def to_csv(self, out_path, out_path_filtered, cutoff, sep, **kw): # write first table with header path = self.pathes[0] table = read_csv(path, self.options.get("delim_in")) df = self.scorer.score(table) df.to_csv(out_path, sep, header=True, **kw) self._update_scores(df) df = df[df.d_score > cutoff] df.to_csv(out_path_filtered, sep, header=True, **kw) # now append and do not write headers again: for path in self.pathes[1:]: with open(out_path, "a") as fp, open(out_path_filtered, "a") as fp2: table = read_csv(path, self.options.get("delim_in")) df = self.scorer.score(table) df.to_csv(fp, sep, header=True, **kw) self._update_scores(df) df = df[df.d_score > cutoff] df.to_csv(fp2, sep, header=True, **kw)
def to_csv(self, out_path, out_path_filtered, cutoff, sep, **kw): table = read_csv(self.path, self.options.get("delim_in")) df = self.scorer.score(table) df.to_csv(out_path, sep, **kw) df = df[df.d_score > cutoff] df.to_csv(out_path_filtered, sep, **kw) self.decoys = df[df["decoy"] == 1]["d_score"].values self.targets = df[df["decoy"] == 0]["d_score"].values tops = df[df["peak_group_rank"] == 1] self.top_decoys = tops[tops["decoy"] == 1]["d_score"].values self.top_targets = tops[tops["decoy"] == 0]["d_score"].values
def read_tables_iter(self, pathes, delim): logging.info("process %s" % ", ".join(pathes)) for path in pathes: part = read_csv(path, delim) yield part
################################### # temporary file for code testing # ################################### # import python packages import os # import local files import generators as gen import data_handling as data inputfile = 'data/random_subset_MainDiagonal.txt' df = data.read_csv(inputfile, -1) print('Input dataframe shape: ', df.shape) df = df.loc[df['Json'] == True] print('Input golden dataframe shape: ', df.shape) (hist, runnbs, lsnbs) = data.get_hist_values(df) print('Shape of histogram array: ' + str(hist.shape)) selhist = hist[102:103, :] print('Shape of selected histogram array: ' + str(selhist.shape)) outfolder = 'testresults' if not os.path.exists(outfolder): os.makedirs(outfolder) gen.fourier_noise_on_mean(hist, outfilename=os.path.join(outfolder, 'fnom'), figname=os.path.join(outfolder, 'fnom'), nresamples=10, nonnegative=True)