def calcium_to_peak_times(inputs, low=1, high=20):
"""Returns a pd.DataFrame with peak timing for calcium events
Params:
inputs: str, h5py.File, tuple, or np.ndarray
if str/h5py.File: string that represents the filename of hdf5 file
if tuple: (path, animal, day), that describes the file location
if np.ndarray: array C of calcium traces
out: str
Output path for saving the metrics in a hdf5 file
outfile: Animal_Day.csv
columns: neuron number
"""
hyperparams = "low_{}_high_{}".format(low, high)
if isinstance(inputs, np.ndarray):
C = inputs
animal, day = None, None
path = './'
savepath = os.path.join(path, 'sample_IBI_{}.csv'.format(hyperparams))
else:
if isinstance(inputs, str):
opts = path_prefix_free(inputs, '/').split('_')
path = file_folder_path(inputs)
animal, day = opts[1], opts[2]
f = None
hfile = inputs
elif isinstance(inputs, tuple):
path, animal, day = inputs
hfile = os.path.join(path, animal, day,
"full_{}_{}__data.hdf5".format(animal, day))
f = None
elif isinstance(inputs, h5py.File):
opts = path_prefix_free(inputs.filename, '/').split('_')
path = file_folder_path(inputs.filename)
animal, day = opts[1], opts[2]
f = inputs
else:
raise RuntimeError("Input Format Unknown!")
savepath = os.path.join(
path, '{}_{}_rawcwt_{}.csv'.format(animal, day, hyperparams))
cwt = os.path.join(path, 'cwt.txt')
if os.path.exists(savepath):
return
if f is None:
f = h5py.File(hfile, 'r')
C = np.array(f['C'])
f.close()
with open(savepath, 'w') as fh:
cwriter = csv.writer(fh, delimiter=',')
for i in range(C.shape[0]):
print(i)
cwriter.writerow(find_peaks_cwt(C[i, :], np.arange(low, high)))
if animal is not None:
with open(cwt, 'a') as cf:
cf.write(hyperparams + "\n")
return savepath
def digitize_calcium(inputs, source, n):
"""Returns a pd.DataFrame with peak timing for calcium events
Params:
inputs: str, h5py.File, tuple, or np.ndarray
if str/h5py.File: string that represents the filename of hdf5 file
if tuple: (path, animal, day), that describes the file location
if np.ndarray: array C of calcium traces
out: str
Output path for saving the metrics in a hdf5 file
outfile: Animal_Day.csv
columns: neuron number
"""
if isinstance(inputs, np.ndarray):
S = inputs
animal, day = None, None
path = './'
savepath = os.path.join(path, 'sample_IBI_{}.csv')
else:
if isinstance(inputs, str):
opts = path_prefix_free(inputs, '/').split('_')
path = file_folder_path(inputs)
animal, day = opts[1], opts[2]
f = None
hfile = inputs
elif isinstance(inputs, tuple):
path, animal, day = inputs
f1 = os.path.join(path, animal,
"full_{}_{}__data.hdf5".format(animal, day))
f2 = encode_to_filename(path, animal, day)
if os.path.exists(f1):
hfile = f1
elif os.path.exists(f2):
hfile = f2
else:
raise FileNotFoundError("File {} or {} not found".format(
f1, f2))
f = None
elif isinstance(inputs, h5py.File):
opts = path_prefix_free(inputs.filename, '/').split('_')
path = file_folder_path(inputs.filename)
animal, day = opts[1], opts[2]
f = inputs
else:
raise RuntimeError("Input Format Unknown!")
savepath = os.path.join(path, '%s_%s_rawcwt_{}.csv' % (animal, day))
if os.path.exists(savepath):
return
if f is None:
f = h5py.File(hfile, 'r')
S = np.array(f[source])
f.close()
dgs = digitize_signal(S, n)
hyperparams = "n_{}".format(n)
savepath = savepath.format(hyperparams)
with open(savepath, 'w') as fh:
cwriter = csv.writer(fh, delimiter=',')
for i in range(S.shape[0]):
print(i)
cwriter.writerow(dgs[i])
return savepath
def get_peak_times_over_thres(inputs, window, method, tlock=30):
""" Returns Peak Times, organized by window bins and trial bins respectively, that Passes a specific
threshold specified by method. """
if isinstance(inputs, str):
opts = path_prefix_free(inputs, '/').split('_')
path = file_folder_path(inputs)
session_path = path
animal, day = opts[1], opts[2]
f = None
hfile = inputs
elif isinstance(inputs, tuple):
path, animal, day = inputs
session_path = os.path.join(path, animal, day)
hfile = os.path.join(session_path,
"full_{}_{}__data.hdf5".format(animal, day))
f = None
elif isinstance(inputs, h5py.File):
opts = path_prefix_free(inputs.filename, '/').split('_')
path = file_folder_path(inputs.filename)
session_path = path
animal, day = opts[1], opts[2]
f = inputs
else:
raise RuntimeError("Input Format Unknown!")
cwt_pattern = '{}_{}_rawcwt_low_(\d+)_high_(\d+).csv'.format(animal, day)
cwtfile = find_file_regex(session_path, cwt_pattern)
if cwtfile is None:
print("({}, {}) requires preprocessing!".format(animal, day))
cwtfile = calcium_to_peak_times((path, animal, day))
if f is None:
f = h5py.File(hfile, 'r')
C = np.array(f['C'])
trial_start = np.array(f['trial_start'])
trial_end = np.array(f['trial_end'])
array_hit = np.array(f['array_t1'])
array_miss = np.array(f['array_miss'])
blen = f.attrs['blen']
f.close()
print(animal, day)
opt, th = method // 10, method % 10
dispersion = median_absolute_deviation if opt else np.nanstd
T = C.shape[1]
slides = int(np.ceil(T / window))
with open(cwtfile) as cwtstream:
creader = csv.reader(cwtstream)
D_trial = {}
D_window = {}
for i, row in enumerate(creader):
c = C[i]
D_window[i] = {s: [] for s in range(slides)}
D_trial[i] = {t: [] for t in range(len(trial_start))}
t = 0
s = 0
s_end = min(window, T)
thres = np.nanmean(c) + dispersion(
c) * th # Use the entire signal as a criteria for evaluating
# large events
for j in range(len(row)):
p = int(row[j])
if p >= s_end:
s += 1
D_window[i][s] = []
s_end = min(s_end + window, T)
elif c[p] >= thres:
D_window[i][s].append(p)
if p <= blen:
pass
elif t >= len(trial_start):
pass
# # if i == 0:
# # print("Reaches End, dropping future frames ({}/{})".format(p, trial_end[-1] + tlock))
else:
# if t > 0 and trial_start[t] - trial_end[t-1] > tlock and i == 0:
# print("trial {}, out of ({}, {}, prev {}), diff:{}, {}".format(t, trial_start[t], trial_end[t], trial_end[t-1], trial_start[t]-trial_end[t-1], HM))
if p > trial_end[t] + tlock:
# if t < len(trial_start) -1 and p > trial_start[t+1]:
# print("Frame overflow into next trial bin {}, (end: {}, start: {})"
# .format(t, trial_end[t], trial_start[t+1]))`
t += 1
if t < len(trial_start):
if p >= trial_start[t] and c[p] >= thres:
D_trial[i][t].append(p)
elif p >= trial_start[t] and c[p] >= thres:
D_trial[i][t].append(p)
# elif i == 0:
# HM = "hit" if t in array_hit else "miss"
# print("trial {}, Out of bin frame: {}".format(t, p))
return D_trial, D_window