Add script to import extracted df/f from suite2p

examples/read_suite2p_output.py

@@ -0,0 +1,103 @@
+from pathlib import Path
+
+import allensdk.brain_observatory.dff as dff_module
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from allensdk.brain_observatory.r_neuropil import NeuropilSubtract
+from scipy.io import loadmat
+
+
+def neuropil_subtraction(f, f_neu):
+    #  use default parameters for all methods
+    neuropil_subtraction = NeuropilSubtract()
+    neuropil_subtraction.set_F(f, f_neu)
+    neuropil_subtraction.fit()
+
+    r = neuropil_subtraction.r
+
+    f_corr = f - r * f_neu
+
+    # kernel values to be changed for 3-photon data
+    # median_kernel_long = 1213, median_kernel_short = 23
+
+    dff = 100 * dff_module.compute_dff_windowed_median(f_corr)
+
+    return dff, r
+
+
+F_path = Path(
+    "/Users/lauraporta/local_data/rotation/230822_CAA_1120509/suite2p/plane0/F.npy"
+)
+f = np.load(F_path)
+print(f.shape)
+
+Fneu_path = Path(
+    "/Users/lauraporta/local_data/rotation/230822_CAA_1120509/suite2p/plane0/Fneu.npy"
+)
+fneu = np.load(Fneu_path)
+print(fneu.shape)
+
+dff, r = neuropil_subtraction(
+    f=f,
+    f_neu=fneu,
+)
+
+dff = pd.DataFrame(dff.T)
+print(dff.shape)
+print(dff.head())
+
+
+path_randperm = Path(
+    "/Users/lauraporta/local_data/rotation/stimlus_randperm.mat"
+)
+pseudo_random = loadmat(path_randperm)
+rotation_speed = pseudo_random["stimulus_random"][:, 0]
+
+full_rotation_blocks_direction = pseudo_random["stimulus_random"][:, 2] > 0
+direction = np.where(
+    full_rotation_blocks_direction, -1, 1
+)  # 1 is counterclockwise, -1 is clockwise
+
+
+rotated_frames_path = Path(
+    "/Users/lauraporta/local_data/rotation/230822_CAA_1120509/derotated_image_stack_full.csv"
+)
+rotated_frames = pd.read_csv(rotated_frames_path)
+print(rotated_frames.head())
+print(rotated_frames.shape)
+
+full_dataframe = pd.concat([dff, rotated_frames], axis=1)
+
+subset = full_dataframe[
+    (full_dataframe["speed"] == 100) & (full_dataframe["direction"] == -1)
+]
+
+rois_selection = [4, 8, 14, 20, 23]
+
+merged_mean = pd.DataFrame()
+for roi in rois_selection:
+    mean_response = subset.loc[:, [roi, "rotation_count"]]
+    mean_response["counter"] = np.zeros(len(mean_response)) - 1
+    latest_rotation = -1
+    for idx in mean_response.index:
+        if mean_response.loc[idx, "rotation_count"] > latest_rotation:
+            counter = 0
+            latest_rotation = mean_response.loc[idx, "rotation_count"]
+        elif mean_response.loc[idx, "rotation_count"] == latest_rotation:
+            counter += 1
+        mean_response.loc[idx, "counter"] = counter
+
+    mean = mean_response.groupby("counter").mean()
+
+    # plt.plot(mean.loc[:, [roi]])
+
+    merged_mean[f"roi_{roi}"] = mean.loc[:, [roi]]
+
+custom_params = {"axes.spines.right": False, "axes.spines.top": False}
+sns.set_theme(style="ticks", rc=custom_params)
+ax = sns.lineplot(merged_mean)
+ax.set(xlabel="Frames during rotation (100 deg/s)", ylabel="ΔF/F")
+
+
+print("debug")