def show_cond():
results = load_same_loc()
tt = 8
# Graph the histogram of activity at the center of
# of the room from cell 0
acts = [x[0] for x in results]
plt.subplot(2,1,1)
count, bins, _ = plt.hist(acts,bins=40,normed=Normed)
plt.title('Original dist. Pts: %i'%(len(acts),))
xlm = plt.xlim()
xs = np.linspace(xlm[0],xlm[1],1000)
mn, std = gaussian(acts)
plt.plot(xs,norm.pdf(xs,mn,std))
# Graph the histogram of activity at the center of
# of the room from cell 1
plt.subplot(2,1,2)
nb = [x[1] for x in results]
plt.hist(nb,bins=bins,normed=Normed)
mn, std = gaussian(nb)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('Side dist. Pts: %i'%(len(nb),))
plt.xlim(xlm)
mx_i = np.argmax(count)
plt.figure()
plt.subplot(2,1,1)
nb = [x[1] for x in results]
plt.hist(nb,bins=bins,normed=Normed)
plt.title('Side dist. Pts: %i'%(len(nb),))
mn, std = gaussian(nb)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.xlim(xlm)
plt.subplot(2,1,2)
iss = get_iss(mx_i+1, bins, acts)
acts_neighbor = [x[1] for x in results[iss]]
plt.hist(acts_neighbor,bins=bins,normed=Normed)
plt.title('Pts: %i'%(len(acts_neighbor)))
nb = acts_neighbor
mn, std = gaussian(nb)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.xlim(xlm)
plt.figure()
for delt in range(-tt,tt):
iss = get_iss(mx_i+delt, bins, acts)
acts_neighbor = [x[1] for x in results[iss]]
plt.subplot(2,tt,delt+tt+0)
plt.hist(acts_neighbor,bins=bins,normed=Normed)
nb = acts_neighbor
mn, std = gaussian(nb)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('Pts: %i'%(len(acts_neighbor)))
plt.xlim(xlm)
plt.show()
def show_adj3():
''' Shows two rows of distributions. The top row gives
the distribution of the input activity to place cells
at various spatial locations. The second row shows
the same distributions conditioned on the value of
the (0,0) activity taking a certain value. '''
results = load_adj3()
acts = [x[0] for x in results]
plt.subplot(2,1,1)
count, bins, _ = plt.hist(acts,bins=40,normed=Normed)
plt.title('Original dist. Pts: %i'%(len(acts),))
xlm = plt.xlim()
xs = np.linspace(xlm[0],xlm[1],1000)
mn, std = gaussian(acts)
plt.plot(xs,norm.pdf(xs,mn,std))
mx_i = np.argmax(count)
iss = get_iss(mx_i, bins, acts)
plt.figure()
width = 5
scale = 1
plt.subplot(2,width,1)
for delt in range(width):
plt.subplot(2,width,1+delt)
cur_acts = [x[delt*scale] for x in results]
plt.hist(cur_acts,bins=bins,normed=Normed)
mn, std = gaussian(cur_acts)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('(%i,%i) O'%(delt*scale,delt*scale))
plt.xlim(xlm)
for delt in range(width):
plt.subplot(2,width,width+1+delt)
cur_acts = [x[delt*scale] for x in results[iss]]
plt.hist(cur_acts,bins=bins,normed=Normed)
mn, std = gaussian(cur_acts)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('(%i,%i) C'%(delt*scale,delt*scale))
plt.xlim(xlm)
plt.axis(tight=True)
plt.show()
def show_conditional_probabilities():
''' Shows two rows of distributions. The top row gives
the distribution of the input activity to place cells
at various spatial locations. The second row shows
the same distributions conditioned on the value of
the (0,0) activity taking a certain value.
Each file contains a list of 400 activity grids. Each grid is a 21x21 numpy
matrix indicating the activity of cell 0 in a 21x21 grid centered around
the middle of the room.
'''
GRID_SIZE = 21
NORMED = True
# Load data.
results = load_one_cell_large_room()
for grid in results:
assert grid.shape == (GRID_SIZE, GRID_SIZE)
# Computations.
activities = {}; gaussians = {}
for diagonal_distance_from_origin in range(10):
dist = diagonal_distance_from_origin # for ease of reading
activities[dist] = [grid_of_activity[dist,dist] for grid_of_activity in results]
mn, std = gaussian(activities[dist])
gaussians[dist] = {'mean': mn,
'std dev': std}
x_bounds = (np.min(acts), np.max(acts))
xs = np.linspace(np.min(acts), np.max(acts), 1000)
mn, std = gaussian(acts)
# Plot.
plt.figure('Original distribution')
count, bins, _ = plt.hist(acts, bins=40, normed=NORMED)
plt.plot(xs, norm.pdf(xs, mn, std))
plt.title('Original dist. Pts: %i'%(len(acts),))
mx_i = np.argmax(count)
iss = get_iss(mx_i, bins, acts)
plt.figure('Conditional distributions')
width = 10
plt.subplot(2, width**2,1)
for delt in range(width):
plt.subplot(2,width,1+delt)
cur_acts = [x[delt*2,delt*2] for x in results]
plt.hist(cur_acts, bins=bins, normed=NORMED)
mn, std = gaussian(cur_acts)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('(%i,%i) O'%(delt*2,delt*2))
plt.xlim(xlm)
for delt in range(width):
plt.subplot(2,width,width+1+delt)
cur_acts = [x[delt,delt] for x in results[iss]]
plt.hist(cur_acts, bins=bins, normed=NORMED)
mn, std = gaussian(cur_acts)
plt.plot(xs,norm.pdf(xs,mn,std))
plt.title('(%i,%i) C'%(delt,delt))
plt.xlim(xlm)
plt.axis(tight=True)
plt.show()
