clean up helper functions

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()