def generate_output_from_results(results, output, alphabet_info):
"""
Given an iterable of dictionaries, each representing the results of mapping
a single read, output a mapped-read file.
This version outputs to the V8 'chunk' file format (actually containing mapped reads, not chunks)
param: results : an iterable of read dictionaries
(with mappings)
param: output : output filename
param: alphabet_info : taiyaki.alphabet.AlphabetInfo instance
"""
progress = helpers.Progress()
err_types = defaultdict(int)
with mapped_signal_files.HDF5Writer(output, alphabet_info) as f:
for resultdict, mesg in results:
# filter out error messages for reporting later
if resultdict is None:
err_types[mesg] += 1
else:
progress.step()
f.write_read(resultdict)
sys.stderr.write('\n')
# report errors at the end to avoid spamming stderr
if len(err_types) > 0:
for err_str, n_errs in err_types.items():
sys.stderr.write(
('* {} reads failed to produce remapping results ' +
'due to: {}\n').format(n_errs, err_str))
def test_check_HDF5_mapped_read_file(self):
"""Check that constructing a read object which doesn't conform
leads to errors.
"""
print("Creating flawed Read object from test data")
read_dict = construct_mapped_read()
read_dict['Reference'] = "I'm not a numpy array!" # Wrong type!
read_object = mapped_signal_files.Read(read_dict)
print("Checking contents")
check_text = read_object.check()
print("Check result on read object: should fail")
print(check_text)
self.assertNotEqual(check_text, "pass")
print("Writing to file")
alphabet_info = alphabet.AlphabetInfo(DEFAULT_ALPHABET, DEFAULT_ALPHABET)
with mapped_signal_files.HDF5Writer(self.testfilepath, alphabet_info) as f:
f.write_read(read_object)
print("Current dir = ", os.getcwd())
print("File written to ", self.testfilepath)
print("\nOpening file for reading")
with mapped_signal_files.HDF5Reader(self.testfilepath) as f:
ids = f.get_read_ids()
print("Read ids=", ids[0])
print("Version number = ", f.version)
self.assertEqual(ids[0], read_dict['read_id'])
file_test_report = f.check()
print("Test report (should fail):", file_test_report)
self.assertNotEqual(file_test_report, "pass")
def test_HDF5_mapped_read_file(self):
"""Test that we can save a mapped read file, open it again and
use some methods to get data from it. Plot a picture for diagnostics.
"""
print("Creating Read object from test data")
read_dict = construct_mapped_read()
read_object = mapped_signal_files.Read(read_dict)
print("Checking contents")
check_text = read_object.check()
print("Check result on read object:")
print(check_text)
self.assertEqual(check_text, "pass")
print("Writing to file")
alphabet_info = alphabet.AlphabetInfo(DEFAULT_ALPHABET, DEFAULT_ALPHABET)
with mapped_signal_files.HDF5Writer(self.testfilepath, alphabet_info) as f:
f.write_read(read_object)
print("Current dir = ", os.getcwd())
print("File written to ", self.testfilepath)
print("\nOpening file for reading")
with mapped_signal_files.HDF5Reader(self.testfilepath) as f:
ids = f.get_read_ids()
print("Read ids=", ids[0])
print("Version number = ", f.version)
self.assertEqual(ids[0], read_dict['read_id'])
file_test_report = f.check()
print("Test report:", file_test_report)
self.assertEqual(file_test_report, "pass")
read_list = f.get_multiple_reads("all")
recovered_read = read_list[0]
reflen = len(recovered_read['Reference'])
siglen = len(recovered_read['Dacs'])
# Get a chunk - note that chunkstart is relative to the start of the mapped
# region, not relative to the start of the signal
chunklen, chunkstart = 5, 3
chunkdict = recovered_read.get_chunk_with_sample_length(chunklen, chunkstart)
# Check that the extracted chunk is the right length
self.assertEqual(len(chunkdict['current']), chunklen)
# Check that the mapping data agrees with what we put in
self.assertTrue(np.all(recovered_read['Ref_to_signal']==read_dict['Ref_to_signal']))
# Plot a picture showing ref_to_sig from the read object, def setup():
# and the result of searches to find the inverse
if False:
plt.figure()
plt.xlabel('Signal coord')
plt.ylabel('Ref coord')
ix = np.array([0, -1])
plt.scatter(chunkdict['current'][ix], chunkdict['sequence'][ix],
s=50, label='chunk limits', marker='s', color='black')
plt.scatter(recovered_read['Ref_to_signal'], np.arange(reflen + 1), label='reftosig (source data)',
color='none', edgecolor='blue', s=60)
siglocs = np.arange(siglen, dtype=np.int32)
sigtoref_fromsearch = recovered_read.get_reference_locations(siglocs)
plt.scatter(siglocs, sigtoref_fromsearch, label='from search', color='red', marker='x', s=50)
plt.legend()
plt.grid()
plt.savefig(self.plotfilepath)
print("Saved plot to", self.plotfilepath)