def make_ica_maps(data, imgs, img_size_x, img_size_y, num_ica_colors, color_map, colors_ica):
reference = data.seriesMean().pack()
maps = Colorize(cmap=color_map, colors = colors_ica[0:np.size(imgs,0)], scale=num_ica_colors).transform(abs(imgs),background=reference, mixing=1.5)
#Count number of unique colors in the images
#Get number of planes based on map dimesnions
if len(maps.shape)==3:
num_planes = 1
else:
num_planes = np.size(maps,2)
unique_clrs = []
for ii in xrange(0, np.size(colors_ica[0:np.size(imgs,0)])):
unique_clrs.append( np.round(np.array(webcolors.name_to_rgb(colors_ica[ii]), dtype=np.float)/255))
#From maps get number of pixel matches with color for each plane
matched_pixels = np.zeros((np.size(unique_clrs,0),num_planes))
array_maps = np.round(maps.astype(np.float16))
matched_pixels = np.zeros((np.size(unique_clrs,0),num_planes))
if len(maps.shape) == 3:
array_maps_plane = np.reshape(array_maps, (np.size(array_maps,0)*np.size(array_maps,1),3))
matched_pixels[:,0] = [np.size(np.where((np.array(array_maps_plane) == match).all(axis=1))) for match in unique_clrs]
else:
for ii in xrange(0,num_planes):
array_maps_plane = np.reshape(array_maps[:,:,ii,:], (np.size(array_maps,0)*np.size(array_maps,1),3))
matched_pixels[:,ii] = [np.size(np.where((np.array(array_maps_plane) == match).all(axis=1))) for match in unique_clrs]
return maps, matched_pixels
def make_ica_maps(data, imgs, img_size_x, img_size_y, num_ica_colors,
color_map, colors_ica):
reference = data.seriesMean().pack()
maps = Colorize(cmap=color_map,
colors=colors_ica[0:np.size(imgs, 0)],
scale=num_ica_colors).transform(abs(imgs),
background=reference,
mixing=1.5)
#Count number of unique colors in the images
#Get number of planes based on map dimesnions
if len(maps.shape) == 3:
num_planes = 1
else:
num_planes = np.size(maps, 2)
unique_clrs = []
for ii in xrange(0, np.size(colors_ica[0:np.size(imgs, 0)])):
unique_clrs.append(
np.round(
np.array(webcolors.name_to_rgb(colors_ica[ii]), dtype=np.float)
/ 255))
#From maps get number of pixel matches with color for each plane
matched_pixels = np.zeros((np.size(unique_clrs, 0), num_planes))
array_maps = np.round(maps.astype(np.float16))
matched_pixels = np.zeros((np.size(unique_clrs, 0), num_planes))
if len(maps.shape) == 3:
array_maps_plane = np.reshape(
array_maps, (np.size(array_maps, 0) * np.size(array_maps, 1), 3))
matched_pixels[:, 0] = [
np.size(np.where(
(np.array(array_maps_plane) == match).all(axis=1)))
for match in unique_clrs
]
else:
for ii in xrange(0, num_planes):
array_maps_plane = np.reshape(
array_maps[:, :, ii, :],
(np.size(array_maps, 0) * np.size(array_maps, 1), 3))
matched_pixels[:, ii] = [
np.size(
np.where(
(np.array(array_maps_plane) == match).all(axis=1)))
for match in unique_clrs
]
return maps, matched_pixels
def make_pca_maps(data, pca, imgs, required_pcs, img_size_x, img_size_y,
num_pca_colors, num_samples, thresh_pca, color_map):
reference = data.seriesMean().pack()
if required_pcs == 0:
maps = Colorize(cmap=color_map,
scale=num_pca_colors).transform(imgs,
background=reference,
mixing=0.3)
else:
maps = Colorize(cmap=color_map,
scale=num_pca_colors).transform(imgs,
background=reference,
mixing=0.3)
pts = pca.scores.subset(num_samples, thresh=thresh_pca, stat='norm')
if required_pcs == 0:
pca_pts = list()
for ii in xrange(1, size(pca.comps.T, 1)):
pca_pts.append(pts[:, ii][:, newaxis])
clrs = Colorize(cmap=color_map,
scale=num_pca_colors).transform(pca_pts).squeeze()
else:
pca_pts = list()
for ii in xrange(0, size(required_pcs)):
pca_pts.append(pts[:, required_pcs[ii]][:, newaxis])
clrs = Colorize(cmap=color_map,
scale=num_pca_colors).transform(pca_pts).squeeze()
#Reconstruct the scores using the pca components
if required_pcs == 0:
recon = asarray(
map(
lambda x: (x[1] * pca.comps[1, :] + x[2] * pca.comps[2, :] + x[
3] * pca.comps[3, :]).tolist(), pts))
else:
pts_list = pts.tolist()
recon = zeros((size(pts_list, 0), size(pca.comps, 1)))
for ii in range(0, size(pts_list, 0)):
for jj in range(0, size(required_pcs)):
recon[ii, :] += pts_list[ii][required_pcs[jj]] * pca.comps[
required_pcs[jj], :]
#Count number of unique colors in the images
#Get number of planes based on map dimesnions
if len(maps.shape) == 3:
num_planes = 1
else:
num_planes = size(maps, 2)
num_time = size(pca.comps.T, 0)
#Get specific color matches across animals and get mean and standard deviation
array1 = [map(int, single_dim)
for single_dim in clrs] #Convert the colors to RGB integers
new_array = [tuple(row) for row in array1]
unique_clrs = list(set(new_array)) #Get unique combination of colors
unique_clrs.remove((0, 0, 0))
matches = [
where((array(array1) == match).all(axis=1)) for match in unique_clrs
] #Match the colors with the original rows
matches_black = [
where((array(array1) == match).all(axis=1)) for match in [0]
]
pts_nonblack = delete(pts, matches_black, axis=0)
clrs_nonblack = delete(clrs, matches_black, axis=0)
#From maps get number of pixel matches with color for each plane
matched_pixels = zeros((size(unique_clrs, 0), num_planes))
# while sum(matched_pixels) == 0 :
array_maps = round(maps.astype(float16))
matched_pixels = zeros((size(unique_clrs, 0), num_planes))
if len(maps.shape) == 3:
array_maps_plane = reshape(
array_maps, (size(array_maps, 0) * size(array_maps, 1), 3))
matched_pixels[:, 0] = [
size(where((array(array_maps_plane) == match).all(axis=1)))
for match in unique_clrs
]
else:
for ii in xrange(0, num_planes):
array_maps_plane = reshape(
array_maps[:, :, ii, :],
(size(array_maps, 0) * size(array_maps, 1), 3))
matched_pixels[:, ii] = [
size(where((array(array_maps_plane) == match).all(axis=1)))
for match in unique_clrs
]
#Find stats based on the color - but only use the subset of pixels in recon
matched_signals = [
structtype() for i in range(size(matches, 0) * num_planes)
]
mean_signal = zeros((size(matches, 0), num_planes, num_time))
sem_signal = zeros((size(matches, 0), num_planes, num_time))
for ii in xrange(0, size(matches, 0)):
temp_ele = array(matches[ii])
matched_signals[ii].clr_grped_signal = [
array(recon[ele]) for ele in temp_ele[0, :]
] #Get signals from the reconstruction that match the colors
mean_signal[ii, :] = mean(matched_signals[ii].clr_grped_signal, axis=0)
sem_signal[ii, :] = stats.sem(matched_signals[ii].clr_grped_signal,
axis=0)
return maps, pts, pts_nonblack, clrs, clrs_nonblack, recon, unique_clrs, matched_pixels, matched_signals, mean_signal, sem_signal
def make_pca_maps(self, pca, imgs, required_pcs, mixing_parameter):
"""
Make maps and scatter plots of the pca scores with colormaps for plotting
Parameters
----------
pca: PCA model
imgs : scores
required_pcs: List contianing indices of required PCA components. Specify 0 if you need all
mixing_parameter : mixing controls relative scale of the background.
Returns
-------
maps: Images of PCA scores plotted using a colormap
pts_nonblack: Pixels that are not black (not part of the background)
clrs_nonblack: Colors that are not black
recon: PCA reconstructed using user specified components
unique_clrs: Colors that are unique from the images of the PCA scores
matched_pixels: Pixels that correspond to the colors obtained in unique_clrs
matched_signals: Individual time traces from the matched_pixels
mean_signal: Mean of matched_signals
sem_signal: Sem of matched_signals
"""
maps = Colorize(cmap=self.color_map,
scale=self.num_pca_colors).transform(
imgs,
background=self.reference,
mixing=mixing_parameter)
pts = pca.scores.subset(self.num_samples,
thresh=self.thresh_pca,
stat='norm')
if required_pcs == 0:
pca_pts = list()
for ii in xrange(0, size(pca.comps.T, 1)):
pca_pts.append(pts[:, ii][:, newaxis])
clrs = Colorize(
cmap=self.color_map,
scale=self.num_pca_colors).transform(pca_pts).squeeze()
else:
pca_pts = list()
for ii in xrange(0, size(required_pcs)):
pca_pts.append(pts[:, required_pcs[ii]][:, newaxis])
clrs = Colorize(
cmap=self.color_map,
scale=self.num_pca_colors).transform(pca_pts).squeeze()
# Reconstruct the scores using the pca components
if required_pcs == 0:
recon = asarray(
map(
lambda x: (x[0] * pca.comps[0, :] + x[1] * pca.comps[1, :]
+ x[2] * pca.comps[2, :]).tolist(), pts))
else:
pts_list = pts.tolist()
recon = zeros((size(pts_list, 0), size(pca.comps, 1)))
for ii in range(0, size(pts_list, 0)):
for jj in range(0, size(required_pcs)):
recon[ii, :] += pts_list[ii][required_pcs[jj]] * pca.comps[
required_pcs[jj], :]
# Count number of unique colors in the images
# Get number of planes based on map dimensions
if len(maps.shape) == 3:
num_planes = 1
else:
num_planes = size(maps, 2)
num_time = size(pca.comps.T, 0)
# Get specific color matches across animals and get mean and standard deviation
array1 = [map(int, single_dim)
for single_dim in clrs] # Convert the colors to RGB integers
new_array = [tuple(row) for row in array1]
unique_clrs = list(set(new_array)) # Get unique combination of colors
if (0, 0, 0) in unique_clrs:
unique_clrs.remove((0, 0, 0))
matches = [
where((array(array1) == match).all(axis=1))
for match in unique_clrs
] # Match the colors with the original rows
matches_black = [
where((array(array1) == match).all(axis=1)) for match in [0]
]
pts_nonblack = delete(pts, matches_black, axis=0)
clrs_nonblack = delete(clrs, matches_black, axis=0)
# From maps get number of pixel matches with color for each plane
array_maps = round(maps.astype(float16))
matched_pixels = zeros((size(unique_clrs, 0), num_planes))
array_maps_plane = reshape(
array_maps, (size(array_maps, 0) * size(array_maps, 1), 3))
matched_pixels[:, 0] = [
size(where((array(array_maps_plane) == match).all(axis=1)))
for match in unique_clrs
]
# Find stats based on the color - but only use the subset of pixels in recon
matched_signals = [
structtype() for i in range(size(matches, 0) * num_planes)
]
mean_signal = zeros((size(matches, 0), num_planes, num_time))
sem_signal = zeros((size(matches, 0), num_planes, num_time))
for ii in xrange(0, size(matches, 0)):
temp_ele = array(matches[ii])
matched_signals[ii].clr_grped_signal = [
array(recon[ele]) for ele in temp_ele[0, :]
]
mean_signal[ii, :] = mean(matched_signals[ii].clr_grped_signal,
axis=0)
sem_signal[ii, :] = stats.sem(matched_signals[ii].clr_grped_signal,
axis=0)
return maps, pts_nonblack, clrs_nonblack, recon, unique_clrs, matched_pixels, \
matched_signals, mean_signal, sem_signal
def make_pca_maps(data,pca, imgs, required_pcs, img_size_x, img_size_y, num_pca_colors, num_samples, thresh_pca, color_map):
reference = data.seriesMean().pack()
maps = Colorize(cmap=color_map, scale=num_pca_colors).transform(imgs,background=reference, mixing=2)
pts = pca.scores.subset(num_samples, thresh=thresh_pca, stat='norm')
if required_pcs == 0:
clrs = Colorize(cmap=color_map, scale=num_pca_colors).transform([pts[:,0][:,newaxis], pts[:,1][:,newaxis]]).squeeze()
else:
clrs = Colorize(cmap=color_map, scale=num_pca_colors).transform([pts[:,required_pcs[0]][:,newaxis], pts[:,required_pcs[1]][:,newaxis]]).squeeze()
#Reconstruct the scores using the pca components
if required_pcs == 0:
recon = asarray(map(lambda x: (x[1] * pca.comps[1, :] + x[2] * pca.comps[2, :] + x[3] * pca.comps[3, :]).tolist(), pts))
else:
pts_list = pts.tolist()
recon = zeros((size(pts_list,0),size(pca.comps,1)))
for ii in range(0,size(pts_list,0)):
for jj in range(0, size(required_pcs)):
recon[ii,:] += pts_list[ii][required_pcs[jj]]*pca.comps[required_pcs[jj],:]
#Count number of unique colors in the images
#Get number of planes based on map dimesnions
if len(maps.shape)==3:
num_planes = 1
else:
num_planes = size(maps,2)
num_time = size(pca.comps.T,0)
#Get specific color matches across animals and get mean and standard deviation
array1 = [map(int,single_dim) for single_dim in clrs] #Convert the colors to RGB integers
new_array = [tuple(row) for row in array1]
unique_clrs = list(set(new_array)) #Get unique combination of colors
unique_clrs.remove((0,0,0))
matches = [where((array(array1) == match).all(axis=1)) for match in unique_clrs] #Match the colors with the original rows
matches_black = [where((array(array1) == match).all(axis=1)) for match in [0]]
pts_nonblack = delete(pts, matches_black, axis=0)
clrs_nonblack = delete(clrs, matches_black, axis=0)
#From maps get number of pixel matches with color for each plane
matched_pixels = zeros((size(unique_clrs,0),num_planes))
# while sum(matched_pixels) == 0 :
array_maps = round(maps.astype(float16))
matched_pixels = zeros((size(unique_clrs,0),num_planes))
if len(maps.shape) == 3:
array_maps_plane = reshape(array_maps, (size(array_maps,0)*size(array_maps,1),3))
matched_pixels[:,0] = [size(where((array(array_maps_plane) == match).all(axis=1))) for match in unique_clrs]
else:
for ii in xrange(0,num_planes):
array_maps_plane = reshape(array_maps[:,:,ii,:], (size(array_maps,0)*size(array_maps,1),3))
matched_pixels[:,ii] = [size(where((array(array_maps_plane) == match).all(axis=1))) for match in unique_clrs]
#Find stats based on the color - but only use the subset of pixels in recon
matched_signals = [structtype() for i in range(size(matches,0)*num_planes)]
mean_signal = zeros((size(matches,0), num_planes, num_time))
sem_signal = zeros((size(matches,0), num_planes, num_time))
for ii in xrange(0,size(matches,0)):
temp_ele = array(matches[ii])
matched_signals[ii].clr_grped_signal = [array(recon[ele]) for ele in temp_ele[0,:]] #Get signals from the reconstruction that match the colors
mean_signal[ii,:] = mean(matched_signals[ii].clr_grped_signal,axis=0)
sem_signal[ii,:] = stats.sem(matched_signals[ii].clr_grped_signal,axis=0)
return maps, pts, pts_nonblack, clrs, clrs_nonblack, recon, unique_clrs, matched_pixels, matched_signals, mean_signal, sem_signal
