add demo_behavior.py back in

demos/general/demo_behavior.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python
+
+"""
+Demonstrate Caiman functions relating to behavioral experiments and optical flow
+
+This demo requires a GUI; it does not make sense to run it noninteractively.
+"""
+
+
+import argparse
+import cv2
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+
+try:
+    cv2.setNumThreads(0)
+except:
+    pass
+
+import caiman as cm
+from caiman.behavior import behavior
+from caiman.utils.utils import download_demo
+
+
+def main():
+    cfg = handle_args()
+
+    if cfg.logfile:
+        logging.basicConfig(format=
+            "[%(filename)s:%(funcName)20s():%(lineno)s] %(message)s",
+            level=logging.INFO,
+            filename=cfg.logfile)
+        # You can make the output more or less verbose by setting level to logging.DEBUG, logging.INFO, logging.WARNING, or logging.ERROR
+    else:
+        logging.basicConfig(format=
+            "[%(filename)s:%(funcName)20s():%(lineno)s] %(message)s",
+            level=logging.INFO)
+
+    if cfg.input is None:
+        # If no input is specified, use sample data, downloading if necessary
+        fnames = [download_demo('demo_behavior.h5')]
+    else:
+        fnames = cfg.input
+    # If you prefer to hardcode filenames, you could do something like this:
+    # fnames = ["/path/to/myfile1.avi", "/path/to/myfile2.avi"]
+
+    plt.ion()
+
+    # TODO: todocument
+    m = cm._load_behavior(fnames[0])
+
+    # load, rotate and eliminate useless pixels
+    m = m.transpose([0, 2, 1]) # XXX Does this really work outside this dataset?
+    m = m[:, 150:, :] # TODO adopt some syntax for clipping, or make this optional and tell the user to clip before running
+
+    # visualize movie
+    m.play()
+
+    # select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
+    print("Please draw a polygon delimiting the ROI on the image that will be displayed after the image; press enter when done")
+    mask = np.array(behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
+
+    n_components = 4  # number of movement looked for
+    resize_fact = 0.5  # for computational efficiency movies are downsampled
+    # number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
+    num_std_mag_for_angle = .6
+    only_magnitude = False  # if onlu interested in factorizing over the magnitude
+    method_factorization = 'dict_learn'  # could also use nmf
+    # number of iterations for the dictionary learning algorithm (Marial et al, 2010)
+    max_iter_DL = -30
+
+    spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(m, n_components, mask=mask,
+                                                                                           resize_fact=resize_fact, only_magnitude=only_magnitude, verbose=True, method_factorization='dict_learn', max_iter_DL=max_iter_DL)
+
+
+    mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
+        spatial_filter_, time_trace_, of_or, num_std_mag_for_angle=num_std_mag_for_angle, sav_filter_size=3, only_magnitude=only_magnitude)
+
+    idd = 0
+    axlin = pl.subplot(n_components, 2, 2)
+    for mag, dirct, spatial_filter in zip(mags, dircts_thresh, spatial_filter_):
+        pl.subplot(n_components, 2, 1 + idd * 2)
+        min_x, min_y = np.min(np.where(mask), 1)
+
+        spfl = spatial_filter
+        spfl = cm.movie(spfl[None, :, :]).resize(
+            1 / resize_fact, 1 / resize_fact, 1).squeeze()
+        max_x, max_y = np.add((min_x, min_y), np.shape(spfl))
+
+        mask[min_x:max_x, min_y:max_y] = spfl
+        mask[mask < np.nanpercentile(spfl, 70)] = np.nan
+        pl.imshow(m[0], cmap='gray')
+        pl.imshow(mask, alpha=.5)
+        pl.axis('off')
+
+        axelin = pl.subplot(n_components, 2, 2 + idd * 2, sharex=axlin)
+        pl.plot(mag / 10, 'k')
+        dirct[mag < 0.5 * np.std(mag)] = np.nan
+        pl.plot(dirct, 'r-', linewidth=2)
+
+        idd += 1
+
+def handle_args():
+    parser = argparse.ArgumentParser(description="Demonstrate behavioural/optic flow functions")
+    parser.add_argument("--input", action="append", help="File(s) to work on, provide multiple times for more files")
+    parser.add_argument("--logfile",    help="If specified, log to the named file")
+    return parser.parse_args()
+
+########
+main()