condition_config)
else:
raise ValueError('sorting method is not recognized')
sorted_list_of_psths = [x[ixs, :] for x in list_of_psths]
psth = psth[ixs, :]
else:
sorted_list_of_psths = list_of_psths
images.append(psth)
odor_on_times.append(odor_on)
water_on_times.append(water_on)
list_of_odor_names.append(odors_copy)
return images, odor_on_times, water_on_times, list_of_odor_names, sorted_list_of_psths
res = analysis.load_data(data_path)
analysis.add_indices(res)
analysis.add_time(res)
if condition_config.plot_big:
mice = np.unique(res['mouse'])
list_of_days_per_mouse = condition_config.plot_big_days
list_of_images = []
list_of_list_of_psths = []
odor_on_times = []
water_on_times = []
days = [int(not condition_config.plot_big_naive)]
for i, day in enumerate(list_of_days_per_mouse):
mouse = mice[i]
if day != -1:
images, odor_on_times, water_on_times, list_of_odor_names, list_of_psth = helper(
res, mouse, [day], condition_config)
print(baseNum)
np.savetxt('baseNumbersUsed.txt', baseNum)
#==============================================================================================================
# Run Base Odor and Mixture Odor Simulations
#==============================================================================================================
ex.createData(run_params_train, I_arr, states, net) # base odors
for rowNum, aCombo in enumerate(baseNum):
ex.mixtures3(run_params_test,
[I_arr[aCombo[0]], I_arr[aCombo[1]], I_arr[aCombo[2]]],
rowNum, states, net) # mixture odors
#==============================================================================================================
# Load in Base and Mixture Data
#==============================================================================================================
#load in the data from disk
spikes_t_arr, spikes_i_arr, I_arr, trace_V_arr, trace_t_arr, label_arr = anal.load_data(
tr_prefix, num_runs=num_odors_train)
#Counter
number_Of_Mix_Runs = 0 # necessary to increment like this because it is not known without first dividing the grid up
listOfAlpha = []
listOfBeta = []
for i in range(num_alpha):
for j in range(num_alpha):
A_arr = np.linspace(0, 1, num_alpha)
B_arr = np.linspace(0, 1, num_alpha)
if A_arr[i] + B_arr[j] <= 1: # Only using normalized probabilities
listOfAlpha.append(A_arr[i])
listOfBeta.append(B_arr[j])
number_Of_Mix_Runs += 1
spikes_t_test_arr_for_combo = 5 * [None]
run_params_test = dict(num_odors=num_odors_mix,
num_trials=num_alpha,
prefix=te_prefix,
inp=inp,
noise_amp=noise_test,
run_time=run_time,
N_AL=N_AL,
train=False)
# --------------------------------------------------------------
# run the simulation and save to disk
ex.createData(run_params_train, I_arr, states, net)
ex.mixtures2(run_params_test, I_arr[:num_odors_mix], states, net)
# load in the data from disk
spikes_t_arr, spikes_i_arr, I_arr, trace_V_arr, trace_t_arr, label_arr = anal.load_data(tr_prefix,
num_runs=num_odors_train)
spikes_t_test_arr, spikes_i_test_arr, I_test_arr, test_V_arr, test_t_arr, label_test_arr = anal.load_data(te_prefix,
num_runs=num_alpha * num_test)
X = np.hstack(trace_V_arr).T
# normalize training data
mini = np.min(X)
maxi = np.max(X)
X = anal.normalize(X, mini, maxi)
y = np.hstack(label_arr)
# train the SVM
clf = anal.learnSVM(X, y)
mixing_fractions = np.linspace(0,1,num=11)
for reward in r_schema:
for stimulus in u_schema:
moniker = '%s-%s'%(reward,stimulus)
print moniker
print ''
simulation = Network(N=N,duration=2500,downsampling=1, mixing_fraction=mixing_fractions,r_schema=reward,u_schema=stimulus)
active_directory = simulation.basedir
results = [filename for filename in os.listdir(active_directory) if 'results' in filename]
accuracy = np.zeros((len(mixing_fractions),N['memories']))
energies = np.zeros((len(mixing_fractions),N['memories']))
for i,(results_filename,fraction) in enumerate(zip(results,mixing_fractions)):
data = postdoc.load_data(os.path.join(active_directory,results_filename))
accuracy[i,:] = postdoc.accuracy_figure(data,savename=os.path.join(active_directory,'accuracy-%s-%s')%(str(int(fraction*10)),moniker))
# energies[i,:] = postdoc.energy_figure(data,savename=os.path.join(active_directory,'energy-%s')%str(int(fraction*10)))
#postdoc.correlation_visualization(data,savename =os.path.join(active_directory,'correlations-%s')%str(int(fraction*10)))
visualization.track_matrices(data['M'],savename=os.path.join(active_directory,'M-change-%s')%(str(int(fraction*10)),moniker))
visualization.memory_stability(data['memory_stability'],savename=os.path.join(active_directory,'M-stability-%s')%(str(int(fraction*10)),moniker))
visualization.network_stability(data['network_stability'],savename=os.path.join(active_directory,'network-stability-%s')%(str(int(fraction*10)),moniker))
#Don't forget about this.
correl = postdoc.sensitivities(mixing_fractions,accuracy.transpose(), savename = os.path.join(active_directory,'sensitivities-%s'%moniker))
#Transpose so that the x-axis contains mixing fraction and y-axis accuracy
#print correl
del simulation
'''
TODO:
1. Exploring sensitvity of recall to changes in M and initial conditions (mixing parameter)
class MPFC_TWO_PHASE:
# naive odor presentation: day 0
# pretraining start: day 1
# pretraining fully learned: day 3
# discrimination start day: day 4
# discrimination fully learned: day 5
path = os.path.join(rootdir,
'MPFC_TWO_PHASE') #specify path on your local machine
pretraining_odors = ['oct']
discrimination_odors = ['pin', 'msy', 'euy', 'lim']
csp = ['oct', 'pin', 'msy']
condition = OFC_SINGLE_PHASE() #input your condition
res = analysis.load_data(condition.path)
analysis.add_indices(res)
analysis.add_time(res)
for i, data in enumerate(res['data']):
frames_per_trial = res['TRIAL_FRAMES'][i]
data_r = utils.reshape_data(data,
nFrames=frames_per_trial,
cell_axis=0,
trial_axis=1,
time_axis=2)
res['data'][i] = data_r
print(res.keys())
print(res['day'])
day = 1
train = False)
param_labels = ['N_AL', 'in_AL', 'PAL', 'inj', 'eta', 'num_train', 'num_test', 'runtime', 'p_inj']
param_values = [N_AL, in_AL, PAL, inp, noise_test, num_train, num_test, run_time, p_inj]
ex.createData(run_params_train, I_arr, states, net)
ex.createData(run_params_test, I_arr, states, net)
# This saves parameters used in the test folders
#np.savetxt(te_prefix + 'params.txt',list(zip(param_labels,param_values)),fmt='%s')
#""" PCA/SVM Code
_, _, _, trace_V_arr, _, label_arr = anal.load_data(tr_prefix, num_runs = num_odors*num_train)
_, _, _, test_V_arr, _, label_test_arr = anal.load_data(te_prefix, num_runs = num_odors*num_test)
pca = True #do PCA on output or not
if pca: #do PCA on the output
pca_dim = 2
pca_arr, PCA = anal.doPCA(trace_V_arr, k = pca_dim)
X = np.hstack(pca_arr).T
else:
X = np.hstack(trace_V_arr).T
#normalize
mini = np.min(X)