diff --git a/FindPosteriors.py b/FindPosteriors.py
index 7e45f449352beb8cf5247ce44f538de3ad59b8ea..29bc2bb95f14328428029396b119fc2b6d739c7c 100644
--- a/FindPosteriors.py
+++ b/FindPosteriors.py
@@ -4,34 +4,38 @@ from brian2 import *
from sbi import utils as utils
from sbi import analysis as analysis
import matplotlib.pyplot as plt
-from Simulator import MEAnetSimulate, ComputeFeatures
-from MakeFigures import rasterplot, Marginaldiffplot
+from Simulator import MEAnetsimulate, compute_features
+from MakeFigures import rasterplot, marginaldiffplot
from scipy.stats import ks_2samp
-numstats = 15 # Number of summary statistics
-numparams = 10 # Number of free parameters of the model
+example_dir = '../example_observations/' # directory with the example observations
+
+num_stats = 15 # Number of summary statistics
+num_params = 10 # Number of free parameters of the model
prior_min = [1.5, 0.5, 0.1, 0.5, 0.05, 0., 0.1, 150, 0.005, 0.]
prior_max = [7, 2, 10, 10, 1, 1, 0.6, 1200, 0.3, 0.005]
prior = utils.BoxUniform(low=torch.tensor(prior_min), high=torch.tensor(prior_max))
-priorlimits = [[1.5, 7], [0.5, 2], [0.1, 10], [0.5, 10], [0.05, 1], [0., 1], [0.1, 0.6], [150, 1200], [0.005, 0.3], [0., 0.005]]
-numparams = 10
-parlabels = ['noise', '$g_{Na}$', '$g_{K}$', '$g_{AHP}$', '$g_{AMPA}$', '$g_{NMDA}$', 'Conn%', r'$\tau_{D}$', 'U (STD)', 'U asyn']
-SSlabels = ['MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'mean ISI CC', 'sd ISI CC', 'ISI dist', 'mean ISI', 'sd ISI temp', 'sd isi elec', 'MAC']
-
-## LOAD YOUR OWN EXPERIMENTAL DATA TO OBTAIN POSTERIOR
+prior_limits = [[1.5, 7], [0.5, 2], [0.1, 10], [0.5, 10], [0.05, 1], [0., 1], [0.1, 0.6], [150, 1200], [0.005, 0.3],
+ [0., 0.005]]
+par_labels = ['noise', '$g_{Na}$', '$g_{K}$', '$g_{AHP}$', '$g_{AMPA}$', '$g_{NMDA}$', 'Conn%', r'$\tau_{D}$',
+ 'U (STD)', 'U asyn']
+SS_labels = ['MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'mean ISI CC', 'sd ISI CC', 'ISI dist',
+ 'mean ISI', 'sd ISI temp', 'sd isi elec', 'MAC']
+
+# LOAD YOUR OWN EXPERIMENTAL DATA TO INFER POSTERIOR
# Load your own experimental data as APs (first column electrode number, second column AP timestamps):
-# location of your experimental files
-exp_fileloc = '/home/yourlocation'
+# location of your experimental file
+exp_fileloc = '/home/Nina/Documents/SBI_project/Output/Paper_Figures_ver1/APs_Fig_5_CACNClonesb3_sim0.csv'
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
+recordtime = 165 * second # how long the recording was
+fs = 10000 # sampling frequency used for the recording
-# plot the data
+# Plot the data
rasterplot(APs_obs, "observation", 1/fs, 0*second, recordtime, 'black')
# Calculate MEA features
-exp_MEAfeatures = ComputeFeatures(APs_obs, recordtime, 5 * second, fs)
+exp_MEAfeatures = compute_features(APs_obs, recordtime, 5 * second, fs)
# Load the trainedNDE for the posterior
with open('TrainedNDE', 'rb') as f:
@@ -45,48 +49,49 @@ modeparams = posterior.map()
samples = posterior.sample((1000,))
_ = analysis.pairplot(samples,
diag='kde',
- ticks = priorlimits,
- upper='kde', points = modeparams, points_colors=['#EF6F6C'],
+ ticks=prior_limits,
+ upper='kde', points=modeparams, points_colors=['#EF6F6C'],
points_offdiag={'markersize': 8},
- limits=priorlimits,
- figsize=(6,6), labels=parlabels)
+ limits=prior_limits,
+ figsize=(6, 6), labels=par_labels)
plt.show()
-# run simulations with the mode of the posterior
-APs_sim, simtime, transient, fs = MEAnetSimulate(modeparams)
+# Run simulations with the mode of the posterior
+APs_sim, simtime, transient, fs = MEAnetsimulate(modeparams)
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('SCN_WTC_2410.pt'))
-posterior.set_default_x(observation1) # find the maxima of the posterior
+# COMPARE TWO POSTERIORS
+# Calculate or define the MEA features of your two observations
+observation1 = torch.tensor(torch.load(example_dir + 'SCN_WTC_2410.pt'))
+posterior.set_default_x(observation1)
obs1_samples = posterior.sample((1000,))
-observation2 = torch.tensor(torch.load('SCN_GEFS_2410.pt'))
-posterior.set_default_x(observation2) # find the maxima of the posterior
+observation2 = torch.tensor(torch.load(example_dir + 'SCN_GEFS_2410.pt'))
+posterior.set_default_x(observation2)
obs2_samples = posterior.sample((1000,))
-Marginaldiffplot(obs1_samples, obs2_samples, numparams, priorlimits, parlabels, 'WTC_GEFS_diff')
+marginaldiffplot(obs1_samples, obs2_samples, num_params, prior_limits, par_labels, 'WTC_GEFS_diff')
-#Perform Kolmogorov-Smirnov test to test differences between marginals
-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('SCN_GEFS_2410.pt'))
-posterior.set_default_x(observation2) # find the maxima of the posterior
-obs2_samples = posterior.sample((50,))
+# Perform Kolmogorov-Smirnov test to test differences between marginals
+num_samples = 50 # the number of samples drawn from the posterior to perform KS test
+observation1 = torch.tensor(torch.load(example_dir + 'SCN_WTC_2410.pt'))
+posterior.set_default_x(observation1)
+obs1_samples = posterior.sample((num_samples,))
+observation2 = torch.tensor(torch.load(example_dir + 'SCN_GEFS_2410.pt'))
+posterior.set_default_x(observation2)
+obs2_samples = posterior.sample((num_samples,))
-KSs = np.zeros(numparams)
-Pvals = np.zeros(numparams)
-for i in range(numparams):
+KSs = np.zeros(num_params)
+Pvals = np.zeros(num_params)
+for i in range(num_params):
par = i
- KSs[i], Pvals[i] = ks_2samp(obs1_samples[:,par], obs2_samples[:,par])
- print(parlabels[i])
+ KSs[i], Pvals[i] = ks_2samp(obs1_samples[:, par], obs2_samples[:, par])
+ print(par_labels[i])
print("KS statistic:", KSs[i])
print("P-value:", Pvals[i])
-## FIND CONDITIONAL DISTRIBUTIONS AND PEARSON CORRELATIONS
-# show a conditional posterior distribution with one sample from the posterior
-observation = torch.tensor(torch.load('SCN_DS_2410.pt'))
+# FIND CONDITIONAL DISTRIBUTIONS AND PEARSON CORRELATIONS
+# Show a conditional posterior distribution with one sample from the posterior
+observation = torch.tensor(torch.load(example_dir + 'SCN_DS_2410.pt'))
posterior.set_default_x(observation)
condition = posterior.sample((1,))
@@ -95,31 +100,31 @@ _ = analysis.conditional_pairplot(
condition=condition,
diag=['kde'],
upper=['kde'],
- limits=priorlimits,
- figsize=(6,6), labels=parlabels)
+ limits=prior_limits,
+ figsize=(6, 6), labels=par_labels)
plt.show()
-# Compute the correlation coefficient of every pair of parameters for every posterior sample
-numconds = 50
-corrcoefs = np.zeros((numconds, 100))
-for i in range (numconds):
+# Compute the correlation coefficient of every pair of parameters for num_conds conditional distributions
+num_conds = 50 # of how many conditional distributions you want to compute the CCs
+corrcoefs = np.zeros((num_conds, 100))
+for i in range(num_conds):
condition = posterior.sample((1,))
cond_coeff_mat = analysis.conditional_corrcoeff(
density=posterior,
condition=condition,
- limits=torch.tensor(priorlimits),
+ limits=torch.tensor(prior_limits),
)
- corrcoefs[i,:] = np.array(torch.flatten(cond_coeff_mat))
+ corrcoefs[i, :] = np.array(torch.flatten(cond_coeff_mat))
-#take the average correlation coefficients
+# Take the average correlation coefficients
average_corrcoefs = torch.tensor(np.mean(corrcoefs, axis=0))
average_corrcoefs_pl = torch.unflatten(average_corrcoefs, 0, (10, 10))
-#Construct the correlation matrix
+# Construct the correlation matrix of the average correlation coefficients
fig, ax = plt.subplots(1, 1, figsize=(3, 3))
im = plt.imshow(average_corrcoefs_pl, clim=[-0.6, 0.6], cmap="RdBu")
-ax.set_xticks(range(0,10))
-ax.set_xticklabels(parlabels, rotation = 90)
-ax.set_yticks(range(0,10), parlabels)
+ax.set_xticks(range(0, 10))
+ax.set_xticklabels(par_labels, rotation=90)
+ax.set_yticks(range(0, 10), par_labels)
_ = fig.colorbar(im)
plt.show()