def compare_truth_pyfnnd(ca_data, learn_theta=(0, 1, 1, 1, 0), tau=0.6):
'''
'''
from fit_neuron.evaluate import spkd_lib as spkd
n_best, c_best, LL, theta_best = pyfnnd.deconvolve(
ca_data.df_f.reshape(-1, 1).T,
dt=0.0166,
verbosity=1,
learn_theta=learn_theta, #tau = tau,
spikes_tol=1E-5,
params_tol=1E-5)
resamp_spikes = MP_analy.downsample_spike_train(ca_data.spike_train,
ca_data.spike_time,
ca_data.fluor_time)
resamp_spikes[resamp_spikes > 1] = 1
n_best = np.roll(n_best, -1)
pyfnnd.plotting.plot_fit(
ca_data.df_f.reshape(-1, 1).T, n_best, c_best, theta_best, 0.0166)
# from sklearn.metrics import roc_curve, auc
# fpr, tpr, thresholds = roc_curve(resamp_spikes, n_best, pos_label = 1)
# my_auc = auc(fpr, tpr)
# plt.figure()
# plt.plot(fpr, tpr)
# plt.xlabel('False Positive Rate', fontsize = 18)
# plt.ylabel('True Positive Rate', fontsize = 18)
# plt.title('AUC = ' + str(my_auc))
thresholds = np.arange(0.01, 1, 0.01)
true_spk_times = ca_data.spike_time[ca_data.spike_train > 0.5]
max_dist = 2 * len(true_spk_times) # maximum distance for victor purpura
vp = max_dist * np.ones(99) # victor-purpura
vp_cost = 5
for i, thresh in enumerate(thresholds):
if np.sum(
n_best > thresh
) > max_dist: # is you have a lot of bad spikes, calculating vp can be long
continue
else:
vp[i] = spkd.victor_purpura_dist(
ca_data.fluor_time[n_best > thresh], true_spk_times, vp_cost)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.plot(thresholds, 2 * vp / max_dist)
plt.xlabel('Threshold', fontsize=18)
plt.ylabel('Normalized VP distance ', fontsize=18)
plt.title('Minimum distance: ' + str(min(2 * vp / max_dist)))
MP_plot.prettify_axes(ax)
example_spikes = threshold_spike_prob(n_best, 0.07)
ca_data.plot_calcium_spikes() # makes a new figure
plt.plot(ca_data.fluor_time, example_spikes + 0.5)
return np.min(2 * vp / max_dist)
def plot_calcium_spikes(self):
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.plot(self.fluor_time, self.df_f)
resampled_spikes = MP_analy.downsample_spike_train(self.spike_train, self.spike_time, self.fluor_time)
plt.plot(self.fluor_time, resampled_spikes - 0.5)
plt.xlabel( 'Time (s)', fontsize = 18)
plt.ylabel('DF / F // spike probability', fontsize = 18)
MP_plot.prettify_axes(ax)
def plot_calcium_spikes(self):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.plot(self.fluor_time, self.df_f)
resampled_spikes = MP_analy.downsample_spike_train(
self.spike_train, self.spike_time, self.fluor_time)
plt.plot(self.fluor_time, resampled_spikes - 0.5)
plt.xlabel('Time (s)', fontsize=18)
plt.ylabel('DF / F // spike probability', fontsize=18)
MP_plot.prettify_axes(ax)
def compare_truth_pyfnnd( ca_data, learn_theta = (0, 1, 1, 1, 0), tau = 0.6 ):
from fit_neuron.evaluate import spkd_lib as spkd
n_best, c_best, LL, theta_best = pyfnnd.deconvolve( ca_data.df_f.reshape(-1, 1).T,
dt=0.0166, verbosity=1, learn_theta=learn_theta, #tau = tau,
spikes_tol=1E-5, params_tol=1E-5 )
resamp_spikes = MP_analy.downsample_spike_train(ca_data.spike_train, ca_data.spike_time, ca_data.fluor_time)
resamp_spikes[resamp_spikes > 1 ] = 1
n_best = np.roll(n_best, -1)
pyfnnd.plotting.plot_fit(ca_data.df_f.reshape(-1, 1).T, n_best, c_best, theta_best, 0.0166)
# from sklearn.metrics import roc_curve, auc
# fpr, tpr, thresholds = roc_curve(resamp_spikes, n_best, pos_label = 1)
# my_auc = auc(fpr, tpr)
# plt.figure()
# plt.plot(fpr, tpr)
# plt.xlabel('False Positive Rate', fontsize = 18)
# plt.ylabel('True Positive Rate', fontsize = 18)
# plt.title('AUC = ' + str(my_auc))
thresholds = np.arange(0.01, 1, 0.01)
true_spk_times = ca_data.spike_time[ca_data.spike_train>0.5]
max_dist = 2* len(true_spk_times ) # maximum distance for victor purpura
vp = max_dist * np.ones(99) # victor-purpura
vp_cost = 5
for i, thresh in enumerate(thresholds):
if np.sum(n_best > thresh) > max_dist: # is you have a lot of bad spikes, calculating vp can be long
continue
else:
vp[i] = spkd.victor_purpura_dist(ca_data.fluor_time[n_best >thresh], true_spk_times, vp_cost)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.plot(thresholds, 2 * vp / max_dist )
plt.xlabel('Threshold', fontsize = 18)
plt.ylabel('Normalized VP distance ', fontsize = 18)
plt.title('Minimum distance: ' + str(min(2 * vp / max_dist)) )
MP_plot.prettify_axes(ax)
example_spikes = threshold_spike_prob(n_best, 0.07)
ca_data.plot_calcium_spikes() # makes a new figure
plt.plot( ca_data.fluor_time, example_spikes + 0.5)
return np.min(2 * vp / max_dist)
