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[datasets] delete packed data in hapmap dataset

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Max Zwiessele 2014-05-20 14:46:29 +01:00
parent 99699e9e02
commit 533e5fb744
1 changed files with 11 additions and 5 deletions
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16
GPy/util/datasets.py
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@ -687,14 +687,20 @@ def hapmap3(data_set='hapmap3'):
import bz2
except ImportError as i:
raise i, "Need pandas for hapmap dataset, make sure to install pandas (http://pandas.pydata.org/) before loading the hapmap dataset"
if not data_available(data_set):
download_data(data_set)
dirpath = os.path.join(data_path,'hapmap3')
hapmap_file_name = 'hapmap3_r2_b36_fwd.consensus.qc.poly'
unpacked_files = [os.path.join(dirpath, hapmap_file_name+ending) for ending in ['.ped', '.map']]
unpacked_files_exist = reduce(lambda a, b:a and b, map(os.path.exists, unpacked_files))
if not unpacked_files_exist and not data_available(data_set):
download_data(data_set)
preprocessed_data_paths = [os.path.join(dirpath,hapmap_file_name + file_name) for file_name in \
['.snps.pickle',
'.info.pickle',
'.nan.pickle']]
if not reduce(lambda a,b: a and b, map(os.path.exists, preprocessed_data_paths)):
if not overide_manual_authorize and not prompt_user("Preprocessing requires ~25GB "
"of memory and can take a (very) long time, continue? [Y/n]"):
@ -708,8 +714,7 @@ def hapmap3(data_set='hapmap3'):
perc="="*int(20.*progress/100.))
stdout.write(status); stdout.flush()
return status
unpacked_files = [os.path.join(dirpath, hapmap_file_name+ending) for ending in ['.ped', '.map']]
if not reduce(lambda a,b: a and b, map(os.path.exists, unpacked_files)):
if not unpacked_files_exist:
status=write_status('unpacking...', 0, '')
curr = 0
for newfilepath in unpacked_files:
@ -726,6 +731,7 @@ def hapmap3(data_set='hapmap3'):
status=write_status('unpacking...', curr+12.*file_processed/(file_size), status)
curr += 12
status=write_status('unpacking...', curr, status)
os.remove(filepath)
status=write_status('reading .ped...', 25, status)
# Preprocess data:
snpstrnp = np.loadtxt(unpacked_files[0], dtype=str)
@ -796,7 +802,7 @@ def hapmap3(data_set='hapmap3'):
def singlecell(data_set='singlecell'):
if not data_available(data_set):
download_data(data_set)
from pandas import read_csv
dirpath = os.path.join(data_path, data_set)
filename = os.path.join(dirpath, 'singlecell.csv')