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], {}