def plot_sorted_lin_subtracted_(target, lin_subtracted, sorteach, sortind):
#if fig is None:
# fig = plt.figure()
lin_subtracted_sort = sort_lin_subtracted(lin_subtracted, sorteach,
sortind)
lb, ub, mn = ut.bootstrap(lin_subtracted_sort[nub_no[sorteach[:, 0]] > 1],
axis=0,
pct=(16, 84, 50),
fn=np.nanmean)
plt.fill_between(np.arange(1, 33), lb, ub)
plt.xticks((1, 5, 10, 15, 20, 25, 30))
plt.xlabel('Stimulus rank')
plt.ylabel('Normalized linear difference')
plt.axhline(0, linestyle='dotted', c='k')
plt.savefig(target, dpi=300)
def plot_size_tuning(arr, colors=None):
usize = np.array((0, 5, 8, 13, 22, 36))
ucontrast = np.array((0, 6, 12, 25, 50, 100))
arr_sz = gen_size_tuning(arr)
arr_sz = arr_sz / arr_sz.max(1).max(1)[:, np.newaxis, np.newaxis]
lb, ub = ut.bootstrap(arr_sz, np.mean, pct=(2.5, 97.5))
to_plot = arr_sz.mean(0)
ut.plot_bootstrapped_errorbars_hillel(usize,
arr_sz[:, :, 1::].transpose(
(0, 2, 1)),
colors=colors)
plt.ylim(to_plot.min() - 0.1, to_plot.max() + 0.1)
# plt.title('%d%% contrast' % ucontrast[ic])
plt.xlabel('size ($^o$)')
# plt.subplot(1,2,1)
plt.tight_layout()
def plot_size_tuning_by_contrast(arr):
usize = np.array((0, 5, 8, 13, 22, 36))
ucontrast = np.array((0, 6, 12, 25, 50, 100))
arr_sz = gen_size_tuning(arr)
arr_sz = arr_sz / arr_sz.max(1).max(1)[:, np.newaxis, np.newaxis]
lb, ub = ut.bootstrap(arr_sz, np.mean, pct=(2.5, 97.5))
to_plot = arr_sz.mean(0)
for ic in range(1, 6):
plt.subplot(1, 5, ic)
ut.plot_bootstrapped_errorbars_hillel(usize,
arr_sz[:, :, ic].transpose(
(0, 2, 1)),
colors=['k', 'r'],
markersize=5)
# plt.scatter((0,),to_plot[:,0,0].mean(0))
# plt.scatter((0,),to_plot[:,0,1].mean(0))
plt.ylim(0.5 * to_plot.min(), 1.2 * to_plot.max())
plt.title('%d%% contrast' % ucontrast[ic])
plt.xlabel('size ($^o$)')
plt.subplot(1, 5, 1)
plt.ylabel('event rate / max event rate')
plt.tight_layout()
def scatter_size_contrast_errorbar(animal_data,
pct=(16, 84),
mn_plot=None,
mx_plot=None,
opto_color='b',
equality_line=True,
square=True,
xlabel=None,
ylabel=None,
alpha=1,
equate_0=False,
linewidth=1):
if xlabel is None:
xlabel = 'PC event rate, light off'
if ylabel is None:
ylabel = 'PC event rate, light on'
this_animal_data = animal_data.copy()
if equate_0:
this_animal_data[:, :, 0] = animal_data[:, :, 0].mean(1)[:, np.newaxis]
lb, ub, mn = ut.bootstrap(this_animal_data,
pct=pct + (50, ),
fn=np.nanmean,
axis=0)
stats = ut.bootstat(this_animal_data,
fns=[compute_slope_w_intercept_, compute_intercept_])
#print('animal data shape: '+str(this_animal_data.shape))
#print('stats shape: '+str([s.shape for s in stats]))
npt = mn[:, :, 0].size
xerr = np.zeros((2, npt))
xerr[0] = mn[:, :, 0].flatten() - lb[:, :, 0].flatten()
xerr[1] = ub[:, :, 0].flatten() - mn[:, :, 0].flatten()
yerr = np.zeros((2, npt))
yerr[0] = mn[:, :, 1].flatten() - lb[:, :, 1].flatten()
yerr[1] = ub[:, :, 1].flatten() - mn[:, :, 1].flatten()
if mx_plot is None:
mx_plot = 1.1 * np.nanmax(mn)
if mn_plot is None:
mn_plot = np.nanmin(mn)
xx = np.linspace(mn_plot, mx_plot, 100)[:, np.newaxis]
#print([s.shape for s in stats])
YY = xx * stats[0] + stats[1]
lb_YY = np.nanpercentile(YY, 16, axis=1)
ub_YY = np.nanpercentile(YY, 84, axis=1)
mn_YY = np.nanpercentile(YY, 50, axis=1)
plt.fill_between(xx[:, 0],
lb_YY,
ub_YY,
alpha=0.5 * alpha,
facecolor=opto_color)
plt.plot(xx[:, 0], mn_YY, c=opto_color, alpha=alpha, linewidth=linewidth)
plt.errorbar(mn[:, :, 0].flatten(),
mn[:, :, 1].flatten(),
xerr=xerr,
yerr=yerr,
fmt='none',
zorder=1,
c='k',
alpha=0.5 * alpha)
sca.scatter_size_contrast(mn[:, :, 0],
mn[:, :, 1],
equality_line=equality_line,
square=square,
alpha=alpha,
equate_0=equate_0)
plt.xlabel(xlabel)
def analyze_size_contrast(datafiles,
stimfile,
retfile=None,
frame_adjust=None,
rg=(1, 0),
nbefore=nbefore,
nafter=nafter,
criterion=None,
criterion_cutoff=None):
if criterion is None:
criterion = lambda x: np.abs(x) > 100
nbydepth = get_nbydepth(datafiles)
# trialwise,ctrialwise,strialwise = gen_trialwise(datafiles,frame_adjust=frame_adjust)
trialwise, ctrialwise, strialwise, dfof, straces, dtrialwise, trialwise_t_offset = ut.gen_precise_trialwise(
datafiles,
rg=rg,
frame_adjust=frame_adjust,
nbefore=nbefore,
nafter=nafter)
zstrialwise = sst.zscore(strialwise.reshape(
(strialwise.shape[0], -1)).T).T.reshape(strialwise.shape)
result = sio.loadmat(stimfile, squeeze_me=True)['result'][()]
infofile = sio.loadmat(datafiles[0][:-12] + '.mat',
squeeze_me=True) # original .mat file
frame = infofile['info'][()]['frame'][()]
frame = frame[rg[0]:frame.size + rg[1]]
if frame_adjust:
frame = frame_adjust(frame)
data = strialwise #[:,:,nbefore:-nafter]
try:
dxdt = sio.loadmat(datafiles[0], squeeze_me=True)['dxdt']
except:
with h5py.File(datafiles[0], mode='r') as f:
dxdt = f['dxdt'][:].T
trialrun = np.zeros(frame[0::2].shape)
for i in range(len(trialrun)):
trialrun[i] = np.abs(dxdt[frame[0::2][i]:frame[1::2][i]]).mean()
runtrial = criterion(trialrun)
print(runtrial.sum() / runtrial.size)
if criterion_cutoff:
if runtrial.sum() / runtrial.size < criterion_cutoff:
#return Savg,Smean,lb,ub,pval,spont,Smean_stat,proc
return [np.array(())] * 8
#pval = np.zeros(strialwise.shape[0])
#for i in range(strialwise.shape[0]):
# _,pval[i] = sst.ttest_rel(data[i,:,nbefore-1],data[i,:,nbefore+1])
stimparams = result['stimParams']
gratingInfo = result['gratingInfo']
indexlut, stimp = np.unique(stimparams, axis=1, return_inverse=True)
#angle = stimparams[0]
#size = stimparams[1]
#contrast = stimparams[4]
#light = stimparams[5]
angle = gratingInfo['Orientation'][()]
size = gratingInfo['Size'][()]
contrast = gratingInfo['Contrast'][()]
light = gratingInfo['lightsOn'][()]
ucontrast = np.unique(contrast)
uangle = np.unique(angle[~np.isnan(angle)])
usize = np.unique(size)
ulight = np.unique(light)
ncontrast = len(ucontrast)
nangle = len(uangle)
nsize = len(usize)
nlight = len(ulight)
print(nlight)
angle180 = np.remainder(angle, 180)
uangle180 = np.unique(angle180[~np.isnan(angle180)])
nangle180 = len(uangle180)
Smean = np.zeros(
(data.shape[0], nangle180, nsize, ncontrast, nlight, data.shape[2]))
Smean_stat = np.zeros(
(data.shape[0], nangle180, nsize, ncontrast, nlight, data.shape[2]))
Stavg = np.zeros(
(data.shape[0], nangle180, nsize, ncontrast, nlight,
int(data.shape[1] / nangle / nsize / ncontrast / nlight)))
Strials = {}
Sspont = {}
for i in range(nangle180):
for j in range(nsize):
for k in range(ncontrast):
for ii in range(nlight):
lkat = np.logical_and(
np.logical_and(
runtrial,
np.logical_and(
angle180 == uangle180[i],
np.logical_and(size == usize[j],
contrast == ucontrast[k]))),
light == ulight[ii])
Smean[:, i, j, k, ii, :] = data[:, lkat, :].mean(1)
Strials[(i, j, k, ii)] = data[:, lkat,
nbefore:-nafter].mean(2)
Sspont[(i, j, k, ii)] = data[:, lkat, :nbefore].mean(2)
stat = np.logical_and(
np.logical_and(
np.logical_not(runtrial),
np.logical_and(
angle180 == uangle180[i],
np.logical_and(size == usize[j],
contrast == ucontrast[k]))),
light == ulight[ii])
Smean_stat[:, i, j, k, ii] = data[:, stat].mean(1)
lb = np.zeros((strialwise.shape[0], nangle180, nsize, ncontrast, nlight))
ub = np.zeros((strialwise.shape[0], nangle180, nsize, ncontrast, nlight))
# mn = np.zeros((strialwise.shape[0],nangle180,nsize,ncontrast))
for i in range(nangle180):
for j in range(nsize):
for k in range(ncontrast):
for ii in range(nlight):
if Strials[(i, j, k, ii)].size:
lb[:, i, j, k,
ii], ub[:, i, j, k,
ii] = ut.bootstrap(Strials[(i, j, k, ii)],
np.mean,
axis=1,
pct=(16, 84))
else:
lb[:, i, j, k, ii] = np.nan
ub[:, i, j, k, ii] = np.nan
# mn[:,i,j,k] = np.nanmean(Strials[(i,j,k)],axis=1)
pval = np.zeros((strialwise.shape[0], nangle180))
# for i in range(pval.shape[0]):
# print(i)
for j, theta in enumerate(uangle180):
_, pval[:, j] = sst.ttest_rel(Sspont[(j, 0, ncontrast - 1, 0)],
Strials[(j, 0, ncontrast - 1, 0)],
axis=1)
Savg = np.nanmean(np.nanmean(Smean[:, :, :, :, :, nbefore:-nafter],
axis=-1),
axis=1)
Storiavg = Stavg.mean(1)
# _,pval = sst.ttest_ind(Storiavg[:,0,-1].T,Storiavg[:,0,0].T)
#suppressed = np.logical_and(pval<0.05,Savg[:,0,-1]<Savg[:,0,0])
#facilitated = np.logical_and(pval<0.05,Savg[:,0,-1]>Savg[:,0,0])
spont = np.zeros((Savg.shape[0], ))
keylist = list(Sspont.keys())
nkeys = len(keylist)
for key in Sspont.keys():
spont = spont + Sspont[key].mean(1) / nkeys
proc = {}
proc['runtrial'] = runtrial
proc['trialrun'] = trialrun
proc['angle'] = angle
proc['size'] = size
proc['contrast'] = contrast
proc['light'] = light
proc['trialwise'] = trialwise
proc['strialwise'] = strialwise
#proc['ctrialwise'] = ctrialwise
proc['dtrialwise'] = dtrialwise
proc['trialwise_t_offset'] = trialwise_t_offset
proc['dfof'] = dfof
proc['frame'] = frame
return Savg, Smean, lb, ub, pval, spont, Smean_stat, proc