Skip to content
GitLab
Commit dcb08a54 authored by Ines Filipa Fernandes Ramos's avatar Ines Filipa Fernandes Ramos
Browse files

commit

parent 7dbbff76
Expand all Hide whitespace changes
Inline Side-by-side
MEI.py
View file @ dcb08a54
......@@ -184,7 +184,7 @@ def unprocess(x, mu=0.4, sigma=0.224):
def deepdraw(net, base_img, octaves, random_crop=True, original_size=None,
bias=None, scale=None, device='cuda', **step_params):
bias=None, scale=None, track=False, device='cuda', **step_params):
""" Generate an image by iteratively optimizing activity of net.
Arguments:
net (nn.Module or function): A backpropagatable function/module that receives
......@@ -227,14 +227,15 @@ def deepdraw(net, base_img, octaves, random_crop=True, original_size=None,
print("starting drawing")
src = torch.zeros(1, c, w, h, requires_grad=True, device=device)
# start figure to plot activation values at each 10 iterations
fig, ax = plt.subplots(11, figsize=(16,14))
x = np.linspace(0,octaves[0]['iter_n'],10)
ax[0].set_xlabel('iter number')
ax[0].set_ylabel('activation for generated MEI')
ax[0].set_xlim(0,1000)
pl_index = 1
if track:
# start figure to plot activation values at each 10 iterations
fig, ax = plt.subplots(11, figsize=(16,14))
x = np.linspace(0,octaves[0]['iter_n'],10)
ax[0].set_xlabel('iter number')
ax[0].set_ylabel('activation for generated MEI')
ax[0].set_xlim(0,1000)
pl_index = 1
for e, o in enumerate(octaves):
if 'scale' in o:
......@@ -273,9 +274,10 @@ def deepdraw(net, base_img, octaves, random_crop=True, original_size=None,
if i % 100 == 0:
print('finished step %d in octave %d' % (i, e))
ax[0].scatter(i,net(src).mean().cpu().detach().numpy())
ax[pl_index].imshow(src.cpu().detach().numpy()[0,0,:,:])
pl_index += 1
if track:
ax[0].scatter(i,net(src).mean().cpu().detach().numpy())
ax[pl_index].imshow(src.cpu().detach().numpy()[0,0,:,:])
pl_index += 1
# insert modified image back into original image (if necessary)
image[:, ox:ox + w, oy:oy + h] = src.data[0].cpu().numpy()
......@@ -341,7 +343,7 @@ def contrast_tuning(model, img, bias, scale, min_contrast=0.01, n=1000, linear=T
return cont, vals, lim_contrast
def MEI_multi_seed(dataset_name, dat, dataloaders, models, n_seeds, MEIParameter, TargetUnit):
def MEI_multi_seed(dataset_name, dat, dataloaders, models, n_seeds, MEIParameter, TargetUnit, track):
"""
n_seeds : int # number of distinct seeded models used
mei : longblob # most exciting images
......@@ -396,7 +398,7 @@ def MEI_multi_seed(dataset_name, dat, dataloaders, models, n_seeds, MEIParameter
gen_image = deepdraw(adj_model, gen_image, octaves, clip=True,
random_crop=False, blur=blur, jitter=jitter,
precond=precond, step_gain=step_gain,
bias=bias, scale=scale, norm=norm, train_norm=train_norm)
bias=bias, scale=scale, norm=norm, train_norm=train_norm, track=track)
mei = gen_image.squeeze()
......
RGC_sim.ipynb
View file @ dcb08a54
%% Cell type:markdown id: tags:
# Simple RGCs simulation
%% Cell type:code id: tags:
``` python
import numpy as np, array
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from scipy import signal
import matplotlib.image as mpimg
import matplotlib.cm as cm
import math
from mpl_toolkits.mplot3d import axes3d
import torch
from collections import OrderedDict
import neuralpredictors as neur
from neuralpredictors.data.datasets import StaticImageSet, FileTreeDataset
from numpy import save
```
%% Cell type:code id: tags:
``` python
#Save notebook session
#dill.dump_session('Natural_images_dataset_session_09122020.db')
```
%% Cell type:code id: tags:
``` python
#Restore notebook session
#dill.load_session('Natural_images_dataset_session_09122020.db')
```
%% Cell type:markdown id: tags:
##### Simple RGCs simulation:
%% Cell type:code id: tags:
``` python
_default_2Dgaussian_p = (1,1,1,0,0,0,0)
def gaussian_2D(xz, sigma_x, sigma_z, amp, theta, x0, z0, y0):
"""Two dimensional Gaussian function
params:
- xz: meshgrid of x and z coordinates at which to evaluate the points
- sigma_x: width of the gaussian
- sigma_z: height of the gaussian
- amp: amplitude of the gaussian
- theta: angle of the gaussian (in radian)
- x0: shift in x of the gaussian
- z0: shift in z of the gaussian
- y0: shift in y of the gaussian
"""
(x,z) = xz
x0, z0 = float(x0), float(z0)
a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_z**2)
b = -(np.sin(2*theta)) /(4*sigma_x**2) + (np.sin(2*theta)) /(4*sigma_z**2)
c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_z**2)
g = amp * np.exp( -(a*((x-x0)**2) + 2*b*(x-x0)*(z-z0) + c*((z-z0)**2))) + y0
return g.ravel()
def mexicanHat(xz, sigma_x_1, sigma_z_1, amp_1, theta_1, x0_1, z0_1,
sigma_x_2, sigma_z_2, amp_2, theta_2, x0_2, z0_2, y0):
"""Sum of two 2D Gaussian function. For the params, see `gaussian_2D`.
However, both share the y0 parameter."""
return (gaussian_2D(xz, sigma_x_1, sigma_z_1, amp_1, theta_1, x0_1, z0_1, 0)
+ gaussian_2D(xz, sigma_x_2, sigma_z_2, amp_2, theta_2, x0_2, z0_2, 0) + y0)
def ELU(r):
if r>0:
return r+1
else:
return np.exp(r) + 1
def RF(vis_field_width, vis_field_height, x_rf_center, z_rf_center, polarity, plot=False):
x,y = np.meshgrid(np.linspace(0,vis_field_width,vis_field_width),np.linspace(0,vis_field_height,vis_field_height))
if polarity==1:
sigma_x_1, sigma_z_1, amp_1, theta_1, x0_1, z0_1 = 2, 2, 1, 0, x_rf_center, z_rf_center
sigma_x_2, sigma_z_2, amp_2, theta_2, x0_2, z0_2, y0 = 3, 3, -0.5, 0, x_rf_center, z_rf_center, 0
else:
sigma_x_1, sigma_z_1, amp_1, theta_1, x0_1, z0_1 = 2, 2, -1, 0, x_rf_center, z_rf_center
sigma_x_2, sigma_z_2, amp_2, theta_2, x0_2, z0_2, y0 = 3, 3, 0.5, 0, x_rf_center, z_rf_center, 0
z = mexicanHat((x,y), sigma_x_1, sigma_z_1, amp_1, theta_1, x0_1, z0_1,
sigma_x_2, sigma_z_2, amp_2, theta_2, x0_2, z0_2, y0).reshape(vis_field_height,vis_field_width)
if plot==True:
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(x, y, z, rstride=3, cstride=3, label=f"x_rf_center={x_rf_center} z_rf_center={z_rf_center} \n amp_center={amp_1} amp_surround={amp_2}")
_ = ax.legend()
return z
def RGC_response(rf, image, plot=False, seed=None):
Img_barHat = image * rf
if plot==True:
fig, ax = plt.subplots(3, figsize=(7,7))
ax[0].imshow(image, cmap='gray')
ax[0].set_title("Image")
ax[1].imshow(rf, vmin=-1, vmax=1, cmap="gray")
ax[1].set_title("RGC RF")
ax[2].imshow(Img_barHat, cmap=cm.Greys_r)
ax[2].set_title("RGC Response")
plt.tight_layout()
if seed is not None:
np.random.seed(seed)
g = ELU(sum(Img_barHat.ravel()))
spikes = np.random.poisson(lam=g, size=None)
return spikes
```
%% Cell type:code id: tags:
``` python
#Generate the receptive field of one RGC
rf = RF(64, 36, 50, 30, -1, plot=True)
```
%% Output
%% Cell type:code id: tags:
``` python
#Generate the response of one RGC
RGC_response(rf=rf, image=ds_imgs, plot=True)
```
%% Output
155
%% Cell type:markdown id: tags:
##### RGCs response generation to natural images dataset:
%% Cell type:code id: tags:
``` python
#Retrieve image sets from evaluation data set of lurz2020 #5993 images randomly selected as train, validation or test
paths = 'D://inception_loop/RGC_sim/data/static27012021/data/images'
paths2 = 'D://inception_loop/RGC_sim/data/static27012021/data/images2'
images = []
for n in range(5993):
x = np.load(paths+'/'+str(n)+'.npy')
x_padded = np.pad(x[0], pad_width=20, mode='constant',
constant_values=0)
np.save(paths2+'/'+str(n)+'.npy', [x_padded])
images.append(x_padded)
#images = np.vstack(images)
```
%% Cell type:code id: tags:
``` python
#Generate receptive fields of several RGCs #2304 RGCs - haf ON/half OFF
rf_ON = []
rf_OFF = []
i = 0
image = images[0]
for width_center in range(image.shape[1]):
for height_center in range(image.shape[0]):
if (i % 2) == 0:
rf = RF(image.shape[1], image.shape[0], width_center, height_center, 1, plot=False)
rf_ON.append(rf)
else:
rf = RF(image.shape[1], image.shape[0], width_center, height_center, -1, plot=False)
rf_OFF.append(rf)
i+=1
```
%% Cell type:code id: tags:
``` python
#Generate responses of simulated RGCs to the image set from evaluation data set of lurz2020
import time
start_time = time.time()
i=0
for image in images:
responses = []
for rfon, rfoff in zip(rf_ON, rf_OFF):
rgc_on_response = RGC_response(rf=rfon, image=image, plot=False)
responses.append(rgc_on_response)
rgc_off_response = RGC_response(rf=rfoff, image=image, plot=False)
responses.append(rgc_off_response)
# save numpy array as npy file
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/data/responses/'+str(i)+'.npy', responses)
if (i % 1000) == 0:
print(i)
i+=1
print("--- %s seconds ---" % (time.time() - start_time))
```
%% Output
0
1000
2000
3000
4000
5000
--- 6485.6336970329285 seconds ---
%% Cell type:code id: tags:
``` python
#Generate data - pupil_center npy files
#Array with list of two values- coordinates of pupil center - [759.87785056, 472.71767702]
for j in range(len(images)):
pupil_center = np.array([0.0,0.0])
# save numpy array as npy file
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/data/pupil_center/'+str(j)+'.npy', pupil_center)
```
%% Cell type:code id: tags:
``` python
#Generate data - behavior npy files -> use include_behavior= False in static_loaders
#Array with list of three values - pupil dilation, temporal derivative and absolute running speed - [99.2678426 , 6.66429682, 0. ]
```
%% Cell type:code id: tags:
``` python
#Generate metadata - neurons - unit_ids npy array
unit_ids = np.array(range(1, len(responses)+1))
#Generate metadata - neurons - animal_ids npy array
animal_ids = np.repeat(1, len(responses))
#Generate metadata - neurons - area npy array
area = ['retina']*len(responses)
#Generate metadata - neurons - layer npy array
layer = ['RGC']*len(responses)
#Generate metadata - neurons - scan_idx npy array
scan_idx = np.repeat(14, len(responses))
#Generate metadata - neurons - sessions npy array
sessions = np.repeat(6, len(responses))
# save numpy arrays as npy arrays
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/unit_ids.npy', unit_ids)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/animal_ids.npy', animal_ids)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/area.npy', area)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/layer.npy', layer)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/scan_idx.npy', scan_idx)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/neurons/sessions.npy', sessions)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - trials - animal_id npy array
animal_id = np.repeat(1, len(responses))
#Generate metadata - trials - scan_idx npy array
scan_idx = np.repeat(14, len(responses))
#Generate metadata - trials - session npy array
session = np.repeat(6, len(responses))
# save numpy arrays as npy arrays
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/trials/animal_id.npy', animal_id)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/trials/scan_idx.npy', scan_idx)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/trials/session.npy', session)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - statistics - pupil_center - all
pupil_center_max_all = np.array([0.0,0.0])
pupil_center_mean_all = np.array([0.0,0.0])
pupil_center_median_all = np.array([0.0,0.0])
pupil_center_min_all = np.array([0.0,0.0])
pupil_center_std_all = np.array([0.0,0.0])
#Generate metadata - statistics - pupil_center - stimulus_frame
pupil_center_max_sf = np.array([0.0,0.0])
pupil_center_mean_sf = np.array([0.0,0.0])
pupil_center_median_sf = np.array([0.0,0.0])
pupil_center_min_sf = np.array([0.0,0.0])
pupil_center_std_sf = np.array([0.0,0.0])
# save numpy arrays as npy arrays - all
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/all/max.npy', pupil_center_max_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/all/mean.npy', pupil_center_mean_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/all/median.npy', pupil_center_median_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/all/min.npy', pupil_center_min_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/all/std.npy', pupil_center_std_all)
# save numpy arrays as npy arrays - stimulus_frame
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/stimulus_frame/max.npy', pupil_center_max_sf)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/stimulus_frame/mean.npy', pupil_center_mean_sf)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/stimulus_frame/median.npy', pupil_center_median_sf)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/stimulus_frame/min.npy', pupil_center_min_sf)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/pupil_center/stimulus_frame/std.npy', pupil_center_std_sf)
```
%% Cell type:code id: tags:
``` python
#Generate responses of simulated RGCs to the image set from evaluation data set of lurz2020
import time
start_time = time.time()
responses_all = []
for k in range(len(images)):
responses_all.append(np.load('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/data/responses/'+str(k)+'.npy'))
if (k % 1000) == 0:
print(k)
print("--- %s seconds ---" % (time.time() - start_time))
```
%% Output
0
1000
2000
3000
4000
5000
--- 47.54899740219116 seconds ---
%% Cell type:code id: tags:
``` python
#Generate metadata - statistics - responses - all
responses_max_all = np.max(responses_all, axis=0)
responses_mean_all = np.mean(responses_all, axis=0)
responses_median_all = np.median(responses_all, axis=0)
responses_min_all = np.min(responses_all, axis=0)
responses_std_all = np.std(responses_all, axis=0)
#Generate metadata - statistics - responses - stimulus_frame
# save numpy arrays as npy arrays - all
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/all/max.npy', responses_max_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/all/mean.npy', responses_mean_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/all/median.npy', responses_median_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/all/min.npy', responses_min_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/all/std.npy', responses_std_all)
# save numpy arrays as npy arrays - stimulus_frame
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/stimulus_frame/max.npy', responses_max_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/stimulus_frame/mean.npy', responses_mean_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/stimulus_frame/median.npy', responses_median_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/stimulus_frame/min.npy', responses_min_all)
save('./Lurz_2020_code/notebooks/data/RGC_sim/static27012021/meta/statistics/responses/stimulus_frame/std.npy', responses_std_all)
```
%% Cell type:markdown id: tags:
----------------------------------------------------------------
%% Cell type:markdown id: tags:
##### RGCs response generation to random checkerboard stimuli:
%% Cell type:code id: tags:
``` python
#Generate random checkerboard stim dataset #10 993 images selected as train or validation
from numpy import random
paths = 'D://inception_loop/RGC_sim/data/static20022021/data/images'
checkerboard_set = []
for n in range(10993):
checkerboard_image = [random.choice([0.0, 255.0], size=(36, 64))]
checkerboard_set.append(checkerboard_image)
np.save(paths+'/'+str(n)+'.npy', checkerboard_image)
```
%% Cell type:code id: tags:
``` python
#Generate random checkerboard stim dataset #10 993 images selected as train or validation
repeated_set = []
idx = 10993
for n in range(10):
checkerboard_image = [random.choice([0.0, 255.0], size=(36, 64))]
for i in range(100):
checkerboard_set.append(checkerboard_image)
np.save(paths+'/'+str(idx)+'.npy', checkerboard_image)
idx+=1
```
%% Cell type:code id: tags:
``` python
#Add padding for comparison reasons
paths = 'D://inception_loop/RGC_sim/data/static20022021/data/images'
images = []
for n in range(11993):
x = np.load(paths+'/'+str(n)+'.npy')
x_padded = np.pad(x[0], pad_width=20, mode='constant',
constant_values=0)
np.save(paths+'/'+str(n)+'.npy', [x_padded])
images.append(x_padded)
```
%% Cell type:code id: tags:
``` python
#Generate receptive fields of several RGCs #2304 RGCs - haf ON/half OFF to checkerboard set
rf_ON = []
rf_OFF = []
i = 0
image = checkerboard_set[0][0]
for width_center in range(image.shape[1]):
for height_center in range(image.shape[0]):
if (i % 2) == 0:
rf = RF(image.shape[1], image.shape[0], width_center, height_center, 1, plot=False)
rf_ON.append(rf)
else:
rf = RF(image.shape[1], image.shape[0], width_center, height_center, -1, plot=False)
rf_OFF.append(rf)
i+=1
```
%% Cell type:code id: tags:
``` python
#Generate responses of simulated RGCs to the image set from evaluation data set of lurz2020
import time
start_time = time.time()
i=0
for image in checkerboard_set:
responses = []
for rfon, rfoff in zip(rf_ON, rf_OFF):
rgc_on_response = RGC_response(rf=rfon, image=image, plot=False)
responses.append(rgc_on_response)
rgc_off_response = RGC_response(rf=rfoff, image=image, plot=False)
responses.append(rgc_off_response)
# save numpy array as npy file
save('D://inception_loop/RGC_sim/data/static20022021/data/responses/'+str(i)+'.npy', responses)
if (i % 1000) == 0:
print(i)
i+=1
print("--- %s seconds ---" % (time.time() - start_time))
```
%% Output
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
--- 12647.280105352402 seconds ---
%% Cell type:code id: tags:
``` python
#Generate data - pupil_center npy files
#Array with list of two values- coordinates of pupil center - [759.87785056, 472.71767702]
for j in range(len(checkerboard_set)):
pupil_center = np.array([0.0,0.0])
# save numpy array as npy file
save('D://inception_loop/RGC_sim/data/static20022021/data/pupil_center/'+str(j)+'.npy', pupil_center)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - neurons - unit_ids npy array
unit_ids = np.array(range(1, len(responses)+1))
#Generate metadata - neurons - animal_ids npy array
animal_ids = np.repeat(1, len(responses))
#Generate metadata - neurons - area npy array
area = ['retina']*len(responses)
#Generate metadata - neurons - layer npy array
layer = ['RGC']*len(responses)
#Generate metadata - neurons - scan_idx npy array
scan_idx = np.repeat(14, len(responses))
#Generate metadata - neurons - sessions npy array
sessions = np.repeat(6, len(responses))
# save numpy arrays as npy arrays
path = 'D://inception_loop/RGC_sim/data/static20022021/'
save(path+'/meta/neurons/unit_ids.npy', unit_ids)
save(path+'/meta/neurons/animal_ids.npy', animal_ids)
save(path+'/meta/neurons/area.npy', area)
save(path+'/meta/neurons/layer.npy', layer)
save(path+'/meta/neurons/scan_idx.npy', scan_idx)
save(path+'/meta/neurons/sessions.npy', sessions)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - trials - animal_id npy array
animal_id = np.repeat(1, len(responses))
#Generate metadata - trials - condition_hash npy array
condition_hash = np.repeat(" ", len(responses))
#Generate metadata - trials - frame_image_class npy array
frame_image_class = np.repeat("imagenet", len(responses))
#Generate metadata - trials - frame_image_id npy array
frame_image_id = np.arange(0, len(responses), 1)
#Generate metadata - trials - frame_last_flip npy array
frame_last_flip = np.random.randint(11000, 30000, size=(len(responses)))
#Generate metadata - trials - frame_pre_blank_period npy array
frame_pre_blank_period = np.random.uniform(0.3, 0.5, size=(len(responses)))
#Generate metadata - trials - frame_presentation_time npy array
frame_presentation_time = np.repeat(0.5, len(responses))
#Generate metadata - trials - frame_trial_ts npy array
frame_trial_ts = np.repeat("Timestamp('2021-02-23 17:53:43')", len(responses))
#Generate metadata - trials - scan_idx npy array
scan_idx = np.repeat(14, len(responses))
#Generate metadata - trials - tiers npy array
#tiers = np.repeat(" ", 11993)
#tiers[0:10000] = np.repeat("train", 10000)
#tiers[10000:10993] = np.repeat("validation", 993)
#tiers[10993:11993] = np.repeat("test", 1000)
tiers = []
tiers[0:10000] = np.repeat("train", 10000)
tiers[10000:10993] = np.repeat("validation", 993)
tiers[10993:11993] = np.repeat("test", 1000)
np.asarray(tiers)
#Generate metadata - trials - session npy array
session = np.repeat(6, len(responses))
#Generate metadata - trials - trial_idx npy array
trial_idx = np.repeat(0, len(responses))
# save numpy arrays as npy arrays
path = 'D://inception_loop/RGC_sim/data/static20022021/'
save(path+'/meta/trials/animal_id.npy', animal_id)
save(path+'/meta/trials/condition_hash.npy', condition_hash)
save(path+'/meta/trials/frame_image_class.npy', frame_image_class)
save(path+'/meta/trials/frame_image_id.npy', frame_image_id)
save(path+'/meta/trials/frame_last_flip.npy', frame_last_flip)
save(path+'/meta/trials/frame_pre_blank_period.npy', frame_pre_blank_period)
save(path+'/meta/trials/frame_presentation_time.npy', frame_presentation_time)
save(path+'/meta/trials/frame_trial_ts.npy', frame_trial_ts)
save(path+'/meta/trials/scan_idx.npy', scan_idx)
save(path+'/meta/trials/tiers.npy', tiers)
save(path+'/meta/trials/session.npy', session)
save(path+'/meta/trials/trial_idx.npy', trial_idx)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - statistics - pupil_center - all
pupil_center_max_all = np.array([0.0,0.0])
pupil_center_mean_all = np.array([0.0,0.0])
pupil_center_median_all = np.array([0.0,0.0])
pupil_center_min_all = np.array([0.0,0.0])
pupil_center_std_all = np.array([0.0,0.0])
#Generate metadata - statistics - pupil_center - stimulus_frame
pupil_center_max_sf = np.array([0.0,0.0])
pupil_center_mean_sf = np.array([0.0,0.0])
pupil_center_median_sf = np.array([0.0,0.0])
pupil_center_min_sf = np.array([0.0,0.0])
pupil_center_std_sf = np.array([0.0,0.0])
# save numpy arrays as npy arrays - all
path = 'D://inception_loop/RGC_sim/data/static20022021/'
save(path+'/meta/statistics/pupil_center/all/mean.npy', pupil_center_mean_all)
save(path+'/meta/statistics/pupil_center/all/median.npy', pupil_center_median_all)
save(path+'/meta/statistics/pupil_center/all/min.npy', pupil_center_min_all)
save(path+'/meta/statistics/pupil_center/all/std.npy', pupil_center_std_all)
# save numpy arrays as npy arrays - stimulus_frame
save(path+'/meta/statistics/pupil_center/stimulus_frame/max.npy', pupil_center_max_sf)
save(path+'/meta/statistics/pupil_center/stimulus_frame/mean.npy', pupil_center_mean_sf)
save(path+'/meta/statistics/pupil_center/stimulus_frame/median.npy', pupil_center_median_sf)
save(path+'/meta/statistics/pupil_center/stimulus_frame/min.npy', pupil_center_min_sf)
save(path+'/meta/statistics/pupil_center/stimulus_frame/std.npy', pupil_center_std_sf)
```
%% Cell type:code id: tags:
``` python
#Generate metadata - statistics - responses - all
#Generate metadata - statistics - responses - all and stimulus_frame
responses_max_all = np.max(responses, axis=0)
responses_mean_all = np.mean(responses, axis=0)
responses_median_all = np.median(responses, axis=0)
responses_min_all = np.min(responses, axis=0)
responses_std_all = np.std(responses, axis=0)
#Generate metadata - statistics - responses - stimulus_frame
# save numpy arrays as npy arrays - all
path = 'D://inception_loop/RGC_sim/data/static20022021/'
save(path+'/meta/statistics/responses/all/max.npy', responses_max_all)
save(path+'/meta/statistics/responses/all/mean.npy', responses_mean_all)
save(path+'/meta/statistics/responses/all/median.npy', responses_median_all)
save(path+'/meta/statistics/responses/all/min.npy', responses_min_all)
save(path+'/meta/statistics/responses/all/std.npy', responses_std_all)
# save numpy arrays as npy arrays - stimulus_frame
save(path+'/meta/statistics/responses/stimulus_frame/max.npy', responses_max_all)
save(path+'/meta/statistics/responses/stimulus_frame/mean.npy', responses_mean_all)
save(path+'/meta/statistics/responses/stimulus_frame/median.npy', responses_median_all)
save(path+'/meta/statistics/responses/stimulus_frame/min.npy', responses_min_all)
save(path+'/meta/statistics/responses/stimulus_frame/std.npy', responses_std_all)
```
%% Cell type:code id: tags:
``` python
images = np.vstack(checkerboard_set)
#Generate metadata - statistics - responses - all
images_max_all = np.max(images)
images_mean_all = np.mean(images)
images_median_all = np.median(images)
images_min_all = np.min(images)
images_std_all = np.std(images)
#Generate metadata - statistics - responses - stimulus_frame
# save numpy arrays as npy arrays - all
path = 'D://inception_loop/RGC_sim/data/static20022021/'
save(path+'/meta/statistics/images/all/mean.npy', images_mean_all)
save(path+'/meta/statistics/images/all/median.npy', images_median_all)
save(path+'/meta/statistics/images/all/min.npy', images_min_all)
save(path+'/meta/statistics/images/all/std.npy', images_std_all)
# save numpy arrays as npy arrays - stimulus_frame
save(path+'/meta/statistics/images/stimulus_frame/max.npy', images_max_all)
save(path+'/meta/statistics/images/stimulus_frame/mean.npy', images_mean_all)
save(path+'/meta/statistics/images/stimulus_frame/median.npy', images_median_all)
save(path+'/meta/statistics/images/stimulus_frame/min.npy', images_min_all)
save(path+'/meta/statistics/images/stimulus_frame/std.npy', images_std_all)
```
%% Cell type:markdown id: tags:
------------------------------------------
......
example_notebook_RGC_sim.ipynb
View file @ dcb08a54
This diff is collapsed.
example_notebook_RGC_sim_checkerboard.ipynb
View file @ dcb08a54
This diff is collapsed.
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment