files_status_for_dpdash.py

#!/usr/bin/env python

# cleaned up Kevin's work to make DPdash compatible files
# inserted codes for mtime: the latest time when a subject's data was downloaded

from pathlib import Path
import pandas as pd
import numpy as np
from datetime import time, timedelta, datetime, date

flow_test_root = Path('/data/predict/kcho/flow_test')
pronet_phoenix_dir = flow_test_root / 'Pronet/PHOENIX'
prescient_phoenix_dir = flow_test_root / 'Prescient/PHOENIX'
outdir = flow_test_root/ 'ctime_experiment'


from os import stat
from os.path import isfile
from datetime import datetime

def _latest_mtime(p):

    latest= -1
    for file in p.rglob('*'):
        if file.is_file():
            mtime= file.stat().st_mtime
            if mtime>latest:
                latest= mtime

    return datetime.fromtimestamp(latest).strftime('%Y-%m-%d')


def get_summary_from_phoenix(phoenix_dir: Path) -> pd.DataFrame:
    '''Get summary from the PHOENIX structure'''

    subject_paths = list(phoenix_dir.glob('*/*/*/*'))

    df = pd.DataFrame({'p': subject_paths})
    df['subject'] = df.p.apply(lambda x: x.name)
    df['site'] = df.p.apply(lambda x: x.parent.parent.name)
    df['level0'] = df.p.apply(lambda x: x.parent.parent.parent.name)
    df['level1'] = df.p.apply(lambda x: x.parent.name)

    df['surveys'] = df.p.apply(lambda x: len(list((x / 'surveys').glob('*json'))) > 0)
    df['eeg'] = df.p.apply(lambda x: len(list((x / 'eeg').glob('*zip'))) > 0)
    df['eeg_ss'] = df.p.apply(lambda x: (x / 'eeg' / f'{x.name}.Pronet.Run_sheet_eeg.csv').is_file())
    df['actigraphy'] = df.p.apply(lambda x: len(list((x / 'actigraphy').glob('*zip'))) > 0)
    df['actigraphy_ss'] = df.p.apply(lambda x: (x / 'actigraphy' / f'{x.name}.Pronet.Run_sheet_actigraphy.csv').is_file())
    df['mri'] = df.p.apply(lambda x: len([x for x in (x / 'mri').glob('*') if x.is_dir()]) > 0)
    df['mri_ss'] = df.p.apply(lambda x: (x / 'mri' / f'{x.name}.Pronet.Run_sheet_mri.csv').is_file())
    df['interviews'] = df.p.apply(lambda x: len([x for x in (x / 'interviews').glob('*') if x.is_dir()]) > 0)
    df['interviews_ss'] = df.p.apply(lambda x: (x / 'interviews' / f'{x.name}.Pronet.Run_sheet_interviews.csv').is_file())


    df['mtime']= df.p.apply(lambda x: _latest_mtime(x))

    return df


df = get_summary_from_phoenix(pronet_phoenix_dir)

df_pivot = pd.pivot_table(df, index=['subject', 'site', 'mtime'], columns=['level0', 'level1'], fill_value=False).astype(int)

for (subject, site, mtime), row in df_pivot.iterrows():
    df_tmp = row.reset_index()
    df_tmp.columns = ['datatype', 'level0', 'level1', 'count']
    df_tmp_pivot = pd.pivot_table(df_tmp, columns=['datatype', 'level0', 'level1']).reset_index()
    df_tmp_pivot['col'] = df_tmp_pivot['datatype'] + '_' + df_tmp_pivot['level1'] + '_' + df_tmp_pivot['level0']
    subject_series_tmp = df_tmp_pivot.set_index('col')[0]

    subject_series_tmp['mtime']= mtime

    # https://gist.github.com/tashrifbillah/cea43521588adf127cae79353ae09968
    # suggestion from Tashrif to link outputs to DPdash
    subject_df_tmp = pd.DataFrame({
        'day': [1],
        'reftime': '',
        'timeofday': '',
        'weekday': ''
    })

    subject_df_tmp = pd.concat([subject_df_tmp, pd.DataFrame(subject_series_tmp).T], axis=1)

    out_file_name = f"{site[-2:]}-{subject}-flowcheck-day1to1.csv"

    subject_df_tmp.to_csv(outdir/out_file_name, index=False)

In reference to gist https://gist.github.com/tashrifbillah/24efeec3219ba3c58c92adc419aac7be
mtime has been made to appear in the final data frame through following modifications:

43 df_pivot = pd.pivot_table(df, index=['subject', 'site', 'mtime'], columns=['level0', 'level1'], fill_value=False).astype(int)

49 for (subject, site, mtime), row in df_pivot.iterrows():

And finally inserting after line 54:

subject_series_tmp['mtime']= mtime

Updated the gist with _latest_mtime()
https://gist.github.com/tashrifbillah/24efeec3219ba3c58c92adc419aac7be

Now final data frame looks like the following

day,reftime,timeofday,weekday,actigraphy_processed_GENERAL,actigraphy_processed_PROTECTED,actigraphy_raw_PROTECTED,actigraphy_ss_processed_GENERAL,actigraphy_ss_processed_PROTECTED,actigraphy_ss_raw_PROTECTED,eeg_processed_GENERAL,eeg_processed_PROTECTED,eeg_raw_PROTECTED,eeg_ss_processed_GENERAL,eeg_ss_processed_PROTECTED,eeg_ss_raw_PROTECTED,interviews_processed_GENERAL,interviews_processed_PROTECTED,interviews_raw_PROTECTED,interviews_ss_processed_GENERAL,interviews_ss_processed_PROTECTED,interviews_ss_raw_PROTECTED,mri_processed_GENERAL,mri_processed_PROTECTED,mri_raw_PROTECTED,mri_ss_processed_GENERAL,mri_ss_processed_PROTECTED,mri_ss_raw_PROTECTED,surveys_processed_GENERAL,surveys_processed_PROTECTED,surveys_raw_PROTECTED,mtime
1,,,,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2021-12-21

This work has been moved to https://github.com/AMP-SCZ/utility/blob/3ac55b59f3da337e544b4b34c87aa9502d524c9a/files_status_for_dpdash.py