def apply_query_w_genotype_select(c, query, use_header):
"""
Execute a query that contains gt* columns in only in the SELECT.
"""
# construct a mapping of sample names to list indices
sample_to_idx = util.map_samples_to_indicies(c)
(select_cols, all_cols_new, all_cols_orig, gt_col_map) = _split_select(query, sample_to_idx)
query = add_gt_cols_to_query(query.lower())
c.execute(query)
# what are the columns that were actually selected by the user.
all_query_cols = [str(tuple[0]) for tuple in c.description if not tuple[0].startswith("gt")]
if "*" in select_cols:
select_cols.remove("*")
# all_cols_orig.remove("*")
all_cols_new.remove("*")
select_cols += all_query_cols
if use_header:
h = [col for col in all_query_cols] + [col for col in oset(all_cols_orig) - oset(select_cols)]
yield OrderedDict(itertools.izip(h, h))
report_cols = all_query_cols + list(oset(all_cols_new) - oset(select_cols))
for row in c:
gts = compression.unpack_genotype_blob(row["gts"])
gt_types = compression.unpack_genotype_blob(row["gt_types"])
gt_phases = compression.unpack_genotype_blob(row["gt_phases"])
gt_depths = compression.unpack_genotype_blob(row["gt_depths"])
fields = OrderedDict()
for idx, col in enumerate(report_cols):
if col == "*":
continue
if not col.startswith("gt") and not col.startswith("GT"):
fields[col] = row[col]
else:
fields[col] = eval(col.strip())
yield fields
def filter_query(c, query, gt_filter, use_header):
"""
Execute a base SQL query while applying filters on the returned
rows based on filters applied to the genotype-specific columns.
For example:
--gt_filter "(gt_types.1478PC0011 == 1 or gt_types.1478PC0012 == 1)
"""
def correct_genotype_filter(gt_filter, sample_to_idx):
"""
This converts a "raw" genotype filter supplied by the user
to a filter than can be eval()'ed. Specifically, we must
convery a _named_ genotype index to a _numerical_
genotype index so that the appropriate value can be
extracted for the sample from the genotype numpy arrays.
For example, converts:
--gt-filter "(gt_types.1478PC0011 == 1)"
to
(gt_types[11] == 1)
"""
corrected_gt_filter = []
tokens = re.split(r"[\s+]+", gt_filter)
for token in tokens:
if token.find("gt") >= 0 or token.find("GT") >= 0:
corrected = _correct_genotype_col(token, sample_to_idx)
corrected_gt_filter.append(corrected)
else:
corrected_gt_filter.append(token)
return " ".join(corrected_gt_filter)
# construct a mapping of sample names to list indices
sample_to_idx = util.map_samples_to_indicies(c)
gt_filter = correct_genotype_filter(gt_filter, sample_to_idx)
(select_cols, all_cols_new, all_cols_orig, gt_col_map) = _split_select(query, sample_to_idx)
query = add_gt_cols_to_query(query.lower())
c.execute(query)
# what are the columns that were actually selected by the user.
all_query_cols = [str(tuple[0]) for tuple in c.description if not tuple[0].startswith("gt")]
if "*" in select_cols:
select_cols.remove("*")
all_cols_orig.remove("*")
all_cols_new.remove("*")
select_cols += all_query_cols
if use_header:
h = [col for col in all_query_cols] + [col for col in oset(all_cols_orig) - oset(select_cols)]
yield OrderedDict(itertools.izip(h, h))
report_cols = all_query_cols + list(oset(all_cols_new) - oset(select_cols))
for row in c:
gts = compression.unpack_genotype_blob(row["gts"])
gt_types = compression.unpack_genotype_blob(row["gt_types"])
gt_phases = compression.unpack_genotype_blob(row["gt_phases"])
gt_depths = compression.unpack_genotype_blob(row["gt_depths"])
if not eval(gt_filter):
continue
fields = OrderedDict()
for idx, col in enumerate(report_cols):
if col == "*":
continue
if not col.startswith("gt") and not col.startswith("GT"):
fields[col] = row[col]
else:
fields[col] = eval(col.strip())
yield fields