def load_conf(conf_path):
dummy_conf = {
"auth_file_path": "headers_auth.json",
"online_catalog_cache_file_path": "cache.json",
"auto_create_playlist_format": "Upload List (%Y/%m/%d %H:%M:%S)"
}
conf = object()
if conf_path == "" or not os.path.exists(conf_path):
conf = dummy_conf
conf_path = os.path.expanduser("~/.yootto/config.json")
os.makedirs(os.path.dirname(conf_path), exist_ok=True)
try:
with open(conf_path, "w") as f:
json.dump(dummy_conf, f)
except:
pass
else:
try:
with open(conf_path, "r") as f:
conf = json.load(f)
except json.JSONDecodeError as e:
logging.FATAL("JSONDecodeError: ", e)
return dummy_conf
basedir = os.path.dirname(os.path.abspath(conf_path))
if not Path(conf["auth_file_path"]).is_absolute():
conf["auth_file_path"] = os.path.join(basedir, conf["auth_file_path"])
if not Path(conf["online_catalog_cache_file_path"]).is_absolute():
conf["online_catalog_cache_file_path"] = os.path.join(
basedir, conf["online_catalog_cache_file_path"])
return conf
def main(files, array):
if array == '450k':
mlh1_probes, cimp_cutoff, cimp_probes = MLH1_PROBES_450k, CIMP_CUTOFF_450k, CIMP_PROBES_450k
elif array == '27k':
mlh1_probes, cimp_cutoff, cimp_probes = MLH1_PROBES_27k, CIMP_CUTOFF_27k, CIMP_PROBES_27k
else:
logging.FATAL('Unrecognized array type %s', array)
sys.exit(1)
sys.stdout.write('Sample\tMLH1\tCIMP\t{}\t{}\n'.format(
'\t'.join(sorted(mlh1_probes)), '\t'.join([
'\t'.join(
['{}_{}'.format(gene, x) for x in sorted(cimp_probes[gene])])
for gene in sorted(cimp_probes)
])))
for f in open(files, 'r'):
f = f.strip('\n')
logging.info('processing %s...', f)
mlh1_values = {}
cimp_values = collections.defaultdict(list)
probes = {}
for line in open(f, 'r'):
fields = line.strip('\n').split('\t')
if fields[0] in mlh1_probes:
mlh1_values[fields[0]] = fields[1]
for gene in cimp_probes: # each cimp gene
if fields[0] in cimp_probes[gene]: # relevant probe
try:
cimp_values[gene].append(float(fields[1]))
except ValueError:
logging.debug(
'skipping non-float value %s in probe %s',
fields[1], fields[0])
probes[fields[0]] = fields[1]
sample = 'TCGA{}'.format(
f.split('TCGA')[-1].replace('.gdc_hg38.txt', ''))
sys.stdout.write('{}\t{:.2f}\t'.format(
sample,
sum([float(mlh1_values[x])
for x in mlh1_values]) / len(mlh1_values)))
cimp_count = 0
for gene in cimp_probes:
if sum(cimp_values[gene]) / len(cimp_values[gene]) > cimp_cutoff:
cimp_count += 1
sys.stdout.write('{}\t'.format(cimp_count))
# mlh1 probes
sys.stdout.write('\t'.join(
mlh1_values.get(x, 'NA') for x in sorted(mlh1_probes)))
# cimp probes
for gene in sorted(cimp_probes):
for probe in sorted(cimp_probes[gene]):
sys.stdout.write('\t{}'.format(probes.get(probe, 'NA')))
sys.stdout.write('\n')
logging.info('processing %s: done', f)
def BaseLoss(feature_maps=None, bboxes=None, orig_size=None):
assert feature_maps is not None, logging.FATAL(
"No feature maps have been provided.")
assert bboxes is not None, logging.FATAL(
"No bounding boxes have been provided.")
assert orig_size is not None, logging.FATAL(
"Original image size has not been provided.")
#Here orig_size is assumed to be of the type 3 x width x height
batchsize = feature_maps.shape[0]
losses = []
#print(orig_size)
H = orig_size[0].numpy()
W = orig_size[1].numpy()
C = orig_size[2].numpy()
#print(len(orig_size))
#print(np.shape(bboxes))
print(bboxes)
bboxes = bboxes[0]
bboxes = bboxes[1]
#print(bboxes)
for batch in range(batchsize):
feat2 = feature_maps[batch, :, :, :].cpu().detach().numpy()
feat2 = np.transpose(feat2, (2, 1, 0))
#print(feat2.shape)
#print(" W = {}".format(W[batch]))
#print(" H = {}".format(H[batch]))
feat2 = cv2.resize(feat2, (W[batch], H[batch]), cv2.INTER_CUBIC)
#at = scipy.misc.imresize(feat,(orig_size[1],orig_size[2]))
feat2 = np.transpose(feat2, (2, 1, 0))
mask, bbox_pixels, non_bbox_pixels = create_mask(
feat2.shape[1:], bboxes[batch])
feat = np.square(feat2)
feat = np.sum(feat, axis=0)
bboxvalues = float(np.sum(feat[mask]) / (bbox_pixels * feat.shape[0]))
restvalues = float(
np.sum(feat[~mask]) / (non_bbox_pixels * feat.shape[0]))
losses.append(restvalues - bboxvalues)
losses = Variable(torch.DoubleTensor(losses), requires_grad=True)
return sum(losses) / len(losses), feat
def bbplot_img_from_df(df):
imagefilename = df['file_name'].iloc[0]
savefilename = df['save_name'].iloc[0]
try:
img = cv2.imread(imagefilename)
except:
logging.FATAL("The image {} could not be read.".format(imagefilename))
plotonimage(img, df, savefilename)
return None
def _wrapper(*args, **kwargs):
# print(f"stuff stuff: {func.__doc__}")
# _wrapper.__doc__ = func.__doc__
try:
print(f"Running: {func.__name__}( {args} , {kwargs} )")
v = func(*args, **kwargs)
except Exception as e:
logging.FATAL(f"Failed running {func.__name__}: {e}")
report_failure(func.__name__, *args, failure=e, **kwargs)
rep_id = report_invocation(func.__name__, *args, scope='id', **kwargs)
report_results(func.__name__,
*args,
invocation_report_id=rep_id,
**kwargs)
print(f"Finished: {func.__name__}( {args} , {kwargs} )")
return v
def parse_doc_to_nlss(doc_info):
l_nlss = [] # e_id, sent id, sent, l_e
docno = doc_info.get('docno', "")
if not docno:
docno = doc_info.get(title_field)
e_grid = doc_info.get(E_GRID_FIELD, {})
if type(e_grid) is not dict:
logging.FATAL('%s is not dict from [%s]', json.dumps(e_grid), docno)
raise TypeError
for p, sent_grid in enumerate(
doc_info.get(E_GRID_FIELD, {}).get(body_field, [])):
sent_id = docno + '_s%d' % p
sent = sent_grid['sent']
l_ana = sent_grid[SPOT_FIELD]
l_e = list(set([ana['id'] for ana in l_ana]))
l_e = [e for e in l_e if e]
for e in l_e:
l_nlss.append([e, sent_id, sent, l_e])
logging.debug('[%s] [%d] nlss pair', docno, len(l_nlss))
return l_nlss
def draw(self):
if len(self.Game) == 2: # counts if the game hasnt changed
pyxel.cls(7)
''' text on screen'''
pyxel.text(90, 40, "Adventure", 0)
pyxel.text(90, 60, "Pong", 0)
pyxel.text(0, 0, f'{datetime.datetime.now()}', 0)
if self.choice == 1: # shows the choice the player can select
pyxel.rectb(85, 35, 50, 15, 0)
if self.choice == 2: # shows the choice the player can select
pyxel.rectb(85, 55, 50, 15, 0)
elif self.Game == {2}: # starts the adventure game
Adventure.App()
Adventure.API_find()
elif self.Game == {1}: # starts pong
Pong.Start()
else: # error occured
logging.FATAL("game softlocked ")
raise Game_exception("Game doesnt equel 2 or 1")
def load_playlist(playlist_path, encoding):
ret = []
try:
fileobj = open(playlist_path, "r", encoding=encoding)
while True:
line = fileobj.readline().replace("\n", "")
if line and line[0] != "#" and ord(line[0]) != 65279:
if not Path(line).is_absolute():
if platform.system() == "Windows":
line = line.replace("/", "\\")
else:
line = line.replace("\\", "/")
basedir = os.path.dirname(os.path.abspath(playlist_path))
line = os.path.join(basedir, line)
ret.append(get_tag_from_file(line))
if line:
pass
else:
break
except:
logging.FATAL("Playlist load error.")
return []
return ret