def test_simple_fit(self):
# fit a 2D gaussian function centered at (64, 64) with width 8
# inpars = [height,amplitude,center_x,center_y,width_x,width_y,rota]
inpars = [0, 1, 64, 64, 8, 8,]
in_data = gf.twodgaussian(inpars, shape=(128, 128), rotate=False)
fit = gf.gaussfit(in_data, rotate=False)
for inval, outval in zip(inpars, fit):
self.assertAlmostEqual(inval, outval)
def test_simple_fit(self):
# fit a 2D gaussian function centered at (64, 64) with width 8
# inpars = [height,amplitude,center_x,center_y,width_x,width_y,rota]
inpars = [0, 1, 64, 64, 8, 8, 0]
in_data = gf.twodgaussian(inpars, shape=(128, 128))
fit = gf.gaussfit(in_data)
for inval, outval in zip(inpars, fit):
self.assertAlmostEqual(inval, outval)
def getfit(frame):
if np.max(frame) != np.max(np.abs(frame)):
onoroff = -1
else:
onoroff = 1
pars = gfit.gaussfit(frame * onoroff)
f = gfit.twodgaussian(pars)
return f, pars, onoroff
def test_masked_fit(self):
inpars = [0, 1, 64, 64, 8, 8, 0]
in_data = gf.twodgaussian(inpars, shape=(128, 128))
masked_data = np.ma.array(in_data)
# fit should be independent of masked data value
masked_data[64, 64] = 1e6
masked_data[64, 64] = np.ma.masked
fit1 = gf.gaussfit(masked_data)
masked_data[64, 64] = 0
masked_data[64, 64] = np.ma.masked
fit2 = gf.gaussfit(masked_data)
for v1, v2 in zip(fit1, fit2):
self.assertAlmostEqual(v1, v2)
def test_masked_fit(self):
inpars = [0, 1, 64, 64, 8, 8]
in_data = gf.twodgaussian(inpars, shape=(128,128), rotate=False)
masked_data = np.ma.array(in_data)
# fit should be independent of masked data value
masked_data[64,64] = 1e6
masked_data[64,64] = np.ma.masked
fit1 = gf.gaussfit(masked_data, rotate=False)
masked_data[64,64] = 0
masked_data[64,64] = np.ma.masked
fit2 = gf.gaussfit(masked_data, rotate=False)
for v1, v2 in zip(fit1, fit2):
self.assertAlmostEqual(v1, v2)
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}')
# filename = 'img'
data = np.load('%s.npy' %filename)
fit = gf.gaussfit(data)
cmap = cm.gist_gray
cmap = cm.jet
fig = pl.figure(num=1, figsize=(11.69, 8.27))
pl.subplot(221)
pl.imshow(data, cmap=cmap)
pl.title('Image')
pl.xlabel('x')
pl.ylabel('y')
pl.subplot(224)
fdata = np.ravel((gf.twodgaussian(fit,0,1,1)\
(*np.indices(data.shape)))).reshape(np.shape(data))
pl.imshow(fdata)
pl.xlabel('Fitted data')
xc = fit[2]
yc = fit[3]
xcr, ycr = np.round(xc), np.round(yc)
# Cross sections
pl.subplot(223)
xp, = pl.plot(range(640), data[ycr, :], 'k-')
xp2, = pl.plot(range(640), fdata[ycr, :], 'b-', linewidth=2)
pl.xlabel('x cross section at y=%d' %(ycr))
pl.xlim([0, 640])
pl.ylim([0, 255])
xp.axes.set_ylim(xp.axes.get_ylim()[::-1])
def test_elliptical_fit(self):
inpars = [0, 1, 64, 64, 4, 12, 30]
in_data = gf.twodgaussian(inpars, shape=(128, 128))
fit = gf.gaussfit(in_data)
for inval, outval in zip(inpars, fit):
self.assertAlmostEqual(inval, outval)
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.subplottext('A', ax, x=0, y=1.3)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax)
import gaussfitter
input_parameters = [0, 1, 25, 25, 3, 6, 30]
print("Input parameters are: {0}".format(input_parameters))
example_gaussian = gaussfitter.twodgaussian(input_parameters, shape=[50,50],)
moments = gaussfitter.moments(example_gaussian, circle=False, rotate=True, vheight=True)
print("These are the input guesses computed using 'moments':")
print(moments)
print("This is from the gaussian fit; it works well:")
print(gaussfitter.gaussfit(example_gaussian))
