def __init__(self, file_names, chan_group=0):
self.file_names = file_names
if file_names['mda']:
spikes = readmda(file_names['mda']).astype(int)
self.spk = spikes[1,:] - 1 # really, 1 indexing?
self.clu = spikes[2,:]
with open(file_names['param'], 'r') as f:
params = json.load(f)
self.s_f = params['samplerate']
else: # its a kilosort conversion
if file_names['clu']:
self.clu = np.squeeze(np.load(file_names['clu']))
elif file_names['temp']:
self.clu = np.squeeze(np.load(file_names['temp']))
else:
raise IOError('both spike_clusters.npy and spike_templates.npy weren\'t found')
self.spk = np.load(file_names['spk'])
with open(file_names['par'], 'r') as f:
exec(f.read())
self.s_f = sample_rate
if 'grp' in file_names and file_names['grp']:
self.grp = load_grp_file(file_names['grp'])
else:
self.grp = [(i, 'unsorted') for i in np.unique(self.clu)]
self.rec_kwik = None
self.spk_kwik = None
self.kwf = None
self.chan_group = chan_group
self.create_kwf()
def run(self,args): ### The processing input_path=args['input'] output_path=args['output'] factor=float(args['factor']) X=readmda(input_path) #read X=X*factor #scale the array writemda64(X,output_path) #write return True
def get_firing_info(file_path, prm):
firing_times_path = file_path + '/Electrophysiology' + prm.get_sorter_name(
) + '/firings.mda'
units_list = None
firing_info = None
if os.path.exists(firing_times_path):
firing_info = mdaio.readmda(firing_times_path)
units_list = np.unique(firing_info[2])
else:
print('I could not find the MountainSort output [firing.mda] file.')
return units_list, firing_info
def run(self,args): ### The processing input_path=args['input'] output_path=args['output'] X=readmda(input_path) #read M=X.shape[0] N=X.shape[1] print(X.shape) for m in range(0,M): row=X[m,:] print(row.shape) stdev0=np.std(row) row=row*(1/stdev0) X[m,:]=row writemda64(X,output_path) #write return True
def get_firing_info(file_path, prm):
firing_times_path = file_path + '/Electrophysiology' + prm.get_sorter_name() + '/firings.mda'
units_list = None
firing_info = None
if os.path.exists(firing_times_path):
firing_info = mdaio.readmda(firing_times_path)
units_list = np.unique(firing_info[2])
else:
print('I could not find the MountainSort output [firing.mda] file. I will check if the data was sorted earlier.')
spatial_firing_path = file_path + '/MountainSort/DataFrames/spatial_firing.pkl'
if os.path.exists(spatial_firing_path):
spatial_firing = pd.read_pickle(spatial_firing_path)
os.mknod(file_path + '/sorted_data_exists.txt')
return units_list, firing_info, spatial_firing
else:
print('There are no sorting results available for this recording.')
return units_list, firing_info, False
def get_snippets(firing_data, prm):
print('I will get some random snippets now for each cluster.')
file_path = prm.get_local_recording_folder_path()
filtered_data_path = []
filtered_data_path = file_path + '/Electrophysiology' + prm.get_sorter_name(
) + '/filt.mda'
snippets_all_clusters = []
if os.path.exists(filtered_data_path):
filtered_data = mdaio.readmda(filtered_data_path)
for cluster in range(len(firing_data)):
#TODO: cluster ID problems
# cluster = firing_data.cluster_id.values[cluster] - 1
firing_times = firing_data.firing_times[cluster]
snippets = extract_random_snippets(filtered_data, firing_times,
firing_data.tetrode[cluster],
50, prm)
snippets_all_clusters.append(snippets)
firing_data['random_snippets'] = snippets_all_clusters
#plt.plot(firing_data.random_snippets[4][3,:,:])
return firing_data
a = np.array([[1,0],[2,0]]).astype(np.int16)
np.savetxt("geom.csv", a, delimiter=",")
#np.readtxt('/home/nel/Code/VolPy/mountainsort_examples-master/bash_examples/001_ms4_bash_example/dataset/geom.csv',delimiter=',')
name = '/home/nel/Dropbox_old/SimultaneousEphys/09212017Fish1-1/RoiSet.zip'
dims=(44,96)
from caiman.base.rois import nf_read_roi_zip
img = nf_read_roi_zip(name,dims)
plt.figure();plt.imshow(img.sum(axis=0))
path='/tmp/mountainlab-tmp/output_47670870cb9afee72b43d0c03d477d36581a055e_timeseries_out.mda'
path = '/home/nel/Dropbox_old/SimultaneousEphys/MountainSort/dataset/raw.mda'
path = '/tmp/mountainlab-tmp/output_47670870cb9afee72b43d0c03d477d36581a055e_timeseries_out.mda'
X = readmda(path)
path = '/home/nel/Dropbox_old/SimultaneousEphys/MountainSort/output/firings.mda'
Y = readmda(path)
plt.figure()
plt.plot(X[0])
plt.scatter(Y[1],np.repeat(0.3, len(Y[1])))
#%% Yichun
fname = '/home/nel/Dropbox_old/Voltron_fly_data/PPL1_ap2a2/axon/180319-1voltron/Data3/Data3_mini.mat'
import h5py
import numpy as np
arrays = {}
f = h5py.File(fname)
gt = f['wholeCell']['voltage'][0]
def get_firing_info(prm):
firing_times_path = prm.get_filepath() + '\\sorting_out_t2\\firings.mda'
firing_info = mdaio.readmda(firing_times_path)
units_list = np.unique(firing_info[2])
return units_list, firing_info
from mdaio import readmda, writemda64
X = readmda("test_firings.mda")
writemda64(X, "test2.mda")
return filtered_trace
if __name__ == "__main__":
#tet4_firings = readmda('/Volumes/coxfs01/guitchounts/MountainSortCluster-master/TetrodeData/Tetrode4/data/firings2-4.mda')
### starting in a dir like GRat54/636xxxxx/0/
#tetrode_num = 1
waveform_stats = {}
for tetrode_num in range(1, 17):
waveform_stats[tetrode_num] = {}
tet_raw = readmda('./TetrodeData/Tetrode%d/data/rawT%d.mda' %
(tetrode_num, tetrode_num))
## ^^ can also load from original RHD file.
#### this stuff will actually be on /n/regal (or wherever you put it)
clust_dir = './Tetrode%d/mountainlab/prvbucket/_mountainprocess/' % tetrode_num
firings_files = [
out_file for out_file in os.listdir(clust_dir)
if out_file.startswith('output_firings_out')
]
output_firings_out = firings_files[np.argmax(
[os.path.getsize(clust_dir + file) for file in firings_files])]
cluster_times_ids = readmda(clust_dir + output_firings_out)
metrics_files = [
out_file for out_file in os.listdir(clust_dir)
from mdaio import readmda,writemda64
X = readmda("test_firings.mda")
writemda64(X,"test2.mda")
