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}.')
stripesta = np.array(data['stas'])[choose][0]
stripemax = np.array(data['max_inds'])[choose][0]
stx_w = stx_h
frame_duration = data['frame_duration']
fits = np.array(data['fits'])[choose]
onoroff = data['polarities'][choose]
cut_time = int(100/(frame_duration*1000)/2)
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
stripesta, stripemax_i = msc.cutstripe(stripesta, stripemax, fsize*2)
fitv = np.mean(stripesta[:, stripemax[1]-cut_time:stripemax[1]+cut_time+1],
axis=1)
s = np.arange(fitv.shape[0])
ax3 = plt.subplot(rows, columns, 3)
plf.stashow(stripesta, ax3)
plf.subplottext('C', ax3)
ax4 = plt.subplot(rows, columns, 4)
ax4.plot(onoroff*fitv, -s, color='C2')
plf.subplottext('D', ax4, x=-.25, y=1.1)
plf.spineless(ax4)
ax4.axvline(0, color='k', alpha=.5, linestyle='dashed', linewidth=1)
onoroff = data['polarities'][index]
csi = data['cs_inds'][index]
fit = fits[index]
popt = all_parameters[index]
cut_time = int(100/(frame_duration*1000)/2)
# 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])
"""
Created on Thu Feb 1 11:29:37 2018
@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')
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)
