diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 623508b41..6d9d1f9a6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -9,19 +9,13 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - name: Install Python - uses: actions/setup-python@v4 + - uses: actions/checkout@v5 + - name: Install & run Ruff + uses: astral-sh/ruff-action@v3 with: - python-version: "3.11" - - # Pin ruff version to make sure we do not break our builds at the worst times - - name: Install Ruff 0.5.1 - run: pip install ruff==0.5.1 - - # Include `--output-format=github` to enable automatic inline annotations. - - name: Run Ruff - run: ruff check --output-format=github . + # Pin ruff version to make sure we do not break our builds at the + # worst times + version: "0.14.5" test: # name: Test (${{ matrix.python-version }}, ${{ matrix.os }}) @@ -33,16 +27,16 @@ jobs: fail-fast: false matrix: # os: ["ubuntu-latest", "macos-latest", "windows-latest"] - python-version: ['3.8', '3.9', '3.10', '3.11', '3.12'] + python-version: ['3.9', '3.10', '3.11', '3.12'] defaults: run: shell: bash -l {0} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v5 - name: Set up Python ${{ matrix.python-version }} - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: auto-update-conda: true environment-file: environment.yml diff --git a/doc/source/changes.rst b/doc/source/changes.rst index 75d0c4d72..c433ea8ec 100644 --- a/doc/source/changes.rst +++ b/doc/source/changes.rst @@ -1,6 +1,20 @@ Change log ########## +Version 0.35 +============ + +In development. + +CORE +---- +.. include:: ./changes/version_0_35.rst.inc + +EDITOR +------ +.. include:: ./changes/editor/version_0_35.rst.inc + + Version 0.34.6 ============== diff --git a/doc/source/changes/version_0_35.rst.inc b/doc/source/changes/version_0_35.rst.inc new file mode 100644 index 000000000..754a05e52 --- /dev/null +++ b/doc/source/changes/version_0_35.rst.inc @@ -0,0 +1,88 @@ +.. py:currentmodule:: larray + + +Syntax changes +^^^^^^^^^^^^^^ + +* renamed ``Array.old_method_name()`` to :py:obj:`Array.new_method_name()` (closes :issue:`1`). + +* renamed ``stacked`` argument of :py:obj:`Array.plot()` to ``stack``. This + also impacts all the relevant kind-specific sub-methods + (:py:obj:`Array.plot.area()`, :py:obj:`Array.plot.bar()`, + :py:obj:`Array.plot.barh()`, and :py:obj:`Array.plot.line()`). + + +Backward incompatible changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Plots made with Array.plot() in a Python script will be shown by default, + unless either the filepath (see below) or ax arguments are used. Shown plots + will open a window and pause the running script until the window is closed by + the user. To revert to the previous behavior, use show=False. + + +New features +^^^^^^^^^^^^ + +* Array.plot now has an ´animate´ argument to produce animated plots. The + argument takes an axis (it also supports several axes but that is rarely + useful) and will create an animation, with one image per label of that axis. + For example, + + >>> arr.plot.bar(animate='year') + + will create an animated bar plot with one frame per year. + +* implemented Array.plot `filepath` argument to save plots to a file directly, + without having to use the matplotlib API. + +* implemented Array.plot `show` argument to display plots directly, without + having to use the matplotlib API. This is the new default behavior. + +* implemented a new kind of plot: `heatmap`. It can be used like this: + + >>> arr.plot.heatmap() + +* added a feature (see the :ref:`miscellaneous section ` for details). It works on :ref:`api-axis` and + :ref:`api-group` objects. + + Here is an example of the new feature: + + >>> arr = ndtest((2, 3)) + >>> arr + a\b b0 b1 b2 + a0 0 1 2 + a1 3 4 5 + + And it can also be used like this: + + >>> arr = ndtest("a=a0..a2") + >>> arr + a a0 a1 a2 + 0 1 2 + +* added another feature in the editor (closes :editor_issue:`1`). + + .. note:: + + - It works for foo bar ! + - It does not work for foo baz ! + + +.. _misc: + +Miscellaneous improvements +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* :py:obj:`Array.plot()` ``stack`` argument (previously called ``stacked``) can + now take an axis (or several axes) to specify which axes to stack, instead of + always stacking the last axis. For example, a plot with genders stacked + could be specified as: + + >>> arr.plot.bar(stacked='gender') + + +Fixes +^^^^^ + +* fixed something (closes :issue:`1`). diff --git a/doc/source/tutorial/tutorial_plotting.ipynb b/doc/source/tutorial/tutorial_plotting.ipynb index 8133065a0..c452a8a9a 100644 --- a/doc/source/tutorial/tutorial_plotting.ipynb +++ b/doc/source/tutorial/tutorial_plotting.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,9 +32,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "country gender\\time 2013 2014 2015 2016 2017\n", + "Belgium Male 5.472856 5.493792 5.524068 5.569264 5.589272\n", + "Belgium Female 5.665118 5.687048 5.713206 5.741853 5.762455\n", + " France Male 31.772665 32.045129 32.174258 32.247386 32.318973\n", + " France Female 33.827685 34.120851 34.283895 34.391005 34.485148\n", + "Germany Male 39.380976 39.556923 39.835457 40.514123 40.697118\n", + "Germany Female 41.14277 41.21054 41.36208 41.661561 41.824535" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "demography_eurostat = load_example_data('demography_eurostat')\n", "population = demography_eurostat.population / 1_000_000\n", @@ -52,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -63,147 +80,313 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In a Python script, add the following import on top of the script:" + "Create and show a simple plot (last axis define the different curves to draw):\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import matplotlib.pyplot as plt" + "population['Belgium'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Create and show a simple plot (last axis define the different curves to draw):\n" + "- Create a Line plot with grid, user-defined x ticks, y label and title. \n", + "- Save the plot as a png file.\n", + "- and show it " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot()\n", - "# shows the figure\n", - "plt.show()" + "population['Belgium'].plot(grid=True, \n", + " xticks=[2013, 2014, 2016, 2017], \n", + " ylabel='population (millions)', \n", + " title='Belgium population by gender',\n", + " # saves figure in a file\n", + " filepath='Belgium_population.png',\n", + " # by default, when the plot is saved to a file, it is *not* shown\n", + " show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "- Create a Line plot with grid, user-defined xticks, label and title. \n", - "- Save the plot as a png file (using `plt.savefig()`).\n", - "- Show the plot:" + "Specify line styles and width:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot(grid=True, xticks=population.time, ylabel='population (millions)', title='Belgium')\n", - "# saves figure in a file (see matplotlib.pyplot.savefig documentation for more details)\n", - "plt.savefig('Belgium_population.png')\n", - "# WARNING: show() resets the current figure after showing it! Do not call it before savefig\n", - "plt.show()" + "# line styles: '-' for solid line, '--' for dashed line, '-.' for dash-dotted line and ':' for dotted line\n", + "population['Male'].plot(style=['-', '--', '-.'], \n", + " linewidth=2, \n", + " title='Male population by country')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Specify line styles and width:" + "Configuring the legend can be done by passing a dict to the legend argument. For example, to put the legend in a specific position inside the graph, one would use `legend={'loc': }`.\n", + "\n", + "Where `` can be: \n", + " `'best'` (default), `'upper right'`, `'upper left'`, `'lower left'`, `'lower right'`, `'right'`, `'center left'`, `'center right'`, `'lower center'`, `'upper center'` or `'center'`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# line styles: '-' for solid line, '--' for dashed line, '-.' for dash-dotted line and ':' for dotted line\n", - "population['Male'].plot(style=['-', '--', '-.'], linewidth=2, \n", - " xticks=population.time, ylabel='population (millions)', title='Male')\n", - "plt.show()" + "population['Belgium'].plot(title='Belgium population by gender', \n", + " legend={'loc': 'lower right'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Configuring the legend can be done by passing a dict to the legend argument. For example, to put the legend in a specific position inside the graph, one would use `legend={'loc': }`.\n", - "\n", - "Where `` can be: \n", - " `'best'` (default), `'upper right'`, `'upper left'`, `'lower left'`, `'lower right'`, `'right'`, `'center left'`, `'center right'`, `'lower center'`, `'upper center'` or `'center'`." + "There are many other ways to customize the legend, see the \"Other parameters\" section of [matplotlib's legend documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html). For example, to put the legend outside the plot:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot(xticks=population.time, ylabel='population (millions)', title='Male', legend={'loc': 'lower right'})\n", - "plt.show()" + "population['Belgium'].plot(title='Belgium population by gender', \n", + " legend={'bbox_to_anchor': (1.25, 0.6)})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "There are many other ways to customize the legend, see the \"Other parameters\" section of [matplotlib's legend documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html). For example, to put the legend outside the plot:" + "Create a Bar plot:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot(xticks=population.time, ylabel='population (millions)', title='Male',\n", - " legend={'bbox_to_anchor': (1.25, 0.6)})\n", - "plt.show()" + "population['Belgium'].plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Create a Bar plot:" + "Specify bounds for the y axis:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot.bar(ylabel='population (millions)', title='Belgium')\n", - "plt.show()" + "population['Belgium'].plot.bar(ylim=[5.3, 5.8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Create a _stacked_ Bar plot:" + "Create a _stacked_ bar plot:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "population['Belgium'].plot.bar(title='Belgium', ylabel='population (millions)', stacked=True)\n", - "plt.show()" + "population['Belgium'].plot.bar(stack='gender')" ] }, { @@ -215,14 +398,161 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[ ,\n", + " ],\n", + " [ ,\n", + " ],\n", + " [ ,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population.plot(subplots=('country', 'gender'),\n", + " sharex=True,\n", + " ylabel='population (millions)',\n", + " figsize=(8, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now demonstrate heatmaps using some random data (because the population array does not lend itself well to heatmaps)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "population.plot(subplots=('country', 'gender'), sharex=True, \n", - " xticks=population.time, ylabel='population (millions)',\n", - " figsize=(8, 10))\n", - "plt.show()" + "from larray.random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "random_data = randint(0, 100, axes='a=a0..a29;b=b0..b29')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_data.plot.heatmap(title='Some random data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For full control over plots, one can use Matplotlib API directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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i19k5EHBsCxiZ1mvJRERENREWFoZvv/0WK1asQI8ePZCRkYHLly8jPz8fgwcPRnBwME6ePAm5XI5XX30V06ZNQ3R0dLUeY9myZVi4cCHef/99/PLLL3jjjTfQq1cv+Pn54e+//8ZTTz2FvXv3wt/fX6sXet++fbCyssKePXsgCAJsbGwQERGBkydPomvXrgCAc+fOIS4uDj///HNtPi16i4G6luXk5GDDhg3YtGkT+vfvDwCIioqCi4sLAOD69ev48ccfkZqaqtk2e/Zs7Ny5E1FRUVi0aBEA9RrNX3/9NVq2bAkAmDZtGhYsWKB5nJUrVyIsLAwvvPACAODrr7/Grl27NPvz8vKwfPly7N+/H926dQMAeHt748iRI/jmm2+0AvWCBQswYMCAunpKiIiqRxDUs2w/OGFYwV3dtlIjdXi+f9i2gz9gZFKPBRMREdWOnNw8rFq1Cl988QUmTpwIAGjZsiV69OiBb7/9FgUFBdi4cSPMzc0BAF988QWGDRuGJUuWwNHR8bEfZ+jQoZg6dSoAYO7cuVixYgUOHjwIPz8/2NvbAwBsbW3h5OSkdT9zc3P873//0wrZgwYNQlRUlCZQR0VFoXfv3vD29q75E9GAMFDXsoSEBBQXF+Opp57SbLO2tkbr1q0BAKdPn4YgCPD19dW6n1KphK2trea2mZmZJkwDgLOzM+RyOQAgOzsbGRkZmqAMAIaGhujSpYtm2PfFixdRWFioE5SLiooQGKg90UGXLl2e5JSJiGpOEIC7ibo9z4X3dNtKjdQ9zZqe546Aoz9gKKvHghuH8PBwREREaG1zdHREZmYmAPUlQxEREVi7di3u3r2LoKAgrFmzBv7+/mKUS0SNlOd7f4hdgkaSnnwOe+lqIpRKpaZjTmvfpUvo0KGDJkwDQPfu3aFSqRAfH1+tQN2+fXvN9xKJBE5OTpqsUZWAgACd66Zfe+01hIaGYvny5TAwMMAPP/yAZcuWPXYtDR0DdS0rD7QPLihevl2lUsHAwACnTp2CgYGBVhsLCwvN90ZGRlr7JBKJzjXSVVGpVACAP/74A66urlr7ZDLtPz7vf1ESEdUZlaosPMfdF57PAcps3bYGxuqw7Nzhvp7ntgzPtcjf3x979+7V3L7/PWnp0qVYvnw5oqOj4evri8jISAwYMADx8fGwtLQUo1wioibB1ET9PnflzhXkW+Zr7cvKz0J+ST4uZF3QbMtR5AAAkrKTYJtli4LSAmTlZ2m1uZN3BzCBZluxqhi3lbe12ihLlcjMzcSFrAtIu5MGALh+9zqMsozgb1fxYWpluWHYsGGQyWTYtm0bZDIZlEqlZhRtU1CtQP2oT7QfNGnSJGzYsEFne9u2bXHhgvoHGB0djcmTJ+u0KSgogImJnnxUVA0tW7aEkZER/v77b7i5uQEAFAoFrl69it69eyMwMBClpaWQy+Xo2bNnjR7D2toazs7OOHHiBHr16gUAKCkpwalTp9CpUycA6udYJpMhOTlZa3g3EVG9UKmAOwllwTlO3euccRZQKnTbGsjU4fn+Ydv2bQBDzhxalwwNDXWG8gHqD4BXrlyJefPmYeTIkQCADRs2wNHREZs2bcKUKVPqu1QioibDx8sdJqYmOHHoBEZNGKW1r2Xrlvht82/Iz8uHmbl6Es24v+IglUrh0dIDANDMthmyblasbFFaWoprl6/hqe5P4XEZGas79kpVpY/V3tDQEBMnTkRUVBRkMhn+85//wMys6UzyWe0e6qo+0X7QqlWr8Mknn2hul5SUoEOHDnjxxRe12llZWSE+Pl5rW0MM0wBgaWmJiRMn4t1330Xz5s3h4OCAjz76CFKpFBKJBL6+vhg/fjxefvllLFu2DIGBgcjKysL+/fsREBCAoUOHPtbjTJ8+HZ988gl8fHzQpk0bLF++HPfu3dOqY/bs2XjnnXegUqnQo0cPKBQKHDt2DBYWFpprMoiInphKBdy5rj1sO/Pcw8OzUzvtYdsObQADI922VKeuXr0KFxcXyGQyBAUFYdGiRfD29kZiYiIyMzMxcOBATVuZTIbevXvj2LFjDw3USqUSSqVSc1uhqOTnT0REVTIxkSH0rVAsX7AcRsZGCHwqEHdv38W1y9fwzAvPYM2SNZg3bR6mzpmKu7fvYtH7izDsxWGwc7ADAAT1DMKnH36K2N2xcPNyw3dff4ec7Jxq1dDcrjlMTE1wZN8RODo7ItsoG9bW1lXe59VXX0WbNm0AAEePHq3ZyTdQ1Q7UD/tEuzLW1tZaT/6vv/6Ku3fv6vRIl4/bbyyWL1+O119/Hc8++yysrKwwZ84cpKSkaD4kiIqKQmRkJGbNmoW0tDTY2tqiW7dujx2mAWDWrFnIyMjApEmTIJVKERoaihEjRiA7u2Lo5MKFC+Hg4IDFixcjISEBNjY26NSpE95///1aP2ciaiJUpcDta+reZs2EYeeAokrerA1N1EtVafU8+zE864GgoCBs3LgRvr6+uHnzJiIjIxESEoILFy5oRp09eC2eo6Mjbty48dBjLl68WGcUGxERVd/rs16HgYEB1ixZA3mmHPaO9hg9cTRMzUzxzU/f4JN5n+A/A/8DE1MTDHh2AOYsmKO574hxIxB/IR7vT3sfBoYGeHnKy3iqx+P3TgPqvPfex+/h62VfY82SNejZs6fWEr+V8fHxQUhICG7fvo2goKCanHaDJRGqcWFueHg4Pv30U1hbW+t8ov04hg0bBqVSid27d2u2RUdH49VXX4WrqytKS0vRsWNHLFy4UGfirAdV9km4m5sbsrOzYWVlpdW2sLAQiYmJ8PLyEqXnOy8vD66urli2bBleeeWVen98fSP2z4OIHpOqFMi6qj3bduZ5oChXt62hKeAUUBGenTuUheemPVWHQqGAtbV1pe9N+iQvLw8tW7bEnDlzEBwcjO7duyM9PR3Ozs6aNq+99hpSUlKwc+fOSo9RnfdlIiJA3yYlGwcAKLRwQ2L3ZfBytYeJoeQR96obF4z165Kn+6+hfhhBEODn54cpU6Zg5syZ9VDVw1WVNerifblaf+lU9Yn2/TNUVyYjIwN//vknNm3apLXdz88P0dHRCAgIgEKhwKpVq9C9e3ecPXtWsyZzZfT5k/C4uDhcvnwZTz31FLKzszXLXT333HMiV0ZE9BCqUiDrygPDts8DxXm6bY3M1OH5/mHbdr5NPjw3ZObm5ggICMDVq1fx/PPPAwAyMzO1ArVcLq9yBlmZTKYz6SURETV+crkc3333HdLS0iqdG6uxq9ZfP0OGDNF8HxAQgG7duqFly5bYsGHDIz+JiI6Oho2NjeaNulxwcDCCg4M1t7t3745OnTrh888/x+rVqx96vLCwMK3HLP8kXF989tlniI+Ph7GxMTp37ozDhw/Dzs5O7LKIiIDSEnV4frDnuThft62RGeDUXnvYtp0vIH34/BnU8CiVSly6dAk9e/aEl5cXnJycsGfPHs1osaKiIsTGxmLJkiUiV0pERPrG0dERdnZ2WLt2LZo1ayZ2OfXuiboT7v9EuyqCIGD9+vWYMGGCzrplD5JKpejatesjj6nPn4QHBgbi1KlTYpdBRKQOz7cua6/xnHkeKCnQbWtsoQ7Pzh3u63n2YXhuhGbPno1hw4bB3d0dcrkckZGRUCgUmDhxIiQSCWbMmIFFixbBx8cHPj4+WLRoEczMzDBu3DixSxedPg1RBYCkT54RuwSNgA0BYpegcX7iebFLIGoyqrO0b2P0RIH6/k+0qxIbG4tr16491vXDgiDgzJkzCAjQn/+UiYgahNJidXi+f9j2zX+BkkLdtsYW2ms8O3cEbFsyPDcRqampGDt2LLKysmBvb4/g4GCcOHECHh7qZVfmzJmDgoICTJ06FXfv3kVQUBB2797NNaiJiIgeUK1AXdUn2oB6GHZaWho2btyodb9169YhKCgI7dq10zlmREQEgoOD4ePjA4VCgdWrV+PMmTNYs2bNE5wWEVEjV1oMyC89MGz7X6BUqdvW2FK719mlI9C8JSCV1mfFpEdiYmKq3C+RSBAeHo7w8PD6KYiIiKiBqlagftQn2hkZGUhOTta6T3Z2NrZs2YJVq1ZVesx79+7hv//9LzIzM2FtbY3AwEAcOnQITz1VvendiYgarZIi4NalB3qeL1QenmVWZT3PHQCXQHWAbu7N8ExERERUB6oVqB/1iXZ0dLTONmtra+TnVzLRTZkVK1ZgxYoV1SmDiKjxKi0Bbp7XDs/yi0BpkW5bmTXg8sCw7WZeDM9ERERE9YRrnBARie3uDeD6PuDaPiDxEKBU6LYxsdYOzi4d1eFZIs4amUSkJ8Ktxa6ggpe72BUQEdU7BmoiovqmzAVuHFUH6Ov7gNvXtPebWFcM19b0PHsyPBMRERHpGQbqJiIpKQleXl6Ii4tDx44dxS6HqGkRBPVSVeW90MknAFVxxX6JAeD2FNCyP9CynzpEc7ZtIiIiqkfzps1DjiIHqzeufuz7eHp6YsaMGZgxY0bdFabnGKj12KRJk7BhwwZMmTIFX3/9tda+qVOn4quvvsLEiRMrvXadiESWewtIOFDWC70fyJNr77dxVwfoVv0Br17qXmkiIiLSS56r0+v18f6Y7Vmt9vOmzcNvm3/T3LZuZo12Hdth5kcz0dq/dS1XV+HkyZMwNzevs+M3BAzUes7NzQ0xMTFYsWIFTE1NAQCFhYX48ccf4e7Oa5WI9EZJEZDyV0UvdOY57f1G5oBXz4oQ3dybQ7iJiIio1vTo1wORqyMBAFnyLKxevBpvjn8Te8/srbPHtLe3r7NjNxScClbPderUCe7u7ti6datm29atW+Hm5obAwEDNtp07d6JHjx6wsbGBra0tnn32WVy/fr3KY1+8eBFDhw6FhYUFHB0dMWHCBGRlZdXZuRA1KoIA3L4O/LUW2PQfYKkXsOFZ4MiKijDt1B7oPgOY+H/A3ERg3GYg6L+AbUuGaSIiIqpVxjJj2Dnawc7RDn4BfnjlrVeQmZaJO1l3AAA3M25i1quzENIqBN19u+OtCW8hLTntocfLy83D3NfnwtzcHM7OzlixYgX69OmjNbzb09MTK1euBKC+xFQikeDMmTOa/ffu3YNEIsHBgwcBAAcPHoREIsGuXbsQGBgIU1NT9OvXD3K5HH/++SfatGkDKysrjB07tsqVovRJkwzUgiAgv6hElC9BEKpd7+TJkxEVFaW5vX79eoSGhmq1ycvLw8yZM3Hy5Ens27cPUqkUI0aMgEqlqvSYGRkZ6N27Nzp27Ih//vkHO3fuxM2bNzF69Ohq10fUZBQqgEu/A7+/A6zqAHzeCfjzXeDKn0BRLmBuD7QfA4xYC8y+Crx+GBgQoR7SbSgTu3oiIiJqIvJz8/H7lt/h7uUOm+Y2KMgvQOjzoTAzN0P09mhs/H0jzMzN8PqY11FcVFzpMZbOX4q4v+Owfft27NmzB4cPH8bp06drpb7w8HB88cUXOHbsGFJSUjB69GisXLkSmzZtwh9//IE9e/bg888/r5XHqmtNcsh3QXEp2n64S5THvrhgEMyMq/e0T5gwAWFhYZpPfY4ePYqYmBjNJz0A8MILL2jdZ926dXBwcMDFixfRrl07nWN+9dVX6NSpExYtWqTZtn79eri5ueHKlSvw9fWt3okRNUYqFZARB1zbrx7KnfI3IJRW7JcaAe7B6iHcLfsBjgFcA5qIiIhEEbs7Fl09ugIACvILYO9ojzU/rIFUKsWf2/6EVCrFgpULICkbJRe5OhLdWnXD30f/Rve+3bWOlZebh982/4alXy9F//79AQBRUVFwcXGplVojIyPRvbv6MV955RWEhYXh+vXr8Pb2BgCMGjUKBw4cwNy5c2vl8epSkwzUDY2dnR2eeeYZbNiwAYIg4JlnnoGdnZ1Wm+vXr2P+/Pk4ceIEsrKyND3TycnJlQbqU6dO4cCBA7CwsNDZd/36dQZqaroUGepJxK7vA64fAAruaO+3baUOzy37A549AJnua4iIiIiovnXt0RUfLv0QAJB9Lxsx62Pw+tjXEbMrBhfPXkRyYjKe8nxK6z7KQiVSklJ0jpWSlIKS4hIEdArQbLO2tkbr1rUzwVn79u013zs6OsLMzEwTpsu3/f3337XyWHWtSQZqUyMDXFwwSLTHronQ0FBMmzYNALBmzRqd/cOGDYObmxu+/fZbuLi4QKVSoV27digqKqr0eCqVCsOGDcOSJUt09jk7O9eoRqIGqbgQSD5WMRu3/KL2fpmVesh2eS90M09RyiQiIiKqipmZGdy9KyYtbtuhLYK9g/HLd79AJajQtkNbLPlK92//ZnbNdA9WfpXqA1O+VHX5qrRslN79bYqLKx9ObmRkpPleIpFo3S7f9rBLV/VNkwzUEomk2sOuxTZ48GBNOB40SPvDgNu3b+PSpUv45ptv0LNnTwDAkSNHqjxep06dsGXLFnh6esLQsGE9F0RPRBCAW/EVvdBJR4GSgvsaSADXThW90C26AAZGDz0cERERkT6SSCSQSqVQFirRtn1b7Px1J5rbN4eF5aNH17l5ucHQyBD/nv4XT3d4GgCgUChw9epV9O7du9L7lM/4nZGRoZk8+f4JyhorJqkGwsDAAJcuXdJ8f79mzZrB1tYWa9euhbOzM5KTk/Hee+9Vebw333wT3377LcaOHYt3330XdnZ2uHbtGmJiYvDtt9/qPAZRg1ZwF0g4WNYLfQBQpGrvt3QuW86qH+DdFzBrLkqZRERERDVVpCxC1k31ij3Z2dn48X8/Ij8vH70H9UZAYACivojCWxPewrS50+Do4oiM1Azs/WMvJk+bDCcXJ61jmVuY47kxz2FZxDJ08OwABwcHfPTRR5BKpZprsB9kamqK4OBgfPLJJ/D09ERWVhY++OCDOj9vsTFQNyBWVlaVbpdKpYiJicHbb7+Ndu3aoXXr1li9ejX69Onz0GO5uLjg6NGjmDt3LgYNGgSlUgkPDw8MHjxYM1yDqMEqLQHSTpVdB71f/b1w37AhAxng2b2iF9qhDZexIiIiogbtyP4j6NOuDwB1IPby8cLydcvxVHf1ddMbtm/A8gXLMWPyDOTl5sHB2QHBPYMf2mM9Z+EcLJi9AM8++yysrKwwZ84cpKSkwMTE5KE1lK9G1KVLF7Ru3RpLly7FwIEDa/1c9YlEqMk6TnpIoVDA2toa2dnZOsGzsLAQiYmJ8PLyqvIXgOoHfx5UJ+4lq8PztX1AYixQmK29396vohfaoztgZCpOndSkVPXe1Ng11nP3fO8PsUvQkmQyTuwSNAK83B/dqJ6cn3he7BLoMejT66n8tVRo4YbE7svg5WoPE0NxPmy/YGwsyuM+jL+dPwD1Mr2urq5YtmwZXnnlFZGreriqskZdvDexh5qIGqaiPPX1z9f3qUP07ava+01sgJZ91SG6ZV/AuoUoZRIRERE1RJfOXULitUSY9DdBdnY2FixYAAB47rnnRK5MvzBQE1HDIAjAzX8reqGTjwOl981iLzEAWnRVD+Nu1R9wCQSknAuAiIiIqKai10QjYmYEjI2N0blzZxw+fFhn+d6mjoGaiPRXXpZ6ErHya6Fzb2rvt3ZXD+Fu2V+9tJWpjShlEhERETU2bdq3wU/7ftIM+abKMVATkf4oKQJS/67ohc44i4qFEAEYmQGePSt6oW1bcTIxIiIiIhINAzURietOQtlyVvuBxENAUa72fseAil5o92DAUCZOnURERERED2CgJqL6pcxRB+dr+9RDue8mae83s6vogfbuC1g6ilImEREREdGjMFATUd1SqYCMM+oe6Ov7gZS/AFVJxX6pkbrnuXxGbqf2ANdCJyIiIqIGgIGaiGpfTmbFddAJB4D829r7m3uXrQndH/DsAcgsxamTiIiIiOgJMFAT0ZMrLlQvY1XeC33zX+39xpaAd++KXujmXuLUSURERERUixioqVo8PT0xY8YMzJgxQ+xSSEyCAGRdVV8DfW0fkHQEKCm4r4EEcOlY0QvdoitgYCRWtUREREREdYKBWo9NmjQJGzZs0Nl+9epVtGrVSoSKqEkruAskxJatCX0AyE7R3m/hpA7PLfupJxMztxWnTiIiIqLatrZPvT2UP4AL045V6z7zps3Db5t/09m+468dcPd2r6XKqDIM1Hpu8ODBiIqK0tpmb28vUjXUpKhKgbTTFb3Qaf8Agqpiv4EM8OhW0Qvt0JZrQhMRERGJpEe/HohcHam1rZldM63bxUXFMDLmqMHaxKl09ZxMJoOTk5PWl4GBAf7v//4PnTt3homJCby9vREREYGSkoqZkyUSCb755hs8++yzMDMzQ5s2bXD8+HFcu3YNffr0gbm5Obp164br169r7nP9+nU899xzcHR0hIWFBbp27Yq9e/dWWV92djb++9//wsHBAVZWVujXrx/Onj1bZ88H1SFBALKuAf9EAT+9DCz1AtY9DRxcDKT+rQ7Tdq2B4KnA+C3A3CTg5d+A7m8Djv4M00REREQiMpYZw87RTuvrlZGv4OO5H2Pp/KXo0boHXhv1GgBgw1cbMKLXCHT16Ir+Hfpj4ZyFyM/N1xzr1x9/RbeW3XB0/1G0adMGFhYWGDx4MDIyMrQec/369fD394dMJoOzszOmTZum2ddUckLT7KEWBKA4/9Ht6oKR2RMHj127duGll17C6tWr0bNnT1y/fh3//e9/AQAfffSRpt3ChQuxfPlyLF++HHPnzsW4cePg7e2NsLAwuLu7IzQ0FNOmTcOff/4JAMjNzcXQoUMRGRkJExMTbNiwAcOGDUN8fDzc3XWHigiCgGeeeQbNmzfHjh07YG1tjW+++Qb9+/fHlStX0Lx58yc6T6pjggDcTQQSD6uvgU46DORo/ycJExvAu0/FutDWLcSolIiIiIhq6LfNv2HM5DH47o/vIAgCAEAqkSJsURhc3FyQlpyGyDmRWLZgGeYvna+5X0FBAaK+jMJ3330HqVSKl156CbNnz8YPP/wAAPjqq68wc+ZMfPLJJxgyZAiys7Nx9OhRAE0rJzTNQF2cDyxyEeex308HjM0fu/nvv/8OCwsLze0hQ4bg5s2beO+99zBx4kQAgLe3NxYuXIg5c+ZoBerJkydj9OjRAIC5c+eiW7dumD9/PgYNGgQAmD59OiZPnqxp36FDB3To0EFzOzIyEtu2bcP27du1Pm0qd+DAAZw/fx5yuRwymQwA8Nlnn+HXX3/FL7/8ogn5pEfu3lAH5/IQrUjV3m9gDLR4CvDqqR7K7doJkBqIUysRERERPbbY3bHo6tFVc7tn/54AAHcvd8z6aJZW2wmvT9B838KjBaa9Nw0L5yzUCtQlxSX48NMP0aVLFwDAtGnTsGDBAs3+yMhIzJo1C9OnT9ds69pV/fhNKSc0zUDdgPTt2xdfffWV5ra5uTlatWqFkydP4uOPP9ZsLy0tRWFhIfLz82FmZgYAaN++vWa/o6MjACAgIEBrW2FhIRQKBaysrJCXl4eIiAj8/vvvSE9PR0lJCQoKCpCcnFxpbadOnUJubi5sbbUnnyooKNAaSk4iupdS0fuceBjIfuBnKTUCWnQBPHuqQ3SLroCRqTi1EhEREVGNde3RFR8u/VBz29TMFO9OeRf+Hf112v595G+sXbkWCfEJyM3JRWlpKZSFSuTn5cPM3Exzf3evilGqzs7OkMvlAAC5XI709HT079+/0lqaUk5omoHayEzdUyzWY1dDeYC+n0qlQkREBEaOHKnT3sTEpOKhjComHJCUDTOvbJtKpZ5o6t1338WuXbvw2WefoVWrVjA1NcWoUaNQVFRUaW0qlQrOzs44ePCgzj4bG5vHO0GqXYr0st7nsq+7Sdr7pYaAa2fAs4c6RLsFAcbV+50kIiIiIv1jZmZW6YzepmbanSXpKel4Y+wbGD1xNN567y1YN7PG6ROn8eGMD7XmZDI01I6KEolEM2Tc1LTqDpimlBOaZqCWSKo17FrfdOrUCfHx8bW+dNbhw4cxadIkjBgxAoD6muqkpKQq68jMzIShoSE8PT1rtRZ6TDmZ2j3Qdx74xE9iALgEqnufPXsAbsGAzKLyYxERERFRo3fhzAWUlpTi3QXvQipVz1G969dd1TqGpaUlPD09sW/fPvTt21dnf1PKCU0zUDdwH374IZ599lm4ubnhxRdfhFQqxblz53D+/HlERkY++gAP0apVK2zduhXDhg2DRCLB/PnzNb3XlXn66afRrVs3PP/881iyZAlat26N9PR07NixA88//7zmeguqRbnyigCddATIuqK9XyIFnDuUDeHupe6BNrESp1YiIiIi0jtunm4oKSnBD9/+gD6D+iDu7zj8tOGnah8nPDwcr7/+OhwcHDBkyBDk5OTg6NGjeOutt5pUTmCgboAGDRqE33//HQsWLMDSpUthZGQEPz8/vPrqq0903BUrViA0NBQhISGws7PD3LlzoVAoHtpeIpFgx44dmDdvHkJDQ3Hr1i04OTmhV69emmu26Qnl3QZuHKkYxn3r8gMNJIBTgDo8e/ZUrwttYi1KqURERESk//wC/DBn4Rys/3w9Vn28Cp2DO2P6B9Px/pvvV+s4EydORGFhIVasWIHZs2fDzs4Oo0aNAtC0coJEKB8I38ApFApYW1sjOzsbVlbaPXKFhYVITEyEl5eX1jXGJA7+PKqQfwe4cVTd+5x4GJBf0G3jGKAevu3VE/AIAUyb1X+dRPRYqnpvauwa67l7vveH2CVoSTIZJ3YJGgFeutduiuX8xPNil0CPQZ9eT+WvpUILNyR2XwYvV3uYGD7ZUrc1dcHYWJTHfRh/O91JzfRZVVmjLt6bpNVpHB4eDolEovXl5OT00PaTJk3SaS+RSODvr/1D2bJlC9q2bQuZTIa2bdti27ZtNTsbooam4B5weQewMwz4ugew1BvY/BLw19cVYdqhLfDUFGD0d8CcROCNI8CQTwC/ZximiYiIiIhEVO0h3/7+/ti7d6/mtoHBw9eoXbVqFT755BPN7ZKSEnTo0AEvvviiZtvx48cxZswYLFy4ECNGjMC2bdswevRoHDlyBEFBQdUtj0i/FWYDySeAxEPqIdwZ5wA8MEjErnXFJGIePQALe1FKJSIiIiKiqlU7UBsaGlbZK30/a2trWFtXXM/566+/4u7du5g8ebJm28qVKzFgwACEhYUBAMLCwhAbG4uVK1fixx9/rG55RPpFmaMO0OWzcGecAYQHJnqzbVWxDrRHD8CycV1XQkRERETUWFU7UF+9ehUuLi6QyWQICgrCokWL4O3t/Vj3XbduHZ5++ml4eHhoth0/fhzvvPOOVrtBgwZh5cqV1S2NSHxFeRUBOukIkHYaEEq12zT3LlsHupf6XytncWolIiIiIqInUq1AHRQUhI0bN8LX1xc3b95EZGQkQkJCcOHCBdja2lZ534yMDPz555/YtGmT1vbMzEydmd4cHR2RmZlZ5fGUSiWUSqXmdlWzUZdrJPOvNXiN6udQXACk/FU2C/cRIO0UoCrWbmPjUTaEu2wYt3ULcWolIiIiIqJaVa1APWTIEM33AQEB6NatG1q2bIkNGzZg5syZVd43OjoaNjY2eP7553X2SSTaM+gJgqCz7UGLFy9GRETEY9Vdfp13UVERTE1NH+s+VHfy8/MBAEZGRiJXUgPFhUDqyYoh3Gn/AKVF2m2s3SqGcHv2AGz0Z9ZTIiIiIiKqPU+0DrW5uTkCAgJw9erVKtsJgoD169djwoQJMH5gGngnJyed3mi5XP7I9cnCwsK0QrxCoYCbm1ulbQ0NDWFmZoZbt27ByMgIUmm1JjenWiIIAvLz8yGXy2FjY1PlhHZ6o0QJpP6j7n1OOgyk/A2UKrXbWLpU9EB79VT3SD/iAyEiIn2yePFivP/++5g+fbrmkitBEBAREYG1a9fi7t27CAoKwpo1a3RW6iAiImrKnihQK5VKXLp0CT179qyyXWxsLK5du4ZXXnlFZ1+3bt2wZ88ereuod+/ejZCQkCqPKZPJIJPJHqtOiUQCZ2dnJCYm4saNG491H6o7NjY2jz2xXb0rKQLST5cN4S4L0CUF2m0sHO/rge6pviaaAZqIGqiTJ09i7dq1aN++vdb2pUuXYvny5YiOjoavry8iIyMxYMAAxMfHw9LSUqRqiYiI9Eu1AvXs2bMxbNgwuLu7Qy6XIzIyEgqFAhMnTgSg7jVOS0vDxo0bte63bt06BAUFoV27djrHnD59Onr16oUlS5bgueeew2+//Ya9e/fiyJEjT3BauoyNjeHj44OioqJHN6Y6Y2RkpF8906XFQPoZIOmQOkSn/AUU52u3MbevuP7Zq5d6Vm4GaCJqBHJzczF+/Hh8++23iIyM1GwXBAErV67EvHnzMHLkSADAhg0b4OjoiE2bNmHKlClilUxERKRXqhWoU1NTMXbsWGRlZcHe3h7BwcE4ceKEZtbujIwMJCcna90nOzsbW7ZswapVqyo9ZkhICGJiYvDBBx9g/vz5aNmyJTZv3lwna1BLpVKYmJjU+nGpASktATLOls3CfVg9I3dRrnYbM9uyWbjLeqDtWzNAE1Gj9Oabb+KZZ57B008/rRWoExMTkZmZiYEDB2q2yWQy9O7dG8eOHWOgJiIiKlOtQB0TE1Pl/ujoaJ1t1tbWmkmoHmbUqFEYNWpUdUohejyqUiDzXMUs3MnHAeUDM8KbNgM8uqt7nz17APZtAF5nT0SNXExMDE6fPo2TJ0/q7Cuf26SyVTgedulUTVbfICJqKAL2vFyvjxfzTNW562Gybmbh21Xf4tCeQ7iZcRMWVhbw8PLAsy8+i+Gjh8PUjBM017YnuoaaSO+oVMDNfyvWgb5xFCjM1m5jYq0O0OXXQTv4M0ATUZOSkpKC6dOnY/fu3VWO3KrOKhzVWX2DiIhqX0pSCiY8MwGW1paYPm86fNr6oLSkFDeu38DWTVvh4OSAvoP7Vvu4xcXFDXN1nnrCFEENm0oF3LwAnPgaiBkPLPUCvukJ7HofiN+hDtMyK8B3MDAwEvhvLDAnERj7I9BtKuAUwDBNRE3OqVOnIJfL0blzZxgaGsLQ0BCxsbFYvXo1DA0NNT3T1VmFIywsDNnZ2ZqvlJSUOj8PIiKqEDknEgaGBti8ZzMGPz8YLX1bwretLwYMG4CvfvwKfQb1AQDkKHIQPjMcvdr0QpBXEEJHhOLyv5c1x1mzdA1e6PMCtv6wFYO7DIZMJtN8oPrNN9/g2WefhZmZGdq0aYPjx4/j2rVr6NOnD8zNzdGtWzdcv35dc6zr16/jueeeg6OjIywsLNC1a1fs3btXq25PT08sWrQIoaGhsLS0hLu7O9auXavZ369fP0ybNk3rPrdv34ZMJsP+/ftr/4msJiYJalgEAZBfAv7+Ftg8AfisFfBVCLBzLnD5d6DwHmBsAbQaAAxYALy2Xx2gx20GQt4CXDoCUj2aFI2ISAT9+/fH+fPncebMGc1Xly5dMH78eJw5cwbe3t5wcnLCnj17NPcpKipCbGzsQ1fhkMlksLKy0voiIqL6ce/OPRw7eAxjQ8fCzNys0jYSiQSCIGDq2KnIkmfhyx+/xE97f0Kb9m3w6guvIvtuxajO5MRk7PptF1asX4EzZ85oti9cuBAvv/wyzpw5Az8/P4wbNw5TpkxBWFgY/vnnHwDQCr+5ubkYOnQo9u7di7i4OAwaNAjDhg3TmXdr2bJl6NKlC+Li4jB16lS88cYbuHxZHfJfffVVbNq0Seuyoh9++AEuLi7o27f6Pe61jUO+Sb8JApB1VT0Ld9IR9VfeLe02RmaAe3DZEO5egHMHwIDDUoiIHsbS0lJn5Q1zc3PY2tpqts+YMQOLFi2Cj48PfHx8sGjRIpiZmWHcuHFilExERFVITkyGIAjwbOWptb1H6x5QFqqD6NhXxqJ73+64eukqDl06BGOZMQDg3Yh3sX/Hfuz+v9148eUXAaiHeS/+cjGa2zWHv52/5niTJ0/G6NGjAQBz585Ft27dMH/+fAwaNAiAegWnyZMna9p36NABHTp00NyOjIzEtm3bsH37dq3gPXToUEydOlVz3BUrVuDgwYPw8/PDCy+8gLfeegu//fab5rGjoqIwadKkh16GVJ8YqEm/qFTA7avAjWMV10Hn3tRuY2gCuAWVrQPdC3AJBAyNxamXiKiRmjNnDgoKCjB16lTcvXsXQUFB2L17N9egJiLSYw8GzB93/QhBJWDuG3NRpCzChbMXkJ+Xj+6+3bXaKQuVSEmquFTHpYULmts11zl++/btNd+XXwIUEBCgta2wsBAKhQJWVlbIy8tDREQEfv/9d6Snp6OkpAQFBQU6PdT3H1cikcDJyQlyuRyAegTUSy+9hPXr12P06NE4c+YMzp49i19//bWaz07dYKAmceVkAqn/AGmn1F/pcbqzcBvIALenKiYRc+0MGMrEqZeIqJE6ePCg1m2JRILw8HCEh4eLUg8RET0+dy93SCQSJF5N1Nru5ukGAJCZqP92FlQC7B3tEfVrlM4xLK0rPjB92Gzg909OVh7eK9umUqkAAO+++y527dqFzz77DK1atYKpqSlGjRqFoqKihx63/DjlxwDUw747duyI1NRUrF+/Hv3799cs3Sw2BmqqP8ocIP0MkFYeoE8DijTddoamgGunirWgW3QFjLh+OBERERFRZWya26Bb7274cd2PGPfquIdeR92mfRtkybNgYGgAV3fXOq/r8OHDmDRpEkaMGAFAfU11UlJStY8TEBCALl264Ntvv8WmTZvw+eef13KlNcdATXWjtBiQXyzrfT6tDtC3LgMQtNtJpOp1n107qXueW3RR3zbgryYRERER0eP6YOkHmPDMBIwZMAZT350KX39fSKVS/Bv3LxKvJsK/gz+69e6GDl064O2X38bMD2fCs5Un5JlyHN57GP2G9kO7ju0e/UDV0KpVK2zduhXDhg2DRCLB/PnztXqeq+PVV1/FtGnTYGZmpgno+oCphZ6cIAB3kyqGbaedAjLOAiWFum2t3SrCs2sX9QRiMot6L5mIiIiIqDFx93LHL/t/wbcrv8Wqj1chMz0TxsbGaNm6JSa9OQn/mfwfSCQSfBXzFVZ9vArzp8/Hndt3YOdgh87dOsPW3rbWa1qxYgVCQ0MREhICOzs7zJ07FwqF4tF3rMTYsWMxY8YMjBs3DiYm+jN6VSIIgvDoZvpPoVDA2toa2dnZXKqjruXdBtLLep3Lr38uuKPbTmZ9X3gu+7KsfP1SIqLGqCm/NzXWc/d87w+xS9CSZKI/s64HeLmLXYLG+YnnxS6BHoM+vZ7KX0uFFm5I7L4MXq72MDEUZwbpC8b6Ndnu/bN8iyklJQWenp44efIkOnXq9NB2hYWFSExMhJeXl07wrov3JvZQU9WKC4CMc2U9z2Xh+W6SbjsDY8Ap4L7w3AVo7g1IudQ5ERERERHVTHFxMTIyMvDee+8hODi4yjAtBgZqqqAqBbKuVAzbTv1HfR20qkS3ra2Pds+zUzvOvE1ERERERLXq6NGj6Nu3L3x9ffHLL7+IXY4OBuqmLDtN+7rn9DigKFe3nbmDerKw8uHbLp0AU5t6L5eIiIiIiJqWPn36QJ+vUmagbioKs9WBuXy5qrRTQE6Gbjsjc8AlUHvWbStXQCLONSRERERERET6ioG6MSopAm7+qx2es65Ad8kqA8CxrfbQbXs/QGogStlERERERKIq6wnV4w5ReoT67s1moG7oBAG4k/DAklXngFKlblsbD+3w7NweMDav/5qJiIiIiPSQQXEuoCpBkQowFbsYqpGioiIAgIFB/XQSMlA3NLm3tMNz2img8J5uO9Nm2uHZpRNgYV/v5RIRERERNRSGRdkwu3UGt8ybwaiZCaQiXPWokqjq/0GrUFhYKHYJj02lUuHWrVswMzODoWH9RF0Gan1WlA9knNEOz/eSddsZyADnDvcF6E7qJat43TMRUaNy7949/P3335DL5VCptP/gevnll0Wqioio8ZBAgPPl9Ui08sKNguYA6v/vaXk9BcHHZXhPv+p5FKlUCnd3d0jqKQs1rGenMVOVArcuq5eqKr/2WX4REEofaCgB7FtXBGfXzoCDP2CoXwvAExFR7fq///s/jB8/Hnl5ebC0tNT6Q0EikTBQExHVEuPCLPgcfgtFpg6izC003dWl3h+zKttHbBe7hGoxNjaGVCqtt8djoBaDIADZqWXB+R91eE4/AxTn6ba1dH5g6HZHwMS6vismIiKRzZo1C6GhoVi0aBHMzMzELoeIqFGTCiUwyU8X5bEzivRrlKmJiYnYJeg1Bur6UHBXvWRV6n1Dt/Pkuu2MLQHXQO0AbaVfn1AREZE40tLS8PbbbzNMExER6REG6tpWogQy/72v9/kUcPuabjupIeDoD7h2qQjPdj5csoqIiCo1aNAg/PPPP/D29ha7FCIiIirDQP0kVCrgzvWKXufUf4DM84CqWLdtMy+gxX3h2SkAMOJk/ERE9HieeeYZvPvuu7h48SICAgJgZGSktX/48OEiVUZERNR0MVBXR87N+2bc/gdIiwOU2brtzGy1e55dOwFmzeu/XiIiajRee+01AMCCBQt09kkkEpSWPjiJJREREdU1BuqHUeZqL1mVegpQpOq2MzQBnDuW9T6Xzbpt48Elq4iIqFY9uEwWERERiY+B+kG3rgA/TwJuXQKEB/94kQAObcqCc1kPtEMbwMCosiMRERERERFRI8ZA/SBLJ/X6zxAAqxbq8Fx+7bNzB0BmKXaFRETURMXGxuKzzz7DpUuXIJFI0KZNG7z77rvo2bOn2KURERE1SQzUDzKxAiZsU/c8WzqJXQ0REREA4Pvvv8fkyZMxcuRIvP322xAEAceOHUP//v0RHR2NcePGiV0iERFRk8NAXZmWfcWugIiISMvHH3+MpUuX4p133tFsmz59OpYvX46FCxcyUBMREYlAKnYBRERE9GgJCQkYNmyYzvbhw4cjMTFRhIqIiIiIgZqIiKgBcHNzw759+3S279u3D25ubiJURERERBzyTURE1ADMmjULb7/9Ns6cOYOQkBBIJBIcOXIE0dHRWLVqldjlERERNUkM1ERERA3AG2+8AScnJyxbtgw//fQTAKBNmzbYvHkznnvuOZGrIyIiapoYqImIiBqIESNGYMSIEWKXQURERGV4DTURERERERFRDbCHmoiISE81b94cV65cgZ2dHZo1awaJRPLQtnfu3KnHyoiIiAioZqAODw9HRESE1jZHR0dkZmY+9D5KpRILFizA999/j8zMTLRo0QLz5s1DaGgoACA6OhqTJ0/WuV9BQQFMTEyqUx4REVGjsmLFClhaWmq+rypQExERUf2rdg+1v78/9u7dq7ltYGBQZfvRo0fj5s2bWLduHVq1agW5XI6SkhKtNlZWVoiPj9faxjBNRERN3cSJEzXfT5o0SbxCiIiIqFLVDtSGhoZwcnJ6rLY7d+5EbGwsEhIS0Lx5cwCAp6enTjuJRPLYxyQiImqKDAwMkJGRAQcHB63tt2/fhoODA0pLS0WqjIiIqOmq9qRkV69ehYuLC7y8vPCf//wHCQkJD227fft2dOnSBUuXLoWrqyt8fX0xe/ZsFBQUaLXLzc2Fh4cHWrRogWeffRZxcXGPrEOpVEKhUGh9ERERNVaCIFS6XalUwtjYuJ6rISIiIqCaPdRBQUHYuHEjfH19cfPmTURGRiIkJAQXLlyAra2tTvuEhAQcOXIEJiYm2LZtG7KysjB16lTcuXMH69evBwD4+fkhOjoaAQEBUCgUWLVqFbp3746zZ8/Cx8fnobUsXrxY53puIiKixmb16tUA1KO5/ve//8HCwkKzr7S0FIcOHYKfn59Y5RERETVpEuFhH3k/hry8PLRs2RJz5szBzJkzdfYPHDgQhw8fRmZmJqytrQEAW7duxahRo5CXlwdTU1Od+6hUKnTq1Am9evXS/BFRGaVSCaVSqbmtUCjg5uaG7OxsWFlZ1fSUiIiIao1CoYC1tfUTvTd5eXkBAG7cuIEWLVpozV1ibGwMT09PLFiwAEFBQbVSc22pjXPXR57v/SF2CVqSTMaJXYJGgJe72CVonJ94XuwS6DHo0+uJr6WHa0yvp7p4b3qiZbPMzc0REBCAq1evVrrf2dkZrq6umjANAG3atIEgCEhNTa20B1oqlaJr164PPWY5mUwGmUz2JOUTERHpvcTERABA3759sXXrVjRr1kzkioiIiKhcta+hvp9SqcSlS5fg7Oxc6f7u3bsjPT0dubm5mm1XrlyBVCpFixYtKr2PIAg4c+bMQ49JRETUFB04cIBhmoiISM9Uq4d69uzZGDZsGNzd3SGXyxEZGQmFQqFZ1iMsLAxpaWnYuHEjAGDcuHFYuHAhJk+ejIiICGRlZeHdd99FaGioZrh3REQEgoOD4ePjA4VCgdWrV+PMmTNYs2ZNLZ8qERFRw5aamort27cjOTkZRUVFWvuWL18uUlVERERNV7UCdWpqKsaOHYusrCzY29sjODgYJ06cgIeHBwAgIyMDycnJmvYWFhbYs2cP3nrrLXTp0gW2trYYPXo0IiMjNW3u3buH//73v5rrrAMDA3Ho0CE89dRTtXSKREREDd++ffswfPhweHl5IT4+Hu3atUNSUhIEQUCnTp3ELo+IiKhJqlagjomJqXJ/dHS0zjY/Pz/s2bPnofdZsWIFVqxYUZ0yiIiImpywsDDMmjULCxYsgKWlJbZs2QIHBweMHz8egwcPFrs8IiKiJumJrqEmIiKi+nHp0iXNJVaGhoYoKCiAhYUFFixYgCVLlohcHRERUdPEQE1ERNQAmJuba5aLdHFxwfXr1zX7srKyxCqLiIioSXuiZbOIiIiofgQHB+Po0aNo27YtnnnmGcyaNQvnz5/H1q1bERwcLHZ5RERETRJ7qImIiBqA5cuXIygoCAAQHh6OAQMGYPPmzfDw8MC6deuqdayvvvoK7du3h5WVFaysrNCtWzf8+eefmv2CICA8PBwuLi4wNTVFnz59cOHChVo9HyIiosaAPdREREQNgLe3t+Z7MzMzfPnllzU+VosWLfDJJ5+gVatWAIANGzbgueeeQ1xcHPz9/bF06VIsX74c0dHR8PX1RWRkJAYMGID4+HhYWlo+8bkQERE1FuyhJiIiamKGDRuGoUOHwtfXF76+vvj4449hYWGBEydOQBAErFy5EvPmzcPIkSPRrl07bNiwAfn5+di0aZPYpRMREekV9lATERHpqWbNmkEikTxW2zt37tToMUpLS/Hzzz8jLy8P3bp1Q2JiIjIzMzFw4EBNG5lMht69e+PYsWOYMmVKpcdRKpWaSdMAQKFQ1KgeIiKihoSBmoiISE+tXLmyzo59/vx5dOvWDYWFhbCwsMC2bdvQtm1bHDt2DADg6Oio1d7R0RE3btx46PEWL16MiIiIOquXiIhIHzFQExER6anydafrQuvWrXHmzBncu3cPW7ZswcSJExEbG6vZ/2DPuCAIVfaWh4WFYebMmZrbCoUCbm5utV84ERGRHmGgJiIiagCSk5Or3O/u7l6t4xkbG2smJevSpQtOnjyJVatWYe7cuQCAzMxMODs7a9rL5XKdXuv7yWQyyGSyatVARETU0DFQExERNQCenp5V9hCXlpY+0fEFQYBSqYSXlxecnJywZ88eBAYGAgCKiooQGxuLJUuWPNFjEBERNTYM1ERERA1AXFyc1u3i4mLExcVh+fLl+Pjjj6t1rPfffx9DhgyBm5sbcnJyEBMTg4MHD2Lnzp2QSCSYMWMGFi1aBB8fH/j4+GDRokUwMzPDuHHjavOUiIiIGjwGaiIiogagQ4cOOtu6dOkCFxcXfPrppxg5cuRjH+vmzZuYMGECMjIyYG1tjfbt22Pnzp0YMGAAAGDOnDkoKCjA1KlTcffuXQQFBWH37t1cg5qIiOgBDNREREQNmK+vL06ePFmt+6xbt67K/RKJBOHh4QgPD3+CyoiIiBo/BmoiIqIG4MF1nQVBQEZGBsLDw+Hj4yNSVURERE0bAzUREVEDYGNjU+lSVm5uboiJiRGpKiIioqaNgZqIiKgBOHDggNZtqVQKe3t7tGrVCoaGfDsnIiISA9+BiYiIGoDevXuLXQIRERE9gIGaiIiogYiPj8fnn3+OS5cuQSKRwM/PD9OmTYOfn5/YpRERETVJUrELICIiokf75Zdf0K5dO5w6dQodOnRA+/btcfr0aQQEBODnn38WuzwiIqImiT3UREREDcCcOXMQFhaGBQsWaG3/6KOPMHfuXLz44osiVUZERNR0sYeaiIioAcjMzMTLL7+ss/2ll15CZmamCBURERERAzUREVED0KdPHxw+fFhn+5EjR9CzZ08RKiIiIiIO+SYiImoAhg8fjrlz5+LUqVMIDg4GAJw4cQI///wzIiIisH37dq22REREVPcYqImIiBqAqVOnAgC+/PJLfPnll5XuAwCJRILS0tJ6rY2IiKipYqAmIiJqAFQqldglEBER0QN4DTURERERERFRDTBQExERNRCxsbEYNmwYWrVqBR8fHwwfPrzSicqIiIiofjBQExERNQDff/89nn76aZiZmeHtt9/GtGnTYGpqiv79+2PTpk1il0dERNQk8RpqIiKiBuDjjz/G0qVL8c4772i2TZ8+HcuXL8fChQsxbtw4EasjIiJqmthDTURE1AAkJCRg2LBhOtuHDx+OxMREESoiIiIiBmoiIqIGwM3NDfv27dPZvm/fPri5uYlQEREREXHINxERUQMwa9YsvP322zhz5gxCQkIgkUhw5MgRREdHY9WqVWKXR0RE1CQxUBMRETUAb7zxBpycnLBs2TL89NNPAIA2bdpg8+bNeO6550SujoiIqGlioCYiImogRowYgREjRohdBhEREZWp1jXU4eHhkEgkWl9OTk5V3kepVGLevHnw8PCATCZDy5YtsX79eq02W7ZsQdu2bSGTydC2bVts27at+mdCRETUBPzzzz/47rvv8P333+PUqVNil0NERNSkVbuH2t/fH3v37tXcNjAwqLL96NGjcfPmTaxbtw6tWrWCXC5HSUmJZv/x48cxZswYLFy4ECNGjMC2bdswevRoHDlyBEFBQdUtj4iIqFFKTU3F2LFjcfToUdjY2AAA7t27h5CQEPz444+cmIyIiEgE1Q7UhoaGj+yVLrdz507ExsYiISEBzZs3BwB4enpqtVm5ciUGDBiAsLAwAEBYWBhiY2OxcuVK/Pjjj9Utj4iIqFEKDQ1FcXExLl26hNatWwMA4uPjERoaildeeQW7d+8WuUIiIqKmp9rLZl29ehUuLi7w8vLCf/7zHyQkJDy07fbt29GlSxcsXboUrq6u8PX1xezZs1FQUKBpc/z4cQwcOFDrfoMGDcKxY8eqWxoREVGjdfjwYXz11VeaMA0ArVu3xueff47Dhw+LWBkREVHTVa0e6qCgIGzcuBG+vr64efMmIiMjERISggsXLsDW1lanfUJCAo4cOQITExNs27YNWVlZmDp1Ku7cuaO5jjozMxOOjo5a93N0dERmZmaVtSiVSiiVSs1thUJRnVMhIiJqUNzd3VFcXKyzvaSkBK6uriJURERERNXqoR4yZAheeOEFBAQE4Omnn8Yff/wBANiwYUOl7VUqFSQSCX744Qc89dRTGDp0KJYvX47o6GitXmqJRKJ1P0EQdLY9aPHixbC2ttZ88doxIiJqzJYuXYq33noL//zzDwRBAKCeoGz69On47LPPRK6OiIioaar2kO/7mZubIyAgAFevXq10v7OzM1xdXWFtba3Z1qZNGwiCgNTUVACAk5OTTm+0XC7X6bV+UFhYGLKzszVfKSkpT3IqREREem3SpEk4c+YMgoKCYGJiAplMhqCgIJw+fRqhoaFo3ry55ouIiIjqxxOtQ61UKnHp0iX07Nmz0v3du3fHzz//jNzcXFhYWAAArly5AqlUihYtWgAAunXrhj179uCdd97R3G/37t0ICQmp8rFlMhlkMtmTlE9ERNRgrFy5UuwSiIiI6AHVCtSzZ8/GsGHD4O7uDrlcjsjISCgUCkycOBGAutc4LS0NGzduBACMGzcOCxcuxOTJkxEREYGsrCy8++67CA0NhampKQBg+vTp6NWrF5YsWYLnnnsOv/32G/bu3YsjR47U8qkSERE1XOXvtURERKQ/qjXku3wNzNatW2PkyJEwNjbGiRMn4OHhAQDIyMhAcnKypr2FhQX27NmDe/fuoUuXLhg/fjyGDRuG1atXa9qEhIQgJiYGUVFRaN++PaKjo7F582auQU1ERERERER6rVo91DExMVXuj46O1tnm5+eHPXv2VHm/UaNGYdSoUdUphYiIiIiIiEhUTzQpGREREREREVFTxUBNRESkp86dOweVSiV2GURERPQQDNRERNTkCYKA1Lv52PlvJpbvjsfZlHtilwQACAwMRFZWFgDA29sbt2/fFrkiIiIiut8TLZtFRETU0JSqBCRm5eFCejYupCtwIT0b/6YpkF1QrGljZCBFBzcb8YosY2Njg8TERDg4OCApKYm91URERHqGgZqIiBqtohIVrspzcCGtLDinK3ApQ4H8olKdtoZSCXwcLdHOxQrtXK1FqFbXCy+8gN69e8PZ2RkSiQRdunSBgYFBpW0TEhLquToiIiJioCYiokYhv6gElzJy1D3PaQr8m56NKzdzUFwq6LQ1MZKijbMV2rlYw78sQPs4WkBmWHlYFcvatWsxcuRIXLt2DW+//TZee+01WFpail0WERERlWGgJiKiBic7v1gzZPvfsn8TbuVCpZudYWliqBWc/V2s4G1vAQOppP4Lr4HBgwcDAE6dOoXp06czUBMREekRBmoiItJrckWhOjinVQTo1LsFlba1t5Spg/N9AbpFM1NIJA0jPFclKipK831qaiokEglcXV1FrIiIiIgYqImISC8IgoCUOwVl1zqXTximwK0cZaXt3Zqbwt/ZGu1creBfFqAdrEzquer6o1KpEBkZiWXLliE3NxcAYGlpiVmzZmHevHmQSrlwBxERUX1joCYionpXqhKQcCtXHZzLrne+mK6AorBEp61UAnjbW6CdS1lwdrWCv7M1rM2MRKhcPPPmzcO6devwySefoHv37hAEAUePHkV4eDgKCwvx8ccfi10iERFRk8NATUREdUpZUoormblaPc+XMhQoLNZdAsrYQApfJwvNkO22LtZo42wJM2O+XW3YsAH/+9//MHz4cM22Dh06wNXVFVOnTmWgJiIiEgH/QiEiolqTqyzBpQwFLmiud1bg6s0clFQyW5iZsQHaOlvB38UK/mWThfk4WMLYkEOXK3Pnzh34+fnpbPfz88OdO3eqdazFixdj69atuHz5MkxNTRESEoIlS5agdevWmjaCICAiIgJr167F3bt3ERQUhDVr1sDf3/+Jz4WIiKixYKAmIqIauZtXpDXL9oW0bCTezoNQyUzbNmZGmsnC2pZNFuZpa95gZtrWBx06dMAXX3yB1atXa23/4osv0KFDh2odKzY2Fm+++Sa6du2KkpISzJs3DwMHDsTFixdhbm4OAFi6dCmWL1+O6Oho+Pr6IjIyEgMGDEB8fDxnGiciIirDQE1ERFUSBAE3FUqtWbYvpiuQdq/ymbYdrWSaIdvlPc+uNo1jpm0xLV26FM888wz27t2Lbt26QSKR4NixY0hJScGOHTuqdaydO3dq3Y6KioKDgwNOnTqFXr16QRAErFy5EvPmzcPIkSMBqIecOzo6YtOmTZgyZUqtnRcREVFDxkBNREQaKpWA5Dv5Oj3Pt/OKKm3vYWum1evs72IFOwtZPVfdNPTu3RtXrlzBmjVrcPnyZQiCgJEjR2Lq1KlwcXF5omNnZ2cDAJo3bw4ASExMRGZmJgYOHKhpI5PJ0Lt3bxw7doyBmoiIqAwDNRFRE1VSqsK1W7maWbYvpCtwKV2BHKXuTNsGUgla2Vto9Tq3dbGClUnTmmlbbC4uLrU++ZggCJg5cyZ69OiBdu3aAQAyMzMBAI6OjlptHR0dcePGjUqPo1QqoVRWLHGmUChqtU4iIiJ9xEBNRNQEFBaXIj4zR6vX+XJmDpQllcy0bShFGydLtC0btt3O1Rp+TpYwMTIQoXKqa9OmTcO5c+dw5MgRnX0PDtMXBOGhQ/cXL16MiIiIOqmRiIhIXzFQExE1MjmFxbiYrtC63vmqPBellcy0bSEzVM+07ape47mdqxVa2lvAyIAzbTcFb731FrZv345Dhw6hRYsWmu1OTk4A1D3Vzs7Omu1yuVyn17pcWFgYZs6cqbmtUCjg5uZWR5UTERHpBwZqIqIG7HauEv+mK3Dhvp7npNv5lbZtbm6sHrJdFpz9Xazh0dwMUs603eQIgoC33noL27Ztw8GDB+Hl5aW138vLC05OTtizZw8CAwMBAEVFRYiNjcWSJUsqPaZMJoNMxuvniYioaWGgJiJqAARBQHp2IS6kZePfdAUupmfj3zQFMhWFlbZ3sTbRXOvczsUa/q5WcLIy4UzbBAB48803sWnTJvz222+wtLTUXDNtbW0NU1P1jOwzZszAokWL4OPjAx8fHyxatAhmZmYYN26cyNUTERHpDwZqIiI9o1IJSLqdV9HznKb+925+caXtve3MtWbZ9nexRnNz43qumupTVlYW/vrrL5SWlqJr165aw7Ifx1dffQUA6NOnj9b2qKgoTJo0CQAwZ84cFBQUYOrUqbh79y6CgoKwe/durkFNRER0HwZqIiI9cTblHqKOJmLPxZvIKyrV2W8olaCVg4UmOLdztUYbZytYyPhfeVOyZcsWvPLKK/D19UVxcTHi4+OxZs0aTJ48+bGPIQi619M/SCKRIDw8HOHh4U9QLRERUePGv8KIiERUUqrCrgs3sf5oIk7duKvZbmIkhZ+TleZaZ38XK/g6cqbtpig3NxcWFhaa2xEREfj777/h6+sLAPjjjz/w2muvVStQExERUe1goCYiEkF2fjF+PJmMjceSkJ6tvg7ayECCYe1dMKGbBwJcrWHImbYJQOfOnbF06VI899xzAABDQ0PI5XJNoL558yaMjTnEn4iISAwM1ERE9eiaPBfRxxKx5VQaCorVw7ptzY0xPsgdLwV7wMHKROQKSd/s2rULU6dORXR0NNasWYNVq1ZhzJgxKC0tRUlJCaRSKaKjo8Uuk4iIqElioCYiqmOCIODQ1SysP5KI2Cu3NNv9nCwR2sMLwzu4cCg3PZSnpyd27NiBTZs2oXfv3pg+fTquXbuGa9euobS0FH5+fjAx4QcxREREYmCgJiKqIwVFpdgal4qoo0m4Js8FAEgkQH8/R4T28EQ3b1suY0WPbdy4cRgyZAhmz56NPn36YO3atejYsaPYZRERETVpDNRERLUs/V4BNh6/gR//TkZ2gXqpKwuZIV7s0gKTQjzhYWsucoXU0Pz555+4ePEiOnTogHXr1uHgwYMYN24chg4digULFsDU1FTsEomIiJokznhDRFRLTiffxbRNp9Fz6QF8HXsd2QXFcG9uhg+fbYvjYf3w0TB/hmmqtjlz5mDSpEk4efIkpkyZgoULF6JPnz6Ii4uDTCZDx44d8eeff4pdJhERUZPEHmoioidQXKrCjvMZWH80CWdT7mm2B3s3R2h3L/Rv4wgDKYd1U82tX78eu3btQufOnXHnzh0EBwdj/vz5MDY2RmRkJMaOHYspU6ZgyJAhYpdKRETU5DBQExHVwN28Imz6OxnfHb+BTIV62StjAyme6+iCSd094e9iLXKF1FiYmZkhMTERnTt3RkpKis4EZP7+/jhy5IhI1RERETVtDNRERNVw5WYOoo4mYuvpNChLVAAAOwsZJgR7YFyQO+wtZSJXSI3N4sWL8fLLL+Ptt99Gfn4+NmzYIHZJREREVIaBmojoEVQqAbFXbmH90UQcvpql2e7vYoVXenjhmfbOkBly2SuqG+PHj8fgwYORkJAAHx8f2NjYiF0SERERlWGgJiJ6iDxlCbacTkX00SQkZOUBAKQSYGBbJ4T28EJXz2Zc9orqha2tLWxtbcUug4iIiB7AQE1E9ICUO/nYeDwJMSdTkFNYAgCwlBniP0+54eVunnBrbiZyhURERESkDxioiYgACIKAf27cxfojidh1IRMqQb3dy84ck0I88ULnFrCQ8b9MIiIiIqpQrXWow8PDIZFItL6cnJwe2v7gwYM67SUSCS5fvqxpEx0dXWmbwsLCmp8VEdFjKipRYevpVAz/4ihe/Po4/vxXHaZ7tLLD+kldsG9mb0wM8WSYJiIiIiId1f4L0d/fH3v37tXcNjB49EQ88fHxsLKy0ty2t7fX2m9lZYX4+HitbQ8uC0JEVJuycpXY9FcyvjtxA7dylAAAmaEUIwJdMbm7F1o7WYpcIRERERHpu2oHakNDwyp7pSvj4OBQ5aykj+rpJiKqLRfTFYg6mojfzqajqGzZK0crGV7u5omxT7mjubmxyBUSERERUUNR7UB99epVuLi4QCaTISgoCIsWLYK3t3eV9wkMDERhYSHatm2LDz74AH379tXan5ubCw8PD5SWlqJjx45YuHAhAgMDqzymUqmEUqnU3FYoFNU9FSJqIkpVAvZflmP9kUQcT7it2d6hhTVCe3hhSDtnGBtW6woYIiIiIqLqBeqgoCBs3LgRvr6+uHnzJiIjIxESEoILFy5UupyHs7Mz1q5di86dO0OpVOK7775D//79cfDgQfTq1QsA4Ofnh+joaAQEBEChUGDVqlXo3r07zp49Cx8fn4fWsnjxYkRERFTzdImoKckpLMbP/6Qi+lgSku/kAwAMpBIMbueE0O5e6ORuw2WviIiIiKjGJIIgCDW9c15eHlq2bIk5c+Zg5syZj3WfYcOGQSKRYPv27ZXuV6lU6NSpE3r16oXVq1c/9DiV9VC7ubkhOztb63ptImp6km/nI/pYEn76JwW5SvWyV9amRhj7lDte7uYBFxtTkSukpkKhUMDa2rpJvjc11nP3fO8PsUvQkmQyTuwSNAK83MUuQeP8xPNil0CPQZ9eT3wtPVxjej3VxXvTE01ba25ujoCAAFy9evWx7xMcHIzvv//+ofulUim6du36yGPKZDLIZLLHflwiatwEQcCJhDtYfzQRey/dRPlHhS3tzTG5uxdGdnKFmTFn6iYiIiKi2vNEf10qlUpcunQJPXv2fOz7xMXFwdnZ+aH7BUHAmTNnEBAQ8CSlEVETUVhciu1n0xF1NAmXMirmUujta4/J3T3Ry8ceUimHdRMRERFR7atWoJ49ezaGDRsGd3d3yOVyREZGQqFQYOLEiQCAsLAwpKWlYePGjQCAlStXwtPTE/7+/igqKsL333+PLVu2YMuWLZpjRkREIDg4GD4+PlAoFFi9ejXOnDmDNWvW1OJpElFjI88pxPcnkvHDiRu4nVcEADAxkuKFTi0wubsnWjlw2SsiIiIiqlvVCtSpqakYO3YssrKyYG9vj+DgYJw4cQIeHh4AgIyMDCQnJ2vaFxUVYfbs2UhLS4OpqSn8/f3xxx9/YOjQoZo29+7dw3//+19kZmbC2toagYGBOHToEJ566qlaOkUiakz+TcvG+qOJ+L+z6SguVY/rdrY2wcQQT/ynqxtszLjsFRERERHVj2oF6piYmCr3R0dHa92eM2cO5syZU+V9VqxYgRUrVlSnDCJqYkpVAvZczMT6I0n4O+mOZntnj2aY3N0Tg/ydYGTAZa+IiIiIqH5xhh4i0lvZBcX46WQKNhxPQurdAgCAoVSCZ9o7Y3J3L3R0sxG3QCIiIiJq0hioiUjvJGblIfpoIn4+lYr8olIAQDMzI4wLcseEYE84WZuIXCEREREREQM1EekJQRBw9NptrD+aiP2X5Zrtvo4WCO3uhecDXWFiZCBihURERERE2hioiUhUhcWl+DUuDeuPJuLKzVzN9n5+Dgjt7oXurWwhkXDZKyIiIiLSPwzURCSKzOxCfHciCZv+Ssbd/GIAgJmxAV7s3AITQzzhbW8hcoVERERERFVjoCaienUm5R6ijibij3MZKFGpl71ytTHF5O6eeLGLG6xNjUSukIiIiIjo8TBQE1GdKylVYeeFTKw/kojTyfc025/ybI7QHp54uo0jDLnsFRERERE1MAzURFRn7uUX4ce/U/Dd8SSkZxcCAIwMJBjWwQWh3b3QztVa5AqJiIiIiGqOgZqIat01eQ6ijiZhy+lUFBarAAC25sYYH+yBl4Ld4WDJZa+IiIiIqOFjoCaiWqFSCTh09RaijiYh9sotzfY2zlYI7e6JYR1cuOwVERERETUqDNRE9ETyi0qw9XQaoo4m4vqtPACARAI83cYRod29EOzdnMteEREREVGjxEBNRDWSdq8AG48nIebvFGQXqJe9spAZYnQXN0wM8YCHrbnIFRIRERER1S0GaiJ6bIIg4HTyPaw/moid/2aitGzZK/fmZpgU4okXu7SApQmXvSIiIiKipoGBmogeqahEhT//zcD6I4k4m5qt2d7N2xahPbzQz88BBlIO6yYiIiKipoWBmoge6k5eEX78OxkbjyfhpkIJADA2lOK5Di6Y3N0LbV2sRK6QiIiIiEg8DNREpCM+MwdRRxOxLS4NyhL1slf2ljJMCPbAuCB32FnIRK6QiIiIiEh8DNREBEEQcCFdgf2X5dh/WY4zKfc0+9q5WiG0uxeeae8MmSGXvSIiIiIiKsdATdRE5SpLcOTqLRy4fAsH4uWQ5yg1+6QSYJC/E0J7eKGLRzMue0VEREREVAkGaqImJOFWLvZfluNAvBx/J95Bcamg2WdmbIAerezQz88Bff0c4GhlImKlRERERET6j4GaqBFTlpTir4Q7OBAvx4HLciTdztfa72lrhr5+Dujn54CnvJpzSDdRE3Lo0CF8+umnOHXqFDIyMrBt2zY8//zzmv2CICAiIgJr167F3bt3ERQUhDVr1sDf31+8oomIiPQMAzVRI5OZXYgD8eproY9ey0J+Ualmn5GBBEFetujT2h79/BzgbW8hYqVEJKa8vDx06NABkydPxgsvvKCzf+nSpVi+fDmio6Ph6+uLyMhIDBgwAPHx8bC0tBShYiIiIv3DQE3UwJWqBJxJuYsDl29h/2U5LmYotPY7WMrQt7V6GHcPHztYyPiyJyJgyJAhGDJkSKX7BEHAypUrMW/ePIwcORIAsGHDBjg6OmLTpk2YMmVKfZZKRESkt/iXNVEDdC+/CLFXbuHAZTlir9zC3fxizT6JBOjoZoO+rdVDuf1drDipGBFVS2JiIjIzMzFw4EDNNplMht69e+PYsWMM1ERERGUYqIkaAEEQEH8zRz2h2GU5Tt24C1XFfGKwMjFEL1/1MO7evvaw5TrRRPQEMjMzAQCOjo5a2x0dHXHjxo1K76NUKqFUVqwWoFAoKm1HRETUmDBQE+mp/KISHLt2G/vj5Th4WY707EKt/a0dLdHHzx79Wjugs0czGBpIRaqUiBqrB0e3CILw0BEvixcvRkRERH2URUREpDcYqIn0SMqdfOy/rJ5Q7HjCbRSVqDT7TIykCGlph75+Dujb2h4tmpmJWCkRNWZOTk4A1D3Vzs7Omu1yuVyn17pcWFgYZs6cqbmtUCjg5uZWt4USERGJjIGaSETFpSqcTLqDA2Uh+vqtPK39rjam6Fe2rFW3lrYwMeKyVkRU97y8vODk5IQ9e/YgMDAQAFBUVITY2FgsWbKk0vvIZDLIZLzchIiImhYGaqJ6ditHiYPxchyIl+PwlSzkKEs0+wykEnTxaKYJ0a0cLDihGBHVidzcXFy7dk1zOzExEWfOnEHz5s3h7u6OGTNmYNGiRfDx8YGPjw8WLVoEMzMzjBs3TsSqiYiI9AsDNVEdU6kEnE/LVk8oFi/HudRsrf225sboXbYudE8fe1ibGolUKRE1Jf/88w/69u2ruV0+XHvixImIjo7GnDlzUFBQgKlTp+Lu3bsICgrC7t27uQY1ERHRfRioieqAorAYR65mYf9lOQ7G30JWrlJrf4CrNfqW9UK3d7WGVMpeaCKqX3369IEgCA/dL5FIEB4ejvDw8PorioiIqIFhoCaqBYIg4PqtXM2EYv8k3UXJfetaWcgM0aOVHfr5OaBPa3s4WJmIWC0REREREdUGBmqiGiosLsWJhNvqCcXi5Ui5U6C139veHP1aq3uhu3g2h7Ehl7UiIiIiImpMGKiJqiH9XoH6WujLchy9noXC4oplrYwNpAjybq6ZUMzD1lzESomIiIiIqK4xUBNVoaRUhbiUe5oQfTkzR2u/k5WJ5lro7q1sYWbMlxQRERERUVPBv/6JHnAnrwixV+TYf/kWDl25heyCYs0+qQQIdFcva9W3tQPaOFtyWSsiIiIioiaqWoE6PDwcERERWtscHR2RmZlZafuDBw9qLclR7tKlS/Dz89Pc3rJlC+bPn4/r16+jZcuW+PjjjzFixIjqlEZUY4Ig4GKGQn0t9GU5zqTcw33zicHGzAi9fe3Rt7UDevvao5m5sXjFEhERERGR3qh2D7W/vz/27t2ruW1gYPDI+8THx8PKykpz297eXvP98ePHMWbMGCxcuBAjRozAtm3bMHr0aBw5cgRBQUHVLY/oseQpS3DkWhYOxstx4PItZCoKtfb7OVlqroXu6GYDQwNOKEZERERERNqqHagNDQ3h5ORUrfs4ODjAxsam0n0rV67EgAEDEBYWBgAICwtDbGwsVq5ciR9//LG65RE9VFJWnvpa6Hg5/kq4g6LSignFTI0M0P2+Za1cbExFrJSIiIiIiBqCagfqq1evwsXFBTKZDEFBQVi0aBG8vb2rvE9gYCAKCwvRtm1bfPDBB1rDwI8fP4533nlHq/2gQYOwcuXKKo+pVCqhVCo1txUKRXVPhRq5ohIV/k68gwPx6gnFErLytPa7NzdTXwvt54Agr+YwMXr0aAsiIiIiIqJy1QrUQUFB2LhxI3x9fXHz5k1ERkYiJCQEFy5cgK2trU57Z2dnrF27Fp07d4ZSqcR3332H/v374+DBg+jVqxcAIDMzE46Ojlr3q+q67HKLFy/WuZ6bSK4oxIF49bXQR65mIa+oVLPPUCrBU17Ny3qhHdDS3pwTihERERERUY1VK1APGTJE831AQAC6deuGli1bYsOGDZg5c6ZO+9atW6N169aa2926dUNKSgo+++wzTaAGoBNqBEF4ZNAJCwvTekyFQgE3N7fqnA41AiqVgLOp99QTisXL8W+a9kgFOwsZ+ra2Rz8/B/TwsYOliZFIlRIRERERUWPzRMtmmZubIyAgAFevXn3s+wQHB+P777/X3HZyctLpjZbL5Tq91g+SyWSQyWTVK5gaheyCYhy6cgsHLssRe+UWbucVae3v0MJaszZ0OxdrSKXshSYiIiIiotr3RIFaqVTi0qVL6Nmz52PfJy4uDs7Ozprb3bp1w549e7Suo969ezdCQkKepDRqRARBwFV5LvaXLWt16sZdlN63rpWlzBC9fO3R10+9rJW9JT9oISIiIiKiuletQD179mwMGzYM7u7ukMvliIyMhEKhwMSJEwGoh2GnpaVh48aNANQzeHt6esLf3x9FRUX4/vvvsWXLFmzZskVzzOnTp6NXr15YsmQJnnvuOfz222/Yu3cvjhw5UounSQ1NQVEpjidkqWflvnwLafcKtPa3crBQTyjW2gFdPJvBiMtaERERERFRPatWoE5NTcXYsWORlZUFe3t7BAcH48SJE/Dw8AAAZGRkIDk5WdO+qKgIs2fPRlpaGkxNTeHv748//vgDQ4cO1bQJCQlBTEwMPvjgA8yfPx8tW7bE5s2buQZ1EyIIAlLvFuBcajbOp2XjXOo9nLpxF8qSimWtZIZSdGtpqwnRbs3NRKyYiIiIiIiomoE6Jiamyv3R0dFat+fMmYM5c+Y88rijRo3CqFGjqlMKNVCCICA9uxDnU++VhWd1iL6XX6zT1sXaRHMtdEhLO5gac1krIiIiIiLSH090DTVRVQRBwE2FEufKwvP5tGycT83WmUQMAIwMJPBzskJAC2u0d7VGoHsz+DpacFkrIiIiIiLSWwzUVGvkOYU4n1rR63w+LRu3cpQ67QylErR2skSAq3VZgLaBr5MFZIbsgSYiIiIiooaDgZpqJCtXqelxVgfoe7ip0A3PBlIJfBwsEOBqjfYtrBHQwgZ+TpYwMWJ4JiIiIiKiho2Bmh7pbl6Rpsf5XOo9nE/NRnp2oU47qUQ9+3aAqw0CXK0Q0MIGbZ2teO0zERERERE1SgzUpCU7v7jieue0eziXmo3UuwU67SQSwNvOHO1b2GiGbrd1toK5jL9SRERERETUNDD9NGGKwmL8m5aNf++bbfvG7fxK23rZmWuGbbdztYa/ixUsTYzquWIiIiIiIiL9wUDdROQqS3AhLVtrtu2ErLxK27o3N9PMth3gag1/V2tYmzI8ExERERER3Y+BuhHKLyrBxXQFzqWW9T6nZeP6rVwIgm5bVxvTssnC1LNtt3O1go2Zcf0XTURERERE1MAwUDdwhcWluJih0My2/W9aNq7Kc6CqJDy7WJug3X2zbQe4WqO5OcMzERERERFRTTBQNyDKklJczsjBubRsnE+9h/NpCly5mYPSStKzo5VMPVmYq43mumd7S5kIVRMRERERETVODNR6qqhEhSs3czRrPJ9LzcaVmzkoLtUNz3YWxmUzbduor3tuYQ1HKxMRqiYiIiIiImo6GKj1QHGpOjzfP9v25YwcFJWqdNo2Ny8Lz2XBuX0LazhZmUAikYhQORERERERUdPFQF3PSkpVuHYrF+fLgvO51GxczFCgqEQ3PFubGqmvd74vQLvamDI8ExERERER6QEG6jpUqhKQcCtX0+t8Pi0bF9KzUVisG54tTQw1oTnAVT3jtltzhmciIiIiIiJ9xUBdS1QqAYm387Rm2/43PRv5RaU6bc2NDTSzbav/tYFHczNIpQzPREREREREDQUDdQ0IgoAbt/Pvm207G/+mKZCrLNFpa2pkgHauVlqzbXvbmTM8ExERERERNXAM1I8gCAJS7xbgXGo2zqXdw79p2Tifmg1FoW54lhlK4e9ihfZlazy3b2ENb3sLGDA8ExERERERNToM1A/IVZbgyNVbWtc938sv1mlnbChFG2crzTJV7VtYo5W9BQwNpCJUTURERERERPWNgfoBckUhXv/+tNY2IwMJ/Jys1MG5LED7OlrCiOGZiIiIiIioyWKgfoCnrTm6eDRDKweLsgBtA18nC8gMDcQujYiIiIiIiPQIA/UDpFIJfnkjROwyiIiIiIiISM9xzDIRERERERFRDTBQExEREREREdUAAzURERERERFRDTBQExEREREREdUAAzURERERERFRDTBQExEREREREdUAAzURERERERFRDTBQExER0UN9+eWX8PLygomJCTp37ozDhw+LXRIREZHeYKAmIiKiSm3evBkzZszAvHnzEBcXh549e2LIkCFITk4WuzQiIiK9wEBNRERElVq+fDleeeUVvPrqq2jTpg1WrlwJNzc3fPXVV2KXRkREpBcMxS6gtgiCAABQKBQiV0JERKRW/p5U/h7VkBQVFeHUqVN47733tLYPHDgQx44d02mvVCqhVCo1t7OzswE0vvdllTJf7BK0KCT687tVWlAqdgkaje33rrHSp9cTX0sP15heT3XxvtxoAnVOTg4AwM3NTeRKiIiItOXk5MDa2lrsMqolKysLpaWlcHR01Nru6OiIzMxMnfaLFy9GRESEzna+L9ct/fqtuiR2ARrWb+jXM0P6T79+Y/TntQQ0ztdTbb4vN5pA7eLigpSUFFhaWkIikTzRsRQKBdzc3JCSkgIrK6taqpAeF59/cfH5Fxeff3HV9vMvCAJycnLg4uJSC9WJ48H3VEEQKn2fDQsLw8yZMzW3VSoV7ty5A1tb2yd+X6a6w/9ziGoPX0/6ry7elxtNoJZKpWjRokWtHtPKyoovBhHx+RcXn39x8fkXV20+/w2tZ7qcnZ0dDAwMdHqj5XK5Tq81AMhkMshkMq1tNjY2dVki1SL+n0NUe/h60m+1/b7MScmIiIhIh7GxMTp37ow9e/Zobd+zZw9CQkJEqoqIiEi/NJoeaiIiIqpdM2fOxIQJE9ClSxd069YNa9euRXJyMl5//XWxSyMiItILDNSVkMlk+Oijj3SGrlH94PMvLj7/4uLzLy4+/9rGjBmD27dvY8GCBcjIyEC7du2wY8cOeHh4iF0a1RL+zhPVHr6emiaJ0BDX8iAiIiIiIiISGa+hJiIiIiIiIqoBBmoiIiIiIiKiGmCgJiIiIiIiIqoBBmoiIiIiIiKiGmiUgXrx4sXo2rUrLC0t4eDggOeffx7x8fFabQRBQHh4OFxcXGBqaoo+ffrgwoULWm3Wrl2LPn36wMrKChKJBPfu3dN5rOHDh8Pd3R0mJiZwdnbGhAkTkJ6eXpenp/fq8/kvp1Qq0bFjR0gkEpw5c6YOzqrhqM/n39PTExKJROvrvffeq8vTaxDq+zXwxx9/ICgoCKamprCzs8PIkSPr6tQahPp6/g8ePKjz+1/+dfLkybo+TSIiItIDjTJQx8bG4s0338SJEyewZ88elJSUYODAgcjLy9O0Wbp0KZYvX44vvvgCJ0+ehJOTEwYMGICcnBxNm/z8fAwePBjvv//+Qx+rb9+++OmnnxAfH48tW7bg+vXrGDVqVJ2en76rz+e/3Jw5c+Di4lIn59PQ1PfzX76cTvnXBx98UGfn1lDU589gy5YtmDBhAiZPnoyzZ8/i6NGjGDduXJ2en76rr+c/JCRE63c/IyMDr776Kjw9PdGlS5c6P08iIiLSA0ITIJfLBQBCbGysIAiCoFKpBCcnJ+GTTz7RtCksLBSsra2Fr7/+Wuf+Bw4cEAAId+/efeRj/fbbb4JEIhGKiopqrf6Grq6f/x07dgh+fn7ChQsXBABCXFxcXZxGg1WXz7+Hh4ewYsWKuiq90airn0FxcbHg6uoq/O9//6vT+hu6+noPKCoqEhwcHIQFCxbUav1Edenq1avCzp07hfz8fEEQ1K8PIqqeu3fvCt9++63w3nvvCbdv3xYEQRBOnTolpKamilwZ1YdG2UP9oOzsbABA8+bNAQCJiYnIzMzEwIEDNW1kMhl69+6NY8eO1fhx7ty5gx9++AEhISEwMjJ6sqIbkbp8/m/evInXXnsN3333HczMzGqv6Eakrn//lyxZAltbW3Ts2BEff/wxioqKaqfwRqSufganT59GWloapFIpAgMD4ezsjCFDhugMXW7q6us9YPv27cjKysKkSZOeqF6i+nD79m08/fTT8PX1xdChQ5GRkQEAePXVVzFr1iyRqyNqOM6dOwdfX18sWbIEn332mebyoG3btiEsLEzc4qheNPpALQgCZs6ciR49eqBdu3YAgMzMTACAo6OjVltHR0fNvuqYO3cuzM3NYWtri+TkZPz2229PXngjUZfPvyAImDRpEl5//XUOr3yIuv79nz59OmJiYnDgwAFMmzYNK1euxNSpU2un+EaiLn8GCQkJAIDw8HB88MEH+P3339GsWTP07t0bd+7cqaUzaNjq4z2g3Lp16zBo0CC4ubnVvGCievLOO+/A0NAQycnJWh9IjxkzBjt37hSxMqKGZebMmZg0aRKuXr0KExMTzfYhQ4bg0KFDIlZG9cVQ7ALq2rRp03Du3DkcOXJEZ59EItG6LQiCzrbH8e677+KVV17BjRs3EBERgZdffhm///57jY7V2NTl8//5559DoVDw078q1PXv/zvvvKP5vn379mjWrBlGjRql6bWmuv0ZqFQqAMC8efPwwgsvAACioqLQokUL/Pzzz5gyZcoTVN441Md7AACkpqZi165d+Omnn2p0f6L6tnv3buzatQstWrTQ2u7j44MbN26IVBVRw3Py5El88803OttdXV2f6ENaajgadQ/1W2+9he3bt+PAgQNabxhOTk4AoPNLLpfLdXosHoednR18fX0xYMAAxMTEYMeOHThx4sSTFd8I1PXzv3//fpw4cQIymQyGhoZo1aoVAKBLly6YOHFiLZxBw1Zfv//3Cw4OBgBcu3btiY7TWNT1z8DZ2RkA0LZtW802mUwGb29vJCcnP0npjUJ9vgaioqJga2uL4cOH17xgonqUl5dX6aVSWVlZkMlkIlRE1DCZmJhAoVDobI+Pj4e9vb0IFVF9a5SBWhAETJs2DVu3bsX+/fvh5eWltd/LywtOTk7Ys2ePZltRURFiY2MREhLyxI8NqJdxaqrq6/lfvXo1zp49izNnzuDMmTPYsWMHAGDz5s34+OOPa+dkGiAxf//j4uIAVAS9pqq+fgadO3eGTCbTWhKquLgYSUlJ8PDwePITaaDq+zUgCAKioqLw8ssvc/4MajB69eqFjRs3am5LJBKoVCp8+umn6Nu3r4iVETUszz33HBYsWIDi4mIA6tdScnIy3nvvPc3oMWrk6nsWtPrwxhtvCNbW1sLBgweFjIwMzVf5DJaCIAiffPKJYG1tLWzdulU4f/68MHbsWMHZ2VlQKBSaNhkZGUJcXJzw7bffCgCEQ4cOCXFxcZrZ+/766y/h888/F+Li4oSkpCRh//79Qo8ePYSWLVsKhYWF9X7e+qK+nv8HJSYmcpZvof6e/2PHjgnLly8X4uLihISEBGHz5s2Ci4uLMHz48Ho/Z31Tn6+B6dOnC66ursKuXbuEy5cvC6+88org4OAg3Llzp17PWZ/U9/9Be/fuFQAIFy9erLdzJHpSFy5cEOzt7YXBgwcLxsbGwqhRo4Q2bdoIjo6OwrVr18Quj6jByM7OFrp37y7Y2NgIBgYGgpubm2BkZCT06tVLyM3NFbs8qgeNMlADqPQrKipK00alUgkfffSR4OTkJMhkMqFXr17C+fPntY7z0UcfVXmcc+fOCX379hWaN28uyGQywdPTU3j99deb/BT59fX8P4iBWq2+nv9Tp04JQUFBgrW1tWBiYiK0bt1a+Oijj4S8vLx6PFv9VJ+vgaKiImHWrFmCg4ODYGlpKTz99NPCv//+W09nqp/q+/+gsWPHCiEhIfVwZkS1KyMjQ/jwww+FZ555RhgyZIgwb948IT09XeyyiBqkffv2CZ9++qmwZMkSYc+ePWKXQ/VIIghlY5SJiIiIiIiI6LE1+lm+iYiIiEi9Xu7jat++fR1WQtSwrV69+rHbvv3223VYCekD9lATERERNQFSqRQSiQSP+tNPIpGgtLS0nqoiangenOzyYSQSCRISEuq4GhIbAzURERFRE1Cd9aWb8koBRETVwUBNREREREREVAO8hpqIiIioibp48SKSk5NRVFSktX348OEiVUTU8KSmpmL79u2VvpaWL18uUlVUXxioiYiIiJqYhIQEjBgxAufPn9e6rloikQAAr6Emekz79u3D8OHD4eXlhfj4eLRr1w5JSUkQBAGdOnUSuzyqB1KxCyAiIiKi+jV9+nR4eXnh5s2bMDMzw4ULF3Do0CF06dIFBw8eFLs8ogYjLCwMs2bNwr///gsTExNs2bIFKSkp6N27N1588UWxy6N6wEBN1IQcPHgQEokE9+7dE7sUIiIS0fHjx7FgwQLY29tDKpVCKpWiR48eWLx4MZf5IaqGS5cuYeLEiQAAQ0NDFBQUwMLCAgsWLMCSJUtEro7qAwM1USPWp08fzJgxQ3M7JCQEGRkZsLa2Fq8oIiISXWlpKSwsLAAAdnZ2SE9PB6Ce3Ts+Pl7M0ogaFHNzcyiVSgCAi4sLrl+/rtmXlZUlVllUj3gNNVETYmxsDCcnJ7HLICIikbVr1w7nzp2Dt7c3goKCsHTpUhgbG2Pt2rXw9vYWuzyiBiM4OBhHjx5F27Zt8cwzz2DWrFk4f/48tm7diuDgYLHLo3rAHmqiRmrSpEmIjY3FqlWrIJFIIJFIEB0drTXkOzo6GjY2Nvj999/RunVrmJmZYdSoUcjLy8OGDRvg6emJZs2a4a233tKaoKaoqAhz5syBq6srzM3NERQUxGvuiIgakA8++AAqlQoAEBkZiRs3bqBnz57YsWMHVq1aJXJ1RA3H8uXLERQUBAAIDw/HgAEDsHnzZnh4eGDdunUiV0f1gT3URI3UqlWrcOXKFbRr1w4LFiwAAFy4cEGnXX5+PlavXo2YmBjk5ORg5MiRGDlyJGxsbLBjxw4kJCTghRdeQI8ePTBmzBgAwOTJk5GUlISYmBi4uLhg27ZtGDx4MM6fPw8fH596PU8iIqq+QYMGab739vbGxYsXcefOHTRr1kwz0zcRPdr9IzrMzMzw5ZdfilgNiYGBmqiRsra2hrGxMczMzDTDvC9fvqzTrri4GF999RVatmwJABg1ahS+++473Lx5ExYWFmjbti369u2LAwcOYMyYMbh+/Tp+/PFHpKamwsXFBQAwe/Zs7Ny5E1FRUVi0aFH9nSQREVVLaGjoY7Vbv359HVdC1Pjk5uZqRn6Us7KyEqkaqi8M1ERNnJmZmSZMA4CjoyM8PT01k9WUb5PL5QCA06dPQxAE+Pr6ah1HqVTC1ta2foomIqIaiY6OhoeHBwIDAzVrTxNRzSUmJmLatGk4ePAgCgsLNdsFQYBEIuGa7k0AAzVRE2dkZKR1WyKRVLqt/BNXlUoFAwMDnDp1CgYGBlrt7g/hRESkf15//XXExMQgISEBoaGheOmll9C8eXOxyyJqsMaPHw9AParD0dGRl0w0QQzURI2YsbFxrX8yGhgYiNLSUsjlcvTs2bNWj01ERHXryy+/xIoVK7B161asX78eYWFheOaZZ/DKK69g4MCBDANE1XTu3DmcOnUKrVu3FrsUEgln+SZqxDw9PfHXX38hKSkJWVlZOtf11ISvry/Gjx+Pl19+GVu3bkViYiJOnjyJJUuWYMeOHbVQNRER1SWZTIaxY8diz549uHjxIvz9/TF16lR4eHggNzdX7PKIGpSuXbsiJSVF7DJIROyhJmrEZs+ejYkTJ6Jt27YoKChAVFRUrRw3KioKkZGRmDVrFtLS0mBra4tu3bph6NChtXJ8IiKqH+XLKgqCUCsfuhI1Nf/73//w+uuvIy0tDe3atdO5bK59+/YiVUb1RSJwRgoiIiKiJkOpVGqGfB85cgTPPvssJk+ejMGDB0Mq5eBFouo4ceIExo0bh6SkJM228g+pOClZ08AeaiIiIqImYurUqYiJiYG7uzsmT56MmJgYrtBA9ARCQ0MRGBiIH3/8kZOSNVHsoSYiIiJqIqRSKdzd3REYGFjlH/5bt26tx6qIGi5zc3OcPXsWrVq1ErsUEgl7qImIiIiaiJdffpk9aES1qF+/fgzUTRx7qImIiIiIiGpg7dq1iIyMRGhoKAICAnQmJRs+fLhIlVF9YaAmIiIiIiKqgaom8uOkZE0DAzURERERERFRDXBtBCIiIiIioidUWFgodgkkAgZqIiIiIiKiGigtLcXChQvh6uoKCwsLJCQkAADmz5+PdevWiVwd1QcGaiIiIiIiohr4+OOPER0djaVLl8LY2FizPSAgAP/73/9ErIzqCwM1ERERERFRDWzcuBFr167F+PHjYWBgoNnevn17XL58WcTKqL4wUBMREREREdVAWlpapWtQq1QqFBcXi1AR1TcGaiIiIiIiohrw9/fH4cOHdbb//PPPCAwMFKEiqm+GYhdARERERETUEH300UeYMGEC0tLSoFKpsHXrVsTHx2Pjxo34/fffxS6P6gHXoSYiIiIiIqqGhIQEeHl5QSKRYNeuXVi0aBFOnToFlUqFTp064cMPP8TAgQPFLpPqAQM1ERERERFRNRgYGCAjIwMODg4AgDFjxmDVqlVwcnISuTKqb7yGmoiIiIiIqBoe7JP8888/kZ+fL1I1JCYGaiIiIiL6//buPC6qcv8D+OewDTsCyqaApOKG5hpKhrhrhrmWWipWpon+XDKX1ERTSCuzey29lmLe3CrRvGqmZeBKmkriEoKAojLiwiY7zPn9QU6OAzgzDJwZ+Lxfr/PKOed5zvk+aMN859mIqBo46Lf+YkJNRERERESkBUEQIAiC2jmqf7jKNxERERERkRZEUURISAhkMhkAoLCwEFOmTIGNjY1KuaioKCnCo1rEhJqIiIiIiEgLEyZMUHn9+uuvSxQJSY2rfBMRERERERHpgHOoiYiIiIiIiHTAhJqIiIiIiIhIB0yoiYiIiIiIiHTAhJqIiIiIiIhIB0yoiYiIiIiMWEhICIYOHSp1GET1EhNqIiIiIiIiIh0woSYiIiIiqsdEUURpaanUYRAZJSbURERERER6kJubi9deew02NjZwd3fHZ599hqCgIMycORMAUFxcjLlz56Jx48awsbGBv78/oqOjlfU3b96MBg0a4Oeff0br1q1ha2uLgQMHIj09XVmmrKwMs2fPRoMGDeDs7Iy5c+dCFEWVOERRxKpVq/DMM8/AysoKzz77LH744Qfl9ejoaAiCgJ9//hldunSBTCbDsWPHavRnQ1RXMaEmIiIiItKD2bNn48SJE9i7dy8OHz6MY8eO4dy5c8rrEydOxIkTJ7Bjxw5cuHABo0aNwsCBA5GYmKgsk5+fj08++QT//e9/cfToUdy4cQNz5sxRXv/000+xadMmbNy4EcePH8eDBw+we/dulTgWLVqEyMhIrFu3DpcuXcKsWbPw+uuvIyYmRqXc3LlzERERgStXrqB9+/Y19FMhqtsE8cmvtIiIiIiISCu5ublwdnbGtm3bMHLkSABAdnY2PDw8MGnSJEyfPh0tWrTAzZs34eHhoazXt29fPPfccwgPD8fmzZsxceJEJCUloVmzZgCAL7/8EsuWLYNcLgcAeHh4YMaMGZg3bx4AoLS0FD4+PujcuTP27NmDvLw8NGzYEEeOHEH37t2Vz3nrrbeQn5+Pbdu2ITo6Gr169cKePXvw8ssv19aPiKhOMpM6ACIiIiIiY5ecnIySkhI899xzynMODg5o2bIlAODcuXMQRRG+vr4q9YqKiuDs7Kx8bW1trUymAcDd3R0ZGRkAyhP09PR0lUTZzMwMXbp0UQ77vnz5MgoLC9GvXz+V5xQXF6Njx44q57p06VKdJhMRmFATEREREVXbo4RWEIQKzysUCpiamuLs2bMwNTVVKWNra6v8s7m5uco1QRDU5khXRaFQAAD279+Pxo0bq1yTyWQqr21sbDS+LxFVjAk1EREREVE1NWvWDObm5jh9+jQ8PT0BADk5OUhMTETPnj3RsWNHlJWVISMjAy+88IJOz3BwcIC7uztiY2MRGBgIoHzI99mzZ9GpUycAQJs2bSCTyXDjxg307NlTP40jokoxoSYiIiIiqiY7OztMmDAB7733HpycnODi4oIlS5bAxMQEgiDA19cXr732GsaPH49PP/0UHTt2xL1793DkyBG0a9cOL774okbPmTFjBj766CO0aNECrVu3xurVq5GVlaUSx5w5czBr1iwoFAr06NEDOTk5OHnyJGxtbTFhwoQa+gkQ1U9MqImIiIiI9GD16tWYMmUKXnrpJdjb22Pu3LlIS0uDpaUlACAyMhLLly/Hu+++i1u3bsHZ2Rndu3fXOJkGgHfffRfp6ekICQmBiYkJ3njjDQwbNgzZ2dnKMh9++CFcXFwQERGB5ORkNGjQAJ06dcL777+v9zYT1Xdc5ZuIiIiIqAbk5eWhcePG+PTTT/Hmm29KHQ4R1QD2UBMRERER6cH58+fx119/4bnnnkN2djaWLVsGANyaiqgOY0JNRERERKQnn3zyCRISEmBhYYHOnTvj2LFjaNiwodRhEVEN4ZBvIiIiIiIiIh2YSB0AERERERERkTFiQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybURERERERERDpgQk1ERERERESkAybUREYmKCgIQUFBtfrMsLAwCIKAe/fu1crzvvzyS2zevLlWnkVERMbn8uXLCAsLQ2pqqtq1bdu2Yc2aNbUek6YEQUBYWJjW9W7fvo2wsDDExcXpPSYi0h0TaiIj8+WXX+LLL7+UOowaxYSaiIiqcvnyZSxdutQoE2pd3b59G0uXLmVCTWRgzKQOgIi006ZNG6lDqDH5+fmwtraWOgwiIiI1BQUFsLKykjoMIjIw7KEmMgCPhlSfP38ew4cPh729PRwcHPD666/j7t27KmUrGvJdXFyM5cuXo1WrVpDJZGjUqBEmTpyoVrcyv//+O4KDg+Hs7AxLS0s0a9YMM2fOVCt3584djBkzBg4ODnB1dcUbb7yB7OxslTJffPEFAgMD4eLiAhsbG7Rr1w6rVq1CSUmJWjv8/Pxw9OhRBAQEwNraGm+88QaaNm2KS5cuISYmBoIgQBAENG3aVKN2EBGRNO7evYu3334bnp6eyt9Dzz//PH755ReVcgcPHkSfPn3g4OAAa2trtG7dGhEREcrrf/zxB0aPHo2mTZvCysoKTZs2xZgxY3D9+nVlmc2bN2PUqFEAgF69eil/V2zevBlBQUHYv38/rl+/rjwvCIKyrqa/L5s2bYqXXnoJUVFR6NixIywtLbF06dJK2//od9qxY8fQrVs3WFlZoXHjxli8eDHKysqe+vO7ePEiXn75ZTg6OsLS0hIdOnTAN998o7weHR2Nrl27AgAmTpyobJcuQ8eJSL/YQ01kQIYNG4ZXXnkFU6ZMwaVLl7B48WJcvnwZv//+O8zNzSuso1Ao8PLLL+PYsWOYO3cuAgICcP36dSxZsgRBQUH4448/qvxG/eeff0ZwcDBat26N1atXw8vLC6mpqTh06JBa2REjRuDVV1/Fm2++ifj4eCxYsAAAsGnTJmWZa9euYezYsfDx8YGFhQX+/PNPrFixAn/99ZdKOQBIT0/H66+/jrlz5yI8PBwmJiaYN28eRo4cCQcHB+XQdplMpvXPkoiIas+4ceNw7tw5rFixAr6+vsjKysK5c+dw//59ZZmNGzdi0qRJ6NmzJ9avXw8XFxdcvXoVFy9eVJZJTU1Fy5YtMXr0aDg5OSE9PR3r1q1D165dcfnyZTRs2BCDBw9GeHg43n//fXzxxRfo1KkTAKBZs2Z47rnn8Pbbb+PatWvYvXu3Soza/r48d+4crly5gkWLFsHHxwc2NjZV/gzkcjlGjx6N+fPnY9myZdi/fz+WL1+OzMxMrF27ttJ6CQkJCAgIgIuLC/71r3/B2dkZ3377LUJCQnDnzh3MnTsXnTp1QmRkJCZOnIhFixZh8ODBAIAmTZpo/pdERDVDJCLJLVmyRAQgzpo1S+X81q1bRQDit99+qzzXs2dPsWfPnsrX27dvFwGIu3btUql75swZEYD45ZdfVvnsZs2aic2aNRMLCgqeGt+qVatUzk+dOlW0tLQUFQpFhfXKysrEkpISccuWLaKpqan44MEDlXYAEH/99Ve1em3btlVpIxERGTZbW1tx5syZlV7Pzc0V7e3txR49elT6O6MipaWl4sOHD0UbGxvx888/V57//vvvRQDib7/9plZn8ODBore3t9p5bX5fent7i6ampmJCQoJGcT76nfbjjz+qnJ80aZJoYmIiXr9+XXkOgLhkyRLl69GjR4symUy8ceOGSt1BgwaJ1tbWYlZWlkqckZGRGsVERLWDQ76JDMhrr72m8vqVV16BmZkZfvvtt0rr7Nu3Dw0aNEBwcDBKS0uVR4cOHeDm5obo6OhK6169ehXXrl3Dm2++CUtLy6fGN2TIEJXX7du3R2FhITIyMpTnzp8/jyFDhsDZ2RmmpqYwNzfH+PHjUVZWhqtXr6rUd3R0RO/evZ/6XCIiMmzPPfccNm/ejOXLlyM2NlZtms/JkyeRk5ODqVOnqgzBftLDhw8xb948NG/eHGZmZjAzM4OtrS3y8vJw5cqVasWo7e/L9u3bw9fXV+P729nZqf2eHDt2LBQKBY4ePVppvSNHjqBPnz7w9PRUOR8SEoL8/HycOnVK4xiIqPYxoSYyIG5ubiqvzczM4OzsrDJk7kl37txBVlYWLCwsYG5urnLI5fIqt7p6NGdM0yFjzs7OKq8fDcUuKCgAANy4cQMvvPACbt26hc8//xzHjh3DmTNn8MUXX6iUe8Td3V2j5xIRkWHbuXMnJkyYgK+//hrdu3eHk5MTxo8fD7lcDkDz3zdjx47F2rVr8dZbb+Hnn3/G6dOncebMGTRq1Ejtd4i2tP19qe3vKFdXV7Vzj36vV/V7/P79+xU+y8PD46l1iUh6nENNZEDkcjkaN26sfF1aWor79++rJbKPa9iwIZydnXHw4MEKr9vZ2VVat1GjRgCAmzdv6hixqj179iAvLw9RUVHw9vZWnq9si4+qeimIiMh4NGzYEGvWrMGaNWtw48YN7N27F/Pnz0dGRgYOHjyo0e+b7Oxs7Nu3D0uWLMH8+fOV54uKivDgwQO9xKjN70ttf0fduXNH7dyjLxSq+j3u7OyM9PR0tfO3b98GUB43ERku9lATGZCtW7eqvP7uu+9QWlqqtqr341566SXcv38fZWVl6NKli9rRsmXLSuv6+vqiWbNm2LRpE4qKiqod/6MPH48vIiaKIr766iut7iOTyardE0FERNLw8vLCtGnT0K9fP5w7dw4AEBAQAAcHB6xfvx6iKFZYTxAEiKKothDl119/rbZS9pMjpJ68VtH56vy+1ERubi727t2rcm7btm0wMTFBYGBgpfX69OmDI0eOKBPoR7Zs2QJra2t069ZN2S6g4jYTkXTYQ01kQKKiomBmZoZ+/fopV/l+9tln8corr1RaZ/To0di6dStefPFFzJgxA8899xzMzc1x8+ZN/Pbbb3j55ZcxbNiwSut/8cUXCA4ORrdu3TBr1ix4eXnhxo0b+Pnnn9US/Kfp168fLCwsMGbMGMydOxeFhYVYt24dMjMztbpPu3btsGPHDuzcuRPPPPMMLC0t0a5dO63uQUREtSM7Oxu9evXC2LFj0apVK9jZ2eHMmTM4ePAghg8fDgCwtbXFp59+irfeegt9+/bFpEmT4OrqiqSkJPz5559Yu3Yt7O3tERgYiI8//hgNGzZE06ZNERMTg40bN6JBgwYqz/Tz8wMAbNiwAXZ2drC0tISPjw+cnZ3Rrl07REVFYd26dejcuTNMTEzQpUuXav++fBpnZ2e88847uHHjBnx9fXHgwAF89dVXeOedd+Dl5VVpvSVLlmDfvn3o1asXPvjgAzg5OWHr1q3Yv38/Vq1aBQcHBwDlq5hbWVlh69ataN26NWxtbeHh4aEcGk5EEpF4UTQiEv9ZRfvs2bNicHCwaGtrK9rZ2YljxowR79y5o1L2yVW+RVEUS0pKxE8++UR89tlnRUtLS9HW1lZs1aqVOHnyZDExMfGpzz916pQ4aNAg0cHBQZTJZGKzZs1UVhx/FN/du3dV6kVGRooAxJSUFOW5//3vf8o4GjduLL733nviTz/9pLYaa8+ePcW2bdtWGE9qaqrYv39/0c7OTgRQ4WqtRERkGAoLC8UpU6aI7du3F+3t7UUrKyuxZcuW4pIlS8S8vDyVsgcOHBB79uwp2tjYiNbW1mKbNm3ElStXKq/fvHlTHDFihOjo6Cja2dmJAwcOFC9evCh6e3uLEyZMULnXmjVrRB8fH9HU1FRl9esHDx6II0eOFBs0aCAKgiA+/nFX09+X3t7e4uDBgzX+GTz6nRYdHS126dJFlMlkoru7u/j++++LJSUlKmXxxCrfoiiK8fHxYnBwsOjg4CBaWFiIzz77bIWreW/fvl1s1aqVaG5uXuF9iKj2CaJYybgbIqo1YWFhWLp0Ke7evcu5UkREREYmKCgI9+7dU9lTm4jqB86hJiIiIiIiItIBE2oiIiIiIiIiHTChJjIAYWFhEEWRw72JiIiMUHR0NId7Ez0hIiICXbt2hZ2dHVxcXDB06FAkJCSolBFFEWFhYfDw8ICVlRWCgoJw6dIllTIbNmxAUFAQ7O3tIQgCsrKyVK5HR0dDEIQKjzNnzlQanybP1gQTaiIiIiIiItKrmJgYhIaGIjY2FocPH0ZpaSn69++PvLw8ZZlVq1Zh9erVWLt2Lc6cOQM3Nzf069cPubm5yjL5+fkYOHAg3n///QqfExAQgPT0dJXjrbfeQtOmTdGlS5dK49Pk2ZrgomRERERERERUo+7evQsXFxfExMQgMDAQoijCw8MDM2fOxLx58wAARUVFcHV1xcqVKzF58mSV+tHR0ejVqxcyMzPVttJ7XElJCZo0aYJp06Zh8eLFFZbR9tlVYQ81ERERERER1ajs7GwAgJOTEwAgJSUFcrkc/fv3V5aRyWTo2bMnTp48qfNz9u7di3v37iEkJKTSMvp8tpmugRIRERFVRqFQ4Pbt27Czs4MgCFKHQ0RklERRRG5uLjw8PGBior++0MLCQhQXF+sc05Pv6zKZDDKZrMo6s2fPRo8ePeDn5wcAkMvlAABXV1eVsq6urrh+/bpOsQHAxo0bMWDAAHh6elZaRp/PZkJNREREenf79u0qP8wQEZHm0tLS0KRJE73cq7CwED7etpBnlOlU39bWFg8fPlQ5t2TJEoSFhVVaZ9q0abhw4QKOHz+udu3J5LyihF1TN2/exM8//4zvvvtOo/L6eDYTaiIiItI7Ozs7AOUfAu3t7SWOhojIOOXk5MDT01P5nqoPxcXFkGeUIeWsN+zttOv1zslVwKfzdbX39qp6p6dPn469e/fi6NGjKl8KuLm5ASjvLXZ3d1eez8jIUOs51lRkZCScnZ0xZMiQKsvp89lMqImIiEjvHn3Db29vz4SaiKiaamLqjI1t+aGNsr+Xs9bkvV0URUyfPh27d+9GdHQ0fHx8VK77+PjAzc0Nhw8fRseOHQGUJ/sxMTFYuXKldoH9/bzIyEiMHz8e5ubmVZbV57O5KBkREREREVE9o4Co06Gp0NBQfPvtt9i2bRvs7Owgl8shl8tRUFAAoPxLgpkzZyI8PBy7d+/GxYsXERISAmtra4wdO1Z5H7lcjri4OCQlJQEA4uPjERcXhwcPHqg878iRI0hJScGbb75ZYTytWrXC7t27tXq2JthDTURERERERHq1bt06AEBQUJDK+cjISOUK3HPnzkVBQQGmTp2KzMxM+Pv749ChQypD3NevX4+lS5cqXwcGBqrdByhfjCwgIACtW7euMJ6EhATlSuOaPlsT3IeaiIiI9C4nJwcODg7Izs7mkG8iIh3VxHvpo3veTmii0xxqj5Y3+d7+GPZQExERERER1TNloogyLftWtS1fHzChJiIiIiIiqme0nRP9qA6pYkJNRERERERUzyggoowJdbUxoSYiIiIiItIzUSwDik8BZXLA3Bcwa1cj21/pij3U+sGEmoiIiIiISI/E0msQMycBZTf/OWneBXBcB8HEQbrAHsM51PrBfaiJqNalpqZCEATExcVJHQoRERGRXoli2d/JdLrqhZLzELMXSxMU1Rgm1ESkkZCQEAiCgClTpqhdmzp1KgRBUNkLkIiIiKheKj71d8902RMXyoCinyGW3ZciKjUKHQ9SxYSaiDTm6emJHTt2oKCgQHmusLAQ27dvh5eXl4SRERERERmIJ3umVYiA4k6thVKVsr8XJdP2IFVMqIlIY506dYKXlxeioqKU56KiouDp6YmOHTsqzx08eBA9evRAgwYN4OzsjJdeegnXrl2r8t6XL1/Giy++CFtbW7i6umLcuHG4d+9ejbWFiIiIqEaYt6ziogVgahidEGWibgepYkJNRFqZOHEiIiMjla83bdqEN954Q6VMXl4eZs+ejTNnzuDXX3+FiYkJhg0bBoWi4oFC6enp6NmzJzp06IA//vgDBw8exJ07d/DKK6/UaFuIiIiI9M6sXfkCZDB94oIAWL8OwcRWiqjUcMi3fjChJiKtjBs3DsePH0dqaiquX7+OEydO4PXXX1cpM2LECAwfPhwtWrRAhw4dsHHjRsTHx+Py5csV3nPdunXo1KkTwsPD0apVK3Ts2BGbNm3Cb7/9hqtXr9ZGs4iMVlhYGARBUDnc3NwqLf9oPYQnj7Zt26qUy8rKQmhoKNzd3WFpaYnWrVvjwIEDNd0cIiKjJwgCBMd1gKwvgEfbZFkA1hMh2M2RMjQVCggo0/JQwHC2/TIU3DaLiLTSsGFDDB48GN988w1EUcTgwYPRsGFDlTLXrl3D4sWLERsbi3v37il7pm/cuAE/Pz+1e549exa//fYbbG3Vv7G9du0afH19a6YxRHVE27Zt8csvvyhfm5o+2Svyj88//xwfffSR8nVpaSmeffZZjBo1SnmuuLgY/fr1g4uLC3744Qc0adIEaWlpsLOzq5kGEBHVMYKJAwTHf0MsuwcoMgBTL4PpmX5EIZYf2tYhVUyoiUhrb7zxBqZNmwYA+OKLL9SuBwcHw9PTE1999RU8PDygUCjg5+eH4uLiCu+nUCgQHByMlStXql1zd3fXb/BEdZCZmVmVvdKPc3BwgIPDP3ug7tmzB5mZmZg4caLy3KZNm/DgwQOcPHkS5ubmAABvb2/9Bk1EVA8Ipg0B04ZPLyiBR73O2tYhVRzyTURaGzhwIIqLi1FcXIwBAwaoXLt//z6uXLmCRYsWoU+fPmjdujUyMzOrvF+nTp1w6dIlNG3aFM2bN1c5bGxsarIpRHVCYmIiPDw84OPjg9GjRyM5OVnjuhs3bkTfvn1VEua9e/eie/fuCA0NhaurK/z8/BAeHo6ysie3gPlHUVERcnJyVA4iIqK6jgk1EWnN1NQUV65cwZUrV9SGljo6OsLZ2RkbNmxAUlISjhw5gtmzZ1d5v9DQUDx48ABjxozB6dOnkZycjEOHDuGNN96o8gM8EQH+/v7YsmULfv75Z3z11VeQy+UICAjA/ftP3+c0PT0dP/30E9566y2V88nJyfjhhx9QVlaGAwcOYNGiRfj000+xYsWKSu8VERGh7P12cHCAp6dntdtGREQ1R9v507r0aNcHTKiJSCf29vawt7dXO29iYoIdO3bg7Nmz8PPzw6xZs/Dxxx9XeS8PDw+cOHECZWVlGDBgAPz8/DBjxgw4ODjAxIRvU0RVGTRoEEaMGIF27dqhb9++2L9/PwDgm2++eWrdzZs3o0GDBhg6dKjKeYVCARcXF2zYsAGdO3fG6NGjsXDhQqxbt67Sey1YsADZ2dnKIy0trVrtIiKimqUQBZ0OUsU51ESkkc2bN1d5fc+ePco/9+3bV21Fb1H8ZxWLpk2bqrwGgBYtWqjsb01EurGxsUG7du2QmJhYZTlRFLFp0yaMGzcOFhYWKtfc3d1hbm6uMgKldevWkMvlKC4uVisPADKZDDKZTD+NICKiGsc51PrBrh8iIqI6pKioCFeuXHnqgn4xMTFISkrCm2++qXbt+eefR1JSksre8VevXoW7u3uFyTQRERmfMpjodJAq/kSIiIiM2Jw5cxATE4OUlBT8/vvvGDlyJHJycjBhwgQA5UOxx48fr1Zv48aN8Pf3r3Aru3feeQf379/HjBkzcPXqVezfvx/h4eEIDQ2t8fYQEVHtEHUY7i1yyLcaDvkmIiIyYjdv3sSYMWNw7949NGrUCN26dUNsbKxy1e709HTcuHFDpU52djZ27dqFzz//vMJ7enp64tChQ5g1axbat2+Pxo0bY8aMGZg3b16Nt4eIiGoHh3zrhyA+OZGRiIiIqJpycnLg4OCA7OzsChcwJCKip6uJ99JH9zwU7w0bO+0GLOflKtC/3XW+tz+GPdRERERERET1TJlogjJRu4S6jF2xaphQExERERER1TMKCFBouaSWAsyon8SEmqieyS0sQVZ++ZGZX4zM/GJk5ZfgYVEpBAEwEQSY/P1fQRBgYWYCK3PT8sPCBJbmprC2MIOLnQyu9pYwNeFcGiIiIiJjwznU+sGEmqgOyc4vQcKdXCTcyUXinVzIswsfS5xLkF1QjBI9jtUxMxHg5mCJxg2s0MTRGo0drdCkgRUaO1qhcQMreDSwgoUZNxMgIiIiMjS6DflmD/WTmFATGaGC4jJc/Ttxvir/+793cnEnp6hW4yhViLiZWYCbmQX4PeWB2nUTAWhkJ0OzRrbo6NUAnbwc0dHLEU423MeWiIiISErlQ76163HWtnx9wISayAik3svDscS7OHntPi7dzsHNzHwojOALQoUI3Mkpwp2cIpy8dl95vqmzdXly7e2ITl4N0MrNnkPHiYiIiGqRAiYoq8E51BEREYiKisJff/0FKysrBAQEYOXKlWjZsqWyjCiKWLp0KTZs2IDMzEz4+/vjiy++QNu2bZVlNmzYgG3btuHcuXPIzc1FZmYmGjRooPa8/fv3Y9myZbhw4QJsbGwQGBiIqKioSuMLCQnBN998o3LO398fsbGxGrcRYEJNZJByCktwMukejibew/HEe7jxIF/qkPQq9X4+Uu/nI+r8LQCAtYUp2jdxQCcvR/g/44zuzzhzqDgRERFRDarpId8xMTEIDQ1F165dUVpaioULF6J///64fPkybGxsAACrVq3C6tWrsXnzZvj6+mL58uXo168fEhISYGdnBwDIz8/HwIEDMXDgQCxYsKDCZ+3atQuTJk1CeHg4evfuDVEUER8f/9QYBw4ciMjISOVrCwvtR1FyH2oiA1CmEBGXlomjV+/hWOJd/HkzG2XG0AVdQ+wszdC3tSsG+bkh0LcRLM1NpQ6JiLTEfaiJiKqvJveh3hHXBtZ22n3Gys8tw+gOl3WK5+7du3BxcUFMTAwCAwMhiiI8PDwwc+ZMzJs3DwBQVFQEV1dXrFy5EpMnT1apHx0djV69eqn1UJeWlqJp06ZYunQp3nzzTY3jCQkJQVZWFvbs2aNVO57EHmoiiZQpRMRczcCuc7dw9Opd5BaWSh2SwcgtLMXu87ew+/wt2FiYIqiVC170c0evVo1gbcG3LSIiIqLqUsCkVrfNys7OBgA4OTkBAFJSUiCXy9G/f39lGZlMhp49e+LkyZNqCXVlzp07h1u3bsHExAQdO3aEXC5Hhw4d8Mknn6gMHa9IdHQ0XFxc0KBBA/Ts2RMrVqyAi4uLVu3iJ1OiWpaU8RDfn03D7nO3kJFbu4uIGaO84jLsv5CO/RfSYWlugsAWjfBiO3f0ae0CO0tzqcMjIiIiMkplooAyUctts/4un5OTo3JeJpNBJpNVWk8URcyePRs9evSAn58fAEAulwMAXF1dVcq6urri+vXrGseUnJwMAAgLC8Pq1avRtGlTfPrpp+jZsyeuXr2qTOCfNGjQIIwaNQre3t5ISUnB4sWL0bt3b5w9e7bKtjyJCTVRLcgtLMG+C+n4/o80nLuRJXU4RquwRIFDl+/g0OU7sDA1Qd82Lpj4vA+6Nq34jZKIiIiIKlamw6JkZX/3UHt6eqqcX7JkCcLCwiqtN23aNFy4cAHHjx9XuyYIqkm9KIpq56qiUCgAAAsXLsSIESMAAJGRkWjSpAm+//77Snu6X331VeWf/fz80KVLF3h7e2P//v0YPny4xs9nQk1UQ0RRxKlr9/H92Zs4eFGOgpIyqUOqU4rLFDgQL8eBeDnaNXbAxOeb4qX2HlzMjIiIiEgDCtEECi0XJVP8vfxWWlqayhzqqnp0p0+fjr179+Lo0aNo0qSJ8rybmxuA8p5qd3d35fmMjAy1XuuqPKrbpk0blXieeeYZ3LhxQ6v7eHt7IzExUeM6ALT8SoKInqqguAxfH0vGC6t+w9ivf8fu87eYTNew+FvZmP3dn3h+5RF8/ksi7j3kUHoiIiKiqjzqodb2AAB7e3uVo6KEWhRFTJs2DVFRUThy5Ah8fHxUrvv4+MDNzQ2HDx9WnisuLkZMTAwCAgI0bkfnzp0hk8mQkJCgPFdSUoLU1FR4e3trfJ/79+8jLS1NJbnXBHuoifQkv7gUW05dx1dHk3E/r1jqcOqlu7lF+OyXq/giOglDnvXAxOeboq2Hg9RhERERERkcBaD1HGqFFmVDQ0Oxbds2/Pjjj7Czs1POmXZwcICVlRUEQcDMmTMRHh6OFi1aoEWLFggPD4e1tTXGjh2rvI9cLodcLkdSUhIAID4+HnZ2dvDy8oKTkxPs7e0xZcoULFmyBJ6envD29sbHH38MABg1apTyPq1atUJERASGDRuGhw8fIiwsDCNGjIC7uztSU1Px/vvvo2HDhhg2bJhWPxMm1ETV9LCoFN+cTMXG4yl4wETaIBSXKvDD2Zv44exN+Ps44a0XnkG/NpoPHSIiIiKi6lm3bh0AICgoSOV8ZGQkQkJCAABz585FQUEBpk6diszMTPj7++PQoUPKPagBYP369Vi6dKnydWBgoNp9Pv74Y5iZmWHcuHEoKCiAv78/jhw5AkdHR2W9hIQE5UrjpqamiI+Px5YtW5CVlQV3d3f06tULO3fuVHm2JrgPNZGOcgpLsPlEKjadSEFWfonU4dBTdPZ2xMLBrdHJy/HphYmo2rgPNRFR9dXkPtTrznWFla12/asFD0vxTqczfG9/DHuoibSUXVCCTcdTEHkiBTncO9ponL2eieFfnsTgdu6YN7AVvJytpQ6JiIiISDJlognKtFyUTNvy9QETaiINFZcq8NWxZKyPuYZcJtJGa398Og5fvoNx3b3xf71bwMGae1kTERFR/aOAAAW0nUOtXfn6gAk1kQZ+T76PhXsuIinjodShkB4Ulymw8XgKfjh7E9N7N8f47k253RYRERHVK+yh1g8m1ERVeJBXjPADV/DD2ZtSh0I1ILugBMv3X8GWU9cxb2ArDG6v3TYJRERERMbq8W2wtKlDqphQE1VAFEV8f/YmIg5cQSYXHKvzbjzIR+i2c9h80hGfjHoW3s42UodEREREVKMUogCFtttmaVm+PuBXDERPSMrIxasbYjH3hwtMpuuZM6mZGPT5MXwbe13qUIiIiIhqlOLvHmptDgXTRzXsoSb6W2FJGf59JBFfHU1BcZk229ZTXZJfXIZFey7i8OU7WDWyPVztLaUOiYiIiIgMFL9iIEL5lkoD1hzFF79dYzJNAICYq3cxYM1R/O/P21KHQkRERKR3CtFEp4NU8SdC9d7Xx5IxesMpXL+fL3UoZGCy8kswfft5TN9+Htkc/k9ERER1SBkEnQ5SxSHfVG/lFpbgve8v4OAludShkIH735+3cTrlPlaNfBY9fRtJHQ4RERFRtenS48weanX8iVC9dPl2DoL/fZzJNGnsTk4RJmw6jcV7LqKwpEzqcIiIiIiqpQy69FLTk9hDTfXOjtM3sGTvJRSVcq40ae+/sddx8XY2NozrgkZ2MqnDISIiItIJe6j1gz8RqjcKisvw7nd/Yn5UPJNpqpbzN7Iw9IsT+EueI3UoRERERDopE010OkgVfyJUL1y7+xBDvziBXeduSh0K1RG3sgow4suT+PXKHalDISIiIiKJMKGmOu/IX3fw8toTSLiTK3UoVMfkFZdh0pY/8PWxZKlDISIiItKKCAEKLQ+Rq3yr4RxqqtO+O5OG93fHo1QhSh0K1VEKEVi+/wqu3X2IZS/7wdyU31MSERGR4dNlCDeHfKtjQk111r9/TcSnh69KHQbVE9tPp+H6/Xyse60zHKzNpQ6HiIiIqEoKUYBC1K7HWdvy9QG/YqA6R6EQ8cGPF5lMU607ee0+hn15Ain38qQOhYiIiKhKZTDR6SBV/IlQnVJSpsD/7TiPLaeuSx0K1VPJ9/Iw/MsTuHQ7W+pQiIiIiCr1qIda24NUMaGmOqOotAzvfHsO+y6kSx0K1XOZ+SV47evfcfEWk2oiIiIyTAqY6HSQKv5EqE4oLCnDW9/8gV+4hREZiKy/k+r4m0yqiYiIiOoqJtRk9PKKSjF+02kcS7wndShEKrILSvDa17G4cDNL6lCIiIiIVJSJgk4HqWJCTUatsKQMIZGncTrlgdShEFUop7AUr3P4NxERERkYzqHWDybUZLQUChEzd8ThTGqm1KEQVSmnsBQTNp1GUsZDqUMhIiIySKWKUvzx4Cz+d3s/Yu//jmJFidQh1XmiaAKFlofIfajVcB9qMlrL9l3GwUtyqcMg0sj9vGK8/vXv+H5Kd3g6WUsdDhERkcHIKLyLlX99gnvF92ACEyiggIO5A+a2fBdNrBtLHV6dVQYBZdCux1nb8vUBv2Igo7Th6DVsPpkqdRhEWpHnFOK1r3/HnZxCqUMhIiIyGF9eW48HxeXT9xRQAAByS3LxeeJaKESFlKHVaQpRl2HfUkdteJhQk9H535+3EfHTX1KHQaSTGw/yMW7j78gt5FA2IiKiWwW3kZKXqkykH1FAgYyiDCQ9vCZRZHWftsO9Hx2aioiIQNeuXWFnZwcXFxcMHToUCQkJKmVEUURYWBg8PDxgZWWFoKAgXLp0SaXMhg0bEBQUBHt7ewiCgKysrAqft3//fvj7+8PKygoNGzbE8OHDq4xPk2drggk1GZXY5Pt49/s/IfLbMTJiV+88xKydf0LkP2QiIqrnckpyqnWddKeAoNOhqZiYGISGhiI2NhaHDx9GaWkp+vfvj7y8PGWZVatWYfXq1Vi7di3OnDkDNzc39OvXD7m5ucoy+fn5GDhwIN5///1Kn7Vr1y6MGzcOEydOxJ9//okTJ05g7NixVcanybM1wYSajMbVO7l4e8sfKC7l0B8yfr9cuYPPf02UOgyqA8LCwiAIgsrh5uZWafmQkBC18oIgoG3bthWW37FjBwRBwNChQ2uoBURUn3laNYGpYFrp9aY23rUYDenTwYMHERISgrZt2+LZZ59FZGQkbty4gbNnzwIo7yFes2YNFi5ciOHDh8PPzw/ffPMN8vPzsW3bNuV9Zs6cifnz56Nbt24VPqe0tBQzZszAxx9/jClTpsDX1xctW7bEyJEjK41N02drggk1GYU7OYUI2XQaOYWlUodCpDef/5qIw5fvSB0G1QFt27ZFenq68oiPj6+07Oeff65SNi0tDU5OThg1apRa2evXr2POnDl44YUXajJ8IqrHbM1t0delt9p5AQKebxiAhrKGEkRVP9T2PtTZ2eVbiDo5OQEAUlJSIJfL0b9/f2UZmUyGnj174uTJkxrf99y5c7h16xZMTEzQsWNHuLu7Y9CgQVUO39bXswEm1GQEHhaVIiTyDG5ncyEnqltEEZi9M47baVG1mZmZwc3NTXk0atSo0rIODg4qZf/44w9kZmZi4sSJKuXKysrw2muvYenSpXjmmWdquglEVI+N9noFQxsPgZWpFQBAZiLDILcBeKPpBIkjq9uqM4c6JydH5SgqKqryWaIoYvbs2ejRowf8/PwAAHJ5+W49rq6uKmVdXV2V1zSRnJwMoHzE1qJFi7Bv3z44OjqiZ8+eePDgQYV19PVsgAk1GYH3o+JxJZ3zZ6huyi0qxdv//YOLlFG1JCYmwsPDAz4+Phg9erTyw4UmNm7ciL59+8LbW3VY5bJly9CoUSO8+eabGt2nqKhI7QMWEZEmTAQTDGv8MtZ2XIPPO3yKLzp9jle9RsHMhDv81iQFtF3h+5851J6ennBwcFAeERERVT5r2rRpuHDhArZv3652TRBUe71FUVQ7V2U7FOXTQRcuXIgRI0agc+fOiIyMhCAI+P7776usW91nA9yHmgzc93+kYe+ft6UOg6hGJd/Nw6ydcfhqfBet38SJ/P39sWXLFvj6+uLOnTtYvnw5AgICcOnSJTg7O1dZNz09HT/99JPafLETJ05g48aNiIuL0ziOiIgILF26VJcmEBEBAMxMzNDAooHUYdQbopaLjD2qAwBpaWmwt7dXnpfJZJXWmT59Ovbu3YujR4+iSZMmyvOP1vuQy+Vwd3dXns/IyFDrOa7Ko7pt2rRRieeZZ57BjRs3Kqyjr2cD7KEmA5ZyLw9he7Vfup7IGP1yJQOf/cJFykh7gwYNwogRI9CuXTv07dsX+/fvBwB88803T627efNmNGjQQGXBsdzcXLz++uv46quv0LCh5nMXFyxYgOzsbOWRlpamdVuIiKj2aL8HdfkBAPb29ipHRQm1KIqYNm0aoqKicOTIEfj4+Khc9/HxgZubGw4fPqw8V1xcjJiYGAQEBGjcjs6dO0Mmk6lsyVVSUoLU1FS10Vf6fjbAHmoyUMWlCkzffg55xWVSh0JUa/59JBFtPewxoG3lKzQTPY2NjQ3atWuHxMSqv6ARRRGbNm3CuHHjYGFhoTx/7do1pKamIjg4WHnu0XA6MzMzJCQkoFmzZmr3k8lkVfZQEBGRYdF2X+lHdTQVGhqKbdu24ccff4SdnZ1ybrKDgwOsrKwgCAJmzpyJ8PBwtGjRAi1atEB4eDisra1VtrySy+WQy+VISkoCAMTHx8POzg5eXl5wcnKCvb09pkyZgiVLlsDT0xPe3t74+OOPAUBlwc1WrVohIiICw4YN0/jZmmBCTQbp45//wsVbnH9H9YsoAu9+9yfazLCHp5O11OGQkSoqKsKVK1eeujJ3TEwMkpKS1OZIt2rVSm2V8EWLFiE3Nxeff/45PD099R4zERHVPevWrQMABAUFqZyPjIxESEgIAGDu3LkoKCjA1KlTkZmZCX9/fxw6dAh2dnbK8uvXr1eZUhQYGKh2n48//hhmZmYYN24cCgoK4O/vjyNHjsDR0VFZLyEhQbnSuKbP1oQgiqKoVQ2iGhZz9S5CIk+D/zKpvgpo5oxtkyrea5HoSXPmzEFwcDC8vLyQkZGB5cuXIyYmBvHx8fD29saCBQtw69YtbNmyRaXeuHHjkJiYiNjY2Kc+IyQkBFlZWdizZ4/GceXk5MDBwQHZ2dkq8+yIiEhzNfFe+uieLx96A+Y2Fk+v8JiSvGL82H8T39sfwznUZFDu5hbh3e/+ZDJN9drJa/ex/XTFi2gQPenmzZsYM2YMWrZsieHDh8PCwgKxsbHKeWPp6elqi7JkZ2dj165dGq/gTUREdY/i70XJtD1IFYd8k8EQRRHvfv8n7j2seh87ovogfP8VBLVsBHcHK6lDIQO3Y8eOKq9v3rxZ7ZyDgwPy8/M1fkZF9yAiIuP2+CJj2tQhVeyhJoPx9bEUHL16V+owiAxCblEpFu6+KHUYREREVEdVZ5Vv+gcTajIIqffy8PHPCU8vSFSPHPkrA7vP35Q6DCIiIqqDmFDrBxNqMgjL9l1GcZlC6jCIDM6y/13mNAgiIiLSOybU+sGEmiT3218ZOPJXhtRhEBmkzPwSfPAjh34TERGRfonQfmEyrhusjgk1Saq4VIFl+y5LHQaRQTsQL8fBi+lSh0FERERET2BCTZLaeDwFKffypA6DyOAt/vESsvKLpQ6DiIiI6ggO+dYPJtQkmYycQqw9kih1GERG4W5uEf59JEnqMIiIiKiOYEKtH0yoSTIRP/2FvOIyqcMgMhrfxl5HenaB1GEQERFRHcCEWj+YUJMkzl5/gD1xt6QOg8ioFJUq8K9fOaqDiIiIqo8JtX4woaZap1CICNt7GSKXCSTS2vd/3EQq1x0gIiKiahJFQaeDVDGhplr33R9piL+VLXUYREapVCFi9eGrUodBRERERk7bLbMeHaSKCTXVqpIyBT7nkFWiavnfhdu4kp4jdRhERERE9R4TaqpVe87fQnp2odRhEBk1UQQ++TlB6jCIiIjIiHEOtX4woaZaI4oiNhxNljoMojrh178ycPZ6ptRhEBERkZHiHGr9YEJNtebXKxlIzHgodRhEdcbHP/8ldQhERERkpNhDrR9MqKnWrI+5JnUIRHVKbPIDHEu8K3UYREREZITYQ60fTKipVvyR+gB/cHgqkd7xiyoiIiLShahD7zQTanVMqKlW8EM/Uc04ee0+ku9yKgURERFpR0T5QqdaHVIHbYCYUFONu3onF7/+lSF1GER1kigC236/IXUYREREZGS4D7V+MKGmGvefmGSI/DqLqMbsOncThSVlUodBREREVO8woaYalZ5dgL1/3pI6DKI6LTO/BAfi06UOg4iIiIwIFyXTDybUVKO2xt5ASRm7p4lq2rex16UOgYiIiIwIt83SDybUVKP2xLF3mqg2nLuRhSvpOVKHQUREREZC6wXJ/j5IFRNqqjFnrz/AzcwCqcMgqje2/s5eaiIiItIMh3zrBxNqqjE/xt2WOgSiemXP+dvIKyqVOgwiIiIyAjWdUEdERKBr166ws7ODi4sLhg4dioSEhCdiEBEWFgYPDw9YWVkhKCgIly5dUimzYcMGBAUFwd7eHoIgICsrS+1ZTZs2hSAIKsf8+fOrjC8kJEStTrdu3TRu3yNMqKlGlJYpsP8CF0kiqk0Pi0o5zYKIiIg0UtNzqGNiYhAaGorY2FgcPnwYpaWl6N+/P/Ly8pRlVq1ahdWrV2Pt2rU4c+YM3Nzc0K9fP+Tm5irL5OfnY+DAgXj//ferfN6yZcuQnp6uPBYtWvTUGAcOHKhS58CBAxq37xEzrWsQaeBY0j3czyuWOgyiemfH6TS85u8tdRhERERUzx08eFDldWRkJFxcXHD27FkEBgZCFEWsWbMGCxcuxPDhwwEA33zzDVxdXbFt2zZMnjwZADBz5kwAQHR0dJXPs7Ozg5ubm1YxymQyres8iT3UVCP2crg3kSTib2XjdhbXLiAiIqKq1faiZNnZ2QAAJycnAEBKSgrkcjn69++vLCOTydCzZ0+cPHlS6/uvXLkSzs7O6NChA1asWIHi4qd37kVHR8PFxQW+vr6YNGkSMjIytH4ue6hJ7wpLynDoklzqMIjqrV//ysC4buylJiIiosqVJ8jaLTL2KKHOyVHdWUQmk0Emk1VRT8Ts2bPRo0cP+Pn5AQDk8vJ8wdXVVaWsq6srrl/XbqHVGTNmoFOnTnB0dMTp06exYMECpKSk4Ouvv660zqBBgzBq1Ch4e3sjJSUFixcvRu/evXH27Nkq2/IkJtSkd4cv30FecZnUYRDVW0eu3GFCTURERFXSZdXuR+U9PT1Vzi9ZsgRhYWGV1ps2bRouXLiA48ePq10TBNUYRFFUO/c0s2bNUv65ffv2cHR0xMiRI5W91hV59dVXlX/28/NDly5d4O3tjf379yuHoGuCCTXpHVf3JpLWyWv3UVBcBisLU6lDISIiIgMl/n1oWwcA0tLSYG9vrzxfVY/u9OnTsXfvXhw9ehRNmjRRnn80d1kul8Pd3V15PiMjQ63XWluPVutOSkqqNKF+kru7O7y9vZGYmKjVsziHmvQqO78ER6/elToMonqtqFSB40n3pA6DiIiIDFh1ts2yt7dXOSpKqEVRxLRp0xAVFYUjR47Ax8dH5bqPjw/c3Nxw+PBh5bni4mLExMQgICCgWm07f/48AKgk6k9z//59pKWlaVUHYEJNenYs6S6KyxRSh0FU7/165Y7UIRAREZEhE3U8NBQaGopvv/0W27Ztg52dHeRyOeRyOQoKyhdPFQQBM2fORHh4OHbv3o2LFy8iJCQE1tbWGDt2rPI+crkccXFxSEpKAgDEx8cjLi4ODx48AACcOnUKn332GeLi4pCSkoLvvvsOkydPxpAhQ+Dl5aW8T6tWrbB7924AwMOHDzFnzhycOnUKqampiI6ORnBwMBo2bIhhw4Zp9WPkkG/Sq9jk+1KHQEQAjvyVodMcJCIiIqondJhDDS3Kr1u3DgAQFBSkcj4yMhIhISEAgLlz56KgoABTp05FZmYm/P39cejQIdjZ2SnLr1+/HkuXLlW+DgwMVLmPTCbDzp07sXTpUhQVFcHb2xuTJk3C3LlzVZ6bkJCgXGnc1NQU8fHx2LJlC7KysuDu7o5evXph586dKs/WhCCK1Vn8nEhVv9UxSMx4KHUYRARg77Tn0b5JA6nDoHoqJycHDg4OyM7OVplnR0REmquJ99JH93xm80KYWFtqVVeRX4jkkBV8b38Mh3yT3tx/WMRkmsiA/HpF+70UiYiIqH6o7X2o6yom1KQ3p1MeSB0CET3m1784j5qIiIgqVp1FyegfTKhJbzh/msiwXLyVg3sPi6QOg4iIiAyRKOh2kAom1KQ3v7OHmsjgxN/MljoEIiIiMkAc8q0fTKhJLzLzipFwJ1fqMIjoCfG3mFATERFRBWp426z6gttmkV6cTn3Ab6yIDNBFJtRERERUAV3mRHMOtTr2UJNecP40kWG6dDtH6hCIiIiI6iwm1KQXvydz/jSRIbqVVYAHecVSh0FERESGiMO9q40JNVVbSZmC86eJDBiHfRMREdGTuG2WfjChpmq7fj8fZQp+ZUVkqC7eZkJtyLKysvD1119jwYIFePCgfLTPuXPncOvWLY3qh4WFQRAElcPNza3S8iEhIWrlBUFA27ZtlWW++uorvPDCC3B0dISjoyP69u2L06dPV6+hRERkWLgomV4woaZqS72XJ3UIRFQF9lAbrgsXLsDX1xcrV67EJ598gqysLADA7t27sWDBAo3v07ZtW6SnpyuP+Pj4Sst+/vnnKmXT0tLg5OSEUaNGKctER0djzJgx+O2333Dq1Cl4eXmhf//+Gif5RERkDAQdD3ocV/mmakthQk1k0C7e4sJkhmr27NkICQnBqlWrYGdnpzw/aNAgjB07VuP7mJmZVdkr/TgHBwc4ODgoX+/ZsweZmZmYOHGi8tzWrVtV6nz11Vf44Ycf8Ouvv2L8+PEax0VERAZMlx5n9lCrYQ81VVvKfSbURIbsxoN8ZBeUSB0GVeDMmTOYPHmy2vnGjRtDLpdrfJ/ExER4eHjAx8cHo0ePRnJyssZ1N27ciL59+8Lb27vSMvn5+SgpKYGTk5PG9yUiIgPHId96wYSaqi3lLhNqIkN3mdtnGSRLS0vk5Kj/3SQkJKBRo0Ya3cPf3x9btmzBzz//jK+++gpyuRwBAQG4f//p2xmmp6fjp59+wltvvVVlufnz56Nx48bo27dvpWWKioqQk5OjchAREdV1TKip2lLZQ01k8G5nFUgdAlXg5ZdfxrJly1BSUj6CQBAE3LhxA/Pnz8eIESM0usegQYMwYsQItGvXDn379sX+/fsBAN98881T627evBkNGjTA0KFDKy2zatUqbN++HVFRUbC0tKy0XEREhHI4uYODAzw9PTWKn4iIJCIKuh2kggk1VUtBcRnkOYVSh0FET5GRWyR1CFSBTz75BHfv3oWLiwsKCgrQs2dPNG/eHHZ2dlixYoVO97SxsUG7du2QmJhYZTlRFLFp0yaMGzcOFhYWlcYXHh6OQ4cOoX379lXeb8GCBcjOzlYeaWlpOsVPRES1QxR1O0gVFyWjakm9n8f/sYiMQEYuv/gyRPb29jh+/DiOHDmCc+fOQaFQoFOnTlUOrX6aoqIiXLlyBS+88EKV5WJiYpCUlIQ333yzwusff/wxli9fjp9//hldunR56nNlMhlkMplOMRMRkQS4KJleMKGmauEK30TGgT3Uhq13797o3bu3TnXnzJmD4OBgeHl5ISMjA8uXL0dOTg4mTJgAoLzn+NatW9iyZYtKvY0bN8Lf3x9+fn5q91y1ahUWL16Mbdu2oWnTpsoF0mxtbWFra6tTnEREZGB0GcLNId9qmFBTtTChJjIOd3OYUBuKf/3rXxqX/b//+7+nlrl58ybGjBmDe/fuoVGjRujWrRtiY2OVq3anp6fjxo0bKnWys7Oxa9cufP755xXe88svv0RxcTFGjhypcn7JkiUICwvTOH4iIjJcglh+aFuHVDGhpmq5mZkvdQhEpAEO+TYcn332mUblBEHQKKHesWNHldc3b96sds7BwQH5+ZW/f6empj71uUREZOQ45FsvmFBTtWTmcW9bImPAId+GIyUlReoQiIiIOORbT7jKN1VLdgETaiJjkF9chodFpVKHQURERFSnsIeaqiWnkAk1kbHIyCmEbSMuKGVobt68ib179+LGjRsoLi5WubZ69WqJoiIiojqPQ771ggk1VQt7qImMR0ZuEZ5hQm1Qfv31VwwZMgQ+Pj5ISEiAn58fUlNTIYoiOnXqJHV4RERUlzGh1gsO+aZqyWFCTWQ0OI/a8CxYsADvvvsuLl68CEtLS+zatQtpaWno2bMnRo0aJXV4RERUl4k6HqSCPdRULb83/BAlJpYoMbVGkYkVCgUrFAiWKIAl8mCJh6IlchWWyFFYILtMhsxSi/KjxBz3Syxwt9gMWSXmUjeDqF7gF2CG58qVK9i+fTsAwMzMDAUFBbC1tcWyZcvw8ssv45133pE4QiIiqrO4KJleMKEm3ZWVwOpePKyqcw9TQDQzASxsoDCzQZm5NcpMrVFiZoNiE6vHknQr5MMSeZDhoWiF3DIL5ChkyFZYILNUhswSCzz4O0nPKDbDw1L+0yZ6UmmZQuoQ6Ak2NjYoKiofOeDh4YFr166hbdu2AIB79+5JGRoREdVxNb0PdUREBKKiovDXX3/BysoKAQEBWLlyJVq2bKksI4oili5dig0bNiAzMxP+/v744osvlL8LAWDDhg3Ytm0bzp07h9zcXGRmZqJBgwYqz2ratCmuX7+ucm7evHn46KOPKo1Pk2drglkH6a5UP/vaCqICKMqFaVEuTP8+V60k3QwQLcwAc2sozG1RZmaNUjNrlJhaodjU5u8k3RIFghXyxL970hUy5IqWyC6zQHbZY0l6qTnuF5vjXrEF8so4Q4KMW6mC47QMTbdu3XDixAm0adMGgwcPxrvvvov4+HhERUWhW7duUodHRER1WQ3PoY6JiUFoaCi6du2K0tJSLFy4EP3798fly5dhY2MDAFi1ahVWr16NzZs3w9fXF8uXL0e/fv2QkJAAOzs7AEB+fj4GDhyIgQMHYsGCBZU+b9myZZg0aZLyta1t1evGaPJsTTChJt2VGu58TEFRChTlwLQoB6YALKp7Q3NAlJlDtLCBwtwGZWY2KDW1QomZNYpNrFH4qCcdlsiHFR6KMjwUZchVPErSZcgqNceDUhnul5jjXrEZ7hVboEjBJJ1qTxkTaoOzevVqPHz4EAAQFhaGhw8fYufOnWjevDk+++wziaMjMjyFpSU4kXYDRaVleK5xEzS0tpY6JCKqxMGDB1VeR0ZGwsXFBWfPnkVgYCBEUcSaNWuwcOFCDB8+HADwzTffwNXVFdu2bcPkyZMBADNnzgQAREdHV/k8Ozs7uLm5aRSbps/WBBNq0p2eeqiNhaAogVCYBZPCLJgBkFX3hhaAaCorT9LNrFFmboMSU+u/56Nb/9OTDkvkqSXp5Ql6VplM2Yt+v9gC90rMUaLg3BaqGHuoDc8zzzyj/LO1tTW+/PJLCaMhMmyHryXhvcMHkVNc/oW+mYkJQrv64/+e6w5B4O8+IkOXnZ0NAHBycgIApKSkQC6Xo3///soyMpkMPXv2xMmTJ7VKagFg5cqV+PDDD+Hp6YlRo0bhvffeg4VFxd1q+nw2E2rSnQH3UBsLoawIQkERTPCg+km6gPIk3cwKorkNFObWKDWzQamZNYpNrFBsYoVCE2sUCpbIR/lRvmicDDmiDDllMmSVypBZao6HZeYQRSZfdU0j04dSh0BVePjwIRQK1Xnu9vb2EkVDZFhSsjIx9cBeKB773VSqUODz30/B26EBhrZqI2F0RMZJgA5zqP/+b05Ojsp5mUwGmazyT7KiKGL27Nno0aMH/Pz8AAByuRwA4OrqqlLW1dVVbT7008yYMQOdOnWCo6MjTp8+jQULFiAlJQVff/11heX1+Wwm1KQ7fhtskITSAgilBTAp4P/g9ARxEYAOUkdBj0lJScG0adMQHR2NwsJ/Rv2IoghBEFBWViZhdESGY+fFCwDUp28KEBAZd44JNZEuqrHKt6enp8rpJUuWICwsrNJq06ZNw4ULF3D8+HG1a0+OMHn0O1Abs2bNUv65ffv2cHR0xMiRI7Fy5Uo4OztXWk8fz+bnbdKdheaT9YnIAJiYPr0M1arXXnsNALBp0ya4urpy2CpRJdJyclDRrBURItJysms/IKK6oBqLkqWlpamMoqqqd3r69OnYu3cvjh49iiZNmijPP5rvLJfL4e7urjyfkZGh1nOsrUcLeyYlJVWYUOvz2UyoSXeyqlfOIyIDY8K3fENz4cIFnD17VmULESJS19zJCYIAPDkbyUQQ0MKpoTRBERm7aiTU9vb2T52WJIoipk+fjt27dyM6Oho+Pj4q1318fODm5obDhw+jY8eOAIDi4mLExMRg5cqVWgam6vz58wCgkizX1LO5xDDpztwKENjjRWQ0TM2ljoCe0LVrV6SlpUkdBpHBG922PWSmpjB5YhSHQhQxuXNXiaIiMm6P9qHW9tBUaGgovv32W2zbtg12dnaQy+WQy+UoKCgof74gYObMmQgPD8fu3btx8eJFhISEwNraGmPHjlXeRy6XIy4uDklJSQCA+Ph4xMXF4cGDBwCAU6dO4bPPPkNcXBxSUlLw3XffYfLkyRgyZAi8vLyU92nVqhV2796t1bM1we4Kqh6ZLVDIoVZERoE91Abn66+/xpQpU3Dr1i34+fnB3Fz1S4/27dtLFBmRYXG3s8OWYSMx59BBXM/OAgDYy2RY0KMnevs8U3VlIqpYDe9DvW7dOgBAUFCQyvnIyEiEhIQAAObOnYuCggJMnToVmZmZ8Pf3x6FDh1T2gV6/fj2WLl2qfB0YGKhyH5lMhp07d2Lp0qUoKiqCt7c3Jk2ahLlz56o8NyEhQbnSuKbP1oQgcilfqo7P/IBs9q4QGYWX1gBdJkodBT0mNjYWY8eORWpqqvKcIAh1YlGynJwcODg4IDs7m6uVk96Iooi/7t1FYWkp2jRygcyMXxRS3VYT76WP7tl0+QqYWFpqVVdRWIjURQv53v4YvgtR9VhwHjWR0bCufJVLksYbb7yBjh07Yvv27VyUjEgDgiCgdSMXqcMgqhtquIe6vmBCTdXDhcmIjIedm9QR0BOuX7+OvXv3onnz5lKHQkRE9Yy2c6If1SFVXJSMqoc91ETGgwm1wenduzf+/PNPqcMgIqL66NE+1NoepII91FQ9Mu5FTWQ0bJlQG5rg4GDMmjUL8fHxaNeundqiZEOGDJEoMiIiqvM45FsvmFBT9TChJjIOVk6AmYXUUdATpkyZAgBYtmyZ2jVjX5SMiIgMG4d86wcTaqoeJtRExsHOXeoIqAIKhULqEIiIqL5iD7VecA41VY99Y6kjICJN2LlKHQE9RWFhodQhEBERkZaYUFP1NGwhdQREpAn2UBuksrIyfPjhh2jcuDFsbW2RnJwMAFi8eDE2btwocXRERFSnif8M+9b0YA+1OibUVD0NfaWOgIg0YcseakO0YsUKbN68GatWrYKFxT9z3Nu1a4evv/5awsiIiKjOE3U8SAUTaqqeBt6AifnTyxGRtNhDbZC2bNmCDRs24LXXXoOpqanyfPv27fHXX39JGBkREdV5TKj1ggk1VY+pGeDkI3UURPQ03IPaIN26dQvNmzdXO69QKFBSUiJBREREVF9oO9xbl1XB6wMm1FR9zpxHTWTwHLiAoCFq27Ytjh07pnb++++/R8eOHSWIiIiIiLTBbbOo+ho2BxKkDoKIKmViBri0kToKqsCSJUswbtw43Lp1CwqFAlFRUUhISMCWLVuwb98+qcMjIqK6jNtm6QV7qKn62ENNZNgatQLMraSOgh6TnJwMURQRHByMnTt34sCBAxAEAR988AGuXLmC//3vf+jXr5/UYRIRUR3GId/6wR5qqj5unUVk2Dw6SB0BPaFFixZIT0+Hi4sLBgwYgE2bNiEpKQlubpzrTkREZEzYQ03Vxx5qIsPmwbm4hkYUVb/i/+mnn5Cfny9RNEREVG9xhe9qY0JN1WfjDFg5Sh0FEVXGnQm1oXsywSYiIqpx3DZLL5hQk340ai11BERUERNzwM1P6ijoCYIgQBAEtXNERES1hXOo9YNzqEk/vAOAGyeljoKInuTSGjCTSR0FPUEURYSEhEAmK/+7KSwsxJQpU2BjY6NSLioqSorwiIioPuAq33rBhJr0wycQOPaJ1FEQ0ZM4f9ogTZgwQeX166+/LlEkRERUX+nS48weanVMqEk/PP0BUxlQViR1JET0OK7wbZAiIyOlDoGIiOo79lDrBedQk36YWwKez0kdBRE9iT3URERERDWGCTXpT9MXpI6AiB5nagG4tJU6CiIiIjJEXOVbL5hQk/74BEodARE9ztMfMLOQOgoiIiIyQFzlWz+YUJP+NOkCmNs8vRwR1Y5Wg6WOgIiIiAwVe6j1ggk16Y+pOeDlL3UURPRIyxeljoCIiIgMFRNqvWBCTfrFYd9EhsHVD3D0ljoKIiIiMlA1PeQ7IiICXbt2hZ2dHVxcXDB06FAkJCSolBFFEWFhYfDw8ICVlRWCgoJw6dIllTIbNmxAUFAQ7O3tIQgCsrKyKn1mUVEROnToAEEQEBcXV2V8ISEhEARB5ejWrZvmDfwbE2rSr6ZMqIkMAnun642wsDC1DwRubm6Vlq/oA4QgCGjbVnUBu127dqFNmzaQyWRo06YNdu/eXdNNISKi2lTDPdQxMTEIDQ1FbGwsDh8+jNLSUvTv3x95eXnKMqtWrcLq1auxdu1anDlzBm5ubujXrx9yc3OVZfLz8zFw4EC8//77T33m3Llz4eHhoXGMAwcORHp6uvI4cOCA5g38G/ehJv3y6ADIHICibKkjIarfWjGhrk/atm2LX375Rfna1NS00rKff/45PvroI+Xr0tJSPPvssxg1apTy3KlTp/Dqq6/iww8/xLBhw7B792688sorOH78OPz9ObWHiIie7uDBgyqvIyMj4eLigrNnzyIwMBCiKGLNmjVYuHAhhg8fDgD45ptv4Orqim3btmHy5MkAgJkzZwIAoqOjq3zeTz/9hEOHDmHXrl346aefNIpRJpNV+SW0JthDTfplYgr4DpA6CqL6zb4x95+uZ8zMzODm5qY8GjVqVGlZBwcHlbJ//PEHMjMzMXHiRGWZNWvWoF+/fliwYAFatWqFBQsWoE+fPlizZk0ttIaIiGpDba/ynZ1d3uHm5OQEAEhJSYFcLkf//v2VZWQyGXr27ImTJ09qde87d+5g0qRJ+O9//wtra2uN60VHR8PFxQW+vr6YNGkSMjIytHouwISaaoLfCKkjIKrffAdKHQHVssTERHh4eMDHxwejR49GcnKyxnU3btyIvn37wtv7nzn3p06dUvmAAwADBgzQ+gMOEREZsGoM+c7JyVE5ioqKqn6UKGL27Nno0aMH/Pz8AAByuRwA4OrqqlLW1dVVeU2jZogiQkJCMGXKFHTp0kXjeoMGDcLWrVtx5MgRfPrppzhz5gx69+791LY8iUO+Sf+a9wGsHIGCTKkjIaqfONy7XvH398eWLVvg6+uLO3fuYPny5QgICMClS5fg7OxcZd309HT89NNP2LZtm8p5uVyu9QecoqIilQ8hOTk5OrSGiIhqjS6rdv9d3tPTU+X0kiVLEBYWVmm1adOm4cKFCzh+/LjaNUEQVB8himrnqvLvf/8bOTk5WLBggcZ1AODVV19V/tnPzw9dunSBt7c39u/frxyCrgn2UJP+mZoDrYdIHQVR/SSz5+KA9cygQYMwYsQItGvXDn379sX+/fsBlM9De5rNmzejQYMGGDp0qNo1bT/gREREwMHBQXk8+WGLiIgMi6DjAQBpaWnIzs5WHlUls9OnT8fevXvx22+/oUmTJsrzj+YuP/llbUZGhtqXulU5cuQIYmNjIZPJYGZmhubNmwMAunTpggkTJmh8H3d3d3h7eyMxMVHjOgATaqop7UZKHQFR/dS8D2BmIXUUJCEbGxu0a9fuqR8IRFHEpk2bMG7cOFhYqP6bcXNz0/oDzoIFC1Q+XKWlpeneCCIiqnnVGPJtb2+vcshkMvXbiyKmTZuGqKgoHDlyBD4+PirXfXx84ObmhsOHDyvPFRcXIyYmBgEBARo341//+hf+/PNPxMXFIS4uTrlS986dO7FixQqN73P//n2kpaXB3d1d4zoAE2qqKd49ADvNl6wnIj1pM1TqCEhiRUVFuHLlylM/EMTExCApKQlvvvmm2rXu3burfMABgEOHDlX5AUcmk6l9wCIiIsNV04uShYaG4ttvv8W2bdtgZ2cHuVwOuVyOgoKC8ucLAmbOnInw8HDs3r0bFy9eREhICKytrTF27FjlfeRyOeLi4pCUlAQAiI+PR1xcHB48eAAA8PLygp+fn/Lw9fUFADRr1kylR7xVq1bKLSAfPnyIOXPm4NSpU0hNTUV0dDSCg4PRsGFDDBs2TKufI+dQU80wMQGeHQ0cXy11JET1h40L0Gqw1FFQLZszZw6Cg4Ph5eWFjIwMLF++HDk5OcphbgsWLMCtW7ewZcsWlXobN26Ev7+/cnGYx82YMQOBgYFYuXIlXn75Zfz444/45ZdfKpz7RkRERqoac6g1sW7dOgBAUFCQyvnIyEiEhIQAKN83uqCgAFOnTkVmZib8/f1x6NAh2NnZKcuvX78eS5cuVb4ODAxUu48mEhISlCuNm5qaIj4+Hlu2bEFWVhbc3d3Rq1cv7Ny5U+XZmhBEUazG4udEVbh/Dfh3Z2j/fyoR6aTHLKBvmNRRUC0bPXo0jh49inv37qFRo0bo1q0bPvzwQ7Rp0wYAEBISovz2/ZHs7Gy4u7vj888/x6RJkyq87w8//IBFixYhOTkZzZo1w4oVK7RapCUnJwcODg7Izs5mbzURkY5q4r300T3bTg6HqcxSq7plRYW49J/3+d7+GPZQU81xbgZ4BwDXT0gdCVE9IACdNF94g+qOHTt2VHl98+bNauccHByQn59fZb2RI0di5Eiuh0FEVKex36vaOIeaalbHcVJHQFQ/PBMEOPk8tRgRERERUPNzqOsLJtRUs9q8XL6NDxHVrC4TpY6AiIiIjEk1VvmmfzChppplYQ10Gi91FER1m4Mn0OolqaMgIiIiI8Ieav1gQk01L2A6YKbdggdEpIXn3gZMTKWOgoiIiIwJe6j1ggk11Tw7N6Dj61JHQVQ3WdgCnbkYGREREWmHPdT6wYSaasfzMwETc6mjIKp7OrwGWDpIHQURERFRvcSEmmpHA0/g2VeljoKobhFMgG5TpI6CiIiIjBGHfOsFE2qqPT1mAwLneRLpTZuhgNMzUkdBRERExogJtV4woaba49wM8BsudRREdYOpBdDnA6mjICIiIiPFOdT6wYSaatcL7wIQpI6CyPh1eQNw8pE6CiIiIjJW7KHWCybUVLtcWgOtBksdBZFxk9kDgXOljoKIiIiMmCCKOh2kigk11b7A96SOgMi4PT8DsHGWOgoiIiIyZuyh1gsm1FT7PDoAvgOljoLIONm5A92mSh0FERERGTnOodYPJtQkjQHhgKlM6iiIjE/QAsDCWuooiIiIiAhMqEkqzs2AHrOkjoLIuDRqBXR8XeooiIiIqC7gkG+9YEJN0nlhNvfQJdJG3zDAhHu5ExERUfVxyLd+MKEm6ZjJgBc/kToKIuPg/TzQcpDUURAREVFdwR5qvWBCTdJq3gdoO0zqKIgMm2AC9P9Q6iiIiIioDmEPtX4woSbpDYgALOykjoLIcAVMBxp3ljoKIiIiqkvYQ60XTKhJevbuQO+FUkdBZJhc2gC9+P8HERER6R97p6uPCTUZhufeBtzaSx0FkWExMQeGrS9fb4CIiIiIDA4TajIMJqbAS5+VzxUlonI95wLuz0odBREREdVFoqjbQSqYvZDhaNIF6DxR6iiIDINHJ6DHbKmjICIiojqKi5LpBxNqMiwDVgAubaWOgkhaZpblQ71NzaSOhIiIiOoqLkqmF0yoybCYWwGvbOGq31S/9V4MNGopdRRERERUhwkK3Q5SxYSaDE/D5sCQf0kdBZE0vJ8Huk2VOgoiqkH5BcXIzi2AyLmIRCSlGu6hjoiIQNeuXWFnZwcXFxcMHToUCQkJqiGIIsLCwuDh4QErKysEBQXh0qVLKmU2bNiAoKAg2NvbQxAEZGVlVfrMoqIidOjQAYIgIC4ururma/BsTTChJsPkNxzoOknqKIhql4UtMPRLwIRvzUR10S15Fuas2IUBr/8Lg0O+QMjsb3Dmz+tSh0VE9VRNz6GOiYlBaGgoYmNjcfjwYZSWlqJ///7Iy8tTllm1ahVWr16NtWvX4syZM3Bzc0O/fv2Qm5urLJOfn4+BAwfi/ffff+oz586dCw8PD43i0+TZmhBEfj1Khqq0GNjUH7h9XupIiGrHsA3As69KHQWRXuTk5MDBwQHZ2dmwt7eXOhzJ5TwsxOszIpGdk48yRflHL0EABEHAl8vHwK+lZh8Aiah+qYn30kf3fG7IhzAzt9SqbmlJIU7vXaxTPHfv3oWLiwtiYmIQGBgIURTh4eGBmTNnYt68eQDKe5hdXV2xcuVKTJ48WaV+dHQ0evXqhczMTDRo0EDt/j/99BNmz56NXbt2oW3btjh//jw6dOhQYSzaPrsq7AYhw2VmAYz6BrBsIHUkRDWvxywm00R12P4j8cjMzlMm08A/u89s2RUrUVRERLUnOzsbAODk5AQASElJgVwuR//+/ZVlZDIZevbsiZMnT2p17zt37mDSpEn473//C2tr66eW1+ezmVCTYXP0Ll/tGILUkRDVnFYvAX2WSB0FEdWgK4nyCs8rFCIuJtyu5WiIiKo35DsnJ0flKCoqqvJZoihi9uzZ6NGjB/z8/AAAcnn5+6Krq6tKWVdXV+U1TYiiiJCQEEyZMgVdunTRqI6+ng0woSZj0HIQEDBd6iiIaoZbe2D4hvKxn0RUZzWwt4JJJf+fN3B4em8KEZHeVWNRMk9PTzg4OCiPiIiIKh81bdo0XLhwAdu3b1e7Jjzx3iiKotq5qvz73/9GTk4OFixYoHEdfT0bYEJNxqLPEsArQOooiPTL1g0YswOwsJE6EiKqYS/29lMZ7v2IIAAv92svQUREVN9Vp4c6LS0N2dnZyqOqZHb69OnYu3cvfvvtNzRp0kR53s3NDQDUeoQzMjLUeo6rcuTIEcTGxkImk8HMzAzNmzcHAHTp0gUTJkyosI6+ng0woSZjYWpWvj+1o4/UkRDph5kVMGYb4NBY6kiIqBa0auaGmW/2hokgQBAEmJiU94D0eb4VRrzYSeLoiKheEkXdDgD29vYqh0wmq+D2IqZNm4aoqCgcOXIEPj6qn+N9fHzg5uaGw4cPK88VFxcjJiYGAQGad6T961//wp9//om4uDjExcXhwIEDAICdO3dixYoVFdbR17MBwEyr0kRSsm0EvL4L2DQAyLsrdTRE1SAAQ78AGneWOhAiqkUjX+yEF55rjpjYRBQVl6Jre2+0au4mdVhEVE9puw3WozqaCg0NxbZt2/Djjz/Czs5O2Rvs4OAAKysrCIKAmTNnIjw8HC1atECLFi0QHh4Oa2trjB07VnkfuVwOuVyOpKQkAEB8fDzs7Ozg5eUFJycneHl5qTzX1tYWANCsWTOVHvFWrVohIiICw4YN0/jZmmBCTcbFuRkw9jvgm2Cg+KHU0RDppuc8wG+E1FEQkQRcG9rjlZf4ZRoRGYDH5kRrVUdD69atAwAEBQWpnI+MjERISAiA8n2jCwoKMHXqVGRmZsLf3x+HDh2CnZ2dsvz69euxdOlS5evAwEC1+2giISFBudK4ps/WBPehJuOU9Cuw7VVAUSJ1JETaaTscGLmJi5BRncd9qImIqq8m96HuPnCZTvtQnzr4Ad/bH8M51GScmvcBhq4DBP4TJiPi3QMY+iWTaSIiIpJcdRYlo39wyDcZr/ajgJI84H8zof14FaJa5v088Np3gLmV1JEQERERAQqx/NC2Dqlg9x4Zt84hwMCPpI6CqGpe3cvn/nN7LCIiIjIU1diHmv7BHmoyft2mAKUFwC9hUkdCpM6zG/DaD4DMVupIiIiIiJQE6LDKd41EYtyYUFPd0GMWUFoEREdIHQnRPzy7Aa8zmSYiIiID9Ni+0lrVIRUc8k11R9B84MVPAMFU6kiIgGa9gXG7AZl2Wy8QERER1QYuSqYfTKipbnluEvDqt4C5tdSRUH3W6iVgzE7Agv8OiYiIyEBxDrVeMKGmuqfVi8CEfYB1Q6kjofqo/WjglS2AmYXUkRARERFRDWNCTXVTk87AW4cBp2ZSR0L1if87wLD1gAmnHRAREZFhE0RRp4NUMaGmusvpGeDNw0CT56SOhOo6Uxnw8pfAoI8AgetfUu0KCwuDIAgqh5ubW5V1ioqKsHDhQnh7e0Mmk6FZs2bYtGmTSpk1a9agZcuWsLKygqenJ2bNmoXCwsKabAoREdUmhY4HqeAq31S32TgDE/YCu94C/tondTRUF9k3Bl79L9C4s9SRUD3Wtm1b/PLLL8rXpqZVj5J45ZVXcOfOHWzcuBHNmzdHRkYGSktLlde3bt2K+fPnY9OmTQgICMDVq1cREhICAPjss89qpA1ERFS7dOlxZg+1OibUVPeZWwGv/Bf4eQHw+3qpo6G6xLsHMGozYNtI6kionjMzM3tqr/QjBw8eRExMDJKTk+Hk5AQAaNq0qUqZU6dO4fnnn8fYsWOV18eMGYPTp0/rNW4iIpKQLouMMZ9WwyHfVD+YmACDVgIDVwKmXCyK9MB/CjD+RybTZBASExPh4eEBHx8fjB49GsnJyZWW3bt3L7p06YJVq1ahcePG8PX1xZw5c1BQUKAs06NHD5w9e1aZQCcnJ+PAgQMYPHhwpfctKipCTk6OykFERAbs0T7U2h6kgj3UVL90mwJ4dSsfAn4/UepoyBiZWQIvrQE6jJE6EiIAgL+/P7Zs2QJfX1/cuXMHy5cvR0BAAC5dugRnZ2e18snJyTh+/DgsLS2xe/du3Lt3D1OnTsWDBw+U86hHjx6Nu3fvokePHhBFEaWlpXjnnXcwf/78SuOIiIjA0qVLa6ydRESkX7rsK819qNUJosivGageKs4HDs4Dzm2ROhIyJg6e5fOlPTpKHQlRpfLy8tCsWTPMnTsXs2fPVrvev39/HDt2DHK5HA4ODgCAqKgojBw5Enl5ebCyskJ0dDRGjx6N5cuXw9/fH0lJSZgxYwYmTZqExYsXV/jcoqIiFBUVKV/n5OTA09MT2dnZsLe3r5nGEhHVcTk5OXBwcNDre+mje/YMWAwzM0ut6paWFiLm5Id8b38Me6ipfrKwBob8G2jeD/jf/wEFmVJHRIau6Qvl86VtuL85GTYbGxu0a9cOiYkVj8Jxd3dH48aNlck0ALRu3RqiKOLmzZto0aIFFi9ejHHjxuGtt94CALRr1w55eXl4++23sXDhQpiYqM8Yk8lkkMlkNdMoIiLSP12GcLMvVg3nUFP91mYIMOVEebJEVBFzG2DgR8D4vUymySgUFRXhypUrcHd3r/D6888/j9u3b+Phw4fKc1evXoWJiQmaNGkCAMjPz1dLmk1NTSGKIjiwjYiobhAUuh2kigk1kUPj8mSpzweACQdt0GOe6QVMPQV0e6d8YTsiAzRnzhzExMQgJSUFv//+O0aOHImcnBxMmDABALBgwQKMHz9eWX7s2LFwdnbGxIkTcfnyZRw9ehTvvfce3njjDVhZWQEAgoODsW7dOuzYsQMpKSk4fPgwFi9ejCFDhjx1Sy4iIjISXJRML5g9EAHlydIL7wI+QcCuN4HMFKkjIilZNgAGhAMdX5M6EqKnunnzJsaMGYN79+6hUaNG6NatG2JjY+Ht7Q0ASE9Px40bN5TlbW1tcfjwYUyfPh1dunSBs7MzXnnlFSxfvlxZZtGiRRAEAYsWLcKtW7fQqFEjBAcHY8WKFbXePiIiqiHcNksvuCgZ0ZOKHgJHPgTOfA0oSqWOhmpbm5eBFz8BbF2kjoTIqNXEQjpERPVNTS5K1qvL+zotSvbbH+F8b38MxzASPUlmW75n9TsngWZ9pI6GaoutG/Dqt8ArW5hMExERUd3HId96wYSaqDKNWgLjooAxOwHn5lJHQzWp4zgg9HegdbDUkRARERGREeEcaqKnaTkQaN4H+H09EPMxUJQtdUSkL02eA/ouAZr2kDoSIiIiotolAtB21W52UKthQk2kCVNzIGA68OyY8vnV57YAIvcNMFruzwK9FgG+/aWOhIiIiEgSgihC0HIIt7bl6wMO+SbShk1DIPhz4O0YwJu9mkbHpU35POnJR5lMExERUf0mQoc51FIHbXjYQ02kC/f2wMT9QMJPwLFPgZtnpI6IquLcAgiaD7Qdzv2kiYiIiADdFhljD7UafrIkqo6Wg4C3fgFC9nNFcEPk2BQYuq58wbF2I5lMExERET2i0PHQUEREBLp27Qo7Ozu4uLhg6NChSEhIUCkjiiLCwsLg4eEBKysrBAUF4dKlSyplNmzYgKCgINjb20MQBGRlZak9a8iQIfDy8oKlpSXc3d0xbtw43L59u8r4QkJCIAiCytGtWzfNG/g3frok0oemPcpXBJ98DPAbAZhw8IekHH2Al9YA084CHcYCJqZSR0RERERkUB7Nodb20FRMTAxCQ0MRGxuLw4cPo7S0FP3790deXp6yzKpVq7B69WqsXbsWZ86cgZubG/r164fc3Fxlmfz8fAwcOBDvv/9+pc/q1asXvvvuOyQkJGDXrl24du0aRo4c+dQYBw4ciPT0dOVx4MABjdv3iCCK7Lcn0rvsW8CZr4Fz3wD596WOpn4QTAHfAUDXN8tHCwiC1BER1Ws5OTlwcHBAdnY27O3tpQ6HiMgo1cR76aN79mn7HsxMZVrVLS0rwq+XPtYpnrt378LFxQUxMTEIDAyEKIrw8PDAzJkzMW/ePABAUVERXF1dsXLlSkyePFmlfnR0NHr16oXMzEw0aNCgymft3bsXQ4cORVFREczNzSssExISgqysLOzZs0erdjyJPdRENcGhcfl2TLMuA0PWAm7tpI6o7rJxAV54F5jxJzBmO9C8L5NpIiIiIgOTnV2+9ayTkxMAICUlBXK5HP37/7NQrEwmQ8+ePXHy5Emdn/PgwQNs3boVAQEBlSbTj0RHR8PFxQW+vr6YNGkSMjIytH4ex6US1SRzS6DTuPLj1jng0m7g8h4g64bUkRk3E/Py3ugOY4EW/cu3NSMiIiIizVVjUbKcnByV0zKZDDJZ5b3doihi9uzZ6NGjB/z8/AAAcrkcAODq6qpS1tXVFdevX9cuLgDz5s3D2rVrkZ+fj27dumHfvn1Vlh80aBBGjRoFb29vpKSkYPHixejduzfOnj1bZVuexB5qotrSuBPQ/0NgZjww6Uj5vtYOXlJHZVzcnwUGrgTeTQBGbwVaDWYyTURERKQLrbfM+icB9/T0hIODg/KIiIio8lHTpk3DhQsXsH37drVrwhMjC0VRVDuniffeew/nz5/HoUOHYGpqivHjx6Oq2c2vvvoqBg8eDD8/PwQHB+Onn37C1atXsX//fq2eyx5qIik07lx+9F8O3DwLXN4NXPoRyGbPtQpzG8DnhfJh3M37Ak4+UkdEREREVDcoAGibt/69yndaWprKHOqqenSnT5+OvXv34ujRo2jSpInyvJubG4Dynmp3d3fl+YyMDLVea000bNgQDRs2hK+vL1q3bg1PT0/Exsaie/fuGtV3d3eHt7c3EhMTtXouE2oiqTXpXH48Sq4vRZXvb/3gmtSRScOlDdC8T3kC7RUAmFlIHRERERFRnaPtqt2P6gCAvb39UxclE0UR06dPx+7duxEdHQ0fH9WOER8fH7i5ueHw4cPo2LEjAKC4uBgxMTFYuXKlVnFV9GygfJEzTd2/fx9paWkqyb0mmFATGZJHyfWAFcDDu0Da7/8ct+OAMs3fFIyGzAF4puc/vdAOjaWOiIiIiKjuq8Ycak2EhoZi27Zt+PHHH2FnZ6ecM+3g4AArKysIgoCZM2ciPDwcLVq0QIsWLRAeHg5ra2uMHTtWeR+5XA65XI6kpCQAQHx8POzs7ODl5QUnJyecPn0ap0+fRo8ePeDo6Ijk5GR88MEHaNasmUrvdKtWrRAREYFhw4bh4cOHCAsLw4gRI+Du7o7U1FS8//77aNiwIYYNG6bVj4QJNZGhsm0EtH6p/ACA0mIgPe6xJPs08PCOpCFqzbIB4Nq2vBfatQ3g1h5w7wCY8q2IiIiIqFYpREDQMqFWaF5+3bp1AICgoCCV85GRkQgJCQEAzJ07FwUFBZg6dSoyMzPh7++PQ4cOwc7OTll+/fr1WLp0qfJ1YGCgyn2srKwQFRWFJUuWIC8vD+7u7hg4cCB27NihMhQ9ISFBudK4qakp4uPjsWXLFmRlZcHd3R29evXCzp07VZ6tCe5DTWTMMlPLE+t7ieUrhz86ctMBsUy6uEzMgYa+5cmzaxvA1a88iWbvM1G9wX2oiYiqryb3oe7bbKZO+1D/cm0N39sfw24hImPm2LT8eFJZKZBzUzXJzkor/2/OLaD4IVCcD5QWAKJCs2eZmAFWToC1E2DtDFg5lv9X+frv/zbwLE+mufo2ERERkeGq4SHf9QUTaqK6yNSs8mT7SSUF/xxi2d9vroq/DxEwMS1Pmi0dajpqIiIiIqo1OiTUYEL9JCbURPWduVX5QURERET1B3uo9YIJNRERERERUX2jEKF1j7MWi5LVF0yoiYiIiIiI6ptHU/y0rUMqmFATERERERHVNxzyrRcmUgdARDWjadOmWLNmjdRhEBEREZEhUoi6HaSCCTWRHoSEhEAQBLUjKSlJ6tCIiIiIiKiGcMg3kZ4MHDgQkZGRKucaNWokUTRERERERFXgkG+9YA81kZ7IZDK4ubmpHKampvjf//6Hzp07w9LSEs888wyWLl2K0tJSZT1BEPCf//wHL730EqytrdG6dWucOnUKSUlJCAoKgo2NDbp3745r164p61y7dg0vv/wyXF1dYWtri65du+KXX36pMr7s7Gy8/fbbcHFxgb29PXr37o0///yzxn4eRERERGTARPyTVGt8SB204WFCTVSDfv75Z7z++uv4v//7P1y+fBn/+c9/sHnzZqxYsUKl3Icffojx48cjLi4OrVq1wtixYzF58mQsWLAAf/zxBwBg2rRpyvIPHz7Eiy++iF9++QXnz5/HgAEDEBwcjBs3blQYhyiKGDx4MORyOQ4cOICzZ8+iU6dO6NOnDx48eFBzPwAiIiIiMkxaJ9M69GjXAxzyTaQn+/btg62trfL1oEGDcOfOHcyfPx8TJkwAADzzzDP48MMPMXfuXCxZskRZduLEiXjllVcAAPPmzUP37t2xePFiDBgwAAAwY8YMTJw4UVn+2WefxbPPPqt8vXz5cuzevRt79+5VSbwf+e233xAfH4+MjAzIZDIAwCeffII9e/bghx9+wNtvv63HnwQRERERGTyFAoCW22ApuG3Wk5hQE+lJr169sG7dOuVrGxsbNG/eHGfOnFHpkS4rK0NhYSHy8/NhbW0NAGjfvr3yuqurKwCgXbt2KucKCwuRk5MDe3t75OXlYenSpdi3bx9u376N0tJSFBQUVNpDffbsWTx8+BDOzs4q5wsKClSGkhMRERFRPcE51HrBhJpITx4l0I9TKBRYunQphg8frlbe0tJS+Wdzc3PlnwVBqPSc4u9vBd977z38/PPP+OSTT9C8eXNYWVlh5MiRKC4urjA2hUIBd3d3REdHq11r0KCBZg0kIqoFoijiz+hLuBBzGVZ2Vgh6NQCNmjg/vSIREWmHCbVeMKEmqkGdOnVCQkKCWqJdXceOHUNISAiGDRsGoHxOdWpqapVxyOVymJmZoWnTpnqNhYhIX4oKivDByytx7pd4mJqZQKEQ8fX8bzFz/dsY9GYfqcMjIiJSw4SaqAZ98MEHeOmll+Dp6YlRo0bBxMQEFy5cQHx8PJYvX67zfZs3b46oqCgEBwdDEAQsXrxY2Xtdkb59+6J79+4YOnQoVq5ciZYtW+L27ds4cOAAhg4dii5duugcCxGRvmxdvgvnj1wEAJSVlr+niRDx2eT/oN0LrdHE10PK8IiI6haFCK2X7Vawh/pJXOWbqAYNGDAA+/btw+HDh9G1a1d069YNq1evhre3d7Xu+9lnn8HR0REBAQEIDg7GgAED0KlTp0rLC4KAAwcOIDAwEG+88QZ8fX0xevRopKamKudsExFJ7cDXv0Ks4MOaIAj45b9HJYiIiKjuEkWFTgepEkSRA+GJiIhIv3JycuDg4IDs7GzY29trVGegxavKnunHmZqZYtCbvTFjHXckIKL6RZf3Uk3v2afBeJgJFlrVLRWL8WvWFr3GY+zYQ01EREQGoWXX5jAxEdTOl5WWoXU3XwkiIiKqw7gPtV4woSYiIiKDMGHpqxDFf3Y2AAATUxN4NHdDz1e6SxgZEVEdpFDodpAKJtRERERkEDr1bY8P/zcf3m2bAABMzUzQc1R3fHZ0GWRWMomjIyKqY9hDrRdc5ZuIiIgMhv+LneD/YifkZefB3NICFjJzqUMiIqqTRIUCoqBdjzMXJVPHhJqIiIgMjo2DjdQhEBERPRUTaiIiIiIiovpG1GEfag75VsOEmoiIiIiIqL5RiIDAhLq6mFATERERERHVN6IIQMs50Uyo1XCVbyIiIiIionpGVIg6HZqKiIhA165dYWdnBxcXFwwdOhQJCQmqMYgiwsLC4OHhASsrKwQFBeHSpUsqZTZs2ICgoCDY29tDEARkZWWpPWvIkCHw8vKCpaUl3N3dMW7cONy+fbvq9mvwbE0woSYiIiIiIqpvRIVuh4ZiYmIQGhqK2NhYHD58GKWlpejfvz/y8vKUZVatWoXVq1dj7dq1OHPmDNzc3NCvXz/k5uYqy+Tn52PgwIF4//33K31Wr1698N133yEhIQG7du3CtWvXMHLkyCrj0+TZmhBEkf32REREpF85OTlwcHBAdnY27O3tpQ6HiMgo1cR76aN7BgnDYCZotzVhqViCaHG3TvHcvXsXLi4uiImJQWBgIERRhIeHB2bOnIl58+YBAIqKiuDq6oqVK1di8uTJKvWjo6PRq1cvZGZmokGDBlU+a+/evRg6dCiKiopgbq7eRm2fXRX2UBMREREREVGNys7OBgA4OTkBAFJSUiCXy9G/f39lGZlMhp49e+LkyZM6P+fBgwfYunUrAgICKkym9f1sLkpGREREevdoAFxOTo7EkRARGa9H76E1Mai4VCzSagg3AJSiRCWuR2QyGWQyWaX1RFHE7Nmz0aNHD/j5+QEA5HI5AMDV1VWlrKurK65fv65VXAAwb948rF27Fvn5+ejWrRv27dtXaVl9PpsJNREREendozlonp6eEkdCRGT8cnNz4eDgoJd7WVhYwM3NDcflB3Sqb2trq/bevmTJEoSFhVVaZ9q0abhw4QKOHz+udk0QBJXXoiiqndPEe++9hzfffBPXr1/H0qVLMX78eOzbt6/Ke+nj2UyoiYiISO88PDyQlpYGOzs7nT4Y6VNOTg48PT2RlpZWZ+Zz17U21bX2AGyTsTD0NomiiNzcXHh4eOjtnpaWlkhJSUFxcbHOMT35vl5V7/T06dOxd+9eHD16FE2aNFGed3NzA1DeW+zu7q48n5GRodZzrImGDRuiYcOG8PX1RevWreHp6YnY2Fh0795draw+n82EmoiIiPTOxMRE5YOTIbC3tzfID8zVUdfaVNfaA7BNxsKQ26SvnunHWVpawtLSUu/3fZwoipg+fTp2796N6Oho+Pj4qFz38fGBm5sbDh8+jI4dOwIAiouLERMTg5UrV1b72UD5QmMV0eezmVATERERERGRXoWGhmLbtm348ccfYWdnp5y37ODgACsrKwiCgJkzZyI8PBwtWrRAixYtEB4eDmtra4wdO1Z5H7lcDrlcjqSkJABAfHw87Ozs4OXlBScnJ5w+fRqnT59Gjx494OjoiOTkZHzwwQdo1qyZSu90q1atEBERgWHDhmn8bE0woSYiIiIiIiK9WrduHQAgKChI5XxkZCRCQkIAAHPnzkVBQQGmTp2KzMxM+Pv749ChQ7Czs1OWX79+PZYuXap8HRgYqHIfKysrREVFYcmSJcjLy4O7uzsGDhyIHTt2qAxFT0hIUK40rumzNcF9qImIiKhOKyoqQkREBBYsWFDlPD9jUtfaVNfaA7BNxqIutolqFxNqIiIiIiIiIh2YSB0AERERERERkTFiQk1ERERERESkAybURERERERERDpgQk1ERERGISwsDIIgqBxubm4a1T1x4gTMzMzQoUMHtWtr1qxBy5YtYWVlBU9PT8yaNQuFhYV6jr5i2rYpOjparbwgCPjrr79Uyu3atQtt2rSBTCZDmzZtsHv37ppuilJNtOmrr77CCy+8AEdHRzg6OqJv3744ffp0bTQHQM39PT2yY8cOCIKAoUOH1lAL1NVUm7KyshAaGgp3d3dYWlqidevWOHDgQE03p8baI+X7AxkHbptFRERERqNt27b45ZdflK9NTU2fWic7Oxvjx49Hnz59cOfOHZVrW7duxfz587Fp0yYEBATg6tWryu1cPvvsM73GXhld2pSQkAB7e3vl60aNGin/fOrUKbz66qv48MMPMWzYMOzevRuvvPIKjh8/Dn9/f/0GXwl9tyk6OhpjxoxBQEAALC0tsWrVKvTv3x+XLl1C48aN9Rt8JfTdpkeuX7+OOXPm4IUXXtBPoFrQd5uKi4vRr18/uLi44IcffkCTJk2Qlpam9TZEutJ3ewzh/YEMHxNqIiIiMhpmZmYa90o/MnnyZIwdOxampqbYs2ePyrVTp07h+eefx9ixYwEATZs2xZgxY2q191OXNrm4uKBBgwYVXluzZg369euHBQsWAAAWLFiAmJgYrFmzBtu3b69uuBrRd5u2bt2q8vqrr77CDz/8gF9//RXjx4/XNUyt6LtNAFBWVobXXnsNS5cuxbFjx5CVlVW9ILWk7zZt2rQJDx48wMmTJ2Fubg4A8Pb2rm6YGtN3ewzh/YEMH4d8ExERkdFITEyEh4cHfHx8MHr0aCQnJ1dZPjIyEteuXcOSJUsqvN6jRw+cPXtW+QE5OTkZBw4cwODBg/Uee2W0bRMAdOzYEe7u7ujTpw9+++03lWunTp1C//79Vc4NGDAAJ0+e1GvcVdF3m56Un5+PkpISODk56Svkp6qJNi1btgyNGjXCm2++WRMhP5W+27R37150794doaGhcHV1hZ+fH8LDw1FWVlZTTVCh7/YYwvsDGT72UBMREZFR8Pf3x5YtW+Dr64s7d+5g+fLlCAgIwKVLl+Ds7KxWPjExEfPnz8exY8dgZlbxR57Ro0fj7t276NGjB0RRRGlpKd555x3Mnz+/ppsDQPs2ubu7Y8OGDejcuTOKiorw3//+F3369EF0dDQCAwMBAHK5HK6urir1XF1dIZfLjbZNT5o/fz4aN26Mvn371nRzANRMm06cOIGNGzciLi6uVtrwpJpoU3JyMo4cOYLXXnsNBw4cQGJiIkJDQ1FaWooPPvjA6Noj9fsDGQmRiIiIyAg9fPhQdHV1FT/99FO1a6WlpWKXLl3EdevWKc8tWbJEfPbZZ1XK/fbbb6Krq6v41VdfiRcuXBCjoqJET09PcdmyZTUdfoWqalNlXnrpJTE4OFj52tzcXNy2bZtKmW+//VaUyWR6i1Mb+mjT41auXCk6OjqKf/75p75C1Fp125STkyM2bdpUPHDggPL6hAkTxJdfflnfoWpMH39PLVq0ED09PcXS0lLluU8//VR0c3PTa6ya0Ed7DO39gQwTe6iJiIjIKNnY2KBdu3ZITExUu5abm4s//vgD58+fx7Rp0wAACoUCoijCzMwMhw4dQu/evbF48WKMGzcOb731FgCgXbt2yMvLw9tvv42FCxfCxKR2Z8dV1abKdOvWDd9++63ytZubm1pvdEZGhlqvdW3RR5se+eSTTxAeHo5ffvkF7du312eYWqlum65du4bU1FQEBwcrrysUCgDl84ATEhLQrFkz/Qb9FPr4e3J3d4e5ubnKYmCtW7eGXC5HcXExLCws9BpzVfTRHkN7fyDDxH8FREREZJSKiopw5coVuLu7q12zt7dHfHw84uLilMeUKVPQsmVLxMXFKVe7zs/PV/tQbGpqClEUIYpirbTjcVW1qTLnz59XKd+9e3ccPnxYpcyhQ4cQEBCgtzi1oY82AcDHH3+MDz/8EAcPHkSXLl30HaZWqtumVq1aqf37HDJkCHr16oW4uDh4enrWVOiV0sff0/PPP4+kpCTllwMAcPXqVbi7u9dqMg3opz2G9v5ABkrS/nEiIiIiDb377rtidHS0mJycLMbGxoovvfSSaGdnJ6ampoqiKIrz588Xx40bV2n9ioZ8L1myRLSzsxO3b98uJicni4cOHRKbNWsmvvLKKzXZFCVt2/TZZ5+Ju3fvFq9evSpevHhRnD9/vghA3LVrl7LMiRMnRFNTU/Gjjz4Sr1y5In700UeimZmZGBsba7RtWrlypWhhYSH+8MMPYnp6uvLIzc012jY9qbaHfNdEm27cuCHa2tqK06ZNExMSEsR9+/aJLi4u4vLly42yPVK/P5Bx4JBvIiIiMgo3b97EmDFjcO/ePTRq1AjdunVDbGysclue9PR03LhxQ6t7Llq0CIIgYNGiRbh16xYaNWqE4OBgrFixoiaaoEbbNhUXF2POnDm4desWrKys0LZtW+zfvx8vvviiskxAQAB27NiBRYsWYfHixWjWrBl27txZa3tQ10SbvvzySxQXF2PkyJEqz1qyZAnCwsKMsk1Sq4k2eXp64tChQ5g1axbat2+Pxo0bY8aMGZg3b55Rtkfq9wcyDoIocrwCERERERERkbY4h5qIiIiIiIhIB0yoiYiIiIiIiHTAhJqIiIiIiIhIB0yoiYiIiIiIiHTAhJqIiIiIiIhIB0yoiYiIiIiIiHTAhJqIiIiIiIhIB0yoiYiIiIiIiHTAhJqIiIiICEBqaioEQUBcXJzUoRCRkWBCTURERERGKyQkBIIgYMqUKWrXpk6dCkEQEBISUvuBEVG9wISaiIiIiIyap6cnduzYgYKCAuW5wsJCbN++HV5eXhJGRkR1HRNqIiIiIjJqnTp1gpeXF6KiopTnoqKi4OnpiY4dOyrPHTx4ED169ECDBg3g7OyMl156CdeuXavy3pcvX8aLL/dbkmgAAALCSURBVL4IW1tbuLq6Yty4cbh3716NtYWIjAsTaiIiIiIyehMnTkRkZKTy9aZNm/DGG2+olMnLy8Ps2bNx5swZ/PrrrzAxMcGwYcOgUCgqvGd6ejp69uyJDh064I8//sDBgwdx584dvPLKKzXaFiIyHmZSB0BEREREVF3jxo3DggULlAuLnThxAjt27EB0dLSyzIgRI1TqbNy4ES4uLrh8+TL8/PzU7rlu3Tp06tQJ4eHhynObNm2Cp6cnrl69Cl9f3xprDxEZBybURERERGT0GjZsiMGDB+Obb76BKIoYPHgwGjZsqFLm2rVrWLx4MWJjY3Hv3j1lz/SNGzcqTKjPnj2L3377Dba2tmrXrl27xoSaiJhQExEREVHd8MYbb2DatGkAgC+++ELtenBwMDw9PfHVV1/Bw8MDCoUCfn5+KC4urvB+CoUCwcHBWLlypdo1d3d3/QZPREaJCTURERER1QkDBw5UJscDBgxQuXb//n1cuXIF//nPf/DCCy8AAI4fP17l/Tp16oRdu3ahadOmMDPjx2YiUsdFyYiIiIioTjA1NcWVK1dw5coVmJqaqlxzdHSEs7MzNmzYgKSkJBw5cgSzZ8+u8n6hoaF48OABxowZg9OnTyM5ORmHDh3CG2+8gbKysppsChEZCSbURERERFRn2Nvbw97eXu28iYkJduzYgbNnz8LPzw+zZs3Cxx9/XOW9PDw8cOLECZSVlWHAgAHw8/PDjBkz4ODgABMTfowmIkAQRVGUOggiIiIiIiIiY8Ov1oiIiIiIiIh0wISaiIiIiIiISAdMqImIiIiIiIh0wISaiIiIiIiISAdMqImIiIiIiIh0wISaiIiIiIiISAdMqImIiIiIiIh0wISaiIiIiIiISAdMqImIiIiIiIh0wISaiIiIiIiISAdMqImIiIiIiIh0wISaiIiIiIiISAf/D7ybGC+CcaqOAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# create a matplotlib figure with 4 subplots in a 2 rows/2 columns grid\n", + "fig, ax = plt.subplots(2, 2, figsize=(10, 8), tight_layout=True)\n", + "\n", + "# line plot with 2 curves (Males and Females) in the top left corner (ax=ax[0, 0])\n", + "# we do not want to see the plot yet, so we could specify show=False but this is not\n", + "# necassary because show is False by default if the ax argument is used.\n", + "population['Belgium'].plot(ax=ax[0, 0],\n", + " title='line plot')\n", + "\n", + "# bar plot in the top right corner (0, 1)\n", + "population[2017].percent('gender').plot.bar(ax=ax[0, 1], \n", + " ylabel='% of population', \n", + " title='bar plot')\n", + "\n", + "# pie chart in the bottom left corner (1, 0)\n", + "population['Belgium', 2017].plot.pie(ax=ax[1, 0], \n", + " title='pie chart')\n", + "\n", + "# scatter plot in the bottom right corner (1, 1)\n", + "population['Belgium'].plot.scatter(ax=ax[1, 1], \n", + " x='Male', y='Female', \n", + " # using the year as color index\n", + " c=population.time,\n", + " # use a specific color map (otherwise we get a gray gradient)\n", + " colormap='viridis',\n", + " title='scatter plot',\n", + " # since this is the last command to create our plot, we want to display it\n", + " show=True)" ] }, { @@ -252,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.12" }, "livereveal": { "autolaunch": false, @@ -260,5 +590,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/larray/__init__.py b/larray/__init__.py index f4f4d9aa9..eb3d91aaa 100644 --- a/larray/__init__.py +++ b/larray/__init__.py @@ -1,4 +1,4 @@ -__version__ = '0.34.6' +__version__ = '0.35-dev' from larray.core.axis import Axis, AxisCollection, X diff --git a/larray/core/array.py b/larray/core/array.py index f8680d935..e4aec2ea7 100644 --- a/larray/core/array.py +++ b/larray/core/array.py @@ -7116,8 +7116,6 @@ def to_clipboard(self, *args, **kwargs) -> None: def plot(self) -> PlotObject: r"""Plot the data of the array into a graph (window pop-up). - The graph can be tweaked to achieve the desired formatting and can be saved to a .png file. - Parameters ---------- kind : str @@ -7132,6 +7130,16 @@ def plot(self) -> PlotObject: - 'pie' : pie plot - 'scatter' : scatter plot (if array's dimensions >= 2) - 'hexbin' : hexbin plot (if array's dimensions >= 2) + - 'heatmap': heatmap plot (if array's dimensions >= 2). + See Array.plot.heatmap for more details. + filepath : str or Path, default None + Save plot as a file at `filepath`. Defaults to None (do not save). + When saving the plot to a file, the function returns None. In other + words, in that case, the plot is no longer available for further + tweaking or display. + show : bool, optional + Whether to display the plot directly. + Defaults to True if `filepath` is None and `ax` is None, False otherwise. ax : matplotlib axes object, default None subplots : boolean, Axis, int, str or tuple, default False Make several subplots. @@ -7184,15 +7192,41 @@ def plot(self) -> PlotObject: Colormap to select colors from. If string, load colormap with that name from matplotlib. colorbar : boolean, optional If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots) - position : float + position : float, optional Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). - Default is 0.5 (center) - yerr : array-like + Defaults to 0.5 (center). + yerr : array-like, optional Error bars on y axis - xerr : array-like + xerr : array-like, optional Error bars on x axis - stacked : boolean, default False in line and bar plots, and True in area plot. - If True, create stacked plot. + stack : boolean, Axis, int, str or tuple, optional + Make a stacked plot. + - if an Axis (or int or str), stack that axis. + - if a tuple of Axis (or int or str), stack each combination of labels of those axes. + - True is equivalent to all axes (not already used in other arguments) except the last. + Defaults to False in line and bar plots, and True in area plot. + animate : Axis, int, str or tuple, optional + Make an animated plot. + - if an Axis (or int or str), animate that axis (create one image per label on that axis). + One would usually use a time-related axis. + - if a tuple of Axis (or int or str), animate each combination of labels of those axes. + Defaults to None. + anim_params: dict, optional + Optional parameters to control how animations are saved to file. + - writer : str, optional + Backend to use. Defaults to 'pillow' for images (.gif .png and + .tiff), 'ffmpeg' otherwise. + - fps : int, optional + Animation frame rate (per second). Defaults to 5. + - metadata : dict, optional + Dictionary of metadata to include in the output file. + Some keys that may be of use include: title, artist, genre, + subject, copyright, srcform, comment. Defaults to {}. + - bitrate : int, optional + The bitrate of the movie, in kilobits per second. Higher values + means higher quality movies, but increase the file size. + A value of -1 lets the underlying movie encoder select the + bitrate. **kwargs : keywords Options to pass to matplotlib plotting method @@ -7206,44 +7240,59 @@ def plot(self) -> PlotObject: Examples -------- - >>> import matplotlib.pyplot as plt - >>> # let us define an array with some made up data - >>> arr = Array([[5, 20, 5, 10], [6, 16, 8, 11]], 'gender=M,F;year=2018..2021') + Let us first define an array with some made up data + + >>> import larray as la + >>> arr = la.Array([[5, 20, 5, 10], + ... [6, 16, 8, 11]], 'gender=M,F;year=2018..2021') Simple line plot - >>> arr.plot() - >>> # show figure (it also resets it after showing it! Do not call it before savefig) - >>> plt.show() + >>> arr.plot() # doctest: +SKIP + - Line plot with grid and a title + Line plot with grid and a title, saved in a file - >>> arr.plot(grid=True, title='line plot') - >>> # save figure in a file (see matplotlib.pyplot.savefig documentation for more details) - >>> plt.savefig('my_file.png') + >>> arr.plot(grid=True, title='line plot', filepath='my_file.png') 2 bar plots (one for each gender) sharing the same y axis, which makes sub plots easier to compare. By default sub plots are independant of each other and the axes ranges are computed to "fit" just the data for their individual plot. - >>> arr.plot.bar(subplots='gender', sharey=True) - >>> plt.show() + >>> arr.plot.bar(subplots='gender', sharey=True) # doctest: +SKIP + array([, + ], dtype=object) + + A stacked bar plot (genders are stacked) + + >>> arr.plot.bar(stack='gender') # doctest: +SKIP + + + An animated bar chart (with two bars). We set explicit y bounds via ylim so that the + same boundaries are used for the whole animation. + + >>> arr.plot.bar(animate='year', ylim=(0, 22), filepath='myanim.avi') # doctest: +SKIP Create a figure containing 2 x 2 graphs + >>> import matplotlib.pyplot as plt >>> # see matplotlib.pyplot.subplots documentation for more details - >>> fig, ax = plt.subplots(2, 2, figsize=(10, 8), tight_layout=True) # doctest: +SKIP + >>> fig, ax = plt.subplots(2, 2, figsize=(10, 8), tight_layout=True) # doctest: +SKIP >>> # line plot with 2 curves (Males and Females) in the top left corner (0, 0) - >>> arr.plot(ax=ax[0, 0], title='line plot') # doctest: +SKIP + >>> arr.plot(ax=ax[0, 0], title='line plot') # doctest: +SKIP + >>> # bar plot with stacked values in the top right corner (0, 1) - >>> arr.plot.bar(ax=ax[0, 1], stacked=True, title='stacked bar plot') # doctest: +SKIP + >>> arr.plot.bar(ax=ax[0, 1], stack='gender', title='stacked bar plot') # doctest: +SKIP + >>> # area plot in the bottom left corner (1, 0) - >>> arr.plot.area(ax=ax[1, 0], title='area plot') # doctest: +SKIP + >>> arr.plot.area(ax=ax[1, 0], title='area plot') # doctest: +SKIP + >>> # scatter plot in the bottom right corner (1, 1), using the year as color >>> # index and a specific colormap >>> arr.plot.scatter(ax=ax[1, 1], x='M', y='F', c=arr.year, colormap='viridis', - ... title='scatter plot') # doctest: +SKIP - >>> plt.show() # doctest: +SKIP + ... title='scatter plot') # doctest: +SKIP + + >>> plt.show() # doctest: +SKIP """ return PlotObject(self) diff --git a/larray/core/plot.py b/larray/core/plot.py index fb571dbee..68d426584 100644 --- a/larray/core/plot.py +++ b/larray/core/plot.py @@ -1,7 +1,13 @@ +from pathlib import Path +import warnings + import numpy as np import pandas as pd -from larray import IGroup, Axis, AxisCollection, Group +from larray.core.abstractbases import ABCArray +from larray.core.axis import Axis, AxisCollection +from larray.core.group import Group, IGroup +from larray.util.misc import deprecate_kwarg def _use_pandas_plot_docstring(f): @@ -16,7 +22,7 @@ def __init__(self, array): self.array = array @staticmethod - def _handle_x_y_axes(axes, x, y, subplots): + def _handle_x_y_axes(axes, animate, subplots, x, y): label_axis = None if np.isscalar(x) and x not in axes: @@ -34,15 +40,21 @@ def _handle_x_y_axes(axes, x, y, subplots): def handle_axes_arg(avail_axes, arg): if arg is not None: arg = avail_axes[arg] + avail_axes = avail_axes - arg if isinstance(arg, Axis): arg = AxisCollection([arg]) - avail_axes = avail_axes - arg return avail_axes, arg + available_axes = axes + if animate: + available_axes, animate_axes = handle_axes_arg(available_axes, animate) + else: + animate_axes = AxisCollection() + if label_axis is not None: - available_axes = axes - label_axis + available_axes = available_axes - label_axis else: - available_axes, x = handle_axes_arg(axes, x) + available_axes, x = handle_axes_arg(available_axes, x) available_axes, y = handle_axes_arg(available_axes, y) if subplots is True: @@ -82,7 +94,7 @@ def handle_axes_arg(avail_axes, arg): assert isinstance(subplot_axes, AxisCollection) assert y is None - return subplot_axes, series_axes, x, y + return animate_axes, subplot_axes, x, y, series_axes @staticmethod def _to_pd_obj(array): @@ -91,8 +103,119 @@ def _to_pd_obj(array): else: return array.to_frame() + @staticmethod + def _plot_heat_map(array, x=None, y=None, numhaxes=1, axes_names=True, maxticks=10, ax=None, + # TODO: we *might* want to default to False for wildcard axes (for label axes, even + # numeric ones, an inverted axis is more natural) + # TODO: rename to topdown_yaxis or zero_top_yaxis or y0_top or whatever where + # the name actually helps knowing the direction + invert_yaxis=True, + x_ticks_top=True, colorbar=False, **kwargs): + from larray.util.plot import MaxNMultipleWithOffsetLocator + + assert ax is not None + + # TODO: check if we should handle those here??? + kwargs.pop('kind') + kwargs.pop('legend') + # This is needed to support plotting using imshow (see below) + if 'aspect' not in kwargs: + kwargs['aspect'] = 'auto' + if 'origin' not in kwargs: + kwargs['origin'] = 'lower' + title = kwargs.pop('title', None) + if title is not None: + ax.set_title(title) + if array.ndim < 2: + array = array.expand(Axis([''], '')) + + # TODO: see how much of this is already handled in _plot_array + axes = array.axes + if x is None and y is None: + x = axes[:-numhaxes] + + if y is None: + y = array.axes - x + else: + if isinstance(y, str): + y = [y] + y = array.axes[y] + + if x is None: + x = array.axes - y + else: + if isinstance(x, str): + x = [x] + x = array.axes[x] + + array = array.transpose(y + x).combine_axes([y, x]) + + # block size is the size of the other (non-first) combined axes + x_block_size = int(x[1:].size) + y_block_size = int(y[1:].size) + c = ax.imshow(array.data, **kwargs) + + # place major ticks in the middle of blocks so that labels are centered + xlabels = x[0].labels + ylabels = y[0].labels + + def format_x_tick(tick_val, tick_pos): + label_index = int(tick_val) // x_block_size + return xlabels[label_index] if label_index < len(xlabels) else '' + + def format_y_tick(tick_val, tick_pos): + label_index = int(tick_val) // y_block_size + return ylabels[label_index] if label_index < len(ylabels) else '' + + # A FuncFormatter is created automatically. + ax.xaxis.set_major_formatter(format_x_tick) + ax.yaxis.set_major_formatter(format_y_tick) + + if invert_yaxis: + ax.invert_yaxis() + + # offset=0 because imshow has some kind of builtin offset + x_locator = MaxNMultipleWithOffsetLocator(min(maxticks, len(xlabels)), offset=0) + y_locator = MaxNMultipleWithOffsetLocator(min(maxticks, len(ylabels)), offset=0) + ax.xaxis.set_major_locator(x_locator) + ax.yaxis.set_major_locator(y_locator) + + if x_ticks_top: + ax.xaxis.tick_top() + ax.xaxis.set_label_position('top') + + # enable grid lines for minor ticks on axes when we have several "levels" for that axis + if len(x) > 1: + # place minor ticks for grid lines between each block on the main axis + ax.set_xticks(np.arange(x_block_size, x.size, x_block_size), minor=True) + ax.grid(True, axis='x', which='minor') + # hide all ticks on x axis + ax.tick_params(axis='x', which='both', bottom=False, top=False) + + if len(y) > 1: + ax.set_yticks(np.arange(y_block_size, y.size, y_block_size), minor=True) + ax.grid(True, axis='y', which='minor') + # hide all ticks on y axis + ax.tick_params(axis='y', which='both', left=False, right=False) + + # set axes names + if axes_names: + ax.set_xlabel('\n'.join(x.names)) + ax.set_ylabel('\n'.join(y.names)) + + if colorbar: + ax.figure.colorbar(c) + return ax + @staticmethod def _plot_array(array, *args, x=None, y=None, series=None, _x_axes_last=False, **kwargs): + kind = kwargs.get('kind', 'line') + if kind is None: + kind = 'line' + # heatmaps are special because they do not go via Pandas + if kind == 'heatmap': + return PlotObject._plot_heat_map(array, x=x, y=y, **kwargs) + label_axis = None if array.ndim == 1: pass @@ -122,9 +245,6 @@ def _plot_array(array, *args, x=None, y=None, series=None, _x_axes_last=False, * # move label_axis last (it must be a dataframe column) array = array.transpose(..., label_axis) - kind = kwargs.get('kind', 'line') - if kind is None: - kind = 'line' lineplot = kind == 'line' # TODO: why don't we handle all line plots this way? if lineplot and label_axis is not None and series is not None and len(series) > 0: @@ -166,55 +286,74 @@ def _plot_array(array, *args, x=None, y=None, series=None, _x_axes_last=False, * return PlotObject._to_pd_obj(array).plot(*args, x=x, y=y, **kwargs) + @deprecate_kwarg('stacked', 'stack') def __call__(self, x=None, y=None, ax=None, subplots=False, layout=None, figsize=None, sharex=None, sharey=False, tight_layout=None, constrained_layout=None, title=None, legend=None, - **kwargs): + animate=None, filepath=None, show=None, **kwargs): from matplotlib import pyplot as plt array = self.array legend_kwargs = legend if isinstance(legend, dict) else {} - subplot_axes, series_axes, x, y = PlotObject._handle_x_y_axes(array.axes, x, y, subplots) + if 'stack' in kwargs: + if y is not None: + raise ValueError("in Array.plot(), cannot use both the 'y' argument and " + "give axes for the 'stack' argument") + # checking that 'stacked' is not also given is done in deprecate_kwarg + y = kwargs.pop('stack') + if y is True: + y = None + warnings.warn("in Array.plot(), using stack=True is deprecated, please use " + "stack=axis_name instead", FutureWarning) + kwargs['stacked'] = True + animate_axes, subplot_axes, x, y, series_axes = PlotObject._handle_x_y_axes(array.axes, animate, subplots, x, y) + if show is None: + show = filepath is None and ax is None if constrained_layout is None and tight_layout is None: constrained_layout = True - if subplots: - if ax is not None: - raise ValueError("ax cannot be used in combination with subplots argument") + if ax is None: fig = plt.figure(figsize=figsize, tight_layout=tight_layout, constrained_layout=constrained_layout) - - num_subplots = subplot_axes.size - if layout is None: - subplots_shape = subplot_axes.shape - if len(subplots_shape) > 2: - # default to last axis horizontal, other axes combined vertically - layout = np.prod(subplots_shape[:-1]), subplots_shape[-1] - else: - layout = subplot_axes.shape - - if sharex is None: - sharex = True - ax = fig.subplots(*layout, sharex=sharex, sharey=sharey) - # it is easier to always work with a flat array - flat_ax = ax.flat - # remove blank plot(s) at the end, if any - if len(flat_ax) > num_subplots: - for plot_ax in flat_ax[num_subplots:]: - plot_ax.remove() - # this not strictly necessary but is cleaner in case we reuse flax_ax - flat_ax = flat_ax[:num_subplots] - if title is not None: - fig.suptitle(title) - for i, (ndkey, subarr) in enumerate(array.items(subplot_axes)): - title = ' '.join(str(ak) for ak in ndkey) - self._plot_array(subarr, x=x, y=y, series=series_axes, ax=flat_ax[i], legend=False, title=title, - **kwargs) + if subplots or layout is not None: + if layout is None: + subplots_shape = subplot_axes.shape + if len(subplots_shape) > 2: + # default to last axis horizontal, other axes combined vertically + layout = np.prod(subplots_shape[:-1]), subplots_shape[-1] + else: + layout = subplot_axes.shape + if sharex is None: + sharex = True + ax_to_return = fig.subplots(*layout, sharex=sharex, sharey=sharey) + ax = ax_to_return if subplots else ax_to_return.flat[0] + else: + ax = fig.add_subplot() + ax_to_return = ax else: - if ax is None: - fig = plt.figure(figsize=figsize, tight_layout=tight_layout, constrained_layout=constrained_layout) - ax = fig.subplots(1, 1) - self._plot_array(array, x=x, y=y, series=series_axes, ax=ax, legend=False, title=title, **kwargs) + fig = ax.figure + ax_to_return = ax + + anim_kwargs = kwargs.pop('anim_params', {}) + if animate: + from matplotlib.animation import FuncAnimation + + if subplots: + def run(t): + for subplot_ax in ax.flat: + subplot_ax.clear() + self._plot_many(array[t], ax, kwargs, series_axes, subplot_axes, title, x, y) + else: + def run(t): + ax.clear() + self._plot_many(array[t], ax, kwargs, series_axes, subplot_axes, title, x, y) + # TODO: add support for interpolation between frames/labels. Would be best to implement this via + # a generic interpolation API in larray though. + # see https://github.com/julkaar9/pynimate for inspiration + ani = FuncAnimation(fig, run, frames=animate_axes.iter_labels()) + else: + ani = None + self._plot_many(array, ax, kwargs, series_axes, subplot_axes, title, x, y) if legend or legend is None: first_ax = ax.flat[0] if subplots else ax @@ -233,16 +372,93 @@ def __call__(self, x=None, y=None, ax=None, subplots=False, layout=None, figsize # use figure to place legend to add a single legend for all subplots legend_parent = first_ax.figure if subplots else ax legend_parent.legend(handles, labels, **legend_kwargs) - return ax + if filepath is not None: + if ani is None: + fig.savefig(filepath) + else: + if not isinstance(filepath, Path): + filepath = Path(filepath) + if filepath.suffix in {'.htm', '.html'}: + # TODO: we should offer the option to use to_jshtml instead of to_html5_video. Even if it makes the + # files (much) bigger (because they are stored as individual frames) it also adds some useful + # play/pause/next frame/... buttons. + filepath.write_text(f'{ani.to_html5_video()}', encoding='utf8') + else: + writer = anim_kwargs.pop('writer', None) + fps = anim_kwargs.pop('fps', 5) + metadata = anim_kwargs.pop('metadata', None) + bitrate = anim_kwargs.pop('bitrate', None) + if writer is None: + # pillow only supports .gif, .png and .tiff ffmpeg supports .avi, .mov, .mp4 (but needs the + # ffmpeg package installed) + writer = 'pillow' if filepath.suffix in {'.gif', '.png', '.tiff'} else 'ffmpeg' + from matplotlib.animation import writers + if not writers.is_available(writer): + raise Exception(f"Cannot write animation using '{filepath.suffix}' extension " + f"because '{writer}' writer is not available.\n" + "Installing an optional package is probably necessary.") + + ani.save(filepath, writer=writer, fps=fps, metadata=metadata, bitrate=bitrate) + + if show: + # The following line displays the plot window. Note however that + # it is only blocking when no Qt loop is already running (i.e. we are + # not running inside the editor). + # When using the Qt backend this boils down to: + # manager = fig.canvas.manager + # manager.show() + # if block: + # manager.start_main_loop() + # the last line just gets the current Qt QApplication instance (created during + # the first canvas creation) and .exec() it + plt.show(block=True) + # It is important to return ani, because otherwise in the non-blocking case + # (i.e. when run in the editor), the animation is garbage-collected before + # it is drawn, and we get a blank animation. + return (ax_to_return, ani) if ani is not None else ax_to_return + elif filepath is not None: # filepath and not show + plt.close(fig) + return None + else: # no filepath and not show + return (ax_to_return, ani) if ani is not None else ax_to_return + + def _plot_many(self, array, ax, kwargs, series_axes, subplot_axes, title, x, y): + if len(subplot_axes): + num_subplots = subplot_axes.size + if not isinstance(ax, (np.ndarray, ABCArray)) or ax.size < num_subplots: + raise ValueError(f"ax argument value is not compatible with subplot axes ({subplot_axes})") + # it is easier to always work with a flat array + flat_ax = ax.flat + if title is not None: + fig = flat_ax[0].figure + fig.suptitle(title) + # remove blank plot(s) at the end, if any + if len(flat_ax) > num_subplots: + for plot_ax in flat_ax[num_subplots:]: + plot_ax.remove() + # this not strictly necessary but is cleaner in case we reuse flat_ax + flat_ax = flat_ax[:num_subplots] + if kwargs.get('kind') == 'heatmap' and 'x_ticks_top' not in kwargs: + kwargs['x_ticks_top'] = False + for i, (ndkey, subarr) in enumerate(array.items(subplot_axes)): + subplot_title = ' '.join(str(ak) for ak in ndkey) + self._plot_array(subarr, x=x, y=y, series=series_axes, ax=flat_ax[i], legend=False, title=subplot_title, + **kwargs) + else: + self._plot_array(array, x=x, y=y, series=series_axes, ax=ax, legend=False, title=title, **kwargs) + + @deprecate_kwarg('stacked', 'stack') @_use_pandas_plot_docstring def line(self, x=None, y=None, **kwds): return self(kind='line', x=x, y=y, **kwds) + @deprecate_kwarg('stacked', 'stack') @_use_pandas_plot_docstring def bar(self, x=None, y=None, **kwds): return self(kind='bar', x=x, y=y, **kwds) + @deprecate_kwarg('stacked', 'stack') @_use_pandas_plot_docstring def barh(self, x=None, y=None, **kwds): return self(kind='barh', x=x, y=y, **kwds) @@ -257,6 +473,35 @@ def box(self, by=None, x=None, **kwds): ax.get_xaxis().set_visible(False) return ax + def heatmap(self, x=None, y=None, **kwds): + """plot an ND array as a heatmap. + + By default, it uses the last array axis as the X axis and other array axes as Y axis (like the viewer table). + Only the first axis in each "direction" will have its name and labels shown. + + Parameters + ---------- + arr : Array + data to display. + y_axes : int, str, Axis, tuple or AxisCollection, optional + axis or axes to use on the Y axis. Defaults to all array axes except the last `numhaxes` ones. + x_axes : int, str, Axis, tuple or AxisCollection, optional + axis or axes to use on the X axis. Defaults to all array axes except `y_axes`. + numhaxes : int, optional + if x_axes and y_axes are not specified, use the last numhaxes as X axes. Defaults to 1. + axes_names : bool, optional + whether to show axes names. Defaults to True + ax : matplotlib axes object, optional + **kwargs + any extra keyword argument is passed to Matplotlib imshow. + Likely of interest are cmap, vmin, vmax or norm. + + Returns + ------- + matplotlib.AxesSubplot + """ + return self(kind='heatmap', x=x, y=y, **kwds) + @_use_pandas_plot_docstring def hist(self, by=None, bins=10, y=None, **kwds): if y is None: @@ -279,6 +524,7 @@ def kde(self, by=None, bw_method=None, ind=None, y=None, **kwds): y = by return self(kind='kde', bw_method=bw_method, ind=ind, y=y, **kwds) + @deprecate_kwarg('stacked', 'stack') @_use_pandas_plot_docstring def area(self, x=None, y=None, **kwds): return self(kind='area', x=x, y=y, **kwds) diff --git a/larray/random.py b/larray/random.py index c45455b16..4378bf22c 100644 --- a/larray/random.py +++ b/larray/random.py @@ -468,7 +468,7 @@ def choice(choices=None, axes=None, replace=True, p=None, meta=None) -> Array: Using an N-dimensional array as probabilities: >>> proba = Array([[0.15, 0.25, 0.10], - ... [0.20, 0.10, 0.20]], 'a=a0,a1;b=b0..b2') # doctest: +SKIP + ... [0.20, 0.10, 0.20]], 'a=a0,a1;b=b0..b2') # doctest: +SKIP >>> proba # doctest: +SKIP a\b b0 b1 b2 a0 0.15 0.25 0.1 diff --git a/larray/tests/common.py b/larray/tests/common.py index 9f9c3bb21..a2ba248cf 100644 --- a/larray/tests/common.py +++ b/larray/tests/common.py @@ -150,6 +150,9 @@ def meta(): @contextmanager def must_warn(warn_cls=None, msg=None, match=None, check_file=True, num_expected=1): + # makes this function not appear in pytest tracebacks + __tracebackhide__ = True + if num_expected == 0: yield [] else: @@ -210,8 +213,6 @@ def must_warn(warn_cls=None, msg=None, match=None, check_file=True, num_expected def must_raise(exception_cls=None, msg=None, match=None): - from _pytest.python_api import RaisesContext - if msg is not None and match is not None: raise ValueError("BAD TEST: can't use both msg and match arguments") elif msg is None and match is None: @@ -219,8 +220,4 @@ def must_raise(exception_cls=None, msg=None, match=None): elif msg is not None: match = f'^{re.escape(msg)}$' - # This version starts the traceback at the right level. Unfortunately, it uses - # pytest private API, so it might break in the future. Given that our end-users should - # not use this function, I think it is worth it. - return RaisesContext(exception_cls, f"DID NOT RAISE {exception_cls}", match) - + return pytest.raises(exception_cls, match=match) diff --git a/larray/tests/test_array.py b/larray/tests/test_array.py index 3a5bf7b21..656314f47 100644 --- a/larray/tests/test_array.py +++ b/larray/tests/test_array.py @@ -3,6 +3,7 @@ import pytest import numpy as np import pandas as pd +import matplotlib.figure from io import StringIO @@ -5377,11 +5378,22 @@ def test_broadcast_with(): def test_plot(): - pass - # small_h = small['c0'] - # small_h.plot(kind='bar') - # small_h.plot() - # small_h.hist() + import matplotlib.pyplot as plt + + fig, ax = plt.subplots() # doctest: +SKIP + assert isinstance(fig, matplotlib.figure.Figure) + arr = Array([[1, 5, 2], + [3, 2, 4]], axes='a=a0,a1;b=b0,b1,b2') + arr.plot(kind='bar', ax=ax) + fig.savefig('bar.png') + + fig, ax = plt.subplots() # doctest: +SKIP + arr.plot(ax=ax) + fig.savefig('plot.png') + + fig, ax = plt.subplots() # doctest: +SKIP + arr.plot.hist(ax=ax) + fig.savefig('hist.png') # large_data = np.random.randn(1000) # tick_v = np.random.randint(ord('a'), ord('z'), size=1000) diff --git a/larray/tests/test_checked_session.py b/larray/tests/test_checked_session.py index fffd173d3..a86d25c18 100644 --- a/larray/tests/test_checked_session.py +++ b/larray/tests/test_checked_session.py @@ -267,8 +267,11 @@ def test_add_cs(checkedsession): test_add(cs) u = Axis('u=u0..u2') - with must_warn(UserWarning, msg=("Session.add() is deprecated. Please use Session.update() instead.", - f"'u' is not declared in '{cs.__class__.__name__}'")): + messages = ( + "Session.add() is deprecated. Please use Session.update() instead.", + f"'u' is not declared in '{cs.__class__.__name__}'" + ) + with must_warn((FutureWarning, UserWarning), msg=messages): cs.add(u) diff --git a/larray/util/misc.py b/larray/util/misc.py index c4279576d..5dd470029 100644 --- a/larray/util/misc.py +++ b/larray/util/misc.py @@ -665,7 +665,7 @@ def wrapper(*args, **kwargs): # deprecate_kwarg is derived from pandas.util._decorators (0.21) -def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, arg_converter=None, stacklevel=2): +def deprecate_kwarg(old_arg_name: str, new_arg_name: str, mapping=None, arg_converter=None, stacklevel=2): if mapping is not None and not isinstance(mapping, dict): raise TypeError("mapping from old to new argument values must be dict!") diff --git a/larray/util/plot.py b/larray/util/plot.py new file mode 100644 index 000000000..5c29aa568 --- /dev/null +++ b/larray/util/plot.py @@ -0,0 +1,37 @@ +import numpy as np +from matplotlib.ticker import MaxNLocator + + +class MaxNMultipleWithOffsetLocator(MaxNLocator): + def __init__(self, nbins=None, offset=0.5, **kwargs): + super().__init__(nbins, **kwargs) + self.offset = offset + + def tick_values(self, vmin, vmax): + # matplotlib calls them vmin and vmax but they are actually the limits and vmin can be > vmax + invert = vmin > vmax + if invert: + vmin, vmax = vmax, vmin + + max_desired_ticks = self._nbins + # not + 1 because we place ticks in the middle + num_ticks = vmax - vmin + desired_numticks = min(num_ticks, max_desired_ticks) + if desired_numticks < num_ticks: + step = np.ceil(num_ticks / desired_numticks) + else: + step = 1 + vmin = int(vmin) + vmax = int(vmax) + # when we have an offset, we do not add 1 to vmax because we place ticks in the middle + # (by adding the offset), and would result in the last "tick" being outside the limits + stop = vmax + 1 if self.offset == 0 else vmax + new_ticks = np.arange(vmin, stop, step) + if invert: + new_ticks = new_ticks[::-1] + return new_ticks + self.offset + + def __call__(self): + """Return the locations of the ticks.""" + vmin, vmax = self.axis.get_view_interval() + return self.tick_values(vmin, vmax) diff --git a/larray/viewer/__init__.py b/larray/viewer/__init__.py index d15b0b269..dd7b8a25e 100644 --- a/larray/viewer/__init__.py +++ b/larray/viewer/__init__.py @@ -73,19 +73,21 @@ def edit(obj=None, title='', minvalue=None, maxvalue=None, readonly=False, depth raise Exception('edit() is not available because the larray_editor package is not installed') -def debug(depth=0): +def debug(title='Debugger', depth=0): r""" Open a new debug window. Parameters ---------- + title : str, optional + Window title suffix. Defaults to 'Debugger'. depth : int, optional Stack depth where to look for variables. Defaults to 0 (where this function was called). """ try: from larray_editor import debug - debug(depth + 1) + debug(title, depth + 1) except ImportError: raise Exception('debug() is not available because the larray_editor package is not installed') diff --git a/pyproject.toml b/pyproject.toml index 9e95dba91..485b6fd4d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,9 +16,6 @@ addopts = [ "--deselect=larray/inout/xw_reporting.py", # doctest is copied from numpy (and fails for some Python + numpy version combinations) "--deselect=larray/core/array.py::larray.core.array.Array.astype", - # doctest fails (because the plot method returns a matplotlib axis object, - # which we do not mention in the doctest to make it nicer) - "--deselect=larray/core/array.py::larray.core.array.Array.plot", # skip Pandas-leeched doctests because they are not larray-specific and, # without Pandas-specific documentation build infrastructure, they leave # some plots open @@ -70,4 +67,7 @@ ignore = [] # fixable = ["A", "B", "C", "D", "E", "F", "..."] # unfixable = [] -per-file-ignores = {} +[tool.ruff.lint.per-file-ignores] +# F403 `from larray import *` used; unable to detect undefined names +# F405 `abc` may be undefined, or defined from star imports +"doc/source/tutorial/*.ipynb" = ["F403", "F405"] diff --git a/setup.py b/setup.py index 453ee20a5..db6a8d818 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ def readlocal(fname): DISTNAME = 'larray' -VERSION = '0.34.6' +VERSION = '0.35-dev' AUTHOR = 'Gaetan de Menten, Geert Bryon, Johan Duyck, Alix Damman' AUTHOR_EMAIL = 'gdementen@gmail.com' DESCRIPTION = "N-D labeled arrays in Python"