def copy_attributes(mapfile, newdir, filenames, key_lstrip=None, key_rstrip=None):
if os.path.exists(newdir):
shutil.rmtree(newdir)
os.makedirs(newdir)
metadata = pd.read_csv(mapfile, sep='\t')
filenames = pd.DataFrame(filenames, columns=['filename'])
filenames['dataset_key'] = filenames['filename'].apply(lambda x: strip_key(x, key_lstrip, key_rstrip))
joined = pd.merge(metadata, filenames, on='dataset_key', how='inner')
assert len(joined) == len(metadata) == len(filenames)
for index, row in joined.iterrows():
id = row['id']
filename = row['filename']
link_name = os.path.join(newdir, '%s.txt' % (id))
#os.symlink(filename, link_name)
shutil.copy(filename, link_name)
def main(edit_file, metadata_in, metadata_out, key_rstrip=None):
# load the table of edits
edits = pd.read_csv(edit_file, sep='\t', names=['filename', 'var', 'value'], encoding='UTF8')
# load metadata
metadata = pd.read_csv(metadata_in, sep='\t', encoding='UTF8', dtype=str)
# metadata as the var's in columns. if any of the var's we are editing don't already
# exist, add them as new cols initialized to missing
edit_vars = pd.Series(edits['var'].unique())
new_vars = edit_vars[~edit_vars.isin(metadata.columns)]
for new_var in new_vars:
metadata[new_var] = np.NAN
# cfg file name should match the dataset key in the metadata file
edits['dataset_key'] = edits['filename'].apply(lambda x: strip_key(x, None, key_rstrip))
# log all edit files we don't have in the metadata table
missing_files = edits[~edits['dataset_key'].isin(metadata['dataset_key'])]['dataset_key']
if len(missing_files) > 0:
print("metadata edits for these missing files will be skipped: ",
' '.join(list(missing_files)))
# apply remaining edits. could probably vectorize this into some kind
# of join, but its not much data, and simpler to think about in a loop,
# at least for me :)
edits = edits[edits['dataset_key'].isin(metadata['dataset_key'])]
for index, row in edits.iterrows():
filename, var, value = row['dataset_key'], row['var'], row['value']
metadata.loc[metadata['dataset_key'] == filename, var] = value
# write new edited metadata file
metadata.to_csv(metadata_out, sep='\t', header=True, index=True, encoding='UTF8')
