def test_yield_wells():
experiments, well = dummy_data({
'FITC-A': [100],
})
experiment_i, condition_i, well_i = next(fcmcmp.yield_wells(experiments))
assert experiment_i == experiments[0]
assert condition_i == 'dummy'
assert well_i.label == 'A1'
def test_yield_wells():
experiments, well = dummy_data({
'FITC-A': [100],
})
experiment_i, condition_i, well_i = next(fcmcmp.yield_wells(experiments))
assert experiment_i == experiments[0]
assert condition_i == 'dummy'
assert well_i.label == 'A1'
def get_channel_label(experiments, baseline=False):
channels = set(x.channel for _, _, x in fcmcmp.yield_wells(experiments))
control_channels = set(x.control_channel
for _, _, x in fcmcmp.yield_wells(experiments))
control_channels.discard(None)
control_expts = set(x.control_expt
for _, _, x in fcmcmp.yield_wells(experiments))
control_expts.discard(None)
get_label = lambda x: x.split('-')[0]
if len(channels) == 1:
channel = next(iter(channels))
label = get_label(channel)
elif channels.issubset(['GFP-A', 'RFP-A']):
label = 'fluorescence'
elif channels.issubset(['FSC-A', 'SSC-A']):
label = 'size'
else:
raise ValueError("inconsistent channels: {}".format(
','.join(channels)))
if len(control_channels) > 1 or len(control_expts) > 1:
label = 'normalized {}'.format(label)
else:
if len(control_channels) == 1:
control_channel = next(iter(control_channels))
label = '{} / {}'.format(label, get_label(control_channel))
if len(control_expts) == 1:
control_expt = next(iter(control_expts))
label = '{} / {}'.format(label, control_expt)
if baseline:
label += f' / {"baseline" if baseline is True else baseline}'
return label
def analyze_wells(experiments, **kwargs):
control_expt_kwarg = kwargs.pop('control_expt', None)
# Convert all the normal wells to "analyzed" wells.
for i, experiment in enumerate(experiments):
for condition in experiment['wells']:
experiment['wells'][condition] = [
AnalyzedWell(experiments, i, condition, j, **kwargs)
for j, _ in enumerate(experiment['wells'][condition])
]
# Normalize by the indicated control experiment(s).
wells = [w for _, _, w in fcmcmp.yield_wells(experiments)]
expt_map = {x['label']: x for x in experiments}
control_locs = []
control_labels = []
for well in wells:
control_label = pick_control_experiment(well.experiment,
control_expt_kwarg)
if not control_label:
continue
control_expt = expt_map[control_label]
try:
control_loc = np.mean([
control_expt['wells'][conc][well.index].linear_loc
for conc in control_expt['wells']
])
except IndexError:
raise IndexError(
"Make sure you have the same number of replicates for each experiment."
)
control_locs.append(control_loc)
control_labels.append(control_label)
for well, label, loc in zip(wells, control_labels, control_locs):
well.control_expt = label
well.normalize(loc)
def check_wells(experiments, **expected_wells):
for expriment, condition, well in fcmcmp.yield_wells(experiments):
assert condition in expected_wells
assert well.label in expected_wells[condition]
assert isinstance(well.meta, dict)
assert isinstance(well.data, pd.DataFrame)
def check_wells(experiments, **expected_wells):
for expriment, condition, well in fcmcmp.yield_wells(experiments):
assert condition in expected_wells
assert well.label in expected_wells[condition]
assert isinstance(well.meta, dict)
assert isinstance(well.data, pd.DataFrame)
def _yield_wells(self):
yield from fcmcmp.yield_wells(self.experiments)
import docopt, fcmcmp, analysis_helpers
import matplotlib.pyplot as plt
from pprint import pprint
args = docopt.docopt(__doc__)
experiments = fcmcmp.load_experiments(args['<yml_path>'])
shared_steps = analysis_helpers.SharedProcessingSteps()
shared_steps.early_event_threshold = float(args['--time-gate'])
shared_steps.small_cell_threshold = float(args['--size-gate'])
shared_steps.low_fluorescence_threshold = float(args['--expression-gate'])
shared_steps.process(experiments)
table = []
for experiment, condition, well in fcmcmp.yield_wells(experiments,
args['<keyword>']):
well = analysis_helpers.AnalyzedWell(experiment, well, normalize_by=True)
percent_on = sum(well.measurements < -0.3) / len(well.measurements)
table.append((experiment['label'], condition, percent_on))
previous_label = None
max_label_len = max_condition_len = 0
for label, condition, percent_on in table:
max_label_len = max(len(label), max_label_len)
max_condition_len = max(len(condition), max_condition_len)
for label, condition, percent_on in table:
print('{:{}s} {:{}s} {:.2f}%'.format(label, max_label_len, condition,
max_condition_len, 100 * percent_on))