{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many different python packages that can be useful for geophysical analysis. These include reading data from files (e.g., pandas), data handling routines (e.g., xarray), plotting (matplotlib) and others. We will go through several example cases in this section. \n", "\n", "Most of our plotting will be done using the Matlab equivalent “matplotlib” package. This is typically imported in scripts, specifically the pyplot routines, as “plt”. In the examples below we will look at plotting data using matplotlib, reading data from a from a file using pandas, and then creating our own data arrays with numpy.\n", "\n", "Matplotlib is the most popular plotting library in python. Using matplotlib, you can create pretty much any type of plot and the syntax is much like Matlab (so it should be easy going back and forth between the two).\n", "\n", "The standard way to import matplotlib is by just loading the pyplot object. Here we will alias this to simply plot.\n", "\n", "To plot a series of numbers, with the independent (x-axis) and dependent variables (y-axis) already defined, use the plot function. Note that if the independent variable is not supplied, the x-axis will be the list position. For example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "plt.plot(x,y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# change color or line\n", "# add three character sequence in quotes\n", "# first is color, linestyle, third is symbol\n", "#\n", "# e.g., red = 'r'\n", "#plt.plot(x,y,'k')\n", "# e.g., dotted = '.'\n", "#plt.plot(x,y,'rs')\n", "# line style\n", "plt.plot(x,y,'r*:')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# can also use key word arguments (aka kwargs)\n", "plt.plot(x,y,color='cyan',\n", " linestyle='dashed',\n", " marker='o',linewidth='4',\n", " markersize='6');" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plt.bar(x,y,facecolor='red',\n", "# edgecolor='black')\n", "plt.bone();" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Annotation',\n", " 'Arrow',\n", " 'Artist',\n", " 'AutoLocator',\n", " 'Axes',\n", " 'Button',\n", " 'Circle',\n", " 'Figure',\n", " 'FigureCanvasBase',\n", " 'FixedFormatter',\n", " 'FixedLocator',\n", " 'FormatStrFormatter',\n", " 'Formatter',\n", " 'FuncFormatter',\n", " 'GridSpec',\n", " 'IndexLocator',\n", " 'Line2D',\n", " 'LinearLocator',\n", " 'Locator',\n", " 'LogFormatter',\n", " 'LogFormatterExponent',\n", " 'LogFormatterMathtext',\n", " 'LogLocator',\n", " 'MaxNLocator',\n", " 'MouseButton',\n", " 'MultipleLocator',\n", " 'Normalize',\n", " 'NullFormatter',\n", " 'NullLocator',\n", " 'Number',\n", " 'PolarAxes',\n", " 'Polygon',\n", " 'Rectangle',\n", " 'ScalarFormatter',\n", " 'Slider',\n", " 'Subplot',\n", " 'SubplotSpec',\n", " 'Text',\n", " 'TickHelper',\n", " 'Widget',\n", " '_INSTALL_FIG_OBSERVER',\n", " '_IP_REGISTERED',\n", " '_IoffContext',\n", " '_IonContext',\n", " '__builtins__',\n", " '__cached__',\n", " '__doc__',\n", " '__file__',\n", " '__loader__',\n", " '__name__',\n", " '__package__',\n", " '__spec__',\n", " '_api',\n", " '_auto_draw_if_interactive',\n", " '_backend_mod',\n", " '_copy_docstring_and_deprecators',\n", " '_get_required_interactive_framework',\n", " '_interactive_bk',\n", " '_log',\n", " '_pylab_helpers',\n", " '_setup_pyplot_info_docstrings',\n", " '_warn_if_gui_out_of_main_thread',\n", " '_xkcd',\n", " 'acorr',\n", " 'angle_spectrum',\n", " 'annotate',\n", " 'arrow',\n", " 'autoscale',\n", " 'autumn',\n", " 'axes',\n", " 'axhline',\n", " 'axhspan',\n", " 'axis',\n", " 'axline',\n", " 'axvline',\n", " 'axvspan',\n", " 'bar',\n", " 'bar_label',\n", " 'barbs',\n", " 'barh',\n", " 'bone',\n", " 'box',\n", " 'boxplot',\n", " 'broken_barh',\n", " 'cbook',\n", " 'cla',\n", " 'clabel',\n", " 'clf',\n", " 'clim',\n", " 'close',\n", " 'cm',\n", " 'cohere',\n", " 'colorbar',\n", " 'colormaps',\n", " 'connect',\n", " 'contour',\n", " 'contourf',\n", " 'cool',\n", " 'copper',\n", " 'csd',\n", " 'cycler',\n", " 'delaxes',\n", " 'disconnect',\n", " 'docstring',\n", " 'draw',\n", " 'draw_all',\n", " 'draw_if_interactive',\n", " 'errorbar',\n", " 'eventplot',\n", " 'figaspect',\n", " 'figimage',\n", " 'figlegend',\n", " 'fignum_exists',\n", " 'figtext',\n", " 'figure',\n", " 'fill',\n", " 'fill_between',\n", " 'fill_betweenx',\n", " 'findobj',\n", " 'flag',\n", " 'functools',\n", " 'gca',\n", " 'gcf',\n", " 'gci',\n", " 'get',\n", " 'get_backend',\n", " 'get_cmap',\n", " 'get_current_fig_manager',\n", " 'get_figlabels',\n", " 'get_fignums',\n", " 'get_plot_commands',\n", " 'get_scale_names',\n", " 'getp',\n", " 'ginput',\n", " 'gray',\n", " 'grid',\n", " 'hexbin',\n", " 'hist',\n", " 'hist2d',\n", " 'hlines',\n", " 'hot',\n", " 'hsv',\n", " 'importlib',\n", " 'imread',\n", " 'imsave',\n", " 'imshow',\n", " 'inferno',\n", " 'inspect',\n", " 'install_repl_displayhook',\n", " 'interactive',\n", " 'ioff',\n", " 'ion',\n", " 'isinteractive',\n", " 'jet',\n", " 'legend',\n", " 'locator_params',\n", " 'logging',\n", " 'loglog',\n", " 'magma',\n", " 'magnitude_spectrum',\n", " 'margins',\n", " 'matplotlib',\n", " 'matshow',\n", " 'minorticks_off',\n", " 'minorticks_on',\n", " 'mlab',\n", " 'new_figure_manager',\n", " 'nipy_spectral',\n", " 'np',\n", " 'pause',\n", " 'pcolor',\n", " 'pcolormesh',\n", " 'phase_spectrum',\n", " 'pie',\n", " 'pink',\n", " 'plasma',\n", " 'plot',\n", " 'plot_date',\n", " 'plotting',\n", " 'polar',\n", " 'prism',\n", " 'psd',\n", " 'quiver',\n", " 'quiverkey',\n", " 'rc',\n", " 'rcParams',\n", " 'rcParamsDefault',\n", " 'rcParamsOrig',\n", " 'rc_context',\n", " 'rcdefaults',\n", " 'rcsetup',\n", " 're',\n", " 'register_cmap',\n", " 'rgrids',\n", " 'savefig',\n", " 'sca',\n", " 'scatter',\n", " 'sci',\n", " 'semilogx',\n", " 'semilogy',\n", " 'set_cmap',\n", " 'set_loglevel',\n", " 'setp',\n", " 'show',\n", " 'specgram',\n", " 'spring',\n", " 'spy',\n", " 'stackplot',\n", " 'stairs',\n", " 'stem',\n", " 'step',\n", " 'streamplot',\n", " 'style',\n", " 'subplot',\n", " 'subplot2grid',\n", " 'subplot_mosaic',\n", " 'subplot_tool',\n", " 'subplots',\n", " 'subplots_adjust',\n", " 'summer',\n", " 'suptitle',\n", " 'switch_backend',\n", " 'sys',\n", " 'table',\n", " 'text',\n", " 'thetagrids',\n", " 'threading',\n", " 'tick_params',\n", " 'ticklabel_format',\n", " 'tight_layout',\n", " 'time',\n", " 'title',\n", " 'tricontour',\n", " 'tricontourf',\n", " 'tripcolor',\n", " 'triplot',\n", " 'twinx',\n", " 'twiny',\n", " 'uninstall_repl_displayhook',\n", " 'violinplot',\n", " 'viridis',\n", " 'vlines',\n", " 'waitforbuttonpress',\n", " 'winter',\n", " 'xcorr',\n", " 'xkcd',\n", " 'xlabel',\n", " 'xlim',\n", " 'xscale',\n", " 'xticks',\n", " 'ylabel',\n", " 'ylim',\n", " 'yscale',\n", " 'yticks']" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(plt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# put multiple plots on a page\n", "# \"subplot\", number of rows, number of columns\n", "# and plot id number\n", "#\n", "# Note, python starts counting at 0, but not\n", "# matplotlib in subplots" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(2,2,1)\n", "plt.plot(x,y,'r')\n", "plt.subplot(2,2,2)\n", "plt.plot(x,y,'g')\n", "plt.subplot(2,2,3)\n", "plt.bar(x,y)\n", "plt.subplot(2,2,4)\n", "plt.bar(x,y,facecolor='red')\n", "\n", "# add title to panel 2:\n", "plt.subplot(2,2,2)\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'data')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# adding titles and labels\n", "# plt.title: plot title\n", "# plt.xlabel: x-axis\n", "# plt.ylabel: y-axis\n", "plt.plot(x,y)\n", "plt.title('My plot')\n", "plt.xlabel('time',color='green')\n", "plt.ylabel('data')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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WLoV+/WBs7F2QzXG/zHxP4GlpaezYsaMISWtPQnzMcddLRNixYwdpaSculmzGTFd83Md6mWG9zCiiV9Om8NlncPvt3uocg/tl5vuamEeOHGH9+vXH9KvOn01Afis9+437XmlpadSqVYvU1NQ4zmLXLDTDepkRQi8ReOABuPxyaH3C8pIJwP01MX1vA09NTaV+/fpF2PM6gvkPJqheFovlGHbuhEmT9IyCviRw9/E9gRedYX4LRMF6mWG9zLBeZuTjlZOjR1VWqQLz5+tnX3C/zHxvAy86QWw+AetlivUyw3qZcZxXdrZe/uz++3VctapO5r7gfpmFKIHf6bdAFKyXGdbLDOtlxnFeSkFKCpQqpdvAfcX9MgtRE4rFYrEUkSNHYO9e3VwybpyPV93eEqIr8K5+C0TBeplhvcywXmY4XgMGQJcucOhQgJK3+2XmezfCorMfKO+mjktYLzOslxnWywzH65NP4Kef4JZb/BbKQ+xlFq0bYYiuwDv7LRCFzn4LRKGz3wJR6Oy3QBQ6+y0Qhc5+C0Shs98CJ3LgAHzZSr++6KKAJW/wosxClMAtFoulAIYPh24rYdMmv00SRqEJXClVWyn1mVIqXSm1Qil1u7P9ZKXUx0qplc5zZW9Vq3p7+pixXmZYLzOsV5EZORLeag41gtrF0f0yK7QNXClVA6ghIt8rpSoAC4FewABgp4j8Qyl1L1BZRP6voHPF1wZusVgsx7F7Nzz9NPztb5Cc7LeNZ8TcBi4im0Tke+f1PiAdqAlcDuSuLTYRndQ9pLe3p48Z62WG9TLDehXI1KkwejREliYMiFe+uO9m1A9cKVUPaAF8C5wqIptAJ3mlVLUoxwwCBgHUqXMyekIXgKFAI+BmJ+4AjOHoys3l0EsQDUBPhJ4OrETP6PWKs88w9Oim3A7yXYERHL1ZUBWYhS64X5xt76KndXzTiR8A0oDc+YB7Ok6XOHEt4D30/0/rnW0zgeeBaY7XR0Am8JDz/jVAH+AKJ24AvA5cDGx3ts0BRgMfO/GT6Imxxjjx9Y5DHyduDEwAOgEZzrZvnDL40omfd3yedZ7HAe2BG5z32wBjgXZANpCMrsohwHxnn/HAPOdYMK8n0OUbrZ7SgftIfD0BPEL0ekp3Pj/R9QT66xGtnpY7z4muJyj4+zQN/V1OdD3BMd+n/tvhgrOgQSP0v6tcr0TXU1G+T+noa91Y6il/ityNUClV3jnzaBGZopTaLSIn5Xl/l4gU2A4eXxNKCGc/8xXrZYb1MsNHr82b4frr4fnnoUGD494ManmBF7MRFqkXilIqFXgHmCwiuasKb3Hax3PbybfGZFZk3vX29DFjvcywXmZYrxPYuRNWroSNG/N5M6jlBV64FaUXigJeAtJF5Mk8b70P9Hde9wemum53DCFfASThWC8zrJcZPnhlOE0d55yjB+l07JjPTkEtL/BrRZ4L0JNed1FKLXYefwD+AXRVSq1EN5b9w3W7Y3iz8F18wXqZYb3MsF4AbNgAjRvDiy/quFSpKDsGtbzAC7dCb2KKyFwg2mQCF7qrY7FYLPlwyinQqRO0bOm3SaAI0WyED/gtEAXrZYb1MqOEe61cqQfmlC8PEyYU4YCglhd44RaiofTxLvLrFdbLDOtlRgn2ysiAzp1h4ECDg4JaXuCFW4gS+PDCd/EF62WG9TKjBHuVK6dHWT74oMFBQS0v8MItRE0oFoulRDB/vl6Q4fzz4aqr/LYJNCFK4D39FoiC9TLDeplRwrxE9DSwhw/r4fFJpo0EQS0v8MItRAs6bANOcVPHJayXGdbLjBLotWGDXkm+du0YDg5qeUE8bsVgQYdLCt/FF6yXGdbLjBLiNXu2ns9bBGrWjDF5Q3DLC7xwC1ECt1gsxZZZs2DaNNi/32+TUBGiBF7Lb4EoWC8zrJcZxdwrO1s/P/44fPUVVKgQ5wmDWl7ghVuI2sAtFkux4t13dRfBjz6CavnORm1xKAZt4L38FohCL78FotDLb4Eo9PJbIAq9/BaIQi+/BaLQK/5TVKmih8iXLh3/uSL0cvFcbtPL9TOGKIGvL3wXX7BeZlgvM4qh17p1+rljR331XamSO0pAcMsLvHALUQK3WCyhZ/p0vQjDZ5/pWEWbJ89SFEKUwGf6LRAF62WG9TKjmHl16gS33w7t2rmrEyGo5QVeuIUogT/vt0AUrJcZ1suMYuI1fboeHl+hgu5xUrasN1qBLS/wwi1ECXxa4bv4gvUyw3qZUQy8Fi2Cnj3huee804kQ1PICL9xCNBeKxWIJJS1awPvvw8UX+21S7AjRFfgjfgtEwXqZYb3MCKmXiG4qSU/Xcc+ekJrqvVZgywu8cAtRAs/0WyAK1ssM62VGSL22b4ennoLx4xOjEyGo5QVeuIVoJGZrIIijOK2XGdbLjJB5iRztGrh+PZx2WgxTwnrgFQhidysGIzEtFkugycmBoUPhySd1XKtWgpN3ySNENzGv8VsgCtbLDOtlRoi8cnJg61YPuwgWhaCWF3jhFqImlHVArHMEe4n1MsN6mRECr+xsOHhQrxyfna2vun0bYRnU8oJ43IpBE8oVfgtEwXqZYb3MCIHXTTdB9+56GbTkZJ+Hxwe1vMALtxAlcIvF4juTJ0O9epC0UD9Pnqy7CF5+OZQq5bddiSNECbyB3wJRsF5mWC8zAuQ1eTIMGgRr1oCgnwcNgsxMuOcev+0cAlReJ+C+W4jawC0Wi6/Uq6eT9vHUrQu//ZZomxJFMWgDD+owXOtlhvUyI0Bea9eabfeFAJXXCbjvFqIEvt1vgShYLzOslxkB8qpSJf/tdeok1qNAAlReJ+C+W4gSuMVi8ZXOnU8cmFO2LIwe7YuOJVQJfI7fAlGY47dAFOb4LRCFOX4LRGGO3wJRmOPvx//6KyxZol+/8Yae26RuXd1VsG5dGDcO+vb11/EY5vgtUABzXD9jiBJ4UP+Xt15mWC8zfPTKyYErrtD9vEX01ff11+sbljn/p58DlbwhuPUIXrgVmsCVUi8rpbYqpZbn2TZSKbVBKbXYefzBdbMT+Nj7j4gJ62WG9TLDB68jR44m7AkT4O238xmcY8vLHPfdinIFPoH8b58+JSLNnccH7mpZLBZf2LtXrxb/z3/quGVL3X3QEkgKTeAi8gWwMwEuhfCk3wJRsF5mWC8zEuxVoQKcc04RkrYtL3Pcd4unDfxWpdRSp4mlsmtGUdnk/UfEhPUyw3qZkQCvrCx4+GHYskU3lbz0Elx1lf9eMRFUL/DCLdbpZMcCo9ADakcB/wRuzG9HpdQgYBBAnTonoyc1BxgKNAJuduIOwBjgPCcuB3wODACWA+lAS2Am8IqzzzCgBnCnE3cFRgCdnbgqMAvoDfzibHsXeA1404kfANKA4U7c03G6xIlrAe8BvYD1zraZ6BWmpzleldGrbTzkvH8N0Iejk9c0AF5Ht0Tl9gWdg76pkdsu9iS6gsc48fWOQx8nboxuzeoEZDjbvnHK4Esnft7xedZ53gO0B25w3m+Drrp2QDaQDHwLDAHmO/uMB+YB45zYtJ5Al2+0ekoHFpP4egK9pFW0ekp3PjPR9QT66xGtnpY7n+lhPf06Dkb/AJV+haE3ULTv0zTn/ImuJyj4+/SM45XoeirK9ynd+btiqaf8KdJQeqVUPWC6iDQ2ee947Io8icR6mVECvVavhvr19etVq+D00w0OLoHlFTcBWZFHKVUjT3gFR/+r8JDrvf+ImLBeZlgvMzzymjYNzjwTPv1Ux0bJG0pcebmC+26FNqEopV5D/4aqqpRaD/wd6KyUao5uQvkN+IvrZidwSeG7+IL1MsN6meGR14UXwn33Qbt2MZ6ghJWXK7jvFqLZCIP608h6mWG9zHDR68sv4V//gtdec2Hu7hJQXq4TkCYUi8USQrZsgWXLYFOQe2pYTAhRAi/0HqlPWC8zrJcZcXrt2AFffKFfX3WVTuB168avVVzLy1PcdwtRE4rFYjHmmmvgs8/0vCXlyvltY4mRYtCE0slvgShYLzOslxkxeInoBYYBnngCPvjAg+RdjMorYbjvFutAHh/IKHwXX7BeZlgvMwy9cnKgd289JP6ll/RiC54suFBMyiuhuO8WogRusVgKJSkJzj1XX3GL5DOLoKU4EaI28CyC+f+N9TLDeplRBC8RvbBC+/bQtGlCrEJdXr4Ru1sxaAMf5rdAFKyXGdbLjCJ47d0LDz4IY8d6rxMhxOXlG+67BfW/qnz4svBdfMF6mWG9zCjA67ffdJfASpXg668TvLhwCMvLd9x3C9EVuMViibB8uZ63+4UXdFyv3okLDluKPSGq8ef9FoiC9TLDepkRxeucc+Cee6BXr4TaHCVk5RUI3HcLUQJP91sgCtbLDOtlRh6vn3+GHj1g+3Z9tT1yJFSv7r9XoAiqF3jhFqIE/mzhu/iC9TLDepmRx2vfPliyBH791T+dCCEor8DhvluIErjFUgI5lAOzZunXrVrp5B3zFLCW4kaIEvggvwWiYL3MsF5GPHIaXHrp0avu0qX99YkQ0PIKrBd44RaiBN7eb4EoWC8zrFeRyJ3LZNj9MH06nHGGvz4nELDyihBUL/DCLUQJ/IbCd/EF62WG9SqU+++Hiy7Sq8WXvwUuvthvo3wIUHkdQ1C9wAu3EA3ksVhKCI0awe7demIqi6UAQpTA2/gtEAXrZYb1ypeZM3XC7tED/vQn/QB894qK9TLHfbcQTWZlsRRTcnJ0z5Ly5fUq8XYGQctxFIPJrILadcp6mWG9ImzeDJmZelDO1Kl64YUTkrctLzOC6gVeuIUogWf7LRAF62WG9QJg1y5o3hzuvVfHp50GZcrks6MtLzOC6gVeuIWoDTzZb4EoWC8zrBcAlSvr5N2tWyE72vIyI6he4IWbbQO3WBLFtm0waBA88ojuaWKxFJFi0AY+xG+BKFgvM0qw1+HD8P33sGKFwUEluLxiIqhe4IVbiJpQ5vstEAXrZUYJ88rJgRkz9HD4mjX1jIJGw+FLWHnFTVC9wAu3EF2BWywh5I034LLL4OOPdRyYuUwsxYEQXYGP91sgCtbLjBLidfgwlCoF116rk3bXrjGeqISUl2sE1Qu8cAvRFfg8vwWiYL3MKAFeEybo1eF379Z9vK+8Mo7BOSWgvFwlqF7ghVuIEvg4vwWiYL3MKAFeZ58N554LrvTwKgHl5SpB9QIv3EKUwC2WALNoEbz8sn7dvj28847u622xeEiIEvhQvwWiYL3MKKZeTzwBo0bBwYPu6EQopuXlGUH1Ai/cQnQTM6gDH6yXGcXIa/9+PZdJ1arw/PP6xmW+w+ET7JUQrJc57rsVegWulHpZKbVVKbU8z7aTlVIfK6VWOs8J+K14s/cfERPWy4xi4pWdDZ06Qb9+uq27UiU45RT/vRKG9TLHfbeiNKFMAI5fEuReYLaInAnMdmKLpeSQnAx33w333GOnf7X4RqEJXES+AHYet/lyYKLzeiLQy12t/Ojg/UfEhPUyI8ReBw/Cn/8MH36o4z59oEsXb7XCXF6+EFQv8MIt1jbwU0VkE4CIbFJKVYu2o1JqEM5yzHXqnAzkzscyFN0mlPuzogMwBjjPicsBnwMDgOWAACuBmcArzj7DgBrAnU7cFRgBdHbiqsAsoDfwi7PtXeA14E0nfgBIA4Y7cU/H6RInrgW8h/4/ar2zbSbwPDDN8foIyAQect6/BugDXOHEDYDX0T9ktjvb5gCjAWeEHk8Cm5wyALjecejjxI3RP4Y6ARnOtm+cMvjSiZ8H0oFnHa9x6IVUc9fiawOMRc9LnI2eHe1b9BwNucN8x6P7q+Z2eTKtJ9DlG62eBLiPxNcTwCNErydxPr+AepIcmL8PzjwA3Uc478dbT6C/HtHqKct5TnQ9QcHfp5Xo73Ki6wkK/j594Xi5/X2CguupKN8nQV/vxlJP+VOk2QiVUvWA6SLS2Il3i8hJed7fJSKFtoPHNxthayCIMxlaLzNC6DVzJlx4oR5ZmZkJaWnB8PIV62VO7G5uz0a4RSlVwzlxDWBrjOexWILNokXwhz/A2LE6TmjytlgKJtYE/j7Q33ndH5jqjk5BlPP+I2LCepkREq8jR/Rzixbw7rtws1+9G0JSXoEhqF7ghVuhTShKqdfQjWBVgS3A39GNWG8CdYC1wNUicvyNzhOwCzpYQsFnn8GAATB7NjRo4LeNxRJ7E4qI9BGRGiKSKiK1ROQlEdkhIheKyJnOc6HJO34GeP8RMTHAb4EoDPBbIAoD/BY4lsmToV49SFL6efJkOOMMaNhQdxX0nQF+C0RhgN8CURjgt0ABDHD9jCEaibm88F18wXqZESCvyZP1EmcHDuh4zRodjxt3tKug7wSovI7BepnjvluI5kKxWFxmxIijyTuXAwf0doslBIQogUfvC+kv1suMgHiJwNq1+b8XbbsvBKS8TsB6meO+W4gS+Ey/BaJgvcwIiFdWll5sIT/q1EmsS4EEpLxOwHqZ475biBL4K4Xv4gvWywwfvX75Be66S09ElZoKjz0GZcseu0/ZsjB6tD9++WLr0YygeoEXbiFK4BZLnCxZAi+8AEuX6viuu/QNy7p1QaGfx42Dvn191bRYikqIEvgwvwWiYL3MSKDXkSMwciRMdOZdu/JKWL1aD87JpW9f+O03yHldPwcuedt6NCOoXuCFW4gSeA2/BaJgvcxIoFdKCnz6Kcx3JhZSqoA5u215mWG9zHHfLUQJ/M7Cd/EF62WGx15z5sD558PevTphf/QR/Pvf/nvFjPUyI6he4IVbiBK4xVIA2dn6uVw5vdTZxo06tpNPWYoxIUrgXf0WiIL1MsNlr6wsuPRS+NvfdNymjb5Z2bChv16uYb3MCKoXeOFWpPnA3SK+yaz2A+Xd1HEJ62WGS1779kGFCvr1bbfBmWfC0HhW/S7m5eU61suc2N3cng/cBzr7LRCFzn4LRKGz3wJR6Bz/Kd56C2rV0nOXADzzTJzJG4p1eXlCZ78FotDZb4EC6Oz6GUOUwC0lmoMHYds2/bp9e7jmGr1CjsVSgglRAq/qt0AUrJcZMXhlZem+27fdpuNateDFF6GGm92yilF5JQTrZY77biFqA7eUKERgwQJ9UxLgpZf04gqdOvnrZbH4QDFoA+/tt0AUrJcZRfQaPx7atoXvvtPxTTd5nLxDXl4Jx3qZ475biBZ0+MVvgShYLzMK8PrpJzh0CJo21W3cOTnHDnv3y8tXrJcZQfUCL9xClMAtxZrsbOjeXXcH/PhjKF8eBg7028piCTQhakJ512+BKFgvM/J47d0Lzz6rr7STk+HVV/UyZ357BQrrZUZQvcALtxAl8KCutGG9zMjj9f77umfJvHk6Pv98qFbNH60wlFegsF7mlOgVed70WyAK1qvI5OTA6/+BqVN13KcPLFoEF1zgrxcQyPICrJcpQfUCL9xClMAtxYIntugugaCbTZo391XHYgkzIUrgD/gtEAXrVSArVkC/fnq196QkmPYcvPee31b5EJDyOgHrZUZQvcALtxAl8KBOC2q9CmTHDpg5E5Yv13GN06IvJuwrASmvE7BeZgTVC7xwC+I3KQrD/RaIgvU6hpwcuPNOvWAwQMeOsHatHpTjp1ehWC8zrJc57rvZfuAWd8jJ0VfWSUmwbp0eCp9LuXL+eVksxZgQXYH39FsgCtaLTz/VCyhs2qTjN96Ap56KsrMtLzOslxlB9QIv3EKUwG/2WyAKJdRLRE/xClC3Lpx2GuzZo+MC27hLaHnFjPUyI6he4IVbiBL4JX4LRKEEeuXkwEUXwa236viMM/RiwkVaxqwElldcWC8zguoFXrjZNnBL0dm0Sc/BnZQEF17o46hJi8UCoboCr+W3QBRKiNc770CdOnrBYIDhw2OcbKqElJdrWC8zguoFXrjFlcCVUr8ppZYppRYrpTxeqeE9b08fM+/5LRCF9+I/xZ498Ntv+nWXLvDXv0LNmnGe9L04j/eK9/wWiMJ7fgtE4T2/BaLwnt8C+bJp3yY6TdjF5v2bXT2vG1fgvxeR5vmtFuEuvbw9fcz08lsgCr3iOzwnR689mXuVXbkyPP44VI13Wag4vTyjl98CUejlt0AUevktEIVefgvky6gvRjF37Rc89PlDrp43RG3g6/0WiEIx8srJgVmz4JJLdDv3o49C7dr+eyUE62WG9TqeQ1mHKJ1SGoD5G+bz9bqvuXf2vWRmZUb2GbtgLGMXjCUtJY2DIw7G/ZnxXoEL8JFSaqFSalB+OyilBimlFiilFmzbthpo7TwmAt/lif8KZOWJc5fPGuDE6cBK4Jk8+7wBfJEnvg/Ynye+2DlH7zzb1gGP54nfBz7KEz8IbMsT93LO0SvPtm3OfrleHznnyX3/cedzcuPcpZQuzrNtv+ObG3/h/D258TPO35sbD3DO0SnPtiyn3HLj75xyzfUaByzL8/4Q5xztnLidEw/R8RsNoEcPmH2nji8fBS2XGdZT60LqKd2nempdSD2l+1RPrQupp/QT64nWzv7j8sSm36fC6qmw71M6/tRTYd+nXC936+ngkZakb3uQI9nfA635am1Dhkw/lwNHDgDteHJebdJGp3HwyEFgCB/92os7PryDFTdP4U+N21ImRQFQNqUUfZt0Z/XtZxnWUxREJOYHcJrzXA1YAnQsaP9WrVpJ7GyN41gvCbnXggUin3+uXx8+LPL22yLZ2d5phb28Eo71MiM2rx0Hdsj0n6bLzgM7RUTkq7VfSddXusrqXatFROSVxa8II5Gftv8kIiITF0+Uqo9XlTW714iIyNdrv5ZRn4+SfYf2iYjInsw9sidzj4iIDJ42WJIeTJK0h0tL0oNJMmT6EGM/YIHkk1PjugIXkY3O81b0chNtCz4iHp737tRxEWIvEejfH+67T8epqfDHP3o82VSIy8sXrJcZR72yc7I5nH0YgO0HtvPst8+ycoe+ml28eTENnmnA5799DsCSzUu49LVLWbR5UeT4vYf2sv/wfgA61u3I5CsnU62c7jp7XdPr2DZsG3Uq1QGgfe32/K3j3yhfqjwAFUtXpGLpigBsydjC4FaD+eamGxjcarC7NzLzy+pFeQDlgAp5Xn8NXFzQMfFdgcdzrJeEzGvrVpEHHhA5dEjHy5eL7N6dOK2wlZfvWK/8yM7JllU7V8mW/VtERF/x3jHzDvls9VkiIrJq5ypJfjBZJiyaICIiK3esFEYiExdPFBGRtbvXSu+3e8v8DfMjx89bN0/2Zu710Dr2MsODK/BTgblKqSXoxqIZIjIrvv9OLJ6zcCGMHg1z5+r43HOhUiV/nSyWfPh09acs3bIUgBzJ4bLXLuOl7/ViIIezD3P6M6czbuE4AEoll+KlRS/xwzZ9Y7BGhRrc+7t7aVytMQD1T6rP1ru3cl3T6wCoXak2r/3xNVqf1hrQV8zn1TqPCqUrJPRvjJeUWA8UkVVAMxddCuGRxH2UEQHzmjwZRozQU7jWqaeT9f79evWbgQP1yu+//AL16vkkGLDyimC9isqmfZvo/c5h3rhqM9XLVy/ycSJCZlYmZVLLADB56WQqlK7AZWdfBkDnCZ1pVaMV/+z+TwB6v92bKxtdyX8u/Q9JKom9h/ZyMEsn6LSUNF7p9Qota7SMxHvv24u+Aarjh7s8HPns5KRkTil3Stx/e3y4X5cxJ/DEk1n4Lr4QIK/Jk2HQIL36DcCaNTo+4wyoX18ncKV8TN4QqPI6ButVVHSf5uU89PlDPN/jaJvztoxt7MrcxVlVzgLgue+e48CRAwy7YBgAF7x8AZXLVGbGn2YA8M95/6RGhRqRBN6qRqvIsQAz/jSD0yqcFonnDJhzjMd1za7Lxy545XUU992U5J232WNat24tCxbEOmCzNeDxYM+YCJBXvXo6aR9P7dp6u1IJVzqRAJXXMVivwkh7OI1D2YdO3O70ab789ctZvWs1S4foZo+r37qafYf2Maufbll9edHLlE4uTd+mfQHYcWAHFUtXJDU51UXL4JTXicTuppRaKPkMlgzRFbglKgcO6LlK1q7N//316wOSvC1B48CRA5RNLQvAdxu+44s1X3D3+XcD8Njcxxi/eDw/3vojAH0a92HyssmkJqVyIOsAqUmpnFr+VOb/eT4Ad7W/y+kHrXnr6reO+awbW9x4TFylbBXP/q6SQogms7rGb4Eo+OS1bRusdDr4HzkCN90EFSvmv2+dOonzKhRbj2bE7pVxOINlW5ZFutJ9tfYrBr4/MNI17sl5T1LukXKR+LPVnzHs42FkHM4A4PTKp9OhTgeyc7IBeKDTA/Rq2IvM7EzSUlLIlmx6ntUz0g7esW5HujfoHrOvOwS1HsELtxAl8D5+C0QhgV6ZThuaCLRrB3frKyUqVYJly+Df/4ayZY89pmxZfSMzMNh6NCO6144DO5j641R2HNgBwLx18+gysQu/7vwVgKk/TaXpf5qyatcqANbuWcuMlTPYfmA7AB3qdODRCx/N7RbMLW1vIWN4BuVK6SXwrj73al687EWSk5IBqF+5Plk5WU6f5unu92l2haDWI3jill/fQq8eth94HNx+u8g55xyNZ8wQWbz4xP0mTRKpW1dEoZ8nTUqMX5Ep4fVYCIezDkvmkUwREdmWsU3++XUt+XHbjyIisnjTYqn7VF355NdPRETk898+F0YiH/3ykYiIfLv+Wzn/pfNl6ealIqL7Or+x/A3ZdXCXB6bBKK8TCaqXSND6gVu85MMPoVOno8uW/e530Lu3bi4B+MMfoFk+vTj79tVTwOa00s99+ybK2FIIOZLDyh0r2bRPrx2699Bebv3gVmavmg3oK+TSD5dm8rLJAOw7tI+7PlrP1+u+BuCUcqfQoW4HTko7CYCWNVoy/8/zaV+7PQBta7blqxu/osmpTQDd1/mac6+J7G8pfoQogTfwWyAKLnlt2QIPPXR0/u2kJJ28N27U8VVXwf336+HuifRyneB56bma17rSHPDxrx/z/abvAf3rtserPXhhwQsAZOVkcda/z+LF718EoHRyaV5d9io/7fgJgFPLncoDnR6gefXmANSpVIed91zBgOYDADitwmn874r/0eq0VgCUL1We1qe1jgzfTizBq0dNUL3AE7f8Lsu9esTXhFLMyMkRWbhQ5Ef981hWrRJRKoBNHsWfIdOHRJ1kKDsnOzJBkYie1GjKD1MicYeXO8jQD4ZG4lPHnCoDpw6MxBe9cpE8++2zkfi1Za/JD1t/cPtPsBRziNKEEqJ+4BcDQRypb+CVkwM7d+pFETIz4ZRToE8fGKeHA7N9uwsLJsTglVCC41VmdJlj5mrOJSUphSP366aq9i+1p1LpSpG+zG1ebMMpZU/hg74fAHDvJ/dyeuXTGdRKz6a8ePNiTi13KjUq1HDJMjjldSzWy5zY3YpBP/DtfgtEwcCrWzfIzobPPoO0NJg6FZo2Pfq+a8nb0CuhJNZr+dblrN61mp5n9wRgzFdjWLhpIa9f9TqrbltF2xfbsmHfBgShbEpZqpWvxuknnR45fkjrIaQmHW22+vi6j6lQ6uh8Gf+46B/HfF5u84d72Ho0I6he4IVbiNrAQ8h//gOtW+srb9BD2QflWfeiSxeXk3bJYO+hvZHX367/lke+PDrHxONfPU79p+tH4hcXvki/d/tF4mzJJisnC9ATHuVOZpSWosjMzuSSBpcwu//syP7XN7uePk2Odv86Ke2kSLc6i8VvQpTA5/gtEIU5R1+uWAE33gi7d+u4ShU9D8leJ+H07q2bTBLtFSjmFPju/sP7WbRpEYey9JDtuWvnMuC9AZGk/dS8p6j0j0qR+Is1XzDi0xGR+MyTz+SSBpdEkvRd59/FtwO/jfR1vvd39/L2NW9HPk8pxZDWQ/jmpq8C2q95jt8CUZjjt0AU5vgtUABz3D9lfg3jXj3iu4l5bxzHesTBgyJv9xL59Vcdz5snUqmSyNy5vmppAlheIrIt4zZ5e8XbsnW/Xjnl67VfS4eXO0RWOnlt2WvCSGT5luUiIvLm8jel1pO15NeduowXblwoY74aE1ntJONwRqTfdHwEs7yslylB9RKJx43w9wP/2G8Bzd69sG7d0dfXvAevvqrjtm1h61a44ALf9I6SuPLKzMqMzIGx/cB2xnw1hh+2/QDA0i1LqflkTT76VU/z+dP2D7nqratYuGkhoOdxVkpFbiZ2qNOBd655h5oVawJ6NOC6v67j9Mq6XbpljZbcff7dkdVOyqaWjSwkGx8B+fd1AtbLjKB6gRduIUrgPpKt54JARC+AcM89Oq5WDb5rCPfeq+OkJChVyh9Hj8iRHNK3pbNh7wZAN3EMnj6YD3/5EIB1e9ZRZnQZJi2dBOj5N+755B6+Wf8NANXKVaP7Gd2pUkZPXNS8ehkW/2UxHet2BKDVaa34fMDnND1V38ytWbEmVza60g4+sViKQIgS+JP+fOydd+pRkKBn9HviCbj99qPvt3oBUoLVmUcPTMkqcnvurF9mMX+DnlFORLhk8iU8991zgF5XsPHYxrywUA9GKZ1cmnd/fJdfd+n5NqqXr86o34+K3AysXak2e+7dE5l5rnr56rx8+cuRwSflSj1Ns+rNIjPgBQef/n0VivUyI6he4IVbsDJPgWxKzMd8/DE88wxMmaJHPTZtCqVL66vw5GS49lp/vAx48PMHmbtmaWTC/YmLJ1ImtQzXnKtnQ+swvgONT2nM2EvHAjDw/YF0O6MbbWq2QSlFsjrayyI1OZU3r3qTc6udG4m33L3lmPf/1vFvkThJJUWaN/IneOWlsV5mWC9z3HcL0UAejyZq37YNJk6Efv2genV47z34299g2jS9io1fXgWwYe8Gdh7cGZnz4plvn2F35m4enftovgNTFIpuZ3SLDEa5/9P7qXtSXQa2HAjovtLVylWLrLjtLUGdcN96mWG9zHF/QYcQNaG4yE8/HV25ZutWGDZMD64BuPxyWL68iMnbG5ZtWcaU9CmR+LG5j3HlG1dG4js+vIOr37o6En+/6Xu+3fAtq25bxZ8a/4lSybodvmxKWfo26cvPt/7MzL4zI/uP6jIqkrwBGldrnKDkbbFY3CRECfz62A8VgX379Ov9+3WzyL/+peNzztHJPLd/tvHKNYV7iQg7D+6M9EX+Zv03PPT5Q5H4sbmPUfPJmpF4/OLxXPfudZE4JSmFlKSjrV13t7+bsT3GRuIJvSYw408zqFGhBhVLVyQrJ4u0lBQyszOpWLoiDao0QAVmRZ446tFTrJcZ1ssc991ClMAvif3Qbt2OJujy5eGtt44uhqBUnCvWXMK+Q/uYv2F+pPli7tq59JvSj92ZuwHdxFHl8SqR+Ot1XzNyzkj2HNoDQKNTGvHHRn+MDD65s/2dLBm8JPIJd51/F29e/WYkblerHb+v//t8bbZkbHEm3H8noANT4qhHT7FeZlgvczxwy69zuFePhC3oMG6cyEUX6Rn/RERefFHk5Zdj/uRtGdvk9WWvy+Z9m0VEZN66edL+v+2dWeVayZvL3xRGIks2LxERkSk/TJF6/6onv+z4RUT0RPxPzXsqMvjk4JGDcjjrcMw+RSOoMz9aLzOslxlB9RKxCzpEIz0dbrtNL+4LultfWhpk6LX9GDgQbrjhmEMyDmdw4Ijef8eBHTz65aMs27IM0G3Q1Z+ozgcr9Yxzv+z8hd7v9GbBRn0DokxKGcqklomsNdihbgem9p5K3Up1Abii0RWsvn01Z5x8BgDNqjfjjvPuiPTOSEtJc3klbovFUhIJfgKfPJlNjWrR6YaFbG5UGyZPhsOHYdYs2KAHl7BxI/z3v7B0KQA5A/qz7L+PsC57F6CT9cD3BzLj5xl6930bKf9oeV5Z8gqgRxIO/3Q43234DtB9l3ue1ZNTy50KQLNTm7F8yHK61O+i4+rNmH39bJpVbwY0pnr56lx29mVUSquUqFIpAo39FoiC9TLDepkRVC/wxC2/y3KvHsZNKJMmiZQtK0N6IEkPIEN6IFK2rMi//iXTzkLmPXqLiIjkHDkiXcd3kafmPSUiIlnZWZL8YLKMmD0iEtf8Z0157rvnIvEjXzwiizYtEhE9aX/G4QwzN4vFYkkQRGlCCfRAnjI/XkfmPUf7qY9tA2PbHCBt+1855YZq/P7M3ZwHqJQUypepFJm3OTkpmSnXTqFh1YaReP2d6yPnSU5K5r4O90XiJJUUx8jATsDnMR7rJdbLDOtlhvUyx323QCfwVU8Ld3eF9xrCgVJQKguu/gGe+EjYvfpzqpY9Opf2lGunHHPsZWdfliDLjAR9jinWywzrZYb1Msd9t0An8Bon16XioTVkpkDaETicDBUPQfUqdanuXF1bLBZLSSXYNzFHj2ZLxSQGL4Bv/guDF8DmikkwerTfZnn4xm+BKFgvM6yXGdbLHPfdgp3A+/ZlSo9XeG5FXZpthedW1GVKj1egb1+/zfIwzG+BKFgvM6yXGdbLHPfdAt2EAuhk3bcvwZ2k5ku/BaJgvcywXmZYL3Pcd4vrClwpdbFS6iel1C9KqXvdkrJYLBZL4cScwJVSycBz6AH+5wB9lFLnuCV2Is97d+q4sF5mWC8zrJcZQfUCL9ziuQJvC/wiIqtE5DDwOnC5O1r5ke7dqePCeplhvcywXmYE1Qu8cIunDbwmsC5PvB5od/xOSqlBwCCAOnVORrdlAwwFGgE3O3EHYAxwnhOXQ3d6HwAsR//x5wMzgVecfYYBNYA7nbgrMALo7MRVgVlAb+AXZ9u7wGtA7ux+DwBpwHAn7uk45c4cVgt4D+jl/Ik4Ds8D0xyvGkAm8JDz/jVAH+AKJ26A/v/tYmC7s20OMJqjC50+iV6xY4wTX+84OLMo0hiYgB4MkNuf9BunDHLb1p53fJ51ng8B7YHceWDaAGPR1ZQNJAPfAkOA+c4+44F5wDgnNq0n0OUbrZ7SgR9JfD0BPEL0ekp39k90PYH+ekSrp+XO/omuJyj4+zTN8U90PUHB36dnHK9E11NRvk+5CTyWesqfmFfkUUpdDXQXkYFOfB3QVkSGRjsmkCvyxI31MsN6mWG9zAiqF3ixIk88V+Drgdp54lrAxoIOWLhw4Xal1JoYP68qqO2F75ZwrJcZ1ssM62VGUL0gPre6+W2M5wo8BfgZuBDYgP7N8CcRWRGjYGGftyC//4H8xnqZYb3MsF5mBNULvHGL+QpcRLKUUrcCH6Ibf172KnlbLBaL5UTiGsgjIh8AH7jkYrFYLBYDgj2U/ljGFb6LL1gvM6yXGdbLjKB6gQduMbeBWywWi8VfwnQFbrFYLJY82ARusVgsISXwCVwpVVsp9ZlSKl0ptUIpdbvfTgBKqTSl1HdKqSWO14N+O+VFKZWslFqklJrut0suSqnflFLLlFKLlVKBGW2hlDpJKfW2UupH599Z+wA4ne2UU+5jr1LqDr+9AJRSf3X+zS9XSr2mlErz2wlAKXW747TCz7JSSr2slNqqlFqeZ9vJSqmPlVIrnefKbnxW4BM4kAXcJSKN0ONNb/F20qwicwjoIiLNgObAxUqp8wo+JKHcTjAnhvi9iDQPWF/dp4FZItIQaEYAyk1EfnLKqTnQCjiAHrfuK0qpmsBtQGsRaYzuQtzbXytQSjUG/oyeo6kZcKlS6kyfdCagx/nn5V5gtoicCcx24rgJfAIXkU0i8r3zeh/6y1XTXytwFove74SpziMQd4SVUrWAHsB//XYJOkqpikBH4CUAETksIrt9lTqRC4FfRSTWUcxukwKUcQbzlaWQEdgJohHwjYgcEJEs9IQiVxRyjCeIyBfAzuM2Xw5MdF5PRE8GEzeBT+B5UUrVA1qgZ4zxHaeZYjGwFfhYRALhBfwLuAfI8dnjeAT4SCm10JnkLAicDmwDxjtNTv9VSpXzW+o4elPQjEYJREQ2AE8Aa9GzRe0RkY/8tQL0zE8dlVJVlFJlgT9w7FQffnOqiGwCfVEKVHPjpKFJ4Eqp8sA7wB0istdvHwARyXZ+4tYC2jo/43xFKXUpsFVEFvrtkg8XiEhL9LRwtyilOvothL6abAmMFZEW6KnpArM4iVKqFHAZ8JbfLgBO2+3lQH3gNKCcUqqfv1YgIunAY+hpI2cBS9DNr8WaUCRwpVQqOnlPFpEpfvscj/OTew4ntnv5wQXAZUqp39BzbnZRSk3yV0kjIhud563o9ty2/hoBelK29Xl+Pb2NTuhB4RLgexHZ4reIw0XAahHZJiJHgCnoeZ59R0ReEpGWItIR3YSx0m+nPGxRStUAcJ63unHSwCdwpZRCt0+mi8iTfvvkopQ6RSl1kvO6DPof9o++SgEicp+I1BKReuif3p+KiO9XSEqpckqpCrmvgW4cnfDYN0RkM7BOKXW2s+lC4AcflY6nDwFpPnFYC5ynlCrrfDcvJAA3fQGUUtWc5zrAlQSr3N4H+juv+wNT3Thp8Bc11leU1wHLnPZmgOHOPCx+UgOY6CwtlwS8KSKB6bIXQE4F3tXfeVKAV0Vklr9KEYYCk53milUcnbHfV5y23K7AX/x2yUVEvlVKvQ18j26iWERwhq+/o5SqAhwBbhGRXX5IKKVeQ6+CUVUptR74O/AP4E2l1E3o/wSvduWz7FB6i8ViCSeBb0KxWCwWS/7YBG6xWCwhxSZwi8ViCSk2gVssFktIsQncYrFYQopN4BaLxRJSbAK3WCyWkPL/S1Pmtwpp89cAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# adding a legend\n", "z = [1, 2, 3, 4, 5]\n", "plt.plot(x,y,'ro:',label='scores')\n", "plt.plot(x,z,'g*:',label='grades')\n", "plt.legend()\n", "plt.grid(linestyle='dashed',color='yellow')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(z)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", " plt.subplot(2,2,1)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'hello')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plt.suplots(nrow=2,ncols=2)\n", "plt.subplot(2,2,1)\n", "plt.plot(x,y)\n", "plt.subplot(2,2,2)\n", "plt.bar(x,y)\n", "plt.subplot(2,2,1)\n", "plt.title(\"hello\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot shows y plotted as a function of x. Just like in matlab, if we just plot y (plt.plot(y), we'd get a plot of y as a function of index (0, 1, 2, …) since the independent variable was not specified.\n", "\n", "There are a few things to note. First, in this notebook the plot is shown \"in line\". Normally, in an interactive session, the plot doesn’t actually appear until the “show” command is issued (e.g., plt.show()). Second, the generated plot is a line using the default color and style (and no symbol). We will see how to change this next. Finally, to save the figure to an image, the plt.savefig function can be used (more on this later).\n", "\n", "The plot can be spruced up a bit by optionally specifying the color, symbol and line style (in that order). These are specified with a single character for each. Colors, for example, can be specified as red (‘r’), green (‘g’), etc.; symbols as circles (‘o’), squares (‘s’), etc.; and lines as solid (‘-‘), dashed (‘- -‘), etc. Further examples:\n", "
    \n", "
  • 'r*--' : ‘red stars with dashed lines’\n", "
  • 'ks.' : ‘black squares with dotted line’ (‘k’ stands for black)\n", "
  • 'bD-.' : ‘blue diamonds with dash-dot line’.\n", "
\n", "For a complete list of colors, markers and linestyles, check out the help(plt.plot) command.\n", "Let’s repeat the example above but use green dots:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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q6vwIKcm5SV554jrwVlZf9naqqv4DWEryusGmGeBfOhzpZNcxIssnAz8Erkzy8sHfzRlG4D99AZJcMLj8JeC3Ga2f25eB6wfXrwe+NIwnbeG30l8FvA94dLDeDPDhweewdGkvcPvgV8u9BPhCVY3MW/ZG0IXAvat/59kDfL6q7u92pJ/7A+DOwXLF94Hf63geAAZruW8BPtD1LCdU1UNJvgg8zOoSxTcZndPX707yGuCnwA1V9Z9dDJHkLqAHnJ/kCeAjwMeALyR5P6v/CL5nKPvyVHpJatPIL6FIktZnwCWpUQZckhplwCWpUQZckhplwCWpUQZckhr1/y0C43qRTDrDAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "plt.plot(x,y,'g.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like Matlab, multiple lines can be plotted in the same panel by calling plt.plot multiple times. With multiple lines, it’s usually helpful to include a legend. Legends, and more generally plot labels are done using specific function calls." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#import matplotlib.pyplot as plt\n", "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "z = [ 2, 5, 10, 17, 26 ]\n", "plt.plot(x,y,'go',label='Green Dots')\n", "plt.plot(x,z,'b*',label='Blue Stars')\n", "plt.title('Example plot')\n", "plt.xlabel('time')\n", "plt.ylabel('amplitude')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rX0f2wY0xRackunogP8uY9fbxg9+Cv/POOwmFQgdtc+655zJ79uy+dW97l2usra3ljjvu4JJLLiGVSgFw0003ceqppx60/09+8hOuueYaEokEruvS0NBAKBRi6dKlfPCDH+TBBx/k1ltv5frrr+fiiy9m+vTpnH322WzZsuWwMd9999389Kc/JRwOM2XKlEOuKRhjxr8xmZZ5pBYtWqRr1649qMymCR5b9n0bU3wKNi2zMcaY44slfmOMKTFFnfiLoZtqPLDv2ZjxpWgTfywWo7W11ZJSnqkqra2txGKxQodijBklRTuqZ8aMGTQ2Nh4ybt6MvlgsxowZMwodhjFmlBRt4g+HwwfdnWqMMWZwirarxxhjzPBY4jfGmBJjid8YY0qMJX5jjCkxlviNMabEWOI3xpgSY4nfGGNKjCV+Y4wpMZb4jTGmxFjiN8aYEmOJ3xhjSowlfmOMKTGW+I0xpsTkLfGLyIki8riIvCAim0Tk6qB8oog8KiKvBI/V+YrBGGPMofLZ4s8Cn1HV04CzgStF5HTgc8AqVT0FWBW8NsYYM0bylvhVtUlVnwmedwAvANOB9wN3BpvdCXwgXzEYY4w51Jj08YtIHXAG8Gdgsqo2gf/jAJxwhH2WichaEVlrq2wZY8zoyXviF5Fy4F7gn1V1/2D3U9WVqrpIVRfV1tbmL0BjjCkxeU38IhLGT/oNqnpfUNwsIlOD96cCu/MZgzHGmIPlc1SPAD8EXlDVb/d765fA5cHzy4EH8xWDMcaYQ+VzsfVzgY8AG0VkQ1D2BeBm4B4R+TiwHbg4jzEYY4wZIG+JX1VXA3KEt5fk67jGGGOOzu7cNcaYEmOJ3xhjSowlfmOMKTGW+I0xpsRY4jfGmBJjid8YY0qMJX5jjCkxlviNMabEWOI3xpgSY4nfGGNKjCV+Y4wpMZb4jTGmxFjiN8aYEmOJ3xhjSowlfmPMca+pCc47D3btKnQk44MlfmPMce/GG2H1arjhhkJHMj5Y4jfGHLficRCB228Hz/MfRfxyM3yW+I0xx63Nm+HSSyGR8F8nElBfD1u2FDauYmeJ3xhz3Jo6FSoroacHYjH/sbISpkwpdGTFzRK/Mea41twMV1wBa9b4j3aBd+Tytti6McaMhvvuO/D8ttsKF8d4Yi1+Y8xxrWFjA3W31OF81aHuljoaNjYUOqSiZy1+Y8xxq2FjA8seWkYykwRgW/s2lj20DID6ufWFDK2oWYvfGHPcWr5qeV/S75XMJFm+anmBIhofLPEbY45b29u3D6ncDM6gEr+InCoiq0TkueD1PBH5Yn5DM8aUuplVM4dUbgZnsC3+/wt8HsgAqOpfgQ/lKyhjjAFYsWQFiXDioLJEOMGKJSsKFNH4MNjEn1DVvwwoy452MMYY01/93HpWLl3JrKpZCMKsqlmsXLrSLuyO0GBH9bSIyMmAAojIB4GmvEVljDGB+rn1luhH2WBb/FcC3wfeKCKvA/8MfPJoO4jIj0Rkd+91gaDsehF5XUQ2BH/vGW7gxhhjhmdQLX5V3Qy8U0TKAEdVOwax2x3A94D/GlD+HVX91pCiNMYYM2qOmvhF5P8coRwAVf32kfZV1d+LSN1IgjPGGDP6jtXVUxH8LcLv2pke/F0BnD7MY35KRP4adAVVH2kjEVkmImtFZO2ePXuGeShjjDEDiaoeeyORR4D/t7eLR0QqgJ+r6oXH2K8O+JWqzgleTwZa8C8S3whMVdWPHev4ixYt0rVr1x4zTmOMMQeIyDpVXTSwfLAXd2cC6X6v00DdUINQ1WZVzamqh39vwOKh1mGMMWZkBjuc8yfAX0TkfvzW+kUcetH2mERkqqr2DgO9CHjuaNsbY4wZfYMd1bNCRH4N/K+g6H+r6vqj7SMidwHnAzUi0gh8BThfRBbg/3hsBT4xvLCNMcYM16ASv4jMxO+bv79/maoecaYkVb3kMMU/HHKExhhjRtVgu3oeJrhrF4gDs4GXgDflIyhjjDH5M9iunrn9X4vIQqybxhhjitKw5uNX1WeAM0c5FmOMMWNgsH38/e/gdYCFgN1VZYwxRWiwffwV/Z5n8fv87x39cIwxxuTbYBP/86r68/4FInIx8PMjbG+MMeY4Ndg+/s8PsswYY8xx7lizc/4N8B5guoh8t99bldgKXMYYU5SO1dWzE1gLvA9Y16+8A7gmX0EZY4zJn6MmflV9FnhWRBpU1Vr4xhgzDhyrq+ceVf17YL2IHDJ/s6rOy1tkxhhj8uJYXT1XB4/vzXcgxhhjxsaxunqagsdtYxOOMcaYfDtWV08HByZnA5DgtQCqqpV5jM0YY0weHKvFX3G0940xxhSfwd652zsj51vxW/yrj7UQizHGmOPToO7cFZEvA3cCk4Aa4A4R+WI+AzPGGJMfg23xXwKcoao9ACJyM/AMcFO+AjPGGJMfg52rZysQ6/c6Crw26tEYY4zJu8G2+FPAJhF5FL+P/13A6t75e1T103mKzxhjzCgbbOK/n34LrQNPjH4oxhhjxsJg19y9M9+BGGOMGRuDHdXzXhFZLyJ7RWS/iHSIyP58B2eMMWb0Dbar5xbg74CNqnrIZG3GGGOKx2BH9ewAnrOkb4wxxW+wLf5/Af5HRJ7EH+EDgKp+Oy9RGWOMyZvBJv4VQCf+WP5I/sIxxhiTb4NN/BNV9YK8RmKMMWZMDLaP/zERscRvjDHjwGAT/5XAb0Ske7DDOUXkRyKyW0Se61c2UUQeFZFXgsfqkQRvTClqaoLzzoNduwodiSlWg0r8wbz8NcD5wFL8pRiXHmO3O4ALB5R9DlilqqcAq4LXxpghuPFGWL0abrih0JGYYiWDGaEpIv+Iv/7uDGADcDbwJ1Vdcoz96oBfqeqc4PVLwPmq2iQiU4EnVPUNxzr+okWLdO3atceM05jxLB6Hnp5Dy2Mx6O4e+3jM8U9E1qnqooHlg+3quRo4E9imqm8HzgBahhHH5H7r+DYBJxxpQxFZJiJrRWTtnj17hnEoY8aXzZvh0kshkfBfJxJQXw9bthQ2LlN8Bpv4e/rNxR9V1ReBY7bUR0JVV6rqIlVdVFtbm89DGVMUpk6Fykq/1R+L+Y+VlTBlSqEjM8VmsIm/UUQmAA8Aj4rIg8DOYRyvOejiIXjcPYw6jClZzc1wxRWwZo3/aBd4zXAMdnbOi4Kn14vI40AV8JthHO+XwOXAzcHjg8Oow5iSdd99B57fdlvh4jDFbdCLrfdS1ScHs52I3IU/CqhGRBqBr+An/HtE5OPAduDioR7fGGPMyAw58Q+Wql5yhLeOOhLIGHNkDRsbWL5qOdvbtzOzaiYrlqygfm59ocMyRSZvid8YM7oaNjaw7KFlJDNJALa1b2PZQ8sALPmbIRnsxV1jTIEtX7W8L+n3SmaSLF+1vEARmWJlid+YIrG9ffuQyo05Ekv8xhSJmVUzh1RuzJFY4jemSKxYsoJEOHFQWSKcYMWSFQWKyBQrS/zGFIn6ufWsXLqSWVWzEIRZVbNYuXSlXdg1QzaoSdoKzSZpM8aYoRvpJG3GGGPGCUv8xhhTYizxG2NMibHEb4wxJcYSvzHGlBhL/MYYU2Is8RtjTImxxG+MMSXGEr8xxpQYS/zGGFNiLPEbY0yJscRvjDElxhK/McaUGEv8xhhTYizxG2NMibHEb4wxJcYSvzHGlBhL/MYYU2Is8RtjTImxxG+MMSXGEr8xxpQYS/zGGFNiCpL4RWSriGwUkQ0isrYQMZjxoakJzjsPdu0qdCTGFI9CtvjfrqoLVHVRAWMwRe7GG2H1arjhhkJHYkzxsK4eU5TicRCB228Hz/MfRfxyY8zRFSrxK/CIiKwTkWWH20BElonIWhFZu2fPnjEOzxzvNm+GSy+FRMJ/nUhAfT1s2VLYuIwpBoVK/Oeq6kLgb4ArReRtAzdQ1ZWqukhVF9XW1o59hOa4NnUqVFZCTw/EYv5jZSVMmVLoyIw5/hUk8avqzuBxN3A/sLgQcZji1twMV1wBa9b4j3aB15jBccf6gCJSBjiq2hE8vwCwS3NmyO6778Dz224rXBzGFJsxT/zAZOB+Eek9/s9U9TcFiMMYY0rSmHf1qOpmVZ0f/L1JVVeMdQxmfGjY2EDdLXU4X3Wou6WOho0NhQ7JmKJQiBa/MSPWsLGBZQ8tI5lJArCtfRvLHvIHiNXPrS9kaMYc92wcvylKy1ct70v6vZKZJMtXLS9QRMYUD0v8pihtb98+pHJjzAGW+E1Rmlk1c0jlxpgDLPGborRiyQoS4cRBZYlwghVLbKyAMcdiid8Upfq59axcupJZVbMQhFlVs1i5dKVd2DVmEERVCx3DMS1atEjXrrXZm40xZihEZN3hZkC2Fr8xxpQYS/zGGFNiLPEbY0yJsTt3BynneXSn0iRTKZKpFDnPwxEhFg6TiMVIRCOEXfs6jTHHv5LIVKpKe3sHra37aG5upbV1H9lsllDIoaqqkqlTT2DSpGqqqytxHOeg/bp6Uuzau49dbW2oKqr+Sk8igqqS8zw8FPWUinicydUTqCiLE3ZCuKEQruPghkIEk9IZY0zBjevEn05neO21baxZs4Hdu1v6knYkEulL3JlMBs9THEeoOWEiC988lxNnTkMch227d9PW2UXIcYiGXUQcFH8UlKqSU8UJOUjwOpnq4ZWdO4mFw9RNPoFEPAbBMaPhMFE3TMix3jVjTGGNy8SvqrzyylZ+/esn6O5OkUjEqa2ddNhWdywRo3JiFWWV5eRyWXbt28eO1lY06hIOh3FEyHkebZ050t0pstksrusSjUWIxaJEwmHCbgjHcYhGIkSBdDbLizteZ8rEambUTERE6MlkSGUyxMMRouGwnQEYYwpm3CX+ZLKbRx/9I5s2vcSECVVUVlb0vZfL5di1q4nXXnuVpqaddCT305NMkiivoKJyApNqT2DarNlUTKzGkRCJsjjxRIxINEwkFiESjeDGouS8HO2dXbR1dBGNRvwfgWiERCSC6zhEXBc3FGLXvn30pNOcPG0KruP4ZwXpNOlclrJobNRb/01N8KEPwd132xKExpgjG1eJv6Ojk7vvfpjW1n1MnFhNd3c3e/fuo7u7hx07tvPqq6+wd+9ulBzxsjgTa6cwo+6NhNwwqko4niCLQ+ueFlAh5LokEgkSZWVEYhGqa6uoqEgQdl1c1yXdkyLZ1U0y2U1ZWZx0IkbUdSmLRnEch0Q0SntXks1NzZw8bQqOCCEHcp7S0dNNRSw+qsn/xhth9Wq44Qb4j/8YtWqNMePMuLlzt6srSUPDgzQ2NtHR0UFLy15ASKdT7Nz5Ou3t7TiOEA6HiZdXkCgrJ5vJkMtlAYjEokw+cTrhSIRYPEYsFiMcCZPN5YjFokyYUA0ITijEtBNPIFEex1MllUqTTqfJZnK4bojKCRWEw37yj7hu0PefYmZtDVMmVvfFm/MUR6AiFj/ogvJwxOP+YuMDxWLQ3T2iqo0xRexId+6Oixa/qnLPPQ/z1FNryWSyuG6I8vIykskuNm9+mWQyCSiZjNLTk6Sjsw0UQq5LNJYgGo8TK4vT0tRMLpcjEokw8YQaJp5QQzwRJ51K07xrF5NqaoiFXbZv3klVdQXTZ05GohH27Wlj5+Ym9u/rACAWjzLxhGpOeeMsTpgyiVgkwo6WVqrKyohHIwCEHCHreSTTacqi0RH1+W/eDNdeCw88AMkkJBJw0UXwrW+N/Ls1xow/4yLxP/DAb3n44ccoL09QUVGGiJBK9bB+/To6O9vJZNKAEI5EcMMRgoE5aC5Drns/qXQXnR17qZ5US/WkE1CFXTt20ta6jykzp1FRWUnIdWnZs4eJNZMoL69gf1snHW2dtO1pI51Kk8t5xMpiqCoh12VfSztrnlhPVVU5C98yl1hZlB17Wjh1xrS+uEMipHNZwtkQ0XB42J9/6lSorPRb/bGY/1hZaf38xpjDK/rEv3r1X/jZzx6gsrKCWCwKQCaTZs2aP9DWtpdQyMV1I8QSCRzHwfM8RIRYLIYTclBPQfzfgr2tu9nbspvJ005kUu1kOvd3sP2VLcw4aRaxeIxILMbellZCoRCprhSvvbSZ7s4OMumeYFiohxsOkygrZ1LNJCadUEt3Os2ax9ex+G0LyHlKTzpDLOIneRHBAbrTaSKuO6JWf3MzXHEFLFsGK1f6F3qNMeZwirqP/+WXN3PrrT9m3772vtE7PT3drF37J1pa9hCJxnHECVr64b6kH41FyWVzZDN+/z4iCBAKuziOQybdQ6K8grqT30CyK4mqcuIpdbghl2w2Q0vTHjrbOnAch1DYJRKJ4Lou6nnkch6ZTJpcLkco5FA7eQrTp00nHIly5vkLmDVtMjNqJh30ObI5j7JodEStfmOMGWjc9fF3dSV54IHf0NHRRTweB/yk/9eNz5DKpKmdOp1Udwo37BIKuXi5HKqK67qkulOA3+IW/zZcQIMfAiUcidKT7OK1lzdx0qlvorN9P7tfbyYajdDSvBtRIRKNE4tFe3uNUNW+i7ROKObXD+xpbmZvSwszps9kd+MeqirKD0n8jkAqm7HEb4wZE0V7G+njj/+Jffv243lKOOyS83K88tpL4AgVVdWkunsQAcdxUPUQx+/jRwRxHMSRvi4eL5cjl8uB+mcEmVSakBsmnU6xfcvLRBNx9jTupLnxdSZMqGLipIlUVJYFPxg+z/P8H4HeMvWfxxMJQqEQW7a8ylNP/JnO7m5ynnfQZ5HgJjFvBGdfDRsbqLulDuerDnW31NGwsWHYdRljxreibPF3diZZv/65oE/fnxNhV8tuunu6QRxS3d3+RdZQCBBUg0SrijgObtglm8mSDbpk/PeCygW/D79HiZfF6dy/n+bXdxAORaisrGDa9CkIwsknzWTO3NPo7u7hD3/4C61795HL5ujuTpHJZP3mvvr1uWGXMrecrZtf49mnn2XeyXUkotG+zyMiEMz744RCQ/4+GjY2sOyhZSQzSQC2tW9j2UPLAGxFKmPMIYqyxb9p04uoKp2dXYRCIdKaY2fjVsKRCOopmUwWx3GCJNo7hAe/Na6Keh7ZTJpsNtvX3SNO8CdCLpcjnUqRTWdx3TAtza+TKI+T7EzSnewhEg4zd85pJLuSRKMR3n3BecTiUcoryzhh8iSiwcXbA/P6gOOEiMXiPLPmGZp37TnkM6lCzssN6/tYvmp5X9LvlcwkWb5q+bDqM8aMb0WZ+J9//hXKyspIJruJxOM0Nb2OI/4IHVXPb9n39t+rBo1vRRAUSPX04Hle3xnBQL37Jru6yKRThFyX9n17QSCVShEOh/u6jNKpDOGIP/laOpXF8zyqJ004tFZVQm6IkBviVw/9loEX1UXAG2ZPz/b27UMqN8aUtqJL/Llcjp07m0kkYuRyHoQc9ux6nWg8Ts7zL+D6jfsDqTccDlNZUc6EqgoqyxNEImGcY0yVLCJ4uRwohMNR9u1rBaA72U1bezt79rQyoaqSyspyXntta/AjArmcRyjkEI4cfKG2d0rniqoKdjQ20bhj52GOOrzMP7Nq5pDKjTGlrej6+Ds6uvpa604oRGd3Es/Tvta9qvblT8dxqKooIxIJ4wXNaceBmpqJeJ5Ha2sb6XQa1d4O+YOJ4wCC53nkslm6urqIRmN4nvLUmnVMmzaZTDZLW1s74bD/Vfa25B3HOeiHJZfL9U32pin489PPcOLM6Qcf7zBnH4OxYsmKg/r4ARLhBCuWrBhWfcaY8a3oWvx+v7z/PBKLsr+9zR+hMyBviyNMqKogHA6TyfqjdnK5HJlM1r/4ilBbM7EvYR+0r/gjgJygz793yGcmnSadyZJKZ0inM7Tu3ce+fe2HjTOdzhyUxrOZHOUTygk5DpUTKnnphdcO6u5RGPaEbfVz61m5dCWzqmYhCLOqZrFy6Uq7sGuMOayCtPhF5ELg34EQ8ANVvXkI+wYtdKgoL6O9fS9hN9LvfQcRiEWjhF2XTDbb917/3wYvl8MJu1RUlNPa2tb/AIRcF3GE/t3wjuOQSiVx3RDpTMavO5Pt++HI5XLBIi9hOjqSeJ7XN64/m84SiYaJl8UIuy5hN8z+1H469ndSWeXfeCaA4wz/zt36ufWW6I0xgzLmLX4RCQG3AX8DnA5cIiKnD3b/srIEqh6qSjweI5vJ+Ak2yJm9XSxlZfFDxsvDwZdys9kciXicUMjpe8cJRvho0DXkebm+Vn82myEarKqlKMmublKpNKGQQzbrEXJDtLd30t6237+QrEomlSHkhqidNglxpN9NWsL+/f6kbr0t/5AU3QmYMaYIFaLFvxh4VVU3A4jIfwPvB54fzM6xWJTq6ipSqRTlZf4duxqM4kEkaDULISeEN/BGKfx+ey+X87uH8BN4KBTyLxT31kVwc1dwobivfoRIJHLgRybkL66yZ88+9ra0+z80wXWGHDkEoawyQXVNlX8m4YT8Hxn179btjc9TiIRGNlePMcYMViGamNOBHf1eNwZlBxGRZSKyVkTW7tlz8Lj3k06aRWdnklg0Qjwew/Ny9CZ7CW6YyqTT/sa9/TVBUnWcYI1cT/33NNgmeO7/gEBvx5BIcHE3l/Pn8k/48//EYhEi0TDpTJZkTwpx/TMNJ+QQjoaprpnAtNlTmDR5ov8DAcSj4b71eR3HIeSGgtE+atM1GGPGTCES/+GatYcMqVHVlaq6SFUX1dbWHvTe/Pmnk05n/MnTpk0jk8kA2pdII9EoqVQap2/6hIOrD7nugRa9KulMxm/5uyEQyPYuwB5y+n4wspkM5ZUT6O5J4XkeHZ1d7NrVwv6OZHAxOEyiIkHt9Bpqp9VQUV2O67qAkvP8xB4OuYDgBfcWVFdPwFNwQ44twm6MGTOFyDaNwIn9Xs8ADjeo/YimT5/ClCk1dHZ2MWP6VBzxL8T6iRZCbggPSKfT/ebOOZD8xXFw3TDhcJiOzk5yuRye55HNZv1hn+qfNQh+X38u54FARdUEstkcqUyazi5/aSsJBhSp5xGNRfy7dINuIVA8VdyQvw6v4zgoSjabYUJVJfF4DFDKojHr5jHGjJlCJP6ngVNEZLaIRIAPAb8cSgUiwoUXvp2Ojk4mTqyhoixGLptFxE/o6nn+XbXpNL1z+fTbGfC7fDxVurtTuK4/g6fjOEQiEeLlZX6XkCPkclly2SyRaIxYPI7rBjNwivTNq6PBfQRu2J8iwgk59Lb0Q45DPBLBvwQheJ6S7uph3rzTyXlKPByx1r4xZkyNecZR1SzwKeC3wAvAPaq6aaj11NWdyOLFZ9DWtp+TT/5/CJEjk04TiUYJhf07c7Oe0tnRBerP4OmGXVw3RMgRUqkULS178VRxQv5UCk4oRC6bJZNKIY5DOBxBPQ8cZeKkWrJZf7EVRxwcJ+SPJlLwch7RuH/R1w2F/DJPibgu8WjUn0HUdfHUw0EIifCm+acTdV3r2zfGjLmCjONX1f8B/mek9SxZ8lZ27dpNV1cX4fCrxEJh2to6UQEcIRqLk06n2LuvnZDjEA67eJ5HKp0mk8kedANV7zmBqkcm43f7uK5LJBYjl81QMaEaVSWVShFPJPz5ejSYosF1CEcjwWRv/tDSRMRvyXs5f+ipOIIgJPfvZ86805laW0tihGvtGmPMcBR1H0M0GuGSSz7A6ae/galTTySTSVFTMwFRpWPfPlp27aS9tZXOjnYy2Rzt7fvp6Ogkk8n6N0zJgUkSFH/Mvud5qOcnawS8XIaqCZOIRuOEI1Eccfomfcuph6pHLBHDEQi7LrGwSzwc6Zs0zvM8f24gcSCbIx6P8Z6/WWJJ3xhTMEWd+AHi8Rj19RfxkY9cQiQSo6urkylTTqCmpgbHCaZlVo9UTxdllZX+1M3qJ2QvSPD+a3+CNyfk4EbChCMumXSKRHk5NZOn0NbSQntrazAUM0Q2m8PL5iivTJCIRagoS5CIRgk5/sgdBf/CrutSFovieDm6k0k+8uGLmVQ9wZK+MaZgim6StsMJh8NccMF5nHjiNL7ylRtpamqmrCzB9OnTaW5upqenm0w6xf59rUyYWOPP5JnNBHfnKoh/cbb/rJ6ZdIryigqmzzqJXDaLG3FxcHBDIVLd3eQyOU6orWZSZWXfKKJ+a7n4C687IarKy0h2+hPLfeyjl3DySXUF+Y6MMaZXUS+2fjhtbW38+79/j+eeex7XjdDV1U1r615aW1tJpdJ4mqOiqppoLIaXy/oXXB3HH4IpQi6bJZXqobyikinTZ+J5HrFYjDfNm08mleHF554nm8lQM7GasrIyxBFCIcefLdTxR/OkUhkcR6idVEVXV5KZM2dw0UXvYfLk2mPGb4wxo+VIi60XReIXkT3AtmHsWgO0jHI4xzv7zKXBPnPpGMnnnqWqh7Q4iyLxD5eIrD3cr914Zp+5NNhnLh35+NxFf3HXGGPM0FjiN8aYEjPeE//KQgdQAPaZS4N95tIx6p97XPfxG2OMOdR4b/EbY4wZwBK/McaUmHGZ+EXkQhF5SUReFZHPFTqefBORE0XkcRF5QUQ2icjVhY5prIhISETWi8ivCh3LWBGRCSLyCxF5Mfg3P6fQMeWbiFwT/Lf9nIjcJSKxQsc02kTkRyKyW0Se61c2UUQeFZFXgsfq0TjWuEv8I13MvUhlgc+o6mnA2cCVJfCZe12NP713Kfl34Deq+kZgPuP884vIdODTwCJVnQOE8NfxGG/uAC4cUPY5YJWqngKsCl6P2LhL/PRbzF1V00DvYu7jlqo2qeozwfMO/ERwyDrG442IzAD+FvhBoWMZKyJSCbwN+CGAqqZVta2gQY0NF4iLiAskGOKqfcVAVX8P7B1Q/H7gzuD5ncAHRuNY4zHxD2ox9/FKROqAM4A/FziUsXAL8C+AV+A4xtJJwB7gx0EX1w9EpKzQQeWTqr4OfAvYDjQB7ar6SGGjGjOTVbUJ/AYecMJoVDoeE/+gFnMfj0SkHLgX+GdV3V/oePJJRN4L7FbVdYWOZYy5wELgdlU9A+hilE7/j1dBv/b7gdnANKBMRD5c2KiK23hM/CNezL0YiUgYP+k3qOp9hY5nDJwLvE9EtuJ3571DRH5a2JDGRCPQqKq9Z3S/wP8hGM/eCWxR1T2qmgHuA95S4JjGSrOITAUIHnePRqXjMfGPeDH3YiP+IgI/BF5Q1W8XOp6xoKqfV9UZqlqH/2/8O1Ud961AVd0F7BCRNwRFS4DnCxjSWNgOnC0iieC/9SWM8wva/fwSuDx4fjnw4GhUOi4WYulPVbMi0ruYewj40XAWcy8y5wIfATaKyIag7AvB2sZm/LkKaAgaNpuB/13gePJKVf8sIr8AnsEfwbaecTh9g4jcBZwP1IhII/AV4GbgHhH5OP4P4MWjciybssEYY0rLeOzqMcYYcxSW+I0xpsRY4jfGmBJjid8YY0qMJX5jjCkxlviNGSCY/fL/C55PC4YSGjNu2HBOYwYI5jv6VTATpDHjzri7gcuYUXAzcHJwM9wrwGmqOkdEPoo/O2IImAP8GxDBv3kuBbxHVfeKyMn4U4PXAkngn1T1xbH+EMYciXX1GHOozwGvqeoC4LMD3psDXIo//fcKIBlMlvYUcFmwzUrgKlV9M3At8B9jEbQxg2UtfmOG5vFgzYMOEWkHHgrKNwLzghlS3wL83J9WBoDo2IdpzJFZ4jdmaFL9nnv9Xnv4/z85QFtwtmDMccm6eow5VAdQMZwdg3UQtojIxeDPnCoi80czOGNGyhK/MQOoaivwx2DR628Oo4p64OMi8iywiXG+9KcpPjac0xhjSoy1+I0xpsRY4jfGmBJjid8YY0qMJX5jjCkxlviNMabEWOI3xpgSY4nfGGNKzP8P6KA2GHEKHpAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "plt.plot(x,y)\n", "\n", "plt.plot(x,y,color='red',\n", " linestyle='dashed',\n", " marker='o',linewidth='4',\n", " markersize='12')\n", "\n", "plt.bar(x,y,facecolor='red',\n", " edgecolor='black')\n", "\n", "plt.subplot(2,2,1)\n", "plt.plot(x,y)\n", "plt.subplot(2,2,2)\n", "plt.bar(x,y)\n", "plt.subplot(2,2,1)\n", "plt.title(\"hello\")\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "z = [ 2, 5, 10, 17, 26 ]\n", "plt.plot(x,y,'go',label='Green Dots')\n", "plt.plot(x,z,'b*',label='Blue Stars')\n", "plt.title('Example plot')\n", "plt.xlabel('time')\n", "plt.ylabel('amplitude')\n", "plt.legend(loc='upper left')\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "\n", "N = 50\n", "x = np.random.rand(N)\n", "y = np.random.rand(N)\n", "colors = np.random.rand(N)\n", "area = (30 * np.random.rand(N))**2 # 0 to 15 point radii\n", "\n", "# x vs. y, sized by variable s, colored by\n", "# variable c\n", "plt.scatter(x, y, s=area, \n", " c=colors, \n", " alpha=0.5)\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "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", "\n", "# Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "\n", "N = 50\n", "x = np.random.rand(N)\n", "y = np.random.rand(N)\n", "colors = np.random.rand(N)\n", "area = (30 * np.random.rand(N))**2 # 0 to 15 point radii\n", "\n", "# x vs. y, sized by variable s, colored by\n", "# variable c\n", "plt.scatter(x, y, s=area, \n", " c=colors, \n", " alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# load numpy package\n", "import numpy as np\n", "\n", "# set N to 50 (numbers)\n", "N = 50\n", "# generate \"N\" number of random numbers\n", "x = np.random.rand(N)\n", "y = np.random.rand(N)\n", "z = np.random.rand(N)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot of x vs y\n", "plt.scatter(x,y,c=z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before proceeding, it should be noted that matplotlib is somewhat different in the sense that is has both a Matlab-like syntax and object-oriented syntax, and this can lead to confusion. Thus, there is no one consistent way to make the same plot (for example). We will see examples of this when making a figure with more than one panel. One way to achieve this is using “axes”, whereby a figure with subplots is defined as an object, and then formatted later. The Matlab syntax, on the other hand, is considered “stateful”. In this case plt keeps track of which subplot is active, and formats that specific one. So whatever you draw with plt.{anything} will reflect only on the current subplot. Practically speaking, the main difference between the two syntaxes is, in Matlab-like syntax, all plotting is done using plt methods instead of the respective axes‘s method as in object oriented syntax.\n", "\n", "As an example, let’s draw our two sets of points (green rounds and blue stars) in two separate plots side-by-side instead of the same plot. You can do that by creating two separate subplots, aka, axes, using plt.subplots(1, 2). This creates and returns two objects:\n", "\n", "
    \n", "
  • the figure\n", "
  • the axes (subplots) inside the figure\n", "
\n", "\n", "Previously, I called plt.plot() to draw the points. Since there was only one axes by default, it drew the points on that axes itself. But now, since you want the points drawn on different subplots (axes), you have to call the plot function in the respective axes (ax1 and ax2 in below code) instead of plt. Notice in below code, calls are made to ax1.plot() and ax2.plot() instead of calling plt.plot() twice." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# import functions\n", "from matplotlib import pyplot as plt\n", "\n", "# Create Figure and Subplots\n", "fig, (ax1, ax2) = plt.subplots(1,2, \n", " figsize=(10,4), \n", " sharey=True, dpi=120)\n", "\n", "# Create variables\n", "x = [ 2, 4, 6, 8, 10 ]\n", "y = [ 1, 4, 9, 16, 25 ]\n", "z = [ 2, 5, 10, 17, 26 ]\n", "\n", "# Plot\n", "ax1.plot(x,y, 'go') # green dots\n", "ax2.plot(x,z, 'b*') # blue stars\n", "\n", "# Title, X and Y labels, X and Y Lim\n", "ax1.set_title('Scatterplot Green dots')\n", "ax2.set_title('Scatterplot Blue stars')\n", "# set x-axis label\n", "ax1.set_xlabel('X1')\n", "ax2.set_xlabel('X2')\n", "# set y-axis label\n", "ax1.set_ylabel('Y')\n", "ax2.set_ylabel('Y')\n", "# set axis limits/ranges\n", "ax1.set_xlim(0, 12)\n", "ax2.set_xlim(0, 12)\n", "ax1.set_ylim(0, 26)\n", "ax2.set_ylim(0, 26)\n", "\n", "# ax2.yaxis.set_ticks_position('none') \n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting \"sharey=True\" in plt.subplots() shares the Y axis between the two subplots.\n", "\n", "Note that the ax1 and ax2 objects, like plt, has equivalent set_title, set_xlabel and set_ylabel functions. In fact, the plt.title() actually calls the current axes set_title() to do the job.\n", "\n", "
    \n", "
  • plt.xlabel() → ax.set_xlabel()\n", "
  • plt.ylabel() → ax.set_ylabel()\n", "
  • plt.xlim() → ax.set_xlim()\n", "
  • plt.ylim() → ax.set_ylim()\n", "
  • plt.title() → ax.set_title()\n", "
\n", "\n", "Alternately, you can set multiple things in one go using the ax.set():\n", "\n", "
\n",
    "ax1.set(title='Scatterplot Green dots', xlabel='X', ylabel='Y', xlim=(0,6), ylim=(0,12))\n",
    "ax2.set(title='Scatterplot Blue stars', xlabel='X', ylabel='Y', xlim=(0,6), ylim=(0,12))\n",
    "
\n", "\n", "Just to complete the example we can now remake the sample multi-panel plot using Matlab syntax rather than object oriented. To do this we need to manually create one subplot at a time (using plt.subplot() or plt.add_subplot()) and immediately call plt.plot() or plt.{anything} to modify that specific subplot (axes). Whatever method you call using plt will be drawn in the current axes.\n", "\n", "Always remember: plt.plot() or plt.{anything} will always act on the plot in the current axes, whereas, ax.{anything} will modify the plot inside that specific ax. In the following example we will make a four-panel figure using the object oriented approach. The plot will be of random numbers, and to generate these we will use the numpy package. Note in this example we will also use dictionaries to store plot variables." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# import packages, numpy for numerics, random for random numbers, and matplotlib for plotting\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from numpy.random import seed, randint\n", "seed(100)\n", "\n", "\n", "# Create Figure and Subplots\n", "fig, axes = plt.subplots(2,2, \n", " figsize=(10,6), \n", " sharex=True, sharey=True, \n", " dpi=120)\n", "\n", "# Define the colors and markers to use\n", "colors = {0:'g', 1:'b', 2:'r', 3:'y'}\n", "markers = {0:'o', 1:'x', 2:'*', 3:'p'}\n", "\n", "# Plot each axes\n", "for i, ax in enumerate(axes.ravel()):\n", " ax.plot(sorted(randint(0,10,10)), \n", " sorted(randint(0,10,10)), \n", " marker=markers[i], \n", " color=colors[i]) \n", " ax.set_title('Ax: ' + str(i))\n", " ax.yaxis.set_ticks_position('none')\n", "\n", "#plt.suptitle('Four Subplots in One Figure', verticalalignment='bottom', fontsize=16) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we can turn off the y-axis tick marks with \n", "\n", "ax.yaxis.set_ticks_position('none') \n", "\n", "to turn off the Y-axis ticks. This is another advantage of the object-oriented interface. You can actually get a reference to any specific element of the plot and use its methods to manipulate it.\n", "\n", "The plt.suptitle() added a main title at figure level title. plt.title() would have done the same for the current subplot (axes). The verticalalignment='bottom' parameter denotes the hinge point should be at the bottom of the title text, so that the main title is pushed slightly upwards.\n", "\n", "The next step will be to modify the figure properties such as labels and tick marks. There are 3 basic things you will probably ever need in matplotlib when it comes to manipulating axis ticks:\n", "\n", "
    \n", "
  1. How to control the position and tick labels? (using plt.xticks() or ax.setxticks() and ax.setxticklabels())\n", "
  2. How to control which axis’s ticks (top/bottom/left/right) should be displayed (using plt.tick_params())\n", "
  3. Functional formatting of tick labels\n", "
\n", "\n", "If you are using ax syntax, you can use ax.set_xticks() and ax.set_xticklabels() to set the positions and label texts respectively. If you are using the plt syntax, you can set both the positions as well as the label text in one call using the plt.xticks(). Actually, if you look at the code of plt.xticks() method (by typing plt.xticks in jupyter notebook), it calls ax.set_xticks() and ax.set_xticklabels() to do the job. plt.xticks takes the ticks and labels as required parameters but you can also adjust the label’s fontsize, rotation, ‘horizontalalignment’ and ‘verticalalignment’ of the hinge points on the labels:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Sine and Cosine Waves\\n(Notice the ticks are on all 4 sides pointing inwards, radians converted to degrees in x axis)')" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "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 matplotlib.ticker import FuncFormatter\n", "\n", "\n", "def rad_to_degrees(x,pos):\n", " return round(x*57.2985,2)\n", "\n", "plt.figure(figsize=(12,7), dpi=100)\n", "X = np.linspace(0,2*np.pi,1000)\n", "plt.plot(X,np.sin(X))\n", "plt.plot(X,np.cos(X))\n", "\n", "# 1. Adjust x axis Ticks\n", "plt.xticks(ticks=np.arange(0, 440/57.2985, \n", " 90/57.2985), \n", " fontsize=12, rotation=30, \n", " ha='center', va='top') # 1 radian = 57.2985 degrees\n", "\n", "# 2. Tick Parameters\n", "plt.tick_params(axis='both',bottom=True, \n", " top=True, left=True, \n", " right=True, direction='in', \n", " which='major', \n", " grid_color='blue')\n", "\n", "# 3. Format tick labels to convert radians to degrees\n", "formatter = FuncFormatter(rad_to_degrees)\n", "plt.gca().xaxis.set_major_formatter(formatter)\n", "\n", "plt.grid(linestyle='--', linewidth=0.5, alpha=0.15)\n", "plt.title('Sine and Cosine Waves\\n(Notice the ticks are on all 4 sides pointing inwards, radians converted to degrees in x axis)', fontsize=14)\n" ] }, { "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }