Late_sessions_average_US_err = np.zeros((2,1))
Rats_early_averageCS = np.zeros((n_rats,2))
Rats_late_averageCS = np.zeros((n_rats,2))
Rats_early_averageUS = np.zeros((n_rats,2))
Rats_late_averageUS = np.zeros((n_rats,2))
for iti, u_iti in enumerate(u_itis):
filename = 'Variable ITIs/' + Distribution_name + ' distribution/Mixed population simulations with intertrial ' + str(iti) + '.npz'
data = np.load(filename)
plt.figure(1)
IndividualAverageDA_CS = np.mean(data['dopamineCS'], axis = 0)
Session_Average_DA_CS = np.mean(IndividualAverageDA_CS, axis = 1)
Session_Average_DA_CS_err = sem(IndividualAverageDA_CS.transpose())
plt.errorbar(x, Session_Average_DA_CS, Session_Average_DA_CS_err, label=ITI_labels[iti], color=ITI_colours[iti], marker = 'o')
Rats_SessionAverage_DA_CS = np.mean(IndividualAverageDA_CS, axis = 0)
All_session_average_CS[iti] = np.mean(Rats_SessionAverage_DA_CS)
All_session_average_CS_err[iti] =sem(Rats_SessionAverage_DA_CS)
for rat in range(n_rats):
Rats_early_averageCS[rat,iti] = np.mean(IndividualAverageDA_CS[0:3,rat])
Rats_late_averageCS[rat,iti] = np.mean(IndividualAverageDA_CS[3:,rat])
Early_sessions_average_CS[iti] = np.mean(Rats_early_averageCS[:,iti])
Early_sessions_average_CS_err[iti] = sem(Rats_early_averageCS[:,iti])
Late_sessions_average_CS[iti] = np.mean(Rats_late_averageCS[:,iti])
Late_sessions_average_CS_err[iti] = sem(Rats_late_averageCS[:,iti])
u_itis = np.array([0.01, 0.1])
n_blocks = 60
n_trials = 25
x = range(1, n_blocks + 1)
for iti, u_iti in enumerate(u_itis):
filename = 'Variable ITIs/' + Distribution_name + ' distribution/Mixed population long simulations with intertrial ' + str(
iti) + '.npz'
data = np.load(filename)
a = data['state1_FMFValue']
V_L = np.mean(a[:, 1, :], axis=1).reshape((n_blocks, n_trials)).transpose()
V_M = np.mean(a[:, 2, :], axis=1)
plt.figure(iti)
yerr = sem(V_L).transpose()
y = np.mean(V_L, axis=0)
plt.errorbar(x,
np.mean(V_L, axis=0),
color=ITI_colours[iti],
marker='o',
label='Lever')
plt.errorbar(x,
np.mean(V_M, axis=0),
color=ITI_colours[iti],
marker='+',
label='Food cup')
plt.legend()
plt.savefig('Variable ITIs/' + Distribution_name + ' distribution/' +
ITI_labels[iti] + ' feature values.eps')
#filename = ITIcondition + '/' + phenotype + 'Simulations with intertrial ' + str(iti_scale) + '.npz'
filename = 'replication Lesaint 2014/' + phenotype + 'Simulations with intertrial ' + str(
iti_scale) + '.npz'
#filename = 'magazine ' + magazine + '/' + phenotype + 'Simulations.npz'
#filename = phenotype + 'Simulations.npz'
data = np.load(filename)
plt.figure(counter)
plt.plot(x, data['goL_counter'].transpose() / n_trials)
plt.figure(counter + n_phenotypes)
plt.plot(x, data['goM_counter'].transpose() / n_trials)
plt.figure(10)
y = np.mean(data['goL_counter'] / n_trials, axis=0)
yerr = sem(data['goL_counter'] / n_trials)
plt.errorbar(x, y, yerr, color=pheno_colours[counter], label=phenotype)
plt.figure(11)
y = np.mean(data['goM_counter'] / n_trials, axis=0)
yerr = sem(data['goM_counter'] / n_trials)
plt.errorbar(x, y, yerr, color=pheno_colours[counter], label=phenotype)
plt.figure(10)
plt.axis([0, 9, 0, 1])
plt.savefig('replication Lesaint 2014/' + 'Approach to lever when ' +
ITIcondition + ' during ' + duration + ' ITI.png')
plt.legend(loc='best')
plt.figure(11)
plt.axis([0, 9, 0, 1])
plt.savefig('replication Lesaint 2014/' + 'Approach to magazine when ' +
AverageDistribution_err = np.zeros((3,3))
AverageFMFvalues = np.zeros((3,3))
AverageFMFvalues_err = np.zeros((3,3))
AverageAdvantage = np.zeros((3,3))
AverageAdvantage_err = np.zeros((3,3))
for iti, u_iti in enumerate(u_itis):
filename = 'Variable ITIs/' + Distribution_name + ' distribution/Mixed population long simulations with intertrial ' + str(iti) + '.npz'
data = np.load(filename)
sio.savemat('goL_counter for ' + ITI_labels[iti] + ' ITI', {'goL': data['goL_counter']})
sio.savemat('goM_counter for ' + ITI_labels[iti] + ' ITI', {'goM': data['goM_counter']})
plt.figure(1)
y = np.mean(data['goL_counter'] / n_trials, axis = 0)
yerr = sem(data['goL_counter'] / n_trials)
plt.errorbar(x, y, yerr, label=ITI_labels[iti], color=ITI_colours[iti], marker = 'o')
plt.figure(2)
y = np.mean(data['goM_counter'] / n_trials, axis = 0)
yerr = sem(data['goM_counter'] / n_trials)
plt.errorbar(x, y, yerr, label=ITI_labels[iti], color=ITI_colours[iti], marker = 'o')
plt.figure(3)
IndividualAverageDA_CS = np.mean(data['dopamineCS'], axis = 0)
y = np.mean(IndividualAverageDA_CS, axis = 1)
yerr = sem(IndividualAverageDA_CS.transpose())
plt.errorbar(x, y, yerr, label=ITI_labels[iti], color=ITI_colours[iti], marker = 'o')
plt.figure(4)
IndividualAverageDA_US = np.mean(data['dopamineUS'], axis = 0)
ITIcondition = 'magazine absent'
elif lever_present:
ITIcondition = 'lever present'
else:
ITIcondition = 'lever absent'
print(ITIcondition)
for counter, phenotype in enumerate(phenotypes):
#filename = ITIcondition + '/' + phenotype + 'Simulations with intertrial ' + str(iti_scale) + '.npz'
filename = 'replication Lesaint 2014/' + phenotype + 'Simulations with intertrial ' + str(iti_scale) + '.npz'
data = np.load(filename)
plt.figure()
IndividualAverageDA_CS = np.mean(data['dopamineCS'], axis = 0)
plt.plot(x, IndividualAverageDA_CS)
plt.figure()
IndividualAverageDA_US = np.mean(data['dopamineUS'], axis = 0)
plt.plot(x, IndividualAverageDA_US)
plt.figure()
y = np.mean(IndividualAverageDA_CS, axis = 1)
yerr = sem(IndividualAverageDA_CS.transpose())
plt.errorbar(x, y, yerr, color='r', label='CS')
y = np.mean(IndividualAverageDA_US, axis = 1)
yerr = sem(IndividualAverageDA_US.transpose())
plt.errorbar(x, y, yerr, color='b', label='US')
plt.axis([0, 9, 0, 1])
plt.legend(loc='best')
plt.savefig('replication Lesaint 2014//Patterns of Da activity in ' + phenotype + ' for ' + duration + ' intertrial ' + ITIcondition + '.pdf')
plt.show()
iti_scales = [0.5, 1, 2]
print(iti_scales)
n_blocks = 8
n_trials = 50
x = range(1, n_blocks + 1)
for counter, iti_scale in enumerate(iti_scales):
filename = ITIcondition + '/' + phenotype + 'Simulations with intertrial ' + str(
iti_scale) + '.npz'
#filename = phenotype + 'Simulations.npz'
data = np.load(filename)
plt.figure(1)
y = np.mean(data['goL_counter'] / n_trials, axis=0)
yerr = sem(data['goL_counter'] / n_trials)
plt.errorbar(x, y, yerr, label='u_iti=' +
str(iti_scale)) #, color=pheno_colours[counter]
plt.figure(2)
y = np.mean(data['goM_counter'] / n_trials, axis=0)
yerr = sem(data['goM_counter'] / n_trials)
plt.errorbar(x, y, yerr, label='u_iti=' +
str(iti_scale)) #, color=pheno_colours[counter]
plt.figure(3)
IndividualAverageDA_CS = np.mean(data['dopamineCS'], axis=0)
y = np.mean(IndividualAverageDA_CS, axis=1)
yerr = sem(IndividualAverageDA_CS.transpose())
plt.errorbar(x, y, yerr, label='u_iti=' + str(iti_scale))
import numpy as np import matplotlib.pyplot as plt from SEM import sem phenotype = ['ST'] iti_scales = 1 n_blocks = 8 n_trials = 50 x = range(1, 3) filename = phenotype[0] + 'Simulations with intertrial ' + str(iti_scales) + '.npz' data = np.load(filename) a = np.mean(data['goL_counter1'] / n_trials, axis = 0) a_err = sem(data['goL_counter1'] / n_trials) b = np.mean(data['goL_counter2'] / n_trials, axis = 0) b_err = sem(data['goL_counter2'] / n_trials) y1 = np.stack((a, b), axis = 1) y1_err = np.stack((a_err, b_err), axis = 1) a = np.mean(data['goM_counter1'] / n_trials, axis = 0) a_err = sem(data['goM_counter1'] / n_trials) b = np.mean(data['goM_counter2'] / n_trials, axis = 0) b_err = sem(data['goM_counter2'] / n_trials) y2 = np.stack((a, b), axis = 1) y2_err = np.stack((a_err, b_err), axis = 1) a = np.mean(data['exp_counter1'] / n_trials, axis = 0) a_err = sem(data['exp_counter1'] / n_trials) b = np.mean(data['exp_counter2'] / n_trials, axis = 0)
width = 0.2
epsilon = 0.1
x = [1 - (width + epsilon) / 2, 1 + (width + epsilon) / 2]
filename = Distribution_name + '/Simulations with intertrial ' + str(
u_iti) + '.npz'
data = np.load(filename)
a = np.mean(data['goL_counter1'] / n_trials, axis=1)
b = np.mean(data['goL_counter2'] / n_trials, axis=1)
y1 = np.stack((a, b), axis=1)
a = np.mean(data['goM_counter1'] / n_trials, axis=1)
b = np.mean(data['goM_counter2'] / n_trials, axis=1)
y2 = np.stack((a, b), axis=1)
y2err = sem(y2)
#plt.figure(2)
#plt.boxplot(y2)
#plt.plot(x, np.mean(y2, axis = 0), color = 'black')
#for rat in range(n_rats):
# plt.figure(1)
# plt.plot(x, y1[rat,:], color = 'lightgray')
#
# plt.figure(2)
# plt.plot(x, y2[rat,:], color = 'lightgray', marker = '.')
#
#t_goL, p_goL = stats.ttest_rel(y1[:,0], y1[:,1])
#t_goM, p_goM = stats.ttest_rel(y2[:,0], y2[:,1])
import numpy as np
import matplotlib.pyplot as plt
from SEM import sem
u_iti = 0.8
n_blocks = 8
n_trials = 50
x = range(1, 3)
#filename = phenotype[0] + 'FMFonly_Simulations with intertrial ' + str(u_iti) + '.npz'
filename = 'Decaying MB Simulations.npz'
data = np.load(filename)
a = np.mean(data['goL_counter1'] / n_trials, axis=0)
a_err = sem(data['goL_counter1'] / n_trials)
b = np.mean(data['goL_counter2'] /
(data['goL_counter2'] + data['goM_counter2']),
axis=0)
b_err = sem(data['goL_counter2'] /
(data['goL_counter2'] + data['goM_counter2']))
y1 = np.stack((a, b), axis=1)
y1_err = np.stack((a_err, b_err), axis=1)
a = np.mean(data['goM_counter1'] / n_trials, axis=0)
a_err = sem(data['goM_counter1'] / n_trials)
b = np.mean(data['goM_counter2'] /
(data['goL_counter2'] + data['goM_counter2']),
axis=0)
b_err = sem(data['goM_counter2'] /
(data['goL_counter2'] + data['goM_counter2']))
elif lever_present:
ITIcondition = 'lever present'
else:
ITIcondition = 'lever absent'
print(ITIcondition)
#filename = 'flupenthixol inhibition/ST flupenthixol inhibition = ' + str(flupenthixol) + '.npz'
filename = 'replication Lesaint 2014/ST flupenthixol inhibition = ' + str(flupenthixol) + '.npz'
data_flu = np.load(filename)
filename = 'replication Lesaint 2014/STSimulations with intertrial ' + str(iti_scale) + '.npz'
data_control = np.load(filename)
plt.figure()
y1 = np.mean(data_flu['goL_counter'] / n_trials, axis = 0)
y1err = sem(data_flu['goL_counter'] / n_trials)
plt.errorbar(x, y1[0:7], y1err[0:7], color='r', label='flu')
y2 = np.mean(data_control['goL_counter'] / n_trials, axis = 0)
y2err = sem(data_control['goL_counter'] / n_trials)
plt.errorbar(x, y2[0:7], y2err[0:7], color='k', label='veh')
plt.axis([0, 9, 0, 1])
plt.legend(loc='best')
plt.savefig('replication Lesaint 2014/ST Approach to lever under flupenthixol treatment.png')
plt.figure()
plt.bar([7.75, 8.25], [y2[7], y1[7]], align='center', width = 0.4, yerr = [y2err[7], y1err[7]], color=['k', 'r'])
plt.savefig('replication Lesaint 2014/ST Approach to lever after flupenthixol treatment.png')
filename = 'replication Lesaint 2014/GT flupenthixol inhibition = ' + str(flupenthixol) + '.npz'
data_flu = np.load(filename)