def drop_failed_sweeps(
dataset: EphysDataSet,
stimulus_ontology: Optional[StimulusOntology] = None,
qc_criteria: Optional[Dict] = None
) -> List[Dict]:
"""A convenience which extracts and QCs sweeps in preparation for dataset
feature extraction. This function:
1. extracts sweep qc features
2. removes sweeps tagged with failure messages
3. sets sweep states based on qc results
Parameters
----------
dataset : dataset from which to draw sweeps
Returns
-------
sweep_features : a list of dictionaries, each describing a sweep
"""
if stimulus_ontology is None:
stimulus_ontology = StimulusOntology.default()
if qc_criteria is None:
qc_criteria = qcp.load_default_qc_criteria()
sweep_features = sweep_qc_features(dataset)
sweep_props.drop_tagged_sweeps(sweep_features)
sweep_props.remove_sweep_feature("tags", sweep_features)
sweep_states = qcp.qc_sweeps(
stimulus_ontology, sweep_features, qc_criteria
)
sweep_props.assign_sweep_states(sweep_states, sweep_features)
dataset.sweep_info = sweep_features
def extract_qc_features(data_set):
cell_features, cell_tags = cell_qc_features(
data_set,
# manual_values=cell_qc_manual_values
)
sweep_features = sweep_qc_features(data_set)
drop_tagged_sweeps(sweep_features)
return cell_features, cell_tags, sweep_features
def run_sweep_extraction(input_nwb_file,
input_h5_file,
stimulus_ontology_file,
input_manual_values=None):
"""
Parameters
----------
input_nwb_file
input_h5_file
stimulus_ontology_file
input_manual_values
Returns
-------
"""
lu.log_pretty_header("Extract QC features", level=1)
if input_manual_values is None:
input_manual_values = {}
manual_values = {}
for mk in MANUAL_KEYS:
if mk in input_manual_values:
manual_values[mk] = input_manual_values[mk]
if stimulus_ontology_file:
mso.make_stimulus_ontology_from_lims(stimulus_ontology_file)
else:
stimulus_ontology_file = StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(
F"Ontology is not provided, using default {StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}"
)
ont = StimulusOntology(ju.read(stimulus_ontology_file))
ds = create_data_set(nwb_file=input_nwb_file,
h5_file=input_h5_file,
ontology=ont)
cell_features, cell_tags = qcfe.cell_qc_features(ds, manual_values)
for tag in cell_tags:
logging.warning(tag)
sweep_features = qcfe.sweep_qc_features(ds)
return dict(
cell_features=cell_features,
cell_tags=cell_tags,
sweep_features=sweep_features,
)
def run_sweep_extraction(input_nwb_file,
stimulus_ontology_file=None,
input_manual_values=None,
update_ontology=True,
**unused_args):
"""
Parameters
----------
input_nwb_file
stimulus_ontology_file
input_manual_values
Returns
-------
"""
log_pretty_header("Extract QC features", level=1)
if input_manual_values is None:
input_manual_values = {}
manual_values = {}
for mk in MANUAL_KEYS:
if mk in input_manual_values:
manual_values[mk] = input_manual_values[mk]
if stimulus_ontology_file and update_ontology:
make_stimulus_ontology_from_lims(stimulus_ontology_file)
if stimulus_ontology_file is None:
stimulus_ontology_file = \
StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(f"Ontology is not provided, using default "
f"{StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}")
ont = StimulusOntology(json_utilities.read(stimulus_ontology_file))
ds = create_ephys_data_set(nwb_file=input_nwb_file, ontology=ont)
cell_features, cell_tags = cell_qc_features(ds, manual_values)
for tag in cell_tags:
logging.warning(tag)
sweep_features = sweep_qc_features(ds)
return {
"cell_features": cell_features,
"cell_tags": cell_tags,
"sweep_features": sweep_features,
}
def plot_data_set(
data_set,
clamp_mode: Optional[str] = None,
stimuli: Optional[Collection[str]] = None,
stimuli_exclude: Optional[Collection[str]] = None,
show_amps: Optional[bool] = True,
qc_sweeps: Optional[bool] = True,
figsize=(15, 7),
):
nwb_file_name = str(data_set._data.nwb_file)
if qc_sweeps:
drop_failed_sweeps(data_set)
elif show_amps:
data_set.sweep_info = sweep_qc_features(data_set)
sweep_table = data_set.filtered_sweep_table(
clamp_mode=clamp_mode,
stimuli=stimuli,
stimuli_exclude=stimuli_exclude)
if len(sweep_table) == 0:
warnings.warn("No sweeps to plot")
return
height_ratios, width_ratios = axes_ratios(sweep_table)
fig, ax = plt.subplots(len(height_ratios),
3,
figsize=figsize,
gridspec_kw={
'height_ratios': height_ratios,
'width_ratios': width_ratios
})
if len(height_ratios) == 1:
# ensure 2d array
ax = ax[None, :]
for fig_row, (stimulus_code, sweep_set_table) in enumerate(
sweep_table.groupby("stimulus_code")):
sweep_set_table = sweep_set_table.copy().sort_values("sweep_number",
ascending=False)
sweep_numbers = sweep_set_table["sweep_number"]
ss = data_set.sweep_set(sweep_numbers)
if qc_sweeps:
ss.select_epoch('experiment')
annot = sweep_numbers.astype(str)
if show_amps:
annot += sweep_set_table['stimulus_amplitude'].apply(
": {:.3g} pA".format)
ax_a = ax[fig_row, 0]
ax_i = ax[fig_row, 1]
ax_v = ax[fig_row, 2]
plot_waveforms(ax_i, ss.i, ss.sampling_rate, annot)
plot_waveforms(ax_v, ss.v, ss.sampling_rate)
ax_v.get_shared_x_axes().join(ax_i, ax_v)
clamp_mode = sweep_set_table["clamp_mode"].values[0]
ax_a.text(0, 0.0, "%s \n%s " % (stimulus_code, clamp_mode))
ax_a.axis('off')
ax[0, 0].set_title("Description")
ax[0, 1].set_title("Current")
ax[0, 2].set_title("Voltage")
ax[-1, 1].set_xlabel("time (s)")
ax[-1, 2].set_xlabel("time (s)")
fig.suptitle("file: " + nwb_file_name, fontsize=12)
mng = plt.get_current_fig_manager()
if hasattr(mng, 'window'):
mng.resize(*mng.window.maxsize())
plt.subplots_adjust(left=0.01, right=0.98, bottom=0.02, top=0.92)
import pandas as pd
from ipfx.dataset.create import create_ephys_data_set
from ipfx.qc_feature_extractor import sweep_qc_features
import ipfx.sweep_props as sweep_props
import ipfx.qc_feature_evaluator as qcp
from ipfx.stimulus import StimulusOntology
# Download and access the experimental data from DANDI archive per instructions in the documentation
# Example below will use an nwb file provided with the package
nwb_file = os.path.join(os.path.dirname(os.getcwd()), "data",
"nwb2_H17.03.008.11.03.05.nwb")
data_set = create_ephys_data_set(nwb_file=nwb_file)
# Compute sweep QC features
sweep_features = sweep_qc_features(data_set)
# Drop sweeps that failed to compute QC criteria
sweep_props.drop_tagged_sweeps(sweep_features)
sweep_props.remove_sweep_feature("tags", sweep_features)
stimulus_ontology = StimulusOntology.default()
qc_criteria = qcp.load_default_qc_criteria()
sweep_states = qcp.qc_sweeps(stimulus_ontology, sweep_features, qc_criteria)
# print a few sweeps and states
print(pd.DataFrame(sweep_features).head())
print(sweep_states[0:len(pd.DataFrame(sweep_features).head())])