initials

tubingen/code/position_heatmap.py

@@ -0,0 +1,183 @@
+from numpy.lib.function_base import angle
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import ticker
+import glob
+import os
+from IPython import embed
+import pycircstat as stats
+
+from tracking_result import TrackingResult
+
+fish_color = {'2019lepto03': {'main': 'red', 'dark': 'darkred', 'light': 'indianred'},
+              '2020lepto20': {'main': 'orange','dark': 'sienna', 'light': 'sandybrown'},
+              '2020lepto48': {'main': 'gold', 'dark': 'darkgoldenrod', 'light': 'yellow'},
+              '2020lepto40': {'main': 'yellowgreen', 'dark': 'seagreen', 'light': 'mediumspringgreen'},
+              '2020lepto42': {'main': 'turquoise', 'dark': 'darkslategrey', 'light': 'aquamarine'},
+              '2020lepto43': {'main': 'cornflowerblue', 'dark': 'midnightblue', 'light': 'dodgerblue'},
+              '2020lepto46': {'main': 'm', 'dark': 'darkmagenta', 'light': 'violet'}}
+
+stimulus_angles = {1: 0, 2: 300 / 180 * np.pi, 3: 240/180 * np.pi, 4: np.pi, 5: 120/180 * np.pi, 6: 60/120 * np.pi}
+light_conditions = ["light", "dark"]
+stimulus_conditions = ["m", "f", "n"]
+
+def plot_angles(angles, stimulus="", dark_light="", ax=None, color=None):
+    if color is None:
+        dark_color = "tab:blue"
+    else:
+        dark_color = color
+
+    if ax is None:
+        fig = plt.figure()
+        ax = fig.add_subplot(1,1,1, polar=True)
+    edges = np.linspace(0, 2*np.pi, 25)[:-1]
+    n, bins, patches = ax.hist(angles / 180 * np.pi, bins=edges, density=True, align="left", color=dark_color, alpha=1.0 if dark_light == "dark" else 0.5)
+    mid_angles = bins[1:] - np.diff(bins)/2
+    mean_angle = stats.mean(angles/ 180 * np.pi)
+    std_angle = stats.std(angles/ 180 * np.pi)
+
+    ax.set_title(r"stim: %s, light: %s, $\sigma$=%.2f" %(stimulus, dark_light, std_angle/np.pi*180),
+                 fontsize=7)
+    ax.set_xticks(np.linspace(0.0, 2*np.pi, 7)[:-1])
+    #ax.set_xticklabels(map(str, np.linspace(0, 300, 6)))
+    ax.xaxis.set_major_formatter(ticker.FixedFormatter(list(map(lambda x:"%i$^\circ$" %x, np.linspace(0, 300, 6)))))
+    ax.tick_params(which="major", labelsize=8)
+    ax.set_yticklabels([])
+    ymax = ax.get_ylim()[-1]
+    ax.plot([mean_angle, mean_angle],[0.0, ymax], lw=1, ls="solid", color="red")
+    if "n" not in stimulus:
+        ax.scatter(0.0, ymax, marker="*", color="red", s=50, zorder=2)
+    if ax is None:
+        plt.show()
+
+
+def get_trial_metadata(df, results_filename):
+    conditions = get_trial_conditions(df, results_filename)
+    stim_angle = None
+    if not np.isnan(conditions.signal_output.values):
+        stim_angle = stimulus_angles[int(conditions.signal_output)]
+    release_angle = stimulus_angles[int(conditions.release)]
+    subject = conditions.subject.values[0]
+    stimulus_type = conditions.stimulus.values[0]
+    light_condition = conditions.dark_light.values[0]
+    return stim_angle, release_angle, subject, stimulus_type, light_condition
+
+
+def get_trial_conditions(df, results_filename):
+    movie_name = results_filename.split("/")[-1].split("DLC")[0] + ".mp4"
+    trial_condition = df[df.dateiname == movie_name]
+    return trial_condition
+
+
+def get_angles(results_filename, min_likelihood=0.95):
+    tr = TrackingResult(results_filename)
+    angles = tr.angle_to_center("snout", min_likelihood=min_likelihood)
+    return angles
+
+
+def get_all_angles(df, subject, stimulus_condition="n", light_condition="dark", data_folder=os.path.join("..", "data"), min_likelihood=0.95, rotate_no_stim=False):
+    subject_trials = df[df.subject == subject]
+    trials = subject_trials[(subject_trials.stimulus == stimulus_condition) & (subject_trials.dark_light == light_condition)]
+    all_angles = []
+    all_angles_joined = []
+    print(len(trials))
+    for movie in trials.dateiname.values:
+        results_files = glob.glob(os.path.join(data_folder, "%s%s"% (movie.split(".mp4")[0], "*.h5")))
+        results_file = results_files[0] if len(results_files) > 0 else None
+        if results_file is None:
+            print("Could not find tracking results for movie %s!" % movie)
+            continue
+        angles = get_angles(results_file, min_likelihood=min_likelihood)
+        if len(angles) == 0:
+            print("Skipping movie %s, no data!" % movie)
+            continue
+        stimulus_angle, release_angle, _, _, _ = get_trial_metadata(df, results_file)
+        if stimulus_condition != "n":
+            if stimulus_angle is None:
+                print("Stimulus angle is None for movie %s, stimulus %s" % (movie, stimulus_condition))
+                continue
+            angles += (360 - stimulus_angle)
+            angles[angles>360] -= 360
+        else:
+            if rotate_no_stim:
+                angles += (360 - release_angle)
+                angles[angles>360] -= 360
+
+        all_angles.append(angles)
+        all_angles_joined.extend(angles)
+        
+    return all_angles, all_angles_joined
+
+
+def characterize_behavior(phis):
+    """[summary]
+
+    Zugewandt =  3/2 π   <   𝞅   ≤   π/2 
+    Zugewandt =  270   <   𝞅   ≤   90 
+    
+    Abgewandt =  π/2  <  𝞅   ≤   3/2 π
+    Abgewandt =  90  <  𝞅   ≤   270
+
+    Interagierend = 7/8 π  <  𝞅  ≤  1/4 π
+    Interagierend = 315  <  𝞅   ≤ 45
+
+    Vermeiden = 3/4 π <  𝞅   ≤  5/4 π
+    Vermeiden = 135 <  𝞅   ≤  225
+    """
+    attracted = []
+    repelled = []
+    interacting = []
+    avoiding = []
+
+    for i in range(len(phis)):
+        phi = phis[i]
+        attracted.append(len(phi[(phi > 270) | (phi <= 90)]) / len(phi))
+        repelled.append(len(phi[(phi > 90) & (phi <= 270)]) / len(phi))
+        interacting.append(len(phi[(phi > 315) | (phi <= 45)]) / len(phi))
+        avoiding.append(len(phi[(phi > 135) & (phi <= 225)]) / len(phi))
+
+    return attracted, repelled, interacting, avoiding
+
+
+def characterize_behavior_all(phis):
+    attracted = len(phis[(phis > 270) | (phis <= 90)]) / len(phis)
+    repelled = len(phis[(phis > 90) & (phis <= 270)]) / len(phis)
+    interacting = len(phis[(phis > 315) | (phis <= 45)]) / len(phis)
+    avoiding = len(phis[(phis > 135) & (phis <= 225)]) / len(phis)
+
+    return attracted, repelled, interacting, avoiding
+
+if __name__ == "__main__":
+    df = pd.read_csv("../data/Tuebingen_tabelle_fertig.csv", sep=",", index_col=0)
+    files = glob.glob("../data/*.h5")
+    
+    for f in files[:1]:
+        angles = get_angles(f)
+        stimulus_angle, release_angle, subject, stimulus_condition, light_condition = get_trial_metadata(df, f)
+    
+    subjects = df.subject.unique()
+    subplot_labels = ["A", "B", "C"]
+    results = []
+    for subject in subjects:
+        fig = plt.figure(figsize=(7, 5))
+        for i, light in enumerate(light_conditions):
+            for j, stim in enumerate(stimulus_conditions):
+                print(subject, light, stim)
+                axis = fig.add_subplot(len(light_conditions), len(stimulus_conditions), i * len(stimulus_conditions) + j + 1, polar=True)
+                all, all_joined = get_all_angles(df, subject, stimulus_condition=stim, light_condition=light, min_likelihood=0.85, rotate_no_stim=True)
+
+                if len(all_joined) > 0:
+                    attracted, repelled, interacting, avoiding = characterize_behavior_all(np.array(all_joined))
+                    results.append({"subject": subject, "light_condition": light, "stimulus": stim, "attracted": attracted, 
+                                    "repelled": repelled, "interacting": interacting, "avoiding": avoiding})
+                    plot_angles(np.array(all_joined), stim, light, ax=axis, color=fish_color[subject]["dark"])
+                if i == 0:
+                    axis.text(-0.2, 1.15, subplot_labels[j], transform=axis.transAxes, fontsize=12, fontweight="bold")
+        fig.subplots_adjust(wspace=0.45, hspace=0.25, left=0.07, top=0.925, right=0.975, bottom=0.05)
+        fig.savefig("%s_preferred_angles.pdf" % subject)
+        plt.close()
+    results_df = pd.DataFrame(results)
+    embed()
+
+# does the fish go to the previous stimulus location?

tubingen/data/Tuebingen_tabelle_fertig.csv

@@ -0,0 +1,316 @@
+,subject,dateiname,date,day,dark_light,trial_number,signal_output,release,stimulus
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+4,2020lepto40,2021.02.05_5.mp4,05_02_21,1,light,4,,1,n
+5,2020lepto40,2021.02.05_6.mp4,05_02_21,1,light,5,,3,n
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