clean up helper functions
This commit is contained in:
a.ott
parent
3ffa6d5dbe
commit
0adb8e98b9
@ -4,68 +4,8 @@ import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from warnings import warn
|
||||
import scipy.stats
|
||||
|
||||
|
||||
def get_subfolder_paths(basepath):
|
||||
subfolders = []
|
||||
for content in os.listdir(basepath):
|
||||
content_path = basepath + content
|
||||
if os.path.isdir(content_path):
|
||||
subfolders.append(content_path)
|
||||
|
||||
return sorted(subfolders)
|
||||
|
||||
|
||||
def get_traces(directory, trace_type, repro):
|
||||
# trace_type = 1: Voltage p-unit
|
||||
# trace_type = 2: EOD
|
||||
# trace_type = 3: local EOD ~(EOD + stimulus)
|
||||
# trace_type = 4: Stimulus
|
||||
|
||||
load_iter = dl.iload_traces(directory, repro=repro)
|
||||
|
||||
time_traces = []
|
||||
value_traces = []
|
||||
|
||||
nothing = True
|
||||
|
||||
for info, key, time, x in load_iter:
|
||||
nothing = False
|
||||
time_traces.append(time)
|
||||
value_traces.append(x[trace_type-1])
|
||||
|
||||
if nothing:
|
||||
print("iload_traces found nothing for the BaselineActivity repro!")
|
||||
|
||||
return time_traces, value_traces
|
||||
|
||||
|
||||
def get_all_traces(directory, repro):
|
||||
load_iter = dl.iload_traces(directory, repro=repro)
|
||||
|
||||
time_traces = []
|
||||
v1_traces = []
|
||||
eod_traces = []
|
||||
local_eod_traces = []
|
||||
stimulus_traces = []
|
||||
|
||||
nothing = True
|
||||
|
||||
for info, key, time, x in load_iter:
|
||||
nothing = False
|
||||
time_traces.append(time)
|
||||
v1_traces.append(x[0])
|
||||
eod_traces.append(x[1])
|
||||
local_eod_traces.append(x[2])
|
||||
stimulus_traces.append(x[3])
|
||||
print(info)
|
||||
|
||||
traces = [v1_traces, eod_traces, local_eod_traces, stimulus_traces]
|
||||
|
||||
if nothing:
|
||||
print("iload_traces found nothing for the BaselineActivity repro!")
|
||||
|
||||
return time_traces, traces
|
||||
from numba import jit
|
||||
import numba as numba
|
||||
|
||||
|
||||
def merge_similar_intensities(intensities, spiketimes, trans_amplitudes):
|
||||
@ -183,47 +123,8 @@ def calculate_mean_frequency(trial_times, trial_freqs):
|
||||
return time, mean_freq
|
||||
|
||||
|
||||
def crappy_smoothing(signal:list, window_size:int = 5) -> list:
|
||||
smoothed = []
|
||||
|
||||
for i in range(len(signal)):
|
||||
k = window_size
|
||||
if i < window_size:
|
||||
k = i
|
||||
j = window_size
|
||||
if i + j > len(signal):
|
||||
j = len(signal) - i
|
||||
|
||||
smoothed.append(np.mean(signal[i-k:i+j]))
|
||||
|
||||
return smoothed
|
||||
|
||||
|
||||
def plot_frequency_curve(cell_data, save_path: str = None, indices: list = None):
|
||||
contrast = cell_data.get_fi_contrasts()
|
||||
time_axes = cell_data.get_time_axes_mean_frequencies()
|
||||
mean_freqs = cell_data.get_mean_isi_frequencies()
|
||||
|
||||
if indices is None:
|
||||
indices = np.arange(len(contrast))
|
||||
|
||||
for i in indices:
|
||||
plt.plot(time_axes[i], mean_freqs[i], label=str(round(contrast[i], 2)))
|
||||
|
||||
if save_path is None:
|
||||
plt.show()
|
||||
else:
|
||||
plt.savefig(save_path + "mean_frequency_curves.png")
|
||||
plt.close()
|
||||
|
||||
|
||||
def rectify(x):
|
||||
if x < 0:
|
||||
return 0
|
||||
return x
|
||||
|
||||
|
||||
def calculate_coefficient_of_variation(spiketimes: list) -> float:
|
||||
# @jit(nopython=True) # only faster at around 30 000 calls
|
||||
def calculate_coefficient_of_variation(spiketimes: np.ndarray) -> float:
|
||||
# CV (stddev of ISI divided by mean ISI (np.diff(spiketimes))
|
||||
isi = np.diff(spiketimes)
|
||||
std = np.std(isi)
|
||||
@ -232,17 +133,31 @@ def calculate_coefficient_of_variation(spiketimes: list) -> float:
|
||||
return std/mean
|
||||
|
||||
|
||||
def calculate_serial_correlation(spiketimes: list, max_lag: int) -> list:
|
||||
# @jit(nopython=True) # maybe faster with more than ~60 000 calls
|
||||
def calculate_serial_correlation(spiketimes: np.ndarray, max_lag: int) -> np.ndarray:
|
||||
isi = np.diff(spiketimes)
|
||||
if len(spiketimes) < max_lag + 1:
|
||||
raise ValueError("Given list to short, with given max_lag")
|
||||
|
||||
cor = []
|
||||
cor = np.zeros(max_lag)
|
||||
for lag in range(max_lag):
|
||||
lag = lag + 1
|
||||
first = isi[:-lag]
|
||||
second = isi[lag:]
|
||||
|
||||
cor.append(np.corrcoef(first, second)[0][1])
|
||||
cor[lag-1] = np.corrcoef(first, second)[0][1]
|
||||
|
||||
return cor
|
||||
|
||||
|
||||
def __vector_strength__(relative_spike_times: np.ndarray, eod_durations: np.ndarray):
|
||||
# adapted from Ramona
|
||||
|
||||
n = len(relative_spike_times)
|
||||
if n == 0:
|
||||
return 0
|
||||
|
||||
phase_times = (relative_spike_times / eod_durations) * 2 * np.pi
|
||||
vs = np.sqrt((1 / n * np.sum(np.cos(phase_times))) ** 2 + (1 / n * np.sum(np.sin(phase_times))) ** 2)
|
||||
|
||||
return vs
|
||||
|
||||
100
introduction/old_helper_functions.py
Normal file
100
introduction/old_helper_functions.py
Normal file
@ -0,0 +1,100 @@
|
||||
|
||||
import pyrelacs.DataLoader as dl
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_subfolder_paths(basepath):
|
||||
subfolders = []
|
||||
for content in os.listdir(basepath):
|
||||
content_path = basepath + content
|
||||
if os.path.isdir(content_path):
|
||||
subfolders.append(content_path)
|
||||
|
||||
return sorted(subfolders)
|
||||
|
||||
|
||||
def get_traces(directory, trace_type, repro):
|
||||
# trace_type = 1: Voltage p-unit
|
||||
# trace_type = 2: EOD
|
||||
# trace_type = 3: local EOD ~(EOD + stimulus)
|
||||
# trace_type = 4: Stimulus
|
||||
|
||||
load_iter = dl.iload_traces(directory, repro=repro)
|
||||
|
||||
time_traces = []
|
||||
value_traces = []
|
||||
|
||||
nothing = True
|
||||
|
||||
for info, key, time, x in load_iter:
|
||||
nothing = False
|
||||
time_traces.append(time)
|
||||
value_traces.append(x[trace_type-1])
|
||||
|
||||
if nothing:
|
||||
print("iload_traces found nothing for the BaselineActivity repro!")
|
||||
|
||||
return time_traces, value_traces
|
||||
|
||||
|
||||
def get_all_traces(directory, repro):
|
||||
load_iter = dl.iload_traces(directory, repro=repro)
|
||||
|
||||
time_traces = []
|
||||
v1_traces = []
|
||||
eod_traces = []
|
||||
local_eod_traces = []
|
||||
stimulus_traces = []
|
||||
|
||||
nothing = True
|
||||
|
||||
for info, key, time, x in load_iter:
|
||||
nothing = False
|
||||
time_traces.append(time)
|
||||
v1_traces.append(x[0])
|
||||
eod_traces.append(x[1])
|
||||
local_eod_traces.append(x[2])
|
||||
stimulus_traces.append(x[3])
|
||||
print(info)
|
||||
|
||||
traces = [v1_traces, eod_traces, local_eod_traces, stimulus_traces]
|
||||
|
||||
if nothing:
|
||||
print("iload_traces found nothing for the BaselineActivity repro!")
|
||||
|
||||
return time_traces, traces
|
||||
|
||||
|
||||
def crappy_smoothing(signal:list, window_size:int = 5) -> list:
|
||||
smoothed = []
|
||||
|
||||
for i in range(len(signal)):
|
||||
k = window_size
|
||||
if i < window_size:
|
||||
k = i
|
||||
j = window_size
|
||||
if i + j > len(signal):
|
||||
j = len(signal) - i
|
||||
|
||||
smoothed.append(np.mean(signal[i-k:i+j]))
|
||||
|
||||
return smoothed
|
||||
|
||||
|
||||
def plot_frequency_curve(cell_data, save_path: str = None, indices: list = None):
|
||||
contrast = cell_data.get_fi_contrasts()
|
||||
time_axes = cell_data.get_time_axes_mean_frequencies()
|
||||
mean_freqs = cell_data.get_mean_isi_frequencies()
|
||||
|
||||
if indices is None:
|
||||
indices = np.arange(len(contrast))
|
||||
|
||||
for i in indices:
|
||||
plt.plot(time_axes[i], mean_freqs[i], label=str(round(contrast[i], 2)))
|
||||
|
||||
if save_path is None:
|
||||
plt.show()
|
||||
else:
|
||||
plt.savefig(save_path + "mean_frequency_curves.png")
|
||||
plt.close()
|
||||
Loading…
Reference in New Issue
Block a user