{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Powers Example

\n", "

Meteorology 227


\n", "Example power plotting script to demonstrate several features available for simple plots.
\n", "
\n", "First, let's import our desired packages and make sure our images appear in our notebook.
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's setup a simple linear space and plot three powers of x on a single plot with a legend and gridines." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-2, 2, -10, 10]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(-5, 5, 100)\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel('x')\n", "ax.set_ylabel('f(x)')\n", "ax.set_title('Powers of x', fontdict={'size':22})\n", "ax.plot(x,x,label='$x$')\n", "ax.plot(x,x**2,'rs',label = '$x^2$')\n", "ax.plot(x,x**3,'g^',label = '$x^3$')\n", "ax.plot(x,x**4,'bo',label = '$x^4$')\n", "ax.grid(True)\n", "ax.legend(loc='lower right')\n", "plt.axis([-2,2,-10,10])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make the plots a little less busy, let's make two plots and put two powers on on each.
\n", "
\n", "Notice the use of markevery to reduce the number of marks on the lines and the use of subplots_adjust
\n", "to help with spacing between the plots for readability.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax0,ax1) = plt.subplots(nrows=2)\n", "ax0.set_xlabel('x')\n", "ax0.set_ylabel('f(x)')\n", "ax0.set_title('Powers of x', fontdict={'size':22})\n", "ax0.plot(x,x,label='$x$')\n", "ax0.plot(x,x**2,'rs',label = '$x^2$', markevery=5)\n", "ax1.set_xlabel('x')\n", "ax1.set_ylabel('f(x)')\n", "ax1.plot(x,x**3,'g^',label = '$x^3$', markevery=5)\n", "ax1.plot(x,x**4,'bo',label = '$x^4$', markevery=5)\n", "ax0.grid(True)\n", "ax0.legend(loc='lower right')\n", "ax1.grid(True)\n", "ax1.legend(loc='lower right')\n", "plt.subplots_adjust(hspace=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's put each power on its own plot. Key additions here are the addtion of a linestyle (although I didn't use
\n", "it all that creatively), an addtional subplots_adjust command with wspace to adjust the spacing between the plots in each row,
\n", "and the suptitle command with sizing to make a single \"super\" title for the whole plot." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ([ax0, ax1], [ax2, ax3]) = plt.subplots(2,2)\n", "plt.suptitle('Powers of x',fontsize=22)\n", "ax0.set_xlabel('x')\n", "ax0.set_ylabel('f(x)')\n", "ax0.plot(x,x,'b+',label='$x$', markevery=10, linestyle=\"-\")\n", "ax1.set_xlabel('x')\n", "ax1.set_ylabel('f(x)')\n", "ax1.plot(x,x**2,'rs',label='$x^2$', markevery=10, linestyle=\"-\")\n", "ax2.set_xlabel('x')\n", "ax2.set_ylabel('f(x)')\n", "ax2.plot(x,x**3,'g^',label='$x^3$', markevery=10, linestyle=\"-\")\n", "ax3.set_xlabel('x')\n", "ax3.set_ylabel('f(x)')\n", "ax3.plot(x,x**4,'bo',label='$x^4$', markevery=10, linestyle=\"-\")\n", "ax0.grid(True)\n", "ax1.grid(True)\n", "ax2.grid(True)\n", "ax3.grid(True)\n", "plt.subplots_adjust(hspace=0.5)\n", "plt.subplots_adjust(wspace=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }