import DataParserFactory as dpf
from warnings import warn
import os
import helperFunctions as hf
import numpy as np
import matplotlib.pyplot as plt
COUNT = 0
def icelldata_of_dir(base_path, test_for_v1_trace=True):
global COUNT
for item in sorted(os.listdir(base_path)):
item_path = base_path + item
if not os.path.isdir(item_path) and not item.endswith(".nix"):
print("ignoring path: " + item_path)
print("It isn't expected to be cell data.")
continue
try:
data = CellData(item_path)
if test_for_v1_trace:
try:
trace = data.get_base_traces(trace_type=data.V1)
if len(trace) == 0:
COUNT += 1
print("NO V1 TRACE FOUND: ", item_path)
print(COUNT)
continue
except IndexError as e:
COUNT += 1
print(data.get_data_path(), "Threw Index error!")
print(COUNT)
print(str(e), "\n")
continue
except ValueError as e:
COUNT += 1
print(data.get_data_path(), "Threw Value error!")
print(COUNT)
print(str(e), "\n")
yield data
else:
yield data
except TypeError as e:
warn_msg = str(e)
warn(warn_msg)
print("Currently throw errors: {}".format(COUNT))
class CellData:
# Class to capture all the data of a single cell across all experiments (base rate, FI-curve, .?.)
# should be abstract from the way the data is saved in the background .dat vs .nix
# traces list of lists with traces: [[time], [voltage (v1)], [EOD], [local eod], [stimulus]]
TIME = 0
V1 = 1
EOD = 2
LOCAL_EOD = 3
STIMULUS = 4
def __init__(self, data_path):
self.data_path = data_path
self.parser = dpf.get_parser(data_path)
self.base_traces = None
self.base_spikes = None
self.fi_traces = None
self.fi_intensities = None
self.fi_spiketimes = None
self.fi_trans_amplitudes = None
self.mean_isi_frequencies = None
self.time_axes = None
# self.metadata = None
self.sam_spiketimes = None
self.sam_contrasts = None
self.sam_delta_fs = None
self.sam_eod_freqs = None
self.sam_durations = None
self.sam_trans_amplitudes = None
self.sampling_interval = None
self.recording_times = None
def get_data_path(self):
return self.data_path
def get_cell_name(self):
return os.path.basename(self.data_path)
def get_baseline_length(self):
return self.parser.get_baseline_length()
def get_fi_curve_contrasts_with_trial_number(self):
return self.parser.get_fi_curve_contrasts()
def get_base_traces(self, trace_type=None):
if self.base_traces is None:
self.base_traces = self.parser.get_baseline_traces()
if trace_type is None:
return self.base_traces
else:
return self.base_traces[trace_type]
def get_base_spikes(self, threshold=2.5, min_length=5000, split_step=1000, re_calculate=False, only_first=False):
if self.base_spikes is not None and not re_calculate:
return self.base_spikes
saved_spikes_file = "base_spikes_ndarray.npy"
full_path = os.path.join(self.data_path, saved_spikes_file)
if os.path.isdir(self.data_path) and os.path.exists(full_path) and not re_calculate:
self.base_spikes = np.load(full_path, allow_pickle=True)
print("Baseline spikes loaded from file.")
return self.base_spikes
if self.base_spikes is None or re_calculate:
print("Baseline spikes are being (re-)calculated...")
times = self.get_base_traces(self.TIME)
v1_traces = self.get_base_traces(self.V1)
spiketimes = []
for i in range(len(times)):
if only_first and i > 0:
break
spiketimes.append(hf.detect_spiketimes(times[i], v1_traces[i], threshold=threshold, min_length=min_length, split_step=split_step))
# plt.plot(times[0], v1_traces[0])
# idx_pos = np.array(spiketimes) / self.get_sampling_interval()
# idx_pos = np.array(np.rint(idx_pos), np.int)
#
# plt.plot(spiketimes[0], np.array(v1_traces[0])[idx_pos][0, :], 'o')
# plt.show()
self.base_spikes = np.array(spiketimes)
if os.path.isdir(self.data_path):
np.save(full_path, self.base_spikes)
print("Calculated spikes saved to file")
return self.base_spikes
def get_base_isis(self):
spikestimes = self.get_base_spikes()
isis = []
for spikes in spikestimes:
isis.extend(np.diff(spikes))
return isis
def get_fi_traces(self):
if self.fi_traces is None:
warn("Fi traces not sorted in the same way as the spiketimes!!!")
self.fi_traces = self.parser.get_fi_curve_traces()
return self.fi_traces
def get_fi_spiketimes(self):
self.__read_fi_spiketimes_info__()
return self.fi_spiketimes
def get_fi_intensities(self):
self.__read_fi_spiketimes_info__()
return self.fi_intensities
def get_fi_contrasts(self):
if self.fi_intensities is None:
self.__read_fi_spiketimes_info__()
contrast = []
for i in range(len(self.fi_intensities)):
contrast.append((self.fi_intensities[i] - self.fi_trans_amplitudes[i]) / self.fi_trans_amplitudes[i])
return contrast
def get_sam_spiketimes(self):
self.__read_sam_info__()
return self.sam_spiketimes
def get_sam_contrasts(self):
self.__read_sam_info__()
return self.sam_contrasts
def get_sam_delta_frequencies(self):
self.__read_sam_info__()
return self.sam_delta_fs
def get_sam_durations(self):
self.__read_sam_info__()
return self.sam_durations
def get_sam_eod_frequencies(self):
self.__read_sam_info__()
return self.sam_eod_freqs
def get_sam_trans_amplitudes(self):
self.__read_sam_info__()
return self.sam_trans_amplitudes
def get_mean_fi_curve_isi_frequencies(self):
if self.mean_isi_frequencies is None:
self.time_axes, self.mean_isi_frequencies = hf.all_calculate_mean_isi_frequency_traces(
self.get_fi_spiketimes(), self.get_sampling_interval())
return self.mean_isi_frequencies
def get_time_axes_fi_curve_mean_frequencies(self):
if self.time_axes is None:
self.time_axes, self.mean_isi_frequencies = hf.all_calculate_mean_isi_frequency_traces(
self.get_fi_spiketimes(), self.get_sampling_interval())
return self.time_axes
def get_base_frequency(self):
base_freqs = []
for freq in self.get_mean_fi_curve_isi_frequencies():
delay = self.get_delay()
sampling_interval = self.get_sampling_interval()
if delay < 0.1:
warn("FICurve:__calculate_f_baseline__(): Quite short delay at the start.")
idx_start = int(0.025 / sampling_interval)
idx_end = int((delay - 0.025) / sampling_interval)
base_freqs.append(np.mean(freq[idx_start:idx_end]))
return np.median(base_freqs)
def get_sampling_interval(self) -> float:
if self.sampling_interval is None:
self.sampling_interval = self.parser.get_sampling_interval()
return self.sampling_interval
def get_recording_times(self) -> list:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times
def get_time_start(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times[0]
def get_delay(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return abs(self.recording_times[0])
def get_time_end(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times[2] + self.recording_times[3]
def get_stimulus_start(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times[1]
def get_stimulus_duration(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times[2]
def get_stimulus_end(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.get_stimulus_start() + self.get_stimulus_duration()
def get_after_stimulus_duration(self) -> float:
if self.recording_times is None:
self.recording_times = self.parser.get_recording_times()
return self.recording_times[3]
def get_eod_frequency(self, recalculate=False):
eod_freq_file_name = "eod_freq_peak_based.npy"
eod_freq_file_path = os.path.join(self.get_data_path(), eod_freq_file_name)
if os.path.exists(eod_freq_file_path) and not recalculate:
print("Loaded eod_freq from file")
return np.load(eod_freq_file_path)
else:
eods = self.get_base_traces(self.EOD)
sampling_interval = self.get_sampling_interval()
frequencies = []
for eod in eods:
frequencies.append(hf.calculate_eod_frequency(eod, sampling_interval))
mean_freq = np.mean(frequencies)
np.save(eod_freq_file_path, mean_freq)
print("Saved eod freq to file.")
return mean_freq
def __read_fi_spiketimes_info__(self):
if self.fi_spiketimes is None:
self.fi_trans_amplitudes, self.fi_intensities, self.fi_spiketimes = self.parser.get_fi_curve_spiketimes()
if os.path.exists(self.get_data_path() + "/redetected_spikes.npy"):
print("overwriting fi_spiketimes with redetected ones.")
contrasts = self.get_fi_contrasts()
spikes = np.load(self.get_data_path() + "/redetected_spikes.npy", allow_pickle=True)
trace_contrasts_idx = np.load(self.get_data_path() + "/fi_traces_contrasts.npy", allow_pickle=True)
trace_max_similarity = np.load(self.get_data_path() + "/fi_traces_contrasts_similarity.npy", allow_pickle=True)
spiketimes = []
for i in range(len(contrasts)):
contrast_list = []
for j in range(len(trace_contrasts_idx)):
if trace_contrasts_idx[j] == i and trace_max_similarity[j][0] > trace_max_similarity[j][1] + 0.15:
contrast_list.append(spikes[j])
spiketimes.append(contrast_list)
self.fi_spiketimes = spiketimes
def __read_sam_info__(self):
if self.sam_spiketimes is None:
spiketimes, contrasts, delta_fs, eod_freqs, durations, trans_amplitudes = self.parser.get_sam_info()
self.sam_spiketimes = spiketimes
self.sam_contrasts = contrasts
self.sam_delta_fs = delta_fs
self.sam_eod_freqs = eod_freqs
self.sam_durations = durations
self.sam_trans_amplitudes = trans_amplitudes
# def get_metadata(self):
# self.__read_metadata__()
# return self.metadata
#
# def get_metadata_item(self, item):
# self.__read_metadata__()
# if item in self.metadata.keys():
# return self.metadata[item]
# else:
# raise KeyError("CellData:get_metadata_item: Item not found in metadata! - " + str(item))
#
# def __read_metadata__(self):
# if self.metadata is None:
# # TODO!!
# pass