def multi_files_tracking(start_frame=0,
plot_particle=False,
single_particle=False,
set_frame_num=None):
file_list = glob.glob("*.nd2")
window_centers_caches = {}
for file in file_list:
center_file_name = file.split(".")[0] + "_window_centers.csv"
if os.path.isfile(center_file_name):
window_centers_caches[file] = pd.read_csv(center_file_name,
index_col=0)
else:
pic = pims.ND2_Reader(file)[0]
window_centers_caches[file] = show_and_select(
pic,
file.split(".")[0])
for file in file_list:
window_centers_xy = window_centers_caches[file]
single_file_tracking(file,
window_centers_xy,
start_frame=start_frame,
set_frame_num=set_frame_num,
plot_particle=plot_particle,
single_particle=single_particle)
def _open_nd2(filename, nd2_m, nd2_c, debug=False):
""" open nd2 file using pims-nd library """
if debug: print('... read %s with nd2_reader library' % filename)
meta = Meta(filename)
meta['openMethod'] = 'pims-nd'
with pims.ND2_Reader(filename) as tif:
if 't' in tif.sizes:
tif.bundle_axes = 'tyx'
if 'm' in tif.sizes:
if tif.sizes['m'] > 1:
tif.iter_axes = 'm'
print('... multistack image %i of %i selected' % (nd2_m + 1, tif.sizes['m']))
if 'c' in tif.sizes:
tif.default_coords['c'] = nd2_c
print('... multichannel image %i of %i selected' % (nd2_c + 1, tif.sizes['c']))
imgarray = np.array(tif[nd2_m])
meta.update_dim(tif.sizes['t'], tif.sizes['x'], tif.sizes['y'])
meta.update_exp(tif[0].metadata['t_ms'])
meta._mpp = tif.metadata['calibration_um']
return meta, imgarray
def nd2frames(nd_fh):
if nd_fh.endswith('nd2'):
frames=pims.ND2_Reader(nd_fh)
elif nd_fh.endswith('mp4'):
frames=pims.Video(nd_fh)
from pimsviewer.utils import wrap_frames_sequence
frames=wrap_frames_sequence(frames)
if nd_fh.endswith('nd2'):
if len(np.shape(frames))==4:
frames = average_z(frames)
else:
frames.default_coords['c'] = 1
frames.bundle_axes='yx'
frames.iter_axes = 't'
return frames
def single_file_tracking(file_name,
window_centers_xy=None,
half_window_size=HALF_WINDOW_SIZE,
start_frame=0,
length_per_pixel=SIZE_PER_PIXEL,
set_frame_num=None,
plot_particle=True,
sigma=1.0,
single_particle=False):
"""Tracking particles in a single file"""
frames = pims.ND2_Reader(file_name)
frame_shape = frames[0].shape
if not set_frame_num:
frame_num = frames.sizes['t'] - start_frame
else:
frame_num = min(set_frame_num, frames.sizes['t'] - start_frame)
if window_centers_xy is None:
if single_particle:
window_centers_xy = pd.DataFrame([], columns=["x", "y", "color"])
window_centers_xy.loc[1] = {
"x": frame_shape[1] // 2,
"y": frame_shape[0] // 2,
"color": "g"
}
else:
window_centers_xy = show_and_select(
frames[0],
file_name=file_name,
half_window_size=half_window_size)
for i in window_centers_xy.index:
color = window_centers_xy.loc[i, "color"]
roi_origin_rc = (window_centers_xy.loc[i, "y"] - half_window_size,
window_centers_xy.loc[i, "x"] - half_window_size)
single_window_tracking(frames,
frame_num,
roi_origin_rc,
i,
file_name,
start_frame,
color=color,
half_window_size=half_window_size,
length_per_pixel=length_per_pixel,
plot_particle=plot_particle,
sigma=sigma)
return
def nd2msd(nd_fh):
# print nd_fh
frames=pims.ND2_Reader(nd_fh)
logging.info('number of frames = %d' % len(np.shape(frames)))
if len(np.shape(frames))==4:
frames = average_z(frames)
threshold=np.percentile(frames,75)
f_batch = tp.batch(frames,diameter=11,threshold=threshold)
t = tp.link_df(f_batch, search_range=11, memory=3)
t_flt = tp.filter_stubs(t, 3*int(len(frames)/4))
try:
d = tp.compute_drift(t_flt)
t_cor = tp.subtract_drift(t_flt, d)
except:
t_cor=t_flt
logging.info("drift correction excepted")
# plt.figure()
# tp.plot_traj(t_flt)
# plt.figure()
# d.plot()
imsd=tp.imsd(t_cor,0.1,0.2, max_lagtime=100, statistic='msd')
emsd=tp.emsd(t_cor,0.1,0.2, max_lagtime=100)
return imsd,emsd