Update master branch to Pastas 1.5 (#741)

.github/workflows/ci.yml

@@ -19,10 +19,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - name: Test suite with py38-ubuntu
-            python: "3.8"
-            toxenv: py38
-            experimental: false
           - name: Test suite with py39-ubuntu
             python: "3.9"
             toxenv: py39

README.md

@@ -0,0 +1,129 @@
+# Pastas: Analysis of Groundwater Time Series
+
+> [!IMPORTANT]
+> As of Pastas 1.5, noisemodels are not added to the Pastas models by default anymore. [Read more about this change here](https://github.com/pastas/pastas/issues/735).
+
+![image](/doc/_static/logo_small.png)
+[![image](https://github.com/pastas/pastas/actions/workflows/ci.yml/badge.svg?branch=master)](https://github.com/pastas/pastas/actions/workflows/ci.yml)
+[![image](https://img.shields.io/pypi/v/pastas.svg)](https://pypi.python.org/pypi/pastas)
+[![image](https://img.shields.io/pypi/l/pastas.svg)](https://mit-license.org/)
+[![image](https://img.shields.io/pypi/pyversions/pastas)](https://pypi.python.org/pypi/pastas)
+[![image](https://img.shields.io/pypi/dm/pastas)](https://pypi.org/project/pastas/)
+[![image](https://zenodo.org/badge/DOI/10.5281/zenodo.1465866.svg)](https://doi.org/10.5281/zenodo.1465866)
+[![image](https://app.codacy.com/project/badge/Grade/952f41c453854064ba0ee1fa0a0b4434)](https://app.codacy.com/gh/pastas/pastas/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
+[![image](https://api.codacy.com/project/badge/Coverage/952f41c453854064ba0ee1fa0a0b4434)](https://app.codacy.com/gh/pastas/pastas/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage9)
+[![image](https://readthedocs.org/projects/pastas/badge/?version=latest)](https://pastas.readthedocs.io/en/latest/?badge=latest)
+[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pastas/pastas/master?filepath=examples%2Fnotebooks%2F1_basic_model.ipynb)
+
+## Pastas: what is it?
+
+Pastas is an open source python package for processing, simulating and
+analyzing groundwater time series. The object oriented structure allows
+for the quick implementation of new model components. Time series models
+can be created, calibrated, and analysed with just a few lines of python
+code with the built-in optimization, visualisation, and statistical
+analysis tools.
+
+## Documentation & Examples
+
+-   Documentation is provided on the dedicated website
+    [pastas.dev](http://www.pastas.dev/)
+-   Examples can be found on the [examples directory on the
+    documentation
+    website](https://pastas.readthedocs.io/en/dev/examples/index.html)
+-   View and edit a working example notebook of a Pastas model in
+    [MyBinder](https://mybinder.org/v2/gh/pastas/pastas/master?filepath=examples%2Fnotebooks%2F1_basic_model.ipynb)
+-   A list of publications that use Pastas is available in a [dedicated
+    Zotero
+    group](https://www.zotero.org/groups/4846685/pastas/items/32FS5PTW/item-list)
+
+## Get in Touch
+
+-   Questions on Pastas can be asked and answered on [Github
+    Discussions](https://github.com/pastas/pastas/discussions).
+-   Bugs, feature requests and other improvements can be posted as
+    [Github Issues](https://github.com/pastas/pastas/issues).
+-   Pull requests will only be accepted on the development branch (dev)
+    of this repository. Please take a look at the [developers
+    section](http://pastas.readthedocs.io/) on the documentation website
+    for more information on how to contribute to Pastas.
+
+## Quick installation guide
+
+To install Pastas, a working version of Python 3.9, 3.10, 3.11, or 3.12
+has to be installed on your computer. We recommend using the [Anaconda
+Distribution](https://www.continuum.io/downloads) as it includes most of
+the python package dependencies and the Jupyter Notebook software to run
+the notebooks. However, you are free to install any Python distribution
+you want.
+
+### Stable version
+
+To get the latest stable version, use:
+
+    pip install pastas
+
+### Update
+
+To update pastas, use:
+
+    pip install pastas --upgrade
+
+### Developers
+
+To get the latest development version, use:
+
+    pip install git+https://github.com/pastas/pastas.git@dev#egg=pastas
+
+## Related packages
+
+-   [Pastastore](https://github.com/pastas/pastastore) is a Python
+    package for managing multiple timeseries and pastas models
+-   [Metran](https://github.com/pastas/metran) is a Python package to
+    perform multivariate timeseries analysis using a technique called
+    dynamic factor modelling.
+-   [Hydropandas](https://github.com/ArtesiaWater/hydropandas/blob/master/examples/03_hydropandas_and_pastas.ipynb)
+    can be used to obtain Dutch timeseries (KNMI, Dinoloket, ..)
+-   [PyEt](https://github.com/phydrus/pyet) can be used to compute
+    potential evaporation from meteorological variables.
+
+## Dependencies
+
+Pastas depends on a number of Python packages, of which all of the
+necessary are automatically installed when using the pip install
+manager. To summarize, the dependencies necessary for a minimal function
+installation of Pastas
+
+-   numpy\>=1.7
+-   matplotlib\>=3.1
+-   pandas\>=1.1
+-   scipy\>=1.8
+-   numba\>=0.51
+
+To install the most important optional dependencies (solver LmFit and
+function visualisation Latexify) at the same time with Pastas use:
+
+    pip install pastas[full]
+
+or for the development version use:
+
+    pip install git+https://github.com/pastas/pastas.git@dev#egg=pastas[full]
+
+## How to Cite Pastas?
+
+If you use Pastas in one of your studies, please cite the Pastas article
+in Groundwater:
+
+-   Collenteur, R.A., Bakker, M., Caljé, R., Klop, S.A., Schaars, F.
+    (2019) [Pastas: open source software for the analysis of groundwater
+    time
+    series](https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12925).
+    Groundwater. doi: 10.1111/gwat.12925.
+
+To cite a specific version of Pastas, you can use the DOI provided for
+each official release (\>0.9.7) through Zenodo. Click on the link to get
+a specific version and DOI, depending on the Pastas version.
+
+-   Collenteur, R., Bakker, M., Caljé, R. & Schaars, F. (XXXX). Pastas:
+    open-source software for time series analysis in hydrology (Version
+    X.X.X). Zenodo. <http://doi.org/10.5281/zenodo.1465866>

doc/about/courses.rst

@@ -6,4 +6,4 @@ Every now and then (on-site) Pastas courses and workshops are held. Please check
 private courses on request. To organize such a workshop please contact one of the following:
 
 - For courses in the Netherlands, please contact Artesia (info[AT]artesia-water.nl)
-- For international courses, please contact Raoul Collenteur (Raoul.Collenteur[AT]eawag.ch)
+- For international courses, please contact Raoul Collenteur (Raoul.Collenteur[AT]hydroconsult.ch)

doc/api/index.rst

@@ -25,6 +25,7 @@ left-hand menu you will find the different categories of the API documentation.
     utils
     timer.SolveTimer
     stats
+    dataset
 
 .. toctree::
     :hidden:

doc/benchmarks/autocorrelation.ipynb

@@ -394,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/check_response_functions.ipynb

@@ -976,7 +976,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/noisemodel.ipynb

@@ -54,7 +54,7 @@
     "\n",
     "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha}$$\n",
     "\n",
-    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. This is the default noise model that is added to a Pastas model, but it can also be added manually as follows: `ml.add_noisemodel(ps.NoiseModel())`\n",
+    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. It can be added to a Pastas model as follows: `ml.add_noisemodel(ps.ArNoiseModel())`\n",
     "\n",
     "## 2. Generate synthetic head time series"
    ]
@@ -169,9 +169,9 @@
     "ml = ps.Model(head_noise)\n",
     "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", report=False)\n",
     "\n",
     "# Plot the results\n",
     "axes = ml.plots.results(figsize=(10, 5))\n",
@@ -295,14 +295,14 @@
     "ml = ps.Model(head_irregular)\n",
     "sm = ps.StressModel(rain, ps.Gamma(cutoff=0.99), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "# ml.set_parameter(\"noise_alpha\", 9.5, vary=False)\n",
     "# ml.set_parameter(\"recharge_A\", 800, vary=False)\n",
     "# ml.set_parameter(\"recharge_n\", 1.2, vary=False)\n",
     "# ml.set_parameter(\"recharge_a\", 200, vary=False)\n",
     "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", report=False)\n",
     "axes = ml.plots.results(figsize=(10, 5))\n",
     "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
     "\n",
@@ -376,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/pastas_synthetic.ipynb

@@ -230,7 +230,7 @@
     "ml = ps.Model(head)\n",
     "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.solve(noise=False, ftol=1e-8)\n",
+    "ml.solve(ftol=1e-8)\n",
     "ml.plots.results();"
    ]
   },
@@ -345,9 +345,10 @@
    ],
    "source": [
     "ml2 = ps.Model(head_noise)\n",
+    "ml2.add_noisemodel(ps.ArNoiseModel())\n",
     "sm2 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml2.add_stressmodel(sm2)\n",
-    "ml2.solve(noise=True)\n",
+    "ml2.solve()\n",
     "ml2.plots.results();"
    ]
   },
@@ -465,9 +466,10 @@
    ],
    "source": [
     "ml3 = ps.Model(head_noise_corr)\n",
+    "ml3.add_noisemodel(ps.ArNoiseModel())\n",
     "sm3 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml3.add_stressmodel(sm3)\n",
-    "ml3.solve(noise=True)\n",
+    "ml3.solve()\n",
     "ml3.plots.results();"
    ]
   },
@@ -524,7 +526,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,