def plot_regressors_as_RGB(self, fig1, gs, regressors, rsq, b, colors, brightness_scale=3):
b_pos = b * (b > 0) # Get positive beta values
# Set up colorize function
c = Colorize(cmap="indexed", scale=brightness_scale, flag_scale=1)
for ii in xrange(0, len(regressors)):
ax1 = fig1.add_subplot(gs[0, ii])
c.colors = [colors[ii]]
img = c.transform([b_pos[ii, :, :]], mask=rsq, background=self.reference, mixing=0.5)
self.image(img)
# Plot the unique stimuli in subplots
subplot_count = 0
for ii in self.unique_stimuli:
b_pos_list = []
color_mat = []
for keys in regressors.iterkeys():
if ii in keys:
index = regressors.keys().index(keys)
b_pos_list.append(b_pos[index, :, :])
color_mat.append(colors[index])
ax1 = fig1.add_subplot(gs[1, subplot_count])
c.colors = color_mat
img = c.transform(b_pos_list, mask=rsq, background=self.reference, mixing=0.5)
self.image(img)
subplot_count += 1
# Plot the different stimuli parameters in subplots
for ii in self.parameters:
b_pos_list = []
color_mat = []
for keys in regressors.iterkeys():
if ii in keys:
index = regressors.keys().index(keys)
b_pos_list.append(b_pos[index, :, :])
color_mat.append(colors[index])
ax1 = fig1.add_subplot(gs[1, subplot_count])
c.colors = color_mat
img = c.transform(b_pos_list, mask=rsq, background=self.reference, mixing=0.5)
self.image(img)
subplot_count += 1
# Plot all together
b_pos_list = []
color_mat = []
for ii in xrange(0, len(regressors)):
b_pos_list.append(b_pos[ii, :, :])
color_mat.append(colors[ii])
ax1 = fig1.add_subplot(gs[2:4, 1:3])
c.colors = color_mat
img = c.transform(b_pos_list, mask=rsq, background=self.reference, mixing=0.5)
self.image(img)
subplot_count += 1
ax1 = fig1.add_subplot(gs[2, 0])
self.create_colorbar(ax1, regressors, colors)
def plot_all_together_inseperateplot(self,
fig1,
gs,
regressors,
rsq,
b,
colors,
brightness_scale=3,
gridspecs='[0, 0]'):
b_pos = b * (b > 0) # Get positive beta values
# Set up colorize function
c = Colorize(cmap='indexed', scale=brightness_scale, flag_scale=1)
b_pos_list = []
color_mat = []
for ii in xrange(0, len(regressors)):
b_pos_list.append(b_pos[ii, :, :])
color_mat.append(colors[ii])
ax1 = eval('fig1.add_subplot(gs' + gridspecs + ')')
c.colors = color_mat
img = c.transform(b_pos_list,
mask=rsq,
background=self.reference,
mixing=0.5)
self.image(img)
def masks(self, dims=None, binary=True, outline=False, base=None):
"""
Composite masks combined across sources as an iamge.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of image in which to create masks, must either provide
these or provide a base image
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
base : array-like, optional, deafult = None
Base background image on which to put masks.
"""
from thunder import Colorize
if dims is None and base is None:
raise Exception(
"Must provide image dimensions for composite masks "
"or provide a base image.")
if base is not None and isinstance(base, SourceModel):
outline = True
if dims is None and base is not None:
dims = asarray(base).shape
combined = zeros(dims)
for s in self.sources:
combined = maximum(s.mask(dims, binary, outline), combined)
if base is not None:
if isinstance(base, SourceModel):
base = base.masks(dims)
baseColor = 'silver'
else:
baseColor = 'white'
clr = Colorize(cmap='indexed', colors=[baseColor, 'deeppink'])
combined = clr.transform([base, combined])
return combined
def plot_all_together_inseperateplot(
self, fig1, gs, regressors, rsq, b, colors, brightness_scale=3, gridspecs="[0, 0]"
):
b_pos = b * (b > 0) # Get positive beta values
# Set up colorize function
c = Colorize(cmap="indexed", scale=brightness_scale, flag_scale=1)
b_pos_list = []
color_mat = []
for ii in xrange(0, len(regressors)):
b_pos_list.append(b_pos[ii, :, :])
color_mat.append(colors[ii])
ax1 = eval("fig1.add_subplot(gs" + gridspecs + ")")
c.colors = color_mat
img = c.transform(b_pos_list, mask=rsq, background=self.reference, mixing=0.5)
self.image(img)
def masks(self, dims=None, binary=True, outline=False, base=None):
"""
Composite masks combined across sources as an iamge.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of image in which to create masks, must either provide
these or provide a base image
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
base : array-like, optional, deafult = None
Base background image on which to put masks.
"""
from thunder import Colorize
if dims is None and base is None:
raise Exception("Must provide image dimensions for composite masks "
"or provide a base image.")
if base is not None and isinstance(base, SourceModel):
outline = True
if dims is None and base is not None:
dims = asarray(base).shape
combined = zeros(dims)
for s in self.sources:
combined = maximum(s.mask(dims, binary, outline), combined)
if base is not None:
if isinstance(base, SourceModel):
base = base.masks(dims)
baseColor = 'silver'
else:
baseColor = 'white'
clr = Colorize(cmap='indexed', colors=[baseColor, 'deeppink'])
combined = clr.transform([base, combined])
return combined
def plot_regressors_as_RGB(self,
fig1,
gs,
regressors,
rsq,
b,
colors,
brightness_scale=3):
b_pos = b * (b > 0) # Get positive beta values
# Set up colorize function
c = Colorize(cmap='indexed', scale=brightness_scale, flag_scale=1)
for ii in xrange(0, len(regressors)):
ax1 = fig1.add_subplot(gs[0, ii])
c.colors = [colors[ii]]
img = c.transform([b_pos[ii, :, :]],
mask=rsq,
background=self.reference,
mixing=0.5)
self.image(img)
# Plot the unique stimuli in subplots
subplot_count = 0
for ii in self.unique_stimuli:
b_pos_list = []
color_mat = []
for keys in regressors.iterkeys():
if ii in keys:
index = regressors.keys().index(keys)
b_pos_list.append(b_pos[index, :, :])
color_mat.append(colors[index])
ax1 = fig1.add_subplot(gs[1, subplot_count])
c.colors = color_mat
img = c.transform(b_pos_list,
mask=rsq,
background=self.reference,
mixing=0.5)
self.image(img)
subplot_count += 1
# Plot the different stimuli parameters in subplots
for ii in self.parameters:
b_pos_list = []
color_mat = []
for keys in regressors.iterkeys():
if ii in keys:
index = regressors.keys().index(keys)
b_pos_list.append(b_pos[index, :, :])
color_mat.append(colors[index])
ax1 = fig1.add_subplot(gs[1, subplot_count])
c.colors = color_mat
img = c.transform(b_pos_list,
mask=rsq,
background=self.reference,
mixing=0.5)
self.image(img)
subplot_count += 1
# Plot all together
b_pos_list = []
color_mat = []
for ii in xrange(0, len(regressors)):
b_pos_list.append(b_pos[ii, :, :])
color_mat.append(colors[ii])
ax1 = fig1.add_subplot(gs[2:4, 1:3])
c.colors = color_mat
img = c.transform(b_pos_list,
mask=rsq,
background=self.reference,
mixing=0.5)
self.image(img)
subplot_count += 1
ax1 = fig1.add_subplot(gs[2, 0])
self.create_colorbar(ax1, regressors, colors)
