{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimizing DTW-Based Audio-to-MIDI Alignment and Matching\n", "\n", "This notebook provides an informal overview of the experiments presented in \"Optimizing DTW-Based Audio-to-MIDI Alignment and Matching\". The goal behind this work is to automatically tune the parameters of dynamic time warping systems used for audio-to-MIDI alignment so that the system is both highly accurate and capable of reporting when an alignment was successful or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthetic data\n", "\n", "To automatically tune alignment systems, we first need a dataset of MIDI/audio pairs where we know what a \"correct\" alignment would be. That way, we can rapidly evaluate the performance of a given system by using it to align all of the pairs and measuring the alignment error. This allows much faster evaluation than, say, listening to alignments manually.\n", "\n", "We create this dataset by applying a series of \"corruptions\" to MIDIs file which mimic the characteristics of real-world differences between MIDI files and the audio files they are a transcription of. These include slowly warping time, cropping out sections, changing instrument numbers, dropping instruments, and changing note velocities. The code for this step is in `corrupt_midi.py`; here's an example of corrupting a MIDI file." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pretty_midi\n", "import corrupt_midi\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import librosa\n", "import create_data\n", "import scipy.spatial\n", "import djitw\n", "import tabulate\n", "import db_utils\n", "import glob\n", "import json\n", "import find_best_aligners\n", "import IPython.display\n", "import csv" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Some utility functions\n", "def compute_cqt(audio_data):\n", " \"\"\" Compute the log-magnitude L2 normalized CQT \"\"\"\n", " cqt, times = create_data.extract_cqt(audio_data)\n", " cqt = librosa.logamplitude(cqt, ref_power=cqt.max())\n", " return librosa.util.normalize(cqt, 2).T, times\n", "def display_cqt(cqt):\n", " \"\"\" Plot a CQT with sane defaults \"\"\"\n", " plt.imshow(cqt.T, aspect='auto', interpolation='nearest',\n", " origin='lower', cmap=plt.cm.hot,\n", " vmin=np.percentile(cqt, 5), vmax=np.percentile(cqt, 95))\n", " plt.xlabel('Frame')\n", " plt.ylabel('Frequency bin')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RUniuIWfu7FI8ka/LZxVpzNbZP4P2V1hZmCyUj4+BZHnWRizS+1PQvHXJNrlh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ECuHfILfBniLbdD118beDE7QO5FJmnlts4XAEludYpLUwJzoiylFSRhUu4qycPm3mwFJAdTl3Fq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\nTczYBzPqLu6gzMzMzOzSVmqDUn19+hH5prP3rVWi3EL+7XM2h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pbjiX8zP2hPCGpvq8JZhDtg/z2Fbgz1eD+c57zMVaV3NksqX18hxX896OgDlY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5C8BQBJkvwxgH9Ncvkv95E9IiIiIiIiIiIi4jcHj9PC+imow17AXX8WERERERERERER8VuLx/mP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\nBl7OsuxQlmXdgGHAsCzLOvy4s9CN+KNv0n4CgUDglxJxoxwIB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Grab a MIDI file from the clean MIDIs we used in this experiment\n", "midi_file = 'data/mid/Come as You Are.mid'\n", "# Parse the MIDI file with pretty_midi\n", "midi_object = pretty_midi.PrettyMIDI(midi_file)\n", "\n", "# For illustration, we'll plot a CQT of the MIDI object\n", "# before and after corruptions.\n", "plt.figure(figsize=(12, 3))\n", "original_cqt, original_times = compute_cqt(midi_object.fluidsynth(22050))\n", "display_cqt(original_cqt)\n", "plt.title('Original MIDI CQT')\n", "\n", "# This is the wrapper function to apply all of the corruptions \n", "# defined in corrupt_midi\n", "adjusted_times, diagnostics = corrupt_midi.corrupt_midi(\n", " midi_object, original_times,\n", " # This defines the extent to which time will be warped\n", " warp_std=10,\n", " # These define how likely we are to crop out sections\n", " # We'll set them to 1 and 0 here for illustration; in the \n", " # paper they are adjusted according to the desired corruption level\n", " start_crop_prob=0., end_crop_prob=0., middle_crop_prob=1.,\n", " # The likelihood that each instrument is removed\n", " remove_inst_prob=.3,\n", " # The likelihood that an instrument's program number is changed\n", " change_inst_prob=1.,\n", " # The standard deviation of velocity adjustment\n", " velocity_std=.2)\n", "\n", "# Now, we can plot the CQT after corruptions.\n", "plt.figure(figsize=(12, 3))\n", "corrupted_cqt, corrupted_times = compute_cqt(midi_object.fluidsynth(22050))\n", "display_cqt(corrupted_cqt)\n", "plt.title('After corruption')\n", "\n", "# We can also plot the timing offset, which we will try to reverse\n", "plt.figure()\n", "plt.plot(original_times, original_times - adjusted_times)\n", "plt.xlabel('Original time')\n", "plt.ylabel('Offset from original time')\n", "plt.title('Time warping applied')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measuring Error\n", "\n", "Given a synthetically corrupted MIDI file for which we know the timing distortion applied, we can measure the success of a given alignment system by computing how well it is able to reverse this distortion. We will do this by aligning the corrupted MIDI to the original MIDI and then comparing the original alignment against the known warping curve. A straightforward way to compare the difference between the curves is the mean absolute error in seconds. We also threshold this error at .5, because above about .5 seconds it doesn't matter how bad the alignment is." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vDBbwxphiLOCdwQLeGFOMBbwzWMAbY4qxnazOYAFvjCnGWvDOYAFvjCnGAt4ZLOCNMcVY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\nZdu2bXTp0sUj86/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute a pairwise distance matrix of the original and corrupted CQTs\n", "distance_matrix = scipy.spatial.distance.cdist(\n", " original_cqt, corrupted_cqt, 'sqeuclidean')\n", "# Compute the lowest-cost path via DTW\n", "# SPOILER ALERT! These are the parameters of the \"golden standard\" system \n", "# that we propose in the paper. See below for details.\n", "p, q, score = djitw.dtw(\n", " distance_matrix, .96, np.median(distance_matrix), inplace=0)\n", "\n", "# Plot the aligned corrupted times and ground-truth times\n", "plt.figure()\n", "plt.plot(original_times[p], original_times[p] - corrupted_times[q])\n", "plt.plot(original_times[p], original_times[p] - adjusted_times[p])\n", "plt.legend(['Fixed corrupted offset', 'Ground-truth offset'], loc='lower right')\n", "plt.title('Timing correction')\n", "\n", "# Compute the absolute error, clipped to within .5 seconds\n", "plt.figure()\n", "error = np.abs(np.clip(\n", " corrupted_times[q] - adjusted_times[p], -.5, .5))\n", "plt.plot(error)\n", "plt.title('Correction error')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizing alignment performance\n", "\n", "Now that we have a rapid way to compute a performance metric for a given alignment system, we can rapidly test out differently-designed systems. We created a dataset of 1,000 corrupted MIDI files using the process described above. Instead of tuning parameters by hand, we used Bayesian optimization, which models the relationship between parameters and an objective value (here, the mean alignment error across all files in the dataset) and then predicts new parameter values which should achieve a better objective value. We optimized the representation used for MIDI and audio files, the distance metric, normalization scheme, whether to beat synchronize, the penalty scale, etc. Below is a table of the top-performing systems. The best-performing system achieved a mean absolute error of about 18 milliseconds, which is pretty good! Note that an alignment system using the \"cosine\" metric is equivalent to a `norm = 2, metric = 'sqeuclidean'` system regardless of the norm used." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " band_mask add_pen standardize metric feature beat_sync objective gully norm\n", "----------- --------- ------------- ----------- --------- ----------- ----------- -------- ------\n", " 0 1.03507 1 sqeuclidean gram 0 0.0180094 0.967203 2\n", " 0 0.840218 0 cosine gram 0 0.018069 0.97018 inf\n", " 0 0.944247 0 cosine gram 0 0.018116 0.967956\n", " 0 0.822652 0 cosine gram 0 0.0181264 0.974826\n", " 0 0.929215 0 cosine gram 0 0.0181429 0.971404\n", " 0 0.920111 1 cosine gram 0 0.0181909 0.962121 1\n", " 0 0.947526 0 cosine gram 0 0.0181951 0.973988\n", " 0 0.89384 1 cosine gram 0 0.0181988 0.963922 2\n", " 0 0.906607 1 cosine gram 0 0.0182004 0.965711 1\n", " 0 0.937798 1 cosine gram 0 0.0182236 0.969747 1\n", " 0 0.973437 1 cosine gram 0 0.0182453 0.964828 2\n", " 0 0.753343 1 cosine gram 0 0.0182517 0.962572 1\n", " 0 0.965885 1 cosine gram 0 0.0182559 0.967337 2\n", " 0 0.894526 1 cosine gram 0 0.0182779 0.974022 1\n", " 0 0.951461 1 cosine gram 0 0.0182823 0.97054 1\n", " 0 0.7484 1 cosine gram 0 0.018291 0.963632 2\n", " 0 0.816493 1 cosine gram 0 0.0182972 0.971567 2\n", " 0 0.870056 1 cosine gram 0 0.0183188 0.976841 2\n", " 0 0.978028 0 cosine gram 0 0.0183209 0.968555\n", " 0 0.933795 1 cosine gram 0 0.0183289 0.975997 1\n" ] } ], "source": [ "# Load in the results from the parameter search experiment\n", "params, objectives = db_utils.get_experiment_results(\n", " 'results/parameter_experiment_gp/*.json')\n", "# Truncate to the top 20 results\n", "good = np.argsort(objectives)[:20]\n", "params = [params[n] for n in good]\n", "objectives = [objectives[n] for n in good]\n", "# Pretty-print using tabulate\n", "for param, objective in zip(params, objectives):\n", " param['objective'] = objective\n", "print tabulate.tabulate(params, headers=dict((k, k) for k in params[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizing confidence score normalization\n", "\n", "DTW provides a distance-like metric by computing the total distance between aligned frames. There are various ways to normalize this metric to report a reliable confidence score. So, we performed an exhaustive search over these normalization strategies by computing the Kendall rank correlation between every normalized DTW distance variant and the alignment error for all of the systems generated by Bayesian optimization. The rank correlation was computed over the dataset we created above, plus an additional dataset of 1,000 synthetic pairs which were most likely to fail due to a higher amount of corruption, which allows us to verify that a system can report when it fails. The highest correlation among the best-accuracy systems was .710; below, we explore the correlation for the absolute best accuracy-system." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kendall rank correlation: 0.692705347799\n" ] }, { "data": { "image/png": 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PASVCiD8APgl8LeWe5jhS3arppGTl26wjmb+3OZfIbIcd0WVju6snMjPXfNVr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\nFEuSk66g5oLxEubAtLlIlHaYTYtQKribA8lc3L1wQvxfB7Y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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load in all confidence reporting experiment trials\n", "trials = []\n", "for trial_file in glob.glob('results/confidence_experiment/*.json'):\n", " with open(trial_file) as f:\n", " trials.append(json.load(f))\n", "# Retrieve the lowest-achieved mean absolute error\n", "best_easy_error = objectives[0]\n", "# Retrieve the confidence reporting trial for this system\n", "best_trial = [t for t in trials\n", " if np.allclose(np.mean(t['results']['easy_errors']),\n", " best_easy_error)][0]\n", "# Retrieve the results from this trial\n", "best_result = best_trial['results']\n", "\n", "# Plot a scatter plot of mean alignment error vs. confidence score\n", "errors = np.array(best_result['hard_errors'] + best_result['easy_errors'])\n", "scores = np.array(best_result['hard_penalty_len_norm_mean_norm_scores'] +\n", " best_result['easy_penalty_len_norm_mean_norm_scores'])\n", "plt.scatter(errors, scores, marker='+', c='black', alpha=.3, s=40)\n", "plt.gca().set_xscale('log')\n", "plt.ylim(0., 1.)\n", "plt.xlim(.9*np.min(errors), np.max(errors)*1.1)\n", "plt.xlabel('Alignment error')\n", "plt.ylabel('Normalized DTW distance')\n", "\n", "# Print the correlation achieved\n", "print \"Kendall rank correlation: {}\".format(\n", " find_best_aligners.kendall_ignore_ties(errors, scores))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The \"golden standard\"\n", "\n", "Now, we have a large collection of DTW systems for which we know both how well they will be able to correctly aligned songs and how reliably they can report their confidence. We have one more goal: To produce a system which is straightforward to explain and implement. Investigating the results, we chose the following system, whose performance was not significantly different than the highest-performing system but is much more straightforward to implement:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confidence score: 0.60321857026\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Audio/CQT parameters\n", "FS = 22050\n", "NOTE_START = 36\n", "N_NOTES = 48\n", "HOP_LENGTH = 1024\n", "# DTW parameters\n", "GULLY = .96\n", "\n", "\n", "def compute_cqt(audio_data):\n", " \"\"\" Compute the CQT and frame times for some audio data \"\"\"\n", " # Compute CQT\n", " cqt = librosa.cqt(audio_data, sr=FS, fmin=librosa.midi_to_hz(NOTE_START),\n", " n_bins=N_NOTES, hop_length=HOP_LENGTH, tuning=0.)\n", " # Compute the time of each frame\n", " times = librosa.frames_to_time(\n", " np.arange(cqt.shape[1]), sr=FS, hop_length=HOP_LENGTH)\n", " # Compute log-amplitude\n", " cqt = librosa.logamplitude(cqt, ref_power=cqt.max())\n", " # Normalize and return\n", " return librosa.util.normalize(cqt, 2).T, times\n", "\n", "# We'll use a real-world MIDI/audio pair\n", "audio_file = 'data/real_world/0.mp3'\n", "midi_file = 'data/real_world/0.mid'\n", "\n", "# Load in the audio data\n", "audio_data, _ = librosa.load(audio_file, sr=create_data.FS)\n", "# Compute the log-magnitude CQT of the data\n", "audio_cqt, audio_times = compute_cqt(audio_data)\n", "# Load and synthesize MIDI data\n", "midi_object = pretty_midi.PrettyMIDI(midi_file)\n", "midi_audio = midi_object.fluidsynth(fs=create_data.FS)\n", "# Compute log-magnitude CQT\n", "midi_cqt, midi_times = compute_cqt(midi_audio)\n", "# Nearly all high-performing systems used cosine distance\n", "distance_matrix = scipy.spatial.distance.cdist(\n", " midi_cqt, audio_cqt, 'cosine')\n", "# Get lowest cost path\n", "p, q, score = djitw.dtw(\n", " distance_matrix,\n", " # The gully for all high-performing systems was near 1\n", " GULLY,\n", " # The penalty was also near 1.0*median(distance_matrix)\n", " np.median(distance_matrix),\n", " # Don't modify the distance matrix in place, as we will\n", " # use it to normalize the score below\n", " inplace=False)\n", "# Normalize by path length\n", "score = score/len(p)\n", "# Normalize by distance matrix submatrix within path\n", "score = score/distance_matrix[p.min():p.max(), q.min():q.max()].mean()\n", "# Adjust the MIDI file\n", "midi_object.adjust_times(midi_times[p], audio_times[q])\n", "# Synthesize aligned MIDI\n", "midi_audio_aligned = midi_object.fluidsynth(fs=create_data.FS)\n", "# Adjust to the same size as audio\n", "if midi_audio_aligned.shape[0] > audio_data.shape[0]:\n", " midi_audio_aligned = midi_audio_aligned[:audio_data.shape[0]]\n", "else:\n", " trim_amount = audio_data.shape[0] - midi_audio_aligned.shape[0]\n", " midi_audio_aligned = np.append(midi_audio_aligned,\n", " np.zeros(trim_amount))\n", "print \"Confidence score: {}\".format(score)\n", "# Play the result!\n", "IPython.display.Audio([midi_audio_aligned, audio_data], rate=create_data.FS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Real-world performance\n", "\n", "In the real world, we don't know the correct alignment a priori, so in order to decide whether an alignment system was successful we have to listen to it. We listened to 500 real-world MIDI/audio pairs and rated them from 1 to 5:\n", "\n", "5 - Perfect transcription and alignment\n", "\n", "4 - Perfect alignment, but the transcription is missing instruments and/or has embellishments\n", "\n", "3 - Alignment is somewhat sloppy\n", "\n", "2 - Alignment failed, potentially because of a poor transcription\n", "\n", "1 - The MIDI file is not correctly matched to the audio file\n", "\n", "We can get some idea as to how well the system is performing and reporting confidence scores by looking at the histograms of the confidence scores for each rating." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Rating Normalized DTW dist Note\n", "-------- --------------------- ----------------------------\n", " 1 1.0047 Audio is a remix\n", " 2 0.751496 Audio is remix\n", " 1 0.97841 Audio is remix?\n", " 1 0.969465 Audio is probably remix\n", " 3 0.84312 Remix?\n", " 1 0.96302 Audio is remix\n", " 2 1.01551 Very sloppy/remix?\n", " 2 0.951068 Very sloppy/audio is remix?\n", " 2 0.89077 Audio is remix/wrong section\n", " 1 0.992035 Audio is remix\n", " 1 0.965641 Audio may be remix\n", " 1 0.739597 Audio sounds like a remix\n", " 2 0.99414 Audio is remix? Very sloppy\n", " 1 0.987043 Audio is remix\n", " 2 0.89037 Audio is remix\n", " 2 0.926759 Different versions (remix?)\n", " 1 0.919555 Audio is remix/live\n", " 2 0.982309 Audio is remix\n", " 2 0.994153 Audio is remix\n", " 2 0.975751 Different versions (remix?)\n", " 2 0.93653 Audio is remix\n" ] } ], "source": [ "# Read in ratings\n", "with open('results/alignment_ratings.csv') as f:\n", " reader = csv.reader(f)\n", " # Cast each entry in each row to the correct type\n", " ratings = [[int(alignment_id), int(rating), float(score), note]\n", " for alignment_id, rating, score, note in reader]\n", "# We made notes about each alignment, too.\n", "# Here are all the alignments where a transcription was matched to a remix\n", "\n", "print tabulate.tabulate([r[1:] for r in ratings if ('remix' in r[-1].lower())], headers=['Rating', 'Normalized DTW dist', 'Note'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IftKzFmWVtCizXz/v1+8/mzG6Wgr3RkRMdUmUEREVkigjIir0cjAnYsLKbUVTSxJlREtS\nDm4qmXSJMjeBR0SnTbpEWRjvX/teVOSOiImiZ4M5khZLekjSTyVd1qs4IiKq9CRRSpoOfAFYDBwB\nXCDpd3sRS0RElV51vRcAj9jeCCDpG8DbgA2NG7300kvdjyyiRhktn5h6lShfCzze8P5nwInDN5ox\nY+a4DmqnUlv0u1wLn4h6lSib+mmxHx7XQadPP5KXXnqhpYAiIkbTq0T5BDCn4f0cilblMIeN66A7\ne+qt/AVu9a929st+9e+X2956q1ePgtgDeBg4A/g5cDdwge0NY+4YEdEDvXoUxHZJl1AU7J0OXJ0k\nGRH9qm/rUUZE9ItUD4qIqJBEGRFRIYkyIqJCEmVERIUkyoiICrUlSkl7SVotaY2kn0i6omHd+yVt\nkLRe0mfqiiEiohNqu4/S9lZJC21vKW8wXyXpVGAGcDZwtO1tkmbXFUNERCfU2vW2vaV8OZPixvJf\nABcBV9jeVm7zbJ0xRES0q9ZEKWmapDXA08Adth8E5gFvlHSXpEFJJ9QZQ0REu2qdwuii7tnrJe0D\nrJQ0UJ5zX9tvkDQfuBE4tM44IiLa0ZW53rZ/KelW4ASKKkHfLJffI2mHpP1sb2rcR1LmVkZELWyP\nqxxTbYlS0v7AdtubJe0NnAX8GfArYBFwp6R5wMzhSXJI5qFHRKe1UrKuzhblQcA1kqZRXAv9mu3b\nJH0fWC5pHfAi8O4aY4iIaFvfVg+S5H6NLSImLknj7npnZk5ERIUkyoiICkmUEREVkigjIiokUUZE\nVEiijIiokEQZEVEhiTIiokKdUxjnANcCBwAGvmT7Skk3UFQQApgFbLZ9bF1xRES0q84pjNuAS22v\nkfQK4D5J37O9ZGgDSZ8DNtcYQ0RE2+qscP4U8FT5+nlJG4CDgQ0AKmamnw8srCuGiIhO6Mo1Sklz\ngWOB1Q2LTwOetv1oN2KIiGhV7Ymy7HavAJbafr5h1QXAdXWfPyKiXbUW7pU0A7gJ+LrtbzUs3wM4\nBzhurP2XLVv28uuBgQEGBgZqiTMiJq/BwUEGBwfbOkZtZdbKa5DXAJtsXzps3WLgMtujXp9MmbWI\nqEO/lVk7BXgXsFDSA+XX4nLdEuD6Gs8dEdExlS1KSafaXjVs2Sm2f1hrYGlRRkQN6mpRfn6EZV8Y\nz0kiIiayUQdzJJ0EnAzMlvQhYCgDv5JMfYyIKWSsUe+ZFElxevnvkOeAd9QZVEREP2nmGuVc2xu7\nE84u5801yojouFauUTZzH+Wekr4MzG3Y3rYXjTO+iIgJqZkW5VrgKuB+4KVysW3fV2tgaVFGRA3q\nalFus31VizFFREx4zYxe3yLpfZIOkvTqoa9mDi5puaSnJa1rWLZA0t3lDej3SJrfcvQREV3QTNd7\nI0Xh3V3YPqTy4NJpwPPAtbaPKpcNAlfYXinpTcCHR5rKmK53RNShlq637bmtBmT7B2WJtUZPAvuU\nr2cBT7R6/IiIbqhMlJLew8gtymtbPOdHgFVldfNpwEktHicioiuaGcyZz85EuTewiGIEvNVEeTXw\nAds3SzoPWA6c1eKxIiJq10zX+5LG95JmATe0cc4Fts8sX68AvjLahqlHGRHt6kk9SkkzgfW251Vu\nzMuPgbilYTDnfoqHjt0p6Qzg07Z3G/nOYE5E1KGWwRxJtzS8nQYcAdzYZEDXA6cD+0t6HPgE8F7g\ni5L2BH5Tvo9oWVEjujX5YxzNaOb2oIHypYHtwGO2H685rrQoo2lFomzlZ0VJlFNQLfUobQ8CDwGv\nAvYFXmgpuoiICaoyUUo6n+Ixs+dRPIf77nK0OiJiSmi2KMaZtp8p388GbrN9dK2BpesdTUrXO8aj\nrkdBCHi24f0mdlY7j4iY9Jq54fy7wEpJ11EkyCXAd2qNKiKij4za9ZZ0GHCg7VWSzqV4/CzAZuA6\n24/UGli63tGkdL1jPFrpeo+VKG8FPmp77bDlRwOX235ry5E2E1gSZTRpoiTK3O/ZHzp9w/mBw5Mk\ngO21kipLrEXESFpL6NFbYw3mzBpj3V7tnljSUknrJK2XtLTd40VE1GWsRHmvpN2mF0q6EGjreTmS\njgT+iKIy0THAWyT9TjvHjIioy1hd7w8CN0t6JzsT4/HAnsA5bZ73cGC17a0Aku4E3g58ts3jxgTW\nzjW8iDqNmihtPyXpZGAhcCTFxZVv2769A+ddD1xePntnK/Bm4O4OHDcmvO5ew0tyjmaMu8xax04s\n/SFwMfBr4EHgBduXNqzPqPcU087o9WTfL78LnVPX42prYXs5RXVzJH0KeGz4Ninc21u5naV/tPq9\nyPehR4V7O0XSAbafkfQ6YCVwou3nGtanRdlj3b4/MS3Kzu+X36HdTagWJbBC0n7ANuDixiQZEdFP\netairJIWZe+lRTnx98vv0O4mWosyJrGMJsdkkkQZNclUvZg8mqlHGRExpSVRRkRUSKKMiKiQRBkR\nUSGDOT3Q7RkvGYGOaE8SZc90e1Q4o9ARrepZ11vSYkkPSfqppMt6FcdEI2ncXxHj0crP2GT/WevJ\nzBxJ04GHgTOBJ4B7gAtsb2jYZtLOzMkMlOzXvf1aNXlnAtX1XO86LAAesb3R9jbgG8DbehRLxCTm\nFr5iuF5do3wt8HjD+58BJ3biwN1u/k+Ev6AR3TJZy8H1KlE29amsWLFiXAedN2/eeA7foBddm4jJ\naHL+HvUqUT4BzGl4P4eiVbmL8847r8XDt/LBt/bNar0Fm/2yX/Z7ea8+Hwjq1WDOHhSDOWcAP6d4\nXs4ugzkREf2iJy1K29slXUJR2Xw6cHWSZET0q74t3BsR0S8y1zsiokISZUREhSTKiIgKSZQRERWS\nKCMiKtSWKCXtJWm1pDWSfiLpioZ175e0QdJ6SZ+pK4aIiE6o7T5K21slLbS9pbzBfJWkU4EZwNnA\n0ba3SZpdVwwREZ1Qa9fb9pby5UyKG8t/AVwEXFFWDcL2s3XGEBHRrloTpaRpktYATwN32H4QmAe8\nUdJdkgYlnVBnDBER7ap1CqPtHcDrJe0DrJQ0UJ5zX9tvkDQfuBE4tM44IiLa0ZW53rZ/KelW4ASK\nKkHfLJffI2mHpP1sb2rcR1LmVkZELcZb4by2RClpf2C77c2S9gbOAv4M+BWwCLhT0jxg5vAkOSTz\n0CMmjnYecdLN3/VWSrrV2aI8CLhG0jSKa6Ffs32bpO8DyyWtA14E3l1jDBExTv1eG7IX+rZ60GR+\nuFhEP+vFw++63aKcKA8Xi4iYMJIoIyIqJFFGRFRIooyIqJBEGRFRIYkyIqJCEmVERIU6Z+bMAa4F\nDqC4uepLtq+UdANFYQyAWcBm28fWFUdERLvqnJmzDbjU9hpJrwDuk/Q920uGNpD0OWBzjTFERLSt\nzsK9TwFPla+fl7QBOBjYAKDi9v/zgYV1xRAR0QlduUYpaS5wLLC6YfFpwNO2H+1GDBERrao9UZbd\n7hXAUtvPN6y6ALiu7vNHRLSr1nqUkmYANwFft/2thuV7AOcAx421/7Jly15+PTAwwMDAQC1xRkRv\ntVqxqJliGoODgwwODrZ0/CG1VQ8qr0FeA2yyfemwdYuBy2yPen0y1YMieqMX1YO6WXWo36oHnQK8\nC1go6YHya3G5bglwfY3njojomNSjjIhdpEW5u8oWZfks7uHLThnPSSIiJrJmut6fH2HZFzodSERE\nvxp11FvSScDJwGxJH6JoHwO8kswRj4gpZKzbg2ZSJMXp5b9DngPeUWdQERH9pHIwR9Jc2xu7E84u\n581gTkQPZDBnd83ccL6npC8Dcxu2t+1F44wvImJCaqZFuRa4CrgfeKlcbNv31RpYWpQRPZEW5e6a\naVFus33VuKOJiJgkmhm9vkXS+yQdJOnVQ1/NHFzScklPS1rXsGyBpLvLmTr3SJrfcvQREV3QTNd7\nIyO0i20fUnlw6TTgeeBa20eVywaBK2yvlPQm4MMjzflO1zuiN9L13l1l19v23HFHsnPfH5S1KBs9\nCexTvp4FPNHq8SMiuqEyUUp6DyO3KK9t8ZwfAVaVj4GYBpzU4nEiIrqimcGc+exMlHsDiyhGwFtN\nlFcDH7B9s6TzgOXAWS0eKyKids10vS9pfC9pFnBDG+dcYPvM8vUK4CujbZjCvRHRrp4U7pU0E1hv\ne17lxrz8vJxbGgZz7qd4OuOdks4APm17t5HvDOZE9EYGc3bXzDXKWxreTgOOAG5sMqDrgdOB/SU9\nDnwCeC/wRUl7Ar8p30dE9K1mbg8aKF8a2A48ZvvxmuNKizKiR9Ki3F3lDee2B4GHgFcB+wIvjDuy\niIgJrJkK5+dTPI/7POB84O5ytDoiYkpotijGmbafKd/PBm6zfXStgaXrHdGWVh8BW0jXu1Ez91EK\neLbh/SZ2VjuPiL7WauKKRs0kyu8CKyVdR/EJLgG+U2tUERF9ZNSut6TDgANtr5J0LsVzugE2A9fZ\nfqTWwNL1jmjLRBq97veu91iJ8lbgo7bXDlt+NHC57beOO8LxBJZEGdGWJMpR9urw7UEHDk+SAOWy\nyhJrERGTxViJctYY6/Zq98SSlkpaJ2m9pKXtHi8ioi5jJcp7Je02vVDShUBbz8uRdCTwRxSViY4B\n3iLpd9o5ZkREXcYa9f4gcLOkd7IzMR4P7Amc0+Z5DwdW294KIOlO4O3AZ9s8bkREx42aKG0/Jelk\nYCFwJMXV1m/bvr0D510PXF4+e2cr8Gbg7g4cN2LSae/G8eiEcZdZ69iJpT8ELgZ+DTwIvGD70ob1\nGfWOYGqMXvf7qHczN5zXwvZyiurmSPoU8NjwbVK4NyLa1ZPCvZ0i6QDbz0h6HbASONH2cw3r06KM\nIC3Ksfab9C1KYIW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot a histogram for each rating\n", "plt.figure(figsize=(5, 7))\n", "# Keep track of the largest bin in all histograms\n", "max_count = 0\n", "# Compute the range of scores\n", "hist_range = (min(r[2] for r in ratings), max(r[2] for r in ratings))\n", "# Plot 5 histograms\n", "for n in [5, 4, 3, 2, 1]:\n", " plt.subplot(5, 1, 6 - n)\n", " # Plot a histogram for this rating\n", " counts, _, _ = plt.hist(\n", " [r[2] for r in ratings if r[1] == n], 20, hist_range)\n", " # Update the max count seen over all histograms\n", " max_count = max_count if max(counts) < max_count else int(max(counts))\n", " # Force the x limits\n", " plt.xlim(hist_range)\n", " # Keep track of the ticks to set later\n", " ticks = plt.xticks()[0]\n", " plt.ylabel('Count')\n", " plt.xticks([])\n", "# Force the Y limits to the largest count range\n", "for n in [1, 2, 3, 4, 5]:\n", " plt.subplot(5, 1, n)\n", " plt.ylim(0, max_count)\n", " plt.yticks(range(0, max_count + 1, max_count/4))\n", "# Set ticks of the final subplot to the tick range\n", "plt.xticks(ticks)\n", "plt.xlabel('Normalized DTW Distance')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }