tapioca/preprocess.py at main · FunctionLab/tapioca · GitHub

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importpreprocessing_helpersasph
importnumpyasnp


defprocess_in_file(in_file, ref_norm=True, logistic_curve_fit=True):
# ------ load in data -------
non_normed=ph.load_data.load_protein_data(in_file+'.csv')
in_file=in_file.replace('raw_input','normalized_input')
ifref_norm:
# ------- reference normalization -------

ref_normed_data=ph.normalization.normalize_to_reference_channel(non_normed)
cols=list(ref_normed_data.columns)
del_cols=set()
forcolincols:
if'unnamed'incol.lower():
del_cols.add(col)

forcolindel_cols:
delref_normed_data[col]

# ------- MOM normalization -------
MOMy_normed_data=ph.normalization.MOMy_normalization(ref_normed_data, logistic_curve_fit=logistic_curve_fit)
MOMy_normed_data.to_csv(in_file+'_normalized.csv')
normalized_file_address=in_file+'_normalized.csv'

else:
MOMy_normed_data=ph.normalization.MOMy_normalization(non_normed, logistic_curve_fit=logistic_curve_fit)
MOMy_normed_data.to_csv(in_file+'_normalized.csv')
normalized_file_address=in_file+'_normalized.csv'


returnnormalized_file_address


defpreprocess(address,ref_norm,logistic_curve_fit):
address=address.replace('.csv','')
normalized_file_address=process_in_file(address,ref_norm=ref_norm,
logistic_curve_fit=logistic_curve_fit)
returnnormalized_file_address


defnormalzie_cofrac_dict(cf_dict):
del_list=set()
forprot, rep_dictincf_dict.items():
forrep, cond_dictinrep_dict.items():
forcond, val_dictincond_dict.items():
max_val=np.max(list(val_dict.values()))
ifmax_val==0:
del_list.add((prot, rep, cond))
continue
forfrac, valinval_dict.items():
cf_dict[prot][rep][cond][frac] =val/max_val

forthingindel_list:
delcf_dict[thing[0]][thing[1]][thing[2]]

returncf_dict