From ba482208d96fb71937b14360f5d828cf435e52e0 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Thu, 6 Jun 2024 20:24:19 +0000 Subject: [PATCH 01/15] updates to documentation for additional gallery --- docs/conf.py | 35 +- docs/getting_started/index.rst | 2 +- docs/getting_started/plots.md | 3 - docs/index.rst | 2 +- {examples => docs/sphinxext}/__init__.py | 0 docs/sphinxext/gallery_order.py | 84 ++ {examples => galleries/examples}/README.txt | 0 galleries/examples/Untitled.ipynb | 810 ++++++++++++++++++ .../examples}/histograms/README.txt | 0 .../examples}/histograms/histogram.py | 0 .../examples}/histograms/layered_histogram.py | 0 .../examples}/line_plots/README.txt | 0 .../examples}/line_plots/SkewT.py | 0 galleries/examples/line_plots/Untitled.ipynb | 207 +++++ .../line_plots/inverted_log_scale.py | 0 .../examples}/line_plots/line_plot.py | 0 .../examples}/line_plots/line_plot_options.py | 0 .../examples}/line_plots/multi_line_plot.py | 0 .../examples}/map_plots/README.txt | 0 .../map_plots/Test_Example_Plots.ipynb | 145 ++++ .../examples}/map_plots/custom_map_domain.py | 0 .../examples}/map_plots/map_gridded.py | 0 .../examples}/map_plots/map_plot_no_data.py | 0 .../examples}/map_plots/map_scatter.py | 0 .../examples}/map_plots/map_scatter_2D.py | 0 .../examples}/scatter_plots/README.txt | 0 .../scatter_plots/density_scatter.py | 0 .../examples}/scatter_plots/scatter.py | 0 .../scatter_with_regression_line.py | 0 galleries/plot_types/README.txt | 20 + galleries/plot_types/basic/line.py | 46 + src/emcpy/plots/create_plots.py | 4 +- 32 files changed, 1336 insertions(+), 22 deletions(-) delete mode 100644 docs/getting_started/plots.md rename {examples => docs/sphinxext}/__init__.py (100%) create mode 100644 docs/sphinxext/gallery_order.py rename {examples => galleries/examples}/README.txt (100%) create mode 100644 galleries/examples/Untitled.ipynb rename {examples => galleries/examples}/histograms/README.txt (100%) rename {examples => galleries/examples}/histograms/histogram.py (100%) rename {examples => galleries/examples}/histograms/layered_histogram.py (100%) rename {examples => galleries/examples}/line_plots/README.txt (100%) rename {examples => galleries/examples}/line_plots/SkewT.py (100%) create mode 100644 galleries/examples/line_plots/Untitled.ipynb rename {examples => galleries/examples}/line_plots/inverted_log_scale.py (100%) rename {examples => galleries/examples}/line_plots/line_plot.py (100%) rename {examples => galleries/examples}/line_plots/line_plot_options.py (100%) rename {examples => galleries/examples}/line_plots/multi_line_plot.py (100%) rename {examples => galleries/examples}/map_plots/README.txt (100%) create mode 100644 galleries/examples/map_plots/Test_Example_Plots.ipynb rename {examples => galleries/examples}/map_plots/custom_map_domain.py (100%) rename {examples => galleries/examples}/map_plots/map_gridded.py (100%) rename {examples => galleries/examples}/map_plots/map_plot_no_data.py (100%) rename {examples => galleries/examples}/map_plots/map_scatter.py (100%) rename {examples => galleries/examples}/map_plots/map_scatter_2D.py (100%) rename {examples => galleries/examples}/scatter_plots/README.txt (100%) rename {examples => galleries/examples}/scatter_plots/density_scatter.py (100%) rename {examples => galleries/examples}/scatter_plots/scatter.py (100%) rename {examples => galleries/examples}/scatter_plots/scatter_with_regression_line.py (100%) create mode 100644 galleries/plot_types/README.txt create mode 100644 galleries/plot_types/basic/line.py diff --git a/docs/conf.py b/docs/conf.py index 00dd0d47..0b37b155 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,7 +17,7 @@ sys.path.insert(0, str(Path(__file__).parent.resolve())) import matplotlib -from sphinx_gallery.sorting import ExampleTitleSortKey, ExplicitOrder +# from sphinx_gallery import emcpy @@ -38,39 +38,44 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'myst_parser', + # 'myst_parser', 'sphinx.ext.githubpages', 'sphinx_gallery.gen_gallery' ] # Sphinx gallery configuration -subsection_order = ExplicitOrder([ - '../examples/line_plots', - '../examples/scatter_plots', - '../examples/histograms', - '../examples/map_plots' -]) +# gallery_order.py from the sphinxext folder provides the classes that +# allow custom ordering of sections and subsections of the gallery +from sphinxext.gallery_order import ( + sectionorder as gallery_order_sectionorder, + subsectionorder as gallery_order_subsectionorder) + +# Create gallery dirs +gallery_dirs = ["examples", "plot_types"] +example_dirs = [] +for gd in gallery_dirs: + gd = gd.replace('gallery', 'examples') + example_dirs += [f'../galleries/{gd}'] sphinx_gallery_conf = { 'capture_repr': (), 'filename_pattern': '^((?!skip_).)*$', - 'examples_dirs': ['../examples'], # path to example scripts - 'gallery_dirs': ['gallery'], # path to where to save gallery generated output + 'examples_dirs': example_dirs, + 'gallery_dirs': gallery_dirs, # path to where to save gallery generated output 'backreferences_dir': '../build/backrefs', - 'subsection_order': subsection_order, - 'within_subsection_order': ExampleTitleSortKey, - 'matplotlib_animations': True, + 'subsection_order': gallery_order_sectionorder, + 'within_subsection_order': gallery_order_subsectionorder, + 'matplotlib_animations': True } - # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '.ipynb'] # -- Options for HTML output ------------------------------------------------- diff --git a/docs/getting_started/index.rst b/docs/getting_started/index.rst index 919554b3..9f9b7723 100644 --- a/docs/getting_started/index.rst +++ b/docs/getting_started/index.rst @@ -10,7 +10,7 @@ The following links provide further documentation on the different branches with calculations.md io.md - plots.md +# galleries/plot_types/index statistics.md utilities.md diff --git a/docs/getting_started/plots.md b/docs/getting_started/plots.md deleted file mode 100644 index 331c6339..00000000 --- a/docs/getting_started/plots.md +++ /dev/null @@ -1,3 +0,0 @@ -## Plots - -Coming soon! \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 008e3c74..2789e960 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,7 +10,7 @@ EMCPy :hidden: getting_started/index - gallery/index + galleries/examples/index installing diff --git a/examples/__init__.py b/docs/sphinxext/__init__.py similarity index 100% rename from examples/__init__.py rename to docs/sphinxext/__init__.py diff --git a/docs/sphinxext/gallery_order.py b/docs/sphinxext/gallery_order.py new file mode 100644 index 00000000..2a601c00 --- /dev/null +++ b/docs/sphinxext/gallery_order.py @@ -0,0 +1,84 @@ +""" +Configuration for the order of gallery sections and examples. +Paths are relative to the conf.py file. +""" + +from sphinx_gallery.sorting import ExplicitOrder + +# Gallery sections shall be displayed in the following order. +# Non-matching sections are inserted at the unsorted position + +UNSORTED = "unsorted" + +# Sphinx gallery configuration +examples_order = [ + '../galleries/examples/line_plots', + '../galleries/examples/scatter_plots', + '../galleries/examples/histograms', + '../galleries/examples/map_plots', + UNSORTED +] + +plot_types_order = [ + '../galleries/plot_types/basic', + UNSORTED +] + +folder_lists = [examples_order, plot_types_order] + +explicit_order_folders = [fd for folders in folder_lists + for fd in folders[:folders.index(UNSORTED)]] +explicit_order_folders.append(UNSORTED) +explicit_order_folders.extend([fd for folders in folder_lists + for fd in folders[folders.index(UNSORTED):]]) + +class MplExplicitOrder(ExplicitOrder): + """For use within the 'subsection_order' key.""" + def __call__(self, item): + """Return a string determining the sort order.""" + if item in self.ordered_list: + return f"{self.ordered_list.index(item):04d}" + else: + return f"{self.ordered_list.index(UNSORTED):04d}{item}" + +# Subsection order: +# Subsections are ordered by filename, unless they appear in the following +# lists in which case the list order determines the order within the section. + +list_all = [ + # **Examples** + # line + "line_plot", "line_plot_options", "multi_line_plot", + "inverted_log_scale", "SkewT" + # scatter + "scatter", "scatter_with_regression_line", + "density_scatter" + # histograms + "histogram", "layered_histogram" + # map plots + "map_plot_no_data", "custom_map_domain", "map_scatter_2D", + "map_scatter", "map_gridded" + + # **Plot Types** + # Basic + "line" + ] +explicit_subsection_order = [item + ".py" for item in list_all] + +class MplExplicitSubOrder(ExplicitOrder): + """For use within the 'within_subsection_order' key.""" + def __init__(self, src_dir): + self.src_dir = src_dir # src_dir is unused here + self.ordered_list = explicit_subsection_order + + def __call__(self, item): + """Return a string determining the sort order.""" + if item in self.ordered_list: + return f"{self.ordered_list.index(item):04d}" + else: + # ensure not explicitly listed items come last. + return "zzz" + item + +# Provide the above classes for use in conf.py +sectionorder = MplExplicitOrder(explicit_order_folders) +subsectionorder = MplExplicitSubOrder diff --git a/examples/README.txt b/galleries/examples/README.txt similarity index 100% rename from examples/README.txt rename to galleries/examples/README.txt diff --git a/galleries/examples/Untitled.ipynb b/galleries/examples/Untitled.ipynb new file mode 100644 index 00000000..e0d247ae --- /dev/null +++ b/galleries/examples/Untitled.ipynb @@ -0,0 +1,810 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import sys\n", + "sys.path.append('/scratch1/NCEPDEV/da/Kevin.Dougherty/emcpy/src/')\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "from emcpy.plots.map_plots import MapGridded\n", + "\n", + "# Create 2d gridded plot on global domian\n", + "lats = np.linspace(25, 50, 25)\n", + "lons = np.linspace(245, 290, 45)\n", + "X, Y = np.meshgrid(lons, lats)\n", + "Z = np.random.normal(size=X.shape)\n", + "\n", + "# Create gridded map object\n", + "gridded = MapGridded(X, Y, Z)\n", + "gridded.cmap = 'plasma'\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [gridded]\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'conus'\n", + "plot1.add_map_features(['coastline'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='2D Gridded Data', loc='center')\n", + "plot1.add_grid()\n", + "plot1.add_colorbar(label='colorbar label',\n", + " fontsize=12, extend='neither')\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "fig.save_figure('map_gridded.png')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Histogram\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Generate test data for histogram plots\n", + "mu = 100 # mean of distribution\n", + "sigma = 15 # standard deviation of distribution\n", + "data = mu + sigma * np.random.randn(437)\n", + "\n", + "# Create histogram object\n", + "hst = Histogram(data)\n", + "hst.color = 'tab:green'\n", + "hst.alpha = 0.7\n", + "hst.label = 'data'\n", + "\n", + "# Create histogram plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [hst]\n", + "plot1.add_title(label='Test Histogram Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "plot1.add_legend()\n", + "\n", + "# Create figure and save as png\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cCxSQEdYV8JSeVCRJksZs1ECvqjn9LESSJI3fes+hp+M1Sf5X83q3JM/tfWmSJKlb3VwU9yngQOBVzeuVwJk9q0iSJI3Zus6hr/G8qto/yXUAVfWLJJv3uC5JkjQG3fTQVyXZlM6FcCSZATza06okSdKYdBPopwMXATsnOQVYBPzvnlYlSZLGZL1D7lV1fpJrgcOaRcdW1dLeliVJksaim3PoAFsBa4bdt+xdOZIkaTy6+dnaB4Bzge2BHYHPJ3l/rwuTJEnd66aHfjzwrKp6CCDJqcB3gb/vZWGSJKl73VwU9xNg+rDXWwA/6kk1kiRpXNY1Ocsn6Zwzfxi4KclVzesj6FzpLkmSJol1DbkvaR6vpfOztTWu7lk1kiRpXNY1Ocu5/SxEkiSN33ovikuyB/BhYC+GnUuvKqdPlSRpkujmorjPA58GVgMvBL4AnNfLoiRJ0th0E+hbVtVCIFX106o6GfiD3pYlSZLGopvfoT+UZBPgtiTzgTuBnXpbliRJGotueujvoHPr17cBzwZeA/xZD2uSJElj1M3kLIubpw8ArwNIchpwTQ/rkiRJY9BND30kfzqhVUiSpA0y3kDPhhw0ybZJLkxyS5KlSQ5Msn2Sq5Lc1jxutyHHkCRpYzJqoDcBO9LfDmxgoAOfAP61qp4B7AssBU4CFlbVHsDC5rUkSerCus6hX0vn3u0jhfcj4z1gkicChwAnAFTVI8AjSY4BDm02O5fOLWbfM97jSJK0MVnXrV/n9OiYTwGW05lXfV86XxzeDuxcVcuaYy9L4k/jJEnqUje/Q+/FMfcH3lpV1yT5BGMYXk9yInAiwG677dabCiW1wvyF8wddwjqdcdgZgy5BLTLei+I2xBAwVFVrfvZ2IZ2AvyfJLgDN470jvbmqzqqquVU1d8aMGX0pWJKkya7vgV5VdwM/S/L0ZtFhwM3ApcC8Ztk84JJ+1yZJ0lTVzWxrT6XTo344yaHAPsAXqur+DTjuW4Hzk2wO/JjODWs2ARYkeT1wB/CKDdi/JEkblW7OoX8ZmJvkd4Gz6fSk/xl4yXgPWlXXA3NHWHXYePcpSdLGrJsh90erajXwMuDjVfWXwC69LUuSJI1FN4G+KsnxdM5r/0uzbLPelSRJksaqm0B/HXAgcEpV3Z5kDvDF3pYlSZLGopvZ1m6mM3Xqmte3A6f2sihJkjQ2owZ6kgVV9adJvk/nFrCPU1X79LQySZLUtXX10N/ePB7Vj0IkSdL4rete7suap09oht0f0/we/ae9K0uSJI1FNxfFLUjynnRsmeSTwId7XZgkSepeN4H+PGBX4FvAYuAu4Pm9LEqSJI1NV79DBx4EtgSmA7dX1aM9rUqSJI1JN4G+mE6gPwc4GDg+yYU9rUqSJI1JN/dyf31VLWme3w0ck+S1PaxJkiSN0Xp76MPCnCRPSPJq4LieViVJksZkvYGeZPMkxyZZACwDDgc+0/PKJElS19Z1p7gjgOOBFwFfB84DnltVr+tTbZIkqUvrOod+JfBN4ODm/u0k+URfqpIkSWOyrkB/Np1z5f+W5MfABcCmfalKkiSNyajn0Kvquqp6T1U9FTgZeBaweZIrkpzYrwIlSdL6dfM7dKrqP6tqPjAT+Did+dElSdIk0c3v0B/T3CHuyuZPkiRNEl310CVJ0uQ2aqAn+WqS2X2sRZIkjdO6htzPAb6W5FzgI1W1qj8lSWqbWYsm12U3Qwd/e9AlSBNu1ECvqgVJLgc+ACxJch7w6LD1H+tDfZIkqQvruyhuFfArYAtgG4YFuiRJmjzWdevXFwMfAy4F9q+qX/etKkkbZLINcUvqvXX10N8HvKKqbupXMZIkaXzWdQ79Bf0sRJIkjZ+/Q5ckqQUMdEmSWsBAlySpBQx0SZJawECXJKkFDHRJklrAQJckqQUMdEmSWsBAlySpBQx0SZJawECXJKkFDHRJklrAQJckqQUMdEmSWsBAlySpBQx0SZJawECXJKkFDHRJklpgYIGeZNMk1yX5l+b19kmuSnJb87jdoGqTJGmqGWQP/e3A0mGvTwIWVtUewMLmtSRJ6sJAAj3JLOClwD8NW3wMcG7z/Fzg2D6XJUnSlDWoHvrHgXcDjw5btnNVLQNoHncaQF2SJE1JfQ/0JEcB91bVteN8/4lJliRZsnz58gmuTpKkqWkQPfTnA0cn+QlwAfAHSb4I3JNkF4Dm8d6R3lxVZ1XV3KqaO2PGjH7VLEnSpNb3QK+q91bVrKqaDRwH/HtVvQa4FJjXbDYPuKTftUmSNFVNpt+hnwockeQ24IjmtSRJ6sK0QR68qq4Grm6erwAOG2Q9kiRNVZOphy5JksbJQJckqQUMdEmSWsBAlySpBQx0SZJawECXJKkFDHRJklrAQJckqQUMdEmSWsBAlySpBQx0SZJaYKD3cpc2xOVn3rBB759134ETVEnH0MHfntD9SdJY2EOXJKkFDHRJklrAIXdpgsxaNLFD+JI0FvbQJUlqAQNdkqQWMNAlSWoBA12SpBYw0CVJagGvcldPzF84v+fHmOgbw0jSVGYPXZKkFrCHLkkD0o+RrA11xmFnDLoEdckeuiRJLWCgS5LUAga6JEktYKBLktQCBrokSS1goEuS1AIGuiRJLWCgS5LUAga6JEktYKBLktQCBrokSS1goEuS1AIGuiRJLeBsa5I2OrMWHTjoEh5n6OBvD7oEtYA9dEmSWsBAlySpBQx0SZJawECXJKkFDHRJklrAQJckqQUMdEmSWsBAlySpBQx0SZJaoO+BnmTXJF9PsjTJTUne3izfPslVSW5rHrfrd22SJE1Vg+ihrwb+uqr2BA4A3pJkL+AkYGFV7QEsbF5LkqQu9D3Qq2pZVX23eb4SWArMBI4Bzm02Oxc4tt+1SZI0VQ30HHqS2cCzgGuAnatqGXRCH9hpgKVJkjSlDCzQk2wNfBl4R1X9cgzvOzHJkiRLli9f3rsCJUmaQgYS6Ek2oxPm51fVV5rF9yTZpVm/C3DvSO+tqrOqam5VzZ0xY0Z/CpYkaZIbxFXuAc4GllbVx4atuhSY1zyfB1zS79okSZqqpg3gmM8HXgt8P8n1zbK/AU4FFiR5PXAH8IoB1CZJ0pTU90CvqkVARll9WD9rkSSpLbxTnCRJLWCgS5LUAga6JEktYKBLktQCBrokSS1goEuS1AIGuiRJLWCgS5LUAga6JEktYKBLktQCBrokSS0wiMlZNEVdfuYNXW87674De1iJJGlt9tAlSWoBA12SpBYw0CVJagEDXZKkFjDQJUlqAQNdkqQWMNAlSWoBA12SpBbwxjKSNGCzFk2eGzENHfztQZegcbKHLklSCxjokiS1gIEuSVILGOiSJLWAF8VNUfMXzu/7MZ1BTZImL3vokiS1gIEuSVILGOiSJLWAgS5JUgsY6JIktYCBLklSCxjokiS1gL9DlySNahD3vBiLMw47Y9AlTBr20CVJagEDXZKkFnDIXZI0ZXlK4LfsoUuS1AIGuiRJLeCQ+yR2+Zk3jLrOmc8k9cKsRZPr35ahg7896BKmDHvokiS1gIEuSVILOOQ+gsly1aTD6pKkbtlDlySpBQx0SZJawECXJKkFJl2gJ3lxkluT/DDJSYOuR5KkqWBSXRSXZFPgTOAIYAhYnOTSqrq5XzVcfuYNXowmSZpyJlsP/bnAD6vqx1X1CHABcMyAa5IkadKbbIE+E/jZsNdDzTJJkrQOk2rIHcgIy+pxGyQnAic2Lx9IcmvPq+qdHYH7Bl1Ey9imE8v2nHi26cSbtG16Jmf2Yre7j7RwsgX6ELDrsNezgLuGb1BVZwFn9bOoXkmypKrmDrqONrFNJ5btOfFs04lnm3ZMtiH3xcAeSeYk2Rw4Drh0wDVJkjTpTaoeelWtTjIfuBLYFPhcVd004LIkSZr0JlWgA1TVV4GvDrqOPmnFqYNJxjadWLbnxLNNJ55tCqSq1r+VJEma1CbbOXRJkjQOBnqfJNk2yYVJbkmyNMmBSbZPclWS25rH7QZd51SS5C+T3JTkxiRfSjLdNh2bJJ9Lcm+SG4ctG7UNk7y3uS3zrUleNJiqJ7dR2vSjzf/730tyUZJth62zTddjpDYdtu6dSSrJjsOWbZRtaqD3zyeAf62qZwD7AkuBk4CFVbUHsLB5rS4kmQm8DZhbVXvTuYjyOGzTsToHePFay0ZswyR70Wnj32ve86nmds16vHP4n216FbB3Ve0D/AB4L9imY3AO/7NNSbIrnVuF3zFs2UbbpgZ6HyR5InAIcDZAVT1SVffTua3tuc1m5wLHDqK+KWwasGWSacBWdO5ZYJuOQVX9B/DztRaP1obHABdU1cNVdTvwQzq3a9YwI7VpVX2tqlY3L79D5x4bYJt2ZZT/TgH+EXg3j78B2UbbpgZ6fzwFWA58Psl1Sf4pyROAnatqGUDzuNMgi5xKqupO4DQ638yXAf9dVV/DNp0Io7Wht2aeGH8OXNE8t03HKcnRwJ1VdcNaqzbaNjXQ+2MasD/w6ap6FvArHAreIM153WOAOcCTgSckec1gq2q99d6aWeuW5H3AauD8NYtG2Mw2XY8kWwHvAz4w0uoRlm0UbWqg98cQMFRV1zSvL6QT8Pck2QWgebx3QPVNRYcDt1fV8qpaBXwFOAjbdCKM1obrvTWzRpdkHnAU8Or67e+FbdPxeSqdL/M3JPkJnXb7bpInsRG3qYHeB1V1N/CzJE9vFh0G3EzntrbzmmXzgEsGUN5UdQdwQJKtkoROmy7FNp0Io7XhpcBxSbZIMgfYA/ivAdQ35SR5MfAe4Oiq+vWwVbbpOFTV96tqp6qaXVWz6YT4/s2/tRttm066O8W12FuB85t71P8YeB2dL1QLkryeTkC9YoD1TSlVdU2SC4Hv0hnCvI7O3aK2xjbtWpIvAYcCOyYZAj4InMoIbVhVNyVZQOfL6GrgLVX1m4EUPomN0qbvBbYArup8/+Q7VfUXtml3RmrTqjp7pG035jb1TnGSJLWAQ+6SJLWAgS5JUgsY6JIktYCBLklSCxjokiS1gIEuTWFJdk1ye5Ltm9fbNa93H2X7lzUzUz2ji33PTXL6GGp5YAzbnpzknd1uP9b9SxsjA12awqrqZ8Cn6fx2nObxrKr66ShvOR5YRGc2qvXte0lVvW1CCpXUcwa6NPX9I5275r0DOBj4h5E2SrI18Hzg9QwL9KbX/m/p2CXJD5I8KcmhSf6l2eb3k1zf/F2XZJtuCkvyR0muad7zb0l2HrZ63yT/3sy7/sZh73lXksXN3OF/O9bGkDZWBro0xTX3sn8XnWB/R1U9MsqmxwL/WlU/AH6eZP/m/RcBdwNvAT5L5y5cd6/13nfSuePWfsALgAe7LG8RcEAzKdEFdKa6XGMf4KXAgcAHkjw5yR/SuVXnc4H9gGcnOaTLY0kbNQNdaocj6Uwju/c6tjmeTqjSPB4/bN1b6dye9OGq+tII7/1P4GNJ3gZsO2xu7/WZBVyZ5Pt0vnT83rB1l1TVg1V1H/B1OiH+h83fdXRu6/sMOgEvaT28l7s0xSXZDzgCOABYlOSCNfOZD9tmB+APgL2TFLApUEne3cz8NRN4FNg5ySZV9ejw91fVqUkuB14CfCfJ4VV1SxflfRL4WFVdmuRQ4OThu11r26Iz9eWHq+r/drFvScPYQ5emsGamuU/TGWq/A/gocNoIm/4J8IWq2r2ZoWpX4Hbg4CTTgM8Dr6IzY91fjXCcpzYzXP0fYAmdnnM3fge4s3k+b611xySZ3nzZOBRYDFwJ/Hlzvp8kM5Ps1OWxpI2aPXRpansjcEdVXdW8/hRwQpLfr6pvDNvueH57JfwaX6YT4i8EvllV30xyPbC46Y0P944kLwR+Q2cWqytGqGWrZiasNT5Gp0f+/5LcCXyHzhzWa/wXcDmwG/ChqroLuCvJnsC3m1nJHgBeg/PaS+vlbGuSJLWAQ+6SJLWAgS5JUgsY6JIktYCBLklSCxjokiS1gIEuSVILGOiSJLWAgS5JUgv8f8DxSGbKAwTYAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Histogram\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Generate test data for histogram plots\n", + "mu = 100 # mean of distribution\n", + "sigma = 15 # standard deviation of distribution\n", + "data1 = mu + sigma * np.random.randn(450)\n", + "data2 = mu + sigma * np.random.randn(225)\n", + "\n", + "# Create histogram objects\n", + "hst1 = Histogram(data1)\n", + "hst1.color = 'tab:green'\n", + "hst1.alpha = 0.7\n", + "hst1.label = 'data 1'\n", + "\n", + "hst2 = Histogram(data2)\n", + "hst2.color = 'tab:purple'\n", + "hst2.alpha = 0.7\n", + "hst2.label = 'data 2'\n", + "\n", + "# Create histogram plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [hst1, hst2]\n", + "plot1.add_title(label='Test Histogram Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "plot1.add_legend()\n", + "\n", + "# Create figure and save as png\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import LinePlot\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "x = [1, 2, 3, 4, 5]\n", + "y = [1, 2, 3, 4, 5]\n", + "\n", + "# Create line plot object\n", + "lp = LinePlot(x, y)\n", + "lp.label = 'line'\n", + "\n", + "# Add line plot object to list\n", + "plt_list = [lp]\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [lp]\n", + "plot1.add_title('Test Line Plot')\n", + "plot1.add_xlabel('X Axis Label')\n", + "plot1.add_ylabel('Y Axis Label')\n", + "plot1.add_legend(loc='upper right')\n", + "\n", + "# Create figure and save as png\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import LinePlot\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "\n", + "def _getLineData():\n", + " # generate test data for line plots\n", + "\n", + " x1 = [0, 401, 1039, 2774, 2408]\n", + " x2 = [500, 250, 710, 1515, 1212]\n", + " x3 = [400, 150, 910, 1215, 850]\n", + " y1 = [0, 2.5, 5, 7.5, 12.5]\n", + " y2 = [1, 5, 6, 8, 10]\n", + " y3 = [1, 4, 5.5, 9, 10.5]\n", + "\n", + " return x1, y1, x2, y2, x3, y3\n", + "\n", + "\n", + "# create line plot with multiple lines\n", + "\n", + "x1, y1, x2, y2, x3, y3 = _getLineData()\n", + "lp1 = LinePlot(x1, y1)\n", + "lp1.label = 'line 1'\n", + "\n", + "lp2 = LinePlot(x2, y2)\n", + "lp2.color = 'tab:green'\n", + "lp2.label = 'line 2'\n", + "\n", + "lp3 = LinePlot(x3, y3)\n", + "lp3.color = 'tab:red'\n", + "lp3.label = 'line 3'\n", + "\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [lp1, lp2, lp3]\n", + "plot1.add_title('Test Line Plot')\n", + "plot1.add_xlabel('X Axis Label')\n", + "plot1.add_ylabel('Y Axis Label')\n", + "plot1.add_legend(loc='upper right')\n", + "\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch1/NCEPDEV/da/Kevin.Dougherty/emcpy/src/emcpy/plots/create_plots.py:713: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " **yticklabels['kwargs'])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import LinePlot\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "x = [0, 401, 1039, 2774, 2408, 512]\n", + "y = [0, 45, 225, 510, 1200, 1820]\n", + "\n", + "# Create line plot object\n", + "lp = LinePlot(x, y)\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [lp]\n", + "plot1.add_title(label='Test Line Plot, Inverted Log Scale')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "\n", + "# Set y-scale to log and invert\n", + "plot1.set_yscale('log')\n", + "plot1.invert_yaxis()\n", + "\n", + "# Set new y labels\n", + "ylabels = [0, 50, 100, 500, 1000, 2000]\n", + "plot1.set_yticklabels(labels=ylabels)\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "# fig.save_figure('inverted_log_scale_line_plot.png')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BqzE9FpMYPSobqBq/c2mtmMBtv0PSSZIuU2dRlwttfzoi3lV2cGWh1YgmorKBqvE7l9YgPfBNkg6LiF9Iku3zJN0kKdsETqsRTVXHyka/4SqGsvJXx9+5thgkgX9X0l6SflF8vaekb5cVUBVoNQLV6DdcxVAWsDbLPkZm+0O2Pyhpp6TbbV9k+0JJt0l6oKoAy8KqTUD5+j0y2ObHCXn0CqPQrwc+U/y9VZ3HyBZ9vrRoADRKv+Gqtg5lUXnAqPTbzOTvqgwEQPP0G65q61BWykm0zDlolkFmoR8i6d2SDlVnLFySFBFsJwpgRf0mObVxAlSqygM9/+YZZBLbhZL+TNJfSjpG0uvVe49wAMAKUlUeeHy2eQZZC/1REfFPkhwRd0XEOZJeWG5YANBcKSbRLvb8JbVqzkGTDdID/4XtR0j6pu3TJG2X9LhywwIAjFJb5xw02SA98DPUWUr1dEnPkvRaSb9XYkwAgBLw+GyzDLKZyY3FPx9QZ/xbtt8v6cslxoWEmKkKAPU36GYmS/17SWeOMhDUAzNVASAPg5TQe2EWekO1eXUsAMjJsj1w2/std0gk8MZq6+pYAJCbfiX0rers/90rWf+ynHCQGjNVASAP/ZZSPbjKQFAPTGADgDysdhIbGogJbACQj9VOYkMDMYENAPJBAseDWGoRi9ivGqi/QXYje5KkeyJip+2jJT1d0scj4idrvbjt4yR9QNIekv57RJy31nO2RRlj1Uxgg8RQCpCLQXrgl0n6le0nS/qYpIMl/Y+1Xtj2HpI+Iukl6mxVusn2oWs9bxssfsBu2bJF09PTI+0lsdQiyhhKoUcPjN4gCXxXRCxIeoWkv4qIP5L06yO49uGSvhUR34mIX0r6lKQTRnDexmOsGmUa9VBKmQ1OoM0GmYU+b3uTpJMlHV+8NjaCa6+XdHfX1/dIes4Iztt4LLaCMo16KIV9qIFyDJLAXy/pDySdGxF32j5Y0sUjuHavBWLiYW+yT5V0qiT+py8wVo2yLQ6ljMJqG5ysSQD054iH5cxqLmwfKemciJgqvn6bJEXEu5f7nsnJyZiZmakoQgCjMmwyZiId0GF7a0RM9jq27Bi47UuLv2+1/ZWlf0YQ142SDrF9sO1HSnqVpM+O4LzAyDEJa22GnRzJPA9gZf1K6G8u/n5ZGReOiAXbp0maVucxsgsi4vYyrgWsBb3B6jHPA1hZv7XQ7y3++eiI+Gr3seJ58LvWevGIuFLSlWs9D1AmJmENZpRj1szzAFY2yCS2S21/QtJ7Je1V/D0p6cgyAwPqgt7gysqoUoxyIh3QRIMk8OdIeo+kL0raR9Ilko4qMyigTugNrowqBVC9QRZymZf0c0mPUqcHfmdE7Co1KjRSzhPBWKGuP9bRB6o3SA/8RkmfkfRsSY+V9Le2T4yIE0uNDI3CRLA8rHYcmyoFUL1BEvgpEbH48PX3JZ1g+3UlxoQGosRaf2ttZLV1zJoFZ5DKiiX0ruQt24+2/Rp1ntkGBkaJtf549np4rPOOlAbZTvSRkl4q6dWSjlNnd7KPlhwXGoYSa/0x2354uVSWqBI007IJ3PaLJW2SNCXpWkmfkHR4RLy+otjQMG0tseaCRtbwcmj0MP+kufr1wKclXS/puRFxpyTZ/kAlUQFIgkbWcHJo9ORSJcDw+iXwZ6kz1n2N7e+os1/3HpVEBQCZqHujJ4cqAVan31KqN0u6WdJbbR+lTjn9kbavknR5RGyuKEYAWJM2jwHnUCXA6gzyGJki4guSvmD7dEkvVqdnTgIHUHuMAde/SoDVGWQltgdFxK6ImGYiG4Bc8HgcmmqoBA4AuWENAjRVv8fIrpT0nyLiu9WFAwCjxRgwmqpfD/wiSZ+zfbbtsYriAYCRYzMaNFG/WeiX2r5C0jskzRR7gu/qOn5+BfEBA2vzTGMA7bPSLPR5ST+TtKc6e4GzjShqiZnGANqm3xj4cZLOl/RZSc+MCFbpR22x2hSAtunXAz9b0kkRcXtVwQCrxWpTaAqGgjCofmPgz6syEGAtmGmMJmAoCMPgOXA0BjONdzc3N6dt27Yl36O6LnHkgEVnMIyBllIFkJdePTlJlVco6FEOh6EgDIMEDjTQ0p7c9u3bddttt/VNpGWMvTK5cDgMBWEYJHCggZb25CT1TaRl9ZTpUQ6PjUcwKBI4UKGqZhgv7clJ2q0HvjSRltVTpkcJlIcEDlSk6vHgpT25fom0zJ4yPUqgHCRwoCKpx4P7JVJ6ykB+SOBAReo+HkxPGcgLCRyoCL1cAKNEAgcqRC8XwKiwEhsAABkigQMAkCESOAAAGSKBAw3GRiK7436gSZjEhuywX/Jg2Ehkd9wPNA0JHFlJ9SGcY6Mh9cIxdbH4s9u5cyf3A41CAkdWUiSlKhsNo2wo1H3hmCp0/+zWrVundevWaWFhobX3A81CAkdWUiSlqhoNo24osHDM7j+7hYUFHXbYYdpzzz1bez9QrqordSRwZCVFUqqq0VBGQ6HtC8cs/dmtX7+exI1SpBjeI4EjO6tNSqttHVfVaKDkPXpUIVCVFMN7JHC0wlpbx1X0ZPslm1ST6HKcvLdU26sQqEaKBjgJHK2Qy4zsXskm5cz7HCfvASmkqPawkAtaYbF1LCm78nSvxkeTrrvYUNiyZYump6dZZAXZWmyAV9UIpQeOVsh5LDTV2HjOk/eANiCBozVGMRaaotSbqvHB5D2g3hwRqWMY2OTkZMzMzKQOAzVURWJlKc7yMAYO9GZ7a0RM9jpGDxzZqyqxUuotDzPFgeExiQ3Zq2qyVc4T4VAddjxDVeiBI3tVjaHmPBEO1WCYBVUigSN7VSZWSr3oh2EWVIkEjkYgsaIOmFGPKpHAkRVmK6POGGZBlUjgyAbji8gB1SBUhVnoyEaqJUUBoI5I4MgGj3EBwEOSlNBtnyTpHElPkXR4RLC8GlbE+CIAPCRVD/w2Sa+UdF2i62OVUi9SUfVuPwBQV0l64BFxhyTZTnF5rBKTyACgPmo/Bm77VNsztmd27NiROpxWYxIZcpK6WgSUrbQeuO1rJB3Y49DZEfGZQc8TEZslbZY6u5GNKDysAotUIBdUi9AGpSXwiDi2rHMjDSaRIRcsaYo2qH0JHfXCJDLURb8SOY8cog1SPUb2CkkfkrS/pCts3xIRUyliAdqkKUvRrlQip1qENkg1C/1ySZenuDbQVk0aFx6kRF7Gkqa5NYByixfDYS10oCWaNC6cYkJlbg2g3OLF8EjgQEs06SmCFCXy3BpAucWL4ZHAgYSqLHE2bVy46l2/cmsA5RYvhueIfB6tnpycjJkZlk1HM1DizE+VDa5RXKstY+BN/u+0vTUiJnsdowcOJEKJMz9V9fpH1bhrw97kbW4I8xw4kAjPKmM5LFs8uDbfK3rgQCJNG5PG6DB+Pbg23yvGwAHURpPHMofFvRhck+8VY+AAaq/NY5m9tGH8elTaeq8YAwdQC20eywRWgwQOoBaY1Lc79jPHSiihAw2R+zggk/oewnACBkECBxqgKR/4bR3LXIo1AjAISugYGqW9+mH8uFkYTsAg6IFjKE3p6TVNm5+FbSKGEzAIEjiGQmmvnvjAf7gmzAng/y30QwLHUOjp1Rcf+A/JqVKUe0MD6ZDAMRR6eshBLpWinBoaqB8msWFoiz09PmhQV7lMAmPyIdaCHjiAxsmlUsSQFNaCBA6gkXKYE5BLQwP1RAIHgIRyaGignhgDBwAgQyRwAAAyRAJH67AULIAmYAwcrcJztwCagh44WoXnbgE0BQkcrZLLAh8oB8MnaBJK6GgVnrttL4ZP0DT0wNE6LAXbTgyfoGlI4ABageETNA0ldACtwPBJO7Rpe1YSOIDWYNnSZmvbPAdK6ACARmjbPAcSOACgEdo2z4ESOgCgEdo2z4EEDgBojDbNc6CEDgBAhkjgAABkiAQOAECGSOAAAGSIBA4AQIZI4AAAZIgEDgBAhkjgAABkiAQOAECGSOAAAGSIBA4AQIZI4AAAZIgEDgBAhkjgAABkiAQOAECGSOAAAGSIBA4AQIZI4AAAZChJArf9Pttfs/0V25fb3jdFHAAA5CpVD/xqSU+NiKdL+oaktyWKAwCALCVJ4BHxuYhYKL7cIukJKeIAACBXdRgDf4Okq1IHAQBATtaVdWLb10g6sMehsyPiM8V7zpa0IOmSPuc5VdKpkrRhw4YSIgUAID+lJfCIOLbfcdsnS3qZpBdFRPQ5z2ZJmyVpcnJy2fcBANAmpSXwfmwfJ+mtkl4QEXMpYgAAIGepxsA/LGkfSVfbvsX2RxPFAQBAlpL0wCPiySmuCwBAU9RhFjoAABgSCRwAgAyRwAEAyBAJHACADJHAAQDIEAkcAIAMkcABAMgQCRwjNTc3p23btmlujgX2AKBMSRZyQTPNzc1penpa8/PzGhsb09TUlMbHx1OHBQCNRA8cIzM7O6v5+XlJ0vz8vGZnZxNHBADNRQLHyExMTGhsbEySNDY2pomJicQRAUBzUULHyIyPj2tqakqzs7OamJigfA4AJSKBY6TGx8e1YcOG1GEAQONRQgcAIEMkcAAAMkQCBwAgQyRwAAAyRAIHACBDJHAAADJEAgcAIEMkcAAAMkQCBwAgQyRwAAAyRAIHACBDjojUMQzM9g5Jd6WOoyYmJLFf5+hwP0eL+zla3M/Ryul+PjEi9u91IKsEjofYnomIydRxNAX3c7S4n6PF/RytptxPSugAAGSIBA4AQIZI4PnanDqAhuF+jhb3c7S4n6PViPvJGDgAABmiBw4AQIZI4Bmz/T7bX7P9FduX2943dUw5s32S7dtt77Kd/QzVVGwfZ/vrtr9l+09Sx5Mz2xfY/qHt21LH0gS2D7J9re07iv/X35w6prUggeftaklPjYinS/qGpLcljid3t0l6paTrUgeSK9t7SPqIpJdIOlTSJtuHpo0qaxdJOi51EA2yIOmPI+Ipko6Q9Kacfz9J4BmLiM9FxELx5RZJT0gZT+4i4o6I+HrqODJ3uKRvRcR3IuKXkj4l6YTEMWUrIq6TdF/qOJoiIu6NiJuKf98v6Q5J69NGtXok8OZ4g6SrUgeB1lsv6e6ur+9Rxh+QaC7bGyUdJunLiUNZtXWpA0B/tq+RdGCPQ2dHxGeK95ytTmnokipjy9Eg9xNr4h6v8agLasX23pIuk3RGRPw0dTyrRQKvuYg4tt9x2ydLepmkFwXPBK5opfuJNbtH0kFdXz9B0vcSxQI8jO0xdZL3JRHx96njWQtK6BmzfZykt0r6dxExlzoeQNKNkg6xfbDtR0p6laTPJo4JkCTZtqSPSbojIs5PHc9akcDz9mFJ+0i62vYttj+aOqCc2X6F7XskHSnpCtvTqWPKTTGp8jRJ0+pMELo0Im5PG1W+bH9S0pck/abte2yfkjqmzB0l6XWSXlh8Zt5i+6Wpg1otVmIDACBD9MABAMgQCRwAgAyRwAEAyBAJHACADJHAAQDIEAkcyEixm9Kdtvcrvv614usnLvP+V9gO2781wLknbX9wiFgeGOK959g+c9D3D3t+oI1I4EBGIuJuSX8j6bzipfMkbY6Iu5b5lk2SblBnQZWVzj0TEaePJFAApSOBA/n5S0lH2D5D0nMl/UWvNxXrPR8l6RR1JfCiV36NO37d9jdsH2j7aNv/u3jPC7oWurjZ9j6DBGb7eNtfLr7nGtsHdB3+Hdv/bPubtt/Y9T1vsX1jsa/9O4e9GUBbkcCBzETEvKS3qJPIzyi27ezl5ZL+T0R8Q9J9tp9ZfP/lkr4v6U2S/pukP4uI7y/53jMlvSkiniHpeZJ+PmB4N0g6IiIOU2cr0bO6jj1d0r9VZ6W7d9h+vO3flXSIOtuQPkPSs2w/f8BrAa1GAgfy9BJJ90p6ap/3bFIniar4e1PXsT+U9DZJOyPikz2+9wuSzrd9uqR9u/adX8kTJE3bvlWdRsZvdx37TET8PCJmJV2rTtL+3eLPzZJukvRb6iR0ACtgNzIgM7afIenFko6QdIPtT0XEvUve81hJL5T0VNshaQ9JYfusYte69ZJ2STrA9iMiYlf390fEebavkPRSSVtsHxsRXxsgvA9JOj8iPmv7aEnndJ92yXtDne1H3x0RfzvAuQF0oQcOZKTYTelv1Cmdb5P0Pknv7/HWEyV9PCKeGBEbI+IgSXdKeq7tdZIulPRqdTYc+c89rvOkiLg1It4jaUadnvEgHiNpe/Hvk5ccO8H2XkXj4mh1di6blvSGYrxettfbftyA1wJajR44kJc3StoWEVcXX/+1pN+3/YKI+Jeu923SQzPVF12mTtI+RtL1EXG97Vsk3Vj0trudYfsYSb+S9FVJV/WIZbzYvW3R+er0uD9te7ukLZIO7jr+fyVdIWmDpP8aEd+T9D3bT5H0pU7bRA9Ieq2kH/a/DQDYjQwAgAxRQgcAIEMkcAAAMkQCBwAgQyRwAAAyRAIHACBDJHAAADJEAgcAIEMkcAAAMvT/ARBfF3GbIPTgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Scatter\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Create test data\n", + "rng = np.random.RandomState(0)\n", + "x = rng.randn(100)\n", + "y = rng.randn(100)\n", + "\n", + "# Create Scatter object\n", + "sctr1 = Scatter(x, y)\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [sctr1]\n", + "plot1.add_title(label='Test Scatter Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "fig.save_figure('scatter_plot.png')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Scatter\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Create test data\n", + "rng = np.random.RandomState(0)\n", + "x = rng.randn(100)\n", + "y = rng.randn(100)\n", + "\n", + "# Create Scatter object\n", + "sctr1 = Scatter(x, y)\n", + "\n", + "# Add linear regression feature in scatter object\n", + "sctr1.do_linear_regression = True\n", + "sctr1.add_linear_regression()\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [sctr1]\n", + "plot1.add_title(label='Test Scatter Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "plot1.add_legend()\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Scatter\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Create test data\n", + "rng = np.random.RandomState(0)\n", + "x = rng.randn(100)\n", + "y = rng.randn(100)\n", + "\n", + "# Create Scatter object\n", + "sctr1 = Scatter(x, y)\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [sctr1]\n", + "plot1.add_title(label='Test Scatter Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "from emcpy.plots.map_plots import MapScatter\n", + "\n", + "lats = np.linspace(35, 50, 30)\n", + "lons = np.linspace(-70, -120, 30)\n", + "data = np.linspace(200, 300, 30)\n", + "\n", + "# Create scatter plot on CONUS domian\n", + "scatter = MapScatter(lats, lons, data)\n", + "# change colormap and markersize\n", + "scatter.cmap = 'Blues'\n", + "scatter.markersize = 25\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [scatter]\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'conus'\n", + "plot1.add_map_features(['coastline', 'states'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='EMCPy Map', loc='center',\n", + " fontsize=20)\n", + "plot1.add_colorbar(label='colorbar label',\n", + " fontsize=12, extend='neither')\n", + "\n", + "# annotate some stats\n", + "stats_dict = {\n", + " 'nobs': len(np.linspace(200, 300, 30)),\n", + " 'vmin': 200,\n", + " 'vmax': 300,\n", + "}\n", + "plot1.add_stats_dict(stats_dict=stats_dict, yloc=-0.175)\n", + "\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "\n", + "# Create global map with no data using\n", + "# PlateCarree projection and coastlines\n", + "plot1 = CreatePlot()\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'global'\n", + "plot1.add_map_features(['coastline', 'land', 'ocean'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "from emcpy.plots.map_plots import MapGridded\n", + "\n", + "# Create 2d gridded plot on global domian\n", + "lats = np.linspace(25, 50, 25)\n", + "lons = np.linspace(245, 290, 45)\n", + "X, Y = np.meshgrid(lons, lats)\n", + "Z = np.random.normal(size=X.shape)\n", + "\n", + "# Create gridded map object\n", + "gridded = MapGridded(X, Y, Z)\n", + "gridded.cmap = 'plasma'\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [gridded]\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'conus'\n", + "plot1.add_map_features(['coastline'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='2D Gridded Data', loc='center')\n", + "plot1.add_grid()\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Scatter\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "\n", + "# Create test data\n", + "x = np.random.normal(size=1000)\n", + "y = x * 10 + np.random.normal(size=1000)\n", + "\n", + "# Create Scatter object\n", + "sctr = Scatter(x, y)\n", + "# Add density scatter feature in object\n", + "sctr.density_scatter()\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [sctr]\n", + "plot1.add_title(label='Test Density Scatter Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "plot1.add_legend()\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots.plots import Scatter\n", + "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", + "from emcpy.stats import get_linear_regression\n", + "\n", + "# Create test data\n", + "rng = np.random.RandomState(0)\n", + "x = rng.randn(100)\n", + "y = rng.randn(100)\n", + "\n", + "# Create Scatter object\n", + "sctr1 = Scatter(x, y)\n", + "# Add linear regression feature in scatter object\n", + "sctr1.do_linear_regression = True\n", + "sctr1.add_linear_regression()\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [sctr1]\n", + "plot1.add_title(label='Test Scatter Plot')\n", + "plot1.add_xlabel(xlabel='X Axis Label')\n", + "plot1.add_ylabel(ylabel='Y Axis Label')\n", + "plot1.add_legend()\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "from emcpy.plots.map_plots import MapGridded\n", + "\n", + "# Create 2d gridded plot on global domian\n", + "lats = np.linspace(25, 50, 25)\n", + "lons = np.linspace(245, 290, 45)\n", + "X, Y = np.meshgrid(lons, lats)\n", + "Z = np.random.normal(size=X.shape)\n", + "\n", + "# Create gridded map object\n", + "gridded = MapGridded(X, Y, Z)\n", + "gridded.cmap = 'plasma'\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [gridded]\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'conus'\n", + "plot1.add_map_features(['coastline'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='2D Gridded Data', loc='center')\n", + "plot1.add_colorbar(label='colorbar label',\n", + " fontsize=12, extend='neither')\n", + "\n", + "# Create figure\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/histograms/README.txt b/galleries/examples/histograms/README.txt similarity index 100% rename from examples/histograms/README.txt rename to galleries/examples/histograms/README.txt diff --git a/examples/histograms/histogram.py b/galleries/examples/histograms/histogram.py similarity index 100% rename from examples/histograms/histogram.py rename to galleries/examples/histograms/histogram.py diff --git a/examples/histograms/layered_histogram.py b/galleries/examples/histograms/layered_histogram.py similarity index 100% rename from examples/histograms/layered_histogram.py rename to galleries/examples/histograms/layered_histogram.py diff --git a/examples/line_plots/README.txt b/galleries/examples/line_plots/README.txt similarity index 100% rename from examples/line_plots/README.txt rename to galleries/examples/line_plots/README.txt diff --git a/examples/line_plots/SkewT.py b/galleries/examples/line_plots/SkewT.py similarity index 100% rename from examples/line_plots/SkewT.py rename to galleries/examples/line_plots/SkewT.py diff --git a/galleries/examples/line_plots/Untitled.ipynb b/galleries/examples/line_plots/Untitled.ipynb new file mode 100644 index 00000000..2faf0e18 --- /dev/null +++ b/galleries/examples/line_plots/Untitled.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "fb108175-eb0e-4e9d-bc1b-22afde0e9b82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_stale': True, 'stale_callback': , '_axes': , 'figure':
, '_transform': , '_transformSet': True, '_visible': True, '_animated': False, '_alpha': None, 'clipbox': None, '_clippath': None, '_clipon': True, '_label': 'Quadratic', '_picker': None, '_rasterized': False, '_agg_filter': None, '_mouseover': False, '_callbacks': , '_remove_method': None, '_url': None, '_gid': None, '_snap': None, '_sketch': None, '_path_effects': [], '_sticky_edges': _XYPair(x=[], y=[]), '_in_layout': True, '_dashcapstyle': , '_dashjoinstyle': , '_solidjoinstyle': , '_solidcapstyle': , '_linestyles': None, '_drawstyle': 'default', '_linewidth': 1.5, '_unscaled_dash_pattern': (0, None), '_dash_pattern': (0.0, None), '_linestyle': 'None', '_invalidx': True, '_color': '#1f77b4', '_marker': , '_gapcolor': None, '_markevery': [1], '_markersize': 6.0, '_antialiased': True, '_markeredgecolor': 'auto', '_markeredgewidth': 1.0, '_markerfacecolor': 'auto', '_markerfacecoloralt': 'none', '_pickradius': 5, 'ind_offset': 0, '_xorig': array([ 0., 10., 20.]), '_yorig': array([3.5, 3.5, 3.5]), '_invalidy': True, '_x': None, '_y': None, '_xy': None, '_path': None, '_transformed_path': None, '_subslice': False, '_x_filled': None}\n", + "{'_stale': True, 'stale_callback': , '_axes': , 'figure':
, '_transform': , '_transformSet': True, '_visible': True, '_animated': False, '_alpha': None, 'clipbox': None, '_clippath': None, '_clipon': True, '_label': 'Linear', '_picker': None, '_rasterized': False, '_agg_filter': None, '_mouseover': False, '_callbacks': , '_remove_method': None, '_url': None, '_gid': None, '_snap': None, '_sketch': None, '_path_effects': [], '_sticky_edges': _XYPair(x=[], y=[]), '_in_layout': True, '_dashcapstyle': , '_dashjoinstyle': , '_solidjoinstyle': , '_solidcapstyle': , '_linestyles': None, '_drawstyle': 'default', '_linewidth': 1.5, '_unscaled_dash_pattern': (0, None), '_dash_pattern': (0.0, None), '_linestyle': 'None', '_invalidx': True, '_color': '#ff7f0e', '_marker': , '_gapcolor': None, '_markevery': [1], '_markersize': 6.0, '_antialiased': True, '_markeredgecolor': 'auto', '_markeredgewidth': 1.0, '_markerfacecolor': 'auto', '_markerfacecoloralt': 'none', '_pickradius': 5, 'ind_offset': 0, '_xorig': array([ 0., 10., 20.]), '_yorig': array([3.5, 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Sample data\n", + "x = [1, 2, 3, 4]\n", + "y1 = [1, 4, 9, 16]\n", + "y2 = [1, 2, 3, 4]\n", + "\n", + "fig, ax = plt.subplots()\n", + "line1, = ax.plot(x, y1, 'o', label='Quadratic')\n", + "line2, = ax.plot(x, y2, 's', label='Linear')\n", + "\n", + "# Create legend\n", + "legend = ax.legend()\n", + "\n", + "# Adjust the size of the legend handles\n", + "for handle in legend.legend_handles:\n", + " print(handle.__dict__)\n", + "# handle._sizes = [20] # Change the size to your desired value\n", + "\n", + "# for i, key in enumerate(leg.legend_handles):\n", + "# print(\n", + "# leg.legendHandles[i]._sizes = [20]\n", + "\n", + "# plt.show()\n", + "\n", + "# legend.__dict__()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8bb8fbdd-9961-4883-9003-cec3011faadd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_stale': False,\n", + " 'stale_callback': ,\n", + " '_axes': ,\n", + " 'figure':
,\n", + " '_transform': ,\n", + " '_transformSet': False,\n", + " '_visible': True,\n", + " '_animated': False,\n", + " '_alpha': None,\n", + " 'clipbox': None,\n", + " '_clippath': None,\n", + " '_clipon': True,\n", + " '_label': '',\n", + " '_picker': None,\n", + " '_rasterized': False,\n", + " '_agg_filter': None,\n", + " '_mouseover': False,\n", + " '_callbacks': ,\n", + " '_remove_method': >,\n", + " '_url': None,\n", + " '_gid': None,\n", + " '_snap': None,\n", + " '_sketch': None,\n", + " '_path_effects': [],\n", + " '_sticky_edges': _XYPair(x=[], y=[]),\n", + " '_in_layout': True,\n", + " 'prop': ,\n", + " '_fontsize': 10.0,\n", + " 'texts': [Text(0, 0, 'Quadratic'), Text(0, 0, 'Linear')],\n", + " 'legend_handles': [,\n", + " ],\n", + " '_legend_title_box': ,\n", + " '_custom_handler_map': None,\n", + " 'numpoints': 1,\n", + " 'markerscale': 1.0,\n", + " 'scatterpoints': 1,\n", + " 'borderpad': 0.4,\n", + " 'labelspacing': 0.5,\n", + " 'handlelength': 2.0,\n", + " 'handleheight': 0.7,\n", + " 'handletextpad': 0.8,\n", + " 'borderaxespad': 0.5,\n", + " 'columnspacing': 2.0,\n", + " 'shadow': False,\n", + " '_ncols': 1,\n", + " '_scatteryoffsets': array([0.375]),\n", + " '_legend_box': ,\n", + " 'isaxes': True,\n", + " 'parent': ,\n", + " '_mode': None,\n", + " '_bbox_to_anchor': None,\n", + " '_shadow_props': {'ox': 2, 'oy': -2},\n", + " 'legendPatch': ,\n", + " '_alignment': 'center',\n", + " '_legend_handle_box': ,\n", + " '_loc_used_default': True,\n", + " '_outside_loc': None,\n", + " '_loc_real': 0,\n", + " '_draggable': None}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "legend.__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fee8ddf6-c752-4cb7-a970-cccbc86ffe45", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'matplotlib' has no attribute 'subplots'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m y1 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m9\u001b[39m, \u001b[38;5;241m16\u001b[39m]\n\u001b[1;32m 6\u001b[0m y2 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplots\u001b[49m()\n\u001b[1;32m 9\u001b[0m line1, \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mplot(x, y1, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQuadratic\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m line2, \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mplot(x, y2, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLinear\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/scratch1/NCEPDEV/da/Kevin.Dougherty/pyenv/EMCPy/lib/python3.10/site-packages/matplotlib/_api/__init__.py:217\u001b[0m, in \u001b[0;36mcaching_module_getattr..__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[0;32m--> 217\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'subplots'" + ] + } + ], + "source": [ + "import matplotlib as plt\n", + "\n", + "# Sample data\n", + "x = [1, 2, 3, 4]\n", + "y1 = [1, 4, 9, 16]\n", + "y2 = [1, 2, 3, 4]\n", + "\n", + "fig, ax = plt.subplots()\n", + "line1, = ax.plot(x, y1, 'o', label='Quadratic')\n", + "line2, = ax.plot(x, y2, 's', label='Linear')\n", + "\n", + "# Create legend\n", + "legend = ax.legend()\n", + "\n", + "# Adjust the size of the legend handles\n", + "for handle in legend.legendHandles:\n", + " handle._sizes = [50] # Change the size to your desired value\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdcd6e2d-4229-4963-bc25-6193290f35b0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/line_plots/inverted_log_scale.py b/galleries/examples/line_plots/inverted_log_scale.py similarity index 100% rename from examples/line_plots/inverted_log_scale.py rename to galleries/examples/line_plots/inverted_log_scale.py diff --git a/examples/line_plots/line_plot.py b/galleries/examples/line_plots/line_plot.py similarity index 100% rename from examples/line_plots/line_plot.py rename to galleries/examples/line_plots/line_plot.py diff --git a/examples/line_plots/line_plot_options.py b/galleries/examples/line_plots/line_plot_options.py similarity index 100% rename from examples/line_plots/line_plot_options.py rename to galleries/examples/line_plots/line_plot_options.py diff --git a/examples/line_plots/multi_line_plot.py b/galleries/examples/line_plots/multi_line_plot.py similarity index 100% rename from examples/line_plots/multi_line_plot.py rename to galleries/examples/line_plots/multi_line_plot.py diff --git a/examples/map_plots/README.txt b/galleries/examples/map_plots/README.txt similarity index 100% rename from examples/map_plots/README.txt rename to galleries/examples/map_plots/README.txt diff --git a/galleries/examples/map_plots/Test_Example_Plots.ipynb b/galleries/examples/map_plots/Test_Example_Plots.ipynb new file mode 100644 index 00000000..9f49448d --- /dev/null +++ b/galleries/examples/map_plots/Test_Example_Plots.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/scratch1/NCEPDEV/da/Kevin.Dougherty/emcpy/src/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "\n", + "# Create dictionary with information pertaining to\n", + "# Africa domain\n", + "africa_dict = {\n", + " \"extent\": (-20, 55, -35, 35),\n", + " \"xticks\": (-15, 0, 15, 30, 45),\n", + " \"yticks\": (-30, -15, 0, 15, 30),\n", + " \"cenlon\": 20.,\n", + " \"cenlat\": -10.\n", + "}\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.projection = 'plcarr'\n", + "# Add data as a tuple with 'custom' as domain name\n", + "# and `africa_dict` as dictionary\n", + "plot1.domain = ('custom', africa_dict)\n", + "plot1.add_map_features(['coastline'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='Custom Africa Domain', loc='center',\n", + " fontsize=12)\n", + "\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from emcpy.plots import CreatePlot, CreateFigure\n", + "from emcpy.plots.map_tools import Domain, MapProjection\n", + "from emcpy.plots.map_plots import MapScatter\n", + "\n", + "lats = np.linspace(35, 50, 30)\n", + "lons = np.linspace(-70, -120, 30)\n", + "\n", + "# Create scatter plot on CONUS domian\n", + "scatter = MapScatter(lats, lons)\n", + "# change colormap and markersize\n", + "scatter.color = 'tab:red'\n", + "scatter.markersize = 25\n", + "\n", + "# Create plot object and add features\n", + "plot1 = CreatePlot()\n", + "plot1.plot_layers = [scatter]\n", + "plot1.projection = 'plcarr'\n", + "plot1.domain = 'conus'\n", + "plot1.add_map_features(['coastline', 'states'])\n", + "plot1.add_xlabel(xlabel='longitude')\n", + "plot1.add_ylabel(ylabel='latitude')\n", + "plot1.add_title(label='EMCPy Map', loc='center',\n", + " fontsize=20)\n", + "\n", + "fig = CreateFigure()\n", + "fig.plot_list = [plot1]\n", + "fig.create_figure()\n", + "fig.save_figure('map_scatter_2D.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/map_plots/custom_map_domain.py b/galleries/examples/map_plots/custom_map_domain.py similarity index 100% rename from examples/map_plots/custom_map_domain.py rename to galleries/examples/map_plots/custom_map_domain.py diff --git a/examples/map_plots/map_gridded.py b/galleries/examples/map_plots/map_gridded.py similarity index 100% rename from examples/map_plots/map_gridded.py rename to galleries/examples/map_plots/map_gridded.py diff --git a/examples/map_plots/map_plot_no_data.py b/galleries/examples/map_plots/map_plot_no_data.py similarity index 100% rename from examples/map_plots/map_plot_no_data.py rename to galleries/examples/map_plots/map_plot_no_data.py diff --git a/examples/map_plots/map_scatter.py b/galleries/examples/map_plots/map_scatter.py similarity index 100% rename from examples/map_plots/map_scatter.py rename to galleries/examples/map_plots/map_scatter.py diff --git a/examples/map_plots/map_scatter_2D.py b/galleries/examples/map_plots/map_scatter_2D.py similarity index 100% rename from examples/map_plots/map_scatter_2D.py rename to galleries/examples/map_plots/map_scatter_2D.py diff --git a/examples/scatter_plots/README.txt b/galleries/examples/scatter_plots/README.txt similarity index 100% rename from examples/scatter_plots/README.txt rename to galleries/examples/scatter_plots/README.txt diff --git a/examples/scatter_plots/density_scatter.py b/galleries/examples/scatter_plots/density_scatter.py similarity index 100% rename from examples/scatter_plots/density_scatter.py rename to galleries/examples/scatter_plots/density_scatter.py diff --git a/examples/scatter_plots/scatter.py b/galleries/examples/scatter_plots/scatter.py similarity index 100% rename from examples/scatter_plots/scatter.py rename to galleries/examples/scatter_plots/scatter.py diff --git a/examples/scatter_plots/scatter_with_regression_line.py b/galleries/examples/scatter_plots/scatter_with_regression_line.py similarity index 100% rename from examples/scatter_plots/scatter_with_regression_line.py rename to galleries/examples/scatter_plots/scatter_with_regression_line.py diff --git a/galleries/plot_types/README.txt b/galleries/plot_types/README.txt new file mode 100644 index 00000000..c7e3ed68 --- /dev/null +++ b/galleries/plot_types/README.txt @@ -0,0 +1,20 @@ +## Plots + +The plotting section of EMCPy is the most mature and is used as the backend plotting for (eva)[https://github.com/JCSDA-internal/eva]. It uses declarative, object-oriented programming approach to handle complex plotting routines under the hood to simplify the experience for novice users while remaining robust so more experienced users can utilize higher-level applications. + +### Design +The design was inspired by Unidata's (MetPy)[https://github.com/Unidata/MetPy] declarative plotting syntax. The structure is broken into three different levels: plot type level, plot level, figure level + +#### Plot Type Level +This is the level where users will define their plot type objects and associated plot details. This includes adding the related data the user wants to plot and how the user wants to display the data i.e: color, line style, marker style, labels, etc. + +#### Plot Level +This level is where users design how they want the overall subplot to look. Users can add multiple plot type objects and define titles, x and y labels, colorbars, legends, etc. + +#### Figure Level +This level where users defines high-level specifics about the actual figure itself. These include figure size, layout, defining information about subplot layouts like rows and columns, saving the figure, etc. + +Plot Types +---------- + +Here is a collection of the current plot types that are currently available using EMCPy. \ No newline at end of file diff --git a/galleries/plot_types/basic/line.py b/galleries/plot_types/basic/line.py new file mode 100644 index 00000000..e041cc9c --- /dev/null +++ b/galleries/plot_types/basic/line.py @@ -0,0 +1,46 @@ +""" +Creating a simple line plot +--------------------------- + +Below is an example of how to plot a basic +line plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import LinePlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + + x = [1, 2, 3, 4, 5] + y = [1, 2, 3, 4, 5] + + # Create line plot object + lp = LinePlot(x, y) + lp.label = 'line' + + # Add line plot object to list + plt_list = [lp] + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [lp] + plot1.add_title('Test Line Plot') + plot1.add_xlabel('X Axis Label') + plot1.add_ylabel('Y Axis Label') + plot1.add_legend(loc='upper right') + + # Create figure and save as png + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/src/emcpy/plots/create_plots.py b/src/emcpy/plots/create_plots.py index df755e6b..d3c3b2c2 100644 --- a/src/emcpy/plots/create_plots.py +++ b/src/emcpy/plots/create_plots.py @@ -773,8 +773,8 @@ def _plot_legend(self, ax, legend): """ leg = ax.legend(**legend) - for i, key in enumerate(leg.legendHandles): - leg.legendHandles[i]._sizes = [20] + for handle in leg.legend_handles: + handle._sizes = [20] def _plot_text(self, ax, text_in): """ From 0b01df64d30d8da3f2976b2b0aa053c3d0cd48e2 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Wed, 26 Jun 2024 16:49:44 +0000 Subject: [PATCH 02/15] testing changes --- docs/conf.py | 2 +- docs/getting_started/index.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 0b37b155..60b30348 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -38,7 +38,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - # 'myst_parser', + 'myst_parser', 'sphinx.ext.githubpages', 'sphinx_gallery.gen_gallery' ] diff --git a/docs/getting_started/index.rst b/docs/getting_started/index.rst index 9f9b7723..7e9cfeb1 100644 --- a/docs/getting_started/index.rst +++ b/docs/getting_started/index.rst @@ -10,7 +10,7 @@ The following links provide further documentation on the different branches with calculations.md io.md -# galleries/plot_types/index + galleries/plot_types/index statistics.md utilities.md From 17cc9918a5395e3083e5955e24ea4678a5528d9c Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Wed, 26 Jun 2024 16:57:38 +0000 Subject: [PATCH 03/15] pycodestyle --- docs/sphinxext/gallery_order.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/sphinxext/gallery_order.py b/docs/sphinxext/gallery_order.py index 2a601c00..399b7cd5 100644 --- a/docs/sphinxext/gallery_order.py +++ b/docs/sphinxext/gallery_order.py @@ -32,6 +32,7 @@ explicit_order_folders.extend([fd for folders in folder_lists for fd in folders[folders.index(UNSORTED):]]) + class MplExplicitOrder(ExplicitOrder): """For use within the 'subsection_order' key.""" def __call__(self, item): @@ -41,6 +42,7 @@ def __call__(self, item): else: return f"{self.ordered_list.index(UNSORTED):04d}{item}" + # Subsection order: # Subsections are ordered by filename, unless they appear in the following # lists in which case the list order determines the order within the section. @@ -62,9 +64,10 @@ def __call__(self, item): # **Plot Types** # Basic "line" - ] +] explicit_subsection_order = [item + ".py" for item in list_all] + class MplExplicitSubOrder(ExplicitOrder): """For use within the 'within_subsection_order' key.""" def __init__(self, src_dir): @@ -79,6 +82,7 @@ def __call__(self, item): # ensure not explicitly listed items come last. return "zzz" + item + # Provide the above classes for use in conf.py sectionorder = MplExplicitOrder(explicit_order_folders) subsectionorder = MplExplicitSubOrder From 3c1207346e9795949ba351efb1aa6c234d29fca0 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Wed, 26 Jun 2024 17:04:46 +0000 Subject: [PATCH 04/15] pycodestyle --- src/emcpy/plots/create_plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/emcpy/plots/create_plots.py b/src/emcpy/plots/create_plots.py index d3c3b2c2..b5f5b728 100644 --- a/src/emcpy/plots/create_plots.py +++ b/src/emcpy/plots/create_plots.py @@ -774,7 +774,7 @@ def _plot_legend(self, ax, legend): leg = ax.legend(**legend) for handle in leg.legend_handles: - handle._sizes = [20] + handle._sizes = [20] def _plot_text(self, ax, text_in): """ From bd8d326287a58c2da364a55e88e47d8ca571ac17 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Thu, 27 Jun 2024 13:02:59 +0000 Subject: [PATCH 05/15] try new matplotlib --- requirements-github.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-github.txt b/requirements-github.txt index f03954f5..24f862d0 100644 --- a/requirements-github.txt +++ b/requirements-github.txt @@ -1,7 +1,7 @@ pyyaml>=6.0 pycodestyle>=2.9.1 netCDF4>=1.6.1 -matplotlib==3.5.2 +matplotlib==3.9.0 cartopy>=0.21.1 scikit-learn>=1.1.2 xarray>=2022.6.0 From f14651c4131044a883933fd80880823d30cc4bcb Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Mon, 1 Jul 2024 19:45:50 +0000 Subject: [PATCH 06/15] add plot type example files for gallery --- docs/conf.py | 8 +- docs/getting_started/index.rst | 2 +- docs/index.rst | 3 +- galleries/examples/README.txt | 8 +- galleries/examples/Untitled.ipynb | 810 ------------------ galleries/plot_types/README.txt | 18 +- galleries/plot_types/basic/README.txt | 4 + galleries/plot_types/basic/bar.py | 53 ++ galleries/plot_types/basic/horizontal_bar.py | 54 ++ galleries/plot_types/basic/horizontal_line.py | 45 + galleries/plot_types/basic/line.py | 8 +- galleries/plot_types/basic/scatter.py | 52 ++ galleries/plot_types/basic/vertical_line.py | 45 + galleries/plot_types/gridded/README.txt | 4 + galleries/plot_types/gridded/contour.py | 55 ++ galleries/plot_types/gridded/contourf.py | 55 ++ galleries/plot_types/gridded/gridded.py | 56 ++ galleries/plot_types/map/README.txt | 4 + galleries/plot_types/map/map_gridded.py | 51 ++ galleries/plot_types/map/map_scatter.py | 60 ++ galleries/plot_types/map/map_scatter_2D.py | 49 ++ galleries/plot_types/statistical/README.txt | 4 + galleries/plot_types/statistical/boxplot.py | 55 ++ galleries/plot_types/statistical/density.py | 53 ++ .../plot_types/statistical/density_scatter.py | 43 + galleries/plot_types/statistical/histogram.py | 55 ++ src/emcpy/plots/plots.py | 1 + 27 files changed, 816 insertions(+), 839 deletions(-) delete mode 100644 galleries/examples/Untitled.ipynb create mode 100644 galleries/plot_types/basic/README.txt create mode 100644 galleries/plot_types/basic/bar.py create mode 100644 galleries/plot_types/basic/horizontal_bar.py create mode 100644 galleries/plot_types/basic/horizontal_line.py create mode 100644 galleries/plot_types/basic/scatter.py create mode 100644 galleries/plot_types/basic/vertical_line.py create mode 100644 galleries/plot_types/gridded/README.txt create mode 100644 galleries/plot_types/gridded/contour.py create mode 100644 galleries/plot_types/gridded/contourf.py create mode 100644 galleries/plot_types/gridded/gridded.py create mode 100644 galleries/plot_types/map/README.txt create mode 100644 galleries/plot_types/map/map_gridded.py create mode 100644 galleries/plot_types/map/map_scatter.py create mode 100644 galleries/plot_types/map/map_scatter_2D.py create mode 100644 galleries/plot_types/statistical/README.txt create mode 100644 galleries/plot_types/statistical/boxplot.py create mode 100644 galleries/plot_types/statistical/density.py create mode 100644 galleries/plot_types/statistical/density_scatter.py create mode 100644 galleries/plot_types/statistical/histogram.py diff --git a/docs/conf.py b/docs/conf.py index 60b30348..10ad5d93 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -61,11 +61,11 @@ sphinx_gallery_conf = { 'capture_repr': (), 'filename_pattern': '^((?!skip_).)*$', - 'examples_dirs': example_dirs, - 'gallery_dirs': gallery_dirs, # path to where to save gallery generated output + 'examples_dirs': ['../galleries/examples', '../galleries/plot_types'], + 'gallery_dirs': ['examples', 'plot_types'], # path to where to save gallery generated output 'backreferences_dir': '../build/backrefs', - 'subsection_order': gallery_order_sectionorder, - 'within_subsection_order': gallery_order_subsectionorder, + # 'subsection_order': gallery_order_sectionorder, + # 'within_subsection_order': gallery_order_subsectionorder, 'matplotlib_animations': True } diff --git a/docs/getting_started/index.rst b/docs/getting_started/index.rst index 7e9cfeb1..919554b3 100644 --- a/docs/getting_started/index.rst +++ b/docs/getting_started/index.rst @@ -10,7 +10,7 @@ The following links provide further documentation on the different branches with calculations.md io.md - galleries/plot_types/index + plots.md statistics.md utilities.md diff --git a/docs/index.rst b/docs/index.rst index 2789e960..58f301c1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,7 +10,8 @@ EMCPy :hidden: getting_started/index - galleries/examples/index + plot_types/index + examples/index installing diff --git a/galleries/examples/README.txt b/galleries/examples/README.txt index e8462aa8..560cd7ec 100644 --- a/galleries/examples/README.txt +++ b/galleries/examples/README.txt @@ -1,6 +1,8 @@ -Gallery -------- +.. _examples: + +Examples +-------- The following examples show off the functionality of EMCPy. The examples give reference to what can be done with these collection of tools. Please -do not hesitate to issue a pull request to add further examples! \ No newline at end of file +do not hesitate to issue a pull request to add further examples! diff --git a/galleries/examples/Untitled.ipynb b/galleries/examples/Untitled.ipynb deleted file mode 100644 index e0d247ae..00000000 --- a/galleries/examples/Untitled.ipynb +++ /dev/null @@ -1,810 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "sys.path.append('/scratch1/NCEPDEV/da/Kevin.Dougherty/emcpy/src/')\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots import CreatePlot, CreateFigure\n", - "from emcpy.plots.map_tools import Domain, MapProjection\n", - "from emcpy.plots.map_plots import MapGridded\n", - "\n", - "# Create 2d gridded plot on global domian\n", - "lats = np.linspace(25, 50, 25)\n", - "lons = np.linspace(245, 290, 45)\n", - "X, Y = np.meshgrid(lons, lats)\n", - "Z = np.random.normal(size=X.shape)\n", - "\n", - "# Create gridded map object\n", - "gridded = MapGridded(X, Y, Z)\n", - "gridded.cmap = 'plasma'\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [gridded]\n", - "plot1.projection = 'plcarr'\n", - "plot1.domain = 'conus'\n", - "plot1.add_map_features(['coastline'])\n", - "plot1.add_xlabel(xlabel='longitude')\n", - "plot1.add_ylabel(ylabel='latitude')\n", - "plot1.add_title(label='2D Gridded Data', loc='center')\n", - "plot1.add_grid()\n", - "plot1.add_colorbar(label='colorbar label',\n", - " fontsize=12, extend='neither')\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "fig.save_figure('map_gridded.png')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Histogram\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Generate test data for histogram plots\n", - "mu = 100 # mean of distribution\n", - "sigma = 15 # standard deviation of distribution\n", - "data = mu + sigma * np.random.randn(437)\n", - "\n", - "# Create histogram object\n", - "hst = Histogram(data)\n", - "hst.color = 'tab:green'\n", - "hst.alpha = 0.7\n", - "hst.label = 'data'\n", - "\n", - "# Create histogram plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [hst]\n", - "plot1.add_title(label='Test Histogram Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "plot1.add_legend()\n", - "\n", - "# Create figure and save as png\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Histogram\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Generate test data for histogram plots\n", - "mu = 100 # mean of distribution\n", - "sigma = 15 # standard deviation of distribution\n", - "data1 = mu + sigma * np.random.randn(450)\n", - "data2 = mu + sigma * np.random.randn(225)\n", - "\n", - "# Create histogram objects\n", - "hst1 = Histogram(data1)\n", - "hst1.color = 'tab:green'\n", - "hst1.alpha = 0.7\n", - "hst1.label = 'data 1'\n", - "\n", - "hst2 = Histogram(data2)\n", - "hst2.color = 'tab:purple'\n", - "hst2.alpha = 0.7\n", - "hst2.label = 'data 2'\n", - "\n", - "# Create histogram plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [hst1, hst2]\n", - "plot1.add_title(label='Test Histogram Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "plot1.add_legend()\n", - "\n", - "# Create figure and save as png\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import LinePlot\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "x = [1, 2, 3, 4, 5]\n", - "y = [1, 2, 3, 4, 5]\n", - "\n", - "# Create line plot object\n", - "lp = LinePlot(x, y)\n", - "lp.label = 'line'\n", - "\n", - "# Add line plot object to list\n", - "plt_list = [lp]\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [lp]\n", - "plot1.add_title('Test Line Plot')\n", - "plot1.add_xlabel('X Axis Label')\n", - "plot1.add_ylabel('Y Axis Label')\n", - "plot1.add_legend(loc='upper right')\n", - "\n", - "# Create figure and save as png\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import LinePlot\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "\n", - "def _getLineData():\n", - " # generate test data for line plots\n", - "\n", - " x1 = [0, 401, 1039, 2774, 2408]\n", - " x2 = [500, 250, 710, 1515, 1212]\n", - " x3 = [400, 150, 910, 1215, 850]\n", - " y1 = [0, 2.5, 5, 7.5, 12.5]\n", - " y2 = [1, 5, 6, 8, 10]\n", - " y3 = [1, 4, 5.5, 9, 10.5]\n", - "\n", - " return x1, y1, x2, y2, x3, y3\n", - "\n", - "\n", - "# create line plot with multiple lines\n", - "\n", - "x1, y1, x2, y2, x3, y3 = _getLineData()\n", - "lp1 = LinePlot(x1, y1)\n", - "lp1.label = 'line 1'\n", - "\n", - "lp2 = LinePlot(x2, y2)\n", - "lp2.color = 'tab:green'\n", - "lp2.label = 'line 2'\n", - "\n", - "lp3 = LinePlot(x3, y3)\n", - "lp3.color = 'tab:red'\n", - "lp3.label = 'line 3'\n", - "\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [lp1, lp2, lp3]\n", - "plot1.add_title('Test Line Plot')\n", - "plot1.add_xlabel('X Axis Label')\n", - "plot1.add_ylabel('Y Axis Label')\n", - "plot1.add_legend(loc='upper right')\n", - "\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/scratch1/NCEPDEV/da/Kevin.Dougherty/emcpy/src/emcpy/plots/create_plots.py:713: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " **yticklabels['kwargs'])\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import LinePlot\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "x = [0, 401, 1039, 2774, 2408, 512]\n", - "y = [0, 45, 225, 510, 1200, 1820]\n", - "\n", - "# Create line plot object\n", - "lp = LinePlot(x, y)\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [lp]\n", - "plot1.add_title(label='Test Line Plot, Inverted Log Scale')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "\n", - "# Set y-scale to log and invert\n", - "plot1.set_yscale('log')\n", - "plot1.invert_yaxis()\n", - "\n", - "# Set new y labels\n", - "ylabels = [0, 50, 100, 500, 1000, 2000]\n", - "plot1.set_yticklabels(labels=ylabels)\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "# fig.save_figure('inverted_log_scale_line_plot.png')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Scatter\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Create test data\n", - "rng = np.random.RandomState(0)\n", - "x = rng.randn(100)\n", - "y = rng.randn(100)\n", - "\n", - "# Create Scatter object\n", - "sctr1 = Scatter(x, y)\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [sctr1]\n", - "plot1.add_title(label='Test Scatter Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()\n", - "fig.save_figure('scatter_plot.png')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Scatter\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Create test data\n", - "rng = np.random.RandomState(0)\n", - "x = rng.randn(100)\n", - "y = rng.randn(100)\n", - "\n", - "# Create Scatter object\n", - "sctr1 = Scatter(x, y)\n", - "\n", - "# Add linear regression feature in scatter object\n", - "sctr1.do_linear_regression = True\n", - "sctr1.add_linear_regression()\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [sctr1]\n", - "plot1.add_title(label='Test Scatter Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "plot1.add_legend()\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Scatter\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Create test data\n", - "rng = np.random.RandomState(0)\n", - "x = rng.randn(100)\n", - "y = rng.randn(100)\n", - "\n", - "# Create Scatter object\n", - "sctr1 = Scatter(x, y)\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [sctr1]\n", - "plot1.add_title(label='Test Scatter Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4iouLozJlypBYLCaJRJKnNcaMGUOMMdq0aZOWruLHo0OHDuTs7ExERBMnTiQAZGhoSKampvme89y5c1S1alXl/aNv375kY2NDAO6Slr9fOcvFd0zdunWxf/9+iMVitG3bFiEhIboWqVD5Ucyy5cqVQ3p6uq7FyDedOnVCuXLlEB0djXHjxulanGKPlZUVQkJC4Orqijdv3uD06dO4d+8eBg8ejJSUFMyePRumpqZwcXGBnp4e4uPj8+xDsWbNGhgZGeHZs2dauoofjxcvXkBfXx9r167Ftm3bMHDgQHh6ehaosm2nTp0QExODHTt2AACsra0Lz19P29pLUW7fu+UiA4lEQiKRiFq3bq1rUQoNW1tbmj59uq7FKBTS0tIIACUlJelalHyhr69P+/fv17UYPyT9+vUjHo9Ha9euzXT8xYsXNGfOHOrUqRMNGTKEZDJZnucuUaIEVa1aldLS0jQl7g/NwYMHyc7OjoyMjKhs2bIUEhJCjDHy9fXN95xCoZB2795NvXv3JmdnZzI0NCQAhWK54BL1FwMEAgEGDBiALVu2YPLkyfj3338xfvx4DBgwgKvFUAzIcMZ7+vQpvLy8dC0Ox3eEr68vSpcujbFjxyIkJARLly4FAJQpUwazZ88u0NwnT55EgwYNULNmTWVyLY7806VLF3Tp0iXTMYFAADMzs3zPaWhoiD59+gAABg8eDE9PT9SpUweVK1cuiKi5gtsWKSYsWbIERISVK1ciNTUVQ4cORcOGDXUtFoeGEIlEP0wKcA7NMnXqVDRt2lTjIY5VqlTBkSNH8PDhQyQmJmp0bg7g7t27kEgk2LFjhzJ5WV7p0aOH8veQkBD07du30GqZcMpFMUEsFkMmk0EqleLFixcAFHt4S5YswaxZs3D27FkdS8hREIyNjb/rLJ2MMV2L8MNiYWGBs2fPQi6Xw8PDA9HR0RqbOyYmBkSENWvWaGxODgXJyckAgGPHjqFEiRJYsGCB8lxQUBBcXFzw008/oUuXLrh165bKOcLDw+Hq6opjx47h1q1b6Nu3b6HIDnDKRbGCx+Mpf965cwdubm74888/sWrVKrRq1QrXr1/XsYQc+cXU1BTBwcG6FoPjO+Ts2bO4ceMGbt++jfj4eFhZWaFMmTLYvHlzjmOJCCNHjkSpUqXAGEP//v2V5/bt24cePXpg6NChmD59ujYv4Yekfv366NevH4YPH46FCxdi1qxZsLGxAWMMFStWhEgkQo0aNfDo0SP8/PPP8PLyypQgbcSIEThx4gTGjx+P9u3bQygU4tixY9i4cWPhXIC2nTqKcisuDp25oVOnTsQYo8WLF+taFI3wIzl0EhHVqlWLmjZtqmsx8oVIJKKDBw/qWgyOL1y8eJE6dOhAAoGA7O3t6eHDh2r7pqSkZDgAEgA6ffo0ERGNGjWKGGM0cuTIwhL7h+fq1as0ZMgQOnDgAPn4+GQKH3727BlZWlqStbU13b59m4iIzMzMaNq0aco+pUqVIgBkYmJSKA6dOv+C12X7kZQLIqKVK1cSj8ej1q1bk7m5Oa1fv17XIuWbH0256NixI3l4eOhajHwhEono0KFDuhaD4xvi4uKodu3axBijJUuWqO0nkUjo/PnzFBwcTEQK5YQxRitXriwkSTlyQ1JSEllYWBBjjIiI+Hw+rVmzRmXfwlAuuG2RH4jx48ejefPmOH/+PGrUqIGxY8diz549uhaLIxeULFlSo3vlHBxisRjXr1/HihUr8Ntvv+HOnTsq+wkEAkRGRmLgwIH477//8PnzZxDRd+0DVBwxNDREy5YtwePx4OHhAZlMptMigFyc4g/Gv//+q/x95MiR6Nu3L16+fIk5c+boTiiOHPneU4Bn+ANxFD3Gjx+P48ePo3379iqjEjp37owjR47AwsICderUQfny5bF161YMHToUtra2+P3333UgNYcqfH198eHDB6SnpyMwMFCnxQC5T/wPzMaNG7F+/XrMnTsXly5d0rU4HNlQtmxZpKSk6FqMfMMpF0WbVq1a4ePHj+jVqxf27dundAzcsGEDjhw5AgCIiorChQsXEBMTg6FDh0IkEqFFixa6FJvjGwQCAS5duoQbN27ovMow94n/wRk5ciSaNWuGxo0bc0XQijAVKlSARCLRtRj5QrHFy1EUefv2LRhj+O233wAA165dw4ABA2BoaIgqVapg1qxZKFeuHExNTQEADRs2xL59+wAAEyZM4JK6caiFUy444O/vj8ePHyMgIACOjo4YM2bMD1O343uhRIkSABQly79HuDwXRZMyZcoAAIYPHw6ZTIZ3794hLS0NV69eBaCwVrx79y6Tgli/fn1MnjwZv/76q05k5vg+4JSLr4iPj/9hv1QrVKiAJ0+eoH379ti2bRssLS0hlUp1LRbHF3g8Hvh8/nebpZPbFimaxMbGQiKRYNOmTZn+R3Xq1MGDBw/QunVrJCUlIT4+PlPp7yVLlsDKykoXInN8J3Cf+C/cuXMHpqamWLVqla5F0RnlypXDhg0bsH79esTGxqJnz574/PmzrsXi+IK+vj6eP3+uazE4ihHGxsbZ1h/6559/MGTIEACAq6trYYnFUQz4oaNFvs6Hf/ToUQBARESErsQpMgwcOBA8Hg+//vorrK2t8erVK5QsWVKnYU0cgImJCV6/fq1rMTh+MDw8PCASiWBjY6NrUTi+I35oy8WrV68wZcoUTJw4EX/++ScAoHXr1jqWqmjQv39/fP78GU5OTnBzc4O+vj7GjRuna7F+aMzNzREaGqprMfIFty3y/cIYg0gkwrt373QtCsd3xA9tueDz+Vi+fLny7759+6J+/fo6lEjhWX/h1jP4Xw+ChakherWpiZJ25jqT5/Xr1wgICEBISAh69uyJyMhI7N27V2fy/MjY2NjkuzqiLuGiRb5/uP9h0UMulyMoKAglS5aEWCzOdDwxMVF5TC6XK+8bDg4OhaboFwnlgjEWDCABgAyAlIi8GGMWAA4AKAUgGEA3Ior50n8pgEYAfiWiy4yxUgDeAhhLRGu/9FkHRYrTnerWTU9PBwBYW1sjNjYWhw8fxs6dO1W++Hfu3EHVqlWz3Z/UBOP//BtbDl4FYwARsNjHDyc3jkadqqW1uq46BAIBatasiZo1a8LS0hJNmzbFpk2bMr2ZOQoHe3t7PHjwQNdi5AsuWuT7hbM66Yb//vsP69evh1AohEAgwIEDB5CSkgJjY2MkJSUhPT0djDFFqm0eDwKBADKZDDKZDMD/LU5paWlK5VAgEMDOzq5Q5C9K75pGRFSFiDICp6cCOE9EZQCc//I3GGPuX87XB/DLV+M/ARjHGMu1Y4ClpSUAIDIyEhKJBMnJySq3RRITE1GjRg3o6elpNYLi3tN32HJQEQKW8aCQJpHi1yUHtbZmXmjSpAnEYjFmz56ta1F+SJycnBAbG6trMfIF9wXFwZE7nj9/jnLlyqF27dq4cuUKLl26hH/++Qe9evXCgQMH8Ouvv+LgwYP4+PEj5HI5YmJicOfOHezfvx+XLl1CTEwMJBIJHj16hK1bt+LRo0cgIshkMvz111/o1KlToVxHkbBcqKE9gIZffvcFcAnAbwD4AORQVOn7+nEoEsB1AP0BbM3NAqVKlYJQKMTHjx9hbGwMADhz5gxWrFiBiRMnKvvdvn1b+bs2b5I3HmR11pPLCQ+fvUdqmgT6Ij2trZ1bunXrhu3bt2PlypW6FuWHo0yZMkhISNC1GBwcHFpALpfD09MTDx48gLu7O968eQMXF5cs/Tp27JjpbzMzM1SrVg3VqlVTHnv79i3++usvhISEICAgQJkosWfPnujZsyfWrFmj9espKsoFAfBnjBGAzUS0BYAtEX0EACL6yBiz+fL7E8aYIYBrACZ/M88iAP8yxrarW4gxNgzAMEDxJPjmzRuULVsWCQkJCAwMhK+vLyZNmgQfHx9MnjwZHz58wB9//AEA2Lt3r1aVixK2qn0rLEwNIdTjqx136NwjbDx0A1GxSWj+c1lMHdAYFqaGWpHRzMyMM3HrCHd3d2Va5u8NznLx/cJ93guHQYMGISgoCCEhIXBycirQXBlhw3Z2dmCMYd26dZBKpRAKhcpsq9qmqHzi6xBRNQCtAPzCGMvWq5KIxhCRJxFd+Ob4WwC3AfTKZuwWIvIiIi9ra2tl7gBLS0u4urqiS5cuICIEBQVh8ODBWLx4sfKGPmzYMLi5uWHfvn2Qy+UYO3Ys/vvvvwJffAbe9SvCzcla+WHO+ExP6N9M7c1527Fb6DtzL24+DsHzkEis238NLX7ZAolUpjG5vqZy5cpISkrSytwc2VO+fHnIZLLvMtHb9ygzx//hHDq1y+HDh/HXX3/h0KFDBVYsAGDt2rVwdnZGREQE4uLi0LBhQ5w+fRr79u3DiBEjNCBxzhQJ5YKIwr78/ATgKIAaACIYY/YA8OVnbvMe/wHF9kmur01fXx/79u1DWloa9u3bhzNnzsDIyAhEhMTERBgZGQFQ+F68fv0aMpkMTZo0wdq1a3H//v3cX2gOCPUE8PcZj77tasLK3BhlnG2xalo3/Dqgqcr+RISF284DUGyfAAoTUODrcPx7/ZnG5PqaSZMmQSqVfreOhd8zFhYWAICQkBAdS5I3GGPfbV0UDs7qVBgMGjQIffv2RZs2bTQy3+jRoxEcHIzExETMnz8fYWFhaN26Nfbs2YN58+ZpZI2c0Pm7hjFmxBgzyfgdQHMAgQBOQOE/gS8/j+dmPiJ6BuApgDz9l7y8vLB8+XLMnz8fvXr1Qrdu3TBz5kyYmpri119/ha2tLQDFtsD06dOVVUTbtWuHjRs34q+//srLcmqxtzbF5jl9EHphER4enYnh3eqrNUtKpDJ8/Ky6DPebD1HZriOTyRH6KR4paXm76fv6+oIx9kNnMtUlenp6ePLkia7FyBNEBD8/P7x8+VLXonBwFClSU1OxY8cOxMfHY+HChRqf39DQEBMnTsSTJ09w5coVHD9+HN7e3hpfRxVFwefCFsDRL1+gAgB7iegMY+wOgL8ZY4MBvAPQNQ9zLgSQZ5PCqFGj0LFjRyxduhQnTpxAWFgYBAIBLl++jPPnz8PExASbN2/Go0eP0LBhQ+zatQslS5YEADRq1Aj9+vXL65IFQqgnQIXSdgh6EwH5N2ZLr/Il1Y47dvUZJq71x8eoRBiK9DCuaw3MHKBeifmaFi1aYMaMGViwYAHMzc2xfPly7smmEDEwMPjusnTK5XJs3boV69atg6+vb6F/TjgKRka4I0fB8PPzw86dO/H48WOEhYUhPj4eMpkMPB4P48ePVxYn/Jb4+HhcunQJkZGRiIyMxKtXr/D27VskJyfDysoK7u7uqFKlCmrVqgUXFxe19+O6devi2LFj+OWXX1Se1zhE9MM2T09P+pqyZcuSSCRS/i2RSMjX15fc3d2Jz+fTokWL6ODBg8rzAoGAAFCPHj0oLS2NdMH5Wy/IpO40Mvj5NzKqPZX0a/1GvabvIrlcrrL/49cRZNj0DzJospD0G/+/bTp2N0/rbt26lfh8PvH5fNq+fbsmLiVP2Nra0vTp0wt9XV3j6OhIEyZM0LUYeUIoFNLJkyfJ2dmZBg8erGtxOPLI1q1bycjISNdifLdcvHiRqlWrRgDI2dmZunTpQn/88QddvXpV5fdGZGQkTZ06lcqXL0+GhoYEgPT09MjQ0JDMzMzIxcWFfv75Z2rWrBlVrVqV7OzsSF9fnwCQQCAgLy8vOnfuXLYyQZEDSqvfr4x+YI3U1taWLCwsIBAIEBkZiYiICFhYWCAqKuuWwty5czFnzhwACtP0q1evsH37dsydOxeAItuni4sLDAwMCvMSAABp6VLExKdAKpfD2FAIM2PVMqSlpSE8Kg4JKgIORAIebEz18uxJ/Pz5c6Snp6NMmTLQ19fPj/j54unTp7CwsCi0hDAFIS4uDkZGRhpJwPbixQvo6+trxOkrL8THx8PAwAB6enkPh378+DHOnTuH9evX4969ewgODta8gFomOTkZq1atwuvXr1GtWjW0bNkSrq6uP0QkhY+PD8aPH5+pFhNHzkilUlSqVAnPnz+Hs7MzatWqhT179mSxLMjlcty4cQPbtm3DmTNnEB4eDnNzc9SvXx+tWrVC586dc1WBVi6X4+DBg1i9ejVu3ryJ2rVr49ixYyrHMsYC6P85pbTCD61c8Pl8at++Pe7fv4/g4GAYGBggLCwMZmZmKvtXrFgRQUFBSs93mUyGpk2bYvbs2ejTpw8+fPiADh06FNltgoCAALwP+wi5qVuWc3qSGMiTItGuXTuVY6UyOcJjkkFygq2FIYSC/4fGRkREwNraulCvOzo6GsbGxt9FMbUTJ07A3d0dP/30U4Hnio+Ph0AggKGhdkKN1XHhwgXweDw0bNgwT+OICCKRCNu3b0dERARcXV2xZcsWDB48WDuCagFfX1+8fPkSCxcuRNOmTfHixQtERkaCx+Ohffv2qFOnDtq0aQMbGxskJSXByMgIjx8/RqVKlQpV4dYW27Ztw7hx4zjlIpekp6ejT58+OHbsGAwNDfHixYssRd9SU1Pxxx9/YPfu3Xj37h2ICI6OjmjdujUmTZqE0qXzn5H58+fPcHV1RUJCAhhjKiO1CkO50PnWhC5bxrZI2bJliTFGixcvznZ7IyUlhUxMTAiAcvskJSWF2rZtSw0bNiQAxBij6OhotXPokiFDhpCdQ8lM2yEZrUmXkWRqaqpy3LXAD2TddTPpt15L+q3XklnHDXTq1pvCFf47xsLCgv744w9di1Egli1bRoaGhgWep2PHjsTn88nd3Z3at29PU6dOpQMHDlBKSooGpNQ8gYGBZGNjQ/b29tS4cWPi8/kERVCWcku0X79+ZGVlRXp6eiQWi4kxRj/99BOJxWJyc3OjZ8+e6foyCoSPjw+3LZJLPn78SHZ2diQWi2nTpk0kk8mU5xISEmjJkiVUpUoV4vF4ZGxsTD169KAzZ85k6qcJGb5+j6oChbAtUjQfsQuZGzduoFu3bpgzZw709fXh5uaGESNGZAm31NfXx65duwAothhGjhwJS0tLnDx5EpcuXcLy5cshFApx4sQJHVxFzvD5fOgL+RjbpUam43UqlUTT6lkzwQGKqJL+S/2RlPL/qJI0iQwDl51FcioXXpgbGGPKfP/fK71790ZycnKB5zly5Ai2bduGSpUq4e3bt9ixYwf69u0LAwMD1KhRA6tXr0a3bt0gEonAGAOfz4dYLEbjxo1x/vx5DVxJzkRFReHvv//G8OHD0a9fP3z69AkfP37ErVu30KdPH/z333+IjIzEs2fPsHv3bvj6+iIyMhLp6emIi4uDRCLB8+fP8eTJE4wdOxatWrVCXFxcociuDYqqJbao8fLlS7i6usLExAQfPnzA8OHDwePx8ODBA3h5eUEsFmPu3LmwsrLCyZMnkZCQgH379qFFixYafY3t7OxAROjbt28Wi0mhom3tpSi3bx06iYiOHDlCLVq0IAcHB2KMkaGhIdWvX5+WL19Or169IiKiJk2aEAAqWbJkJg0xw6lm1apVKrVFXTNixAhydnYmIqKXoVG01/8xXX/8juRyOa1cuVKl5eLRm0ilxeLbduH+u8K9gO8US0tLmjt3rq7FKBD3798nxpjW5r9z5w45OzuTWCwmV1dXWrlyJQUHB1NAQAAtXryYqlSpQowxsrGxoWvXrmlFhvT0dOrWrRuJxeJMn2sA1KdPH7p48aJaR+nsaNu2La1fv14LEhcO27dv14jV6ntCJpMRj8cjPp9P7dq1o7lz55K/v79aC1taWhpZWVlRlSpVlFaIgIAA8vb2Jh6PR1WrVtXa+/ZbQkJCaPHixWRqakrNmzdX2QeFYLnQ+Re8Lpsq5cLLy4sAkLGxMTVp0oQ6duxI1atXJ2NjY+W2B2OMypQpQ2vWrKHg4GBatGiRclsEAJmYmKj8h+qakSNHKpWLb1GnXLz9GKdWubjzPFztWmHRSTRvXwD1WX6Blhx+QFHxqRq6iu8Pa2trmj17tq7FKBChoaEEQKPm27wSGRlJnp6epKenRwYGBmRoaEg1atSgjx8/amT+hw8fkrGxMSUmJio/y/v27SM/P78CzXv58mVydnYmqVSqETkLm507d/5wygURZbqnf914PB516NCBLl++TEREjx8/JisrK7K0tKSUlBTy9/cnOzs7YoyRq6sr7d+/v9Bk7tu3LwEgsVhMDRo0UKsMFYZywdm7vsHU1BSenp6YNGkSoqOj8e+//+LBgwewtbWFp6cnqlevjlatWqFt27a4e/cupk6dijFjxiA6Olo5R0JCQpHMosjn89WmYVZ3vJSdGA0rl8DXTvE8xlCxlCU8y6g2ub2LTEStScex6PBDHLsVgjn77qHubycQpSpM5QegOGyLODg4AIBOa5tYWVnB398f1apVw6RJk7Bs2TJER0ejRIkS6Nq1K8LCwgAAr1+/RtOmTSEUCsHn8zFq1Chs3rwZBw4cwD///IPNmzfj2rVrmeaOjY3F3Llz8csvv8DIyAhhYWF49eoVgoKCMHHixAJVo61Xrx5KliyJRYsWFeTydcaPEBGjiosXL4KIkJaWhri4OPj7+8PDwwNyuRzHjh1DgwYNwBiDh4cH3Nzc8O7dO4wYMQItWrRA1apVER0djdevX6N79+75Wj88PBzr1q3Djh07sG3bNtSqVQulSpVS1gnJQC6XY968ebCxscGePXtw7tw5xMXF4dKlSzp1KC4KSbSKFPr6+pBIJJg9ezZmz54NuVyOXbt24eLFi4iMjERUVBTevHmDBw8eQE9PD/Hx8cr04EKhEOnp6QAUFVcjIyNzFUJUWOR0k1CnYOya0gKj113EyZtvICegcZUS2DC2sdr5Vh5/rFQkMtKSh0QmYsuZZ5jWtUr+L+A7pTgoFzweD4aGhli0aFGhpQ9WhYWFBW7evKn8e+TIkdi4cSMWLlwIR0dHCAQCSKVSuLq6YseOHXj69ClWr14NxhikUilkMhmEQiGSk5PRsmVLfP78GbGxsYiPj0eNGjXw66+/4u+//8bGjRuVWXjLli2rrJqcHxhj2L9/Pxo1aoS3b99i7NixKF++PPh8PqKiopCYmAiZTAZDQ0PY29sX9CXSCoqH3R8ToVAIoVCIZs2a4eHDhwAUUVsdOnTAxYsX8fbtW9y4cQMuLi6Ij4/H0aNH0b59+yzzpKenY9iwYZkU9GHDhqFx48bKv9+/f4/p06fj0KFDSElJyTTeyckJVatWxaRJkzB27FiULFkS1tbWCAwMhFwux5AhQzB16lQkJSWhR48eWi+0mSPaNo0U5aZqW+TAgQPE4/Ho8OHDKs1JqoiLi6P9+/dnMp3x+XyKiorK9RyFwfjx48nR0VHluWfPnpFAIKD69eurNX3HJ6VRbGLO2xv1p54gwy7bMzXjrjuo66KzBZL/e8XOzo6mTp2qazEKzIQJE4p01MCbN2/o9OnTFBcXl20/qVRKgwYNyvR53bt3LxFRpmM1a9ak7du3U3p6ukbki46OpilTppCbmxvx+XzS09MjMzMzcnJyolKlShEAOn36tEbW0iS7du0iAwMDXYtR5ChRogR5e3uTr68vMcaoY8eOFBMTk6VfWlpapvdVhQoVqEKFCmRqakqMMapYsSK5uroqfX0sLCyofPny1LJlSzp16hR16tRJeU4oFNLQoUPp+vXrNHjwYGrRogWNHDmSFixYQDExMRQcHKyMaEpISFArOzifi8JXLoiIhg8fTowx8vHxUfe/yYSvr2+Wfbncji1MJkyYoFa5IFLsORsYGFCdOnUKtM7gNZfJuGtm5cKoy3aa5ntb7ZhPcSn0y5b/yGX4Qao47igtPfqYJFLd7e9rEnt7e5o0aZKuxSgwUVFRxBgr1D1kTSCXy+nBgwe0YsUKat++PZmbm1OlSpVo1qxZ9OLFi0x9R44cSV27dqU1a9ZoXI5//vlHeX/w8vLKtB/u5+dHJUuWpKSkJI2vW1A45SIzMpmMvL29SU9Pj9avX088Hi/bz3dcXBwBoMDAQIqIiMh07uTJk1S3bl3q1asXLVmyhFatWqVWKUhKSqINGzYQY4zWrFlDw4cPJz09PeLxeMr3lVAoVPoMZgenXOhIuSAiGj16dK6e0g4ePKjS6efMmTM5ji1sJk6cSA4ODtn2efz4MfF4PNq9e3e+13kUHEUWPX2VCoZx1+1k3283hXxS/aGRymRUb/ppsuy3lyz6/r/N2BOQbxmKEt9jym51dO3alWxsbHQtRq6QSCS0bds2qlKlCrm4uNDw4cNp79699OHDB53I8+jRIxo0aBCNGTOGNm3alOlchw4d6Pfff9eJXDmxe/duTrn4wsePH8nZ2ZkMDAyoa9euxBijKVOmZDsmMjJSbb6J/DBkyBDi8XhkampKq1atIplMRlFRUTR58mQCQN7e3jnmjeGUCx0qFwkJCQQgy5PN13z48EGpTJw+fZpcXFyUWyLaePIpKJMmTSJ7e/sc+xkYGFCzZs0KtNbDt1HUf+UlqjHxKA1ff5VefIhV2/dS4MdMSkVGsx+0n5JSJQWSoyhQokQJGjt2rK7F0Ah37twhHo+nazFy5Pnz5+Tp6UkNGjQgPz+/Ake5vH79mtauXUt9+vQhT09PqlmzZrb3hryyZ88esrS0pIkTJ+a4rVPY9OvXj6ytrXUths6ZM2cOCYVCKlOmDJUvX54EAkEmC/Xly5epUaNGNGXKFJJIJJSWlkarVq0iCwsLsrKy0rp8YrGYxo8fn6u+haFccNEiajA2Noa9vT1+++03tX3279+v/L179+7KqnYymaxI1k/Irdd3vXr1cPbsWbRo0QLPnz/P11oepSywc3wD3FreAZtG1UUZB/U1S8JjU1QeT5PIEZecnu06oVFJuP82GinpRddhsjhVlbSzs4NcLs/krV6UICLs2LEDderUweDBg3Hx4kU0b948345taWlp+O2331CzZk08ePAAu3fvRkBAAG7duqXREvK9evVCQEAAPnz4AE9PzyKVdMvIyAhJSUlKZ/UfkREjRmD+/PnKaKIXL17g7du3yjT2fn5+aNy4McLCwrBkyRI4OjpCX18fv/76K8zMzGBoaAg7OzssWbJEK/LNmjULCQkJmDlzplbmzxfa1l6KcsvOckFEdOjQIWKMUWRkpMrzMpmMpkyZkmVLxMvLS2dVUrNj6tSpZGdnl6u+58+fJxcXF2KMab36aPCnBLL8xmph1W8vVfv1hNqkRUmpEhq88T+yG3qI7IYeop/GHqejt4pmUi9nZ2caOXKkrsXQGAAoNDRU12Jk4ePHj9S5c2eqWLEiPX78uEBzJSUl0bx588jKyooA0O+//06NGjWicuXK0dmz2nNM9vf3J2Nj4yJVQkAmk5GhoSEtXrxY16LohNmzZxOPx6O//vqLzMzMqESJEhQYGKg8f/r0aeLxeDRgwAAiImUyxQzHShMTE6pfvz4NGTKEBAIBOTs7a8zqFRgYqKzavXnz5lyPA7ctolvlgkhRVt3R0VHtzWrx4sWZkmsV5TLg06ZNI1tb2zyN8fHxIR6PR506ddKSVAqWHn2ceUtk4H66+Fh9cqSZ+x8oFQu7oYfIfughchh2iN5EqPeQ1hXOzs40YsQIXYuhMfh8Pt24cUPXYihJT0+n9evXk7W1NU2bNo2Sk5Oz9JFKpTR+/PhMXwqqiI2NpePHj5OHhwd16dKFatWqpXxoaN68OUkk2tumk8lk5O7uroxcKUp4eXlRuXLldC1GobJnzx4yMzMjxhg1bdqU9PX1qXTp0pkeHE+dOkU8Ho8GDx6sPJaUlETLly+ntWvXZvF9iIyMpGrVqhGPx1P70JoXMt6bPB6PjIyMsiiAEomEOnfuTH/++ee34zjlQpstN8qFtbW18h/YuHHjHPsXZWbMmJEvZ7xr166Rnp4e5eb1Kgh3X32mhQcf0qqTT+j958Rs+5YbfyKTcpHRVp0K0qqM+SHDmbC4YGJiUiSuRy6X07Fjx6hs2bLUpEkTevjwIREpUpVv3ryZWrVqRUKhkJydnQkAderUKYvFRSqVUmhoKN28eZOWLFlCNjY21KxZM1q3bp2yj7u7Ozk5OZGHhwfVq1eP+vXrR/PmzSuwdeRb0tLSyNHRkZycnMjR0bFIRZy9evWK9PT0qEmTJroWReskJCRkuu8DICMjIxo7dmwm351jx44Rj8ejoUOH5nkNHo9HN2/eLLCsly9fpuHDh9PBgwdJKBRmCnn/+PEjWVhYEAAqXbp0pnGFoVxwSbRyYO7cuRg1ahR69eqFzZs361qcApHfTHt16tTBmzdvULp0abi4uGDMmDGYOHGihqUDPEtbwrO0Za76Kj4fWZGrOZ4x5tbraNx+HQU7U314V3GAsb72PwIZCZyKC/b29oiKitKpDJ8+fULNmjUhEAgwb948WFlZwc/PD5MmTcLjx49Rt25ddOzYEe/evcOTJ08AKEqHm5mZAQBCQkIwd+5cHDt2DCKRCA4ODqhWrRr8/PxQpUqVTGuVLl0aly5dwvbt20FEeP/+PQIDA9G0aVP8+uuvmDx5cq5kTktLQ2xsLEQiEQwMDBAWFoZLly4hICAAgYGB+PPPPxESEoJ+/fph7969uHz5cpEpTV+6dGncvn0bNWvWRP/+/eHr66trkTSKVCrFvn37MGvWrEz+ciVKlMCaNWvQsWPHTP0PHz6Mbt26Yfjw4diwYUOe1+PxeFmSZOWH+vXro379+gAUfn/u7u4AFEm+3N3dYWpqitjYWLRt27bAa+UZbWsvRbnl5kk8LS1NuYdWFMNL88Ls2bML5PX94cMHcnd3JwBkbm5OxsbGGqvrkFem7rlH9t9si9gPO0QvwlR72svlcpq0+z45jz2pbDVm+lNIZPYWEk3g5uam3I8tDlSsWJHatWuns/Xv3LlDjDEyMTEhKysrsrCwoPr169O4cePo4MGDlJiYqOyHL0+eI0eOpJ07d9KiRYuoRYsWZG5uTr///juFhYXluF5SUpLK3AOhoaHk5OSUrS/C7du3aeDAgWRtbU0ikYisra1JLBYTn88nBwcH6tatG61cuTJTAq158+YRALp+/TolJibmq1iatjh+/Dgxxuj8+fO6FqVApKSk0KJFi1SmERCJRLRv375sxxsZGRXoM62np0f+/v75Hq+K/v37K/PQTJo0SZkIztzcnPT09OjmzZvK9zE4y4XuEQqFynStEsn3XWK8oFELDg4OCAoKwj///IP9+/djz549GpQub/zeqSJCIpNw8UkEAEBfyMefvaqgjL1YZf+rzz/j4O33mY5Fxqdh0cln2DDQU6uyMsbUplb/HmnevDl27typs/WfPn0KHo+H2NjYbKNAGGMYPHgwWrVqhXv37mHAgAEAFKn5Q0NDlWn7c8LQ0FDl8RIlSuDSpUuoV68eDh06pJSpc+fO0NPTw927d/H48WP88ssvmDFjBpycnCAQKG65RKS0JMpkMkyYMAH79u3D48ePcfHiRQBA06ZNkZKSgp9//hk3btzI7cujVdq1a4cePXqgZcuWuHjxIurUqaNrkfLMnTt3UKNGDeXfTZo0QdmyZTFq1ChUqFAhx/GfPn1CUlISVq5cmW8ZGGMasVx8zc6dOxEYGIh169bh5MmTiI2NxdmzZyEUCsHj8VCrVi0AUL4HtQ2nXOSBMmXKICgoCOXKldO1KPkiLi5OIzUu2rRpg1OnTsHW1hZ2dnYakCzvGOvrYe+4ungVnoBPcanwcDaDsb6e2v7XX3zOckxOwNVnkdoUE0DxCkUFgAkTJmD16tWYPn06/vjjD53IIBKJcgwv9fT0hI+PDwCgc+fOWLhwIU6fPo22bduiV69e6Nq1K9q1awexWLVCmhtcXFxw9+5dvHz5EkSE1NRUnD9/HvHx8ahTpw6OHDkCoVCYZdzXW5R8Ph/+/v7477//EBYWBhMTE5QvXx42Nja4dOkS7ty5g19//RVdu3aFs7Mz7OzsdFpMbO/evUhNTUW9evXg4uKC5s2bo1OnTqhXr55OC2VlR0axsc6dOyuP+fj45HnbKT09HTVr1oStra1yiy0/8Hg8rYT2jhgxAkOHDkVERAS2bt0KX19fpVINAGPHjkX16tXRt29fja/9Law43fTyipeXF929ezfbPnK5HHw+P8txY2NjTJgwAaamphg4cCAsLCy0JabGePnyJdzd3bF37958V+rLwN7eHi1btsSOHTs0JJ128bn4BguOPc10jAFwsTbChRmN1I7zexyOw3c/IDVdhiYVbNCzlhOEgrzlTChXrhyqVaumU0uPppk7dy6WLl2KxMTEQl/b19cXo0aNQlJSUr7Gb9iwAcuXL8eHDx+QlpYGfX19lChRApUrV0bjxo3RrVu3IlFwMCQkBKVKlQKgeNoUiUQQiUSwsrJC5cqV4eLiAhcXFxgbG+PTp08wMDCAtbU1YmNjUbduXZQtW1arSsiVK1ewceNGXL58GZGRkZBKpRAKhbC0tISTkxOqVauG6dOnK/P/6IKwsDDUr18fr1+/Vh5buHAhpk6dmq/cJ926dYOfnx/evn1boHu+oaEhNm3ahH79+uV7DlVIpVI4OTkhPDwcDg4OMDc3R82aNVGmTBnMnz8f6enpaNy4Mfz8/AKIyEuji38Dp1zkoFwAwOrVqzF+/Hg4OjpCJBJBLBbjwYMHABQJZlJSUnDkyBGVlfC0gVQqzXRT/9bknp0Jvnz58mjUqBEOHDhQIBkYYzhw4AC6detWoHkKi88JaWi04CKS0qSQf/WWX9itEnrXcVY5ZvuVYCw5/Rw8BhApNmSbV7TFmj5V8rR2+fLlUblyZezbty//F1DEiI+Ph6mpKWJiYgr0BJdXkpOT0bVrV1y6dCnfysXXREdH4/jx4zh37hzu37+Pd+/eITk5Ge7u7ujevTs8PDxgZ2eHypUrq90eyStyuRzr1q3D7t27YWJigho1aqBjx46oUaMGkpKScOzYMTRq1Ag8Hg/Dhg3DyZMnAQAVKlSAXC7HwIEDUaJECbx9+xZv3rxBSkoKrKysEBERAYlEAj6fj8uXLyMmJgaGhoYwMTFBdHQ0BAIBgoODYWZmpvz/aZLo6GhcvHgR169fx6NHj/DkyRNERESgTJkyOHHiBMqWLavR9XLi1KlTaNOmjfLvpk2b4uTJkwWyrpiammLixImYPXt2gWQzNjbGihUrMGzYsALNo4rw8HDs2LEDkZGRePbsGc6dOweJRAJPT0+0bdsWJ06cwL179zjlQpvkVrk4fvw4unfvnqlU7tf0798fhw4dQkJCgkZL3Mrlcty7dw8nTpzAjRs3EBERgaSkJISEhBRoD19PTw9BQUEoXbp0vucwNjZGkyZNcPz48XzPUdg8/RCPBUef4PbraFiLRRjRpDT61Sul8ukuTSJD7QUXkZSWdRvp2Nif4e6Qe1N6qVKlEBUVVWTLaeeXly9fIjw8HLa2tlpfKzExEd27d4efnx/09fUxY8YMTJ06VStr3bt3D1OmTMH9+/eRmJgIqVQKuVwOIyMjtG/fXq0FSi6X4/jx4+jQoQNkMhnCwsJQokQJzJs3D/7+/jA0NER4eDhCQ0NRoUIFLFq0CMnJybh+/Tp2796N8uXLQygU4u7du3j//j0EAgHEYjGio6NVrlezZk3I5XLI5XK8f/8eQUFBWLBgAf79919YWVnB3d0dly9fRoMGDbBjxw5lxJKdnR3Cw8PRo0cPSCQS9O7dO0s0hKZ49OgRevfujeDgYDx58gRisRiXL19G27ZttVoO/PXr13BzcwMArF27FqNGjdLIenw+H2fOnEGzZs0KNI+pqSmmTZum8fdwYmIiWrVqhb/++gslS5ZE7dq1cefOHQCAl5cXdu7cibS0NHh6empdueB8LnJBTo5j27Ztw/79+7FhwwaMHj063+tkKBNHjx7FgQMH8ObNGwCAubk5SpcujbJly0IsFqNDhw5o165dvtaQSqWoVasWypQpg86dO+PAgQN5/tCdP38eycnJGndI0jblHcXYO/rnXPWNTEhTqVgAwKtPSdkqF0SEFIkMBnp8MMaUpuLatWvnS+6iSEBAAAAUitVCLpejevXqiIiIgK+vL3r37q3V9apVq4Zz585lOhYdHY3ly5fjjz/+AJ/Pxx9//JHF3L9t2zYMGzYMI0aMwKZNmwAA3t7eiIyMxLJlyxAVFQVjY2N4enriw4cPABShhC1btkS/fv0wfvx4HDt2DIMGDcKVK1cwa9YstGzZEh8+fEBKSgo+fPiAmJgY3Lx5E7t374aBgQEuXbqkXD/DTL99+3Y8e/YM/v7+mD9/vnILtF27dti9ezfCwsIQGBiItLQ0pKWlYcSIERAIBFoJV/Tw8MD9+/dRuXJllCpVCiKRCKmpqWjXrp1WHkxSU1Nx/Phx9OjRAwAQGhqqsW2ZQYMGgTGm3KoqKDExMRqZ52tiY2Nx7do1uLq6AlBY1s+dO4fY2FiMGjUKFStWLDx/HW2HoxTlltukUNeuXSM+n59tnzZt2pCFhQVNnTqVNm/eTJcvX6ZTp05RnTp1SCQSZQp1EgqFVKtWLWrevDm5ubmRsbGxMlUsY4wsLCyoQ4cOdPXq1QIXXFLH8ePHSV9fn7p3757nsQsWLCA9PT3l3927dydbW1s6cuSIJkXUKWkSGVWfc47K/nYmS3sZrj4LqP+TCPJedZ2qzj1PLVZco5MPwqhSpUpaz3BamDRr1owYYzR79uxCWW/IkCEkEonozZs3hbKeOiQSCY0ePZocHR2JMZYpPPXx48dkb29PAKhs2bLKz/r48ePp06dPmeZJT09XlgkgUoRFlitXjgDQqFGjyMHBgQCQu7s7/fXXX9mGoqanp9O5c+do8+bNNGjQIAJAzs7ONHDgQHrw4AFdvXpVKcuoUaNUznH69GlydHSkgwcParXku7+/P/Xt25d8fX2JMUaXL1/O1bi0tDQ6cOAA9e3blxo1akSdOnWiQ4cOZeoTGhpKBgYGme6zV69e1YjcSUlJVL9+fQKgscRmBgYGZG9vTwsWLKCAgIJXf05JSaEZM2YoQ54BkKWlpcoQ6qSkJC5Dp7ZbbpWL4OBgApDtF/2HDx/I09OTbG1tycjIiPh8PvF4PKpUqRLt3LmTQkNDKSkpiUJDQ8nHx4eqV69O1atXp379+tGaNWvo8uXLFBUVlSt5NIWnpyfVrl07z+Pmzp1LAKh+/fpkYmJCQqGQPD09CYBGUtoWFfbdfEdlfztD5aYqWtnfztDUvx+p7X8/JIaqzT1P1eaep6pfNbdyFaljx46FKLn2kMlkBICGDBlSaGv27t2bAGRKsaxLMl6DjC+FhIQEcnV1JR8fH0pKSqL09HRau3Yt7dy5U+X45ORkqlixIm3cuJGIFCXNa9asqUwrLZVK6fr16+Tj40OVK1emnj170u7duyk1NTVH2eRyOT19+pTmzZtHdnZ2yi9aY2PjbBWHNWvWEAD67bff8vpy5Ivu3bsTj8ejZs2a0dixY6lLly40efJkSklJobS0NDpz5gx17dpVeQ1CoZBcXV2pbt26VL58eeV1McaoQoUKWXJVzJo1S2OylilThiwtLenixYsamzMwMJAaN26svLb8cubMGWrUqBHp6ekp8zExxmjgwIHZjuOUiyKiXOzfv79Ab4CiyP3794kxRufOncvX+CNHjpC7uzsNGDBAmT9fLBbTmDFjNCmmzrnx8jP9uu8h/eJ7j47cfU9SmfqnyBlHn5DnvMyKhee882RTyl2nSac0zfr164kxRi4uLnTt2rVCWdPHx4cUhlbdM336dAJAdevWpbi4OOrSpYtS2RoyZAi1bNky13O9fPmShg4dSgCof//+Wc6Hh4fTvHnzqHnz5iQWi6lNmzbk4+Ojsn7Kt6SkpNCbN2+oYcOG1LNnzxz7r1+/nqpVq0b16tXLUotCG2zYsIGqVKlCjo6OVL58eTIxMSHGmLLol5ubG40ePVplPRh7e3tydHRUmQQLQIELR8pkMtq/fz/179+fANAff/xRoPnUUaNGDRKLxTnK4unpSUZGRspjx44dI2dnZ2KMUZUqVWjt2rXE4/GUqcpFIhGZmJiQtbU1mZqakru7O7m5uZG5uTlZWlpyyoW2W26Vi8WLF5OpqWmu+n4PyGQysre3pzp16mh03smTJ5NQKNS5+VpXjN37IItyUW3uebJ0dqfWrVurHZeSLqXrr6Lo1ttoSpdqZxtM0wQGBlLNmjXJxMSkUNbz9vbOdUVfbXPt2jXlE3W3bt2oXr169PnzZ6pUqRIZGBjQuHHjsh0vl8vpv//+o/3795OVlRWNGTOGVq1aRU+fPs12XGRkJO3du5caNWpEzZs3z5PMkyZNotq1a1PHjh1p+PDhKq2w6enpymyhulDkZDIZ3b59m+LiVGfZ/RqRSERisTiTQlGvXj3y8fEpUNbg/v37k1AoVH5RA6CBAwdqrWBd165dycDAgA4ePKi2UuquXbsyXaO+vj7xeDxq3LgxffjwgYgUW3aVK1cmgUCgrCcCgPr166es7PtN45QLbbbcKhft27enSpUq5arv98D06dNJKBTm6kOcF2QyGVWqVImMjY1/SAVj/+3QTIpFRitVthJ5e3urHHMnOJqarb5OtZdcodpLrlC7DTfpZYT2U5JrgmPHjpFIJCqUtVxcXKhHjx6FslZuOHnypNK/4dOnT+Tm5kYAyMHBQWX/gIAAKlu2LL148YJ++eUXAkAdOnTIl/k+IiKCAGTxO8iOXr16Ka0jACg+Pl5t31WrVhEAevLkSZ5lKwzGjx+f6Yvy66J1BeHcuXPEGKNly5ZRyZIlqUKFCrR//34NSKyehw8fkpGRUabr8fT0pJYtW5KZmRlZW1sTY4zKli1LHTt2pJo1a9L69eszKTsymYz++OMPsrCwIMYYlShRgho1aqScL8MXMCQkRDmGUy6KiHLh5uZGffv2zVXf7wGRSERz5szRytwpKSlka2ubJ9OwKk6ePEkikei7akKRiPgCPWL8/zeeQE/5xPEtqelSarH6BtVZekWpXNRdeoV6+twpUvUk1FGYyoWVlVWRqMRKpPCJWLp0KQGg5cuX04ABA8jY2JiEQqHaL7mEhATq2LEjNW7cmGxtbZU3/kWLFuV5/fnz5+fLVL93794Mkzi5uLhQnz59KDg4OEu/+Ph4qlChAtWsWZNevXqVZ/k0zblz56hu3bpkZGSk3DbJaAKBQGXDlzLkqs7x+XyVDYDyIXLw4MHk6upaKNcnk8lIIpGQQCCgNm3akLOzM1WpUoWmTZtG/fr1U1t9VyKR0JQpU8jQ0JBEIhENHTo00wPjkSNHCAA1aNAgy9jCUC64UNRckJHlrTiwcOFCyGQyzJw5Uyvz6+vrw9nZGVevXkWXLl0wffp0VKtWLc/zBAYGQiAQfHdZLYkIrz8lIiwuFXZifbjZGqNXz54qQ4cfvI9HQlrmaqlyAkKiUxAakwInC80kbSoO6OnpqUyjrQs2bdqEnTt3olevXvjrr7/w5MkTCIVCrF69Gh4eHirH+Pj44M6dO5g6dSr69u2LgQMHAkC+qp527twZmzdvzlK9NSd69uyJhw8f4uLFi7h//z52796Ntm3bwtn5/0nkzp07hw4dOqBChQq4desW3NzcsGrVKowbNy7PchaUJ0+eoEuXLnj+/DmqVauGhQsXwsLCAjweDzY2NplC6L8Nr2zSpAnmz5+fKfz76/7fjo2Ojkb79u2Vn1NbW9tCyz7L4/HA4/Egl8vh7++PNWvWwMzMTG0W5dTUVIwdOxa+vr4QCAQYO3Ys5s+fn6VmSMeOHUFEuqtrpG3tpSi33FoueDwe3bx5M1d9izLnzp0jHo9HS5Ys0eo6KSkp1LFjR3J1dSU+n6/y6UgVz549I4FAQCKRiH755ReysLDQqpyFhYmJCa1duzbL8XvvYpQWi2/bx9gUtfNFJ6XR3jvvaeWF1+Qf9ElnfhqFabmwtLSkn376iUJDQwtlPXW8ePGCLC0tacaMGeTk5ESXLl2ismXL0tixY9WOmTVrFhkYGNC///5LRIptDUNDQ1q/fn2+5Vi0aBGNHz8+z+MkEgmJxWJq2bKl8um/Tp06NGnSJJLL5fT582fy9vZWWtsA0LJly/ItZ36ZNm0a8Xg8qlatWq7vH18jEAjo1KlTue4/ceJEMjQ0VPqirF27Vuv+RDKZjJYtW0bVq1enKlWqEI/Ho8qVKxMAldWrZTIZnT9/PlOkTG5SFTx79kxpUVuxYgV1796ds1wUFbRRwa6w+fz5M1q1aoWePXti8uTJWl1LX18fR44cAaCoQTJhwgT89ddfOHbsGFxcXPD582dMmjRJqWnr6ekhLS0Nb968gYWFBUqVKoX169drLN2yrlFXuMzD0RT2YhEiEtKUKckZA6qUMIWdqeoUxe9jUjD20BMkpEnBGHDqySf8+/QTFrcvBz2+9jIe6prLly+jXbt2KFmyJExNTeHr61to6fa/5uTJk4iKisKhQ4dw+PBhhIeHQyaTYeHChWrHPHz4EGvXrkXLli0BADY2NgVOXR4REZGvzKj37t0DYwwHDx7EkydP0KtXL0yePBkLFixAhQoV0LlzZ0ycOBHBwcGYOXNmgTNR5pWXL1/C29sbwcHB2LBhA4YPH56veaRSKaytrXPdv3nz5lixYoXy75IlSyItLS1fawOKyqleXl74999/wePxkJKSgoMHD6JNmzbg8Xho3rw5eDwekpKSULNmTdjY2KBXr17w9PREs2bNMHfu3Ezz+fn5Kd8/gKKI5o0bN7JNgLhgwQIsX74csbGxAICZM2di7969meqsaBVtay9FueXGciGTyYgxRvfv38+xb1Gmffv25OjoWOjrrlmzhhhjxOfzSSgUKvc2GzRoQL169aKePXtShw4dqGfPntS9e3fy9fWltLQ0cnZ2JgMDg0KXVxuIxWJatWqVynPBn5Oo/84ApcVi9L6HFJmgPoxu/r/Pqdm6/6jp2szNP+iT2jHaojAtFxl8+PCBevbsSYwxMjExoeXLlxfq+nFxcbRv3z5lKGizZs1o37592Y5p0KABnTlzRqNy9OzZk3bt2pWnMYcOHSI7Ozs6ePBglnNyuZyuXr1Kv/32G9WtW5fatGmjDDEvDEJDQ2nFihWkr69PQqGwwBYqJyenXIXfRkRE0JQpU0goFFL16tWVx+/fv088Hi9faz9+/Fh5n2vUqJHy96+bh4cHTZ8+PccIISKFFSVjXJkyZXIth0gkorJly5KFhQVNmjSJDA0NicfjUZcuXTiHTm233CgXu3btIoFAUKgfNG3QsWNHEgqFVLduXWX4UmGxefNm6ty5s9KElxtT3p9//llstkVMTU1p5cqVas/L5XIKi02hT/E5J0nqvu1uFsWi2br/aPXFwo/O0YVykUFUVBRNmjSJeDyeTp2tzc3NKSwsTO35p0+fkoWFhcpMiflBIpFQnz59CIBaRz9VvHjxgqysrOjChQsakSM/pKamUkpKCsnlcgoMDKS9e/dS3759ydzcnPh8Punr65OXlxc9e/aswGuVKlUqx0RSGblTTExMaObMmZnuSzExMTkmTlTHkCFDyNDQUGX+japVq9LkyZNzPdfLly+VY7t27ZrrcZs3byYAaiOsOOWiCCgX48eP18kTv6aRSCQ0d+5cEolE1LRpU12LkyPFSbkwMzPT2L71uEOBKi0X+wPUK4zRSem0/kowDd77kH49+oQuvvysEVl0qVxkcP78eeLxeMqwQW2ly1fFrVu3yMDAQO1TdkhICLm5udG2bds0tubhw4epVKlSysRScrmcoqKiso0uksvl1KRJE5o5c6bG5MgNiYmJVLp0abWJrlxcXJS/Gxoa0oEDBzQSJXXz5k1ijOXoc9GiRQvK7jsAQJ5zZshkMuLxeLRp0yaaNm0aAaB27dpRx44d8+XrlpiYSO3atSMej5frB9zWrVsTY4w6d+6sNj9HYSgXxXeTVkMEBwfDyspK12IUGIFAgFmzZkFPTw+1atXStTg/HJry2O7p6QAiIMM3njFArC9Ai3Kq95fTpXJMP/kM/s8iEZmYjleRyVh58S38giI1Io+uady4MQIDA2FmZobevXtDLBYXSoRRamoqOnfujD179qgtjLVs2TK4u7tj0KBBBV7vwYMHGDRoEHr37o3ly5fDzMwM06ZNg6enJxwcHFC1alU8ePBA5diQkBA8fvy4wGXC1UFECAoKwsGDB7F27VrMmDEDbdq0gbGxsdr9/UWLFsHPzw9DhgwBACQnJ8PMzEwjRbX27t0LW1tbeHt7Z9vv+vXr2fbh8XgICQnJdo5Lly7By8sLY8eOhYeHB2xtbWFgYIChQ4diwYIFCA0NxfHjx3HkyJF8+boZGRmhRYsWMDAwyLFUfHx8PLy8vHD27FmcPXsWhw4dyhJBUphwykUOxMbGQizOfXntokxYWBiSkpK0Vl6ZQzXqHDrzQ81S5ljQpiwq2JvAykiIhm6WWN2lAswM9FT2vxkSi7D4NGSsnvHz7/sfNSJPUaBcuXK4du0akpOT0alTJwwYMEDr4XcXLlyAq6trtp+lIUOG4MmTJxpZr0+fPnBwcMDDhw/RsmVLlChRAsHBwVixYgUSExMxceJEtGnTRqWjqFgsRnp6Ovh8foHlSEpKwtq1a9G3b1/Ur18f5ubmEIlEaN26Nfbt24fnz59DKBSib9++uHfvHgYNGgQnJ6dMc/Ts2RO//fYbypQpg61btyodU01MTAosH/D/SrTZMXfuXKSmpmLKlClq+wiFQpXKRVBQEBo0aIAyZcqgUaNGCAgIwNq1a2FpaQlvb2+8f/9eGV6qiYqsvXr1QlJSEv7++29cv35dZZ93797B3d0dISEhePDgAZo0aVLgdQsKFy2SA7GxsbC3t9e1GBrh8OHDMDQ0zFfeCY6Cockvu5qlzFGzlHmu+n5KUO3x/jkpHXIi8LJ5UoxPlSJVIoO1sbDwyjQXAKFQiJ07d2L37t24c+cOatasqdW1csqD8Pr1a43k5khOTsaTJ09w4sQJSKVStGvXDm3btsWePXuU0QL9+vXDrl274O/vn0Xh0YRym5KSgjNnzmD8+PGoXr06WrduDWdnZ1SqVAkmJiZqn6q3bdsGAPj48SMCAgJQo0YN2NjYZOrj4uKCpk2b4ueffy6QjBlUrVoVMplM7fkHDx5g/vz5mDNnDoyNjdX2E4lECAsLy3TswIEDynLuGezatQsNGzbUWGn3bzEzM4Obm5sy78Xu3bvRu3dvAMDIkSNx4sQJhIeHo2zZsrhw4QLs7Oy0Ikde4ZSLbPj8+TMePXqEkSNH6loUjSCXy3VqJvtR0aTlIq+422a9eTIAbtZGahWLFIkMW268w+13cQAAWxMhRtZxRhlro0z9JBIJiAjR0dEal/tbcqOcZbzGBgYGuHfvnlaVi6NHj6Ju3brZ9lm7di0mTpxY4LUMDQ0xa9YseHl5ITU1FdOmTcOECROyhCG2bdsWp06dyqJcCIVCpKWlgYjypSTu27cPo0aNQunSpbFt2zY0bdo0z3PY29ujTZs2mY4RER49eoSffvqpQGGf33LgwAEAQP369XHlypUs5wcMGICKFStixowZ2c5jaGiYRbkYPnw4GGOFnpgqKCgId+7cwaZNmzBixAjcuHEDT548weXLlzFq1CjUrl1bqXAUFbhvmmzo2bMn5HK5RvZMiwq6+pLLD9+TrDmhqyx5FeyMUdfVHNfexIAxgAjQ4zMMrqX+Kcv39nulYgEAnxLSsfj8a6zpVAGGQoVp/dq1a+jatSsAwNLSUrsXkQ8uXLigtYeC1NRU7N+/X62PQwa9e/fG33//jWHDhhV4zblz52LGjBlITk6Gqampyj4NGjTAxo0bsxwnUmRpzI9y8ejRI4wZMwb+/v6oXr16lvOxsbEIDQ1FXFwc0tPTYWJighIlSsDW1jbbHAwPHz7EsGHDEBkZiUqVKmHMmDF5kis7unbtiitXruDw4cMqzz9+/BgXL17McR6hUIiEhAQAis9v+/btkZCQgIMHD2pM1twiEAjw888/w9PTE0+ePMHRo0cRHx+PTp06Yf369YUuT27glAs1REdH49y5cwCQ7Yfke+J7uo7vSdac0KXlgjGGiY1cUcc1Fg/ex8PUQICmP1nBVixS2V8ik+P625hMxwhAikSOe+/jUNfVAgBQo0YNMMawZ88e9OzZU9uXkScmTJiAVatW4fTp0zk69eUHHx8f1K1bFyVLlsy2X58+fTB//nxcunQJDRs2LPC6enp6ahULAKhYsSLCwsIQFRWlVPhiYmLw66+/KpM35YX09HQ0bNgQv//+O6pXrw4iwoMHD7Bq1SrExcUhICAAMTExcHJygrm5ufLLODQ0FElJSWjWrBm8vb3x4cMHJCcnQyQSITk5Gf/99x8CAgKwceNGDBgwQOOfdTs7O4wfPx5Hjx7Ncu7z58+Qy+W5cmp3c3PDkSNHsHz5cnTq1AlXrlzBtWvXNLZ9kx+EQiHu3r2rs/XzQvG5g2uYpUuXwtzcXBFSU0y+6L6HffPiis7y+wPg8xhqu5hjVD1n9PZyVKtYAIraJnI1epBE9v8TQqEQfD4fS5cuBaB4On4TlYwLr6Lw+GMC5Dq0Oi1fvhyMMUil0pw754NXr17luCUCKPbsFy9ejN9++00rcnwLn89H7dq1cenSJRAR/vrrL7i5uYExpvR9yAt6enro2rUr/vzzT5iamsLW1hadO3fGX3/9hTp16uDSpUtISEjA06dPcf36dVy8eBF3795FREQEQkND0aJFC9y4cQMymQyWlpYQCARITk7G9OnT8enTJwwaNEhr99bExESVc/v4+EAsFufKF+bQoUNISUlB06ZN4e/vjzNnzuhUsfje4CwXati3bx9atGihazE0TnHaavhekEql2LdvX45mdF3SoEEDjBs3DiIBD5XsTRD4MQFfv1N4DKjimDlqqnPnzrh8+TLkRNh+6z1uh8Yrz5Uy18f4BqVgqFfwCIW8EhsbCyLKssevKQwMDBAcHJyrvl26dMHUqVNx7969QnGkbtq0KaZMmYJJkybBzMwMFy5cQOXKlfM1F2MMmzdvxqZNmxAbG4vExEQ4ODhAIBBgypQpaNWqldoHFnNzc4wYMQIjRowoyOXkm4SEBJXKxdmzZ+Hm5parOczMzDBv3jxMmDABvXv3LjbFKwsLTrlQwe3bt/Hu3TucPXtW16JolOJigfneICLExMTgzZs3uhZFJUSEf/75B7Nnz8aoUaNQpkJl7NzyFz6Hv4dcKgFjDBXcSuFT9RUwL1sWgMIS4+fnh4EDB+JuaHwmxQIAgmNScSboMzp55L3+RUGxsLAAn8/HmTNntLIt0qRJE4wbNy5XPgx8Ph9t2rTBxYsXC0W5SE9PR2hoKAICAlCxYsV8Wyvj4uLw5s0bPHjwAESEjx8/4tOnTzA0NISjoyM+fPiA+Pj4nCfSEUlJSVlCb5OTk3Hx4kXs27cvx/Hv37/HlClT4OfnB7FYjN27d2tL1GKL1pULxhgfwF0AH4ioDWNsKYC2ANIBvAYwkIhiVYwLBpAAQAZASkReX447ANj95VxvIkpkjM0BMAVAKSL69KVfIhGpjzPKhn///RdCoTBTKWIOjvwiEokwbNgwzJs3T9eiqCU1NRXjx4/Hxo0bER8fDzc3N5Sxs4dAKIKRkI+XL57B3d0dJUuWRNOmTWFsbIyEhATMmzcPfz+NVTqLfs2jjwk6US4AoHbt2ujevTumT5+OoUOHajQRXsOGDZGYmIg7d+6gRo0aOfa3srJCXFxcjv0KQnJyMp4/f46TJ0+iZs2aqFSpUp7nSE9Px+rVq7Fu3TpERUWhVKlSqFKlCng8HhwcHFCqVCkkJiaibt26uHLlCt69e5eppHlRIsPP46effoJQKIRQKASPxwMRYebMmdi9ezdEIhH09fWhr6+f6fdnz57h6NGjsLe3R1xcHMqUKYP4+Phik++osCgMy8U4AEEAMv4zZwFMIyIpY2wxgGkA1G1KNiKiz98cGwtgDABXAH0AbPpy/DOAX7OZK9f8/vvvWL58OUaPHo0tW7YUdDqOHxxdOnTmFn19fWzatAmbNm1S2ycoKAiLFy+Gn5+fMkRv586dsKrTGfjm8hgAI6H6LREiQmB4Iu6HJQAEVHU0QUU7Y435Bfn7+6Nv3774888/MXPmTBw+fFhjVVQzwrktLCxy1T8iIgLu7u4aWVsVMpkMXl5eCAoKAqDwFVDFlStXsHTpUty/fx/R0dGQyWSwtrZGhQoVUKNGDSxYsAAA4OXlhcOHD8PLyyvTeLlcjgMHDmDt2rVYunSpMu9CUeTx48cwMTHBzz//jJSUFKSkpCA9PR1Vq1aFVCpFWFgYJBIJ0tPTIZFIIJVKlc3Q0FDpqLx69WqMHz8e5ubm2ebO4MiKVpULxlgJAK0BLAQwEQCIyP+rLjcBdMnjtHwA8i/t6zvRdgADGGOLiahAgfcCgQC1a9fGvXv3CjJNkYNz6NQdunTo1BTlypXDzp07ASjKNy9YsADjx4/H7SdtcIkBIGTKBNrQTf2Xr/+LKPi/iFJ+gB9+TEDTMpZo5a4ZC4O+vr4yZLBGjRro0KEDgKxbg19/JjJ+//anqmOpqam5Vi6uXr2Kvn375uMqcsehQ4fA4/Hg5eWFu3fv4vz58+jcubPyfEJCAsaMGYPz589j/vz5WLduHaytrcEYw6dPn/Dw4UPcuXMHYrEY8fHxuHv3LubOnYuTJ08q5/D398f48eMhEolw+vTpLIpHUePkyZOYNGkS5syZk+857t69i19//RXOzs6YP3++5oT7QdC25WIVFNsV6vK6DgJwQM05AuDPGCMAm4kow4SwDsAuAHEAen3VPxEKBWMcALVJ9BljwwAMA5AlLe3XJCcnw8jISO3575Wi/gSdQXFShL4Hy0VemT9/PqZOnYpSpUph3eJ5GL9wNf5+EI5PiekwEfHRprw1qpdUHTaZlC7DuZdRADIbPC68ikI9FzMYizR7W7p48SLc3d3x/v17HDhwAK6urpDL5ZDJZJBKpco8EN/+LpPJlPkh5HK5coxMJsPAgQNzlZDu+vXrSEpK0kpCLyLChAkTsHXrVlSrVg21atWCvb09Tp48iQ0bNkAmk+Hhw4eoXr06+vTpg6dPn2ZJse3s7AxnZ2e0a9cuyxcoEeHYsWPYunUrnj59ivXr18Pb27vIfzb/+ecfpKSkYOrUqfme4/nz56hevTocHBwQGBiYbSZPDtVoTblgjLUB8ImIAhhjDVWc/x2AFIC6KkN1iCiMMWYD4Cxj7BkRXSGiEADq3HbXAHjAGFuuTq4vSsoWAPDy8lJ7x69cubJOkqVoE86hUzcUR+UCUBRVatCgAS5cuIDt9iaoZGeMNKkcQgEv27TinxLTVIa7ygmISEzXuHJhZGSE0NBQVKlSBQMHDsS2bdvQrVu3As05bdo0vHv3DhUrVlTbJzk5GaNGjcK8efO08tk7fvw41q5di7Fjx6JChQqIjo6GhYUFxo0bhwkTJmD37t34/Fmxq7xlyxaIROpDkL8mNjYWu3fvxoYNGxAUFIQNGzbg6NGjuR6va+bPnw8vL68cC31lh62tLcqWLYt3797B3NwcgYGBKPvFmZkjd2jTclEHQDvGmDcAfQBixthuIurDGOsPoA2AJqTmrktEYV9+fmKMHQVQA0DWXK6Zx8QyxvYCGFVQ4SdOnIj169eje/fuygQ4hfnlHBsbCxMTE40UG8rg6tWrkEgkKrP4FYSYmBiNVTTM4L///isWWwnFnRUrVsDFxQVjx47FmjVroJ+L0FMrIyG+7KJkgn05p44n4Qm4/Doa0SkS2JuI0PQnKzibG+Ra1tu3b2PAgAHo0aMHXFxcVGaczC2jRo1Cnz59cOfOHejpqS4aN27cOFSqVElraZnXrl0LQFFLw87ODlFRURAKhejevTsGDhyIW7duwdXVNVdzRUdH49KlSzh16hSOHDmC5s2bY+PGjWjYsCFkMhm2b9+eqX9298Ls7gN5Hefu7o46derk4gr+z927d2FqaorKlSsrHTXDwsJQunRp6OnpQSAQgM/ng8/nK3Nv2NjYwNjYGDKZDL/99hscHBzw7NkzAEDp0qXh7e2N169fY9asWZg/fz4cHBwQGhrKPbBlAyuMJ6ovlotJX6JFWgJYAaABEaksXccYMwLAI6KEL7+fBTCPiM6o6T8HQCIRLWOMWQG4A8CeiLJVXb28vCi7bGebN2/GiBEjYGJiUuhPnomJiUovZ01BRJBIJBp/AklISIC+vr7am2x+SEtLA4/HQ0pKisbm1BX29vbo168fFi9erGtRtMLff/+N7t27IykpCYaGhrkaczzwE668jVH6XBCAui5m6FhRdXTJy8gk7Hvw/0quDIrcG8NqOcHaOG+fEaFQiAMHDhS4OnC9evUwYcIEdOrUKcu5EydOYNSoUXj69KlWogwWLlyIFStW4M8//8SZM2cQHR0NDw8PrF27Ftu2bctVyYI3b97g0KFDOHPmDC5evAgDAwPMnTsX/fr1U1Yqtbe3z1JpNb/3wvyMS05ORseOHbFt2zaYmZnlaoyenh48PDzg7OysdOZ89OgRrKysIBaLlVtbGdtcz549g0AggK2tLT58+IDmzZvDz89POd9vv/2GJUuWYPPmzWjRogUqVqyIxMREtG7dGv/880+er6kowBgLyIjA1Ba6yHOxDoAIiq0OALhJRCO+hJj6EJE3AFsAR7+cFwDYq06x+BYi+vzF0jGhoIJmaP3v378v9DAkGxsbjBo1qkAOSYWFoaEhtm7dqtEntKVLl+LPP//U2Hy6pLhui2TQrVs35ZP8kSNHcjWmbQVr2ImFuPchASBCVUcxajipT219KzQ2k7WDAMgIuPchDi3KWudJVgBo3bp1rseoY/DgwVi6dCnatWun9L94+PAhGjZsiNjYWLi5uaFdu3Yqx6p7uv/pp58waNAgxMfHIzExEUlJSUhKSlL+npKSguDgYFy4cAHXrl1DuXLlMGzYMBARnJyc8PfffyudV1WRlJQEX19f7Ny5EyEhIejUqRMmTpwIoVCIyMhITJ48OVP/jx8/qpmpcNi3bx8GDx4MDw8PvHv3Lldj5HI5Nm/enGunUycnJ7Rv3x5r166FgYEB/P398csvvyhrdgwYMAC3b9/G5MmT8fDhQ1SpUgXXrl3DmTO5+kr6YSkU5YKILgG49OV3lenRvmyDeH/5/Q2AXKeVI6I53/w9EV+iUwpCzZo1YWFhgW7duunkjcRtCxQPGGPFPoytQYMGePnyZa778xhDTScz1HQyy1X/xDSZym2UpPTsX1epXI6IhHQIBTzcvOCHQ4cO4dKlSxqxCPbq1QsDBw6EiYkJunTpgtevX+PNmzews7NDmTJl1FpxiEilsvn06VNcunQJ27ZtA2MMfD4fPB5Pab7n8/nQ09NDTEwMTE1N0aFDBwwdOhTDhw9HVFQUWrVqBS8vL5UWRCLC9u3bMW3aNNStWxcLFixA48aNlUrR1q1bC/x6aIOePXtCIpFg8ODBueovlUohl8vzFPrL4/GUqeIzqrNu2LABCxcuRIcOHXD58mVUqlQJ6enp2LBhg3Jcbi0pPypchs5sEIvFWLBggUbKJueVou6RzZF7irvlAvh/kSc/Pz+tpM13tTDEp8T0TMcIQKlsfC6CwqKxbu9xBAX8h8fXzuLz+2D07T9AY2mchUIhBAIBpk+fDicnJ/Tv3x/16tXL97bj6tWrMWvWrFwl3CIiXL58GQsXLsxkbZg1axbCw8Nx/vx53LlzBx8/fgSPx0NQUBD09PRw9uzZfKcD1xWdOnXCoEGD0KpVK8yZMyfbyJuQkBAAyFN0B4/Hg0wmQ3R0dKbPqbm5ORwcHFChQgU8fvwYFhYW6NmzJ6pXr46ff/4ZpUqVyvc1/Qhw3ig50L17d6SmpnJWBA6ObFi8eDEcHR01Wjr7a+q6mMPKMPMTuauFASo7qN6uPHv+Iio522LT1GG4feYwbJ1LY4rvGXSerPmttjp16qB///5o2rRpgfyZpFJprh0EGWNo2LAhZs2apTxWqlQpVKxYEeXKlcORI0fg6OiIjh07ok2bNli1ahVu3br13SkWgEJROHHiBAIDA1GrVi3Uq1cP0dGqUxk9f/48i+UmOTlZef+WSqXYs2cP/v77b/z222/o0KED3r59i2PHjsHV1RX6+vowNTVFjx49EBwcjN69e+PJkycAgEqVKsHHxweGhoaoWrUqIiIitHvh3zmc5SIHfHx8YGRkxHkFcxSI4m65AIC2bdti06ZNkMvlGv+8GAr5GFarJII+JSEqOR0OYn24WRmqDXldtGw5TCysMfvQ9UzH30anIF0mh5CvOfk0da0SiSTPFst69eplem8lJyeDMQYDg9xH0XwPeHt7w8DAAKampggKCoK1tTUaN26MLVu24MWLF/D29kZoaCiCg4MhkUjw+vVrPHjwAFu2bMHZs2czvUYCgQCMMZibm8Pe3h4lSpSAk5MTateujWnTpmVKjubk5ARTU1N07NgRo0aNwtatW5XF2JYuXQofH59Cfy2+FzjlIgf8/PyyjWXn0B5yuRyxsbF5HpebglKFCREhJSUlz9fC4/FgaGiYq2RNRYHZs2dj8+bNuHHjRq5KkucVAV9RsTU39BkyAhe7tsPtM0dQo2XmaI7s3hmpEhleRSUhVSKHg6k+7E1E2b6XiEhjyoVMJivwXLmN1vmeSE5ORosWLRAcHIx3797Bzs4Ox48fx6+//orSpUujXLlykMvl8PT0RHh4OACgQoUKkEqlqFChAnbu3ImmTZvC0NAQs2bNUobwdu7cGf7+/liyZAl69uypcu3Ro0dj9OjRyr+rV6+Onj17IiAgAPXq1dP+xX/HfB93LR0ikUg4q4UOcHBwQFxcHMzNzXUtikbYvHkzNm/enOdxJUuWzLWXvK6xs7ODiYkJAgICtKJc5IWe7VphrJkFggMDMikXpS0NoafGahGdnI5TQRFIlymech9+jEdZayPUKWWRrYKhqVw0Uqm0SCnFRYVWrVrh5s2buH//Puzs7AAA7du3R/v27XHnzh2MHDkSgCJ8H1D4wpiamqJ06dJ48eIFRo4cCalUivT0zD47Gfl+Hj16pFa5UIVYLEajRo00cWnFGu5bMxsSEhJw9epV9OnTR9ei/HD07t1b6VWf1yYUCnH8+PF8jy8qbf78+UhOTtb1vyJPCAQCrVcAzQ3bt25GclwM2g8eqzzmKBahiZv62iU3Q2IgkWXevnoemYSIxLRs1ypKloviSFxcHCwsLFRakKtXrw57e3sA/1cuypcvj1q1akEoFGLo0KGYM2cO6tSpo1RMAEUG5gMHDoCIik3Ie1GDs1xkQ8abmVMucuZH8CngyJn09HTcvn0bJ06cABEpa3Rk/J5Rp+PrREZElCmp0dd/Z/ye8XdG+/o4EcHDwwN9+vRBcnIypk6divXr1+P333/HpDY1EZmUDhGfB3PD7JO8hSekZQl3zThuZ6I6Hx8RaWzbSleWC6lUipcvXxZJp/X379/j4cOHKlMByOVyVKlSBY8fPwagcPzctGkTXr58iZs3byImJgYbNmxAfHw8rK2t4eHhgQULFmDgwIGcElcIcMqFGmJjYxEaGopNmzYVegItDg7g+wxHnjZtGn7//Xf8+++/Ssc5ddVGv/757e/q/s74UmCMZfo9PDwcJ06cwMuXL/Hy5UssWrRIGaJpZ5K7CA4DPT6SJVnzZhjmkNL8+adERD+LgJFQgLI2xrA2zl/ESF6iRTTJlClTEBQUBDc3lSmIdEqGD8nWrVuzhDjPnDlTqVgACsvFgAEDYGpqilKlSsHR0RGdO3fG6NGjc13BlkNzcMqFGh48eAAej4fhw4frWhQOju+G6dOnK6t0Hj58uNDWPXDgAPr27QuJRILJkydnyTSZGyrZm+DWu9hMxwz0eHCxUO0kmS6TgwgIjU+DXooUsSlSfIhLRYPSlrAT571olq6Uiz59+mDlypVF8iHKwsICFy9eRKNGjdCnTx/s3r1bea506dLK38uWLYunT59yFokiBPefUEOtWrUgl8uxZ4+6oq0cHByqmDhxIo4ePYouXboU2poZxbr09fUz5X7ICxVsTfCzszlMRHzo8RiczQ3QppytWgfQ4OhkAATeN/EnT8MT8rW+rpSLmzdvFvqaeaFhw4Y4duwY9u/fj/79+yuPDxo0CESE6dOnK5OFcRQduP+GGvT19TFu3Dj07dsX/fr107U4HBzfDWPGjMG5c+dw+PBhZQKiwiAoKAg1a9bMU3bGr2GMobytCbpVdkQ/r5JoWsYaYn31fhoJqYqU0eybL7X4NGmOa0nlBIkss4+Drhw6lyxZApFIVKT9pmrWrImyZcvir7/+QmBgYKZzZ86cQdWqVXUkGYc6uG2RbFi5ciW8vb3RqlUryOXyTCY5Dg5t8/nzZ0RFRUFfP+8m9qKCh4cHVq9ejVGjRmn9i9PR0REBAQFaXeNrMhxEv1UuLLJxHE2XyRH4IR5h8YoIFEsjPVR2NIWhkA+ZTFbofjZ+fn549+4dGjdurDbrpa755ZdfsHHjRpQsWRK7du3KFDVy9+5d3Lt3D0uWLNGhhByq4JSLHGjWrBn8/f3RvHlzuLm5fRdVSjmKBxYWFjA2Nv5ulVq5XI7jx49j/PjxGDNmDGxsbLRqvh4wYAD+/vtvJCYm5tt6kReczRW+GF/nueAzhor26n0XHryPw6eE/+dbiEqS4FZwDBqWsdSJ5WLixImoX78+jI2Ni4xy8csvv+DYsWOIiopSFhIrU6YMXrx4obIvoLhPcxQtOOUiFzRu3Bi///47li5dyikXHIWGQCCAUChE+/btdS1KvunYsSPWrVuHOnXq4NGjR0hMTNSa42CLFi1gY2ODihUr4tGjR1p3UOTzFFaG8nZimJjpw1BPADdrI5iIVN9WUyWyTIpFBknpMkQnSwrd50IqleLp06e4ffs2lixZggcPHqi1kkkkEmX0z9fjMxSrrxNU8Xi8LH1zi1wuh1Qqha2tLdLS0lCuXDksWbIEbdq0ydI3NTUVt2/fBgBUqVIlz2txaBdOucglU6dOxbx58/D+/XuUKFFC1+Jw/AAUl2qqV65cwePHj/Hw4UOtf+E/fvwYHh4ecHR0hL+/P37++WetrgcApa1NUMbFMsd+Urn6/6VURtlaLhJTpUhMl8FYxIexGuUlL7x79w4NGzaEqakpPD09sW3bNvTq1Utt/06dOmHz5s0wNTVFUlISTp06hf379ysLtaWnp8PSUmF9iY2NhUQiwZ49e3Lc0svIrXHu3Dls2rRJWTgsPDwcenp6ePr0qcpxJ06cQI8ePQAAXbt2zc9LwKFlcvUuZYz9BGAjAFsiqsgY8wDQjogWaFW6IkRG1kGhUKhjSYomRTEBz/dOcfF+9/DwAJ/Px9ixY3Hp0iWtrmVlZYV3796hbdu2qFOnDubMmZPv6JGcyEjmldskWkZCPgyFfCSnZ86lwWeAhZGeSsuFnAhPwxIR+VW5eRsTIcrZG6st2qaOwMBArFmzBsnJydi3bx9++uknZfimWCxGx44dsx3fqVMn9OzZE6dPnwZjDIMHD0Z4eDhiY2MRHh4OoVCIM2fOgIjg4uKCU6dO5bild+fOHTRu3FiZXTM1NVVZ1VQikeD69euoU6dO5tdELsf8+fORkpKC6dOnY+HChXl6HTgKh9zevbYCmAZAAgBE9AhAD20JVRQJCQkBY6xQCwN9L0+t32Oyp++B4mK5KFGiBAIDA3Ht2jWsWLFC6+sJBAL8+++/WLduHebMmYPVq1drZZ1Ro0ZBKBQq00/nBGMMVUuIIeD9//PCY0DlEqbQ4/NUVpMNi03NpFgAwKeEdITHZZ+SXJWsVatWxf79+3Hx4kVs2LABQUFBcHBwyHFsxoODo6Mjrly5gqtXryI2NhY+Pj74559/sGPHDrx+/RqxsbFwcXHB+vXr4eLigj179qBy5coICQlROWeTJk3w888/KxWLDNLT07F3714AQN26ddGoUSPcvXtXed7JyQn37t2Dr68vp1gUZXJT4wDAnS8/73917IGuay8UtHl6elJeMDMzowkTJuRpTH6xsbGhGTNmFMpaBcXQ0JB8fX11LYYSoVBIx48f17UYBWbZsmVkZmamazE0xowZM4gxRjVr1iSZTFYoay5ZsoQYY/ThwweNz+3q6kp169bN8ziJVEYfYlMoNDqZUiX/fx28vb0JAI0ePZomTpxIz549o/vv4ujCs89Z2oPQuFyvd+TIEWKM0ezZs/MsKxGRTCYjoVBIw4cPp6SkpCznmzZtSiVLliQiorVr15JQKCQAtHDhQuLxeFStWrUs42QyGQEgAOTq6koAKCUlha5du0Z8Pp+IiH766Sdq2LAh2djYEGOMTE1Nafjw4aSnp0cfP37M17VwKABwl7T8/Zpb5eJfAKUB3PvydxcA/2pbOG23vCoXrVu3pipVquRpTH6xtbWlHj16FMpaBYVTLrTD8uXLydTUVNdiaJT79+8TAJJIJIW2pp2dHbVv3562b99Oz54909i8zZo1IwcHB43NV6JECTIwMCBLS0uysbEhHo9HG/ccUalcPP4Qr3YeuVxO0YnptHTtVnIpXYb4fD5VrVpVY3JmkJKSQj179iQej0cXL15UHo+Li6Ndu3YREdHcuXPJ2NiYBAIB1axZk9q3b6/sl6FcZLQ3b95QSEgIKZ55ifr160disZjS0tLo5MmTBIAYY3TgwAGNX8uPRlFSLlwBnAOQDOADgGsASmlbOG23vCoXkydPJpFIRPfv38/TuPywatUq4vF45OnpSTExMVpfryBwyoV2WLlyZbFTLoio0JWL/fv3E4/HI319fQJARkZGdPXq1QLPGxUVRQDo1atXGpCSyMnJiYyNjZV/d+jQgQyNjGjhxr105lFYJuXic2Ka2nlCIpNp57HLxBiPTMSmBIAmTPpNIzISEd28eZPGjx9PIpGIDAwMcvVZmzx5slKJWL58ORERlSpViqytrZXHiYgkEgkBoLS0NEpLSyMzMzPy8PCgrVu3kpGRES1evFhj1/EjU2SUC2VnwAiAibaFKqyWV+VCJpNR48aNic/nF4r2/OzZM3J0dCSRSFSktXVOudAOq1atIrFYrGsxNE5hKxdfExMTQwBo06ZNBZ4rNDSUGGN07dq1fI3v0aMHWVtbk5mZGdna2hIAatGihfJ8SkoK1a9fn/h8vmL7oGwFGjR2KnXo3I0CAgIoMjKSxowZQ9WqVVNeT0q6jG6+iiWvn+tTyVKl6VJgOJ24+oTuvokjmVxe4Gv++PEj8Xg8srGxob59++ZpXIYSERkZSUSKrd8aNWooj2dsnXytsL169Yr09fXJ3d29wLJz/B+dKxcAJmbXtC2ctltelYsMxo8fT4wx2rlzZ77G5wWZTEZDhw4lxhh5e3tTWpr6JxZdwSkX2mHNmjXFVrnQ1fu4X79+ZG5urpG5WrVqpfQ1yCsymYwEAgE1bdqUBAIBjRw5kl68eKG2//37D6i0mxsZGRkpfRQAkIWFBVWvXp34fD5du3aNPiekk9/dN2RgaETlPKrRzVexypaSLs2znMHBwfTzzz8rtzkqVqxIpUuXztMcaWlpSnkzLA/29vbKexpjTHk+LS2N+Hw+Xb58mYj+b8nI8MPg0AxFQbmY/aXtBfASwPIv7QUAH20Lp+2WX+WCiGjKlCnEGCs0H4yrV6+SWCwmU1PTfD8paQtOudAOa9euJRMTE12LoXG+fkotTFJSUojP52vEajFw4MB87/8nJSWRtbU1GRoaUlRUVL7WT0hIyGT96dy5MwEgO3t7srFzIBNTMzp986VSsbj1OpakMvWWi+jEdHoWlkiPQhPozadkSk1XOJq2atVKuZ1kZWVFPB6P3rx5kydZt2/frtyOql+/PgUGBiqViQzFYsKECbRhwwaSyWSkp6dH+vr6ZGNjQzY2Nsq+8fHq/Uw48kZhKBfZhqIS0VwimgvACkA1IvqViH4F4Angh84ktXjxYrx79w6BgYEYOHCg1terW7cuIiMjUbt2bdSrVw8jRowoUrklFO9XDk1SXPJcFBWWLVsGfX19DB8+PN9zyOVyeHt7Y+fOnTh27Bi6deuWp/Fnz56Fu7s70tPTERUVBQsLi3zJYWxsnCm/xqFDh9C1a1eEf/yIn9zLY+exy7CwslaetzcVKTOKfktMkgQhUalIkcghkxPiUqR4EZGM8+cv4syZM/Dx8cGNGzcwaNAg3LlzBy4uLnmSNSM3UFJSEq5cuYKKFSuiQ4cOABT3jRIlSmDNmjUYO3Ys+Hw+tm7dikmTJmHYsGEYPHiwch4jI6M8rcuhY3KjgQB4BkD01d8iAM+0rflouxXEcpHBggULCAB5e3sTEdGLFy+oR48e9Ndff1FgYCCtWbOGZDIZyWQyje0z79+/n0QiETk6OmZrSi0sDA0NC2WLKLcUF8vFhg0bMjn4FRcAUEJCQqGvu337duLxeHTkyJF8z3H58mUCQCtXrsyxr0QioRo1alCNGjVIJpNRWloaCQQCql27dp6f/nNDmzZtyMXFhaQyOYVGpdDDkHh6HBpP4bGpJM/G3yIoLJHuh8RnapcfvSOhUEjt2rXTiGxv3ryhSpUqZYkQAUCNGzem9evXKy0kXzvI1qlTR7n9w6E5oOttEWUn4HcADwHMgWKb5AGA6doWTttNE8rF7t27CQCJxWKSSCRUsWLFTHuI37Zjx44VeE0ihWNatWrViMfj0R9//KGROfMLp1xoh02bNhVb5SIuLvd5GjTJ6NGjiTFGbdq0ydcXfI0aNUggEKh9YIiKiqKdO3dSZGQkde3aVfm5d3d3p+rVq2t1m2vatGkkEAjyHM32KDQ+i3JRrUZtsrK2yXacXC6nlHQZSaS5y1lSq1Yt5etRu3Zt5e8ZPhwvXrwgAGRtbU0dOnSgEiVKKPsEBwfn6Zo4sqfIKBcKWVANwLgvraq2BSuMpgnl4saNG1S5cmUyMTEhAwMD5YfB2NiY5s6dS6tWraJp06bRjBkzaOvWrRr3kl+yZAnx+Xzy8PDI9/5tQeGUC+2wefNmMjIy0rUYGgeATsOrjx8/TiVLliTGGPXu3TtPCb2EQiFZW1uTpaUl8Xg8pd9A+fLlaejQocTj8UgkEpFAICAej0dz5syhp0+fUp06dcjV1ZXOnz+vxSsjqlKlCpUoUSJPY15/SsqkWDRq3poEAgFduPqf2jHxKRJ69jGRnnxQtNColFxFo/zxxx8EgAQCAZ07d44cHR2Jz+crLbAeHh4EgAwNDZX30rZt2+bpejhypsgoFwCcVDVtC6ftpgnlIgOJREJz5szJ5Kh0584djc2fHW/evCFnZ2cSCoW0e/fuQlnzazjlQjtkxPYXN3StXGRw6NAhMjQ0JGtr61xl8ExKSlJ+vitVqkQuLi40cuRIunr1KtWuXZt4PB7Nnz+fiBQJ0MaPH19omUgziIqKIj09PRo+fHiux6SkS5XWiyFjphCPz6dD/1xQu5WSJpEplYqv28fY1FytN3PmTOXreOrUKQJAAQEBRET0559/EmOMGjRoQIaGhtS/f/9cXwdH7ilKysVjAI++tJcApACeaFs4bTdNKhcZmJubKz84tra2Gp8/O0aNGkWMMercuXOh3tQ45UI7+Pj4FFvlQldWtm9JSkoiV1dXcnV1zbHvkiVLSF9fX2NJs7TFkSNHiMfjkZeXF/3zzz+5GiORyigiLo0srayp1s+1s/XRiExIU6lcBIUl5lrGa9euZXoQI1LkDeHxeLR8+XJijNGWLVtyPR9H3igM5SJX7uhEVImIPL60MgBqQJGlk+MbLCwsUKlSJYjFYkRERGDHjh2Ftvb69etx7tw5nD59GiVLllRZMIjj+4HH42Uo98WOohLpZGhoiGXLliEkJCRLAa0M5HI5Nm7ciO3bt4PH46F06dKFLGXe6NixI27evIm7d++iTZs28PX1Vdv3xIkTOHToEHr26I76NTwQHfUZU3+bkm0xwvy+JWVyQnyKFKvWbUTjxo1x//59GBkZgYjw6NEjTJkyBUKhENOnT4eenh769u2bv4U4igT5inUjonsAqmtYlmLB4sWL8eTJE0RGRuLPP/9Es2bNCnX9xo0bIywsDBYWFnBzc8ux5LGmKCpfFsWJ4hyKWpTeL61bt4ZcLsfVq1dVnreyssK4ceMgFotx4cKFQpYu7/j4+KBBgwawtLSEg4MDZsyYgQcPHkAqlWLy5Mno1KkTdu/ejREjRqB9+/bo2bMn7t27BwMDA2zZsgXt27fPdn6xvuoS82ID9aXn69SpC319ETyrVEJ4ZAzS09NRtWpVJCUlAVCUXj9z5gxSU1NRr149pKSkQF9fP/8vAofuyY15A5kzc06CIqmWn7bNKtpu2tgWyaj2p23HrdyQkUm0S5cuWt0mMTIyou3bt2tt/rxSXLZFfH19ycDAQNdiaBwAFBERoWsxlISGhhIACg0NJYlEQp06daJq1apR9+7d6erVq6Svr09LlizRtZg5smzZMrK2tiY+n0/Dhw8nmUxGHz58oPLlyxNjTJmcys7OjkQiETk7O+fbpyE6MZ2efrUl8iYyWW2SLrlcTpWreqqNoGvYsGGmKqlFfdupOIBC2BZRr2pmxuSr36UATgE4XEC9pljC4/HQo0cPNG/eHL6+vujZs6fOnkBXrlyJNm3aoG3btnBycsKNGzfg5OSk8XXkcjn8/PwQExOT6bi661Zlcs3uNVJnolU1hjEGmUyG06dP48OHD9mJXSSQSqVISkqCqalplnO3b9+GVCrFxo0bdSCZdilKlosSJUqgbNmyKFmyJADAwMAAjRs3xp07d1C/fn0QER4/fqxjKbPn999/x59//olevXph3bp1MDMzAwA4ODjgyZMnePLkCa5du4ahQ4dq5H5kbqQHsYEAyekyCPgM+gKe2s+pnIDDZxRWoY9hH/CXz3psXb8KAGBiYoJLly5BLpdj/vz5+PTpU5HfduLIHYxysYHGGOtKRAdzOva94eXlRXfv3tXK3CNHjsSWLVtARHBxccGTJ090ZuaLjY1F3bp18fz5c+zcuRO9e/fW6PxCoRACgQAikUh5LDfvq/z0zc2YhIQE6OvrQ09PL8/zFjZpaWmQSCQwNjbOck4ul0MikWR6XYsDCQkJePfunfLLvCgQGxuLevXq4cmTJ6hatSoCAgIAKP4HL1++RJkyZYrkNlV4eDiGDh2K06dPY8WKFRg3bpyuRcqCnAhvI1MzHbt68SwG9vj/9otYLEbJkiVx5swZlCihSP4skxMSUmWQyOQQ8BmMRXzo8Yve/+B7hDEWQEReWl0kN+YNAPdyc+x7a9rYFvmWwMBAMjIyokmTJml9rZwYO3YsMcaoe/fuGt0m4aJF8s/06dMLPapI1wDIVeinLmjXrh15eHioPV/YoaXqePPmDY0cOZL4fD7Z29sX+ff7p/g0ehWRnKn9ffIcVa9enWbOnElLliwhU1NT6tKlCxERSWVy+hCTSqHR/2/vo1MpPZcJuziyB7reFmGMtQLgDcCRMbbmq1NiKLZHOHJAX18f6enpSjOlLlm9ejXatGmDdu3aoVSpUrhx44byKaGgZOddzsHxLUXJCiCXyxEeHo4pU6bA398fY8eOVduXz+cDUNT2MDExgUwmg1wuV9noi3WNz+dDIBAom56enrIJhUIIhUKIRCJlMzAwgL6+PvT19WFgYABDQ0MYGRlBKBRi3bp1+Pjxo1Ken376CY0aNcK///4LPz8/5evKGANjDGlpaQgJCUFKSgoqVqwIc3Nz8Pl88Hg88Pl8lU1PTw98Ph+MMQgEgiznvz6W8btQKMw0b8a1ft0nMRVIlgI8Ph8mBkI0ruOJ1pcuKfuePHkShw4dAgAkpskg/8Y4SQASUmWwMCo67x0O9eTkcxEG4C6AdgACvjqeAGCCtoQqTowcORL29vaYNm2arkUBADRr1gwfP35E7dq14erqioMHD+boHZ4TGTdRjrxz6dIlREZGanyfmTGGXbt24eeff9bovJqiZs2amQpvFSb6+vr4559/lAW4GjVqhCtXrijPHzp0CIcPZ3Yp+1p5Hj58OMzNzSGTyZTbgSKRSKksCAQC6OvrQygUQiaTISkpCYmJiUhOTkZycjJSUlKQkpKC5ORkpKamIjU1FWlpaUhLS0NCQgLS09ORnp4OiUSibFKpFBKJBMnJyTAyMoKlpSUEAgEkEgn8/Py+tihn+skYg42NDQICAnD//n3o6+tnerrM8H35+ue3c6n6+9t1vj2W3e85cfToUdRr1kblOYks53ky1uIeeHRLtp9uInoI4CFjbA8RcZaKfGBpaQnGWJF6UjMzM8PTp08xZMgQdOzYEaNHj8aaNWtyHpgNRen6viciIiJgYWGBJk2aaHTeXbt24ezZs0VSueDz+fjpp59QqlQpnaz/77//olWrVnj27BkA4O3btwCA3r17q/SL+tr5tEGDBli0aFGRsETmhnfv3qFVq1ZgjCEmJqZIf07t7e0RHh6O8ePH49GztkiVZO2jx1evMMiJkJgqR5pUoVzo6zEYiXjgcUqGTshpW+RvIuoG4P6XYlyZICIPrUlWTJgxYwaqVq0KLy8v3LhxQ1l+uCjg4+ODZs2aoU+fPrh69SquX78OQ0PDHMeFh4fj2bNnqFatGsRiMaRSKfeUkE94PB68vb2xZcsWjc579OhRjc6nSSwtLVGtWjUsXry40NeOjo7GhQsXlPkVjh49itDQUMyYMQPz588vdHm0RXh4OORyOSpXrgwTExP88ccfRUaxCAoKQt++fZHhTL9161YMGzZMWVL93bt3MNRjSEpDpq0RBsBEn69yTiJCXLIM0q+CkFIlBLlcBlND3VjIfnRyerdluB63AdBWRePIgQoVKiAoKAivXr1C5cqVi1QIHgB0794dL168wIcPH2Bvb48HDx5k279Hjx6wt7dH06ZNYWZmBm9vb0ilUtSrV69wBC5mEFGRuekXFs7Ozrh//36hr5uYmIjSpUsjOTkZV65cQXJyMp48eQLG2HevWKSmpmLu3Lk4fvw4XFxcYG9vD0dHR/D5fLx69QpTp07VtYhK/vnnHwQEBGDhwoWwtLTEsGHDAADt2rVT9hk2dAhsxUIYi/gQCRQWCBuxntpoEZkcmRSLDNJliqgTjsIn27saEWV4Do0iopCvG4BR2heveFC6dGk8ffoUISEhaN26ta7FyYKLiwvCwsLg6ekJT09PrF27Vm3f06dPY8iQIZBKpVi4cCFu376NZcuWaSV/xo9Axr64tuYuilSsWBEvX74s9HUXL16M2NhYhIeHw9XVFUZGRpg5cyZcXV0LXRZNMn36dJiZmWHu3Lno0KED9PX1ERERgdu3byM4OLhIWUsBKLcAZ8yYgejoaOjp6YExhoCAANjY2AAAdu7ciZTkJJgZCmBtIoS5oXrFAlBsiag/l708MjlBIqNs5+DIO7l9ZFKVw7qVJgUp7jg4OODy5cvw8/PD5MmTdS1OFgQCAS5cuIC5c+di3Lhx6NChg0orS/369XH79m0AwLRp0/D582dMnDixsMUtNhCRMgJBkxTlbaratWsjIiKi0NedP38+kpKSIJFIQERYtmwZAGDMmDGFLosmePnyJTp06IDFixfD29sbqampuHr1KoKCgmBjY4Pq1aurzJ+ia6pUqYL58+dj9OjRmD59OpYuXYq2bdvixYsXMDAwUPb72pKRE+p8MRgDBGq+5YgIyelyJKUTUiSExDRS+mtwaIDs4lQBjISiImoS/l8V9RGAtwB2aztOVtutMPJcfMvOnTuJMUYzZ84sMjHz33L16lUyMDCgkiVL0sePHzOdmzBhAllYWOhIstzxPeW5cHFxoaFDh2p8Xmtra5o9e7bG59UEISEhBIAkEonOZDh58iQxxmjs2LE6kyG/3L9/nxo0aECMMSpdunSR/T/nhZYtWxIAmjt3LvH5fPpfe+cZHkXVBeD3JCEJIZ2E3pEiSO+gKApSpdhQUFDBAqggCgiKoGABwYLgBxZQBCmCCIgiHakCUqQX6R1CbwnZPd+PnawJpGc3m8B9n2eenb3l3DN3dmbO3nvnnMaNG6vj8ZR6rsbY9OSF6wm2a9eTvsdeu27X81dtN23XY5OOCHurQBaIivojjrUVs0i41qKaqj7lelPn1qdjx4707duXjz76iMaNGxMbm/Vewrn77rs5fPgw/v7+FCtWjN9//92ZV65cOa5evepB7W4t7Ha7W0YusjJFihTBy8uL1atXe0yHpUuXEhQUxOeff+4xHdJC7969CQ0NxcvLiypVqnDq1CnmzJnDnj17GDhwoKfVSxWzZs2icOHCLF++nEWLFlG+fHn+/fdfAMaNG0erVq0YOHAgzzzzDG3atAHS5ibeP4cX4bm8yeXnRaCfY98vqWELkn6t9bpZo+ESUlpzcV5V96vqk+pYZ3EVhy+TQBExk+zp5P3332flypWsWrWK0NBQYmJiPK3STYSHh7Njxw4ef/xxmjdvTu/evQGoWrUq165d4+DBgx7W8NbBXVMYWW3xcHxCQkJYsmSJR9petGgRw4cPd/6mszqPPvoon376Ka+88gr//PMP169fZ+vWrTRtmr1mpgsUKMDhw4e55557eOCBB9i2bRtlypShRo0atGzZkl9//ZWJEyfyzTffsHLlSiDtr7h7ewkBvl7k9PXC28v115WqY/HodZtjAalZppE0qXpHR0QeAj4BCgAngaLAdqB8RhUQEX/gT8DP0meaqg4QkYHA88Apq2g/Vf3NqvMx0AB4XVWXikgxHFM1r6rqF1aZkTiGfr7LqI7uoHr16hw/fpygoCDWr19P7dq1Pa3STXh5eTF+/HgeeOABOnXqxJIlS/jzzz/JmTMnc+fOda7yNqQfddOCzqy85gIcjqzOnz/vkbZHjhxJmTJleOuttzzSfmqZOHEib7zxBsePH2fmzJmpWoNw6dIlvvjiC3x9fQkJCSE4OJiQkBDCw8MJDQ0lLCyM0NBQjzgwq169OjabjXHjxvHZZ5+xZcsWatSoQUBAAGfPnmXp0qX4+fnx8ccfM378eLfrk8NbEl1jkZwvjVg7OAc81OGm2tcb3GDHZHtS+wsbDNQGFqhqFRFpADzpIh2igftV9ZKI5ACWi0jcOPynqjosfmERKWvt1ge+A5Za308C3UVkjKpmvaGARAgMDKRSpUrUqVOHjh078t1333lapUTp2LEjtWvXpm7duuTPn59r166RN29eT6t1S6C34auo4IgGG3/xXmZx8OBBVq1aRenSpdNcN24kKLPO14ABA4iMjOTrr7+mRYvEPVYCrFixgm+//Za5c+dy7NgxAgICyJEjB7GxsdhsNmJjY2/yvAkOA9QVDvRSS61atVizZg1Hjhzh5MmTvPPOO0yZMsX5plmZMmXYtWsXxYoVw9vbm9mzZ7tVH19vx8hDjO2/NH8fwScJS8Gu8QyLeFy3gZ9xpXETqe2S66oaJSJeIuKlqotFxCUecKzFJZesrzmsLbnBJm/AbpWJ/ys4BawAOgJfu0K3zGDjxo20b9+eRYsWeVqVZClTpgzHjh3jgQceYPny5YSFhXlapWTJLg9sdxoXmoXHbG02m0eMi1KlShETE8Pp06dTjFJ84+jPtWvXkih5c/kb9+O+x33a7XZ8fHycefE3Ly9H+PLz58/j7+9P+/btExgHcVucPiJCoUKFaNCgAe+9916KruRjY2NZvHgxzz//PKNGjSJHjhwMHz482TquYMCAATRv3pyCBQs60+rWrcuzzz7L3XffzQcffICPj0+GwxGkFhHBP4fg66OoOkYfkhvxS2ophmPlqePNFMN/pNa4OCcigTimLyaKyElcGLhMRLxxxC65Axilqn9ZQdNeFpEOOOKbvK6qZ1V1q4gEAMuBG9/p/Aj4XUTGJtPWC8ALQJbxzRAUFJSl58fj8PX1ZdmyZfj5+bFhwwbq16/vaZWyPbfzyEVqvMG6CrvdTrly5YiJieHTTz91xhW58bqL+x7fMItLi4qKomvXrhw5csS5Vio2NtYZByRuhCB+XBC73Z4gNkhsbCxnz56lffv2jBs3Dh8fH2w2G9evX3d+xo04nDhxgoiICGe8kriAYr6+vvzxxx/88MMPPP7440ycODFN0xw+Pj40atSI/fv306FDBz755BOWLFmSIIBa/OBp1apVc8nrus2aNWPlypV06NCBy5cvkzNnTvbu3cvgwYNp3rw5v/76a4bbSA9eIgn/piaBsR3SRmp/ka2AaziClbUHQoD3XKWEqtqAyiISCswQkbuA/wGDcBiGg4DhwHNW+UR/6aq6T0TWAO2Saesr4CuA6tWrZ4m/diVLluTixYueViPVdOrUid69e/PYY49RoEABT6uT7bkdF3TabDanu+fM4IEHHmDv3r0cO3aMfPnypUvGli1bEBHnbz4jxtFzzz2HiPDkk+mbXe7atSvVqlVj0qRJGTJO33rrLWfgtKtXrxIdHc358+c5efKk00iaMGEC06dPZ9GiRWlu69y5c4SHhzNy5Eg+/PBDRISYmJgEfk4qVqzoMcMiLXhZNsiNDw0fLzNqkRip+qWo6mVVtalqrKp+r6ojVDXK1cqo6jlgCdBEVU9YbdpxTHPUTKWYD4A+pN5BmMe5evUqly5dYuHChdjtdueFnVX58ssvKVasGI0bN/a0Ktkedy3o9Pb2Zvjw4cyaNcvlsl1BZk+LrF69mpEjR6bbsABYuXKly3QuUqQIv/32W7rrDxgwgM2bN1O4cGG2bt2abjllypRh6tSpzJw5k3nz5rF06VJWr17Nhg0b2Lp1K7t27WLjxo2sW7eOEiVKcO7cuTTJP3XqFKpKt27dqFq1Kq1bt6Zjx47ONSSFCxdm06ZN6dY/MxFxrNOIW+8pOAyLZNZ/3tYk+wAWkYsiciGR7aKIXHCFAiISaY1YICI5gYbADhHJH69YG2BLauSp6g5gG454KNmCnj170rRpUxo2bIi3tzd58+bFz8+PiIgIatasyV133UXv3r1TnPPNTL799lu2bt3Kww8/zKVLl1KuYEgUd02LrFixgubNm9O6dWuqV69OuXLl8PPzyzI+Eex2e6Z4j7Tb7XTo0IHo6Og0eXxMjI0bN5I7d26X6FWtWjVn4K700LNnT06dOkX+/Pm566673OrC/K677uLgwYOoKoULF2bLllTdipkyZQoVKlTA39+fY8eOMXPmTEaMGMGQIUOYPXs2qprtXmkXgRze4O/jWMRpRi2SJiU/F0GqGpzIFqSqwS7SIT+wWET+AdYC81X1V2CoiGy20hvgmJJJLe8DhVykn9sJDAzk119/5eLFi5w/fx5VZf/+/Tz77LOEh4dTvHhxRo8eTXBwMP/73/88rS7gcLQ1ffp0fvnlF5o1a+ZpdbIt7jIuihcvzvTp05k/fz6+vr6EhoZSv3593n33Xb7//ntnmHFPYbfbM2XNxcsvv8yPP/7IiBEjMjRqAbBz584EixEzQpMmTTh06FCGZAQHB7Nu3TqioqI4dOgQ77//vkt0S4zw8HD27dtH1apVqVy5MtOnT0+2/AsvvMCTTz7JM888w+XLlzPc94ZsiLtdgGblzRPuv5Pj4sWLeurUKef3ZcuWaVRUlPN7hw4d1MfHR/v163eTW+7M5vz581q0aFH19fXVX375xaO63Iivr6/Onj3b02qkinz58umbb76Zae3VqFFDc+TIoYAGBQVpaGioNm3aVKtVq6Z33HGH3nnnnZnilt7b21tXr17t1jY+/PBDFRH95ptvXCKvVKlS2qFDB5fIOn/+vAJ64sQJl8j78MMP1dvbWw8dOuQSecnRpUsXFRF99913E83HeoFizpw5btfFkD7IBPffHn/Ae3LLasaFt7e388IMDAx07gPauXNntdls2qlTJw0LC1NA/fz81MfHR0NDQ3XLli2Zqmu5cuU0f/78evbs2UxtNzVkJ+MiT5482q9fv0xvNyoqSocOHaoDBw7UAgUKaLly5bRDhw4KaKVKlfTEiRPaqFEjnTBhQoJ6s2fP1g0bNiQr+/r167pgwQK12Wy6a9cuHT58uEZHRycoIyK6efNmVx+Wk8WLF6uPj48WLFjQZTJDQkJ06NChLpMXEBCgo0aNcpm8yMhIFZEEf1DcxejRo9XLy0sffvhhtdlsevHiRb169aru379fgSwff+h2xxgXt5lxsWXLFv3www+dBkWLFi20TJky2rBhw5uMh2PHjumUKVN03rx5WqVKFZfeRFPi77//VhHR/fv3Z1qbaSG7GRdvv/22p9VwsmXLFhURFRENDQ1VQMuWLau5cuXSYsWKKaBeXl4aEBCggYGBumfPHrXZbNq3b18tWrSoRkREqIgooAEBAerl5aXe3t7q6+ub4J8uoHv37nXbcQwePFi9vLxcOsLn7e2tS5cudZm88uXLa6tWrVwm78SJExoeHq5+fn66YMECl8lNipkzZzp/D/H/CAH6/fffu719Q/oxxsVtZlzEER0dfdM/veT47rvvFFAfHx/Nly+fLly40I3a/fcAyqpkJ+MiK0YvnTt3rvPhsGzZMq1Zs6aGh4c7Dd5vvvlGO3TooCVKlFBARUT9/f21Xbt2+sYbb+jkyZP1yJEjOmzYMJ05c6babDYdNGiQent7a2RkpL766qsKuO0f9oYNG9TPz0/r1avnMplx0xhXr151mcxnn31WixYt6jJ5qqo2m02feOIJ9fLycutoZtw0TP78+fXLL790Rri12WxZNtqz4T+McXGbGhdpJTo6Wvv166cffvihNm/eXEVEH3zwQbdNWYwYMUL9/f3dItsVZCfjIiIiQgcOHOhpNVIkOjpav/nmm5seHGvXrtV+/fql6oFy6tQp7dixo+bLl08BLVy4sBYrVkzvvffeNBnTKfHkk08q4NKH3Ny5c9XHx8dl8lRVf/75Z82RI4dLZcZRv359zZEjh8vWm8Rn2LBhKiIunSIyZC7GuDDGRYqMHj1aAW3Xrp3z38Ps2bM1f/78mjNnTpcPj86cOVP9/Py0Y8eOLpXrSrKTcZE7d+4kF8bdyixdulQfeOAB7dSpkwI6c+ZMl8keO3asAnr+/HmXyRw0aJCGhYW5TJ6qw2Bz5/RQz549VUR0ypQpLpEXHR2tL7/8soqIfvrppy6RafAMmWFcZBtHU7czEyZMoGPHjok61qpVqxYAP/74I88//zwALVq04PDhw7Rs2ZJGjRoxePBgl+ny2muvUbJkScaOTdLDuiGN3I7uv+vXr8+CBQv45ptv8PPz4/r16y6TXbduXQCXRv7cunWry4P1+fr6EhwczJQpU1wqN47hw4dTt25dOnfuzJkzZzIsr2HDhowcOZKmTZvSo0ePjCtouKW5/e5q2ZBZs2Yxfvx4vvrqq5vyKleuzK5du+jfv38C//9eXl5MnjyZUaNG8c477zB//nyX6OLj40O5cuVuyweiO1BVvL29Pa2GRxERbDZbygVTScmSJfHx8WHkyJEuk7lnzx6KFi3qMnlxlCpVisWLF7tcbhyLFi0iV65c3H333Rk2MOICvQ0bNiyFkgZD6mOLGDzI1KlTiYmJwdfX96a8MWPGMGPGDObOnZto3S5dujBnzhw6duzI0aNHM6zLM888w1tvvcUTTzzB5MmTMyzvdkfVPe6/sxN2u5233347Qehvx8jtzfvJfY//GRsbS6NGjVym49GjR7n77rtdJi+OunXrMm3aNJfLjcPX15fly5dz7733kjdvXrZt20apUqXSJGP48OF8/vnnHD58mDfffJM777zTTdoabiXM389sQmKGBcDhw4f5448/KF++fJJ1R40axbFjx7hy5UqG9ejbty8zZsxgypQpHvfyeCtgRi4c+Pj4OKNw+vv7kzNnTgICAggICCBXrlwEBgY6t6CgIIKDg51bSEgIoaGhhIWFER4e7nTR/d1337lMv7Nnz1K5cmWXyYujbdu2HD9+3K2u/UuWLMnhw4cpX748bdu2TXW9mJgYZs6cSe/evalRowb//PMPH374odv0NNxamJGLbM6gQYOYPHky27Zt4/jx44m62Y2bKz59+nSawsyrKleuXOH06dNERUURFRXFkiVLnNERXRVn4Xbndp9i8vb2pl+/fjz11FMuk+nr6+sy9+J2u52rV68613K4knr16uHn58eoUaN4/fXXXS4/Pn379uXpp5/Gbren+Jtbu3YtNWvWRETo0KGDSw01w+2BMS5uAXbv3p1svr+/PyJC8eLF2bFjRwJjIW5LLC0qKsppROTOnZuwsDBWrVpFhQoVmDZtGsHBrgovc/tipkXcw/Xr17l8+bJLZMWN0JUsWdIl8m6kffv2DB482O3GxSOPPMLTTz/Niy++yNdff50g78KFCwQEBLBjxw569+7N2rVr8fPz4/Dhw0RERLhVL8OtiTEubhPOnTtHSEgIjRs3JjIy0mkwxG2VK1e+KS137twJ/v2NHTuWJUuW0L59e7cMEd+u3O4jF4DLDax8+fK5ZI0ROCLM5syZ023naeTIkXz//fd89dVXvPDCC25pAxxTT2PGjKFz586UKFGCvn37smLFClasWEGfPn2c5UqWLEmrVq0YNmwYoaGhbtPHcIvj7ndds/J2K/i5SAutWrXSL774It31r127prNmzdLnnntOIyIitFKlSjpgwADdsGGD2u12F2qaMbKTn4vg4GAdMWKEp9XwKDlz5rwphklGGTVqlIqIDho0KMOyevTooQUKFHCBVklz1113ae3atd3aRhwffPCB+vj46PXr17VatWpOl90+Pj66bdu2TNHB4HoOHz6sgHbr1i3Fshg/FwZX8t577/H+++/z888/p6u+n58fDz30EN9++y3Hjx9nxIgRXLx4kUceeYTixYvTo0cPlixZQmxsrIs1v3VRMy0CuH705oknniA0NJTly5dnWNb27dspUKCAC7RKmosXL7J9+3a3thFH3759sdvtLFy4kKFDhwKQI0cOPvnkE/MmSDYmbqrrpZde8rAmDsy0yG1ExYoV+eqrrxg6dCgPP/xwhmR5e3tTv3596tevz7Bhw9iyZQszZ87kjTfeYP/+/bRo0YLWrVvz4IMPumxhXWayfv16Vq9enSDNbrenqq7e8KpkcsTExNz2b4ukpb9SS9OmTfH29mbq1KkZlnXgwAEqVarkAq0SZ+fOnRw4cIDNmze7rY0bqVKlCs2bN8dms5E/f362bdtmpkCyOQMHDmTgwIGeVuM/3D00kpW3221aRNUxtVGmTBlt27atnjhxItX1zp8/r++//75evHgxxbIHDx7UL774Qhs2bKhBQUHasmVLHTt2bKaEglZ1zbRIqVKlFNCgoKAEW3BwsEu30NBQXblypYuOPHtSuHBhLViwYKp+Wylhs9m0WbNmKiIumxoLCwvTwYMHu0RWYrRr104LFy7sNvmJ8c0332hgYGCWDkBocB9kwrSIqBv+NWQXqlevruvWrfO0GpnOlStXKFWqFC1btuR///tfqupcuHCBkJAQADp16sSbb77JHXfckWK9s2fPMmfOHGbOnMn8+fOpVKkSrVu3plWrVpQoUSJDx5EUfn5+TJ8+nRYtWiRIHzZsGJs2bcLLywsvLy9ExLnF/+7l5cX27dtZsmSJ8w2bpPyMGDLOuXPnKFWqFOXKlWPp0qUZktW5c2cmTJjAwoULqVevnkv0CwwMZPjw4bz44osukXcj5cqVo1y5cm51phWfmJgYQkJCiI6OZvPmzcn6yDHcmojI36pa3Z1tmDUXtyE5cuTg6NGj5MmTJ9V1goODmThxIj4+Ppw4cYLatWszePBgYmJiOHfuHJ988glbt269qV5YWBhPPfUUP/30E8ePH6dXr15s27aNOnXqULFiRd555x3Wr1/vlqHxGxk3bhx33nknDRo04J577qFevXrUqVOHWrVqUa1aNapUqUKlSpUoX748jzzyCJ07dyYwMJBOnTq5XbfbmdDQUN5++202bNiQITl2u50ff/yR999/32WGBThe5T516pTL5N1IjRo1WLlypdvk38jbb7/NtWvXmDRpkjEsDO7D3UMjWXm7HadF4njhhRf0xRdfTNNbHq+99pr26dNHVVUPHDigDz30kBYuXFjLlCmjJUqU0Pz58+tTTz2lsbGxKcqKjY3V5cuX6+uvv64lS5bUwoUL6yuvvKILFy7UmJiYdB+XqmNaZM6cOTelly1bVrdu3ZomWZcvX9YyZcro4MGDM6yXIWk2bNigIqKjRo1Kt4x27dqpn5+fS8O3q6oWLFhQX331VZfKjI87wrknh5eXlzpu/YbbFTJhWsQs6LxN+eijj2jatCldunThf//7X6reWPj333955plnAChSpAizZs1i06ZNHD9+nPr16wNw5513MnnyZNq0aZPsQk5vb2/q1atHvXr1+Pjjj9m2bRszZsygT58+7N27l+bNm3PfffeRI0eOBKMacfvxP+PnA9hsNv744w+nn4O4/LNnz6b5zYyAgAAWLlzI008/zTfffMOECRNc+q/Y4KBy5co8/fTT9OnTh65du6ZLxty5c+nevbvLp7ACAgI4d+6cS2XG559//iFXrlxukx+f/v37Y7fbefXVVzOlPcPti1lzcRuuuYjj0qVL3H333bzwwgupuqHXrl2bTz75JFk3yNOmTWPIkCFs2bKF/Pnz07p1awYMGOBcr5EaDh8+zKxZs1i9erXTMIhvFMTtx/+Mn79jxw7uuOOOBA8ZEcHX15chQ4YQGBiYal3iM2fOHOd6k+7du5tXSF3MuXPnyJ07N5988gndu3dPU91+/frx4Ycfsnr1amrVquVSvapWrUrBggWZPXu2S+XG8dprrzF16lSOHDniFvlxxMTEUKBAAaKiorDZbMZ5221MZqy5MCMXtzGBgYF89dVXPPPMM6kyLry8vDh58mSyZR599FEeffRRbDYbGzZsoEaNGlSuXJkOHTqkWq9ChQrRtWvXdP+DdRfNmzdnzZo1NGvWjL179/Lpp5/e9q+RupLQ0FCeeOIJ3nvvvTQbF2fPniUwMJCSJUveFFo8uYeoj49PisZmcHAwFy5cSDQvoyMadrud0qVLc/LkSWJjY/Hxcd8tedasWURFRdGlSxdjWBjcj7vnXbLydjuvuYgjKipKg4KC9K+//kqx7Ny5c7VIkSKpntO+ePGiAnr48OGMqpmlOHfunD7wwAPapEmTNL3Oa0iZXr16aa5cudJc7+6773Z6mkzrtmzZsmRlt2rVSitXrnxTepcuXdLdZmLbCy+8kObjTgtPPPGEAnr58mW3tmPI+mA8dBrcTXh4OOPGjXN63kyOxo0bkzdv3lR7PfT29sbf398ZlfVWISQkhN9//50qVapQpUoVJkyYYLySuogtW7ZQrFixNNdbtmxZmm9+c+bMQUSoXj350eHQ0NBEg6BduHCByMhIoqOjM3wjHjBgAOPHj3fp7+j06dNUqFCBihUrMnToUKdnXn9/f5e1YTAkhVlzcRuvuYjP8uXLefnll9m4cWOy5fr06UNwcDBvvfVWijJjY2MpWLAgf/75J2XKlHGRplmLZcuW8dZbbxEVFcXEiRNNQLcMcPr0afLnz8/HH39Mjx493N7e/fffT1RUFJs2bUq2XM+ePZk6dSqHDx+mS5curFy5kg0bNnD69GnKlClDcHAwBw4cyJAudrsdPz8/YmNjERF8fHzw9fXFz8+PnDlzEhAQQFBQEMHBwYSGhhIWFkbu3LmJjIwkX7585M+fnwIFClC4cGFntOIuXbrwww8/UK9ePVauXMmlS5cQkVR7mjXcupg1F4ZMo3jx4hw/fjzFctWrV+fLL79MlXHh4+ND7969efbZZ/nzzz/dOp/sKe655x6WLl3KxIkTadq0KVu2bCF37tyeVitb8uqrrxIREZEphgXA3XffzQcffMBff/2V7CLQyMhIrl69CsA333xDbGwskyZNon379owZM4b27dszffp02rdvj81m49SpU4SGhrJ+/Xq+++47wLFep3Hjxkm24eXlRXR0NGfOnOHw4cMcPXqUY8eOcfLkSU6dOkVUVBRnzpzh/Pnz7Nu3j82bN3PlyhWuXr1KdHQ0169fx2azJXhzqmXLluTKlYs//vgDcPyBuPfee13QcwZDKnD3vEtW3syai/+w2+2aJ08efeedd5JdU3H06FHNkydPquXabDZt0KCBfvbZZ65QM0vTtWtX7d69u6fVyLZUrlxZGzVq5NY2fvnlF61Xr5727NlTbTab1q9f3+l6+8cff9QBAwZow4YNNTg4WPPly6ePPvqoVqxYUQGtU6eOiojmy5dPixUrpjabTQsXLqyA03cEoIULF9bcuXPftKZiw4YNbj02Vcf1FhUVpQULFlQvLy+98847E+SJiG7atMntehiyNmTCmguPP+A9uRnjIiHbtm3T2rVr6+jRo5Mss2PHDi1VqlSa5YaHh+ulS5cyqmKW5ujRoxoWFqbHjh3ztCrZkrJly2qzZs3cJn/16tXq5eWl1apVU39/f82dO7c2b95cAX3ggQdURDQsLEyrVq2qgwcP1kaNGimghQoV0jx58txkLOTIkcO5H2dclClTRp988knt37+/7tixQ++77z4NDQ1VQP38/LRZs2aaM2dO9fb2dttxqqouX75cmzdvrrt27UqQHh4erv369XNr24asjzEujHGR6QwZMkR79uyZZH5UVJSGhISk+S2JZs2a6cSJEzOqXpbn5Zdf1v79+3tajWxJ06ZNtUqVKm6TX7lyZQ0ODtbly5fr4MGDExgKIqLly5dXQE+ePKn79+/Xn376Sb28vPTFF1/UH3/8Uf39/bVcuXJaoEABrVatmvbt21f37dun169fT7Ht69ev64gRI7RAgQLONlPi559/1ieffFJr166tX3/9tSu6QB955BEtUaKES2QZsi/GuDDGRaYzdOhQbdKkSbJlXn/9dW3QoIGePXs21XI/+ugj7dq1awa1y/qMHj1aO3bs6Gk1sh3ffvutAvrGG2+4rY0NGzZoqVKlVETU399f8+fPr4AGBAQkMDRUVYsXL+42F9lz5sxRQKtVq5boNXT16lX97LPPFNDSpUtrvXr11NvbWyMjI3XKlCkZanvx4sXq5eWlNpstQ3IM2ZvMMC7M2yLmbZEEbNq0ifr167N582aKFCmSaJnY2Fi6devG9u3bmTRpEgULFkxR7qFDh6hevTq//vorNWrUcLXaWYa9e/dSs2ZNdu3aRXh4eJrq2u12jhw5wvDhw/n9999p0aIFFStWJDw8HD8/P/bu3UudOnWoVKmSm7R3L3a7nStXrtzktComJgY/Pz8APv30UyIiIjh37hznz5+nefPmSb6Bs3v3bl5++WXy5s3L2LFj07xg+NKlS9SsWZN///2XMmXKsHnzZuLuh3fddRdbt27FXffHnTt3UrZsWQCuX7+Ol5cXs2fPZsaMGUycOBG73U6JEiXYvXs34HjttXPnzkybNo0ZM2bQqlWrdLVrt9vx9fVl9uzZNG3a1GXHY8heZMbbIh4fPfDkZkYuEqdPnz5as2ZN3bJlS5Jlrl27pq+//rqGhYXpTz/9lCq5Y8aM0dq1a7s8sFRWo3v37tq2bds0BYU7d+6c1q9fX/Pmzatt27bVJUuW6Hvvvaft27fX5s2ba8OGDbVSpUoaHh6un3zyiRu1dw81a9ZUX19fBXTu3LnO9ClTpmjDhg2dowa+vr6aM2dODQoK0rCwMBURDQgI0C5duuj58+ed9S5fvuxcXOnj46OAPvvss2nWa8GCBc6227Rpo6qq0dHRCmh4eHjGDzwZ4toNCgpKsCD07bffTrLOiy++qD4+Prp58+Z0t1uuXDlt0KBBuusbsj+YaRFjXHgCm82mo0aN0sjISH311Vf14sWLSZbdsGGD5smTR4cPH642m02nTZumDz/8sB44cOCmsrGxsdq6det0PQSyE5cvX9ZatWrpQw89lKyBFp+XX35ZO3TokOJw9b59+7RgwYLarVu3ZM9LViNuAWXcAzQ8PFwDAgLU29tbq1atqi1btkzUS+zly5f13XffdS6K9PX11bCwMOeURtxWrVo1FRGtW7euFilSRBcsWJCkLm+88YYOHjxYz549q3feeacCunbtWmf+1atXtXr16mma9ksPmzdv1tatW2uNGjWcx/Hoo4+mWO+ee+7RwMBAjYqKSle7P//8s3p5eWWr34/BtRjjwhgXHuXUqVPaoUMHveOOO3TgwIG6b9++RMvt3btXa9Wqpa1bt9aXX375plfg4nP58mX19/fXM2fOuFFzz3Px4kUdPny4RkRE6A8//JDsKMaJEyc0LCws1Ytkz5w5o0899ZRWrVpVr1275iqV3coTTzyhOXPmTGAQDBo0KE2jWAcOHNCff/5ZP/zwQ+3Ro4dOmzZNX3nlFQ0ICNA333xTp0yZonXq1FFAGzdunKQcf39/pw733nuv5sqVSyMjI3XgwIHZwp27zWbTIkWKaO7cuXXPnj3pkhEWFqadO3d2sWaG7IIxLoxxkSVYtGiRdu/eXfPly5fkcGxMTIy2atVKW7du7fyHWrBgwURfy7z//vu1cOHCeujQIXer7nHWr1+vlStX1mLFiumrr76a6MPg66+/1latWqVJrt1u14cfflgbNGiQ5R+I0dHRWqVKFecD/bvvvnNre3HtxP1Wo6OjtUaNGhoUFKQVKlTQ77//Xr29vXXChAnO0ZCQkBANCgpSQNu3b5+qN0A8yeXLl7Vq1arq4+Oj33//fZrrDx8+XHPkyKFXr151g3aGrI4xLoxxkSqOHTumgL7zzjtubWfSpEkaGRmpixcvTjT/4sWLWr58ee3Zs6fTwLjxxvfdd98584YNG+ZWfbMKdrtdt27dqv369dOIiAjt1q2bnjx5UlUdhltYWJh+8803aZYbGxur/fr10+LFi2dp3xqlSpVyPvCnTZvm9vbeeecd57RJzpw5NXfu3BocHKwjRozQ4OBg9fHx0bJly6qq6qFDh7Rfv36aI0cObdu2rfbq1UsBfe2119yupyvo2bOnioi2a9cuTW+A2Gw2DQkJ0eeff96N2hmyKsa4MMZFqjh79myq353PKAsXLkzWwFi2bJmWKVNGf/nll0RfLWzdurVOmjRJCxUqpBUrVtSYmBi365yVOH36tL7yyisaGhqqDz/8sEZEROjChQszJPP111/Xl156yUUaupb+/fsroK1atcr0hbwXL17UMWPG6LPPPuscMZo5c6bec889umPHjgRlhw8f7ryGQkJCstWo2ty5c9XPzy/ZqaDE+Oyzz9Tb21uPHj3qJs0MWRVjXBjjItUMGjRIQ0JCMqWtxYsXa2RkZKKhqq9fv6516tTRnDlz6siRI29aE1CsWDGdNm2aVqpUSWvVqqXNmjVL98K07MyJEyd0/PjxLnHFvHfvXs2dO3eWGr1YuHCh5s+fX318fPTLL7/0tDopcurUKX3iiSd02bJleurUKU+rk2ZWr16tIpKk0Z8UpUqV0nLlyrlHKUOWxRgXxrhINXa7XWNjYzOtvXnz5mlkZKTOmzcv0fyxY8dqnjx5dPr06QnSf/jhBy1UqJAGBATo5cuXtX379vrcc89lhsq3NP369dPmzZun6fXXjPD9999rzZo19ciRI7pw4ULn3P3mzZu1QoUKKiL64IMPuv2NC8N/BAYGptmQK1KkiAI6ePBgN2llyIoY48IYF1maxYsXa/78+XXChAmJ5o8fPz5Rb5/xDaEzZ85o8eLFk41nYkiZmJgYrVmzZqaNEgwcOPCmWBtxW9WqVU1wLA/g5+eXZg+e8d/g2b9/v5s0M2Q1MsO48Mq4Gy7D7cp9993HrFmz6NWrlzMkdXweeeQRtmzZQpMmTVixYoUzXUTw9vYGICwsjAULFtC/f3/69u3L4cOHM03/W4kcOXLw7rvvMnjwYLe3tXDhQooVK4aIAFCvXj0AKlSowP79+/n777+pWLGi2/Uw/Ifdbuf69evky5cvTfX69OmT6L7BkFGMcWFIli5dulCmTJkk8ytXrszFixeJjo6+KS8gIICtW7dSunRpPv744yRllChRglWrVnH58mXq1q3L2bNnXaL77cZ9991HTEwM7nRpb7fbefHFF3nmmWccQ584XHZfv36df/75h6JFi7qtbUPSjBs3Dh8fH+6+++401evQoYNzv2PHjq5Wy3AbY4wLQ5LYbDY2bdrErl27+PfffxMts2rVKooVK0ZoaGii+cHBwbzxxhssWbKEY8eOJdlWyZIlGTFiBK1ataJLly7OB5ch9fj7+/PVV1/RpEkTJk2a5FLZR48epW7dunh7eyf4LfTs2ZMaNWqkOa6HwbV89dVX1KpVCy+vtN3Sixcv7owNZLPZ3KGa4TbFGBeGJDl27Bg7duwAHKMQN6KqfPzxx7z44ovJyilSpAj58uVj586dKbY5dOhQdu/ezauvvproaIghedq0acPixYvp0aNHgqmojLJx40ZWrVrl/P70009js9kYPny4y9owpJ+tW7dSqFChVF1jN/Ljjz8C8Pjjj7taLcNtjDEuDElSqFAhjhw5wtq1azl8+DDLly9n79692O12oqOjadu2LbNnz6ZUqVIpymrRogXLly9PsVzOnDlZtGgR+/fvJ2/evFStWpU6derwwgsvOCNEGpKnQoUKjBo1ihdeeIErV65kSFZMTAzt2rVjzJgxzrQvvviC8ePHp/lfssE9HD9+nMuXLzNp0iTKli2b5qmR+vXrA3D16tUM/14MBifuWikKFAYWA9uBrUB3K30KsNHa9gMbk6i/H9hslVsXL70AsAiYCQRaaQOBK0CeeOUupaSjeVsk9WCtKC9UqJAGBQVpYGCgMy01r7HNmjVLixQpcpPzouQ4ePCgzp8/X5cuXapAssGoDAmx2+3asWNHbdiwodMbaHrYvHmzAs54HA888IALtTS4gl27dimgOXLkcF6TafHWqao6YsQIBTQiIsJNWhqyEmTnV1GB/EBVaz8I2AWUu6HMcOCdJOrvByISSf8IKA88BLyk/xkXB4Eh8coZ48KFzJkzRwsVKqSvvfaanjx5Us+cOaO7d+/WgICAVHsGHTdunEZGRupXX32VppvfyZMnNSgoKNN8ONwqxMbG6uuvv64RERFJ+iNJDY899pjzoWViUWRN4rvc/+CDD9Jc32azpdswMWQ/MsO4cNu4pqoeU9X11v5FHCMYBePyxfEe2+NAWleeeQN2a5N46WOBtiISnhG9DYnTrFkzNm3axJYtW2jevDlHjhzhjjvu4PLly6lefPnMM88wb948vv76awYOHJjqtnPnzk2OHDnYv39/+pS/TfH29mbYsGH89NNPPPXUU2laJPv5558zcuRIABYtWgTA5MmT8ff3d4uuhozxxBNPEBUVRcOGDenbt2+a63t5efHQQw8B8M4777haPcNtiKTlhpPuRkSKAX8Cd6nqBSutPvCJqlZPos4+4CwOa3qMqn5lpRcFfgDOA+1U9aKIDAQuAQGAt6oOEJFLqhqYiNwXgBcAihQpUu3AgQMuPdZbHbvdzpgxYxg6dCh79+51+jpICytXruT5559n8+bNqZ63f+edd1i/fj2//PKLeTMhjZw+fZrChQtz5cqVVJ+vChUqsGXLFiIjIzl16hSAeYMnCxMbG0uJEiWIjIzk77//TpeMCxcuEBISAphzfasjIn8n9ex1FW5fkSUigcB0oEecYWHxJMmPWtRT1apAU6CbZYygqgdUtb6qPmSNiMRnBNBRRIKTEqqqX6lqdVWtHhkZma5jup3x8vLipZdeIkeOHPTv358lS5YQGxubJhm1a9cmZ86cDBo0iJiYmFTV6devHzExMTRr1oxly5aZN0nSgL+/P8HBwfzzzz+prjNnzhxy5crlNCx69erlLvUMLsDHx4dff/2VDRs28Ndff6VLRnBwsHNxtlnYacgobjUuRCQHDsNioqr+HC/dB3gYx+LORFHVo9bnSWAGUDOl9lT1HPAj0DVDihuSRUT4448/OHPmDL169aJGjRrs3r071f92vLy8mDBhAvPmzeOxxx5LVR1/f3/mzJnDAw88wGuvvUZ4eDj9+vXLyGHcNgQGBlKmTBkOHjyY6jpFihTh0qVL5MmTB8B43MwGVKxYkdq1a/PUU0+lW8aiRYvw8fGhf//+LtTMcFvirsUcONZDjAc+SySvCbA0mbq5gKB4+yuBJsmUHwi8Ye1HAPuAaynpaBZ0Zhy73a79+/fXyMhIvffee/XKlSvOvE8//VQ/+uijJOvu3r1bQ0JCdNasWXr8+PE0tbt161bNnTt3pgZry858+eWXescdd+iJEydSXef8+fPORX5Dhgxxo3YGV3HgwAH18fHRUqVKKaB58uRJs4wePXpoZGRkunV46qmnnL+bxx9/3ASvy4KQnRd0AvWAp4H7RWSjtTWz8p7ghikRESkgIr9ZX/MCy0VkE7AGmKOqc1PTqKqexjHS4eeKgzAkj4jw3nvvcfz4cSIiInj22Wfp27cv999/P6+99hpjx45Nsu4dd9zBgAEDaNmyJfny5WPhwoWpbrdcuXKULVuW//3vf0RFRXHixAlXHM4tS5cuXWjRokWapjeCg4OdztM2btzoJs0MrqRIkSJMmjSJffv2AaTZ5wX8tzg0NYwYMYIPPviAQ4cOsXbtWtatW0eOHDmcvjOmTp1KWFhYmkbNDLcI7rZesvJmRi5cy4ULF7Rbt27avXt3/eOPP3T+/PmaO3fuZH1bHDp0yPkvJygoSC9dupTq9rZu3aqlSpXS0NBQDQoK0iFDhpjXVZPh3LlzGhgYqBcvXkx1nbvvvlsB9fLycqNmBlczZswYBZz+aNIyYhUVFZXs6+ULFixQQIcOHapeXl7O19Hjb+vWrVNVx0hKXNqKFSsyfFwG10B29nORHTZjXLifsWPHar58+fS3335LssywYcM0b968+uCDD2r//v3T1c6hQ4e0UqVK+vHHH6dX1duCJk2a6JgxY1Jd/s0331Rvb28FNDo62o2aGVxJ48aNnQ91EUlT3VOnTiXr78Jms2nt2rWd8kNCQrR169Y6bNgwnTlzpq5evTpB3ZUrVzrL/vLLLxk6LoNrMMaFMS5uCZYsWaKFChXS119/XU+fPp1kudWrV2vx4sU1JiYmXe38+eef6u/vr+fOnUuvqrc869at0zx58mj58uW1a9eueurUqWTLx3noBNJklBg8y+jRoxXQggULpnnUacqUKern55diueXLl+vvv/+eKsdqDRs21GrVqunrr7+eJl0M7iEzjAsTHMDgdu699142bNjAyZMnKVGiBI888ginT5++qVzNmjUJCAhg5cqV6Wpn+/btXLt2jd69e2dU5VuWatWqsX//fiZMmICPjw/lypXj8ccfZ+LEidjt9pvK33XXXdx1110ADB48OLPVNaSTF198EVXl4MGDqCpbtmxJdd0VK1YQERGRYrl69erRpEmTVDlWW7BgAX///Xe61oAYsieZ4kQrq1K9enVdt26dp9W4rbh48SJvvPEGZ86cYerUqTc5dRo1ahR//vknFSpUoG/fvnh7e6dK7oYNG2jcuDELFiygZMmS5MqVyx3q33Ls2bOHVatWMWrUKKKjo2nUqBFFihQhb9681K5dm8KFC7N7925Kly6Nt7d3mn2aGDxPnjx5aNKkCePHj09V+YYNG3LlypV0G/k3Mn36dB599FFatGjB7NmzXSLTkDFuCSdaBkN8goKC+Pzzz9myZQtTp069Kb9EiRLs2rWL/v37M2rUqFTLnTZtGp06daJixYrGsEgDd9xxB08//TQrVqxg+PDhBAcHs23bNiZOnEjVqlWpV68ea9asITAwEJvN5ml1Dengk08+YcKECXTr1o0LFy6kWP7gwYOEh7suisKzzz4LQJkyZVwm05ANcPe8S1bezJoLz7FgwQItX778TW937NixQ4OCgtK8gHD06NHauXNn5/fr16+bN0cyiM1m06+//lpLly6tnTt31goVKnhaJUM6GTNmjAYHByugvXv3TrJc3GLOli1buqztK1euaLdu3XTLli0uk2nIGJg1F4ZblQYNGrB3714uXbqUIL106dJUqlSJ4cOH4+vrm2p5FStWZN68eaxfv55JkyaRI0cOFixY4Gq1byu8vLzo3LkzO3fupEqVKlSqVMnTKhnSyQsvvMD58+d55ZVX+OKLLzhz5kyi5dq2bUtERASTJqU1nmTS5MyZk5EjR1K+fHmXyTRkfYxxYfAIXl5elC1bljlz5iRIFxG+/fZb3n///TRFTq1Tpw49evSgbdu2tGvXDoBvvvkm1bFLDEljs9kYM2aMs18N2ZfPPvuM3Llz06RJk0TzFy9ezI8//uh0nuYOjh8/TpUqVdi7d6/b2jB4HmNcGDzGvffey+HDh29KL126NBs2bGDYsGE3jWwkx2uvvcbu3bvp27cvr7zyCn/99RdvvvlmquaZDUnz3XffERQUlOQDyZB98PLyYsaMGaxdu5YxY8YkyPvnn39QVapWrepWHX755Rc2btxIyZIladeuXaq9gRqyGe6ed8nKm1lz4Tn+/vtvDQ0N1Z9//jnJMg8//LCOGjUq3W0cOnRI27dvr5GRkfrdd9+ZOCTp4NKlS5o/f35ds2aNp1UxuIj4MWM6dOjgvE58fX0V0NGjR6dL7t69e3X//v0plrPZbHrp0qUEMUj++uuvdLVpSB8YJ1rGuLhV6dq1qwK6Z8+eJMv8+eefWrRoUZ09e7auX78+3W1t2LBBS5curYD26tVLL1y4kG5ZtxvffvutPvTQQ55Ww+BCLl++rD4+PgncdefJk8e5n5RnzpSoVauWAjp48GBn2tq1a/Xbb7/VZcuWJVonOjra2e7evXvT1a4h7RjjwhgXtyw5c+ZUIMXRhL59+2ru3LkV0K1bt6a7vb179xoXxOmgQYMGyY4uGbIne/bs0SlTpujly5d1586dzmtj27Zt6ZbZq1cv7dChgwYHB+vp06f14sWLTrlFixZNsl7ctbl69ep0t21IG5lhXJg1F4ZM59KlS1y9epWgoKAUHfu8//77FChQAMAZ6TE9FC9enPHjxxMeHm7et08lV65cYc2aNTRq1MjTqhhcTMmSJWnWrBm9evXiueeew9/fny1btnDnnXemS96lS5eYMWMG7du3p127dgwYMIDAwECOHTvG9u3bk1y8efnyZdq3bw84fNwYbh2McWHIdOIWaV68eJFBgwbRo0cPxzBaIogIISEhzJgxg+bNm2eo3aeffpru3bvzwQcfZEjO7cLKlSupVKkSgYGBnlbF4AL69OmDiFC/fn1CQ0MJCgpixowZPPjgg1y9ejXdr4rGxsby9ttvY7PZePDBB3nvvff47bffmDhxIvny5aNs2bJ4eSX+qBERVq1aBUBkZCSqSu/evc1r5LcAxv23cf/tEXbu3EnZsmWpXbs2W7ZsoUGDBowYMYJixYrdVDZnzpwcO3aM0NDQDLd79OhRatSoQePGjWnSpAmlSpWicuXKN7khN0C/fv3w9vZm0KBBnlbFkAFatGjB5s2bOXjwIGXKlCF37tw89dRTqCovvfRSkg/+1DJnzhxatGgBwIULFwgKCmLIkCGcPn2ajz/+OMX6O3bs4Mcff+S9997DZrORK1cuoqOjnbIMricz3H97fN2DJzez5sKzTJs2TUVEAQ0KCtLixYvr33//fVO5e++9V2fPnq2qjrcX0rvgLI6oqCiNjIx0zgf/8ccfGZJ3q1KrVi1dtGiRp9UwZJAPP/xQBw0apAcOHHCLfLvdrkOGDFFAK1eurNu2bdNq1appv3790iVv3759CmiDBg1crKkhDsyaC8OtzCOPPILNZuPrr78mMjKSQYMG0bhxY3r37p3AN0Xbtm354IMPOHfuHKGhodSpU4c9e/YgIvzxxx9pbjc8PJwjR47QsGFDAObPn5/ktMztiqryzz//UKNGDU+rYsggb775Jm+//TZFihRxi3wRoXfv3pw7d44DBw5Qrlw5goODOXz4cJr81MRRrFgxPv/8cxYvXsxbb73lBo0NmYExLgweRUTo3Lkz//77L+3bt2fz5s2cOnWKO++80+mC+MUXX6RChQqEhYURGxtLo0aNqFOnDgDvvPNOutrNkSMH8+fPZ+PGjSxYsIAWLVpw5MgRlx1XdufUqVMEBASY9RaGVBMSEsLkyZMBh0Ou8ePHU7NmzXTJinPY9sEHH3D27NkEeZcvX2bIkCHG+VZWx91DI1l5M9MiWZfVq1driRIl9P3333cGIFu6dKkOGzZMVVWnTJmiERERzumSjBAdHa0DBgzQyMhI/fXXXzMs71ZgzZo1WrVqVU+rYciG9OzZU2vVqqUffvih+vn5aUxMTJplXLhwQX/88UddsmTJTQEIx40bp4CuW7fOVSrfdmD8XBjj4nbm8OHDWr16dW3Tpo1eu3btpvyxY8dqhQoV9OLFiy5pb+XKlZo3b16dMWOGS+RlZ2bPnq3NmjXztBqGbMiVK1c0MDBQd+/erZGRkbpv3z6Xyv/rr7+0X79+GV57dTuTGcaFmRYxZFkKFizIihUrEBGaNWvGxo0bE+R37NiRYsWK3RQjIb3UqVOH2bNn88ILL7Bnzx6XyMyunD59Gj8/P0+rYciG5MyZk8aNG9O4cWN8fHycfmpSYseOHcTGxiZIO3HixE3latasyfvvv5/ht1wM7sWcHUOWxtfXl0mTJtGiRQuaNWvGiy++6BhywxGEqWfPngwePJinn34am82W4fZq1KjBwIEDadOmDadPn86wvOzKtWvXiIiI8LQahmzKt99+y5QpU1izZg2+vr4plo+KiuLOO+8kR44cCZzl5cuXj6FDh7pTVYObMMaFIcvj6+vrjHi6Zs0apk2b5sy77777OHz4MIcOHWLs2LEuaa9Lly40a9aMhg0bcuXKFZfIzG6cOHGCvHnzeloNQzYlJCSE6tWrU6hQoVSVDw8PZ968eYDDU+f8+fMBxyhFnz59iI6OdpuuBvdgjAtDtiFXrlwMHz6cV199lWvXriVIL1++fLpee0sMEeGjjz7izjvvZMCAAS6Rmd0wxoUhMxERGjVqRHR0NKNHj3a6IZ89ezYAPXv29KR6hnTg42kFDIa0cP/991O8eHGWL1/u9FPx77//8uuvvyYY0cgoIsKIESOoUKEC1atXp23bti6TnR0wHkuzJ6dPn2by5MmsWbOGffv2ERgYSKFChbjzzjspXbo0V65cYdu2bbz88sv4+flx/vx5rly5QmRkJGFhYZ5WH19fX1588UXn94sXLwLw5Zdf8v7777vES68hczDGhSHb0aRJE3777TcaNmzIb7/9RocOHejXr5/LHT5FRkYyd+5c2rRpw8GDB+nVq5dL5WdlgoODOX/+vKfVMCSBzWbjr7/+YteuXRw4cICDBw/y77//snHjRlq0aMEDDzxAp06duHTpEocOHWLr1q3Mnz+fXLlysWDBAoYOHYqXlxehoaH4+/tz8eJFvv76a5o2bUpsbCx+fn5ZYsFkREQELVu2ZNasWYSFhbFv375EQwQYsh7GuDBkO9q3b0/t2rXZvn07//zzD7NmzaJu3bpuaaty5cosX76cu+++m7CwMDp37uyWdrIaIkJMTIyn1TDcwO7duxk3bhzff/89kZGRVKxYkaJFi1K7dm3atm1L7dq1CQ4OTlaGqrJv3z6KFCmCj4/jETB37lwGDhzIww8/jLe3N7lz56ZRo0a0b9+e+++/31kuswkJCWHmzJmcOHGCfPny8dVXXzkDDx44cICAgAAiIyM9opsheUzgMhO4LFuyfv16/v33X5o2bZopXiR37dpFw4YNefrppxk8ePAtPW0QGxtLkSJFmDNnDlWqVPG0Orc9ly5dYtq0aYwdO5adO3fy9NNP8+yzzyaIYnr16lV27dqFt7f3TVtISEiqpzyuX7+Oj48P69ato0WLFpw8edKZt3//fooWLery40sty5Yto1ChQhQvXhyAevXqsXLlSrp3707RokW57777zO81lWRG4DJjXBjjwpBKoqKiaNSoEY899hh9+/b1tDpuY926dXTo0IFt27Z5WpXbnl9//ZVOnTpRs2ZNOnXqRLNmzW56tXPdunW0b98eLy8vvL29iY2NxWazObezZ8/SrFkzfvzxxzRPdfz99980atTI6YL7xufF7t27yZkzJ3a73W2xS5JiyZIlNGjQwPm9T58+fPTRR5mqQ3bFREU1HjoNWYz9+/dreHi4RkVFeVoVt7Ft2zYtWbKkp9W47ZkwYYLmy5dPV61alWi+3W7XXr16af78+fWHH364Kf/69eu6dOlSfffddxXQ48ePp0uPefPmaUREhL7++ut68uRJvXTpkkZFRemGDRuckYUB7dGjh27dujVdbWSE1atXK6CPPfZYgvSzZ8/qiBEjdNWqVXr16tVM1ysrg3H/bYwLQ9bjueee03fffdfTariN69eva2hoaLofRoaMc/z4cQ0LC9Nt27Ylmr9371596KGHtGbNmkkaum+99ZYC2rVrV124cGGCvMuXL6dJn4MHD2r79u01ODhYc+bMqSEhIRoWFqaVKlXSjh07aosWLZxGxvTp09MkOyNMnjzZ2e7p06cT5P3www8JjJ/58+dnml5ZncwwLjy/HNhgyGbcf//97Ny509NquA0fHx9q1qzJ2rVrPa3KbcvBgwcpUKCA09/D1KlTGTRoEM899xwtW7akTp061KpViyVLlhAeHp6ojN69e9OiRQsWLVrEuHHj6NKlCx988AHTp08nV65cztc847jR9XZ8ChcuzIQJE5yvrp47d44tW7bw9ttvs3PnTnbt2kXfvn0RER555BHGjRvnus5IgsmTJ/PEE09QqlQpjh49Su7cuRPkt2/fnoMHD7J9+3Zeeukls/AzkzFrLsyaC0MaGT9+PPPnz+eHH37wtCpuo2nTpoSHhzNx4kRPq3JboaqsWrWKefPm8e6773L+/Hl27txJq1ateO6558ibNy+5c+emQoUKVKhQIVXyli5dysGDB7l8+TK//fYbv//+O6pKjRo1uPPOO4mJieH06dPMmzePtWvXUr162qbi49r44IMP2L17N4cOHcJms9GkSRNGjhxJyZIl09sdybJ371727NnDgw8+6Bb5tzJmzYWZFjFkQX777TctV67cLT2PW79+fQX033//9bQqtw0TJ05MMIzfrVs37dKlixYtWlQHDx6caJ0///xT77nnHm3Xrp3OmjUrVe3YbDaNiYnRWbNm6dixY3XChAk6depU/eCDD7RAgQK6evXqdB/D119/rcHBwc5jGDRokJ4/fz7d8tzBtWvX9MqVK3rw4EEFdPTo0bf0GqrEwKy5MMaFwTPExsbqmTNnEs2z2+3apk0bfeeddzJZq8xj27ZtzgdEYuHuDa6nYcOGCuj777+vgObNm1cHDx6sK1eu1NjY2ETr3HXXXTp06FD96quvNDw8PMMLKqdMmaIFCxbUunXr6ldffaVHjx5Ns4zNmzcnWIMB6KZNmzKklyvp27evAvrMM89oUFCQU8cff/zR06plGsa4MMaFwUMMGzZMAV2xYkWi+f/++6+Gh4frnj17MlmzzMFutyd4OBw5csTTKnmMc+fO6f79+3X9+vX6+eef68yZM/XUqVPO/DNnzjjzdu3aleH27Ha7zpo1Sy9evJhsuVWrVmmNGjW0W7duqqo6ZMgQbdeuXYbbj4mJ0ZkzZ+rjjz+uoaGhWrlyZe3bt6/++eefeuHCBd28ebNOnDhRR40aleyi3z179uikSZP0qaeeUkDbtGmjMTExGdYvo6xZs0Y7dOigGzduVFXVrVu36uDBg3Xv3r0e1izzMMaFMS4MHmLevHkp/pv5/PPPNXfu3Prll19momaZR+/evTUgIEABfeKJJzytTqZis9l0zZo12qJFCw0MDNTChQtrsWLF9LHHHtMHH3xQg4KCtHjx4tqlS5cERliePHmSNEhdTffu3Z0jHBMnTtQqVapo8+bNXdrG9evXddmyZdqvXz+tWrWq+vv7a+nSpfWxxx7Tp556SgsXLpzi6EafPn2c/VOvXj09ePCgS3VMjldeeUWBJEd+blcyw7gwCzrNgk5DEkRFRREWFpas46EffviBn376iVmzZmWiZplDTEwMQUFBxMTE8O233/Lcc895WiWXc+bMGXbt2sWuXbvYvXt3gv0CBQrw0ksv0bVrV/z9/RPUs9ls7Nixgzlz5rBmzRrWrFnDt99+y5UrV+jQoQMbNmygRIkSbtU9NjaWjz76iN9//53w8HDatWvHo48+So4cOdzabnzeeOMNrl+/zueff55suXHjxlGhQgVmzpzJ4MGD6d69O++++y4hISFu1W/48OEcPHgwRf1uN4yHTjdjjAtDRpk8eTI///wzU6dO9bQqbuF///sfXbt2dbqFvhW4evUqP/zwA1988QUHDhygTJkylC5dOsFWqlSpFGN0JEXZsmXZuXMnNpstSwT/cifbtm2jefPm7Nu3L1Xlr127xjvvvMOhQ4eYOnUqOXPmpE6dOjz//PM8+uijGe6v2NhYxo8fj4+PD4ULF+b++++na9eujBo1KkNybzUyw7i4Ne4WBoOH2Llz503v199KvPjii4wcOZIFCxbQpEkTT6uTIVSVSZMm0bNnT2rWrMnnn3/Offfd53IDoHr16uTLl++Wjj8TR9myZYmKiuL06dNERESkWN7f35+hQ4cCMHHiRM6ePcuSJUsYOnQonTp1Il++fAQFBdG3b18ee+yxNOly8uRJSpUqRYUKFYiIiGDXrl0A1K5dO+0HZsg47p53ycqbWXNhyAjz5s3TPHny3LKLOuP46aeftEqVKmqz2TytSoaYM2eOFitWTP/66y+3tWG327VMmTKZtu4iK/DSSy9plSpVMtSvdrtdz5w5ozt27NARI0ZoUFBQmmX8/fffCuhzzz2njz32mJYrV04bNWpk1lskAsZDp8GQNfnnn3948MEHeeONN9zmJCir8Mgjj+Dt7c3PP//saVUyxJQpU5yjFu5i0qRJzqH+24VRo0bx+uuv06JFC8aOHZusp8+kEBHCwsIoWrQo77333k3eQ1ND5cqVWbhwIbVq1aJ169Z89913zJ49G29v7zTLMmQcs+bCrLkwpIOjR4/St29f5s2bx7fffkuzZs08rZJbGTVqFIsWLWL69OmeViVdxMTEEBkZyY4dO8ifP79b2li7di3NmjVj7ty5VKtWzS1tZGU2b97M888/z99//01AQAB58uRxhnuvVq0avXv3TtJVeRynTp0iT548AAQGBpIrVy5iY2MpXbo0NWrUoFu3bpQuXTozDueWJjPWXJiRC4MhHezdu5dy5cqRM2dODhw44Gl13M4zzzzD9u3bGTJkiKdVSRf79u0jMjLSbYbF5MmTadasGY888gjVq1enZ8+ebmknK1OhQgVWr17NpUuX2L9/P7/++itffvklPXv2ZOvWrXTu3BmbzZasjMjISOx2O3a7nSNHjrB+/Xq2bt3KRx99RGhoKPXq1WPQoEHczn+Ksw3unnfJyptZc2FID9OnT9egoCB94403dMaMGWq32z2tUqZw5MgRveOOO/Szzz7ztCppZtu2bVqmTBm3yF6/fr1GREQ4faMAunjxYre0lV2JiorSgIAA3bx5c4bkHDt2TKtVq6Zt2rTRdevWpcuDqOE2WXMhIoVFZLGIbBeRrSLS3UoPF5H5IrLb+gyLV+djEVknIvda34uJiIrIK/HKjBSRZzL9gAy3PEWLFsXPz4/+/fvTunXr2+KtAIACBQqwcOFCPvvsM0aPHu1pddJEbGysW/w/XL16lSeffJLPP//cGcF06NCh3HfffS5vK7uyevVq6tevz8MPP0y5cuUyJCtfvnwsXryYKlWq0KFDB+644w5+++03F2lqcCUeNy6AWOB1Vb0TqA10E5FywJvAQlUtBSy0viMiZa169YFu8eScBLqLiG+maW64LalWrRrVq1dn/vz5nlYl0ylSpAgLFixg0KBB2eqmHhMT4xY/HbNnz6ZQoUK0a9eOQoUKcfjwYXr16uXydrIjsbGx9OjRgzZt2tC/f3/Gjx+f4dd+L1++zIYNGyhbtiw9evQgf/78nDlzJtk606ZN47333mPDhg0ZatuQNjzu50JVjwHHrP2LIrIdKAi0Au6zin0PLAH6AN6AHcfwY/y/jKeAFUBH4OtMUN1wG9OmTRtGjx7Nww8/fNuMXMRRsmRJpk6dSps2bVi5ciV33HGHp1VKFeqGefpDhw45RywAChYs6PI2siuDBw9m48aNbNmyhaCgIC5cuMCFCxdYu3Ythw8f5ty5cxw7doxDhw5x5coVQkNDyZ07N4ULF6Zp06aULVuWK1eu0LJlS5o0acJff/3F8uXLueuuuyhYsCDBwcH07t2b9u3bJ6mDqvLSSy8RFRXFgAED+O2332jatGkm9sLtS5Z6W0REigF/AncBB1U1NF7eWVUNs/a/AOoCvVR1kVXvV+Ah4HegPPA5jnml725o4wXgBetrcSB1ruUMWZEI4LSnlTCkC3Pusjfm/GVv/FX1Lnc24PGRizhEJBCYDvRQ1QvJ/RtU1VeSSN8nImuAdsnU/Qr4ympznbr5dRyD+zDnL/tizl32xpy/7I2IuN0HQ1ZYc4GI5MBhWExU1ThPPSdEJL+Vnx/HmorU8AGO6ZMscWwGg8FgMNxuePwBLI4him+B7ar6SbysWTjWT2B9zkyNPFXdAWwDWrhST4PBYDAYDKkjK0yL1AOeBjaLyEYrrR/wETBVRDoBB4G0RLF5H0jN0uCv0iDTkPUw5y/7Ys5d9sacv+yN289fllrQaTAYDAaDIfvj8WkRg8FgMBgMtxbGuDAYDAaDweBSso1xISJjReSkiGyJl/axiOwQkX9EZIaIhFrpxUTkqohstLZEfRWLyEARORKvXLMbZMd3MT5DRFrHy98pIm/H+z5dRB52/ZHfuiRxTh+z3MDbRaT6DeX7isgeq+8bx0u/zzpXQ63vrUTklxvrxfv+kIjMcuvB3cKISHcR2WKdpx435L1hueKPSKLufhHZbF1v6+KlFxCRRSIyU0QCRSRURKKsBd+ISB1LbiHre4iInBGRbHMPy6qISJl498CNInJBRHqISCURWWWdr9kiEhyvjrk/egjr2phmPfu2W9fGIOs5uFFE5olIgSTqZtr1l50uzO+AJjekzQfuUtWKwC6gb7y8f1W1srW9lIzcT+OV+w2SdDG+EofjLkQkN3AJqBNPTh2rjCH1fMfN53QL8DAOZ2pOxOES/gkcDtKaAF+KiLeV3QW4B/C2zt1Kbj43F0Qkj/W9Lg5vroY0IiJ3Ac8DNYFKQAsRKWXlFQYa4ViAnRwNrOstvvH4KvAK8A3wlKqeA44Dce4v6+JYpF3X+l4b+EtV7Rk+qNscVd0Zdw8EqgFXgBk4zsWbqlrB+t4LzP0xC/A5MFdVy+K4BrcDH6tqResc/gq8k0z9TLn+so1xoap/AmduSJunqrHW19VAIRc1l5iL8RX817F1cZzASHFQHLiqqsdd1P5tQRLndLuq7kykeCtgsqpGq+o+YA+OBxw4fseK45yJqp4CzotInF/qgjj8qMQ/f+ZGlz7uBFar6hXr2lsKtLHyPgV64zgXaSXumrOT9DX3KeYcupsHcPwxOwCU4T8jfz7wiLVv7o8ewho9qo/DfQOqGqOq51T1QrxiuUj7Nejy6y/bGBep4Dkcrr/jKC4iG0RkqYjck0y9l63hpLFiRV5V1a1AALAc+J9V7m/gLnEERqsLrAJ24rjZmn/C7qcgcCje98NWGjis7ZWAl6put9JWAnVFpAywG4fxWVdEfICKwNpM0frWYwtQX0Ryi0gA0AwoLCItgSOquimF+grME5G/xeGKP46RwBjgJWCCleb8NwyUAH4C4v5tmWvOPTwBTLL2twAtrf3HgMJg7o8epgSOOFrjrOfbNyKSC0BE3heRQ0B7kh65yLTr75YwLkTkLRzRVSdaSceAIqpaBegJ/Bh/vjAe/wNKApWtOsPjMlT1FVWtpqqLrO/RwFagKtaQEI4LqC7mX1RmkJg/eAVQ1T9Utaqqvh4vL87qjrvRrQFqAVWAnap6zc363pJYxtsQHP9k5wKbcFx7b5H8UGwc9VS1KtAURwTk+pbcA6paX1UfUtWLVtkVOAzC4sB+65yJOEIFVMNxTg0uwjIMWuJ4iIDjD1s3EfkbCAJi4sqa+6PH8MHRx/+znm+XsSKGq+pbqloYx3Pw5STqZ9r1l+2NCxHpiMMbZ3u1nHZYQ+dR1v7fwL9A6RvrquoJVbVZ80Zf898we1KsxDEkFaSqZ7H+DWMs88zgMNY/J4tCwNFkysdZ3XWBVdYF448j0q45VxlAVb+1jLn6OKa19uMIArhJRPbjODfrRSRfInWPWp8ncczjJ3nNqepuIAxHQMJVVvLfwLPAPlW95KpjMgCOB856VT0BDm/HqvqgqlbDMZrxbwr1zf3R/RwGDqvqX9b3aTiMjfj8yH9TWAnIzOsvWxsXItIERxyRlqp6JV56ZNxiPxEpAZQC9iZSP3+8r21wDAMmxwrgRRz/1gD+wWGlF8FhtRvcxyzgCRHxsyzpUiRvOW8DCuBY6BnnrXUjjmE/8y8qA8QtjBWRIjgW345X1TyqWkxVi+G4AVa9cY5dRHKJSFDcPvAgKV9zq4Du/HdzWwX0wJxDd/Ak/02JxD/PXsDbQKJv3cXD3B/djHVNHbKme8GxRmZb3KJqi5bAjhvrZvb1l22MCxGZhOPAyojIYXG4BR+JY7huviR85bQ+8I+IbMJh2b2kqmcsOd/If684DrVey/kHaAC8loIaK3HMPa0CsBa0ncQR2t2sWk8jiZ1TEWkjIodxrC6fIyJ/gHOedyoOo2Eu0E1VbUnJtkax/gJOq+p1K3kVjvNnHkwZY7qIbANm4zgPZ5MqaL3i9pv1NS+w3Lou1wBzVHVuCm2twDFiFffanDmHbsBaP9MI+Dle8pMisgvHg+ooMC4FMeb+mDm8Aky0nluVcQTr/Egcr4f/g8No6A6evf6M+2+DwWAwGAwuJduMXBgMBoPBYMgeGOPCYDAYDAaDSzHGhcFgMBgMBpdijAuDwWAwGAwuxRgXBoPBYDAYXIoxLgwGQ5KIiMsdVYlISxF509pvbQWlS6uMJXJD1FyDwZB1MMaFwWDIVFR1lqp+ZH1tDaTZuDAYDFkbY1wYDIYUsaJbfmw56tksIm2t9PusUYRpIrJDRCaKiFh5zay05SIyQkR+tdKfEZGRIlIXhzfBjy0neCXjj0iISITlThwRySkik60gg1OAnPF0e1BEVonIehH5yYp9YDAYPIiPpxUwGAzZgodxeAOsBEQAa0UkLhx3FaA8Di+OK4B6IrIOR5TF+qq6z/LGmgBVXSkis4BfVXUagGWXJEYX4IqqVhSRisB6q3wEDtfUDVX1soj0wRGs8D0XHLPBYEgnxrgwGAyp4W5gkuVy/YSILAVqABeANap6GEBENgLFgEvAXlXdZ9WfBLxwo9A0UB8YAaCq/1hujsERu6IcsMIyTHz5Lw6CwWDwEMa4MBgMqSHJIQUgOt6+Dcd9JbnyyRHLf9O1/jfkJRarQID5qvpkOtszGAxuwKy5MBgMqeFPoK2IeItIJI6RhOSi0u4ASohIMet72yTKXcQRfDCO/UA1a//RG9pvDyAidwEVrfTVOKZh7rDyAkSkdGoOyGAwuA9jXBgMhtQwA0cI7U3AIqD3jSHV46OqV4GuwFwRWQ6cAM4nUnQy0EtENohISWAY0EVEVuJY2xHH/4BAazqkN5Zho6qngGeASVbeaqBsRg7UYDBkHBMV1WAwuAURCVTVS9bbI6OA3ar6qaf1MhgM7seMXBgMBnfxvLXAcysQguPtEYPBcBtgRi4MBoPBYDC4FDNyYTAYDAaDwaUY48JgMBgMBoNLMcaFwWAwGAwGl2KMC4MhA7gqOqeIfCsim6zYGdPi4mNYMT1GiMgeK69qxrVOt44rM1C3vaX/PyKyUkQqxctrIiI7rWN8M156uIjMF5Hd1mdYRo/B04jIXOs8bxWR0SLibaX7icgUqw/+iucfBBHpaPXBbhHp6DHlDYY0YIwLgyFr8JqqVlLVisBB4GUrvSlQytpewOHvwSOoat0MVN8H3Gsd3yDgKwDr4ToKx3GWA56MF4L9TWChqpYCFlrfszuPq2ol4C4gEnjMSu8EnFXVO4BPgSHgMLCAAUAtoCYw4FYwsgy3Psa4MBgsRKSYiGwXka+tf5bzRCSnlVdZRFZb/7xn3HCDf8r6N75FRGpa5e+1In1utBxEBSXaqIWqXrDqCY6In3GvcbUCxquD1UCoiORPx7GFiMh+EfGyvgeIyCERyWGNvnwqIn9ax19DRH62/ikPjifjkvWZZCTUZI5vpaqetb6uBgpZ+zWBPaq6V1VjcDjVahXv2L+39r/HEZ49zYhIUxGZGu/7fSIyO+6YRGSIiPwtIgtEpKZ1bHtFpKVVppiILBNH1NX14ojmioi0seqIiOQXkV0iki+Ffrhg7frgiIMS/zzHHes04AGrTxvjcG9+xuq/+UCT9PSDwZCZGOPCYEhIKWCUqpYHzgGPWOnjgT7WP+/NOP5NxpHL+lffFRhrpb0BdFPVysA9wFVwBvZKFBEZBxzH4WHyCyu5IHAoXrHDVlqaUNXzOLxr3mslPQT8oarXre8xqlofGA3MBLrh+Hf9jIjkTkRkFaAHjtGGEkA96xjei3soJ0Mn4HdrP7njy6uqxyz9jwF5Uj7SRJkP1BaRXNb3tsAUaz8XsERVq+FwRT4YaAS04b/IqieBRqpa1aobF0BtBo7z1Q34GhigqsdFpICI/JaUMiLyhyXzIg5DAuL1g6rG4vBmmhsXnX+DIbMxxoXBkJB9qrrR2v8bKCYiIUCoqi610r/HEVsjjkkAqvonECwioThCj38iIq9adWOtMpWTalhVnwUKANv5LxZHYiMC6XVOMyWe3Cf47wELMMv63AxsVdVjqhoN7AUKJyJrjaoeVlU7sBFHJFRU9R1VnZVIeQBEpAEO46JPXFIixVzqfMfq+7nAQyLiAzTHYUABxFh54Dj2pZbBtRnrmIAcwNcishn4CYdBFccrQF8gWlXjfgdHVbVZMvo0BvIDfsD9VnJS/eD2/jEY3IExLgyGhCQW4TMlbrzZq6p+BHTGMcWxWkRSFe/CCmk+hf9GTA6T8OFeCDiaGlmJMAtoas3jV8MRIySOuOO2k7AP7CTeB2nuJxGpCHwDtFLVKCs5ueM7ETcFZH2eTKmNZJgCPI7jYb5WVS9a6df1P0+CzmO3jKa4Y3oNR2yUSkB1HNMZcRS06uWNm3JKDap6Dcf5iJsCcvaDZQCFAGdw7fk3GDINY1wYDClgTSmcFZF7rKSngaXxirQFEJG7gfOqel5ESqrqZlUdAqwjmWBa1px9XFRPwTFlscPKngV0sMrUtuQfS+dxXMIR8Otz4FfLkMkURKQI8DPwtKruipe1FiglIsVFxBfHiErcyMcsIO7tiI78N9qQHpYAVYHnSThikxpCgGOWwfE0EPeGhw8wDmiHY7SpZ3JCRCQwnrHkAzQj4XmOO9ZHgUWW0fMH8KCIhFnrfB600gyGLE1q/pUZDAbHjX+0iATgmCp4Nl7eWXG8phkMPGel9bCmAGzANqw1BiKyMZGpEQG+F5Fga38T0MXK+w3HQ2gPcOWGdtPDFBxD+/dlUE6iiMh7wLpEpkbewbGG4Etr7WesqlZX1VgReRnHA9MbGKuqW606HwFTRaQTjjdoHiOdqKpNRH7FEUE1ra9zfglMF5HHgMXAZSu9H7BMVZdZa2nWisgcHOslvklkaiQXMEtE/HAc6yIca1wAvgV+EJE9OEYsnrD0PiMig3AYYQDvqeqZNOpvMGQ6JraIwWAwGAwGl2KmRQwGg8FgMLgUY1wYDAaDwWBwKca4MBgMBoPB4FKMcWEwGAwGg8GlmLdFUsmDjZto1OnTafJeozftJJGfgtTUrrm9qVgK9TSZb/GT0nXMHtThpvLxTkRychLN08TSU3fmNIkCydVOTO/E2ki6XDI9mkZ5SZZN5AeZeLkU2rlJr8R7M/VyEuuBNMrSVJ7bZDI18R9NQrnJdESq7xspyUjuWNNR96bSKd2YEvyOUncSNWGlZESndGPQxD5uKJJU5cQqJaZsCldScjfDZE9EYr+fpPrPOmdXT/2hqlnKLbwxLlJJ1OnTrPhrHao3/7CV/35n8W8scT+H+HXi/x4T1HGWI0FlReOV+y/vRnnxdSAReanVLz3HlGQdp64u0iEN8uyqiZazJ9qvatVJpF8V7Df19X914+f9154lL95+/Dz7DeXi17XHKx9fjlMXTaiLXeMdUyK62hOtE092/DQSpsVvL7G0//ogDW3cdEya4Fic/RFv355YH+p/+zfWiZ93U7/eIDsuT2+Ql/hvIfF2bzx2TURewnZTyk+tnOTKJV7Xbk+7ro6dm9McdZPPJ1G9Uic7I3Xj59+cFvdpT7jvPHh7KvNTKccev1wa66ay3LWNoyLIYphpEYPBYDAYDC7FGBcGg8FgMBhcijEuDAaDwWAwuBRjXBgMBoPBYHApxrgwGAwGg8HgUoxxYTAYDAaDwaUY48JgMBgMBoNLMcaFwWAwGAwGl2KMC4PBYDAYDC7FGBcGg8FgMBhcijEuDAaDwWAwuBRjXBgMBoPBYHApxrgwGAwGg8HgUoxxYTAYDAaDwaUY48JgMBgMBoNLMcaFwWAwGAwGl2KMC4PBYDAYDC7FGBcGg8FgMBhcijEuDAaDwWAwuBRRVU/rkC0QkS3ANU/rcYsRAZz2tBK3GKZPXY/pU9dj+tS1+KvqXZ5WIj4+nlYgG3FNVat7WolbCRFZZ/rUtZg+dT2mT12P6VPXIiLrPK3DjZhpEYPBYDAYDC7FGBcGg8FgMBhcijEuUs9XnlbgFsT0qesxfep6TJ+6HtOnriXL9adZ0GkwGAwGg8GlmJELg8FgMBgMLsUYFwaDwWAwGFyKMS4sRKSwiCwWke0islVEulvp4SIyX0R2W59h8er0FZE9IrJTRBp7TvusRzL9+bGI7BCRf0RkhoiExqtj+jMZkurTePlviIiKSES8NNOnyZBcn4rIK1a/bRWRofHSTZ8mQzLXfmURWS0iG0VknYjUjFfH9GkyiIi/iKwRkU1Wn75rpWfd55Oqms2x7iQ/UNXaDwJ2AeWAocCbVvqbwBBrvxywCfADigP/At6ePo6ssiXTnw8CPlb6ENOfGe9T63th4A/gABBh+jRjfQo0ABYAflZeHtOnGe7TeUBTK70ZsMT0aar7VIBAaz8H8BdQOys/n8zIhYWqHlPV9db+RWA7UBBoBXxvFfseaG3ttwImq2q0qu4D9gA1MQBJ96eqzlPVWKvYaqCQtW/6MwWS+Y0CfAr0BuKv0DZ9mgLJ9GkX4CNVjbbyTlpVTJ+mQDJ9qkCwVSwEOGrtmz5NAXVwyfqaw9qULPx8MsZFIohIMaAKDuswr6oeA8dFA+SxihUEDsWrdpj/bvSGeNzQn/F5Dvjd2jf9mQbi96mItASOqOqmG4qZPk0DN/xOSwP3iMhfIrJURGpYxUyfpoEb+rQH8LGIHAKGAX2tYqZPU4GIeIvIRuAkMF9Vs/TzyRgXNyAigcB0oIeqXkiuaCJp5r3eG0iqP0XkLSAWmBiXlEh105+JEL9PcfThW8A7iRVNJM30aSIk8jv1AcJwDD33AqaKiGD6NNUk0qddgNdUtTDwGvBtXNFEqps+vQFVtalqZRyjvTVFJLlYIh7vU2NcxENEcuC4GCaq6s9W8gkRyW/l58dhNYLDEiwcr3oh/hvmM5BkfyIiHYEWQHu1Jggx/ZkqEunTkjjmVDeJyH4c/bZeRPJh+jRVJPE7PQz8bA1HrwHsOIJtmT5NBUn0aUcgbv8n/humN32aBlT1HLAEaEIWfj4Z48LC+lfyLbBdVT+JlzULx0WB9TkzXvoTIuInIsWBUsCazNI3q5NUf4pIE6AP0FJVr8SrYvozBRLrU1XdrKp5VLWYqhbDcVOpqqrHMX2aIslc978A91tlSgO+OKJ4mj5NgWT69Chwr7V/P7Db2jd9mgIiEhn3Zp2I5AQaAjvIws8nExX1P+oBTwObrXktgH7ARziGRDsBB4HHAFR1q4hMBbbhGJrupqq2TNc665JUf47AsYJ5vuMexGpVfcn0Z6pItE9V9bfECps+TRVJ/U7HAmNFZAsQA3S0RtlMn6ZMUn36PPC5iPgA14AXwPxOU0l+4HsR8cYxKDBVVX8VkVVk0eeTcf9tMBgMBoPBpZhpEYPBYDAYDC7FGBcGg8FgMBhcijEuDAaDwWAwuBRjXBgMBoPBYHApxrgwGAwGg8HgUoxxYTAYAIerZnFEVU3XK+oiMlBEJrhar/TIF5HvRGRwOttJd12DweDAGBcGg8FgMBhcijEuDAZDhknvaEdmyTMYDJmLMS4MhlsQESksIj+LyCkRiRKRkVa6l4i8LSIHROSkiIwXkZAkZBQQkVkickZE9ojI8/HyBorINBGZICIXgGesLH8RmSIiF0VkvYhUilfnTRH518rbJiJt4uU9IyIrRORTETkDDEzFMf4kIsdF5LyI/Cki5W8oEiEi8632lopI0Xh1y1p5Z0Rkp4g8nnKvGgyG1GKMC4PhFsNyEfwrcAAohiPU8mQr+xlrawCUAAKBkUmImoQjVkkB4FHgAxF5IF5+K2AaEMp/0W1b4QhKFQ78CPxiBbEC+Be4BwgB3gUmxAVdsqgF7MURNvr9VBzq7zhiJuQB1sfTIY72wCAcAcc2xuWLSC5gvqVfHuBJ4MtEjBODwZBOjHFhMNx61MRhEPRS1cuqek1Vl1t57YFPVHWvql4C+uIIcJRgGkJECgN3A32s+huBb3DEjIhjlar+oqp2Vb1qpf2tqtNU9TrwCeCPI2w5qvqTqh61yk/BEbiqZjx5R1X1C1WNjScvSVR1rKpeVNVoHCMdlW4YhZmjqn9a+W8BdazjagHsV9VxVlvrcUTwfDSlNg0GQ+owxoXBcOtRGDigqrGJ5BXAMaIRxwEcAQzzJlLujKpevKFswXjfDyUi35mmqnb+G/lARDqIyEYROSci54C7cIwqJCcvUUTEW0Q+sqZZLgD7raxE5VmG1BlLl6JArTg9LF3aA/lS277BYEges2jKYLj1OAQUERGfRAyMozgernEUwRE18QRQ6IZy4SISFM/AKAIciVcmsaiHheN2RMTLknnUWu/wNfAAjhEPmxUxU1KQlxTtcEzBNMRhWIQAZ2+QF1+XQBxTNUdx9M9SVW2UhvYMBkMaMCMXBsOtxxrgGPCRiOQSEX8RqWflTQJeE5Hi1gP3A2DKjUaIqh4CVgIfWvUrAp24eV3DjVQTkYetaZYeQDSwGsiFw3g4BSAiz+IYuUgvQZbsKCDAOo4baSYid4uIL461F39Zx/UrUFpEnhaRHNZWQ0TuzIA+BoMhHsa4MBhuMVTVBjwE3AEcxDE10dbKHgv8APwJ7AOuAa8kIepJHAtCjwIzgAGqOj+F5mdabZ3FsT7jYVW9rqrbgOHAKhyjJBWAFek4vDjG45imOQJsw2HA3MiPwAAc0yHVcEx9YI3EPAg8YR3bcWAI4JcBfQwGQzxENS0jkQaDwWAwGAzJY0YuDAaDwWAwuBRjXBgMBoPBYHApxrgwGAwGg8HgUoxxYTAYDAaDwaUY48JgMBgMBoNLMcaFwWAwGAwGl2KMC4PBYDAYDC7FGBcGg8FgMBhcyv8BIftPfz6TTeYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots import CreatePlot, CreateFigure\n", - "from emcpy.plots.map_tools import Domain, MapProjection\n", - "from emcpy.plots.map_plots import MapScatter\n", - "\n", - "lats = np.linspace(35, 50, 30)\n", - "lons = np.linspace(-70, -120, 30)\n", - "data = np.linspace(200, 300, 30)\n", - "\n", - "# Create scatter plot on CONUS domian\n", - "scatter = MapScatter(lats, lons, data)\n", - "# change colormap and markersize\n", - "scatter.cmap = 'Blues'\n", - "scatter.markersize = 25\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [scatter]\n", - "plot1.projection = 'plcarr'\n", - "plot1.domain = 'conus'\n", - "plot1.add_map_features(['coastline', 'states'])\n", - "plot1.add_xlabel(xlabel='longitude')\n", - "plot1.add_ylabel(ylabel='latitude')\n", - "plot1.add_title(label='EMCPy Map', loc='center',\n", - " fontsize=20)\n", - "plot1.add_colorbar(label='colorbar label',\n", - " fontsize=12, extend='neither')\n", - "\n", - "# annotate some stats\n", - "stats_dict = {\n", - " 'nobs': len(np.linspace(200, 300, 30)),\n", - " 'vmin': 200,\n", - " 'vmax': 300,\n", - "}\n", - "plot1.add_stats_dict(stats_dict=stats_dict, yloc=-0.175)\n", - "\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from emcpy.plots import CreatePlot, CreateFigure\n", - "from emcpy.plots.map_tools import Domain, MapProjection\n", - "\n", - "# Create global map with no data using\n", - "# PlateCarree projection and coastlines\n", - "plot1 = CreatePlot()\n", - "plot1.projection = 'plcarr'\n", - "plot1.domain = 'global'\n", - "plot1.add_map_features(['coastline', 'land', 'ocean'])\n", - "plot1.add_xlabel(xlabel='longitude')\n", - "plot1.add_ylabel(ylabel='latitude')\n", - "\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots import CreatePlot, CreateFigure\n", - "from emcpy.plots.map_tools import Domain, MapProjection\n", - "from emcpy.plots.map_plots import MapGridded\n", - "\n", - "# Create 2d gridded plot on global domian\n", - "lats = np.linspace(25, 50, 25)\n", - "lons = np.linspace(245, 290, 45)\n", - "X, Y = np.meshgrid(lons, lats)\n", - "Z = np.random.normal(size=X.shape)\n", - "\n", - "# Create gridded map object\n", - "gridded = MapGridded(X, Y, Z)\n", - "gridded.cmap = 'plasma'\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [gridded]\n", - "plot1.projection = 'plcarr'\n", - "plot1.domain = 'conus'\n", - "plot1.add_map_features(['coastline'])\n", - "plot1.add_xlabel(xlabel='longitude')\n", - "plot1.add_ylabel(ylabel='latitude')\n", - "plot1.add_title(label='2D Gridded Data', loc='center')\n", - "plot1.add_grid()\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Scatter\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "\n", - "# Create test data\n", - "x = np.random.normal(size=1000)\n", - "y = x * 10 + np.random.normal(size=1000)\n", - "\n", - "# Create Scatter object\n", - "sctr = Scatter(x, y)\n", - "# Add density scatter feature in object\n", - "sctr.density_scatter()\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [sctr]\n", - "plot1.add_title(label='Test Density Scatter Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "plot1.add_legend()\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots.plots import Scatter\n", - "from emcpy.plots.create_plots import CreatePlot, CreateFigure\n", - "from emcpy.stats import get_linear_regression\n", - "\n", - "# Create test data\n", - "rng = np.random.RandomState(0)\n", - "x = rng.randn(100)\n", - "y = rng.randn(100)\n", - "\n", - "# Create Scatter object\n", - "sctr1 = Scatter(x, y)\n", - "# Add linear regression feature in scatter object\n", - "sctr1.do_linear_regression = True\n", - "sctr1.add_linear_regression()\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [sctr1]\n", - "plot1.add_title(label='Test Scatter Plot')\n", - "plot1.add_xlabel(xlabel='X Axis Label')\n", - "plot1.add_ylabel(ylabel='Y Axis Label')\n", - "plot1.add_legend()\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from emcpy.plots import CreatePlot, CreateFigure\n", - "from emcpy.plots.map_tools import Domain, MapProjection\n", - "from emcpy.plots.map_plots import MapGridded\n", - "\n", - "# Create 2d gridded plot on global domian\n", - "lats = np.linspace(25, 50, 25)\n", - "lons = np.linspace(245, 290, 45)\n", - "X, Y = np.meshgrid(lons, lats)\n", - "Z = np.random.normal(size=X.shape)\n", - "\n", - "# Create gridded map object\n", - "gridded = MapGridded(X, Y, Z)\n", - "gridded.cmap = 'plasma'\n", - "\n", - "# Create plot object and add features\n", - "plot1 = CreatePlot()\n", - "plot1.plot_layers = [gridded]\n", - "plot1.projection = 'plcarr'\n", - "plot1.domain = 'conus'\n", - "plot1.add_map_features(['coastline'])\n", - "plot1.add_xlabel(xlabel='longitude')\n", - "plot1.add_ylabel(ylabel='latitude')\n", - "plot1.add_title(label='2D Gridded Data', loc='center')\n", - "plot1.add_colorbar(label='colorbar label',\n", - " fontsize=12, extend='neither')\n", - "\n", - "# Create figure\n", - "fig = CreateFigure()\n", - "fig.plot_list = [plot1]\n", - "fig.create_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/galleries/plot_types/README.txt b/galleries/plot_types/README.txt index c7e3ed68..65c59018 100644 --- a/galleries/plot_types/README.txt +++ b/galleries/plot_types/README.txt @@ -1,20 +1,6 @@ -## Plots - -The plotting section of EMCPy is the most mature and is used as the backend plotting for (eva)[https://github.com/JCSDA-internal/eva]. It uses declarative, object-oriented programming approach to handle complex plotting routines under the hood to simplify the experience for novice users while remaining robust so more experienced users can utilize higher-level applications. - -### Design -The design was inspired by Unidata's (MetPy)[https://github.com/Unidata/MetPy] declarative plotting syntax. The structure is broken into three different levels: plot type level, plot level, figure level - -#### Plot Type Level -This is the level where users will define their plot type objects and associated plot details. This includes adding the related data the user wants to plot and how the user wants to display the data i.e: color, line style, marker style, labels, etc. - -#### Plot Level -This level is where users design how they want the overall subplot to look. Users can add multiple plot type objects and define titles, x and y labels, colorbars, legends, etc. - -#### Figure Level -This level where users defines high-level specifics about the actual figure itself. These include figure size, layout, defining information about subplot layouts like rows and columns, saving the figure, etc. +.. _plot-types: Plot Types ---------- -Here is a collection of the current plot types that are currently available using EMCPy. \ No newline at end of file +Here is a collection of the plot types that are currently available using EMCPy. diff --git a/galleries/plot_types/basic/README.txt b/galleries/plot_types/basic/README.txt new file mode 100644 index 00000000..02ad2572 --- /dev/null +++ b/galleries/plot_types/basic/README.txt @@ -0,0 +1,4 @@ +.. _basic: + +Basic +===== diff --git a/galleries/plot_types/basic/bar.py b/galleries/plot_types/basic/bar.py new file mode 100644 index 00000000..6c77aa6b --- /dev/null +++ b/galleries/plot_types/basic/bar.py @@ -0,0 +1,53 @@ +""" +Bar Plot +-------- + +Below is an example of how to plot a bar +plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import BarPlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create bar plot + + # Grab sample bar plot data + x_pos, heights = _getBarData() + + # Create bar plot object + bar = BarPlot(x_pos, heights) + bar.color = 'tab:red' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [bar] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title("Bar Plot") + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getBarData(): + # Generate test data for bar graphs + + x = ['a', 'b', 'c', 'd', 'e', 'f'] + heights = [5, 6, 15, 22, 24, 8] + + x_pos = [i for i, _ in enumerate(x)] + + return x_pos, heights + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/basic/horizontal_bar.py b/galleries/plot_types/basic/horizontal_bar.py new file mode 100644 index 00000000..aabeecfd --- /dev/null +++ b/galleries/plot_types/basic/horizontal_bar.py @@ -0,0 +1,54 @@ +""" +Horizontal Bar Plot +------------------- + +Below is an example of how to plot a horizontal +bar plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import HorizontalBar +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create horizontal bar plot + + # Grab sample bar plot data + y_pos, widths= _getBarData() + + # Create horizontal bar plot object + bar = HorizontalBar(y_pos, widths) + bar.color = 'tab:green' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [bar] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title("Horizontal Bar Plot") + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getBarData(): + # Generate test data for bar graphs + + x = ['a', 'b', 'c', 'd', 'e', 'f'] + heights = [5, 6, 15, 22, 24, 8] + + x_pos = [i for i, _ in enumerate(x)] + + return x_pos, heights + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/basic/horizontal_line.py b/galleries/plot_types/basic/horizontal_line.py new file mode 100644 index 00000000..e1f88428 --- /dev/null +++ b/galleries/plot_types/basic/horizontal_line.py @@ -0,0 +1,45 @@ +""" +Horizontal Line Plot +-------------------- + +Below is an example of how to plot a horizontal +line using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import HorizontalLine +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + + y = 5 + + # Create vertical line plot object + hlp = HorizontalLine(y) + hlp.label = 'Horizontal Line' + + # Add vertical line plot object to list + plt_list = [hlp] + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [hlp] + plot1.add_title('Horizontal Line Plot') + plot1.add_xlabel('X Axis Label') + plot1.add_ylabel('Y Axis Label') + plot1.add_legend(loc='upper right') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/basic/line.py b/galleries/plot_types/basic/line.py index e041cc9c..bf055e87 100644 --- a/galleries/plot_types/basic/line.py +++ b/galleries/plot_types/basic/line.py @@ -1,6 +1,6 @@ """ -Creating a simple line plot ---------------------------- +Line Plot +--------- Below is an example of how to plot a basic line plot using EMCPy's plotting method. @@ -29,12 +29,12 @@ def main(): # Create plot object and add features plot1 = CreatePlot() plot1.plot_layers = [lp] - plot1.add_title('Test Line Plot') + plot1.add_title('Line Plot') plot1.add_xlabel('X Axis Label') plot1.add_ylabel('Y Axis Label') plot1.add_legend(loc='upper right') - # Create figure and save as png + # Create figure fig = CreateFigure() fig.plot_list = [plot1] fig.create_figure() diff --git a/galleries/plot_types/basic/scatter.py b/galleries/plot_types/basic/scatter.py new file mode 100644 index 00000000..f5bce87c --- /dev/null +++ b/galleries/plot_types/basic/scatter.py @@ -0,0 +1,52 @@ +""" +Scatter Plot +------------ + +Below is an example of how to plot a basic +scatter plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import Scatter +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + + # Create scatter plot object + x1, y1, x2, y2 = _getScatterData() + sctr1 = Scatter(x1, y1) + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [sctr1] + plot1.add_title(label='Scatter Plot') + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + +def _getScatterData(): + # Generate test data for scatter plots + + rng = np.random.RandomState(0) + x1 = rng.randn(100) + y1 = rng.randn(100) + + rng = np.random.RandomState(0) + x2 = rng.randn(30) + y2 = rng.randn(30) + + return x1, y1, x2, y2 + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/basic/vertical_line.py b/galleries/plot_types/basic/vertical_line.py new file mode 100644 index 00000000..2562e333 --- /dev/null +++ b/galleries/plot_types/basic/vertical_line.py @@ -0,0 +1,45 @@ +""" +Vertical Line Plot +------------------ + +Below is an example of how to plot a vertical +line using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import VerticalLine +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + + x = 5 + + # Create vertical line plot object + vlp = VerticalLine(x) + vlp.label = 'Vertical Line' + + # Add vertical line plot object to list + plt_list = [vlp] + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [vlp] + plot1.add_title('Vertical Line Plot') + plot1.add_xlabel('X Axis Label') + plot1.add_ylabel('Y Axis Label') + plot1.add_legend(loc='upper right') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/gridded/README.txt b/galleries/plot_types/gridded/README.txt new file mode 100644 index 00000000..9be9c8ad --- /dev/null +++ b/galleries/plot_types/gridded/README.txt @@ -0,0 +1,4 @@ +.. _gridded + +Gridded +======= diff --git a/galleries/plot_types/gridded/contour.py b/galleries/plot_types/gridded/contour.py new file mode 100644 index 00000000..7e9f6d0c --- /dev/null +++ b/galleries/plot_types/gridded/contour.py @@ -0,0 +1,55 @@ +""" +Contour +------- + +Below is an example of how to plot a contour +plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import ContourPlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + +def main(): + # Create contourf plot + + # Grab sample data + x, y, z = _getContourData() + + # Create contour plot object + cp = ContourPlot(x, y, z) + cp.linestyles = '--' + cp.colors = 'green' + + # Create plot and add features + plot1 = CreatePlot() + plot1.plot_layers = [cp] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title('Contour Plot') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getContourData(): + # Generate test data for contour plots + + x = np.linspace(-3, 15, 50).reshape(1, -1) + y = np.linspace(-3, 15, 20).reshape(-1, 1) + z = np.cos(x)*2 - np.sin(y)*2 + + x, y = x.flatten(), y.flatten() + + return x, y, z + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/gridded/contourf.py b/galleries/plot_types/gridded/contourf.py new file mode 100644 index 00000000..9a28618b --- /dev/null +++ b/galleries/plot_types/gridded/contourf.py @@ -0,0 +1,55 @@ +""" +Filled Contour +-------------- + +Below is an example of how to plot a filled +contour plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import FilledContourPlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + +def main(): + # Create contourf plot + + # Grab sample data + x, y, z = _getContourfData() + + # Create filled contour plot object + cfp = FilledContourPlot(x, y, z) + cfp.cmap = 'Greens' + + # Create plot and add features + plot1 = CreatePlot() + plot1.plot_layers = [cfp] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title('Filled Contour Plot') + plot1.add_colorbar(orientation='vertical') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getContourfData(): + # Generate test data for contourf plots + + x = np.linspace(-3, 15, 50).reshape(1, -1) + y = np.linspace(-3, 15, 20).reshape(-1, 1) + z = np.cos(x)*2 - np.sin(y)*2 + + x, y = x.flatten(), y.flatten() + + return x, y, z + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/gridded/gridded.py b/galleries/plot_types/gridded/gridded.py new file mode 100644 index 00000000..bcfea42e --- /dev/null +++ b/galleries/plot_types/gridded/gridded.py @@ -0,0 +1,56 @@ +""" +Gridded +------- + +Below is an example of how to plot a gridded +plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt +from scipy.ndimage import gaussian_filter + +from emcpy.plots.plots import GriddedPlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create gridded plot + + # Grab sample data + x, y, z = _getGriddedData() + + # Create gridded object + gp = GriddedPlot(x, y, z) + gp.cmap = 'plasma' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [gp] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title('Gridded Plot') + plot1.add_colorbar(orientation='vertical') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getGriddedData(): + # Generate test data for gridded data + + x = np.linspace(0, 1, 51) + y = np.linspace(0, 1, 51) + r = np.random.RandomState(25) + z = gaussian_filter(r.random_sample([50, 50]), sigma=5, mode='wrap') + + return x, y, z + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/map/README.txt b/galleries/plot_types/map/README.txt new file mode 100644 index 00000000..7d1a2266 --- /dev/null +++ b/galleries/plot_types/map/README.txt @@ -0,0 +1,4 @@ +.. _map_plots: + +Map Plots +========= diff --git a/galleries/plot_types/map/map_gridded.py b/galleries/plot_types/map/map_gridded.py new file mode 100644 index 00000000..198b47d2 --- /dev/null +++ b/galleries/plot_types/map/map_gridded.py @@ -0,0 +1,51 @@ +""" +Gridded Map Plot +---------------- + +Below is an example of how to plot +gridded data on a map plot using EMCPy's +plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt +from emcpy.plots import CreatePlot, CreateFigure +from emcpy.plots.map_tools import Domain, MapProjection +from emcpy.plots.map_plots import MapGridded + + +def main(): + # Create 2d gridded plot on global domian + lats = np.linspace(25, 50, 25) + lons = np.linspace(245, 290, 45) + X, Y = np.meshgrid(lats, lons) + Z = np.random.normal(size=X.shape) + + # Create gridded map object + gridded = MapGridded(X, Y, Z) + gridded.cmap = 'plasma' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [gridded] + plot1.projection = 'plcarr' + plot1.domain = 'conus' + plot1.add_map_features(['coastline']) + plot1.add_xlabel(xlabel='longitude') + plot1.add_ylabel(ylabel='latitude') + plot1.add_title(label='2D Gridded Data', loc='center') + plot1.add_grid() + plot1.add_colorbar(label='colorbar label', + fontsize=12, extend='neither') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/map/map_scatter.py b/galleries/plot_types/map/map_scatter.py new file mode 100644 index 00000000..67cd3439 --- /dev/null +++ b/galleries/plot_types/map/map_scatter.py @@ -0,0 +1,60 @@ +""" +Scatter Map Plot +---------------- + +Below is an example of how to plot +scatter data on a map plot using EMCPy's +plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots import CreatePlot, CreateFigure +from emcpy.plots.map_tools import Domain, MapProjection +from emcpy.plots.map_plots import MapScatter + + +def main(): + # Create test data + lats = np.linspace(35, 50, 30) + lons = np.linspace(-70, -120, 30) + data = np.linspace(200, 300, 30) + + # Create scatter plot on CONUS domian + scatter = MapScatter(lats, lons, data) + # change colormap and markersize + scatter.cmap = 'Blues' + scatter.markersize = 25 + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [scatter] + plot1.projection = 'plcarr' + plot1.domain = 'conus' + plot1.add_map_features(['coastline', 'states']) + plot1.add_xlabel(xlabel='longitude') + plot1.add_ylabel(ylabel='latitude') + plot1.add_title(label='EMCPy Map', loc='center', + fontsize=20) + plot1.add_colorbar(label='colorbar label', + fontsize=12, extend='neither') + + # annotate some stats + stats_dict = { + 'nobs': len(np.linspace(200, 300, 30)), + 'vmin': 200, + 'vmax': 300, + } + plot1.add_stats_dict(stats_dict=stats_dict, yloc=-0.175) + + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/map/map_scatter_2D.py b/galleries/plot_types/map/map_scatter_2D.py new file mode 100644 index 00000000..8ffdf3de --- /dev/null +++ b/galleries/plot_types/map/map_scatter_2D.py @@ -0,0 +1,49 @@ +""" +2D Scatter Map Plot +------------------- + +Below is an example of how to plot +2D data on a map plot using EMCPy's +plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots import CreatePlot, CreateFigure +from emcpy.plots.map_tools import Domain, MapProjection +from emcpy.plots.map_plots import MapScatter + + +def main(): + # Create test data + lats = np.linspace(35, 50, 30) + lons = np.linspace(-70, -120, 30) + + # Create scatter plot on CONUS domian + scatter = MapScatter(lats, lons) + # change colormap and markersize + scatter.color = 'tab:red' + scatter.markersize = 25 + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [scatter] + plot1.projection = 'plcarr' + plot1.domain = 'conus' + plot1.add_map_features(['coastline', 'states']) + plot1.add_xlabel(xlabel='longitude') + plot1.add_ylabel(ylabel='latitude') + plot1.add_title(label='EMCPy Map', loc='center', + fontsize=20) + + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/statistical/README.txt b/galleries/plot_types/statistical/README.txt new file mode 100644 index 00000000..131ae9d4 --- /dev/null +++ b/galleries/plot_types/statistical/README.txt @@ -0,0 +1,4 @@ +.. _statistical_distributions + +Statistical distributions +========================= diff --git a/galleries/plot_types/statistical/boxplot.py b/galleries/plot_types/statistical/boxplot.py new file mode 100644 index 00000000..61659b0f --- /dev/null +++ b/galleries/plot_types/statistical/boxplot.py @@ -0,0 +1,55 @@ +""" +Box Plot +-------- + +Below is an example of how to plot a box +plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import BoxandWhiskerPlot +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create box and whisker plot + + # Grab sample data + data = _getBoxPlotData() + + # Create box plot object + bwp = BoxandWhiskerPlot(data) + bwp.label = 'Box Plot data' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [bwp] + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_title('Test Box and Whisker Plot') + plot1.add_legend(loc='upper left') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getBoxPlotData(): + # Generate test data for box and whisker plot + + # Fixing random state for reproducibility + np.random.seed(19680801) + + data = [np.random.normal(0, std, 100) for std in range(6, 10)] + + return data + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/statistical/density.py b/galleries/plot_types/statistical/density.py new file mode 100644 index 00000000..994ee6be --- /dev/null +++ b/galleries/plot_types/statistical/density.py @@ -0,0 +1,53 @@ +""" +Density +------- + +Below is an example of how to plot a density +histogram plot using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import Density +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Test density plot + + # Grab sample data + data = _getHistData() + + # Create density object + den1 = Density(data) + den1.label = 'Density' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [den1] + plot1.add_title(label='Density Plot') + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getHistData(): + # Generate test data for histogram plots + + mu = 100 # mean of distribution + sigma = 15 # standard deviation of distribution + data = mu + sigma * np.random.randn(437) + + return data + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/statistical/density_scatter.py b/galleries/plot_types/statistical/density_scatter.py new file mode 100644 index 00000000..d67f1f2d --- /dev/null +++ b/galleries/plot_types/statistical/density_scatter.py @@ -0,0 +1,43 @@ +""" +Density Scatter Plot +-------------------- + +The following example shows how to create +a density scatter plot. +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import Scatter +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create test data + x = np.random.normal(size=1000) + y = x * 10 + np.random.normal(size=1000) + + # Create Scatter object + sctr1 = Scatter(x, y) + # Add density scatter feature in object + sctr1.density_scatter() + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [sctr1] + plot1.add_title(label='Density Scatter Plot') + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_legend() + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/galleries/plot_types/statistical/histogram.py b/galleries/plot_types/statistical/histogram.py new file mode 100644 index 00000000..b4bbc8c8 --- /dev/null +++ b/galleries/plot_types/statistical/histogram.py @@ -0,0 +1,55 @@ +""" +Histogram +--------- + +Below is an example of how to plot a histogram +using EMCPy's plotting method. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from emcpy.plots.plots import Histogram +from emcpy.plots.create_plots import CreatePlot, CreateFigure + + +def main(): + # Create histogram plot + + # Grab sample data + data = _getHistData() + + # Create histogram object + hst1 = Histogram(data) + hst1.color = 'tab:green' + hst1.label = 'data' + + # Create plot object and add features + plot1 = CreatePlot() + plot1.plot_layers = [hst1] + plot1.add_title(label='Histogram Plot') + plot1.add_xlabel(xlabel='X Axis Label') + plot1.add_ylabel(ylabel='Y Axis Label') + plot1.add_legend(loc='upper right') + + # Create figure + fig = CreateFigure() + fig.plot_list = [plot1] + fig.create_figure() + + plt.show() + + +def _getHistData(): + # Generate test data for histogram plots + + mu = 100 # mean of distribution + sigma = 15 # standard deviation of distribution + data = mu + sigma * np.random.randn(437) + + return data + + +if __name__ == '__main__': + main() diff --git a/src/emcpy/plots/plots.py b/src/emcpy/plots/plots.py index 713097b9..91f681be 100644 --- a/src/emcpy/plots/plots.py +++ b/src/emcpy/plots/plots.py @@ -203,6 +203,7 @@ def __init__(self, x, y, z): self.extent = None self.locator = None self.extend = None + self.levels = None self.colorbar = False From d82ff9abb56842fc9baa3176ce2f8dee93d0f7fc Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Mon, 1 Jul 2024 19:49:42 +0000 Subject: [PATCH 07/15] pycodestyle --- galleries/plot_types/basic/bar.py | 1 + galleries/plot_types/basic/horizontal_bar.py | 2 +- galleries/plot_types/basic/scatter.py | 1 + galleries/plot_types/gridded/contour.py | 1 + galleries/plot_types/gridded/contourf.py | 1 + 5 files changed, 5 insertions(+), 1 deletion(-) diff --git a/galleries/plot_types/basic/bar.py b/galleries/plot_types/basic/bar.py index 6c77aa6b..e3d007b6 100644 --- a/galleries/plot_types/basic/bar.py +++ b/galleries/plot_types/basic/bar.py @@ -49,5 +49,6 @@ def _getBarData(): return x_pos, heights + if __name__ == '__main__': main() diff --git a/galleries/plot_types/basic/horizontal_bar.py b/galleries/plot_types/basic/horizontal_bar.py index aabeecfd..10e03c6e 100644 --- a/galleries/plot_types/basic/horizontal_bar.py +++ b/galleries/plot_types/basic/horizontal_bar.py @@ -18,7 +18,7 @@ def main(): # Create horizontal bar plot # Grab sample bar plot data - y_pos, widths= _getBarData() + y_pos, widths = _getBarData() # Create horizontal bar plot object bar = HorizontalBar(y_pos, widths) diff --git a/galleries/plot_types/basic/scatter.py b/galleries/plot_types/basic/scatter.py index f5bce87c..fa4aa2b2 100644 --- a/galleries/plot_types/basic/scatter.py +++ b/galleries/plot_types/basic/scatter.py @@ -34,6 +34,7 @@ def main(): plt.show() + def _getScatterData(): # Generate test data for scatter plots diff --git a/galleries/plot_types/gridded/contour.py b/galleries/plot_types/gridded/contour.py index 7e9f6d0c..c4be8e66 100644 --- a/galleries/plot_types/gridded/contour.py +++ b/galleries/plot_types/gridded/contour.py @@ -13,6 +13,7 @@ from emcpy.plots.plots import ContourPlot from emcpy.plots.create_plots import CreatePlot, CreateFigure + def main(): # Create contourf plot diff --git a/galleries/plot_types/gridded/contourf.py b/galleries/plot_types/gridded/contourf.py index 9a28618b..6dae3ffe 100644 --- a/galleries/plot_types/gridded/contourf.py +++ b/galleries/plot_types/gridded/contourf.py @@ -13,6 +13,7 @@ from emcpy.plots.plots import FilledContourPlot from emcpy.plots.create_plots import CreatePlot, CreateFigure + def main(): # Create contourf plot From 52344e59f137b14051fa54d66038411cb10ad0b9 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Mon, 1 Jul 2024 20:10:22 +0000 Subject: [PATCH 08/15] update plots markdown --- docs/getting_started/plots.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 docs/getting_started/plots.md diff --git a/docs/getting_started/plots.md b/docs/getting_started/plots.md new file mode 100644 index 00000000..ed5efc12 --- /dev/null +++ b/docs/getting_started/plots.md @@ -0,0 +1,18 @@ +## Plots + +The plotting section of EMCPy is the most mature and is used as the backend plotting for [eva](https://github.com/JCSDA-internal/eva). It uses declarative, object-oriented programming approach to handle complex plotting routines under the hood to simplify the experience for novice users while remaining robust so more experienced users can utilize higher-level applications. + +### Design +The design was inspired by Unidata's [MetPy](https://github.com/Unidata/MetPy) declarative plotting syntax. The structure is broken into three different levels: plot type level, plot level, figure level + +#### Plot Type Level +This is the level where users will define their plot type objects and associated plot details. This includes adding the related data the user wants to plot and how the user wants to display the data i.e: color, line style, marker style, labels, etc. + +#### Plot Level +This level is where users design how they want the overall subplot to look. Users can add multiple plot type objects and define titles, x and y labels, colorbars, legends, etc. + +#### Figure Level +This level where users defines high-level specifics about the actual figure itself. These include figure size, layout, defining information about subplot layouts like rows and columns, saving the figure, etc. + + +For the current available plot types in EMCPy, see [Plot Types](../plot_types/index.rst). From 6a7ceb7e63921040d133879598bd178517689ff2 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Tue, 9 Jul 2024 15:44:14 +0000 Subject: [PATCH 09/15] histograms/histogram.py --- galleries/examples/line_plots/line_plot.py | 46 -------------- galleries/examples/map_plots/map_gridded.py | 49 --------------- galleries/examples/map_plots/map_scatter.py | 60 ------------------- .../examples/scatter_plots/density_scatter.py | 43 ------------- galleries/examples/scatter_plots/scatter.py | 42 ------------- 5 files changed, 240 deletions(-) delete mode 100644 galleries/examples/line_plots/line_plot.py delete mode 100644 galleries/examples/map_plots/map_gridded.py delete mode 100644 galleries/examples/map_plots/map_scatter.py delete mode 100644 galleries/examples/scatter_plots/density_scatter.py delete mode 100644 galleries/examples/scatter_plots/scatter.py diff --git a/galleries/examples/line_plots/line_plot.py b/galleries/examples/line_plots/line_plot.py deleted file mode 100644 index e041cc9c..00000000 --- a/galleries/examples/line_plots/line_plot.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Creating a simple line plot ---------------------------- - -Below is an example of how to plot a basic -line plot using EMCPy's plotting method. - -""" - -import numpy as np -import matplotlib.pyplot as plt - -from emcpy.plots.plots import LinePlot -from emcpy.plots.create_plots import CreatePlot, CreateFigure - - -def main(): - - x = [1, 2, 3, 4, 5] - y = [1, 2, 3, 4, 5] - - # Create line plot object - lp = LinePlot(x, y) - lp.label = 'line' - - # Add line plot object to list - plt_list = [lp] - - # Create plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [lp] - plot1.add_title('Test Line Plot') - plot1.add_xlabel('X Axis Label') - plot1.add_ylabel('Y Axis Label') - plot1.add_legend(loc='upper right') - - # Create figure and save as png - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() diff --git a/galleries/examples/map_plots/map_gridded.py b/galleries/examples/map_plots/map_gridded.py deleted file mode 100644 index 920ebfc1..00000000 --- a/galleries/examples/map_plots/map_gridded.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Create a map plot with gridded data ------------------------------------ - -The following example plots gridded data over -a CONUS domain. -""" - -import numpy as np -import matplotlib.pyplot as plt -from emcpy.plots import CreatePlot, CreateFigure -from emcpy.plots.map_tools import Domain, MapProjection -from emcpy.plots.map_plots import MapGridded - - -def main(): - # Create 2d gridded plot on global domian - lats = np.linspace(25, 50, 25) - lons = np.linspace(245, 290, 45) - X, Y = np.meshgrid(lats, lons) - Z = np.random.normal(size=X.shape) - - # Create gridded map object - gridded = MapGridded(X, Y, Z) - gridded.cmap = 'plasma' - - # Create plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [gridded] - plot1.projection = 'plcarr' - plot1.domain = 'conus' - plot1.add_map_features(['coastline']) - plot1.add_xlabel(xlabel='longitude') - plot1.add_ylabel(ylabel='latitude') - plot1.add_title(label='2D Gridded Data', loc='center') - plot1.add_grid() - plot1.add_colorbar(label='colorbar label', - fontsize=12, extend='neither') - - # Create figure - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() diff --git a/galleries/examples/map_plots/map_scatter.py b/galleries/examples/map_plots/map_scatter.py deleted file mode 100644 index 1b0ff5e3..00000000 --- a/galleries/examples/map_plots/map_scatter.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Creating a map plot with scatter data -------------------------------------- - -The following example plots scatter data -on a map plot over a CONUS domain. This -example also shows how to annotate stats -on the plot. -""" - -import numpy as np -import matplotlib.pyplot as plt - -from emcpy.plots import CreatePlot, CreateFigure -from emcpy.plots.map_tools import Domain, MapProjection -from emcpy.plots.map_plots import MapScatter - - -def main(): - # Create test data - lats = np.linspace(35, 50, 30) - lons = np.linspace(-70, -120, 30) - data = np.linspace(200, 300, 30) - - # Create scatter plot on CONUS domian - scatter = MapScatter(lats, lons, data) - # change colormap and markersize - scatter.cmap = 'Blues' - scatter.markersize = 25 - - # Create plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [scatter] - plot1.projection = 'plcarr' - plot1.domain = 'conus' - plot1.add_map_features(['coastline', 'states']) - plot1.add_xlabel(xlabel='longitude') - plot1.add_ylabel(ylabel='latitude') - plot1.add_title(label='EMCPy Map', loc='center', - fontsize=20) - plot1.add_colorbar(label='colorbar label', - fontsize=12, extend='neither') - - # annotate some stats - stats_dict = { - 'nobs': len(np.linspace(200, 300, 30)), - 'vmin': 200, - 'vmax': 300, - } - plot1.add_stats_dict(stats_dict=stats_dict, yloc=-0.175) - - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() diff --git a/galleries/examples/scatter_plots/density_scatter.py b/galleries/examples/scatter_plots/density_scatter.py deleted file mode 100644 index 146ed706..00000000 --- a/galleries/examples/scatter_plots/density_scatter.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Creating a Density Scatter Plot -------------------------------- - -The following example shows how to create -a density scatter plot. -""" - -import numpy as np -import matplotlib.pyplot as plt - -from emcpy.plots.plots import Scatter -from emcpy.plots.create_plots import CreatePlot, CreateFigure - - -def main(): - # Create test data - x = np.random.normal(size=1000) - y = x * 10 + np.random.normal(size=1000) - - # Create Scatter object - sctr1 = Scatter(x, y) - # Add density scatter feature in object - sctr1.density_scatter() - - # Create plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [sctr1] - plot1.add_title(label='Test Density Scatter Plot') - plot1.add_xlabel(xlabel='X Axis Label') - plot1.add_ylabel(ylabel='Y Axis Label') - plot1.add_legend() - - # Create figure - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() diff --git a/galleries/examples/scatter_plots/scatter.py b/galleries/examples/scatter_plots/scatter.py deleted file mode 100644 index d5ca4e70..00000000 --- a/galleries/examples/scatter_plots/scatter.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Creating a simple scatter plot ------------------------------- - -Below is an example of how to plot a basic -scatter plot using EMCPy's plotting method. - -""" - -import numpy as np -import matplotlib.pyplot as plt - -from emcpy.plots.plots import Scatter -from emcpy.plots.create_plots import CreatePlot, CreateFigure - - -def main(): - # Create test data - rng = np.random.RandomState(0) - x = rng.randn(100) - y = rng.randn(100) - - # Create Scatter object - sctr1 = Scatter(x, y) - - # Create plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [sctr1] - plot1.add_title(label='Test Scatter Plot') - plot1.add_xlabel(xlabel='X Axis Label') - plot1.add_ylabel(ylabel='Y Axis Label') - - # Create figure - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() From 953dc6ba44f73c1f58fb919ff61e4ed6205de0c7 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Tue, 9 Jul 2024 17:11:26 +0000 Subject: [PATCH 10/15] update order and remove examples --- docs/conf.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 10ad5d93..51749559 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,7 +17,7 @@ sys.path.insert(0, str(Path(__file__).parent.resolve())) import matplotlib -# from sphinx_gallery +from sphinx_gallery.sorting import ExplicitOrder import emcpy @@ -58,6 +58,18 @@ gd = gd.replace('gallery', 'examples') example_dirs += [f'../galleries/{gd}'] +# Sphinx gallery configuration +subsection_order = ExplicitOrder([ + '../plot_types/basic', + '../plot_types/statistical', + '../plot_types/gridded', + '../plot_types/map', + '../examples/line_plots', + '../examples/scatter_plots', + '../examples/histograms', + '../examples/map_plots' +]) + sphinx_gallery_conf = { 'capture_repr': (), 'filename_pattern': '^((?!skip_).)*$', From dc54a4c1404612c819109c3099b0529d1a9f2257 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Tue, 9 Jul 2024 17:13:23 +0000 Subject: [PATCH 11/15] add subsection order --- docs/conf.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 51749559..ff15942a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -76,8 +76,7 @@ 'examples_dirs': ['../galleries/examples', '../galleries/plot_types'], 'gallery_dirs': ['examples', 'plot_types'], # path to where to save gallery generated output 'backreferences_dir': '../build/backrefs', - # 'subsection_order': gallery_order_sectionorder, - # 'within_subsection_order': gallery_order_subsectionorder, + 'subsection_order': subsection_order, 'matplotlib_animations': True } From 367bb91270f62f036f840c7ab189b9b8fac7744d Mon Sep 17 00:00:00 2001 From: Kevin Dougherty Date: Tue, 9 Jul 2024 17:20:42 +0000 Subject: [PATCH 12/15] one last fix --- docs/conf.py | 16 ++++---- galleries/examples/histograms/histogram.py | 46 ---------------------- 2 files changed, 8 insertions(+), 54 deletions(-) delete mode 100644 galleries/examples/histograms/histogram.py diff --git a/docs/conf.py b/docs/conf.py index ff15942a..0c3c7814 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -60,14 +60,14 @@ # Sphinx gallery configuration subsection_order = ExplicitOrder([ - '../plot_types/basic', - '../plot_types/statistical', - '../plot_types/gridded', - '../plot_types/map', - '../examples/line_plots', - '../examples/scatter_plots', - '../examples/histograms', - '../examples/map_plots' + '../galleries/plot_types/basic', + '../galleries/plot_types/statistical', + '../galleries/plot_types/gridded', + '../galleries/plot_types/map', + '../galleries/examples/line_plots', + '../galleries/examples/scatter_plots', + '../galleries/examples/histograms', + '../galleries/examples/map_plots' ]) sphinx_gallery_conf = { diff --git a/galleries/examples/histograms/histogram.py b/galleries/examples/histograms/histogram.py deleted file mode 100644 index d345dbee..00000000 --- a/galleries/examples/histograms/histogram.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Creating a simple histogram ---------------------------- - -Below is an example of how to plot a basic -histogram plot using EMCPy's plotting method. - -""" - -import numpy as np -import matplotlib.pyplot as plt - -from emcpy.plots.plots import Histogram -from emcpy.plots.create_plots import CreatePlot, CreateFigure - - -def main(): - # Generate test data for histogram plots - mu = 100 # mean of distribution - sigma = 15 # standard deviation of distribution - data = mu + sigma * np.random.randn(437) - - # Create histogram object - hst = Histogram(data) - hst.color = 'tab:green' - hst.alpha = 0.7 - hst.label = 'data' - - # Create histogram plot object and add features - plot1 = CreatePlot() - plot1.plot_layers = [hst] - plot1.add_title(label='Test Histogram Plot') - plot1.add_xlabel(xlabel='X Axis Label') - plot1.add_ylabel(ylabel='Y Axis Label') - plot1.add_legend() - - # Create figure and save as png - fig = CreateFigure() - fig.plot_list = [plot1] - fig.create_figure() - - plt.show() - - -if __name__ == '__main__': - main() From e6ed1ea936fb827024ca9b4d01987d117c7a25e8 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty <69815622+kevindougherty-noaa@users.noreply.github.com> Date: Tue, 9 Jul 2024 13:22:09 -0400 Subject: [PATCH 13/15] Delete galleries/examples/line_plots/Untitled.ipynb --- galleries/examples/line_plots/Untitled.ipynb | 207 ------------------- 1 file changed, 207 deletions(-) delete mode 100644 galleries/examples/line_plots/Untitled.ipynb diff --git a/galleries/examples/line_plots/Untitled.ipynb b/galleries/examples/line_plots/Untitled.ipynb deleted file mode 100644 index 2faf0e18..00000000 --- a/galleries/examples/line_plots/Untitled.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Sample data\n", - "x = [1, 2, 3, 4]\n", - "y1 = [1, 4, 9, 16]\n", - "y2 = [1, 2, 3, 4]\n", - "\n", - "fig, ax = plt.subplots()\n", - "line1, = ax.plot(x, y1, 'o', label='Quadratic')\n", - "line2, = ax.plot(x, y2, 's', label='Linear')\n", - "\n", - "# Create legend\n", - "legend = ax.legend()\n", - "\n", - "# Adjust the size of the legend handles\n", - "for handle in legend.legend_handles:\n", - " print(handle.__dict__)\n", - "# handle._sizes = [20] # Change the size to your desired value\n", - "\n", - "# for i, key in enumerate(leg.legend_handles):\n", - "# print(\n", - "# leg.legendHandles[i]._sizes = [20]\n", - "\n", - "# plt.show()\n", - "\n", - "# legend.__dict__()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8bb8fbdd-9961-4883-9003-cec3011faadd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'_stale': False,\n", - " 'stale_callback': ,\n", - " '_axes': ,\n", - " 'figure':
,\n", - " '_transform': ,\n", - " '_transformSet': False,\n", - " '_visible': True,\n", - " '_animated': False,\n", - " '_alpha': None,\n", - " 'clipbox': None,\n", - " '_clippath': None,\n", - " '_clipon': True,\n", - " '_label': '',\n", - " '_picker': None,\n", - " '_rasterized': False,\n", - " '_agg_filter': None,\n", - " '_mouseover': False,\n", - " '_callbacks': ,\n", - " '_remove_method': >,\n", - " '_url': None,\n", - " '_gid': None,\n", - " '_snap': None,\n", - " '_sketch': None,\n", - " '_path_effects': [],\n", - " '_sticky_edges': _XYPair(x=[], y=[]),\n", - " '_in_layout': True,\n", - " 'prop': ,\n", - " '_fontsize': 10.0,\n", - " 'texts': [Text(0, 0, 'Quadratic'), Text(0, 0, 'Linear')],\n", - " 'legend_handles': [,\n", - " ],\n", - " '_legend_title_box': ,\n", - " '_custom_handler_map': None,\n", - " 'numpoints': 1,\n", - " 'markerscale': 1.0,\n", - " 'scatterpoints': 1,\n", - " 'borderpad': 0.4,\n", - " 'labelspacing': 0.5,\n", - " 'handlelength': 2.0,\n", - " 'handleheight': 0.7,\n", - " 'handletextpad': 0.8,\n", - " 'borderaxespad': 0.5,\n", - " 'columnspacing': 2.0,\n", - " 'shadow': False,\n", - " '_ncols': 1,\n", - " '_scatteryoffsets': array([0.375]),\n", - " '_legend_box': ,\n", - " 'isaxes': True,\n", - " 'parent': ,\n", - " '_mode': None,\n", - " '_bbox_to_anchor': None,\n", - " '_shadow_props': {'ox': 2, 'oy': -2},\n", - " 'legendPatch': ,\n", - " '_alignment': 'center',\n", - " '_legend_handle_box': ,\n", - " '_loc_used_default': True,\n", - " '_outside_loc': None,\n", - " '_loc_real': 0,\n", - " '_draggable': None}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "legend.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fee8ddf6-c752-4cb7-a970-cccbc86ffe45", - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'matplotlib' has no attribute 'subplots'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m y1 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m9\u001b[39m, \u001b[38;5;241m16\u001b[39m]\n\u001b[1;32m 6\u001b[0m y2 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplots\u001b[49m()\n\u001b[1;32m 9\u001b[0m line1, \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mplot(x, y1, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQuadratic\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m line2, \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mplot(x, y2, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLinear\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/scratch1/NCEPDEV/da/Kevin.Dougherty/pyenv/EMCPy/lib/python3.10/site-packages/matplotlib/_api/__init__.py:217\u001b[0m, in \u001b[0;36mcaching_module_getattr..__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[0;32m--> 217\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'subplots'" - ] - } - ], - "source": [ - "import matplotlib as plt\n", - "\n", - "# Sample data\n", - "x = [1, 2, 3, 4]\n", - "y1 = [1, 4, 9, 16]\n", - "y2 = [1, 2, 3, 4]\n", - "\n", - "fig, ax = plt.subplots()\n", - "line1, = ax.plot(x, y1, 'o', label='Quadratic')\n", - "line2, = ax.plot(x, y2, 's', label='Linear')\n", - "\n", - "# Create legend\n", - "legend = ax.legend()\n", - "\n", - "# Adjust the size of the legend handles\n", - "for handle in legend.legendHandles:\n", - " handle._sizes = [50] # Change the size to your desired value\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bdcd6e2d-4229-4963-bc25-6193290f35b0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 63081d4e22367b92f084194add1c693ad6aa7567 Mon Sep 17 00:00:00 2001 From: Kevin Dougherty <69815622+kevindougherty-noaa@users.noreply.github.com> Date: Tue, 9 Jul 2024 13:23:35 -0400 Subject: [PATCH 14/15] Delete docs/sphinxext/gallery_order.py --- docs/sphinxext/gallery_order.py | 88 --------------------------------- 1 file changed, 88 deletions(-) delete mode 100644 docs/sphinxext/gallery_order.py diff --git a/docs/sphinxext/gallery_order.py b/docs/sphinxext/gallery_order.py deleted file mode 100644 index 399b7cd5..00000000 --- a/docs/sphinxext/gallery_order.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -Configuration for the order of gallery sections and examples. -Paths are relative to the conf.py file. -""" - -from sphinx_gallery.sorting import ExplicitOrder - -# Gallery sections shall be displayed in the following order. -# Non-matching sections are inserted at the unsorted position - -UNSORTED = "unsorted" - -# Sphinx gallery configuration -examples_order = [ - '../galleries/examples/line_plots', - '../galleries/examples/scatter_plots', - '../galleries/examples/histograms', - '../galleries/examples/map_plots', - UNSORTED -] - -plot_types_order = [ - '../galleries/plot_types/basic', - UNSORTED -] - -folder_lists = [examples_order, plot_types_order] - -explicit_order_folders = [fd for folders in folder_lists - for fd in folders[:folders.index(UNSORTED)]] -explicit_order_folders.append(UNSORTED) -explicit_order_folders.extend([fd for folders in folder_lists - for fd in folders[folders.index(UNSORTED):]]) - - -class MplExplicitOrder(ExplicitOrder): - """For use within the 'subsection_order' key.""" - def __call__(self, item): - """Return a string determining the sort order.""" - if item in self.ordered_list: - return f"{self.ordered_list.index(item):04d}" - else: - return f"{self.ordered_list.index(UNSORTED):04d}{item}" - - -# Subsection order: -# Subsections are ordered by filename, unless they appear in the following -# lists in which case the list order determines the order within the section. - -list_all = [ - # **Examples** - # line - "line_plot", "line_plot_options", "multi_line_plot", - "inverted_log_scale", "SkewT" - # scatter - "scatter", "scatter_with_regression_line", - "density_scatter" - # histograms - "histogram", "layered_histogram" - # map plots - "map_plot_no_data", "custom_map_domain", "map_scatter_2D", - "map_scatter", "map_gridded" - - # **Plot Types** - # Basic - "line" -] -explicit_subsection_order = [item + ".py" for item in list_all] - - -class MplExplicitSubOrder(ExplicitOrder): - """For use within the 'within_subsection_order' key.""" - def __init__(self, src_dir): - self.src_dir = src_dir # src_dir is unused here - self.ordered_list = explicit_subsection_order - - def __call__(self, item): - """Return a string determining the sort order.""" - if item in self.ordered_list: - return f"{self.ordered_list.index(item):04d}" - else: - # ensure not explicitly listed items come last. - return "zzz" + item - - -# Provide the above classes for use in conf.py -sectionorder = MplExplicitOrder(explicit_order_folders) -subsectionorder = MplExplicitSubOrder From 1a0572a0256fbf972f92e07e4a5d18dd9ce1c4cf Mon Sep 17 00:00:00 2001 From: Kevin Dougherty <69815622+kevindougherty-noaa@users.noreply.github.com> Date: Tue, 9 Jul 2024 13:24:03 -0400 Subject: [PATCH 15/15] Delete docs/sphinxext/__init__.py --- docs/sphinxext/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docs/sphinxext/__init__.py diff --git a/docs/sphinxext/__init__.py b/docs/sphinxext/__init__.py deleted file mode 100644 index e69de29b..00000000