def plotcheckersurround(exp_name, stim_nr, filename=None, spikecutoff=1000,
ratingcutoff=4, staqualcutoff=0, inner_b=2,
outer_b=4):
"""
Divides into center and surround by fitting 2D Gaussian, and plot
temporal components.
spikecutoff:
Minimum number of spikes to include.
ratingcutoff:
Minimum spike sorting rating to include.
staqualcutoff:
Minimum STA quality (as measured by z-score) to include.
inner_b:
Defined limit between receptive field center and surround
in units of sigma.
outer_b:
Defined limit of the end of receptive field surround.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
stim_nr = str(stim_nr)
if filename:
filename = str(filename)
if not filename:
savefolder = 'surroundplots'
label = ''
else:
label = filename.strip('.npz')
savefolder = 'surroundplots_' + label
_, metadata = asc.read_spikesheet(exp_name)
px_size = metadata['pixel_size(um)']
data = iof.load(exp_name, stim_nr, fname=filename)
clusters = data['clusters']
stas = data['stas']
stx_h = data['stx_h']
exp_name = data['exp_name']
stimname = data['stimname']
max_inds = data['max_inds']
frame_duration = data['frame_duration']
filter_length = data['filter_length']
quals = data['quals'][-1, :]
spikenrs = data['spikenrs']
c1 = np.where(spikenrs > spikecutoff)[0]
c2 = np.where(clusters[:, 2] <= ratingcutoff)[0]
c3 = np.where(quals > staqualcutoff)[0]
choose = [i for i in range(clusters.shape[0]) if ((i in c1) and
(i in c2) and
(i in c3))]
clusters = clusters[choose]
stas = list(np.array(stas)[choose])
max_inds = list(np.array(max_inds)[choose])
clusterids = plf.clusters_to_ids(clusters)
t = np.arange(filter_length)*frame_duration*1000
# Determine frame size so that the total frame covers
# an area large enough i.e. 2*700um
f_size = int(700/(stx_h*px_size))
del data
for i in range(clusters.shape[0]):
sta_original = stas[i]
max_i_original = max_inds[i]
try:
sta, max_i = mf.cut_around_center(sta_original,
max_i_original, f_size)
except ValueError:
continue
fit_frame = sta[:, :, max_i[2]]
if np.max(fit_frame) != np.max(np.abs(fit_frame)):
onoroff = -1
else:
onoroff = 1
Y, X = np.meshgrid(np.arange(fit_frame.shape[1]),
np.arange(fit_frame.shape[0]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'.*divide by zero*.', RuntimeWarning)
pars = gfit.gaussfit(fit_frame*onoroff)
f = gfit.twodgaussian(pars)
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z-pars[0])/pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm*-2)
ax = plt.subplot(1, 2, 1)
plf.stashow(fit_frame, ax)
ax.set_aspect('equal')
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax.contour(Y, X, Zm, [inner_b, outer_b],
cmap=plf.RFcolormap(('C0', 'C1')))
barsize = 100/(stx_h*px_size)
scalebar = AnchoredSizeBar(ax.transData,
barsize, '100 µm',
'lower left',
pad=1,
color='k',
frameon=False,
size_vertical=.2)
ax.add_artist(scalebar)
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'.*invalid value encountered in*.',
RuntimeWarning)
center_mask = np.logical_not(Zm < inner_b)
center_mask_3d = np.broadcast_arrays(sta,
center_mask[..., None])[1]
surround_mask = np.logical_not(np.logical_and(Zm > inner_b,
Zm < outer_b))
surround_mask_3d = np.broadcast_arrays(sta,
surround_mask[..., None])[1]
sta_center = np.ma.array(sta, mask=center_mask_3d)
sta_surround = np.ma.array(sta, mask=surround_mask_3d)
sta_center_temporal = np.mean(sta_center, axis=(0, 1))
sta_surround_temporal = np.mean(sta_surround, axis=(0, 1))
ax1 = plt.subplot(1, 2, 2)
l1 = ax1.plot(t, sta_center_temporal,
label='Center\n(<{}σ)'.format(inner_b),
color='C0')
sct_max = np.max(np.abs(sta_center_temporal))
ax1.set_ylim(-sct_max, sct_max)
ax2 = ax1.twinx()
l2 = ax2.plot(t, sta_surround_temporal,
label='Surround\n({}σ<x<{}σ)'.format(inner_b, outer_b),
color='C1')
sst_max = np.max(np.abs(sta_surround_temporal))
ax2.set_ylim(-sst_max, sst_max)
plf.spineless(ax1)
plf.spineless(ax2)
ax1.tick_params('y', colors='C0')
ax2.tick_params('y', colors='C1')
plt.xlabel('Time[ms]')
plt.axhline(0, linestyle='dashed', linewidth=1)
lines = l1+l2
labels = [line.get_label() for line in lines]
plt.legend(lines, labels, fontsize=7)
plt.title('Temporal components')
plt.suptitle(f'{exp_name}\n{stimname}\n{clusterids[i]}')
plt.subplots_adjust(wspace=.5, top=.85)
plotpath = os.path.join(exp_dir, 'data_analysis',
stimname, savefolder)
if not os.path.isdir(plotpath):
os.makedirs(plotpath, exist_ok=True)
plt.savefig(os.path.join(plotpath, clusterids[i])+'.svg',
format='svg', dpi=300)
plt.close()
print(f'Plotted checkerflicker surround for {stimname}')
def stripesurround_SVD(exp_name, stimnrs, nrcomponents=5):
"""
nrcomponents:
first N components of singular value decomposition (SVD)
will be used to reduce noise.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
if isinstance(stimnrs, int):
stimnrs = [stimnrs]
for stimnr in stimnrs:
data = iof.load(exp_name, stimnr)
_, metadata = asc.read_spikesheet(exp_dir)
px_size = metadata['pixel_size(um)']
clusters = data['clusters']
stas = data['stas']
max_inds = data['max_inds']
filter_length = data['filter_length']
stx_w = data['stx_w']
exp_name = data['exp_name']
stimname = data['stimname']
frame_duration = data['frame_duration']
quals = data['quals']
# Record which clusters are ignored during analysis
try:
included = data['included']
except KeyError:
included = [True] * clusters.shape[0]
# Average STA values 100 ms around the brightest frame to
# minimize noise
cut_time = int(100 / (frame_duration * 1000) / 2)
# Tolerance for distance between center and surround
# distributions 60 μm
dtol = int((60 / px_size) / 2)
clusterids = plf.clusters_to_ids(clusters)
fsize = int(700 / (stx_w * px_size))
t = np.arange(filter_length) * frame_duration * 1000
vscale = fsize * stx_w * px_size
cs_inds = np.empty(clusters.shape[0])
polarities = np.empty(clusters.shape[0])
savepath = os.path.join(exp_dir, 'data_analysis', stimname)
for i in range(clusters.shape[0]):
sta = stas[i]
max_i = max_inds[i]
# From this point on, use the low-rank approximation
# version
sta_reduced = sumcomponent(nrcomponents, sta)
try:
sta_reduced, max_i = msc.cutstripe(sta_reduced, max_i,
fsize * 2)
except ValueError as e:
if str(e) == 'Cutting outside the STA range.':
included[i] = False
continue
else:
print(f'Error while analyzing {stimname}\n' +
f'Index:{i} Cluster:{clusterids[i]}')
raise
# Isolate the time point from which the fit will
# be obtained
if max_i[1] < cut_time:
max_i[1] = cut_time + 1
fitv = np.mean(sta_reduced[:, max_i[1] - cut_time:max_i[1] +
cut_time + 1],
axis=1)
# Make a space vector
s = np.arange(fitv.shape[0])
if np.max(fitv) != np.max(np.abs(fitv)):
onoroff = -1
else:
onoroff = 1
polarities[i] = onoroff
# Determine the peak values for center and surround
# to give as initial parameters for curve fitting
centerpeak = onoroff * np.max(fitv * onoroff)
surroundpeak = onoroff * np.max(fitv * -onoroff)
# Define initial guesses for the center and surround gaussians
# First set of values are for center, second for surround.
p_initial = [centerpeak, max_i[0], 2, surroundpeak, max_i[0], 8]
if onoroff == 1:
bounds = ([0, -np.inf, -np.inf, 0, max_i[0] - dtol, 4], [
np.inf, np.inf, np.inf, np.inf, max_i[0] + dtol, 20
])
elif onoroff == -1:
bounds = ([
-np.inf, -np.inf, -np.inf, -np.inf, max_i[0] - dtol, 4
], [0, np.inf, np.inf, 0, max_i[0] + dtol, 20])
try:
popt, _ = curve_fit(centersurround_onedim,
s,
fitv,
p0=p_initial,
bounds=bounds)
except (ValueError, RuntimeError) as e:
er = str(e)
if (er == "`x0` is infeasible."
or er.startswith("Optimal parameters not found")):
popt, _ = curve_fit(onedgauss, s, fitv, p0=p_initial[:3])
popt = np.append(popt, [0, popt[1], popt[2]])
elif er == "array must not contain infs or NaNs":
included[i] = False
continue
else:
print(f'Error while analyzing {stimname}\n' +
f'Index:{i} Cluster:{clusterids[i]}')
import pdb
pdb.set_trace()
raise
fit = centersurround_onedim(s, *popt)
popt[0] = popt[0] * onoroff
popt[3] = popt[3] * onoroff
csi = popt[3] / popt[0]
cs_inds[i] = csi
plt.figure(figsize=(10, 9))
ax = plt.subplot(121)
plf.stashow(sta_reduced, ax, extent=[0, t[-1], -vscale, vscale])
ax.set_xlabel('Time [ms]')
ax.set_ylabel('Distance [µm]')
ax.set_title(f'Using first {nrcomponents} components of SVD',
fontsize='small')
ax = plt.subplot(122)
plf.spineless(ax)
ax.set_yticks([])
# We need to flip the vertical axis to match
# with the STA next to it
plt.plot(onoroff * fitv, -s, label='Data')
plt.plot(onoroff * fit, -s, label='Fit')
plt.axvline(0, linestyle='dashed', alpha=.5)
plt.title(f'Center: a: {popt[0]:4.2f}, μ: {popt[1]:4.2f},' +
f' σ: {popt[2]:4.2f}\n' +
f'Surround: a: {popt[3]:4.2f}, μ: {popt[4]:4.2f},' +
f' σ: {popt[5]:4.2f}' + f'\n CS index: {csi:4.2f}')
plt.subplots_adjust(top=.85)
plt.suptitle(f'{exp_name}\n{stimname}\n{clusterids[i]} ' +
f'Q: {quals[i]:4.2f}')
os.makedirs(os.path.join(savepath, 'stripesurrounds_SVD'),
exist_ok=True)
plt.savefig(os.path.join(savepath, 'stripesurrounds_SVD',
clusterids[i] + '.svg'),
bbox_inches='tight')
plt.close()
data.update({
'cs_inds': cs_inds,
'polarities': polarities,
'included': included
})
np.savez(os.path.join(savepath, f'{stimnr}_data_SVD.npz'), **data)
print(f'Surround plotted and saved for {stimname}.')
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z-pars[0])/pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm*-2)
ax1 = plt.subplot(rows, columns, 1)
plf.subplottext('A', ax1)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax1)
ax1.set_aspect('equal')
plf.spineless(ax1)
ax1.set_xticks([])
ax1.set_yticks([])
checkercolors = ['black', 'orange']
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax1.contour(Y, X, Zm, [inner_b, outer_b], linewidths=.5,
cmap=plf.RFcolormap(checkercolors))
barsize_set_checker = 100 # micrometers
checker_scalebarsize = barsize_set_checker/(stx_h*px_size)
f = gfit.twodgaussian(pars)
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z - pars[0]) / pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm * -2)
ax = plt.subplot(1, 2, 1)
plf.subplottext('A', ax, x=0, y=1.3)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax)
ax.set_aspect('equal')
plf.spineless(ax)
ax.set_axis_off()
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax.contour(Y,
X,
Zm, [inner_b, outer_b],
cmap=plf.RFcolormap(('C0', 'C1')))
barsize = 100 / (stx_h * px_size)
scalebar = AnchoredSizeBar(ax.transData,
barsize,
def sumcomponent(nr, u, s, v):
cumulative = np.zeros((u.shape[0], v.shape[-1]))
for comp in range(nr + 1):
cumulative += component(comp, u, s, v)
return cumulative
for i, clusterid in enumerate(clusterids):
sta = stas[i]
rows = 2
cols = 5
plt.figure(figsize=(12, 12))
ax1 = plt.subplot(rows, cols, 1)
plf.stashow(sta, ax1)
u, s, v = np.linalg.svd(sta)
#componentnr = 1
comp_range = 9
sta_dn = np.zeros(sta.shape)
for componentnr in range(comp_range):
#u = u[:, :componentnr]
#v = v[:componentnr, :]
#
#for i in range(componentnr):
# sta_dn += np.dot(u[:, i], v[i, :])
sta_dn += component(componentnr, u, s, v)
@author: ycan
"""
import plotfuncs as plf
import matplotlib.pyplot as plt
import miscfuncs as msc
import iofuncs as iof
data = iof.load('20180124', 12)
index = 5
sta = data['stas'][index]
max_i = data['max_inds'][index]
sta, max_i = msc.cutstripe(sta, max_i, 30)
a = 'Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Vega10, Vega10_r, Vega20, Vega20_r, Vega20b, Vega20b_r, Vega20c, Vega20c_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spectral, spectral_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r'
b = a.split(',')
c = [i.strip(' ') for i in b if not i.endswith('_r')]
c = ['bwr_r', 'RdBu', 'seismic_r', 'bwr', 'RdBu_r', 'seismic']
dims = plf.numsubplots(len(c))
plt.figure(figsize=(20, 20))
for i, cm in enumerate(c):
ax = plt.subplot(dims[0], dims[1], i + 1)
im = plf.stashow(sta, ax, cmap=cm, ticks=[])
plt.axis('off')
im.axes.get_xaxis().set_visible(False)
im.axes.get_yaxis().set_visible(False)
ax.set_title(cm, size='x-small')
plt.savefig('cmaps.svg', bbox_inches='tight')
plt.close()
randnrs = [1 if i > .5 else -1 for i in randnrs]
stim = np.reshape(randnrs, (sy, filter_length), order='F')
for frame in range(total_frames):
randnrs, seed = randpy.ran1(seed, sy)
randnrs = [1 if i > .5 else -1 for i in randnrs]
stim = np.hstack((stim[:, 1:], np.array(randnrs)[..., None]))
spike = np.random.poisson()
spike = 1
if spike != 0:
sta += stim * spike
spikect += spike
sta /= spikect
# %%
plt.figure(figsize=(6, 14))
plf.stashow(sta, plt.gca())
plt.show()
#%%
bar = sta[:, -1]
clusters = data['clusters']
stas = data['stas']
clusterids = plf.clusters_to_ids(clusters)
rows = 1
columns = 2
for i in range(clusters.shape[0]):
orig_sta = stas[i]
plt.figure(figsize=(14, 14))
import matplotlib.pyplot as plt
import plotfuncs as plf
multiplier = 100
sy = 160
total_frames = 80000 * multiplier
filter_length = 40
seed = -1000
length = sy * total_frames
randnrs, seed = randpy.ran1(seed, length)
randnrs = [1 if i > .5 else -1 for i in randnrs]
stimulus = np.reshape(randnrs, (sy, total_frames), order='F')
del randnrs
sta = np.zeros((sy, filter_length))
spikecounter = 0
for k in range(filter_length, total_frames - filter_length + 1):
stim_small = stimulus[:, k - filter_length + 1:k + 1][:, ::-1]
spike = np.random.poisson()
if spike != 0:
sta += spike * stim_small
spikecounter += spike
del stimulus
sta = sta / spikecounter
ax = plt.subplot(111)
plf.stashow(sta, ax)
plt.show()
# Changed width from 700 micrometer to 400 to zoom in on the
# region of interest. This shifts where the fit is drawn,
# it's fixed when plotting.
fsize_original = int(700/(stx_w*px_size))
fsize = int(400/(stx_w*px_size))
fsize_diff = fsize_original - fsize
t = np.arange(filter_length)*frame_duration*1000
vscale = fsize * stx_w*px_size
sta, max_i = msc.cutstripe(sta, max_i, fsize*2)
ax1 = axes[2*j]
plf.subplottext(['A', 'C'][j], ax1, x=-.4)
plf.subplottext(['Mesopic', 'Photopic'][j],
ax1, x=-.5, y=.5, rotation=90, va='center')
plf.stashow(sta, ax1, extent=[0, t[-1], -vscale, vscale])
ax1.set_xlabel('Time [ms]')
# ax1.set_ylabel(r'Distance [$\upmu$m]')
ax1.set_ylabel(r'Distance [μm]')
fitv = np.mean(sta[:, max_i[1]-cut_time:max_i[1]+cut_time+1],
axis=1)
s = np.arange(fitv.shape[0])
ax2 = axes[2*j+1]
plf.subplottext(['B', 'D'][j], ax2, x=-.1)
plf.subplottext(f'Center-Surround Index: {csi:4.2f}',
ax2, x=.95, y=.15, fontsize=8, fontweight='normal')
plf.spineless(ax2)
ax2.set_yticks([])
# Changed width from 700 micrometer to 400 to zoom in on the
# region of interest. This shifts where the fit is drawn,
# it's fixed when plotting.
fsize_original = int(700/(stx_w*px_size))
fsize = int(400/(stx_w*px_size))
fsize_diff = fsize_original - fsize
t = np.arange(filter_length)*frame_duration*1000
vscale = fsize * stx_w*px_size
sta, max_i = msc.cutstripe(sta, max_i, fsize*2)
ax1 = axes[2*j]
plf.subplottext(['A', 'C'][j], ax1, x=-.4)
plf.subplottext(['Mesopic', 'Photopic'][j],
ax1, x=-.5, y=.5, rotation=90, va='center')
plf.stashow(sta, ax1, extent=[0, t[-1], -vscale, vscale],
cmap=texplot.cmap)
ax1.set_xlabel('Time [ms]')
ax1.set_ylabel(r'Distance [$\upmu$m]')
fitv = np.mean(sta[:, max_i[1]-cut_time:max_i[1]+cut_time+1],
axis=1)
s = np.arange(fitv.shape[0])
ax2 = axes[2*j+1]
plf.subplottext(['B', 'D'][j], ax2, x=-.1)
plf.subplottext(f'Center-Surround Index: {csi:4.2f}',
ax2, x=.95, y=.15, fontsize=8, fontweight='normal')
plf.spineless(ax2)
ax2.set_yticks([])
ax2.set_xticks([])
#%% sp = sp1c rows = 2 columns = 2 plt.figure(figsize=(12, 10)) f, pars0, pol0 = getfit(sp) X, Y = np.meshgrid(np.arange(sp.shape[0]), np.arange(sp.shape[1])) Z0 = f(Y, X) Z0m = mahalonobis_convert(Z0, pars0) ax0 = plt.subplot(rows, columns, 1) plf.stashow(sp, ax0) ax0.contour(X, Y, Z0m, [2]) d1 = sp - Z0 * pol0 f1, pars1, pol1 = getfit(d1) Z1 = f1(Y, X) Z1m = mahalonobis_convert(Z1, pars1) ax1 = plt.subplot(rows, columns, 2) plf.stashow(d1, ax1) ax1.contour(X, Y, Z1m, [1.4]) d2 = sp - Z1 * pol1 f2, pars2, pol2 = getfit(d2) Z2 = f2(Y, X) Z2m = mahalonobis_convert(Z2, pars2)
def stripesurround(exp_name, stimnrs):
exp_dir = iof.exp_dir_fixer(exp_name)
if isinstance(stimnrs, int):
stimnrs = [stimnrs]
for stimnr in stimnrs:
data = iof.load(exp_name, stimnr)
_, metadata = asc.read_spikesheet(exp_dir)
px_size = metadata['pixel_size(um)']
clusters = data['clusters']
stas = data['stas']
max_inds = data['max_inds']
filter_length = data['filter_length']
stx_w = data['stx_w']
exp_name = data['exp_name']
stimname = data['stimname']
frame_duration = data['frame_duration']
quals = data['quals']
clusterids = plf.clusters_to_ids(clusters)
fsize = int(700 / (stx_w * px_size))
t = np.arange(filter_length) * frame_duration * 1000
vscale = fsize * stx_w * px_size
#%%
cs_inds = np.empty(clusters.shape[0])
polarities = np.empty(clusters.shape[0])
savepath = os.path.join(exp_dir, 'data_analysis', stimname)
for i in range(clusters.shape[0]):
sta = stas[i]
max_i = max_inds[i]
sta, max_i = msc.cutstripe(sta, max_i, fsize * 2)
plt.figure(figsize=(12, 10))
ax = plt.subplot(121)
plf.stashow(sta, ax)
# Isolate the time point from which the fit will
# be obtained
fitv = sta[:, max_i[1]]
# Make a space vector
s = np.arange(fitv.shape[0])
if np.max(fitv) != np.max(np.abs(fitv)):
onoroff = -1
else:
onoroff = 1
polarities[i] = onoroff
# Determine the peak values for center and surround
# to give as initial parameters for curve fitting
centerpeak = -onoroff * np.max(fitv * onoroff)
surroundpeak = -onoroff * np.max(fitv * -onoroff)
# Define initial guesses for the center and surround gaussians
# First set of values are for center, second for surround.
p_initial = [centerpeak, max_i[0], 2, surroundpeak, max_i[0], 4]
bounds = ([0, -np.inf, -np.inf, 0, -np.inf, -np.inf], np.inf)
try:
popt, _ = curve_fit(centersurround_onedim,
s,
fitv,
p0=p_initial,
bounds=bounds)
except ValueError as e:
if str(e) == "`x0` is infeasible.":
print(e)
popt, _ = curve_fit(onedgauss,
s,
onoroff * fitv,
p0=p_initial[:3])
popt = np.append(popt, [0, popt[1], popt[2]])
else:
raise
fit = centersurround_onedim(s, *popt)
# Avoid dividing by zero when calculating center-surround index
if popt[3] > 0:
csi = popt[0] / popt[3]
else:
csi = 0
cs_inds[i] = csi
ax = plt.subplot(122)
plf.spineless(ax)
ax.set_yticks([])
# We need to flip the vertical axis to match
# with the STA next to it
plt.plot(onoroff * fitv, -s, label='Data')
plt.plot(onoroff * fit, -s, label='Fit')
plt.axvline(0, linestyle='dashed', alpha=.5)
plt.title(f'Center: a: {popt[0]:4.2f}, μ: {popt[1]:4.2f},' +
f' σ: {popt[2]:4.2f}\n' +
f'Surround: a: {popt[3]:4.2f}, μ: {popt[4]:4.2f},' +
f' σ: {popt[5]:4.2f}' + f'\n CS index: {csi:4.2f}')
plt.subplots_adjust(top=.82)
plt.suptitle(f'{exp_name}\n{stimname}\n{clusterids[i]}')
os.makedirs(os.path.join(savepath, 'stripesurrounds'),
exist_ok=True)
plt.savefig(
os.path.join(savepath, 'stripesurrounds',
clusterids[i] + '.svg'))
plt.close()
data.update({'cs_inds': cs_inds, 'polarities': polarities})
np.savez(os.path.join(savepath, f'{stimnr}_data.npz'), **data)
f = gfit.twodgaussian(pars)
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z - pars[0]) / pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm * -2)
ax = plt.subplot(1, 2, 1)
plf.subplottext('A', ax, x=0, y=1.3)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax, cmap=texplot.cmap)
ax.set_aspect('equal')
plf.spineless(ax)
ax.set_axis_off()
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax.contour(Y,
X,
Zm, [inner_b, outer_b],
cmap=plf.RFcolormap(('C0', 'C1')))
barsize = 100 / (stx_h * px_size)
scalebar = AnchoredSizeBar(ax.transData,
barsize,