{ "cells": [ { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. _colors:\n", "\n", "Colors\n", "======\n", "\n", "Color choices are critical in visualization because good color choices can reveal or emphasize patterns in your data while poor choices will obscure them. This section of the user guide will introduce Toyplot's basic functionality for creating and manipulating colors. See :ref:`color-mapping` for details on how to represent your data using color.\n", "\n", "Color Values\n", "------------\n", "\n", "Colors in Toyplot are represented as red-green-blue-alpha (RGBA) tuples, where each component can range from zero (off) to one (full strength). Alpha is used to represent the opacity of a color, from zero (completely transparent) to one (completely opaque). There are a variety of functions in Toyplot for creating color tuples using CSS strings, RGBA data, and even other color spaces:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "array((1., 0., 0., 1.),\n", " dtype=[('r', '
" ], "text/plain": [ "array((1., 0., 0., 0.5),\n", " dtype=[('r', '
" ], "text/plain": [ "array((0.23529412, 0.70196078, 0.44313725, 1.),\n", " dtype=[('r', '
" ], "text/plain": [ "array((0.13333333, 0.26666667, 0.53333333, 1.),\n", " dtype=[('r', '
" ], "text/plain": [ "array((0., 0.77157504, 1., 1.),\n", " dtype=[('r', '` objects, which store ordered collections of colors. For example, consider Toyplot's default palette:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.Palette()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will likely recognize the colors in the default palette, since they are used to specify per-series colors when adding multiple data series to a figure. Although you can create your own custom palettes by passing a sequence of color values to the :class:`toyplot.color.Palette` constructor, we strongly recommend that you use the high-quality palettes from [Color Brewer](http://colorbrewer2.org) which are included with Toyplot and ideal for visualization. As we will see shortly, the Toyplot default palette is itself an example of a Color Brewer palette.\n", "\n", "Each of the Color Brewer palettes is assigned to one of three categories, \"sequential\", \"diverging\", or \"qualitative\", which we will discuss in-turn:" ] }, { "cell_type": "markdown", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "### Color Brewer Sequential Palettes\n", "\n", "Sequential color palettes are designed to visualize magnitudes for some quantity of interest. Colors at one end of the palette are mapped to low values and colors at the opposite end map to high values. Toyplot includes the complete set of Color Brewer sequential palettes, ordered so that colormaps based on these palettes always map low values to low luminance and high values to high luminance:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "BlueGreen" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueGreenYellow" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BluePurple" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Blues" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BrownOrangeYellow" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenBlue" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenBluePurple" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenYellow" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Greens" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Greys" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Oranges" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleBlue" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleRed" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Purples" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedOrange" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedOrangeYellow" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedPurple" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Reds" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import IPython.display\n", "for name, palette in toyplot.color.brewer.palettes(\"sequential\"):\n", " IPython.display.display_html(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(palette)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Color Brewer Diverging Palettes\n", "\n", "Diverging palettes are especially useful when visualizing signed magnitudes, or magnitudes relative to some well-defined reference point, such as a mean, median, or domain-specific critical value. Once again, Toyplot includes the complete set of Color Brewer diverging palettes, ordered consistent so that low/negative values map to cooler colors and high/positive values map to warmer colors:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "BlueGreenBrown" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueRed" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueYellowRed" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GrayRed" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenYellowRed" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PinkGreen" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleGreen" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleOrange" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Spectral" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, palette in toyplot.color.brewer.palettes(\"diverging\"):\n", " IPython.display.display_html(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(palette)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Color Brewer Qualitative (Categorical) Palettes\n", "\n", "Qualitative or categorical palettes are designed for visualizing unordered information. Adjacent colors typically have high contrast in hue or luminance, to emphasize boundaries between values. Toyplot includes the full set of qualitative palettes from Color Brewer, without modification:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Accent" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Dark2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Paired" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Pastel1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Pastel2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Set1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Set2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Set3" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, palette in toyplot.color.brewer.palettes(\"qualitative\"):\n", " IPython.display.display_html(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(palette)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Usage\n", "\n", "Color brewer palettes are created by name. For example, the following is the equivalent of Toyplot's default palette:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.palette(\"Set2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is a palette that is commonly used for diverging data:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.palette(\"BlueRed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can lookup the set of all available palette names:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Accent',\n", " 'BlueGreen',\n", " 'BlueGreenBrown',\n", " 'BlueGreenYellow',\n", " 'BluePurple',\n", " 'BlueRed',\n", " 'BlueYellowRed',\n", " 'Blues',\n", " 'BrownOrangeYellow',\n", " 'Dark2',\n", " 'GrayRed',\n", " 'GreenBlue',\n", " 'GreenBluePurple',\n", " 'GreenYellow',\n", " 'GreenYellowRed',\n", " 'Greens',\n", " 'Greys',\n", " 'Oranges',\n", " 'Paired',\n", " 'Pastel1',\n", " 'Pastel2',\n", " 'PinkGreen',\n", " 'PurpleBlue',\n", " 'PurpleGreen',\n", " 'PurpleOrange',\n", " 'PurpleRed',\n", " 'Purples',\n", " 'RedOrange',\n", " 'RedOrangeYellow',\n", " 'RedPurple',\n", " 'Reds',\n", " 'Set1',\n", " 'Set2',\n", " 'Set3',\n", " 'Spectral']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.names()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or you can lookup names for a specific category:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['BlueGreenBrown',\n", " 'BlueRed',\n", " 'BlueYellowRed',\n", " 'GrayRed',\n", " 'GreenYellowRed',\n", " 'PinkGreen',\n", " 'PurpleGreen',\n", " 'PurpleOrange',\n", " 'Spectral']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.names(\"diverging\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, given a palette name, you can lookup its category:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'diverging'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.category(\"BlueRed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When creating a palette, you can reverse the order of its color values (though this should almost never be necessary, thanks to the careful ordering of the palettes):" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.palette(\"BlueRed\", reverse=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of the Color Brewer palettes comes in multiple variants with different numbers of colors. By default, when you create a Color Brewer palette, the one with the maximum number of colors is returned. However, you can query for all of the available variants, and request one with fewer colors if necessary:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 4, 5, 6, 7, 8, 9, 10, 11]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.counts(\"BlueRed\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.palette(\"BlueRed\", count=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Palette Manipulation\n", "\n", "Sometimes you'll find yourself needing categorical palettes with more categories than the existing Color Brewer palettes provide. For these cases, you'll have to create larger palettes on your own, but Toyplot can still help. First, the :func:`toyplot.color.spread` function can create a family of colors based on a single color value:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.spread(\"mediumseagreen\", lightness=0.9)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.spread(toyplot.color.rgb(1, 0.8, .05), lightness=0.2, count=6)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Of course, this isn't a complete solution, because you can only create so many colors of a single hue before they become indistinguishable. However, Toyplot makes it easy to concatenate palettes:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.spread(\"steelblue\", count=6) + toyplot.color.spread(\"crimson\", count=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this approach, you can, within reason, build-up arbitrarily large palettes. Often, the two-level hierarchy created by the combination of palette hues and lightness is useful for visually grouping families of related categories." ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Color Maps\n", "----------\n", "\n", "While palettes group together related collections of color values, Toyplot uses *color maps* to perform the real work of mapping data values to colors. Some color maps are based on the colors contained in a palette, while others calculate color values based on the subtleties of the human visual cortex; regardless, all color maps perform the basic function of mapping a collection of scalar values to a collection of colors.\n", "\n", "Categorical Color Maps\n", "~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "The simplest type of color map in Toyplot is a :class:`toyplot.color.CategoricalMap`, which uses scalar values as an index to lookup color values from a palette. As you might have guessed, if you create a default categorical map, it uses Toyplot's default palette:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.CategoricalMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, you can create a categorical map explicitly using any palette:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.CategoricalMap(toyplot.color.brewer.palette(\"Set1\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a convenience, :meth:`toyplot.color.BrewerFactory.map` can be used to create a map from any categorical Color Brewer palette:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.map(\"Set1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Color Maps\n", "\n", "The most used type of color map in Toyplot is a :class:`toyplot.color.LinearMap`, which uses linear interpolation to map a continuous range of data values to a continuous range of colors, specified using a palette. Unsurprisingly, Toyplot provides a default linear colormap, which is based on a diverging palette:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.LinearMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And as before, you can create linear maps either explicitly using palettes, or using the :meth:`toyplot.color.BrewerFactory.map` convenience function:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.LinearMap(toyplot.color.brewer.palette(\"BlueRed\"))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.brewer.map(\"BlueRed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are complete lists of the linear maps that can be created from the sequential and diverging Color Brewer palettes:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "BlueGreen" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueGreenYellow" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BluePurple" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Blues" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BrownOrangeYellow" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenBlue" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenYellow" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Greens" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Greys" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Oranges" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleBlue" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Purples" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedOrange" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedOrangeYellow" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "RedPurple" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Reds" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, colormap in toyplot.color.brewer.maps(\"sequential\"):\n", " IPython.display.display(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(colormap)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "BlueGreenBrown" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueYellowRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GrayRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenYellowRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PinkGreen" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleGreen" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleOrange" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Spectral" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, colormap in toyplot.color.brewer.maps(\"diverging\"):\n", " IPython.display.display(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(colormap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Miscellaneous Linear Maps\n", "\n", "In addition to linear maps based on the Color Brewer palettes, Toyplot provides a handful of additional high-quality color maps that can be created by name using :meth:`toyplot.color.LinearFactory.map`, for example:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.linear.map(\"Blackbody\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the full list of additional linear maps:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Blackbody', 'ExtendedBlackbody', 'Kindlmann', 'ExtendedKindlmann']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.linear.names()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Blackbody" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "ExtendedBlackbody" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Kindlmann" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "ExtendedKindlmann" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, colormap in toyplot.color.linear.maps():\n", " IPython.display.display(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(colormap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Moreland Diverging Maps\n", "\n", "As an alternative to linear maps, Toyplot also provides a set of nonlinear diverging color maps based on “Diverging Color Maps for Scientific Visualization” by Ken Moreland at http://www.sandia.gov/~kmorel/documents/ColorMaps. The Moreland maps are carefully crafted to provide a perceptually uniform mapping that takes both color and luminance into account to eliminate Mach banding effects. Unsurprisingly, they are created by name using :meth:`toyplot.color.DivergingFactory.map`:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.diverging.map(\"PurpleOrange\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here are examples of the available maps:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['BlueBrown', 'BlueRed', 'GreenRed', 'PurpleGreen', 'PurpleOrange']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toyplot.color.diverging.names()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "BlueBrown" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "BlueRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "GreenRed" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleGreen" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "PurpleOrange" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, colormap in toyplot.color.diverging.maps():\n", " IPython.display.display(IPython.display.HTML(\"%s\" % name))\n", " IPython.display.display(colormap)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Mapping\n", "-------\n", "\n", "To use a categorical colormap, you pass the scalar values to be mapped to the :meth:`toyplot.color.CategoricalMap.colors` method:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colormap = toyplot.color.brewer.map(\"Set1\")\n", "colormap" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "array([(0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.21568627, 0.49411765, 0.72156863, 1.),\n", " (1. , 0.49803922, 0. , 1.),\n", " (0.6 , 0.6 , 0.6 , 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colormap = toyplot.color.brewer.map(\"Set1\", count=3)\n", "colormap" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "array([(0.30196078, 0.68627451, 0.29019608, 1.),\n", " (0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.21568627, 0.49411765, 0.72156863, 1.),\n", " (0.30196078, 0.68627451, 0.29019608, 1.),\n", " (0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.21568627, 0.49411765, 0.72156863, 1.),\n", " (0.30196078, 0.68627451, 0.29019608, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.89411765, 0.10196078, 0.10980392, 1.),\n", " (0.21568627, 0.49411765, 0.72156863, 1.),\n", " (0.21568627, 0.49411765, 0.72156863, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colormap = toyplot.color.brewer.map(\"BlueYellowRed\")\n", "colormap" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "array([(0.19215686, 0.21176471, 0.58431373, 1.),\n", " (1. , 1. , 0.74901961, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(0.19215686, 0.21176471, 0.58431373, 1.),\n", " (1. , 1. , 0.74901961, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.5627451 , 0.76470588, 0.86666667, 1.),\n", " (1. , 1. , 0.74901961, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(1. , 1. , 0.74901961, 1.),\n", " (0.9745098 , 0.55490196, 0.32156863, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.),\n", " (0.64705882, 0. , 0.14901961, 1.)],\n", " dtype=[('r', '
" ], "text/plain": [ "array([(0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.19215686, 0.21176471, 0.58431373, 1.),\n", " (0.19215686, 0.21176471, 0.58431373, 1.)],\n", " dtype=[('r', '0.00.51.0050100BlueGreen0.00.51.0050100BlueGreenYellow0.00.51.0050100BluePurple0.00.51.0050100Blues0.00.51.0050100BrownOrangeYellow0.00.51.0050100GreenBlue0.00.51.0050100GreenBluePurple0.00.51.0050100GreenYellow0.00.51.0050100Greens0.00.51.0050100Greys0.00.51.0050100Oranges0.00.51.0050100PurpleBlue0.00.51.0050100PurpleRed0.00.51.0050100Purples0.00.51.0050100RedOrange0.00.51.0050100RedOrangeYellow0.00.51.0050100RedPurple0.00.51.0050100Reds
" ], "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import docs\n", "docs.plot_luma(toyplot.color.brewer.maps(\"sequential\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that for each of the maps, the relationship between data and luminance is close to linear (the ideal). Similarly, here are the other linear color maps provided by Toyplot, which provide nearly perfect luminance:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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0.00.51.0050100Blackbody0.00.51.0050100ExtendedBlackbody0.00.51.0050100Kindlmann0.00.51.0050100ExtendedKindlmann
" ], "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "docs.plot_luma(toyplot.color.linear.maps())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, here are the Color Brewer diverging colormaps which, once again, are close to linear, while providing a crisp transition between positive and negative slopes:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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0.00.51.0050100BlueGreenBrown0.00.51.0050100BlueRed0.00.51.0050100BlueYellowRed0.00.51.0050100GrayRed0.00.51.0050100GreenYellowRed0.00.51.0050100PinkGreen0.00.51.0050100PurpleGreen0.00.51.0050100PurpleOrange0.00.51.0050100Spectral
" ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "docs.plot_luma(toyplot.color.brewer.maps(\"diverging\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In most situations, the preceding colormaps should be ideal. However, there are some circumstances where you will need to account for more than just luminance. In particular, some data may trigger an optical illusion called \"Mach Bands\" that can exaggerate contrasts in color. In this case, the Moreland diverging color maps may be a good choice:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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P4bAtOHL0xOBquKQ6VeB8v39lou22h1jWPBSgZJgisOJine8CQL1yn38Vz/OexIS4250x0T8CACN/jLhcnjdPnSn4z4WOLWIsYhH7aIRAFHExAED9zPTPTEbjQ8p74mKdvlNnCke6XO6FNEKGevVSF5qMxr7KMQDg4FVfBJ0q43DYJgAAuFyeD5WBBwAgwHFnjx/PvwnRqB7PC0cALtyPXOw3ZhjGVj8zfQmNUCf173HOo8eP5/cKcNzhK+03LtfWAABSkhL/43DYXtN8zf6rung6IQQC3C75oQkAIC7W+SkAxJT7/HMkgWWIl8eQ45LACj532O0PMIyhe7nPv0n5XFys8yEAuE76LuAKCotvKyktm1v5uJeysbpq4zUJinQDqkJGeur87CYNS7KbNCxJS03eAQClBYXFQyt7nKpKYkL81wCQWHy2ZNSxE/kJAY771WQ09k1MiH9c+z6WNV+vHLdxoywXy5pvC3DcdyUlZd9p38cwhiEeb/kXHm/5zyajMdXhsH0BAKT4bMnok/mnWwY4bjGNUPu4OOdrPp9/GwAU0Ai1ZhjGajIZVcMwGY05RpOxKQDYeV5Yp3g8ZCynzhT2LT5bMgoAKkxG4yCWNadfyfnrVI2q/v5VhWGYGGdM9DcA4D6Zf7r1yfzTjUSM9zscthcdDntn7XsTE+IezkhP/SgjPfWj+pnpsxnG0I7nhdUul3uZw2HvYzIaH+J5Ye2xE/lpBYXFPQCATklKmMIwjMXpjL7TZDT2FTHefTL/dLeS0rKHAaBBNVwSnSpiMhpbAQB4vN51mteasqy5O6KREvcU4unR9iOX+o3jY53P0gh18njLPzp2Ij/O5fJMpBHKTEtN/rJSUy7Zb1yNrbGsuanDYXsFADwFhcW3njpTOAAAoqv3av7zMBmNjVnW3NpmtbSPi3M+AwAQ4LjLjS9OBwBTSWnZa1jEBcqLAY7znsw/neNyef4DAMbEhLjPaYTOSXi7lI3VJRuPFHXFg1UIAMpUIAIAZ7TdNtrlcm+53C8yGY1xDGPoAgAF5T5/IQA093rL/zQZjbeyrHkgAEzSvD0AAMflxywANGQYph/Lmht5vOV7lTe5XJ5Jp84UvAkAEB/nvA8AjAGO+6GouGS6/JYH01KTD9usliEnMX7E5/MvZlnzBJY1d2TN5s4ixltphDJY1tyRppEdAMDj9S7Wtru0tOwTl8u9FAAgLtb5IgC0BAAnAJy43GugU2Wq9PtXFZvV0gsArAGOWydi7AQAp8/nX2ezWppYzOaB2mlHk9E4vNLHXUVnS14SMRZsVssQAACfz78OABoFOA54XtjNMIbrWNbcxWq1DAcAKCgsnujxlq/xeMvXsKx5sMlo7HcF10DnymABAESM/coLiQlxz7GsebzyvPhsyStFxSWvKs+1/UhaavJnABf5jW2WwfLfcwGgpcfr3eZw2HiGMfRiGMakfGdV+o2rsTWTMao9ACCXy/NlSWnZLOn7oj50xkRPAp0rJjEh7udKL/mKi0veuMyv8Rw7kd+r8kxPcXHJ4x5v+VaPt3yj1WYZTiPUhmXN14kYh3i5q2BjdcbGI0WdEFjHTuTfrYkRMDVokLmZZc1POBz2GS6Xe9PlfBeiUar8MDEjPXWl9m8MY0jTPvf5/Ju1MTjxcc5xcbHOyYkJ8e97vOUDlNd5gdfGIyQBAHg1A7DHW34EADBIdxTg8ZYvZlnzBIvZ3JllzR093vIlDGMoNBmNOTSSBJbP519UqenaQMEr8tzpXB5V/f2rCk2jFAAAk9HYLyM9NUTsVLa9gsLiuwMctxkAINpu7+dw2N5OSUqY6nK569FI+h6Hw/aMw2F7Rvu5KMaQbjIaEwEAeJ7fp7yORbwXAHSBVUPwvHCUYQxJNquljc/n3wQAUOb2zPX5/YdZszmbZc2jzvmMph+51G9MI6kfS0yImw6VYBhDiubpJfuNarE1QWNrGO8DnasiwHHTsIiLAAB4XvCUud1TfD7/5U69lp0vjEbE+IjymAtwe1nW3MZkMqaV+/whAutSNlaXbDxS1AmBpUXEOMDz/CnaaGwexRgaAUBVBJZ2KtQl73cfO5F/Q6X3XfTHCQS4QwAAjHTcC1EMAGA0GusrL5iMxlS5DcUAAD6ffykAYKvNcgsANOQ47mUs4mKHw/YkolEsAJzy+fy7zvPdOhGkir9/ZbS25wYACHDcNwWFxc9q34TF0KDMAMcd0pQcOe5w2N4GgHSGYaJAtmGXyzO+zO3+Tfs5nhfKHXb7OIYxAMMwmQD+wwAAGpe9Tg3g8/kXOhy2Tg6H/ZHi4pLJIsacy+WeAwBzMtJTv67CV1z0N46Ldf4HAGIKCosbVE6F5wKcCy6Dq7E1xsA8BwDAGJhM5XWEUK2YnqnLFBQWv3MZJYcuK9SHRigJAEoBABiGyQAACAS4gvO81QUXsbG6ZOORok7EYEXb7RPi45wvx8c5X66fmT7VZDT2BgDO4ylffZ63K1OJDZ0x0TfYrJbWDrv9HuWPPp//qIjxfgBozDBMIy7AlUXb7WMy0lO3x0s/qArDMJnKcVOSEiclJyf+CADg8Zb/caG2lvv8SwBAtFktY5wx0d1MRqMjOTnhA/nYCwGkIECeFzbRCLWXP/NXgONyAcBCI5Tt8/kXixiTK79iOtXBFfz+5QAAVqtlMMuaGyQmxN0HAKpnyuMpXwEAAsMwPbCIGZ4XfIkJce9npKdut9ksN1WtTQaLx1u+BADAaIoaxAW4cgCISktN/j0jPXUrwxiSfD7/HwAA8bHOV21WS5YzJnq4yWgceOVXQudyKTpb8rmIcT6NUMv6mfVWOmOib3E47P0z0lO/Z1nzPZf6/KV+Y+XvJqNxiM/nLzUZjY0z0lO3pqUmzwcpqeGqqYqtlfslW3M4bPc7HPYcm9XS1uGwP1Edx9e5IF4AgGi7/TaWNTeKj3U+c6kPaImLc77FsubUxIS4sQxj6AQAxQGu4pxwm0vZ2LVg4+GmTniwHA7bvyq95CopLbsvwHH5ld/r8/kPiRjn0gjlJCbEKenpIa7VgsLiR1KSEuanJCXkQlKCBwBsIsYHCgqLP9S+j2EMmXGxzle0r/G8sKS4uOTFC7XV5/MfcLk8LzkctjcSE+JUAShifLCgsPg5zfv+cDhsOQBwxufzH8EiVgPaPd7yytODOhHgcn9/j7d8Gsuax5iMxn9lpKf+C6TYwTKQg34DHHfc5fK87nDYXq2fmZ4PADwAmAMcN6+k5NwsHgUuwHkAgAMAI8MwTVwu9y8Oh+0Ok9E4onGjrIEgZSTSLpfnWZ/Pf5znhS+sNssEhjFcn5aarKRgnwKAlAsdQ6d64Xm+tKCw+KbEhLj5DGPonJgQN0f5m8vlec3hsN1/sc9f6jfGIn6DZc39HQ7bBw6H7VUAsAGAt6Cw+M6ruTm7XFsD8B93xjimmYzG0SlJCRvkrzl1pcfXuTQeb/k0m9XyjMNh+8LhsAFcZtamyWhsk5GeelJ+iktKyx7jeZ5jGAOrfV9xcclFbUzEuE7aeE1Sq+uVsKy5E8ipqRq8PC/s0c4ts6y5PQBYuQC3WcTYyzBMtDPGcbfJaEzxeMv/8vn8axGNmmARnwlw3F75M42i7fZRDGNw+vz+XWUuz0zlOxmGSWIYQ9NKxyUAcFRJvweQMiYQjZJ4XtjL8/wZ7ZsdDnsHi9k8iGEMVp/fn1fm8kxT6nfIx4hnGENzAHDJmYXAsuauAMBwAW6jiLHvQseofL5Xc411zqWqvz/DMHEMY2iBRVwc4Li/AQAcDnsPm9VyMxaxp8zt/gEkUWPiAtwGpQSIw2G/0Wa19AcA2ufzr3S53KrHUrF5LsBtF7FU/FF+vT0AWHleOMrz/FG5PsytNqulA88L3nK/f47L5d6uaZvNGeP4l8lorO/xlq/2+fy7EY3SeF44yPO80rnqhBmGYVib1TKUZc1tAYD3eMt/d7nca1jW3I7nhbM8zx+9UD9Shd84xhnjGGMyGhsGOO6Ex1s+zefznwaoWr9RXbYm11y71Wa1dApw3EGXyzMP0aiR9t+FTtXQjAEX7NtphGiHwz7aZrVcF+C4Iy6XZyqiUSsACPh8/g00QiajydhJea58TqkSX1BY3NRkNI5CNLJ6vOWzXC53rvy9BqPJeD0A8HLg+UVtTKE223i1/Cg6Ojo6Ojo6OhdCLjlDWNZceSkenTBRJ2KwdHR0dHR0dHTqEnUiBktHR0dHR0fnyik+WzIOAKJ4XtCnznR0dHR0dHR0dOomFw1yZ1lzpjMm+mGGMbQ3GY3RPC+4Ahy3paS07GNtsK+OzuWg25VOONDtSicc6HalU+0wDGPJbtJwZ3yccxzLmhuxrDmWZc2N4uOcoxs3ytrMMIwl0m3UqXvodqUTDnS70gkHul3phAWWNbfOSE/96nx/S0lKfIdlzTk13Saduo9uVzrhQLcrnXCg25XO1XDOCtpBKG9iQtyrFAVeAPAxDEMYhkmLdtj7Ohz2sWdLyt7FOHR5Dx2dS6PblU440O1KJxzodqVz5VQlBusxk9HYnmEMNp4XPAGO2yzPPR+toTbqXGPodqUTDnS70gkHul3pXCmXrORuMhqdDGNobTIZoyt4wcUFuB0BjjtbE43TuXbR7UonHOh2pRMOdLvSuRIuKrAy0lPfMpqMt3ABbhsAlACA02gytuYC3LxjJ/JfqJkm6lxr6HalEw50u9IJB7pd6VQ7LGvOapiV+QeNUEi1dxohKiM99ReWNTeOVNt06i66XemEA92udMKBblc6V8Ollsq50IrVpAqf1dG5ELpd6YQD3a50woFuVzpXxAWXyvH5/Id5nt/aoEHmbi7AbQGAYgCIM5qMrbgA97vP599bc83UuVbQ7UonHOh2pRMOdLvSuRqqEuQeZzQZ20QxBlsFL3i4ALc9wHHFNdE4nWsX3a50woFuVzrhQLcrHR0dHR0dHR0dnVrABT1YNEImo8nY5EJ/9/n8O8LTJJ1rGd2udMKBblc64UC3K52r4YIxWIimLfGxzudY1jzI5/P/Ufnvx3z5w8LbtMhis9tRdnazjjk5OR2bN2vawmaz1QfA4Ha7j+Tl5e3Jzd2Ym5e3N9ft9uBIt7Uu8U+3KwCAjjmds3NyOnZr3qxpa4fN2ogiBDxu94E9eXt2bMjdtOavjZv2RLqNdQ3drgCaZmfXy+mU0615drMOaakpTSggJP/kybwNubkbNuRuWnYyP78k0m2sa+h2JZGTk9O0d58+t2Y3y26HAZiTJ08ezMvL27xpQ+6fe/Pyjke6fbWVi8Zg0Qihxo2ycvP2HexQUw2KJI2zm2cPGT5yYE5OTt8W2U27GSgxigIMhIgAIAIQDADSRqTHQl7evj9zc7f8MXvOb7/l5R3QB8Yq8E+zKwCAHr1v6ty7T7+xffv0HhzjYNMQEYAmIiAiAiIYKILVvdvtPrlo+aoFi1asnrpo+er1kW57XeGfaFc39unVvlefPqM653QakJmWlk0TApRsV5RsTxTBQAEhu/P2rv1lzoLpi1es+vVE/umiSLe9rvBPtCuFjjmdOjz62KOv5uR0vEkAABEI4JANIP/kybwtuRsXrl66fObqpcs3R7rNtYlLBrmzrLmlz+ffVRONiQSNsls0HjDstnE9e/cbmZGe0tgAGAwgAk2JQIEoCykRQBZZktjC8nMMQBSxRcDj9hxeunztr3PmLv4hd+OOvIieWC3nWrcrAACbzWG5fcL9d9wyfOQj9dKSGzOUADQlACIi0ETeY2UwFAFhaS8NigQACBw/dXr/9HmLP/3qp1+/d3m85ZE+p9rOP8GuktJSEkf9a/ydQ4YPuzPWHt2QAQoYoIAmBBARNLaENfaktSsQ12/aOnvm/EVfzJi3aFWET6dO8E+wKy1NspvVf/aFl969vnOH4QQIiEBAAAAMBETAIMqvaQUXAQCv231w0ax53836Yep3Z06eKoj0eUSaSwqsaxGrzWHqN3T0baMm3Ht/vfS0HAMlgoGShRVgMFAioBDPlQgAQuhzIpma+ljxbsliK/9UwbYfp8z9bM7c5TPcnnJfRE9Yp0ax2hzWux9+7pExE+59ikGC00CJQFMCGDTiiiYCICwCTUSg5MeVB0XAkvdBKrdDlXw5fc77k76Z+onL4/VG+hx1ap5WOe1b/evRB57q2KnT7UZABgYQSBsFNAFAsmgPCnUREJYEF4UxUCDbkyK0CIHjpwu2vvv15DemL1gyJ9LnpxN5rDY7+9TzLz83bPjwp40IRyFKEVeKoMIhj0UgIJLKQosAEBB2btw8/adPvn5vV+6WnZE+r0jxjxJYian1EsY++NzDNw+7/cEoSow1IAwGCquiigYMBsCy9woDIZJTNOi9EmRBJWrElezVUgUXVjdJ00Pp3Hkr//fZVz9/mH+q6HRkr4BOuPnXgy/86/YJ9/83xmFNNCBBsitKBAMIQFOK50oAGotAY+kxwqLkecAYKCxIIgvLg6JqTwQoIFDm9RV8MXP+c2/9b/r3kT5XnZqhRU67duMevf/V9jkdB0RRNEQBDQxQYJDFlQFQcLoZC6rAkmxKlDYQz2NTqkcLjp8u3PH2//309PSFy5dE+HR1IsQNPfv1fOW/H3wb57BmMggDg6QxTNAILEEWVYIitkhQbGGCg0KLEGX8g925WxfO/Oz/Xt6du3VLhE+xxqlWgcUwTCIAAM/ztco1mJCSkXz7g88933/o6LujEDYZKElYGSgMNBUUWTRIniwEWJ4elDxXqojSPiahjwnBoYJLidOSp3oAQJi74M//ff717DfzTxWfjOT1qGvUVrvS0qp9t+xn3/zm/+qlpXY1IBEMSJTFFZa8VyACoqS4K5rwQYElb5KXQR4cQzZlIFQGRQAAAscKitfd99bn963Ztnt3pM+9rlLb7apZx7ZZtz167zvX5XQcbqQMwFAIomSvlUHeJIFFybF8gmxTUp+kFVsUFirZU/AxyN4sAIBdB4/+8fzH3z2ydtvuAxE+/TpLbberylhtdtubH3/3bk6nTvdFIQwGWVwZKEkk8RpxJVTai0TZY1lkkeCeYHXqEIDA3o3bZ8367Lun8zZuOxzhU64x6Or8slhn9FMmk7G9z+dfXZ3fe6XEp2TE3DHx3Vcff/OraY2btejCUNhAUwRoigDS7BEFgIBIGyV5CihQhBHW7JVNDNkTCBVXikcr+JoAgAXUtFFq+3G3934kNTk2bt/+k5s9Xp8+dVgFaptdabHYoqk7H33jmSde+mRGjMNen0YYEJKEu2Rb8h4IUBSW7QwDwgQQBAORkTzoIYJlTxaWB0BZaCl7eaCMZo31xvXrdrfDahE35h1cx1Xwkb4UdY7aaleszWq589WnXrvrxSenpaWlt2AogySoKAQ0IEBAafYUIKCAku1KOxUYTJ4I2hko3lEiqlPQWkGfGG1tOLr/jfc5rBbD5j0H13MVvBjp61HXqK12dT669Ojf4aPvZi1v0DCrtwERoBEAjZSxUXqPJrVLsyeACahThFgWVYLsyRKIPJWoPCYYolPim3W9pf+DcalJ7L6NOzbwFdd+p1WtAsvCmrsDAETasFhbND3ojqcef/j1b35t1qZDbwMiBpoKNRyaCgqqoMCSXHoUBbLAOp+4Uu74sMZrpZhdMPAdKgktQkQALAAQgW7aMDln3Kge9wEFZO+Bk5sqKgS9E7sItcWuKlO/cevEFz+YNb9b74H3GBAx0IgAjRSbCt0oIEBRRBr8FJFFzjMQ4qB3ISiqRAAsyoOjZkAUBTqnSVavvh1bdd+Yd2hxQalLj826DGqjXfUeN3zAvz+ftKhZ29YDGIqmGQoBTSGgqaCYCm6g2STbQoAriXZZcOGgyFLj+2SbAlXAq/ZFd2iS1f3OwT2H7j9xeuOBE6f10IbLoDba1fm496nXnnz4mddmWExRTgMCoCmQxkJlTKQAQPZASaNgUGBVziZUniuCSpS9W5gEvVsiEUEgmE5uUv/6bqMGjhMq+P1HduZd057Sa05gdR04vvvDkyYv7NRz0BjWZDRLokoRV7LxAMgDniymNF4rSvViAYBqWpU2UklwKd4sImcdquILA8FCiMgiWPJoESyY2rfJ6j3yli6jz5Z49u47cOpQJK5XXaA22FVlug+Y0PXpST+tTE1Lb0Ej2a404oqSbU1rX4pdUYABEQIUIcFBUBFPBAPCwQERlKkc1aMlPQYsynsBEh2WzJE3dhhfUObO3Xn4hF6TporUJrtypiTGPPDJq9/3vn3oWxYT62CQ5LFCIAksRFUWV4rnSvpPeizFVEkbPndTRLsafyWLK9m+FLEliXkRTDRKGH5jhzujraywbPPfayN9jeoKtcmuzofF5rC/9/3vM7r16v+oAREk9V/BjZKdDQik55KbgWhEVmVhpUwNEllMSTFYWnGFsSKypI2Koh1Nul43ulGHVk0Pbtq1wu8pD0T2qoSHa0ZgxSZnxDz07q9f3jTmkY8c0fZ4miJBUYUgZNCTDEc7+ElCC4CSB0Dpf4qCB9n7EPRonSuyQrMIg9ODIeKqcpwWFiHKAM4e12eP69A2q/nm7UdWebwBfdqwErWtw7r1rpfvvfvJ939hzUa71isanF6GUIEFABCypyS7IyToeTjfoIg1g6ASgyXKdqUMhLLYMtHIMiin1TgKqMI//z7wjwsmvRJqi111HNK77/2fvro8vUH9TgyiwUDRGlGlEVcUhHivgvIKAOT3gvZGUUmOuJBdacS7akshdoYBRBF1aJzRa2CXNjdu2ndkUWGpWy8Vcglqi12dj8xGLZp+OHX5srS09Oul6UCNsFIFltbxIPVVQfdCpWnCELF1bgyWCBhEjAFrxJUAorTHItiT41q0H9LzX96zZTtP7TtyzTkZrgmB1X3ko8PvfWPq4vSsJl0NqsdKo8pBMhqkGfxA8V4BqP9Tn2s6LYoK5gFQsuBSahRRynShKqw0xUjV0g2SsCLyQKlmHGpjtrAIyQm25oP7t7nLGMXkb95x9B9Tb6Uq1KYO6/7nv/9w0OhH3zQgQKrnShZYFCVPAyqdlPIh1c6CdhUU8+T84koNRA7GyoAckxUUVvJAqOxFEXVr3mBgRmJszIKNuxZH5ALVISJtVyabJWrM2xPf6X/3bV+YTazVQNGAKMVjpRVWknSiKNBKKlCsiGj6K6BQSAwpUqYINdOGqrAiGq+ocvOnCi7tJkKi3VJ/+A3txu3PL1x/IL9AT9K5CJG2qwtx482j+r708Yw/WLMxTSuuKNAILAC5nyIgdVySddGacVAVWpTyLiVYAAAgAElEQVT0WDtdqIgoLHu2pMeKuJKEVdCzJf1HMbS1afcO41Ka1rcfWL99lXANxf3VaYEVk5QRffekWd93H37Pa2aTyWKgpHnk4Fxy0HBCNymvIXTQk/5HKKVrUiJlaLw3b99fa9as+2Vv3r71QCGIj49NoYAgNUOQKGJL48HCoSIrRFRhfI7Qkr1Z5nat0oa1a53ZdsuOY8s85ZzuzYLa0WGx1mjm5S//+um6Lv3uNtAQ9Ixqp5pBEeHSY3XgA+UPaog7YKCh1OM/tHLtul+27di5OT0tJcYUFeVUB8PzxMxIYkoMFVZaD4TseWidkdxpcE7L7F/Wb5/H8QKOyAWrA0TSrpIaZ9a793+vL2rUpvmtDGWAEHEFlEZcSTd5qiTX3AQqaNNxMAVQ6i0/s3rt+rnFxWcPpqUm16cIMYQEucuCXp0evJC4IqHPTTRlHd6l7XiKgoK1uw/pXtILUBv6q8oMu2PiPXc+/voMAwKzKqw0Y6Q6DkLozaEi1QEAaJCSLE7n5/8NAJgxGq1EFVdYFlja55KYUr1ZivjSbKpXi4gQk5HUuUXfLv2ObdmzxHvW5arpaxQOLrgWYW2n+fWDu9/+/LdTHQ5HqnR3R1TLICDHoUuyR3VxYqK535NGQOm9FAKkTtcQ8Lo9p/9ctmjeltz1v61ctni5x+0OmR+2221RHTte17NPr66DevXMGWKzRKVKAe4UhJonAAABIgfFAyGyNysotIJxW0StCH9d88Qh0z4f3/n9r1eOW7D0b70uTYRhrdHsMx+unJPZuHVfRZwDyDamsTcMFFAYASAMQBAguWvCQAEiSBb2xPvbrOlTVi5b9PXKZYvUhWL/DQBdO1zXcvSgfveNHdBjPMGCjcKyHSkHUKamLzAIEk26fcvk2FF/vHhPTL/X/2+oy+fXhXotovXAbr0HTrxrhsMRHYsABVU5BGNdggOXJIZESpr+UxwL0vBFgSiL+HK3p+jPpStmrl66fOqqpctyle+z22zsTT27DbtvzLC7WjWo153C53EOKHZDyPlti2hsiwDzzNAeX6fHOpo/N3nB465yvy7gazn3PPvJ2z0GjHpa61wAULoVCjAFQBFp6o8KEe4UYEIkgU8A3G73kXffevWxebN/XQAAcGOfXjld+vQY27F391EGmzleJEoSj+b7QZt1GIzLwpU2ItfNMifYO97+xbNbl3847badC9csq7GLFCaoS7+l6sTHOV8GACgqLnm1Or9Xi8kajQY8+P7LOQPG/yeKBopBBAxyaqmBkoPZ5bgYyZulLccQjL9SsgjVGCwg/Nrlv89Ys+z3KYvmTF96OW3q3atrj6FDeo7v1f262wjmTESsAIJ5zSYAyHuCRWkve62UuCzJq6UVWgAAFCxcsffdD75Z/bynnBOq/WLWEWrCri6E2Rptm/jeyoUNmra5wWAgwCACNB3MSKVRMLg9xM5CsgcBCAH373N/+vDbzyd9dDr/eNnFjlkvJcnx3D1jHh/X74bHER+wUwIPoGwir0wHSl4r2ZMliXV5gNSw82TB2n5vfXezyxfwhPVC1UEiYVe9Hx/z7+vHDHo/io5CUcgADJK8V8pGU0h+LGcPgpJBKE8bgpRNKDkdKCg4mb9l8idffbB26YpfvG7PRdPeu7Zv0+rpe8Y8d0PrJqMQX0GBWAGUIMg2JYTalewVJarg0ogwAAAKYNeJgvkD3/i/0S5fQI/L0hDJ/koLa3UYHnn1+29btO86nqEBaBrAQAfHSqSUZaA0CWCacAcltIYA8NMnf/vm/z774G2Px31OMLrVbmM69+4xYuQjdz/hSIlvFyAC8FgAXtljEQQigoAFqTgpFmWhJQLGGpElOyMIEEAUwlt/XvbUyo9mfFjzV676qFMCK7FB64RhE7+dmdGkdXeGBjAgAKV2hxp7RQWNBp1jOJoAd5CMqNztOr107rRPZ0/54puC/ONnr6Z9qakJ0UMH97hz3O19HreyhjQicqrIAswDFgVZZMlFSuU9lju00AxEydAoANh/tOSv1z9eeev+I8WnquVC1jEi1WGZLdGWJ95duSSrSZsujAGApoNiXhFWqn2FlGcIZqkCAGzb+OeUt1+876kzp44VXs7x6yUnJHz9/MPv3diiwTiKrwDgK6TBUBBCBkRlEFQHw5CBkIKdJwvX93/nx34uX0Av46ChJu3KaGWZm/9z15fNe3a6KwoxwCADMIhRxZQB0UBTNNCAwIAUYaWZMtRMGwJQUJB/6q/pn3zzyvLZCy7bw921Xatmz9458t0bWjS6mRIqQoW7IGiEuwhE1HixKossANh1smDj2E+m33S8uKykOq9XXaY2CCzW4mCfem/29KwmLQYbDIqwgmCdK7UsAwGkjpdyTGhwggdO55/YPOnFxyds2/TXnqoct+ewgX2HPnzHK7bkuM4c5qECB8WWgEUQiAAiluOxsCiJrPN4sog803Rk7c5vl745+QHO66uTNbPqjMBq3Hlwp6ETv51jc0QnqeKKBjBQwaB2bSkGGgXFVDDDK7j3elwnF0z9/PX5Uz7/vtzjqtYfz2azGMaP7jdu3G09X7Ka6UyCOSAiD1jUerQ03izZk6XslXgaAsHOzOvjiz/6fuOIhSv3r6rOttYFItFhmS3Rxsf+u3Jx/SZtujOG4J2fYmdI2WvtSw1wl/Yet+vU+y/dM2H9yvlX5eoedH2H3t88ddcP0VF0KgicNAgK0oBIFJGlxGTJXixSSWTtyi9a1f/9qTe5fIFrMh36SqgpuzJazY4Rn02cm5bdoHsUigKGlr1WGu8VTQUFFQ0IENIUFaWCoe1ej+fwj299/O9VsxcuuNp2jb65R/9JD475IjqKri/ZU9CugjYl3/yJSqwWCQoteb7SHajYPejdyf12nTiTf9UX6xog0gLLbLHbnnxn9sL6jVvcYDBQkucKSR4srZgKxmIF614p/RchFFk8b+brX7770mtej/uyg867Db150IhnH/iItkRlcViACsyDgAXgFU8WDgqsEE+W4sUiQZFVcujUqnmPfXwL5/XXubisOhHk3nX0f+4d8OgXv5jMJkdQeVcKYAfQBIDKT+RnBChp3CEUeNzus79++8F/Pnh6wtit65Zt5Cu4ao8hqKjg8aYtedtnzl71eQWPC5o2TusQxSCLtj6Wul6h6rlSxJWg8WQFhRdjIGy3DmnjrRajK3f7qdxLt+LaoaaDRs2WaPTwWytmZzRq24+mNXaGpL9TqmkFw0KVtAlMKMAEwf68XXOfubdP3727Nlbpzu9i7D9+6vCvq3K/69Ymu3Giw5p93ixCRViFeLOCMTUJVjazT/OsNr9u3juTEwRy6aNe+9SEXcU2TE0d/f0LK2PTkjrSFA00oiVPlMYzdb6sU5BjYoic/s4T7Pvtx5mvfPDQc6MP7NidVx1t23Xg6MHv5y//X6OMVGPjtMTOgEUKKtmNZEtE7augsrcUYzAiKmFYh2ZDV+w5vLDQXf6P92RFMsjdmZAe/chr05dmNG7RhUaUlIiDND1VSL+lQIESPYUJBWUud8FHbzwzYOrX7/9YUcFdUV9xbO+B/atnLvjSaDIFMls17SwCYZSMQkVMiSCeE/CuTh1qNmMMm9lscNdBJzftm+cv8dSpUIdaLbCMlmg0cOLUL9sNuO8VAw20Iq4UURVMKZUIllSgAIgmG5BQIBAKr5o/9cOPnxs/YvPKBSv5Ci7sqaAVFTzetHXfpp/nrv0mPi4aNWmUnANEpNUK79oYrErPsSY7TBFhBItU8wbO/o0ynfVyd55ZXMGL10w668Wo6Q7r0bfX/5jRqM0ImpY6KOocIa8JBKWCtoYJBSKmyNxpnz49aeLwx8o9rmrzFrm8vsD/Fq76OdpmKe/YOKMPiMpgWFlUSQMiCSlGqogsc+M+zbOyvlu3Y051tasuE267im2Y2uyWjx9bbXHYG6riChBQcs0qSsnekgPXFdQAdzn76nDe/tVfPfnagFUz58/jKyqq9YaQq+D52Ss3LNl1+OSK3u2b9zYZkCNYcPTckg1EJEHRpRFjRoRihrXLHrE878jCQnd5cXW2sa4RKYGVmtk85qFXpi1PyWzUnkYACFHn9l0AITeHwQQd6ac8sHf32jeevKPX1r9WXvUap3xFBd69dtOaQ5v//iWtSYNWllh7Jg4RUecGu2PN6+oUIsYADJWQ1aP1yPxN+//wl3qKrrZtNUWtFVhGS7Rj+OtLF9ZvfeMIA6IkdyYKrdlRuW5VsPgCpWZIiISCglPH13381IiBS6Z/NtVXjYNeVamoELgVf+5atmX7kZlNG6W2iI2x1FeKkIZmEkqvYRwUW+rf1TgIEeols21zWiX3yt1VMNfr4/01fT41TU12WCMe+u6/zdv3f1AqThvaQWldpGpqhJJ8RSgQMHJ/9vqdw+ZOeWdyuNq3dMvu9ceLSjcO6thyEIiisXK5BklcicHHoiS2iCg9T7CYWmfEOiwL/z50WYkc1yLhtKuGfdtd1/ulCSssDlsSLZdgUDaKksQVRaSbQiJ3Xmq1bDm1nScit2zyrGe+fuyV+86eKgirZ+jAidPHZ/25+fsbWjVtnuiwNAlmpgb3iqgK9kWhni4jomzDrms6YsXeYwsLPf9ckRUJgeWMT4u+/6WfVsSn1LtO8VxRmj6sstMqWIpIckCImIK/Vi7+6r8T7xhVePqEuzrbdvZUQcn6Xxb9YLHbPOktG3cXCTZoBZZIRPmGQhP4jrVlHqTXwUDZM7u3uo0r8y4rPVQ3lm+qlQIrNrN15m3vb1rhTM7sqLo5qfN5EkKnBAmAmsmMJXHlmfd/b0z84ukR9589fXkBxuHg1JmSs7/Mz/2RQuhEu9aZNwLBJrUeFtYUJCXniis161DeYmxM+k031Bu+cVfRHyUu7qqC82s7NdVh3Tz+/bu79r//baR4rjR3gME5nGBdGGkKR3Kru12ugrefuLnX5jXzwr6kyM4jJw+u2X3o90EdWwwz0ciqDnqiZkAUyTmeLWVrkRTbNZo1n1q+79jWcLe1NhMuu8rqc13Xbs/ettRkYmOkKUHZa0XJi9toa+/JxoRVcSV5rdwu156vH3ip3/pfFs2rzrZdDFe5P/Dd4jXTo22WQIdG9XqpXlK1DhvRLAFGKtmW6smyDm3bePiKfccWFHp813S/dCFqPKSBtTsee2v+ivjkjOsQoqR+CwXrW4UUuAJNGSMi/WwiBvzbjG8e/OLNx14NR8iMwr71W/86umXPvOweHW5EDB2viKyQqUEsCawQAaZMJWIRgKHY1M5Nb/cVutaUHT5zIlxtrS5qncDKaD/ouv5P/7LKYovJlOJfpCUiKHQhYRVEGexEQsGx/TtXf/7EwL5blv1c6+pIbd5+ZNuqdfuntmyW1jI22txAW3hUDXDXerPUNQyDwosQERiaOHt2TB5T6uZXHTzhvmYDTGuiw2pzw/g+/cdOmkHTFAp6riRD08wInnPnhzEF7jLXwUn/7tnz0J4N1RIbUxWOFZUULN2xf/aIzq1vNiIqtnI24cUeE4yhfXrCgBMu74Zdp4uvueUpqko47Kp+nzbdr584YpGBZmxqvJU8LUipweqU2ocRZRkSIhVnFIgIBzbumPrFhGdvKTqaH5GK6cu25a07XlS6eUCH5gMBi0aoJKIIJnL2KtF4TYO2FYWQ9ZbWjYauPHB8bqHHd9GSJNciNSmwzKzddv9LPy9NrteovXRjSKk3hoqNhda20kwLEgBBBO9Xbz46fMFPn04Pd1sBAEpPFRZunrVscnrLxvVsSc5WarwVxoAhtGyDuuHg6yIRQRRFY0rnxqN8he71rsMFR2ui3VdKrRJYDW8Y27/Xv6f+bjKZY5DquVJqvkCosNIOepo5ZJGAsOrnT56bMenue0vOHKu1/7jPlno9sxZumwIUKmvXMq07EGwgIdOG2gWjtR4txZuliixz1zbxYwrOcnsOnfDsjfR5hYNwd1iJ9Vo3Hf/MwiW0gTJLdqeZGtTEKih75e5PxAAeV9m+d57s0ePEoe01vshyQZmndOnOA7NG5LQcYKRRXDBO5lxBFToYEiAipm7Ozhz8+95jcwu9/n/kdE5121VGr1Z9Oz81fIEBGVialupeaz1XQJ072IGcNYWl+kDCup9+e/SX5z9+TqjgK6qjTVfKrqOnDizcvGfB8M6tBhkRcoDsIQ0paouVcAap41WnDEUMRoTst7RqNGjVgRM/F3p9/6jyIDUlsEysnb3vhZmL0hq06ELTSBZWwZvD0LgrKrTvIgAet+vsF6/d3zt35dwajRUTKviKnb/9OYe1W4uTmtfviwlGWJNNqA1+F7ES7C4LLdnpIGIxKrlTo1H+Ivcm15HCWnuTWGsEVtO+D43pevdnPxsMlImmpEFOmRYErcjSUFmNe91lR356bUz/dbM+/UWoqBvZ6Ft2nsjd+nf+whs7ZfVgaIg7X8A70VR9x5pYLMV1T7DIdGkdO9LGMic27SnZHulzqm7C2WE54jIcd/5nzXKj2ZyK5A4KKd4rgNCAUKIR8xjA4y7b//7EHj1OHt4esXiAApfXs/Tvg7Nu7dh8sBFRsUrtohBxJWpElqgZEEVsGtqiQZ85uw9PcQcquEidQ6SoTrtK79Wid8cnbpnH0IxZirWig/FWAKrA0kJIcP02r8tTsnDStzevn7zg16ttS3VR6PIULd91YNqwnJZ9jAglaWOyACuCilQS8EGPVhSiYm5p2fCmeX8fmuYO1JEOuRqoCYFlYu3MPc/OmJOW1aIXQtK6laAVVtrBUpmKVsJnMIDX7T7+zpPDeuzbsT5i694e3rBrk+dMyerUto2HAIPM2gD3oKCSxr7gVKI0Hsrii0ns2OBWf5En132k6HCkzuNioEg3AACg411f39dh3LtTKEQZ1LAEElo8WBkblE1Q9iIALwIc27dj/qf3tm+bt27+5gifzmWzZefJ7bfcPaXjtrzCmWotAACQ7m6VmkaScidYqk9DsAhYFAGLPGCxAkSeQ0NuiP/uqXGNHovYidQxjKyDuvWhn2eYrdFN1A6IaLLUVZsjgDGRHsu1GF1lpcc+eLpHr0iKK4Wdx8+cuem9KT1dXMUxAAiphaUVWiDIYksQgQgCEEEEG6Ka/Di850yHKep8s+46VSCtR/Mb2z02cB5CyKy8RiB0mkMtrChvAhakukAiD66y0n1TH3iz066Fa/+M5Hmcj13HThe0nvhht7/zi9Ypd7jKlCYhRBVaRPZmERFL/ZMoAhFEsCKq+bcjei22m6LYyJ7JtYOJtVN3T5w+OTWj2U2SaJfTI4j8u2AALPdZ2mRiEUt9l7vMtfedJ4d2OXHo732RPpfdv6/7c86jH3YSyiv2hXp3g4VHRSx7suRNxCKIogCCyEOFwJmbPdBjXmqP7BsjeBoXJOIerPZ3fPVCw27jPjDQiFKzHyhKXj1emwFx7lSNPACSrX9Mfn76iwMfDpS76uxdeAUvcgtX7P/VZjW5mzeK7UMwpoLL6IjqdGEwo0eJyVJib6T3ZaWY+ifFmg1/7SpdGelzqi7CdUfYd/Snrzds1e9ONXZBsbdggIz25k91rXvdZUWfPd+zZ/6R7Ueqsz1XQ4G73LNsz5Hfh1/X9DYjRVmkoHcxKK6UbEIS9Dhg2bMVbzY1TLSy9KIDJ64Zm6kK1WFXKd2zO7V97KZFBgNjpZFBU4JBibWiQqcF5QKKiug6ve/IX78+/H7P0uO1t0gnxwvc7E27Z/Rq0aBtgsXcSK3uLk01q17S4NQhaOKyCMSZTWk9GqS1np935GdOEK/5Gmzh9mCNvPujbxo06zyBojWxffKmmhoJWp2SUY8xAa/bvevDZ4f1OHlk95lwtO1K8JW4Sw6u2PpTatuG3YzRlvTg1KBkWyLg0ClC+d+O5NkSQcSYiW9ff3ig2LvSe7Q4InGLFyKiAuu6CV++3eCGcf8x0EgNzkNaN6ecsRViKErRUMl75frtg7uGrp32+g/VeR6RZMP2/A1nin1r2jZLGMTQxBycMgwWJVWC3M8JXpaFV/0UY7ckp9H519+uxZE+n+ogHB1Ws5yxN98w5MWvaJqiEKURV8GaDAAQLFhNsJRQJQrEO+X90b0O7V79d3W1pboocJefPVBYsmx4m8ajiYiNRHH3aoKQsaiUb5CKkxL5783io2/I9/g27y4qPRDp86gprtauYpqltGn3zKBlBgNjR5psQTWYXTvNrJZhwPKityKc2Xds9tzHPr7FV+Ku9cUTOV7g52zJ+7VX86zm8RZTdmgNNqJWeCf4XM8WwRjizKbGDeOiUxbsPXrVFehrO+EUWEPH/ffV1jmDn6BoJVtQFvGVbw5Bmf2h1CTQclfZjo9fGN4j/8juWpfdWeH1Bw6t3P5TWsemzaKizdlK4LuIJWGlTBNirEwTatcyFEHEojGufcYIV96ZxYEib60RjxETWG3Gf/lp/evHPk7TlYKKNQOdZCcatzQEp3DKPWX7p028oc/RrUs2VOc51AYOHC09unHHmVm9utTrzdAQr3irlCxDRVwFhZUYmmmIRaifFJWT5DQmb9jt+S3S53O1VHeHFZ/aMmP4w3OX0DQya+/8Qn0NSragPGVIAASR4Jmf3XXLzvUzw16K4UrZX1By5nipZ+uAZvVvJ6KIJJFFNOJKiZchlQQ6ofo1SL3pj8P5Pxf5ArU2OaQ6uRq7smbEZl/3wqCVjNnkVD1XiJJjrirFvyirqxGiFlM8sXXfj4ue+WZshddfZ9ZY43hBnLMlb1av7Kzm8ay5mZJQofVWqXutyBKlsiENom3XpUdbhSWHTq6J9LmEk3AJrP7Dnru3441j3lM8V2qGs6qpgmOmskoWAek38LrLdnz20oge+Ud3l1Znm6oTsUIQjqza8WtCdkamOcHeBmsD3LX7kExDTYYhFk2x7TNuLdt16jfeVTsSdyIisFqO/eKLzC5jH9KKq+AUjXaI04orSp2iOXNox5pZL/XpXZq/r9bXwbhSSlyB0nnLD/+U0zqpQ4zNkKVmDiqBpiGeLE1AvBynhbEI9ZOY9q0aWOot3+qeH+nzuRqqtYCt2WEc9vDvC1lbTENtSnPoKgDSXivoRRFg7cKPH1gz/52ZV9uGcLPrVNEhioLTXTKTBxGhUqB7yFShtOGgWDd3z0ju/Mu+Y1M4EV/zqwRcqV1Z6jlT27w0cLXJbklCSFpHkFLEVeUpQVVcKWnoIuxdnPvFipcn3ytW1L0lizhBJHO37ZvTMzuzZTxrahpccULjwVLtTE6mIEGhlR0b3dPDCwe2nzkbseDqcBMOgdW6wy3D+gyZOJmiKYqSC0NSmgqiIaKeaLKdMUC527Xj81dG9jx1rPaKKwWxQiCHl26da0uMSbDXj+8gyqEvoWIqmE1I5Kx6xatFaGDjcjKHuHad/pl3BSLuGa5xgdV89Gdf1us85gE6pGZH5TnkoGudEEoq6CiLq4LDO6bNe6XvcH9ZQXl1tr02UsFjbkVu/k8ZKbb09ERzWxLivRIBQFvCQVSNTX2ORYh3oLZZyaZ62w74F/B1rz8HgOrtsPqO/e6d1KycEVSlcgwAoKYyqxXag6sVwZZVP37y2/cPv3G1x68p1h4+tbVejD2meUJ0J5DjrYKDnbZcA1FddEQkYGMMqY2cdnbBoZO1rn5cdXMldsXWi3G2fKH/SpPd2kCpcxUqrLRxMBDiuRKxCPv/2PTehvdm/zsMp1NjcIKI5+7YP6dnk4yO8WZTw5A4P6JZPUDxZMkFShXx1S09aeApr295XnFZrYqXqS6qW2C1bjek05DRk+ZRNGK046Va/JhS56ABSHB1CVlc7f3q9ZHdTx3bU6fWiMz/a+/v1qQYqy0zrosS2K6dIgyWbNC+Lk8f0mB35mT0d/99ZgbvCkR0pZMaFVhNb/v0y/ROY+6nNTFXoVM0ANpK2YQEp2hEDLB35ZT3Fk8afL/IB675u2uFCh6TFRtPz0uOs9BZqZYbtdOFWrGlFVlKwLtSziHFidqmxUelbjsY+K0uiqzq6rAat7u9f/vej3+OEFKTKRTPlTyTA4r9KRmsGAOcOrJ9+ewvx40V+ECdunjrjpxa0qthWtd4szGLVCoMicXzeB3kfZbd2jm/PJC7p8R1MNLnEE4u165olmGbPNptiSUxui1CNCCEQsWVNnYPzhVXB5dsmbTpg3nPhuVkahhOEMV5Ow/O7tG4Xvd4s7FecOUAcp5pw2ChQrlUiKFjSvyQNScLfyn2X3vT0dUpsBKTmzQdffdXKxBN25TxMqQwJIDab0kPgx7Dco/r0Ndvjup5+viegqttRyQ4tWH/UktitNGaGXuDmkEoiytyjrjSLLGDMQAN8ZYGsd3LNp2cRngcsWn4GivT0GjkJ5+kdBx9vzKgSWm+yhQMkVdiICEp8YJIQBAJVAgE8lZMfmTNV/dMrKn21jbe/v7vl5bmFt5HUUiqYqGkSRMc4rECURFXmhoXBEOHRoa77+hn+zzS5xEpYpNbJPUY8flUJeKYqCnNRC7LIKc1ixhEgQAWCQgCAY+75PDPn4y4NeArC9sSEuHCFajA439ePsJdwUuF+LSByEpqvaiU+8AgCiKIvAhihQD/7dxyajOnPSnCp1BroFkGNX66+zRLRmwOUJR0LYk89afU6JEHgGAauVSGgRcq4OAfmyZt/nD+85E+j+rEFeB8w7+fP8Bdwe9QXiNYLhegiCup3hpgUQRRxIAFDKKAgQUU92O/rgvsUYwtkudQm0lMahI37p5vFwKFYgBALcOgilW5JINyw4RFAliQtnJX2Zlv3hrV7/TxPbU2O7UqbPv49+dPr9o7SQ3hkLOOgrWycLB0AxZAxAIImAderAA6yZRT/4ku02iWiVg5qho5cObA1x9K7nD7I5QS8Ek0y0PIQksVWZiAgElQXPFYXPPVPWPX/9+9n9VEW2sz7/yY9817Px0aTVFIUNzC6h2LpoTD+RcSoqBHK/MDd/S1/yPrZN0w9NMfAde4crEAACAASURBVCAWSFBcKaKKKPVisCTwRYxBEDDwguj79dORQ11na++KAJfiRJm3bMKslcOAonxq/SLZkxXMKCSABaxuIi+CyAmx/+3U8sdIt7+2kDKy5eeWes4hFEWppRYU75SaLi7fXYui1NGLsrg6tGTLh1s//v2aElcK7kCFa8SU3/t5eF4qWaIKz+BUoXTjEhRaWBAB8yKwgFr80LfrlAifQq3EZLJZBg59ZaGJtWcpr0k31Np/w0rfJde+EqXrXO4uK5ny8b19Th/fU2srnF8OOz794/kzq/d/SKlxsUEbUzxbQZEl//uT62ShJOOQhOFNI+ZYCPsUoTEmPabJ6K/n0zRlUgPZERUaVCzvQzMFCQiYcLnf3jfq6Lqffq7OdtZlDp0s/7uglN/cuYVjKMEiQ7BS5V2Jy1JLtZ4LRUHjVKbrqp3+r8oDpM5UVr5al3uHfq89kdViwIMIoWBQO2iWwdE4+0ApMooJrJ3/5l27N0xdWl3nESlOuMoLgKJOdEqOG4rlqUJlGkcVWap3KzitExsV1cBhjPKsOVP8V6TPIRxU1a5SxreaGH99/edompamBBECJC+OGuzHyLnTgkSE48t2fbnzsyWPhvlUIkqR11+++kj+4sFNM2+PAorVBrljHGpXIEKIFzU2Kqppqs3CrDh5ZkWkz6O6uNr+ymSyUqPHf/1Tclp2H0peAifE1kILXAGlycYhAi6fP/XFvnk7lm27+jOpPRRuOvyHOcGeyKbHdAjGYGliszSV30MC4rEIUanW9lFxrK98Z9H6mm532D1YMc1uugkoygGgnbGS/+ERkC+C4jkgIAanBctz/3ffwKPrfpoT7jbWNZbmFi36cObxfhSF5EB/OaJReXwhpNxdW4/W5pvD3MRaQ2JG5+bNOt2rBqcTonirtNWOpUw6USQgCJL36sie1V//9ftb18zd9fvrd03ZcKroazlzJCSLkGDJc0WUKRweg8gLIFYIMDYz9c2O8THNI93+SGHPSRkZ07ne2xSlZJfK04IhUxPBzly5cxZEHgp2HP5x1+dLH4z0OdQEuwtK9r26avPNgEI9pWpShSh7SkXJc6r1lg5KTX5hbKP6IyJ9DrWFXr0ffzcxucmt0r1y0GsFIZ5BJfuXqNdT5EVh1g8TR23bMHtjpM8hHOR9uepBz74C1auunaLXBr1L3mMRBGWaXqgA83Wxb9s6Jo2s6TZXSWDRCBlY1hzDMAxzibe65U3FYLI3BNVDQOQAYmlqUJTjXyRhhUEQCfDStKBn03f33XT8r5+WXeF5XfMs3Xh27Uc/5/ekEC1d78sLv24YjjZdLpdhV1dElNHOdB744VSKoszBmKugwMdYjluQ465EUYoPKTlzZMdv/7v98XC0KZLctXDd4/le33YgECqulOkbQZ421Ax+AieYXm/ddKqNMYTlNwoH1WVXbMu4nKRxLX6gKIoimoxAIhcKDXbo8tI3YnD5m7MH82ftfHfxHdV1TnWBX/8+tPHpZbkTKIpSqzCFJFLI/9ZUoSVgwDwGsUKEJ5s3/q59nLNlZM/g4oS7vwIAuL7rnfe0aj3wSQBQiz9K4R/aaUF8znUUeRFW/PbxXdtz5ywMV9tqA3s+WH6H/6RrFkBwVQS1vlzoYtBSPJZ8s1MhVFCOkVk/sC1ic2qyvYaL/TE+zjk4Jib6PzRCCQDgAgCHiHFxaWnZy0XFJecUsCwqLvnwPF/DS3d9lBr3AoiSDIWSLpJSXJQAAYyJe+fUB/ufyp1+TU5LVCfLNpduJAT3fOSW2GUE+OjL+GhElxS6XLu6Utr2evkla3S9NqrnARMglOTBQiCV/QiWaZDGBCwS79JpD4zk/K6IpveGAzfH++9ZsmHkb4O6bQUAa+VpQWW6MHjHDAAUQCJtaPN6qyYv/3vL7hcjfQ4XozrtikmxZCaMbrqAoih5fcFgRw6YkgwIAxCKAEXh4HQzEPAcLV7995uLxwm+ijqVdVod/Lrn8K8dk+KeGVI/7R3lpgbUmxpNeRAc9MoAAIBAWd9r13r26DUb2p/y+V2RPYtQaqq/atG8f6+uXe/6MqTMhzwFSDABAAxEWSGAkv9xgvSe7blznl+9+PPJ1dWW2orgqyB73lo6rskzPeLoZPONigAlmqB37bQhyCIMAIDClNk2ImMBX+LvyJ/yHa2J9l5QYJmMxoSYmOiXT+afHubz+Y8pr7OsOS0lKXG6x1O+McBxhZc6AFd6YgMmABQhQEmKSu6YKKAoApgKFmbABFy7pz3U9/TG6dekizMcLN/i2kII7vXwYMdyEHGVRFZhmRgx8VpddnUpUhr06VC/xYjnARThLtsbJqqgoihKGiCBUj0Tm5b896FTh9fuv9rj11b2FLsOfLx9/0MPN8v6ERSPnsazELLciSIPKIAbY2Ke65EQO29l4dlNET2BC1CddkWZaWvCgy3m05aoeMU2MMZAURiASMMalsU6RWHQZkbz3sD2XW8uHiz6Kq45gV5Vnl6x8d2UgeYm7ZzRd2mDslUvlsarpWT0AgCYKarhu21bTR2zLndQRE9AQ031VwlxDbNv7vf8LIqiaCW2KiiuACiEgWAkPZfFFaEkgbp31/Kv5898YdLVtqGuIPgq/HvfXjm42aR+q4GBNmrCHNbEYWGsCisFAgTASMVH39tkXvHbO7sSv+gNd1svOEWIaJTA8/xOrVEBAPh8/pM+n38LolG9qhygZM/i1YLftROT0HgXZQvGXGHvrqkP6uLqClix1bP107mlvYGiXOfPIAxSHiDbNu7jIrZURXXZ1cVgjHZzh37v/iitGR4UDFj11kh2J4oEREGKuxIFDCf2/zljy/J3rvm7wI+37Zu8sbBkRkhGjpKRpImREZWNxyBwAnq5SYMfbQaDOdLtPx/VaVcx4xt9bGCjWqriALAmY0m5QxYBYyVbUJqKCLi9R3dPWnqT6KtwX/oo1zYPLln/4N4yt9TPaDxZ2n+DBEslBYhA1OnCrCjzwJeaZ78U4ear1ER/5bAlOm8b/sFvFJJjleWYmpBlrUSlhp0oJ6iIQAQBTh/bvXDBzy/+I+L8tIi+CvfB9/68iQTEowDBFBO1wvtFYmYoE2oVPa7BxzXRzgtmEWIRF8fFxT5qtbDdoqKYBAtrzrCw5uvjY533shZzdlHR2fcIIZesDUQEjviLDq6LbTnkdgJgAgB1jbdgtiB2753xSP/CLTOuuXUFa4qjZ/jTRWV4RccmxhEAlOkCbyv5cLZrwNECvqhGG6fhSuzqcrNyOvb78E1nUotbKBS6+G4Q6R9fsFwDBn956fE/Jt82oCLgjuj0aU2RW1iybFj9lNsZDNGq90qs5GEQQ/cMUPH1LSyzpOhsrYuNrC67isqyZdn7pn9HI5pCiv0ACllhQp5xBiwLL5GIwHn9JfvfWdXTf8J1tIZOuVbDiVhcm18475YG6bcxANFqYHalvVJeIGhrAA0t1hvPcNxfB7zew5E+j3D3V8YoS9StA99aFB2T2pqiEQBC8kLhECwmSpRCyHINLLk6flnxyS0/fj1hQCDgqaj+M6/9CG7O691T9Ie9XfJoQhOzFA+JgcClZAkFdExUa/6wd6pYWhHW5YMuKLAIIaSszPUThRAYo6KamYzGxgAU6/P7/zx9uuAlEWOhqgcJFB0sdB9ZP8dWv0sz2uTIUoqMioSA/+zx5XmTJwwt2bNoe/Wc0j+XowV8/u5jFbObZ0Q1tJhQQ9Ak9BaWiYvf/rls+PZDXESnv67Eri6nw0rK7NUxu9Nj3yEaUSFLMCkSSw0cDWahiCLG6xdMHFJw7K9rdmqwMp4KgSsOVGztmRQ/gQiYUj1X2tINlQKTiUignsnU+aDfv/iYP1CrChhWl12Z2sWNNjWIHoAopRRDcGFdgOCsKdYWGRX5wImp2/p5dxfpfZgGTwXv33m2dPmQzLQJWMCMmlxSSbgH18RUYwKpbrFxA9aUlEwv4SPrDQx3f3XrTW98k5LafAhF02opBnV1Ce3KAFJ2mGSAIoaA13V85pRHerhcp2v9+oLhRHBzxaKnYg3bMm4sxqJBJCJUMeOLEksrDvCHPWENebhgDBaNkMFoMrYpKS2bCwBznTHRw2xWy80A0MrHMOtEjrssL4j78Pr929/t0Nue1SXLXr9LOwAA95H1W9yH10f8LuVaYvexigMPfFp0c2Yik94iM6oVwP+3d+fxbVTXHsDPjDQeabTZsrxgxwvZSEggUMJSSqCsZV8SCpSWUijQ19JCW5Y+oI+tFB4Q3muhC8sDWgihkBSajT2EpIEEEgLB2QjZvCbeZMuSRhrNzL3vDy2WTRw7sWXJzu/7+czHzoxknTvcOId7z9xLtGFX7ItdzXpObIw91P0qnZTnko8+9aHnBBJEzpOjVPFf3KLIEnVYyW2Z4r+8GONUt+WN2dvXv7piKNo3kry+q2nFqSW+2acU5N+efLIwVYhspH2fXKCOiASRxDsrq577NNB1TMg0c2a0bwj7lS+5gC/jjATOKFFfTFzgJAqcuND9f8iMM2p64YsfBVY34qGcvVjT3F7zyPrN19wyeeIr3dMWybq/xDRYr38POScyDeb9/cRJ866t+XxGyDCyttVJJn9fnT3j5lsqyo68Nrl0Sqr2ijESSCROLLUGVmqf1Pi0T9f8f9xyfnPz1hG5Bc5QC6xuXMU4uzr/srH/2M+3+jISUJo+a7Bkm+yqqij/PyKiIp/3Cm9B/s1qJLLEZGZ7ZWX5Yoso7vMJxL507fhoR8PS2fMals6eh+Qqc3Y16/WLPw4vWfxxeEmuJFdEmetXREQTvvGzOyXZPSW14jHrrrtKjdAktpQwTZM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0.00.51.0050100BlueBrown0.00.51.0050100BlueRed0.00.51.0050100GreenRed0.00.51.0050100PurpleGreen0.00.51.0050100PurpleOrange
" ], "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "docs.plot_luma(toyplot.color.diverging.maps())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the diverging colormaps use a much narrower range of available luminance that *isn't* linear, they provide a perceptually uniform mapping that takes both color and luminance into account to eliminate Mach banding effects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we say that a palettes and colormaps in Toyplot are \"high quality\", this is what we mean - they provide perceptually uniform relationships between data and color, so that viewers can intuitively relate changes in color to changes in magnitude for the underyling data.\n", "\n", "As a counterexample, here is a low-quality colormap (mis)used by many mainstream visualization libraries: " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/html": [ "
0.00.51.0050100jet
" ], "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "jet = toyplot.color.Palette(numpy.load(\"jet.npy\"))\n", "docs.plot_luma(\"jet\", jet)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this palette provides a complex, nonlinear luminance profile that makes it a poor choice no matter what type of data you wish to display, sequential *or* diverging!" ] }, { "cell_type": "raw", "metadata": { "collapsed": true, "raw_mimetype": "text/restructuredtext" }, "source": [ "Now that you are familiar with palettes and colormaps, see :ref:`color-mapping` to see how they are used to display data in a plot." ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 1 }