Gait (steps) example

In this example, we use DiCoDiLe on an open dataset of gait (steps) IMU time-series to discover patterns in the data. We will then use those to attempt to detect steps and compare our findings with the ground truth.

import matplotlib.pyplot as plt
import numpy as np

from dicodile.data.gait import get_gait_data
from dicodile.utils.dictionary import init_dictionary
from dicodile.utils.viz import display_dictionaries
from dicodile.utils.csc import reconstruct
from dicodile import dicodile

Retrieve trial data

trial = get_gait_data(subject=6, trial=1)
Downloading data from http://dev.ipol.im/~truong/GaitData.zip (192.3 MB)


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file_sizes:   5%|#4                          | 10.6M/202M [00:40<28:16, 113kB/s]
file_sizes:   5%|#4                         | 10.6M/202M [00:41<32:53, 96.8kB/s]
file_sizes:   5%|#4                         | 10.6M/202M [00:41<36:03, 88.3kB/s]
file_sizes:   5%|#4                         | 10.6M/202M [00:41<47:04, 67.6kB/s]
file_sizes:   5%|#4                         | 10.6M/202M [00:41<55:06, 57.8kB/s]
file_sizes:   5%|#3                       | 10.6M/202M [00:42<1:09:29, 45.8kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:42<1:04:50, 49.1kB/s]
file_sizes:   5%|#4                         | 10.7M/202M [00:43<55:01, 57.8kB/s]
file_sizes:   5%|#4                         | 10.7M/202M [00:43<58:40, 54.2kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:43<1:01:10, 52.0kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:43<1:11:17, 44.6kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:44<1:05:17, 48.7kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:44<1:12:53, 43.7kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:44<1:09:25, 45.8kB/s]
file_sizes:   5%|#3                       | 10.7M/202M [00:44<1:06:39, 47.7kB/s]
file_sizes:   5%|#3                       | 10.8M/202M [00:44<1:04:32, 49.3kB/s]
file_sizes:   5%|#3                       | 10.8M/202M [00:44<1:02:53, 50.6kB/s]
file_sizes:   5%|#3                       | 10.8M/202M [00:45<1:01:41, 51.6kB/s]
file_sizes:   5%|#3                       | 10.8M/202M [00:45<1:00:48, 52.3kB/s]
file_sizes:   5%|#3                       | 10.8M/202M [00:45<1:17:19, 41.1kB/s]
file_sizes:   5%|#4                         | 10.8M/202M [00:45<56:05, 56.7kB/s]
file_sizes:   5%|#4                         | 10.8M/202M [00:45<56:40, 56.1kB/s]
file_sizes:   5%|#4                         | 10.8M/202M [00:46<57:12, 55.6kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:46<48:15, 65.9kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:46<42:11, 75.4kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:46<45:14, 70.3kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:46<48:06, 66.1kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:47<40:05, 79.3kB/s]
file_sizes:   5%|#4                         | 10.9M/202M [00:47<48:55, 65.0kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:47<40:07, 79.2kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:47<42:10, 75.4kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<48:32, 65.5kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<48:13, 65.9kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<50:09, 63.4kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<48:25, 65.6kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<50:46, 62.6kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:48<45:36, 69.7kB/s]
file_sizes:   5%|#4                         | 11.0M/202M [00:49<48:29, 65.5kB/s]
file_sizes:   5%|#4                         | 11.1M/202M [00:49<46:52, 67.8kB/s]
file_sizes:   5%|#4                         | 11.1M/202M [00:49<43:10, 73.6kB/s]
file_sizes:   5%|#4                         | 11.1M/202M [00:49<42:49, 74.2kB/s]
file_sizes:   6%|#4                         | 11.1M/202M [00:49<37:31, 84.6kB/s]
file_sizes:   6%|#4                         | 11.1M/202M [00:49<37:38, 84.4kB/s]
file_sizes:   6%|#5                          | 11.1M/202M [00:50<29:46, 107kB/s]
file_sizes:   6%|#5                          | 11.1M/202M [00:50<29:38, 107kB/s]
file_sizes:   6%|#5                          | 11.2M/202M [00:50<29:33, 107kB/s]
file_sizes:   6%|#5                          | 11.2M/202M [00:50<29:33, 107kB/s]
file_sizes:   6%|#5                          | 11.2M/202M [00:50<29:28, 108kB/s]
file_sizes:   6%|#5                          | 11.2M/202M [00:50<25:36, 124kB/s]
file_sizes:   6%|#5                          | 11.2M/202M [00:50<26:36, 119kB/s]
file_sizes:   6%|#5                          | 11.3M/202M [00:51<27:20, 116kB/s]
file_sizes:   6%|#5                          | 11.3M/202M [00:51<24:25, 130kB/s]
file_sizes:   6%|#5                          | 11.3M/202M [00:51<21:54, 145kB/s]
file_sizes:   6%|#5                          | 11.3M/202M [00:51<21:52, 145kB/s]
file_sizes:   6%|#5                          | 11.3M/202M [00:51<23:42, 134kB/s]
file_sizes:   6%|#5                          | 11.4M/202M [00:51<22:15, 142kB/s]
file_sizes:   6%|#5                          | 11.4M/202M [00:52<18:50, 168kB/s]
file_sizes:   6%|#5                          | 11.4M/202M [00:52<19:01, 167kB/s]
file_sizes:   6%|#5                          | 11.5M/202M [00:52<19:09, 165kB/s]
file_sizes:   6%|#5                          | 11.5M/202M [00:52<19:15, 165kB/s]
file_sizes:   6%|#5                          | 11.5M/202M [00:52<17:51, 177kB/s]
file_sizes:   6%|#6                          | 11.5M/202M [00:52<18:03, 176kB/s]
file_sizes:   6%|#6                          | 11.6M/202M [00:52<17:03, 186kB/s]
file_sizes:   6%|#6                          | 11.6M/202M [00:53<16:12, 195kB/s]
file_sizes:   6%|#6                          | 11.6M/202M [00:53<15:40, 202kB/s]
file_sizes:   6%|#6                          | 11.7M/202M [00:53<14:11, 223kB/s]
file_sizes:   6%|#6                          | 11.7M/202M [00:53<13:20, 237kB/s]
file_sizes:   6%|#6                          | 11.8M/202M [00:53<10:53, 291kB/s]
file_sizes:   6%|#6                          | 11.8M/202M [00:53<10:31, 301kB/s]
file_sizes:   6%|#6                          | 11.9M/202M [00:53<10:16, 308kB/s]
file_sizes:   6%|#6                          | 11.9M/202M [00:54<09:22, 337kB/s]
file_sizes:   6%|#6                          | 12.0M/202M [00:54<08:36, 367kB/s]
file_sizes:   6%|#6                          | 12.1M/202M [00:54<08:09, 387kB/s]
file_sizes:   6%|#6                          | 12.1M/202M [00:54<09:42, 326kB/s]
file_sizes:   6%|#6                          | 12.2M/202M [00:54<07:33, 418kB/s]
file_sizes:   6%|#7                          | 12.3M/202M [00:55<09:45, 323kB/s]
file_sizes:   6%|#7                          | 12.3M/202M [00:55<08:21, 378kB/s]
file_sizes:   6%|#7                          | 12.4M/202M [00:55<09:13, 342kB/s]
file_sizes:   6%|#7                          | 12.4M/202M [00:55<09:45, 323kB/s]
file_sizes:   6%|#7                          | 12.5M/202M [00:55<10:12, 309kB/s]
file_sizes:   6%|#7                          | 12.5M/202M [00:55<09:35, 328kB/s]
file_sizes:   6%|#7                          | 12.6M/202M [00:55<09:37, 327kB/s]
file_sizes:   6%|#7                          | 12.6M/202M [00:56<09:38, 327kB/s]
file_sizes:   6%|#7                          | 12.7M/202M [00:56<09:39, 326kB/s]
file_sizes:   6%|#7                          | 12.7M/202M [00:56<09:39, 326kB/s]
file_sizes:   6%|#7                          | 12.8M/202M [00:56<09:39, 326kB/s]
file_sizes:   6%|#7                          | 12.8M/202M [00:56<09:40, 325kB/s]
file_sizes:   6%|#7                          | 12.9M/202M [00:56<09:56, 316kB/s]
file_sizes:   6%|#7                          | 12.9M/202M [00:56<09:51, 319kB/s]
file_sizes:   6%|#7                          | 13.0M/202M [00:57<09:47, 321kB/s]
file_sizes:   6%|#8                          | 13.0M/202M [00:57<09:31, 330kB/s]
file_sizes:   6%|#8                          | 13.1M/202M [00:57<09:33, 329kB/s]
file_sizes:   7%|#8                          | 13.1M/202M [00:57<09:35, 328kB/s]
file_sizes:   7%|#8                          | 13.2M/202M [00:57<09:37, 326kB/s]
file_sizes:   7%|#8                          | 13.2M/202M [00:57<09:38, 326kB/s]
file_sizes:   7%|#8                          | 13.3M/202M [00:58<08:55, 352kB/s]
file_sizes:   7%|#8                          | 13.3M/202M [00:58<08:36, 364kB/s]
file_sizes:   7%|#8                          | 13.4M/202M [00:58<08:06, 387kB/s]
file_sizes:   7%|#8                          | 13.4M/202M [00:58<08:32, 367kB/s]
file_sizes:   7%|#8                          | 13.5M/202M [00:58<08:51, 354kB/s]
file_sizes:   7%|#8                          | 13.5M/202M [00:58<08:18, 378kB/s]
file_sizes:   7%|#8                          | 13.6M/202M [00:58<08:38, 363kB/s]
file_sizes:   7%|#8                          | 13.6M/202M [00:58<08:10, 384kB/s]
file_sizes:   7%|#8                          | 13.7M/202M [00:59<08:34, 365kB/s]
file_sizes:   7%|#9                          | 13.7M/202M [00:59<08:06, 386kB/s]
file_sizes:   7%|#9                          | 13.8M/202M [00:59<08:31, 367kB/s]
file_sizes:   7%|#9                          | 13.9M/202M [00:59<07:26, 420kB/s]
file_sizes:   7%|#9                          | 13.9M/202M [00:59<07:59, 392kB/s]
file_sizes:   7%|#9                          | 14.0M/202M [00:59<07:44, 404kB/s]
file_sizes:   7%|#9                          | 14.0M/202M [01:00<07:34, 413kB/s]
file_sizes:   7%|#9                          | 14.1M/202M [01:00<07:27, 419kB/s]
file_sizes:   7%|#9                          | 14.2M/202M [01:00<07:24, 422kB/s]
file_sizes:   7%|#9                          | 14.2M/202M [01:00<07:28, 418kB/s]
file_sizes:   7%|#9                          | 14.3M/202M [01:00<07:22, 423kB/s]
file_sizes:   7%|#9                          | 14.3M/202M [01:00<07:19, 426kB/s]
file_sizes:   7%|##                          | 14.4M/202M [01:00<06:39, 469kB/s]
file_sizes:   7%|##                          | 14.5M/202M [01:00<06:43, 464kB/s]
file_sizes:   7%|##                          | 14.5M/202M [01:01<06:52, 454kB/s]
file_sizes:   7%|##                          | 14.6M/202M [01:01<06:58, 447kB/s]
file_sizes:   7%|##                          | 14.7M/202M [01:01<06:31, 477kB/s]
file_sizes:   7%|##                          | 14.7M/202M [01:01<06:12, 501kB/s]
file_sizes:   7%|##                          | 14.8M/202M [01:01<08:10, 381kB/s]
file_sizes:   7%|##                          | 14.9M/202M [01:01<05:38, 552kB/s]
file_sizes:   7%|##                          | 15.0M/202M [01:02<06:30, 478kB/s]
file_sizes:   7%|##                          | 15.1M/202M [01:02<06:17, 494kB/s]
file_sizes:   8%|##1                         | 15.2M/202M [01:02<06:29, 478kB/s]
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file_sizes:   8%|##1                         | 15.8M/202M [01:03<08:00, 387kB/s]
file_sizes:   8%|##1                         | 15.8M/202M [01:04<08:23, 369kB/s]
file_sizes:   8%|##2                         | 15.9M/202M [01:04<07:23, 419kB/s]
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file_sizes:   8%|##2                         | 16.0M/202M [01:04<08:46, 353kB/s]
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file_sizes:   8%|##3                         | 16.6M/202M [01:07<14:37, 211kB/s]
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file_sizes:   9%|##6                         | 18.8M/202M [01:14<07:36, 401kB/s]
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file_sizes:  57%|################5            | 115M/202M [09:19<06:15, 230kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:56, 243kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:44, 251kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:27, 264kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:16, 273kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:18, 271kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:12, 276kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<05:03, 284kB/s]
file_sizes:  57%|################5            | 115M/202M [09:20<04:59, 287kB/s]
file_sizes:  57%|################6            | 115M/202M [09:21<07:58, 180kB/s]
file_sizes:  57%|################6            | 116M/202M [09:21<05:21, 268kB/s]
file_sizes:  57%|################6            | 116M/202M [09:21<07:04, 203kB/s]
file_sizes:  57%|################6            | 116M/202M [09:21<05:31, 259kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:10, 232kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:16, 229kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:45, 212kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:43, 213kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:41, 214kB/s]
file_sizes:  57%|################6            | 116M/202M [09:22<06:39, 215kB/s]
file_sizes:  57%|################6            | 116M/202M [09:23<06:38, 215kB/s]
file_sizes:  57%|################6            | 116M/202M [09:23<06:37, 216kB/s]
file_sizes:  57%|################6            | 116M/202M [09:23<06:37, 216kB/s]
file_sizes:  58%|################6            | 116M/202M [09:23<06:36, 216kB/s]
file_sizes:  58%|################6            | 116M/202M [09:23<06:36, 216kB/s]
file_sizes:  58%|################6            | 116M/202M [09:23<06:35, 216kB/s]
file_sizes:  58%|################6            | 116M/202M [09:23<06:34, 217kB/s]
file_sizes:  58%|################6            | 116M/202M [09:24<07:06, 201kB/s]
file_sizes:  58%|################6            | 116M/202M [09:24<06:56, 206kB/s]
file_sizes:  58%|################7            | 116M/202M [09:24<07:23, 193kB/s]
file_sizes:  58%|################7            | 116M/202M [09:24<07:07, 200kB/s]
file_sizes:  58%|################7            | 116M/202M [09:24<07:32, 189kB/s]
file_sizes:  58%|################7            | 116M/202M [09:24<07:52, 181kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<06:50, 208kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<06:45, 210kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<07:15, 196kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<06:30, 219kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<06:04, 234kB/s]
file_sizes:  58%|################7            | 116M/202M [09:25<08:03, 176kB/s]
file_sizes:  58%|################7            | 116M/202M [09:26<06:43, 211kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<06:42, 212kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<07:06, 200kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<06:52, 206kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<07:00, 202kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<06:50, 207kB/s]
file_sizes:  58%|################7            | 117M/202M [09:26<06:41, 212kB/s]
file_sizes:  58%|################7            | 117M/202M [09:27<06:07, 231kB/s]
file_sizes:  58%|################7            | 117M/202M [09:27<06:13, 227kB/s]
file_sizes:  58%|################7            | 117M/202M [09:27<06:19, 224kB/s]
file_sizes:  58%|################7            | 117M/202M [09:27<05:48, 244kB/s]
file_sizes:  58%|################8            | 117M/202M [09:27<06:01, 235kB/s]
file_sizes:  58%|################8            | 117M/202M [09:27<05:53, 240kB/s]
file_sizes:  58%|################8            | 117M/202M [09:27<06:04, 232kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<06:11, 228kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<05:44, 246kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<05:58, 236kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<05:35, 252kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<05:51, 241kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<06:02, 233kB/s]
file_sizes:  58%|################8            | 117M/202M [09:28<05:37, 250kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:53, 239kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:45, 245kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:26, 258kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:12, 270kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:35, 251kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:18, 265kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:20, 263kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:08, 273kB/s]
file_sizes:  58%|################8            | 117M/202M [09:29<05:00, 280kB/s]
file_sizes:  58%|################8            | 117M/202M [09:30<07:55, 177kB/s]
file_sizes:  58%|################9            | 118M/202M [09:30<05:12, 269kB/s]
file_sizes:  58%|################9            | 118M/202M [09:30<05:28, 256kB/s]
file_sizes:  58%|################9            | 118M/202M [09:30<05:42, 245kB/s]
file_sizes:  58%|################9            | 118M/202M [09:30<05:38, 248kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:50, 239kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:30, 254kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:16, 265kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:07, 273kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:07, 272kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:29, 254kB/s]
file_sizes:  58%|################9            | 118M/202M [09:31<05:22, 260kB/s]
file_sizes:  58%|################9            | 118M/202M [09:32<05:01, 278kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<05:01, 278kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<05:03, 276kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<04:49, 289kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<04:52, 285kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<04:42, 296kB/s]
file_sizes:  59%|################9            | 118M/202M [09:32<04:39, 298kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<04:40, 298kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<04:32, 305kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<04:43, 294kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<06:14, 223kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<06:15, 222kB/s]
file_sizes:  59%|#################            | 118M/202M [09:33<04:47, 289kB/s]
file_sizes:  59%|#################            | 118M/202M [09:34<05:11, 267kB/s]
file_sizes:  59%|#################            | 119M/202M [09:34<07:36, 182kB/s]
file_sizes:  59%|#################            | 119M/202M [09:34<07:46, 178kB/s]
file_sizes:  59%|#################            | 119M/202M [09:34<08:50, 157kB/s]
file_sizes:  59%|#################            | 119M/202M [09:35<09:48, 141kB/s]
file_sizes:  59%|#################            | 119M/202M [09:35<09:40, 143kB/s]
file_sizes:  59%|#################            | 119M/202M [09:35<09:42, 142kB/s]
file_sizes:  59%|################4           | 119M/202M [09:35<15:22, 90.0kB/s]
file_sizes:  59%|#################            | 119M/202M [09:35<11:58, 115kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<13:20, 104kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<13:13, 104kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<12:54, 107kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<12:52, 107kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<11:23, 121kB/s]
file_sizes:  59%|#################            | 119M/202M [09:36<11:43, 118kB/s]
file_sizes:  59%|#################            | 119M/202M [09:37<11:59, 115kB/s]
file_sizes:  59%|#################            | 119M/202M [09:37<10:59, 125kB/s]
file_sizes:  59%|#################            | 119M/202M [09:37<10:08, 136kB/s]
file_sizes:  59%|#################            | 119M/202M [09:37<10:47, 128kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:37<10:00, 138kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:37<10:39, 129kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<09:54, 139kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<10:30, 131kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<09:48, 140kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<09:21, 147kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<09:04, 152kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<08:52, 155kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:38<08:44, 157kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<08:16, 166kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<08:19, 165kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<07:03, 195kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<07:24, 185kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<07:03, 194kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:39<06:50, 201kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:40<06:40, 205kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:40<06:34, 209kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:40<09:21, 146kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:40<06:53, 199kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:40<07:12, 190kB/s]
file_sizes:  59%|#################1           | 119M/202M [09:41<07:28, 183kB/s]
file_sizes:  59%|#################1           | 120M/202M [09:41<07:41, 178kB/s]
file_sizes:  59%|#################1           | 120M/202M [09:41<07:24, 185kB/s]
file_sizes:  59%|#################1           | 120M/202M [09:41<07:40, 178kB/s]
file_sizes:  59%|#################1           | 120M/202M [09:41<07:16, 188kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:41<07:00, 195kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:41<06:44, 203kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:42<09:10, 149kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:42<09:46, 140kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:42<07:16, 188kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:42<09:12, 148kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<09:00, 151kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<09:24, 145kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<09:53, 138kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<09:30, 143kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<10:06, 135kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:43<09:35, 142kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:44<09:50, 138kB/s]
file_sizes:  59%|#################2           | 120M/202M [09:44<09:31, 143kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:44<09:23, 145kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:44<09:04, 150kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:44<08:51, 154kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:44<08:12, 166kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:44<07:34, 179kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<10:19, 132kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<08:54, 152kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<08:57, 152kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<09:10, 148kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<09:09, 148kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:45<08:53, 153kB/s]
file_sizes:  60%|#################2           | 120M/202M [09:46<08:02, 169kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:46<08:37, 157kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:46<07:52, 172kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:46<07:23, 183kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:46<09:46, 139kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:47<08:07, 166kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:47<08:10, 166kB/s]
file_sizes:  60%|#################3           | 120M/202M [09:47<08:12, 165kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:47<08:13, 164kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:47<08:38, 156kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:47<07:55, 170kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<07:30, 180kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<08:56, 151kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<08:24, 160kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<09:02, 149kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<08:49, 153kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:48<08:49, 153kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:49<10:52, 124kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:49<10:08, 133kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:49<09:04, 148kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:49<09:11, 147kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:49<09:06, 148kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<09:52, 136kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<09:20, 144kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<10:05, 133kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<09:28, 142kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<09:04, 148kB/s]
file_sizes:  60%|#################3           | 121M/202M [09:50<08:49, 152kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:50<08:38, 155kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<09:30, 141kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<09:05, 148kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<08:49, 152kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<08:39, 155kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<07:04, 190kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<07:23, 182kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:51<06:58, 192kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<07:18, 183kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<06:55, 193kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<06:41, 200kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<06:31, 205kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<06:24, 209kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:52<06:20, 211kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:53<06:17, 213kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:53<08:47, 152kB/s]
file_sizes:  60%|#################4           | 121M/202M [09:53<06:51, 195kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:53<07:09, 187kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:53<07:25, 180kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:53<07:00, 190kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:54<07:18, 182kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:54<06:25, 208kB/s]
file_sizes:  60%|#################4           | 122M/202M [09:54<06:19, 211kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:54<06:16, 212kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:54<07:57, 167kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:54<06:13, 214kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<06:40, 200kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<07:23, 180kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<07:00, 190kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<06:43, 197kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<06:40, 199kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<06:29, 205kB/s]
file_sizes:  60%|#################5           | 122M/202M [09:55<06:25, 207kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<06:14, 213kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<05:42, 233kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<05:50, 227kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<05:55, 224kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<05:59, 221kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<06:01, 220kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:56<05:38, 234kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:57<08:16, 160kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:57<06:48, 194kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:57<06:57, 190kB/s]
file_sizes:  61%|#################5           | 122M/202M [09:57<06:50, 193kB/s]
file_sizes:  61%|#################6           | 122M/202M [09:57<07:07, 185kB/s]
file_sizes:  61%|#################6           | 122M/202M [09:58<06:48, 194kB/s]
file_sizes:  61%|#################6           | 122M/202M [09:58<06:35, 200kB/s]
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file_sizes:  76%|#####################1      | 153M/202M [12:23<08:18, 98.3kB/s]
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file_sizes:  76%|######################       | 153M/202M [12:25<05:35, 145kB/s]
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Successfully downloaded file to /github/home/data/dicodile/gait/GaitData.zip

Let’s have a look at the data for one trial.

trial.keys()
dict_keys(['Subject', 'Trial', 'Code', 'Age', 'Gender', 'Height', 'Weight', 'BMI', 'Laterality', 'Sensor', 'WalkedDistance', 'WalkingSpeed', 'PathologyGroup', 'IsControl', 'LeftFootActivity', 'RightFootActivity', 'data'])

We get a dictionary whose keys are metadata items, plus a ‘data’ key that contains a numpy array with the trial time series for each sensor axis, at 100 Hz resolution.

# right foot acceleration (vertical)
plt.plot(trial['data']['RAV'])
plot gait
[<matplotlib.lines.Line2D object at 0x7f8a44d55df0>]

Let’s look at a small portion of the series for both feet, overlaid on the same plot

fig, ax = plt.subplots()
ax.plot(trial['data']['LAV'][5000:5800],
        label='left foot vertical acceleration')
ax.plot(trial['data']['RAV'][5000:5800],
        label='right foot vertical acceleration')
ax.set_xlabel('time (x10ms)')
ax.set_ylabel('acceleration ($m.s^{-2}$)')
ax.legend()
plot gait
<matplotlib.legend.Legend object at 0x7f8a18a48670>

We can see the alternating left and right foot movements.

In the rest of this example, we will only use the right foot vertical acceleration.

Convolutional Dictionary Learning

Now, let’s use DiCoDiLe to learn patterns from the data and reconstruct the signal from a sparse representation.

First, we initialize a dictionary from parts of the signal:

X = trial['data']['RAV'].to_numpy()
X = X.reshape(1, *X.shape)

print(X.shape)

D_init = init_dictionary(X, n_atoms=8, atom_support=(200,), random_state=60)
(1, 18639)

Note the use of reshape to shape the signal as per dicodile requirements: the shape of the signal should be (n_channels, *sig_support). Here, we have a single-channel time series so it is (1, n_times).

Then, we run DiCoDiLe!

D_hat, z_hat, pobj, times = dicodile(
    X, D_init, n_iter=3, n_workers=4, window=True,
    dicod_kwargs={"max_iter": 10000}, verbose=6
)


print("[DiCoDiLe] final cost : {}".format(pobj))
[DEBUG:DICODILE] Lambda_max = 1.7133212673372127
Started 4 workers in 1.94s
[INFO:DICODILE] - CD iterations 0 / 3 (0s)
[DEBUG:DICODILE] lambda = 1.713e-01

[INFO:DICOD-4] converged in 1.578s (1.103s) with 22320 iterations (9923 updates).
[DEBUG:DICODILE] Objective (z) : 1.446e+01 (2s)

[INFO:Update D]: 4 iterations
[DEBUG:DICODILE] Objective (d) : 1.403e+01  (1s)
[INFO:DICODILE] - CD iterations 1 / 3 (3s)
[DEBUG:DICODILE] lambda = 1.713e-01

[INFO:DICOD-4] converged in 0.760s (0.458s) with 11916 iterations (4766 updates).
[DEBUG:DICODILE] Objective (z) : 1.377e+01 (1s)

[INFO:Update D]: 4 iterations
[DEBUG:DICODILE] Objective (d) : 1.375e+01  (1s)
[INFO:DICODILE] - CD iterations 2 / 3 (5s)
[DEBUG:DICODILE] lambda = 1.713e-01

[INFO:DICOD-4] converged in 0.760s (0.494s) with 19079 iterations (4936 updates).
[DEBUG:DICODILE] Objective (z) : 1.360e+01 (1s)

[INFO:Update D]: 3 iterations
[DEBUG:DICODILE] Objective (d) : 1.356e+01  (1s)

[INFO:DICOD-4] converged in 0.527s (0.327s) with 9525 iterations (3438 updates).
[INFO:DICODILE] Finished in 7s
[DiCoDiLe] final cost : [37.92317851302482, 14.463153013563076, 14.03018341403462, 13.772255722079938, 13.750831983050613, 13.595845203316816, 13.562848526082101, 13.485498715112117]

We can order the dictionary patches by decreasing sum of the activations’ absolute values in the activations z_hat, which, intuitively, gives a measure of how they contribute to the reconstruction.

sum_abs_val = np.sum(np.abs(z_hat), axis=-1)

# we negate sum_abs_val to sort in decreasing order
patch_indices = np.argsort(-sum_abs_val)

fig_reordered = display_dictionaries(D_init[patch_indices],
                                     D_hat[patch_indices])
plot gait

Signal reconstruction

Now, let’s reconstruct the original signal

X_hat = reconstruct(z_hat, D_hat)

Plot a small part of the original and reconstructed signals

fig_hat, ax_hat = plt.subplots()
ax_hat.plot(X[0][5000:5800],
            label='right foot vertical acceleration (ORIGINAL)')
ax_hat.plot(X_hat[0][5000:5800],
            label='right foot vertical acceleration (RECONSTRUCTED)')
ax_hat.set_xlabel('time (x10ms)')
ax_hat.set_ylabel('acceleration ($m.s^{-2}$)')
ax_hat.legend()
plot gait
<matplotlib.legend.Legend object at 0x7f8a13d925b0>

Check that our representation is indeed sparse:

584

Besides our visual check, a measure of how closely we’re reconstructing the original signal is the (normalized) cross-correlation. Let’s compute this:

np.correlate(X[0], X_hat[0]) / (
    np.sqrt(np.correlate(X[0], X[0]) * np.correlate(X_hat[0], X_hat[0])))
array([0.97643375])

Multichannel signals

DiCoDiLe works just as well with multi-channel signals. The gait dataset contains 16 signals (8 for each foot), in the rest of this tutorial, we’ll use three of those.

# Left foot Vertical acceleration, Y rotation and X acceleration
channels = ['LAV', 'LRY', 'LAX']

Let’s look at a small portion of multi-channel data

colors = plt.rcParams["axes.prop_cycle"]()
mc_fig, mc_ax = plt.subplots(len(channels), sharex=True)

for ax, chan in zip(mc_ax, channels):
    ax.plot(trial['data'][chan][5000:5800],
            label=chan, color=next(colors)["color"])
mc_fig.legend(loc="upper center")
plot gait
<matplotlib.legend.Legend object at 0x7f8a13c95790>

Let’s put the data in shape for DiCoDiLe: (n_channels, n_times)

X_mc_subset = trial['data'][channels].to_numpy().T
print(X_mc_subset.shape)
(3, 18639)

Initialize the dictionary (note that the call is identical to the single-channel version)

D_init_mc = init_dictionary(X_mc_subset,
                            n_atoms=8,
                            atom_support=(200,),
                            random_state=60)

And run DiCoDiLe (note that the call is identical to the single-channel version here as well)

D_hat_mc, z_hat_mc, pobj_mc, times_mc = dicodile(X_mc_subset,
                                                 D_init_mc,
                                                 n_iter=3,
                                                 n_workers=4,
                                                 dicod_kwargs={"max_iter": 10000},  # noqa: E501
                                                 verbose=6,
                                                 window=True)


print("[DiCoDiLe] final cost : {}".format(pobj_mc))
[DEBUG:DICODILE] Lambda_max = 903.7323963288766
Started 4 workers in 1.81s
[INFO:DICODILE] - CD iterations 0 / 3 (0s)
[DEBUG:DICODILE] lambda = 9.037e+01

[INFO:DICOD-4] converged in 1.656s (1.167s) with 29044 iterations (12468 updates).
[DEBUG:DICODILE] Objective (z) : 5.744e+06 (2s)

[INFO:Update D]: 5 iterations
[DEBUG:DICODILE] Objective (d) : 5.553e+06  (1s)
[INFO:DICODILE] - CD iterations 1 / 3 (4s)
[DEBUG:DICODILE] lambda = 9.037e+01

[INFO:DICOD-4] converged in 1.012s (0.571s) with 20933 iterations (6717 updates).
[DEBUG:DICODILE] Objective (z) : 5.484e+06 (1s)

[INFO:Update D]: 3 iterations
[DEBUG:DICODILE] Objective (d) : 5.481e+06  (1s)
[INFO:DICODILE] - CD iterations 2 / 3 (7s)
[DEBUG:DICODILE] lambda = 9.037e+01

[INFO:DICOD-4] converged in 0.620s (0.386s) with 11210 iterations (3944 updates).
[DEBUG:DICODILE] Objective (z) : 5.469e+06 (1s)

[INFO:Update D]: 2 iterations
[DEBUG:DICODILE] Objective (d) : 5.469e+06  (1s)

[INFO:DICOD-4] converged in 0.220s (0.107s) with 5756 iterations (1112 updates).
[INFO:DICODILE] Finished in 8s
[DiCoDiLe] final cost : [20304139.366815556, 5743922.414714775, 5552921.847982835, 5483810.728770616, 5481227.555007235, 5469421.625393614, 5469121.2254714, 5468286.657784177]

Signal reconstruction (multichannel)

Now, let’s reconstruct the original signal

(3, 18639)

Let’s visually compare a small part of the original and reconstructed signals along with the activations.

viz_start_idx = 4000
viz_end_idx = 5800
viz_chan = 2

max_abs = np.max(np.abs(z_hat_mc), axis=-1)
max_abs = max_abs.reshape(z_hat_mc.shape[0], 1)
z_hat_normalized = z_hat_mc / max_abs
fig_hat_mc, ax_hat_mc = plt.subplots(2, figsize=(12, 8))
ax_hat_mc[0].plot(X_mc_subset[viz_chan][viz_start_idx:viz_end_idx],
                  label='ORIGINAL')
ax_hat_mc[0].plot(X_hat_mc[viz_chan][viz_start_idx:viz_end_idx],
                  label='RECONSTRUCTED')
for idx in range(z_hat_normalized.shape[0]):
    ax_hat_mc[1].stem(z_hat_normalized[idx][viz_start_idx:viz_end_idx],
                      linefmt=f"C{idx}-",
                      markerfmt=f"C{idx}o")
ax_hat_mc[0].set_xlabel('time (x10ms)')
ax_hat_mc[0].legend()
plot gait
<matplotlib.legend.Legend object at 0x7f8a11ac3d30>

Total running time of the script: ( 18 minutes 2.488 seconds)

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