ipfx package¶
Subpackages¶
- ipfx.attach_metadata package
- ipfx.bin package
- Submodules
- ipfx.bin.generate_fx_input module
- ipfx.bin.generate_pipeline_input module
- ipfx.bin.generate_qc_input module
- ipfx.bin.generate_se_input module
- ipfx.bin.get_fx_output module
- ipfx.bin.make_stimulus_ontology module
- ipfx.bin.mcc_get_settings module
- ipfx.bin.nwb_to_pdf module
- ipfx.bin.pipeline_from_specimen_id module
- ipfx.bin.plot_ephys_nwb module
- ipfx.bin.run_chirp_fv_extraction module
- ipfx.bin.run_feature_collection module
- ipfx.bin.run_feature_extraction module
- ipfx.bin.run_feature_vector_extraction module
- ipfx.bin.run_pipeline module
- ipfx.bin.run_pipeline_from_nwb_file module
- ipfx.bin.run_qc module
- ipfx.bin.run_sweep_extraction module
- ipfx.bin.run_synphys_feature_vector_extraction module
- ipfx.bin.run_x_to_nwb_conversion module
- ipfx.bin.validate_experiment module
- Module contents
- ipfx.dataset package
- ipfx.x_to_nwb package
- Submodules
- ipfx.x_to_nwb.ABFConverter module
- ipfx.x_to_nwb.DatConverter module
- ipfx.x_to_nwb.conversion_utils module
- ipfx.x_to_nwb.hr_bundle module
- ipfx.x_to_nwb.hr_nodes module
- ipfx.x_to_nwb.hr_segments module
- ipfx.x_to_nwb.hr_stimsetgenerator module
- ipfx.x_to_nwb.hr_struct module
- ipfx.x_to_nwb.hr_treenode module
- Module contents
Submodules¶
ipfx.chirp module¶
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ipfx.chirp.chirp_amp_phase(sweep_set, start=0.6, end=20.6, down_rate=2000, min_freq=0.2, max_freq=40.0)[source]¶ Calculate amplitude and phase of chirp responses
- sweep_set: SweepSet
- Set of chirp sweeps
- start: float (optional, default 0.6)
- Start of chirp stimulus in seconds
- end: float (optional, default 20.6)
- End of chirp stimulus in seconds
- down_rate: int (optional, default 2000)
- Sampling rate for downsampling before FFT
- min_freq: float (optional, default 0.2)
- Minimum frequency for output to contain
- max_freq: float (optional, default 40)
- Maximum frequency for output to contain
- amplitude: array
- Aka resistance
- phase: array
- Aka reactance
- freq: array
- Frequencies for amplitude and phase results
ipfx.data_set_features module¶
-
ipfx.data_set_features.extract_cell_features(data_set, ramp_sweep_numbers, short_square_sweep_numbers, long_square_sweep_numbers, subthresh_min_amp)[source]¶
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ipfx.data_set_features.extract_data_set_features(data_set, subthresh_min_amp=None)[source]¶ -
- data_set : EphysDataSet
- data set
subthresh_min_amp
cell_features :
sweep_features :
cell_record :
sweep_records :
-
ipfx.data_set_features.extractors_for_sweeps(sweep_set, dv_cutoff=20.0, thresh_frac=0.05, reject_at_stim_start_interval=0, min_peak=-30, thresh_frac_floor=None, est_window=None, start=None, end=None)[source]¶ Extract data from sweeps
sweep_set : SweepSet object
dv_cutoff : float thresh_frac : reject_at_stim_start_interval : thresh_frac_floor est_window : start : end :
spx : SpikeExtractor object spfx : SpikeTrainFeatureExtractor object
-
ipfx.data_set_features.select_subthreshold_min_amplitude(stim_amps, decimals=0)[source]¶ Find the min delta between amplitudes of coarse long square sweeps. Includes failed sweeps.
stim_amps: list of stimulus amplitudes decimals: int of decimals to keep
subthresh_min_amp: float min amplitude min_amp_delta: min increment in the stimulus amplitude
ipfx.data_set_utils module¶
A shim for backwards compatible imports of create_data_set
ipfx.ephys_data_set module¶
A shim for backwards compatible imports of EphysDataSet
ipfx.epochs module¶
-
ipfx.epochs.get_experiment_epoch(i, hz, test_pulse=True)[source]¶ Find index range for the experiment epoch. The start index of the experiment epoch is defined as stim_start_idx - PRESTIM_DURATION*sampling_rate The end index of the experiment epoch is defined as stim_end_idx + POSTSTIM_DURATION*sampling_rate
i : float np.array of current hz : float sampling rate test_pulse: bool True if present, False otherwise
(expt_start_idx,expt_end_idx): int tuple with start, end indices of the epoch
-
ipfx.epochs.get_last_stability_epoch(idx1, hz)[source]¶ Get epoch lasting LAST_STABILITY_EPOCH before idx1
idx1 : int last index of the epoch hz : float sampling rate
(idx0,idx1) : int tuple of epoch indices
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ipfx.epochs.get_recording_epoch(response)[source]¶ Detect response epoch defined as interval from start to the last non-nan value of the response
response: float np.array
- start,end: int
- indices of the epoch
-
ipfx.epochs.get_stim_epoch(i, test_pulse=True)[source]¶ Determine the start index, and end index of a general stimulus.
- i : numpy array
- current
- test_pulse: bool
- True if test pulse is assumed
start,end: int tuple
ipfx.error module¶
ipfx.feature_extractor module¶
-
class
ipfx.feature_extractor.SpikeFeatureExtractor(start=None, end=None, filter=10.0, dv_cutoff=20.0, max_interval=0.005, min_height=2.0, min_peak=-30.0, thresh_frac=0.05, reject_at_stim_start_interval=0)[source]¶ Bases:
object-
AFFECTED_BY_CLIPPING= ['trough_t', 'trough_v', 'trough_i', 'trough_index', 'downstroke', 'downstroke_t', 'downstroke_v', 'downstroke_index', 'fast_trough_t', 'fast_trough_v', 'fast_trough_i', 'fast_trough_indexadp_t', 'adp_v', 'adp_i', 'adp_index', 'slow_trough_t', 'slow_trough_v', 'slow_trough_i', 'slow_trough_index', 'isi_type', 'width', 'upstroke_downstroke_ratio']¶ Feature calculation for a sweep (voltage and/or current time series).
-
spike_feature(spikes_df, key, include_clipped=False, force_exclude_clipped=False)[source]¶ Get specified feature for every spike.
key : feature name include_clipped: return values for every identified spike, even when clipping means they will be incorrect/undefined
spike_feature_values : ndarray of features for each spike
-
ipfx.feature_record module¶
ipfx.feature_vectors module¶
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ipfx.feature_vectors.first_ap_vectors(sweeps_list, spike_info_list, target_sampling_rate=50000, window_length=0.003, skip_clipped=False)[source]¶ Average waveforms of first APs from sweeps
- sweeps_list: list
- List of Sweep objects
- spike_info_list: list
- List of spike info DataFrames
- target_sampling_rate: float (optional, default 50000)
- Desired sampling rate of output (Hz)
- window_length: float (optional, default 0.003)
- Length of AP waveform (seconds)
- ap_v: array of shape (target_sampling_rate * window_length)
- Waveform of average AP
- ap_dv: array of shape (target_sampling_rate * window_length - 1)
- Waveform of first derivative of ap_v
-
ipfx.feature_vectors.first_ap_waveform(sweep, spikes, length_in_points)[source]¶ Waveform of first AP with length_in_points time samples
- sweep: Sweep
- Sweep object with spikes
- spikes: DataFrame
- Spike info dataframe with “threshold_index” column
- length_in_points: int
- Length of returned AP waveform
- first_ap_v: array of shape (length_in_points)
- The waveform of the first AP in sweep
-
ipfx.feature_vectors.identify_subthreshold_depol_with_amplitudes(features, sweeps)[source]¶ Identify subthreshold responses from depolarizing steps
- features: dict
- Output of LongSquareAnalysis.analyze()
- sweeps: SweepSet
- Long square sweeps
- amp_sweep_dict: dict
- Amplitude-sweep pairs
- deflect_dict: dict
- Dictionary of (base, deflect) tuples with amplitudes as keys
-
ipfx.feature_vectors.identify_subthreshold_hyperpol_with_amplitudes(features, sweeps)[source]¶ Identify subthreshold responses from hyperpolarizing steps
- features: dict
- Output of LongSquareAnalysis.analyze()
- sweeps: SweepSet
- Long square sweeps
- amp_sweep_dict: dict
- Amplitude-sweep pairs
- deflect_dict: dict
- Dictionary of (base, deflect) tuples with amplitudes as keys
-
ipfx.feature_vectors.identify_suprathreshold_spike_info(features, target_amplitudes, shift=None, amp_tolerance=0)[source]¶ Find spike information for sweeps matching desired amplitudes relative to rheobase
- features: dict
- Output of LongSquareAnalysis.analyze()
- target_amplitudes: array
- Amplitudes (relative to rheobase) for each desired step
- shift: float (optional, default None)
- Amount to consider shifting “rheobase” to identify more matching sweeps if only a single sweep matches. A value of None means that no shift is attempted.
- amp_tolerance: float (optional, default 0)
- Tolerance for matching amplitude (pA)
- info_list: list
- Spike info in order of desired amplitudes. If a given amplitude cannot be found, the list has None at that location
-
ipfx.feature_vectors.identify_suprathreshold_sweeps(sweeps, features, target_amplitudes, shift=None, amp_tolerance=0)[source]¶ Find spike information for sweeps matching desired amplitudes relative to rheobase
- sweeps: Sweep set
- Long square sweeps
- features: dict
- Output of LongSquareAnalysis.analyze()
- target_amplitudes: array
- Amplitudes (relative to rheobase) for each desired step
- shift: float (optional, default None)
- Amount to consider shifting “rheobase” to identify more matching sweeps if only a single sweep matches. A value of None means that no shift is attempted.
- amp_tolerance: float (optional, default 0)
- Tolerance for matching amplitude (pA)
- sweeps: list
- Sweeps in order of desired amplitudes. If a given amplitude cannot be found, the list has None at that location
-
ipfx.feature_vectors.identify_sweep_for_isi_shape(sweeps, features, duration, min_spike=5)[source]¶ Find lowest-amplitude spiking sweep that has at least min_spike or else sweep with most spikes
- sweeps: SweepSet
- Sweeps to consider for ISI shape calculation
- features: dict
- Output of LongSquareAnalysis.analyze()
- duration: float
- Length of stimulus interval (seconds)
- min_spike: int (optional, default 5)
- Minimum number of spikes for first preference sweep (default 5)
- selected_sweep: Sweep
- Sweep object for ISI shape calculation
- selected_spike_info: DataFrame
- Spike info for selected sweep
-
ipfx.feature_vectors.inst_freq_vector(spike_info_list, start, end, width=20)[source]¶ - Create binned instantaneous frequency feature vector,
- concatenated across sweeps
- spike_info_list: list
- Spike info DataFrames for each sweep
- start: float
- Start of stimulus interval (seconds)
- end: float
- End of stimulus interval (seconds)
- width: float (optional, default 20)
- Bin width in ms
- output: array
- Concatenated vector of binned instantaneous firing rates (spikes/s)
-
ipfx.feature_vectors.isi_shape(sweep, spike_info, end, n_points=100, steady_state_interval=0.1, single_return_tolerance=1.0, single_max_duration=0.1)[source]¶ Average interspike voltage trajectory with normalized duration, aligned to threshold
- sweep: Sweep
- Sweep object with at least one action potential
- spike_info: DataFrame
- Spike info for sweep
- end: float
- End of stimulus interval (seconds)
- n_points: int (optional, default 100)
- Number of points in output
- steady_state_interval: float (optional, default 0.1)
- Interval for calculating steady-state for sweeps with only one spike (seconds)
- single_return_tolerance: float (optional, default 1)
- Allowable difference from steady-state for determining end of “ISI” if only one spike is in sweep (mV)
- single_max_duration: float (optional, default 0.1)
- Allowable max duration for finding end of “ISI” if only one spike is in sweep (seconds)
- isi_norm: array of shape (n_points)
- Averaged, threshold-aligned, duration-normalized voltage trace
-
ipfx.feature_vectors.noise_ap_features(noise_sweeps, stim_interval_list=[(2.02, 5.02), (10.02, 13.02), (18.02, 21.02)], target_sampling_rate=50000, window_length=0.003, skip_first_n=1)[source]¶ Average AP waveforms in noise sweeps
- noise_sweeps: SweepSet
- Noise sweeps
- stim_interval_list: list
- Tuples of start and end times (in seconds) of analysis intervals
- target_sampling_rate: float (optional, default 50000)
- Desired sampling rate of output (Hz)
- window_length: float (optional, default 0.003)
- Length of AP waveform (seconds)
- skip_first_n: int (optional, default 1)
- Number of initial APs to exclude from average (default 1)
- ap_v: array of shape (target_sampling_rate * window_length)
- Waveform of average AP
- ap_dv: array of shape (target_sampling_rate * window_length - 1)
- Waveform of first derivative of ap_v
-
ipfx.feature_vectors.psth_vector(spike_info_list, start, end, width=50)[source]¶ - Create binned “PSTH”-like feature vector based on spike times, concatenated
- across sweeps
- spike_info_list: list
- Spike info DataFrames for each sweep
- start: float
- Start of stimulus interval (seconds)
- end: float
- End of stimulus interval (seconds)
- width: float (optional, default 50)
- Bin width in ms
- output: array
- Concatenated vector of binned spike rates (spikes/s)
-
ipfx.feature_vectors.spike_feature_vector(feature, spike_info_list, start, end, width=20)[source]¶ - Create binned feature vector for specified features,
- concatenated across sweeps
- feature: string
- Name of feature found in members of spike_info_list
- spike_info_list: list
- Spike info DataFrames for each sweep
- start: float
- Start of stimulus interval (seconds)
- end: float
- End of stimulus interval (seconds)
- width: float (optional, default 20)
- Bin width in ms
- output: array
- Concatenated vector of binned spike features
-
ipfx.feature_vectors.step_subthreshold(amp_sweep_dict, target_amps, start, end, extend_duration=0.2, subsample_interval=0.01, amp_tolerance=0.0)[source]¶ Subsample set of subthreshold step responses including regions before and after step
- amp_sweep_dict : dict
- Amplitude-sweep pairs
- target_amps: list
- Desired amplitudes for output vector
- start: float
- start stimulus interval (seconds)
- end: float
- end of stimulus interval (seconds)
- extend_duration: float (optional, default 0.2)
- Duration to extend sweep before and after stimulus interval (seconds)
- subsample_interval: float (optional, default 0.01)
- Size of subsampled bins (seconds)
- amp_tolerance: float (optional, default 0)
- Tolerance for finding matching amplitudes
output_vector: subsampled, concatenated voltage trace
-
ipfx.feature_vectors.subthresh_depol_norm(amp_sweep_dict, deflect_dict, start, end, extend_duration=0.2, subsample_interval=0.01, steady_state_interval=0.1)[source]¶ Largest positive-going subthreshold step response that does not evoke spikes, normalized to baseline and steady-state at end of step
- amp_sweep_dict: dict
- Amplitude-sweep pairs
- deflect_dict:
- Dictionary of (baseline, deflect) tuples with amplitude keys
- start: float
- start stimulus interval (seconds)
- end: float
- end of stimulus interval (seconds)
- extend_duration: float (optional, default 0.2)
- Duration to extend sweep on each side of stimulus interval (seconds)
- subsample_interval: float (optional, default 0.01)
- Size of subsampled bins (seconds)
- steady_state_interval: float (optional, default 0.1)
- Interval before end for normalization (seconds)
- subsampled_v: array
- Subsampled, normalized voltage trace
-
ipfx.feature_vectors.subthresh_norm(amp_sweep_dict, deflect_dict, start, end, target_amp=-101.0, extend_duration=0.2, subsample_interval=0.01)[source]¶ Subthreshold step response closest to target amplitude normalized to baseline and peak deflection
- amp_sweep_dict: dict
- Amplitude-sweep pairs
- deflect_dict:
- Dictionary of (baseline, deflect) tuples with amplitude keys
- start: float
- start stimulus interval (seconds)
- end: float
- end of stimulus interval (seconds)
- target_amp: float (optional, default=-101)
- Search target for amplitude (pA)
- extend_duration: float (optional, default 0.2)
- Duration to extend sweep on each side of stimulus interval (seconds)
- subsample_interval: float (optional, default 0.01)
- Size of subsampled bins (seconds)
- subsampled_v: array
- Subsampled, normalized voltage trace
ipfx.lab_notebook_reader module¶
A shim for backwards compatible imports of lab_notebook_reader
ipfx.lims_queries module¶
-
ipfx.lims_queries.get_nwb_path_from_lims(ephys_roi_result)[source]¶ Try to find NWBIgor file preferentially If not found, look for a processed NWB file
well known file type ID for NWB files is 475137571 well known file type ID for NWBIgor files is 570280085
ephys_roi_result: int
full path of the nwb file
ipfx.logging_utils module¶
-
ipfx.logging_utils.log_pretty_header(header, level=1, top_line_break=True, bottom_line_break=True)[source]¶ Decorate logging message to make logging output more human readable
- header: str
- header message
- level: int
- 1 or 2 as in markdown
- top_line_break: bool (True)
- add a blank line at the top
- bottom_line_break: bool (True)
- add a blank line at the bottom
ipfx.nwb_append module¶
-
ipfx.nwb_append.append_spike_times(input_nwb_path: Union[str, pathlib.Path], sweep_spike_times: Dict[int, List[float]], output_nwb_path: Union[str, pathlib.Path, None] = None)[source]¶ Appends spiketimes to an nwb2 file
input_nwb_path: location of input nwb file without spiketimes
spike_times: Dict of sweep_num: spiketimes
- output_nwb_path: optional location to write new nwb file with
- spiketimes, otherwise appends spiketimes to input file
ipfx.plot_qc_figures module¶
-
ipfx.plot_qc_figures.display_features(qc_fig_dir, data_set, feature_data)[source]¶ -
- qc_fig_dir: str
- directory name for storing html pages
data_set: NWB data set feature_data: dict
cell and sweep features
-
ipfx.plot_qc_figures.make_cell_html(image_files, metadata, file_name, img_sub_dir, required_fields=('electrode_0_pa', 'seal_gohm', 'initial_access_resistance_mohm', 'input_resistance_mohm'))[source]¶
-
ipfx.plot_qc_figures.make_cell_page(data_set, feature_data, working_dir, save_cell_plots=True)[source]¶
-
ipfx.plot_qc_figures.plot_fi_curve_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_hero_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_instantaneous_threshold_thumbnail(data_set, sweep_numbers, cell_features, lims_features, sweep_features, color='red')[source]¶
-
ipfx.plot_qc_figures.plot_ramp_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_rheo_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_sag_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_short_square_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
-
ipfx.plot_qc_figures.plot_single_ap_values(data_set, sweep_numbers, lims_features, sweep_features, cell_features, type_name)[source]¶
-
ipfx.plot_qc_figures.plot_subthreshold_long_square_figures(data_set, cell_features, lims_features, sweep_features, image_dir, sizes, cell_image_files)[source]¶
ipfx.py2to3 module¶
ipfx.qc_feature_evaluator module¶
-
ipfx.qc_feature_evaluator.evaluate_blowout(blowout_mv, blowout_mv_min, blowout_mv_max, fail_tags)[source]¶
-
ipfx.qc_feature_evaluator.evaluate_electrode_0(electrode_0_pa, electrode_0_pa_max, fail_tags)[source]¶
-
ipfx.qc_feature_evaluator.evaluate_input_and_access_resistance(input_access_resistance_ratio, input_vs_access_resistance_max, initial_access_resistance_mohm, access_resistance_mohm_min, access_resistance_mohm_max, fail_tags)[source]¶
-
ipfx.qc_feature_evaluator.qc_cell(cell_data, qc_criteria=None)[source]¶ Evaluate cell state across different types of stimuli
- cell_data : dict
- cell features
- qc_criteria : dict
- qc criteria
- cell_state : dict
- cell state including qc features
-
ipfx.qc_feature_evaluator.qc_current_clamp_sweep(sweep, is_ramp, qc_criteria=None)[source]¶ QC for the current-clamp sweeps
- is_ramp: bool
- True for Ramp otherwise False
- sweep : dict
- features of a sweep
- qc_criteria : dict
- qc criteria
- fails : int
- number of fails
- fail_tags : list of str
- tags of the failed sweeps
-
ipfx.qc_feature_evaluator.qc_experiment(ontology, cell_features, sweep_features, qc_criteria=None)[source]¶ -
- ontology: StimulusOntology object
- stimulus ontology
- cell_features : dict
- cell features
- sweep_features: list of dicts
- sweep features
- qc_criteria : dict
- qc criteria
cell_state : list sweep_states : list
ipfx.qc_feature_extractor module¶
-
ipfx.qc_feature_extractor.cell_qc_features(data_set, manual_values=None)[source]¶ -
- data_set : EphysDataSet
- dataset
- manual_values : dict
- default (manual) values that can be passed in through input.json.
- features : dict
- cell qc features
- tags : list
- warning tags
-
ipfx.qc_feature_extractor.extract_blowout(data_set, tags)[source]¶ Measure blowout voltage
data_set: EphysDataSet tags: list
warning tags- blowout_mv: float
- blowout voltage in mV
-
ipfx.qc_feature_extractor.extract_clamp_seal(data_set, tags, manual_values=None)[source]¶ -
data_set: EphysDataSet tags: list
warning tagsmanual_values
-
ipfx.qc_feature_extractor.extract_electrode_0(data_set, tags)[source]¶ Measure electrode zero
data_set: EphysDataSet tags: list
warning tagse0: float
-
ipfx.qc_feature_extractor.extract_initial_access_resistance(breakin_sweep, tags, manual_values)[source]¶
-
ipfx.qc_feature_extractor.extract_input_and_access_resistance(data_set, tags, manual_values=None)[source]¶ - Measure input and series (access) resistance in two steps:
- finding the breakin sweep
- and then analyzing it
if the value is unavailable then check to see if it was set manually
data_set: EphysDataSet tags: list
warning tags- manual_values: dict
- manual/default values
- ir: float
- input resistance
- sr: float
- access resistance
ipfx.qc_features module¶
-
ipfx.qc_features.get_r_from_stable_pulse_response(v, i, t)[source]¶ Compute input resistance from the stable pulse response
v : float membrane voltage (V) i : float input current (A) t : time (s)
ir: float input resistance
ipfx.script_utils module¶
-
ipfx.script_utils.categorize_iclamp_sweeps(data_set, stimuli_names, sweep_qc_option='none', specimen_id=None)[source]¶
-
ipfx.script_utils.dataset_for_specimen_id(specimen_id, data_source, ontology, file_list=None)[source]¶
-
ipfx.script_utils.organize_results(specimen_ids, results)[source]¶ Build dictionary of results, filling data from cells with appropriate-length nan arrays where needed
-
ipfx.script_utils.preprocess_long_square_sweeps(data_set, sweep_numbers, extra_dur=0.2, subthresh_min_amp=-100.0)[source]¶
ipfx.spike_detector module¶
-
ipfx.spike_detector.check_thresholds_and_peaks(v, t, spike_indexes, peak_indexes, upstroke_indexes, start=None, end=None, max_interval=0.005, thresh_frac=0.05, filter=10.0, dvdt=None, tol=1.0, reject_at_stim_start_interval=0.0)[source]¶ Validate thresholds and peaks for set of spikes
Check that peaks and thresholds for consecutive spikes do not overlap Spikes with overlapping thresholds and peaks will be merged.
Check that peaks and thresholds for a given spike are not too far apart.
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of spike indexes peak_indexes : numpy array of indexes of spike peaks upstroke_indexes : numpy array of indexes of spike upstrokes start : start of time window for feature analysis (optional) end : end of time window for feature analysis (optional) max_interval : maximum allowed time between start of spike and time of peak in sec (default 0.005) thresh_frac : fraction of average upstroke for threshold calculation (optional, default 0.05) filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dvdt : pre-calculated time-derivative of voltage (optional) tol : tolerance for returning to threshold in mV (optional, default 1) reject_at_stim_start_interval : duration of window after start to reject potential spikes (optional, default 0)
spike_indexes : numpy array of modified spike indexes peak_indexes : numpy array of modified spike peak indexes upstroke_indexes : numpy array of modified spike upstroke indexes clipped : numpy array of clipped status of spikes
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ipfx.spike_detector.detect_putative_spikes(v, t, start=None, end=None, filter=10.0, dv_cutoff=20.0, dvdt=None)[source]¶ Perform initial detection of spikes and return their indexes.
v : numpy array of voltage time series in mV t : numpy array of times in seconds start : start of time window for spike detection (optional) end : end of time window for spike detection (optional) filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dv_cutoff : minimum dV/dt to qualify as a spike in V/s (optional, default 20) dvdt : pre-calculated time-derivative of voltage (optional)
putative_spikes : numpy array of preliminary spike indexes
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ipfx.spike_detector.filter_putative_spikes(v, t, spike_indexes, peak_indexes, min_height=2.0, min_peak=-30.0, filter=10.0, dvdt=None)[source]¶ - Filter out events that are unlikely to be spikes based on:
- Height (threshold to peak)
- Absolute peak level
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of preliminary spike indexes peak_indexes : numpy array of indexes of spike peaks min_height : minimum acceptable height from threshold to peak in mV (optional, default 2) min_peak : minimum acceptable absolute peak level in mV (optional, default -30) filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dvdt : pre-calculated time-derivative of voltage (optional)
spike_indexes : numpy array of threshold indexes peak_indexes : numpy array of peak indexes
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ipfx.spike_detector.find_clipped_spikes(v, t, spike_indexes, peak_indexes, end_index, tol)[source]¶ Check that last spike was not cut off too early by end of stimulus by checking that the membrane potential returned to at least the threshold voltage - otherwise, drop it
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of spike indexes peak_indexes : numpy array of indexes of spike peaks end_index: int index of the end of time window for feature analysis
tol: float tolerance to returning to threshold
clipped: Boolean np.array
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ipfx.spike_detector.find_downstroke_indexes(v, t, peak_indexes, trough_indexes, clipped=None, filter=10.0, dvdt=None)[source]¶ Find indexes of minimum voltage (troughs) between spikes.
v : numpy array of voltage time series in mV t : numpy array of times in seconds peak_indexes : numpy array of spike peak indexes trough_indexes : numpy array of threshold indexes clipped: boolean array - False if spike not clipped by edge of window filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dvdt : pre-calculated time-derivative of voltage (optional)
downstroke_indexes : numpy array of downstroke indexes
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ipfx.spike_detector.find_peak_indexes(v, t, spike_indexes, end=None)[source]¶ Find indexes of spike peaks.
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of preliminary spike indexes end : end of time window for spike detection (optional)
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ipfx.spike_detector.find_trough_indexes(v, t, spike_indexes, peak_indexes, clipped=None, end=None)[source]¶ Find indexes of minimum voltage (trough) between spikes.
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of spike indexes peak_indexes : numpy array of spike peak indexes end : end of time window (optional)
trough_indexes : numpy array of threshold indexes
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ipfx.spike_detector.find_upstroke_indexes(v, t, spike_indexes, peak_indexes, filter=10.0, dvdt=None)[source]¶ Find indexes of maximum upstroke of spike.
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of preliminary spike indexes peak_indexes : numpy array of indexes of spike peaks filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dvdt : pre-calculated time-derivative of voltage (optional)
upstroke_indexes : numpy array of upstroke indexes
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ipfx.spike_detector.refine_threshold_indexes(v, t, upstroke_indexes, thresh_frac=0.05, filter=10.0, dvdt=None)[source]¶ Refine threshold detection of previously-found spikes.
v : numpy array of voltage time series in mV t : numpy array of times in seconds upstroke_indexes : numpy array of indexes of spike upstrokes (for threshold target calculation) thresh_frac : fraction of average upstroke for threshold calculation (optional, default 0.05) filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default 10) dvdt : pre-calculated time-derivative of voltage (optional)
threshold_indexes : numpy array of threshold indexes
ipfx.spike_features module¶
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ipfx.spike_features.analyze_trough_details(v, t, spike_indexes, peak_indexes, clipped=None, end=None, filter=10.0, heavy_filter=1.0, term_frac=0.01, adp_thresh=0.5, tol=0.5, flat_interval=0.002, adp_max_delta_t=0.005, adp_max_delta_v=10.0, dvdt=None)[source]¶ Analyze trough to determine if an ADP exists and whether the reset is a ‘detour’ or ‘direct’
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of spike indexes peak_indexes : numpy array of spike peak indexes end : end of time window (optional) filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (default 1) heavy_filter : lower cutoff frequency for 4-pole low-pass Bessel filter in kHz (default 1) thresh_frac : fraction of average upstroke for threshold calculation (optional, default 0.05) adp_thresh: minimum dV/dt in V/s to exceed to be considered to have an ADP (optional, default 1.5) tol : tolerance for evaluating whether Vm drops appreciably further after end of spike (default 1.0 mV) flat_interval: if the trace is flat for this duration, stop looking for an ADP (default 0.002 s) adp_max_delta_t: max possible ADP delta t (default 0.005 s) adp_max_delta_v: max possible ADP delta v (default 10 mV) dvdt : pre-calculated time-derivative of voltage (optional)
isi_types : numpy array of isi reset types (direct or detour) fast_trough_indexes : numpy array of indexes at the start of the trough (i.e. end of the spike) adp_indexes : numpy array of adp indexes (np.nan if there was no ADP in that ISI slow_trough_indexes : numpy array of indexes at the minimum of the slow phase of the trough
(if there wasn’t just a fast phase)
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ipfx.spike_features.estimate_adjusted_detection_parameters(v_set, t_set, interval_start, interval_end, filter=10)[source]¶ Estimate adjusted values for spike detection by analyzing a period when the voltage changes quickly but passively (due to strong current stimulation), which can result in spurious spike detection results.
v_set : list of numpy arrays of voltage time series in mV t_set : list of numpy arrays of times in seconds interval_start : start of analysis interval (sec) interval_end : end of analysis interval (sec)
new_dv_cutoff : adjusted dv/dt cutoff (V/s) new_thresh_frac : adjusted fraction of avg upstroke to find threshold
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ipfx.spike_features.find_widths(v, t, spike_indexes, peak_indexes, trough_indexes, clipped=None)[source]¶ Find widths at half-height for spikes.
Widths are only returned when heights are defined
v : numpy array of voltage time series in mV t : numpy array of times in seconds spike_indexes : numpy array of spike indexes peak_indexes : numpy array of spike peak indexes trough_indexes : numpy array of trough indexes
widths : numpy array of spike widths in sec
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ipfx.spike_features.fit_prespike_time_constant(t, v, start, spike_time, dv_limit=-0.001, tau_limit=0.3)[source]¶ Finds the dominant time constant of the pre-spike rise in voltage
v : numpy array of voltage time series in mV t : numpy array of times in seconds start : start of voltage rise (seconds) spike_time : time of first spike (seconds) dv_limit : dV/dt cutoff (default -0.001)
Shortens fit window if rate of voltage drop exceeds this limit- tau_limit : upper bound for slow time constant (seconds, default 0.3)
- If the slower time constant of a double-exponential fit is twice that of the faster and exceeds this limit, the faster one will be considered the dominant one
tau : dominant time constant (seconds)
ipfx.spike_train_features module¶
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ipfx.spike_train_features.average_rate(t, spikes, start, end)[source]¶ Calculate average firing rate during interval between start and end.
t : numpy array of times in seconds spikes : numpy array of spike indexes start : start of time window for spike detection end : end of time window for spike detection
avg_rate : average firing rate in spikes/sec
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ipfx.spike_train_features.basic_spike_train_features(t, spikes_df, start, end, exclude_clipped=False)[source]¶
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ipfx.spike_train_features.burst(t, spikes_df, tol=0.5, pause_cost=1.0)[source]¶ Find bursts and return max “burstiness” index (normalized max rate in burst vs out).
max_burstiness_index : max “burstiness” index across detected bursts num_bursts : number of bursts detected
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ipfx.spike_train_features.delay(t, v, spikes_df, start, end)[source]¶ Calculates ratio of latency to dominant time constant of rise before spike
delay_ratio : ratio of latency to tau (higher means more delay) tau : dominant time constant of rise before spike
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ipfx.spike_train_features.detect_bursts(isis, isi_types, fast_tr_v, fast_tr_t, slow_tr_v, slow_tr_t, thr_v, tol=0.5, pause_cost=1.0)[source]¶ Detect bursts in spike train.
isis : numpy array of n interspike intervals isi_types : numpy array of n interspike interval types fast_tr_v : numpy array of fast trough voltages for the n + 1 spikes of the train fast_tr_t : numpy array of fast trough times for the n + 1 spikes of the train slow_tr_v : numpy array of slow trough voltages for the n + 1 spikes of the train slow_tr_t : numpy array of slow trough times for the n + 1 spikes of the train thr_v : numpy array of threshold voltages for the n + 1 spikes of the train tol : tolerance for the difference in slow trough voltages and thresholds (default 0.5 mV)
Used to identify “delay” interspike intervals that occur within a burst- bursts : list of bursts
- Each item in list is a tuple of the form (burst_index, start, end) where burst_index is a comparison index between the highest instantaneous rate within the burst vs the highest instantaneous rate outside the burst. start is the index of the first ISI of the burst, and end is the ISI index immediately following the burst.
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ipfx.spike_train_features.detect_pauses(isis, isi_types, cost_weight=1.0)[source]¶ Determine which ISIs are “pauses” in ongoing firing.
Pauses are unusually long ISIs with a “detour reset” among “direct resets”.
isis : numpy array of interspike intervals isi_types : numpy array of interspike interval types (‘direct’ or ‘detour’) cost_weight : weight for cost function for calling an ISI a pause
Higher cost weights lead to fewer ISIs identified as pauses. The cost function also depends on the difference between the duration of the “pause” ISIs and the average duration and standard deviation of “non-pause” ISIs.pauses : numpy array of indices corresponding to pauses in isis
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ipfx.spike_train_features.fit_fi_slope(stim_amps, avg_rates)[source]¶ Fit the rate and stimulus amplitude to a line and return the slope of the fit.
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ipfx.spike_train_features.get_isis(t, spikes)[source]¶ Find interspike intervals in sec between spikes (as indexes).
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ipfx.spike_train_features.norm_diff(a)[source]¶ Calculate average of (a[i] - a[i+1]) / (a[i] + a[i+1]).
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ipfx.spike_train_features.norm_sq_diff(a)[source]¶ Calculate average of (a[i] - a[i+1])^2 / (a[i] + a[i+1])^2.
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ipfx.spike_train_features.pause(t, spikes_df, start, end, cost_weight=1.0)[source]¶ Estimate average number of pauses and average fraction of time spent in a pause
Attempts to detect pauses with a variety of conditions and averages results together.
Pauses that are consistently detected contribute more to estimates.
avg_n_pauses : average number of pauses detected across conditions avg_pause_frac : average fraction of interval (between start and end) spent in a pause max_reliability : max fraction of times most reliable pause was detected given weights tested n_max_rel_pauses : number of pauses detected with max_reliability
ipfx.stim_features module¶
ipfx.stimulus module¶
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class
ipfx.stimulus.StimulusOntology(stim_ontology_tags=None)[source]¶ Bases:
object-
DEFAULT_STIMULUS_ONTOLOGY_FILE= '/home/docs/checkouts/readthedocs.org/user_builds/ipfx/checkouts/v1.0.0.2/ipfx/defaults/stimulus_ontology.json'¶
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find(tag, tag_type=None)[source]¶ Find stimuli matching a given tag Parameters ———- tag: str tag_type: str
matching_stims: list of Stimuli objects
Find stimulus based on a tag stim and then check if it has any tags Parameters ———- stim: str
tag to find stimulus- tags: str
- tags to check in any belong to the stimulus
tag_type
bool: True if any tags match, otherwise False
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ipfx.stimulus_protocol_analysis module¶
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class
ipfx.stimulus_protocol_analysis.LongSquareAnalysis(spx, sptx, subthresh_min_amp, tau_frac=0.1, require_subthreshold=True, require_suprathreshold=True)[source]¶ Bases:
ipfx.stimulus_protocol_analysis.StimulusProtocolAnalysis-
HERO_MAX_AMP_OFFSET= 61.0¶
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HERO_MIN_AMP_OFFSET= 39.0¶
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SAG_TARGET= -100.0¶
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SUBTHRESH_MAX_AMP= 0¶
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class
ipfx.stimulus_protocol_analysis.RampAnalysis(spx, sptx)[source]¶ Bases:
ipfx.stimulus_protocol_analysis.StimulusProtocolAnalysis
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class
ipfx.stimulus_protocol_analysis.ShortSquareAnalysis(spx, sptx)[source]¶ Bases:
ipfx.stimulus_protocol_analysis.StimulusProtocolAnalysis
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class
ipfx.stimulus_protocol_analysis.StimulusProtocolAnalysis(spx, sptx)[source]¶ Bases:
object-
MEAN_FEATURES= ['upstroke_downstroke_ratio', 'peak_v', 'peak_t', 'trough_v', 'trough_t', 'fast_trough_v', 'fast_trough_t', 'slow_trough_v', 'slow_trough_t', 'threshold_v', 'threshold_i', 'threshold_t', 'peak_v', 'peak_t']¶
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ipfx.subthresh_features module¶
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ipfx.subthresh_features.baseline_voltage(t, v, start, baseline_interval=0.1, baseline_detect_thresh=0.3, filter_frequency=1.0)[source]¶
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ipfx.subthresh_features.fit_membrane_time_constant(t, v, start, end, rmse_max_tol=1.0)[source]¶ Fit an exponential to estimate membrane time constant between start and end
v : numpy array of voltages in mV t : numpy array of times in seconds start : start of time window for exponential fit end : end of time window for exponential fit rsme_max_tol: minimal acceptable root mean square error (default 1e-4)
a, inv_tau, y0 : Coefficients of equation y0 + a * exp(-inv_tau * x)
returns np.nan for values if fit fails
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ipfx.subthresh_features.input_resistance(t_set, i_set, v_set, start, end, baseline_interval=0.1)[source]¶ Estimate input resistance in MOhms, assuming all sweeps in passed extractor are hyperpolarizing responses.
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ipfx.subthresh_features.sag(t, v, i, start, end, peak_width=0.005, baseline_interval=0.03)[source]¶ Calculate the sag in a hyperpolarizing voltage response.
peak_width : window width to get more robust peak estimate in sec (default 0.005)
sag : fraction that membrane potential relaxes back to baseline
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ipfx.subthresh_features.time_constant(t, v, i, start, end, max_fit_end=None, frac=0.1, baseline_interval=0.1, min_snr=20.0)[source]¶ Calculate the membrane time constant by fitting the voltage response with a single exponential.
v : numpy array of voltages in mV t : numpy array of times in seconds start : start of stimulus interval in seconds end : end of stimulus interval in seconds max_fit_end : maximum end of exponential fit window. If None, end of fit
window will always be the time of the peak hyperpolarizing deflection. If set, end of fit window will be max_fit_end if it is earlier than the time of peak deflection (default None)- frac : fraction of peak deflection (or deflection at present_fit_end if used)
- to find to determine start of fit window. (default 0.1)
baseline_interval : duration before start for baseline Vm calculation min_snr : minimum signal-to-noise ratio (SNR) to allow calculation of time constant.
If SNR is too low, np.nan will be returned. (default 20)tau : membrane time constant in seconds
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ipfx.subthresh_features.voltage_deflection(t, v, i, start, end, deflect_type=None)[source]¶ Measure deflection (min or max, between start and end if specified).
- deflect_type : measure minimal (‘min’) or maximal (‘max’) voltage deflection
- If not specified, it will check to see if the current (i) is positive or negative between start and end, then choose ‘max’ or ‘min’, respectively If the current is not defined, it will default to ‘min’.
deflect_v : peak deflect_index : index of peak deflection
ipfx.sweep module¶
ipfx.sweep_props module¶
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ipfx.sweep_props.assign_sweep_states(sweep_states, sweep_features)[source]¶ Assign sweep state to all sweeps
sweep_states: dict of sweep states sweep_features: list of dics of sweep features
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ipfx.sweep_props.count_sweep_states(sweep_states)[source]¶ Count passed and total sweeps
- sweep_states: list of dicts
- Sweep state dict has keys:
- “reason”: list of strings “sweep_number”: int “passed”: True/False
- num_passed_sweeps: int
- number of sweeps passed QC
- num_sweeps: int
- number of sweeps QCed
ipfx.time_series_utils module¶
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ipfx.time_series_utils.average_voltage(v, t, start=None, end=None)[source]¶ Calculate average voltage between start and end.
v : numpy array of voltage time series in mV t : numpy array of times in seconds start : start of time window for spike detection (optional, default None) end : end of time window for spike detection (optional, default None)
v_avg : average voltage
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ipfx.time_series_utils.calculate_dvdt(v, t, filter=None)[source]¶ Low-pass filters (if requested) and differentiates voltage by time.
v : numpy array of voltage time series in mV t : numpy array of times in seconds filter : cutoff frequency for 4-pole low-pass Bessel filter in kHz (optional, default None)
dvdt : numpy array of time-derivative of voltage (V/s = mV/ms)
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ipfx.time_series_utils.find_time_index(t, t_0)[source]¶ Find the index value of a given time (t_0) in a time series (t).
t : time array t_0 : time point to find an index
idx: index of t closest to t_0
ipfx.utilities module¶
Module contents¶
Top-level package for ipfx.