cooperative_channels_in_biological_neurons_via_dynamic_clamp/scripts/extract_stimulations.py

import re

import pandas as pd

import tools.pyrelacs as pr
from tools.helper import stimulation_metadata_raw


def extract_value_unit(value_unit_string):
    value_unit_pattern = re.compile('(\d+[e][+]\d*|[-+]?\d*\.\d+|\d+)\s*(\w+)')
    value, unit = value_unit_pattern.match(value_unit_string).groups()
    return float(value), unit


def convert_to_sec(time_value, time_unit):
    if time_unit == "ms":
        time_value *= 0.001
    return time_value


overview_of_recordings = "metadata/cells.csv"
test_protocol_for_pa = "SingleStimulus"

print "# Extract stimulation protocols"
print "Search the recordings listed in {}".format(overview_of_recordings)

print "Collect all instances of the stimulation protocol {} used to test for persistent activity".format(
    test_protocol_for_pa)

cell_recordings = pd.read_csv(overview_of_recordings, header=0)

cell_recordings.columns = cell_recordings.columns.str.strip()
for column in cell_recordings.columns:
    cell_recordings[column] = cell_recordings[column].str.strip()

cells = cell_recordings["CellId"].unique()
stimulation_id = []
cell_ids = []
recordings = []
before_protocols = []
after_protocols = []
stimulus_lengths = []
pulse_periods = []
pulse_lengths = []
pulse_amplitudes = []
pulse_offsets = []
dts = []
start_indices = []
end_indices = []

parameter_dict={}

before_stimulation_interval = 2
after_stimulation_interval = 8

for cell in cells:
    recording_counter = 0
    for _, recording in cell_recordings[cell_recordings["CellId"] == cell].iterrows():
        cell_id = cell
        recording_dir = "recordings/"+recording["Recording"]
        for info, key, dat in pr.iload('%s/stimuli.dat' % (recording_dir,)):
            if info[1]['RePro'] != test_protocol_for_pa:
                continue
            repro_starting_idx = dat[1, 0]
            left_appendum, right_appendum = pr.get_trace_indices(info, before_stimulation_interval,
                                                                 after_stimulation_interval)
            dt = pr.get_sampling_interval(info)

            stimulation_id.append("{:s}_R{:03d}".format(cell_id, recording_counter))
            cell_ids.append(cell_id)
            recordings.append(recording_dir)
            available_model_parameters = map(lambda key: info[0][key], filter(lambda key: key.startswith("identifier") and key != "identifier1", info[0].keys()))
            parameter_values = [dat[0][key[1].index(param)+1] for param in available_model_parameters]
            for param, value in zip(available_model_parameters, parameter_values):
                try:
                    parameter_dict[param].append(value)
                except KeyError as err:
                    parameter_dict[param] = [value]
            dts.append(dt)

            stimulus_length, unit = extract_value_unit(info[1]["duration"])
            stimulus_length = convert_to_sec(stimulus_length, unit)
            pulse_period, unit = extract_value_unit(info[1]["period"])
            pulse_period = convert_to_sec(pulse_period, unit)
            pulse_length, unit = extract_value_unit(info[1]["pulseduration"])
            pulse_length = convert_to_sec(pulse_length, unit)
            pulse_amplitude, _ = extract_value_unit(info[1]["amplitude"])
            pulse_offset, _ = extract_value_unit(info[1]["offset"])

            before_protocols.append(before_stimulation_interval)
            after_protocols.append(after_stimulation_interval)
            stimulus_lengths.append(stimulus_length)
            pulse_periods.append(pulse_period)
            pulse_lengths.append(pulse_length)
            pulse_amplitudes.append(pulse_amplitude)
            pulse_offsets.append(pulse_offset)
            start_indices.append(repro_starting_idx - left_appendum)
            end_indices.append(repro_starting_idx + right_appendum)
            recording_counter += 1

dictionary_of_stimulations = {
    "stimulation_id": stimulation_id,
    "cell_id": cell_ids,
    "recording": recordings,
    "dt": dts,
    "before_protocol": before_protocols,
    "after_protocol": after_protocols,
    "stimulus_length": stimulus_lengths,
    "pulse_period": pulse_periods,
    "pulse_length": pulse_lengths,
    "pulse_amplitude": pulse_amplitudes,
    "pulse_offset": pulse_offsets,
    "start_index": start_indices,
    "end_index": end_indices
}

dictionary_of_stimulations.update(parameter_dict)

pd.DataFrame(data=dictionary_of_stimulations).set_index("stimulation_id").to_csv(path_or_buf=stimulation_metadata_raw)

print "Gathered meta data for each stimulation and saved to {}".format(stimulation_metadata_raw)
print ""