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Optimize gctx subsetting order for memory usage. #72

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Jun 17, 2021
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Optimize gctx subsetting order for memory usage. #72

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24 changes: 17 additions & 7 deletions cmapPy/pandasGEXpress/parse_gctx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -369,16 +369,26 @@ def parse_data_df(data_dset, ridx, cidx, row_meta, col_meta):
-row_meta (pandas DataFrame): the parsed in row metadata
-col_meta (pandas DataFrame): the parsed in col metadata
"""
if len(ridx) == len(row_meta.index) and len(cidx) == len(col_meta.index): # no subset
total_rows = len(row_meta.index)
total_cols = len(col_meta.index)
if len(ridx) == total_rows and len(cidx) == total_cols: # no subset
data_array = np.empty(data_dset.shape, dtype=np.float32)
data_dset.read_direct(data_array)
data_array = data_array.transpose()
elif len(ridx) <= len(cidx):
first_subset = data_dset[:, ridx].astype(np.float32)
data_array = first_subset[cidx, :].transpose()
elif len(cidx) < len(ridx):
first_subset = data_dset[cidx, :].astype(np.float32)
data_array = first_subset[:, ridx].transpose()
else:
# We can only subset on a single dimension at a time with h5py.
# For the first dimension to use, pick the one that minimizes
# the size of the intermediate array.
row_first_count = total_cols * len(ridx)
col_first_count = total_rows * len(cidx)

if row_first_count < col_first_count:
first_subset = data_dset[:, ridx].astype(np.float32)
data_array = first_subset[cidx, :].transpose()
else:
first_subset = data_dset[cidx, :].astype(np.float32)
data_array = first_subset[:, ridx].transpose()

# make DataFrame instance
data_df = pd.DataFrame(data_array, index=row_meta.index[ridx], columns=col_meta.index[cidx])
return data_df
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