Update FindPosteriors.py

FindPosteriors.py

@@ -3,12 +3,9 @@ import pickle
 from brian2 import *
 from sbi import utils as utils
 from sbi import analysis as analysis
-from sbi.inference import SNPE
 import matplotlib.pyplot as plt
 from Simulator import MEAnetSimulate, ComputeFeatures
 from MakeFigures import rasterplot, Marginaldiffplot
-import importlib
-import os
 from scipy.stats import ks_2samp
 
 numstats = 15        # Number of summary statistics
@@ -25,7 +22,7 @@ SSlabels = ['MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'm
 ##  LOAD YOUR OWN EXPERIMENTAL DATA TO OBTAIN POSTERIOR
 # Load your own experimental data as APs (first column electrode number, second column AP timestamps):
 # location of your experimental files
-exp_fileloc = '/home/Nina/Documents/SBI_project/Output/Paper_Figures_ver1/APs_Fig_5_CACNClonesb3_sim0.csv'
+exp_fileloc = '/home/yourlocation'
 APs_obs = numpy.loadtxt(exp_fileloc, delimiter=",", dtype='int')
 recordtime = 165 * second   # how long the recording was
 fs = 10000              # sampling frequency used for the recording
@@ -61,20 +58,20 @@ rasterplot(APs_sim, "simulation", 1/fs, transient, simtime, 'black')
 
 ## COMPARE TWO POSTERIORS
 # calculate or define the MEA features of your two observations
-observation1 = torch.tensor(torch.load('/home/Nina/Documents/SBI_project/Analyzed_experimental/SCN_WTC_2410.pt'))
+observation1 = torch.tensor(torch.load('SCN_WTC_2410.pt'))
 posterior.set_default_x(observation1)                    # find the maxima of the posterior
 obs1_samples = posterior.sample((1000,))
-observation2 = torch.tensor(torch.load('/home/Nina/Documents/SBI_project/Analyzed_experimental/SCN_GEFS_2410.pt'))
+observation2 = torch.tensor(torch.load('SCN_GEFS_2410.pt'))
 posterior.set_default_x(observation2)                    # find the maxima of the posterior
 obs2_samples = posterior.sample((1000,))
 
 Marginaldiffplot(obs1_samples, obs2_samples, numparams, priorlimits, parlabels, 'WTC_GEFS_diff')
 
 #Perform Kolmogorov-Smirnov test to test differences between marginals
-observation1 = torch.tensor(torch.load('/home/Nina/Documents/SBI_project/Analyzed_experimental/SCN_WTC_2410.pt'))
+observation1 = torch.tensor(torch.load('SCN_WTC_2410.pt'))
 posterior.set_default_x(observation1)                    # find the maxima of the posterior
 obs1_samples = posterior.sample((50,))
-observation2 = torch.tensor(torch.load('/home/Nina/Documents/SBI_project/Analyzed_experimental/SCN_GEFS_2410.pt'))
+observation2 = torch.tensor(torch.load('SCN_GEFS_2410.pt'))
 posterior.set_default_x(observation2)                    # find the maxima of the posterior
 obs2_samples = posterior.sample((50,))
 
@@ -89,7 +86,7 @@ for i in range(numparams):
 
 ##  FIND CONDITIONAL DISTRIBUTIONS AND PEARSON CORRELATIONS
 # show a conditional posterior distribution with one sample from the posterior
-observation = torch.tensor(torch.load('/home/Nina/Documents/SBI_project/Analyzed_experimental/SCN_DS_2410.pt'))
+observation = torch.tensor(torch.load('SCN_DS_2410.pt'))
 posterior.set_default_x(observation)
 condition = posterior.sample((1,))