def filter_plot_tar(metrics, src_tar, pass_tar, fail_tar, tempdir, filters):
allplots = os.path.join(tempdir, 'allplots')
helpers.makedirs(allplots)
helpers.extract_tar(src_tar, allplots)
metrics_data = csvutils.read_csv_and_yaml(metrics)
all_cells = metrics_data.cell_id.tolist()
metrics_data = helpers.filter_metrics(metrics_data, filters)
good_cells = metrics_data.cell_id.tolist()
bad_cells = [cell for cell in all_cells if cell not in good_cells]
plotdir = os.path.join(tempdir, 'segs_pass')
helpers.makedirs(plotdir)
for cell in good_cells:
src_path = os.path.join(allplots, 'segments',
'{}_{}.png'.format(cell, 'segments'))
dest_path = os.path.join(plotdir, '{}_{}.png'.format(cell, 'segments'))
shutil.copyfile(src_path, dest_path)
helpers.make_tarfile(pass_tar, plotdir)
plotdir = os.path.join(tempdir, 'segs_fail')
helpers.makedirs(plotdir)
for cell in bad_cells:
src_path = os.path.join(allplots, 'segments',
'{}_{}.png'.format(cell, 'segments'))
dest_path = os.path.join(plotdir, '{}_{}.png'.format(cell, 'segments'))
shutil.copyfile(src_path, dest_path)
helpers.make_tarfile(fail_tar, plotdir)
def get_good_cells(metrics, cell_filters):
metrics_data = csvutils.read_csv_and_yaml(metrics)
if not cell_filters:
return metrics_data.cell_id.tolist()
metrics_data = helpers.filter_metrics(metrics_data, cell_filters)
return metrics_data.cell_id.tolist()