbKYCF7vMmLjtT05u5X1qM9WpgbOuVCKBuL+syL+EZWlaIltoJ5LE9ssXpuZL0M+qdvbVm6G5JND4zugmOv/nhrafXvn817ztnhtT1Q1rOtXRORhVcPbtvgHUNlUK9ow0kpwTWvYwgRSNN3VqiB1DSMZZFCz+rqV8coG//fMxOfT0q4wgE9sCCGEEEIIIbWHNzaEEEIIIYSQ2sMbG0IIIYQQQkjtqZ/HJgeg+4z2E6urtXpGHQUdb2uatlZT+V+Ap0bE99XMMT4av813pGiPjZ1vUPVFHpsRI7Iec+PjbG/74lXPtxP4ZlQ/EP28AxWBaESavp7UF2xq/01pPRhe/KQVfiq9aq+sMiDiN2+gyKCon5mG3hygjw3GTIxwttPQR9O2bXo+sw8T25CfwLbpbeqaptbUZKD3BgQeMhB96m1vENMZyOITPVQJ4WBkkpQtkSLhypka/xz4qWDccwHa1GfkSbCRt7ptwLSBSOc2iMDVnhoRkd32GPfVPHHY99G8cO97O58/tM9Pvbb3P3ho5/PNrQO9tt8V8zuft4pP6f20ARs4dMH5U+A3BPLf6Gjahn3dQKp30e5f5JVEHryqpPptTN/K9ZxxHocRzsgXZutb1bD21Ij4vpqhPXxf2H57bup8PmLBfV7bh/e9o/P5jAef47XpGl5fzPPaHtPrFfw01xsueImAmVZemYpR0OHrHYD/RtdwRl3mXrfKjLhnPrEhhBBCCCGE1B7e2BBCCCGEEEJqT+2laA7m3IImL6oxPQrae/xsY/e0NMvGPatpJD0T8WVktm2oOdq1n4jIYFNFajb93Mq5je3Rtsfa489nHzMZnrqtIeD72m2olAjb/ZYgUtqpbWNfPt/Q0iEbqYliudW0nQ/GfaYqkCajN6sqG0icD0aIolIHj5hhFLOZbth4U1WmjTFTQ1puZuZrjLpoW3O7ervzHBONqaJ4SxvL6z3+9tu0JMfWuo109qSHweP+eH2hZOZUOSFKdEYEJRtXEBBAqhStcnQukJt5sp4c6Q6Qouk43EB6ZmU9Ki7XzTEngUElkx7yrzHDu40LbQ6c9/+8tpfP+7Ga8lfulfPv6HzeMDLfa9s6On5tGh3159ve1q8w8FfT36hY1qNlZPa06h3aVtajzmtBrK4eyDR6MjWrzwHnB4fOHT0iNdLZxuhjKZr6nCGfRPHltoa1vLI0cc9OT5t6bqkanr/bNq/twD0e7nz+o+Efe22l2lHHDt/htT20fVx+tm3MyP7Hxr/kdlMXXg2jk4KIVwBWBlmC61YD7Cd9nXbBtV/J1FBdmgPRRSe6QykaIYQQQgghZFbBGxtCCCGEEEJI7eGNDSGEEEIIIaT21N5jkwzUwce7BhaMVB+N8d/otpaZD0U6Dxk/jO4b+GjU9Nym72zZozkeVziv6etFNxdDnc/WH6G9BtZ3gMiJy/UCna3/Rgk6A6m055fw79F1/HOBfA6B6STyecdA0ndS/TA5sYqepjveBqNHJ4h7hpHOylfTsG3aR2P9N6PjCw28OSNa9OuPWbaBWFgRRkwm6unF/x5WQ+9pykFEbOit0BNmTOSvSvTm0EfTG1yzS4y4dPHyaTJqBFlAoCeh0f2ziO+jCfw3Og53Qr+Cmh4w1z/lUZgzx4973mMg7vHcVI5ff/70nhd7bW/d7987n3dv+ZG7uw2MjzM46I/ZVn6F9pjx4OnvGxwvcc9Nw+4M9RXR+SL4DYFi9NW09QD6bWY+9YecqPgA6AsDMfap9ZzhsfF8YaCey1bcU2PbXVDP6hozx3id1fTuc/zfU7qGN6v6FRH5s3te1Pn89v3/zWvTNazrV8SvYV2/O6bHN4AzPqGghkGcuf4tZK1Qnv+mTG8TVJe6zS5Q90uoURdcaOPwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ76idFA49A87Q76iOUmxkJjJq2q6LlZ00rU1Nj2ghn23dOY0x99vv6cjMrRRt/XKqlZyK+/Gy4ad/T3HuspMxrs9G5qm8oRVPRp+Y5J4ouTI3sdpPKbZ5GMqRK6DDwZExQphZvs9OB3EzHPY+afaFlaqP+oJ6EbcQftLFd6UCCXagf49vn5o1oiy8f8Qe1Es1StVvpnX9usfnlup7NbOit49GJLtOkr5TNQgobI94NIM9Bklc/OtfMh+RmyW9qN7WsJT+BrMdMq3kL07fZir+mwF7jNOvHhjuf73jgSV7bg/uMx+Paa8MAeC1CQ02XA0aKpqWqZrVslK3z5KlWpg2kWXp1bFsZ6Sf+PrTnWK8tiNWNR+5WBdas7Zsqn0Q1a9tA3LMnL7PyyUBqqcYx9SxqnGbL3+C6plqB/mqc9aN7etM/un+8hh/cd57Xpmt4AFgQ9LEk4tewV78iIkZ6p68r9pjRdgL7O8y7/pm69NrQbwFwTassk368scx4DMMnNoQQQgghhJDawxsbQgghhBBCSO3hjQ0hhBBCCCGk9tTCYwPsGskgH03gwdA+msCfEffReG1mPi8KurCeGhMzqLSWg00/NlNPDzZ8j81uymOzm/HY7NYYn9694beNKgFr2+gutQ7T6jW9Njiff/88Zr5/qXwHTSOkdGq7tU3cn47bLkurF1WaY/FxXj/TBuOepefkxDYjIWp1H01c6Ip8M0E0dFt/tm0u2qa9K0Gk8/bxQRuj/goUynPTCA9u9dn8241eRobvoTTL0N8/iBPXumKrGwdtnuY4oy58/40taBpweo0X95x4TkB+haAN+Q6A/wZ5ErQ3xnoStK/Gxj1bz40oH0JhPAnN5vj0QNP45VQBjxkTxMPtPcaX/9BQtG3MfKmGdy3217Ol1q1t1tMpf5Sz1xtzHdGnOXu4ligKWo+JTk8gCtruX+Rl8JaHDnnblnNNS/SMBb4wVLOJHpsg0hnEl4c1q6aNv0zXcMPUbEtN29+BI+pAebi9u7+83w+qtj28Jv1bqGHG1MdM06zLmFpPN2B9yOZVF6qGG9aXrHZG8PoO9J4T4BlDvjDvXFbx90ynu/WyAvjEhhBCCCGEEFJ7eGNDCCGEEEIIqT21kKIlkyMdQi/RLVzXzyL+40PbpuVmLSNTa8E2/xmbjhbU0c922sY979bY3vXzjumRrp9FRLZ7UjTzWFM9Z2w3/Tbdt22eTevpshmXqYn4UZVts230OFZu0NZSKdNWlvH95GsBQBtiErK05FlzYn1TJWwojhHGPbtom223bVpiFsjUdNuYkXaq6cJK0UbVcWHqq1DSxlClVnTtZ/sG8wFZCJKT2DhZLXMVI3vxFAUVH9unxGaSyVG2RIqUK2dipHNWG4h09tqsdEfLegKZmpowUh0x51Ud8WzPuU0g3dGMGL3bo+1x+dmcR/wv/MjYbp3Po3bF9bLNdUOvW3Bt0N/RfN9AxuTJQ83O0NsUvOEdxcH3Q9YzJXHPFesZxpfbmGYtnwS1HsrU4hHldn831LSVf6EaHlMroOtXRGTgkXibrf3Y8uy66Bq2ce12WkvvgppNjXsOfvdq+aZZbz2doYROllc/TkkpGiGEEEIIIWQ2wRsbQgghhBBCSO3hjQ0hhBBCCCGk9tTfY5PoowkinYGeEGntUcRkE/lvlBCxNUHc86Dy0QyYvrptyMQ96/jnocJv2115bsK451bXzyIio42mavOFrnraRkHrSE8b79wyouOxxvj9ddMIOHX8cxv4nUqwD9G+h1HifYh3nhCwQsmRzsBHg7wbyGODvDkifhy0jW3WPpMg0nlUxW1aj81oosem5ddlofT2hfGFNZQxy/n2NSmUkaYwmm6rk/e9Oqa+1D4MYptBnCvUyYMoaH/Z0dlIj/DinjXgfJFznkERuH4UdNxHA/03wFcSeEyA56YArzuwUbaaMfOlNisfwsCj/sbYWs7pfB4xHht7zfFWU58D7Hqqc0KwLcr4ucs2oYhn81IBvwm9UiDRrwDPD6Ypx3OTWqeoX1bcs67ZnHrWNQuizUXEr9nAu6J9YWY2sOG03+vRsUGvbeDR8c9b23O8Nlv7seXZZesoautZdnYavMLBNzxFVyVA/74q2vF6zoooT/UMR8ZG8IkNIYQQQgghpPbwxoYQQgghhBBSe+ovRdNUlRUFUiUdFZn+uF1P47hnE+8MpgeN3EzLz4I2JT+zMrUhJUWzMjWvzQ14bVpupmVptm3MvMFZf4eWeYYYTo9vm9Eyvk1tpGdbx/qaZ+N+ZLd9dIpkAqANUXW+1CEtGW2ehM3GNifGRKN4Zztt2xrteJs3n42CbqvjYsxowXTbqK8pK9Sj+caYiS9XNWNjYLWEwT5uD+KeEyV8tg1FOnuxzZN8bJ/URirhGmo/Jh7qvXtTu9qhUNbjt5Wp0h170gHxuEHcsz7n+qN4sjEb26wjcFtb/fkea49fj8aAFM1ei/Xy7Xpq6Wgo9bPfPy4rdfoPSJtlt6n3FncgU7PHLpL1SLytZ6cAJEVLjXS259zU+PIg4lhNBNIzs3x9Xrf7F/zW85ZnpfZq5UZK/zdTa4uoNiPtB5HlXs3a35aenM7Us9027fixbl8/EF0DGPVt5ZuqybzCQMD1Dkoru+AY90wIIYQQQgiZTfDGhhBCCCGEEFJ7eGNDCCGEEEIIqT3189jk2BeQcM/zYJgmoBXWEc/W86Gnm0HEsfbY+G020nkA9m13/Szi+2oCH4323xRjoM2fT/tqthn/jV6+Xc9BlaVrtdE2/rmhxJd2u+lt2jY61waShIJ9X4B+ztOZBsGZ0TERlb0yGX2TvTJAt418NDAKWkwcctCmxjFxz4WKXxYb96ynSzOo9twMmPm0xtiMKS0V6Wz9Pm2gY4Y+GuPH8eI2TZu3L0ztqb5Zsc1o35Oe41o7/puwX2p0buCxiXtAvGnQFkbeqs8ZbUGUuedJiLdZtB/GRt5uVz6E5mP+GGP6lQIZ598Gur57kdXW82GjdOPnQC9mNzDFgYuTHiPw94I4Xr3ZkFeiX+eAih4b6AsD0eZ62yNfGIp3ttPWK+l5V/wmr55t7WlPsfXRtFQNW/+NPg5QXLk9lrT/p2G+RBn4wvT1xwysPWOoUAI/S0X/DfrtkRk9zbhnQgghhBBCyKyCNzaEEEIIIYSQ2lM/KVpF7KM9JEfST4ODGEkgU0NvjvXij61MDcQ9W7mZnrayMa9NRTjbvkNWwubFRPvzafkZkqmNmmfF+lGt/X4N++gWREPr+Gf0qDjcT937BQQyxPHPUEEW1FMfIp5TpWemL2qDUdCoLYhpNn1B3DNs0/Kvto10Vm+FBnHPts01VVvLr6eGkqa1zRubC1XCUGpn+ubEZLvEfYHinrNkagjK1irhGl1iVLv1S3zFQFbcs5cJa9p6Id2xseZAbgbPqwArwdERuK1t/pgj7fFrhX2lgI3gTV1Pfa2AMh4RT/IV7HPdtWnXRTXaN7XrcYKLmtpP9vxQpsnbsuLfs6T9ahi03jnySShhU5+RfDL4IRaXU6LfejkgaWVrZHwZo6YNySkboGYLIPcK5ZT6eAbSSrMuwTXHbx3/aCOdvZWJzxZeJ120qVsNBzUA4BMbQgghhBBCSO3hjQ0hhBBCCCGk9vDGhhBCCCGEEFJ7di2PDdQlmq6pcc9An2t9HXraxh/rtgHjOWmacVCkM24bj1geEDQfiJAO5hvr2s9ON4woUn//ARPVa7//dqWxDrxJal1HwfYONalp+zdLZFx1PksvvA3AnxG2ue79bN9A0x0ZX0KvjOcBabt4Xzufqg07nxfx3PZryGn/jWnz50PL8/9dB3qB0LaxfhwQ24y2aVX/TdU2Ug3XcL6OfeffE+Od7XQwXyPe5vsVjLa+qfvZNn2A2uWpug/8CWKmU8+rPg7E3GqPgvYniPgeBRSPiwjWq4h/X+ud9DwK9uvp7Q3NK/YclOZRCPx5yH8DfTVVr3FgmIy4Z1TryEfjeZpghDTw38hEHpv47zmNrb1SrfioeZ1FU9WwfdVFatxzAxxbNmbd+sT0qwocuE4HNeTVM6pRO6Pqa2PIU/1eCdemFF/jTvjEhhBCCCGEEFJ7eGNDCCGEEEIIqT28sSGEEEIIIYTUnl3LY9MHcjSZTSWKDTwnsC3HR6Pbxry2OaptjplvjhI/Dpjl67ZgPrA8tJ5NNWbDvrcHTNtto7d30/iWCvVeA+yxARprq/n1Xt7Sm3fTQJDnpSKV32ODfDPBe2vi07ZNvHfVmH2oPTDGi+W9n8a0yZiqxXLAa/KWYedTvprCiIz9d+qYbWHfU6G3DfDRwP0L2nr2rhrSe5oV3mODPAl2RuRXUHr68L0faiS7fsCbA9tyzquJWG9BW003zXtsSs9jk/5OEOyxVJ/BO09EzDa1nhe1OoX5TqnvBIHvEsl6Jwhoq3r2CK6NoK/2yqC2YPt277djWte6rdm4xwb6xEA956BrL3iPzbayaz+RdJ9YjmfY/g71fsNY3xKoE1h63h/sAQX8ew6c6HI9NhmPYfjEhhBCCCGEEFJ7eGNDCCGEEEIIqT27thQNPL4T8EgdPZ5EEcN+FHQ87rnViMvLRPw45IGGlX+NT1vZmB/NbOdTUjSzLfy29OVpuVmwnm68tAZKf76RjG3TAFHQ/vb2miYTxjxzQKqQDAlb6nxAsdclCto+x1bHhZV4IbkbiHT2npu326atjLehMUG8tI7JnEzcs79N7TggClr1DeI2kUyNOrUpxSVK0bx57B+ATM0BuRmS9cA2XTRAumNlPPac68m4gvOxRNGSHCsp05G4ze3+weTF41Y8q0NZj+2LpGj2C7pIPzEx6znRuToK2p5X9Gx2VVLPARP1S4wsz5JPFon13IzXc/BP8AVoA7UPZYlZcc/x2ObGiHpFBYh7zsH7ukaS3zZfSkvvgrhnFF+O6lmtgY02L1TN2q+nr6NBjabK8nbORykaIYQQQgghZDbBGxtCCCGEEEJI7eGNDSGEEEIIIaT21M5j45ARoKKxItDgqs/Iu4E9H7ZtXBfZBPOJ+JHHTaNn1H4c6+MZEBQTrSOdi2hbMJ+A5YG4Z/0dwljs+LbJ2aaeTB3oY0ONtZ6w9VQPd05OpDOaD/tv4r6ZwHOjo5IDH40W75p90Y63aX+MM14ZbzqIkAZxz3rStrl4ZmngufEinY3GOdULhfTuYD+h2RgT3X9ckSgP13J24PGEsboo0hm12eVBv0KaF3VHe9yfkkoQgaumG8ZjM+bFPfcqOjfeZjeAngx8S95k3H9jj0LkuSlU3+Dr9SI6N2enJfptRGzNOtBm5kuuZ+sVifvC0O+5HD81AnlsmqNltE37yxyoX/Q7KIh7ttu71NvGDKx9nKiGbAw5iij3zDngBzP8fSFRdjYFHiwAn9gQQgghhBBCag9vbAghhBBCCCG1p3ZStMogOZIBRTrH+tlpJL9qSHy+HX3j0rCmJymLt1kJm54eMPezOrbZzqeXkbM8XzIXl9rt6Ds+zvYJZHpV2iqry8DbwqcCKDdD5MgUwNP+nHhpr29p29S+MBHL3uNoEM0sJjLci3h2VlIGpG9qTGeiOLXcLJDeBfHWRbyvt01B0WTsC6BswTikBSBVcI3Et2Cnys3AeSYr0tmTm4ED1F4LgeQnjMdNl/ymouU6je1t0DMdFD1doG0BoreDw0dHM9u3v6t9YaU7eiAYBR3sQiQjisfIVwUq/YKLhZoPRDPDegZjhpHOaesiYuSTqXUxAUiKVmwHOd0A9HvG/w7px2hhDgQv/jmQqUU+71g51WSXryPKzXHgjRmXbAffvMumSDrnPg6f2BBCCCGEEEJqD29sCCGEEEIIIbWHNzaEEEIIIYSQ2lMPj01V0WiiJyLVb2OnA68MiDj223wNZlPiMcq2b0P1bYD5BoJoZv0dbNyz/myWV6QtL/hOwCfUapho6HK8DOE2Bd6kIAo6Va8a7TUB/fLbVPTRoEMEtwGhq5aiB74ZsD45kc7AD+N7bECb9eYo/4317TgQYe3pj7O+L+hbcT+laI7JNNFwoYelC8k+GhSxDPwK4ZhxT48ex8bj6r7BkEDPjzwBCBTTbD02yMuQCvpOE3mI/FcDGL+C57+Jx9xaX4nnu7NRvWrGwGcHzyvAf1MVZP+BvjDbFq9nFPcsKO4Z1HpY38ifkraxUDRzab5wY/vYeBuINkfkvb4ix0+nJpytZz1hFup5c+K+HevpQb8LYJ122d45hz+f2BBCCCGEEEJqz6RubM455xwpikJWr17d+du2bdtk1apVsmDBAtljjz3k+OOPlw0bNkx2PQkhhJAkeG0ihJDZSWUp2q233ir/5//8H3n2s5/t/f3UU0+Vb33rW3LllVfK8PCwnHzyyXLcccfJ//2//3fSKzshGY+qqsZWJkdBZ8imwmkdo+xrYnTkchNI2mz8csPrV4A2fz5vTLi8uIQNzWf7om1RdZ/lxDjqvlOh/slRc6T2DeKH0RipErZgPiuv0BIv0Neum5aKQZkakpSlxz17j9SD7xAZQwRKyqxMD8U29yTSuaoyF3+lXYZ+X5vS457BGJ7EyewJL8rWLhvIzcDb36F0R0vRMl6LkAOSkem2YjQuResV6PuGnfVnu03jkjL4FnfVtyjN91NtzrShLTElqe5ABplaz0HcM9y+3ccQmaBmJb1vVZA0Tddwr+o3T5qmPpu+Dp0j9GRwfotHOntR0DmvPkC/L7p93wT5b6drck/Fo48+KieeeKJ85jOfkSc84Qmdvz/yyCPyuc99Tj7xiU/IMcccI4cddphccskl8oMf/EBuuummKosihBBCkuC1iRBCZjeVbmxWrVolxx57rCxfvtz7+2233Sajo6Pe3w8++GBZsmSJ3HjjjV3HGhkZkU2bNnn/EUIIIbnw2kQIIbObbCnaFVdcIbfffrvceuutQdv69etlzpw5sueee3p/X7hwoaxfv77reGvXrpUPfehDuatBCCGEdOC1iRBCSNaNzQMPPCDvec975JprrpGhoaGerMDpp58ua9as6Uxv2rRJFi9e3JOxNUjpOKloPUWqV8R6TEIPCvLjxH003nxBmxrDPKjz2+JjBr4d8H3RulSlaqRz2NaT1ZlZpHoyUj01pq/17eTEPXvTgR8l7nnx9LpB3HO8DUU6Q/8N8AnBcapmf2f4nSqPOUuY0mtTIZ1dniyhR/4BGJ2b4UkA/hvoM0DxsGC6ql/B+g48j42Kys0dR6PXzV432l6/+Hw7ppV/AMZrW69MZIGmzZkD1vPc2Cho7xxr/DfodJSzm6r6whLrOa8NGBJRtLkdBv0WwLMmYSOcrU+s1yAP0Y7p+DFaeCeCuGcsq9Yl7Vpoa92LmU85kWboy7KkaLfddps8+OCD8rznPU9arZa0Wi25/vrr5fzzz5dWqyULFy6U7du3y8aNG735NmzYIIsWLeo65uDgoMyfP9/7jxBCCEmF1yZCCCEimU9sXvayl8lPfvIT729vetOb5OCDD5a/+qu/ksWLF8vAwICsW7dOjj/+eBERueuuu+T++++XZcuW9W6tCSGEkMfhtYkQQohI5o3NvHnz5JBDDvH+tvvuu8uCBQs6f3/LW94ia9askb322kvmz58vp5xyiixbtkxe+MIX9m6tNX2QFaU+YkexzQ0rL0Nxz+BZsZWt+fOhiGUjDVOPD5vmUaLfBuRmVkLnydTi6xKsd7Bu8e2W+nZr1C9PsgYe1aJiy6nDVGlAjmysyvh2nJxXVhv5lyfVguuNZGpxuVkYI1lG2wogU6sqi8uJX0Yx2UWqhC1Dplakbfpdmqm8NrnChRKxbkD9c7wfjnRWbcEb3iP97LSVrujPk5D1pJ6rIaO+FE2/1R1KzzIWAa/vYLsF20ZLyuy6of2k5WZg3wdR0H5Pf6qITYTLqAySf/WinsGYwbYHbVVl6I1ebSdVw72Ke85ZtyL5PBCPGg/jl0G0uVen6PeUadKLS3gXQdAHUPk9NjH+/u//XhqNhhx//PEyMjIiK1askAsvvLDXiyGEEEKS4bWJEEJ2fSZ9Y/P973/fmx4aGpILLrhALrjggskOTQghhFSC1yZCCJl9VHqPDSGEEEIIIYTMJHouRZtJ9CLyF/loqhJ6TJBXBvhaUJuNZlYCRxv3rKdQhLRdntcvow15jEKPD/AmQb13tGn2UbFkU30zcD4R38uCfDzARyNlaZq0Ft3Unp4PRjobnXp0IsRPIrXLKLr2s+Mi+TOZwai45+DvaB6FS/UroHFA5G5VT0LO6w36QTGaHvfck+WhOFzxNze0S4DoXOgrqerbhJHOPdpnOcvXEeUoChqNAyOkQV1OcCLt9+sdrI8G1XCq5wb/tvHbShjJHq9n7HfyGz2flPV+wa/kGWnS5+vW1q+4Z0IIIYQQQgiZifDGhhBCCCGEEFJ7dmkpWipVH7cjaVQgqcp4PGzlYF6blqlljamlaAVoA1I024bkZ6pvznevSs/iPnu1kIpUjXFO/Uo50ihveRPJpuKlEEq1dJuOY0ZR0EimlhNTHRtfJO87eDHSU6t7TEjG7NqXSrceUUGKBuU5WRInMB+Q50DpTkV6Ff2sI51lzH9ru32rexV6JaezkiatrEFtUKYG0nFtpDiOf+4DvZJWevNVrGew/MlslarWAliX20crrk2cqjWMpKXhiPFXXRSqLVDTpUor0RWoR7HYO+ETG0IIIYQQQkjt4Y0NIYQQQgghpPbwxoYQQgghhBBSe2aNxyZHo9jvqOCmEfMjD0oDCf+DcbWvpf/odbPr2YD+m3hfuC36EL0dsAvERE97bHCJtLTAR9MrgDdHe3qwNwVEQQfLS1+1qtArM4OIeWwMyb6DYHwUBd0Hf6AXD2uacpJde4AL/AnNni8jJ97a8yTYWF/Pr5CxZZD/BngNdD0VOdG5VemVZyy1nlHt5WzejDjzfuCMT6zX5NRsuFET45dRnDjyjCHTmAWN2Y2c3/DJPQkhhBBCCCFkhsIbG0IIIYQQQkjt2bWkaH14zNirJ7xImpXT15N/FVbShWRc49+kWTRAm51PydsyvoMGxUJPRC/kZruAuiwkY7P05el7EA2dIT+LUZo60ZI22+ZAWypWpua0tMSuW/qwnowtiMkGUa9eIq+Vwu2SVVxLXOFCKU43ct+sPckxUqU7011J8O3rY/at7b2XolUm2OfxeFxf1mOzkSP9JmrzutlzV4/yjwFQWtmLekazBXKrauNMCaPbOx9LN2caVwRvp6AJSCS9QzZLBlnxtQiT/M3CJzaEEEIIIYSQ2sMbG0IIIYQQQkjt4Y0NIYQQQgghpPbsWh6bDKrG/vmek3j8MIottiAPSrPielqvTL+pup79YipiHWtJ4PmId9VlmbM5YV8UBd0rUr05qWN0YcaW10xdr9lAVU8N8mfkjINmQ1r7GeRXcNu3m78Mjrchb06/SI3H7dmY1ZZnPTf+kNVWNMlHlkJqPQfHQdryp/tabz1jblT7xKbeY5N6rFfdaoFlrPJAehDQhv4WgU9sCCGEEEIIIbWHNzaEEEIIIYSQ2jNrpWhk8lSNce5FhHPPmEnrMt1Mx6ao/BybkGmikDRZRC+UU1ArGpfuTLc8pyouiHuuCSB6O1CCTfGu0ZIyK0vLkpslxoln1WwfmO7an+oarqyItJHOKDIcysZSI53NjGh5jHsmhBBCCCGEzHZ4Y0MIIYQQQgipPbyxIYQQQgghhNQeemxqRlVfy1TTlHqsJyGTpp52BkICpt2fEMQ9Ty1THn3dK28B8kDoppp6rwJm8Pfw457Tme5jD0aNTzWJ9RyDT2wIIYQQQgghtYc3NoQQQgghhJDawxsbQgghhBBCSO3hjQ0hhBBCCCGk9vDGhhBCCCGEEFJ7eGNDCCGEEEIIqT2Me64ZbVePe9E275nJbGGakzEJ6RUuiHmd2gjaYs6cKV2exU114m6vlpc4TmH2b23jn/X3mGHfoRio9rM6PPammOlevmaSu5S/PgkhhBBCCCG1hzc2hBBCCCGEkNrDGxtCCCGEEEJI7aHHhlSmqt+nnFFazhm0LtPNdGyKgtuf1Awn3TXgyJ5Stczt+Un7CWybbjLrV0yxV6YqRaumP0nM5vW2/zRveuurSW0L/Dfoe+hhcmq2D/6YafeJTXENV/120NNjB0UL0eOg+XKWN0n4xIYQQgghhBBSe3hjQwghhBBCCKk9NX3uO3mqRutpGZWVVHltGdoDJOlqV1zP9hQ//q66nv1i2qMTZypWvYKeDjfS+gXzWbWBnmhMwX7Ry2iYYytV+jbBes7Y8pqp6zUbsOfcIrUNSGd6JG/T0ih7CEx5xDEAxT0X0xHr2w/5DBwzcXlWatiHE1LPoqFRzSKpkotO+N3MfFMtu2yY7VJWjHvuFeh47sWx3rPzBZS3ZfY38IkNIYQQQgghpPbwxoYQQgghhBBSe3hjQwghhBBCCKk9u5bHpg86017JCcuMaGTUt1T3oraf5/kxm6JUwsi2K0GbnU97iqrdB1eNhbbLr8oMkpD3jkl4XvqyfORdSfa1mDpJ9crYtlQKqyEH65mxCG+cYBl6+XZ9EteFTCuFK5I8Da4XXpkM347no0E6ezDkVGA9CR4zOe45xxuDInC9fhlt3v6dgujcIJl5/A8OLaRqPaPZgu07PtCMO1UOjPvEYK1PAdBvA/qG27ua9ys5CrrH8IkNIYQQQgghpPbwxoYQQgghhBBSe2bwc9/ekhP/W/b56WHb3E+iaOgy496zrcYpQb9e4cni7HdCEdagL9wWIF67Z+wCujUYtzwVoKhkqxvoh44AScES46azpGBTsIFnbLz0bMRJ9/NEjnQHynWQ3kzXdsbJKvHN4c5oV6yUpd+nx2LOgPlL769kzvu+RbSt23RqG14B/Tl9jGT52RRcwwpTwFCaBvWTWqoUr72sUg8kmvH93Q+KVlNNjfV8/MnUbGVJGRojNbI7Z8yqfR6HT2wIIYQQQgghtYc3NoQQQgghhJDawxsbQgghhBBCSO2ZNR4bRFXdJfJ8tG1bhhC/Dfrq6GTULxxzXOBYGrGj3xZfF7s8FOPs+336r2vNkRX3RBvdJ0BiN9Tlw/ki/Wxn1GYHDfqCfyLRfpUg9VZ5XgrkvwnaGvG2VOx8id9hR9/pM73klC+9OX0g5rEBZHkSPEtCPOY2J1oV+hUqRtKi82iO/7FRqKtOa47fJu3s9bL0ylcRROe6tDboOwgWMv6xKKvNNx0+UV3f0E9WtZ6Dgo7PlrO3q/p0G2gjez6x3nhsqtYw9N9keHEcqq/USOcpvBjxiQ0hhBBCCCGk9vDGhhBCCCGEEFJ7dmkpmgOPMtEbWTX9iBi2Y1hJl44/tm2eNAy1mQeypfe5jLYFcjOwPK9fRluwTb1lxLd3zr7od2R3raiqvPOkYOkb1Mq2tGoMysaQ3KzRME1FtA3K1NQ0jHSeYJv5T/Tj+do50j/KxmpCTIqGNDFWneMdW2aYhou2YYmT6/ZxxzBANpUTf9xv3ID9SbK9v8tDkhsxu61yBC7oC6U7GW1oXapSUbIYyC7d5OsZSaOC1Qr69vfHQMOswFhQw/G+MfBvm/S4Z1TP+DxgF1q1ZhPjpVPaMpLf+cSGEEIIIYQQUnt4Y0MIIYQQQgipPbyxIYQQQgghhNSe+nts+iCfTNUVI89Haf0vyCuC4p3BvWcpyJtjfTyl+mzintU08vvY5fmenvh8wXoH6xbfbqmeJtQP7U+4r7NyddO7JnteMlatiE5gvHECzwn4Uibu2It0hutt/Dd62kYogyhop3w0QUx0A4zpLU/ibcA3E4C8MkFb4s4B3hwLvTlTS+GKThQv3Pb68MnwqOmYX89vs6N1/KM1EsJYXf3ZpTYF9CriOYrxJ+goaORP6FncPzC+BV4G6DvQ+yndZ1AkexnMeTTXrxADriowjaEhU+sZGEKqesYsqL575stVNZzqqZmInHVzoE6gv8urPXD+QDHkOV4zRZFw7kjpsxM+sSGEEEIIIYTUHt7YEEIIIYQQQmpP/aVoicAntRlSJfwoXsm2ApmakolZSReIbcbjGEmXni9oU2OY3Dy/LT5mECENvi9al6pMFHOY3taT1ZlZpEqlciRNINLY2UfsSNJWUf6lJWYOyNSs3KyAkrK0tvAJPhinKhlys8pjkt6j4p6h0gRIw3y5ohlEp5Ub2Ycv5TE7W5/WzZhInlKANhwlW63YrDxHT7s56T9JoDQNSL/9fvH5gvac2GYUTava7P5Fkp8iUdYzKXk+kguBc3xqPWetd3IsNv7C8LcAnDONhn2VyECzB6PGCTZFVnx7937BOFn7Im0/BVKy1HreCeOeCSGEEEIIIbOJ7Bub3/zmN/KGN7xBFixYIHPnzpVDDz1UfvjDH3banXNy5plnyn777Sdz586V5cuXy913393TlSaEEEI0vDYRQgjJurH57//+bznqqKNkYGBAvvOd78jPfvYz+bu/+zt5whOe0OnzsY99TM4//3z59Kc/LTfffLPsvvvusmLFCtm2bVvPV54QQgjhtYkQQohIpsfm3HPPlcWLF8sll1zS+dvSpUs7n51zct5558n73/9+edWrXiUiIl/84hdl4cKF8tWvflVOOOGEHq12hKxIvGr+jDJRY2x9JSXQ/Ob4cba7cf1m6M2J+29Kr58DbdYroyOk0fJQ9HR8PtsXbYvqnpp0LXhV3XhVYGwz6Iv72ValYa8aIxzMZzXWcX8K9LU0G/E2b76GaYq3weU14m0w+hqlZga+ociYE7WB5SW3AXblWOipvDYV5Y7/JgIdP579JvDKqPOzLW0UnYsiWb0Fxv03UHc/CZAfxvPYGH9Cr+JyNej7hp3153Q/DPIdwEhn1RZ8deBJSPbf5BC/jITXLe98GK/nwgzq1XCwfVWbOd68rrZmreclZ38nUiB/l6rhXtVvng9cfQ46q8+oZm1bGeln2rJ8NNBv1eX7oth0Q9YTm69//ety+OGHy2te8xrZd9995bnPfa585jOf6bTfe++9sn79elm+fHnnb8PDw3LEEUfIjTfe2HXMkZER2bRpk/cfIYQQkgqvTYQQQkQyb2x+9atfyUUXXSRPfepT5bvf/a68853vlHe/+93yhS98QURE1q9fLyIiCxcu9OZbuHBhp82ydu1aGR4e7vy3ePHiKt+DEELILIXXJkIIISKZUrSyLOXwww+Xj370oyIi8tznPlfuvPNO+fSnPy0rV66stAKnn366rFmzpjO9adOm8ALiP3dMHzyxK34bLZCUBXIvIDfz2rA0a1TJzWzfUvUtwXyjzrbp7+CibXY+TyaWsZ5afqb7iYiMlf508jbNkPelPrqt/KC4X5HRqU9bwVPkQEEA23RjPMLZyq2gpM1KvJpKXpETv9xQCw3inlVb06xcc7y+nG3zJHNAppb1fUFf1CbpbYxxxkzptakskmQRnlwFHa9AumllNTA615M/mcZSd/MXWCg5UBglG5e5oBhlBJLnlHPiUrSqsh70nSaOe473FSQp07s+J9K5jPSTCeRmaj17pd5DuzeQYsFzV7yetTQtqHU9KJDFBdLKQIap92H67wR/CCSl9HVypYost1HQdjrGpF5B4p0HTN8S1XPksx3HtBWgDUk5YZ12acup66wnNvvtt58885nP9P72jGc8Q+6//34REVm0aJGIiGzYsMHrs2HDhk6bZXBwUObPn+/9RwghhKTCaxMhhBCRzBubo446Su666y7vb7/85S/lgAMOEJEdZs1FixbJunXrOu2bNm2Sm2++WZYtW9aD1SWEEEJ8eG0ihBAikilFO/XUU+XII4+Uj370o/La175WbrnlFrn44ovl4osvFpEdMpPVq1fLRz7yEXnqU58qS5culTPOOEP2339/efWrX92P9SeEEDLL4bWJEEKISOaNzfOf/3y56qqr5PTTT5ezzjpLli5dKuedd56ceOKJnT7ve9/7ZMuWLfL2t79dNm7cKEcffbRcffXVMjQ01POVzyIjxjI50hn5b0xbG/lIgr5xf4pu2w7awtjm8elRI2DUbXY+vYyc5fn+m7hvZkdfFPecvv1jbZUjS3PiCfsAitSEZPhBUPwwjnsGfQN/itoXTX/Gwmszkc6ex8avPe2jyYl7dt6YZj2Vxwb6b0TMdgOa7hzfDNgXcD5EH+JyZyJTeW1KjXtGdZDsv7HxuOokhNomyJf259MRw/aYmMCDktqG0N4Z67GpSgnK3iEPAvLRIK+M9Q9488XHtHWEPDbQR9OHuGd4vQFR+YWNW06tZzsmiHv2hrHn5iDOPO23QE79Iu+Xm5P93nsRwb9nHPhNio5RB70yZnll/LjwIp0zoqCxLyzSL0LSOfdxsm5sRERe+cpXyitf+cr4wotCzjrrLDnrrLNyhyaEEEIqwWsTIYSQareWhBBCCCGEEDKDyH5iM93YN5t6j+Eqao6CR3vqs32k7belS9F0HHIbzCfiS7WwxMvEL0uza78d01rC1o6OGcwnYHlgPhR9HX7/NClaEOmsP1d8My98m/QMBsrUwBP1PLkZkFTZx+9KYuba5nG0lgrkREEraVoRRDqntXmx0CL+P+XYNk/C5jfZt2l701UlfBlys1SVRI9erE0AhUtU+KE+oNYExC+jeFyvza5g6XU0y9MyF3vBs+dOvfxqoAjc0sh4Wkp/YiU/qfHP8M3sE5z/vc2BpDuB3Cw+X1F2/2z7Bm1x9WL6zpioH5AselJZm/as28zaadklrOcC1bOZUceX2+1kz9VqqVXjni1IitYeaETbdDQ0ipBGv4OCJPcSHKO2DUkrQT0jaWUBYs9TpZWInZupaKfvKz6xIYQQQgghhNQe3tgQQgghhBBCag9vbAghhBBCCCG1p3YemyxcmnY4R3eJoqB9P0gj2jZWGh+LibIdVe2jhb+L9LSNXx51ra6fd0yPqs/WY6Pjpe188eW11X3xaGnmK4H/JmPbYN+SbvOa6mKVwQCfRRDFmeirQfPl+G+s58TTWIMo0GA+7XOxXhk9TtPEwOpcWtuGxlRtNnrai3u2Ou3g+3f/vGPd9HxgWwTbG+xEMF9W/DOZNEV7x38TAY873c/q0r0iAZ0DzyGIztWTRiPvgB+kNMdBA/hTUMSy9tE0jHmk1RjfmG3jsfG8DBXP6uj6HmzeHB+N9ryA+YJIZ1U7wXwoAhdE58JY3aqg64/tq3ebPTa8GjLeTG9x8XqGXkXgU9oxUNyfAv1WCuTvsm3l4PjGGGj4G6NRVPvJ7Vm9yuB9Cv4kiF/2vV/xhaBI58Dr4s1nx1THwWQjyjPinvnEhhBCCCGEEFJ7eGNDCCGEEEIIqT27thStB6DH2GGMsYotNs9Ox2CblZSh2Gbd5u8+LRWzsrHtWjZmlq/bgvnA8tB6apmalZ6NgWm7bfT2bptHsPhtvPE29GhakAykH/RBVpQTBY1kYvrZsZVboelAmqWjoI00zDVV/KWJX3at8Zoqxoy+oaVq0c6nluGCNh0hWsTbjEwt+CcgJClrdO8XTGfIAskMIiJFC4676ITgOtD1Y2VjqrN9w7sXzx60gZXRGjIg4xGZ4LyaiJXuNNV0e8hI37y4Z39jIGkaetu8Jz9Ccbgi8G3sOAJXfW7bNjAflPVEPksXmY83X8Z+QgOhmtWz2XMlkkbpfQHq2Uo5oUwaSS2txLhqDat1bZkdNTbU6Npvx+KrRpTH6zl4DYbua2VjIO7Zk5jZVzb0O6I8YbMEYwP4xIYQQgghhBBSe3hjQwghhBBCCKk9vLEhhBBCCCGE1J5dy2MTaA1V/GWgs0Ua3LiOODV+eMz4QXRs5qiJNJ7TGPOm0z02IO5Z0HyNeFswX6trPzsd+IbU9w+WlxHp7PlvwPbO0aRW1dX2zPjQi2FQFGfQpn0l/saA/hvtmwnagB/HxigrvW5h5wPxyygKumjG2/y45/Tl4bjnnL7dP+/oG29DGnaoKa/YRqpRlEUYhypdbAdoe+vj0P7zIvIreLp0ZHQwbcrnYc8BWiMP/Qnie1ByzqsFiMfVHoWxQX+MAc9j05u4Z89jOYGnCMbj6thm60lAbV52rz+k5yOwlhPtZYCemoy2YJehfaiGCc5dnlnGX6Sq4QL5b4BxJ/SNFtE2mzvu12z8d4L9faEJ457HV9xGOrdVDbeCuOf4caAJXl+h13MCX5jnq4Ex5PYcEffKwIhy7b8Bkc4w7tnSpS0lYn8nfGJDCCGEEEIIqT28sSGEEEIIIYTUHt7YEEIIIYQQQmrPruWxAQTaSq3fDHSX459hRrhZBvSKKF/JWKOMtomIjCmB42gj7mvZ5ga8tiE3Ot5WzvHathWqzfmeHj2OnQ8tb6TU8/ltej77/eC2sd4ksL2RPtZ7VwESu4N3HCCKPr1oBHllYv1s38rvsUFt1kfSMDpm3R684wb4UbTPpenXSaHecaPfabOjrRltEzVt32NTtuL+G/QuHmMvS35XTfBOn6p+GKRvrwo9N5UoykS9N/TY6B1q2+J+BfxPkXqBwEyB6g74E0Sw/zQV6y3QHoUx8x6bweb4tarV9q+bBfAopPpkAw+Cfe+H6ht4ZbRfwb6rRvsVrFdG9w08NsBHg95xE+nXU+D5CdSzq1jPehlm2zvkUQMeFPRbLwddw8F7bJTHZiDj3UvYM6wnsMdG0HuSvHfVmDb07iVQz4LecQM9NomesZ3d7bEJ4BMbQgghhBBCSO3hjQ0hhBBCCCGk9tRPipbz6NB7ZGc1R2mxlXZxbfWYr21kU231PK1tJDA6ttjKrUZN3xbsG4971nKwAfMs0ZObKcla2OZLyvSYKO7ZrudIqWOi49tCxI+Kbps2vY3DuOfxz0imZnGJ/Xql+QmkYdGJCUhc1UDChmRqSH7lScis9Mz0VaVhH0druZlr2cfo4wMVLbOMtmobMwvU8jNz/HjytpaZL1EWF0rv4tNou2HpX7wwskoPSURIzynGRIqd5Qe2t3f1AcdkqBqrKOuJLsBId6ykoxFvCyLRUexsYlyule7o1x205/pjtNR1DMl4LCW6vnvx1vHoWhEj3TF9YaQzkOdUjs5FbZppkaLFl194+8LujNQF2kETpZUiMM5c1zD6DWFrr6XsBIPmdR1jqoYHG/5vrdS450A+CX4HQTll0Db+MajZMSUNAzHkgdzMazNjej+2zHxIptYFKL808IkNIYQQQgghpPbwxoYQQgghhBBSe3hjQwghhBBCCKk99fPYIKrqTkGkc2m8I1qrHHo+xqet56SpzAxjhYl3BnHPI4XveWmV423WR6OnB0rjsSnmqM/GY6Minm1ss/bcjNg24L/xI5xtWyM6jbap9TTpfRPGI8Y11tDAUNVX04f458reHKQ5tl4RZVSC3hzkIzHttq/e/VbX63lVjJ5fxz8XLSOwHVODDvinMafmK43HRnt8yiDuWa+LmDYzDbdNvK0Xcc8BVdtIJYoyoveuuB8CT5qumRy/AqCh9POlHRT4xaznpmzHj5828KZqP4GOdxbxPQpju/nzzW2OX6taZr4GuBbr5QdeIBCHiz0JfpMXgYt8NBn+G+ijgdG5EiXFv9AZJrWGUT/gDbLXH9HnWVvP3jnYXgzV6zrshcv4QXUNB54q8FvPW5zZiNonZn00Y7uLavP9N7b2vVVRn4PfQdoLZK+h1jPnxZBbX5hqG7Nt6jPy2IB6Ds5XqC3z93oxFv4tBp/YEEIIIYQQQmoPb2wIIYQQQgghtaf+UrTUx1mBVEk9yoRvrfeH9KVR5lG8eszacvYxvZKimef9241Uy4vGNI8udYzygJGpeVI089x8i5KbDZhnerrNxj1r+ZmVqel10Z9F/Ihn+33HjM5HS8yCCO3EN0hnvakXRRBKYlu/8J7P2sjWaJOfEGtlAmqTBtKHREkVlMuILzezK9BUcreyZaUAWrflb/CGms+ZY0Ta4/XmrNxsQEkUjVymbKo2Ox+Ke7ayNbU6VgqB9pO33ZD0L0emFutH+kLRDmVJ3Tuqjzn7E7xh3qsfK9100Ql/tax0Bcoj4zIXZ2XaWtYDCtHGPc9rbut8Ht3DX+/dGts7nweBFM3iyZTtenpvX49Ldex0A0l3kEwNveEdvandyoF6GJ0bQ39DFFGO6tkuH711Q/cNztXwAqy2r10gkkm3/Voo9TUG/NazaEnZHq0Rr210j/HPuzW3e2229mPLC6Roar3td0A1DOVmqC5NW8OLkAbzgXq2wHru1p9xz4QQQgghhJDZBG9sCCGEEEIIIbWHNzaEEEIIIYSQ2lN/j40mIwJRtwWzVfR1aH/ImNF9ah2ojn7e0dfEITfGxYTbjXelqcZ5zIgdm0qE2DCCxKbqO8cIKLeWg+rzHNM2Pv1Y2/fY6HWz6+m1tU1b23xf7ccBum3raYKRnmA/Ie9Vsq9mEjrmuIvGUDXyF82HPB9WG+3FFtsZnemr/Sl+m/bV2CROz89m9z302Ki2Ab9NT2u/jYgf9xxEOqsynTDuGUQ6w7aiiLYl70NDsq+G/pue0BgTaeysj8RtGkS3Az9VAepHkC4dxeOqA80OqU+dwXFuvWU6Ntr45draBwCK0kbg7qE8NtuH/S813Nra+byhMT86ZvgqgKLr5x2dgcfGehLUNIp0Rv6bHE9CanRublRutA3Ub9CEahZ4Nb02cP4PUsi15yTYGKqf2fZ2d4vnsbHeFfX7oh3/7WHRXhldvyIio8PxNlv7seXZdUFxz7aGRdesWZyuZ5s8DSPKQRR0L3xhlm717Rj3TAghhBBCCJlN8MaGEEIIIYQQUntqIUXzIgErjoHePh/EE+rHfjY60JNG+feFTaWzaZvHmA0dadyYIO65rSRl5hvr6aCtHW9rqmkbBe1J0dqDflt7XIo2YuOelcRsBMjNbNxz207rbQPkfYEsELS5xJoJ6yLyeaKBKpITqZksR8qQCXjHQel/Qd3XKsEadns3E+PTzaNqb5E2ClprawKZgoqQnuOvXKnkZ/bt6Fo+Y6U0uq+dL5SbAUmZlj4gWURGBLBuw28Hj0s2SG+oEveMpIVBdK73pnYzWzwdHb+mwA/yNaui5Cmmfkp7/VMr4Mb8vlo+M2qlNKpo7SsM9mo+Or78fbZF21pmo6NXL4yN6fU0G1FNh9Izv6te1aBNS36QrAe+xd1vQ7IeGOnco7hnb0hUs7Zv6qsBcq63ioZZIoqCLmz8PohI17VRmpodA9JKLSnbq7nFX4G9R1Tbo15To9hnfHlmTH3MBLI4XcOjfputYa12C9ra8bbkKOheySczZf/2+EPwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ7eGNDCCGEEEIIqT218Nh4QI0mivW187luHx+fjkdFav2mscp4Ot/CRDpr7fKoiTu2fpjtxfhusZrnfrC5PdT5/Kjx2DzWjsc962nrE9Jxz/b72kjnUe2xMW16m1r/DdKUI1MCinGsDTmRzpX9N5HPXfrq7W/9OP68RvOrPpdhEK36bAdVvazHRvto5hit/4COe7Y+obhvBnluUDR04NFLjN7GPpoJpklfabSdNMbC87ILTHHjIL8c8mFBvwI6zqwnTTcFWb3Kr1bY61183ZzR6LfHxlfInvPteV2zqPVI5/NzFv+X17Zva/P4os21cLSML69U0w54ElC8847p8c9BpLP2MiBPQh/8CkFbcAGM94UgH00Rb9TrBusZReObevY2TVDP8Shom1guo2pMez5W023jxRoFPmHNooGN3vQfLBmvYV2/O9ZN/Q40F0q9PH0sifg1HEQxj8ZrOIh01m02Chr5whI9Yzk16/VLqFHrz0XwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ76idFqwqUKrloVyxTM7Ip9bko7ONR9SjRrEpRxGU2OZTe41kTj6jWe9Q889WSssfKOdG2INJZPUrV0jMR/7GqlakhmYKN7dRtdnuXXltcboYjnTNydfuvCsQSlarxsVoKAN/8bGccbwzmC2QD6hG32VAoalavuJUp6D/oOGkzm7SN3MyhSGc1baVoukyRvCzoC+QV9p+OfJma35a6f1EbjJAmPSEW91yAt6NbHDxe45JIT/ID5KFVZT3o+BQRKb36Ndc/dYxuH/CvB4+OxiXN8xvjEc//ctC1XttN28Y39JYx8yqCUXVtGvHHbG9X62YlZEq60zAyHivP8SKdR/02vS/wW9zj8wWRzl48rt3Buk3S23qE0wOj84yRe3k1C6Lxw0h/1WTPx+C6FUjTJH48aT9B2TRy+oHx6S3b47+L5jX8iPJ/OujqzudbRvx10zWs61fEr2GvfkW8Gi62m5oFNWzbkHzSq+EyvS010tm+TiLWL0bRRf4bg09sCCGEEEIIIbWHNzaEEEIIIYSQ2sMbG0IIIYQQQkjtqb3HptBeiiCTNj4f0mgWoM2pGVEUdOC/8TTVZsXa1Tw2pVk3HUloowRHGuO7epPRKupxRoxXRo+53awninTWvpogwtmsN/TRAK8M2odepCn0V9k2SWMyOmbkl+jBfIEuX2uVkXcjiHvW236CY0vHPQeRzi7a5nmDAj+K0hUbfW45oP038fkCr0Ez3qa3WxDvbA9R4JVBHoleeGWq+mbQYUDSaYyFUf/dQNsXxazrWke1FezQVL+CadOHViMoSusbUrVtDlj9fbc3ff/AI625nc/3DSzw2r4z+OzO5xcO/dRr++am54zP9+he/phbx8ccHTE/ZUbGD9jGiHn1gvIoNIxvBnkSwujceJsA/43nozHntVS/Qhj3HG+rTPxUHdS2X89xrxk8V9pLTNn9s4iI/pkSHopxz42t79JbN3+kMVXDm1pDXtt9c8Zr8WpVvyIiR+17R+fztx55jtf2q83jtb9xy1yvbXSb+lIj/gVH13Aj8NiImdZx5qYNeb8q+sK8Gs6oy9yadWMT99kJn9gQQgghhBBCag9vbAghhBBCCCG1p/ZSNEjqoy4gcbJyL/3s0sZ0amla2za2wT1k0zz3A9I0L9LZrFtLPa8dM5KubcX4rrZvcNbjhPI29fZdM6aWuwVt+i26dswg7rmI9tXbNIx71o+xrRRNktp69di+ZxGbnlQJRK8iGZNtQrIXtC203Kri26tF/JoN38CuIqWttBNIa7C8Li4F82Oa43KzQDIBZGvBOLoNRG8Hy0DSsMTo4CxpI6lEJSkakBZaaYfXFkh30mRqSNZjU1e1csimDzdABLz9Um11wnDmp8VW9fm/3LDX9r2xp3U+3/XoQq/tt4+O9/1/j+7mtT22ZTw61231l9fYpqQ7JnJXS3eCqFwb6awuzfBN7Ui60wZyM7vBXbwtWdZjmcS52xsGRZT3oJ7tbw/dF9UsfJ2D+HYFHGdufl+oz9vEjxr/rarh68ee4rW9bss+4/0e9Wv9YVXD27b4EdLuMfUbbZuRT6oankg+qduDSGcQ9+zVM5CbhW1qwm57W9+azIhyStEIIYQQQgghswre2BBCCCGEEEJqD29sCCGEEEIIIbWnfh6bHC0pyidE3gIXF/A7HU0cCAjH2wIvQWO8r/USiPgCfteICxMd8MOMKh1kszBxgYkmEDumXt6YEXXrmGbrsdHTQdyz+f5jIO7Z+752u3lxxGD/gvkCwGYqoAmiIqneCTFeGeCxsbsaaqOBjwWuDNxQYJzAshaPdPY1uOnb3otGRv6XQO+tJlCbmDjoquOgfYFiuYFPKYCem57TaDtpjIXbPDheoxMiTh+IKNrb/tOjmob+G+tz0PUK/De2lMrAA6LHsZ2RH3L8p8bWUb9tZNu412D9f8/z59NezW3G6KYjcG2kc6InIWgDkc6BX8Hz2BgfDfQrxNuwxyYxVhcxUb+KXj5Yz/qcG//JJE0b6a92d8Ncw70o6Ak8NnqRTevjAbVf6N8lbX++baqG12/zf0Y/pGq4PWaOAxXjXASRzuPLaNpI5+3qM4gkF0n30QS1rmo4qD3dF/howm0ocbzr+8QF7Lqcc2PwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ76idFs6TKZ8Cb04OnYPqxZ/BsTc1nH4+qvoFsSt1D2tQ6+1Z3/UTWSsN0HHLTPO7XcrMmkLOhuGeLlpGhdbFyM0/CZqKuw0hnLUWLxz2Hb8wG8aagzZchIh2IxMmK0EQayfjiA/EXkJQhqVIgZ/Ea1eesf+bIkKaBdfGjIuMygWDpMO45sQ3IxOAb30X8KGwgFwrakMxILw+pJyvGQlOW1huKMZGd5YCOV38mf1JfK7AULS6lDGSdStliZWqlOglORtajCeLZgcSqMTq+cuV2I895TMnNzFvr9TLCWFslY7VyHCQ301Id8GZ22x5K0dKkO1iKhuS38fmwZDreNhmwFA3Usxd/ny4N1j9hXNP/Uuh1DjazXG+3shVvC+pZ70NTe6WSirnHjAxfT9rjYAzUs6q1QG6mI8pBjdpxsBStD/JJZ4/f7v0sKTVbtifusxM+sSGEEEIIIYTUHt7YEEIIIYQQQmoPb2wIIYQQQgghtaf+HhtNupVhAp+F+gh0/kEEbOCriQHEpCIiynNi/TdaT9o2wkTtq7E+lqqUiT4aG+HstQFPzY7peKSz57EprfEB+GH0ODlR34g+aZejoFhfa7IBX9crk7j1a3JfT61PGFOttOgwkt1vgt6g5PUCYyItuPXUwL4V27IinePzwUOdvpqeU7SdFI97QXJ8NRrfr2C9muqz8Zyg+tUegdIkI/fDr+DMMjz9/pg5j6tfGtbz4h1rwLoX6P61X2HMtqnl5fhm7DiJPppgGcBHg3wHKDpX76iwDcxXkbz4ct0GfDRFvJ5hND+IKHfWpwQ8ZHYfer40VF/ml3Kpa9iuN7Bo6+PJHiOexwXUZVij8WkbQ94Ay0A+Gl3D2GMTHzM08MXn60a3iP1o3+SeItJut+WMM86QpUuXyty5c+Wggw6SD3/4w96Pb+ecnHnmmbLffvvJ3LlzZfny5XL33XfnLIYQQghJhtcmQgghIpk3Nueee65cdNFF8g//8A/y85//XM4991z52Mc+Jp/61Kc6fT72sY/J+eefL5/+9Kfl5ptvlt13311WrFgh27Zt6/nKE0IIIbw2EUIIEcmUov3gBz+QV73qVXLssceKiMiBBx4o//iP/yi33HKLiOz4F7HzzjtP3v/+98urXvUqERH54he/KAsXLpSvfvWrcsIJJ1Rby148Wg0e3cajoB16rAsf26vYYvsMH62a0ato+UFhH926eJuWg1kljSYcM965BN9XT9soaD1t5WV2HCRFc54sD8SUBsvQE+LTj0jnHOKl5z/Fz5ARpUZBB19Jz2dlU+mLj45pVyj4vlpqg96mjVYmVfJj+troUQEysZxIZxjNnCxPAvOhCPpZKj2bymtTY8wFkfldgfs6rldBb2pHsi3vuhFIbrSk18xXUdYTSNHUtAPyLyjzBFI0JNsK36Le/bOdtvIWGNsMpDtBdC6U9agJEI+L5WbxmGgLfPs7AEqBAylaYj0HNavqEsgug+u7rjUgmxIRKcHvBFizus1GKqPjEP32SqwLJEUL6zkuNwvipmGkM5BdwrjnuEQS1h6q5y70TYp25JFHyrp16+SXv/yliIj86Ec/khtuuEFe/vKXi4jIvffeK+vXr5fly5d35hkeHpYjjjhCbrzxxpxFEUIIIUnw2kQIIUQk84nNaaedJps2bZKDDz5Yms2mtNttOfvss+XEE08UEZH169eLiMjChQu9+RYuXNhps4yMjMjIyEhnetOmTVlfgBBCyOyG1yZCCCEimU9s/vmf/1kuu+wyufzyy+X222+XL3zhC/Lxj39cvvCFL1RegbVr18rw8HDnv8WLF1ceixBCyOyD1yZCCCEimU9s3vve98ppp53W0SMfeuih8utf/1rWrl0rK1eulEWLFomIyIYNG2S//fbrzLdhwwZ5znOe03XM008/XdasWdOZ3rRpE76ABCJNre+zPguQc+vi2n7kwfCGtHpkrbu0kcZK7Gi9Ig2rLVULCdp0dK4RNOpRkzTgCWivTJg+WXT9LGJimkHbjnb1GURKB7Jibz8BoSvQKsMo6BkG8mBoiTPSRlvNq9fX6mpzoqD1+lhdLYh0Tj7WqpIRmyxoG2bEPaNxHFoGGpNApvLa1Bh10kg5UQA/lz53233tRSoH0bloviLapg+mIFa37P55x5jm+qM0+tq3I2J8D/aNBlU9aalRssBLELapayjwzdh229ZAUdAg7hlGWCfH6qZ7bLL6aetIO95WuZ6D2ovPB/03ntcrXs8i4u9/40fRcc92nAKdj1Nj+20TivpG/pfE2HHbHsaQV/PR9KIus+brQmO0Tx6brVu3SqPhz9JsNqV83J21dOlSWbRokaxbt67TvmnTJrn55ptl2bJlXcccHByU+fPne/8RQgghqfDaRAghRCTzic3/+l//S84++2xZsmSJPOtZz5L/+I//kE984hPy5je/WUREiqKQ1atXy0c+8hF56lOfKkuXLpUzzjhD9t9/f3n1q1/dj/UnhBAyy+G1iRBCiEjmjc2nPvUpOeOMM+Rd73qXPPjgg7L//vvLO97xDjnzzDM7fd73vvfJli1b5O1vf7ts3LhRjj76aLn66qtlaGio5ys/IUCJhuVI6vGojQfU3ezzLv3I2UZaaomVeaYeKHe8OOD4I99Aiqa6Bk+RE6VpKPoZScHCKOj4mGE0c+I4pd2mcZlaL+RmVtoIx+wVQCrlR0GjnOiMMdXnsJ5Vv4mioFOPNSuF07OhfVgVFMVZUaYmYrYVkqnB2Ob0dUOqSzhmrN8uxlRemxrt7nHPMOYV7E8Yzx7Ic/TnuEzNHjsNfY7NidUF0lUbM4ukaDCmOrEuYfyxkXuh6FwoucmQ7vjRuSA2OpDQASkckMjj6Nx45G6vQDXb73qGMjUrl7Qx5EiiCWRqSG7sx1tLOkB+BSOVQVtluRmKKM+o2WSJpB0T1WkXaZpdX0ThXE+U7D1j06ZNMjw8LAd89CPSePyCA/Xs+iQdFF+8TVCbHtP+EPSWZ28s1M63OlN0Q4LGCX7cpt3YWGbdjY3uG8ynFxD/JVig+ewNL/jxHp5cinjf2PLMOHDMDC06elcMfKdCzjhg20T7dRmnEjP4xgZd5MNzme5nf7WCMYvu/cK28THLbdvk13/9fnnkkUcov1LsvDYd9bIPSqsV3gzBH+jWK5Nah8GPvdQ2s26eX8G2gTGbqK9E+/LGpvt6BmPW9MZmRtdzULPxvtpjE5wfeWPTfcxpurEZG9smP7j2g0nXJVpUCSGEEEIIIbWHNzaEEEIIIYSQ2pPlsZmRgEdd8BGhp+0Aj8/sbECq5EfnWrmIehxqxizawEcTyNS8KdBmllHx+TSKW471s32DCOdgZq2XBeOCmOhAbobqAlH1MX7V+ZAfBfRFEqdgSN1mH6nrx892Pt3XPka2fdH2Bv4bXZbQt1OVHNkL2r4ZEbWwDUjY8P6Ny2Mr2736pVnZxSnGnBRdijPYDd4fjH7fkwHa64g+58XP/1gKVtGvAGJ1w76mrS3RNi11zInH9RdgZkuU/+ZEMVftC+NxgVQIytQC+S9oA4dySpRuN4JIZ39Q01dN5NQzlJtFxpAJ6tn+TmjG+yL/DYzmB+djSEXpd/WaNW3teA1Vr1k1YeV13jEK2hCP92uMpdcxn9gQQgghhBBCag9vbAghhBBCCCG1hzc2hBBCCCGEkNpTf48NoPD8IcAkgKKCbbRqGdcj47x0rcm0RgN/0rMkAP8NEimGfptqQnwoz0UxzZF+XccE0dD4XTXxiOPKbZH1mrBvBp7nJdFTEywfeDAc8F4F/i7vHRbxxQeraesblCX2ZsXn6wc9i3tO9eoE2uy0NqhhT1z2hH1JJRqjpTTswSIC/5kw8CvoOrDHnT6Wbd014wez1rr3w3+T1TfHd9aLuOeqUdDAG5PTt7KPxkbuQg+G9keAkyWKhc4g8JF5MfYV67lp6kn3tf6X1Gjx4PdF3CeWU88wproH51XkcYH+m4lq1qX1hTULXudglycoXhrEl0f7RWi00wubT2wIIYQQQgghtYc3NoQQQgghhJDaUzspWiBzQXqZ5Dag+UFtQdyzfiQHHl2iKFcR/7kukKnZdYPSoR4Ax0Rys0CahKKZ0ThWpgba0HyxfuLLF6cDpDZz8bIwbeZRvHqMbh8Ve8vLiYI208nFByJbg9l6UcRo1+dI0cC4ULaWEffsUscE60L6T9Eupej2Gnj7Rm51Hg+OSX282re4K7mOlRR7yg4rV0QyNT1fjqyntOcS/dlKqoFMLVVKmQOQovkRtLYNSWdA3xzpTqLcLJS+xcfUOz+U3iG9b7wJYveT+v5Bzep6tnIzfRwE9aSk5k3x25BMLVF2GSwjq55VW1AYMnnAMTrtNWvH1DHRVormHYdWpqb7IdnlxEVajFGKRgghhBBCCJlF8MaGEEIIIYQQUnt4Y0MIIYQQQgipPbXz2ASg7Fyt/TOiSC8SEBkWAu0fMBPoaGKgkQ9001CjD5aPYqJ7pGPGcc9ovsS4ZbsM6JUB49i2EmyA1O8E4wnBGDnkRDrH+pm+YZOu9figwXxafwziVEUm8Mr4K4MX2meSLVRZxyjomxUpDc5Jif4bWNr04vSExlj3uGe4fQP9fvy65b82wPgovXja+Hk08BnolQORxhP6FXTEP/ArZHlMqwI8Np4fJSO6tlcRuMiTkOpzCE660MsA/Dc9wtuHqJ6tB8OLATfz6foCVmNYz8F2slHU6pgJfDz6WIv7aMJzbg+KGHpOzOK039XOlxU1HuknGT6atm2r6r9JK9Sd24Vxz4QQQgghhJBZBW9sCCGEEEIIIbWn/lI0DVKNTdQ3Np+VNOnnhfZxbOKgUN0m5hFsxZjXYBGJz6eDKGbYGbWB7wv6hnKzSL+gDY0fn86Kd54KLQ94wu6JIOPJlAHeMPZxuxooUHKiR+MZfeF8qTHRvSI1/rlfcc9AZpr8dvacMqT8rPeMOQlery5dNrX+Z0NzHdFxuUH9NEBx6XHsP0sCWY8+7otAwtZ7WU9ObYPFYVkVlKLpNiDdAVG5O6bjbfCN6yjuGcjNfJla+nz+epl1SYjS7QwLJFZF1Xpuao0iuP6AmGhUz2LmC15poNcb1bOVYerXJJjFo8JE7gi/ox2ymoQsq2/FmkVyM1iz3u8C8KMhRWVm1wnAJzaEEEIIIYSQ2sMbG0IIIYQQQkjt4Y0NIYQQQgghpPbUw2OjpXU5mnGk9deeF9zRjAliDb0ITzsOiHK18ceJeuScxEHXB7E9joLOMABV9dGkLiPQsqZ6euLDZ9GriOPEr5uXEq00xmZOOA74TkhXXDU+vGdU9axV9dEEy3DxfqnrBg4Remr6TzHalqJshw32nwl1F3uy1tPGI+D7aEwb8t8gvwLyPOrrmF0X5AfN8Cv4vlHg44i2dOmb6EmAXgYUtyzmsg28MoGXITnuOcN/04635XhuEN72t/XseajifpjC7nu92sh/E/ho9BjGswXq2Xq/4DVdfyXrzUHnXPBIILWG7fJgNLJuC+aL14m9FletWT/u2V8B6P0CMdV+Pv3EF/+i3eWcG4FPbAghhBBCCCG1hzc2hBBCCCGEkNpTDymaBkXgVtT8FGa+ZGka0uNYvAxAOyaI70Nvip9u2UlVWRGSm6FxKsrbsiKdpwIt2ciRf+nvlBP1jcb0+oGIVis7AQsJnion6uT69cZsTdX49DyZ2uTlZjOtZMk4RbsthXSRRdhXA3gzAelOkHEMZGpIA4MkN3o2K9Xx5G4grlX8CNzgQAexut6YKCrXypiQjApdppHczJP1xONw7fLz3tQOpGhaUlaW8Ta7fT2J0eRkPUmAenYNf0ehKGi9T61EslCx6S6odS03A9dJdL0RMZI201XVc3gYxu0DqC79Yxv9JoxPh/HlQF4WRDPr9cyo2TJeX5VrVq83iJAO6NJGKRohhBBCCCFkVsEbG0IIIYQQQkjt4Y0NIYQQQgghpPbUz2PTDwLfTg+ioAMPQjyaEhomEqNjp4VUI8BEGtgqY9pxgkjPalHQyNNTpM43HfQiChpFOCP/jf2D1SMnbpteScMhqSU7UT/ko0HLS/XRoPlQXxQ9Pd3ni12FsbaIS9B7e/H/5lzieVX8f1/UXgN7HvP8CraAtF/A/JtlcgRthgweXv8CI0DaykBPjQWe/3Vb3DeE/Ak71ifuEYCxzSoSF8fq2ozhRE9COz5frnfBA9Ss161p/Rmq9qz/Rh8rqNZz/p1dL36C2Qq0afT+NdHq/rkTtNnlpXq7M3w03noGnjFUlwLaZlDNWrq10WNDCCGEEEIImU3wxoYQQgghhBBSe3ZtKZqfVxvvByU4IAoa5eqix/SWLNla4phTTa/kZVnjjn8MZBmJ84WSshm0TVHJAKVHqizN9kVKpQmTzYv4Y3S4SVEMer9BsbNZr0CfYDo2boYUzYG2ysygUq8TRbvty2uiHbU8B8Q2B2+7b6huVlKm4nGh3MyXfei+cLfn/FOnHagB2vrw2oJUuVm4fdVn8IZ1kYx4XPQ29kDCVia2AemOjdxFb3+PjdENIEWDUd864tjK1HQ9m7rU8rOgTReGlYk1E+tZBNd0gb4TGKMXNWsp49dQGOGMYsFBfdemZjv9KUUjhBBCCCGEzCJ4Y0MIIYQQQgipPbyxIYQQQgghhNSe+ntsPD+MaYJ+lGrRp9Zz44+Y6I0JBzUD5Zgkakiq/8WQHOE8UVviNpyOdFyUmOphJfvgOEiOOAbzBesCDifsqbHjzKCCnoyvRpEc41zRC4XGnEkWsV2W0TGRRnPHZxTVr3wBhYm51Xp67RewhIeZ9hbk+GjSvDnOzAmja+13RychtUhbo1VPAak+mjAKGvlmTN9+xOPCNjVtPQm6zXoSdF+7Lpocj43xtRRl0b2f4Br2xjC1p301QVtTzWeLpp3qNfNrOqhnL3Udvd4BmVqrAX00dnmoDcWQB56xGtZspw89NoQQQgghhJBZBG9sCCGEEEIIIbWn/lI0gJeajHVqZsZ4E1xeorbEBXm4cNBq9EqS0gul0ERPv1P1MxUlbDlR1FAW0Q8JG8hGDiQbaF1AaqXXrWJdTBz3rD72o56ztmnFZejF9UiWliU368GYYd8ZJPXbVRhrizTGdnyGUjT174YNsx90XK05YJ2Ny1X4l6b+R0FDaVoj3jcYR0fZCsDGYqdIVCRP1pP8Znaz/GAcFDddNR5Xtdk4XkmN1UVvaM+SojXibU1QX2Yb4stBxShovW72+LH71FsXU8+NeC1411Q7X+o1DkrrkWTQ9NU1hOrZzDslkc6q3oKa9eoS1LOVt3UboxyL9zHwiQ0hhBBCCCGk9vDGhhBCCCGEEFJ7eGNDCCGEEEIIqT3189ig2NkM3X8/oqC92YCQOPCU5MTj6iYk9JwCaX2yN8aSs26pfTN8NJrKnpp+bV9gDPOioCsODyXWGWVZOe45WKHEfr3yjE3xOJV9NBl9GfE8tbixMXE7Nf4FiGrWXhmrIddZttavoLwMrohrz4NDsAF8O57nJB7THKTa2tVOjM4NVq5IPHm1M06sif4FGOkcxOpaH40ax66b54eJjxPG8ybG42bE6mqfgwtidfXJGXgZRPx6bqT7WjwPWavptyH/hB4fREFLYcYs9H4xA5muXu2jejY+OK8tJ+654m8Wbxl2k3n1NME4OtI56IvqsgeRzmg+6/3S5x3rv/H6lY//jx4bQgghhBBCyCyCNzaEEEIIIYSQ2lM/KVoOQBqGVVyJGa05UrCqjy5zJG0ziapSrcrzVZSb9Wr5OaTGiedEQSfKIHOYKBnUX4FKTb2ThvWYSZVB6nfK+O7Jhzoq9hm6rWtHe0yk7CJFM1HFTst+mr4+Bh8TIDZZS7qsbEpJSQKZsvfWeCDNCuKrwasJSr+vlvKgmGh4fc0gOS53ErIeX8aVIWnTbYEMEY2ZFgXtxowsx4uwbsfbcqRoRiLpRXbbSHJdN1aqpMcIlofqUh0HRkPmxZkb6VkY2wzWILGeLb2o4aB+QT3BaHHUN5CNxeWxBapLT5KZEemsJZJAPhkeB11q1lGKRgghhBBCCJlF8MaGEEIIIYQQUnt4Y0MIIYQQQgipPfX32KR6C3JibhMF9lk6yxwvRep8uwoVvUJZ3hlveVM8HyInaTwxCjpncZU79yaVtTp98BRVpmrqedX1rlz4pBe40TFxO70IOmLZ+lOaVvyvAF4ZT6duxyxBWxHXyGv9fJBc6+Lr4swyvEXkGEl15G4/zggoOldsm54vw6+A+qI2Gzed6ldAngQ7pvbcmFhdh8Y0FLpmrR9HtTnjJytslK/XqLwyZr1do1rN+oPY+UCcufV+oXrWnhvzCAD6u1LJ8n55K2rGiddX1eUHY+q6Qecre/7wPGOmRkAUdLeadfTYEEIIIYQQQmYTvLEhhBBCCCGE1J76S9E0VeNpKz5V7JkCpqImZSalPU+LOqYfy5xulU/VKOhEehW1imPIe7OIqsvvB1NyrPViw82gc8Kuihtriyt2yCK0/MyhSGcbBa3fDh5IyoBMzRsEtAWRt1raASRsMCpXcH1plZx9i7v3newywJjeIPGmnOjc1DGzQLKeYJlov8VlY748x77FXUVBB5IfFblrZVtm/3rttp51FLRdBqrZxBjhQP4E/tm9APKyAD1uMy5TK8w4njTNHjOanMcDE6Rtp5Alg6talzn1i6SOKC5d12VCzToH5I4GPrEhhBBCCCGE1B7e2BBCCCGEEEJqD29sCCGEEEIIIbWnFh4bkHJbnV5oa6tqhXvErEt9ncHfty/7oh+1PgVFM5O8X7WF23DGsiPu+fEdpHwIQXK7joK2/oFCR6Sm//uiHieYrRcRtBOhl2H9N5XHVJ9z4u8rLy/R42L75vgVUvshv4LF898YDxOK3PXajG/HLkPXs92/el77ugHPq2LQ65pTs962N6OiQybIM+/zybSqbyarLuJdYTQ0GDfHq1Mgrwxatq4ZW7O6r417boeeMcY9E0IIIYQQQmYVvLEhhBBCCCGE1J5aSNE0M1p+NZPXjcxeKGsipHeUbZHHo2+9U759G3sZlwAl/5tiRXmZlZn0LObdW4Y/jSSoXjwvkgZlfN2evP29X4B1C2SJFcaA2MhdHQU9ZuQ8DRvpDCRHXmxytVXrGUimBtYtOC76LVPLoB+/bSsfI/04tuw5UNWpjSH34p93Rpsz7pkQQgghhBAym+CNDSGEEEIIIaT2zDgp2s5HUuW2bdO8JoQQMvvYee618oDZzs7tMSaj45IpJa+wso/Ca4vLMIrgbd1KclGYf3ssxnU2rm01N6qviUxzahwHUthcadOu7PL1Z9NXS/HAP5n2Sv4DZTZ6k9r94iU8mTHb/r7wlmGUMN5+a5tlaCmNHVPvX7TvS5MU5UkbfUmZlxjlRk1bu3u/HYP66wZq1psO1lvXs5UMqWIo/Zp1XqHE6zms2QK0+Xg13QA1a+sZ1HpPCM4X8TZdp8F5xkob1XTRtm1lUptNKfNq3e5f0OZKXZfxmnVGZubX8462MRl9vG3i69KMu7HZvHmziIg8cNZHpnlNCCFk9rJ582YZHh6e7tWYMey8Nt0g3x7/o/59N2JmsNOEzDSs9UtPjwohM46U61LhZtg/y5VlKb/97W/FOSdLliyRBx54QObPnz/dqzWj2LRpkyxevJjbxsDt0h1ulzjcNiHOOdm8ebPsv//+0mhQrbwTXpswPJbicNt0h9slDreNT851acY9sWk0GvKkJz1JNm3aJCIi8+fP506NwG3THW6X7nC7xOG28eGTmhBem9LgdonDbdMdbpc43DbjpF6X+M9xhBBCCCGEkNrDGxtCCCGEEEJI7ZmxNzaDg4PygQ98QAYHB6d7VWYc3Dbd4XbpDrdLHG4bkgtrpjvcLnG4bbrD7RKH26Y6My48gBBCCCGEEEJymbFPbAghhBBCCCEkFd7YEEIIIYQQQmoPb2wIIYQQQgghtYc3NoQQQgghhJDaM2NvbC644AI58MADZWhoSI444gi55ZZbpnuVppS1a9fK85//fJk3b57su+++8upXv1ruuusur8+2bdtk1apVsmDBAtljjz3k+OOPlw0bNkzTGk8P55xzjhRFIatXr+78bTZvl9/85jfyhje8QRYsWCBz586VQw89VH74wx922p1zcuaZZ8p+++0nc+fOleXLl8vdd989jWvcf9rttpxxxhmydOlSmTt3rhx00EHy4Q9/WHRuymzcLiSf2X5dEuG1KRVem8bhdak7vDb1CTcDueKKK9ycOXPc5z//effTn/7Uve1tb3N77rmn27Bhw3Sv2pSxYsUKd8kll7g777zT3XHHHe4Vr3iFW7JkiXv00Uc7ff7iL/7CLV682K1bt8798Ic/dC984QvdkUceOY1rPbXccsst7sADD3TPfvaz3Xve857O32frdnn44YfdAQcc4E466SR38803u1/96lfuu9/9rvvP//zPTp9zzjnHDQ8Pu69+9avuRz/6kfvjP/5jt3TpUvfYY49N45r3l7PPPtstWLDAffOb33T33nuvu/LKK90ee+zhPvnJT3b6zMbtQvLgdWkHvDZNDK9N4/C6FIfXpv4wI29sXvCCF7hVq1Z1ptvtttt///3d2rVrp3GtppcHH3zQiYi7/vrrnXPObdy40Q0MDLgrr7yy0+fnP/+5ExF34403TtdqThmbN292T33qU90111zjXvziF3cuHrN5u/zVX/2VO/roo6PtZVm6RYsWub/927/t/G3jxo1ucHDQ/eM//uNUrOK0cOyxx7o3v/nN3t+OO+44d+KJJzrnZu92IXnwutQdXpt8eG3y4XUpDq9N/WHGSdG2b98ut912myxfvrzzt0ajIcuXL5cbb7xxGtdsennkkUdERGSvvfYSEZHbbrtNRkdHve108MEHy5IlS2bFdlq1apUce+yx3vcXmd3b5etf/7ocfvjh8prXvEb23Xdfee5znyuf+cxnOu333nuvrF+/3ts2w8PDcsQRR+zS2+bII4+UdevWyS9/+UsREfnRj34kN9xwg7z85S8Xkdm7XUg6vC7F4bXJh9cmH16X4vDa1B9a070Clt///vfSbrdl4cKF3t8XLlwov/jFL6ZpraaXsixl9erVctRRR8khhxwiIiLr16+XOXPmyJ577un1Xbhwoaxfv34a1nLquOKKK+T222+XW2+9NWibzdvlV7/6lVx00UWyZs0a+eu//mu59dZb5d3vfrfMmTNHVq5c2fn+3Y6tXXnbnHbaabJp0yY5+OCDpdlsSrvdlrPPPltOPPFEEZFZu11IOrwudYfXJh9em0J4XYrDa1N/mHE3NiRk1apVcuedd8oNN9ww3asy7TzwwAPynve8R6655hoZGhqa7tWZUZRlKYcffrh89KMfFRGR5z73uXLnnXfKpz/9aVm5cuU0r9308c///M9y2WWXyeWXXy7Petaz5I477pDVq1fL/vvvP6u3CyGThdemcXht6g6vS3F4beoPM06Ktvfee0uz2QySQjZs2CCLFi2aprWaPk4++WT55je/Kd/73vfkSU96UufvixYtku3bt8vGjRu9/rv6drrtttvkwQcflOc973nSarWk1WrJ9ddfL+eff760Wi1ZuHDhrNwuIiL77befPPOZz/T+9oxnPEPuv/9+EZHO959tx9Z73/teOe200+SEE06QQw89VN74xjfKqaeeKmvXrhWR2btdSDq8LoXw2uTDa1N3eF2Kw2tTf5hxNzZz5syRww47TNatW9f5W1mWsm7dOlm2bNk0rtnU4pyTk08+Wa666iq57rrrZOnSpV77YYcdJgMDA952uuuuu+T+++/fpbfTy172MvnJT34id9xxR+e/ww8/XE488cTO59m4XUREjjrqqCB29Ze//KUccMABIiKydOlSWbRokbdtNm3aJDfffPMuvW22bt0qjYZ/qms2m1KWpYjM3u1C0uF1aRxem7rDa1N3eF2Kw2tTn5ju9IJuXHHFFW5wcNBdeuml7mc/+5l7+9vf7vbcc0+3fv366V61KeOd73ynGx4edt///vfd7373u85/W7du7fT5i7/4C7dkyRJ33XXXuR/+8Idu2bJlbtmyZdO41tODTp5xbvZul1tuucW1Wi139tlnu7vvvttddtllbrfddnNf/vKXO33OOecct+eee7qvfe1r7sc//rF71atetctHR65cudI98YlP7ERq/uu//qvbe++93fve975On9m4XUgevC7tgNemdHht4nUJwWtTf5iRNzbOOfepT33KLVmyxM2ZM8e94AUvcDfddNN0r9KUIiJd/7vkkks6fR577DH3rne9yz3hCU9wu+22m/uTP/kT97vf/W76VnqasBeP2bxdvvGNb7hDDjnEDQ4OuoMPPthdfPHFXntZlu6MM85wCxcudIODg+5lL3uZu+uuu6ZpbaeGTZs2ufe85z1uyZIlbmhoyD35yU92f/M3f+NGRkY6fWbjdiH5zPbrknO8NuXAa9MOeF3qDq9N/aFwTr3ilBBCCCGEEEJqyIzz2BBCCCGEEEJILryxIYQQQgghhNQe3tgQQgghhBBCag9vbAghhBBCCCG1hzc2hBBCCCGEkNrDGxtCCCGEEEJI7eGNDSGEEEIIIaT28MaGEEIIIYQQUnt4Y0MIIYQQQgipPbyxIYQQQgghhNQe3tgQQgghhBBCag9vbAghhBBCCCG15/8DDbwFDayzgUwAAAAASUVORK5CYII=",
"text/plain": [
- "<Figure size 720x432 with 2 Axes>"
+ "<Figure size 1000x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -624,7 +713,7 @@
"Ex, Ey = np.gradient(SOR_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"axes[1].imshow(E)\n",
- "axes[1].set_title('Electric Field');"
+ "_ = axes[1].set_title('Electric Field')"
]
},
{
@@ -639,7 +728,14 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:10.809584Z",
+ "iopub.status.busy": "2024-08-14T18:39:10.809382Z",
+ "iopub.status.idle": "2024-08-14T18:39:11.999833Z",
+ "shell.execute_reply": "2024-08-14T18:39:11.998970Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -651,14 +747,12 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 720x432 with 2 Axes>"
+ "<Figure size 1000x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -678,7 +772,7 @@
"Ex, Ey = np.gradient(SOR_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"axes[1].imshow(E)\n",
- "axes[1].set_title('Electric Field');"
+ "_ = axes[1].set_title('Electric Field')"
]
},
{
@@ -693,18 +787,23 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:39:12.008455Z",
+ "iopub.status.busy": "2024-08-14T18:39:12.008267Z",
+ "iopub.status.idle": "2024-08-14T18:41:03.640235Z",
+ "shell.execute_reply": "2024-08-14T18:41:03.638498Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -715,7 +814,7 @@
" SOR_info = solverinfo()\n",
" SOR_info.w = w\n",
" tol = 1e-9\n",
- " SOR_sol = solve_poisson(u, tol, SOR_info, SOR)\n",
+ " _ = solve_poisson(u, tol, SOR_info, SOR)\n",
" return SOR_info.iterations\n",
"\n",
"w = np.arange(1.0, 1.99, 0.01)\n",
@@ -723,7 +822,7 @@
"plt.plot(w, iterations)\n",
"plt.title('Number of iterations vs SOR parameter')\n",
"plt.xlabel('w')\n",
- "plt.ylabel('Iterations');"
+ "_ = plt.ylabel('Iterations')"
]
},
{
@@ -740,11 +839,18 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:03.645445Z",
+ "iopub.status.busy": "2024-08-14T18:41:03.644042Z",
+ "iopub.status.idle": "2024-08-14T18:41:03.657693Z",
+ "shell.execute_reply": "2024-08-14T18:41:03.656439Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
- "def jacobi(x, source, solverinfo):\n",
+ "def jacobi(x, source, solverinfo): # pylint: disable=function-redefined\n",
" \"\"\"Performs one iteration of the jacobi method\n",
" \n",
" Arguments:\n",
@@ -782,7 +888,14 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:03.661618Z",
+ "iopub.status.busy": "2024-08-14T18:41:03.661004Z",
+ "iopub.status.idle": "2024-08-14T18:41:03.670789Z",
+ "shell.execute_reply": "2024-08-14T18:41:03.669648Z"
+ }
+ },
"outputs": [],
"source": [
"# This spec is required for Numba jit compilation\n",
@@ -796,7 +909,7 @@
"class NAG_solverinfo:\n",
" \"\"\"A class used to get information to/from a solver\n",
" \"\"\"\n",
- " def __init__(self, N=50, w=1):\n",
+ " def __init__(self, N=50, _w=1):\n",
" self.iterations = 0\n",
" self.w = 1 # Used for SOR and ignored for everything else\n",
" self.source = np.zeros((N, N))"
@@ -805,7 +918,14 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:03.674740Z",
+ "iopub.status.busy": "2024-08-14T18:41:03.674371Z",
+ "iopub.status.idle": "2024-08-14T18:41:03.684314Z",
+ "shell.execute_reply": "2024-08-14T18:41:03.683156Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
@@ -818,7 +938,7 @@
" solverinfo - Takes a solverinfo object to get info in/out of the solver\n",
" \"\"\"\n",
" N = int(np.sqrt(x.size))\n",
- " x.shape = (N, N) # Make x 2D because that's how I think\n",
+ " x = x.reshape(N, N) # Make x 2D because that's how I think\n",
" nextx = np.zeros((N, N))\n",
" h = 1/(N-1)\n",
" # loop over the grid\n",
@@ -826,10 +946,10 @@
" # the boundary condition that x = 0 at the edges.\n",
" for i in range(1, N - 1):\n",
" for j in range(1, N - 1):\n",
- " nextx[j, i] = 0.25 * (x[j+1, i] + x[j, i+1] + x[j-1, i] + x[j, i-1]) + 0.25*h**2*solverinfo.source[j, i]\n",
+ " nextx[j, i] = 0.25 * (x[j+1, i] + x[j, i+1] + x[j-1, i] + x[j, i-1]) + 0.25*h**2*solverinfo.source[j, i]\n",
" solverinfo.iterations += 1\n",
" nextx -= x # NAG requires this rather than nextx itself\n",
- " nextx.shape = N*N # Make nextx 1D since that's what NAG needs\n",
+ " nextx = nextx.reshape(N*N) # Make nextx 1D since that's what NAG needs\n",
" return nextx"
]
},
@@ -845,12 +965,19 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:03.689523Z",
+ "iopub.status.busy": "2024-08-14T18:41:03.689154Z",
+ "iopub.status.idle": "2024-08-14T18:41:13.704343Z",
+ "shell.execute_reply": "2024-08-14T18:41:13.703337Z"
+ }
+ },
"outputs": [],
"source": [
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_jacobi_info = NAG_solverinfo()\n",
"nag_source = source(N)\n",
"NAG_jacobi_info.source = nag_source \n",
@@ -862,7 +989,14 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:13.710321Z",
+ "iopub.status.busy": "2024-08-14T18:41:13.710130Z",
+ "iopub.status.idle": "2024-08-14T18:41:13.932287Z",
+ "shell.execute_reply": "2024-08-14T18:41:13.931225Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -874,14 +1008,12 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 720x432 with 2 Axes>"
+ "<Figure size 1000x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -889,13 +1021,13 @@
"print(f\"NAG driven Jacobi on a {N} x {N} grid with no acceleration\")\n",
"print(f\"Solution found in {NAG_jacobi_info.iterations} iterations\")\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
- "NAG_jacobi_sol.shape = (N, N)\n",
+ "NAG_jacobi_sol = NAG_jacobi_sol.reshape(N, N)\n",
"axes[0].imshow(NAG_jacobi_sol)\n",
"axes[0].set_title('Potential')\n",
"Ex, Ey = np.gradient(NAG_jacobi_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"axes[1].imshow(E)\n",
- "axes[1].set_title('Electric Field');"
+ "_ = axes[1].set_title('Electric Field')"
]
},
{
@@ -908,7 +1040,14 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:13.939861Z",
+ "iopub.status.busy": "2024-08-14T18:41:13.939642Z",
+ "iopub.status.idle": "2024-08-14T18:41:17.115177Z",
+ "shell.execute_reply": "2024-08-14T18:41:17.114172Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -922,7 +1061,7 @@
"source": [
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_jacobi_info = NAG_solverinfo()\n",
"NAG_jacobi_info.source = source(N)\n",
"tol = 1e-9\n",
@@ -942,35 +1081,47 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:17.121312Z",
+ "iopub.status.busy": "2024-08-14T18:41:17.121112Z",
+ "iopub.status.idle": "2024-08-14T18:41:17.126394Z",
+ "shell.execute_reply": "2024-08-14T18:41:17.125466Z"
+ }
+ },
"outputs": [],
"source": [
"def find_best_m(m):\n",
" N = 100\n",
" x0 = init_problem(N)\n",
- " x0.shape = N*N\n",
+ " x0 = x0.reshape(N*N)\n",
" NAG_jacobi_info = NAG_solverinfo()\n",
" NAG_jacobi_info.source = source(N)\n",
" tol = 1e-9\n",
- " NAG_jacobi_sol, fvec = roots.sys_func_aa(NAG_jacobi, x0, tol, eps, m, data=NAG_jacobi_info)\n",
+ " _ = roots.sys_func_aa(NAG_jacobi, x0, tol, eps, m, data=NAG_jacobi_info)\n",
" return NAG_jacobi_info.iterations"
]
},
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:41:17.132790Z",
+ "iopub.status.busy": "2024-08-14T18:41:17.132530Z",
+ "iopub.status.idle": "2024-08-14T18:48:45.664367Z",
+ "shell.execute_reply": "2024-08-14T18:48:45.663269Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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nz8e//vUvREZGwt/fH3369MGkSZPw888/47777sPTTz+NqKgoFBQU4OjRo1ixYgWSk5Ph6+uL+++/H127dsXrr7+O5ORktG7dGitXrqzSYPPrue+++/DNN9/Aw8MDrVu3RkJCAjZv3gwfHx+bdpMmTcKKFSvwyCOP4JlnnkFUVBSys7Px888/Y8GCBejQoUO1f88iIyPRrVs3PP/88ygpKcGcOXPg4+Nz3dOURNWiyDV7RLeo+++/Xzg6OoqCgoLrtnn66aeFRqMRmZmZ8mXSs2bNqtAOgHjnnXdslv3444+iVatWQqfTidatW4uVK1dWuOTd6osvvhBRUVHCyclJuLm5iXbt2onXXntNXLp0SW4TGhoq4uPjK7x2y5YtYuDAgSI4OFhotVoRHBwshg0bVuHy9fT0dDFixAjh6+srtFqtaNeuXYVL1Ku7j+W9+OKLAoA4e/bsddtMmzZNABBHjhwRQlgud3/zzTdFeHi40Gg0IjAwUDz88MM22zAajWLWrFmiZcuWQqvVCj8/PzFgwABx4MABm23/+OOPolu3bsLFxUW4uLiIli1birFjx4rExES5TV18/kII8fPPP4v27dsLR0dHERYWJj744AN5SoWkpCS5XVpamoiPjxdubm4CgM30A3l5eWLKlCkiMjJSaLVa4evrK+6++27xf//3f8JgMMjtsrKyxJNPPinc3d2Fh4eHePLJJ8WhQ4eqNOWAdQqH8tMGXLlyRf69cHV1FXFxceLkyZMiNDRUDB8+3Ob1WVlZYty4caJRo0ZCq9WKkJAQMXz4cJGZmSm3qe7v2YcffigaN24sdDqd6N69u/y7YcUpB6imJCFqMNKUiIgIlt7VgQMHYvPmzejbt6/S5RDVKY5pIiKiGrNe2XjtfGFEtyKOaSIiomrbuHEjduzYgQ8//BD33HMPgoKClC6JqM7x9BwREVVb7969cfDgQcTFxeHTTz+97mSeRLcShiYiIiKiKuCYJiIiIqIqYGgiIiIiqgKGJiKqYPv27ZAkCStWrFC6lCpJT0/Hww8/DB8fH0iShDlz5ly3rSRJmDZtWr3V9k8lJydDkiQsWrRI6VKIbnsMTUQKWbRoESRJgqOjo81tH6x69eqFtm3bKlCZ/ZkwYQI2bNiAKVOm4JtvvkH//v2r/Nrdu3dj2rRpVbq9SV367rvvbhj2iEh5nHKASGElJSV4//33/9G9vm53W7duxcCBA697a5PyioqK4OBw9U/f7t27MX36dDz99NPw9PSswypv7LvvvsOxY8cwfvx4m+WhoaEoKiq67g2Iiaj+sKeJSGEdO3bEf//7X1y6dEnpUupdQUFBrWwnIyOjyoHH0dHRJjTVlcLCwlrZjrU30noTXyJSDkMTkcLeeOMNmEwmvP/++zdsd6OxLdeO05k2bRokScKpU6fwxBNPwMPDA35+fnj77bchhMCFCxcwcOBAuLu7IzAwEB9++GGl72kymfDGG28gMDAQLi4ueOCBB3DhwoUK7fbu3Yv+/fvDw8MDzs7O6NmzJ3bt2mXTxlrTiRMn8Nhjj8HLy0u++e/1nDt3Do888gi8vb3h7OyMu+66C2vXrpXXW09xCiEwb948SJIESZJuuM3yn9W0adMwadIkAEB4eLj8+uTkZLn9t99+i6ioKDg5OcHb2xtDhw6t8BlYT6UeOHAAPXr0gLOzM9544w0AwOrVqxEfH4/g4GDodDpERETgvffeg8lksnn92rVrcf78ebmGsLAwANc/7lu3bkX37t3h4uICT09PDBw4EH/99Veln/mZM2fknjQPDw+MGDGiQqjbtGkTunXrBk9PT7i6uqJFixbyPlxP27Zt0bt37wrLzWYzGjVqZHNj4h9++AFRUVFwc3ODu7s72rVrh48//viG27fu+//93/9h3rx5aNq0KZydndGvXz9cuHABQgi89957CAkJgZOTEwYOHIjs7OwbbpPon+DpOSKFhYeH46mnnsJ///tfvP766wgODq61bQ8ZMgStWrXC+++/j7Vr1+Jf//oXvL298fnnn6NPnz744IMPsGTJErz66qvo3LkzevToYfP6f//735AkCZMnT0ZGRgbmzJmD2NhYHD58GE5OTgAsX94DBgxAVFQU3nnnHahUKixcuBB9+vTBb7/9hi5duths85FHHkGzZs3wn//8BzeaJi49PR133303CgsL8dJLL8HHxweLFy/GAw88gBUrVuDBBx9Ejx498M033+DJJ5/EPffcg6eeeqpan8/gwYNx6tQpfP/995g9ezZ8fX0BAH5+fvL+v/3223j00UcxatQoXL58GXPnzkWPHj1w6NAhm96trKwsDBgwAEOHDsUTTzyBgIAAAJZg5+rqiokTJ8LV1RVbt27F1KlTodfrMWvWLADAm2++idzcXPz999+YPXs2AMDV1fW6dW/evBkDBgxA06ZNMW3aNBQVFWHu3Lno2rUrDh48KAcuq0cffRTh4eGYMWMGDh48iC+//BL+/v744IMPAADHjx/Hfffdh/bt2+Pdd9+FTqfDmTNnKgTfaw0ZMgTTpk1DWloaAgMD5eW///47Ll26hKFDhwKwBLJhw4ahb9++8nv+9ddf2LVrF15++eUbvgcALFmyBAaDAS+++CKys7Mxc+ZMPProo+jTpw+2b9+OyZMn48yZM5g7dy5effVVfPXVVzfdJlGNKHWnYKLb3cKFCwUA8ccff4izZ88KBwcH8dJLL8nre/bsKdq0aSM/t97FvbK7zgMQ77zzjvzcehf30aNHy8uMRqMICQkRkiSJ999/X15+5coV4eTkZHP3+W3btgkAolGjRkKv18vLly1bJgCIjz/+WAghhNlsFs2aNRNxcXHCbDbL7QoLC0V4eLi45557KtQ0bNiwKn0+48ePFwDEb7/9Ji/Ly8sT4eHhIiwsTJhMJpv9Hzt2bJW2e+1nNWvWLAFAJCUl2bRLTk4WarVa/Pvf/7ZZfvToUeHg4GCzvGfPngKAWLBgQYX3KywsrLBszJgxwtnZWRQXF8vL4uPjRWhoaIW2lR33jh07Cn9/f5GVlSUvO3LkiFCpVOKpp56Sl1k/82eeecZmmw8++KDw8fGRn8+ePVsAEJcvX67w/jeSmJgoAIi5c+faLH/hhReEq6urvO8vv/yycHd3F0ajsVrbt+67n5+fyMnJkZdPmTJFABAdOnQQpaWl8vJhw4YJrVZr87kS1SaeniNqAJo2bYonn3wSX3zxBVJTU2ttu6NGjZJ/VqvV6NSpE4QQGDlypLzc09MTLVq0wLlz5yq8/qmnnoKbm5v8/OGHH0ZQUBB+/fVXAMDhw4dx+vRpPPbYY8jKykJmZiYyMzNRUFCAvn37YufOnTCbzTbbfO6556pU+6+//oouXbrYnMJzdXXF6NGjkZycjBMnTlTtQ6ihlStXwmw249FHH5X3KzMzE4GBgWjWrBm2bdtm016n02HEiBEVtmPtkQOAvLw8ZGZmonv37igsLMTJkyerXVdqaioOHz6Mp59+Gt7e3vLy9u3b45577pGPTXnXfubdu3dHVlYW9Ho9AMg9ZqtXr65wvG6kefPm6NixI5YuXSovM5lMWLFiBe6//3553z09PVFQUIBNmzZVedvlPfLII/Dw8JCfR0dHAwCeeOIJm/Fp0dHRMBgMlV6NSlQbGJqIGoi33noLRqPxpmObqqNJkyY2zz08PODo6Cifhiq//MqVKxVe36xZM5vnkiQhMjJSHvNz+vRpAMDw4cPh5+dn8/jyyy9RUlKC3Nxcm22Eh4dXqfbz58+jRYsWFZa3atVKXl+XTp8+DSEEmjVrVmHf/vrrL2RkZNi0b9SoEbRabYXtHD9+HA8++CA8PDzg7u4OPz8/PPHEEwBQ4bOpCut+X++zsYbW8q79PfDy8gIA+ZgPGTIEXbt2xahRoxAQEIChQ4di2bJlVQpQQ4YMwa5du+Sgsn37dmRkZGDIkCFymxdeeAHNmzfHgAEDEBISgmeeeQbr16+v8j5X9nsMAI0bN650eWW/y0S1gWOaiBqIpk2b4oknnsAXX3yB119/vcL66w1wLj+g+FqVXXF1vauwRA1uQ2n9Up01axY6duxYaZtrx+aU73lpyMxmMyRJwrp16yr9zKqyXzk5OejZsyfc3d3x7rvvIiIiAo6Ojjh48CAmT55crV6df+Jmx9zJyQk7d+7Etm3bsHbtWqxfvx5Lly5Fnz59sHHjxhteuTdkyBBMmTIFy5cvx/jx47Fs2TJ4eHjYzJXl7++Pw4cPY8OGDVi3bh3WrVuHhQsX4qmnnsLixYtrXH9t/i4TVQVDE1ED8tZbb+Hbb7+VB8uWZ+0duHYSxrrscbH2JFkJIXDmzBm0b98eABAREQEAcHd3R2xsbK2+d2hoKBITEysst57SCg0NrZX3uV4YjYiIgBAC4eHhaN68eY22vX37dmRlZWHlypU2g+yTkpKqXMe1rPt9vc/G19cXLi4u1a5VpVKhb9++6Nu3Lz766CP85z//wZtvvolt27bd8NiGh4ejS5cuWLp0KcaNG4eVK1di0KBB0Ol0Nu20Wi3uv/9+3H///TCbzXjhhRfw+eef4+2330ZkZGS16yVSAk/PETUgEREReOKJJ/D5558jLS3NZp27uzt8fX2xc+dOm+WfffZZndXz9ddfIy8vT36+YsUKpKamYsCAAQCAqKgoRERE4P/+7/+Qn59f4fWXL1+u8Xvfe++92LdvHxISEuRlBQUF+OKLLxAWFobWrVvXeNvlWQPGtWF08ODBUKvVmD59eoWeCyEEsrKybrpta09I+dcbDIZKj5mLi0uVTtcFBQWhY8eOWLx4sU3Nx44dw8aNG3HvvffedBvXquwyfWvPYUlJyU1fP2TIEOzZswdfffUVMjMzbU7NAajwWalUKjl4V2X7RA0Fe5qIGpg333wT33zzDRITE9GmTRubdaNGjcL777+PUaNGoVOnTti5cydOnTpVZ7V4e3ujW7duGDFiBNLT0zFnzhxERkbi2WefBWD58vvyyy8xYMAAtGnTBiNGjECjRo1w8eJFbNu2De7u7vjll19q9N6vv/46vv/+ewwYMAAvvfQSvL29sXjxYiQlJeHHH3+ESlU7/88XFRUFwPK5Dx06FBqNBvfffz8iIiLwr3/9C1OmTEFycjIGDRoENzc3JCUlYdWqVRg9evRNZyC/++674eXlheHDh+Oll16CJEn45ptvKj19FBUVhaVLl2LixIno3LkzXF1dcf/991e63VmzZmHAgAGIiYnByJEj5SkHPDw8anRfvXfffRc7d+5EfHw8QkNDkZGRgc8++wwhISE3nUsLsExp8Oqrr+LVV1+Ft7d3hZ6pUaNGITs7G3369EFISAjOnz+PuXPnomPHjvIYNSJ7wNBE1MBERkbiiSeeqHSsx9SpU3H58mWsWLECy5Ytw4ABA7Bu3Tr4+/vXSS1vvPEG/vzzT8yYMQN5eXno27cvPvvsMzg7O8ttevXqhYSEBLz33nv49NNPkZ+fj8DAQERHR2PMmDE1fu+AgADs3r0bkydPxty5c1FcXIz27dvjl19+QXx8fG3sHgCgc+fOeO+997BgwQKsX78eZrMZSUlJcHFxweuvv47mzZtj9uzZmD59OgDL4ON+/frhgQceuOm2fXx8sGbNGrzyyit466234OXlhSeeeAJ9+/ZFXFycTdsXXngBhw8fxsKFCzF79myEhoZeNzTFxsZi/fr1eOeddzB16lRoNBr07NkTH3zwQZUH2pf3wAMPIDk5We4p8vX1Rc+ePTF9+nSbq9auJyQkBHfffTd27dqFUaNGVbjli3Ws3meffYacnBwEBgbKczzVVvglqg+S4Ig5IiIioptixCciIiKqAoYmIiIioipgaCIiIiKqAoYmIiIioipgaCIiIiKqAoYmIiIioirgPE21xGw249KlS3Bzc6vy7RCIiIhIWUII5OXlITg4+KbzhjE01ZJLly5VuOM2ERER2YcLFy4gJCTkhm0YmmqJm5sbAMuH7u7urnA1REREVBV6vR6NGzeWv8dvhKGpllhPybm7uzM0ERER2ZmqDK3hQHAiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoChiYiIiKiKmBoIiIiIqoC3rC3gSs0GJFdYIDWQQV/N0elyyEiIrptsaepgdt4PB3dPtiGCUsPK10KERHRbY2hqYFTqyQAgNEkFK6EiIjo9sbQ1MA5lIUms2BoIiIiUhJDUwOnsvY0mRmaiIiIlMTQ1MDJPU0MTURERIpiaGrg2NNERETUMDA0NXDWniYTQxMREZGiGJoaODVDExERUYPA0NTAqSWGJiIiooaAoamBc1CXhSZOOUBERKQohqYGTiVxcksiIqKGgKGpgXNQWQ4RJ7ckIiJSFkNTA1eWmTjlABERkcIYmho4uaeJoYmIiEhRDE0NnJqTWxIRETUIDE0NHOdpIiIiahgYmho4zghORETUMDA0NXAqhiYiIqIGgaGpgZN7mjjlABERkaIYmho4VbnbqAgGJyIiIsUwNDVw1p4mAOAZOiIiIuUwNDVwavXV0GQ0mxWshIiI6PbG0NTAqaWroYmDwYmIiJTD0NTAqVUMTURERA0BQ1MDx9BERETUMDA0NXA8PUdERNQwMDQ1cCqVBGtnE0MTERGRchia7ICaE1wSEREpjqHJDlhDk9HE0ERERKQUhiY7oJZ4/zkiIiKlMTTZAZ6eIyIiUh5Dkx2QQxN7moiIiBSjaGiaMWMGOnfuDDc3N/j7+2PQoEFITEy0adOrVy9IkmTzeO6552zapKSkID4+Hs7OzvD398ekSZNgNBpt2mzfvh133nkndDodIiMjsWjRogr1zJs3D2FhYXB0dER0dDT27dtX6/tcE2qV5TAxNBERESlH0dC0Y8cOjB07Fnv27MGmTZtQWlqKfv36oaCgwKbds88+i9TUVPkxc+ZMeZ3JZEJ8fDwMBgN2796NxYsXY9GiRZg6darcJikpCfHx8ejduzcOHz6M8ePHY9SoUdiwYYPcZunSpZg4cSLeeecdHDx4EB06dEBcXBwyMjLq/oO4CQf2NBERESlOEqLhDJS5fPky/P39sWPHDvTo0QOApaepY8eOmDNnTqWvWbduHe677z5cunQJAQEBAIAFCxZg8uTJuHz5MrRaLSZPnoy1a9fi2LFj8uuGDh2KnJwcrF+/HgAQHR2Nzp0749NPPwUAmM1mNG7cGC+++CJef/31m9au1+vh4eGB3NxcuLu7/5OPoYKu72/FxZwirB7bFR0ae9bqtomIiG5n1fn+blBjmnJzcwEA3t7eNsuXLFkCX19ftG3bFlOmTEFhYaG8LiEhAe3atZMDEwDExcVBr9fj+PHjcpvY2FibbcbFxSEhIQEAYDAYcODAAZs2KpUKsbGxcptrlZSUQK/X2zzqijzlAHuaiIiIFOOgdAFWZrMZ48ePR9euXdG2bVt5+WOPPYbQ0FAEBwfjzz//xOTJk5GYmIiVK1cCANLS0mwCEwD5eVpa2g3b6PV6FBUV4cqVKzCZTJW2OXnyZKX1zpgxA9OnT/9nO11FHAhORESkvAYTmsaOHYtjx47h999/t1k+evRo+ed27dohKCgIffv2xdmzZxEREVHfZcqmTJmCiRMnys/1ej0aN25cJ+/F0ERERKS8BhGaxo0bhzVr1mDnzp0ICQm5Ydvo6GgAwJkzZxAREYHAwMAKV7mlp6cDAAIDA+X/WpeVb+Pu7g4nJyeo1Wqo1epK21i3cS2dTgedTlf1nfwHOLklERGR8hQd0ySEwLhx47Bq1Sps3boV4eHhN33N4cOHAQBBQUEAgJiYGBw9etTmKrdNmzbB3d0drVu3ltts2bLFZjubNm1CTEwMAECr1SIqKsqmjdlsxpYtW+Q2SuLklkRERMpTtKdp7Nix+O6777B69Wq4ubnJY5A8PDzg5OSEs2fP4rvvvsO9994LHx8f/Pnnn5gwYQJ69OiB9u3bAwD69euH1q1b48knn8TMmTORlpaGt956C2PHjpV7gp577jl8+umneO211/DMM89g69atWLZsGdauXSvXMnHiRAwfPhydOnVCly5dMGfOHBQUFGDEiBH1/8Fcw0Ft7WkyK1wJERHRbUwoCEClj4ULFwohhEhJSRE9evQQ3t7eQqfTicjISDFp0iSRm5trs53k5GQxYMAA4eTkJHx9fcUrr7wiSktLbdps27ZNdOzYUWi1WtG0aVP5PcqbO3euaNKkidBqtaJLly5iz549Vd6X3NxcAaBCbbVh4Ke/i9DJa8TG42m1vm0iIqLbWXW+vxvUPE32rC7naXp4/m7sP38FC564E/3bBtXqtomIiG5ndjtPE1VOxXmaiIiIFMfQZAd4GxUiIiLlMTTZAc7TREREpDyGJjvA0ERERKQ8hiY7wNNzREREymNosgMqiQPBiYiIlMbQZAesk1uaOTsEERGRYhia7IDc02RiaCIiIlIKQ5MdsI5pYk8TERGRchia7IBaZTlMHNNERESkHIYmO6AuO0q8eo6IiEg5DE12wNrTxNBERESkHIYmO2DtaeLpOSIiIuUwNNkBh7KeJjNDExERkWIYmuwAJ7ckIiJSHkOTHeDklkRERMpjaLID1hv2cnJLIiIi5TA02QG1xJ4mIiIipTE02QG5p8lsVrgSIiKi2xdDkx2whibO00RERKQchiY7wNBERESkPIYmO+Cg4pQDRERESmNosgPWniZObklERKQchiY7oGZPExERkeIYmuyA3NPEKQeIiIgUw9BkBzi5JRERkfIYmuyAdXJLXj1HRESkHIYmOyBPOcDTc0RERIphaLID1hv2sqeJiIhIOQxNdkDF03NERESKY2iyAw4qy2HilANERETKYWiyA+qyo8TJLYmIiJTD0GQH1OxpIiIiUhxDkx1w4A17iYiIFMfQZAdUDE1ERESKY2iyA+xpIiIiUh5Dkx2Qpxzg5JZERESKYWiyA5zckoiISHkMTXaAk1sSEREpj6HJDnBMExERkfIYmuyA9Ya9RrNZ4UqIiIhuXwxNdkAt9zQpXAgREdFtjKHJDlw9PcfUREREpBSGJjvAyS2JiIiUx9BkBzgQnIiISHkMTXaAk1sSEREpj6HJDnBySyIiIuUxNNmBq1MOMDQREREphaHJDqjLTs8JAZgZnIiIiBTB0GQHHFRXDxPHNRERESlD0dA0Y8YMdO7cGW5ubvD398egQYOQmJho06a4uBhjx46Fj48PXF1d8dBDDyE9Pd2mTUpKCuLj4+Hs7Ax/f39MmjQJRqPRps327dtx5513QqfTITIyEosWLapQz7x58xAWFgZHR0dER0dj3759tb7PNVEuM3FcExERkUIUDU07duzA2LFjsWfPHmzatAmlpaXo168fCgoK5DYTJkzAL7/8guXLl2PHjh24dOkSBg8eLK83mUyIj4+HwWDA7t27sXjxYixatAhTp06V2yQlJSE+Ph69e/fG4cOHMX78eIwaNQobNmyQ2yxduhQTJ07EO++8g4MHD6JDhw6Ii4tDRkZG/XwYN2DT08TQREREpAzRgGRkZAgAYseOHUIIIXJycoRGoxHLly+X2/z1118CgEhISBBCCPHrr78KlUol0tLS5Dbz588X7u7uoqSkRAghxGuvvSbatGlj815DhgwRcXFx8vMuXbqIsWPHys9NJpMIDg4WM2bMqFLtubm5AoDIzc2t5l7fXEmpSYROXiNCJ68RuUWGWt8+ERHR7ao6398NakxTbm4uAMDb2xsAcODAAZSWliI2NlZu07JlSzRp0gQJCQkAgISEBLRr1w4BAQFym7i4OOj1ehw/flxuU34b1jbWbRgMBhw4cMCmjUqlQmxsrNzmWiUlJdDr9TaPumK9eg4ATCb2NBERESmhwYQms9mM8ePHo2vXrmjbti0AIC0tDVqtFp6enjZtAwICkJaWJrcpH5is663rbtRGr9ejqKgImZmZMJlMlbaxbuNaM2bMgIeHh/xo3LhxzXa8CsplJk47QEREpJAGE5rGjh2LY8eO4YcfflC6lCqZMmUKcnNz5ceFCxfq7L0kSZJ7m8y8eo6IiEgRDkoXAADjxo3DmjVrsHPnToSEhMjLAwMDYTAYkJOTY9PblJ6ejsDAQLnNtVe5Wa+uK9/m2ivu0tPT4e7uDicnJ6jVaqjV6krbWLdxLZ1OB51OV7MdrgG1SoLJLNjTREREpBBFe5qEEBg3bhxWrVqFrVu3Ijw83GZ9VFQUNBoNtmzZIi9LTExESkoKYmJiAAAxMTE4evSozVVumzZtgru7O1q3bi23Kb8NaxvrNrRaLaKiomzamM1mbNmyRW6jNOsEl5zckoiISBmK9jSNHTsW3333HVavXg03Nzd5/JCHhwecnJzg4eGBkSNHYuLEifD29oa7uztefPFFxMTE4K677gIA9OvXD61bt8aTTz6JmTNnIi0tDW+99RbGjh0r9wQ999xz+PTTT/Haa6/hmWeewdatW7Fs2TKsXbtWrmXixIkYPnw4OnXqhC5dumDOnDkoKCjAiBEj6v+DqYQDb6VCRESkrLq/mO/6AFT6WLhwodymqKhIvPDCC8LLy0s4OzuLBx98UKSmptpsJzk5WQwYMEA4OTkJX19f8corr4jS0lKbNtu2bRMdO3YUWq1WNG3a1OY9rObOnSuaNGkitFqt6NKli9izZ0+V96UupxwQQogO0zeI0MlrxOn0vDrZPhER0e2oOt/fkhAcWVwb9Ho9PDw8kJubC3d391rfftR7m5BVYMCG8T3QItCt1rdPRER0O6rO93eDuXqObkwtn54zK1wJERHR7YmhyU7IUw4wMxERESmCoclOsKeJiIhIWQxNdoKTWxIRESmLoclOyD1NvPccERGRIhia7IR1niYT52kiIiJSBEOTnVCVzQhu4uk5IiIiRTA02QkHNWcEJyIiUhJDk53gveeIiIiUxdBkJ9S89xwREZGiGJrshIPKcqjY00RERKQMhiY7UZaZ2NNERESkEIYmO2HtaeKUA0RERMpgaLITKs7TREREpCiGJjvByS2JiIiUxdBkJzi5JRERkbIYmuyEA6ccICIiUhRDk51Qqzm5JRERkZIYmuyEdUZw9jQREREpg6HJTlwdCG5WuBIiIqLbE0OTnbg65YDChRAREd2mGJrsBHuaiIiIlMXQZCfY00RERKQshiY7wZ4mIiIiZTE02Qm1ipNbEhERKYmhyU5wygEiIiJlMTTZCevkliYTQxMREZESGJrshJr3niMiIlIUQ5OduDoQnKGJiIhICQxNdkKtshwqhiYiIiJlMDTZCXXZkWJoIiIiUgZDk51gTxMREZGyGJrsBHuaiIiIlMXQZCesPU2cp4mIiEgZDE12omyaJk45QEREpJAahaaioiIUFhbKz8+fP485c+Zg48aNtVYY2VKXnZ/j5JZERETKqFFoGjhwIL7++msAQE5ODqKjo/Hhhx9i4MCBmD9/fq0WSBYOvPccERGRomoUmg4ePIju3bsDAFasWIGAgACcP38eX3/9NT755JNaLZAs5BnBOaaJiIhIETUKTYWFhXBzcwMAbNy4EYMHD4ZKpcJdd92F8+fP12qBZKHmjOBERESKqlFoioyMxE8//YQLFy5gw4YN6NevHwAgIyMD7u7utVogWTA0ERERKatGoWnq1Kl49dVXERYWhujoaMTExACw9DrdcccdtVogWVhDk9FsVrgSIiKi25NDTV708MMPo1u3bkhNTUWHDh3k5X379sWDDz5Ya8XRVdaB4MxMREREyqhRaAKAwMBABAYG2izr0qXLPy6IKqdiTxMREZGiahSaCgoK8P7772PLli3IyMiA+Zov8nPnztVKcXTV1SkHFC6EiIjoNlWj0DRq1Cjs2LEDTz75JIKCgiCVXQ5PdUclDwRnTxMREZESahSa1q1bh7Vr16Jr1661XQ9dh9zTxMxERESkiBpdPefl5QVvb+/aroVu4OrklkxNRERESqhRaHrvvfcwdepUm/vPUd26OuUABzUREREpoUan5z788EOcPXsWAQEBCAsLg0ajsVl/8ODBWimOrnJQW6ccYGgiIiJSQo1C06BBg2q5DLoZlcSeJiIiIkUJBe3YsUPcd999IigoSAAQq1atslk/fPhwAcDmERcXZ9MmKytLPPbYY8LNzU14eHiIZ555RuTl5dm0OXLkiOjWrZvQ6XQiJCREfPDBBxVqWbZsmWjRooXQ6XSibdu2Yu3atdXal9zcXAFA5ObmVut1VfXnhRwROnmNiPnP5jrZPhER0e2oOt/fNRrTZHXgwAF8++23+Pbbb3Ho0KFqv76goAAdOnTAvHnzrtumf//+SE1NlR/ff/+9zfrHH38cx48fx6ZNm7BmzRrs3LkTo0ePltfr9Xr069cPoaGhOHDgAGbNmoVp06bhiy++kNvs3r0bw4YNw8iRI3Ho0CEMGjQIgwYNwrFjx6q9T3VFVXak2NNERESkkJqksvT0dNG7d28hSZLw8vISXl5eQpIk0adPH5GRkVGTTV63p2ngwIHXfc2JEycEAPHHH3/Iy9atWyckSRIXL14UQgjx2WefCS8vL1FSUiK3mTx5smjRooX8/NFHHxXx8fE2246OjhZjxoypcv113dN0MlUvQievEVHvbayT7RMREd2O6ryn6cUXX0ReXh6OHz+O7OxsZGdn49ixY9Dr9XjppZdqM9Nh+/bt8Pf3R4sWLfD8888jKytLXpeQkABPT0906tRJXhYbGwuVSoW9e/fKbXr06AGtViu3iYuLQ2JiIq5cuSK3iY2NtXnfuLg4JCQkXLeukpIS6PV6m0ddUrOniYiISFE1Ck3r16/HZ599hlatWsnLWrdujXnz5mHdunW1Vlz//v3x9ddfY8uWLfjggw+wY8cODBgwACaTCQCQlpYGf39/m9c4ODjA29sbaWlpcpuAgACbNtbnN2tjXV+ZGTNmwMPDQ340btz4n+3sTajLzs+ZeB8VIiIiRdTo6jmz2VxhmgEA0Gg0Fe5D908MHTpU/rldu3Zo3749IiIisH37dvTt27fW3qcmpkyZgokTJ8rP9Xp9nQanq/eeY2giIiJSQo16mvr06YOXX34Zly5dkpddvHgREyZMqNMw07RpU/j6+uLMmTMAgMDAQGRkZNi0MRqNyM7ORmBgoNwmPT3dpo31+c3aWNdXRqfTwd3d3eZRl1Sc3JKIiEhRNQpNn376KfR6PcLCwhAREYGIiAiEh4dDr9dj7ty5tV2j7O+//0ZWVhaCgoIAADExMcjJycGBAwfkNlu3boXZbEZ0dLTcZufOnSgtLZXbbNq0CS1atICXl5fcZsuWLTbvtWnTJsTExNTZvlSXtaeJk1sSEREpo0an5xo3boyDBw9i8+bNOHnyJACgVatWFQZT30x+fr7cawQASUlJOHz4MLy9veHt7Y3p06fjoYceQmBgIM6ePYvXXnsNkZGRiIuLk9+zf//+ePbZZ7FgwQKUlpZi3LhxGDp0KIKDgwEAjz32GKZPn46RI0di8uTJOHbsGD7++GPMnj1bft+XX34ZPXv2xIcffoj4+Hj88MMP2L9/v820BErj5JZEREQKq4er+a5r27ZtFSavBCCGDx8uCgsLRb9+/YSfn5/QaDQiNDRUPPvssyItLc1mG1lZWWLYsGHC1dVVuLu7ixEjRtxwcstGjRqJ999/v0Ity5YtE82bNxdarVa0adOmwU1umZ1fIkInrxGhk9cIo8lcJ+9BRER0u6nO97ckRNVGFn/yyScYPXo0HB0d8cknn9ywbW1PO2AP9Ho9PDw8kJubWyfjm/KKS9Fu2kYAQOK/+kPnoK719yAiIrrdVOf7u8qn52bPno3HH38cjo6ONqe2riVJ0m0ZmuqaRn11+FmpSUBXoxOrREREVFNV/upNSkqq9GeqHzahyWgGdAoWQ0REdBuq0dVz7777LgoLCyssLyoqwrvvvvuPi6KK1CoJ6rIr6EpNtTcXFhEREVVNjULT9OnTkZ+fX2F5YWEhpk+f/o+Lospp1JbQZGBoIiIiqnc1Ck1CCEhll8CXd+TIEXh7e//joqhy1lN0pbyVChERUb2r1nBiLy8vSJIESZLQvHlzm+BkMpmQn5+P5557rtaLJAutHJrY00RERFTfqhWa5syZAyEEnnnmGUyfPh0eHh7yOq1Wi7CwsAY1i/atxtrTZDAyNBEREdW3aoWm4cOHAwDCw8Nx9913V3rTXqo7GgcOBCciIlJKjWb76dmzp/xzcXExDAaDzfq6vnnt7YpjmoiIiJRTo4HghYWFGDduHPz9/eHi4gIvLy+bB9UNjmkiIiJSTo1C06RJk7B161bMnz8fOp0OX375JaZPn47g4GB8/fXXtV0jlZHHNDE0ERER1bsanZ775Zdf8PXXX6NXr14YMWIEunfvjsjISISGhmLJkiV4/PHHa7tOwtV5mko5EJyIiKje1ainKTs7G02bNgVgGb+UnZ0NAOjWrRt27txZe9WRDY5pIiIiUk6NQlPTpk3l+8+1bNkSy5YtA2DpgfL09Ky14siW1oFjmoiIiJRSo9A0YsQIHDlyBADw+uuvY968eXB0dMSECRMwadKkWi2QruKYJiIiIuXUaEzThAkT5J9jY2Nx8uRJHDhwAJGRkWjfvn2tFUe25DFNDE1ERET1rto9TaWlpejbty9Onz4tLwsNDcXgwYMZmOqYPKaJA8GJiIjqXbVDk0ajwZ9//lkXtdBNaDkQnIiISDE1GtP0xBNP4H//+19t10I3wTFNREREyqnRmCaj0YivvvoKmzdvRlRUFFxcXGzWf/TRR7VSHNniveeIiIiUU6PQdOzYMdx5550AgFOnTtmskyTpn1dFldLwNipERESKqVFo2rZtW23XQVXAMU1ERETKqdGYJqszZ85gw4YNKCoqAgAIwS/zuiSPaeLVc0RERPWuRqEpKysLffv2RfPmzXHvvfciNTUVADBy5Ei88sortVogXcXTc0RERMqpUWiaMGECNBoNUlJS4OzsLC8fMmQI1q9fX2vFkS0OBCciIlJOjcY0bdy4ERs2bEBISIjN8mbNmuH8+fO1UhhVxDFNREREyqlRT1NBQYFND5NVdnY2dDrdPy6KKsd5moiIiJRTo9DUvXt3fP311/JzSZJgNpsxc+ZM9O7du9aKI1u8jQoREZFyanR6bubMmejbty/2798Pg8GA1157DcePH0d2djZ27dpV2zVSGd6wl4iISDk16mlq27YtTp06hW7dumHgwIEoKCjA4MGDcejQIURERNR2jVRG68AxTUREREqpUU9TSkoKGjdujDfffLPSdU2aNPnHhVFFHNNERESknBr1NIWHh+Py5csVlmdlZSE8PPwfF0WV4zxNREREyqlRaBJCVHqPufz8fDg6Ov7joqhyHNNERESknGqdnps4cSIAy9Vyb7/9ts20AyaTCXv37kXHjh1rtUC6Sp6nycgxTURERPWtWqHp0KFDACw9TUePHoVWq5XXabVadOjQAa+++mrtVkgyjQNPzxERESmlWqFp27ZtAIARI0bgk08+gZubW50URZXjQHAiIiLlVCs0DR48WP55+PDh1223cuXKmldE18UxTURERMqpVmjy8PCoqzqoCnjvOSIiIuVUKzQtXLiwruqgKuBtVIiIiJRToykHSBnWgeAc00RERFT/GJrsCMc0ERERKYehyY5YxzSZBWAyc1wTERFRfWJosiPWMU0Ae5uIiIjqG0OTHSkfmjiuiYiIqH4xNNkR65gmgFfQERER1TeGJjsiSVK5weAc00RERFSfGJrsjIOK958jIiJSAkOTnbH2NHFMExERUf1iaLIzWgf2NBERESmBocnOXL2VCsc0ERER1SdFQ9POnTtx//33Izg4GJIk4aeffrJZL4TA1KlTERQUBCcnJ8TGxuL06dM2bbKzs/H444/D3d0dnp6eGDlyJPLz823a/Pnnn+jevTscHR3RuHFjzJw5s0Ity5cvR8uWLeHo6Ih27drh119/rfX9rQ3W0MTTc0RERPVL0dBUUFCADh06YN68eZWunzlzJj755BMsWLAAe/fuhYuLC+Li4lBcXCy3efzxx3H8+HFs2rQJa9aswc6dOzF69Gh5vV6vR79+/RAaGooDBw5g1qxZmDZtGr744gu5ze7duzFs2DCMHDkShw4dwqBBgzBo0CAcO3as7na+hngrFSIiIoWIBgKAWLVqlfzcbDaLwMBAMWvWLHlZTk6O0Ol04vvvvxdCCHHixAkBQPzxxx9ym3Xr1glJksTFixeFEEJ89tlnwsvLS5SUlMhtJk+eLFq0aCE/f/TRR0V8fLxNPdHR0WLMmDFVrj83N1cAELm5uVV+TU3Ezd4hQievETtPZdTp+xAREd0OqvP93WDHNCUlJSEtLQ2xsbHyMg8PD0RHRyMhIQEAkJCQAE9PT3Tq1EluExsbC5VKhb1798ptevToAa1WK7eJi4tDYmIirly5Ircp/z7WNtb3qUxJSQn0er3Noz5wIDgREZEyGmxoSktLAwAEBATYLA8ICJDXpaWlwd/f32a9g4MDvL29bdpUto3y73G9Ntb1lZkxYwY8PDzkR+PGjau7izUij2niQHAiIqJ61WBDU0M3ZcoU5Obmyo8LFy7Uy/tyTBMREZEyGmxoCgwMBACkp6fbLE9PT5fXBQYGIiMjw2a90WhEdna2TZvKtlH+Pa7Xxrq+MjqdDu7u7jaP+iBPOcDQREREVK8abGgKDw9HYGAgtmzZIi/T6/XYu3cvYmJiAAAxMTHIycnBgQMH5DZbt26F2WxGdHS03Gbnzp0oLS2V22zatAktWrSAl5eX3Kb8+1jbWN+nIdEyNBERESlC0dCUn5+Pw4cP4/DhwwAsg78PHz6MlJQUSJKE8ePH41//+hd+/vlnHD16FE899RSCg4MxaNAgAECrVq3Qv39/PPvss9i3bx927dqFcePGYejQoQgODgYAPPbYY9BqtRg5ciSOHz+OpUuX4uOPP8bEiRPlOl5++WWsX78eH374IU6ePIlp06Zh//79GDduXH1/JDd1dZ4mjmkiIiKqV/VwNd91bdu2TQCo8Bg+fLgQwjLtwNtvvy0CAgKETqcTffv2FYmJiTbbyMrKEsOGDROurq7C3d1djBgxQuTl5dm0OXLkiOjWrZvQ6XSiUaNG4v33369Qy7Jly0Tz5s2FVqsVbdq0EWvXrq3WvtTXlAPjvjsoQievEf/77Vydvg8REdHtoDrf35IQgl0WtUCv18PDwwO5ubl1Or5p4rLDWHnwIqYMaIkxPSPq7H2IiIhuB9X5/m6wY5qochzTREREpAyGJjvDMU1ERETKYGiyM5xygIiISBkMTXZG41A2uaWRoYmIiKg+MTTZGY5pIiIiUgZDk53hmCYiIiJlMDTZmcrGNJWazJiy8k/8fOSSUmURERHd8hia7ExlN+w9lJKD7/ddwOxNp5Qqi4iI6JbH0GRntA4Ve5pyiyz31cspNChSExER0e2AocnOyGOajFfHNOWXWEKTvtgITvBORERUNxia7ExlY5ryi40AAJNZoKjUpEhdREREtzqGJjtT2ZimvBKj/LO+yFjhNURERPTPMTTZmcrmabL2NAFAXnFpvddERER0O2BosjOVzdOUX76niaGJiIioTjA02RmN9eo5Y+U9Tfpinp4jIiKqCwxNdubmY5rY00RERFQXGJrszM3HNLGniYiIqC4wNNmZq1MOVD6miaGJiIiobjA02ZmrA8HL9TRxIDgREVGdY2iyM1qHSsY0ccoBIiKiOsfQZGfk03Plr54ruRqUOLklERFR3WBosjPXjmkqNZlRXFq+14k9TURERHWBocnOlB/TJIRAQYltzxLnaSIiIqobDE12xjrlAAAYzaLC1XLsaSIiIqobDE12RlM2EBywnJrLv7aniWOaiIiI6gRDk53RlOtpKjUKOTSpVZYwxZ4mIiKiusHQZGccVFd7mgwmszwbeKC7IwCgwGCCsdx0BERERFQ7GJrsjCRJNrdSsd53LtjTUW5z7Sk7IiIi+ucYmuxQ+Zv2WnuaPJ21cNKoAXBcExERUV1gaLJDGoerPU3WiS3ddA5wc3QAwFupEBER1QWGJjskz9VkFHJPk6ujA9ydNAB4014iIqK6wNBkhyob0+TKniYiIqI6xdBkhyob0+Tq6AB3R/Y0ERER1RWGJjtU/v5z1ivlbMY0FbGniYiIqLYxNNkhjbr8QPCrPU1u7GkiIiKqMwxNdshVZ+lRulJokAOSq04DdyeOaSIiIqorDE12KMLfBQBwOj3/ak+TrvyYJoYmIiKi2sbQZIea+bsBAE5n5MkDwd0cHeAuj2ni6TkiIqLaxtBkh5oHlIWma3qa5DFNJexpIiIiqm0OShdA1dc8wBUAkJxVALOwLLNMbsmeJiIiorrCniY75Oemg4eTRg5MwDU9TRzTREREVOsYmuyQJElo5u8qP9eoJegcVPJAcD2nHCAiIqp1DE12qlnZuCbA0sskSZI8uWVecSmEENd7KREREdUAQ5Odso5rAizjmQDIN+wtNQmUGM2K1EVERHSrYmiyU81teposYclFq4bKcls63kqFiIioljE02alm5Xqa3MpmCJckCR5lvU1XChmaiIiIahNDk53yc9XJAcl6eg4AfFx1AICs/BJF6iIiIrpVMTTZKUmS5HFN1nvRAYCPixYAkFlgUKQuIiKiWxVDkx2zXkFXvqfJlz1NREREdYKhyY49eEcjRPq7YkDbQHmZj6ulpymbPU1ERES1qkGHpmnTpkGSJJtHy5Yt5fXFxcUYO3YsfHx84Orqioceegjp6ek220hJSUF8fDycnZ3h7++PSZMmwWi0nfxx+/btuPPOO6HT6RAZGYlFixbVx+79Y53DvLF5Yk90b+YnL/NxsfQ0ZeYzNBEREdWmBh2aAKBNmzZITU2VH7///ru8bsKECfjll1+wfPly7NixA5cuXcLgwYPl9SaTCfHx8TAYDNi9ezcWL16MRYsWYerUqXKbpKQkxMfHo3fv3jh8+DDGjx+PUaNGYcOGDfW6n7XF2tPE03NERES1q8HfsNfBwQGBgYEVlufm5uJ///sfvvvuO/Tp0wcAsHDhQrRq1Qp79uzBXXfdhY0bN+LEiRPYvHkzAgIC0LFjR7z33nuYPHkypk2bBq1WiwULFiA8PBwffvghAKBVq1b4/fffMXv2bMTFxdXrvtYGX2to4uk5IiKiWtXge5pOnz6N4OBgNG3aFI8//jhSUlIAAAcOHEBpaSliY2Plti1btkSTJk2QkJAAAEhISEC7du0QEBAgt4mLi4Ner8fx48flNuW3YW1j3cb1lJSUQK/X2zwaAk45QEREVDcadGiKjo7GokWLsH79esyfPx9JSUno3r078vLykJaWBq1WC09PT5vXBAQEIC0tDQCQlpZmE5is663rbtRGr9ejqKjourXNmDEDHh4e8qNx48b/dHdrhXXKgSyOaSIiIqpVDfr03IABA+Sf27dvj+joaISGhmLZsmVwcnJSsDJgypQpmDhxovxcr9c3iOBk7WnKKzGiuNQER41a4YqIiIhuDQ26p+lanp6eaN68Oc6cOYPAwEAYDAbk5OTYtElPT5fHQAUGBla4ms76/GZt3N3dbxjMdDod3N3dbR4NgbujAzRqyw3orp12YPOJdBw4f0WJsoiIiOyeXYWm/Px8nD17FkFBQYiKioJGo8GWLVvk9YmJiUhJSUFMTAwAICYmBkePHkVGRobcZtOmTXB3d0fr1q3lNuW3YW1j3Ya9kSRJnnag/Cm6dH0xnv1mP0Yu/gNCCKXKIyIislsNOjS9+uqr2LFjB5KTk7F79248+OCDUKvVGDZsGDw8PDBy5EhMnDgR27Ztw4EDBzBixAjExMTgrrvuAgD069cPrVu3xpNPPokjR45gw4YNeOuttzB27FjodJZg8dxzz+HcuXN47bXXcPLkSXz22WdYtmwZJkyYoOSu/yPWaQcyC64OBk/JLoQQQE5hKS7ncZA4ERFRdTXoMU1///03hg0bhqysLPj5+aFbt27Ys2cP/PwskznOnj0bKpUKDz30EEpKShAXF4fPPvtMfr1arcaaNWvw/PPPIyYmBi4uLhg+fDjeffdduU14eDjWrl2LCRMm4OOPP0ZISAi+/PJLu5xuwOrqFXRXe5rScovln89nF8Lf3bHe6yIiIrJnDTo0/fDDDzdc7+joiHnz5mHevHnXbRMaGopff/31htvp1asXDh06VKMaG6KrV9Bd7VFK118NTSlZhegc5l3vdREREdmzBn16jmpGDk0FtmOarM5nF9Z7TURERPaOoekWZD09l1mupylNX258U1ZBvddERERk7xiabkFX7z9Xrqcplz1NRERE/wRD0y3o6v3nyo1pyrMd00RERETVw9B0C7LO05Rd1tMkhLC5ei6rwID8EqMitREREdkrhqZb0NV5mgwQQiC3qBQlRjMAwE1nuWDyPMc1ERERVQtD0y3I2tNkMJqRX2JEWtmVc57OGkT4uwIALnBcExERUbUwNN2CnLRquGgtN+rNyjcgvezKuUB3R4T6OAMAznNcExERUbUwNN2i5FnBC0rkK+f83R3RxLssNLGniYiIqFoYmm5R8rimfIN8ei7QXSeHJl5BR0REVD0MTbco37KepgvZhfJs4JbTcy4AgPPZHAhORERUHQxNt6hOoV4AgJ2nM+XQFOBxdUzTpZxilJrMitVHRERkbxiablF9WvoDAPacy0JSpqVXKdDdEf5uOjhqVDCZBS5eKVKyRCIiIrvC0HSLivR3RYiXEwxGM85etoSmAHdHSJKEsLJTdGcy8pUskYiIyK4wNN2iJEmSe5usAtwdAQCtg90BAMcu5dZ7XURERPaKoekW1rtcaNKoJfi4WK6oaxvsAQA4dlFf4TVZ+SXIKHefOiIiIrJgaLqFxTT1gaPGcoj93RyhUkkAgHYhltB0/JqeJqPJjAc+3YUBc36Dvri0foslIiJq4BiabmGOGjW6RvgCAPzddfLyVkHukCQgNbcYmfkl8vLkrEJczClCVoEBW//KqPd6iYiIGjKGplvcve2CAFiCkpWrzgHhvpbB4McuXu1tOp2eJ//869HUeqqQiIjIPjgoXQDVrcF3NkKgh6M8jsmqXSMPnLtcgOOX9OjVwjL2KbFcaNpx6jIKSoxw0VX8FTmUcgUXc4pwX/vgui2eiIioAWFP0y1OkiR0jfSFh7PGZrk1RB39u3xP09UpCEqMZmxLrPwU3QtLDmLcd4dsXktERHSrY2i6TbVpVHHaAWtPU+uyU3nrjqVVeJ2+uBSpZTcA/u3M5bouk4iIqMFgaLpNtSnrafr7ShGuFBhQYjQhuWzm8Jf6NgMAbDuZgeJSk83ryt/oN+FsVj1VS0REpDyGptuUh5NGvg/d8Ut6JGUWwGgWcNM5oF/rADTydEKhwYTfTmfavO58udD0R3I2DEbev46IiG4PDE23Meu4poMpV3CqbDxTswBXqFQSejS3TFWwL8m2N+l8doH8c3GpGYcv5NRPsURERApjaLqNWYPRsv0XcDLVMjt4i0A3AECnUG8AwIHzV2xeU/70HADsPmvbE2WVW1RqM4UBERGRvWNouo090KERPJw0+PtKEb7flwIAaOZfFprCvABYbrVSflyT9fRcp1DL+t3XGdf0yrLDiJuzEwfOZ9dZ/URERPWJoek25qRVY2jnxgCAK4WW26ZYe5qaeDvD11UHg8mMo+UmwDyfZTk9N7RLEwDA4ZQcFBlsB4ubzQK7z2bBLIAVBy7W+X4QERHVB4am29wTd4Wi7JZ0ACxjmgDL/E7W3qT9yZZTdCVGE1L1lukGejb3Q5CHIwwmc8VTeNmFKCwLUhuOp8Fo4mBxIiKyfwxNt7nG3s6IbRUAAPBy1sDP9eo96qyn6Kyn2C5kF0EIwEWrhq+rFneX3ddu52nb+ZpOpunln7MLDNibxFN0RERk/xiaCGN6NoVGLaFbMz9I0tVup6hQa2i6AiEEUsqunGvi4wJJktC7pR8AYOtJ25nDT6TaDgBfy/vYERHRLYChiRAV6o3fJ/fBrIfb2yxvE+wBnYMKVwpLcfZygTwIPNTbMr9T92Z+cFBJOJORL491AiBfidenpeWeduuP8RQdERHZP4YmAgAEuDvCUaO2WaZ1UKFDY08AllN0cmgqmxTTw0mDzmGWqQm2/HW1t+mvstNzI7qGwctZw1N0RER0S2BoohvqXDauadWhi3JvUpOy0AQAfVtZepOsp+jyiktxIbsIgGXyzLg2gQCAnw9fqrDtRbuSEP2fzdh2svIbAwOWU4NDPk+wGSdFRESkBIYmuqFHOzWGo0aFPeeysbPsliqh3i7yeuspuL1JWcgrLkVimmU8U6C7I7xctBh0RyMAlnFNhQajzbYX7k5Gur4Ez317AHvOVT7f0/ztZ7A3KRtzt565bo3p+mK8suwI/kplsCIiorrD0EQ3FOrjglf7tQAAmMyibNnVnqamfq4I93VBqUng99OZ+KssNLUMssz3FB3ujSbezsgvMWLd0TT5dcmZV8dIlRjNGLV4P46Vmw8KAIwmM/acs5zWq+zmwVZzt57Gjwf/xsz1J2tjl4mIiCrF0EQ3NaJrOO5s4gkAcFBJCPJwtFlv7W364Y8LOF4WfFoFuQOwzPf0cFQIAGD5gQvya3acskxTEBXqhbuaeiO/xIipq4/ZbPfI37nIL7H0ThUaTPJryjOZBdYfSwcA7DmXjRJj5cGKiIjon2JooptSqyTMfLgDvJw16BrpCwe17a/Ng3c0glolYcepy1i23xKMWpbNLA4AD0WFQJIsocZ67zprALqndQA+GXYHNGoJB1NybHqbdp+xva/d+mNpuNb+5Gxk5pcAAIpKTRUm2rS6nFeCiUsPY3vi9cdPERER3QhDE1VJpL8rdr/eFwuf7lxhXdtGHvh02B1wUEkoO4OH1mU9TQDQyNMJ3SItE2GuOHABxaUmJJTds65ncz/4uzliQNsgAMDi3cny63aV3Qx4UMdgAMDmv9Ir9CStuyZI7TxV+Q2E31x1FCsPXcTbq4/BbC2SiIioGhiaqMqctGqoyt9zpZwB7YIw7/E7oVFL8HXVIdzXxWa99RTdV7uS8U3CeRSVmuDvppN7pIbfHQoAWH3kEq4UGFBkMOHg+RwAwLg+kfB30yGv2IjdZ64OGDebhdz7FN/eErp+O13xFN6G42nYeMJyCu9CdhH2X6c3qsRowkebTlV6GpCIiIihiWpNXJtAbJ7YE6vHda1wCu/edkHy2KV///oXAEsvk3UG8jubeKFtI3cYjGYs3X8B+89nw2AyI9DdERF+rujf1jJ1wYIdZ3ExxzKlwaELOUjTF8NV54A37m0FADh+SY/LeSXy++YWluKd1ccBAG6ODgCAHw/8La8v3+v0/rqT+GTLaYz77iDyikuvu59nL+cjo+wefEREdPtgaKJaFerjgkaeThWWa9QqfP5kJzQvuyEwAPRs4Sf/LEkSnooJAwB8vPk0/r3WEqzujvSRB5OrVRL2JmWj96ztePJ/ezFpxREAlrmiGnk6yacEfzp0EZOWH0GXf29Gh3c3Ik1fjFAfZ8wddgcAy/QH+uJSPP/tAXR8dyOW7D2PbSczsHBXMgAgr9iIb/ekAAB2nrqM8T8cwpELOQCAr35PQt8Pd+DeT35DdoGh9j64f4inHImI6h5DE9UbDycNFo3ogkaeTvBy1qB7pJ/N+gc6BKNLmDeKSk04WTZ1QdeymwK3D/HE8udiENPUBwaTGb+dzsS5y5bJNgd1tMwF1b25pe2/f/0Lyw/8jYyyHid/Nx0+fKQDejTzQyNPJ+SXGPHgvF1YdywN+mIj3lx1DKO+3g/g6gD2//1+DgdTrmD0N/vx0+FLePCzXXjiy714d80JAEBmvkEOdjtOXcaoxX9g3dFUCGEJL0aTWf75n9h4PA3/WnMCOYXXD2j/XnsCd7y3CWv+rDiBqNXJND3e/ukYTqXnXbcNERHdmCRq4y87Qa/Xw8PDA7m5uXB3d7/5C25jhQYjSk0CHk6aCuuEENh5OhNf/Z6EIoMJXz7dCe6Otu32J2fj7OV8OGrUCHB3RHS4NyRJwu6zmXjsv3sBAO1DPPB6/5ZoFeQOT2eNfBrwo42J+KRsokwHlYTHo5vg+z8uwGA0o2WgG1Y8fzfiZu/ExZwiaB1UMBjNCHDXIV1/9ZTf0M6NsXT/BQgBPH13GL7Zc16ew6prpA+cNGr8fiYTHk4aTH+gLXq39MP/fk/C9/tSENPUByO7NYWrowP2nM2CADCgbSBcdA44lHIF3+9LwV1NfTCoYyOsOnQRr644AiGApr4u+OrpzvBy0eLPv3PQ1M8VjTyd8MO+FLy+8igAy1WOHw/tiPh2QcgpLIW7kwZqlYTM/BLEf/Ib0vUlcNU54NPH7kCvFv4wmsxQqyT5szmfVYCFu5JxX/sgdCq7PQ4R0a2uOt/fDE21hKFJeUIILN6dDFdHjTwNwrWSMwvQ+8PtAICPh96BBzoEIzEtDz8fuYhhXZogxMsZ3yQk4+2ycVAhXk74ZVw3HLpwBV/9noyHohrhwTtCMHX1MXydcF7ebucwLxz5OxcGY8UbE/u4aJF1g1N5bjoHtA52t7k/X+sgd5xM08MsACeNGkWlJjhp1DCYzDCZBRxUEgbd0Qg/H7kEg9GMZv6uOJ2RD7VKgpujA3IKSxHo7oi372uNJXvPY/fZLKhVEkxmAZUENPJywqWcYjT2csJHQzrC21mLoV/sQZq+GCoJeKFXJPq1CcCRv3Ph46JF/zaBUKkkJGcW4MNNp3BnE088Hh0KrQM7q4nIvjE0KYChyX5sS8yAVq1C17JpEK5VXGpC7Ec7kF1gwPLnYtAm2KNCm7ziUvSbvROpucUY07MpXu/fEinZhfh2z3m4O2rQq4U/1h1LxYIdZ2EWgK+rDi/0isD+89lYfywNkiShXSMP5BaVIinTcppRJVkmCt19NguFBsvUCkM7N8bEe5rj2a/348jfljmsAt0dkVZuIHpsK38seCIKr688ihXlBrmX56xVY8Vzd+N/vyfhx4O2bRxUEjycNMgqMMDTWYOcwoqD4Ae0DcSo7uEY880BZOZbAmCojzO6RvrifFYBtGoVJsW1ROtgd+iLS/HDvhSE+bggtlUAVCoJxaUmZOaXIMTLucK2iYiUxNCkAIamW0tuYSkMJjP83HTXbZORV4y/rxThziZe121z7GIuDpy/gsF3NoJb2WnG3MJSqNUSXHUOMJsFdpy6jBOpesS1CUSkvytSc4vw6dYz8HbRYkJsczl07D6biRaB7mjk6YTdZzMxZ/NpAMB/n+oEDycNzGaBvUnZ8HDSoJGnE778/RwW7DiLUpPAx0M7YmDHRhBC4NCFHBhNAgHuOszakIg1f6YCACL8XPDD6BjsTcrCu7+cQInRjDbB7vgjORulpqt/JiL9XZFTWCpPKmqlVavwWHQTrPkzVV7XIsANzQPdsPWvdBQYTBgeE4o34lshObMQ87efgVlYgqKbowN+OXIJ5zILMKRzYzzWpQkA4K/UPDhqVGjqZ7mAYPHuZMzdehq9W/hjwj3NoVZJ+OXIJQgBDOnSGO6OGmxPzMDcrWfQKsgNQztbtvPLn5dQUGLEiK7hiPBzxcGUK5i96RQi/FwxukdTeDprsOrQRZxMzcOIrmFo6ueKlKxCzNqYiAA3HV7s2wweThrsPpuJbScz8HBUY7QIdEORwYTFCclwUEl44q5QOGrUOHFJj58OX8SAtoG4o4mXfMo5t6gU8e2CoFZJyC8x4tejqegc5i1Pz1FoMEJfZETgNTPuE1HdYmhSAEMTNUQXsguRVWBAx8aela4XQmD5gb+xPzkbr/ZrAX93R3k5YLmqcX9yNp771tLD1DnMC18O7wwHlYTv96Ugu8CAcF8XbDiehs1/XZ1tvYm3M64UGJBXYqzwnk28nfH3lULc6IK/uyN8kFdsxNGyGeIHdgyGq84BS/amyG20ahWMZrO8HR8XLbqEe1eY8LQ8jVpC10hf7Dh1Gda/fFq1CjqNCnnFRvn5fe2DsO5YGorK7nfo66pFM383JJTdWFqtkjCoYyMknM3EpVxLr19jbyd0DvPGT4cuyjUNvrMRUrIK5bnBWge544GOwfjytyRk5pfAQWW5alSnUWHJnvPIKzHiiehQvNgnEr8eTcU3e86jVZA7JsW1gLeLFt/tTUFiWh5iWwfgntYBSM4swKa/0uHmqEH/NoFw1Kjwy5FUHL2Ygx7N/HBP6wBcyinG6sMXAVjmMwv2dMK2kxnYf/4K2od4oF/rQFzKLcLqw5cgARh+dxi8XbQ4dzkfqw5dRIiXE/q1DkSBwYj1x9KQmW9A7xZ+iAq1nJLefSYTAe6O6NPKHxqVCr+duYy/rxQhOtwbHUI8cT67ELvPZsJV54Bukb5w0Tng4PkrOHs5H+1DPNG2kQeyCkqw91w2VJKE6Kbe8HLW4mSaHqfS89DM3w2tgtxRYDDiQPIVlBjN6BTmBR8XLS5kF+FEqh4hXk5oFeQOo9mMYxf10BeVokNjT3i7aHGlwIDjl/TwcdWieYAb1CoJ2QUGZBcY0NjbCToHNYQQuJxfAkeNWh5DKYSA0SygUfMU9K2OoUkBDE10K0vXF2PPuSzEtQmEo0ZdYb0QAt/uOY8le1MwsGMjjOwWjiKDCd//kYLcolLc0zoAOYUGTFx2RD79N6BtIJr6uWDLXxnIKzbintYB8HXVYu7WMygpGxumVatQajaj/F+pMT2b4siFHPlmzp1CvZBdaJCvpgSAx6ObQF9sxIZjaZAky7QURQYTtiVenbh0UMdgpOYWy2PJwnycEezphN1nr06g2iXMG9mFBpzJyAdgCV3tQzxtbtfTyNMJJrOwOWXaIcRDPp0KADoHFbQOV4MZgOueCq2MRi3BUaO2eb2zVi2fxgUsp3e1DioUl14dV+flrMGVa97DUWPbRuegkj9vAHB3dECvFv749WgqjGXpzzoe7tqayvdAShKgkmzbXVujJFmmHyk/9s9Fq0aBwXam/2tf56K1jOsrX4K7owP05T4PV50DDCazzbb93XTyVbTWNo4alXyKWSVZTndnFxrkz8TLWQMXnQMy80tQXGqGq84BPq5alJSakVNkgEqS4Omkga7seBQajHDWOsDDyQEms0B+iRFCWK4W1mnUyC8pRX6xEU4aNdydNDCZBfKKjTCaBTycHOCqc0B+iRF5xUY4adXwdtFCApBT1tsd4uWEYA8nZBYY8Hd2IdycNGgd5A5XnRqn0vORri9GswA3tA5yR7q+GCdS9dA5qHBHY0/4uTvi+MVcJGUWoFmAK6JCvWAwmnH2cgGMJoEWga4I8XJGZn4JLueVwNtFi0aeThAAMvJKYDCa4e+mg7eLFrlFpcgqMMBZo4avmw5qScKVQgOKS03wctHC00mD4lIz8kpKoVGr4KpzgINKQqHBBIPJDEcHNZy0KhjNAiWlZqgkCY4aFRzUKpSWHTeVJEGlAtSSBJVKgkqSoJYkSBJQajKj1CTg6uhQ6bQ2/wRDkwIYmohu7mJOEb7dcx7dm/ni7ojKx5SdvZyPeVvPIMLfFUM7N8alnGL8a+0JnEzLw/uD22FAuyAIIXAyLQ8uWgc08XFGqcmMb/ecx2+nMzG6R1Pc1dQHgOWUlwQJTlpL0Nt6Mh2/HEnFw1Eh8pi2P//OQZHBhM5h3pAkYOOJdHyTcB5xbQPxeJcmMJoFvk5IxoXsQozs1hRNfJyx+0wmvvjtHKKaeOHZHk1hMgss2HEWR/7OxfM9IxAT4YMD569gzuZTaOLtjBf7NINGLeGjTaew8/RlPHVXGIbfHYY957Lw4cZE6DRqjOwWDjedA95efQxnLxegkacTRnQNw45Tl/HbacvtgSL8XNC9mR/W/HkJmfkGaNQSujfzQ1Z+iRzSIvxccFdTH6w/loasAgMkCegW6QsHlYSdpzNhMgsEeziia6Qv9iRl4UJ2EVSSZbLZNH0J/krVy8eia6QPrhSU4kSqHpJkCZHBnk7Y/Fc68oqNcHN0QPdmvkjJLsSxi5bXRfq7IszHBXvOZSG/xAiNWkJUqBdyi4zytv3cdGgR4IYjF3KQV2KEJFl64UxmIU834qpzQLMAV5xOz5dv3B3m4wxHjVpuo1FLaObvhgvZhXKvpo+LFp7OGpwtF6KbeDsjK7/EJpxdG9YkCeC3YcM3qGMw5gy9o1a3ydD0D8ybNw+zZs1CWloaOnTogLlz56JLly43fR1DE1HdEkLI0yPcygxGM45dykXbYA9oHVQQQiDhXBZKSs3o2dwPKpUkt4nwdYWHs+V00oXsQuQVG9EqyA2SJKHEaMKB5Cto7O2Mxt6WAfhZ+SXIyCtBiwA3qFQShBA4lZ4PLxcN/N0cYTIL/PBHCn4/nYkn7gqVg+Wlsik4fF0tY/xKjCYkZxYi3NdFvoIyXV9sCWRlvQAGoxmnM/IQ6uMCV51lNv4MfTHyS4wI93WBJEkwmsw4lZ6PQA9HeLto5Roz8w2I8HOBg1oFo8mM0xn58HLWyuO9rhQY8PeVIjQLcIWjRg2TWeB0Rh60apW87ewCA5Iy8xHh5wpPZ63cptQoEOHvAieNGpfzSnDhSiF8XHRo5OWEEqMZKVmFKCo1wddVCzdHDa4UWk7lOTqo4eGkgYBATmEpSoyWXihnrRoFZePRNGoJro4OEMIySW5RqQlujpbepOJSE3KLSqGWJLg5WqYDySkyoKDEBFedA9wcHVBUakJWvgGAgJezFmqVhAtXCnEppxh+rjqEeDkhu9ByurHIYELzADf4u+mQmJ6Hk2l58HfToU2wu3zz8qx8A9oEuyPc1wUn0/Jw+EIOnLVqRPi5QiVJSEzXIy23GH5uOvi46nClwICLOUVQSxL83HTQOqhwOa8EVwoN8HSyhNGiUhMu55XALCw16jRq5BQakFtUCkcHNVx06rIeNxNMZjOctQ7QqCUUl5pRVGqCg0qCzkEFActN1o0mAa2DCg4qCQKWf+cms+VhFoDJLCBgOU2qVasQ1zYQ/3mwXa3+m2NoqqGlS5fiqaeewoIFCxAdHY05c+Zg+fLlSExMhL+//w1fy9BERERkf6rz/c0RbuV89NFHePbZZzFixAi0bt0aCxYsgLOzM7766iulSyMiIiKFMTSVMRgMOHDgAGJjY+VlKpUKsbGxSEhIqNC+pKQEer3e5kFERES3LoamMpmZmTCZTAgICLBZHhAQgLS0ipcwz5gxAx4eHvKjcePG9VUqERERKYChqYamTJmC3Nxc+XHhwgWlSyIiIqI65KB0AQ2Fr68v1Go10tPTbZanp6cjMDCwQnudTged7vqzRRMREdGthT1NZbRaLaKiorBlyxZ5mdlsxpYtWxATE6NgZURERNQQsKepnIkTJ2L48OHo1KkTunTpgjlz5qCgoAAjRoxQujQiIiJSGENTOUOGDMHly5cxdepUpKWloWPHjli/fn2FweFERER0++HklrWEk1sSERHZH05uSURERFTLGJqIiIiIqoChiYiIiKgKGJqIiIiIqoChiYiIiKgKOOVALbFehMgb9xIREdkP6/d2VSYTYGiqJXl5eQDAG/cSERHZoby8PHh4eNywDedpqiVmsxmXLl2Cm5sbJEn6x9vT6/Vo3LgxLly4cMvO+8R9vDVwH28N3MdbA/ex+oQQyMvLQ3BwMFSqG49aYk9TLVGpVAgJCan17bq7u9+yv/hW3MdbA/fx1sB9vDVwH6vnZj1MVhwITkRERFQFDE1EREREVcDQ1EDpdDq888470Ol0SpdSZ7iPtwbu462B+3hr4D7WLQ4EJyIiIqoC9jQRERERVQFDExEREVEVMDQRERERVQFDUwM1b948hIWFwdHREdHR0di3b5/SJdXYjBkz0LlzZ7i5ucHf3x+DBg1CYmKiTZtevXpBkiSbx3PPPadQxdU3bdq0CvW3bNlSXl9cXIyxY8fCx8cHrq6ueOihh5Cenq5gxdUXFhZWYR8lScLYsWMB2Ocx3LlzJ+6//34EBwdDkiT89NNPNuuFEJg6dSqCgoLg5OSE2NhYnD592qZNdnY2Hn/8cbi7u8PT0xMjR45Efn5+Pe7F9d1o/0pLSzF58mS0a9cOLi4uCA4OxlNPPYVLly7ZbKOy4/7+++/X855c382O4dNPP12h/v79+9u0acjHELj5Plb271KSJMyaNUtu09CPY1W+J6rydzQlJQXx8fFwdnaGv78/Jk2aBKPRWGt1MjQ1QEuXLsXEiRPxzjvv4ODBg+jQoQPi4uKQkZGhdGk1smPHDowdOxZ79uzBpk2bUFpain79+qGgoMCm3bPPPovU1FT5MXPmTIUqrpk2bdrY1P/777/L6yZMmIBffvkFy5cvx44dO3Dp0iUMHjxYwWqr748//rDZv02bNgEAHnnkEbmNvR3DgoICdOjQAfPmzat0/cyZM/HJJ59gwYIF2Lt3L1xcXBAXF4fi4mK5zeOPP47jx49j06ZNWLNmDXbu3InRo0fX1y7c0I32r7CwEAcPHsTbb7+NgwcPYuXKlUhMTMQDDzxQoe27775rc1xffPHF+ii/Sm52DAGgf//+NvV///33Nusb8jEEbr6P5fctNTUVX331FSRJwkMPPWTTriEfx6p8T9zs76jJZEJ8fDwMBgN2796NxYsXY9GiRZg6dWrtFSqowenSpYsYO3as/NxkMong4GAxY8YMBauqPRkZGQKA2LFjh7ysZ8+e4uWXX1auqH/onXfeER06dKh0XU5OjtBoNGL58uXysr/++ksAEAkJCfVUYe17+eWXRUREhDCbzUII+z+GAMSqVavk52azWQQGBopZs2bJy3JycoROpxPff/+9EEKIEydOCADijz/+kNusW7dOSJIkLl68WG+1V8W1+1eZffv2CQDi/Pnz8rLQ0FAxe/bsui2ullS2j8OHDxcDBw687mvs6RgKUbXjOHDgQNGnTx+bZfZ0HIWo+D1Rlb+jv/76q1CpVCItLU1uM3/+fOHu7i5KSkpqpS72NDUwBoMBBw4cQGxsrLxMpVIhNjYWCQkJClZWe3JzcwEA3t7eNsuXLFkCX19ftG3bFlOmTEFhYaES5dXY6dOnERwcjKZNm+Lxxx9HSkoKAODAgQMoLS21OaYtW7ZEkyZN7PaYGgwGfPvtt3jmmWds7rVo78ewvKSkJKSlpdkcNw8PD0RHR8vHLSEhAZ6enujUqZPcJjY2FiqVCnv37q33mv+p3NxcSJIET09Pm+Xvv/8+fHx8cMcdd2DWrFm1erqjPmzfvh3+/v5o0aIFnn/+eWRlZcnrbrVjmJ6ejrVr12LkyJEV1tnTcbz2e6Iqf0cTEhLQrl07BAQEyG3i4uKg1+tx/PjxWqmL955rYDIzM2EymWwOOgAEBATg5MmTClVVe8xmM8aPH4+uXbuibdu28vLHHnsMoaGhCA4Oxp9//onJkycjMTERK1euVLDaqouOjsaiRYvQokULpKamYvr06ejevTuOHTuGtLQ0aLXaCl9EAQEBSEtLU6bgf+inn35CTk4Onn76aXmZvR/Da1mPTWX/Fq3r0tLS4O/vb7PewcEB3t7edndsi4uLMXnyZAwbNszmfl4vvfQS7rzzTnh7e2P37t2YMmUKUlNT8dFHHylYbdX1798fgwcPRnh4OM6ePYs33ngDAwYMQEJCAtRq9S11DAFg8eLFcHNzq3D6356OY2XfE1X5O5qWllbpv1frutrA0ET1auzYsTh27JjNeB8ANuMH2rVrh6CgIPTt2xdnz55FREREfZdZbQMGDJB/bt++PaKjoxEaGoply5bByclJwcrqxv/+9z8MGDAAwcHB8jJ7P4a3s9LSUjz66KMQQmD+/Pk26yZOnCj/3L59e2i1WowZMwYzZsywi1mnhw4dKv/crl07tG/fHhEREdi+fTv69u2rYGV146uvvsLjjz8OR0dHm+X2dByv9z3REPD0XAPj6+sLtVpd4YqA9PR0BAYGKlRV7Rg3bhzWrFmDbdu2ISQk5IZto6OjAQBnzpypj9JqnaenJ5o3b44zZ84gMDAQBoMBOTk5Nm3s9ZieP38emzdvxqhRo27Yzt6PofXY3OjfYmBgYIULNIxGI7Kzs+3m2FoD0/nz57Fp06ab3jU+OjoaRqMRycnJ9VNgLWvatCl8fX3l38tb4Rha/fbbb0hMTLzpv02g4R7H631PVOXvaGBgYKX/Xq3ragNDUwOj1WoRFRWFLVu2yMvMZjO2bNmCmJgYBSurOSEExo0bh1WrVmHr1q0IDw+/6WsOHz4MAAgKCqrj6upGfn4+zp49i6CgIERFRUGj0dgc08TERKSkpNjlMV24cCH8/f0RHx9/w3b2fgzDw8MRGBhoc9z0ej327t0rH7eYmBjk5OTgwIEDcputW7fCbDbLobEhswam06dPY/PmzfDx8bnpaw4fPgyVSlXhlJa9+Pvvv5GVlSX/Xtr7MSzvf//7H6KiotChQ4ebtm1ox/Fm3xNV+TsaExODo0eP2oRg6/8ItG7dutYKpQbmhx9+EDqdTixatEicOHFCjB49Wnh6etpcEWBPnn/+eeHh4SG2b98uUlNT5UdhYaEQQogzZ86Id999V+zfv18kJSWJ1atXi6ZNm4oePXooXHnVvfLKK2L79u0iKSlJ7Nq1S8TGxgpfX1+RkZEhhBDiueeeE02aNBFbt24V+/fvFzExMSImJkbhqqvPZDKJJk2aiMmTJ9sst9djmJeXJw4dOiQOHTokAIiPPvpIHDp0SL567P333xeenp5i9erV4s8//xQDBw4U4eHhoqioSN5G//79xR133CH27t0rfv/9d9GsWTMxbNgwpXbJxo32z2AwiAceeECEhISIw4cP2/zbtF5ptHv3bjF79mxx+PBhcfbsWfHtt98KPz8/8dRTTym8Z1fdaB/z8vLEq6++KhISEkRSUpLYvHmzuPPOO0WzZs1EcXGxvI2GfAyFuPnvqRBC5ObmCmdnZzF//vwKr7eH43iz7wkhbv531Gg0irZt24p+/fqJw4cPi/Xr1ws/Pz8xZcqUWquToamBmjt3rmjSpInQarWiS5cuYs+ePUqXVGMAKn0sXLhQCCFESkqK6NGjh/D29hY6nU5ERkaKSZMmidzcXGULr4YhQ4aIoKAgodVqRaNGjcSQIUPEmTNn5PVFRUXihRdeEF5eXsLZ2Vk8+OCDIjU1VcGKa2bDhg0CgEhMTLRZbq/HcNu2bZX+bg4fPlwIYZl24O233xYBAQFCp9OJvn37Vtj3rKwsMWzYMOHq6irc3d3FiBEjRF5engJ7U9GN9i8pKem6/za3bdsmhBDiwIEDIjo6Wnh4eAhHR0fRqlUr8Z///McmcCjtRvtYWFgo+vXrJ/z8/IRGoxGhoaHi2WefrfA/oA35GApx899TIYT4/PPPhZOTk8jJyanwens4jjf7nhCian9Hk5OTxYABA4STk5Pw9fUVr7zyiigtLa21OqWyYomIiIjoBjimiYiIiKgKGJqIiIiIqoChiYiIiKgKGJqIiIiIqoChiYiIiKgKGJqIiIiIqoChiYiIiKgKGJqIiIiIqoChiYiIiKgKGJqIiIiIqoChiYiIiKgKGJqIiK7Rq1cvvPjiixg/fjy8vLwQEBCA//73vygoKMCIESPg5uaGyMhIrFu3TulSiageMTQREVVi8eLF8PX1xb59+/Diiy/i+eefxyOPPIK7774bBw8eRL9+/fDkk0+isLBQ6VKJqJ5IQgihdBFERA1Jr169YDKZ8NtvvwEATCYTPDw8MHjwYHz99dcAgLS0NAQFBSEhIQF33XWXkuUSUT1hTxMRUSXat28v/6xWq+Hj44N27drJywICAgAAGRkZ9V4bESmDoYmIqBIajcbmuSRJNsskSQIAmM3meq2LiJTD0ERERERUBQxNRERERFXA0ERERERUBbx6joiIiKgK2NNEREREVAUMTURERERVwNBEREREVAUMTURERERVwNBEREREVAUMTURERERVwNBEREREVAUMTURERERVwNBEREREVAUMTURERERVwNBEREREVAUMTURERERV8P8x4yDH0f4uGgAAAABJRU5ErkJggg==",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -980,7 +1131,7 @@
"plt.plot(mlist, iterations)\n",
"plt.title('Anderson Accelerated Jacobi\\nNumber of iterations vs m')\n",
"plt.xlabel('m')\n",
- "plt.ylabel('Iterations');"
+ "_ = plt.ylabel('Iterations')"
]
},
{
@@ -993,7 +1144,14 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:45.673975Z",
+ "iopub.status.busy": "2024-08-14T18:48:45.672703Z",
+ "iopub.status.idle": "2024-08-14T18:48:48.851295Z",
+ "shell.execute_reply": "2024-08-14T18:48:48.850318Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -1005,21 +1163,19 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 720x432 with 2 Axes>"
+ "<Figure size 1000x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_jacobi_info = NAG_solverinfo()\n",
"NAG_jacobi_info.source = source(N)\n",
"tol = 1e-9\n",
@@ -1029,13 +1185,13 @@
"print(f\"Anderson Accelerated Jacobi with m={m} on a {N} by {N} grid\")\n",
"print(f\"Solution found in {NAG_jacobi_info.iterations} iterations\")\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
- "NAG_jacobi_sol.shape = (N, N)\n",
+ "NAG_jacobi_sol = NAG_jacobi_sol.reshape(N, N)\n",
"axes[0].imshow(NAG_jacobi_sol)\n",
"axes[0].set_title('Potential')\n",
"Ex, Ey = np.gradient(NAG_jacobi_sol)\n",
"E = np.sqrt(Ex**2+Ey**2) # Magnitude of Electric field\n",
"axes[1].imshow(E)\n",
- "axes[1].set_title('Electric Field');"
+ "_ = axes[1].set_title('Electric Field')"
]
},
{
@@ -1057,13 +1213,20 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:48.859423Z",
+ "iopub.status.busy": "2024-08-14T18:48:48.859201Z",
+ "iopub.status.idle": "2024-08-14T18:48:48.865957Z",
+ "shell.execute_reply": "2024-08-14T18:48:48.865151Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
"def NAG_gauss_seidel(x, solverinfo):\n",
" N = int(np.sqrt(x.size))\n",
- " x.shape = (N, N) # Make x 2D because that's how I think\n",
+ " x = x.reshape(N, N) # Make x 2D because that's how I think\n",
" nextx = np.copy(x)\n",
" h = 1/(N-1)\n",
" for i in range(1, N - 1):\n",
@@ -1071,21 +1234,28 @@
" nextx[j, i] = 0.25 * (nextx[j+1, i] + nextx[j, i+1] + nextx[j-1, i] + nextx[j, i-1]) + 0.25*h**2*solverinfo.source[j, i]\n",
" solverinfo.iterations += 1\n",
" nextx -= x # NAG requires this rather than nextx itself\n",
- " nextx.shape = N*N # Make nextx 1D since that's what NAG needs\n",
+ " nextx = nextx.reshape(N*N) # Make nextx 1D since that's what NAG needs\n",
" return nextx"
]
},
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:48.871228Z",
+ "iopub.status.busy": "2024-08-14T18:48:48.870942Z",
+ "iopub.status.idle": "2024-08-14T18:48:49.509652Z",
+ "shell.execute_reply": "2024-08-14T18:48:49.508644Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anderson Accelerated Gauss-Seidel with m=4 on a 100 by 100 grid\n",
- "Solution found in 577 iterations\n"
+ "Solution found in 593 iterations\n"
]
}
],
@@ -1094,7 +1264,7 @@
"\n",
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_gs_info = NAG_solverinfo()\n",
"NAG_gs_info.source = source(N)\n",
"tol = 1e-9\n",
@@ -1114,14 +1284,21 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:49.515770Z",
+ "iopub.status.busy": "2024-08-14T18:48:49.515567Z",
+ "iopub.status.idle": "2024-08-14T18:48:49.841770Z",
+ "shell.execute_reply": "2024-08-14T18:48:49.840830Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anderson Accelerated Gauss--Seidel with m=7 on a 100 by 100 grid\n",
- "Solution found in 445 iterations\n"
+ "Solution found in 434 iterations\n"
]
}
],
@@ -1130,7 +1307,7 @@
"\n",
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_gs_info = NAG_solverinfo()\n",
"NAG_gs_info.source = source(N)\n",
"tol = 1e-9\n",
@@ -1150,35 +1327,47 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:49.847699Z",
+ "iopub.status.busy": "2024-08-14T18:48:49.847512Z",
+ "iopub.status.idle": "2024-08-14T18:48:49.852471Z",
+ "shell.execute_reply": "2024-08-14T18:48:49.851563Z"
+ }
+ },
"outputs": [],
"source": [
- "def find_best_m(m):\n",
+ "def find_best_m(m): # pylint: disable=function-redefined\n",
" N = 100\n",
" x0 = init_problem(N)\n",
- " x0.shape = N*N\n",
+ " x0 = x0.reshape(N*N)\n",
" NAG_gs_info = NAG_solverinfo()\n",
" NAG_gs_info.source = source(N)\n",
" tol = 1e-9\n",
- " NAG_gs_sol, fvec = roots.sys_func_aa(NAG_gauss_seidel, x0, tol, eps, m, data=NAG_gs_info)\n",
+ " _ = roots.sys_func_aa(NAG_gauss_seidel, x0, tol, eps, m, data=NAG_gs_info)\n",
" return NAG_gs_info.iterations"
]
},
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:48:49.858160Z",
+ "iopub.status.busy": "2024-08-14T18:48:49.857901Z",
+ "iopub.status.idle": "2024-08-14T18:52:51.380163Z",
+ "shell.execute_reply": "2024-08-14T18:52:51.378173Z"
+ }
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -1200,10 +1389,10 @@
" facecolor=\"black\", width=0.5,\n",
" headwidth=4, shrink=0.1)\n",
"\n",
- "plt.annotate(\n",
+ "_ = plt.annotate(\n",
" \"Best SOR result\", xy=(arrow_x, arrow_y),\n",
" xytext=(label_x, label_y),\n",
- " arrowprops=arrow_properties);"
+ " arrowprops=arrow_properties)"
]
},
{
@@ -1216,7 +1405,14 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:52:51.386952Z",
+ "iopub.status.busy": "2024-08-14T18:52:51.386701Z",
+ "iopub.status.idle": "2024-08-14T18:52:53.873980Z",
+ "shell.execute_reply": "2024-08-14T18:52:53.872450Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -1232,7 +1428,7 @@
"\n",
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_gs_info = NAG_solverinfo()\n",
"NAG_gs_info.source = source(N)\n",
"tol = 1e-9\n",
@@ -1254,13 +1450,20 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:52:53.880726Z",
+ "iopub.status.busy": "2024-08-14T18:52:53.880423Z",
+ "iopub.status.idle": "2024-08-14T18:52:53.889930Z",
+ "shell.execute_reply": "2024-08-14T18:52:53.888495Z"
+ }
+ },
"outputs": [],
"source": [
"@jit\n",
"def NAG_SOR(x, solverinfo):\n",
" N = int(np.sqrt(x.size))\n",
- " x.shape = (N, N) # Make x 2D because that's how I think\n",
+ " x = x.reshape(N, N) # Make x 2D because that's how I think\n",
" nextx = np.copy(x)\n",
" h = 1/(N-1)\n",
" w = solverinfo.w\n",
@@ -1270,14 +1473,21 @@
" nextx[j, i] += w * (new - nextx[j, i])\n",
" solverinfo.iterations += 1\n",
" nextx -= x # NAG requires this rather than nextx itself\n",
- " nextx.shape = N*N # Make nextx 1D since that's what NAG needs\n",
+ " nextx = nextx.reshape(N*N) # Make nextx 1D since that's what NAG needs\n",
" return nextx"
]
},
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:52:53.896868Z",
+ "iopub.status.busy": "2024-08-14T18:52:53.896571Z",
+ "iopub.status.idle": "2024-08-14T18:52:56.818369Z",
+ "shell.execute_reply": "2024-08-14T18:52:56.817349Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -1292,7 +1502,7 @@
"# Define and run the simulation using SOR and NAG Anderson Acceleration\n",
"N = 100\n",
"x0 = init_problem(N)\n",
- "x0.shape = N*N\n",
+ "x0 = x0.reshape(N*N)\n",
"NAG_SOR_info = NAG_solverinfo()\n",
"NAG_SOR_info.w = 1.94\n",
"NAG_SOR_info.source = source(N)\n",
@@ -1320,25 +1530,39 @@
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:52:56.824423Z",
+ "iopub.status.busy": "2024-08-14T18:52:56.824206Z",
+ "iopub.status.idle": "2024-08-14T18:52:56.829371Z",
+ "shell.execute_reply": "2024-08-14T18:52:56.828456Z"
+ }
+ },
"outputs": [],
"source": [
"def find_best_mw(m, w):\n",
" N = 100\n",
" x0 = init_problem(N)\n",
- " x0.shape = N*N\n",
+ " x0 = x0.reshape(N*N)\n",
" NAG_SOR_info = NAG_solverinfo()\n",
" NAG_SOR_info.source = source(N)\n",
" NAG_SOR_info.w = w\n",
" tol = 1e-9\n",
- " NAG_SOR_sol, fvec = roots.sys_func_aa(NAG_SOR, x0, tol, eps, m, data=NAG_SOR_info)\n",
+ " _ = roots.sys_func_aa(NAG_SOR, x0, tol, eps, m, data=NAG_SOR_info)\n",
" return NAG_SOR_info.iterations"
]
},
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-14T18:52:56.834573Z",
+ "iopub.status.busy": "2024-08-14T18:52:56.834371Z",
+ "iopub.status.idle": "2024-08-15T01:09:17.435841Z",
+ "shell.execute_reply": "2024-08-15T01:09:17.434827Z"
+ }
+ },
"outputs": [],
"source": [
"# This will take a LONG LONG time!\n",
@@ -1350,21 +1574,28 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-15T01:09:17.452298Z",
+ "iopub.status.busy": "2024-08-15T01:09:17.452039Z",
+ "iopub.status.idle": "2024-08-15T01:09:17.469929Z",
+ "shell.execute_reply": "2024-08-15T01:09:17.469009Z"
+ }
+ },
"outputs": [
{
"data": {
"text/plain": [
- "[(199, 1.6700000000000006, 230),\n",
- " (198, 1.6600000000000006, 231),\n",
- " (198, 1.6700000000000006, 231),\n",
- " (198, 1.6800000000000006, 231),\n",
- " (198, 1.7300000000000006, 231),\n",
- " (199, 1.6400000000000006, 231),\n",
- " (199, 1.6500000000000006, 231),\n",
- " (199, 1.6600000000000006, 231),\n",
- " (199, 1.6800000000000006, 231),\n",
- " (199, 1.6900000000000006, 231)]"
+ "[(np.int64(199), np.float64(1.6700000000000006), 230),\n",
+ " (np.int64(198), np.float64(1.6600000000000006), 231),\n",
+ " (np.int64(198), np.float64(1.6700000000000006), 231),\n",
+ " (np.int64(198), np.float64(1.6800000000000006), 231),\n",
+ " (np.int64(198), np.float64(1.7300000000000006), 231),\n",
+ " (np.int64(199), np.float64(1.6400000000000006), 231),\n",
+ " (np.int64(199), np.float64(1.6500000000000006), 231),\n",
+ " (np.int64(199), np.float64(1.6600000000000006), 231),\n",
+ " (np.int64(199), np.float64(1.6800000000000006), 231),\n",
+ " (np.int64(199), np.float64(1.6900000000000006), 231)]"
]
},
"execution_count": 36,
@@ -1382,22 +1613,28 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-15T01:09:17.487646Z",
+ "iopub.status.busy": "2024-08-15T01:09:17.487206Z",
+ "iopub.status.idle": "2024-08-15T01:09:17.502926Z",
+ "shell.execute_reply": "2024-08-15T01:09:17.501638Z"
+ },
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
- "[(5, 1.9800000000000009, 1543),\n",
- " (6, 1.9800000000000009, 1479),\n",
- " (8, 1.9800000000000009, 1442),\n",
- " (7, 1.9800000000000009, 1352),\n",
- " (9, 1.9800000000000009, 1326),\n",
- " (11, 1.9800000000000009, 1281),\n",
- " (10, 1.9800000000000009, 1192),\n",
- " (12, 1.9800000000000009, 1175),\n",
- " (15, 1.9800000000000009, 1122),\n",
- " (13, 1.9800000000000009, 1107)]"
+ "[(np.int64(9), np.float64(1.9800000000000009), 1571),\n",
+ " (np.int64(7), np.float64(1.9800000000000009), 1475),\n",
+ " (np.int64(6), np.float64(1.9800000000000009), 1466),\n",
+ " (np.int64(11), np.float64(1.9800000000000009), 1369),\n",
+ " (np.int64(8), np.float64(1.9800000000000009), 1365),\n",
+ " (np.int64(10), np.float64(1.9800000000000009), 1305),\n",
+ " (np.int64(16), np.float64(1.9800000000000009), 1134),\n",
+ " (np.int64(12), np.float64(1.9800000000000009), 1121),\n",
+ " (np.int64(15), np.float64(1.9800000000000009), 1113),\n",
+ " (np.int64(13), np.float64(1.9800000000000009), 1092)]"
]
},
"execution_count": 37,
@@ -1440,7 +1677,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1454,7 +1691,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.12.0"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/special_functions/mathieu_functions.ipynb b/special_functions/mathieu_functions.ipynb
index 1219326..98d0d4f 100644
--- a/special_functions/mathieu_functions.ipynb
+++ b/special_functions/mathieu_functions.ipynb
@@ -16,7 +16,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 2 Axes>"
]
@@ -82,7 +82,7 @@
{
"data": {
"text/plain": [
- "'1.5.0'"
+ "'1.8.0'"
]
},
"execution_count": 2,
@@ -91,7 +91,6 @@
}
],
"source": [
- "import scipy as sp\n",
"sp.__version__"
]
},
@@ -103,7 +102,7 @@
{
"data": {
"text/plain": [
- "'27.0.2.0'"
+ "'28.3.0.1'"
]
},
"execution_count": 3,
@@ -119,7 +118,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -133,7 +132,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/special_functions/opt_imp_vol/graphs.PNG b/special_functions/opt_imp_vol/graphs.PNG
new file mode 100644
index 0000000..0b51c1c
Binary files /dev/null and b/special_functions/opt_imp_vol/graphs.PNG differ
diff --git a/special_functions/opt_imp_vol/imp_vol_demo.ipynb b/special_functions/opt_imp_vol/imp_vol_demo.ipynb
new file mode 100644
index 0000000..e08dbb3
--- /dev/null
+++ b/special_functions/opt_imp_vol/imp_vol_demo.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fast Implied Volatilities using the NAG Library"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Black-Scholes formula for the price of a European call option is\n",
+ "\n",
+ "$$P = S_0\\Phi\\left(\\frac{\\ln\\left(\\frac{S_0}{K}\\right)+\\left[r+\\frac{\\sigma^2}{2}\\right]T}{\\sigma\\sqrt{T}}\\right) - Ke^{-rT}\\Phi\\left(\\frac{\\ln\\left(\\frac{S_0}{K}\\right)+\\left[r-\\frac{\\sigma^2}{2}\\right]T}{\\sigma\\sqrt{T}}\\right),$$\n",
+ "\n",
+ "where $T$ is the time to maturity, $S_0$ is the spot price of the underlying asset, $K$ is the strike price, $r$ is\n",
+ "the interest rate and $\\sigma$ is the volatility. A similar formula applies for European put options.\n",
+ "\n",
+ "An important problem in finance is to compute the implied volatility, $σ$, given values for $T$, $K$, $S_0$,\n",
+ "$r$ and $P$. An explicit formula for $\\sigma$ is not available. Furthermore, $\\sigma$ cannot be directly measured from\n",
+ "financial data. Instead, it must be computed using a numerical approximation. Typically, multiple values\n",
+ "of the input data are provided, so the Black-Scholes formula must be solved many times.\n",
+ "\n",
+ "As shown in the figure below, the volatility surface (a three-dimensional plot of how the volatility varies\n",
+ "according to the price and time to maturity) can be highly curved. This makes accurately computing\n",
+ "the implied volatility a difficult problem.\n",
+ "\n",
+ "<img src=\"impvolsurf.png\" width=500 />\n",
+ "\n",
+ "Before introducing our new NAG Library routine, let’s demonstrate how one might naively\n",
+ "compute implied volatilities using a general purpose root finder. First we need to import a few things:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import time\n",
+ "from naginterfaces.library import rand, roots, specfun"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's generate some input data using a random number generator from the NAG Library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n = 10000 # This is the number of volatilities we will be computing\n",
+ "statecomm = rand.init_nonrepeat(1)\n",
+ "p = rand.dist_uniform(n, 3.9, 5.8, statecomm)\n",
+ "k = rand.dist_uniform(n, 271.5, 272.0, statecomm)\n",
+ "s0 = rand.dist_uniform(n, 259.0, 271.0, statecomm)\n",
+ "t = rand.dist_uniform(n, 0.016, 0.017, statecomm)\n",
+ "r = rand.dist_uniform(n, 0.017, 0.018, statecomm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have chosen the limits of the various uniform distributions above to ensure the input data takes\n",
+ "sensible values.\n",
+ "\n",
+ "There are various standard root finding techniques that we could use to compute implied volatilities,\n",
+ "a common example being bisection. The NAG Library routine ```contfn_cntin```, is a general\n",
+ "purpose root finder based on the secant method. It uses a *callback*, with data passed in via a communication object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def black_scholes(sigma, data):\n",
+ " try:\n",
+ " price = specfun.opt_bsm_price('C', [data['k']], data['s0'], [data['t']], sigma, data['r'], 0.0)\n",
+ " except:\n",
+ " price = np.zeros((1,1))\n",
+ " return price[0, 0] - data['p']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " ```contfn_cntin``` operates on scalars, so we need to call the routine\n",
+ "once for every volatility we want to compute. We will time the computation and count how many errors are caught:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using a general purpose root finder:\n",
+ " Time taken: 51.561 seconds\n",
+ " There were 4 failures\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = {}\n",
+ "errorcount = 0\n",
+ "tic = time.perf_counter()\n",
+ "for i in range(n):\n",
+ " data['p'] = p[i]\n",
+ " data['k'] = k[i]\n",
+ " data['s0'] = s0[i]\n",
+ " data['t'] = t[i]\n",
+ " data['r'] = r[i]\n",
+ " try:\n",
+ " sigma = roots.contfn_cntin(0.15, black_scholes, 1.0e-14, 0.0, 500, data)\n",
+ " if sigma < 0.0:\n",
+ " errorcount += 1\n",
+ " except:\n",
+ " errorcount += 1\n",
+ "toc = time.perf_counter() \n",
+ "print('Using a general purpose root finder:')\n",
+ "print(' Time taken: {0:.3f} seconds'.format(toc-tic))\n",
+ "print(' There were {} failures'.format(errorcount))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Can a bespoke implied volatility routine do better? Our new routine at Mark 27.1 is called ```opt_imp_vol```. We call it as follows:\n",
+ "\n",
+ "```sigma, ivalid = specfun.opt_imp_vol('C', p, k, s0, t, r, mode=2)```\n",
+ "\n",
+ "The return argument ```ivalid``` is an array recording any data points for which the volatility could not be computed. The argument ```mode``` allows us to select which algorithm to use – more on that in a moment, but\n",
+ "for now we choose ```mode=2```. This selects the algorithm of Jäckel (2015), a very accurate method based\n",
+ "on third order Householder iterations.\n",
+ "\n",
+ "Here is the call surrounded by some timing code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "omp_imp_vol with mode = 2 (Jäckel algorithm):\n",
+ " Time taken: 0.01415 seconds\n",
+ " There were 0 failures\n"
+ ]
+ }
+ ],
+ "source": [
+ "tic = time.perf_counter()\n",
+ "sigma, ivalid = specfun.opt_imp_vol('C', p, k, s0, t, r, mode=2)\n",
+ "toc = time.perf_counter()\n",
+ "print('omp_imp_vol with mode = 2 (Jäckel algorithm):')\n",
+ "print(' Time taken: {0:.5f} seconds'.format(toc-tic))\n",
+ "print(' There were {} failures'.format(np.count_nonzero(ivalid)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The new routine is several orders of magnitude faster than the root finder, with no failures reported. We could try\n",
+ "tweaking the convergence parameters and iteration limits in ```nag_roots_contfn_cntin```, and we could certainly\n",
+ "improve the way data is passed through the callback, but we are unlikely to match the\n",
+ "performance of ```opt_imp_vol```.\n",
+ "\n",
+ "Recently NAG embarked upon a collaboration with mathematicians at Queen Mary University of\n",
+ "London, who have been developing an alternative algorithm for computing implied volatilities. The new\n",
+ "algorithm (based on Glau et. al. (2018)) uses Chebyshev interpolation to remove branching and give\n",
+ "increased SIMD performance. We access it by setting ```mode=1``` in the call to ```opt_imp_vol```:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "omp_imp_vol with mode = 1 (Glau algorithm):\n",
+ " Time taken: 0.01770 seconds\n",
+ " There were 0 failures\n"
+ ]
+ }
+ ],
+ "source": [
+ "tic = time.perf_counter()\n",
+ "sigma, ivalid = specfun.opt_imp_vol('C', p, k, s0, t, r, mode=1)\n",
+ "toc = time.perf_counter()\n",
+ "print('omp_imp_vol with mode = 1 (Glau algorithm):')\n",
+ "print(' Time taken: {0:.5f} seconds'.format(toc-tic))\n",
+ "print(' There were {} failures'.format(np.count_nonzero(ivalid)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Depending on your system, you should find that, for similar accuracy, there is a modest speedup over the Jäckel algorithm. Our numerical experiments have shown that for very small arrays (containing fewer than 100 elements) the Jäckel algorithm actually\n",
+ "outperforms that of Glau et.al., but for larger arrays the converse is true. As vector units continue\n",
+ "to improve in the future, we expect the performance of the highly vectorizable Glau et.al. approach to\n",
+ "improve similarly.\n",
+ "\n",
+ "So far, we have been computing implied volatilities with a relative accuracy as close as possible to\n",
+ "double precision. However, in some applications implied volatilities are only required with a few decimal\n",
+ "places of precision. One advantage of the Glau et.al. algorithm is that it can be run in a lower accuracy\n",
+ "mode, aiming only for seven decimal places of accuracy. This is accessed by setting ```mode=0``` in the call\n",
+ "to ```opt_imp_vol```. It roughly doubles the speed of the routine:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "omp_imp_vol with mode = 0 (lower accuracy Glau algorithm):\n",
+ " Time taken: 0.01556 seconds\n",
+ " There were 0 failures\n"
+ ]
+ }
+ ],
+ "source": [
+ "tic = time.perf_counter()\n",
+ "sigma, ivalid = specfun.opt_imp_vol('C', p, k, s0, t, r, mode=0)\n",
+ "toc = time.perf_counter()\n",
+ "print('omp_imp_vol with mode = 0 (lower accuracy Glau algorithm):')\n",
+ "print(' Time taken: {0:.5f} seconds'.format(toc-tic))\n",
+ "print(' There were {} failures'.format(np.count_nonzero(ivalid)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The charts below summarize the results, using timings collected on an Intel Skylake machine. We can see that the Glau et.al. algorithm outperforms the Jäckel algorithm for large arrays but not for small arrays. Note that the general purpose root finder\n",
+ "is omitted here as it is so much slower ```opt_imp_vol```.\n",
+ "<img src=\"graphs.PNG\" width=800 />\n",
+ "\n",
+ "In summary, NAG’s new state-of-the art algorithm can be run in three different modes, according to\n",
+ "the length of the input arrays and the required accuracy. For more information, and to access the NAG\n",
+ "Library, go to: https://www.nag.co.uk/content/nag-library.\n",
+ "\n",
+ "### References\n",
+ "\n",
+ "P. Jäckel (2015). Let’s be rational. *Wilmott* 2015, 40-53.\n",
+ "\n",
+ "K. Glau, P. Herold, D. B. Madan, C. Pötz (2019). The Chebyshev method for the implied volatility.\n",
+ "*Journal of Computational Finance*, 23(3).\n",
+ "\n",
+ "### NAG Library for Python Setup\n",
+ "\n",
+ "Find instructions to install the NAG Library for Python in the documentation here: https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/special_functions/opt_imp_vol/impvolsurf.png b/special_functions/opt_imp_vol/impvolsurf.png
new file mode 100644
index 0000000..20b4c23
Binary files /dev/null and b/special_functions/opt_imp_vol/impvolsurf.png differ
diff --git a/time_series_analysis/cp_pelt.ipynb b/time_series_analysis/cp_pelt.ipynb
index beb1afc..e7768a0 100644
--- a/time_series_analysis/cp_pelt.ipynb
+++ b/time_series_analysis/cp_pelt.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Change Point Analysis\n",
"\n",
- "Change points are time points at which some feature of a data set changes. For detecting change points in a univariate time series we can use [`tsa.cp_pelt`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.tsa.html#naginterfaces.library.tsa.cp_pelt).\n",
+ "Change points are time points at which some feature of a data set changes. For detecting change points in a univariate time series we can use [`tsa.cp_pelt`](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.tsa.cp_pelt.html).\n",
"\n",
"Consider the following time series, showing the EUR/GBP exchange rate between January 1999 and February 2020 (numbers indicate Euros per Pound, with FX data from www.macrotrends.net)"
]
@@ -160,36 +160,38 @@
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- " };\n",
- "}\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
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"\n",
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- " var warnings = document.getElementById(\"mpl-warnings\");\n",
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" if (warnings) {\n",
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- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
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+ " canvas.setAttribute(\n",
+ " 'height',\n",
+ " entry.devicePixelContentBoxSize[0].blockSize\n",
+ " );\n",
+ " } else {\n",
+ " canvas.setAttribute('width', width * fig.ratio);\n",
+ " canvas.setAttribute('height', height * fig.ratio);\n",
+ " }\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
" });\n",
+ " this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'dblclick',\n",
+ " on_mouse_event_closure('dblclick')\n",
+ " );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
"\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
- " mouse_event_fn(event);\n",
+ " on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
"\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+ " event.preventDefault();\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
- " // put a spacer in here.\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
"\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
"\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(option);\n",
" }\n",
"\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ "mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
+ "mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+ " var x0 = msg['x0'] / fig.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+ " var x1 = msg['x1'] / fig.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
@@ -507,78 +590,96 @@
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / fig.ratio,\n",
+ " fig.canvas.height / fig.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
+ "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+ " fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
+ " this.send_message('ack', {});\n",
+ "};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
+ " var img = evt.data;\n",
+ " if (img.type !== 'image/png') {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " img.type = 'image/png';\n",
+ " }\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " img\n",
+ " );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -591,9 +692,12 @@
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -602,62 +706,74 @@
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
+ "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
- " if (!e)\n",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\n",
"\n",
- " // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
+ " * https://stackoverflow.com/a/24161582/3208463\n",
" */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
+ " var x = canvas_pos.x * this.ratio;\n",
+ " var y = canvas_pos.y * this.ratio;\n",
"\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -665,265 +781,337 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "};\n",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.key === this._key) {\n",
" return;\n",
- " else\n",
- " this._key = event.which;\n",
+ " } else {\n",
+ " this._key = event.key;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\n",
"\n",
" var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
+ " if (event.ctrlKey && event.key !== 'Control') {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " else if (event.altKey && event.key !== 'Alt') {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " else if (event.shiftKey && event.key !== 'Shift') {\n",
+ " value += 'shift+';\n",
+ " }\n",
"\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
+ " value += 'k' + event.key;\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+ " if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+ "// prettier-ignore\n",
+ "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
- " ws.close = function() {\n",
- " comm.close()\n",
+ " ws.binaryType = comm.kernel.ws.binaryType;\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " function updateReadyState(_event) {\n",
+ " if (comm.kernel.ws) {\n",
+ " ws.readyState = comm.kernel.ws.readyState;\n",
+ " } else {\n",
+ " ws.readyState = 3; // Closed state.\n",
+ " }\n",
+ " }\n",
+ " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+ " comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+ "\n",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\n",
+ " ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
+ " comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
+ " var data = msg['content']['data'];\n",
+ " if (data['blob'] !== undefined) {\n",
+ " data = {\n",
+ " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+ " };\n",
+ " }\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
+ " ws.onmessage(data);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ "mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
- " fig.parent_element = element.get(0);\n",
+ " fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
+ " fig.cell_info[0].output_area.element.on(\n",
+ " 'cleared',\n",
+ " { fig: fig },\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
"};\n",
"\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / fig.ratio;\n",
+ " fig.cell_info[0].output_area.element.off(\n",
+ " 'cleared',\n",
+ " fig._remove_fig_handler\n",
+ " );\n",
+ " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
+ " var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
+ " this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
+ "mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\n",
+ " for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
- " if (!name) { continue; };\n",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
"\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\n",
+ "\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+ " var fig = event.data.fig;\n",
+ " if (event.target !== this) {\n",
+ " // Ignore bubbled events from children.\n",
+ " return;\n",
+ " }\n",
+ " fig.close_ws(fig, {});\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
+ " if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\n",
+ "mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
+ " for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " if (cell.cell_type === 'code') {\n",
+ " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
- " if (data['text/html'] == html_output) {\n",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
- "}\n",
+ "};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -936,7 +1124,7 @@
{
"data": {
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\" 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -968,13 +1156,13 @@
" '(FX data from www.macrotrends.net)\\n'\n",
" 'Showing change points determined in the mean')\n",
"plt.legend(handles=[vl, hl], loc='upper right')\n",
- "plt.show();"
+ "plt.show()"
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -988,7 +1176,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/time_series_analysis/inhom_iema.ipynb b/time_series_analysis/inhom_iema.ipynb
index 7eb566c..824705a 100644
--- a/time_series_analysis/inhom_iema.ipynb
+++ b/time_series_analysis/inhom_iema.ipynb
@@ -27,7 +27,7 @@
"\n",
"The value of $\\nu$ depends on the method of interpolation chosen.\n",
"\n",
- "See the NAG Library for Python documentation for the further details on this function: [NAG Library for Python docs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.tsa.html#naginterfaces.library.tsa.inhom_iema)"
+ "See the NAG Library for Python documentation for the further details on this function: [NAG Library for Python docs](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.tsa.inhom_iema.html)"
]
},
{
@@ -944,7 +944,7 @@
"plt.ylabel('Value')\n",
"plt.suptitle('Exponential Moving Averages')\n",
"plt.title(r'3 $\\tau$' + ' values applied to simulated data', fontsize=10)\n",
- "plt.show();"
+ "plt.show()"
]
},
{
@@ -1004,7 +1004,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1018,7 +1018,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.10"
}
},
"nbformat": 4,