def create_zarr_count_assay(z: zarr.Group,
assay_name: str,
chunk_size: Tuple[int, int],
n_cells: int,
feat_ids: Union[np.ndarray, List[str]],
feat_names: Union[np.ndarray, List[str]],
dtype: str = 'uint32') -> zarr.hierarchy:
"""
Creates and returns a Zarr array with name 'counts'.
Args:
z (zarr.Group):
assay_name (str):
chunk_size (Tuple[int, int]):
n_cells (int):
feat_ids (Union[np.ndarray, List[str]]):
feat_names (Union[np.ndarray, List[str]]):
dtype (str = 'uint32'):
Returns:
A Zarr array.
"""
g = z.create_group(assay_name, overwrite=True)
g.attrs['is_assay'] = True
g.attrs['misc'] = {}
create_zarr_obj_array(g, 'featureData/ids', feat_ids)
create_zarr_obj_array(g, 'featureData/names', feat_names)
create_zarr_obj_array(g, 'featureData/I',
[True for _ in range(len(feat_ids))], 'bool')
return create_zarr_dataset(g,
'counts',
chunk_size,
dtype, (n_cells, len(feat_ids)),
overwrite=True)
def write_csr(self, group: zarr.Group, name: str,
matrix: csr_matrix) -> None:
sub_group = group.create_group(name, overwrite=True)
sub_group.attrs.update(data_type='csr_matrix', shape=matrix.shape)
sub_group.create_dataset('data',
data=matrix.data,
shape=matrix.data.shape,
chunks=calc_chunk(matrix.data.shape),
dtype=matrix.data.dtype,
compressor=COMPRESSOR,
overwrite=True)
sub_group.create_dataset('indices',
data=matrix.indices,
shape=matrix.indices.shape,
chunks=calc_chunk(matrix.indices.shape),
dtype=matrix.indices.dtype,
compressor=COMPRESSOR,
overwrite=True)
sub_group.create_dataset('indptr',
data=matrix.indptr,
shape=matrix.indptr.shape,
chunks=calc_chunk(matrix.indptr.shape),
dtype=matrix.indptr.dtype,
compressor=COMPRESSOR,
overwrite=True)
def write_dataframe(self, group: zarr.Group, name: str,
df: Union[pd.DataFrame, np.recarray]) -> None:
data_type = 'data_frame' if isinstance(
df, pd.DataFrame) else 'record_array'
sub_group = group.create_group(name, overwrite=True)
attrs_dict = {'data_type': data_type}
cols = list(df.columns if data_type ==
'data_frame' else df.dtype.names)
attrs_dict['columns'] = cols
if data_type == 'data_frame':
attrs_dict[
'index_name'] = df.index.name if df.index.name is not None else 'index'
sub_group.create_group(
'_categories', overwrite=True
) # create a group to store category keys for catigorical columns
self.write_series(sub_group, '_index', df.index.values, data_type)
for col in cols:
self.write_series(
sub_group, col,
(df[col].values if data_type == 'data_frame' else df[col]),
data_type)
sub_group.attrs.update(**attrs_dict)
def conv_chrom(fname: str, block_size: int,
root: Group, chrom: int) -> None:
num_positions = 0
with open(fname) as tfam:
for line in tfam: # redo this with generators
tokens = line.rstrip().split(' ')
l_chrom = int(tokens[0])
if l_chrom < chrom:
continue
elif l_chrom > chrom:
break
num_positions += 1
tfam = open(fname)
chrom_group = root.create_group(f'chromosome-{chrom}')
all_calls = chrom_group.zeros('calls', shape=(num_positions, NUM_SAMPLES),
dtype='B')
block = []
all_positions = []
all_alleles = []
current_position = 0
for line in tfam: # redo this with generators
tokens = line.rstrip().split(' ')
l_chrom = int(tokens[0])
if l_chrom < chrom:
continue
elif l_chrom > chrom:
break
position = int(tokens[3])
all_positions.append(position)
calls = tokens[4:]
alleles = ''.join(set(calls[4:]) - set(['0']))
if len(alleles) == 1:
alleles += alleles
all_alleles.append(alleles)
sample_calls = np.empty(shape=NUM_SAMPLES, dtype='B')
for sample_position, sample in enumerate(range(NUM_SAMPLES)):
a1, a2 = calls[2 * sample: 2 * sample + 2]
try:
sample_calls[sample_position] = encode_alleles(a1, a2, alleles)
except Exception:
print(chrom, current_position, sample_position)
raise
block.append(sample_calls)
current_position += 1
if current_position % block_size == 0:
all_calls[current_position - block_size:current_position, :] = block
block = []
if len(block) > 0:
all_calls[- len(block):, :] = block
chrom_group.array('positions', all_positions)
chrom_group.array('alleles', all_alleles)
