clean up helper functions

helperFunctions.py

@@ -4,68 +4,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 from warnings import warn
 import scipy.stats
-
+from numba import jit
-
+import numba as numba
-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 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:
+# @jit(nopython=True)  # only faster at around 30 000 calls
-    smoothed = []
+def calculate_coefficient_of_variation(spiketimes: np.ndarray) -> float:
-
-    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:
     # 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

introduction/old_helper_functions.py

@@ -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()