{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from pylab import * \n", "from matplotlib.patches import Polygon\n", "\n", "def func(x):\n", " return (x-3)*(x-5)*(x-7)+85\n", "\n", "ax = subplot(111)\n", "for i in range(2):\n", " a, b = 2, 9 # integral area\n", " x = arange(0, 10, 0.1)\n", " y = func(x)\n", " plot(x, y, linewidth=1)\n", " show()\n", "\n", "\n", "#smart" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# implement the example graphs/integral from pyx\n", "from pylab import *\n", "from matplotlib.patches import Polygon\n", "%matplotlib inline\n", "def func(x):\n", " return (x-3)*(x-5)*(x-7)+85\n", "\n", "ax = subplot(111)\n", "\n", "a, b = 2, 9 # integral area\n", "x = arange(0, 10, 0.01)\n", "y = func(x)\n", "plot(x, y, linewidth=1)\n", "\n", "# make the shaded region\n", "ix = arange(a, b, 0.01)\n", "iy = func(ix)\n", "verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]\n", "poly = Polygon(verts, facecolor='0.8', edgecolor='k')\n", "ax.add_patch(poly)\n", "\n", "text(0.5 * (a + b), 30,\n", " r\"$\\int_a^b f(x)\\mathrm{d}x$\", horizontalalignment='center',\n", " fontsize=20)\n", "\n", "axis([0,10, 0, 180])\n", "figtext(0.9, 0.05, 'x')\n", "figtext(0.1, 0.9, 'y')\n", "ax.set_xticks((a,b))\n", "ax.set_xticklabels(('a','b'))\n", "ax.set_yticks([])\n", "show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "ename": "ValueError", "evalue": "invalid literal for int() with base 10: '23.90'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/home/jdn/off\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mint_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;31m# print int_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '23.90'" ] } ], "source": [ "from pylab import *\n", "from matplotlib.patches import Polygon\n", "\n", "# all numbers on one line\n", "with open(\"/home/jdn/off\") as f:\n", " for line in f:\n", " int_list = [int(i) for i in line.split()]\n", " # print int_list\n", " \n", "ax = subplot(111)\n", " \n", "\n", "plot(int_list, linewidth=1)\n", "show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[' ', 1, 2, 4, 56, 7, 4, 2, 1, 124, 5, 7, 8, 7, 5, 234, 243, 4, 4, 5, 55, 5, 5, 5]\n" ] }, { "ename": "ValueError", "evalue": "could not convert string to float: ", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0myy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mii\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mii\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mint_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint_list\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0myy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'int_list' is not defined" ] } ], "source": [ "from pylab import *\n", "from matplotlib.patches import Polygon\n", "\n", "# all numbers on one line\n", "\n", "ii= 1\n", "with open(\"/home/jdn/data1\") as f:\n", " for line in f:\n", " l = [int(i) for i in line.split()]\n", " yy= yy + l[2]\n", " ii = ii+1\n", " int_list = int_list +yy\n", "\n", " \n", "ax = subplot(111)\n", " \n", "\n", "plot(int_list, linewidth=1)\n", "show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.6666666667\n", "\n", "48.1666666667\n", "\n", "73.1666666667\n", "\n", "45\n", "\n", "46.3333333333\n", "\n", "43.3333333333\n", "\n", "52.5\n", "\n", "65\n", "\n", "71.3333333333\n", "\n", "65\n", "\n", "69.3333333333\n", "\n", "48\n", "\n", "47.8333333333\n", "\n", "51.5\n", "\n", "56.8333333333\n", "\n", "20\n", "\n", "48.6666666667\n", "\n", "41.5\n", "\n", "48\n", "\n", "27\n", "\n", "39.3333333333\n", "\n", "35.3333333333\n", "\n", "27.6666666667\n", "\n", "54\n", "\n", "66.3333333333\n", "\n", "57.6666666667\n", "\n", "17.5\n", "\n", "44.6666666667\n", "\n", "39.6666666667\n", "\n", "65\n", "\n", "75.6666666667\n", "\n", "72.8333333333\n", "\n", "53.3333333333\n", "\n", "77.6666666667\n", "\n", "77\n", "\n", "79.1666666667\n", "\n", "46.3333333333\n", "\n", "93\n", "\n", "64.3333333333\n", "\n", "71.6666666667\n", "\n", "49.1666666667\n", "\n", "0\n", "\n", "67.5\n", "\n", "74.6666666667\n", "\n", "60.6666666667\n", "\n", "49\n", "\n", "54\n", "\n", "48.8333333333\n", "\n", "59.5\n", "\n", "0\n", "\n", "48.1666666667\n", "\n", "39.1666666667\n", "\n", "51.3333333333\n", "\n" ] }, { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0myy\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0myy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0mii\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mii\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mint_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint_list\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0myy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "from pylab import *\n", "from matplotlib.patches import Polygon\n", "\n", "# all numbers on one line\n", "\n", "ii= 1\n", "yy = 0\n", "with open(\"/home/jdn/Desktop/shit1\") as f:\n", " for line in f:\n", " print line\n", " \n", " l = [float(i) for i in line.split()]\n", " yy= yy + l[1]\n", " ii = ii+1\n", " int_list = int_list +yy\n", "\n", " \n", "ax = subplot(111)\n", " \n", "\n", "plot(int_list, linewidth=1)\n", "show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 3]\n", "[2, 4]\n", "[3, 5]\n" ] } ], "source": [ "# Corey Goldberg - 2009\n", "\n", "\n", "# Waterfall plot using Matplotlib\n", "# under construction\n", "\n", "\n", "from pylab import *\n", "\n", "\n", "\n", "def waterfall_graph(time_lines):\n", " fig = figure(figsize=(8, 3)) # image dimensions \n", " ax = fig.add_subplot(111)\n", " ax.set_xlabel('Transfer Time (millisecs)', size='x-small')\n", " xticks(size='xx-small')\n", " ax.yaxis.set_major_formatter(NullFormatter())\n", " #ax.set_yticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n", "\n", " for i, line in enumerate(time_lines):\n", " print line\n", " ax.plot(line, [i + 1, i + 1])\n", " \n", " ax.set_ylim(0, len(time_lines) + 1)\n", " savefig('foo.png')\n", " \n", "\n", "a = [1, 3]\n", "b = [2, 4]\n", "c = [3, 5]\n", "\n", "waterfall_graph([a, b, c])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from pylab import plotfile, show, gca\n", "import matplotlib.cbook as cbook\n", "\n", "fname = cbook.get_sample_data('msft.csv', asfileobj=False)\n", "fname2 = cbook.get_sample_data('data_x_x2_x3.csv', asfileobj=False)\n", "\n", "# test 1; use ints\n", "plotfile(fname, (0,5,6))\n", "\n", "# test 2; use names\n", "plotfile(fname, ('date', 'volume', 'adj_close'))\n", "\n", "# test 3; use semilogy for volume\n", "plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'})\n", "\n", "# test 4; use semilogy for volume\n", "plotfile(fname, (0,5,6), plotfuncs={5:'semilogy'})\n", "\n", "#test 5; single subplot\n", "plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False)\n", "\n", "# test 6; labeling, if no names in csv-file\n", "plotfile(fname2, cols=(0,1,2), delimiter=' ',\n", " names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])\n", "\n", "# test 7; more than one file per figure--illustrated here with a single file\n", "plotfile(fname2, cols=(0, 1), delimiter=' ')\n", "plotfile(fname2, cols=(0, 2), newfig=False, delimiter=' ') # use current figure\n", "gca().set_xlabel(r'$x$')\n", "gca().set_ylabel(r'$f(x) = x^2, x^3$')\n", "\n", "# test 8; use bar for volume\n", "plotfile(fname, (0,5,6), plotfuncs={5:'bar'})\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from pylab import plotfile, show, gca\n", "import matplotlib.cbook as cbook\n", "\n", "fname = cbook.get_sample_data('/home/jdn/offf', asfileobj=False)\n", " \n", "# test 1; use ints\n", "plotfile(fname, cols=(0,1))\n", " \n", "show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "with open(\"/home/jdn/off2.txt\") as f:\n", " data = f.read()\n", "\n", "data = data.split('\\n')\n", "\n", "x = [row.split(' ')[0] for row in data]\n", "y = [row.split(' ')[1] for row in data]\n", "\n", "fig = plt.figure()\n", "\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.set_title(\"Plot title...\") \n", "ax1.set_xlabel('your x label..')\n", "ax1.set_ylabel('your y label...')\n", "\n", "ax1.plot(x,y, c='r', label='the data')\n", "\n", "leg = ax1.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ " #Need to import the plotting package:\n", "import matplotlib.pyplot as plt\n", "\n", "# Read the file.\n", "f2 = open('/home/jdn/off2', 'r')\n", "# read the whole file into a single variable, which is a list of every row of the file.\n", "lines = f2.readlines()\n", "f2.close()\n", "\n", "# initialize some variable to be lists:\n", "x1 = []\n", "y1 = []\n", "\n", "# scan the rows of the file stored in lines, and put the values into some variables:\n", "for line in lines:\n", " p = line.split()\n", " x1.append(float(p[0]))\n", " y1.append(float(p[1]))\n", " \n", "xv = np.array(x1)\n", "yv = np.array(y1)\n", "\n", "\n", "# now, plot the data:\n", "plt.plot(xv, yv)\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.4\n", "1.49666295471\n", "qed\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Need to import the plotting package:\n", "import matplotlib.pyplot as plt\n", "import statistics\n", "\n", "# Read the file.\n", "f2 = open('/home/jdn/off2', 'r')\n", "# read the whole file into a single variable, which is a list of every row of the file.\n", "# every line format two numbers \n", "lines = f2.readlines()\n", "f2.close()\n", "\n", "# initialize some variable to be lists:\n", "x1 = []\n", "y1 = []\n", "\n", "# scan the rows of the file stored in lines, and put the values into some variables:\n", "for line in lines:\n", " p = line.split()\n", " x1.append(float(p[0]))\n", " y1.append(float(p[1]))\n", " \n", "xv = np.array(x1)\n", "yv = np.array(y1)\n", "\n", "print (statistics.mean(yv))\n", "print (statistics.pstdev(yv))\n", "# now, plot the data:\n", "plt.plot(xv, yv)\n", "print \"qed\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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WrdbyBRfAN99AUpK9uSQsrV69msTERBo1amR3FBEJMO3et5vTCTfeCD/9ZC0n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\nve9yWXfQy53alJEjw6bwJXw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Plot demonstrating the integral as the area under a curve.\n", "\n", "Although this is a simple example, it demonstrates some important tweaks:\n", "\n", " * A simple line plot with custom color and line width.\n", " * A shaded region created using a Polygon patch.\n", " * A text label with mathtext rendering.\n", " * figtext calls to label the x- and y-axes.\n", " * Use of axis spines to hide the top and right spines.\n", " * Custom tick placement and labels.\n", "\"\"\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Polygon\n", "\n", "\n", "def func(x):\n", " return (x - 3) * (x - 5) * (x - 7) + 85\n", "\n", "\n", "a, b = 2, 9 # integral limits\n", "x = np.linspace(0, 10)\n", "y = func(x)\n", "\n", "fig, ax = plt.subplots()\n", "plt.plot(x, y, 'r', linewidth=2)\n", "plt.ylim(ymin=0)\n", "\n", "# Make the shaded region\n", "ix = np.linspace(a, b)\n", "iy = func(ix)\n", "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", "ax.add_patch(poly)\n", "\n", "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", " horizontalalignment='center', fontsize=20)\n", "\n", "plt.figtext(0.9, 0.05, '$x$')\n", "plt.figtext(0.1, 0.9, '$y$')\n", "\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.xaxis.set_ticks_position('bottom')\n", "\n", "ax.set_xticks((a, b))\n", "ax.set_xticklabels(('$a$', '$b$'))\n", "ax.set_yticks([])\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "\"\"\"\n", "Plot demonstrating the integral as the area under a curve.\n", "\n", "Although this is a simple example, it demonstrates some important tweaks:\n", "\n", " * A simple line plot with custom color and line width.\n", " * A shaded region created using a Polygon patch.\n", " * A text label with mathtext rendering.\n", " * figtext calls to label the x- and y-axes.\n", " * Use of axis spines to hide the top and right spines.\n", " * Custom tick placement and labels.\n", "\"\"\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Polygon\n", "\n", "\n", "def func(x):\n", " return (x - 3) * (x - 5) * (x - 7) + 85\n", "\n", "\n", "a, b = 2, 9 # integral limits\n", "x = np.linspace(0, 10)\n", "y = func(x)\n", "\n", "fig, ax = plt.subplots()\n", "plt.plot(x, y, 'r', linewidth=2)\n", "plt.ylim(ymin=0)\n", "\n", "# Make the shaded region\n", "ix = np.linspace(a, b)\n", "iy = func(ix)\n", "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", "ax.add_patch(poly)\n", "\n", "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", " horizontalalignment='center', fontsize=20)\n", "\n", "plt.figtext(0.9, 0.05, '$x$')\n", "plt.figtext(0.1, 0.9, '$y$')\n", "\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.xaxis.set_ticks_position('bottom')\n", "\n", "ax.set_xticks((a, b))\n", "ax.set_xticklabels(('$a$', '$b$'))\n", "ax.set_yticks([])\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Plot demonstrating the integral as the area under a curve.\n", "\n", "Although this is a simple example, it demonstrates some important tweaks:\n", "\n", " * A simple line plot with custom color and line width.\n", " * A shaded region created using a Polygon patch.\n", " * A text label with mathtext rendering.\n", " * figtext calls to label the x- and y-axes.\n", " * Use of axis spines to hide the top and right spines.\n", " * Custom tick placement and labels.\n", "\"\"\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Polygon\n", "\n", "\n", "def func(x):\n", " return (x - 3) * (x - 5) * (x - 7) + 85\n", "\n", "\n", "a, b = 2, 9 # integral limits\n", "x = np.linspace(0, 10)\n", "y = func(x)\n", "\n", "fig, ax = plt.subplots()\n", "plt.plot(x, y, 'r', linewidth=2)\n", "plt.ylim(ymin=0)\n", "\n", "# Make the shaded region\n", "ix = np.linspace(a, b)\n", "iy = func(ix)\n", "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", "ax.add_patch(poly)\n", "\n", "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", " horizontalalignment='center', fontsize=20)\n", "\n", "plt.figtext(0.9, 0.05, '$x$')\n", "plt.figtext(0.1, 0.9, '$y$')\n", "\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.xaxis.set_ticks_position('bottom')\n", "\n", "ax.set_xticks((a, b))\n", "ax.set_xticklabels(('$a$', '$b$'))\n", "ax.set_yticks([])\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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GRkJ8jthyKEhHewcgpK/qQBOKsOwdPxsG0BfIFbF3VHn6UeXpu9k7LIodcK+9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\nCcy6efRRKX1ZCJP+d7/7XbzyyiuYO3c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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "x = np.linspace(0, 3*np.pi, 500)\n", "plt.plot(x, np.sin(x**2))\n", "plt.title('A simple chirp');\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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qqadYunQp4eEXL+Ps2yR03WDnzp3s+f33Mr3kt6z5A/9xOPgeiPoFgqcZoEDn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\nYt/1QB2gBhAA/ANYCPgBhTg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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#!/usr/bin/env python\n", " \n", "import matplotlib.pyplot as pyplot\n", " \n", "x_list = [1, 3, 6]\n", "label_list = [\"bells\", \"whistles\", \"pasta\"]\n", " \n", "pyplot.axis(\"equal\")\n", "pyplot.pie(\n", " x_list,\n", " labels=label_list,\n", " autopct=\"%1.1f%%\"\n", " )\n", "pyplot.title(\"Pastafarianism expenses\")\n", "pyplot.show()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%http://stackoverflow.com/questions/25735153/plotting-a-fast-fourier-transform-in-python\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.fftpack\n", "\n", "# Number of samplepoints\n", "N = 600\n", "# sample spacing\n", "T = 1.0 / 800.0\n", "x = np.linspace(0.0, N*T, N)\n", "y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)\n", "yf = scipy.fftpack.fft(y)\n", "xf = np.linspace(0.0, 1.0/(2.0*T), N/2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(xf, 2.0/N * np.abs(yf[0:N/2]))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 0.20000000000000001, 1]\n", "[1, 0.40000000000000002, 1]\n", "[1, 0.60000000000000009, 1]\n", "[1, 0.80000000000000004, 1]\n", "[1, 1.0, 1]\n", "[1, 1.2, 1]\n", "[1, 1.4000000000000001, 1]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "[1, 1.6000000000000001, 1]\n", "[1, 1.8, 1]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n", "ERROR: Line magic function `%Specify` not found.\n" ] }, { "data": { "image/png": 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lRNmtG3DiBFeP27KFa0W8/TbXgShZUnfN2Fi6ZXbtolDcvcvMqe7duQxq+fKm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\n+/bAzZu0EPz8aEF4eDA+sWcPK8dbtWK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x5AgtiKAgZl9lZ/N1BwdaOUuWsCblrbesrZyVK4GNG2nlaAID6aJ6+mnj3Dvv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\nUixCjIxky5OdO9k3q0kTWj3B/9/evYVE1fVxHP+W+aSmFT1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Pavczz4h8+ql2jxypG15QkL7OyNCDByDyyCMie/aYy1heruEAaMAsWqTdM2dq\nyAN60Dp8WHdsDw/dyRcuFOncWQ/GO3eKdOmiO1FmpvbXu7eWycKFugNGRor8+9+6s3bvrsMEB2sA\nLlyoj+BgnZdt27QsfHxEkpN1J77ooiUyY4bIgw+KhIdrEGzapCEREKAH2/BwXfbx47W/iAgdz/r1\n2t+ll+o8jRih4XPttbodjBihQbV2rfYfHq4BNHasHqhHjdIdcehQ7W/NGjPYHnxQJCpK5Fe/0gN6\nQoLIoEHm+Hx9dfz33aehNXq0htNzz2k5+frq/NntOr5779UDeESEHsDXrdOQ9PXVsujfX8vinntE\n7r9fy2bAAD0w+Ppq+b77rh74+vcXuftuDSrjQJ6ergfubt10fV99tQ53zz0iL76o4d+/v4aLt7du\nu6mpIlOn6rZ+771a7oD2V1qq8w5oBco4CM2fb25nAQG6TYeF6etdu0SefFK7Fy4096s+fXS7jY7W\n13v2iCQmavfvfy9y/Lh2d++u+8HMmbqP7N2rywKILF5sVt6Mfc7YRz77TOTrr7X7j3/Uzzp3Nvt7\n4QUzL44d0+7//m/9bMAAs7/kZLM/Y1r33aefXXWV2d/evdqdlWXmxfTp2j15stlfYaGZHSK6/H/9\nq3a/+qrIFVdod12dHrgLC83xGRVZfe2iwf/GG2+cVfCPHy+yebN2r12rNTQR3YgArY3pMHrEF9EN\nxBhFTY0ZkEZ/kydrtxHUIubK++orfd2hg4aCiIb8z/v7+GN9bbdrGIiY3wbq6sz+kpL0syFDNKhE\ntBYLaA3R6G/VKv3smmu0VieiNS9giRw7ZvbncOhnkyadWVuovzEsWqTds2eb/WVlaXdmptlfXJx2\nP/igOe+5udr9ySdmf8a3KaOmU1VlHiyNb1MdO2pIioisXGl+mzIOvq+/bpZZSIh2v/yyfnb0qHkw\nN8oiLEwDSURrssASyc8X+fFH7W/ZMv3s8st1Z6ir01qssaOdPGke6EREbrrJrIkZ6+Dzz7UGB2jw\niWjoGusnLc0Mqupq7b7tNu3PCLiKCnMdvP++jh/QSouIBodRFt98o92bNun8enmZO+5TT5mVmfx8\n7X7tNbPMgoPrb49L5IcfzDJ76SX9bPBgPViKmLXNggKdNqDTENFwstm0OyXFDB1jvzJqm/W3s/oh\nZmyP8+frZ8a37Lo63b4A/dZjbD8zZmi3UdmqqzPDLj39zO3sL38x+zPW986d+lm3bmYQ6vazROrq\nzHnftk2W4Pf1AAAF40lEQVQ/69dPtyERM6hra815N7bHESP0gCOi+/XPc8Uo2+uvN8vMOHhUV5v9\nPfmkdhvfGkREfvhBuysrzf4WLtTuu+82+zPmvaxMX992m8grr2j3pk36TdHQo8fpFd7f/Mb8zGWD\nf9euXac19Tz55JPiMNLsJ8HBwQKADz744IOPs3gEG7WDVmATEUErqampwaBBg/D++++jb9++GDNm\nDNatW4fBgwe31iSIiOgcebbqyDw98dxzz+GGG25AbW0t5syZw9AnInIxrVrjJyIi19emV+6ezcVd\n7iAvLw/XXXcdhgwZgqFDh+KZn062LykpQUxMDEJDQzFu3Dg4jftJA0hISEBISAjCwsKwbdu29pr1\n86K2thaRkZGYMGECAOuWAwA4nU5MnToVgwcPRnh4ONLS0ixZHgkJCRgyZAiGDRuG6dOno7Ky0jLl\nMHv2bNjtdgwbNuzUey1Z9k8//RTDhg1DSEgI7r///uZNvNV+LfgFNTU1EhwcLDk5OVJVVSURERGS\nkZHRVpNvF4WFhbJv3z4RETl+/LiEhoZKRkaG/P73v5elS5eKiIjD4ZCHH35YREQOHDggERERUlVV\nJTk5ORIcHCy1tbXtNv+t7amnnpLp06fLhAkTREQsWw4iIjNnzpTVP503XF1dLU6n03LlkZOTIwMG\nDJCKigoREYmNjZVXXnnFMuXw73//W9LT02WoceqgnN0+UVdXJyIio0ePlrS0NBER+c1vfiPvvPPO\nL067zYK/ORd3ububb75Z3nvvPRk0aJAUFRWJiB4cBg0aJCJnngV1ww03yK5du9plXltbXl6eREdH\ny/bt22X8T+f1WbEcREScTqcMGDDgjPetVh5Hjx6V0NBQKSkpkerqahk/frxs27bNUuWQk5NzWvCf\n7bIXFBRImHFeq4isW7dOfmdcONSENmvqyc/PRz/jemYAgYGByM/Pb6vJt7vc3Fzs27cPY8eORXFx\nMew/XdNtt9tRXFwMACgoKEBgYOCpYdypjBYsWIBly5bBw8Pc5KxYDgCQk5ODPn36YNasWRg5ciTu\nuusunDx50nLl4ePjg0WLFqF///7o27cvevXqhZiYGMuVQ31nu+w/fz8gIKBZZdJmwW8z7rZmQSdO\nnMCUKVOwcuVK9Kh/AyJouTRVNu5Qbps3b4afnx8iIyMhjZxLYIVyMNTU1CA9PR1z585Feno6unXr\nBofDcVo/ViiP7OxsrFixArm5uSgoKMCJEyewdu3a0/qxQjk05peW/Vy0WfAHBAQgr94dpPLy8k47\nUrmr6upqTJkyBTNmzMCkSZMA6JG8qKgIAFBYWAi/n+4w9/MyOnToEAICAtp+plvZzp07kZycjAED\nBmDatGnYvn07ZsyYYblyMAQGBiIwMBCjR48GAEydOhXp6enw9/e3VHns3bsXV1xxBXx9feHp6YnJ\nkydj165dliuH+s5mnwgMDERAQAAOHTp02vvNKZM2C/5Ro0YhKysLubm5qKqqwoYNGzBx4sS2mny7\nEBHMmTMH4eHheMD4nzYAEydORGJiIgAgMTHx1AFh4sSJWL9+PaqqqpCTk4OsrCyMGTOmXea9NT35\n5JPIy8tDTk4O1q9fj+uvvx5r1qyxXDkY/P390a9fP2RmZgIAUlJSMGTIEEyYMMFS5REWFobdu3ej\nvLwcIoKUlBSEh4dbrhzqO9t9wt/fHz179kRaWhpEBGvWrDk1TJNa4weK5tqyZYuEhoZKcHCwPGnc\n/MKN7dixQ2w2m0RERMiIESNkxIgR8s4778jRo0clOjpaQkJCJCYmRkpLS08N8+c//1mCg4Nl0KBB\nstW4q5wbSU1NPXVWj5XL4bPPPpNRo0bJ8OHD5ZZbbhGn02nJ8li6dKmEh4fL0KFDZebMmVJVVWWZ\ncrj99tvl4osvFi8vLwkMDJSXX365Rcu+d+9eGTp0qAQHB8t9xt3kfgEv4CIisph2/etFIiJqewx+\nIiKLYfATEVkMg5+IyGIY/EREFsPgJyKyGAY/EZHFMPiJiCzm/wHtDgAFTews0wAAAABJRU5ErkJg\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import *\n", "from pylab import * # Not needed if you use ipython -pylab\n", "a = zeros(1000)\n", "a[:100]=1\n", "b = fft(a)\n", "plot(abs(b))\n", "show() " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " # http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.bode.html\n", "from scipy import signal\n", "import matplotlib.pyplot as plt\n", "\n", "s1 = signal.lti([1,0.5], [1, 1])\n", "w, mag, phase = signal.bode(s1)\n", "\n", "plt.figure()\n", "plt.semilogx(w, mag) # bode magnitude plot\n", "plt.figure()\n", "plt.semilogx(w, phase) # bode phase plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "s + 0.5\n", "-------\n", " s + 1\n", "\n", "A = [[-1.]]\n", "\n", "B = [[ 1.]]\n", "\n", "C = [[-0.5]]\n", "\n", "D = [[ 1.]]\n", "\n" ] }, { "data": { "image/png": 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a3jiKi4s7/P147LHHmsba+LcxPz8fr9fLrFmz2tRxDKe3CvbNubkAOAD8CqgA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "from matplotlib.pyplot import * # Grab MATLAB plotting functions\n", "from control.matlab import * # MATLAB-like functions\n", "\n", "\n", "# Transfer functions for dynamics\n", "G1_modele = tf([1], [1,1]);\n", "G2_modele = tf([1,0.5], [1,1]);\n", "\n", "# Use state space versions\n", "#G_modele = tf2ss(G_modele);\n", "print (G2_modele)\n", "G_modele = tf2ss(G2_modele);\n", "print (G_modele)\n", "\n", "#figure(1); \n", "bode(G1_modele, dB=1); # 1 color /JDN\n", "bode(G2_modele, dB=1); # 1 color /JDN\n", "show();" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import signal\n", "#from matplotlib import *\n", "import matplotlib.pyplot as plt\n", "\n", "s1 = signal.lti([1], [1,2244.5926792592,39261722.568816 ])\n", "w, mag, phase = signal.bode(s1)\n", "\n", "plt.figure()\n", "semilogx(w, mag) # bode magnitude plot\n", "plt.figure()\n", "semilogx(w, phase) # bode phase plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from matplotlib.pyplot import * # Grab MATLAB plotting functions\n", "from control.matlab import * # MATLAB-like functions\n", "\n", "\n", "# Transfer functions for dynamics\n", "G_modele = tf([1,0.5], [1,1]);\n", "\n", "# Use state space versions\n", "#G_modele = tf2ss(G_modele);\n", "print G_modele\n", "G_modele = tf2ss(G_modele);\n", "print G_modele\n", "\n", "figure(1); \n", "bode(G_modele, dB=1); # 1 color /JDN\n", "show();" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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AuJ5jxLHFiDgT+CzwKrAZsAVwPfARYGpmromI9wOzM3PmEI93bFFS1wyeYT/rLJg5sz9n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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from pylab import * \n", "from matplotlib.patches import Polygon\n", " \n", "ax = subplot(111)\n", "\n", "a, b = 2, 9 # integral area\n", "x = [21,30,40,50,60,70]\n", "y = [60,56,51,45,41,37]\n", "plot(x, y, linewidth=1)\n", "show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26.3063063063\n", "63.7837837838\n", "37.4774774775\n" ] }, { "data": { "image/png": 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TT0KZMqFTiYRz1FFWHGrWtDMRTz0VOpGkq+KhA4jsqZUr4e9/t3nxzz9vZx9ExJa+fvNN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\n10cf1aULERFJbXXqwLBh8Pj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "import numpy as np\n", "\n", "\n", "x = np.linspace(0, pi/2, 1000) # 1000 evenly-spaced values from 0 to 50\n", "z = np.linspace(0, 90, 1000)\n", "\n", "y = pow(np.sin(2*x), 3)\n", "\n", "\n", "low = 1\n", "for i in range(1000):\n", " if y[i] < 0.5:\n", " v[i] = 0.0\n", " if low == 0:\n", " low = 1\n", " a = z[i]\n", " print (z[i])\n", " else:\n", " if low == 1:\n", " low = 0\n", " b = z[i]\n", " print(z[i])\n", " \n", " v[i] = 0.5\n", " \n", "print(a-b)\n", "yy = 10 * log10(y+0.01)\n", "pylab.plot(z, yy) \n", "pylab.plot(z, v) \n", "show()\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n" ] } ], "source": [ "for i in range(4):\n", " print (i)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000\n", "in Celcius (47 kOhm NTC)\n", "296.769673003\n", "308.487894455\n", "2\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "from pylab import *\n", "k47=[47000.0,3.354016e-3,2.519107e-4,3.510939e-6,1.105179e-7]\n", "\n", "def ntcTemp(r,l): \n", " f = numpy.log(r/l[0])\n", " return ( 1.0/(l[1]+l[2]*f + l[3]*f*f + l[4]*f*f*f) ) # steinhart & hart formula KELVIN\n", "\n", " \n", "def doY(fct,k):\n", " print (fct(50000,k)) \n", " print (fct(30000,k)) \n", " return\n", "\n", "x = numpy.linspace(1000, 200000, 1000) # 100 evenly-spaced values from 0 to 50\n", " \n", "z=[]\n", "for i in range(1000): \n", " z.append(ntcTemp(x[i],k47)-273.15) \n", "\n", "calcVal(x,k47)\n", "\n", "print(\"in Celcius (47 kOhm NTC)\")\n", "\n", "doY(ntcTemp,k47)\n", "\n", "plot(x,z)\n", "xx = []\n", "yy=[]\n", "xx.append(1000)\n", "xx.append(55000)\n", "yy.append(25)\n", "yy.append(25)\n", "plot(xx,yy)\n", "\n", "xx = []\n", "yy=[]\n", "xx.append(47000)\n", "xx.append(47000)\n", "yy.append(10)\n", "yy.append(30)\n", "plot(xx,yy)\n", "\n", "print(len(yy))\n", "\n", "show()\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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mq81sg5mNMbM6Zf3cJ56AmjXh1lvL+k4iIiJSWhXVI/EVUBeoFzzO\niDj3DHARcBnQDmgAjC3Lh61ZA8OGwe23Q+3aZXknERERiUVFrWy50zm3KvqgmdUGegM9nHMfBceu\nAxaaWWvn3KfxfNiwYeAc3HxzmWoWERGRGFVUj8TvzWyZmX1vZqPNrFFwPAMfXqYWXeicWwwsAU6L\n54O2b4dBg/wqlr/7XZnrFhERkRhURJCYA/QCOgF9gGOAj82sFv42x3bn3Pqo16wMzsVszBhYvhz6\n9Yu/YBEREYlPud/acM5Ninj6lZl9CvwMdAe2luW9s7KySEtL2+3YggWZdOiQyUknleWdRUREEl92\ndjbZ2dm7HcvPz6/QzzTnXIV+AEAQJqYAHwaPQyJ7JczsJ+Bp59zfS3h9OpCTk5NDenr6f45/9RWc\nfLLvlbjssopsgYiISGLKzc0lIyMDIMM5l1ve71/h60iY2YHAccByIAfYCbSPOH8CcCQwO9b3fukl\nOPxw6NKlnIoVERGRmJT7rQ0zewJ4D3874wjgAXx4eN05t97MRgADzWwdsAF4Fvgk1hkb27fDqFFw\nzTVQvXr5tkFERERKpyKmfzYEXgMOA1YBM4G2zrk1wfksoAAYA9QAJgIxL2o9fjysWgW9e5dLzSIi\nIhKHihhsmbmX89uAW4NH3F5+GVq2RIMsRUREQpSQe21s2AATJ0KPHmFXIiIiktoSMkhMmADbtmmm\nhoiISNgSMkiMGeNvaxx9dNiViIiIpLaECxJbtvgeCfVGiIiIhC/hgsTMmbB5M3TuHHYlIiIiknBB\nYsoUqF8fmjULuxIRERFJuCAxeTJ06ABmYVciIiIiCRUk1qyBefPgvPPCrkREREQgwYLEZ5/5/+3Q\nIdw6RERExEuoIDFvHhx/vB8jISIiIuFLqCDx9dfQpk3YVYiIiEiRhAoSixdD69ZhVyEiIiJFEipI\n7NypICEiIlKVJFSQqFYNmjcPuwoREREpklBBokEDqFEj7CpERESkSEIFiSOPDLsCERERiaQgISIi\nInFLqCBx1FFhVyAiIiKREipINGoUdgUiIiISKaGChG5tiIiIVC0JFSQOOSTsCkRERCRSQgWJAw4I\nuwIRERGJlFBBwizsCipednZ22CVUCrUzuaRKOyF12qp2SmmFFiTM7BYz+9HMtpjZHDNrFVYtVUmq\n/FCrncklVdoJqdNWtVNKK5QgYWZXAk8B9wEtgHnAJDM7PIx6REREJD5h9UhkAUOdc6845xYBfYDN\nQO+Q6hEREZE4VHqQMLP9gAxgatEx55wDPgROq+x6REREJH77hvCZhwP7ACujjq8ETijhNfsDLFy4\nsALLqhry8/PJzc0Nu4wKp3Yml1RpJ6ROW9XO5BHxu3P/inh/850BlcfM6gPLgNOcc3Mjjj8GtHPO\n/VevhJldBbxaeVWKiIgknZ7OudfK+03D6JFYDRQAdaOO1wXySnjNJKAn8BOwtcIqExERST77A0fj\nf5eWu0rvkQAwsznAXOdcv+C5AUuAZ51zT1R6QSIiIhKXMHokAAYCI80sB/gUP4ujJjAypHpEREQk\nDqEECefcm8GaEQ/ib2n8G+jknFsVRj0iIiISn1BubYiIiEhySKi9NkRERKRqUZAQERGRuFX5IJFs\nm3uZ2X1mVhj1WBBxvoaZDTaz1Wa2wczGmFmdMGsuLTM708zeNbNlQbsuLuaaB81suZltNrMpZtY4\n6vwhZvaqmeWb2TozG25mtSqvFXu3t3aa2UvFfMcToq6p0u00s7vM7FMzW29mK83sbTM7Puqavf6s\nmlkjMxtvZpvMLM/MHjezKvP3TinbOT3quywwsyFR11TpdgKYWR8zmxf8zOWb2SwzOz/ifMJ/n1Cq\ndibF9xnJzO4M2jIw4lilfZ9V9v8YSOrNvb7CDzKtFzzOiDj3DHARcBnQDmgAjK3sAuNUCz9w9mbg\nvwbfmFl/oC9wI9Aa2IT/PqtHXPYa0BRoj///oR0wtGLLjtke2xn4gN2/48yo81W9nWcCzwFtgA7A\nfsBkMzsg4po9/qwGfyFNwA/qbgv8EeiFH2RdVZSmnQ4Yxq7vsz5wR9HJBGknwFKgP5CO36ZgGvCO\nmTUNzifD9wl7b2eyfJ8AmP/H9Y3434+RKu/7dM5V2QcwB/h7xHMDfgHuCLu2MrTpPiC3hHO1gW1A\nt4hjJwCFQOuwa4+xnYXAxVHHlgNZUe3dAnQPnjcNXtci4ppOwE6gXthtiqGdLwHj9vCaJgnYzsOD\nms+I+O72+LMKXADsAA6PuOYmYB2wb9htKk07g2P/Agbu4TUJ186IOtcA1yXr9xndzmT7PoEDgcXA\nuZHtquzvs8r2SFhyb+71+6Bb/HszG21mjYLjGfh0GNnmxfjFuhK6zWZ2DD79R7ZtPTCXXW1rC6xz\nzn0R8dIP8f+CaFNJpZaXs4Ou8kVmNsTMDo04dxqJ186D8fWtDZ6X5me1LTDfObc64n0mAWlAs4ou\nOE7R7SzS08xWmdl8M3s4qsci4dppZtXMrAd+/Z7ZJOn3GdXOWRGnkuX7HAy855ybFnW8JZX4fYa1\nIFVpxLO5VyKYg+8+WozvUrsf+NjMTsL/ot0e/IKNtDI4l8jq4f+CLu77rBdxza+RJ51zBWa2lsRq\n/wf4LsQfgeOAR4AJZnZaEIYTqp1mZvhu0pnOuaLxPKX5Wa1H8d930bnorthQldBO8Pv8/IzvUTsF\neBw4Hrg8OJ8w7Qz+npmNXzJ5A/5frIvMrAVJ9H2W0M7Fwemk+D6DgHQqPjREq0slfp9VOUgkJedc\n5FrnX5nZp/gf6u5oH5Gk4Jx7M+Lp12Y2H/geOBvf/ZhohgAnsvtYnmRU1M7TIw8654ZHPP3azPKA\nqWZ2jHPux8ossBwsAprj/9V5OfCKmbULt6QKUWw7nXOLkuH7NLOG+NDbwTm3I+x6quytDeLb3Cvh\nOOfygW+Axvh2VTez2lGXJUOb8/BjXPb0feYB0aOK9wEOJYHbH/zltBr/HUMCtdPMBgEXAmc755ZH\nnCrNz2oexX/fULXbuWIvlxftWhz5fSZEO51zO51zPzjnvnDO3YP/V2c/kuz73EM7i5OI32cG8Dsg\n18x2mNkO4Cygn5ltx/cs1Kis77PKBokgZeXgR7UD/+l6bM/u97oSmpkdiO/+Xo5v7052b/MJwJH4\nbrqEFfwyzWP3ttXGjwko+j5nAwcH3axF2uMDyFwSVPCvh8OAol9QCdHO4JdrV+Ac59ySqNN7+lmN\n/D5Pjppl1RHIByJvHYRqL+0sTgv8bbrI77PKt7ME1YAaJNH3WYKidhYnEb/PD4GT8bc2mgePz4HR\nEX/eQWV9n2GPOt3LiNTuwGbgWvxI96H40be/C7u2MrTpCfxUnKOAPwBT8OnxsOD8EPy99bPxqfMT\nYEbYdZeybbWCH+JT8aODbw+eNwrO3xF8f12C/wj+CXwLVI94jwnBfwSt8F3Mi4FRYbettO0Mzj2O\nD0hHBf8hfw4sBPZLlHYGP4fr8NMj60Y89o+6psSfVfxf3vPwY0ZOwc9MWQkMCLt9pW0ncCxwL34q\n4VHAxcB3wLREamdQ58NBO48CTsKP3dkJnJss3+fe2plM32cx7d5tNkplfp+hN74U/+fcDPyEnyY4\nG2gZdk1lbE82fgrrFvwI2teAYyLO18DPa1+NHyT0FlAn7LpL2baz8L9YC6IeL0Zccz++92UzfoRw\n46j3OBifqvODv+BfAGqG3bbSthM/uGsivvdlK/AD8DxR4beqt7OE9hUA18bys4oPV+8DG4O/pB4D\nqoXdvtK2E2gITAdWBT+zi/G/mA5MpHYGNQ4Pfh63BD+fkwlCRLJ8n3trZzJ9n8W0exq7B4lK+z61\naZeIiIjErcqOkRAREZGqT0FCRERE4qYgISIiInFTkBAREZG4KUiIiIhI3BQkREREJG4KEiIiIhI3\nBQkRERGJm4KEiIiIxE1BQkREROKmICEiIiJx+//R6JyI1+eIyAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "from pylab import *\n", "x = linspace(0,400,1000)\n", "f = 331.0*np.sqrt(x/273.15)\n", "\n", "pylab.plot(x,f)\n", "show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy import signal\n", "import matplotlib.pyplot as plt\n", "border = 2\n", "b, a = signal.butter(border, 300, 'low', analog=True)\n", "w, h = signal.freqs(b, a)\n", "plt.plot(w, 20 * np.log10(abs(h)))\n", "plt.xscale('log')\n", "plt.title('Butterworth filter frequency response')\n", "plt.xlabel('Frequency [radians / second]')\n", "plt.ylabel('Amplitude [dB]')\n", "plt.margins(0, 0.1)\n", "plt.grid(which='both', axis='both')\n", "plt.axvline(300, color='green') # cutoff frequency\n", "plt.axhline(-3, color='green') # cutoff frequency\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3000\n", "3000\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "dt = 0.01\n", "t = np.arange(0, 30, dt)\n", "nse1 = np.random.randn(len(t)) # white noise 1\n", "nse2 = np.random.randn(len(t)) # white noise 2\n", "\n", "# Two signals with a coherent part at 10Hz and a random part\n", "s1 = np.sin(2 * np.pi * 10 * t) + nse1\n", "s2 = np.sin(2 * np.pi * 10 * t) + nse2\n", "\n", "fig, axs = plt.subplots(2, 1)\n", "axs[0].plot(t, s1, t, s2)\n", "axs[0].set_xlim(0, 2)\n", "axs[0].set_xlabel('time')\n", "axs[0].set_ylabel('s1 and s2')\n", "axs[0].grid(True)\n", "\n", "cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)\n", "axs[1].set_ylabel('coherence')\n", "print(len(s1))\n", "print(len(nse1))\n", "fig.tight_layout()\n", "plt.show()" ] }, { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }