diff --git a/LICENSE b/LICENSE
index 0190061..77da6be 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,15 +1,21 @@
-# Copyright (C) 2014-2017 Primož Čermelj, Matjaž Mršnik, Miha Pirnat, Janko Slavič, Blaž Starc (in alphabetic order)
-#
-# This file is part of OpenModal.
-#
-# OpenModal is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, version 3 of the License.
-#
-# OpenModal is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with OpenModal. If not, see .
\ No newline at end of file
+MIT License
+
+Acknowledgement: from 2014-2017 this package was part of the OpenModal project
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/pyuff/__init__.py b/pyuff/__init__.py
index 58592df..b611b33 100644
--- a/pyuff/__init__.py
+++ b/pyuff/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.2"
+__version__ = "2.3"
from .pyuff import *
from .datasets import *
diff --git a/pyuff/pyuff.py b/pyuff/pyuff.py
index 1545637..1d317a4 100644
--- a/pyuff/pyuff.py
+++ b/pyuff/pyuff.py
@@ -1,27 +1,9 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2014-2017 Primož Čermelj, Matjaž Mršnik, Miha Pirnat, Janko Slavič, Blaž Starc (in alphabetic order)
-#
-# This file is part of pyuff.
-#
-# pyFRF is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, version 3 of the License.
-#
-# pyuff is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with pyuff. If not, see .
"""
==========
pyuff module
==========
-This module is part of the www.openmodal.com project and
-defines an UFF class to manipulate with the
+This module defines an UFF class to manipulate with the
UFF (Universal File Format) files, i.e., to read from and write
to UFF files. Among the variety of UFF formats, only some of the
formats (data-set types) frequently used in structural dynamics
@@ -44,14 +26,13 @@
* This source (py2.7) was first written in 2007, 2008 by Primoz Cermelj (primoz.cermelj@gmail.com)
* As part of the www.openmodal.com project the first source was adopted for Python 3 by
Matjaz Mrsnik
+ * 2014-2017 the package was part of the OpenModal project maintained by: Martin Česnik,
+ Matjaž Mršnik, Miha Pirnat, Janko Slavič, Blaž Starc (in alphabetic order)
* The package is maintained by Janko Slavič
Notes:
- * 58 data-set is always written in double precision, even if it is
- read in single precision.
-
- * ``numpy`` module is required as all the vector/matrix-type data are read
- or written using ``numpy.array`` objects.
+ * by default 58 data-set is written in double precision (see option `force_double=True`),
+ even if it is read in single precision ().
Example:
>>> import pyuff
diff --git a/pyuff_Showcase.ipynb b/pyuff_Showcase.ipynb
index 0a002f6..4e4eb0d 100644
--- a/pyuff_Showcase.ipynb
+++ b/pyuff_Showcase.ipynb
@@ -2,129 +2,126 @@
"cells": [
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Aug 2021: Janko Slavič (janko.slavic@fs.uni-lj.si), Klemen Zaletelj (klemen.zaletelj@fs.uni-lj.si), Matej Razpotnik (matej.razpotnik@gmail.com), Blaž Starc (sbtlaarzc@gmail.com), Matjaž Mršnik (matjaz.mrsnik@gmail.com), Matija Brumat (matija.brumat@gmail.com)"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"# pyuff Showcase"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"See the [documentation](https://pyuff.readthedocs.io/en/latest/index.html) for the ``pyUFF`` package!"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "This module is part of the [www.openmodal.com](www.openmodal.com) project and defines an UFF class to manipulate with the UFF (Universal File Format) files."
- ],
- "metadata": {}
+ "This module was part of the [www.openmodal.com](www.openmodal.com) project and defines an UFF class to manipulate with the UFF (Universal File Format) files."
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"UFF file format was originally developed by the Structural Dynamics Research Corporation (SDRC) to standardize data transfer mainly between various modal analysis, measurement or CAD geometry software products. UFF file is composed into several *datasets*, most important being the *dataset* 58 for measurement data and *dataset* 55 for modal data. More info on specific *datasets* can be obtained at https://www.ceas3.uc.edu/sdrluff"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"If required, install ``pyuff`` and ``matplotlib``"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 1,
- "source": [
- "#!!pip install pyuff\r\n",
- "#!!pip install matplotlib"
- ],
- "outputs": [],
"metadata": {
"tags": []
- }
+ },
+ "outputs": [],
+ "source": [
+ "#!!pip install pyuff\n",
+ "#!!pip install matplotlib"
+ ]
},
{
"cell_type": "code",
"execution_count": 2,
+ "metadata": {},
+ "outputs": [],
"source": [
- "import numpy as np\r\n",
- "import matplotlib.pyplot as plt\r\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
"%matplotlib inline"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"To analyse UFF file we first load the uff module and example file:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 3,
+ "metadata": {},
+ "outputs": [],
"source": [
- "import pyuff\r\n",
+ "import pyuff\n",
"uff_file = pyuff.UFF('data/beam.uff')"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"First we can check which *datasets* are written in the file:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 4,
- "source": [
- "uff_file.get_set_types()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"array([ 151, 164, 2420, 2411, 58, 58, 58])"
]
},
+ "execution_count": 4,
"metadata": {},
- "execution_count": 4
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "uff_file.get_set_types()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"we see that first 4 *datasets* are *151: Header, 164: Units, 2420: Coordinate Systems* and *2411: Nodes - Double Precision*. Next we have several *datasets* 58 containing measurement data. To check what is written in the header (first *dataset*) use:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
- "source": [
- "uff_file.read_sets(0)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"{'type': 151,\n",
@@ -143,187 +140,3315 @@
" 'time_file_written': '14:48:16'}"
]
},
+ "execution_count": 5,
"metadata": {},
- "execution_count": 5
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "uff_file.read_sets(0)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"We see that each *dataset* consists number of dictionary-like *keys*. We read and write directly to *keys*."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"## Reading from the UFF file"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"To load all *datasets* from the UFF file to `data` object use:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 6,
+ "metadata": {},
+ "outputs": [],
"source": [
"data = uff_file.read_sets()"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"The first *dataset* 58 (this is the fifth in the example file) contains following *keys*:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 7,
- "source": [
- "data[4].keys()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['type', 'binary', 'id1', 'id2', 'id3', 'id4', 'id5', 'func_type', 'func_id', 'ver_num', 'load_case_id', 'rsp_ent_name', 'rsp_node', 'rsp_dir', 'ref_ent_name', 'ref_node', 'ref_dir', 'ord_data_type', 'num_pts', 'abscissa_spacing', 'abscissa_min', 'abscissa_inc', 'z_axis_value', 'abscissa_spec_data_type', 'abscissa_len_unit_exp', 'abscissa_force_unit_exp', 'abscissa_temp_unit_exp', 'abscissa_axis_lab', 'abscissa_axis_units_lab', 'ordinate_spec_data_type', 'ordinate_len_unit_exp', 'ordinate_force_unit_exp', 'ordinate_temp_unit_exp', 'ordinate_axis_lab', 'ordinate_axis_units_lab', 'orddenom_spec_data_type', 'orddenom_len_unit_exp', 'orddenom_force_unit_exp', 'orddenom_temp_unit_exp', 'orddenom_axis_lab', 'orddenom_axis_units_lab', 'z_axis_spec_data_type', 'z_axis_len_unit_exp', 'z_axis_force_unit_exp', 'z_axis_temp_unit_exp', 'z_axis_axis_lab', 'z_axis_axis_units_lab', 'x', 'data'])"
]
},
+ "execution_count": 7,
"metadata": {},
- "execution_count": 7
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "data[4].keys()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"To read specific *key* form the desired *dataset* use:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 8,
- "source": [
- "data[4]['func_type']"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"4"
]
},
+ "execution_count": 8,
"metadata": {},
- "execution_count": 8
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "data[4]['func_type']"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"e.g. *key* `'func_type'`: 4 in the *dataset* 58 means that the function type in this *dataset* is frequency response function."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Most important *keys* are 'x': x_axis and 'data': y_axis that define the stored response:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 9,
- "source": [
- "plt.semilogy(data[4]['x'], np.abs(data[4]['data']))\r\n",
- "plt.xlabel('Frequency [Hz]')\r\n",
- "plt.ylabel('FRF Magnitude [dB m/N]')\r\n",
- "plt.xlim([0,1000])\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
+ "image/png": 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",
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+ "\r\n",
+ "\r\n"
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""
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RpRQXOONcLOeSHJa+v4u8HGHWuN3FZfHanYHY0Ke4iMiB/X1QVZf2936YiS2rbWrrpLIkGHHxQgRhFJem9k5KC11xMc/F2APtkS7e3dbIMTNHU5LviYt5Lsng6be2Uls9LCriHuOqitny+ia6upScnOR2o+4vLPYz928RUAsswwmR7Q8sBg5LqmUppCUmkd/Q1kFlSX6g+60qyae8KC9U4tLcFqGkII9RZYWB3RmlA56kmLgMzKqtjXR0KnuPKycvN4eC3BwTlySwcVcLb25u4Fun7rXbe+Mqi+joVD5oamN0eVFS7ejzllxVj1XVY4FNwIGqWquqBwFzgA1JtSrFtHV0UeoqfpCtTDxxKcrPZUJVMevcEbbpTleX0tzRSWlBLpOHl7BxVwttIevqnCje+BZL6A/Myk1OM/VZ45y+t8UFubRYWMx3Hl2xGYBjZ47e7T2viWUQeZd44j0zVfV174WqLgf2Tp5JPRGRqSLyRxG5N6h9tnR0MqrcKQcOUlxaY8Rl6qhSVn/QFNi+h0JrpBNVKCnMY+qoMroU3t8eDmEcKiYq8bNyUz0FeTnUuJVipQW55rn4zKqtjfzyyVXMnljJ9DHlu70/zm2/H0TFWDzi8pqI/EFEjnEfvwdei2fjInKLiGwVkeW9lp8sIm+JyCoRuaq/bajqalW9IJ79+UVrRycThzl5j+0B5g88ISsvymPqyDLe39Ecit5LXgv10gJHFAHe3RYOYRwqmtmFk76ycnM9M8eURwtkik1cfOX/PfgGJ1z/DAr87MwD9rhOVFx2tSTdnnjE5Xyc1i9fcR9vuMvi4Vbg5NgFIpIL3AScAswCzhGRWSKyn4g82Ouxu1+XZFSVlo7OlIw1qWvpoCg/h8I85yLd2aXR/kDpjFfxU1KQF70rfWdLQypNCgxLtcSHqrJyUwN7j+u+my4rzMvKbg7J4OHlm/jDc+8BcPdFh/YYPBnL8NICCvJy2FSffM+lv4Q+AKraCtzgPgaFqj4rItW9Fs8FVnkj/kXkbuB0Vb0WOG2w+/AQkYuAiwAmT56c6GZo73SaR46vLCI3RwJNqtc1d0T7AO07wSkhfG193R7d23TCu/ssLcylvCifGWPKWJQlSX1L5MfH1oY2djS1s/e47nkGy4vyaWjNnm4OiXD/so38/PG3eeiyD+9W+eXR2aX83yNvAXDfJUcwo5/rhYgwrrKITbtSGBYTkZsH+nA86+yBCcC6mNfr3WV97WOEiPwWmCMiV/e1nqre7BYd1I4alXjj5tZ2JwxVWpjHiNKCYMWlpYOKIkdcpo0qo7wwj6Xvp/9FOtZzATikZgSL1+wIRUhvqFjOJT7ecJP5PcUlL6vm/kmEy+96hdXbmqKDIvfEg69t5N1tTdz06QOZPalqwG2OrQhmIGV/nssZItKfBQIc67M9u6Gq24GL41lXROYD86dNm5bw/mIrtkaVF7I1QHGpb+32XHJyhAMmV/HK+7sC23+ieDmXEvfO6piZo7jjxbW8uHo7R83I7BkaYjtXBzF2IKw89eZWCnJzoh45QEVRPvXmufTJio3d3d3f2drIIVNH7LZOZ5dy4xPvMHNMOafsOzau7Y6vKmbRmh2+2dkX/eVcvo7T/qWvx2Lg2wnscwMwKeb1RNKotNmr2CouyGF8VTHrdwZX9VTX0i0uALVThrNycz33Llmf1vNe9PZcjpg2kuL8XB5xSyIzmdioWHMa/49SyZV/W8btC9cyf/Z4ygq772fNc+mfb/2zuw5qVx+zRz6wbCOrtzXxlROmx31jM7ayiC31rXQm2e3ub5zLbXE87klgn4uA6SJSIyIFwNnA/Yl+gV42D7nlvue5FOfnMnVkKWu2Nyf9n+DRW1xOnDUGVefH+c2/x1WglxKi1WKFjudSlJ/LvH3GcP+rGzM+YRubcwnToNeguO2FNdy7ZD0A//ORniMYyovyoy2WjJ7Ut3awbN0uRpYVUFqQy46m3T28jbta+OpfX2X66DJO3ic+rwWgZkQpHZ2a9BvnpPY1EZG7gIXATBFZLyIXqGoEuBR4BFgJ3KOqK5Jpx2DwPIRCd6xJe6SLjQGU7YGbc4kRl9jKmkXvJd+NTZTengvAeYdX09AW4e6X30+VWYEQ67lsqgvmPAkLrR2d/PrpVQAs//5JDHMnq/KoKHbOF/NedufBZZsA+M5psxhWWtDDc3l4+Waqr3qIw3/8ZHSdwYRjp41xKsne2dLoo8W7k1RxUdVzVHWcquar6kRV/aO7fIGqzlDVD6nqD/3anx8zUUZzLnm51Ix0/glBDGbs7FIaWiM9PBcR4fAPOXHWjQF1Mk2EpvaengvAnMnD+PD0kfzyyVXRqW0zkVjPJahus+mOqnNX/JOH32RLfRt3fuGQHuEwj3K3eMXEBVZva6T6qoeovuohVm1t4M8vrmX66DI+Ons8w0oK2OGKywurPuDiPy+Jfu6y46YNOq/plSm/szVNxEVESpJpiB/4ERbzQjxlhXnMdEv6Xu+nUsMvvJLMWHEBJ3/hka4hpua2CCKOIMfyPx+ZRUt7J1fc82qPZqCZRGzE9N1tyf2xhgFV5TN/eIkjf/IUf3p+DfNnj+9xDsfitd3P9qS+qnLcz56Jvj7h+md5Y1M9nzu8GhFhWGkBO5s7WL6hjk//4SXAKTle8+OP8LV5Mwe9v4qifEaXF7Iq1eIiIoeLyBvAm+7r2SLy66RalSB+eC7eRb68KI/Kknxmjinn5TXJLwf2Zm+s6CUunztsSnSq0t+4IYZ0o6m9k5L83N1c85ljy/neR2fx9FvbuOTOpRk6MZSjLiLw+ob6FNuSGt7d1sgZNz1PQ2sHT6zcygvvbo++93+f3L/Pz3ll99ksLq+u20XN1QsAKMzL4RMHToy+99HZ4wEYVpLPsnW7OO2XzwFwZu3EuEqO+2P6mDLe2Zrcgc4DDqLEGTx5Em7SXVWXichRSbUqQVT1AeCB2traCxPdhucdeHdVB9cM41+vbEz6vC6euPT2XMqL8nnia8fwtXuW8eun3+WQmhFpV97b3N5JyR7CHgCfOWQKkU7lmgdW8LFfP891n5rdoxw17Hiey9zq4bz83vbdijIynYbWDo5377r3u+ZRwJky4omvHU3+AL+XqOfSkp4e+VBQVepaOqgqcfJM63Y08+U7l/LXLx4azU1O//aC6NxRAC9/+wQqi/P53GFTmDy8JHoeDSvpzlUV5uXw00/OHrJ9M8dU8JeX19LZpeQmqXw+HnFBVdeJ9DAgE29Bge74b5l74h85bSR/fvF9Xng3uWM2vB9YXxemH5yxD8s31HHh7Yv5xTlzOGkQ1SHJprk9Eu0ivSfOPbya6pGlfPXuVzjtl89xzMxRXHrsNGqrhwdoZXLwci5nHTyJl97bwQ2Pvc335s+i1+8lI1BVRIR3tzVy1u8WMmt8JXu6Ll33qdkDCgt0ey5hHqVf39rBC6u2UzOylKmjSsnPzWFHUzsH/uAxAD59yGT2HlfBd/7llBXP+u4jfP2kmWytb40Ky8RhxTz5tWMoyHOOWW+vxJvfCeDNH/ToppUwe40tp7Wji+Ub6obsBfVFPOKyTkQOB1RE8nH6i61MijVDxBtEOaE68UGU9a0dFOQ5/b0Ajt1rNBVFefx96fqkiktfnotHSUEed154CBfctpiL/7yE73xkFucfUZ0WF7Gmtk6KC/o/lY6eMYpnvnEsdyxcyx+fe49P/nYhB06u4vi9xzC3ZjizJ1ZFf1ypYltDGwf/8HH+dN7BHLtXfG3tvHz+/hMrOe/wam59YQ0bd7Vw3ZmzoxfPVNDYFuEvL63lM4dMobQPr9LDu3uNdHaxZnsTU0eWIQLX3L+CRWt28rvPHsTPH3+Hvy9d3+Nzz769DYDzj6jme/P3YcXGOjo6lQPivFh5nks6J/RvX7iGNzbWc+3H99vtt7aruZ0D/vexHstO3mcsD8eM7/rLS7tXS3qtWgBeuOo4xlcV92vDWQdP4vlVH+zRhkSpcUPtp9/0PGt+/BFfttmbeMTlYuBGnBYtG4BHgUuSYs0Q8cJio6fOSjws1hqhoqj7sBTm5fLxAydy50trufqUvRlbmZwJdgYSF4CRZYXcfeGhXH73K/zvg2/wzNvb+OoJ05kzeVhSbIqXgTwXj4qifC45dhrnH1HNXS+v494l66M/tKL8HA6aMozaKcOZM7mKWeMrKMzLpaIoLzAB9eYb+f1/VsctLp7nIiJ897RZTKgq5icPv8mpN/6HD08fxYSqIo6eMZq9x5XTFumKXugbWjt4f0czHxpVRluki20NrUwZ4dz5ejM2Th5ewnWPvsWmXa1cMW8Gk4eX8PqGOiqL85k0rIQ7X1pLaWEeZ9VOYs32Jk775XN897RZnHXwJPb93iMAPPnmVm7+XC37uyGrkWUF/O6ztQwvLeALty2Kq3v1h3/61G7LXv728dz05CpKC/P46gkzANhn/ODCneme0F++oY7v3ueMkrjgyBoWr93J1f94nStOnMHlx0/nM25yPZZYYXnqymM49rqnATcC8oVDuO/VDXzl7lcBWPw/JzCyrHBAO0oL8/jjeQcP/QvFMGN08vsVxtO48gPgM0m3JE1oaI3sVjZ5wZE13PHiWm56ahU/OGPfpOw3HnEBp0357/7rIG59YQ03PP42H/v1Cxw5bSSXHjeNudXDU9J+pKm9c1B5hpKCPC44soYLjqxhZ1M7i9bsYOHq7by0egc3PvFOj3XHVBSy34QqSgpyKS/KY9b4CiYOK6GkINdZVphPZUk+xfm55OfKkITIK6UezJ2057kITsueC4+ayuxJVfxowUrucsf4XPfo2z0+8+HpI1m0ZgetHV3kSM+Ks4/OHs9jb2zpMRsq9LxoAT0+d/U/otMtcdU/XuenMXfGL67eERUWgA8a2/nEb16I+/t99YTp/Pxx53+y4vsn9fCCvn/60H4Lebk5lBbkpiTn0tWl0XZLy9bX0dHZRe2UYT3OHy+BDvDTR97isTe2AHD9Y2+z74QKVrvC/N61pyIiPLBsI5fd9QoAr3znRIaVFrDqh6fQ0BqJjvE5/YAJnH5An60UA6OyJJ9po8uSOvC3T3ERkV9C35NVqOrlSbEoxTS0dkTr7z0mDS/hs4dO4baFazj9gPFJyRXUtXRQkJtDUf7AoaGcHOHzR9Zw1sGTuPOltdz87GrOvvlFRpQWcNxeozl77iQOnDwssDv+5rYI4xP06IaVFjBvn7HMc3NITo3/+3xoVCktHZ2s2FjPyk31tEe62N7Uzp17CDN45OUIpYV5lBXmUVqYG31eVpjHhKpiSgrzaGjtoDAvl4K8HOpbOhhWUsDh00ZQXpQXHVT2+oY6Xlj1AeOripkyoqTf4+h5Ljkx68ytGc6/LjkCcOYD+vfyzSxcvZ3po8u486X3Wb2tiVP3G8dR00fx1pYGtjW0UVKQyz2L1/Hcqg84YtoIWjo6Kc7P45iZozh6xij+vXwTD72+mUhnF6fsO5bm9k6O33s0ty9cy32vbgTghL3H8PjKLdFxRat/dCrf/Ptr/G3Jen5w+j589rBqvnffcm5buBaAqaNKefCyI8kRYXNdK5OGl5Cb4+RUWto7o4UXnmeSDCqKg+kv1tmlfOhbTlXW41ccxQnXPzuoz3vC4vH5WxcDcMNZs6Pnx/zZ4zlh7zEA0Q7Gebk5uw0eTRcOmjyMe5euj+bS/Ea0j5bhInKu+/QInHlX/uq+/hTwhqrG1UwySLycS+WEaRfuWv/OgOvviU/+5gUK8nL4y4WH9lje1BZh3g3PUpSfw0OXf5ii/IHDQIPh6n+8zmNvbGHx/5ww6M82t0d4ZMVmnnlrG4+9sSU6qHF4aQFXnbIX82aNiVatJIMjfvwkh04dwc/OHHoVS384g/Na2NrQRnN7hKa2ThpaO6hr6aClvZPmjk6a2iI0tkVoanPeb2yL0NDawbqdLbRHuigrzKM90kV7ZxflhXk0tUf67Wy819jy6Nw6h04dwZm1k3rcvf99yXq+9rdlPPv1Y5k8IjVDwd7e0sDCd7fz2UOnsPqDJr71z9f5zWcOZEQcIZdUc9INzzJlRAk3f652yNtSVb54xxK+8OGpzK0ZTvVVDwHwzg9P4dbn1/DDBYNPFd978WGs2trIVa53uOx78zjuuqfZ7gr4W//v5Gh+NmzcsXAN37lvBS9efXw03C8iS1R16P8M+vFcVPU2d2dfAo5027bgtr//jx879xsv5zKqZgg5l7YIk0t3v0iUFubxo4/vx7m3vMx371vOjz62n6+lyfUtHdF2GIOlpCCPj82ZyMfmTKSxLcKC1zbxjb+/xo6mdr5x72t8A2dU7v4TKjl65iiOnDbS1wtPU3ukx+j8ZCEiTBpeEp3IbTCoKl1KtOzS62C8q7mdRWt20tHZxZfvXArA/ZceQX1LhHe2NvDga5t4a3MDkS7lkRVb+OFDKzlqxij+9/R9mDisJCbn4t/3HCwzxpRH5/CYNrqMe754WOqMGSSVQ/BcVJUTrn+GhtYIL3/7hOh4kUff2ELtlO485LUL3uSW59/b7fMvXHUc3/nXcn772YPYXNcazS3dePYB0bzIQVOGccCkKu5ftpFLj51GZXE+L3/7BH768Jucf0RNaIUFYMoIJ6n//o7mpOSS47maDQMqAK+5VZm7LCNpaI3sFhbzOHqGU0L7q6dWsbWhjZs+feCAlTjx4tf4iLLCPM48eBKnzxlPjggrNtbz/KoPeOX9nTz7zjb+8YrTgHpcZRGn7jeOk/cdy77jKynMy0k4X9Pc1tmjr1g6IiLkxnw977tWlRRw4iwnlPGfbxzLzuZ29p9YBcCR00dy/hE10c8sWbuTBa9v4p5F6/j071/iwcuPjMaN06BoL5RUFOexYRATV63cVM8pN/6H5686jh888Ea0IOFyN9fhsThmsjpPWE6cNYbf/ddB/Hv5ZubtM4b83JxoonzS8JIeVVOxeZG8XOkRycjNEa4+tWcTzjAyxfW012xvYm6N/6H+eK4IPwZeEZGncPKWRwHX+G5JmlDf2hGtYtkTV540k3FVRXz3vhWc8/sXueW8g+Oq+Ihnv8N9jM16d1QHTKqKloZ2dSmvrNvJ0rW7WLRmB7cvXMMf3alRR5cX8p3TZnHyvmPjGqPg0dHphJjiqRZLdwbyig6aMoyDpgzjlH3HctbNL/Ljf7/J7IlOXiLH1CUhKoryWdkS30hxVeWUG52gyRFu00aP+5c5eacrTpzB9Y85BRQfnzOBV9bt4j23N+D1Z84mJ0f4yP7j/DI/1Hgl0Lc89x5n1k4aYO3BE0+12J9E5N/AIe6ib6pqRk7Uoao0tkX6FRdwRp2PrSjikr8s5cTrn+GLR3+Icw+r7nMa0nioa+mIzj+fLHJyhIOmDOegKcO58Kip1LV08OLq7fxt8ToeX7mVy+56hYqiPObPHs/5R1QzLY5yRW+K46F897BRWz2csw+exN8Wr2eC+wM1bUmMwST0e48p2ROXHTeNCz88lUhXF+VF+agq+1/zKN/6yN59RiSyFe8mcnuSGssOKC4xrV48P3OGiMxQ1cGVWwRATEI/oc83tXeiyoDiAnD83mP4x5eO4CcPv8mP//0mf1u8jivnzWTePmMTaqeQirYhlcX5nLTPWE7aZyxdXcrjK7dw36sb+cfSDfx10TouPGoqlx83vV/h8Nrt+xUeDAtn1k7izpfejw4kNM8lMSqK82lojfTbhuSRFZv54h3dnYAfuvxIPvILp0z45W8dz4iyQh5YtpFj9xqNiLjnq3POigivf/+kpH+PsHLuYVO4d8n6pMyiGs8V4esxz4uAuTgzUR7nqyU+0J3Q3zuhhH5308r4LvKzxldw2+fn8uzb2/jm31/jS3cu5aApw7jx7AOYOCz+pHNXl1Kf4p5UOTkSLQne3tjGtf9+k988/S73LlnPTz6xH8ftNWaPn+s9xXG2sO+ESiqL83nJnWfHpCUxvAHLja0RKkt2P/9b2jt7CMsFR9awz/jK3UaVnzEn9WNHwkhlcT5N7Z3c8eJazj282tdtDxhcV9X5MY8TgX3p9mLSkkTnjWxs7dm0Ml6OmjGKx644mjmTq1iydicnXv8s970a/8zNjW45bLo0PBxRVsh1n5rNPV88jKL8HL54xxKeWLllj+tGPZc0T+j7TW6OcOjU7iRoOrThCSPeOd9XaGzv7z4cff7OD0/hO6fNCsSubOEYtxPFpiTMRZRILe16IK1LJRLtVVTvNa1MIMRTVpjHP798BM9981j2nVDBV+5+lcvueoXtjQOPgK1rdtvtp1lMeG7NcB687MPsPa6Cy+56ZY/zlUQ9lwBKkdONqaPKos9T0BghI/CmmPA6VHh0dml0nArAsu/OG1ShiREfB7iVkb995l3ftx3PfC6/FJFfuI9f4YxxWeq7JT7TlcC894MNi+2JicNK+MuFh/K1E2fw4GsbOej/Pc6aAWay7Gsul3Sgsjifmz9bS2FeDpfcuTQ6DbRHtnouQI+uBOa5JEZ0Tpde4nLh7Yujz783f9YeQ2bG0Elmu6h4bgUW4+RYlgALcarF/itpFvnEqwnMHul5PBWDDIv1Jj83h8uOn85dbm38+bcuYmc/FRn1cfYVSxVjK4u4/swDeHNzQ7TPlMeepjjOFsZVdnezNc8lMfoKiz3jFkocv9foHmONjOTRHvF3tth4xKVKVW9zH3eq6vMi8hVfrfAJbyZKgKVrB58W8iYKKxuiuHgcOnUE9158GBt2tXDRHYt3u+v3qO9jiuN04ti9RnP6AeO5feEadjV3C2Wze8wGarmfiYws7x7fZJ5LYnhdKWLDYqpKZ5dSnJ/rezdgo29eXL194JUGQTzicu4elp3nqxU+oaoPqOpFAK+8v2vQn/cjLNab2urh/OxTs1m0ZiffuPc19tTLLdoROc1d/y8fM43m9k7+/OLa6DJvnEsmDKIcLGUx3pppS2J4oeDYzshL3BvDg6ZkbCOQtOJ3nz0IgJ3N/o536VNcROQcEXkAqBGR+2MeT9HdCiZteXnNjj1eyPujsTWCCJT43JRy/uzxXHHiDO5ftpHnVn2w2/vxtttPNTPHlnNIzfBoF14g2hre70aeYSB2bI+Nc0mMsoI8cqRnWOzZd5zfyOePrE6RVdnFXLfL++I1/hYB9+e5vAD8DHjT/es9vgak/aikbQ1tLF67k1fX7Yr7Mw1tzlwuyUhyffHoqYyrLOKGx97eTfTqWjrIzZFQ3P2fut843tnayGq3cqylvZMcceb2zjZi+6mZtCRGTo5QXpTfIyy2fEMdeTnCsTPjm7DNGBpVbsTkjpiIhB/0eUVQ1bWq+rSqHqaqz8Q8lnodktOdT/12IWfc9Dz3LF7XZ74jlobWCOVJGmlemJfLJcdOY+n7u6LJSg9vdH4Y4vYfnj4ScCahAtx5R3JDYbvfxN4MmOeSOKUFudHwaqSziyff3EqkKzlzjBi7IyKUFeYxZ3KVr9vtLyz2nPu3QUTqYx4NIlLvqxVJ5hv3vsYx//d0NKfSF439dET2gzNrJzGusohbnl/TY3ldS2TIFWpBUTOylNHlhbz0npP8a27vzMpkPtBjygW7DiZOUX5uNLz6xJtbU2xNduK3sED/nsuR7t9yVa2IeZSraoXvlvjMab06n26ub2W/ax7l1Bv/E50rvTcNbR2+VYrtiYK8HM6YM4HnV30QnS0QSHnrl8EgIuw/sYo3NjrHsLWjk+KC7AuJ9cbEJXEK83Npc8Xl9fV1ANz++bmpNCnr2N7YnlARVH/EdVUQkVwRGS8ik72Hr1YkgV99+kDe/dGpXDN/Fq9fM4/f/teBHLfXaN7YVM+X/ryESOfuNd2O55Lcu/CT9hlLZ5fyfExiv7EtklRR85sZY8p474Mm2iNdtLR3UpIfHtuThYXFEqc4P4fWDuf3+KunVgEw2x05bgTDG33ccA+FeEboXwZsAR4DHnIfD/puSf82nCEivxeRv4rIvHg/l5sjnHdEDeVF+Zy87zhuOe9gfvXpOazZ3swnfrtwt/W9hH4y2Xd8BWWFeT1qyhtbk79fP5k5tpxIl/LeB000d3RSFIJChGRj0pI4Rfm5u+VE070s3xiYeDyXrwAzVXUfVd3Pfewf7w5E5BYR2Soiy3stP1lE3hKRVSJyVX/bUNV/qeqFwMXAWfHue0+cuq8TLlu2bhct7T1P6IYAPJe83Bzm1gzvKS5tEcoKw/Njmu7O8/LO1gZa2zt9L90OI+a5JE5szsVIDT84Y1/ftxmPuKwD6oawj1uBk2MXiEgucBNwCjALOEdEZonIfiLyYK9HbD3i/7ifS5icHOGy45z5Xu5dsq7He8lO6HscOnU4725rYmuD04k0ngnK0okJw5y2J5t2tdLcEcmqicL6wrQlcYpdz2Wz25k3G8vaU838JMzOGc8VbTXwtIg8BERb/Krq9fHsQFWfFZHqXovnAqtUdTWAiNwNnK6q1wKn9d6GODWJPwb+rapDbpp5xYkzeGn1Dn755CrOPHgShXm5dHR20dLRGUh46qApzqClZevqOGHvQhrbIqHqzVVRlEdJQS6b6lppae80ccHavwyFQjfnsmKjcw9r0xAHTzI6Tsezxfdx8i0FQHnMYyhMwPGIPNa7y/riMuAE4JMicvGeVhCRi0RksYgsHl3Qf8mxiHDBh2vY2tAWrZBoaktsLpdEmD7GadW+amsjrR1ddHZpqGZyFBHGVhaxub7FTeibuBiJU5SfS1ukk9fcSrErTpyRYouyj2SEdQe8oqnq933f6yBR1V8AvxhgnZtFZBMwv6gg/6CBtnlozQhEYNF7Ozh06ohoR+QgPJeKonzGVhTxztaGaKw5bC3rx5QXsaW+zRlEaZ6LMQSK8nJpae/kxiecjtujy4sG+IThNzlJiEQOeEVz+4v1btJVh9OK/3eqmsgUZhuASTGvJ7rLhoQ3zXFtbe2A0xxXluQzc0w5L69xRpo3RGehDCaxPnVUKe990BQVl+KQ3f0PK83nrc0N0RH6hpEoxQU5tEa6mFsznJff20GB5VwCJxmeSzz/xdVAI/B791EPNAAz3NeJsAiYLiI1IlIAnA3cn+C2ongt9+vq4qs/mFsznCVrdxLp7IpOehXUXPDjq4rZtKs1WrEWtnLeqpICdjV30B7psgSsMSSK8nKjLfb3GZ/247MzktwUicvhqvppt539A+5EYQer6iXAgQN9WETuwplkbKaIrBeRC9zeZJcCjwArgXtUdcUQvgfQ3XK/srIyrvUPrh5Oc3snKzbWRz2IwMSlsoitDa3ROWTCdvc/rCSf7U3tdGlykoFG9uB11H7m7W27TXdsBIMIPH7F0b5uM55Af5mITFbV9x0jZDLgTR4+4AQAqnpOH8sXAAviNTQZHDCpCoCVm+oZUeZM/BRU6/hxVcV0Kazd7kyBHD5xKYg+zzfPxRgCRfnd58/6nS0ptCR7ERGmjS4beMVBEI+4fA14TkTexRmIXAN8WURKgdt8tWaIiMh8YP60adPiWn9cZRG5OcKGXS2UuIn8oMRlTIUjZu9vb3b3G64LdGwvNPNcjKGQjXMBZQPxVIstEJHpwF7uordikvg/T5ZhiTCYhD44o+XHVhSxfmcLk4aVAARW+eTd+W90B46F7QcWO5dJQa6N8TASJ/bc/8KRNSm0xPCTeOtfpwMzgSJgtoigqrcnz6zEGKznAjChqpgNO1sCr9oaXuqKyy4nDBC2ct7YTsjmuRhDIVZcqqynWMYQT+PK7wG/dB/HAj8FPppkuxJisAl9cFqZbNgVvLgM6y0uIfNcYi8IJi7GUIg998M0mNjon3iuCp8Ejgc2q+r5wGwg/qt3mjOhqpjN9a3REfpBldWWF+aRlyOhFZdYey2hbwyF2HyjiUvmEM9VoUVVu4CIiFQAW+k5ADJtGOw4F3DCU51dyraGNoryc8jJCSZ/ICKUFeXR5I1zCZu4xITxLOdiDIXYcz9MU08Y/ROPuCwWkSqcAZNLgKU441bSjkTCYl6Md1Nda+AX+NiWL2EbiFhsYTHDJ8xzyUziqRb7svv0tyLyMFChqq8l16zg8Epqt9S3Bh6a8gZs5udKYB6TX5i4GH4Re1MXtvCw0Td9iouI9Dn6XkQO9KP1fTrgicumulZGlBYMsLa/eOJSEMKLc2y7GhMXYyjEiktuyG6yjL7pz3NZDCwHvMneY//rChyXLKMSJZFSZC8sVtfSwbjKYLuxemNFwtioL1YQC/LsgmAkTqy45Jm4ZAz9XdWuwGlS2QL8CZivqse6j7QTFkgs51IRM9I86Iu8N0FYGMUl1lsxz8UYCkUx5795LplDn1cFVf25qh6JM1HXJOAJEblHRA4IyrggSGUbk+IQey6xFwETF2Mo5OWauGQiA14V3KmI7wMexZmeOKOmiSvMy41Wq+QHXFJb7O43jDmXWCyUYfiFnUuZQ38J/ak486ycjjMl8d3Aj1Q1bduWJpJzASf30drRHvgduOexFOSFu0ImbJVuRvpi51Lm0F9CfxXwGo7XUg9MBr4k7qQyqnp90q0bJINtXOnhlT8GLS7e/sIYFoslGbPYGdmJeS6ZQ3/i8r90T2/sb6P/NMMLiwV9YnuiUhjysJhdDwy/sBuVzKFPcVHVawK0I6V4rUyC7pFVYJ6LYfQgz1oJZQzWawFnDm8IPrEe9kS+RzZry4tXH09jm03N6xfJmMvdSA0mLnR7LkGHxTxPKdLVFeh+/Uay+IIwtrIIZ5ojww+sFDlzyIxb5yHiJdZTFRYLubZYzsXwDROXzKHPq6mI3Brz/NxArBkiibTch26PJfCwWIZ4LpZzMfzCxCVz6O9qOjvm+VeSbYgfJNL+Bbo9lsCrxVwx6+zSAdZMb0xbDL8wcckc+hOXcF/xBkG+e0IHHhaLei7hPtTmuRh+YeKSOfSX0J8oIr/A6YbsPY+iqpcn1bIA8Xob5Qd8YnujkTXc2mLiYviGVYtlDv2Jy9djni9OtiGpxNOUvIBzLpnyQ8qMb2GkA+a5ZA79DaK8ra/3RGRycsxJDd4JHfSJ7e0u7BpjnovhF9lc1p5p9HurLiKHicgnRWS0+3p/EfkL8Hwg1gVEqi6OmdKkT6yg3TCMXvRXivx/wC3AJ4CHROT/4bTdfwmYHox5weBViQUtMplyx58p38MwDP/oL+fyEWCOqraKyDCctvv7quqaQCxzEZG9cUqhRwJPqOpv/N5HTlRc/N5y/2RI9xcbRGkYxm70d3lrVdVWAFXdCbwzWGERkVtEZKuILO+1/GQReUtEVonIVf1tQ1VXqurFwJnAEYPZf7ykKrGeKfFl81wMw+hNf57LVBG5P+Z1TexrVf1oHNu/FfgVcLu3QERygZuAE4H1wCJ3u7nAtb0+/3lV3SoiHwW+BNwRxz4HjZfID3q4SaZUixnGULly3gzuenldqs0wfKQ/cTm91+ufDXbjqvqsiFT3WjwXWOVOn4yI3A2crqrXAqf1sZ37gftF5CHgL3taR0QuAi4CmDw5sWK2oK/13h1/2DXGPBdjqFx63HQuPS6jUrlZT3/i8p6qvp+EfU7Ayd94rAcO6WtlETkG+DhQCCzoaz1VvRm4GaC2tjYUwxJzLOdiGEaG0p+4/As4EEBE/q6qnwjEol6o6tPA0/GsKyLzgfnTpk1LcF8JfSxhMuWOP1O+h2EY/tHfvXPsFWOqj/vcAEyKeT3RXTZkEm1cmSoyZTSyaYthGL2Jt3Gln/f0i4DpIlIjIgXA2cD9A3wmLhJtue/JqAbcqzNDtCVjqt4Mw/CPflvui0i9iDQA+7vP60WkQUTq49m4iNwFLARmish6EblAVSPApcAjwErgHlVdMdQvAol7LpKi7lh2UTYMI1Ppr7dY7lA3rqrn9LF8Af0k5xNlqDmXoIlWi1nrR8MwMowMqVdyCFvOxSTFMIxMJaPExTAMw0gPMkpcEk7oG4ZhGL6SUeIStrCYh+X1DcPINDJKXMJK2Kc5NgzD6E1GictQw2JBX+QlReNrDMMwkk1GiUvC41xSFJbySpHNczEMI9PIKHFJlFSlPDxR6wy6179hGEaSMXHB3942g8E8F8MwMpWMEpdEcy6purgX5ztNEEZXFKbGAMMwjCTRX8v90KGqDwAP1NbWXjioz7m+S9C5l+qRpdxw1myOmTE62B37xFNXHsPGXS2pNsMwjDQko8QlYVzPJRU9vj42Z2Lg+/SLmpGl1IwsTbUZhmGkIRkVFksULypmgxkNwzD8IaPEJfGcixsWS4ZRhmEYWUhGiUtY278YhmFkGhklLkPFwmKGYRj+YOKCjTMxDMPwGxMXYhL6lnUxDMPwBRMXuj0XC4sZhmH4g4mLYRiG4TsZJS42E6VhGEZ6kFHikmgpss2nYhiG4S8ZJS5DRSzpYhiG4QsmLlgpsmEYht+YuABzJlcBsPfY8tQaYhiGkSFYV2Tg9AMmUFs9nAlVxak2xTAMIyMwz8XFhMUwDMM/TFwMwzAM3wmFuIhIqYgsFpHTUm2LYRiGMTBJFRcRuUVEtorI8l7LTxaRt0RklYhcFcemvgnckxwrDcMwDL9JdkL/VuBXwO3eAhHJBW4CTgTWA4tE5H4gF7i21+c/D8wG3gCKkmyrYRiG4RNJFRdVfVZEqnstngusUtXVACJyN3C6ql4L7Bb2EpFjgFJgFtAiIgtUtWsP610EXAQwefJkH7+FYRiGMVhSUYo8AVgX83o9cEhfK6vqtwFE5Dzggz0Ji7vezcDNALW1tTYs0jAMI4WEZpyLqt460DoiMh+YP23atOQbZBiGYfRJKqrFNgCTYl5PdJcNmUQbVxqGYRj+kgpxWQRMF5EaESkAzgbu92PD1nLfMAwjPUh2KfJdwEJgpoisF5ELVDUCXAo8AqwE7lHVFX7szzwXwzCM9CDZ1WLn9LF8AbDA7/1ZzsUwDCM9CMUI/Xgxz8UwDCM9yChxMQzDMNKDjBIXS+gbhmGkBxklLhYWMwzDSA8ySlwMwzCM9CCjxMXCYoZhGOlBRomLhcUMwzDSg4wSF8MwDCM9ENXMayAsIg3AW6m2I00YCXyQaiPSBDsW3dix6MaORTczVbXcjw2FpivyIHlLVWtTbUQ6ICKL7Vg42LHoxo5FN3YsuhGRxX5ty8JihmEYhu+YuBiGYRi+k6nicnOqDUgj7Fh0Y8eiGzsW3dix6Ma3Y5GRCX3DMAwjtWSq52IYhmGkEBMXwzAMw3cySlxE5GQReUtEVonIVam2J9mIyCQReUpE3hCRFSLyFXf5cBF5TETecf8Oc5eLiPzCPT6viciBqf0G/iMiuSLyiog86L6uEZGX3O/8V3dqbUSk0H29yn2/OqWG+4yIVInIvSLypoisFJHDsvW8EJH/dn8fy0XkLhEpypbzQkRuEZGtIrI8ZtmgzwMROddd/x0ROTeefWeMuIhILnATcAowCzhHRGal1qqkEwG+pqqzgEOBS9zvfBXwhKpOB55wX4NzbKa7j4uA3wRvctL5Cs702R4/AW5Q1WnATuACd/kFwE53+Q3uepnEjcDDqroXMBvnmGTdeSEiE4DLgVpV3RfIBc4me86LW4GTey0b1HkgIsOB7wGHAHOB73mC1C+qmhEP4DDgkZjXVwNXp9qugI/BfcCJON0JxrnLxuEMKgX4HXBOzPrR9TLhAUx0fyzHAQ8CgjPyOq/3OQI8AhzmPs9z15NUfwefjkMl8F7v75ON5wUwAVgHDHf/zw8CJ2XTeQFUA8sTPQ+Ac4DfxSzvsV5fj4zxXOg+iTzWu8uyAtd9nwO8BIxR1U3uW5uBMe7zTD9GPwe+AXS5r0cAu1Q14r6O/b7RY+G+X+eunwnUANuAP7khwj+ISClZeF6o6gbgOuB9YBPO/3kJ2XleeAz2PEjo/MgkcclaRKQM+DvwVVWtj31PnVuNjK83F5HTgK2quiTVtqQBecCBwG9UdQ7QRHfoA8iq82IYcDqO4I4HStk9TJS1JPM8yCRx2QBMink90V2W0YhIPo6w3Kmq/3AXbxGRce7744Ct7vJMPkZHAB8VkTXA3TihsRuBKhHxeujFft/osXDfrwS2B2lwElkPrFfVl9zX9+KITTaeFycA76nqNlXtAP6Bc65k43nhMdjzIKHzI5PEZREw3a0CKcBJ2t2fYpuSiogI8EdgpapeH/PW/YBX0XEuTi7GW/45tyrkUKAuxj0ONap6tapOVNVqnP/9k6r6GeAp4JPuar2PhXeMPumunxF38qq6GVgnIjPdRccDb5CF5wVOOOxQESlxfy/esci68yKGwZ4HjwDzRGSY6wnOc5f1T6qTTT4nrk4F3gbeBb6dansC+L5H4ri0rwGvuo9TcWLETwDvAI8Dw931Baei7l3gdZwKmpR/jyQcl2OAB93nU4GXgVXA34BCd3mR+3qV+/7UVNvt8zE4AFjsnhv/AoZl63kBfB94E1gO3AEUZst5AdyFk2vqwPFoL0jkPAA+7x6TVcD58ezb2r8YhmEYvpNJYTHDMAwjTTBxMQzDMHzHxMUwDMPwHRMXwzAMw3dMXAzDMAzfMXExMhIR6RSRV2Me1am2yW9E5BgRqRORBe7r6tjut+6ya0Tkyn628d8i8r6I/CrZ9hrZRd7AqxhGKGlR1QP29IY7mE5UtWtP74eM/6jqaYl+WFVvEJGdQK2PNhmGeS5GduDe1b8lIrfjDKabJCJfF5FF7twV349Z99si8raIPOfO/3Glu/xpEal1n490W814c8j8X8y2vuguP8b9jDevyp2usCEiB4vICyKyTEReFpFyEXlWRA6IseM5EZnt0/cf38uT6xSRKX5s2zD2hHkuRqZSLCKvus/fA/4bZ56Kc1X1RRGZ576eizMy+X4ROQqnyePZOCPc84ClOF10++MCnFYZB4tIIfC8iDzqvjcH2AfYCDwPHCEiLwN/Bc5S1UUiUgG04LTyOQ/4qojMAIpUddkgv/eHYr43wFjgOlXd6H4nROQS4GhVXTvIbRtG3Ji4GJlKj7CYm3NZq6ovuovmuY9X3NdlOGJTDvxTVZvdz8XTn24esL+IeL2qKt1ttQMvq+p6d1uv4sytUQdsUtVFAOp2shaRvwHfEZGv47TbuHWQ3xng3V7f+5rYN0XkCOBCnNZBhpE0TFyMbKIp5rkA16rq72JXEJGv9vP5CN2h5KJe27pMVXs08xORY4C2mEWd9PObU9VmEXkMp0X8mcBB/dgyaNwOuH8EPqqqjX5u2zB6YzkXI1t5BPi8OxcOIjJBREYDzwJniEixiJQD82M+s4buC/4ne23rS+JMf4CIzBBncq6+eAsYJyIHu+uXx7R//wPwC2CRqu4c0jeMwbXtb8A3VfVtv7ZrGH1hnouRlajqoyKyN7DQzbE3Av+lqktF5K/AMpx5LhbFfOw64B4RuQh4KGb5H3DCXUvdhP024Ix+9t0uImcBvxSRYpx8ywlAo6ouEZF64E/+fNMoh+NUhH0/pnjhVDcXYxi+Y12RDaMf3JxFo6peF9D+xgNPA3sNVCrtht2uHEopsrud83Daq186lO0YRiwWFjOMNEFEPge8hDMXUTxjcNqBfb1BlAnu87+Bq4H6gdY1jMFgnothGIbhO+a5GIZhGL5j4mIYhmH4jomLYRiG4TsmLoZhGIbvmLgYhmEYvvP/AZf4t5Ul7XocAAAAAElFTkSuQmCC"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "plt.semilogy(data[4]['x'], np.abs(data[4]['data']))\n",
+ "plt.xlabel('Frequency [Hz]')\n",
+ "plt.ylabel('FRF Magnitude [dB m/N]')\n",
+ "plt.xlim([0,1000])\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Other keys in the *dataset* 58 are explained here: http://www.sdrl.uc.edu/sdrl/referenceinfo/universalfileformats/file-format-storehouse/universal-dataset-number-58 Similarly we could read the data from *dataset* 55 that is used for modal data."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"## Write measurement data to UFF file"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Here you can see a minimal working example for writing three measured accelerance FRF data to the UFF file *measurement_123.uff*. First we load the accelerances:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 10,
+ "metadata": {},
+ "outputs": [],
"source": [
- "measurement_point_1 = np.genfromtxt('data/meas_point_1.txt', dtype=complex)\r\n",
- "measurement_point_2 = np.genfromtxt('data/meas_point_2.txt', dtype=complex)\r\n",
+ "measurement_point_1 = np.genfromtxt('data/meas_point_1.txt', dtype=complex)\n",
+ "measurement_point_2 = np.genfromtxt('data/meas_point_2.txt', dtype=complex)\n",
"measurement_point_3 = np.genfromtxt('data/meas_point_3.txt', dtype=complex)"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 11,
+ "metadata": {},
+ "outputs": [],
"source": [
"measurement_point_1[0] = np.nan*(1+1.j)"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 12,
- "source": [
- "measurement_point_1"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"array([ nan +nanj, -0.17515115-0.12042372j,\n",
@@ -331,63 +3456,39 @@
" -0.5316 +0.00659625j, -0.53797177+0.01138392j])"
]
},
+ "execution_count": 12,
"metadata": {},
- "execution_count": 12
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "measurement_point_1"
+ ]
},
{
"cell_type": "code",
"execution_count": 13,
+ "metadata": {},
+ "outputs": [],
"source": [
"measurement = [measurement_point_1, measurement_point_2, measurement_point_3]"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"In the next step we create a UFF file where we add *dataset* 58 for each measurement point consisting of the dictionary-like *keys* containing the measurement data and the information about the mesurement point."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 14,
- "source": [
- "for i in range(3):\r\n",
- " print('Adding point {:}'.format(i + 1))\r\n",
- " response_node = 1\r\n",
- " response_direction = 1\r\n",
- " reference_node = i + 1\r\n",
- " reference_direction = 1\r\n",
- " acceleration_complex = measurement[i]\r\n",
- " frequency = np.arange(0, 1001)\r\n",
- " name = 'TestCase'\r\n",
- " data = {'type':58, \r\n",
- " 'func_type':4, \r\n",
- " 'rsp_node': response_node, \r\n",
- " 'rsp_dir': response_direction, \r\n",
- " 'ref_dir': reference_direction, \r\n",
- " 'ref_node': reference_node,\r\n",
- " 'data': acceleration_complex,\r\n",
- " 'x': frequency,\r\n",
- " 'id1': 'id1', \r\n",
- " 'rsp_ent_name': name,\r\n",
- " 'ref_ent_name': name,\r\n",
- " 'abscissa_spacing':1,\r\n",
- " 'abscissa_spec_data_type':18,\r\n",
- " 'ordinate_spec_data_type':12,\r\n",
- " 'orddenom_spec_data_type':13}\r\n",
- " uffwrite = pyuff.UFF('./data/measurement.uff')\r\n",
- " uffwrite.write_sets(data, 'add')"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Adding point 1\n",
"Adding point 2\n",
@@ -395,62 +3496,59 @@
]
}
],
- "metadata": {}
+ "source": [
+ "for i in range(3):\n",
+ " print('Adding point {:}'.format(i + 1))\n",
+ " response_node = 1\n",
+ " response_direction = 1\n",
+ " reference_node = i + 1\n",
+ " reference_direction = 1\n",
+ " acceleration_complex = measurement[i]\n",
+ " frequency = np.arange(0, 1001)\n",
+ " name = 'TestCase'\n",
+ " data = {'type':58, \n",
+ " 'func_type':4, \n",
+ " 'rsp_node': response_node, \n",
+ " 'rsp_dir': response_direction, \n",
+ " 'ref_dir': reference_direction, \n",
+ " 'ref_node': reference_node,\n",
+ " 'data': acceleration_complex,\n",
+ " 'x': frequency,\n",
+ " 'id1': 'id1', \n",
+ " 'rsp_ent_name': name,\n",
+ " 'ref_ent_name': name,\n",
+ " 'abscissa_spacing':1,\n",
+ " 'abscissa_spec_data_type':18,\n",
+ " 'ordinate_spec_data_type':12,\n",
+ " 'orddenom_spec_data_type':13}\n",
+ " uffwrite = pyuff.UFF('./data/measurement.uff')\n",
+ " uffwrite.write_sets(data, 'add')"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Note: If we run upper example twice we add the data two-times! `._write_set` Does not check if the nodal responses are allready written in the file, it just writes the defined dataset at the end of choosen file!"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"If you would like to write is as dataset 58b, all you need to add is the key `binary: 1` (which is by default `0`). \n",
"\n",
"The data dictionary in the example below is built using the ``prepare_58()`` function."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 16,
- "source": [
- "for i in range(3):\r\n",
- " print('Adding point {:}'.format(i + 1))\r\n",
- " response_node = 1\r\n",
- " response_direction = 1\r\n",
- " reference_node = i + 1\r\n",
- " reference_direction = 1\r\n",
- " acceleration_complex = measurement[i]\r\n",
- " frequency = np.arange(0, 1001)\r\n",
- " name = 'TestCase'\r\n",
- " \r\n",
- " data = pyuff.prepare_58(\r\n",
- " binary=1,\r\n",
- " func_type=4,\r\n",
- " rsp_node=response_node,\r\n",
- " rsp_dir=response_direction,\r\n",
- " ref_dir=reference_direction,\r\n",
- " ref_node=reference_node,\r\n",
- " data=acceleration_complex,\r\n",
- " x=frequency,\r\n",
- " id1='id1',\r\n",
- " rsp_ent_name=name,\r\n",
- " ref_ent_name=name,\r\n",
- " abscissa_spacing=1,\r\n",
- " abscissa_spec_data_type=18,\r\n",
- " ordinate_spec_data_type=12,\r\n",
- " orddenom_spec_data_type=13)\r\n",
- " \r\n",
- " uffwrite = pyuff.UFF('./data/measurement_58b.uff')\r\n",
- " uffwrite.write_sets(data, 'add')"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Adding point 1\n",
"Adding point 2\n",
@@ -458,36 +3556,64 @@
]
}
],
- "metadata": {}
+ "source": [
+ "for i in range(3):\n",
+ " print('Adding point {:}'.format(i + 1))\n",
+ " response_node = 1\n",
+ " response_direction = 1\n",
+ " reference_node = i + 1\n",
+ " reference_direction = 1\n",
+ " acceleration_complex = measurement[i]\n",
+ " frequency = np.arange(0, 1001)\n",
+ " name = 'TestCase'\n",
+ " \n",
+ " data = pyuff.prepare_58(\n",
+ " binary=1,\n",
+ " func_type=4,\n",
+ " rsp_node=response_node,\n",
+ " rsp_dir=response_direction,\n",
+ " ref_dir=reference_direction,\n",
+ " ref_node=reference_node,\n",
+ " data=acceleration_complex,\n",
+ " x=frequency,\n",
+ " id1='id1',\n",
+ " rsp_ent_name=name,\n",
+ " ref_ent_name=name,\n",
+ " abscissa_spacing=1,\n",
+ " abscissa_spec_data_type=18,\n",
+ " ordinate_spec_data_type=12,\n",
+ " orddenom_spec_data_type=13)\n",
+ " \n",
+ " uffwrite = pyuff.UFF('./data/measurement_58b.uff')\n",
+ " uffwrite.write_sets(data, 'add')"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"## The ``prepare_xx()`` functions"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"The new ``prepare_xx()`` functions enable easy dictionary construction. \n",
"\n",
"Each function contains the arguments that are available for a certain dataset and the arguments are documented in the docstring along with the reference to the **record** and **field** in the UFF file.\n",
"\n",
"All of the docstrings are available in the ``pyUFF`` [documentation](https://pyuff.readthedocs.io/en/latest/index.html). To see the docstring for, e.g., ``prepare_15()`` function:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 17,
- "source": [
- "? pyuff.prepare_15"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"\u001b[1;31mSignature:\u001b[0m\n",
" \u001b[0mpyuff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprepare_15\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
@@ -537,22 +3663,27 @@
]
}
],
- "metadata": {}
+ "source": [
+ "? pyuff.prepare_15"
+ ]
},
{
"cell_type": "code",
"execution_count": null,
- "source": [],
+ "metadata": {},
"outputs": [],
- "metadata": {}
+ "source": []
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
+ "interpreter": {
+ "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
+ },
"kernelspec": {
- "name": "python3",
- "display_name": "Python 3.7.4 64-bit ('base': conda)"
+ "display_name": "Python 3.7.4 64-bit ('base': conda)",
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -615,11 +3746,8 @@
"_Feature"
],
"window_display": false
- },
- "interpreter": {
- "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
}
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/requirements.txt b/requirements.txt
index 9e030eb..1cc18fc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,2 @@
-numpy>=1.12.0
-pytest>=3.0.5
+numpy
+pytest
diff --git a/setup.py b/setup.py
index cbcee38..7e0e845 100644
--- a/setup.py
+++ b/setup.py
@@ -1,21 +1,3 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2014-2017 Primož Čermelj, Matjaž Mršnik, Miha Pirnat, Janko Slavič, Blaž Starc (in alphabetic order)
-#
-# This file is part of pyuff.
-#
-# pyFRF is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, version 3 of the License.
-#
-# pyuff is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with pyuff. If not, see .
-
import os
import re
from setuptools import setup