def process(line_sources):
"""
@param line_sources: sources of line iterables
"""
# get the headers and data from all of the input sources
header_data_pairs = [hud.decode(lines) for lines in line_sources]
header_list, data_list = zip(*header_data_pairs)
# get the header to index map for each input source
h_to_i_list = [Util.inverse_map(x) for x in header_list]
# get the intersection of headers in all lists
header_sets = [set(x) for x in header_list]
header_intersection = set.intersection(*header_sets)
# get the ordered list of all headers
unique_headers = list(
iterutils.unique_everseen(itertools.chain.from_iterable(header_list)))
# get the ordered list of headers present in every input source
out_headers = [h for h in unique_headers if h in header_intersection]
out_data = []
for h in out_headers:
row = []
for data, h_to_i in zip(data_list, h_to_i_list):
if h in h_to_i:
row.extend(data[h_to_i[h]])
out_data.append(row)
return hud.encode(out_headers, out_data) + '\n'
def get_response_content(fs):
lines = Util.get_stripped_lines(fs.data.splitlines())
if len(lines) < 2:
raise ValueError('expected at least two lines')
rows = [line.split() for line in lines]
headers = rows[0]
data_rows = [[int(x) for x in row] for row in rows[1:]]
for row in data_rows:
for x in row:
if x not in (-1, 0, 1, 2):
msg = 'invalid diploid data value: %d' % x
raise ValueError(msg)
# impute the missing data
if fs.use_mode:
imputed_data_rows = []
for row in data_rows:
non_missing_row = [x for x in row if x != -1]
if not non_missing_row:
msg = 'a variable has missing data for each individual'
raise ValueError(msg)
counts = [0]*3
for x in non_missing_row:
counts[x] += 1
imputed_value = counts.index(max(counts))
imputed_row = [imputed_value if x == -1 else x for x in row]
imputed_data_rows.append(imputed_row)
data_rows = imputed_data_rows
# return the hud table
return hud.encode(headers, zip(*data_rows))
def process(line_sources):
"""
@param line_sources: sources of line iterables
"""
# get the headers and data from all of the input sources
header_data_pairs = [hud.decode(lines) for lines in line_sources]
header_list, data_list = zip(*header_data_pairs)
# get the header to index map for each input source
h_to_i_list = [Util.inverse_map(x) for x in header_list]
# get the intersection of headers in all lists
header_sets = [set(x) for x in header_list]
header_intersection = set.intersection(*header_sets)
# get the ordered list of all headers
unique_headers = list(iterutils.unique_everseen(
itertools.chain.from_iterable(header_list)))
# get the ordered list of headers present in every input source
out_headers = [h for h in unique_headers if h in header_intersection]
out_data = []
for h in out_headers:
row = []
for data, h_to_i in zip(data_list, h_to_i_list):
if h in h_to_i:
row.extend(data[h_to_i[h]])
out_data.append(row)
return hud.encode(out_headers, out_data) + '\n'
def process(fs, raw_lines):
headers, sequences = Phylip.decode(raw_lines)
binary_rows = Carbone.get_binary_rows(sequences)
if fs.hud:
return hud.encode(headers, binary_rows) + '\n'
elif fs.phy:
binary_seqs = [''.join(str(x) for x in row) for row in binary_rows]
return Phylip.encode(headers, binary_seqs) + '\n'
def get_response_content(fs):
# get the headers and data from all of the input sources
headers, sequences = hud.decode(fs.hud.splitlines())
h_to_s = dict((h, s) for h, s in zip(headers, sequences))
headers_out = []
sequences_out = []
for p, hs in process_headers(headers):
headers_out.append(p)
data = np.vstack(h_to_s[h] for h in hs).sum(axis=0)
if fs.combine_exist:
data = np.minimum(1, data)
sequences_out.append(data)
if fs.remove_invariant:
sequences_out = remove_invariant_columns(sequences_out)
return hud.encode(headers_out, sequences_out) + '\n'
def get_response_content(fs):
# get the headers and data from all of the input sources
headers, sequences = hud.decode(fs.hud.splitlines())
h_to_s = dict((h, s) for h, s in zip(headers, sequences))
headers_out = []
sequences_out = []
for p, hs in process_headers(headers):
headers_out.append(p)
data = np.vstack(h_to_s[h] for h in hs).sum(axis=0)
if fs.combine_exist:
data = np.minimum(1, data)
sequences_out.append(data)
if fs.remove_invariant:
sequences_out = remove_invariant_columns(sequences_out)
return hud.encode(headers_out, sequences_out) + '\n'
def process(raw_lines):
headers, binary_rows = read_microsatellite_lines(raw_lines)
return hud.encode(headers, binary_rows) + '\n'
def process(raw_lines):
headers, binary_rows = read_microsatellite_lines(raw_lines)
return hud.encode(headers, binary_rows) + '\n'
def get_response_content(fs):
headers, binary_rows = read_microsatellite_lines(fs.data.splitlines())
return hud.encode(headers, binary_rows) + '\n'
