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View Code? Open in Web Editor NEWCode for the technical report on the ActiLife counts algorithm.
License: GNU General Public License v3.0
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muschellij2 amandamcgow robmarcotte trybnetic huangzijian037 srigot55 khannay ranazia517 mshakil-ahmed digitalinteraction neurophysicole ambiqai le-chengagcounts's Issues
Formula for magnitude:
Hi Ali, thanks for the great package!
I have tried to run the test/demo code, but I had one issue and one important question?
- The issue was I had to comment out the following line to run the test in the get_counts_csv :
ts = ts[0:counts.shape[0]]
-
The question is, calculating the magnitude of the count vector, I think ^ does a binary xor operation and not a power operation, is that what it meant to do here? cause it affects the outputs.
counts["AC"] = (counts["Axis1"]^2 + counts["Axis2"]^2 + counts["Axis3"]^2) ** 0.5
Your response and feedback is highly appreciated. Thanks!
Request: extract fast but one column at a time
I have high density data (80Hz for 7 days) and memory can be an issue when running _extract. The _extract_slow allows us to run one at a time, which is slower but saves memory. Would it be possible to add _extract_one or something of the like so that we have a fast, memory-saving implementation?
`NA/NaN/Inf in 'y'` induced by `gcalibrateC`
NHANES File is located at https://figshare.com/articles/dataset/Accelerometer_file_that_s_failing_for_Resampling/25627848?file=46327363 and can be downloaded via: https://figshare.com/ndownloader/files/46327363,
url = "https://figshare.com/ndownloader/files/46327363"
file = tempfile(fileext = ".csv.gz")
download.file(url, file, quiet = FALSE)
library(agcounts)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(readr)
data = readr::read_csv(file,
col_types =
readr::cols(
# HEADER_TIMESTAMP = col_datetime_with_frac_secs(),
HEADER_TIMESTAMP = vroom::col_datetime(),
X = vroom::col_double(),
Y = vroom::col_double(),
Z = vroom::col_double()
))
probs = readr::problems(data)
stopifnot(nrow(probs) == 0)
readr::stop_for_problems(data)
head(data)
#> # A tibble: 6 × 4
#> HEADER_TIMESTAMP X Y Z
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2000-01-03 20:24:00 -0.032 0.053 -0.974
#> 2 2000-01-03 20:24:00 -0.032 0.053 -0.982
#> 3 2000-01-03 20:24:00 -0.032 0.047 -0.982
#> 4 2000-01-03 20:24:00 -0.032 0.053 -0.979
#> 5 2000-01-03 20:24:00 -0.032 0.053 -0.977
#> 6 2000-01-03 20:24:00 -0.032 0.053 -0.979
data = data %>%
dplyr::rename(time = HEADER_TIMESTAMP)
# needed for fix of agcounts
# PR at https://github.com/bhelsel/agcounts/pull/32
data = as.data.frame(data)
attr(data, "sample_rate") = 80L
attr(data, "last_sample_time") = max(data$time)
xyz = c("X", "Y", "Z")
mat = as.matrix(data[, xyz])
# calibrated = agcalibrate(df, verbose = TRUE)
C <- agcounts:::gcalibrateC(dataset = mat, sf = 80L)
#> Loading chunk: 1 2
#> Error in (function (x, y, w, offset = NULL, method = "qr", tol = 1e-07, : NA/NaN/Inf in 'y'Created on 2024-05-14 with reprex v2.1.0
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.3.1 (2023-06-16)
#> os macOS Sonoma 14.4.1
#> system x86_64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2024-05-14
#> pandoc 3.1.11.1 @ /usr/local/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> agcounts * 0.6.6 2024-04-10 [1] local
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#> vroom 1.6.5 2023-12-05 [1] CRAN (R 4.3.0)
#> withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.0)
#> xfun 0.41 2023-11-01 [1] CRAN (R 4.3.0)
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#> zoo 1.8-12 2023-04-13 [1] CRAN (R 4.3.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/johnmuschelli/miniconda3/bin/python3
#> libpython: /Users/johnmuschelli/miniconda3/lib/libpython3.11.dylib
#> pythonhome: /Users/johnmuschelli/miniconda3:/Users/johnmuschelli/miniconda3
#> version: 3.11.4 (main, Jul 5 2023, 08:41:25) [Clang 14.0.6 ]
#> numpy: /Users/johnmuschelli/miniconda3/lib/python3.11/site-packages/numpy
#> numpy_version: 1.25.2
#> pygt3x: /Users/johnmuschelli/miniconda3/lib/python3.11/site-packages/pygt3x
#>
#> NOTE: Python version was forced by RETICULATE_PYTHON
#>
#> ──────────────────────────────────────────────────────────────────────────────Discrepancy from ActiLife Output
I downloaded some open data from https://springernature.figshare.com/collections/Upper_limb_activity_of_twenty_myoelectric_prosthesis_users_and_twenty_healthy_anatomically_intact_adults_/4457855 in order to ensure the output from @bhelsel and the agcounts R package match up with ActiLife output. That package is using the code from this repo, adapted for R.
The GT3X files are available at https://springernature.figshare.com/articles/dataset/Unprocessed_raw_30Hz_acceleration_data_stored_as_gt3x/7946189?backTo=/collections/Upper_limb_activity_of_twenty_myoelectric_prosthesis_users_and_twenty_healthy_anatomically_intact_adults_/4457855 or raw CSVs: https://springernature.figshare.com/articles/dataset/Unprocessed_raw_30Hz_acceleration_data_stored_as_csv/7946186?backTo=/collections/Upper_limb_activity_of_twenty_myoelectric_prosthesis_users_and_twenty_healthy_anatomically_intact_adults_/4457855
I picked out the odd case and uploaded it to figshare.
Here is a reproducible example demonstrating the difference. I don't know why this occurs, but would be interested in your thoughts on where the discrepancy is.
library(readr) # install.packages("readr")
library(dplyr) # install.packages("dplyr")
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(agcounts) # remotes::install_github("bhelsel/agcounts")
library(SummarizedActigraphy) # remotes::install_github("muschellij2/SummarizedActigraphy")
options(digits.secs = 3)Read in the data
raw_url = "https://figshare.com/ndownloader/files/42434487"
onesec_url = "https://figshare.com/ndownloader/files/42434481"
read_it = function(url) {
file = tempfile(fileext = ".csv.gz")
curl::curl_download(url, file)
x = SummarizedActigraphy::read_acc_csv(file)
readr::stop_for_problems(x)
x
}
data = read_it(raw_url)
data$header
#> $serial
#> [1] "NEO1B41100262"
#>
#> $start_time
#> [1] "14:00:00"
#>
#> $start_date
#> [1] "18/07/2017"
#>
#> $epoch
#> [1] "00:00:00"
#>
#> $download_time
#> [1] "19:44:43"
#>
#> $download_date
#> [1] "31/07/2017"
#>
#> $battery_voltage
#> [1] "4"
#>
#> $memory_address
#> [1] "0"
#>
#> $mode
#> [1] "12"
#>
#> $firmware
#> [1] "3.2.1"
#>
#> $actilife_version
#> [1] "6.11.9"
#>
#> $filter
#> [1] "Normal"
#>
#> $date_format
#> [1] "dmy"
#>
#> $sample_rate
#> [1] 30
csv = read_it(onesec_url)
#> Warning in extract_acc_header(file): NAs introduced by coercion
#> Warning in parse_acc_header(hdr): NAs introduced by coercion
#> Warning in parse_acc_header(hdr): NAs introduced by coercion
#> Warning in SummarizedActigraphy::read_acc_csv(file): X/Y/Z not in the data, but
#> only_xyz = TRUE, setting only_xyz = FALSE
#> Warning in get_sample_rate(df): Guessing sample_rate from the data
csv$header
#> $serial
#> [1] "NEO1B41100262"
#>
#> $start_time
#> [1] "14:00:00"
#>
#> $start_date
#> [1] "18/07/2017"
#>
#> $epoch
#> [1] "00:00:01"
#>
#> $download_time
#> [1] "19:44:43"
#>
#> $download_date
#> [1] "31/07/2017"
#>
#> $battery_voltage
#> [1] "4"
#>
#> $memory_address
#> [1] "0"
#>
#> $mode
#> [1] "61"
#>
#> $firmware
#> [1] "3.2.1"
#>
#> $actilife_version
#> [1] "6.11.9"
#>
#> $filter
#> [1] "Normal"
#>
#> $date_format
#> [1] "dmy"
#>
#> $sample_rate
#> [1] NADownloading/Reading 1s Counts from AciLife
x = csv$data
x = x %>%
mutate(time = lubridate::dmy_hms(paste0(Date, " ", Time)))
x = x %>%
select(time, Axis1, Axis2, Axis3)
head(x)
#> # A tibble: 6 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-18 14:00:00.000 0 0 0
#> 2 2017-07-18 14:00:01.000 0 0 0
#> 3 2017-07-18 14:00:02.000 0 0 0
#> 4 2017-07-18 14:00:03.000 0 0 0
#> 5 2017-07-18 14:00:04.000 102 89 55
#> 6 2017-07-18 14:00:05.000 131 76 92
y = data
freq = data$freq
y = tibble::as_tibble(y$data)
attr(y, "sample_rate") = freq
head(y)
#> # A tibble: 6 × 4
#> time X Y Z
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-18 14:00:00.000 0.314 0.968 -0.196
#> 2 2017-07-18 14:00:00.033 0.331 0.971 -0.22
#> 3 2017-07-18 14:00:00.066 0.299 0.974 -0.229
#> 4 2017-07-18 14:00:00.099 0.296 0.974 -0.208
#> 5 2017-07-18 14:00:00.133 0.328 0.968 -0.196
#> 6 2017-07-18 14:00:00.166 0.317 0.956 -0.211Calculate AC from Open Source Version
res = calculate_counts(raw = y, epoch = 1)
res = res %>%
select(time, Axis1, Axis2, Axis3)
res = tibble::as_tibble(res)
head(res)
#> # A tibble: 6 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-18 14:00:00.000 0 0 0
#> 2 2017-07-18 14:00:01.000 0 0 0
#> 3 2017-07-18 14:00:02.000 0 0 0
#> 4 2017-07-18 14:00:03.000 0 0 0
#> 5 2017-07-18 14:00:04.000 102 89 55
#> 6 2017-07-18 14:00:05.000 131 76 92
the_check = all.equal(res, x)
if (!isTRUE(the_check)) {
print(the_check)
}
#> [1] "Component \"Axis2\": Mean relative difference: 0.5087719"
#> [2] "Component \"Axis3\": Mean relative difference: 0.0754717"
ind = rowSums(res != x) > 0
res[ind,]
#> # A tibble: 3 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0 42 39
#> 2 2017-07-23 22:57:31.000 4 2 14
#> 3 2017-07-23 22:57:32.000 0 13 2
x[ind,]
#> # A tibble: 3 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0 17 41
#> 2 2017-07-23 22:57:31.000 4 4 12
#> 3 2017-07-23 22:57:32.000 0 11 2
y %>%
filter(between(time, lubridate::as_datetime("2017-07-23 22:57:30"),
lubridate::as_datetime("2017-07-23 22:57:33"))) %>%
print(n = nrow(y))
#> # A tibble: 91 × 4
#> time X Y Z
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0.768 0.384 0.196
#> 2 2017-07-23 22:57:30.033 0.328 0.106 0.073
#> 3 2017-07-23 22:57:30.066 0.455 0.158 0.27
#> 4 2017-07-23 22:57:30.099 0.311 0.446 0.504
#> 5 2017-07-23 22:57:30.133 1.20 0.839 1.27
#> 6 2017-07-23 22:57:30.166 1.37 0.93 1.12
#> 7 2017-07-23 22:57:30.200 0.977 0.674 0.405
#> 8 2017-07-23 22:57:30.233 1.07 0.525 0.39
#> 9 2017-07-23 22:57:30.266 0 0 0
#> 10 2017-07-23 22:57:30.299 0.252 0.015 0.205
#> 11 2017-07-23 22:57:30.333 0.428 0.317 0.543
#> 12 2017-07-23 22:57:30.366 1.35 0.76 1.09
#> 13 2017-07-23 22:57:30.400 1.40 1.02 1.12
#> 14 2017-07-23 22:57:30.433 1.07 0.771 0.384
#> 15 2017-07-23 22:57:30.466 1.08 0.566 0.457
#> 16 2017-07-23 22:57:30.500 0.161 0.088 0.065
#> 17 2017-07-23 22:57:30.533 0.337 0.097 0.235
#> 18 2017-07-23 22:57:30.566 0.261 0.399 0.698
#> 19 2017-07-23 22:57:30.599 0.903 0.672 0.757
#> 20 2017-07-23 22:57:30.633 1.47 0.918 1.17
#> 21 2017-07-23 22:57:30.666 0.959 0.827 0.651
#> 22 2017-07-23 22:57:30.700 0.314 0.487 0.343
#> 23 2017-07-23 22:57:30.733 0.633 0.27 0.267
#> 24 2017-07-23 22:57:30.766 0.337 0.106 0.199
#> 25 2017-07-23 22:57:30.799 0.413 0.284 0.34
#> 26 2017-07-23 22:57:30.833 0.748 0.654 0.76
#> 27 2017-07-23 22:57:30.866 1.33 0.897 1.15
#> 28 2017-07-23 22:57:30.900 1.01 0.724 0.572
#> 29 2017-07-23 22:57:30.933 0.856 0.572 0.317
#> 30 2017-07-23 22:57:30.966 0.997 0.581 0.51
#> 31 2017-07-23 22:57:31.000 0.067 0.018 0.123
#> 32 2017-07-23 22:57:31.033 0.46 0.144 0.334
#> 33 2017-07-23 22:57:31.066 0.516 0.343 0.569
#> 34 2017-07-23 22:57:31.099 1.07 0.672 0.613
#> 35 2017-07-23 22:57:31.133 1.34 1.01 1.12
#> 36 2017-07-23 22:57:31.166 0.944 0.818 0.677
#> 37 2017-07-23 22:57:31.200 0.575 0.628 0.326
#> 38 2017-07-23 22:57:31.233 1.01 0.44 0.434
#> 39 2017-07-23 22:57:31.266 -0.167 -0.097 -0.021
#> 40 2017-07-23 22:57:31.299 0.548 0.185 0.323
#> 41 2017-07-23 22:57:31.333 0.66 0.405 0.572
#> 42 2017-07-23 22:57:31.366 1.14 0.894 0.853
#> 43 2017-07-23 22:57:31.400 1.30 1.07 1
#> 44 2017-07-23 22:57:31.433 1.10 0.824 0.543
#> 45 2017-07-23 22:57:31.466 0.739 0.554 0.384
#> 46 2017-07-23 22:57:31.500 0.305 0.232 0.358
#> 47 2017-07-23 22:57:31.533 0.015 -0.056 0.158
#> 48 2017-07-23 22:57:31.566 0.402 0.161 0.364
#> 49 2017-07-23 22:57:31.599 0.856 0.475 0.677
#> 50 2017-07-23 22:57:31.633 1.37 0.988 1.23
#> 51 2017-07-23 22:57:31.666 1.18 0.991 0.806
#> 52 2017-07-23 22:57:31.700 0.71 0.61 0.323
#> 53 2017-07-23 22:57:31.733 0.903 0.405 0.258
#> 54 2017-07-23 22:57:31.766 0.39 0.123 0.223
#> 55 2017-07-23 22:57:31.799 0.375 0.117 0.408
#> 56 2017-07-23 22:57:31.833 0.457 0.475 0.554
#> 57 2017-07-23 22:57:31.866 1.07 0.809 0.891
#> 58 2017-07-23 22:57:31.900 1.24 0.924 0.985
#> 59 2017-07-23 22:57:31.933 1.11 0.73 0.66
#> 60 2017-07-23 22:57:31.966 0.525 0.516 0.37
#> 61 2017-07-23 22:57:32.000 0.557 0.346 0.434
#> 62 2017-07-23 22:57:32.033 0.155 0.07 0.226
#> 63 2017-07-23 22:57:32.066 0.399 0.191 0.293
#> 64 2017-07-23 22:57:32.099 0.46 0.501 0.493
#> 65 2017-07-23 22:57:32.133 1.15 0.789 0.979
#> 66 2017-07-23 22:57:32.166 1.36 0.909 1.08
#> 67 2017-07-23 22:57:32.200 1.42 0.798 0.789
#> 68 2017-07-23 22:57:32.233 0.625 0.469 0.296
#> 69 2017-07-23 22:57:32.266 0.589 0.287 0.431
#> 70 2017-07-23 22:57:32.299 0.276 0.041 0.208
#> 71 2017-07-23 22:57:32.333 0.396 0.217 0.384
#> 72 2017-07-23 22:57:32.366 0.625 0.525 0.566
#> 73 2017-07-23 22:57:32.400 1.25 0.824 1.15
#> 74 2017-07-23 22:57:32.433 1.24 0.871 0.865
#> 75 2017-07-23 22:57:32.466 0.809 0.654 0.196
#> 76 2017-07-23 22:57:32.500 0.947 0.501 0.317
#> 77 2017-07-23 22:57:32.533 0.246 0.026 0.147
#> 78 2017-07-23 22:57:32.566 0.416 0.141 0.39
#> 79 2017-07-23 22:57:32.599 0.496 0.343 0.616
#> 80 2017-07-23 22:57:32.633 1.22 0.654 0.941
#> 81 2017-07-23 22:57:32.666 1.43 1.01 1.07
#> 82 2017-07-23 22:57:32.700 1.14 0.883 0.534
#> 83 2017-07-23 22:57:32.733 0.839 0.604 0.393
#> 84 2017-07-23 22:57:32.766 0.457 0.24 0.355
#> 85 2017-07-23 22:57:32.799 -0.018 -0.076 0.196
#> 86 2017-07-23 22:57:32.833 0.331 0.238 0.46
#> 87 2017-07-23 22:57:32.866 1.10 0.452 0.718
#> 88 2017-07-23 22:57:32.900 1.17 0.818 0.93
#> 89 2017-07-23 22:57:32.933 1.05 0.9 0.657
#> 90 2017-07-23 22:57:32.966 1.23 0.733 0.572
#> 91 2017-07-23 22:57:33.000 0.754 0.51 0.587Fixing the zeros
Here we “fix” any row with all zeroes with a last observation carried forward, to mimic what we think what happens in idle sleep mode in ActiLife. Is that correct?
y2 = fix_zeros(y)We see the there are no zero rows
y2 %>%
filter(between(time, lubridate::as_datetime("2017-07-23 22:57:30"),
lubridate::as_datetime("2017-07-23 22:57:33"))) %>%
print(n = nrow(y2))
#> # A tibble: 91 × 4
#> time X Y Z
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0.768 0.384 0.196
#> 2 2017-07-23 22:57:30.033 0.328 0.106 0.073
#> 3 2017-07-23 22:57:30.066 0.455 0.158 0.27
#> 4 2017-07-23 22:57:30.099 0.311 0.446 0.504
#> 5 2017-07-23 22:57:30.133 1.20 0.839 1.27
#> 6 2017-07-23 22:57:30.166 1.37 0.93 1.12
#> 7 2017-07-23 22:57:30.200 0.977 0.674 0.405
#> 8 2017-07-23 22:57:30.233 1.07 0.525 0.39
#> 9 2017-07-23 22:57:30.266 1.07 0.525 0.39
#> 10 2017-07-23 22:57:30.299 0.252 0.015 0.205
#> 11 2017-07-23 22:57:30.333 0.428 0.317 0.543
#> 12 2017-07-23 22:57:30.366 1.35 0.76 1.09
#> 13 2017-07-23 22:57:30.400 1.40 1.02 1.12
#> 14 2017-07-23 22:57:30.433 1.07 0.771 0.384
#> 15 2017-07-23 22:57:30.466 1.08 0.566 0.457
#> 16 2017-07-23 22:57:30.500 0.161 0.088 0.065
#> 17 2017-07-23 22:57:30.533 0.337 0.097 0.235
#> 18 2017-07-23 22:57:30.566 0.261 0.399 0.698
#> 19 2017-07-23 22:57:30.599 0.903 0.672 0.757
#> 20 2017-07-23 22:57:30.633 1.47 0.918 1.17
#> 21 2017-07-23 22:57:30.666 0.959 0.827 0.651
#> 22 2017-07-23 22:57:30.700 0.314 0.487 0.343
#> 23 2017-07-23 22:57:30.733 0.633 0.27 0.267
#> 24 2017-07-23 22:57:30.766 0.337 0.106 0.199
#> 25 2017-07-23 22:57:30.799 0.413 0.284 0.34
#> 26 2017-07-23 22:57:30.833 0.748 0.654 0.76
#> 27 2017-07-23 22:57:30.866 1.33 0.897 1.15
#> 28 2017-07-23 22:57:30.900 1.01 0.724 0.572
#> 29 2017-07-23 22:57:30.933 0.856 0.572 0.317
#> 30 2017-07-23 22:57:30.966 0.997 0.581 0.51
#> 31 2017-07-23 22:57:31.000 0.067 0.018 0.123
#> 32 2017-07-23 22:57:31.033 0.46 0.144 0.334
#> 33 2017-07-23 22:57:31.066 0.516 0.343 0.569
#> 34 2017-07-23 22:57:31.099 1.07 0.672 0.613
#> 35 2017-07-23 22:57:31.133 1.34 1.01 1.12
#> 36 2017-07-23 22:57:31.166 0.944 0.818 0.677
#> 37 2017-07-23 22:57:31.200 0.575 0.628 0.326
#> 38 2017-07-23 22:57:31.233 1.01 0.44 0.434
#> 39 2017-07-23 22:57:31.266 -0.167 -0.097 -0.021
#> 40 2017-07-23 22:57:31.299 0.548 0.185 0.323
#> 41 2017-07-23 22:57:31.333 0.66 0.405 0.572
#> 42 2017-07-23 22:57:31.366 1.14 0.894 0.853
#> 43 2017-07-23 22:57:31.400 1.30 1.07 1
#> 44 2017-07-23 22:57:31.433 1.10 0.824 0.543
#> 45 2017-07-23 22:57:31.466 0.739 0.554 0.384
#> 46 2017-07-23 22:57:31.500 0.305 0.232 0.358
#> 47 2017-07-23 22:57:31.533 0.015 -0.056 0.158
#> 48 2017-07-23 22:57:31.566 0.402 0.161 0.364
#> 49 2017-07-23 22:57:31.599 0.856 0.475 0.677
#> 50 2017-07-23 22:57:31.633 1.37 0.988 1.23
#> 51 2017-07-23 22:57:31.666 1.18 0.991 0.806
#> 52 2017-07-23 22:57:31.700 0.71 0.61 0.323
#> 53 2017-07-23 22:57:31.733 0.903 0.405 0.258
#> 54 2017-07-23 22:57:31.766 0.39 0.123 0.223
#> 55 2017-07-23 22:57:31.799 0.375 0.117 0.408
#> 56 2017-07-23 22:57:31.833 0.457 0.475 0.554
#> 57 2017-07-23 22:57:31.866 1.07 0.809 0.891
#> 58 2017-07-23 22:57:31.900 1.24 0.924 0.985
#> 59 2017-07-23 22:57:31.933 1.11 0.73 0.66
#> 60 2017-07-23 22:57:31.966 0.525 0.516 0.37
#> 61 2017-07-23 22:57:32.000 0.557 0.346 0.434
#> 62 2017-07-23 22:57:32.033 0.155 0.07 0.226
#> 63 2017-07-23 22:57:32.066 0.399 0.191 0.293
#> 64 2017-07-23 22:57:32.099 0.46 0.501 0.493
#> 65 2017-07-23 22:57:32.133 1.15 0.789 0.979
#> 66 2017-07-23 22:57:32.166 1.36 0.909 1.08
#> 67 2017-07-23 22:57:32.200 1.42 0.798 0.789
#> 68 2017-07-23 22:57:32.233 0.625 0.469 0.296
#> 69 2017-07-23 22:57:32.266 0.589 0.287 0.431
#> 70 2017-07-23 22:57:32.299 0.276 0.041 0.208
#> 71 2017-07-23 22:57:32.333 0.396 0.217 0.384
#> 72 2017-07-23 22:57:32.366 0.625 0.525 0.566
#> 73 2017-07-23 22:57:32.400 1.25 0.824 1.15
#> 74 2017-07-23 22:57:32.433 1.24 0.871 0.865
#> 75 2017-07-23 22:57:32.466 0.809 0.654 0.196
#> 76 2017-07-23 22:57:32.500 0.947 0.501 0.317
#> 77 2017-07-23 22:57:32.533 0.246 0.026 0.147
#> 78 2017-07-23 22:57:32.566 0.416 0.141 0.39
#> 79 2017-07-23 22:57:32.599 0.496 0.343 0.616
#> 80 2017-07-23 22:57:32.633 1.22 0.654 0.941
#> 81 2017-07-23 22:57:32.666 1.43 1.01 1.07
#> 82 2017-07-23 22:57:32.700 1.14 0.883 0.534
#> 83 2017-07-23 22:57:32.733 0.839 0.604 0.393
#> 84 2017-07-23 22:57:32.766 0.457 0.24 0.355
#> 85 2017-07-23 22:57:32.799 -0.018 -0.076 0.196
#> 86 2017-07-23 22:57:32.833 0.331 0.238 0.46
#> 87 2017-07-23 22:57:32.866 1.10 0.452 0.718
#> 88 2017-07-23 22:57:32.900 1.17 0.818 0.93
#> 89 2017-07-23 22:57:32.933 1.05 0.9 0.657
#> 90 2017-07-23 22:57:32.966 1.23 0.733 0.572
#> 91 2017-07-23 22:57:33.000 0.754 0.51 0.587Calculating AC on LOCF Data
res2 = calculate_counts(raw = y2, epoch = 1)
res2 = res2 %>%
select(time, Axis1, Axis2, Axis3)
res2 = tibble::as_tibble(res2)
the_check = all.equal(res2, x)
if (!isTRUE(the_check)) {
print(the_check)
}
#> [1] "Component \"Axis2\": Mean relative difference: 0.5087719"
#> [2] "Component \"Axis3\": Mean relative difference: 0.0754717"Still does not match up
ind = rowSums(res2 != x) > 0
res2[ind,]
#> # A tibble: 3 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0 42 39
#> 2 2017-07-23 22:57:31.000 4 2 14
#> 3 2017-07-23 22:57:32.000 0 13 2
x[ind,]
#> # A tibble: 3 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0 17 41
#> 2 2017-07-23 22:57:31.000 4 4 12
#> 3 2017-07-23 22:57:32.000 0 11 2
res[ind,]
#> # A tibble: 3 × 4
#> time Axis1 Axis2 Axis3
#> <dttm> <dbl> <dbl> <dbl>
#> 1 2017-07-23 22:57:30.000 0 42 39
#> 2 2017-07-23 22:57:31.000 4 2 14
#> 3 2017-07-23 22:57:32.000 0 13 2Created on 2023-09-22 with reprex v2.0.2
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.1 (2022-06-23)
#> os macOS Big Sur ... 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2023-09-22
#> pandoc 3.1.5 @ /usr/local/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> agcounts * 0.6.4 2023-07-26 [1] local
#> anytime 0.3.9 2020-08-27 [1] CRAN (R 4.2.0)
#> bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
#> bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
#> blob 1.2.4 2023-03-17 [1] CRAN (R 4.2.0)
#> bslib 0.5.0 2023-06-09 [1] CRAN (R 4.2.0)
#> cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
#> cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
#> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
#> crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
#> curl 5.0.1 2023-06-07 [1] CRAN (R 4.2.0)
#> data.table 1.14.9 2023-08-21 [1] local
#> DBI 1.1.3 2022-06-18 [1] CRAN (R 4.2.0)
#> digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.0)
#> dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
#> evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.0)
#> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0)
#> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
#> fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.0)
#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
#> GGIR 2.9-0 2023-03-24 [1] CRAN (R 4.2.0)
#> ggplot2 3.4.2 2023-04-03 [1] CRAN (R 4.2.0)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
#> gsignal 0.3-5 2022-05-15 [1] CRAN (R 4.2.0)
#> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0)
#> hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
#> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0)
#> htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
#> httpuv 1.6.11 2023-05-11 [1] CRAN (R 4.2.0)
#> jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
#> jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.2.0)
#> knitr 1.43 2023-05-25 [1] CRAN (R 4.2.0)
#> later 1.3.1 2023-05-02 [1] CRAN (R 4.2.0)
#> lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.0)
#> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
#> lubridate 1.9.2 2023-02-10 [1] CRAN (R 4.2.0)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
#> Matrix 1.6-0 2023-07-08 [1] CRAN (R 4.2.1)
#> memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
#> mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
#> png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
#> pracma 2.4.2 2022-09-22 [1] CRAN (R 4.2.0)
#> promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
#> purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0)
#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0)
#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0)
#> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
#> Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.0)
#> reactable 0.4.4 2023-03-12 [1] CRAN (R 4.2.0)
#> read.gt3x 1.2.0 2022-06-30 [1] CRAN (R 4.2.0)
#> readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0)
#> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.0)
#> reticulate 1.30 2023-06-09 [1] CRAN (R 4.2.0)
#> rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
#> rmarkdown 2.23 2023-07-01 [1] CRAN (R 4.2.0)
#> RSQLite 2.3.1 2023-04-03 [1] CRAN (R 4.2.0)
#> rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
#> sass 0.4.7 2023-07-15 [1] CRAN (R 4.2.0)
#> scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
#> shiny 1.7.4.1 2023-07-06 [1] CRAN (R 4.2.0)
#> stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
#> stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
#> styler 1.10.1 2023-06-05 [1] CRAN (R 4.2.0)
#> SummarizedActigraphy * 0.6.0 2023-08-18 [1] Bioconductor
#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
#> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
#> timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
#> tsibble 1.1.3 2022-10-09 [1] CRAN (R 4.2.0)
#> tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.0)
#> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
#> vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.0)
#> vroom 1.6.3 2023-04-28 [1] CRAN (R 4.2.0)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
#> xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
#> xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
#> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
#> zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/johnmuschelli/miniconda3/bin/python3
#> libpython: /Users/johnmuschelli/miniconda3/lib/libpython3.11.dylib
#> pythonhome: /Users/johnmuschelli/miniconda3:/Users/johnmuschelli/miniconda3
#> version: 3.11.4 (main, Jul 5 2023, 08:41:25) [Clang 14.0.6 ]
#> numpy: /Users/johnmuschelli/miniconda3/lib/python3.11/site-packages/numpy
#> numpy_version: 1.25.2
#> pygt3x: /Users/johnmuschelli/miniconda3/lib/python3.11/site-packages/pygt3x
#>
#> NOTE: Python version was forced by RETICULATE_PYTHON
#>
#> ──────────────────────────────────────────────────────────────────────────────Example to run
I wanted to create an issue to show how to potnetially use this data.
The file is from https://ftp.cdc.gov/pub/pax_g/62172.tar.bz2
And the file is located at
GT3XPLUS-AccelerationCalibrated-1x8x0.NEO1G75911139.2000-01-06-13-00-00-000-P0000.sensor.csv.gz
It has a header timestamp and X/Y/Z and I created an example.
Run data in chunks?
So I have 8 days worth of 80Hz data, and it would be useful for memory-saving to be able to run some of this in chunks, but it's unclear how to break up the data to get the same result.
The recursive nature of the filtering doesn't make it easy for me to determine how to do it, including how the epoch/sample rate combination would affect how to chunk the data.
Is there any clear way to perform this operation, even if you need to pass in some of the previous data into the next chunk?
Time of generated counts not correspond to that of raw data
Dear developer,
thank you for your great commitment on this python package!
I am collecting data using Actigraph wGT3X-BT for several days, and use this package to calculate activity counts. While my raw data starts from 2023-04-13 16:15:38 and ends at 2023-04-17 16:56:06. The calculated activity data starts from 2023-04-13 16:16:00 and ends at 2023-04-17 13:11:00.
I suppose the reason is in the raw data, some timepoint is missing (I don't know the reason for that, I would be grateful if you can indicate in which circumstance Actigraph stops collecting data).
If I understand correctly, this package is processing timestamp outside the pipeline using:
if time_column is not None:
ts = raw[time_column]
ts = pd.to_datetime(ts)
time_freq = str(epoch) + "S"
ts = ts.dt.round(time_freq)
ts = ts.unique()
ts = pd.DataFrame(ts, columns=[time_column])
ts = ts[0 : counts.shape[0]]
I am wondering if it is better to deal with the timestamp inside _extract method? I doubt using round and delete the rest of data to fit to the correct sample size is the most accurate way to deal with time.
Thank you for your time and best regards,
Fan
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