{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "criminal-dividend", "metadata": {}, "outputs": [], "source": [ "# This tutorial demonstrates how to create log-log scale plots and semilog\n", "# (log-linear & linear-log) plots.\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "id": "prospective-stevens", "metadata": {}, "outputs": [], "source": [ "# Log-log plots are created using the 'loglog' command. It has all the\n", "# same formating options as 'plot' which are discussed in the basic_plots.m\n", "# script.\n", "\n", "# First, we define a function to plot. This function is the output of a\n", "# simple low-pass RC filter.\n", "def lowpass(f, f0):\n", " return 1/np.sqrt(1 + (f/f0)**2)" ] }, { "cell_type": "code", "execution_count": 5, "id": "innovative-chick", "metadata": {}, "outputs": [], "source": [ "# Next, we generate a logarithmically-spaced array of values using \n", "# 'np.logspace(a, b, n)' where 10^a is the first entry in the array and\n", "# 10^b is the last entry. n is the number of elements in the array.\n", "ff = np.array(np.logspace(0, 5, 1000))" ] }, { "cell_type": "code", "execution_count": 6, "id": "alive-adelaide", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here's the first log-log plot.\n", "f0 = 1e4\n", "plt.loglog(ff, lowpass(ff, f0), 'k-', linewidth = 2.5)\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('Low-pass Filter')\n", "plt.axis((10, 1e5, 0.1, 1.2));" ] }, { "cell_type": "code", "execution_count": 7, "id": "fabulous-traffic", "metadata": {}, "outputs": [ { "data": { "image/png": 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NjQWgV69eHD9+3HKqoq1IFRQdHFIVCVdeCQMGwOjRsGCB7TSuVq1atcxeX+vXr+eFF16wnKho015eSoWj1FSoWxdKlICVK/VGvR/S09Np2rQpS5YswePxsGTJEho2bGg7VtjRXl5KFVVxcfD227BxI/zwg+00rubxeHjnnXeIjIwkPT2dnj176lhfQaIFRalw1bo1bNrkzJ+i/FK/fv3Mnl7Lly9nzJgxlhMVTVpQlApnF1/s/Penn5yBJFWhPfXUU1StWjXz671791pOVPRoQVEq3E2fDldfDZ9/bjuJq8XGxjJs2DAADhw4wJNPPmk5UdGjBUWpcNe6NdSv74z3lZpqO42rtW3blptvvhmA9957j0WLFllOVLRoQVEq3EVGwogRsG0bvPSS7TSuJiIMHz6c6OhoAPr06aM36ANIC4pSbnDdddC5M7z+OqxbZzuNq9WoUeOsG/TvvPOO5URFhxYUpdzi//7PuUm/fr3tJK735JNPZt6gf/rpp3XwyADRgqKUW1x0kdM68d4DUIUXGxvLa6+9BsD+/ft58cUXLScqGrSgKOUmHg+kp8Onn8Lp07bTuFr79u259tprARg+fDgbNmywnMj98iwoIhIhIqtDFUYp5YPZs+Gee5y5U1ShiQhvvPEGAKdOneIJHYzTb3kWFGNMBvCziFQJUR6lVH5uvtm5Sf/MM3DkiO00rtakSRM6deoEwKRJkzJneVSF48slr4uANSLyjYhMPbMEO5hSKhciMGQI7NkDr7xiO43rvfzyy8R4B98cOHAgGRkZlhO5ly8F5QXgNuCfwJAsS9jR+VBUsdGkCXTqBG+8AVu32k7japUrV+axxx4DYOnSpYwfP95yIvfyafh6EakKXGqMmSMisYDHGBO2bW0dvl4VC1u3wh13wJgx0KiR7TSudvToUS699FJ27dpFpUqVWLduXWarpbgIyfD1ItILmASM9q66GPjSn4MqpQKgShVYulSLSQCUKlUqcyKu7du3M2rUKMuJ3MmXS159gGuBwwDGmHVA+WCGUkr5SAQOHYJx42wncb1u3bqRlJQEwEsvvYReOi84XwrKSWPMqTMvRCQS0HG0lQoX778P990H8+bZTuJqkZGRmQ847t+/nyFDwvJWcVjzpaDMF5FBQEkRuQmYCHwV3FhKKZ898IAzJMuTT+qcKX668847ufJK5zbCG2+8we7duy0nchdfCsoTwF7gF+ABYIYx5qmgplJK+a5kSXjuOVi0CL7Sv/X8ISK84u2KnZqaqkOyFFC+vbxEpL8xZlh+68KJ9vJSxc7p01CnDkRFwc8/O0O0qEK76aabmDNnDlFRUaSkpFCtWjXbkYIuJL28gK45rOvmz0GVUgEWGQmDB0OlSnDggO00rvfyyy8DkJaWxnPPPWc5jXvk2kIRkbuBe4DrgAVZNsUD6caYG4Mfr3C0haKKJWOcXl8qIJKTk5k4cSIiwsqVK6lXr57tSEEV7BbKDzhPxK/l7CfkHwX+6s9BlVJBcKaYbN4Mc+ZYjVIUDB48GI/HgzFGWyk+yrWgGGO2GGPmGWOaGmPmZ1mWG2N03GylwlWvXs7sjseP207iaklJSXTp0gWAL7/8khUrVlhOFP5yLSgickREDuewHBGRw6EMqZQqgKefhl27YPTo/N+r8vT0008TGRkJwPPPP283jAvk1UKJN8aUzmGJN8aUDmVIpVQBXH893HCDMxLxsWO207ha9erV6datGwBTp05l2bJldgOFubxaKKW9/z0/pyV0EZVSBfbCC7B7N7z9tu0krvfUU09pK8VHed2UPzOG87IcFu1CpVQ4a9YM/vpX2LfPdhLXS0xMpHv37gBMmzaNxYsXW04UvvLqNtzUGPNjiPMEhHYbVgrIyIAIXx41U/nZunUrNWrUIC0tjZtvvpkZM2bYjhRwwe42PDLLgVxZWJQq1s4Uk0WLIDXVbhaXq1KlCj169ABg5syZLFq0yHKi8JRXQcn6hFTxmmlGqaJizRpo2hR0fg+/DRo0iOjoaEDvpeQmr4ISISLniUjZLF/rTXml3KROHWjVCl59VVspfqpcuTK9evUCYPbs2fzwww+WE4WfvApKAn/egC8NLCfMb8rrnPJK5eD5552b8yNH5vtWlbcnn3ySEiVKAGTO8Kj+5NOc8m6jN+WVyqZ1a1ixwhmWJTbWdhpX69OnT+YUwUuXLqVREZmCOVSjDSul3O7ppyEtDVatsp3E9R5//PHM51J0vpSzaUFRqjho1gy2b4err7adxPWqVq1K586dAfjiiy9Ys2aN5UThQwuKUsVFXJzzbMrWrbaTuN4TTzxBhLdb9ksvvWQ5TfjIt6CIyCUiUsL7dQsReVhEygQ9mVIq8Dp3hpYtnRkeVaFddtllJCcnAzBhwgTWr19vOVF48KWFMhlIF5EawHtANf4clkUp5SZ33QUbNsB//mM7iesNGjQIgIyMjMx56Is7XwpKhnf+kzuAocaYgcBFwY2llAqKtm2dZ1Neesm5/KUK7fLLL+f2228H4MMPP2Tbtm12A4UBXwpKmnc64K7ANO+6qOBFUkoFTUQEDBrkPEE/ZYrtNK731FNPAc7c86+99prlNPb5UlDuA5oCLxpjNolINeDj4MZSSgVNcjJccgl89JHtJK535ZVX0rp1awDeeecddu/ebTmRXfkWFGPMr8aYh40xn4rIeUC8MUYvGCrlVpGRMHu23kcJkDOtlBMnTvDGG29YTmOXL7285olIae/4XT8D74tI8T5rSrndJZc4heXkSdtJXK9Zs2Y0b94cgFGjRnHgwAHLiezx5ZJXgjHmMHAn8L4xphFwY3BjKaWCbsUKqFoVvv/edhLXO9Pj6+jRo7z11luW09jjS0GJFJGLgGT+vCmvlHK7pCTneZRXX7WdxPVatWpF/fr1ARg2bBjHjx+3nMgOXwrKP4HZwHpjzBIRqQ6sC24spVTQxcZCv37w1VdOry9VaCLC448/DsCePXv44IMPLCeyQ0cbVqo4278fqlRxen69/77tNK52+vRpLr30UjZv3swll1xCSkoKHo/HdiyfhWS0YRGJEZE+IjJKRMaeWfw5qFIqTJQtCz16wCefwM6dttO4WmRkJI8++igAGzZsYPLkyZYThZ4vl7w+AioArYH5QCXgSDBDKaVC6O9/h1mzoEIF20lcr3v37pQtWxaAV199laJ4BSgvvhSUGsaYZ4BUY8wHwK3A5cGNpZQKmcqVnQEjRWwncb3Y2Fj69esHwLJly/j2228tJwotn4Ze8f73oIjUxZkaODFoiZRSoZeeDgMHwtChtpO4Xt++fYn1zor5f//3f5bThJYvBWWM9wn5Z4CpwK9A8TpLShV1Hg+sXQuvvAInTthO42ply5alZ8+eAHz99dcsX77ccqLQ8WXolXeNMQeMMfONMdWNMeWNMaNDEU4pFUKPPw67d8OHH9pO4nqPPPJIZg+vV4vRcz6+9PIqKyL/FpHlIrJMRIaKSNlQhFNKhVCLFtC4Mbz2mnMJTBVa1apV6dixIwATJ05k48aNlhOFhi+XvCYAe4C/Ae2BfcBnwQyllLJAxGmlrF8PX3xhO43rnXnQMSMjgyFDhlhOExq+FJTzjTGDjTGbvMu/gDJBzqWUsuGOO+CRR6BuXdtJXK9evXrcfPPNAIwdO5Y9e/ZYThR8vhSUuSLSUUQivEsyMD3YwQpDRNqIyJhDhw7ZjqKUO3k8MGQI1KxpO0mR8I9//ANwhrYfNWqU5TTBl+/QKyJyBIgD0gHBKUKp3s3GGFM6qAkLQYdeUcpPK1bAN9/AY4/ZTuJqxhiaNGnC0qVLKVeuHFu3bqVkyZK2Y+UoJEOvGGPijTERxpgoY0yk9+t47xJ2xUQpFQBffeU8Qf/bb7aTuJqI8MgjjwCwb98+Piris2T6cskrk4g8H6QcSqlw8uCDEBMDb75pO4nrtW/fnsqVKwPw5ptvkpGRYTlR8BSooABtg5JCKRVeLrgAunRxnknZu9d2GleLioqif//+AKxdu5aZM2daThQ8BS0oOtiPUsXFwIHOFMHF4GZysPXs2ZP4+HiAIt2F2Kfh67O8bBTELEqpcFKzJnTqBFFRtpO4XkJCAr169QJg7ty5rFixwnKi4PClhbJaRBaKyCvAX0UkIdihlFJh4uOPwTtfuvJP//79M4djKaqtFF96edUA7gZ+AW4DfhaRlUHOpZQKF8bA3LlQhG8mh0KVKlW46667APjss8/Yvn275USB58slr0rAtUAzoCGwBh16Rani48svnflSZs+2ncT1znQhPn36NMOHD7ecJvB8ebAxA1gCvGSMmRKSVH7SBxuVCqC0NKhWzbmnMmeO7TSu17x5cxYsWEBCQgLbtm3LvFlvW0gebMRplXwI3CMiP4rIhyLSw5+DKqVcJCoKHn7YeXJ+5UrbaVzvzLzzhw4dYuzYsZbTBFa+LRQAESkFXIdz2etenCFXEoMbrfC0haJUgB08CJUqwZ136nwpfsrIyKBmzZqsW7eOxMRE1q1bR2RkpO1YoWmhiMhS4EfgDmAt0Dyci4lSKgjKlIEePeD7751nU1ShRUREMHDgQAA2b97MF0VoqgBf7qFcYIxx1aOy2kJRKggOHYKSJSE62nYS1zt27BhVqlRh//79XHXVVfz444+I2H1uPFT3UE6JyBsistS7DNFnUZQqhhISnGKSlqatFD/Fxsby4IMPAvDTTz/xww8/WE4UGL4UlLHAESDZuxwG3g9mKKVUmNqzx+nxNWaM7SSu16dPH6K9rb2i8qCjLwXlEmPMc8aYjd7lBaB6sIMppcJQ+fJQpQoMHarzzvupQoUKdOrUCYApU6awadMmy4n850tBOS4i1515ISLXAseDF0kpFdYeeQQ2boRp02wncb0BAwYATs+vESNG2A0TAL7clK+P8xzKmfsmB4CuxphVQc5WaHpTXqkgOn0aLrkEqld3hmRRfmnZsiVz586ldOnSbN++3dqDjqGasfFnY0x9oB5QzxjTEGjpz0GVUi4WGQl9+8K8efDLL7bTuN6ZVsrhw4f54IMP7Ibxk08PNp7zIZGtxpgqQcgTENpCUSrIDhyAxYuhVSuw3N3V7dLT00lKSmLDhg3UqFGDlJQUIiIKOlWV/0LVbTjHY/tzUKWUy513HrRurcUkADweDw8//DAA69evZ8aMGZYTFV5hC0rBmzVKqaIlIwMef1znnQ+Abt26Zd47GTp0qN0wfsi1oIjIERE5nMNyBKgYwoxKqXAUEeHcQ3n1VTh1ynYaVytdujQ9ejhj7n7zzTesXr3acqLCybWgGGPijTGlc1jijTH2RzJTStk3YADs2gX/+Y/tJK7Xr1+/zOFXhg0bZjlN4YT+zo9Squho1Qpq1XIuexWig4/6U/Xq1WnXrh0AH3/8Mfv27bOcqOC0oCilCk/EmStl+XJYuNB2Gtfr378/ACdOnGCMC4e30YKilPJP587QsyeUK2c7ietdf/311K9fH4CRI0dyymX3prSgKKX8ExcH77zjTBGs/CIimQ867tixg0mTJtkNVEBaUJRSgbFqFUycaDuF63Xs2JHy5csDThfiwjx8bosWFKVUYLz4IvTqBUeP2k7iajExMfTu3RuAJUuWsGjRIsuJfKcFRSkVGAMGOLM66pzzfnvwwQeJiooC3PWgoxYUpVRgXH01NGkCw4Y5T9GrQqtQoQJ33303AJMnT2bbtm2WE/lGC4pSKjBEoH9/+N//YNYs22lc70wX4vT0dEaOHGk5jW+KVEERkTYiMubQoUO2oyhVPLVvD0lJ4JK/qMPZFVdcQbNmzQAYM2YMqamplhPlr0gVFGPMV8aY+xMSEvJ/s1Iq8KKjYc0aeOAB20mKhDNdiA8cOMBHH31kN4wPilRBUUqFAY/HGYZl3TrbSVyvXbt2JCYmAs74Xhlhfm9KC4pSKvCefx7q14f9+20ncTWPx0Pfvn0BWLt2LXPmzLGcKG9aUJRSgde+PRw/Du+9ZzuJ6/Xo0YPY2FgA/v3vf1tOkzctKEqpwLv8cmjRAkaNgvR022lcrUyZMnTu3BmA6dOns2HDBsuJcqcFRSkVHP36wZYt8NVXtpO4Xr9+/QAwxoR1F2ItKEqp4GjbFqpU0SfnA6BOnTq0bNkSgLFjx3I0TIe30YKilAqOyEiYORM+/dR2kiLh4YcfBuDQoUN8/PHHltPkTAuKUip4ateGEiV0NscAuO222zK7EP/73/8Oy1GItaAopYJr9mznJv2BA7aTuJrH4+Ghhx4C4Ndff+Xbb7+1nOhcWlCUUsFVoYLz9PzYsbaTuF6PHj0oWbIkEJ5diLWgKKWCq359aNYMRo7ULsR+Ov/887n33nsBmDp1Kps2bbKc6GxaUJRSwdevH2zaBDNm2E7ielm7EI8aNcpymrNpQVFKBd/tt0OlSjB8uO0krnf55ZfTokULAN59992wGoVYC4pSKviiomDIEGe+FOW3M62UgwcP8sknn1hO8ycJx65n/rryyivN0qVLbcdQSqmgOH36NNWrV2fbtm3UrVuXVatWISJ+7VNElhljrvRnH9pCUUqFzq5d8MwzcPCg7SSuFhkZSZ8+fQBYvXo18+bNsxvISwuKUip0fv8d/vUvGDfOdhLX69mzJzExMUD4dCHWgqKUCp1GjeCaa2DECAjzyaLCXdmyZbnnnnsAmDJlClu2bLGcSAuKUirU+vWDDRuccb6UX87cnM/IyAiLLsRaUJRSofW3v0HFihAml2ncrEGDBjRr1gxwuhAfO3bMah4tKBY8+uij1K5dO/OvC390796d8uXLU7du3bPWz5o1i6SkJGrUqMErr7yS42c3b958zufOKFWq1Fmvx40blzkVqVJ+iYpyWikJCXD6tO00rnfm98gff/zB+PHjrWbRghJiGzduZOHChfz6668BuZHWrVs3Zs2adda69PR0+vTpw8yZM/n111/59NNP+fXXX/0+llIB88QT8NlnzhD3yi+33347lSpVAuyPQqwFJYRSUlK4/vrr2bJlCw0bNgzIE67Nmzfn/PPPP2vd4sWLqVGjBtWrVyc6OpqOHTsyZcqUPPezceNGGjZsyJIlS/J839tvv02DBg1o0KAB1apV44YbbvD7e1DF2G+/wZEjtlO4WlRUFA8++CAAq1atYsGCBdayFMs/DwYMGMDKlSsDvt8GDRowdOjQXLcnJSXRtWtXEhMT6dmzZ577atasGUdy+If2+uuvc+ONN+b52d9//53KlStnvq5UqRI//fRTru9PSUmhY8eOvP/++zRo0ACA48ePZ34NTnO6bdu29O7dm969e5OWlkbLli155JFH8syiVK7WrnXmSxk+3LkEpgqtV69e/POf/+TkyZMMHz6c5s2bW8lRLAvKypUrmT9/vpVj//LLL7Rr147U1FQeeughoqOjadGiBZ06dTrrff78lZFTkze3p2j37t1Lu3btmDx5MnXq1MlcX7JkybOK7rhx48g6+kD//v1p2bIlbdq0KXROVczVrAlXXeV0Ie7TByL0gklhXXDBBdx9992MGzeOL7/8km3btp31R2WoFMuCkvUv71Dvd82aNdSpU4fJkyfTvn172rRpQ4cOHc4pKP60UCpVqsS2bdsyX2/fvp2KFSvm+N6EhAQqV67MwoULzyooeRk3bhxbtmxhxIgRPr1fqVz16wf33gtffw2tW9tO42r9+vVj3LhxpKen89Zbb/HSSy+FPEOxLCh5XZYKpiNHjhAVFUVsbCzbt2/n8ssvB5yZ2LLzp4XSuHFj1q1bx6ZNm7j44ouZMGFCrr0/oqOj+fLLL2ndujWlSpXKfFAqN8uWLeP1119nwYIFROhflMpfd90Fjz3mXPbSguKXK664gmuuuYYffviBMWPG8Mwzz2ROxhUq+hshhFavXp3ZTbdSpUps374dcB5KKqy7776bpk2bkpKSQqVKlXjvvfeIjIxkxIgRtG7dmlq1apGcnJxn6yMuLo5p06bx5ptv5nvzfsSIEfzxxx/ccMMNNGjQIN97QUrlKToaHngA5s+H/fttp3G9hx9+GID9+/czYcKEkB9fRxu2JDU1lb59+xITE8N11113ziUvpYqNgwedYViy9VZUBZeWlkZiYiI7duygYcOGLFu2zOdRiAMx2rAWFKVUeDDGKSw5XAJWvhs8eDDPPvss4Fw6v+6663z6nA5fr5QqGg4edHp8jR5tO4nr3X///URFRQEwcuTIkB5bC4pSyr6EBKd1MnKk01JRhXbhhReSnJwMwKRJk9i5c2fIjq0FRSllnwj07Qu//gphMlmUm50Zd+/06dOMGTMmZMfVgqKUCg8dOjg35vX5Jr9dddVVNGrUCIDRo0eTlpYWkuNqQVFKhYeSJaFnT5gyBbI8mKsKTkQyWyk7d+7kiy++CMlxtaAopcLHQw85N+bLlbOdxPU6dOhA2bJlAUI2qoUWFKVU+KhaFXr0cForyi8lS5bMfPB4wYIF/Pzzz0E/phYUpVR4SUtzhmKZPt12Etfr3bt35hBJoehCrAVFKRVePB6n+/CLL9pO4nqJiYmZI4J//PHHHDhwIKjH04JiQSimAM4qt6l+dZpfFZYiIpzh7H/8EZYts53G9fr06QM4cxy9//77QT2WFpQQC8UUwEq5XteuEBfntFSUX/7yl7+QlJQEOJe9/BmMNj9aUEIoVFMA50Wn+lWukJAAnTvD+PE6CrGfIiIiMlspGzduDOofoMVyPhQAWrQ4d11ystNt8dgxuOWWc7d36+Ys+/ZB+/Znb/Ph6d5QTQGcm+xT/eY2zS+gU/0q+/r0cZ6c37sXvN1fVeF07dqVQYMGcfToUUaMGMEtOf1+C4DiW1AsCcUUwDnJaarf/Kb5BZ3qV1lUt64zT4ryW+nSpenSpQujRo1i1qxZrF+/nho1agT8OMW3oOTVooiNzXt7uXKFHm8oFFMA50Sn+lWutXs3HDjgzEGvCq1Pnz6MGjUKYwxvvfUWQ4YMCfgxim9BsSBUUwDnRKf6Va5kDFxzDdSoAbNn207jarVr16Zly5Z8++23jB07ln/+85/ExcUF9Bj6myKEQjUFcG50ql/lOiJw333w3//C//5nO43rnXks4ODBg4wfPz7g+9cZGy3RKYCV8tHu3VC5stNhZuhQ22lc7fTp01SvXp1t27ZRr149Vq5cmTlFsE4BnAs3FBSlVAF06gTTpsHvv0O2B3JVwbz88ssMGjQIgO+++45mzZoBOgWwUqq46NMHjhyB776zncT1evbsSXR0NBD4UYi1oCilwl/TprBlS87Ph6kCueCCC+jQoQMAn3/+OTt27AjYvrWgKKXCn4hzHwUgPd1uliIgWFMEa0FRSrlH166QT5d3lb8mTZrQuHFjwJki+NSpUwHZrxYUpZR7XHghTJ7s3JxXfjnTStm1axeff/55QPapBUUp5R4PPggZGc40wcovycnJlPNOtRyom/NaUJRS7lGtGtx6K4wZAwG6TFNcxcTE0KtXLwAWLlwYkH2GfUERkeoi8p6ITLKdRSkVBvr2dR52nDzZdhLXyzpFcCAEtaCIyFgR2SMiq7Ot/6uIpIjIehF5Iq99GGM2GmN6BDOnUspFbroJhg2Dv/zFdhLXq1KlCu3atQvY/oLdQhkH/DXrChHxACOBm4HawN0iUltELheRadmW8kHOp5Rym4gIePhhKK+/HgIhkNN+B3W0YWPMdyKSmG11E2C9MWYjgIhMANoZY14GbivssUTkfuB+78uT2VtFQZAAHAryZ/N7X17bc9pWmHXlgH35JvVfYc9nQT5n43xmfx2K8xkOP5t5vacg6/V85r/d1/OZ3/lNyj9mPowxQV2ARGB1ltftgXezvO4MjMjj82WBt4ENwJM+HnNpCL6vMcH+bH7vy2t7TtsKsy4U59Kf81mQz9k4nzm8LhY/m3m9pyDr9Xzmv93X8xmKf+s25kORHNblOkKlMWY/0Dt4cQrtqxB8Nr/35bU9p23+rAu2wh6zIJ+zcT7ddC4L8llf3pfbewqyXs9n/tt9PZ9B/7ce9NGGvZe8phlj6npfNwWeN8a09r5+EsA4l7wCdcylxs9RM5VDz2Vg6fkMLD2fgROIc2mj2/AS4FIRqSYi0UBHYGqAjxG4wWmUnsvA0vMZWHo+A8fvcxnUFoqIfAq0wLlxtht4zhjznojcAgwFPMBYY8yLQQuhlFIqJIrkBFtKKaVCL+yflFdKKeUOWlCUUkoFRJEvKDoWWGCJyO0i8o6ITBGRVrbzuJ2I1BKRt0Vkkog8aDuP24lInIgsE5FCPyStHCLSQkQWeH8+W/jyGVcWlIKMEWZ0LLB8FfB8fmmM6QV0AzpYiBv2Cng+fzPG9AaSAe3+mk0hxgP8B/Cf0KZ0jwKeTwMcBWKA7T4dINhPmQZjAZoDV3D2E/genKfpqwPRwM9A7SzbJ9nOHa5LIc/nEOAK29nDcSno+QTaAj8A99jOHm5LQc4lcCPOYwjdgNtsZw/HpYDnM8K7/ULgE1/278oWijHmO+CPbKszxwgzxpwCJgCBG0azCCvI+RTH/wEzjTHLQ53VDQr682mMmWqMuQboFNqk4a+A5/IG4GrgHqCXiLjy91swFeR8GmMyvNsPACV82b+NoVeC5WJgW5bX24GrRKQs8CLQUESeNAF8Ir+Iy/F8Av1w/hJMEJEaxpi3bYRzodx+PlsAd+L8g50R+liulOO5NMb0BRCRbsC+LL8QVd5y+9m8E2gNlAF8mtKxKBWUHMcIM+E7Fli4y+18DgeGhzpMEZDb+ZwHzAttFNfLczxAY8y40EUpEnL72fwcKNBk80WpSbgdqJzldSVgh6UsRYGez8DS8xk4ei4DK2DnsygVlFCMEVac6PkMLD2fgaPnMrACdj5dWVC8Y4T9CCSJyHYR6WGMOQ30BWYDvwH/McassZnTLfR8Bpaez8DRcxlYwT6fOpaXUkqpgHBlC0UppVT40YKilFIqILSgKKWUCggtKEoppQJCC4pSSqmA0IKilFIqILSgqCJNRNJFZGWWJdF2pkARkYYi8q73624iMiLb9nkikuuQ+CIyQUQuDXZOVXwUpbG8lMrJcWNMg5w2iIjgPIvl1kEEBwH/8uPzbwGPA70CE0cVd9pCUcWKiCSKyG8iMgpYDlQWkb+LyBIRWSUiL2R571PeSYfmiMinIvKYd33mX/4iUk5ENnu/9ojIa1n29YB3fQvvZyaJyFoR+cRbzBCRxiLyg4j8LCKLRSRenFnyGmTJsVBE6mX7PuKBesaYn334nttmaaGliMgm76YFwI0ion9YqoDQHyRV1JUUkZXerzcBA4Ek4D5jzEPiTGN8Kc6cEAJMFZHmQCrOmEYNcf6dLAeW5XOsHsAhY0xjESkBLBSR/3q3NQTq4Ay6txC4VkQWA58BHYwxS0SkNHAceBdnkqgBInIZUMIYsyrbsa4EVmdb10FErsvyugY4863gHZtJRP4DzPeuzxCR9UB9H743pfKlBUUVdWdd8vLeQ9lijFnkXdXKu6zwvi6FU2DigS+MMce8n/NlsLxWQD0Rae99neDd1ylgsTFmu3dfK4FE4BCw0xizBMAYc9i7fSLwjIj8HegOjMvhWBcBe7Ot++zMnCDe/czLulFEHsc5HyOzrN4DVEQLigoALSiqOErN8rUALxtjRmd9g4gMIMscG9mc5s/LxTHZ9tXPGDM7275aACezrErH+bcnOR3DGHNMRL7GmYUwt7nmj2c7dp5E5C/AXThTwGYV492XUn7TeyiquJsNdBeRUgAicrGIlAe+A+4QkZLe+xVtsnxmM9DI+3X7bPt6UESivPu6TETi8jj2WqCiiDT2vj8+y/2Md3EmMltijMk+ZSs4o8LW8OUbFJGqwCgg2RiTvXhcBuhIvSogtIWiijVjzH9FpBbwo/c++VHgXmPMchH5DFgJbMG5gX3G68B/RKQz8G2W9e/iXMpa7r3pvhe4PY9jnxKRDsC/RaQkTkvhRuCoMWaZiBwG3s/ls2tFJEFE4o0xR/L5NrsBZYEvvN/jDmPMLSJyIc4lsJ35fF4pn+jw9Ur5QESex/lF/3qIjlcRZ2rgmrl1axaRgcARY8y7hTzGQOCwMea9QgdVKgu95KVUmBGRLsBPwFP5PCPzFmffmymog8AHfnxeqbNoC0UppVRAaAtFKaVUQGhBUUopFRBaUJRSSgWEFhSllFIBoQVFKaVUQGhBUUopFRD/D+BGVpqlJkOfAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's add the curve from a second low-pass filter with\n", "# a different cut-off frequency.\n", "\n", "# First, the origonal plot.\n", "f0 = 1e4\n", "plt.loglog(ff, lowpass(ff, f0), 'k-', linewidth = 2.5)\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('Low-pass Filter')\n", "plt.axis((10, 1e5, 0.1, 1.2))\n", "\n", "# Then the new plot.\n", "f0 = 1e3\n", "plt.loglog(ff, lowpass(ff, f0), 'r--', linewidth = 1.5)\n", "plt.legend((r'$f_0 = 10$ kHz', r'$f_0 = 1$ kHz'), loc = 'lower left', frameon = False);" ] }, { "cell_type": "code", "execution_count": 8, "id": "welcome-wallpaper", "metadata": {}, "outputs": [ { "data": { "image/png": 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AtwGHga3AC652EqXsV6QIbNsGzz0HFy7YncbRunbtStOm1kWKiRMnsnbtWpsTqezypM3ieaARcBbAGLMXKO3B5ySDeemved0PbAbKAbWBCSJy3fmpiHQXkQ0isuHYsWMe7FqpPBASYjV0HzyovdPmkogwdepUgoODMcbQrVs3rly5YncslQ2eFIvEtG0IIlKI6//oZyQeqJjmfQWsM4i0OgPzjWUfsB+4Nf2GjDHTjDExxpiYUqVKebBrpfJI48bw7LPw7ruwcaPdaRwtMjKSV119cG3bto0xY8bYnEhlhyfFYpWrIbqwiNwLfAF87cHn1gO3iEgVEQkGOgDp76L6H/BXABEpA0QCcZ6GV8onRo6EMmVgwAC7kzjegAEDiIqKAuCNN95g//79NidSnvKkWAwEjmG1KTwLLDHGuG2hMsYkAb2AZcBO4HNjzHYR6SEiPVyrvQU0FJGtwHfAAGPM8Rx8H0p5T0QEfPmljnuRB4KDg1P7jkpMTKSvPvzoGJ7cOvuCMeY9d/N8RW+dVbZKSYHERChc2O4kjtapUyf+9a9/AfDtt99y77332pzI/3n91lng6QzmxeZ0h0o51pUr0KiRXo7KA6NGjUrtyvyFF17Qxm4HyLRYiEhHEfkaqJL2yW0RWQGc8F1EpfKJoCC44w6YNAm2brU7jaOVK1eO1157DYCdO3cyadIkmxMpdzK9DCUilYAqwAisdourzgG/uNokfE4vQylbnTgB1atDdDT85z/W8KwqRxITE6lZsyZ79+4lPDycPXv2ULq0J3flq5zw2mUoY8xBY8xKY8xdxphVaaZNdhUKpWxXsiQMHw4rV8JXX9mdxtFCQkJ49913AThz5gyvv/66rXlU1rI6szhHxs9TCGCMMbZ07qJnFsp2SUnWmUWlStoVSB5o2bIly5YtIzAwkO3bt6eO463yljfPLIoZY4pnMBWzq1AolS8UKgRff61nFnlk9OjRiAjJyckMGjTI7jgqE1k1cBd3fb0ho8l3EZXKh/7yF6s7kIsXrUnlWHR0NE8/bd10+eWXX7JmzRqbE6mMZHXr7KeurxszmPQ6kFJnzlhjXowcaXcSx3vzzTcJDQ0FoH///jhtuOeCIKtiMRzAGFMlg6mqj/IplX+Fh0PDhjB2LBxO3+2Zyo6KFSvy4osvAvDTTz/x5Zdf2htIXSerYjHx6gsR+ckHWZRynrffth7WGzrU7iSON3DgQEqWLJn6Wh/Uy1+yKhZpbyAP9XYQpRzpL3+xxruYMQP27LE7jaOFh4enPqi3d+9eZs2aZW8gdY2sikWAiJQQkZJpXmsDt1LpDR4MoaHg6utI5VyPHj24+eabAasdIyEhweZE6qqsikU4fzZmF8caSlUbuJVKr0wZ2LwZ3njD7iSOFxISwhDXQFPx8fG8//77NidSV7ntdTa/0YfyVL529iwU18eQcuPKlStERUWxb98+ypQpQ1xcHEWKFLE7luP5otdZpZQnvvsOypXTEfVyKSgoKLXrjyNHjjBx4sSsP6B8QouFUnmlXj3rQT1XI63KuQ4dOqSOqDdq1CjOnj1rcyKlxUKpvFK8uDXWxdKloE8h50pgYCBvvvkmACdOnOC992wZa02l4clIeX8B4o0xiSLSDIgGPjLGnPZ6ugxom4XK1y5csG6njYqyujBXOZaSkkJMTAz//e9/CQ8PZ//+/ZQoUcLuWI7lizaLeUCyiFQDpmONcfFp1h9RqoAKC7NupV2xArZtszuNowUEBKSeXZw5c4Z33nnH5kQFmyfFIsU1fsXDwLvGmD7ATd6NpZSDdesGv/wCNWrYncTxHnzwQerVqwfAuHHjOH36tL2BCjBPisUVEemINRb3Ite8IO9FUsrhCheGmjWt10k6TlhuiEjqcxdnzpxh/PjxNicquDwpFp2Bu4Dhxpj9IlIF0EdVlXKnd29o08buFI734IMPUqdOHQDeeecdvTPKJm6LhTFmhzGmtzFmtoiUAIoZY7RPZqXcqVDBujNq3Tq7kziaiKT2GXXq1CkmTZpkc6KCyW2xEJGVIlLc1R/UFmCmiPyf96Mp5XA9e1pjdr/1lt1JHK9t27bUdF3aGzt2LOfPn7c5UcHjyWWocGPMWaAdMNMYUxe4x7uxlPIDRYvCSy/B4sWwaZPdaRwtICCAV199FYDjx48zZcoUmxMVPJ4Ui0IichPQnj8buJVSnujVCyIiYMQIu5M43t///nduu+02AMaMGcNFHc7WpzwpFm8Cy4B9xpj1IlIV2OvdWEr5ieLF4bPPYNw4u5M4XmBgYOrZxZEjR7RHWh/TXmeVUo6RnJxMVFQUe/bs4aabbiIuLi517G6VNa8/wS0ioSLyvIhMEpEZV6ec7lCpAmnPHmjaFLZvtzuJowUGBjJ48GAAfv/9d6ZPn25zooLDk8tQHwNlgfuBVUAF4Jw3Qynld0qWtBq5hw+3O4njPf7441StWhWAkSNHcvnyZZsTFQyeFItqxpjXgAvGmA+BB4Ga3o2llJ8pWdK6lfazz3Ss7lwKCgpi0KBBgDWa3kcffWRzooLBo+4+XF9Pi0gNrOFWK3uycRFpKSK7RWSfiAzMZJ1mIrJZRLaLyCqPUivlRH37WuNd6J1RufbUU09RsWJFAEaMGEGSdqvidZ4Ui2muJ7dfAxYCO4BR7j4kIoHARKAVEAV0FJGodOtEAJOANsaY24FHs5VeKScpXRq6d4ePP4YDB+xO42jBwcEMGDAAgLi4OGbPnm1zIv/ntbuhROQu4HVjzP2u94MAjDEj0qzTEyhnjHnV0+3q3VDK0X77zboU9eyzVnfmKscuXbpE1apV+eOPP7j11lvZtm0bgYGBdsfKt3xxN1RJERkvIptEZKOIvCsiJT3YdnngUJr38a55aVUHSri6FNkoIk9lkqG7iGwQkQ3Hjh3zYNdK5VPly1tPdWuhyLXChQvTv39/AHbt2sX8+fNtTuTfPLkMNQc4CvwdeAQ4Dnzmweckg3npT2MKAXWxGs3vB14TkerXfciYacaYGGNMTKlSpTzYtVL53Mcfwz//aXcKx3v22WcpWdL633XYsGE47bkxJ/GkWNxgjHnLGLPfNQ0DIjz4XDxQMc37CsDhDNb5xhhzwRhzHFgN1PJg20o523ffwdChcOSI3UkcLSwsjJdeegmAX375ha+//trmRP7Lk2KxQkQ6iEiAa2oPLPbgc+uBW0SkiogEAx2wGsjTWgA0EZFCIlIEuBPYmZ1vQClHGjwYEhJAhwrNtV69ehEREQHo2YU3eVIsnsUaczsRuIx1WeolETknIpmOQuIairUXVr9SO4HPjTHbRaSHiPRwrbMT+Ab4BfgZ+MAYowMXK/9XvTo89hhMnAgnT9qdxtGKFy9O7969AVi/fj3//ve/bU7kn7RvKKXssm2bNfzqkCHwxht2p3G0kydPUqlSJc6fP0/jxo1ZvXo1Ihk1mxZcXr8bKt3OXs/pjpRS6dSoAS+/DHfcYXcSx7vhhht4/vnnAfjhhx9YvXq1zYn8T7bOLERkkzHG1p9sPbNQSmXk6NGjVK5cmUuXLnHPPffo5ah0fHpmQca3wyqlcuPMGRg1Ci5csDuJo5UuXZru3bsDsHz5ctauXWtzIv/iURflad7W9WIWpQqm7dth4ECYOtXuJI7Xv39/goODAevOKJV3PDmz2CYia0RkJNBSRMK9HUqpAqVhQ2jRwnpILyHB7jSOVr58ebp06QLA4sWL+e9//2tzIv/htlgYY6oBHYGtwEPAFhHZ7OVcShUsr74Kf/wBOphPrg0YMCC1j6jhOn5InvHkMlQFoBHQBKgDbMez7j6UUp5q1gwaNbLaLnQwn1ypXLkynTp1AmDevHls19EJ84Qnl6H+B7wILDXG3GWMeTBtz7FKqTwgYp1dREXBiRN2p3G8QYMGERBg/Xl7++23bU7jHzwpFnWAj4DHReQnEflIRLp6OZdSBU/LlvDNN3DTTXYncbzq1avz2GOPATBnzhz27t1rcyLn86TNYgvwITAT+A/QFGsgJKWUNxw8COvW2Z3C8QYPHgxASkoKI3R0wlzzpM1iA/AT8DCwC7jbGFPZy7mUKrjatYPYWEhJsTuJo9WoUYN27doB8PHHH3NARyfMFU8uQ7UyxtQ0xjxrjPnYGHPQ66mUKshefhl27QIdzCfXXnnlFQCSkpIYPXq0zWmczZNicVlE/u/qSHUiMlaftVDKix55BCIjYdgwcFhHn/nNHXfcwQMPPADA9OnTOXw4/ZA6ylOeFIsZwDmgvWs6i9V+oZTyhsBAa7yLLVtg0SK70zjeq6++CsDly5f5p45OmGOeFIu/GGOGGmPiXNMbQFVvB1OqQOvY0RrzYscOu5M43l133cVf//pXAKZOncrRo0dtTuRMnhSLSyLS+OobEWkEXPJeJKUUQUHwyy8wYIDdSfzC1bOLS5cu8Y6OTpgjnhSLHsBEETkgIgeACVij5ymlvCkkxPqqTyDnWtOmTWnc2Pqfd8KECZzU0QmzzaPnLIwxtYBoINoYUwdo4fVkSinrjqgaNUAH88kVEUk9uzh//jzjxo2zOZHz5GhYVRH5nzHmZi/kcUsHP1IFyqVLUKUKREfDt9/ancbRjDHUr1+fDRs2EBERwcGDBylevLjdsXzG14Mfpe43pztUSmVD4cLQrx/8+9/6VHcuiQivvWZ1PnH69GkmTZpkcyJnyWmx0Ju/lfKVHj3ghhus5y5Urjz00ENER0cDMHbsWC7o6IQey7RYiMg5ETmbwXQOKOfDjEoVbEWLQp8+8P33cOyY3WkcLSAgIPWp7uPHjzNVRyf0WI7aLOykbRaqQDp/HpKSICLC7iSOl5ycTI0aNdi1axelS5cmLi6OsLAwu2N5nV1tFkopXypa1CoUKSk63kUuBQYGMnToUACOHj2qd0Z5SIuFUk7SqhV06GB3Csdr3759atvF6NGjOX36tL2BHECLhVJO0qoVLF8OK1bYncTRAgICGOa6YeD06dOMGTPG5kT5n7ZZKOUkCQlwyy1QsSKsWWMNx6pyxBjDXXfdxbp16wgLC+PXX3+lTJkydsfyGm2zUKogCQ2FIUPgp59g8WK70ziaiKSOz33hwgUdTc8NLRZKOU1sLFSrBtOn253E8Vq0aEGLFlbvRZMnT+Z///ufzYnyLy0WSjlNUBAsXQqff253Er8wfPhwwBrv4q233rI5Tf7l1WIhIi1FZLeI7BORgVmsV09EkkXkEW/mUcpvVKtmFY2rz1+oHGvQoAGtW7cGYObMmezZs8fmRPmT14qFiAQCE4FWQBTQUUSiMllvFLDMW1mU8kuHDlmN3R98YHcSxxs2bBgiQnJyMoMGDbI7Tr7kzTOL+sA+1+h6l4E5QNsM1vsHMA/Q4auUyo4KFaxiMXQonDtndxpHi46OplOnTgDMnz+fH374weZE+Y83i0V54FCa9/GuealEpDzwMDDFizmU8k8iMGYMHD0Ko0fbncbxhg0bRmhoKAD9+vXDaY8VeJs3i0VGN4CnP/rvAgOMMclZbkiku4hsEJENx7QjNaX+VL++NV732LHw2292p3G0ihUr0qdPHwDWrVvHF198YXOi/MWbxSIeqJjmfQXgcLp1YoA5ruFaHwEmicjf0m/IGDPNGBNjjIkpVaqUl+Iq5VBvvw3JyfCvf9mdxPEGDhzI1b8xAwcOJDEx0eZE+Yc3i8V64BYRqSIiwUAHYGHaFYwxVYwxlY0xlYG5QE9jzFdezKSU/6lcGX75BV5+2e4kjle8eHFef/11APbv38/EiRPtDZSPeK1YGGOSgF5YdzntBD43xmwXkR4i0sNb+1WqQIqMtNowjup9IrnVrVs3IiMjAasd4+TJkzYnyh+8+pyFMWaJMaa6MeYvxpjhrnlTjDHXNWgbY2KNMXO9mUcpv/bjj1CpEixZYncSRwsKCmK064aBU6dO8eabb9qcKH/QJ7iV8hcxMXDzzfDii6DX2nOldevWNGvWDIAJEyawbds2ewPlA1oslPIXwcHw3nuwdy+8+67daRxNRBg3bhyBgYEkJyfTu3fvAn8rrRYLpfxJy5bQpg289ZbeSptLNWvWpGfPngCsWLGCuXML9lVyLRZK+Zt33gFj4Ntv7U7ieG+88QY33ngjAH379uXChQs2J7KPFgul/E3VqrB/P3TubHcSxytRokTqOBeHDh1i5MiRNieyjxYLpfxR6dLW102btFfaXOrSpQsxMdYAc//85z/Zt2+fzYnsocVCKX+1YQPUrQvjx9udxNECAgKYMGECAImJiTz33HMFsrFbi4VS/qpuXXjwQXjtNdAR4HLlzjvvpHv37gAsX76cTz75xOZEvqfFQil/JQITJ1qN3T17Wl9Vjo0aNYqyZcsC0KdPH44fP25zIt/SYqGUP6tUybqNdvFiKOC3fuZWREQE7733HgDHjx+nf//+NifyLS0WSvm73r2hSRM4fdruJI736KOP8sADDwAwa9Ys/vOf/9icyHfEaQ01MTExZsOGDXbHUMpZjLEuS6lcO3jwIFFRUVy8eJFq1aqxZcsWihQpYncst0RkozEmJqef1zMLpQoCEatgzJ4NBei/YW+oVKkSb731FgD79u1j8ODBNifyDT2zUKqgSEyEWrXg4kXYuhXCw+1O5FjJyck0adKEn376CbC6A7na8WB+pWcWSinPhITAhx9afUa99JLdaRwtMDCQWbNmUbhwYQA6d+7MuXPnbE7lXVos8ljfvn2JioriH//4R6631aVLF0qXLk2NGjWumf/NN98QGRlJtWrVMu1+4MCBA9d97qqiRYte837WrFn06tUr13mVA9x5JwwcCDNmwKJFdqdxtOrVqzNq1CjA+n3r16+fzYm8S4tFHoqLi2PNmjXs2LGD8Xnw1GxsbCzffPPNNfOSk5N5/vnnWbp0KTt27GD27Nns2LEj1/tSBciQIVCzJnTrBufP253G0Z5//nmaN28OwLRp0677ffUnWizyyO7du2natCkHDx6kTp06edI75d13380NN9xwzbyff/6ZatWqUbVqVYKDg+nQoQMLFizIcjtxcXHUqVOH9evXZ7nelClTqF27NrVr16ZKlSqpvwTKz4SEwEcfwYQJkO4sU2VPQEAAM2bMoFixYoB1Oeqonw5tW8juAHntxRdfZPPmzXm+3dq1a/NuFgPKREZG8vTTT1O5cmWeeeaZLLfVpEmTDK9vjhkzhnvuuSfLz/72229UrFgx9X2FChVYt25dpuvv3r2bDh06MHPmTGrXrg3ApUuXUl8DnDx5kjZt2tCjRw969OjBlStXaNGiBS/pdW3/Vbu2NQGcOaON3blQuXJl3nvvPbp06cIff/xBbGwsixYtIiDAv/4X97tisXnzZlatWmXLvrdu3Urbtm25cOECPXv2JDg4mGbNmvHEE09cs97333+f431kdPeaZHL//LFjx2jbti3z5s3j9ttvT51fuHDhawrqrFmzSHuH2QsvvECLFi1o3bp1jnMqh1iwAGJj4YcfIM3PiMqe2NhYvv32W+bMmcPSpUt555136Nu3r92x8pTfFYu0/zH7ervbt2/n9ttvZ968eTzyyCO0bt2axx577LpikZsziwoVKnDo0KHU9/Hx8ZQrVy7DdcPDw6lYsSJr1qy5plhkZdasWRw8eDC1l03l5xo0sC5LPfYYrF8Prrt7VPaICFOmTOHnn38mLi6OgQMHcvfdd1OvXj27o+UZvysWWV0q8qZz584RFBREkSJFiI+Pp2bNmoB1i116uTmzqFevHnv37mX//v2UL1+eOXPm8Omnn2a4bnBwMF999RX3338/RYsW5fHHH89y2xs3bmTMmDF8//33fncKrTJRpozVfnH//dCnD0yZYncixwoPD2fOnDk0bNiQpKQkOnTowKZNmwj3k0t8+hchj2zbti31VtUKFSoQHx8PQEpKSo632bFjR+666y52795NhQoVmD59OoUKFWLChAncf//93HbbbbRv3z7Ls4awsDAWLVrEO++847YhfMKECZw8eZLmzZtTu3Ztt20vyk/cdx8MGABTp1pPeKscq1evXurt7HFxcXTp0sVvxr7QJ7i94MKFC/Tq1YvQ0FAaN2583WUopfKdK1fgr3+Fli2hgHRf4S0pKSm0bt2aJUuWAPD2228zaNAgm1Pl/gluLRZKKcuVKxAUZHcKv3Dq1CliYmKIi4tDRFiyZAktW7a0NZN296GUyhtXC8Xq1fDUU5CcbG8eBytRogRfffUVRYoUwRhDx44dHT92txYLpdS1du+Gjz+GV1+1O4mj1axZk5kzZwJw+vRpHn74Yc47+Il5LRZKqWt16wbdu8PIkVbRUDnWvn17BgwYAFg3wXTo0IGkpCSbU+WMFgul1PXGj4fmzaFrV1i50u40jjZ8+HBatWoFwOLFi+ndu7cj75DSYqGUul5wMMyfD7fcAv/6l91pHC0wMJDPPvss9cHeyZMnM3bsWHtD5YAWC6VUxiIiYNUqmDbN7iSOV6xYMRYtWkSFChUA6N+/P1988YXNqbLHq8VCRFqKyG4R2SciAzNY/oSI/OKafhSRWt7Mo5TKphtvhIAAOHgQOnWCs2ftTuRY5cuXZ/Hixak91Hbq1InvvvvO5lSe81qxEJFAYCLQCogCOopIVLrV9gNNjTHRwFuA/gujVH60YwfMmQOtW1vDsqociY6OZt68eRQqVIjExETatGnDjz/+aHcsj3jzzKI+sM8YE2eMuQzMAdqmXcEY86Mx5pTr7VqgghfzKKVyqlUr686o77+Hdu2s8bxVjtx777188sknBAQEcPHiRR544AE2bdpkdyy3vFksygOH0ryPd83LTFdgaUYLRKS7iGwQkQ3Hjh3Lw4h5zxfDqqaV2fCpOnSqynMdOsD778OyZdCxIzj0FtD8oH379nzwwQcAnDlzhvvuu4/t27fbnCpr3iwWGQ2ykOH9YiLSHKtYDMhouTFmmjEmxhgTU6pUqTyMmLd8MayqUrbq2hXefRf279f2i1zq3Llz6t+JEydO0KJFC7Zu3Wpzqsx5s1jEAxXTvK8AHE6/kohEAx8AbY0xJ7yYx6t8NaxqVnT4VOUTL7wAa9fCDTdAQoJeksqFXr16MWLECACOHj1Ks2bN2Lhxo82pMubN8SzWA7eISBXgN6ADcM2ACiJyMzAf6GSM2ZNne27W7Pp57dtDz55W49wDD1y/PDbWmo4fh0ceuXaZBw8l+WpY1cykHz41s6FTAR0+VeVeSAgYY12aunwZ5s3TgZNyaODAgRQqVIj+/ftz8uRJWrRowZIlS2jUqJHd0a7htWJhjEkSkV7AMiAQmGGM2S4iPVzLpwBDgJLAJNfQoEm56RXRbr4YVjUjGQ2f6m7oVNDhU1UuicBDD1ldgzz4ICxcCOnaypRn+vXrR5EiRXj++ec5e/Ys9913HwsWLMjxP4/e4NWR8owxS4Al6eZNSfP6GSDvR9jJ6kygSJGsl994Y467N/DFsKoZ0eFTlW2eecY6o3j6aeuMftEiKFvW7lSO1LNnT4oUKULXrl25ePEirVq14oMPPuDpp5+2Oxrgh8Oq2sVXw6pmRIdPVbZ64gnrae/27aFNG1i3zjrrUNkWGxtLWFgYTz75JJcvXyY2NpYDBw4wZMgQxOZjqn8p8oivhlXNjA6fqmz14IPWOBgTJmihyKVHH32U5cuXU6JECQBef/11OnfuzOXLl23NpSPleYEOq6oKvDfegHLlrO7OVY7s3r2bVq1asX//fgAaNmzIF198Qbly5XK0PR1WVSmVv1y5YnULsmyZVSzGj7funlLZduTIEdq2bcu6desAKFu2LHPnzs3RnVI6rKpSKn8JCoLFi2HQIOuJ76ZNwXVZVmVPmTJlWLlyJV27dgXgjz/+oFmzZkycONHnY2JosVBK5b3AQHj7bZg7F7Ztg0aN9OG9HAoNDeX9999n6tSpBAUFkZSURK9evXjkkUc4efKkz3JosVBKec/f/w4//wyjR/95KUr7lMo2EaF79+6sXr2a8uWtLvbmz59PdHQ0K300kqEWC6WUd0VFwWOPWa8//RRiYqwuz1W2NWjQgC1btvC3v/0NgN9++40WLVowcOBAEhISvLpvLRZKKd+JiIDffoM77oARI6zGcJUtJUuWZP78+UydOpXChQtjjGHUqFHUrl2bNWvWeG2/WiyUUr7zwAOwdavVTcjgwXDnnbBli92pHOfqZamNGzdSt25dwLrVtkmTJvTu3Zvz58/n+T61WCilfKtsWavhe+5cOHwYfv3V7kSOddttt7F27VpGjRpFSEgIxhjGjx9PZGQkn376aZ7eMaXPWSil7HPuHLjGpGbCBAgPt7oP0W5osm3Pnj107dqVH374IXVeo0aNGD9+PHXq1NHnLJRSDna1UBhjdXP+1FPQuDG4GZNFXa969eqsWrWKmTNnUrp0aQDWrFlD3bp16d69e663r8VCKWU/EfjuO5gxw7osVb++Na6MXqLKloCAAGJjY9mzZw99+/alUKFCGGN4//33c71tx12GEpFzwG4v7yYcOOODz7pbN7Pl2Zmffl769zcCx90mzR1fHM+cHsuslrk7dhnN0+Pp2XHKaJ6/HktP1vXF73qkMaaY+6iZMMY4agI2+GAf03zxWXfrZrY8O/PTz8vgvV8cz5wey+wcTw+Prx5PD46TJ8fXX45lbo5nfvpd18tQGfvaR591t25my7MzP/283HxvOeWL45nTY5nVMk+OnR5Pz5YV5J9NT9bN97/rTrwMtcE4eOjV/EaPZ97S45l39FjmrdweTyeeWUyzO4Cf0eOZt/R45h09lnkrV8fTcWcWSimlfM+JZxZKKaV8TIuFUkopt7RYKKWUcsvxxUJEqorIdBGZa3cWfyAifxOR90VkgYjcZ3ceJxOR20RkiojMFZHn7M7jD0QkTEQ2ishDdmdxOhFpJiLfu35Gm7lbP18WCxGZISJHRWRbuvktRWS3iOwTkYEAxpg4Y0xXe5I6QzaP51fGmG5ALPCYDXHztWwey53GmB5Ae0BvAc1Ado6nywDgc9+mdI5sHk8DnAdCAfeDpHv7CckcPhl5N3AHsC3NvEDgV6AqEAxsAaLSLJ9rd+78OuXweI4F7rA7e36bsnssgTbAj8DjdmfPj1N2jidwD9AB6x+Zh+zOnh+nbB7PANfyMsAn7radL88sjDGrgfQjkdcH9hnrTOIyMAdo6/NwDpSd4ymWUcBSY8wmX2fN77L7s2mMWWiMaQg84dukzpDN49kcaAA8DnQTkXz598tO2TmexpgU1/JTQIi7bRfK06TeVR44lOZ9PHCniJQEhgN1RGSQMWaELemcJ8PjCfwD6z+4cBGpZoyZYkc4h8nsZ7MZ0A7rF3GJ72M5VobH0xjTC0BEYoHjaf7Yqaxl9vPZDrgfiAAmuNuIk4qFZDDPGGNOAD18HcYPZHY8xwHjfB3G4TI7liuBlb6N4hcyPJ6pL4yZ5bsofiGzn8/5wHxPN+Kk07h4oGKa9xWAwzZl8Qd6PPOOHsu8pcczb+XJ8XRSsVgP3CIiVUQkGKuha6HNmZxMj2fe0WOZt/R45q08OZ75sliIyGzgJyBSROJFpKsxJgnoBSwDdgKfG2O225nTKfR45h09lnlLj2fe8ubx1I4ElVJKuZUvzyyUUkrlL1oslFJKuaXFQimllFtaLJRSSrmlxUIppZRbWiyUUkq5pcVCOZaIJIvI5jRTZbsz5RURqSMiH7hex4rIhHTLV4pIpt2ei8gcEbnF2zlVweGkvqGUSu+SMaZ2RgtERLCeI3JqZ3ODgWG5+Pxk4GWgW97EUQWdnlkovyEilUVkp4hMAjYBFUWkv4isF5FfROSNNOu+4hoMZrmIzBaRfq75qf+xi8iNInLA9TpQRP6ZZlvPuuY3c31mrojsEpFPXIUKEaknIj+KyBYR+VlEiok1MlntNDnWiEh0uu+jGBBtjNniwffcJs2Z1W4R2e9a9D1wj4joP4QqT+gPknKywiKy2fV6P9AHiAQ6G2N6ijUs7C1Y/fkLsFBE7gYuYPWPUwfrd2ATsNHNvroCZ4wx9UQkBFgjIt+6ltUBbsfqnG0N0EhEfgY+Ax4zxqwXkeLAJeADrMF7XhSR6kCIMeaXdPuKAbalm/eYiDRO874aWONl4OrnR0Q+B1a55qeIyD6glgffm1JuabFQTnbNZShXm8VBY8xa16z7XNN/Xe+LYhWPYsCXxpiLrs950qnafUC0iDzieh/u2tZl4GdjTLxrW5uBysAZ4HdjzHoAY8xZ1/IvgNdEpD/QBZiVwb5uAo6lm/fZ1fEcXNtZmXahiLyMdTwmppl9FCiHFguVB7RYKH9zIc1rAUYYY6amXUFEXiTN+AjpJPHn5dnQdNv6hzFmWbptNQMS08xKxvq9koz2YYy5KCL/xhr5LbOxuS+l23eWROSvwKNYQ2qmFerallK5pm0Wyp8tA7qISFEAESkvIqWB1cDDIlLY1T7QOs1nDgB1Xa8fSbet50QkyLWt6iISlsW+dwHlRKSea/1iadoPPsAaYGq9MSb9EJhg9QxazZNvUEQqAZOA9saY9IWhOqC9tao8oWcWym8ZY74VkduAn1xtzueBJ40xm0TkM2AzcBCrMfiqMcDnItIJ+E+a+R9gXV7a5GrAPgb8LYt9XxaRx4DxIlIY6z/8e4DzxpiNInIWmJnJZ3eJSLiIFDPGnHPzbcYCJYEvXd/jYWPMAyJSBuuy1O9uPq+UR7SLclXgicjrWH/Ex/hof+Wwhlu9NbNbe0WkD3DOGPNBDvfRBzhrjJme46BKpaGXoZTyIRF5ClgHvOLmGZDJXNsWkl2ngQ9z8XmlrqFnFkoppdzSMwullFJuabFQSinllhYLpZRSbmmxUEop5ZYWC6WUUm5psVBKKeXW/wPQIBan3iA9QwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Semilog plots are created using 'plt.semilogx' and 'plt.semilogy'. Here's\n", "# plt.semilogx.\n", "f0 = 1e4\n", "plt.semilogx(ff, lowpass(ff, f0), 'k-', linewidth = 2.5)\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('Low-pass Filter')\n", "plt.axis((10, 1e5, 0.1, 1.2))\n", "\n", "# Add another curve to the log-linear plot.\n", "f0 = 1e3\n", "plt.semilogx(ff, lowpass(ff, f0), 'r--', linewidth = 1.5)\n", "plt.legend((r'$f_0 = 10$ kHz', r'$f_0 = 1$ kHz'), loc = 'lower left', frameon = False);" ] }, { "cell_type": "code", "execution_count": 9, "id": "fiscal-synthesis", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here's semilogy.\n", "f0 = 1e4\n", "plt.semilogy(ff, lowpass(ff, f0), 'k-', linewidth = 2.5)\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('Low-pass Filter')\n", "plt.axis((10, 1e5, 0.1, 1.2))\n", "\n", "# Add another curve to the linear-log plot.\n", "f0 = 1e3\n", "plt.semilogy(ff, lowpass(ff, f0), 'r--', linewidth = 1.5)\n", "plt.legend({r'$f_0 = 10$ kHz', r'$f_0 = 1$ kHz'}, loc = 'upper right', frameon = False);" ] }, { "cell_type": "code", "execution_count": 13, "id": "assured-palmer", "metadata": {}, "outputs": [ { "data": { "image/png": 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SQcCqiJjZ17XccG1mQ1UNy5EOVF8N16VOy9FXtdS+gw0MICIeB75B0s12E/BsRNwMHBgRm9JjNpEM3it2/zpJnZI6t2zZMpRQzMxYu3YDNTV7ltXUJOVjUanjJG6TNL13oaT3AmuGEkCaZE4CDgMOBvaRdHqp50dEa0TURkTtpEmThhKKmVm6HOmeZdW0HOlAlZokHga6JNUBSHqJpBaSOZ2+P8QY3gs8GhFb0lHc1wNvA55Mq5lI3zcP8T5mZv2q9uVIB6rUcRKnpRP5/Yek+cAU4FXAsRHx6yHGsAE4RtJE4C/AcSRdbZ8HFgEXpO83DPE+Zmb9qvblSAdqQA3Xkr4GfBnYSZIgfjUsQUjLgH9Mr9tF0h325cC1JAlpA/CRiHi6r+u44drMhotHXCdKepKQtD+wAngH8HHg3cDNks6LiIuGGmBENAANvYp3kDxVmJlZTkodcd0NrAWOjIgNwJWSbgJaJb0/Ik4YsQjNzCw3pTZcfwt4T5ogAIiI64E3ABqJwMzMymmszOo6ULkPphtObpMwMxu4IbdJFFzoYJKG5L0KyyNi9eDDMzOzSlVqw/XBQDvwLpK5mpS+9xg//KGZmVneSm2T+Cawi2TSvW3AO4GPAA8AJ45IZGZmlrtSq5veDXwgIh6UFMCWiPi1pB1AE7uXMzUzsypS6pPE3sBT6fbT7J5s736SHk5mZlaFSk0SDwKz0u27gc9ImgqcBTw+AnGZmVkFKLW66SLgNen2vwI/A04jGRW9aATiMjOzClDqBH9tBdt3SZpG8mSxISKeyjzRzMxGtVKrm14k6e3Aroi4ywnCzKy6DThJAD8FJg93IGZmVnkGkyQ8V5OZVazeczBlvcbaHEyDNeC5myT9GXhjRDwyMiENnuduMrPextK6EIPV19xNg3mS+DTw5NBC2pOkV0u6TtKDkh6Q9FZJ+0m6RdLD6fu+w3lPM6t+7e1trFlzG6tXdzB79jTa29v6P8n2UFKSkPRjSfMljYuI9oh4fpjjuAj4WUTMAt5IMt3HucCtETEduDX9bGZWkvb2NpYuraO+fgc33wx1detZurTOiWKASqpuktQGnAw8C1wOrIiIdcMSgPRK4B7g8CgIRtJDwLyI2CTpIGBVRMzs61qubjKzHrNnT6Oubj1z5+4u6+qC1tapPPDAY7nFVYmGXN0UEQuBg0jmaXovsFbSakkfk7T3EOM7HNgCfF9Sl6TLJO0DHBgRm9L7b2L3VCBmZv1au3YDNTV7ltXUJOVWupLbJCLiTxHxnYg4GqgB7gQuAf4g6RJJswcZwwTgTcB3ImIu8DwDqFqSVCepU1Lnli1bBhmCmVWbGTOm0N29Z1l3d1JupRvMYLqDgZOA+cBO4DrgUGCNpC8NIoaNwMaIuD39fB1J0ngyrWYifd9c7OSIaI2I2oionTRp0iBub2bVqL6+mZaWiXR1wc6dSVVTS8tE6uub8w5tVCl10aGXkCSGTwLHA13AcuDqiHguPeYUoBX4xkACiIg/SPq9pJkR8RBwHMnssveTzAt1Qfp+w0Cua2Zj24IFCwFYvPgMtm7dwcyZU1m+vPnFcitNqQ3XT5EMomsHLo2INUWO2Re4KyIOG3AQ0pHAZSTLoj4CfILkKedakuVSNwAfiYin+7qOG67NrDePk+jfcKxx/QXgRxGxPeuAiHgGGHCCSM+9GygW4HGDuZ6ZjV2NjY0sW7bs78qlPSeLaGho8KjrEgx4xHUl85OEmdnADfeIazMzGyOcJMzMLJOThJmZZXKSMDOzTE4SZmaWyUnCzMwyOUmYmVkmJwkzqzhegrRyeDCdmVU8T60xsjyYzsxGLS9Bmq9S524yMyu7wiVIa2qguztZghTwbK5l4uomM6tYXoK0PFzdZGajkpcgzZ+ThJlVLC9Bmr+KSRKSxkvqkvST9PN+km6R9HD6vm/eMZpZeXkJ0vxVUsP154EHgFemn88Fbo2ICySdm34+J6/gzKz8vARp/iriSULSIcAHSJYw7XEScEW6fQVwcpnDMrOcFA6mW7jwdJ55ZgcR8OCD61m48HQPpiujiujdJOk64N+AVwBfioj5krZGxKsLjnkmIvqscnLvJjOzgavo3k2S5gObI+LOQZ5fJ6lTUueWLVuGOTozs7Et9yQBvB34oKTHgGuAYyVdBTwp6SCA9H1zsZMjojUiaiOidtKkSeWK2cxsTMg9SUTEeRFxSERMA04FfhERpwM3AovSwxYBN+QUopnZmJV7kujDBcDxkh4Gjk8/m5lZGVVUkoiIVRExP93+Y0QcFxHT0/en847PzErX3t7G7NnTGD9+nCfmG8UqKkmY2ehW2HV18eLTqatbz8qVQV3dehYvdtfV0agiusAOF3eBNasMnphvdKnoLrBmVn08MV/1cJIws2Hnifmqh5OEmQ2bnjaJBx9cT1MTe0zM19SUTKvhNonRxW0SZjYi2tvbaGo6n7VrNzBjxhTq6z0xX6Xqq03CScLMbIxzw7WZmQ2Kk4SZmWVykjAzs0xOEmZmlslJwszMMjlJmJlZJicJMwM8a6sV5yRhNoZ51lbrT+6D6SQdClwJvAZ4AWiNiIsk7Qf8EJgGPAacEhHP9HUtD6YzGxzP2jq2Vfpgup3AkoiYDRwDnCVpDnAucGtETAduTT+b2QjwrK2WJfckERGbIuKudPvPwAPAZOAk4Ir0sCuAk3MJ0GwM8KytliX3JFFI0jRgLnA7cGBEbIIkkQAH5BiaWVWrr2+mpWXiHrO2trRMpL6+Oe/QLGe5t0n0kPRyoANojojrJW2NiFcX7H8mIvYtcl4dUAcwZcqUo9avX1+ukM1GvcbGRpYtW/bi5733hu3b4WUvg7/8ZfdxDQ0NbryuYhU/C6yklwA/AVZGxIVp2UPAvIjYJOkgYFVEzOzrOm64NjMbuIpuuJYk4HvAAz0JInUjsCjdXgTcUO7YzPJS2DVVEhMninHjkvfCcv91byMt9yQBvB34KHCspLvT1/uBC4DjJT0MHJ9+NhsTGhsbiQja2q5i8uSJNDfDzTdDczNMnjyRtrariAgnCRtxFVHdNFxc3WTVxuMXrBwqurrJzLJ5/ILlzUnCrIJ5/ILlzUnCrAL1NFw/+OB6mprYY/xCUxM8+OB6N1xbWbhNwqzCtbe30dR0PmvXbmDGjCnU1zezYMHCvMOyKlLx4ySGi5OEmdnAueHazMwGxUnCrA9eiMfGugl5B2BWqdrb21i6tI4lS7ZRUwPd3etZurQOwG0CNmb4ScKsl56eRWeeeTpLlmxj7lyYMAHmzoUlS7Zx5pmnu2eRjRlOEmYZtm+n6EC27dvziccsD04SZr30zJs0c+bUogPZZs6c6nmTbMxwkrCyGI0NwF6Ix8wN11YGo7UBuCe2woFsy5d7IJuNLR5MZyOmZ9WzvfdOprjuPZPp+ecnq5951TOzfHkwneXKDcBmo5eTxACMxnr1PLkB2Gz0q/gkIelESQ9JWifp3Lzi6KlXr6tbz8qVQV1dUq/uRNE/NwCbjWIRUbEvYDzwO+BwYC/gHmBO1vFHHXVUDLeGhoYAYu+9iQsvJH75y92vCy9MyoFoaGgY9ntXk7a2q2LWrKkxbpxi1qyp0dZ2Vd4hmVkK6IyM39VK7910NLAuIh4BkHQNcBJwf7kDcb360CxYsNC9gsxGoYru3STpw8CJEXFm+vmjwFsiYnHBMXVAXfrxCOC+QdzqVcCzfcdCzeTJ7DVx4u6ybdvg8cf5awTd2WdaL/3+W1eoSoi7XDEM932G63pDuc5gzx3oefsDTw3iPnmbGhGTiu2o9CcJFSnbI6tFRCvQCiCpNSLqipzT900GeJ6kzsjoLmZ9G+x/o7xVQtzlimG47zNc1xvKdfzbMHiV3nC9ETi04PMhwBN9HP+/B3mfwZ5nAzda/60rIe5yxTDc9xmu6w3lOv5tGKRKr26aAKwFjgMeB34LLIiIwVQpDWdcVffXgpkNXTX+NlR0dVNE7JS0GFhJ0tNpRd4JItWadwBmVpGq7rehop8kzMwsX5XeJmFmZjlykjAzs0xOEmZmlslJYogk7SPpCkmXSvKQYjMDQNLhkr4n6bq8YxkKJ4kiJK2QtFnSvb3Ki002+CHguoj4FPDBsgdrZmUzkN+GiHgkIs7IJ9Lh4yRR3OXAiYUFksYD3wL+CzAHOE3SHJIBfr9PD9tVxhjNrPwup/TfhqrgJFFERKwGnu5V/OJkgxHxV6BnssGNJIkC/O9pVtUG+NtQFfyjVrrJ7H5igCQ5TAauB/6bpO/gIfxmY1HR3wZJ/yDpu8BcSeflE9rQVfSI6wpTdLLBiHge+ES5gzGzipH12/BH4DPlDma4+UmidAOdbNDMxoaq/m1wkijdb4Hpkg6TtBdwKnBjzjGZWf6q+rfBSaIISVcDvwFmStoo6YyI2An0TDb4AHBthUw2aGZlMhZ/GzzBn5mZZfKThJmZZXKSMDOzTE4SZmaWyUnCzMwyOUmYmVkmJwkzM8vkJGE2DCRdLuknQzh/laSLB3D8PEkhaf/B3jO9zpDiturnuZvMhsfnKT6Hj9mo5iRhNgwi4tm8YzAbCa5usqohaZKkTZL+paDsDZK2S/pwH+e9WdLNkp6S9CdJv5L01oL975b0N0nzCso+kx57ePp5j2obSe+SdJuk5yQ9K+l2Sa8fwHc5XdJvJf05XQntR5ImFzn0GEl3p9/xTklH9brO2yR1SNom6XFJ35H0ylLjMHOSsKoREVuAjwNflfRWSXsDVwNXR0Rf6wy/AvgB8E6SBWTuBv5PT31/RHQAXwd+IGk/SbOAFuBzEfFI74tJmgDcAPwKeCPwFuAiBrZy4V5AQ3r+fGD/9Lv09g3gHKAWeAS4SdLENI4a4GaSyebeSLLU7pHAigHEYWOc526yqiPpmyTrjXeQ/PAfGRHPDeB8kUz1/OWIuCotewnw/0gWl5kGPBwR/1hwzuXA/hExX9J+wB+BeWmCKeWeq4B7I2Jxxv5ZJJPHHRoRG9Onml8Cp0dEW3rMy0mmrf5SRFwm6Urgb4XrLEs6EugCDoyIzYVxlxKnjT1+krBqdA7wV+BjwMKeBCFpSlr90/P6Slp+gKRLJK2V9CzwZ+AAYErPBSPib8ACkr/qDwA+nXXziHiaZC3klZJukvRFSYdmHV+MpDdJukHSekl/BjrTXVN6Hfqbgvs+B3STrLMMcBRweuF3Bn6d7nvtQOKxscsN11aNppEsAhPA4cDtafkTJNUtPXrWKr4COBD4AvAYsAO4laTKp9AxJH9YvRqYBGzNCiAiPpE+0ZxI8lTTLOnkiFjZX/CS9iGZdvrnwEeBzSTVTf+3SEx9GQdcBvx7kX2PD+A6NoY5SVhVSauF2kjq4W8HviPp1xGxIZ33f12R094BnB0RN6XXOBA4qNd1pwEXA2eR/PC3SXpbes2iIuIe4B7ga5J+Ciwi+fHvzyySpPCViHg0vf+HMo49hqQtoie5vB64Mt13F3BERBT7zmYlcXWTVZsmkuqgz5I0Ft9G0uDc1//ra0mqZeZIejNwDUl1FQCSxgNXAR0RcQlwJskSlQ3FLpauUHZB2rNoqqT3AG8A7i/xO2wgeZpZLOlwSR9Iv1cxX5V0vKQjSBqk/wq0p/u+Bhwt6buS5kp6naT5ki4pMQ4zJwmrHpLeDSwBPhYRWyPplfFxYDZJO0WWTwIvB+4kSRArSKqdenwFeB1wBkC6wP0i4FxJ7yhyvW3ADOBHJAnoCpKnm6+V8j3SXlqLgJNJEksD8MWMw88l6Wl1FzAdmB8Rz6fXWQO8i6T6rYPkqebfgCdLicMM3LvJzMz64CcJMzPL5CRhZmaZnCTMzCyTk4SZmWVykjAzs0xOEmZmlslJwszMMjlJmJlZJicJMzPL9P8B3TcviJ1+UaUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# The easiest way that I've found to do plots with error bars on log scales\n", "# is to use 'errorbar' (as shown in basic_plots.m) and then modify whatever\n", "# axis you want to be on a log scale afterwards. Below, 'plt.xscale('log')'' is\n", "# used to change the x-axis to be on a log scale.\n", "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "y = [1.02, 4.3, 8.6, 16, 22, 34, 45, 60.2, 80.1, 96, 125, 144]\n", "dy = [.3, .3, .5, 2, 2, 2, 4, 4, 6, 10, 20, 20]\n", "formattedPlot = plt.errorbar(x, y, dy, fmt = 'ko', markersize = 6,\\\n", " capsize = 5, linewidth = 1.5, markerfacecolor = 'y');\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.xscale('log')\n", "plt.axis((0.8, 15, 0, 180));" ] }, { "cell_type": "code", "execution_count": 14, "id": "varying-clause", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here's an error bar plot with the y-axis on a log scale.\n", "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "y = [1.02, 4.3, 8.6, 16, 22, 34, 45, 60.2, 80.1, 96, 125, 144]\n", "dy = [.3, .3, .5, 2, 2, 2, 4, 4, 6, 10, 20, 20]\n", "formattedPlot = plt.errorbar(x, y, dy, fmt = 'ko', markersize = 6,\\\n", " capsize = 5, linewidth = 1.5, markerfacecolor = 'y');\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.yscale('log')\n", "plt.axis((0, 15, 0.4, 180));" ] }, { "cell_type": "code", "execution_count": 15, "id": "sunrise-junior", "metadata": {}, "outputs": [ { "data": { "image/png": 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uh4aSdjNrbFlDYjtwZvX5t4HLJC0iufz0ZA51WYl0dvbQ29vE1q1w4EByqam3t4nOzp6iSzOzI8jaJ7ESeElE3CDpV4F/Iumj2A9cEhFfybfMI9Y3ssb1pTt37iyyFBulUqmwfv36g6/nzoV9++DYY+GFFw7t19XV5c5rswJN1CcxpXESkppIzix2RcSzR9q/XtxxbWY2eTUbJ1E92BuBFyPiW40UEGZmVnuTDgngH4EFtS7EzMwaz1RCovC5mszMrD6mEhJmZjZDTCUkPgJ8v9aFmJlZ48k6d9M/SLpI0qyIGIiI5/MuzMzMipf1TOJ54O+A3ZL+TNLiHGsyM7MGkSkkImIl8EqSeZreAuyQdK+kD0iam2eBZmZWnMx9EhHxHxFxTUScA7QCW4Brgf8n6VpJLXkVeSRemc7MLB9TGUz3KuDdwEXAAeA24BTgIUl/VNvysomIOyKifd68eUV8vJlZaWXtuH6JpPdJugsYBt4DbABeGRGrI+IdwErgytwqNTOzusu6nsS/kwyiGwCuiIiHxtnnHuBHtSrMzMyKlzUkPgF8JSL2pe0QET8CTq1JVWZm1hAyhURE3Jx3IWZm1ng8LYeZmaVySJiZWSqHhJmZpXJImJlZKoeEmZmlckiYmVkqh4SZmaVySJiZWSqHhJmZpSpFSHiqcDOzfJQiJDxVuJlZPkoREmZmlg+HhJmZpXJImJlZKoeEmZmlckiYmVkqh4SZmaVySJiZWSqHhJmZpXJImJlZKoeEmZmlckiYmVkqh4SZmaVySJiZWSqHxBFUKhUkHfFRqVSKLtXMrOYUEUXXUDNLly6NzZs35/oZy5YtA2DTpk25fo6ZWb1I2hIRS8fb5jMJMzNL5ZAwM7NUDgkzM0vlkDAzs1QNGxKSjpN0o6TrJK0suh6AgYF+HnroAe69d5CWlmYGBvqLLsnMLFd1DQlJ10t6WtLDY9ovlPRdSY9KuqLa/F7gtoi4FHhXPescz8BAP2vXttPZuZ+774b29mHWrm13UJhZqdX7TOIG4MLRDZJmA1cDbwfOAi6WdBZwMvBEdbcX61jjuLq717FmzV7a2mDOHGhrgzVr9tLdva7o0szMclPXkIiIe4Efjmk+B3g0Ih6LiJ8CXwLeDewmCQqYoE5J7ZI2S9r8zDPP5FE2ADt27KK19fC21tak3cysrBqhT2IBh84YIAmHBcBXgd+SdA1wR9qbI6IvIpZGxNL58+fnVuSSJQsZGjq8bWgoaTczK6tGCAmN0xYR8XxEfCgifj8iCr/w39nZQ29vE1u3woEDsHUr9PY20dnZU3RpZma5mVN0ASRnDqeMen0y8FRBtaRasSK5waqjYzXPPbefM85YxIYNPQfbzczKqBFC4kHgdEmnAk8C7wdWTOYAkpYDyxcvXpxDeYesWLGSvr7rAM/dZGYzQ71vgb0VuB84Q9JuSasj4gDQAWwEtgFfjohHJnPciLgjItrnzZtX+6LNzGawup5JRMTFKe13AXfVsxYzMzuyRui4NjOzBlWKkJC0XFLfnj17ii7FzKxUShES7pMwM8tHKULCzMzy4ZAwM7NUDokjqFQqSDr4GBwcZHBw8LA2SVQqlaJLNTOrOUVE0TUctVGD6S7duXNn0eWYmU0rkrZExNLxtpXiTMId12Zm+ShFSJiZWT4cEmZmlsohYWZmqRwSZmaWqhQh4Wk5zMzyUYqQ8N1NZmb5KEVImJlZPhwSZmaWyiFhZmapHBJmZpaqFCHhu5vMzPJRipDw3U1mZvkoRUiYmVk+HBJmZpbKIWFmZqkcEmZmlsohYWZmqRwSZmaWak7RBdTCyBrXwF5J2yb59nnAZAdYnAA8O8n3WGIqf9+NoBHqrkcNeXxGrY55NMeZyntn0m/DotQtEVGaB9BXp/dsLvq7TtfHVP6+G+HRCHXXo4Y8PqNWxzya4/i3YeqPsl1uuqNO77Gpm65/341Qdz1qyOMzanXMozmOfxumSNX0s0mQtDkilhZdh5k1ljL+NpTtTKJe+oouwMwaUul+G3wmYWZmqXwmYWZmqRwSZmaWyiFhZmapHBJHSdJxkm6UdJ2klUXXY2aNQdJpkr4o6baiazkaDolxSLpe0tOSHh7TfqGk70p6VNIV1eb3ArdFxKXAu+perJnVzWR+GyLisYhYXUylteOQGN8NwIWjGyTNBq4G3g6cBVws6SzgZOCJ6m4v1rFGM6u/G8j+21AKDolxRMS9wA/HNJ8DPFr938FPgS8B7wZ2kwQF+O/TrNQm+dtQCv5Ry24Bh84YIAmHBcBXgd+SdA0exm82E4372yDpFZI+D7RJ+lQxpR29UswCWycapy0i4nngQ/UuxswaRtpvww+Ay+pdTK35TCK73cApo16fDDxVUC1m1jhK/dvgkMjuQeB0SadKOgZ4P3B7wTWZWfFK/dvgkBiHpFuB+4EzJO2WtDoiDgAdwEZgG/DliHikyDrNrL5m4m+DJ/gzM7NUPpMwM7NUDgkzM0vlkDAzs1QOCTMzS+WQMDOzVA4JMzNL5ZAwO0qSbpD09aN4/yZJn5vE/sskhaQTpvqZ1eMcVd02M3juJrOj93HGn7/HbNpzSJgdpYjYU3QNZnnx5SYrBUnzJf27pP8+qu21kvZJet8E7/s1SXdLelbSf0j6V0nnjdr+Jkk/k7RsVNtl1X1Pq74+7LKNpPMlPSDpJ5L2SPqmpNdM4ruskvSgpB9XV0H7iqQF4+x6rqRvV7/jFklnjznOGyQNStor6UlJ10j6z1nrMAOHhJVERDwDfBC4UtJ5kuYCtwK3RsREawz/J+Bm4DdIFo/5NnDXyPX+iBgE/gK4WdLLJZ0J9AIfjYjHxh5M0hzga8C/Aq8D/gvwV0xu1cJjgK7q+y8CTqh+l7H+EvhjYCnwGHCnpKZqHa3A3SQTzb2OZJnd1wPXT6IOM8/dZOUi6bMka40Pkvzwvz4ifjKJ94tkmudPRsQt1baXAP+HZGGZZmBnRPzOqPfcAJwQERdJejnwA2BZNWCyfOYm4OGI6EjZfibJxHGnRMTu6lnNvwCrIqK/us/LSKas/qOI+IKkm4CfjV5jWdLrga3ASRHx9Oi6s9RpM5PPJKxs/hj4KfABYOVIQEhaWL38M/L4dLX9REnXStohaQ/wY+BEYOHIASPiZ8AKkv/Vnwh8JO3DI+KHJOsgb5R0p6TLJZ2Stv94JP2qpK9JGpb0Y2BzddPCMbveP+pzfwIMkayxDHA2sGr0dwbuq277lcnUYzObO66tbJpJFoAJ4DTgm9X2p0gut4wYWaf4RuAk4BPA48B+4J9JLvmMdi7Jf6p+CZgPPJdWQER8qHpGcyHJWU2PpPdExMYjFS/pOJIpp78B/C7wNMnlpv89Tk0TmQV8Afif42x7chLHsRnOIWGlUb0s1E9yHf6bwDWS7ouIXdU5/x8d522/DnwsIu6sHuMk4JVjjtsMfA74Q5If/n5Jb6gec1wR8W/AvwGfkfSPwCUkP/5HciZJKHw6Ir5X/fz3pux7LklfxEi4vAa4qbrtW8CrI2K872yWmS83WZl0k1wO+n2SzuIHSDqcJ/p3voPkssxZkn4N+BLJ5SoAJM0GbgEGI+Ja4PdIlqfsGu9g1dXJ/rx6Z9EiSb8JvBb4TsbvsIvkbKZD0mmS3ln9XuO5UtJbJb2apEP6p8BAddtngHMkfV5Sm6TFki6SdG3GOswAh4SVhKQ3AWuAD0TEc5HckfFBoIWknyLNh4GXAVtIAuJ6kstOIz4NLAZWA1QXt78EuELSr49zvL3AEuArJAF0I8nZzWeyfI/qXVqXAO8hCZYu4PKU3a8gudPqW8DpwEUR8Xz1OA8B55NcfhskOav5H8D3s9RhNsJ3N5mZWSqfSZiZWSqHhJmZpXJImJlZKoeEmZmlckiYmVkqh4SZmaVySJiZWSqHhJmZpXJImJlZqv8PU2e9Q/RE96wAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here's an error bar plot with both axes on a log scale.\n", "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "y = [1.02, 4.3, 8.6, 16, 22, 34, 45, 60.2, 80.1, 96, 125, 144]\n", "dy = [.3, .3, .5, 2, 2, 2, 4, 4, 6, 10, 20, 20]\n", "formattedPlot = plt.errorbar(x, y, dy, fmt = 'ko', markersize = 6,\\\n", " capsize = 5, linewidth = 1.5, markerfacecolor = 'y');\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.axis((0.8, 15, 0.4, 180));" ] }, { "cell_type": "code", "execution_count": 16, "id": "minimal-enhancement", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Just for fun, we can add a line to this last plot.\n", "# We just use plot and the axes will still be on log scales.\n", "\n", "# Here's the origonal plot.\n", "formattedPlot = plt.errorbar(x, y, dy, fmt = 'ko', markersize = 6,\\\n", " capsize = 5, linewidth = 1.5, markerfacecolor = 'y');\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.axis((0.8, 15, 0.4, 180))\n", "\n", "# Now add the line.\n", "xx = np.array(np.logspace(-1, 2, 1000))\n", "plt.plot(xx, xx**2, 'b-', linewidth = 1.5);" ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }