diff --git a/README.md b/README.md index a6f6d471..7f17ae5b 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,6 @@ # Py-FEAT [![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) -[![Coverage Status](https://coveralls.io/repos/github/cosanlab/feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/feat?branch=master) - +[![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) Python Facial Expression Analysis Toolbox (FEAT) Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial muscle movements (e.g., action units), and facial landmarks, from videos and images of faces, as well as methods to preprocess, analyze, and visualize FEX data. diff --git a/feat/data.py b/feat/data.py index d214de19..2cdde1ba 100644 --- a/feat/data.py +++ b/feat/data.py @@ -7,8 +7,6 @@ import numpy as np import pandas as pd from pandas import DataFrame, Series, Index -import six -import abc from copy import deepcopy from functools import reduce from nltools.data import Adjacency, design_matrix @@ -31,7 +29,7 @@ from feat.plotting import plot_face, draw_lineface, draw_muscles from nilearn.signal import clean from scipy.signal import convolve -from scipy.stats import ttest_1samp +from scipy.stats import ttest_1samp, ttest_ind class FexSeries(Series): @@ -689,7 +687,7 @@ def regress(self, X, y, fit_intercept=True, *args, **kwargs): res_df = pd.DataFrame(res, columns=my.columns) return b_df, t_df, p_df, df_df, res_df - def ttest(self, popmean=0, threshold_dict=None): + def ttest_1samp(self, popmean=0, threshold_dict=None): """Conducts 1 sample ttest. Uses scipy.stats.ttest_1samp to conduct 1 sample ttest @@ -703,6 +701,24 @@ def ttest(self, popmean=0, threshold_dict=None): """ return ttest_1samp(self, popmean) + def ttest_ind(self, col, sessions, threshold_dict=None): + """Conducts 2 sample ttest. + + Uses scipy.stats.ttest_ind to conduct 2 sample ttest on column col between sessions. + + Args: + col (str): Column names to compare in a t-test between sessions + session_names (tuple): tuple of session names stored in Fex.sessions. + threshold_dict ([type], optional): Dictonary for thresholding. Defaults to None. [NOT IMPLEMENTED] + + Returns: + t, p: t-statistics and p-values + """ + sess1, sess2 = sessions + a = self[self.sessions == sess1][col] + b = self[self.sessions == sess2][col] + return ttest_ind(a, b) + def predict(self, X, y, model=LinearRegression, *args, **kwargs): """Predicts y from X using a sklearn model. @@ -755,6 +771,25 @@ def downsample(self, target, **kwargs): return df_ds # return self.__class__(df_ds, sampling_freq=target, features=ds_features) + def isc(self, col, index="frame", columns="input", method="pearson"): + """[summary] + + Args: + col (str]): Column name to compute the ISC for. + index (str, optional): Column to be used in computing ISC. Usually this would be the column identifying the time such as the number of the frame. Defaults to "frame". + columns (str, optional): Column to be used for ISC. Usually this would be the column identifying the video or subject. Defaults to "input". + method (str, optional): Method to use for correlation pearson, kendall, or spearman. Defaults to "pearson". + + Returns: + DataFrame: Correlation matrix with index as colmns + """ + if index == None: + index = 'frame' + if columns == None: + columns = "input" + mat = pd.pivot_table(self, index = index, columns=columns, values=col).corr(method=method) + return mat + def upsample(self, target, target_type="hz", **kwargs): """Upsample Fex columns. Relies on nltools.stats.upsample, but ensures that returned object is a Fex object. diff --git a/feat/plotting.py b/feat/plotting.py index 130a3a66..16c6732a 100644 --- a/feat/plotting.py +++ b/feat/plotting.py @@ -863,7 +863,7 @@ def plot_face( Args: model: sklearn PLSRegression instance - au: vector of action units (same length as model.x_mean_) + au: vector of action units (same length as model.n_components) vectorfield: (dict) {'target':target_array,'reference':reference_array} muscles: (dict) {'muscle': color} ax: matplotlib axis handle @@ -883,7 +883,7 @@ def plot_face( raise ValueError("make sure that model is a PLSRegression instance") if au is None: - au = np.zeros(len(model.x_mean_)) + au = np.zeros(model.n_components) warnings.warn( "Don't forget to pass an 'au' vector of len(20), " "using neutral as default" @@ -927,7 +927,7 @@ def plot_face( vectorfield["target"] = landmarks draw_vectorfield(ax=ax, **vectorfield) ax.set_xlim([25, 172]) - ax.set_ylim((-240, -50)) + ax.set_ylim((240, 50)) ax.axes.get_xaxis().set_visible(False) ax.axes.get_yaxis().set_visible(False) return ax @@ -948,20 +948,20 @@ def predict(au, model=None): elif not isinstance(model, PLSRegression): raise ValueError("make sure that model is a PLSRegression instance") - if len(au) != len(model.x_mean_): + if len(au) != model.n_components: print(au) - print(len(model.x_mean_)) - raise ValueError("au vector must be len(", len(model.x_mean_), ").") + print(model.n_components) + raise ValueError("au vector must be len(", model.n_components, ").") if len(au.shape) == 1: au = np.reshape(au, (1, -1)) landmarks = np.reshape(model.predict(au), (2, 68)) - landmarks[1, :] = -1 * landmarks[1, :] # this might not generalize to other models + # landmarks[1, :] = -1 * landmarks[1, :] # this might not generalize to other models return landmarks -def _create_empty_figure(figsize=(4, 5), xlim=[25, 172], ylim=[-240, -50]): +def _create_empty_figure(figsize=(4, 5), xlim=[25, 172], ylim=[240, 50]): """Create an empty figure""" plt.figure(figsize=figsize) ax = plt.gca() diff --git a/feat/resources/pyfeat_aus_to_landmarks.h5 b/feat/resources/pyfeat_aus_to_landmarks.h5 new file mode 100644 index 00000000..e9c95726 Binary files /dev/null and b/feat/resources/pyfeat_aus_to_landmarks.h5 differ diff --git a/feat/tests/test_feat.py b/feat/tests/test_feat.py index b499361f..29de3a72 100644 --- a/feat/tests/test_feat.py +++ b/feat/tests/test_feat.py @@ -410,5 +410,15 @@ def test_stats(): clf = openface.predict(X = openface[["AU02_c"]], y = openface["AU04_c"]) assert clf.coef_ < 0 - t, p = openface[["AU02_c"]].ttest() - assert t > 0 \ No newline at end of file + t, p = openface[["AU02_c"]].ttest_1samp() + assert t > 0 + + a = openface.aus().assign(input = "0") + b = openface.aus().apply(lambda x: x+np.random.rand(100)).assign(input = "1") + doubled = pd.concat([a, b]) + doubled.sessions = doubled['input'] + t, p = doubled.ttest_ind(col="AU12_r", sessions = ("0", "1")) + assert(t < 0) + + frame = np.concatenate([np.array(range(int(len(doubled)/2))), np.array(range(int(len(doubled)/2)))]) + assert(doubled.assign(frame = frame).isc(col = "AU04_r").iloc[0,0] == 1) \ No newline at end of file diff --git a/feat/tests/test_plot.py b/feat/tests/test_plot.py index 5907b4fa..13f0c7a5 100644 --- a/feat/tests/test_plot.py +++ b/feat/tests/test_plot.py @@ -13,7 +13,7 @@ def assert_plot_shape(ax): - assert ax.get_ylim() == (-240.0, -50.0) + assert ax.get_ylim() == (240.0, 50.0) assert ax.get_xlim() == (25.0, 172.0) diff --git a/feat/tests/test_utils.py b/feat/tests/test_utils.py index 58a8e94f..f0eb376c 100644 --- a/feat/tests/test_utils.py +++ b/feat/tests/test_utils.py @@ -17,7 +17,6 @@ neutral, softmax, load_h5, - load_pickled_model, ) from nltools.data import Adjacency import unittest @@ -47,9 +46,4 @@ def test_utils(): assert softmax(0) == 0.5 # Test badfile. with pytest.raises(Exception): - load_h5("badfile.h5") - - # Test loading of pickled model - out = load_pickled_model() - with pytest.raises(Exception): - load_pickled_model("badfile.pkl") + load_h5("badfile.h5") \ No newline at end of file diff --git a/feat/utils.py b/feat/utils.py index 3b7de184..86013c84 100644 --- a/feat/utils.py +++ b/feat/utils.py @@ -12,7 +12,6 @@ __all__ = [ "get_resource_path", - "load_pickled_model", "read_facet", "read_affdex", "read_affectiva", @@ -27,6 +26,10 @@ import os, math, pywt, pickle, h5py from sklearn.cross_decomposition import PLSRegression +# setattr(PLSRegression, "_x_mean", None) +# setattr(PLSRegression, "_y_mean", None) +# setattr(PLSRegression, "_x_std", None) +from sklearn import __version__ import numpy as np, pandas as pd from scipy import signal from scipy.integrate import simps @@ -197,30 +200,7 @@ def get_resource_path(): # return ("F:/feat/feat/") # points to the package folder. # return os.path.join(os.path.dirname(__file__), 'resources') - -def load_pickled_model(file_name="pls_python27.pkl"): - """Load the pickled PLS model for plotting. - - Args: - file_name ([type], optional): Specify model to load. Defaults to pls_python27.pkl. - - Returns: - model: PLS model - """ - file_name = os.path.join(get_resource_path(), file_name) - try: - with open(file_name, "rb") as f: - model = pickle.load(f) - except UnicodeDecodeError as e: - with open(file_name, "rb") as f: - model = pickle.load(f, encoding="latin1") - except Exception as e: - print("Unable to load data ", file_name, ":", e) - raise - return model - - -def load_h5(file_name="blue.h5"): +def load_h5(file_name="pyfeat_aus_to_landmarks.h5"): """Load the h5 PLS model for plotting. Args: @@ -237,9 +217,14 @@ def load_h5(file_name="blue.h5"): d4 = hf.get("x_std") model = PLSRegression(len(d1)) model.coef_ = np.array(d1) - model.x_mean_ = np.array(d2) - model.y_mean_ = np.array(d3) - model.x_std_ = np.array(d4) + if int(__version__.split(".")[1]) < 24: + model.x_mean_ = np.array(d2) + model.y_mean_ = np.array(d3) + model.x_std_ = np.array(d4) + else: + model._x_mean = np.array(d2) + model._y_mean = np.array(d3) + model._x_std = np.array(d4) hf.close() except Exception as e: print("Unable to load data ", file_name, ":", e) diff --git a/feat/version.py b/feat/version.py index 80eb7f98..40ed83d9 100644 --- a/feat/version.py +++ b/feat/version.py @@ -1 +1 @@ -__version__ = '0.3.3' +__version__ = '0.3.5' diff --git a/notebooks/_build/.doctrees/api/index.doctree b/notebooks/_build/.doctrees/api/index.doctree index 99dbe66a..0f468ba7 100644 Binary files a/notebooks/_build/.doctrees/api/index.doctree and b/notebooks/_build/.doctrees/api/index.doctree differ diff --git a/notebooks/_build/.doctrees/content/analysis.doctree b/notebooks/_build/.doctrees/content/analysis.doctree index 0202e5c4..cf99995d 100644 Binary files a/notebooks/_build/.doctrees/content/analysis.doctree and b/notebooks/_build/.doctrees/content/analysis.doctree differ diff --git a/notebooks/_build/.doctrees/content/intro.doctree b/notebooks/_build/.doctrees/content/intro.doctree index d2141ebd..11c33f52 100644 Binary files a/notebooks/_build/.doctrees/content/intro.doctree and b/notebooks/_build/.doctrees/content/intro.doctree differ diff --git a/notebooks/_build/.doctrees/content/loadingOtherFiles.doctree b/notebooks/_build/.doctrees/content/loadingOtherFiles.doctree index 17e07b6b..239d1dfa 100644 Binary files a/notebooks/_build/.doctrees/content/loadingOtherFiles.doctree and b/notebooks/_build/.doctrees/content/loadingOtherFiles.doctree differ diff --git a/notebooks/_build/.doctrees/content/plotting.doctree b/notebooks/_build/.doctrees/content/plotting.doctree new file mode 100644 index 00000000..ae71e482 Binary files /dev/null and b/notebooks/_build/.doctrees/content/plotting.doctree differ diff --git a/notebooks/_build/.doctrees/content/trainAUvisModel.doctree b/notebooks/_build/.doctrees/content/trainAUvisModel.doctree new file mode 100644 index 00000000..d923ddee Binary files /dev/null and b/notebooks/_build/.doctrees/content/trainAUvisModel.doctree differ diff --git a/notebooks/_build/.doctrees/environment.pickle b/notebooks/_build/.doctrees/environment.pickle index 9b5cfaf2..ff4268ce 100644 Binary files a/notebooks/_build/.doctrees/environment.pickle and b/notebooks/_build/.doctrees/environment.pickle differ diff --git a/notebooks/_build/html/.buildinfo b/notebooks/_build/html/.buildinfo index 5505a0f0..ad7b0931 100644 --- a/notebooks/_build/html/.buildinfo +++ b/notebooks/_build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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b/notebooks/_build/html/_sources/content/analysis.ipynb @@ -62,8 +62,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:48:17.995310Z", - "start_time": "2021-03-24T18:48:17.859908Z" + "end_time": "2021-03-26T01:35:57.204484Z", + "start_time": "2021-03-26T01:35:56.969949Z" } }, "outputs": [ @@ -81,6 +81,8 @@ "import os, glob\n", "import numpy as np\n", "import pandas as pd\n", + "import seaborn as sns\n", + "sns.set_context(\"talk\")\n", "\n", "clip_attrs = pd.read_csv(\"clip_attrs.csv\")\n", "videos = np.sort(glob.glob(\"*.mp4\"))\n", @@ -119,11 +121,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:56:25.057270Z", - "start_time": "2021-03-24T18:56:24.564396Z" + "end_time": "2021-03-26T01:36:04.630894Z", + "start_time": "2021-03-26T01:36:02.098727Z" } }, "outputs": [], @@ -142,11 +144,11 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:29.835552Z", - "start_time": "2021-03-24T19:42:29.826112Z" + "end_time": "2021-03-26T01:36:04.662326Z", + "start_time": "2021-03-26T01:36:04.632024Z" } }, "outputs": [ @@ -238,7 +240,7 @@ "4 5 gn 5 your application has been accepted 005.mp4 goodNews" ] }, - "execution_count": 94, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -262,11 +264,11 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:35.182738Z", - "start_time": "2021-03-24T19:42:35.082327Z" + "end_time": "2021-03-26T01:36:04.916876Z", + "start_time": "2021-03-26T01:36:04.663369Z" } }, "outputs": [ @@ -409,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:38.745631Z", - "start_time": "2021-03-24T19:42:38.187813Z" + "end_time": "2021-03-26T01:36:05.612696Z", + "start_time": "2021-03-26T01:36:04.917989Z" } }, "outputs": [ @@ -641,11 +643,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:44.544102Z", - "start_time": "2021-03-24T19:42:44.530159Z" + "end_time": "2021-03-26T01:37:30.117739Z", + "start_time": "2021-03-26T01:37:29.898669Z" } }, "outputs": [ @@ -755,17 +757,65 @@ "mean_AU24 -4.533345 1.419377e-03" ] }, - "execution_count": 97, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map)\n", - "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest(.5)\n", + "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest_1samp(.5)\n", "pd.DataFrame({\"t\": t, \"p\": p}, index= average_au_intensity_per_video.au_columns)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two sample independent t-test\n", + "You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T01:54:46.741635Z", + "start_time": "2021-03-26T01:54:46.568587Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-test between goodNews vs badNews: t=17, p=2.32e-12\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "columns2compare = \"mean_AU12\"\n", + "sessions = (\"goodNews\", \"badNews\")\n", + "t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions)\n", + "print(f\"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}\")\n", + "sns.barplot(x = average_au_intensity_per_video.sessions, \n", + " y = columns2compare, \n", + " data = average_au_intensity_per_video);" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -776,11 +826,11 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:47.876293Z", - "start_time": "2021-03-24T19:42:47.862905Z" + "end_time": "2021-03-26T01:33:23.062956Z", + "start_time": "2021-03-26T01:33:23.046675Z" } }, "outputs": [ @@ -866,11 +916,11 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:49:09.446263Z", - "start_time": "2021-03-24T19:49:09.423695Z" + "end_time": "2021-03-26T01:33:24.774852Z", + "start_time": "2021-03-26T01:33:24.727406Z" } }, "outputs": [ @@ -1001,6 +1051,43 @@ " p.round(3).loc[[0]].rename(index={0:\"p-values\"})])\n", "display(results)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intersubject correlations\n", + "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T02:03:17.392348Z", + "start_time": "2021-03-26T02:03:16.179007Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fex.sessions = fex.input()\n", + "isc = fex.isc(col = \"AU01\")\n", + "sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap=\"RdBu_r\");" + ] } ], "metadata": { diff --git a/notebooks/_build/html/_sources/content/intro.md b/notebooks/_build/html/_sources/content/intro.md index a56c2824..017b2d55 100644 --- a/notebooks/_build/html/_sources/content/intro.md +++ b/notebooks/_build/html/_sources/content/intro.md @@ -2,10 +2,10 @@ Py-Feat: Python Facial Expression Analysis Toolbox ============================ [![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) -[![Coverage Status](https://coveralls.io/repos/github/cosanlab/feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/feat?branch=master) -[![GitHub forks](https://img.shields.io/github/forks/cosanlab/feat)](https://github.com/cosanlab/feat/network) -[![GitHub stars](https://img.shields.io/github/stars/cosanlab/feat)](https://github.com/cosanlab/feat/stargazers) -[![GitHub license](https://img.shields.io/github/license/cosanlab/feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) +[![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) +[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/feat/network) +[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/feat/stargazers) +[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data. diff --git a/notebooks/_build/html/_sources/content/loadingOtherFiles.ipynb b/notebooks/_build/html/_sources/content/loadingOtherFiles.ipynb index e31a92a1..346676ee 100644 --- a/notebooks/_build/html/_sources/content/loadingOtherFiles.ipynb +++ b/notebooks/_build/html/_sources/content/loadingOtherFiles.ipynb @@ -14,11 +14,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:04.115339Z", - "start_time": "2021-03-24T22:20:04.002648Z" + "end_time": "2021-03-26T05:11:11.802818Z", + "start_time": "2021-03-26T05:11:10.161089Z" } }, "outputs": [ @@ -60,39 +60,7 @@ " gaze_1_x\n", " gaze_1_y\n", " gaze_1_z\n", - " pose_Tx\n", - " pose_Ty\n", - " pose_Tz\n", - " pose_Rx\n", - " pose_Ry\n", - " pose_Rz\n", - " x_0\n", - " x_1\n", - " x_2\n", - " x_3\n", - " x_4\n", - " x_5\n", - " x_6\n", - " x_7\n", - " x_8\n", - " x_9\n", " ...\n", - " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -118,41 +86,9 @@ " -0.124401\n", " 0.066311\n", " -0.990014\n", - " 16.6573\n", - " 43.4571\n", - " 597.876\n", - " 0.297590\n", - " -0.063753\n", - " -0.019235\n", - " 277.276\n", - " 278.163\n", - " 280.209\n", - " 283.258\n", - " 288.537\n", - " 297.109\n", - " 307.888\n", - " 320.240\n", - " 333.596\n", - " 346.018\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -174,41 +110,9 @@ " -0.123464\n", " 0.069979\n", " -0.989879\n", - " 16.2613\n", - " 43.2594\n", - " 596.885\n", - " 0.326858\n", - " -0.058038\n", - " -0.022072\n", - " 276.980\n", - " 277.893\n", - " 279.952\n", - " 283.021\n", - " 288.339\n", - " 296.925\n", - " 307.663\n", - " 319.981\n", - " 333.443\n", - " 346.086\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -230,41 +134,9 @@ " -0.122873\n", " 0.070540\n", " -0.989912\n", - " 15.8624\n", - " 43.2698\n", - " 593.122\n", - " 0.350392\n", - " -0.051172\n", - " -0.022723\n", - " 276.902\n", - " 277.785\n", - " 279.844\n", - " 282.911\n", - " 288.141\n", - " 296.601\n", - " 307.278\n", - " 319.657\n", - " 333.284\n", - " 346.138\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -286,41 +158,9 @@ " -0.122172\n", " 0.070736\n", " -0.989985\n", - " 15.5008\n", - " 43.1023\n", - " 592.867\n", - " 0.352725\n", - " -0.047812\n", - " -0.022291\n", - " 276.757\n", - " 277.626\n", - " 279.664\n", - " 282.699\n", - " 287.902\n", - " 296.353\n", - " 307.023\n", - " 319.397\n", - " 333.035\n", - " 345.927\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -342,41 +182,9 @@ " -0.121321\n", " 0.070529\n", " -0.990104\n", - " 14.7702\n", - " 43.1363\n", - " 594.135\n", - " 0.349325\n", - " -0.045551\n", - " -0.022963\n", - " 276.342\n", - " 277.208\n", - " 279.248\n", - " 282.285\n", - " 287.478\n", - " 295.913\n", - " 306.562\n", - " 318.911\n", - " 332.540\n", - " 345.428\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -392,12 +200,26 @@ "" ], "text/plain": [ - " frame timestamp ... AU28_c AU45_c\n", - "0 1 0.000 ... 0 0\n", - "1 2 0.001 ... 0 0\n", - "2 3 0.002 ... 0 0\n", - "3 4 0.003 ... 0 0\n", - "4 5 0.004 ... 0 0\n", + " frame timestamp confidence success gaze_0_x gaze_0_y gaze_0_z \\\n", + "0 1 0.000 0.883333 1 0.109059 0.062474 -0.992070 \n", + "1 2 0.001 0.883333 1 0.110256 0.065356 -0.991752 \n", + "2 3 0.002 0.883333 1 0.108539 0.064244 -0.992014 \n", + "3 4 0.003 0.883333 1 0.108724 0.064943 -0.991948 \n", + "4 5 0.004 0.883333 1 0.109766 0.065250 -0.991813 \n", + "\n", + " gaze_1_x gaze_1_y gaze_1_z ... AU12_c AU14_c AU15_c AU17_c AU20_c \\\n", + "0 -0.124401 0.066311 -0.990014 ... 0 0 0 0 0 \n", + "1 -0.123464 0.069979 -0.989879 ... 0 0 0 0 0 \n", + "2 -0.122873 0.070540 -0.989912 ... 0 0 0 0 0 \n", + "3 -0.122172 0.070736 -0.989985 ... 0 0 0 0 0 \n", + "4 -0.121321 0.070529 -0.990104 ... 0 0 0 0 0 \n", + "\n", + " AU23_c AU25_c AU26_c AU28_c AU45_c \n", + "0 1 0 0 0 0 \n", + "1 1 0 0 0 0 \n", + "2 1 0 0 0 0 \n", + "3 1 0 0 0 0 \n", + "4 1 0 0 0 0 \n", "\n", "[5 rows x 431 columns]" ] @@ -426,11 +248,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:46.501974Z", - "start_time": "2021-03-24T22:20:46.429372Z" + "end_time": "2021-03-26T05:11:11.831292Z", + "start_time": "2021-03-26T05:11:11.803938Z" } }, "outputs": [ @@ -465,39 +287,7 @@ " x_7\n", " x_8\n", " x_9\n", - " x_10\n", - " x_11\n", - " x_12\n", - " x_13\n", - " x_14\n", - " x_15\n", - " x_16\n", - " x_17\n", - " x_18\n", - " x_19\n", - " x_20\n", - " x_21\n", - " x_22\n", - " x_23\n", - " x_24\n", - " x_25\n", " ...\n", - " y_42\n", - " y_43\n", - " y_44\n", - " y_45\n", - " y_46\n", - " y_47\n", - " y_48\n", - " y_49\n", - " y_50\n", - " y_51\n", - " y_52\n", - " y_53\n", - " y_54\n", - " y_55\n", - " y_56\n", - " y_57\n", " y_58\n", " y_59\n", " y_60\n", @@ -523,39 +313,7 @@ " 320.240\n", " 333.596\n", " 346.018\n", - " 356.266\n", - " 365.507\n", - " 372.289\n", - " 376.285\n", - " 378.485\n", - " 380.039\n", - " 380.562\n", - " 286.383\n", - " 292.931\n", - " 303.077\n", - " 313.934\n", - " 323.874\n", - " 340.883\n", - " 350.207\n", - " 359.633\n", - " 368.524\n", " ...\n", - " 186.658\n", - " 181.683\n", - " 181.337\n", - " 184.018\n", - " 187.718\n", - " 188.326\n", - " 237.529\n", - " 235.506\n", - " 233.715\n", - " 234.922\n", - " 233.408\n", - " 234.625\n", - " 235.620\n", - " 240.460\n", - " 242.560\n", - " 243.216\n", " 243.032\n", " 241.552\n", " 237.763\n", @@ -579,39 +337,7 @@ " 319.981\n", " 333.443\n", " 346.086\n", - " 356.530\n", - " 365.888\n", - " 372.591\n", - " 376.430\n", - " 378.497\n", - " 379.960\n", - " 380.414\n", - " 286.137\n", - " 292.527\n", - " 302.614\n", - " 313.488\n", - " 323.464\n", - " 340.515\n", - " 349.885\n", - " 359.369\n", - " 368.252\n", " ...\n", - " 186.681\n", - " 181.790\n", - " 181.413\n", - " 183.969\n", - " 187.708\n", - " 188.342\n", - " 237.534\n", - " 235.528\n", - " 233.778\n", - " 234.985\n", - " 233.450\n", - " 234.572\n", - " 235.499\n", - " 240.720\n", - " 243.024\n", - " 243.725\n", " 243.535\n", " 241.895\n", " 237.777\n", @@ -635,39 +361,7 @@ " 319.657\n", " 333.284\n", " 346.138\n", - " 356.717\n", - " 366.159\n", - " 372.821\n", - " 376.590\n", - " 378.645\n", - " 380.110\n", - " 380.536\n", - " 285.955\n", - " 292.222\n", - " 302.268\n", - " 313.181\n", - " 323.186\n", - " 340.184\n", - " 349.622\n", - " 359.211\n", - " 368.176\n", " ...\n", - " 186.788\n", - " 181.864\n", - " 181.463\n", - " 183.978\n", - " 187.807\n", - " 188.454\n", - " 238.026\n", - " 235.976\n", - " 234.216\n", - " 235.456\n", - " 233.928\n", - " 235.091\n", - " 236.081\n", - " 241.759\n", - " 244.207\n", - " 244.885\n", " 244.668\n", " 242.816\n", " 238.284\n", @@ -691,39 +385,7 @@ " 319.397\n", " 333.035\n", " 345.927\n", - " 356.552\n", - " 366.024\n", - " 372.690\n", - " 376.460\n", - " 378.517\n", - " 379.986\n", - " 380.415\n", - " 285.736\n", - " 292.013\n", - " 302.060\n", - " 312.962\n", - " 322.970\n", - " 339.908\n", - " 349.373\n", - " 358.981\n", - " 367.970\n", " ...\n", - " 186.682\n", - " 181.764\n", - " 181.370\n", - " 183.889\n", - " 187.717\n", - " 188.355\n", - " 237.935\n", - " 235.940\n", - " 234.204\n", - " 235.451\n", - " 233.921\n", - " 235.070\n", - " 236.026\n", - " 241.618\n", - " 244.027\n", - " 244.707\n", " 244.484\n", " 242.662\n", " 238.209\n", @@ -747,39 +409,7 @@ " 318.911\n", " 332.540\n", " 345.428\n", - " 356.057\n", - " 365.517\n", - " 372.168\n", - " 375.910\n", - " 377.945\n", - " 379.397\n", - " 379.831\n", - " 285.234\n", - " 291.509\n", - " 301.494\n", - " 312.317\n", - " 322.265\n", - " 339.345\n", - " 348.778\n", - " 358.342\n", - " 367.285\n", " ...\n", - " 186.682\n", - " 181.778\n", - " 181.384\n", - " 183.907\n", - " 187.712\n", - " 188.348\n", - " 237.788\n", - " 235.827\n", - " 234.104\n", - " 235.327\n", - " 233.797\n", - " 234.910\n", - " 235.821\n", - " 241.386\n", - " 243.801\n", - " 244.494\n", " 244.297\n", " 242.514\n", " 238.063\n", @@ -797,17 +427,31 @@ "" ], "text/plain": [ - " x_0 x_1 ... y_66 y_67\n", - "0 277.276 278.163 ... 237.104 236.823\n", - "1 276.980 277.893 ... 237.579 237.295\n", - "2 276.902 277.785 ... 238.606 238.292\n", - "3 276.757 277.626 ... 238.453 238.137\n", - "4 276.342 277.208 ... 238.267 237.974\n", + " x_0 x_1 x_2 x_3 x_4 x_5 x_6 x_7 \\\n", + "0 277.276 278.163 280.209 283.258 288.537 297.109 307.888 320.240 \n", + "1 276.980 277.893 279.952 283.021 288.339 296.925 307.663 319.981 \n", + "2 276.902 277.785 279.844 282.911 288.141 296.601 307.278 319.657 \n", + "3 276.757 277.626 279.664 282.699 287.902 296.353 307.023 319.397 \n", + "4 276.342 277.208 279.248 282.285 287.478 295.913 306.562 318.911 \n", + "\n", + " x_8 x_9 ... y_58 y_59 y_60 y_61 y_62 \\\n", + "0 333.596 346.018 ... 243.032 241.552 237.763 238.249 238.527 \n", + "1 333.443 346.086 ... 243.535 241.895 237.777 238.333 238.601 \n", + "2 333.284 346.138 ... 244.668 242.816 238.284 238.824 239.104 \n", + "3 333.035 345.927 ... 244.484 242.662 238.209 238.804 239.092 \n", + "4 332.540 345.428 ... 244.297 242.514 238.063 238.688 238.958 \n", + "\n", + " y_63 y_64 y_65 y_66 y_67 \n", + "0 237.790 236.296 236.401 237.104 236.823 \n", + "1 237.848 236.234 236.828 237.579 237.295 \n", + "2 238.397 236.835 237.882 238.606 238.292 \n", + "3 238.379 236.786 237.723 238.453 238.137 \n", + "4 238.232 236.595 237.525 238.267 237.974 \n", "\n", "[5 rows x 136 columns]" ] }, - "execution_count": 21, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -818,11 +462,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:48.115597Z", - "start_time": "2021-03-24T22:20:48.079943Z" + "end_time": "2021-03-26T05:11:11.868256Z", + "start_time": "2021-03-26T05:11:11.832354Z" } }, "outputs": [ @@ -857,21 +501,7 @@ " AU10_r\n", " AU12_r\n", " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", + " ...\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -897,21 +527,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -935,21 +551,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -973,21 +575,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1011,21 +599,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1049,21 +623,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1077,20 +637,35 @@ " \n", " \n", "\n", + "

5 rows × 35 columns

\n", "" ], "text/plain": [ - " AU01_r AU02_r AU04_r ... AU26_c AU28_c AU45_c\n", - "0 0.6773 0.4275 0.2435 ... 0 0 0\n", - "1 0.5958 0.3507 0.3347 ... 0 0 0\n", - "2 0.6017 0.3078 0.4339 ... 0 0 0\n", - "3 0.6545 0.3294 0.5075 ... 0 0 0\n", - "4 0.5636 0.2709 0.5708 ... 0 0 0\n", + " AU01_r AU02_r AU04_r AU05_r AU06_r AU07_r AU09_r AU10_r AU12_r \\\n", + "0 0.6773 0.4275 0.2435 0.3434 0 0.0 0.0 0 0 \n", + "1 0.5958 0.3507 0.3347 0.3434 0 0.0 0.0 0 0 \n", + "2 0.6017 0.3078 0.4339 0.2920 0 0.0 0.0 0 0 \n", + "3 0.6545 0.3294 0.5075 0.2899 0 0.0 0.0 0 0 \n", + "4 0.5636 0.2709 0.5708 0.1455 0 0.0 0.0 0 0 \n", + "\n", + " AU14_r ... AU12_c AU14_c AU15_c AU17_c AU20_c AU23_c AU25_c \\\n", + "0 0 ... 0 0 0 0 0 1 0 \n", + "1 0 ... 0 0 0 0 0 1 0 \n", + "2 0 ... 0 0 0 0 0 1 0 \n", + "3 0 ... 0 0 0 0 0 1 0 \n", + "4 0 ... 0 0 0 0 0 1 0 \n", + "\n", + " AU26_c AU28_c AU45_c \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", "\n", "[5 rows x 35 columns]" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1109,11 +684,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:01.787253Z", - "start_time": "2021-03-24T22:21:01.692699Z" + "end_time": "2021-03-26T05:11:11.907622Z", + "start_time": "2021-03-26T05:11:11.869247Z" } }, "outputs": [ @@ -1155,39 +730,7 @@ " EventSource\n", " Timestamp\n", " MediaTime\n", - " PostMarker\n", - " Annotation\n", - " FrameNo\n", - " FrameTime\n", - " NoOfFaces\n", - " FaceRectX\n", - " FaceRectY\n", - " FaceRectWidth\n", - " FaceRectHeight\n", - " Joy\n", - " Anger\n", - " Surprise\n", - " Fear\n", - " Contempt\n", - " Disgust\n", - " Sadness\n", " ...\n", - " IsMaleProbability\n", - " Yaw\n", - " Pitch\n", - " Roll\n", - " LEFT_EYE_LATERALX\n", - " LEFT_EYE_LATERALY\n", - " LEFT_EYE_PUPILX\n", - " LEFT_EYE_PUPILY\n", - " LEFT_EYE_MEDIALX\n", - " LEFT_EYE_MEDIALY\n", - " RIGHT_EYE_MEDIALX\n", - " RIGHT_EYE_MEDIALY\n", - " RIGHT_EYE_PUPILX\n", - " RIGHT_EYE_PUPILY\n", - " RIGHT_EYE_LATERALX\n", - " RIGHT_EYE_LATERALY\n", " NOSE_TIPX\n", " NOSE_TIPY\n", " 7X\n", @@ -1213,39 +756,7 @@ " Emotient FACET\n", " 35\n", " 35\n", - " NaN\n", - " NaN\n", - " 0\n", - " 35\n", - " 1\n", - " 388.0\n", - " 258.0\n", - " 258.0\n", - " 258.0\n", - " -0.462367\n", - " -3.642415\n", - " -1.254946\n", - " -1.967231\n", - " 0.428609\n", - " -2.541990\n", - " -1.224254\n", " ...\n", - " 0.977411\n", - " -1.102638\n", - " 8.205247\n", - " -3.704682\n", - " 445.2972\n", - " 328.3713\n", - " 0.0\n", - " 0.0\n", - " 500.4211\n", - " 334.8774\n", - " 554.7314\n", - " 335.0555\n", - " 0.0\n", - " 0.0\n", - " 604.1805\n", - " 327.6500\n", " 525.0905\n", " 394.8224\n", " 0.0\n", @@ -1269,39 +780,7 @@ " Emotient FACET\n", " 68\n", " 68\n", - " NaN\n", - " NaN\n", - " 1\n", - " 68\n", - " 1\n", - " 389.0\n", - " 252.0\n", - " 257.0\n", - " 257.0\n", - " -4.371406\n", - " -3.821383\n", - " -2.094306\n", - " -3.278980\n", - " -0.516405\n", - " -4.306592\n", - " -1.787594\n", " ...\n", - " 0.995149\n", - " -2.236120\n", - " 7.103536\n", - " -2.550549\n", - " 447.2003\n", - " 322.2042\n", - " 0.0\n", - " 0.0\n", - " 499.8117\n", - " 328.6136\n", - " 555.1335\n", - " 330.2828\n", - " 0.0\n", - " 0.0\n", - " 607.3446\n", - " 323.2844\n", " 525.9382\n", " 385.9986\n", " 0.0\n", @@ -1325,39 +804,7 @@ " Emotient FACET\n", " 101\n", " 101\n", - " NaN\n", - " NaN\n", - " 2\n", - " 102\n", - " 1\n", - " 387.0\n", - " 242.0\n", - " 264.0\n", - " 264.0\n", - " -5.124269\n", - " -2.969738\n", - " -1.833445\n", - " -3.807125\n", - " -0.724873\n", - " -4.186139\n", - " -1.579113\n", " ...\n", - " 0.994264\n", - " -0.855230\n", - " 4.500861\n", - " -3.289358\n", - " 446.9748\n", - " 315.9877\n", - " 0.0\n", - " 0.0\n", - " 498.4589\n", - " 322.1017\n", - " 559.3203\n", - " 323.0681\n", - " 0.0\n", - " 0.0\n", - " 611.6814\n", - " 315.5635\n", " 525.2611\n", " 378.3876\n", " 0.0\n", @@ -1381,39 +828,7 @@ " Emotient FACET\n", " 134\n", " 134\n", - " NaN\n", - " NaN\n", - " 3\n", - " 135\n", - " 1\n", - " 391.0\n", - " 239.0\n", - " 255.0\n", - " 255.0\n", - " -5.310449\n", - " -3.882663\n", - " -2.479799\n", - " -4.622220\n", - " -0.881652\n", - " -4.608058\n", - " -2.352544\n", " ...\n", - " 0.989092\n", - " -0.936105\n", - " 5.009235\n", - " -3.453575\n", - " 450.5946\n", - " 308.2809\n", - " 0.0\n", - " 0.0\n", - " 499.7155\n", - " 314.7372\n", - " 559.8370\n", - " 317.5146\n", - " 0.0\n", - " 0.0\n", - " 607.8320\n", - " 312.9327\n", " 524.6653\n", " 372.0715\n", " 0.0\n", @@ -1437,39 +852,7 @@ " Emotient FACET\n", " 168\n", " 168\n", - " NaN\n", - " NaN\n", - " 4\n", - " 168\n", - " 1\n", - " 393.0\n", - " 236.0\n", - " 253.0\n", - " 253.0\n", - " -5.206233\n", - " -4.181856\n", - " -2.407882\n", - " -4.627919\n", - " -0.880042\n", - " -5.046319\n", - " -2.624082\n", " ...\n", - " 0.994389\n", - " -0.632709\n", - " 5.729938\n", - " -2.903348\n", - " 452.0932\n", - " 305.5123\n", - " 0.0\n", - " 0.0\n", - " 501.3641\n", - " 309.6560\n", - " 561.4435\n", - " 312.6160\n", - " 0.0\n", - " 0.0\n", - " 608.5308\n", - " 309.9495\n", " 525.9166\n", " 366.4871\n", " 0.0\n", @@ -1487,12 +870,33 @@ "" ], "text/plain": [ - " StudyName ... SceneParent\n", - "0 RotationTest ... NaN\n", - "1 RotationTest ... NaN\n", - "2 RotationTest ... NaN\n", - "3 RotationTest ... NaN\n", - "4 RotationTest ... NaN\n", + " StudyName ExportDate Name Age Gender StimulusName \\\n", + "0 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "1 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "2 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "3 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "4 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "\n", + " SlideType EventSource Timestamp MediaTime ... NOSE_TIPX NOSE_TIPY \\\n", + "0 TestImage Emotient FACET 35 35 ... 525.0905 394.8224 \n", + "1 TestImage Emotient FACET 68 68 ... 525.9382 385.9986 \n", + "2 TestImage Emotient FACET 101 101 ... 525.2611 378.3876 \n", + "3 TestImage Emotient FACET 134 134 ... 524.6653 372.0715 \n", + "4 TestImage Emotient FACET 168 168 ... 525.9166 366.4871 \n", + "\n", + " 7X 7Y LiveMarker KeyStroke MarkerText SceneType SceneOutput \\\n", + "0 0.0 0.0 NaN NaN NaN NaN NaN \n", + "1 0.0 0.0 NaN NaN NaN NaN NaN \n", + "2 0.0 0.0 NaN NaN NaN NaN NaN \n", + "3 0.0 0.0 NaN NaN NaN NaN NaN \n", + "4 0.0 0.0 NaN NaN NaN NaN NaN \n", + "\n", + " SceneParent \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", "[5 rows x 78 columns]" ] @@ -1519,11 +923,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:59.557509Z", - "start_time": "2021-03-24T22:21:59.539415Z" + "end_time": "2021-03-26T05:11:11.918287Z", + "start_time": "2021-03-26T05:11:11.908503Z" } }, "outputs": [ @@ -1643,17 +1047,22 @@ "" ], "text/plain": [ - " Joy Anger ... Positive Negative\n", - "0 -0.462367 -3.642415 ... -0.462367 0.428609\n", - "1 -4.371406 -3.821383 ... -4.371406 -0.516405\n", - "2 -5.124269 -2.969738 ... -5.124269 -0.724873\n", - "3 -5.310449 -3.882663 ... -5.310449 -0.881652\n", - "4 -5.206233 -4.181856 ... -5.206233 -0.880042\n", + " Joy Anger Surprise Fear Contempt Disgust Sadness \\\n", + "0 -0.462367 -3.642415 -1.254946 -1.967231 0.428609 -2.541990 -1.224254 \n", + "1 -4.371406 -3.821383 -2.094306 -3.278980 -0.516405 -4.306592 -1.787594 \n", + "2 -5.124269 -2.969738 -1.833445 -3.807125 -0.724873 -4.186139 -1.579113 \n", + "3 -5.310449 -3.882663 -2.479799 -4.622220 -0.881652 -4.608058 -2.352544 \n", + "4 -5.206233 -4.181856 -2.407882 -4.627919 -0.880042 -5.046319 -2.624082 \n", "\n", - "[5 rows x 12 columns]" + " Confusion Frustration Neutral Positive Negative \n", + "0 -3.051880 -2.369417 1.784896 -0.462367 0.428609 \n", + "1 -2.694475 -2.190143 2.697488 -4.371406 -0.516405 \n", + "2 -2.003944 -1.487295 2.437310 -5.124269 -0.724873 \n", + "3 -2.248295 -1.931136 3.032463 -5.310449 -0.881652 \n", + "4 -2.401067 -1.991587 3.191700 -5.206233 -0.880042 " ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1671,11 +1080,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:23:40.991510Z", - "start_time": "2021-03-24T22:23:40.443246Z" + "end_time": "2021-03-26T05:11:12.547489Z", + "start_time": "2021-03-26T05:11:11.919098Z" } }, "outputs": [ @@ -1683,7 +1092,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jcheong/packages/feat/feat/data.py:1303: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + "/home/jcheong/packages/feat/feat/data.py:1338: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " wavs = np.array(wavs)[::-1]\n" ] }, @@ -1718,39 +1127,7 @@ " neg1_hz_0.66_Joy\n", " neg2_hz_0.66_Joy\n", " neg3_hz_0.66_Joy\n", - " neg4_hz_0.66_Joy\n", - " neg5_hz_0.66_Joy\n", - " pos0_hz_0.47_Joy\n", - " pos1_hz_0.47_Joy\n", - " pos2_hz_0.47_Joy\n", - " pos3_hz_0.47_Joy\n", - " pos4_hz_0.47_Joy\n", - " pos5_hz_0.47_Joy\n", - " neg0_hz_0.47_Joy\n", - " neg1_hz_0.47_Joy\n", - " neg2_hz_0.47_Joy\n", - " neg3_hz_0.47_Joy\n", - " neg4_hz_0.47_Joy\n", - " neg5_hz_0.47_Joy\n", - " pos0_hz_0.33_Joy\n", - " pos1_hz_0.33_Joy\n", " ...\n", - " neg4_hz_0.12_Negative\n", - " neg5_hz_0.12_Negative\n", - " pos0_hz_0.08_Negative\n", - " pos1_hz_0.08_Negative\n", - " pos2_hz_0.08_Negative\n", - " pos3_hz_0.08_Negative\n", - " pos4_hz_0.08_Negative\n", - " pos5_hz_0.08_Negative\n", - " neg0_hz_0.08_Negative\n", - " neg1_hz_0.08_Negative\n", - " neg2_hz_0.08_Negative\n", - " neg3_hz_0.08_Negative\n", - " neg4_hz_0.08_Negative\n", - " neg5_hz_0.08_Negative\n", - " pos0_hz_0.06_Negative\n", - " pos1_hz_0.06_Negative\n", " pos2_hz_0.06_Negative\n", " pos3_hz_0.06_Negative\n", " pos4_hz_0.06_Negative\n", @@ -1776,39 +1153,7 @@ " 0\n", " 0\n", " 2\n", - " 1\n", - " 4\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 3\n", - " 0\n", - " 1\n", " ...\n", - " 1\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", " 2\n", " 0\n", " 0\n", @@ -1826,13 +1171,28 @@ "" ], "text/plain": [ - " pos0_hz_0.66_Joy ... neg5_hz_0.06_Negative\n", - "0 2 ... 1\n", + " pos0_hz_0.66_Joy pos1_hz_0.66_Joy pos2_hz_0.66_Joy pos3_hz_0.66_Joy \\\n", + "0 2 0 0 0 \n", + "\n", + " pos4_hz_0.66_Joy pos5_hz_0.66_Joy neg0_hz_0.66_Joy neg1_hz_0.66_Joy \\\n", + "0 3 3 0 0 \n", + "\n", + " neg2_hz_0.66_Joy neg3_hz_0.66_Joy ... pos2_hz_0.06_Negative \\\n", + "0 0 2 ... 2 \n", + "\n", + " pos3_hz_0.06_Negative pos4_hz_0.06_Negative pos5_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg0_hz_0.06_Negative neg1_hz_0.06_Negative neg2_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg3_hz_0.06_Negative neg4_hz_0.06_Negative neg5_hz_0.06_Negative \n", + "0 0 1 1 \n", "\n", "[1 rows x 1152 columns]" ] }, - "execution_count": 30, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1852,11 +1212,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:24:52.073973Z", - "start_time": "2021-03-24T22:24:52.028491Z" + "end_time": "2021-03-26T05:11:12.578290Z", + "start_time": "2021-03-26T05:11:12.548607Z" } }, "outputs": [ @@ -1898,18 +1258,7 @@ " Valence\n", " Engagement\n", " Timestamp\n", - " FileName\n", - " AU12\n", - " AU01\n", - " AU02\n", - " AU04\n", - " AU09\n", - " AU10\n", - " AU15\n", - " AU17\n", - " AU18\n", - " AU24\n", - " AU28\n", + " ...\n", " AU25\n", " Smirk\n", " AU43\n", @@ -1935,18 +1284,7 @@ " 78.124275\n", " 99.921013\n", " 0\n", - " JinHyunCheong.jpg\n", - " 100\n", - " 6.299417\n", - " 0.01924\n", - " 6.175626e-09\n", - " 7.965685\n", - " 29.55155\n", - " 2.398794e-12\n", - " 0.002099\n", - " 0.000042\n", - " 0.00536\n", - " 8.028388e-08\n", + " ...\n", " 99.999939\n", " 0\n", " 5.448178e-07\n", @@ -1960,11 +1298,18 @@ " \n", " \n", "\n", + "

1 rows × 32 columns

\n", "" ], "text/plain": [ - " Joy Sadness ... AU06 AU20\n", - "0 99.930573 0.000002 ... 94.181007 0.262457\n", + " Joy Sadness Disgust Contempt Anger Fear Surprise \\\n", + "0 99.930573 0.000002 0.002528 0.000107 0.000096 4.059986e-07 1.76971 \n", + "\n", + " Valence Engagement Timestamp ... AU25 Smirk AU43 \\\n", + "0 78.124275 99.921013 0 ... 99.999939 0 5.448178e-07 \n", + "\n", + " Attention AU07 AU26 AU14 AU05 AU06 AU20 \n", + "0 91.699234 11.572159 82.992149 0.000003 0.000744 94.181007 0.262457 \n", "\n", "[1 rows x 32 columns]" ] @@ -1981,6 +1326,260 @@ "print(type(detections))\n", "display(detections.head())" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading a completely new file as a Fex class\n", + "It's easy to cast a dataframe which might be neither an OpenFace or FACET outputs into a Fex class. Simply cast your dataframe as a Fex class." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:45.226073Z", + "start_time": "2021-03-26T05:15:45.221254Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from feat import Fex\n", + "import pandas as pd, numpy as np\n", + "au_columns = [f\"AU{i}\" for i in range(20)]\n", + "fex = Fex(pd.DataFrame(np.random.rand(20,20)))\n", + "fex.columns = au_columns\n", + "print(type(fex))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To take full advantage of Py-Feat's features, make sure you set the attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:51.277149Z", + "start_time": "2021-03-26T05:15:51.259222Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " AU0 AU1 AU2 AU3 AU4 AU5 AU6 \\\n", + "0 0.003699 0.042126 0.532839 0.410701 0.079237 0.612938 0.560122 \n", + "1 0.887544 0.882457 0.265571 0.078094 0.341208 0.563913 0.294769 \n", + "2 0.222848 0.007902 0.806356 0.392177 0.842465 0.765245 0.260304 \n", + "3 0.161766 0.678874 0.994342 0.076421 0.618708 0.880437 0.302860 \n", + "4 0.802379 0.951092 0.047695 0.914416 0.651683 0.183344 0.446120 \n", + "\n", + " AU7 AU8 AU9 AU10 AU11 AU12 AU13 \\\n", + "0 0.999783 0.889522 0.169887 0.374350 0.631952 0.040375 0.852979 \n", + "1 0.307120 0.480530 0.321297 0.321300 0.513947 0.566524 0.816932 \n", + "2 0.372778 0.648263 0.118601 0.695544 0.800680 0.870643 0.588052 \n", + "3 0.191823 0.443328 0.221810 0.321260 0.154832 0.330742 0.281754 \n", + "4 0.872419 0.468856 0.118990 0.985744 0.793074 0.155625 0.178448 \n", + "\n", + " AU14 AU15 AU16 AU17 AU18 AU19 \n", + "0 0.329220 0.196842 0.118240 0.631301 0.709566 0.003093 \n", + "1 0.493617 0.541507 0.624186 0.138642 0.845837 0.638981 \n", + "2 0.266168 0.247223 0.479493 0.928013 0.061631 0.158413 \n", + "3 0.707123 0.500014 0.457013 0.575017 0.955154 0.216029 \n", + "4 0.217853 0.966267 0.455350 0.970413 0.781624 0.193531 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fex.au_columns = au_columns\n", + "display(fex.aus().head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/_build/html/_sources/content/plotting.ipynb b/notebooks/_build/html/_sources/content/plotting.ipynb new file mode 100644 index 00000000..e58e0106 --- /dev/null +++ b/notebooks/_build/html/_sources/content/plotting.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:51:41.015128Z", + "start_time": "2021-03-26T04:51:39.430936Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:02:39.899481Z", + "start_time": "2021-03-26T05:02:39.823101Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:19.202362Z", + "start_time": "2021-03-26T04:54:19.119350Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JOHRyksG/swKdHGVQKmRQyO59repzsB3lUjjKpfBWOqA3qlrRcr0Z6koTSiqN0FbPLIKq2UNkkob3XUskXK3dWGVlZZg/fz7S0tKwf/9+DBw4sM6/S0tLq/PHsJrRaERxcTG6dOlyz2MDBgzA77//joSEBLz77rtYsWIFPvroI0ybNq3e12sNJpMJUqlU0PPbBVmzVCq1i1/ORx55BIMHD8aFCxfwzTff4MCBAw1+qQDAy8sLCoWi3sednJzg5eVV52NdunTB7t27MW3aNERGRmLz5s0AquapMvN1n35obWXvtJRaowU8AB9XB4T6Kq2tbLhfZwR5OMPDWVFngIGqQai63p+DrCrQvXyUGBbkgRHdPTGwqxv8OztBcmd+aY3BDK3BAmMdLbWsxnnFCQkJGDx4MFxdXZGSklJvgAHA19e33scAgOd5uLm5Nfic0NBQPPnkk1Cr1Vi6dCl0ura9XtWCBQuwfv36Nl3n3QQJsUwms243CmnevHlIS0tDbm4uYmNjsXTpUvTs2RMfffQRbt68WeffTJ069b7dzRkzZtT7mEQiweLFi7F3714sX74cc+bMQUGJGsCdbVmeqrZl7wSn0sjDSSFFD09nDOzmhpHdPTGiuydCVa7o0skRSgdZo6+UWDWSfP+usoNMAi8XBUK8lYgJ9MDIYE8M6uqGAHcnyCQSaI1VtWkMFhjNVYeJavRGLF++HI8++ii++OILrF27Fs7Ozg2uZ/HixXBxcanzMZlMhscff7zO1yAiHD58GE8//TQCAwNx4MAB/Otf/0JOTk6bX9xcr9fXudnVpuq7cHFdi60uMj5mzBjat2+fTV7Llniep5MnT9JLL71EnTt3pilTptB//vMfMplMtZ63ZcsWcnZ2JlSN6VgXJycn2rBhQ6PWpdPpKCkpiUJCQsg/MIji9p2k/ReK6OjlEsrIL6ebZToq15vIYsOLYk+YMIF27tzZ4tcxmC1UUmGgnGItJeaW0s6z162fwfvvv09Hjx6l4uLi+76OxWKhWbNmkYuLyz2fY2BgIBUWFtZ6/vXr12n58uXUo0cP6t+/P3355ZdUVFTU4vfTEtOnT6etW7e2+nrQwEXGBdkmtpeW+G4cxyEiIgIRERH4/PPP8dNPP2H58uWYN28e5s6di5CQECiVSri7u2PVqlXYunUrTp48CSJCbGwsli1bhuHDh1tfj4hQXFyM8+fPIzs7u9Zy48YNdO/eHf369UPPkF6IClYhOMATDvV0hW1BIpHY5AR+hVQCT2cFPJ0VCPZygdHcCS++PA+B/n64ePEidu7ciezsbMhkMvTp0we9e/dGnz59rEv37t0hk8kgkUiwYcMGPPjgg1izZg1ycnKgVCqtl+TZvXs3NBoNNBoN4uPjkZiYiOnTp2Pr1q2IiIiwi5lO9Xp9m7f+dxMkxPayTdwQpVKJ559/Hs8//zzOnj2LzZs349ChQ9BqtbUWHx8faLVaHDlyBLt374azszOUSiWUSiVKS0sBoNYXODY2Fn369EFwcDDkcnmbvqf6tolbSiGTYv26tbX+jYhQVFRU64fr0KFDyM7ORn5+PlxcXKDRaGCxWODq6gpXV1eoVCq4urriypUrKCkpgVKptD42c+ZMbNu27b5d9Lam0+kE706zlrgRwsLC8Mknn9z3eRaLBZWVldaAu7m5wdvb2y5aDKDx28S2WpdKpYJKpcKoUaNqPabT6aDRaODq6gpHR0e7+Xyawx62iVlLbENSqdTactijtgxxQ5ycnATvgtqKPXSnO/TodEdjq21i5r/soTst2H5iFuK211rbxB1Zh26J22N32t7ZS3e6PbGHbWLWEncgLMS212G706wlFgbbJra9DtudZi2xMNg2se112O40a4mFIZFIWIhtyGQyAaj6PguJtcQdCPvxtC176EoDbD9xhyKXy62tB9Ny9tCVBjr4+cQdDQuxbdnDyDTAWuIOhXWnbatDd6dZSywM1hLbVofvTrOWuO2xENuWTqfruC0x69YJg4XYtjp0SyyTydiXSQAsxLbVoUPs4+ODgoICIVbdobEQ29bt27fRqVMnocsQJsSBgYG4evWqEKvu0ORyOduMsaGcnBz06NFD6DJYiDsSthljW5cuXULPnj2FLkOYEAcEBOD69evsON42xrrTttWhQ+zo6Ah3d3e2XdzGWIhtq0OHGGBdaiGwENtORUUFbt++bRcXPhcsxAEBASzEbYyF2HZycnIQHBws6IXUqrGWuANhIbYde+lKAyzEHQo7Us52WIjBQiwE1hLbDgsxWIiFwA72sB0WYvw3xGz2xbbDDvawnUuXLtnF0VqAgCHu3LkzJBIJ1Gq1UCV0OKw7bRs6nQ5FRUXw9/cXuhQAAoYYYF3qtsZCbBtXrlxBYGCg4LNcVmMh7kDY6LRt2NP2MGAHIc7LyxOyhA6FtcS2wUJcA2uJ2xYLsW2wENfAQty2WHfaNliIa2AhblusJbYNFuIa2EkQbYuFuOWMRiNu3LiBwMBAoUuxEjTEKpUK5eXlqKysFLKMDoMdsdVyubm58PPzg0KhELoUK0FDLJFI4O/vz0ao2wg7Yqvl7K0rDQgcYqBquzg3N1foMjoE1p1uGbOFR/zJ0/APCha6lFoED7GvXyAOnDwLrYF181qbi4sLZs6cKXQZoqQ1mHHyWhkSkpIwOGKo0OXUIniI+w8YiLNnTiMp7zby1JXg2QkRrcbFxQX/93//J3QZokJEKCjXIznvNowWC86fSUPssBihy6pF8BBHDBmEK9mZcJZLcKG4AqnXb6PCyFplRnhmnsf5ogqcLdDAUcah/FYRdBVa9OvbW+jSahE8xEOHDEZezgXwZhNcHaSoMFiQdFWNPLWOtcqMYCqMZqRcK8ONMj06OUghk0pwOvUkBoYPtYt5tWoSvBq3Tq7w6dINORcvgOM4OCukcJJLcb5Yi7TrZaxVZtoUEaFQo0dS3m3oTTxcHaXgOA4AkJGWgsioKIErvJfgIQaAfmFhyM48a/1vqYRDJwcptAYzkq7exrXbrFVmWp+FJ1worsCZfA0cpBycFbXjkZWegpHDhglUXf3sIsSDBw/Ghcwztf7tv62yBOeLtDh1owyVRnZNY6Z1VBotSL1e1WC4Okghl9aOhsloxMWsMxgxjLXEdQofPBg52Zl1PiaVcHB1kKJcb0Zinho3ynRsSh/Gpoq1BiTnqVFptKCTowySO93nmrKzMtDVLwCe7u4CVNgwu5iaIGpoOHLOZYDn+ToHDTiOg4tCCjNPyCrUolBjRF+VEk5yqQDVMu2FhSdcvlWBXLUOznLJPa1vTadTTyI80v5aYcBOWuJuXXzh4OiIG9evNfg82Z1t5TK9CQlX1bjZjltlMRxZJebPXmeyIO16Ga6q6+4+3+1ceiqGRUe3UXVNYxchBoC+/QfgXMaZ+z6vulV2lEmQWahF+o1y6E3tb1t59OjReOihh7Bx40aUl5cLXU4tN27cwMcff4xevXohM7PuzSB7VqI1IOmqGhVGM1wdpHV2n++WlZ6CEcPtb1ALsKMQDxw0CBcaEeJq1a3ybd2dVrlcL+qW4W579uzBM888g23btsHf3x/Tp0/Hb7/9BoPBIEg9RqMRv/76Kx5++GGEhYUhNzcXP/zwA0JDQwWppzl4IlwqqcCpm+WQS6sGTrlGBLikuBhl6lIMDuvXBlU2nd2EOHzIEFw6l9Gkv+E4Di4OUjhIOWTma3D6ZvtplV1cXDBjxgzs2LEDly9fxrhx47Bq1Sp07doVL7zwAg4ePAiLpfXfa2ZmJpYsWQJ/f3989dVXePLJJ3Ht2jV8++23iIqKalQI7IHeZMGpG2XILa1sVPe5ptOpJxE2OBxSqX2OwdhNiCOHDG5yiKvJpBJ0cpRCXWlC4lU18ttZq+zp6YmXXnoJhw8fRnp6Ovr06YOlS5fC398fixcvxr59+1BRUWGTdRERLl++jHXr1iE6Ohrjx4+Hg4MDjh07hiNHjuDpp5+Gi4uLTdbVVkorjUjKu41yfeO7zzVlnEpBlJ1uDwMA15Qve0REBKWkpLRKIRaLBW6d3bEtPg0enp7Nfh2ThUeliYeP0gF9fFzgILPPX09bOHfuHH788UccOHAAp06dQlhYGGJjYxEbG4vhw4fDvRG7Q4gI2dnZiI+Pty5msxmjR4/GrFmzMGHCBLuZX7mpeCLkllbi8q1KOMokUMia12bNe/IveOftN/HEo4/YuMLG4zgulYgi6nzMXkIMAEOihmHOa29i+KgHWvQ6RIQKEw8JgL4qJXyUDqLp9jWXTqdDUlKSNYhJSUno0aOHNdQjR46ESqWCxWLB2bNnER8fjyNHjuDo0aNwcXFBbGwsRo0ahdjYWPTo0UP0n5fBbEFWgQa3Kk1QKqSQSJr3fsxmMx4cEIwrV67Ax9vLxlU2XkMhtquf2AGDBiE783SLQ8xxHJQKKUwWHmfyNfB1NaKXtxIOzfwlFgMnJyc88MADeOCBBwBUDUSlpaUhPj4ecXFxeOGFF+Dt7Y2SkhKoVCqMGjUKjz/+OL788ksEBAQIW7yNqStNOJNfDgsRXB0aN3hVn4vZ5+Cl6iJogO/HrkIcPngwftu5x2avJ5dK0EnCoURrRGllKfr6KOHdAVplAFAoFIiOjkZ0dDTeeustWCwWZGZmQqVSQaVSCV1eq+CJkKfW4VJJBRxkEpscDHQ6NRnhQ+1rEoC72VXTFBE+GDnNHNyqT/UItkzC4fRNDTIKNDCaeZuuQwykUikGDBjQbgNsNPM4fbMcF0sq4KKQ2qzXlZWeipho+5oE4G52FeLBA8JQcD0PlZW2GWmtSX5nBLtYa0TCVTWKtYZ2NYLdkd3WmZCUp4a60oRODlJIm7n9W5dz6akYOYKFuNEcHRwQ1CME57OyWuX1OY6D0kEKmQRIv1mOrA7aKrcXRIQ8dSVSrt8GAChbuP17N3VpKUoK8xE+cIDNXrM12FWIAaBf2ACcz2z8kVvNIZdK0MlBigKtgbXKImU0Vw1aXiiugIvcdt1nIoLBzENjMCPx+FEMGBIBuVxuk9duLXYX4iFDBuNijQkCWgvHcXB1kEFa3SoXaWG0sFZZDMr1JiTnqXGrwgBXG3SfLTyhwmiBRm+G1miBo1yKXt5KpOz9Dc/NecpGVbceuxqdBoCYqKH4x/r1KNebwHEc5BIOcqnEpts5NSmkEsglHArK9bhVYUSojxJeSodWWRfTMkSE67f1uFBcAbmMg9KheV9fIoLRQlU/2lR1xJ+vqwO8XBRwc5JDIZWguLgYR48cxuZNG238LmzP7kL8wIjhkMMC5F9Ar4HhKK0wQq0zwUIAQJBwHBRSDjIJZ7Ptn+pW2WjmcepmOfzcHNHTy6VJx9cyrctk4ZFdpEWhxgAXRdNbXwtf1U22EAAiuDrKEeDuBA9nBZR1nAixefNmTJkyBZ06dbLhu2gddhdiiUSCl19+GT/EfYd//nME/Ds7gYhQaarq7tzWm1BaaYLGYKn64AlQyDjIpVyTj4m9m0ImgVzK4Wa5HiUVRoSqXOHpYj/X3OmoyvVmnM0vh95safTBG0QEk4VgsPAAOMgkgLfSAT5KBdwc5Q0egklEiIuLw5dffmnDd9F67C7EAPDMM8+gZ8+eUKvVcHd3v3MOsQwuChl8OzkCAIwWHlqDGeV6M25VGlGuM4MHgQDIOA4KmQRSDk1uratbZYOZR9qNMvh3dkIPT2fWKguAiHCzXI/sogrIJYDrfbrP/21tqwYplQoZ/Do7wcNZDqVD3dPu1CU9PR3l5eXWo9/snV2G2MvLCw8//DA2btyI119/vc7nKKQSeDgr4OGsQJCHM3iqHpwwQa0zo7TSCJ2ZAI7A3Xm+XNr4LrjDnVb5RpkOJVoDQn1d4eHc/ltlIsLVq1fh7OwMb29vwY5uM1l4nC/SIv9O91lWR/eZiGDiCQZzVWilXFVr632ntW3uiHVcXBzmzp1rd/NL18euToCoKT4+Hi+//DKysrKa9UUiqupKaQ0WlOlMuFVpgsZwZ8obAmRSDopGDpgZzDwMZt7aKsvauFU2GAy4fv06rl27hry8POttzftOTk6IiYnBsGHDEBMTg4iICDg5OTXqtVNTU3H8+HEcP34cJ06cgEwmg9FohFarhb+/P/z9/REQEFDnraurq83fr9Zgxpn8cuhMlnu2V3meoDfzsPAEcKjqnbk6wN1ZDtcmtLb1MRgM8PPzQ3JyMrp3797St2IzojmLqSYiQr9+/bBu3TrExsba5DUtPEFrNEOjr2qp1TozzHzVCKVEUtVa1zdgxhNBa7TASSZFqEoJdxu2ypWVlcjIyKg3oGq1Gl27dq0zSNX3y8vLkZCQgISEBJw4cQKZmZno16+fNdTDhg2Dv78/SkpKcOLECWto09PT0atXLwwfPty6+Pv7W+u6+4fj7lsHB4daNfXp0weRkZEYNGhQo35EaiIi5GsMyC7UQCrh4CSX/re1NVX1qiQcBy8XBXyUDujsJLP5qabbt2/H119/jUOHDtn0dVtKlCEGgK+++gpJSUnYsmVLq7w+EUFn4qE1mqGuNKFUZ7wztzUHEEEuq2qta/66V7fKAe5OCPZ0hqyJXa7qk+4TExORkJCAxMREnDt3Dr169UJQUFCtYFbfV6lUTZ5VQqfTISUlpVawzWYzzGYzoqKirIGNiopqdmtKRCgtLa0V6szMTCQnJ+PcuXPo27cvIiMjMXToUERGRqJv3771vg8zz+NCcQVulOnhLJfAzBPMPAHEwcVBAh+lAzydFXCtZ0pZW5k8eTKmT5+Op59+utXW0RyiDbFarUZwcDAuXLgAb2/vNlmn6U4XvFxvwq1KI8ruDJiBqubAVkg5SCQcKgw8nOQShKqU6NxAq6zVanHy5ElrYBMTE6FQKBATE4Po6GjExMRg8ODBTW61moqIkJ+fDx8fnzY5yV+n0yE9PR3JyclITk7GyZMnkZ+fj/DwcERGRloXf39/VBotOJOvQXGFAc7yqt1Hni4K+Lgo0NlJDsc2mpo4Pz8foaGhuH79ut3NXiLaEANVI9X9+vXDm2++2abrrcYTodJogcZghlpnQmmFEQYzD3BVrTLHcQjxckZ3DxdIOODChQu1WtmLFy9i0KBB1sBGR0fDz89PkPcitNLSUqSkpCA5ORmJiYnYvXs3FAoFxk2Zhv/3ySr4Kh3g4aK4cyRd2w+orVy5EtnZ2diwYUObr/t+GgoxiKjRS3h4OLW1hIQE6tGjB1ksljZfd114nie9yUIlWgPlFGspOU9NBy4UEQDr8uCDD9KqVasoKSmJ9Hq90CXbjbKyMtq1axeNGTOG5HI5cRxHEomEfv1tB/E8L2htPM9T37596ejRo4LWUR8AKVRPLu1yF1NNUVFRUCqVOHjwIMaNGyd0OeA4Dg4yDg4yBTxdFAhG1YDZ4CFDcCotDU5OTti3bx+OHz+O3r17Y8SIEejZsyd8fHzg7e0NHx8f+Pj4wNPTU7RzV9VFr9ffM4Jec8nNzYVeXzWBYWhoKFavXo1//etfeOyxx/DYX4Sbu6pacnIyTCYThg8fLnQpTWb33yKO4/Dyyy9j3bp1dhHiukglHNJSU/Hxxx9j69at+OOPP7Bjxw5s27YN3333HXx9feHv7w+JpOqY3KKiIqjVari5ud0T7vruu7u7C7bf0mw2Iz8/v86AVv93WVkZunXrVmuketCgQRgyZAj++OMP3Lx5E0uWLMGiRYvQuXNnLFu2DJ06dcKSJUsEeU93++c//4lnnnlGlLO+2P02MQCUl5cjMDAQWVlZ6NKlS5uvv7GICK+//jpOnz6NPXv2wNHREVqtFlu2bMHatWuh0Wgwb948PPvss+jcuTNKS0tRVFRkDXZD9zUaDTw9PesMukqlQkBAAIKCghAYGAhHR8cm111YWIhLly7h4sWLuHjxIi5fvmwNaGFhIby9ve/ZZ1zzv318fGr9yGRnZ2P58uXYs2cPFi5ciIULF8LNzQ0AsH//fsydOxenTp2Cj4+PTf8fNIdOp4Ofnx/S09Otu9fsjagHtqq99NJLCAwMxLJlywRZf2PxPI+ZM2fCbDbj559/tu5SISIkJiZi7dq12LFjB/7yl7/glVdeafQE7CaTCSUlJbXCXX1bWFiIq1ev4sqVK7h27Rq8vLwQFBSE7t27o3v37tb7/v7+KC4utga1erl06RIUCgVCQkKsS3BwsDWsXbt2hULRuP3iWVlZ+PDDD3HgwAEsWrQIr732Wq2TCAoLCzFkyBBs3LgRY8eObd6HbGNbt25FXFwc9u7dK3Qp9WoXIU5NTcXUqVORk5NjtzPxVzMYDJg0aRJ69+6NNWvW3BPSkpISxMXFYd26dXBzc8Orr76KmTNn2mS3hsViwc2bN3HlyhXk5ubiypUr1vt5eXnw9vZGSEgIevbsWSu0jZmjuiEZGRn48MMPcfjwYbzxxhuYP3/+PfufeZ7HxIkTMXToUCxfvrxF67OlCRMm4JlnnsHMmTOFLqVeoh6drikiIoL+/PNPQWtorLKyMho0aBB98MEH9T7HYrHQrl27aMqUKeTh4UELFy6krKysNqyy5U6fPk1Tp04llUpFK1asII1GU+9zP/30Uxo+fDiZTKY2rLBheXl55OHhQZWVlUKX0iA0MDotqhB/9913NGXKFEFraIr8/Hzq3r07rV+//r7PvXr1Ki1btoxUKhU98MAD9PPPP5PRaGyDKpvn1KlT9Nhjj5Gvry999tlnpNVqG3x+QkIC+fj40NWrV9uowsZZvnw5vfzyy0KXcV/tJsRarZbc3d0pLy9P0Dqa4sKFC+Tr60u//fZbo55vMBjoxx9/pNjYWOrSpQv9z//8D127dq2Vq2y8lJQUmjJlCnXp0oW++OILqqiouO/fqNVqCgoKol9//bUNKmw8nuepZ8+elJiYKHQp99VuQkxENH/+fPrf//1foctokpMnT5K3tzcdO3asSX+XkZFB8+fPJ3d3d3r00Udpz549gh30kpycTJMnT6auXbvSV1991ejuJ8/z9MQTT9D8+fNbucKm++GHH2jQoEGCH2jSGO0qxGfOnKFu3brZ1XZVY+zZs4d8fHwoIyOjyX+r0Wjo22+/pYEDB1LPnj3ps88+o1u3brVClfdKTEykiRMnUrdu3Wj16tWk0+ma9PfVdTf171rb9evXydvbm9LS0oQupVHaVYiJiIYNG0YbN24Uuowm++GHH8jf37/ZmwM8z9OJEydo9uzZ5ObmRnPnzqWkpCSbtiQ6nY6ys7Pp999/pwkTJpC/vz998803zTp89MyZM+Tl5UXZ2dk2q88WeJ6n8ePHNzjoaG8aCrHdH7FVl6+++gqTJk1CdHQ0QkJChC6n0WbNmoWCggJMmDABx44dg4eHR5P+nuM4xMTEICYmBsXFxYiLi8OMGTPAcRz8/f3RpUsX+Pr6okuXLrXu+/r6wtPTExzHged55Ofn48qVK7h8+bL1tvp+cXExAgIC0L17d0ydOhW///47HByaPvtnRUUFnnzySXz22Wfo3bt3k/++Na1btw63b9/GO++8I3QpNiGa/cR3W7t2LdatW4eEhAQ4OzsLXU6TLF26FAkJCdi3b1+La+d5HufPn0d+fj7y8/NRUFBQ521FRQW8vLxQWloKNzc3dO/eHcHBwQgODq51v1u3bjbZD//iiy9Cr9dj48aNdnUo46VLlxATE4OjR4+iT58+QpfTaO1mP3FNPM/TU089Rc8++6zQpTSZxWKhWbNm0SOPPNJm2/Y6nY7y8vLuuyvIFrZs2UIhISFUXl7e6utqCrPZTMOHD6dVq1YJXUqTob1tE1fTaDTUt29f2rBhg9ClNJnBYKDx48fTc889Z9f7g5tCq9XSkiVLyMfHh1JTU4Uu5x6ffvopjR492m5Oa22KhkIsjun86qFUKrF9+3a8/fbbSE9PF7qcJlEoFNi+fTtu3LiB/v3745dffgHPi/cyMnv37kVYWBgKCgqQkZGBIUOGCF1SLWfPnsXKlSsRFxcnmlksG62+dNe12FtLXG3r1q3Uo0cPUqvVQpfSZDzP0969eyk8PJyGDBlCe/bsEcV+y2rFxcU0e/ZsCgoKol27dgldTp0MBgMNHDiQvv/+e6FLaTa01+50TfPnz6dHH31UVAGoied5+uWXX6hXr140evRouz+KiOd52rhxI6lUKlq8eHGbbGs317Jly+iRRx4R7XeDqIOEWK/X09ChQ+mzzz4TupQWMZlM9I9//IP8/Pzo0UcfpczMTKFLusfly5dp/PjxNHDgQDp58qTQ5TQoMTGRVCoV5efnC11Ki3SIEBMR5ebmkkqlstt5kpqisrKSVq5cSd7e3vTMM89Qbm6u0CWRyWSilStXkqenJ33yySd2PyBXUVFBvXr1ol9++UXoUlqsw4SYiGjnzp3UrVs3KigoELoUm7h9+za999575OHhQYsWLaKioiJB6khLS6MhQ4bQmDFj6OLFi4LU0FQLFiygp556SugybKJDhZiI6L333qPRo0eT2WwWuhSbyc/Pp9dee408PDzob3/7W5vtg62oqKA333yTfHx8KC4uTjTblfv37yc/Pz8qLS0VuhSbaCjE7Wysvcrf/vY3cByHv/71r0KXYjO+vr5YvXo1Tp48iUuXLqFnz55YtWoV9Hq9Tdej1+tx7tw57NixA59//jnCwsJw/fp1nD17VjQTyZWVleG5557Dd9991+IZS8RAtIdd3k9RURHCw8Oxbt06PPzww0KXY3Nnz57FsmXLkJiYiJCQEKhUKvj6+tZ5q1Kpah3eqdfrcfnyZevEeDVvCwoKEBAQYJ2+Z9KkSZgwYYKA77Tpnn32WTg6OmLt2rVCl2Iz7WKOreY4fvw4Hn/8cSQnJyMwMFDoclpFbm6udUbKgoKCWrc17ysUCqhUKhiNRhQUFCAwMLDWXFvVtwEBAZDL5UK/rWb7/fffsWTJEqSnp0OpVApdjs00FGJRnsXUWMOHD8fbb7+NadOm4dixY806G8feBQUFISgoqMHnEBHKy8tRUFAAmUyGwMDAdjVxfbXi4mLMmzcPv/zyS7sK8P2065YYqPoCT506FV26dMGaNWuELodpJTzPY9q0aQgJCcGnn34qdDk211BL3C4HtmriOA5xcXHYs2dPq10ilREWz/N49dVXUVRUhA8++EDoctpcuw8xALi5uWH79u14/fXXkZWVJXQ5jA3xPI/58+fj7Nmz2LVrV7vcZLqfDhFiABg4cCA+//xzjB8/XnRnPDF1IyK89tprOH36NHbt2tXsi6WLXfsb3WjA008/DScnJ4wfPx6bNm0S3a4T5r+ICPPnz0d6ejp2795d61IxHU2HaYmrPfHEE/j1118xd+5cfP/990KXwzRDdQt86tSpDh9goIO1xNVGjBiBI0eOYNKkSbh69ar1CC/G/hERFixYgNTUVOzZs6fDBxjogC1xtd69e+PEiRPYuXMnnn32WRiNRqFLYu6DiLBw4UKkpKRgz5491kuldnQdNsQAoFKpcPjwYdy6dQsPP/wwysvLhS6JqQdR1bWfk5OTWYDv0qFDDAAuLi7497//jZCQEIwcORI3btwQuiTmLkSERYsWITExkQW4Dh0+xAAgk8mwZs0azJo1CzExMTh79qzQJTF3EBHeeOMNJCQkYO/evejcubPQJdmdDjmwVReO4/DWW2/B398fY8eOxdatW+3mSvYdFRFh8eLFOH78OPbt28cCXA/WEt9l5syZ+OWXX/DUU09h06ZNQpfTYRERlixZgqNHj7IW+D5YS1yHUaNG4dChQ5g0aRLy8vLw7rvvsl1QbYiIsHTpUsTHx2Pfvn0d4sT+lmAtcT1CQ0ORkJCA7du346WXXoLJZBK6pA6BiPDmm2/i8OHDLMCNxELcgC5duiA+Ph43btzAlClToNVqhS6pXSMivPXWWzh48CALcBOwEN+HUqnEjh074Ofnh1GjRiE/P1/oktolIsLbb7+NAwcOYP/+/U2+7GtHxkLcCDKZDOvXr8fUqVMxbNgwdjqjjanVakyfPh0HDx5kAW4GFuJG4jgO7777Lj788EOMHj0a+/fvF7qkdiE+Ph6DBg1C165dm3XhdQbtc97p1nbgwAHy8/OjWbNm0fXr14UuR5SMRiO999575OvrS3/88YfQ5dg9dLR5p1vbmDFjkJ2djcDAQAwcOBCffvopDAaD0GWJxuXLlxEbG4vk5GScOnWqXU4p3JZYiJvJxcUFH330EZKSknD8+HGEhYVh586dQpdl97Zs2YKoqChMnz4du3btgq+vr9AliV99TXRdC+tO12/nzp0UEhJCkydPFs21itpSWVkZzZkzh/r06UNpaWlClyM6YN3p1jdx4kScPXsWI0aMQHR0NJYtW4aKigqhy7ILSUlJGDx4MJycnJCSkoLBgwcLXVK7wkJsQw4ODnj77bdx5swZXL16FX369MGPP/5YdeW6DshiseDvf/87pkyZgpUrV+Lbb7+Fi4uL0GW1O+1+8nghHTt2DAsWLECnTp2wevVqDBgwQOiS2sy1a9cwZ84ccByHTZs2wc/PT+iSRK1DTx4vpBEjRiAlJQUzZ87Egw8+iNdeew2lpaVCl9Xqfv31V0RERGDChAnYv38/C3ArYyFuZVKpFPPmzUNWVhZ4nkffvn2xfv16WCwWoUuzuYqKCrz00kt46623sGPHDrzzzjuQSqVCl9XusRC3EU9PT3zzzTfYs2cPNm3ahMjISJw4cULosmzm1KlTCA8Ph8FgQFpaGqKiooQuqeOob9i6roXtYrINnudp8+bN1K1bN5ozZw7dvHlT6JKazWKx0BdffEHe3t60efNmoctpt8B2MdkXjuPw1FNPITs7G127dkVYWBhWrlwpimlzLRYLMjMz8f3332PevHkICwvDzz//jKSkJDz11FNCl9chsdFpO3Dx4kUsWrQI8fHxCA0NRb9+/axLaGgo/P39BZtZ5ObNm0hOTkZSUhKSkpKQkpICHx8fREVFISoqCpGRkYiIiGiX1zu2Jw2NTrMQ25HS0lJkZWUhMzMTmZmZ1vsVFRXWcNcMuZ+fn03DrdVqkZqaiqSkJGtwKysrERkZWSu0np6eNlsn0zgsxCJXWlpaK9TVi06nQ2ho6D2td7du3e4b7upucc1WNicnB2FhYdawRkVFoUePHmx+MTvAQtxO3bp1655gZ2VlWcNdM9gBAQG1QpuamoquXbvWamUHDBjQIa/vKwYsxB1MSUnJPd3y3NxchIaGWlvZoUOHshPwRYSFmGFEjh12yTDtGAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIHAsxw4gcCzHDiBwLMcOIXJMuqMZxXDGAq61XDsMw9QgkIu+6HmhSiBmGsT+sO80wIsdCzDAix0LMMCLHQswwIsdCzDAix0LMMCLHQswwIsdCzDAix0LMMCL3/wEnyj00EAtqiAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FmPjZyWMxHGMxo8esiCEo5NNnZnL6zHSKqpzISudCbr6T2529N0f7itvrx2zSM2FEassF6FKgr3OEKEK0SU9hvZsHP9rbpWVR6/Dy0Mc5qKqKRd/3y6B5XSyKItPGZaIo6lGzaBRFxeXxMX18BiZj5xF7DU4fAgLXnz6eCHP4VU7pjmNaxBAU8tlzszl5ehoFlU7kTuJ+BUFg7PDklnC/UCMrCl5fgJkTstA3rR+bo7T6s3nfLOScKid//TQHpYMbl9sn88BH+2j0BYg09f1skhBcvzdjsxiZOibtqFk0jS4vw9PjSYzrvPODyxvA7vZz/enjSYwO38IFXXFMBHt0hyAInHfCMPwBhS92lpGVYOtwTWQxGYiLtuJwejAfEeTh8zgp2fc9NXlbUWU/os6EqDcg6k1IehOS3ohOb0IyNP82oTOYMEfEEhnXvtaUqqrYHV7Gj0gmJvJwDezOorR6S7OQt5fa22U+KYrCY5/tp6TBQ3Q/481FwK8E1/FSkyWRnhxDRU0jpVUNRNk6LpOkKAqy34uqKiiyjKLIqIrS9Dv4o8hy8HFFBlUlJmVYqwIEzTfbrLS4TsfnDyhU1Lu59tRxjEg5dlu8HHURl5eX4/V6ycrKOqrnFUWBixYMJ6CofL27nKxEW9AZ04as1Fi27ilCIijcqgPf0liyh4TsCUyduQiLLQKvx4XP46G6rICKgt34PC6M1ihMEXG4ZT8+r4eAz4OjppTorKmMXXIJZlt0yzmcHj9x0VZGZia0OndXUVq95cjMJ9uGfK5YMByA/67PZ2dZIzG92ErqjOaPz6uARWr+m8Ck0WnU1Dvw+gIYjwiDVVSVwl3ryV//MgGvA0EQEUSp6beIqioIgoikNyGKUvAxUURVZDzOBhLGzCdz6lKiEtLxBWSsFiOR1s5uFCpF1Q4unD+c6SOGftHE/nDURfzyyy9TWFjIo48+erRPjSSKXLJoBAFZYdP+SrISbK2cIY7GBnI2r2XvFx/gqsghIWsCM088iRlz78YWcTjIUFVV3n3+YXK+/5yAP2g66u016KqLuPqWf5CUFsxFdTocvPfK02x87mbSZ53DiNkrkEQJWZaZMDKlVV6wqqq4FOggVLnv77cp8+mzvVXYjDpsRh2r91cRZdT1W8DNCIBbUbEcsSdmNOiYMDKVLXsKW0RcW5bH/rXPQ8DNuT+7lQlTDteh/uazN/j8zaeDn4cq4He5WXDqj1hy9hUt309RYR7ffP4+W1+7G1N0MtEj57NwxVkdOrPUpr3gkyansnxq2sC80W7YtGkTHo+HxYsXH5XzHclRF7HBYMDrPXrOo7ZIoshlS0YRkFW+P1BJnFll64Y17P9+DWUHf2DCtLmcdPLpxA+7jez0xA6Pse+Hb9ix6XP8Pk/L3/w+D36fh1f+eTs33rcqGNhhs/Gjn95EwdIzeee/j/HNni8Yvuhy4jLGtTKj4XCUVm8zg7pClsGvqAiiwN9ffA+vs56Rc09GVoLRYAMhZIlgHHXb1MTEuAhEQcDlsJOz/jXqDn7L3NN+wtLTzmtV2mfHps/5/I2n8PtaXxPrP34Fk9XGvJMvACAjcxgXXXUjvp9cw5Zv1rFxzbu8cc9rjJy1nBOWn8ew0YercBTXuJiUHcuF84cfte2uNWvW4HQ6jw8RG41GfL6jF1jRETpJ5Mqlo3nwph+Ru/cHAP7y0D/42eXvk5QQy7ofCrj12a+o89cSadZjMeowGyRMegmjXuLbNW/j93o6PHZDTSVVpQUkpmW3/C1r2EhuuP0fbPpqNWtef5za7InMGPUn4hMPRxe1RGn18ppTFPDLKj5VISCrqKiICKgE16yRJh2ZMWZkuZzymkJGxp1NeaOXBo//cPykIKCXBAyiiE4UeiVuUQCfEoylPnKFLYlQd3A9h75+lWGT53HZ/auIim6fqPH5m8+2EzAEb4pfvreKE5af30qIBr2BcdMXcOKSkzljRhIP/+3fvPrXmzBFxDFx4UpGzVhGWlIsVy8fE7IQ2o7w+XwYB6mbx3E3Ezej14l88cl7vP3OO7zx+ms8/tf78NSX89Of/pTCCjuNngDUuymrcwWdTS3Xu0B1bX2nxxVEEZ+3ffijKAqcsGg58cMm07B/DU/84WKmn3IpJ593GXq9AVcXUVqKAgFFxa8q+AMqSptZz2oQSbWayIgxkxFjJjnaTFqMmaRIY4vJ/tdDsRTpndx1TrC2ssvrJ6/KSWGNi7xqJ/k1LqocPpz+4LpcVVVEUcAoiRgkke704JVV9E1rgT3bN/PJqocwG40sv/IO5s3pMG4fgIaais6P6XJSVt2AKhpwegM4PH6cngBV9Q5+e0GwT/SqJ/+K52/3c/U11/PSfx8kf+tavt/41YDnlneHz+cjMnJwnGeDMhMPBREDZGZm8Ksbb+BXN97Anj17eOqpp5g5cybWhGySJy4F8yQiLa0dJ6qqkjB6Lo2VBSiB9haFokKJPxpHcQM2sw6TQYdZL2EySLg8PrJTE7jrz//mu23Xc/UvruOx317IsktvJnbyPCQV3IqCX1YIKCoioArBogMmvUisWU9KpInMODMp0RZSY8ykRZt7lLEly3IrM9Zi1DMhPZoJ6dGHx64o1Dp9TeJ2klvloqjORZ070BLKraoqOknAKEropeCsLQjgVsFTWcEHqx6lLGcrt97xF351zZXc/Pjn+ANyq0qdqgq+gILXL2ONiqOxrrLDMQs6I9sKGxEIZkvpJAFJFEAQ2VdQRW5uLv/5z394/vnnSUtL46mnnuKiiy4iMuLoNw/wer0YDIOTznhcmtMdMX78eB577DEuu+Y3/OaOx8jZ/CHVFU+TMfkk0qcsxRwV9CQLgkDWtOWUbl+N216NKh/ORBJ1BkaddAWNHpU6lwNZUREFmsrOgM/r4+enT6GoqIj6qlKu/vE5/O0f/+KNh29g+iV/YsKSs4k26UiINZMVZyEt2kxKtIn0WCuR/QxSaCvijhBFkfgIE/ERJmYNP7x1E5AVimtdFNa4yK9xcqjS2cokVxSZV+66lIbSXOYtOYWv9u8hOTHoEZ4xJpUPNh3EbDLi8PhxeQO4vDIqwVTLhBln4/zy+XY3RFFnYPjcs4lqEwvtcrsJlGzlP5//lSf+UMJPfvITPvnkEyZOHNzuDT6f7/gR8VAxpztj26EaJs5ZxpIVZ/HOmm+p2fslG5//PVGpo7ClTUDSGxF1etJnnU3Noa3UF+9GDfixxKaSPu1kYtLGoXjs6PUGcNXjrCnBWVuCo6aYhsoirnu+nMS4GMaNG8e4ceO4+Te/RrDEo4vJ5mdnzQxZJcueiLgzdJJIdoKN7AQbR/ZHcHn95Fc7ya9y8nJpLlnZwygvOEB2ZjqZmZmMGTMGhxDJ/mqJuNQsbLEpmCPjsJl0LcsB2/RliJ56cje+jdC0D6zKATKmLmfY3LOC/1cVGisLKN6xhrI9G0jKHM3YOSv47XVXcNq8cf36XAaK42pNPFRnYgiGQW7eW0pSrBVJFBg9egwl8RmMWXwJRbs3UJK3H0Hxo8h+VNmHKvvRx2SgBvz4An4ObXqPgxveRJWDzzFaIomIT8cal0Zc5iQypp1CI1H87bdnsXzq4QZe2w+Ws+NgeUhL0fZHxJ1hMeoZnxbNmORI/vzUGv74kwVYTHq8Xi+5ubns3buXB599H399LsVF3+OqLUMOeLHGpCBZ45EVJfhZBnwYYtKRvQ5UOYCgFynevZ6iHV+gygFU2Y8hIpaMyUuYfNE9LD9hEqqi8M3uMk49YeyQKGx3XJnTQ3km3rCzCL+stHg1U2ItFFU7kUxGsqYsIZA4A0kUepQB4/HJxEeZiGuzPpPrHaz6fB8Lxqe2lBHSSWLIS++GQsTNuH2BYM3mps/FaDQybtw4RGsCE/frkEY4iLToAQG/x4G9upSCggL0ooQo6RF0+uBvSY+oMyBKekRJ1/J3nyyRmRSBXpKClU2NOkClsMJOcVUjGYmDH411XJnTQ3UmLqq08/oXe0iJs7b8LcYaLNUjKwqSKBJlNVDn8GIUuxeDJAm4vYF2haisZiO5RdVs2FfB0qZqlTpJxBnifGZZlkNm7rm9fmRFaRcBt2l3CUZD0KkXkIMOMb3JhjlhOFYhEVMPHHKKoiIJKmaDDofHz6iUyKZIMQFREPh6ewE/Xj4pJO+rNwymOX3UEyCG4kzs9QV45v2tmA06jEdUpJREgbRYCy5vMCsnwqynp3H9kiDg8cm0LdFhMujw+3y8sSGXxqbZVyeJOD2hF3GoZmKXx99qJobgZ/rd3hISoi1EmPUEjkjE8PnlHlcu8csKkZZgjrKqQkLU4SSG1Hgbn2/OY09+1YC9l74ymDPxURfxUJyJ312/n5LqRuKjLe0eS4k1t2TkmAwSOknoNBPqSEQx+LxAm3hoQQgWnc8vrubLXcF6UEER+0Oa+RNKEXuazekjZuK9BdX4mraWIs0G/IHD783tC/Q4oEQFIi0GArKCQS+18tJLkkh8lJln3t92VFNIO2Iw18TH/Uy8J7+KT749REZCxwVYo60G9DqxKRdZINpm7LbkTwtCcD+0LREWI9W1Dbz2VQ51Dm9Q8AElOEOFCEVRQj4TtyRE+AK8tW4vEU0dGyLMulY3KI9P7lGRdkVRkUQBk17C7ZNJi7W0K5YQaTXi8vp4cfXOo17Q4UiOK3N6KM3EdqeXZ97fRnyUudNi5qIgkB5nbTGpbU3pez26YFQ6FKYoitjMBvYeLOHdbwuC625Vxd1J9cuBQJblkHm/XV4/inq49vY7X++jrMZBbGTQ9DUbdC3iU1QVn6x0W8kTgqZ0lMXQZEqrJEV3HMSRFh/B93tL2bCzaEDeT184rszpoTQTe3wBBAHqGj1U17vaVZ1sJiXG3FJLyqgXMerEHprUdCpMo0GPToRVn+ygttGLoih4QiziUM3EDpeP5kXu7rxKPvsul/QjLBuLUWpZAvsDCgL0aFtIVYN+CFlR0Uki0Z0UeBcEgZQ4Gy9+tpPy2v6V7u0rx5U5PZRm4sQYKw9eu4wbzp9NVko0RZV2iirt7cQUZTFg1EtNdboEoq1GAj0QsSSKXda6irKZqa5t4LlP9yIrwULooSKUIq53eJAEkQaHh2c+2EZcdGvLxqCTEAUhOAv3sHRPULgCJoOE2xcgOabr8reSKNDg9PL++px+v5++cNxtMQ2VmRiCTqXJI5KYPCKJqnon3+4pYc2WPCpqnVhNemIjgxdPRpyV3IpGIswiVrMe6t3ddgYUBfD6FRRF7fACFASB+Cgrn3ybw/i0yOCWVIgIpYjtTi+CCC+u3onH6yeuTSKAIARnVK9fbvLYd09AVoiNMAECiqKS0kFpHVVVqW300OjyYTLoOHvBGBZMObrFJpo5riK2DAYDPp+vX60xQ0VCtJUz5o3mlNkj2JNfxdot+ezJr0IUgnvGzetgvSRiNkr4Agr6LgpEC4KAIIAvIGPQSfgDMv6ATEBu6q7YlHpoMhpocHrCVsQNDg+VtU4a3SWMSO24L1SkWU+px9/k1Or6e29ucxph1gcztkSBGNthgbi9fqobgm1mx2TFs2RaFhOHJbaqInK0Oa4itkRRRJIkAoEAev3QrDyo10lMGZnMlJHJVNY52bS7mLVb8/D5/dQ2BJAkAZ2g0uDxIrcI43AycDC4P1jT1S+r1Da4iLAYiLAaSYqLICrSjM1swmI2IEkidU4fDeVluH3haU47XF78skqy1URRpR1RFAhuC6uYjTrMRj02k46ArOLxB7rN81VUFYMkYtSLuLwyiZEmBEGlos6FxxsgwmJk5fwxzBqbSmKMtctjHS2OK3MaDs/GQ1XER5IYY2Xl/DGsmDOCv3+wmwOldmIjDHi8Ab7eVdbitGlpiq0E832VJmdMQBHITolm2qiEDmt6FdU4OWVKKu9WVjQ5iEJDKLeY7C4fI1Kj+b/rTsbl8VNjd1Nrd1NZ56SgooGSKjsudyONDjcerx9DKxE3NRIXaLrxgV+WibIYaXB4cHkCJEXqKa1yMHV0MoumZjE6I+6oJvz3hOPKnIbD62KrdWjcRXvC3hI7BytdZCdHBde3EZCZ7KbW4e0yAd3rl3F4Ax0KOCAriAgsHJ/MB1+INDg7rhYyEIRqi0lVVZxuH9ERppZeU1azgcyk1l0ay2pd3PTsRg6W1GHUifh9fhBAFEREUWhaegQ/I6c3wNyxydjMeqrsHuaMTuSSRSOJtA6OSHrCcTsThwt1Di//W3eIxEhTKwdVRoKNioaum5jpdSJ2l79D51Zlg4cF45OIagooCWXUUahmYq9fxhfoPngjPtKETwaD0UiUtfOL3euXsdlEslKCfZxjoizsKLKzqNbF+CEs4uNqiwmGnoe6KxRF5cWvcwkEFKxtCq0nRJmDWyddbDeJTf1OnG2cVrKiIisqiycG62zpJZHGEJrToZqJXR4//oCC1E0HOL1ORFG6D/Lw+GWykiION20XReIijPz3y0PYQ/j59AdVVY+vYA8Ir5n4qz0V7C6sJzmm/RaHXieSGmvB1Y1XWUVo16OossHN7FHxJESamo4l0egKv5nY7fU3ZRp1v9MgK13vSKhqsFhgUpvtpAizHo9P5uX1eV3eMAeLQCCATqcLaT54V2gzcReU1Lp4c1M+KTHmTi++jAQbcjchmAIqDc7DNy1FVfEH1JZURAgGRITSsRWqmdjt9QMqYjeOJm9T5lJX4apev0K0zYC1g84UKTFmfsirZVNOx/W4BpPBNKVhkEQcFxdHdXX1YJy6x3j9Ms+tPYDZIHVZiC42whSMfe4iKUKvE6lpPOy0qrZ7mZgZTVrs4awpvT60OcWh2mJyewOoKt3u/VY2uDEbdXRVk9cbkMnqJBFFEASSo828uiGf8vq+N1MPBZWVlSQkJHT/xBAxKCLOysoiPz9/ME7dYz7cUkxZnbtdZY62SKJARrwNVxeRSAadRL3D17IN5fYFOGVaWrvnNKf0hYJQmdMurx8FtVvHVkW9G4NO6FTDzTN0YgfLlmZMBgmDJPL82gP4O8gOGywKCwvJzBy8puWDIuLs7OwhLeJ9xfWs3l5KWmzPuuilxVm6XKtJooCsKHh8MnVOHyOSIxiWaGv1HL1Owi8rTebpwBMyx5bbDz2YiQurHF1uxXn8MnERJszdRF3FR5korHbyybaSPo03FBQVFZGRkdH9E0OEJuI22N1+nvviIHERxh7lvAJEW40Y9VK3ecaNbh+Nbj+nTU9vt8Y26CUCATlkoZehmonrHR50ktitiHPLG4m2GJDEjr35Pr9CVoKtg1e2Jy3WwsfbijlYbu/TmAea43YmLigoGIxTd4mqqry2Pg+3N9CrZtSiKJCVGNGlAFUVKhrcpMRYGJMa1e5xvS6YJRVuM7Hd5UUSxS6PrSgqhVVObGY9EWZ9u5tdMI4eEnrYP1gniURbDTy35gAOT2gLDPaE41bEQ3Em3pRTxZbcGlJj25fp6Y6UWEtL+GVH6HUipTVuzpiZ3mFGk14n4pdDl1McKsdWg8MT7MzQxT5xndOLX5bRSSJRZn27kkVun0xCVM86WTQTZTFgd/t545v8Qa3oAcepiNPT0yktLSUQCF3WTm+pqHfz6vo8kqM7307qikiLHqtR36XDJRCQmZTZcZaPQSchh3AmVhQlNDOx04skiV0uPaoaPC3+rEiLvl1BBX9AIaOHpvSRpMZa+PZANVsO1fT6tQNJUVHR8Sdig8FAYmIiJSVDwznhDyj898uDLUnofUEQBLITbZ0WAfAF5GDoYSciN+olFJWQrolDIeJGlze4Ju5iJi6tddKsW7NR16pOltIUAJIY1TNT+khEQSAp2sSLX+dSbQ9d3HlXqKpKQUHB8efYgqFlUn+2vYT8Sgfxkf1rxJXcZIa3Ne8CsoJRryMh0kRFJ3uchqZSua4QrfGCXQ4H/ut2uHzdzsS55Yc902aDrlW12uaqHXpd38ZmNugQEVi17lDItue6oqGhAUEQiIpq7+c4Whz3Ij5YZuejLcWkxlr6XaTAatITbTPg9be+mFxemZHJwSCGijpXh6816qVgiRlHaGYURVEGvAhDQFbwBWREQehSxHkV9pa4c3PTurdZyAFZJT2+f9lsCVFGDpbZWbuzrF/H6QvN6+HBLHBxXIvY6Q3w/BcHibIY0A9QfmpWQgTeI+pIyU1lV9PirBj1IrkVjR2+zqAP1qGyhyh+OhSVVFwef4tp3Fn9K49PpqbR29LtQRQFrEYdAflw2aKEPpjSRyIIAqkxFt79rpCCqqNbKG+wnVpwnIu40e3H7ZNpcPmod/oGxMuZ1BRx1HwspzfAsCQbBp2IzaznYFnHe5tGvQ5JEkKWBBEKEbu9/pZysp3tE1c2uBGPyBWGYEJDQFZxeQOkxln7neCvqioev4zbLx/12XiwnVowiCLOysoa9L3i5Ggz9/x4GlcsGUW01UBRjYvSWle/QvpMBh0JkWbcPjlYHwrIjA96Xi1GHeV17g6PL0oC+hAmQYRMxAhNsdMdX0rlda6Wcr/NRFn0BGQFWVVJj+u7Ke2XFcpqXRTVuNBLIlcuGcl5J2T3+Xh9obCwcFCdWjBIRQFgaMzEEHSMzBoVz8yRcZTWuvhmfxXf7K/E55eJshha+gD1hswEK1V2N7JHJTPB1uLxFoVg7HCV3U1qbOuLVyeJ6HShyykOlTmtEqwp1pl3urDa2W6mtZr0BBQVvRTMFe4NqqrS4PLT6PajkwTmjE7ghDGJZCVYB2VdWlhYyIoVK476eY9k0ESckZFBSUlJSy7mYCMIwXXrBfOsnDEznV0F9azZWUZRjQOdKJIQaeqxB7U58khWVLLbxEirikpFvadDEeslCUeIGquFwjvt9gWakjoEdJ0cO7fc3q6YgsUg4fPLDE+K6LTzRlu8fpnqRi+KopKZYOW8uVlMyIzuNtY61AyFNfGgfQJGo5GEhARKS0sH/UNoy5Gzc0mti41Ns7MsB8MDmyOzBEFAVdRWkVqiGGy5qdeJmPVSUy/dw4iiQGGVg2nD41r9XSeJ6CQRjyfQaZ3q/hAK77TL7UNRVFSx4yym5nDLxKjWW3dmgy7o8JNESmqcNH+ozZ9tK9Rg7VCTXmTZ5BRmjYzvc0BOKBgKa+JBvY01m9SD/SF0htDUh6l5dq5p9BKQVfyygiyrBBQFv6wQkIOldnwBGZ9fwRuQGZ5o5Yfc2nbHtJo6dm7pJDFY6gcVjy+ApYPE+P4QCnO60eVDJzWtiTswp2sd3pYWLEeiqCrThsVxzanjMBkk9E3BIjpRRCcJ6MRg50idKLQkV5gN0pCrcCnLMmVlZaSlpXX/5BAyqCJuziteuHDhYA6jR5gNOtLjev5xeSen8rvnvsMfUFqZ4TaTjoJKR7vZVieJwbKtatBhFA4iDmYwSfjljgvlVda7O0wfrrZ7OG1mBnNGD14i/UBQVlZGXFzcoFb1gEH0TsPQcW6FAqNeYvbohHbhgDpJxC/L1LWpbNk8ywhC503Y+kMoRGx3etHrRFSVDmfJsjpXh9t2sqoyY0Rcu7+HG0NhPQxDQMSDvc0USmaPTugwx1hAoLJN+KVOEltmrVA0VguFiBtaRNyxd/pQeWNTSZ7DuH0BIs0GshM7LsMTTgyF9TAMAREfqzMxwIjkCKwmHZ42PYpVVaWkpnX4ZbOXVg1REkQovNMOtxedJHVaYyuvorGdZ7rG7uXEcUkD7rgbDLSZmGNfxJIocuK4JGramNQWk66dc0sShZY911CIOBTe6UanD71ORBDaB3u4fQHqHIfDLVvGoartPPPhylAI9IBBFnFGRgbFxcXIcs/aXYYjM0YktCtpazXpyWsTQ62TxKY9V3B7B36vePny5URHRw/Y8RRFxeXxtywDhLbdLeo97cItXd4AsTYjGf1MeBgqaDMxYDKZiIuLo7S0dDCHEVIy4q3EWo2tCswbdSINLl+r0jI6SYQmB1FDCNq5PPLII2RlDVzvXo8vEPSmCwIgtOs1VV7fPtyyttHDggnJQ2aPtz/4AwoHDuWRnp4+2EMZXBEDJKdmsHbT9sEeRsgQRYGFE5NbmdTNzcOOzC3WSSJq0+9Q9mQaKFxHOt+E9llMhZWOVplhqqqiqDBlWOzRGmLIKK9zcf+r33PgwAFGjBw92MMZfBEnZY3msVUf8t81OTS6B7/oWSiYOiyuXf0tRVVb5RY3m6XhImK3199q9m07Ex8qb+3UcnoCJEWbSYnpff2yoYKqqmzcV8FfXt3Gd1u2kJCaRWzM4BUDaGbQRbxo4XwaiveyaX8ld760hW2Hqge98NlAkxRtJjXW2uomZdJLHCo/vC5unon1OjGk7VwGCrcn2L4FgnvbR5rIiqJSXONsJeJap5cF48PXlHZ6/Pxn9X6eXb2fGJuB2sK9TJk+a7CHBQwBEa9YuoiKvN2kxpox6EX+9dEenvp0H3WOoT8b9RRBEFg0MZn6I/ox2Uw6co8QsSQ277eKNIawnctA4fYFWuKcVbX1TFzj8CIrSovHurlRWria0ofK7Nz9ylY2H6wiK9GG2aCjInc380+cN9hDA4aAiCePH42ASmV5CTaTnuxEG9vzarjjpS1s2lcxJLvg9YWJWcEql81Whtmoo7zeja+pCojYEi8s4ggDEbs8ftSm70YAjtxhahvI0uj2kx5v7XcFj6NNQFb4aHMhD7z5A4qqkh5va7lZVeTtYsXSRYM8wiCDLmJRFBkzaToHdm8DDqcERpj1PLN6P//4YDdVDYNTyXAgiYswMTIlsmU2FgUBAVq9N50kIooCLvfAVBkJJW2bvx05E5fWujhy+PVOHwvGJx+toQ0IVQ0eHn1nJ29vyict1kr0EQ3OK8pLUOQA0yaNHcQRHmbQRQwwb948inJ2tPqbxagjO9HGgbIG7nhpM1/uLO2y82A4sGB8cqt1sapCed3hWUuvC0Y/VRTlYHd0XFBvqFBQXIoSaFrytPFO55bbW1Iwm29GzZbIUEdVVbYcrOLuV7ZQVO0kK8HWLo/84O5tjJ4wbdD6EbdlSIxi2eIFVObtavd3QRBIibEQF2nihS8P8vDbOyirHdoXd1eMz4xBEISW4umSJFBQdXhdrJdEZEVh6+evMGJ4Nr/61a/Yvn3obL8pisJnn33GBRdcwI0/OY3Kwn3BB9qsiYPhlsEsrHqnjxHJkd12lxwKuLwBVn1xgMc/3kOExUBSJ32piw7sZO4JcwdhhB0zNES8aB71FYW4XB1XKjTpJbITbZTWurjrlS18srVoSLW27CkRZj2TsmJanHbWNuGXhqaZeMWVf2bDhm+Ijo7mzDPPZPr06fzzn/+ktrZ9fvLRoKSkhHvuuYcRI0bwxz/+kZNOOokX3vuK5BFTgaB3unkmdnkD1Dt9GPXBS8vu9jN/fNKgjLs35Fc0cs9r29i4t4LshIh2xRyOpDJvN0sWzj+Ko+uaISFiq8VM2rAxHNyzo9PnCIJAYrSZpGgzb36Tx32vb6OgsuPyr0OZeWMTW6K3rEYdhVXOFuedXh+ciQVBZPSokdx1113k5eXx4IMP8s033zB8+HAuuugiPv3005CHqgYCAd577z1WrlzJpEmTKC4u5o033mDr1q1ce+21ZKS2FmaLw6fejSgGg1mUpkZp4ztpXTMUkGWF1duKuf+NH/D6ZTISbF0mZ3g9HmpLczl58YlHcZRdMyREDDBj1hzy9v3Q7fMMOonsxAjqnT7ufX0b72zKx+sPn9jrsekx6CSRgKygazKfa5pmZoNOwh9QsBh1LWacJEksX76cl156iby8PBYvXsxtt91GdnY2t9xyC+vWrcPjGRjHn9/vZ9OmTdx2221kZWVx//33c/bZZ1NYWMi///1vZsyY0fLcKKuxdbCHeFjEzcuFOoePsWnRRFkGN2m+M2obvfz9/V28uj6X5Bgzsbbui/Yd2r+TpPRhxERHHoUR9owhI+JFC+dTkdt+XdwZ8ZEm0mKtfLSliLtf2UpOSX3oBjeAmAwSM0fGtyoW0Lwlo9cFC8h1VtUjJiaGa6+9lu+//56PPvoIRVH43e9+R3x8PIsXL+aOO+5g7dq1uFw98xt4PB6++uor7rnnHpYvX05sbCzXXHMNbrebTz/9lI0bN3LVVVdhs7VvdhZhNaK27BQfjp0uqHJgaHIEOTx+Thw39ExpVVXZkVfDXS9vIbeikexEGwZdz3pw5e3dztQZs0M8wt4x+GUmmzh16SJ+/+vretWCUyeJZCXYqHd6+b+3drB0SipnzcnusiP9UGDumEQ27a8K/keF4honEzJjMOgl/IHORXwkkyZN4oEHHgCgsbGRDRs2sG7dOm677TZ27NjB1KlTWbRoEYsWLWLevHnYbDacTicbN25k3bp1fPXVV2zZsoXx48ezcOFCbrzxRl599VViY3sWkBFlNbbaBmueiQ+V2bGZgp0PJUFgXEZ07z6cEOPxBXh7UwGfby8mMcqMrZdlkMrzdnHWlZeEaHR9Y8hc7aOGZ2K2RlBckEvW8FG9em201UiEWc+XO8vYeqiGy08axYQmT/BQZERyJCaDiLdp1j1UZodpQXPaF5CxmHpnfkZERLBixYqW+sdHivWee+5hy5atRMclUl9TybRpQXHfcsstzJs3j4iIvlXYMBl0GPS6liZmQa+7QkmNk+QYM3UOLxOzYnotklBSXO3g6c/2UV7nJishotOuFZ2hKAoVubs55aShVRNuyIgYYPyUmRzas63XIoZg2GJmgg27y8dj7+1i3tgkzj9xGJFDcD2m14nMG5vEV7vKiIs0kVfRiKqq6HXBmdhm7t+YrVYry5YtY+GiJcz+7hBvfbGDhuoSohPSOWfxJE6ZPQJDLxp6d4QgCMRHWVqaoouiQG2jF6WpG4TLG+CEsYn9OsdAoSgq63aV8er6XMwGicw+9EIGqCwvAVQmjx/8zKUjGVIinn/iPN757Cvgwj4fI9JiwGbWs/lAFTsKavjJ4lFMHxE/5GblmSPjWbuzFINOpLI+2NFArxPxBRSs5v7PXodKannuox+oqHWSmRKHPiMRf0DmvfU5fLenhCtPn8rw1P55jROirRwsDjb4Fo9IrQzICpIoMDY9ur9vo980OH2s+uIA2/NqSI21YOzHzevg7h8YM3HoBHk0M6RGs/ykhVT2wrnVGaIgkBZvxWzQ8cTHe3n8oz3UNg6thIrsxAiizAY8frklt9ioDzq2+jMTuzx+Xlmzi/v+tx63109WchT6JqeNXieRlRyJ0+Pjvv99zWtrd+PuR1G+5Dhby86AKAqU1LpAValt9DJteNygd2fYU1THXa9sYV9xHdmJtn4JGKDowA5OOOGEARrdwDGkRLxw7kycDdXY6+sG5HjNCRV7Cuu5/aXNbNhTPmQSKkRRYMGEJGrsXhRVpbzOjUGv67M5raoqu/MquePZL1m7JY/MxEhiIjpOOIiNNJOeEMnnm/O44z/r2Jtf1af3kBhtwd+cwCEIHCq3Y2kqDHjC2MHzSvv8Mm9syOWv7+zEoBNJjR2YPk2Vebs5adGCARjhwDKkRGww6Bk2ZhI5TckQA4EgCKTGWYi2GnhuTQ6PvbezVUWNwWTa8HgUVcVkkDhUbseol1AUFbOxd+a03enlPx/+wF9f3YQgQGZSVLc9jnSSSGZSJKqi8PArG/nvx9t73VY1ymZq2VoSBIH8CgdGfbCjw6jUwdlHLat18eCb2/nsh2IyE6xE9NO/0IzH46auPJ/lQyjIo5khJWKA2XPmUrB/4OOFzYZgQkVeRSN3vryFNT+UtHhWB4vUWAtJTc3XcisaMeglJEnEqO+ZGaqqKt/vLeG2Z75g875SspKiiLT2rstglM1EVlIUG3cVcfuzX7Itp6zHGVRRNlOwH1VT6xm7y0ej28/s0Qk93ncdKFRVZcOecu5+dSs1Dk+T93ngLu9De3eSnDmCyIihV+RvyIl4yaKFVOTtDsmxBUEgOcZCQqSJl78+xENv7Qg29BokBEFg4YRk3D452PKkqfeQ0dC9AKobXPzzre/59ztbsBp1pCVE9LmWsygKpCdGYtRL/PPN73nqva3UO7qPAots3itWobrRiyAI+AMKs0Yd3fYsjW4/T326j+fW5BAfYSQhcuDzlnP3/cC0IVLJoy1DTsSnLVtIVcFeAoHQ1dsyNiVUlNe7uPuVrXy0uRDfIIVuTh4WFyz5Kgg0uv0YdCKmLhxCsqyw7ocCbn/mC/blV5GdEoV1gExGm9lAdkoU2w+W8+env+Db3cVd+hAiLMGoLQWVWrsHj0/GZJAYkXz0TOmckgbufmUr23NryE60dfnZ9YeK3F0sWDD0TGkYgiJOTownJiGV/AP7QnoeQRBIjAoWbnt7Uz73vr6tXS3oo0FCpInsxAjqHF7qnX70OqnTPdzS6kYefmUjqz7ZTmykmZT4iAHfOhMEgdT4CCIsBp56fyt/f/M7quo7tlZ0kkikxYTPJ1NY7aLR7eOEMYk97uPcH/wBhXc35fPQW9sRBEiLD12TcUVRqMjbzSlLh1aQRzNDTsQAk6fP4tDegXNudYVeJ5KdGEGj2899r2/jjQ25LQEMR4sFE5Jw+wJUNngw6KV2s4k/IPPxxgPc9dw6SqrsZCdHhWzGacZi0pOdHMXBohpuf+ZLvtyW32FRhoSm6pXNMdMzRobelK6od/PIOzv4YHMh6fHWkCRYBGSFqgY3hVUOtm/fgdlsZuKYkQN+noFgSIp4wfwTKdr/AxX17pZu9KEmLsJEepyVz34o4c6Xt7KveGC2uXrCxKxY4iJMlNa40OukVo6t/LJ67l31NW+u20tSjJXEmNDNOG0RBIHkOBtxUWb+9+kOHnp5I6XVra2VhJigo6es1kVKrJVhSX2LhuoJqqryXU4ld7+ylbI6F1kJtgHrWayqKg63n+JqJ0XVDqrsHsZlRHP18jHE1W/hul9cPeSCPJoZUhFbzfzsJxfw8D23MSZBorBBoarB01SMTQhGZJl0/b6QO+oSeGRCxSPv7GTRhBTOnpuNbQAiqLoiymJg6rA4Nu4uJrZpTezxBfjgmxw+2XSQCKuR7JToTl/v8/nQ6XQhu8hMBh3ZyVGUVjdy53/Wce7CsSydOQy9TiI5NgJvQMHvCXDqzIwB9QgfidPj55Wvc/lmbzkpsZYBCSTxywp1Di8eX9Afkhxj4YxZGYxNjyGzqSyPx+PhzddfYfPmzf0+X6gYkiJOSU7inLPPwnXwKx64+WYaXD6Kqp0cLG1gV2EdRdVOREFAVVVsZj0RZn2v7sgVjV4qG70kRRiJsehbdSqA5oQKA+v3lPNDXg2XLRnFpOzYkM6AJ45LZP3OIhSCIZPPf7ydukY36YmR7d6bx+MmZ/d2Du7ZSlnuLqqKDmA020gdNYXsMdOYMH0O8Qk9K0xXXVXOvh1bKTywg/K83TTWVpA8bAIZo6cxftpcMoeNRBTFoA8hxhIMpFi3l2/3lHDFaVOIizIjKypGvci0EfEh+GSCNbue/GQvDW4/2UkR7QrVA3gDMtUOPw0eP6MSrO2+U2iabT1+Gpw+VDW4lJqUHcuMEfEMT44kpoN84nfeeYdp06aRnZ0dirc2IAi9MVVnzpypHq070vfff8+FF17IwYMH26Umur0BSmqd5Fc42FVYy8FSOwFFQVHAbJSIshi6DLErrHNTZvciCsFyqzEWPQk2AxHG9jN8o9tPld3DnNEJXDR/BFHW0CRUuLwBfv63tZSXVpCVFEVspIkoW7AuVbNoD+3ZRlnuTqpKDpGaOYIT5s3nlJOXcdZpy8krKOaNdz9g9erV7Nr2LWZbDGmjpjJi/HTGT5mF1RaBoiiUlxazf9cWig/uojxvNz6Pk7GTprNw4UJOPXkpE8aO5p0PPuWDjz7m2w3r8Pt9pI+axrDxM5gwbS4xscGOhtUNLpxuP1NGJrH6+zzGjMnmgctnD2jL0kBT1Y23N+UTZTW0qjgJQVE2egNUNPqobypAqKowOTWipRujP6BQ5zw826bFWZk5Mp5xGTFkxFu7vfkvX76cq6++mosvvnjA3ldfEARhi6qqMzt8bKiKGGDu3LnceuutnHnmmV0+T5YVyuuDTog9RXXsKarH6fG3dLCPtOixHiHQwjo3VQ4fJn2wy703oKCoKnpJJNFmIM6qx3hEsIKiqpTVutBLIpcsHsmsUQkDOiurqsoPB8r59T8/p7rewTknjuDAnu0cbBZt8UFSM4Yz98T5rDh5OWeuWEp8XOfJC35/gM/XbeCd9z7iiy/Wkpezi5ikTFwNNSCoTJw6m8WLFnLaKcs4cc70TvO3FUVh6/Y9vP72e3z22Wfs2b6ZyLhk0kdPZ/SkWYwcN4WCSiff7i3lt5cu5JrTJw3YZ1Jt9/D8mv3sK24gPc7ayuPtlxVqXX7K7V58soIkCBh1IgjBm2F2jLnFOWnQiUwZFse04fEMT45odyPoiry8PGbPnk1RUREm0+AW+gtbEb/wwgusWrWKzz77rFevU1WVmkYvxTVOckrq2V1YR3mdm2bdNfoUnH4Fi7H1xSsrKr6AggpEGHUkRRiJNOla8k6d3gCV9S6mZMfxo0UjiY/s/xdb1+jmlTW7+X53MT98u5YD33+Kp7awRbSnLF/GylOXdSna7miwN/LJ518xcng20yaP6/Pa2ePx8v6na3n3vQ/5at1ayovzSc6egN2UxUkrzub8pVM4a/6Yfu1bq6rKtkM1PL8mBxVIim6KClNVXD6ZSoePGqcPFTBIIjpJICArON1+ArKCN6Bw4ugEFoxNYmx6NBnx1m5DUDvj9ttvp6Ghgb/97W99fj8DRdiK2Ov1kpmZybp16xg7tn+Fup0eP8U1TnLLG/lwWwl7iusxSCIKKka9hNmoO2xaqeCTFQKKiigIJNgMxFkNWJoqOJbXuVGBixcMZ97YpD5dJIqisnFXES9+toNtm75g3/o3iIyI4Pe//z0XnXtmv0R7tCgqKef1dz7gv/97iQO7f2D4nJUsPf18fnbWbCYM67214vYGeOObPL7cVUZStBmrUYesqNS7/ZTZvbibMr6MkoDXL+PyBGdbvU4kKzmS7ORIdEY9V56QSXQ/nZGyLJOdnc2HH37I5MmT+3WsgSBsRQxw2223Ybfb+fvf/z5gx9yQW8uOEjuqLFPT4KG4spHSagc+WYGmfkhmow6DrsnclhVUFcx6iaQIAzFmPQFZoazOxZi0aC5dMqpX3f4qah2s+mQ7b7z5Njkb3sRqMXLnnXdx1aUXDNltjO747IsN/PZ3fyDv0AHGLzifn//0Si5cOqnHsdwFlY08/dk+qho8pMdZ8ckKVQ4flQ4fihKsmunzBggoCiAQH2VmeFoUKXFW4iLNLWvxGqePi6en9lvEH3/8MXfccQffffddv44zUIS1iIuLi5k8eTIFBQV9LiXTlg25tewudxBjOfxFq6qK3emjxu6mrNpJUVVjU3dCAUEIJlBIOpGAoiIAsZbg2tnj8eNXVM6dm82SyaldOkoCssLaLXk89K/n2fzZi5iNOv7859v5xZU/DlvxtuX1dz/mj3/8E9XVNZx4xuXc/ftrmTE2tdNZWZYVvthZyusb8rAYdej0EhV2Lw0eP16fjCIrCIKAQSeSnRxJVkoUiTEWzJ3UURsoEZ9//vksX76cX/ziF/06zkAR1iIGuOCCC1i8eDHXXXfdgByvIxF3hNsboKbBTUWdi+LKRqobgimMiqIiiCIGg4hJryPWLOHxBBiVEsnlJ40mo4PyLwXl9fz5oaf56NWn0AsKt9x2O7/86aU9LgoYTiiKwnMvvsGf/3wbvoDKxVfdyF2/vZq4qNbWSp3Dy3/X5vBDXg1Go54Kuw97UzqkThJIjLYwPDWK1HgbMZGmDreW2jIQIq6srGT06NEUFhYSGTk0StOGvYjXrVvHNddcw549ewbEK9xTEbfFH1Coa/RQVR8UdVmNE19AwRdQ0OlETJJAlFnPBfOyOW1GJka9hMfr58F/reKJvz+M4vfwp1tu48ZrrjgmxdsWWZZ57In/cP+9f8ESEcutt9/Jz358FqIosD2vmn98uJeCagf+pkvQatQzIi2KzKRIkmItfQotHQgRP/LII+zcuZPnn3++z8cYaMJexKqqMnnyZB577DGWLl3a7+P1VcRtUVSVBoeXmgY3xVUO8svtNDh9+P0yEzKjKfjoMXZu24yqqvzuD7dw03U/Rd+DXGFVVfF4PLhcLpxOJy6Xq+Wn7f9dLhdut5v4+HiGDRtGdnY2GRkZGAw98xA3NDSQl5fX8lNQUACAxWLp049e3/4z9fn83HLXA/zzkQdIHzaKJZf8ioP+ZPyySkqclXFZMcHZNqJns21X9FfEqqoyYcIEnnzySRYsGDpVPMJexABPPvkkn3zyCW+//Xa/jzVQIm6Lqqq4PAEq6lxs3LqLd/98QctjRqORqKgo4uLiiI2NxWw24/V6OxSo2+3GaDR2KBKr1drubyaTiaqqKvLz88nPz6e0tJTExESys7Nb/fh8PnJzc1uJ1uv1MmzYsJaf7OxsJElqd6Po7OfIm4rT6UQURcxmMzqdDlmW8Xq9+HzBVq2iKKIoCvFjT+QX9/2bsWlRGAc4kaO/Iv7mm2+48sor2bdv35AqrtiViIdk2GVHXHrppdx6660UFBSQlZU12MPpEEEQMBokrBEmfrTiBBzVT7Lh2T/y69/8lmkTRrF9+3b27t3L3r17KSgoYNy4ccybN49FixYxffp0bDYbFosFs9ncL3M7EAhQUlLSIur8/Hy+/vprDAYDw4YNY8aMGS2iTUgYmMCVyspKPvzwQ95//33WrFlDdnY2ixYtYs6cOUyZMoWRI0dy6jk/4oc9h7j0Dw8zOi0aV0DGOMQqCj/77LNcffXVQ0rA3RE2IrZarVx22WU88cQTLZ0Phhpuv4zDG2DB8BgERSVh+HimnfsH/vbXB/jr3//Fvffe2/Jcu93Ol19+yerVq7ntttuoqalh6dKlLF++nGXLlvXrRqXT6cjKyiIrK4tFi0LTzV5VVX744Qc+/PBDPvjgA/bt28fy5ctZuXIlTzzxBElJrQvl/ez637F1y1YmXHAHRouFlRMT+TynhiqHlzirYUiIprGxkbfeeou9e/cO9lB6RdiIGOCXv/wlJ5xwAnfccQdm88CXYOkPDW4/giBw1qRk0qJMfLazgpgoC97hY1lx5Z+56YZfYjGbuOzicwGIjIxk5cqVrFy5EoCioiJWr17N6tWr+dOf/kR0dDTLly9n+fLlLFmyhKioqMF8ewC4XC7WrFnDBx98wIcffojZbOaMM87g3nvvZcGCBZ2uwx949AleeXEVJ//0PoS45GDHRAVWTkxi7YFqDlW7iLcaBjTuui+8+uqrLF68mOTkniWPDBXCanNy5MiRzJo1i1dffXWwh9KCoqpUO3xEm/VcMDWFtKhgKGZhrZPESBNRcdHEpo7i7J/9mWt+dhWvv/txh8fJyMjgqquu4uWXX6a8vJzXXnuN7OxsHn/8cdLT05k3bx533HEH69evx+8PXemithQUFPD4449z2mmnkZyczKOPPsqYMWNYu3YtBw4c4NFHH2Xp0qWdCvi1tz/krj//iYuuvwedNYbkxGgEQaDe5cOgEzl5TAJT0yKpcvrwD3LhwmZTOtwIq5kY4IYbbuC2227j8ssvH3QTzC8r1Ln8TEiJ4MRhMS3pb6qqUlLnIdKsIy4miip7PWMnz8B/+R+5/NIfYX7jbc44ZUmnxxVFkalTpzJ16lR+97vf4Xa72bBhA6tXr+bGG28kNzeXhQsXtszUY8aMGbDPwu12s3Xr1hYzuaysjNNOO40rr7ySl19+uVcWwabNP3Dl5Zdy+hV/JCF9OM5KB5ERZhRVpdbpYzjBHPF5w2KIMutZd7CGSJOuJQPpaLJ7924KCwtb+lmFE2En4lNOOYUbbriBTZs2DWo1fqc3gNuvsGRUHOOSbK1E5PAG8PhkYq0G9AaJrJRYqhscTJ01D7//N1x0wbm898FHLF3Ys/GbzWaWLVvGsmXLePDBB6mqqmLNmjWsXr2ahx56CK/Xy/Dhw8nMzOzwJzb2cC603++nqKiI/Pz8Vl7q5p+6ujrGjh3L6aefzpNPPsns2bP75GTLKyjmtNNOZ+7pVzB11jyKKuxEx0VhMxlw+2TKGw7XuBYEgYkpEdiMEp/urSKgqNiOcmfLZ599liuuuAKdLuwkEX4iFkWR6667jn/+85+DImJVValzBzDpRM6dkkxSRPvY4BqHr0U0ekkkITGGssp6AGbNW4zf5+GsM8/g088+48Q5M9q9vjsSEhK4+OKLufjii4OzfkkJhYWFLT/79+9n9erVLf/3er1kZGTg8XgoKysjOTm51bbSihUryM7OZtiwYaSmpvY7BLTB3sjiZSsYNmUBS1acg6qqBBQFi9mC1SChqiplHRTwz461cO6UFD7cXUG9y0/0AG8BdobX6+WFF15g48aNR+V8A03YiRjgiiuu4O6776a8vPyoOiEURaXG6Sc92siyMQlYOqkPXd0YbM0CYDPqcKoKVrMBjy+AyaBj3uIV+H0+Tj31VL5Y+wUzpk7o85gEQSA9Pb1l3dwRjY2NFBUVYTQaexUI0hf8/gBLTz0bvS2Bs358DRDsD2U2GbBZg4kKJr1ERUPH3SYSbAbOm5LCR3srqXb4iLPqQ75seu+995g4cSIjRowI6XlCRVg5tpqJiYnhwgsv5Omnnz5q5/QGFKqcPqalR3D6hKROBQxQXOvG0JTEbtKLNLgDzJ2YQVW9q+U5i05eyfSlF7F02TJ27c0J6dgjIiIYP348I0aMCKmAAS687OeUlFXy42tvaZnRa+1uRmQmBsuoAHpJwOkL4Omk1neEScfZk5LJjDFT7fC33BBDgcvl4o477uCGG24I2TlCTViKGOD666/niSeeoK4u9FUp7Z4ATm+AFeMSOGFYbLfNqYtqXS0iF4RgFlRmaiyKoraq3LnsjAuYeOKZLF6ylAOH8kP5Fo4KN996D1+sWc1Pfn0vBmNwmaGqKooKosmMWX/4MxEFgXpX5152o05kxbgEJqbYqHL4QtZy5/e//z1Tp07lnHPOCcnxjwZhK+JJkyZx4YUXctlll6EoofmCVVWlxunDpBM5f2oKI+O778MjKyrlDd52Hla3LJCZFEWDs7UZueLcSxk+bTHzFy6hsLhsIId/VHni2Rd44h+PcsmvHyAyKrrl706Pn8QYK7UeGZvp8OpNVVXqnb4ujymJAgtGxDJ/WAy1Lj/ewMB+z80RZo8//viAHvdoE7YiBnjooYeoq6trFQk1UMiKSpXDR1aMmfOmJBPXwwJ59a5gnPCRs7XVoONAhYOlM4bR4Gi/Flx58c9IGzuL2SfM53+vvB2ym1IoKC2v5IKf/Jybbvwl5157JylpGa0er7V7mDI6Fb9MqwqUqhp0AHaHIAhMTY/i1HEJNHoDOAeosH9lZSU/+9nPWLVqFdHR0QNyzMEirEWs1+t5/fXX+fe//83HH3ccRNEXPH6ZGqePOdnRnDIuoVf7ljUOH7RxxFiNOopq3YwbloAkih12Ujj70l8yefF53HDjDYwYO4XnXnxjSIu5wd7IL371B0aOHM3OnEJ+ccfTjBk/pdVzVDW4fIiPi0Cl9brWqBcpre++aVszw+OtnDMpCUUJRsf1B1VVueqqq7j88stDFpZ6NAlrEQOkpKTw6quvcsUVV5CXl9fv4zW4/XgCCmdOSGRmRnSvU+Mq7R7UNk3IJLGp0JtfZebYFKoaXO1eJ4oiC5edwU0PrGLM3NP5zW9+w7Axk3lm1atDSsw+n59b7/4/0rOG89na9fzkd49y6S9vISEppd1zHW4fKXE26r1quzrQJr1EWUPv+kQnR5o4b0oyVoOOWqevz51B/v3vf1NeXs5dd93Vp9cPNcJexADz58/nlltu4dxzz8Xt7lsDcbUpfDLCpOPCqSlkxva8ZtaRFNW6O5y5FaCk1s38yZl4fZ13YJQkHfOXnsav7/8vE+av5Pe//wNZoybw7/+8OKhiVhSFRx//D6mZI3juvy9w9s9v5+qb7ycju/NtmbpGDwunZnGwwtEueMOkl6iy916IUWY950xOIiXSSLXD12vP9b59+7j99tt58cUXQ+6pP1ocEyIGuPHGGxk3bhzXXnttry8Mf1NRttGJVs6ZnExUP6pCFNW425XCBTDrJA5WOhiVHkuk1Yjb27VJKEkS8xav4Ff3PceURedz621/JmP4OP7x5H+R5aPbhvWl198je9RE7r33Xhaddw3X3Pb3dqZzW4KmNAxPi6PO5cekb32pSaKArKg0enq/xjXpJU6bkMS45KDnWu6i/eqR+Hw+LrnkEu655x7GjBnT6/MOVY4ZEQuCwNNPP82WLVt48skne/w6l0+mwR1g4cg4ThoVh6EfDbp8AYU6ly9YyLwNNpOOQ5UORFFgyfTslnpd3SFJEnMXLefGe55l+vKLuevuu0kfNoZHH/9PyMX86dr1jJ92Atdc+wvGn7iSG/7yDNNmz+9RRFejy0d6QgR+hJb+y20RBLrcZuoKnSiwaGQcJ2THUOP04euB5/r2228nPT2dn//8530651DlmBExBHOO33rrLW6//Xa+/fbbbp9f7woGEpw9OYlJKf3v9Vvj8DbtC7c/jkEn4vLJ1Lv8zByb2m7PuDskSWLO/KXceM+zzD71Mu67735Ss0bx8N+forpmYPbKK6pq+HzdN/zjqVXMWbSCc88+i4QR07np/heYv/S0XsVQ1zuCpnRxrbutn68FRVWp62abqStEQWBGRhQnj0nA7gng6mKZ8uWXX7Jq1SqeeeaZQU+cGWjCMuyyK0aNGsUzzzzDBRdcwObNm0lMTOzweQ1uPyPjrSwfGz9gwfY1Dl87p9aRCEBpvYcJaZGMSIuhqt5FTETvukiIosiseYuZMXch275bzz0PPsIff3sDoiQRFZNAbEIiycmppKWnkZGeTnZWJsOyMxk1PJvICBu79x1gX85BDhw8RG5usKZWWWkxdVVlyHKAiNgkDLZ4zAnD+fWDL2C2dL833pZmU3ri8ERe21KG1djx8kQUBCobOw6/7A2jEq1YjRIf7anscC+5rq6Oyy67jGeffZaEhND3Tz7aHHMiBli5ciXfffcdF110EatXr26XmZJoMzBveAxzsmLQDWAielmDp8vEdlEUyKt2MiEtkiXTsnnmg229FvHhY4lMnHEiRXIaw1MiyY7TU1tVQW1NJfU1VewvqmLb7lxc9hqc9lqcDTUEfB5sMYlExiZhi0kkIiaJ5LEnMHFRKkkpaURERiOKIut3lVBr96Az9K3wgt3pJTMpiiibmdI6D4kdJIlAMCS1rK7n20xdkRpl4vypKXxXUN9qOaOqKtdeey1nnXUWp5566oCca6hxTIoY4K677uK0007jlltu4f/+7/9aPTYq0caoEJyzoNqFuYuYaptRx4FyB0yBSSOS0EkiAVnpc6PsyjoXshLsaD95eAKRUdFkj+yfw8bnl6lr9IIQrAudGN17Idc7vZwxbzQVDR5UlU5vbH3ZZuqKaLOek8e2nmlfeOEFdu7cOaT7C/eXY2pNfCSSJPHSSy/x2muv8cYbb4T8fKqqUlrvbokP7giLQaLS7sHtk7GY9MwZn9YqKaK35FfYiTAbcHkCLa07+0ttk3kriQIVdc5ev15VVWgypUvq3O2CPI7EqBOpd/lDVtEjLy+P3/zmN7z44otDrpzTQHLMihggLi6ON998k2uvvTbkxc+cXhm3V27JXuqI5mSIsoagCXnipAz8gb6Jz+nxU+fwYmya+dvGZPeV8jonkihgNugorXHS23iKBqeXYanRxEVZyCl3dHlTa06EaOijh7orAoEAP/nJT/jjH//I1KlTB/z4Q4ljWsQAM2bM4MEHH+Tcc8+lsbExZOepcXgRerC+VhUorg3OviPSYomymnB5en8RV9S5gls3BLdqerpl1eXYVCircWJq6hDp88s0unvnPW5weFkwORNFUcmtcrRKeuj4nCp1IRDxAw88gMlk4qabbhrwYw81jnkRA1x11VUsWLCAK6+8ss+het1R3ejt0jPdjNWoI6fcAQTXiktnDOu1AFUV8svtmJqCSkwGHeV9MH3b0uDyBtfoR9yMauw9H5uqqiAETeng3m37cMv2r4G6AbIimvnuu+/4xz/+wfPPP3/MNKrrimP/HTbxj3/8g8LCQh5++OGQHL+4ztPtBQvBoI+CaldLlNGMsSktiQI9pdHtw+HxYdAFRazXiQOyLq5pczMx6iVKqh09fn29w8vI1FhiIsyU1Xe9Hm5GrxMpqx84ETscDi655BL+9a9/kZ6ePmDHHcocNyI2Go288cYbPPLII6xdu3bAj19Y27VnuhlJFJBVhaomB1JCtJVRGXHUNfZ8q6WsxhlcXzf9v/l3f9fFJdUOTEe8B6Neoq7Ri6+TChxtsTu9LJiSCcChSmePbmomvUhpB/W2+oKiKFx33XXMnz+f888/f0COGQ4cNyIGyMzM5MUXX+SSSy5paRw2ECiKSmWDt0cihuC6uLTu8IW7ZFoWja6erT1VNeiVtrQJUOnvutjrl6l3+jAc4YhqDmyq7UFARrARuMD4YcEtngPl7ZMeOsKkl6i0e/u9zPH5fFx22WUcOnRoQBvShwPHlYgBli5dyq233sr8+fPZunXrgByz3uUnoCjdlu1pxqATOVR5eA07cXgiBp3UI091ncODzy+3T+0z6Kio6/t2VY3dA6i0fQeSKPRovV3v8DA6I5ZomwmHJ0Cdy9cu6aEjdKKAxy/j7uFs3xEOh4OVK1fS2NjI6tWrB6wZfbhw3IkYgvW5HnvsMU455RTefffdfh+vxulrd/F3RYQpWOmjefYxG/XMndizPePSakeHwRN6nYjT4+/zuri81tlh0InZqKOsxonSjdOu0eVj/qSgKV1a7+406aEtzcuCOmffPNRVVVWcdNJJpKWl8eabbx7T+8GdcVyKGOC8887jo48+4rrrruORRx7plzlXaff0aj/VoBNp9Pixuw+n4c2bmIHXL+PpovyMrKgUVjW2M6Whf+tiRVEpr3Ni7qDNqCQKBBQFeydbTaqqUlHnxKCXWkzpohpXp0kPndGXbKaCggLmz5/P8uXLeeaZZ8Ky8PtAcNyKGGDWrFls3LiRVatWcc011/S5x1FRjQtjL0r4NM8+5Q2HnVnDUmK46KQJNDi8FJQ3dCjmGruHgKx2arYLQu+2hJqxu3zIXRwXtf16W1VVauxuCsobGJYSw+8vOZFIazBGel95I9ZeJpVUd1B7rCt27tzJ/Pnzuf7667n33nuPucyk3nBcixiCjczWr19PSUkJp556KvX19b0+RnGtu8s61B0hCAIF1YfNZ1EUOHn2CB68dhkXnjQBu9NHfnlDq+IBxVWN6KTOL1aTQUd5be/XxR2VC2p93NZbTXZn8EYTF2nm5h/N46YL55CVFOzR5AsolNZ5sPaiebhRJ7Vy9HXH119/zbJly3jooYfCul70QHHcixiCxdXfffddJk6cyAknnEBubm6PX+sLKMGytj1w4hyJzahjf3n7CDKLSc/yWcN58Nql/GjpBBxuPwXlDdidXkprHB2a0s30dV1cUu1stbXUFoNewu70UWv3kF9Wj04Sue7cWdzykwWMzYpvNQt2l/TQESaD2Ko3U1e88847nHfeebz44otcfPHFPT7HsYwm4iYkSeKxxx7j+uuv58QTT2TDhg09el1tk1Ort+acxShRVu/ptCKF2ahn6czhPHjtMn68fCIlNQ7sTm+HlTKb6cu62O0NYHe13lpqixyQcbi8VNY7uer0adz90yVMG53SoVC7S3roCKNOCra+6cZ59swzz3Dttdfy0UcfsWzZsl6d41hGE3EbrrvuOp577jnOOeccXnrppW6fX93o7eUlG0QUBBBar4s7wmTQsWT6MKaNzWDC8CQCskq9w4Ovk+2o3q6Laxs73loCkBWFBocHj19mdHYC86YMY96kjC5TJ7tLeuiI5mqgnZWiVVWVe++9l/vuu4+vvvqKmTNn9ur4xzrHpzuvG1asWMHatWs544wzOHDgALfffnunM215gwehVxtMh1EVlZI6N5lxXVfWbHB6ySmuY9KIJCYMT6Sksp59BdXUOzxYjPpWs2jzunh8VlyPxlBW62xT1F0lICvBtbggMCoznuFpwdaoewpru8x/bk56iOxDocHmelsxbYr0y7LMr3/9a77++ms2bNhASkr70rjHO5qIO2HixIls2rSJs846i5ycHJ599llMpvZVOApqXL12ajVjMUrklDdywsiuBbcrrxqV4DpTRCArJZb0xGhKKhvYV1BFg9OLAKgERejyBCiuCuYaG/Q6jHqpxfRVFBWvX8brC+D0+jlUUofJoEOWlSYzWMBi0pOVEsvIjDjMR5TW8ftlCivtDE+J7nCcPU166AhFbb/N5PV6ueyyy6ioqGDdunW9anB+PKGJuAuSk5P58ssvueKKK1i6dCnvvPNOqxpNqqpSVufpcbhlW6xGHblVwUCKrhxB63YUE2VpPUNJkkhmSgzpSdF4/QF8/gBev4zPJ1NY2UB6YjQmnUiN3U1pTSOKQpPQVWIjLaQmRCCKIsW1brKSojAZdJiMeox6qcv1/Z6C2k5F3NOkh46QBIEK++Glhd1u55xzziE6OppPPvmkwxuoRhBNxN1gNpt5+eWXueOOO5g7dy7vv/8+48ePB8Dpk3H6AkSa+/Yx6iURfyDYtC2hkzpUlfUu8ivsZCTYOnxcFAXMRn2rGVOQJMYOS+T8BaOB5tnZjy8gE2kxIjXNlO9tPMS+EjvJcT0LU4yJMPLdvnLOmDu8w8d7mvTQESaDREnTNlNFRQWnnXYas2bN4l//+levqmwej2iOrR4giiJ/+ctfuOOOO1i8eDGff/45ALUOHyK990wfiYJKWRd7pNsPVUIvzxFlNbLjUHXL/wVBwGo2EBNhbhGwqqp8t6+caFvHN4+OsJr0lNU5O3Wc9TTpoSNMepHKBi+HDh3ixBNP5Mwzz+SJJ57QBNwDNBH3gssuu4w33niDSy+9lKeeeiq4LdLPYxokkUNVHScYqKrKl9tLiOlklu4MqylYJMDu6nyrqbbRQ0W9C6up506o4I1E5WBJfbvHepP00BEGSSRn7w4WLlzIzTffzJ133nlcR2H1Bs2c7iULFy7k66+/5owzzmD8+q2MP+OX/TqezajjYEXHiffF1Q4q611kJnZsSndGcy2vwspGJmZ3fAMIClHttVCsRj2bD1QwZ1xrL3Fvkh7a4vV4eP+FJ9j88Us88/RT/OiiC3p9jOMZbSbuA6NGjWLjxo0UHdrPU789l2+/Wt3nZmcmvUit04fD2z5WetuBSiSx444S3SEKAvuLOu8MsflARadF3bsi2mZkd351u0IBfUl6APh+/Vru/fnpeCpz+X7zZk3AfUATcR+JjY3lu6/XcP/99/P5qkd45DeXsG/ntl4fpyUZok2vXllWWLezmLiovnllo6xGth+q6vAxn19md351r9bDzegkEVlRya+wt/p7b5MeykuL+Odt1/DR0/fy6KOP8e0XHzN+dOcdFjU6RxNxPxBFkZ9eegGFB/Zy9eWX8cK91/P4nTdQUpjfuwMJUFjTOgkhr9xOo8vXYXpgT7CadJTVOjusGJJfYUdW1D4XrRcEgV35hx1nvoBCSa27R0kPPp+Xt5//J4/ecC4L5s4k/8Berrj4nD6NQyOIJuIBwGDQc8fvbyA/9yALZk/j7zddwKrH7qK+rqZHr7cadOS0SYb4bn9Zn0UGTTWdRSiotLd7bFd+db+cRjE2I9/vK2/Jwa5oCh3tLulh27dfc9/Pz6Q+fycbN27imb89gM3atz7QGofRRDyAxERF8NSj97Fnz25SIvXc/9NTefu//+q28bnVqKOw1tXSCcHnl9m4p4z4yP4FOHS0LlZVle/3VxDTB1O6GYtRR7XdTWVTgbtg0kPnVFWU8fidN/DOP/7Mfffdx+avVzNlwrHTH3iw0UQcArLTU3nv5f+w7quvcJflcM9VJ/P5u68iBzqu2iGJAqhQaQ9uCR0oqcfrl7vMLOoJUVZDu3VxVYObGru7y5TG7mhu33qgOHiD6CzpIRDw8/6LT/HwL1cyc9JY8g7u4xeXX3xc1II+mmifZgiZNXUiG9d8yAsvvsSedW9z7zUr+X792g492QpQUhuc2TbsLulVpZDOsJr07dbFB4rrUNXeby21xWbS831ORaedHnZu3cR915xF2e6NfLnuK/7370eJiujdVplGz9BEfBRYecoS9m//jr/cdRef/OcBHr35MnJ2b2/1HLNO4mClA5fHz9aDlcT105SG5l5HrdfFm3MqsPUhy6gtUTYD+4tqKalztUp6qK2u5Kl7fsOrD93MbbfexvZv1zF72qR+n0+jczQRHyVEUeSaK35E4cF9XHLxBTx/9zX8++5fU15SCAQ7QxysdLCnsAalH57jdueVBHKazF6PL8DeolqirX1fDzcjiSKKqrLlYBUqKnIgwEevPsf//eIMxg9PJ/fAfn7188s00/kooH3CRxmT0cC9t/6W/EMHmTNlHI/ecC4v/P0vuB31uH0yn20pwtpNE7LeEGUxsD03uC7OK29AVdWW+On+IokC63eXUbBvOw9cdy55mz/n088/59XnHicuRksbPFpoIh4k4mKi+M8/H2LHzp3EGRXuu3oFa197mi37i4mxDVzandWkp6wmuC7elVcTrCjST3xeL7k5e9j/3WpefOxW3n/0Jm769a/Zs3Uj82dPH4BRa/QGLXZ6kBmZncFHb/yPjZu3c+2vfss7bz/B5zGJRCdmEBmfRmxyBklp2aRmDCM2PqHX5mlzRFhBhZ3vc8qJjej5DUJRFCrLSyjKzaGs8BC1ZXnUluZhrykjJjGV4aPGcsZJc7nn1tdJSuhZJRGNgUcT8RDhhJlT+GHD57jdHrbu3MP2nXvYvWcf+/btZ/PH66goKcDv8xCVkE5UQhpRCenEp2SSnJ5NakY2VlvnOcGiJPDd/nLqHV4yEzt+nsPeQEFuDiX5OVSV5FFXlkdteT5Gk4WMYaOZMGEiZ510PnNmTmfuzClakMYQQuhN54OZM2eqmzdvDuFwNLqivLKazT/sYvvO3ezdu5+cAzkU5edSXV6EwWRpEXh0YgaJqZkkpw8jOTUDr6xSUedCJ4mkxJgoLsilOC+HyuLc4OxalovP7SQlcwSjx45n8uRJzJ4xnXmzp5ORljzYb1sDEARhi6qqHVYI1ER8DCDLMvsO5rFt+x527t7Dvv37OXggh9KifBrrqrDFJCFa4lA99TQ2mcLDRo5h8qRJTJ82hXmzZzJp/CgtAX8I05WINXP6GECSJCaMGcmEMSOBla0ec7rcbNu5hx279zNyeDbzZk3VTOFjDE3ExzhWi5n5c2Ywf86MwR6KRojQtpg0NMIcTcQaGmGOJmINjTBHE7GGRpijiVhDI8zRRKyhEeZoItbQCHM0EWtohDmaiDU0whxNxBoaYY4mYg2NMEcTsYZGmKOJWEMjzNFErKER5mgi1tAIczQRa2iEOZqINTTCHE3EGhphjiZiDY0wRxOxhkaYo4lYQyPM0USsoRHmaCLW0AhzNBFraIQ5mog1NMIcTcQaGmGOJmINjTBHE7GGRpjTq9amgiBUAQWhG46GhkYnZKmqmtDRA70SsYaGxtBDM6c1NMIcTcQaGmGOJmINjTBHE7GGRpijiVhDI8zRRKyhEeZoItbQCHM0EWtohDmaiDU0wpz/B0jFPtJSBRPpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/_build/html/_sources/content/trainAUvisModel.ipynb b/notebooks/_build/html/_sources/content/trainAUvisModel.ipynb new file mode 100644 index 00000000..5f891170 --- /dev/null +++ b/notebooks/_build/html/_sources/content/trainAUvisModel.ipynb @@ -0,0 +1,451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training AU visualization model\n", + "You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:01.906078Z", + "start_time": "2021-03-26T04:01:59.634560Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:16.245590Z", + "start_time": "2021-03-26T04:02:14.666837Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:17.089755Z", + "start_time": "2021-03-26T04:10:15.301127Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:21.609846Z", + "start_time": "2021-03-26T04:10:20.554202Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/_build/html/api/index.html b/notebooks/_build/html/api/index.html index 4e96c02a..2587ccf6 100644 --- a/notebooks/_build/html/api/index.html +++ b/notebooks/_build/html/api/index.html @@ -56,7 +56,7 @@ - + @@ -113,7 +113,12 @@

Py-Feat

@@ -196,7 +206,7 @@

Py-Feat

  • - + GitHub Repository @@ -258,11 +268,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -801,6 +811,21 @@

    API Reference +
    +isc(col, index='frame', columns='input', method='pearson')
    +

    [summary]

    +
    +
    Args:

    col (str]): Column name to compute the ISC for. +index (str, optional): Column to be used in computing ISC. Usually this would be the column identifying the time such as the number of the frame. Defaults to “frame”. +columns (str, optional): Column to be used for ISC. Usually this would be the column identifying the video or subject. Defaults to “input”. +method (str, optional): Method to use for correlation pearson, kendall, or spearman. Defaults to “pearson”.

    +
    +
    Returns:

    DataFrame: Correlation matrix with index as colmns

    +
    +
    +
    +
    itersessions()
    @@ -983,8 +1008,8 @@

    API Reference -
    -ttest(popmean=0, threshold_dict=None)
    +
    +ttest_1samp(popmean=0, threshold_dict=None)

    Conducts 1 sample ttest.

    Uses scipy.stats.ttest_1samp to conduct 1 sample ttest

    @@ -996,6 +1021,21 @@

    API Reference +
    +ttest_ind(col, sessions, threshold_dict=None)
    +

    Conducts 2 sample ttest.

    +

    Uses scipy.stats.ttest_ind to conduct 2 sample ttest on column col between sessions.

    +
    +
    Args:

    col (str): Column names to compare in a t-test between sessions +session_names (tuple): tuple of session names stored in Fex.sessions. +threshold_dict ([type], optional): Dictonary for thresholding. Defaults to None. [NOT IMPLEMENTED]

    +
    +
    Returns:

    t, p: t-statistics and p-values

    +
    +
    +

    +
    upsample(target, target_type='hz', **kwargs)
    @@ -1310,7 +1350,7 @@

    API Reference
    Args:

    model: sklearn PLSRegression instance -au: vector of action units (same length as model.x_mean_) +au: vector of action units (same length as model.n_components) vectorfield: (dict) {‘target’:target_array,’reference’:reference_array} muscles: (dict) {‘muscle’: color} ax: matplotlib axis handle @@ -1348,7 +1388,7 @@

    API Reference
    -feat.utils.load_h5(file_name='blue.h5')
    +feat.utils.load_h5(file_name='pyfeat_aus_to_landmarks.h5')

    Load the h5 PLS model for plotting.

    Args:

    file_name (str, optional): Specify model to load.. Defaults to ‘blue.h5’.

    @@ -1358,18 +1398,6 @@

    API Reference -
    -feat.utils.load_pickled_model(file_name='pls_python27.pkl')
    -

    Load the pickled PLS model for plotting.

    -
    -
    Args:

    file_name ([type], optional): Specify model to load. Defaults to pls_python27.pkl.

    -
    -
    Returns:

    model: PLS model

    -
    -
    -

    -
    feat.utils.read_affectiva(affectivafile, orig_cols=False)
    @@ -1529,7 +1557,7 @@

    API ReferenceContributing new FEX models + Contributing new detectors Contribute

    diff --git a/notebooks/_build/html/content/analysis.html b/notebooks/_build/html/content/analysis.html index 5b54135b..b38bda15 100644 --- a/notebooks/_build/html/content/analysis.html +++ b/notebooks/_build/html/content/analysis.html @@ -55,8 +55,8 @@ - - + + @@ -113,7 +113,12 @@

    Py-Feat

    @@ -196,7 +206,7 @@

    Py-Feat

  • - + GitHub Repository @@ -258,11 +268,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -283,21 +293,21 @@

    Py-Feat

    aria-label="Launch interactive content">
    - - - @@ -343,6 +353,11 @@

    Py-Feat

    Simple t-test
    • +
  • + + Two sample independent t-test + +
  • Prediction @@ -353,6 +368,11 @@

    Py-Feat

    Regression
    • +
  • + + Intersubject correlations + +
    • @@ -400,6 +420,8 @@

    How to preprocess and analyze facial expression data with Feat.
    import os, glob
 import numpy as np
 import pandas as pd
+import seaborn as sns
+sns.set_context("talk")
 
 clip_attrs = pd.read_csv("clip_attrs.csv")
 videos = np.sort(glob.glob("*.mp4"))
@@ -828,7 +850,7 @@ 
    Simple t-test
 
    
 
    average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map)
-t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions=="goodNews"].aus().ttest(.5)
+t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions=="goodNews"].aus().ttest_1samp(.5)
 pd.DataFrame({"t": t, "p": p}, index= average_au_intensity_per_video.au_columns)
 
    
 
    
@@ -922,6 +944,29 @@ 
    Simple t-test
+
    Two sample independent t-test
    
+
    You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews.
    
+
    
+
    
+
    columns2compare = "mean_AU12"
+sessions = ("goodNews", "badNews")
+t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions)
+print(f"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}")
+sns.barplot(x = average_au_intensity_per_video.sessions, 
+            y = columns2compare, 
+            data = average_au_intensity_per_video);
+
    
+
    
+
    
+
    
+
    T-test between goodNews vs badNews: t=17, p=2.32e-12
+
    
+
    
+../_images/analysis_17_1.png
+
    
+
    
+
    
 
    
 
    Prediction
    
 
    If you want to know what combination of features predic the good news or bad news conditions. To investigate this problem, we can train a Logistc Regression model using emotion labels to predict the conditions. Results suggest that detections of happy expressions predict the delivery of good news.
    
@@ -1093,6 +1138,22 @@ 
    Regression
+
    Intersubject correlations
    
+
    To compare the similarity of signals over time between subjects or videos, you can use the isc method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions.
    
+
    
+
    
+
    fex.sessions = fex.input()
+isc = fex.isc(col = "AU01")
+sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap="RdBu_r");
+
    
+
    
+
    
+
    
+../_images/analysis_23_0.png
+
    
+
    
+
    - + @@ -113,7 +113,12 @@

    Py-Feat

    @@ -196,7 +211,7 @@

    Py-Feat

  • - + GitHub Repository @@ -258,11 +273,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -283,21 +298,21 @@

    Py-Feat

    aria-label="Launch interactive content">
    - - - @@ -377,7 +392,7 @@

    Installation example Py-Feat: Python Facial Expression Analysis Toolbox - Detecting FEX from images and videos + Detecting FEX from images

    diff --git a/notebooks/_build/html/content/intro.html b/notebooks/_build/html/content/intro.html index b16beb36..dc481339 100644 --- a/notebooks/_build/html/content/intro.html +++ b/notebooks/_build/html/content/intro.html @@ -131,8 +131,8 @@

    Py-Feat

  • - - Visualizations + + Plotting examples
  • @@ -140,6 +140,11 @@

    Py-Feat

    Loading data from other detectors
    • +
  • + + Training AU visualization model + +
  • Contributing new detectors @@ -200,7 +205,7 @@

    Py-Feat
  • - + GitHub Repository @@ -262,11 +267,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -366,10 +371,10 @@

    Py-Feat

    Py-Feat: Python Facial Expression Analysis Toolbox

    Build Status -Coverage Status -GitHub forks -GitHub stars -GitHub license

    +Coverage Status +GitHub forks +GitHub stars +GitHub license

    Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data.

    Why you should use Py-Feat

    diff --git a/notebooks/_build/html/content/loadingOtherFiles.html b/notebooks/_build/html/content/loadingOtherFiles.html index 8677c511..b15be8ae 100644 --- a/notebooks/_build/html/content/loadingOtherFiles.html +++ b/notebooks/_build/html/content/loadingOtherFiles.html @@ -55,8 +55,8 @@ - - + + @@ -118,7 +118,7 @@

    Py-Feat

  • - Detecting FEX from videos. + Detecting FEX from videos
  • @@ -132,8 +132,8 @@

    Py-Feat

  • - - Visualizations + + Plotting examples
  • @@ -141,6 +141,11 @@

    Py-Feat

    Loading data from other detectors
    • +
  • + + Training AU visualization model + +
  • Contributing new detectors @@ -201,7 +206,7 @@

    Py-Feat
  • - + GitHub Repository @@ -263,11 +268,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -288,21 +293,21 @@

    Py-Feat

    aria-label="Launch interactive content">
    - - - @@ -336,6 +341,11 @@

    Py-Feat

    Loading Affectiva API file
    • +
  • + + Loading a completely new file as a Fex class + +
    • @@ -399,39 +409,7 @@

    Loading OpenFace datagaze_1_x gaze_1_y gaze_1_z - pose_Tx - pose_Ty - pose_Tz - pose_Rx - pose_Ry - pose_Rz - x_0 - x_1 - x_2 - x_3 - x_4 - x_5 - x_6 - x_7 - x_8 - x_9 ... - AU14_r - AU15_r - AU17_r - AU20_r - AU23_r - AU25_r - AU26_r - AU45_r - AU01_c - AU02_c - AU04_c - AU05_c - AU06_c - AU07_c - AU09_c - AU10_c AU12_c AU14_c AU15_c @@ -457,41 +435,9 @@

    Loading OpenFace data-0.124401 0.066311 -0.990014 - 16.6573 - 43.4571 - 597.876 - 0.297590 - -0.063753 - -0.019235 - 277.276 - 278.163 - 280.209 - 283.258 - 288.537 - 297.109 - 307.888 - 320.240 - 333.596 - 346.018 ... 0 0 - 0.0 - 0 - 0.4543 - 0.0 - 0.0 - 0.02348 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 - 0 - 0 0 0 0 @@ -513,41 +459,9 @@

    Loading OpenFace data-0.123464 0.069979 -0.989879 - 16.2613 - 43.2594 - 596.885 - 0.326858 - -0.058038 - -0.022072 - 276.980 - 277.893 - 279.952 - 283.021 - 288.339 - 296.925 - 307.663 - 319.981 - 333.443 - 346.086 ... 0 0 - 0.0 - 0 - 0.3076 - 0.0 - 0.0 - 0.12580 - 0 - 0 - 0 - 1 - 0 - 0 - 0 - 0 - 0 - 0 0 0 0 @@ -569,41 +483,9 @@

    Loading OpenFace data-0.122873 0.070540 -0.989912 - 15.8624 - 43.2698 - 593.122 - 0.350392 - -0.051172 - -0.022723 - 276.902 - 277.785 - 279.844 - 282.911 - 288.141 - 296.601 - 307.278 - 319.657 - 333.284 - 346.138 ... 0 0 - 0.0 - 0 - 0.2163 - 0.0 - 0.0 - 0.16820 - 1 - 1 - 0 - 1 - 0 - 0 - 0 - 0 - 0 - 0 0 0 0 @@ -625,41 +507,9 @@

    Loading OpenFace data-0.122172 0.070736 -0.989985 - 15.5008 - 43.1023 - 592.867 - 0.352725 - -0.047812 - -0.022291 - 276.757 - 277.626 - 279.664 - 282.699 - 287.902 - 296.353 - 307.023 - 319.397 - 333.035 - 345.927 ... 0 0 - 0.0 - 0 - 0.1961 - 0.0 - 0.0 - 0.15160 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 - 0 - 0 0 0 0 @@ -681,41 +531,9 @@

    Loading OpenFace data-0.121321 0.070529 -0.990104 - 14.7702 - 43.1363 - 594.135 - 0.349325 - -0.045551 - -0.022963 - 276.342 - 277.208 - 279.248 - 282.285 - 287.478 - 295.913 - 306.562 - 318.911 - 332.540 - 345.428 ... 0 0 - 0.0 - 0 - 0.1634 - 0.0 - 0.0 - 0.16890 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 - 0 - 0 0 0 0 @@ -766,39 +584,7 @@

    Loading OpenFace datax_7 x_8 x_9 - x_10 - x_11 - x_12 - x_13 - x_14 - x_15 - x_16 - x_17 - x_18 - x_19 - x_20 - x_21 - x_22 - x_23 - x_24 - x_25 ... - y_42 - y_43 - y_44 - y_45 - y_46 - y_47 - y_48 - y_49 - y_50 - y_51 - y_52 - y_53 - y_54 - y_55 - y_56 - y_57 y_58 y_59 y_60 @@ -824,39 +610,7 @@

    Loading OpenFace data320.240 333.596 346.018 - 356.266 - 365.507 - 372.289 - 376.285 - 378.485 - 380.039 - 380.562 - 286.383 - 292.931 - 303.077 - 313.934 - 323.874 - 340.883 - 350.207 - 359.633 - 368.524 ... - 186.658 - 181.683 - 181.337 - 184.018 - 187.718 - 188.326 - 237.529 - 235.506 - 233.715 - 234.922 - 233.408 - 234.625 - 235.620 - 240.460 - 242.560 - 243.216 243.032 241.552 237.763 @@ -880,39 +634,7 @@

    Loading OpenFace data319.981 333.443 346.086 - 356.530 - 365.888 - 372.591 - 376.430 - 378.497 - 379.960 - 380.414 - 286.137 - 292.527 - 302.614 - 313.488 - 323.464 - 340.515 - 349.885 - 359.369 - 368.252 ... - 186.681 - 181.790 - 181.413 - 183.969 - 187.708 - 188.342 - 237.534 - 235.528 - 233.778 - 234.985 - 233.450 - 234.572 - 235.499 - 240.720 - 243.024 - 243.725 243.535 241.895 237.777 @@ -936,39 +658,7 @@

    Loading OpenFace data319.657 333.284 346.138 - 356.717 - 366.159 - 372.821 - 376.590 - 378.645 - 380.110 - 380.536 - 285.955 - 292.222 - 302.268 - 313.181 - 323.186 - 340.184 - 349.622 - 359.211 - 368.176 ... - 186.788 - 181.864 - 181.463 - 183.978 - 187.807 - 188.454 - 238.026 - 235.976 - 234.216 - 235.456 - 233.928 - 235.091 - 236.081 - 241.759 - 244.207 - 244.885 244.668 242.816 238.284 @@ -992,39 +682,7 @@

    Loading OpenFace data319.397 333.035 345.927 - 356.552 - 366.024 - 372.690 - 376.460 - 378.517 - 379.986 - 380.415 - 285.736 - 292.013 - 302.060 - 312.962 - 322.970 - 339.908 - 349.373 - 358.981 - 367.970 ... - 186.682 - 181.764 - 181.370 - 183.889 - 187.717 - 188.355 - 237.935 - 235.940 - 234.204 - 235.451 - 233.921 - 235.070 - 236.026 - 241.618 - 244.027 - 244.707 244.484 242.662 238.209 @@ -1048,39 +706,7 @@

    Loading OpenFace data318.911 332.540 345.428 - 356.057 - 365.517 - 372.168 - 375.910 - 377.945 - 379.397 - 379.831 - 285.234 - 291.509 - 301.494 - 312.317 - 322.265 - 339.345 - 348.778 - 358.342 - 367.285 ... - 186.682 - 181.778 - 181.384 - 183.907 - 187.712 - 188.348 - 237.788 - 235.827 - 234.104 - 235.327 - 233.797 - 234.910 - 235.821 - 241.386 - 243.801 - 244.494 244.297 242.514 238.063 @@ -1132,21 +758,7 @@

    Loading OpenFace dataAU10_r AU12_r AU14_r - AU15_r - AU17_r - AU20_r - AU23_r - AU25_r - AU26_r - AU45_r - AU01_c - AU02_c - AU04_c - AU05_c - AU06_c - AU07_c - AU09_c - AU10_c + ... AU12_c AU14_c AU15_c @@ -1172,21 +784,7 @@

    Loading OpenFace data0 0 0 - 0 - 0.0 - 0 - 0.4543 - 0.0 - 0.0 - 0.02348 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 + ... 0 0 0 @@ -1210,21 +808,7 @@

    Loading OpenFace data0 0 0 - 0 - 0.0 - 0 - 0.3076 - 0.0 - 0.0 - 0.12580 - 0 - 0 - 0 - 1 - 0 - 0 - 0 - 0 + ... 0 0 0 @@ -1248,21 +832,7 @@

    Loading OpenFace data0 0 0 - 0 - 0.0 - 0 - 0.2163 - 0.0 - 0.0 - 0.16820 - 1 - 1 - 0 - 1 - 0 - 0 - 0 - 0 + ... 0 0 0 @@ -1286,21 +856,7 @@

    Loading OpenFace data0 0 0 - 0 - 0.0 - 0 - 0.1961 - 0.0 - 0.0 - 0.15160 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 + ... 0 0 0 @@ -1324,21 +880,7 @@

    Loading OpenFace data0 0 0 - 0 - 0.0 - 0 - 0.1634 - 0.0 - 0.0 - 0.16890 - 0 - 1 - 0 - 1 - 0 - 0 - 0 - 0 + ... 0 0 0 @@ -1352,6 +894,7 @@

    Loading OpenFace data +

    5 rows × 35 columns

    @@ -1401,39 +944,7 @@

    Loading FACET iMotions data -
    /home/jcheong/packages/feat/feat/data.py:1303: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
+
    /home/jcheong/packages/feat/feat/data.py:1338: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
   wavs = np.array(wavs)[::-1]
 
    
 
    
@@ -1893,39 +1244,7 @@ 
    Loading FACET iMotions data
+
    Loading a completely new file as a Fex class
    
+
    It’s easy to cast a dataframe which might be neither an OpenFace or FACET outputs into a Fex class. Simply cast your dataframe as a Fex class.
    
+
    
+
    
+
    from feat import Fex
+import pandas as pd, numpy as np
+au_columns = [f"AU{i}" for i in range(20)]
+fex = Fex(pd.DataFrame(np.random.rand(20,20)))
+fex.columns = au_columns
+print(type(fex))
+
    
+
    
+
    
+
    
+
    <class 'feat.data.Fex'>
+
    
+
    
+
    
+
    
+
    To take full advantage of Py-Feat’s features, make sure you set the attributes.
    
+
    
+
    
+
    fex.au_columns = au_columns
+display(fex.aus().head())
+
    
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+
    AU0AU1AU2AU3AU4AU5AU6AU7AU8AU9AU10AU11AU12AU13AU14AU15AU16AU17AU18AU19
    00.0036990.0421260.5328390.4107010.0792370.6129380.5601220.9997830.8895220.1698870.3743500.6319520.0403750.8529790.3292200.1968420.1182400.6313010.7095660.003093
    10.8875440.8824570.2655710.0780940.3412080.5639130.2947690.3071200.4805300.3212970.3213000.5139470.5665240.8169320.4936170.5415070.6241860.1386420.8458370.638981
    20.2228480.0079020.8063560.3921770.8424650.7652450.2603040.3727780.6482630.1186010.6955440.8006800.8706430.5880520.2661680.2472230.4794930.9280130.0616310.158413
    30.1617660.6788740.9943420.0764210.6187080.8804370.3028600.1918230.4433280.2218100.3212600.1548320.3307420.2817540.7071230.5000140.4570130.5750170.9551540.216029
    40.8023790.9510920.0476950.9144160.6516830.1833440.4461200.8724190.4688560.1189900.9857440.7930740.1556250.1784480.2178530.9662670.4553500.9704130.7816240.193531
    @@ -2139,8 +1595,8 @@

    Loading Affectiva API fileVisualizations - Contributing new detectors + Plotting examples + Training AU visualization model

    diff --git a/notebooks/_build/html/content/modelContribution.html b/notebooks/_build/html/content/modelContribution.html index 10191a9f..5be4b886 100644 --- a/notebooks/_build/html/content/modelContribution.html +++ b/notebooks/_build/html/content/modelContribution.html @@ -56,7 +56,7 @@ - + @@ -118,7 +118,7 @@

    Py-Feat

  • - Detecting FEX from videos. + Detecting FEX from videos
  • @@ -132,8 +132,13 @@

    Py-Feat

  • - - Visualizations + + Two sample independent t-test + +
    • +
  • + + Plotting examples
  • @@ -141,6 +146,11 @@

    Py-Feat

    Loading data from other detectors
    • +
  • + + Training AU visualization model + +
  • Contributing new detectors @@ -201,7 +211,7 @@

    Py-Feat
  • - + GitHub Repository @@ -263,11 +273,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    @@ -413,7 +423,7 @@

    3. Add models to the toolbox.Loading data from other detectors + Training AU visualization model API Reference

    diff --git a/notebooks/_build/html/content/plotting.html b/notebooks/_build/html/content/plotting.html new file mode 100644 index 00000000..518dfd3b --- /dev/null +++ b/notebooks/_build/html/content/plotting.html @@ -0,0 +1,672 @@ + + + + + + + + Plotting examples — Py-Feat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + + + +
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    + Contents +
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    +
    + +
    + +
    +

    Plotting examples

    +

    written by Jin Hyun Cheong

    +

    Included in the toolbox are two models for Action Units to landmark visualization. The ‘pyfeat_aus_to_landmarks.h5’ model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy’s least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data.

    +

    Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended.

    +
    +
    +
    # Load modules
+%matplotlib inline
+from feat.plotting import plot_face
+import numpy as np
+import matplotlib.pyplot as plt
+
+plot_face(au=np.zeros(20))
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_2_1.png +
    +
    +
    +

    Draw lineface using input vector

    +

    Affectiva vectors should be divided by twenty for use with our ‘blue’ model.

    +
    +
    +
    from feat.plotting import plot_face
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Add data, AU is ordered as such: 
+# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43
+
+# Activate AU1: Inner brow raiser 
+au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+# Plot face
+fig, axes = plt.subplots(1,2)
+plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')
+plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_5_1.png +
    +
    +
    +
    +

    Add a vectorfield with arrows from the changed face back to neutral and vice versa

    +
    +
    +
    from feat.plotting import plot_face, predict
+from feat.utils import load_h5
+import numpy as np
+import matplotlib.pyplot as plt
+
+model = load_h5('pyfeat_aus_to_landmarks.h5')
+# Add data activate AU1, and AU12
+au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])
+
+# Get neutral landmarks
+neutral = predict(np.zeros(len(au)))
+
+# Provide target landmarks and other vector specifications
+vectors = {'target': predict(au),
+           'reference':  neutral, 'color': 'blue'}
+
+fig, axes = plt.subplots(1,2)
+# Plot face where vectorfield goes from neutral to target, with target as final face
+plot_face(model = model, ax = axes[0], au = np.array(au), 
+            vectorfield = vectors, color='k', linewidth=1, linestyle='-')
+
+# Plot face where vectorfield goes from neutral to target, with neutral as base face
+plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), 
+            vectorfield = vectors, color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_7_1.png +
    +
    +
    +
    +

    Add muscle heatmaps to the plot

    +
    +
    +
    from feat.plotting import plot_face
+from feat.utils import load_h5
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Add data
+model = load_h5()
+
+au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+# Add some muscles
+muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': "blue"}
+muscles = {'all': 'heatmap'}
+
+plot_face(model=model, au = np.array(au), 
+          muscles = muscles, color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_9_1.png +
    +
    +
    +
    +
    from feat.plotting import plot_face
+from feat.utils import load_h5
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Add data
+au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, 
+      0.450328, 1.02961, 0.871225, 0, 1.1977,  0.457218, 0, 0, 0, 0]
+au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+# Add some muscles
+muscles = {'all': 'heatmap'}
+
+# Plot face
+plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_10_1.png +
    +
    +
    +
    +

    Make sure muscle array contains ‘facet’ for a facet heatmap

    +
    +
    +
    from feat.plotting import plot_face
+from feat.utils import load_h5
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Add data
+au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, 
+      0.450328, 1.02961, 0.871225, 0, 1.1977,  0.457218, 0, 0, 0, 0])
+
+# Load a model 
+model = load_h5()
+
+# Add muscles
+muscles = {'all': 'heatmap', 'facet': 1}
+
+# Plot face
+plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_12_1.png +
    +
    +
    +
    +

    Add gaze vectors

    +

    Add gaze vectors to indicate where the eyes are looking.
    +Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards.

    +
    +
    +
    from feat.plotting import plot_face
+from feat.utils import load_h5
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Add data
+au = np.zeros(20)
+
+# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)
+gaze = [-1, 5, 1, 5]
+
+# Plot face
+plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_14_1.png +
    +
    +
    +
    +

    Call plot method on Fex instances

    +

    It is possible to call the plot_aus method within openface, facet, affdex fex instances

    +

    OpenFace

    +
    +
    +
    from feat.plotting import plot_face
+import numpy as np
+import matplotlib.pyplot as plt
+from feat.utils import  load_h5, get_resource_path, read_openface
+from feat.tests.utils import get_test_data_path
+from os.path import join
+
+test_file = join(get_test_data_path(),'OpenFace_Test.csv')
+openface = read_openface(test_file)
+openface.plot_aus(12, muscles={'all': "heatmap"}, gaze = None)
+
    +
    +
    +
    +
    <AxesSubplot:>
+
    +
    +../_images/plotting_17_1.png +
    +
    +
    +
    + + + + +
    + + +
    + + Preprocessing FEX data + Loading data from other detectors + +
    + +
    +
    +
    +
    +

    + + By Jin Hyun Cheong
    + + © Copyright 2020.
    +

    +
    +
    +
    + + +
    +
    + + + + + + + + \ No newline at end of file diff --git a/notebooks/_build/html/content/trainAUvisModel.html b/notebooks/_build/html/content/trainAUvisModel.html new file mode 100644 index 00000000..26ba9008 --- /dev/null +++ b/notebooks/_build/html/content/trainAUvisModel.html @@ -0,0 +1,726 @@ + + + + + + + + Training AU visualization model — Py-Feat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    +
    + +
    + + +
    + +
    + Contents +
    + + +
    +
    +
    +
    +
    + +
    + +
    +

    Training AU visualization model

    +

    You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following.

    +
    +
    +
    %matplotlib inline
+import pandas as pd, numpy as np, matplotlib.pyplot as plt
+from sklearn.cross_decomposition import PLSRegression
+from sklearn.model_selection import KFold
+from feat.plotting import predict, plot_face
+from feat.utils import registration, neutral
+from natsort import natsorted
+import os, glob 
+import pandas as pd, numpy as np
+import seaborn as sns
+sns.set_style("white")
+
+base_dir = "/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train"
+
+labels_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_labels.csv"))
+landmarks_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_landmarks.csv"))
+print("EmotioNet: ", len(labels_emotionet))
+labels_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_labels.csv"))
+landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_landmarks.csv"))
+# Disfa is rescaled to 0 - 1
+disfaplus_aus = [col for col in labels_disfaplus.columns if "AU" in col]
+labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5
+print("DISFA Plus: ", len(labels_disfaplus))
+labels_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_labels.csv"))
+landmarks_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_landmarks.csv"))
+bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index
+print("BP4D: ", len(labels_bp4d))
+
    +
    +
    +
    +
    EmotioNet:  24587
+DISFA Plus:  57668
+BP4D:  143951
+
    +
    +
    +
    +

    We aggregate the datasets and specify the AUs we want to train.

    +
    +
    +
    labels = pd.concat([
+                    labels_emotionet.replace({999: np.nan}), 
+                    labels_disfaplus,
+                    labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]
+                   ]).reset_index(drop=True)
+landmarks = pd.concat([
+                       landmarks_emotionet, 
+                       landmarks_disfaplus,
+                       landmarks_bp4d.iloc[bp4d_pruned_idx,:]
+                      ]).reset_index(drop=True)
+
+landmarks = landmarks.iloc[labels.index]
+
+au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]
+au_cols = [f"AU{au}" for au in au_cols]
+labels = labels[au_cols].fillna(0)
+
    +
    +
    +
    +

    We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package.

    +
    +
    +
    min_pos_sample = 500
+
+print('Pseudo balancing samples')
+balY = pd.DataFrame()
+balX = pd.DataFrame()
+for AU in labels[au_cols].columns:
+    if np.sum(labels[AU]==1) > min_pos_sample:
+        replace = False
+    else:
+        replace = True
+    newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)
+    balX = pd.concat([balX, newSample])
+    balY = pd.concat([balY, landmarks.loc[newSample.index]])
+X = balX[au_cols].values
+y = registration(balY.values, neutral)
+
+# Model Accuracy in KFold CV
+print("Evaluating model with KFold CV")
+n_components=len(au_cols)
+kf = KFold(n_splits=3)
+scores = []
+for train_index, test_index in kf.split(X):
+    X_train,X_test = X[train_index],X[test_index]
+    y_train,y_test = y[train_index],y[test_index]
+    clf = PLSRegression(n_components=n_components, max_iter=2000)
+    clf.fit(X_train,y_train)
+    scores.append(clf.score(X_test,y_test))
+print('3-fold accuracy mean', np.round(np.mean(scores),2))
+
+# Train real model
+clf = PLSRegression(n_components=n_components, max_iter=2000)
+clf.fit(X,y)
+print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))
+
    +
    +
    +
    +
    Pseudo balancing samples
+Evaluating model with KFold CV
+3-fold accuracy mean 0.13
+N_comp: 20 Rsquare 0.15
+
    +
    +
    +
    +

    We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces.

    +
    +
    +
    # Plot results for each action unit
+f,axes = plt.subplots(5,4,figsize=(12,18))
+axes = axes.flatten()
+# Exaggerate the intensity of the expression for clearer visualization. 
+# We do not recommend exceeding 2. 
+intensity = 2
+for aui, auname in enumerate(axes):
+    try:
+        auname=au_cols[aui]
+        au = np.zeros(clf.n_components)
+        au[aui] = intensity
+        predicted = clf.predict([au]).reshape(2,68)
+        plot_face(au=au, model=clf,
+                  vectorfield={"reference": neutral.T, 'target': predicted,
+                               'color':'r','alpha':.6},
+                 ax = axes[aui])
+        axes[aui].set(title=auname)
+    except:
+        pass
+    finally:
+        ax = axes[aui]
+        ax.axes.get_xaxis().set_visible(False)
+        ax.axes.get_yaxis().set_visible(False)
+
    +
    +
    +
    +../_images/trainAUvisModel_7_0.png +
    +
    +

    Here is how we would export our model into an h5 format which can be loaded using our load_h5 function.

    +
    +
    +
    # save out trained model
+# import h5py
+# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')
+# hf.create_dataset('coef', data=clf.coef_)
+# hf.create_dataset('x_mean', data=clf._x_mean)
+# hf.create_dataset('x_std', data=clf._x_std)
+# hf.create_dataset('y_mean', data=clf._y_mean)
+# hf.close()
+
    +
    +
    +
    +
    +

    Preprocessing datasets

    +

    Here we provide sample code for how you might preprocess the datasets to be used in this tutorial.

    +
    +
    +
    from PIL import Image, ImageOps
+import math, cv2, csv
+from scipy.spatial import ConvexHull
+from skimage.morphology.convex_hull import grid_points_in_poly
+from feat import Detector
+import os, glob, pandas as pd, numpy as np
+import matplotlib.pyplot as plt
+from skimage import data, exposure
+from skimage.feature import hog
+from tqdm import tqdm
+
+def padding(img, expected_size):
+    desired_size = expected_size
+    delta_width = desired_size - img.size[0]
+    delta_height = desired_size - img.size[1]
+    pad_width = delta_width // 2
+    pad_height = delta_height // 2
+    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
+    return ImageOps.expand(img, padding)
+
+def resize_with_padding(img, expected_size):
+    img.thumbnail((expected_size[0], expected_size[1]))
+    delta_width = expected_size[0] - img.size[0]
+    delta_height = expected_size[1] - img.size[1]
+    pad_width = delta_width // 2
+    pad_height = delta_height // 2
+    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
+    return ImageOps.expand(img, padding)
+
+def align_face_68pts(img, img_land, box_enlarge, img_size=112):
+    """
+    img: image
+    img_land: landmarks 68
+    box_enlarge: relative size of face
+    img_size = 112
+    
+    """
+    leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +
+                img_land[2 * 41]) / 6.0
+    leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +
+                img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0
+    rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +
+                 img_land[2 * 47]) / 6.0
+    rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +
+                 img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0
+    deltaX = (rightEye0 - leftEye0)
+    deltaY = (rightEye1 - leftEye1)
+    l = math.sqrt(deltaX * deltaX + deltaY * deltaY)
+    sinVal = deltaY / l
+    cosVal = deltaX / l
+    mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])
+    mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],
+                   [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])
+    mat2 = (mat1 * mat2.T).T
+    cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5
+    cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5
+    if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):
+        halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))
+    else:
+        halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))
+    scale = (img_size - 1) / 2.0 / halfSize
+    mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])
+    mat = mat3 * mat1
+    aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))
+    land_3d = np.ones((int(len(img_land)/2), 3))
+    land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))
+    mat_land_3d = np.mat(land_3d)
+    new_land = np.array((mat * mat_land_3d.T).T)
+    new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)
+    return aligned_img, new_land
+
+def extract_hog(image, detector):
+    im = cv2.imread(image)
+    detected_faces = np.array(detector.detect_faces(im)[0])
+    if np.any(detected_faces<0):
+        orig_size = np.array(im).shape
+        if np.where(detected_faces<0)[0][0]==1: 
+            new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))
+        else:
+            new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])
+        im = resize_with_padding(Image.fromarray(im), new_size)
+        im = np.asarray(im)
+        detected_faces = np.array(detector.detect_faces(np.array(im))[0])
+    detected_faces = detected_faces.astype(int)
+    points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)
+
+    aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)
+
+    hull = ConvexHull(points)
+    mask = grid_points_in_poly(shape=np.array(aligned_img).shape, 
+                               verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped
+                               )
+
+    mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True
+    aligned_img[~mask] = 0
+    resized_face_np = aligned_img
+
+    fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),
+                    cells_per_block=(2, 2), visualize=True, multichannel=True)
+
+    return fd, hog_image, points
+
    +
    +
    +
    +

    Replace the paths so that it points to your local dataset directory.

    +
    +
    +
    detector = Detector(face_model = "retinaface", landmark_model="mobilenet")
+# Correct path to your downloaded dataset.
+EmotioNet_images = np.sort(glob.glob("/Storage/Data/EmotioNet/imgs/*.jpg"))
+labels = pd.read_csv("/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv")
+labels = labels.dropna(axis=0)
+for col in labels.columns:
+    if "AU" in col:
+        kwargs = {col.replace("'", '').replace('"', '').replace(" ",""): labels[[col]]}
+        labels = labels.assign(**kwargs)
+        labels = labels.drop(columns = col)
+labels = labels.assign(URL = labels.URL.apply(lambda x: x.split("/")[-1].replace("'", "")))
+labels = labels.set_index('URL')
+labels = labels.drop(columns = ["URL orig"])
+
+aus_to_train = ['AU1','AU2','AU4','AU5', "AU6", "AU9","AU10", "AU12", "AU15","AU17", 
+                "AU18","AU20", "AU24", "AU25", "AU26", "AU28", "AU43"]
+
+with open('emotionet_labels.csv', "w", newline='') as csvfile:
+    writer = csv.writer(csvfile, delimiter=',')
+    writer.writerow(["URL"] + aus_to_train)
+    
+landmark_cols = [f"x_{i}" for i in range(68)] + [f"y_{i}" for i in range(68)]
+with open('emotionet_landmarks.csv', "w", newline='') as csvfile:
+    writer = csv.writer(csvfile, delimiter=',')
+    writer.writerow(landmark_cols)
+    
+for ix, image in enumerate(tqdm(EmotioNet_images)):
+    try:
+        imageURL = os.path.split(image)[-1]
+        label = labels.loc[imageURL][aus_to_train]
+        fd, _, points = extract_hog(image, detector=detector)
+        with open('emotionet_labels.csv', "a+", newline='') as csvfile:
+            writer = csv.writer(csvfile, delimiter=',')
+            writer.writerow([imageURL]+list(label.values))
+        with open('emotionet_landmarks.csv', "a+", newline='') as csvfile:
+            writer = csv.writer(csvfile, delimiter=',')
+            writer.writerow(points.T.flatten())
+    except:
+        print(f"failed {image}")
+
    +
    +
    +
    +
    +
    + + + + +
    + + +
    + + Loading data from other detectors + Contributing new detectors + +
    + +
    +
    +
    +
    +

    + + By Jin Hyun Cheong
    + + © Copyright 2020.
    +

    +
    +
    +
    + + +
    +
    + + + + + + + + \ No newline at end of file diff --git a/notebooks/_build/html/content/visualizations.html b/notebooks/_build/html/content/visualizations.html index a197976d..43f074e6 100644 --- a/notebooks/_build/html/content/visualizations.html +++ b/notebooks/_build/html/content/visualizations.html @@ -55,8 +55,6 @@ - - @@ -110,10 +108,15 @@

    Py-Feat

    Tutorials

    -
    • - - + @@ -531,8 +538,6 @@

    L

  • load_h5() (in module feat.utils) -
    • -
  • load_pickled_model() (in module feat.utils)
    • @@ -653,7 +658,9 @@

    T

    diff --git a/notebooks/_build/html/objects.inv b/notebooks/_build/html/objects.inv index 0f74848b..8887b923 100644 Binary files a/notebooks/_build/html/objects.inv and b/notebooks/_build/html/objects.inv differ diff --git a/notebooks/_build/html/py-modindex.html b/notebooks/_build/html/py-modindex.html index 81386ca9..f74469dc 100644 --- a/notebooks/_build/html/py-modindex.html +++ b/notebooks/_build/html/py-modindex.html @@ -133,8 +133,8 @@

    Py-Feat

  • - - Visualizations + + Plotting examples
  • @@ -142,6 +142,11 @@

    Py-Feat

    Loading data from other detectors
    • +
  • + + Training AU visualization model + +
  • Contributing new detectors @@ -202,7 +207,7 @@

    Py-Feat
  • - + GitHub Repository @@ -247,11 +252,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    diff --git a/notebooks/_build/html/search.html b/notebooks/_build/html/search.html index 0861a49c..5403f06a 100644 --- a/notebooks/_build/html/search.html +++ b/notebooks/_build/html/search.html @@ -135,8 +135,8 @@

    Py-Feat

  • - - Visualizations + + Plotting examples
  • @@ -144,6 +144,11 @@

    Py-Feat

    Loading data from other detectors
    • +
  • + + Training AU visualization model + +
  • Contributing new detectors @@ -204,7 +209,7 @@

    Py-Feat
  • - + GitHub Repository @@ -249,11 +254,11 @@

    Py-Feat

    aria-label="Connect with source repository">
    diff --git a/notebooks/_build/html/searchindex.js b/notebooks/_build/html/searchindex.js index 7ae85b77..0539792a 100644 --- a/notebooks/_build/html/searchindex.js +++ b/notebooks/_build/html/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/analysis.ipynb b/notebooks/_build/jupyter_execute/content/analysis.ipynb index 1c702851..32355cb3 100644 --- a/notebooks/_build/jupyter_execute/content/analysis.ipynb +++ b/notebooks/_build/jupyter_execute/content/analysis.ipynb @@ -62,8 +62,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:48:17.995310Z", - "start_time": "2021-03-24T18:48:17.859908Z" + "end_time": "2021-03-26T01:35:57.204484Z", + "start_time": "2021-03-26T01:35:56.969949Z" } }, "outputs": [ @@ -81,6 +81,8 @@ "import os, glob\n", "import numpy as np\n", "import pandas as pd\n", + "import seaborn as sns\n", + "sns.set_context(\"talk\")\n", "\n", "clip_attrs = pd.read_csv(\"clip_attrs.csv\")\n", "videos = np.sort(glob.glob(\"*.mp4\"))\n", @@ -119,11 +121,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:56:25.057270Z", - "start_time": "2021-03-24T18:56:24.564396Z" + "end_time": "2021-03-26T01:36:04.630894Z", + "start_time": "2021-03-26T01:36:02.098727Z" } }, "outputs": [], @@ -142,11 +144,11 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:29.835552Z", - "start_time": "2021-03-24T19:42:29.826112Z" + "end_time": "2021-03-26T01:36:04.662326Z", + "start_time": "2021-03-26T01:36:04.632024Z" } }, "outputs": [ @@ -238,7 +240,7 @@ "4 5 gn 5 your application has been accepted 005.mp4 goodNews" ] }, - "execution_count": 94, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -262,11 +264,11 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:35.182738Z", - "start_time": "2021-03-24T19:42:35.082327Z" + "end_time": "2021-03-26T01:36:04.916876Z", + "start_time": "2021-03-26T01:36:04.663369Z" } }, "outputs": [ @@ -409,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:38.745631Z", - "start_time": "2021-03-24T19:42:38.187813Z" + "end_time": "2021-03-26T01:36:05.612696Z", + "start_time": "2021-03-26T01:36:04.917989Z" } }, "outputs": [ @@ -641,11 +643,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:44.544102Z", - "start_time": "2021-03-24T19:42:44.530159Z" + "end_time": "2021-03-26T01:37:30.117739Z", + "start_time": "2021-03-26T01:37:29.898669Z" } }, "outputs": [ @@ -755,17 +757,68 @@ "mean_AU24 -4.533345 1.419377e-03" ] }, - "execution_count": 97, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map)\n", - "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest(.5)\n", + "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest_1samp(.5)\n", "pd.DataFrame({\"t\": t, \"p\": p}, index= average_au_intensity_per_video.au_columns)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two sample independent t-test\n", + "You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T01:54:46.741635Z", + "start_time": "2021-03-26T01:54:46.568587Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-test between goodNews vs badNews: t=17, p=2.32e-12\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/analysis_17_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "columns2compare = \"mean_AU12\"\n", + "sessions = (\"goodNews\", \"badNews\")\n", + "t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions)\n", + "print(f\"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}\")\n", + "sns.barplot(x = average_au_intensity_per_video.sessions, \n", + " y = columns2compare, \n", + " data = average_au_intensity_per_video);" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -776,11 +829,11 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:47.876293Z", - "start_time": "2021-03-24T19:42:47.862905Z" + "end_time": "2021-03-26T01:33:23.062956Z", + "start_time": "2021-03-26T01:33:23.046675Z" } }, "outputs": [ @@ -866,11 +919,11 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:49:09.446263Z", - "start_time": "2021-03-24T19:49:09.423695Z" + "end_time": "2021-03-26T01:33:24.774852Z", + "start_time": "2021-03-26T01:33:24.727406Z" } }, "outputs": [ @@ -1001,6 +1054,46 @@ " p.round(3).loc[[0]].rename(index={0:\"p-values\"})])\n", "display(results)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intersubject correlations\n", + "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T02:03:17.392348Z", + "start_time": "2021-03-26T02:03:16.179007Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/analysis_23_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fex.sessions = fex.input()\n", + "isc = fex.isc(col = \"AU01\")\n", + "sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap=\"RdBu_r\");" + ] } ], "metadata": { diff --git a/notebooks/_build/jupyter_execute/content/analysis.py b/notebooks/_build/jupyter_execute/content/analysis.py index 2f443d41..6859867f 100644 --- a/notebooks/_build/jupyter_execute/content/analysis.py +++ b/notebooks/_build/jupyter_execute/content/analysis.py @@ -24,6 +24,8 @@ import os, glob import numpy as np import pandas as pd +import seaborn as sns +sns.set_context("talk") clip_attrs = pd.read_csv("clip_attrs.csv") videos = np.sort(glob.glob("*.mp4")) @@ -75,9 +77,20 @@ You can use a simple t-test to test if the average activation of a certain AU is significantly higher than .5 (chance). The results suggests that AU10 (upper lip raiser), 12 (lip corner puller), and 14 (dimpler) is significantly activitated when providing good news. average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map) -t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions=="goodNews"].aus().ttest(.5) +t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions=="goodNews"].aus().ttest_1samp(.5) pd.DataFrame({"t": t, "p": p}, index= average_au_intensity_per_video.au_columns) +## Two sample independent t-test +You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews. + +columns2compare = "mean_AU12" +sessions = ("goodNews", "badNews") +t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions) +print(f"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}") +sns.barplot(x = average_au_intensity_per_video.sessions, + y = columns2compare, + data = average_au_intensity_per_video); + ## Prediction If you want to know what combination of features predic the good news or bad news conditions. To investigate this problem, we can train a Logistc Regression model using emotion labels to predict the conditions. Results suggest that detections of happy expressions predict the delivery of good news. @@ -100,4 +113,11 @@ results = pd.concat([b.round(3).loc[[0]].rename(index={0:"betas"}), t.round(3).loc[[0]].rename(index={0:"t-stats"}), p.round(3).loc[[0]].rename(index={0:"p-values"})]) -display(results) \ No newline at end of file +display(results) + +## Intersubject correlations +To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. + +fex.sessions = fex.input() +isc = fex.isc(col = "AU01") +sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap="RdBu_r"); \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/analysis_17_1.png b/notebooks/_build/jupyter_execute/content/analysis_17_1.png new file mode 100644 index 00000000..397a92dd Binary files /dev/null and b/notebooks/_build/jupyter_execute/content/analysis_17_1.png differ diff --git a/notebooks/_build/jupyter_execute/content/analysis_23_0.png b/notebooks/_build/jupyter_execute/content/analysis_23_0.png new file mode 100644 index 00000000..120e1ec5 Binary files /dev/null and b/notebooks/_build/jupyter_execute/content/analysis_23_0.png differ diff --git a/notebooks/_build/jupyter_execute/content/loadingOtherFiles.ipynb b/notebooks/_build/jupyter_execute/content/loadingOtherFiles.ipynb index 1d58ff92..1b6d5686 100644 --- a/notebooks/_build/jupyter_execute/content/loadingOtherFiles.ipynb +++ b/notebooks/_build/jupyter_execute/content/loadingOtherFiles.ipynb @@ -14,11 +14,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:04.115339Z", - "start_time": "2021-03-24T22:20:04.002648Z" + "end_time": "2021-03-26T05:11:11.802818Z", + "start_time": "2021-03-26T05:11:10.161089Z" } }, "outputs": [ @@ -60,39 +60,7 @@ " gaze_1_x\n", " gaze_1_y\n", " gaze_1_z\n", - " pose_Tx\n", - " pose_Ty\n", - " pose_Tz\n", - " pose_Rx\n", - " pose_Ry\n", - " pose_Rz\n", - " x_0\n", - " x_1\n", - " x_2\n", - " x_3\n", - " x_4\n", - " x_5\n", - " x_6\n", - " x_7\n", - " x_8\n", - " x_9\n", " ...\n", - " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -118,41 +86,9 @@ " -0.124401\n", " 0.066311\n", " -0.990014\n", - " 16.6573\n", - " 43.4571\n", - " 597.876\n", - " 0.297590\n", - " -0.063753\n", - " -0.019235\n", - " 277.276\n", - " 278.163\n", - " 280.209\n", - " 283.258\n", - " 288.537\n", - " 297.109\n", - " 307.888\n", - " 320.240\n", - " 333.596\n", - " 346.018\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -174,41 +110,9 @@ " -0.123464\n", " 0.069979\n", " -0.989879\n", - " 16.2613\n", - " 43.2594\n", - " 596.885\n", - " 0.326858\n", - " -0.058038\n", - " -0.022072\n", - " 276.980\n", - " 277.893\n", - " 279.952\n", - " 283.021\n", - " 288.339\n", - " 296.925\n", - " 307.663\n", - " 319.981\n", - " 333.443\n", - " 346.086\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -230,41 +134,9 @@ " -0.122873\n", " 0.070540\n", " -0.989912\n", - " 15.8624\n", - " 43.2698\n", - " 593.122\n", - " 0.350392\n", - " -0.051172\n", - " -0.022723\n", - " 276.902\n", - " 277.785\n", - " 279.844\n", - " 282.911\n", - " 288.141\n", - " 296.601\n", - " 307.278\n", - " 319.657\n", - " 333.284\n", - " 346.138\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -286,41 +158,9 @@ " -0.122172\n", " 0.070736\n", " -0.989985\n", - " 15.5008\n", - " 43.1023\n", - " 592.867\n", - " 0.352725\n", - " -0.047812\n", - " -0.022291\n", - " 276.757\n", - " 277.626\n", - " 279.664\n", - " 282.699\n", - " 287.902\n", - " 296.353\n", - " 307.023\n", - " 319.397\n", - " 333.035\n", - " 345.927\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -342,41 +182,9 @@ " -0.121321\n", " 0.070529\n", " -0.990104\n", - " 14.7702\n", - " 43.1363\n", - " 594.135\n", - " 0.349325\n", - " -0.045551\n", - " -0.022963\n", - " 276.342\n", - " 277.208\n", - " 279.248\n", - " 282.285\n", - " 287.478\n", - " 295.913\n", - " 306.562\n", - " 318.911\n", - " 332.540\n", - " 345.428\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -392,12 +200,26 @@ "
    " ], "text/plain": [ - " frame timestamp ... AU28_c AU45_c\n", - "0 1 0.000 ... 0 0\n", - "1 2 0.001 ... 0 0\n", - "2 3 0.002 ... 0 0\n", - "3 4 0.003 ... 0 0\n", - "4 5 0.004 ... 0 0\n", + " frame timestamp confidence success gaze_0_x gaze_0_y gaze_0_z \\\n", + "0 1 0.000 0.883333 1 0.109059 0.062474 -0.992070 \n", + "1 2 0.001 0.883333 1 0.110256 0.065356 -0.991752 \n", + "2 3 0.002 0.883333 1 0.108539 0.064244 -0.992014 \n", + "3 4 0.003 0.883333 1 0.108724 0.064943 -0.991948 \n", + "4 5 0.004 0.883333 1 0.109766 0.065250 -0.991813 \n", + "\n", + " gaze_1_x gaze_1_y gaze_1_z ... AU12_c AU14_c AU15_c AU17_c AU20_c \\\n", + "0 -0.124401 0.066311 -0.990014 ... 0 0 0 0 0 \n", + "1 -0.123464 0.069979 -0.989879 ... 0 0 0 0 0 \n", + "2 -0.122873 0.070540 -0.989912 ... 0 0 0 0 0 \n", + "3 -0.122172 0.070736 -0.989985 ... 0 0 0 0 0 \n", + "4 -0.121321 0.070529 -0.990104 ... 0 0 0 0 0 \n", + "\n", + " AU23_c AU25_c AU26_c AU28_c AU45_c \n", + "0 1 0 0 0 0 \n", + "1 1 0 0 0 0 \n", + "2 1 0 0 0 0 \n", + "3 1 0 0 0 0 \n", + "4 1 0 0 0 0 \n", "\n", "[5 rows x 431 columns]" ] @@ -426,11 +248,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:46.501974Z", - "start_time": "2021-03-24T22:20:46.429372Z" + "end_time": "2021-03-26T05:11:11.831292Z", + "start_time": "2021-03-26T05:11:11.803938Z" } }, "outputs": [ @@ -465,39 +287,7 @@ " x_7\n", " x_8\n", " x_9\n", - " x_10\n", - " x_11\n", - " x_12\n", - " x_13\n", - " x_14\n", - " x_15\n", - " x_16\n", - " x_17\n", - " x_18\n", - " x_19\n", - " x_20\n", - " x_21\n", - " x_22\n", - " x_23\n", - " x_24\n", - " x_25\n", " ...\n", - " y_42\n", - " y_43\n", - " y_44\n", - " y_45\n", - " y_46\n", - " y_47\n", - " y_48\n", - " y_49\n", - " y_50\n", - " y_51\n", - " y_52\n", - " y_53\n", - " y_54\n", - " y_55\n", - " y_56\n", - " y_57\n", " y_58\n", " y_59\n", " y_60\n", @@ -523,39 +313,7 @@ " 320.240\n", " 333.596\n", " 346.018\n", - " 356.266\n", - " 365.507\n", - " 372.289\n", - " 376.285\n", - " 378.485\n", - " 380.039\n", - " 380.562\n", - " 286.383\n", - " 292.931\n", - " 303.077\n", - " 313.934\n", - " 323.874\n", - " 340.883\n", - " 350.207\n", - " 359.633\n", - " 368.524\n", " ...\n", - " 186.658\n", - " 181.683\n", - " 181.337\n", - " 184.018\n", - " 187.718\n", - " 188.326\n", - " 237.529\n", - " 235.506\n", - " 233.715\n", - " 234.922\n", - " 233.408\n", - " 234.625\n", - " 235.620\n", - " 240.460\n", - " 242.560\n", - " 243.216\n", " 243.032\n", " 241.552\n", " 237.763\n", @@ -579,39 +337,7 @@ " 319.981\n", " 333.443\n", " 346.086\n", - " 356.530\n", - " 365.888\n", - " 372.591\n", - " 376.430\n", - " 378.497\n", - " 379.960\n", - " 380.414\n", - " 286.137\n", - " 292.527\n", - " 302.614\n", - " 313.488\n", - " 323.464\n", - " 340.515\n", - " 349.885\n", - " 359.369\n", - " 368.252\n", " ...\n", - " 186.681\n", - " 181.790\n", - " 181.413\n", - " 183.969\n", - " 187.708\n", - " 188.342\n", - " 237.534\n", - " 235.528\n", - " 233.778\n", - " 234.985\n", - " 233.450\n", - " 234.572\n", - " 235.499\n", - " 240.720\n", - " 243.024\n", - " 243.725\n", " 243.535\n", " 241.895\n", " 237.777\n", @@ -635,39 +361,7 @@ " 319.657\n", " 333.284\n", " 346.138\n", - " 356.717\n", - " 366.159\n", - " 372.821\n", - " 376.590\n", - " 378.645\n", - " 380.110\n", - " 380.536\n", - " 285.955\n", - " 292.222\n", - " 302.268\n", - " 313.181\n", - " 323.186\n", - " 340.184\n", - " 349.622\n", - " 359.211\n", - " 368.176\n", " ...\n", - " 186.788\n", - " 181.864\n", - " 181.463\n", - " 183.978\n", - " 187.807\n", - " 188.454\n", - " 238.026\n", - " 235.976\n", - " 234.216\n", - " 235.456\n", - " 233.928\n", - " 235.091\n", - " 236.081\n", - " 241.759\n", - " 244.207\n", - " 244.885\n", " 244.668\n", " 242.816\n", " 238.284\n", @@ -691,39 +385,7 @@ " 319.397\n", " 333.035\n", " 345.927\n", - " 356.552\n", - " 366.024\n", - " 372.690\n", - " 376.460\n", - " 378.517\n", - " 379.986\n", - " 380.415\n", - " 285.736\n", - " 292.013\n", - " 302.060\n", - " 312.962\n", - " 322.970\n", - " 339.908\n", - " 349.373\n", - " 358.981\n", - " 367.970\n", " ...\n", - " 186.682\n", - " 181.764\n", - " 181.370\n", - " 183.889\n", - " 187.717\n", - " 188.355\n", - " 237.935\n", - " 235.940\n", - " 234.204\n", - " 235.451\n", - " 233.921\n", - " 235.070\n", - " 236.026\n", - " 241.618\n", - " 244.027\n", - " 244.707\n", " 244.484\n", " 242.662\n", " 238.209\n", @@ -747,39 +409,7 @@ " 318.911\n", " 332.540\n", " 345.428\n", - " 356.057\n", - " 365.517\n", - " 372.168\n", - " 375.910\n", - " 377.945\n", - " 379.397\n", - " 379.831\n", - " 285.234\n", - " 291.509\n", - " 301.494\n", - " 312.317\n", - " 322.265\n", - " 339.345\n", - " 348.778\n", - " 358.342\n", - " 367.285\n", " ...\n", - " 186.682\n", - " 181.778\n", - " 181.384\n", - " 183.907\n", - " 187.712\n", - " 188.348\n", - " 237.788\n", - " 235.827\n", - " 234.104\n", - " 235.327\n", - " 233.797\n", - " 234.910\n", - " 235.821\n", - " 241.386\n", - " 243.801\n", - " 244.494\n", " 244.297\n", " 242.514\n", " 238.063\n", @@ -797,17 +427,31 @@ "" ], "text/plain": [ - " x_0 x_1 ... y_66 y_67\n", - "0 277.276 278.163 ... 237.104 236.823\n", - "1 276.980 277.893 ... 237.579 237.295\n", - "2 276.902 277.785 ... 238.606 238.292\n", - "3 276.757 277.626 ... 238.453 238.137\n", - "4 276.342 277.208 ... 238.267 237.974\n", + " x_0 x_1 x_2 x_3 x_4 x_5 x_6 x_7 \\\n", + "0 277.276 278.163 280.209 283.258 288.537 297.109 307.888 320.240 \n", + "1 276.980 277.893 279.952 283.021 288.339 296.925 307.663 319.981 \n", + "2 276.902 277.785 279.844 282.911 288.141 296.601 307.278 319.657 \n", + "3 276.757 277.626 279.664 282.699 287.902 296.353 307.023 319.397 \n", + "4 276.342 277.208 279.248 282.285 287.478 295.913 306.562 318.911 \n", + "\n", + " x_8 x_9 ... y_58 y_59 y_60 y_61 y_62 \\\n", + "0 333.596 346.018 ... 243.032 241.552 237.763 238.249 238.527 \n", + "1 333.443 346.086 ... 243.535 241.895 237.777 238.333 238.601 \n", + "2 333.284 346.138 ... 244.668 242.816 238.284 238.824 239.104 \n", + "3 333.035 345.927 ... 244.484 242.662 238.209 238.804 239.092 \n", + "4 332.540 345.428 ... 244.297 242.514 238.063 238.688 238.958 \n", + "\n", + " y_63 y_64 y_65 y_66 y_67 \n", + "0 237.790 236.296 236.401 237.104 236.823 \n", + "1 237.848 236.234 236.828 237.579 237.295 \n", + "2 238.397 236.835 237.882 238.606 238.292 \n", + "3 238.379 236.786 237.723 238.453 238.137 \n", + "4 238.232 236.595 237.525 238.267 237.974 \n", "\n", "[5 rows x 136 columns]" ] }, - "execution_count": 21, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -818,11 +462,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:48.115597Z", - "start_time": "2021-03-24T22:20:48.079943Z" + "end_time": "2021-03-26T05:11:11.868256Z", + "start_time": "2021-03-26T05:11:11.832354Z" } }, "outputs": [ @@ -857,21 +501,7 @@ " AU10_r\n", " AU12_r\n", " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", + " ...\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -897,21 +527,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -935,21 +551,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -973,21 +575,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1011,21 +599,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1049,21 +623,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1077,20 +637,35 @@ " \n", " \n", "\n", + "

    5 rows × 35 columns

    \n", "" ], "text/plain": [ - " AU01_r AU02_r AU04_r ... AU26_c AU28_c AU45_c\n", - "0 0.6773 0.4275 0.2435 ... 0 0 0\n", - "1 0.5958 0.3507 0.3347 ... 0 0 0\n", - "2 0.6017 0.3078 0.4339 ... 0 0 0\n", - "3 0.6545 0.3294 0.5075 ... 0 0 0\n", - "4 0.5636 0.2709 0.5708 ... 0 0 0\n", + " AU01_r AU02_r AU04_r AU05_r AU06_r AU07_r AU09_r AU10_r AU12_r \\\n", + "0 0.6773 0.4275 0.2435 0.3434 0 0.0 0.0 0 0 \n", + "1 0.5958 0.3507 0.3347 0.3434 0 0.0 0.0 0 0 \n", + "2 0.6017 0.3078 0.4339 0.2920 0 0.0 0.0 0 0 \n", + "3 0.6545 0.3294 0.5075 0.2899 0 0.0 0.0 0 0 \n", + "4 0.5636 0.2709 0.5708 0.1455 0 0.0 0.0 0 0 \n", + "\n", + " AU14_r ... AU12_c AU14_c AU15_c AU17_c AU20_c AU23_c AU25_c \\\n", + "0 0 ... 0 0 0 0 0 1 0 \n", + "1 0 ... 0 0 0 0 0 1 0 \n", + "2 0 ... 0 0 0 0 0 1 0 \n", + "3 0 ... 0 0 0 0 0 1 0 \n", + "4 0 ... 0 0 0 0 0 1 0 \n", + "\n", + " AU26_c AU28_c AU45_c \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", "\n", "[5 rows x 35 columns]" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1109,11 +684,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:01.787253Z", - "start_time": "2021-03-24T22:21:01.692699Z" + "end_time": "2021-03-26T05:11:11.907622Z", + "start_time": "2021-03-26T05:11:11.869247Z" } }, "outputs": [ @@ -1155,39 +730,7 @@ " EventSource\n", " Timestamp\n", " MediaTime\n", - " PostMarker\n", - " Annotation\n", - " FrameNo\n", - " FrameTime\n", - " NoOfFaces\n", - " FaceRectX\n", - " FaceRectY\n", - " FaceRectWidth\n", - " FaceRectHeight\n", - " Joy\n", - " Anger\n", - " Surprise\n", - " Fear\n", - " Contempt\n", - " Disgust\n", - " Sadness\n", " ...\n", - " IsMaleProbability\n", - " Yaw\n", - " Pitch\n", - " Roll\n", - " LEFT_EYE_LATERALX\n", - " LEFT_EYE_LATERALY\n", - " LEFT_EYE_PUPILX\n", - " LEFT_EYE_PUPILY\n", - " LEFT_EYE_MEDIALX\n", - " LEFT_EYE_MEDIALY\n", - " RIGHT_EYE_MEDIALX\n", - " RIGHT_EYE_MEDIALY\n", - " RIGHT_EYE_PUPILX\n", - " RIGHT_EYE_PUPILY\n", - " RIGHT_EYE_LATERALX\n", - " RIGHT_EYE_LATERALY\n", " NOSE_TIPX\n", " NOSE_TIPY\n", " 7X\n", @@ -1213,39 +756,7 @@ " Emotient FACET\n", " 35\n", " 35\n", - " NaN\n", - " NaN\n", - " 0\n", - " 35\n", - " 1\n", - " 388.0\n", - " 258.0\n", - " 258.0\n", - " 258.0\n", - " -0.462367\n", - " -3.642415\n", - " -1.254946\n", - " -1.967231\n", - " 0.428609\n", - " -2.541990\n", - " -1.224254\n", " ...\n", - " 0.977411\n", - " -1.102638\n", - " 8.205247\n", - " -3.704682\n", - " 445.2972\n", - " 328.3713\n", - " 0.0\n", - " 0.0\n", - " 500.4211\n", - " 334.8774\n", - " 554.7314\n", - " 335.0555\n", - " 0.0\n", - " 0.0\n", - " 604.1805\n", - " 327.6500\n", " 525.0905\n", " 394.8224\n", " 0.0\n", @@ -1269,39 +780,7 @@ " Emotient FACET\n", " 68\n", " 68\n", - " NaN\n", - " NaN\n", - " 1\n", - " 68\n", - " 1\n", - " 389.0\n", - " 252.0\n", - " 257.0\n", - " 257.0\n", - " -4.371406\n", - " -3.821383\n", - " -2.094306\n", - " -3.278980\n", - " -0.516405\n", - " -4.306592\n", - " -1.787594\n", " ...\n", - " 0.995149\n", - " -2.236120\n", - " 7.103536\n", - " -2.550549\n", - " 447.2003\n", - " 322.2042\n", - " 0.0\n", - " 0.0\n", - " 499.8117\n", - " 328.6136\n", - " 555.1335\n", - " 330.2828\n", - " 0.0\n", - " 0.0\n", - " 607.3446\n", - " 323.2844\n", " 525.9382\n", " 385.9986\n", " 0.0\n", @@ -1325,39 +804,7 @@ " Emotient FACET\n", " 101\n", " 101\n", - " NaN\n", - " NaN\n", - " 2\n", - " 102\n", - " 1\n", - " 387.0\n", - " 242.0\n", - " 264.0\n", - " 264.0\n", - " -5.124269\n", - " -2.969738\n", - " -1.833445\n", - " -3.807125\n", - " -0.724873\n", - " -4.186139\n", - " -1.579113\n", " ...\n", - " 0.994264\n", - " -0.855230\n", - " 4.500861\n", - " -3.289358\n", - " 446.9748\n", - " 315.9877\n", - " 0.0\n", - " 0.0\n", - " 498.4589\n", - " 322.1017\n", - " 559.3203\n", - " 323.0681\n", - " 0.0\n", - " 0.0\n", - " 611.6814\n", - " 315.5635\n", " 525.2611\n", " 378.3876\n", " 0.0\n", @@ -1381,39 +828,7 @@ " Emotient FACET\n", " 134\n", " 134\n", - " NaN\n", - " NaN\n", - " 3\n", - " 135\n", - " 1\n", - " 391.0\n", - " 239.0\n", - " 255.0\n", - " 255.0\n", - " -5.310449\n", - " -3.882663\n", - " -2.479799\n", - " -4.622220\n", - " -0.881652\n", - " -4.608058\n", - " -2.352544\n", " ...\n", - " 0.989092\n", - " -0.936105\n", - " 5.009235\n", - " -3.453575\n", - " 450.5946\n", - " 308.2809\n", - " 0.0\n", - " 0.0\n", - " 499.7155\n", - " 314.7372\n", - " 559.8370\n", - " 317.5146\n", - " 0.0\n", - " 0.0\n", - " 607.8320\n", - " 312.9327\n", " 524.6653\n", " 372.0715\n", " 0.0\n", @@ -1437,39 +852,7 @@ " Emotient FACET\n", " 168\n", " 168\n", - " NaN\n", - " NaN\n", - " 4\n", - " 168\n", - " 1\n", - " 393.0\n", - " 236.0\n", - " 253.0\n", - " 253.0\n", - " -5.206233\n", - " -4.181856\n", - " -2.407882\n", - " -4.627919\n", - " -0.880042\n", - " -5.046319\n", - " -2.624082\n", " ...\n", - " 0.994389\n", - " -0.632709\n", - " 5.729938\n", - " -2.903348\n", - " 452.0932\n", - " 305.5123\n", - " 0.0\n", - " 0.0\n", - " 501.3641\n", - " 309.6560\n", - " 561.4435\n", - " 312.6160\n", - " 0.0\n", - " 0.0\n", - " 608.5308\n", - " 309.9495\n", " 525.9166\n", " 366.4871\n", " 0.0\n", @@ -1487,12 +870,33 @@ "" ], "text/plain": [ - " StudyName ... SceneParent\n", - "0 RotationTest ... NaN\n", - "1 RotationTest ... NaN\n", - "2 RotationTest ... NaN\n", - "3 RotationTest ... NaN\n", - "4 RotationTest ... NaN\n", + " StudyName ExportDate Name Age Gender StimulusName \\\n", + "0 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "1 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "2 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "3 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "4 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "\n", + " SlideType EventSource Timestamp MediaTime ... NOSE_TIPX NOSE_TIPY \\\n", + "0 TestImage Emotient FACET 35 35 ... 525.0905 394.8224 \n", + "1 TestImage Emotient FACET 68 68 ... 525.9382 385.9986 \n", + "2 TestImage Emotient FACET 101 101 ... 525.2611 378.3876 \n", + "3 TestImage Emotient FACET 134 134 ... 524.6653 372.0715 \n", + "4 TestImage Emotient FACET 168 168 ... 525.9166 366.4871 \n", + "\n", + " 7X 7Y LiveMarker KeyStroke MarkerText SceneType SceneOutput \\\n", + "0 0.0 0.0 NaN NaN NaN NaN NaN \n", + "1 0.0 0.0 NaN NaN NaN NaN NaN \n", + "2 0.0 0.0 NaN NaN NaN NaN NaN \n", + "3 0.0 0.0 NaN NaN NaN NaN NaN \n", + "4 0.0 0.0 NaN NaN NaN NaN NaN \n", + "\n", + " SceneParent \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", "[5 rows x 78 columns]" ] @@ -1519,11 +923,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:59.557509Z", - "start_time": "2021-03-24T22:21:59.539415Z" + "end_time": "2021-03-26T05:11:11.918287Z", + "start_time": "2021-03-26T05:11:11.908503Z" } }, "outputs": [ @@ -1643,17 +1047,22 @@ "" ], "text/plain": [ - " Joy Anger ... Positive Negative\n", - "0 -0.462367 -3.642415 ... -0.462367 0.428609\n", - "1 -4.371406 -3.821383 ... -4.371406 -0.516405\n", - "2 -5.124269 -2.969738 ... -5.124269 -0.724873\n", - "3 -5.310449 -3.882663 ... -5.310449 -0.881652\n", - "4 -5.206233 -4.181856 ... -5.206233 -0.880042\n", + " Joy Anger Surprise Fear Contempt Disgust Sadness \\\n", + "0 -0.462367 -3.642415 -1.254946 -1.967231 0.428609 -2.541990 -1.224254 \n", + "1 -4.371406 -3.821383 -2.094306 -3.278980 -0.516405 -4.306592 -1.787594 \n", + "2 -5.124269 -2.969738 -1.833445 -3.807125 -0.724873 -4.186139 -1.579113 \n", + "3 -5.310449 -3.882663 -2.479799 -4.622220 -0.881652 -4.608058 -2.352544 \n", + "4 -5.206233 -4.181856 -2.407882 -4.627919 -0.880042 -5.046319 -2.624082 \n", "\n", - "[5 rows x 12 columns]" + " Confusion Frustration Neutral Positive Negative \n", + "0 -3.051880 -2.369417 1.784896 -0.462367 0.428609 \n", + "1 -2.694475 -2.190143 2.697488 -4.371406 -0.516405 \n", + "2 -2.003944 -1.487295 2.437310 -5.124269 -0.724873 \n", + "3 -2.248295 -1.931136 3.032463 -5.310449 -0.881652 \n", + "4 -2.401067 -1.991587 3.191700 -5.206233 -0.880042 " ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1671,11 +1080,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:23:40.991510Z", - "start_time": "2021-03-24T22:23:40.443246Z" + "end_time": "2021-03-26T05:11:12.547489Z", + "start_time": "2021-03-26T05:11:11.919098Z" } }, "outputs": [ @@ -1683,7 +1092,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jcheong/packages/feat/feat/data.py:1303: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + "/home/jcheong/packages/feat/feat/data.py:1338: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " wavs = np.array(wavs)[::-1]\n" ] }, @@ -1718,39 +1127,7 @@ " neg1_hz_0.66_Joy\n", " neg2_hz_0.66_Joy\n", " neg3_hz_0.66_Joy\n", - " neg4_hz_0.66_Joy\n", - " neg5_hz_0.66_Joy\n", - " pos0_hz_0.47_Joy\n", - " pos1_hz_0.47_Joy\n", - " pos2_hz_0.47_Joy\n", - " pos3_hz_0.47_Joy\n", - " pos4_hz_0.47_Joy\n", - " pos5_hz_0.47_Joy\n", - " neg0_hz_0.47_Joy\n", - " neg1_hz_0.47_Joy\n", - " neg2_hz_0.47_Joy\n", - " neg3_hz_0.47_Joy\n", - " neg4_hz_0.47_Joy\n", - " neg5_hz_0.47_Joy\n", - " pos0_hz_0.33_Joy\n", - " pos1_hz_0.33_Joy\n", " ...\n", - " neg4_hz_0.12_Negative\n", - " neg5_hz_0.12_Negative\n", - " pos0_hz_0.08_Negative\n", - " pos1_hz_0.08_Negative\n", - " pos2_hz_0.08_Negative\n", - " pos3_hz_0.08_Negative\n", - " pos4_hz_0.08_Negative\n", - " pos5_hz_0.08_Negative\n", - " neg0_hz_0.08_Negative\n", - " neg1_hz_0.08_Negative\n", - " neg2_hz_0.08_Negative\n", - " neg3_hz_0.08_Negative\n", - " neg4_hz_0.08_Negative\n", - " neg5_hz_0.08_Negative\n", - " pos0_hz_0.06_Negative\n", - " pos1_hz_0.06_Negative\n", " pos2_hz_0.06_Negative\n", " pos3_hz_0.06_Negative\n", " pos4_hz_0.06_Negative\n", @@ -1776,39 +1153,7 @@ " 0\n", " 0\n", " 2\n", - " 1\n", - " 4\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 3\n", - " 0\n", - " 1\n", " ...\n", - " 1\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", " 2\n", " 0\n", " 0\n", @@ -1826,13 +1171,28 @@ "" ], "text/plain": [ - " pos0_hz_0.66_Joy ... neg5_hz_0.06_Negative\n", - "0 2 ... 1\n", + " pos0_hz_0.66_Joy pos1_hz_0.66_Joy pos2_hz_0.66_Joy pos3_hz_0.66_Joy \\\n", + "0 2 0 0 0 \n", + "\n", + " pos4_hz_0.66_Joy pos5_hz_0.66_Joy neg0_hz_0.66_Joy neg1_hz_0.66_Joy \\\n", + "0 3 3 0 0 \n", + "\n", + " neg2_hz_0.66_Joy neg3_hz_0.66_Joy ... pos2_hz_0.06_Negative \\\n", + "0 0 2 ... 2 \n", + "\n", + " pos3_hz_0.06_Negative pos4_hz_0.06_Negative pos5_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg0_hz_0.06_Negative neg1_hz_0.06_Negative neg2_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg3_hz_0.06_Negative neg4_hz_0.06_Negative neg5_hz_0.06_Negative \n", + "0 0 1 1 \n", "\n", "[1 rows x 1152 columns]" ] }, - "execution_count": 30, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1852,11 +1212,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:24:52.073973Z", - "start_time": "2021-03-24T22:24:52.028491Z" + "end_time": "2021-03-26T05:11:12.578290Z", + "start_time": "2021-03-26T05:11:12.548607Z" } }, "outputs": [ @@ -1898,18 +1258,7 @@ " Valence\n", " Engagement\n", " Timestamp\n", - " FileName\n", - " AU12\n", - " AU01\n", - " AU02\n", - " AU04\n", - " AU09\n", - " AU10\n", - " AU15\n", - " AU17\n", - " AU18\n", - " AU24\n", - " AU28\n", + " ...\n", " AU25\n", " Smirk\n", " AU43\n", @@ -1935,18 +1284,7 @@ " 78.124275\n", " 99.921013\n", " 0\n", - " JinHyunCheong.jpg\n", - " 100\n", - " 6.299417\n", - " 0.01924\n", - " 6.175626e-09\n", - " 7.965685\n", - " 29.55155\n", - " 2.398794e-12\n", - " 0.002099\n", - " 0.000042\n", - " 0.00536\n", - " 8.028388e-08\n", + " ...\n", " 99.999939\n", " 0\n", " 5.448178e-07\n", @@ -1960,11 +1298,18 @@ " \n", " \n", "\n", + "

    1 rows × 32 columns

    \n", "" ], "text/plain": [ - " Joy Sadness ... AU06 AU20\n", - "0 99.930573 0.000002 ... 94.181007 0.262457\n", + " Joy Sadness Disgust Contempt Anger Fear Surprise \\\n", + "0 99.930573 0.000002 0.002528 0.000107 0.000096 4.059986e-07 1.76971 \n", + "\n", + " Valence Engagement Timestamp ... AU25 Smirk AU43 \\\n", + "0 78.124275 99.921013 0 ... 99.999939 0 5.448178e-07 \n", + "\n", + " Attention AU07 AU26 AU14 AU05 AU06 AU20 \n", + "0 91.699234 11.572159 82.992149 0.000003 0.000744 94.181007 0.262457 \n", "\n", "[1 rows x 32 columns]" ] @@ -1981,6 +1326,260 @@ "print(type(detections))\n", "display(detections.head())" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading a completely new file as a Fex class\n", + "It's easy to cast a dataframe which might be neither an OpenFace or FACET outputs into a Fex class. Simply cast your dataframe as a Fex class." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:45.226073Z", + "start_time": "2021-03-26T05:15:45.221254Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from feat import Fex\n", + "import pandas as pd, numpy as np\n", + "au_columns = [f\"AU{i}\" for i in range(20)]\n", + "fex = Fex(pd.DataFrame(np.random.rand(20,20)))\n", + "fex.columns = au_columns\n", + "print(type(fex))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To take full advantage of Py-Feat's features, make sure you set the attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:51.277149Z", + "start_time": "2021-03-26T05:15:51.259222Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    40.8023790.9510920.0476950.9144160.6516830.1833440.4461200.8724190.4688560.1189900.9857440.7930740.1556250.1784480.2178530.9662670.4553500.9704130.7816240.193531
    \n", + "
    " + ], + "text/plain": [ + " AU0 AU1 AU2 AU3 AU4 AU5 AU6 \\\n", + "0 0.003699 0.042126 0.532839 0.410701 0.079237 0.612938 0.560122 \n", + "1 0.887544 0.882457 0.265571 0.078094 0.341208 0.563913 0.294769 \n", + "2 0.222848 0.007902 0.806356 0.392177 0.842465 0.765245 0.260304 \n", + "3 0.161766 0.678874 0.994342 0.076421 0.618708 0.880437 0.302860 \n", + "4 0.802379 0.951092 0.047695 0.914416 0.651683 0.183344 0.446120 \n", + "\n", + " AU7 AU8 AU9 AU10 AU11 AU12 AU13 \\\n", + "0 0.999783 0.889522 0.169887 0.374350 0.631952 0.040375 0.852979 \n", + "1 0.307120 0.480530 0.321297 0.321300 0.513947 0.566524 0.816932 \n", + "2 0.372778 0.648263 0.118601 0.695544 0.800680 0.870643 0.588052 \n", + "3 0.191823 0.443328 0.221810 0.321260 0.154832 0.330742 0.281754 \n", + "4 0.872419 0.468856 0.118990 0.985744 0.793074 0.155625 0.178448 \n", + "\n", + " AU14 AU15 AU16 AU17 AU18 AU19 \n", + "0 0.329220 0.196842 0.118240 0.631301 0.709566 0.003093 \n", + "1 0.493617 0.541507 0.624186 0.138642 0.845837 0.638981 \n", + "2 0.266168 0.247223 0.479493 0.928013 0.061631 0.158413 \n", + "3 0.707123 0.500014 0.457013 0.575017 0.955154 0.216029 \n", + "4 0.217853 0.966267 0.455350 0.970413 0.781624 0.193531 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fex.au_columns = au_columns\n", + "display(fex.aus().head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/_build/jupyter_execute/content/loadingOtherFiles.py b/notebooks/_build/jupyter_execute/content/loadingOtherFiles.py index b5d41f41..e080baa5 100644 --- a/notebooks/_build/jupyter_execute/content/loadingOtherFiles.py +++ b/notebooks/_build/jupyter_execute/content/loadingOtherFiles.py @@ -47,4 +47,20 @@ facet = os.path.join(get_test_data_path(), "sample_affectiva-api-app_output.json") detections = read_affectiva(facet) print(type(detections)) -display(detections.head()) \ No newline at end of file +display(detections.head()) + +## Loading a completely new file as a Fex class +It's easy to cast a dataframe which might be neither an OpenFace or FACET outputs into a Fex class. Simply cast your dataframe as a Fex class. + +from feat import Fex +import pandas as pd, numpy as np +au_columns = [f"AU{i}" for i in range(20)] +fex = Fex(pd.DataFrame(np.random.rand(20,20))) +fex.columns = au_columns +print(type(fex)) + +To take full advantage of Py-Feat's features, make sure you set the attributes. + +fex.au_columns = au_columns +display(fex.aus().head()) + diff --git a/notebooks/_build/jupyter_execute/content/plotting.ipynb b/notebooks/_build/jupyter_execute/content/plotting.ipynb new file mode 100644 index 00000000..c81f483d --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/plotting.ipynb @@ -0,0 +1,547 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:51:41.015128Z", + "start_time": "2021-03-26T04:51:39.430936Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_2_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:02:39.899481Z", + "start_time": "2021-03-26T05:02:39.823101Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_5_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:19.202362Z", + "start_time": "2021-03-26T04:54:19.119350Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_7_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_9_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_10_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_12_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_14_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/plotting_17_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/plotting.py b/notebooks/_build/jupyter_execute/content/plotting.py new file mode 100644 index 00000000..c9d5aa70 --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/plotting.py @@ -0,0 +1,149 @@ +# Plotting examples +*written by Jin Hyun Cheong* + +Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. + +Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. + +# Load modules +%matplotlib inline +from feat.plotting import plot_face +import numpy as np +import matplotlib.pyplot as plt + +plot_face(au=np.zeros(20)) + +## Draw lineface using input vector + +Affectiva vectors should be divided by twenty for use with our 'blue' model. + +from feat.plotting import plot_face +import numpy as np +import matplotlib.pyplot as plt + +# Add data, AU is ordered as such: +# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43 + +# Activate AU1: Inner brow raiser +au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + +# Plot face +fig, axes = plt.subplots(1,2) +plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-') +plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-') + +## Add a vectorfield with arrows from the changed face back to neutral and vice versa + +from feat.plotting import plot_face, predict +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +model = load_h5('pyfeat_aus_to_landmarks.h5') +# Add data activate AU1, and AU12 +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]) + +# Get neutral landmarks +neutral = predict(np.zeros(len(au))) + +# Provide target landmarks and other vector specifications +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +fig, axes = plt.subplots(1,2) +# Plot face where vectorfield goes from neutral to target, with target as final face +plot_face(model = model, ax = axes[0], au = np.array(au), + vectorfield = vectors, color='k', linewidth=1, linestyle='-') + +# Plot face where vectorfield goes from neutral to target, with neutral as base face +plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), + vectorfield = vectors, color='k', linewidth=1, linestyle='-') + +## Add muscle heatmaps to the plot + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +model = load_h5() + +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + +# Add some muscles +muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': "blue"} +muscles = {'all': 'heatmap'} + +plot_face(model=model, au = np.array(au), + muscles = muscles, color='k', linewidth=1, linestyle='-') + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, + 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0] +au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + +# Add some muscles +muscles = {'all': 'heatmap'} + +# Plot face +plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-') + +## Make sure muscle array contains 'facet' for a facet heatmap + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, + 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]) + +# Load a model +model = load_h5() + +# Add muscles +muscles = {'all': 'heatmap', 'facet': 1} + +# Plot face +plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-') + +## Add gaze vectors +Add gaze vectors to indicate where the eyes are looking. +Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards. + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = np.zeros(20) + +# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y) +gaze = [-1, 5, 1, 5] + +# Plot face +plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-') + +## Call plot method on Fex instances +It is possible to call the `plot_aus` method within openface, facet, affdex fex instances + +OpenFace + +from feat.plotting import plot_face +import numpy as np +import matplotlib.pyplot as plt +from feat.utils import load_h5, get_resource_path, read_openface +from feat.tests.utils import get_test_data_path +from os.path import join + +test_file = join(get_test_data_path(),'OpenFace_Test.csv') +openface = read_openface(test_file) +openface.plot_aus(12, muscles={'all': "heatmap"}, gaze = None) \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/plotting_10_1.png b/notebooks/_build/jupyter_execute/content/plotting_10_1.png new file mode 100644 index 00000000..97a89170 Binary files /dev/null and b/notebooks/_build/jupyter_execute/content/plotting_10_1.png differ diff --git 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b/notebooks/_build/jupyter_execute/content/trainAUvisModel.ipynb new file mode 100644 index 00000000..d29203b7 --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/trainAUvisModel.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training AU visualization model\n", + "You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:01.906078Z", + "start_time": "2021-03-26T04:01:59.634560Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:16.245590Z", + "start_time": "2021-03-26T04:02:14.666837Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:17.089755Z", + "start_time": "2021-03-26T04:10:15.301127Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:21.609846Z", + "start_time": "2021-03-26T04:10:20.554202Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/trainAUvisModel_7_0.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/trainAUvisModel.py b/notebooks/_build/jupyter_execute/content/trainAUvisModel.py new file mode 100644 index 00000000..c413b24a --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/trainAUvisModel.py @@ -0,0 +1,271 @@ +# Training AU visualization model +You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. + +%matplotlib inline +import pandas as pd, numpy as np, matplotlib.pyplot as plt +from sklearn.cross_decomposition import PLSRegression +from sklearn.model_selection import KFold +from feat.plotting import predict, plot_face +from feat.utils import registration, neutral +from natsort import natsorted +import os, glob +import pandas as pd, numpy as np +import seaborn as sns +sns.set_style("white") + +base_dir = "/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train" + +labels_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_labels.csv")) +landmarks_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_landmarks.csv")) +print("EmotioNet: ", len(labels_emotionet)) +labels_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_labels.csv")) +landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_landmarks.csv")) +# Disfa is rescaled to 0 - 1 +disfaplus_aus = [col for col in labels_disfaplus.columns if "AU" in col] +labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5 +print("DISFA Plus: ", len(labels_disfaplus)) +labels_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_labels.csv")) +landmarks_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_landmarks.csv")) +bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index +print("BP4D: ", len(labels_bp4d)) + +We aggregate the datasets and specify the AUs we want to train. + +labels = pd.concat([ + labels_emotionet.replace({999: np.nan}), + labels_disfaplus, + labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:] + ]).reset_index(drop=True) +landmarks = pd.concat([ + landmarks_emotionet, + landmarks_disfaplus, + landmarks_bp4d.iloc[bp4d_pruned_idx,:] + ]).reset_index(drop=True) + +landmarks = landmarks.iloc[labels.index] + +au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43] +au_cols = [f"AU{au}" for au in au_cols] +labels = labels[au_cols].fillna(0) + +We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package. + +min_pos_sample = 500 + +print('Pseudo balancing samples') +balY = pd.DataFrame() +balX = pd.DataFrame() +for AU in labels[au_cols].columns: + if np.sum(labels[AU]==1) > min_pos_sample: + replace = False + else: + replace = True + newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0) + balX = pd.concat([balX, newSample]) + balY = pd.concat([balY, landmarks.loc[newSample.index]]) +X = balX[au_cols].values +y = registration(balY.values, neutral) + +# Model Accuracy in KFold CV +print("Evaluating model with KFold CV") +n_components=len(au_cols) +kf = KFold(n_splits=3) +scores = [] +for train_index, test_index in kf.split(X): + X_train,X_test = X[train_index],X[test_index] + y_train,y_test = y[train_index],y[test_index] + clf = PLSRegression(n_components=n_components, max_iter=2000) + clf.fit(X_train,y_train) + scores.append(clf.score(X_test,y_test)) +print('3-fold accuracy mean', np.round(np.mean(scores),2)) + +# Train real model +clf = PLSRegression(n_components=n_components, max_iter=2000) +clf.fit(X,y) +print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2)) + +We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. + +# Plot results for each action unit +f,axes = plt.subplots(5,4,figsize=(12,18)) +axes = axes.flatten() +# Exaggerate the intensity of the expression for clearer visualization. +# We do not recommend exceeding 2. +intensity = 2 +for aui, auname in enumerate(axes): + try: + auname=au_cols[aui] + au = np.zeros(clf.n_components) + au[aui] = intensity + predicted = clf.predict([au]).reshape(2,68) + plot_face(au=au, model=clf, + vectorfield={"reference": neutral.T, 'target': predicted, + 'color':'r','alpha':.6}, + ax = axes[aui]) + axes[aui].set(title=auname) + except: + pass + finally: + ax = axes[aui] + ax.axes.get_xaxis().set_visible(False) + ax.axes.get_yaxis().set_visible(False) + +Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. + +# save out trained model +# import h5py +# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w') +# hf.create_dataset('coef', data=clf.coef_) +# hf.create_dataset('x_mean', data=clf._x_mean) +# hf.create_dataset('x_std', data=clf._x_std) +# hf.create_dataset('y_mean', data=clf._y_mean) +# hf.close() + +## Preprocessing datasets +Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. + + + +from PIL import Image, ImageOps +import math, cv2, csv +from scipy.spatial import ConvexHull +from skimage.morphology.convex_hull import grid_points_in_poly +from feat import Detector +import os, glob, pandas as pd, numpy as np +import matplotlib.pyplot as plt +from skimage import data, exposure +from skimage.feature import hog +from tqdm import tqdm + +def padding(img, expected_size): + desired_size = expected_size + delta_width = desired_size - img.size[0] + delta_height = desired_size - img.size[1] + pad_width = delta_width // 2 + pad_height = delta_height // 2 + padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height) + return ImageOps.expand(img, padding) + +def resize_with_padding(img, expected_size): + img.thumbnail((expected_size[0], expected_size[1])) + delta_width = expected_size[0] - img.size[0] + delta_height = expected_size[1] - img.size[1] + pad_width = delta_width // 2 + pad_height = delta_height // 2 + padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height) + return ImageOps.expand(img, padding) + +def align_face_68pts(img, img_land, box_enlarge, img_size=112): + """ + img: image + img_land: landmarks 68 + box_enlarge: relative size of face + img_size = 112 + + """ + leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] + + img_land[2 * 41]) / 6.0 + leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] + + img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0 + rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] + + img_land[2 * 47]) / 6.0 + rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] + + img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0 + deltaX = (rightEye0 - leftEye0) + deltaY = (rightEye1 - leftEye1) + l = math.sqrt(deltaX * deltaX + deltaY * deltaY) + sinVal = deltaY / l + cosVal = deltaX / l + mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]]) + mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1], + [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]]) + mat2 = (mat1 * mat2.T).T + cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5 + cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5 + if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))): + halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0]))) + else: + halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1]))) + scale = (img_size - 1) / 2.0 / halfSize + mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]]) + mat = mat3 * mat1 + aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128)) + land_3d = np.ones((int(len(img_land)/2), 3)) + land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2)) + mat_land_3d = np.mat(land_3d) + new_land = np.array((mat * mat_land_3d.T).T) + new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int) + return aligned_img, new_land + +def extract_hog(image, detector): + im = cv2.imread(image) + detected_faces = np.array(detector.detect_faces(im)[0]) + if np.any(detected_faces<0): + orig_size = np.array(im).shape + if np.where(detected_faces<0)[0][0]==1: + new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0]))) + else: + new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1]) + im = resize_with_padding(Image.fromarray(im), new_size) + im = np.asarray(im) + detected_faces = np.array(detector.detect_faces(np.array(im))[0]) + detected_faces = detected_faces.astype(int) + points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int) + + aligned_img, points = align_face_68pts(im, points.flatten(), 2.5) + + hull = ConvexHull(points) + mask = grid_points_in_poly(shape=np.array(aligned_img).shape, + verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped + ) + + mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True + aligned_img[~mask] = 0 + resized_face_np = aligned_img + + fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8), + cells_per_block=(2, 2), visualize=True, multichannel=True) + + return fd, hog_image, points + +Replace the paths so that it points to your local dataset directory. + +detector = Detector(face_model = "retinaface", landmark_model="mobilenet") +# Correct path to your downloaded dataset. +EmotioNet_images = np.sort(glob.glob("/Storage/Data/EmotioNet/imgs/*.jpg")) +labels = pd.read_csv("/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv") +labels = labels.dropna(axis=0) +for col in labels.columns: + if "AU" in col: + kwargs = {col.replace("'", '').replace('"', '').replace(" ",""): labels[[col]]} + labels = labels.assign(**kwargs) + labels = labels.drop(columns = col) +labels = labels.assign(URL = labels.URL.apply(lambda x: x.split("/")[-1].replace("'", ""))) +labels = labels.set_index('URL') +labels = labels.drop(columns = ["URL orig"]) + +aus_to_train = ['AU1','AU2','AU4','AU5', "AU6", "AU9","AU10", "AU12", "AU15","AU17", + "AU18","AU20", "AU24", "AU25", "AU26", "AU28", "AU43"] + +with open('emotionet_labels.csv', "w", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(["URL"] + aus_to_train) + +landmark_cols = [f"x_{i}" for i in range(68)] + [f"y_{i}" for i in range(68)] +with open('emotionet_landmarks.csv', "w", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(landmark_cols) + +for ix, image in enumerate(tqdm(EmotioNet_images)): + try: + imageURL = os.path.split(image)[-1] + label = labels.loc[imageURL][aus_to_train] + fd, _, points = extract_hog(image, detector=detector) + with open('emotionet_labels.csv', "a+", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow([imageURL]+list(label.values)) + with open('emotionet_landmarks.csv', "a+", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(points.T.flatten()) + except: + print(f"failed {image}") \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/trainAUvisModel_34_0.png 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a/notebooks/_build/jupyter_execute/content/trainAUvisModel_7_0.png b/notebooks/_build/jupyter_execute/content/trainAUvisModel_7_0.png new file mode 100644 index 00000000..d5c1cc4b Binary files /dev/null and b/notebooks/_build/jupyter_execute/content/trainAUvisModel_7_0.png differ diff --git a/notebooks/_config.yml b/notebooks/_config.yml index 3e0114a1..764fa121 100644 --- a/notebooks/_config.yml +++ b/notebooks/_config.yml @@ -60,9 +60,9 @@ launch_buttons: colab_url : https://colab.research.google.com # The URL of Google Colab (e.g., https://colab.research.google.com) repository: - url : https://github.com/cosanlab/feat # The URL to your book's repository + url : https://github.com/cosanlab/py-feat # The URL to your book's repository path_to_book : "notebooks" # A path to your book's folder, relative to the repository root. - branch : book # Which branch of the repository should be used when creating links + branch : master # Which branch of the repository should be used when creating links ####################################################################################### # Advanced and power-user settings diff --git a/notebooks/_toc.yml b/notebooks/_toc.yml index f01e677b..8245128d 100644 --- a/notebooks/_toc.yml +++ b/notebooks/_toc.yml @@ -12,8 +12,9 @@ chapters: - file: content/detector - file: content/analysis - - file: content/visualizations + - file: content/plotting - file: content/loadingOtherFiles + - file: content/trainAUvisModel - file: content/modelContribution - part: API @@ -23,5 +24,5 @@ - part: Contributing chapters: - file: content/contribute - - url: https://github.com/cosanlab/feat + - url: https://github.com/cosanlab/py-feat title: GitHub Repository \ No newline at end of file diff --git a/notebooks/content/analysis.ipynb b/notebooks/content/analysis.ipynb index 22f364f3..a0640419 100644 --- a/notebooks/content/analysis.ipynb +++ b/notebooks/content/analysis.ipynb @@ -62,8 +62,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:48:17.995310Z", - "start_time": "2021-03-24T18:48:17.859908Z" + "end_time": "2021-03-26T01:35:57.204484Z", + "start_time": "2021-03-26T01:35:56.969949Z" } }, "outputs": [ @@ -81,6 +81,8 @@ "import os, glob\n", "import numpy as np\n", "import pandas as pd\n", + "import seaborn as sns\n", + "sns.set_context(\"talk\")\n", "\n", "clip_attrs = pd.read_csv(\"clip_attrs.csv\")\n", "videos = np.sort(glob.glob(\"*.mp4\"))\n", @@ -119,11 +121,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:56:25.057270Z", - "start_time": "2021-03-24T18:56:24.564396Z" + "end_time": "2021-03-26T01:36:04.630894Z", + "start_time": "2021-03-26T01:36:02.098727Z" } }, "outputs": [], @@ -142,11 +144,11 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:29.835552Z", - "start_time": "2021-03-24T19:42:29.826112Z" + "end_time": "2021-03-26T01:36:04.662326Z", + "start_time": "2021-03-26T01:36:04.632024Z" } }, "outputs": [ @@ -238,7 +240,7 @@ "4 5 gn 5 your application has been accepted 005.mp4 goodNews" ] }, - "execution_count": 94, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -262,11 +264,11 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:35.182738Z", - "start_time": "2021-03-24T19:42:35.082327Z" + "end_time": "2021-03-26T01:36:04.916876Z", + "start_time": "2021-03-26T01:36:04.663369Z" } }, "outputs": [ @@ -409,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:38.745631Z", - "start_time": "2021-03-24T19:42:38.187813Z" + "end_time": "2021-03-26T01:36:05.612696Z", + "start_time": "2021-03-26T01:36:04.917989Z" } }, "outputs": [ @@ -641,11 +643,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:44.544102Z", - "start_time": "2021-03-24T19:42:44.530159Z" + "end_time": "2021-03-26T01:37:30.117739Z", + "start_time": "2021-03-26T01:37:29.898669Z" } }, "outputs": [ @@ -755,17 +757,65 @@ "mean_AU24 -4.533345 1.419377e-03" ] }, - "execution_count": 97, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map)\n", - "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest(.5)\n", + "t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions==\"goodNews\"].aus().ttest_1samp(.5)\n", "pd.DataFrame({\"t\": t, \"p\": p}, index= average_au_intensity_per_video.au_columns)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two sample independent t-test\n", + "You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T01:54:46.741635Z", + "start_time": "2021-03-26T01:54:46.568587Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-test between goodNews vs badNews: t=17, p=2.32e-12\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "columns2compare = \"mean_AU12\"\n", + "sessions = (\"goodNews\", \"badNews\")\n", + "t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions)\n", + "print(f\"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}\")\n", + "sns.barplot(x = average_au_intensity_per_video.sessions, \n", + " y = columns2compare, \n", + " data = average_au_intensity_per_video);" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -776,11 +826,11 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:42:47.876293Z", - "start_time": "2021-03-24T19:42:47.862905Z" + "end_time": "2021-03-26T01:33:23.062956Z", + "start_time": "2021-03-26T01:33:23.046675Z" } }, "outputs": [ @@ -866,11 +916,11 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T19:49:09.446263Z", - "start_time": "2021-03-24T19:49:09.423695Z" + "end_time": "2021-03-26T01:33:24.774852Z", + "start_time": "2021-03-26T01:33:24.727406Z" } }, "outputs": [ @@ -1001,6 +1051,43 @@ " p.round(3).loc[[0]].rename(index={0:\"p-values\"})])\n", "display(results)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intersubject correlations\n", + "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T02:03:17.392348Z", + "start_time": "2021-03-26T02:03:16.179007Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fex.sessions = fex.input()\n", + "isc = fex.isc(col = \"AU01\")\n", + "sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap=\"RdBu_r\");" + ] } ], "metadata": { diff --git a/notebooks/content/intro.md b/notebooks/content/intro.md index a56c2824..017b2d55 100644 --- a/notebooks/content/intro.md +++ b/notebooks/content/intro.md @@ -2,10 +2,10 @@ Py-Feat: Python Facial Expression Analysis Toolbox ============================ [![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) -[![Coverage Status](https://coveralls.io/repos/github/cosanlab/feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/feat?branch=master) -[![GitHub forks](https://img.shields.io/github/forks/cosanlab/feat)](https://github.com/cosanlab/feat/network) -[![GitHub stars](https://img.shields.io/github/stars/cosanlab/feat)](https://github.com/cosanlab/feat/stargazers) -[![GitHub license](https://img.shields.io/github/license/cosanlab/feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) +[![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) +[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/feat/network) +[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/feat/stargazers) +[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data. diff --git a/notebooks/content/loadingOtherFiles.ipynb b/notebooks/content/loadingOtherFiles.ipynb index e31a92a1..346676ee 100644 --- a/notebooks/content/loadingOtherFiles.ipynb +++ b/notebooks/content/loadingOtherFiles.ipynb @@ -14,11 +14,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:04.115339Z", - "start_time": "2021-03-24T22:20:04.002648Z" + "end_time": "2021-03-26T05:11:11.802818Z", + "start_time": "2021-03-26T05:11:10.161089Z" } }, "outputs": [ @@ -60,39 +60,7 @@ " gaze_1_x\n", " gaze_1_y\n", " gaze_1_z\n", - " pose_Tx\n", - " pose_Ty\n", - " pose_Tz\n", - " pose_Rx\n", - " pose_Ry\n", - " pose_Rz\n", - " x_0\n", - " x_1\n", - " x_2\n", - " x_3\n", - " x_4\n", - " x_5\n", - " x_6\n", - " x_7\n", - " x_8\n", - " x_9\n", " ...\n", - " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -118,41 +86,9 @@ " -0.124401\n", " 0.066311\n", " -0.990014\n", - " 16.6573\n", - " 43.4571\n", - " 597.876\n", - " 0.297590\n", - " -0.063753\n", - " -0.019235\n", - " 277.276\n", - " 278.163\n", - " 280.209\n", - " 283.258\n", - " 288.537\n", - " 297.109\n", - " 307.888\n", - " 320.240\n", - " 333.596\n", - " 346.018\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -174,41 +110,9 @@ " -0.123464\n", " 0.069979\n", " -0.989879\n", - " 16.2613\n", - " 43.2594\n", - " 596.885\n", - " 0.326858\n", - " -0.058038\n", - " -0.022072\n", - " 276.980\n", - " 277.893\n", - " 279.952\n", - " 283.021\n", - " 288.339\n", - " 296.925\n", - " 307.663\n", - " 319.981\n", - " 333.443\n", - " 346.086\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -230,41 +134,9 @@ " -0.122873\n", " 0.070540\n", " -0.989912\n", - " 15.8624\n", - " 43.2698\n", - " 593.122\n", - " 0.350392\n", - " -0.051172\n", - " -0.022723\n", - " 276.902\n", - " 277.785\n", - " 279.844\n", - " 282.911\n", - " 288.141\n", - " 296.601\n", - " 307.278\n", - " 319.657\n", - " 333.284\n", - " 346.138\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -286,41 +158,9 @@ " -0.122172\n", " 0.070736\n", " -0.989985\n", - " 15.5008\n", - " 43.1023\n", - " 592.867\n", - " 0.352725\n", - " -0.047812\n", - " -0.022291\n", - " 276.757\n", - " 277.626\n", - " 279.664\n", - " 282.699\n", - " 287.902\n", - " 296.353\n", - " 307.023\n", - " 319.397\n", - " 333.035\n", - " 345.927\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -342,41 +182,9 @@ " -0.121321\n", " 0.070529\n", " -0.990104\n", - " 14.7702\n", - " 43.1363\n", - " 594.135\n", - " 0.349325\n", - " -0.045551\n", - " -0.022963\n", - " 276.342\n", - " 277.208\n", - " 279.248\n", - " 282.285\n", - " 287.478\n", - " 295.913\n", - " 306.562\n", - " 318.911\n", - " 332.540\n", - " 345.428\n", " ...\n", " 0\n", " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", @@ -392,12 +200,26 @@ "" ], "text/plain": [ - " frame timestamp ... AU28_c AU45_c\n", - "0 1 0.000 ... 0 0\n", - "1 2 0.001 ... 0 0\n", - "2 3 0.002 ... 0 0\n", - "3 4 0.003 ... 0 0\n", - "4 5 0.004 ... 0 0\n", + " frame timestamp confidence success gaze_0_x gaze_0_y gaze_0_z \\\n", + "0 1 0.000 0.883333 1 0.109059 0.062474 -0.992070 \n", + "1 2 0.001 0.883333 1 0.110256 0.065356 -0.991752 \n", + "2 3 0.002 0.883333 1 0.108539 0.064244 -0.992014 \n", + "3 4 0.003 0.883333 1 0.108724 0.064943 -0.991948 \n", + "4 5 0.004 0.883333 1 0.109766 0.065250 -0.991813 \n", + "\n", + " gaze_1_x gaze_1_y gaze_1_z ... AU12_c AU14_c AU15_c AU17_c AU20_c \\\n", + "0 -0.124401 0.066311 -0.990014 ... 0 0 0 0 0 \n", + "1 -0.123464 0.069979 -0.989879 ... 0 0 0 0 0 \n", + "2 -0.122873 0.070540 -0.989912 ... 0 0 0 0 0 \n", + "3 -0.122172 0.070736 -0.989985 ... 0 0 0 0 0 \n", + "4 -0.121321 0.070529 -0.990104 ... 0 0 0 0 0 \n", + "\n", + " AU23_c AU25_c AU26_c AU28_c AU45_c \n", + "0 1 0 0 0 0 \n", + "1 1 0 0 0 0 \n", + "2 1 0 0 0 0 \n", + "3 1 0 0 0 0 \n", + "4 1 0 0 0 0 \n", "\n", "[5 rows x 431 columns]" ] @@ -426,11 +248,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:46.501974Z", - "start_time": "2021-03-24T22:20:46.429372Z" + "end_time": "2021-03-26T05:11:11.831292Z", + "start_time": "2021-03-26T05:11:11.803938Z" } }, "outputs": [ @@ -465,39 +287,7 @@ " x_7\n", " x_8\n", " x_9\n", - " x_10\n", - " x_11\n", - " x_12\n", - " x_13\n", - " x_14\n", - " x_15\n", - " x_16\n", - " x_17\n", - " x_18\n", - " x_19\n", - " x_20\n", - " x_21\n", - " x_22\n", - " x_23\n", - " x_24\n", - " x_25\n", " ...\n", - " y_42\n", - " y_43\n", - " y_44\n", - " y_45\n", - " y_46\n", - " y_47\n", - " y_48\n", - " y_49\n", - " y_50\n", - " y_51\n", - " y_52\n", - " y_53\n", - " y_54\n", - " y_55\n", - " y_56\n", - " y_57\n", " y_58\n", " y_59\n", " y_60\n", @@ -523,39 +313,7 @@ " 320.240\n", " 333.596\n", " 346.018\n", - " 356.266\n", - " 365.507\n", - " 372.289\n", - " 376.285\n", - " 378.485\n", - " 380.039\n", - " 380.562\n", - " 286.383\n", - " 292.931\n", - " 303.077\n", - " 313.934\n", - " 323.874\n", - " 340.883\n", - " 350.207\n", - " 359.633\n", - " 368.524\n", " ...\n", - " 186.658\n", - " 181.683\n", - " 181.337\n", - " 184.018\n", - " 187.718\n", - " 188.326\n", - " 237.529\n", - " 235.506\n", - " 233.715\n", - " 234.922\n", - " 233.408\n", - " 234.625\n", - " 235.620\n", - " 240.460\n", - " 242.560\n", - " 243.216\n", " 243.032\n", " 241.552\n", " 237.763\n", @@ -579,39 +337,7 @@ " 319.981\n", " 333.443\n", " 346.086\n", - " 356.530\n", - " 365.888\n", - " 372.591\n", - " 376.430\n", - " 378.497\n", - " 379.960\n", - " 380.414\n", - " 286.137\n", - " 292.527\n", - " 302.614\n", - " 313.488\n", - " 323.464\n", - " 340.515\n", - " 349.885\n", - " 359.369\n", - " 368.252\n", " ...\n", - " 186.681\n", - " 181.790\n", - " 181.413\n", - " 183.969\n", - " 187.708\n", - " 188.342\n", - " 237.534\n", - " 235.528\n", - " 233.778\n", - " 234.985\n", - " 233.450\n", - " 234.572\n", - " 235.499\n", - " 240.720\n", - " 243.024\n", - " 243.725\n", " 243.535\n", " 241.895\n", " 237.777\n", @@ -635,39 +361,7 @@ " 319.657\n", " 333.284\n", " 346.138\n", - " 356.717\n", - " 366.159\n", - " 372.821\n", - " 376.590\n", - " 378.645\n", - " 380.110\n", - " 380.536\n", - " 285.955\n", - " 292.222\n", - " 302.268\n", - " 313.181\n", - " 323.186\n", - " 340.184\n", - " 349.622\n", - " 359.211\n", - " 368.176\n", " ...\n", - " 186.788\n", - " 181.864\n", - " 181.463\n", - " 183.978\n", - " 187.807\n", - " 188.454\n", - " 238.026\n", - " 235.976\n", - " 234.216\n", - " 235.456\n", - " 233.928\n", - " 235.091\n", - " 236.081\n", - " 241.759\n", - " 244.207\n", - " 244.885\n", " 244.668\n", " 242.816\n", " 238.284\n", @@ -691,39 +385,7 @@ " 319.397\n", " 333.035\n", " 345.927\n", - " 356.552\n", - " 366.024\n", - " 372.690\n", - " 376.460\n", - " 378.517\n", - " 379.986\n", - " 380.415\n", - " 285.736\n", - " 292.013\n", - " 302.060\n", - " 312.962\n", - " 322.970\n", - " 339.908\n", - " 349.373\n", - " 358.981\n", - " 367.970\n", " ...\n", - " 186.682\n", - " 181.764\n", - " 181.370\n", - " 183.889\n", - " 187.717\n", - " 188.355\n", - " 237.935\n", - " 235.940\n", - " 234.204\n", - " 235.451\n", - " 233.921\n", - " 235.070\n", - " 236.026\n", - " 241.618\n", - " 244.027\n", - " 244.707\n", " 244.484\n", " 242.662\n", " 238.209\n", @@ -747,39 +409,7 @@ " 318.911\n", " 332.540\n", " 345.428\n", - " 356.057\n", - " 365.517\n", - " 372.168\n", - " 375.910\n", - " 377.945\n", - " 379.397\n", - " 379.831\n", - " 285.234\n", - " 291.509\n", - " 301.494\n", - " 312.317\n", - " 322.265\n", - " 339.345\n", - " 348.778\n", - " 358.342\n", - " 367.285\n", " ...\n", - " 186.682\n", - " 181.778\n", - " 181.384\n", - " 183.907\n", - " 187.712\n", - " 188.348\n", - " 237.788\n", - " 235.827\n", - " 234.104\n", - " 235.327\n", - " 233.797\n", - " 234.910\n", - " 235.821\n", - " 241.386\n", - " 243.801\n", - " 244.494\n", " 244.297\n", " 242.514\n", " 238.063\n", @@ -797,17 +427,31 @@ "" ], "text/plain": [ - " x_0 x_1 ... y_66 y_67\n", - "0 277.276 278.163 ... 237.104 236.823\n", - "1 276.980 277.893 ... 237.579 237.295\n", - "2 276.902 277.785 ... 238.606 238.292\n", - "3 276.757 277.626 ... 238.453 238.137\n", - "4 276.342 277.208 ... 238.267 237.974\n", + " x_0 x_1 x_2 x_3 x_4 x_5 x_6 x_7 \\\n", + "0 277.276 278.163 280.209 283.258 288.537 297.109 307.888 320.240 \n", + "1 276.980 277.893 279.952 283.021 288.339 296.925 307.663 319.981 \n", + "2 276.902 277.785 279.844 282.911 288.141 296.601 307.278 319.657 \n", + "3 276.757 277.626 279.664 282.699 287.902 296.353 307.023 319.397 \n", + "4 276.342 277.208 279.248 282.285 287.478 295.913 306.562 318.911 \n", + "\n", + " x_8 x_9 ... y_58 y_59 y_60 y_61 y_62 \\\n", + "0 333.596 346.018 ... 243.032 241.552 237.763 238.249 238.527 \n", + "1 333.443 346.086 ... 243.535 241.895 237.777 238.333 238.601 \n", + "2 333.284 346.138 ... 244.668 242.816 238.284 238.824 239.104 \n", + "3 333.035 345.927 ... 244.484 242.662 238.209 238.804 239.092 \n", + "4 332.540 345.428 ... 244.297 242.514 238.063 238.688 238.958 \n", + "\n", + " y_63 y_64 y_65 y_66 y_67 \n", + "0 237.790 236.296 236.401 237.104 236.823 \n", + "1 237.848 236.234 236.828 237.579 237.295 \n", + "2 238.397 236.835 237.882 238.606 238.292 \n", + "3 238.379 236.786 237.723 238.453 238.137 \n", + "4 238.232 236.595 237.525 238.267 237.974 \n", "\n", "[5 rows x 136 columns]" ] }, - "execution_count": 21, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -818,11 +462,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:20:48.115597Z", - "start_time": "2021-03-24T22:20:48.079943Z" + "end_time": "2021-03-26T05:11:11.868256Z", + "start_time": "2021-03-26T05:11:11.832354Z" } }, "outputs": [ @@ -857,21 +501,7 @@ " AU10_r\n", " AU12_r\n", " AU14_r\n", - " AU15_r\n", - " AU17_r\n", - " AU20_r\n", - " AU23_r\n", - " AU25_r\n", - " AU26_r\n", - " AU45_r\n", - " AU01_c\n", - " AU02_c\n", - " AU04_c\n", - " AU05_c\n", - " AU06_c\n", - " AU07_c\n", - " AU09_c\n", - " AU10_c\n", + " ...\n", " AU12_c\n", " AU14_c\n", " AU15_c\n", @@ -897,21 +527,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.4543\n", - " 0.0\n", - " 0.0\n", - " 0.02348\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -935,21 +551,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.3076\n", - " 0.0\n", - " 0.0\n", - " 0.12580\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -973,21 +575,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.2163\n", - " 0.0\n", - " 0.0\n", - " 0.16820\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1011,21 +599,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1961\n", - " 0.0\n", - " 0.0\n", - " 0.15160\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1049,21 +623,7 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.1634\n", - " 0.0\n", - " 0.0\n", - " 0.16890\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " ...\n", " 0\n", " 0\n", " 0\n", @@ -1077,20 +637,35 @@ " \n", " \n", "\n", + "

    5 rows × 35 columns

    \n", "" ], "text/plain": [ - " AU01_r AU02_r AU04_r ... AU26_c AU28_c AU45_c\n", - "0 0.6773 0.4275 0.2435 ... 0 0 0\n", - "1 0.5958 0.3507 0.3347 ... 0 0 0\n", - "2 0.6017 0.3078 0.4339 ... 0 0 0\n", - "3 0.6545 0.3294 0.5075 ... 0 0 0\n", - "4 0.5636 0.2709 0.5708 ... 0 0 0\n", + " AU01_r AU02_r AU04_r AU05_r AU06_r AU07_r AU09_r AU10_r AU12_r \\\n", + "0 0.6773 0.4275 0.2435 0.3434 0 0.0 0.0 0 0 \n", + "1 0.5958 0.3507 0.3347 0.3434 0 0.0 0.0 0 0 \n", + "2 0.6017 0.3078 0.4339 0.2920 0 0.0 0.0 0 0 \n", + "3 0.6545 0.3294 0.5075 0.2899 0 0.0 0.0 0 0 \n", + "4 0.5636 0.2709 0.5708 0.1455 0 0.0 0.0 0 0 \n", + "\n", + " AU14_r ... AU12_c AU14_c AU15_c AU17_c AU20_c AU23_c AU25_c \\\n", + "0 0 ... 0 0 0 0 0 1 0 \n", + "1 0 ... 0 0 0 0 0 1 0 \n", + "2 0 ... 0 0 0 0 0 1 0 \n", + "3 0 ... 0 0 0 0 0 1 0 \n", + "4 0 ... 0 0 0 0 0 1 0 \n", + "\n", + " AU26_c AU28_c AU45_c \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", "\n", "[5 rows x 35 columns]" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1109,11 +684,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:01.787253Z", - "start_time": "2021-03-24T22:21:01.692699Z" + "end_time": "2021-03-26T05:11:11.907622Z", + "start_time": "2021-03-26T05:11:11.869247Z" } }, "outputs": [ @@ -1155,39 +730,7 @@ " EventSource\n", " Timestamp\n", " MediaTime\n", - " PostMarker\n", - " Annotation\n", - " FrameNo\n", - " FrameTime\n", - " NoOfFaces\n", - " FaceRectX\n", - " FaceRectY\n", - " FaceRectWidth\n", - " FaceRectHeight\n", - " Joy\n", - " Anger\n", - " Surprise\n", - " Fear\n", - " Contempt\n", - " Disgust\n", - " Sadness\n", " ...\n", - " IsMaleProbability\n", - " Yaw\n", - " Pitch\n", - " Roll\n", - " LEFT_EYE_LATERALX\n", - " LEFT_EYE_LATERALY\n", - " LEFT_EYE_PUPILX\n", - " LEFT_EYE_PUPILY\n", - " LEFT_EYE_MEDIALX\n", - " LEFT_EYE_MEDIALY\n", - " RIGHT_EYE_MEDIALX\n", - " RIGHT_EYE_MEDIALY\n", - " RIGHT_EYE_PUPILX\n", - " RIGHT_EYE_PUPILY\n", - " RIGHT_EYE_LATERALX\n", - " RIGHT_EYE_LATERALY\n", " NOSE_TIPX\n", " NOSE_TIPY\n", " 7X\n", @@ -1213,39 +756,7 @@ " Emotient FACET\n", " 35\n", " 35\n", - " NaN\n", - " NaN\n", - " 0\n", - " 35\n", - " 1\n", - " 388.0\n", - " 258.0\n", - " 258.0\n", - " 258.0\n", - " -0.462367\n", - " -3.642415\n", - " -1.254946\n", - " -1.967231\n", - " 0.428609\n", - " -2.541990\n", - " -1.224254\n", " ...\n", - " 0.977411\n", - " -1.102638\n", - " 8.205247\n", - " -3.704682\n", - " 445.2972\n", - " 328.3713\n", - " 0.0\n", - " 0.0\n", - " 500.4211\n", - " 334.8774\n", - " 554.7314\n", - " 335.0555\n", - " 0.0\n", - " 0.0\n", - " 604.1805\n", - " 327.6500\n", " 525.0905\n", " 394.8224\n", " 0.0\n", @@ -1269,39 +780,7 @@ " Emotient FACET\n", " 68\n", " 68\n", - " NaN\n", - " NaN\n", - " 1\n", - " 68\n", - " 1\n", - " 389.0\n", - " 252.0\n", - " 257.0\n", - " 257.0\n", - " -4.371406\n", - " -3.821383\n", - " -2.094306\n", - " -3.278980\n", - " -0.516405\n", - " -4.306592\n", - " -1.787594\n", " ...\n", - " 0.995149\n", - " -2.236120\n", - " 7.103536\n", - " -2.550549\n", - " 447.2003\n", - " 322.2042\n", - " 0.0\n", - " 0.0\n", - " 499.8117\n", - " 328.6136\n", - " 555.1335\n", - " 330.2828\n", - " 0.0\n", - " 0.0\n", - " 607.3446\n", - " 323.2844\n", " 525.9382\n", " 385.9986\n", " 0.0\n", @@ -1325,39 +804,7 @@ " Emotient FACET\n", " 101\n", " 101\n", - " NaN\n", - " NaN\n", - " 2\n", - " 102\n", - " 1\n", - " 387.0\n", - " 242.0\n", - " 264.0\n", - " 264.0\n", - " -5.124269\n", - " -2.969738\n", - " -1.833445\n", - " -3.807125\n", - " -0.724873\n", - " -4.186139\n", - " -1.579113\n", " ...\n", - " 0.994264\n", - " -0.855230\n", - " 4.500861\n", - " -3.289358\n", - " 446.9748\n", - " 315.9877\n", - " 0.0\n", - " 0.0\n", - " 498.4589\n", - " 322.1017\n", - " 559.3203\n", - " 323.0681\n", - " 0.0\n", - " 0.0\n", - " 611.6814\n", - " 315.5635\n", " 525.2611\n", " 378.3876\n", " 0.0\n", @@ -1381,39 +828,7 @@ " Emotient FACET\n", " 134\n", " 134\n", - " NaN\n", - " NaN\n", - " 3\n", - " 135\n", - " 1\n", - " 391.0\n", - " 239.0\n", - " 255.0\n", - " 255.0\n", - " -5.310449\n", - " -3.882663\n", - " -2.479799\n", - " -4.622220\n", - " -0.881652\n", - " -4.608058\n", - " -2.352544\n", " ...\n", - " 0.989092\n", - " -0.936105\n", - " 5.009235\n", - " -3.453575\n", - " 450.5946\n", - " 308.2809\n", - " 0.0\n", - " 0.0\n", - " 499.7155\n", - " 314.7372\n", - " 559.8370\n", - " 317.5146\n", - " 0.0\n", - " 0.0\n", - " 607.8320\n", - " 312.9327\n", " 524.6653\n", " 372.0715\n", " 0.0\n", @@ -1437,39 +852,7 @@ " Emotient FACET\n", " 168\n", " 168\n", - " NaN\n", - " NaN\n", - " 4\n", - " 168\n", - " 1\n", - " 393.0\n", - " 236.0\n", - " 253.0\n", - " 253.0\n", - " -5.206233\n", - " -4.181856\n", - " -2.407882\n", - " -4.627919\n", - " -0.880042\n", - " -5.046319\n", - " -2.624082\n", " ...\n", - " 0.994389\n", - " -0.632709\n", - " 5.729938\n", - " -2.903348\n", - " 452.0932\n", - " 305.5123\n", - " 0.0\n", - " 0.0\n", - " 501.3641\n", - " 309.6560\n", - " 561.4435\n", - " 312.6160\n", - " 0.0\n", - " 0.0\n", - " 608.5308\n", - " 309.9495\n", " 525.9166\n", " 366.4871\n", " 0.0\n", @@ -1487,12 +870,33 @@ "" ], "text/plain": [ - " StudyName ... SceneParent\n", - "0 RotationTest ... NaN\n", - "1 RotationTest ... NaN\n", - "2 RotationTest ... NaN\n", - "3 RotationTest ... NaN\n", - "4 RotationTest ... NaN\n", + " StudyName ExportDate Name Age Gender StimulusName \\\n", + "0 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "1 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "2 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "3 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "4 RotationTest 20171006 separate_rotation 0 MALE gopro \n", + "\n", + " SlideType EventSource Timestamp MediaTime ... NOSE_TIPX NOSE_TIPY \\\n", + "0 TestImage Emotient FACET 35 35 ... 525.0905 394.8224 \n", + "1 TestImage Emotient FACET 68 68 ... 525.9382 385.9986 \n", + "2 TestImage Emotient FACET 101 101 ... 525.2611 378.3876 \n", + "3 TestImage Emotient FACET 134 134 ... 524.6653 372.0715 \n", + "4 TestImage Emotient FACET 168 168 ... 525.9166 366.4871 \n", + "\n", + " 7X 7Y LiveMarker KeyStroke MarkerText SceneType SceneOutput \\\n", + "0 0.0 0.0 NaN NaN NaN NaN NaN \n", + "1 0.0 0.0 NaN NaN NaN NaN NaN \n", + "2 0.0 0.0 NaN NaN NaN NaN NaN \n", + "3 0.0 0.0 NaN NaN NaN NaN NaN \n", + "4 0.0 0.0 NaN NaN NaN NaN NaN \n", + "\n", + " SceneParent \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", "[5 rows x 78 columns]" ] @@ -1519,11 +923,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:21:59.557509Z", - "start_time": "2021-03-24T22:21:59.539415Z" + "end_time": "2021-03-26T05:11:11.918287Z", + "start_time": "2021-03-26T05:11:11.908503Z" } }, "outputs": [ @@ -1643,17 +1047,22 @@ "" ], "text/plain": [ - " Joy Anger ... Positive Negative\n", - "0 -0.462367 -3.642415 ... -0.462367 0.428609\n", - "1 -4.371406 -3.821383 ... -4.371406 -0.516405\n", - "2 -5.124269 -2.969738 ... -5.124269 -0.724873\n", - "3 -5.310449 -3.882663 ... -5.310449 -0.881652\n", - "4 -5.206233 -4.181856 ... -5.206233 -0.880042\n", + " Joy Anger Surprise Fear Contempt Disgust Sadness \\\n", + "0 -0.462367 -3.642415 -1.254946 -1.967231 0.428609 -2.541990 -1.224254 \n", + "1 -4.371406 -3.821383 -2.094306 -3.278980 -0.516405 -4.306592 -1.787594 \n", + "2 -5.124269 -2.969738 -1.833445 -3.807125 -0.724873 -4.186139 -1.579113 \n", + "3 -5.310449 -3.882663 -2.479799 -4.622220 -0.881652 -4.608058 -2.352544 \n", + "4 -5.206233 -4.181856 -2.407882 -4.627919 -0.880042 -5.046319 -2.624082 \n", "\n", - "[5 rows x 12 columns]" + " Confusion Frustration Neutral Positive Negative \n", + "0 -3.051880 -2.369417 1.784896 -0.462367 0.428609 \n", + "1 -2.694475 -2.190143 2.697488 -4.371406 -0.516405 \n", + "2 -2.003944 -1.487295 2.437310 -5.124269 -0.724873 \n", + "3 -2.248295 -1.931136 3.032463 -5.310449 -0.881652 \n", + "4 -2.401067 -1.991587 3.191700 -5.206233 -0.880042 " ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1671,11 +1080,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:23:40.991510Z", - "start_time": "2021-03-24T22:23:40.443246Z" + "end_time": "2021-03-26T05:11:12.547489Z", + "start_time": "2021-03-26T05:11:11.919098Z" } }, "outputs": [ @@ -1683,7 +1092,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jcheong/packages/feat/feat/data.py:1303: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + "/home/jcheong/packages/feat/feat/data.py:1338: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " wavs = np.array(wavs)[::-1]\n" ] }, @@ -1718,39 +1127,7 @@ " neg1_hz_0.66_Joy\n", " neg2_hz_0.66_Joy\n", " neg3_hz_0.66_Joy\n", - " neg4_hz_0.66_Joy\n", - " neg5_hz_0.66_Joy\n", - " pos0_hz_0.47_Joy\n", - " pos1_hz_0.47_Joy\n", - " pos2_hz_0.47_Joy\n", - " pos3_hz_0.47_Joy\n", - " pos4_hz_0.47_Joy\n", - " pos5_hz_0.47_Joy\n", - " neg0_hz_0.47_Joy\n", - " neg1_hz_0.47_Joy\n", - " neg2_hz_0.47_Joy\n", - " neg3_hz_0.47_Joy\n", - " neg4_hz_0.47_Joy\n", - " neg5_hz_0.47_Joy\n", - " pos0_hz_0.33_Joy\n", - " pos1_hz_0.33_Joy\n", " ...\n", - " neg4_hz_0.12_Negative\n", - " neg5_hz_0.12_Negative\n", - " pos0_hz_0.08_Negative\n", - " pos1_hz_0.08_Negative\n", - " pos2_hz_0.08_Negative\n", - " pos3_hz_0.08_Negative\n", - " pos4_hz_0.08_Negative\n", - " pos5_hz_0.08_Negative\n", - " neg0_hz_0.08_Negative\n", - " neg1_hz_0.08_Negative\n", - " neg2_hz_0.08_Negative\n", - " neg3_hz_0.08_Negative\n", - " neg4_hz_0.08_Negative\n", - " neg5_hz_0.08_Negative\n", - " pos0_hz_0.06_Negative\n", - " pos1_hz_0.06_Negative\n", " pos2_hz_0.06_Negative\n", " pos3_hz_0.06_Negative\n", " pos4_hz_0.06_Negative\n", @@ -1776,39 +1153,7 @@ " 0\n", " 0\n", " 2\n", - " 1\n", - " 4\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 3\n", - " 0\n", - " 1\n", " ...\n", - " 1\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", " 2\n", " 0\n", " 0\n", @@ -1826,13 +1171,28 @@ "" ], "text/plain": [ - " pos0_hz_0.66_Joy ... neg5_hz_0.06_Negative\n", - "0 2 ... 1\n", + " pos0_hz_0.66_Joy pos1_hz_0.66_Joy pos2_hz_0.66_Joy pos3_hz_0.66_Joy \\\n", + "0 2 0 0 0 \n", + "\n", + " pos4_hz_0.66_Joy pos5_hz_0.66_Joy neg0_hz_0.66_Joy neg1_hz_0.66_Joy \\\n", + "0 3 3 0 0 \n", + "\n", + " neg2_hz_0.66_Joy neg3_hz_0.66_Joy ... pos2_hz_0.06_Negative \\\n", + "0 0 2 ... 2 \n", + "\n", + " pos3_hz_0.06_Negative pos4_hz_0.06_Negative pos5_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg0_hz_0.06_Negative neg1_hz_0.06_Negative neg2_hz_0.06_Negative \\\n", + "0 0 0 0 \n", + "\n", + " neg3_hz_0.06_Negative neg4_hz_0.06_Negative neg5_hz_0.06_Negative \n", + "0 0 1 1 \n", "\n", "[1 rows x 1152 columns]" ] }, - "execution_count": 30, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1852,11 +1212,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T22:24:52.073973Z", - "start_time": "2021-03-24T22:24:52.028491Z" + "end_time": "2021-03-26T05:11:12.578290Z", + "start_time": "2021-03-26T05:11:12.548607Z" } }, "outputs": [ @@ -1898,18 +1258,7 @@ " Valence\n", " Engagement\n", " Timestamp\n", - " FileName\n", - " AU12\n", - " AU01\n", - " AU02\n", - " AU04\n", - " AU09\n", - " AU10\n", - " AU15\n", - " AU17\n", - " AU18\n", - " AU24\n", - " AU28\n", + " ...\n", " AU25\n", " Smirk\n", " AU43\n", @@ -1935,18 +1284,7 @@ " 78.124275\n", " 99.921013\n", " 0\n", - " JinHyunCheong.jpg\n", - " 100\n", - " 6.299417\n", - " 0.01924\n", - " 6.175626e-09\n", - " 7.965685\n", - " 29.55155\n", - " 2.398794e-12\n", - " 0.002099\n", - " 0.000042\n", - " 0.00536\n", - " 8.028388e-08\n", + " ...\n", " 99.999939\n", " 0\n", " 5.448178e-07\n", @@ -1960,11 +1298,18 @@ " \n", " \n", "\n", + "

    1 rows × 32 columns

    \n", "" ], "text/plain": [ - " Joy Sadness ... AU06 AU20\n", - "0 99.930573 0.000002 ... 94.181007 0.262457\n", + " Joy Sadness Disgust Contempt Anger Fear Surprise \\\n", + "0 99.930573 0.000002 0.002528 0.000107 0.000096 4.059986e-07 1.76971 \n", + "\n", + " Valence Engagement Timestamp ... AU25 Smirk AU43 \\\n", + "0 78.124275 99.921013 0 ... 99.999939 0 5.448178e-07 \n", + "\n", + " Attention AU07 AU26 AU14 AU05 AU06 AU20 \n", + "0 91.699234 11.572159 82.992149 0.000003 0.000744 94.181007 0.262457 \n", "\n", "[1 rows x 32 columns]" ] @@ -1981,6 +1326,260 @@ "print(type(detections))\n", "display(detections.head())" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading a completely new file as a Fex class\n", + "It's easy to cast a dataframe which might be neither an OpenFace or FACET outputs into a Fex class. Simply cast your dataframe as a Fex class." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:45.226073Z", + "start_time": "2021-03-26T05:15:45.221254Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from feat import Fex\n", + "import pandas as pd, numpy as np\n", + "au_columns = [f\"AU{i}\" for i in range(20)]\n", + "fex = Fex(pd.DataFrame(np.random.rand(20,20)))\n", + "fex.columns = au_columns\n", + "print(type(fex))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To take full advantage of Py-Feat's features, make sure you set the attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:15:51.277149Z", + "start_time": "2021-03-26T05:15:51.259222Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " AU0 AU1 AU2 AU3 AU4 AU5 AU6 \\\n", + "0 0.003699 0.042126 0.532839 0.410701 0.079237 0.612938 0.560122 \n", + "1 0.887544 0.882457 0.265571 0.078094 0.341208 0.563913 0.294769 \n", + "2 0.222848 0.007902 0.806356 0.392177 0.842465 0.765245 0.260304 \n", + "3 0.161766 0.678874 0.994342 0.076421 0.618708 0.880437 0.302860 \n", + "4 0.802379 0.951092 0.047695 0.914416 0.651683 0.183344 0.446120 \n", + "\n", + " AU7 AU8 AU9 AU10 AU11 AU12 AU13 \\\n", + "0 0.999783 0.889522 0.169887 0.374350 0.631952 0.040375 0.852979 \n", + "1 0.307120 0.480530 0.321297 0.321300 0.513947 0.566524 0.816932 \n", + "2 0.372778 0.648263 0.118601 0.695544 0.800680 0.870643 0.588052 \n", + "3 0.191823 0.443328 0.221810 0.321260 0.154832 0.330742 0.281754 \n", + "4 0.872419 0.468856 0.118990 0.985744 0.793074 0.155625 0.178448 \n", + "\n", + " AU14 AU15 AU16 AU17 AU18 AU19 \n", + "0 0.329220 0.196842 0.118240 0.631301 0.709566 0.003093 \n", + "1 0.493617 0.541507 0.624186 0.138642 0.845837 0.638981 \n", + "2 0.266168 0.247223 0.479493 0.928013 0.061631 0.158413 \n", + "3 0.707123 0.500014 0.457013 0.575017 0.955154 0.216029 \n", + "4 0.217853 0.966267 0.455350 0.970413 0.781624 0.193531 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fex.au_columns = au_columns\n", + "display(fex.aus().head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/content/plotting.ipynb b/notebooks/content/plotting.ipynb new file mode 100644 index 00000000..e58e0106 --- /dev/null +++ b/notebooks/content/plotting.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:51:41.015128Z", + "start_time": "2021-03-26T04:51:39.430936Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T05:02:39.899481Z", + "start_time": "2021-03-26T05:02:39.823101Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:19.202362Z", + "start_time": "2021-03-26T04:54:19.119350Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/content/trainAUvisModel.ipynb b/notebooks/content/trainAUvisModel.ipynb new file mode 100755 index 00000000..5f891170 --- /dev/null +++ b/notebooks/content/trainAUvisModel.ipynb @@ -0,0 +1,451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training AU visualization model\n", + "You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:01.906078Z", + "start_time": "2021-03-26T04:01:59.634560Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:02:16.245590Z", + "start_time": "2021-03-26T04:02:14.666837Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:17.089755Z", + "start_time": "2021-03-26T04:10:15.301127Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:10:21.609846Z", + "start_time": "2021-03-26T04:10:20.554202Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/content/visualizations.ipynb b/notebooks/content/visualizations.ipynb deleted file mode 100644 index d0c9e080..00000000 --- a/notebooks/content/visualizations.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualizations\n", - "\n", - "## How to visuzalize facial expression data with Feat.\n", - "*Written by Jin Hyun Cheong*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/requirements.txt b/requirements.txt index a1696e72..e9eee7c6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ numpy>=1.9 seaborn>=0.7.0 matplotlib>=2.1 nltools>=0.3.6 -scikit-learn>=0.18.1, <0.24.0 +scikit-learn>=0.18.1 pywavelets>=0.3.0 h5py>=2.7.0 Pillow>=6.0.0 diff --git a/tutorials/TrainLandmarkPredictionModel.ipynb b/tutorials/TrainLandmarkPredictionModel.ipynb deleted file mode 100755 index 39875ff5..00000000 --- a/tutorials/TrainLandmarkPredictionModel.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook trains a model to predict landmark changes from action units using a partial least squares regression\n", - "\n", - "You will need to download the preprocessed files 'ck_openface_data.csv' and 'ck_facet_Data.csv'." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2018-07-27T17:46:55.414252", - "start_time": "2018-07-27T17:46:53.889869Z" - } - }, - "outputs": [], - "source": [ - "# load modules\n", - "%matplotlib inline\n", - "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", - "from sklearn.cross_decomposition import PLSRegression\n", - "from sklearn.model_selection import KFold\n", - "from feat.plotting import predict, plot_face\n", - "from feat.utils import registration\n", - "from natsort import natsorted\n", - "\n", - "# Load data \n", - "# Landmarks are extended CK images processed by OpenFace v 2.0\n", - "openface_dat = pd.read_csv('ck_openface_data.csv')\n", - "facet_dat = pd.read_csv('ck_facet_data.csv')\n", - "facet_dat = facet_dat[natsorted(facet_dat.columns)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train model with FACET AUs on OpenFace landmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2018-07-27T17:47:00.462306", - "start_time": "2018-07-27T17:46:55.416168Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N_comp: 23 Rsquare 0.61\n", - "3-fold accuracy mean 0.53\n" - ] - }, - { - "data": { - "image/png": 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ii8D27aIlk9+gTKegAIiIEJWPjAwx14fVS5IkzJw1y9zFYOyO3P/UU+YuAtC9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\nqhUObN2KPsePY1S3bhjn64t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HqGGurFSKVp6jR0WlVS4Xm6yWlUF+8SIqSkoMcpnmTEME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Bgzv+9PT0hKS3GwRPnwaqq3t8MGd9fT2Ki4tRUlLS45+tLS2YPmECXnvtNdxwww29H4OZpz4u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\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UV1drvT9DztmIiAjh6uoqlixZopPfpbE6dOiQcHNzE3PmzBFFOtjvG3LOSoZLDsKk1kYT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\n18cfJ/SPPwgNDWXo0KHExsbqO7qWrX17KqZO5Q1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Subset the landmark data \n", - "lm_cols = ['x_'+str(i) for i in range(0,68)]+['y_'+str(i) for i in range(0,68)]\n", - "# Delete rows with na and mask each array\n", - "mask =np.any(~np.isnan(np.array(facet_dat)),axis=1)\n", - "X = np.array(facet_dat)[mask]\n", - "y = registration(openface_dat[lm_cols].as_matrix() [mask])\n", - "\n", - "# Train model\n", - "n_components= np.shape(X)[1]\n", - "clf_facet = PLSRegression(n_components=n_components,max_iter=1000)\n", - "clf_facet.fit(X,y)\n", - "print('N_comp:',n_components,'Rsquare', np.round(clf_facet.score(X,y),2))\n", - "# Model Accuracy in KFold CV\n", - "kf = KFold(n_splits=3)\n", - "scores = []\n", - "for train_index, test_index in kf.split(X):\n", - " X_train,X_test = X[train_index],X[test_index]\n", - " y_train,y_test = y[train_index],y[test_index]\n", - " clf_facet = PLSRegression(n_components=n_components)\n", - " clf_facet.fit(X_train,y_train)\n", - " scores.append(clf_facet.score(X_test,y_test))\n", - "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", - "\n", - "# Plot results for each action unit\n", - "f,axes = plt.subplots(5,5,figsize=(15,15))\n", - "axes = axes.flatten()\n", - "intensity = 15\n", - "for aui, auname in enumerate(facet_dat.columns):\n", - " au = np.zeros(len(clf_facet.x_mean_))\n", - " au[aui] = intensity\n", - " plot_face(au=np.zeros(n_components),model=clf_facet,vectorfield=\n", - " {'target':predict(au,model=clf_facet),'color':'r','alpha':.6},\n", - " ax = axes[aui])\n", - " axes[aui].set(title=auname)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train model with OpenFace AUs on OpenFace landmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2018-07-27T17:47:27.592622", - "start_time": "2018-07-27T17:47:22.681919Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N_comp: 20 Rsquare 0.63\n", - "3-fold accuracy mean 0.49\n" - ] - }, - { - "data": { - "image/png": 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xYp6pe/fE90OHAh07Frp5YmIijhw5Au89e3Dyn3+gkEqLjMdXf1alShWYmpoiLS0Nt27d\nyhebYWFhqF+/fr7YdHZ2hrm5uZh359tvxfC4IspWILlcaz139DU29TYuC5GdnY07d+7kqzdDQ0NR\nv379vHrTzQ1NateG8ZIlSEtPx+OEBDweNgyPoqMLjE2ZTIYaNWqI2KxRAy0dHDBwzBjY2trq+u2q\nTWxsLA4ePAjvrVtx7upVGJuaFltf2tjYwDosDFVOn4YxgOThw3E9IwPBYWEIDg5GUFAQwsPD0bhx\n49y4dHFxgZOTE8yKW4RAj3BsqkdOQ8er9eb9+/fh6OiYV2+amqLhmTMwatoUKaNG4dHjx/li8fU6\nVKFQiAfH996DXfXqaNu+PfoNGADL4lYpNSBRUVHwWb8e3seO4dKNGyhbrlyx9aWNjQ1sjI1ReelS\nlJFKEW9ri2vu7ggOC8utO6OiouDk5JQbm66urmjcuDGMi+vdrkc4NtUjIyMjX0NHQEAAIiMj4ezs\nnFdvNmqEer/9BumXXyKxRo0i72cfPXqEMmXK5CZ1alarhk5duqB3nz5vVWN4eHg4vL294b1tG4LC\nwlChQgWl7mdtbGxgaWkJoxUrEJORgWuxsQi2tc2NzefPn+cmXXNis0GDBjBSsue6PlA2Nt+qJM75\nU6dwZutWBMTFISAwEMbGxvkeClu0aIHKlSuX+PgymQxRUVEiyG7fhn9ICLz370e1atXwwQcfYMiQ\nIahdu3b+nYiAxYvFpMgzZuhuWBERsHq1GEY1Z07eilt45eHQ2xunT5+Gu7s7Bg4ciD7vv49q1apB\nooaMZlZWFu7cuZMbZEFBQbh58ybee+89uNStiw+7dEHvr74q9Xk0SV8rPEC/Y5PWrcPxCxdwwd4e\nAYGBCAwMRKVKlXIfCN3c3ODq6lqqhF56enpeJRgRAd9Ll+Dj44OGDRtiyJAhGDx4MN57vedZdjYw\ncaIYkuTiIoY/1qtXyneroogI0bNv8GARk61a5ft1bGwsDh06BG9vb1y8eBEdOnTAwIED0atXL1Sp\nUkUtRZDJZAgJCckXm7dv30atWrXg8t57GFulCtpu21b80MtXRUYCDx9qrXeTvsamPsclIJI2B3bt\nwuXAQARcv47g4GBUrVo1X73ZvHlzlHt9CfvQUMDHRwxHBMTQWXf3As+RkpKSG5sPT53CGR8fHH32\nDC3c3DBkyBAMGDBAbZ9lbYqKioKPjw+8vb0RHByMbt26YaCpKbo7O6Pi1KnKHeTPP/MmcG/YEJg0\nKV/iMy0tDTdu3MhNuAYFBeHevXtwqF8fro0aYdKMGWjWrJn635wacWyWjOzJE+w7fRpXXiZtbt68\niVq1auWrN52dnWH66oqfgYGiYWDePDHnYTGSkpJEnbltGyKDg3Hi6VOcjoxEu/btMWTIEPTt29eg\nGtpy5Dwc7t+/H/fu3UPvWrUwsEYNdHZyQrmOHYFOnYq/Fw8PB37+WcydN2UK4OCQ79dJSUm4/vKa\nmVN3RkREoLGDA1q4umLqzJmoW7euBt9l6XFslkB8PJKNjLDP2zs3oXr79m04ODjkqzcdHR3zEnr+\n/qIRfd8+4KefgLJli1wNOKfH2KNHj/Do4UM8+OknHM3OxqXbt9GtWzcMGTIEPXv2fLNe1nNEhDt3\n7ojEjbc3njx5gn79+mEgEdq7ucF0zBjlD7Z7d968mZUri/vol168eIFr167li80nT56gadOmaNGs\nGWZOmICqjo4aeIfqo3RsEpHSX66urqSP5HI5TZ06leyrVqWZzZrR/o8+osc3bmjt3GfOnKFx48aR\ntbU1ubm50dKlSykyMjJvo8hIonHjiHx9tVKmAp06Jcpw7x4REcXGxtKmTZuoR48eVKFCBerduzf9\n8ccf9Pz5c60VSS6XU2hoKG1ZvJhq2NrSDz/8QAqFQmvnVxWAQFIhXrT5pfHYVCjEl4rS09Np5NCh\n1KhePZo3bx4dOXKEnj17poECvkkmk9HRo0dp5MiRZGlpSe3ataM1a9bQ06dP8za6coVo+3aiOXNK\n9P5K7eBBom++yff3ffz4Ma1evZo6duxIFhYWNHjwYNq9ezclJSVprViZmZl07do1WrduHdlYW9OG\nDRuU3/nff4kmT8691miDvsamvtaZREQJCQnUq1cvamFvTz907UonDh9W/vqfnU10/DjRhAlEY8cS\nLV6s3H5ZWUTz5lHaokW0b+9eGtK/P1lYWFC3bt3o999/pxcvXpT8DWnBf//9R0uWLCEPDw+ytLSk\njz76iA4cOEBpfn5Ed+6Iv0VwsPIHXLFC7DN/PlFamlK7pKen09UdO2iphwdVqVyZ9u/fX8J3ox0c\nm6p7+vAheVavTu0dHWnxzz/T2bNnKTExsfgdg4PFvZ6qTp4Un8O5cykxIYG2b99O77//PllYWFC/\nfv1o165dlJycrPpxtSg0NJQWLFhAzZo1IxsbGxo7diydOHGCZDIZ0dKl4v2NHUt0/rxyBwwKUm17\nIkpJSSG/xYvpOzc3sq1Shc6rsK8ucGyq7t9588jR1pZ6ubjQ8p9/pou+vpSamlr0TufP533+pk8n\nUiaWc6Slif1u3aK4uDjauHEjde7cmSpWrEjDhg2jAwcOUHp6eunelAYpFAoKDAykWbNmUYMGDahG\njRo0efJkOn/+PMmjooguXCD68ktx3VLlfSQlEX39dd7ftBgJCQl07tw5+srLi2pWrEjXAwNL8a40\nT9nYNPjAio+Pp+7du1PHjh0p9vZt8eBw5w7R/ftafyjLysqif/75h0aPHk1WVlbUqlUrWrFiBT1+\n/Jho1y6iKVOIUlK0WiYiInr4kGjCBIr64498D4eDBg2iP//8U7mbAw2LioqiFi1a0IgRI/T2gqSv\nFR5pIzYVCqLDh4nkcqV3iY6OppYtW9KgQYMoRRef+1dkZGTQwYMHacSIEVSxYkXq1KkTrV+/nmJj\nY4nCw0VF8O+/2i/YggVE27dTeHg4LV26lFq1apX7cOjj40NpSj7YadLdu3fJwcGBpkyZQvLi/v/v\n3CGaOJHo88+JZDLtFJD0Nzb1sc4kIrp37x41bNiQxo8fT7KkJKKwsJIl3R4/FgmIsWPFa2X8959o\nUNixg2jTJkpJSaHdu3dT/5cJnV69etG2bdsoISFB9fJowO3bt3MfDq2trWnMmDF0/Phx8XCY49Ah\n8Zn//HOiEyeUv0mfNUvcgMbHq1aoLVuIzp6lgKtXqXr16rRo0SK9bQDh2FRNYGAg2Vlb07x27Sg7\nNFS1nWNjRYJVVWFhIobPnMn34/j4ePrjjz+oR48eufeMe/fuLf6hVQsUCgUFBQXRrFmzqGHDhrkP\nh+fOnXuznlq5Ury/n35S/rng9GlxjVLV8uVEa9bQyWPHyMbGhn7//XfVj6ElHJuqOXnyJNmUL0+r\nW7cmxZgxRMr+3964kZfE8fZW7aSxsWK///7L9+Nnz57RunXrqEOHDlSpUiX66KOP6MiRI/nrJR3J\nzs4mPz8/+uqrr6hWrVpUt25d+uabb+jKlSv566nk5Ly/y1dfEV28qNqJchKt33yj/D5Pn9Lurl2p\nioUFHTx4ULXzadE7kcS5c+cOOTg40KRJkygzM1PXxcknMzOT/v77bxo1ahRZWlpS29ataXWnTvR0\nzRqtliMrM5OW9uxJnvb2ZGlpSR9++CH5+PjoRSX8utTUVBoyZAi1atUqf28JPaGvFR5pKza3bRM3\nKEp8dq5cuULVq1enBQsW6N3DRVpaGnl7e9MHH3xAFStWpK5du9LmPn3o+erVWi1HcnQ0/eDmRs0b\nNsx9ODx27JheVMKve/HiBXXq1Il69epVeNL31q28nhkLF2q1fPoam/pWZxIRHT9+nKytrem3335T\nzwGzsogOHCDavbv4bRUKouvXiebNE5+TKVPyPXQmJSXRjh07qE+fPmRhYUF9+/bVSS+AmKgomv3y\n4bB69eo0adKkgh8Oc/z5Z97N6Pbtyp0kK0u8/6go1QqnUOTb5/Hjx+Ti4kIff/wxZWRkqHYsLeDY\nVN6uXbuoSpUqtO+nn8TnQ1tSUogmTSqyN1hOL4AuXbpQxYoVaejQoeTj46P1RreIiIg3Hg79/f0p\nu6jk1Zo1IsH66JHyJ7p1S/X/g+xskfh5eZ0ICwujevXq0dSpU4tvANEBjk3lKBQKWrFiBdna2tLZ\nTz4R1/lvv1W+58jDh2KfSZNE4kJZ9+7lJSoiIoji4grcLDo6mn799Vdq06YNVa5cmT755BM6fvy4\n1p+LbwcG0oQJE6hatWrk6OhIc+fOpRs3bhT+DKBQEI0fL97fnDklS0Bv2EA0dapq+5w7R1f696f3\nbGxo8c8/k0LP8gdEysemwQbW0aNHydramjZt2qTrohQrIyODDh06RB/27EmtmjbV6rkVCgVN//JL\n+vuvv/Ty4fB12dnZNHfuXLK3t6ebN2/qujj56GuFR9qKzeBgcbGdN0+0DhRi27ZtZG1tTQcOHNB8\nmUopJSWF/vrrLxrYuTP16dhRq+eWyWQ0ZcIEOnvyJGVp84a9hDIzM2ncuHHUpEkTevDgQf5fJiQQ\n/fZb3oPsX39ptWz6Gpv6VGcqFApaunQpVa1alS5cuKD+Ezx5olwr94MH4qYr57PyWgtjjvj4eNqy\nZQv19PKisZ6eqnW1LqW4rVtpWvv2xT8c5ti8WbyXBQuIlL0hfP6c6O7d0hX0pZSUFBowYAC1bt2a\nYmJi1HJMdeHYLJ5cLqfp06dT7dq16YaWpgJ4w7FjSm8aExNDv82YQR2bNaM5I0cWGsNqd+oU3Vuw\ngOa0aUPXFy8mhbLXhPXrifbt02zZiETy5rVrYFxcHHXo0IH69Omjd0PSODaLl5GRQZ988gk5OTlR\neHg40ezZIvEQEaH8QZKSRP2g6j1xaGhePfnFF0RKNG4/evSIfvnlF/Jo3JjWfvWVaucrjexs8h89\nmhZ+/DGFhYUpv9+MGeL9BQSU7LzJyUTffafaPgoF0a+/0sMJE6hZvXr0Sd++evd8rGxsGtwaXESE\nn3/+GWPGjMGBAwcwevRoXRepWKampnj//fex/ehR+F2/rtVzSyQS/LRiBXoMGWIQS5VKpVJ8//33\nWLRoEby8vHDkyBFdF4nlaNRITAL85ImYnC08PN+v5XI5pk6diu+//x5nz55F3759dVRQ5Zmbm2PI\nkCHYd/IkDpw+rdVzm5iY4Jc1a9Chc2eDWKrU2NgY69atw2effQZPT09cunQp75cVKwJOTmKyyDp1\nxAp4TG9kZGRg5MiR2LFjB/z9/dFWExNOV62q3MT9tWqJ1RpzJhq/davAzSpVqoSRI0fi6P79+M3F\nBdBWfBLBKiwMi7/4Ai1btlRuqdLUVDFZ5bhxyk8AXrnyG5OllpS5uTn27t2L9u3bo2XLlggNDVXL\ncZnmJSYmok+fPrhy5QquXr2Kpi9XcdS6zp2V3tTa2hrjmjXDGXd3fG9iIiYY1YbKlVH/0SPMb9wY\nzkZGkCi7SputLdC7t2bLBoj7o9eugVZWVjhx4gRsbGzQunVrPHz4UPPlYGrx5MkTdOjQAYmJibh0\n6ZJYuEYmAwYMUG0l0/LlAQsLoEsX1QpQu3be56lmTfE5LkaNGjUwZcoUXJ48GZ9r8z4sKAgty5TB\nzEWL0KBBA+X3q1QJsLMDXF1Ldt7y5YHhw1XbRyIBhgyBnUQC37ZtEf/oEbp06YK4uLiSlUGHDCqJ\nk5aWhhEjRmDv3r24cuUKPD09dV0klUl0tTqVgRk2bBgOHTqEsWPH4pdffoFITDKdMjMD6tcXrzMy\ngGvXRIUGID4+Hr169cKNGzdw9epVOOr5zO8F4dgsnkQiwZdffonNmzejX79+2LFjh/iFTAYcOAC0\nbg2MH5/3OWHalZEhEgqviIqKQrt27SCTyXDx4kXYq3LzqSmVKwPTpwOOjkBISNHbWlhA0rUr8M8/\nYoUPTdYF4eFixbjnz4EWKizakpYGfPIJYG2tsaIVRyqV4scff8T8+fPRsWNHHDt2TGdlYcq5e/cu\nWrZsiTp16uCff/7R7SptqjYkvHxAlEgkb6yqqDENG4rVogCgeXPl9+veHXh1FS8tMzExwYYNGzBq\n1Ci0atUK/v7+OisLU05AQADc3d3Ro0cP7N27N29p73r1AC8v1Q72MmkAVVeTKlsWqFZNvG7dWrV9\nzc0hSUtTbZ+SUiiAI0dEIub1VWCLU7Ei0K9f6VZuVvV+MysLOHgQyMpCeWNjeLdti1YtW8LDwwN3\n7twpeTl0wGCSOI8ePULbtm3BRF64AAAgAElEQVQhkUjg6+uLGjVq6LpITMM8PDzg7++PrVu3Yuyo\nUcjMzNR1kViTJuLmLStLtESYmuLOnTtwd3dH48aNcezYMVSuXFnXpWQa1qNHD5w9exZz587F7Nmz\noTh2TCQQ+vYVLU4GuCztW8HUFFi3DoiNBQD4+/ujZcuW6N+/P3bv3g1zc3MdF/AVZmbAxImi51Zi\nYtHbdukiWrmPHwf+/ltziZwLF4D160XrnkwGKFvntG4NODtrpkwq+vDDD3N7Ka9cuZIbQPTUsWPH\n0LZtW0ydOhWrVq3KW47YUOS08tvZAdq6Hy9bNu+8zZopv58OEzg5JBIJpkyZgg0bNqBPnz74888/\ndV0kVogdO3agZ8+eWLVqFebOnZu/N+bw4SVLOKjSKPCqOnUAExPVe6qUKycaFzQtNVX0xHv2DOjV\nS/X9XV1FY442GRsDY8YAAwcCEgmk6en4aeRIfPvtt2jfvj1OnDih3fKUgkEkcfz8/NCyZUsM7dgR\nO379FWXLltV1kZiW1KxZExd37sSzy5fRtV07JBZ3s880q3lz4IsvAE9PYPduHNm3D+3bt8fs2bOx\nfPlygxgWxNTD0dERV65cwflz5zB4xgzIOnfm5I2uSSSAlRXw00/YumQJ+vTpg3Xr1mHmzJn62dNM\nKgWGDi2+hTIjA3B3B86cAQ4dKj7pU1JZWUB8PJCSAly6JG6elaFqK6mG5Qx33LR+PcZ37cqJHH0h\nk4FiY7FkyRKMHj0aPj4++Oyzz3RdqpKxtBQ96rTVCyeHo6MYkmlpqd3zqkmvXr1w+vRpzJo5E9/P\nm6fr4rBXZGdnY9q0aZg3bx7Onj2Lfv36vblRhQolO3hJ69+6dQEXF9HooQpz8zd65apdWppoNCpp\nLxxAJLd0cW8ikQBduwJffin+VkFBGDVqFLy9vTFy5Ej89uOP2i9TCej9E9fRo0fx6bBh2DphAro7\nOQHffSeyfZ07q979kxmesDBU2LABPu3b43e5XL9akt9FOd29Bw3CH3v3Ys6OHTh06BA8PDx0Wy6m\nE9bW1jh95gy2//47TLp313VxGAA0aYKF27bhj4gInDt3Do0bN9Z1iYomkRQ/j0xCAuDvL7ptA0Bk\npBhLr245PW+srIBhw5TfTw8TZLVq1YLf6dM4sGKFfibw3jV37wLbtmFSQAAuP3+OK1euwM7OTtel\nKh0HB5Fc1aYmTbR7Pg1wcnLClTVrcN7Ahm68zejMGQxetAiJCgWuXr0KKysrXRdJqFMn775bWcHB\noqEjNRXYvx9o0wawsVF/2aKigPv3xWsrKyAsTAx5VIWu66ZGjYDZs4Ht24HsbLRt2xaX9u5F2MKF\nwI0betPDtjB6nwVp6eaGC/Pno0FysujqDAA+PqKVbPhw1T8wzHBcvw7s2AFUrw6j997DGB5Cpz/M\nzdFx5UpcMTND9erVdV0apkOmpqb4bPx4XReD5XByQv9VqzC+enVYGmhr9Rtq1QL+9z9g+XLRK+fh\nQ83cXGVmit5Bo0eLoRsGzqJaNXz888+6Lsa7LSMD8PbOvX8dXa8eFu/fj3I5c2wYst69S94zoaRq\n1ND+OTXApkMHDC7J8BOmfgoFJPv3Y0aFCnD5/HOU0acpAWxtlZrQOJ8yZfIWAvD1Bfr3V3+5ACA6\nOu+1qanaJurXOisrMcIgKwswMkKdtm1RZ8AAYNcu8Z70+F5A75M4VWxsUOV//xNdnBctyutG/ewZ\nsHKl6MY8YIDqE0Yx/UYkZmZfskT3mVpWoFq8AhFj+sfMDI3egtbqN9SqJbo+r1wpeuJoQlaW6OnL\n1zamLsbGwODB4gtA85yfvQ10MZG3RKKZXnjaxr3K9Ud0NJCVBff69UXjgD49c5SkLI6OItGZnCx6\n8mjq/URF5Z1v9Oi8SccNkbFx/uvyBx8A8+aJTiOqrn6lRXqfxMllZARMnSrGqOd88XCqt5dEImYt\nZ4wxxgBxQzp5MrBli0j0q/vm1M4O6NlTvcdk7zYjI/HFGNNPEREiMfj115oZdqRtRkaAhwdw8qRm\nGySio8VqXZ9//vY9j1tYiBXFtmwRQ0br1dN1iQpkOGkzCwsRXJUqiV43b9sHhjHGGGNFq1sXGDUK\nSE9X/7EHDDDs1kTGGGOqSUx8exI4OXImHK9TRzPHJxLJookTlV8AwNB4eIg5c7ZvF7109RDfrTDG\nGGPMcNStq5kh1HqwFDFjjDEt6tr17UrgAED16mJKitq1NXN8uRz47DO9ni+m1CQS4MMPgefPgWPH\nxM9kMt2W6TXcnYUxxhhjRcvOFr1UlBnClJ4uEiLveq8WIuDJE+WWXlUo+O/F1IM/S4wp722Zo+p1\ngwdrrmHi9Tlk3lZVqgB9+4pVvszNRfKqWzddlyoXX+UZY4wxVrw//gBu3xbJiaIYGQG//grcu6ed\ncumj9HRg/XqxCENxEhKAI0c0Xyb2brh8WTxsqCItTTNl0Seq/k2IxDLNjBkinqC/9BQKMSTN3BzY\nswcICdF1ifLhJA5jjDHGimZkBLi6itWhli0D7t8vfFsTEzGWfNkyYONGsbrkuyQ6Gli4ELhzByhu\npbB794AffgDeluXgme7dvAncuKH89leuqLa9oYqIAMLClN/+yJF379rFGMsjkQAPHgApKeL7f/8F\nMjJ0W6ZXcBLHUHBrAGOMMV1q2hRo0EAkcJYuFb1tkpML3rZ9e9F6FRgIzJ0LnDpVfA+et0FICLBo\nERATI5arLazLORHwzz/A8uWifm/eXLvlZG+vyEjgwgXltr1wQfSw09QEqPokIQE4cEC569DNmyKJ\nU7685svF2OvehbrSEEgkQOfOYgUuY2PRM+fOnbzf67i3Mc+JAyA+Ph5/7d6NLJkMMDICvQweeiWI\nXn3dzcsLjZyctFvIo0eBHj2AChW0dsrU2FjMnTEDkYmJMDExgbGxMUxMTAr8MiZCY2dnvN+nDyTq\nXvaVvbOehIdj/99/Q0FUaDzmvJYQof/AgahZs6bWy6ltz+PiMPurrxCXmAgTc/M34/G1WHVr0gSd\nevXSdbGZoZNIxDj7H3/Ef4mJOBYeDtqypfDYTEkBQkJgZGKCoVOnwvodqBseVayIOUFBSI2Nhcnj\nxzDx9S247jQygsnDh+jw4gXcO3Tgh0WmHsnJuBUdjXPXrwMyGeiVCcDfuLfNyICJnx8+ql0bFd62\niV0LcCc8HAv27YPc3x/GBdSbuXVnmTIwuXYNvUxM4MhxydRFJoP/li0IkMkAiaTAejP3Z//+i/IW\nFvho3jyYvgMT7l89dgxL58wB6tQp9n7WNDoagz79FLVcXbVXwGbNgGnTgNWrRUNNTqOLtzcwbhxQ\nubL2yvKKdzqJQ0TYs2cPpkyejLZWVrBxdASqVgWA3ETEqwkJiUSCzNBQ/Pjtt/hl3Tp89NFH2its\naiqwc6f4sGjhRjgkJASD+/ZFS2NjDP36a2SamyMzMxOZmZnIysrKfZ3zlerri9kbN2L9hg1Yt27d\nO/EgzTRHoVBgw4YNmDNtGno4OaHiy4v16/GY+xpAyunTWPjDD/hj+3b07NlT20XWGj8/PwwbNgwD\nbGzwQefOyGra9I14fDVOk//7D6MXLoRnt25YsWYNrK2tdf0WmAHLqloVy54/x9KTJ9FvwACUfdkS\nVVBsSkxMAHNzxJmbY0nbtti9ezc8PT21W+DYWEBLn/mjR49i9OjRGDd2LJpYWyOzYkVkZmcXWHdm\nZGYivmJF9D18GINMTbHw009RQYuNNOztk5aWhu/nz8cfx49jYP/+KBMVBaCQ2Hz570OJBKv+/hv7\nxo2Do6Oj9gutJdu2bcPXX3+N6dOno2bNmoXey+bEZqydHTrs3InPv/8es2fPhpmZma7fAjNg8fHx\nmD59Ov4+cgR9+/eH9OXE44XGplSK25cvY2O7dtizZw/s7e11Um5NIyIsX74cP/30E77/7DNYNWtW\n5P1sWloaHkdEoEWXLpj17beYPHkyypTRUirD3h6YMUM8ixOJZ/GMDLEE+eTJWnk2fwO9bOFW5svV\n1ZXeFhEREdSzZ09ydHQkv3PniObMIbpwofgdw8Lo5hdfkIODA40dO5bS09M1X1giStiyhb5q04bo\nxQuNnkehUNDGjRupSpUqtHXGDKKrV5XZiWj6dJL99x8tWLCArKysaMWKFSSXyzVaVm0DEEgqxIs2\nv96m2AwJCSFPT0/ycHGhm4MGEQUGFr9TRATR2LHku38/1ahRg2bPnq2dz59cThH37tH8WbPE9wqF\nxk6VnZ1NixYtIhsbGzp8+DDR+vVEK1YUv2NGBqVMnEhf9+1LNjY2tHXrVlJosJy6oK+xaTBxmZ1N\ndP26+LcI/v7+5OTkRN27dKHw//5T7tgyGRERHT58mGxsbGj58uVa+/xdv3qVfv30U42fJzMzk6ZO\nnUp2dnZ08eJFlfZ9/vw5ffrpp2RnZ0eHDh0qfMOICCIt3W+oE8emdpw4cYLq1KlDQ4cOpSdPnqi0\n75YtW6hKlSq0fft2DZXuTefOnaNty5Zp/DwpKSk0atQoatiwId28eVOlfR8/fkz9+/enBg0a0Pnz\n5zVUQt3h2NQ8hUJBf/31F1WrVo3Gjx9PCQkJKu27dOlSsrW1pb///luDpXxFZiYdWLCADh08qPFT\nPX/+nN5//31yd3enBw8eqLTvvXv3qGPHjuTq6krXrl3TTAELk5ZGlJkpXs+bRzR2LJGK9X5xlI3N\ndy6wsrKy6JdffiErKyv68ccfSfbyBpPCw5VL4hARnT1LiYmJNGTIEGrevDn9p+zNbClEP3xItra2\nGj1HUlISDR8+nJo0aUK3b98mSkxUbsfUVKI7d3K/DQsLo3bt2pG7u7vKlaY+09cKj96S2ExPT6c5\nc+ZQlSpVaM2aNZS9aZNIrhbzYElERKdOEX35JZFCQc+ePaPOnTtTp06d6OnTp5ot9IULFDBxIrlY\nWxP9+qu4uGtATEwMde/enVq3bk0PHz4UPzx1imjyZOX+PocPE02eTAG+vtSsWTPq0qWLVq5b2qKv\nsWlQcXniBNGsWaIezMrK96vExESaOHEiVa1alXbt2lXiJEx4eDi5urrSwIEDKSEujkjDn8GDBw9S\n7969NXPwFy+IoqIoIiKCWrZsSb1796a4uLgSH+7MmTNUr149Gjx4cP6HcIWC6OxZosWLS19mHeDY\n1Kxnz57RiBEjqFatWqV60Lt58yY5ODjQuHHjtNI4ufbXX2lc69YavQaEhIRQ48aN6eOPP6bk5OQS\nH8fb25uqV69OY8eOpfj4eDWWULc4NtWogORMREQE9erVS3QW8PMr8aF9fX210ziZnU106hR9278/\nfT93rubOQ0R+fn5Us2ZN+vrrr/Oew1WkUCho8+bNZG1tTd988w2lpqaquZRKyEniTJ6s1k4Wysbm\nOzWx8bVr1+Dh4YHDhw/j8uXLmDVrFkxMTMQva9cGWrRQ7kDt28PCwgK7d+/GJ598Ag8PDxw8eFBz\nBQdARkYanWvm+vXraNGiBczNzXH16lU0atQIsLBQbudy5YCGDXO/bdCgAc6ePYvPPvsMXl5emD17\nNjL0aDZvVowXL7R+yvPnz8PZ2RmhoaG4/vvvmODlBWlgINCtGyAt4jJFeeOHUacOIJHAxsYGx48f\nR5s2beDq6ooLyk7wWBIODqDHjyHJKUfZsmo/xYULF+Di4oJmzZrh7NmzsLOzE7+oW1d05YyOLv4g\nHTsCRGiRlISrV6+is5cX3N3dsWTJEshVXXaVvZ06dQLKlAF27ABmzwZOnwYyM3Hw4EE4OjoiPT0d\noaGhGDZsWInrotq1a8PPzw+2trZo4eGBG7//LiYa1dBnkIg0U28+egQsXowDvr5wd3fH4MGDcejQ\nIVhZWZX4kB07dsTNmzdRr149NG3aFJs2bQKlpwObNwN//skTHzPhZf1MRNiyZQucnJxQtWpVhISE\noEePHiU+rJOTEwICAvDixQu0bt0a4eHh6ipxgcjICBInJ+C999R/bCJs3rwZHTp0wLRp07B161aU\nL8XcNgMGDEBISAgkEgkcHR3h7e0NIp54lr3iyhXg7FmACNnZ2VixYgVcXV3RqlUrBAcHl2oYcZs2\nbRAUFAR/f3907doVz549U2PBXyGVAl5eoMaNIdHQECWFQoHFixdjwIABWL16NZYuXZr3HK4iiUSC\nTz/9FLdu3UJkZCSaNm2K06dPi1/K5UBwsBpLXgxjY+DwYe1PSK1MpocMOTtKohXx66+/JhsbG/rj\njz/U3pX78uXLVLNmTZo2bRpl5nSxUrPHjx9TtWrV1H5chUJBa9eupSpVqtDOnTvVfvzo6GgaNGgQ\n1a9bl86ePav242sT9LTVgtQVmwqF6LFx+XLpj6WkmJgYGj16NNWoUYN8fHzED3fuJPr8c6KJE4lu\n3Ch6CEFcHNGxY0TTphEdPUr0WivFsWPHyNbWln7++WfNDOFQKOjKyJHUwtpa7d0p5XI5LViwgKpW\nrUrHjh0raAOiL74gOnNGDIUpjrc30ZQpYojknTv077//kpeXFzVv3pyClBmypsf0NTYNrs68c0e0\nKo0dS5FffUUDuncnBwcHce1OSVGu1xcRkRKt1Tt37qQqlpa0uV07oh9+IIqOLl3ZC+Dj40N9+vRR\n70Fv3SLZhAn0ZevWZG9vT5eLu15GRSnX2yAtTVzPiOj69evk5uZG7Zs3p3tDhxKNG6fU31QfvXWx\nWYreVmrh40N3fHyoU6dO5OrqSkFBQWo9vEKhoJUrV5K1tTUd1OCQitWrV9P48ePVftzk5GQaMWIE\nOTo6UmhoqNqPf+HCBWrYsCH17dOHHu/eTXTgwBv3HYbirYtNXYqOJsWYMRTw3Xfk6upKHTt2pLt3\n7765nbK9NR4/fuNHcrmc5syZQ9WrV88b3vfoUSkKXbBZs2bRggUL1HOwhITcUR0xMTHUo0cP8vT0\npMjIyDe3LeU9+uHDh8nOzo5GfvABPf/6a6JS9H5S2u+/E61ZQ7RokVoPq2xsvhM9cSpWrIhly5Zh\n/RdfYOTIkWpvmfPw8EBQUBBu3rwJLy8vRCvTMq4iIsqdCEtd7t+/j549e2L9+vXw8/PD8OHD1Xp8\nAKhcuTL6dO2KxKdPMWvsWLUfn6kJkZhl/fBhoF49rZwyKywMNjY22Lx5M3bt2oV+/fqJXzx7Jpbx\ny8wEbt0CipqZv2xZwMcHSEwEzp8HfH3z/bp79+4ICAiAj48P+vXrh4SEBPW+CYkEVLs2pBKJWH5Z\nTYKDg9GuXTucPHkSgYGB6N69e/4NsrLE5GpmZsC+fUBAQNEHjI8HLC3FBOmbNgFPnsDW1hYDBw7E\n/fv3sfDTT4GgILWVnxmohg2BFi3wzNgY9r/8gv3Hj2Pv3r3o0KGD+P2yZUBYWPHHuXpVfD5TUgrd\nZPjw4Th/4QKWhobi023bkPbHH4UvV15Caq83ExJwbsUKtNi9G5EKRW7v3kLduQMsWQLY2hZ93Lg4\nsWT7y558NWvWxMCBA+F36xZWZ2UBrVsDlSqp732wkgsPB7ZsAdLTdXL6KxkZaNS/P/z9/bF37164\nuLio9fgSiQSTJ0/GoUOHMGnSJEyfPh3y58/Veg5AM/e0Bw8cgLOzM8zMzHD16lU0btxYrccHgPr1\n66N///44dPgwdvz9t+jBaGSk9vMwA1OtGjalpcHtu++QlJSEP//8Ew4ODvm3yc4W99nK2LtX3Oe9\nwsjICPPnz8fGjRsxZMgQLJkxA3T+vJreQB61xmaFCsCaNdgyeTKaNWuGpk2b4ty5cwUvfhMVBURE\nKHfcJ0/e+FGTJk3Qq1cvbNuzB4ezsoCWLUtXdmWMHAl4eIhyJyVp/nyvefuTOKdO4d8xY/CLhwem\nrV2LZs2aYf369Ugp4uZSVZmZmbh9+zbc3Nxw9epVfDJggNq7VCkUCrUnn9q3aYPjx49DJpNh1apV\n2LNnj9oSUFFRUZgzZw7s7e2xY/NmbG7TBr6dOwOhoWo5PlMjhUI8cJ08KR4USjEkQClZWUBqKoy3\nbMHtmTOxYP58DB8+HJ6enti5cydkjx+L7Zo3B4YNK3rG91dXjDAzA9q2zffrtLQ0hIWFwdPTE0eO\nHMFXQ4eq/e0oatWCxMxMVFZq4urqikuXLiE1NRXLly/HgQMHEBsbm7eBsTFQrZp46JXLAXPzog9Y\nsaJ4oATwb2IipixZAnt7e5w8eRKHDh3C3q++EjPsa+BmnRmYQYNgO3YsgoOCMGPGDHTu3BldunTB\ngZMnIa9fH1i+HFi7FoiJKfwY7dqJpOCcOcCZM+Lm9TVJSUkIj4hAa1dX/HHvHhY+fqzWGAI0UG9W\nqoSOmzfj1osXiJPLsWTJEvz9998FJ4d9fYFffxVxWlR8/vcf8NNPQLlyuPXffxg7dizq1KmDkJAQ\n+Pn5YeW+feI6yPRD8+aicWH+fODuXa2fvuXQofDz88Po0aPh6uqKfv364eTJk1AoFGo7x4sXLxAV\nFQUPDw8sXrwYGz/+GFi5UgxbVhN1xyY9f45+/fsjPDwcERERWLx4MU6dOqWWe30igr+/P0aMGIHG\njRsjPj4et27dwvStW9V+zSpWRobak91vpYcPgYMHgWPHxNBgX1/RuHD9usYSsJ9t3YqTJ0+ibdu2\naNCgAUaMGIFLly6Bcp4HIyLEEJ+0tKIPJJcD9+4V2Dj39OlTxMfHo3nz5vjm559x6OlTtb8Ptcam\nVIrM3r3xyapViI6Oxs2bN7FkyRL4+vq+Oc3Gw4eiMbY4MplovIWIzdOnT6Nfv365U4L8+++/GLly\npXaSq1Ip0LixONetW5o/32ve/iXGW7ZE3bNnMaVpU3w5aBBOOTlh7dq1mDlzJj788ENMmDABDV+Z\nz0UZCoUC169fx+nTp3H69GlcunQJDg4O8PLywqHff0eb+Hi1LzVGpP6x/VH37iHr+nUEm5ri4sWL\n2LVrFyZMmAALCwu0adMGbdq0Qdu2bdGwYUOlzk1EuHjxIlatWoVTp05hxIgROHfuHBpeuSIuXOPH\n55s7h+mJ4OC8i0+9eppdJi8hATh+XCQLiNBo5kx8W6ECZsycicOHD2Pt6tX46vJljG7VCuOmTYN9\nca0BUqlI3mRkAIMHQ06EgMuXc2MzICAAzs7O8PLywpmffoKHBsYSU82akJRivH1BFN9/j4x69RBQ\nowZ8fX2xfv16jBo1ClWrVs2NyzYeHqhTpQokcXFiXqqijgfgn5o1sWrVKlyNiMBoT08EBwfnLVsp\nk4mHyc2bgalTxZwfb+mSlgYlIwN4+hSoVUt757S0BCwt0RxAcxcXfPfdd9i7dy8WL16MSQ8fYlzN\nmhiTlgbbkBBg4EDAy+vNY5iZAV27ihutv/4CAgMhGz0al2/dyo3Nmzdvwt3dHV6tW+PynDloUVSP\nlhLSRL2pUCiQkpyMK1euwPfiRSxbtgwffPABateuLeKyTRu0qVABdidOiAR5kyaFHywyEvI1a3Dw\n1i2sOnkS93/5BZ9//jnCwsJg+2rvHW0tocqKV6aM6Bl14oTo1fjFF9qNTwCenp7w9PTEwoULsXPn\nTnz99dfIyMjAhAkTMHLkSFhaWqp0vLS0NPj6+ubG5v3799G6dWt4eXlhxvTpcDYzA2xsgMqV1fYe\n1B2bEjMzKLKzkZCYiEuXLuHixYv4/vvvce3aNTRq1Cj3nrZNmzb5Y6sIstRU/OXtjVWrVuHFixeY\nOHEi1qxZg0q67BV3+bL4v3iLl4NXC6kU8PMTvbRzWFsDQ4dqZO5CAJAYGaFz587o3LkzlixZgi1b\ntmDkyJEwNzfHhAkTMMLKCubZ2SKRVNQcOU+eiIaP8+eR6OiI8+fP58ZmVFQU2rdvj549e2LZsmVi\n/lI1U3dsmjg5QaFQIDY2Fn5+frh48SKmTp2K0NBQODs758Zl65gYVL52DRg0qOiGj/v3kRIcjO0r\nVmD1xo2QSqWYNGkSdu7cCfPiGjQ1wcwMcHAAbtwQdYMWSUiFHiMtWrSgwMDAEp+sr4cHPp80CT1G\njCjxMUokJka0dJmZAQsXAgAiIyOxYcMGbNq0CU2aNIFb06ZQEEFRpgwUCkXBX9nZiHv2DOcvXoS1\njQ28vLzg5eWFDh06oHJO5SaTiQdUNU/WFhERgfbt2yMyMlKtx4Vcnu8GUaFQ4O7du7h48aL48vVF\nwosXqGRpCZJIcsfhKRSK/GPzFApkpqfDytoaEydPxsiRI2FhYSF6JC1eDHz4IVC9unrLXpzYWFCV\nKmq7GEkkkiAiUnL2a+0qbWy6N26MjQMGwLlzZyBn6IQm/PNPXnfSyZPfvBGJjMTdZcuwLjsb23fv\nRms3NzRyds4Xhzmfv9wvf38oypTBE2tr+Pr5wd7ePjc227Vrhwo5LWU+PiJZNXeuWt+Sn58fpk2d\nikuXL6vvoN9/L4Zn9e+f+6Ps7GyEhITkxqavry8yk5NRQaEAlS8PMjMrMDYVCgVkaWmwr1oVk/73\nPwyLjkbZzEzg55/zn/PRI3GdrFdPtCpMnqy+91MAksshkUqLnrhaSfoam6WNSwCobm2N67dvw9ra\nWk2lKrng4GCsmzULe8+eRbfatVHr/fehAAquL7OyQP7+UGRlIaJSJVwOCUGjRo1yY7N169Yoq6Gb\n6Rx79+7FX3/9hX379mn0PFlZWbh+/Xq+2Cwjl6MsIGLTyOjNOvNlbKYnJ6OJlRUmzZyJAaNHw9jY\nWKNlLRARcOkScPMmIJWCKleGZOBAjs3CxMYi89dfUXnpUqToaFjVq4gIfn5+WLt2LY4dPYo+HTqg\nasOGhd/LvvJ1PygIgffuoXnz5rmx2bJlyxJPNqqsFStW4MGDB1i5cqVGz5ORkYGgoCD4+vri4sWL\n8Lt4EeUVCphYWxd8L/vKz1KTkuD53nuY9L//ocf48Wof/qUyItCcOZC0bw906VLqw72VsQnxfNel\nZUvcGzBAJEOMjYEePUTDgpavrwqFAidPnsTatWtx8cwZDLS3h2XDhlDUrl14XMbEIPvhQ4S+eIGQ\nmBi0bNkyNzZdXFxQRsZlxPcAACAASURBVMNJ/W+++QZWVlaYPn26Rs+TmpqKK1eu5Nab/n5+qGxu\nDqNy5UBSaeGxKZcjNSUFXTp1wqSpU9G+fXuNLvyjlLNnQd7ekCxfrpbPmLKxqb3mnaQkGD17hrSi\numBrio0NMHEisGJFbtLC3t4eP/74I+b+73/w+eYbRPj7Q9q2LaQ2NpBKpQV/PXiAChER+PW331Bj\nyJCCz2VqqrHZ9jXyIX3tYiCVStGoUSM0atQIY8aMARQKPBs7Fsl9+kDi6AipVAqJRAKJRJLvtSQp\nCdKFC2HzySeQduqUd0CZDBg3Tjfj+XftwoIzZ0ANG2Le6w+sLB+phQXSevYEnJ01dxIi0YqU4+BB\noGbN/N2R5XI0mD8fKypVwo/jx2PvxIl4CkBqbV14XEZFQWpqispmZth87x5sbGzePHdYmOjCWq6c\n6A1kYaGWBxTxtkgkI9QhI0N0oyUSNx+hobmJLiMjIzg7O8PZ2RlffPEFiAjREyciPSYG0iFDIGnR\nosDYlEqlMDp0CNb370Py2WdiXpwlS8S5Xh2OFhcn5u4ICxN/m+RkjXYVH9+lC1q4uOCzZcs0do63\ngdTMDOl68KAIAC4uLth44AAWb9+OPQkJiM/OLjwupVJIy5aFtHp19HJwwJ4OHbTegq2xevM1xsbG\ncHNzg5ubG6ZMmQIiwqNHj5AZEwOppSUkL+MxX5358ntjY2NUMTMTLY+6uhGVSETLsJERsHs3+p46\nhXHlyqFXr166KY++s7aG8ZQpSF+4EFlZWbpJvL1CIpHktmY/e/IEe7ZsQerLz1dxXwM6dULb3r1R\nXsvDgrQVm2ZmZmjdujVav2whV2RnIzIwEIqXDXyF1ZkSiQSm6emobG2t/SFThcnIgPu+fdjQogV4\nrbrCmZqaIlGhAAYPFsPIhwwBqlTRSVmkUim6deuGbt26ITIiAvt9fJCZmVl0XEokkEilGF6vHjw9\nPWH26n2aFmgrNs3NzdGpUyd0evnMKJfLczsqFPic+cr35cqVQ8WKFTVeRqU5O6PGwIEI+uorVK1T\nR2un1U4Shwh4+BDlypRBmi66OgFi+eFPPxW9ZF7pSmlqbY2hXl6AiYnoLVLU5KSpqcC8eWKMu5Zp\nK6jeIJXCtmVL2PbuXfRDr6Ul0KDBm704zMzyPyhqS3Q0cPs2kiIiYFu2rJiHRcc3WvqsXLlySEtP\nL35uldJ4/DhvOeyOHUWXyddbFOrWzX1pfvYsRnXtCsycWfTDjYcHsGeP6MZYUAIHEEs/3rolzrdh\nA/DNN6V8M3nUGptmZmJCu5gY0aXW2LjQbtMSiQTVnZyA2FgRe7VrF37cDz4Qy0ZfuCCGv3z5pZh0\n9tXYrFkzL0YUCjGniQZ7ZSXZ2KAcL5tcrHLlyiGtuDH02mRmBstPPsE4ZVoDiXSXmIDu6k2JRCIm\nbixo8kZ9JZGIa2mDBkj099dNt3QDIqlcGeXKlUN6errOkzivsq1WDZNmztR1MYqlq9iUGhmhtjYm\nPNWEsmWRkJ2N8jyUqki597OuruJeU0/Y16qFKVOm6LoYxdJVbJYpUwZ1X3kGMCiVKyM+IwMVlByq\nqS7a6Rt44AAQECAqvLQ0seqMLjRrVvBD3gcfADVqFD/Zlbm5yOzqYHy6zpI4gHjYLq6ngYkJMHy4\n2no3lNrp0wCApHLlYOHmxgmcYpQtW1bzrf3+/qInzOefi3HJRcVRRAQQEgL07l38Q+B774nVl4qa\nb6luXdGzRSYD3N1LVPzCqD02X51Do1mzoretVUvMiF/cuOhKlcSkz8ePi4Smjc2bLVNWVsC0aXk3\nPVevqlx0VSSlpqKCPrWk6KmcB0W9omwdqOMuzjqtNw2VpSWSZDIxHJoVSS9j00BwbJZMUlJS3hBx\nVqCc+1niv5Pq7t8HaWAhnbedXC6HTCZDuWLmp1Q37Txxp6cD/v4oq1Ag7eRJ3T5QF/TBNDYGxo4V\nrc/FcXcXExhpmU4rPGUmyStTRvR20gfJySIBMGoUkmvVQgUd9JwyNBpv7VcoxHCdb78Vq3sUJmeO\nrqNHATs7wMmp+GPfvStiuKieKDnLphsZAW5uypdbCRpL4lSqVHxLvr29SOIoswJH9+5iSNmFC+L7\nguZDK1NGJNjGjBHLPb54oVrZVZCcnMwPikooW7asfvXEMSD8oFgyycnJ/KCoBI7NkuPYLBmuN4tn\nbGwMiUSCrNeW6GZKePAAFBYGjkzV5NSZ2r6maadLycvW1nJlyiC9fn2dt84VyNZWuaWVJRLNL8Fc\nAJLJIJHJtH5egxQfL4aOWFgg6YcfuMJTgsZbFLOzRaK0uCX/zpwR14ubN8VqZsVdK27cEHO41K9f\ndO+AnKFCTZqod8jYy8nW1HrhdnAQZW3WrPj3n7OCVGQkUL580QnyV3vj5EwyXljvpRYtRO/E6Gi1\nrkjyKm5RVA639pccZWdDEhur62IYnKSkJK43lcCxWXJEBElSkuiZr+FJlN8WcrkcmZmZGp8Q/m2Q\nE5uanqD7rePgAHr0CJJHj3RdEoOiqzpTOz1xcpI4NjZIU/NSvGqlx8t4UkoKJJxVVk7NmmLiWnCL\norI03qJobFx8AgcQy1xv3CiGXSkUYuhPUTZvFgkfuRw4f77w7cqUEUOPWrVSqdjFOn4c9PffkDx+\nDPj6ismCS8vYWMxxU9x8MZmZoreMlZV473/9VfT2qakiaZOUBKxcCTx8WPT2VasWvTxyKXGLonL0\nbk4cA0ISCSQqLrfMuN5UFsdmyeUmcZTpRcoA6K613xBxbJaQnR2oWTNIchoImVJ0VWdqNYlT1sVF\nTDbFVEaVKkHC80f8n73zDmvyXP/4N+wZNsgICIpMRRFwIcutWOtqrVp72tN6tL/T1i491g5rW9tq\na0+X3a2e6vG42qqouADFhVYKKhgQlD3CDDMh4/n98QqKrCQkeRN4PteViwjJ+3wT8839vvdzP/ej\nNHRGUTF0JuC1Nz5uaWEa9va19NLIiEng3L7dqSlyt4wZ02OTYJWxtATJzgantRW4elV9TbzHjWOq\ni3rDxAQ4fpxp1p6drVjfquRk5r5cfv+9ZglaiaMYdMmG6hBCwDE1ZVuGXiGRSCCRSOhsvwJQb6oO\nIYS5UNRQpedAhMZMxaHeVBFDQxB7e/XtuDpIGPiVOKNGwYLHo6Wn/YBm35WHzigqhlYaG/eFVApU\nVjL3Z8xgbn3RXiobF8cs/+mN6Gj1V9vdm63gcDjMUiV1ER6uWOXS/Pn37/cVQIyNgeefv9+7qrRU\ndX1qgFbiKAZdstE/aNxUDjrbrzjUm/2DfsaUg8ZMxaHe7B/Um8oxsCtxbG2BefNoZrQfkO6akFL6\nhFbiKIZOVOIIBEyFyOTJnZMTvWFiwiSJH3mk78dqYrmkuzuIoSGTcFHndtmKBlAe7/5uW4p8zk1N\ngRdeYBJe5eWKNXPXAO2z/WbqqlwawNC4qTo0bioPjZmKQ72pOtSbykMrcRSHelN1qDeVZ2BX4nC5\ngIeHblwo6im0k79q0EocxdAJb5aVMQ11ly5VPIlhbAwsXqy+ZUzKYmQEODoySx3Z6qk1bx6TRFL0\nc25hAbz0ElPGzlLT1/YZRfqd1jc64U09hcZN5aExU3GoN/sH9aZy0EocxaHeVB0aN5VnYFfi3IOW\nt/UPairlaGtrg0wmo7P9CqATy6lsbYGnn+67t8uDhIQwiR8WIS4ujHa2cHQEoqIUq8Rph8sFXn4Z\nYOn/nM4oKg6Nm/2Dxk3loJU4ikO9qTp0tl95aNxUHOrN/kHjpnIM7Eqce9DyNtWhAU956Np+xdGJ\nWYvhw5WvZpk9W/GqHU0xZgz7zVPnzGF2qVIGOztmxy4WoDOKikPjpurQuKk8tBJHcag3+wc9N1MO\nGjcVh3pTRWpradxUAVqJQ+kVWt6mPHRGUXF0ohJHFdhawvQAhM0qnHasrdmtBlISOqOoODRuqg6N\nm8pD46biUG+qDr1QVB4aNxWHelNFTpwAaWmhcVNRJBIAg6QSRydm+/UYairloLMWikO9qTr0QlF5\nqDcVh3qzf1BvKgf1puJQb6oOjZvKQ72pONSbKtLUBOTnU28qytGjACGseVOr09i0vE116KyF8tBZ\nC8WhAa9/0ICnHNSbikPjpurQuKk81JuKY25ujiqWmsMPBGjcVI6GhgY4OTmxLUMvoHFTRUQikOpq\nZvdSSt/w+YCDA2txky6n0hPorIXy0FkLxemynIpe/CgMvVBUHupNxaFxU3Vo3FQe6k3Fod5UnY64\nWVfHrhA9gnpTcag3VUQsZuLmtWuAVMq2Gv3gwAE01tbS5VSU3qEno8pBZxQVp8ObhACnT7O2a5G+\nQr2pHNSbikPjZj8Qi8ERidhWoVdQbypASwsgk1Fv9geRCJw//wQyM9lWojdQbyoO9aaKiMUAhwOO\niQlw4wbbavQDkQgNeXmwtrLS+tBa352KZkZVJDsbEAqBtja2legNdNZCcTq8+csvzEmVhQXbkvSD\n06eBy5eBykqgsJDOXChII22eqjA0bvaD0lKgrIxtFXoFjZsKYGgIfPklzGUy6k1VMTFhmvFHR7Ot\nRG9obGwE19QUaGhgW4rOY25ujlaaxFGe2bOB4GAgKgoYM4ZtNXpDY309uAUFWh9Xqz1xzMzMIBKJ\nIJfLYWCg1fyR/tPYCMhkTOCjKASdtVAcC4kELQIBkJYGPPYY23L0B5GIec9qaoADB4BXX2VbkV7Q\ncOMGXMaNY1uGXmBhYYEWoZBtGfqJjw9N4iiKQADY2NC4qQimpgCXC4s//kBLYyPbavQTAwPA1xeg\nVawK01BfD+uTJ4H4eLal6DwWFhZoycsD1q0DuNz7N2trwN0dCA9nPoOUzowZA9jb04lcZTAzQwMh\nsDYxYVYzaPE7TaufYAO5vCORQ1GS9i8fSt/I5YBUSmcUlcBCLkdLe5XX6NHsitEnhgy5f3/6dPZ0\n6BmNOTng0qolhbAwMGASrBSKJjE1BTZtYrxpbs62Gt1n3DhYyGRoqaigFdIUzdPWhsa7d8ElBDAz\nY1uNzmNhYYGWqiqgvh4oKgJu3gT+/JOpohs1iiZwKOphzhwgPh6Nra3gxsRoPSmt3U/xpUuwMzRE\nDe16rTy+voCxMdsq9IM7d4CTJ1F39ixs2NaiJ1j7+6NFLkfbk08CDg5sy9Ef2pM4pqZMCSpFIers\n7WHj7My2DL3AbsgQ1MjlbMugDHRsbIDHHkNdczNsHB3ZVqP7BATAbvFi1JiZ0QppiuYxMUEdAJuY\nGLaV6AV2dnaokUgAR0cmYRMVBbz/PjBvHkCT1BR1MXIkyOjRqJdKYTNge+KUlADXrwMnT4Ln7Izi\nykqtDDugMDKiu+AoSkYGcOgQigsKwLO3ZypzKL1iZGQEFxcXlHl4sC1Fv3BxATgcEHt7WhauBMVt\nbeD5+7MtQy9wGTIEdXV1EIvFbEvRS2jcVIKQEBS3toLH47GtRPcxMABv1iwUl5SwrURvod5UHJlM\nhrKqKrgvXMi2FL2Ax+OhWCIBRo4ENm4Eli1jEtWU3rm3UoZ6U3EEcjm4NjYwY+HzpZ0kTmUl8M03\ngEAAnocHim/d0sqwAwm6+42CEMIkcQAUNTXBs6wMkEhYFqUfeHp6ori4mG0Z+oWxMTiurvTkQEmK\niorg6enJtgy9wNDQEK6urigtLWVbit5B46ZySKVSVFRUwN3dnW0peoGTkxMaGxtpc2MVoN5UjsrK\nStjZ2cGMLqVSCB6Px5zPLlnCTLZRFOPCBXDohJFSsHk+q70kzr1qCE+pFEW0EoeiKcrLgaoqAEBh\nWxu81qxhlrpQ+oTH46GoqIhtGfoHC+tg9Zm2tjZUV1fDzc2NbSl6g6enJ/UmReOUlpbCxcUFxnTp\ntkIYGBjAw8ODTn5QNE5hYSG8vLzYlqE3ODo6oqWlBc3NzWxL0S/KypjVMxSFYdOb2knitDdldHAA\nb+5cFFdUaGVYyiAkIwMwMUHrU0+hQSSCM83AK0zHzAVFOWhvF6UoKSmBq6srDA0N2ZaiN1BvUrQB\nrZBTHupNijag3lQODodDvakKAgEzGU6rcRRm4FfiCASApSXw0kvg+fpSU1E0R3ExsHYtih0d4eHh\nQbeyVwIa8CjaoKioiM4oKgn1JkUb0AtF5aHepGgDGjeVh3pTBQQCZuVMbi7bSvSGgZ/Eqa8H/vlP\nwMWFLtnoB7TRVB/I5cDSpQCPR0tPVYD2xFEd6k3FKSwspBeKSkLjpupQbyoOjZvKQy8UVYcQwvQx\npJtP9AmNm8pD46aSiMXM9ToAkpPT0eSY0jsDezmVWAw8/jjg4wOAXiiqCm0CpwAGBoC1NQA6o6gK\nNOCpBvWmclBvKg+Nm6pBvakc1JvKQ/tVqQaHwwFkMmDvXuYnpVeoN5WHxk0lqaoCLCzAAQB7e2ZX\naUqfDOxKHFNTICSk45/Ozs5oaGiAiGb4KBqEzlooD51RpGgDOtuvPNSbFG1A46byUG+qSGsrkJYG\nNDQAtJF2n9C4qTzUm0pibg5s2sRMiHt6AhERbCvSC9iMm1pvGGJgYAA3NzeU0O7XiiOV0nJTJaHr\nh5XHyckJLS0taGlpYVuK/tDYCLS1MSXhFIWgM4rKQ09G+0FjI/WngtC4qTzUmyoiEABCIRAayrYS\nvYDGTeWh3lQSBwdmNYO5OZNkpfRJc3Mzmpub4czSBiesdH2lyzaUpKYGzr/+ipfc3YGjR9lWoxfQ\ngKc8HA6HbpeqLGfOYPjJk3ja2ho4fZpeLCoAvVBUHnt7e4jFYjQ2NrItRa8IDQ3FwvHjAbojZp8Q\nQmjcVAF6oagaMU89hWlvvAEEB7MtRedpaGiARCKBvb0921L0CnqtqRqPbNyIcX//O9sy9ILi4mLw\neDzWlm4bsTEoXaeoJE5OcGppwUpvb8DWlm01egEtC1eN9hNSPz8/tqXoBzY28BaJ4O3sDEgkAO3B\n0SvtF4o8Ho9tKXoFh8PpiJuBgYFsy9EbRo0ahVGjRrEtQy+oq6uDgYEBbGxs2JaiV7S/X0KhkL53\nSjBx4kS2JegN7clV2uNLOdrPZwkh9L1TghmzZ7MtQW9g+1qTtUocmsRRAgMDwMMDsLAAwsPZVqPz\nyOVylJSU0AtFFaAzF0rSnlS1sgLi4tjVogdUV1fD3NwcVlZWbEvRO2jcpGgSWiGnGhwOh8ZNikah\nFXKqweVyYWxsjLq6OralUAYobMdNupxKX/DwACIjARMTtpXoPBUVFbC1tYW5uTnbUvQOWiWnJHZ2\nzM9Zs5gm7pReoc0ZVYfGTYomYXtGUZ+hCVaKJqFxU3Vo3KRoErbjJq3E0Rc8PYHoaLZV6AVsZ0b1\nGepNJbG1BWxsqDcVhM4oqg71JkWT0LipOtSbFE1C46bqUG9SNAnbcZOVJA6d7VeBceMAR0e2VegF\nNOCpDg14SsLlAvHxdItUBWE74OkzNG5SNAmNm6pDvUnRJDRuqs6g8uaVK2wrGHSwHTdpJY6+QJdq\nKAzb5W36jKenZ9fSU7rjUs8YGDDLHCk909gIZGcDoN7sDzRuUjQJ9abq0CUbFE1SWFgITw8PtmXo\nJYMqbp44AfD5bKsYHFRXA2A/brKSxLG1tYVMJoNQKGRjeMoAh85aqM6D3fwhEgEnTwLXrrEtS7cx\nYOVrVH+wtgYOHABOnWJ91kKfoReKFI0gkQD3do2jcVM1eC4ug+dCkaJ1ioqK4FlQwLYMvWRQxU0u\nF/j+e6Cqqvu/i0SATKZ5HYPhs5qQANmtWygrK2N1Ex1Wrj7au/nToEfRBGxnRvUZLpcLQ0ND1O/e\nDaxfD6SlAaGhbMui6DshIcCBAyi8dg1e7u5sq9FLeDweSkpKQORytqVQBhJtbcCuXTRu9gOeRILi\n/Hy2ZVAGIJK2NlSUlcHdyIhtKXoJj8dD8Z07QHMz21I0j50d8zq//ppJ2DxMXR0zMdtfeqv2aWxk\nJu0GOu7uKP/4Y9hzuTBlcaWM9pM497KANIlD0Qj3ZhTpyaiKSCTgWVmh+I8/gJYWYNkyWmlC6T+j\nRwMAiqqq4GloyLIY/cTKygqmRkaoGQyzXBTtYWkJcWUlaqqq4OriwrYavcTDxQUlZWWQNzWxLYUy\nwChLSYGLmRmMnZ3ZlqKX8Hg8FBcVAa+8AqxdC3zxBXDwIHD58sBL7NjaMj/Ly4EffgAenvCRy4GE\nBKCysudj9DVJ1NwM/P57z38/dAiorVVMrz7j44Oiujp4mpt3nzDTEtq/Ojt1CsjNhVdLCwpu3dL6\n8JQBCiEAISDXr+POrVvwpiejqmFsDC9fXxQYGgKTJwM+PmwrogwEPD3ROHUqmgmBc3g422r0Fi9X\nVxTQyQ+KmikYMwY8Hg+GtEG7Sli4uYFrbY3K3i6OKOphkPXou2NkBO/gYGDkSLal6CUeHh4oEwgg\nkcsBoZDpz1ddDTg4ABYWbMtTL3Z2AIfD3Pfz65pMkcsBqRTYtatnHyUl9e6xmzeZ5VL19V3/VlgI\nnD8PtLaqJF+v8PTEnZAQeIeHA2ZmrMnQbhJHKgXOnAE++wyBFhbIzs3V6vBsU9XTOkUN09raiqaB\nPkMkFALJySjbswfmJiawp03gVCYwPBzZvr7A/PlsS9E8EglQX8+aNxsbGyFqbQXEYlbG1xocDvhe\nXvDz94cBrexSmcCxY5FdWMi2DK0hl8tRc+kSKxdutfn5kEmlWh+XDW41NiIgKIhtGfoLj4fAkBBk\nD6IqOXFrKxp+/hn45BPmpq3X3tqK6uPHtTOWDnCLz0dASAjg6sq2FL3EzMwMnp6eyCMEmDED2LwZ\n+Mc/AF/f+wmPgYKTE/DCC2gyMEBLc3PXHY3bq2xyc5lkS3dcvQr0VmCRkcH8vHGj8+8JAY4fR3Vr\nK1OZMtCTrcbGuCWTIWDUKFZlaPds+upVoKEBkMsRbG2Nm1evanV4NpGJxQgLCsLIoCCsX78eFy5c\ngEyDDaYaGxuxd+9ePPbYYxji5ISj27drbCydoKwM2LcPt27cgL+XFyAQsK1IbwkODsbNggLA0pJt\nKZrn1i3Ub90K/2HDEB4ejnfffRfXrl2DXIN9R2pra7Fjxw488sgjcHd1xeV//YuZ3Rjg3OLz4e/v\nz7YMvSY4OBg3B8FnpZ38vDz4TJ+OyVFR+Pjjj5GVlcU0XdcQFRUV+PbbbzFt2jR4BwUhNzlZY2Pp\nErdu3aLe7CeDzZt/pqfD/fnnMfXbb/G5WIz8ns5n5XLVlq08lEAtLCzEZ599hskzZsD38cdRzdLE\ni7ah3uw/wUFBuDllCrBgAWBvz7YczeHnBwQF4YSHB4Y8+STi4+Px3XffoaSkhPn7g+e1Bw8yk98P\nU10NHDvW/fGlUiAri7n/QBInJycHH374IcJ+/BGjjx2D5Ikn+l6WNQDQBW9qL4lDCLOUCgC4XASv\nXIkbhYUaPSHTJQxNTXHnrbfww48/wtDQEM8//zyGDBmCFStWYN++fWrZqaumpgY7duzA3Llz4e7u\njp07d2L69OnI+/JLPB4RoYZXocOUlQGE4FZ9PQLMzQErK7YV6S2D6mT0r79gW12NiqefxtZnn0Vj\nYyOWLVsGDw8PrFy5EocPH0azGtZNl5eX45tvvmEuDr29ceTIETw+bRqKnnkGMSIRU4Y6wLl16xYC\nAgLYlqHXDCpvAvAdMQKVVVXYsGEDiouLMXv2bPj4+OCFF17AyZMnIVZDBVtBQQE+++wzREZGIiAg\nAKmpqVi1ahXKqqsRMG2aGl6F7kO92X8GmzcnTZqE8tu38c+PP8aNu3c7/PP666/j7NmzkNbWAseP\nM1U6vVU89HSxd+oU+Hw+Nm/ejLCwMISFhSErKwvr169HRVUVHJ2cNPPCdAzqzf4THBKCm4Nh6+17\nPlu4YQMKi4uxfPlypKamIiQkBGPGjMFbn36KtLY2yNurkhoaOj9fJAKamoDbt5nbw+TkAGIxCCHI\nSE3F22++iaCgIMTFxaG0rAxbtmxBQXk5jKOigEHQ+1AXvKm9dud8PlBayvTZWLAAQ8zNQQiBQCCA\nyyDpX2L4xBMY7+iI8RMm4P3330dRURGOHj2KnTt34tlnn0VoUBDiZs5EbFwcxo0bBxMTk16PJxaL\nceXKFaSkpCDp2DGkZ2dj6tSpWLJkCXbt2gUbGxvmgaWlWnh1LFNWBgDgi8UIWLRo4K111SIBAQHI\nzc2FVCqF0UDeEUEuBzIzAQ4HxnPmIGbaNMRwOPjkk09w+/ZtJCQk4PNPPsHypUsxMTwcsTNnIjY2\nFqGhoX2+L83Nzbh06RJSUlJw5tgx8PPyMGfuXKxevRp//PEHLNurnLKygO3bB8WWjHw+H8uWLWNb\nhl4z2C4UAaYcfubMmZg5cya+/PJL3Lx5EwkJCXj33XdxMzMT0f7+iF22DLGxsRg1alSfy/WEQiHO\nnz+PlKQknNmzB8VtbZj36KPYsGED4uLiWN1pgi34fD5WrVrFtgy9Jjg4GDt37mRbhlax4vHwKI+H\nRxcuhFwuR3p6Oo4cOYJXXnkFd+/cwRQnJ8Q+/jhiCwvh7+8PzsPJnOPHgbg44J7nampqcO7cOaQk\nJ+PUwYMQAliwYAG2bt2KyZMnD+zzkR7g8/msXyjqO8HBwdi3bx/bMrSKnZ0dlixZgiVLlkAqleLy\n5cs4sn8/nklLQ3VxMaaVliJ2wQLExsbC29ub8eaD1W3HjgEvvdTxz/Lycpw9dgwpAgFOpqaCY26O\nBfb2+OmnnxARETHolslLJBLcvXsXI0aMYFUHR5lKmLCwMPLnn3+qNtK+fcwOJQ+84OjoaLz99tuY\nMmWKasccQDQXFuLs228j2dkZycnJyMnJwfjx4xEbG4vY2FiEhYVBLpd3JG1SUlJw5coVBAQEICYm\nBtFNTYj9xz9gERLS/QCEDLz1nw/y0UdAbS3iLl3Cv956C9OnT1f7EBwO5xohJEztB1YD/fJmN/j6\n+uLIkSOslwpqFD4f+O474NlngZ76Qdy9C+GmTUhqaECypSWS//oLxcXFiIyMRExMDGJjYzF69GiI\nxWJcvHixw5sZ12wtEAAAIABJREFUGRkYM2YMYnx9ESMUYrKDA0y++657D968CezYAWzdOqA96u/v\njwMHDiA4OFjtx9ZVb6rbl3K5HFwuF6WlpfeT9IOYqvx8nDl5EskZGUhJSUF1dTWio6M74mZQUBAa\nGhqYpM09b/L5fERERCAmJgYxw4ZhwuLFMBrEDX0JIbCxsUFBQQHsNbDUYLB4s66uDl5eXhAKhV2T\nFYOQ0tJSnDlyBMlpaUhOToZYLO6ImbGxsRjO56P2wgWcGzeuw5sFBQWYNGlSx+PGjh076C4OH6Sh\noQGurq5obGzUyPswWLyZlZWFBQsWICcnR23H1Gfu3LmDM2fOIDk5GcnJyTAxMWF86eODmJwceFlZ\nodzKCmeHDkVKVhZSUlIgEAgQFRWFmJgYxMXFYeTIkYP6e47P5yM+Ph55eXkaOb6i3tROWpsQZi3i\nQ1n09llFmsQBLL28MHvNGsweMwYAUF9fj3PnziE5ORmrV6/Gnfx8EEIQEBiImJgYvPrqq4iMjASX\ny2UOkJnZ84UoMKAvDkEIUxa4Zg1u/frrwE48aIl2bw7o91IgANavB3rbulMqhY2JCeZPmYL5q1cD\nHA6qqqo6Tjp//vFHlJWVQSqTYXRICGLi4rBx40ZMmDCBqbaRyYD33mN2CejJg8HBwN/+xjzGwUEj\nL5Vt2traUFBQAF9fX7al6DUGBgYIDAxEVlYWJk6cyLYc1nEaNgxLVq/Gknv/Li0tRUpKCpKTk/Hv\nf/8bdRUVkACIGD8eMTEx2LZtGyIiIgZltU1PlJaWwtzcXCMJnMGEnZ0duFwuioqK4OXlxbYc1nF3\nd8eKVauw4l6F1927dzsuGjdt2gRRYyMkUikmXb+OmJgY/PDDDwpVuQ4m+Hw+/Pz8BnUiSx34+vqi\nqKgIra2tMDc3Z1sO6/j4+MDHxwfPPfccCCHIyclBcnIyjh4+jNevXAFpa4PMyIhJ2sTGYtWqVQpV\nuQ4mdKEfDqCtJA6H0yWBAzAXiunp6VqRoBeMHt1x19bWFo888ggeeeQRAEDt9u0wBGDz/PPdP3fU\nqIGdqOmNlhZg5UrUW1igsbERPB6PbUV6T1BQEG7evIlFixaxLUVzTJ7ct2ekUsDaGnjyyY7HOjk5\nYfHixVi8eDGQmQnBtm2wtLKC5WefAQ8vgTQ0BJYuBb7/vvdxgoMHdDf//Px8eHp60otnNdCeYKVJ\nnK64u7tj2bJlHcv2yjMzYT9sGExpj7Qeocs11Ed73KRJnK54e3vD29sbzzzzDAghKC0thYuzM4z7\naBswmKHeVA8mJiYYPnw4+Hw+xtybKKcwcDgc+Pv7w9/fH6tXrwYhBCUlJXBzc4PhIOhroyq64k1W\n02qDcX1/r/RyQWk/YwZseluGMFgTOACzi9LQoeDf2/1mMJf4qYtB4U1FPidSKbBiBZPI6Q4/Pzhb\nWMBy1KiuCZx2RowAoqLUo0dP0YUGcAOFQeFNNeEaEkITOH1Avak+qDcVg8PhwMPDgyZw+oB6U31Q\nbyoGh8MBj8ejCZw+0BVvsprECQoK0viWoQMGH59OlTqUruiKqQYCNODdw9eXqXLrCTMzxpuhob0f\nZ/Zs9erSM3Sl9HQgQL1JUSc0bqoP6k2KOqFxU31Qb1LUia7ETVaTOPb29rC2tkZRURGbMvQDDgdo\n739D6Zb2ShxK/xkxYgQKCwshEonYlsIuZmZ9PyYkBBg5svfHDPJ1/rpSejoQoCejFHVC46b6oN6k\nqBMaN9XHoPSmRDKgl+mzBSFEZ+Im612KBqWxKBpBVzKjAwETExMMGzYMfD6fbSm6T2ysYsmeQQz1\npvpwdXWFVCqFQCBgWwplAEC9qT4CAwORk5MDqVTKthSKntO+GcDw4cPZljIgGLTXmr/+ymywoW5K\nStR/TD2htLQUlpaWsLOzY1sKTeJQBg70ZFS9UG8qyCDenlgR5HK5zsxaDAQ4HA71JkUt1NfXo6mp\nCR4eHmxLGRBYWlrC1dUV+fn5bEuh6Dl5eXl0MwA14u3tjerqajQ0NLAtRXsYGzO7sH7zDVOVoy7k\ncuC//1Xf8fQMXbrW1IkkTlZWFtsyKHqOSCRCcXExnbVQI/RCkaIOSkpKwOVyYWNjw7aUAQONmxR1\n0N5zg24GoD5o3KSoA126UBwIGBgYIDAwENnZ2WxL0S6+vsCNG8DnnwOtrT0/TpkkT2YmkJ8PNDX1\nX58eokve1IkkDg14lP6Sl5eHoUOHwphWRagN6k2K0tTXAxcvdlqHTdf1qx/qTYo6oBVy6od6k6IO\naNxUP4PSm76+zM/bt4Ft24DGxu4fd+UKU7WjCCdPMj8rK/uvTw/RpbjJehInMDAQfD4fMk2s2aMM\nGnQpMzpQGJQBj9I/bG2BtDTgq6+YhA7oDhuagHqTog5o3FQ/1JsUdUDjpvrRa28WFDDLmJTFxwcw\nuHepL5cDPb1+sZjpn9NXI+T8fODOHeZ+RYXyegYAuhQ3WU/i0DXEFHWgS6YaKHh7e6OqqmpwrSGm\n9J+YGOZEYeNG4NIl3MrOpt5UM0FBQbh58yYI3XmCoipyOY2bGkCvLxQpukFDA27duEG9qWb02pst\nLcCmTcxSJmXivpkZ4O3N7KBaX8/sptodhAC5uUBqau/Ha6/CAWgSRwdgPYkD6LmxKDoBLT1VP4aG\nhggICLi/hpgQZk1teTlw6xaTkadQHiYkBLCzYz4rO3aAn5SEAB6PbVUDCgcHB1haWqJkEO8QQekn\niYk0bmoAPz8/FBQUQCQSsS2FoqfI+Xzk5ObSShw1o9fXmgEBgI0NsH07sHWrcuffzzwDPP00U4mT\nkND9Y9oTQwcPAnV13T+mrY1JBjk6An5+vffY6Yv+PJdF6urq0NzcDHd3d7alAKBJHMoAgZaeaobg\n4GDc/M9/gLffBl56CVizhqmwOHECcHVlWx5FFzEwAKKjO/55q7oaAaGhLAoamAQHB+NmcjLbMih6\niujGDRQXFmLYsGFsSxlQmJiYwMfFBTl8PttS2IcQoLQUOHIEyMtT/Hn5+cpVGwwwStLSYGNsDBsu\nl20pAwo3NzeIxWJUVVWxLUV5OBxg/nzmfn4+sGUL8P33gCLJYkdHwNISmDcPSE4Gysq6PqZ9qZZI\nxOw81Z3/TEyASZOYvjphYcCSJcq/jpYWJlF0+bLyz9UB2vvh6MpmAOwnccrLEeztTZM4FJWR1NQg\nNycH/n5+bEsZcAQHBeFmSQnTwEwsZn45ZQrw4ouAhQW74ii6S2Qk4O6O6qFD0SoSwZVuYax2gn18\ncPPwYbZlUPSUHA4H3k5OMDZg/zRwoBFsb4+bR46wLYNdUlOBd95hloAIhUB3O4f2lKjJzQWOH9es\nPh0mSyCAv7t7z01oKSrB4XAQHBCArFdeAT7+GNixg/mcVVezLU0xhg4Fxo69/++JE5nlUooSFQW4\nuQF793b1HiHMsTgcphnyvZ6GXWhsZK4FnJwAIyPFx5ZKgdOngQ0bmCbKkZGKP1eHyEpIQIAOJVeV\n+B/QEKdPY8zVq1ibkgK5XA6DQXZCIZFIcPHiRSTu2oVqmQwxU6YgNjYWbm5ufT5XJpMhMzMTqamp\nuLBnD9x8fRH/1FOIioqCiYmJFtSzDCEAh4Pkb7/FSBsbWNOkgtoJHTsWe7/6ism6V1QAy5YxmfhB\ngEgkwrlz55D4888Qt7Yi9sknERMTA0dHxz6fK5FIcO3aNaSmpuLynj3wjYhA/PLlGD9+PIyUCXz6\nirU18PzzSPj9d0ydNUtnZi0GEmNGj8bB/fvxOttCWKCpqQlJSUk4cfw4TKqrEbt8OaKio2Fra9vn\nc0UiEa5cuYJz587h2oULCImIQPzcuQgNDR1U5x8JbW2YumABYGjItpQBR2hUFC7cuYNlbAthgbq6\nOpw+fRqnDhyAfU4OYkNDETl3LiwffqBUylzMTZzY8aumpiZcunQJqb//jhtXr2JcejriH30UQUFB\ngyqGJNTWYuqKFYAOXSwOFMaMGoULN24gxsKCqU6JjGQqVfSFefOY6jYHB+CHH5gKeR8fhZ4qqK7G\nCVNTJP38M3jl5YibPRvjx4+HmZkZc4zXXwfeew/g8Zgl8Q9QX1+PCxcu4FxiIm5fuIAoT0/EP/44\nhneXnH0QQoBr14Dff7+fLJs/H9DTnYQT/voLC2bPZltGBxxlGiOGhYWRP//8U32jNzQA69cDUilG\nHzqEzz7/HLGPP66+4+soJSUlOH78OBITE3HmzBkM9/HBzOBgOI8ahZSLF3H27Fk4OTkhLi4OsbGx\niImJgZOTE8RiMa5evYrU1FScO3cOly5dgpubGyZPnoxJI0aguL4eCadP49atW5g6dSri4+Mxe/Zs\nODs7s/2SNUNjI3D1Kp5buxb+vr54dc8ejQ7H4XCuEULCNDqIiqjdm/eQSqXg8XhI3rIF/hMnAgO8\n9D4/Px/Hjx/H8ePHkZqaipEjR2LWpEkwMzZGckYGzp8/j6FDh3Z4MyoqCra2tmhpacHly5c7vHnl\nyhUMGzYMkydPxngvL/BrapCQmIji4mLMnDkT8fHxmDFjBuweCpQDjTlz5mD58uV44oknNDqOrnpT\nU74EAKFQCC8vL+Tn58PBwUEjY+gKhBBkZ2d3xM20tDRERERg5qRJkFy8iOT6elzOyUFAQABiY2MR\nFxeHyMhIWFpaoqGhARcvXsS5c+eQmpqK9PR0BAUFISoqCmMlEqQbGyPh6FHU19djzpw5iI+Px9Sp\nU2FlZcX2y9Yoo0ePxhdffIGoqCiNjjMYvXn37l1ERESgtLR0wE+oyeVyZGRkdMTNzMxMTJ48GTOm\nT0ft9etI5vORfv06xowZ0+HN8WFhMNu5EzUODjhvbt4RN7OyshAaGorJkZEYOWoULl68iISEBBBC\nEB8fj/j4eMTExDAXnQMUmUwGd3d3nD9/vu8L5H4yGL158eJF/P2JJ5B98SI4OtLXRGnKyphKmG++\nYZZW9ZDIkclkSLt4EcdPnkRiYiJu376NuLg4TImJQUl5OZKSkpCdnY2IiAjExcUhLi4OYQkJMA4J\nQUVUVIcvU1NTkZ+fj4iICERFRWH48OFISUnB0aNHweVyO+JmZGQkjB9OzkgkzI6lu3YxCR0nJ+Dd\nd/Vy8qChoQE8Hg+FhYUKTRj1B0W9yW4S59Ah4NgxwMEBn8lkyKysxI4dO9R3fB2hra0NFy5c6Ahy\nZWVlmD59OmbNmoUZM2bAxcWl0+PlcjkyMzORlJSE5ORkpKamwsXEBGUtLfDz90dUVBQmT56MyMjI\nbhM0AoEAx48fR0JCAk6dOgV/f/+OABgSEjJwZjSqqyFZvx6uv/6Kaxs2wOuRR3ruvK4GdDXgAZoN\neq+99hpMjI2x+cMPNXJ8NmltbUVKSkqHN5uamjBz5kzMnDkT06ZNg729fafHt1fYJCcnIykpCZcv\nX4abmRlKWloQEhKCyZMnIyoqCpMmTer2S764uBjHjh1DQkICzp49i9DQUMTHx2POnDk6tc5WHdTV\n1WHo0KEoKSmBtbW1RsfSVW9q0pcAsHTpUkyaNAn/93//p7Ex2KKhoQFnzpxBYmIijh8/Dg6Hg1mz\nZmHWrFmIi4vr/JkSCCDmcpF25QqSkpKQlJSE9PR0uDs7o1QgQFhYWEfcnDBhwv0EjUTSMSOYl5eH\no0ePIiEhAZcvX8akSZM6Tk69vb1ZeAc0R05ODmJjY1FcXAxDDZ9MD1ZvRkdHY82aNZjf3sdiICCR\nAGVlqM3OxsmbN3H85k2cOHECNjY2Hd6MioqCubl5p6c1Nzfj4sWLHd7MysqCm4UFKlpaMH7iRERF\nRSEqKgrh4eFdntuewE1ISEBCQgKuX7+OmJgYxM+YgTlDh8Jt9GhgyJD72yjrOSkpKXjllVeQnp6u\n8bEGozcJIRgxYgR2796NiIgIjYyhNSSS+4mcNWsAb29UVFQgMTERiYmJOHXqFNytrDBrzhzMeuwx\nTJw4sUtSWSgUIjU1teN6Mz8nB05cLmrFYkRGRnac04aGhnZ5rlwiwV/Xr3d4My8vD9OnT0d8fDxm\nzZrFVK23tQFffw0UFTGJm8ceA/T0fd+9ezf+97//4YgWlsoq7E1CiMK3sWPHErUhEhHy6quEHDhA\niEhEKioqiK2tLWlsbFTfGCwil8tJwjffkHmTJxMul0vCw8PJ22+/TS5evEikUqlSx5JIJCTz0CEi\nrK1VWodYLCanT58ma9asIcOGDSPu7u5k5XPPkQtff03IrVuEyOVKH1NnKCkhibNmkfHOzoS8+SYh\nra0aHQ7An0QJv2jzplZvPsT169eJh4eH0p9bXUUmk5H/7t5NZkRHEysrKxIZGUk++OADkp6eTmQy\nmVLHEolE5K+DB0lzc7PSOpqbm0lCQgJZtWoV8fDwID4+PuTFVatIRkaG0sfSRX7++WeyYMECrYyl\nq97UpC8JIeT48eMkPDxco2NoE4lEQr775hsSM2ECsbKyIlOnTiWffvopyc7OJnIlY1VzczPJ+OIL\nIqqqUlqHUCgkBw4cIH/729+Is7MzCQwMJGtfeYXcvn1b6WPpIu+99x554YUXtDLWYPXmTz/9RObN\nm6fRMbRJS0sL+fSll8h4Z2dibWZG5syeTb766iuSn5+v9LHq6+tJZmYmkYjFSj+3urqa7Nq1iyxZ\nsoTYcbkk1NGRvB0TQ0oKC5U+li6yevVq8uGHH2plrMHqzU2bNpHnn39eo2NojbY2Ivz4Y7IpKoqM\nGTOG2NrakoULF5Iff/yRlJSUEPLXX4SsXk1IYiIhfZ3fpqeT6upqcvPmzb7PhYuLCXnoXLWsrIz8\n+OOP5NFHHyVcLpdMnDiRfLh4MaleuZKQggJCLl3S62vORx55hPznP//RyliKepM9Y+XnE1JU1OlX\nc+bM0dobpEn++usvEhcXRwKGDSM73nuPCAQCtiURQpjEEp/PJ1u3biVeLi4kfuZMcv36dbZlqU5+\nPvm7nx/5dOJE5gtCw+hqwCNaCHpjxowhp0+f1ugY2uDcuXMkPDychAUFkb1Tp5I6LXxuFEEul5OM\npCSyadIkMsTFhSxdulSlk2NdYubMmWTPnj1aGUtXvalpX0qlUuLq6kqys7M1Oo6mkcvl5MiRI8Tf\n35/ETZpEDj/5JGlUV8Kkn8lnmUxGLqekkH9FRhIHBweyatUqUlZWph5tLDFq1Chy7tw5rYw1WL0p\nFAqJjY0NqVIhiahLyGQysnv3buLp6Unmz5tHTm7eTFpVmLQgIlHX3/V0QadgckcikZCzP/1EXli8\nmNjb25N169aRWhUmO3UFqVRKnJ2dSV5enlbGG6zevHv3LnFwcCCi7j6TeoREIiHbt28nLi4u5KnH\nHiNnz54lbW1tnR8kkxGydi0hK1cSsmULIb19H739NiGKxDaZjJDNmwm5erXHh4hEInLixAnyzLJl\nxMHOjrz//vukqalJwVemewiFQsLlckl9fb1WxlPUm+zVH/r4MM2THuCpp57Czp07WRLUf0pLS/H0\n009j5syZWLRoEa7z+XjqzTfh5OTEtjQATGd2Pz8/vPbaa8gpLMTUmTMxdepUrFixAnfv3mVbntJI\nmprwR0EBFq1eDXh5sS1nQLNixQq99ubt27exYMECLF++HC+99BLSrl/HY598AtuiIralAWC8GeLu\njrcSE5F7+zb8/f0RERGBf/7zn6ioqGBbntLU1tbi4sWLiI+PZ1vKgMbQ0BDLly/Hf/7zH7alqExG\nRgamTp2K119/HZ988glOp6Zi7n/+Ayt19Svo53IhAwMDjBs+HB8ePYqcnBxYWloiODgYb7zxBup7\n2sFDh+Hz+aiursakQdKgni3ae0Xs0XCvPk1y/vx5jB8/Hp999hl+/fVX/PbHH5i2fj3MlNlEoqEB\n2LkTKC7u/HtCmMbG3XHmTM+74zyAkZERop55Bl/s3Yvr16+jrq4OI0aMwEcffYSWlhbFNeoI586d\ng4eHB4YN8N6DbDN06FCMHDkSR48eZVuKShBCcPToUYwaNQoHDhxAYmIiduzdi6ioqK49aQwM7m9G\nkpfH7BbX027QHA7TLFki6V1AUhJQUNDz7nIATE1NMX36dPy0axcupaXhxo0b8PX1xfbt29HW1qb4\ni9URDh8+jOjoaNjY2LAtpRM6tYh07ty5+Ouvv1D88Je9jtPU1IR33nkHo0aNwpAhQ5CTk4PVq1cr\ntgtNczNz0zKmpqZ46aWXcPv2bfj4+CAsLAwvvvgiKisrta5FVc6cP48Rrq7wXDYY94DQLkuXLsXh\nw4fR1NTEthSlqK2txZo1azBhwgSEh4eDz+dj2bJlzC40ISG6tdPWiBGAlRWsra3x1ltvgc/nw8TE\nBEFBQXjzzTchFArZVqgwf/zxB6ZNmzbgm8PqAitWrMCuXbsgk8nYlqIUD056LFy4ENevX8ecOXPu\n94V6qDcGq7i7A1wuHBwc8MknnyAjIwMCgQC+vr7YsmULWltb2VaoMPv378eiRYsG1U5cbPHUU0/p\nZYI1Ly8PCxcuxLJly5hJj7Q05RtgS6XAiRPAW28BtbVdtxi/dAnIzu7+uRIJ8MsvgFyu2FgcDtzd\n3fHdd9/h/PnzSE9Ph6+vL7777jtI+rog1SH27duHxx57jG0Zg4IVK1aw783ycuD6dSbRqSCZmZmY\nNm0aXnvtNWzZsgWnT5/G6NGje39SZCSToAGAoCDm1h2mpszOVwcO9Hys6mrgjz+Y+wr609fXF//7\n3/+QkJCAw4cPIyAgAP/9738hV9TfOoCuelOnoriZmRkWL16MXbt2sS1FIWQyGX766Sf4+fkhLy8P\n6enp+PDDD5XL1BkZAdu3M82feqKhodeMJwCgsrL3Y/QAl8vFxo0bcevWLXA4HAQGBuKdd95BgxJf\nKmyx/8wZLF658v6XE0VjODs7IyoqCgcPHmRbikKIxWJs27YNfn5+EIvFyM7Oxvr167s0TdTlZoiO\njo7Ytm0b/vrrL5SWlsLX1xeffvopRCIR29L6ZN++fVi8eDHbMgYFwcHBcHZ2RnJyMttSFKK5uRkb\nN27EqFGj4OLigpycHDz//PNdZxC1QVub4heKD8Dj8fDjjz/i3LlzSEtLg6+vL3744QdIpVINiFQv\n+/fvp97UElOmTEFZWRmye0pW6Bi1tbV4+eWXMX78eISFhXWe9FCGujrgww+B334DRCLg4YrMpibm\nQrEnv5iZAXw+kJio9Gvw8/PDvn37cOjQIRw4cACBgYHYu3evzl8wSqVS/Pbbb9SbWmLRokVISUlB\nVVUVeyKGDAEyMpitvf/1L+Dbb5nNfnbtAnbvBlJSgNxcoKEBZaWleOaZZzBjxgwsWLAA169fR3x8\nvGKbYdjbA2PHAitWAOnpwNmz3T+ufde3lBTgr7+6/p0Q4MiR+9dcfV2XPkRoaCgSExPx448/4vPP\nP0doaCiOHzgAUl7e+btALAb+/JO5rtUB3wqFQpw9exZz585lW0oX2N2dqhsuXbqEp59+uiOpoKuc\nOnUKr732GqytrbFt2zaFu5w3NTWhsrISFRUV93/++isqGhshCQuDnYMD7OzsYGdnB1tbW+a+QAAb\nqRT1I0agTCBAWVkZSktLUVZWdv/+3buQEIIh7u4YMmQIXFxcOv18+HeWlpbd6isoKMA777yDxMRE\nrF+/HqtWrYKZqSljVh264G1ra4OrqysyMjLAe2hZnqbQ1U7+gHa8eeDAAWzfvh1JSUkaHac/EEJw\n8OBBrFu3Dv7+/ti6dSsCAwMVel5jY2NnX7b/LCsDkcth5+R035MP3KytrVFTU9Phx4f9WVZSAnA4\ncLnnw9786eLi0jXRJJMBhCArJwdvvvkmrl27ho3vvIMVTz2lWLWflqmpqYGPjw9KS0u1Vomjq97U\nhi8B4IsvvsDVq1fx66+/anwsVZHJZNi5cyfeeustREdHY/PmzRg6dGifzyOEQCgUdvJk+/3KsjIY\n1NXBzs+viy/t7Oxg2dyMKg6nsx8f9GdxMYyNjTHE3b3bWPngfWdnZ5iamnarMS0tDevXr0dpaSk+\n+OADLFy4UCfPX27duoWpU6eiuLhYa5U4g92b69atAwB8/PHHGh9LVdra2vD1119j8+bNWLRoETZu\n3Nhl19TuIISgtra2S8ysqKiAIDMTJkVFsHN1hd306Z3Pa9PTYX7tGird3FAWFtbVnwUFKBcIYGFl\n1eu5bPt9Z2fn+0lguZyp/KmqAoyMcPr2baxfvx5yiQQfrliBafPmgRMQoOF3VHmSkpKwdu1aaOMz\n2c5g9+aTTz6J8PBwvPjiixofq0cIAX79Fbhw4f7vHn2USYSePYtmiQRbMzPxZXY2nluwAOu3b1eo\nUEAul6Ompua+L4uLUVlbi4rz51GVnw/zCRM6rjc7zmvPnYNdaSlMuFyUe3qizMsLZZWVXfxZUV4O\na3NzDHFygouXV6/ntU5OTt3ugEgIwe+//44Nb7wBl5YWfBgaiglBQcwW5NbWTBJHLAYsLZlKWB4P\n8PAAgoMBBwd1/g/0ya+//ooDBw7g0KFDWhtTP7YY7wZCCPz8/LBr1y6d3P4tPS0Nb777LnJzc7Fl\n82Y8GhsLkYUFBFVV3V8APnTSKZfLu37Qb9+GS10djENCUB8YiLr6etTV1aGurg719fWoKyxEfWEh\nbEeMgJu3N9zd3eHm5gY3Nze4u7vDzs4OBj/9BLGHByqDgiAQCFBVVYWqqipUV1ejpqYGtbW1qK2t\nRV1dHYRCIQwMDGBjYwNra+uOm6WxMaRFRWiUSlHc2oqyykpYmJsj5eWXEf7220ypnY5w/PhxvP/+\n+7jw4BefhtHVgAdox5tisRhubm5IT0+Hl471ICKEIDU1FW+sW4fGlhZ8+umniIuLQ4tQiMrcXFTK\nZH3609DQsNtA5FJUBANDQ9QNG9bZl3V1qKutRUN5ORyGDIGbt3eHJ9v9aWNjA4NffkHL0KGo9PFB\nVVUVBAIBqqurO3nzweOamJiAy+WCy+XC2soKVo2NsBwyBGITEzQ1NeHu3buoqqoC18oK2Xw+3NXV\nO0RN/PTtpAuxAAAgAElEQVTTT0hMTMT+/fu1NqauelNbJ6NVVVXw9fVFcXGxxrdzVxapRILTJ05g\n3YYNsLa2xqefforw8HA0NjZCIBD06svKykpUVlbC1NS0qy9dXOBiaQn59euo43JRb27e4aN2bzYV\nFMDJ3x/uPF6nmOnq6goulwtOWRmazMxQWV/fETe782Z9fT2EQiFMTU07xU0rKytYGBtDJJOhqakJ\nubm5EAqFcLK3R0l5eZctWdlm06ZNqK2txb///W+tjTnYvZmVlYXp06ejqKhI49u5K4uoqQlH1q3D\n+oMH4Td0KLa++Sb8Q0Mh/OUXVLa0oFIqRQUhqHRxQUVNTRd/CgQCWFpadhs3nZ2c0Hb7Nurb2lBn\nbNzJm/X19WiprYWLoyPcfH07xUw3N7eOScb2iZX289mqqirU1NR0iZuNjY2wsLAA19gYXEJgZWwM\nKyMjmLu7o1UqRWNFBfjV1WiWSjHM3R15JSUsv/NdWbVqFXx8fLB27VqtjTnYvXnq1CmsX79eq4mz\nbnk4kWNhgeboaPzv9Gm8/csviHZ1xeZJk8B76inUjxyJyj6uNysrK1FVVQUbG5vOvrS1hUtrK5wi\nIiAipHO8rKtD/Z07qBOJIJZI4Dp0aKeY6ebmhiFDhsDiXj+s+ooKCBoausTN2tpa1NTUdPi8qakJ\nlpaWneKmpaUlzFpa0FJZiYbmZtyqrYVIJkOEtTXSeDzA0ZFJ2jxYbBAUBMTGMj+1XFAwd+5cLFmy\nBMu02LpDb5M4APD++++jvLwcX3/9tcbHUoY1Tz+Nz3fs6PUxHA4HHA4HBgYGnW6GhoYdt4f/ZiAW\nw4AQWDk7w87BAfb29rCzs4O9vT1zv6gIVnfvonr0aJTJ5SgvL+90E4vFcHV0BNfGBkZmZjAyMup0\nMzY27vI7QggkEgna2trQ1tYGsViM1uZmGDU1wdrDA61tbTh//jwiIyNx5MAB2Dg7a+dNVpBnnnkG\no0aNwpo1a7Q2pq4GPEB73ly9ejU8PDywYcMGjY+lDOPHjUNaT00S79GdN3v1ZftNKATXzg52Xl6d\nvGlnZwd7LhfmJ09CEBSEcrG4w5NlZWUoLy8HIQRDjI1h5eQEIy63Rz8+7M22tja0iURoy8+HqLER\nIgMDmAwdCmtra9RXVCAtMxPxw4fj92vXYMTlauldVowZM2bg73//u1bXD+uqN7XlSwCYN28e5s+f\nj7/97W9aGU9RHLhc1DY29vqY3rxpZGTU6fcPPs5QLoeNiUn33rS0hEltLSpMTbvEzPLyciZpa2YG\nSycnGFlYdOvFB71qaGgIuVwOiUQCsVjMxM3KSogaGmDG48HK2hrlZWW4fv06VkyZgh2nTuncUt+R\nI0fi22+/1WpTY+pNICwsDJs3b8b06dO1Mp6iKFIt9rA322Nld9580KNGMhlsDAxg7+oKO0/PLv40\nNDRERUkJyquqOuJleXk5KioqYGZkBBcrK5jb23fyZk+x09DQEDKZjDmnFQohLi+HuL4eYgcHmDs4\nwNrKCnk3b+JOaSnW/OMf2Lp9uxbeXcWRSqVwc3NDWloavL29tTbuYPemTCaDl5cXTpw4gaCe+sRo\ni/ZEDo8H1NaCs2hRn09p91q7P9u92e6JTr6Uy2HQ0gIDDgdGXC5sXVy6xkw7O9hZWYEYG3cbMysq\nKmBlZQUXc3OYEgJjd/cez2PbvWpoaAipVNoRN9tvbXV1sCAE1ra2uJGTA0FDA96dORNrX3iB2ajm\nxg3g/Hmmb2V0NKBAZaAmqK+vh5eXF4qLi8HV4rm2ot7UvVp8AMuXL0dYWBi2bdvWY/kyG3zyww+I\nmDwZL69fj8WLF+Ojjz6ChYUFDAwMQAiBXC7vuD3874dvnf7e1gYZh4OmpqaO2YUHK2ey5HI02tnB\nUSqFq6srRo8eDTc3N7i6usLV1RW2trZqLd3evXs31qxZg2+//RbPPfeczpWFt7W14dChQ9i0aRPb\nUgYdTz31FFasWIE33nhDpz4XKd99h19eegkb0tLwz+eew5uffgqTO3eAL78EkUggnzkT8rlzVfKo\ntLwcjYSgtrW1w5vtP4vz8tBSUQHnoUPhMXQowsPDO3zZMdsvkQDKzsg3NQFffME0X3ZzA9zdQaZN\nwxfffosP3nsPB99+GwtefRVgI4FTVwfY2XX7p+rqaly+fBm//fablkVRVqxYga+++krnkjh37t7F\nd999h81btmDjxo144YUXmIqEkhIQZ2fI7yVHeo2RPdxkFRUQ5uaizsQEtYaGnbxZkJUFUWEhXCZM\ngLe3NyZNmtTJm/1e6ieXA/n5wPDhkMpkePfdd/Hzzz/j9PHjiJs5Uz1vnhrJzs5GXV0dJkyYwLaU\nQUf7zqu6lsSpqKjAv9eswXfHjuGLr77C0nv9b8jZsyAjRjD+VPQ89sEbnw/J/v0QVlejzsUFtbGx\nHb6sEQiQl5oKSWkpXD09MWLKFERHR8PV1fX+bP9PPzE76MTGAkuWqPbiysoAc3OIzM3x8ssvIyc3\nF2lpaQgNCVHvm6gGUlJS4OXlpdUEDuX+7o47d+7Eli1b2BXD4QBPPgk0NgJcLgru3MFHW7bg999/\nx4/r1mH2smUwcHQE4XA6fKdIjCRyOeQXLkD+xx+Qy2SQA5DMnIn6wMBO15m1tbWoLC0F/8wZEA8P\nuI0di+DgYEybNq0jZg4ZMgRm1dXAl18yu8e99x7Qjwl+oVCIZ599FvY1NTh95gx8x4y5/0cDA+CR\nR1hfAXL48GHExsZqNYGjFIrsQ95+Gzt2rLq2QO+TmJgYcvDgQa2Npww1NTVk8eLFJDAwkKSnp7Mt\nR220tLSQZ599lowYMYJkZGSwLadHjh49SiZNmqT1cQH8SZTwizZv2vKmXC4nI0aMIJcuXdLKeEpR\nUECK33yTTPX1JePHjye3b98mpLKSkIMHCXn9dULy8thWqDh1dYS0tHT8s7a2ljz66KMkbOxYks/2\n60hMJGTPnk762vnhhx/I4sWLtS5JV72pzZgpEomIg4MDKSgo0NqYysDn80l4eDiZMWMGKS0tZVuO\n2igtLSXR0dFkypQppLy8nG05PbJx40ayZs0arY9LvUlIVVUVsbGxIUKhUGtjKsPVq1eJn58fWbp0\nKamtre3/AaurCdm6lZCVK5lbVVXnv3//PfP7n3/u/vk7dhDywguE9PP9ys3NJaNHjyaLFy8m9fX1\n/TqWJlm5ciXZsmWL1sel3iQkOzubuLm5EalUqrUxleH06dOEx+ORf/7zn6S5uVm5J8vlhJw/T8g3\n3xCyezchR44QcvYsITduMH97mJs3CXnxxb7PlWtrCXnvPUKSkpTT8wDXrl0jw4YNI6tXryatra0q\nH0fTzJkzh+zevVvr4yrqTd3pVPsQ7TMXuoi9vT327t2L9evXY/r06fjoo4+63961sFD74lSEz+dj\n3LhxaG5uxp9//okQHZytaGf//v06udXbYIDD4WDFihW66U0vL3hs2oQT+/fjiblzMWHCBPxw6BDI\n/PnA5s0AG7vfqIqtbccWy1euXMHYsWPh6emJ8xcuwGfYMHa1TZ4MXLwIvPMO03yO3F+Sq6vbMA4G\nTE1N8fjjj+tsc2M/Pz9cuHAB48aNw5gxY/Rmp7veOHnyJMaOHYu4uDicOHECQ4YMYVtSj9C4yR6O\njo6IiYnBgd627mWRsLAwpKenw97eHiEhIThz5kz/DujgALzyCjB/PjOj/nDvwiVLgN4q4WxsgClT\n+lVpunfvXkyaNAkrV67E3r17lds1VovQXanYJSAgAO7u7jh9+jTbUrplypQpyMzMRG1tLcaOHatc\n/x4Oh1mOtGoVsHQps0tcVBTTHLi7SvrKSuDll4G+zjHt7IDXXlNpuTAhBF9//TVmzJiBDz74ANu3\nb4dZ+65YOkZ9fT1SU1N1cleqDhTJ9BAWsqMNDQ3ExsaGCAQCrY2pCoWFhSQmJoZMmjSJ3Llzp/Mf\nv/xSsZkEiUQz4vqipYUQsZjs2rWLODo6ku+//57Iu8vO6hBisZjY2dmRkpISrY8NHZ21IFr2ZmFh\nIbG3tycikUhrY6pCVlYWGTNmDImPjycVFRVsy1EauVxO/v3vfxMnJyfdq0rct+/+LOvnnxMiEBCB\nQEC4XK7ys0VqQFe9qU1fEkLI5cuXia+vr85/j1+6dIkMHz6crFixQqdnyHtCIpGQN998k7i5uZGk\nfsxGaoubN28SDw8PIpPJtD429SbDb7/9RqKjo7U6piokJiYSd3d38vLLL6tnhryggJBt2wh5+LP3\n55+E/PRT98+5fLnbSk9FaG1tJatWrSLDhg0j165dU+kY2uTkyZMkPDyclbGpNxm+/PJLsnTpUq2O\nqQp79uwhTk5O5L333iMSTVw3trUp93glzzPq6+vJokWLyOjRo0lubq5yY7HAjh07yKOPPsrK2Ip6\nU2crcaytrTF37lzs2bOHbSm94unpiTNnzmD+/PmIiIjAL7/8AkII0NwMZGUBOTl9H+TkyU6z2d3S\nXaXPwxACtLQoJryhAa1ffYXnnn8emzZtwunTp3Wy/83DnDp1CkFBQTq3I89gwtPTEyEhIThy5Ajb\nUnolMDAQly9fxsiRIzF69GgcPnyYbUkKU1dXhwULFmDXrl24fPkyFixYwLakzkydCrTvtFJSAuTl\n4ffffsPMmTM7di+gaJ+IiAgYGBjg8uXLbEvplfHjxyMjIwPm5uYICQnBuXPn2JakMGVlZZg6dSou\nXbqE9PR0xMbGsi2pT/bv34/FixdrbVtxSldmz56NrKws3L17l20pvTJjxgxkZmaiuLgYYWFhyMjI\n6N8BvbyA//s/oK2t8+/HjmVu3RER0VGJqgy3b9/GhAkTUFNTg/T0dISGhqogWIu0tdHqVR3giSee\nwNGjR9HQ0MC2lF5ZsmQJ0tPTkZKSgqioKOTn56t3AGWr1ZW4XkxPT8fYsWPh5OSES5cuwdfXV0lx\n2kcfvKnTEV2Xl1Q9iIGBAV599VUkJSXhs88+w8KFC1GVlMQ0PlQkiZOezjRx6w1Fyluzs4Hbt/t+\nnEAA/uuvY9znn6O5pUXnl089CC0J1w26ePPuXeBf/wLWrmV+btgAvPUWsHs3IBKxptPExASbN2/G\n/v37sWbNGjz77LNo7GOnHLbptHzq/Hn4+PiwLakrdnbAuHHMVpBCIUAI9h84QL3JMhwOR2/ipqWl\nJb799lt8/fXXWLJkCdauXQuxWMy2rF5pXz41ZcoUnDhxAi4s7ZihFHw+jZs6gK4vd3wQBwcH7Nu3\nD+vWrcO0adPw8ccfd98yQFFMTIDulkz0dN6p7GSiVNpl+ZTONiJ9AMnhw/jjjz/oUiqWcXBwQFxc\nHPbv38+2lD7x8PDAyZMn8fjjj2P8+PH44YcfmMIBHYWQ+8unNm/erNPLpx6krq4O58+fR3x8PNtS\nekWnkzixsbGorKzEzb4SHDrCyJEjcfXqVQwbNgwhy5fjSGEhSF9JHEKAqirg2LGeq3FkMuD4caYb\neG+cPg2Ul/f4Z7FYjBMnTuD/1q/H5N278cLSpdi9ezesra37eGW6gVgsxuHDh7Fw4UK2pQx6Fi5c\niNTUVAhKS5kE5JUrzEybUMjsXtTcDEyfzqzD1YEv7MjISGRmZoIQgtGjR+PcuXM6FfhaWlpweNcu\n/H3yZMTPmYNPPvkEn3/+uU7tzteF6dOBv/8dmDYNVd9/jyuXL2PWrFlsqxr0LF++HPv374eIxeSp\nMsyZMweZmZnIzc1FREQEMtLT2ZbUCaFQiL1792LJ/7N333FVlX8cwD8XwYkyBAQEHGxFlO0mzVFu\nf5kDZ24rLUeWo6zcu7QsR25Rc4/c5kBDpluWG5nKEFHW5Z7fHydQEhFknHvg83697kvIey9fyC/P\neb7neb5P16745JNP4O3tjW+//VY8YUsGbh46hGeJifDw8JA6lApv6JAh2Lx5s1qNPW+iUCgwePBg\nBAYG4ujRo2jXrh3u3Lr19lXjZejJkyfY9MMP6OHujhkzZuD48eMYN26c2q8oz3HmyBE0NDJCvXr1\npA6lwhsyZAg2b9ggzqGUSqnDKZCGhga++OwznPvpJ6xatQo9u3RBdHS01GHlER0djdWrV6N927ZY\nt2QJfM+fl9WNhAMHDuD9999X+/mx+hZxVCpUunYNg+3tsWbVKqmjKbQqVapg8eLF8N6+HVPDwmC6\nZg2GDhmCrVu3Ii4u7vUXPH8urlS4excID8//Te/eFbdJFbSsNTpaXInznyJOUlIStm3bhr59+6JO\nnTr44YcfYGFjA79r1zBq9mzZDHY4cwarv/oKzvXqwdTUVOpoKjxtbW306NoV6z/+WDxuMCQEqF9f\n/EtnZ+CHH8QGuGr076tmzZr4448/sHTpUgzu0wcN6tXD6NGjsXv3biQmJpZ5PHFxcfjjjz/Qo0cP\nGBsb46dffoGjuzuu+Pmp3/ap/JiYAA0bAh99hCVJSejZuTO3UqkBc3NzONWrh12LF0sdSqEZGhpi\n3759+GLCBHRu1w62lpYYP348Dh06JMnKuYcPH+LXX39Fp06dYG5ujk0bN6KdiQmuXbwoi+1Tr1pw\n4QIG9ukjn7G+HHO1tIRWWhr+Lm7j4DJUr149nD59Gt27dYObmxuaWltjypQpOHHiBNLS0so8noiI\nCCxduhRt27aFpaUlDm7dio90dXF57144vXpEsZpTqVRYdOMGBnl5SR0KQdzuGBIaipAvvgDGjwc2\nbBCP+1ZHqanAzz+j0e3b8Bs+HA6xsbCztYX7v8XMc+fOIfO/2xdLmSAIuH79OubMmQN3d3c4ODjg\n/PnzGNu7N3w//BBW+/YBiYnAnTuSrs4vjIywMCz/4QcMatlSrYrW+VEU5Y6Aq6urUKTO2MWRkgKE\nhyPu1i04ff01tm7bhvbt25fN1y5Bd+7cwcmTJ3HixAmcOXMG9erVQ6dOndCpUye0bt0aVWNigAUL\nxCfb2Ymdwf9r/35xJc6b/h4ANm8WTwCoVw/3BgzAgQMHcPDgQQQGBuK9995Dz5490a1bN3ks/87H\n9fHj0X7dOviOGgWrrl3FVQBlfFGqUCiCBEFwLdMvWkhlmpv/CgsLQ+vmzXH6wAE4tm0rbgusVOnN\nS6TViPDiBUL8/XHy6lWcOHECPj4+sLe3z83N5s2bQ6uET7MSBAGhoaG5uXnr1i107twZPXr0QJcu\nXaCnp1eiX6+s/P333xg8eDCuXLkCQ0NDSWJQ19yUIi8BwHfXLvT69FMEBgfD3Ny8zL9+caiSknD1\n2DGcjIzEiRMn4OfnBycnp9zcdHFxKfFVMIIg4MqVK7m5+fDhQ3Tt2hU9evRA586doV29ujjeyKwQ\n4u3tjR9//BFBQUGoUaOGJDEwN/Pav2cPJk6ZguDgYHn9zk9Ph/LvvxG4dy9OGBri5IULuHLlClq0\naJGbm02aNCnxYmF2djb8/f1zczMpKQk9evRAzx490D4mBlX19MQbRkZGJfp1S9uSJUuwf/9+nD17\nFpqampLEwNzMa9Xy5Vi3fDl8jx5FlUaN1PP3fXQ08OuvwJMn4ucWFkCHDshs0gS+AQE4ceIETpw4\ngfDwcLRt2zY3N21sbEo8N7OysuDj44ODBw/i4MGDUKlU6NmzJ3r27Ik2bdq8vIaOiQF++01ctGBm\nBujrA0OHlmgsJWnSyJF4cPo0dk+bBsXo0ZLEUNjcVN8izitOnjyJYcOG4fLlyzCS2S/qVymVSgS8\nkmTXrl1Dy8aN0alSJbQzNoZJ48bQHTwYVa2s8ibb3LnAw4fiUY1LlgA1aiArKwvx8fGIjY1FbGQk\nYo8cwe2AAByOiUGcSoXu3bujR48e6Nixo+zvjqelpcGtXj181bgxhtrYABMmAI0bl3kc6jrgAdLl\n5ubNmzF//nwEBgZKNkkoCRkZGfjnn39w4sQJnDx5Erdv34anpyc6duiAtra2MHJ0hJ6eXqG2N2Vm\nZiIuLg6xsbGIiYlB7L17CD13DoeuX0d6erp4AdqzJzw9PdV7u1QhJCQkoFmzZvjjjz/QqVMnyeJQ\n19yUKi8BYMGCBTh8+LCkk4SS8OLFC5w/fz43N6Ojo/H++++jY8eOaOXuDgMTE+jq6qJy5cpvfa+0\ntLTc3IyNjUVsdDSu37iBQ4cPo3LlyrkXoC1btpT1zwwA7t27Bw8PDxw/flzSFQrMzdeNHz8e0dHR\n2L17t/xWSGVni9um9fXx9OlTnD17Nvea9tmzZ+jYsSM6deoED3d36GdlQcfWtlA3RF68eJGblzEx\nMYi9exeXQ0Nx6PBhGBkZ5Y6brq6uYoPu7Gyx72QJ32wpC0FBQfjwww8REBAg6VYq5mZegiDg448/\nhqmpKVasWFHmX/+tbtwA1q7Nu5KlQQNxTvSfeV5CQgJOnz6dm5sAcgs6zs7O0NPTg46OzlvHOUEQ\nkJqa+nLM/Pdx6cIFHD15EpaWlrm5WWARNz0dWL4cuH9f/HzMGHHVvpo5duwYRo8ahSvt20N/zhxA\noptg5aqIAwDTp09HcHAwjhw5Um5OWEhOTsaZnTtx0tcXPhcu4HFqKpKSkgAAurq60NPTg26tWtBV\nKlEzPR1PK1dGTFoaYhMTkZycDAMDAxgbG8PExATGxsYwNzVF506d4NGiBSrJcGB7k88//xwJZ87A\nu1UrKNq3B/r3lyQOdR3wAGlzc+jQodDQ0MCGDRsk+fql4fHjx+IAeOAAfE+dQqKGBpKSklCpUiXo\n6urmPvR0dVEjPh6JtWoh9t+i6rNnz2BkZARjY2Pxoa+P+tnZ6DJ5MpycnOR30f4GgiDgf//7Hywt\nLbFkyRJJY1HX3JQyL1UqFT788EO4ublhzpw5ksRQGqKionDq1CmcOHECASdPIikrC8mpqdDS0hLH\nzFfys7qmJp6kpIiTwthYpKWloU6dOi/HzWrVYFmnDrqNGQN7e/tyk5tKpRJt27ZFnz59MGnSJElj\nYW6+Lj09HS1atMCoUaPw6aefShJDabh375648vz4cVw+exbJ6el4mpGBqlWrvrym/Tc3qygUiE9I\nQOzjx4iNjUVmZmbutaxxnTowvnMHtk2bovv338PSykrqb63EpKamwtnZGT/++CP6S3Qtm4O5+brk\n5GQ4OTlh+fLl6NWrlyQx5Cs+XtxtoasrHiyR86hZ860rhgRBQFhYGE6eOIHjq1fjZnIynqalISUl\nBdWrV897TVuzJjQ1NBCXlJQ7bgqC8DI3jY1holLBITUV3devR93CFjlCQoBNm8SemQBQowbw3Xfi\n96Mm4uLi4OTkBG9vb7x39y4wfLhksZS7Io5SqYSnpyd69uyJqVOnShJDWUlPT0dycnLuIykpCSkp\nKdDV1c1NIgMDA9k0ViyOQ4cOYcKECbjcsyd0dXTEU48Kcce1NKjrgAdIm5upqalwdXXFzJkzMWjQ\nIEliKAuCICAtLS1PbiYnJ+NZQAD0PTxgYmYGY2Nj6Ovrl5tCc0FWr16N1atXw9fXV/IVReqam1Lm\nJSBelDg7O2PTpk3o0KGDZHGUmoQEICICgpMTXiiVueNlTm4+v3IFhk2awNjaGsbGxtDT08tbqElL\nExuvl5PiTY5Zs2bh0qVLOHr0qOS/i5ib+QsPD0erVq1w8uRJNGvWTLI4SsXVq8AffwAZGRAmTkRq\n3bp5rmeTb95E+t69MOrfHyatW8PY2Bi1atV6mZvx8cCPPwKTJol918qRkSNHIjs7Wy1uejE383fp\n0iX07NkTAQEBsLCwkCyOEnfpErBxIzB9OmBhAZVKhWfPnuW9pj1+HJkRETCeMCF3vqmtrZ133IyN\nBWbNKvquCJUKuHkT8PEBrl8XW4RMmKAW468gCOjatSucnJwwd+5csaWLhCfcFTo3BUEo9MPFxUWQ\n0oMHDwQjIyPB19dX0jiobERFRQl16tQRLly4IAhTpwrCgweSxgMgUChCvpTlQ+rcvHLlimBgYCCE\nhYVJGgeVjZs3bwoGBgZCaGio1KEIgqC+uSl1XgqCIJw6dUowNTUVYmNjpQ6FysD58+cFY2NjISYm\nRupQBEFgbhZk69atgo2NjfDs2TOpQyl5SUmCsH69ICxeLAgqVd6/U6kE4ZdfBCE4OP/XXr8uPsqZ\nP//8U7CyshJSUlKkDkUQBOZmQRYuXCi0bNlSyMzMlDqU4rtxQxAiI8V51MaNBT9340ZBWLbs7e85\nd64grFsn5vK7/IySkgThyBFBuHat6K8tBT///LPg7u6uNv+/C5ubsrpdbGFhgTVr1mDAgAG5246o\nfFKpVBg6dCjGjRuHVq1aAR9+KDbwIrXUtGlT/Pjjj+jXr59sjjamd5Oeng4vLy/Mnz8ftra2UodD\nb/H+++9j+PDhGDx4MFQqldThUClKSkrCoEGDsG7dOhgbG0sdDr3FwIED0apVK3z22WdSh1LydHWB\nTz4B+vQR72q/SqEABg8GqlXL/7V2doCDQ+nHWIYePnyIzz77DN7e3mp/bDEBU6ZMQa1atTBr1iyp\nQym+kBBg0SLxVKtu3fJ/jkoFPHoEJCeLufu2a4XmzYHLlwFvb7HZclHp6orzuiZNiv7aEnbt2jXM\nnj0b3t7eJX6gSWmTVREHAHr27IkePXpgxIgREItVVB4tW7YMaWlpmDFjhvgfPD2lDYjeauzYsbC0\ntMSUKVOkDoVK0bRp02BlZYURI0ZIHQoV0qxZs5Ceno6FCxdKHQqVEkEQMHbsWPTs2RNdu3aVOhwq\npJUrVyIgIACbNm2SOpTSUb8+oKPz+n+vVQt4000AmTcV/6/s7GwMGjQIkydPhpubm9ThUCFoaGhg\n8+bN2Lx5c25jYNm6cwfIyACUSvHo9PxutGpoAL//Dty+LT5/y5Y3v194uNifJysLOH9e3JYsUy9e\nvMCAAQOwbNkyWFpaSh1OkcmuiAMAixYtwoMHD7Bq1SqpQ6FSEBQUhEWLFmHbtm0vO6erwZ5JKphC\nocC6detw5MgR7N27V+pwqLASE8XBvRCOHj2KPXv2YM2aNeWmCWxFoKmpCW9vb/z888+4ePGi1OFQ\nKagZYsMAACAASURBVNi4cSNu3bqFRYsWSR0KFUGNGjWwc+dOTJkyBaGhoVKHU7YqyBgyf/58aGlp\n4auvvpI6FCoCQ0NDbNmyBcOGDUNsbKzU4bybrCzxdGMAsLcHxo8X+8Dlx9QUyMwEHj8GXAtox2Jj\nA7x6qpqMizhTpkxBs2bNZNvPU5ZFnCpVqmDHjh34/vvvceXKFanDoRKUmpqKAQMGYMWKFZIevUjv\nRldXFzt27MDYsWNxP+coQVJvmprAggXi3ZcCxMXFYcSIEdiyZQv09fXLKDgqKWZmZli7di28vLyQ\nmJgodThUgsLDwzF16lRs374dVd90gU5qq0mTJpg7dy769euHNBlPiOh1vr6++OWXX7B582bJm4xT\n0bVr1w6jRo3CoEGDkJ2dLXU4RffwoXiTztER+OwzoKBDKHJaVhgZAY0aFfy+/fuLRR8g/5U9MnDg\nwAEcO3YMq1atku1NSdn+RrG2tsaKFSvQr18/pKamSh0OlZAvv/wSrVq1kvzoRXp37u7umDp1KgYM\nGICsrCypw6G3qVVLHLwXLQK2b893QFapVBg2bBiGDx8OT25tlK3u3bvjo48+4nbkciQzMxNeXl74\n4Ycf4FDO+ohUJKNGjYKdnR0mT54sdShUQp4+fYqBAwfi999/R926daUOh97Rd999B6VSiQULFkgd\nStHduQO4uABjxgBv6/eSc1y4p+fbV8lVrgyMHi2+pwwLz1FRURgzZgy2bdsGnfy2e8qEbIs4ADBg\nwAC0adMG48aN4wVpObBr1y6cO3cOK1askDoUKqZJkyZBT08P3377rdShUGHkHD999qx4dOS1a3n+\nesWKFUhKSiofTf4quAULFuDRo0f45ZdfpA6FSsDMmTNhamqKcePGSR0KFYNCocCaNWtw/Phx7N69\nW+pwqJgEQcCnn36Kzp07o1evXlKHQ8VQqVIlbNu2DStXroSPj4/U4RSNoSEwcmTh+kxZWIhFmZYt\nC/feJiaAl5fsVuJkZ2djyJAh+Oyzz9CiRQupwykWWRdxAHFyERwcXH6bwlUQDx48YOf+ckRDQwOb\nNm3Ctm3bcOzYManDobcxNX25fDY5GQgNBZ49AwBcuXIFc+fOlWXnfnpd5cqVsWPHDsyePRvBwcFS\nh0PFcOrUKXh7e2P9+vWyXQ5OL+no6GDHjh349NNPce/ePanDoWLYunUrLl++jKVLl0odCpWAunXr\nYv369Rg4cCASEhKkDqfwnJzEpsWFoasLtG8PVK9e+Pdv0QJo3PjdYpPIkiVLkJWVhenTp0sdSrHJ\nvgV89erVsXPnTrRr1w4eHh6wt7eXOiQqInbuL58MDQ2xdetW9O/fH0FBQTDN2T+b49o14N49cSnm\nq48mTcSVIdw/XrY6dABy+hglJgLa2rmd+5cvX46GDRtKGh6VHEtLS6xcuRL9+vVDcHAwC+cy9Pjx\nYwwdOhSbNm2CgYGB1OFQCXFzc8O0adPQv39/+Pj4oHLlylKHREV0584dTJo0CadOnUL1okyISa11\n6dIFffv2xbBhw3Dw4MHyVzhXKICinmyoUORtcqzmAgICsHTpUgQGBqJSpUpSh1Ns5WKW5ODggHnz\n5rEpnEzNmzcPlStXZuf+csjT0xNjx47NvylczZrAyZPAmTPApUvArVuAuzvQsSMLOFJo1Ajo10/c\nO331KrB3LyZNmgQXFxfZdu6nN+vXrx/atWuHsWPHcjuyzAiCgBEjRmDgwIHokLMVksqNL7/8EkZG\nRpgxY4bUoVARZWVlwcvLCzNnzkTTpk2lDodK2Lx58xAfH4+ff/5Z6lBKR0GNj2Xu2bNn8PLywi+/\n/AKLnCbOMlduZkojR45EYzs7TPryS6lDocLKysKZU6fYub+cmzlzJgRBwLx584AXL4Dz58UmugsW\nADmFnTp1gG++Adq0qTDHjqodhQJo3hywswMGD8aOVatw4sgR/Prrr1JHRqXkp59+wrVr17Bhwwap\nQ6EiWD5+PKLu3cOcOXOkDoVKgUKhwMaNG7Fz504cOXJE6nCokARBwDQvL9SuWRMTJkyQOhwqBTnb\nkefNm4fAwECpw6HCUCqR/ewZxo0Zg7Zt26Jv375SR1RiZL+dKodCocDq1avhbGOD9U2bYvinn0od\nEr3F+sWL8c3cudg+eDDqsoBTbuU0hXNxcoLzpUvoWr++eNzhp58CT58Ct28DAweW6zsAciIIAhae\nP4+VN2/i0JEjsu7cTwXL2Y7s2aIFGuvpwaN3b6lDogIolUp8/fXXOLB/P457e3OrTTlWu1YtbPvq\nK3w8aBDO+frC1tZW6pCoAGlpaRg9ejRuXrmCY/v3l7+tNpSrQYMGWLVqFfp99BHOf/016g4cCPA6\nSW0lp6TAy90d6WlpOPjTT0BKingqazlQrmbOtfT0cPDkScxevBjTp0+HSqWSOiTKh1KpxBdffIGF\n69fjfLdueL9uXcDYWOqwqBSZmppiz969GOnnh+UWFhBGjwaaNhWbrn3yCQs4auLFixcYOHAg9uzZ\nA7/AQDg7O0sdEpWyRo0aYcP336PbqFHYtm2b1OHQGyQlJaFr1664du0a/K9dg2XbtlKHRKUpPBxt\nzM2xcPRotGnTBkePHpU6InqDqKgoeHp6IisrCxeuXoWRzBq9UtH16dMHY/r0gcfMmfBbt05cZU5q\nJywsDM1btoSltjaOd+kC7ejoclPAAcpZEQcAGjVrBn9/f/zzzz/o2bMnUlJSpA6JXpGQkIDOnTsj\nPDwcfv7+sNPXB7p14xaaCqBlq1a4FBSETdu345NPPkF6errYF4f/79VCZGQk2rRpAw0NDZw/fx5m\nZmZSh0RlpNvEiThz9iy+++47TJ069fX+VSSpkJAQuLu7o1GjRjh69Cj09fWlDolKW+PGQK9eGLpg\nAfbt24cRI0Zg8eLF7F+lZi5dugR3d3f07t0b27dvZyPjCmTq0qX4bdMmdF+4EJt27ZI6HPqPo0eP\nok2bNpgyZQpWdu0KrUqVgI8+kjqsElXuijiAeCrOyZMnYWFhgebNmyMiIkLqkAjAjRs34O7uDhcX\nFxw+fBi6+vqAgwPQrJnUoVEZqVevHi5evIgXL17gvffeQ0xMjNQhEYCLFy/Cw8MDAwYMwJYtW1Ct\nWjWpQ6Iy5uDgAH9/fwQFBaF79+5ITk6WOiQCcPjwYXh6emLGjBlYvnw5NDXLzS54KqRWrVrBz88P\nO3bswJAhQ3iAh5rYuHEjevTogdWrV2PatGncQlUBde/eHWfPnsWcOXMwefJkKJVKqUOq8ARBwOLF\nizFixAjs27cPI0eOBLS1gZYtgXJ2c7JcFnEAQEtLC7/++iu++OILtG7dGidPnpQ6pArtwIEDaNeu\nHX744QcsWrTo5dFu/ftzJUYFU6NGDezcuRPdunWDu7s7AgICpA6pQlu3bh169+6NP/74A1OmTOGF\naAVWu3ZtHDt2DFZWVmjevDnCw8OlDqnCEgQB8+fPx5gxY3Dw4EEMGzZM6pBIQubm5vDx8YFSqYSn\npyeioqKkDqnCUiqVmDhxIubOnYtz586hW7duUodEEmrUqBH8/Pxw7do1dO3aFUlJSVKHVGGlpaVh\n8ODB2LFjB/z8/NCqVSvxL2rXBnr0kDa4UlBuizg5xowZg127dmHIkCH46aefuBS1jAmCgNmzZ+Pz\nzz/HX3/99fpRxbVrSxMYSUqhUGDmzJlYuXIlunTpwl4cxXHnDvAOqyaysrIwfvx4LF68GD4+Pvjw\nww9LITiSGy0tLaxYsQJTpkxB69atcezYMalDqnBevHgBLy8v7Nu3D/7+/mjevLnUIZEaqF69Ory9\nvdG7d294eHjAz89P6pAqnMTERHTp0gW3bt2Cv78/7O3tpQ6J1IC+vj6OHj2KRo0awcPDAyEhIVKH\nVOFERUWhbdu2yM7Oho+PD8zNzV/+ZffugK6udMGVknJfxAGAtm3bwtfXFxs3bsTw4cORkZEhdUgV\nwvPnz9G3b18cOXIE/v7+cHd3lzokUjO9evXCmTNn2IujOAwMgDlzgH37Ct1cL6c31Z07d+Dn58eT\nT+g1I0eOxN69ezF8+HAsWbKEN0DKyMOHD9G6dWtoamri3LlzqFu3rtQhkRpRKBSYNm0afvvtN3Tv\n3h2bNm2SOqQK4+bNm3B3d0eTJk3w119/QU9PT+qQSI1oampi+fLlmDZtGjw9PfHXX39JHVKF4evr\nC3d3d3z00Ufw9vZ+vTeViYk0gZWyClHEAYD69evj4sWLSE1NZS+OMnD//n20bNkS2traOHPmDEzK\naQJR8bEXRzHp6ACensCxY8CMGcDx40Bm5huffv36dbi5ucHNzQ2HDh2Cbjm8O0Elo3Xr1rh06RK8\nvb0xZMgQsRk5lZoLFy6gefPm8PLywubNm9mbit6IvTjK1qFDh/Dee+/hu+++w9KlS9mbit7ok08+\nwYEDBzB69GgsXLiQN0BK2YYNG9CzZ0+sWbMG33zzTYVqCVBhijiA2Ivjzz//RJcuXdiLoxSdO3cO\nLVq0wCeffIL169ejatWqUodEao69OIqpY0fxpK8XL4C9e4H584F89mXv27cP77//PmbPno2FCxe+\n7E1F9AYWFha4cOECsrKy4OnpiejoaKlDKpfWrl2Ljz76COvXr2dvKioU9uIofYIgYN68eRg3bhwO\nHz6MIUOGSB0SyUCLFi3g5+eHXbt2YdCgQWxGXgqUSiW+/PJLzJ8/H+fOnUPXrl2lDqnMVagiDiAu\nRf3222+xcuVKdO3aNf9eHLyj8c5+++039O3bF1u2bMGXX37JC1EqtFd7cbRp06bgXhyCAISHi6tO\nVKqyC1JdVa0KvNpcsVkz4JWl3iqVCj/++CO++OILHDlyBAMHDpQgSJKr6tWrY/v27ejVqxfc3d3Z\ni6MEZWVl4fPPP8fSpUvh4+ODDz74QOqQSEbYi6P0PH/+HP3798eBAwfg7+8PDw8PqUMiGTEzM4OP\njw8AoE2bNnj06JHEEZUfCQkJ+OCDDxAaGgo/P78K25uqwhVxcvTq1Qt///03vvvuO3z99dcve3HE\nxwOBgdIGJ0OZmZkYO3YsfvnlF1y8eBEdOnSQOiSSqQJ7ccTGAvv3A9OnAz//DDRuDGhU2F9jebVp\nA5ibA336AEeOAAcPAoKA1NRU9O3bF8eOHYO/vz9cXV2ljpRk6L+9ODZv3ix1SLL35MkTdOrUCffu\n3YOfnx9sbGykDolkKKcXx/Tp09mLo4Tk9KaqWrUqzp07B1NTU6lDIhmqVq0atm7dir59+8LDwwP/\n/POP1CHJ3s2bN+Hh4QEnJ6cK35uqQs9+cnpxBAQEoEePHnj69Cmwaxfw7JnUoclKfGQkOrRvj5iY\nGPj6+sLKykrqkEjmWrVqlbcXR3w8sHQpMGsWcPQokJgI9O0LmJlJHar6qFQJ+PxzcWvVwIHAX3/h\n/tq1aNWqFXR0dHDmzBkYGxtLHSXJXE4vjh9//JG9OIrhenAw3N3d0bx5cxw8eBA6OjpSh0QyN2zY\nMPbiKC5BgI+PD5o3b44hQ4Zg48aNbAlAxaJQKDB16lSsXbsWvXr1wvr166UOSbYOHDiAdu3aYdas\nWVi8eHGFbwlQoYs4gNiL4/jx42jYsCE8mjVD+PnzAJs3Fkp2djbWrVuHZq6uaJuZiX3DhqEWBzsq\nIa/24mjbrRuitLVf/qWzM9C2rXTBqat/mxRneHhgmZYW3KdMwcgRI7Bu3TpUqVJF4uCovGjUqBH8\n/f1f9uKIjZU6JNlITU3FrOnT0a5VK8z9/nvMnz+/wl+IUsl5tRfHQC8vvCjkiYUkbtGY2KcP+vTu\njY0bN2LixIlsCUAlpkuXLjh//jwWLFiAL7/8Esr0dM43Cynq3j0M79sXn48ahcOHD2Pw4MFSh6QW\nKnwRBxB7caxcuRKTJ05Ey2PH8P2RI0hJSZE6LLUlCAKOHDmCpk2bYsuWLTiwaxfmHDkCjV69gMqV\npQ6PypGcXhy9e/WC81dfYVliItJ1dYHBgwFeXL1GpVJh+/btsLOzw9mHD3Hu0iWMnzCBF6JU4nJ6\ncdgbGMDRzg7r1q3jqpwCKJVK/P7777C2tsbdyEgEBQdjAJukUikwMzODz99/QyMiAs2aNMGff/4J\nFXvHvVF6ejoWLVoEO1tbZMbH4/r+/ejUqZPUYVE5ZGdnBz8/P4TdvAl3a2scW7qUK+YKkJKSghkz\nZsDR0RHG8fG40bs33G/cADhHB8AiTh6jJkxAwJUruPv8OaytrbFs2TIeqfofQUFBeP/99zF58mTM\nnz8fZ8+ehVvbtoCBASfVVCoUCgWmTZ+O0xcv4nxaGqy3bME6b29OGP/j3Llz8PDwwPLly7Fx40Yc\nPHgQ9o0aSR0WlWOampr4aetW7DpwAN7e3mjcuDEnjP8hCAIOHjyIJk2aYNeuXfjrr7+wZcsW1Kug\njRipbFS7dAlbOnTAr998g8WLF8PV1RVHjx7lhPEVKpUKW7Zsga2tLS5duoQLf/+NX318YNS6tdSh\nUTmmp6eHI1OnYoadHSYuW4b3PD3ZK+c/srKy8Ouvv8LGxgZRUVG44u2Neba20AGAp0+BWrWkDlE9\nCIJQ6IeLi4tQUdy4cUPo1auXYG5uLqxdu1bIysqSOiRJ3bt3T/Dy8hKMjY2F33//vUL+PAAECkXI\nl7J8VKTc9PX1Fdq1aydYW1sLO3bsELKzs6UOqXgyM4v18ps3bwrdunUTGjRoIGzfvl3+P493oK65\nWZHyUqVSCSdPnhRcXV0FJycn4ciRI4JKpZI6LEn5+fkJbdu2FRo3biz89ddfFfLnwdyUQHa2IFy4\nkDu2qFQqYffu3YKdnZ3Qpk0b4cKFCxIHKL1Tp04JTk5OQvPmzQUfHx+pw5EEc1NCKpUgXL4sZE2d\nKmz4+muhXr16Qrdu3YQrV65IHZmkVCqVsGfPHsHa2lro2LHjy59HQoIgjB4tPnx9pQ2yDBQ2N5lY\nb3Hp0iWhffv2grW1tbBz5853nyDJqejxyoVmYmKiMGXKFEFfX1+YNWuWkJKSImFg0lLXAU+ogLlZ\nriaMe/cKwrVrRX5ZdHS0MGrUKMHQ0FBYunSpkJ6eXgrByYO65mZFy0tBeHkRZm9vX2EnjHfu3BH6\n9esnmJqaCuvWrauQNz1yMDfVR1ZWlrBhwwahXr16QteuXSvkhPHatWvCBx98IFhaWgq7du2S73VD\nCWBuqoGMDEE4f15IT0sTfv75Z8HY2FgYMGCAEBERIXVkZe7ixYtCy5YtBUdHR+H48eOvP+GbbwTh\n888FoQJc6xY2N7md6i08PDxw+vRprFq1CkuWLHm3JamRkcD586UXZElJSQH27AGePkVGRgaWLVsG\nW1tbPH36FDdu3MD333+PmjVrSh0lERQKBTp06AB/f3/MnDkTkydPhqenJy5evFj4N4mPL70Ai8LJ\nCfjlF2DDBuD587c+PTU1Fd9//z0cHBygo6ODsLAwTJo0iY2LSS0oFAr873//w/Xr1zF8+HAMHDgQ\n3bt3x9WrV6UOrdQlJCRg4sSJcHNzQ+PGjREeHo4RI0ZAU1NT6tCIoKmpiWHDhiEsLAydOnXCBx98\nAC8vL9y+fVvq0EpdVFQURowYgQ4dOuDDDz/ErVu30KdPH/aLI2lVrgy0aYMqVatiwoQJiIiIQKNG\njdC8eXOMHTsW0dHRUkdY6iIiItCnTx/069cPo0ePRnBwcP49qaysxENNeK2bi0WcQurQoQP8/PyK\nPmEMDQWWLAFMTUsnsOBgICmpeO/x7BmwezcwfbrYGPXoUdjZ2eHMmTM4c+YM1qxZAxMTk5KJl6gE\n5Tdh7Nat29snjKGhgJ9f8QMoid4f9esDDRsCly4B338PXL6c79OUSiVWr14NGxsbREREICgoCIsX\nL4aenl7xYyAqYZUqVaowE8bcxqh2dsjIyMCtW7fw7bffokaNGlKHRvSaKlWq5DthjIqKkjq0EpeS\nkoKZM2fC0dERRkZGCA8Px4QJE1CZh3CQGtLW1sbMmTMRFhYGHR0dNGnSBFOnTkVCQoLUoZW4x48f\nY/z48WjRogVcXFwQHh6OoUOHvvm0RisroHnzsg1SzbGIUwSvThhHjBjx9gljYCCwYgWgVAKWliUb\nTEYGsHkzcOwY8K6TuOfPgX37gBkzgJMnce7xY3gsWIClS5di/fr1OHToEBo3blyycROVglcnjJ07\ndy54wpieLuZOSTh1CijKqrw3ad9e/DMlBdi4EXilyZ0gCDh06BAcHR2xc+dOHDp0CNu2bUP9+vWL\n/3WJSlmVKlUwfvx4REREoHHjxuVqwqhSqbB161bY2trin3/+gY+PD1atWoU6depIHRrRW/13wujo\n6FhuJoyvNkaNjIzE5cuXMX/+fOjo6EgdGtFb1a5dGwsXLsT169fx7Nkz2NraYs6cOUhNTZU6tGJ7\n8eIF5s2bB3t7eygUCoSEhGDatGmoVq1awS90dARsbcsmSJlgEecdVKpUCUOHDi14wnjmDLBuHZCd\nLd5l19IquQAePADmzAEuXgRatXr394mKAq5fx63YWPQ4dgxDz5/HxMmT4e/vj3bt2pVcvERlJL8J\n45gxY/JOGPfsARISSuY0tYgI4O+/i/8+Tk5it30NDUBXV/wcQEBAANq1a4dv/j1h5PTp03BxcSn+\n1yMqY9ra2pgxY0a5mTCePn0arq6uWLlyJbZu3Yr9+/fDzs5O6rCIiiy/CePs2bNlOWEUBAH79u2D\ng4MD9u/fj2PHjmHTpk2wsLCQOjSiIjM1NcVvv/2GS5cuISQkBNbW1lixYgUyMjKkDq3IsrOzsXHj\nRtja2iI4OBi+vr5YsWIFDA0NC/cGenriNTLl4k+jGN44YbxxA3j48OUd+pK6sBME4PhxYOFCsZ+H\nlhbg5vbObxd19y7G7NgBz8OH4Wlnh9A7d+Dl5QUNJgnJXM6EMTw8HLq6unB0dMRXX32FBF/fl/2p\nSqKIo6EhrmaLiyve+2hqAu3aARMnQkhNxe25czGgf3/06tULgwYNwtWrV9G1a1fu3yfZk/OEURAE\nXL16FV26dMHo0aPxzTff4NKlS2jTpo3UoREV26sTxtDQUFhZWeHnn3+WxYRRlZmJixcvok2bNpg1\naxZWrFiBEydOoFmzZlKHRlRsVlZW2LZtG44fP46TJ0/CxsYGGzZsgFKplDq0t8rOzsbx48fh7OyM\nNWvWYOfOndi9ezesra2lDk322G2vBORMGMeNG4eFCxfC0dMTw9u2RS9NTVhYW8PEyqpkftDp6UC1\nai/7cDg7A9WrF/gSQRAQGRmJ0NBQhISEICQkJPfjtOfPMeq99xC2fj30dXTe+l5EcqOvr4+FCxfi\niy++wOwff4TtBx9ggqMjOhkZwSIxEcYqVfGKlhoaQFaWuAXqq6+KdJcgOzsbDx48eJmTt24hNCwM\nITdvAtnZ+GLyZKz74w/21aByKWfCOHnyZMyaNQtWVlaY+tVXaNO2LSwsLGBkZCRZ0VKpVOLu3bt5\nxsucj7U1NfHV9OnYt28fm4lTuZQzYbx27RpmzpyJZUuXYvqMGXBzc4OFhQVq164tWW5mZmbi9u3b\nr13Pht28iTq1a2Pm7NkYUlBfDSIZc3R0xKFDh3Dx4kVMnz4dixYtwvSvv0ZTe3uYV68OXQcHyXIz\nLS0N4eHhr42ZEeHhaFi7Nn5Ytgz/690bipLcmVLBKYpyypKrq6sQGBhYiuGUD9HR0Vg4Zw4CfH3x\nMC4OjxMSYGxsDAsLi9yHubl5ns91dHTenniCAKxaBdy/DxgYAD175q7yyRnY/ps8YWFhqFmzJuzs\n7GBvb5/7p729PUxNTcWvKQglsyqhnFMoFEGCILhKHUd+mJuFc+fOHSz4/ntcvXEDkVFRSHz6FHXr\n1i0wNws8kW3tWrH3FQD8739A586vPSU9PR3h4eGvTQgjIiJgYGCQJydzPpZyAitH6pqbzMvCu+bn\nh8WTJ+NWWhoePnyI1NRUmJmZ5cnFV3PU3Ny82AXO58+fIyws7LUJ4d27d2FiYpInJ3P+rH32rJjr\nzM9CYW7K3z/Tp2P50aOIUCrxMCoK6enp+eblq7lZtWrVYn3NlJSUfG8+PnjwABYWFnmvZxs0gO3G\njdB1cADGjeMNyUJibsqbIAg4ceQIfp00CfeSk/Hg2TMIlSq9MTctLCxQt27dYjf1TkxMzPcGR1RU\nFBo2bJj3etbQELZVqkB7+3agRQvAwQFwVbt/cmqnsLnJIk4ZyMzMRHR0NB4+fPjGB4A3Doi5iXf6\nNFJ270bohx8iNCoKITExCAkNRWhoKO7fv//awGZnZwc7Ozvo6upK/BMoH9R1wAOYm+8qIyMDjx49\nKjA3K1eu/ObcvHgRpvfvQ1NbG0murggxMUFIRESewe3Vge3Vgo2trS20tbWl/hGUC+qam8zLIkhK\nAmrUEI9chdj8sKDcjIyMRI0aNQq8YK1Tpw40NDTw5MmT3Hx89eIzPj4e1tbWrxVRra2tUf1NE8GM\nDB5xWgTMTZkTBODrrwFzc2DsWEBLC6mpqYiMjHxjbj569Ai6uroF5qahgQEUGhqIiYnJt1jz9OlT\n2NravlZEtbKyen0FXGAgcP06MHiwuDWZCoW5KXNZWcDWrUBQkPhxw4Z4OnZs7viYX27GxMSgdu3a\nBeZm7dq1AQCRkZH5FmvS0tJeu561s7NDw4YNofXfVTZPn4qH52RliZ9/9ZV4yhQVqLC5yd92ZaBy\n5cqoX79+gafJPH369LVkO3r0aJ7Eq1mlCtKzsmB7+XJu4gwaNOjNAxsRFahKlSqwtLSE5RtOjxME\nAYmJia8NipcvXxY/vnMH8YmJqFG5MrIrVcozsI0cORL29vb5D2xElNd/TlmsXr06bGxsYGNjk+/T\nBUHA48ePX8tNPz+/3I8TExNRrUoVaGhq5snNjh07ws7ODvXr1y/6tguOs1SRJCUB9esDo0fnFki0\ntbVzr0Hzo1KpEBcXl+d69sGDB/Dx8cn9PCU5GZWrVEHVatXyTAS7d+8OOzs7mJubF36rs6kpnU2q\nIAAAIABJREFU4OLC1XFUsWhpAd27A3fvin1S79+HTpUqaNKkCZo0aZLvS7KzsxETE5MnNyMiInD6\n9Oncz9PS0qAhCNDR188dMx0cHPDxxx/D3t4eJiYmhV8prqMDeHqKJ7kCgL5+CX3zBLCIozZ0dHQK\nTDylUomkpCTU1teHBvf6EpUJhUKB2rVro3bt2nD698So/8rKykJycjIMDAy4BYqojCgUChgZGcHI\nyAiub1ienREcjNT796Hfuzdzk+hdaGoCY8YARbju1NDQgImJCUxMTODh4fH6E8LD8WLhQmSYm0Pv\nm2+AYm69gqlp8V5PJFcGBsDUqcDKleLJxbdvi1uW3qBSpUowMzODmZkZWrZsme9zUv39kf3bb9CZ\nP19cHVtQS4HC6NwZOHcOUCrF01epxPAYIpnQ1NSEoaEhCzhEakZLSwuGhoacJBKpmSoNGqB2r17M\nTaJ3VatWkQo4hXLnDqobG0Pv88+LX8Ahquhq1gQmTQLs7YHQ0GK/nbarK3QMDIA//wR27ix+fLVq\niaev6uryiPASxp8mERERlT96erxoJFI3z5+LvTEMDKSOhKh8qFoV+PxzsWBSHNnZwG+/iach37wp\n9rQpCZ06AcbGJfNelItXN0REREREVPp69uS2CqKSpqkJdOxYvPeoVAnw8hL77QDAixfFjwsQVwv1\n7l0y70W5WMQhIiIiIqLSx0b/RKWjJLYO6+kBw4eLH6elFf/9ctSrV3LvRQBYxCEiIiIiIiIiBwfg\ngw9KbiUOlQoWcYiIiIiIiIhI3PZobg4IgtSR0BuwiENERERERERE4qEAI0aIR4OTWtKUOgAiIiIi\nIiIiUhNsQK7WuBKHiIiIiIiIiEgGWMQhIiIiIiIiIpIBFnGIiIiIiIiIiGSARRwiIiIiIiIiIhlg\nEYeIiIiIiIiISAZYxCEiIiIiIiIikgEWcYiIiIiIiIiIZIBFHCIiIiIiIiIiGWARh4iIiIiIiIhI\nBljEISIiIiIiIiKSARZxiIiIiIiIiIhkgEUcIiIiIiIiIiIZYBGHiIiIiIiIiEgGWMQhIiIiIiIi\nIpIBFnGIiIiIiIiIiGSARRwiIiIiIiIiIhlQCIJQ+CcrFI8BPCi9cIjUWj1BEAylDiI/zE2q4NQy\nN5mXRMxNIjXF3CRST4XKzSIVcYiIiIiIiIiISBrcTkVEREREREREJAMs4hARERERERERyQCLOERE\nREREREREMsAiDhERERERERGRDLCIQ0REREREREQkAyziEBERERERERHJAIs4REREREREREQywCIO\nEREREREREZEMsIhDRERERERERCQDLOIQEREREREREckAizhERERERERERDLAIg4RERERERERkQyw\niENEREREREREJAMs4hARERERERERyQCLOEREREREREREMsAiDhERERERERGRDLCIQ0REREREREQk\nAyziEBERERERERHJAIs4REREREREREQywCIOEREREREREZEMsIhDRERERERERCQDLOIQERERERER\nEckAizhERERERERERDLAIg4RERERERERkQywiENEREREREREJAMs4hARERERERERyQCLOERERERE\nREREMsAiDhERERERERGRDLCIQ0REREREREQkAyziEBERERERERHJAIs4REREREREREQywCIOERER\nEREREZEMsIhDRERERERERCQDLOIQEREREREREckAizhERERERERERDLAIg4RERERERERkQywiENE\nREREREREJAMs4hARERERERERyQCLOEREREREREREMsAiDhERERERERGRDLCIQ0REREREREQkAyzi\nEBERERERERHJAIs4REREREREREQywCIOEREREREREZEMsIhDRERERERERCQDLOIQEREREREREcmA\nZlGebGBgINSvX7+UQiFSb0FBQU8EQTCUOo78MDepIlPX3GReUkXH3CRST8xNIvVU2NwsUhGnfv36\nCAwMfPeoiGRMoVA8kDqGN2FuUkWmrrnJvKSKjrlJpJ6Ym0TqqbC5ye1UREREREREREQywCIOERER\nEREREZEMsIhDRERERERERCQDLOIQEREREREREckAizhERERERERERDLAIg4RERERERERkQywiENE\nREREREREJAMs4hARERERERERyQCLOEREREREREREMsAiDhERERERERGRDLCIQ0REREREREQkAyzi\nEBERERERERHJAIs4REREREREREQywCIOEREREREREZEMsIhDRERERERERCQDLOIQEREREREREckA\nizhERERERERERDLAIg4RERERERERkQywiENEREREREREJAMs4hARERERERERyQCLOERERERERERE\nMsAiDhERERERERGRDLCIQ0REREREREQkAyziEBERERERERHJAIs4REREREREREQywCIOERERERER\nEZEMsIhDRERERERERCQDLOIQEREREREREckAizhERERERERERDLAIg4RERERERERkQywiENERERE\nREREJAMs4hARERERERERyQCLOEREREREREREMsAiDhERERERERGRDLCIQ0REREREREQkAyziEBER\nERERERHJAIs4REREREREREQywCIOEREREREREZEMsIhDRERERERERCQDLOIQEREREREREckAizhE\nRERERERERDLAIg4RERERERERkQywiENEREREREREJAMs4hARERERERERyQCLOEREREREREREMsAi\nDhERERERERGRDLCIQ0REREREREQkAyziEBERERERERHJAIs4REREREREREQywCIOERERUVkSBKkj\nICIiIpliEYeIiIioLF29CiQlSR0FERERyRCLOERERERlSaEAfvsNyMp6+3NDQoBnz0o/JpKvFy+k\njoCIiMoQizhERET0brKzpY5AnmrVAh48ALZte/vWqipVgMWLgeTksomN5GfnTiAjo2ivefQIiIoq\nnXjUWXQ0kJYmdRRERMXCIg4REREV3YMHQHCw1FGoh/v3gdTUwj+/Vi3xT19f4MyZgp9rYQEkJIiF\nnCdP3jlEKsdSU4E//gBUqsK/pmpVYNUq4Pnz0otL3Tx/Dvz+u/i9F1ZGBlfCEZHaYRGHiIiIiiY+\nHli5EtDVlToS9ZCYCOzbV/jn5xRxAGDXLiA8/M3P1dQE6tcXCziLFwNxce8cJpVTBgZin6Xduwv/\nGh0d8d/UmjVFK/7IVXY2sHo1kJkpbmcsrL17i1agJSosQRD/Xaani4XCxEQ2vQfE30dhYeKNoidP\nxO2ib/u5VITfYf+hKXUAREREJCPJycBPP4kXncbGRX/9nTtAw4ZFm0ipu6ws4MIFoGVLwNLy7c/X\n0gKqVxcnlLa2gFJZ8PMtLYHbt8Wf/dKlwOTJQJ06JRM7yZ+hofjn6dPix+3avf01WlpAjRpAaKhY\n/Onbt3RjlNquXeLEsEGDwr8mNBQ4exZo377UwqIK6OhR8ZGZ+bI4UasW8MkngL6+tLGpAw0N8fHr\nr8DTp+J/UyjEMXPgQMDF5fXXBASIK1ttbMRH/friDZByrHx/d0QkT4JQviZ4ROru+XPxjuCrK0Ty\n8+IFsGKFuL2nRg2gZs2ifZ0HD8QJ49dfv3us6iinQbG3NzBjhngB+ja9e4snVJ0/L150FsTSUtwC\nkp4OeHmxgEN51a798uMTJwBzc8DK6u2v09ERc//0acDMTCxClkcXLrzctljY1YPp6cCmTeLHvB6h\ngqhUQEqKOD6++nj+XCwm5BT2lUrg1i0gJiZvAadZM2DwYEBb++1fKyUFiIwEjIxeFm/VXXo6sGeP\nWJCpVSvvw8JCXEn4X9bWwMyZ4jbR0FDxZ6VUin286tV7/TUeHuJ1yYED4udaWuLP3dMTcHYu/e9R\nAhW2iJOdnY1bt24hICAAUTduoK6pKcybNoW5uTnMzMygXZhEquAyMzPx5MkTxMfH4/Hjx4iPjET8\nrVuobmUFZxcXNGnSBFWLsu+YCEBWVhau//QT/JVKJGRnw8zcHOb/PszMzFCtWjWpQ1R7GRkZYk7G\nx7/Mz7g46FWuDOfmzdHYwQFa1atLHSapi7g4YO3atxZW0tPTceXiRQTExOD59esws7CA+blzMDc3\nR926dVGlSpW3f50VK8SVJ+VNThHn0SPg77+BDh3yfVpaWtrLnExNRXxkJJ788w/qLF0Kl+7dYWtr\ni0qVKr3+woYNxbu0fn7ixbCDQ7m/y1ghveMNjOc1aiA4OxsBoaHIdnWFWUAAzGNjYW5uDlNTU2hp\naeX/Qh0dcUKpry9ORFWqtxcglUrg8WPAxKTIcZa4sDBxsldQzC9eiHmpqSnG/oYizvPnz/OOmdev\n48nly7BQKOBy7x4sDQygUZjiLFVMhw8DPj4vP1cogJ498bR2bQSePo2gEydQ6Z9/YF6lCsytrWGu\nrw/jp0+hOWAA0KpV/nmvUgFBQWLR5tEj8c+UFOC994D+/cvsWyu2qlWBPn2ALVvy/oxMTYGpU9/8\nulq1IEyYgNRduxB/4ADiTUzweP16xC9diiRTU1h+/DGcnZ1Rr149KBQK4MMPxa2Pp0+LY3JUlFgk\nKqcUQhH23rm6ugqBgYGlGE7pEAQB9+7dg7+/PwICAhAQEIDLly/DxMQEbk2bwuL+fURnZyNSVxeP\nHj1CZGQkqlatmmfiaP6fiaS5uTmqJSWJVcTyVvARBDzdswd/Z2cj6pWJYO7A9u/HqampMDAwgJGR\nkfjIyIBhUhJSGjRA8MOHCA8Ph42NDVxcXODs7AwXFxc4OjqiukwnjwqFIkgQBFep48iPXHNTpVIh\nPDwcAQEBufl5/do1NKhaFW516qCOkxMeaWggMjISjx49QlRUFLS1tQvMTTMzs7dPJmXsyZMnOHPi\nBKLj4vA4ISHf/ExLS4OhoeHL3DQygmF6Oh7fuIHglBTcT0pCo8aN8+Smg4ODbH9u6pqbssjL0FCx\nT4SNDTBuXO5/ViqVCAkJyTNuhoSEwNbWFm5ubtCpXh2Pbt9GZHIyHj16hOjoaOjp6b05N/X1UffP\nP6EVEwN06wZ07y7hN10Kjh9H1J9/4nxMDGLr1kW8vn6e/MzJzaysrJc5+W+OGtSsiagnTxAcHIyY\nmBg4OjrC2dk5Nzft7e1fTsKfPAFmzRJX8eQUigRB3Nr2tlVUEmBuFpFKBRw6JG7fecMqt8zMTFy/\nfj3PuHn37l042NvDzdkZVWrWRGRkZO64GRcXBwMDg/xzMzYWZmZmMDl6FJrz5hXuzr5SCSxZIk6+\npCxqBASIq9gmT37rU++dP48Ls2Yh3soK8SkpiK9e/bUbHYIg5B0zDQ2hr6uL+3fuIOjqVSQlJ8PJ\nySk3L52dnWFjY5N/0VUGmJslJD4euHQJuHQJ6XFxuJKQAP9nzxCgrY2AsDA8evQIzZo1g2uzZkBU\nFCKVSkTGxeHR3bt4kpKCOnXq5JubOR/XCQ9Hpe3bX369Tp2A//1PnqvDBEEssOzenbsKKaRKFfhb\nWiI+MzPfuWZ8fDy0tLRgqKMDo9q1YWRhASMtLehoayMiORlBQUFIT09/OWY6O8P53j1YPnwIRc5K\nwy5dxPFSJjc+Cpub5bKIExsbm2dwCwwMRNWqVeHm5gY3Nze4u7vDxcUFenp64j+imzeBY8cAd3eg\nbVsIgoCEhITcAfDVwTDn46ioKJhpa+NjGxv0mzULTTt1EquAMpaQkIADBw5gz6pV8Ll2DS3t7WHZ\nuvVrF5w5D11d3Zd3JZKSxAtLd3fxAl1XF+np6bh+/TqCg4MRFBSE4OBg3Lp1Cw0bNnw5eXR2RjMn\nJ1msfFLXAQ9Q89xUKgFNTQiCgMjIyNwJob+/P4KCgqCvrw93d/fc/HR+8AA1L14UX6uvL04s/62k\nq1QqPH78uMDcjH70CDZWVujr5YV+/frBthzc9Y+Ojsa+ffuwd+9eBAYGwtPEBPUaNoRRixb55qeO\njk7e30fJyeJe4f37AQDPO3TA1bp1c/MyODgYERERsLOzyzN5dHR0lMXKJ3XNTbXOSwC4eBHYuhVC\ndjbuduiAAJUqd9y8cuUKTE1N84ybzZo1e+O/h+zsbMTFxRWYm3FxcXCsWxf9evRA30mTUK9evTL+\nhkvevXv3sHfvXuzx9kbo3bto36YNzBo2fL2I+u/nNWvWLPBa4enTp7hy5UpubgYFBeHhw4dwcHB4\nOXlMTobDgweoPG/ey4n+2rXiXUgzszL6zguHufkOTp8GjhwB+veHytkZYf/e6MgZN2/cuIEGDRrk\nGTcdHR1RuXLlfN9OqVQiJiamwNx8Eh8PDxcX9Bs8GH369IFxQb2usrOBTz8VJ5KdO5fSD+EtzpwR\nj1X/4AOgV698nxISEoI9e/Zg7969iIqKQjtXV5ja2MDI0BCGdeq8lp81atQoMDcTEhJyx8uc3IyL\ni0PTpk3zFHbstbWhWZS+OxJhbhZDWhqUfn64deAAAoKD4Z+cjICUFIRGRcHOyAhuHTvCrVUruLu7\no1GjRtB8Q/EgMzMT0dHReXLxv7mZnJQEzzp10K9hQ/QePRr6Xl7yLOD8SxAEXN67F3sXLcKeJ0+Q\nmpwMz06dYFy3br7Xs4aGhm+9+R8bG5snL4ODg/H0yRM4ubnBRU8PzqmpcLG2hvXYsdBwdBRfVJhV\nhxKpcEWcpKQkTBw3DqfPnsXzjAy4vTK4ubm5wdTU9O1vEhtb6CaNqufPceW77/Cnjw92hoWhsq4u\n+g4Zgn79+uH/7J13eJPVF8e/SVvahi66oC1t2RQKBTvYGwQE2VNE/SFDpoiKoIKgKIiK7KWi7CVT\ntpVVSilDViktmy666Z5J3vP749JCpaVJmuRN2vt5njy0NnnvScz3Pfeec+65zZo1q+C70R/x8fHY\nv38/9u7diytXruD15s0xxMEBfXv0gI2PD9Cxo2pf8hs32B79cj6/wsJChIeHP188hoYi7PZt9O7R\nAxt37IC1uv0V9IihOjzAgLWpUCBu8WJ8FByMoGdHERctCAMCAuDv7w+nFzN/gsCye1lZ7Ps0YYJ6\ni5IDB6A8cQIXe/bErjNn8Oeff8LZ2RnDhw/HiBEjUF+VhqMGwuPHj9nicO9eREREoG/fvhgyZAh6\n9eoFy8uXgW3bgClTgCKH9CqI2Mk5J06w3xs1eil7mZeXh7CwsBKBnYiICIzu2BGrDh0qc4FgCBiq\nNg1Wl8+4ExGBjwcNQmhUFCzt7dGqdetin+nv7w87LZ88JZfLERQUhF07d2Lf/v1o0KABRowYgWHD\nhqG2gQUfEBfHKltK8UmRkZHYu3cv9u7di9jYWAwcOBCDBw9Gt27ddKKT7Oxs3Lhx43lg58oVPLx3\nD9MnTsS3S5eyZMrRoywZNWEC22plIHBtakB+Pi6PG4fZN2/iSlQUHBwcSvhNX19frSe+8vPz8c8/\n/2DXrl04fPgwXnvtNQwfPhxDhgwp6aMB5qcnTWJZ7Tlz9LutiohtWzl8mP3+0UdAkybP/kS4fv16\nsd/MzMzE4MGDMWTIEHTo0EEnFTPp6em4du1aicVjbHQ0FixYgI8+/tigk7tcm5oR+Pff+GbcOFyL\nj4ebvT1aBQQgoEcPBLRujZYSCSxbtdJqcCAnOxtHf/4Zu/7+G4FhYWjfvj1GjBiBgQMHwtbWVmvj\n6BJBEHDx4sXioKpUKsWQPn0wePhwBLRrp5NtiinJybhapM2QEFy9cAGpOTlY9csvGD16NNvWZW8P\neHtrfeyKorI2iUjlh5+fHxki4eHh1MDdnT7086N7I0aQ8OiRXscXCgvpYmAgffLxx+Tu7k5Nmzal\nr7/+miIiIvRqh6o8evSIlixZQu3bt6caNWrQ6NGjad++fZSTmUmUkaFfY06fpvyxY2lchw7k4+ND\njx8/1u/4agDgCqmhF30+DFKbSiVd+OwzcpXJaH6fPhT1+DEJgvDq19y8STRhAtHGjUT5+SqPQ8nJ\nROHhRB98QHT6dPGfFAoFnT17liZPnkzOzs7k5+dHixcvpkd6vkeoSmRkJH333Xfk6+tLTk5ONG7c\nODp27BgVFBSUfKIgEP36K9FHHxGlpLDfy/tsiYiOH2ef75QpRHJ5uU/PvH+fBnp7U6dWrSg5OVnD\nd6V7DFWbBqnLZxw5coScnJxo2dKl9OTSJb2PX1hYSMePH6cxY8aQvb09tW/fnlasWEFPnjx5+cmC\nQJSZqV8DV60iiol5NrxA165dozlz5lCTJk3Izc2Npk2bRqdPnya5CjrSBUlJSdSpUycaOHAgZWVl\nET1+zLT9wQdEZ86IYlNpcG2qz5YtW8jRwYE2/vGHKPfdvLw82r9/P40cOZJsbW3p9ddfp19//ZVS\nU1PZEwSBfdcmTCBauJD5YH0gCETbtz8fe9IkUubl0YULF+jTTz+levXqUb169WjmzJkUGhpKSn3Z\n9R8eP35MPj4+NH78+Jd9twHBtakegiDQkiVLqFatWrR39WpKi43V3+B5eURElJmZSdu3b6cBAwaQ\njY0N9evXj7Zu3UqZr/KPqswN1aWwkOjkSaKgIKKwMOYrs7JKjCWXy+nUqVM0ZcoUcnV1JW9vb/rq\nq6/o+vXr5a8FdETY1atUt25d+uKLL0iZkkI0cSK7pxiYTlXVptEL66+//iJHR0f6Y906ojlz2I39\n+nXR7FEqlRQSEkLTp08nV1dX8vHxoe+++47u3bvHnpCfr/riVBs8mxBHRkbSwoULyc/PjxwdHWns\n2LF09OhR8R3Mr78SLV5MQk4OLVmyhFxcXOjChQvi2lQGhurwyEC1+ce8eeRka0uH1q9XKWBARESb\nNhGFhqo30K1bRMuXE33yCdHatWU6LLlcTidPnqQJEyaQo6MjtW7dmpYsWULR0dHqjadFBEGg69ev\n09y5c6lp06bk6upKU6dOVW1xmJdHNHcu0aJF7DMruseUx7lzbKF3965KT1cqFDTrww+pfv36dPv2\nbdXG0DOGqk1D1KUgCLR48WJycXGh4OBgsc0hIqKCggI6fPgwvfPOO2RnZ0ddunShtWvXUmJiItPz\njh3FARW9cO8eKcePpwvbthUvDuvWrUuffvopXbhwQbTF4X8pKCigMWPGUMuWLSk6KordA4sWuH/+\nqZvJu5pwbaqOQqEo/r6FhYWJbQ4REeXk5NCff/5JQ4cOJRsbG3rjjTfoj99/p7T33mPfs2nTWHJA\nX8jlJP/iCzo9cCBNbdeO3NzcqGnTpjR37lxRF4f/JTMzk958803q0qXL8+CXgcG1qTp5eXn07rvv\nUosWLejxnTtim0NEROnp6bR582bq27cv2djY0ODBg2nnzp2UnZ3NnnDvHtHBg7rzA4mJRPPmlQiq\nFly+TEePHqWxY8eSk5MT+fn50cKFCykyMlI3NmhAUlIStW/fnoYMGULZS5Yw2+fOJTKg5G6lD+II\ngkDfffcdubq6Pl/0P3lC9OGHJTLxYqJUKikoKIimTJlCNWvWJF9fX/p51iz2ZdHHwvH2bZJPn04t\nGzUiV1dXmjJlCp06dUq0zGGpbNpUIqh16NAhcnR0pG3btoloVOkYqsMjA9OmXC6n6dOnU8OGDdVb\n9AsC0dOn6g/422/PM9DnzxdnLF5FYWEhnThxgt5//31WBRAQQL//+qv6Y1eAp0+fUn03N6rr5qb5\n4jAmhlXVTJpEtHmz6q/791+iEyfUGuqPP/4gJycnOqHm6/SBoWrTkHRJRJSbm0ujRo0iX19fUYOX\nryIvL48OHDhAb731Ftna2lKP9u1pX69eLPOnJ+6dPEluNWpQ0/r1ac6cOXTt2jWDWRz+F0EQ6Icf\nfiBXV1e6WJTImjKFKCJCr59ZWXBtqsbTp0+pV69e1L17d0pJSRHbnFLJysqiHTt20MCBA8nG3Jz6\nNWxIgfPn6/V7duH4cXKqXp18mzWj78aNM9iKd6LnQbkGDRoY1CK2CK5N1YiLjaVWLVrQsPbtKfub\nb4gMMJn19OlT+v3336lXr15ka2tLw3v3posDB+o+MJGbS7RiBdGECXSoVy+qYWtL7du3p59//tlg\nK96JiPLz8+ndd98l3yZNKPbtt5nfnDiRzYsNwNdX6iBOdnY2DR8+nFq1akWx/y1nCwsjOnBAHMNe\ngUKhoFOnTtGKUaPYl2XyZKLAQN19WYKC2BdywgQKnzePlAqFbsapCIJQqvO/efMm1alTh+bOnWsw\nGU8iw3V4ZEDaTElJoe7du1PPnj3pqSYBGXXJy2MLlqIb8NGjamuqICKCjvTrRxs++khHRpaBIFDY\nt9+SMGGCZoFnuZzol1+eZ0E+/FC9yXRWltpDBgUFUc2aNWnlypVqv1aXGKo2DUWXREQxMTHk5+dH\nb731FuXk5Ihtjkrk5OTQnj17aPfGjXodVy6XG2zVWVkcPHiQHO3saOfnnxNNn060c6fYJhER16Yq\n3L59mxo2bEjTp083rCTbK8hITKQtv/5Kx44e1eu4WVlZ9PDBA/aLASy2VGHDhg3k7OxMgYGBYptS\nAq7Ncrh1i0K//JLcrKzoW39/EsaPN6itqmWRnJxMv6xfTyG7dulnQKWSaM8eStm9u/Tt0AaKIAi0\naOFCqm1tTVcGDWIFFmLvTnlGpQ3iREVFUcuWLemdd96hvLIy7klJ+jVKVZRKouBgVnr6119Ee/bo\nZutXWhrbXnH/PvvZSBzdiyQmJlLbtm1p2LBhBrPgMFSHRwaizVu3blH9+vXp008/1d9ENCSEBTBm\nzVJ9OxERu1E/ecL6SEyfzrZjiTF5FgR2L5gwgd0PBIHo6lXVXy+XE+3e/TyQo4feJg8ePKCmTZvS\n5MmTDWbBYajaNARdUmYmnd+9m1xdXWnx4sUGW1HCqTjXL18mDw8Pmj9mDAsOP3wotklcm+Vw6NAh\ncnJyot9//11sUzg65MyZM1SzZk1au3at2KYUw7X5ajauWkVOlpZ0sGdPNr/SV1DEWMnNFdsCjdg3\nezY52trSn716sf44BoCq2jSOA9Ofce7cOQwfPhwzZ87EjBkzyu76/t9O+oaCVAq0b69Ql7tHAAAg\nAElEQVT7cezsgNatdT+ODnF2dsapU6cwbtw4dO7cGQcPHlTthDGOKBw8eBDjx4/HkiVL8M477+hv\n4NBQdjrT//4HVK+u+uvOngUiI4FHj9jx5UUnbegbiQTo1w9wcAC2bAGio5lNdeoANWqU/3pTU2DY\nMMDLC/jjD3aMeECATk2uV68eQkJCMHLkSPTp0we7d+/W+ilGHC0gCMCZM9iweDE+v3oVGzdtQp8+\nfcS2iqNDWvj74+LFixg4cCAiCwrw+++/w3LePHHubZxXQkT4/vvvsWrVKhw8eBBt27YV2ySODunc\nuTPOnz+PN998ExEREViyZEmZx05zxEWhUOCz8eNx6OBBnBk8GE3t7NgJaEOHim2aYWNpKbYFGjFo\n7lzUGTYMA/r0wZ3UVHzh4QGJPtbqWsAwD0gvhfXr12Po0KHYtGkTPjbwY/s42sHCwgJbtmzBoEGD\n0Lp1a1x9dkQ1x3AgIixYsABTp07F4cOH9RvAycxkAZzJk9UL4OTns6N4b90CTEyA998HzMx0Z6cq\ntGsHDB7MAksFBcCOHewoVVVp3hz46itAqQTS03Vn5zNsbW1x6NAhNGnSBG3btsX9c+eAS5fYZ8oR\nn7t3IZ8/Hx9++CF+uHkTQefO8QBOFaFWrVo4ffo0JHXrouuvvyJh506xTeL8h9zcXIwaNQr79+/H\npUuXeACnilC/fn1cuHABkZGR6Pfmm8jIyBDbJM5/SHvyBH1eew23AgNxceZMNF2+HGjTBhg3TqvH\nhnMMCJkMr/n6IvTqVRxIS8M7Y8Yg/84dsa1SCYP/RsrlckyePBnLly9HcHAwevbsKbZJHD0ikUjw\nxRdfYFm/fuj7+utITU0V2yQOERAZiZxDhzB8+HAcPXoUly5dQqtWrfRrh7U10L07q2ZRh1OngOxs\n9rNcDly9ql7ARBdkZgKxsc8nCTduANevq3cNOztg+nQWmNIDpqamWL58OaZPn45eI0agICQEaNBA\nL2NzXkF+PlIDA9F7wwbcy8zExeBgeHl5iW0VR49YWlpi29696NOzJ95csAAk9v2NU0z0jRvo0L49\nTE1NcfbsWbi5uYltEkeP2NnZ4ci+faifnY23Bw8W2xzOC9w+fBitvL3RzMwMR3fuhP3s2axKetQo\nwNxcbPM4OsbV1RVnr1+H3MYG0+fOFdsclTD4Wr4969cjNigIoUFBsOEl+1WT1FQMUSrRdcgQ2MfG\nspsqRxzCw4Fdu4DERKyNiIBV/fo4ffo0LCws9G+LJtV4ubnA33+zYEnnzsCbbwJWVtq3TV1sbIAx\nY4CBA4HTp4GgIFaN4+WlXomqVMqCW3pk4sSJGD5gAMxr1uSZKkPAwgILw8Ph5+ODRcOGwaR+fbEt\n4oiARCLBV5s3Y+rTp7xy2YCYPXYsRnXtik+WLOH/X6oopoGBWNWiBZ6+/rrYpnCeQUSYvmgR5gwc\niPeWL2dzsiL4vKbKILOywo5Ll4ymSs7ggzgj334bIyIiID1/nm0TeO89sU3i6JvISACA/ZAhbPsM\nRzy8vYG5c4HoaHwcFwdJhw6QGJOD+/tvoFEjYMgQoGZNsa15mRo12LaqPn2A8+dZMKdXL7GtKhd7\nFxexTeC8wI+rVkEqkehlax3HsLG3txfbBM4LbL10CVJj8pkc7ZKezvqVffcd7Hli2mCQSCQ4ce4c\n1yYHUqkUNVTpSWkAGHwQR5KYCEm9esDRo0C1asA77/CoaFXj7l1WpdCmjdiWcADWP6Z+fUiNLcNP\nBLRsyapdDB0LC7ZVrLJug0hPZ9u/ODqheCJqJBMRDqeqwBeJVRxbW5ao4RgcXJscY8Pwv7GWlkBM\nDPu5sBCIjxfXHo7+6dGDB3C0TWUNDrwKiYSd+mRMVNZy+7NngeRksa3gcDgcDkd/VFafzuFw9I7h\nB3E8PYEZM573hYiKEtcejv5xdxfbgsrHtWuAQiG2FZyqyv377Hh4DofD4XA4HA6HoxaGH8QBngdy\nZDLg8WOxreFwjJ+4OHbMNoejb5RK4NEj4MKFqlkRxuFwOBwOh8PhVADjCOIALJDz0UdAUpLYlnA4\nxo9cDhw7BiQmim0Jp6oRE8O+f6mpwL17YlvD4XA4HA6Hw+EYFcYTxAFYIGfYsKqbvU1NFdsCTmVB\nqWTbqbZurbp64ojDgwfPfw4JEc8ODkdMeEKKw+FwOByOhhhXEAcA3NyqZmMwImD3brGt4FQWivrh\n3L0LXLwori2cqkVMDODiAlhbA/n5QEGB2BZxONojPBzIzS3/edevA8HBureHw+FwOBxOpcP4gji6\nwtCbvD55wiZ9aWl6HbYwJQVZmZnIz8+HIAh6HZujQ5RK9q+FBXD1KltMc4yK/Px8ZGVloaCgwLi0\nOXw40Lo1UK0aMHEi+5djfAgC244ZFaXaqZFKJZCXp3u7xKawEHmrVyM7MxOFhYWgsiodXV1ZJeSV\nK/q1j1P5KUr6HToE3LjB5o1lfQ9zcqrOtup795CTkYHs7OyytVlQAJw/D5w7p3/7OByA+dYqWCGf\nnZ2NnNhYyF/lN1+kCn5G/8VUbAPEhoiwY8cOLJs3D/K8PMDKCmRhUfy3F58HAMjIwOCuXTF3wwaY\nmJjoz9CrV9m/t28D7dvrZcjAwEC8O2QIcgQBhQoFCgsLIZVKUa1aNZiZmaFatWrsYWYGM7kcTV97\nDUtXrkTdunX1Yh+nAri4AAMGAIcPA2PHAubmYlv0EkqlEutWrcLvv/wCwcwML96uS9OmRC7HuPff\nx9RPP4Wkklfr7dq8GZOmTIFcECBXKktos+hRrFGpFAHNm+OndetQq1YtsU1nDeplMrZ4AKpmZaWR\nU1hYiB8WL8a+tWtBUinI3h6QspxQado0USgw08sLo1aten7SZCWEiLDmxAnMXrAA+OYbFBYWorCw\nEKampi/r0tQU1XJz0S0jA4vWrYOtra3Y5nMqAdnZ2fj666/xz+bNgFLJ/KapKWBjA3qmvRc1Wk2p\nxLeenujdrRvQsSPg6wuYmYljvK6Qy6Hctw/f/vgjFt+6BampKQoLCyGXy2FmZsY0aWqKagCqCQLM\nJBJUs7DAoLFj8dVXX8GyEt+zOPrjaXAwPl+5EpciIgATk1J9JRGxQGJhIaxsbPDzunVo07atWCbr\nhYKCAsz8+GP8+uuvMJFIUEgEhUJRwm++6DvNpVK816MHPh42DGbduoltvqhU6SDOw4cPMWnSJCQk\nJOCH2bPhbGUF1KwJ2NhA8mxC+uJiUCKRoDApCbO+/Ra9evXC9u3b4ezs/PKFMzMBGxvtGlsUxAkP\n124QRxCKJ99FKBQKzJ8/Hxs3bsSOAwfQ5ZlIiAhKpRJyubx4clpYWIjC/HzIMzJw4MQJBAQEYPbs\n2fjoo49galqlv16GTffuLDt38CDbUtW8udgWlSAsLAzjx4+HqVyOJfXrw3bePEAqfUmPL/6cvX07\npm3ahHOXL+O3336DjbY1aADk5eVhxowZOHnyJE6+/TZee+cdoH37Ym2+qMsinRbs3o1NgYHw8fHB\nokWL8P7774sf5JLJWPVXKfcfjmETEhKC8ePHo27dulj1yy+wdHUFJJJStVn0b0pSEiZOmoTgb7/F\n0qVLYW6AQeOKkp6ejvHjx+Phw4e4duMGGjRoAID5TcWzJEgJ31lQgLzcXKxcvRre3t5YsWIFBg8e\nLPK74BgzR48exeTJk9GxY0f8MmUKTOVywM8PEk/P4mD5f7UZfeUKxn36Kd53csK8wYNhok/fQKT7\nID4REo4dw9vffgsC8ODECbh07vzsT8+0mZuLwjt3UPjwIQofPoQ8KgpZDg5Y+OABfHx8sH79enSr\n4otFjuYUFQt8MnkyhtWujd86doS0d2/Aw6N0vxkaCgQG4nZ2Nvr36YM58+dj2ocf6n7eVlAAJCcD\nKSmAj49e5mb379/HiOHDUcfEBPFvvw27atWAkSNBXbqU8JfFP1+9iqeHD2PO4cPYcfQoftu7F/7+\n/jq302AhIpUffn5+VBmQy+X0ww8/kIODAy1evJgKCwvVer2ioIDmzJlDtWvXpnPnzpX8oyAQ/f67\nFq0lIrmc4kNCyNXBgejuXe1dNz+f6D/2x8TEUMeOHalnz56UmJhYPL6q3L9/n7p3706+vr7077//\nas9WAwDAFVJDL/p8aKzNefOIdu7U7LU6IDc3lz7//HNydHSk9evXk1KpJNq0SbUX37hBeTk59MEH\nH1CjRo3o5s2bujWWiCg3l66cPEn+DRsSXbxIpFDobKjIyEjy8fGhkSNHUkZGBlFoqGr3g8uXiYKC\n6Nq1a+Tv709dunShu9q8j2jCrVtEEyYQZWdr5XKGqs3K4jOJiNLT02nSpEnk4uJCu3fvJkEQ1H79\nkCFDyM/Pjx49eqQbI//DX/v2UX8/P+brdMilS5eobt26NHXqVMrXYKygoCDy8vKigQMHUmxsrA4s\nFA+uTd2TkJBAI0eOpHr16tHff//N/qMa38OEhATq2rUrde/e/fm8T8esW7OGJnXqRJSZqdNxAgMD\nycXFhebNm0cKdfxzRgaRINBff/1F7u7uNGbMGEpNTdWdoSLAtal7Hj58SL169SIfHx8K/eorNu/5\n+We2ViyLX39lz5s6lR788w/5+vrSsGHD2LxPVwgC0ZUrRAsW0Fft2tE3X3+tu7GesWvXLnJycqJV\nq1aREBtLNHMme9+//FL2i3Jzib75hoTx42nziBHk7OxMM2bMoGwtzSUNBVW1WeVSoJcvX0ZAQAAC\nAwNx8eJFfPbZZzD7b+kovWKfHRFMzpzBggUL8Msvv2DIkCFYsmQJqOg1sbHApUuqNTZUFVNTCJ6e\nEMzMgIYNtXfd69eBW7eKfz169Cj8/f3Ru3dvHDt2jFUZCQJw6lTZ11AoSnxe9evXR2BgIKZNm4Y3\n3ngDM2fMQG5ysvZs5miXpk3ZFj0D4OTJk2jevDnu37+PmzdvYsKECZBKpcCQIapdoHlzWMhkWLdu\nHebOnYtu3bph06ZNujU6KwvCL7+A0tKAvXsBHW2x3Lp1Kzp06IApU6Zg+/btrMooIABwdy//xV5e\nQMuWaNmyJUJDQ9G/f3+0bdsWCxcuhFwu14m95VK9Ovu3aEsVx2AhIuzduxdNmzaFUqlEeHg4hg0b\npnZW0NbWFn/++SdGjx6N1q1b4/Dhwzqy+DlCQQEoLw+IjNTJ9YkIy5YtQ9++ffHjjz9i5cqVGlUZ\ndezYEdevX4ePjw9atmyJtatWQVClzxCnSkNE2LBhA5o3bw5PT0+EhYXh9ddfZ39U43tYs2ZNBAYG\nok2bNvDz88P58+d1ZPFzlHl5oGrV2JZuHaBQKDBnzhy899572Lp1K+bPn69eCwQbG0AiQb9+/RAe\nHg4rKyt4e3tj586dz+f7HE4ZKBQK/PTTTwgICEDXrl1x5coVtPbyYlsbR416dQVadDTb0jhtGup1\n747z58/D3t4eAQEBCAsL043BEgng5wd8+SWU/v46rZDLy8vDpEmT8MUXX+D48eOYMmUKJG5uwGef\nAY6OwL17Za/DLS2BDz+ExNER77i54datW0hOTkazZs1w/PhxndlssKgS6SFjj44WFlLWv//SR/37\nU01bW9qycWPZWcSUFKKQkLKv9e+/RD/+WPzr48ePqVWrVjRw4EBKS0sjOniQRRK1XIUSGxtLLi4u\nWr0mLV9O9PHHVFhQQDNnziR3d3cKCgoq+Zy4OKJZs4iUytKvUVhItH17qVHlxMREeuutt6ieoyP9\n/dlnREaeYYSBZi2oItoMC2PfVxEzTMnJyfTee++Rh4cHHTp0SGvXvXXrFjVu3JjGjRtHubm5Wrvu\nf7n4+efk7+RU4r6gLXJycuj999+nxo0b040bN7R23UePHlHv3r2pefPmFBoaqrXrqkxCAvveaaki\nw1C1abQ+8xnR0dHUv39/atKkyctVpxXg/Pnz5O7uTrNnzya5GpWe6rJ//37q36+fditYn5GamkoD\nBgyggIAAevDggdaue+vWLWrbti21b9CAwhctInr6VGvXFoNKr00dV3mVSkoKRV65Qp07d6ZWrVrR\n9evXtXbpI0eOkLOzM/30009qV9upw6pVq2jSpEmsEqes+aWGxMbGUqdOnahHjx6UkJCgteteuHCB\nvL29qU/nzhS1d6/WrisWlV6bInH58mVq2bIl9ejRg+7fv//8D7t3szXiq8jLI5o2jSgi4qU/bd68\nmRwdHWnjxo1atrgkX3zxBS1YsEAn1y6qKB8xYkTplUVpaUTz5xOVVxGYkED08cfF1dzHjx+nOnXq\n0KhRoygpKUkHlusXVbVZJSpxPhk6FK5t2iDt8WPcun4do997r/Qs4t27wMKFgIND6RcSBNbt/4UT\nojw9PREUFITatWvD398f144eZX94ocJFGxCRdvdDZmYCt28jOiEBndu1Q3h4OK5evYqOHTuWfN6j\nR+z9llWtYWbGnrN5M/t8XsDZ2Rnbt2/HypUrMXrlSkwaOfKl53BEpmFDlhmIiND70ESEd/r0Qd26\ndWFvb4/w8HC8+eabWru+t7c3Ll++jOzsbLRr1w4PHjzQ2rVfhNq0gQQASuuPVQFu376NVq1aobCw\nEFeuXIGPj4/Wrl2nTh0cPXoUs2fPRq9evfD18OFau7ZKyGTsX21WLHK0hlKpRK+OHdG0cWP4NWmC\na9euoUOHDlq7frt27fDvv//i33//RY8ePRCvo6oTImL97bRZwQogNDQUvr6+qFu3LoKDg1GvXj2t\nXdvb2xvBwcEY+f77aDV3LtbNmsVP4TBUUlKAixf1OmReXh4COndGm/btMcTNDSHbt6NFixZau36f\nPn1w6dIl7Nq1C0OGDEF6errWrv0ixXNaa2ut9t44fvw4/P390bNnT5w4cQI1a9bU2rXbvPYarn72\nGdrk5KDZqFHYu2OH1q7NNW78JDx5Am9vb/Tt2xcff/wx/v77b9SvX//5E9zcgDfeKOciCcCkSayC\n+j+88847OH36NBYtWoTx48cjT0enPWp9vfmMbdu2oUOHDpg8eTJ27NhRet9KOzvg009Zf55XUbMm\nMHUqkJEBAOjVqxdu3boFBwcHeHl54fSOHcDly1p/D4ZGlQji/PzXX8iSy2HSvDmiUlNffgIRcOYM\nsHQp+/1ZQ8KX+PdfdtR3enqJG665uTlWrlyJb2fORLdNm7Dl/n3m3LV4U9a6qKKi8NHt2/Dcvh11\nHBxw4MABODo6vvy8R4/Yv8HBZV+rbl0gJATYtKlEkEahUGDv3r34cf16mFpbo1G/fryJqaFhbs6+\n7yJsqVIoFNh67Biys7OhUCgQExOj9TGsra2xfft2jB07FgEBATh27JjWxyBnZ0hkMsDJSWvXHDhg\nALy9vdG7d29s2rQJVlZWWrs2wE4D2LJlC5YtWwYHe3vU8fDQ6vXLhQdxXo1CAYi11Q3A06dP8Xdw\nMLLz8pAXF4f46Gitj+Hk5IRjx46hU6dO8PX1ReiZM1ofQxeT0eZ166Jt27aYNm0afv75Z1SrVk2r\n18/Ozsb69euxdutW1KlbF65vvMFPcDNUDh0CSptT6pB79+7hSng40gsKkHH3LpLnzwcWL34+V9MC\nnp6eOHfuHJydneHv74+IkBCtXbsIbWuTLl2CTCbDG2+8gR9//BFffvkl246tLSIj8XT2bKxYswZ/\n3LmDpnZ2cNTm4R337wPffMMeS5cCv/0G7NoF/PMPoFRqbxyOzri0YAFu374NeWEhUlJSXg6Atm1b\n/slvnp5AkyZl/rlZs2a4fPky0pKS0K5NG8TGxmrB8pJoW5tyuRwSiQSjR4/G1q1b8cEHH7z6+tWr\nq9YqoG5ddtIugISEBPz000/Ys2cPWtavjxp79gDHj1f64GiVWFETERJjY9HA2xuDBw9G69atsXnz\nZuTn57PJ8tatwI4dLADRokXpgYaiKhyAvSYrCwDw5MkTbNmyBf/73/8w89tvUd3BAQ98fYHp0w07\niNO8ORx79kSzZs1wPSYGjo6O6N27N7799lucOXMGuUWLq6KJwY0brHqnNOrUYf+GhgIbNyIlKQmL\nFi1CvXr1sHTpUkycOBGPY2Mx47PPtGc/R3s0bcoqcfRcJWVmZgYiQlRUFGxsbNC1a1d0794de/fu\nhUKhqPD1Hz16hN9++w2jRo3CggULUEMmw8OzZ7VgeUmICBI7O61W4tSysoK/uzsOHDgAJycn9O/f\nHz/88ANCQkJQUF6G4hXExcVhzpw58PDwwLZt2/DVV1/h7r17eO+nn7Rmu0qYmLAAIg/ilI6JCfNL\nR44U+xp94uTkBBIEREZGIt/REX5t2qBfv344fvw4hAreJ4gIkZGRWL16NYYNG4ZVq1ahRo0aeLR3\nL/D4sXbewAtjaTuIU9fNDe0CArBs2TK4uLhg6NChWLZsGa5cuaL+feuF59+/fx8zZsyAp6cnAgMD\nsWrVKoTduYP+gwZp1X6OloiPZ1U4OqpUKQsfHx9QcDCuLVyIaBcXNPnrL7x19SrOxcaCKjjnFAQB\nN27cwM8//4zBgwdjx44dsJbJEPP998DNm1p6BwytajM3F5Jdu9DMzg6d2rXDJ598Ag8PD4waNQpr\n165FWFiY+vet/Hz2r1KJsDVrMGHECNRfuxY3s7Oxa9AghI4Zg86NGmnHfoBVC06dyiqjIyNZFcGp\nU+wewZOfRkF/NzcIS5bg0KFDuHLlCurWrYtx48bhatHpwqp838t4jlKpxKVLl7Bo0SIMGjQIJwID\nYZ6YiPhXJdg1RNt+0zQ/H562tuhcrx7eeustNGjQAP/73/+wYcMG3LlzR+P7FhEh9OJFvP3222ja\ntCkSEhIQGBiIk9u2oaW3N+tRe+eO1t6HISJR58Pz9/enK1euaDzY6dOn0bhxY7i6ump8jYqiVCpx\n5MgRrF69GteuXcOYQYMw0cUFdYvKuadMYUer/ZerV4Hjx5F29y7OJCTgZM2aOHnpEhITE4sXn927\nd0ejRo10UoYWFRWFjh07IloHGVEASElJQUhICIKDgxEcHIybN2+imbc3OggC2tvbw97dHdSsGahF\nCxARBEF4vi8vNRW0ZQsKlUocUCqx/8oVDBo8GFOnToWvr69O7BUDiUTyLxEZ5Fl2FdJmTAyOTpiA\ntt9/jxpaLMtWl4KCAuzbtw9r1qzBo0ePMGHCBIwfNw4uKt4vkpKScOrUKXb89smTyMnJKdZl9+7d\nUcfSkgVatKzPkJAQfPLJJ7hw+jRgYaG9C6enA5aWiH/6tFiXwcHBuHPnDvz8/NChQwe0bdsW1tbW\nJfbIltDms9/z8/Oxc+dOBAYG4u2338bUqVPRuHFj7dmqCbNnA507l19erAKGqs0K6TIlBfj6a+xJ\nTka/X36BeWmlx3oiJycHO3bswOrVq5GVlYVJkyZhzJgxsLe1VamZd2x0NE6ePl2sTRMTk2JdduvW\nTWdzgj179mDHjh3Yu3evTq4fFRVVQptRUVFo3bo1OnTogNa1asGycWMQUKouiQjC7dvISk3FpmvX\ncOnSJYwdOxaTJk2Cp6enTuxVCUHQ6qKxUmoTgPLoUfy5bBlG9u/PFt8ikZ6ejk2bNmHNmjUwNzfH\n5MmTMXr0aFglJgIvbuUog4e3b+Of4GCcPHkSp0+fhq2tbbE2u3btCkcHB+DhQzYH7t9frYbJr2L5\n8uV48OABVqxYUfGL7d7NElF9+wK+viCJBPfv3y+hzaSkJLRr1w4dmjeHf7duqGZuXrYu4+JAZ88i\ntX17/LZtG+7fv4+JEydi/PjxWt2eVSpyOUsqnz/PAjoKBds60qEDq+SwttbaUJVVm9nZ2Th58CAG\njBjBPkN98dNPwLvvFif0EhMTsWHDBqxbtw5ubm6YPHkyhg0bBovy5omCAMrNRUR0dLHPPHv2LNzc\n3Iq12fnaNdhmZADjx7+yckcTZs2ahRo1amD27Nnau2hICLBjB4QlSxBx714JbWZnZ6NDhw7o0KED\nXvPxgYlEAjI1LV2bz/7bk7g4rFu3Dk/T0jB16lSMGTMGdnZ2z8eTy1kSLCEBmDhRe+9DT6iqTf0E\ncfLzgf378ebXX+ODIUPQb8gQwAAW9/fu3cO6deuw6fffIVUoICiVECwsIAhCmQ9zU1N0aNUK3Tt2\nRPfBg9GyZUv1Ot5ryOPHj9G5c2dERUXpfCwAyM3NxeXgYASfP4+QM2eQpVBAYmoKiUQCqVQKiURS\n4iFNSoLEygqd+/bFuPHjS9+aJQI3b96EoFSi5WuvVfhahurwgAo6PSL4Nm+OXzduhJ+/Yby9Gzdu\nYO3atdi5aRPMLCwgSKXFGiy6kf/3Ud3SEp1fCKh6e3vrJKD6X86fP4+ZM2ciRAcl56WRlZWF0NBQ\nBJ87h9C//kJetWqQWFqWrU2pFCapqeg9bBjemzix9H3IIhA6YQLsmzdHo2nTKnwtQ9VmRSejOHsW\nNYcOxY2wMNSqVUt7hmkIESE0NBRr1qzBwQMHYE4EoVo1CGVoskirdhYW6NqxI7oPGIDu3bujQYMG\netHmn3/+iV27dmHPnj06Hwtg29AuXLiA4LNncXnfPhTWrAmJqWmZupQAMJPLMWj0aLz11luwtLTU\ni51lQgRs24bTdnZo0KIF3FUpaS+HyqpNpVIJMzMzKFNTIalRQ4uWaQYR4eTJk1izZg0CT5yAhSBA\nMDODYGLyyjmts0yG7p06ofvQoejWrZveAojLli3Do0ePsHz58opdqKCABXBatHhlgiYxMRHnjx9H\n8IoVuCaXQ2lnV6ouJampkKakQALA0s0NI2fMwODBg18+xVbXnDsHREWx4E1wMDv1VqHA0erV0Xr2\nbDiU1btTDSqrNqOjotDexwcxc+ey/jJFp2Hqmrt3gVKqsxQKBY4cOYI1P/6IkGvXYCGTvVKTglIJ\nQRDg7uFRItlRPAd4+hRYvx744APA3l7rb+Ozzz6Dg4MDZs2apd0LX7rEtj/9x6/ExsaygM6ZMwj7\n5x9Q9eqQ1qhRus+USCABYJOUhPfefhtvfPzxq7dNxsWxoJoe9Ltn9270ef11yP1fcKUAACAASURB\nVLTgD1TVpn5ClBYWwN27kMnlyL14EXjrLb0MWx4NGzbEkiVLsHDhQmRkZEAaFweppyekUmm5D32j\nq0ZTZSGTydC5Z0907tlTtRcQGeS+/d3ffw/zp0/RsioePacqEglkdnbI1VGTNE1o0aIF1q1bh5+/\n+grZEgmkZmav1uSJEzBp0gSSsvpZ6RB9a9Pa2hqvv/46O0q2Vy/Wi6e8su5r14CWLQ1Ko7/k5KCD\nqSm0WJBe+ejcGTIrq+fbW0VGIpGgbdu2aNu2LbIjI5EbGgqpqSmkr78Oqbl56doEYPLgASQZGSx5\no+UeMq9C39q0t7dH37590bdvX+CHH/Q2rtYIDgbOncP3f/+NGStXaiWIU1kxMTGBmZkZCiwtocX6\nS42RSCTo0aMHevTogYyjR1Fw7Bik+flsTvvuu5A+C1qUeCQkwGTrVkhSUoCBAwE9BqO0pk1zc+bb\nyqGmszMGp6Rg8JtvAl27Ap06law4y85mFTCPHrFrmpuzreb9+onjNzt2ZPfL6tVZy4KhQ4ErVzDr\n3Xex/d13tRLEqazIqldHrlQKJCcD338PTJum9YMnSqWMeZipqSkGDBiAAWlpSMvOhmLo0LLntE+e\nQPrDD5DY2cF06tTSe8MIAjBzps6qjHTmN1u1KrVtQ+3atTFy5EiMHD6c+aCgIODzz19d5XvzJtvO\nWt563M2tgkarzoQxY3Bv0SLIPvxQb2Pqr86sQQPITE2RZ2bGbowGhLm5OZydnfUjcg3R92RUbQzR\nNiJkKZVwzMpipXxOTlo/paSyYGlpqbNO9xVB5uoKmSpP7NJFq2XG6iCqNtu1U62vjIEFcAAgq7AQ\n1gZSsWfIyGQyg9SmlZcXrEo5QaNUtFzurSoG7zcNibQ04FnFUlZeHqzj4kQ2yPAp0ma52yP0jG2f\nPkCfPuwXQWCNcUvLRHt4AF98wfr7xMYaZxBHVRISgDffZAvt0sa1smJbUwyJFytILCyADh2QJQiw\nFmmuYyzIZDLkFRSwQMCqVSyQM2mSuPN/IqBhQ9Ro167suZhSCYSFsbYezZqVHaDQ5rypqC/bCwEh\nnWrzVUEXqZQFV197jfUCfHF71H/x8WEBLgMpICAiNqe9eZP1kNVTawr9lZQ0aABLExPk1q7Nm3Rp\nAOXnQ1rJu2xrnfBwZN65AxtTU3ZyFv/elYlMJjOYbL9GiDipISJRqvMAMOelSqmwATi5/5KZmWkw\nW7sMGUtLS+PWpoiIqk1jggjYvp0t9N98E5n29rBp105sqwweo9CmVFr+VgIXF6B5c/3Y8wy9a9PF\nBWjc2CB9oTpwv1k+FhYWyM/Ph2BjwypW6tcHli1jlRtiIZEA7du/+vtnYsIq4nx89LdeMTEBdu5k\nJ9z9+Sdw9Spbb4rpN62tXx3AKaJGDYPRc35+PkxMTFCtTRt2KEVOjl7G1WsQR2ZqilxdNwWrpAhJ\nSZAUdcvnqIa3N7IAWBeV7xtYtsyQMPogjogIgsCz/RqQlZXFM4oqwLWpOVybKvLkCct+LloE9OuH\nrJwcrk0V4NrUHK5N9SEi7jdVQCqVwtzcnJ1AbG7OqnA6dwZ+/52dMkzEKtR4YpwFQUaNYr11/vkH\nWL8eQnAwJEWH/XBUoliXI0ey79Xu3XoZV39BHHt7yNzckKelzvZVDSoshATQ+zHQRo1EgkxbW9gU\nfed4EKdMDHU7lTFAggBJXh47TYijMjyjqBqGup3KGKC8PEhEOKLd6HBzY1szn1VscG2qBtem5lBB\nASSpqWKbYVTk5+fD1NRU/02WjZAS2pRKgeHDWT/WI0dYMOfOHeDCBXGNNBSkUuD994u3ABEAiZOT\nuDYZGcU+08qKBcVCQ1nfHh2jvyCORAJLX1+etdCUOnUgsbHhW4LUJKuwENZt27JfeBCnTHhGsQLE\nxkISFwc8fiy2JUYFzyiqhlFs2TBUYmIguXOHnfKip5MdjR2e7Vcdrs0KEBcHyfXrrPcFz/qrBNel\n6pSqzS5dgKlTWc+StWvZ9qHMTFHsMzhMTIAJEwBvb8DPDxJecKEWJbTp6wv4+QHbtqnWs7IC6DUi\nIHNy4lkLDVHnKHjOczIzM2HTuzdgY8PKKjmlwitxNIdq1wYcHICkJLFNMSp4tl81eLZfQyIiQI8e\nsZ+//x7g25FVIi8vD2ZmZjzbrwJcmxqiUIBcXFhScvlydpw2p1y4z1SdMrVpb89O+yooYAvsHTv0\nbpvBYmoKTJoEkql0nAjnBV7S5ltvsabRf/6p03H1G8Th2X6N4adsaEZWVhY7AWfECJ0dx1cZ4Nqs\nGBJHR37ymRrwbL/qcG1qSOPGoJyc59uQLS3Ftsgo4LpUHa5NDSECQkIgUSqBmBhWjcMpF65N1SlT\nmw4OrEqiqD/r1avA9ev6Nc6QMTMDSSR8vakmL2nT2poFckJCgFu32H/TQTGGXoM4vPS0YnBRqU9x\ndNTPT2xTDBo+GdWc4io5HsRRDSLk5eWhWrVqMOWB1XLhflNDpFKgdWsUe02+nVYleLZfdbg2NcTM\nDNSq1fNTs3gQRyW4NlWnTG2am7Mmx19/zbZWeXmxahyu4xLw9aZ6lKpNPz92YMCWLUB4OAsYahm9\nV+Lw0lPN4Nup1IeIkJ2dzaKj/IZUOnI5AL6dqiLwKjk1CQ7mGUU14H5Tc6hmTcDRkf3CK3FUIisr\niy8UVYRrU3PI0hKSli3Z3Cw7W2xzjALuN1WnXG1KJEDz5sCMGcC0aUBsrP6MM3D4elN9SvWbEgmr\nxsnPB1atYr2YtAyvxDES+EJRfXJzc3m2vzwOHQIEgVfiVBCuTTU4dQqZd+/yhaKKcL+pOUQEiacn\ny/jzII5KZGZm8oWiinBtVgyJoyMwcCCvxFERXomjOmpps3ZtoFEj3RpkRPD1pvqU6jcVCmDnThbE\nEQQgLEzrJ0zzShwjgotKPXhGUQWio4Hjx3klTgXgWQs1UCiAhARknTrFF4oqwv1mxZCYmwP9+vGe\naCrC/abqcG1qTrHf7NWLbUXmfrRceCWO6nBtlkNBAasMUShK/TNfb6pHqX7T1BQYPx7o2ZP9npsL\nPHig1XF5Y2MjgS8U1YdnFFXAxAQ4dAiynByuzQrAHZ6KJCUBgoDMa9dgU7262NYYBdxvak6x3+zR\nQ1xDjAjuN1WHa7NiSCQStuVg9GgexFEBXomjOlyb5WBuzpqKz5oF7N4NxMUV/4mvN9WnTL8plQJD\nhgDvvcfWW1reUqXX1BQvPdUcXt6mPtzhqYCJCdtOFRSE3Jwcsa0xSrjDU4MnTwAAmXl5sC4oENkY\n44D7Tc0p9psmJmKbYjRwv6k6XJuaU8Jv8qbjKsEDrKrDtakCffoAERHAyZPs4ekJtGsHUir5elNN\nyvWb7doBzs7Avn1aHZdvpzIiuKjUg5eeqsCzxY1lZibyeGM3jeHaVJFnQZwshQI2OTnFjbU5ZcP9\nZsXg2lQP7jdVh2uzYnBtqgff6qg6XJsqIJUC77//vF9cVBQgkwH8iHG1UclvNmgAjB3LtrJpCb6d\nykjg2X714RlFFTAxAapXh8zODrlEfFGtAVybauDiAgwahEylEtbt2gFKpdgWGTzcb2oO16b6cL+p\nOlybmsO1qT68Ekd1uDZVxMGBbWcEgBo1gN27Qenp4tpkhKjsNx0c2FY2LcFPpzIW0tMhycwU2wqj\ngmcUVaBDB2DsWFjm5SFXENgpLhy1kfCqEtUICAA8PJCVl8d64vAy+rLJzQUePuR+syIIAiRcl2rB\n/abqcG1WACJI8vPFtsKo4JU4qsO1qQb+/qzB+FdfAfXqAWFhkPAqJrUQy2/qtScOL2+rAGZm/HQN\nNeEZRRXw8gIUCsisrZGXkSG2NcaLXM4alvn5sUaNnLJxcECmXA5rqV5zCMaHTAbs2QOZVMr9pqaE\nh2u9kWBlh/tN1ZHJZMjLzWX3f54AUY87d4CHD1lDY+4zSyc/nx0G4OwMWFjwShw1kMlkSEpKEtsM\n42HQIKbDSZOA8+fZ/IOjMmL5Tb1GBczMzCAIAhQKBUx5QEI9qldnD47K8IyiipiaQubjg9zdu8W2\nxHipUYNlMzjlY2+PLLkcTjyIUz6tW0O2YgVyo6KAnBzuA9TFzg4oLGRVTXxSqhLcb6qOTCZD7sOH\nbFsoD+Koh5MTcPs2EB8PuLqKbY34JCezgHNiIpCQwII36elAo0bAxx8D4JU46sC3U6lJUSBVIgH4\nd0xtxPKbep1FS6KiILOwQE5mJj9OkKNzMjMzYWNtzRY/nFcia9sWOXI571HC0T1mZsgEYM19QPkE\nBEDm5IQciYQHcDTB1pb9Gx8vrh3GgFIJHD6MzOhovlBUEZlEgpzcXL4tVBPs7Vng69YtsS0xDBwd\nWTArKQm4e5cFcAD28yefAN9/j8wnT3iAVUVkFhbIuX2bBcc4HB2TmZkJMbymflOhW7aglqkp4pcu\n1euwnCrI3btIPn8ejqGhwIEDYltj8Fi1agWYmiKTB7w4eiDZzQ2OHTuKbYbhI5Oh1pdfIj49nTcC\n1QQrK3YCx7NT0ThlkJAALF4MHDqE5Lg4ODo6im2RUVCrbl3Ec11qhkTCAhdhYWJbYhhIJEDTpsCM\nGcAXX7DKXokE6NoV6NsX8PREcmYm16aK1KpRA/FPngCLFgH374ttDqcSU1hYiNzsbNj+8Yfeiwb0\nG8RxcIB79eqIcXHhe2A1gE/i1cDKClEPHsCTN+tVCYlEAg8PD8TExIhtilHCtakeUXFx8KxbV2wz\njAJbd3dIJBJk8J5V6iOVgiwteRDnVRCxrP+AAcCMGYjKyYGnh4fYVhkF7u7uiImJ4fd/DSEnJ7bA\n5j2/SuLpCYwfD3zzDQvsdO+OvIEDkZGbi5o1a4ptnVHg3qgRYqRSoG5dYOlS4NIl9oesLHENE5v8\nfECFuQS/p6lOTEwMXF1dYVJQACxbxrZv6wn9BnGcnODh7IzoatX0OmxlQMKDXurh6opoQYCHlRUv\ndVYRd3d3REdHi22G0cG1qT7R0dHw4AtFlfHw8ODa1AAJEeuF8+QJEBICCILYJhkeEglrcO/tjSw3\nN+QXFsKBZ/tVwsrKChYWFkhNTRXbFKNDIpGwvjiCAEREiG2OYeLsDPj4AGALxdq1a0PKe8mphLu7\nO2JiY0GTJwMdOwIbNgCHDwMbNwJVWa/m5sD27cCmTWUmN/icVj2io6PhUacOq6JLTwdWrmTBMj2g\n37uBoyPcfX0RExur12E5VQ8iQnR6Ojxq1eJBHBUpyipyOLokIyMDgiDAzs5ObFOMBq5NDbl4kWUd\n79wB/vqLba3ilElMTAw8PDz4JF4NuDYrQLVqrOrk1i3Wk4n35CsTnvhQD5lMBisrKySnpgIjR7LH\n4cPsu7ZlS9XtyyqRAMOHA1euAF9/zQIOkZFV9/PQAsXarFmTBXKSkoDVq9mhCnK5Tj9b/c5oGjeG\ne6tW3OFxdE5qairMzc1hPXw4D+KoCJ+McvRBVFQUPD09+UJRDbg2NaRpUzaRIgIcHMS2xuAp0iZH\ndbg2K4BCATRvzvrirF3Lt1W9Aq5N9SnWpiAAaWnPg/gREUBoqLjGiYmDA9C/P/v51i223ezgQR7I\n0ZAS2nR1BT76CIiNZfe0s2dZkExH6DeI4+oKd09PXhauIXyPouoUR0bbtwcaNBDbHKOA98TRHK5N\n1eEZRfXhWx01xNYWVBS84VuEyoVrU314EEdz6MkT4O+/gcxMFsjhlXJlwrWpPsV+UyplQYt33wXc\n3dkfd+9m37uqSvfuzz+LatXYOumFxBqf06rOS9p0dwc+/BB4+BDYs4cdrqOjz1Pvd0y+UNQMnrVW\nj2JRSaUsMsopF75Q1AyuTfWIjo7mGUU14X5TMyQSCVC7Npuc8kqccuELRfXh/ao0o1ibLza45760\nTLjfVJ8SftPUFGjTBvjyS3Zke8OGLJBTVZFKgXfeAZo0AV57DVi1CggKAsDntOrykt8kYlu4idjj\n8WOdncKn9yAO7+bP0Qe89FR9eEaRow+ioqL4QlFNuDYrgEwG+PryShwV4H5Tfbg2K4BEAowe/fwE\nUV6JUybcb6pPqdqUSIBGjYDJk1l1jp4a0Boknp4skDNmDPDGG8C2bcDevXxblZq85DclEqBnT2DE\nCMDGhv03HW1X0/sd09raGmZmZkhLS9P30JwqBM8oqo+7uztiY2N5gFVdlEp2bOW2bUB2ttjWGDxc\nm+rDF4oVpE8fHsRRAa5N9eHarCDOzs/7c/AKgDLh2lSfcrXp7Mx7Zjo4MN0NGMC2m/3zD+sZxJuM\nqwQRITo6Gu5FW9OKkEpZO48FC9j8IzERuHZN6+OLEvbm2zY04MGD5w0aOeXCsxbqU716dchkMqSk\npIhtinEhlbJjBVu2BKysxLbG4OHZfvWpXbs2YmNjIfAjstXj6VMgOZlt2+C90cqF+0314UEcDcnI\nYM0/AaBHD1YVwCtxSkUQBMTGxr68UOS8Er7WVJP27YFp01gysqBAbGuMguTk5OKT0ErFwoIFyBYs\nYKdWaXkNb6rVq6lI0T7Fli1bijG8UdI4Ph5nZs4U2wyjge8f1oyiCamTk5PYphgNnTp3ht/Zs+x4\nQU658Iyi+lhaWsLW1hZJSUmoVauW2OYYDSOaNsXAu3fZxIkvEF+JQqFAfHw8ateuLbYpRoWbmxvi\n4+OhVCphYmIitjlGw4cdO4KOHGEnB0mlrAqAa7RUEhMTYWtrC0tLS7FNMSp4LzkNaNoUiwIDYVK0\nxZHzSlRea9aoAfTurfXxRavE4cJSD9P69eE0fjwvN1URvlDUDJ65UB9zc3PY8wCOSsjlciQnJ8OV\nNxtXG+431cfS3R12RHybowrEx8fDyckJ1apVE9sUo8Lc3Bz29vZISEgQ2xSjwuq112AtCKzpJ8Cq\n5XgQp1R4UlIzXF1dkZiYCIVCIbYpRoWNnR2qV68uthlGgdhrTb6dyljo3p0HcFQkPz8faWlpPGOt\nATxzwdElsbGxqFWrFkxNRSkCNWq439SAoorCpCRx7TAC+FYqzeEBVg1wdQWsrYHISLEtMXi4NjXD\nzMwMTk5OePLkidimcCopYmuTV+IYCzyAozIxMTGoXbs2pDyrozZcmxxdInbWwpjh2tQAR0eW3edB\nnHLh2X7N4drUAIkE8PLiQRwV4H5Tc7g2ObpEbL/JgzicSofYkVFjhmuTo0t4U2PN4drUAKmUBXJ4\nEKdcuN/UHK5NDfHyYod2yOViW2LQcL+pOVybHF0itt8UJYjDt2xwdInYkVGj5M4dQC6Hh4cH37LB\n0Q0JCTyjqClXr8KDT0Y1w9mZB3FUgPtNzeF+U0O8vACFggVyOGXC/abm8PUmR5eI7TdFCeLUrl0b\ncXFxUPJz6Dk6gDs8DVAogMWL4V69Ond4HN1w+TKib97kC0VNuHcP7o8f84WiJvAgjkpwv6k5PNuv\nIY6O7MG3VL0SsReKxgzvJcfRJWL7TVGCOObm5qhRowYSExPFGJ5TyRG7vM0o8fIC0tLgtnEjEp4d\nl8rhaJWMDERdvw4Pd3exLTE+XF3hfusWYu7fF9sS46MoiEMktiUGDfebmsODOBXAywuIiBDbCoOG\na1NzuDY5uiI3NxeZmZlwdnYWzQbROr9yYekZIiA2Vmwr9ALPWmiAiQng6wuzggI4VquG+I0bAUEQ\n2ypOZSIjA9HJyfDIyxPbEuPD1RWuMhmS09IgP3NGbGuMC2dnID+fHTPOAzklycwENm0CBQYi6vFj\n7jc1hM9nK4CXFxAVBeTmim2JYUEEREUha9s2FBQUwMHBQWyLjBKuTT1RBY9xj4mJgbu7u6iH6Ihz\nzqtSWbyHuHXr1qKYoDfkciA8HGjRQtwTpo4eBezsgNq1xbNBT0Q9fAgPc3OxzTA+/P2BoCB4WFkh\n2twctQWBNQatSuTksOCVtbXYllQ6yN4e0Tk58KgC9yCt4+ICUxMT1LS0RFxCAuooFAA/pr18UlIA\nMzP288mTgIsLUNnnHOpgYwN06YL0776DRBBga2srtkXGhyCgVnw8nqamoqCgAOZ87qEejRuzgMW9\ne2yeXNVJSAAuXwYuXQKSkhCdnQ0PFxdI+Am1GuHh4YHooiChTCa2Odrj4UMgI4NpRxCeP8zMAF9f\n/a83r1xh82Zvb/2OKyJRUVHwsLYGnj4F7O1FsUGcFdrDh6gXH4/7hw9X/syYmRnb77tokXglo5cv\nA3/9BVSBSH7hgweIjYlBnZAQsU0xPho2BDp3Rr1WrXBfEKrWIjE6Gti8GVi7FqheXT9jXrkCFBTo\nZywDIK5DB9g4OMCaL6LVRyYDxoxBvWbNcN/RsWppsyKYmwMrVrCfjx2rXJN4beHpibu9e6NhkyZ8\noaguBQXAwYMwOXIEHo6OePjwodgWGR82NoCrK99SBTxfkFtZAW5ugEyGu2lpaNiggdiWGS3Ozs7I\ny8pCxvz5bFttfDxL1hk7zs7A9evA+vXAr78CGzYAf/zBAjsSCfseFRZqZ6yEhPLX6g0aAKtWAWfP\namdMI+BueDgaSqXAt9+Kdv8SJ4hjZoZm5uYIj48XtzpFX/TtCyQmYsWnn+LyuXMQ9LhNJS0pCZvX\nrMHNtLQqEcS5l58PD0dHmPNFjvpIpcDIkWjWti3Cw8PFtkZvKPPy8NPEiQg7ehQ0apTeqo8S09Px\ny6BBiLpzRy/jiU1EZCS8vLzENsN4ad0azfz8qpQ2MzMzsfTnn3Hv7l3NLmBtDbRt+/z3mjVVell0\ndDSWL1uG9JQUzcY1MiIyM7k2NcHcHBg0CJg3D81at65S2oyJicGa1au10zS2SROVmxvfu3cPS376\nCYrKuC1XImEBra5dgYkTgZ9/RkTz5vBq3Fhsy4wWqVSKpt7eCE9PZ8n0gweBXbvENqviWFkBY8YA\nH35Ysgpk1y6ET5yI35ctQ+KePcCXXwKrVwP79wMXLwIxMWyHiDqkpLBAxeXLZbZZIAcH3ASw4vvv\ngfR0zd+XERFx/z68Roxg1YTLl7NEERH7jPWEeEGcZs1wq6qcGGFtDeXo0Yht0ADvffABXF1dMXbs\nWOzfvx/ZGRksMlzRiqSCAuDxYwBAQkIC1q9fj169eqFOw4bYn56O/P/9D6hRo8JvxdCJjIxEE39/\nlt3hqI9UyrR565bYluiNXIUC0e7u6B8UhDpt22LKlCk4duwY8vPzSz4xKUmzvlJpacU/RkdHY/ny\n5ejUqRMaDx2K02ZmyK0ivYciIyPRpEkTsc0waqqaNnP+/ReRv/2GLl27onHjxvjkk09w+vRpyNWZ\nhL7+OlscmZiwk3DK4O7du/j+++/RqlUr+Pr64samTcg+dkwL78Lw4dqsOFVNm/khIbi4ejX8/PzQ\nokULfPnll7hw4YJmhyJ4ebF5cGzsS8eNExFu3ryJ+fPno3nz5ujUqRMebN+O7H/+0dI7MWAkEkTG\nx6NJy5ZiW2LUNGvZErdatGDVTdeusWDGjRtim6UdvL2B+fOBbt2AOnWAGTOQX68eTly4AK8pU9B6\n3z4s2LgR17ZuBW3YwIINjx6x16alscBMeZVJzZqxyp/ffgO++goICgLkcgiCgIsXL2LWrFlo1KgR\n+h86hKiGDSHXVzW7yERGRqKJjw8wYQIwZAjb8bJmDbBlC6AnXyBOEMfcHE2mTcOdO3egqCLNkEwC\nAvDD8uW4ffs2zp8/Dx8fH6xZswYutWujd79+WNWnDx4eOqTx9R9t2oRlS5eiU6dOaNKkCc6ePYvx\n48cjLi4O+/fvR6s+fapECX5ERASaNGsGDB4stilGS1WbjFpbW2PF+vV4GBeHY8eOwdPTEwsXLkTN\nmjUxcOBA/LZmDeK++46Vijo5qXVtIkLE8uVYtHAhAgIC2OLwxg3MmjULCQkJ2HHwYJVZPEVERFSZ\n96orqpo2XdzcsL5jR8SGhWHHjh2wtbXFZ599BmdnZ4wYMQJbt25FcnLyqy/i7Mx6BDg5laiyEwQB\n169fx7x589C8eXN06dIFMTExWLRoEeLj4/H7hAn/Z++8w5q+vj/+DmHvvQVcoICKCxRlOlEqddat\nrX5ba6u2ah39tY5q1S5bbWsd1TrqFqsIIogMERUFFWW7RfaQGci8vz8uCSABg0ISIK/nyWPgM+5J\n5HzOveeeAevy8jb+hPKBQjffnc6mmz3t7XHIwwN5WVn466+/QAjBJ598AgsLC8yfPx9nzpxBqSQ7\n8tnZdQuebduAggLw+XzcvHkTq1atQs+ePREQEICKigrs2bMH2dnZ2LVwIfSFC9EOjkI33x1nZ2ck\nx8fTjTgh//7bMdKqABoR+MEHwNy5QK9eGLhqFU6ePo38/Hxs/fNPvOrZE9MiItDl2DF8cvEiLuze\njaqSErqxn5UFrFgB/PgjjSR5+VJ8UMG0aYCaGjh5eYgODMTSpUtha2uL+fPnQ0VFBSdPnsTTrCz8\nsnMnVIR16Do4It1kMOhm0ZdfUif08+fAwYO0cUAbI5tVvbExtIyNYWVlhUePHnW6MN7u3btj2bJl\nWLZsGcrLy3E5PBzBO3di84IFUNfUhI+Pj+jVpYl2vM+ePUNMTAyio6MRHR2N6qoqjB81CqtXr8aI\nESOgrq4u5U8lH6SlpWH06NHN7rgqaB47OzsUFxejvLwcup0ooonBYMDR0RGOjo5YtWoViouLcenS\nJQQHB2N1cDCMTUzgU1AAHx8feHt7w6x+akZeHqCtDaKlhYcPH4r0Mjo6GsoAJrz/Pn744Qd4enpC\nuRM4U8WRlpaGiRMnylqMdo2TkxNSUlJACOkc9Utqa9gwqqsxYMAADBgwAOvWrUNubi4uXryIM/v3\n47OPP4ZN9+4im+nl5QXD14sMjhkDwYULSHnwQKSXMTExMGAyEfDee9izZw+GDBnSsMtEt25AeDgt\niFlTI7PChdJAsVB8d5ycnLB+/XpZiyE9tLUBAEwWC+7u7nB3d8eWLVvw7NkzhISE4O+ff8aHc+bA\nwclJpJseHh7Qeb1pgKUl+Lm5uFdYiOjcXEQnJSH2wQN00dLC+1On4vTpUXNKMwAAIABJREFU03Bx\ncWn4vOvXDzh2jOplB57rEkKQrkhDfmecnZ0RHBwMHDhAI3Giouhi+9QpmpLUUbCyavCjqqoqfH19\n4evri+0PHiDjp58QrKOD7cHBmLF3L/r16wcfb2/4qKvDPSMDGo8fA+fOAQMH0u9FRQUcDgcJCQnU\nbsbF4UZKChx69sTE6dMRHh7eae1GWVkZysvLYS1s1EEIra0p7LJXUQEcOgR8/nmblo2R6WpCuHPR\nmR9Qurq6mDxlCiZPngwCGp4VFRWFCxcuYMWKFdDT04OPtze8HRzAMzZG9NWr1GlTXQ1vb294e3tj\n9erVcHBw6ByT+jeQnp6OpUuXylqMdo2SkhJ69+6NlJQUDK1fT6KTYWRkhFmzZmHWrFkQ8Pm4/+AB\noqKicPToUSxatAgWFhbUodOjB15FRyNaS4s6bZSV4ePjg1GjRuH777+HnZ2dQjehSNloDQwNDaGj\no4MXL150/HbQAkFdkXEWi+bZ6+sDACwsLLBgwQIs6NoVvKAgJM6YgejoaOzZswfz5s1D91qnjoeH\nB16+fEmdNpGRMDA2hre3NyZNmoQdO3bAescOYMQIwN294dhpaUBxMW1LvnEj8OmnHdaJw2az8eLF\nC/RQFE99J+zt7fH8+XPU1NR0jk00oTOmoqKBbtjZ2eGzzz7DZ926gR0RgVvvv4+oqCj89NNPmDZt\nGpydneHj44OhQ4eKNjxir16FNZMJbwsLzPvoI+w/fRqm27YB/v5A//6Nx3Z2pi2N09LEH+8gZGdn\nQ1tbG/q1zz0Fb4coSk5ZGRg8mL5evKDOnPR0ms7X0enTBw7r18Ohe3esAMBisXD9+nVERUXh2/h4\n3L9zBwONjOBtYYFBJiZ48NNPiI6Oxo0bN9CzZ094e3vj82+/xUknJxj07CnrTyNz0tPT4eDgULfx\nw2AAI0cCLi5AXBxw/TqNMIyJAby920wOuXDiTJkyRZZiyAcMBhgAevfujd69e2Px4sUQCARITU1F\nVFQUAi9cgIqBAXx8fBROmyYQCATIyMjo1E7B1kKom53ZiVMfJSYTLi4ucHFxwZdffgk+n4979+4h\nKioKh8PDYaCqipEjR2Lz5s3o2rWrQjdfo7S0FJWVlbB6badIQcsR6maHd+JkZQFnztD3Bw8CY8cC\nHh4NzzE0hHJ1NdxcXODm5obVq1eDy+Xi9u3biIqKwt4NG2BtY4NJ06dTp83r7e1tbOhk/nVsbIC9\ne+n70lLpdayTAY8ePYKtrS1UVVVlLUq7RlVVFd27d0d6ejpcOkMNE6ETp7JSfESMlRXUKivhMWAA\nPDw8sG7dOtTU1ODGjRuIiozEH2vWoKezM+bOnYv9+/fD9Pp1ms4xbhxgbg7Y29MF9siRjcfW0wNs\nbYH792n9w+7d2/7zygBFhFzrYGFhAR6Ph4KCApiamtJf2tgA8+YBb1PDqb1ST080NTUxcuRIjKzV\nr8rsbMR9+SWi2GzsDA9Hn+HD8fnnn+PkyZMw6AT1VFtKk7ppbAwEBADvvQekptL6S717S9xUoaXI\n3IlzRjhJU9AIpdois87OzliyZImsxZF7srKyoKenBz09PVmL0u7pbPn9LYXJZGLgwIEYOHAgVq5c\nKWtx5J60tDT06tVL4dxqBYS6OX78eFmL0rbY2NBdfoB2xxC3UBN2XCwupl1dAKioqIjSO6CtTV+z\nZjU9xu3bjX+vpUW7Sp4+XfdzB0WxUGw9hLrZ4Z04+fnAlSv0fVAQ3X3282t4jtBhn51NHTIA1NXV\nRalV0NSk+jd1Kj1v3Djg1i1A6Ex0cKD3Fggad4y8f59G/9y5Q9NjfvutjT5oExAilc66Qrup4N1g\nMBgi3fT19W14kMmUjVByhraVFcZs344xBgYd2t61Fm+0m0pKNGLQ2fndGxc1g2wKG9eiWCgqaE0U\nk9HWQ6GbCloThW62Hp1GNxmMuvbgmpqAhUXjc4RpHCUl4u9haUkXkU1hYwMUFtJ0LWEuuxBv77pi\nyBoaLRa/vaDQzdaj0+imqWldF6lnz6gevY6hIY3OaUr/7OxoAVAhqqq0OKuwKGqvXjTCp/45QtTV\nqfOmpqbl7ZLfhepq6liSEgrdbD06jW6+C9bWCgeOhLRIN9vQ4StTJ079HGIFCt4VRQG41kNh8BS0\nJop6OK1Hp9JNNzc6AerWrfFEiMWiofCqqrRl6o0bja+3sqJFx8WFzOfm1jlu9uyhi8L6KCvTLoda\nWlLZdZcVCrvZeshENzkc6Y4HUH0YPrzuZzs78ec050S1s6Mpk/WdMP36iSLqYGlJU7bS0xtfa28P\neHm9rfRvx+PHwKZN9LkgpeeBwm62Hp3Kbipoc+TFbsrUiSPMIc7IyJClGAo6CIpdi9bD0tISHA4H\nBfVbMipQ8JYodLP1cHR0REZGBvidIZdfXx9wdBSfSqWqCmzZQheBwcHio3GsrKgDJz+/8TFDQ+C/\n/+j79HRaZ+N1+venC8sOjEI3Ww+ZLBTz84GTJ2mhX2ni5kajZkxNm969t7Jq3okjENCWxvURdm9k\nMGhKVUYGbQX9+mbvpEnSKTYuENDny88/02eMFOsEKnSz9XB2dkZKSoqsxVDQAZCnZgAydeIACu+o\ngtZDYfBaD2EOscLoKWgNFLn9rYeWlhYsLCzwWJjO0NFxdwfETZaUlWmhY2G+ubii2RYWdDGYk0MX\nY/VRU6N1b4SIc+IwGMDkyW8vu5yjaAbQunTt2hWFhYUoLy+X3qDW1kBmJrBtm3hnZVuhqQkMGCA+\nCkeIlRXVvYICWteqPubmNC3q2TPx1z5+TJ24jx4BP/zQOOJIXR2YPbtto2KKi4F164Dz5+nzw9ER\nkFKR11evXoHFYsFSGJmk4J1wcnJCcnIySBvWJ1HQOXj48CHs7OzkohmAwomjoMOgCD1tXRS6qaA1\nqKmpQXZ2Nrp30A4isqBT6Wa/fk0vFD0963buX1/scDh0gWhkRDtEnDzZ+HoPj7riyE0VxNfUfBup\n2wVZWVkwMDCArq6urEXpEDCZTPTu3RupqanSG1RYOyorC/j+e9raVloLVQ+PpnXz5k0gMZFG0Gza\n1DilkcGgHaaePhV/vZISEBFBI+3y8+sKHtfHyQkYNuydPkKTlJfTSL3i4rrCym01lhiE6RqKZgCt\ng5GREbS0tPDy9cgvBZLz+kZIJ0We1poKJ46CDkFxcTHYbDbMzc1lLUqHQaGbClqDhw8fomvXrlAR\nFqxU8G4UFXUu3VRREb+AA2jNDFdXetzEpOExVVUgJIRGANy/L37HXlmZtgJVUqprmdzRIUQUsaGI\nkGt9ZKKbrq70b5jNBrZvBw4ckE7r5B49aDSOOPr3p3WnAOpQFZf6ZGfXdCRO164N6+409QyYOFFS\naSWDwwEuXgS++Qa4do1+j4MG0agiKaZWKiLLW59OYTdratouIi8uTrqFxOUUebKbCieOgg6B0OAp\ndi1aD4VutiKdOIRXngxeh+DqVTgbGip0U8iIETQKR9yz/7336t43VT/DzY3W3ugstoPBAA4fBths\nxUKxDZCJ3dTVpa1sAer0TE6m0WdtvXPOYDSdXqSmBoweTd/r6NR1naqPnR1dcL7eGU7IxIm03o6y\ncuM240LU1VssdpMkJNSlT1VX02gcKytg7lxgyZK6qD8poLCbrU+nmNOqqwOhoTQFMTaW/h23Fi9f\nAmFhrXe/doo82U2ZO3G6du2KgoICVFRUyFoUBe0YeVKqjoIih/g1CAEePHi765KSWl+edoJCN1sZ\nXV0437uH5E78N9UAa2vAx0f8sR49AOHfXlNOHCUlYObMtpFNXikrA44eRVpqqkI3WxmZLRSHDgWm\nTKGOkcxM4OBBmsaUlCS7TQRvb0Bbu2ndE6ZiPX8uvpW4tjZ15Eir9oSTE6ChQd8/eQIYGwOLFlGH\nlJRq4QhR2M3Wp1M4cQBg+nSgshL491/gq6+Av/8GUlMbO3WfPWtZxB6HQx1Er9e36mTIk27K3Ikj\nkxxiBR0OecpR7CgYGxtDU1NTkUMM0EnwmTN0YtdSHj+mdQo6KQrdbGX09eGgpoanT56AXVYma2nk\nAze3po/5+9N/hbVvxGFq2rryyDtaWkB8PNJv3VLoZisjs4Vi376Ary/wySe0vtOLF7So8K5dwKFD\n0u9eBVDnx5gx4p045eVAVBSNbtm/H7hzR/w9hg2jkXJtDSHAqVO0CLOwRtSiRYCZWduPLQaF3Wx9\nOo0TR10dWLgQYDJp+tPt20BeXmMnDpsNbN0q3oEqDjabPkfE1ZfrJAgEAmRmZspNlJzMnThAJ1Is\nBW2GIvS0bVDoJujk7vx5WmSxSxfx55SW0gKI4rh8mRrQTopCN1sZfX2oMpnopqODjG3bFMUGgeZT\noYTRONJoR9xeqG0JnZaZiV7a2jIWpmNhZWWFmpoaFBYWSndgZWW6aLOzA/73P7oT/+oV/b27u1RT\ngRrg5UULGL+Ori5t2c3jARUVTdekUlKikQVvg6QRSIQAx47RYswBAfR7mzu36Xo/bYywGUC3bt1k\nMn5HxdHREenp6eBLo16UrLG1BSZNou/V1Ghq1evPJHt7+re/bRsQGNi4A9zrCI/fv09fb0Np6dtd\nJye8ePECBgYG0JGTGnoKJ46CDoE8hbd1GAiBc48eSL57V9aSSIemJnwXL9IQUkC8E4cQWmNCnAHM\nz6fh7IWFstkJlTF8Pl+udi06BPr6AABnAwMkZ2TQXTYFzfP++3W76woATU0U1dSASwjMs7I6dc2u\n1obBYMDZ2RkpKSmyE8Lbm0agFRXRAsG//05TrGSBmhowcqT4Y++/Tx1PQPOFxfX1qVOqpVy79uZz\nCAFOn6aL3NmzgZgYoFcv+l5GZGZkKJoBtAHa2towMzPDk7eJqJYVAgF1Kr7NM3rECFqUe906WpNq\n61bg1q264wwG1U2BAAgPB777DkhPb/p+bDa9xtCQlhZoyQaSQACcOEHr6rRj0tLS0FuO5rPy4cTp\n3RsP3qbWRAdCIBCA8yYvaDOUl5eD1wkXiQBQmJ2N4vx8dFUYvHeHEFoQcdcuYNUqOKen40FHr71B\nCHDvHjVir5ObC35aGngCAQ1RNTZufE5sLJCSUjcZrU9MDEoFAgj4fBqmLQ4ut8NGU6Tevg0LfX1o\n1+78K2gF9PUBW1s4d+mCB0DzqUQdHB6PJ9muqp2d2GidV69edc6aX1ZWSOjbF32dnMCYNKnzFHVu\nS+7do/9WVcHZyEi2c1oGA1i2DBg4kG4e9OoldUcOh8Op062m5mYmJoCnJ31fz8lKCMGr+gtXLpem\nOrWErCy6AdMchNA24pGRwPz59P+Qw6GRTOLsuTTg8ZCwbRv62th02HmBLHF2dsaD8+fbj+NaSYlG\nvXz5JXXCHDpE56oPHlDnTnMwGMCHH9J566pVtG7W/v1gHzwIIlwvDh5cp3uVlfTV1N+dqyvImDF4\nVV1N68g1VWz8ddhs4K+/qFNVGqmRbUjC9evoW1FBCzwLBPQ5I8O1t4ziKxviVlmJhLg4lBYVQV/c\nIqmDUlJSgvDwcISGhiLs4kVUVFVhyNCh8PHxga+vLwYPHtykJz43NxexsbG4evUqYmNj8SgzE+oa\nGhgzdiz8/f0xduxYGHaS8PH/QkIwztkZylVVshal/cNg0Ie6tjbAZMKztBT/FxQELpfb8XaFBAKa\nh3/xIo2U+e470aG8vDyEhYUhNDQUl8PDwa2pwfAePeDz88/w9fWFi4sLmEwmdcycPk0vqp30PX/+\nvE43r17F8+fPoautjXGVlfCfMgWjRo1qGIrJZAJ799LQ1w5WmyMwNBQB/fsrFomtibIysGwZPPv1\nw5K1a7GFkE7Vle/FixcIDQ1FaGgooiIiwFRSgqevL3xrX05OTmK/D0IIHj58iNjYWJF+5mVnw9jA\nAP6TJsHf3x8+Pj7QEBY27ciMHYvAs2cR0NkKOrcFhNAJfVgY8OgRcPUqvDgcHDp3DkuWLJGdXBoa\nwNKlNFXC25vamd9/p12W7O0lu0dmJk1HlGCxJtSv0NBQXLp0CVejo6GtpgbvMWPg6+sLHx8f9OzZ\ns7Fu+vlBcP06UrOzERsdLZrTFhcUoIupKfynTYO/qiqG6+tD4vLGPB4t7CwQ0BefL96RFBJC/9/m\nzqUL4uRkYPlyWlNIVuTnIzAuDrO9vKjski6UFUiEp6MjQv7+G5OUlOj/+4sXNFpNyoWrW4SXF02B\nPXCAFiMGqLzLlr35WmVlEEKQnJaG0KwshCYm4saBAzD+5huRzfRxdIRtTg6Nknn5kkbv1MLn85GU\nlCTSy9iYGFRWVqJHZCT8/f3h7+8PNzc3Oh8WR2kp8Oef9Ht2cmraodtOCLxwATunT6clFu7coQ4y\nDQ1g1iyZzHMZLdmFGjRoEElISGh9KcLCMOX77zF69mx8/PHHrX9/OUEgECAxMVE0AU1JSYGXlxfG\njh0Lvy5dYDRoEGLv3EFUVBQiIyPx+PFjDBs2DL6+vhg6dCgePXpUZ+CKizF8+HB4enrCw8MDA4yM\nUMDj4WJsLIKDgxEVFQUXFxeRknXk9tsjR47E4oULMcndHbCxabNxGAxGIiFk0JvPlD5tppsVFXAf\nMwZff/01/IUFQts7fD4NKQ0NpelOAHjvvYcbenq4dOkSQkND8fTpU4wYMQJ+fn4YO3Ys1NTUEHPl\nCqJiYxEZGYnc3Fx4eXnB19AQA8vKkFJYiKvm5oiNj0dNTY1ILz09PdGnTx88f/4cISEhCA4OxvXr\n1zF06FD4+/tj/Pjx6N69OzUIV64A06bRQo4dQFcJIXBycsKBXbswxNu7TceSV91sM70EtSfdunXD\nf//9h/79+7fJGPIAm81GbGysyG4WFhZizJgxGDt2LMYQAt6tW4h2d0dkZCQiIyNRUVEh2ghxdnbG\nnTt3RE4bFRWVBrrZ68oVZGRnI9jICMHBwbh79y68vb1FumllZSXrj98mcLlcWFhYIDExEbbi6pW0\nIh1aN4WpOFeu0J+1tYGRI8FydYV1z55ITk6GpaXluwv7LsTG0i41ixbRKNvkZODzz2l0zpsICqKL\nFHd3sYerqqoQFRUlsps1NTV0Puvnh5FlZSg7cABRCxcisnZOC6CBQyf+8mVcvXYN127fhoGxMTy8\nvET62e3hQyTt349gBgPBMTHIrKzEqNr5rJ+fH0xMTBoKw2bTRW5ODu3Ek5lJF4yTJtEir+bmDc+/\ndIlG4cycSTdPduyg5wrbosuIV5GRsBs3Di+zsqDz+mdsZTq0bjZBbm4uHHv1QvZnn0GzoqKucPVX\nX9HUP3kmJQXYvbsudd/JCRg/HujevdGpZWVliIiIEDlVVVVV4efnBz8/P3h7eyM3N1dkM6MiI6Gj\nqQnfvn3hw2LBavJkxJWWIjY2FtevX4eVlZVILz08PGBlZYVbt24hODgYwcHByMnJgZ+fH8aPH48x\nY8ZAvzblG9nZ1HEsjBiaNo2meLVTMjIy4OPjg6ysLDBzc6mjOCuLHmzlzyapbsqHEycjAxfS0vDD\nzz/jmiQ5rO2IoqIihIWF4dKlSwgLC4OxsbHIyHl4eEBdXZ2eSEijhVtxcTFiYmIQGRmJmxER6OHi\nIpp8Ojk5QakZD311dTWio6NFSsZkMkUOHS8vL6jJ+8NKQgoKCmBvb4/c3Nw230GVV4MHtK3R27Nn\nD65cuYJTLQ1nllc4HODqVWQfPIiwrCyEFhYiIjcXdnZ2IiM3ZMiQZiOPcnNzER0djcjISCTGxsK5\na1d4+vvDY+RI2NvbN+swraioQEREBIKDgxESEgJDQ0P4jxiB8c+eYZi5OZRHjAA++KDdO3KSk5Mx\nfvx4PHv2rM0dyPKqm22plwCwbt06VFRU4Ndff22zMWTB06dPRZPPmJgYODo6iuzmwIED63b90tKA\n336jrZS5XMDKCs+fP0dUVBSiwsORHBeH/sOHw3PMGHh6esLW1rbh32JCAm2/Om0aYGiIEhsbhIWF\nITg4GJcuXYKtra3IoTN48OBmbW57IiwsDBs2bMCNGzfafKwOq5s8Hq2FFh9f9zsdHeogsbPDwoUL\n4eDggK+++urdhX0XCAH++YemX6xdSx0XDx5I5sj591/q9Nm0CSgqAlFXR3penshpc+PGDQwcOFBk\nN/v06VOnX6dPA+vX080SW1sQQvDo0SO6aAwLw6OEBLhZW8PDzw8es2bBys6uYdQJITR95PRpwM4O\neQMHItTKCsHBwYiIiICjo6NoTtvXyQmMjAzqTIuPB+7epdf36kWvf72o85UrND1r6lSacrZ5M9Cz\nJ+3sJWO7+8+6dQhOSkLg+fNtPlaH1c034OfnhzkzZmBmRUVdgd7+/eXi//+NPHlCU5PmzKGRZM+e\nAQ4OIOPHI6m6WrTZcffuXQwbNkxkN5ublxJCkJKSQgMITpxATnU13GsdqsOHD2/sMH2NFy9eICQk\nBCEhIbh69SoGDhxIddPDA/bp6WDExdETv/tOZt3eWoPNmzejoKAAO3fuBHJzgT/+qGu3zmDQZ6qz\nc6uMJbFuEkIkfg0cOJC0FRwOh5iampKHDx+22RjSJDMtjUxydye6urpkwoQJ5K+//iJPnz59+xvm\n57/1pQKBgNy/f59s2bKFuLu7E11NTbJg1Cjy4sWLt5dHTti9ezeZPn26VMYCkEBaoC/SfLWlbpaU\nlBA9PT1SUlLSZmNIk7t375KRHh7EQEODTOvWjRz47juSk5Mj2cVc7tsNymaL/TWfzye3bt0i69at\nIwOsrYmBujr5YtEiUlBQ8HbjyBHffvstWb58uVTGklfdbEu9JISQzMxMYmpqSjgcTpuOIy2uXr1K\n3Pr1I6YGBmTOnDnk2OHDpKioSPzJ+fmEhIYS8vHHhKxaRUhkZMPjPB4hS5YQEhEh/vqcHEL++INe\n//HHhMTFNTjM5XLJ1atXyapVq4hj797E1MiIrFu5kpQ9f94Kn1S2LFiwgPzyyy9SGatD6iaHQ8iJ\nE/Tv58wZQq5dI+TRI0IqK0WnxMTEECcnJyIQCN5+nNaipoaQ9esJ2bKFvt+9m5DPPiMkLa356/78\nk5CPPyaC8HAS/N9/xLlLF2JtbU3+97//kcDAQFJWVtb0tZs2EdKrFyH79xNy5UrDYzweIZ98Qoiv\nLyFhYeKvr64mZM0aQvr3J8TGhpBp0wgpLKz9ODUkPDycLFu2jHTr1o10MTMjPw8ZQqrXryfk/fcJ\nGTaMkKlTCVm7tvF9o6OpvoeGUnu+bRsh33xDCIvV/HchJcaOHUuOHz8ulbE6pG5KwLFjx8hoLy9C\nfv6ZkEWL6mzAuXNtOm6rIZyvCgSE/+ABOTpjBulqZES6d+9OPv/8cxISEkKqqqpkIlpVVRW5cOEC\n+eSTT4iVlRXpaW5O9n78MeH+8Qch8vAsfAf69u1Lrl69WvcLFouQ27fpM+6LLwhZurTu/+YdkVQ3\n5Uqxli1bRtatW9emY7Q1xcXFZNmyZcTIyIhs3bqVsOTEMNSnIDOTfP3FF8TQ0JAsX7686UlyO8DX\n15ecPXtWKmPJq8EjUtDNqVOnkt27d7fpGG3Ny5cvyfz584mZmRn5888/CYfNbnoCKY6bNwl5/Fj8\nseYe3DU1hISEvPn+WVnkxfPn5PPPPydGRkZk48aNpLy8XHL55AiBQEB69epFbt68KZXx5FU321ov\nCSHE3d2dXLhwoc3HaUsyMzPJxIkTiY2NDfl3zRrCX7yYkPPnCUlKavoigYAuwIQT8OvXG5/z55+E\n7NxJF4Q1NY2PHztWd/3rTqD67NtHMubOJXN79yamBgbk119/JdXV1S3/oHIAh8MhRkZG5LmUnFGd\nVTf5fD7p2rUrSUxMbNNxJCYnh5DPP6fOJx6PkD17qCMnNbXpa7ZsIXcnTSIjunQhvRwcyIULFyRz\nSj15QoirKyE9ehAyYQIh27c3PC4QELJwISF2doQcPSpyzjSAwyGkoICQsWMJMTYmZMgQ+kwgr99K\nQO7Gx5P3/fxIly5dyP6FCwl39WpC/v2XkPDwhidfu0Z1PSiI/nz8OP0OXr5882eSAsXFxURXV5dU\nVFRIZbzOqpssFosYGBiQly9fUsdrfDwh+/bRhbiU5iytQWxsLBk8eDAZNGgQibl8WTqDZmZKfKpA\nICDXrl0jPj4+xL5nT3L69Gn5cGq/BWlpacTS0pLw+XzxJ/D59LuJiWkVZ5WkuilXscHz5s3D4cOH\nIWiHFdnZbDa2b98OBwcHcDgcpKamYs2aNXJZJNGkZ098/+uvSE5ORnV1NRwcHLB582ZUNtXCsaJC\nugJKSEFBARITEzF27FhZi9LhmTdvHg4dOiRrMd6KyspKrF+/Hn379oWZmRkyMjKwePFiqKiqAqNG\nSXaTO3doSLq4UFAeDzh6tOlrb9+m3S/ehLU1utjY4Pfff8etW7eQmZmJnj17YufOnWCz2ZLJKSck\nJyeDxWLB1dVV1qJ0eNqzbpaUlODLL7/E0KFDMXjwYKRfv45ZAwZAicejoeJ5eU1fzGDQtEMhwtRk\nIdnZgJUVrY3x889AdXXje0yZAlhb0/c1NU2P1a8f7NXVccjDAxEXLiAyMhIODg44ePCgZN2x5Igr\nV67A3t4eNm1YP04BoKSkhLlz58qPblpY0OKbkZG0C9OCBUC/frToaGpqo9Ozs7Px4fHjGBsaikm2\ntrj//ffw9/eXLDU2OpqmLQu73ZDXyjY8eACUlQEsFq27ExLS+B4qKsD167RYtKEhLR56/XqjzjkM\nBgMurq747+JFnDx5EodSUtBnzx6cLSwEqd/a/NYt4MgRYMwY2n799m0gKoq2EpeT2lfnzp3DqFGj\noK2tLWtROjQaGhqYPHkyjh49SgsGu7rSukk//0w7psn5GvTRo0eYPHkyZs2ahWXLliE+Ph6e9f/W\n79+nqZ6BgbRxR3Q0TTNMSWmoi4QAjx/TVGRJiY5uvg15PRgMBoYNG4YrV67g9z/+wJYtW+Dq6oqI\niAjJx5MTTp8+jSlTpjSdUq2kRFMyPT2lmpInV04cFxcXaGtrIzaKNf5lAAAgAElEQVQ2VtaiNObF\nC2pw6sPlghCCM2fOwNHREZGRkYiJicGuXbtg2tIuM+/aWYnFqqtaLiEWFhbYtWsXbt68idTUVPTs\n2RN//PADOPUns1wucO7cu8nWRpw9exbjxo2TS0dZR2P06NF48uQJMqXYovRd4fP52L9/PxwcHPDw\n4UMkJiZi27Zt0KvfeUKSh+2DB8C+fUBxsfhOEWFhQGJi09efPVuXoy8h3bp1w7///ouwsDCEh4ej\nV69eOHLkSLtZMJ46dQpTp07tsMXU5Ylp06bh8uXLKCkpkbUoElN/06OmpgapqalYu3YtNKysGrb2\nbc6JA9DW4cKiq6/bgaoqOoHlcGjxQXE1rlRUaDthVVVaGLUp+val55qZoc+wYQgKCsKxY8ewf/9+\n9O3bF+fPnwdpgX7LktOnT2Pq1KmyFqNTMGfOHBw/fhwcYSFSWTNkCDB8OF3gFReLdeRUVVVhw4YN\ndNPDygoZy5dj8f/+BxUvL8nG4PPpQlhZmbYuft1mCQRATAytZ8Xl0nmrsK5EfQgBTpygGyf29tRW\nv3pFr2uCoUOHInrjRvzq6YlNBw9iyJAhiIqKqtuE8fEBJk6k9SwOH6YLriFDJPzy2h6h3VTQ9ggd\nrA2e20wm0K2b3HYEE256DBkyBIMGDUJ6ejpmzZrV2LHQpw8tdhwVRZtmHD9Oi36Xljac8zIYdHNj\nxQpg505aKyo/v/m5qqoqvVdTm/5iYDAYGD16NBISEvDVV1/h008/xahRoyC27hEhNHCAxaI2mceT\ni3bw8mo35aLFuBAGgyGKxvGS1GBICy6XLuSWLBEpePyvv2JFYCAq2Wzs2bMHI+t7QlsIuXwZxN0d\nSk05f54/B17rIsFms5GXl4fs7GzkhIWBW1wM88mTYW5uDjMzMxgYGEi0iOrRoweOHTuGu4mJ+HrW\nLGzfswebNm3CjBkzoBQRUVd9W844deoUli5dKmsxOgUqKiqYOXMmjhw5gk2bNslanDdy+fJlrFy5\nEjo6Ojh77BjcevUCysvpZK68nLaUlKQry6tXEJw9C2RmQklDo/FCMT8fOHmykQO1uroaubm5yElO\nRk5EBFBTA7P//oO5kxPMzc2hq6srXjcLCujCU1kZ0NREPy0tBK9ejdiUFKzZsQM/bt6MrYsXY3y/\nfmAQQlvHypmjhBCCU6dO4ciRI7IWpVOgr6+PMWPG4NSpU1i0aJGsxWkWQggCAwOxevVq9OrVS1S0\nuAETJ9JJ3PXrb3TiCAQCMN5/H4w7dxrrpr09dfBcv05/rnUOVVVVIScnR/RSYTJh7ugI82fPYFZR\nAW1t7ca6qaZGF7taWqJfDRs2DFevXsXFixexdu1abNu2Ddu2bZO/uUs9OBwOzp07hw0bNshalE5B\n9+7d4eDggEuXLmHChAmyFocyfTq1V3v2AGvWUEfOgQPg//47Dlla4tvdu+Hl5YXEhATY2dlR+/bo\nEe1SJQlMJqCrCz6XC6UuXcDo2bPhIozBAD79FDh0iHbz0tQUOXEqKiqQk5OD7Oxs5CYkQDMqCmYq\nKjBnMGDu6gpNXV2qz05OTQ7PSErCWH9/jF6wACdPnsTCuXPRA8DWTz7BgGnT6MJw924amVQ/kk/G\nFBcX48aNGzhz5oysRekUDB8+HDU1Nbhz5w4GDhwoa3GahcPh4M8//8TWrVsxefJkpKSkwKy5AsEM\nBu1yamdH9by2CyuOHQOSk8EfOhRKwiLkzs40IvXoURqpc+oU3bRYuFDUrYsQgrKyMmozMzORl5gI\nnT//hLm3N8zMzGBmZibRZrqSkhKmTZuGiRMn4sCBAwgYOxbuVlbYPGIEHHR1qW7y+TSqNi+v4dzW\n2JhGzfXu/Q7f5NuRlpaG4uJiuDfRpU+WyEd3qnrk5ubC0dER2dnZ0NTUbNOxWkRxMfD114CvL565\nuWHt2rW4GhGBzQMGYO4ff4DZs2ejSwghKC8vR15eHvLy8pCfn9/kv/l5eeALBNDV1YWBgQEMDAyg\nr69P3+vrQ+/JE5Ta2SEnL09k5MrKymBubg5LS0tYKitDRVUV+QKBaLzq6mqRgpmbm4te9X8WvtfW\n1gYjKAiIjkZ0QADWrFkDVmUl1tvaYlLfvmBs3SqDL71p8vPz0atXL+Tm5tZ1+Gpj5LWSPyAd3bx3\n7x4CAgLw9OlT+ezScvkyUv77D19duYLMkhL8EBCASdXVYFRW0t08BoO+AgJAxozBq9JSkQ42p5+F\n+fkghEBPTQ36lpYi/TQwMIABnw+dW7dQzOEgx8VFtDCsrKyEhYUFrExNYVFYCAaDgTwLC+QXFiIv\nLw9cLlesHpqbm9NJ6/XrMK+uhpmGBrSqqgAWC8TcHMFZWVh76xb0VFSw4bPPMGrdOll/6424f/8+\nJkyYgKdPn0otEkdedVMaegkAFy9exKZNm6TSbehtiY+Px4oVK1BRUYFffvmlyU0PgUCAkqIi5O3Y\ngfz0dORNnIj8ggKxelpUu/jT19KCgZERDIyNG+inlpISCiMjkV1RgRxdXeTk5IDNZlObaWkJSz4f\n3KIi5GtpIa+4GHnFxQAg3k5yODA3MoKZu7vod8KJK5/Px/Hjx/Htt9/CwcEB69etw1A5nPCFhoZi\n8+bNiBN2C5ECnV039+3bh0uXLiEwMLDNx5KY/HxgyxZg8GBg9mxEhIdjxYIF0OHx8Mv27XCbMaPu\n3Js3qcNl507wlZRQVFTUpK0UvX/6FCVVVWAyGNDX1oaBujoMunWrm9dyONCIjEQ+g4EcBgPZfD5y\n+HwIBAJYWVnB0tIS5mVlqHnyBHkCAfK5XORxuVBWU4O5nh7Mu3aFmaVl43mtnh7M9+6F6aJFUBs+\nHEhNBWfnTvzNYmFzRASGDRuG9Y6OcK6oAP7v/wAjI9n9H7zG/v37cenSJZw+fVpqY3Z23dy4cSOK\ni4tptyFZQQhNLXz+nGZ8vHhBHYyTJoGUleHsnj1YvX07HCws8NPRo403PWrh8XgorJ1fNtDLrCzk\nX7uGvOJiuj4sLUVpdTWUVVQarjXZbBhUVMBAVRWqzs7IVVJqsNnBZDJhaWkJK319mFlbo5LDaaDz\nGhoaTa4v6783NTWlnV8rKsCKjsbOzZvxc1ISJtja4htXV3RzcwNycuiaG6Bzdl9fICBAZi3gv/vu\nO5SUlOC3336T2pjtq8X4a/j5+WH27NmYNWtWm48lMTwebk+bBq8LF1DN48HExARjvL2hnJQEjo4O\nKszNUV5ZifLycpSVlaG8vBylpaVQVlaGvr6+6GVoaAgDAwMYGhqKXsbGxjCMiYHexIlQNjICg8EA\nn88X3eNVTg5KHzyAvqsrLC0tYWxsDCUlJXA4HOTn5yM3Nxe5ly6hksEAz8YGPB4PPB4PNTU1qKys\nRFVVFaqqqsBisVBdXY3q6mqw2WzRi1ubFqbMZIIIBODXFkwScmXHDvjKWcTLX3/9hbi4OPz7779S\nG1NeDR4gPd3s168ffvvtN/j4+LT5WC0hMDAQ0z/4ADw+H120tOBjaQklZWWwuVyUs9mo4HJRxuOh\nXE0NZdXVKCsrg5qamkgvhYZMqJ9GRkYwMjKCoUAAw1u3oFdRAea4cWAMGAAej4fy8nK8evUKr2Jj\nUX77NoxMTGD52WcwMDAAk8lEdXU18vLykHv7NvISElBVUwNez57gqatLrJuc6mpweDwwADCVlEBq\nnwv1n9jPnj2DrSQRRVLkm2++AYfDwY8//ii1MeVVN6WllzweD9bW1oiJiYGDg0Obj9cSfvnlF6xc\nuRIAYG9vD3d3dxBCUMNioaKqCuXl5SK7WVZWhoqKCmhoaEBPVxcGfD70u3WDoYmJaMJpaGhIddPQ\nEEYREdAtLobSihVQYjLBAVBWVkZ1Mz8flSdOwMTBAVZFRdD79lswlJTAYrGobubmIi8oCNUpKeAF\nBIAHgMfno6amBuXl5SK9FOpmTXU1ampqwOZwwOFwwOVywWAwoKykBD4hEAgEIrvJrB1HVUaTzgYU\nFdHIPn19fPTRR+jXrx+WLVsmteE7u26WlZXB1tYWjx8/hpEcOQ2QkIBl8+djZ0oKAMClXz/019aG\noKgI1Q4OqOByqV4WF6P85UuUMhiorKqClpZWA7sp1Emh3TTU1IRRZiYMT52CtqEhmJMmAd27g+Pk\nROezr16h9PBhsK5dg5m2NiydnaGjqwvGokWoUFamupmTg/zjx8EuKwOXxQJPWxs8Q0OwCEGVsjIq\nVVTAqq6u082aGqqb1dXgstngEgIlJSUwGQzwBQII6s1pNdXVUZWQ0Gw0jywYM2YMFi5cKNWUjc6u\nm0+ePIGbmxuys7Ohqqra5uM1IiWFRroJI2UAGoXKYGBqTAzOZGcDAIaamqKXrS34urqoNjQU2cz6\ndrOqqgo6OjpUN3V0YKCiAgNCYMDlwojJhAGDASN3d6qjTk7Q6tYNSkpKIDwe2Dk5KH3yBKUXL+JV\nTQ3YY8fCont3WFhYQEtLCwwGA+Xl5aLo8oLUVHDs7ERrTR6PBxaLhcrKSlRWVja0m7W6yalnN5lM\nJphAnW7WfnRzXV3krlxJdTM+nkbqzZlDI4pkSJ8+fbBnzx6pRuK0ayfOiRMn8M8//yAsLKzNx2oJ\nf3z7LZZs3gwAsLa2hpqaGpS4XCipq0NDUxNqampQV1eHmpoaVFVVoaamBgaDIZrcCQSCJl98FguV\nXC5KSkrw6tUrlJeXQ1tbmxpHXV1oV1ejSFkZOTk5YLFYMDc3h4WFRYOXrq4ulJWVG71UVFTE/r7+\ncQ6Hg9LSUqirqcHYxASHDx/GsWPHcOjQIYwePVrG33xjfHx88MUXXyAgIEBqY8qrwQOkp5vbt2/H\ngwcP8M8//7T5WC1h4cKF2L9/PwDAVlsbylpaUNLWBpPNhgafD3UmE2q2tlDT1oaqqipUVVVFuln/\nJU5PeSwWKoqK8IrNRklZGSorK0URc4aamtCoqkJBdTVyWSyw2exGemlhYQFtLS2qbyoqEumjsrIy\nlJlMMJOSwL51C2WjRkHT3ByGOjr4/dtvEXnjBk4GBcFVzlI3CCFwcHDA0aNHMXjwYKmNK6+6KS29\nhECAFStXQkNTE5trbZS84OHigmtJSQCArl27gslkglFTA2UWCxq2tlBTV4e6urrIZgon0wKBAAIu\nFwIeDwImU6ye8rKzUVZYiFfa2igpKQGLxRItLg0NDaH66BHylJSQW14OoqzcSC/N9fSgdekSlF1d\nqc717Sux7WQymWCfPo0yFRXojh4NTW1tbN68GY9v38apP/5A7/ffl49Ux+hogMEAZ+hQWFhYICkp\nCdbCYs5SoNPrJoAZM2bAw8MDixcvlsp4kmJjbo6s2gVkt27doMRggMHhQEVHRzSXVVdXh/qTJ1C1\ntoaKhQUANDuXFQgEEPD54KakoIzBwCseDyU1NWCz2XUbJRwOmHl5yAOQy+OBqaICC1NTWNjawtLS\nEhYWFjAzMYGGqiqUz52D8rBhULa3f7PtDAmBMpcLpVmzwGKxUFFeDgN9fSirqmLt2rXgcrk4fvw4\nunTpIsNvvTFFRUXo3r07cnJyoFUvZbOtUegm4OnpiRUrVkh1LdEANhtISKB1op4/p5E5lpbQ3LwZ\n1Xw+GAC66uhASUUFDBUVqBobi3Sz/ktVVbVu/lpWBsGrVxCUllIbCkBACATKyhAIBOAaGqKUz6fr\nzeJicDkcGKipwVBVFQbq6iA2NsitjX5VV1dvaDfNzWFmYgI1Tc1m9bGpY0pKSnQDs6AAhqam4N2+\njeUHD8Kie3cc+ucfGJmY0O8gOprWrKpfI08GpKamYvTo0Xjx4oVUMxAk1k1JWlgJX9Jol0rIa+3f\n5IzKykqyePFiYmNjQyKba0n6jvD5fFJSUkIePXpEbt++TSIjI8n9+/dJYWFhm7Zoy8vLIyNHjiSe\nnp4kOzu7zcZ5F3Jzc4m+vr7UW7xCTtsxEinqpvC7r6yslMp4LaG4uJhMGzKE9O7Ro2Fb17g4Qtjs\nVhuHx+ORoqIi8vDhQxIfH0+ioqJISlISKSkpaRvdrNXDx48fk0GDBpH333+flAhbY8oZ9+7dI3Z2\ndlJvIymvuiktvSR8Pkn68UdiY2PTdAtMWfHoEXk5dy4ZNXgwcXV1JZmZmYSUlRHy9deElJS8/X2L\niwm5d4+2DL5wgZDkZMLhcEhBQQHJyMggN27cINGrVpG05ctJ2d27RPC6vlRXE7J/PyFLl9J7HDrU\nsvE5HPo5CCFJSUnE3t6eLPjwQ1IlpfbAErNzJyE7dpCQkBAybNgwqQ/f6XWTEHLx4kXi6uoqtfFa\nQkZGBhk8eDAZPXp003O+H38k5OBByW8qEBDyySe0dXPtPdlsNsnPzydpaWnk+vXrJOajj0imvz+p\n+PBDeo44e5abS3Xz6dM3j8nhELJkCSFXrjT49dWrV4m1tTVZs2YN4XA4kn8GKbJv3z4ydepUqY+r\n0E1C/v77bzJx4kSpjdcsz55RO5SURAghJOHWLdKrWzcy3c2NlJw40fL78fmEPHxIyMmThKxZQ8ij\nR2JPq2GxSG5YGElZvpxcW7KExMbEkMePH5Oqqqp3+TRv5Pz588TU1JT89NNP8jdvqWXDhg3kiy++\nkPq4kuqm3CrWwoULyQ8//CC18VrKxYsXiaWlJVmxYoXUnQmtgphFVmRkJLG0tCTffPMN4XK5MhBK\nMv744w8ye/ZsqY8rrwaPSFk3x40bR44cOSK18VqCgM8nR48eJSYmJuT7778nPB5P1iK1CoGBgcTE\nxIT89ttvUneQtISvv/6arFq1SurjyqtuSlMvybZtpJ+NDYl8bREjF9y8SfjnzpGdO3cSIyMjsnv3\nbiLIzSUkK+vt7/niBSGLFtFF3scf0wlwfe7eJeTAAUI+/ZSQlSsJKS9vfI8bN+quP3CgxSIIBAKy\nd+9eYmxsLJ/PRDabkM8+I+TTT8m82bPJjh07pC6CQjcJ4XK5xNzcnKSlpUltzJbA4XDIhg0biKmp\nKTl16lTjE06eJOS77yS/YXU11anaxahYNm0i5NQpQiIjCTl7Vvw5Qicti/XmMe/fp+cWFRFC6Ebo\nli1biJmZGQkJCZFcdhkwatQocvr0aamPq9BNQkpLS4menh4pqv27kQvqzfFYLBZZsmQJsba2Jpcv\nX363e0rilCksfPsxJITNZpPly5cTGxsbEhcX1+bjvQuOjo7k+vXrUh9XUt2Uw+qklHnz5jVu/yZH\n+Pn5ISkpCU+fPsXgwYORVBsu3i4oLaXdBmrh8/nYtGkTZs6ciX/++QebNm2CsrJcNS5rgLy2euss\nCFszyiMMJSXMnDkTCQkJiIiIgKenJ548eSJrsZqnqAhITxd7iM1mY9myZVixYgWCg4OxbNkyuW3b\nTQhRtEiVJTY2mGdpiUMbN8pFS84GuLlBafBgLFmyBLGxsdi7dy/8FyxA3rvYmS5dgP79637W1294\n3MwMuHWLdrsoL2/cvapWLtE9Xm+F/AYqKiowe/Zs7Ny5E7GxsZg9e3YLP4AUyMgAuFxwOBwEBQVh\n8uTJspaoU6KsrIxZs2bh8OHDshZFLCoqKli/fj2CgoLwf//3f5gzZw7KysrqTrC1pcVGJW2VXlND\n/22u6UR1Ne325uMDjBgh/py8PNqmXILON0hKAqytASMjFBYWYvz48QgJCUFCQgLGjRsnmdwyoLCw\nEPHx8XItY0dGT08P48aNw4kTJ2QtSh315ngaGhrYuXMn9u/fj/nz5+OLL75AdXX1291TkmZBknah\ne0ueP38OT09PZGRk4M6dO3LZ8UlISkoKysvL4ebmJmtRmkRunTjDhg1DTU0NEhMTZS1KkxgbG+PM\nmTNYuXIlRo4ciZ9++gl84USwvFy2wjVHZiad3IJ2eRo7diyuXLmCxMREuax/U5/c3FwkJSXJvZwd\nmYCAANy5cwdZctp6HgBsbGwQERGBKVOmwM3NDQcOHGjaIVxcDISGyk5njYyA06eBw4cBFkv06ydP\nnmD48OF48eIF7ty5A1dXV9nIJyH37t0Dn8+X+3adHRYbG8zs0QPnb95E5b//yp8jx9ISANC7d2/c\nuHED/fv3h4uLC86dO/f293zvPTo5VVICdHQaHrOwAIQ1o1RVaXHf12EwgFmz6LXNOXFYrAbPh/v3\n72PQoEHQ1NREfHw8evXq9fafoS1JTgYAXM7OhpOpKaysrGQsUOdl3rx5OHLkSN0cUQ5xc3PD3bt3\noaOjg379+iEmJoYesLMDBALg5UvJbiR04jTnfKmurjuuqyv+nPx86ox9E4RQJ46LC2JjYzFgwAC4\nuLggKipKqvWf3ob//vsPY8eOla9uvJ0MYdCAPDN69Gjcv38fubm5GDRoEO7evStrkVpMUFAQXF1d\nMWXKFAQFBclXoXcxCAMG5LIbby1yKxmDwcDcuXPldudCCIPBwLx583D79m1cuHABvr6+ePbsGRAR\n0bDiuDyRkQEkJiIqIgIDBgzAkCFDEBERAcvaSbY8ExgYiPfee09qbcUVNEZdXR1TpkzB0aNHZS1K\nsygpKeHLL79EVFQUdu7ciYkTJ6KgoIAe5HBo9ftffwW+/poWT2tqItnWCFsoxsUBGzYASUk4e/Ys\nhgwZgtmzZ+Ps2bMwMDCQjWwt4PTp05g2bZrcRgp1eGxsYKapiWHm5vjv5ElaLFFOUVVVxebNm3H2\n7FmsWLECH330EcrfxolqYUGjafT0qCPndd57j+4+NrdA0tGhHTCaW1wrKQH79oHw+di3bx9GjBiB\nb7/9Fvv27ZPvxZe2NtCrF07l52Oaj4/8OfY6EX369IGJiQmio6NlLUqzaGlpYdeuXdi1axdmzpyJ\nr776Cmw9PRpV8/y5ZDd5UyQOIQ2dOE2RlweYm795vGfPICgrw9Zr1zB16lTs3bsXW7dupa2M5Ryh\n3VQgO0aOHImXL18iLS1N1qI0i6GhIU6cOIGvv/4aY8aMwdatW+XaKQwA4PPBycnBio8+wpKPP8a5\njRuxcvlyuXaMCDl16pTc66Zcf4tz587FiRMnwJE0hFOG2NnZISoqCuPHj8fgwYNxODIS5O+/JQvR\n5nLfXQAeD5DwAcRJS8Oma9cwc8aMdpE+VR9FKpV8IEypktd0x/o4OzsjPj4eDg4OcHFxQfDff1PH\nzYEDNI3J0REYNerNNxJOTN+Ghw+bP+7qCmhrg1VcjGUrVmDlihUICQmR6/Sp+ihSqeQACwtAWxvz\nBgzAocePASl2B3tb3N3dce/ePTCZTLi4uODatWstv8l779FoNnFoaQETJrw5jLxfP2DIkKaPq6mh\nNC0Ns3195Tt96nXeew9sLS1cyMjA5A0b5KNbVidGnlORX2fcuHFISkrC48ePMdjVFfeVlSV34gjT\nPZpy4nA4NLLnTU6c/HyJnDh50dEYHxGBkLg4JCQkwM/PTzI5ZUxhYSFu3brVbuTtqDCZTMyePVvu\ngwYAGjgwa+ZMJJw4gcuXL8PLy0uuSwY8uXgRnv37I/PKFdz58EMM/eAD8RsuckZKSgoqy8rgJuP2\n5m9Crr/Jrl27onfv3rh48aKsRZEIJpOJVatW4fLly/jxwgVMOnwYoUeOoKqqqvkL4+KaT+XIz3+z\no+fFC9qirglKS0tx7NgxTP/gA5jt2oXrGhpIvHGjXaUlvXz5Eg/u38doSRbcCtoU9759weVycfv2\nbVmLIhFqamr44YcfcOLECSz5/nvMj41FZHY22BoawIcfvnlxU1ICnDnT9PEHD5o+Rghw7FiTDt2C\nggIcOHIE71+7Bovjx5Gvo4M7d+9KtUX3u5IYHw9CCAYMGCBrUTovysrAkiV4b+VK3H36FFnx8bKW\nSCJ0dHSwb98+/Prrr5g6dSqWjhuHuM8+A+/ECclSHI2NAX//po97egLdu7/5PmL+drOysrBr1y6M\nGTsWNsePQ6dHD/lOnxJDWGoqnM3N20WkbUdn5syZCAoKQkVFhaxFkQhjY2MEBgZi+fLlGLFvH745\ncgSJiYkQCATNX/imSBxh2nBzTpyqKqCyssl0qkePHmH79u3w8vKCw7Jl6O/ri+joaLlPnwJA5wQ1\nNThz8iT8/PzkO5qvkzB37lya7shmy1qUpiEEuHMH2LwZNvfvI+L33zGpTx+4ubhg63ffITk5WeYb\nq4QQJCcnY8uWLXBzc8PAOXMwxdkZQV99BaMNG4B2EFUOACeOHcPUvn3B+PHHBjVk5Q25duIAwAIP\nD2xbtw7c1ohWkRIuLi5ISEqC+4IF+OHgQZibm8PX1xfbtm3DnTt3GhvAwkKgOUfVy5dAVFTzgz55\nAty/T41eLc+ePcOOHTswYsQI2NjY4MSJExg5ahTS0tMRGhMDyx493uFTShdCCBZ/+ikWDRoEtTt3\nZC1Op4dx7hwW9OuHzf/3fzI3Gi3B09MTSUlJ6Orhga9TU2Hy998YN306fvvtN6SkpIj/LDk5wA8/\n0JQNcWRnN6+/Dx/Se9SrJ5Ceno4ff/wRw4YNg729PS5duoQpH3+Mp1lZOPHff9B/vUirHMONisJn\nM2fiy4EDwQAUKRuyxM4O6u7umOnjgy1//SVraVpEQEAA7t27Bw19fXx+6RKMFy3CxHnz8Ndff+Hx\n48fNX9y7d9PHmExg4sSmj3M4QG3tPUII7t27h40bN2LgwIFwcXHBzZs38b///Q/ZeXnYvX9/u1pw\nVVRUYEVICL788UdZi9J5ETrvuVyY3rgBX0tL/LZ9u2xlagEMBgPz58/HrYQEVDo7Y86cOTA1NcX0\n6dNx4MAB8bXxamqo3jUV4S2M1GlOl4TlCGqdOAKBADdv3sTatWvh5OQEDw8PpKenY9WqVcjLz8eW\nv/9uNxHlYDCQu2EDvluzBkvt7IDr12UtUafH2dkZ1oaGODRpEv3/EK7T3rQJLw0EAuD2bWDjRmDP\nHjqXTEuD0s6dWK6ujqiFC/H8yRNMmDABVlZWmDdvHo4ePYp8KZX04PF4iIqKwpdffokePXpg/Pjx\nyMvLw5YtW5BfUICVe/eCsXgxoKYmFXnelfT0dOzevRuf6LI2fSYAACAASURBVOvT//9ff5Xb9HS5\nf+LNtrTECR0drFu3Dlu3bpW1OBKjrq+Pr1atwlerVqGiogIxMTEIDw/HrFmzUFxcjJEjR2LUqFEY\nNWoUrMvL6R/IiBGAiUnjm5WX08Krw4c3bfQeP4aAx0PioUMIysvD+fPnkZeXB39/fyxduhQjR46E\nlpZW237oNuSvv/5CTm4uzsTF0agIQhSh4bKCECAlBcsNDOAeH48/fv4ZS776StZSSYyuri7W//wz\n1n/zDV4RgsjISISHh2PHjh3gcDgYPXo0Ro8ejZEjR8KktBTYtYvuHDo4NL4ZIcC//zbftSMuDnyB\nANfPnUPQy5cICgpCVVUVJkyYgHXr1sHb2xtq7cS4iWPDxo0w4vHwmZ0dTVH76CNZi9S5YTCw+cQJ\n9O/fH4GBge2qI5GZmRl+OHYMP4AW3b9y5QrCw8OxadMmqKuri3TT19eXOjpZLODpU8DJqemb1tTQ\ntKomjnF++w0x6uoIOngQQUFBUFZWRkBAALZv345hw4a1n4WhGJYuXQovLy9MbEd/Ax2KrCxacLdH\nD+DoUaC0FDu/+QaDli+Hz4gRGD58uKwllJiu9vb4bccOADQq+vLlywgPD8fq1athYmKCUaNGYfTo\n0fDy8oJ2TQ2NwmlqjiZ04jQXiZOXh2pCEHHjBoKCg3HhwgUYGxtjwoQJOHDgAAYPHtwu6mqIQyAQ\nYH5gID52cID7q1eyFkdBLX/v2gUfPz8M1dZG77AwICAASE2l9qV+J0RpwuUCly7RIvX1Numhqgqs\nXQtYWMCZwcDu2l8/fvwY4eHhCAwMxOeffw47OzuMdnfHqLFjMXzUqDfXE+Vw6L3fQHl5OcLCwnD+\n/HmEhoaia9euCAgIwNmzZ9G3b9+GZQAkiYSVE9hsNmbOnInNc+bAXvic4vGAfftoE5TRo+Vq7clo\nyS76oEGDSIK0vVHx8Sjs1g39+/fHgQMH2lX6T1O8ePFCZACvXLkCMzU1+JiZwWLoUOg7OkJfXx/6\n+vowMDCg72/ehE5sLMrc3ZHr4oK8vLzGr8xMPM7Kgr6hISZMmYKAgAAMGTIETCZT1h/3nUlOToaP\njw/i4uJgb28vMzkYDEYiIWSQzARoBqnqZm4uLcCrr49H/v4YOmkSwsLC2n0qDSFEZADDw8MRHR2N\nbl26wFNNDaaqqtCfMQMGxsYi/dTX14dBejq0LlxAia4u8mbMaKSXubm5yHv8GI+ePYO1pSUmfPAB\nAgICMGDAgHZR6+ZNREdHY2ZAAO4FBMBUQwNYtUomBltedVMmNrOWW7duwd/fH7du3YKdnOd1vwlC\nCFJTU0W6GRcXBydHRwxjMmE8fDj0u3ZtaDP19aGvpwfNBw9QVFCA3K5dxdvN7Gw8TEuDQ+/eCJgy\nBRMmTICjo2OH0M0TJ05g/fr1SExMhLa2tszk6LS6+eoVsG0bXRSxWICzMzBzJmBkhODgYCxevBh3\n796V+w4tb0IgEODu3buiOe3t27cxqHt3uGlpwTAgoKG9FOpnbi7U/vkHBcuXI6+sTLzdfPoUD58+\nxQA3NwQEBGDChAno3o4Wg82xfft2nPnzT1z19YWyiQmwaRONXJIynVY3m2Hfvn34/ddfEf/pp9BI\nTq6r37JgATBIDr4qNps6E4qLAX19oEuXJk/l5f8/e/cdFdXVtQH8GcDeKIIgINgQ7FLGHo09xt6w\nG2OM3dhjixrFaGz5NL5Go8YSGxYUgy2JFTVS7QISDQg2QClKhznfHzegRFSQcufC81trliNM2YNu\n7j37nrPPU3ivW4ff//wTv6em4mZgIFrWqgX7Tz+FgaFh1uNlhQrQDwyEQVIS9Jyd8TQiQsrD/x4z\nHz/Gk+Bg/B0RgZYtW6Jnz57o3r27MpYx5sCMGTNw7949uLm5QZWWBkRFvbpFRkqzfhs0KPA4cpqb\n2l/E+XfGxdmzZzFkyBD4+/vDNCfd6hUiPT0dV3184Hn5MiKjohATE/PGLToyEnEvX0LfwACmZmYw\nNTXNcjP792uWlpawsrKS+yPlq8TERKjVakybNg0jR46UNRZtPeABhZybZ84Anp7A5MmAgQH27duH\nBQsWwM/PDxX+u82vgqWmpsLLywt/XbiA56GhiNHRQXR09Bv5+SI2FoYVKsDM2vqN3My4WVlZFbnt\nfZ89e4bGjRtj88CB6BIXBzRsCEyYIEss2pqbcp6MAsDq1atx8OBBXLhwQRE7teRUUlISLl26BJ+L\nFxGdkJD1ePlajsa/eAFjY2OYVq361uNmdWtrmJiYaNXVtbwKCQmBWq3GiRMn4ODgIGssxTI3ExOB\nlSulpbYAYGsLTJqUZXnR9OnT8ffff+PIkSNFomiYIT4+HufPn8fVq1ezP5+NjkZMdDSSEhJgYmaW\nmYfZ3WrWrAlDQ0O5P1K+8vf3R5cuXeD17beofu0aMGSI1LdLBsUyN99DCIFBgwbBQF8fPzk4vFpG\no1JJ/RObNpUlrlx58UJa4n/+fJZejDEqFc4kJuK2pSVik5Je5ePjx4gJCUHMy5eISUlBip5etmPN\nzGOnqSlq166NCnLt6FpATp06hS+++ALXrl2TvbhedIo4r1m0aBE8PT3x+++/F4kZJvR+kyZNQkRE\nBPbt2yf7iY62HvCAQs7NP/8EWrTIsrRv9OjRSEhIwK5du2T/d6KCJ4RA3759YW1tjTVt20onDPPn\nAzJdjdHW3JT7mKnRaNCjRw/Uq1cP33//vWxxUOFJS0uTllD17o0ZM2bIHU7xy830dODHH1/tFmph\nIS3HaNEiy05LKSkpaNWqFYYMGYKvvvoq/+MgrRMfHw97e3ssWrQIg8zNpeOmi8vbewcVsGKXmzkU\nFxcH+8aNsWzkSPSvWVPaOCY8XFpWM2IE0Ly5bLG9140bgJsbEBGRdTONPn3eXAqUkCBt2HHp0quv\n6epKy7TeMcOnKIqIiEDjxo2xa9cutGvXTu5wcpybilrs/c0336B9+/ZYtmwZ5s+fL3c4VMA8PDxw\n9OhRXLt2jYUBbdK+/RtXrdeuXQu1Wo1t27bhc/ZEKfI2b96M+/fvY+/evYCXl7SddRGZTluU6Ojo\nYPv27WjSpAnatm3LrWyLARcXF5QtWxbTpk2TO5TiRwjA3R2oUEG6al+3LvCWq9UlS5bEvn370LRp\nU7Rs2RKO2rBUgwrUlClT0KxZMwwaNEjqtdK5s2wFHHq7ihUrYt/+/ejatSscrlxBjcGDpebCT55I\nfa7i49/eZ01uDRtKNyGkOOPigNhYaRnWf5UtCwwfLs0GS0mR+u+kpABFaNZuTgghMHLkSIwYMUIr\nCji5oajfHrq6uti9ezccHBzQpk0btG7dWu6QqIA8fvwYX3zxBQ4cOAADhWxJV2xkU1ArW7YsXF1d\n0aZNGzRr1gx169aVITAqDAEBAZg7dy48PT2lhsyGhkCPHnKHRW9RuXJl7N69G87OzvDz8+NW00VJ\nXFyWIsHFixexceNGXL16VbFNXxWvd+8cL82rUaMG/ve//2HgwIHw9/dHxSK2PIFeOXjwIM6ePYur\nV69KX7CwAGrXljcoeitHR0fMnTsXAwcOxMWLF1GyZEmgalXppgQqFVC+vHR7X8y6ulKT8Xc1Gi/C\n1q9fj8jISCxevFjuUHJNcUd5c3Nz/PLLL5m7PFHRo9FoMGLECIwZM4aFOgXJWLLh7OyMxIyu7lSk\nJCcnY9CgQfjuu+9gl7Gtc9262e+qR1rjo48+wrhx4zB06FCkvz7FmpQrPh44dizzrzExMRg6dCg2\nb94MMzMzGQMrxlSqXPdWGjBgADp06IAxY8YgN+0NSDnCwsIwfvx47Nmz51XfwIoVi92MB6X56quv\nYGpqirlz58odChWQGzduYPHixdizZ48i+wYqrogDAF27doWzszM+++wzHvSKoB9++AEvX77EN998\nI3colEuff/45GjRogClTpsgdChWAOXPmoEaNGhg9evSrL/KKvyLMmzcP0GiwdNYsuUOh/PDHH8Cj\nRwCk6eBjxoxBt27d0L17d5kDo9z64YcfcPv2bWzdulXuUCifpaenY+jQoZg6dSrUarXc4VAuqFQq\nbNu2Dfv378ex1wrmVDQkJiZi0KBBWLVqFWrVqiV3OB9EsWffS5cuRUREBNauXSt3KJSP/P39sXz5\ncuzevRt6XCusOCqVChs3bsSZM2fg6uoqdziUj06ePIkDBw5g8+bN7FGlQLq6uti9dy9+2rwZ53/8\nUVozT8r04oW0S2B0NABgx44duH37NlauXClzYPQhypQpA1dXV8yZMwe3bt2SOxzKR8uXL4eOjg5m\nsXiuSEZGRtizZw9GjRqF8PBwucOhfDRjxgw0bNgQw4cPlzuUD6bYIk5GU7jvvvsOcnYxp/wTHx+P\nQYMGYd26dahevbrc4dAHqlixIlxdXTFp0iTcu3dP7nAoHzx9+hSff/45du7cKfvWi/ThzMzMsG32\nbAydMweRa9dm3+yQtN+pU9K/XXQ0gu/excyZM7F3716UKaY9DYoCOzs7rFy5Es7OzkhISJA7HMoH\nV65cwbp16/Drr79yR10Fa9WqFSZNmoQhQ4YgLS1N7nAoHxw9ehTHjh3DTz/9pOiLkoot4gBA9erV\nsWHDBjg7OyM2NlbucCiPsnTuJ0Wzt7fH/PnzMXDgQKSkpMgdDuVBRuf+zz77DB9//LHc4VAedRk/\nHoNr1cJna9ZA4+ICPHwod0iUG7GxwLlzAICU5GQMHjgQCxcuRIMGDeSNi/JsxIgRsLe3x+TJk+UO\nhfIoLi4OgwcPxk8//QQL7tyoeLNnz0aJEiWwZMkSuUOhPHr06BG+/PJL7N69G/r6+nKHkyeKLuIA\nQL9+/dCpUyc2hVO4jM7969evlzsUyieTJk2Cubk5Zs+eLXcolAc//vgjoqKi8O2338odCuUHfX24\nDB2K58nJ+OHPP4ETJ6QmuaQMYWFA8+YAgAUBAahiYIAJEybIHBTlB5VKhQ0bNuDChQvYu3ev3OFQ\nHkyYMAEdO3ZEnz595A6F8oGuri527dqFzZs34+zZs3KHQx9Io9Fg+PDhGDduHFq2bCl3OHmm+CIO\nAKxZswYBAQHYsmWL3KHQB8i2cz8pnkqlwi+//IJDhw7ht99+kzsc+gA3btzAkiVLFNu5n7JXonlz\n7O3ZEyuuX4d3SgpQrpzcIVFO1a8PGBvj9PPn+DUkBNvYo6pIqVChAvbv34/JkycjODhY7nDoA+za\ntQu+vr5Ys2aN3KFQPjI1NcWOHTswbNgwREREyB0OfYDVq1cjKSlJ2uihCCgSRZyMpnBz587FzZs3\n5Q6HciE9PR1DBw3C1ClT2Lm/CDI0NMSePXvwxRdfICwsTO5wKBcSEhIwaNAgrF69WrGd++ktmjSB\n9bhx+GnyZAycMwcxbKaqKFEBARjxxx/Ytm0bjGvUkDscymeNGzfGokWLMHDgQCSzb5Wi3L9/H1On\nTsWePXtQjsXxIqdjx44YPnw4hg8fDo1GI3c4lAu+np5Y+d132L1lS5HZOKdIFHEAwNbWFqtWrYKz\nszPiOTVcEVJTUzF+7FjoPH2KWXXryh0OFZCWLVtiypQpGDx4MJvCKUR8fDyGdO6MRra2GDZsmNzh\nUH4rWxZo0gR9vvsOXevVw+gBAyCSkuSOinLg2bNn6Lt1KwZ26YJOnTrJHQ4VkPHjx8PKyoq7GilI\n2IMH6N2xI+ZOnYomTZrIHQ4VkMWLF+PFixdYtXIld3lUiICAAPQfOBDrHBxgtX498MsvQHCw4v/9\nikwRBwBGDBwItZER+vTogZiYGLnDoXeIiopCp06dEH7vHo60bQvdEyeA69flDosKyNdff42yKSkY\n3rMnEhMT5Q6H3iE0NBStmjVDxadP8cvMmVyqUZTp6GDVgQO49/Ilpk6bxiKrlrt16xbUajXUnTvj\n+9275Q6HCpBKpcLWRYvw2969WObiwp6PWu7SuXNoqlZjqLExpjg7yx0OFSA9PT3s3bsXa1aswNae\nPYHTp4HXd5SLjpYvOHqDh4cH2rRpgwVTp2LgJ58A7doBTZoAxsaAws9vi1QRB+Hh2GJrC9vy5dG0\naVMEBQXJHRFl48aNG1Cr1WjWrBmO/vEHKq1dC4wcCfj5Ac+eyR0e5bfUVOhcu4bDn3wCER2Nj9Rq\nPOSOOFrpwoULaObkhOFOTtjeowdKOznJHRIVsNLm5jh9/Tru/P03unbtimiegGold3d3tGvXDosW\nLcLKlSu5ZXFRJYS0fXxsLAx274bnuHE47O6OwYMHc+txLbVlyxb07tkTW/v1w0wHB6jKls06qKci\np1q1ajjv5oYVPj6YPHUq0mbMAHbsAEJCgJ9+ArhEWXZCCCxbtgxjxoyBu7s7Rs6YAUyfDvTpIxVx\nFL4zFVDUijhhYdDr3h1r3d0xa9YstG7dGidOnJA7KnrNoUOH0L59eyxduhTLli2TTkTLlgWaNQM+\n/xwwNJQ7RMpvJUoAHh4o+/gx9tSvj74NG0KtVuPKlStyR0av2bhxI/r3748dLVpgqr4+VI0b84pS\nMWFgYIDjx4+jfv36UKvVCAgIkDsk+pcQAkuWLMHEiRPh4eHB5Y1F3b17wO+/Axs2ACVKwHzWLJy/\ncAF6enpo3bo1e8tpkdTUVEyaNAkrV66E5/Ll+CQ1FUhLAzZtAkqVkjs8KmB12rSBV0AAgk1N0fnK\nFTy7fRtYtgwIDQXWrwdOnlT8ch2lSkhIwODBg3H48GF4e3uj+b87OhY1RauIU6sW0L07AGDUqFE4\nfPgwRo0ahZUrV3Iqqsw0Gg0WLlyIqVOn4uTJkxg0aFD2D1T41DZ6i397HqlUKsxevBibNm1Cjx49\nsH37dnnjIqk31fjxWLduHS6ePIlOVapIW057egKcMVVs6OnpYc2aNZg3bx7atGkDDw8PuUMqHt5x\nxT4+Ph4DBgzA8ePH4e3tzeb/xcHZs4CHh/S7d/x4oFw5lClTBjt37sSgQYPQtGlTXLp0Se4oi71n\nz56hc+fOuHfvHry8vFDHxubVN/v3BzhTrljQ19eHx4kTcOjQAepDh3DLyEj6hhDA4cPA5s3SzDoq\nNA8ePECrVq2gp6eH8+fPw9zcXO6QCkzRKuJYWGQpArRs2RJeXl7Yt28fhg8fzl4cMnnx4gX69euH\n06dPw8fHBw4ODnKHRIUto3G1vj5Qowa6deuG8+fPY+nSpZjGXhyyiYyMRIcOHRAeHo4rV66gdpUq\nr77ZtSvQqJF8wZEsPvvsMxw9ehRjxozB8uXLeQGkIIWFAd7e2X4rJCQELVq0QPny5XH27FmYmZkV\ncnBU6GJjAX9/6X5qKnD8uDSzA9IFkBkzZmDr1q3o3bs3tm7dKmOgxdvNmzehVqvh5OSE3377Dfr6\n+kCZMtI3GzQA6tWTN0AqVLq6ulixYgW+/fprtPvpJ7hXrgx89JF0/vTsGbBlC2c1F5KLFy+iWbNm\nGDx4MHbu3IkyGXlZRBWtIk42LC0t4enpibS0NLRp04a9OArZ/fv30aJFCxgZGeH06dOo8vogkYqP\n2rUBPT1pHeq/hVY7Ozt4e3vj1q1b7MUhg+vXr0OtVqNVq1Y4cuQIKlasCMTFSd+sVy9zViMVP82a\nNYO3tzcOHTqEIUOGSL047t8Hbt+WO7SiQwjg4EHgxYs3vnX+/Hk0b94cI0eOxC+//ILSpUvLECAV\nOk9PQKMBSpYEBg0CRo2Sjpuv+eSTT+Dp6YkVK1Zg8uTJvABSyA4fPoz27dtj8eLF+P7771/1pipT\nBtDRkWbhULE09Msv4XHqFCbu3ImlISEQ48YBc+YAEyYABgZyh1fkbd68GX379sUvv/yCGTNmFItN\nOYp8EQcAypYtiz179qB3795o2rQpe3EUkjNnzqBFixYYM2YMfv75Z5TiGuHiq0QJqZBjb5/ly+zF\nIY+DBw+iQ4cOWL58OZYuXQodnX8PBS9eAJUrS4MHnWJxeKC3MDc3x4ULF6CjoyP14tDRAXbulJo3\nsmln3t2+DQQGAi9fZvnyxo0bMWDAAOzcuRNTpkwpFieiBGnGzfnzgI0NsHAh0LbtW5eX16lTB15e\nXggODkbnzp3xjBtCFDiNRoPFixdj8uTJOH78OIYMGZL1AWXLSrve8EJlsaZWq+Hl5YXffvsNzs7O\niI+PlzukIi81NRUTJ07E6tWr4enpiS5dusgdUqEpNmfpKpUKc+bMwcaNG9GjRw/s2LFD7pCKLCEE\n1q9fj8GDB2PPnj2YOHEiT0RJal5dq9YbX87oxTF//nz24ihgGo0GCxYswPTp03Hq1Ck4/3cr1ORk\nYNw4oFw5eQIkrVKmTBn8+uuvGDhwIJq2aIHLxsbA5cvAokXAjRtyh6dcGg1w6JB0/98iTkpKCsaO\nHYt169bh0qVL6Nixo4wBUqG7c0dawjptmlRIfw99fX14eHjAwcEBarUat7gbToF5+fIlBgwYgJMn\nT8Lb2xuOjo5vPsjICPj008IPjrRO1apVce7cOZQtWxatWrVCaGio3CEVWVFRUejcuTP++ecfeHl5\nweb13lTFQLEp4mTo1q0bzp07BxcXF0yfPp1TUfNZcnIyRo8ejU2bNuHy5cto166d3CGRtmja9J2z\nO0aMGMFeHAXoxYsX6Nu3L86ePQtvb2/Y/2dWFADp38jCovCDI62lUqkwc+ZMbNmyBb2+/x6/BAdL\nvTv+9z9g61YgKUnuEJXn8mXg0SPp/suXiIiIQIcOHfDo0SNcuXIFtbIpdlMRV68e8PHHudrcIbMX\nx7ffol27dnB3dy/AAIunkJAQtGzZEpUqVXp3b6rKlaXZOEQASpcujW3btmH48OFo1qwZPD095Q6p\nyMnoTaVWq3H06FFUqlRJ7pAKXbEr4gBA3bp14eXlhZs3b+LTTz9lL4588vTpU7Rr1w7Pnz/HX3/9\nhRo1asgdEmmTHJycZvTicHNze9WLg/Ls3r17aN68OUxMTN7dm6pEicINjBSja9euuODpie/v3MFX\nly8jTQigVSuA/VpyRwhpOZqVFVChAq7duwe1Wo2PPvroVW8qKn7ysJvR0KFD4eHhgYkTJ8LFxYUX\nQPLJuXPn0Lx5c4waNQpbtmxhSwDKFZVKhalTp2L79u3o168ffv75Z7lDKjLc3NzQrl07uLi4YPny\n5a96UxUzxbKIAwCGhoY4fvw46tWrx14c+cDv8mU4OTmhY8eOOHjwIMqXLy93SKRQ5ubmOH/+PHR1\ndaVeHGFhcoekaKdPn0bLli0xfvx4bNy4ESVLlpQ7JFIoW1tbeP3+O+6mp6PL6dN4tnkz++PklkoF\ndOoEJCXhAICOe/dixYoVcHFxedWbiiiX1Go1vL294eHhwV4ceSSEwIYNGzBw4EDs2rULkydPZksA\n+mCdO3fGxYsX8cMPP2DChAlITU2VOyTF0qSlYdFXX2HKuHE46eGBwYMHyx2SrIr1GUNGL4558+ah\nTZs2OHbsmNwhKY4QAju3bUOXrl3xw2efYdGiRTwRpTwrU6YMdu7cKfXiaNoUly9fljskxUlPTcUP\nq1ZhyJAh2LdvH8aPH88TUcoz/YYN4XHhApp07YqmW7fi9rffAunpcoelKCkxMZj322+YuWcP/jh9\nGgMGDJA7JCoCzMzM2Isjj+JfvsTYsWOxYcMGXLp0Ce3bt5c7JCoCateujStXriA0NBSdOnVCVFSU\n3CEpzvOnT9G/YUP8sX8/vA8fhkPTpnKHJDuOtgF89tlncD9yBF+OGoWlLi6skubQ5cuX0apVK6xZ\nuhSnO3RA38hIICRE7rCoiMjSi6NXL2zcsAEajUbusBTh1KlTaGJnB7e1a/HXuXNo27at3CFREaJb\nuTJW/vgjFn7zDdquW4f98+ZxCUcOCCGwf/9+2Dk44IaxMbyvXEHjxo3lDouKkP/24vjjjz/kDkkR\n0tLSsHnzZthYWyPRxwd/Xb6MmjVryh0WFSGVKlWCu7s7mjZtCrVaDZ9ffwUyZsxduABw7JmtpKQk\nrF69GrYNGsC8fn2cOXIEpi1ayB2WVmAR51/N69SBV9eu8Ny9G7Y2Nti9ezcHjG8RHByMfv36YeDA\ngfhy2DD4ffEFGhoZSTtubN4MJCbKHSIVIV27doWnmxt2uLigcf36+O233zhgfItr166hU6dOmDxp\nEhY3aIALLi6obmsrd1hURA2bNg3HduzAEg8PtGrVCufPn5c7JK3l6emJZs2a4fvvv8fmzZvx2++/\nw6RaNbnDoiIooxfHjl69MGbYMHTp0gX+/v5yh6WVhBA4duwYGjdqhN27d8N98GDsHDcOFdibigqA\nrq4uli9fjmULF6Ln2LEY0KgRgr79Fjh7Fli+HHj6VO4QtYZGo8GePXtgZ2eHCxcu4Pz581jn6opS\nnIGTiUWcDMHBsChRAid79sTW1auxfv16NG7cmAPG10RGRmLSpElo3rw5HB0dERQUhBFjx0J32jTg\niy+k3RWePQN27ZKaNxLlkzrNm+OyszNc+vfH3Nmz0bJlS5w7d07usLTGgwcPMGLECHzyySfo3aAB\nbn3/PXpZWkLVq5fcoVERpx4wANeuX8e4ceMwcuRIDhj/IzAwEL169cKwYcMwefJk+Pj4cNdGKnix\nsegkBALd3NCjRw9069YN/fv3R2BgoNyRaQ0/Pz+0b98eM2fOxPLx43F27Fg4pqQATZrIHRoVcc4j\nRiA4NBQOHTqg1YoV+GLvXoQFBgLffQf4+ckdnuzOnj0LtVqN//u//8P27dvh7u4OOzu7XO3eVxyw\niJPh778BExNg5ky07dMHly9fxtKlSzFv3rxiP2BMSEjAd999Bzs7O+jo6CAgIACzZ89GmTJlpAeU\nLAk4OQGTJ0uVZCsr4P59eYOmouXFC6gaNkQPIXBtwACMHz8en3/+OTp37gy/YnzAi42NxezZs9Gk\nSRNUq1YNd69cwbiEBJQ4fRro2BEohlsuUuHT1dXF0KFDERgYiJ49e6J79+5Fd8CYw4axT58+xbhx\n49C6dWu0atUKgYGBGDJkCHvGUcH76y/AxwcoUQIlH0GKywAAIABJREFUHRwwfvx4BAcHw9HREa1b\nt8aoUaPw4MEDuaOUTUhICIYMGYIePXpg0KBBuHHjBrq1bg3V2bPSBcgjR3gOSwWuXOXK+PrHHxG8\nYAGqlCmDxocOYeqZM4hcuxbYtw9IS5M7xEJ3+/ZtdOvWDaNGjcLMmTNx5coVtGnTRu6wtBbPJjIk\nJgIzZwJGRgCk6ajdu3fH1atXMWHCBIwaNarYDRjT09Oxfft21KlTB1evXsWVK1ewdu1aGBsbv/1J\n+vrSzhtcS0z56coV6fb0KXSfP88cMPbq1Qs9evQougPGt0hJScHatWthY2ODqKgo3Lx5E0uWLEGF\nyEhpWWNqKnDyJHDnjtyhUjFSsmRJjBs37v0DxmvXgORkeYLMi/h44Pff3/OQeCxZsgT16tVDmTJl\nEBgYiBkzZqA0t2KnwnLmDHD4sHRh0tcXEALlypXD119/jeDgYJiamqJJkyaYOnUqIiMj5Y620ERH\nR2PGjBlwcHCAjY0NgoKCMHr0aOjp6WVtzt6oEVCjhnyBUvFRogT0Z83C0r/+wu2rV5Hm6Ajbo0ex\n8MwZxN29K3d0hebx48cYPXo0Pv74Y7Rv3x4BAQFwdnbmRY/34E8HkCrvQ4YA2ayB1dXVxZAhQxAQ\nEFCsBoynTp2Cvb09Nm/ejP379+PAgQOoVauW3GFRcdWp06uTqn9PtnI8YCxChBA4cOAA6tati1On\nTuHPP//Eli1bULVqVekBGVcPS5QAJk4E6taVL1gqtsqWLfvuAaOODjB/PnDunLJ2tvr7b8DLK9vl\nwunp6diyZQtsbGxw584deHt7Y82aNTD698IQUaFJTpau4oeHA6VKZVmCoK+vj6VLl+L27dtIS0uD\nra0tFi5ciLi4OBkDLljJyclYs2YN6tSpgxcvXuDWrVtYuHAhypcv/+pBGb+HzMwALkOmwqRSAaVL\nw9TODj9u2wbfq1cRkpyM2h9/jNWrVyOxCPcZffnyJRYuXIj69evDwMAAQUFBmDp1KkqVKiV3aIrA\nIg4gJVDZsu98yOsDRicnJ3z00UdFcsB4/fp1dOrUCZMmTcKiRYtw8eJFNG/eXO6wqLjT0QFGjpSW\n7v2n4fh7B4xFREYuLlu2DJs2bcLx48fRoEGDrA+6f186af/qK8DOTp5Aif71+oAxPT0ddnZ20oDR\nygqoUgXYuxdYuFBa+pFffdQKsh/b3btAdDQQHPza2wkcP34cjRo1wq+//orDhw9j7969qMEr+SSX\njFlurVoBjo7ZPsTU1BQ//vgjfH19ERISgtq1axe5AaNGo8HevXtha2uLc+fO4fz589i0aRPMzMze\nfHBamnSeMWqUdBGESCbVq1fHjh07cObMGVy6dAk2NjbYvHkz0orQ8qq0tDRs3LgRtWvXxr179+Dv\n748VK1bAwMBA7tAUhUWcXCpbtixmzZqFu3fvwszMrMgMGMPCwjBixAh07twZPXv2xO3bt9G7d2+o\n2ESKtIWJCdC3r3TFLJuB2tsGjLGxsTIEm3+CgoLQu3dvDBkyBBMnToSvry/at2//5gNTU4GoKGDK\nFKB27cIPlOgtTE1NsW7dOvj6+iI0NBS1bWyw+vlzJKalAZGRwJYtwLZt+dMDICgIKKiZshnT2729\nAQD+/v5o3749pk+fju+++w7nzp2DWq0umPcmyqmkJMDUFBgw4L0PzW7A+PPPPyNV4dsdnz9/Hk2b\nNsWaNWuwbds2HD16VGqM+jbp6UD37oClZeEFSfQO9erVg5ubGw4dOgRXV1fUrVsXrq6uit45WQiB\no0ePokGDBti/fz88PDywa9cuWFlZyR2aIrGI84H09fXh4uKi+AFjbGws5syejcaNG8PS0hJ3797F\nhAkTUIJXIkgbtWkjzTB5x9X2/w4YbWxssGrVKsVdYYyIiMCECRPQqlUrtGjRAkFBQRg6dOjb1wg/\nfw5MmsS1/KS1rK2tsX37dmnAGBgIm8OH8XNAAFIBoGlTQE8v729iaQmsXw/k9w5ZiYlAWBgAIPTs\nWQwdPBjdunWDs7Mzbt68iR49evCiB8lPCGm26ujR0qzMHHp9wLh//37Uq1cP+/btU9yA8c6dO+jR\nowdGjhyJ6dOnw8vLC23btn3/Ey0tgS5dCjw+otxSq9X4888/8dNPP2H16tVwcHDA8ePHlbVzskYD\nHw8PtG3bFnPnzsXq1atx+vRpODg4yB2ZorGIk0dKGzBqNBr8888/OH78OFwWL4ZNjRp4GhSE69ev\nw8XFBRWz6QtEpDVUKmDEiBw9NGPAePbsWVy+fFnrrzCmpaXh7t27OHrkCObOnIm6deuiZMmSCAwM\nxMyZM9/fGLVKFWlnOCItlzlg3L0b+yMiUM/dHfumToXmxo28v3i5ctIshJ9/Bjw98/56kBqJ3/nj\nD7ilp2PK5cuw378fNY2MEBQUhDFjxkiNUYm0QWqqNGPVwuKDnv76gPGHH37Q+gFjUlISbty4Adfd\nu/HFqFFo27Yt2rZti4CAAAwcODDnjVGrVZOWUxFpqfbt28PLywsLFizAzJkz8dFHH+HixYvAy5dS\n3muZ+Ph4+Pv7Y/evv8LZwQG9Ro3CsGHDcO3aNXTt2pUXPfKBKje/mB0dHYWvr28BhqN8d+7cwfyx\nY+ETHIxOn3yCalZWqFatWubN0tKyUHapSEpKwt27dxEYGIiAgAAEBAQgMDAQd+/eReXKlWFnZwc7\nPT18XqYMGn70kbQ9OL2TSqXyE0Jkv8BcZszN9/P29sa8efPwz99/o13r1qhmY5MlNy0sLFCyZMkC\njyM+Ph5BQUFv5Oa9e/dgZmYGu6pVUTchAWN37EDN//a8oWxpa24yL3MgLg6nvbwwd8wYJCYkoGWv\nXm8cN6tWrZq72aEHDgB//ind79VLusKegxPGuLi4zLx8PT9DQ0NRrVo12NnZoWH9+hg3YcKrZuL0\nTszNQpZxTp8PAyQhBI4cOYL58+ahfKlScGrR4o3cNDMzg66ubp7f632eP3/+xjEzICAAjx49Qo0a\nNWBraAh7lQrjjxyBgaFhgcdTFDA3lSs9PR27du3CwoULUd3QEI10dGDZogWqtWqVmaNVqlQp8N2d\nhBCIjIzMkpMZf0ZGRqJ27dqwtbVF0/r1MWbqVJR7vZk4vVVOc5NFnPwmBDBnDq4FB8O3alU80NfH\ng7AwPHjwAA8ePEB4eDgqVaoES0vLLAfC128mJiY5Trzo6Og3DmqBgYF4+PAhqlevLhVr7Oxga2sL\nOzs71KlTR+rI//gxsGmT9CcAfPutdPWS3kpbD3gAczM3/ho7FteTkhBmbp6Zlw8ePMCjR49gZGSU\nWWzNLjcrV66co6sHGQe27AaErx/YXs9NGxsblCldGli2DNDXB8aPL4SfRtGgrbnJvMw5kZ6OsydP\nIiAkBA8ePEDYa8fNJ0+eoEqVKlkuhvw3Nw0MDF7l5vXrwIYN0n1LS6BfP8DWVnofIfD48eNsB4Sx\nsbGoU6dOZk5m5GetWrW4W8YHYm4qX/rt2/h9xgwEt22LB5GRmXkZFhaGqKgomJmZvTM3K1WqlKP3\nEUIgLCws2wFhUlLSG8dMW1tb1KhRQyrw/vCDtEHJmDEF/NMoOpibypecnIwT33yDf3x98SApCQ80\nGjxIS8ODsDDExsbC3Nz8jXx8PUfL57Cokp6ejtDQ0DfOZwMDAyGEeGOsaWdnBysrq0Ip8BZFOc1N\nzgHOb8+fA9HRaGxujsbjx2eeOGbQaDSIiIjIMnh88OABLl26lHk/NjYWFhYWbySbiYkJQkJCsiRR\nYmJilsQZPXp01gPb25iZAfPmAceOAadOAWfPAoMGFfAPh0h+zbt0QXMvL8DFJcvVyvT0dDx58iRL\nXv799984c+ZM5glrQkJCZk6+npuGhoa4d+9eltz874GtQ4cOsLW1hbW1dfYHtvh4adeb0FDA2bkQ\nfyJE8lPp6qLdp5+iXTbfS0tLw6NHj7Lk5p07d3Dy5MnM3ExNTX11olq1KqoFBcHSwAAVdHUR7O6O\ngOXLM/OzdOnSWQaC3bt3h52dHSwsLAr8yiWR0ug+f45PatbEJ7NmvTHDJyUlBQ8fPsySm9euXcPR\no0cz/66jo5PtQLJ06dIIDAzMPGYGBQWhUqVKmbnZoEED9O/fH3Z2djAzM3v7BZSkJOnYOXRoIfw0\niLRHqVKl0GvFimy/l5SUhPDw8Cy56eXlhQMHDmT+vXTp0m9esLSwgCowEAEpKQj8d8wZHBwMY2Pj\nzGOmk5MThg0bBjs7OxgbG3NplExYxMlvwcHSmvzJkwFr6ze+raOjA1NTU5iamr51F4vExMQ3Eu/K\n0aOIuH8fVjVron6nTjk7sL1PiRLSVPMmTQBXV6lxY5kyH/ZaREphZwecOAFEREhblv+7paGuri7M\nzc1hbm6O5s2bZ/vU+Pj4LDMEHjx4gAseHngWGIia7dvn7cC2fr3U2LVGDTYnJnqNnp5e5gnm28TF\nxWXNzdKl8eejR4i7fx+1y5ZF69atMy9yGBkZFWL0RAoXEQEYG2e7RKtkyZKoXr06qlevnu1ThRCI\njY1948Llzd9/R+KLF6jToQM6duyIyZMno06dOjmetZPFnTvS7lL16+f+uURFVOnSpVGrVi3UqlUr\n2+8LIfDs2bM3zmn9/PyQHhMDO7Ua3bt3x6xZs2BjY5PjWTtUeFjEyW+RkcDMmdJMlw9UpkwZ1K5d\nG7Vf3yb45Eng8GGgVStg2LB8CPQ1VlbAtGlSEYeoKHv5UvpTTw/YulUq6PTuneOnlytXDra2trB9\nfYadl5e0PfK6dR/ehyA5Gbh/X7pvZgZcuCDtxEVEOVKxYkXUq1cP9erVkzsUoqIlMlIq4nwAlUoF\nfX196Ovro2HDhq++sX69dCExL8ufNBrpmHvzpnTRlBtzEOWYSqVC5cqVUblyZTRp0kTucOgDcN5w\nfuvcOU8FnLdycpL+tLHJ/9cGpEFthQoF89pE2qJsWcDNDUhLk5Yt5ceuMi9fSrPv8jKd9NmzV/dL\nlgSaNct7XERERHmVhyLOWz18CJib5+01YmKA3bulIk7Dhq+aOhMRFQMs4uS3gtrdxsgIqF274Io4\nRMWBjo60bj6j4JJfRZy8TjONjJT+NDICJk4E2ESViIjkJkT+F3ESE6X+kXkt4qhUgKcn8OKFNHv1\nr7/yJz4iIgVgEUdJPv00s38HEX0gS0ugfXvpfm62Lc6OuzsQHi4VXa5ckdblf4jISGmW0KRJnBJO\nRETyu3dP2uktORmoXBmIi8uf1330SPrTwiJ/Xg8ATEyAt/SyIyIqiljEURI7O7kjICoaevSQCqJ5\nnYnz8iVw44a0NOvqVeBDt1OMjgbGjSuYpZhERES5VaIE8NNP0v3Nm4EnT/LndTMufFSunLfXyZhR\nW6KE1CuSO+QQUTHCIg4RFT+lSgEDB+a9iPP6jhwtW37467Rrx6WSRESkPapUyXr/9c02PoQQ0u3h\nQ+mCRX4VXXr2lGbiEBEVIyziEFHx1LgxkNedbDK2Aq9UKW/bm3LLYyIi0ialSr1awt+pU/4UXXbs\nAP75R+qHk7Gs6kOpVNKuVBnLo4mIihFuMU5ExVdee0xVqSL1smneXGqaTEREVFSYmkrLhO3t8/5a\nKpXUZyciAggLk/q/9er14a+npweMGMFjLxEVSyziEBF9KJVKWlKVl6VURERE2sjUFGjUKP8KJYaG\nUhGnVCmgY8e8vVa5ctKNiKgYYhGHiCgvOnXienwiIip6atUCGjTIv9czNJT+bNeOBRgiojxgEYeI\nKC9sbeWOgIiIKP85OOTvrk+GhkDp0kCHDvn3mkRExRAXkhIRERERUVb5vW23oaHUiJizcIiI8oQz\ncYiIiIiIqGCZmwNNmsgdBRGR4rGIQ0REREREBcvaWu4IiIiKBC6nIiIiIiIiIiJSABZxiIiIiIiI\niIgUgEUcIiIiIiIiIiIFYBGHiIiIiIiIiEgBWMQhIiIiIiIiIlIAFnGIiIiIiIiIiBSARRwiIiIi\nIiIiIgVgEYeIiIiIiIiISAFYxCEiIiIiIiIiUgAWcYiIiIiIiIiIFIBFHCIiIiIiIiIiBVAJIXL+\nYJUqEkBowYVDpNWshBDGcgeRHeYmFXNamZvMSyLmJpGWYm4Saacc5WauijhERERERERERCQPLqci\nIiIiIiIiIlIAFnGIiIiIiIiIiBSARRwiIiIiIiIiIgVgEYeIiIiIiIiISAFYxCEiIiIiIiIiUgAW\ncYiIiIiIiIiIFIBFHCIiIiIiIiIiBWARh4iIiIiIiIhIAVjEISIiIiIiIiJSABZxiIiIiIiIiIgU\ngEUcIiIiIiIiIiIFYBGHiIiIiIiIiEgBWMQhIiIiIiIiIlIAFnGIiIiIiIiIiBSARRwiIiIiIiIi\nIgVgEYeIiIiIiIiISAFYxCEiIiIiIiIiUgAWcYiIiIiIiIiIFIBFHCIiIiIiIiIiBWARh4iIiIiI\niIhIAVjEISIiIiIiIiJSABZxiIiIiIiIiIgUgEUcIiIiIiIiIiIFYBGHiIiIiIiIiEgBWMQhIiIi\nIiIiIlIAFnGIiIiIiIiIiBSARRwiIiIiIiIiIgVgEYeIiIiIiIiISAFYxCEiIiIiIiIiUgAWcYiI\niIiIiIiIFIBFHCIiIiIiIiIiBWARh4iIiIiIiIhIAVjEISIiIiIiIiJSABZxiIiIiIiIiIgUgEUc\nIiIiIiIiIiIFYBGHiIiIiIiIiEgBWMQhIiIiIiIiIlIAFnGIiIiIiIiIiBSARRwiIiIiIiIiIgVg\nEYeIiIiIiIiISAFYxCEiIiIiIiIiUgAWcYiIiIiIiIiIFIBFHCIiIiIiIiIiBWARh4iIiIiIiIhI\nAVjEISIiIiIiIiJSAL3cPLhy5crC2tq6gEIh0m5+fn5RQghjuePIDnOTijNtzU3mJRV3zE0i7cTc\nJNJOOc3NXBVxrK2t4evr++FRESmYSqUKlTuGt2FuUnGmrbnJvKTijrlJpJ2Ym0TaKae5yeVURERE\nREREREQKwCIOEREREREREZECsIhDRERERERERKQALOIQERERERERESkAizhERERERERERArAIg4R\nERERERERkQKwiENEREREREREpAAs4hARERERERERKQCLOERERERERERECsAiDhERERERERGRArCI\nQ0RERERERESkACziEBEREREREREpAIs4REREREREREQKwCIOEREREREREZECsIhDRERERERERKQA\nLOIQERERERERESkAizhERERERERERArAIg4RERERERERkQKwiENEREREREREpAAs4hARERERERER\nKQCLOERERERERERECsAiDhERERERERGRArCIQ0RERERERESkACziEBEREREREREpAIs4RERERERE\nREQKwCIOEREREREREZECsIhDRERERERERKQALOIQERERERERESkAizhERERERERERArAIg4RERER\nERERkQKwiENEREREREREpAAs4hARERERERERKQCLOERERERERERECsAiDhERERERERGRArCIQ0RE\nRERERESkACziEBEREREREREpAIs4REREREREREQKwCIOEREREREREZECsIhDRERERERERKQALOIQ\nERERERERESkAizhERERERERERArAIg4RERERERERkQKwiENEREREREREpAAs4hARERERERERKQCL\nOERERERERERECsAiDhERERERERGRArCIQ0RERERERESkACziEBEREREREREpAIs4REREREREREQK\nwCIOEREREREREZECsIhDRERERERERKQALOIQERERERERESkAizhEREREREQF6epVQKOROwoiKgJY\nxCEiIiIqDHFx0kBOCLkjIaLC9vgxsH+/3FEQURHAIg4RERF9mOJ8VTk5GYiNzd1zKlYELl8G1qwB\nwsJy9pwXL4r3z5moqDA0BM6eBc6dkzsSIlI4FnGIiIgod4QAjh8H4uPljkQ+JUsC27cDnp65m1nT\nowdw9y6wdCmwc6c0O+d9fvgBiIj44FCJSAsYGkp/uroCt2/LGwsRKRqLOERERJRzQgAHDgA+PkCF\nCrl/rpdXwcRV2FQqoGVLYNcuYPVq4MmTnD3P0hJo0kT6WVy6BCxcKBV13qZCBWkGj4tL7gtGRKQ9\nMoo4Gg3w88/S8ioiog/AIg4RaS8OVogKR3Q0EBT0/scJIRUtTp8GbGxy/z4nTgCBgbl/nraytweM\njIDgYGDJEsDDA0hLe//zunV7db9WLen2Lh99JC3f2rUL+N//cjZ7h4i0i76+VPxVqYA6daTfu0RE\nH0BP7gAKmxAC9+7dg4+PD7wvXoSPry8ehofD3NwcljVrwtLSEhYWFrC0tMy8GRsbQ0enGNW7NJpX\nB5l/CSEQFxeHiIiIzFtkZGTW+yEhiIiMRNkKFWDfrBkcHBxgb2+P+vXro2TJkjJ+IFKC9PR0BAUF\nwcfHR7qdOYOoqChY2NllycfX89PIyAiq1/6fFkcajQYxMTFZ8jHb/Hz4EAYGBrBv2jQzN+3s7KCn\nV+wOA/RfN25Iy4Lmzcv222lpabh9+7Z03PT2hs9ffyH+6VNY+PvD8uTJbI+b+vr6b+amlxfg7g70\n7Fnwn6mw6OgA7dpJM5PS0oD794GHDwErK2g0Gjx//vyNfMz8u68vIlJTEXX4MKq4usJBrYa9vT0c\nHBxQu3btrOcdNjaAiYm0pOrWLWD3buCzz4AyZWT76CS/5ORk3LhxI/O46Xv2LNJKloSFlVW2x0xL\nS0tUrFhR7rBll56ejmfPnmV7vHz971EPH6KauTkcWrfOzM3q1at/+HmHnh7QtavU5+r2bcDWNn8/\nGGmNxMREXL16FT4XL8L72jX4XbwI3bJlYWlt/dbcLFeunNxhyy4tLQ1RUVHvPp+NiED048eoWbMm\n7Fu0yDyntbCwKFZjApXIxZVuR0dH4evrW4Dh5L9Hjx69OvH08YGvry/Kly8PJycnOFWvDqe//0a1\nMmXwyNgYYWo1wsPDERYWlnkLDw9HXFycVOT5T9I1sLNDSwMD6DZpIvfHzFePL1zA4YMHceLWLTyM\niclMmlKlSsHExCTzZmxs/OrvhoYwOXgQxmXLIq5GDfjr6sIvNBT+/v64d+8e7OzsMpPM3t4eDRs2\nROnSpeX+qLmiUqn8hBCOcseRHaXlphACoaGhrwo2Pj7w8/ODsbEx1Gq1lJ+NGqGKgQHCY2Iyc/G/\nuZmYmJjlIGhhYQHL8uVhb2cHp549i9wv85CQELgdOIA/Tp7Eo8hIREZFISoqCuXKlcuaj//NTxMT\nGIeEIDI9Hf6xsfDz84O/vz/CwsLQoEGDzNx0cHBA3bp1FVd01dbc1Pq8TEsDDh8G/vwTqF0bmDED\nQgj8/fffWY6b169fh6WlpZSX/94qVayI8NBQhD1+nG1upqenZ81NU1NY3r+PpmlpaDBrFlRqtdyf\nPv8kJSFw9GgcCgvDuehoPNFoEBERgefPn6NixYrZ5qOJiQmMy5WDibU1Khsa4uHjx/D398/MzcjI\nSDRu3DjzmOng4IA6Dx5A7/hxICUF+PxzoGlTuT/5ezE38096ejoCAwOzHDdv3bqFWrVqScfNqlXh\ndPs2Sn35JcKEeOO4mXFfR0fnzQsj5uZo3aYNateuLffHzFdCCFy/fh2H9u/HxT//RER8vDQAjI6G\ngYHBW4+XGV8zPHYMIXFx8DM1zczPly9fZslLe3t71KpVK+cXe4WQll4uWgSMGwc0blygP4O3YW7m\nn9TU1KwXOnx8cPfuXdStWxdOtrZwSkqCIwC0bYuwMmUQnpb2xjEzLCwMpUuXzpqXZmaoVqMGPv74\nY1haWsr9MfOVRqOBl5cXDh04AJ8LFzJzMy4uDoaGhtnm4+t/r7R9O4KNjOCnqwv/q1fh5+cHjUb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A9wxQpa9EqXlJQA69YBEglMOA4XpkzBso8+4lfctVcvwNtb/XN6egKa1MxQ5Qu7uwPNm6NJ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HGk6eeELqyePHpT41NpYpkOoIup495fFnzjywnr548SLz587Fz9ub15s3Z2W7dlj++KPs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IlA8G2D+ys0maHD7MoC81lQHT33/zWrK2lrws/P1x1dER5yws0LdTJ8hyckjwaDQ8nvAEcnLi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OuGMzbmdry3b6ekds4cMltNSqJ1gqEhvdbFi0ToeHsDP/xACePZs5T0ajqZAFSdbVJTX0vi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\nsdTg5M6dWUrZtCmfK+78s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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "AUcols = [col for col in openface_dat.columns if 'AU' in col and '_r' in col]\n", - "AUcols.extend(['pose_Rx','pose_Ry','pose_Rz'])\n", - "# Delete rows with na and mask each array\n", - "mask = np.any(~np.isnan(np.array(openface_dat[AUcols])),axis=1)\n", - "X = np.array(openface_dat[AUcols])[mask]\n", - "y = registration(openface_dat[lm_cols].as_matrix() [mask])\n", - "\n", - "# Train model\n", - "n_components=np.shape(X)[1]\n", - "clf_facet = PLSRegression(n_components=n_components,max_iter=1000)\n", - "clf_facet.fit(X,y)\n", - "print('N_comp:',n_components,'Rsquare', np.round(clf_facet.score(X,y),2))\n", - "# Model Accuracy in KFold CV\n", - "kf = KFold(n_splits=3)\n", - "scores = []\n", - "for train_index, test_index in kf.split(X):\n", - " X_train,X_test = X[train_index],X[test_index]\n", - " y_train,y_test = y[train_index],y[test_index]\n", - " clf_facet = PLSRegression(n_components=n_components)\n", - " clf_facet.fit(X_train,y_train)\n", - " scores.append(clf_facet.score(X_test,y_test))\n", - "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", - "\n", - "# Plot results for each action unit\n", - "f,axes = plt.subplots(4,5,figsize=(20,20))\n", - "axes = axes.flatten()\n", - "intensity = 10\n", - "for aui, auname in enumerate(AUcols):\n", - " au = np.zeros(len(clf_facet.x_mean_))\n", - " au[aui] = intensity\n", - " plot_face(au=np.zeros(n_components),model=clf_facet,vectorfield=\n", - " {'target':predict(au,model=clf_facet),'color':'r','alpha':.6},\n", - " ax = axes[aui])" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python [conda env:py36]", - "language": "python", - "name": "conda-env-py36-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" - }, - "toc": { - "nav_menu": { - "height": "81px", - "width": "252px" - }, - "navigate_menu": true, - "number_sections": true, - "sideBar": true, - "threshold": 4, - "toc_cell": false, - "toc_section_display": "block", - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/plotting.ipynb b/tutorials/plotting.ipynb deleted file mode 100644 index 622fb3f3..00000000 --- a/tutorials/plotting.ipynb +++ /dev/null @@ -1,676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook contains examples of how to use the feat plotting functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Draw a standard neutral face. \n", - "Figsize can be altered but a ratio of 4:5 is recommended. \n", - "Included in the toolbox are two models trained on the CK+ dataset for use with the plotting functions. The 'blue' model was created by using openface landmarks to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using OpenFace Action Unit vectors along with the pitch, yaw and roll of each face to predict transformed landmark data. The 'facet' model was trained using the same procedure, but with AU vectors from Facet instead of OpenFace." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:44:30.205629Z", - "start_time": "2018-08-17T00:44:30.151877Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load modules\n", - "%matplotlib inline\n", - "from feat.plotting import plot_face\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plot_face(au=np.zeros(20))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Draw lineface using input vector" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Affectiva vectors should be divided by twenty for use with our 'blue' model. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:44:33.416435Z", - "start_time": "2018-08-17T00:44:33.332759Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Add data, AU is ordered as such: \n", - "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", - "\n", - "# Activate AU1: Inner brow raiser \n", - "au = [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", - "\n", - "# Plot face\n", - "fig, axes = plt.subplots(1,2)\n", - "plot_face(model=None, ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", - "plot_face(model=None, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Draw using Facet model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-16T16:37:16.259414Z", - "start_time": "2018-08-16T16:37:16.176778Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Activate AU1: Inner Brow Raiser and AU12: Lip Corner Puller\n", - "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", - "au = [5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", - "\n", - "# Load a model \n", - "model = load_h5()\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "plot_face(model=model, ax = axes[0], au = np.zeros(len(au)), color='k', linewidth=1, linestyle='-')\n", - "plot_face(model=model, ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add a vectorfield, in this case the arrow plots from the changed back to neutral. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:44:43.946912Z", - "start_time": "2018-08-17T00:44:43.844443Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face, predict\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "model = load_h5('blue.h5')\n", - "# Add data activate AU1, and AU12\n", - "au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", - "\n", - "# Get neutral landmarks\n", - "neutral = predict(np.zeros(len(au)))\n", - "\n", - "# Provide target landmarks and other vector specifications\n", - "vectors = {'target': predict(au),\n", - " 'reference': neutral, 'color': 'blue'}\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "# Plot face where vectorfield goes from neutral to target, with target as final face\n", - "plot_face(model = model, ax = axes[0], au = np.array(au), \n", - " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", - "\n", - "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", - "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", - " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add some muscles" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:44:47.244857Z", - "start_time": "2018-08-17T00:44:47.147638Z" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Add data\n", - "model = load_h5()\n", - "\n", - "au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", - "\n", - "# Add some muscles\n", - "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", - "muscles = {'all': 'heatmap'}\n", - "\n", - "plot_face(model=model, au = np.array(au), \n", - " muscles = muscles, color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:45:10.673345Z", - "start_time": "2018-08-17T00:45:10.581711Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Add data\n", - "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", - " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", - "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", - "\n", - "# Add some muscles\n", - "muscles = {'all': 'heatmap'}\n", - "\n", - "# Plot face\n", - "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make sure muscle array contains 'facet' for a facet heatmap" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:45:24.700552Z", - "start_time": "2018-08-17T00:45:24.609976Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Add data\n", - "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", - " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", - "\n", - "# Load a model \n", - "model = load_h5()\n", - "\n", - "# Add muscles\n", - "muscles = {'all': 'heatmap', 'facet': 1}\n", - "\n", - "# Plot face\n", - "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add gaze vectors\n", - "Add gaze vectors to indicate where the eyes are looking. \n", - "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking downward." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:45:29.118260Z", - "start_time": "2018-08-17T00:45:29.022477Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "from feat.utils import load_h5\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Add data\n", - "au = np.zeros(20)\n", - "\n", - "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", - "gaze = [-1,5,1,5]\n", - "\n", - "# Plot face\n", - "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Call plot method on Fex instances\n", - "It is possible to call the plot method within openface, facet, affdex fex instances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OpenFace" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:46:13.423332Z", - "start_time": "2018-08-17T00:46:13.297349Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.plotting import plot_face\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from feat.utils import load_pickled_model, load_h5, get_resource_path, read_openface\n", - "from feat.tests.utils import get_test_data_path\n", - "from feat.data import Openface, Affdex, Facet\n", - "from os.path import join\n", - "\n", - "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", - "openface = Openface(read_openface(test_file))\n", - "openface.plot(12, muscles={'all': \"heatmap\"}, gaze = None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Facet" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:46:25.842846Z", - "start_time": "2018-08-17T00:46:25.688670Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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/DNkfIYFmogTE3edS6HcrCCDHYSbNbtaXMbLum+j3qFMmZtAFPbtkcpKCpoWgrRYLqi96D2s0qCpEWlrmJutvO1wW1IBHpWPAOyJsFisauhnZ2uelY8BHboqZZIusF1qMIurAetosMWpjvOE8c6iDnGRrVDHq7Se6eftET9TnBr0Nk1keOQubJEZkx6maRueAN7hOngw0v7ne2uclySJjliUkIUi1yvS5pi49U1V8uAd6J6132bQQtNlijssaejiqqiFJIiSzyWGRgmu9ocf1uHx0Or2oauyz8Who6DNVc48HiyzITbFgt8gIRcUT4T4LONMUrxZTsX80MeoTXYP8dW/0Hm3QY8m2MCa1gJBrGXB4tfV7gwKLhDzYjVA8CMUDig9UH/h8aKoXfF40xYvm86KpXjSfD2G2ImavQrWMXk6qi9pDkT/uKwSYZRGxBXCicfV2YXWkYrUkPgEKpomg4+UUC8dQUQsg3XYqq0lRNToGvPQM6uusRP7JNcCtaDR0u7GZJXJTLFhkIooaiHk9HYhRf/u8OWQkjSzz63f7uPeNEzF5tAPjCMdQU1vTNOq73GPmEJi76qh64j727dpBWno6FrMFi9WKxWLG6/Xidrmx2qwUFBSSnJyMxWLBYrHQ0dnBWz/5Bms3fojS9Rejla1Gk8OHgpweFadHIckf33dYJTqdUzNLO3s6cKRnT9rrTR9BK+OPQ49FQNRZSaHxybZ+D71RmGOapiH73Hp4Rc/8QNP0mUkTAqFpaMGv/Z8jhW3QvbV1nS6SrTKZSeawzfCEEKBpMa2nh8aof3DR/JA6at2jXROTRxvAbpIipLFqQYcfQM+gb1Qxy4PdtL34B9566Rm+/vWv88LT/8Rq1QXZ0dHBxRdfzJHDh9E0DZPJRPXx4/z617/m+uuvD56jq6uLJ554ggcefJC97/47Z553MYVrLsRXvBSkU7eyBnQ5ddNbCEGyWaaTqRH0YE8nqZkfMEFbLVZUJTEzdABV1bANiZ26vOqYayvhc6G+/wrbnvgjDfV1CCFQVTWY8BDpA2D5ylWs2fJlfEXLwp5bA/rdCi6vSkm6lXATcWA9rS+7oxQ14WPU971Rx4BHicmjbZZF2EITTdMwDQmvKapG+4A3rJiFz4VrxxP86+H7+dSnrucPVVVkZoa2WPr4xz/O3r178XpD74HbbruNRYsWsWrVKgAyMjK48cYbufHGG2lqauKRRx7hoYf/h7oTJzjzgo+Ss+pCPHkVCCFwD3sIxupojBeDPR1kZE2eoKdFHNpqTZzJHcA2zDxs6XNHnE1Mzg4GXvkd/7z9EtoPbOM3v/4VAwMDwY9vfetb2O12LBYLJpOJ8vJyXnnllaDYFUXha1/9Ci//6nsc/c3tmNurI45LUTV63T6sEZKbAhlnsYbIu10+fu6PUT++v5m6GD3asj8HO5yYFZUQB1l7v2dk+F9V4eBL/OvrV+I+eYwdO3Zwzz0/HSHmmpoatm/fPkLMAC6Xix//+Mdhx1dYWMjtt9/Orp072bZtG67OZn57+9X0bP87EPoAlCRBrmNy1rDDGezuICd38pr5T5MZemKpn9GQZjv1Vp1eFU+Yp7W57Sh1r/yVHa+/xJbrruOtt95i/vz5Icd8+ctf5ve//z1OpzP4vUOHDnHJJZfw4osvsmHDBiRJ4pprruHKK6/k/vvv5wc/uJVla89k1kduwZcSWretAZ0DPtJsplFnEbtJipj5FImaLhc/21pDbVdsBReRkkcCXUZ8amiYf3g/MHPjft76849Jslt59NFH2LBhQ8TXamhowGKxMDg4OOJnqqpSXR35YQiwa9cuvvmtb3H48BE+/a2fwMJzAEIsCIEeq54KBnvaWTB38jq6fmBm6KGJDy7vkBtQ9SEdfZP999zMcz++nc1nrqW6uppf/PznI8Tc0tLC/fffHyLmAE6nk69+9ash37NYLHzuc5/j6NGjbFi9jCf+/Vq6n7kXebA75DgNaBvwIksixJIIoBcg+KuWxmBo5pQGfH/LZnrbmsb8vaGEc4Jpml5s4PJpJJtF0HRX1FOe+N6Db3Lol1/khf/5Nt/51jfY/vbbo4oZoLKyEo8nfKGJxWJh48aNYX/23nvvcdnll3P5FVdQuOJDbP7hE7Do/OBaenhs3CyJiM69ROLu6aQgf/Kab0yPGdpqjVumWDhSraGzjcs/0x164a9s/+MPSU1N5fe//z2XX345JlPkS7J9+3YsFgsuV3jzdefOnWG/73A4+M63v81tt97KD37wnzzwlcu54OobSF5/NaolCYBB/yJaF7XGoFdXSTDzSQjMMiiaPkNq/rJRwancaofVRKbdREaSmaxkC/kOCw+4+vi/584nJSMbt0/F7VNx+fSdHD2Kgtun4VUC/1dp6B6kN0wiiEnSizGs/gy7wM99qobff8ff7vwsAAsWLOSFF1+ir6+PdevWsWDBAmQ5/AyZlZXFTTfdNMLqEUJgs9m44447Qo4/cuQI3/7ud3nt1Ve58Lpbuejj3xrh7RZAxpDEIatJr8hKs8kMxmjlTBRnTwdFBR84kzsxcegAScNqdgP50sXLN7Lu3Is5cWgv9//ud2RmZnLOOedE9AKnpaWN2jcsKSlp1HHk5ORw770/4/bbv8xnP/tZXnr8I1z8hTsxlZ+JT9WC4SRZkrDIusiGlnsHSi0/XJlLeXYSafax14Uet4uS7DRSU8eun97X1MuR1oFQ81kSbJqTwTnzsoLfe/loO7vq9cQUs78ND8AND+1DKF7MndUM1lfx2FPP858//BFtrS2sWLmSdevWcca6daxdu5bi4uLg+e655x7S0tK49957UVUVr9fLwoULueuuu+jo6KC2tpa+vj4eeeRRnn76KS649jN89O47UM22sH6QJIsU8je3mSUkSZCRZOJkHDMSo8HZ3cGsoslJ+4RpImiLxYISxikSL1JtcojX02bWs4dScoup/Mx/sUjxoh56hVtv+yyO5GS+9rX/x1VXXTVitt60aRPJycn09fWNeA2r1cqNN94YcQyKorBr1y6eefZZnn32OY4fO8qaM88GW/jMrhyHmauXFXL/jgacnlMzpqrBzrpuVkbZtcTlcmG3jy3m1n43/3i/NSQ7zSQJzi/PZv3s9JBj181KZ3d9T7A7i0kS/plagMmCN3cBptwFFKy6nIJPgOTuQ2o5QsOJg/z8V/dz4JZbsFgszCsvZ9DppL+vn77+PkwmE319fUiSRENDA7feehvJDgf25GTsSQ6KFyzjsp/8E9XiINI8K4DcFEvI/wPrabtZr9+ezCxQZ3c7s4snJ+0Tpomg7db4FWeEI9kio2m+YBJHskWibcjPNdmMWHwhGyvPRz6xk5/87D6+9vWvc8ftt3PjjTeSnKxnJ8myzBNPPMEFF1ygJ0G4dWdTcnIy8+bN4/vf/37I67a3t/Ovf/2Lp55+hpdfepGc3DwWbziXVVtuZ1n+wpDYqWlYbfE587LISrZw6/oSfru9ngH/FjEa0DHg5YUj7Vy8YPTcbZ/Ph6qqoy4jAFw+hT/tbtKb9w0Zz6WLclkR5sHhsJrIdVho6dfXvlnJJlr7woetAFRrCuqsVdhmraJsE8zWNMz9rdDTjDDbwGJHM+sfitkecl1GnGuU9xHI5TbLp9bK9pBdQ/WWy92TlAqqqSqDfV3MmaQ8bpgmgrZarQkzuQP1uQ6rHMwWMst6XW334LBcY0lCKTuDxV86A3NrFU/96wHuvPNOPnPzzSyqrERVVRRF4Xvf+x6vvfYaBw8exGKxsGbNGpYsWcJvfvMbVFWlu7ubl15+mcNVVaxev4lZKzdxxV2fwZesxyPD+fOTLaduQgEsyE0BIN1u5tb1s/jt9nr63D5UTc/x3lXXw7ysJObnRE6FdLvd2Gy2MTugPLr3JE7PqVGZJMHHl+ZRmZ8S8fdWFafxr8NtKBqk2vRrOTz2GwkhBL6UPEiJz9pSoFthWQ5LiHfbJOnr/QCSQP+7T5Kg3QO9mG1JJNkmp7kBTBNB26wWlAQlljj8AV5ZkhCcMl2zHGZ6XL6I5dPe3AWUfOoHzLmskcNvPcm77z+JJEsISUYICSk5gyXrzkLIEv1IbH//OELSZwOTxcbyqz/PkvxFaLK+zh0tKCcEZA/ZkWF2ZqiJnGozcduGEn67vYGeQW+wR/Sj+07yxU2lpERoRDc4OIjNNnovq63VXdR2DgbLOs2S4NoVBaM+KAAqch3863Cbf/yCvBQr9V2uSd3OJhCOyk4OnZUDBKrcgscLoXcc7Zqc8Tm720mexLRPmDaCTtwMnWI91TTAbj4Vy5WEIC/Fwslez6g3oTe1iLSLPkfkbdnCE21UXQDZyeaQxgabykZuVpdsMXHr+hJ+t6OBDqcHRQWPovLQO8185ozisB08XC7XqIKu6XTy2vHOYBKGRRZcv7qI0igaEFpNEksLU9nf1Iui4c9PN9PnUnD5VP1BKeLe7g3005Jslch2WLCEETLo+dvhmkVI/kSdycgaG+zpIDUja+wD48i0iEPbExi2Gjp72YfETwFSbCZKM22k2uRg8cZkIgSYZEG6PXQtXZgWXoR2s8xnzigm12HFJAlUDVr63Lx2rDPs8aMJutfl46F3moNitpokblxXHJWYA5w7LyukTW2a3Uxxho15OUmUZdkpSLWQkWTScwAY2lg/dgK/n2yRmJVpozDNFlHMFllgi9AiRjfPJ2ceG+zuID07d1JeK8C0mKHttsR4uSVxqhc0gCRJpCdB15DKG4tJIj/VSo5Do8vppWvQF+xvlQiECNyYMg6rTPKwjd0uWTi6o8tmkrlpbTH/u7uR5l43XlXjjZou5mYnjRBjJA+3T9X4y55GPIoazAq7aV0JOTFuxGaWJa5ams+fdjWMKDAxyQKHbMLhXz4GdsxweVWcXoVBr4pP0QK1LnrnzqGfJYHs/1r2dw21maUxd4EUQIotcp9wSRK6E68/8eErZ08HOTkfQEHbrFYUX3zb0oIejwyYfgFMkkSyRRtRlihLgmyHhcxkMz2DPjoHvEGv8kQIJF1YZEGKTSbZYorYrmd2ho3KvMiOqAAWk8Sn1xTxlz1N1He78KkaD73TxJc2zQ6Jv0aaoZ852ErHgH5DJ1tlbl5XEpKIEQu5DitnlmWytTq8lRBACIHFJLCYJFLtibntTJJ+jcdqGZxklvwFLwkZRpDB7g7mlkxeUglME5O7OD8Hd38vqhJf76PDIoc18ZIs4RvIgb/BXJKZOdl28lIt/iIE/K13/PtOjfEh0D87rDL5KRbmZtuZnWUnK9mCLUIfM5MkuHpZ9AkIZlni+tVFzMmyY5IELp/KI/uaQxJfwjnF9jX1sq+pD0XTSLWZ+Oz6WeMWc4BFecmjbr6XaEySIN0uk5FkCvFyR8IiS+Q6LAlfYrl7O8jPn1xBT4sZOiU5CXtqBgOdLaTkxC9ml2aP/LROt8t0jFL0LoQg1WYixarvpaSo+lQvILjRmv/L4H/8SZpIgrDtekZjy4rCmNvxmiTBlhWFPLbvJIfbBqjrcrGttpuNZfpeX8Nn6JY+N08eaEXRNLKTLNy0rnhEFt146PcoZCWbaPFnYU2Wp1v2PzRjvdY+VeOsOen8/b22cW2hEy2DPR0UFUxelhhMkxkaIKtgFr0t9XE7n1kWIRVWw5EkidQoKnD0TpwyDqtJX/P6171J/g+7RcZu1j9sZhmbWcISpmf1aBSnWSI6wsZClgRXL89ncb4DVdN44XA7jT16rvlQQbt8Cn/e3YhP1ShIsXLz+viIGaDP7UMSUJhqJiPJFPUGA+NFEnrcOSPJFPO1Bv2arShK56YzijEncKA9LQ1Uzp+TsPOHY9oIunBWKb0tDXE5l0UWnFmaNqYDxWqSyEqScVikSfdwBzAJWJjrmNA5JCG4ckkeq4pT0YAH3mnC5VOCTrFA8kivW2F2hp2b1hVjM8WvnLB3UPc3mGWJzfOzyUgyk5lsJjvZRIbdhMOq93GbqFUuCT0MmZlkCtvoPxpMkmB9aQapNhOL81P48MKcYPJRPFEVhZ6WBtYum9w9y6eFyQ0wZ85cDjZOfIaWJShJt/Hx5QW09Xt4rko3MSMhSRJ2C9gtMm6fSr9LGTW9MB7IAmwmiVSbhMUk44zDpuFCCD5amYuIIJI/AAAgAElEQVRZlnirtpu/7W9B8a+ht1Z3crTdSXlOEteuKIxqnRkL3S496i4JwZzMJPY399Lp9AZb6ZpkmUAdicur0OeO7gpbZEi2mobsFT2xnU0kAQWpVuZnn0qauaA8i7ouF+8198XUZXUs+toaSUrLIs0xeoJOvJk2M3T5vLkMtE18hnZYTNy2YZY/ccTKonxH1Dew1SSR5TCTHmHXhfEi0Nvyptlkchz67JVqN7FpTjayEHEr6RNCcPGCHD40N5Oq1gEON3fjxcRLRztZWuDgupXxFzPoJjfobZOFEGycnRlxBxAlyrcq0J2XJkkEw1YT3dnELEucPSd7RPbYDWsKyXFYJmxBDKW7qZb8WZNrbsM0EnRlxTx6JmhyW2TBlzaVkjxkbbiqOD1iamQkzLJEZrKZzCSZidbEZ9olsh1m0pPMwaZ9AshLsVKWmYRJErh98fXub56fxQXlWbxzoo3aPoU1Jal8bGl+wnaAdPqrXgLnz3FYmZVmD3tzRdsbDYjrw0cWgnPnZYddhplliS9unIXdHL9lSHdjDbPnzR/7wDgzbQS9orKCnpPjN7nNsuDf1hRRkBqaCC8JwXnzs8d1c8iSRHqSmRxH6Ee0IRoBhNtoRpYEZ5Vl+WufBe4EBEQ3zcmkIsPM3Lw0PlqZm5B9uwB/p09dpPKQ11hXmo4U5jpFK2jLKFvrxIpJggW5yRSmRnY8ptnNfP7MWXFzkvU117KwoiIu54qFaSPosuJ8fF437oGRtcZjYZEF58/PYlmEGuFUm5l1s9Lj9sSPtPXLcPQm+6HfM0mCdbPSgx5mWUpcE/h0s8rcvIyEiRn0Xt8BIQ+1AJItJpYWpIZs0qdvDTv2OQVETN0cD0lmE2tKMsY8bnamnU+uLIiLk6y7qZalixZO+DyxMm0ELUkSGfmz6IvR7DZJgvKcZD5SOXrKZEWOg7wUa1zWSdYY7HDfkEWjADLsZipyTnm19eYAiXHDDQ4OjtlFZaIE1s+gOySHsqQgFdOQ2LqiRp8vH6+ZUm/SkBP1rp5rZ6WzqSxjwqLuaqxhzbJFEzrHeJg2ggbIKy6ltzV6s1ug17feuK54zFlICMGH5mSF3GDjJdClIxqGt5P90NxQp0ygyCKWtWW0DA4ORtWtZCL0ub3BscvD/gYmSbBhdmbwWvlULaqkk+FbFY0XkyRYXZJGehStmoZy5dI8Zmfax23ReZz9uJ39VM6bPa7fnwjTStCls8tiikVbTRJf2lQatXlmM8ucOy8rpj2YI54rSrM70NDPJAlWFqaNqPQJ1PF64uwYg8kR9NAmEeGu6+wMezDBJxpLRKCXYk4UCchJtlCZO3Zu/IjfFYJb15eQ6g+ZxUp3Uy2ZRaWYIjRGTCTTStBz586hL8psMbMsuGV9CdnJsVUIFaXZKc9OnnBYKhazW9P0vPLFBSNvrsAskAjH2GQIusd1qmopnKCFEGwsy0IWgmjC7RrxMbdNsuCcednjnuntZpkvnVUasp1QtHQ31VI0e+64XneiTCtBL5g/N6o1tEXW+10tyB1f0H7drAySLBPLqYnF7FY1OGdedtiwUeDmdZ2mgh4Y0rpouMkdIDvZwuxMuz8ffnTiYW7L/qXNRMNQuQ4Lt4wjPbSnqYb58yffww3TTNDLFlbQM8YMbZYESwtS2DxvZFePaJElwfnzsydsekdrdmclmckMsxskDDW5Tz9Bq5oW7HEO4WfoAMsL08ZcP8fD3JaFYG5mEiXp8XnfC/McXLYoNyYnWW9zLYsWLojL68fKtBL00gVz6Ws/GXEnSklAjsPC9asLJ/wUz0iysLo4bUKijtbslkTk4wJOOne0KVQx4HQ6EyroQY8SEmsezWLp9yhjWjQaTMi7HHggrC8dO0QVC+fOy2RZYUrUM3VXYy0rlkx+yAqmmaAdSXaS07Ppbw+/IbnNJPGFjbPCNoQbD4vzU7lkQS5FqTZkEX1IJYDk3x1yLFoHIu8tFXiguBLkFEtk2CpQZRVgtIdjW//Y+2uN19wW6PnxZZlJfLQyD1Oc7o/g+YXgU6sKyXNYxvS9aKpKd/MJ1k5ByAqmmaABsotmhfV0m2XB5zfOijkEMRa5DisXLcjliiUFzPM7y2KZJMaq6AK9f5caYf0YmLVccSjQGE6iTe4+d2jX1NEE3dzrHjU0N15zWxaCknQ7Vywu4Jx52SRP0DcSCbMs8YWNpWOuywc6W7AmOcjLiq+VEC3TTtCFs2aPqIu2yIJrludTlpm42SbNZuasOVl8YnkRi/NTMEnROb2iCZnJkqBrMHwPq4AIYt1ZMhomQ9BDRTqaoE/2jT5Dx2puy5KgMNXKpYvyOL88J6ptgSZKqs3EFzfNGnWc3U215JSUJXwskZh2gp5TNoe+1lMztFnWUyU3zJ6cJ57dLLO6JIMtK4pYVZyGzSTpFT9+r/bwD6tJGjO3WwBtA+F7pp3OM3TX4Kn3JBHZy61pGp3O0ZvyRdt1xCQJ8hxWPrwgl4sX5EV0NiaKknQ7168qjLie7m6soXTu5BdlBJg29dAByufPZfvudwHd9C1Os/GJ5ZO3HWcAsyyxOD+VyrwU6rsHcXnVYIdKgQipzz3aNsA+f3/qcHgUfYP5BWEaGQRmtdMxDt3rGuK8FESs5ur3jP2wSjZLuqjxtzDyN2hU/fnfkoBMu4V1penkp4yvu0u8WFWSRl23i9eOd47Iw+9prmXD0qkJWcE0FPTiivnB0FWyReazG0riktk1XiQhKM0YY1fJZAv7mnpHPaaxN/wWtCb/lrCJEHSivdxDGzMIRMS/U1u/B1lE3nxAEnDW3CySLTJmSRe2WfZ/liRMskhY6ed4uWxxLvXdLo62O0OWHT1NtSy55rIpG9e0M7mXV5bT01KPRRZ8cVNpSCP36YrDaiJrjIy19v7IJrcQ4I1zgYamabjd7oQJ2qfofbUDBDqihqO13z1qN5CCVL19cWlGEoVpNnIcVtLtZpItes+w6SZm0B/0N68vId0emh7a1VjD6qVT4+GGaSjoWQW5oKqszoGicTbOmwqWFqSM6kRz+dSwySOBbhw+RRt17+lYcblcWCyWmDuJRku/RwmZkfXQUfj339Tjilg2aZYES/In1lNtqrD5awkCkQ6f24Wzu53lC6duDT3tBC1JEsXli3jj7R3UdjqnejhRU5HrGHUfJ5MkaA/jGAuKQsS34moyPNzD5RuuoQEQ3HY2HKqmUZ5zegoa9LTW2zaUYJYFPc0nSM8vwWpJvMc9EtNO0ADzl6yk7uBe/n7gZFQJCdOBFKuJzFEa1qsatIa5sU2SvruHLOLbuSTajd7HS5/bF9p8UYSfoVVVC6mZHk6Owxq3dsJTRXlOMlcuyaOnqZbC0qkpyggwLQW9dt1aThzcC8CD7zbR60r8PkTxYGlhZLPbp2qc7BvpGJMlgYa+x1O8BT3WVrITocffunco4WbozkFvRGeZyZ+XPxNYW5KG2lE7pSErmKaCvmzzWdRV7Qd0586f9zQyEEXoY6qpyHGMWoDQ3DvS2gg0OIDTS9DdYR6y4UKzbf2eiCm1mqZRPsY+1KcDPYNefrujjsbD+1mxetWUjmVaCnrlgjmYLVbaG08ghMCjqDywpyEhyRfxJNWmN5aPRKfTO8LxFZi8NI24dv9MtKD7w5jR4bzRrf3uiD3TMpMsp0UUYzRa+tz8cXcDiqpSd2g/l24+a0rHMy0FDVBauZw6v9ktCYHTq/DQ3qaElBnGkyWjeLtVRiZZCP+WqaqmxbXiKpGC1jSNweEPVy28yR3Ylmc4Jkmw5DQ3t2s6nTy8twlN0+g62YBsNrN6obGGDsvi5aup9Qsa9Bu/Z9DLY/ubE9ZULx4syI1sdstC0BbGMSYJ3ew+XUzucM0YNMI7xcJ59kG3SCrG2aBiOvBecy//eL8F0O/NuoN7KV24bIpHNY0FfdbG9dQd2hfyPSEErf1u/n6gJaruF1NBms1MeoRN8nyqSmsYr31ACPHYEidAIgXd7/aFdXQNF7THp0bsxJJmN5Fmm7rwznjRNI03azp5+VhHyPdrD+6lctnUrp9hGgv6ys0bOVlzBK87VABCCBp7XDxzqAU1jokY8WRJQUpYB5Gq6UkWwwnkfgzG0fGXSEEPL5sEfYYensPSPuAJu/yQJViSf/qZ24qq8WxVK7sbekb8rO7QPjaduX4KRhXKtBV0TkYq2cVlNB47GPbnNZ2DvHC4La7ZVfFiQa6DsDvNEz7JIjCzxbOvWEIF7fKNTILRtBEzdNuAJ2yyjUCELVSZzrh9Ko/tb+Zo+8hkJ5/XQ/PxKq48f9MUjCyUaStogDmLlgfj0cPRgCPtA7x6vGPaiTrdbo64N3Wfe2Szg2AbotPEyx0uZKXP0KGCbu51hc3hTrbIZIyShDPd6HP7ePDdxog13U3Hq8gsKKEgK32SRzaSaS3oFavWRBQ06Cbs+yf7eOtE1ySOKjqW5KeM2EkC9ESSzmHNDgLrUc9p4uXuCSdobWTYKlzcXRaw+DQyt9v63Tz4TmNoqegw6g7upWzR8kkcVWSmtaAv+NDGEY6x4SgavNvYy6767kkaVXQszHMgwqRUCBjh6Q6sM71x3OMqkYIOl+Sje7mH/F/TRjy4QPeBLMw7PcztE11OHt3XPOZSqPbgXpavXDNJoxqdaS3oC9cvZ6Cnk/7ujlGP86kaO+q6x6xJnkzS7WZSrCNzlAPNDoYSslVMnJYPiRK0qmoRcwGGdhwZ8ChhK6zsZinmzRGmgveae3nqYGtUm8DXHdrP5rPPnIRRjc20FrTZZKKkYiknxpilQRfDGzWdVLXGvntloliSnxrW2z282YHJf5AkiNtOlIODgwkRdL8nfMhq+Lfa+j2YhpngkoDKaT47B8JSr1d3RlX95uzrobe9hY9sWj0JoxubaS1ogPJlqzjx/rtRHetTNV462sHx9oEEjyo6FuY5wvbJGp5sEXCKSSJ+m78naobudythHfjD18+t/e4RCUCyECzMm77rZ0XVeKaqlb1NvVGXstYd2k9R+SKs5unh5Jv2gj5j3TpOHBx7hg7gUzWeO9xGXdfU11JnJJlxhCkNdPvUkKywQMO5eFZcJUrQupd+5PeHi7yp1z2ix5pZlshzTE9z2+VTeGx/M7WdgzHVpZ84+C7zFq9I4MhiY9oL+vLzz6K+aj9qDOmePlXjnwdbaY7Qx2syWVyQMsIcHd7sYOjGAdNd0L0ub2gdtJ/hM3TLsIw4QWSLZarpdfl46J0m2vpH7x0ejhMH97F27doEjSx2pr2gF88pweZIpa2+Jqbf86kaT0yDBgmVeY4RN7uqhXq6A22AtTjmcydK0N0R+osPfWipmjYizGOSBYum4fq5td/Ng+82+hs2xPa7mqZRX7WPSzdPfUJJgGkvaIDZlZETTEbDq2g8tv8knc7ILXASTWaSheRhZrdP1WgaYj3ofcX8FVdxWkNfccUVrFgRf1MwUveRoQ+tLufIpgayEBSkWuM+nolQ06mHpdw+NaqN6IfT2dyAZDKzZtHUNjUYymkh6MXLV41L0KAnazy6r3lKu54szh+Z2z0060j2d/6MZ8XVlVdeyZIlS+JyrqFEKiAZKui2gdCmBgJYkJs8rczt/c29PHOodUJ93E4c2kvpgqmvsBrKaSHoszeuH7egQRfJI/uaQ/YynkzCrR2HNjswSSKYhBLPiqt44/GpEQtihk7ILX2hTQ3MsqBymni3NU1ja3UHW6MMS43GiYN7Wbh8ZZxGFh9OC0Fffu4GWuuO4x4cn+daQxfKI3ubRxbmTwLZyRaSwmxy1ufWx2KSpOCMNhXji5ZetxdnT/g026GFGU1hnJHF6VPfktmnajxzqJX9zX1x6bB64uBeNm6Y+gqroZwWgs5KS2H2ktUc2PbSuDOpNE1Pinhsf3NCdqkYi0X5jpBZTBLQ5t9mNrDe9HrcVB18f9LHNhYul4s//OEPnLVuNX/5yffDHjO0MKN9IHR5U5Ez0jE42bi8Co/ta6K2yxkXMSs+r15hdd7GOIwufpwWggbYsuU6tj37BC6fGnFr1rFQNd1L+7f3mvEmYIP10ajMSwmZxXyqRluf7qwzSfpGWSfravjuLVs455xz+Pvf/46iTO1s3dTUxLe//W1KS0v529/+xt0/vpubv39P2GMD782jqCFWhkUWLJriRvq9Li8PvttI+4CHeD3LAxVWRTmZ8TlhnDhtBP35T2+h8eC7VNU00drvRVW1cTU4UDXoGPDy5CR3PclONofsf6xqp1JAAzN0ybwF/OGFndxyyy3cfffdzJ07l7vvvpvOzs5JGyfA7t27+eQnP8nixYvp6upi69atPPPMM3z4ootIirD/ciCU3jGsqYGm6Ts2ThUtfW4efLeJfrcSc1hqNE4c3MvsyulRYTWU00bQ2emprDzrfI5te44el0JNpxuXd3yztaJptPS7eOrg5HU9EUKwKC80ySTQeF+WBIG4id1m5ZprruGtt97i8ccf58CBA8ydO5dbbrmFAwcOJGx8Pp+Pxx57jI0bN/Kxj32M5cuXc/z4cX7xi19QUXFqN8X8lPChp8AM3dof2tRgXnbSlG02WNPpDC6x4vlXVjWNQzu2sn7j9DK34TQSNMBNn/4/HNv6FKCbrPXdHlr6vSjjqFLyqdDQ4+L5qsnrelKZ7wgxu/vcPhRVC5nRhm4mvnr1av70pz9RVVVFUVERF1xwAZs3b+Yf//gHPl98PPY1NTV897vfZc6cOdx3333cfvvtHDt2jK985StkZIzck7swzR6hrZD+vZN9pzam083tqfFu72uaeFhqOAGr8ERdPcf37+Jbt90Qt3PHi9NK0NdfeQkDXW10NRwPfq/XpVDT4WLAE/ts7VM1qjudvHysfVJEnZNsCW5sBv5mB04vJiGCM4jFNNIbnpeXx3e+8x1qa2u58cYbueuuuygsLOSWW27hpZdeilncTqeTBx54gM2bN7N27Vq6urr45z//yRtvvMFVV12FyRS5V3aewxr2Wsn+ApOhTQ1UDWZnjr4Vb7zRNI3Xj3fwRs3Ew1IBVE1DUTU6nT6Ot7vY9o+/ctYlV5GZNj1CcUM5rQRtNpk49yNXcdQ/SwdQNGjs8dDc50GJcW3tUzWqWgd4sybxXU+E0B1EgYuuNztwI0siOGZruHpLPxaLhS1btvD222+zY8cO5s+fzze/+U0KCgq46aabeP755/F4IrXN1di5cye33norxcXFPPjgg9x66600NDRw3333sXx5dOtBh9UUrA4bSmCGHpqVV5YZfjZPFD5V46mDrbx3Mj5hKVXVhdzW7+V4u4sOpw+v10vVy3/ja1/+XBxGHH9OK0EDfOHmT3P0jafRwhRr9LtVqjtc9LuVmEW9r7mXnXWJF3VlXsqQlkN6swOTJNA0XeBmU3R/krKyMr7yla+wY8cOdu/ezaJFi7jzzjspKCjghhtu4JlnnsHtdtPa2spPf/pTlixZwpYtW5g1axb79+/nueee4+Mf/zhWa+zpmLmOkb9jEjDg8QUdjRZZTGqrIZdX4dF9TdR1TzwspagaXkWlpd/LsXYX3YNK0II6sfs1sotK2bx+6lv2huO0E/T5Z67B7kil6eDusD9XNWju9dLU48EXw2ztUzV21vewt3Fki9Z4kuuwYBki2sZet3/DOj02bR7Hfs6lpaXcfvvtbNu2jX379rFy5Up++KO7yMzJY868cna+s5df/vKXHD16lG984xsUFxdP6D0UptlGdPiUZUlvauB/WCkqzMmaHHO7J05hqUA3lpY+L9UdbnpdI8OGVS8+yqdv+swERptYTsuNhS792LVs2/pPihZHLlsb8KjUdLjIdZhJsclRJTb4VI03a7uwmKSEpSoKIajMc7C7vgcN6HB6EMKf+ClO1UaPl8KiIlZf+ila5l/Eyo42ZKsNe3IKx2xJlPW4KYlDxlZ+ilWvfx7yrDRJgtZ+T3B2nJVhCykLTRQtfW6eeO8kHmV8nmxN09A03VpqH/Ay4In8ROhpPkFbzSHuuOmT4x9wgjktBX3HLTfwl2VL2XDjNzFbI8c4VQ1O9nnpcSkUplqQRORNyQP4VI1XjnVgkQXzshOTELEoL4W9Tb14FQ23T8XlU5AkXdTjmaEDNPW6ePjdZjqdXjyKhi09GzjlJzjW7iQ/xcqFFdmU5ySNu1gi3W4OEY9AL85o7h1E0fSH0mTsW1Xd4eTZqvF5sjVNQwNcXpX2AR+D3rGn9qqXHuO8yz9BSvLkOvpi4bQU9MK5pcxasJQTu15l3sZLxjx+0KuvrXMcZtLsY8/WPlXj+cPtfESSEuKlzUuxYJYkvIqiNzvo9yAL3Uwdzwzt9qk8V9XGjrqeiDe3BnhVjfoeF3/e04jDauLC8myWFabEHCcWQpCVZAlu6yOEvlxo8We+qarG3KzE7lu1t6mHN2u6YhZzQMhOj0r7gBe3L7rfV7weql79B7/a+sY4Rjt5nHZr6ABbrruOY288NfaBfjSgtd9LfZcHrxK5aiiAT9V4+lBr2K1rJkrA7Bb4mx0MePQN69AwxWimHjjZxw9frmbnKGIejkfR6HR6+dt7J/mPF4+ztboz5vz24jRbMEkmMEMH+nUXpdlCwnPxRNM0XjveEbOYVU3zN15QqO1009jjiVrMANXbX6B4fiXrllWOZ9iTxmkr6M9/egtNh97F2d0e0++5fCo1HW66nD5UbfSEFJ+q8fcDJ8NuMDdRFuU7MMnC3+zA7xjTTnUvGYsup5ffbK/noXebcXqVqNrNDsejaDi9Cs8fbuc/XjzGM4fawu77HI68lFOOMSEEbp+KJATmBHq3farKPw+2cCCGsFRAyD2Der7CyT7vuPqfH3rxUW6+5ZaYf2+yOW0FPTQVNFY0oH3AR12XG68yuifcq2o8vr857l1P8lOsQY/wSb+ggTEdSYp/jX/3azVUdzjj0pzfq2h4FI03a7r4z5ereWRvc8RtYANkJ1tCeov1e3wgdHO7PCf+5vagv/y1vtsVlZhVVUNVNbqcPqrbXbT2e8ftAe+sO0pPcx2fv/7q8Z1gEjltBQ2hqaDjwe3TqOl00zkw+qzkUTQe3dccdguY8TLU7O4a9AZnu9HW0LVdg9z9Wg0vHe3Aq2phG9lPBJ+q4VM13mns5Sev1/L7HfXUdw+GPVaWRHA7WAH0uRS8ikZeihVbmNrvidAz6OXBdxrpcHqiErOianQ4fRzvcNE+EHuvsOEcevFRLrn6k9is07Nj6VBOa0GHSwUdDx1OH619HrKSzBEzm9w+lUf2NkdtkkZDZV5KsMl+wE8XLgvL6VF4+N1mfvt2PR3O8ZmMsaBqurgPtzn51dv13PtGLVWt/SOWJ0Vpp0JgXYP6jB5v7/ZJf7VUpJ04Apglgd0s0TGgZ3V1On1xeeB5XU6OvPEM377j8xM/2SRwWgs6UiroeOj3qFxQnsMZs9L9LYFC0fCbffua4tZVpDDViixEMFdYQIjHWdM09tT38KNXqtnf3DuudfJE0NDN8YYeN3/Z08SPXqlhT0NPMBssP8WmWxQCegb1B11FTvxCfcc7Bnh8f/OoMWazJEixypw7P5tz5mbT545vZdWxbc8xf9kalpTPieNZE8dpLWgYPRU0FgRQ1TrA6pJ0PrmyiFyHZcRsrQEDbj3FMB5dTwIbtwVmxKFibu338PM363jiQAsunxq3wvzx4lE0uga9PPFeC//x4jFeP95Jms2si1uDQZ9KnsNCUpiNBcbDO409PFfVFtHENkuCTLuZixbk8G9rSliY6+Cdht6I+26Nl0MvPspnb7s1rudMJKe9oMdKBY0Wj6Kxt0nfFyvdbuaa5YVsKsvUZ+uhPaeBHpePx/fHp+tJoLzQ5VWRJYFXUXm2qo17ttbS0OOK215X8UL3jKv860g7d71aHUzK0IiPua1pGq8e6+Ct2vBhKZMkyHNY+GhlHp9aVcTcLL2bqKZp7Gnojevs3HrsAK7eLm76xGVxPGtiOS0TS4Zz6ceu5a03nho1FTQajnc4UfwzpRCCZYWpzMlK4vmqNlqG7KqganpV0RMHTnLVkoIJVRQVpVqxmSRcPhWhwI9eqcblVeNax5sIAuv4ln4vJ/u8WE1iwrtK+hSVpw+10tAT6skOLEUKUq1sKM2gIHVk+mpDjzuu+2uDnhl2xZYbMI9STjrdOO1naNBTQY/veAmfe2JJILIQ1HaFenVTrCY+tjSfc+dlYZZF8IIpGrT2uXnq/ZMT6noihKAs0059t5uj7S763OOLKccDz+AA3hivod/ixuXT+M32Bu7fUU9dV3jP+GgMehX+uq8pRMwSupDnZiVxzfJCrlpSEFbMQMjaPh64B/o4/vYLfOvLt8XtnJPB6fPoGYWFc0uZVbGEuj2vMnfDxeM2u7yKyvsn+5k7rEpIDzGlUJph54Uj7TT6bzpF06ulnq1q5cMLcmPOjVY1jbdru9leF91uh5qm0d/ZSkfdMTrqj9PX1kRO2QKKKleRllcc8+t7nP00HHyH+ve2U79/Bx311aiqghACe0o69pR0bClp2FPSsDnSsDrSsKakB/9vS0nDlpKOPTUDR2YuPhWOtjmp6awnN9nChRXZUTXY7x708th+vcWyqvk3jheCipxk1s1KD4bHwr4Hn0rrgIdtNd1x7Rl2dOtTLFp3FvNKi+J30klgRgga4NobbuJXP72LpWdfhCSbUDU9Hqn447WKv6pmNBQNttV0sbIolcI064ic72SLiSsW53OkrZ+Xjraj+OO2tZ2DvHi0nfPnZ0ctqsYevZCia9A7QsyaqtLb3kxH3XE66o/RVX+cjvrjtNUdw2yxkj97PnPLF7BsbjH79r7JG3/6KQgoWbSawspVFFeuJrt0PmJYCMzrdtFctZe6/dtpOLCTluoqZi1YyoZNZ/OVG3/GR87dRJLNSm//APXNrTS3tdPS1kFrewdt7R20t3fQ0dlJZ0MVbd1d9HZ3MdDbTW9nGwJByaJVFCxcSdGiVbjLFvDAO2qOmM4AAB6gSURBVB6SLTIXVGSzvDA17NKkudfF3w+cxKNoyEJgkmBJfgqrS9JIHtKQcMCj0NbvobXfTXOfm8YeN+0DHn2pAvR74tchVdM0Dr3wCD/52b1xO+dkIWJpvbN69Wpt9+6JOZ8ShaIozF21kXlnbGb1pdcHvx9Ixg/cSgFx690oAqmBoefKTjaBEMzOsLMoz8G87CSykswhYh30Krx8tJ3aLn37UZMkWJKfwtlzs0Ydp8un8uyhVnbXnwpDufp7OLr9JRre20ln/XHa66uxJTsoKCtnXnkFixctYuXSxZyxcgmlhfkjzqmqKnveP8zTL77K61vf5MCe7fR3dVBcuYLChatQFR8NB3bSdHg/BXMqOGPjWVxy4XlcccE5pDrik9X13pHj/ONfr/L661vZv2c73S1NFC1YTsHClRQvWsXcRSu4YFExZ8xOx+bP8z7WPsAzVa3BlNcVRanMy06mx+Wjtd9DY4+L5l43nU79oWeSJVRVG7EkcXmVmPKyx6L50B7e+M13aTtxDGkC1W/xRAixR9O0MXeVnzGCBvjH67u49tILufGXT5OckT3qscPft+YXuqKCST6V4GGWTmVwleckszDXwdwsOw6rPntUdwzwwpH2oENmbUk6Z5SObK6naRoHTvbz+H59Nurv7eHYjpc5uu15Gt7fQ+XaTVx44QWsWbGc9SuXUJAz+oNhLI7XNfLPF1/jlddeR5ZlLjr/PK665DxyMtImdN5oqWtu5amXXuPlV7eyZ8dbNB+vInt2OSWVqzjvnE0sXrWGFiUp2NSh160Ed6yUJKE/cKO8NZ1uH+qQzIGxbKSxjKgX7vkq5565nl/+8JvRDWAS+EAKGuBD19xMR3sbH77jv8b1+4HrEcl0NkmgqpBuN7Ew10FFbjKFqVa21XZxqLUfgLPmZLKy6JRwOp1eHt3XzJHGdg6//TJHtz3Hif07Wbj6TK6++mo+c93HJk1oU4Gqqjzz3PN8+7vfZd8e/f5JzS3kjgdeY9A7sZk18Pca7SzhkoQiHVN/YBf//O87OPT++5Tk50xobPEkWkHPmDV0gAfuu4uFlZU0vL+b4kVjvv8RjLUGDuQtdA762Haim7frutE0yEwyU5Zho83pYWt1J1ZZYkGug+f2neA3D/+Nqjeep27/dspXrOOT136Cm59+nPzs6bXrwkTQNI2Ojg4aGxtpaGigrq6ON998kx07dlBbW4umacyaNYuKRUuoa2jkU//1lwmLGU79vWJxB0Y61j3Qx7P3fJ2v/uc900rMsTDjBF2cm8nNX/s+f/nlf3DDfU8gyYl9i4HlXIfTS4fTiwAcVolPbrmWva8+A0BWYQk3f/H/8vl/PExh7uhLgemIx+OhqamJxsbGkI+Ghobg101NTdjtdjIyMvD5fLS1tZGVlcXGjRu57777uOiii3j6lTe55qrLuep7vyWvuIRz52Xx1MHYyl8TyYu/vpPyNWfx3Vuvm+qhjJsZZ3JDZAfZZPLU3V/h4GtPIUkSQggURcFms5Gdnc2cOXNYunQpJSUlpKSk4HA4SElJCfl66GezOXLYJh50d3dz5MgRjhw5Qm1tbYhQGxsb6erqIi8vj6KiIoqKiiguLg5+XVhYSF9fH6+//jqPP/44DoeD6667jmuuuYY5c07lP7978DBnbTqL8z/3PSrOOJcMu4kPL8wlzWbil2/Xx71yLFaq3nyerf/7U97bv5ei7PSpHUwYPrBr6ACxOMjijQAKUsxsfflFnr/vW7y+dSvFORm8/PLLbNu2jT179nDgwAHsdjulpaXk5uZiNpvp7++nr69vxGeTyRRW9CkpKeTm5pKXlxf2w+E4VSjhdruprq7m8OHDQfEGvnY6nZSXl1NeXk5ZWdkI0ebm5iLLoTna1dXVPPzwwzz00EMMDAxw7bXXcu2117JkyZIRy5bGlnZWrDmDpZdcy9pLP0WSRc/E21Cazjnzsul0evjZGyemLM21r6OF//3iFdz3x4e58fLzp2QMY/GBFzRM3EE2Hiyy4PMbSvjj7kb63T7e/sdD7HvmAd7duZ3iIesyRVHYsWMHTz75JE8++SQul4vLLruMyy67jLPPPjs4K2uahtvtDiv0np4e2traOHnyJC0tLSM+hBDk5eUB+k6SpaWlQeGWl5dTUVFBeXk5BQUFUcXPT548yaOPPspDDz1EdXU1V199Nddeey3r16+PGN5xDrpYduY5pM+q4EM3/TuygBSrBEJQmmHnkyv1xI0Bj8LPttbSF8d4cjRomsZj37mJiqWreO6PP5vU144FQ9BAQ2snCysruexrPx2XgyxWMu0mPrthFm6fyu931aOoeuubV353F521h9i37VWSk0Z2KdU0jaqqqqC4jx49yiWXXMJll13GRRddREpK7EUPmqbR399PS0sLqqpSVlY2LtO9u7ubJ554gocffpjdu3dz6aWXsmXLFjZv3jzqljmge7c3ffRq2rt6+MjX7kWWZRxWKVhVlmyR+fKmsuDxbp/KL9+qo6U/vt1hRmPPUw/w/itPcnTvLpLtsW86MFlEK+jpETVPEAEH2Qu//A9UJX6NCYYjgDmZdr581mwcVhNNva5gA0CLSeL/t3fm8TGd+x//nJlJIoskgoSsRJDE1oo9qa2W2oO6lHIpbVxVVKmt90d7Lbe0/GjdKqUoXSxt7k9sRYVSghBbCLEkIfs6k5nMds7z+2Myk0lyZs1EYvK8Xy/CnJlznpnM5zzf5/t8l74zPobQ2R1DJkwFx5PmyTAMwsLCsGzZMiQkJODOnTuIiorCzp074efnhxEjRmD79u3Izs42f0wMg8aNGyMkJATt2rWzSMxlZWU4ePAgxo0bh6CgIMTFxSEmJgaZmZnYs2cPhg4dalLMADD1gyV4kvoAwxZugEAohIOwcp55mYqtlLHmJBLgg8hAhDR9Me1nCzIe4eKPX2Hn93vqtZgtwa5naKD2HWRCBujq746xHX10oaJH7+UiKVNcfpyBWM5CJpPhwIppGDBoKPZv3WD2+cViMY4fP47Y2FicOHECoaGhiI6ORnR0dKU2rzWhsLAQycnJSE5OxoULF3DkyBF069YNkydPxtixY+HpabmTaOXGb7Bl/Vq8teFnnQ/DxUEAV0cG2hLYjkIGb3f1q5ZwwRGCgzezkZQpsWk6pD6sWoV9iyZhyNjJ2PP5slq6iu2gJrceteUgEzHAoLZN0a+NV6U16LZLaSiQaeuPEUgUmphyaVE+flw8EQuWfIJVH1qeNK9UKhEfH4/Y2Fj897//hZOTE0JCQhAYGIiAgAAEBARU+reLS0WSCSEEubm5OuEmJyfj3r17SE5OhkwmQ3h4OMLDwxEREYHx48ejRYvqIabmsudwHObMmo6Ja/eiaUAbzWclYBDg6YSisoq6bA5CBm+0b47OLd2rnYMQgpMp+Tj3uKhWRH1+7/8i99FdpFz+o5rDrz5CBV0FWzvIRAwwoUsLdPGt/GVkOYL18Y902zCOQgZOQhEyyut756en4sDyadi+Zz+mjHnD6utzHIeUlBSkpaUhPT0dGRkZup/aP25ubggICICzszNSUlJACEGHDh104g0PD0dYWBj8/Pys7qJRlXNXbmD4kEEYuWQTAjv11D0uEjDwaewAaRWnV48ADwxuZziI48KTIhy7lwdbZjo/v3cDv62ei/OXEtA19OUoLdRgI8UMUdMIMn0chQymd/dDME9XjdxSTTdJ7RYMSwj8PR2RK1VCoebQLDAEIxZ/iZjpbyPw1Bm81q2LVWMQCAQICwtDWFgY73FCCPLy8pCRkYHS0lKEhobC29vyFE9LePj0GcaMHoX+M5dWEjOgadInV1X3Y2SKjdc8j2rdBG5OQhy8mW2T9EhlmRRxX36MeavWvzRitgS7dorpYwsHGQONZ3ZuZCCvmAHNF1Q/SILlNDm7+pZQUJfeeO3vH2H0qJF4kpFp1VhMjpVh4O3tjYiICPTr1w8+Pj61KuaiEgkGvjEcHQeNQ/iA0ZWOOQoZtPFy5m25U2BGvfNXfN0xLcIXIoG2uZz1yj6zYx2CO/fA2vkzrD5HfabBCBoA1i+cBRePJrh+9Eeznk90aZaa9Mhmrg6YHxXE2x9ZS3p5OqU+WRIlOrdsXMnD23HQOIT1G4X+Q0dAXCq15u3UG8QyOfqNHA9P/xD0mjin2nFtyipf4IhCzZlVcLG9txtiegVAwGi6jVgj6oeXzyAt6RJ+27PN4te+LDQoQQuFQmzevAV//fQfSIuMxxATQqBiCSQKFoQQ+Hs0wtzIILg3Mr5KeSauXsJHoWbRxde9WhH9PlPmwc3HHwNG/w1q9sUGVNgCpVqNDz//BoHBbSHnGAx+/1NeKyDcxw3pBgr2iwSMyS4dWgI8ndG+uStkSg4KtWWilhYX4OTXK7F2y3YE+tQsNbU+06AEDQBj+nVHj2FvIv57/q0jrUknU3K6FqOOQgHe7elvsgGbUs3xFuIXChgIGFLt9QzDYMi8tcjLzcWbsz6w8h3VDRt2H0ZgaBf8vGsbhs5fgzHLtkDoUL1IoKOQQZeWbpAbmIVJebM+cyCEIDlHk6KqUGt+P+aImhCC45tXoOewNzHnb8PMutbLSoMTNKBxkD1NuoRndyt77El5wXuJnK1kNptb3iZbouBtNqdiCZ6XKNA70BNV7wkiB0eMXv4V/jx1HH2ip+Lc9WTL39ALZN+xcwh+NRLrVnyEnhNiMOWLX6o5wPQRCQQgIDBUGFXFEWSbcIxpeS5WVDLPVSyBVMGabDp488QBSPKz8duOjWZd52WmQQq6qoNMOyvLVRykyuqdF4QMgzQzKllmiuW8FTsJgKdFMnQL8ABfNq6zexNMXPcD1EInDOsfibCoofjm4HGw9cgMP3P1NjoNGInZUyegTZ8hmL41Du0ihxp1tAkZoEegO1ILZEYTL7Ik5lUavf5MXK2yJ0uAUrmmuCCfqIsy03B+70Zs2/k93F34K4baEw1S0ICegyxuPzgClCpYg186FcvhTnapyXM+LSozmAaYW6qEp7MIPm78tatdmzRHvxmL8e7OM/Dt2APL5/8D/mGvYMG6rRDLbN+j2lzuPE5H5NhpGPV6X3j6t8XMb0/ilWGTIBSZDiUVMAy6+3vgaaHxm2FFEI5xrqSX8G5dEWh+f2q28kzNsWrEfbEYY2YuwNj+hq0Ie6LBClooFGLlvzfh4s/fIC0l2Wg+LkuAm+VdNYyRZcR0ZKD54vZp5QlHIx0mHZ1d0XXk25i57Th6T5qDX3/+AX4BQYie/TFS0rJMjsFWPM8rwoiZC9HjlS5QcALM+OYYek2cDcdG/Nt1fHi5OEAkZEzWy1axnMl+YXmlSl3NMUPIVBqPuVbUlw5sh5OzK/asrz+1wWqbBitoAIiM6ITXZy3DyS8WQCE1LtgcidLol06mZKFQG/lSMhqTvHPLxmYl8zMCAdr0GIiJa/Zg3KrtSE1NRZcOoeg5ajLi/qydaD2WZXErNQ3TPl6D9u3b4+njR5iy8RAGvLsMLh6Wl0tycxKanJ0BTSFGU57uG5nmtblRqDUOzef3buB63D78uHc3HF+izhc1peG8UwNEDIlGevJ1xG9dgSGLNxtcEzoIGaTkSvGKX/W4Y0AjVpFAANZAOxYVS5BWVIbOLd3RtpkL7uWav/fs3ToUwxasg7QoD0nHfsLE0W+gRUg4Jk17B53bhyAk0BehQX5mZwxJyxSIT7yDyzfu4O69e3ia+hDZ6Y9Q+OwxBEIRfENfxfhV2+EdzB+FZi7PiuVwEMBkJxCWaJYkAZ6Gs6wup5WY3R4o+/E9HF39PiYv+Td6dWpr0Zhfdhq8oBUswfx/rsH/zBiLW0d2o8to/ggiuZrDrSyJQUE/L5Gb7K2UUVwGjhCIrewx7dqkOSKnzEPPCTG4dy4O+7//DjuK8iErKUSZuAiOzq5w8fCCi6cX3DybwsOrGbyaNYOHpxdys7Pw7Ekq8jIeQZybicbNWsLLvzW8/IPhHdoVoYPeRBO/1nDxqF6C2FrURBNUYypATc0RZEsML1dKylTIMzNHujAjFcdWx2DAu8sxZNgIS4ZrFzRoQQc1ccaUV1uiYws3RMQeQmSfPmge0gm+4fyx3neNOMbSispMmoQlcjUO385Gfg0T+EWOTug0eDw6DR6ve4xwHOSlJZCVFEJWXKD5WVKA4uICZOU8gJuXN0L7j0Yf/2B4+gZBxLNnbEsEDMq3qvTbHBgmiycgR0tSpsTgtpc+xZlPcXT1e1i8cjWmT5sKVxu1tn2ZaNCCdhIJdC1QXwlri41bt+OjD2Zj3OcH4NqkegZQmYpDXqkSzat4qgkhyCk1vZfKALidVQobNnmoOLdAAGf3JnB2b6JLWaxrhBZ4aArLDHu6E9JLTNYbE+dkIO6zmXh/0XKsWhBj/oXtjAbtFKtKzOSxGDlxKs5sWsSbwMEwmqbwVRHL1WY5utQcASmfx2vQgfalgGGY8t7a5r1RjgOkyuqfuUzJIr3Y+LadJC8TcZ/OxDtzF+LzpfOsGq+9QAVdhR+2/BtuLs64+mP1gnFKluDGc3G1xzPFCrMEyjAMhNrC8LWY+VTXaD8LI7tz1RAJGN518u1sidH+29LCXMR9NhNvvTMbm1cutnSodgcVdBUcRCKcjD2A1L9O4knC6WrHU8ubwuvzrKTM7BK0+maovUqaYRiLLRCWcLwx3QlpJQazscpKChD36UyMmTgV29Z9Ys1Q7Q4qaB5a+bXA3v0/4fy3n6I4K63SMb4wUHPCQrUwqBCyPU7S2rdkIo+lGmpOs/Wnj1LN4WG+jPf5ckkx4j6bhcGjorFn02orRmqfUEEbIHpwX7y3cClOf/khVIoKwapYTpfxA2gK2uWbGbqoRTtL26PZrX1LlqyfteRIKs/QyTmlvOa2QirG0X/NQtTAIfjJgoKLDQEqaCNs/OdHCG4XjovfrdaFE7IEuPG8IqqsQKqy2LzUbyRvb5rWitgap19RmapSLPaVjJJqaZdKWSmOrYlBRM9I/Prd5nrTv7m+QD8NIwgEAhw/sAcFj+/i/ulDusdzSivCQM3NFNKi8f7q/d8mI60fWOMMq4pEoflcWY5U2/dXyWU4se4fCO/UBUf3fUvFzAP9REzg5dEYv/56GAk/bUbeo7sANLHHD/I0a7v04jKoLKxepz972aPZzVc7zKzXMQzypJr9/Ad50kqWjFohx8nP56JVcBv8/sv3VMwGoJ+KGUR17YRV6/8Xp778EHJJsS4MFAAyTOyRGkL/O28Pe9ICpuLmJBJYd6NSc5wuSeNqRoV3m1Up8fsX89GihQ/iY3+E6CWoo11XUEGbyZKYaYgaPALxXy8D4TjcyZJAzREUG4lwMkZNzNL6BIPKYgasv0GxBHheogBHiK5rBqtS4vTGhfByb4wLcYfg0IAyp6yBCtoCDu/8Cpxcihu/7UCZikNKLr8X1hQMw1QySxmGeenW0lohC6p4s2t6o8opVeBpoaZQBMeqcXbLErg4CHDxxG9wcqzdPtn2AL3dWUAjJ0ccjz2EHj16wKdtZ9xuNQwmEqwMUjXOWSBgNGWD67jxuSEIIbobD8MYNqmtucHpU1KmwrWMEsgVKsR/vRwCtRyX40/ApZF9NJOrbegMbSHhIa2w9bvdOLNlKW4+eAzWyqLvfF977cwtYOqP91tbb40j5bOyif1loZXrZy0MwyAxowTnvl0FtaQACaePwo2nBS+FHypoK/j7uOGYMGM29n36AZRy86PEqmLIPGUYBoJyYXOE6Kpa1qRjhKUQ3TU1UVwcgVkVQ2rq4FOxHE7851PIctKQcPYkPN3danbCBgYVtJV8t2EVfFu1wa5FUyEtLrDqHOakF7Kc5o+a09Swrm1h64QMzTX1d+RMVQyxNNyzKkp5GX7dsBQFT5Jx5dwpNPPkLyZBMQwVtJWIhEL8deQXdO71Gr6dNxEFmWmmX6RHVccYH1V1y+rPlnqztjUC11oH+ufgOI1prb2J8I3H2LWENTC1c548wDfvjwNDWCScPwufprarnNKQoIKuAUKhEEe//wrj35mDHQvewrP7Ny17vRmlefjgyoXNls+gGhFqBClggOauDnB1FOqqhjgINea7q6MQrZo4o08rT/i6O0HNEqjLZ3+13rmM3R6MTdIioeXrZ0IIrh79GTsXvY2/zZyDK8cPw8/b8oKEFA3Uy20Dtv/rYwQF+GPdkvcw9qO1CO39utmvZcAvIHM83qT8L/2nMRwQ0zsQHuU9uJRqDlIlCzcnIRzKbXyOECw/9tCqRuosR3gtC/0sMnORSyWI3fQJ8tIe4eDR3zE8smZtfilU0DZjxXuTEejbAnP+PgkD83PRY9RbZr1OKNDMjlWxtheyUACk5ssQ4a9ZfzqKBHCssrhNL5JbJWagwtyvOhNbUm4IAJ6n3MYvaxYgrPtriL9xFU09Gls5Ioo+1OS2IVNHDkTsidO4eGgnTu3aaNba1tC6k7VS0Qo1QbKJLh9JmWKozGjhagg+y8HcdElCCC4e2oW9K2YhZtE/cSF2HxWzDaEztI15vXtnnP/zAt4YPhKH87IQvXCNwQqbGscYAarU5+cIsXoGBYCUPOM1v288l8B6OfOb3ebM0NKSQvy6YSmkxQU4/sefeO3V8BqMgsIHnaFrgfDW/ki8dB5KqQQ/rHgXciNdOfgc3dbOzloUag6FBoou5EgUkJloO2OKqo3hzFk/P7l1BVtnj0FAcFskJyZQMdcSVNC1RHNPd1yPP46WgcH47sPJEOfnGHyuvqgJIVavn7UwDPAwn3+WvpUlsck+tr7ZbWx25lgWf/zwNX7513wsXbsJp/Z/i8YNoAtkXUEFXYs4OTgg/tBuRA2Lxvb5E5Hz9CHv8/S3r2wRy61kqxcH0JL4TMzrhLMU/UKJhtbPkoJc7F4yHU+TLuPsxUtYNmtSzS9MMQoVdC0jFApx8Ot1mLFgKXYtmoonNxMqHa8aYGJu/yZTpObLqs3EJXI18qXWpXtWRd/s5osQe3DlPLb+Ixodu/dG8tU/0S0sxCbXpRiHOsVeEBuXzkWgnx8+mTcLI+euRKf+w3XHtCarNnbaFqg4gnypqlKXjztZEpvWMOMI4FBFzKxahVO7NuHW2SP4/D+7MGfiSNtdkGISKugXyIKpYxHo1wIzJo1DSV4WoibMBFDhULLV7KzlQb60kqATn4ktLpdkDJYjaCSquEMUZT/DL6sXwNWjCS4lXEV4a3+bXYtiHtTkfsGMG9gbv/9xHtdPHsbRravBsRqPswDE6txqPlQswd2sinW0XMUizcpySYbgSEW65N3zJ7Bt7nj0HTYaty+comKuI6ig64CeHdsi4a8LyHl8Hz+vng+VQl4rCdCPCst069z7udIaZ0PxoVIo8H+bV+LEjvXYtu8Q9m/8jJYJqkOooOuIVi29cf3CH3B0cMDuJdMhLiqy+TUIIcguL15/7ZkYChu1vSSEoCQ7A/fPHcGOeROgKC1G0vXreHvEAJucn2I99FZah3i4ueDKyd8wYvpc7F0wDq26vobmwR3g3SYcTYPa1biHMyGa/WhvN0eT0WPGUCvkyHl0F9kpSchJScLz+0kQCAUI6dQNk2M+wJeLZ0NIK3HWC6ig6xgHkQi/79uG2NNTEH/hEhITE3H2xE8oeP4UXv6t4dOmA5q2DoNPSAc0C2oPkZP5QRkqjuB2Vil83JwgZBiozAwoleRlIatcvFkpSchLewifoBB0iuiB4W+/hZGDtqFD29a0NnY9hLEkaqhbt27k2rVrtTgcipaSUin++OsaLiRcw7XERDy8exN56Y/RxDcQ3m06oFnrcHiHdEDzVu3h0MjF4HkchAy6+rnjSnoJr5xZlRJ5T+4h675GwJkpSWBVSrTpFIEePXvh9b5RGNa/Dy0FVMcwDJNICDGZX0oF/RIhlZXh7OVE/Hn5Gq5eu4YHd28h5+lDeLTwh09wOJoFh8O7TTiaB4fD0dkVAOAoBASMQNcjSlqUh6yUJGTfT0L2gyTkPr6Ppr5B6Ni1O6Ii+2D4630R0aE9nX3rGVTQDQSZXIHzV27g/OWruHotESm3byLrSQoaN/NBizYd0DQ4HCIHJ+Q8SELm/SQopBK07vAquvfshYF9IzF8QBSae3nW9dugmIAKugEjVyhx4dpNnLt8FZevXIVcLkffqEgMHfAa+rzakbaSeQkxV9DUKWaHNHJyxKDI7hgU2b2uh0J5wdCFEoViR1BBUyh2BBU0hWJHUEFTKHYEFTSFYkdQQVModgQVNIViR1BBUyh2BBU0hWJHUEFTKHYEFTSFYkdQQVModgQVNIViR1BBUyh2BBU0hWJHUEFTKHYEFTSFYkdQQVModgQVNIViR1BBUyh2BBU0hWJHUEFTKHYEFTSFYkdQQVModgQVNIViR1BBUyh2hEW9rRiGyQOQVnvDoVAoBggihDQ39SSLBE2hUOo31OSmUOwIKmgKxY6ggqZQ7AgqaArFjqCCplDsCCpoCsWOoIKmUOwIKmgKxY6ggqZQ7Ij/B4ZqETx6L8lPAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from feat.tests.utils import get_test_data_path\n", - "from feat.utils import load_h5, read_facet\n", - "import matplotlib.pyplot as plt\n", - "from feat.data import Openface, Affdex, Facet\n", - "from os.path import join\n", - "\n", - "model = load_h5()\n", - "test_file = join(get_test_data_path(),'iMotions_Test_v2.txt')\n", - "fig, ax = plt.subplots(figsize=(4,5))\n", - "facet = Facet(read_facet(test_file))\n", - "facet = facet/20\n", - "facet.plot(3, ax=ax, muscles={'all': \"heatmap\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Affdex" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-17T00:46:56.217179Z", - "start_time": "2018-08-17T00:46:56.099927Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import feat.plotting as plot\n", - "from feat.utils import load_pickled_model, load_h5, get_resource_path\n", - "from feat.tests.utils import get_test_data_path\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from feat.data import *\n", - "from os.path import join\n", - "\n", - "test_file = join(get_test_data_path(),'sample_affectiva-api-app_output.json')\n", - "fig, ax = plt.subplots(figsize=(4,5))\n", - "openface = Affdex(read_affectiva(test_file))\n", - "openface.plot(0, ax=ax, muscles={'all': \"heatmap\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python [conda env:anaconda3]", - "language": "python", - "name": "conda-env-anaconda3-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "toc": { - "base_numbering": 1, - "nav_menu": { - "height": "171px", - "width": "252px" - }, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}