From 42b784a908feb770177f8b8118e8f594172ea7e4 Mon Sep 17 00:00:00 2001 From: pgleeson Date: Tue, 27 Jun 2023 16:46:48 +0100 Subject: [PATCH 1/7] Adding intiial python script for plotting in python --- .gitignore | 1 + WormSim/MatlabSupport/WormView.m | 2 +- WormSim/Model/load_data.py | 62 ++++++++++++++++++++++++++++++++ 3 files changed, 64 insertions(+), 1 deletion(-) create mode 100644 WormSim/Model/load_data.py diff --git a/.gitignore b/.gitignore index e0b7fb8..b8f2522 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,4 @@ /WormSim/sundials-2.3.0/config.log /WormSim/sundials-2.3.0/config.status /WormSim/sundials-2.3.0/src/*/.libs +/WormSim/Model/objects.csv diff --git a/WormSim/MatlabSupport/WormView.m b/WormSim/MatlabSupport/WormView.m index 8eeebf1..e7303fd 100644 --- a/WormSim/MatlabSupport/WormView.m +++ b/WormSim/MatlabSupport/WormView.m @@ -8,7 +8,7 @@ %during previous runs. usingObjects = exist('../Model/objects.csv','file'); if usingObjects - Objects = importdata('../Model/objects.csv'); + Objects = importdata('../Model/objects.csv', ','); Sz = size(Objects); Nobj = Sz(1); end diff --git a/WormSim/Model/load_data.py b/WormSim/Model/load_data.py new file mode 100644 index 0000000..3c7fc58 --- /dev/null +++ b/WormSim/Model/load_data.py @@ -0,0 +1,62 @@ + +from matplotlib import pyplot as plt + +from numpy import genfromtxt +my_data = genfromtxt('simdata.csv', delimiter=',').T + +t = my_data[0] + +x_offset = 1 +y_offset = 2 + +''' +for i in [(j*3)+y_offset for j in range(49)]: + plt.plot(t,my_data[i],label=i) + +plt.legend()''' + + +fig, ax = plt.subplots() +ax.set_aspect('equal') + +import os +usingObjects = os.path.isfile('objects.csv') +if usingObjects: + Objects = genfromtxt('objects.csv', delimiter=',') + for o in Objects: + x = o[0] + y= o[1] + r = o[2] + print("Circle at (%s, %s), radius %s"%(x,y,r)) + circle1 = plt.Circle((x, y), r, color='b') + plt.gca().add_patch(circle1) +else: + print("No objects found") + +num_t = 20 +timesteps = len(t) + +for ti in [int(timesteps * i/num_t) for i in range(num_t)]: + + f = ti/timesteps + + color = "#%02x%02x00" % (int(0xFF*(f)),int(0xFF*(1-f)*0.8)) + print("Color at ti: %s, f %f: %s"%(ti,f,color)) + xs = [] + ys = [] + + for i in [(j*3)+x_offset for j in range(49)]: + xs.append(my_data[i][ti]) + + for i in [(j*3)+y_offset for j in range(49)]: + ys.append(my_data[i][ti]) + + print('\n t=%sms'%t[ti]) + print("xs: %s"%xs) + print("ys: %s"%ys) + + plt.plot(xs,ys,color=color,label='t=%sms'%t[ti],markersize=3 if ti==0 else 0.4) + + +ax.plot() #Causes an autoscale update. +plt.show() From 3e499c116ffd7b3152bd1acd4def01dbcef684dd Mon Sep 17 00:00:00 2001 From: pgleeson Date: Thu, 29 Jun 2023 11:08:26 +0100 Subject: [PATCH 2/7] Add images --- images/MatlabView.png | Bin 0 -> 13381 bytes images/PythonView.png | Bin 0 -> 51757 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 images/MatlabView.png create mode 100644 images/PythonView.png diff --git 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zXKc5S$}tKmj_#Q$yr#=vM^-I@eMce3JPSvI5j!@1msuW!d&di0KH5viQG9`cC+3%@ z@W*_k3~KwLfjjt%2dnFHw1Ov{iGlWSqi>=aEzoW%i!YyItLJgbN;Jzd> YvW{GCRX=--0{*+Jq^VdgZx;0b06 Date: Thu, 29 Jun 2023 11:18:30 +0100 Subject: [PATCH 3/7] Update WormView.py --- .gitignore | 3 + WormSim/Model/Player.py | 133 +++++++++++++++++++++++++++++++++++++ WormSim/Model/Test.ipynb | 93 ++++++++++++++++++++++++++ WormSim/Model/WormView.py | 121 +++++++++++++++++++++++++++++++++ WormSim/Model/load_data.py | 62 ----------------- 5 files changed, 350 insertions(+), 62 deletions(-) create mode 100644 WormSim/Model/Player.py create mode 100644 WormSim/Model/Test.ipynb create mode 100644 WormSim/Model/WormView.py delete mode 100644 WormSim/Model/load_data.py diff --git a/.gitignore b/.gitignore index b8f2522..3e48dbc 100644 --- a/.gitignore +++ b/.gitignore @@ -25,3 +25,6 @@ /WormSim/sundials-2.3.0/config.status /WormSim/sundials-2.3.0/src/*/.libs /WormSim/Model/objects.csv +.DS_Store +.ipynb_checkpoints +__pycache__ diff --git a/WormSim/Model/Player.py b/WormSim/Model/Player.py new file mode 100644 index 0000000..a62efbb --- /dev/null +++ b/WormSim/Model/Player.py @@ -0,0 +1,133 @@ +# Based on example at https://stackoverflow.com/questions/46325447/animated-interactive-plot-using-matplotlib + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +import mpl_toolkits.axes_grid1 +import matplotlib.widgets + + +class Player(FuncAnimation): + def __init__( + self, + fig, + func, + frames=None, + init_func=None, + fargs=None, + save_count=None, + mini=0, + maxi=100, + pos=(0.125, 0.92), + **kwargs + ): + self.i = 0 + self.min = mini + self.max = maxi + self.runs = True + self.forwards = True + self.fig = fig + self.func = func + self.setup(pos) + FuncAnimation.__init__( + self, + self.fig, + self.update, + frames=self.play(), + init_func=init_func, + fargs=fargs, + save_count=save_count, + **kwargs + ) + + def play(self): + while self.runs: + self.i = self.i + self.forwards - (not self.forwards) + if self.i > self.min and self.i < self.max: + yield self.i + else: + self.stop() + yield self.i + + def start(self): + self.runs = True + self.event_source.start() + + def stop(self, event=None): + self.runs = False + self.event_source.stop() + + def forward(self, event=None): + self.forwards = True + self.start() + + def backward(self, event=None): + self.forwards = False + self.start() + + def oneforward(self, event=None): + self.forwards = True + self.onestep() + + def onebackward(self, event=None): + self.forwards = False + self.onestep() + + def onestep(self): + if self.i > self.min and self.i < self.max: + self.i = self.i + self.forwards - (not self.forwards) + elif self.i == self.min and self.forwards: + self.i += 1 + elif self.i == self.max and not self.forwards: + self.i -= 1 + self.func(self.i) + self.slider.set_val(self.i) + self.fig.canvas.draw_idle() + + def setup(self, pos): + playerax = self.fig.add_axes([pos[0], pos[1], 0.64, 0.04]) + divider = mpl_toolkits.axes_grid1.make_axes_locatable(playerax) + bax = divider.append_axes("right", size="80%", pad=0.05) + sax = divider.append_axes("right", size="80%", pad=0.05) + fax = divider.append_axes("right", size="80%", pad=0.05) + ofax = divider.append_axes("right", size="100%", pad=0.05) + sliderax = divider.append_axes("right", size="500%", pad=0.07) + self.button_oneback = matplotlib.widgets.Button(playerax, label="$\u29CF$") + self.button_back = matplotlib.widgets.Button(bax, label="$\u25C0$") + self.button_stop = matplotlib.widgets.Button(sax, label="$\u25A0$") + self.button_forward = matplotlib.widgets.Button(fax, label="$\u25B6$") + self.button_oneforward = matplotlib.widgets.Button(ofax, label="$\u29D0$") + self.button_oneback.on_clicked(self.onebackward) + self.button_back.on_clicked(self.backward) + self.button_stop.on_clicked(self.stop) + self.button_forward.on_clicked(self.forward) + self.button_oneforward.on_clicked(self.oneforward) + self.slider = matplotlib.widgets.Slider( + sliderax, "", self.min, self.max, valinit=self.i + ) + self.slider.on_changed(self.set_pos) + + def set_pos(self, i): + self.i = int(self.slider.val) + self.func(self.i) + + def update(self, i): + self.slider.set_val(i) + + +if __name__ == "__main__": + ### using this class is as easy as using FuncAnimation: + + fig, ax = plt.subplots() + x = np.linspace(0, 6 * np.pi, num=100) + y = np.sin(x) + + ax.plot(x, y) + (point,) = ax.plot([], [], marker="o", color="crimson", ms=15) + + def update(i): + point.set_data(x[i], y[i]) + + ani = Player(fig, update, maxi=len(y) - 1) + + plt.show() diff --git a/WormSim/Model/Test.ipynb b/WormSim/Model/Test.ipynb new file mode 100644 index 0000000..4f6d41c --- /dev/null +++ b/WormSim/Model/Test.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4c87c8e3-c1a7-48c1-a5c6-d8ae331e556b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded data: (148, 500)\n", + "Sz: 148, Nt: 500, Nbar: 49, NSEG: 48\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "df410ab3df4442feb89a6442755a9178", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+ +ventral_plot = None +midline_plot = None +dorsal_plot = None + +from Player import Player + + +def update(ti): + global dorsal_plot, ventral_plot, midline_plot + f = ti / timesteps + + color = "#%02x%02x00" % (int(0xFF * (f)), int(0xFF * (1 - f) * 0.8)) + print("Time step: %s, fract: %f, color: %s" % (ti, f, color)) + ds = [] + xs = [] + ys = [] + + for i in [(j * 3) + d_offset for j in range(Nbar)]: + ds.append(data[i][ti]) + + for i in [(j * 3) + x_offset for j in range(Nbar)]: + xs.append(data[i][ti]) + + for i in [(j * 3) + y_offset for j in range(Nbar)]: + ys.append(data[i][ti]) + + for j in range(Nbar): + dX = R[j] * math.cos(ds[j]) + dY = R[j] * math.sin(ds[j]) + + Dorsal[j, 0] = xs[j] + dX + Dorsal[j, 1] = ys[j] + dY + Ventral[j, 0] = xs[j] - dX + Ventral[j, 1] = ys[j] - dY + + if dorsal_plot == None: + (dorsal_plot,) = ax.plot(Dorsal[:, 0], Dorsal[:, 1], color="grey", linewidth=1) + else: + dorsal_plot.set_data(Dorsal[:, 0], Dorsal[:, 1]) + + if ventral_plot == None: + (ventral_plot,) = ax.plot( + Ventral[:, 0], Ventral[:, 1], color="grey", linewidth=1 + ) + else: + ventral_plot.set_data(Ventral[:, 0], Ventral[:, 1]) + + if midline_plot == None: + (midline_plot,) = ax.plot( + xs, ys, color="g", label="t=%sms" % t[ti], linewidth=0.5 + ) + else: + midline_plot.set_data(xs, ys) + + +ax.plot() # Causes an autoscale update. + +ani = Player(fig, update, maxi=timesteps - 1) + +plt.show() diff --git a/WormSim/Model/load_data.py b/WormSim/Model/load_data.py deleted file mode 100644 index 3c7fc58..0000000 --- a/WormSim/Model/load_data.py +++ /dev/null @@ -1,62 +0,0 @@ - -from matplotlib import pyplot as plt - -from numpy import genfromtxt -my_data = genfromtxt('simdata.csv', delimiter=',').T - -t = my_data[0] - -x_offset = 1 -y_offset = 2 - -''' -for i in [(j*3)+y_offset for j in range(49)]: - plt.plot(t,my_data[i],label=i) - -plt.legend()''' - - -fig, ax = plt.subplots() -ax.set_aspect('equal') - -import os -usingObjects = os.path.isfile('objects.csv') -if usingObjects: - Objects = genfromtxt('objects.csv', delimiter=',') - for o in Objects: - x = o[0] - y= o[1] - r = o[2] - print("Circle at (%s, %s), radius %s"%(x,y,r)) - circle1 = plt.Circle((x, y), r, color='b') - plt.gca().add_patch(circle1) -else: - print("No objects found") - -num_t = 20 -timesteps = len(t) - -for ti in [int(timesteps * i/num_t) for i in range(num_t)]: - - f = ti/timesteps - - color = "#%02x%02x00" % (int(0xFF*(f)),int(0xFF*(1-f)*0.8)) - print("Color at ti: %s, f %f: %s"%(ti,f,color)) - xs = [] - ys = [] - - for i in [(j*3)+x_offset for j in range(49)]: - xs.append(my_data[i][ti]) - - for i in [(j*3)+y_offset for j in range(49)]: - ys.append(my_data[i][ti]) - - print('\n t=%sms'%t[ti]) - print("xs: %s"%xs) - print("ys: %s"%ys) - - plt.plot(xs,ys,color=color,label='t=%sms'%t[ti],markersize=3 if ti==0 else 0.4) - - -ax.plot() #Causes an autoscale update. -plt.show() From cde4cd909b279a8b1f5b683ddd6ed58eb4615b15 Mon Sep 17 00:00:00 2001 From: pgleeson Date: Thu, 29 Jun 2023 11:38:26 +0100 Subject: [PATCH 4/7] Updating Readmes and images --- README.md | 5 +-- WormSim/README.md | 70 +++++++++++++++++++++++++++++------------- images/MatlabView.png | Bin 13381 -> 64527 bytes 3 files changed, 52 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 87cbf25..507ff29 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,9 @@ [![C/C++ CI](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/actions/workflows/build.yml/badge.svg)](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/actions/workflows/build.yml) -![](http://www.opensourcebrain.org/attachments/download/33/Selection_095.png) +MatlabView   PythonView -A model of C. elegans locomotion described in Boyle, Berri and Cohen, [Gait modulation in C. elegans: an integrated neuromechanical model](http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2012.00010/abstract), Front. Comput. Neurosci., 2012. + +A model of C. elegans locomotion described in Boyle, Berri and Cohen, [Gait modulation in C. elegans: an integrated neuromechanical model](http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2012.00010/abstract), Front. Comput. Neurosci., 2012. For information on installing and running this code, see [here](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/tree/master/WormSim). diff --git a/WormSim/README.md b/WormSim/README.md index e79cae8..c737303 100644 --- a/WormSim/README.md +++ b/WormSim/README.md @@ -1,4 +1,4 @@ -## WormSim c elegans simulation engine from Boyle, Berri and Cohen 2012 +## WormSim C. elegans simulation engine from Boyle, Berri and Cohen 2012 See Boyle, Berri and Cohen, [Gait modulation in C. elegans: an integrated neuromechanical model](http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2012.00010/abstract), Front. Comput. Neurosci., 2012. @@ -10,22 +10,30 @@ Installation guide: 1) Clone this project: - git clone https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation.git +``` +git clone https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation.git +``` 2) Go to "WormSim" directory. - cd WormSim +``` +cd WormSim +``` 3) Set idaInstallDir: - idaInstallDir=`pwd`/Sundials +``` +idaInstallDir=`pwd`/Sundials +``` 4) Go to sundials-2.3.0 directory, configure & make: - cd sundials-2.3.0 - ./configure CC=g++ --prefix=$idaInstallDir --disable-mpi --disable-fcmix - make - make install +``` +cd sundials-2.3.0 +./configure CC=g++ --prefix=$idaInstallDir --disable-mpi --disable-fcmix +make +make install +``` 5) Look carefully at the resulting output and check for any error messages (there shouldn't be any...). Fix and repeat step 4 if necessary. @@ -37,26 +45,46 @@ Installation guide: 2) To compile the program, make sure you're in the "Model" directory. -3) Enter the command "make". +3) Enter the command: + +``` +make +``` 4) Check the resulting output for errors (some warnings are unfortunately inevitable, but they shouldn't be a problem), fix and re-run "make" if necessary. -5) Enter the command "./program" and wait for it to complete. +5) Enter the following command and wait for it to complete. + +``` +./program +``` + +6) `program` will generate a file called `simdata.csv`, and possibly also `objects.csv` (if objects are being used). + +7) Open the Matlab [viewer program](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/MatlabSupport/WormView.m), found in `WormSim/MatlabSupport/`. + +``` +matlab WormView.m +``` + +If you don't have access to Matlab, try [Octave](https://www.gnu.org/software/octave/it), or should be possible to reproduce this viewer in another language by examining the code and translating it as appropriate. -6) "program" will generate a file called "simdata.csv", and possibly also "objects.csv" (if objects are being used). +MatlabView -7) Open the appropriate [viewer program](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/MatlabSupport/WormView.m), found in "WormSim/MatlabSupport/", -in Matlab. If you don't have access to Matlab, try [Octave](https://www.gnu.org/software/octave/it), or should be possible to reproduce this viewer -in another language by examining the code and translating it as appropriate. +8) Try also the [new Python viewer](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/Model/WormView.py): -8) Run the viewer to visualize the model behaviour. -9) Summary: +``` +python WormView.py +``` - cd ../Model - make - ./program - cd ../MatlabSupport - matlab WormView.m +PythonView +**Note:** to get the objects shown in the visualisations above, change the following values in [worm.cc](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/Model/worm.cc), then remake the main program, and rerun: +``` +#define DURATION 20 //duration of simulation (in seconds) +#define MEDIUM 1.0 //change in the range 0.0 (water) to 1.0 (agar) +#define OBJECTS 20 //set number of objects (>= 0) +#define 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WormSim/README.md | 1 - 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/WormSim/Model/Test.ipynb b/WormSim/Model/Test.ipynb index 4f6d41c..26da3fd 100644 --- a/WormSim/Model/Test.ipynb +++ b/WormSim/Model/Test.ipynb @@ -19,18 +19,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df410ab3df4442feb89a6442755a9178", + "model_id": "2722f7fd8d1243faab072ddc29a70cc5", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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ERESkMV7vA0hERORuQgC//QYUFABlZUBFBRAUBAQHA61aAc2bAzqd0lESqQcTQCIi8jmnTgHr1gHbtgFbtgDbtwOlpdWXb9wYiI8HevUCevYEbr8daNDAe/ESqQ0TQCIi8hlbtgALFgBLlgCVlUBAgPx6NadPA2vWAN9+C1gsQFgYkJYGPPoo0L695+MmUhv2ASQiIlWz2YD33we6dwcSEoCPPrqY9NUk+bMTQiZ/AHD2LDB/PtChA3DbbUBmptvDJlI1JoBERKRav/0G9OkDjBgB7Nkjt9mTuPqyn+fHH4GUFGDwYODkSfecm0jtmAASEZHq2GzAG28AnTsDmzdf3OYJVqv8+tlnQLt28iuRv2MCSEREqnLqlGyWffxxOaLXXTV+V2O1AsXFwN/+BgwdKq9N5K+YABIRkWocPQrcdBOwcaMy17cvjfDJJ0C/frKvIJE/4ihgIiJShYIC4OabZRJob5ZVis0G/PQT8Ne/ypHD/jxljBDAsWNATg6wbx9w7hxgNALR0cCNNwI33CDnUyT/wgSQiIgUZ2/2PXrUe02+V2O1Alu3AgMGACtXyoml/cnJk8DixXI09O+/y20GA6D//7ZBi0Umh0YjcNddwPjx8v+IE2r7BzYBExGRooQAUlOBX35RT/JnZ7UC69cDzzyjdCTuY7EA//kPEBsLTJ58MfkD5P1WVsqXvTncYgG++kpOnt2jB7B7tzJxk3sxASQiIkUtXiwnaVa62bc6Nhswdy7www9KR1J/Bw7I1VCmTpVJXk1HVtsT89xcIC5OJpD2BJF8ExNAIiJSzB9/AE88oXQUV2cwAMOHy/5xvmrPHqB3b/m1rsmbxSJf//wnMHas56bmIc9jAkhERIoQQi7HVl6udCRXZ7XKZPUf/1A6kro5dEj23yspcV9N6zvvAM8+655zkfcxASQiIkV88QWwdq36+v1Vx94UnJurdCS1Y7PJ2kt3Jn92L78s/w/J9zABJCIiRcydK5tWfYnRCCxcqHQUtfPmm3K5O08k2nq9XKbPbHb/ucmzmAASEZHX5ecDGzaod+BHdSwWOWiltFTpSGqmvFwO+PAUmw0oKgLefttz1yDPYAJIRERe9+absjbNF124AHz4odJR1MxnnwFnznj2GjYbMG8eB4T4GiaARETkVefPA+++6zt9/1yZO1fpCGrmnXcuTuzsSYcPA99/7/nrkPswASQiIq/assV3mlBdEQLYv18un6ZmViuwebN3auYMBiAry/PXIfdhAkhERF6Vk+OdWilPy8lROoIr+/lnoKzMO9cSQi6bR77DD34FiYjIl2zb5vvryRqN6k8ADxzw3rVsNiAvz3vXo/pjAkhERF61ebPvjf69nNWq/hovb9X+KXU9qh8mgERE5DWlpXJVCl/nC02egYHevV5QkHevR/XDBJCIiLzm5EmlI3AfT0+vUl+tWnnvWjod0KaN965H9ccEkIiIvMafmgktFnU3ZXfuDAQEeOdaBgPQq5d3rkXuwQSQiIi8xpfn/nOlslLpCKoXGAjccIN3BtxYLEB8vOevQ+7DBJCIiLzG3/qJqf1+HnrIO9dp3Bjo29c71yL3YAJIREReExysdATuExio/ulsHnzQ80mqwQA88oj6k2FyxgSQiIi8JjbW+6NTPaV1a6UjuDqTCZg0yXOJqk4HhIQA48d75vzkOUwAiYjIa4xG2S/N1xmNQGKi0lHUzHPPAe3ayZo6dxMCeOMNoFkz95+bPMuodADkX8rLgd275Qz5Bw4AFy7IUX8Gg2z6CQ8HuncH4uLkX89qbz4hIvdLSAB27VL3AIqrsVrl+5gvCAoCPvoIuPlmmbC5a21gvR645x5g+HD3nI+8iwkg1YvNBnz7LfDpp3Ih8P375WgwnU7+hQzINxxAbhPi4ihAkwno2RO45RZg5EjOIUWkFT17AvPnKx1F/QjhOwkgIGP94gvg7rvle3B9k0C9HujTB1iyhH/I+yqdEPaPZ6ovs9mM8PBwlJSUwGQyKR2OR50+DaSnA/PmyVn9jca6Te+g08k3EpsN6NdP9iNJTvZMUwURqUNuLtCli9JR1I9eD5jNQIMGSkdSO99/D9x/v3wPr8schvb36wcfBN55x3cG9Wjp87mm2AeQauXECWD0aNnf4+mngd9/l9vrOreXEPJNSAjgm2+AlBRZE/jOOxdrDonIv3TqBHTs6Ls1R0YjMGCA7yV/gKy1278fGDJEfm+sYTugTidfjRoBn38OfPCB7yR/5BoTQKqxzz4D2rcHFi8GKirkX4HuTNLsf43+8QcwZgzwl79cTDCJyH/odMATTygdRd1ZLMC4cUpHUXeNG8sEbutWYNiwi6Oy9XqZENpbZi5dRaRdOznY47ffZPJLvo9NwG7kr1XMJ07IN7uMjIv9+LzBaJRvQK++KhNCX60tIKKqSkuBmBjg/HmlI6kdnQ649lrg55/95z3JbAa2bZOD93Jz5f+J0Qg0bSr7DsbF+XaNLeC/n8/1wUEgdEVZWbJZ1myW33vzzwWLRb4eeQRYvlzWQIaFee/6ROQ5DRsCo0YBb73le8vDPfGEbydDlzOZZIvLX/6idCTkTWwCpmqtXSvfEEpKlF/wfN06GcuZM8rGQUTu42vNwPY+cJz2hPwBE0Byae1a4I47Lvb1U5rVCmzfDvz5zzIhJSLf164d8MILvlObJoQcoBYernQkRPXHBJCqyMqSc0XZbOpI/uysVtk/5Y47fK/fEBG59vTTQI8e6p/6yWAABg0C7rtP6UiI3IMJIDk5eRK480711PxdzmoFNm8GHn9c6UiIyB2MRuD999VdC6jTyVo/X5+8muhSTADJybhxsolVjcmfnc0GLFoErFmjdCRE5A6dOwMvvaR0FFe2eDEQGal0FETuwwSQHD77DFi2TPkBHzWh18vl49gfkMg/PPUUMHGi0lG4tmCB7BZD5E+YABIA2fQ7erS6m2EuZbPJ+QnV+oFBRLWj0wEvvwyMHat0JM5efllORUXkb5gAEgBgyhQ5158vTQtutcqm4KwspSMhInfQ6YCFC4Fnn1U2Dr1exvLWW8CkScrGQuQpTAAJp0/LTti+0PR7OaMRmDtX6SiIyF10OmDWLPnHXYMGNV+r1l0MBiAqCsjMlCsQEfkrJoCE9HSgslLpKOrGYgE+/RQoKlI6EiJyp1GjgLw84LbbvHM9/f9/Gg4bBuzfD/Tr553rEimFCaDG2WzAvHm+1fR7OZsNePddpaMgIndr0UKO9n/3XVkbqNO5v5+yPfGLigK+/lqO9o2IcO81iNSICaDGffstcOiQ0lHUj80GvPGGbzZhE9GV6XTAQw8BR48Cr78OXHut3F7fpmH78TfeKLvAHDokJ5kn0gomgBr36afe72PjCQUFwJYtSkdBRJ4SHi4ngP/5Z2D9ejktS3Dwxf0BAVc+/tL9YWFyPd9t24CtW2Wz76XnItICP/jop/rIypL96HydTgfk5ACJiUpHQkSepNPJfoG33SZr/ffvl4lcTg6QnQ0cOwaUlcl+zYGBMrFr2xaIjwfi4uSrbduLTb9EWsUEUMPKy+Wbpz8wGOQHABFph8EgVxHp3BkYMULpaIh8C/8G0rA9e/yj9g+Q97F5s9JREBER+QbWAGpYTo5sTvHlEcCX+vln4Px5IDRU6UiIau/cOWDFCuCPP2R/tW7dgL/8hU2VROQZTAA17MABOQDEV+cAvJzNJkfydeqkdCRENXf+PDB1KvDOO8DZs/J3UgjZv61NG7nvoYeUjpKI/A0TQA27cEHpCNzPH++J/NfZs7KWLydH/gEDOHfLOHgQSEuTtduzZikTIxH5JzYuaFhZmf80/9qVlSkdAVHNPfwwsH37xeSvOi+9BCxZ4p2YiEgbFEkA58+fj9atWyM4OBgJCQnYcpUJ3DIyMtChQwcEBweja9euyMzMdNovhMC0adPQrFkzhISEICkpCQcOHHAqc/r0aaSmpsJkMiEiIgJpaWk4e/asY/+hQ4eg0+mqvDb78cgCg0HpCNzPH++J/NPvvwPLltVsAnO9Hpg50//+YCMi5Xg9Afzkk08wceJETJ8+Hdu3b0e3bt2QnJyM48ePuyy/adMmDBkyBGlpadixYwcGDBiAAQMGYO/evY4ys2fPxty5c/Hmm28iOzsbDRo0QHJyMsouqQ5KTU1Fbm4u1q5di5UrV+KHH37AGBcrfX/77bcoKChwvOLi4tz/EFQiONj9yyopjZO5kq94992aD/Cw2YC9e+WkxUREbiG8LD4+XowbN87xvdVqFbGxsWLmzJkuyw8aNEikpKQ4bUtISBBjx44VQghhs9lETEyMmDNnjmN/cXGxCAoKEh9//LEQQoh9+/YJAGLr1q2OMqtWrRI6nU4cPXpUCCHEwYMHBQCxY8eOOt9bSUmJACBKSkrqfA5veu45IYxGIWS9gn+8fvtN6adKVDMDBwqh09Xu53vxYqWjJvJNvvb57A1erQGsqKhATk4OkpKSHNv0ej2SkpKQlZXl8pisrCyn8gCQnJzsKH/w4EEUFhY6lQkPD0dCQoKjTFZWFiIiItCzZ09HmaSkJOj1emRnZzud++6770bTpk1xyy234Msvv6zfDavcDTf4zzyAgFwsvlUrpaMgqpmr9ftzRSvrXdtsckBXaan/zFJApDZeHQV88uRJWK1WREdHO22Pjo7G/mqWpCgsLHRZvrCw0LHfvu1KZZo2beq032g0onHjxo4yYWFheOWVV3DzzTdDr9fjs88+w4ABA7BixQrcfffdLmMrLy9HeXm543uz2XzF+1cbf2vdvvFGzplGvqNNG9lntTZ/hLVt67l4lHDqlBwBnZMjl3PLzgaOH6+a9On1QEiInOIpIeHikm4dO/rHWuZESuCvzv+LjIzExIkTHd/36tULx44dw5w5c6pNAGfOnIkZM2Z4K0S3a90aMJkAH8tbXQoIkGt9EvmKtDTgv/+teflWrYA+fTwXjzfYbMA33wCLFwM//STX7QUuDt6qrobTZpMTZW/dCuzceTFBDAoCunYF7roLGD0aaNbM47dA5De8Wl8SGRkJg8GAoqIip+1FRUWIiYlxeUxMTMwVy9u/Xq3M5YNMLBYLTp8+Xe11ASAhIQG//PJLtfunTJmCkpISx+uPP/6otqwa6XRAr17+MRCkshK4pIWfSPU6dQJuu63mI9efesp3a7hPnQJeflnWevbvD3z22cXkD5CJX02bty+tHSwvlzWHM2YALVoA998PfPcdR0sT1YRX304CAwMRFxeHdevWObbZbDasW7cOiYmJLo9JTEx0Kg8Aa9eudZRv06YNYmJinMqYzWZkZ2c7yiQmJqK4uBg5OTmOMuvXr4fNZkNCQkK18e7cuRPNrvAnZVBQEEwmk9PL19xyi+9+qFyud2+lIyCqnSVLgObNr9yMqdPJxObxx70Xl7ts2waMHClr5p59Fjh8WG53d19Gm02ec8UKmVR36AC88YbsQ0hE1fD2qJOlS5eKoKAgkZ6eLvbt2yfGjBkjIiIiRGFhoRBCiGHDhonJkyc7ym/cuFEYjUbx8ssvi7y8PDF9+nQREBAg9uzZ4ygza9YsERERIb744guxe/ducc8994g2bdqICxcuOMr069dP9OjRQ2RnZ4uffvpJXH/99WLIkCGO/enp6WLJkiUiLy9P5OXliRdffFHo9XqxaNGiGt+bL44yOniw9iMR1fYyGIS47TalnyRR3RQWCnHXXfL30GCQI/MDAuT3oaFC/OMfQlgsSkdZOyUlQoweLX8/lZhpQKeTr2bNhFizRumnQWrgi5/Pnub1BFAIIebNmydatmwpAgMDRXx8vNi8ebNjX58+fcSIESOcyi9btky0a9dOBAYGis6dO4uvv/7aab/NZhPPPfeciI6OFkFBQeL2228X+fn5TmVOnTolhgwZIsLCwoTJZBKjRo0SpaWljv3p6emiY8eOIjQ0VJhMJhEfHy8yMjJqdV+++gN2xx3yg0fpRK4+r+XLlX6KRPVz8KAQM2YI8fDDQjz6qBBvvinEJW9RPuObb4SIjVXHe4peL7+mpcmklLTLVz+fPUknhBDK1kH6D7PZjPDwcJSUlPhUc/CqVcAddygdRd1FRwNHjnA0IJGSzGbg738H3nlHdiupyzQ3nmIwAE2bAunpQN++SkdDSvDVz2dP8pPeX1QfycmyA7Uv0uuBceOY/BEpadMmOSXLu+/K79WU/AGyf2BRkXyvGzMGqKhQOiIi5TEBJOj1wLRpSkdRezod0LAhMHas0pEQadfq1cBf/gIUFqov8buUPbZ33wXuvhs4f17ZeIiUxgSQAMg5yW67zbdq0oQAFiyQTTtE5H0ZGXIOvspKdSd/l7LZgLVrgaQkoKRE6WiIlMMEkADI2rTFi+WEyr7AaJR/xQ8ZonQkRNr0xRfAAw/I5lVfSf7sbDZgyxY5JyFrAkmrmACSQ6tWwKuvKh3F1el0ct3ft97yj0msiXzNunXA3/52cbytL7Ja5dJz997LPoGkTUwAycmYMbKjdE1XJ1CCEMDbbwNXWMSFiDwkP182+9psvpv82dlswLffAo8+qnQkRN7HBJCc6HRymaa4OPUmga+9BgwapHQURNpjtQLDh/tWn7+rsdmARYuAr79WOhIi72ICSFU0aCBH9nXpor4k8F//Ap58UukoiLTptddk3zmLRelI3EuvBx56CDhzRulIiLyHCSC51KgR8P33QEKC8msF2/v5vfYaMHWqoqEQaVZ+PvCPfygdhWfYbMCpU8BTTykdCZH3MAGkaoWHy+kSHnpIfq9EImg0AiYTsGwZa/6IlGJv+vWXZl9XrFbgvffYFEzawQSQrig0VC7ttGaNXHLNW03C9lq/O+4A9u8H7r/fO9cloqpef90/m34vZ28KLi5WOhIiz2MCSDXSty+QlweMHCm/9+SE0Xq9rPX76CNgxQqO9iVSUlmZ7HurBTYbcPIk8L//KR0JkecxAaQaCw+Xb4ybNskaOaPRfc3C9prF6Gjg+eeBn38Ghg7lPH9ESvv0U23ViNlswLx5/t3cTQQwAaQ6SEwEliwBjhwB/v1vIDZWbtfpalczeOmqI336AMuXy3M+9xyXdyNSi7lzlR8I5m2HDwPffKN0FESepRPC16fyVA+z2Yzw8HCUlJTAZDIpHY7XWK3A1q1ATo58bd4sRwxW9xd0WBhw441AfLycb7B3b6B1a6+GTEQ1sGOH/F3VGqNRToi/cqXSkZC7aPXz+Uo82JOLtMJgkElc794Xt124ABw6JL9euCDLhITIZuSWLbVXo0Dki+bPl8mQvw/+uJzFAmRmyvcw/nFK/ooJIHlESAjQsaPSURBRXRUXAx9+qL3kz06vl+uNz5ypdCREnsF6GCIiquK774DycqWjUI7VKmchIPJXTACJiKiKbds8O92TL/j5Z+D8eaWjIPIMJoBERFTF1q2yFkzLbDZg1y6loyDyDCaARETkRAiZAGp9jgidTs5sQOSPmAASEZGTI0eAM2eUjkJ5BgMTQPJfTACJiMgJkx7JYpHzmhL5IyaARETkZO9eDgCx+/ln9oUk/8QEkIiInJSWch1uO5sNKCtTOgoi92MCSERETi5cUDoCdeHzIH/EBJCIiJyUlXEE8KVYA0j+iAkgERE5MRjYBHwpg0HpCIjcjwkgERE5CQlROgJ14fMgf8QEkIiInAQHKx2BuvB5kD9iAkhERE5iYjj1iV1YGBAUpHQURO7HBJCIiJzceKOc/oTks2B/SPJHTACJiMhJ9+5MegAgIACIj1c6CiLPYAJIREROwsKAa69VOgrlVVYCcXFKR0HkGUwAiYioit69Of0JwASQ/BcTQCIiqiIujpNBN2jAmlDyX0wAiYioivh4bQ8E0euBXr3kVyJ/ZFQ6ACIiNamoAPLzgZIS2QQaGwu0bKm9QRG9ewOtWwOHDikdiTJsNmDUKKWjIPIc/m1DRJp34gQwaxbQo4ds9rvhBuDWW4GbbpJJUKNGQEoK8PnngMWidLTeodcDjz+uvcTXLjwcGDRI6SiIPIcJIBFp1rlzwIQJspbvn/8Edu50neCVlABr1gD33SdrAz/7zNuRKmPkSDkVitYYDMCYMVwBhPwbE0Ai0qTsbKBTJ2DePJn0Xa2/m31ljMJC4G9/k7VDpaWej1NJjRsDQ4cCRo11FrLZgLFjlY6CyLOYABKR5qxfD/TpAxw9WvuBDvaRscuXA3/+s6wd9Gfjxmmn2RuQtX99+3L0L/k/JoBEpCl79sj+fJWV9Vvv1moFdu0C7rrLv9fN7dlTTgmjlTkBrVbgiSeUjoLI85gAEpFmVFYCqak1a/KtCasV+PFH4LXX6n8uNVu4UBtzAhqNwB13AP37Kx0JkecxASQizXj1VWDvXvc3af7jH/49XUqvXsCzz/r3iGCdDggJAd55x7/vk8iOCSARaUJlJfDKK56pybJagQUL3H9eNZk+Hbj+ev9tChYCeOMNOSKcSAuYABKRJnz1FXD8uGfObbUCb78NlJV55vxqEBQEfPihfzYF25t+hw1TOhIi72ECSESasGaNZ6czKSkBtm/33PnVwB+bgtn0S1rFBJCINCE727PTmeh0QE6O586vFtOny2Xi/Kkp+P332fRL2sMEkIg0IT/fs+c3GoG8PM9eQw2CgoDMTDmJtj9MEP3uu8CAAUpHQeR9TACJSBMqKjx7fiGACxc8ew21iIgAvv0WaNXKt2sCX30VGDVK6SiIlMEEkIg0wdO1VTodEBjo2WuoSdOmwKZNQMeOvpcE6nRy1PaECUpHQqQcJoBEpAmtW3v2/FYrcN11nr2G2jRtKifC7tUL0PvAp4nBIOP88EPg0UeVjoZIWT7wK0tEVH+JiZ6tBbTZ5LJpWhMRIddWnjhR1qyptTZQr5dN1j/+CAwdqnQ0RMpjAkhEmvDnP3t2FHBwsDYTQEBOozJnDrBxo0yy1FQbaDDIxHTCBLkKzE03KR0RkTqo6NeUiMhzBg0CGjb0zLmNRmD4cM+d31ckJsokSy21gfZav59+kqvAhIQoGw+RmjABJCJNCA2V/b48UTtltQLjx7v/vL7o0trADh3kNm9PF2MfkPPUU6z1I6oOE0Ai0oznnpMT/rozCdTrgUmTgK5d3XdOf5CYCOzZI/vc/e1vMgn0ZNOwPcls3Rp4+WWgoEB+Za0fkWtMAIlIM8LCgA8+cN96tkajHPn7wgvuOZ+/0emAW24BPv4YOHIE+Ne/gGbN5D77iNz6CAi4eJ3+/eVyf7/+KpugGzeu37mJ/J1OCH9c2lsZZrMZ4eHhKCkpgclkUjocIqrG++8DI0fKf9f1HdBoBKKj5Vx4LVu6LTS/Z7EAGzbIpfm2bZNfCwvlPntSKMTFl0538VVZKcsFBMga14QEIC4O+Otf+X9AV8bP56oUqQGcP38+WrdujeDgYCQkJGDLli1XLJ+RkYEOHTogODgYXbt2RWZmptN+IQSmTZuGZs2aISQkBElJSThw4IBTmdOnTyM1NRUmkwkRERFIS0vD2bNnncrs3r0bt956K4KDg9GiRQvMnj3bPTdMRKoyfDiQkQE0aFC3gQo6HdCtG7B5MxOP2jIaZcI2dSqwYoVsqi0qksvLPf88MHq0/P8ZPBgYOBBITQUeekj2sfzf/4AdO4Bz5+S6ywsWAGlp/D8gqhPhZUuXLhWBgYFi0aJFIjc3V4wePVpERESIoqIil+U3btwoDAaDmD17tti3b5+YOnWqCAgIEHv27HGUmTVrlggPDxcrVqwQu3btEnfffbdo06aNuHDhgqNMv379RLdu3cTmzZvFjz/+KK677joxZMgQx/6SkhIRHR0tUlNTxd69e8XHH38sQkJCxFtvvVXjeyspKREARElJSR2eDBF525EjQvTvL+uajMZL651cv/R6IQIDhZg1S4jKSqWjJ6Ka4udzVV5PAOPj48W4ceMc31utVhEbGytmzpzpsvygQYNESkqK07aEhAQxduxYIYQQNptNxMTEiDlz5jj2FxcXi6CgIPHxxx8LIYTYt2+fACC2bt3qKLNq1Sqh0+nE0aNHhRBCLFiwQDRq1EiUl5c7yjz77LOiffv2Nb43/oAR+aacHCHS0oQwmapP/q6/Xog5c4Q4eVLpaImotvj5XJVXm4ArKiqQk5ODpKQkxza9Xo+kpCRkZWW5PCYrK8upPAAkJyc7yh88eBCFhYVOZcLDw5GQkOAok5WVhYiICPS8ZJbWpKQk6PV6ZGdnO8r86U9/QuAli3kmJycjPz8fZ86ccRlbeXk5zGaz04uIfM+NN8rmxeJiOYhg+XIgPV0OGFm3DjhzBvj5Z+DvfweaNFE6WiKi+vPq7EwnT56E1WpFdHS00/bo6Gjs37/f5TGFhYUuyxf+f69h+9erlWnatKnTfqPRiMaNGzuVadOmTZVz2Pc1atSoSmwzZ87EjBkzqr9hIvIpOh3Qtq18ERH5M04DUw9TpkxBSUmJ4/XHH38oHRIRERHRVXk1AYyMjITBYEBRUZHT9qKiIsTExLg8JiYm5orl7V+vVub48eNO+y0WC06fPu1UxtU5Lr3G5YKCgmAymZxeRERERGrn1QQwMDAQcXFxWLdunWObzWbDunXrkJiY6PKYxMREp/IAsHbtWkf5Nm3aICYmxqmM2WxGdna2o0xiYiKKi4uRk5PjKLN+/XrYbDYkJCQ4yvzwww+otE809f/Xad++vcvmXyIiIiKf5e1RJ0uXLhVBQUEiPT1d7Nu3T4wZM0ZERESIwsJCIYQQw4YNE5MnT3aU37hxozAajeLll18WeXl5Yvr06S6ngYmIiBBffPGF2L17t7jnnntcTgPTo0cPkZ2dLX766Sdx/fXXO00DU1xcLKKjo8WwYcPE3r17xdKlS0VoaCingSEiIvJx/HyuyusJoBBCzJs3T7Rs2VIEBgaK+Ph4sXnzZse+Pn36iBEjRjiVX7ZsmWjXrp0IDAwUnTt3Fl9//bXTfpvNJp577jkRHR0tgoKCxO233y7y8/Odypw6dUoMGTJEhIWFCZPJJEaNGiVKS0udyuzatUvccsstIigoSDRv3lzMmjWrVvfFHzAiIiL14edzVVwKzo241AwREZH68PO5Ko4CJiIiItIYr84DSERE2lBWBpSUyK+VlUBQEBAcDDRqJNcDJiJl8deQiIjq5fx5YOdOICcH2LYN2LwZ+OUXwGarWjYoCLjhBiAhAYiLk6+OHZkUEnkbf+WIiKhOtm0D5s8HliwBKirkSioGA2CxVH9MeTmwdatMGO2zbkVGAo8+CowZA1xzjVdCJ9I89gEkIqIau3BBrpN8441Ar17Ahx/K5A8AhLhy8nepS6ZcxcmTwH/+A7RqBdx7r1x/mcMTiTyLCSAREdXIN98A114LjBoF7Nolt9U04bsaq1U2Ga9cCSQlAbfeCvz6q3vOTURVMQEkIqIrMpuBhx8GkpMB+4qZrvr3uYM9oczOBrp0AebN89y1iLSMCSAREVXrm2+ADh1ksy/gvWTMYpEjiJ94AvjTn1gbSORuTACJiMilN964WOtntSoXR3a27HO4ebNyMRD5GyaARERUxX/+Azz+uPy30k2wFgtw7hxw223A+vXKxkLkL5gAEhGRkzlzgH/+U+konFmtcrTxHXcAGzcqHQ2R72MCSEREDu++CzzzjNJRuGazyelj+vUDdu9WOhoi38YEkIiIAAB5eXJCZjWz2eRchPffLyeVJqK6YQJIRESwWoHhw31jAmarFThwAJgxQ+lIiHwXE0AiIsJ//yuXdnPXxM6eJgQwa5ZcVo6Iao8JIBGRxuXlqW/QR03o9cCDD7IpmKgumAASEWncpEnKT/VSF/am4IULlY6EyPcwASQi0rDffgNWr1Z2ouf6mjvXNxNYIiUxASQi0rC33pJNqb5KCODgQU4QTVRbPvxrT0RE9VFWJhNAX679AwCjUS5bR0Q1xwSQiEijMjKAkhKlo6g/iwX48kvgyBGlI9EWi0VOzE2+yah0AEREpIzly2Xzr7/0n/vyS+Cxx5SOwv+UlABZWUBOjpwqKDsbOH78Ys2xXg80bgz06iVfcXFA795A06bKxk1XxgSQiEijsrP9J/kzGGSCQu6TkwPMnw8sWSKn2jEYZJ/Ly39mbDbg5ElgzRpg7VpZM2gwAPfeC4wbB/TpA+h0ytwDVY9NwEREGnTiBFBQoHQU7mOxAJs3Kx2Ff1i1Stbi9ewJfPDBxXkWrdYr/8Fgs12cSNxqBVasAG67DWjfXp7HF1aZ0RImgEREGuSPtWX5+XJgC9XNmTPAiBHAHXcAO3fKbfVZGcZ+7C+/yGUGU1KAo0frHSa5CRNAIiINysmRzXT+xGoFdu9WOgrflJkJdOgAfPSR/N6dXQPsNX/ffCOv8d577js31R0TQCIiDcrPVzoCz/DX+/KkBQuAO++U/fg8OSWQ1QqcOweMHAlMmcImYaUxASQi0qDz5/1nAMilzp9XOgLf8uqrcqCGq8EdnmBP+mbNAiZMYBKoJCaAREQadOGC/3346vXyvqhm3n0XmDhRuevPnQs8/7xy19c6JoBERBrky8u/XYm/3pe77doFjB2rdBTACy/IUcfkffxVISLSoJAQ/5ubzWaT90VXVlkJPPig0lFIej0wahRQXKx0JNrDBJCISIMaNPC/UcAAEBqqdATqN3MmkJurjjWg7ZNIP/WU0pFoDxNAIiIN6tzZPweBdOmidATq9ssvstlVTf0/rVYgPR34/nulI9EWJoBERBoUF+d/CWBAANCpk9JRqNuCBeps+jca5Yhk8h4mgEREGnTjjUpH4H5du8okkFw7fx743//qt7qHp1gswJdfAkeOKB2JdjABJCLSoPBwoFUrpaNwn4AAICFB6SjU7eOPgdJSpaOonl4PvPWW0lFoBxNAIiKNSkz0n4EglZVAz55KR6Fuixers/nXzmqVcxOSdzABJCLSqAceUMdIUHcICADuuUfpKNTLapXrP6tp8IcrBQVAYaHSUWgDE0AiIo1KSQGaNVM6ivozGoGhQ4EmTZSORL327wfKypSOomZycpSOQBuYABIRaZTRKNeB9fXVMywW4LHHlI5C3XwlqTIYfCdWX+fjv/ZERFQfaWm+nQDq9UC3bkCvXkpHom67d/vGCGkh5DJ15Hk+/GtPRET1FRMDDBrku4NBbDZgwgR1D25QA7NZ/f3/APn/WVKidBTawASQiEjj5syRS6j5WhJlNMqRzMOHKx2J+pWX+0YCCMj5CsnzmAASEWlcbCwwb57vJAh2BgPw/vu+3YTtLUaj7yT4gYFKR6AN/LUhIiIMHw707y8TBV/x0kvAddcpHYVvCAnxnQSwQQOlI9AGJoBERASdTi4T5guJgr3p9/HHlY7Ed1x3nW/M+RgQAFx/vdJRaAMTQCIiAiCbgj/5RDapqjUJNBqBqChg6VI2/dZGXJwcYKF2lZUyVvI8/voQEZFD//7ARx8pHYVrBgPQsCHw3XdAy5ZKR+NbevRQb1J/OSaA3sEEkIiInAweDHzwgbpqAo1GoHFj4IcfgHbtlI7G94SFAddeq3QUVxccDLRvr3QU2sAEkIiIqkhNBT7/XCZeSg8MMRjkknVZWUCXLsrG4sv+9jd1z/doNMr1nNUcoz9hAkhERC7dfTewYwdwww3K1ATa+/g9+KBcHcIXarDUbOxYdfcDtFjk0oTkHUwAiYioWp07A9nZwH/+I2tovFU7YzAAkZHAypVAejrQqJF3ruvPWreWfTzVWMOm0wEdOgC33KJ0JNrBBJCIiK7IaAQmT5a1cN27X9zmCQaDTAYefBDYvx9ISfHMdbRq/Hj1TgfzxBPq6XOqBUwAiYioRjp1ArZulQMxBg6USaA7pmKxnyM8HJg4EThwgLV+ntKvH5CUpHy/zksZDLL2Ly1N6Ui0hQkgERHVmE4H3HqrnIfvjz+AF14AWrS4uL8mSaHBcLGMTienKElPBwoKgNmz2dfPk3Q6YNEidS23JoQcda6mmLRAJ4Svrf6oXmazGeHh4SgpKYHJZFI6HCIiryksBHJy5GvbNllTWFwMVFTIgQdGo/yAb9ZMruIRFydf3bvLuf3Iu/73P2D0aKWjkAnpP/8J/Otfnr0OP5+rYgLoRvwBIyKqymbjqh1qIwRw773AV18pNzLYYJC1vxs3er72j5/PVfFXkoiIPIrJn/rodLIZ/5ZblBkVbDDINX/XrGHTr1L4a0lERKRBwcFAZibw5z97N0k3GICOHYHvv5eru5AyvJoAnj59GqmpqTCZTIiIiEBaWhrOnj17xWPKysowbtw4NGnSBGFhYRg4cCCKioqcyhw+fBgpKSkIDQ1F06ZN8fTTT8NisTiV+e6773DjjTciKCgI1113HdLT0532P//889DpdE6vDh06uOW+iYiI1KhBA+Drr4Fhw+T33kgEk5KAH38Emjb1/LWoel5NAFNTU5Gbm4u1a9di5cqV+OGHHzBmzJgrHvPUU0/hq6++QkZGBr7//nscO3YM9913n2O/1WpFSkoKKioqsGnTJrz33ntIT0/HtGnTHGUOHjyIlJQU3Hbbbdi5cycmTJiAhx9+GGvWrHG6VufOnVFQUOB4/fTTT+59AERERCoTFCRHYX/5payR80STsNEok8133wVWrQIiItx/Daol4SX79u0TAMTWrVsd21atWiV0Op04evSoy2OKi4tFQECAyMjIcGzLy8sTAERWVpYQQojMzEyh1+tFYWGho8zChQuFyWQS5eXlQgghnnnmGdG5c2encw8ePFgkJyc7vp8+fbro1q1bve6xpKREABAlJSX1Og8REZESTp0SYtgwIQAhDAb5tT4vo1F+7ddPiMOHlbsvfj5X5bUawKysLERERKBnz56ObUlJSdDr9cjOznZ5TE5ODiorK5GUlOTY1qFDB7Rs2RJZWVmO83bt2hXR0dGOMsnJyTCbzcjNzXWUufQc9jL2c9gdOHAAsbGxaNu2LVJTU3H48OH63TQREZEPadwYeP99ORn3U0/JybmB2k0cbV/NJThYTu68c6es9bt0vkhSntfmAi8sLETTyxr8jUYjGjdujMLCwmqPCQwMRMRldcXR0dGOYwoLC52SP/t++74rlTGbzbhw4QJCQkKQkJCA9PR0tG/fHgUFBZgxYwZuvfVW7N27Fw2rmaSqvLwc5eXlju/NZvNVngIREZH6XXcdMGeOnOg7IwNYvx7YvFkmhleaNqZNG6B3bzlZ+NChFxNIUp96J4CTJ0/GSy+9dMUyeXl59b2Mx/Xv39/x7xtuuAEJCQlo1aoVli1bhrRq1qeZOXMmZsyY4a0QifyGEIDFAlRWyv5HalycnoiAkBBg+HD5AoDz5+Wa0AUFwIUL8nc5JASIipKTenOKPd9R7wRw0qRJGDly5BXLtG3bFjExMTh+/LjTdovFgtOnTyMmJsblcTExMaioqEBxcbFTLWBRUZHjmJiYGGzZssXpOPso4UvLXD5yuKioCCaTCSEhIS6vHRERgXbt2uGXX36p9r6mTJmCiRMnOr43m81owTpuIoczZ4Dt2+XKEDk5QHY2cPz4xdUh7AwGmQi2bu28SsQNN8hmJCJSh9BQ+TtKvq/eCWBUVBSioqKuWi4xMRHFxcXIyclBXFwcAGD9+vWw2WxISEhweUxcXBwCAgKwbt06DBw4EACQn5+Pw4cPI/H/fwITExPx4osv4vjx444m5rVr18JkMqFTp06OMpmZmU7nXrt2reMcrpw9exa//vorhtnHxrsQFBSEoKCgq947kVZUVADLl8vX5s1yrVjgYg2f1er6OKtV1izs2wf8/LNcq1QIeVz79sDNN8saiJtvln2LiIionrw54qRfv36iR48eIjs7W/z000/i+uuvF0OGDHHsP3LkiGjfvr3Izs52bHvkkUdEy5Ytxfr168W2bdtEYmKiSExMdOy3WCyiS5cuom/fvmLnzp1i9erVIioqSkyZMsVR5rfffhOhoaHi6aefFnl5eWL+/PnCYDCI1atXO8pMmjRJfPfdd+LgwYNi48aNIikpSURGRorjx4/X+P44yoi06vffhfjnP4Vo3Nh9owddjSTs2FGIhQuFMJuVvmMi8iX8fK7KqwngqVOnxJAhQ0RYWJgwmUxi1KhRorS01LH/4MGDAoDYsGGDY9uFCxfEY489Jho1aiRCQ0PFvffeKwoKCpzOe+jQIdG/f38REhIiIiMjxaRJk0RlZaVTmQ0bNoju3buLwMBA0bZtW7F48WKn/YMHDxbNmjUTgYGBonnz5mLw4MHil19+qdX98QeMtMRqFWLNGiHuuksInc79SZ+rl04nX6GhQowbJ8TevUo/BSLyBfx8rkonhBCKVkH6ES42TVqxZ49cOWDXLtlMW13TricZjXIgydChwNy5QJMm3o+BiHwDP5+r4lrARFRjlZXAiy8CN94I7N0rtymR/AEy+QOATz6R/QS/+EKZOIiIfBETQCKqkT17gF69gOeek8mXUonf5axWOdp4wAAgNRU4fVrpiIiI1I8JIBFdkcXiXOunxk4j9illPvkEaNeOtYFERFfDBJCIqlVWBtx7r/pq/apzaW3g7NlKR0NEpF5MAInIpdJSIDkZyMxUZ61fdey1gc8+C0yZ4luxExF5CxNAIqqitBT4y1+AjRuvvO6n2s2aBUyYwCSQiOhyTACJyElZGXDnncCOHepv8q2JuXOB559XOgoiInVhAkhEDhYLcP/9wE8/+UfyZ/fCC8CrryodBRGRejABJCKH2bOBr7/27Wbf6kyaBGzapHQURETqwASQiADIKV6mT/ff/nJ6vVy95MIFpSMhIlIeE0AiQmUl8OCDSkfhWVYrcOiQnNKGiEjrmAASEebMAXbvvri8mr+y2YD//pdNwURETACJNM7fm34vx6ZgIiImgESaZrPJZEhL7E3B06crHQkRkXKYABJp2Pr1wM6d/t/0ezmbDZg3DzCblY6EiEgZTACJNOyNNwCjUekolFFeDnzwgdJREBEpgwkgkUYdOQJ8+aX2av8uNXeudvo+EhFdigkgkUa9/bYcEKFVQgA//wz88IPSkRAReZ+G3/6JtKuiAli40L+We6sLoxGYP1/pKIiIvI8JIJEGff45cPKk0lEoz2IBli8HCgqUjoSIyLuYABJp0OefAwaD0lGog9Uq1z8mItISJoBEGrR5M5t/7QICgJwcpaMgIvIuJoBEGlNcDPz+u9JRqEdlpUyIiYi0hAkgkcZs3650BOqzd68cGENEpBVMAIk0JieH/f8uZ7EAublKR0FE5D1MAIk0hv3dXONzISItYQJIpDEcAFIVB4IQkdYwASTSmBMnlI5AfSorgaIipaMgIvIeJoBEGlNernQE6nT+vNIREBF5DxNAIg2xWNj8Wx0mgESkJUwAiTTEYlE6AvVizSgRaQkTQCINCQxUOgL1CglROgIiIu9hAkikIXq9HPFKVYWGKh0BEZH3MAEk0pigIKUjUB+djgkgEWkLE0AijWnbVukI1MdoBK69VukoiIi8hwkgkcYkJLAZ+HKVlUBcnNJREBF5DxNAIo2Ji+NoYFeYABKRljABJNKYuDhACKWjUJeGDdk0TkTawgSQSGO6dpV93uiiuDg5EISISCuYABJpTFAQ0KmT0lGoR0AAEB+vdBRERN7FBJBIg266ibWAdpWVTACJSHuYABJp0PDhHAhiZzIBd9yhdBRERN7FBJBIg3r3Brp0kSuDaJnBAIwZw2XgiEh7NP72T6RNOh3wxBOAzaZ0JMqyWoGxY5WOgojI+5gAEmnU0KFAgwZKR6EcgwFISgKuu07pSIiIvI8JIJFGNWgApKVpdzCI1Qo8/rjSURARKYMJIJGGPfqoNgeD6HRAbCyQkqJ0JEREymACSKRhHToAw4bJ5lAtEQKYOVN7901EZMcEkEjjXn8daNxYOyOCDQY57cuwYUpHQkSkHI285RNRdRo1AhYt0saIYJ0OCA0F3nmHS78RkbZptPs3XaqgAMjJAQ4dAi5cAMrK5IdjSIh8tWsn10pt1EjpSMlT7rxT1ogtWSIHR/grIYA33pD9/4iItIwJoMbYbMCGDcBPPwHbtgHZ2cCJE3KfTiebAfV6+UEphCwvhNzfogWQkAD07AncdhvQqxdrUfzJ668Dq1cDp075Z22gwQAkJ7Ppl4gIAHRC2D/eqb7MZjPCw8NRUlICk8mkdDhOTp0CFi+WtR+//y6n/rDZav9Bb+80b7UCXbvKyYSHDpXNauT7Vq4E7rpL6SjcT68HwsKAvDzW/hFpkZo/n5XCPoB+LicHGDkSaNYMePZZmfwBcuqPutTyWK0Xmwhzc4HRo4HoaGDCBOCXX9wVNSnlzjuBl19WOgr30uuBwEBg1Somf0REdkwA/dS5c3KS2549gY8+Aior3d+sZz/f2bPA/PlAx45yag0tzivnTyZNAqZOVToK97B3a/jiC+Cmm5SOhohIPZgA+qHvvwc6dQIWLJDfeyMhs1jk65//lP0Ec3M9f03ynBdeACZOVDqK+tHrZZeFZcuAvn2VjoaISF2YAPoRe63fn/8MHD2qTEd+IYBdu4Du3Vkb6Mt0OtkUPGOG0pHUjb3Z9+uvgXvvVToaIiL1YQLoJ06eBG699WKtn5JTeVitF2sD77tPTitDvkenA6ZNA+bOlf/2lVUzjEY54GPdOtb8ERFVhwmgHzh6VPZv2r1bXdN3CCFrYPr1A0pLlY6G6urxx4GsLKBNG3WvFmKfkig5Gdi/n33+iIiuRMVv51QTx48DffoABw+qcwJfm03OOXjnnawJ9GUJCcCePcDf/67O2kCDQdb6vf8+8NVXctQ7ERFVz6sJ4OnTp5GamgqTyYSIiAikpaXh7NmzVzymrKwM48aNQ5MmTRAWFoaBAweiqKjIqczhw4eRkpKC0NBQNG3aFE8//TQsl3Q+KygowNChQ9GuXTvo9XpMmDDB5bUyMjLQoUMHBAcHo2vXrsjMzKz3PXtSSQmQlCRX8FBzXzurVSaB998vRyOTbwoOBl56SV21gfZav379gPx8OckzJycnIro6r76Fp6amIjc3F2vXrsXKlSvxww8/YMyYMVc85qmnnsJXX32FjIwMfP/99zh27Bjuu+8+x36r1YqUlBRUVFRg06ZNeO+995Ceno5p06Y5ypSXlyMqKgpTp05Ft27dXF5n06ZNGDJkCNLS0rBjxw4MGDAAAwYMwN69e91z8x4wZgywb586a/4uZ7PJ5uB//1vpSKi+7LWBkyfLCcB1Ou8nXfYayNhY1voREdWJ8JJ9+/YJAGLr1q2ObatWrRI6nU4cPXrU5THFxcUiICBAZGRkOLbl5eUJACIrK0sIIURmZqbQ6/WisLDQUWbhwoXCZDKJ8vLyKufs06ePePLJJ6tsHzRokEhJSXHalpCQIMaOHVvjeywpKREARElJSY2PqatPP7Uv1uZbL4NBiO3bPf54yEvMZiHefFOIjh3l/6/R6PmfH51OiORkIb76SgiLReknQES+wJufz77CazWAWVlZiIiIQM+ePR3bkpKSoNfrkZ2d7fKYnJwcVFZWIikpybGtQ4cOaNmyJbKyshzn7dq1K6Kjox1lkpOTYTabkVuLyeiysrKcrmM/j/06rpSXl8NsNju9vOHECbkCh682dT34IFBRoXQU5A4NGwJjx8p5H3/8Efjb3+QoXAAICHDPNeznCw+XcxMeOCDXLL7zTvX1RSQi8hVeSwALCwvRtGlTp21GoxGNGzdGYWFhtccEBgYiIiLCaXt0dLTjmMLCQqfkz77fvq828bk6z5XOMXPmTISHhzteLVq0qPH16mPcOMBslnUivsZqleuxsinYv+h0wC23AB9/DBw5Arz7rvwjJS5OzsdnFxBQ/R8uer1z0tiwIfCXv8iVST77DCgoAGbPBq691rP3QkSkBcb6nmDy5Ml46aWXrlgmLy+vvpdRpSlTpmDiJcslmM1mjyeBK1YAGRkevYTHCQG8+CIwcCBQTZdM8mHR0cBDD8kXIAf+7Nsn16XOyQGKioDz5+WrvFz2IwwNBUJCgLZtZdLYsyfQurXv1nITEaldvRPASZMmYeTIkVcs07ZtW8TExOD48eNO2y0WC06fPo2YmBiXx8XExKCiogLFxcVOtYBFRUWOY2JiYrBlyxan4+yjhKs7b3XXunx08aXXcSUoKAhBQUE1voY7vPiirClR03x/daHXA6+8Ijvwk38LCJCJfrduF5NCIiJSVr2bgKOiotChQ4crvgIDA5GYmIji4mLk5OQ4jl2/fj1sNhsSEhJcnjsuLg4BAQFYt26dY1t+fj4OHz6MxMREAEBiYiL27NnjlFyuXbsWJpMJnTp1qvF9JCYmOl3Hfh77ddQgJwfYts33kz9ATluzdKlcwYSIiIi8y2t9ADt27Ih+/fph9OjR2LJlCzZu3Ijx48fjgQceQGxsLADg6NGj6NChg6NGLzw8HGlpaZg4cSI2bNiAnJwcjBo1ComJiejduzcAoG/fvujUqROGDRuGXbt2Yc2aNZg6dSrGjRvnVDu3c+dO7Ny5E2fPnsWJEyewc+dO7Nu3z7H/ySefxOrVq/HKK69g//79eP7557Ft2zaMHz/eW4/oqhYsuNgh3h9YrcDixUpHQUREpEHeHHJ86tQpMWTIEBEWFiZMJpMYNWqUKC0tdew/ePCgACA2bNjg2HbhwgXx2GOPiUaNGonQ0FBx7733ioKCAqfzHjp0SPTv31+EhISIyMhIMWnSJFFZWelUBkCVV6tWrZzKLFu2TLRr104EBgaKzp07i6+//rpW9+fJYeanTwsRFKT8NC7ufrVsKYTV6vbHRURE5MBpYKrSCeGLY0nVyWw2Izw8HCUlJTCZTG499+uvA0895Zsjf69m1Sq5kgMREZEnePLz2VepYDEnqonVq5WOwDMCAoBvvlE6CiIiIm1hAugDhAC2bvXP2r/KSuCyQdxERETkYUwAfcDRo8CpU0pH4Tnbt/vHyGYiIiJfwQTQB1wyc45funAB+PlnpaMgIiLSDiaAPiAnx7+mf3HF35NcIiIiNWEC6AP27ZNz5vmrgAB5j0REROQdTAB9wLlz/jkA5FLnzysdARERkXYwAfQB584pHYFnCSH7ARIREZF3MAH0ARaL0hF4nj83cRMREakNE0AfEBqqdASepdMBwcFKR0FERKQdTAB9QIMGSkfgeSEhSkdARESkHUwAfUDz5nKkrL+yWuU9EhERkXcwAfQBPXvKJdP8lc0m75GIiIi8gwmgD4iLUzoCz9Lrge7dlY6CiIhIO5gA+oBOnfy7Cfjaa7XRz5GIiEgtmAD6gIAAoGtXpaPwDKMR6N1b6SiIiIi0xc9XmPUft94K7N7tf3MCWq1AYqLSUZCaWK3A8eNycnAh5Ajx6GjAYFA6MiIi/8EE0EeMGgW8/rrSUbhfYCDwwANKR0FKqqgAVq4EfvwR2LIF2L4dKCtzLhMcDPToAcTHAzffDNx9NxAUpEy8RET+QCeEv68y6z1msxnh4eEoKSmByWRy+/kTE+UHpM3m9lMrwmgEhg0DFi1SOhJSwuHDwNtvA2++CZw6Jbs6XG20u71Mo0bAI48AY8cCrVp5J14i8l2e/nz2RewD6EMef9x/kj9ANmePG6d0FORtFRXAc88BbdoAs2bJ5A+o2VRH9jJnzgCzZwNt2wL/+AdQXu65eImI/BETQB8ycKCs+fAHej1w443+P8UNOcvJkVP+vPii/GOmPmtAW63yHLNmATfcAGzd6rYwiYj8HhNAHxIUBDz2mEyefJ3NBkyYoHQU5E1LlwIJCcDPP8vBHe4iBPDrr3I0+QcfuO+8RET+zA9SCW2ZPFkum+bLSaDRKDvyp6YqHQl5y/vvA0OH1r/Wrzr22sDhw4H//c/95yci8jc+nEZoU1iY/DD15b6ARiPw3nu+ncRSzX35JTBypKyp88aQszFjgM8+8/x1iIh8GT+CfdCf/+zbTcGzZ8vVP8j/FRbKkd7eJIRMOI8e9e51iYh8iY+mEPTSS77XFGw0ArfcwpG/WiGErI07d847NX+XKisDHn7Y+9clIvIVPpQ+0KXCwmSneoMB0OmUjubqDAagYUPZfO1LSSvV3SefAF995Zk+f1djsQCrVwMffuj9axMR+QJ+FPuwm26SfZ3UngDq9XIlh3Xr5Nxv5P+EAGbMUPZnU6eTMbAWkIioKiaAPu6uu4CPPpJJlhoTQYNBJn9r1silvEgbfvoJ2L9f2eTLPj3Mhg3KxUBEpFZMAP3AAw8An38u+9ipqXnV3uz7ww9y2hfSjvnz5c+j0oxGYN48paMgIlIfFaULVB933w2sXQtERcnES2k6HXD99cCmTVztQ2ssFvkHicWidCQyhpUruVQcEdHlmAD6kT59ZLPb8OHyeyVqAw0Ged1//hPYuRPo2NH7MZCy9u2T6/2qhcUC7NmjdBREROrCBNDPREQAixYBmZnerw201/pt2QL8619y6TrSnpwcpSNwptOpLyYiIqUxAfRT/fsD+flyzr2QEPkh6IlBIvZaxkaN5IjLnTvZ5Kt1O3YAAQFKR3GR0ShjIiKii5gA+rHwcOD11+VqDG+8IWvnAPd0zrefIz5ejkIuKACee461fgScPq2upQotFhkTERFdxARQA0wmuXTc/v3A998DgwYBsbEX99v77VXn0v06HdC2rVxlYccOICsLGDqUiR9dVF6urgRQCA4CISK6nAomaiBv0emAP/1JvgDg5Elg+3bZP2rbNuDAAeDCBbmMlk4n5+8LDQW6dJHNunFxci6/hg2VvQ9St8BA+QeDEiuAuKLTyZiIiOgiJoAaFhkJ9O0rX0TuEhGhrgTQYJAxERHRRWwCJiK36t4dqKxUOoqLrFYZExERXcQEkIjcSm2jwIVQX0xEREpjAkhEbtWli7qmgdHrgW7dlI6CiEhd2AeQiNwqMFDOQ5mZqfxycAYD8Ne/yrkwidQqNxd4913g11/liPUmTeTynvfeywFM5DlMAInI7caPB778UukoZP+/8eOVjoLItc2bgaefBn76Sc6tav+DyWAAliyRieDEicCzz6pjjXfyL2wCJiK3u/12oE0bz6w+UxstWgD9+ikbA5ErX30l12/ftEl+f2ltuX0E/alTwNSpwP33q2tgFfkHJoBE5HZ6PfCPf8gBGEqaMoU1J6Q+W7YAf/ubTOquNmm6EMCKFXJZTyJ3YgJIRB6RliZrONyx9GBtGY3AzTcDY8d6/9pEVzN5sqzlq+kfSEIA77wD5OV5Ni7SFiaAROQROh2Qnq7MiGCjEXjvvSsvcUikhPx8YMOG2k+UbjQCb77pmZhIm/j2SEQe07o18Pbb3r/uggXAtdd6/7pEV7N4cd1qxS0WOVJY6ZH15D+YABKRRz34IPDqq9673ksvAaNGee96RLVx8ODV+/1V59w54MwZ98ZD2sUEkIg8bsIEYP58+W9PNMvaz/naa8Azz7j//ETuUlFRv8FRFRXui4W0jQkgEXnFY48B334LNGvm3iTQYACio4E1a4Ann3TfeYk8oUmT+o1Mb9TIfbGQtjEBJCKvuf12OZJxzBj5fX1GCNuPfeghYP9+oG/f+sdH5GkDBtStH5/BACQlAaGhbg+JNIoJIBF5VcOGwMKFwO7dwMMPA8HBcsRwTZJBg0GWDQ6Wid+OHXKQicnk+biJ3KF/f+Caa2p/nNUKPPGE++Mh7dIJofRUrf7DbDYjPDwcJSUlMPETiahGzGbg44/lclibN8v1UC9/V9LpgLZtgd695fx+Q4YAERGKhEtUb/Pm1S6ZMxrliPr9+zmxeV3x87kqJoBuxB8wovo7e1aOlCwrk4lgSIj88GvYUOnIiNxDCDlS/b33rl7WaJQ13Fu2cGqj+uDnc1UKzNFPRFS9sDCga1eloyDyHJ0OWLQIiIoCXnlFDoq6fGJog0Fua9sWWLVKfiVyJ/YBJCIi8jK9HpgzB/jtNzl1UePGzvv69ZOJX14ekz/yDDYBuxGrmImIqC6EkBM9l5XJ/q1KrKHtz/j5XBV/xIiIiBSm08nuD2FhSkdCWuHVJuDTp08jNTUVJpMJERERSEtLw9mzZ694TFlZGcaNG4cmTZogLCwMAwcORFFRkVOZw4cPIyUlBaGhoWjatCmefvppWC6ZaKmgoABDhw5Fu3btoNfrMWHChCrXSU9Ph06nc3oFBwe75b6JiIiI1MSrCWBqaipyc3Oxdu1arFy5Ej/88APG2GeErcZTTz2Fr776ChkZGfj+++9x7Ngx3HfffY79VqsVKSkpqKiowKZNm/Dee+8hPT0d06ZNc5QpLy9HVFQUpk6dim7dulV7LZPJhIKCAsfr999/r/9NExEREamN8JJ9+/YJAGLr1q2ObatWrRI6nU4cPXrU5THFxcUiICBAZGRkOLbl5eUJACIrK0sIIURmZqbQ6/WisLDQUWbhwoXCZDKJ8vLyKufs06ePePLJJ6tsX7x4sQgPD6/j3UklJSUCgCgpKanXeYiIiMh9+PlclddqALOyshAREYGePXs6tiUlJUGv1yM7O9vlMTk5OaisrERSUpJjW4cOHdCyZUtkZWU5ztu1a1dER0c7yiQnJ8NsNiM3N7dWMZ49exatWrVCixYtcM8999T6eCIiIiJf4LUEsLCwEE2bNnXaZjQa0bhxYxQWFlZ7TGBgICIum/I/OjracUxhYaFT8mffb99XU+3bt8eiRYvwxRdf4MMPP4TNZsNNN92EI0eOVHtMeXk5zGaz04uIiIhI7eqdAE6ePLnK4InLX/v373dHrB6VmJiI4cOHo3v37ujTpw+WL1+OqKgovPXWW9UeM3PmTISHhzteLVq08GLERERERHVT72lgJk2ahJEjR16xTNu2bRETE4Pjx487bbdYLDh9+jRiYmJcHhcTE4OKigoUFxc71QIWFRU5jomJicGWLVucjrOPEq7uvDUREBCAHj164Jdffqm2zJQpUzBx4kTH92azmUkgERERqV69E8CoqChERUVdtVxiYiKKi4uRk5ODuLg4AMD69eths9mQkJDg8pi4uDgEBARg3bp1GDhwIAAgPz8fhw8fRmJiouO8L774Io4fP+5oYl67di1MJhM6depU5/uyWq3Ys2cP7rjjjmrLBAUFISgoqM7XICIiIlKC1/oAduzYEf369cPo0aOxZcsWbNy4EePHj8cDDzyA2NhYAMDRo0fRoUMHR41eeHg40tLSMHHiRGzYsAE5OTkYNWoUEhMT0bt3bwBA37590alTJwwbNgy7du3CmjVrMHXqVIwbN84pOdu5cyd27tyJs2fP4sSJE9i5cyf27dvn2P/CCy/gm2++wW+//Ybt27fjwQcfxO+//46HH37YW4+IiIiIyCu8uhLIRx99hPHjx+P222+HXq/HwIEDMXfuXMf+yspK5Ofn4/z5845tr776qqNseXk5kpOTsWDBAsd+g8GAlStX4tFHH0ViYiIaNGiAESNG4IUXXnC6do8ePRz/zsnJwZIlS9CqVSscOnQIAHDmzBmMHj0ahYWFaNSoEeLi4rBp06Z61SISERERqRHXAnYjrjVIRESkPvx8rsqrK4EQERERkfKYABIRERFpjFf7APo7e2s6J4QmIiJSD/vnMnu9XcQE0I1KS0sBgHMBEhERqVBpaSnCw8OVDkMVOAjEjWw2G44dO4aGDRtCp9PV6lj7JNJ//PEHO6hegs+lenw2rvG5VI/PxjU+F9f86bkIIVBaWorY2Fjo9ez9BrAG0K30ej2uueaaep3DZDL5/C+aJ/C5VI/PxjU+l+rx2bjG5+KavzwX1vw5YxpMREREpDFMAImIiIg0hgmgSgQFBWH69OlcW/gyfC7V47Nxjc+lenw2rvG5uMbn4t84CISIiIhIY1gDSERERKQxTACJiIiINIYJIBEREZHGMAEkIiIi0hgmgG5w+vRppKamwmQyISIiAmlpaTh79uwVjykrK8O4cePQpEkThIWFYeDAgSgqKnIqc/jwYaSkpCA0NBRNmzbF008/DYvF4ti/fPly/PWvf0VUVBRMJhMSExOxZs0ap3M8//zz0Ol0Tq8OHTq47+avQs3PBgDmz5+P1q1bIzg4GAkJCdiyZYt7bvwqlHouBQUFGDp0KNq1awe9Xo8JEyZUuU56enqVn5ng4GC33HdNqPnZAEBGRgY6dOiA4OBgdO3aFZmZmfW+55pQ6rkAwHfffYcbb7wRQUFBuO6665Cenu6039vvM7X9vb3a/5kQAtOmTUOzZs0QEhKCpKQkHDhwwKlMTZ7/7t27ceuttyI4OBgtWrTA7Nmz3XPDNaTG53Lo0KEqPxs6nQ6bN292341T3Qiqt379+olu3bqJzZs3ix9//FFcd911YsiQIVc85pFHHhEtWrQQ69atE9u2bRO9e/cWN910k2O/xWIRXbp0EUlJSWLHjh0iMzNTREZGiilTpjjKPPnkk+Kll14SW7ZsET///LOYMmWKCAgIENu3b3eUmT59uujcubMoKChwvE6cOOH+h1ANNT+bpUuXisDAQLFo0SKRm5srRo8eLSIiIkRRUZH7H8RllHouBw8eFE888YR47733RPfu3cWTTz5Z5TqLFy8WJpPJ6WemsLDQbfd+NWp+Nhs3bhQGg0HMnj1b7Nu3T0ydOlUEBASIPXv2uO3+q6PUc/ntt99EaGiomDhxoti3b5+YN2+eMBgMYvXq1Y4y3nyfqe3vbU3+z2bNmiXCw8PFihUrxK5du8Tdd98t2rRpIy5cuOAoc7XnX1JSIqKjo0VqaqrYu3ev+Pjjj0VISIh46623PPIcLqfW53Lw4EEBQHz77bdOPx8VFRWeexhUI0wA62nfvn0CgNi6datj26pVq4ROpxNHjx51eUxxcbEICAgQGRkZjm15eXkCgMjKyhJCCJGZmSn0er3TB+/ChQuFyWQS5eXl1cbTqVMnMWPGDMf306dPF926davr7dWL2p9NfHy8GDdunON7q9UqYmNjxcyZM2t/s7WglufSp0+fahPA8PDwOt5d/aj92QwaNEikpKQ4bUtISBBjx46t1X3WlpLP5ZlnnhGdO3d2OvfgwYNFcnKy43tvvs/U9vf2av9nNptNxMTEiDlz5jj2FxcXi6CgIPHxxx8LIWr2/BcsWCAaNWrk9PP07LPPivbt29fzjmtGrc/FngDu2LHDLfdJ7sMm4HrKyspCREQEevbs6diWlJQEvV6P7Oxsl8fk5OSgsrISSUlJjm0dOnRAy5YtkZWV5Thv165dER0d7SiTnJwMs9mM3Nxcl+e12WwoLS1F48aNnbYfOHAAsbGxaNu2LVJTU3H48OE6329tqPnZVFRUICcnx+k6er0eSUlJjut4ipqeS3XOnj2LVq1aoUWLFrjnnntqfXxdqf3ZZGVlOV3Hfh5//pmp6T17432mLr+3V4v/4MGDKCwsdCoTHh6OhIQEp+d0teeflZWFP/3pTwgMDHS6Tn5+Ps6cOVPPO78yNT8Xu7vvvhtNmzbFLbfcgi+//LJ+N0xuwQSwngoLC9G0aVOnbUajEY0bN0ZhYWG1xwQGBiIiIsJpe3R0tOOYwsJCpzdl+377PldefvllnD17FoMGDXJsS0hIQHp6OlavXo2FCxfi4MGDuPXWW1FaWlqr+6wLNT+bkydPwmq1ujxPdedwFzU9F1fat2+PRYsW4YsvvsCHH34Im82Gm266CUeOHKnxOepK7c+muvP4889MdWXMZjMuXLgAwHvvM3X5vb3a/5n969XKXO35u+tnrC7U/FzCwsLwyiuvICMjA19//TVuueUWDBgwgEmgCjABrMbkyZNddly99LV//36lw3RYsmQJZsyYgWXLljn9Qvbv3x/3338/brjhBiQnJyMzMxPFxcVYtmxZna/lL8/G3XztuVQnMTERw4cPR/fu3dGnTx8sX74cUVFReOutt+p8Tn95Nu7mL8/FE+8z5B8iIyMxceJEJCQkoFevXpg1axYefPBBzJkzR+nQNM+odABqNWnSJIwcOfKKZdq2bYuYmBgcP37cabvFYsHp06cRExPj8riYmBhUVFSguLjY6a/zoqIixzExMTFVRnDZR+9dft6lS5fi4YcfRkZGRpUq/ctFRESgXbt2+OWXX65Y7kr84dlERkbCYDBUGRF56XVqy5eeS20EBASgR48emvmZuZKYmBjN/cxUd88mkwkhISEur+2O9xlX6vJ7e7X/M/vXoqIiNGvWzKlM9+7dHWWu9vyru86l1/AUNT8XVxISErB27dqa3Rx5jtKdEH2dvRPstm3bHNvWrFlTo87Zn376qWPb/v37XXbOvnQE11tvvSVMJpMoKytzbFuyZIkIDg4WK1asqFG8paWlolGjRuL111+v1X3WhdqfTXx8vBg/frzje6vVKpo3b+61QSBKPRe76gY6XM5isYj27duLp556qqa3WGdqfzaDBg0Sd955p9O2xMRErw0CUeK5PPPMM6JLly5O5x4yZIjTIJDLefJ9pra/t1f7P7MPdnj55Zcd+0tKSlwOdrjS87cPArl0dOuUKVO8OghEjc/FlYcfflj06NGj9jdJbsUE0A369esnevToIbKzs8VPP/0krr/+eqdh8EeOHBHt27cX2dnZjm2PPPKIaNmypVi/fr3Ytm2bSExMFImJiY799ukZ+vbtK3bu3ClWr14toqKinKZn+Oijj4TRaBTz5893Gl5fXFzsKDNp0iTx3XffiYMHD4qNGzeKpKQkERkZKY4fP+7hpyKp+dksXbpUBAUFifT0dLFv3z4xZswYERER4ZUpT5R6LkIIsWPHDrFjxw4RFxcnhg4dKnbs2CFyc3Md+2fMmCHWrFkjfv31V5GTkyMeeOABERwc7FTGk9T8bDZu3CiMRqN4+eWXRV5enpg+fbpXp4FR4rnYp4F5+umnRV5enpg/f36VaWC8+T5ztd/bYcOGicmTJzvK1+T/bNasWSIiIkJ88cUXYvfu3eKee+5xOd3JlZ5/cXGxiI6OFsOGDRN79+4VS5cuFaGhoV6dBkaNzyU9PV0sWbJE5OXliby8PPHiiy8KvV4vFi1a5IWnQlfCBNANTp06JYYMGSLCwsKEyWQSo0aNEqWlpY799mHwGzZscGy7cOGCeOyxx0SjRo1EaGiouPfee0VBQYHTeQ8dOiT69+8vQkJCRGRkpJg0aZKorKx07O/Tp48AUOU1YsQIR5nBgweLZs2aicDAQNG8eXMxePBg8csvv3jsWVxOzc9GCCHmzZsnWrZsKQIDA0V8fLzYvHmzR57D5ZR6LkIIl8+lVatWjv0TJkxwPJPo6Ghxxx13OM2f6GlqfjZCCLFs2TLRrl07ERgYKDp37iy+/vprtz8DV5R8Lhs2bBDdu3cXgYGBom3btmLx4sVO+739PnOl39s+ffpU+T2/2v+ZzWYTzz33nIiOjhZBQUHi9ttvF/n5+U5lrvb8hRBi165d4pZbbhFBQUGiefPmYtasWe698atQ43NJT08XHTt2FKGhocJkMon4+HinqYlIOTohhPBoGzMRERERqQpHARMRERFpDBNAIiIiIo1hAkhERESkMUwAiYiIiDSGCSARERGRxjABJCIiItIYJoBEREREGsMEkIiIiEhjmAASERERaQwTQCIiIiKNYQJIREREpDFMAImIiIg0hgkgERERkcYwASQiIiLSGCaARERERBrDBJCIiIhIY5gAEhEREWkME0AiIiIijWECSERERKQxTACJiIiINIYJIBEREZHGMAEkIiIi0hgmgEREREQawwSQiIiISGOYABIRERFpDBNAIiIiIo1hAkhERESkMUwAiYiIiDSGCSARERGRxjABJCIiItIYJoBEREREGvN/nwwOe7e/TjAAAAAASUVORK5CYII=", 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\n", - " \n", + " \n", "
\n", " " ], @@ -47,7 +47,7 @@ "from matplotlib import pyplot as plt\n", "\n", "\n", - "import load_data\n" + "import WormView\n" ] }, { diff --git a/WormSim/README.md b/WormSim/README.md index c737303..9a38f09 100644 --- a/WormSim/README.md +++ b/WormSim/README.md @@ -73,7 +73,6 @@ If you don't have access to Matlab, try [Octave](https://www.gnu.org/software/oc 8) Try also the [new Python viewer](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/Model/WormView.py): - ``` python WormView.py ``` From e3547c93e159be39b2f6b753d8db426b3be7c490 Mon Sep 17 00:00:00 2001 From: pgleeson Date: Fri, 30 Jun 2023 17:21:34 +0100 Subject: [PATCH 6/7] Add animated gif --- README.md | 2 +- WormSim/Model/WormView.py | 1 + WormSim/README.md | 2 +- images/Replay.gif | Bin 0 -> 310673 bytes 4 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 images/Replay.gif diff --git a/README.md b/README.md index 507ff29..fec7294 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![C/C++ CI](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/actions/workflows/build.yml/badge.svg)](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/actions/workflows/build.yml) -MatlabView   PythonView +MatlabView   PythonView A model of C. elegans locomotion described in Boyle, Berri and Cohen, [Gait modulation in C. elegans: an integrated neuromechanical model](http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2012.00010/abstract), Front. Comput. Neurosci., 2012. diff --git a/WormSim/Model/WormView.py b/WormSim/Model/WormView.py index 870524c..96d9df1 100644 --- a/WormSim/Model/WormView.py +++ b/WormSim/Model/WormView.py @@ -21,6 +21,7 @@ plt.legend()""" fig, ax = plt.subplots() +plt.get_current_fig_manager().set_window_title("2D WormSim replay") ax.set_aspect("equal") usingObjects = os.path.isfile("objects.csv") diff --git a/WormSim/README.md b/WormSim/README.md index 9a38f09..bb16fed 100644 --- a/WormSim/README.md +++ b/WormSim/README.md @@ -77,7 +77,7 @@ If you don't have access to Matlab, try [Octave](https://www.gnu.org/software/oc python WormView.py ``` -PythonView +PythonView **Note:** to get the objects shown in the visualisations above, change the following values in [worm.cc](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/blob/master/WormSim/Model/worm.cc), then remake the main program, and rerun: diff --git a/images/Replay.gif b/images/Replay.gif new file mode 100644 index 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index fec7294..9dd8dd0 100644 --- a/README.md +++ b/README.md @@ -8,3 +8,5 @@ A model of C. elegans locomotion described in Boyle, Berri and Cohen, [Gait modulation in C. elegans: an integrated neuromechanical model](http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2012.00010/abstract), Front. Comput. Neurosci., 2012. For information on installing and running this code, see [here](https://github.com/OpenSourceBrain/CelegansNeuromechanicalGaitModulation/tree/master/WormSim). + +