diff --git a/DataRepresentation.ipynb b/DataRepresentationGraphsHists.ipynb similarity index 99% rename from DataRepresentation.ipynb rename to DataRepresentationGraphsHists.ipynb index c5f372320f99ef4e561aab76aa97fe180b7c4cba..f8a2cff44f56c8ee9e0b460316dc1e493569a0b2 100644 --- a/DataRepresentation.ipynb +++ b/DataRepresentationGraphsHists.ipynb @@ -783,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/basicMatplotlib.ipynb b/basicMatplotlib.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8400e82403f57e08ec4d47905def6edd91f5f01b --- /dev/null +++ b/basicMatplotlib.ipynb @@ -0,0 +1,547 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic plotting with matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n", + "[1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n" + ] + } + ], + "source": [ + "# equally spaced numbers:\n", + "\n", + "# give range and number of points \n", + "print(np.linspace(1, 2.0, 11))\n", + "\n", + "# give range and step size\n", + "print(np.arange(1, 2.1, 0.1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# dataset is a parabola\n", + "x = np.arange(1, 21., 1.0)\n", + "y = x**2 +1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(-3.0, 300, 'LaTex: $\\\\alpha \\\\beta \\\\gamma$')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# the simplest way to plot\n", + "plt.plot(x, y, '.b') # format is'[marker][line][color]'\n", + "\n", + "# axis ranges\n", + "plt.axis([-5, 25, 0, 500])\n", + "\n", + "# group relevant font stuff in one place\n", + "font = {'family': 'Courier New', # Arial\n", + " 'color': 'black',\n", + " 'size': 16,\n", + " }\n", + "\n", + "plt.xlabel('x-axis (units)', fontdict=font)\n", + "plt.ylabel('y-axis (units)', fontdict=font)\n", + "\n", + "plt.title('Title of the plot', fontdict=font)\n", + "\n", + "plt.text(-3.0, 350, r'some text', fontdict=font, color='g')\n", + "plt.text(-3.0, 300, r'LaTex: $\\alpha \\beta \\gamma$', fontdict=font, color='b')\n", + "\n", + "# make the plot appear\n", + "#plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Markers: \n", + "character\tdescription \n", + "'.'\tpoint marker \n", + "','\tpixel marker \n", + "'o'\tcircle marker \n", + "'v'\ttriangle_down marker \n", + "'^'\ttriangle_up marker \n", + "'<'\ttriangle_left marker \n", + "'>'\ttriangle_right marker \n", + "'1'\ttri_down marker \n", + "'2'\ttri_up marker \n", + "'3'\ttri_left marker \n", + "'4'\ttri_right marker \n", + "'s'\tsquare marker \n", + "'p'\tpentagon marker \n", + "'*'\tstar marker \n", + "'h'\thexagon1 marker \n", + "'H'\thexagon2 marker \n", + "'+'\tplus marker \n", + "'x'\tx marker \n", + "'D'\tdiamond marker \n", + "'d'\tthin_diamond marker \n", + "'|'\tvline marker \n", + "'_'\thline marker \n", + "\n", + "\n", + "### Line Styles\n", + "\n", + "character\tdescription \n", + "'-'\tsolid line style \n", + "'--'\tdashed line style \n", + "'-.'\tdash-dot line style \n", + "':'\tdotted line style \n", + "\n", + "\n", + "### Colors\n", + "\n", + "The supported color abbreviations are the single letter codes\n", + "\n", + "character\tcolor \n", + "'b'\tblue \n", + "'g'\tgreen \n", + "'r'\tred \n", + "'c'\tcyan \n", + "'m'\tmagenta \n", + "'y'\tyellow \n", + "'k'\tblack \n", + "'w'\twhite " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x11bbad410>,\n", + " <matplotlib.lines.Line2D at 0x11bbad610>]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# you can overlap several plot in one call\n", + "plt.plot(x, x, '-b', x, x**2, ':r')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plots\n", + "a = np.random.randn(50)\n", + "b = a + np.random.randn(50)\n", + "plt.scatter(a, b)\n", + "plt.xlabel('entry a')\n", + "plt.ylabel('entry b')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting categorical variables\n", + "names = ['group_a', 'group_b', 'group_c']\n", + "values = [1, 10, 100]\n", + "plt.bar(names, values)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# A figure is composed of subplots\n", + "def f(t):\n", + " return np.exp(-t) * np.cos(2*np.pi*t)\n", + "\n", + "t1 = np.arange(0.0, 5.0, 0.1)\n", + "t2 = np.arange(0.0, 5.0, 0.02)\n", + "\n", + "\n", + "# create a figure (by default the figure 1)\n", + "plt.figure() \n", + "\n", + "# The subplot() command specifies numrows, numcols, plot_number \n", + "# where plot_number ranges from 1 to numrows*numcols. \n", + "plt.subplot(211) \n", + "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mauro/anaconda3/envs/fits/lib/python3.7/site-packages/ipykernel_launcher.py:18: 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" + ] + } + ], + "source": [ + "# Move around figures and subplots\n", + "\n", + "plt.figure(1) # the first figure\n", + "plt.subplot(221) # the first subplot in the first figure\n", + "plt.plot([1, 2, 3])\n", + "plt.subplot(222) # the second subplot in the first figure\n", + "plt.plot([4, 5, 6])\n", + "plt.subplot(223) # the first subplot in the first figure\n", + "plt.plot([1, 2, 8])\n", + "plt.subplot(224) # the second subplot in the first figure\n", + "plt.plot([4, 3, 6])\n", + "\n", + "\n", + "plt.figure(2) # a second figure\n", + "plt.plot([4, 5, 6]) # creates a subplot(111) by default\n", + "\n", + "plt.figure(1) # figure 1 current; subplot(212) still current\n", + "plt.subplot(221) # make subplot(211) in figure1 current\n", + "plt.title('Easy as 1, 2, 3') # subplot 211 title\n", + "\n", + "#Save a figure\n", + "# plt.savefig(\"test.pdf\") \n", + "\n", + "# Close and release the memory for figure(1)\n", + "plt.close(1)\n", + "plt.close(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# A figure is composed of subplots\n", + "def f(t):\n", + " return np.exp(-t) * np.cos(2*np.pi*t)\n", + "\n", + "t1 = np.arange(0.0, 5.0, 0.1)\n", + "t2 = np.arange(0.0, 5.0, 0.02)\n", + "\n", + "\n", + "# Create two subplots sharing y axis\n", + "fig, (ax1, ax2) = plt.subplots(2, sharey=True)\n", + "ax1.plot(t1, f(t1), 'bo')\n", + "ax1.set(title='A tale of 2 subplots', ylabel='Damped oscillation')\n", + "\n", + "ax2.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", + "ax2.set(xlabel='time (s)', ylabel='Undamped')\n", + "\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(432x288) AxesSubplot(0.125,0.125;0.775x0.755)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 4 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# by default all subplots in a figure are ordered on a rectangular grid\n", + "# to position plots in any arbitrary place use axes\n", + "\n", + "# create some data to use for the plot\n", + "a = np.random.randn(50)\n", + "b = a + np.random.randn(50)\n", + "\n", + "fig, main_ax = plt.subplots()\n", + "print (fig, main_ax)\n", + "main_ax.scatter(a, b)\n", + "main_ax.set_xlim(-5, 5)\n", + "main_ax.set_ylim(-5, 20)\n", + "main_ax.set_xlabel('x')\n", + "main_ax.set_ylabel('y')\n", + "main_ax.set_title('title')\n", + "\n", + "# this is an inset axes over the main axes\n", + "right_inset_ax = fig.add_axes([.65, .6, .2, .2])\n", + "right_inset_ax.scatter(a,b)\n", + "right_inset_ax.set_title('title')\n", + "#remove ticks\n", + "right_inset_ax.set_xticks([])\n", + "right_inset_ax.set_yticks([])\n", + "\n", + "# this is another inset axes over the main axes \n", + "left_inset_ax = fig.add_axes([.2, .6, .2, .2])\n", + "left_inset_ax.scatter(a,b)\n", + "left_inset_ax.set_title('title')\n", + "#remove ticks\n", + "left_inset_ax.set_xticks([])\n", + "left_inset_ax.set_yticks([])\n", + "\n", + "# this is another inset axes below the main axes\n", + "left_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n", + "left_inset_ax.scatter(a,b)\n", + "left_inset_ax.set_xlim(0, 0.2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n" + ] + } + ], + "source": [ + "# plots styles\n", + "print (plt.style.available)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histograms and text\n", + "\n", + "mu, sigma = 100, 15\n", + "x = mu + sigma * np.random.randn(10000)\n", + "\n", + "# the histogram of the data\n", + "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n", + "\n", + "plt.xlabel('Smarts')\n", + "plt.ylabel('Probability')\n", + "plt.title('Histogram of IQ')\n", + "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n", + "plt.axis([40, 160, 0, 0.03])\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Useful links\n", + "\n", + "Gallery of examples:\n", + "https://matplotlib.org/gallery/index.html\n", + "\n", + "pyplot.plot ('Markers', 'Line Styles', 'Colors'\n", + "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n", + "\n", + "pyplot.figure:\n", + "https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure\n", + " \n", + "line properties:\n", + "https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D\n", + "\n", + "matplotlib fonts:\n", + "http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samples/\n", + "\n", + "stlye sheet reference:\n", + "https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dataToolkit_1.ipynb b/dataToolkit_1.ipynb deleted file mode 100644 index 8209043128ecb5370bc17a3defb445667b04b719..0000000000000000000000000000000000000000 --- a/dataToolkit_1.ipynb +++ /dev/null @@ -1,575 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Analysis Toolkit\n", - "# 1: Basic plotting with matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n", - "[1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n" - ] - } - ], - "source": [ - "# equally spaced numbers:\n", - "\n", - "# give range and number of points \n", - "print(np.linspace(1, 2.0, 11))\n", - "\n", - "# give range and step size\n", - "print(np.arange(1, 2.1, 0.1))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# dataset is a parabola\n", - "x = np.arange(1, 21., 1.0)\n", - "y = x**2 +1" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(-3.0, 300, 'LaTex: $\\\\alpha \\\\beta \\\\gamma$')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# the simplest way to plot\n", - "plt.plot(x, y, '.b') # format is'[marker][line][color]'\n", - "\n", - "# axis ranges\n", - "plt.axis([-5, 25, 0, 500])\n", - "\n", - "# group relevant font stuff in one place\n", - "font = {'family': 'Courier New', # Arial\n", - " 'color': 'black',\n", - " 'size': 16,\n", - " }\n", - "\n", - "plt.xlabel('x-axis (units)', fontdict=font)\n", - "plt.ylabel('y-axis (units)', fontdict=font)\n", - "\n", - "plt.title('Title of the plot', fontdict=font)\n", - "\n", - "plt.text(-3.0, 350, r'some text', fontdict=font, color='g')\n", - "plt.text(-3.0, 300, r'LaTex: $\\alpha \\beta \\gamma$', fontdict=font, color='b')\n", - "\n", - "# make the plot appear\n", - "#plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Markers: \n", - "character\tdescription \n", - "'.'\tpoint marker \n", - "','\tpixel marker \n", - "'o'\tcircle marker \n", - "'v'\ttriangle_down marker \n", - "'^'\ttriangle_up marker \n", - "'<'\ttriangle_left marker \n", - "'>'\ttriangle_right marker \n", - "'1'\ttri_down marker \n", - "'2'\ttri_up marker \n", - "'3'\ttri_left marker \n", - "'4'\ttri_right marker \n", - "'s'\tsquare marker \n", - "'p'\tpentagon marker \n", - "'*'\tstar marker \n", - "'h'\thexagon1 marker \n", - "'H'\thexagon2 marker \n", - "'+'\tplus marker \n", - "'x'\tx marker \n", - "'D'\tdiamond marker \n", - "'d'\tthin_diamond marker \n", - "'|'\tvline marker \n", - "'_'\thline marker \n", - "\n", - "\n", - "### Line Styles\n", - "\n", - "character\tdescription \n", - "'-'\tsolid line style \n", - "'--'\tdashed line style \n", - "'-.'\tdash-dot line style \n", - "':'\tdotted line style \n", - "\n", - "\n", - "### Colors\n", - "\n", - "The supported color abbreviations are the single letter codes\n", - "\n", - "character\tcolor \n", - "'b'\tblue \n", - "'g'\tgreen \n", - "'r'\tred \n", - "'c'\tcyan \n", - "'m'\tmagenta \n", - "'y'\tyellow \n", - "'k'\tblack \n", - "'w'\twhite " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x113e70dd8>,\n", - " <matplotlib.lines.Line2D at 0x113e70f28>]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# you can overlap several plot in one call\n", - "plt.plot(x, x, '-b', x, x**2, ':r')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plots\n", - "a = np.random.randn(50)\n", - "b = a + np.random.randn(50)\n", - "plt.scatter(a, b)\n", - "plt.xlabel('entry a')\n", - "plt.ylabel('entry b')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plotting categorical variables\n", - "names = ['group_a', 'group_b', 'group_c']\n", - "values = [1, 10, 100]\n", - "plt.bar(names, values)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# A figure is composed of subplots\n", - "def f(t):\n", - " return np.exp(-t) * np.cos(2*np.pi*t)\n", - "\n", - "t1 = np.arange(0.0, 5.0, 0.1)\n", - "t2 = np.arange(0.0, 5.0, 0.02)\n", - "\n", - "\n", - "# create a figure (by default the figure 1)\n", - "plt.figure() \n", - "\n", - "# The subplot() command specifies numrows, numcols, plot_number \n", - "# where plot_number ranges from 1 to numrows*numcols. \n", - "plt.subplot(211) \n", - "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n", - "\n", - "plt.subplot(212)\n", - "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Easy as 1, 2, 3')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 4 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Move around figures and subplots\n", - "\n", - "plt.figure(1) # the first figure\n", - "plt.subplot(221) # the first subplot in the first figure\n", - "plt.plot([1, 2, 3])\n", - "plt.subplot(222) # the second subplot in the first figure\n", - "plt.plot([4, 5, 6])\n", - "plt.subplot(223) # the first subplot in the first figure\n", - "plt.plot([1, 2, 8])\n", - "plt.subplot(224) # the second subplot in the first figure\n", - "plt.plot([4, 3, 6])\n", - "\n", - "\n", - "plt.figure(2) # a second figure\n", - "plt.plot([4, 5, 6]) # creates a subplot(111) by default\n", - "\n", - "plt.figure(1) # figure 1 current; subplot(212) still current\n", - "plt.subplot(221) # make subplot(211) in figure1 current\n", - "plt.title('Easy as 1, 2, 3') # subplot 211 title\n", - "\n", - "#Save a figure\n", - "# plt.savefig(\"test.pdf\") \n", - "\n", - "# Close and release the memory for figure(1)\n", - "plt.close(1)\n", - "plt.close(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# A figure is composed of subplots\n", - "def f(t):\n", - " return np.exp(-t) * np.cos(2*np.pi*t)\n", - "\n", - "t1 = np.arange(0.0, 5.0, 0.1)\n", - "t2 = np.arange(0.0, 5.0, 0.02)\n", - "\n", - "\n", - "# Create two subplots sharing y axis\n", - "fig, (ax1, ax2) = plt.subplots(2, sharey=True)\n", - "ax1.plot(t1, f(t1), 'bo')\n", - "ax1.set(title='A tale of 2 subplots', ylabel='Damped oscillation')\n", - "\n", - "ax2.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", - "ax2.set(xlabel='time (s)', ylabel='Undamped')\n", - "\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Figure(432x288) AxesSubplot(0.125,0.125;0.775x0.755)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 4 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# by default all subplots in a figure are ordered on a rectangular grid\n", - "# to position plots in any arbitrary place use axes\n", - "\n", - "# create some data to use for the plot\n", - "a = np.random.randn(50)\n", - "b = a + np.random.randn(50)\n", - "\n", - "fig, main_ax = plt.subplots()\n", - "print (fig, main_ax)\n", - "main_ax.scatter(a, b)\n", - "main_ax.set_xlim(-5, 5)\n", - "main_ax.set_ylim(-5, 20)\n", - "main_ax.set_xlabel('x')\n", - "main_ax.set_ylabel('y')\n", - "main_ax.set_title('title')\n", - "\n", - "# this is an inset axes over the main axes\n", - "right_inset_ax = fig.add_axes([.65, .6, .2, .2])\n", - "right_inset_ax.scatter(a,b)\n", - "right_inset_ax.set_title('title')\n", - "#remove ticks\n", - "right_inset_ax.set_xticks([])\n", - "right_inset_ax.set_yticks([])\n", - "\n", - "# this is another inset axes over the main axes \n", - "left_inset_ax = fig.add_axes([.2, .6, .2, .2])\n", - "left_inset_ax.scatter(a,b)\n", - "left_inset_ax.set_title('title')\n", - "#remove ticks\n", - "left_inset_ax.set_xticks([])\n", - "left_inset_ax.set_yticks([])\n", - "\n", - "# this is another inset axes below the main axes\n", - "left_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n", - "left_inset_ax.scatter(a,b)\n", - "left_inset_ax.set_xlim(0, 0.2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n" - ] - } - ], - "source": [ - "# plots styles\n", - "print (plt.style.available)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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b9vV225pHNvdaWb1h1JBR/VKnvvp5qeefTaj/9vVUygCjEmnFcyVd5pF0KNmw2Ym7UGaWGLEQWAjQ1NQU06dPr1Dd2tXa2kq9tq+327Zg8YJeK6s3NI9sZmnH0j7/3JWn9s1y/fX8swn1376eSjlE1g4cULDfCDxTLo+kocDewAv5fiPwA+DMiHi8IH9jhTLNzGwASBlgVgETJY2XNAyYA7QU5Wkhm8QHOA1YEREhaR/gJ8B5EXFHZ+aIeBZ4VdKxkgScCdyUsA1mZtZNyQJMRGwD5gO3AL8CboiItZIuknRKnm0RMFpSG/BXwBfy9PnABOB8SWvyr7H5sU8DVwNtwOPAT1O1wczMui/pczARsZzsWZXCtAsKtrcCp5c472Lg4jJlrgYm925Nzcyst3mpGDMzS8IBxszMknCAMTOzJLzYpdkgVG5NtpVz++b5GBsc3IMxM7MkHGDMzCyJqgKMpO9JOlmSA5KZmVWl2oBxFfAnwGOSLpF0cMI6mZlZHagqwETE7RFxBnAksAG4TdIvJX1C0ttSVtDMzGpT1UNekkYDZwF/DtxP9qbKI4HbktTMzMxqWlW3KUv6PnAw2ZslP5IvOglwvaTVqSpnZma1q9rnYK7O1xV7i6ThEfFaRByVoF5mZlbjqh0iK7Xw5J29WREzM6svXfZgJP0e2SuJ3y7p9/ndGyX3AnZPXDezbiv3pLqZ9Z1KQ2QnkU3sNwJfLUh/Ffj7RHUyM7M60GWAiYjFwGJJp0bE9/qoTmZmVgcqDZH9aURcB4yT9FfFxyPiqyVOMzMzqzhEtkf+fWTqiphZ//Mqy9abKg2R/Xv+fUHfVMfMzOpFpSGyb3R1PCL+onerY2Zm9aLSENm9fVILMzOrO9XcRWZmZrbLKg2RfT0iPi/pR0AUH4+IU5LVzMzMalqlIbJr8++Xpa6ImZnVl0pDZPfm338uaRjZisoBrIuI1/ugfmY2APj2ZeuOapfrPxn4N+BxsvXIxks6JyJ+mrJyZmZWu6pdrv+fgRkR0QYg6T3ATwAHGDMzK6na5fo3dQaX3BPApkonSZopaZ2kNklfKHF8uKTr8+N3SxqXp4+WtFJSh6Qris5pzctck3+NrbINZmbWhyrdRfZ/8s21kpYDN5DNwZwOrKpw7hDgSuAEoB1YJaklIh4pyHY28GJETJA0B7gUmA1sBc4HJudfxc6ICL9J08xsAKs0RPaRgu3ngD/Mt38DvKPCuVOBtoh4AkDSMmAWUBhgZgEX5ts3AldIUkRsAX4haULFFpiZ2YBU6S6yT/Sg7AZgY8F+O3BMuTwRsU3Sy8BoYHOFsr8l6U3ge8DFEbHTMzqS5gHzAMaMGUNra2t32lATOjo66rZ93W1b88jm3q9MAqOGjKqZupZS6drU888m1H/7eqrau8hGkA1nHQqM6EyPiE92dVqJtOJAUE2eYmdExNOS9iQLMH8GLNmpkIiFwEKApqammD59eoVia1drayv12r7utm3B4tpYn7V5ZDNLO5b2dzW6beWpXd+mXM8/m1D/7eupau8iuxZ4lOwNlxcBZwC/qnBOO3BAwX4j8EyZPO2ShgJ7Ay90VWhEPJ1/f1XSd8mG4nYKMDY4+NXIZgNXtXeRTYiI84Et+fpkJwOHVThnFTBR0vj8Ic05QEtRnhZgbr59GrCi1HBXJ0lDJe2bb78N+DDwcJVtMDOzPlRtD+aN/PtLkiYDvwbGdXVCPqcyH7gFGAJcExFrJV0ErI6IFmARcK2kNrKey5zO8yVtAPYChkn6KHAi8BRwSx5chgC3A9+ssg1mZtaHqg0wCyW9g+zW4RayN1yeX+mkiFgOLC9Ku6BgeyvZLc+lzh1Xptgp1VXZzMz6U1UBJiKuzjd/DhyYrjpmZlYvqpqDyZ+s/xdJ90m6V9LXJY1OXTkzM6td1U7yLyNbGuZUssn4zcD1qSplZma1r9o5mFER8eWC/YvziXczM7OSqu3BrJQ0R9Ju+dfHyVZTNjMzK6nSYpevkj1ZL+CvgOvyQ7sBHcCXktbOzMxqVqW1yPbsq4qYmVl9qXYOBkmnANPy3daI+HGaKpmZWT2o9jblS4DPkS21/wjwuTzNzMyspGp7MH8MvDcitgNIWgzcD+z0lkozMzOo/i4ygH0Ktvfu7YqYmVl9qbYH84/A/ZJWkt1RNg04L1mtzMys5lUMMJIE/AI4FjiaLMD8XUT8OnHdzMyshlUMMBERkn4YEVPY+X0uZmZmJVU7B3OXpKOT1sTMzOpKtXMwM4BP5S8B20I2TBYRcXiqipmZWW2rNsB8KGktzKwmzVg8o2T6yrkr+7gmNhBVWotsBPApYALwELAoIrb1RcXMzKy2VZqDWQwcRRZcPgT8c/IamZlZXag0RDYpIg4DkLQIuCd9lczMrB5U6sG80bnhoTEzM9sVlXowR0h6Jd8W8PZ8v/Musr2S1s7MzGpWpffBDOmripiZWX2p+n0wZv2p3O2wZjZwOcDYgFIcSJpHNrNg8YJ+qo2Z9cSuLNdvZmZWNQcYMzNLImmAkTRT0jpJbZJ2evulpOGSrs+P3y1pXJ4+WtJKSR2Srig6Z4qkh/JzvpG/TsDMBpAZi2cwY/EM1j+//q1tz6MNPskCjKQhwJVkKwBMApolTSrKdjbwYkRMAL4GXJqnbwXOB/66RNFXAfOAifnXzN6vvZmZ9VTKHsxUoC0inoiI14FlwKyiPLPIlqMBuBE4XpIiYktE/IIs0LxF0n7AXhFxZ0QEsAT4aMI2mJlZN6W8i6wB2Fiw3w4cUy5PRGyT9DIwGtjcRZntRWU2lMooaR5ZT4cxY8bQ2tq6i9WvHR0dHXXTvuaRzTvsjxoyaqe0ejLY2lcvP6ed6ul3L4WUAabU3Eh0I0+38kfEQmAhQFNTU0yfPr2LYmtba2sr9dK+4luSm0c2s7RjaT/VJr3B1r6Vp9bXMv719LuXQsohsnbggIL9RuCZcnkkDQX2Bl6oUGZjhTLNzGwASBlgVgETJY2XNAyYA7QU5WkB5ubbpwEr8rmVkiLiWeBVScfmd4+dCdzU+1U3M7OeSjZEls+pzAduAYYA10TEWkkXAasjogVYBFwrqY2s5zKn8/z89cx7AcMkfRQ4MSIeAT4NfBt4O/DT/MvMzAaYpEvFRMRyYHlR2gUF21uB08ucO65M+mpgcu/V0szMUvCT/GZmloQDjJmZJeEAY2ZmSTjAmJlZEn4fjPULL3xoVv/cgzEzsyQcYMzMLAkHGDMzS8IBxszMknCAMTOzJBxgzMwsCQcYMzNLws/BmFmfKff808q59fUiMsu4B2NmZkm4B2NJ+Yl9s8HLPRgzM0vCAcbMzJJwgDEzsyQcYMzMLAkHGDMzS8IBxszMknCAMTOzJBxgzMwsCQcYMzNLwgHGzMyS8FIxZjZgeXHM2uYAY73Ca46ZWbGkQ2SSZkpaJ6lN0hdKHB8u6fr8+N2SxhUcOy9PXyfppIL0DZIekrRG0uqU9Tczs+5L1oORNAS4EjgBaAdWSWqJiEcKsp0NvBgREyTNAS4FZkuaBMwBDgX2B26XdFBEvJmfNyMiNqequ5mZ9VzKHsxUoC0inoiI14FlwKyiPLOAxfn2jcDxkpSnL4uI1yLiSaAtL8/MzGpEyjmYBmBjwX47cEy5PBGxTdLLwOg8/a6icxvy7QBulRTAv0fEwgR1N7M+5Dm8+pQywKhEWlSZp6tz/yAinpE0FrhN0qMR8Z87fbg0D5gHMGbMGFpbW6uueK3p6Ojo9/Y1j2xOUu6oIaOSlT0QuH3d098/750Gwu/eQJYywLQDBxTsNwLPlMnTLmkosDfwQlfnRkTn902SfkA2dLZTgMl7NgsBmpqaYvr06T1v0QDV2tpKf7dvweIFScptHtnM0o6lScoeCNy+7ll56sC4TXkg/O4NZCnnYFYBEyWNlzSMbNK+pShPCzA33z4NWBERkafPye8yGw9MBO6RtIekPQEk7QGcCDycsA1mZtZNyXow+ZzKfOAWYAhwTUSslXQRsDoiWoBFwLWS2sh6LnPyc9dKugF4BNgGnBsRb0p6J/CD7D4AhgLfjYibU7XBzMy6L+mDlhGxHFhelHZBwfZW4PQy534F+EpR2hPAEb1fUzMz621ei8zMzJLwUjFmVnO8RlltcA/GzMyScA/GdokfiDOzajnAmFnd8NDZwOIhMjMzS8I9GCvJQ2Fm1lPuwZiZWRIOMGZmloQDjJmZJeEAY2ZmSTjAmJlZEg4wZmaWhAOMmZkl4edgBjk/72JmqbgHY2ZmSTjAmJlZEh4iM7O650Uw+4cDjJkNWg48aXmIzMzMknCAMTOzJBxgzMwsCc/BmJkV8dxM73APxszMknAPps74yXwzGygcYGpUYSBpHtnMgsUL+rE2ZoND8X/gOn/3PHRWmofIzMwsCfdgBjgPeZlZrUoaYCTNBC4HhgBXR8QlRceHA0uAKcDzwOyI2JAfOw84G3gT+IuIuKWaMgc6351iVn/8e11asgAjaQhwJXAC0A6sktQSEY8UZDsbeDEiJkiaA1wKzJY0CZgDHArsD9wu6aD8nEplDgi72vNwT8Ws/vRW4KnVAJayBzMVaIuIJwAkLQNmAYXBYBZwYb59I3CFJOXpyyLiNeBJSW15eVRRppnZgNZb/6Hc1cDT14EqZYBpADYW7LcDx5TLExHbJL0MjM7T7yo6tyHfrlQmAJLmAfPy3dckPdyNNtSEVlr3BTb3dz1SqOe2gdtX6wZq+3SWeit/U0/qkTLAlKpxVJmnXHqpu96Ky8wSIxYCCwEkrY6Io8pXtbbVc/vquW3g9tW6wdC+npyf8jblduCAgv1G4JlyeSQNBfYGXuji3GrKNDOzASBlgFkFTJQ0XtIwskn7lqI8LcDcfPs0YEVERJ4+R9JwSeOBicA9VZZpZmYDQLIhsnxOZT5wC9ktxddExFpJFwGrI6IFWARcm0/iv0AWMMjz3UA2eb8NODci3gQoVWYV1VnYy80baOq5ffXcNnD7ap3b1wVlHQYzM7Pe5aVizMwsCQcYMzNLoi4DjKQhku6X9ON8f7ykuyU9Jun6/AaBmiRpH0k3SnpU0q8kHSdplKTb8vbdJukd/V3P7pL0l5LWSnpY0lJJI2r5+km6RtKmwuewyl0vZb4hqU3Sg5KO7L+aV6dM+/4p//l8UNIPJO1TcOy8vH3rJJ3UP7WuTqm2FRz7a0khad98vy6uXZ7+2fz6rJX0/wvSd/na1WWAAT4H/Kpg/1LgaxExEXiRbImaWnU5cHNEHAwcQdbOLwA/y9v3s3y/5khqAP4COCoiJpPdyNG5hFCtXr9vAzOL0spdrw+R3TE5kewh4av6qI498W12bt9twOSIOBxYD5wHULQE1EzgX/MlpQaqb7Nz25B0ANlyVf9dkFwX107SDLLVUQ6PiEOBy/L0bl27ugswkhqBk4Gr830Bf0S2FA3AYuCj/VO7npG0FzCN7O47IuL1iHiJ7AdicZ6tZtuXGwq8PX8uanfgWWr4+kXEf5LdIVmo3PWaBSyJzF3APpL265uadk+p9kXErRGxLd+9i+x5NShYAioingQKl4AacMpcO4CvAX/Ljg9518W1Az4NXJIv00VEbMrTu3Xt6i7AAF8nu/jb8/3RwEsFP/CFy87UmgOB3wDfyocAr5a0B/DOiHgWIP8+tj8r2V0R8TTZ/5j+myywvAzcS/1cv07lrlep5ZVqva2fBH6ab9d8+ySdAjwdEQ8UHar5tuUOAj6QD0n/XNLReXq32ldXAUbSh4FNEXFvYXKJrLV6b/ZQ4Ejgqoj4fWALNTocVko+FzELGE+2ivYeZEMPxWr1+lVSTz+rSPoi2XNs3+lMKpGtZtonaXfgi8AFpQ6XSKuZthUYCrwDOBb4G+CGfBSoW+2rqwAD/AFwiqQNwDKyoZWvk3VXOx8qreXlZdqB9oi4O9+/kSzgPNfZHc+/bypz/kD3QeDJiPhNRLwBfB94H/Vz/TqVu151sxSSpLnAh4Ez4ncP29V6+95D9p+fB/K/MY3AfZJ+j9pvW6d24Pv5UN89ZCNB+9LN9tVVgImI8yKiMSLGkU1IrYiIM4CVZEvRQLY0zU39VMUeiYhfAxslda5wejzZageFS+7UbPvIhsaOlbR7/r+mzvbVxfUrUO56tQDw/ry7AAAC+ElEQVRn5nckHQu83DmUVkuUvRTw74BTIuK3BYfKLQFVEyLioYgYGxHj8r8x7cCR+e9lXVw74Idk/zFH2Tu4hpGtFt29axcRdfkFTAd+nG8fmP9jtAH/AQzv7/r1oF3vBVYDD+Y/DO8gm2f6GfBY/n1Uf9ezB+1bADwKPAxcCwyv5esHLCWbT3qD7A/S2eWuF9kwxJXA48BDZHfT9XsbutG+NrLx+jX5178V5P9i3r51wIf6u/672rai4xuAfevs2g0Drst//+4D/qgn185LxZiZWRJ1NURmZmYDhwOMmZkl4QBjZmZJOMCYmVkSDjBmZpaEA4xZD0j6Yr7q7IOS1kg6JsFn/H1vl2nWF3ybslk3SToO+CowPSJey5duHxYRvfIEd8ESHa9ExMjeKNOsL7kHY9Z9+wGb43crz26OiGckbZD0D5LulLRa0pGSbpH0uKRPAUgaKelnku6T9JCkWXn6OGXv+flXsgfdFpGtLr1G0nck7SHpJ5IeUPbOnNn91XizStyDMesmSSOBX5C9VuB24PqI+Hm+TtWlEXGVpK+RLXnzB8AIYG1EjO18HUFEvJL3fO4iW37j3cATwPsiW/YdSR2dPRhJpwIzI+L/5vt7R8TLfdhss6q5B2PWTRHRAUwhe8HUb4DrJZ2VH27Jvz8E3B0Rr0bEb4Ctyt7wKOAfJD1IFpwagHfm5zzVGVxKeAj4oKRLJX3AwcUGsqGVs5hZORHxJtAKtEp6iN8tYvla/n17wXbn/lDgDGAMMCUi3sh7PSPyPFu6+Lz1kqYAfwz8o6RbI+KiXmqOWa9yD8asmyQ1SZpYkPRe4KkqT9+b7N1Fb+SvqX13F3nfkPS2/DP3B34bEdeRvZxtwL/73QYv92DMum8k8C/5kNc2slWE55G9B6WS7wA/krSabMXhR7vIuxB4UNJ9wBLgnyRtJ1sF99M9qL9ZUp7kNzOzJDxEZmZmSTjAmJlZEg4wZmaWhAOMmZkl4QBjZmZJOMCYmVkSDjBmZpbE/wKHOvnvwq3fAgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Histograms and text\n", - "\n", - "mu, sigma = 100, 15\n", - "x = mu + sigma * np.random.randn(10000)\n", - "\n", - "# the histogram of the data\n", - "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n", - "\n", - "plt.xlabel('Smarts')\n", - "plt.ylabel('Probability')\n", - "plt.title('Histogram of IQ')\n", - "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n", - "plt.axis([40, 160, 0, 0.03])\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Useful links\n", - "\n", - "Gallery of examples:\n", - "https://matplotlib.org/gallery/index.html\n", - "\n", - "pyplot.plot ('Markers', 'Line Styles', 'Colors'\n", - "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n", - "\n", - "pyplot.figure:\n", - "https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure\n", - " \n", - "line properties:\n", - "https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D\n", - "\n", - "matplotlib fonts:\n", - "http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samples/\n", - "\n", - "stlye sheet reference:\n", - "https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html\n", - "\n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}