def test_with_spike_rec():
directory = "../data/spikefinder_train/"
dataset = "8"
calcium = np.genfromtxt(directory + dataset + '.train.calcium.csv',
delimiter=",",
skip_header=1).transpose()
spikes = np.genfromtxt(directory + dataset + '.train.spikes.csv',
delimiter=",",
skip_header=1).transpose()
t = np.arange(calcium.shape[1]) / 100.0
#plt.plot(t, calcium[2])
#plt.plot(t, spikes[2])
#plt.show()
spks_deconv = np.empty_like(spikes) * np.nan
for i, arr in enumerate(tqdm(calcium)):
spks_tmp = deconvolve(arr[~np.isnan(arr)][None, :], 100, tau=1.5)[0]
spks_deconv[i, :len(spks_tmp)] = spks_tmp
k_arr = np.arange(1, 100)
coefficients = np.zeros_like(k_arr, dtype=float)
for i, arr in enumerate(tqdm(spikes[:])):
coeff_res = mre.coefficients(arr[~np.isnan(arr)], k_arr)
coefficients += coeff_res.coefficients
coefficients_deconv = np.zeros_like(k_arr, dtype=float)
for i, arr in enumerate(tqdm(spks_deconv[:])):
coeff_res = mre.coefficients(arr[~np.isnan(arr)], k_arr)
coefficients_deconv += coeff_res.coefficients
print(coefficients)
f = plt.figure(figsize=(4.3, 3))
t = np.arange(0, len(coefficients)) / 100
plt.plot(t,
coefficients / len(spikes),
label="from spikes, 'ground truth'")
plt.plot(t,
coefficients_deconv / len(spks_deconv) / 8.5,
label='from deconvolved calcium imaging')
plt.xlabel("time interval $\Delta T$ [s]")
plt.ylabel("autocorrelation (normed)")
plt.tick_params(
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
left=False, # ticks along the bottom edge are off
right=False, # ticks along the top edge are off
labelleft=False) # labels along the bottom edge are off
plt.legend()
plt.tight_layout()
plt.savefig(
"../reports/estimating_timescales/figures/comparison3_normed.pdf")
plt.show()
def plot_map_tau():
directory = "../data/Spontaneous/suite2p/plane0/"
# load traces and subtract neuropil
iscell = np.load(directory + "iscell.npy")
F = np.load(directory + "F.npy")
Fneu = np.load(directory + "Fneu.npy")
Fc = F - 1 * Fneu
stat = np.load(directory + 'stat.npy', allow_pickle=True)
x_pos = np.array([np.median(neur["xpix"]) for neur in stat])
y_pos = np.array([np.median(neur["ypix"]) for neur in stat])
mask_iscell = iscell[:, 1] > 0.5
Fc = Fc[mask_iscell, :]
Fc = Fc[:]
x_pos = x_pos[mask_iscell]
y_pos = y_pos[mask_iscell]
x_pos = x_pos[:]
y_pos = y_pos[:]
Fc_conv = Fc
spks = deconvolve(Fc_conv, fs=30, tau=1.5)
t = np.linspace(0, Fc_conv.shape[1] / 30, Fc_conv.shape[1])
k_arr = np.arange(1, 40)
taus = []
for i, arr in enumerate(tqdm(spks[:])):
coeff_res = mre.coefficients(arr, k_arr, dt=1 / 30 * 1000)
plt.plot(coeff_res.coefficients)
plt.show()
fit_result = mre.fit(coeff_res, steps=np.arange(1, 40))
taus.append(fit_result.tau)
cm = plt.cm.get_cmap("inferno")
sc = plt.scatter(x_pos,
y_pos,
c=taus,
vmin=0,
vmax=300,
s=35,
cmap=cm,
norm=colors.PowerNorm(0.7, 10, 300))
plt.xlabel("x position")
plt.ylabel("y position")
cbar = plt.colorbar(sc)
cbar.set_label("Timescale [ms]")
plt.tight_layout()
plt.savefig("../reports/estimating_timescales/figures/map_taus.pdf")
plt.show()
def test_deconv():
directory = "../data/Spontaneous/suite2p/plane0/"
# load traces and subtract neuropil
iscell = np.load(directory + "iscell.npy")
F = np.load(directory + "F.npy")
Fneu = np.load(directory + "Fneu.npy")
Fc = F - 1 * Fneu
N = 3
mask_iscell = iscell[:, 1] > 0.5
Fc = Fc[mask_iscell, :]
Fc = Fc[:]
#Fc_conv = np.empty((Fc.shape[0], Fc.shape[1]-N+1))
#for i in range(len(Fc)):
# Fc_conv[i] = np.convolve(Fc[i], np.ones((N,))/N, mode='valid')
Fc_conv = Fc
spks = deconvolve(Fc_conv, fs=30, tau=1.3)
t = np.linspace(0, Fc_conv.shape[1] / 30, Fc_conv.shape[1])
print(iscell[0])
plt.plot(t, spks[0] * 10)
plt.plot(t, Fc_conv[0], alpha=0.8)
plt.show()
k_arr = np.arange(1, 40)
coefficients = np.zeros_like(k_arr, dtype=float)
for i, arr in enumerate(tqdm(spks[:])):
coeff_res = mre.coefficients(arr, k_arr)
coefficients += coeff_res.coefficients
f = plt.figure(figsize=(4, 3))
t = np.arange(0, len(coefficients)) / 30
plt.plot(t, coefficients / len(spks))
#plt.gca().set_yscale('log')
#plt.gca().set_xscale('log')
plt.xlabel("time interval $\Delta T$ [s]")
plt.ylabel("autocorrelation")
plt.tight_layout()
plt.savefig(
"../reports/estimating_timescales/figures/packer_example_lin_pil1.pdf")
plt.show()
def correlation_different_resolutions5():
cells = {
1: [8, 2, 14, 0],
2: [15, 11, 87, 1],
3: [19, 240, 196, 4],
4: [16, 51, 124, 2],
5: [24, 255, 305, 3],
6: [46, None, None, 9],
7: [26, 438, None, 17],
8: [28, 250, 793, 6]
}
labels = [
'60 Hz, 1.37$\mu$m, 15 min', '30 Hz, 1.37$\mu$m, 15 min (downsampled)',
'20 Hz, 1.37$\mu$m, 15 min (downsampled)',
'15 Hz, 1.37$\mu$m, 15 min (downsampled)',
'10 Hz, 1.37$\mu$m, 15 min (downsampled)',
'7.5 Hz, 1.37$\mu$m, 15 min (downsampled)',
'5 Hz 1.37$\mu$m, 15 min (downsampled)'
]
tau = 1.5
fs_list = [60, 30, 20, 15, 10, 7.5, 5]
k_arr_list = []
Fc_list = []
directory = '/data.nst/share/data/packer_calcium_mice/2019-11-08_RL065/'
subpath_F = 'suite2p/plane0/F.npy'
subpath_Fneu = 'suite2p/plane0/Fneu.npy'
paths = ['2019-11-08_RL065_t-002']
for path in paths:
F = np.load(os.path.join(directory, path, subpath_F))
Fneu = np.load(os.path.join(directory, path, subpath_Fneu))
Fc_list.append(F - 0.7 * Fneu)
del F, Fneu
spks_2dlist = []
for cell in cells.keys():
spks_2dlist.append([])
for Fc, cell_num, fs in zip(Fc_list, cells[cell][1:], fs_list):
if cell_num is not None:
spks_2dlist[-1].append(
deconvolve(Fc[cell_num][None, :], fs, tau=tau)[0])
else:
spks_2dlist[-1].append(None)
subsampling_list = [30, 20, 15, 10, 7.5, 5]
for i, subs in enumerate(subsampling_list):
for i_cell, cell in enumerate(cells.keys()):
if cells[cell][1] is not None:
cell_num = cells[cell][1]
Fc = Fc_list[0][cell_num][None, ::round(60 // subs)]
spks1 = deconvolve(Fc, subs, tau=tau)[0]
spks_2dlist[i_cell].append(spks1)
else:
spks_2dlist[i_cell].append(None)
mpl.rcParams["axes.spines.right"] = False
mpl.rcParams["axes.spines.top"] = False
f, axes_list = plt.subplots(4, 2, figsize=(16, 20))
axes_list = [ax for axes in axes_list for ax in axes]
colors = [
'tab:blue', 'tab:pink', 'tab:red', 'tab:cyan', 'tab:olive',
'tab:green', 'tab:purple', 'tab:gray'
]
tau_res_list2d = []
def mult_coeff_res(coeff_res, mult):
return coeff_res._replace(
coefficients=coeff_res.coefficients * mult,
stderrs=coeff_res.stderrs *
mult if coeff_res.stderrs is not None else None)
to_plot = [0, 1, 3, 4]
for cell in range(len(cells)):
taus = []
plot = mre.OutputHandler([], ax=axes_list[cell])
tau_res_list2d.append([])
for i in range(len(labels)):
k_arr = np.arange(1, fs_list[i] * 1)
if spks_2dlist[cell][i] is not None:
act = spks_2dlist[cell][i]
print(act.shape)
len_trial = round(40 * fs_list[i])
act = act[:-(len(act) % len_trial)].reshape((-1, len_trial))
coeff_res = mre.coefficients(act,
k_arr,
dt=1 / fs_list[i] * 1000,
numboot=500,
method='ts')
#norm = 1/coeff_res.coefficients[0]
#coeff_res = mult_coeff_res(coeff_res, norm)
#for j, bootstrapcrs in enumerate(coeff_res.bootstrapcrs):
# coeff_res.bootstrapcrs[j] = mult_coeff_res(bootstrapcrs, norm)
#axes_list[cell].plot(k_arr/fs_list[i]*1000 ,coeff_res.coefficients, color=colors[i],
# label = labels[i] if cell==0 else None)
if i in to_plot:
plot.add_coefficients(
coeff_res,
color=colors[i],
label=labels[i] if cell == 0 else None)
tau_res_list2d[-1].append(
mre.fit(coeff_res,
fitfunc='exponentialoffset',
numboot=500))
else:
tau_res_list2d[-1].append(None)
#axes_list[cell].set_ylim(-0.3,1.2)
axes_list[cell].set_title('cell {}'.format(cell))
plt.legend()
plt.tight_layout()
plt.savefig(
'../reports/different_zoom_levels/autocorrelation_plots_tempSubs2.pdf')
#axes_list[0].set_ylabel('Autocorrelation')
plt.show()
x_ticks = np.arange(len(cells))
offsets = np.linspace(-0.25, 0.25, len(labels))
f, axes_list = plt.subplots(figsize=(10, 6))
for i_ana, offset in enumerate(offsets):
res_list = [
tau_res_list2d[i_cell][i_ana] for i_cell in range(len(cells))
]
taus = np.array(
[res.tau if res is not None else None for res in res_list],
dtype=np.float)
yerr_lower = taus - np.array([
res.tauquantiles[0] if res is not None else None
for res in res_list
],
dtype=np.float)
yerr_upper = np.array([
res.tauquantiles[-1] if res is not None else None
for res in res_list
],
dtype=np.float) - taus
plt.errorbar(x_ticks + offset,
y=taus,
yerr=[yerr_lower, yerr_upper],
color=colors[i_ana],
label=labels[i_ana],
marker="x",
elinewidth=1.5,
capsize=2,
markersize=6,
markeredgewidth=1,
linestyle="")
plt.ylim(0, 400)
plt.legend()
plt.xlabel("Cell number")
plt.ylabel('Timescale (ms)')
#fit_result = mre.fit(coeff_res, steps=k_arr)
#taus.append(fit_result.tau)
plt.savefig('../reports/different_zoom_levels/timescales_tempSubs2.pdf')
plt.show()
def correlation_spatial_subsampling():
#cells = {1: [8,0,1,1,8,8],
# 2: [15,4,3,3,15,15],
# 3: [19,23,19,18,19,19],
# 4: [16,40,88,75,16,16],
# 5: [24,25,43,46,24,24],
# 6: [46,58,48,109,46,46],
# 7: [26,41,37,41,26,26],
# 8: [28,45,31,37,28,28]}
cells = {
1: [8, 0, 1, 1, 1, 2, 2],
2: [15, 4, 3, 3, 2, 1, 11],
3: [19, 23, 19, 18, 18, 19, 240],
4: [16, 40, 88, 75, 85, 22, 51],
5: [24, 25, 43, 46, 38, 39, 255],
6: [46, 58, 48, 109, 44, 42, None],
7: [26, 41, 37, 41, 39, 40, 438],
8: [28, 45, 31, 37, 29, 25, 250]
}
labels = [
'60 Hz, 0.54x0.54$\mu$m, 30 min',
'60 Hz, 0.54x1.08$\mu$m, 30 min (subs.)',
'60 Hz, 1.08x1.08$\mu$m, 30 min (subs.)',
'60 Hz, 1.62x1.62$\mu$m, 30 min (subs.)',
'60 Hz, 2.16x2.16$\mu$m, 30 min (subs.)',
'60 Hz, 2.7x2.7$\mu$m, 30 min (subs.)', '60 Hz, 1.37$\mu$m, 15 min'
]
tau = 1.5
fs_list = [60, 60, 60, 60, 60, 60, 60]
k_arr_list = []
Fc_list = []
directory = '/data.nst/jdehning/packer_data/calcium_subsampled/'
subpath_F = 'suite2p/plane0/F.npy'
subpath_Fneu = 'suite2p/plane0/Fneu.npy'
paths = [
'2019-11-08_RL065_t-001_1x1', '2019-11-08_RL065_t-001_1x2',
'2019-11-08_RL065_t-001_2x2', '2019-11-08_RL065_t-001_3x3',
'2019-11-08_RL065_t-001_4x4', '2019-11-08_RL065_t-001_5x5',
'2019-11-08_RL065_t-002'
]
for path in paths:
F = np.load(os.path.join(directory, path, subpath_F))
Fneu = np.load(os.path.join(directory, path, subpath_Fneu))
Fc_list.append(F - 0.7 * Fneu)
del F, Fneu
spks_2dlist = []
for cell in cells.keys():
spks_2dlist.append([])
for Fc, cell_num, fs, i_rec in zip(Fc_list, cells[cell], fs_list,
range(1000)):
if cell_num is not None:
if i_rec == 4:
beg, end = (0, Fc.shape[1] // 2)
elif i_rec == 5:
beg, end = (Fc.shape[1] // 2, -1)
else:
beg, end = (0, -1)
spks_2dlist[-1].append(
deconvolve(Fc[cell_num][None, beg:end], fs, tau=tau)[0])
else:
spks_2dlist[-1].append(None)
mpl.rcParams["axes.spines.right"] = False
mpl.rcParams["axes.spines.top"] = False
f, axes_list = plt.subplots(4, 2, figsize=(16, 20))
axes_list = [ax for axes in axes_list for ax in axes]
colors = [
'tab:blue', 'tab:pink', 'tab:red', 'tab:cyan', 'tab:olive',
'tab:green', 'k'
]
tau_res_list2d = []
def mult_coeff_res(coeff_res, mult):
return coeff_res._replace(
coefficients=coeff_res.coefficients * mult,
stderrs=coeff_res.stderrs *
mult if coeff_res.stderrs is not None else None)
to_plot = [0, 1, 2, 3, 4, 5]
for cell in range(len(cells)):
taus = []
plot = mre.OutputHandler([], ax=axes_list[cell])
tau_res_list2d.append([])
for i in range(len(labels)):
k_arr = np.arange(1, fs_list[i] * 1)
if spks_2dlist[cell][i] is not None:
act = spks_2dlist[cell][i]
print(act.shape)
len_trial = round(40 * fs_list[i])
act = act[:-(len(act) % len_trial)].reshape((-1, len_trial))
coeff_res = mre.coefficients(act,
k_arr,
dt=1 / fs_list[i] * 1000,
numboot=500,
method='ts')
#norm = 1/coeff_res.coefficients[0]
#coeff_res = mult_coeff_res(coeff_res, norm)
#for j, bootstrapcrs in enumerate(coeff_res.bootstrapcrs):
# coeff_res.bootstrapcrs[j] = mult_coeff_res(bootstrapcrs, norm)
#axes_list[cell].plot(k_arr/fs_list[i]*1000 ,coeff_res.coefficients, color=colors[i],
# label = labels[i] if cell==0 else None)
if i in to_plot:
plot.add_coefficients(
coeff_res,
color=colors[i],
label=labels[i] if cell == 0 else None)
tau_res_list2d[-1].append(
mre.fit(coeff_res,
fitfunc='exponentialoffset',
numboot=500))
else:
tau_res_list2d[-1].append(None)
#axes_list[cell].set_ylim(-0.3,1.2)
axes_list[cell].set_title('cell {}'.format(cell))
plt.legend()
plt.tight_layout()
plt.savefig(
'../reports/spatial_subsampling/autocorrelation_plots_spatialSubs3.pdf'
)
#axes_list[0].set_ylabel('Autocorrelation')
plt.show()
x_ticks = np.arange(len(cells))
offsets = np.linspace(-0.25, 0.25, len(labels))
f, axes_list = plt.subplots(figsize=(10, 6))
for i_ana, offset in enumerate(offsets):
res_list = [
tau_res_list2d[i_cell][i_ana] for i_cell in range(len(cells))
]
taus = np.array(
[res.tau if res is not None else None for res in res_list],
dtype=np.float)
yerr_lower = taus - np.array([
res.tauquantiles[0] if res is not None else None
for res in res_list
],
dtype=np.float)
yerr_upper = np.array([
res.tauquantiles[-1] if res is not None else None
for res in res_list
],
dtype=np.float) - taus
plt.errorbar(x_ticks + offset,
y=taus,
yerr=[yerr_lower, yerr_upper],
color=colors[i_ana],
label=labels[i_ana],
marker="x",
elinewidth=1.5,
capsize=2,
markersize=6,
markeredgewidth=1,
linestyle="")
plt.ylim(0, 400)
plt.legend()
plt.xlabel("Cell number")
plt.ylabel('Timescale (ms)')
#fit_result = mre.fit(coeff_res, steps=k_arr)
#taus.append(fit_result.tau)
plt.savefig('../reports/spatial_subsampling/timescales_spatialSubs3.pdf')
plt.show()