Source code for

# Lib
import logging
from ftplib import FTP
import socket
from pathlib import Path, PurePath
import os
from tarfile import ReadError
import re
import zipfile
import gzip
import tarfile
import shutil
from bs4 import BeautifulSoup
import dateutil # python-dateutil, non-built-in
import datetime
import pickle
from urllib.request import urlopen
import pandas as pd
from tqdm import tqdm
from collections import Counter
import sys

# unique to find_betas_any_source()...
    import methylcheck # not required generally, but needed for one of these functions; handled within.
except ImportError:
import io
import json
import time
import tempfile
import requests
import zipfile
import random
import subprocess

# app
from .miniml import sample_sheet_from_miniml, sample_sheet_from_idats, convert_miniml
# cannot relative-import here because process_data uses
#from .process_data import confirm_dataset_contains_idats, get_attachment_info, run_series

__all__ = [

#logging.basicConfig(level=logging.DEBUG) # always verbose
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel( logging.INFO )

def geo_download(geo_id, series_path, geo_platforms, clean=True, decompress=True):
    """Downloads the IDATs and metadata for a GEO series

        geo_id [required]
            the GEO Accension for the desired series (e.g. GSE134293)
        series_path [required]
            the directory to download the data to
        geo_platforms [required]
            the list of supported GEO platforms
            whether or not to delete files once they are no longer need (True by default)

    Note about GEO IDs:
        You can use the NIH online search to find data sets, then click "Send to:" at the button of a results page,
        and export a list of unique IDs as text file. These IDs are not GEO_IDs used here. First, remove the first
        three digits from the number, so Series ID: 200134293 is GEO accension ID: 134293, then include the GSE part,
        like "GSE134293" in your CLI parameters.

    This function returns True or False, depending on whether the downloaded data is correct."""
    success = True
    series_dir = Path(series_path)
    raw_filename = f"{geo_id}_RAW.tar"
    miniml_filename = f"{geo_id}_family.xml"

    if not os.path.exists(series_path):
        raise FileNotFoundError(f'{geo_id} directory not found.')

    for platform in geo_platforms:
        if not Path(f"{series_path}/{platform}").exists():
            Path(f"{series_path}/{platform}").mkdir(parents=True, exist_ok=True)

    ftp = FTP('', timeout=120) # 2 mins

        filesize = ftp.size(f"miniml/{miniml_filename}.tgz") # -- gives 550 error because CWD puts it in ASCII mode."DEBUG ftp.size WORKED: miniml/{miniml_filename}.tgz -- {filesize}")
    except Exception as e:
        LOGGER.error(f"ftp.size ERROR: {e}")

    if not Path(f"{series_path}/{miniml_filename}").exists():
        if not Path(f"{series_path}/{miniml_filename}.tgz").exists():
  "Downloading {miniml_filename}")
            miniml_file = open(f"{series_path}/{miniml_filename}.tgz", 'wb')
                #filesize = ftp.size(f"miniml/{miniml_filename}.tgz") -- gives 550 error because CWD puts it in ASCII mode.
                for filename,filestats in ftp.mlsd(path="miniml", facts=["size"]):
                    if filename == miniml_filename:
                        filesize = filestats['size']
                with tqdm(unit = 'b', unit_scale = True, leave = False, miniters = 1, desc = geo_id, total = filesize) as tqdm_instance:
                    def tqdm_callback(data):
                    ftp.retrbinary(f"RETR miniml/{miniml_filename}.tgz", tqdm_callback)
            except Exception as e:
      'tqdm: Failed to create a progress bar, but it is downloading...')
                ftp.retrbinary(f"RETR miniml/{miniml_filename}.tgz", miniml_file.write)
  "Downloaded {miniml_filename}")
        #ftp.quit() # instead of 'close()'"Unpacking {miniml_filename}")
        min_tar ="{series_path}/{miniml_filename}.tgz")
        for file in min_tar.getnames():
            if file == miniml_filename:
                min_tar.extract(file, path=series_path)
        if clean:

    if list(series_dir.glob('*.idat.gz')) == [] and list(series_dir.glob('**/*.idat')) == []:
        if not Path(f"{series_path}/{raw_filename}").exists():
            ftp = FTP('',
                      timeout=59)  # see issue (must be <60s because of a bug)
            raw_file = open(f"{series_path}/{raw_filename}", 'wb')
            filesize = ftp.size(f"suppl/{raw_filename}")
                    with tqdm(unit = 'b', unit_scale = True, leave = False, miniters = 1, desc = geo_id, total = filesize) as tqdm_instance:
                        def tqdm_callback(data):
                        ftp.retrbinary(f"RETR suppl/{raw_filename}", tqdm_callback)
                except Exception as e:
          'tqdm: Failed to create a progress bar, but it is downloading...')
                    ftp.retrbinary(f"RETR suppl/{raw_filename}", raw_file.write)
            except socket.timeout as e:
                LOGGER.warning(f"FTP timeout error.")
                # seems to happen AFTER download is done, so just ignoring it.
  "Closing file {raw_filename}")
  "Downloaded {raw_filename}")"Unpacking {raw_filename}")
            tar ="{series_path}/{raw_filename}")
            # let user know if this lack idats
            if not any([(True if '.idat' in else False) for member in list(tar.getmembers())]):
                file_endings = Counter([tuple(PurePath( for member in list(tar.getmembers())])
                file_endings = [(k,v) for k,v in file_endings.most_common() if v > 1]
                LOGGER.warning(f'No idat files found in {raw_filename}. {len(list(tar.getmembers()))} files found: {file_endings}.')
                success = False
            for member in tar.getmembers():
                if re.match('.*.idat.gz',
                    tar.extract(member, path=series_path)
        except ReadError as e:
            raise ReadError(f"There appears to be an incomplete download of {geo_id}. Please delete those files and run this again.")
            success = False
        if clean:

    if not decompress:
        pass"Not decompressing {geo_id} IDAT files")
    else:"Decompressing {geo_id} IDAT files")
        for gz in series_dir.glob("*.idat.gz"):
            gz_string = str(gz)
            with, 'rb') as f_in:
                with open(gz_string[:-3], 'wb') as f_out:
                    shutil.copyfileobj(f_in, f_out)
            if clean:
                gz.unlink() #os.remove(gz_string)

    if not decompress:"Downloaded {geo_id} idats without decompressing")
    else:"Downloaded and unpacked {geo_id} idats")
    return success

def geo_metadata(geo_id, series_path, geo_platforms, path):
    """Reads the metadata for the given series (MINiML file) and creates a metadata dictionary and sample sheet for each platform

        geo_id [required]
            the GEO Accension for the desired series
        series_path [required]
            the directory containing the series data
        geo_platforms [required]
            the list of supported GEO platforms
            the path to the directory containing dictionaries for each platform

        A list of platforms that the series contains samples of"""
    pipeline_kwargs = {}
    miniml_filename = f"{geo_id}_family.xml"
    with open(f"{series_path}/{miniml_filename}", 'r') as fp:
        soup = BeautifulSoup(fp, "xml")

    meta_dicts = {}
    samples_dict = {}

    for platform in geo_platforms:
        meta_dicts[platform] = {}
        samples_dict[platform] = {}

    samples = soup.MINiML.find_all("Sample")
    for sample in samples:
        platform = sample.find('Platform-Ref')['ref']
        accession = sample.Accession.text
        title = sample.Title.text
        attributes_dir = {}
        attributes_dir['source'] = sample.Source.text
        attributes_dir['platform'] = platform
        attributes_dir['title'] = title
        for char in sample.find_all('Characteristics'):
            attributes_dir[char['tag']] = char.text.strip()
        if sample.find('Description'):
            attributes_dir['description'] = sample.find('Description').text.strip()

        # only some MINiML files have this.
            split_idat = sample.find('Supplementary-Data').text.split("/")[-1].split("_")
            attributes_dir['Sample_ID'] = f"{split_idat[1]}_{split_idat[2]}" # matches beta_value column names
            attributes_dir['Sentrix_ID'] = f"{split_idat[1]}"
            attributes_dir['Sentrix_Position'] = f"{split_idat[2]}"
   "MINiML file does not provide (Sentrix_ID_R00C00)" )

        if platform in geo_platforms:
            for idat in sample.find_all('Supplementary-Data'):
                if idat['type'] == 'IDAT':
                    file_name = (idat.text.split("/")[-1]).strip()[:-3]
                    if (not(Path(f"{series_path}/{file_name}").is_file())) and Path(f"{series_path}/{file_name}.gz").is_file():
                        file_name = file_name+".gz"
                        shutil.move(f"{series_path}/{file_name}", f"{series_path}/{platform}/{file_name}")
                    except FileNotFoundError:
                        # this doesn't throw an error if file is already in the right folder
                        if not Path(f"{series_path}/{platform}/{file_name}").is_file():
                            raise FileNotFoundError (f"Could not move file {series_path}/{file_name} after downloading.")

            meta_dicts[platform][accession] = attributes_dir
            samples_dict[platform][accession] = title
            # this ought to keep other idat files from being included in the package.
            LOGGER.warning(f'Sample: {title[:40]} has unrecognized platform: {platform}; not moving data file')"Found {len(attributes_dir)} tags for {len(samples)} samples: {attributes_dir}")

    seen_platforms = []

    for platform in geo_platforms:
        if meta_dicts[platform]:
            meta_dict_filename = f"{geo_id}_{platform}_dict.pkl"
            pickle.dump(meta_dicts[platform], open(f"{series_path}/{meta_dict_filename}", 'wb'))
            if not os.path.exists(f"{path}/{platform}_dictionaries/{geo_id}_dict.pkl"):
                shutil.move(f"{series_path}/{meta_dict_filename}", f"{path}/{platform}_dictionaries/{geo_id}_dict.pkl")
                sample_sheet_from_miniml(geo_id, series_path, platform, samples_dict[platform], meta_dicts[platform], save_df=True)
            except ValueError:
                # in this case, the samplesheet meta data wasn't consistent, so using idat filenames instead
                    sample_sheet_from_idats(geo_id, series_path, platform, samples_dict[platform])
                except ValueError as e:
          "{e}; will try run_pipeline's create samplesheet method.")
                    pipeline_kwargs['make_sample_sheet'] = True
            if platform not in seen_platforms:

    return seen_platforms, pipeline_kwargs

[docs]def pipeline_find_betas_any_source(**kwargs): """Sets up a script to run methylprep that saves directly to path or S3. The slowest part of processing GEO datasets is downloading, so this handles that. STEPS - uses `methylprep alert -k <keywords>` to curate a list of GEO IDs worth grabbing. note that version 1 will only process idats. also runs methylcheck.load on processed files, if installed. - downloads a zipfile, uncompresses it, - creates a samplesheet, - moves it into foxo-test-pipeline-raw for processing. - You get back a zipfile with all the output data. required kwargs: - project_name: string, like GSE123456, to specify one GEO data set to download. to initialize, specify one GEO id as an input when starting the function. - beforehand, you can use `methylprep alert` to verify the data exists. - OR you can pass in a string of GEO_ID separated by commas without any spaces and it will split them. optional kwargs: - function: 'geo' (optional, ignored; used to specify this pipeline to run from command line) - data_dir: - default is current working directory ('.') if omitted - use to specify where all files will be downloaded, processed, and finally stored, unless `--cleanup=False`. - if using AWS S3 settings below, this will be ignored. - verbose: False, default is minimal logging messages. - save_source: if True, it will retain .idat and/or -tbl-1.txt files used to generate beta_values dataframe pkl files. It will use local disk by default, but if you want it to save to S3, provide these: - bucket (where downloaded files are stored) - efs (AWS elastic file system name, for lambda or AWS batch processing) - processed_bucket (where final files are saved) - clean: default True. If False, does not explicitly remove the tempfolder files at end, or move files into data_dir output filepath/folder. - if you need to keep folders in working/efs folder instead of moving them to the data_dir. - use cleanup=False when embedding this in an AWS/batch/S3 context, then use the `working tempfolder` path and filenames returned to copy these files into S3. returns: - if a single GEO_ID, returns a dict with "error", "filenames", and "tempdir" keys. - if mulitple GEO_IDs, returns a dict with "error", "geo_ids" (nested dict), and "tempdir" keys. Uses same tempdir for everything, so clean should be set to True. - "error" will be None if it worked okay. - "filenames" will be a list of filenames that were created as outputs (type=string) - "tempdir" will be the python tempfile tempory-directory object. Passing this out prevents garbage collector from removing it when the function ends, so you can retrive these files and run tempdir.cleanup() manually. Otherwise, python will remove the tempdir for you when python closes, so copy whatever you want out of it first. This makes it possible to use this function with AWS EFS (elastic file systems) as part of a lambda or aws-batch function where disk space is more limited. NOTE: v1.3.0 does NOT support multiple GEO IDs yet. """ if 'methylcheck' not in sys.modules: assert ImportError("You cannot run this without `methylcheck` installed.") import zipfile # FOR SOME REASON, importing zipfile at top of file doesn't work in this function :( -- prob because I imported this function without loading the whole file. Or I reassigned var 'zipfile' by accident somewhere. BATCH_SIZE=33 # for processing idats if kwargs.get('verbose') == False: LOGGER.setLevel( logging.WARNING ) if not kwargs.get('project_name'): return {"filenames": [], "tempdir": None, "error": "`project_name` is required (to specify one GEO_ID or a list of them as a comma separated string without spaces)"} geo_ids = kwargs['project_name'].split(',') # always a list."Your command line inputs: {kwargs}")"Starting batch GEO pipeline processor for {geo_ids}.") #1: set working folder, and make sure /mnt/efs exists if used. if kwargs.get('efs') and kwargs['efs'] is not None: EFS = kwargs['efs'] mounted = False try: #efs_files = list(Path(EFS).glob('*')) mounted = Path(EFS).exists()"{EFS} exists: {mounted}") except Exception as e: LOGGER.error(f"{EFS} error: {e}") if not mounted: result =["df", "-h"], stdout=subprocess.PIPE) LOGGER.warning(f"EFS mount [df -h]: {result.stdout.decode('utf-8')}") raise FileNotFoundError("This batch function has no {EFS} mounted. Cannot proceed.") working = tempfile.TemporaryDirectory(dir=EFS) elif not kwargs.get('efs') and kwargs.get('data_dir') != None: if not Path(kwargs.get('data_dir')).exists(): Path(kwargs.get('data_dir')).mkdir(parents=True, exist_ok=True) working = tempfile.TemporaryDirectory(dir=kwargs['data_dir']) EFS = elif not kwargs.get('efs') and kwargs.get('data_dir') == None: working = tempfile.TemporaryDirectory(dir='.') EFS = if kwargs.get('data_dir') == None: kwargs['data_dir'] = '.' # CLI seems to pass in None and the get() doesn't get it. #2: set output folder(s), if specified. Otherwise, use data_dir or '.' for both. s3 = False if kwargs.get('bucket') and kwargs.get('output_bucket'): s3 = True BUCKET = kwargs['bucket'] PROCESSED_BUCKET = kwargs['output_bucket'] else: BUCKET = kwargs.get('data_dir', '.') PROCESSED_BUCKET = kwargs.get('data_dir', '.') #2b: test outgoing requests work (if, for example, you're on AWS behind a NAT gateway) #test = requests.get('')"testing outgoing request returned: {test.status_code} headers {test.headers['content-type']} encoding {test.encoding} text length {len(test.text)}") #3: run EACH geo series through downloader/processor each_geo_result = {} zipfile_names = [] for geo_id in geo_ids: # this download processed data, or idats, or -tbl-1.txt files inside the _family.xml.tgz files. # also downloads meta_data # download_geo_processed() gets nothing if idats exist. result = download_geo_processed(geo_id, working, verbose=kwargs.get('verbose', False)) if result['found_idats'] == False and result['processed_files'] == False and result['tbl_txt_files'] == False: LOGGER.warning(f"No downloadable methylation data found for {geo_id}.") continue # result dict tells this function what it found. if result['tbl_txt_files'] == True:"Found -tbl-1.txt files with meta data for {geo_id}.") if result['tbl_txt_files'] == True:"Found processed csv files for {geo_id}.") if result['found_idats'] == True: try: # dict_only: should download without processing idats.,, dict_only=True, batch_size=BATCH_SIZE, clean=True, abort_if_no_idats=True) except Exception as e: LOGGER.error(f"run_series ERROR: {e}") each_geo_result[geo_id] = result #4: memory check / debug if != 'nt' and kwargs.get('verbose') == True: total_disk_space = subprocess.check_output(['du','-sh', EFS]).split()[0].decode('utf-8')"Tempfolder {EFS} contains {len(list([str(k) for k in Path(EFS).rglob('*')]))} files, {total_disk_space} total.")"DEBUG {EFS} all files: {list([str(k) for k in Path('*')])}")"Downloaded and extracted: {geo_ids}. Zipping and moving to S3 for processing in pipeline now.") zipfile_names = [] # one for each GSExxx, or one per pkl file found that was too big and used gzip. #5: compress and package files zipfile_name = f"{geo_id}.zip" outfile_path = Path(, zipfile_name) # check if any one file is too big, and switch to gzip if so. use_gzip = False for k in Path('*'): if k.stat().st_size >= zipfile.ZIP64_LIMIT: # this next line assumes only one GSE ID in function."Switching to gzip because {str(k)} is greater than 2GB. This probably breaks if processing IDATS. Found_idats = {result['found_idats']}") use_gzip = True break if use_gzip or result['found_idats'] == False: for k in Path('*.pkl'): # gets beta_values.pkl and meta_data.pkl # gzip each file in-place, then upload them. These are big pkl files. # this will also catch the GSExxxxx-tbl-1.txt pickled beta_values.pkl dataframe, if it exists. with open(k, 'rb') as file_in: gzip_name = Path(f"{str(k)}.gz") with, 'wb') as file_out: shutil.copyfileobj(file_in, file_out) zipfile_names.append( #file_size =, io.SEEK_END)"File: {} -- {round(file_size/1000000)} MB") if != 'nt': result =["du", "-chs", EFS], stdout=subprocess.PIPE) result = result.stdout.decode('utf-8').split('\t')[0]"{} -- {result} total") else: with zipfile.ZipFile(outfile_path, mode='w', compression=zipfile.ZIP_DEFLATED, allowZip64=True, compresslevel=9) as zip: for k in Path('*'): if k.is_dir(): continue if == zipfile_name: continue # there is an empty file created in the same folder I'm zipping up, so need to skip this guy. zip.write(str(k), # 2nd arg arcname will drop folder structure in zipfile (the /mnt/efs/tmpfolder stuff) zipinfo = zip.getinfo("{} ({round(zipinfo.file_size/1000)} --> {round(zipinfo.compress_size/1000)} KB)")"In ZipFile {outfile_path}: {zip.namelist()}") zipfile_names.append(zipfile_name) """ #6: upload, if s3. -- this won't work inside methylprep because of all the AWS config stuff, so this function passes the data out. #my_s3 = boto3.resource('s3') # using multipart so handle >5GB files, but test with smaller files for now. if s3: for zipfile_name in zipfile_names: if result['found_idats'] == False: s3_key = PurePath(zipfile_name) local_file = Path(, zipfile_name) file_size = round(local_file.stat().st_size/1000000)"S3: Uploading processed file {zipfile_name} ({file_size} MB) to s3://{PROCESSED_BUCKET}/{s3_key}") with open(local_file, 'rb') as f: provider.upload_s3_object(PROCESSED_BUCKET, str(s3_key), f, multipart=True) else: s3_key = PurePath(START_PIPELINE_FOLDER, zipfile_name) #mp_id = ''.join([random.choice('abcdefghjiklmnopqrstuvwxyz1234567890') for i in range(16)])"S3: Uploading zipfile {zipfile_name} to s3://{BUCKET}/{s3_key}") # multipart needs you to pass in the fileobj, not the part. with open(Path(, zipfile_name), 'rb') as f: provider.upload_s3_object(BUCKET, str(s3_key), f, multipart=True) if != 'nt': total_disk_space = subprocess.check_output(['du','-sh', EFS]).split()[0].decode('utf-8')"DEBUG {EFS} {len(list([str(k) for k in Path(EFS).rglob('*')]))} files, total: {total_disk_space}") """ #7: delete/move tempfile/efs stuff #efs_files = list(Path('*')) # NOTE: if running a bunch of GEO_IDs, and clean is False, everything will be in the same temp workdir, so make sure to clean/move each one. if kwargs.get('clean') == False: pass # skip this step if you need to keep folders in working/efs folder instead of moving them to the data_dir. else: # moving important files out of working folder, then returning these as a list. # this would break in lambda/aws-batch because efs is huge but the host drive is not. #zipfiles = [_zipfile for _zipfile in list(Path('*')) if _zipfile in zipfile_names] for _zipfile in zipfile_names: if kwargs.get('verbose') == True:"Copying {_zipfile} to {kwargs.get('data_dir','.')}") shutil.copy(Path(, _zipfile), kwargs.get('data_dir','.')) efs_exists = Path( if len(geo_ids) > 1 and geo_id != geo_ids[-1]: # remove files, but don't let folder go away with cleanup(), unless this is the last one. for _file in Path('*'): if _file.is_file(): _file.unlink()"Removed temp files; left working dir intact.") else: working.cleanup() final_files = list(Path(kwargs.get('data_dir','.')).glob('*')) if kwargs.get('verbose') == True:"Removing temp_files: (exists: {efs_exists} --> exists: {Path(})")"Files saved: {final_files} vs zipfile_names: {zipfile_names}") #total_disk_space = subprocess.check_output(['du','-sh', EFS]).split()[0].decode('utf-8') each_geo_result[geo_id]['filenames'] = zipfile_names if len(geo_ids) > 1: LOGGER.warning("Returning a different, nested DICT structure because more than one GEO_ID was processed in this function.") return {"error": None, "geo_ids": each_geo_result, "tempdir": working} return {"error":None, "filenames": zipfile_names, "tempdir": working}
# version for def download_geo_processed(geo_id, working, verbose=False): """ uses methylprep/methylcheck to get processed beta values. """ filename_keywords = ['matrix', 'processed', 'signals', 'intensities', 'normalized', 'intensity', 'raw_data', 'mean', 'average', 'beta'] filename_exclude_keywords = ['RNA','Illumina'] if verbose:"Searching GEO NIH for {geo_id}") result_df = search(geo_id,, verbose=verbose) """ note: each df row columns are: ROW = {'title': 'GSE: 'url': 'samples': 'date': 'platform': 'idats': file_name_1, file_size_1, file_link_1 ... } """ # confirm no idats, then # find the links and download if match pattern. found_idats = False downloaded_files = False tbl_txt_files = False for idx, row in result_df.iterrows(): if verbose:"{idx}: {dict(row)}") if row.get('idats') == '1': if verbose: LOGGER.warning(f"Skipping this row because idats exist.") found_idats = True return {'found_idats': found_idats, 'processed_files': downloaded_files, 'tbl_txt_files': tbl_txt_files} for i in range(3): if row.get(f'file_name_{i}') and row.get(f'file_size_{i}') and row.get(f'file_link_{i}'): file_name = row.get(f'file_name_{i}') file_size = row.get(f'file_size_{i}') file_link = row.get(f'file_link_{i}') if (any([keyword.lower() in file_name.lower() for keyword in filename_keywords]) and all([keyword.lower() not in file_name.lower() for keyword in filename_exclude_keywords]) ): if verbose:"Matched {file_name}; size: {file_size}") if 'ftp://' in file_link: raw_filename = file_link series_path = saved_file = file_name.replace(' ','_') ftp = FTP('', timeout=120) # 2 mins ftp.login() ftp.cwd(f"geo/series/{geo_id[:-3]}nnn/{geo_id}") raw_file = open(f"{series_path}/{saved_file}", 'wb') filesize = ftp.size(f"suppl/{file_name}")"FTPing {saved_file} -- {round(filesize/1000000)} MB") # from geo/series/{geo_id[:-3]}nnn/{geo_id} try: #ftp.retrbinary(f"RETR suppl/{raw_filename}", raw_file.write) ftp.retrbinary(f"RETR suppl/{file_name}", raw_file.write) except Exception as e: LOGGER.error("error: {e}, trying {file_link} instead of geo/series/{geo_id[:-3]}nnn/{geo_id}/suppl/{raw_filename}") ftp.retrbinary(f"RETR {file_link}", raw_file.write) raw_file.close() if verbose:"Downloaded {raw_filename}") downloaded_files = True elif 'https://' in file_link: saved_file = file_name.replace(' ','_') if verbose:"Downloading {saved_file} from {file_link}") with requests.get(file_link, stream=True) as r: with open(Path(, saved_file), 'wb') as f: shutil.copyfileobj(r.raw, f) if verbose:"Downloaded {saved_file}") downloaded_files = True else: LOGGER.error(f"Unrecognized protocol in {file_link}") # pandas supports reading zipped files already. """ if '.gz' in PurePath(saved_file).suffixes: gzip.decompress(Path(, saved_file)) if '.zip' in PurePath(saved_file).suffixes: """ this = Path(, saved_file) try: if verbose:"Trying read_geo() on {this}") beta_df = methylcheck.read_geo(this, verbose=verbose) is_df = isinstance(beta_df, pd.DataFrame) if is_df and verbose:"Result is a dataframe.") else: if verbose:"Result is NOT a dataframe.") except Exception as e: import traceback"ERROR: {e}") return if is_df: # save to disk, then load again. don't overwriting pre-existing file of same name df_file = Path(, f"{geo_id}_beta_values.pkl") if df_file.exists(): # could use a while loop here... if verbose:"{df_file} already exists. Trying an alternate name.") alt_name = Path(saved_file).stem.replace(geo_id,'') df_file = Path(, f"{geo_id}_beta_values_from_{alt_name}.pkl") if verbose:"Alt name: {df_file}") if df_file.exists(): # guaranteed to work, but less informative. random_id = ''.join([random.choice('abcdefghjiklmnopqrstuvwxyz1234567890') for i in range(16)]) df_file = Path(, f"{geo_id}_beta_values_{random_id}.pkl") beta_df.to_pickle(df_file) if verbose:"Saved {}") try: if verbose:"reopening {}") beta_df = methylcheck.load(df_file, verbose=verbose, silent=(not verbose)) if isinstance(beta_df, pd.DataFrame) and verbose:"df shape: {beta_df.shape}") except Exception as e: import traceback"ERROR: {e}") else: if verbose:"Skipped {file_name}") if downloaded_files: if verbose:"Getting MINiML meta data") ftp = FTP('', timeout=120) # 2 mins ftp.login() ftp.cwd(f"geo/series/{geo_id[:-3]}nnn/{geo_id}/miniml") # look for additional parts of xml files file_parts = [] for filename,filestats in ftp.mlsd(facts=["size"]): if geo_id in filename:"DEBUG {filename} -- {round(int(filestats['size'])/1000000)} MB") file_parts.append(filename) if len(file_parts) > 1:"The {geo_id}_family.xml miniml file for this series contains multiple ({len(file_parts)}) files.") try: # local_files = convert_miniml(geo_id,, merge=False, download_it=True, extract_controls=False, require_keyword=None, sync_idats=False, verbose=verbose) # HERE -- copy all of these (.xml files) into the S3 output folder for local_file in local_files: if '.tgz' in Path(local_file).suffixes: if verbose == True:"Unpacking: {local_file}") shutil.unpack_archive(local_file) all_files = list(Path('*')) gsm_files = list(Path('GSM*.txt')) non_gsm_files = [file for file in all_files if file not in gsm_files] if verbose == True:"{len(all_files)} local_files, {len(gsm_files)}, non-GSM: {len(non_gsm_files)} | GSMs: {gsm_files}") else: if verbose == True:"local_file skipped: {local_file}") if len(gsm_files) > 0: tbl_txt_files = True"DEBUG: gsm_files detected: {gsm_files} -- to_df()") beta_df = betas_from_tbl_txt_files(gsm_files) # overwrites each time, but last cycle should be a complete list. beta_file = f"{geo_id}_beta_values-tbl-1.pkl" beta_df.to_pickle(beta_file) if verbose:"{beta_file} written, exists: {Path(,beta_file).exists()}") # uploading to s3 will happen in outside function, because it is a .pkl file in dir. if Path(, f"{geo_id}_family.xml").exists(): # check again, as unpack_archive should create this from multi-part _family files # this should create the meta_data.pkl file that gets auto-saved in pipeline. local_files = convert_miniml(geo_id,, merge=False, download_it=False, extract_controls=False, require_keyword=None, sync_idats=False) except Exception as e: LOGGER.error(f"convert_miniml error: {e}") import traceback LOGGER.error(traceback.format_exc()) return {'found_idats': found_idats, 'processed_files': downloaded_files, 'tbl_txt_files': tbl_txt_files} def betas_from_tbl_txt_files(file_list, remove_after=True): """input: list of file paths to be converted into one beta DF and saved, returning saved file path.""" samples = [] # list of dfs to merge, with cols being sample names and probes as index for file in file_list: FILE = Path(file) if FILE.suffix == '.txt' and'GSM') and '-tbl' in sample_id ='-')[0] sample = pd.read_csv(FILE, sep='\t', header=0, names=['IlmnID', sample_id]) sample = sample.set_index('IlmnID') samples.append(sample)"{len(samples)}: {sample_id} -> {len(sample)}") df_ok = False try: df = pd.concat(samples, axis=1, sort=False) df_ok = True except Exception as e: LOGGER.error("Could not concat these samples into a dataframe. The probe names in rows don't line up.") LOGGER.error(e) if df_ok == False: return if remove_after == True: for file in file_list: # deleting source files FILE = Path(file) if FILE.suffix == '.txt' and'GSM') and '-tbl' in FILE.unlink()"Found {df.shape[1]} samples with {df.shape[0]} probes from {len(file_list)} GSM-txt files.") return df