def see(filename=None):
"""shows you the important data about a certain test file ( just give the number or name)
if you give nothing it will give you back the groundtruth infos
Parameters:
-----------
self: dictionnary
the object of this class tha tcontains every value
filename:
( just give the number or name)
See Also:
---------
@image html caiman/tests/comparison/data.pdf
"""
if filename == None:
dr = os.path.join(caiman_datadir(), "testdata", "groundtruth.npz")
else:
dr = os.path.join(caiman_datadir(), "testdata", filename,
filename + ".npz")
print(dr)
with np.load(dr) as dt:
print('here is the info :\n')
see_it(dt)
def evaluate_components_CNN(A, dims, gSig, model_name=os.path.join(caiman_datadir(), 'model', 'cnn_model'), patch_size=50, loaded_model=None, isGPU=False):
""" evaluate component quality using a CNN network
"""
import os
if not isGPU:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# try:
os.environ["KERAS_BACKEND"] = "tensorflow"
from keras.models import model_from_json
# except:
# print('PROBLEM LOADING KERAS: cannot use classifier')
if loaded_model is None:
if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".json")):
model_file = os.path.join(caiman_datadir(), model_name + ".json")
model_weights = os.path.join(caiman_datadir(), model_name + ".h5")
elif os.path.isfile(model_name + ".json"):
model_file = model_name + ".json"
model_weights = model_name + ".h5"
else:
raise FileNotFoundError("File for requested model {} not found".format(model_name))
with open(model_file, 'r') as json_file:
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_name + '.h5')
loaded_model.compile('sgd', 'mse')
print("Loaded model from disk")
half_crop = np.minimum(
gSig[0] * 4 + 1, patch_size), np.minimum(gSig[1] * 4 + 1, patch_size)
dims = np.array(dims)
coms = [scipy.ndimage.center_of_mass(
mm.toarray().reshape(dims, order='F')) for mm in A.tocsc().T]
coms = np.maximum(coms, half_crop)
coms = np.array([np.minimum(cms, dims - half_crop)
for cms in coms]).astype(np.int)
crop_imgs = [mm.toarray().reshape(dims, order='F')[com[0] - half_crop[0]:com[0] + half_crop[0],
com[1] - half_crop[1]:com[1] + half_crop[1]] for mm, com in zip(A.tocsc().T, coms)]
final_crops = np.array([cv2.resize(
im / np.linalg.norm(im), (patch_size, patch_size)) for im in crop_imgs])
predictions = loaded_model.predict(
final_crops[:, :, :, np.newaxis], batch_size=32, verbose=1)
return predictions, final_crops
def evaluate_components_CNN(A, dims, gSig, model_name=os.path.join(caiman_datadir(), 'model', 'cnn_model'), patch_size=50, loaded_model=None, isGPU=False):
""" evaluate component quality using a CNN network
"""
import os
if not isGPU:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# try:
os.environ["KERAS_BACKEND"] = "tensorflow"
from keras.models import model_from_json
# except:
# print('PROBLEM LOADING KERAS: cannot use classifier')
if loaded_model is None:
if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".json")):
model_file = os.path.join(caiman_datadir(), model_name + ".json")
model_weights = os.path.join(caiman_datadir(), model_name + ".h5")
elif os.path.isfile(model_name + ".json"):
model_file = model_name + ".json"
model_weights = model_name + ".h5"
else:
raise FileNotFoundError("File for requested model {} not found".format(model_name))
with open(model_file, 'r') as json_file:
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_name + '.h5')
loaded_model.compile('sgd', 'mse')
print("Loaded model from disk")
half_crop = np.minimum(
gSig[0] * 4 + 1, patch_size), np.minimum(gSig[1] * 4 + 1, patch_size)
dims = np.array(dims)
coms = [scipy.ndimage.center_of_mass(
mm.toarray().reshape(dims, order='F')) for mm in A.tocsc().T]
coms = np.maximum(coms, half_crop)
coms = np.array([np.minimum(cms, dims - half_crop)
for cms in coms]).astype(np.int)
crop_imgs = [mm.toarray().reshape(dims, order='F')[com[0] - half_crop[0]:com[0] + half_crop[0],
com[1] - half_crop[1]:com[1] + half_crop[1]] for mm, com in zip(A.tocsc().T, coms)]
final_crops = np.array([cv2.resize(
im / np.linalg.norm(im), (patch_size, patch_size)) for im in crop_imgs])
predictions = loaded_model.predict(
final_crops[:, :, :, np.newaxis], batch_size=32, verbose=1)
return predictions, final_crops
def handle_args():
global sourcedir_base
parser = argparse.ArgumentParser(
description="Tool to manage Caiman data directory")
parser.add_argument("command",
help="Subcommand to run. install/check/test/demotest")
parser.add_argument(
"--inplace",
action='store_true',
help=
"Use only if you did an inplace install of caiman rather than a pure one"
)
parser.add_argument(
"--force",
action='store_true',
help=
"In installs, overwrite parts of an old caiman dir that changed upstream"
)
cfg = parser.parse_args()
if cfg.inplace:
# In this configuration, the user did a "pip install -e ." and so the share directory was not made.
# We assume the user is running caimanmanager right out of the source tree, and still want to try to
# copy the correct files out, which is a little tricky because we never kept track of that before.
sourcedir_base = os.getcwd()
cfg.userdir = caiman_datadir()
return cfg
def test_tf():
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
try:
model_name = os.path.join(caiman_datadir(), 'model', 'cnn_model')
if use_keras:
model_file = model_name + ".json"
with open(model_file, 'r') as json_file:
print('USING MODEL:' + model_file)
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_name + '.h5')
loaded_model.compile('sgd', 'mse')
else:
model_file = model_name + ".h5.pb"
loaded_model = load_graph(model_file)
except:
raise Exception('NN model could not be loaded. use_keras = ' +
str(use_keras))
A = np.random.randn(10, 50, 50, 1)
try:
if use_keras:
predictions = loaded_model.predict(A, batch_size=32)
else:
tf_in = loaded_model.get_tensor_by_name('prefix/conv2d_20_input:0')
tf_out = loaded_model.get_tensor_by_name('prefix/output_node0:0')
with tf.Session(graph=loaded_model) as sess:
predictions = sess.run(tf_out, feed_dict={tf_in: A})
pass
except:
raise Exception('NN model could not be deployed. use_keras = ' +
str(use_keras))
def main():
fnames = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')]
movie = cm.load(fnames)
movie = movie.astype(np.float)
# makes estimation numerically better:
movie -= movie.mean()
# use one every 200 frames
temporal_stride = 200
# use one every 8 patches (patches are 8x8 by default)
spatial_stride = 8
movie_train = movie[::temporal_stride]
t = timeit.default_timer()
estimation_res = est.estimate_vst_movie(movie_train, stride=spatial_stride)
print('\tTime', timeit.default_timer() - t)
alpha = estimation_res.alpha
sigma_sq = estimation_res.sigma_sq
movie_gat = compute_gat(movie, sigma_sq, alpha=alpha)
# save movie_gat here
movie_gat_inv = compute_inverse_gat(movie_gat, sigma_sq, alpha=alpha,
method='asym')
# save movie_gat_inv here
return movie, movie_gat_inv
def download_demo(name='Sue_2x_3000_40_-46.tif', save_folder=''):
"""download a file from the file list with the url of its location
using urllib, you can add you own name and location in this global parameter
Parameters:
-----------
name: str
the path of the file correspondong to a file in the filelist (''Sue_2x_3000_40_-46.tif' or 'demoMovieJ.tif')
save_folder: str
folder inside ./example_movies to which the files will be saved. Will be created if it doesn't exist
Raise:
---------
WrongFolder Exception
"""
#\bug
#\warning
file_dict = {
'Sue_2x_3000_40_-46.tif':
'https://www.dropbox.com/s/09z974vkeg3t5gn/Sue_2x_3000_40_-46.tif?dl=1',
'demoMovieJ.tif':
'https://www.dropbox.com/s/8j1cnqubye3asmu/demoMovieJ.tif?dl=1',
'demo_behavior.h5':
'https://www.dropbox.com/s/53jmhc9sok35o82/movie_behavior.h5?dl=1',
'Tolias_mesoscope_1.hdf5':
'https://www.dropbox.com/s/t1yt35u0x72py6r/Tolias_mesoscope_1.hdf5?dl=1',
'Tolias_mesoscope_2.hdf5':
'https://www.dropbox.com/s/i233b485uxq8wn6/Tolias_mesoscope_2.hdf5?dl=1',
'Tolias_mesoscope_3.hdf5':
'https://www.dropbox.com/s/4fxiqnbg8fovnzt/Tolias_mesoscope_3.hdf5?dl=1',
'data_endoscope.tif':
'https://www.dropbox.com/s/dcwgwqiwpaz4qgc/data_endoscope.tif?dl=1'
}
# ,['./example_movies/demoMovie.tif','https://www.dropbox.com/s/obmtq7305ug4dh7/demoMovie.tif?dl=1']]
base_folder = os.path.join(caiman_datadir(), 'example_movies')
if os.path.exists(base_folder):
if not os.path.isdir(os.path.join(base_folder, save_folder)):
os.makedirs(os.path.join(base_folder, save_folder))
path_movie = os.path.join(base_folder, save_folder, name)
if not os.path.exists(path_movie):
url = file_dict[name]
print("downloading " + name + "with urllib")
f = urlopen(url)
data = f.read()
with open(path_movie, "wb") as code:
code.write(data)
else:
print("File already downloaded")
else:
raise Exception('You are in ' + os.getcwd() +
' and must be in caiman folder')
return path_movie
def demo(parallel=False):
p = 2 # order of the AR model (in general 1 or 2)
if parallel:
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
else:
n_processes, dview = 2, None
# LOAD MOVIE AND MEMORYMAP
fname_new = cm.save_memmap([os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')],
base_name='Yr',
order = 'C')
Yr, dims, T = cm.load_memmap(fname_new)
# INIT
cnm = cnmf.CNMF(n_processes, method_init='greedy_roi', k=30, gSig=[4, 4], merge_thresh=.8,
p=p, dview=dview, Ain=None, method_deconvolution='oasis')
# FIT
images = np.reshape(Yr.T, [T] + list(dims), order='F')
cnm = cnm.fit(images)
if parallel:
cm.cluster.stop_server(dview=dview)
# verifying the spatial components
npt.assert_allclose(cnm.A.sum(), 281.1, 1e-2)
# verifying the temporal components
npt.assert_allclose(cnm.C.sum(), 66271668, 1e-2)
try:
dview.terminate()
except:
pass
def main():
fnames = [
os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')
]
movie = cm.load(fnames)
movie = movie.astype(np.float)
# makes estimation numerically better:
movie -= movie.mean()
# use one every 200 frames
temporal_stride = 200
# use one every 8 patches (patches are 8x8 by default)
spatial_stride = 8
movie_train = movie[::temporal_stride]
t = timeit.default_timer()
estimation_res = est.estimate_vst_movie(movie_train, stride=spatial_stride)
print('\tTime', timeit.default_timer() - t)
alpha = estimation_res.alpha
sigma_sq = estimation_res.sigma_sq
movie_gat = compute_gat(movie, sigma_sq, alpha=alpha)
# save movie_gat here
movie_gat_inv = compute_inverse_gat(movie_gat,
sigma_sq,
alpha=alpha,
method='asym')
# save movie_gat_inv here
return movie, movie_gat_inv
def download_demo(name:str='Sue_2x_3000_40_-46.tif', save_folder:str='') -> str:
"""download a file from the file list with the url of its location
using urllib, you can add you own name and location in this global parameter
Args:
name: str
the path of the file correspondong to a file in the filelist (''Sue_2x_3000_40_-46.tif' or 'demoMovieJ.tif')
save_folder: str
folder inside ./example_movies to which the files will be saved. Will be created if it doesn't exist
Returns:
Path of the saved file
Raise:
WrongFolder Exception
"""
#\bug
#\warning
file_dict = {'Sue_2x_3000_40_-46.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/Sue_2x_3000_40_-46.tif',
'demoMovieJ.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/demoMovieJ.tif',
'demo_behavior.h5': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/demo_behavior.h5',
'Tolias_mesoscope_1.hdf5': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_1.hdf5',
'Tolias_mesoscope_2.hdf5': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_2.hdf5',
'Tolias_mesoscope_3.hdf5': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_3.hdf5',
'data_endoscope.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/data_endoscope.tif',
'gmc_960_30mw_00001_red.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/gmc_960_30mw_00001_red.tif',
'gmc_960_30mw_00001_green.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/gmc_960_30mw_00001_green.tif',
'msCam13.avi': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/msCam13.avi',
'alignment.pickle': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/alignment.pickle',
'data_dendritic.tif': 'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/2014-04-05-003.tif'}
# ,['./example_movies/demoMovie.tif','https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/demoMovie.tif']]
base_folder = os.path.join(caiman_datadir(), 'example_movies')
if os.path.exists(base_folder):
if not os.path.isdir(os.path.join(base_folder, save_folder)):
os.makedirs(os.path.join(base_folder, save_folder))
path_movie = os.path.join(base_folder, save_folder, name)
if not os.path.exists(path_movie):
url = file_dict[name]
logging.info(f"downloading {name} with urllib")
logging.info(f"GET {url} HTTP/1.1")
try:
f = urlopen(url)
except:
logging.info(f"Trying to set user agent to download demo")
from urllib.request import Request
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
f = urlopen(req)
data = f.read()
with open(path_movie, "wb") as code:
code.write(data)
else:
logging.info("File " + str(name) + " already downloaded")
else:
raise Exception('Cannot find the example_movies folder in your caiman_datadir - did you make one with caimanmanager.py?')
return path_movie
def do_run_demotests(targdir: str) -> None:
out, err, ret = runcmd([os.path.join(caiman_datadir(), "test_demos.sh")])
if ret != 0:
print("Demos failed with return code " + str(ret))
sys.exit(ret)
else:
print("Demos success!")
def mrcnn():
summary_image = np.load(
'/home/nel/data/voltage_data/volpy_paper/memory/summary.npz'
)['arr_0']
config = neurons.NeuronsConfig()
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MAX_DIM = 512
RPN_NMS_THRESHOLD = 0.7
POST_NMS_ROIS_INFERENCE = 1000
config = InferenceConfig()
config.display()
model_dir = os.path.join(caiman_datadir(), 'model')
DEVICE = "/cpu:0" # /cpu:0 or /gpu:0
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference",
model_dir=model_dir,
config=config)
weights_path = download_model('mask_rcnn')
model.load_weights(weights_path, by_name=True)
results = model.detect([summary_image], verbose=1)
r = results[0]
ROIs = r['masks'].transpose([2, 0, 1])
return ROIs
def main():
pass # For compatibility between running under Spyder and the CLI
# %% load data
fname = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')]
# %% set up some parameters
fr = 10 # frame rate (Hz)
decay_time = .75 # approximate length of transient event in seconds
gSig = [6, 6] # expected half size of neurons
p = 1 # order of AR indicator dynamics
min_SNR = 1 # minimum SNR for accepting candidate components
thresh_CNN_noisy = 0.65 # CNN threshold for candidate components
gnb = 2 # number of background components
init_method = 'cnmf' # initialization method
# set up CNMF initialization parameters
init_batch = 400 # number of frames for initialization
patch_size = 32 # size of patch
stride = 3 # amount of overlap between patches
K = 4 # max number of components in each patch
params_dict = {'fr': fr,
'fnames': fname,
'decay_time': decay_time,
'gSig': gSig,
'p': p,
'min_SNR': min_SNR,
'nb': gnb,
'init_batch': init_batch,
'init_method': init_method,
'rf': patch_size//2,
'stride': stride,
'sniper_mode': True,
'thresh_CNN_noisy': thresh_CNN_noisy,
'K': K}
opts = cnmf.params.CNMFParams(params_dict=params_dict)
# %% fit with online object
cnm = cnmf.online_cnmf.OnACID(params=opts)
cnm.fit_online()
# %% plot contours
logging.info('Number of components:' + str(cnm.estimates.A.shape[-1]))
Cn = cm.load(fname[0], subindices=slice(0,500)).local_correlations(swap_dim=False)
cnm.estimates.plot_contours(img=Cn)
# %% pass through the CNN classifier with a low threshold (keeps clearer neuron shapes and excludes processes)
use_CNN = True
if use_CNN:
# threshold for CNN classifier
opts.set('quality', {'min_cnn_thr': 0.05})
cnm.estimates.evaluate_components_CNN(opts)
cnm.estimates.plot_contours(img=Cn, idx=cnm.estimates.idx_components)
# %% plot results
cnm.estimates.view_components(img=Cn, idx=cnm.estimates.idx_components)
def get_run_logdir():
""" Returns the path to the directory where the model will be saved.
The directory will be locates inside the caiman_data/my_logs.
"""
root_logdir = os.path.join(caiman_datadir(), "my_logs")
if not os.path.exists(root_logdir):
os.mkdir(root_logdir)
run_id = time.strftime("run_%Y_%m_%d-%H_%M_%S")
return os.path.join(root_logdir, run_id)
def download_demo(name='Sue_2x_3000_40_-46.tif', save_folder=''):
"""download a file from the file list with the url of its location
using urllib, you can add you own name and location in this global parameter
Args:
name: str
the path of the file correspondong to a file in the filelist (''Sue_2x_3000_40_-46.tif' or 'demoMovieJ.tif')
save_folder: str
folder inside ./example_movies to which the files will be saved. Will be created if it doesn't exist
Raise:
WrongFolder Exception
"""
#\bug
#\warning
file_dict = {
'Sue_2x_3000_40_-46.tif':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/Sue_2x_3000_40_-46.tif',
'demoMovieJ.tif':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/demoMovieJ.tif',
'demo_behavior.h5':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/demo_behavior.h5',
'Tolias_mesoscope_1.hdf5':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_1.hdf5',
'Tolias_mesoscope_2.hdf5':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_2.hdf5',
'Tolias_mesoscope_3.hdf5':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/Tolias_mesoscope_3.hdf5',
'data_endoscope.tif':
'https://users.flatironinstitute.org/~neuro/caiman_downloadables/data_endoscope.tif'
}
# ,['./example_movies/demoMovie.tif','https://users.flatironinstitute.org/~neuro/caiman_downloadables/demoMovie.tif']]
base_folder = os.path.join(caiman_datadir(), 'example_movies')
if os.path.exists(base_folder):
if not os.path.isdir(os.path.join(base_folder, save_folder)):
os.makedirs(os.path.join(base_folder, save_folder))
path_movie = os.path.join(base_folder, save_folder, name)
if not os.path.exists(path_movie):
url = file_dict[name]
logging.info("downloading " + str(name) + " with urllib")
f = urlopen(url)
data = f.read()
with open(path_movie, "wb") as code:
code.write(data)
else:
logging.info("File " + str(name) + " already downloaded")
else:
raise Exception(
'Cannot find the example_movies folder in your caiman_datadir - did you make one with caimanmanager.py?'
)
return path_movie
def mrcnn_inference(img, weights_path, display_result=True):
""" Mask R-CNN inference in VolPy
Args:
img: 2-D array
summary images for detection
weights_path: str
path for Mask R-CNN weight
display_result: boolean
if True, the function will plot the result of inference
Return:
ROIs: 3-D array
region of interests
(# of components * # of pixels in x dim * # of pixels in y dim)
"""
from caiman.source_extraction.volpy.mrcnn import visualize, neurons
import caiman.source_extraction.volpy.mrcnn.model as modellib
config = neurons.NeuronsConfig()
class InferenceConfig(config.__class__):
# Run detection on one img at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MAX_DIM = 512
RPN_NMS_THRESHOLD = 0.7
POST_NMS_ROIS_INFERENCE = 1000
config = InferenceConfig()
config.display()
model_dir = os.path.join(caiman_datadir(), 'model')
DEVICE = "/cpu:0" # /cpu:0 or /gpu:0
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference",
model_dir=model_dir,
config=config)
model.load_weights(weights_path, by_name=True)
results = model.detect([img], verbose=1)
r = results[0]
ROIs = r['masks'].transpose([2, 0, 1])
if display_result:
_, ax = plt.subplots(1, 1, figsize=(16, 16))
visualize.display_instances(img,
r['rois'],
r['masks'],
r['class_ids'], ['BG', 'neurons'],
r['scores'],
ax=ax,
title="Predictions")
return ROIs
def download_model(name: str = 'mask_rcnn', save_folder: str = '') -> str:
"""download a NN model from the file list with the url of its location
using urllib, you can add you own name and location in this global parameter
Args:
name: str
the path of the file correspondong to a file in the filelist
save_folder: str
folder inside caiman_data/model to which the files will be saved. Will be created if it doesn't exist
Returns:
Path of the saved file
Raise:
WrongFolder Exception
"""
#\bug
#\warning
file_dict = {
'mask_rcnn':
'https://caiman.flatironinstitute.org/~neuro/caiman_downloadables/model/mask_rcnn_neurons_0040.h5'
}
base_folder = os.path.join(caiman_datadir(), 'model')
if os.path.exists(base_folder):
if not os.path.isdir(os.path.join(base_folder, save_folder)):
os.makedirs(os.path.join(base_folder, save_folder))
path_movie = os.path.join(base_folder, save_folder, name)
if not os.path.exists(path_movie):
url = file_dict[name]
logging.info(f"downloading {name} with urllib")
logging.info(f"GET {url} HTTP/1.1")
try:
f = urlopen(url)
except:
logging.info(f"Trying to set user agent to download demo")
from urllib.request import Request
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
f = urlopen(req)
data = f.read()
with open(path_movie, "wb") as code:
code.write(data)
else:
logging.info("File " + str(name) + " already downloaded")
else:
raise Exception(
'Cannot find the model folder in your caiman_datadir - did you make one with caimanmanager.py?'
)
return path_movie
def download_demo(name='Sue_2x_3000_40_-46.tif', save_folder=''):
"""download a file from the file list with the url of its location
using urllib, you can add you own name and location in this global parameter
Parameters:
-----------
name: str
the path of the file correspondong to a file in the filelist (''Sue_2x_3000_40_-46.tif' or 'demoMovieJ.tif')
save_folder: str
folder inside ./example_movies to which the files will be saved. Will be created if it doesn't exist
Raise:
---------
WrongFolder Exception
"""
#\bug
#\warning
file_dict = {'Sue_2x_3000_40_-46.tif': 'https://www.dropbox.com/s/09z974vkeg3t5gn/Sue_2x_3000_40_-46.tif?dl=1',
'demoMovieJ.tif': 'https://www.dropbox.com/s/8j1cnqubye3asmu/demoMovieJ.tif?dl=1',
'demo_behavior.h5': 'https://www.dropbox.com/s/53jmhc9sok35o82/movie_behavior.h5?dl=1',
'Tolias_mesoscope_1.hdf5': 'https://www.dropbox.com/s/t1yt35u0x72py6r/Tolias_mesoscope_1.hdf5?dl=1',
'Tolias_mesoscope_2.hdf5': 'https://www.dropbox.com/s/i233b485uxq8wn6/Tolias_mesoscope_2.hdf5?dl=1',
'Tolias_mesoscope_3.hdf5': 'https://www.dropbox.com/s/4fxiqnbg8fovnzt/Tolias_mesoscope_3.hdf5?dl=1',
'data_endoscope.tif': 'https://www.dropbox.com/s/dcwgwqiwpaz4qgc/data_endoscope.tif?dl=1'}
# ,['./example_movies/demoMovie.tif','https://www.dropbox.com/s/obmtq7305ug4dh7/demoMovie.tif?dl=1']]
base_folder = os.path.join(caiman_datadir(), 'example_movies')
if os.path.exists(base_folder):
if not os.path.isdir(os.path.join(base_folder, save_folder)):
os.makedirs(os.path.join(base_folder, save_folder))
path_movie = os.path.join(base_folder, save_folder, name)
if not os.path.exists(path_movie):
url = file_dict[name]
print("downloading " + name + "with urllib")
f = urlopen(url)
data = f.read()
with open(path_movie, "wb") as code:
code.write(data)
else:
print("File already downloaded")
else:
raise Exception('Cannot find the example_movies folder in your caiman_datadir - did you make one with caimanmanager.py?')
return path_movie
def get_caiman_version() -> Tuple[str, str]:
""" Get the version of CaImAn, as best we can determine"""
# This does its best to determine the version of CaImAn. This uses the first successful
# from these methods:
# 'GITW' ) git rev-parse if caiman is built from "pip install -e ." and we are working
# out of the checkout directory (the user may have since updated without reinstall)
# 'RELF') A release file left in the process to cut a release. Should have a single line
# in it whick looks like "Version:1.4"
# 'FILE') The date of some frequently changing files, which act as a very rough
# approximation when no other methods are possible
#
# Data is returned as a tuple of method and version, with method being the 4-letter string above
# and version being a format-dependent string
# Attempt 'GITW'.
# TODO:
# A) Find a place to do it that's better than cwd
# B) Hide the output from the terminal
try:
rev = subprocess.check_output(
["git", "rev-parse", "HEAD"],
stderr=subprocess.DEVNULL).decode("utf-8").split("\n")[0]
except:
rev = None
if rev is not None:
return 'GITW', rev
# Attempt: 'RELF'
relfile = os.path.join(caiman_datadir(), 'RELEASE')
if os.path.isfile(relfile):
with open(relfile, 'r') as sfh:
for line in sfh:
if ':' in line: # expect a line like "Version:1.3"
_, version = line.rstrip().split(':')
return 'RELF', version
# Attempt: 'FILE'
# Right now this samples the utils directory
modpath = os.path.dirname(
inspect.getfile(caiman.utils)
) # Probably something like /mnt/home/pgunn/miniconda3/envs/caiman/lib/python3.7/site-packages/caiman
newest = 0
for fn in os.listdir(modpath):
last_modified = os.stat(os.path.join(modpath, fn)).st_mtime
if last_modified > newest:
newest = last_modified
return 'FILE', str(int(newest))
def demo():
fname = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')]
fr = 10 # frame rate (Hz)
decay_time = .75 # approximate length of transient event in seconds
gSig = [6, 6] # expected half size of neurons
p = 1 # order of AR indicator dynamics
min_SNR = 1 # minimum SNR for accepting candidate components
thresh_CNN_noisy = 0.65 # CNN threshold for candidate components
gnb = 2 # number of background components
init_method = 'cnmf' # initialization method
# set up CNMF initialization parameters
init_batch = 400 # number of frames for initialization
patch_size = 32 # size of patch
stride = 3 # amount of overlap between patches
K = 4 # max number of components in each patch
params_dict = {
'fr': fr,
'fnames': fname,
'decay_time': decay_time,
'gSig': gSig,
'p': p,
'motion_correct': False,
'min_SNR': min_SNR,
'nb': gnb,
'init_batch': init_batch,
'init_method': init_method,
'rf': patch_size // 2,
'stride': stride,
'sniper_mode': True,
'thresh_CNN_noisy': thresh_CNN_noisy,
'K': K
}
opts = cnmf.params.CNMFParams(params_dict=params_dict)
cnm = cnmf.online_cnmf.OnACID(params=opts)
cnm.fit_online()
cnm.save('test_online.hdf5')
cnm2 = cnmf.online_cnmf.load_OnlineCNMF('test_online.hdf5')
npt.assert_allclose(cnm.estimates.A.sum(), cnm2.estimates.A.sum())
npt.assert_allclose(cnm.estimates.C, cnm2.estimates.C)
def demo(parallel=False):
p = 2 # order of the AR model (in general 1 or 2)
if parallel:
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
else:
n_processes, dview = 2, None
# LOAD MOVIE AND MEMORYMAP
fname_new = cm.save_memmap(
[os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')],
base_name='Yr',
order='C')
Yr, dims, T = cm.load_memmap(fname_new)
# INIT
cnm = cnmf.CNMF(n_processes,
method_init='greedy_roi',
k=30,
gSig=[4, 4],
merge_thresh=.8,
p=p,
dview=dview,
Ain=None,
method_deconvolution='oasis')
# FIT
images = np.reshape(Yr.T, [T] + list(dims), order='F')
cnm = cnm.fit(images)
if parallel:
cm.cluster.stop_server(dview=dview)
# verifying the spatial components
npt.assert_allclose(cnm.estimates.A.sum(), 281.1, 1e-2)
# verifying the temporal components
npt.assert_allclose(cnm.estimates.C.sum(), 66271668, 1e-2)
try:
dview.terminate()
except:
pass
import caiman as cm
from caiman.components_evaluation import estimate_components_quality_auto
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.paths import caiman_datadir
#%% start a cluster
c, dview, n_processes =\
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
#%% save files to be processed
# This datafile is distributed with Caiman
fnames = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')]
# location of dataset (can actually be a list of filed to be concatenated)
add_to_movie = -np.min(cm.load(fnames[0], subindices=range(200))).astype(float)
# determine minimum value on a small chunk of data
add_to_movie = np.maximum(add_to_movie, 0)
# if minimum is negative subtract to make the data non-negative
base_name = 'Yr'
name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name,
add_to_movie=add_to_movie)
name_new.sort()
fname_new = cm.save_memmap_join(name_new, base_name='Yr', dview=dview)
#%% LOAD MEMORY MAPPABLE FILE
Yr, dims, T = cm.load_memmap(fname_new)
d1, d2 = dims
images = np.reshape(Yr.T, [T] + list(dims), order='F')
def main():
pass # For compatibility between running under Spyder and the CLI
# %% start a cluster
c, dview, n_processes =\
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
# %% set up some parameters
fnames = [
os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')
]
# file(s) to be analyzed
is_patches = True # flag for processing in patches or not
fr = 10 # approximate frame rate of data
decay_time = 5.0 # length of transient
if is_patches: # PROCESS IN PATCHES AND THEN COMBINE
rf = 10 # half size of each patch
stride = 4 # overlap between patches
K = 4 # number of components in each patch
else: # PROCESS THE WHOLE FOV AT ONCE
rf = None # setting these parameters to None
stride = None # will run CNMF on the whole FOV
K = 30 # number of neurons expected (in the whole FOV)
gSig = [6, 6] # expected half size of neurons
merge_thresh = 0.80 # merging threshold, max correlation allowed
p = 2 # order of the autoregressive system
gnb = 2 # global background order
params_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'rf': rf,
'stride': stride,
'K': K,
'gSig': gSig,
'merge_thr': merge_thresh,
'p': p,
'nb': gnb
}
opts = params.CNMFParams(params_dict=params_dict)
# %% Now RUN CaImAn Batch (CNMF)
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = cnm.fit_file()
# %% plot contour plots of components
Cns = local_correlations_movie_offline(fnames[0],
remove_baseline=True,
swap_dim=False,
window=1000,
stride=1000,
winSize_baseline=100,
quantil_min_baseline=10,
dview=dview)
Cn = Cns.max(axis=0)
cnm.estimates.plot_contours(img=Cn)
# %% load memory mapped file
Yr, dims, T = cm.load_memmap(cnm.mmap_file)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# %% refit
cnm2 = cnm.refit(images, dview=dview)
# %% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier (this will pick up only neurons
# and filter out active processes)
min_SNR = 2 # peak SNR for accepted components (if above this, acept)
rval_thr = 0.85 # space correlation threshold (if above this, accept)
use_cnn = True # use the CNN classifier
min_cnn_thr = 0.99 # if cnn classifier predicts below this value, reject
cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
cnm2.params.set(
'quality', {
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': use_cnn,
'min_cnn_thr': min_cnn_thr,
'cnn_lowest': cnn_lowest
})
cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview)
# %% visualize selected and rejected components
cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components)
# %% visualize selected components
cnm2.estimates.view_components(images,
idx=cnm2.estimates.idx_components,
img=Cn)
#%% only select high quality components (destructive)
# cnm2.estimates.select_components(use_object=True)
# cnm2.estimates.plot_contours(img=Cn)
#%% save results
cnm2.estimates.Cn = Cn
cnm2.save(cnm2.mmap_file[:-4] + 'hdf5')
# %% play movie with results (original, reconstructed, amplified residual)
cnm2.estimates.play_movie(images, magnification=4)
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('Yr*_LOG_*')
for log_file in log_files:
os.remove(log_file)
def main():
pass # For compatibility between running under Spyder and the CLI
#%% Select file(s) to be processed (download if not present)
fnames = [
os.path.join(caiman_datadir(), 'example_movies/Sue_2x_3000_40_-46.nwb')
]
# estimates save path can be same or different from raw data path
save_path = os.path.join(
caiman_datadir(),
'example_movies/Sue_2x_3000_40_-46_CNMF_estimates.nwb')
# filename to be created or processed
# dataset dependent parameters
fr = 15. # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
#%% load the file and save it in the NWB format (if it doesn't exist already)
if not os.path.exists(fnames[0]):
fnames_orig = 'Sue_2x_3000_40_-46.tif' # filename to be processed
if fnames_orig in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fnames_orig = [download_demo(fnames_orig)]
orig_movie = cm.load(fnames_orig, fr=fr)
# save file in NWB format with various additional info
orig_movie.save(fnames[0],
sess_desc='test',
identifier='demo 1',
exp_desc='demo movie',
imaging_plane_description='single plane',
emission_lambda=520.0,
indicator='GCAMP6f',
location='parietal cortex',
experimenter='Sue Ann Koay',
lab_name='Tank Lab',
institution='Princeton U',
experiment_description='Experiment Description',
session_id='Session 1',
var_name_hdf5='TwoPhotonSeries')
#%% First setup some parameters for data and motion correction
# motion correction parameters
dxy = (2., 2.) # spatial resolution in x and y in (um per pixel)
# note the lower than usual spatial resolution here
max_shift_um = (12., 12.) # maximum shift in um
patch_motion_um = (100., 100.) # patch size for non-rigid correction in um
pw_rigid = True # flag to select rigid vs pw_rigid motion correction
# maximum allowed rigid shift in pixels
max_shifts = [int(a / b) for a, b in zip(max_shift_um, dxy)]
# start a new patch for pw-rigid motion correction every x pixels
strides = tuple([int(a / b) for a, b in zip(patch_motion_um, dxy)])
# overlap between patches (size of patch in pixels: strides+overlaps)
overlaps = (24, 24)
# maximum deviation allowed for patch with respect to rigid shifts
max_deviation_rigid = 3
mc_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'dxy': dxy,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': 'copy',
'var_name_hdf5': 'acquisition/TwoPhotonSeries'
}
opts = params.CNMFParams(params_dict=mc_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = False
if display_images:
m_orig = cm.load_movie_chain(fnames,
var_name_hdf5=opts.data['var_name_hdf5'])
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames,
dview=dview,
var_name_hdf5=opts.data['var_name_hdf5'],
**opts.get_group('motion'))
# note that the file is not loaded in memory
# %% Run (piecewise-rigid motion) correction using NoRMCorre
mc.motion_correct(save_movie=True)
# %% compare with original movie
if display_images:
m_orig = cm.load_movie_chain(fnames,
var_name_hdf5=opts.data['var_name_hdf5'])
m_els = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate([
m_orig.resize(1, 1, ds_ratio) - mc.min_mov * mc.nonneg_movie,
m_els.resize(1, 1, ds_ratio)
],
axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit
# %% MEMORY MAPPING
border_to_0 = 0 if mc.border_nan is 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
# memory map the file in order 'C'
fname_new = cm.save_memmap(mc.mmap_file,
base_name='memmap_',
order='C',
border_to_0=border_to_0) # exclude borders
# now load the file
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# load frames in python format (T x X x Y)
# %% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
# %% parameters for source extraction and deconvolution
p = 1 # order of the autoregressive system
gnb = 2 # number of global background components
merge_thr = 0.85 # merging threshold, max correlation allowed
rf = 15
# half-size of the patches in pixels. e.g., if rf=25, patches are 50x50
stride_cnmf = 6 # amount of overlap between the patches in pixels
K = 4 # number of components per patch
gSig = [4, 4] # expected half size of neurons in pixels
# initialization method (if analyzing dendritic data using 'sparse_nmf')
method_init = 'greedy_roi'
ssub = 2 # spatial subsampling during initialization
tsub = 2 # temporal subsampling during intialization
# parameters for component evaluation
opts_dict = {
'fnames': fnames,
'fr': fr,
'nb': gnb,
'rf': rf,
'K': K,
'gSig': gSig,
'stride': stride_cnmf,
'method_init': method_init,
'rolling_sum': True,
'merge_thr': merge_thr,
'n_processes': n_processes,
'only_init': True,
'ssub': ssub,
'tsub': tsub
}
opts.change_params(params_dict=opts_dict)
# %% RUN CNMF ON PATCHES
# First extract spatial and temporal components on patches and combine them
# for this step deconvolution is turned off (p=0)
opts.change_params({'p': 0})
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = cnm.fit(images)
# %% ALTERNATE WAY TO RUN THE PIPELINE AT ONCE
# you can also perform the motion correction plus cnmf fitting steps
# simultaneously after defining your parameters object using
# cnm1 = cnmf.CNMF(n_processes, params=opts, dview=dview)
# cnm1.fit_file(motion_correct=True)
# %% plot contours of found components
Cn = cm.local_correlations(images, swap_dim=False)
Cn[np.isnan(Cn)] = 0
cnm.estimates.plot_contours(img=Cn)
plt.title('Contour plots of found components')
#%% save results in a separate file (just for demonstration purposes)
cnm.estimates.Cn = Cn
cnm.save(fname_new[:-4] + 'hdf5')
#cm.movie(Cn).save(fname_new[:-5]+'_Cn.tif')
# %% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution
cnm.params.change_params({'p': p})
cnm2 = cnm.refit(images, dview=dview)
# %% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier
min_SNR = 2 # signal to noise ratio for accepting a component
rval_thr = 0.85 # space correlation threshold for accepting a component
cnn_thr = 0.99 # threshold for CNN based classifier
cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
cnm2.params.set(
'quality', {
'decay_time': decay_time,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': True,
'min_cnn_thr': cnn_thr,
'cnn_lowest': cnn_lowest
})
cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview)
#%%
cnm2.estimates.Cn = Cn
cnm2.save(fname_new[:-4] + 'hdf5')
# %% PLOT COMPONENTS
cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components)
# %% VIEW TRACES (accepted and rejected)
if display_images:
cnm2.estimates.view_components(images,
img=Cn,
idx=cnm2.estimates.idx_components)
cnm2.estimates.view_components(images,
img=Cn,
idx=cnm2.estimates.idx_components_bad)
#%% update object with selected components
# cnm2.estimates.select_components(use_object=True)
#%% Extract DF/F values
cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250)
#%% Show final traces
cnm2.estimates.view_components(img=Cn)
#%% reconstruct denoised movie (press q to exit)
if display_images:
cnm2.estimates.play_movie(images,
q_max=99.9,
gain_res=2,
magnification=2,
bpx=border_to_0,
include_bck=False) # background not shown
#%% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
#%% save the results in the original NWB file
cnm2.estimates.save_NWB(save_path,
imaging_rate=fr,
session_start_time=datetime.now(tzlocal()),
raw_data_file=fnames[0])
def save_with_compare(self,
istruth=False,
params=None,
dview=None,
Cn=None):
"""save the comparison as well as the images of the precision recall calculations
depending on if we say this file will be ground truth or not, it wil be saved in either the tests or the ground truth folder
if saved in test, a comparison to groundtruth will be added to the object
this comparison will be on
data : a normized difference of the normalized value of the arrays
time : difference
in order for this function to work, you must
have previously given it the cnm objects after initializing them ( on patch and full frame)
give the values of the time and data
have a groundtruth
Args:
self: dictionnary
the object of this class tha tcontains every value
istruth: Boolean
if we want it ot be the ground truth
params:
movie parameters
dview :
your dview object
n_frames_per_bin:
you need to know those data before
they have been given to the base/rois functions
dims_test:
you need to know those data before
they have been given to the base/rois functions
Cn:
your correlation image
Cmap:
a particular colormap for your Cn
See Also:
Example of utilisation on Demo Pipeline
\image caiman/tests/comparison/data.pdf
Raises:
('we now have ground truth\n')
('we were not able to read the file to compare it\n')
"""
# getting the DATA FOR COMPARISONS
assert (params != None and self.cnmpatch != None)
logging.info('we need the parameters in order to save anything\n')
# actions on the sparse matrix
cnm = self.cnmpatch.__dict__
cnmpatch = deletesparse(cnm)
# initialization
dims_test = [self.dims[0], self.dims[1]]
dims_gt = dims_test
dt = datetime.datetime.today()
dt = str(dt)
plat = plt.platform()
plat = str(plat)
pro = plt.processor()
pro = str(pro)
# we store a big file which contains everything (INFORMATION)
information = {
'platform': plat,
'time': dt,
'processor': pro,
'params': params,
'cnmpatch': cnmpatch,
'timer': {
'cnmf_on_patch': self.comparison['cnmf_on_patch']['timer'],
'cnmf_full_frame': self.comparison['cnmf_full_frame']['timer'],
'rig_shifts': self.comparison['rig_shifts']['timer']
}
}
rootdir = os.path.abspath(cm.__path__[0])[:-7]
file_path = os.path.join(caiman_datadir(), "testdata",
"groundtruth.npz")
# OPENINGS
# if we want to set this data as truth
if istruth:
# we just save it
if os._exists(file_path):
os.remove(file_path)
logging.debug("nothing to remove\n")
np.savez_compressed(
file_path,
information=information,
A_full=self.comparison['cnmf_full_frame']['ourdata'][0],
C_full=self.comparison['cnmf_full_frame']['ourdata'][1],
A_patch=self.comparison['cnmf_on_patch']['ourdata'][0],
C_patch=self.comparison['cnmf_on_patch']['ourdata'][1],
rig_shifts=self.comparison['rig_shifts']['ourdata'])
logging.info('we now have ground truth\n')
return
else: # if not we create a comparison first
try:
with np.load(file_path, encoding='latin1') as dt:
rig_shifts = dt['rig_shifts'][()]
A_patch = dt['A_patch'][()]
A_full = dt['A_full'][()]
C_full = dt['C_full'][()]
C_patch = dt['C_patch'][()]
data = dt['information'][()]
# if we cannot manage to open it or it doesnt exist:
except (IOError, OSError):
# we save but we explain why there were a problem
logging.warning('we were not able to read the file ' +
str(file_path) + ' to compare it\n')
file_path = os.path.join(caiman_datadir(), "testdata",
"NC" + dt + ".npz")
np.savez_compressed(
file_path,
information=information,
A_full=self.comparison['cnmf_full_frame']['ourdata'][0],
C_full=self.comparison['cnmf_full_frame']['ourdata'][1],
A_patch=self.comparison['cnmf_on_patch']['ourdata'][0],
C_patch=self.comparison['cnmf_on_patch']['ourdata'][1],
rig_shifts=self.comparison['rig_shifts']['ourdata'])
return
# creating the FOLDER to store our data
# XXX Is this still hooked up to anything?
i = 0
dr = os.path.join(caiman_datadir(), "testdata")
for name in os.listdir(dr):
i += 1
i = str(i)
if not os.path.exists(dr + i):
os.makedirs(dr + i)
information.update({'diff': {}})
information.update({
'differences': {
'proc': False,
'params_movie': False,
'params_cnm': False
}
})
# INFORMATION FOR THE USER
if data['processor'] != information['processor']:
logging.info(
"you don't have the same processor as groundtruth.. the time difference can vary"
" because of that\n try recreate your own groundtruth before testing. Compare: "
+ str(data['processor']) + " to " +
str(information['processor']) + "\n")
information['differences']['proc'] = True
if data['params'] != information['params']:
logging.warning(
"you are not using the same movie parameters... Things can go wrong"
)
logging.warning(
'you must use the same parameters to compare your version of the code with '
'the groundtruth one. look for the groundtruth parameters with the see() method\n'
)
information['differences']['params_movie'] = True
# We must cleanup some fields to permit an accurate comparison
if not normalised_compare_cnmpatches(data['cnmpatch'], cnmpatch):
if data['cnmpatch'].keys() != cnmpatch.keys():
logging.error(
'DIFFERENCES IN THE FIELDS OF CNMF'
) # TODO: Now that we have deeply nested data structures, find a module that gives you tight differences.
diffkeys = [
k for k in data['cnmpatch']
if data['cnmpatch'][k] != cnmpatch[k]
]
for k in diffkeys:
logging.info("{}:{}->{}".format(k, data['cnmpatch'][k],
cnmpatch[k]))
logging.warning(
'you are not using the same parameters in your cnmf on patches initialization\n'
)
information['differences']['params_cnm'] = True
# for rigid
# plotting part
information['diff'].update({
'rig':
plotrig(init=rig_shifts,
curr=self.comparison['rig_shifts']['ourdata'],
timer=self.comparison['rig_shifts']['timer'] -
data['timer']['rig_shifts'],
sensitivity=self.comparison['rig_shifts']['sensitivity'])
})
try:
pl.gcf().savefig(dr + str(i) + '/' + 'rigidcorrection.pdf')
pl.close()
except:
pass
# for cnmf on patch
information['diff'].update({
'cnmpatch':
cnmf(Cn=Cn,
A_gt=A_patch,
A_test=self.comparison['cnmf_on_patch']['ourdata'][0],
C_gt=C_patch,
C_test=self.comparison['cnmf_on_patch']['ourdata'][1],
dview=dview,
sensitivity=self.comparison['cnmf_on_patch']['sensitivity'],
dims_test=dims_test,
dims_gt=dims_gt,
timer=self.comparison['cnmf_on_patch']['timer'] -
data['timer']['cnmf_on_patch'])
})
try:
pl.gcf().savefig(dr + i + '/' + 'onpatch.pdf')
pl.close()
except:
pass
# CNMF FULL FRAME
information['diff'].update({
'cnmfull':
cnmf(Cn=Cn,
A_gt=A_full,
A_test=self.comparison['cnmf_full_frame']['ourdata'][0],
C_gt=C_full,
C_test=self.comparison['cnmf_full_frame']['ourdata'][1],
dview=dview,
sensitivity=self.comparison['cnmf_full_frame']['sensitivity'],
dims_test=dims_test,
dims_gt=dims_gt,
timer=self.comparison['cnmf_full_frame']['timer'] -
data['timer']['cnmf_full_frame'])
})
try:
pl.gcf().savefig(dr + i + '/' + 'cnmfull.pdf')
pl.close()
except:
pass
# Saving of everything
target_dir = os.path.join(caiman_datadir(), "testdata", i)
if not os.path.exists(target_dir):
os.makedirs(
os.path.join(caiman_datadir(), "testdata", i)
) # XXX If we ever go Python3, just use the exist_ok flag to os.makedirs
file_path = os.path.join(target_dir, i + ".npz")
np.savez_compressed(
file_path,
information=information,
A_full=self.comparison['cnmf_full_frame']['ourdata'][0],
C_full=self.comparison['cnmf_full_frame']['ourdata'][1],
A_patch=self.comparison['cnmf_on_patch']['ourdata'][0],
C_patch=self.comparison['cnmf_on_patch']['ourdata'][1],
rig_shifts=self.comparison['rig_shifts']['ourdata'])
self.information = information
fontScale=1.2,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
'Frame = ' + str(t),
(vid_frame.shape[1] // 2 - vid_frame.shape[1] // 10,
vid_frame.shape[0] - 20),
fontFace=5,
fontScale=1.2,
color=(0, 255, 255),
thickness=1)
return vid_frame
#%% Prepare object for OnACID
cnm2 = deepcopy(cnm_init)
path_to_model = os.path.join(caiman_datadir(), 'model',
'cnn_model_online.h5')
if save_init:
cnm_init.dview = None
save_object(cnm_init,
os.path.abspath(fls[0]) + '_DS_' + str(ds_factor) + '.pkl')
cnm_init = load_object(
os.path.abspath(fls[0]) + '_DS_' + str(ds_factor) + '.pkl')
t1 = time()
cnm2._prepare_object(np.asarray(Yr),
T1,
expected_comps,
idx_components=None,
min_num_trial=min_num_trial,
def main():
pass # For compatibility between running under Spyder and the CLI
# %% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fnames = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
logging.info(fnames)
# %% Set up some parameters
fr = 10 # frame rate (Hz)
decay_time = 2 # approximate length of transient event in seconds
gSig = (3, 3) # expected half size of neurons
p = 1 # order of AR indicator dynamics
min_SNR = 1 # minimum SNR for accepting new components
ds_factor = 1 # spatial downsampling factor (increases speed but may lose some fine structure)
gnb = 2 # number of background components
gSig = tuple(np.ceil(
np.array(gSig) /
ds_factor).astype('int')) # recompute gSig if downsampling is involved
mot_corr = False # flag for online motion correction
pw_rigid = True # flag for pw-rigid motion correction (slower but potentially more accurate)
max_shifts_online = 6 # maximum allowed shift during motion correction
sniper_mode = True # use a CNN to detect new neurons (o/w space correlation)
rval_thr = 0.9 # soace correlation threshold for candidate components
# set up some additional supporting parameters needed for the algorithm
# (these are default values but can change depending on dataset properties)
init_batch = 200 # number of frames for initialization (presumably from the first file)
K = 2 # initial number of components
epochs = 2 # number of passes over the data
show_movie = False # show the movie as the data gets processed
params_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'gSig': gSig,
'p': p,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'ds_factor': ds_factor,
'nb': gnb,
'motion_correct': mot_corr,
'init_batch': init_batch,
'init_method': 'bare',
'normalize': True,
'sniper_mode': sniper_mode,
'K': K,
'epochs': epochs,
'max_shifts_online': max_shifts_online,
'pw_rigid': pw_rigid,
'dist_shape_update': True,
'min_num_trial': 10,
'show_movie': show_movie
}
opts = cnmf.params.CNMFParams(params_dict=params_dict)
# %% fit online
cnm = cnmf.online_cnmf.OnACID(params=opts)
cnm.fit_online()
# %% plot contours (this may take time)
logging.info('Number of components: ' + str(cnm.estimates.A.shape[-1]))
images = cm.load(fnames)
Cn = images.local_correlations(swap_dim=False, frames_per_chunk=500)
cnm.estimates.plot_contours(img=Cn, display_numbers=False)
# %% view components
cnm.estimates.view_components(img=Cn)
# %% plot timing performance (if a movie is generated during processing, timing
# will be severely over-estimated)
T_motion = 1e3 * np.array(cnm.t_motion)
T_detect = 1e3 * np.array(cnm.t_detect)
T_shapes = 1e3 * np.array(cnm.t_shapes)
T_track = 1e3 * np.array(cnm.t_online) - T_motion - T_detect - T_shapes
plt.figure()
plt.stackplot(np.arange(len(T_motion)), T_motion, T_track, T_detect,
T_shapes)
plt.legend(labels=['motion', 'tracking', 'detect', 'shapes'], loc=2)
plt.title('Processing time allocation')
plt.xlabel('Frame #')
plt.ylabel('Processing time [ms]')
#%% RUN IF YOU WANT TO VISUALIZE THE RESULTS (might take time)
c, dview, n_processes = \
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
if opts.online['motion_correct']:
shifts = cnm.estimates.shifts[-cnm.estimates.C.shape[-1]:]
if not opts.motion['pw_rigid']:
memmap_file = cm.motion_correction.apply_shift_online(
images, shifts, save_base_name='MC')
else:
mc = cm.motion_correction.MotionCorrect(fnames,
dview=dview,
**opts.get_group('motion'))
mc.y_shifts_els = [[sx[0] for sx in sh] for sh in shifts]
mc.x_shifts_els = [[sx[1] for sx in sh] for sh in shifts]
memmap_file = mc.apply_shifts_movie(fnames,
rigid_shifts=False,
save_memmap=True,
save_base_name='MC')
else: # To do: apply non-rigid shifts on the fly
memmap_file = images.save(fnames[0][:-4] + 'mmap')
cnm.mmap_file = memmap_file
Yr, dims, T = cm.load_memmap(memmap_file)
# images = np.reshape(Yr.T, [T] + list(dims), order='F')
# min_SNR = 2 # peak SNR for accepted components (if above this, acept)
# rval_thr = 0.85 # space correlation threshold (if above this, accept)
# use_cnn = True # use the CNN classifier
# min_cnn_thr = 0.99 # if cnn classifier predicts below this value, reject
# cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
#
# cnm.params.set('quality', {'min_SNR': min_SNR,
# 'rval_thr': rval_thr,
# 'use_cnn': use_cnn,
# 'min_cnn_thr': min_cnn_thr,
# 'cnn_lowest': cnn_lowest})
#
# cnm.estimates.evaluate_components(images, cnm.params, dview=dview)
cnm.estimates.Cn = Cn
cnm.save(os.path.splitext(fnames[0])[0] + '_results.hdf5')
dview.terminate()
def main():
pass # For compatibility between running under Spyder and the CLI
c, dview, n_processes =\
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
# %% set up some parameters
fnames = [os.path.join(caiman_datadir(), 'split', 'first3000-ch1.tif'),
os.path.join(caiman_datadir(), 'split', 'second3000-ch1.tif')]
is_patches = True # flag for processing in patches or not
fr = 1.5 # approximate frame rate of data
decay_time = 5.0 # length of transient
if is_patches: # PROCESS IN PATCHES AND THEN COMBINE
rf = 20 # half size of each patch
stride = 4 # overlap between patches
K = 2 # number of components in each patch
else: # PROCESS THE WHOLE FOV AT ONCE
rf = None # setting these parameters to None
stride = None # will run CNMF on the whole FOV
K = 10 # number of neurons expected (in the whole FOV)
gSig = [6, 6] # expected half size of neurons
merge_thresh = 0.80 # merging threshold, max correlation allowed
p = 2 # order of the autoregressive system
gnb = 2 # global background order
params_dict = {'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'rf': rf,
'stride': stride,
'K': K,
'gSig': gSig,
'merge_thr': merge_thresh,
'p': p,
'nb': gnb}
opts = params.CNMFParams(params_dict=params_dict)
# %% Now RUN CaImAn Batch (CNMF)
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
#cnm.estimates.normalize_components()
cnm = cnm.fit_file()
# %% plot contour plots of components
Cn = cm.load(fnames[0], subindices=slice(1000)).local_correlations(swap_dim=False)
cnm.estimates.plot_contours(img=Cn)
# %% load memory mapped file
Yr, dims, T = cm.load_memmap(cnm.mmap_file)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# %% refit
cnm2 = cnm.refit(images, dview=dview)
# %% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier (this will pick up only neurons
# and filter out active processes)
min_SNR = 2 # peak SNR for accepted components (if above this, acept)
rval_thr = 0.85 # space correlation threshold (if above this, accept)
use_cnn = False # use the CNN classifier
min_cnn_thr = 0.99 # if cnn classifier predicts below this value, reject
cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
cnm2.params.set('quality', {'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': use_cnn,
'min_cnn_thr': min_cnn_thr,
'cnn_lowest': cnn_lowest})
cnm2.estimates.detrend_df_f()
cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview)
# %% visualize selected and rejected components
cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components)
# %% visualize selected components
cnm2.estimates.nb_view_components(images, idx=cnm2.estimates.idx_components, img=Cn)
cnm2.estimates.view_components(images, idx=cnm2.estimates.idx_components_bad, img=Cn)
#%% only select high quality components
cnm2.estimates.select_components(use_object=True)
#%%
cnm2.estimates.plot_contours(img=Cn)
cnm2.estimates.detrend_df_f()
import pickle
f = open("/home/david/zebraHorse/df_f_day55.pkl", "wb")
pickle.dump(cnm2.estimates.F_dff, f)
f.close()
# %% play movie with results (original, reconstructed, amplified residual)
for j in range(10):
cnm2.estimates.play_movie(images, magnification=4.0, frame_range = range(100 * j, 100 * (j + 1)))
#import time
#time.sleep(1000)
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('Yr*_LOG_*')
for log_file in log_files:
os.remove(log_file)
def evaluate_components_CNN(A,
dims,
gSig,
model_name: str = os.path.join(
caiman_datadir(), 'model', 'cnn_model'),
patch_size: int = 50,
loaded_model=None,
isGPU: bool = False) -> Tuple[Any, np.array]:
""" evaluate component quality using a CNN network
"""
import os
if not isGPU:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
try:
os.environ["KERAS_BACKEND"] = "tensorflow"
from tensorflow.keras.models import model_from_json
use_keras = True
logging.info('Using Keras')
except (ModuleNotFoundError):
use_keras = False
logging.info('Using Tensorflow')
if loaded_model is None:
if use_keras:
if os.path.isfile(
os.path.join(caiman_datadir(), model_name + ".json")):
model_file = os.path.join(caiman_datadir(),
model_name + ".json")
model_weights = os.path.join(caiman_datadir(),
model_name + ".h5")
elif os.path.isfile(model_name + ".json"):
model_file = model_name + ".json"
model_weights = model_name + ".h5"
else:
raise FileNotFoundError(
"File for requested model {} not found".format(model_name))
with open(model_file, 'r') as json_file:
print('USING MODEL:' + model_file)
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_name + '.h5')
#loaded_model.compile('sgd', 'mse')
else:
if os.path.isfile(
os.path.join(caiman_datadir(), model_name + ".h5.pb")):
model_file = os.path.join(caiman_datadir(),
model_name + ".h5.pb")
elif os.path.isfile(model_name + ".h5.pb"):
model_file = model_name + ".h5.pb"
else:
raise FileNotFoundError(
"File for requested model {} not found".format(model_name))
loaded_model = load_graph(model_file)
logging.debug("Loaded model from disk")
half_crop = np.minimum(gSig[0] * 4 + 1,
patch_size), np.minimum(gSig[1] * 4 + 1, patch_size)
dims = np.array(dims)
coms = [
scipy.ndimage.center_of_mass(mm.toarray().reshape(dims, order='F'))
for mm in A.tocsc().T
]
coms = np.maximum(coms, half_crop)
coms = np.array([np.minimum(cms, dims - half_crop)
for cms in coms]).astype(np.int)
crop_imgs = [
mm.toarray().reshape(
dims, order='F')[com[0] - half_crop[0]:com[0] + half_crop[0],
com[1] - half_crop[1]:com[1] + half_crop[1]]
for mm, com in zip(A.tocsc().T, coms)
]
final_crops = np.array([
cv2.resize(im / np.linalg.norm(im), (patch_size, patch_size))
for im in crop_imgs
])
if use_keras:
predictions = loaded_model.predict(final_crops[:, :, :, np.newaxis],
batch_size=32,
verbose=1)
else:
tf_in = loaded_model.get_tensor_by_name('prefix/conv2d_20_input:0')
tf_out = loaded_model.get_tensor_by_name('prefix/output_node0:0')
with tf.Session(graph=loaded_model) as sess:
predictions = sess.run(
tf_out, feed_dict={tf_in: final_crops[:, :, :, np.newaxis]})
sess.close()
return predictions, final_crops
def main():
pass # For compatibility between running under Spyder and the CLI
# %% Load demo movie and ROIs
fnames = download_demo('demo_voltage_imaging.hdf5', 'volpy') # file path to movie file (will download if not present)
path_ROIs = download_demo('demo_voltage_imaging_ROIs.hdf5', 'volpy') # file path to ROIs file (will download if not present)
# %% Setup some parameters for data and motion correction
# dataset parameters
fr = 400 # sample rate of the movie
ROIs = None # Region of interests
index = None # index of neurons
weights = None # reuse spatial weights by
# opts.change_params(params_dict={'weights':vpy.estimates['weights']})
# motion correction parameters
pw_rigid = False # flag for pw-rigid motion correction
gSig_filt = (3, 3) # size of filter, in general gSig (see below),
# change this one if algorithm does not work
max_shifts = (5, 5) # maximum allowed rigid shift
strides = (48, 48) # start a new patch for pw-rigid motion correction every x pixels
overlaps = (24, 24) # overlap between pathes (size of patch strides+overlaps)
max_deviation_rigid = 3 # maximum deviation allowed for patch with respect to rigid shifts
border_nan = 'copy'
opts_dict = {
'fnames': fnames,
'fr': fr,
'index': index,
'ROIs': ROIs,
'weights': weights,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'gSig_filt': gSig_filt,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': border_nan
}
opts = volparams(params_dict=opts_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = False
if display_images:
m_orig = cm.load(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %%% MOTION CORRECTION
# Create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run piecewise rigid motion correction
mc.motion_correct(save_movie=True)
dview.terminate()
# %% motion correction compared with original movie
display_images = False
if display_images:
m_orig = cm.load(fnames)
m_rig = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate([m_orig.resize(1, 1, ds_ratio) - mc.min_mov * mc.nonneg_movie,
m_rig.resize(1, 1, ds_ratio)], axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit
# % movie subtracted from the mean
m_orig2 = (m_orig - np.mean(m_orig, axis=0))
m_rig2 = (m_rig - np.mean(m_rig, axis=0))
moviehandle1 = cm.concatenate([m_orig2.resize(1, 1, ds_ratio),
m_rig2.resize(1, 1, ds_ratio)], axis=2)
moviehandle1.play(fr=60, q_max=99.5, magnification=2)
# %% Memory Mapping
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
fname_new = cm.save_memmap_join(mc.mmap_file, base_name='memmap_',
add_to_mov=border_to_0, dview=dview, n_chunks=10)
dview.terminate()
# %% change fnames to the new motion corrected one
opts.change_params(params_dict={'fnames': fname_new})
# %% SEGMENTATION
# Create mean and correlation image
use_maskrcnn = True # set to True to predict the ROIs using the mask R-CNN
if not use_maskrcnn: # use manual annotations
with h5py.File(path_ROIs, 'r') as fl:
ROIs = fl['mov'][()] # load ROIs
opts.change_params(params_dict={'ROIs': ROIs,
'index': list(range(ROIs.shape[0])),
'method': 'SpikePursuit'})
else:
m = cm.load(mc.mmap_file[0], subindices=slice(0, 20000))
m.fr = fr
img = m.mean(axis=0)
img = (img-np.mean(img))/np.std(img)
m1 = m.computeDFF(secsWindow=1, in_place=True)[0]
m = m - m1
Cn = m.local_correlations(swap_dim=False, eight_neighbours=True)
img_corr = (Cn-np.mean(Cn))/np.std(Cn)
summary_image = np.stack([img, img, img_corr], axis=2).astype(np.float32)
del m
del m1
# %%
# Mask R-CNN
config = neurons.NeuronsConfig()
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MAX_DIM = 512
RPN_NMS_THRESHOLD = 0.7
POST_NMS_ROIS_INFERENCE = 1000
config = InferenceConfig()
config.display()
model_dir = os.path.join(caiman_datadir(), 'model')
DEVICE = "/cpu:0" # /cpu:0 or /gpu:0
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference", model_dir=model_dir,
config=config)
weights_path = download_model('mask_rcnn')
model.load_weights(weights_path, by_name=True)
results = model.detect([summary_image], verbose=1)
r = results[0]
ROIs_mrcnn = r['masks'].transpose([2, 0, 1])
# %% visualize the result
display_result = False
if display_result:
_, ax = plt.subplots(1,1, figsize=(16,16))
visualize.display_instances(summary_image, r['rois'], r['masks'], r['class_ids'],
['BG', 'neurons'], r['scores'], ax=ax,
title="Predictions")
# %% set rois
opts.change_params(params_dict={'ROIs':ROIs_mrcnn,
'index':list(range(ROIs_mrcnn.shape[0])),
'method':'SpikePursuit'})
# %% Trace Denoising and Spike Extraction
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False, maxtasksperchild=1)
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
# %% some visualization
print(np.where(vpy.estimates['passedLocalityTest'])[0]) # neurons that pass locality test
n = 0
# Processed signal and spikes of neurons
plt.figure()
plt.plot(vpy.estimates['trace'][n])
plt.plot(vpy.estimates['spikeTimes'][n],
np.max(vpy.estimates['trace'][n]) * np.ones(vpy.estimates['spikeTimes'][n].shape),
color='g', marker='o', fillstyle='none', linestyle='none')
plt.title('signal and spike times')
plt.show()
# Location of neurons by Mask R-CNN or manual annotation
plt.figure()
if use_maskrcnn:
plt.imshow(ROIs_mrcnn[n])
else:
plt.imshow(ROIs[n])
mv = cm.load(fname_new)
plt.imshow(mv.mean(axis=0),alpha=0.5)
# Spatial filter created by algorithm
plt.figure()
plt.imshow(vpy.estimates['spatialFilter'][n])
plt.colorbar()
plt.title('spatial filter')
plt.show()
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
def test_computational_performance(fnames, path_ROIs, n_processes):
import os
import cv2
import glob
import logging
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import h5py
from time import time
try:
cv2.setNumThreads(0)
except:
pass
try:
if __IPYTHON__:
# this is used for debugging purposes only. allows to reload classes
# when changed
get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
except NameError:
pass
import caiman as cm
from caiman.motion_correction import MotionCorrect
from caiman.utils.utils import download_demo, download_model
from caiman.source_extraction.volpy.volparams import volparams
from caiman.source_extraction.volpy.volpy import VOLPY
from caiman.source_extraction.volpy.mrcnn import visualize, neurons
import caiman.source_extraction.volpy.mrcnn.model as modellib
from caiman.paths import caiman_datadir
from caiman.summary_images import local_correlations_movie_offline
from caiman.summary_images import mean_image
from caiman.source_extraction.volpy.utils import quick_annotation
from multiprocessing import Pool
time_start = time()
print('Start MOTION CORRECTION')
# %% Load demo movie and ROIs
fnames = fnames
path_ROIs = path_ROIs
#%% dataset dependent parameters
# dataset dependent parameters
fr = 400 # sample rate of the movie
# motion correction parameters
pw_rigid = False # flag for pw-rigid motion correction
gSig_filt = (3, 3) # size of filter, in general gSig (see below),
# change this one if algorithm does not work
max_shifts = (5, 5) # maximum allowed rigid shift
strides = (
48, 48
) # start a new patch for pw-rigid motion correction every x pixels
overlaps = (24, 24
) # overlap between pathes (size of patch strides+overlaps)
max_deviation_rigid = 3 # maximum deviation allowed for patch with respect to rigid shifts
border_nan = 'copy'
opts_dict = {
'fnames': fnames,
'fr': fr,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'gSig_filt': gSig_filt,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': border_nan
}
opts = volparams(params_dict=opts_dict)
# %% start a cluster for parallel processing
dview = Pool(n_processes)
#dview = None
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run correction
mc.motion_correct(save_movie=True)
time_mc = time() - time_start
print(time_mc)
print('START MEMORY MAPPING')
# %% restart cluster to clean up memory
dview.terminate()
dview = Pool(n_processes)
# %% MEMORY MAPPING
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
# memory map the file in order 'C'
fname_new = cm.save_memmap_join(mc.mmap_file,
base_name='memmap_',
add_to_mov=border_to_0,
dview=dview,
n_chunks=1000) # exclude border
time_mmap = time() - time_start - time_mc
print('Start Segmentation')
# %% SEGMENTATION
# create summary images
img = mean_image(mc.mmap_file[0], window=1000, dview=dview)
img = (img - np.mean(img)) / np.std(img)
Cn = local_correlations_movie_offline(mc.mmap_file[0],
fr=fr,
window=1500,
stride=1500,
winSize_baseline=400,
remove_baseline=True,
dview=dview).max(axis=0)
img_corr = (Cn - np.mean(Cn)) / np.std(Cn)
summary_image = np.stack([img, img, img_corr], axis=2).astype(np.float32)
#%% three methods for segmentation
methods_list = [
'manual_annotation', # manual annotation needs user to prepare annotated datasets same format as demo ROIs
'quick_annotation', # quick annotation annotates data with simple interface in python
'maskrcnn'
] # maskrcnn is a convolutional network trained for finding neurons using summary images
method = methods_list[0]
if method == 'manual_annotation':
with h5py.File(path_ROIs, 'r') as fl:
ROIs = fl['mov'][()] # load ROIs
elif method == 'quick_annotation':
ROIs = quick_annotation(img_corr, min_radius=4, max_radius=10)
elif method == 'maskrcnn':
config = neurons.NeuronsConfig()
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MAX_DIM = 512
RPN_NMS_THRESHOLD = 0.7
POST_NMS_ROIS_INFERENCE = 1000
config = InferenceConfig()
config.display()
model_dir = os.path.join(caiman_datadir(), 'model')
DEVICE = "/cpu:0" # /cpu:0 or /gpu:0
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference",
model_dir=model_dir,
config=config)
weights_path = download_model('mask_rcnn')
model.load_weights(weights_path, by_name=True)
results = model.detect([summary_image], verbose=1)
r = results[0]
ROIs = r['masks'].transpose([2, 0, 1])
display_result = False
if display_result:
_, ax = plt.subplots(1, 1, figsize=(16, 16))
visualize.display_instances(summary_image,
r['rois'],
r['masks'],
r['class_ids'], ['BG', 'neurons'],
r['scores'],
ax=ax,
title="Predictions")
time_seg = time() - time_mmap - time_mc - time_start
print('Start SPIKE EXTRACTION')
# %% restart cluster to clean up memory
dview.terminate()
dview = Pool(n_processes, maxtasksperchild=1)
# %% parameters for trace denoising and spike extraction
fnames = fname_new # change file
ROIs = ROIs # region of interests
index = list(range(len(ROIs))) # index of neurons
weights = None # reuse spatial weights
tau_lp = 5 # parameter for high-pass filter to remove photobleaching
threshold = 4 # threshold for finding spikes, increase threshold to find less spikes
contextSize = 35 # number of pixels surrounding the ROI to censor from the background PCA
flip_signal = True # Important! Flip signal or not, True for Voltron indicator, False for others
opts_dict = {
'fnames': fnames,
'ROIs': ROIs,
'index': index,
'weights': weights,
'tau_lp': tau_lp,
'threshold': threshold,
'contextSize': contextSize,
'flip_signal': flip_signal
}
opts.change_params(params_dict=opts_dict)
#%% Trace Denoising and Spike Extraction
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
# %% STOP CLUSTER and clean up log files
#dview.terminate()
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
time_ext = time() - time_mmap - time_mc - time_start - time_seg
#%%
print('file:' + fnames)
print('number of processes' + str(n_processes))
print(time_mc)
print(time_mmap)
print(time_seg)
print(time_ext)
time_list = [time_mc, time_mmap, time_seg, time_ext]
return time_list
import cv2
from caiman.utils.visualization import plot_contours
from caiman.source_extraction.cnmf.online_cnmf import bare_initialization
from caiman.source_extraction.cnmf.utilities import detrend_df_f_auto
from caiman.paths import caiman_datadir
#%% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fls = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
print(fls)
#%% Set up some parameters
# frame rate (Hz)
fr = 15
# approximate length of transient event in seconds
decay_time = 0.5
# expected half size of neurons
gSig = (3, 3)
def main():
pass # For compatibility between running under Spyder and the CLI
#%% load data
fname = os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')
Y = cm.load(fname).astype(np.float32) #
# used as a background image
Cn = cm.local_correlations(Y.transpose(1, 2, 0))
#%% set up some parameters
# frame rate (Hz)
fr = 10
# approximate length of transient event in seconds
decay_time = 0.5
# expected half size of neurons
gSig = [6, 6]
# order of AR indicator dynamics
p = 1
# minimum SNR for accepting new components
min_SNR = 3.5
# correlation threshold for new component inclusion
rval_thr = 0.90
# number of background components
gnb = 3
# set up some additional supporting parameters needed for the algorithm (these are default values but change according to dataset characteristics)
# number of shapes to be updated each time (put this to a finite small value to increase speed)
max_comp_update_shape = np.inf
# maximum number of expected components used for memory pre-allocation (exaggerate here)
expected_comps = 50
# number of timesteps to consider when testing new neuron candidates
N_samples = np.ceil(fr * decay_time)
# exceptionality threshold
thresh_fitness_raw = log_ndtr(-min_SNR) * N_samples
# total length of file
T1 = Y.shape[0]
# set up CNMF initialization parameters
# merging threshold, max correlation allowed
merge_thresh = 0.8
# number of frames for initialization (presumably from the first file)
initbatch = 400
# size of patch
patch_size = 32
# amount of overlap between patches
stride = 3
# max number of components in each patch
K = 4
#%% obtain initial batch file used for initialization
# memory map file (not needed)
fname_new = Y[:initbatch].save(os.path.join(caiman_datadir(),
'example_movies', 'demo.mmap'),
order='C')
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
Cn_init = cm.local_correlations(np.reshape(Yr, dims + (T, ), order='F'))
#%% RUN (offline) CNMF algorithm on the initial batch
pl.close('all')
cnm_init = cnmf.CNMF(2,
k=K,
gSig=gSig,
merge_thresh=merge_thresh,
fr=fr,
p=p,
rf=patch_size // 2,
stride=stride,
skip_refinement=False,
normalize_init=False,
options_local_NMF=None,
minibatch_shape=100,
minibatch_suff_stat=5,
update_num_comps=True,
rval_thr=rval_thr,
thresh_fitness_delta=-50,
gnb=gnb,
decay_time=decay_time,
thresh_fitness_raw=thresh_fitness_raw,
batch_update_suff_stat=False,
max_comp_update_shape=max_comp_update_shape,
expected_comps=expected_comps,
dview=None,
min_SNR=min_SNR)
cnm_init = cnm_init.fit(images)
print(('Number of components:' + str(cnm_init.estimates.A.shape[-1])))
pl.figure()
crd = plot_contours(cnm_init.estimates.A.tocsc(), Cn_init, thr=0.9)
#%% run (online) OnACID algorithm
cnm = deepcopy(cnm_init)
cnm.params.data['dims'] = (60, 80)
cnm._prepare_object(np.asarray(Yr), T1)
t = initbatch
Y_ = cm.load(fname)[initbatch:].astype(np.float32)
for frame_count, frame in enumerate(Y_):
cnm.fit_next(t, frame.copy().reshape(-1, order='F'))
t += 1
#%% extract the results
C, f = cnm.estimates.C_on[gnb:cnm.M], cnm.estimates.C_on[:gnb]
A, b = cnm.estimates.Ab[:, gnb:cnm.M], cnm.estimates.Ab[:, :gnb]
print(('Number of components:' + str(A.shape[-1])))
#%% pass through the CNN classifier with a low threshold (keeps clearer neuron shapes and excludes processes)
use_CNN = True
if use_CNN:
# threshold for CNN classifier
thresh_cnn = 0.1
from caiman.components_evaluation import evaluate_components_CNN
predictions, final_crops = evaluate_components_CNN(
A,
dims,
gSig,
model_name=os.path.join(caiman_datadir(), 'model', 'cnn_model'))
A_exclude, C_exclude = A[:, predictions[:, 1] < thresh_cnn], C[
predictions[:, 1] < thresh_cnn]
A, C = A[:,
predictions[:,
1] >= thresh_cnn], C[predictions[:,
1] >= thresh_cnn]
noisyC = cnm.estimates.noisyC[gnb:cnm.M]
YrA = noisyC[predictions[:, 1] >= thresh_cnn] - C
else:
YrA = cnm.estimates.noisyC[gnb:cnm.M] - C
#%% plot results
pl.figure()
crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9)
view_patches_bar(Yr, A, C, b, f, dims[0], dims[1], YrA, img=Cn)
def main():
pass # For compatibility between running under Spyder and the CLI
#%% start a cluster
c, dview, n_processes =\
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
#%% save files to be processed
# This datafile is distributed with Caiman
fnames = [
os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')
]
# location of dataset (can actually be a list of filed to be concatenated)
add_to_movie = -np.min(cm.load(fnames[0],
subindices=range(200))).astype(float)
# determine minimum value on a small chunk of data
add_to_movie = np.maximum(add_to_movie, 0)
# if minimum is negative subtract to make the data non-negative
base_name = 'Yr'
fname_new = cm.save_memmap(fnames,
dview=dview,
base_name=base_name,
order='C',
add_to_movie=add_to_movie)
#%% LOAD MEMORY MAPPABLE FILE
Yr, dims, T = cm.load_memmap(fname_new)
d1, d2 = dims
images = np.reshape(Yr.T, [T] + list(dims), order='F')
#%% play movie, press q to quit
play_movie = False
if play_movie:
cm.movie(images[1400:]).play(fr=50, magnification=4, gain=3.)
#%% correlation image. From here infer neuron size and density
Cn = cm.movie(images).local_correlations(swap_dim=False)
plt.imshow(Cn, cmap='gray')
plt.title('Correlation Image')
#%% set up some parameters
is_patches = True # flag for processing in patches or not
if is_patches: # PROCESS IN PATCHES AND THEN COMBINE
rf = 10 # half size of each patch
stride = 4 # overlap between patches
K = 4 # number of components in each patch
else: # PROCESS THE WHOLE FOV AT ONCE
rf = None # setting these parameters to None
stride = None # will run CNMF on the whole FOV
K = 30 # number of neurons expected (in the whole FOV)
gSig = [6, 6] # expected half size of neurons
merge_thresh = 0.80 # merging threshold, max correlation allowed
p = 2 # order of the autoregressive system
gnb = 2 # global background order
#%% Now RUN CNMF
cnm = cnmf.CNMF(n_processes,
method_init='greedy_roi',
k=K,
gSig=gSig,
merge_thresh=merge_thresh,
p=p,
dview=dview,
gnb=gnb,
rf=rf,
stride=stride,
rolling_sum=False)
cnm = cnm.fit(images)
#%% plot contour plots of components
plt.figure()
crd = cm.utils.visualization.plot_contours(cnm.A, Cn, thr=0.9)
plt.title('Contour plots of components')
#%%
A_in, C_in, b_in, f_in = cnm.A[:, :], cnm.C[:], cnm.b, cnm.f
cnm2 = cnmf.CNMF(n_processes=1,
k=A_in.shape[-1],
gSig=gSig,
p=p,
dview=dview,
merge_thresh=merge_thresh,
Ain=A_in,
Cin=C_in,
b_in=b_in,
f_in=f_in,
rf=None,
stride=None,
gnb=gnb,
method_deconvolution='oasis',
check_nan=True)
cnm2 = cnm2.fit(images)
#%% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier (this will pick up only neurons
# and filter out active processes)
fr = 10 # approximate frame rate of data
decay_time = 5.0 # length of transient
min_SNR = 2.5 # peak SNR for accepted components (if above this, acept)
rval_thr = 0.90 # space correlation threshold (if above this, accept)
use_cnn = True # use the CNN classifier
min_cnn_thr = 0.95 # if cnn classifier predicts below this value, reject
idx_components, idx_components_bad, SNR_comp, r_values, cnn_preds = \
estimate_components_quality_auto(images, cnm.A, cnm.C, cnm.b, cnm.f,
cnm.YrA, fr, decay_time, gSig, dims,
dview=dview, min_SNR=min_SNR,
r_values_min=rval_thr, use_cnn=use_cnn,
thresh_cnn_min=min_cnn_thr)
#%% visualize selected and rejected components
plt.figure()
plt.subplot(1, 2, 1)
cm.utils.visualization.plot_contours(cnm2.A[:, idx_components],
Cn,
thr=0.9)
plt.title('Selected components')
plt.subplot(1, 2, 2)
plt.title('Discaded components')
cm.utils.visualization.plot_contours(cnm2.A[:, idx_components_bad],
Cn,
thr=0.9)
#%%
plt.figure()
crd = cm.utils.visualization.plot_contours(cnm2.A.tocsc()[:,
idx_components],
Cn,
thr=0.9)
plt.title('Contour plots of components')
#%% visualize selected components
cm.utils.visualization.view_patches_bar(Yr,
cnm2.A.tocsc()[:, idx_components],
cnm2.C[idx_components, :],
cnm2.b,
cnm2.f,
dims[0],
dims[1],
YrA=cnm2.YrA[idx_components, :],
img=Cn)
#%% STOP CLUSTER and clean up log files
cm.stop_server()
log_files = glob.glob('Yr*_LOG_*')
for log_file in log_files:
os.remove(log_file)
import logging
from pytictoc import TicToc
from caiman.source_extraction.cnmf import params as params
from caiman.source_extraction import cnmf as cnmf
import os
from caiman.paths import caiman_datadir
# %% ********* Creating named pipes for communication with MicroManager: *********
timer = TicToc()
timer.tic() # start measuring time
sendPipeName = "/tmp/getPipeMMCaImAn.ser" # FOR SENDING MESSAGES --> TO MicroManager
receivePipeName = "/tmp/sendPipeMMCaImAn.ser" # FOR READING MESSAGES --> FROM MicroManager
MMfileDirectory = '/Applications/MicroManager 2.0 gamma/uMresults'
CaimanFileDirectory = caiman_datadir() # specify where the file is saved
if os.path.exists(sendPipeName):
os.remove(sendPipeName)
os.mkfifo(sendPipeName)
print("Removed old write-pipe, created new write-pipe.")
else:
os.mkfifo(sendPipeName)
print("Write-pipe created sucessfully!")
if os.path.exists(receivePipeName):
os.remove(receivePipeName)
os.mkfifo(receivePipeName)
print("Removed old read-pipe, created new read-pipe.")
else:
os.mkfifo(receivePipeName)
def main():
pass # For compatibility between running under Spyder and the CLI
#%% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fls = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
print(fls)
#%% Set up some parameters
# frame rate (Hz)
fr = 15
# approximate length of transient event in seconds
decay_time = 0.5
# expected half size of neurons
gSig = (3, 3)
# order of AR indicator dynamics
p = 1
# minimum SNR for accepting new components
min_SNR = 2.5
# correlation threshold for new component inclusion
rval_thr = 0.85
# spatial downsampling factor (increases speed but may lose some fine structure)
ds_factor = 1
# number of background components
gnb = 2
# recompute gSig if downsampling is involved
gSig = tuple(np.ceil(np.array(gSig) / ds_factor).astype('int'))
# flag for online motion correction
mot_corr = True
# maximum allowed shift during motion correction
max_shift = np.ceil(10. / ds_factor).astype('int')
# set up some additional supporting parameters needed for the algorithm (these are default values but change according to dataset characteristics)
# number of shapes to be updated each time (put this to a finite small value to increase speed)
max_comp_update_shape = np.inf
# number of files used for initialization
init_files = 1
# number of files used for online
online_files = len(fls) - 1
# number of frames for initialization (presumably from the first file)
initbatch = 200
# maximum number of expected components used for memory pre-allocation (exaggerate here)
expected_comps = 300
# initial number of components
K = 2
# number of timesteps to consider when testing new neuron candidates
N_samples = np.ceil(fr * decay_time)
# exceptionality threshold
thresh_fitness_raw = scipy.special.log_ndtr(-min_SNR) * N_samples
# number of passes over the data
epochs = 2
# upper bound for number of frames in each file (used right below)
len_file = 1000
# total length of all files (if not known use a large number, then truncate at the end)
T1 = len(fls) * len_file * epochs
#%% Initialize movie
# load only the first initbatch frames and possibly downsample them
if ds_factor > 1:
Y = cm.load(fls[0], subindices=slice(0, initbatch, None)).astype(
np.float32).resize(1. / ds_factor, 1. / ds_factor)
else:
Y = cm.load(fls[0], subindices=slice(0, initbatch,
None)).astype(np.float32)
if mot_corr: # perform motion correction on the first initbatch frames
mc = Y.motion_correct(max_shift, max_shift)
Y = mc[0].astype(np.float32)
borders = np.max(mc[1])
else:
Y = Y.astype(np.float32)
# minimum value of movie. Subtract it to make the data non-negative
img_min = Y.min()
Y -= img_min
img_norm = np.std(Y, axis=0)
# normalizing factor to equalize the FOV
img_norm += np.median(img_norm)
Y = Y / img_norm[None, :, :] # normalize data
_, d1, d2 = Y.shape
dims = (d1, d2) # dimensions of FOV
Yr = Y.to_2D().T # convert data into 2D array
Cn_init = Y.local_correlations(swap_dim=False) # compute correlation image
#pl.imshow(Cn_init)
#pl.title('Correlation Image on initial batch')
#pl.colorbar()
bnd_Y = np.percentile(Y, (0.001, 100 - 0.001)) # plotting boundaries for Y
#%% initialize OnACID with bare initialization
cnm_init = bare_initialization(Y[:initbatch].transpose(1, 2, 0),
init_batch=initbatch,
k=K,
gnb=gnb,
gSig=gSig,
p=p,
minibatch_shape=100,
minibatch_suff_stat=5,
update_num_comps=True,
rval_thr=rval_thr,
thresh_fitness_raw=thresh_fitness_raw,
batch_update_suff_stat=True,
max_comp_update_shape=max_comp_update_shape,
deconv_flag=False,
use_dense=False,
simultaneously=False,
n_refit=0)
#%% Plot initialization results
crd = plot_contours(cnm_init.A.tocsc(), Cn_init, thr=0.9)
A, C, b, f, YrA, sn = cnm_init.A, cnm_init.C, cnm_init.b, cnm_init.f, cnm_init.YrA, cnm_init.sn
view_patches_bar(Yr,
scipy.sparse.coo_matrix(A.tocsc()[:, :]),
C[:, :],
b,
f,
dims[0],
dims[1],
YrA=YrA[:, :],
img=Cn_init)
bnd_AC = np.percentile(A.dot(C), (0.001, 100 - 0.005))
bnd_BG = np.percentile(b.dot(f), (0.001, 100 - 0.001))
#%% create a function for plotting results in real time if needed
def create_frame(cnm2, img_norm, captions):
A, b = cnm2.Ab[:, cnm2.gnb:], cnm2.Ab[:, :cnm2.gnb].toarray()
C, f = cnm2.C_on[cnm2.gnb:cnm2.M, :], cnm2.C_on[:cnm2.gnb, :]
# inferred activity due to components (no background)
frame_plot = (frame_cor.copy() - bnd_Y[0]) / np.diff(bnd_Y)
comps_frame = A.dot(C[:, t - 1]).reshape(cnm2.dims, order='F')
bgkrnd_frame = b.dot(f[:, t - 1]).reshape(
cnm2.dims, order='F') # denoised frame (components + background)
denoised_frame = comps_frame + bgkrnd_frame
denoised_frame = (denoised_frame.copy() - bnd_Y[0]) / np.diff(bnd_Y)
comps_frame = (comps_frame.copy() - bnd_AC[0]) / np.diff(bnd_AC)
if show_residuals:
#all_comps = np.reshape(cnm2.Yres_buf.mean(0), cnm2.dims, order='F')
all_comps = np.reshape(cnm2.mean_buff, cnm2.dims, order='F')
all_comps = np.minimum(np.maximum(all_comps, 0) * 2 + 0.25, 255)
else:
all_comps = np.array(A.sum(-1)).reshape(cnm2.dims, order='F')
# spatial shapes
frame_comp_1 = cv2.resize(
np.concatenate([frame_plot, all_comps * 1.], axis=-1),
(2 * np.int(cnm2.dims[1] * resize_fact),
np.int(cnm2.dims[0] * resize_fact)))
frame_comp_2 = cv2.resize(
np.concatenate([comps_frame, denoised_frame], axis=-1),
(2 * np.int(cnm2.dims[1] * resize_fact),
np.int(cnm2.dims[0] * resize_fact)))
frame_pn = np.concatenate([frame_comp_1, frame_comp_2], axis=0).T
vid_frame = np.repeat(frame_pn[:, :, None], 3, axis=-1)
vid_frame = np.minimum((vid_frame * 255.), 255).astype('u1')
if show_residuals and cnm2.ind_new:
add_v = np.int(cnm2.dims[1] * resize_fact)
for ind_new in cnm2.ind_new:
cv2.rectangle(vid_frame,
(int(ind_new[0][1] * resize_fact),
int(ind_new[1][1] * resize_fact) + add_v),
(int(ind_new[0][0] * resize_fact),
int(ind_new[1][0] * resize_fact) + add_v),
(255, 0, 255), 2)
cv2.putText(vid_frame,
captions[0], (5, 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[1], (np.int(cnm2.dims[0] * resize_fact) + 5, 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[2], (5, np.int(cnm2.dims[1] * resize_fact) + 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[3], (np.int(cnm2.dims[0] * resize_fact) + 5,
np.int(cnm2.dims[1] * resize_fact) + 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
'Frame = ' + str(t),
(vid_frame.shape[1] // 2 - vid_frame.shape[1] // 10,
vid_frame.shape[0] - 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 255),
thickness=1)
return vid_frame
#%% Prepare object for OnACID
cnm2 = deepcopy(cnm_init)
save_init = False # flag for saving initialization object. Useful if you want to check OnACID with different parameters but same initialization
if save_init:
cnm_init.dview = None
save_object(cnm_init, fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl')
cnm_init = load_object(fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl')
path_to_cnn_residual = os.path.join(caiman_datadir(), 'model',
'cnn_model_online.h5')
cnm2._prepare_object(np.asarray(Yr),
T1,
expected_comps,
idx_components=None,
min_num_trial=3,
max_num_added=3,
path_to_model=path_to_cnn_residual,
sniper_mode=False,
use_peak_max=False,
q=0.5)
cnm2.thresh_CNN_noisy = 0.5
#%% Run OnACID and optionally plot results in real time
epochs = 1
cnm2.Ab_epoch = [] # save the shapes at the end of each epoch
t = cnm2.initbatch # current timestep
tottime = []
Cn = Cn_init.copy()
# flag for removing components with bad shapes
remove_flag = False
T_rm = 650 # remove bad components every T_rm frames
rm_thr = 0.1 # CNN classifier removal threshold
# flag for plotting contours of detected components at the end of each file
plot_contours_flag = False
# flag for showing results video online (turn off flags for improving speed)
play_reconstr = True
# flag for saving movie (file could be quite large..)
save_movie = False
movie_name = os.path.join(
folder_name, 'sniper_meso_0.995_new.avi') # name of movie to be saved
resize_fact = 1.2 # image resizing factor
if online_files == 0: # check whether there are any additional files
process_files = fls[:init_files] # end processing at this file
init_batc_iter = [initbatch] # place where to start
end_batch = T1
else:
process_files = fls[:init_files + online_files] # additional files
# where to start reading at each file
init_batc_iter = [initbatch] + [0] * online_files
shifts = []
show_residuals = True
if show_residuals:
caption = 'Mean Residual Buffer'
else:
caption = 'Identified Components'
captions = ['Raw Data', 'Inferred Activity', caption, 'Denoised Data']
if save_movie and play_reconstr:
fourcc = cv2.VideoWriter_fourcc('8', 'B', 'P', 'S')
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(
movie_name, fourcc, 30.0,
tuple([int(2 * x * resize_fact) for x in cnm2.dims]))
for iter in range(epochs):
if iter > 0:
# if not on first epoch process all files from scratch
process_files = fls[:init_files + online_files]
init_batc_iter = [0] * (online_files + init_files)
# np.array(fls)[np.array([1,2,3,4,5,-5,-4,-3,-2,-1])]:
for file_count, ffll in enumerate(process_files):
print('Now processing file ' + ffll)
Y_ = cm.load(ffll,
subindices=slice(init_batc_iter[file_count], T1,
None))
# update max-correlation (and perform offline motion correction) just for illustration purposes
if plot_contours_flag:
if ds_factor > 1:
Y_1 = Y_.resize(1. / ds_factor, 1. / ds_factor, 1)
else:
Y_1 = Y_.copy()
if mot_corr:
templ = (cnm2.Ab.data[:cnm2.Ab.indptr[1]] *
cnm2.C_on[0, t - 1]).reshape(cnm2.dims,
order='F') * img_norm
newcn = (Y_1 - img_min).motion_correct(
max_shift, max_shift,
template=templ)[0].local_correlations(swap_dim=False)
Cn = np.maximum(Cn, newcn)
else:
Cn = np.maximum(Cn, Y_1.local_correlations(swap_dim=False))
old_comps = cnm2.N # number of existing components
for frame_count, frame in enumerate(Y_): # now process each file
if np.isnan(np.sum(frame)):
raise Exception('Frame ' + str(frame_count) +
' contains nan')
if t % 100 == 0:
print(
'Epoch: ' + str(iter + 1) + '. ' + str(t) +
' frames have beeen processed in total. ' +
str(cnm2.N - old_comps) +
' new components were added. Total number of components is '
+ str(cnm2.Ab.shape[-1] - gnb))
old_comps = cnm2.N
t1 = time() # count time only for the processing part
frame_ = frame.copy().astype(np.float32) #
if ds_factor > 1:
frame_ = cv2.resize(frame_,
img_norm.shape[::-1]) # downsampling
frame_ -= img_min # make data non-negative
if mot_corr: # motion correct
templ = cnm2.Ab.dot(cnm2.C_on[:cnm2.M, t - 1]).reshape(
cnm2.dims, order='F') * img_norm
frame_cor, shift = motion_correct_iteration_fast(
frame_, templ, max_shift, max_shift)
shifts.append(shift)
else:
templ = None
frame_cor = frame_
frame_cor = frame_cor / img_norm # normalize data-frame
cnm2.fit_next(t, frame_cor.reshape(
-1, order='F')) # run OnACID on this frame
# store time
tottime.append(time() - t1)
t += 1
if t % T_rm == 0 and remove_flag:
prd, _ = evaluate_components_CNN(cnm2.Ab[:, gnb:], dims,
gSig)
ind_rem = np.where(prd[:, 1] < rm_thr)[0].tolist()
cnm2.remove_components(ind_rem)
print('Removing ' + str(len(ind_rem)) + ' components')
if t % 1000 == 0 and plot_contours_flag:
pl.cla()
A = cnm2.Ab[:, cnm2.gnb:]
# update the contour plot every 1000 frames
crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9)
pl.pause(1)
if play_reconstr: # generate movie with the results
vid_frame = create_frame(cnm2, img_norm, captions)
if save_movie:
out.write(vid_frame)
if t - initbatch < 100:
#for rp in np.int32(np.ceil(np.exp(-np.arange(1,100)/30)*20)):
for rp in range(len(cnm2.ind_new) * 2):
out.write(vid_frame)
cv2.imshow('frame', vid_frame)
if t - initbatch < 100:
for rp in range(len(cnm2.ind_new) * 2):
cv2.imshow('frame', vid_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print('Cumulative processing speed is ' +
str((t - initbatch) / np.sum(tottime))[:5] +
' frames per second.')
# save the shapes at the end of each epoch
cnm2.Ab_epoch.append(cnm2.Ab.copy())
if save_movie:
out.release()
cv2.destroyAllWindows()
#%% save results (optional)
save_results = False
if save_results:
np.savez('results_analysis_online_MOT_CORR.npz',
Cn=Cn,
Ab=cnm2.Ab,
Cf=cnm2.C_on,
b=cnm2.b,
f=cnm2.f,
dims=cnm2.dims,
tottime=tottime,
noisyC=cnm2.noisyC,
shifts=shifts)
#%% extract results from the objects and do some plotting
A, b = cnm2.Ab[:, cnm2.gnb:], cnm2.Ab[:, :cnm2.gnb].toarray()
C, f = cnm2.C_on[cnm2.gnb:cnm2.M,
t - t // epochs:t], cnm2.C_on[:cnm2.gnb,
t - t // epochs:t]
noisyC = cnm2.noisyC[:, t - t // epochs:t]
b_trace = [osi.b for osi in cnm2.OASISinstances] if hasattr(
cnm2, 'OASISinstances') else [0] * C.shape[0]
pl.figure()
crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9)
view_patches_bar(Yr,
scipy.sparse.coo_matrix(A.tocsc()[:, :]),
C[:, :],
b,
f,
dims[0],
dims[1],
YrA=noisyC[cnm2.gnb:cnm2.M] - C,
img=Cn)