def threshold_task(exp, block,
es,
T,
base_filename,
):
# def removeTemporaryNegativeFeatures(S, indicator_string = 'negative_feature___'):
# """Remove elements starting with the indicator_string and remove possible duplicates."""
# return S.apply(lambda list_element: set([s.replace(indicator_string, '') for s in list_element]))
"""Computes co-comodules from matrix H by given threshold T."""
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
H = es.get_assay_data_frame()
print(H)
# mu = np.mean(H, axis = 1)
# sigma = np.std(H, axis = 1)
# Z = H.apply(lambda z: (z-mu)/sigma, axis = 0)
# S = []
# S.append(removeTemporaryNegativeFeatures(Z.apply(lambda x: Z.columns[x >= T].tolist(), axis = 1)))
# S = pd.DataFrame(S)
# S = S.apply(lambda x: set.union(*x))
# result = pd.DataFrame(S)
from wrappers.snmnmf.evaluation import EnrichmentInGeneSets
z = 1
x = EnrichmentInGeneSets(z)
result = x.getGeneSet(H, T)
cs = ComoduleSet(exp.get_data_folder(), base_filename)
cs.store_set(result)
return [cs], {}
def threshold_task(
exp,
block,
es,
T,
base_filename,
):
# def removeTemporaryNegativeFeatures(S, indicator_string = 'negative_feature___'):
# """Remove elements starting with the indicator_string and remove possible duplicates."""
# return S.apply(lambda list_element: set([s.replace(indicator_string, '') for s in list_element]))
"""Computes co-comodules from matrix H by given threshold T."""
if settings.CELERY_DEBUG:
import sys
sys.path.append(
'/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg'
)
import pydevd
pydevd.settrace('localhost',
port=6901,
stdoutToServer=True,
stderrToServer=True)
H = es.get_assay_data_frame()
print(H)
# mu = np.mean(H, axis = 1)
# sigma = np.std(H, axis = 1)
# Z = H.apply(lambda z: (z-mu)/sigma, axis = 0)
# S = []
# S.append(removeTemporaryNegativeFeatures(Z.apply(lambda x: Z.columns[x >= T].tolist(), axis = 1)))
# S = pd.DataFrame(S)
# S = S.apply(lambda x: set.union(*x))
# result = pd.DataFrame(S)
from wrappers.snmnmf.evaluation import EnrichmentInGeneSets
z = 1
x = EnrichmentInGeneSets(z)
result = x.getGeneSet(H, T)
gene_sets = GeneSets(exp.get_data_folder(), base_filename)
gs = GS(result, result)
gene_sets.store_gs(gs)
# cs = GeneSets(exp.get_data_folder(), base_filename)
# cs.store_set(result)
return [gene_sets], {}
