def test_seqinfo(self):
with tempfile.TemporaryFile() as tf, \
open(config.data_path('minimal_taxonomy.csv')) as taxtable_fp, \
open(config.data_path('minimal_add_taxonomy.csv')) as extra_nodes_fp:
class _Args(object):
extra_nodes_csv = extra_nodes_fp
taxtable = taxtable_fp
out_file = tf
verbosity = 0
self.assertFalse(add_to_taxtable.action(_Args()))
# No output check at present
self.assertTrue(tf.tell() > 0)
def test_seqinfo(self):
with tempfile.TemporaryFile() as tf, \
open(config.data_path('minimal_taxonomy.csv')) as taxtable_fp, \
open(config.data_path('minimal_add_taxonomy.csv')) as extra_nodes_fp:
class _Args(object):
extra_nodes_csv = extra_nodes_fp
taxtable = taxtable_fp
out_file = tf
verbosity = 0
self.assertFalse(add_to_taxtable.action(_Args()))
# No output check at present
self.assertTrue(tf.tell() > 0)
def test_rollforward(self):
with config.tempdir() as scratch:
rpkg = os.path.join(scratch, 'tostrip.refpkg')
shutil.copytree(config.data_path('lactobacillus2-0.2.refpkg'),
rpkg)
r = refpkg.Refpkg(rpkg, create=False)
original_contents = copy.deepcopy(r.contents)
r.update_metadata('boris', 'hilda')
r.update_metadata('meep', 'natasha')
updated_contents = copy.deepcopy(r.contents)
r.rollback()
r.rollback()
class _Args(object):
refpkg = rpkg
def __init__(self, n):
self.n = n
self.assertEqual(rollforward.action(_Args(3)), 1)
r._sync_from_disk()
self.assertEqual(r.contents['metadata'],
original_contents['metadata'])
self.assertEqual(rollforward.action(_Args(2)), 0)
r._sync_from_disk()
self.assertEqual(r.contents['metadata'],
updated_contents['metadata'])
self.assertEqual(r.contents['rollforward'], None)
self.assertNotEqual(r.contents['rollback'], None)
def test_rollforward(self):
with config.tempdir() as scratch:
rpkg = os.path.join(scratch, 'tostrip.refpkg')
shutil.copytree(config.data_path('lactobacillus2-0.2.refpkg'), rpkg)
r = refpkg.Refpkg(rpkg, create=False)
original_contents = copy.deepcopy(r.contents)
r.update_metadata('boris', 'hilda')
r.update_metadata('meep', 'natasha')
updated_contents = copy.deepcopy(r.contents)
r.rollback()
r.rollback()
class _Args(object):
refpkg = rpkg
def __init__(self, n):
self.n = n
self.assertEqual(rollforward.action(_Args(3)), 1)
r._sync_from_disk()
self.assertEqual(r.contents['metadata'], original_contents['metadata'])
self.assertEqual(rollforward.action(_Args(2)), 0)
r._sync_from_disk()
self.assertEqual(r.contents['metadata'], updated_contents['metadata'])
self.assertEqual(r.contents['rollforward'], None)
self.assertNotEqual(r.contents['rollback'], None)
def test_seqinfo(self):
with tempfile.TemporaryFile() as tf, \
open(config.data_path('simple_seqinfo.csv')) as ifp:
class _Args(object):
database_file = config.ncbi_master_db
taxids = None
taxnames = None
seq_info = ifp
out_file = tf
verbosity = 0
self.assertEqual(taxtable.action(_Args()), 0)
# No output check at present
self.assertTrue(tf.tell() > 0)
def test_action(self):
with config.tempdir() as scratch:
pkg_path = os.path.join(scratch, 'test.refpkg')
r = refpkg.Refpkg(pkg_path)
test_file = config.data_path('bv_refdata.csv')
class _Args(object):
refpkg=pkg_path
changes = ['meep='+test_file, 'hilda='+test_file]
metadata = False
update.action(_Args())
r._sync_from_disk()
self.assertEqual(r.contents['files']['meep'], 'bv_refdata.csv')
self.assertEqual(r.contents['files']['hilda'], 'bv_refdata.csv1')
def test_strip(self):
with config.tempdir() as scratch:
rpkg = os.path.join(scratch, 'tostrip.refpkg')
shutil.copytree(config.data_path('lactobacillus2-0.2.refpkg'), rpkg)
r = refpkg.Refpkg(rpkg, create=False)
r.update_metadata('boris', 'hilda')
r.update_metadata('meep', 'natasha')
class _Args(object):
refpkg = rpkg
strip.action(_Args())
r._sync_from_disk()
self.assertEqual(r.contents['rollback'], None)
self.assertEqual(r.contents['rollforward'], None)
def test_seqinfo(self):
with tempfile.TemporaryFile() as tf, \
open(config.data_path('simple_seqinfo.csv')) as ifp:
class _Args(object):
database_file = config.ncbi_master_db
taxids = None
taxnames = None
seq_info = ifp
out_file = tf
verbosity = 0
full = False
self.assertEqual(taxtable.action(_Args()), 0)
# No output check at present
self.assertTrue(tf.tell() > 0)
def test_strip(self):
with config.tempdir() as scratch:
rpkg = os.path.join(scratch, 'tostrip.refpkg')
shutil.copytree(config.data_path('lactobacillus2-0.2.refpkg'),
rpkg)
r = refpkg.Refpkg(rpkg, create=False)
r.update_metadata('boris', 'hilda')
r.update_metadata('meep', 'natasha')
class _Args(object):
refpkg = rpkg
strip.action(_Args())
r._sync_from_disk()
self.assertEqual(r.contents['rollback'], None)
self.assertEqual(r.contents['rollforward'], None)
def load(self, filename="hmm.json"):
filename = data_path(filename)
fr = open(filename, 'r')
txt = fr.read()
model = json.loads(txt)
# build emit mat
mat = model['emit_mat']
data = []
for state in STATES:
for observe in mat[state]:
data.append((state, observe, mat[state][observe]))
self.emit_mat = self.context.parallelize(data)
# build others
self.trans_mat = model['trans_mat']
self.init_vec = model['init_vec']
self.state_count = model['state_count']
def test_seqinfo(self):
with tempfile.TemporaryFile() as tf, \
open(config.data_path('simple_seqinfo.csv')) as ifp:
class _Args(object):
url = 'sqlite:///' + config.ncbi_master_db
schema = None
valid = False
ranked = False
tax_ids = None
taxnames = None
seq_info = ifp
out = tf
verbosity = 0
clade_ids = None
taxtable = None
self.assertIsNone(taxtable.action(_Args()))
# No output check at present
self.assertTrue(tf.tell() > 0)
def test_action(self):
with config.tempdir() as scratch:
pkg_path = os.path.join(scratch, 'test.refpkg')
r = refpkg.Refpkg(pkg_path, create=True)
test_file = config.data_path('bv_refdata.csv')
class _Args(object):
refpkg=pkg_path
changes = ['meep='+test_file, 'hilda='+test_file]
metadata = False
update.action(_Args())
r._sync_from_disk()
self.assertEqual(r.contents['files']['meep'], 'bv_refdata.csv')
# Second file should have been assigned a non-clashing name
h = r.contents['files']['hilda']
self.assertNotEqual(h, 'bv_refdata.csv')
self.assertTrue(h.startswith('bv_refdata'))
self.assertTrue(h.endswith('.csv'))
self.assertTrue(os.path.exists(r.resource_path('hilda')))
def test_action(self):
with config.tempdir() as scratch:
pkg_path = os.path.join(scratch, 'test.refpkg')
r = refpkg.Refpkg(pkg_path, create=True)
test_file = config.data_path('bv_refdata.csv')
self.args.refpkg = pkg_path
self.args.changes = ['meep=' + test_file, 'hilda=' + test_file]
update.action(self.args)
r._sync_from_disk()
self.assertEqual(r.contents['files']['meep'], 'bv_refdata.csv')
# Second file should have been assigned a non-clashing name
h = r.contents['files']['hilda']
self.assertNotEqual(h, 'bv_refdata.csv')
self.assertTrue(h.startswith('bv_refdata'))
self.assertTrue(h.endswith('.csv'))
self.assertTrue(os.path.exists(r.resource_path('hilda')))
def setUp(self):
self.t1 = data_path('simple_taxtable.csv')
self.t2 = data_path('taxids1.taxtable')
self.parser = argparse.ArgumentParser()
merge_taxtables.build_parser(self.parser)
self.outfile = os.path.join(self.mkoutdir(), 'taxtable.csv')
class _Args(object):
refpkg = config.data_path('lactobacillus2-0.2.refpkg')
output = translateFile(TEMP_FILENAME)
os.remove(TEMP_FILENAME)
return output
def translateFile(input_filename, output_filename=None):
"""
Translates a text file of sentences into a dictionary.
"""
out = subprocess.check_output(['phonetisaurus-g2p', '--model=%s' % (G014B2B_FST),
'--input=%s' % (input_filename), '--words', '--isfile'])
out = parseOutput(out)
if output_filename:
out = '\n'.join(out)
f = open(output_filename, "wb")
f.write(out)
f.close()
return None
return out
if __name__ == "__main__":
translateFile(os.path.expanduser(data_path("/sentences.txt")),
os.path.expanduser(data_path("/dictionary.dic")))
class TestUpdateTaxids(config.TestBase):
def main(self, arguments):
taxit.main(['update_taxids'] + [str(a) for a in arguments])
log_info = 'taxit update_taxids '
thisdata_path = config.data_path('update_taxids', 'TestUpdateTaxids')
seq_info = config.data_path(thisdata_path, 'seq_info.csv')
small_taxonomy_db = 'sqlite:///' + config.data_path('small_taxonomy.db')
def test01(self):
"""
Minimal inputs
"""
args = [self.seq_info, self.small_taxonomy_db]
log.info(self.log_info + ' '.join(map(str, args)))
# ValueError: Unknown or missing tax_ids present
self.assertRaises(ValueError, self.main, args)
def test02(self):
"""
--ignore-unknowns
"""
this_test = sys._getframe().f_code.co_name
thisdata_path = self.thisdata_path
ref = os.path.join(thisdata_path, this_test, 'update.csv')
outdir = self.mkoutdir()
out = os.path.join(outdir, 'update.csv')
args = [
'--ignore-unknowns', '--out', out, self.seq_info,
self.small_taxonomy_db
]
log.info(self.log_info + ' '.join(map(str, args)))
self.main(args)
self.assertTrue(filecmp.cmp(out, ref))
def test03(self):
"""
--unknowns unknowns.csv
"""
this_test = sys._getframe().f_code.co_name
thisdata_path = self.thisdata_path
ref_info = os.path.join(thisdata_path, this_test, 'update.csv')
ref_unknowns = os.path.join(thisdata_path, this_test, 'unknowns.csv')
outdir = self.mkoutdir()
out_info = os.path.join(outdir, 'update.csv')
out_unknowns = os.path.join(outdir, 'unknowns.csv')
args = [
'--unknowns', out_unknowns, '--out', out_info, self.seq_info,
self.small_taxonomy_db
]
log.info(self.log_info + ' '.join(map(str, args)))
self.main(args)
self.assertTrue(filecmp.cmp(out_info, ref_info))
self.assertTrue(filecmp.cmp(out_unknowns, ref_unknowns))
def test04(self):
"""
--ignore-unknowns --name-columns tax_name
"""
this_test = sys._getframe().f_code.co_name
thisdata_path = self.thisdata_path
ref_info = os.path.join(thisdata_path, this_test, 'update.csv')
outdir = self.mkoutdir()
out_info = os.path.join(outdir, 'update.csv')
args = [
'--ignore-unknowns', '--name-column', 'tax_name', '--out',
out_info, self.seq_info, self.small_taxonomy_db
]
log.info(self.log_info + ' '.join(map(str, args)))
self.main(args)
self.assertTrue(filecmp.cmp(out_info, ref_info))
def load_data(self, filename):
self.data = open(data_path(filename), 'r', encoding="utf-8")
lab_files.sort()
return zip(img_files, lab_files)
if __name__ == '__main__':
if __package__ is None:
import sys
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
else:
__package__ = ''
import config
datagen = CamVidGenerator(config.data_path())
batch_size = 3
target_size = (360, 480)
for imgBatch, labelBatch in datagen.flow('train', batch_size, target_size):
print(len(imgBatch))
img = imgBatch[0]
label = labelBatch[0]
colored_class_image = datagen.one_hot_to_bgr(label, target_size,
datagen.n_classes,
datagen.labels)
cv2.imshow("img", img)
def read(self, filename):
filepath = data_path(filename)
self.raw_data = self.context.textFile(filepath)
def load(self, filename="words.txt"):
filepath = data_path(filename)
data = self.context.textFile(filepath)
self.word_dict = data.map(lambda x: tuple(x.split(' ')))\
.map( lambda x: (x[0], int(x[1])) ).toDF(['word', 'num'])
def _stagepath(self):
return data_path() + self._stage + "/"
return filenames
if __name__ == '__main__':
if __package__ is None:
import sys
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
else:
__package__ = ''
import cityscapes_labels
import config
datagen = GTAGenerator(dataset_path=config.data_path())
batch_size = 1
target_size = 256, 512
i = 3
for img, label in datagen.flow('val', batch_size, target_size):
print(i, img.shape, label.shape)
colored_class_image = datagen.one_hot_to_bgr(label[0], target_size,
datagen.n_classes,
datagen.labels)
cv2.imshow("normalized", img[0])
cv2.imshow("gt", colored_class_image)
cv2.waitKey()
TODO: rewrite routines to avoid writing files to disk
TODO: use StringIO() instead of temp files
TODO: pass around file-like objects
"""
import os
import json
from wave import open as open_audio
import audioop
import pyaudio
from config import HM_DIR, data_path
import alteration
THRESHOLD_MULTIPLIER = 1.8
PASSIVE_AUDIO_FILE = data_path("passive.wav")
ACTIVE_AUDIO_FILE = data_path("active.wav")
RATE = 16000
CHUNK = 1024
# number of seconds to allow to establish threshold
THRESHOLD_TIME = 1
# quirky bug where first import doesn't work
try:
import pocketsphinx as ps
except:
import pocketsphinx as ps
help='Input file',
default='/home/mlyko/data/stuttgart_00.mp4')
parser.add_argument('-o', '--output', help='Output file', default=None)
args = parser.parse_args()
return args
args = parse_arguments()
size = config.target_size()
videoEvaluator = VideoEvaluator()
videoEvaluator.select_device(args.gid)
datagen = CityscapesFlowGenerator(config.data_path())
videoEvaluator.load_model({
'model':
ICNet(config.target_size(), datagen.n_classes, for_training=False),
'weights':
config.weights_path() +
'city/rel/ICNet/1612:37e200.b8.lr-0.001000._dec-0.000000.of-farn.h5',
'warp':
False
})
videoEvaluator.load_model({
'model':
ICNetWarp0(config.target_size(), datagen.n_classes,
for_training=False),
return output
def translateFile(input_filename, output_filename=None):
"""
Translates a text file of sentences into a dictionary.
"""
out = subprocess.check_output([
'phonetisaurus-g2p',
'--model=%s' % (G014B2B_FST),
'--input=%s' % (input_filename), '--words', '--isfile'
])
out = parseOutput(out)
if output_filename:
out = '\n'.join(out)
f = open(output_filename, "wb")
f.write(out)
f.close()
return None
return out
if __name__ == "__main__":
translateFile(os.path.expanduser(data_path("/sentences.txt")),
os.path.expanduser(data_path("/dictionary.dic")))
epochs, self.batch_size, losswise_params['optimizer']['lr'],
losswise_params['optimizer']['decay'], self._optical_flow_type)
self.prepare_callbacks(run_name, epochs)
self.model.k.fit_generator(generator=train_generator,
steps_per_epoch=train_steps,
epochs=epochs,
initial_epoch=restart_epoch,
verbose=1,
validation_data=val_generator,
validation_steps=val_steps,
callbacks=self.train_callbacks,
max_queue_size=max_queue,
shuffle=not self.is_debug,
use_multiprocessing=multiprocess,
workers=workers)
# save final model
self.model.save_final(self.get_run_path(run_name, '../../weights/'),
epochs)
if __name__ == '__main__':
trainer = Trainer(model_name='mobile_unet',
dataset_path=config.data_path(),
target_size=(288, 480),
batch_size=2,
n_gpu=1,
debug_samples=0)
y = [np.array(Y), np.array(Y2), np.array(Y3)]
yield x, y
if __name__ == '__main__':
if __package__ is None:
import sys
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
else:
__package__ = ''
import config
datagen = CityscapesGeneratorForICNet(config.data_path(),
flip_enabled=True)
batch_size = 1
# target_size = 288, 480
target_size = 256, 512
# target_size = 1024, 2048 # orig size
for imgBatch, labelBatch in datagen.flow('train', batch_size, target_size):
print(len(imgBatch))
img = imgBatch[0][0]
label = labelBatch[0][0]
colored_class_image = datagen.one_hot_to_bgr(
label, tuple(a // 4 for a in target_size), datagen.n_classes,