From 51b2ca1632fe75e852ebed49efd7b7c5193dc580 Mon Sep 17 00:00:00 2001 From: Seyed Mostafa Kia Date: Tue, 23 Jul 2024 10:29:48 +0200 Subject: [PATCH 01/68] Minor modig=fication in slurm job and log file names --- pcntoolkit/normative_parallel.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pcntoolkit/normative_parallel.py b/pcntoolkit/normative_parallel.py index 4733bf77..2be391d1 100755 --- a/pcntoolkit/normative_parallel.py +++ b/pcntoolkit/normative_parallel.py @@ -1123,13 +1123,13 @@ def sbatchwrap_nm(processing_dir, output_changedir = ['cd ' + processing_dir + '\n'] sbatch_init = '#!/bin/bash\n' - sbatch_jobname = '#SBATCH --job-name=' + processing_dir + '\n' + sbatch_jobname = '#SBATCH --job-name=' + job_name + '\n' sbatch_nodes = '#SBATCH --nodes=1\n' sbatch_tasks = '#SBATCH --ntasks=1\n' sbatch_time = '#SBATCH --time=' + str(duration) + '\n' sbatch_memory = '#SBATCH --mem-per-cpu=' + str(memory) + '\n' - sbatch_log_out = '#SBATCH -o ' + log_path + '%j.out' + '\n' - sbatch_log_error = '#SBATCH -e ' + log_path + '%j.err' + '\n' + sbatch_log_out = '#SBATCH -o ' + log_path + '%x_%j.out' + '\n' + sbatch_log_error = '#SBATCH -e ' + log_path + '%x_%j.err' + '\n' #sbatch_module = 'module purge\n' #sbatch_anaconda = 'module load anaconda3\n' sbatch_exit = 'set -o errexit\n' From 0b4ff50717fbbd1a3080e873913a78e4e6c0f953 Mon Sep 17 00:00:00 2001 From: Seyed Mostafa Kia Date: Sun, 11 Aug 2024 11:35:15 +0200 Subject: [PATCH 02/68] The HBR test is simplified --- pcntoolkit/util/utils.py | 157 +++++++++++++++++++-------------------- tests/testHBR.py | 116 ++++++++++++++--------------- 2 files changed, 129 insertions(+), 144 deletions(-) diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py index 9eb9208b..ac855315 100644 --- a/pcntoolkit/util/utils.py +++ b/pcntoolkit/util/utils.py @@ -5,7 +5,7 @@ import numpy as np from scipy import stats from subprocess import call -from scipy.stats import genextreme, norm +from scipy.stats import genextreme, norm, skewnorm from six import with_metaclass from abc import ABCMeta, abstractmethod import pickle @@ -879,90 +879,83 @@ def calibration_error(Y, m, s, cal_levels): def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, working_dir=None, plot=False, random_state=None, noise=None): - """ This function simulates linear synthetic data for testing pcntoolkit methods. - - :param method: simulate 'linear' or 'non-linear' function. - :param n_samples: number of samples in each group of the training and test sets. - If it is an int then the same sample number will be used for all groups. - It can be also a list of size of n_grps that decides the number of samples - in each group (default=100). - :param n_features: A positive integer that decides the number of features - (default=1). - :param n_grps: A positive integer that decides the number of groups in data - (default=1). - :param working_dir: Directory to save data (default=None). - :param plot: Boolean to plot the simulated training data (default=False). - :param random_state: random state for generating random numbers (Default=None). - :param noise: Type of added noise to the data. The options are 'gaussian', - 'exponential', and 'hetero_gaussian' (The defauls is None.). - - :returns: - X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef - + """ + Simulates synthetic data for testing purposes, with options for linear, non-linear, + or combined data generation methods, and various noise types. + + :param method: Method to simulate ('linear', 'non-linear', or 'combined'). + :param n_samples: Number of samples per group, either an int or a list for each group (default=100). + :param n_features: Number of features to simulate (default=1). + :param n_grps: Number of groups in the data (default=1). + :param working_dir: Directory to save the data (default=None). + :param plot: Boolean flag to plot the simulated training data (default=False). + :param random_state: Seed for random number generation (default=None). + :param noise: Type of noise to add ('homoscedastic_gaussian', 'heteroscedastic_gaussian', + 'homoscedastic_nongaussian', 'heteroscedastic_nongaussian', default=None). + + :returns: Tuple of (X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef) """ + #np.random.seed(random_state) + if isinstance(n_samples, int): - n_samples = [n_samples for i in range(n_grps)] + n_samples = [n_samples for _ in range(n_grps)] X_train, Y_train, X_test, Y_test = [], [], [], [] grp_id_train, grp_id_test = [], [] coef = [] + for i in range(n_grps): bias = np.random.randint(-10, high=10) if method == 'linear': - X_temp, Y_temp, coef_temp = make_regression(n_samples=n_samples[i]*2, - n_features=n_features, n_targets=1, - noise=10 * np.random.rand(), bias=bias, - n_informative=1, coef=True, - random_state=random_state) + X_temp, Y_temp, coef_temp = make_regression( + n_samples=n_samples[i] * 2, n_features=n_features, n_targets=1, + noise=10 * np.random.rand(), bias=bias, n_informative=1, coef=True, + ) elif method == 'non-linear': - X_temp = np.random.randint(-2, 6, [2*n_samples[i], n_features]) \ - + np.random.randn(2*n_samples[i], n_features) + X_temp = np.random.randint(-2, 6, [2 * n_samples[i], n_features]) \ + + np.random.randn(2 * n_samples[i], n_features) Y_temp = X_temp[:, 0] * 20 * np.random.rand() + np.random.randint(10, 100) \ * np.sin(2 * np.random.rand() + 2 * np.pi / 5 * X_temp[:, 0]) coef_temp = 0 elif method == 'combined': - X_temp = np.random.randint(-2, 6, [2*n_samples[i], n_features]) \ - + np.random.randn(2*n_samples[i], n_features) + X_temp = np.random.randint(-2, 6, [2 * n_samples[i], n_features]) \ + + np.random.randn(2 * n_samples[i], n_features) Y_temp = (X_temp[:, 0]**3) * np.random.uniform(0, 0.5) \ + X_temp[:, 0] * 20 * np.random.rand() \ + np.random.randint(10, 100) coef_temp = 0 else: - raise ValueError("Unknow method. Please specify valid method among \ - 'linear' or 'non-linear'.") - coef.append(coef_temp/100) - X_train.append(X_temp[:X_temp.shape[0]//2]) - Y_train.append(Y_temp[:X_temp.shape[0]//2]/100) - X_test.append(X_temp[X_temp.shape[0]//2:]) - Y_test.append(Y_temp[X_temp.shape[0]//2:]/100) - grp_id = np.repeat(i, X_temp.shape[0]) - grp_id_train.append(grp_id[:X_temp.shape[0]//2]) - grp_id_test.append(grp_id[X_temp.shape[0]//2:]) - - if noise == 'hetero_gaussian': - t = np.random.randint(5, 10) - Y_train[i] = Y_train[i] + np.random.randn(Y_train[i].shape[0]) / t \ - * np.log(1 + np.exp(X_train[i][:, 0])) - Y_test[i] = Y_test[i] + np.random.randn(Y_test[i].shape[0]) / t \ - * np.log(1 + np.exp(X_test[i][:, 0])) - elif noise == 'gaussian': - t = np.random.randint(3, 10) - Y_train[i] = Y_train[i] + np.random.randn(Y_train[i].shape[0])/t - Y_test[i] = Y_test[i] + np.random.randn(Y_test[i].shape[0])/t - elif noise == 'exponential': - t = np.random.randint(1, 3) - Y_train[i] = Y_train[i] + \ - np.random.exponential(1, Y_train[i].shape[0]) / t - Y_test[i] = Y_test[i] + \ - np.random.exponential(1, Y_test[i].shape[0]) / t - elif noise == 'hetero_gaussian_smaller': - t = np.random.randint(5, 10) - Y_train[i] = Y_train[i] + np.random.randn(Y_train[i].shape[0]) / t \ - * np.log(1 + np.exp(0.3 * X_train[i][:, 0])) - Y_test[i] = Y_test[i] + np.random.randn(Y_test[i].shape[0]) / t \ - * np.log(1 + np.exp(0.3 * X_test[i][:, 0])) + raise ValueError("Unknown method. Please specify 'linear', 'non-linear', or 'combined'.") + + coef.append(coef_temp / 100) + X_train.append(X_temp[:n_samples[i]]) + Y_train.append(Y_temp[:n_samples[i]] / 100) + X_test.append(X_temp[n_samples[i]:]) + Y_test.append(Y_temp[n_samples[i]:] / 100) + grp_id = np.repeat(i, n_samples[i] * 2) + grp_id_train.append(grp_id[:n_samples[i]]) + grp_id_test.append(grp_id[n_samples[i]:]) + + t = np.random.randint(1,5) + # Add noise to the data + if noise == 'homoscedastic_gaussian': + Y_train[i] += np.random.normal(loc=0, scale=0.2, size=Y_train[i].shape[0]) / t + Y_test[i] += np.random.normal(loc=0, scale=0.2, size=Y_test[i].shape[0]) / t + + elif noise == 'heteroscedastic_gaussian': + Y_train[i] += np.random.normal(loc=0, scale=np.log(1 + np.exp(X_train[i][:, 0])), size=Y_train[i].shape[0]) + Y_test[i] += np.random.normal(loc=0, scale=np.log(1 + np.exp(X_test[i][:, 0])), size=Y_test[i].shape[0]) + + elif noise == 'homoscedastic_nongaussian': + Y_train[i] += skewnorm.rvs(a=10, loc=0, scale=0.2, size=Y_train[i].shape[0]) / t + Y_test[i] += skewnorm.rvs(a=10, loc=0, scale=0.2, size=Y_test[i].shape[0]) / t + + elif noise == 'heteroscedastic_nongaussian': + Y_train[i] += skewnorm.rvs(a=10, loc=0, scale=np.log(1 + np.exp(0.3 * X_train[i][:, 0])), size=Y_train[i].shape[0]) + Y_test[i] += skewnorm.rvs(a=10, loc=0, scale=np.log(1 + np.exp(0.3 * X_test[i][:, 0])), size=Y_test[i].shape[0]) + X_train = np.vstack(X_train) X_test = np.vstack(X_test) Y_train = np.concatenate(Y_train) @@ -970,32 +963,32 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, grp_id_train = np.expand_dims(np.concatenate(grp_id_train), axis=1) grp_id_test = np.expand_dims(np.concatenate(grp_id_test), axis=1) - for i in range(n_features): - plt.figure() - for j in range(n_grps): - plt.scatter(X_train[grp_id_train[:, 0] == j, i], - Y_train[grp_id_train[:, 0] == j,], label='Group ' + str(j)) - plt.xlabel('X' + str(i)) - plt.ylabel('Y') - plt.legend() - - if working_dir is not None: + if plot: + for i in range(n_features): + plt.figure() + for j in range(n_grps): + plt.scatter(X_train[grp_id_train[:, 0] == j, i], Y_train[grp_id_train[:, 0] == j], label='Group ' + str(j)) + plt.xlabel(f'X{i}') + plt.ylabel('Y') + plt.legend() + plt.show() + + if working_dir: if not os.path.isdir(working_dir): os.mkdir(working_dir) + with open(os.path.join(working_dir, 'trbefile.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(grp_id_train), - file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(grp_id_train), file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'tsbefile.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(grp_id_test), - file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(grp_id_test), file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'X_train.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(X_train), file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(X_train), file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'X_test.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(X_test), file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(X_test), file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'Y_train.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(Y_train), file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(Y_train), file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'Y_test.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(Y_test), file, protocol=PICKLE_PROTOCOL) + pickle.dump(pd.DataFrame(Y_test), file, protocol=pickle.HIGHEST_PROTOCOL) return X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef diff --git a/tests/testHBR.py b/tests/testHBR.py index fee7065e..1d2ba679 100644 --- a/tests/testHBR.py +++ b/tests/testHBR.py @@ -16,18 +16,15 @@ import matplotlib.pyplot as plt from pcntoolkit.normative import estimate from warnings import filterwarnings -from pcntoolkit.util.utils import scaler -import xarray - filterwarnings('ignore') -np.random.seed(10) ########################### Experiment Settings ############################### -working_dir = '/home/stijn/temp/' # Specifyexit() a working directory -# to save data and results. +random_state = 29 + +working_dir = '/' # Specify a working directory to save data and results. simulation_method = 'linear' n_features = 1 # The number of input features of X @@ -35,75 +32,70 @@ n_samples = 500 # Number of samples in each group (use a list for different # sample numbers across different batches) -model_types = ['linear', 'polynomial', 'bspline'] # models to try +model_type = 'linear' # modelto try 'linear, ''polynomial', 'bspline' + + ############################## Data Simulation ################################ X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = \ simulate_data(simulation_method, n_samples, n_features, n_grps, - working_dir=working_dir, plot=True) - -################################# Methods Tests ############################### - - -for model_type in model_types: - - nm = norm_init(X_train, Y_train, alg='hbr', likelihood='SHASHb', - model_type=model_type, n_samples=100, n_tuning=10) - nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl') - yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl') - - for i in range(n_features): - sorted_idx = X_test[:, i].argsort(axis=0).squeeze() - temp_X = X_test[sorted_idx, i] - temp_Y = Y_test[sorted_idx,] - temp_be = grp_id_test[sorted_idx, :].squeeze() - temp_yhat = yhat[sorted_idx,] - temp_s2 = ys2[sorted_idx,] - - plt.figure() - for j in range(n_grps): - scat1 = plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,], - label='Group' + str(j)) - plt.plot(temp_X[temp_be == j,], temp_yhat[temp_be == j,]) - plt.fill_between(temp_X[temp_be == j,], temp_yhat[temp_be == j,] - - 1.96 * np.sqrt(temp_s2[temp_be == j,]), - temp_yhat[temp_be == j,] + - 1.96 * np.sqrt(temp_s2[temp_be == j,]), - color='gray', alpha=0.2) - - # Showing the quantiles - resolution = 200 - synth_X = np.linspace(-3, 3, resolution) - q = nm.get_mcmc_quantiles( - synth_X, batch_effects=j*np.ones(resolution)) - col = scat1.get_facecolors()[0] - plt.plot(synth_X, q.T, linewidth=1, color=col, zorder=0) - - plt.title('Model %s, Feature %d' % (model_type, i)) - plt.legend() - plt.show() + working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian', + random_state=random_state) +################################# Fittig and Predicting ############################### + +nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', + linear_sigma='True', random_slope_mu='True', linear_epsilon='True', linear_delta='True') + +nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl') +yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl') -############################## Normative Modelling Test ####################### +################################# Plotting Quantiles ############################### -model_type = model_types[0] -covfile = working_dir + 'X_train.pkl' -respfile = working_dir + 'Y_train.pkl' -testcov = working_dir + 'X_test.pkl' -testresp = working_dir + 'Y_test.pkl' -trbefile = working_dir + 'trbefile.pkl' -tsbefile = working_dir + 'tsbefile.pkl' +for i in range(n_features): + sorted_idx = X_test[:, i].argsort(axis=0).squeeze() + temp_X = X_test[sorted_idx, i] + temp_Y = Y_test[sorted_idx,] + temp_be = grp_id_test[sorted_idx, :].squeeze() + temp_yhat = yhat[sorted_idx,] + temp_s2 = ys2[sorted_idx,] + + plt.figure() + for j in range(n_grps): + scat1 = plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,], + label='Group' + str(j)) + # Showing the quantiles + resolution = 200 + synth_X = np.linspace(-3, 3, resolution) + q = nm.get_mcmc_quantiles( + synth_X, batch_effects=j*np.ones(resolution)) + col = scat1.get_facecolors()[0] + plt.plot(synth_X, q.T, linewidth=1, color=col, zorder=0) + + plt.title('Model %s, Feature %d' % (model_type, i)) + plt.legend() + plt.show() + + +############################## Normative Modelling Test ####################### + +# covfile = working_dir + 'X_train.pkl' +# respfile = working_dir + 'Y_train.pkl' +# testcov = working_dir + 'X_test.pkl' +# testresp = working_dir + 'Y_test.pkl' +# trbefile = working_dir + 'trbefile.pkl' +# tsbefile = working_dir + 'tsbefile.pkl' -os.chdir(working_dir) +# os.chdir(working_dir) -estimate(covfile, respfile, testcov=testcov, testresp=testresp, trbefile=trbefile, - tsbefile=tsbefile, alg='hbr', outputsuffix='_' + model_type, - inscaler='None', outscaler='None', model_type=model_type, - savemodel='True', saveoutput='True') +# estimate(covfile, respfile, testcovfile_path=testcov, testrespfile_path=testresp, trbefile=trbefile, +# tsbefile=tsbefile, alg='hbr', outputsuffix='_' + model_type, +# inscaler='None', outscaler='None', model_type=model_type, +# savemodel='True', saveoutput='True') ############################################################################### From edac27925f64d305468700f87b3a4dbfa0eef896 Mon Sep 17 00:00:00 2001 From: AuguB Date: Thu, 12 Sep 2024 22:58:14 +0200 Subject: [PATCH 03/68] Runs the testHBR script correctly on pymc==5.16 --- pcntoolkit/model/hbr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py index 77c60f1d..f95e3e85 100644 --- a/pcntoolkit/model/hbr.py +++ b/pcntoolkit/model/hbr.py @@ -233,7 +233,7 @@ def get_sample_dims(var): pb.batch_effect_indices = tuple( [ pm.Data( - pb.batch_effect_dim_names[i], + pb.batch_effect_dim_names[i]+"_data", pb.batch_effect_indices[i], mutable=True, dims="datapoints", @@ -520,7 +520,7 @@ def predict( # Compute those indices for the test data indices = list(map(lambda x: valmap[x], batch_effects[:, i])) # Those indices need to be used by the model - pm.set_data({f"batch_effect_{i}": indices}) + pm.set_data({f"batch_effect_{i}_data": indices}) self.idata = pm.sample_posterior_predictive( trace=self.idata, From 88e12adc31a6df79d0a9dc7f442b5b5c450472d6 Mon Sep 17 00:00:00 2001 From: AuguB Date: Tue, 17 Sep 2024 16:22:43 +0200 Subject: [PATCH 04/68] Suppress warnings due to pymc==5.16 --- pcntoolkit/model/SHASH.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py index 39bf0646..8d43908b 100644 --- a/pcntoolkit/model/SHASH.py +++ b/pcntoolkit/model/SHASH.py @@ -152,8 +152,7 @@ def m(epsilon, delta, r): class SHASH(RandomVariable): name = "shash" - ndim_supp = 0 - ndims_params = [0, 0] + signature = "(),()->()" dtype = "floatX" _print_name = ("SHASH", "\\operatorname{SHASH}") @@ -194,8 +193,7 @@ def logp(value, epsilon, delta): class SHASHoRV(RandomVariable): name = "shasho" - ndim_supp = 0 - ndims_params = [0, 0, 0, 0] + signature = "(),(),(),()->()" dtype = "floatX" _print_name = ("SHASHo", "\\operatorname{SHASHo}") @@ -239,8 +237,7 @@ def logp(value, mu, sigma, epsilon, delta): class SHASHo2RV(RandomVariable): name = "shasho2" - ndim_supp = 0 - ndims_params = [0, 0, 0, 0] + signature = "(),(),(),()->()" dtype = "floatX" _print_name = ("SHASHo2", "\\operatorname{SHASHo2}") @@ -286,8 +283,7 @@ def logp(value, mu, sigma, epsilon, delta): class SHASHbRV(RandomVariable): name = "shashb" - ndim_supp = 0 - ndims_params = [0, 0, 0, 0] + signature = "(),(),(),()->()" dtype = "floatX" _print_name = ("SHASHo2", "\\operatorname{SHASHo2}") From cc8352677194e62eeb9dfa74106d8725b1360249 Mon Sep 17 00:00:00 2001 From: AuguB Date: Tue, 17 Sep 2024 16:24:27 +0200 Subject: [PATCH 05/68] Correct error in SHASHb name --- pcntoolkit/model/SHASH.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py index 8d43908b..1ccffcb6 100644 --- a/pcntoolkit/model/SHASH.py +++ b/pcntoolkit/model/SHASH.py @@ -285,7 +285,7 @@ class SHASHbRV(RandomVariable): name = "shashb" signature = "(),(),(),()->()" dtype = "floatX" - _print_name = ("SHASHo2", "\\operatorname{SHASHo2}") + _print_name = ("SHASHb", "\\operatorname{SHASHb}") @classmethod def rng_fn( From 255ecca39cf7bc6920ef527d4a76c65dbccc6f49 Mon Sep 17 00:00:00 2001 From: AuguB Date: Wed, 2 Oct 2024 15:36:01 +0200 Subject: [PATCH 06/68] Address issue 215 Speedup for python==3.12 --- README.md | 56 ++++++++++---- pcntoolkit/model/SHASH.py | 76 +++++++++--------- pcntoolkit/model/architecture.py | 12 ++- pcntoolkit/model/hbr.py | 2 + pcntoolkit/normative.py | 15 ++-- pcntoolkit/normative_model/norm_hbr.py | 3 +- pcntoolkit/normative_parallel.py | 103 ++++++++++++------------- pcntoolkit/util/utils.py | 62 +++++++++------ requirements.txt | 4 +- setup.py | 3 +- tests/testHBR.py | 11 ++- tests/test_normative_parallel.py | 28 +++---- 12 files changed, 215 insertions(+), 160 deletions(-) diff --git a/README.md b/README.md index fa37c7d4..51c7d474 100644 --- a/README.md +++ b/README.md @@ -8,64 +8,92 @@ Methods for normative modelling, spatial statistics and pattern recognition. Doc ## Basic installation (on a local machine) -i) install anaconda3 ii) create enviornment with "conda create --name " iii) activate environment by "source activate " iv) install required conda packages +#### Install anaconda3 + +using the download here: https://www.anaconda.com/download + +#### Create environment +``` +conda create +``` + +#### Activate environment + +``` +source activate +``` + +#### Install torch + +Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. + +#### Install other required conda packages ``` -conda install pip pandas scipy +conda install pip pandas scipy pymc ``` -v) install PCNtoolkit (plus dependencies) +#### Install PCNtoolkit ``` pip install pcntoolkit ``` ## Alternative installation (on a shared resource) -Make sure conda is available on the system. + +#### Make sure conda is available on the system. Otherwise install it first from https://www.anaconda.com/ ``` conda --version ``` -Create a conda environment in a shared location +#### Create a conda environment in a shared location ``` -conda create -y python==3.8.3 numpy mkl blas --prefix=/shared/conda/ +conda create -y python==3.10 numpy mkl blas --prefix=/shared/conda/ ``` -Activate the conda environment +#### Activate the conda environment ``` conda activate /shared/conda/ ``` +#### install torch -Install other dependencies +Using the command that you get from the command builder here: ``` -conda install -y pandas scipy +https://pytorch.org/get-started/locally/ ``` -Install pip dependencies +If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip. + +#### Install other dependencies + +``` +conda install -y pandas scipy pymc +``` + +#### Install pip dependencies ``` pip --no-cache-dir install nibabel scikit-learn torch glob3 ``` -Clone the repo +#### Clone the repo ``` git clone https://github.com/amarquand/PCNtoolkit.git ``` -install in the conda environment +### Install in the conda environment ``` cd PCNtoolkit/ python3 setup.py install ``` - -Test +### Test ``` python -c "import pcntoolkit as pk;print(pk.__file__)" ``` diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py index 1ccffcb6..4be5c947 100644 --- a/pcntoolkit/model/SHASH.py +++ b/pcntoolkit/model/SHASH.py @@ -23,26 +23,42 @@ """ -def numpy_P(q): +def K(q, x): """ - The P function as given in Jones et al. + The K function as given in Jones et al. :param q: + :param x: :return: """ - frac = np.exp(1.0 / 4.0) / np.power(8.0 * np.pi, 1.0 / 2.0) - K1 = numpy_K((q + 1) / 2, 1.0 / 4.0) - K2 = numpy_K((q - 1) / 2, 1.0 / 4.0) - a = (K1 + K2) * frac - return a + return spp.kv(q, x) -def numpy_K(p, x): +def unique_K(q, x): """ - Computes the values of spp.kv(p,x) for only the unique values of p + This is the K function, but it only calculates the unique values of q. + :param q: + :param x: + :return: """ + unique_q, inverse_indices = np.unique(q, return_inverse=True) + unique_outputs = spp.kv(unique_q, x) + outputs = unique_outputs[inverse_indices].reshape(q.shape) + return outputs + - ps, idxs = np.unique(p, return_inverse=True) - return spp.kv(ps, x)[idxs].reshape(p.shape) +CONST = np.exp(0.25) / np.power(8.0 * np.pi, 0.5) + + +def P(q): + """ + The P function as given in Jones et al. + :param q: + :return: + """ + K1 = K()((q + 1) / 2, 0.25) + K2 = K()((q - 1) / 2, 0.25) + a = (K1 + K2) * CONST + return a class K(Op): @@ -58,23 +74,19 @@ def make_node(self, p, x): return Apply(self, [p, x], [p.type()]) def perform(self, node, inputs_storage, output_storage): - # Doing this on the unique values avoids doing A LOT OF double work, apparently scipy doesn't do this by itself - - unique_inputs, inverse_indices = np.unique( - inputs_storage[0], return_inverse=True - ) - unique_outputs = spp.kv(unique_inputs, inputs_storage[1]) - outputs = unique_outputs[inverse_indices].reshape( - inputs_storage[0].shape) - output_storage[0][0] = outputs + output_storage[0][0] = unique_K(inputs_storage[0], inputs_storage[1]) def grad(self, inputs, output_grads): # Approximation of the derivative. This should suffice for using NUTS - dp = 1e-10 + dp = 1e-16 p = inputs[0] x = inputs[1] - grad = (self(p + dp, x) - self(p, x)) / dp - return [output_grads[0] * grad, grad_not_implemented(0, 1, 2, 3)] + grad = (self(p + dp, x) - self(p - dp, x)) / dp + return [ + output_grads[0] * grad, + grad_not_implemented( + "K", 1, "x", "Gradient not implemented for x"), + ] def S(x, epsilon, delta): @@ -102,19 +114,6 @@ def C(x, epsilon, delta): return np.cosh(np.arcsinh(x) * delta - epsilon) -def P(q): - """ - The P function as given in Jones et al. - :param q: - :return: - """ - frac = np.exp(1.0 / 4.0) / np.power(8.0 * np.pi, 1.0 / 2.0) - K1 = K()((q + 1) / 2, 1.0 / 4.0) - K2 = K()((q - 1) / 2, 1.0 / 4.0) - a = (K1 + K2) * frac - return a - - def m(epsilon, delta, r): """ :param epsilon: @@ -298,9 +297,8 @@ def rng_fn( size: Optional[Union[List[int], int]], ) -> np.ndarray: s = rng.normal(size=size) - mean = np.sinh(epsilon / delta) * numpy_P(1 / delta) - var = ((np.cosh(2 * epsilon / delta) * - numpy_P(2 / delta) - 1) / 2) - mean**2 + mean = np.sinh(epsilon / delta) * P(1 / delta) + var = ((np.cosh(2 * epsilon / delta) * P(2 / delta) - 1) / 2) - mean**2 out = ( (np.sinh((np.arcsinh(s) + epsilon) / delta) - mean) / np.sqrt(var) ) * sigma + mu diff --git a/pcntoolkit/model/architecture.py b/pcntoolkit/model/architecture.py index 0dfb09c9..8894ce3f 100644 --- a/pcntoolkit/model/architecture.py +++ b/pcntoolkit/model/architecture.py @@ -46,7 +46,8 @@ def __init__(self, x, y, args): # Conv 1 self.encoder_y_layer_1_conv = nn.Conv3d(in_channels=self.factor, out_channels=self.factor, kernel_size=5, stride=2, padding=0, - dilation=1, groups=self.factor, bias=True) # in:(90,108,90) out:(43,52,43) + # in:(90,108,90) out:(43,52,43) + dilation=1, groups=self.factor, bias=True) self.encoder_y_layer_1_bn = nn.BatchNorm3d(self.factor) d_out_1, h_out_1, w_out_1 = compute_conv_out_size(y.shape[2], y.shape[3], y.shape[4], padding=[ @@ -57,7 +58,8 @@ def __init__(self, x, y, args): # Conv 2 self.encoder_y_layer_2_conv = nn.Conv3d(in_channels=self.factor, out_channels=self.factor, kernel_size=3, stride=2, padding=0, - dilation=1, groups=self.factor, bias=True) # out: (21,25,21) + # out: (21,25,21) + dilation=1, groups=self.factor, bias=True) self.encoder_y_layer_2_bn = nn.BatchNorm3d(self.factor) d_out_2, h_out_2, w_out_2 = compute_conv_out_size(d_out_1, h_out_1, w_out_1, padding=[ @@ -68,7 +70,8 @@ def __init__(self, x, y, args): # Conv 3 self.encoder_y_layer_3_conv = nn.Conv3d(in_channels=self.factor, out_channels=self.factor, kernel_size=3, stride=2, padding=0, - dilation=1, groups=self.factor, bias=True) # out: (10,12,10) + # out: (10,12,10) + dilation=1, groups=self.factor, bias=True) self.encoder_y_layer_3_bn = nn.BatchNorm3d(self.factor) d_out_3, h_out_3, w_out_3 = compute_conv_out_size(d_out_2, h_out_2, w_out_2, padding=[ @@ -79,7 +82,8 @@ def __init__(self, x, y, args): # Conv 4 self.encoder_y_layer_4_conv = nn.Conv3d(in_channels=self.factor, out_channels=1, kernel_size=3, stride=2, padding=0, - dilation=1, groups=1, bias=True) # out: (4,5,4) + # out: (4,5,4) + dilation=1, groups=1, bias=True) self.encoder_y_layer_4_bn = nn.BatchNorm3d(1) d_out_4, h_out_4, w_out_4 = compute_conv_out_size(d_out_3, h_out_3, w_out_3, padding=[ diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py index f95e3e85..4d051b38 100644 --- a/pcntoolkit/model/hbr.py +++ b/pcntoolkit/model/hbr.py @@ -448,6 +448,7 @@ def estimate(self, X, y, batch_effects, **kwargs): init=self.configs["init"], n_init=500000, cores=self.configs["cores"], + nuts_sampler=self.configs["nuts_sampler"], ) self.vars_to_sample = ['y_like'] if self.configs['remove_datapoints_from_posterior']: @@ -559,6 +560,7 @@ def estimate_on_new_site(self, X, y, batch_effects): init=self.configs["init"], n_init=50000, cores=self.configs["cores"], + nuts_sampler=self.configs["nuts_sampler"], ) return self.idata diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py index 1c62737c..14a35643 100755 --- a/pcntoolkit/normative.py +++ b/pcntoolkit/normative.py @@ -522,7 +522,8 @@ def estimate(covfile, respfile, **kwargs): if warp is not None: # TODO: Warping for scaled data if outscaler is not None and outscaler != 'None': - raise ValueError("outscaler not yet supported warping") + raise ValueError( + "outscaler not yet supported warping") warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1] Ywarp[ts, nz[i]] = nm.blr.warp.f( Y[ts, nz[i]], warp_param) @@ -804,7 +805,7 @@ def predict(covfile, respfile, maskfile=None, **kwargs): Y, maskvol = load_response_vars(respfile, maskfile) if len(Y.shape) == 1: Y = Y[:, np.newaxis] - + sample_num = X.shape[0] if models is not None: feature_num = len(models) @@ -853,13 +854,13 @@ def predict(covfile, respfile, maskfile=None, **kwargs): if respfile is not None: if alg == 'hbr': # Z scores for HBR must be computed independently for each model - Z[:,i] = nm.get_mcmc_zscores(Xz, Yz[:, i:i+1], **kwargs) - + Z[:, i] = nm.get_mcmc_zscores(Xz, Yz[:, i:i+1], **kwargs) + if respfile is None: save_results(None, Yhat, S2, None, outputsuffix=outputsuffix) return (Yhat, S2) - + else: if models is not None and len(Y.shape) > 1: Y = Y[:, models] @@ -891,9 +892,9 @@ def predict(covfile, respfile, maskfile=None, **kwargs): Y = Yw else: warp = False - + if alg != 'hbr': - # For HBR the Z scores are already computed + # For HBR the Z scores are already computed Z = (Y - Yhat) / np.sqrt(S2) print("Evaluating the model ...") diff --git a/pcntoolkit/normative_model/norm_hbr.py b/pcntoolkit/normative_model/norm_hbr.py index f972bfd0..b98674d0 100644 --- a/pcntoolkit/normative_model/norm_hbr.py +++ b/pcntoolkit/normative_model/norm_hbr.py @@ -135,13 +135,14 @@ def __init__(self, **kwargs): "random_noise", "True") == "True" self.configs["likelihood"] = kwargs.get("likelihood", "Normal") # sampler settings + self.configs["nuts_sampler"] = kwargs.get("nuts_sampler", "pymc") self.configs["n_samples"] = int(kwargs.get("n_samples", "1000")) self.configs["n_tuning"] = int(kwargs.get("n_tuning", "500")) self.configs["n_chains"] = int(kwargs.get("n_chains", "1")) self.configs["sampler"] = kwargs.get("sampler", "NUTS") self.configs["target_accept"] = float( kwargs.get("target_accept", "0.8")) - self.configs["init"] = kwargs.get("init", "jitter+adapt_diag") + self.configs["init"] = kwargs.get("init", "jitter+adapt_diag_grad") self.configs["cores"] = int(kwargs.get("cores", "1")) self.configs["remove_datapoints_from_posterior"] = kwargs.get( "remove_datapoints_from_posterior", "True") == "True" diff --git a/pcntoolkit/normative_parallel.py b/pcntoolkit/normative_parallel.py index 2be391d1..f6a957e7 100755 --- a/pcntoolkit/normative_parallel.py +++ b/pcntoolkit/normative_parallel.py @@ -132,7 +132,7 @@ def execute_nm(processing_dir, kwargs.update({'batch_size': str(batch_size)}) job_ids = [] start_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") - + for n in range(1, number_of_batches+1): kwargs.update({'job_id': str(n)}) if testrespfile_path is not None: @@ -181,11 +181,10 @@ def execute_nm(processing_dir, memory=memory, duration=duration, **kwargs) - + job_id = sbatch_nm(job_path=batch_job_path) job_ids.append(job_id) - - + elif cluster_spec == 'new': # this part requires addition in different envioronment [ sbatchwrap_nm(processing_dir=batch_processing_dir, @@ -225,7 +224,7 @@ def execute_nm(processing_dir, memory=memory, duration=duration, **kwargs) - + job_id = sbatch_nm(job_path=batch_job_path) job_ids.append(job_id) elif cluster_spec == 'new': @@ -268,11 +267,10 @@ def execute_nm(processing_dir, memory=memory, duration=duration, **kwargs) - - + job_id = sbatch_nm(job_path=batch_job_path) job_ids.append(job_id) - + elif cluster_spec == 'new': # this part requires addition in different envioronment [ bashwrap_nm(processing_dir=batch_processing_dir, func=func, @@ -301,31 +299,31 @@ def execute_nm(processing_dir, if response: if cluster_spec == 'torque': rerun_nm(processing_dir, log_path=log_path, memory=memory, - duration=duration, binary=binary, - interactive=interactive) + duration=duration, binary=binary, + interactive=interactive) elif cluster_spec == 'slurm': sbatchrerun_nm(processing_dir, - memory=memory, - duration=duration, - binary=binary, - log_path=log_path, - interactive=interactive) - + memory=memory, + duration=duration, + binary=binary, + log_path=log_path, + interactive=interactive) + else: success = True else: print('Reruning the failed jobs ...') if cluster_spec == 'torque': rerun_nm(processing_dir, log_path=log_path, memory=memory, - duration=duration, binary=binary, - interactive=interactive) + duration=duration, binary=binary, + interactive=interactive) elif cluster_spec == 'slurm': sbatchrerun_nm(processing_dir, - memory=memory, - duration=duration, - binary=binary, - log_path=log_path, - interactive=interactive) + memory=memory, + duration=duration, + binary=binary, + log_path=log_path, + interactive=interactive) if interactive == 'query': response = yes_or_no('Collect the results?') @@ -508,11 +506,11 @@ def collect_nm(processing_dir, # prediction is made (when test cov is not specified). files = glob.glob(processing_dir + 'batch_*/' + 'yhat' + outputsuffix + file_extentions) - if len(files)>0: + if len(files) > 0: file_example = fileio.load(files[0]) else: - raise ValueError(f"Missing output files (yhats at: {processing_dir + 'batch_*/' + 'yhat' + outputsuffix + file_extentions}") - + raise ValueError(f"Missing output files (yhats at: {processing_dir + 'batch_*/' + 'yhat' + outputsuffix + file_extentions}") + numsubjects = file_example.shape[0] try: # doesn't exist if size=1, and txt file @@ -1129,9 +1127,9 @@ def sbatchwrap_nm(processing_dir, sbatch_time = '#SBATCH --time=' + str(duration) + '\n' sbatch_memory = '#SBATCH --mem-per-cpu=' + str(memory) + '\n' sbatch_log_out = '#SBATCH -o ' + log_path + '%x_%j.out' + '\n' - sbatch_log_error = '#SBATCH -e ' + log_path + '%x_%j.err' + '\n' - #sbatch_module = 'module purge\n' - #sbatch_anaconda = 'module load anaconda3\n' + sbatch_log_error = '#SBATCH -e ' + log_path + '%x_%j.err' + '\n' + # sbatch_module = 'module purge\n' + # sbatch_anaconda = 'module load anaconda3\n' sbatch_exit = 'set -o errexit\n' # echo -n "This script is running on " @@ -1142,8 +1140,8 @@ def sbatchwrap_nm(processing_dir, sbatch_nodes + sbatch_tasks + sbatch_time + - sbatch_memory+ - sbatch_log_out+ + sbatch_memory + + sbatch_log_out + sbatch_log_error ] @@ -1212,7 +1210,7 @@ def sbatch_nm(job_path): # submits job to cluster job_id = check_output(sbatch_call, shell=True).decode( sys.stdout.encoding).replace("\n", "") - + return job_id @@ -1240,11 +1238,11 @@ def sbatchrerun_nm(processing_dir, written by (primarily) T Wolfers, (adapted) S Rutherford. ''' - - #log_path = kwargs.pop('log_path', None) - + + # log_path = kwargs.pop('log_path', None) + job_ids = [] - + start_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") if binary: @@ -1284,15 +1282,16 @@ def sbatchrerun_nm(processing_dir, print(line.replace(memory, new_memory), end='') job_id = sbatch_nm(jobpath) job_ids.append(job_id) - + if interactive: - check_jobs(job_ids, cluster_spec='slurm', start_time=start_time, delay=60) + check_jobs(job_ids, cluster_spec='slurm', + start_time=start_time, delay=60) def retrieve_jobs(cluster_spec, start_time=None): """ A utility function to retrieve task status from the outputs of qstat. - + :param cluster_spec: type of cluster, either 'torque' or 'slurm'. :return: a dictionary of jobs. @@ -1300,7 +1299,7 @@ def retrieve_jobs(cluster_spec, start_time=None): """ if cluster_spec == 'torque': - + output = check_output('qstat', shell=True).decode(sys.stdout.encoding) output = output.split('\n') jobs = dict() @@ -1310,9 +1309,9 @@ def retrieve_jobs(cluster_spec, start_time=None): jobs[Job_ID]['name'] = Job_Name jobs[Job_ID]['walltime'] = Wall_Time jobs[Job_ID]['status'] = Status - + elif cluster_spec == 'slurm': - + end_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") cmd = ['sacct', '-n', '-X', '--parsable2', '--noheader', '-S', start_time, '-E', end_time, '--format=JobName,State'] @@ -1336,9 +1335,9 @@ def check_job_status(jobs, cluster_spec, start_time=None): c = 0 q = 0 u = 0 - + if cluster_spec == 'torque': - + for job in jobs: try: if running_jobs[job]['status'] == 'C': @@ -1352,14 +1351,14 @@ def check_job_status(jobs, cluster_spec, start_time=None): except: # probably meanwhile the job is finished. c += 1 continue - + print('Total Jobs:%d, Queued:%d, Running:%d, Completed:%d, Unknown:%d' - % (len(jobs), q, r, c, u)) - + % (len(jobs), q, r, c, u)) + elif cluster_spec == 'slurm': - + lines = running_jobs.stdout.strip().split('\n') - + for line in lines: if line: parts = line.split('|') @@ -1373,10 +1372,10 @@ def check_job_status(jobs, cluster_spec, start_time=None): c += 1 elif state == 'FAILED': u += 1 - + print('Total Jobs:%d, Pending:%d, Running:%d, Completed:%d, Failed:%d' - % (len(jobs), q, r, c, u)) - + % (len(jobs), q, r, c, u)) + return q, r, c, u diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py index ac855315..5b1550ec 100644 --- a/pcntoolkit/util/utils.py +++ b/pcntoolkit/util/utils.py @@ -469,7 +469,8 @@ def __init__(self): def _get_params(self, param): if len(param) != self.n_params: - raise ValueError('number of parameters must be ' + str(self.n_params)) + raise ValueError( + 'number of parameters must be ' + str(self.n_params)) return param[0], np.exp(param[1]) def f(self, x, params): @@ -570,7 +571,8 @@ def __init__(self): def _get_params(self, param): if len(param) != self.n_params: - raise ValueError('number of parameters must be ' + str(self.n_params)) + raise ValueError( + 'number of parameters must be ' + str(self.n_params)) epsilon = param[0] b = np.exp(param[1]) @@ -896,8 +898,8 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, :returns: Tuple of (X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef) """ - #np.random.seed(random_state) - + # np.random.seed(random_state) + if isinstance(n_samples, int): n_samples = [n_samples for _ in range(n_grps)] @@ -927,7 +929,8 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, + np.random.randint(10, 100) coef_temp = 0 else: - raise ValueError("Unknown method. Please specify 'linear', 'non-linear', or 'combined'.") + raise ValueError( + "Unknown method. Please specify 'linear', 'non-linear', or 'combined'.") coef.append(coef_temp / 100) X_train.append(X_temp[:n_samples[i]]) @@ -938,23 +941,31 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, grp_id_train.append(grp_id[:n_samples[i]]) grp_id_test.append(grp_id[n_samples[i]:]) - t = np.random.randint(1,5) + t = np.random.randint(1, 5) # Add noise to the data if noise == 'homoscedastic_gaussian': - Y_train[i] += np.random.normal(loc=0, scale=0.2, size=Y_train[i].shape[0]) / t - Y_test[i] += np.random.normal(loc=0, scale=0.2, size=Y_test[i].shape[0]) / t + Y_train[i] += np.random.normal(loc=0, + scale=0.2, size=Y_train[i].shape[0]) / t + Y_test[i] += np.random.normal(loc=0, + scale=0.2, size=Y_test[i].shape[0]) / t elif noise == 'heteroscedastic_gaussian': - Y_train[i] += np.random.normal(loc=0, scale=np.log(1 + np.exp(X_train[i][:, 0])), size=Y_train[i].shape[0]) - Y_test[i] += np.random.normal(loc=0, scale=np.log(1 + np.exp(X_test[i][:, 0])), size=Y_test[i].shape[0]) + Y_train[i] += np.random.normal(loc=0, scale=np.log( + 1 + np.exp(X_train[i][:, 0])), size=Y_train[i].shape[0]) + Y_test[i] += np.random.normal(loc=0, scale=np.log( + 1 + np.exp(X_test[i][:, 0])), size=Y_test[i].shape[0]) elif noise == 'homoscedastic_nongaussian': - Y_train[i] += skewnorm.rvs(a=10, loc=0, scale=0.2, size=Y_train[i].shape[0]) / t - Y_test[i] += skewnorm.rvs(a=10, loc=0, scale=0.2, size=Y_test[i].shape[0]) / t + Y_train[i] += skewnorm.rvs(a=10, loc=0, + scale=0.2, size=Y_train[i].shape[0]) / t + Y_test[i] += skewnorm.rvs(a=10, loc=0, + scale=0.2, size=Y_test[i].shape[0]) / t elif noise == 'heteroscedastic_nongaussian': - Y_train[i] += skewnorm.rvs(a=10, loc=0, scale=np.log(1 + np.exp(0.3 * X_train[i][:, 0])), size=Y_train[i].shape[0]) - Y_test[i] += skewnorm.rvs(a=10, loc=0, scale=np.log(1 + np.exp(0.3 * X_test[i][:, 0])), size=Y_test[i].shape[0]) + Y_train[i] += skewnorm.rvs(a=10, loc=0, scale=np.log( + 1 + np.exp(0.3 * X_train[i][:, 0])), size=Y_train[i].shape[0]) + Y_test[i] += skewnorm.rvs(a=10, loc=0, scale=np.log(1 + + np.exp(0.3 * X_test[i][:, 0])), size=Y_test[i].shape[0]) X_train = np.vstack(X_train) X_test = np.vstack(X_test) @@ -967,7 +978,8 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, for i in range(n_features): plt.figure() for j in range(n_grps): - plt.scatter(X_train[grp_id_train[:, 0] == j, i], Y_train[grp_id_train[:, 0] == j], label='Group ' + str(j)) + plt.scatter(X_train[grp_id_train[:, 0] == j, i], + Y_train[grp_id_train[:, 0] == j], label='Group ' + str(j)) plt.xlabel(f'X{i}') plt.ylabel('Y') plt.legend() @@ -976,19 +988,25 @@ def simulate_data(method='linear', n_samples=100, n_features=1, n_grps=1, if working_dir: if not os.path.isdir(working_dir): os.mkdir(working_dir) - + with open(os.path.join(working_dir, 'trbefile.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(grp_id_train), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(grp_id_train), file, + protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'tsbefile.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(grp_id_test), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(grp_id_test), file, + protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'X_train.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(X_train), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(X_train), file, + protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'X_test.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(X_test), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(X_test), file, + protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'Y_train.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(Y_train), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(Y_train), file, + protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(working_dir, 'Y_test.pkl'), 'wb') as file: - pickle.dump(pd.DataFrame(Y_test), file, protocol=pickle.HIGHEST_PROTOCOL) + pickle.dump(pd.DataFrame(Y_test), file, + protocol=pickle.HIGHEST_PROTOCOL) return X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef diff --git a/requirements.txt b/requirements.txt index 58c3f61e..1ec16b74 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,4 +10,6 @@ pandas>=0.25.3 torch>=1.1.0 sphinx-tabs pymc>=5.1.0 -arviz==0.13.0 +arviz +numba +nutpie \ No newline at end of file diff --git a/setup.py b/setup.py index d37cd554..00693595 100644 --- a/setup.py +++ b/setup.py @@ -7,10 +7,11 @@ def parse_requirements(filename): lineiter = (line.strip() for line in f) return [line for line in lineiter if line and not line.startswith("#")] + requirements = parse_requirements('requirements.txt') # Note: to force PyPI to overwrite a version without bumping the version number -# use e.g.: +# use e.g.: # version = '0.29-1' setup(name='pcntoolkit', diff --git a/tests/testHBR.py b/tests/testHBR.py index 1d2ba679..30168317 100644 --- a/tests/testHBR.py +++ b/tests/testHBR.py @@ -24,7 +24,7 @@ random_state = 29 -working_dir = '/' # Specify a working directory to save data and results. +working_dir = '/home/guus/tmp/' # Specify a working directory to save data and results. simulation_method = 'linear' n_features = 1 # The number of input features of X @@ -32,8 +32,7 @@ n_samples = 500 # Number of samples in each group (use a list for different # sample numbers across different batches) -model_type = 'linear' # modelto try 'linear, ''polynomial', 'bspline' - +model_type = 'linear' # modelto try 'linear, ''polynomial', 'bspline' ############################## Data Simulation ################################ @@ -41,13 +40,13 @@ X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = \ simulate_data(simulation_method, n_samples, n_features, n_grps, - working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian', + working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian', random_state=random_state) ################################# Fittig and Predicting ############################### -nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', - linear_sigma='True', random_slope_mu='True', linear_epsilon='True', linear_delta='True') +nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHo', + linear_sigma='True', random_intercept_mu='True', random_slope_mu='False', linear_epsilon='False', linear_delta='False', nuts_sampler='nutpie') nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl') yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl') diff --git a/tests/test_normative_parallel.py b/tests/test_normative_parallel.py index 89644c59..1a861429 100644 --- a/tests/test_normative_parallel.py +++ b/tests/test_normative_parallel.py @@ -12,7 +12,7 @@ # configs # specify your python path. Make sure you are using the Python in the right environement. -python_path = '/path/to/my/python' +python_path = '/path/to/my/python' # specify the working directory to sacve the results. processing_dir = '/path/to/my/test/directory/' @@ -21,29 +21,31 @@ cov_num = 1 # simulating data -pd.DataFrame(np.random.random([sample_num, resp_num])).to_pickle(os.path.join(processing_dir,'train_resp.pkl')) -pd.DataFrame(np.random.random([sample_num, cov_num])).to_pickle(os.path.join(processing_dir,'train_cov.pkl')) -pd.DataFrame(np.random.random([sample_num, resp_num])).to_pickle(os.path.join(processing_dir,'test_resp.pkl')) -pd.DataFrame(np.random.random([sample_num, cov_num])).to_pickle(os.path.join(processing_dir,'test_cov.pkl')) +pd.DataFrame(np.random.random([sample_num, resp_num])).to_pickle( + os.path.join(processing_dir, 'train_resp.pkl')) +pd.DataFrame(np.random.random([sample_num, cov_num])).to_pickle( + os.path.join(processing_dir, 'train_cov.pkl')) +pd.DataFrame(np.random.random([sample_num, resp_num])).to_pickle( + os.path.join(processing_dir, 'test_resp.pkl')) +pd.DataFrame(np.random.random([sample_num, cov_num])).to_pickle( + os.path.join(processing_dir, 'test_cov.pkl')) -respfile = os.path.join(processing_dir,'train_resp.pkl') -covfile = os.path.join(processing_dir,'train_cov.pkl') +respfile = os.path.join(processing_dir, 'train_resp.pkl') +covfile = os.path.join(processing_dir, 'train_cov.pkl') -testresp = os.path.join(processing_dir,'test_resp.pkl') -testcov = os.path.join(processing_dir,'test_cov.pkl') +testresp = os.path.join(processing_dir, 'test_resp.pkl') +testcov = os.path.join(processing_dir, 'test_cov.pkl') job_name = 'nmp_test' batch_size = 1 memory = '4gb' duration = '01:00:00' cluster = 'slurm' -binary='True' +binary = 'True' execute_nm(processing_dir, python_path, job_name, covfile, respfile, - testcovfile_path=testcov, testrespfile_path=testresp, batch_size=batch_size, + testcovfile_path=testcov, testrespfile_path=testresp, batch_size=batch_size, memory=memory, duration=duration, cluster_spec=cluster, log_path=processing_dir, interactive='auto', binary=binary, savemodel='True', saveoutput='True') - - From 053d0c1c27a66ed57c9a89703a6458ab0abff587 Mon Sep 17 00:00:00 2001 From: AuguB Date: Wed, 2 Oct 2024 15:36:12 +0200 Subject: [PATCH 07/68] Formatting --- pcntoolkit/normative_model/norm_hbr.py | 119 ++++++++++++------------- 1 file changed, 59 insertions(+), 60 deletions(-) diff --git a/pcntoolkit/normative_model/norm_hbr.py b/pcntoolkit/normative_model/norm_hbr.py index b98674d0..170af404 100644 --- a/pcntoolkit/normative_model/norm_hbr.py +++ b/pcntoolkit/normative_model/norm_hbr.py @@ -40,7 +40,6 @@ class NormHBR(NormBase): - """HBR multi-batch normative modelling class. By default, this function estimates a linear model with random intercept, random slope, and random homoscedastic noise. @@ -144,8 +143,9 @@ def __init__(self, **kwargs): kwargs.get("target_accept", "0.8")) self.configs["init"] = kwargs.get("init", "jitter+adapt_diag_grad") self.configs["cores"] = int(kwargs.get("cores", "1")) - self.configs["remove_datapoints_from_posterior"] = kwargs.get( - "remove_datapoints_from_posterior", "True") == "True" + self.configs["remove_datapoints_from_posterior"] = ( + kwargs.get("remove_datapoints_from_posterior", "True") == "True" + ) # model transfer setting self.configs["freedom"] = int(kwargs.get("freedom", "1")) self.configs["transferred"] = False @@ -261,8 +261,8 @@ def estimate(self, X, y, **kwargs): """ Sample from the posterior of the Hierarchical Bayesian Regression model. - This function samples from the posterior distribution of the Hierarchical Bayesian Regression (HBR) model given the data matrix 'X' and target 'y'. - If 'trbefile' is provided in kwargs, it is used as batch effects for the training data. + This function samples from the posterior distribution of the Hierarchical Bayesian Regression (HBR) model given the data matrix 'X' and target 'y'. + If 'trbefile' is provided in kwargs, it is used as batch effects for the training data. Otherwise, the batch effects are initialized as zeros. :param X: Data matrix. @@ -291,9 +291,9 @@ def predict(self, Xs, X=None, Y=None, **kwargs): """ Predict the target values for the given test data. - This function predicts the target values for the given test data 'Xs' using the Hierarchical Bayesian Regression (HBR) model. - If 'X' and 'Y' are provided, they are used to update the model before prediction. - If 'tsbefile' is provided in kwargs, it is used to as batch effects for the test data. + This function predicts the target values for the given test data 'Xs' using the Hierarchical Bayesian Regression (HBR) model. + If 'X' and 'Y' are provided, they are used to update the model before prediction. + If 'tsbefile' is provided in kwargs, it is used to as batch effects for the test data. Otherwise, the batch effects are initialized as zeros. :param Xs: Test data matrix. @@ -332,8 +332,8 @@ def estimate_on_new_sites(self, X, y, batch_effects): """ Samples from the posterior of the Hierarchical Bayesian Regression model. - This function samples from the posterior of the Hierarchical Bayesian Regression (HBR) model given the data matrix 'X' and target 'y'. The posterior samples from the previous iteration are used to construct the priors for this one. - If 'trbefile' is provided in kwargs, it is used as batch effects for the training data. + This function samples from the posterior of the Hierarchical Bayesian Regression (HBR) model given the data matrix 'X' and target 'y'. The posterior samples from the previous iteration are used to construct the priors for this one. + If 'trbefile' is provided in kwargs, it is used as batch effects for the training data. Otherwise, the batch effects are initialized as zeros. :param X: Data matrix. @@ -350,7 +350,7 @@ def predict_on_new_sites(self, X, batch_effects): """ Predict the target values for the given test data on new sites. - This function predicts the target values for the given test data 'X' on new sites using the Hierarchical Bayesian Regression (HBR) model. + This function predicts the target values for the given test data 'X' on new sites using the Hierarchical Bayesian Regression (HBR) model. The batch effects for the new sites must be provided. :param X: Test data matrix for the new sites. @@ -373,8 +373,8 @@ def extend( """ Extend the Hierarchical Bayesian Regression model using data sampled from the posterior predictive distribution. - This function extends the Hierarchical Bayesian Regression (HBR) model, given the data matrix 'X' and target 'y'. - It also generates data from the posterior predictive distribution and merges it with the new data before estimation. + This function extends the Hierarchical Bayesian Regression (HBR) model, given the data matrix 'X' and target 'y'. + It also generates data from the posterior predictive distribution and merges it with the new data before estimation. If 'informative_prior' is True, it uses the adapt method for estimation. Otherwise, it uses the estimate method. :param X: Data matrix for the new sites. @@ -427,11 +427,13 @@ def tune( """ This function tunes the Hierarchical Bayesian Regression model using data sampled from the posterior predictive distribution. Its behavior is not tested, and it is unclear if the desired behavior is achieved. """ - - #TODO need to check if this is correct - print("The 'tune' function is being called, but it is currently in development and its behavior is not tested. It is unclear if the desired behavior is achieved. Any output following this should be treated as unreliable.") - + # TODO need to check if this is correct + + print( + "The 'tune' function is being called, but it is currently in development and its behavior is not tested. It is unclear if the desired behavior is achieved. Any output following this should be treated as unreliable." + ) + tune_ids = list(np.unique(batch_effects[:, merge_batch_dim])) X_dummy, batch_effects_dummy = self.hbr.create_dummy_inputs( @@ -516,7 +518,7 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None): Args: X ([N*p]ndarray): covariates for which the quantiles are computed (must be scaled if scaler is set) batch_effects (ndarray): the batch effects corresponding to X - z_scores (ndarray): Use this to determine which quantiles will be computed. The resulting quantiles will have the z-scores given in this list. + z_scores (ndarray): Use this to determine which quantiles will be computed. The resulting quantiles will have the z-scores given in this list. """ # Set batch effects to zero if none are provided if batch_effects is None: @@ -525,9 +527,9 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None): # Set the z_scores for which the quantiles are computed if z_scores is None: z_scores = np.arange(-3, 4) - likelihood = self.configs['likelihood'] + likelihood = self.configs["likelihood"] - # Determine the variables to predict + # Determine the variables to predict if self.configs["likelihood"] == "Normal": var_names = ["mu_samples", "sigma_samples", "sigma_plus_samples"] elif self.configs["likelihood"].startswith("SHASH"): @@ -543,24 +545,21 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None): exit("Unknown likelihood: " + self.configs["likelihood"]) # Delete the posterior predictive if it already exists - if 'posterior_predictive' in self.hbr.idata.groups(): + if "posterior_predictive" in self.hbr.idata.groups(): del self.hbr.idata.posterior_predictive if self.configs["transferred"] == True: - self.predict_on_new_sites( - X=X, - batch_effects=batch_effects - ) - #var_names = ["y_like"] - else: + self.predict_on_new_sites(X=X, batch_effects=batch_effects) + # var_names = ["y_like"] + else: self.hbr.predict( - # Do a forward to get the posterior predictive in the idata + # Do a forward to get the posterior predictive in the idata X=X, batch_effects=batch_effects, batch_effects_maps=self.batch_effects_maps, pred="single", - var_names=var_names+["y_like"], - ) + var_names=var_names + ["y_like"], + ) # Extract the relevant samples from the idata post_pred = az.extract( @@ -568,9 +567,9 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None): ) # Remove superfluous var_nammes - var_names.remove('sigma_samples') - if 'delta_samples' in var_names: - var_names.remove('delta_samples') + var_names.remove("sigma_samples") + if "delta_samples" in var_names: + var_names.remove("delta_samples") # Separate the samples into a list so that they can be unpacked array_of_vars = list(map(lambda x: post_pred[x], var_names)) @@ -586,7 +585,7 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None): quantiles[i] = xarray.apply_ufunc( quantile, *array_of_vars, - kwargs={"zs": zs, "likelihood": self.configs['likelihood']}, + kwargs={"zs": zs, "likelihood": self.configs["likelihood"]}, ) return quantiles.mean(axis=-1) @@ -599,12 +598,12 @@ def get_mcmc_zscores(self, X, y, **kwargs): y ([N*1]ndarray): response variables """ - print(self.configs['likelihood']) + print(self.configs["likelihood"]) tsbefile = kwargs.get("tsbefile", None) if tsbefile is not None: batch_effects_test = fileio.load(tsbefile) - else: # Set batch effects to zero if none are provided + else: # Set batch effects to zero if none are provided print("Could not find batch-effects file! Initializing all as zeros ...") batch_effects_test = np.zeros([X.shape[0], 1]) @@ -624,7 +623,7 @@ def get_mcmc_zscores(self, X, y, **kwargs): exit("Unknown likelihood: " + self.configs["likelihood"]) # Delete the posterior predictive if it already exists - if 'posterior_predictive' in self.hbr.idata.groups(): + if "posterior_predictive" in self.hbr.idata.groups(): del self.hbr.idata.posterior_predictive # Do a forward to get the posterior predictive in the idata @@ -633,7 +632,7 @@ def get_mcmc_zscores(self, X, y, **kwargs): batch_effects=batch_effects_test, batch_effects_maps=self.batch_effects_maps, pred="single", - var_names=var_names+["y_like"], + var_names=var_names + ["y_like"], ) # Extract the relevant samples from the idata @@ -642,9 +641,9 @@ def get_mcmc_zscores(self, X, y, **kwargs): ) # Remove superfluous var_names - var_names.remove('sigma_samples') - if 'delta_samples' in var_names: - var_names.remove('delta_samples') + var_names.remove("sigma_samples") + if "delta_samples" in var_names: + var_names.remove("delta_samples") # Separate the samples into a list so that they can be unpacked array_of_vars = list(map(lambda x: post_pred[x], var_names)) @@ -656,7 +655,7 @@ def get_mcmc_zscores(self, X, y, **kwargs): z_scores = xarray.apply_ufunc( z_score, *array_of_vars, - kwargs={"y": y, "likelihood": self.configs['likelihood']}, + kwargs={"y": y, "likelihood": self.configs["likelihood"]}, ) return z_scores.mean(axis=-1).values @@ -704,19 +703,19 @@ def m(epsilon, delta, r): def quantile(mu, sigma, epsilon=None, delta=None, zs=0, likelihood="Normal"): """Get the zs'th quantiles given likelihood parameters""" - if likelihood.startswith('SHASH'): + if likelihood.startswith("SHASH"): if likelihood == "SHASHo": - quantiles = S_inv(zs, epsilon, delta)*sigma + mu + quantiles = S_inv(zs, epsilon, delta) * sigma + mu elif likelihood == "SHASHo2": - sigma_d = sigma/delta - quantiles = S_inv(zs, epsilon, delta)*sigma_d + mu + sigma_d = sigma / delta + quantiles = S_inv(zs, epsilon, delta) * sigma_d + mu elif likelihood == "SHASHb": true_mu = m(epsilon, delta, 1) - true_sigma = np.sqrt((m(epsilon, delta, 2) - true_mu ** 2)) - SHASH_c = ((S_inv(zs, epsilon, delta)-true_mu)/true_sigma) + true_sigma = np.sqrt((m(epsilon, delta, 2) - true_mu**2)) + SHASH_c = (S_inv(zs, epsilon, delta) - true_mu) / true_sigma quantiles = SHASH_c * sigma + mu - elif likelihood == 'Normal': - quantiles = zs*sigma + mu + elif likelihood == "Normal": + quantiles = zs * sigma + mu else: exit("Unsupported likelihood") return quantiles @@ -724,22 +723,22 @@ def quantile(mu, sigma, epsilon=None, delta=None, zs=0, likelihood="Normal"): def z_score(mu, sigma, epsilon=None, delta=None, y=None, likelihood="Normal"): """Get the z-scores of Y, given likelihood parameters""" - if likelihood.startswith('SHASH'): + if likelihood.startswith("SHASH"): if likelihood == "SHASHo": - SHASH = (y-mu)/sigma - Z = np.sinh(np.arcsinh(SHASH)*delta - epsilon) + SHASH = (y - mu) / sigma + Z = np.sinh(np.arcsinh(SHASH) * delta - epsilon) elif likelihood == "SHASHo2": - sigma_d = sigma/delta - SHASH = (y-mu)/sigma_d - Z = np.sinh(np.arcsinh(SHASH)*delta - epsilon) + sigma_d = sigma / delta + SHASH = (y - mu) / sigma_d + Z = np.sinh(np.arcsinh(SHASH) * delta - epsilon) elif likelihood == "SHASHb": true_mu = m(epsilon, delta, 1) - true_sigma = np.sqrt((m(epsilon, delta, 2) - true_mu ** 2)) - SHASH_c = ((y-mu)/sigma) + true_sigma = np.sqrt((m(epsilon, delta, 2) - true_mu**2)) + SHASH_c = (y - mu) / sigma SHASH = SHASH_c * true_sigma + true_mu Z = np.sinh(np.arcsinh(SHASH) * delta - epsilon) - elif likelihood == 'Normal': - Z = (y-mu)/sigma + elif likelihood == "Normal": + Z = (y - mu) / sigma else: exit("Unsupported likelihood") return Z From 4ade92700dfac402eb7da629d7005524dd411895 Mon Sep 17 00:00:00 2001 From: AuguB Date: Wed, 2 Oct 2024 15:37:00 +0200 Subject: [PATCH 08/68] Git thinks this has changed --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1ec16b74..dda3803d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,6 +10,6 @@ pandas>=0.25.3 torch>=1.1.0 sphinx-tabs pymc>=5.1.0 -arviz +arviz numba nutpie \ No newline at end of file From e43e1881d7ac98d28f2fcc4d37ef089f90e74857 Mon Sep 17 00:00:00 2001 From: AuguB Date: Wed, 2 Oct 2024 18:46:39 +0200 Subject: [PATCH 09/68] Added test notebook --- tests/test_HBR.ipynb | 188 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 188 insertions(+) create mode 100644 tests/test_HBR.ipynb diff --git a/tests/test_HBR.ipynb b/tests/test_HBR.ipynb new file mode 100644 index 00000000..1abbd02d --- /dev/null +++ b/tests/test_HBR.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "\"\"\"\n", + "Created on Mon Jul 29 13:26:35 2019\n", + "\n", + "@author: seykia\n", + "\n", + "This script tests HBR models with default configs on toy data.\n", + "\n", + "\"\"\"\n", + "\n", + "import os\n", + "import numpy as np\n", + "from pcntoolkit.normative_model.norm_utils import norm_init\n", + "from pcntoolkit.util.utils import simulate_data\n", + "import matplotlib.pyplot as plt\n", + "from pcntoolkit.normative import estimate\n", + "from warnings import filterwarnings\n", + "filterwarnings('ignore')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3jPC+F8RrS5cuxaBBg9CvXz/84x//wLXXXguWZbFv3z4cP34cffv2VV3/6aefxp133ok+ffrg5ptvxvr16/H555/j22+/BSBkfwYMGIAFCxbgqquuwsWLF/H888/Lbuull15CbGws4uPj8fe//x2tW7fGuHHjFPddUFCAgoICnDx5EoDQTDUiIgJXXnklYmJi3DshBEEQRIuFMjeexIfitU6dOuHgwYMYNmwY5syZg549e6Jfv35488038dRTT+Hll19WXX/cuHFYtGgRXn/9dXTv3h1vv/02PvjgAwwdOtS+zIoVK2CxWNCvXz/MmDHDXkLuzIIFCzBjxgz07dsX+fn5yMrKQmBgoOK+ly9fjt69e+ORRx4BAAwZMgS9e/dGVhbZohMEQRDGYXipiKIFUFZWhqioKJSWliIyMtLhverqapw+fRodOnRAcHCw+zuRbdLWVghsmrF4bfv27bjxxhtx6dIlREdHe20/HvucCIIgiCaD2v3bGZqW8gYkXiMIgiAIn0HBjbcQxWsEQRAEQTQqFNwQHmPo0KFoYbOcBEEQhB9CgmKCIAiCIJoVFNzIQNkH/4Y+H4IgCEINCm4kmM1mAEBlZaWPR0KoIX4+4udFEARBEFJIcyPBZDIhOjoahYWFAIDQ0FBqG+BH8DyPyspKFBYWIjo6GiYTVZ8RBEEQrlBw40RCQgIA2AMcwv+Ijo62f04EQRAE4QwFN04wDIPExETExcUZagxJNA5ms5kyNgRBEIQqFNwoYDKZ6CZKEARBEE0QEhQTBEEQBNGsoOCGIAiCIIhmBQU3BEEQBEE0Kyi4IQiCIAiiWUHBDUEQBEEQzQoKbgiCIAiCaFZQcEMQBEEQRLOCghuCIAiCIJoVFNwQBEEQBNGsoOCGIAiCIIhmBQU3BEEQBEE0Kyi4IQiCIAiiWUHBDUEQBEEQzQoKbgiCIAiCaFZQcEMQBEEQRLOCghuCIAiCIJoVFNwQBEEQBNGsoOCGIAiCIIhmBQU3BEEQBEE0Kyi4IQiCIAiiWRHg6wEQBEEQzQDOCpzZDVy+AITHA+0HAqzJ16MiWig+zdzMnz8f/fv3R0REBOLi4jBu3Dj8/PPPquts374dDMO4/Dt+/HgjjZogCIJwIC8LyEwBPhwNrHtI+JmZIrxOED7Ap8HNjh07MG3aNOzZswdbtmxBXV0dRowYgYqKCs11f/75Z+Tn59v/XX311Y0wYoIgCMKBvCzg04lA2XnH18vyhdcpwCF8gE+npbKzsx1+/+CDDxAXF4cffvgBQ4YMUV03Li4O0dHRmvuoqalBTU2N/feysjK3xkoQBEE4wVmB7NkAeJk3eQAMkP0s0PVWmqIiGhW/EhSXlpYCAGJiYjSX7d27NxITE3HzzTdj27ZtisvNnz8fUVFR9n/t2rXz2HgJgiBaNGd2u2ZsHOCBsnPCcgTRiPhNcMPzPGbNmoXBgwcjJSVFcbnExES88847WLduHT7//HN06dIFN998M3bu3Cm7/Jw5c1BaWmr/d/bsWW8dAkEQhB0rxyPnVBG+OnQOOaeKYOXkshtNnMsXPLscQXgIv6mWeuyxx/Djjz9i165dqst16dIFXbp0sf+elpaGs2fPYuHChbJTWUFBQQgKCvL4eAmCIJTIPpqPeevzkF9abX8tMSoYc8ckIz0l0Ycj8zDh8Z5djiA8hF9kbh5//HFkZWVh27ZtuOKKKwyvP2DAAPzyyy9eGBlBEIQxso/mY8rqAw6BDQAUlFZjyuoDyD6a76OReYH2A4HIJACMwgIMENlWWI4gGhGfBjc8z+Oxxx7D559/jq1bt6JDhw5ubefgwYNITGxGT0MEQTRJrByPeevzFOW1ADBvfV7zmaJiTUD6a7ZfnAMc2+/pC0hMTDQ6Pp2WmjZtGj755BN89dVXiIiIQEFBAQAgKioKISEhAATNzLlz5/DRRx8BADIzM3HVVVehe/fuqK2txerVq7Fu3TqsW7fOZ8dBEAQBALmni10yNlJ4APml1cg9XYy0TrGNNzBvkpwB3PmRUDUlFRdHJgmBTXKG78ZGtFh8GtwsW7YMADB06FCH1z/44ANMmjQJAJCfn4/ff//d/l5tbS2eeuopnDt3DiEhIejevTs2bNiAUaNGNdawCYIgZCksVw5s3FmuyZCcIZR7k0Mx4ScwPM83k/yoPsrKyhAVFYXS0lJERkb6ejgEQTQjck4VYcK7ezSXW/PIgOaTuSGIRsLI/dsvBMUEQRDNgdQOMUiMClaT1yIxKhipHbS9vAiCcB8KbgiCIDyEiWUwd0wyAEV5LeaOSYaJVQp/CILwBBTcEARBeJD0lEQsu7cPEqKCHV5PiArGsnv7NC+fG4LwU/zGxI8gCKK5kJ6SiOHJCcg9XYzC8mrERQhTUZSxIYjGgYIbgiAIL2BiGRINE4SPoGkpgiAIgiCaFRTcEARBEATRrKBpKYIgiBaIleNJE0Q0Wyi4IQiCaGH4pGs5ZyUHY6LRoOCGIAiiBSF2LXe2phe7lnulXD0vS6H31GvUe4rwCqS5IQiCaCH4pGt5Xhbw6UTHwAYAyvKF1/OyPLcvgrBBwQ1BEEQLwUjXco/AWYWMjVo4lf2ssBxBeBAKbgiCIFoIjd61/Mxu14yNAzxQdk5YjiA8CAU3BEEQLYS4iGDthQwsp8nlC55djiB0QsENQRBEC6HRu5aHx3t2OYLQCQU3BEEQLQQTy+CFW5NlFTBe6VrefqBQFaUWTkW2FZYjCA9CwQ1BEEQLIftoPl7ekCf7nle6lrMmodwbgGuAY/s9fQH53RAeh4IbgiCIFoDob6NULfXCrd28Y+CXnAHc+REQ6bTtyCThdfK5IbwAmfgRBEE0c9T8bQAhh/LyhmMYmZLonRYMyRlA11vJoZhoNCi4IQiCaOYY8bdJ6xTrnUGwJqDD9d7ZNkE4QcENQRCEh/DXZpSN7m9DED6GghuCIAgP4JNmlDppdH8bgvAxJCgmCIJoIEpiXbEZZfbRfB+NTKDR/W0IwsdQcEMQBNEAfNKM0iAmlsHcMckAFAuyPetvQxA+hoIbgiCIBtDozSjdJD0lEcvu7YOEKMepJ6/42xCEjyHNDUEQfoO/CnLVaEpi3fSURAxPTmhy55jwP/z9b5WCG4IgGgfOqupz4s+CXDWamljXxDLeK/cmWgRN4W+VpqUIgvA+eVlAZgrw4Whg3UPCz8wU4XX4vyBXDRLrEi2JpvK3SsENQRDeJS8L+HQiUHbe8fWyfODTibD+9JXfC3LVILEu0VJoCuJ5EQpuCILwHpwVyJ4NqFwO6zbMxoXSSsVN+IsgVw0S6xItgaYingdIc0MQhDc5s9s1Y+MAj6DKfKSyx7GHS1bdlD8IctXwlVjX34WdhtDQZRG+pSmJ5ym4IQjCe1y+oGuxOJRoL+Mnglw1Glus2xSEnbrJyxKyfNJgODIJSH+NOof7CU1JPE/TUgRBeI/weF2L1YXFkSDXIE1F2KkLDV2WKDwnfEtTEs9TcEMQhPdoP1B4+la7HEa2RUbGePE353cBkCDXmaYk7NREhy4L2c8KyzUxrByPnFNF+OrQOeScKmoan4cKTUk8T8ENQRDegzUJ0woAFC+H6QuQ3uMKEuQawJCwk7MCp78Djnwm/PS3IEGHLgtl54TlmhDZR/Mx+LWtmPDuHsxYewgT3t2Dwa9tbVoZNRmainieNDcEQXiX5Azgzo8U9BQL7HoKcs/Vj17Bpunn9cBXr/m3jkWnLkv3cn6AOGXonKcRpwwbKwjwlti8KfytUnBDEIT3Sc4Aut6qWQmjKcilahoA+gSbI9lc9M9dBJfpHlHHcudH/hHg6NRl6V7Ox2hNGTIQpgyHJyd4NRjwttjc352uKbghCKJxYE1Ah+vdX78FVtMoPXmLws6C0mrZm6gJHF4KXAVlHQsj6Fi63ur74FDUZZXlQ368jPB++4GNPTK3MDJl6K3gwF8yR76ENDcEQfg/LbCaRk2zoSXs7M8eRzyKFGXcfqVj0anL8nkQphNfe8E0K7F5A6DghiAI/6YZV9MooafMW03YOWdwtL4d+YuORdRlRTplEyKT/Gf6TCe+9oJpSi7C3sSn01Lz58/H559/juPHjyMkJAQDBw7Ea6+9hi5duqiut2PHDsyaNQs//fQTkpKS8Mwzz2Dy5MmNNGqCIBoVI9U0DZn28hOMaDYUhZ1nAoFcHTvzJx2LTl2Wv6M1ZchACEC95QXj68yRv+DTzM2OHTswbdo07NmzB1u2bEFdXR1GjBiBiooKxXVOnz6NUaNG4frrr8fBgwfx3HPPYfr06Vi3bl0jjpwgiEajGVbTqGH0yVsUdo7t1RZpnWIFkapOfyG/07GIuqwetws/m1hgA/jeC8bXmSN/waeZm+zsbIffP/jgA8TFxeGHH37AkCFDZNdZvnw5rrzySmRmZgIAunXrhv3792PhwoUYP368y/I1NTWoqamx/15WVua5AyAIwvs0s2oaLTzy5C3qWD6dCOGWKs0hND0dS1NDnDJ0rlZKaITWGL7OHPkLflUtVVpaCgCIiVE+6Tk5ORgxYoTDayNHjsT7778Pi8UCs9ns8N78+fMxb948zw+WIIjGoZlV02ghPlGz4JDKHkccSlCIaORyXcFJku2aT946/YWaBE3QAsBXXjBi5mjK6gNKYa3fuAh7E78Jbniex6xZszB48GCkpKQoLldQUID4eMcntPj4eNTV1eHixYtITHSMiOfMmYNZs2bZfy8rK0O7du08O3iCILxHC8tCpHaIwV3hhzDd8h6SmHrR53k+BvMsE7GZS9X/5N0cdCxN2ALAV14wvswc+Qt+E9w89thj+PHHH7Fr1y7NZRnGMeLkeV72dQAICgpCUFCQZwZJEIRvaE5ZCA1Mx9djft3r4J2yVAkoxjJzJqZaZmLcmMn6n7wb6i/kS0QLAH83IvRDmoKLsDfxi+Dm8ccfR1ZWFnbu3IkrrrhCddmEhAQUFBQ4vFZYWIiAgADExvqvWyJBEA2kOWQhtLCVvTPg4fysxjIAB+CNqLUISX7RJ8NrVDQtAPzIiNBP8XcXYW/i0+CG53k8/vjj+OKLL7B9+3Z06NBBc520tDSsX7/e4bXNmzejX79+LnobgiCaGU05C6EHjbJ3FkBIVUGzKXtXpYVZABCexael4NOmTcPq1avxySefICIiAgUFBSgoKEBVVZV9mTlz5mDixIn23ydPnowzZ85g1qxZOHbsGFasWIH3338fTz31lC8OgSAIwnN4qezdyvHIOVWErw6dQ86poqbhTtvCLAAIz+LTzM2yZcsAAEOHDnV4/YMPPsCkSZMAAPn5+fj999/t73Xo0AEbN27EE088gbfeegtJSUlYvHixbBk4QRBEk8ILZe/ebqDoNVqYBQDhWRheVOO2EMrKyhAVFYXS0lJERkb6ejgEQRD1cFYgM0W77H3mEV06E6UGiqKcx68bKHr4XBBNHyP3b+otRRAE4S94sIlkk2+g2MwaahKNCwU3BEEQ/oSHmkg2iwaKzaihJtG4+EUpOEEQBCHBA2XvzaaBYkuwACA8DgU3BEEQ/kgDy96bVQPF5m4BQHgcCm4IgiC0aIK9jaiBItGSoeCGIAhCjSba24gaKBItGRIUEwRBKCH2NnJ2yhV7G+Vl+WZcOhEbKCZEOU49JUQF+3cZOEE0EPK5IQiCkMPus6LUAqDp+KxYOb7FNlAkmg9G7t80LUUQBCGHB3ob+UtQ0ZIbKBItEwpuCIIg5Ghgb6Mm2/aAIBqAvwT0FNwQBNE88HRFUwN6Gym1PSgorcaU1QdI70I0S/wpoCdBMUEQTZ+8LEEf8+FoYN1Dws/MlIYJftsPFDQ1Ltb/IgwQ2VZYTkKTb3tAEG4gBvTOrthiQJ99NL9Rx0PBDUEQTRtvVTS52duoWbQ9IAgD+GNAT8ENQRBNF84qeNCoXVaznxWW04mV45FzqghfHTqHnKBBsN7xoaHeRs2m7QFB6MQfA3rS3BAE0XTxQEWTFHnNQDjmjt6C9PDTuvQ8zartAUHowB8DesrcEATRdGlgRZMdzorcrV9i4ydL0L78AFhw9rcKSqsx5ePDyK7oDPS4XQiSVITKYtsDFaUOEqntAdGM8MeAnoIbgiCaLg2oaLKTlwU+MwWpO+/H4sAlWBv4CnYFTcdINheAcc2A2PYAUFTqUNsDolnhjwE9BTcEQTRd3KxosqMgRk5AMZaZMx0CHCOaAWp7YBDOCpz+DjjymfDTgEaK8D3+GNCT5oYgiKaLWNH06URAqT2kTEUTAAcxsvMll2UAjgfmmldhS00/cLbnQCOagfSURAxPTvALQzO/pok2JiUcEQN6Z81ago98bii4IQiiaZOcIVQuyd4gFyjfIDXEyCwDJKEIqexx7OGEp1KjmgFqe6CBmDlzrnYTy/gVKtII/8SfAnoKbgiC8DyedgvWIjkD6HqrsX3qFCPHoQQMhCdQEgF70F5fs4yfEcr4u97q941JiXr8JaCn4IYgCM/iq2kG1qSr3NuOTjFyIaIBkAgY8LC9vofL+AlCCgmKCcLHOJjGnSpq2rb83nIL9gYaYmSOB87zsTgb3hNv3d0HUSGBzeMzchOP2+t7qoyfIGSgzA1B+BB/ajTXYJraNIOKGJkHA4YBym94GX+PTcHLG4x/Rp7ujuzLbsta9voMhFL54ckJ+sekM3NmDYuDH3xbiCYGBTcE4SOaXefopjjNoCBGZmxi5NNcf0xz4zPydNAqt72YMDNeGZuCUdcmGd6eUYzY6+vWW4iZs7J8yAXEHA8UIBZ3rK3GCxn5TetvgfA5NC1FED5AT6O5l7OOwPrrzqbj/dFUpxmSM4Dph4GRrwKpfxN+Tj8Ea9cxbjUD9PT0jdL2iissmPrJQczfmGdoe+7gFXt9lcak4imdZ7kP58ssPukqTTRtKHNDED5A60l4BJuLuTUfwfSRxDTO370/POEW7AvkBNA5S3Cy9/PIL41WXE0uW+Hp6Ru17Ym8vfM0el7RCqOu9V5mw2v2+rbMGZ89G4zk/BcgFvMs92ETlwrAzWkvokVDmRuC8AFqT7gj2VwsM2ciAU5uuP4oypXSULdgX6AigL5mxzS7Q7Ea0s/S092RtbYn8sJXR70qcvaqvX5yBvaM2YG7ap/H9NrHcFft8xhcs8ge2AC+6SpNNG0ouCEIH6D0hMuCw1zzR8L/u9xJbDev7Gf9c4pKZZpB0y3YF2gKoAWHYmkTTTmkn6Wnp2/0LldUUeuVG79Yyff1j+dxV/929uyTFE/Y6xdWWLCHS0YWNxB7uGS7I7TLco3YVZpo2tC0FEH4APFJuKC02uHWmsoeRxKjdpPyQ1GuFHfdgn2BhgCaAY8kxtGh2PF9V2M/T0/fGJnm8fSNX07EHB1qBgCUVFrsr3nCXt8fu0oTTRsKbgjCB4iN5qasPuBQhByHEn0b8DdRrg0rxyM3aBAKb9yEzpVH0C2iEmxEgvcdit3BoEOxTNcql2yFUtAqXc+I03FqhxjEhJlRXGHRXNaTN36lSr5SW1DzxLBrcFXrUI+VpHv6vBEETUsRhI+Q6xwtuuFq4m+iXAg3xMGvbcWEd/dgxv8dwa3rgUEbYpBd0dn/AhtA9zm8d1h/3d29Pd0d2cQyeCWjGwawechgd2MAmyc7Tea23kUGPZV8a/f9jtHXJiGtU6xHBL7+2FWaaNowPM+3KKvNsrIyREVFobS0FJGRkb4eDkE4mrOFmTFg/Q1gFLw/BFFuEjDziF8FDEpP+uKtyC89ezgrkJmi6LMiPddWsIYM9DzmcyNTyXWej8E8y0SHSiJPnt+cU0WY8O4ezeXWPDLA4z2EmpWpJeFxjNy/aVqKIHyMS6M5BddcvxTlwkvutY2BikOx87k2Aeo3cqdGoenJAxveHVmhY3YCirHMnIkplpn4MWKIx2/8XvG00Yk/dZUmmjYU3BCEv9GURLnwknttY+GJc63QKNSU/hrS3P2sbJVcPHiXaRqWEdpDLI7+PwTMegGmAM9exn0t7vWXrtJE04aCG4LwR5IzhB5MkmyAX4py4dsnfY/QkHOtkF2xexLd+ZF7waitkkvZV4ZHUGU+cDbH41VzDRX3+rIHFkGIUHBDEP4Ka/LPcm8nfP2k7xHcOdceahQqFwwcPXYcPfWMQanii7MKLTvO7BKG0uF64KrBugI2pUo+QFvcS5oZwl+g4IYgiAbR3Mp4dWcePNAoVC4YSIgMQorlMt7TM1i5iq+8LGD9dKDqUv1r370OhMQAYxYByRmaxyhW8rmMTSVQaXaNYIkmDQU3BKEBpdnVaciTvk9wEv9Kp6AMZR4a2ChUMRgoq0EhOuN8UAwSUCzjVC00lrSEJSLIuZVFXhbw6X3y46gqBj69DwfTFmPqgSs0j9GIuLfJisqJZgsFNwShAqXZ9eHOk75PUBD/Iv01ZHP9jWUeGtAoVKshJgcW8ywTscycCY53bMUhtpA6nPIsUqXTTJwV+OYZ1aHwAOJ3/wMXahaDheCIHYcSFJZHY9rqSrx1bz+HY9Qr7vV7UblKQEs0T3wa3OzcuROvv/46fvjhB+Tn5+OLL77AuHHjFJffvn07brzxRpfXjx07hq5du3pxpERLhNLsxvD7Ml4V8S//6URsD3gaPHq5rKaYeRAbhWr55Mg0CtXTEHMTl4oplpmYa/4ISZImqmLH7EldxjiucGY3UJ6vuk0GQBJTjGmmLzAhYJtDq4/zfAwWf/kwhic/Z/gz82tRuUpA62+Vh4Tn8GlwU1FRgZ49e+KBBx7A+PHjda/3888/Oxj4tGnTxhvDI1owlGZ3D78t49Uh/p1ueR+fYpFs00bZzIMBnxxn9N7kN3Gp2FLTrz7Dgmjs47oiLirUVcNkoCXHrIB1Lq8loBivWv6FX3ZchS433qN7W4Afi8q9Vc1G+D26g5s//vgDV1xxhUd3fsstt+CWW24xvF5cXByio6N1LVtTU4Oamhr772VlZYb3R7Q8/D7NThijgU0yRVyCEjd9cozc5Dmw9jGpapgMtORgZOJxlhGmvNrlvgTccJehaRu/FJV7qJqNaJro7i2VkpKCVatWeXMsuunduzcSExNx8803Y9u2barLzp8/H1FRUfZ/7dq1a6RREk0Zv06zE8Yx0CRT9X25oCQ5A5h5FLj/a2D8+8LPmUdUMwJiMKDsYyN04E6I1NfTCoAw/RWhPk2q1WyHZYDQqgIhGDSAX/aGMlLNRjQ7dAc3r776KqZNm4bx48ejqKjIm2NSJDExEe+88w7WrVuHzz//HF26dMHNN9+MnTt3Kq4zZ84clJaW2v+dPXu2EUdMNFX8Ns1OuIfOrIZS41IGGs0pRZ+cHrcLPzUyAXqCgQW39cD3z96ENY8MwKK7emHNIwOwa/ZNyjov1gTc8i/FfRrqIuhG13m5RrCARkDmCURPnyOfCT85q/B6A6vZiKaN7mmpqVOn4pZbbsFDDz2E7t2745133kFGRuPOVXbp0gVdunSx/56WloazZ89i4cKFGDJkiOw6QUFBCAoKaqwhEs0Ev0yzE+6jQ/xbFRKPfdVdG62cXW+FmaFpz+QM4M5Vrj43kJ+KUsTNrvONLipXEws3oJpNN1SF5bcYEhR36NABW7duxZIlSzB+/Hh069YNAU59TQ4cOODRAWoxYMAArF69ulH3STR/mpx3S2PT1C7qDuJfeULGvI63uH5ulbO764XklWBAbCdhcyg+W1yFLw+exePmr/StH9lW+Dzd/IwbTVSuJRa+Y6Xb1Wy6909VWH6L4WqpM2fOYN26dYiJicHYsWNdgpvG5uDBg0hMpHJcwvM0Ge+WxqapXtSTM4CBjwM5SwCeq3+dYYG0x4DkDKQDhoMNNS8kPdvySjDAmoBOQ4FOQ3Hg0Dl8f+ATPA6dwU36AuD4Bv/+jPWIhTc9B4yYD3w2CUar2TTRUYVl7TrGf20RWgCGIpN3330XTz75JIYNG4ajR482uAT78uXLOHnypP3306dP49ChQ4iJicGVV16JOXPm4Ny5c/joo48AAJmZmbjqqqvQvXt31NbWYvXq1Vi3bh3WrXMtayQIT+D33i2NTVMurc3LAna/CZex87zw+hX9geQMQ8GGmhfS5NUHEB1qRkmlxf66Lwwg4yKCkct1xXle2fEYAHiGBXP7B8Iv/v4Z6xULh8U2vOu7MzoCq6r1T2PYl8E4V+bbz74lozu4SU9PR25uLpYsWYKJE5VTu0bYv3+/gynfrFmzAAD3338/Vq5cifz8fPz+++/292tra/HUU0/h3LlzCAkJQffu3bFhwwaMGjXKI+MhCDn81rulsWlgaa1P21h4oSzYyvF4OesIrmPz7B40uVxXcGDte5EGNoBvDCBTO8QgPioUL5VPxFIFx2OGAbjxK2DqNgbITIHfl08bEQv3uN39ru9y6AisQqoK0K72MM6h3laAzD8bF93BjdVqxY8//uhRr5uhQ4eCV5Hwr1y50uH3Z555Bs88o24vThCEl9D7tLx3uXADkdxEfNbGQtSN/LqjwU0unTm54xP8t+Z5JAU6uvzOs0zEJi5VaS+NbgBZrx+rxlTLTLwo43h8IW0ueqf8RdDpePg8eQWjYmF3ur4r4aatAJl/Ni66g5stW7Z4cxwEQfg7ep+WNz1X//+RSTjY/VlM2da68dtYyGmDtNB7jHlZuGbHNPBOR5WAYiwzZ2KKZaZqgOOuAWRDhMuCfiwYg0vrHY/rwuKQkTEe6T1sD61+WD4te8wNaH3RYBpgK0Dmn40HNc4kCEIfbpTM8mX56JUzHSNY15u9V55kxUzNzxuBPUuNr6/nGCVTXM5DFl1+55pXYUtNP9lWDiJGDSAbmv1y1I/1kQ+OGqN82gCqx+xm64sGoxFYcbyQDcvllPsdkvmn99Ft4kcQRAtHvKgr+uq6woAHb7vZs+Bc3pc+yTaYvCxBL/LhaMOBDQ+mvgRaC9v0nNJZYBnYWzmoYcQAUhQuO7cEEbNf2UfVG2aKiPqxsb3aIq1TrGtAqfkZGzhPgLLBng40j5nrL4iFI50Cu8gk74qeRVsBAM7nibf9Ps9yn2pgS+af3ocyNwRB6EO1UaTKagyQBPW+TQ1+klWq4tKBIKjlYR05HyY9T/oNbOVg1ACyUZu4NqAZqAsNsAzQfcyzx8DkSbGwXlR6ij1XeQ821/SSXY3MPxsPCm4IooXgkWolpYu6DtT6NjXoSVa1EkqbAsRiXu19mBQ8GGl6VjCguTBkAKlgmqe3ieueU0VgWabh1WhuNgN1oIGWAYYb1/pC3CyaJUo+M6b9QAzNK8T/kfmnz6HghiBaAB6tVnK+qF++4CgiVkBOYOmRJ1nNKi55FlvGYTefYi/fHqU3e9R+IBASA1TJT6XxNjHrpOETcObrn/UZQKpkOQpr++oa1rRPDqCkykO+KjI3bt0ZEQ+U3TeZxrUyVVhk/ukfUHBDEM0cNaM5e7VScpyxG5n0os5ZBddfBYElDwb5fAz2OQksFZ9kjdr+G6zcEQWfmdbbHXQRurNHxzcoBjaAoDNC+gKkJ1+B4d3bamfLNLIcXW94C1Bo6ClFGtgAHqhGc7d8WqdlwK7/ZcHUcYjsOWnqjWvJ/NP3UHBDEM0YPdqF7V+uwMjNq8G4a7WvodNgAFwYOBdxB0K1n2Td0WkYqNzhbEOTCj4NZY/sWQkVQmKErAR0GEDqyHJcc/CfaBuZifNlFkMTbz7zVdEZbH66bT+y/hcom2FqDo1ryfzTt1C1FEE0Y7S0CyPYXLxq+Zfrk7aojcjL0lfxIuo0FCpXeo+8H7tm34Q1jwzAort6Yc0jA7Br9k2ugc2nE9XHIoeBKq4CxDp40Mhlj6wcj5xTRfjq0DnknCqClZPcXvVMgVUVA7/t0lclpCPLwZSdwxsDKh3GqxePVqPpxaAPjFzFl2g8CLgeM2lXCD1Q5oYgmjFqmgQWHOaahb5trrcI23P/+hnAN88A5ZJSY6VMioZOQ/VJtiE6DVvmiP90Ing4PrGJcckKazq+5frZ9TUiztkjLW0SV16g74nwvxOBqpL635XOmc4sR2qbOiy79zqXsUWHmF2mo+RwV5vilgjdoA+MUoaJtCtEQ6DghiCaMWqahFT2OJIYtSd6Xl5bolbxYkCnIb1xdq44hO4NsP3P5vrjy9oZQmsByTEVIBbzLPcpugUvvL0nBl3dGgCw8cfzmPrJQZdlxMzC34Z0wNmD56HLQUca2ADK58yAaV56B1cdB8fzuOe9vZqrXyyvgZXjDWU63Bahq0xTyk0LAsrOvaRdIdyFghuCaMaoaRfUSrPVaXgDRecbZwa7G4sDdazolOmwcjz2nCrCs+uOoIRLxeaa+tYC0kaWSlysqAEAbPwxH4+tcQ1sgPpb89s7T4NFR5wPikEiisEYur8qnDODbQScs1+1dRxiwgJRXFGruveXNxzDe7tO68546BKhq21HoZxcK9iUyzB5Urvi0+atRKNCwQ1BeAKjFT6NRH3TRFffDbnSbP2430BR7sapeyySTIcYIF0orRQCGlZfQCMlLiIY2UfzMfWTA7qW58Aiq24gHg34Grytm7aIOL2ijMw5a4Bpnnj8WoGNSL7OwMRjpoGSacoTp07ixa0XNT8bb1Y/+aR5q59eF1oCFNwQRENpgBNrY6CkXTgb3hNVpgSEVF2AuwZ4RsuwlW6cuVxXnOdjkIBil35NgFBOzkgyGGKANILNxdwgx6korc7cLDikssdxTWgF+vERGJqlX4/CgkNGwG5B2+M0Tt3P/87nzA3TPKXMih60AhPDBnpq2KYpO7UfjDP7toJX2K63q58anIlyBz+/LjR3KLghiIbQQCfWxkJRu3D8dcPtFBww2EBR6cbJgcU8y0QsM2eC4x0DB07MkNgyGGKAlM7uxVLzIpdtqXXmHsnmYq6oy6kDsPoN/JePwTxWORiSoq1T0oHcOUvOgPWaUTi+dxOqLp1DSKu26HrdSJgCXC/RapkVLfQEJs5TQ2Iw6DzVZ0SkrJZB9Hb1U6O2rxBpIteF5gyVghOEu2hW+EDQWDiVAauWGrs7Dh1lx7JNExVLuNsCIa3gsQaKNtRuiJu4VEyxzEQBHJ/eCxCLfamZ9ptB7uli9CzfjiXmxWAYuGhfxPuTc7POkWwulpkzkeAUnIjB0Eg2V3XsLDgMYo+oH6Aqyucs+2g+Br++A7euB27f1Ra3rgcGv75DtiGmVmZFD2qfg3RqaCSbi11B07E28BUsDlyCtYGvYFfQdIxkcw1PIYkZxIQox/USooK9kzmxYSQT5RHcvC4QnoUyNwThLjqdWKUaC4/P+3si9a1Uwn18g2caKErQuiFu4lKxRUYU/HEXISCwcjwK936KpebFstNXItJmnXu5ZDxxcyc88sMTYKpdwzWWEbJDc82rsKWmn6wmZCSbi/nm9xDDXDZ0vPUonzOjUyaeaDmg9DlYOR4czyM6xIzrar7HMnOmyzKJKMbywExwVb0A/EV2G0qiXV9UPzV6Kwc3rguE56HghiDcRa/exLacx+f9PZn6livhTs6A9Y4PUbfhGQRVFtS/bqSBohNazrOAMEUldg+XajE2/piPF788jCzrv3Tvr0NQOSaN74P0sJPA98qfl1rn8ltMuVhqztQ5a8cIGS9zsC79jDtTJg0R3appW6SBNwsOc4MEDyQXbZHtd9O6B4UtpoyT3YaIc/De2M69rcOCPLqcJgavC4R3oOCGINzFgEeJx+f9PdCcUAnxyXtLXgG+PBSKkoqF9kxKXVgcMoaPR3ryFYa2KaKmvXBGqsX4V/YxvL3zNAaweUgK1D99MGpgb1yfkggc+V7X8s7l8Sw4/CdiDZhq6FcMj1mku+mkO+JdMUA0OjWlpm1xDrx1aYt4DvjsfoBdBSRn+Ea0qwedn9vuUxfBskzDM0kGrguE9yDNDUG4i6btf73GwuPz/kZS3wbIPpqPwa9txYR392DF97+huKLWnknJ4gbim/LOmPLxYVktiF6UtBfO9xNRi8FxPN7eeRqAfm8engfO87FgRX2LwZYAgJBxWDvCipBqnU/Yoa3rs2ViJqzH7cJPD3a/FgNEo7dfJW2LXOBtyAMp+1lY6+pUg3dACN4brC9zg4uXa3Qt99b2U5jw7h4Mfm1rg77fRq4LhPegzA1BuIsBjxKPz/t7IfWtp7zYU9UlctqLvu1b4Yczlxy0GADQ/59b7OsZ8eaZZ7kPhz87gn+MsSA9jANCol2dg+0w4COTMGPM/ZhQYanXgmx+zsBBzdc3VSfxPulcEQIWnKYvzy8XLiPnVJE9q6BU3i+SGBWMF25NRquwQE1ti1zgbcgDqewcju/dhPxS5UUMlY97GKPTeB7ppu6mdxHhOSi4IYiGoNOjRO8FVveF2MOpbyPlxZ66UclpL5x/zzlVhOKK+t5JWn44AFDHs3jc8jg2calIv/wdrv3sEUB1ikXYEJO+AGlXx9W/zFlRc2ANdCsxInTcCJ0E4N0B5ATHYm7tfchWKUVfsu0klmw7Wa9fSY5DethJjLi1GMfKQ3EiOAXFlVbEhAchIdKYSFcuoBbPs14n5qpL5wC0VXxfLCc3HzsHsN0a1cxOj85LikcCeKXrQmgscOu/qQy8EaDghiAaikbDSED7AmvYxMygbb8W7pQXe6y6xMA+tPxwAOAxy2PI5q7DSNYmBNZCSez72/cIqr2kuToPgNEzzaAgAI9DMZaaMzHVMlM1wAGErMKXnyzHDVFrEVIlNPHsDqB7A8zh5AJq8Twv13P+AIS0Ug5sHLyF9kP414hmdkZ0XiIeCeCTMwCOAzbOAiqLhNcqLwKb5gAMSwGOlyHNDUF4Ag2NhXiBBVxn4t0yMRNT32pblKS+tbx13AlUvGmVr7YPNT+cKZaZyOYGOHQ8VzylITHAfV8BM4/I3mhO/XpK/0C1phlUBOAMeDAMg38EOvryyDHCFrAFVRU4viFWyOVl6R+zDTHwlp4mFhxKEY4VdSNh5dW+k4J+pOt1I9E20owBbB4y2N0YwOaBBVfvLQSnzFkDxqsH5+/78OQEWZ2XFg0K4POygM8m1Qc2Il4+dkKAMjcE0Ugo6SQS3PW50Tklpqc812igEh1q9ppVvhSlyiAlPxxRu6Kr2qeqWAhIbK7Hzt4rhXw0rtExxuNdH0M3padwUV9zeoeqAJwBjwQUYf0YFt9c7oQl21wDK4eAzeVd9yvknDMbI6SZFsetu4waAJC+AKYTG/Gt6WmEBNYHXfl8DIIg9L1yDTB1jNfNvkxq3/dds29C7ulifH/yIpZsO6m5LbcDeC9WMxL6oOCGIBoRj5uYaUyJ6S3PNapLaKw+ytIbr/O4xCqu0dcmYs+PjtUtuqt9ft6I7IrOsjfDCf26oaOKvofjgQLEoKTvDPltyxksatA9sgonwyJk39MO2Nw3hxMD7+1frsCrlkyX92U/bzGIBoBPJyLE6ROKV9FFaY7XTXNKI9/3dQf+8Nw0sTNk5OdzaFqKIBoZ2TYIDUFhSkzLWweoL8+VTpvp4VKlxXN29RqIN95EpymFmDAzlt7dG4vu6u0yraK32offsxRffrLcJTNUUFqNN/53CgvZBwHU63lExN8Xmx9Gaqc2rhsW9TUGAhsAQHi8YrZAd8DmpjlcenIc5oeuBsMoTeUxQrn7be8C938tTOd1vRV89mzwMt8y3V9r5/EqnTuN6Rx3v+8emSZ2hoz8fA4FNwTRRDDak8qot44YRESHmHWNpzEExSLpKYnYNfsmrHlkABbd1QtrHhmAfX8fjlHXJsneqMRqHy1bFR7Ai2ZXrYu42jbmOkV9z1TLTAwd96DrDVB1SkKF0NZAu+tkNTCAgfJsd83hzuwGU3ZeJSvHC4LYiER7EJ27fb3GOjqQjrcBfZnc/b57pdcVGfn5HJqWIogmgDs9qdzx1klPSUREsBn3vLdXc73GEBSr9SmS4qxnslf7BGaqbp8FkMQIbRdyua4uGp5LlRYkD7sHd+QOQrvLh+3vnQ3viRfu6CF/7jWnJBSovAgs7glTn0lY3jMG83eVYB/XFVbbM6h2Gbx8hZyV47HnVBFyfr0IQMgaDugokzF0o53Ixm/3ITXQ2GGqjrcB0znuft+90utKbzUjZxUa3hrQFBH6oOCGIPwcd23t3fXWGdAx1rNl625iNKBzvVENAH+iBszeZZr7Gsb8gP8ELXXQtJznYzDPMhFXte6Fnc8OR+7pvsJ2w8xINR2HqeJ74LTMTakhUw1l54Htr6IngLWBwAXE4sXa+7CJSwUHFovND2N+3eu2hbXN4bKP5uPZz4+gpLLeK2jJtpOIDjVjwW1OwZnOLELunwHoa5sCam/E7E8O5yqzBkznuPt990qvK00jPx6wVAKrxta/3Ijl8S0BmpYiCD/GiI7AGaXpDREGQrDgHKRo6W94ABk9E73ayVkM6OS0MFNWH3Cxxxen7L7+UXjqH31tEtI6xYLtequu/T0U8I1LuXICirHMnImul7bX66QCf0Da+htg+mgMsO4h4MPRQGaKow6kyEAJuQZxKMLywEX4dMifWPPIAPzzuefA3PkREOkU3EUmuTRKzT6aj8mrD6Ck0gIWnEOZdlllDSY7nUdruzRcQKziVB5na2kxMycEe34tEqZ4NKb/eF745/I6AGvaY0JzVsl0609lIfpOjEwg5u733WuI1YzOn1VIK+FnlZOHEpWIexTK3BCEH+NOY0URNfMyLdFkekoi/jakg72nkzPv7DyN3le28kojRKNNRlUzPMnq0wMcAJ5nwYBzmephGeH9aw7+E7jhLuD4Bu0u7ACwfb7bx+6M8LnxSD3+LyD9PmFQOkwjrRyPf2T9BMDJRM+GmJWatz7Yfh5zz5RiZe19qgaJ8yz34XxNHXJOCd4taqaKYlAj53DM88CfOZ9gZd1f8dWPhfbPjgWHnOBYxKEYjEFzyoZ8372G82cV1gb44lGFhalE3JNQ5oZocRgV5vqShvakclc0aeV4ZB1Wbx7orUaIRgI6zQxPXqGq2SEDwMS4BjYiLACm7Bxw+jttoes3s90TEmvAAK5NUDVMI3NPF6OgrEbRRE/MSl1bvtMusC0sr9Y0SNxkd1CuP0aldRhGPrABhCAoAUU49H22w2fHgcXc2vvA8zx4HeaUznhVJOwu0s+KYYFytb8r9xreEq5Q5oZoUbgjzPUl7uoInIW4O56+0aUppdoTbEMyRg1FLlATexNJxb4FZdX4V/Zx7QzP7DEwKZgdMsljgT1LtQd1Zpe20LVcp4i45z3A4Y/1LSuBKy/Q/TRaWF6t6tLMMkI2ZoH5Pfx06lqgwzj7d0jLIBEA0jq2xroD5+y6LOk6w5gf8FDAN7p6UsmVt2dzqZhqmYl5gasQD4m7r0KbDGdkRcLto2A6mwMc+d634l1vlIi7aXbY3KHghmgxuCvM9SXu9KRSC+DG9rL1AOKswGnlC6LHu5gbwDlQU5pWyfv1OeSXJiluxyEAU5rKObNbX3DjwWTM4aCeaMN/o9r8U45j5aHornPZuIhgTdM/lgFa4TIG734QOPo8+gybD0CwARANEuWIjwjEgE6xmDu6C1auWeMQAOVyXfGfIB3n00Z7Rj6Lkc2lYnN1P6wfw6J7ZJXhm7aDSDgvC1hs3BDQK3i6RNxNs8OWAE1LES2ChghzfYlRszFdQty8LEEE++FoRVGsx7uYG0AqDFWcVmGKcfORpzGSzdXcXkHJZWFa6acvhBe6/6V+Kkcs2VWToUa29aiL7NsHqzHPMhGAqzmgEhf5SJwM7eH4ImcVjuvIZ8JPifdLaocYXB1yWf+gyvIR+PkkXefz+qvjYDq+HulbhmNt4CtYHLgEawNfwa6g6Zhm+hJJjL5O4jwPPBGwTnGfHFicDOulOPWmCzcNAb2G3u+bnoa3/nZsfgYFN0SLwKjBlz+hV0egJ4Db/uUK8DouiL6sPBEDOtVpFdvPuTIGfFJGsrlIW3+jciCntwHplWmCXkIBHgAPBlXB8TJakfrt1YQmIru8o12nUopwxW0C9dVGz1smIS4yrP4NHQFqz25dVbctdwRa5xMA+lZ9J3tTTWSKMcv8me49igGQ2j7F4NktnVwDDAG9hsGGt4r447H5GTQtRbQIfDnN4gn0mI1pBXAMOEy3vAcw2s38TKzJvcoTD83/p6ckYs0IK5J2KgebDHi7AZ/cFIqY9XHp+iitbkrO0NeA9PR3AK980xc2z2Np+fV4IuAz8Izzk6OwxOGUZ8HtrH8nCpfB88rCWwB4u240DkcMrQ8kxSd2haqtg2mLMPXAFbhQGo6BQWqmf67HoHY+AUH7NOb8Ytd9w9XNRQ8sAyTBdZ/S6Va3dXL+2t9JZ8NbVfz12PwICm6IFoEvp1k8hZbZmFZgZrTxouEu5h6e/09tU6drOTlRqlrWR7bkVqu8WqfA8wyfgCmWmS4aIfHGZQ0aBOzc4zA+pcDGyjN43PIYvuHSsEwMJDWe2HkwiN89DxdqFgEA1tTdhFkBn7mUaauh1sPqOvY4wmuUz4W4Cw7GpgWk+5QGz1vyCtzXyflzfycd5fyq+POx+QkU3BAtAneEuU0NrcDMncaLuu3pNbIJzgZzutApqrx3WH/s+t6M4op6F163OmiLJbsNGEtrpgQrren2yqFrQisw9+6bYLpqEMCakMrxSIwKRvvyAxrjA0wMD4S1wbKxkhu4xhO7mM2aZvoCEwK22fdhxNlFrYfVXd3MgB6PQt4W4OjcsXSfYvA8PDkBg1/bav9GseBwHZuHNDYP4IEcPhkvZ5ntPj0u+Ht/J7Xvmxb+fmx+gE81Nzt37sSYMWOQlJQEhmHw5Zdfaq6zY8cO9O3bF8HBwejYsSOWL1/u/YESTR6vdwH2Ac46hL7tW6nqZNxtvKjZxdxb8/8a4kseDCpDEmBtNxDPj3KcRvF4B21NIajAi+bV2BU0HcPZ/djDJeOjy/2Ry3e3P5GL30O941syJskxM6FzvE8ErHMRYXM2/Y7S1JHoQJzLuWp1WAZ4dEgHjB3cV9f+36gb7+J7Iw8DPrItZjxwv70h6q7ZNyE9JdFhmnUkm4v9QZOxJvBVTA/4EtPNX2JN4KtYX/MATu74RH7TnhTv+hvN+dg8hE8zNxUVFejZsyceeOABjB8/XnP506dPY9SoUXjkkUewevVqfP/995g6dSratGmja32iZWN4msWG3uaNjYmSDiGjZyLe2XlaViej1XiRA8BEtgVj9ILorfl/lf48HADwPJ4ovQub3t+HmDChhFn0w+nM/qFvH3qfbG1j4T+dCB6OT4XOmhnRIE80vfv+5J8O3530lETEDOsP7NSx3z+PC3ofccrCwJO4nLeN6BrsLEOSOhBL/WxGJMfjug4xuC/tKgQGsEKAquL4zINBPh+Dt6x/wVvWvwi+N+x+PGjKlhmT8AuTvgBpV8e5bEucZh3J5mK5OVP2GFvhMlrtmArER7hmBjX7O0GfeNcfac7H5iEYnpfr/NH4MAyDL774AuPGjVNcZvbs2cjKysKxY8fsr02ePBmHDx9GTk6Orv2UlZUhKioKpaWliIyMbOiwiSaIkWDFH03/lPx6xCP425AOyDqc7zDm2LBAFFXU1otsIW+v/8vQpehy4z3GBnTkM6FiR4vx7wtlvUaR0fKc52Mxz3KfxDFX3g9HVawb2RaYeUT3DSD7aD6+/GQ5XnTW08jA8UAxIvGy5V5cQIzdBK++LUQc+MwUoCxfts2Ay7hF7VLXW4WqKKXgQkOcLFJjboUgS31vI7nzCQCL7upV740kYp+CBJxvqjyAybUzXLYj99kgsq2qgDbnVBHueXc3vg+ajgQol5fzAJiQWODJ40CATItyWS2Y+r6bDM352GQwcv9uUsHNkCFD0Lt3byxatMj+2hdffIE777wTlZWVMJvNLuvU1NSgpqbG/ntZWRnatWtHwQ2hiVYQ4QvTPyvHY/BrWxWrokTtkLMjcUFpFZ749DAAJVM84eY26s6/ud7MtPh1B/CRjgvp/V+7rzHgrLD+9j3mfbIVJyrDXBxzlYI2tf5G6H6bECzoEHNKzzsLDpNM2XjRvFr38MVeTpttN/1l9/ZBOrtPNkiQGzNvaxUh9q8SMki8SwZJ8VidODHoP3hxW7GiA7HImkcGyIvYFW6qP/f+O0Zuipbdp9Rl+tFbB6J7WrrwhoKo1srxeHz+Yiy1vKh9QAAQGguMzpS/qTdnF9/mfGxOGAlumpSguKCgAPHxjmnZ+Ph41NXV4eLFi0hMdL3RzJ8/H/PmzWusIRLNBKPNGxsLvX49P5y55HBTEhsdAur2+pOMVovlZQHfPKOxkHKzQ92wJuTy3fHR5XLXt1Qqo1Rv9D99LvwD5Ku6JDeN42UhuFDK2fbG4iIfbWj40qmqzVyqQ1sIPns2GKdpPedxM7ZKKCb7WWDmEZy44S1EbH/eJYOkJ7ABgE6drsaZ/RbsdVdgr1Dt0xksEvdslRXuc2Cxl0tGQlQw3ki7CTi+XrW6zsQyeLR3KKDtKyhQWaQsXm+IeNffac7H1gCanIkf4/TXKyaenF8XmTNnDkpLS+3/zp496/UxEk0EFYdXfzX9M+TXIzm+VOYntI00S0p1BXv9LG4g9nDJ4G1TJoaqxcTpCdVGgLY9jnhVuBHKnGu9KB27WBnVoBjT2dXVySSv+5a7sStout1NV7c424Y4trnmVWDAIb+0Gnt+LUJO0CD8J3kd7qp9HovrxgFQDlAYiXbpeKuhGFyzWFjPor6e81YQ2Ra4ciDu6t9OMbABdAjsZZp36hbuH1+vy13XmBGhjRZuXkcINKnMTUJCAgoKChxeKywsREBAAGJj5f0/goKCEBQU1BjDI5oSGp4s/mr6p9eHp+ul7UDmK/bjMwH4NiQBT7B3YROXqt+UTwFrXR3qvn4agSp+vACEc5oyHtg8x/Vcj5wvTCXoTKcrHfsw5gddY1ZH4n3DccBnk+CsaZFmX7Zw/ZDPxyDeQH8oZ8O6aR8fQEmVWL6uv4IKly8gLiLZHqDGsTrXs3Gw+2xMfX2HYvCuJbDXQlO4nxwHZKpV10k8iGxVQXzZeZ3l7G6K11vQ1E5LoUkFN2lpaVi/fr3Da5s3b0a/fv1k9TYEIYsOT5a4iEG6NtXYpn+pHWIQHWpGSaVF9n0GwF/DD+GaHa/D+fhCqi5gWeAizAl4Gmsv97K/bvRmln00H1lffYqllgLthXvfB+x4zWUsKDsP/Pd+x9c0DP/kvIpGsrl4KOAbXePWxnZj3DjLdbyo76Q917wKrIVHEGrdyhaJQUx9YCNgpFQ/tX0MEiKDUFBWo3+90NY42PNF3LatNXjIBzZPDLsGj93UucFTrar+SKe/M1Zdl/4amE/vMzaAyxf0ByzUfLJZ4tPg5vLlyzh58qT999OnT+PQoUOIiYnBlVdeiTlz5uDcuXP46CNhPn3y5MlYsmQJZs2ahUceeQQ5OTl4//33sWbNGl8dAtHU0PRkEZ4aU6f/6Jemf1vyChQDG0BosTDX/BGYOvnjYwDMD/0YY//6CAorLIZL20WR9Ri2EJApTHEh923oNuXXMPwzsQzmju6CD9d8jDQmDwzD427TVpey5gZTWaT4lph9WWpe5HajcKVgRKtUnwcDxqZdMrEM7u7fFru3fY14FKOIj0ArlCusBzChrWGdmYep/96lGNgwANbu+x2P3dTZzSNzRNFR26i7bnIGcOcqYP0MoErnNHDRKVtVmUbA4g3zScIv8Glws3//ftx4443232fNmgUAuP/++7Fy5Urk5+fj999/t7/foUMHbNy4EU888QTeeustJCUlYfHixeRxQ+hHpyeL6WyO7t5KjeWDI4qc1bgp5CRCqtQzKkzZOaQF/Az0MiZClIqsdWcLqi5pL2NHpi2ClLwspG+cjvRAI9v0HkY/Yo4HCiBvkgcIOqh5lolYZs50aZfAwfa9E71L8rLwyA9PYXpgfaAgNtmUam843qZHHP0Gcs9exoXSSgyQEZIDjjoytTYfDcYdd11RwPzrTuDTe4FapY7nDBDSCtj+qutbzgGLzgcd2e9iE8EfPboaC58GN0OHDoVaJfrKlStdXrvhhhtw4MABL46KaNYYeGpM73G9pumfV31wnNLquXVdVEXOABBWoy+jwpWex95TRYYuelKRtVaWQbjJRBsMbgBFzUReFmB0asIt9LV/1FuVJCL6CC1kH5AtuRYRu4XPNX+EJInDcAEfi/KhL6NLcoY92xDsNE65DFYBYlF+g7CeaeNK7Aqa52QBIJSoS31pvK4jE911Fbx6FKvrWBPQ+UZg3DJFnx3X16Q4BSzNvPmkP3p0NSZNSnNDEA3G4FOjmnZAyQfHpamfO2JFGR1Ar+B4jGQnONyIpN4hhYhGa6ZE1+EtWp+DRRXh9t/1XPSkNz21LIPdk+W6KfJP0HqQBqGcVUe5uafwkO1XYLhDdqE6NAFnU1/E+CtG4vP396quqlSq/0arPugiyTY4BzKiJqiIrzcPPBveEztvGA7kZaF/7kzwKiJp8XvldR1ZQ9111bpq97lf4zsnCViacfNJ3demZgwFN0TLwo2nRjntgG4fHCYXpk3PGhMrKugAgqsLHW5EcmZ8l/gwtaO381tViMPvei56rcMdqw6VsgzlgXHIT5uLzoP/CtOBlSrnWgVpEHpmt0a5uR8iCWz40FiE3voaunQfh84cj5iwQBRX1KquLlZCSYmLCNYU47IM0BpluIAY7OWSsSyjB0zg7AGRXEsGUST9bU0/xEWFNo6OTC1A0eOuq9RV+6cv9O1fXEcPTaz5pL96dDU2FNwQLQsP9WTR44NzbflOsP9dBJcbu5pYUUUHwIAHz9RX67xlXuSyTDRToTpukQtOTQ2lF72busY7uBvbp6xkrpayWYbqruA2sUjcswNL+zyL3jkzoHe6R3ZKwq0nZ9tnOfBx4OhnjjfQ0NZA5UU3tikgHoXcbUFuaoivLAL+OwkM8xFMyRkY1ysJK77/Tff+7OL16l3A1zN0rXNNaAUmjbMFqraASOk2Joqk+7PHMWnMvY13w1MKUPTqW+TM64wELO5Oj/k5Rjy6vKqt8jEU3BAtj4Y+NUJbl1DvmmtQrKihA2ABJDFFeMW8QvjdxclWHTVRq3jRGzD/fw6ZBXHKqqaOk9+mTJYBELJBt21rjc9vXITePy3Q0DdIRu8cXIa21lhPBulnOewfjjfQ8nzg80eMb1MySrG7tmOLBMhGNywAjudRvf5phHS9FcOTEwwFNwCwtM8fMP13BvRmwObefRNMHW0ZOJ3B4ZzB0ejp5lSF28JVT7vrGglYmmnzSX/16GpsKLghWiYNfGrU0iWIrrnKKIgVdd6IYhnXNgRaiJdu587PzkgDGxYc2pcfwMZPtmJInxSwCLev66z3ce5PJN7npx64AruePgLT2Zz6c11RJG/sJxdc6lTv8kGRYG79NxCR6PhZOt9AT3+na3tqyA2Jsf/HFZYBQqoKYP3te6R2uN4+NaV1DmPDAvHPcd3Qe8twGMl8ma6S+DTpzGa45QYMPxOuGg1YPPCg42/o1Uw1tkdXY0PBDdFyacBTo5yhnBQjbrMOeHJ+PyQaqKofR21oIqaX/NWlY7MSLpqeo8D1wbGYW3sfeECm+aZr5Y09BX6mFGmdnM518hh7cGkJicOq84n47ZcatC/6FfelXYXAANtNvuJPXeNdaxmCVuz1SO+gfkO1tktDXWgCAisvyHbkNsLiunEAD0w3f6lr+VO/nsI1HYfglbEp+GrtctVzGBNmRs6cmxF49nsdWS8JztmG9gNRFZKAoKoChZDW/ekXvxSuGg1YGjo95mdoXZt85dHV2FBwQxBuIPbQUfLBMeI264CetHporD7NyB0fAQxrv2AHtEvDj6/vAKNw0ZMi7bItJQ5FWGbOlF1frvJGRDYFzppgbT8YM9YexIYf88GjPhv1z43H8Mj1HTBnVLLugO+r6l7Yq3FDFbMM15bfZT+OhkhMdnMp+gNZAIV8NK4BMCpgH24JzISzE4Z4DqdaZmLcXyYLAZ5ezVFIK2DMYpebd3ZeIb4svQtLzZng4OSfY/PFYdyYfvGVcFXXFJjRgKUZNZ/UujYBxlqtNFWaXONMgvAXxB46CVGO6d2EqGBMmjBBCFIUVTC2BoZyXh7prymvAwCj/q1v21cNhrX9YOSE3oivSjsi90wpXri1m3RLsqh22bb9h2Fc35M2h2ThqM+RS4FnH81Hj39swtc/5rt2kOaBt3eexvyNefUBn8KoOR44z9friOatz4OVc73lilmG/NJqe6VXgZOwGiExQpCggXSfegPZi3ykMF1kE40zUDiHDPBG1FqhBxOgP5s3/gOXwEYMQLIVjrcAsZgT8DSsXcfo24eExmgua+V45JwqwleHziHnVBE2/piPwa9txYR392DG2kOY8O4eDH5tK7KPylTTyTT2bCmoXZtaQhk4QJkbggA4K6y/fY9Tv55CIR8N01WDkNqpja4nG9UeOib1uX/ryPnIPV0i/wQqZ4AX0goYs0i4gbGspq4gO69QVgsxLDkO/ztW6JA1kG5FSy+kGhgxjs0hlVLg2UfzMXm1thnnu9+dxpMjuiLQpqMQPHTqBy7GMFIdkdhxm2UY+7nt274V5q3PAwMO10k0LkNqMtGPPYE4lKAuLA5vPjlN+AzO7AZ+3gjsWQrnc+y8T9HQMBHFsloc8Tw/b5mEjKo6XaLxkKqCej2WZjbPxldTgFv+5RDgSAMQJf8crobFWDcqZwrLqzU1Q+Jy7iCn5ZGjJXm3GEH12tQCoOCGaNnkZaFq/dMIqSrANQCuAXB+Vwz+bn4YQ8c9qOtiqdhDR2Xu/2D32ZiaFY780j32lxOjgrG0zx+20mmZm5i0r46GrmBjXX9M/cQ1eMgvrZa9WUhv+p2ZPzSPWYth7H7stVVQOafA9bSREG+a8SjGgbX7kJrAoLjdSASfy0E4V2pfrgCxmGe5z2UazLHjNhARHICBtbsxN0he45LFDQTKgfvOlAqfZYfrhX9XprmcY+d9cmDxkmUilgZmAk7tD0TerhuNbG4A9n31E0aMuaTvwitOR6mKZCWUF7hYDDgHFkqVbe4EIF0vbceuoOc1dVfuCFeVtDxyGJ0Ca0ktCRSvTS0Ahlfrf9AMKSsrQ1RUFEpLSxEZGenr4RC+JC8L/KcTwfO8iw4BgKB7uHuyW0+DDhfQMDNSTcdhqigEwuORfbkDpnx82OXCbQKH74KmI5EpVsiO2ISfM4/Up9hl3I83Hr2Ax9YchMzMjCxyZoAN5SIfibFB7+OFjB4u5y/nVBEmvLtHYU3t8ZTxwfjUOhTfcv1kMwVSxCBpGPMDHgr4xkVjI56jaZYZuIQIPDUwCv26d3PQZ2Qf+QNZWesQUFEom50QN/dmrz/Q59gCh3Ff5CPxguUBfMNdZ39teOgJvMv9Q3HMdoY+BwydXf97XhbwzWygXE1c7Pgd0TrXImseGWDsJij+7YB3OPvi+ZximYnNXCoSooKxa/ZNhoIHK8dj8GtbNTM2cmgdh19VdhGGMXL/pswN0TLhrOCzZwsXZwXX1hfNq3BH1iDDgkjlC+ggDG+fgHmvbZV9Iu3vTvm4kxAy+2g+pn5yUPdYlYTD4iOP0R5KIq2ZMuy8K7jea0WCWpZAHI/abiNQjQdN2dinEdjIBUlyLQt4HnjTvBgBDA/sh/DP5iKdzfW3BaKdAch3y06w+wD1wuBDSZrTNP+r7Iz8oBgkKAaxNn5YCQx5ylErUqd1w7d9R7bNBzregNT2aZ6vnLFrhpRbQIiOx+4IV7W0PGqofbf8srKL8BoU3BB+i1fTx2d2g9Hh2tru8mHknu6r+6lW6wI6c9g1ihduI+XjcucGgOZ0jxRV4bDNkNjVk06v0zCETJUMStMU0vGoBVWMbQhzzR9hS00/2QBHKWhT2l6Ai4v0efCf3odL7Ghcx/ZSzBDFhJmx4+kbERjAIudUkeK0j7M2ZU3djZhlXqc+sPLzwN7lQkau6JSxPl3fvQ589zpMkUlY2udZ3LattVuVM7J/g1qaIdvfzpoRVqS6ESw0xFxO6btFLQlaHhTcEH6J19PHOstr41Ci+2Kr5wL6we7TiuvrrbrJ/TMAM5zS9olRwbirfztDT7xuCYd1NSe0oVDlo+TDoW18KBkbAySh2C5cBoDoUDNKKi2qQZsRGAATuK8xIfBrWS0JABRXWPDDmUtI6xSreFzyPcDCoYtNz7l/AAD4snz0ypmBN3u9hld+7YyCMvnu9nIo/Q0u7/kreurYd2qbOrfG7I5GRysDRS0JWh4U3BB+R6Okj3WW1xYiWvfFVs8FtKTSovi+WHWTgGKFmzKDqpB4TNhsghWO+ykorcYb3/7i8JpUlBvLlKGIj8QFxNizEHozRWV8ED6sS0dYsBkpvW5F6uDRwIGVKk/v6qZwSj4cRvxiROJQgolp7XFLSiI4jsc97+81FCTpRY+Hj/S4RJQySFG47PKaN2DAg+OBPsdeAxu4DE8MuxpXtQ7TzISq/Q3O31WCtYE6du6mIaWWCZ0zejJQ3m5J0JJEyk0FCm4In+J8URBLdr2ePm4/EHxkEviy87KqDbEH09nwnrr1CHovjNEhZpRWWVy9XWxVN8sCM11KnmH7bZ5lIqwyI3belpooV8xC6M0URTI1eNz8FWAFsPMzVO1LQEjvO4Hdb8rsXV9PHtGHQ5oZ0G18KKEQ0ZiQkoi0TrGwcjwSIoMQd7nE8Ha0kGpJnKfCpMGveFzPfXEUJRXVihkkUesDyE/B8QpVV+6OPQlFuLLiR2R+W4dl9/ZRzU5oZSD3cV1xAbGIQ7GCw7P7jseAugmdHFoZKEB/NuhieQ2sHO8hjZ3TmGTE/y3Je6exIRM/wmdkH3U15Bow/1uvG4MBAFgTmPTXwIBxqSoSf3/Jch9eyOih+0Kn9wL6wKCrALhO+zAQvEgOpS0GE+l0oQ6NRX63SfitMtDFIM8ZMVuQAPlzlGjLQkSjTP/0iISgqgLwu98UOm47jzMySb7buQzpKYnYNfsmPDHsakSFmO2ZKz1VXjwvBGnS4HNLXgGq6zi3giQ9sIzQtDSVPQ5A+LwSZaZC0lMSsWfOzbg59CSSGKUsnM0ZmIHs8XoqsJEiZsaUTA5FtDKQVrB4sfY+KIcdfIMbTiqZ0CVGBWPp3b2x5pEBWHRXL6x5ZAB2zb5JM5MrZoO0TuvLG44pmwLKIDWGlCJmme3bycsCMlOAD0cD6x4CPhyNmoXJyN24EjmnilQ/D8I9KHND+ASltHdxhfK0jRSPdLRNzgBz50dCt+aqAvvLBYjFYvNDGHeHPp8bEb09XR676Wp0SYhwedoTn0B7pyQCw++tN5L78VOg8iKSjn2AtYHyXiIievQmjC1r8KL5Y3xQNwKzzJ/rPkZhH7bb2tF1wPTDwNm9Qqftij+BsDaC2SBn1XVz25JXIJlOYzHPMlFoi6CSuRAzHll1A/HCHULwKf0+5UJreq9hxKFEcyokMIDFY/3CgVzt7ZUiDK1Q4dlBylCIaF3aEj1/W72ZEx4enSsNNqGTZEpM4fGYO7oLpnx8WDYb5CD4Lo/GtNWVeOvefqp//7pFykwuTP+932Wv5ooC9Ns7A1N2ncasiCFUju5hKLghHGiMuWO1i4KIlvOpxzraJmcgpOutLg7F/9TpUCzFSE8XzQs3axIcivcsg/NFUU3/oVdvIk5VHDN3Bx+yDYyzG7IGjFhyfHavMM5v5zpocGpCE3A4ZQ6sXcYofoesHI9nPz9SPyZwuIb5A5UIRjijfIMVK7nuj9yPkOQ4l+8TpzNIcpdCROuaCqkJbqNre1MtM8DbNFCtmRK8aF7tqaECqJ9iFdtTAEBhWQVwOk92ikTrb2uO6WP8LWCjehYk+1mht1MDp12UTOg0r1N5WS7mi+mRSfj8xmcx9cAVDg8VclO45/kYLP7yYQxPfk7xOqBHY3ehtBJ1G56BSeZqJ53qvL60H5WjexgKbgg7jWVwpXVRULrYzLNMtBuDebSjLWuCqeMQXNNxCK5p4KbSUxKx7J6eLqZvcZEhmJB6JWrqOOScKtIev81LRC71r6r/MCjKfSw1CsxVi4FP7zO0np2fN8oGYNKn0mkhg3BbzwT8JfYsukVUgo1IANoPxJ5fL9kF1iPZXMw3v4cYRp/QlkF9i4JcLtnl+yT2jppvfg8xHhTv1pijMeOe+zXbc1g5Hk/sCcV/Ndoy5CMWe7lk+2fIgsPDARs9lnWSa08xks1F+pZZQGV9tlL09UFyhmoG8hZ2L/4WsEE7YHT2Y/IgmtepvCybm7NzeX8+eufMwK47PsTK4h54ecMxRcF3AorxquVf+GXHVehy4z2y49CT4UpljyNIep6dEB8y+rPHsZdLpnJ0D0LBDQGgcQ2u1C4Kt7B7sdS8yOV1h27JYyb77x9/XhbSt8xGuuU8YKsoKQ+Mwz+5SXjj2xr7YtGhZgCO1VMOF2idXiLSUmjAuCi3Z7euwg1o6HPGfFREfvw/aAVgL1fxePCHVY4ZpcgkXEyYAaCdLuM+RS5fQKG1o+xbYi+laaYv8WBANlrpDJzUCOo+SsgkaHz/ck8X41yZBVmmgXg04GuXDFL91FqaQ3DKgcWbAQ/iVevCBo8VcG0Vkc7mYllgJlDptGBZvr11gyk5QzYDyYLDK+YV+jNheruZG0DzOnVPT6RvkX8oECeLTJvmoPWN2apTuOL3t13uS8ANd8lmoPRkj/U+bMShhMrRPQwJignNuWNAW4RoBKWLQjq7B0vMi+1CSymy3ZL9Ac4KnP4OOPIZsN3W/8cpKAmrKcSrln9hJFsvwCiptLiUhYsX6I0/5uPEqZO6du988dQryuWdu5IPeQqITNJpz2cjOAqoLFJ8WxTgLjUvchE382XnkXFiNm5h9+oy7lMkPF71JsOBxZvW29C3Zjnuqn0e79Wlg+NdRbwcLwQcms1oDn0iCEPzslQXE5tKZgTsFm6pCiaJGQE5DgLxW1IS8Mo9N7gX6NmoMbfCzNqpuKv2eQyuWWQPbEzg8KJ4rl3Wsh149rMAZ5UV9KayxxHLlOsfiJul4ErouU5lZa1TfSgQHZw7Vx7BdWyequCbZYBQsYGpDFoiZQZAXZi+a5X0ocQjekKCghvCmMGVJ5C7KIxkc7HUvBgmRvnu4tAt2R9wroDY/iqUshiAkMVQq3QSHYEfW3MAL269qGsIhYjG6GsTYYv97HoTQL4KR4QBHCtaWBMOdn9Wcz0HrnAVNCvh4oBs+7nA/K7qDUYVW3CmpxJGdA5+pW4iplhm4gIcpwULEIO360aDh47jF7McKgFOXESwXf+kdvOUVl8Bwt8i08CMR5DlEu66+TqciejjkBUaGfErklRbPvD100mor2YTK5P+c+05/YMIbe12KbgSeq5TAQqu2M50K/sOywMX69uxwuchauwA+cpHAMjIGC9M+SmcdY4HzvOOeiiP6QlbOBTcEF43uHLG+aIgTQ/rwgvpbsOI8/qqT4n1yN3IlOB47QyMeFHcx3XFD2cu4a2765+yRb3Jn4xCajuyrUu5dvaRP/DajkK8X5eOcoToOiZ0HKprMaWMDAMginGeHzGALTiT+z4NYPOQwe7GADZPNqDkXYJQHgf5zphimYkCaOm5HLMcclyqqDE0JSFSVFGLY+WhutZTY2POIbxwa7JDyfSSMUn6Vpb8fYmC3rHXJiDpd/VslQO3/tvjHi56rj96p2XZvcsQqVeLpZKBUipZT4gKFqbye1whaJkAl05cznooJWsBwj1Ic0PoflLw5BOF1MStffkBY46yGuludyu+dK9na7rpernSRu8NT1rxw/HynaznWe6DFSzyS6vRKiwQu2bfJBn/ALRu/wJwNsexTDsi0cU8zPrTV+i97gmkB9ZPMVl5Bix49aminQuB4FZAdQnkMlbO4/YoNzwrlJwf+QwIj0d68kAsu7cPtn+5AtMt78mK0TdxqSoC0kv2CrTBNYsxyZStUbXEy4pmrRyPPb8W4bkvjqKrzhtte8bRU+VkaA90j0wSMkTGJgrtnKgMw6pPBK3c2F5thRdPJ+ha95djh3B1j9sdX9y5UHUK0oG0x4Hu4/QPVid6rj+5XFfUhCYgqPIC5M+dbc6b53T87eozI9SsfEzOAO78CIxT9ZZUD6XHZZkwBgU3hG5/Fk8/UYgXhVNbfwd26VxJqhORwd2KLyPr5W5fj1SdGRtnjAh+xQzMXPNHSJJoVpxFooDwVCtbNqtVrZKXBfa/96MNzztkzlnbN0E1QKmuLx93dlT2licZB4AJbgXmwIfAjgX1b0QmIT3ldoysexNwmtoUxejTLDPwgnkVAGUBqViBdpGP1jcgSZbD+Tukx2+H54EnAoQGmm9Z/yK0xYgMg3XkArD/vR88HNPrHF/fulQ27uaBYkQiHsW4js3Dy1nm+uqb9gOFm7XKd5fngbCfPkb2kclC1gEQspR6xeZXjwBGvqJvWYPouU7FR4Ui4NZ/Af+9H65NXm2/awqrJOg0I5T923N2JLZ5Qh0+dhxvH6xEdnlH+7ShHmsBwhgMzxv5pJs+ZWVliIqKQmlpKSIjI309HL9BrEIA5P1ZvOq/cPo7QbeihztXKbrfKlVSaB2DkfWyj+Zj4ydLsDhwib7x2hC9RgbXLJLtLq2GlucPAKx5ZICuCguH7FSYGQPW3wAodEfneYADo6qDEt+pNUchyFJqf/0iH4ksaxoeDNik5xB1wdkqjtx5rhVv+q2ZMs1l76p9HjFhgVhqeVF7w/d/DXS4XvE7JK0E0xJM5/MxWGR+GEMyHsTLG/JwbflOGUuEWGTVpeFvAV8DcM3oOe/nPB+D8qGv1Jczb39NV6Ay1fwS3pwzHSZwgq5MbzBvOx/eQvd1SsbnBpFtgeSxwJ6l2jsKiQHGLNLltC2L7P7ry+2pF5V7GLl/U+aGACDf6wdopCcK+xOlShqeMQG3r1C82OippHjuiyOosnBIiKy/mOh2GU0WUvrz1uehvcFyazmvEUPr28SwSrQKNaOgrNrun6PWDFH6+Q5g85AWqHzTYhjImo85LGP7WVNbi3KEo7Wt3Lo1U4Y7TDtU1zVKTUg8QphawTTQICwDtIZ2YAMIU4fpo6cA/1uu+J3kwaA2NAEB7dIAle/QJi4Vb9TdjifNn2nuNx7FmF/3L0xZW4t8LhX5EErZ5QLbg3xnl4ye3KeegGIk7pgGxEcIfzuxnXSdg4CKQqEkmc3TH9hoZFU9ge7rVHKGYCLo3MvpzG59wc3tHwCdhro3SBWfHWm5PZV7excKbgg7DbY7dxfWJDzRfDoRrqlk4bcT1y/CcUt/xCncwLUqKQChtcMT/3cIQP2UU1RIoP5KMc6K9uUHEI9iXOQjEINy2akBZ08TuWkkOZ8bcWrEKJcqXY/LORiVyyy404VbiUimymXsETaXYSMuwc7LitmIY12nITl1BLBqrGcGrEIhovHyxp+R2OdZ9M6ZAefvpHCcPKaX/BU/vr4Dd/Vvp/odOsPr07qIn/+L5lXYbDNnVApsRQ8fsev7i+ZVaCXzfWRtJed2x2Cd5dmFiBYEvCYD4v0G9pPSvRu91ynW5JpF0nyQsuls3M0+qZhv2h+XPOTeTKhDwQ3hgJLdudexie6cU7lVIQmYZ5mItZtjABwCIH8DN1rJVVhaiZWfrMYdXQIwgK2TneqRYvp5PXr8+CrWBtZf7HneVZMi3uD/YxmPM3yi7DTS30d1w4ODOwCAwwX6UkUNpn1y0E0JqYCc6aJSdsrTDSYVY2CmPgumBMcDJQhHDQKRKKMvmpQ6Hajc1uAxVplbIaj2kqpeZT93Dayl1bhtW2t8fuMi9P5pgaIQFKXVkt5Y8hg5z0rmjHKIgc8ANk/Vf8beKuPMbqD9QNSEJsBcUaB4DsRWDTMiggFWp1fN0Ofcn8JxA7evU6oPUrYT0pAgTcN8U0mITngeCm4I/8EplZz7ZwAmbDbB6hR0yN3AjVRyObR3OA2M12hGOZLNRf/cRZB7GnO+P8hlaVwOMzHS/pTpfIFexjL4R9ZPKCirkVtVE+epNBPLKGa1xHJzJcErzwMVTAhCUdUgzwitpI1YqD3H8jC2cI7TMPu4roiLChXE7GcaZgpXjEg8V3EflpoXywqlxamrnUEz7a0+ph64Ajue/BEzX1/q0E7DyPSi1nmWQ8yq6dFb6c7AXb4AsCYc7fEceu+ZrhiYv2S5D/HiOYeOKeOIJMEEsqmg8CAlaGIWuB2kWTkep06d1NfCxR/sLJo5FNwQ/oUtlWzleMx4bSuscL0py93AtSopRLTaOzg3ozSBw7zAVeDBu9zOGNs0QhEfiZct9+ICYnTd+C5WKAcuYsp9ydaTeONb9zovS6fS0jrFKma11MrNAeH4wlHl1hgU6TYWOPM9UFlvUlhujsMzFXfbz7uYsXAuj7W2S8NFxKINX+RWifnndQORzQ3AFAvroleR4vBdKE3Fqr1nsbG8M4DOxncK7fMsRyGiVXusOVTK6cwM7Sow4YcLvyBzR2uMYJWr8DZzqVhmL0nWkem45bWmN8WipMlx8zhEPVv78otYG6hjBQ+7NxOukImfn2PleOScKsJXh84h51SRx1og+DtGXZPV3EJFNNs7wNFFmAHQnz2OBBQp/qGwjCCevYAY7JE0QFRDK8tkYhnMGHY1lt7dBzFheq6U8ohBjdr+xHJzbeM6eYzWWh6OuB546gRyh3yIFwOewF21z6N3+X+wiUt1uenbjdBs2bncM6WYW3uPcIt148/gW64fAOGYh9RkooiPkN2O83fht6IGGA3a2MSlYqplJgrQSnU50ZwxGmVYZs50aVshBl7SVh52w0elbULY5sT/BeCNb0+At41ncM1i3FX7PKbXPmZv1bCFT8Vbd/d21GyJmY5Ip6KCyCQXM8gmhajJ6XG78LMBgc2U1QeE65Fm+xOmUYTXBGVu/JrG6tLtj7jjmqxUSQHUt3dQe2oW9Q6TTNm4yEejLiwOUZLyZjWuCa3AxIzeeHnDMY/4BWUfzcfLG/JQXFGra/9y/HaxAoC2P8gmLhX/q+mDPUHTFEXSShQhEkF8LcJRrUs0PH9XCXriZ7yz0wwe/R3eEwONhwa2w22xZ9Etohhs2EmAiwNYEwpKq1CCSMM9qKQ6EpF+7AlVnYpU+wJcpWs/SlNILDhcx+YhNeQPVHS6E+t/+Rmjue0uXjXiDfFly714UacfDwcWPFi8ZJmIZYFiRtLVb0iuUk9WrMwDrcKCXA9OI9Nh5XjsOVWEnF8vAhD0MAM6xjZOebOzn0wDMjBGcdazqWfpPKDpIXRDwY2f0phduv0Rd12TpZUUBaVVeHnDMZRWVBtq72B3prUAF/kIXcYqc+++CaaOSWBZxqWbMuA6xaJG9pE/sHLNGvRHCQpZV52FHh0GAKzJ/R2P3XS1PaslNy6RfuwJtDbSFNHGK5a7NZx8BcQAYx/XFbnfnVasJUlnc/G3A48jHhI33JBo4LqpuMTeZrjCS+nmrnc714RWoOcV0ViF31WXk5tCKguMQ1mnsWhz6r8Iqi0BrABOAFcDKEcwLAhAjKQFQAFi8ZLlHiQxRaqO3c6i44SoYIwbMxkM29dFR6JHA+aM4oOFXPURhGvVs58fcaj8W7LtJKJDzVhwWw/vXqc0/GS8jVyGWcl8s6GaHsIYFNz4IUa8V5qr8VNDXJOllRQhgSas/GS1sfYOEmJQbs8oyGUMOB6oDk1A6FWDADTcL0iuFYJz+wA9OgwAKCirsetuxHEpiZXdLQuPYS4jhtHXo0dsF6EkihKya5mupVVVJcD2V3GPeSlKmJsMjU/p5q5Xp9Klc2ckRKn32lJq6RBRW4jIY+/KrhPOVIPngX9LqupaoRwvmlfp/q7e0SUAMwYPkJRB12dXTpw6iRe3XjQsfgZ0PFhIMiW5fwZg6maT7D5KKi2YvPoAlnviQUwuO3N8g6afjNcCCdt4zMeOYQBb6nKepaX6cSjBnTf2w+CbMyhj04hQcOOHGNGbNDsjKNtFw3T5AhZdJ1RLcfZmAAJGsiDpKYlIHBwN5Koupog4DcBAuez7TL8X0U1y0XLbL0ihFYKos3inbrTdmVaKkhgacJ22iwg245739rpsw2hZuJiJKeL1uXyvsKarZg+kzVOVTlOgpQRPBHyuOa5iaAu8tSqYxON74VAUIn8+4Na41T5t8b0JAdswuGYxhrP78ZaM0F2N//3B4k3xe1VXC+x7F7j0G9DqKhyLGYU9XJ6h7emaNnXKlKQC2BUkH1yLGcbdX+7D8NCbYLpqkHs3d7nsTEQiUFcDd/1kGuQQLBlPPwBrFaotpdN+f+1wHXJOl5AjcSNCwY0f0thduv0GmQvn0VY2n5vLveyLGXVN7tmtq9vBDVB/0yrmIxCL+qkbMSswsf1Il3UM+3BIzL+UdBYPB2xwGI/z+1IdhojzU/jFy/KVWkbKlaVTPaUI1zw0QBDzqk2npbLHNTMWDABeZWziuD6sGy5T2+a0rFQbAdf+TUD9VFZJlcVhXelxtGZK3M4KMgyQZOsBpRXYOYzdFnhll3cUHnBOZQI5SwC+XlKcwTyPfNMoLLDerW8stp+qDwwKzrtywbVDhrEOwEdvuDddpOT2W54vu3g9Tn4yTtmmJ/aE4lxZ/eeqW8to4BwAwnmNDjXjyU8POWRMW4p20pdQcOOH+KJLt89RuGiEVF3AfLyOB0a+hbzooSi+XIOYsEBEhQTCyvH6nn70tHfQwUuW+3ABMS4351EKAYMhbOZfSkfDMnDKX7m+L9VhKD2FK31n1ISQao7LLDhdGZBWKMeuoOmK02l6p8XUPu0KBMECM540r5PdhzN7gwbho7axGHH2DYdxqelU5KYFG8q97Le6t+cceLXOeQU4ucLVJJHn8KhZyPLpCXA0HxhUnHedg+vh7H7ZaTrD00Wqbr86uXxB9qHpv3wM5rH13wtdWkYD54ADa9e3Xaq0uCzfUrSTvoSCGy/jTvrTV126fYaGZTkDBlfmvoQHrW3ce9rSaO+gF+nUDWOrgGmDMnSuAMAlGE+5S3UEfx53a0zOxKFE9Slc7bslCiEXmN9DK4nQlWGACj4Qe7lu2MX1wEfWEaizXTrUgiLxRpxVlyY75SJ92vWEW3I4asDDMdBUm7IrqbLgHyc74SUs1iXQVtLWNJRRJv1pRWngFYA6dD61Utb9WfyWPxywEf+23gmL5FIvvvfEsKtxVesw9euS+B39dYeq864YXKtnoQy2H9B0+9VB0Slg+3xoZVp0aRk1xiMn9K6yWB2E1iItRTvpS3we3CxduhSvv/468vPz0b17d2RmZuL66+Vtqbdv344bb7zR5fVjx46ha9euMmv4FndLudWqW4zoTZoMOizLQ6oK0K72MM6hvnRVbKGQODhamHpSKwFVdCVtC4x4Fdg8RzGzI7YG+I95ORLlnrC3LAH2Gky5y+kIPEB7psD1KVy8QZXnw1TxJ5b3DMD8XWXYx3V1cX8GgGgZgXAYU4ubTIdxk+kwHg7Y6JANUaoOEat/xGoqtem0ITVv4AJiEYcit7p+A/Idw9Wm7ES0GpMC2pogI/2zXNaFvk7nL1nuxUpruj0rMC1sGxirkruNsM0AhsPUsG1YVDHc/rruaV03vqMD2Z80slAG2g80wMWXA8BEtgXzwwfQm2nR1DLqHM9TA6Ng6TYAHMfjnvdd9W0izVo76Qf4NLj5v//7P8ycORNLly7FoEGD8Pbbb+OWW25BXl4errzySsX1fv75Z4d2523atGmM4RqioaXcblXd+NDvoUHovGhIpy4cpgdyIfzTmtNX8+pgWdnMDm/7vRUuq+d7FFLuspm74+vldQQqiBVbzt4ozss8EfAZhva6Hn1TbhZelLlB9YQggryAWLxYWz/90ibUhMygtWA0TInlsiHO1SFiBkRLSyM+7X41JgAxUW8An03UfbPXi7iPIUEnsL3GvYcgreNwN7AB6j9PJfdicWpPGtgAwF/a1wK/am9/em8TBnQbYFjcbvQ7CgCTTJv0LfjzRu3gRqeLr1LG8OxVd+DKHzMV11Pq46WoZdQ5nn7duwEdYvHVoXO6lm922kk/wafBzX/+8x889NBDePjhhwEAmZmZ2LRpE5YtW4b58+crrhcXF4fo6OhGGqVxPFXKbajqxsd+Dw3CQKdiQGV6QM+cvoJXh1Jmh4lIhKWmEgG1JRoSVdeUu1zmrm2kGd+ankaIwZsGw9iyA1DOEojLtN37Eqw3T4Bpdyaw/VXFbcahCMsCMzGlVghSOlUfQQhXoDkWpWyIXAZEr5amrvQ8+v+vPa6rnYn55vd0l5cbIdLivk7Gkx3UlZCryOPBgGF4zKut9+lJiArG3NFdEHRko67t/s4nuC1u170KAIYHwmXapciyZylwZZr6tUlDK6fVbPWRwLZQfkSux/mzVdQy6u0obnMfbpHaST/CZ8FNbW0tfvjhBzz77LMOr48YMQK7d+9WXbd3796orq5GcnIynn/+edmpKpGamhrU1NTPw5eVlTVs4DrwZCm3rqobpaesxvB7aAiS6RKEtgYqi6B0ERMdZtWnBwzO6Tsjl9nhOQR+pPfc1afcsys6y2bu2l0+jJBA7QBCDj3ZAZYBElCE2n93ganmkvr2IARDYpASr9BrSWk/Wt2rWXDoEFIhmNdpMH9XCUq4JGyCkAGaZvoSDwZko5UHgxwlXY8eU0S9mqCXLPegLS7iwQAhg2Eko1OFINQgAK1QYX/tAmJw7roXMemaMRglPuBU74Jp03DN6SKeB6xg8WPb29FB/zDc0rqIh6n/eHX8napo5cTsjFyzVfHzm95K30jEz1ZTy2iwo3iL0076GT4Lbi5evAir1Yr4eMen9vj4eBQUyF/8ExMT8c4776Bv376oqanBqlWrcPPNN2P79u0YMmSI7Drz58/HvHnzPD5+NRq1lFtDjNugm7030TmfLx6VWB0ygM3zyJy+otDbObNz5DP9x2SDKy/AvA21sp9IY2QAAMCsEdiISIOUWMZ44C89HmmQ0J7Jx4SArUiyqo+DB2P3oxHhwOJN6214yzrOvr0/EYl3zP9GOGpkb6BaRouFjGP7BRG9poh6S+UfDtiINXU3uzVNFcrUINQmiL7Eh2FFXTqWWv8CbgeLZe1qMbZXW+Hv5r/3w/nv3TmbJ56P9+pGoWeUPi8iOwa1LnxoazCVFw3OJerU3iRnwHrHh6jb8AyCKuvvC4VMLP5RW1+x50xiVDC6XneDoIVTyfyID026tYwGOoq3OO2kn+FzQTHjdBXged7lNZEuXbqgS5cu9t/T0tJw9uxZLFy4UDG4mTNnDmbNmmX/vaysDO3atfPAyJVp1HSkDjGubgFfY2FgPp8BUMqE2y8GuoMDlQu083QRCw43h57E1L7h6J3sKEzO/TMA+o3rBc6c+Q39yw/ZWycAcPBFaQyMXi7jUKLbkE+KdKrQOUjQ9+kCc2td+x4BrtNcT1mmYLk5U/FGztv+X05/IbcPpelNqaZoM5eKhKhgLLy9J/74ZS4Sc2faHhnkjy4BlzArwHhA7EwUKvBEwDqc4NthC9cP6z5fi9hfQ9Drp9cQYFOCSXG+ZFrB4r26Ufgw/EHsMpoZ0NuxesjTQIcb8MvJE7jm+1nay8uhEUgJf6vhuFC60P43VBcWhyt73YRN3/2OdDYXL8oEpwtrH8SW48lIV8r82H6KD02GfGfUtHtOusf0rtfh/0ZY8PXuQzhRGWbPKhn16iKM47PgpnXr1jCZTC5ZmsLCQpdsjhoDBgzA6tXKvW2CgoIQFCTTCM6LeDodqVpOrvcpy2m5Bjl0NgQ35vOjcBlLjZYMK1ygnYXe9psyVwzsg/DPplXK5vpj2mYTvgvSZ24HAGBYdPjhFSy2NfMu5gWTO6mGxMozMDHGy9F5HshHDAAeCbhkqMGlFu46FOdyXRWDBM3hhcZiX/e/Y9N3+v7eN3GpmGyZiX+YP3LQWORDyLQAkK3YkvOsUZvelGqKvq3phxduTQYLDrXmKOR3nYTE37NsU6iuSB2tG4K4nfnm9zAXHyKJuwQoGyU78GHdMLxSNxF1CMAydzIDerUlQ+cArAmFJy/iGmN7qEclkHL8W60PdJlyAN/9joUpZ/CXk5kuQ0xAMRZyCzH1kzrg7slIl9PSRbbFid5/x6hWQzHJneufnHZPLhvNsEjlOeEBKRCoCo7H79fNRecb7qaMjZfxWXATGBiIvn37YsuWLfjLX/5if33Lli0YO3as7u0cPHgQiYn+Ff16Mh2pWU6u9ylLspxPu427OZ/Po75kWH16wFHUJ8VZ6K10U+ZtWqXtAU/Dil4qXX5l4B1T5FK/mPoR8kK2QaZ0WQvxJq7HbE8Pzh2zz/MxSESx6nakRnIADLnrOlB5ET1/eg0j2bt0N3ZUqsoSszJq70nRW8U1vXMhvv38PTzFr9BttOepexbLADG4bD/fevmB64rWUeGqf8+qDzcSbYngMuVYPcgADtoS01WDcGlXuEF9lPLfqTg+taIMEzgMPrlQGK5CcPqieRXuyBqE4c+Ogckp08K0H4gurAldXDfvHkrZaKfrQUh1IbrsmAbER/inDrIZYayjmoeZNWsW3nvvPaxYsQLHjh3DE088gd9//x2TJ08GIEwpTZw40b58ZmYmvvzyS/zyyy/46aefMGfOHKxbtw6PPfaYrw5BEbGUOyHKceopISpYtyul+OTiLE4Wy8mzj+bXP2Up3iYZwcvFdhHRtU1v4qZ3BcsASUwR+rEnsKvTU2AYRi45L/yQXHilSIXe6j2BhAvUdMv7YMHZfVwKoJJpY+T/lBjGNeBgGRiOaqxgMdUyA5u4VGziUjGtbjqKEeGwTBlCDW2TlwQpHFi7IR8PqN5QCxBrLwMXgwR3b+iBlRewPDATt7B7MYDNQwa7GwPYPFkdhbgLcbpqZ/AN2MMlO7RaEN/L4gZiD5es2DRS7/Rm999WYSG/EAkGxNZqGM/XGQ+WChGNhbf3dLzGcFbg9HfAkc+Qu/VLDFmwBRPe3YMZaw9hwrt7MPi1rY5/+8kZOJi2CIVO3/kLiMHBAW8AIa0EPdrp75DaIQb/DRht8Kh4xb9TQLsooz97HPEoUryBideLdpcPI/d0cX2mpcftwk9P6g8NZaNty2Q/K6xHeA2fam7++te/oqioCC+99BLy8/ORkpKCjRs3on379gCA/Px8/P777/bla2tr8dRTT+HcuXMICQlB9+7dsWHDBowaNcpXh6CK2w0UYbCcXKeC3y+6jevNNCkQhxK8ca4Pxt/xIUybntUU9UmRCrg1fUvAI4mprwZyzhj8CUGf0gZl6BBSgSesHxg6DsNn9/YVmBgyBOnl1Qg7tRHJP65Ga6a+z5WVZxDFVBraZL7MlI2SId9FPhJf1g3Ct3xfh2xIQwXSDHjwAN40L0aAZKpOTtTLA3jh1m5oHRFk/1vaklfgkoXUg95puJtNB4VxeujPgQELPiQKTJU+wbcRpFm4ixUSp2Y32g9kH83HlG2twWCRQyasFcrwQs5LwJ7674YpMgn9+z+D4pyv0QqXdZ2rnLg7gaBBSFVooaJVbKH3exeHEu/7yBjORvuhDrIZ4nNB8dSpUzF16lTZ91auXOnw+zPPPINnnnmmEUblOQw3ULRhqJxcp4LfL7qNN7DPUyGihTEGD0bazKOGTAulAm4jF0cRMSvgXDrMVHFAoOFD0UdkWyB9AUzJGUgDgLws8EeetoUE9Tj3nXKeohJ1IJ9ah2A3l+LQMdv5eLZw/XRP73iibQIDOAQ2gLxRIAsOXaoPY3C0FWDjAQy0P0As2foL3t75Kypr9T0N661+8lRQUw+HxywzUFRbh3gU4wXzKsQw5Q1OoTv3nLJ/13U2epQ+3NzUNd7+EMRLtC4j2Vy8ZV7suvOyfPTe8wR+7fIgWp14X9d4F/1xDfa8u0dxOlyr2ELv964Q0d73kXHXSbkBDsyENj4Pbgh5DJeTqyn43d2mN3DwitCPszaksLxa0ZBPSU+Q2iEGMWGBKK6oNXRxlCJXFVTBmw0di17qhr2KgIGT6z9Dlc7hzjdh59/F0tlsJ22L3PFc5CPwpXUQvuX64WtugOLUDmCsm7gRnI0CR7C5eMX8AVp/X5+tEoXfW7j+yPz2F0OhsrQnVmNjqryIPZwwTVxtCazvTO4UjBo5n6JwejOXikSxWMFgo0fx4WZVzm8uD0F6/KU6/vEVuP5/A7/vXcUmr85/x0qO7VpFGfu4rriAWLRRmJoS93M2vGd94Ya3HNzdzUY3MIst4rPiED+Hghs/xa1yciX33YZs0xvYMk21XzyGQEup5uLOT6WA8hi1xNKvjE3B1E8OaN6UnS/CgLIAOYyxGG4ZoOXLUoBYZGztiFeiC+sv+hqdwxUZ+SoOhY/Fpo8PO76scDytmXI8HJCNh5Gt2lUb0OgmjoZVDYmi3jcCliDDtMf1XJWdB28TfvPoZXj7m7hUrLCm4+GA7AaM0jjSgFlpGrA6JAE11ZcRxV9W7GVVjhC8YHnAnoUTtUf2YoXTxho9ipwpdp3e1JrGBXig8iLYfe/Yx8dDvixf+nesNB2uVZTBgcX5tLmIy5kBjudl9/OS5T68cEcPYZvedHA3nI1WF1MbwafFIX6OTwXFhDLik4uKTLj+Cc2H23QXa9cxmMU/oWvZYkTa0+dqY9Qjlh51bSIeHdLBflMGXMWzchdhrcaJSueU5+sDGeftywl3pfu+WMk5irzdTWOHxyO9xxWCwD1SCAq1jkdEnL4YyTp2rmbB2QXApQjHNMsMF8H1BcTi35bbMbN2Mir5QJfzoJcM0x6Vd3k8ZVmOsewuFyGyOMax7C48aNoou8z/uD7uDQqCV0pNaCIm1D6H6bWPYULtczjPt1IUY3M8cJ53NRPcxKVicM1i3FX7PE4MzgTu/xqhz+ThtzShdYbSeYtAFaoRaBdOx4QFOmZA3OjZBgDtY1yF6e5oq5y/VlIhuhTpdLgUraKM3iPvB3PnR6gJTXDZz3PmZzDu7snCuRCn5pwDPdHBPS/L8LE5IGajAWiH8+pFD0bweXGIn0OZGz/FG+6W/uSYmXu6GBsvd8Z5FQ8ZngeKEIkBNUtQhwDHMYITnkxtKWZruzTdYuk5o5LR84poPP+VGVOq9HmjaD+5ysMwQDkfjAhJzx1x+4DgYxIjKRdnmXpvHJF/ZP2EiGAz+AITBhseAezp7/SURAzv2gZffPlfnD+Uret45KYvlIzTXrLcixJEOuh0hrP7Md/8HkKZWndGDkBd98IAaM2UYVHgUvs4HDxvZI5RKxslh6yGiWEQcOtrOP1VGHLKBAHvPMv9slksZ9M4Z0Q917HWvXBNh7YAgN7D70XtDy/CXFvietyMMKb55vfsn8vzt3ZzfFo32LNN9N+6L+0qvLfrtMOUkFFtFWP73lzmQ7DSOgK7ue7Yq1K9BshPh2sWZSRnIKTrrbD+9j1O/XoKhXw0TFcNwj87tRGWaSwHdyXdI8M6loNrFD3oxS+KQ/wcCm78GLc6g/tgm+5QWF6tOqUhPv3+3fIg6mxf06gQMx4YdBWGM7lApmOlVF1oAq4tvwv5Tn7CDmLZ8mjknuqBtKvjMOraJIxMSUTu6b7YV/YQOlceQZewCkzfkI9N5R1hdboIN6Qq6NO6G7CF7+8izh3J5iIal11unNG47CD2LCirwT3v7QWLAOwKikECU6wr5crzgtbm4OUOSAeAvCzUrn8at1cVGPrLl05fROEylgZmyhqnLTUvxhTLTGTZ9CTitJfapVVtes4d3x4x06S2mlRMG4Q6Xdt1HkdNaAJCxrwOU3IGJuSfwBvf/gJAeZqpgJc3E3TGYbr1zG4EygQ20jHF4DKmmb7Em9bbkBAV4qi/COuCAZFJYAy2HwgMYF0egnK5rijiIxArqdDTgmWE7NLjAV/hXv5brKhLx1vWvyiX5ytMNWsWZbAmmDoOwTUdh7iaCXrbwd1ZxzP9MHB2b/3v7a5z/N1DOh+/KA7xcyi48XMaUk7emNs0inghU7wZ2LIbEb1uQ/SxCyipsqCkyoK8rR+DDcx00XMEVl5wqa6RE8tWrXsXyFgIJGdILpqxgK1/cEZAPr6RyWy1Z9xrdgkADwZsQq6lG3aF3IBLFYI+RzotJOeDo9R12x4MQltwyjDAasuNWPLxYXw+dAt67ZmJoAYIYZ5Ki0CPY++BqdQeMwDF43MeozvvKcEy6gGT81iftDxqaPvidyLk1nqtxlWtwxyWUbINaB9YgQG1edjPXYN+7AmHYJe3WfI7TLfqnFZ6MCAbn4f9FZcqajH4ta0ON73bQ+/G61gIOJnxabUfcH4I4sDioLUzhgUc1H+yJLRiKvCkeR0eCNiEOZaHHYI8MWPklelwNx3cdaGm4+lxe/1rXij39oviED+HgpsmgLvl5I29TSNIqyE2can4X00fTDRtxpVMIX7n47DKOgLhoSG4dOAP+zpiQODcPwio90v5p3kFgi21uJIpxBMyPX6CqwtVO6XLZbZYcLg7YKtbmQSg3l35wOiH8Pj/HQED/Q65othTzEAFoQ5v1N2ORwPWIxw1iuuLnOETkM7uxbV73gSDhpU292ttBSqVgzzpmAG4NY3nCfR2T09CEQayP+EiH4EYlOuqTmLE/25+DkgeA7Am2YyDOM00ks3Fv83L689FoGv7DWFKbyLGjZns+IChc1qpFXMZd8b9gWmfWFzyM59V9kE5O9MlyNfTfkB8CFr5/WnkfvOh3fOnIbRyykp6fTrcDQd3XSg5Eos6HoXri6fwm+IQP4aCG8InSPU/chqOhwM2YiH/ID5HveBTT0DQGvX6C7lghNExz+6c2Qr4/XskHnD/Ri3eSC9VHrEHTnHlJbrWjUMJbmH34hXzCocpAatOcW57pgCzAj5rYK8jW3VHWBtdS8ehBIyMw7A/8njAVwCE74r+8mvHqQylsmWlajTW2dOHKcaywEVg2L4AJDfE9gPBh0SDqSrRHNGpX0+BR7yLZ1Eu19Ulk3TvsP5IHTpGV/sBE8ugdXiA8FCBhvfMErVCYoYvPirUu9PhevtkGalcaiwdjwqe7l/YHKHghvAZ6SmJ+PzGi+iVk+lSEZLICM3vKtj6aSajuhflJ3jh5mT9dSdMpgDZ+XBpZotjGd1NC9WoLPoDUQkD8Ex6VwSeLdK1zZvZ/bJl0CYdd5mLfATuDtjq3mDtSKo7QlrpWqM9U4BJAZsauN/Gx/CN2zaVISfUV2/v4YhdgeJ0Q8zOK8TvNen4G9ZqDqUQ0bLTsFLxtFjuvXOXGW+1u4QBnWJ1ZUs6Vx7xaBZODPbXj2HR9bobYDqbAxz53rPeM/adSX215MooeKDP/cBPX+jfv7d1PDrwp+IQf4WCG8K7qBlncVb0/mmBix8G4NgsU9SdeMINV8rl1fciStrYMjIJ1pELkBs82FGLFJGgvBEDvHOoElt2C2XNLELQLzgWcSh20EKIcDxQgjCNMmh19lq74daAXO0FbVTxgahEkEOGiI9MAtPnfsBaK3yWKk/BwpjDZacD3UUa9HreLdhx2xwPFPGRWFN3Ix43f6W9kmQqw3k603h1neMNceOP+Zj6yQGwGI3bg5TbGoii4GiUYamMe7Cc03NJpQX3vL9Xtx9KtwhjbT300r18F7D4Ke94z0hRqmQSg/Xtrxrbvzd1PAbwl+IQf4WCG8J7aBlnaZjSOetOPO2GG8lfdniM5svywf53IlbW1t8IEqOCMXd0F6RHJoF3x0AP9Teg/1V2rn8NLObW3oel5kyAkem8zPAI4K0NuqGfQpKh5UOYWjxY+xR4sIhDCdoz+bi3bCvipRf/4GjUF5tKBKpOsY4nHxjlgl9vIE5r7ua7Yzz/HRKZS7KBJw8GtaEJyC5pj7hTRXa9inQ603zsHLDfjUFcvoCNP57HY2sEfQsHFnMsD2OZOdNFayae85ct9+JF8yr7MTgfk5w4HQAKSyux8pPVSBwcjZ7duipmLVi9wf2IfwLfvQ7omEYDAOxZ6vqatzQrzg7uRaeA7fPhlmbGWzoeN/CH4hB/hUz8CO+gxzjLoMmYmvGeEURjPTk9jqgHEI3eCkqrMeXjw/jlivFuBzaAvL9Jtq1S7IKT+R0TmYTjXR9DJONepYNoFpcjcZ3VSxuUYQ+XjBoE4ImAdYjjLzkuUF0i/HSaoipALN6oG48YRt5RVwk1Yz8rz4BhGiewkdIGZfbvmfMkEm8L6qaX/BUz/u8IJry7B/3/uQUbfxS+5+J0Zr/u3dza92c/WzD1k4MO32+lrvSiKd4lRKh2Zhc7ZItCb0DQA30XNB1rA19Bz9yngA9HA5kp8oZ2Nt0Kr/AXwIMReqANmAKMedPwMTtvDYDbXbOtHI+cU0X46uDv+On7DeB+/K/QDZ2z1ju4d/8LcGAllDUzGvsXdTxqdqiRbT3iQKwH8Ts3tldbpOmcamwJUHBDeB5NwR2Ei0doa12bc7arnypzodc9NFtQo1YmLL0RiEew6oR7Sc5CRt6VVWQTl4qB1YtwV+3zmF77GKaaX0L28M0wte4su7wW0mBqL5eM83yMoUCwENFgwWG++T3F6ioegMXK4cjNqzC99jHcVfs8BtcswhnevTS48/DE4HNl3Qi3tucMxwv9v/Seh0JEYxOXihM3vAVEOh5TPh+DybWOn2dxhRCQzN+YV7+g5g3QER4MChCLZ/aHy74vdTGWnvNNXKrhJrCi0DkBTtNmSo69Nt0KA7gEODwY4RXRcTc5A7hzlW59ljySKTpIApZD55BzqghWhQ8y+2g+Br+2FSvfX4z+X96A7lvuBvv5w66BmxHNjByqjsQSjZqXxMSEPmhaqoXi1WZrei8eDKNZyVAVEo+zgT2BMov91cMRQ/Dj6Edwqeww3tmwS+isrLOUtxThaCXV2SiQbms3IBrunagM09X52zrsZRxHB1SV5COkVVucCOmBTZ8eUV1HLBsGAKYc+Objw1gzXN/NwbnCx9ld+SXLvbJaDDku8pHI5briOjYPMYzyOWIAmGtLsOh/J/AtV/906pYmihH/U//55yMWL1vuRSJTZHhzzhU94n3QChMAi8wajssWIBb7uK5IjApG5xvuBm64CzizG1x5AR5bfx7Z5R0VTeje3nkaPa9ohVHXJmoIWV3HzPM85iq4F9vHJ/meSDHSBFZPA0zZSh+bboVxmmZm5Bx3xSmgrMeAQ5/oGpscXHkBNuvsnSS2IhihUKHGl+WDEaebrDrdstUyy0o6Hg85EBMNh4KbFojXm63pFdJV/KlRyQCEjHkdO7sOlw3EXlpfiq+4+s7Kaj40YiuHGZZp+DhwvubQJgVsxiRstlebbOH6oSo4HiHVhVAKxBCZBNPAaeguuSmUnTJ2gxZvzk/uDcW2wGiYa0oU3XsBxxtUER+Bly33OpgYvmherRn0idt63jIJHFikMXnqK9j4S90mfIv66Rd3NFHCYjwutr4OL53vj0JEIxpleNG8ynCFjtynIo4jEtpTfAyEZoscWEmliTCVsfdUETaWa4u7X/jqKEam2CzvdVry5+t0L1bCSBNYXQ0wlSp9nHUratVFrAnodHODgpu/ffEHvq1yLSl07iQutiJgVCvUeCHLlP0sMG6ZvgFoaWaMnA+i0aFpqRZGozRbMyK4E28ATul/RCbZRX1yc8rZR/Ox4vvfAABbuH74r3WIpttta6YMHBjVqRpnDYhYbTKc3Y/fr5srbs1568IPmVS0VrNS2TEAyC+rUUy/K9EK5XjLvAgj2VzFqQcljctma19kcwOEX3QO9kbTIYdmlA3RRMVczMVGLlVo72BerHvcUvL5WEytnYF3THfp6s0sR3hwgGPzSRt6nV6LKmodG0AmZwAzjwL3fw2Mf1/4+fcLyB3yocv0krQZqXODT2ceHdIBibaGkuJ5Zxj5aSMw9ZqvYaxOlfPlC8L08unvgCOfuepWetwu/FS7kbspqBU1Y1ur5Kdmxc923vo8ewZaWqGmFFgzYuDG857TzBg5H0SjQpmbFkSjNVszapyl9wnIVlbOlRcga/15sOiI4ex+xQaJcohiUbl+VnKZH7HaZF7gKrS5/gQQHyGbiraOnI/coEEoPHTOIbtkYhm8cGs3TP3EmLtrKnscIXWlitdfuUCuvjLmQ1EJ4foEq6ChGW76ASO5XGziUlHH63vmCWVqXRpWKrXT0IIFj4mmTXg44Bvd4xbheKFz/JCaN8CBxQt104Xvs8GvMA/gpcDVCE1+zuU9I06vLoGQeAO0YeV4zNgbgXzJlJ6WR419UwywZEIfjLo2Ec+kd5NkNAeAq+4D06ZnXaaNDnWfjc3bWiOdzcWDpmx9B1F0StCpNKRMW+M6INcmQ02A77Au6nsniedbtw9W5UXNjDFpZpo+FNy0IBqt2ZoevYHzxcPpBuDC0S+BjbOAyiKwAJYCKA4KR7QO/YyUQkRjD5eMKZaZeClwFeJRP22kJjJOQBFwNkc2EMu+3AHzsn5Gfmn9tIU4zQcAL284ZmiMgPuNOoXy+UvaCzqtw4PBXPMqMBYOMwM+N9xqwtlPRXTEjUcxFpjfRQijrncBgFTmZ7fM4sQS7n7sCQDut31gGSC0ukD4bNsPdPiMU9unISbMjOIK7ePQCoTEv0PRTXgY8wMeCvjG5a8kAcVYbs7Ef+pux1vWceDAYsmE3oKmB3ItVMYC3Ua7PCT0Zk1YlvQHeq97XE36Y4MRxMDS8n+RsvPGyrRVrgNiEFOCcMRI/oadNWNaiFPVgAHNV3i8cK0hzUyzhoKbFkSjNlsTp5vWzwCqnG42IdHGtrX5BWC3qyhWFAbruQmLuoP93DUYwOYhJohHzIT3gQAWOP41kPuO9kZO76jPKNkCseyj+Zjy8QGXe4boIRKHErRHNC6gq+qTqDOeNizUggGPJKYI/zR/IPxuMOvh7KcSamYBK8CDxR6uG240/ai5jQomyJ2h22lI53Yp+d++icSyR4Dy+ilaU2QSnrvqcTz1U3vVdRN1WN4XllfLZmqcT7mYvXrS/BnuNW/F+bR/oPe1Kt5FKoaZ6eGnARTpmHLkoR4B8cZaCyjojsQgZgvXz6VdhJG/EzFLmhgVjH2l6tojHowggDaaMSaaJBTctCB80mzNObABBJMvHU+AVo7HyW2rcY0tsHFRuui8AYtPiVl1adgZNFO4ofAAPlkkPKn1maRvQztfBw59bE/NK03z6Z1iUMPThoV6kboTG0U0XZxm+hL3m7ejtemi/T1Vsbft5xfWQRhv+t7t/XsqIEw8t8ml6oovy8f4sjnYwiqX9QPAC7d2g4llVKsRu17ajjEyFT1qxKEY8TkzgHatXP5mrByPkzs+wZV75yGkWiLml04j6RX5dxsLHPtKfRmjrQWcgoifykIwZj1nD2LkKsC0YFDfO0naiuAly0QslZtydi5ZF2FNjlk6MWtHAU6Th4KbFkSjNluze93IoVJyaiP7aD5ezjqCrJo5DbbdL0YkDlk74dGAr12Puywf2D4fNYHRMNeWaD8zShxMc4MGuUzzKTVLlLPBV0MUicppg/ydWQGfwVkLqyr2tv3kEOBWQCetBgLgkaDQVTLOKzr9ArBPMYX8/Bv2/NkWT+4NxTmJfYG9GjE5DtccfAU8Y6yawz4e5/5TR/Ox/csVeNXyL5eB87bvKnfHhzDpFvfqlGKXGyw8kGQ7u3I84ndulb0OyTX+dD7X4iFKeyfVtyIIxpRyuGi+ZEvWAW0XdaLJwvC8njqE5kNZWRmioqJQWlqKyMhIXw+n0RGrpQD5ZmtylSJucfo7wTxLi/u/dnkCFMd4HZuHtYGvNGgYpXwIopgq1WV4MLjEh9n1O9o3RUEQ/dWN2Zjxf/UeNiw47AqarlmSO7hmkWzqPSiARU2dY1QglwUygnPGRCmDIn4XPBFDOWc99HI4dSF+3L8L91q/FMaic7oRgEPQKA0wvREU3lX7vEO2QS1T9z+uDyaaNuNKphC/83FIv/FGpO56qGEDsP3NZB/Nx7TV+/GdxneukInFodu2IX3LSO2gJCgSqCnTHsOIfwKJPd2ezpG7Dimdx4Xsg/i8qo/9NTXbCnvGrKwCnSuPoFtEpdA+Qm58oou6S4hlO5GebgFBNBgj92/K3LQwGq3ZmpvN5aRTPQ3RUHC8cImKgHpgAwhP5THMZfzbMh4TArbpqPIRSko7Vzqa82l5iDj3ynJ+Sh14w2j853+/OqzjLM590bwKrXQaFgLAZQQjwsnjRS4TpGdzHC+m93nFGynLuB8g9exyNa49/h+gTP82ShCO5ywPO2TD3K3Y0ku8ZJtamToeDEyMREi76+OGD6A8H9Zfd2L3l1txv+lPze9cAoqwcu2n6NTrdlydp9EeQU9gAwhTtGIrDsBwtsP5OqR0HhOZS/g3/288OvItHG81VNNwtF5kHQvgSuUBaLqoq2eWCf+HgpsWSKM0W3OzuZy0okuvhsI5GyEGNoCxJ/czfCKG1GTitYC3MT5AW/tRe+kPDGDL7MFJvM4baRxKZJ9S+SMrcD78Xqy93MtheakzrWhYqHeq6lHLTHAIsI9RMMhbbfimL2ZI3q27FX8L+Npl/5y76RqRkBihgaiqs7UrrZgKRIcGwrloTgwKJ5my8aJ5dQMG5soL5lWotgRiC9dP0TSOZWzfS6ebJ8PzDU6P8dnPwlRZhJcAwKxvnTiUYNUJs7COJ5AGNoBbDS+HJycgItiMvb/kY/IPU8HUyU8HAgy6HPwnusy8y3OBhpEWDHq1RYRfQcFNC8W1jNSDcFbBgTUkWqVDsJPXjQ1ppZYoqk1EsepUSgkThlaokLzOgGXknsjUac/k1wuOdXDV/lewNrD+SfciH6FvPSYfMwPWuTbuLMvHfLyOS+wM1V5U79SNxiMBG6Bq6c8DlxCOPVyKyxTYtzX9sCdomu6WFQBwGSF42vIoNnGpOMh3dsmKFDKxaD3kb2B3ars/y1JVDJzQ6cEigQEwP/RjjP3rI/j+10tYsu2k/T0OLFZa0/FwwEaPCrNjUI5l5ky8UTde9bsi951tiH5MDNr5yiLD8VEholFYCV0tRBDaWvCCMYSxbIfokn5t+U780/w+QlWF7F4INNzMLBNNB3IobgbobSzXKORlCeZfH2WoBzaArFGWtFJLFNUKvXdkNsMDb1tGo2/127ir9nm8Z7kFPC8YwhmB44FiPhxPBKxzbSaoAA8gindM4ceg3N70UWk/5/kYTAjYKjTvlN0q8Hr4GkV32pFsLv4W8LVLRsBhK7ywpTmWh2W1Pf3YE2jN6A9sAOCjupvRlrmIfwSsRFvmIobW/MehieOh8TsRMPRp1e7R6jDAj5+6sR4Ppuwc0gJ+xtXxrk0n1VyTOR6qn5cS4nl7MGCTG+Otx3m34ng4Xt7hWdyv3EVb/TsniK1zua74k22t6B7N8UBVSAJw678hfDuNfo6SIEQFUW9zbflOLDNnIgY6K/Q8GWi4mVkmmg4U3DRxxE64E97dgxlrD2HCu3sw+LWtnmmjYBRRoKc1tSBpreCMc7sCUT+R79QF/CIfiamWGVhgvRscWORyXTEqYK9hZ1oejMPyRm74slMR4nadbiDibMQ+axckMpdUjN95RNZewE3Bv7juT7XpYT35aKValeWOlmlKwNd40bwakwI240XzahwLegBDmUP4X8D1mHT3vUjvcYV692jNAIIHKi+ikg0zPDYAwOULihYG4nfIuZN8AWLxdt1o8DDeLoJlgFYqzUX14PwRFiAWky0zMcUyEyWQ7w6u9N2We108ppcs9yCVPY7R7B58VHODw3vOy86zTIS121j5lighOqsoVYIQPX2gFPFkoKHZtZ3R34KB8EtoWqoJIz4BOV+XnRvLNQqqAj0bITHA7R+o9mCRelaInqZSUa1Siah2Q0B5mIhE/NJuvLbQ0nk9hdcV+9rYNBhDTOodwkVCawXnZBM49Lcdd2umRNcxPmmZghwupX5MTsLliwo3TjWcn4JY8Hg04GugDvi5oBOGJ/MOzSJdukfrvIHtsXTGTabDhscnuAjHIDrEjJIqVxdhte+Q3DSbXi7x4YjC5QZPeb1kuRcrrengwApBLD5ssAUAywCX+WC8al7h0OW9mBc+f1ln4JpeGHu6GGlyBnecFVg1VnvHKkGIqKkbYOjvVX4Ku0GouqgrZ5aJpgMFN02URusTpRdNgR4EXQVr0rxgyFV0cWBxIqQn9ijY37tdWfWX5bi64k9AXyPsBsEw9a7KWhQiGuNDDuAZ/gOHFhF6aIP66TI54bKVN/59cNEH2YK1hwM2ouu3d2JN7hn8I6O7EExLboxc3ldg972rez+7uB7GgxvbE7aJZfDAoA5449sTsotJhdlSNnGpYC08lgYuEo7NwK5X1KXjiYDPZPuUAfqDuot8tD1YFwJ1Yy00pPuV7jMM1XDOhYmWB/+23I4zfILLw4Jd9+bcEoWzGusZJ4PhPlAi3gg0lLq2UwuGZgEFN02URusTpRcPC/SGJycgIsiMnF8vAhDEz4XlNXji/w7JLu+2O23Fn7CGxaExn88u8WGIQoWqF040yvA6t9gtAap4LpTKa41qkpRgGCAAHCaaNmNF2SjHbKHtxsgCgM7gxsqzKOBboYiPMOSUbO09ESbbje+xmzrj7Z2nUFlr1b2+CRxeMK8SjknnOuLn9JZ1HH4PuBKzsdIh83MZwQhHte4eXdLvrzuBulIwpdZgdULANlnPJUWHcg9kOwz3gQptDYx+w3uBBrVgaLZQcNNEadQ+UXrwoEBPrKSQBm/rDvyBu/q3U1zH7XYF4fF462Qb3MeHoRVTob28BxCe9tfJl1MDeNlyL15Uudkq3TClTr1BLK+oaWio47MzVzKFwrgAPLvuCCKCzRjQMVbIGFbqzzqx4LDE/Ca2WntjWMBB3evN+74aA9vkIz0lESaWwaNDOuKNb101S0qMjPgVSRb9U1L1Wpb7EB8Vij3cQAwu72Of8voTgrnYQ+w3uNF0CCaVYFL6mYlBh9FA3WiTU8DVc0kkNixQ3aG8gdkO/X2gACa0NTDrGBCgp8SrAWg17SWaJCQobqL4pE+UGh4S6Ik6IuesVEFpNRZ9+zOGhZxABrsbA9g8h4oie1UM4ypmlYO3jcfaLg0rdv+OFXXpmus4Y7T6Rqxcecv6F1mBazEisaLuFiQyRUhilIM0NfHoPMt94MCiN46pbsOT/M7H2f+/pMqCe97bK4jaj/wBbJqjeztiBdlA00+G9n+iMgxTVh+wi+gfu+lqRIfWG8Cw4DCAzZP93sSEmbH4VmO6tALEYqpNsH1X/ytxobzWPuVVgwD827wcawJfxbCAgzAxPCp4s2wFlPNnxvFCb6oJd/wVNaEJbladGcM5S/Ty2BTtaezkDGDmUcEpefz7ws+ZR3RlV0RNHQcWLylUsQlGkYyQsfF2YEM0Wyhz00Qx3CdKpWOwJ7CCxcnez+OaHdMAm4ut42igmbJW0xGNELUjfLHdq8O5GeUmLhVTamfipcBVDjoVlxYEkiZ6uWdKUVJlwVv4Cx4I2IRWuKz5FMzxgjNuNcxIQr02ogCxyKpLw98CvhZK0mWyMuKNbBOXih+YVGRYNmIwcwR9TSfQminDw+Zv1HeuQAnCMUfi1OupDtlaWHkGH1lHuLxeUFqNlWvWID3QmCkfwwChqNWVjXDuKSXVmC24rQcmrz6g2cS0uMKCnyvC0F3H2BZbxmE3n4Jcrivio0KxbEyyQ7sMpWnAEFjAQPAdkhXxSqraWkcEYWyvtkDQ68CnE+2O0CJy56UhmThplujRIR0w6lqZQE/p2uFmtsPtPlAEYQAKbpooclVFIuK1zt5YzkvN4cQ+LlvyCvDlofMorojGSHaGay8knRcrJR2RkWaUm7hUfFvdD/3Z45gzOBopIUWo2/cBgioL7OtJL56Fh84BEDI/cywPC5b5KlUqYpAyx/IwtnD6q2+kN7LEqGAs7fMHuh56FSEokN+RATi+XiQq4qkO2Vpss/ZCncxlhEfDAiytG7ZYWv+y5V67ZkSqMUtPScRryb/hjlOZLus6f29OhvZAdw2hLB+ZhP5jXkf7CgtmSBy9c04JQTQLDvPN7wnuMApuxdUIxITa59AGZYpNIe2Z1gZWnWkhDQxjwsx4ZWwKRl2b5LCMrm7jblLvkt4L+8oe0u4DRRAGocaZTRw5fYpDYzkvNYeT26+ItPT43mH9kTp0jK6L1VeHzmHG2kMu23KnGaWYudo1+yaYwClmrXJOFWHCu3vs62k1qjzPuz5ty3UyBuDyWkigGe/c1w+pNd8j4LP7oTWBJla96cH5PLDgsD9osq5MVENwbiIpZYAHGp9q4Zy9W3RXL4zt1RbZR/5Ar3VDEMcXaX5vPn5kINJqvrf9nQCyjwoKfydWjsfg17bi9suf4EnzZ5rjVTtfMWFm7Pv7cMdpIc4K7F0ObHpOc9t6Eb91/9/evUc3Ue17AP9OQpu+oaXQUilQYYFglUp5WASuoJaHIPUCynEdLFf0UkSQy9GFoBdUUDwHXCAqD1+IL0DlInhEpB6k4KJIUUAeokeop9W2p2JpqQVbmsz9Yzohj5lkkiadafh+1mJp00myZ5qZ+WXv3/7tooErYe01TnHpFdfVxp1/3bxrB5G/uHDmFcTjOlFBWhxOrb6OTM4/EAAUfRWBL282uc1Gsq/e69BmpfwgXxejdNw7p9liKl3oA9MSkBAdjqq6BgDu9VDk5FC1b9vehj1kAoDn7+qL2j/+QNWW/0EHUXnxSUe+xCSux+E20yG33pxAch0SUtIO56X8IyEwq40rce2F6RgbgZ3Hy/Hmxo3YFP6b6hvLx2tU7BkMTBsLmO4AJr4J7JjrnATtpdfRbBKwaGwvDPpQ23Cip/XHlijlu5jMvhWvc106ITIBgAhcvDx8KvdcDlTZp8urjb8mNcHtGPp/7SBqKQxuQoDqOlFBWBzOU16MwqsrTkdX6vVJjrNg8oAubkXYtA5tqG1XWfsHYLPCemYvyo7ko67BCmuXIbjmxtEwt2kDs0nAkvHpePC9b+zPUaqH4tguuafmVtMh3Gd2Xw8pWXC+4co9aQCa8lDUb7rN1RHVTpWMg9Fr4zirS62wogk2LAx7ByKCO2tBnmG0KOxtHLfchMyu8fiPZV9ggMbPzfQboi4P3e6a7xzYRCUCI5/12jsxKqYY0DjTrr2gvOq2lO+Sovg7zcGNPLuo9CvnXkrgcs9ldAdpjOzCWaB4n9sQkHx+D/BaZM/3awdRS2JwEyBKPREtUjzPkyAsDuetvo4Sx+noqlWVz9dj5T/cp+9qzR1R2+6ac3tQ/9wCWBqqYZ9I/sNaVH8eg11XL0DqTZMxMj0Z04elYd3eYgDuw0xFtmvwXzd1w4rP/+l12Ep6PiAKAla03YSj/zkTA7t3AAAM+etuzTddf1Wind/VmrWqQAK2Nw7G/4a9rdpj1Zw22AAIPkxvlnthVtx4AV//6xzKa/5Apamdpuf27X2N+tDthd+AD6YCgpfhFx/Onz/C4gGHEjzto8OxeHy6ciKvTJ6J6K1I5pjnpdlFSsFG2lBpPz/K85h7J5/fA0zV2naIC0uSQTG4CQCveS96CcLicP7UzZGHm3zp9ZHZ69cIVYo9AJ6GR0aaDqJnwUrF5JW24u+YdHoB8k5VYm7sMCwa1wer72mH3R+9jrnWN5xuzBcjkxHe6W8ojzmOZy+t1NRuASKiLlYgq833gKkjCk//5tNN11eOx2GB+d2Avq4IAWaHVdYj8Aemt/m729/RcYjIgka/3k9OFPanx2lgh0Zsa/p8HrL1hFUUYIKouqK8IJiBq/oDL/VDs4ZufTh/Ft+bjXHitb59CXIqnqdy9gyeDaTnqL+GWgB3vlx6vCl/Rj6/NSekc2FJMijWuWkmT3VZHGtv6CIIi8P5UjdHgBTkydPR/en1iYuyoGbYkqbJ28774Tq92pE0NLMBUOkBkB9bFPYW/l1zATPe+QadyvOxzPY8Orn0OERe/DfMH+TiKdMr0mv7cOO11UqzoeSbhhysBXbhdgGCIGBV2DTcZjqEaW38m07uytYUaLhWNI7HBQiC8sKhgDREJOcq+cPvQxOTZP989jf9ALOgHNgATZ8k0QrkP6F96FaN/TzzIjYF5m43Iat7e4zPuApZ3dtr792Vi+e5vk9UIjBpA5C9WP25XnPvIAVwNqv9+Hn7nIp+XDuIWpLuwc3q1auRlpaGiIgIZGZmYt++fR63LygoQGZmJiIiInD11Vdj7dq1LdRSd97WdwKk2hvWwN7JtJO/8QFwD3C01Z5x5bpqtxrppmjDC4NqYT6xBSjeh8rzvlcArr5wCdXdRkFQWKW4Au1VV7+W1+fx1AMgCECKUIWBplMwwYaUwqegPINJBCDC0lDtc1G8hz4uw87j5fabhr3YILSslK1RXAqEu97CM4/Nw6p2mwKWzyMK0uXBl7oqJgFIEaS8lTIxwadCdHVimJSj42P7bQDEphut/PnUPA296DVt23kafnE6zzwY/dfmJd8qFc975Afg2hz3bW1WKafm2IfSbCuNAZx8/ESHz6lb8UH5f7iwJBmYrsHN5s2bMWfOHDz++OM4fPgwhg4ditGjR6OkpERx++LiYowZMwZDhw7F4cOHsWDBAsyePRtbtmxp4ZZLfFnfyWeOF6fifdLP/rB/43MZHotL8Wsqp1xfB/B8D7075giOxz+CgXtzgS3TgA1jMSr/Now0HfRxB5p6Pa65HchZCwx9FKXpM/GnhgUYUv+CYmAD+FZjpSOqMcB0Ckn4LWB5vnI14s9qr8aMd77Bubp6e1CYb+uPOvhbOVoAYlOAe7e7VYc1lxbCcqGi+fsQmQBkL4EZNr8TkjuiGhsbR8CXfpho4ZLvgY0Ud+KHGx4HTGb75zPgdX68Db/0uQO46+2m2UkuIhOk3wVi2rRcPO+6idJ/lYKLk9uBlenAhrHSuad1Gvnv/3Y6v3fZBipW0q6PTJa+bHAaOBmYrnVuBg0ahH79+mHNmjX2x3r37o2cnBwsXbrUbft58+Zh+/bt+O677+yP5eXl4ejRoygsLNT0noGsc6NUl0WJXHtDs2AU3QtwhWKlPKOE6DDcmXEV7oo5gp4FM12qFMt9IiJmNMzBToWgRKlejA0mfDayGr0OL3E6HkrTrR35UmNlcsMT6IhqrAp/SdP23sjfdOVeJbnmzoIxvTFr42HMMv+fppoo7rzUFzn2oXQz86ZdN6D6J/XfD54NdOqr7bVU1IiRaCtc1LStKAJ1iECM4Hs+l1x3aMxd/+10ju089jNu2DIMHVTq3GgnSOfenGPazhebFfjpS+kLiQCg6xD1ICQYVOtaaZD7d3sysuP5LZ+XPaPqMHZwhua6VUSB1irq3DQ0NODrr7/GY4895vR4dnY29u9XHt8uLCxEdrZzqfeRI0fi9ddfx6VLlxAWFub2nPr6etTX19t/Pn9eeSqmP4KyvpPGxD+fBXhxONX6OrABKydC6eIqQIQIAQvD3kZ+fX9YNdSL+Yd5GHoWbHN7PaXqxI6knIF4dIL60JQoAuVIwEHbNRhkOunXcVDiWlZf7sFbtP0ETLDhvjbu08cVRcY71SfxWulZa3JnjXLPqN3xLUCPW7W9lgpfAhsRwLrGsZoCvsWX7sFJsZtb3aGpLufYqOs6w2paAeGD3KZPnT/8GLo1mYGr/0P619I85tZ40hTAOeTPuJ/fg40xA5RII92Cm7Nnz8JqtSIpyfmCnJSUhIoK5ZL0FRUVits3Njbi7Nmz6NTJfWbS0qVL8dRTTwWu4Q58Xt/JmyAV3QsWxfo6xZ5r6wgQkSL8hpGxZ7CjtgcAz8sr/Nn2keKNybG+SX59f7eEYim3JVd6XYWkYrm/8qlL97o91zvnBS9ESPX1X28cjc/FTMWy+gBQVdeAG02nEC9oLK438U3p76y1t63rYCCqvfeVuEWb59+f/0U6QHEpEM+Xu/XABZIgACsuTcDL1hz8qc1ur5Wo11vHuFWi7qRyjpmvHS9N4/77HJ9WJ7drbWscea1rpUQ9gFOtn0XUCuieUCy43HVEUXR7zNv2So/L5s+fj5qaGvu/0tLSZrb4Mk/5J27rO2nhS9E9o9JY9+KlcSnY+MCNeOHu67A85j1AZfaNpyMnJ68ONJ1S/P1ntoH4qdc0TQm2HaCxR+/GB93ylxqikpF3aQ6WWKfggK2Px2BJcy5QZLzU0+Ytv8KRyQxcf7e21/fmwllg1F+bwrjgflv/SezkMYFVfv+nXWbFOZ1jsAFnCoB/LAF2LwFO75G+LPS5Q7pxazXyWZ9XujYMf2rO+Jl7R2R0uvXcJCYmwmw2u/XSVFZWuvXOyJKTkxW3b9OmDdq3V/6GYbFYYLFYAtNoBZdXuHWpuOtPnZsgFN1zEuSVwQFoHhoxxSYjK609UHwSaKhs1luqBQwJkSZ0Kv1E8dYsuPT8aE5A7TUGyF7idBzbpGbh22UFEFR68Bxpfp9BM/z72/QaAxxY7fvzXMUkSQGVwuKNWlbs9sWfbx2Aoq8i8FmNlMCqtEr0kWvn4eg3nQGlc8xUBCx7GLjomLi/TAoQx60CYjWeg1GJwKA8Q/SK+kXrsOTIZ6Vtg3UNIDIA3YKb8PBwZGZmIj8/H3feeaf98fz8fIwfP17xOVlZWfj444+dHtu1axf69++vmG/TUjyu7+SLIBTdswvSyuBu7NVU1VdYdhrfD0CF02H90rH9kPvjPeuPI1JUX3Vbrmw7x/whCsU+KBcTkKQyLCICaAxvh6/+WQlz428Y2H2I/e9rBlRXaHdlL0qo8j4ApNk1wx7x8CoeeD3+GkQmXP779LlDGgb9137Yaivw84/H0eGHjc6rRPtN+iwMvHkcvrzZ1HT+ZOBf0TORZD4Fc12l/QZ8g8mML29TqAJ+6mPg/SnKL3/xnPS7iRu0V/htzTd6redeaw7giDTSdbbU5s2bMWXKFKxduxZZWVl45ZVX8Oqrr+LEiRPo2rUr5s+fj19++QVvvSWtk1NcXIz09HRMnz4dDzzwAAoLC5GXl4eNGzdiwoQJmt7T0KuC26zSFE5vFyetMzdkQVoZ3Pv7weU9Fd6veJ80ZdUvAi5GJiH93HKn5GTZHab9Ps2AqhJjEI/f3WqtuPZUlIkJWBV2P27Ouc+pZ05tBllV3eW1sgDnHCP3AEdo/t+jOTNmACm4efRH9c+YzYoThTux7pP9SBSqsTDsHT/eJACfPZsVWHEtUOulUGZsCjBqKfBBrvo2g2crF8Jrid7OQPLl3CNqZXy5f+uac3P33Xdj5cqVePrpp5GRkYG9e/dix44d6Nq1KwCgvLzcqeZNWloaduzYgT179iAjIwOLFy/GqlWrNAc2uvClXk0Qiu75Up3UZ2r7pqW2TtNzbefLcA5x6pVQRbmEnitp7tWcmsmKgQ0An6vktmsKbKoR43G7ZFTh2Ut/w0fvrXWqQD0qvRO+nDdCyiWanIGND9yIA/NvdSt6+JlK/RDEXRWYm498/CPj/Xv+xSrPeV0mM67JGoOi2BHYYB3lX8XlQOR67F3uPbABgNoy4NfvPW/TeYD7Y671YjaMlX4+ud2/9raEANe1ImqtdO250UOL9tz4OxSk+Lyr/Ju5obVnxKHGhSZa9k3tW6/Cc+VPoeI6QCIAkwmCwyyfi5HJmFMzWbXODQBkmY5jY/iz2vcJ8qycBDx6aTpeCluFtqjzOHtnkmUt9j52m8chSHmJDsA5SDPDhgGmU5g/pJ20gGOgewVO7wHeVh7i9WrC61Iiswfyfo00HcRq1Z6oJrEpQOZUoH33wPSAnNyuPhylxHVavROFHtGW7u0MtNbW40SkQauocxPymlOvxiHPodkXp2AkKWvdN6XaOirP9bZMAkSbPRHSGt0RQ9+5gLNeeps0z4ByIOXhVKGXUIp4QX25CDlfJ/X3ozhYnOlxyqxa0nnHtlGYOu7P6BusxVXThvqff6Mhr+vyfkVgRi3cEoERlQhcf5eU5BzIm6u9N9IHqoEN4DQLMW1oqyvJoCjAda2IWhsGN8EQiItjoC5OgU5Sbs6++V1kzKGN103EwdO/4eyFA143b04J/i6CthlcHVGtaaX0gCWd+8JpNWlvqc4y94JunlzerwwUnZ+GHheOoXfsBZhik4PXW+BrPZeIdsAf1d63kwN8X0oyMIAgMiQGN8FgpIujr7OXvGnOvvlVZMxBUwCmJZgALs9M6iSc87kQ3TnLVYCGNKRKtNNcgVqXomhyDobrEKIi//K6Lu9XewBd/G2pdr7OsLvxQWCPhuFJOcAPdkkGIgo63Yv4hSQjXRwDnaTcnH3zc39FCFLOUVMApjWYEGFC+Y1PNu2lth4SEQLqozph9qPPQhRMqiGRKAKNogm/RKdrr0CtF6XVpOXp0Y5aS9Kp1l7G8FhpwcphjzTtq9pnwPnzFdSSDETUIthzEwxGuziqfXv3p7x8c/bNj/0VIUi3JIcATF72wtOK7ADw4uQMZGZcBXSN19xzIQCwjP0bUP61x2UKBAFoAxuez6pvHevtKA1z9hnXOpNOtdTyiUoE5n4HtAmXflYdnlMI8APd20lELY7BTTAY8eIYqCTl5uybl+eKACA4z4oSotpLxdUcAjB52YsZ73yj2rMyfVgaxsqrRCvte91vwK756sHeMW2rdg/s0KhpO0NqrUmnHnOJmoKVsSsuBzaAbwG+ltf3tSQDEbUoTgUPllAuptWcffP23KyHgKMbpbWNZCrT55WK5rWPDsfi8ekYc72GGUiepssGawo9BY4/JRN8mSIdyJIMRNRsvty/GdwEUyhfHJuzb2rPTZ8A7H8RvtQWsdoUSvIHYpgoWNWiKbCCXc+F9WKIDIPBjQctvvxCKF8cm7Nvrs9NHQSs6ushL0aHYCKUe9+IiFoZFvEzktaa16BFc/bN9bnF+4wzfV4WyERsIiJqMQxuyBiMNH3eUSCrRRMRUYtgcEPGYLTp845CufeNiCgEsYgfGYM8TVxroTUiIiIVDG7IGAJdSZmIiK5YDG7IOOQE3jiXGjWtZVkAIiIyBObckLEwgZeIiJqJwQ0ZDxN4iYioGTgsRURERCGFwQ0RERGFFAY3REREFFIY3BAREVFIYXBDREREIYXBDREREYUUBjdEREQUUhjcEBERUUhhcENEREQh5YqrUCyKIgDg/PnzOreEiIiItJLv2/J93JMrLripra0FAKSmpurcEiIiIvJVbW0t2rZt63EbQdQSAoUQm82GsrIyxMbGQhCEZr3W+fPnkZqaitLSUsTFxQWohaGJx0o7HitteJy047HSjsdKGz2OkyiKqK2tRUpKCkwmz1k1V1zPjclkQufOnQP6mnFxcTwJNOKx0o7HShseJ+14rLTjsdKmpY+Ttx4bGROKiYiIKKQwuCEiIqKQwuCmGSwWCxYtWgSLxaJ3UwyPx0o7HitteJy047HSjsdKG6MfpysuoZiIiIhCG3tuiIiIKKQwuCEiIqKQwuCGiIiIQgqDGyIiIgopDG4C6I477kCXLl0QERGBTp06YcqUKSgrK9O7WYby008/Ydq0aUhLS0NkZCS6d++ORYsWoaGhQe+mGdIzzzyDwYMHIyoqCu3atdO7OYayevVqpKWlISIiApmZmdi3b5/eTTKcvXv3Yty4cUhJSYEgCPjoo4/0bpIhLV26FAMGDEBsbCw6duyInJwcfP/993o3y5DWrFmD66+/3l68LysrC59++qnezXLD4CaAhg8fjvfffx/ff/89tmzZgtOnT2PixIl6N8tQTp06BZvNhnXr1uHEiRNYsWIF1q5diwULFujdNENqaGjApEmTMGPGDL2bYiibN2/GnDlz8Pjjj+Pw4cMYOnQoRo8ejZKSEr2bZih1dXXo27cvXnrpJb2bYmgFBQWYOXMmDhw4gPz8fDQ2NiI7Oxt1dXV6N81wOnfujOeeew6HDh3CoUOHMGLECIwfPx4nTpzQu2lOOBU8iLZv346cnBzU19cjLCxM7+YY1rJly7BmzRqcOXNG76YY1ptvvok5c+agurpa76YYwqBBg9CvXz+sWbPG/ljv3r2Rk5ODpUuX6tgy4xIEAVu3bkVOTo7eTTG8X3/9FR07dkRBQQGGDRumd3MMLyEhAcuWLcO0adP0boode26CpKqqCu+++y4GDx7MwMaLmpoaJCQk6N0MaiUaGhrw9ddfIzs72+nx7Oxs7N+/X6dWUSipqakBAF6XvLBardi0aRPq6uqQlZWld3OcMLgJsHnz5iE6Ohrt27dHSUkJtm3bpneTDO306dN48cUXkZeXp3dTqJU4e/YsrFYrkpKSnB5PSkpCRUWFTq2iUCGKIubOnYshQ4YgPT1d7+Y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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "\n", + "########################### Experiment Settings ###############################\n", + "\n", + "\n", + "random_state = 29\n", + "\n", + "working_dir = '/home/guus/tmp/' # Specify a working directory to save data and results.\n", + "\n", + "simulation_method = 'linear'\n", + "n_features = 1 # The number of input features of X\n", + "n_grps = 2 # Number of batches in data\n", + "n_samples = 500 # Number of samples in each group (use a list for different\n", + "# sample numbers across different batches)\n", + "\n", + "model_type = 'linear' # modelto try 'linear, ''polynomial', 'bspline'\n", + "\n", + "\n", + "############################## Data Simulation ################################\n", + "\n", + "\n", + "X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = \\\n", + " simulate_data(simulation_method, n_samples, n_features, n_grps,\n", + " working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian',\n", + " random_state=random_state)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "TypingError", + "evalue": "Failed in nopython mode pipeline (step: nopython frontend)\nFailed in nopython mode pipeline (step: nopython frontend)\nInvalid use of type(CPUDispatcher()) with parameters (readonly array(float64, 2d, C))\nKnown signatures:\n * (Array(float64, 2, 'A', False, aligned=True),) -> array(float64, 1d, A)\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /tmp/tmp01rwehf6 (7)\n\n\nFile \"../../../../../tmp/tmp01rwehf6\", line 7:\ndef numba_funcified_fgraph(_unconstrained_point, y, batch_effect_0_data, X):\n \n # Sum{axis=1}(X)\n tensor_variable_2 = careduce_axis(X)\n ^\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\n\nFile \"../../../anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py\", line 637:\n def extract_shared(x, user_data_):\n \n\n return inner(x, *_shared_tuple)\n ^\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypingError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m################################# Fittig and Predicting ###############################\u001b[39;00m\n\u001b[1;32m 3\u001b[0m nm \u001b[38;5;241m=\u001b[39m norm_init(X_train, Y_train, alg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhbr\u001b[39m\u001b[38;5;124m'\u001b[39m, model_type\u001b[38;5;241m=\u001b[39mmodel_type, likelihood\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSHASHo\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m linear_sigma\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, random_intercept_mu\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, random_slope_mu\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, linear_epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFalse\u001b[39m\u001b[38;5;124m'\u001b[39m, linear_delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFalse\u001b[39m\u001b[38;5;124m'\u001b[39m, nuts_sampler\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnutpie\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mnm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrbefile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworking_dir\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrbefile.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m yhat, ys2 \u001b[38;5;241m=\u001b[39m nm\u001b[38;5;241m.\u001b[39mpredict(X_test, tsbefile\u001b[38;5;241m=\u001b[39mworking_dir\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsbefile.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m 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\u001b[38;5;28mrange\u001b[39m(batch_effects_train\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 284\u001b[0m ]\n\u001b[0;32m--> 286\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhbr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_effects_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pcntoolkit-0.30.post2-py3.11.egg/pcntoolkit/model/hbr.py:444\u001b[0m, in \u001b[0;36mHBR.estimate\u001b[0;34m(self, X, y, batch_effects, **kwargs)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m modeler(X, y, batch_effects, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfigs) \u001b[38;5;28;01mas\u001b[39;00m m:\n\u001b[0;32m--> 444\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 445\u001b[0m \u001b[43m \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn_samples\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 446\u001b[0m \u001b[43m 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Your model is probably not continuous.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 722\u001b[0m )\n\u001b[1;32m 724\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m joined_blas_limiter():\n\u001b[0;32m--> 725\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sample_external_nuts\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 726\u001b[0m \u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnuts_sampler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 727\u001b[0m \u001b[43m \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdraws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 728\u001b[0m \u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtune\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 729\u001b[0m \u001b[43m \u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 730\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mnuts_sampler_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnuts_sampler_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 739\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 740\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m 743\u001b[0m step \u001b[38;5;241m=\u001b[39m CompoundStep(step)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pymc/sampling/mcmc.py:307\u001b[0m, in \u001b[0;36m_sample_external_nuts\u001b[0;34m(sampler, draws, tune, chains, target_accept, random_seed, initvals, model, var_names, progressbar, idata_kwargs, compute_convergence_checks, nuts_sampler_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m var_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 303\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 304\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`var_names` are currently ignored by the nutpie sampler\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 305\u001b[0m \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m 306\u001b[0m )\n\u001b[0;32m--> 307\u001b[0m compiled_model \u001b[38;5;241m=\u001b[39m \u001b[43mnutpie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_pymc_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 309\u001b[0m idata \u001b[38;5;241m=\u001b[39m nutpie\u001b[38;5;241m.\u001b[39msample(\n\u001b[1;32m 310\u001b[0m compiled_model,\n\u001b[1;32m 311\u001b[0m draws\u001b[38;5;241m=\u001b[39mdraws,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnuts_sampler_kwargs,\n\u001b[1;32m 318\u001b[0m )\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py:391\u001b[0m, in \u001b[0;36mcompile_pymc_model\u001b[0;34m(model, backend, gradient_backend, **kwargs)\u001b[0m\n\u001b[1;32m 388\u001b[0m backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_compile_pymc_model_numba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjax\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _compile_pymc_model_jax(\n\u001b[1;32m 394\u001b[0m model, gradient_backend\u001b[38;5;241m=\u001b[39mgradient_backend, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 395\u001b[0m )\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py:207\u001b[0m, in \u001b[0;36m_compile_pymc_model_numba\u001b[0;34m(model, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 201\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m 202\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 203\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cache compiled function .* as it uses dynamic globals\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 204\u001b[0m category\u001b[38;5;241m=\u001b[39mnumba\u001b[38;5;241m.\u001b[39mNumbaWarning,\n\u001b[1;32m 205\u001b[0m )\n\u001b[0;32m--> 207\u001b[0m logp_numba \u001b[38;5;241m=\u001b[39m \u001b[43mnumba\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc_sig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlogp_numba_raw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m expand_shared_names \u001b[38;5;241m=\u001b[39m [var\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m expand_fn_pt\u001b[38;5;241m.\u001b[39mget_shared()]\n\u001b[1;32m 210\u001b[0m expand_numba_raw, c_sig_expand \u001b[38;5;241m=\u001b[39m _make_c_expand_func(\n\u001b[1;32m 211\u001b[0m n_dim, n_expanded, expand_fn, user_data, expand_shared_names, shared_data\n\u001b[1;32m 212\u001b[0m )\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/decorators.py:275\u001b[0m, in \u001b[0;36mcfunc..wrapper\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache:\n\u001b[1;32m 274\u001b[0m res\u001b[38;5;241m.\u001b[39menable_caching()\n\u001b[0;32m--> 275\u001b[0m \u001b[43mres\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_lock.py:35\u001b[0m, in \u001b[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire_compile_lock\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/ccallback.py:68\u001b[0m, in \u001b[0;36mCFunc.compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 65\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mload_overload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig,\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_targetdescr\u001b[38;5;241m.\u001b[39mtarget_context)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cres \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 68\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_uncached\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39msave_overload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig, cres)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/ccallback.py:82\u001b[0m, in \u001b[0;36mCFunc._compile_uncached\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 79\u001b[0m sig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig\n\u001b[1;32m 81\u001b[0m \u001b[38;5;66;03m# Compile native function as well as cfunc wrapper\u001b[39;00m\n\u001b[0;32m---> 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:84\u001b[0m, in \u001b[0;36m_FunctionCompiler.compile\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retval\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:94\u001b[0m, in \u001b[0;36m_FunctionCompiler._compile_cached\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 94\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_core\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mTypingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_failed_cache[key] \u001b[38;5;241m=\u001b[39m e\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:107\u001b[0m, in \u001b[0;36m_FunctionCompiler._compile_core\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 104\u001b[0m flags \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_customize_flags(flags)\n\u001b[1;32m 106\u001b[0m impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_implementation(args, {})\n\u001b[0;32m--> 107\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[43mcompiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_extra\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetdescr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtyping_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetdescr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mimpl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43mpipeline_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline_class\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;66;03m# Check typing error if object mode is used\u001b[39;00m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cres\u001b[38;5;241m.\u001b[39mtyping_error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m flags\u001b[38;5;241m.\u001b[39menable_pyobject:\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:744\u001b[0m, in \u001b[0;36mcompile_extra\u001b[0;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compiler entry point\u001b[39;00m\n\u001b[1;32m 721\u001b[0m \n\u001b[1;32m 722\u001b[0m \u001b[38;5;124;03mParameter\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03m compiler pipeline\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 742\u001b[0m pipeline \u001b[38;5;241m=\u001b[39m pipeline_class(typingctx, targetctx, library,\n\u001b[1;32m 743\u001b[0m args, return_type, flags, \u001b[38;5;28mlocals\u001b[39m)\n\u001b[0;32m--> 744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_extra\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:438\u001b[0m, in \u001b[0;36mCompilerBase.compile_extra\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mlifted \u001b[38;5;241m=\u001b[39m ()\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mlifted_from \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_bytecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:506\u001b[0m, in \u001b[0;36mCompilerBase._compile_bytecode\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;124;03mPopulate and run pipeline for bytecode input\u001b[39;00m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfunc_ir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_core\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:485\u001b[0m, in \u001b[0;36mCompilerBase._compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus\u001b[38;5;241m.\u001b[39mfail_reason \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m 484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_final_pipeline:\n\u001b[0;32m--> 485\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompilerError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll available pipelines exhausted\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:472\u001b[0m, in \u001b[0;36mCompilerBase._compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 470\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 472\u001b[0m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 473\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mcr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 474\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:368\u001b[0m, in \u001b[0;36mPassManager.run\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 365\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed in \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m mode pipeline (step: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \\\n\u001b[1;32m 366\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpipeline_name, pass_desc)\n\u001b[1;32m 367\u001b[0m patched_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_patch_error(msg, e)\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m patched_exception\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:356\u001b[0m, in \u001b[0;36mPassManager.run\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 354\u001b[0m pass_inst \u001b[38;5;241m=\u001b[39m _pass_registry\u001b[38;5;241m.\u001b[39mget(pss)\u001b[38;5;241m.\u001b[39mpass_inst\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pass_inst, CompilerPass):\n\u001b[0;32m--> 356\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_runPass\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpass_inst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLegacy pass in use\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_lock.py:35\u001b[0m, in \u001b[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire_compile_lock\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:311\u001b[0m, in \u001b[0;36mPassManager._runPass\u001b[0;34m(self, index, pss, internal_state)\u001b[0m\n\u001b[1;32m 309\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m check(pss\u001b[38;5;241m.\u001b[39mrun_initialization, internal_state)\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m SimpleTimer() \u001b[38;5;28;01mas\u001b[39;00m pass_time:\n\u001b[0;32m--> 311\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mcheck\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_pass\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m SimpleTimer() \u001b[38;5;28;01mas\u001b[39;00m finalize_time:\n\u001b[1;32m 313\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m check(pss\u001b[38;5;241m.\u001b[39mrun_finalizer, internal_state)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:273\u001b[0m, in \u001b[0;36mPassManager._runPass..check\u001b[0;34m(func, compiler_state)\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck\u001b[39m(func, compiler_state):\n\u001b[0;32m--> 273\u001b[0m mangled \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompiler_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mangled \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 275\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCompilerPass implementations should return True/False. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCompilerPass with name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m did not.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typed_passes.py:112\u001b[0m, in \u001b[0;36mBaseTypeInference.run_pass\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;124;03mType inference and legalization\u001b[39;00m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m fallback_context(state, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFunction \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m failed type inference\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;241m%\u001b[39m (state\u001b[38;5;241m.\u001b[39mfunc_id\u001b[38;5;241m.\u001b[39mfunc_name,)):\n\u001b[1;32m 111\u001b[0m \u001b[38;5;66;03m# Type inference\u001b[39;00m\n\u001b[0;32m--> 112\u001b[0m typemap, return_type, calltypes, errs \u001b[38;5;241m=\u001b[39m \u001b[43mtype_inference_stage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtypingctx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetctx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc_ir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 119\u001b[0m \u001b[43m \u001b[49m\u001b[43mraise_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_errors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m state\u001b[38;5;241m.\u001b[39mtypemap \u001b[38;5;241m=\u001b[39m typemap\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# save errors in case of partial typing\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typed_passes.py:93\u001b[0m, in \u001b[0;36mtype_inference_stage\u001b[0;34m(typingctx, targetctx, interp, args, return_type, locals, raise_errors)\u001b[0m\n\u001b[1;32m 91\u001b[0m infer\u001b[38;5;241m.\u001b[39mbuild_constraint()\n\u001b[1;32m 92\u001b[0m \u001b[38;5;66;03m# return errors in case of partial typing\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m errs \u001b[38;5;241m=\u001b[39m \u001b[43minfer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpropagate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraise_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraise_errors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m typemap, restype, calltypes \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39munify(raise_errors\u001b[38;5;241m=\u001b[39mraise_errors)\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _TypingResults(typemap, restype, calltypes, errs)\n", + "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typeinfer.py:1091\u001b[0m, in \u001b[0;36mTypeInferer.propagate\u001b[0;34m(self, raise_errors)\u001b[0m\n\u001b[1;32m 1088\u001b[0m force_lit_args \u001b[38;5;241m=\u001b[39m [e \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m errors\n\u001b[1;32m 1089\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, ForceLiteralArg)]\n\u001b[1;32m 1090\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m force_lit_args:\n\u001b[0;32m-> 1091\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m errors[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1092\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m reduce(operator\u001b[38;5;241m.\u001b[39mor_, force_lit_args)\n", + "\u001b[0;31mTypingError\u001b[0m: Failed in nopython mode pipeline (step: nopython frontend)\nFailed in nopython mode pipeline (step: nopython frontend)\nInvalid use of type(CPUDispatcher()) with parameters (readonly array(float64, 2d, C))\nKnown signatures:\n * (Array(float64, 2, 'A', False, aligned=True),) -> array(float64, 1d, A)\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /tmp/tmp01rwehf6 (7)\n\n\nFile \"../../../../../tmp/tmp01rwehf6\", line 7:\ndef numba_funcified_fgraph(_unconstrained_point, y, batch_effect_0_data, X):\n \n # Sum{axis=1}(X)\n tensor_variable_2 = careduce_axis(X)\n ^\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\n\nFile \"../../../anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py\", line 637:\n def extract_shared(x, user_data_):\n \n\n return inner(x, *_shared_tuple)\n ^\n" + ] + } + ], + "source": [ + "\n", + "################################# Fittig and Predicting ###############################\n", + "\n", + "nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHo',\n", + " linear_sigma='True', random_intercept_mu='True', random_slope_mu='True', linear_epsilon='False', linear_delta='False', nuts_sampler='nutpie')\n", + "\n", + "nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')\n", + "yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')\n", + "\n", + "\n", + "################################# Plotting Quantiles ###############################\n", + "\n", + "\n", + "for i in range(n_features):\n", + " sorted_idx = X_test[:, i].argsort(axis=0).squeeze()\n", + " temp_X = X_test[sorted_idx, i]\n", + " temp_Y = Y_test[sorted_idx,]\n", + " temp_be = grp_id_test[sorted_idx, :].squeeze()\n", + " temp_yhat = yhat[sorted_idx,]\n", + " temp_s2 = ys2[sorted_idx,]\n", + "\n", + " plt.figure()\n", + " for j in range(n_grps):\n", + " scat1 = plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,],\n", + " label='Group' + str(j))\n", + " # Showing the quantiles\n", + " resolution = 200\n", + " synth_X = np.linspace(-3, 3, resolution)\n", + " q = nm.get_mcmc_quantiles(\n", + " synth_X, batch_effects=j*np.ones(resolution))\n", + " col = scat1.get_facecolors()[0]\n", + " plt.plot(synth_X, q.T, linewidth=1, color=col, zorder=0)\n", + "\n", + " plt.title('Model %s, Feature %d' % (model_type, i))\n", + " plt.legend()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev_216", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 447f383f0e44b6bc7b7772f14ba42041e1acbbee Mon Sep 17 00:00:00 2001 From: AuguB Date: Thu, 3 Oct 2024 10:50:32 +0200 Subject: [PATCH 10/68] Fix shape problem that arises when using nutpie sampler --- pcntoolkit/model/hbr.py | 21 +- tests/test_HBR.ipynb | 605 ++++++++++++++++++++++++++++++++++------ 2 files changed, 537 insertions(+), 89 deletions(-) diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py index 4d051b38..2237a203 100644 --- a/pcntoolkit/model/hbr.py +++ b/pcntoolkit/model/hbr.py @@ -1013,8 +1013,8 @@ def get_samples(self, pb: ParamBuilder): :return: The samples from the parameterization. """ with pb.model: - samples = self.dist[pb.batch_effect_indices] - return samples + return self.dist[pb.batch_effect_indices] + class NonCentralRandomFixedParameterization(Parameterization): @@ -1072,8 +1072,7 @@ def get_samples(self, pb: ParamBuilder): :return: The samples from the parameterization. """ with pb.model: - samples = self.dist[pb.batch_effect_indices] - return samples + return self.dist[pb.batch_effect_indices] class LinearParameterization(Parameterization): @@ -1109,15 +1108,19 @@ def get_samples(self, pb): :return: The samples from the parameterization. """ with pb.model: - intc = self.intercept_parameterization.get_samples(pb) + intercept_samples = self.intercept_parameterization.get_samples(pb) slope_samples = self.slope_parameterization.get_samples(pb) + if pb.configs[f"random_slope_{self.name}"]: - slope = pb.X * slope_samples - slope = slope.sum(axis=-1) + if slope_samples.shape.eval()[1] > 1: + slope = pm.math.sum( + pb.X * slope_samples, axis=1) + else: + slope = pb.X *slope_samples else: - slope = pb.X @ self.slope_parameterization.get_samples(pb) + slope = pb.X @ slope_samples - samples = pm.math.flatten(intc) + pm.math.flatten(slope) + samples = pm.math.flatten(intercept_samples) + pm.math.flatten(slope) return samples diff --git a/tests/test_HBR.ipynb b/tests/test_HBR.ipynb index 1abbd02d..70dd46ee 100644 --- a/tests/test_HBR.ipynb +++ b/tests/test_HBR.ipynb @@ -2,21 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", - "\n", - "\"\"\"\n", - "Created on Mon Jul 29 13:26:35 2019\n", - "\n", - "@author: seykia\n", - "\n", - "This script tests HBR models with default configs on toy data.\n", - "\n", - "\"\"\"\n", - "\n", "import os\n", "import numpy as np\n", "from pcntoolkit.normative_model.norm_utils import norm_init\n", @@ -24,111 +14,560 @@ "import matplotlib.pyplot as plt\n", "from pcntoolkit.normative import estimate\n", "from warnings import filterwarnings\n", - "filterwarnings('ignore')\n" + "filterwarnings('ignore')\n", + "\n", + "plt.rcParams.update({'font.size': 8, 'figure.figsize': (5, 3)})\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(16000,)\n", + "[[-0.25317189]\n", + " [-0.25317189]\n", + " [-0.25317189]\n", + " ...\n", + " [-0.14649218]\n", + " [-0.14649218]\n", + " [-0.14649218]]\n" + ] } ], "source": [ - "\n", - "\n", - "\n", "########################### Experiment Settings ###############################\n", - "\n", - "\n", "random_state = 29\n", - "\n", "working_dir = '/home/guus/tmp/' # Specify a working directory to save data and results.\n", - "\n", "simulation_method = 'linear'\n", "n_features = 1 # The number of input features of X\n", - "n_grps = 2 # Number of batches in data\n", - "n_samples = 500 # Number of samples in each group (use a list for different\n", + "n_grps = 8 # Number of batches in data\n", + "n_samples = 2000 # Number of samples in each group (use a list for different\n", "# sample numbers across different batches)\n", - "\n", - "model_type = 'linear' # modelto try 'linear, ''polynomial', 'bspline'\n", - "\n", - "\n", + "model_type = 'bspline' # modelto try 'linear, ''polynomial', 'bspline'\n", "############################## Data Simulation ################################\n", - "\n", - "\n", "X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = \\\n", " simulate_data(simulation_method, n_samples, n_features, n_grps,\n", " working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian',\n", " random_state=random_state)\n", - "\n" + "plt.tight_layout()\n", + "plt.show()\n", + "print(Y_train.shape)\n", + "\n", + "random_group_offsets = np.random.normal(0, 1, n_grps)\n", + "print(random_group_offsets[grp_id_train])\n", + "Y_train += np.squeeze(np.array(random_group_offsets[grp_id_train]))\n", + "Y_test += np.squeeze(np.array(random_group_offsets[grp_id_test]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb',\n", + " random_intercept_mu='True', random_slope_mu='False', linear_sigma='True', linear_delta='True',linear_epsilon='True', nuts_sampler='nutpie')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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Sampling for a minute

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"output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypingError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m################################# Fittig and Predicting ###############################\u001b[39;00m\n\u001b[1;32m 3\u001b[0m nm \u001b[38;5;241m=\u001b[39m norm_init(X_train, Y_train, alg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhbr\u001b[39m\u001b[38;5;124m'\u001b[39m, model_type\u001b[38;5;241m=\u001b[39mmodel_type, likelihood\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSHASHo\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m linear_sigma\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, random_intercept_mu\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, random_slope_mu\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m, linear_epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFalse\u001b[39m\u001b[38;5;124m'\u001b[39m, linear_delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFalse\u001b[39m\u001b[38;5;124m'\u001b[39m, nuts_sampler\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnutpie\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mnm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrbefile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworking_dir\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrbefile.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m yhat, ys2 \u001b[38;5;241m=\u001b[39m nm\u001b[38;5;241m.\u001b[39mpredict(X_test, tsbefile\u001b[38;5;241m=\u001b[39mworking_dir\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsbefile.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m################################# Plotting Quantiles ###############################\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pcntoolkit-0.30.post2-py3.11.egg/pcntoolkit/normative_model/norm_hbr.py:286\u001b[0m, in \u001b[0;36mNormHBR.estimate\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m batch_effects_train \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros([X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_effects_maps \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 282\u001b[0m {v: i \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(np\u001b[38;5;241m.\u001b[39munique(batch_effects_train[:, j]))}\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(batch_effects_train\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 284\u001b[0m ]\n\u001b[0;32m--> 286\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhbr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_effects_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pcntoolkit-0.30.post2-py3.11.egg/pcntoolkit/model/hbr.py:444\u001b[0m, in \u001b[0;36mHBR.estimate\u001b[0;34m(self, X, y, batch_effects, **kwargs)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m modeler(X, y, batch_effects, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfigs) \u001b[38;5;28;01mas\u001b[39;00m m:\n\u001b[0;32m--> 444\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 445\u001b[0m \u001b[43m \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn_samples\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 446\u001b[0m \u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn_tuning\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[43m \u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn_chains\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 448\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500000\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mcores\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcores\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mnuts_sampler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfigs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnuts_sampler\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogressbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvars_to_sample \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my_like\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfigs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mremove_datapoints_from_posterior\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pymc/sampling/mcmc.py:725\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, blas_cores, model, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 721\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel can not be sampled with NUTS alone. Your model is probably not continuous.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 722\u001b[0m )\n\u001b[1;32m 724\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m joined_blas_limiter():\n\u001b[0;32m--> 725\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sample_external_nuts\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 726\u001b[0m \u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnuts_sampler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 727\u001b[0m \u001b[43m \u001b[49m\u001b[43mdraws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdraws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 728\u001b[0m \u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtune\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 729\u001b[0m \u001b[43m \u001b[49m\u001b[43mchains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 730\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_accept\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnuts\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtarget_accept\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 731\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_seed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 732\u001b[0m \u001b[43m \u001b[49m\u001b[43minitvals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitvals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 733\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogressbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogressbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 736\u001b[0m \u001b[43m \u001b[49m\u001b[43midata_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midata_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompute_convergence_checks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute_convergence_checks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m \u001b[49m\u001b[43mnuts_sampler_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnuts_sampler_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 739\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 740\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m 743\u001b[0m step \u001b[38;5;241m=\u001b[39m CompoundStep(step)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/pymc/sampling/mcmc.py:307\u001b[0m, in \u001b[0;36m_sample_external_nuts\u001b[0;34m(sampler, draws, tune, chains, target_accept, random_seed, initvals, model, var_names, progressbar, idata_kwargs, compute_convergence_checks, nuts_sampler_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m var_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 303\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 304\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`var_names` are currently ignored by the nutpie sampler\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 305\u001b[0m \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m 306\u001b[0m )\n\u001b[0;32m--> 307\u001b[0m compiled_model \u001b[38;5;241m=\u001b[39m \u001b[43mnutpie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_pymc_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 309\u001b[0m idata \u001b[38;5;241m=\u001b[39m nutpie\u001b[38;5;241m.\u001b[39msample(\n\u001b[1;32m 310\u001b[0m compiled_model,\n\u001b[1;32m 311\u001b[0m draws\u001b[38;5;241m=\u001b[39mdraws,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnuts_sampler_kwargs,\n\u001b[1;32m 318\u001b[0m )\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py:391\u001b[0m, in \u001b[0;36mcompile_pymc_model\u001b[0;34m(model, backend, gradient_backend, **kwargs)\u001b[0m\n\u001b[1;32m 388\u001b[0m backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_compile_pymc_model_numba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjax\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _compile_pymc_model_jax(\n\u001b[1;32m 394\u001b[0m model, gradient_backend\u001b[38;5;241m=\u001b[39mgradient_backend, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 395\u001b[0m )\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py:207\u001b[0m, in \u001b[0;36m_compile_pymc_model_numba\u001b[0;34m(model, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 201\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m 202\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 203\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cache compiled function .* as it uses dynamic globals\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 204\u001b[0m category\u001b[38;5;241m=\u001b[39mnumba\u001b[38;5;241m.\u001b[39mNumbaWarning,\n\u001b[1;32m 205\u001b[0m )\n\u001b[0;32m--> 207\u001b[0m logp_numba \u001b[38;5;241m=\u001b[39m \u001b[43mnumba\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc_sig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlogp_numba_raw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m expand_shared_names \u001b[38;5;241m=\u001b[39m [var\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m expand_fn_pt\u001b[38;5;241m.\u001b[39mget_shared()]\n\u001b[1;32m 210\u001b[0m expand_numba_raw, c_sig_expand \u001b[38;5;241m=\u001b[39m _make_c_expand_func(\n\u001b[1;32m 211\u001b[0m n_dim, n_expanded, expand_fn, user_data, expand_shared_names, shared_data\n\u001b[1;32m 212\u001b[0m )\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/decorators.py:275\u001b[0m, in \u001b[0;36mcfunc..wrapper\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache:\n\u001b[1;32m 274\u001b[0m res\u001b[38;5;241m.\u001b[39menable_caching()\n\u001b[0;32m--> 275\u001b[0m \u001b[43mres\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_lock.py:35\u001b[0m, in \u001b[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire_compile_lock\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/ccallback.py:68\u001b[0m, in \u001b[0;36mCFunc.compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 65\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mload_overload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig,\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_targetdescr\u001b[38;5;241m.\u001b[39mtarget_context)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cres \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 68\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_uncached\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39msave_overload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig, cres)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/ccallback.py:82\u001b[0m, in \u001b[0;36mCFunc._compile_uncached\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 79\u001b[0m sig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sig\n\u001b[1;32m 81\u001b[0m \u001b[38;5;66;03m# Compile native function as well as cfunc wrapper\u001b[39;00m\n\u001b[0;32m---> 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:84\u001b[0m, in \u001b[0;36m_FunctionCompiler.compile\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retval\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:94\u001b[0m, in \u001b[0;36m_FunctionCompiler._compile_cached\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 94\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_core\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mTypingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_failed_cache[key] \u001b[38;5;241m=\u001b[39m e\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/dispatcher.py:107\u001b[0m, in \u001b[0;36m_FunctionCompiler._compile_core\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 104\u001b[0m flags \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_customize_flags(flags)\n\u001b[1;32m 106\u001b[0m impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_implementation(args, {})\n\u001b[0;32m--> 107\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[43mcompiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_extra\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetdescr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtyping_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetdescr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mimpl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43mpipeline_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline_class\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;66;03m# Check typing error if object mode is used\u001b[39;00m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cres\u001b[38;5;241m.\u001b[39mtyping_error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m flags\u001b[38;5;241m.\u001b[39menable_pyobject:\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:744\u001b[0m, in \u001b[0;36mcompile_extra\u001b[0;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compiler entry point\u001b[39;00m\n\u001b[1;32m 721\u001b[0m \n\u001b[1;32m 722\u001b[0m \u001b[38;5;124;03mParameter\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03m compiler pipeline\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 742\u001b[0m pipeline \u001b[38;5;241m=\u001b[39m pipeline_class(typingctx, targetctx, library,\n\u001b[1;32m 743\u001b[0m args, return_type, flags, \u001b[38;5;28mlocals\u001b[39m)\n\u001b[0;32m--> 744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_extra\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:438\u001b[0m, in \u001b[0;36mCompilerBase.compile_extra\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mlifted \u001b[38;5;241m=\u001b[39m ()\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mlifted_from \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_bytecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:506\u001b[0m, in \u001b[0;36mCompilerBase._compile_bytecode\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;124;03mPopulate and run pipeline for bytecode input\u001b[39;00m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfunc_ir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compile_core\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:485\u001b[0m, in \u001b[0;36mCompilerBase._compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus\u001b[38;5;241m.\u001b[39mfail_reason \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m 484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_final_pipeline:\n\u001b[0;32m--> 485\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompilerError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll available pipelines exhausted\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler.py:472\u001b[0m, in \u001b[0;36mCompilerBase._compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 470\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 472\u001b[0m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 473\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mcr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 474\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:368\u001b[0m, in \u001b[0;36mPassManager.run\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 365\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed in \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m mode pipeline (step: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \\\n\u001b[1;32m 366\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpipeline_name, pass_desc)\n\u001b[1;32m 367\u001b[0m patched_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_patch_error(msg, e)\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m patched_exception\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:356\u001b[0m, in \u001b[0;36mPassManager.run\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 354\u001b[0m pass_inst \u001b[38;5;241m=\u001b[39m _pass_registry\u001b[38;5;241m.\u001b[39mget(pss)\u001b[38;5;241m.\u001b[39mpass_inst\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pass_inst, CompilerPass):\n\u001b[0;32m--> 356\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_runPass\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpass_inst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLegacy pass in use\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_lock.py:35\u001b[0m, in \u001b[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire_compile_lock\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:311\u001b[0m, in \u001b[0;36mPassManager._runPass\u001b[0;34m(self, index, pss, internal_state)\u001b[0m\n\u001b[1;32m 309\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m check(pss\u001b[38;5;241m.\u001b[39mrun_initialization, internal_state)\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m SimpleTimer() \u001b[38;5;28;01mas\u001b[39;00m pass_time:\n\u001b[0;32m--> 311\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mcheck\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_pass\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m SimpleTimer() \u001b[38;5;28;01mas\u001b[39;00m finalize_time:\n\u001b[1;32m 313\u001b[0m mutated \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m check(pss\u001b[38;5;241m.\u001b[39mrun_finalizer, internal_state)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/compiler_machinery.py:273\u001b[0m, in \u001b[0;36mPassManager._runPass..check\u001b[0;34m(func, compiler_state)\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck\u001b[39m(func, compiler_state):\n\u001b[0;32m--> 273\u001b[0m mangled \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompiler_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mangled \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 275\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCompilerPass implementations should return True/False. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCompilerPass with name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m did not.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typed_passes.py:112\u001b[0m, in \u001b[0;36mBaseTypeInference.run_pass\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;124;03mType inference and legalization\u001b[39;00m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m fallback_context(state, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFunction \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m failed type inference\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;241m%\u001b[39m (state\u001b[38;5;241m.\u001b[39mfunc_id\u001b[38;5;241m.\u001b[39mfunc_name,)):\n\u001b[1;32m 111\u001b[0m \u001b[38;5;66;03m# Type inference\u001b[39;00m\n\u001b[0;32m--> 112\u001b[0m typemap, return_type, calltypes, errs \u001b[38;5;241m=\u001b[39m \u001b[43mtype_inference_stage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtypingctx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetctx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc_ir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 119\u001b[0m \u001b[43m \u001b[49m\u001b[43mraise_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_errors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m state\u001b[38;5;241m.\u001b[39mtypemap \u001b[38;5;241m=\u001b[39m typemap\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# save errors in case of partial typing\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typed_passes.py:93\u001b[0m, in \u001b[0;36mtype_inference_stage\u001b[0;34m(typingctx, targetctx, interp, args, return_type, locals, raise_errors)\u001b[0m\n\u001b[1;32m 91\u001b[0m infer\u001b[38;5;241m.\u001b[39mbuild_constraint()\n\u001b[1;32m 92\u001b[0m \u001b[38;5;66;03m# return errors in case of partial typing\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m errs \u001b[38;5;241m=\u001b[39m \u001b[43minfer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpropagate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraise_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraise_errors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m typemap, restype, calltypes \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39munify(raise_errors\u001b[38;5;241m=\u001b[39mraise_errors)\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _TypingResults(typemap, restype, calltypes, errs)\n", - "File \u001b[0;32m~/anaconda3/envs/dev_215/lib/python3.11/site-packages/numba-0.60.0-py3.11-linux-x86_64.egg/numba/core/typeinfer.py:1091\u001b[0m, in \u001b[0;36mTypeInferer.propagate\u001b[0;34m(self, raise_errors)\u001b[0m\n\u001b[1;32m 1088\u001b[0m force_lit_args \u001b[38;5;241m=\u001b[39m [e \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m errors\n\u001b[1;32m 1089\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, ForceLiteralArg)]\n\u001b[1;32m 1090\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m force_lit_args:\n\u001b[0;32m-> 1091\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m errors[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1092\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m reduce(operator\u001b[38;5;241m.\u001b[39mor_, force_lit_args)\n", - "\u001b[0;31mTypingError\u001b[0m: Failed in nopython mode pipeline (step: nopython frontend)\nFailed in nopython mode pipeline (step: nopython frontend)\nInvalid use of type(CPUDispatcher()) with parameters (readonly array(float64, 2d, C))\nKnown signatures:\n * (Array(float64, 2, 'A', False, aligned=True),) -> array(float64, 1d, A)\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /tmp/tmp01rwehf6 (7)\n\n\nFile \"../../../../../tmp/tmp01rwehf6\", line 7:\ndef numba_funcified_fgraph(_unconstrained_point, y, batch_effect_0_data, X):\n \n # Sum{axis=1}(X)\n tensor_variable_2 = careduce_axis(X)\n ^\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\nDuring: resolving callee type: type(CPUDispatcher())\nDuring: typing of call at /home/guus/anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py (637)\n\n\nFile \"../../../anaconda3/envs/dev_215/lib/python3.11/site-packages/nutpie-0.13.2-py3.11-linux-x86_64.egg/nutpie/compile_pymc.py\", line 637:\n def extract_shared(x, user_data_):\n \n\n return inner(x, *_shared_tuple)\n ^\n" + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [y_like]\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5367d8f840af4551bd83b9c02e9b05d6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "\n",
-    "################################# Fittig and Predicting ###############################\n",
-    "\n",
-    "nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHo',\n",
-    "               linear_sigma='True', random_intercept_mu='True', random_slope_mu='True', linear_epsilon='False', linear_delta='False', nuts_sampler='nutpie')\n",
-    "\n",
-    "nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')\n",
-    "yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')\n",
-    "\n",
-    "\n",
-    "################################# Plotting Quantiles ###############################\n",
-    "\n",
     "\n",
     "for i in range(n_features):\n",
     "    sorted_idx = X_test[:, i].argsort(axis=0).squeeze()\n",
@@ -152,10 +591,16 @@
     "\n",
     "    plt.title('Model %s, Feature %d' % (model_type, i))\n",
     "    plt.legend()\n",
-    "    plt.show()\n",
-    "\n"
+    "    plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From cc6c21c80c21580b703feb96a03c6fdf6f429f40 Mon Sep 17 00:00:00 2001
From: AuguB 
Date: Thu, 3 Oct 2024 10:50:51 +0200
Subject: [PATCH 11/68] Update test HBR notebook

---
 tests/test_HBR.ipynb | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/tests/test_HBR.ipynb b/tests/test_HBR.ipynb
index 70dd46ee..12e03746 100644
--- a/tests/test_HBR.ipynb
+++ b/tests/test_HBR.ipynb
@@ -208,16 +208,16 @@
        "        Finished Chains:\n",
        "        0\n",
        "    
\n",
-       "    
Sampling for a minute
\n",
+       "    
Sampling for 2 minutes
\n",
        "    
\n",
        "        Estimated Time to Completion:\n",
-       "        35 minutes\n",
+       "        an hour\n",
        "    
\n",
        "\n",
        "    \n",
+       "        value=\"58\">\n",
        "    \n",
        "    \n",
        "        \n",
@@ -235,13 +235,13 @@
        "                    \n",
-       "                    \n",
+       "                    \n",
        "                    \n",
-       "                    \n",
-       "                    \n",
+       "                    \n",
+       "                    \n",
        "                \n",
        "            \n",
        "            \n",
@@ -255,6 +255,16 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [

From d4e0631737f0f1938fd362b5181a0196b7ecdc52 Mon Sep 17 00:00:00 2001
From: AuguB 
Date: Thu, 3 Oct 2024 15:19:37 +0200
Subject: [PATCH 12/68] Small bug fix in gradient calculation for SHASH model

---
 pcntoolkit/model/SHASH.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index 4be5c947..a3a48a84 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -81,7 +81,7 @@ def grad(self, inputs, output_grads):
         dp = 1e-16
         p = inputs[0]
         x = inputs[1]
-        grad = (self(p + dp, x) - self(p - dp, x)) / dp
+        grad = (self(p + dp, x) - self(p - dp, x)) / (2*dp)
         return [
             output_grads[0] * grad,
             grad_not_implemented(

From 924681f3a584d64d343fca2ad6aadc1fb2c835bc Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 7 Oct 2024 15:04:22 +0200
Subject: [PATCH 13/68] Refactoring of SHASH Prior tuning

---
 pcntoolkit/model/SHASH.py | 139 ++++++++++++++++++--------------------
 pcntoolkit/model/hbr.py   |  49 +++++++-------
 2 files changed, 91 insertions(+), 97 deletions(-)

diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index a3a48a84..d9139d67 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -1,4 +1,12 @@
+"""
+@author: Stijn de Boer (AuguB)
+See: Jones et al. (2009), Sinh-Arcsinh distributions.
+"""
+
+# Standard library imports
 from typing import Union, List, Optional
+
+# Third-party imports
 import pymc as pm
 from pymc import floatX
 from pymc.distributions import Continuous
@@ -11,61 +19,17 @@
 from pytensor.tensor.random.basic import normal
 from pytensor.tensor.random.op import RandomVariable
 
-
 import numpy as np
 import scipy.special as spp
 import matplotlib.pyplot as plt
 
+CONST1 = np.exp(0.25) / np.power(8.0 * np.pi, 0.5)
+CONST2 = -np.log(2 * np.pi) / 2
 
-"""
-@author: Stijn de Boer (AuguB)
-See: Jones et al. (2009), Sinh-Arcsinh distributions.
-"""
-
-
-def K(q, x):
-    """
-    The K function as given in Jones et al.
-    :param q:
-    :param x:
-    :return:
-    """
-    return spp.kv(q, x)
-
-
-def unique_K(q, x):
-    """
-    This is the K function, but it only calculates the unique values of q.
-    :param q:
-    :param x:
-    :return:
+class KOp(Op):
     """
-    unique_q, inverse_indices = np.unique(q, return_inverse=True)
-    unique_outputs = spp.kv(unique_q, x)
-    outputs = unique_outputs[inverse_indices].reshape(q.shape)
-    return outputs
-
-
-CONST = np.exp(0.25) / np.power(8.0 * np.pi, 0.5)
-
-
-def P(q):
+    Modified Bessel function of the second kind, pytensor wrapper for scipy.special.kv
     """
-    The P function as given in Jones et al.
-    :param q:
-    :return:
-    """
-    K1 = K()((q + 1) / 2, 0.25)
-    K2 = K()((q - 1) / 2, 0.25)
-    a = (K1 + K2) * CONST
-    return a
-
-
-class K(Op):
-    """
-    Modified Bessel function of the second kind, pytensor implementation
-    """
-
     __props__ = ()
 
     def make_node(self, p, x):
@@ -74,7 +38,7 @@ def make_node(self, p, x):
         return Apply(self, [p, x], [p.type()])
 
     def perform(self, node, inputs_storage, output_storage):
-        output_storage[0][0] = unique_K(inputs_storage[0], inputs_storage[1])
+        output_storage[0][0] = spp.kv(inputs_storage[0], inputs_storage[1])
 
     def grad(self, inputs, output_grads):
         # Approximation of the derivative. This should suffice for using NUTS
@@ -84,10 +48,9 @@ def grad(self, inputs, output_grads):
         grad = (self(p + dp, x) - self(p - dp, x)) / (2*dp)
         return [
             output_grads[0] * grad,
-            grad_not_implemented(
-                "K", 1, "x", "Gradient not implemented for x"),
-        ]
+            grad_not_implemented("KOp", 2, "x", "")
 
+        ]
 
 def S(x, epsilon, delta):
     """
@@ -137,17 +100,47 @@ def m(epsilon, delta, r):
             - 4 * np.cosh(2 * epsilon / delta) * P(2 / delta)
             + 3
         ) / 8
-    # else:
-    #     frac1 = ptt.as_tensor_variable(1 / pm.power(2, r))
-    #     acc = ptt.as_tensor_variable(0)
-    #     for i in range(r + 1):
-    #         combs = spp.comb(r, i)
-    #         flip = pm.power(-1, i)
-    #         ex = np.exp((r - 2 * i) * epsilon / delta)
-    #         p = P((r - 2 * i) / delta)
-    #         acc += combs * flip * ex * p
-    #     return frac1 * acc
 
+def m1(epsilon, delta):
+    return np.sinh(epsilon / delta) * P(1 / delta)
+
+def m2(epsilon, delta):
+    return (np.cosh(2 * epsilon / delta) * P(2 / delta) - 1) / 2
+
+def m3(epsilon, delta):
+    return (
+        np.sinh(3 * epsilon / delta) * P(3 / delta)
+        - 3 * np.sinh(epsilon / delta) * P(1 / delta)
+    ) / 4
+
+def numpy_P(q):
+    """
+    The P function as given in Jones et al.
+    :param q:
+    :return:
+    """
+    frac = CONST1
+    K1 = spp.kv((q + 1) / 2, 0.25)
+    K2 = spp.kv((q - 1) / 2, 0.25)
+    a = (K1 + K2) * frac
+    return a
+
+# Instance of the KOp
+my_K = KOp()
+
+def P(q):
+    """
+    The P function as given in Jones et al.
+    :param q:
+    :return:
+    """
+    K1 = my_K((q + 1) / 2, 0.25)
+    K2 = my_K((q - 1) / 2, 0.25)
+    a = (K1 + K2) * CONST1
+    return a
+
+
+##### SHASH Distributions #####
 
 class SHASH(RandomVariable):
     name = "shash"
@@ -181,13 +174,12 @@ def logp(value, epsilon, delta):
         this_S = S(value, epsilon, delta)
         this_S_sqr = ptt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac1 = -ptt.log(ptt.constant(2 * np.pi)) / 2
         frac2 = (
             ptt.log(delta) + ptt.log(this_C_sqr) /
             2 - ptt.log(1 + ptt.sqr(value)) / 2
         )
         exp = -this_S_sqr / 2
-        return frac1 + frac2 + exp
+        return CONST2 + frac2 + exp
 
 
 class SHASHoRV(RandomVariable):
@@ -224,14 +216,13 @@ def logp(value, mu, sigma, epsilon, delta):
         this_S = S(remapped_value, epsilon, delta)
         this_S_sqr = ptt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac1 = -ptt.log(ptt.constant(2 * np.pi)) / 2
         frac2 = (
             ptt.log(delta)
             + ptt.log(this_C_sqr) / 2
             - ptt.log(1 + ptt.sqr(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return frac1 + frac2 + exp - ptt.log(sigma)
+        return CONST2 + frac2 + exp - ptt.log(sigma)
 
 
 class SHASHo2RV(RandomVariable):
@@ -270,14 +261,15 @@ def logp(value, mu, sigma, epsilon, delta):
         this_S = S(remapped_value, epsilon, delta)
         this_S_sqr = ptt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac1 = -ptt.log(ptt.constant(2 * np.pi)) / 2
         frac2 = (
             ptt.log(delta)
             + ptt.log(this_C_sqr) / 2
             - ptt.log(1 + ptt.sqr(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return frac1 + frac2 + exp - ptt.log(sigma_d)
+        return CONST2 + frac2 + exp - ptt.log(sigma_d)
+
+
 
 
 class SHASHbRV(RandomVariable):
@@ -297,8 +289,8 @@ def rng_fn(
         size: Optional[Union[List[int], int]],
     ) -> np.ndarray:
         s = rng.normal(size=size)
-        mean = np.sinh(epsilon / delta) * P(1 / delta)
-        var = ((np.cosh(2 * epsilon / delta) * P(2 / delta) - 1) / 2) - mean**2
+        mean = np.sinh(epsilon / delta) * numpy_P(1 / delta)
+        var = ((np.cosh(2 * epsilon / delta) * numpy_P(2 / delta) - 1) / 2) - mean**2
         out = (
             (np.sinh((np.arcsinh(s) + epsilon) / delta) - mean) / np.sqrt(var)
         ) * sigma + mu
@@ -323,17 +315,16 @@ def dist(cls, mu, sigma, epsilon, delta, **kwargs):
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
-        mean = m(epsilon, delta, 1)
-        var = m(epsilon, delta, 2) - mean**2
+        mean = m1(epsilon, delta)
+        var = m2(epsilon, delta) - mean**2
         remapped_value = ((value - mu) / sigma) * np.sqrt(var) + mean
         this_S = S(remapped_value, epsilon, delta)
         this_S_sqr = np.square(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac1 = -np.log(2 * np.pi) / 2
         frac2 = (
             np.log(delta)
             + np.log(this_C_sqr) / 2
             - np.log(1 + np.square(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return frac1 + frac2 + exp + np.log(var) / 2 - np.log(sigma)
+        return CONST2 + frac2 + exp + np.log(var) / 2 - np.log(sigma)
diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 2237a203..8fff24e9 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -247,22 +247,24 @@ def get_sample_dims(var):
                 "mu_samples",
                 pb.make_param(
                     "mu",
-                    mu_slope_mu_params=(0.0, 3.0),
-                    sigma_slope_mu_params=(3.0,),
-                    mu_intercept_mu_params=(0.0, 3.0),
-                    sigma_intercept_mu_params=(3.0,),
+                    mu_slope_mu_params=(0.0, 10.0),
+                    sigma_slope_mu_params=(10.0,),
+                    mu_intercept_mu_params=(0.0, 10.0),
+                    sigma_intercept_mu_params=(10.0,),
                 ).get_samples(pb),
                 dims=get_sample_dims('mu'),
             )
             sigma = pm.Deterministic(
                 "sigma_samples",
                 pb.make_param(
-                    "sigma", mu_sigma_params=(0.0, 2.0), sigma_sigma_params=(2.0,)
+                    "sigma", sigma_params = (0, 2),
+                    mu_sigma_params=(0.0, 2.0),
+                    sigma_sigma_params=(2.0,)
                 ).get_samples(pb),
                 dims=get_sample_dims('sigma'),
             )
             sigma_plus = pm.Deterministic(
-                "sigma_plus_samples", pm.math.log(1 + pm.math.exp(sigma/3))*3, dims=get_sample_dims('sigma')
+                "sigma_plus_samples", np.exp(sigma), dims=get_sample_dims('sigma')
             )
             y_like = pm.Normal(
                 "y_like", mu, sigma=sigma_plus, observed=y, dims="datapoints"
@@ -285,11 +287,11 @@ def get_sample_dims(var):
                 "mu_samples",
                 pb.make_param(
                     "mu",
-                    slope_mu_params=(0.0, 2.0),
-                    mu_slope_mu_params=(0.0, 2.0),
-                    sigma_slope_mu_params=(2.0,),
-                    mu_intercept_mu_params=(0.0, 2.0),
-                    sigma_intercept_mu_params=(2.0,),
+                    slope_mu_params=(0.0, 10.0),
+                    mu_slope_mu_params=(0.0, 10.0),
+                    sigma_slope_mu_params=(10.0,),
+                    mu_intercept_mu_params=(0.0, 10.0),
+                    sigma_intercept_mu_params=(10.0,),
                 ).get_samples(pb),
                 dims=get_sample_dims('mu'),
             )
@@ -297,23 +299,23 @@ def get_sample_dims(var):
                 "sigma_samples",
                 pb.make_param(
                     "sigma",
-                    sigma_params=(1.0, 1.0),
+                    sigma_params=(0., 2.0),
                     sigma_dist="normal",
-                    slope_sigma_params=(0.0, 1.0),
-                    intercept_sigma_params=(1.0, 1.0),
+                    slope_sigma_params=(0.0, 2.0),
+                    intercept_sigma_params=(0.0, 2.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('sigma'),
             )
             sigma_plus = pm.Deterministic(
-                "sigma_plus_samples", np.log(1 + np.exp(sigma)), dims=get_sample_dims('sigma')
+                "sigma_plus_samples", np.exp(sigma), dims=get_sample_dims('sigma')
             )
             epsilon = pm.Deterministic(
                 "epsilon_samples",
                 pb.make_param(
                     "epsilon",
-                    epsilon_params=(0.0, 1.0),
-                    slope_epsilon_params=(0.0, 0.2),
-                    intercept_epsilon_params=(0.0, 0.2),
+                    epsilon_params=(0.0, 10.0),
+                    slope_epsilon_params=(0.0, 10.0),
+                    intercept_epsilon_params=(0.0, 10.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('epsilon'),
             )
@@ -321,16 +323,16 @@ def get_sample_dims(var):
                 "delta_samples",
                 pb.make_param(
                     "delta",
-                    delta_params=(1.0, 1.0),
+                    delta_params=(0., 2.0),
                     delta_dist="normal",
-                    slope_delta_params=(0.0, 0.2),
-                    intercept_delta_params=(1.0, 0.3),
+                    slope_delta_params=(0.0, 2.0),
+                    intercept_delta_params=(0.0, 2.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('delta'),
             )
             delta_plus = pm.Deterministic(
                 "delta_plus_samples",
-                np.log(1 + np.exp(delta * 10)) / 10 + 0.3,
+                np.exp(delta) + 0.3,
                 dims=get_sample_dims('delta'),
             )
             y_like = SHASH_map[configs["likelihood"]](
@@ -558,7 +560,7 @@ def estimate_on_new_site(self, X, y, batch_effects):
                 chains=self.configs["n_chains"],
                 target_accept=self.configs["target_accept"],
                 init=self.configs["init"],
-                n_init=50000,
+                n_init=500000,
                 cores=self.configs["cores"],
                 nuts_sampler=self.configs["nuts_sampler"],
             )
@@ -751,6 +753,7 @@ def __init__(self, name, dist, params, pb, has_random_effect=False) -> None:
             "hcauchy": pm.HalfCauchy,
             "hstudt": pm.HalfStudentT,
             "studt": pm.StudentT,
+            "lognormal": pm.Lognormal,
         }
         self.make_dist(dist, params, pb)
 

From 500f7cdd22151929b941c2cabb109d4983fc089c Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 8 Oct 2024 10:58:33 +0200
Subject: [PATCH 14/68] Modify test script

---
 tests/testHBR.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/testHBR.py b/tests/testHBR.py
index 30168317..2dab4972 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -32,7 +32,7 @@
 n_samples = 500     # Number of samples in each group (use a list for different
 # sample numbers across different batches)
 
-model_type = 'linear'  # modelto try 'linear, ''polynomial', 'bspline'
+model_type = 'bspline'  # modelto try 'linear, ''polynomial', 'bspline'
 
 
 ############################## Data Simulation ################################
@@ -45,8 +45,8 @@
 
 ################################# Fittig and Predicting ###############################
 
-nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHo',
-               linear_sigma='True', random_intercept_mu='True', random_slope_mu='False', linear_epsilon='False', linear_delta='False', nuts_sampler='nutpie')
+nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='Normal',
+               linear_sigma='True', random_intercept_mu='True', random_slope_mu='False', linear_epsilon='False', linear_delta='False', nuts_sampler='pymc')
 
 nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
 yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')

From 24d84debe9cbd9a9194c6d9d9f6d0e98a8277ade Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 8 Oct 2024 11:09:57 +0200
Subject: [PATCH 15/68] Modify test script

---
 tests/testHBR.py     |    2 +-
 tests/test_HBR.ipynb | 2455 ++++++++++++++++++++++++++++++++++++++----
 2 files changed, 2250 insertions(+), 207 deletions(-)

diff --git a/tests/testHBR.py b/tests/testHBR.py
index 2dab4972..edfad541 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -24,7 +24,7 @@
 
 random_state = 29
 
-working_dir = '/home/guus/tmp/'  # Specify a working directory to save data and results.
+working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.
 
 simulation_method = 'linear'
 n_features = 1      # The number of input features of X
diff --git a/tests/test_HBR.ipynb b/tests/test_HBR.ipynb
index 12e03746..45ec901a 100644
--- a/tests/test_HBR.ipynb
+++ b/tests/test_HBR.ipynb
@@ -2,11 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
+    "from IPython.display import clear_output, DisplayHandle\n",
+    "def update_patch(self, obj):\n",
+    "    clear_output(wait=True)\n",
+    "    self.display(obj)\n",
+    "DisplayHandle.update = update_patch\n",
     "import os\n",
     "import numpy as np\n",
     "from pcntoolkit.normative_model.norm_utils import norm_init\n",
@@ -21,12 +26,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -34,38 +39,22 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "data": {
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(16000,)\n",
-      "[[-0.25317189]\n",
-      " [-0.25317189]\n",
-      " [-0.25317189]\n",
-      " ...\n",
-      " [-0.14649218]\n",
-      " [-0.14649218]\n",
-      " [-0.14649218]]\n"
+      "(80000,)\n"
      ]
     }
    ],
    "source": [
     "########################### Experiment Settings ###############################\n",
     "random_state = 29\n",
-    "working_dir = '/home/guus/tmp/'  # Specify a working directory to save data and results.\n",
+    "working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.\n",
     "simulation_method = 'linear'\n",
     "n_features = 1      # The number of input features of X\n",
-    "n_grps = 8          # Number of batches in data\n",
-    "n_samples = 2000     # Number of samples in each group (use a list for different\n",
+    "n_grps = 80         # Number of batches in data\n",
+    "n_samples = 1000     # Number of samples in each group (use a list for different\n",
     "# sample numbers across different batches)\n",
     "model_type = 'bspline'  # modelto try 'linear, ''polynomial', 'bspline'\n",
     "############################## Data Simulation ################################\n",
@@ -73,129 +62,32 @@
     "    simulate_data(simulation_method, n_samples, n_features, n_grps,\n",
     "                  working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian',\n",
     "                  random_state=random_state)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n",
     "print(Y_train.shape)\n",
     "\n",
-    "random_group_offsets = np.random.normal(0, 1, n_grps)\n",
-    "print(random_group_offsets[grp_id_train])\n",
-    "Y_train += np.squeeze(np.array(random_group_offsets[grp_id_train]))\n",
-    "Y_test += np.squeeze(np.array(random_group_offsets[grp_id_test]))\n"
+    "# random_group_offsets = np.random.normal(0, 1, n_grps)\n",
+    "# print(random_group_offsets[grp_id_train])s\n",
+    "# Y_train += np.squeeze(np.array(random_group_offsets[grp_id_train]))\n",
+    "# Y_test += np.squeeze(np.array(random_group_offsets[grp_id_test]))\n",
+    "s"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb',\n",
-    "              random_intercept_mu='True', random_slope_mu='False',  linear_sigma='True', linear_delta='True',linear_epsilon='True', nuts_sampler='nutpie')"
+    "              random_intercept_mu='True', random_slope_mu='False',  linear_sigma='True', linear_delta='False',linear_epsilon='False', nuts_sampler='nutpie')"
    ]
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",
       "text/plain": [
        "
"
       ]
@@ -593,7 +2636,7 @@
     "                            label='Group' + str(j))\n",
     "        # Showing the quantiles\n",
     "        resolution = 200\n",
-    "        synth_X = np.linspace(-3, 3, resolution)\n",
+    "        synth_X = np.linspace(-4, 4, resolution)\n",
     "        q = nm.get_mcmc_quantiles(\n",
     "            synth_X, batch_effects=j*np.ones(resolution))\n",
     "        col = scat1.get_facecolors()[0]\n",
@@ -621,7 +2664,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev_216",
+   "display_name": "dev_215",
    "language": "python",
    "name": "python3"
   },
@@ -635,7 +2678,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

From 4a7746d052d031c766125074de41b31c2c80a3e2 Mon Sep 17 00:00:00 2001
From: matei4501 <154380373+matei4501@users.noreply.github.com>
Date: Wed, 9 Oct 2024 17:27:13 +0300
Subject: [PATCH 16/68] fix anomaly_detection_auc print

fix anomaly_detection_auc in utils.py to correctly print n_features instead of n_permutations
---
 pcntoolkit/util/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py
index ac855315..1a9afc48 100644
--- a/pcntoolkit/util/utils.py
+++ b/pcntoolkit/util/utils.py
@@ -1457,7 +1457,7 @@ def anomaly_detection_auc(abn_p, labels, n_permutation=None):
             p_values[i] = (np.sum(auc_perm > aucs[i]) + 1) / \
                 (n_permutation + 1)
             print('Feature %d of %d is done: p_value=%f' %
-                  (i, n_permutation, p_values[i]))
+                  (i, p, p_values[i]))
 
     return aucs, p_values
 

From 7bbe9cd21d5535ba80e65a0f0570a0d3e6d4f770 Mon Sep 17 00:00:00 2001
From: Augub 
Date: Thu, 10 Oct 2024 16:09:14 +0200
Subject: [PATCH 17/68] suppress warning

---
 pcntoolkit/model/hbr.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 8fff24e9..50a9f967 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -226,16 +226,15 @@ def get_sample_dims(var):
 
     with pm.Model(coords=pb.coords) as model:
         model.add_coord("datapoints", np.arange(X.shape[0]), mutable=True)
-        X = pm.MutableData("X", X, dims=("datapoints", "basis_functions"))
+        X = pm.Data("X", X, dims=("datapoints", "basis_functions"))
         pb.X = X
-        y = pm.MutableData("y", np.squeeze(y), dims="datapoints")
+        y = pm.Data("y", np.squeeze(y), dims="datapoints")
         pb.model = model
         pb.batch_effect_indices = tuple(
             [
                 pm.Data(
                     pb.batch_effect_dim_names[i]+"_data",
                     pb.batch_effect_indices[i],
-                    mutable=True,
                     dims="datapoints",
                 )
                 for i in range(len(pb.batch_effect_indices))

From faeb44ca827ab6833c8683e42969f300d36763e5 Mon Sep 17 00:00:00 2001
From: Augub 
Date: Thu, 10 Oct 2024 16:10:31 +0200
Subject: [PATCH 18/68] Suppress warning

---
 pcntoolkit/model/hbr.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index f95e3e85..d9872cbc 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -226,16 +226,15 @@ def get_sample_dims(var):
 
     with pm.Model(coords=pb.coords) as model:
         model.add_coord("datapoints", np.arange(X.shape[0]), mutable=True)
-        X = pm.MutableData("X", X, dims=("datapoints", "basis_functions"))
+        X = pm.Data("X", X, dims=("datapoints", "basis_functions"))
         pb.X = X
-        y = pm.MutableData("y", np.squeeze(y), dims="datapoints")
+        y = pm.Data("y", np.squeeze(y), dims="datapoints")
         pb.model = model
         pb.batch_effect_indices = tuple(
             [
                 pm.Data(
                     pb.batch_effect_dim_names[i]+"_data",
                     pb.batch_effect_indices[i],
-                    mutable=True,
                     dims="datapoints",
                 )
                 for i in range(len(pb.batch_effect_indices))

From 531f58be8ba51b50ba71551a463e65c5529e63c5 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 11 Oct 2024 16:23:27 +0200
Subject: [PATCH 19/68] Reset priors to narrower versions

---
 pcntoolkit/model/hbr.py | 43 ++++++++++++++++++++++-------------------
 1 file changed, 23 insertions(+), 20 deletions(-)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 50a9f967..0543c60a 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -246,24 +246,27 @@ def get_sample_dims(var):
                 "mu_samples",
                 pb.make_param(
                     "mu",
-                    mu_slope_mu_params=(0.0, 10.0),
-                    sigma_slope_mu_params=(10.0,),
-                    mu_intercept_mu_params=(0.0, 10.0),
-                    sigma_intercept_mu_params=(10.0,),
+                    slope_mu_params=(0.0, 3.0),
+                    mu_slope_mu_params=(0.0, 3.0),
+                    sigma_slope_mu_params=(3.0,),
+                    mu_intercept_mu_params=(0.0, 2.0),
+                    sigma_intercept_mu_params=(2.0,),
                 ).get_samples(pb),
                 dims=get_sample_dims('mu'),
             )
             sigma = pm.Deterministic(
                 "sigma_samples",
                 pb.make_param(
-                    "sigma", sigma_params = (0, 2),
-                    mu_sigma_params=(0.0, 2.0),
-                    sigma_sigma_params=(2.0,)
+                    "sigma",
+                    sigma_params=(0., 2.0),
+                    sigma_dist="normal",
+                    slope_sigma_params=(0.0, 2.0),
+                    intercept_sigma_params=(0.0, 2.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('sigma'),
             )
             sigma_plus = pm.Deterministic(
-                "sigma_plus_samples", np.exp(sigma), dims=get_sample_dims('sigma')
+                "sigma_plus_samples", np.log(1+np.exp(sigma)), dims=get_sample_dims('sigma')
             )
             y_like = pm.Normal(
                 "y_like", mu, sigma=sigma_plus, observed=y, dims="datapoints"
@@ -286,11 +289,11 @@ def get_sample_dims(var):
                 "mu_samples",
                 pb.make_param(
                     "mu",
-                    slope_mu_params=(0.0, 10.0),
-                    mu_slope_mu_params=(0.0, 10.0),
-                    sigma_slope_mu_params=(10.0,),
-                    mu_intercept_mu_params=(0.0, 10.0),
-                    sigma_intercept_mu_params=(10.0,),
+                    slope_mu_params=(0.0, 3.0),
+                    mu_slope_mu_params=(0.0, 3.0),
+                    sigma_slope_mu_params=(3.0,),
+                    mu_intercept_mu_params=(0.0, 2.0),
+                    sigma_intercept_mu_params=(2.0,),
                 ).get_samples(pb),
                 dims=get_sample_dims('mu'),
             )
@@ -306,15 +309,15 @@ def get_sample_dims(var):
                 dims=get_sample_dims('sigma'),
             )
             sigma_plus = pm.Deterministic(
-                "sigma_plus_samples", np.exp(sigma), dims=get_sample_dims('sigma')
+                "sigma_plus_samples", np.log(1+np.exp(sigma)), dims=get_sample_dims('sigma')
             )
             epsilon = pm.Deterministic(
                 "epsilon_samples",
                 pb.make_param(
                     "epsilon",
-                    epsilon_params=(0.0, 10.0),
-                    slope_epsilon_params=(0.0, 10.0),
-                    intercept_epsilon_params=(0.0, 10.0),
+                    epsilon_params=(0.0, 2.0),
+                    slope_epsilon_params=(0.0, 3.0),
+                    intercept_epsilon_params=(0.0, 3.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('epsilon'),
             )
@@ -324,14 +327,14 @@ def get_sample_dims(var):
                     "delta",
                     delta_params=(0., 2.0),
                     delta_dist="normal",
-                    slope_delta_params=(0.0, 2.0),
-                    intercept_delta_params=(0.0, 2.0),
+                    slope_delta_params=(0.0, 1.0),
+                    intercept_delta_params=(0.0, 1.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('delta'),
             )
             delta_plus = pm.Deterministic(
                 "delta_plus_samples",
-                np.exp(delta) + 0.3,
+                np.log(1+np.exp(delta*5))/5 + 0.3,
                 dims=get_sample_dims('delta'),
             )
             y_like = SHASH_map[configs["likelihood"]](

From d388d4bac45e20a2de97c035402ea7afc08f5b4d Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 11 Oct 2024 17:10:48 +0200
Subject: [PATCH 20/68] More changes to the priors

---
 dist/pcntoolkit-0.30.post2-py3.12.egg | Bin 0 -> 306489 bytes
 pcntoolkit/model/hbr.py               |  23 ++++++++++++-----------
 pcntoolkit/util/utils.py              |   2 +-
 tests/testHBR.py                      |   8 ++++----
 4 files changed, 17 insertions(+), 16 deletions(-)
 create mode 100644 dist/pcntoolkit-0.30.post2-py3.12.egg

diff --git a/dist/pcntoolkit-0.30.post2-py3.12.egg b/dist/pcntoolkit-0.30.post2-py3.12.egg
new file mode 100644
index 0000000000000000000000000000000000000000..69167a632d26aa8504e33decb5180cf35523ffc4
GIT binary patch
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zb&z7`S`-*sz}xE)!me%jX<^^ec#7hKO^Z#^%C7;FEf%ySq-k#0Uz7&&TZFJkDb$Q}
zU>^5^X@?XRhW|BTaN>&)=G@+SR~<|}V!WU*HtE-d{X~Z}qVZ^}!EMfI69j=_JF&QI
z$;@9921-SQVVt
Date: Fri, 11 Oct 2024 21:43:36 +0200
Subject: [PATCH 21/68] Tests with new priors look good

---
 pcntoolkit/model/hbr.py | 20 +++++++++---------
 tests/testHBR.py        | 45 ++++++++++++++++++++++++++++++++---------
 2 files changed, 44 insertions(+), 21 deletions(-)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 296b68dc..7ee500b3 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -246,11 +246,12 @@ def get_sample_dims(var):
                 "mu_samples",
                 pb.make_param(
                     "mu",
-                    slope_mu_params=(0.0, 3.0),
-                    mu_slope_mu_params=(0.0, 3.0),
-                    sigma_slope_mu_params=(3.0,),
-                    mu_intercept_mu_params=(0.0, 2.0),
-                    sigma_intercept_mu_params=(2.0,),
+                    intercept_mu_params=(0.0, 10.0),
+                    slope_mu_params=(0.0, 10.0),
+                    mu_slope_mu_params=(0.0, 10.0),
+                    sigma_slope_mu_params=(10.0,),
+                    mu_intercept_mu_params=(0.0, 10.0),
+                    sigma_intercept_mu_params=(10.0,),
                 ).get_samples(pb),
                 dims=get_sample_dims('mu'),
             )
@@ -258,19 +259,16 @@ def get_sample_dims(var):
                 "sigma_samples",
                 pb.make_param(
                     "sigma",
-                    sigma_params=(0., 2.0),
+                    sigma_params=(10., 10.0),
                     sigma_dist="normal",
-                    slope_sigma_params=(0.0, 2.0),
-                    intercept_sigma_params=(0.0, 2.0),
+                    slope_sigma_params=(0.0, 10.0),
+                    intercept_sigma_params=(10.0, 10.0),
                 ).get_samples(pb),
                 dims=get_sample_dims('sigma'),
             )
             sigma_plus = pm.Deterministic(
                 "sigma_plus_samples", np.log(1+np.exp(sigma/10))*10, dims=get_sample_dims('sigma')
             )
-            y_like = pm.Normal(
-                "y_like", mu, sigma=sigma_plus, observed=y, dims="datapoints"
-            )
 
         elif configs["likelihood"] in ["SHASHb", "SHASHo", "SHASHo2"]:
             """
diff --git a/tests/testHBR.py b/tests/testHBR.py
index 1d312548..6c569b22 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -8,7 +8,7 @@
 This script tests HBR models with default configs on toy data.
 
 """
-
+# %%
 import os
 import numpy as np
 from pcntoolkit.normative_model.norm_utils import norm_init
@@ -22,13 +22,12 @@
 ########################### Experiment Settings ###############################
 
 
-random_state = 34
-
+random_state = 40
 working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.
 
 simulation_method = 'non-linear'
 n_features = 1      # The number of input features of X
-n_grps = 2          # Number of batches in data
+n_grps = 10     # Number of batches in data
 n_samples = 500     # Number of samples in each group (use a list for different
 # sample numbers across different batches)
 
@@ -46,15 +45,13 @@
 ################################# Fittig and Predicting ###############################
 
 nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb',
-               linear_sigma='True', random_intercept_mu='True', random_slope_mu='True', linear_epsilon='True', linear_delta='False', nuts_sampler='nutpie')
+               linear_sigma='True', random_intercept_mu='True', random_slope_mu='True', linear_epsilon='False', linear_delta='False', nuts_sampler='nutpie')
 
 nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
 yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')
 
 
 ################################# Plotting Quantiles ###############################
-
-
 for i in range(n_features):
     sorted_idx = X_test[:, i].argsort(axis=0).squeeze()
     temp_X = X_test[sorted_idx, i]
@@ -62,14 +59,14 @@
     temp_be = grp_id_test[sorted_idx, :].squeeze()
     temp_yhat = yhat[sorted_idx,]
     temp_s2 = ys2[sorted_idx,]
-
+    
     plt.figure()
     for j in range(n_grps):
         scat1 = plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,],
                             label='Group' + str(j))
         # Showing the quantiles
         resolution = 200
-        synth_X = np.linspace(-3, 3, resolution)
+        synth_X = np.linspace(np.min(X_train), np.max(X_train), resolution)
         q = nm.get_mcmc_quantiles(
             synth_X, batch_effects=j*np.ones(resolution))
         col = scat1.get_facecolors()[0]
@@ -77,7 +74,20 @@
 
     plt.title('Model %s, Feature %d' % (model_type, i))
     plt.legend()
-    plt.show()
+    plt.show(block=False)
+    plt.savefig(working_dir + 'quantiles_' + model_type + '_feature_' + str(i) + '.png')
+
+    for j in range(n_grps):
+        plt.figure()
+        plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,])
+        plt.plot(temp_X[temp_be == j,], temp_yhat[temp_be == j,], color='red')
+        plt.fill_between(temp_X[temp_be == j,].squeeze(),
+                         (temp_yhat[temp_be == j,] - 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
+                         (temp_yhat[temp_be == j,] + 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
+                         color='red', alpha=0.2)
+        plt.title('Model %s, Group %d, Feature %d' % (model_type, j, i))
+        plt.show(block=False)
+        plt.savefig(working_dir + 'pred_' + model_type + '_group_' + str(j) + '_feature_' + str(i) + '.png')
 
 
 ############################## Normative Modelling Test #######################
@@ -98,3 +108,18 @@
 
 
 ###############################################################################
+# %%
+
+for j in range(n_grps):
+    # Showing the quantiles
+    resolution = 200
+    synth_X = np.linspace(np.min(X_train), np.max(X_train), resolution)
+    q = nm.get_mcmc_quantiles(
+        synth_X, batch_effects=j*np.ones(resolution))
+    plt.figure()
+    plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,])
+    plt.plot(synth_X, q.T, color='black')
+    plt.title('Model %s, Group %d, Feature %d' % (model_type, j, i))
+    plt.show(block=False)
+    plt.savefig(working_dir + 'pred_' + model_type + '_group_' + str(j) + '_feature_' + str(i) + '.png')
+# %%

From b7ebe5a4d28f178e51ed943bc4862f3a65a3ba5f Mon Sep 17 00:00:00 2001
From: Augub 
Date: Fri, 11 Oct 2024 21:55:46 +0200
Subject: [PATCH 22/68] Mutable coords are default in pymc now

---
 pcntoolkit/model/hbr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 50a9f967..e9d6c5e0 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -225,7 +225,7 @@ def get_sample_dims(var):
         return None
 
     with pm.Model(coords=pb.coords) as model:
-        model.add_coord("datapoints", np.arange(X.shape[0]), mutable=True)
+        model.add_coord("datapoints", np.arange(X.shape[0]))
         X = pm.Data("X", X, dims=("datapoints", "basis_functions"))
         pb.X = X
         y = pm.Data("y", np.squeeze(y), dims="datapoints")

From 694d5d1326a77c36eb4f9de3ac351752feb0cea7 Mon Sep 17 00:00:00 2001
From: Seyed Mostafa Kia 
Date: Mon, 14 Oct 2024 17:48:59 +0200
Subject: [PATCH 23/68] Unbiased kurtosis calculation

---
 pcntoolkit/util/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py
index 1a9afc48..0ce281f6 100644
--- a/pcntoolkit/util/utils.py
+++ b/pcntoolkit/util/utils.py
@@ -350,7 +350,7 @@ def calibration_descriptives(x):
     s1 = np.std(x, axis=0)
     skew = n*m3/(n-1)/(n-2)/s1**3
     sdskew = np.sqrt(6*n*(n-1) / ((n-2)*(n+1)*(n+3)))
-    kurtosis = (n*(n+1)*m4 - 3*m2**2*(n-1)) / ((n-1)*(n-2)*(n-3)*s1**4)
+    kurtosis = (n * (n+1) * m4) / ((n-1) * (n-2) * (n-3) * s1**4) - (3 * (n-1)**2) / ((n-2) * (n-3))
     sdkurtosis = np.sqrt(4*(n**2-1) * sdskew**2 / ((n-3)*(n+5)))
     semean = np.sqrt(np.var(x)/n)
     sesd = s1/np.sqrt(2*(n-1))

From 091980eb96162c6a96fa2c99142a3369f8db039e Mon Sep 17 00:00:00 2001
From: Augub 
Date: Tue, 15 Oct 2024 15:03:02 +0200
Subject: [PATCH 24/68] Add multicore support for Torque jobs

---
 pcntoolkit/normative_parallel.py | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/pcntoolkit/normative_parallel.py b/pcntoolkit/normative_parallel.py
index f6a957e7..cd058e8b 100755
--- a/pcntoolkit/normative_parallel.py
+++ b/pcntoolkit/normative_parallel.py
@@ -112,6 +112,7 @@ def execute_nm(processing_dir,
     cluster_spec = kwargs.pop('cluster_spec', 'torque')
     log_path = kwargs.get('log_path', None)
     binary = kwargs.pop('binary', False)
+    cores = kwargs.pop('cores')
 
     split_nm(processing_dir,
              respfile_path,
@@ -165,7 +166,8 @@ def execute_nm(processing_dir,
                     job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration,
+                                     cores=cores)
                     job_ids.append(job_id)
                 elif cluster_spec == 'slurm':
                     # update the response file
@@ -211,7 +213,8 @@ def execute_nm(processing_dir,
                     job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration, 
+                                     cores=cores)
                     job_ids.append(job_id)
                 elif cluster_spec == 'slurm':
                     sbatchwrap_nm(batch_processing_dir,
@@ -254,7 +257,8 @@ def execute_nm(processing_dir,
                     job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration,
+                                     cores=cores)
                     job_ids.append(job_id)
                 elif cluster_spec == 'slurm':
                     sbatchwrap_nm(batch_processing_dir,
@@ -300,7 +304,7 @@ def execute_nm(processing_dir,
                         if cluster_spec == 'torque':
                             rerun_nm(processing_dir, log_path=log_path, memory=memory,
                                      duration=duration, binary=binary,
-                                     interactive=interactive)
+                                     interactive=interactive, cores=cores)
                         elif cluster_spec == 'slurm':
                             sbatchrerun_nm(processing_dir,
                                            memory=memory,
@@ -316,7 +320,7 @@ def execute_nm(processing_dir,
                     if cluster_spec == 'torque':
                         rerun_nm(processing_dir, log_path=log_path, memory=memory,
                                  duration=duration, binary=binary,
-                                 interactive=interactive)
+                                 interactive=interactive, cores=cores)
                     elif cluster_spec == 'slurm':
                         sbatchrerun_nm(processing_dir,
                                        memory=memory,
@@ -985,7 +989,8 @@ def bashwrap_nm(processing_dir,
 def qsub_nm(job_path,
             log_path,
             memory,
-            duration):
+            duration,
+            cores):
     '''This function submits a job.sh scipt to the torque custer using the qsub command.
 
     Basic usage::
@@ -1005,10 +1010,10 @@ def qsub_nm(job_path,
     # created qsub command
     if log_path is None:
         qsub_call = ['echo ' + job_path + ' | qsub -N ' + job_path + ' -l ' +
-                     'procs=1' + ',mem=' + memory + ',walltime=' + duration]
+                     'nodes=1:ppn='+ cores + ',mem=' + memory + ',walltime=' + duration]
     else:
         qsub_call = ['echo ' + job_path + ' | qsub -N ' + job_path +
-                     ' -l ' + 'procs=1' + ',mem=' + memory + ',walltime=' +
+                     ' -l ' + 'nodes=1:ppn='+ cores + ',mem=' + memory + ',walltime=' +
                      duration + ' -o ' + log_path + ' -e ' + log_path]
 
     # submits job to cluster
@@ -1024,6 +1029,7 @@ def rerun_nm(processing_dir,
              memory,
              duration,
              cluster_spec,
+             cores,
              binary=False,
              interactive=False):
     '''This function reruns all failed batched in processing_dir after collect_nm has identified the failed batches.
@@ -1051,7 +1057,8 @@ def rerun_nm(processing_dir,
             job_id = qsub_nm(job_path=jobpath,
                              log_path=log_path,
                              memory=memory,
-                             duration=duration)
+                             duration=duration,
+                             cores=cores)
             job_ids.append(job_id)
     else:
         file_extentions = '.txt'
@@ -1064,7 +1071,8 @@ def rerun_nm(processing_dir,
             job_id = qsub_nm(job_path=jobpath,
                              log_path=log_path,
                              memory=memory,
-                             duration=duration)
+                             duration=duration,
+                             cores=cores)
             job_ids.append(job_id)
 
     if interactive:

From e35c95255a34b16129c24a8855dcdf0cafbd5b1f Mon Sep 17 00:00:00 2001
From: Augub 
Date: Tue, 15 Oct 2024 15:03:24 +0200
Subject: [PATCH 25/68] Add missing likelihood in Normal model

---
 pcntoolkit/model/hbr.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/pcntoolkit/model/hbr.py b/pcntoolkit/model/hbr.py
index 7b2a4c8e..23b73e0e 100644
--- a/pcntoolkit/model/hbr.py
+++ b/pcntoolkit/model/hbr.py
@@ -269,6 +269,13 @@ def get_sample_dims(var):
             sigma_plus = pm.Deterministic(
                 "sigma_plus_samples", np.log(1+np.exp(sigma/10))*10, dims=get_sample_dims('sigma')
             )
+            y_like = pm.Normal(
+                "y_like",
+                mu=mu,
+                sigma=sigma_plus,
+                observed=y,
+                dims="datapoints",
+            )
 
         elif configs["likelihood"] in ["SHASHb", "SHASHo", "SHASHo2"]:
             """

From 411c47c6cc78706e3454f6c60bf819ae9ead6e0c Mon Sep 17 00:00:00 2001
From: Augub 
Date: Tue, 15 Oct 2024 15:11:22 +0200
Subject: [PATCH 26/68] Correct argument name for cores

---
 pcntoolkit/normative_parallel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/normative_parallel.py b/pcntoolkit/normative_parallel.py
index cd058e8b..6b92b124 100755
--- a/pcntoolkit/normative_parallel.py
+++ b/pcntoolkit/normative_parallel.py
@@ -112,7 +112,7 @@ def execute_nm(processing_dir,
     cluster_spec = kwargs.pop('cluster_spec', 'torque')
     log_path = kwargs.get('log_path', None)
     binary = kwargs.pop('binary', False)
-    cores = kwargs.pop('cores')
+    cores = kwargs.pop('n_cores_per_batch')
 
     split_nm(processing_dir,
              respfile_path,

From 7ac7e29df2b2cda4d01b6a3fca99654f7479452f Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 16 Oct 2024 16:37:09 +0200
Subject: [PATCH 27/68] Initial commit

---
 conda_recipe/meta.yaml | 45 ++++++++++++++++++++++++++++++++++++++++++
 requirements.txt       |  7 +------
 2 files changed, 46 insertions(+), 6 deletions(-)
 create mode 100644 conda_recipe/meta.yaml

diff --git a/conda_recipe/meta.yaml b/conda_recipe/meta.yaml
new file mode 100644
index 00000000..9cd1a2c8
--- /dev/null
+++ b/conda_recipe/meta.yaml
@@ -0,0 +1,45 @@
+package:
+  name: pcntoolkit
+  version: "0.30.0"
+
+source:
+  path: ..
+
+build:
+  number: 0
+  script: python setup.py install
+
+requirements:
+  build:
+    - python
+    - setuptools
+    - numpy
+  run:
+    - python
+    - pymc # includes numpy, scipy, pandas, arviz
+    - six
+    - scikit-learn
+    - matplotlib
+    - pytorch
+    - cpuonly
+    - sphinx-tabs
+    - nibabel
+  pip:
+    - bspline
+
+# test:
+#   imports:
+#     - your_project_name
+
+about:
+  home: http://github.com/amarquand/PCNtoolkit
+  license: 'GNU GPLv3'
+  summary: 'Predictive Clinical Neuroscience toolkit'
+  description: |
+    'Predictive Clinical Neuroscience toolkit'
+  doc_url: https://pcntoolkit.readthedocs.io/en/latest/
+  dev_url: http://github.com/amarquand/PCNtoolkit
+
+extra:
+  recipe-maintainers:
+    - AuguB
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 58c3f61e..271c759c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,8 @@
-argparse
 nibabel>=2.5.1
+pymc>=5.1.0
 six
 scikit-learn
 bspline
 matplotlib
-numpy
-scipy>=1.3.2,<1.13.0
-pandas>=0.25.3
 torch>=1.1.0
 sphinx-tabs
-pymc>=5.1.0
-arviz==0.13.0

From 47bcb4b186690e78a026dfbf58502cab9d1fd755 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 16 Oct 2024 16:39:52 +0200
Subject: [PATCH 28/68] Remove unnecessary requirement specifications

All removed specifications are also dependencies of PyMC
---
 requirements.txt | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 58c3f61e..ca1a2cda 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,9 @@
 argparse
 nibabel>=2.5.1
+pymc>=5.1.0
 six
 scikit-learn
 bspline
 matplotlib
-numpy
-scipy>=1.3.2,<1.13.0
-pandas>=0.25.3
 torch>=1.1.0
 sphinx-tabs
-pymc>=5.1.0
-arviz==0.13.0

From 5d25556e083bdd92ed6b48a7a5ffdb9b174ee530 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 16 Oct 2024 16:52:59 +0200
Subject: [PATCH 29/68] Reset requirements

Will wait with this until after next release
---
 requirements.txt | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index ca1a2cda..58c3f61e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,13 @@
 argparse
 nibabel>=2.5.1
-pymc>=5.1.0
 six
 scikit-learn
 bspline
 matplotlib
+numpy
+scipy>=1.3.2,<1.13.0
+pandas>=0.25.3
 torch>=1.1.0
 sphinx-tabs
+pymc>=5.1.0
+arviz==0.13.0

From 5d901165880a640e0eb368813436204c1e122bf2 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 16 Oct 2024 17:05:00 +0200
Subject: [PATCH 30/68] Add pyproject.toml

---
 pyproject.toml | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..6f7873fb
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "pcntoolkit"
+version = "0.30.2"
+description = "Predictive Clinical Neuroscience Toolkit"
+authors = ["Andre Marquand"]
+license = "GPL v3"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+torch = "2.4.1+cpu"
+pymc = "^5.17.0"
+bspline = "^0.1.1"
+scikit-learn = "^1.5.2"
+matplotlib = "^3.9.2"
+nibabel = "^5.3.1"
+sphinx-tabs = "^3.4.7"
+
+[tool.poetry.group.dev.dependencies]
+pymc = "^5.17.0"
+torch = "2.4.1+cpu"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

From 56f0a6c9c4a31596c7c12fa82f2bc1538b7e7e48 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 17 Oct 2024 11:31:49 +0200
Subject: [PATCH 31/68] Cleaning up the project

---
 .github/workflows/ci.yml         |   2 +-
 .nojekyll                        |   1 -
 MANIFEST.in                      |   1 -
 PCNtoolkit.drawio                | 909 -------------------------------
 build/lib/pcntoolkit/configs.py  |   9 -
 conda_recipe/meta.yaml           |  45 --
 pyproject.toml                   |  25 -
 requirements.txt                 |   8 -
 setup.cfg                        |   3 -
 setup.py                         |  25 -
 {pytest => tests}/test_sanity.py |   0
 11 files changed, 1 insertion(+), 1027 deletions(-)
 delete mode 100644 .nojekyll
 delete mode 100644 MANIFEST.in
 delete mode 100644 PCNtoolkit.drawio
 delete mode 100644 build/lib/pcntoolkit/configs.py
 delete mode 100644 conda_recipe/meta.yaml
 delete mode 100644 pyproject.toml
 delete mode 100644 requirements.txt
 delete mode 100644 setup.cfg
 delete mode 100644 setup.py
 rename {pytest => tests}/test_sanity.py (100%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 66f16af3..50ec6bd5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,7 +30,7 @@ jobs:
 
     - name: Run Tests with coverage and save report
       run: |
-        coverage run -m pytest pytest/ --junitxml=report.xml --html=report.html
+        coverage run -m pytest tests/ --junitxml=report.xml --html=report.html
         coverage xml -o coverage.xml
       continue-on-error: true  # Continue to the next step even if tests fail
 
diff --git a/.nojekyll b/.nojekyll
deleted file mode 100644
index 8b137891..00000000
--- a/.nojekyll
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index f9bd1455..00000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1 +0,0 @@
-include requirements.txt
diff --git a/PCNtoolkit.drawio b/PCNtoolkit.drawio
deleted file mode 100644
index 5bf3ed1d..00000000
--- a/PCNtoolkit.drawio
+++ /dev/null
@@ -1,909 +0,0 @@
-
-  
-    
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-      
-    
-  
-
diff --git a/build/lib/pcntoolkit/configs.py b/build/lib/pcntoolkit/configs.py
deleted file mode 100644
index 98b56f17..00000000
--- a/build/lib/pcntoolkit/configs.py
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Dec  7 12:51:07 2020
-
-@author: seykia
-"""
-
-PICKLE_PROTOCOL = 4
diff --git a/conda_recipe/meta.yaml b/conda_recipe/meta.yaml
deleted file mode 100644
index 9cd1a2c8..00000000
--- a/conda_recipe/meta.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-package:
-  name: pcntoolkit
-  version: "0.30.0"
-
-source:
-  path: ..
-
-build:
-  number: 0
-  script: python setup.py install
-
-requirements:
-  build:
-    - python
-    - setuptools
-    - numpy
-  run:
-    - python
-    - pymc # includes numpy, scipy, pandas, arviz
-    - six
-    - scikit-learn
-    - matplotlib
-    - pytorch
-    - cpuonly
-    - sphinx-tabs
-    - nibabel
-  pip:
-    - bspline
-
-# test:
-#   imports:
-#     - your_project_name
-
-about:
-  home: http://github.com/amarquand/PCNtoolkit
-  license: 'GNU GPLv3'
-  summary: 'Predictive Clinical Neuroscience toolkit'
-  description: |
-    'Predictive Clinical Neuroscience toolkit'
-  doc_url: https://pcntoolkit.readthedocs.io/en/latest/
-  dev_url: http://github.com/amarquand/PCNtoolkit
-
-extra:
-  recipe-maintainers:
-    - AuguB
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 6f7873fb..00000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,25 +0,0 @@
-[tool.poetry]
-name = "pcntoolkit"
-version = "0.30.2"
-description = "Predictive Clinical Neuroscience Toolkit"
-authors = ["Andre Marquand"]
-license = "GPL v3"
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.10"
-torch = "2.4.1+cpu"
-pymc = "^5.17.0"
-bspline = "^0.1.1"
-scikit-learn = "^1.5.2"
-matplotlib = "^3.9.2"
-nibabel = "^5.3.1"
-sphinx-tabs = "^3.4.7"
-
-[tool.poetry.group.dev.dependencies]
-pymc = "^5.17.0"
-torch = "2.4.1+cpu"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 271c759c..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-nibabel>=2.5.1
-pymc>=5.1.0
-six
-scikit-learn
-bspline
-matplotlib
-torch>=1.1.0
-sphinx-tabs
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 9b914123..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-[autopep8]
-max-line-length = 120
-ignore = E226,E302,E41
diff --git a/setup.py b/setup.py
deleted file mode 100644
index d37cd554..00000000
--- a/setup.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from setuptools import setup, find_packages
-
-
-def parse_requirements(filename):
-    """Load requirements from a pip requirements file."""
-    with open(filename, 'r') as f:
-        lineiter = (line.strip() for line in f)
-        return [line for line in lineiter if line and not line.startswith("#")]
-
-requirements = parse_requirements('requirements.txt')
-
-# Note: to force PyPI to overwrite a version without bumping the version number
-#       use e.g.: 
-#       version = '0.29-1'
-
-setup(name='pcntoolkit',
-      version='0.30-2',
-      description='Predictive Clinical Neuroscience toolkit',
-      url='http://github.com/amarquand/PCNtoolkit',
-      author='Andre Marquand',
-      author_email='andre.marquand@donders.ru.nl',
-      license='GNU GPLv3',
-      packages=find_packages(),
-      install_requires=requirements,
-      zip_safe=False)
diff --git a/pytest/test_sanity.py b/tests/test_sanity.py
similarity index 100%
rename from pytest/test_sanity.py
rename to tests/test_sanity.py

From e708f37940bc917ab0fd35f59decf5c152454dd2 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 17 Oct 2024 11:43:40 +0200
Subject: [PATCH 32/68] Update gitignore

---
 .gitignore | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.gitignore b/.gitignore
index ea6f81b1..fcc7cfa6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -83,3 +83,10 @@ dist/pcntoolkit-0.27-py3.11.egg
 # Basic test functions for SHASH
 tests/test_SHASH.ipynb
 
+dist/pcntoolkit-0.30.post2-py3.12.egg
+
+dist/*
+
+dist
+
+build/*

From d870519e14a5e70f104805ba68d1baf4e382f2e0 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 17 Oct 2024 16:35:59 +0200
Subject: [PATCH 33/68] Add pyproject.toml with poetry backend

---
 pyproject.toml | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..1466e7b4
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,26 @@
+[tool.poetry]
+name = "pcntoolkit"
+version = "0.30.2"
+description = "Predictive Clinical Neuroscience Toolkit"
+authors = ["Andre Marquand"]
+license = "GNU GPLv3"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.13"
+bspline = "^0.1.1"              # Install with pip
+nibabel = "^5.3.1"              # Prefer Conda
+pymc = "^5.17.0"                # Prefer Conda
+scikit-learn = "^1.5.2"         # Prefer Conda
+seaborn = "^0.13.2"             # Prefer conda
+six = "^1.16.0"                 # Prefer conda
+
+[tool.poetry.group.dev.dependencies]
+sphinx-tabs = "^3.4.7"
+pytest = "^8.3.3"
+ipywidgets = "^8.1.5"
+black = "^24.10.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

From 8054cba2bd3ea03db5a7649cbf0201f826d19966 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 21 Oct 2024 11:45:48 +0200
Subject: [PATCH 34/68] Add ipykernel to dev dependencies

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 1466e7b4..991ae576 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ sphinx-tabs = "^3.4.7"
 pytest = "^8.3.3"
 ipywidgets = "^8.1.5"
 black = "^24.10.0"
+ipykernel = "^6.29.5"
 
 [build-system]
 requires = ["poetry-core"]

From 9211ebaf0fb23482b33de59eb02d693233bf6cbe Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 21 Oct 2024 11:46:00 +0200
Subject: [PATCH 35/68] Add poetry lock

---
 poetry.lock | 2636 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2636 insertions(+)
 create mode 100644 poetry.lock

diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 00000000..fd9e155c
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,2636 @@
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+
+[[package]]
+name = "alabaster"
+version = "1.0.0"
+description = "A light, configurable Sphinx theme"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b"},
+    {file = "alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e"},
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
+
+[[package]]
+name = "arviz"
+version = "0.20.0"
+description = "Exploratory analysis of Bayesian models"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "arviz-0.20.0-py3-none-any.whl", hash = "sha256:5ec4f2ec180a8305ff3d1108c29e189944ab939663eb5bc3231ff199a1a5dc36"},
+    {file = "arviz-0.20.0.tar.gz", hash = "sha256:a2704e0c141410fcaea1973a90cabf280f5aed5c1e10f44381ebd6c144c10a9c"},
+]
+
+[package.dependencies]
+h5netcdf = ">=1.0.2"
+matplotlib = ">=3.5"
+numpy = ">=1.23.0"
+packaging = "*"
+pandas = ">=1.5.0"
+scipy = ">=1.9.0"
+setuptools = ">=60.0.0"
+typing-extensions = ">=4.1.0"
+xarray = ">=2022.6.0"
+xarray-einstats = ">=0.3"
+
+[package.extras]
+all = ["bokeh (>=3)", "contourpy", "dask[distributed]", "dm-tree (>=0.1.8)", "netcdf4", "numba", "ujson", "xarray-datatree", "zarr (>=2.5.0,<3)"]
+preview = ["arviz-base[h5netcdf]", "arviz-plots", "arviz-stats[xarray]"]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
+[[package]]
+name = "babel"
+version = "2.16.0"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
+]
+
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+
+[[package]]
+name = "black"
+version = "24.10.0"
+description = "The uncompromising code formatter."
+optional = false
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+accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
+complete = ["xarray[accel,dev,io,parallel,viz]"]
+dev = ["hypothesis", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "xarray[complete]"]
+io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
+parallel = ["dask[complete]"]
+viz = ["matplotlib", "nc-time-axis", "seaborn"]
+
+[[package]]
+name = "xarray-einstats"
+version = "0.8.0"
+description = "Stats, linear algebra and einops for xarray"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "xarray_einstats-0.8.0-py3-none-any.whl", hash = "sha256:fd00552c3fb5c859b1ebc7c88a97342d3bb93d14bba904c5a9b94a4f724b76b4"},
+    {file = "xarray_einstats-0.8.0.tar.gz", hash = "sha256:7f1573f9bd4d60d6e7ed9fd27c4db39da51ec49bf8ba654d4602a139a6309d7f"},
+]
+
+[package.dependencies]
+numpy = ">=1.23"
+scipy = ">=1.9"
+xarray = ">=2022.09.0"
+
+[package.extras]
+doc = ["furo", "jupyter-sphinx", "matplotlib", "myst-nb", "myst-parser[linkify]", "numpydoc", "sphinx (>=5)", "sphinx-copybutton", "sphinx-design", "sphinx-togglebutton", "watermark"]
+einops = ["einops"]
+numba = ["numba (>=0.55)"]
+test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.13"
+content-hash = "c429aa38c33951c68b15a3f1ca4089b8589d0acf180a8ae10355bacf33a89704"

From db9544c650f99127f5937d57860b2a0c52056a94 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 21 Oct 2024 14:21:24 +0200
Subject: [PATCH 36/68] Basic tests check out

---
 tests/testHBR.py          | 6 ++++--
 tests/testHBR_transfer.py | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/tests/testHBR.py b/tests/testHBR.py
index 1d2ba679..cd4d2bd2 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+
+# %%
 """
 Created on Mon Jul 29 13:26:35 2019
 
@@ -24,7 +26,7 @@
 
 random_state = 29
 
-working_dir = '/'  # Specify a working directory to save data and results.
+working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.
 
 simulation_method = 'linear'
 n_features = 1      # The number of input features of X
@@ -47,7 +49,7 @@
 ################################# Fittig and Predicting ###############################
 
 nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', 
-               linear_sigma='True', random_slope_mu='True', linear_epsilon='True', linear_delta='True')
+               linear_sigma='True', random_slope_mu='True', linear_epsilon='True', linear_delta='True', nuts_sampler='nutpie')
 
 nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
 yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')
diff --git a/tests/testHBR_transfer.py b/tests/testHBR_transfer.py
index 029ad6b1..1b0cfa57 100644
--- a/tests/testHBR_transfer.py
+++ b/tests/testHBR_transfer.py
@@ -26,7 +26,7 @@
 ########################### Experiment Settings ###############################
 
 
-working_dir = '/home/stijn/temp/'  # Specifyexit() a working directory
+working_dir = '/Users/stijndeboer/temp/'  # Specifyexit() a working directory
 # to save data and results.
 
 simulation_method = 'linear'
@@ -86,7 +86,7 @@
     print("Now Estimating on transfer train data ==============================================")
     nm.estimate_on_new_sites(
         X_train_transfer, Y_train_transfer, grp_id_train_transfer)
-    print("Now Estimating on transfer test data ==============================================")
+    print("Now Predicting on transfer test data ==============================================")
     yhat, s2 = nm.predict_on_new_sites(X_test_transfer, grp_id_test_transfer)
 
     for i in range(n_features):

From 53d9dbc797db10c609454535c040060e6e737570 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 22 Oct 2024 13:48:48 +0200
Subject: [PATCH 37/68] Add numba and nutpie dependencies

---
 poetry.lock               | 156 +++++++++++++++++++++++++++++++++++++-
 pyproject.toml            |   2 +
 tests/testHBR.py          |   8 +-
 tests/testHBR_transfer.py |   7 +-
 tests/test_blr.py         |  13 +++-
 5 files changed, 177 insertions(+), 9 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index fd9e155c..5108b93c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1132,6 +1132,36 @@ files = [
     {file = "kiwisolver-1.4.7.tar.gz", hash = "sha256:9893ff81bd7107f7b685d3017cc6583daadb4fc26e4a888350df530e41980a60"},
 ]
 
+[[package]]
+name = "llvmlite"
+version = "0.43.0"
+description = "lightweight wrapper around basic LLVM functionality"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
+    {file = "llvmlite-0.43.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d4fd101f571a31acb1559ae1af30f30b1dc4b3186669f92ad780e17c81e91bc"},
+    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d434ec7e2ce3cc8f452d1cd9a28591745de022f931d67be688a737320dfcead"},
+    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6912a87782acdff6eb8bf01675ed01d60ca1f2551f8176a300a886f09e836a6a"},
+    {file = "llvmlite-0.43.0-cp310-cp310-win_amd64.whl", hash = "sha256:14f0e4bf2fd2d9a75a3534111e8ebeb08eda2f33e9bdd6dfa13282afacdde0ed"},
+    {file = "llvmlite-0.43.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8d0618cb9bfe40ac38a9633f2493d4d4e9fcc2f438d39a4e854f39cc0f5f98"},
+    {file = "llvmlite-0.43.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e0a9a1a39d4bf3517f2af9d23d479b4175ead205c592ceeb8b89af48a327ea57"},
+    {file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c1da416ab53e4f7f3bc8d4eeba36d801cc1894b9fbfbf2022b29b6bad34a7df2"},
+    {file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:977525a1e5f4059316b183fb4fd34fa858c9eade31f165427a3977c95e3ee749"},
+    {file = "llvmlite-0.43.0-cp311-cp311-win_amd64.whl", hash = "sha256:d5bd550001d26450bd90777736c69d68c487d17bf371438f975229b2b8241a91"},
+    {file = "llvmlite-0.43.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f99b600aa7f65235a5a05d0b9a9f31150c390f31261f2a0ba678e26823ec38f7"},
+    {file = "llvmlite-0.43.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:35d80d61d0cda2d767f72de99450766250560399edc309da16937b93d3b676e7"},
+    {file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eccce86bba940bae0d8d48ed925f21dbb813519169246e2ab292b5092aba121f"},
+    {file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df6509e1507ca0760787a199d19439cc887bfd82226f5af746d6977bd9f66844"},
+    {file = "llvmlite-0.43.0-cp312-cp312-win_amd64.whl", hash = "sha256:7a2872ee80dcf6b5dbdc838763d26554c2a18aa833d31a2635bff16aafefb9c9"},
+    {file = "llvmlite-0.43.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9cd2a7376f7b3367019b664c21f0c61766219faa3b03731113ead75107f3b66c"},
+    {file = "llvmlite-0.43.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:18e9953c748b105668487b7c81a3e97b046d8abf95c4ddc0cd3c94f4e4651ae8"},
+    {file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74937acd22dc11b33946b67dca7680e6d103d6e90eeaaaf932603bec6fe7b03a"},
+    {file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc9efc739cc6ed760f795806f67889923f7274276f0eb45092a1473e40d9b867"},
+    {file = "llvmlite-0.43.0-cp39-cp39-win_amd64.whl", hash = "sha256:47e147cdda9037f94b399bf03bfd8a6b6b1f2f90be94a454e3386f006455a9b4"},
+    {file = "llvmlite-0.43.0.tar.gz", hash = "sha256:ae2b5b5c3ef67354824fb75517c8db5fbe93bc02cd9671f3c62271626bc041d5"},
+]
+
 [[package]]
 name = "logical-unification"
 version = "0.4.6"
@@ -1409,6 +1439,40 @@ test = ["coverage (>=7.2)", "pytest", "pytest-cov", "pytest-doctestplus", "pytes
 typing = ["tox"]
 zstd = ["pyzstd (>=0.14.3)"]
 
+[[package]]
+name = "numba"
+version = "0.60.0"
+description = "compiling Python code using LLVM"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numba-0.60.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d761de835cd38fb400d2c26bb103a2726f548dc30368853121d66201672e651"},
+    {file = "numba-0.60.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:159e618ef213fba758837f9837fb402bbe65326e60ba0633dbe6c7f274d42c1b"},
+    {file = "numba-0.60.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1527dc578b95c7c4ff248792ec33d097ba6bef9eda466c948b68dfc995c25781"},
+    {file = "numba-0.60.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fe0b28abb8d70f8160798f4de9d486143200f34458d34c4a214114e445d7124e"},
+    {file = "numba-0.60.0-cp310-cp310-win_amd64.whl", hash = "sha256:19407ced081d7e2e4b8d8c36aa57b7452e0283871c296e12d798852bc7d7f198"},
+    {file = "numba-0.60.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a17b70fc9e380ee29c42717e8cc0bfaa5556c416d94f9aa96ba13acb41bdece8"},
+    {file = "numba-0.60.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3fb02b344a2a80efa6f677aa5c40cd5dd452e1b35f8d1c2af0dfd9ada9978e4b"},
+    {file = "numba-0.60.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5f4fde652ea604ea3c86508a3fb31556a6157b2c76c8b51b1d45eb40c8598703"},
+    {file = "numba-0.60.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4142d7ac0210cc86432b818338a2bc368dc773a2f5cf1e32ff7c5b378bd63ee8"},
+    {file = "numba-0.60.0-cp311-cp311-win_amd64.whl", hash = "sha256:cac02c041e9b5bc8cf8f2034ff6f0dbafccd1ae9590dc146b3a02a45e53af4e2"},
+    {file = "numba-0.60.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d7da4098db31182fc5ffe4bc42c6f24cd7d1cb8a14b59fd755bfee32e34b8404"},
+    {file = "numba-0.60.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:38d6ea4c1f56417076ecf8fc327c831ae793282e0ff51080c5094cb726507b1c"},
+    {file = "numba-0.60.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:62908d29fb6a3229c242e981ca27e32a6e606cc253fc9e8faeb0e48760de241e"},
+    {file = "numba-0.60.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0ebaa91538e996f708f1ab30ef4d3ddc344b64b5227b67a57aa74f401bb68b9d"},
+    {file = "numba-0.60.0-cp312-cp312-win_amd64.whl", hash = "sha256:f75262e8fe7fa96db1dca93d53a194a38c46da28b112b8a4aca168f0df860347"},
+    {file = "numba-0.60.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:01ef4cd7d83abe087d644eaa3d95831b777aa21d441a23703d649e06b8e06b74"},
+    {file = "numba-0.60.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:819a3dfd4630d95fd574036f99e47212a1af41cbcb019bf8afac63ff56834449"},
+    {file = "numba-0.60.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b983bd6ad82fe868493012487f34eae8bf7dd94654951404114f23c3466d34b"},
+    {file = "numba-0.60.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c151748cd269ddeab66334bd754817ffc0cabd9433acb0f551697e5151917d25"},
+    {file = "numba-0.60.0-cp39-cp39-win_amd64.whl", hash = "sha256:3031547a015710140e8c87226b4cfe927cac199835e5bf7d4fe5cb64e814e3ab"},
+    {file = "numba-0.60.0.tar.gz", hash = "sha256:5df6158e5584eece5fc83294b949fd30b9f1125df7708862205217e068aabf16"},
+]
+
+[package.dependencies]
+llvmlite = "==0.43.*"
+numpy = ">=1.22,<2.1"
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1454,6 +1518,45 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
+[[package]]
+name = "nutpie"
+version = "0.13.2"
+description = "Sample Stan or PyMC models"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "nutpie-0.13.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:4c731b6b32f51407ca973aefdcb0241c6dadfebcf47e781557344d28d346c0fa"},
+    {file = "nutpie-0.13.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b69e62c4d25e62e670ef31244e65556ed562650dfbc56a068972e177c5e5e291"},
+    {file = "nutpie-0.13.2-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:a7cfe73f29769f7185e677587755ba63818e9334d161a69216c8d6cefd9d66b7"},
+    {file = "nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:0202a5b2352b065a269dd1467cacd4b9ef4020665373e4d12eede232425eaea8"},
+    {file = "nutpie-0.13.2-cp310-none-win_amd64.whl", hash = "sha256:fa2f5f46fad31d9cdac486510a656a7e85df470662ffcd6c3c84534eb7d24c28"},
+    {file = "nutpie-0.13.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:024fb04ddcaa2ce8a2cf6864bebe68acfb68518f6199c6d3de0c6b9b49d1ac75"},
+    {file = "nutpie-0.13.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:225f17a15e33f731db43c55f821b988df2781568e2dc6f22ae9798e259386009"},
+    {file = "nutpie-0.13.2-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:1a7a5e7012976327485349b581ae762cd6e60bb1805f9d323e0eed2d945c73a3"},
+    {file = "nutpie-0.13.2-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:be1635cdd6ec19cc541e212ee95e11288dda7a234a2ae7f70c2c91fdaa677fe0"},
+    {file = "nutpie-0.13.2-cp311-none-win_amd64.whl", hash = "sha256:d7d297a975737ca997890cae284adca74e429567503596cbf66a37640faf4f10"},
+    {file = "nutpie-0.13.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:1656a4e45981db30d9ca850e889c10ac69c3e327a994607924c2db1dcefb49c7"},
+    {file = "nutpie-0.13.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:57b6f6640996d88b290285acdcf7978bf9f6257c2a80d38eb5d1903e11bb0301"},
+    {file = "nutpie-0.13.2-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:e1419e53a5ce3bfba39157cb1381eb18f1835bd1b73312d485e1f543f9ce3748"},
+    {file = "nutpie-0.13.2-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6d29babf3773544692153799b3579f9de1e084a06fd2dcc851e97bef4c92768b"},
+    {file = "nutpie-0.13.2-cp312-none-win_amd64.whl", hash = "sha256:5b6f45e2e475eee1519f18b6cbcd56ef225dbcaeb6f35e248d829467097ab385"},
+    {file = "nutpie-0.13.2-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:db240a317b1ded7eddf2ca8e2b4bcfcdbd4624256655aac61625c8f7d5ca39d0"},
+    {file = "nutpie-0.13.2-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:2100024275ec6ba6de899188a3a2111f4b68aee7bfdbd4e4eb02ed4c922a9f22"},
+    {file = "nutpie-0.13.2.tar.gz", hash = "sha256:f14282e2ac045c67a9b262a865b02a243178c55b541b236b21dfcb0c3678bcea"},
+]
+
+[package.dependencies]
+arviz = ">=0.15.0"
+pandas = ">=2.0"
+pyarrow = ">=12.0.0"
+xarray = ">=2023.6.0"
+
+[package.extras]
+all = ["bridgestan (>=2.4.1)", "jax (>=0.4.27)", "numba (>=0.59.1)", "pymc (>=5.15.0)"]
+pymc = ["numba (>=0.59.1)", "pymc (>=5.15.0)"]
+pymc-jax = ["jax (>=0.4.27)", "pymc (>=5.15.0)"]
+stan = ["bridgestan (>=2.4.1)"]
+
 [[package]]
 name = "packaging"
 version = "24.1"
@@ -1783,6 +1886,57 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
+name = "pyarrow"
+version = "17.0.0"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyarrow-17.0.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:a5c8b238d47e48812ee577ee20c9a2779e6a5904f1708ae240f53ecbee7c9f07"},
+    {file = "pyarrow-17.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:db023dc4c6cae1015de9e198d41250688383c3f9af8f565370ab2b4cb5f62655"},
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+    {file = "pyarrow-17.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75c06d4624c0ad6674364bb46ef38c3132768139ddec1c56582dbac54f2663e2"},
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+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:dec8d129254d0188a49f8a1fc99e0560dc1b85f60af729f47de4046015f9b0a5"},
+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:a48ddf5c3c6a6c505904545c25a4ae13646ae1f8ba703c4df4a1bfe4f4006bda"},
+    {file = "pyarrow-17.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:42bf93249a083aca230ba7e2786c5f673507fa97bbd9725a1e2754715151a204"},
+    {file = "pyarrow-17.0.0.tar.gz", hash = "sha256:4beca9521ed2c0921c1023e68d097d0299b62c362639ea315572a58f3f50fd28"},
+]
+
+[package.dependencies]
+numpy = ">=1.16.6"
+
+[package.extras]
+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -2633,4 +2787,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "c429aa38c33951c68b15a3f1ca4089b8589d0acf180a8ae10355bacf33a89704"
+content-hash = "243443b42aa8bbd9d52455e147000ebd4e1065e8de68e79fa93254d3e4e951fc"
diff --git a/pyproject.toml b/pyproject.toml
index 991ae576..bc33c1d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,8 @@ pymc = "^5.17.0"                # Prefer Conda
 scikit-learn = "^1.5.2"         # Prefer Conda
 seaborn = "^0.13.2"             # Prefer conda
 six = "^1.16.0"                 # Prefer conda
+nutpie = "^0.13.2"
+numba = "^0.60.0"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"
diff --git a/tests/testHBR.py b/tests/testHBR.py
index 8929edb9..ebe8405c 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -25,13 +25,11 @@
 
 
 random_state = 40
-working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.
-
-working_dir = '/Users/stijndeboer/temp/'  # Specify a working directory to save data and results.
+working_dir = '/Users/stijndeboer/temp/HBR/'  # Specify a working directory to save data and results.
 
 simulation_method = 'linear'
 n_features = 1      # The number of input features of X
-n_grps = 10     # Number of batches in data
+n_grps = 3     # Number of batches in data
 n_samples = 500     # Number of samples in each group (use a list for different
 # sample numbers across different batches)
 
@@ -49,7 +47,7 @@
 ################################# Fittig and Predicting ###############################
 
 nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', 
-               linear_sigma='True', random_slope_mu='True', linear_epsilon='True', linear_delta='True', nuts_sampler='nutpie')
+               linear_sigma='True', random_slope_mu='True', linear_epsilon='False', linear_delta='Fals', nuts_sampler='nutpie')
 
 nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
 yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')
diff --git a/tests/testHBR_transfer.py b/tests/testHBR_transfer.py
index 1b0cfa57..29ec9c96 100644
--- a/tests/testHBR_transfer.py
+++ b/tests/testHBR_transfer.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+
+# %%
 """
 Created on Mon Jul 29 13:26:35 2019
 
@@ -26,7 +28,7 @@
 ########################### Experiment Settings ###############################
 
 
-working_dir = '/Users/stijndeboer/temp/'  # Specifyexit() a working directory
+working_dir = '/Users/stijndeboer/temp/HBR_transfer/'  # Specifyexit() a working directory
 # to save data and results.
 
 simulation_method = 'linear'
@@ -54,7 +56,7 @@
 
 for model_type in model_types:
     nm = norm_init(X_train, Y_train, alg='hbr', likelihood='Normal', model_type=model_type,
-                   n_chains=4, cores=4, n_samples=100, n_tuning=50, freedom=5, nknots=8, target_accept="0.99")
+                   n_chains=4, cores=4, n_samples=100, n_tuning=50, freedom=5, nknots=8, target_accept="0.99", nuts_sampler='nutpie')
 
     print("Now Estimating on original train data ==============================================")
     nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
@@ -108,6 +110,7 @@
                              color='gray', alpha=0.2)
         plt.title('Transfer model %s, Feature %d' % (model_type, i))
         plt.legend()
+        plt.savefig(os.path.join(working_dir, 'transfer_model_' + model_type + '_feature_' + str(i) + '.png'))
         plt.show()
 
 
diff --git a/tests/test_blr.py b/tests/test_blr.py
index 59be02c2..afaaad58 100644
--- a/tests/test_blr.py
+++ b/tests/test_blr.py
@@ -10,8 +10,9 @@
 from pcntoolkit.model.bayesreg import BLR
 from pcntoolkit.model.gp import GPR
 from pcntoolkit.util.utils import WarpBoxCox, WarpAffine, WarpCompose, WarpSinArcsinh
+import os
 
-
+workingdir = '/Users/stijndeboer/temp/BLR/'
 def create_noise(type_noise, N, parameters=None):
     """Function to create different noise distributions"""
     if type_noise == 'exp':
@@ -112,6 +113,7 @@ def create_noise(type_noise, N, parameters=None):
 plt.fill_between(Xs, yhat-1.96*np.sqrt(s2), yhat+1.96*np.sqrt(s2), alpha=0.2)
 plt.scatter(X, y)
 plt.plot(Xs, yhat)
+plt.savefig(os.path.join(workingdir, 'linear_regression.png'))
 plt.show()
 
 print(B.nlZ)
@@ -151,11 +153,15 @@ def create_noise(type_noise, N, parameters=None):
     mod = (0.5*(1+lam*yhat + np.sqrt((1+lam*yhat)**2 + 4*s2*lam*(lam-1))))**(1/lam)
     plt.plot(Xs, mod, 'b--')
     plt.legend(('median', 'mode'))
+plt.savefig(os.path.join(workingdir, 'warp1.png'))
+
 plt.show()
 
 xx = np.linspace(-5, 5, 100)
 plt.plot(xx, W.invf(xx, warp_param))
 plt.title('estimated warping function')
+plt.savefig(os.path.join(workingdir, 'warp2.png'))
+
 plt.show()
 
 # estimate a model with heteroskedastic noise
@@ -181,6 +187,8 @@ def create_noise(type_noise, N, parameters=None):
 
 print(hyp)
 plt.fill_between(Xs, yhat-1.96*np.sqrt(s2), yhat+1.96*np.sqrt(s2), alpha=0.2)
+plt.savefig(os.path.join(workingdir, 'linear_regression2.png'))
+
 plt.show()
 
 print("Estimate a model with site-specific noise ...")
@@ -232,6 +240,8 @@ def create_noise(type_noise, N, parameters=None):
 
 yhat, s2 = Bh.predict(hyp, Phi, y, Phis, var_groups_test=sids_te)
 
+
+
 for s in range(n_site):
     plt.scatter(X[idx[s]], y[idx[s]])
     plt.plot(Xs[idx_te[s]], yhat[idx_te[s]], color=cols[s])
@@ -239,4 +249,5 @@ def create_noise(type_noise, N, parameters=None):
                      yhat[idx_te[s]] - 1.96 * np.sqrt(s2[idx_te[s]]),
                      yhat[idx_te[s]] + 1.96 * np.sqrt(s2[idx_te[s]]),
                      alpha=0.2, color=cols[s])
+plt.savefig(os.path.join(workingdir, 'linear_regression3.png'))
 plt.show()

From b25bc6ee1190f3a896d9d74001f9fb8b794fe033 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 22 Oct 2024 13:56:04 +0200
Subject: [PATCH 38/68] Changes: - Add nutpie support for HBR - Add
 pyproject.toml with poetry for dependency management - Clean up the project -
 Tune HBR priors - Add multicore support for torque through
 'n_cores_per_batch' kwarg - Adapted readme.py

---
 README.md        | 24 ++++++------------------
 requirements.txt |  0
 setup.py         |  0
 3 files changed, 6 insertions(+), 18 deletions(-)
 delete mode 100644 requirements.txt
 delete mode 100644 setup.py

diff --git a/README.md b/README.md
index 51c7d474..d76af7c1 100644
--- a/README.md
+++ b/README.md
@@ -27,16 +27,16 @@ source activate 
 
 Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. 
 
-#### Install other required conda packages
+#### Install PCNtoolkit
 
+Using pip:
 ```
-conda install pip pandas scipy pymc
+pip install pcntoolkit
 ```
 
-#### Install PCNtoolkit
-
+Using a local clone of the repo:
 ```
-pip install pcntoolkit
+python -m pip install .
 ```
 
 ## Alternative installation (on a shared resource)
@@ -69,18 +69,6 @@ https://pytorch.org/get-started/locally/
 
 If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip.
 
-#### Install other dependencies
-
-```
-conda install -y pandas scipy pymc
-```
-
-#### Install pip dependencies
-
-```
-pip --no-cache-dir install nibabel scikit-learn torch glob3 
-```
-
 #### Clone the repo
 
 ```
@@ -91,7 +79,7 @@ git clone https://github.com/amarquand/PCNtoolkit.git
 
 ```
 cd PCNtoolkit/
-python3 setup.py install
+python -m pip install .
 ```
 ### Test 
 ```
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/setup.py b/setup.py
deleted file mode 100644
index e69de29b..00000000

From 5898249156d4c4413d5c827c6298d33e997ea833 Mon Sep 17 00:00:00 2001
From: Augub 
Date: Tue, 22 Oct 2024 14:19:39 +0200
Subject: [PATCH 39/68] Update README.md

---
 README.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/README.md b/README.md
index d76af7c1..af6078c1 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,12 @@ source activate 
 
 Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. 
 
+### Install nutpie using conda 
+
+```
+conda install nutpie
+```
+
 #### Install PCNtoolkit
 
 Using pip:
@@ -69,6 +75,13 @@ https://pytorch.org/get-started/locally/
 
 If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip.
 
+
+### Install nutpie using conda 
+
+```
+conda install nutpie
+```
+
 #### Clone the repo
 
 ```

From 00458df05b76fba16bb28f34c706d8472c32d3b8 Mon Sep 17 00:00:00 2001
From: Augub 
Date: Tue, 22 Oct 2024 14:30:02 +0200
Subject: [PATCH 40/68] Set default argument for multicore on HPC

---
 pcntoolkit/normative_parallel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/normative_parallel.py b/pcntoolkit/normative_parallel.py
index 6b92b124..d5b95c60 100755
--- a/pcntoolkit/normative_parallel.py
+++ b/pcntoolkit/normative_parallel.py
@@ -112,7 +112,7 @@ def execute_nm(processing_dir,
     cluster_spec = kwargs.pop('cluster_spec', 'torque')
     log_path = kwargs.get('log_path', None)
     binary = kwargs.pop('binary', False)
-    cores = kwargs.pop('n_cores_per_batch')
+    cores = kwargs.pop('n_cores_per_batch','1')
 
     split_nm(processing_dir,
              respfile_path,

From bdb6bfabe8d70d338b01cf943ddea673ef48dfd3 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 24 Oct 2024 12:23:07 +0200
Subject: [PATCH 41/68] Split KvOp and SHASH

---
 pcntoolkit/model/KOp.py   | 34 ++++++++++++++++++++++++++++++++++
 pcntoolkit/model/SHASH.py | 37 +++++--------------------------------
 tests/testHBR.py          |  2 +-
 3 files changed, 40 insertions(+), 33 deletions(-)
 create mode 100644 pcntoolkit/model/KOp.py

diff --git a/pcntoolkit/model/KOp.py b/pcntoolkit/model/KOp.py
new file mode 100644
index 00000000..f4b8a654
--- /dev/null
+++ b/pcntoolkit/model/KOp.py
@@ -0,0 +1,34 @@
+
+# Third-party imports
+from pytensor.scalar.basic import BinaryScalarOp, ScalarOp, upgrade_to_float
+from pytensor.gradient import grad_not_implemented
+
+import scipy.special as spp
+
+class KnuOp(BinaryScalarOp):
+    """
+    Modified Bessel function of the second kind, pytensor wrapper for scipy.special.kv
+    """
+    nfunc_spec = ("scipy.special.kv", 2, 1)
+    
+    @staticmethod
+    def st_impl(p, x):
+        return spp.kv(p, x)
+
+    def impl(self, p, x):
+        return KnuOp.st_impl(p, x)
+    
+    def grad(self, inputs, grads):
+        dp = 1e-16
+        (p, x) = inputs
+        (gz,) = grads
+        dfdp = (knuop(p + dp, x) - knuop(p - dp, x)) / (2*dp)
+        gni = grad_not_implemented(self, 0, 'x', ' Grad with respect to x is not implemented for KvOp')
+        # dfdx = spp.kvp(p, x) #TODO Implement this
+        return [
+            gz * dfdp,
+            gni
+        ]
+
+
+knuop = KnuOp(upgrade_to_float, name='knuop')
\ No newline at end of file
diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index d9139d67..13f84457 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -7,50 +7,23 @@
 from typing import Union, List, Optional
 
 # Third-party imports
-import pymc as pm
 from pymc import floatX
 from pymc.distributions import Continuous
 
-import pytensor as pt
 import pytensor.tensor as ptt
-from pytensor.graph.op import Op
-from pytensor.graph import Apply
-from pytensor.gradient import grad_not_implemented
-from pytensor.tensor.random.basic import normal
 from pytensor.tensor.random.op import RandomVariable
 
 import numpy as np
 import scipy.special as spp
 import matplotlib.pyplot as plt
 
-CONST1 = np.exp(0.25) / np.power(8.0 * np.pi, 0.5)
-CONST2 = -np.log(2 * np.pi) / 2
-
-class KOp(Op):
-    """
-    Modified Bessel function of the second kind, pytensor wrapper for scipy.special.kv
-    """
-    __props__ = ()
-
-    def make_node(self, p, x):
-        p = pt.tensor.as_tensor_variable(p)
-        x = pt.tensor.as_tensor_variable(x)
-        return Apply(self, [p, x], [p.type()])
+from pcntoolkit.model.KOp import KnuOp, knuop
 
-    def perform(self, node, inputs_storage, output_storage):
-        output_storage[0][0] = spp.kv(inputs_storage[0], inputs_storage[1])
+from pytensor.tensor.elemwise import Elemwise
 
-    def grad(self, inputs, output_grads):
-        # Approximation of the derivative. This should suffice for using NUTS
-        dp = 1e-16
-        p = inputs[0]
-        x = inputs[1]
-        grad = (self(p + dp, x) - self(p - dp, x)) / (2*dp)
-        return [
-            output_grads[0] * grad,
-            grad_not_implemented("KOp", 2, "x", "")
+CONST1 = np.exp(0.25) / np.power(8.0 * np.pi, 0.5)
+CONST2 = -np.log(2 * np.pi) / 2
 
-        ]
 
 def S(x, epsilon, delta):
     """
@@ -126,7 +99,7 @@ def numpy_P(q):
     return a
 
 # Instance of the KOp
-my_K = KOp()
+my_K = Elemwise(knuop)
 
 def P(q):
     """
diff --git a/tests/testHBR.py b/tests/testHBR.py
index ebe8405c..2d3baf9e 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -47,7 +47,7 @@
 ################################# Fittig and Predicting ###############################
 
 nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', 
-               linear_sigma='True', random_slope_mu='True', linear_epsilon='False', linear_delta='Fals', nuts_sampler='nutpie')
+               linear_sigma='True', random_slope_mu='True', linear_epsilon='False', linear_delta='True', nuts_sampler='nutpie')
 
 nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
 yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')

From 159e9d16ada2daa2e8623c9fe512de2be26bb13b Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 25 Oct 2024 13:36:05 +0200
Subject: [PATCH 42/68] Refactoring SHASH

---
 pcntoolkit/model/SHASH.py | 465 ++++++++++++++++++++++++++------------
 1 file changed, 325 insertions(+), 140 deletions(-)

diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index 13f84457..441f952d 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -3,126 +3,93 @@
 See: Jones et al. (2009), Sinh-Arcsinh distributions.
 """
 
-# Standard library imports
-from typing import Union, List, Optional
+from functools import lru_cache
 
-# Third-party imports
+import numpy as np
+import pytensor as pt
 from pymc import floatX
 from pymc.distributions import Continuous
-
-import pytensor.tensor as ptt
+from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.random.op import RandomVariable
 
-import numpy as np
-import scipy.special as spp
-import matplotlib.pyplot as plt
+from pcntoolkit.model.KnuOp import knuop
 
-from pcntoolkit.model.KOp import KnuOp, knuop
-
-from pytensor.tensor.elemwise import Elemwise
+##### Constants #####
 
 CONST1 = np.exp(0.25) / np.power(8.0 * np.pi, 0.5)
 CONST2 = -np.log(2 * np.pi) / 2
 
+##### SHASH Transformations #####
+
 
 def S(x, epsilon, delta):
-    """
-    :param epsilon:
-    :param delta:
-    :param x:
-    :return: The sinharcsinh transformation of x
+    """Sinh-arcsinh transformation.
+
+    Args:
+        x: input value
+        epsilon: parameter for skew
+        delta: parameter for kurtosis
+
+    Returns:
+        Sinh-arcsinh transformed value
     """
     return np.sinh(np.arcsinh(x) * delta - epsilon)
 
 
 def S_inv(x, epsilon, delta):
-    return np.sinh((np.arcsinh(x) + epsilon) / delta)
+    """Inverse sinh-arcsinh transformation.
 
+    Args:
+        x: input value
+        epsilon: parameter for skew
+        delta: parameter for kurtosis
 
-def C(x, epsilon, delta):
-    """
-    :param epsilon:
-    :param delta:
-    :param x:
-    :return: the cosharcsinh transformation of x
-    Be aware that this is sqrt(1+S(x)^2), so you may save some compute if you can re-use the result from S.
+    Returns:
+        Inverse sinh-arcsinh transformed value
     """
-    return np.cosh(np.arcsinh(x) * delta - epsilon)
+    return np.sinh((np.arcsinh(x) + epsilon) / delta)
 
 
-def m(epsilon, delta, r):
-    """
-    :param epsilon:
-    :param delta:
-    :param r:
-    :return:  The r'th uncentered moment of the SHASH distribution parameterized by epsilon and delta. Given by Jones et al.
-    The first four moments are given in closed form.
-    """
-    if r == 1:
-        return np.sinh(epsilon / delta) * P(1 / delta)
-    elif r == 2:
-        return (np.cosh(2 * epsilon / delta) * P(2 / delta) - 1) / 2
-    elif r == 3:
-        return (
-            np.sinh(3 * epsilon / delta) * P(3 / delta)
-            - 3 * np.sinh(epsilon / delta) * P(1 / delta)
-        ) / 4
-    elif r == 4:
-        return (
-            np.cosh(4 * epsilon / delta) * P(4 / delta)
-            - 4 * np.cosh(2 * epsilon / delta) * P(2 / delta)
-            + 3
-        ) / 8
-
-def m1(epsilon, delta):
-    return np.sinh(epsilon / delta) * P(1 / delta)
-
-def m2(epsilon, delta):
-    return (np.cosh(2 * epsilon / delta) * P(2 / delta) - 1) / 2
-
-def m3(epsilon, delta):
-    return (
-        np.sinh(3 * epsilon / delta) * P(3 / delta)
-        - 3 * np.sinh(epsilon / delta) * P(1 / delta)
-    ) / 4
-
-def numpy_P(q):
-    """
-    The P function as given in Jones et al.
-    :param q:
-    :return:
-    """
-    frac = CONST1
-    K1 = spp.kv((q + 1) / 2, 0.25)
-    K2 = spp.kv((q - 1) / 2, 0.25)
-    a = (K1 + K2) * frac
-    return a
+def C(x, epsilon, delta):
+    """Cosh-arcsinh transformation.
 
-# Instance of the KOp
-my_K = Elemwise(knuop)
+    Args:
+        x: input value
+        epsilon: parameter for skew
+        delta: parameter for kurtosis
 
-def P(q):
-    """
-    The P function as given in Jones et al.
-    :param q:
-    :return:
+    Returns:
+        The cosh-arcsinh transformation of x.
+
+    Note: C(x) = sqrt(1+S(x)^2)
     """
-    K1 = my_K((q + 1) / 2, 0.25)
-    K2 = my_K((q - 1) / 2, 0.25)
-    a = (K1 + K2) * CONST1
-    return a
+    return np.cosh(np.arcsinh(x) * delta - epsilon)
 
 
 ##### SHASH Distributions #####
 
+
 class SHASH(RandomVariable):
+    """SHASH RV, described by Jones et al., based on a standard normal distribution."""
+
     name = "shash"
     signature = "(),()->()"
     dtype = "floatX"
     _print_name = ("SHASH", "\\operatorname{SHASH}")
 
     @classmethod
-    def rng_fn(cls, rng, epsilon, delta, size=None) -> np.ndarray:
+    def rng_fn(cls, rng, epsilon, delta, size=None):
+        """Draw random samples from SHASH distribution.
+
+        Args:
+            rng: Random number generator
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            size: sample size. Defaults to None.
+
+        Returns:
+            Random samples from SHASH distribution
+        """
         return np.sinh(
             (np.arcsinh(rng.normal(loc=0, scale=1, size=size)) + epsilon) / delta
         )
@@ -134,35 +101,144 @@ def rng_fn(cls, rng, epsilon, delta, size=None) -> np.ndarray:
 class SHASH(Continuous):
     rv_op = shash
     """
-    SHASH described by Jones et al., based on a standard normal distribution.
+    SHASH distribution described by Jones et al., based on a standard normal distribution.
     """
 
+    # Instance of the KOp
+    my_K = Elemwise(knuop)
+
+    @staticmethod
+    @lru_cache(maxsize=128)
+    def P(q):
+        """The P function as given in Jones et al.
+
+        Args:
+            q: input parameter for the P function
+
+        Returns:
+            Result of the P function computation
+        """
+        K1 = SHASH.my_K((q + 1) / 2, 0.25)
+        K2 = SHASH.my_K((q - 1) / 2, 0.25)
+        a = (K1 + K2) * CONST1
+        return a
+
+    @staticmethod
+    def m1(epsilon, delta):
+        """The first moment of the SHASH distribution parametrized by epsilon and delta.
+
+        Args:
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            First moment of the SHASH distribution
+        """
+        return np.sinh(epsilon / delta) * SHASH.P(1 / delta)
+
+    @staticmethod
+    def m2(epsilon, delta):
+        """The second moment of the SHASH distribution parametrized by epsilon and delta.
+
+        Args:
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Second moment of the SHASH distribution
+        """
+        return (np.cosh(2 * epsilon / delta) * SHASH.P(2 / delta) - 1) / 2
+
+    @staticmethod
+    def m1m2(epsilon, delta):
+        """Compute both first and second moments together to avoid redundant calculations.
+
+        Args:
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Tuple containing (mean, variance) of the SHASH distribution
+        """
+        inv_delta = 1.0 / delta
+        two_inv_delta = 2.0 * inv_delta
+
+        # Compute P values once
+        p1 = SHASH.P(inv_delta)
+        p2 = SHASH.P(two_inv_delta)
+
+        # Compute trig terms once
+        eps_delta = epsilon / delta
+        sinh_eps_delta = np.sinh(eps_delta)
+        cosh_2eps_delta = np.cosh(2 * eps_delta)
+
+        # Compute moments
+        mean = sinh_eps_delta * p1
+        raw_second = (cosh_2eps_delta * p2 - 1) / 2
+        var = raw_second - mean**2
+        return mean, var
+
     @classmethod
     def dist(cls, epsilon, delta, **kwargs):
-        epsilon = ptt.as_tensor_variable(floatX(epsilon))
-        delta = ptt.as_tensor_variable(floatX(delta))
+        """Return a SHASH distribution.
+
+        Args:
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            **kwargs: Additional arguments passed to the distribution
+
+        Returns:
+            A SHASH distribution
+        """
+        epsilon = pt.as_tensor_variable(floatX(epsilon))
+        delta = pt.as_tensor_variable(floatX(delta))
         return super().dist([epsilon, delta], **kwargs)
 
     def logp(value, epsilon, delta):
+        """Log-probability of the SHASH distribution.
+
+        Args:
+            value: value to evaluate the log-probability at
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Log-probability of the SHASH distribution
+        """
         this_S = S(value, epsilon, delta)
-        this_S_sqr = ptt.sqr(this_S)
+        this_S_sqr = pt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac2 = (
-            ptt.log(delta) + ptt.log(this_C_sqr) /
-            2 - ptt.log(1 + ptt.sqr(value)) / 2
-        )
+        frac2 = pt.log(delta) + pt.log(this_C_sqr) / 2 - pt.log(1 + pt.sqr(value)) / 2
         exp = -this_S_sqr / 2
         return CONST2 + frac2 + exp
 
 
 class SHASHoRV(RandomVariable):
+    """SHASHo Random Variable.
+
+    Samples from a SHASHo distribution, which is a SHASH distribution scaled by sigma and translated by mu.
+    """
+
     name = "shasho"
     signature = "(),(),(),()->()"
     dtype = "floatX"
     _print_name = ("SHASHo", "\\operatorname{SHASHo}")
 
     @classmethod
-    def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None) -> np.ndarray:
+    def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None):
+        """Draw random samples from a SHASHo distribution.
+
+        Args:
+            rng: Random number generator
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            size: sample size. Defaults to None.
+
+        Returns:
+            Random samples from SHASHo distribution
+        """
         s = rng.normal(size=size)
         return np.sinh((np.arcsinh(s) + epsilon) / delta) * sigma + mu
 
@@ -171,41 +247,84 @@ def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None) -> np.ndarray:
 
 
 class SHASHo(Continuous):
+    """SHASHo distribution, which is a SHASH distribution scaled by sigma and translated by mu."""
+
     rv_op = shasho
-    """
-    This is the transformation where the location and scale parameters have simply been applied as an linear transformation directly on the original distribution.
-    """
 
     @classmethod
     def dist(cls, mu, sigma, epsilon, delta, **kwargs):
-        mu = ptt.as_tensor_variable(floatX(mu))
-        sigma = ptt.as_tensor_variable(floatX(sigma))
-        epsilon = ptt.as_tensor_variable(floatX(epsilon))
-        delta = ptt.as_tensor_variable(floatX(delta))
+        """Return a SHASHo distribution.
+
+        Args:
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            **kwargs: Additional arguments passed to the distribution
+
+        Returns:
+            A SHASHo distribution
+        """
+        mu = pt.as_tensor_variable(floatX(mu))
+        sigma = pt.as_tensor_variable(floatX(sigma))
+        epsilon = pt.as_tensor_variable(floatX(epsilon))
+        delta = pt.as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
+        """The log-probability of the SHASHo distribution.
+
+        Args:
+            value: value to evaluate the log-probability at
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Log-probability of the SHASHo distribution
+        """
         remapped_value = (value - mu) / sigma
         this_S = S(remapped_value, epsilon, delta)
-        this_S_sqr = ptt.sqr(this_S)
+        this_S_sqr = pt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
         frac2 = (
-            ptt.log(delta)
-            + ptt.log(this_C_sqr) / 2
-            - ptt.log(1 + ptt.sqr(remapped_value)) / 2
+            pt.log(delta)
+            + pt.log(this_C_sqr) / 2
+            - pt.log(1 + pt.sqr(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return CONST2 + frac2 + exp - ptt.log(sigma)
+        return CONST2 + frac2 + exp - pt.log(sigma)
 
 
 class SHASHo2RV(RandomVariable):
+    """SHASHo2 Random Variable.
+
+    Samples from a SHASHo2 distribution, which is a SHASH distribution scaled by sigma/delta
+    and translated by mu. This variant provides an alternative parameterization where the
+    scale parameter is adjusted by the kurtosis parameter.
+    """
+
     name = "shasho2"
     signature = "(),(),(),()->()"
     dtype = "floatX"
     _print_name = ("SHASHo2", "\\operatorname{SHASHo2}")
 
     @classmethod
-    def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None) -> np.ndarray:
+    def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None):
+        """Draw random samples from SHASHo2 distribution.
+
+        Args:
+            rng: Random number generator
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            size: sample size. Defaults to None.
+
+        Returns:
+            Random samples from SHASHo2 distribution
+        """
         s = rng.normal(size=size)
         sigma_d = sigma / delta
         return np.sinh((np.arcsinh(s) + epsilon) / delta) * sigma_d + mu
@@ -215,81 +334,147 @@ def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None) -> np.ndarray:
 
 
 class SHASHo2(Continuous):
-    rv_op = shasho2
-    """
-    This is the reparameterization where we apply the transformation provided in section 4.3 in Jones et al.
+    """SHASHo2 distribution, which is a SHASH distribution scaled by sigma/delta and translated by mu.
+
+    This distribution provides an alternative parameterization of the SHASH distribution where
+    the scale parameter is adjusted by the kurtosis parameter. This can be useful in scenarios
+    where the relationship between scale and kurtosis needs to be explicitly modeled.
     """
 
+    rv_op = shasho2
+
     @classmethod
     def dist(cls, mu, sigma, epsilon, delta, **kwargs):
-        mu = ptt.as_tensor_variable(floatX(mu))
-        sigma = ptt.as_tensor_variable(floatX(sigma))
-        epsilon = ptt.as_tensor_variable(floatX(epsilon))
-        delta = ptt.as_tensor_variable(floatX(delta))
+        """Return a SHASHo2 distribution.
+
+        Args:
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            **kwargs: Additional arguments passed to the distribution
+
+        Returns:
+            A SHASHo2 distribution
+        """
+        mu = pt.as_tensor_variable(floatX(mu))
+        sigma = pt.as_tensor_variable(floatX(sigma))
+        epsilon = pt.as_tensor_variable(floatX(epsilon))
+        delta = pt.as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
+        """The log-probability of the SHASHo2 distribution.
+
+        Args:
+            value: value to evaluate the log-probability at
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Log-probability of the SHASHo2 distribution
+        """
         sigma_d = sigma / delta
         remapped_value = (value - mu) / sigma_d
         this_S = S(remapped_value, epsilon, delta)
-        this_S_sqr = ptt.sqr(this_S)
+        this_S_sqr = pt.sqr(this_S)
         this_C_sqr = 1 + this_S_sqr
         frac2 = (
-            ptt.log(delta)
-            + ptt.log(this_C_sqr) / 2
-            - ptt.log(1 + ptt.sqr(remapped_value)) / 2
+            pt.log(delta)
+            + pt.log(this_C_sqr) / 2
+            - pt.log(1 + pt.sqr(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return CONST2 + frac2 + exp - ptt.log(sigma_d)
+        return CONST2 + frac2 + exp - pt.log(sigma_d)
 
 
+class SHASHbRV(RandomVariable):
+    """SHASHb Random Variable.
 
+    Samples from a SHASHb distribution, which is a standardized SHASH distribution scaled by sigma
+    and translated by mu. This variant provides a standardized version of the SHASH distribution
+    where the base distribution is normalized to have zero mean and unit variance before applying
+    the location and scale transformations.
+    """
 
-class SHASHbRV(RandomVariable):
     name = "shashb"
     signature = "(),(),(),()->()"
     dtype = "floatX"
     _print_name = ("SHASHb", "\\operatorname{SHASHb}")
 
     @classmethod
-    def rng_fn(
-        cls,
-        rng: np.random.RandomState,
-        mu: Union[np.ndarray, float],
-        sigma: Union[np.ndarray, float],
-        epsilon: Union[np.ndarray, float],
-        delta: Union[np.ndarray, float],
-        size: Optional[Union[List[int], int]],
-    ) -> np.ndarray:
+    def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None):
+        """Draw random samples from SHASHb distribution.
+
+        Args:
+            rng: Random number generator
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            size: sample size. Defaults to None.
+
+        Returns:
+            Random samples from SHASHb distribution
+        """
         s = rng.normal(size=size)
-        mean = np.sinh(epsilon / delta) * numpy_P(1 / delta)
-        var = ((np.cosh(2 * epsilon / delta) * numpy_P(2 / delta) - 1) / 2) - mean**2
+        mean, var = SHASH.m1m2(epsilon, delta)
         out = (
             (np.sinh((np.arcsinh(s) + epsilon) / delta) - mean) / np.sqrt(var)
         ) * sigma + mu
-        return out
+        return out.eval()
 
 
 shashb = SHASHbRV()
 
 
 class SHASHb(Continuous):
-    rv_op = shashb
-    """
-    This is the reparameterization where the location and scale parameters been applied as an linear transformation on the shash distribution which was corrected for mean and variance.
+    """SHASHb distribution, which is a standardized SHASH distribution scaled by sigma and translated by mu.
+
+    This distribution provides a standardized version of the SHASH distribution where the base
+    distribution is normalized to have zero mean and unit variance before applying the location
+    and scale transformations. This transformation aims to remove the correlation between the
+    parameters, which can be useful in MCMC sampling.
     """
 
+    rv_op = shashb
+
     @classmethod
     def dist(cls, mu, sigma, epsilon, delta, **kwargs):
-        mu = ptt.as_tensor_variable(floatX(mu))
-        sigma = ptt.as_tensor_variable(floatX(sigma))
-        epsilon = ptt.as_tensor_variable(floatX(epsilon))
-        delta = ptt.as_tensor_variable(floatX(delta))
+        """Return a SHASHb distribution.
+
+        Args:
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+            **kwargs: Additional arguments passed to the distribution
+
+        Returns:
+            A SHASHb distribution
+        """
+        mu = pt.as_tensor_variable(floatX(mu))
+        sigma = pt.as_tensor_variable(floatX(sigma))
+        epsilon = pt.as_tensor_variable(floatX(epsilon))
+        delta = pt.as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
-        mean = m1(epsilon, delta)
-        var = m2(epsilon, delta) - mean**2
+        """The log-probability of the SHASHb distribution.
+
+        Args:
+            value: value to evaluate the log-probability at
+            mu: location parameter
+            sigma: scale parameter
+            epsilon: skew parameter
+            delta: kurtosis parameter
+
+        Returns:
+            Log-probability of the SHASHb distribution
+        """
+        mean, var = SHASH.m1m2(epsilon, delta)
         remapped_value = ((value - mu) / sigma) * np.sqrt(var) + mean
         this_S = S(remapped_value, epsilon, delta)
         this_S_sqr = np.square(this_S)

From f3732b56359e1323d85f920bab870525da234a86 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 25 Oct 2024 13:36:24 +0200
Subject: [PATCH 43/68] Refactor KOp (Now called KnuOp

---
 pcntoolkit/model/KOp.py   | 34 -------------------------
 pcntoolkit/model/KnuOp.py | 52 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 52 insertions(+), 34 deletions(-)
 delete mode 100644 pcntoolkit/model/KOp.py
 create mode 100644 pcntoolkit/model/KnuOp.py

diff --git a/pcntoolkit/model/KOp.py b/pcntoolkit/model/KOp.py
deleted file mode 100644
index f4b8a654..00000000
--- a/pcntoolkit/model/KOp.py
+++ /dev/null
@@ -1,34 +0,0 @@
-
-# Third-party imports
-from pytensor.scalar.basic import BinaryScalarOp, ScalarOp, upgrade_to_float
-from pytensor.gradient import grad_not_implemented
-
-import scipy.special as spp
-
-class KnuOp(BinaryScalarOp):
-    """
-    Modified Bessel function of the second kind, pytensor wrapper for scipy.special.kv
-    """
-    nfunc_spec = ("scipy.special.kv", 2, 1)
-    
-    @staticmethod
-    def st_impl(p, x):
-        return spp.kv(p, x)
-
-    def impl(self, p, x):
-        return KnuOp.st_impl(p, x)
-    
-    def grad(self, inputs, grads):
-        dp = 1e-16
-        (p, x) = inputs
-        (gz,) = grads
-        dfdp = (knuop(p + dp, x) - knuop(p - dp, x)) / (2*dp)
-        gni = grad_not_implemented(self, 0, 'x', ' Grad with respect to x is not implemented for KvOp')
-        # dfdx = spp.kvp(p, x) #TODO Implement this
-        return [
-            gz * dfdp,
-            gni
-        ]
-
-
-knuop = KnuOp(upgrade_to_float, name='knuop')
\ No newline at end of file
diff --git a/pcntoolkit/model/KnuOp.py b/pcntoolkit/model/KnuOp.py
new file mode 100644
index 00000000..c9f54fe4
--- /dev/null
+++ b/pcntoolkit/model/KnuOp.py
@@ -0,0 +1,52 @@
+# Third-party imports
+import scipy.special as spp
+from pytensor.scalar.basic import BinaryScalarOp, upgrade_to_float
+
+
+class KnuOp(BinaryScalarOp):
+    """
+    Modified Bessel function of the second kind, pytensor wrapper for scipy.special.kv
+    """
+
+    nfunc_spec = ("scipy.special.kv", 2, 1)
+
+    @staticmethod
+    def st_impl(p, x):
+        return spp.kv(p, x)
+
+    def impl(self, p, x):
+        return KnuOp.st_impl(p, x)
+
+    def grad(self, inputs, grads):
+        dp = 1e-16
+        (p, x) = inputs
+        (gz,) = grads
+        dfdp = (knuop(p + dp, x) - knuop(p - dp, x)) / (2 * dp)
+        return [gz * dfdp, gz * knupop(p, x)]
+
+
+class KnuPrimeOp(BinaryScalarOp):
+    """
+    Derivative of the modified Bessel function of the second kind.
+    """
+
+    nfunc_spec = ("scipy.special.kvp", 2, 1)
+
+    @staticmethod
+    def st_impl(p, x):
+        return spp.kvp(p, x)
+
+    def impl(self, p, x):
+        return KnuPrimeOp.st_impl(p, x)
+
+    def grad(self, inputs, grads):
+        dp = 1e-16
+        (p, x) = inputs
+        (gz,) = grads
+        dfdp = (knupop(p + dp, x) - knupop(p - dp, x)) / (2 * dp)
+        dfdx = -knuop(p, x) - knupop(p, x) / x
+        return [gz * dfdp, gz * dfdx]
+
+
+knuop = KnuOp(upgrade_to_float, name="knuop")
+knupop = KnuPrimeOp(upgrade_to_float, name="knupop")

From 0013ea71d1b575d40122f1db7791cfaabd0cb6b8 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 25 Oct 2024 13:36:58 +0200
Subject: [PATCH 44/68] Add tests for SHASH classes

---
 pytest_tests/test_shash.py | 100 +++++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 pytest_tests/test_shash.py

diff --git a/pytest_tests/test_shash.py b/pytest_tests/test_shash.py
new file mode 100644
index 00000000..2e81460a
--- /dev/null
+++ b/pytest_tests/test_shash.py
@@ -0,0 +1,100 @@
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_array_almost_equal
+
+from pcntoolkit.model.SHASH import SHASH, C, S, S_inv, SHASHb, SHASHo, SHASHo2
+
+
+def test_shash_transformations():
+    """Test the basic SHASH transformations (S, S_inv, C)"""
+    x = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
+    epsilon = 0.5
+    delta = 1.5
+
+    # Test S transformation
+    s_result = S(x, epsilon, delta)
+    # Test inverse relationship
+    s_inv_result = S_inv(s_result, epsilon, delta)
+    assert_array_almost_equal(x, s_inv_result, decimal=6)
+
+    # Test C transformation
+    c_result = C(x, epsilon, delta)
+    # C should always be >= 1 since it's sqrt(1 + S^2)
+    assert np.all(c_result >= 1.0)
+    # Test relationship between S and C
+    assert_array_almost_equal(c_result, np.sqrt(1 + s_result**2), decimal=6)
+
+
+def test_moment_calculations():
+    """Test moment calculation functions"""
+    epsilon = 0.5
+    delta = 1.5
+
+    # Test compute_moments function
+    mean, var = SHASH.m1m2(epsilon, delta)
+    assert_almost_equal(mean.eval(), SHASH.m1(epsilon, delta).eval(), decimal=6)
+    assert_almost_equal(
+        var.eval(), SHASH.m2(epsilon, delta).eval() - mean.eval() ** 2, decimal=6
+    )
+
+
+def test_shash_random_generation():
+    """Test random number generation for SHASH distributions"""
+    rng = np.random.RandomState(42)
+    n_samples = 1000
+
+    # Test base SHASH
+    epsilon, delta = 0.5, 1.5
+    samples = SHASH.rv_op.rng_fn(rng, epsilon, delta, size=n_samples)
+    assert samples.shape == (n_samples,)
+
+    # Test SHASHo
+    mu, sigma = 1.0, 2.0
+    samples_o = SHASHo.rv_op.rng_fn(rng, mu, sigma, epsilon, delta, size=n_samples)
+    assert samples_o.shape == (n_samples,)
+
+    # Test SHASHo2
+    samples_o2 = SHASHo2.rv_op.rng_fn(rng, mu, sigma, epsilon, delta, size=n_samples)
+    assert samples_o2.shape == (n_samples,)
+
+    # Test SHASHb
+    samples_b = SHASHb.rv_op.rng_fn(rng, mu, sigma, epsilon, delta, size=n_samples)
+    assert samples_b.shape == (n_samples,)
+
+
+def test_shash_distribution_properties():
+    """Test statistical properties of SHASH distributions"""
+    rng = np.random.RandomState(42)
+    n_samples = 10000
+    mu, sigma = 1.0, 2.0
+    epsilon, delta = 0.5, 1.5
+
+    # Generate samples from SHASHb
+    samples = SHASHb.rv_op.rng_fn(rng, mu, sigma, epsilon, delta, size=n_samples)
+
+    # Check mean and standard deviation are close to specified values
+    assert_almost_equal(np.mean(samples), mu, decimal=1)
+    assert_almost_equal(np.std(samples), sigma, decimal=1)
+
+
+def test_edge_cases():
+    """Test edge cases and boundary conditions"""
+    x = np.array([-1e10, 0.0, 1e10])  # Test very large/small values
+    epsilon = 0.0  # Test zero skewness
+    delta = 1.0  # Test unit tail weight
+
+    # S transformation should handle extreme values
+    s_result = S(x, epsilon, delta)
+    assert not np.any(np.isnan(s_result))
+    assert not np.any(np.isinf(s_result))
+
+    # C transformation should handle extreme values
+    c_result = C(x, epsilon, delta)
+    assert not np.any(np.isnan(c_result))
+    assert not np.any(np.isinf(c_result))
+    assert np.all(c_result >= 1.0)
+
+    # Test moment calculations with edge case parameters
+    mean, var = SHASH.m1m2(0.0, 1.0)  # Standard case
+    assert not np.isnan(mean.eval())
+    assert not np.isnan(var.eval())
+    assert var.eval() > 0  # Variance should always be positive

From 1679ec57bef97df136d7f303bf1ad38bba2d923e Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 25 Oct 2024 13:37:26 +0200
Subject: [PATCH 45/68] Formating

---
 tests/testHBR.py | 117 +++++++++++++++++++++++++++++++----------------
 1 file changed, 78 insertions(+), 39 deletions(-)

diff --git a/tests/testHBR.py b/tests/testHBR.py
index 2d3baf9e..0e19ac5b 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -10,47 +10,65 @@
 This script tests HBR models with default configs on toy data.
 
 """
+
 # %%
-import os
+from warnings import filterwarnings
+
+import matplotlib.pyplot as plt
 import numpy as np
+
 from pcntoolkit.normative_model.norm_utils import norm_init
 from pcntoolkit.util.utils import simulate_data
-import matplotlib.pyplot as plt
-from pcntoolkit.normative import estimate
-from warnings import filterwarnings
-filterwarnings('ignore')
+
+filterwarnings("ignore")
 
 
 ########################### Experiment Settings ###############################
 
 
 random_state = 40
-working_dir = '/Users/stijndeboer/temp/HBR/'  # Specify a working directory to save data and results.
+working_dir = "/Users/stijndeboer/temp/HBR/"  # Specify a working directory to save data and results.
 
-simulation_method = 'linear'
-n_features = 1      # The number of input features of X
-n_grps = 3     # Number of batches in data
-n_samples = 500     # Number of samples in each group (use a list for different
+simulation_method = "linear"
+n_features = 1  # The number of input features of X
+n_grps = 3  # Number of batches in data
+n_samples = 500  # Number of samples in each group (use a list for different
 # sample numbers across different batches)
 
-model_type = 'bspline'  # modelto try 'linear, ''polynomial', 'bspline'
+model_type = "bspline"  # modelto try 'linear, ''polynomial', 'bspline'
 
 
 ############################## Data Simulation ################################
 
 
-X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = \
-    simulate_data(simulation_method, n_samples, n_features, n_grps,
-                  working_dir=working_dir, plot=True, noise='heteroscedastic_nongaussian',
-                  random_state=random_state)
+X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = simulate_data(
+    simulation_method,
+    n_samples,
+    n_features,
+    n_grps,
+    working_dir=working_dir,
+    plot=True,
+    noise="heteroscedastic_nongaussian",
+    random_state=random_state,
+)
 
 ################################# Fittig and Predicting ###############################
 
-nm = norm_init(X_train, Y_train, alg='hbr', model_type=model_type, likelihood='SHASHb', 
-               linear_sigma='True', random_slope_mu='True', linear_epsilon='False', linear_delta='True', nuts_sampler='nutpie')
+nm = norm_init(
+    X_train,
+    Y_train,
+    alg="hbr",
+    model_type=model_type,
+    likelihood="SHASHb",
+    linear_sigma="True",
+    random_slope_mu="False",
+    linear_epsilon="True",
+    linear_delta="True",
+    nuts_sampler="pymc",
+)
 
-nm.estimate(X_train, Y_train, trbefile=working_dir+'trbefile.pkl')
-yhat, ys2 = nm.predict(X_test, tsbefile=working_dir+'tsbefile.pkl')
+nm.estimate(X_train, Y_train, trbefile=working_dir + "trbefile.pkl")
+yhat, ys2 = nm.predict(X_test, tsbefile=working_dir + "tsbefile.pkl")
 
 
 ################################# Plotting Quantiles ###############################
@@ -61,35 +79,47 @@
     temp_be = grp_id_test[sorted_idx, :].squeeze()
     temp_yhat = yhat[sorted_idx,]
     temp_s2 = ys2[sorted_idx,]
-    
+
     plt.figure()
     for j in range(n_grps):
-        scat1 = plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,],
-                            label='Group' + str(j))
+        scat1 = plt.scatter(
+            temp_X[temp_be == j,], temp_Y[temp_be == j,], label="Group" + str(j)
+        )
         # Showing the quantiles
         resolution = 200
         synth_X = np.linspace(np.min(X_train), np.max(X_train), resolution)
-        q = nm.get_mcmc_quantiles(
-            synth_X, batch_effects=j*np.ones(resolution))
+        q = nm.get_mcmc_quantiles(synth_X, batch_effects=j * np.ones(resolution))
         col = scat1.get_facecolors()[0]
-        plt.plot(synth_X, q.T,  linewidth=1, color=col, zorder=0)
+        plt.plot(synth_X, q.T, linewidth=1, color=col, zorder=0)
 
-    plt.title('Model %s, Feature %d' % (model_type, i))
+    plt.title("Model %s, Feature %d" % (model_type, i))
     plt.legend()
     plt.show(block=False)
-    plt.savefig(working_dir + 'quantiles_' + model_type + '_feature_' + str(i) + '.png')
+    plt.savefig(working_dir + "quantiles_" + model_type + "_feature_" + str(i) + ".png")
 
     for j in range(n_grps):
         plt.figure()
         plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,])
-        plt.plot(temp_X[temp_be == j,], temp_yhat[temp_be == j,], color='red')
-        plt.fill_between(temp_X[temp_be == j,].squeeze(),
-                         (temp_yhat[temp_be == j,] - 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
-                         (temp_yhat[temp_be == j,] + 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
-                         color='red', alpha=0.2)
-        plt.title('Model %s, Group %d, Feature %d' % (model_type, j, i))
+        plt.plot(temp_X[temp_be == j,], temp_yhat[temp_be == j,], color="red")
+        plt.fill_between(
+            temp_X[temp_be == j,].squeeze(),
+            (temp_yhat[temp_be == j,] - 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
+            (temp_yhat[temp_be == j,] + 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),
+            color="red",
+            alpha=0.2,
+        )
+        plt.title("Model %s, Group %d, Feature %d" % (model_type, j, i))
         plt.show(block=False)
-        plt.savefig(working_dir + 'pred_' + model_type + '_group_' + str(j) + '_feature_' + str(i) + '.png')
+        plt.savefig(
+            working_dir
+            + "pred_"
+            + model_type
+            + "_group_"
+            + str(j)
+            + "_feature_"
+            + str(i)
+            + ".png"
+        )
 
 
 ############################## Normative Modelling Test #######################
@@ -110,18 +140,27 @@
 
 
 ###############################################################################
+
 # %%
 
 for j in range(n_grps):
     # Showing the quantiles
     resolution = 200
     synth_X = np.linspace(np.min(X_train), np.max(X_train), resolution)
-    q = nm.get_mcmc_quantiles(
-        synth_X, batch_effects=j*np.ones(resolution))
+    q = nm.get_mcmc_quantiles(synth_X, batch_effects=j * np.ones(resolution))
     plt.figure()
     plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,])
-    plt.plot(synth_X, q.T, color='black')
-    plt.title('Model %s, Group %d, Feature %d' % (model_type, j, i))
+    plt.plot(synth_X, q.T, color="black")
+    plt.title("Model %s, Group %d, Feature %d" % (model_type, j, i))
     plt.show(block=False)
-    plt.savefig(working_dir + 'pred_' + model_type + '_group_' + str(j) + '_feature_' + str(i) + '.png')
+    plt.savefig(
+        working_dir
+        + "pred_"
+        + model_type
+        + "_group_"
+        + str(j)
+        + "_feature_"
+        + str(i)
+        + ".png"
+    )
 # %%

From 445dfabb018f6cedca7c7893fc08945f73389736 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 25 Oct 2024 14:08:51 +0200
Subject: [PATCH 46/68] Correct call to as_tensor_variable Refactor P and m1m2
 Add grad_not_implemented warning for P`

---
 pcntoolkit/model/KnuOp.py |  8 ++----
 pcntoolkit/model/SHASH.py | 54 +++++++++++++++++++++++++++------------
 2 files changed, 40 insertions(+), 22 deletions(-)

diff --git a/pcntoolkit/model/KnuOp.py b/pcntoolkit/model/KnuOp.py
index c9f54fe4..35e93302 100644
--- a/pcntoolkit/model/KnuOp.py
+++ b/pcntoolkit/model/KnuOp.py
@@ -1,5 +1,6 @@
 # Third-party imports
 import scipy.special as spp
+from pytensor.gradient import grad_not_implemented
 from pytensor.scalar.basic import BinaryScalarOp, upgrade_to_float
 
 
@@ -40,12 +41,7 @@ def impl(self, p, x):
         return KnuPrimeOp.st_impl(p, x)
 
     def grad(self, inputs, grads):
-        dp = 1e-16
-        (p, x) = inputs
-        (gz,) = grads
-        dfdp = (knupop(p + dp, x) - knupop(p - dp, x)) / (2 * dp)
-        dfdx = -knuop(p, x) - knupop(p, x) / x
-        return [gz * dfdp, gz * dfdx]
+        return [grad_not_implemented(self, 0, "p"), grad_not_implemented(self, 1, "x")]
 
 
 knuop = KnuOp(upgrade_to_float, name="knuop")
diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index 441f952d..a64317e7 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -9,8 +9,10 @@
 import pytensor as pt
 from pymc import floatX
 from pymc.distributions import Continuous
+from pytensor.tensor import as_tensor_variable
 from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.random.op import RandomVariable
+from scipy.special import kv
 
 from pcntoolkit.model.KnuOp import knuop
 
@@ -190,8 +192,8 @@ def dist(cls, epsilon, delta, **kwargs):
         Returns:
             A SHASH distribution
         """
-        epsilon = pt.as_tensor_variable(floatX(epsilon))
-        delta = pt.as_tensor_variable(floatX(delta))
+        epsilon = as_tensor_variable(floatX(epsilon))
+        delta = as_tensor_variable(floatX(delta))
         return super().dist([epsilon, delta], **kwargs)
 
     def logp(value, epsilon, delta):
@@ -265,10 +267,10 @@ def dist(cls, mu, sigma, epsilon, delta, **kwargs):
         Returns:
             A SHASHo distribution
         """
-        mu = pt.as_tensor_variable(floatX(mu))
-        sigma = pt.as_tensor_variable(floatX(sigma))
-        epsilon = pt.as_tensor_variable(floatX(epsilon))
-        delta = pt.as_tensor_variable(floatX(delta))
+        mu = as_tensor_variable(floatX(mu))
+        sigma = as_tensor_variable(floatX(sigma))
+        epsilon = as_tensor_variable(floatX(epsilon))
+        delta = as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
@@ -357,10 +359,10 @@ def dist(cls, mu, sigma, epsilon, delta, **kwargs):
         Returns:
             A SHASHo2 distribution
         """
-        mu = pt.as_tensor_variable(floatX(mu))
-        sigma = pt.as_tensor_variable(floatX(sigma))
-        epsilon = pt.as_tensor_variable(floatX(epsilon))
-        delta = pt.as_tensor_variable(floatX(delta))
+        mu = as_tensor_variable(floatX(mu))
+        sigma = as_tensor_variable(floatX(sigma))
+        epsilon = as_tensor_variable(floatX(epsilon))
+        delta = as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):
@@ -420,11 +422,31 @@ def rng_fn(cls, rng, mu, sigma, epsilon, delta, size=None):
             Random samples from SHASHb distribution
         """
         s = rng.normal(size=size)
-        mean, var = SHASH.m1m2(epsilon, delta)
+
+        def P(q):
+            K1 = kv((q + 1) / 2, 0.25)
+            K2 = kv((q - 1) / 2, 0.25)
+            a = (K1 + K2) * CONST1
+            return a
+
+        def m1m2(epsilon, delta):
+            inv_delta = 1.0 / delta
+            two_inv_delta = 2.0 * inv_delta
+            p1 = P(inv_delta)
+            p2 = P(two_inv_delta)
+            eps_delta = epsilon / delta
+            sinh_eps_delta = np.sinh(eps_delta)
+            cosh_2eps_delta = np.cosh(2 * eps_delta)
+            mean = sinh_eps_delta * p1
+            raw_second = (cosh_2eps_delta * p2 - 1) / 2
+            var = raw_second - mean**2
+            return mean, var
+
+        mean, var = m1m2(epsilon, delta)
         out = (
             (np.sinh((np.arcsinh(s) + epsilon) / delta) - mean) / np.sqrt(var)
         ) * sigma + mu
-        return out.eval()
+        return out
 
 
 shashb = SHASHbRV()
@@ -455,10 +477,10 @@ def dist(cls, mu, sigma, epsilon, delta, **kwargs):
         Returns:
             A SHASHb distribution
         """
-        mu = pt.as_tensor_variable(floatX(mu))
-        sigma = pt.as_tensor_variable(floatX(sigma))
-        epsilon = pt.as_tensor_variable(floatX(epsilon))
-        delta = pt.as_tensor_variable(floatX(delta))
+        mu = as_tensor_variable(floatX(mu))
+        sigma = as_tensor_variable(floatX(sigma))
+        epsilon = as_tensor_variable(floatX(epsilon))
+        delta = as_tensor_variable(floatX(delta))
         return super().dist([mu, sigma, epsilon, delta], **kwargs)
 
     def logp(value, mu, sigma, epsilon, delta):

From 76340afc5889d0472fcb50ae7135ca2784abe946 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 6 Nov 2024 16:30:13 +0100
Subject: [PATCH 47/68] Fix pandas backwards compatibility

---
 pcntoolkit/normative_model/norm_base.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/pcntoolkit/normative_model/norm_base.py b/pcntoolkit/normative_model/norm_base.py
index 7839e740..268d421e 100644
--- a/pcntoolkit/normative_model/norm_base.py
+++ b/pcntoolkit/normative_model/norm_base.py
@@ -1,8 +1,10 @@
 import os
+import pickle
 import sys
-from six import with_metaclass
 from abc import ABCMeta, abstractmethod
-import pickle
+
+import pandas as pd
+from six import with_metaclass
 
 try:  # run as a package if installed
     from pcntoolkit import configs
@@ -53,7 +55,7 @@ def save(self, save_path):
     def load(self, load_path):
         try:
             with open(load_path, 'rb') as handle:
-                nm = pickle.load(handle)
+                nm = pd.read_pickle(handle)
             return nm
         except Exception as err:
             print('Error:', err)

From 104f28d3527a757de4af88d13d4ea91dbfaadd6c Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 8 Nov 2024 16:17:31 +0100
Subject: [PATCH 48/68] cmake dependency added

---
 pcntoolkit/regression_model/blr/warp.py |   1 +
 poetry.lock                             | 228 ++++++++++++++----------
 pyproject.toml                          |   1 +
 3 files changed, 133 insertions(+), 97 deletions(-)
 create mode 100644 pcntoolkit/regression_model/blr/warp.py

diff --git a/pcntoolkit/regression_model/blr/warp.py b/pcntoolkit/regression_model/blr/warp.py
new file mode 100644
index 00000000..0519ecba
--- /dev/null
+++ b/pcntoolkit/regression_model/blr/warp.py
@@ -0,0 +1 @@
+ 
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 5108b93c..165a1724 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -380,6 +380,36 @@ files = [
     {file = "cloudpickle-3.1.0.tar.gz", hash = "sha256:81a929b6e3c7335c863c771d673d105f02efdb89dfaba0c90495d1c64796601b"},
 ]
 
+[[package]]
+name = "cmake"
+version = "3.30.5"
+description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "cmake-3.30.5-py3-none-macosx_10_10_universal2.whl", hash = "sha256:c518d174cde8c5bda3dc9c906af2e1ad54448019d9a9061c5d85331c67bbaa60"},
+    {file = "cmake-3.30.5-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:085728e950e6096014c99fc46361fbaa508863157bafe0ecf6dc232c16098456"},
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 version = "0.4.6"
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 [[package]]
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 [package.dependencies]
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 [package.dependencies]
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 [[package]]
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-[package.dependencies]
-numpy = ">=1.16.6"
-
 [package.extras]
 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
@@ -1964,13 +1997,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
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-version = "5.17.0"
+version = "5.18.0"
 description = "Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor"
 optional = false
 python-versions = ">=3.10"
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 ]
 
 [package.dependencies]
@@ -2261,13 +2294,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rich"
-version = "13.9.2"
+version = "13.9.4"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
@@ -2401,23 +2434,23 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
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+version = "75.3.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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 python-versions = ">=3.8"
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-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -2740,13 +2773,13 @@ files = [
 
 [[package]]
 name = "xarray"
-version = "2024.9.0"
+version = "2024.10.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "xarray-2024.9.0-py3-none-any.whl", hash = "sha256:4fd534abdf12d5fa75dd566c56483d5081f77864462cf3d6ad53e13f9db48222"},
-    {file = "xarray-2024.9.0.tar.gz", hash = "sha256:e796a6b3eaec11da24f33e4bb14af41897011660a0516fa4037d3ae4bbd1d378"},
+    {file = "xarray-2024.10.0-py3-none-any.whl", hash = "sha256:ae1d38cb44a0324dfb61e492394158ae22389bf7de9f3c174309c17376df63a0"},
+    {file = "xarray-2024.10.0.tar.gz", hash = "sha256:e369e2bac430e418c2448e5b96f07da4635f98c1319aa23cfeb3fbcb9a01d2e0"},
 ]
 
 [package.dependencies]
@@ -2755,12 +2788,13 @@ packaging = ">=23.1"
 pandas = ">=2.1"
 
 [package.extras]
-accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
-complete = ["xarray[accel,dev,io,parallel,viz]"]
-dev = ["hypothesis", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "xarray[complete]"]
+accel = ["bottleneck", "flox", "numba (>=0.54)", "numbagg", "opt-einsum", "scipy"]
+complete = ["xarray[accel,etc,io,parallel,viz]"]
+dev = ["hypothesis", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "sphinx", "sphinx-autosummary-accessors", "xarray[complete]"]
+etc = ["sparse"]
 io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
 parallel = ["dask[complete]"]
-viz = ["matplotlib", "nc-time-axis", "seaborn"]
+viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "xarray-einstats"
@@ -2787,4 +2821,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "243443b42aa8bbd9d52455e147000ebd4e1065e8de68e79fa93254d3e4e951fc"
+content-hash = "6679b5416e0514a42c49c46585cc45ce6d0a6a69d458565976c497bc5a4ca507"
diff --git a/pyproject.toml b/pyproject.toml
index bc33c1d7..1f1740e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,6 +16,7 @@ seaborn = "^0.13.2"             # Prefer conda
 six = "^1.16.0"                 # Prefer conda
 nutpie = "^0.13.2"
 numba = "^0.60.0"
+cmake = "^3.30.5"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"

From 11e2c49bf5e0d046fef98b4b35dbbebcec6fc354 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 12 Nov 2024 11:23:42 +0100
Subject: [PATCH 49/68] Remove nutpie and numba deps

---
 pcntoolkit/normative.py |  68 ++++++++-----
 poetry.lock             | 221 ++++------------------------------------
 pyproject.toml          |   5 +-
 3 files changed, 62 insertions(+), 232 deletions(-)

diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py
index 14a35643..26612c27 100755
--- a/pcntoolkit/normative.py
+++ b/pcntoolkit/normative.py
@@ -11,25 +11,38 @@
 #  Written by A. Marquand
 # ------------------------------------------------------------------------------
 
-from __future__ import print_function
-from __future__ import division
+from __future__ import division, print_function
 
-import os
-import sys
-import numpy as np
 import argparse
-import pickle
 import glob
+import os
+import pickle
+import sys
+import warnings
+from pathlib import Path
 
+import numpy as np
 from sklearn.model_selection import KFold
-from pathlib import Path
+
+try:
+    import nutpie
+except ImportError:
+    warnings.warn("Nutpie not installed. For sampling with the nutpie backend, install it with `conda install nutpie numba`")
+
+
 
 try:  # run as a package if installed
     from pcntoolkit import configs
     from pcntoolkit.dataio import fileio
     from pcntoolkit.normative_model.norm_utils import norm_init
-    from pcntoolkit.util.utils import compute_pearsonr, CustomCV, explained_var
-    from pcntoolkit.util.utils import compute_MSLL, scaler, get_package_versions
+    from pcntoolkit.util.utils import (
+        CustomCV,
+        compute_MSLL,
+        compute_pearsonr,
+        explained_var,
+        get_package_versions,
+        scaler,
+    )
 except ImportError:
     pass
 
@@ -41,10 +54,15 @@
 
     import configs
     from dataio import fileio
-
-    from util.utils import compute_pearsonr, CustomCV, explained_var, compute_MSLL
-    from util.utils import scaler, get_package_versions
     from normative_model.norm_utils import norm_init
+    from util.utils import (
+        CustomCV,
+        compute_MSLL,
+        compute_pearsonr,
+        explained_var,
+        get_package_versions,
+        scaler,
+    )
 
 PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
@@ -953,14 +971,14 @@ def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
         return
     # testing should not be obligatory for HBR,
     # but should be for BLR (since it doesn't produce transfer models)
-    elif (not 'model_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print(f'{kwargs=}')
         print('InputError: Some general mandatory arguments are missing.')
         return
     # hbr has one additional mandatory arguments
     elif alg == 'hbr':
-        if (not 'output_path' in list(kwargs.keys())):
+        if ('output_path' not in list(kwargs.keys())):
             print('InputError: Some mandatory arguments for hbr are missing.')
             return
         else:
@@ -972,7 +990,7 @@ def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
     # or (testresp==None)
     elif alg == 'blr':
         if (testcov == None) or \
-                (not 'tsbefile' in list(kwargs.keys())):
+                ('tsbefile' not in list(kwargs.keys())):
             print('InputError: Some mandatory arguments for blr are missing.')
             return
     # general arguments
@@ -1208,9 +1226,9 @@ def extend(covfile, respfile, maskfile=None, **kwargs):
     if alg != 'hbr':
         print('Model extention is only possible for HBR models.')
         return
-    elif (not 'model_path' in list(kwargs.keys())) or \
-        (not 'output_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+        ('output_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
@@ -1319,9 +1337,9 @@ def tune(covfile, respfile, maskfile=None, **kwargs):
     if alg != 'hbr':
         print('Model extention is only possible for HBR models.')
         return
-    elif (not 'model_path' in list(kwargs.keys())) or \
-        (not 'output_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+        ('output_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
@@ -1427,9 +1445,9 @@ def merge(covfile=None, respfile=None, **kwargs):
     if alg != 'hbr':
         print('Merging models is only possible for HBR models.')
         return
-    elif (not 'model_path1' in list(kwargs.keys())) or \
-        (not 'model_path2' in list(kwargs.keys())) or \
-            (not 'output_path' in list(kwargs.keys())):
+    elif ('model_path1' not in list(kwargs.keys())) or \
+        ('model_path2' not in list(kwargs.keys())) or \
+            ('output_path' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
diff --git a/poetry.lock b/poetry.lock
index 165a1724..6c2d0551 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -380,36 +380,6 @@ files = [
     {file = "cloudpickle-3.1.0.tar.gz", hash = "sha256:81a929b6e3c7335c863c771d673d105f02efdb89dfaba0c90495d1c64796601b"},
 ]
 
-[[package]]
-name = "cmake"
-version = "3.30.5"
-description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "cmake-3.30.5-py3-none-macosx_10_10_universal2.whl", hash = "sha256:c518d174cde8c5bda3dc9c906af2e1ad54448019d9a9061c5d85331c67bbaa60"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:085728e950e6096014c99fc46361fbaa508863157bafe0ecf6dc232c16098456"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:437e9f885cf576f47b45974a92239d7c86da91e99e62ecf1aa8be454703dd1a6"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b3a662e22dba1288b678daa5544a62a05da1992e0b654e799217e22bc7e5194a"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9dbedacaba2daa40726569590762d775246325b609753d7af888dc7eda49c7bd"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4a5c113428aa9f3b4b2ce2e6dc1b932cfe3991be04d215c5ab51c078eae3e674"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9edd4065dbc67a6861255f05135249a3f042f1e09a73c608f84a1a0c6a60a266"},
-    {file = "cmake-3.30.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db049e551e2f0562b3b225ac760888ee38b8ade62d74213f3611420e6b22f36d"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:f9b351ad3b0bacd04fe73a35b71bf603416b0441d295c13881e56eba6ec01b4b"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_1_i686.whl", hash = "sha256:c37f41ac7e7a806c91d16cd3efcd696635634bb63e0d9007582373f5a81c4fa1"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:e4fd409b3455a01fe80860503be4c1b34e2e12cc5433921c7aed1102a3870322"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:4a1ded0183a75bd71ee275ef976e8eb97b4a5f4b58e09c90c1734c509acb7d5a"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:e2fd43ab522695e0ebbb6dfc6a1466daeb66c57f7f4aade02fe8c5a61acc2828"},
-    {file = "cmake-3.30.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:6161031b0c89f3f60b82cd5e1dd8cdef47b0a057f9a5349e18bf1507ac4b2402"},
-    {file = "cmake-3.30.5-py3-none-win32.whl", hash = "sha256:3ee3ab9b4d3fcdb9360104ef2175a47e145693b365983dcd8ccb5d58b1d0ab7f"},
-    {file = "cmake-3.30.5-py3-none-win_amd64.whl", hash = "sha256:dddb1c82e69c8cf79a314f57c2cbb1b3c152758fa0997a5f5ddf8fc8207c994a"},
-    {file = "cmake-3.30.5-py3-none-win_arm64.whl", hash = "sha256:ef454176664c941da91197feb91b68cab91ef0354ab0c4d7bfb70e069cb700f1"},
-    {file = "cmake-3.30.5.tar.gz", hash = "sha256:2815a2b4dde61a78648a2ff4318d2d303eb41600c4bdb32fcb237b4746444ed2"},
-]
-
-[package.extras]
-test = ["coverage (>=4.2)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -941,22 +911,22 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -1162,36 +1132,6 @@ files = [
     {file = "kiwisolver-1.4.7.tar.gz", hash = "sha256:9893ff81bd7107f7b685d3017cc6583daadb4fc26e4a888350df530e41980a60"},
 ]
 
-[[package]]
-name = "llvmlite"
-version = "0.43.0"
-description = "lightweight wrapper around basic LLVM functionality"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
-    {file = "llvmlite-0.43.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d4fd101f571a31acb1559ae1af30f30b1dc4b3186669f92ad780e17c81e91bc"},
-    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d434ec7e2ce3cc8f452d1cd9a28591745de022f931d67be688a737320dfcead"},
-    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6912a87782acdff6eb8bf01675ed01d60ca1f2551f8176a300a886f09e836a6a"},
-    {file = "llvmlite-0.43.0-cp310-cp310-win_amd64.whl", hash = "sha256:14f0e4bf2fd2d9a75a3534111e8ebeb08eda2f33e9bdd6dfa13282afacdde0ed"},
-    {file = "llvmlite-0.43.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8d0618cb9bfe40ac38a9633f2493d4d4e9fcc2f438d39a4e854f39cc0f5f98"},
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-    {file = "setuptools-75.3.0-py3-none-any.whl", hash = "sha256:f2504966861356aa38616760c0f66568e535562374995367b4e69c7143cf6bcd"},
-    {file = "setuptools-75.3.0.tar.gz", hash = "sha256:fba5dd4d766e97be1b1681d98712680ae8f2f26d7881245f2ce9e40714f1a686"},
+    {file = "setuptools-75.4.0-py3-none-any.whl", hash = "sha256:b3c5d862f98500b06ffdf7cc4499b48c46c317d8d56cb30b5c8bce4d88f5c216"},
+    {file = "setuptools-75.4.0.tar.gz", hash = "sha256:1dc484f5cf56fd3fe7216d7b8df820802e7246cfb534a1db2aa64f14fcb9cdcb"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"]
-core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.7.0)"]
+core = ["importlib-metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -2655,13 +2468,13 @@ files = [
 
 [[package]]
 name = "tomli"
-version = "2.0.2"
+version = "2.1.0"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
-    {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
+    {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
 ]
 
 [[package]]
@@ -2821,4 +2634,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "6679b5416e0514a42c49c46585cc45ce6d0a6a69d458565976c497bc5a4ca507"
+content-hash = "83eae095eaf77b411c3945404211e299478b3374ecc52808f5b289f703bb3646"
diff --git a/pyproject.toml b/pyproject.toml
index 1f1740e2..6981a939 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,9 +14,7 @@ pymc = "^5.17.0"                # Prefer Conda
 scikit-learn = "^1.5.2"         # Prefer Conda
 seaborn = "^0.13.2"             # Prefer conda
 six = "^1.16.0"                 # Prefer conda
-nutpie = "^0.13.2"
-numba = "^0.60.0"
-cmake = "^3.30.5"
+scipy = "^1.12"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"
@@ -25,6 +23,7 @@ ipywidgets = "^8.1.5"
 black = "^24.10.0"
 ipykernel = "^6.29.5"
 
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

From 8f3b8ab32e4434807fdde6a3d5ae52b52048019f Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 12 Nov 2024 11:27:16 +0100
Subject: [PATCH 50/68] Improve warning

---
 pcntoolkit/normative.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py
index 26612c27..9d885315 100755
--- a/pcntoolkit/normative.py
+++ b/pcntoolkit/normative.py
@@ -27,7 +27,7 @@
 try:
     import nutpie
 except ImportError:
-    warnings.warn("Nutpie not installed. For sampling with the nutpie backend, install it with `conda install nutpie numba`")
+    warnings.warn("Nutpie not installed. For fitting HBR models with the nutpie backend, install it with `conda install nutpie numba`")
 
 
 

From e595ddea4c9614b6378716397f2ad0929202e75e Mon Sep 17 00:00:00 2001
From: Stijn de Boer <19709783+AuguB@users.noreply.github.com>
Date: Tue, 12 Nov 2024 15:18:37 +0100
Subject: [PATCH 51/68] Created using Colab

---
 notebooks/pcntk_colab_dev_env.ipynb | 4501 +++++++++++++++++++++++++++
 1 file changed, 4501 insertions(+)
 create mode 100644 notebooks/pcntk_colab_dev_env.ipynb

diff --git a/notebooks/pcntk_colab_dev_env.ipynb b/notebooks/pcntk_colab_dev_env.ipynb
new file mode 100644
index 00000000..82b831e6
--- /dev/null
+++ b/notebooks/pcntk_colab_dev_env.ipynb
@@ -0,0 +1,4501 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "name": "pcntk_colab_dev_env.ipynb",
+      "authorship_tag": "ABX9TyPhmRD2D0nfPuaeSc4uS/Q+",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Install condacolab, which will enable us to install using conda\n",
+        "## Outcomment this cell after the kernel has automatically restarted\n",
+        "import os\n",
+        "os.environ['PYTHONPATH'] = \"\"  # Need to clear the pythonpath to avoid some errors\n",
+        "!echo \"pythonpath: $PYTHONPATH\"\n",
+        "!pip install -q condacolab\n",
+        "import condacolab\n",
+        "condacolab.install()"
+      ],
+      "metadata": {
+        "id": "WtVP1BD3eKjV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6e4a9a86-d761-4bf8-80fa-6ed748d9000a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "pythonpath: \n",
+            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.11.0-0/Mambaforge-23.11.0-0-Linux-x86_64.sh...\n",
+            "📦 Installing...\n",
+            "📌 Adjusting configuration...\n",
+            "🩹 Patching environment...\n",
+            "⏲ Done in 0:00:16\n",
+            "🔁 Restarting kernel...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check if condacolab works\n",
+        "import condacolab\n",
+        "condacolab.check()\n",
+        "\n",
+        "# Install libraries with optimized dependencies (BLAS and Rust) using conda\n",
+        "!conda install pymc numba nutpie -c conda-forge\n",
+        "\n",
+        "# Install CPU only verion of torch using conda (go to https://pytorch.org/get-started/locally/ if you need another version for your platform)\n",
+        "!conda install pytorch torchvision torchaudio cpuonly -c pytorch\n",
+        "\n",
+        "# # Install the pcntoolkit\n",
+        "!pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cb4xm679kxnE",
+        "outputId": "0985fdde-0fe6-44f6-cb29-d38e393f9010",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Channels:\n",
+            " - conda-forge\n",
+            "Platform: linux-64\n",
+            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
+            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
+            "\n",
+            "\n",
+            "==> WARNING: A newer version of conda exists. <==\n",
+            "    current version: 23.11.0\n",
+            "    latest version: 24.9.2\n",
+            "\n",
+            "Please update conda by running\n",
+            "\n",
+            "    $ conda update -n base -c conda-forge conda\n",
+            "\n",
+            "\n",
+            "\n",
+            "## Package Plan ##\n",
+            "\n",
+            "  environment location: /usr/local\n",
+            "\n",
+            "  added / updated specs:\n",
+            "    - numba\n",
+            "    - nutpie\n",
+            "    - pymc\n",
+            "\n",
+            "\n",
+            "The following packages will be downloaded:\n",
+            "\n",
+            "    package                    |            build\n",
+            "    ---------------------------|-----------------\n",
+            "    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n",
+            "    arviz-0.20.0               |     pyhd8ed1ab_0         1.4 MB  conda-forge\n",
+            "    atk-1.0-2.38.0             |       h04ea711_2         348 KB  conda-forge\n",
+            "    aws-c-auth-0.7.22          |       h96bc93b_2         103 KB  conda-forge\n",
+            "    aws-c-cal-0.6.14           |       h88a6e22_1          46 KB  conda-forge\n",
+            "    aws-c-common-0.9.19        |       h4ab18f5_0         221 KB  conda-forge\n",
+            "    aws-c-compression-0.2.18   |       h83b837d_6          19 KB  conda-forge\n",
+            "    aws-c-event-stream-0.4.2   |      ha47c788_12          53 KB  conda-forge\n",
+            "    aws-c-http-0.8.1           |      h29d6fba_17         190 KB  conda-forge\n",
+            "    aws-c-io-0.14.8            |       h21d4f22_5         154 KB  conda-forge\n",
+            "    aws-c-mqtt-0.10.4          |       h759edc4_4         160 KB  conda-forge\n",
+            "    aws-c-s3-0.5.9             |       h594631b_3         107 KB  conda-forge\n",
+            "    aws-c-sdkutils-0.1.16      |       h83b837d_2          54 KB  conda-forge\n",
+            "    aws-checksums-0.1.18       |       h83b837d_6          49 KB  conda-forge\n",
+            "    aws-crt-cpp-0.26.9         |       he3a8b3b_0         332 KB  conda-forge\n",
+            "    aws-sdk-cpp-1.11.329       |       hba8bd5f_3         3.5 MB  conda-forge\n",
+            "    binutils_impl_linux-64-2.40|       hf600244_0         5.2 MB  conda-forge\n",
+            "    binutils_linux-64-2.40     |       hb3c18ed_9          29 KB  conda-forge\n",
+            "    blas-2.125                 |         openblas          15 KB  conda-forge\n",
+            "    blas-devel-3.9.0           |25_linux64_openblas          15 KB  conda-forge\n",
+            "    brotli-1.1.0               |       hd590300_1          19 KB  conda-forge\n",
+            "    brotli-bin-1.1.0           |       hd590300_1          19 KB  conda-forge\n",
+            "    c-ares-1.34.3              |       heb4867d_0         201 KB  conda-forge\n",
+            "    ca-certificates-2024.8.30  |       hbcca054_0         155 KB  conda-forge\n",
+            "    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge\n",
+            "    cached_property-1.5.2      |     pyha770c72_1          11 KB  conda-forge\n",
+            "    cachetools-5.5.0           |     pyhd8ed1ab_0          14 KB  conda-forge\n",
+            "    cairo-1.18.0               |       h3faef2a_0         959 KB  conda-forge\n",
+            "    certifi-2024.8.30          |     pyhd8ed1ab_0         160 KB  conda-forge\n",
+            "    cloudpickle-3.1.0          |     pyhd8ed1ab_1          25 KB  conda-forge\n",
+            "    cons-0.4.6                 |     pyhd8ed1ab_0          14 KB  conda-forge\n",
+            "    contourpy-1.3.0            |  py310h3788b33_2         255 KB  conda-forge\n",
+            "    cycler-0.12.1              |     pyhd8ed1ab_0          13 KB  conda-forge\n",
+            "    etuples-0.3.9              |     pyhd8ed1ab_0          17 KB  conda-forge\n",
+            "    expat-2.6.4                |       h5888daf_0         135 KB  conda-forge\n",
+            "    filelock-3.16.1            |     pyhd8ed1ab_0          17 KB  conda-forge\n",
+            "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
+            "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
+            "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
+            "    font-ttf-ubuntu-0.83       |       h77eed37_3         1.5 MB  conda-forge\n",
+            "    fontconfig-2.14.2          |       h14ed4e7_0         266 KB  conda-forge\n",
+            "    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n",
+            "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
+            "    fonttools-4.54.1           |  py310h89163eb_1         2.2 MB  conda-forge\n",
+            "    freetype-2.12.1            |       h267a509_2         620 KB  conda-forge\n",
+            "    fribidi-1.0.10             |       h36c2ea0_0         112 KB  conda-forge\n",
+            "    gcc-13.3.0                 |       h9576a4e_1          53 KB  conda-forge\n",
+            "    gcc_impl_linux-64-13.3.0   |       hfea6d02_1        64.3 MB  conda-forge\n",
+            "    gcc_linux-64-13.3.0        |       hc28eda2_5          31 KB  conda-forge\n",
+            "    gdk-pixbuf-2.42.12         |       hb9ae30d_0         516 KB  conda-forge\n",
+            "    gettext-0.22.5             |       he02047a_3         468 KB  conda-forge\n",
+            "    gettext-tools-0.22.5       |       he02047a_3         2.6 MB  conda-forge\n",
+            "    gflags-2.2.2               |    h5888daf_1005         117 KB  conda-forge\n",
+            "    giflib-5.2.2               |       hd590300_0          75 KB  conda-forge\n",
+            "    glog-0.7.1                 |       hbabe93e_0         140 KB  conda-forge\n",
+            "    graphite2-1.3.13           |    h59595ed_1003          95 KB  conda-forge\n",
+            "    graphviz-9.0.0             |       h78e8752_1         2.2 MB  conda-forge\n",
+            "    gtk2-2.24.33               |       h280cfa0_4         6.2 MB  conda-forge\n",
+            "    gts-0.7.6                  |       h977cf35_4         311 KB  conda-forge\n",
+            "    gxx-13.3.0                 |       h9576a4e_1          52 KB  conda-forge\n",
+            "    gxx_impl_linux-64-13.3.0   |       hdbfa832_1        12.7 MB  conda-forge\n",
+            "    gxx_linux-64-13.3.0        |       h6834431_5          30 KB  conda-forge\n",
+            "    h5netcdf-1.4.0             |     pyhd8ed1ab_0          46 KB  conda-forge\n",
+            "    h5py-3.12.1                |nompi_py310h60e0fe6_102         1.2 MB  conda-forge\n",
+            "    harfbuzz-8.5.0             |       hfac3d4d_0         1.5 MB  conda-forge\n",
+            "    hdf5-1.14.3                |nompi_hdf9ad27_105         3.7 MB  conda-forge\n",
+            "    kernel-headers_linux-64-3.10.0|      he073ed8_18         921 KB  conda-forge\n",
+            "    kiwisolver-1.4.7           |  py310h3788b33_0          70 KB  conda-forge\n",
+            "    lcms2-2.16                 |       hb7c19ff_0         239 KB  conda-forge\n",
+            "    lerc-4.0.0                 |       h27087fc_0         275 KB  conda-forge\n",
+            "    libabseil-20240116.2       | cxx17_he02047a_1         1.2 MB  conda-forge\n",
+            "    libaec-1.1.3               |       h59595ed_0          35 KB  conda-forge\n",
+            "    libarrow-16.1.0            |   hcb6531f_6_cpu         7.9 MB  conda-forge\n",
+            "    libasprintf-0.22.5         |       he8f35ee_3          42 KB  conda-forge\n",
+            "    libasprintf-devel-0.22.5   |       he8f35ee_3          33 KB  conda-forge\n",
+            "    libblas-3.9.0              |25_linux64_openblas          15 KB  conda-forge\n",
+            "    libbrotlicommon-1.1.0      |       hd590300_1          68 KB  conda-forge\n",
+            "    libbrotlidec-1.1.0         |       hd590300_1          32 KB  conda-forge\n",
+            "    libbrotlienc-1.1.0         |       hd590300_1         276 KB  conda-forge\n",
+            "    libcblas-3.9.0             |25_linux64_openblas          15 KB  conda-forge\n",
+            "    libcrc32c-1.1.2            |       h9c3ff4c_0          20 KB  conda-forge\n",
+            "    libcurl-8.8.0              |       hca28451_0         396 KB  conda-forge\n",
+            "    libdeflate-1.20            |       hd590300_0          70 KB  conda-forge\n",
+            "    libexpat-2.6.4             |       h5888daf_0          72 KB  conda-forge\n",
+            "    libgcc-14.2.0              |       h77fa898_1         829 KB  conda-forge\n",
+            "    libgcc-devel_linux-64-13.3.0|     h84ea5a7_101         2.5 MB  conda-forge\n",
+            "    libgcc-ng-14.2.0           |       h69a702a_1          53 KB  conda-forge\n",
+            "    libgd-2.3.3                |       h119a65a_9         219 KB  conda-forge\n",
+            "    libgettextpo-0.22.5        |       he02047a_3         167 KB  conda-forge\n",
+            "    libgettextpo-devel-0.22.5  |       he02047a_3          36 KB  conda-forge\n",
+            "    libgfortran-14.2.0         |       h69a702a_1          53 KB  conda-forge\n",
+            "    libgfortran-ng-14.2.0      |       h69a702a_1          53 KB  conda-forge\n",
+            "    libgfortran5-14.2.0        |       hd5240d6_1         1.4 MB  conda-forge\n",
+            "    libglib-2.80.2             |       hf974151_0         3.7 MB  conda-forge\n",
+            "    libgomp-14.2.0             |       h77fa898_1         450 KB  conda-forge\n",
+            "    libgoogle-cloud-2.24.0     |       h2736e30_0         1.2 MB  conda-forge\n",
+            "    libgoogle-cloud-storage-2.24.0|       h3d9a0c8_0         735 KB  conda-forge\n",
+            "    libgrpc-1.62.2             |       h15f2491_0         7.0 MB  conda-forge\n",
+            "    libhwloc-2.9.3             |default_h554bfaf_1009         2.5 MB  conda-forge\n",
+            "    libjpeg-turbo-3.0.0        |       hd590300_1         604 KB  conda-forge\n",
+            "    liblapack-3.9.0            |25_linux64_openblas          15 KB  conda-forge\n",
+            "    liblapacke-3.9.0           |25_linux64_openblas          15 KB  conda-forge\n",
+            "    libllvm14-14.0.6           |       hcd5def8_4        30.0 MB  conda-forge\n",
+            "    libopenblas-0.3.28         |pthreads_h94d23a6_1         5.3 MB  conda-forge\n",
+            "    libpng-1.6.43              |       h2797004_0         281 KB  conda-forge\n",
+            "    libprotobuf-4.25.3         |       h08a7969_0         2.7 MB  conda-forge\n",
+            "    libre2-11-2023.09.01       |       h5a48ba9_2         227 KB  conda-forge\n",
+            "    librsvg-2.56.3             |       he3f83f7_1         5.6 MB  conda-forge\n",
+            "    libsanitizer-13.3.0        |       heb74ff8_1         3.9 MB  conda-forge\n",
+            "    libstdcxx-14.2.0           |       hc0a3c3a_1         3.7 MB  conda-forge\n",
+            "    libstdcxx-devel_linux-64-13.3.0|     h84ea5a7_101        13.4 MB  conda-forge\n",
+            "    libtiff-4.6.0              |       h1dd3fc0_3         276 KB  conda-forge\n",
+            "    libutf8proc-2.8.0          |       h166bdaf_0          99 KB  conda-forge\n",
+            "    libwebp-1.4.0              |       h2c329e2_0          90 KB  conda-forge\n",
+            "    libwebp-base-1.4.0         |       hd590300_0         429 KB  conda-forge\n",
+            "    libxcb-1.15                |       h0b41bf4_0         375 KB  conda-forge\n",
+            "    llvm-openmp-19.1.3         |       h024ca30_0         3.0 MB  conda-forge\n",
+            "    llvmlite-0.42.0            |  py310h1b8f574_1         3.2 MB  conda-forge\n",
+            "    logical-unification-0.4.6  |     pyhd8ed1ab_0          18 KB  conda-forge\n",
+            "    markdown-it-py-3.0.0       |     pyhd8ed1ab_0          63 KB  conda-forge\n",
+            "    matplotlib-base-3.9.2      |  py310h68603db_2         6.7 MB  conda-forge\n",
+            "    mdurl-0.1.2                |     pyhd8ed1ab_0          14 KB  conda-forge\n",
+            "    minikanren-1.0.3           |     pyhd8ed1ab_0          23 KB  conda-forge\n",
+            "    mkl-2024.2.2               |      ha957f24_16       118.9 MB  conda-forge\n",
+            "    mkl-service-2.4.2          |  py310h22455d7_0          70 KB  conda-forge\n",
+            "    multipledispatch-0.6.0     |     pyhd8ed1ab_1          17 KB  conda-forge\n",
+            "    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n",
+            "    numba-0.59.1               |  py310h7dc5dd1_0         4.1 MB  conda-forge\n",
+            "    numpy-1.26.4               |  py310hb13e2d6_0         6.7 MB  conda-forge\n",
+            "    nutpie-0.13.2              |  py310h4a6dfd8_0         1.2 MB  conda-forge\n",
+            "    openblas-0.3.28            |pthreads_h6ec200e_1         5.5 MB  conda-forge\n",
+            "    openjpeg-2.5.2             |       h488ebb8_0         334 KB  conda-forge\n",
+            "    openssl-3.4.0              |       hb9d3cd8_0         2.8 MB  conda-forge\n",
+            "    orc-2.0.1                  |       h17fec99_1        1006 KB  conda-forge\n",
+            "    pandas-2.2.3               |  py310h5eaa309_1        12.4 MB  conda-forge\n",
+            "    pango-1.54.0               |       h84a9a3c_0         438 KB  conda-forge\n",
+            "    pcre2-10.43                |       hcad00b1_0         929 KB  conda-forge\n",
+            "    pillow-10.3.0              |  py310hf73ecf8_0        39.8 MB  conda-forge\n",
+            "    pixman-0.43.2              |       h59595ed_0         378 KB  conda-forge\n",
+            "    pthread-stubs-0.4          |    hb9d3cd8_1002           8 KB  conda-forge\n",
+            "    pyarrow-core-16.1.0        |py310h6f79a3a_1_cpu         4.2 MB  conda-forge\n",
+            "    pygments-2.18.0            |     pyhd8ed1ab_0         859 KB  conda-forge\n",
+            "    pymc-5.18.0                |       hd8ed1ab_0          11 KB  conda-forge\n",
+            "    pymc-base-5.18.0           |     pyhd8ed1ab_0         326 KB  conda-forge\n",
+            "    pyparsing-3.2.0            |     pyhd8ed1ab_1          90 KB  conda-forge\n",
+            "    pytensor-2.25.5            |  py310ha549d7f_0           9 KB  conda-forge\n",
+            "    pytensor-base-2.25.5       |  py310h89e8f5a_0         1.7 MB  conda-forge\n",
+            "    python-dateutil-2.9.0      |     pyhd8ed1ab_0         218 KB  conda-forge\n",
+            "    python-graphviz-0.20.3     |     pyhe28f650_1          37 KB  conda-forge\n",
+            "    python-tzdata-2024.2       |     pyhd8ed1ab_0         139 KB  conda-forge\n",
+            "    pytz-2024.1                |     pyhd8ed1ab_0         184 KB  conda-forge\n",
+            "    qhull-2020.2               |       h434a139_5         540 KB  conda-forge\n",
+            "    re2-2023.09.01             |       h7f4b329_2          26 KB  conda-forge\n",
+            "    rich-13.9.4                |     pyhd8ed1ab_0         181 KB  conda-forge\n",
+            "    s2n-1.4.15                 |       he19d79f_0         341 KB  conda-forge\n",
+            "    scipy-1.14.1               |  py310hfcf56fc_1        16.1 MB  conda-forge\n",
+            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
+            "    snappy-1.2.1               |       ha2e4443_0          41 KB  conda-forge\n",
+            "    sysroot_linux-64-2.17      |      h4a8ded7_18        14.8 MB  conda-forge\n",
+            "    tbb-2021.11.0              |       h00ab1b0_1         191 KB  conda-forge\n",
+            "    threadpoolctl-3.5.0        |     pyhc1e730c_0          23 KB  conda-forge\n",
+            "    toolz-1.0.0                |     pyhd8ed1ab_0          51 KB  conda-forge\n",
+            "    typing-extensions-4.12.2   |       hd8ed1ab_0          10 KB  conda-forge\n",
+            "    typing_extensions-4.12.2   |     pyha770c72_0          39 KB  conda-forge\n",
+            "    unicodedata2-15.1.0        |  py310ha75aee5_1         359 KB  conda-forge\n",
+            "    xarray-2024.10.0           |     pyhd8ed1ab_0         795 KB  conda-forge\n",
+            "    xarray-einstats-0.8.0      |     pyhd8ed1ab_0          33 KB  conda-forge\n",
+            "    xorg-kbproto-1.0.7         |    hb9d3cd8_1003          30 KB  conda-forge\n",
+            "    xorg-libice-1.1.1          |       hb9d3cd8_1          57 KB  conda-forge\n",
+            "    xorg-libsm-1.2.4           |       he73a12e_1          27 KB  conda-forge\n",
+            "    xorg-libx11-1.8.9          |       h8ee46fc_0         809 KB  conda-forge\n",
+            "    xorg-libxau-1.0.11         |       hb9d3cd8_1          14 KB  conda-forge\n",
+            "    xorg-libxdmcp-1.1.5        |       hb9d3cd8_0          19 KB  conda-forge\n",
+            "    xorg-libxext-1.3.4         |       h0b41bf4_2          49 KB  conda-forge\n",
+            "    xorg-libxrender-0.9.11     |       hd590300_0          37 KB  conda-forge\n",
+            "    xorg-renderproto-0.11.1    |    hb9d3cd8_1003          12 KB  conda-forge\n",
+            "    xorg-xextproto-7.3.0       |    hb9d3cd8_1004          30 KB  conda-forge\n",
+            "    xorg-xproto-7.0.31         |    hb9d3cd8_1008          72 KB  conda-forge\n",
+            "    zlib-1.2.13                |       hd590300_5          91 KB  conda-forge\n",
+            "    zstandard-0.23.0           |  py310ha39cb0e_1         399 KB  conda-forge\n",
+            "    zstd-1.5.6                 |       ha6fb4c9_0         542 KB  conda-forge\n",
+            "    ------------------------------------------------------------\n",
+            "                                           Total:       466.7 MB\n",
+            "\n",
+            "The following NEW packages will be INSTALLED:\n",
+            "\n",
+            "  arviz              conda-forge/noarch::arviz-0.20.0-pyhd8ed1ab_0 \n",
+            "  atk-1.0            conda-forge/linux-64::atk-1.0-2.38.0-h04ea711_2 \n",
+            "  aws-c-auth         conda-forge/linux-64::aws-c-auth-0.7.22-h96bc93b_2 \n",
+            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.6.14-h88a6e22_1 \n",
+            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.9.19-h4ab18f5_0 \n",
+            "  aws-c-compression  conda-forge/linux-64::aws-c-compression-0.2.18-h83b837d_6 \n",
+            "  aws-c-event-stream conda-forge/linux-64::aws-c-event-stream-0.4.2-ha47c788_12 \n",
+            "  aws-c-http         conda-forge/linux-64::aws-c-http-0.8.1-h29d6fba_17 \n",
+            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.14.8-h21d4f22_5 \n",
+            "  aws-c-mqtt         conda-forge/linux-64::aws-c-mqtt-0.10.4-h759edc4_4 \n",
+            "  aws-c-s3           conda-forge/linux-64::aws-c-s3-0.5.9-h594631b_3 \n",
+            "  aws-c-sdkutils     conda-forge/linux-64::aws-c-sdkutils-0.1.16-h83b837d_2 \n",
+            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.18-h83b837d_6 \n",
+            "  aws-crt-cpp        conda-forge/linux-64::aws-crt-cpp-0.26.9-he3a8b3b_0 \n",
+            "  aws-sdk-cpp        conda-forge/linux-64::aws-sdk-cpp-1.11.329-hba8bd5f_3 \n",
+            "  binutils_impl_lin~ conda-forge/linux-64::binutils_impl_linux-64-2.40-hf600244_0 \n",
+            "  binutils_linux-64  conda-forge/linux-64::binutils_linux-64-2.40-hb3c18ed_9 \n",
+            "  blas               conda-forge/linux-64::blas-2.125-openblas \n",
+            "  blas-devel         conda-forge/linux-64::blas-devel-3.9.0-25_linux64_openblas \n",
+            "  brotli             conda-forge/linux-64::brotli-1.1.0-hd590300_1 \n",
+            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.1.0-hd590300_1 \n",
+            "  cached-property    conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1 \n",
+            "  cached_property    conda-forge/noarch::cached_property-1.5.2-pyha770c72_1 \n",
+            "  cachetools         conda-forge/noarch::cachetools-5.5.0-pyhd8ed1ab_0 \n",
+            "  cairo              conda-forge/linux-64::cairo-1.18.0-h3faef2a_0 \n",
+            "  cloudpickle        conda-forge/noarch::cloudpickle-3.1.0-pyhd8ed1ab_1 \n",
+            "  cons               conda-forge/noarch::cons-0.4.6-pyhd8ed1ab_0 \n",
+            "  contourpy          conda-forge/linux-64::contourpy-1.3.0-py310h3788b33_2 \n",
+            "  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_0 \n",
+            "  etuples            conda-forge/noarch::etuples-0.3.9-pyhd8ed1ab_0 \n",
+            "  expat              conda-forge/linux-64::expat-2.6.4-h5888daf_0 \n",
+            "  filelock           conda-forge/noarch::filelock-3.16.1-pyhd8ed1ab_0 \n",
+            "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n",
+            "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n",
+            "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n",
+            "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n",
+            "  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n",
+            "  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n",
+            "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n",
+            "  fonttools          conda-forge/linux-64::fonttools-4.54.1-py310h89163eb_1 \n",
+            "  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n",
+            "  fribidi            conda-forge/linux-64::fribidi-1.0.10-h36c2ea0_0 \n",
+            "  gcc                conda-forge/linux-64::gcc-13.3.0-h9576a4e_1 \n",
+            "  gcc_impl_linux-64  conda-forge/linux-64::gcc_impl_linux-64-13.3.0-hfea6d02_1 \n",
+            "  gcc_linux-64       conda-forge/linux-64::gcc_linux-64-13.3.0-hc28eda2_5 \n",
+            "  gdk-pixbuf         conda-forge/linux-64::gdk-pixbuf-2.42.12-hb9ae30d_0 \n",
+            "  gettext            conda-forge/linux-64::gettext-0.22.5-he02047a_3 \n",
+            "  gettext-tools      conda-forge/linux-64::gettext-tools-0.22.5-he02047a_3 \n",
+            "  gflags             conda-forge/linux-64::gflags-2.2.2-h5888daf_1005 \n",
+            "  giflib             conda-forge/linux-64::giflib-5.2.2-hd590300_0 \n",
+            "  glog               conda-forge/linux-64::glog-0.7.1-hbabe93e_0 \n",
+            "  graphite2          conda-forge/linux-64::graphite2-1.3.13-h59595ed_1003 \n",
+            "  graphviz           conda-forge/linux-64::graphviz-9.0.0-h78e8752_1 \n",
+            "  gtk2               conda-forge/linux-64::gtk2-2.24.33-h280cfa0_4 \n",
+            "  gts                conda-forge/linux-64::gts-0.7.6-h977cf35_4 \n",
+            "  gxx                conda-forge/linux-64::gxx-13.3.0-h9576a4e_1 \n",
+            "  gxx_impl_linux-64  conda-forge/linux-64::gxx_impl_linux-64-13.3.0-hdbfa832_1 \n",
+            "  gxx_linux-64       conda-forge/linux-64::gxx_linux-64-13.3.0-h6834431_5 \n",
+            "  h5netcdf           conda-forge/noarch::h5netcdf-1.4.0-pyhd8ed1ab_0 \n",
+            "  h5py               conda-forge/linux-64::h5py-3.12.1-nompi_py310h60e0fe6_102 \n",
+            "  harfbuzz           conda-forge/linux-64::harfbuzz-8.5.0-hfac3d4d_0 \n",
+            "  hdf5               conda-forge/linux-64::hdf5-1.14.3-nompi_hdf9ad27_105 \n",
+            "  kernel-headers_li~ conda-forge/noarch::kernel-headers_linux-64-3.10.0-he073ed8_18 \n",
+            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.7-py310h3788b33_0 \n",
+            "  lcms2              conda-forge/linux-64::lcms2-2.16-hb7c19ff_0 \n",
+            "  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n",
+            "  libabseil          conda-forge/linux-64::libabseil-20240116.2-cxx17_he02047a_1 \n",
+            "  libaec             conda-forge/linux-64::libaec-1.1.3-h59595ed_0 \n",
+            "  libarrow           conda-forge/linux-64::libarrow-16.1.0-hcb6531f_6_cpu \n",
+            "  libasprintf        conda-forge/linux-64::libasprintf-0.22.5-he8f35ee_3 \n",
+            "  libasprintf-devel  conda-forge/linux-64::libasprintf-devel-0.22.5-he8f35ee_3 \n",
+            "  libblas            conda-forge/linux-64::libblas-3.9.0-25_linux64_openblas \n",
+            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.1.0-hd590300_1 \n",
+            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.1.0-hd590300_1 \n",
+            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.1.0-hd590300_1 \n",
+            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-25_linux64_openblas \n",
+            "  libcrc32c          conda-forge/linux-64::libcrc32c-1.1.2-h9c3ff4c_0 \n",
+            "  libdeflate         conda-forge/linux-64::libdeflate-1.20-hd590300_0 \n",
+            "  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n",
+            "  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n",
+            "  libgcc-devel_linu~ conda-forge/noarch::libgcc-devel_linux-64-13.3.0-h84ea5a7_101 \n",
+            "  libgd              conda-forge/linux-64::libgd-2.3.3-h119a65a_9 \n",
+            "  libgettextpo       conda-forge/linux-64::libgettextpo-0.22.5-he02047a_3 \n",
+            "  libgettextpo-devel conda-forge/linux-64::libgettextpo-devel-0.22.5-he02047a_3 \n",
+            "  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n",
+            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-14.2.0-h69a702a_1 \n",
+            "  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n",
+            "  libglib            conda-forge/linux-64::libglib-2.80.2-hf974151_0 \n",
+            "  libgoogle-cloud    conda-forge/linux-64::libgoogle-cloud-2.24.0-h2736e30_0 \n",
+            "  libgoogle-cloud-s~ conda-forge/linux-64::libgoogle-cloud-storage-2.24.0-h3d9a0c8_0 \n",
+            "  libgrpc            conda-forge/linux-64::libgrpc-1.62.2-h15f2491_0 \n",
+            "  libhwloc           conda-forge/linux-64::libhwloc-2.9.3-default_h554bfaf_1009 \n",
+            "  libjpeg-turbo      conda-forge/linux-64::libjpeg-turbo-3.0.0-hd590300_1 \n",
+            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-25_linux64_openblas \n",
+            "  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-25_linux64_openblas \n",
+            "  libllvm14          conda-forge/linux-64::libllvm14-14.0.6-hcd5def8_4 \n",
+            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.28-pthreads_h94d23a6_1 \n",
+            "  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n",
+            "  libprotobuf        conda-forge/linux-64::libprotobuf-4.25.3-h08a7969_0 \n",
+            "  libre2-11          conda-forge/linux-64::libre2-11-2023.09.01-h5a48ba9_2 \n",
+            "  librsvg            conda-forge/linux-64::librsvg-2.56.3-he3f83f7_1 \n",
+            "  libsanitizer       conda-forge/linux-64::libsanitizer-13.3.0-heb74ff8_1 \n",
+            "  libstdcxx          conda-forge/linux-64::libstdcxx-14.2.0-hc0a3c3a_1 \n",
+            "  libstdcxx-devel_l~ conda-forge/noarch::libstdcxx-devel_linux-64-13.3.0-h84ea5a7_101 \n",
+            "  libtiff            conda-forge/linux-64::libtiff-4.6.0-h1dd3fc0_3 \n",
+            "  libutf8proc        conda-forge/linux-64::libutf8proc-2.8.0-h166bdaf_0 \n",
+            "  libwebp            conda-forge/linux-64::libwebp-1.4.0-h2c329e2_0 \n",
+            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.4.0-hd590300_0 \n",
+            "  libxcb             conda-forge/linux-64::libxcb-1.15-h0b41bf4_0 \n",
+            "  llvm-openmp        conda-forge/linux-64::llvm-openmp-19.1.3-h024ca30_0 \n",
+            "  llvmlite           conda-forge/linux-64::llvmlite-0.42.0-py310h1b8f574_1 \n",
+            "  logical-unificati~ conda-forge/noarch::logical-unification-0.4.6-pyhd8ed1ab_0 \n",
+            "  markdown-it-py     conda-forge/noarch::markdown-it-py-3.0.0-pyhd8ed1ab_0 \n",
+            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.9.2-py310h68603db_2 \n",
+            "  mdurl              conda-forge/noarch::mdurl-0.1.2-pyhd8ed1ab_0 \n",
+            "  minikanren         conda-forge/noarch::minikanren-1.0.3-pyhd8ed1ab_0 \n",
+            "  mkl                conda-forge/linux-64::mkl-2024.2.2-ha957f24_16 \n",
+            "  mkl-service        conda-forge/linux-64::mkl-service-2.4.2-py310h22455d7_0 \n",
+            "  multipledispatch   conda-forge/noarch::multipledispatch-0.6.0-pyhd8ed1ab_1 \n",
+            "  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n",
+            "  numba              conda-forge/linux-64::numba-0.59.1-py310h7dc5dd1_0 \n",
+            "  numpy              conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0 \n",
+            "  nutpie             conda-forge/linux-64::nutpie-0.13.2-py310h4a6dfd8_0 \n",
+            "  openblas           conda-forge/linux-64::openblas-0.3.28-pthreads_h6ec200e_1 \n",
+            "  openjpeg           conda-forge/linux-64::openjpeg-2.5.2-h488ebb8_0 \n",
+            "  orc                conda-forge/linux-64::orc-2.0.1-h17fec99_1 \n",
+            "  pandas             conda-forge/linux-64::pandas-2.2.3-py310h5eaa309_1 \n",
+            "  pango              conda-forge/linux-64::pango-1.54.0-h84a9a3c_0 \n",
+            "  pcre2              conda-forge/linux-64::pcre2-10.43-hcad00b1_0 \n",
+            "  pillow             conda-forge/linux-64::pillow-10.3.0-py310hf73ecf8_0 \n",
+            "  pixman             conda-forge/linux-64::pixman-0.43.2-h59595ed_0 \n",
+            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 \n",
+            "  pyarrow-core       conda-forge/linux-64::pyarrow-core-16.1.0-py310h6f79a3a_1_cpu \n",
+            "  pygments           conda-forge/noarch::pygments-2.18.0-pyhd8ed1ab_0 \n",
+            "  pymc               conda-forge/noarch::pymc-5.18.0-hd8ed1ab_0 \n",
+            "  pymc-base          conda-forge/noarch::pymc-base-5.18.0-pyhd8ed1ab_0 \n",
+            "  pyparsing          conda-forge/noarch::pyparsing-3.2.0-pyhd8ed1ab_1 \n",
+            "  pytensor           conda-forge/linux-64::pytensor-2.25.5-py310ha549d7f_0 \n",
+            "  pytensor-base      conda-forge/linux-64::pytensor-base-2.25.5-py310h89e8f5a_0 \n",
+            "  python-dateutil    conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n",
+            "  python-graphviz    conda-forge/noarch::python-graphviz-0.20.3-pyhe28f650_1 \n",
+            "  python-tzdata      conda-forge/noarch::python-tzdata-2024.2-pyhd8ed1ab_0 \n",
+            "  pytz               conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n",
+            "  qhull              conda-forge/linux-64::qhull-2020.2-h434a139_5 \n",
+            "  re2                conda-forge/linux-64::re2-2023.09.01-h7f4b329_2 \n",
+            "  rich               conda-forge/noarch::rich-13.9.4-pyhd8ed1ab_0 \n",
+            "  s2n                conda-forge/linux-64::s2n-1.4.15-he19d79f_0 \n",
+            "  scipy              conda-forge/linux-64::scipy-1.14.1-py310hfcf56fc_1 \n",
+            "  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n",
+            "  snappy             conda-forge/linux-64::snappy-1.2.1-ha2e4443_0 \n",
+            "  sysroot_linux-64   conda-forge/noarch::sysroot_linux-64-2.17-h4a8ded7_18 \n",
+            "  tbb                conda-forge/linux-64::tbb-2021.11.0-h00ab1b0_1 \n",
+            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.5.0-pyhc1e730c_0 \n",
+            "  toolz              conda-forge/noarch::toolz-1.0.0-pyhd8ed1ab_0 \n",
+            "  typing-extensions  conda-forge/noarch::typing-extensions-4.12.2-hd8ed1ab_0 \n",
+            "  typing_extensions  conda-forge/noarch::typing_extensions-4.12.2-pyha770c72_0 \n",
+            "  unicodedata2       conda-forge/linux-64::unicodedata2-15.1.0-py310ha75aee5_1 \n",
+            "  xarray             conda-forge/noarch::xarray-2024.10.0-pyhd8ed1ab_0 \n",
+            "  xarray-einstats    conda-forge/noarch::xarray-einstats-0.8.0-pyhd8ed1ab_0 \n",
+            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-hb9d3cd8_1003 \n",
+            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.1.1-hb9d3cd8_1 \n",
+            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.4-he73a12e_1 \n",
+            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.8.9-h8ee46fc_0 \n",
+            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.11-hb9d3cd8_1 \n",
+            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n",
+            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h0b41bf4_2 \n",
+            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.11-hd590300_0 \n",
+            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-hb9d3cd8_1003 \n",
+            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-hb9d3cd8_1004 \n",
+            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-hb9d3cd8_1008 \n",
+            "  zlib               conda-forge/linux-64::zlib-1.2.13-hd590300_5 \n",
+            "\n",
+            "The following packages will be UPDATED:\n",
+            "\n",
+            "  c-ares                                  1.24.0-hd590300_0 --> 1.34.3-heb4867d_0 \n",
+            "  ca-certificates                     2023.11.17-hbcca054_0 --> 2024.8.30-hbcca054_0 \n",
+            "  certifi                           2023.11.17-pyhd8ed1ab_0 --> 2024.8.30-pyhd8ed1ab_0 \n",
+            "  libcurl                                  8.5.0-hca28451_0 --> 8.8.0-hca28451_0 \n",
+            "  libgcc-ng                               13.2.0-h807b86a_3 --> 14.2.0-h69a702a_1 \n",
+            "  libgomp                                 13.2.0-h807b86a_3 --> 14.2.0-h77fa898_1 \n",
+            "  openssl                                  3.2.0-hd590300_1 --> 3.4.0-hb9d3cd8_0 \n",
+            "  zstandard                          0.22.0-py310h1275a96_0 --> 0.23.0-py310ha39cb0e_1 \n",
+            "  zstd                                     1.5.5-hfc55251_0 --> 1.5.6-ha6fb4c9_0 \n",
+            "\n",
+            "The following packages will be DOWNGRADED:\n",
+            "\n",
+            "  _openmp_mutex                                   4.5-2_gnu --> 4.5-2_kmp_llvm \n",
+            "\n",
+            "\n",
+            "\n",
+            "Downloading and Extracting Packages:\n",
+            "mkl-2024.2.2         | 118.9 MB  | :   0% 0/1 [00:00 WARNING: A newer version of conda exists. <==\n",
+            "    current version: 23.11.0\n",
+            "    latest version: 24.9.2\n",
+            "\n",
+            "Please update conda by running\n",
+            "\n",
+            "    $ conda update -n base -c conda-forge conda\n",
+            "\n",
+            "\n",
+            "\n",
+            "## Package Plan ##\n",
+            "\n",
+            "  environment location: /usr/local\n",
+            "\n",
+            "  added / updated specs:\n",
+            "    - cpuonly\n",
+            "    - pytorch\n",
+            "    - torchaudio\n",
+            "    - torchvision\n",
+            "\n",
+            "\n",
+            "The following packages will be downloaded:\n",
+            "\n",
+            "    package                    |            build\n",
+            "    ---------------------------|-----------------\n",
+            "    blas-2.116                 |              mkl          13 KB  conda-forge\n",
+            "    blas-devel-3.9.0           |   16_linux64_mkl          12 KB  conda-forge\n",
+            "    cpuonly-2.0                |                0           2 KB  pytorch\n",
+            "    ffmpeg-4.3                 |       hf484d3e_0         9.9 MB  pytorch\n",
+            "    gmp-6.3.0                  |       hac33072_2         449 KB  conda-forge\n",
+            "    gmpy2-2.1.5                |  py310he8512ff_2         199 KB  conda-forge\n",
+            "    gnutls-3.6.13              |       h85f3911_1         2.0 MB  conda-forge\n",
+            "    jinja2-3.1.4               |     pyhd8ed1ab_0         109 KB  conda-forge\n",
+            "    lame-3.100                 |    h166bdaf_1003         496 KB  conda-forge\n",
+            "    libblas-3.9.0              |   16_linux64_mkl          13 KB  conda-forge\n",
+            "    libcblas-3.9.0             |   16_linux64_mkl          12 KB  conda-forge\n",
+            "    liblapack-3.9.0            |   16_linux64_mkl          12 KB  conda-forge\n",
+            "    liblapacke-3.9.0           |   16_linux64_mkl          12 KB  conda-forge\n",
+            "    llvm-openmp-15.0.7         |       h0cdce71_0         3.1 MB  conda-forge\n",
+            "    markupsafe-3.0.2           |  py310h89163eb_0          22 KB  conda-forge\n",
+            "    mkl-2022.1.0               |     h84fe81f_915       199.6 MB  conda-forge\n",
+            "    mkl-devel-2022.1.0         |     ha770c72_916          25 KB  conda-forge\n",
+            "    mkl-include-2022.1.0       |     h84fe81f_915         745 KB  conda-forge\n",
+            "    mkl-service-2.4.0          |  py310h9263142_0          56 KB  conda-forge\n",
+            "    mpc-1.3.1                  |       h24ddda3_1         114 KB  conda-forge\n",
+            "    mpfr-4.2.1                 |       h90cbb55_3         620 KB  conda-forge\n",
+            "    mpmath-1.3.0               |     pyhd8ed1ab_0         428 KB  conda-forge\n",
+            "    nettle-3.6                 |       he412f7d_0         6.5 MB  conda-forge\n",
+            "    networkx-3.4.2             |     pyhd8ed1ab_1         1.1 MB  conda-forge\n",
+            "    openh264-2.1.1             |       h780b84a_0         1.5 MB  conda-forge\n",
+            "    pytorch-2.5.1              |     py3.10_cpu_0        88.1 MB  pytorch\n",
+            "    pytorch-mutex-1.0          |              cpu           3 KB  pytorch\n",
+            "    pyyaml-6.0.2               |  py310ha75aee5_1         178 KB  conda-forge\n",
+            "    sympy-1.13.3               | pypyh2585a3b_103         4.4 MB  conda-forge\n",
+            "    torchaudio-2.5.1           |        py310_cpu         5.0 MB  pytorch\n",
+            "    torchvision-0.20.1         |        py310_cpu         6.9 MB  pytorch\n",
+            "    yaml-0.2.5                 |       h7f98852_2          87 KB  conda-forge\n",
+            "    ------------------------------------------------------------\n",
+            "                                           Total:       331.6 MB\n",
+            "\n",
+            "The following NEW packages will be INSTALLED:\n",
+            "\n",
+            "  cpuonly            pytorch/noarch::cpuonly-2.0-0 \n",
+            "  ffmpeg             pytorch/linux-64::ffmpeg-4.3-hf484d3e_0 \n",
+            "  gmp                conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n",
+            "  gmpy2              conda-forge/linux-64::gmpy2-2.1.5-py310he8512ff_2 \n",
+            "  gnutls             conda-forge/linux-64::gnutls-3.6.13-h85f3911_1 \n",
+            "  jinja2             conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n",
+            "  lame               conda-forge/linux-64::lame-3.100-h166bdaf_1003 \n",
+            "  markupsafe         conda-forge/linux-64::markupsafe-3.0.2-py310h89163eb_0 \n",
+            "  mkl-devel          conda-forge/linux-64::mkl-devel-2022.1.0-ha770c72_916 \n",
+            "  mkl-include        conda-forge/linux-64::mkl-include-2022.1.0-h84fe81f_915 \n",
+            "  mpc                conda-forge/linux-64::mpc-1.3.1-h24ddda3_1 \n",
+            "  mpfr               conda-forge/linux-64::mpfr-4.2.1-h90cbb55_3 \n",
+            "  mpmath             conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_0 \n",
+            "  nettle             conda-forge/linux-64::nettle-3.6-he412f7d_0 \n",
+            "  networkx           conda-forge/noarch::networkx-3.4.2-pyhd8ed1ab_1 \n",
+            "  openh264           conda-forge/linux-64::openh264-2.1.1-h780b84a_0 \n",
+            "  pytorch            pytorch/linux-64::pytorch-2.5.1-py3.10_cpu_0 \n",
+            "  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cpu \n",
+            "  pyyaml             conda-forge/linux-64::pyyaml-6.0.2-py310ha75aee5_1 \n",
+            "  sympy              conda-forge/noarch::sympy-1.13.3-pypyh2585a3b_103 \n",
+            "  torchaudio         pytorch/linux-64::torchaudio-2.5.1-py310_cpu \n",
+            "  torchvision        pytorch/linux-64::torchvision-0.20.1-py310_cpu \n",
+            "  yaml               conda-forge/linux-64::yaml-0.2.5-h7f98852_2 \n",
+            "\n",
+            "The following packages will be DOWNGRADED:\n",
+            "\n",
+            "  blas                                       2.125-openblas --> 2.116-mkl \n",
+            "  blas-devel                      3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
+            "  libblas                         3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
+            "  libcblas                        3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
+            "  liblapack                       3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
+            "  liblapacke                      3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
+            "  llvm-openmp                             19.1.3-h024ca30_0 --> 15.0.7-h0cdce71_0 \n",
+            "  mkl                                  2024.2.2-ha957f24_16 --> 2022.1.0-h84fe81f_915 \n",
+            "  mkl-service                         2.4.2-py310h22455d7_0 --> 2.4.0-py310h9263142_0 \n",
+            "\n",
+            "\n",
+            "\n",
+            "Downloading and Extracting Packages:\n",
+            "mkl-2022.1.0         | 199.6 MB  | :   0% 0/1 [00:00
Date: Tue, 12 Nov 2024 15:19:22 +0100
Subject: [PATCH 52/68] Created using Colab

---
 notebooks/pcntk_colab_env.ipynb | 94 +++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 notebooks/pcntk_colab_env.ipynb

diff --git a/notebooks/pcntk_colab_env.ipynb b/notebooks/pcntk_colab_env.ipynb
new file mode 100644
index 00000000..0dc879ef
--- /dev/null
+++ b/notebooks/pcntk_colab_env.ipynb
@@ -0,0 +1,94 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPhmRD2D0nfPuaeSc4uS/Q+",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Install condacolab, which will enable us to install using conda\n",
+        "## Outcomment this cell after the kernel has automatically restarted\n",
+        "import os\n",
+        "os.environ['PYTHONPATH'] = \"\"  # Need to clear the pythonpath to avoid some errors\n",
+        "!echo \"pythonpath: $PYTHONPATH\"\n",
+        "!pip install -q condacolab\n",
+        "import condacolab\n",
+        "condacolab.install()"
+      ],
+      "metadata": {
+        "id": "WtVP1BD3eKjV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check if condacolab works\n",
+        "import condacolab\n",
+        "condacolab.check()\n",
+        "\n",
+        "# Install libraries with optimized dependencies (BLAS and Rust) using conda\n",
+        "!conda install pymc numba nutpie -c conda-forge\n",
+        "\n",
+        "# Install CPU only verion of torch using conda (go to https://pytorch.org/get-started/locally/ if you need another version for your platform)\n",
+        "!conda install pytorch torchvision torchaudio cpuonly -c pytorch\n",
+        "\n",
+        "# # Install the pcntoolkit\n",
+        "!pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
+      ],
+      "metadata": {
+        "id": "cb4xm679kxnE",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pcntoolkit as ptk\n",
+        "\n",
+        "### If you get an error about a cffi version mismatch, bright the site-packages version of cffi up to the dist-packages version using the following line\n",
+        "# !pip install --upgrade cffi==1.17.1 # works if dist-packages version is 1.17.1"
+      ],
+      "metadata": {
+        "id": "AAtKC6emj8Rn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "JZlmznTwoH_t"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 887a972d510e2934131438972a64843b23483ca0 Mon Sep 17 00:00:00 2001
From: Stijn de Boer <19709783+AuguB@users.noreply.github.com>
Date: Tue, 12 Nov 2024 15:46:41 +0100
Subject: [PATCH 53/68] Created using Colab

---
 notebooks/pcntk_colab_dev_env.ipynb | 4558 +--------------------------
 1 file changed, 114 insertions(+), 4444 deletions(-)

diff --git a/notebooks/pcntk_colab_dev_env.ipynb b/notebooks/pcntk_colab_dev_env.ipynb
index 82b831e6..6d5f5408 100644
--- a/notebooks/pcntk_colab_dev_env.ipynb
+++ b/notebooks/pcntk_colab_dev_env.ipynb
@@ -4,8 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "name": "pcntk_colab_dev_env.ipynb",
-      "authorship_tag": "ABX9TyPhmRD2D0nfPuaeSc4uS/Q+",
+      "authorship_tag": "ABX9TyNfYdKn7+C4d4WSym/CFRMQ",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -30,4469 +29,140 @@
     {
       "cell_type": "code",
       "source": [
-        "## Install condacolab, which will enable us to install using conda\n",
-        "## Outcomment this cell after the kernel has automatically restarted\n",
-        "import os\n",
-        "os.environ['PYTHONPATH'] = \"\"  # Need to clear the pythonpath to avoid some errors\n",
-        "!echo \"pythonpath: $PYTHONPATH\"\n",
-        "!pip install -q condacolab\n",
-        "import condacolab\n",
-        "condacolab.install()"
+        "!pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip\n",
+        "!pip install nutpie"
       ],
       "metadata": {
-        "id": "WtVP1BD3eKjV",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "6e4a9a86-d761-4bf8-80fa-6ed748d9000a"
+        "id": "vIbnkHN9ydb3"
       },
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "pythonpath: \n",
-            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.11.0-0/Mambaforge-23.11.0-0-Linux-x86_64.sh...\n",
-            "📦 Installing...\n",
-            "📌 Adjusting configuration...\n",
-            "🩹 Patching environment...\n",
-            "⏲ Done in 0:00:16\n",
-            "🔁 Restarting kernel...\n"
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "# Check if condacolab works\n",
-        "import condacolab\n",
-        "condacolab.check()\n",
+        "# %%\n",
+        "from warnings import filterwarnings\n",
         "\n",
-        "# Install libraries with optimized dependencies (BLAS and Rust) using conda\n",
-        "!conda install pymc numba nutpie -c conda-forge\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
         "\n",
-        "# Install CPU only verion of torch using conda (go to https://pytorch.org/get-started/locally/ if you need another version for your platform)\n",
-        "!conda install pytorch torchvision torchaudio cpuonly -c pytorch\n",
+        "from pcntoolkit.normative_model.norm_utils import norm_init\n",
+        "from pcntoolkit.util.utils import simulate_data\n",
+        "\n",
+        "filterwarnings(\"ignore\")\n",
+        "\n",
+        "\n",
+        "########################### Experiment Settings ###############################\n",
         "\n",
-        "# # Install the pcntoolkit\n",
-        "!pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cb4xm679kxnE",
-        "outputId": "0985fdde-0fe6-44f6-cb29-d38e393f9010",
-        "collapsed": true
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Channels:\n",
-            " - conda-forge\n",
-            "Platform: linux-64\n",
-            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
-            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
-            "\n",
-            "\n",
-            "==> WARNING: A newer version of conda exists. <==\n",
-            "    current version: 23.11.0\n",
-            "    latest version: 24.9.2\n",
-            "\n",
-            "Please update conda by running\n",
-            "\n",
-            "    $ conda update -n base -c conda-forge conda\n",
-            "\n",
-            "\n",
-            "\n",
-            "## Package Plan ##\n",
-            "\n",
-            "  environment location: /usr/local\n",
-            "\n",
-            "  added / updated specs:\n",
-            "    - numba\n",
-            "    - nutpie\n",
-            "    - pymc\n",
-            "\n",
-            "\n",
-            "The following packages will be downloaded:\n",
-            "\n",
-            "    package                    |            build\n",
-            "    ---------------------------|-----------------\n",
-            "    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n",
-            "    arviz-0.20.0               |     pyhd8ed1ab_0         1.4 MB  conda-forge\n",
-            "    atk-1.0-2.38.0             |       h04ea711_2         348 KB  conda-forge\n",
-            "    aws-c-auth-0.7.22          |       h96bc93b_2         103 KB  conda-forge\n",
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-            "    aws-c-common-0.9.19        |       h4ab18f5_0         221 KB  conda-forge\n",
-            "    aws-c-compression-0.2.18   |       h83b837d_6          19 KB  conda-forge\n",
-            "    aws-c-event-stream-0.4.2   |      ha47c788_12          53 KB  conda-forge\n",
-            "    aws-c-http-0.8.1           |      h29d6fba_17         190 KB  conda-forge\n",
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-            "    llvm-openmp-19.1.3         |       h024ca30_0         3.0 MB  conda-forge\n",
-            "    llvmlite-0.42.0            |  py310h1b8f574_1         3.2 MB  conda-forge\n",
-            "    logical-unification-0.4.6  |     pyhd8ed1ab_0          18 KB  conda-forge\n",
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-            "    mkl-service-2.4.2          |  py310h22455d7_0          70 KB  conda-forge\n",
-            "    multipledispatch-0.6.0     |     pyhd8ed1ab_1          17 KB  conda-forge\n",
-            "    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n",
-            "    numba-0.59.1               |  py310h7dc5dd1_0         4.1 MB  conda-forge\n",
-            "    numpy-1.26.4               |  py310hb13e2d6_0         6.7 MB  conda-forge\n",
-            "    nutpie-0.13.2              |  py310h4a6dfd8_0         1.2 MB  conda-forge\n",
-            "    openblas-0.3.28            |pthreads_h6ec200e_1         5.5 MB  conda-forge\n",
-            "    openjpeg-2.5.2             |       h488ebb8_0         334 KB  conda-forge\n",
-            "    openssl-3.4.0              |       hb9d3cd8_0         2.8 MB  conda-forge\n",
-            "    orc-2.0.1                  |       h17fec99_1        1006 KB  conda-forge\n",
-            "    pandas-2.2.3               |  py310h5eaa309_1        12.4 MB  conda-forge\n",
-            "    pango-1.54.0               |       h84a9a3c_0         438 KB  conda-forge\n",
-            "    pcre2-10.43                |       hcad00b1_0         929 KB  conda-forge\n",
-            "    pillow-10.3.0              |  py310hf73ecf8_0        39.8 MB  conda-forge\n",
-            "    pixman-0.43.2              |       h59595ed_0         378 KB  conda-forge\n",
-            "    pthread-stubs-0.4          |    hb9d3cd8_1002           8 KB  conda-forge\n",
-            "    pyarrow-core-16.1.0        |py310h6f79a3a_1_cpu         4.2 MB  conda-forge\n",
-            "    pygments-2.18.0            |     pyhd8ed1ab_0         859 KB  conda-forge\n",
-            "    pymc-5.18.0                |       hd8ed1ab_0          11 KB  conda-forge\n",
-            "    pymc-base-5.18.0           |     pyhd8ed1ab_0         326 KB  conda-forge\n",
-            "    pyparsing-3.2.0            |     pyhd8ed1ab_1          90 KB  conda-forge\n",
-            "    pytensor-2.25.5            |  py310ha549d7f_0           9 KB  conda-forge\n",
-            "    pytensor-base-2.25.5       |  py310h89e8f5a_0         1.7 MB  conda-forge\n",
-            "    python-dateutil-2.9.0      |     pyhd8ed1ab_0         218 KB  conda-forge\n",
-            "    python-graphviz-0.20.3     |     pyhe28f650_1          37 KB  conda-forge\n",
-            "    python-tzdata-2024.2       |     pyhd8ed1ab_0         139 KB  conda-forge\n",
-            "    pytz-2024.1                |     pyhd8ed1ab_0         184 KB  conda-forge\n",
-            "    qhull-2020.2               |       h434a139_5         540 KB  conda-forge\n",
-            "    re2-2023.09.01             |       h7f4b329_2          26 KB  conda-forge\n",
-            "    rich-13.9.4                |     pyhd8ed1ab_0         181 KB  conda-forge\n",
-            "    s2n-1.4.15                 |       he19d79f_0         341 KB  conda-forge\n",
-            "    scipy-1.14.1               |  py310hfcf56fc_1        16.1 MB  conda-forge\n",
-            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
-            "    snappy-1.2.1               |       ha2e4443_0          41 KB  conda-forge\n",
-            "    sysroot_linux-64-2.17      |      h4a8ded7_18        14.8 MB  conda-forge\n",
-            "    tbb-2021.11.0              |       h00ab1b0_1         191 KB  conda-forge\n",
-            "    threadpoolctl-3.5.0        |     pyhc1e730c_0          23 KB  conda-forge\n",
-            "    toolz-1.0.0                |     pyhd8ed1ab_0          51 KB  conda-forge\n",
-            "    typing-extensions-4.12.2   |       hd8ed1ab_0          10 KB  conda-forge\n",
-            "    typing_extensions-4.12.2   |     pyha770c72_0          39 KB  conda-forge\n",
-            "    unicodedata2-15.1.0        |  py310ha75aee5_1         359 KB  conda-forge\n",
-            "    xarray-2024.10.0           |     pyhd8ed1ab_0         795 KB  conda-forge\n",
-            "    xarray-einstats-0.8.0      |     pyhd8ed1ab_0          33 KB  conda-forge\n",
-            "    xorg-kbproto-1.0.7         |    hb9d3cd8_1003          30 KB  conda-forge\n",
-            "    xorg-libice-1.1.1          |       hb9d3cd8_1          57 KB  conda-forge\n",
-            "    xorg-libsm-1.2.4           |       he73a12e_1          27 KB  conda-forge\n",
-            "    xorg-libx11-1.8.9          |       h8ee46fc_0         809 KB  conda-forge\n",
-            "    xorg-libxau-1.0.11         |       hb9d3cd8_1          14 KB  conda-forge\n",
-            "    xorg-libxdmcp-1.1.5        |       hb9d3cd8_0          19 KB  conda-forge\n",
-            "    xorg-libxext-1.3.4         |       h0b41bf4_2          49 KB  conda-forge\n",
-            "    xorg-libxrender-0.9.11     |       hd590300_0          37 KB  conda-forge\n",
-            "    xorg-renderproto-0.11.1    |    hb9d3cd8_1003          12 KB  conda-forge\n",
-            "    xorg-xextproto-7.3.0       |    hb9d3cd8_1004          30 KB  conda-forge\n",
-            "    xorg-xproto-7.0.31         |    hb9d3cd8_1008          72 KB  conda-forge\n",
-            "    zlib-1.2.13                |       hd590300_5          91 KB  conda-forge\n",
-            "    zstandard-0.23.0           |  py310ha39cb0e_1         399 KB  conda-forge\n",
-            "    zstd-1.5.6                 |       ha6fb4c9_0         542 KB  conda-forge\n",
-            "    ------------------------------------------------------------\n",
-            "                                           Total:       466.7 MB\n",
-            "\n",
-            "The following NEW packages will be INSTALLED:\n",
-            "\n",
-            "  arviz              conda-forge/noarch::arviz-0.20.0-pyhd8ed1ab_0 \n",
-            "  atk-1.0            conda-forge/linux-64::atk-1.0-2.38.0-h04ea711_2 \n",
-            "  aws-c-auth         conda-forge/linux-64::aws-c-auth-0.7.22-h96bc93b_2 \n",
-            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.6.14-h88a6e22_1 \n",
-            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.9.19-h4ab18f5_0 \n",
-            "  aws-c-compression  conda-forge/linux-64::aws-c-compression-0.2.18-h83b837d_6 \n",
-            "  aws-c-event-stream conda-forge/linux-64::aws-c-event-stream-0.4.2-ha47c788_12 \n",
-            "  aws-c-http         conda-forge/linux-64::aws-c-http-0.8.1-h29d6fba_17 \n",
-            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.14.8-h21d4f22_5 \n",
-            "  aws-c-mqtt         conda-forge/linux-64::aws-c-mqtt-0.10.4-h759edc4_4 \n",
-            "  aws-c-s3           conda-forge/linux-64::aws-c-s3-0.5.9-h594631b_3 \n",
-            "  aws-c-sdkutils     conda-forge/linux-64::aws-c-sdkutils-0.1.16-h83b837d_2 \n",
-            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.18-h83b837d_6 \n",
-            "  aws-crt-cpp        conda-forge/linux-64::aws-crt-cpp-0.26.9-he3a8b3b_0 \n",
-            "  aws-sdk-cpp        conda-forge/linux-64::aws-sdk-cpp-1.11.329-hba8bd5f_3 \n",
-            "  binutils_impl_lin~ conda-forge/linux-64::binutils_impl_linux-64-2.40-hf600244_0 \n",
-            "  binutils_linux-64  conda-forge/linux-64::binutils_linux-64-2.40-hb3c18ed_9 \n",
-            "  blas               conda-forge/linux-64::blas-2.125-openblas \n",
-            "  blas-devel         conda-forge/linux-64::blas-devel-3.9.0-25_linux64_openblas \n",
-            "  brotli             conda-forge/linux-64::brotli-1.1.0-hd590300_1 \n",
-            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.1.0-hd590300_1 \n",
-            "  cached-property    conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1 \n",
-            "  cached_property    conda-forge/noarch::cached_property-1.5.2-pyha770c72_1 \n",
-            "  cachetools         conda-forge/noarch::cachetools-5.5.0-pyhd8ed1ab_0 \n",
-            "  cairo              conda-forge/linux-64::cairo-1.18.0-h3faef2a_0 \n",
-            "  cloudpickle        conda-forge/noarch::cloudpickle-3.1.0-pyhd8ed1ab_1 \n",
-            "  cons               conda-forge/noarch::cons-0.4.6-pyhd8ed1ab_0 \n",
-            "  contourpy          conda-forge/linux-64::contourpy-1.3.0-py310h3788b33_2 \n",
-            "  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_0 \n",
-            "  etuples            conda-forge/noarch::etuples-0.3.9-pyhd8ed1ab_0 \n",
-            "  expat              conda-forge/linux-64::expat-2.6.4-h5888daf_0 \n",
-            "  filelock           conda-forge/noarch::filelock-3.16.1-pyhd8ed1ab_0 \n",
-            "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n",
-            "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n",
-            "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n",
-            "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n",
-            "  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n",
-            "  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n",
-            "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n",
-            "  fonttools          conda-forge/linux-64::fonttools-4.54.1-py310h89163eb_1 \n",
-            "  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n",
-            "  fribidi            conda-forge/linux-64::fribidi-1.0.10-h36c2ea0_0 \n",
-            "  gcc                conda-forge/linux-64::gcc-13.3.0-h9576a4e_1 \n",
-            "  gcc_impl_linux-64  conda-forge/linux-64::gcc_impl_linux-64-13.3.0-hfea6d02_1 \n",
-            "  gcc_linux-64       conda-forge/linux-64::gcc_linux-64-13.3.0-hc28eda2_5 \n",
-            "  gdk-pixbuf         conda-forge/linux-64::gdk-pixbuf-2.42.12-hb9ae30d_0 \n",
-            "  gettext            conda-forge/linux-64::gettext-0.22.5-he02047a_3 \n",
-            "  gettext-tools      conda-forge/linux-64::gettext-tools-0.22.5-he02047a_3 \n",
-            "  gflags             conda-forge/linux-64::gflags-2.2.2-h5888daf_1005 \n",
-            "  giflib             conda-forge/linux-64::giflib-5.2.2-hd590300_0 \n",
-            "  glog               conda-forge/linux-64::glog-0.7.1-hbabe93e_0 \n",
-            "  graphite2          conda-forge/linux-64::graphite2-1.3.13-h59595ed_1003 \n",
-            "  graphviz           conda-forge/linux-64::graphviz-9.0.0-h78e8752_1 \n",
-            "  gtk2               conda-forge/linux-64::gtk2-2.24.33-h280cfa0_4 \n",
-            "  gts                conda-forge/linux-64::gts-0.7.6-h977cf35_4 \n",
-            "  gxx                conda-forge/linux-64::gxx-13.3.0-h9576a4e_1 \n",
-            "  gxx_impl_linux-64  conda-forge/linux-64::gxx_impl_linux-64-13.3.0-hdbfa832_1 \n",
-            "  gxx_linux-64       conda-forge/linux-64::gxx_linux-64-13.3.0-h6834431_5 \n",
-            "  h5netcdf           conda-forge/noarch::h5netcdf-1.4.0-pyhd8ed1ab_0 \n",
-            "  h5py               conda-forge/linux-64::h5py-3.12.1-nompi_py310h60e0fe6_102 \n",
-            "  harfbuzz           conda-forge/linux-64::harfbuzz-8.5.0-hfac3d4d_0 \n",
-            "  hdf5               conda-forge/linux-64::hdf5-1.14.3-nompi_hdf9ad27_105 \n",
-            "  kernel-headers_li~ conda-forge/noarch::kernel-headers_linux-64-3.10.0-he073ed8_18 \n",
-            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.7-py310h3788b33_0 \n",
-            "  lcms2              conda-forge/linux-64::lcms2-2.16-hb7c19ff_0 \n",
-            "  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n",
-            "  libabseil          conda-forge/linux-64::libabseil-20240116.2-cxx17_he02047a_1 \n",
-            "  libaec             conda-forge/linux-64::libaec-1.1.3-h59595ed_0 \n",
-            "  libarrow           conda-forge/linux-64::libarrow-16.1.0-hcb6531f_6_cpu \n",
-            "  libasprintf        conda-forge/linux-64::libasprintf-0.22.5-he8f35ee_3 \n",
-            "  libasprintf-devel  conda-forge/linux-64::libasprintf-devel-0.22.5-he8f35ee_3 \n",
-            "  libblas            conda-forge/linux-64::libblas-3.9.0-25_linux64_openblas \n",
-            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.1.0-hd590300_1 \n",
-            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.1.0-hd590300_1 \n",
-            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.1.0-hd590300_1 \n",
-            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-25_linux64_openblas \n",
-            "  libcrc32c          conda-forge/linux-64::libcrc32c-1.1.2-h9c3ff4c_0 \n",
-            "  libdeflate         conda-forge/linux-64::libdeflate-1.20-hd590300_0 \n",
-            "  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n",
-            "  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n",
-            "  libgcc-devel_linu~ conda-forge/noarch::libgcc-devel_linux-64-13.3.0-h84ea5a7_101 \n",
-            "  libgd              conda-forge/linux-64::libgd-2.3.3-h119a65a_9 \n",
-            "  libgettextpo       conda-forge/linux-64::libgettextpo-0.22.5-he02047a_3 \n",
-            "  libgettextpo-devel conda-forge/linux-64::libgettextpo-devel-0.22.5-he02047a_3 \n",
-            "  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n",
-            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-14.2.0-h69a702a_1 \n",
-            "  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n",
-            "  libglib            conda-forge/linux-64::libglib-2.80.2-hf974151_0 \n",
-            "  libgoogle-cloud    conda-forge/linux-64::libgoogle-cloud-2.24.0-h2736e30_0 \n",
-            "  libgoogle-cloud-s~ conda-forge/linux-64::libgoogle-cloud-storage-2.24.0-h3d9a0c8_0 \n",
-            "  libgrpc            conda-forge/linux-64::libgrpc-1.62.2-h15f2491_0 \n",
-            "  libhwloc           conda-forge/linux-64::libhwloc-2.9.3-default_h554bfaf_1009 \n",
-            "  libjpeg-turbo      conda-forge/linux-64::libjpeg-turbo-3.0.0-hd590300_1 \n",
-            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-25_linux64_openblas \n",
-            "  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-25_linux64_openblas \n",
-            "  libllvm14          conda-forge/linux-64::libllvm14-14.0.6-hcd5def8_4 \n",
-            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.28-pthreads_h94d23a6_1 \n",
-            "  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n",
-            "  libprotobuf        conda-forge/linux-64::libprotobuf-4.25.3-h08a7969_0 \n",
-            "  libre2-11          conda-forge/linux-64::libre2-11-2023.09.01-h5a48ba9_2 \n",
-            "  librsvg            conda-forge/linux-64::librsvg-2.56.3-he3f83f7_1 \n",
-            "  libsanitizer       conda-forge/linux-64::libsanitizer-13.3.0-heb74ff8_1 \n",
-            "  libstdcxx          conda-forge/linux-64::libstdcxx-14.2.0-hc0a3c3a_1 \n",
-            "  libstdcxx-devel_l~ conda-forge/noarch::libstdcxx-devel_linux-64-13.3.0-h84ea5a7_101 \n",
-            "  libtiff            conda-forge/linux-64::libtiff-4.6.0-h1dd3fc0_3 \n",
-            "  libutf8proc        conda-forge/linux-64::libutf8proc-2.8.0-h166bdaf_0 \n",
-            "  libwebp            conda-forge/linux-64::libwebp-1.4.0-h2c329e2_0 \n",
-            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.4.0-hd590300_0 \n",
-            "  libxcb             conda-forge/linux-64::libxcb-1.15-h0b41bf4_0 \n",
-            "  llvm-openmp        conda-forge/linux-64::llvm-openmp-19.1.3-h024ca30_0 \n",
-            "  llvmlite           conda-forge/linux-64::llvmlite-0.42.0-py310h1b8f574_1 \n",
-            "  logical-unificati~ conda-forge/noarch::logical-unification-0.4.6-pyhd8ed1ab_0 \n",
-            "  markdown-it-py     conda-forge/noarch::markdown-it-py-3.0.0-pyhd8ed1ab_0 \n",
-            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.9.2-py310h68603db_2 \n",
-            "  mdurl              conda-forge/noarch::mdurl-0.1.2-pyhd8ed1ab_0 \n",
-            "  minikanren         conda-forge/noarch::minikanren-1.0.3-pyhd8ed1ab_0 \n",
-            "  mkl                conda-forge/linux-64::mkl-2024.2.2-ha957f24_16 \n",
-            "  mkl-service        conda-forge/linux-64::mkl-service-2.4.2-py310h22455d7_0 \n",
-            "  multipledispatch   conda-forge/noarch::multipledispatch-0.6.0-pyhd8ed1ab_1 \n",
-            "  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n",
-            "  numba              conda-forge/linux-64::numba-0.59.1-py310h7dc5dd1_0 \n",
-            "  numpy              conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0 \n",
-            "  nutpie             conda-forge/linux-64::nutpie-0.13.2-py310h4a6dfd8_0 \n",
-            "  openblas           conda-forge/linux-64::openblas-0.3.28-pthreads_h6ec200e_1 \n",
-            "  openjpeg           conda-forge/linux-64::openjpeg-2.5.2-h488ebb8_0 \n",
-            "  orc                conda-forge/linux-64::orc-2.0.1-h17fec99_1 \n",
-            "  pandas             conda-forge/linux-64::pandas-2.2.3-py310h5eaa309_1 \n",
-            "  pango              conda-forge/linux-64::pango-1.54.0-h84a9a3c_0 \n",
-            "  pcre2              conda-forge/linux-64::pcre2-10.43-hcad00b1_0 \n",
-            "  pillow             conda-forge/linux-64::pillow-10.3.0-py310hf73ecf8_0 \n",
-            "  pixman             conda-forge/linux-64::pixman-0.43.2-h59595ed_0 \n",
-            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 \n",
-            "  pyarrow-core       conda-forge/linux-64::pyarrow-core-16.1.0-py310h6f79a3a_1_cpu \n",
-            "  pygments           conda-forge/noarch::pygments-2.18.0-pyhd8ed1ab_0 \n",
-            "  pymc               conda-forge/noarch::pymc-5.18.0-hd8ed1ab_0 \n",
-            "  pymc-base          conda-forge/noarch::pymc-base-5.18.0-pyhd8ed1ab_0 \n",
-            "  pyparsing          conda-forge/noarch::pyparsing-3.2.0-pyhd8ed1ab_1 \n",
-            "  pytensor           conda-forge/linux-64::pytensor-2.25.5-py310ha549d7f_0 \n",
-            "  pytensor-base      conda-forge/linux-64::pytensor-base-2.25.5-py310h89e8f5a_0 \n",
-            "  python-dateutil    conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n",
-            "  python-graphviz    conda-forge/noarch::python-graphviz-0.20.3-pyhe28f650_1 \n",
-            "  python-tzdata      conda-forge/noarch::python-tzdata-2024.2-pyhd8ed1ab_0 \n",
-            "  pytz               conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n",
-            "  qhull              conda-forge/linux-64::qhull-2020.2-h434a139_5 \n",
-            "  re2                conda-forge/linux-64::re2-2023.09.01-h7f4b329_2 \n",
-            "  rich               conda-forge/noarch::rich-13.9.4-pyhd8ed1ab_0 \n",
-            "  s2n                conda-forge/linux-64::s2n-1.4.15-he19d79f_0 \n",
-            "  scipy              conda-forge/linux-64::scipy-1.14.1-py310hfcf56fc_1 \n",
-            "  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n",
-            "  snappy             conda-forge/linux-64::snappy-1.2.1-ha2e4443_0 \n",
-            "  sysroot_linux-64   conda-forge/noarch::sysroot_linux-64-2.17-h4a8ded7_18 \n",
-            "  tbb                conda-forge/linux-64::tbb-2021.11.0-h00ab1b0_1 \n",
-            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.5.0-pyhc1e730c_0 \n",
-            "  toolz              conda-forge/noarch::toolz-1.0.0-pyhd8ed1ab_0 \n",
-            "  typing-extensions  conda-forge/noarch::typing-extensions-4.12.2-hd8ed1ab_0 \n",
-            "  typing_extensions  conda-forge/noarch::typing_extensions-4.12.2-pyha770c72_0 \n",
-            "  unicodedata2       conda-forge/linux-64::unicodedata2-15.1.0-py310ha75aee5_1 \n",
-            "  xarray             conda-forge/noarch::xarray-2024.10.0-pyhd8ed1ab_0 \n",
-            "  xarray-einstats    conda-forge/noarch::xarray-einstats-0.8.0-pyhd8ed1ab_0 \n",
-            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-hb9d3cd8_1003 \n",
-            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.1.1-hb9d3cd8_1 \n",
-            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.4-he73a12e_1 \n",
-            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.8.9-h8ee46fc_0 \n",
-            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.11-hb9d3cd8_1 \n",
-            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n",
-            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h0b41bf4_2 \n",
-            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.11-hd590300_0 \n",
-            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-hb9d3cd8_1003 \n",
-            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-hb9d3cd8_1004 \n",
-            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-hb9d3cd8_1008 \n",
-            "  zlib               conda-forge/linux-64::zlib-1.2.13-hd590300_5 \n",
-            "\n",
-            "The following packages will be UPDATED:\n",
-            "\n",
-            "  c-ares                                  1.24.0-hd590300_0 --> 1.34.3-heb4867d_0 \n",
-            "  ca-certificates                     2023.11.17-hbcca054_0 --> 2024.8.30-hbcca054_0 \n",
-            "  certifi                           2023.11.17-pyhd8ed1ab_0 --> 2024.8.30-pyhd8ed1ab_0 \n",
-            "  libcurl                                  8.5.0-hca28451_0 --> 8.8.0-hca28451_0 \n",
-            "  libgcc-ng                               13.2.0-h807b86a_3 --> 14.2.0-h69a702a_1 \n",
-            "  libgomp                                 13.2.0-h807b86a_3 --> 14.2.0-h77fa898_1 \n",
-            "  openssl                                  3.2.0-hd590300_1 --> 3.4.0-hb9d3cd8_0 \n",
-            "  zstandard                          0.22.0-py310h1275a96_0 --> 0.23.0-py310ha39cb0e_1 \n",
-            "  zstd                                     1.5.5-hfc55251_0 --> 1.5.6-ha6fb4c9_0 \n",
-            "\n",
-            "The following packages will be DOWNGRADED:\n",
-            "\n",
-            "  _openmp_mutex                                   4.5-2_gnu --> 4.5-2_kmp_llvm \n",
-            "\n",
-            "\n",
-            "\n",
-            "Downloading and Extracting Packages:\n",
-            "mkl-2024.2.2         | 118.9 MB  | :   0% 0/1 [00:00 WARNING: A newer version of conda exists. <==\n",
-            "    current version: 23.11.0\n",
-            "    latest version: 24.9.2\n",
-            "\n",
-            "Please update conda by running\n",
-            "\n",
-            "    $ conda update -n base -c conda-forge conda\n",
-            "\n",
-            "\n",
-            "\n",
-            "## Package Plan ##\n",
-            "\n",
-            "  environment location: /usr/local\n",
-            "\n",
-            "  added / updated specs:\n",
-            "    - cpuonly\n",
-            "    - pytorch\n",
-            "    - torchaudio\n",
-            "    - torchvision\n",
-            "\n",
-            "\n",
-            "The following packages will be downloaded:\n",
-            "\n",
-            "    package                    |            build\n",
-            "    ---------------------------|-----------------\n",
-            "    blas-2.116                 |              mkl          13 KB  conda-forge\n",
-            "    blas-devel-3.9.0           |   16_linux64_mkl          12 KB  conda-forge\n",
-            "    cpuonly-2.0                |                0           2 KB  pytorch\n",
-            "    ffmpeg-4.3                 |       hf484d3e_0         9.9 MB  pytorch\n",
-            "    gmp-6.3.0                  |       hac33072_2         449 KB  conda-forge\n",
-            "    gmpy2-2.1.5                |  py310he8512ff_2         199 KB  conda-forge\n",
-            "    gnutls-3.6.13              |       h85f3911_1         2.0 MB  conda-forge\n",
-            "    jinja2-3.1.4               |     pyhd8ed1ab_0         109 KB  conda-forge\n",
-            "    lame-3.100                 |    h166bdaf_1003         496 KB  conda-forge\n",
-            "    libblas-3.9.0              |   16_linux64_mkl          13 KB  conda-forge\n",
-            "    libcblas-3.9.0             |   16_linux64_mkl          12 KB  conda-forge\n",
-            "    liblapack-3.9.0            |   16_linux64_mkl          12 KB  conda-forge\n",
-            "    liblapacke-3.9.0           |   16_linux64_mkl          12 KB  conda-forge\n",
-            "    llvm-openmp-15.0.7         |       h0cdce71_0         3.1 MB  conda-forge\n",
-            "    markupsafe-3.0.2           |  py310h89163eb_0          22 KB  conda-forge\n",
-            "    mkl-2022.1.0               |     h84fe81f_915       199.6 MB  conda-forge\n",
-            "    mkl-devel-2022.1.0         |     ha770c72_916          25 KB  conda-forge\n",
-            "    mkl-include-2022.1.0       |     h84fe81f_915         745 KB  conda-forge\n",
-            "    mkl-service-2.4.0          |  py310h9263142_0          56 KB  conda-forge\n",
-            "    mpc-1.3.1                  |       h24ddda3_1         114 KB  conda-forge\n",
-            "    mpfr-4.2.1                 |       h90cbb55_3         620 KB  conda-forge\n",
-            "    mpmath-1.3.0               |     pyhd8ed1ab_0         428 KB  conda-forge\n",
-            "    nettle-3.6                 |       he412f7d_0         6.5 MB  conda-forge\n",
-            "    networkx-3.4.2             |     pyhd8ed1ab_1         1.1 MB  conda-forge\n",
-            "    openh264-2.1.1             |       h780b84a_0         1.5 MB  conda-forge\n",
-            "    pytorch-2.5.1              |     py3.10_cpu_0        88.1 MB  pytorch\n",
-            "    pytorch-mutex-1.0          |              cpu           3 KB  pytorch\n",
-            "    pyyaml-6.0.2               |  py310ha75aee5_1         178 KB  conda-forge\n",
-            "    sympy-1.13.3               | pypyh2585a3b_103         4.4 MB  conda-forge\n",
-            "    torchaudio-2.5.1           |        py310_cpu         5.0 MB  pytorch\n",
-            "    torchvision-0.20.1         |        py310_cpu         6.9 MB  pytorch\n",
-            "    yaml-0.2.5                 |       h7f98852_2          87 KB  conda-forge\n",
-            "    ------------------------------------------------------------\n",
-            "                                           Total:       331.6 MB\n",
-            "\n",
-            "The following NEW packages will be INSTALLED:\n",
-            "\n",
-            "  cpuonly            pytorch/noarch::cpuonly-2.0-0 \n",
-            "  ffmpeg             pytorch/linux-64::ffmpeg-4.3-hf484d3e_0 \n",
-            "  gmp                conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n",
-            "  gmpy2              conda-forge/linux-64::gmpy2-2.1.5-py310he8512ff_2 \n",
-            "  gnutls             conda-forge/linux-64::gnutls-3.6.13-h85f3911_1 \n",
-            "  jinja2             conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n",
-            "  lame               conda-forge/linux-64::lame-3.100-h166bdaf_1003 \n",
-            "  markupsafe         conda-forge/linux-64::markupsafe-3.0.2-py310h89163eb_0 \n",
-            "  mkl-devel          conda-forge/linux-64::mkl-devel-2022.1.0-ha770c72_916 \n",
-            "  mkl-include        conda-forge/linux-64::mkl-include-2022.1.0-h84fe81f_915 \n",
-            "  mpc                conda-forge/linux-64::mpc-1.3.1-h24ddda3_1 \n",
-            "  mpfr               conda-forge/linux-64::mpfr-4.2.1-h90cbb55_3 \n",
-            "  mpmath             conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_0 \n",
-            "  nettle             conda-forge/linux-64::nettle-3.6-he412f7d_0 \n",
-            "  networkx           conda-forge/noarch::networkx-3.4.2-pyhd8ed1ab_1 \n",
-            "  openh264           conda-forge/linux-64::openh264-2.1.1-h780b84a_0 \n",
-            "  pytorch            pytorch/linux-64::pytorch-2.5.1-py3.10_cpu_0 \n",
-            "  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cpu \n",
-            "  pyyaml             conda-forge/linux-64::pyyaml-6.0.2-py310ha75aee5_1 \n",
-            "  sympy              conda-forge/noarch::sympy-1.13.3-pypyh2585a3b_103 \n",
-            "  torchaudio         pytorch/linux-64::torchaudio-2.5.1-py310_cpu \n",
-            "  torchvision        pytorch/linux-64::torchvision-0.20.1-py310_cpu \n",
-            "  yaml               conda-forge/linux-64::yaml-0.2.5-h7f98852_2 \n",
-            "\n",
-            "The following packages will be DOWNGRADED:\n",
-            "\n",
-            "  blas                                       2.125-openblas --> 2.116-mkl \n",
-            "  blas-devel                      3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
-            "  libblas                         3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
-            "  libcblas                        3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
-            "  liblapack                       3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
-            "  liblapacke                      3.9.0-25_linux64_openblas --> 3.9.0-16_linux64_mkl \n",
-            "  llvm-openmp                             19.1.3-h024ca30_0 --> 15.0.7-h0cdce71_0 \n",
-            "  mkl                                  2024.2.2-ha957f24_16 --> 2022.1.0-h84fe81f_915 \n",
-            "  mkl-service                         2.4.2-py310h22455d7_0 --> 2.4.0-py310h9263142_0 \n",
-            "\n",
-            "\n",
-            "\n",
-            "Downloading and Extracting Packages:\n",
-            "mkl-2022.1.0         | 199.6 MB  | :   0% 0/1 [00:00
Date: Tue, 12 Nov 2024 15:56:33 +0100
Subject: [PATCH 54/68] Created using Colab

---
 notebooks/pcntk_colab_env.ipynb | 263 +++++++++++++++++++++++++++-----
 1 file changed, 228 insertions(+), 35 deletions(-)

diff --git a/notebooks/pcntk_colab_env.ipynb b/notebooks/pcntk_colab_env.ipynb
index 0dc879ef..9b5ae128 100644
--- a/notebooks/pcntk_colab_env.ipynb
+++ b/notebooks/pcntk_colab_env.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPhmRD2D0nfPuaeSc4uS/Q+",
+      "authorship_tag": "ABX9TyNpr04OpO9Ta6c7GFsTKsic",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -13,6 +13,89 @@
     },
     "language_info": {
       "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "76210a49be1144d58d8ed3b02aadbe72": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_a868d75a2c2849bca3edd651767b54f1",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "Sampling chain 0, 0 divergences \u001b[38;2;23;100;244m━━━━━━━━━\u001b[0m\u001b[38;2;23;100;244m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 25%\u001b[0m \u001b[36m0:32:20\u001b[0m / \u001b[33m0:06:35\u001b[0m\n",
+                  "text/html": "
Sampling chain 0, 0 divergences ━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  25% 0:32:20 / 0:06:35\n
\n"
+                },
+                "metadata": {}
+              }
+            ]
+          }
+        },
+        "a868d75a2c2849bca3edd651767b54f1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        }
+      }
     }
   },
   "cells": [
@@ -29,14 +112,7 @@
     {
       "cell_type": "code",
       "source": [
-        "## Install condacolab, which will enable us to install using conda\n",
-        "## Outcomment this cell after the kernel has automatically restarted\n",
-        "import os\n",
-        "os.environ['PYTHONPATH'] = \"\"  # Need to clear the pythonpath to avoid some errors\n",
-        "!echo \"pythonpath: $PYTHONPATH\"\n",
-        "!pip install -q condacolab\n",
-        "import condacolab\n",
-        "condacolab.install()"
+        "!pip install pcntoolkit"
       ],
       "metadata": {
         "id": "WtVP1BD3eKjV"
@@ -47,45 +123,162 @@
     {
       "cell_type": "code",
       "source": [
-        "# Check if condacolab works\n",
-        "import condacolab\n",
-        "condacolab.check()\n",
+        "# %%\n",
+        "from warnings import filterwarnings\n",
         "\n",
-        "# Install libraries with optimized dependencies (BLAS and Rust) using conda\n",
-        "!conda install pymc numba nutpie -c conda-forge\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
         "\n",
-        "# Install CPU only verion of torch using conda (go to https://pytorch.org/get-started/locally/ if you need another version for your platform)\n",
-        "!conda install pytorch torchvision torchaudio cpuonly -c pytorch\n",
+        "from pcntoolkit.normative_model.norm_utils import norm_init\n",
+        "from pcntoolkit.util.utils import simulate_data\n",
+        "\n",
+        "filterwarnings(\"ignore\")\n",
+        "\n",
+        "\n",
+        "########################### Experiment Settings ###############################\n",
         "\n",
-        "# # Install the pcntoolkit\n",
-        "!pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
-      ],
-      "metadata": {
-        "id": "cb4xm679kxnE",
-        "collapsed": true
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pcntoolkit as ptk\n",
         "\n",
-        "### If you get an error about a cffi version mismatch, bright the site-packages version of cffi up to the dist-packages version using the following line\n",
-        "# !pip install --upgrade cffi==1.17.1 # works if dist-packages version is 1.17.1"
+        "random_state = 40\n",
+        "working_dir = \"temp\"  # Specify a working directory to save data and results.\n",
+        "os.makedirs(working_dir, exist_ok=True)\n",
+        "simulation_method = \"linear\"\n",
+        "n_features = 1  # The number of input features of X\n",
+        "n_grps = 3  # Number of batches in data\n",
+        "n_samples = 500  # Number of samples in each group (use a list for different\n",
+        "# sample numbers across different batches)\n",
+        "\n",
+        "model_type = \"bspline\"  # modelto try 'linear, ''polynomial', 'bspline'\n",
+        "\n",
+        "\n",
+        "############################## Data Simulation ################################\n",
+        "\n",
+        "\n",
+        "X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef = simulate_data(\n",
+        "    simulation_method,\n",
+        "    n_samples,\n",
+        "    n_features,\n",
+        "    n_grps,\n",
+        "    working_dir=working_dir,\n",
+        "    plot=True,\n",
+        "    noise=\"heteroscedastic_nongaussian\",\n",
+        "    random_state=random_state,\n",
+        ")\n",
+        "\n",
+        "################################# Fittig and Predicting ###############################\n",
+        "\n",
+        "nm = norm_init(\n",
+        "    X_train,\n",
+        "    Y_train,\n",
+        "    alg=\"hbr\",\n",
+        "    model_type=model_type,\n",
+        "    likelihood=\"SHASHb\",\n",
+        "    linear_sigma=\"True\",\n",
+        "    random_slope_mu=\"False\",\n",
+        "    linear_epsilon=\"False\",\n",
+        "    linear_delta=\"False\",\n",
+        "    nuts_sampler=\"nutpie\",\n",
+        ")\n",
+        "\n",
+        "nm.estimate(X_train, Y_train, trbefile=os.path.join(working_dir, \"trbefile.pkl\"))\n",
+        "yhat, ys2 = nm.predict(X_test, tsbefile=os.path.join(working_dir, \"tsbefile.pkl\"))\n",
+        "\n",
+        "\n",
+        "################################# Plotting Quantiles ###############################\n",
+        "for i in range(n_features):\n",
+        "    sorted_idx = X_test[:, i].argsort(axis=0).squeeze()\n",
+        "    temp_X = X_test[sorted_idx, i]\n",
+        "    temp_Y = Y_test[sorted_idx,]\n",
+        "    temp_be = grp_id_test[sorted_idx, :].squeeze()\n",
+        "    temp_yhat = yhat[sorted_idx,]\n",
+        "    temp_s2 = ys2[sorted_idx,]\n",
+        "\n",
+        "    plt.figure()\n",
+        "    for j in range(n_grps):\n",
+        "        scat1 = plt.scatter(\n",
+        "            temp_X[temp_be == j,], temp_Y[temp_be == j,], label=\"Group\" + str(j)\n",
+        "        )\n",
+        "        # Showing the quantiles\n",
+        "        resolution = 200\n",
+        "        synth_X = np.linspace(np.min(X_train), np.max(X_train), resolution)\n",
+        "        q = nm.get_mcmc_quantiles(synth_X, batch_effects=j * np.ones(resolution))\n",
+        "        col = scat1.get_facecolors()[0]\n",
+        "        plt.plot(synth_X, q.T, linewidth=1, color=col, zorder=0)\n",
+        "\n",
+        "    plt.title(\"Model %s, Feature %d\" % (model_type, i))\n",
+        "    plt.legend()\n",
+        "    plt.show(block=False)\n",
+        "    plt.savefig(working_dir + \"quantiles_\" + model_type + \"_feature_\" + str(i) + \".png\")\n",
+        "\n",
+        "    for j in range(n_grps):\n",
+        "        plt.figure()\n",
+        "        plt.scatter(temp_X[temp_be == j,], temp_Y[temp_be == j,])\n",
+        "        plt.plot(temp_X[temp_be == j,], temp_yhat[temp_be == j,], color=\"red\")\n",
+        "        plt.fill_between(\n",
+        "            temp_X[temp_be == j,].squeeze(),\n",
+        "            (temp_yhat[temp_be == j,] - 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),\n",
+        "            (temp_yhat[temp_be == j,] + 2 * np.sqrt(temp_s2[temp_be == j,])).squeeze(),\n",
+        "            color=\"red\",\n",
+        "            alpha=0.2,\n",
+        "        )\n",
+        "        plt.title(\"Model %s, Group %d, Feature %d\" % (model_type, j, i))\n",
+        "        plt.show(block=False)\n",
+        "        plt.savefig(\n",
+        "            working_dir\n",
+        "            + \"pred_\"\n",
+        "            + model_type\n",
+        "            + \"_group_\"\n",
+        "            + str(j)\n",
+        "            + \"_feature_\"\n",
+        "            + str(i)\n",
+        "            + \".png\"\n",
+        "        )"
       ],
       "metadata": {
-        "id": "AAtKC6emj8Rn"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129,
+          "referenced_widgets": [
+            "76210a49be1144d58d8ed3b02aadbe72",
+            "a868d75a2c2849bca3edd651767b54f1"
+          ]
+        },
+        "id": "CuoBYP1g2I20",
+        "outputId": "ca033c81-ab9b-4cda-938d-73e19c7360ca"
       },
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pytensor/tensor/random/op.py:84: FutureWarning: ndim_supp is deprecated. Provide signature instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/pytensor/tensor/random/op.py:94: FutureWarning: ndims_params is deprecated. Provide signature instead.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "76210a49be1144d58d8ed3b02aadbe72"
+            }
+          },
+          "metadata": {}
+        }
+      ]
     },
     {
       "cell_type": "code",
       "source": [],
       "metadata": {
-        "id": "JZlmznTwoH_t"
+        "id": "L9gKL8bz2Jj4"
       },
       "execution_count": null,
       "outputs": []

From 06ad20dc77c8e60b7c677a15633e562a6bb6af6b Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Wed, 13 Nov 2024 18:02:21 +0100
Subject: [PATCH 55/68] Add nicer entrypoint for normative and basic cli
 testing

---
 pcntoolkit/normative.py      | 66 +++++++++++++++++------------------
 pcntoolkit/util/utils.py     |  4 +--
 pyproject.toml               |  4 ++-
 tests/cli_test/split_data.py | 67 ++++++++++++++++++++++++++++++++++++
 tests/cli_test/test_cli.sh   | 18 ++++++++++
 tests/profile_trendsurf.py   |  5 +--
 tests/test_normative.py      |  7 ++--
 7 files changed, 128 insertions(+), 43 deletions(-)
 create mode 100644 tests/cli_test/split_data.py
 create mode 100755 tests/cli_test/test_cli.sh

diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py
index 9d885315..a46a3942 100755
--- a/pcntoolkit/normative.py
+++ b/pcntoolkit/normative.py
@@ -110,71 +110,62 @@ def get_args(*args):
     :returns configparam: Parameters controlling the estimation algorithm
     :returns kw_args: Additional keyword arguments
     """
-
+    args = args[0][0]
+    print(args)
     # parse arguments
     parser = argparse.ArgumentParser(description="Normative Modeling")
-    parser.add_argument("responses")
-    parser.add_argument("-f", help="Function to call", dest="func",
-                        default="estimate")
+    parser.add_argument("respfile", help="Response variables for the normative model")
+    parser.add_argument("-f", help="Function to call", dest="func", default="estimate")
     parser.add_argument("-m", help="mask file", dest="maskfile", default=None)
-    parser.add_argument("-c", help="covariates file", dest="covfile",
-                        default=None)
-    parser.add_argument("-k", help="cross-validation folds", dest="cvfolds",
-                        default=None)
-    parser.add_argument("-t", help="covariates (test data)", dest="testcov",
-                        default=None)
-    parser.add_argument("-r", help="responses (test data)", dest="testresp",
-                        default=None)
+    parser.add_argument("-c", help="covariates file", dest="covfile", default=None)
+    parser.add_argument("-k", help="cross-validation folds", dest="cvfolds", default=None)
+    parser.add_argument("-t", help="covariates (test data)", dest="testcov", default=None)
+    parser.add_argument("-r", help="responses (test data)", dest="testresp", default=None)
     parser.add_argument("-a", help="algorithm", dest="alg", default="gpr")
-    parser.add_argument("-x", help="algorithm specific config options",
-                        dest="configparam", default=None)
-    # parser.add_argument('-s', action='store_false',
-    #                 help="Flag to skip standardization.", dest="standardize")
-    parser.add_argument("keyword_args", nargs=argparse.REMAINDER)
-
-    args = parser.parse_args()
+    parser.add_argument("-x", help="algorithm specific config options", dest="configparam", default=None)
+    parsed_args, keyword_args = parser.parse_known_args(args)
 
-    # Process required  arguemnts
+    # Process required arguments
     wdir = os.path.realpath(os.path.curdir)
-    respfile = os.path.join(wdir, args.responses)
-    if args.covfile is None:
+    respfile = os.path.join(wdir, parsed_args.respfile)
+    if parsed_args.covfile is None:
         raise ValueError("No covariates specified")
     else:
-        covfile = args.covfile
+        covfile = parsed_args.covfile
 
     # Process optional arguments
-    if args.maskfile is None:
+    if parsed_args.maskfile is None:
         maskfile = None
     else:
-        maskfile = os.path.join(wdir, args.maskfile)
-    if args.testcov is None and args.cvfolds is not None:
+        maskfile = os.path.join(wdir, parsed_args.maskfile)
+    if parsed_args.testcov is None and parsed_args.cvfolds is not None:
         testcov = None
         testresp = None
-        cvfolds = int(args.cvfolds)
+        cvfolds = int(parsed_args.cvfolds)
         print("Running under " + str(cvfolds) + " fold cross-validation.")
     else:
         print("Test covariates specified")
-        testcov = args.testcov
+        testcov = parsed_args.testcov
         cvfolds = None
-        if args.testresp is None:
+        if parsed_args.testresp is None:
             testresp = None
             print("No test response variables specified")
         else:
-            testresp = args.testresp
-        if args.cvfolds is not None:
+            testresp = parsed_args.testresp
+        if parsed_args.cvfolds is not None:
             print("Ignoring cross-valdation specification (test data given)")
 
     # Process addtional keyword arguments. These are always added as strings
     kw_args = {}
-    for kw in args.keyword_args:
+    for kw in keyword_args:
         kw_arg = kw.split('=')
 
         exec("kw_args.update({'" + kw_arg[0] + "' : " +
              "'" + str(kw_arg[1]) + "'" + "})")
 
     return respfile, maskfile, covfile, cvfolds, \
-        testcov, testresp, args.func, args.alg, \
-        args.configparam, kw_args
+        testcov, testresp, parsed_args.func, parsed_args.alg, \
+        parsed_args.configparam, kw_args
 
 
 def evaluate(Y, Yhat, S2=None, mY=None, sY=None, nlZ=None, nm=None, Xz_tr=None, alg=None,
@@ -387,7 +378,9 @@ def estimate(covfile, respfile, **kwargs):
     # '_' is in the outputsuffix to
     # avoid file name parsing problem.
     inscaler = kwargs.pop('inscaler', 'None')
+    print(f"inscaler: {inscaler}")
     outscaler = kwargs.pop('outscaler', 'None')
+    print(f"outscaler: {outscaler}")
     warp = kwargs.get('warp', None)
 
     # convert from strings if necessary
@@ -670,6 +663,8 @@ def fit(covfile, respfile, **kwargs):
     outputsuffix = "_" + outputsuffix.replace("_", "")
     inscaler = kwargs.pop('inscaler', 'None')
     outscaler = kwargs.pop('outscaler', 'None')
+    print(f"inscaler: {inscaler}")
+    print(f"outscaler: {outscaler}")
 
     if savemodel and not os.path.isdir('Models'):
         os.mkdir('Models')
@@ -1546,6 +1541,9 @@ def main(*args):
     # Executing the target function
     exec(func + '(' + all_args + ')')
 
+def entrypoint():
+    main(sys.argv[1:])
+
 
 # For running from the command line:
 if __name__ == "__main__":
diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py
index 6e49becc..f04ae211 100644
--- a/pcntoolkit/util/utils.py
+++ b/pcntoolkit/util/utils.py
@@ -126,7 +126,7 @@ def create_design_matrix(X, intercept=True, basis='bspline',
 
     N = X.shape[0]
 
-    if type(X) is pd.DataFrame:
+    if isinstance(X, pd.DataFrame):
         X = X.to_numpy()
 
     # add intercept column
@@ -149,7 +149,7 @@ def create_design_matrix(X, intercept=True, basis='bspline',
     else:
         # site ids are defined
         # make sure the data are in pandas format
-        if type(site_ids) is not pd.Series:
+        if not isinstance(site_ids, pd.Series):
             site_ids = pd.Series(data=site_ids)
         # site_ids = pd.Series(data=site_ids)
 
diff --git a/pyproject.toml b/pyproject.toml
index 6981a939..9a082e4a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,9 @@ ipywidgets = "^8.1.5"
 black = "^24.10.0"
 ipykernel = "^6.29.5"
 
-
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+normative = "pcntoolkit.normative:entrypoint"
diff --git a/tests/cli_test/split_data.py b/tests/cli_test/split_data.py
new file mode 100644
index 00000000..b522700a
--- /dev/null
+++ b/tests/cli_test/split_data.py
@@ -0,0 +1,67 @@
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+# Import train_test_split from sklearn
+from sklearn.model_selection import train_test_split
+
+# Import the StandardScaler from sklearn
+from sklearn.preprocessing import StandardScaler
+
+from pcntoolkit.util.utils import create_design_matrix
+
+
+def main():
+
+    np.random.seed(42)
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_file", type=str, required=True)
+    parser.add_argument("--output_dir", type=str, required=True)
+    args = parser.parse_args()
+
+    print(f"Splitting the data located at {args.input_file} into train and test covariates, responses and batch effects...")
+    df = pd.read_csv(args.input_file)
+    cov = df['age']
+    resp = df[['SubCortGrayVol','Left-Hippocampus','Brain-Stem','CSF']]
+    be = df['site']
+
+    # Standardize the covariates and responses
+    cov = StandardScaler().fit_transform(cov.to_numpy()[:,np.newaxis])
+    resp = StandardScaler().fit_transform(resp.to_numpy())
+
+    # Map the batch effects to integers
+    be_ids = np.unique(be, return_inverse=True)[1]
+
+    # Get the min and max of the covariates
+    xmin = min(cov)
+    xmax = max(cov)
+
+    # Split the data into training and test sets
+    train_idx, test_idx = train_test_split(np.arange(len(cov)), test_size=0.2, random_state=42, stratify=be_ids)
+
+
+    # Create the design matrices
+    mean_basis = 'linear'
+    var_basis = 'linear'
+    Phi_tr = create_design_matrix(cov[train_idx], basis=mean_basis, xmin=xmin, xmax=xmax, intercept=False, site_ids=be_ids[train_idx])
+    Phi_var_tr = create_design_matrix(cov[train_idx], basis=var_basis, xmin=xmin, xmax=xmax, intercept=False)
+    Phi_te = create_design_matrix(cov[test_idx], basis=mean_basis, xmin=xmin, xmax=xmax, intercept=False, site_ids=be_ids[test_idx])
+    Phi_var_te = create_design_matrix(cov[test_idx], basis=var_basis, xmin=xmin, xmax=xmax, intercept=False)
+
+    # Save everything
+    pd.to_pickle(pd.DataFrame(Phi_tr), os.path.join(args.output_dir, f'X_tr_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_var_tr), os.path.join(args.output_dir, f'X_var_tr_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_te), os.path.join(args.output_dir, f'X_te_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_var_te), os.path.join(args.output_dir, f'X_var_te_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(resp[train_idx]), os.path.join(args.output_dir, f'Y_tr_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(resp[test_idx]), os.path.join(args.output_dir, f'Y_te_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(be[train_idx], os.path.join(args.output_dir, f'be_tr_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(be[test_idx], os.path.join(args.output_dir, f'be_te_{args.input_file.split("/")[-1]}.pkl'))
+
+    print(f"Done! The files can be found in: {args.output_dir}")
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/cli_test/test_cli.sh b/tests/cli_test/test_cli.sh
new file mode 100755
index 00000000..6390fe3d
--- /dev/null
+++ b/tests/cli_test/test_cli.sh
@@ -0,0 +1,18 @@
+# Assign the current directory to a variable
+export testdir=$(pwd)
+export tempdir="$testdir/temp"
+mkdir $tempdir
+echo $tempdir
+export data_name="fcon1000"
+export model_config="-a blr warp=WarpSinArcsinh optimizer=l-bfgs-b warp_reparam=True"
+echo "Downloading the data..."
+curl -o $tempdir/$data_name https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/refs/heads/main/data/$data_name.csv
+echo "Splitting the data into train and test covariates, responses and batch effects..."
+python split_data.py --input_file $tempdir/$data_name --output_dir $tempdir
+echo "Fitting the model..."
+normative $tempdir/Y_tr_$data_name.pkl -c $tempdir/X_tr_$data_name.pkl -f fit $model_config
+echo "Predicting the test set..."
+normative $tempdir/Y_te_$data_name.pkl -c $tempdir/X_te_$data_name.pkl -f predict $model_config inputsuffix=fit outputsuffix=predict
+echo "Also doing estimate..."
+normative $tempdir/Y_tr_$data_name.pkl -c $tempdir/X_tr_$data_name.pkl -f estimate $model_config -t $tempdir/X_te_$data_name.pkl -r $tempdir/Y_te_$data_name.pkl outputsuffix=estimate
+echo "Done!"
diff --git a/tests/profile_trendsurf.py b/tests/profile_trendsurf.py
index 3877122f..3e0efefa 100644
--- a/tests/profile_trendsurf.py
+++ b/tests/profile_trendsurf.py
@@ -1,11 +1,12 @@
 # NOTE: must be run with kernprof (otherwise the inmports get screwed up)
 
 # import pcntoolkit
+import os
+import sys
+
 from bayesreg import BLR
 from line_profiler import LineProfiler
 from trendsurf import estimate
-import os
-import sys
 
 sys.path.append('/home/preclineu/andmar/sfw/PCNtoolkit/pcntoolkit')
 
diff --git a/tests/test_normative.py b/tests/test_normative.py
index 666bce49..4a4b8e29 100644
--- a/tests/test_normative.py
+++ b/tests/test_normative.py
@@ -4,12 +4,11 @@
 
 @author: andmar
 """
-# import pcntoolkit
-from normative import estimate
+from pcntoolkit.normative import estimate
 import os
 import sys
 # from pcntoolkit.normative import estimate
-sys.path.append('/home/preclineu/andmar/sfw/PCNtoolkit/pcntoolkit')
+# sys.path.append('/home/preclineu/andmar/sfw/PCNtoolkit/pcntoolkit')
 
 # wdir = '/home/mrstats/andmar/py.sandbox/normative_nimg'
 # wdir = '/Users/andre/data/normative_nimg'
@@ -26,7 +25,7 @@
 covfile = os.path.join(wdir, 'covariates_basic_n500.txt')
 testresp = os.path.join(wdir, 'shoot_data_3mm_last100.nii.gz')
 testcov = os.path.join(wdir, 'covariates_basic_last100.txt')
-estimate(covfile, respfile, maskfile=maskfile, testresp=testresp,
+estimate(covfile=covfile, respfile=respfile, maskfile=maskfile, testresp=testresp,
          testcov=testcov, alg="blr")  # , configparam=4)
 # cvfolds = 2
 

From 48a796b555718eec803b1657f56f19c91d31f093 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 14 Nov 2024 10:34:55 +0100
Subject: [PATCH 56/68] Set read and write permissions in test_cli script

---
 tests/cli_test/test_cli.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/cli_test/test_cli.sh b/tests/cli_test/test_cli.sh
index 6390fe3d..d46e4195 100755
--- a/tests/cli_test/test_cli.sh
+++ b/tests/cli_test/test_cli.sh
@@ -2,7 +2,7 @@
 export testdir=$(pwd)
 export tempdir="$testdir/temp"
 mkdir $tempdir
-echo $tempdir
+chmod -R 766 $tempdir
 export data_name="fcon1000"
 export model_config="-a blr warp=WarpSinArcsinh optimizer=l-bfgs-b warp_reparam=True"
 echo "Downloading the data..."

From 33b746c722b02fd2880c5432cf0d928e8f401d87 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 14 Nov 2024 11:00:42 +0100
Subject: [PATCH 57/68] Remove print statement in normative

---
 pcntoolkit/normative.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py
index a46a3942..436f6064 100755
--- a/pcntoolkit/normative.py
+++ b/pcntoolkit/normative.py
@@ -111,7 +111,6 @@ def get_args(*args):
     :returns kw_args: Additional keyword arguments
     """
     args = args[0][0]
-    print(args)
     # parse arguments
     parser = argparse.ArgumentParser(description="Normative Modeling")
     parser.add_argument("respfile", help="Response variables for the normative model")

From cabfb5aeb99cca1cbcd442e10b0c32f8ffe66171 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 14 Nov 2024 14:39:01 +0100
Subject: [PATCH 58/68] Fix bug in create_design_matrix

---
 pcntoolkit/util/utils.py     | 30 +++++++++++++-------------
 tests/cli_test/split_data.py | 41 +++++++++++++++++++-----------------
 tests/cli_test/test_cli.sh   |  3 +++
 3 files changed, 40 insertions(+), 34 deletions(-)

diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py
index f04ae211..19c6f553 100644
--- a/pcntoolkit/util/utils.py
+++ b/pcntoolkit/util/utils.py
@@ -1,26 +1,26 @@
 from __future__ import print_function
 
 import os
+import pickle
+import re
+import subprocess
 import sys
-import numpy as np
-from scipy import stats
-from subprocess import call
-from scipy.stats import genextreme, norm, skewnorm
-from six import with_metaclass
 from abc import ABCMeta, abstractmethod
-import pickle
+from io import StringIO
+from subprocess import call
+
+import bspline
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
-import bspline
+import pymc as pm
+import scipy.special as spp
 from bspline import splinelab
+from scipy import stats
+from scipy.stats import genextreme, norm, skewnorm
+from six import with_metaclass
 from sklearn.datasets import make_regression
-import pymc as pm
-from io import StringIO
-import subprocess
-import re
 from sklearn.metrics import roc_auc_score
-import scipy.special as spp
-
 
 try:  # run as a package if installed
     from pcntoolkit import configs
@@ -175,8 +175,8 @@ def create_design_matrix(X, intercept=True, basis='bspline',
         Phi = np.concatenate(
             (Phi, np.array([B(i) for i in X[:, basis_column]])), axis=1)
     elif basis == 'poly':
-        Phi = np.concatenate(Phi, create_poly_basis(
-            X[:, basis_column], **kwargs))
+        Phi = np.concatenate((Phi, create_poly_basis(
+            X[:, basis_column], **kwargs)), axis=1)
 
     return Phi
 
diff --git a/tests/cli_test/split_data.py b/tests/cli_test/split_data.py
index b522700a..01506913 100644
--- a/tests/cli_test/split_data.py
+++ b/tests/cli_test/split_data.py
@@ -21,9 +21,12 @@ def main():
     parser.add_argument("--input_file", type=str, required=True)
     parser.add_argument("--output_dir", type=str, required=True)
     args = parser.parse_args()
+    infile=args.input_file.split("/")[-1]
 
     print(f"Splitting the data located at {args.input_file} into train and test covariates, responses and batch effects...")
     df = pd.read_csv(args.input_file)
+
+    # Select the covariates, responses and batch effects
     cov = df['age']
     resp = df[['SubCortGrayVol','Left-Hippocampus','Brain-Stem','CSF']]
     be = df['site']
@@ -34,32 +37,32 @@ def main():
 
     # Map the batch effects to integers
     be_ids = np.unique(be, return_inverse=True)[1]
-
-    # Get the min and max of the covariates
-    xmin = min(cov)
-    xmax = max(cov)
-
+    
     # Split the data into training and test sets
-    train_idx, test_idx = train_test_split(np.arange(len(cov)), test_size=0.2, random_state=42, stratify=be_ids)
-
+    train_idx, test_idx = train_test_split(np.arange(len(cov)), test_size=0.2, stratify=be_ids)
 
     # Create the design matrices
     mean_basis = 'linear'
     var_basis = 'linear'
-    Phi_tr = create_design_matrix(cov[train_idx], basis=mean_basis, xmin=xmin, xmax=xmax, intercept=False, site_ids=be_ids[train_idx])
-    Phi_var_tr = create_design_matrix(cov[train_idx], basis=var_basis, xmin=xmin, xmax=xmax, intercept=False)
-    Phi_te = create_design_matrix(cov[test_idx], basis=mean_basis, xmin=xmin, xmax=xmax, intercept=False, site_ids=be_ids[test_idx])
-    Phi_var_te = create_design_matrix(cov[test_idx], basis=var_basis, xmin=xmin, xmax=xmax, intercept=False)
+    Phi_tr = create_design_matrix(cov[train_idx], basis=mean_basis, intercept=False, site_ids=be_ids[train_idx])
+    Phi_var_tr = create_design_matrix(cov[train_idx], basis=var_basis)
+    Phi_te = create_design_matrix(cov[test_idx], basis=mean_basis, intercept=False, site_ids=be_ids[test_idx])
+    Phi_var_te = create_design_matrix(cov[test_idx], basis=var_basis)
+
+    print(f"{Phi_tr.shape=}")
+    print(f"{Phi_var_tr.shape=}")
+    print(f"{Phi_te.shape=}")
+    print(f"{Phi_var_te.shape=}")
 
     # Save everything
-    pd.to_pickle(pd.DataFrame(Phi_tr), os.path.join(args.output_dir, f'X_tr_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(pd.DataFrame(Phi_var_tr), os.path.join(args.output_dir, f'X_var_tr_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(pd.DataFrame(Phi_te), os.path.join(args.output_dir, f'X_te_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(pd.DataFrame(Phi_var_te), os.path.join(args.output_dir, f'X_var_te_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(pd.DataFrame(resp[train_idx]), os.path.join(args.output_dir, f'Y_tr_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(pd.DataFrame(resp[test_idx]), os.path.join(args.output_dir, f'Y_te_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(be[train_idx], os.path.join(args.output_dir, f'be_tr_{args.input_file.split("/")[-1]}.pkl'))
-    pd.to_pickle(be[test_idx], os.path.join(args.output_dir, f'be_te_{args.input_file.split("/")[-1]}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_tr), os.path.join(args.output_dir, f'X_tr_{infile}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_var_tr), os.path.join(args.output_dir, f'X_var_tr_{infile}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_te), os.path.join(args.output_dir, f'X_te_{infile}.pkl'))
+    pd.to_pickle(pd.DataFrame(Phi_var_te), os.path.join(args.output_dir, f'X_var_te_{infile}.pkl'))
+    pd.to_pickle(pd.DataFrame(resp[train_idx]), os.path.join(args.output_dir, f'Y_tr_{infile}.pkl'))
+    pd.to_pickle(pd.DataFrame(resp[test_idx]), os.path.join(args.output_dir, f'Y_te_{infile}.pkl'))
+    pd.to_pickle(be[train_idx], os.path.join(args.output_dir, f'be_tr_{infile}.pkl'))
+    pd.to_pickle(be[test_idx], os.path.join(args.output_dir, f'be_te_{infile}.pkl'))
 
     print(f"Done! The files can be found in: {args.output_dir}")
 
diff --git a/tests/cli_test/test_cli.sh b/tests/cli_test/test_cli.sh
index d46e4195..d1645981 100755
--- a/tests/cli_test/test_cli.sh
+++ b/tests/cli_test/test_cli.sh
@@ -1,3 +1,6 @@
+#! /bin/bash
+set -x
+
 # Assign the current directory to a variable
 export testdir=$(pwd)
 export tempdir="$testdir/temp"

From d45fb14f24b1a24672bb47e0b6ed59c180baa6dd Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 14 Nov 2024 14:41:16 +0100
Subject: [PATCH 59/68] Update gitignore and remove temp folder

---
 .gitignore                 | 9 ++++++---
 tests/cli_test/test_cli.sh | 1 +
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index fcc7cfa6..c877c0c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -83,10 +83,13 @@ dist/pcntoolkit-0.27-py3.11.egg
 # Basic test functions for SHASH
 tests/test_SHASH.ipynb
 
+# Dist folder
 dist/pcntoolkit-0.30.post2-py3.12.egg
-
 dist/*
-
 dist
-
 build/*
+
+# CLI test folder
+tests/cli_test/*
+!tests/cli_test/test_cli.sh
+!tests/cli_test/split_data.py
\ No newline at end of file
diff --git a/tests/cli_test/test_cli.sh b/tests/cli_test/test_cli.sh
index d1645981..92aa890b 100755
--- a/tests/cli_test/test_cli.sh
+++ b/tests/cli_test/test_cli.sh
@@ -19,3 +19,4 @@ normative $tempdir/Y_te_$data_name.pkl -c $tempdir/X_te_$data_name.pkl -f predic
 echo "Also doing estimate..."
 normative $tempdir/Y_tr_$data_name.pkl -c $tempdir/X_tr_$data_name.pkl -f estimate $model_config -t $tempdir/X_te_$data_name.pkl -r $tempdir/Y_te_$data_name.pkl outputsuffix=estimate
 echo "Done!"
+rm -R $tempdir
\ No newline at end of file

From aa15085cd61c65e6eaaaf30334b056ee77db58a1 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Thu, 14 Nov 2024 16:31:15 +0100
Subject: [PATCH 60/68] Small changes

---
 README.md                    |   4 +-
 pcntoolkit/model/SHASH.py    |  29 ++++----
 pcntoolkit/normative.py      |   5 +-
 poetry.lock                  | 135 ++++++++++++++++-------------------
 pyproject.toml               |   1 +
 tests/cli_test/split_data.py |   5 --
 tests/testHBR.py             |   8 +--
 7 files changed, 85 insertions(+), 102 deletions(-)

diff --git a/README.md b/README.md
index af6078c1..64fc232d 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ using the download here: https://www.anaconda.com/download
 
 #### Create environment 
 ```
-conda create 
+conda create  python==3.12
 ```
 
 #### Activate environment
@@ -57,7 +57,7 @@ conda --version
 #### Create a conda environment in a shared location
 
 ```
-conda create -y python==3.10 numpy mkl blas --prefix=/shared/conda/
+conda create -y python==3.12 numpy mkl blas --prefix=/shared/conda/
 ```
 
 #### Activate the conda environment 
diff --git a/pcntoolkit/model/SHASH.py b/pcntoolkit/model/SHASH.py
index a64317e7..c00585a9 100644
--- a/pcntoolkit/model/SHASH.py
+++ b/pcntoolkit/model/SHASH.py
@@ -6,7 +6,6 @@
 from functools import lru_cache
 
 import numpy as np
-import pytensor as pt
 from pymc import floatX
 from pymc.distributions import Continuous
 from pytensor.tensor import as_tensor_variable
@@ -71,7 +70,7 @@ def C(x, epsilon, delta):
 ##### SHASH Distributions #####
 
 
-class SHASH(RandomVariable):
+class SHASHrv(RandomVariable):
     """SHASH RV, described by Jones et al., based on a standard normal distribution."""
 
     name = "shash"
@@ -97,7 +96,7 @@ def rng_fn(cls, rng, epsilon, delta, size=None):
         )
 
 
-shash = SHASH()
+shash = SHASHrv()
 
 
 class SHASH(Continuous):
@@ -208,9 +207,9 @@ def logp(value, epsilon, delta):
             Log-probability of the SHASH distribution
         """
         this_S = S(value, epsilon, delta)
-        this_S_sqr = pt.sqr(this_S)
+        this_S_sqr = np.log(this_S)
         this_C_sqr = 1 + this_S_sqr
-        frac2 = pt.log(delta) + pt.log(this_C_sqr) / 2 - pt.log(1 + pt.sqr(value)) / 2
+        frac2 = np.log(delta) + np.log(this_C_sqr) / 2 - np.log(1 + np.log(value)) / 2
         exp = -this_S_sqr / 2
         return CONST2 + frac2 + exp
 
@@ -288,15 +287,15 @@ def logp(value, mu, sigma, epsilon, delta):
         """
         remapped_value = (value - mu) / sigma
         this_S = S(remapped_value, epsilon, delta)
-        this_S_sqr = pt.sqr(this_S)
+        this_S_sqr = np.log(this_S)
         this_C_sqr = 1 + this_S_sqr
         frac2 = (
-            pt.log(delta)
-            + pt.log(this_C_sqr) / 2
-            - pt.log(1 + pt.sqr(remapped_value)) / 2
+            np.log(delta)
+            + np.log(this_C_sqr) / 2
+            - np.log(1 + np.log(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return CONST2 + frac2 + exp - pt.log(sigma)
+        return CONST2 + frac2 + exp - np.log(sigma)
 
 
 class SHASHo2RV(RandomVariable):
@@ -381,15 +380,15 @@ def logp(value, mu, sigma, epsilon, delta):
         sigma_d = sigma / delta
         remapped_value = (value - mu) / sigma_d
         this_S = S(remapped_value, epsilon, delta)
-        this_S_sqr = pt.sqr(this_S)
+        this_S_sqr = np.log(this_S)
         this_C_sqr = 1 + this_S_sqr
         frac2 = (
-            pt.log(delta)
-            + pt.log(this_C_sqr) / 2
-            - pt.log(1 + pt.sqr(remapped_value)) / 2
+            np.log(delta)
+            + np.log(this_C_sqr) / 2
+            - np.log(1 + np.log(remapped_value)) / 2
         )
         exp = -this_S_sqr / 2
-        return CONST2 + frac2 + exp - pt.log(sigma_d)
+        return CONST2 + frac2 + exp - np.log(sigma_d)
 
 
 class SHASHbRV(RandomVariable):
diff --git a/pcntoolkit/normative.py b/pcntoolkit/normative.py
index 436f6064..1f160e84 100755
--- a/pcntoolkit/normative.py
+++ b/pcntoolkit/normative.py
@@ -18,7 +18,6 @@
 import os
 import pickle
 import sys
-import warnings
 from pathlib import Path
 
 import numpy as np
@@ -27,8 +26,8 @@
 try:
     import nutpie
 except ImportError:
-    warnings.warn("Nutpie not installed. For fitting HBR models with the nutpie backend, install it with `conda install nutpie numba`")
-
+    # warnings.warn("Nutpie not installed. For fitting HBR models with the nutpie backend, install it with `conda install nutpie numba`")
+    pass
 
 
 try:  # run as a package if installed
diff --git a/poetry.lock b/poetry.lock
index 6c2d0551..4607f4a6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -423,76 +423,65 @@ logical-unification = ">=0.4.0"
 
 [[package]]
 name = "contourpy"
-version = "1.3.0"
+version = "1.3.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
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-    {file = "contourpy-1.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:76c905ef940a4474a6289c71d53122a4f77766eef23c03cd57016ce19d0f7b42"},
-    {file = "contourpy-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:92f8557cbb07415a4d6fa191f20fd9d2d9eb9c0b61d1b2f52a8926e43c6e9af7"},
-    {file = "contourpy-1.3.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:36f965570cff02b874773c49bfe85562b47030805d7d8360748f3eca570f4cab"},
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+    {file = "contourpy-1.3.1.tar.gz", hash = "sha256:dfd97abd83335045a913e3bcc4a09c0ceadbe66580cf573fe961f4a825efa699"},
 ]
 
 [package.dependencies]
@@ -708,13 +697,13 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "h5netcdf"
-version = "1.4.0"
+version = "1.4.1"
 description = "netCDF4 via h5py"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "h5netcdf-1.4.0-py3-none-any.whl", hash = "sha256:d1bb96fce5dcf42908903c9798beeef70ac84e97159eb381f1b151459313f228"},
-    {file = "h5netcdf-1.4.0.tar.gz", hash = "sha256:e959c3b5bd3ca7965ce5f4383a4e038ffcb55034c63d791829bd33a5ac38a962"},
+    {file = "h5netcdf-1.4.1-py3-none-any.whl", hash = "sha256:dd86c78ae69b92b16aa8a3c1ff3a14e7622571b5788dcf6d8b68569035bf71ce"},
+    {file = "h5netcdf-1.4.1.tar.gz", hash = "sha256:7c8401ab807ff37c9798edc90d99467595892e6c541a5d5abeb8f53aab5335fe"},
 ]
 
 [package.dependencies]
@@ -2247,13 +2236,13 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
-version = "75.4.0"
+version = "75.5.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "setuptools-75.4.0-py3-none-any.whl", hash = "sha256:b3c5d862f98500b06ffdf7cc4499b48c46c317d8d56cb30b5c8bce4d88f5c216"},
-    {file = "setuptools-75.4.0.tar.gz", hash = "sha256:1dc484f5cf56fd3fe7216d7b8df820802e7246cfb534a1db2aa64f14fcb9cdcb"},
+    {file = "setuptools-75.5.0-py3-none-any.whl", hash = "sha256:87cb777c3b96d638ca02031192d40390e0ad97737e27b6b4fa831bea86f2f829"},
+    {file = "setuptools-75.5.0.tar.gz", hash = "sha256:5c4ccb41111392671f02bb5f8436dfc5a9a7185e80500531b133f5775c4163ef"},
 ]
 
 [package.extras]
diff --git a/pyproject.toml b/pyproject.toml
index 9a082e4a..5ede1693 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ scikit-learn = "^1.5.2"         # Prefer Conda
 seaborn = "^0.13.2"             # Prefer conda
 six = "^1.16.0"                 # Prefer conda
 scipy = "^1.12"
+matplotlib = "^3.8.1"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"
diff --git a/tests/cli_test/split_data.py b/tests/cli_test/split_data.py
index 01506913..bb2caa98 100644
--- a/tests/cli_test/split_data.py
+++ b/tests/cli_test/split_data.py
@@ -49,11 +49,6 @@ def main():
     Phi_te = create_design_matrix(cov[test_idx], basis=mean_basis, intercept=False, site_ids=be_ids[test_idx])
     Phi_var_te = create_design_matrix(cov[test_idx], basis=var_basis)
 
-    print(f"{Phi_tr.shape=}")
-    print(f"{Phi_var_tr.shape=}")
-    print(f"{Phi_te.shape=}")
-    print(f"{Phi_var_te.shape=}")
-
     # Save everything
     pd.to_pickle(pd.DataFrame(Phi_tr), os.path.join(args.output_dir, f'X_tr_{infile}.pkl'))
     pd.to_pickle(pd.DataFrame(Phi_var_tr), os.path.join(args.output_dir, f'X_var_tr_{infile}.pkl'))
diff --git a/tests/testHBR.py b/tests/testHBR.py
index 0e19ac5b..db875fa2 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -35,7 +35,7 @@
 n_samples = 500  # Number of samples in each group (use a list for different
 # sample numbers across different batches)
 
-model_type = "bspline"  # modelto try 'linear, ''polynomial', 'bspline'
+model_type = "linear"  # modelto try 'linear, ''polynomial', 'bspline'
 
 
 ############################## Data Simulation ################################
@@ -62,9 +62,9 @@
     likelihood="SHASHb",
     linear_sigma="True",
     random_slope_mu="False",
-    linear_epsilon="True",
-    linear_delta="True",
-    nuts_sampler="pymc",
+    linear_epsilon="False",
+    linear_delta="False",
+    nuts_sampler="nutpie",
 )
 
 nm.estimate(X_train, Y_train, trbefile=working_dir + "trbefile.pkl")

From 749b19416172c30be06a8c4d570725f65050130d Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Fri, 15 Nov 2024 11:46:43 +0100
Subject: [PATCH 61/68] Update README Update CHANGES Fix delimiter issue

---
 CHANGES                  | 15 +++++++++++++++
 README.md                |  4 ++--
 pcntoolkit/util/utils.py |  6 +++---
 pyproject.toml           |  4 ++--
 4 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/CHANGES b/CHANGES
index 169180f0..99fa20b5 100644
--- a/CHANGES
+++ b/CHANGES
@@ -87,3 +87,18 @@ version 0.29
 - Addedd functionality to compute SHASH z-scores from normative.py
 - Updated requirements
 - Basic pytest continuous integration framework implemented
+
+version 0.30.0
+- Minor bug fixes
+
+version 0.31.0
+- Major changes:
+    - Move to Poetry for dependency management in pyproject.toml.
+    - PCNToolkit must now be installed using python -m pip install .. See the README for complete instructions.
+    - A CLI command normative is automatically created, and can be used instead of python normative.py.
+    - Nutpie can be used as a sampler for HBR by setting `nuts_sampler='nutpie''. Nutpie and numba must first be installed using conda.
+- Minor changes
+    - torque jobs now support multicore jobs via the keyword 'n_cores_per_batch'
+    - Backwards compatibilty improved by using pd.read_pickle instead of pickle.load
+    - SHASH classes have been refactored and improved
+    - HBR priors improved
diff --git a/README.md b/README.md
index 64fc232d..f18f7801 100644
--- a/README.md
+++ b/README.md
@@ -30,7 +30,7 @@ Use the command that you get from the command builder here: https://pytorch.org/
 ### Install nutpie using conda 
 
 ```
-conda install nutpie
+conda install nutpie numba -c conda-forge
 ```
 
 #### Install PCNtoolkit
@@ -79,7 +79,7 @@ If your shared resource has no GPU, make sure you select the 'CPU' field in the
 ### Install nutpie using conda 
 
 ```
-conda install nutpie
+conda install nutpie numba -c conda-forge
 ```
 
 #### Clone the repo
diff --git a/pcntoolkit/util/utils.py b/pcntoolkit/util/utils.py
index 19c6f553..cd245746 100644
--- a/pcntoolkit/util/utils.py
+++ b/pcntoolkit/util/utils.py
@@ -1078,14 +1078,14 @@ def load_freesurfer_measure(measure, data_path, subjects_list):
                 data = dict()
 
                 a = pd.read_csv(data_path + sub + '/stats/lh.aparc.a2009s.stats',
-                                delimiter='\s+', comment='#', header=None)
+                                delimiter=r'\s+', comment='#', header=None)
                 temp = dict(zip(a[0], a[col]))
                 for key in list(temp.keys()):
                     temp['L_'+key] = temp.pop(key)
                 data.update(temp)
 
                 a = pd.read_csv(data_path + sub + '/stats/rh.aparc.a2009s.stats',
-                                delimiter='\s+', comment='#', header=None)
+                                delimiter=r'\s+', comment='#', header=None)
                 temp = dict(zip(a[0], a[col]))
                 for key in list(temp.keys()):
                     temp['R_'+key] = temp.pop(key)
@@ -1137,7 +1137,7 @@ def load_freesurfer_measure(measure, data_path, subjects_list):
                 else:
                     tiv = a[' ICV']
                 a = pd.read_csv(data_path + sub + '/stats/aseg.stats',
-                                delimiter='\s+', comment='#', header=None)
+                                delimiter=r'\s+', comment='#', header=None)
                 data_vol = dict(zip(a[4]+'_mm3', a[3]))
                 for key in data_vol.keys():
                     data_vol[key] = data_vol[key]/tiv
diff --git a/pyproject.toml b/pyproject.toml
index 5ede1693..d370c791 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pcntoolkit"
-version = "0.30.2"
+version = "0.31.0"
 description = "Predictive Clinical Neuroscience Toolkit"
 authors = ["Andre Marquand"]
 license = "GNU GPLv3"
@@ -10,7 +10,7 @@ readme = "README.md"
 python = ">=3.10,<3.13"
 bspline = "^0.1.1"              # Install with pip
 nibabel = "^5.3.1"              # Prefer Conda
-pymc = "^5.17.0"                # Prefer Conda
+pymc = "^5.18.0"                # Prefer Conda
 scikit-learn = "^1.5.2"         # Prefer Conda
 seaborn = "^0.13.2"             # Prefer conda
 six = "^1.16.0"                 # Prefer conda

From 3e9c7c74a35d79095cf97ce1512aa1ed4810825d Mon Sep 17 00:00:00 2001
From: Stijn de Boer <19709783+AuguB@users.noreply.github.com>
Date: Fri, 15 Nov 2024 12:35:54 +0100
Subject: [PATCH 62/68] Created using Colab

---
 .../pages/apply_normative_models_ct.rst       | 1020 +++++++++++++++++
 1 file changed, 1020 insertions(+)
 create mode 100644 doc/source/pages/apply_normative_models_ct.rst

diff --git a/doc/source/pages/apply_normative_models_ct.rst b/doc/source/pages/apply_normative_models_ct.rst
new file mode 100644
index 00000000..ba552baf
--- /dev/null
+++ b/doc/source/pages/apply_normative_models_ct.rst
@@ -0,0 +1,1020 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "2d8fb4c8-4360-4fdc-b0a2-e1c2e22bd8f9",
+      "metadata": {
+        "id": "2d8fb4c8-4360-4fdc-b0a2-e1c2e22bd8f9"
+      },
+      "source": [
+        "## Using lifespan models to make predictions on new data\n",
+        "\n",
+        "This notebook shows how to apply the coefficients from pre-estimated normative models to new data. This can be done in two different ways: (i) using a new set of data derived from the same sites used to estimate the model and (ii) on a completely different set of sites. In the latter case, we also need to estimate the site effect, which requires some calibration/adaptation data. As an illustrative example, we use a dataset derived from several [OpenNeuro datasets](https://openneuro.org/) and adapt the learned model to make predictions on these data.\n",
+        "\n",
+        "First, if necessary, we install PCNtoolkit (note: this tutorial requires at least version 0.27)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "f606eda6",
+      "metadata": {
+        "id": "f606eda6",
+        "outputId": "dd209f94-142d-4043-81da-e4bceaee19f0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip\n",
+            "  Using cached https://github.com/amarquand/PCNtoolkit/archive/dev.zip\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: bspline<0.2.0,>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (0.1.1)\n",
+            "Requirement already satisfied: matplotlib<4.0.0,>=3.8.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (3.9.2)\n",
+            "Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)\n",
+            "Requirement already satisfied: pymc<6.0.0,>=5.18.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.18.0)\n",
+            "Requirement already satisfied: scikit-learn<2.0.0,>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.5.2)\n",
+            "Requirement already satisfied: scipy<2.0,>=1.12 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.13.1)\n",
+            "Requirement already satisfied: seaborn<0.14.0,>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (0.13.2)\n",
+            "Requirement already satisfied: six<2.0.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.16.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bspline<0.2.0,>=0.1.1->pcntoolkit==0.31.0) (1.26.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (1.3.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (4.54.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (1.4.7)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (24.2)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (11.0.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (3.2.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.8.1->pcntoolkit==0.31.0) (2.8.2)\n",
+            "Requirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (6.4.5)\n",
+            "Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (4.12.2)\n",
+            "Requirement already satisfied: arviz>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.20.0)\n",
+            "Requirement already satisfied: cachetools>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (5.5.0)\n",
+            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.1.0)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.2.2)\n",
+            "Requirement already satisfied: pytensor<2.26,>=2.25.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.25.5)\n",
+            "Requirement already satisfied: rich>=13.7.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (13.9.4)\n",
+            "Requirement already satisfied: threadpoolctl<4.0.0,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.5.0)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.5.2->pcntoolkit==0.31.0) (1.4.2)\n",
+            "Requirement already satisfied: setuptools>=60.0.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (75.1.0)\n",
+            "Requirement already satisfied: xarray>=2022.6.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2024.10.0)\n",
+            "Requirement already satisfied: h5netcdf>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.4.0)\n",
+            "Requirement already satisfied: xarray-einstats>=0.3 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.8.0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2024.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2024.2)\n",
+            "Requirement already satisfied: filelock>=3.15 in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.16.1)\n",
+            "Requirement already satisfied: etuples in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.3.9)\n",
+            "Requirement already satisfied: logical-unification in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.4.6)\n",
+            "Requirement already satisfied: miniKanren in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.3)\n",
+            "Requirement already satisfied: cons in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.4.6)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.18.0)\n",
+            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from h5netcdf>=1.0.2->arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.12.1)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.1.2)\n",
+            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.12.1)\n",
+            "Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "ddd7b3cb-b018-4ed4-8b55-15728d8c5411",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ddd7b3cb-b018-4ed4-8b55-15728d8c5411",
+        "outputId": "d3d408d4-9c55-49d2-cae9-e3a500e168e3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "fatal: destination path 'braincharts' already exists and is not an empty directory.\n"
+          ]
+        }
+      ],
+      "source": [
+        "! git clone https://github.com/predictive-clinical-neuroscience/braincharts.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "b1849f76-b17d-4286-bf57-50ff56e81bf8",
+      "metadata": {
+        "id": "b1849f76-b17d-4286-bf57-50ff56e81bf8"
+      },
+      "outputs": [],
+      "source": [
+        "# we need to be in the scripts folder when we import the libraries in the code block below,\n",
+        "# because there is a function called nm_utils that is in the scripts folder that we need to import\n",
+        "import os\n",
+        "\n",
+        "wdir = 'braincharts'\n",
+        "\n",
+        "os.chdir(wdir) #this path is setup for running on Google Colab. Change it to match your local path if running locally\n",
+        "root_dir=os.getcwd()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b2227bc7-e798-470a-99bc-33561ce4511b",
+      "metadata": {
+        "id": "b2227bc7-e798-470a-99bc-33561ce4511b"
+      },
+      "source": [
+        "Now we import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "ff661cf2-7d80-46bb-bcfb-1650a93eed3d",
+      "metadata": {
+        "id": "ff661cf2-7d80-46bb-bcfb-1650a93eed3d"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import pickle\n",
+        "from matplotlib import pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from pcntoolkit.normative import estimate, predict, evaluate\n",
+        "from pcntoolkit.util.utils import compute_MSLL, create_design_matrix\n",
+        "os.chdir(os.path.join(root_dir, 'scripts'))\n",
+        "from nm_utils import remove_bad_subjects, load_2d\n",
+        "os.chdir(root_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "78719463-28b2-4849-b970-cfbe2f07d214",
+      "metadata": {
+        "id": "78719463-28b2-4849-b970-cfbe2f07d214"
+      },
+      "source": [
+        "We need to unzip the models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "3b1d4d4b-68ab-4bba-87f5-6062995805d0",
+      "metadata": {
+        "id": "3b1d4d4b-68ab-4bba-87f5-6062995805d0"
+      },
+      "outputs": [],
+      "source": [
+        "os.chdir(os.path.join(root_dir,'models'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "d4b7b2f4-c514-4d4f-a6b0-9461e1b20831",
+      "metadata": {
+        "id": "d4b7b2f4-c514-4d4f-a6b0-9461e1b20831",
+        "outputId": "87f49919-54d4-4b2f-a959-28060596ced9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Archive:  lifespan_57K_82sites.zip\n",
+            "replace lifespan_57K_82sites/Right-Pallidum/Models/meta_data.md? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
+          ]
+        }
+      ],
+      "source": [
+        "# we will use the biggest sample as our training set (approx. N=57000 subjects from 82 sites)\n",
+        "# for more info on the other pretrained models available in this repository,\n",
+        "# please refer to the accompanying paper https://elifesciences.org/articles/72904\n",
+        "! unzip lifespan_57K_82sites.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "802b1da6-04cc-4310-af81-f50d38c3e653",
+      "metadata": {
+        "id": "802b1da6-04cc-4310-af81-f50d38c3e653"
+      },
+      "source": [
+        "Next, we configure some basic variables, like where we want the analysis to be done and which model we want to use.\n",
+        "\n",
+        "**Note:** We maintain a list of site ids for each dataset, which describe the site names in the training and test data (`site_ids_tr` and `site_ids_te`), plus also the adaptation data . The training site ids are provided as a text file in the distribution and the test ids are extracted automatically from the pandas dataframe (see below). If you use additional data from the sites (e.g. later waves from ABCD), it may be necessary to adjust the site names to match the names in the training set. See the accompanying paper for more details"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "f52e2a19-9b63-4f0f-97c1-387f1a1872a2",
+      "metadata": {
+        "id": "f52e2a19-9b63-4f0f-97c1-387f1a1872a2"
+      },
+      "outputs": [],
+      "source": [
+        "# which model do we wish to use?\n",
+        "model_name = 'lifespan_57K_82sites'\n",
+        "site_names = 'site_ids_ct_82sites.txt'\n",
+        "\n",
+        "\n",
+        "# where the data files live\n",
+        "data_dir = os.path.join(root_dir,'docs')\n",
+        "\n",
+        "# where the models live\n",
+        "out_dir = os.path.join(root_dir, 'models', model_name)\n",
+        "\n",
+        "# load a set of site ids from this model. This must match the training data\n",
+        "with open(os.path.join(root_dir,'docs', site_names)) as f:\n",
+        "    site_ids_tr = f.read().splitlines()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3aab54a5-2579-48d8-a81b-bbd34cea1213",
+      "metadata": {
+        "id": "3aab54a5-2579-48d8-a81b-bbd34cea1213"
+      },
+      "source": [
+        "### Load test data\n",
+        "\n",
+        "**Note:** For the purposes of this tutorial, we make predictions for a multi-site transfer dataset, derived from [OpenNeuro](https://openneuro.org/)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "262d429a-160b-4ba3-9ba4-9acc195bc644",
+      "metadata": {
+        "id": "262d429a-160b-4ba3-9ba4-9acc195bc644"
+      },
+      "outputs": [],
+      "source": [
+        "test_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_te.csv')\n",
+        "\n",
+        "df_te = pd.read_csv(test_data)\n",
+        "\n",
+        "# extract a list of unique site ids from the test set\n",
+        "site_ids_te =  sorted(set(df_te['site'].to_list()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c636509a-8b12-43f1-811c-08cb22640be2",
+      "metadata": {
+        "id": "c636509a-8b12-43f1-811c-08cb22640be2"
+      },
+      "source": [
+        "### (Optional) Load adaptation data\n",
+        "\n",
+        "If the data you wish to make predictions for is not derived from the same scanning sites as those in the trainig set, it is necessary to learn the site effect so that we can account for it in the predictions. In order to do this in an unbiased way, we use a separate dataset, which we refer to as 'adaptation' data. This must contain data for all the same sites as in the test dataset and we assume these are coded in the same way, based on a the 'sitenum' column in the dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "53551023-aff6-4934-ad2d-d77bc63c562d",
+      "metadata": {
+        "id": "53551023-aff6-4934-ad2d-d77bc63c562d"
+      },
+      "outputs": [],
+      "source": [
+        "adaptation_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_ad.csv')\n",
+        "\n",
+        "df_ad = pd.read_csv(adaptation_data)\n",
+        "\n",
+        "# extract a list of unique site ids from the test set\n",
+        "site_ids_ad =  sorted(set(df_ad['site'].to_list()))\n",
+        "\n",
+        "if not all(elem in site_ids_ad for elem in site_ids_te):\n",
+        "    print('Warning: some of the testing sites are not in the adaptation data')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f73e30e-c693-44b8-98c6-52b71b577ea8",
+      "metadata": {
+        "id": "4f73e30e-c693-44b8-98c6-52b71b577ea8"
+      },
+      "source": [
+        "### Configure which models to fit\n",
+        "\n",
+        "Now, we configure which imaging derived phenotypes (IDPs) we would like to process. This is just a list of column names in the dataframe we have loaded above.\n",
+        "\n",
+        "We could load the whole set (i.e. all phenotypes for which we have models for ..."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "b48e104c-cbac-4ae2-8377-cd3ff80162fd",
+      "metadata": {
+        "id": "b48e104c-cbac-4ae2-8377-cd3ff80162fd"
+      },
+      "outputs": [],
+      "source": [
+        "# load the list of idps for left and right hemispheres, plus subcortical regions\n",
+        "with open(os.path.join(data_dir, 'phenotypes_ct_lh.txt')) as f:\n",
+        "    idp_ids_lh = f.read().splitlines()\n",
+        "with open(os.path.join(data_dir, 'phenotypes_ct_rh.txt')) as f:\n",
+        "    idp_ids_rh = f.read().splitlines()\n",
+        "with open(os.path.join(data_dir, 'phenotypes_sc.txt')) as f:\n",
+        "    idp_ids_sc = f.read().splitlines()\n",
+        "\n",
+        "# we choose here to process all idps\n",
+        "idp_ids = idp_ids_lh + idp_ids_rh #+ idp_ids_sc"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "280731ad-47d8-43e2-8cb5-4eccfd9f3f81",
+      "metadata": {
+        "id": "280731ad-47d8-43e2-8cb5-4eccfd9f3f81"
+      },
+      "source": [
+        "... or alternatively, we could just specify a list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "8b74d75f-77a5-474a-9c9b-29aab1ce53a2",
+      "metadata": {
+        "id": "8b74d75f-77a5-474a-9c9b-29aab1ce53a2"
+      },
+      "outputs": [],
+      "source": [
+        "idp_ids = [ 'Left-Thalamus-Proper', 'Left-Lateral-Ventricle', 'rh_MeanThickness_thickness']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "56ee1f7f-8684-4f1c-b142-a68176407029",
+      "metadata": {
+        "id": "56ee1f7f-8684-4f1c-b142-a68176407029"
+      },
+      "source": [
+        "### Configure covariates\n",
+        "\n",
+        "Now, we configure some parameters to fit the model. First, we choose which columns of the pandas dataframe contain the covariates (age and sex). The site parameters are configured automatically later on by the `configure_design_matrix()` function, when we loop through the IDPs in the list\n",
+        "\n",
+        "The supplied coefficients are derived from a 'warped' Bayesian linear regression model, which uses a nonlinear warping function to model non-Gaussianity (`sinarcsinh`) plus a non-linear basis expansion (a cubic b-spline basis set with 5 knot points, which is the default value in the PCNtoolkit package). Since we are sticking with the default value, we do not need to specify any parameters for this, but we do need to specify the limits. We choose to pad the input by a few years either side of the input range. We will also set a couple of options that control the estimation of the model\n",
+        "\n",
+        "For further details about the likelihood warping approach, see the accompanying paper and [Fraza et al 2021](https://www.biorxiv.org/content/10.1101/2021.04.05.438429v1)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "62312b8e-4972-4238-abf9-87d9bb33cc10",
+      "metadata": {
+        "id": "62312b8e-4972-4238-abf9-87d9bb33cc10"
+      },
+      "outputs": [],
+      "source": [
+        "# which data columns do we wish to use as covariates?\n",
+        "cols_cov = ['age','sex']\n",
+        "\n",
+        "# limits for cubic B-spline basis\n",
+        "xmin = -5\n",
+        "xmax = 110\n",
+        "\n",
+        "# Absolute Z treshold above which a sample is considered to be an outlier (without fitting any model)\n",
+        "outlier_thresh = 7"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "42bc1072-e9ed-4f2a-9fdd-cbd626a61542",
+      "metadata": {
+        "id": "42bc1072-e9ed-4f2a-9fdd-cbd626a61542"
+      },
+      "source": [
+        "### Make predictions\n",
+        "\n",
+        "This will make predictions for each IDP separately. This is done by extracting a column from the dataframe (i.e. specifying the IDP as the response variable) and saving it as a numpy array. Then, we configure the covariates, which is a numpy data array having the number of rows equal to the number of datapoints in the test set. The columns are specified as follows:\n",
+        "\n",
+        "- A global intercept (column of ones)\n",
+        "- The covariate columns (here age and sex, coded as 0=female/1=male)\n",
+        "- Dummy coded columns for the sites in the training set (one column per site)\n",
+        "- Columns for the basis expansion (seven columns for the default parameterisation)\n",
+        "\n",
+        "Once these are saved as numpy arrays in ascii format (as here) or (alternatively) in pickle format, these are passed as inputs to the `predict()` method in the PCNtoolkit normative modelling framework. These are written in the same format to the location specified by `idp_dir`. At the end of this step, we have a set of predictions and Z-statistics for the test dataset that we can take forward to further analysis.\n",
+        "\n",
+        "Note that when we need to make predictions on new data, the procedure is more involved, since we need to prepare, process and store covariates, response variables and site ids for the adaptation data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "07b7471b-c334-464f-8273-b409b7acaac2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "07b7471b-c334-464f-8273-b409b7acaac2",
+        "outputId": "a1de6a82-4430-4e11-de5f-b2874dd3c5d9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Running IDP 0 Left-Thalamus-Proper :\n",
+            "Some sites missing from the training data. Adapting model\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Evaluating the model ...\n",
+            "Evaluations Writing outputs ...\n",
+            "Writing outputs ...\n",
+            "Running IDP 1 Left-Lateral-Ventricle :\n",
+            "Some sites missing from the training data. Adapting model\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Evaluating the model ...\n",
+            "Evaluations Writing outputs ...\n",
+            "Writing outputs ...\n",
+            "Running IDP 2 rh_MeanThickness_thickness :\n",
+            "Some sites missing from the training data. Adapting model\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Evaluating the model ...\n",
+            "Evaluations Writing outputs ...\n",
+            "Writing outputs ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "for idp_num, idp in enumerate(idp_ids):\n",
+        "    print('Running IDP', idp_num, idp, ':')\n",
+        "    idp_dir = os.path.join(out_dir, idp)\n",
+        "    os.chdir(idp_dir)\n",
+        "\n",
+        "    # extract and save the response variables for the test set\n",
+        "    y_te = df_te[idp].to_numpy()\n",
+        "\n",
+        "    # save the variables\n",
+        "    resp_file_te = os.path.join(idp_dir, 'resp_te.txt')\n",
+        "    np.savetxt(resp_file_te, y_te)\n",
+        "\n",
+        "    # configure and save the design matrix\n",
+        "    cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')\n",
+        "    X_te = create_design_matrix(df_te[cols_cov],\n",
+        "                                site_ids = df_te['site'],\n",
+        "                                all_sites = site_ids_tr,\n",
+        "                                basis = 'bspline',\n",
+        "                                xmin = xmin,\n",
+        "                                xmax = xmax)\n",
+        "    np.savetxt(cov_file_te, X_te)\n",
+        "\n",
+        "    # check whether all sites in the test set are represented in the training set\n",
+        "    if all(elem in site_ids_tr for elem in site_ids_te):\n",
+        "        print('All sites are present in the training data')\n",
+        "\n",
+        "        # just make predictions\n",
+        "        yhat_te, s2_te, Z = predict(cov_file_te,\n",
+        "                                    alg='blr',\n",
+        "                                    respfile=resp_file_te,\n",
+        "                                    model_path=os.path.join(idp_dir,'Models'))\n",
+        "    else:\n",
+        "        print('Some sites missing from the training data. Adapting model')\n",
+        "\n",
+        "        # save the covariates for the adaptation data\n",
+        "        X_ad = create_design_matrix(df_ad[cols_cov],\n",
+        "                                    site_ids = df_ad['site'],\n",
+        "                                    all_sites = site_ids_tr,\n",
+        "                                    basis = 'bspline',\n",
+        "                                    xmin = xmin,\n",
+        "                                    xmax = xmax)\n",
+        "        cov_file_ad = os.path.join(idp_dir, 'cov_bspline_ad.txt')\n",
+        "        np.savetxt(cov_file_ad, X_ad)\n",
+        "\n",
+        "        # save the responses for the adaptation data\n",
+        "        resp_file_ad = os.path.join(idp_dir, 'resp_ad.txt')\n",
+        "        y_ad = df_ad[idp].to_numpy()\n",
+        "        np.savetxt(resp_file_ad, y_ad)\n",
+        "\n",
+        "        # save the site ids for the adaptation data\n",
+        "        sitenum_file_ad = os.path.join(idp_dir, 'sitenum_ad.txt')\n",
+        "        site_num_ad = df_ad['sitenum'].to_numpy(dtype=int)\n",
+        "        np.savetxt(sitenum_file_ad, site_num_ad)\n",
+        "\n",
+        "        # save the site ids for the test data\n",
+        "        sitenum_file_te = os.path.join(idp_dir, 'sitenum_te.txt')\n",
+        "        site_num_te = df_te['sitenum'].to_numpy(dtype=int)\n",
+        "        np.savetxt(sitenum_file_te, site_num_te)\n",
+        "\n",
+        "        yhat_te, s2_te, Z = predict(cov_file_te,\n",
+        "                                    alg = 'blr',\n",
+        "                                    respfile = resp_file_te,\n",
+        "                                    model_path = os.path.join(idp_dir,'Models'),\n",
+        "                                    adaptrespfile = resp_file_ad,\n",
+        "                                    adaptcovfile = cov_file_ad,\n",
+        "                                    adaptvargroupfile = sitenum_file_ad,\n",
+        "                                    testvargroupfile = sitenum_file_te)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "75210821-ccb8-4bd2-82f3-641708811b21",
+      "metadata": {
+        "id": "75210821-ccb8-4bd2-82f3-641708811b21"
+      },
+      "source": [
+        "### Preparing dummy data for plotting\n",
+        "\n",
+        "Now, we plot the centiles of variation estimated by the normative model.\n",
+        "\n",
+        "We do this by making use of a set of dummy covariates that span the whole range of the input space (for age) for a fixed value of the other covariates (e.g. sex) so that we can make predictions for these dummy data points, then plot them. We configure these dummy predictions using the same procedure as we used for the real data. We can use the same dummy data for all the IDPs we wish to plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "2d0743d8-28ca-4a14-8ef0-99bf40434b5b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2d0743d8-28ca-4a14-8ef0-99bf40434b5b",
+        "outputId": "e8957652-cfd0-4a4c-998b-463c356bbaed"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "configuring dummy data ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "# which sex do we want to plot?\n",
+        "sex = 1 # 1 = male 0 = female\n",
+        "if sex == 1:\n",
+        "    clr = 'blue';\n",
+        "else:\n",
+        "    clr = 'red'\n",
+        "\n",
+        "# create dummy data for visualisation\n",
+        "print('configuring dummy data ...')\n",
+        "xx = np.arange(xmin, xmax, 0.5)\n",
+        "X0_dummy = np.zeros((len(xx), 2))\n",
+        "X0_dummy[:,0] = xx\n",
+        "X0_dummy[:,1] = sex\n",
+        "\n",
+        "# create the design matrix\n",
+        "X_dummy = create_design_matrix(X0_dummy, xmin=xmin, xmax=xmax, site_ids=None, all_sites=site_ids_tr)\n",
+        "\n",
+        "# save the dummy covariates\n",
+        "cov_file_dummy = os.path.join(out_dir,'cov_bspline_dummy_mean.txt')\n",
+        "np.savetxt(cov_file_dummy, X_dummy)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "126323a3-2270-4796-97c4-94629730ddf7",
+      "metadata": {
+        "id": "126323a3-2270-4796-97c4-94629730ddf7"
+      },
+      "source": [
+        "### Plotting the normative models\n",
+        "\n",
+        "Now we loop through the IDPs, plotting each one separately. The outputs of this step are a set of quantitative regression metrics for each IDP and a set of centile curves which we plot the test data against.\n",
+        "\n",
+        "This part of the code is relatively complex because we need to keep track of many quantities for the plotting. We also need to remember whether the data need to be warped or not. By default in PCNtoolkit, predictions in the form of `yhat, s2` are always in the warped (Gaussian) space. If we want predictions in the input (non-Gaussian) space, then we need to warp them with the inverse of the estimated warping function. This can be done using the function `nm.blr.warp.warp_predictions()`.\n",
+        "\n",
+        "**Note:** it is necessary to update the intercept for each of the sites. For purposes of visualisation, here we do this by adjusting the median of the data to match the dummy predictions, but note that all the quantitative metrics are estimated using the predictions that are adjusted properly using a learned offset (or adjusted using a hold-out adaptation set, as above). Note also that for the calibration data we require at least two data points of the same sex in each site to be able to estimate the variance. Of course, in a real example, you would want many more than just two since we need to get a reliable estimate of the variance for each site."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "cdd68cc6-212b-4149-b86a-24e842078e1a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "cdd68cc6-212b-4149-b86a-24e842078e1a",
+        "outputId": "43a33a06-cffb-4bab-9bd1-293cc8e65d48"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Running IDP 0 Left-Thalamus-Proper :\n",
+            "Making predictions with dummy covariates (for visualisation)\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Writing outputs ...\n",
+            "metrics: {'RMSE': array([0.55690777]), 'Rho': array([0.]), 'pRho': array([1.]), 'SMSE': array([0.]), 'EXPV': array([0.])}\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Running IDP 1 Left-Lateral-Ventricle :\n",
+            "Making predictions with dummy covariates (for visualisation)\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Writing outputs ...\n",
+            "metrics: {'RMSE': array([4205.49266088]), 'Rho': array([0.45898577]), 'pRho': array([5.62632393e-25]), 'SMSE': array([0.81397727]), 'EXPV': array([0.19814613])}\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Running IDP 2 rh_MeanThickness_thickness :\n",
+            "Making predictions with dummy covariates (for visualisation)\n",
+            "Loading data ...\n",
+            "Prediction by model  1 of 1\n",
+            "Writing outputs ...\n",
+            "metrics: {'RMSE': array([0.08652435]), 'Rho': array([0.77666469]), 'pRho': array([2.97430261e-103]), 'SMSE': array([0.40227749]), 'EXPV': array([0.59789079])}\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.set(style='whitegrid')\n",
+        "\n",
+        "for idp_num, idp in enumerate(idp_ids):\n",
+        "    print('Running IDP', idp_num, idp, ':')\n",
+        "    idp_dir = os.path.join(out_dir, idp)\n",
+        "    os.chdir(idp_dir)\n",
+        "\n",
+        "    # load the true data points\n",
+        "    yhat_te = load_2d(os.path.join(idp_dir, 'yhat_predict.txt'))\n",
+        "    s2_te = load_2d(os.path.join(idp_dir, 'ys2_predict.txt'))\n",
+        "    y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))\n",
+        "\n",
+        "    # set up the covariates for the dummy data\n",
+        "    print('Making predictions with dummy covariates (for visualisation)')\n",
+        "    yhat, s2 = predict(cov_file_dummy,\n",
+        "                       alg = 'blr',\n",
+        "                       respfile = None,\n",
+        "                       model_path = os.path.join(idp_dir,'Models'),\n",
+        "                       outputsuffix = '_dummy')\n",
+        "\n",
+        "    # load the normative model\n",
+        "    with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:\n",
+        "        nm = pickle.load(handle)\n",
+        "\n",
+        "    # get the warp and warp parameters\n",
+        "    W = nm.blr.warp\n",
+        "    warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]\n",
+        "\n",
+        "    # first, we warp predictions for the true data and compute evaluation metrics\n",
+        "    med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]\n",
+        "    med_te = med_te[:, np.newaxis]\n",
+        "    print('metrics:', evaluate(y_te, med_te))\n",
+        "\n",
+        "    # then, we warp dummy predictions to create the plots\n",
+        "    med, pr_int = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param)\n",
+        "\n",
+        "    # extract the different variance components to visualise\n",
+        "    beta, junk1, junk2 = nm.blr._parse_hyps(nm.blr.hyp, X_dummy)\n",
+        "    s2n = 1/beta # variation (aleatoric uncertainty)\n",
+        "    s2s = s2-s2n # modelling uncertainty (epistemic uncertainty)\n",
+        "\n",
+        "    # plot the data points\n",
+        "    y_te_rescaled_all = np.zeros_like(y_te)\n",
+        "    for sid, site in enumerate(site_ids_te):\n",
+        "        # plot the true test data points\n",
+        "        if all(elem in site_ids_tr for elem in site_ids_te):\n",
+        "            # all data in the test set are present in the training set\n",
+        "\n",
+        "            # first, we select the data points belonging to this particular site\n",
+        "            idx = np.where(np.bitwise_and(X_te[:,2] == sex, X_te[:,sid+len(cols_cov)+1] !=0))[0]\n",
+        "            if len(idx) == 0:\n",
+        "                print('No data for site', sid, site, 'skipping...')\n",
+        "                continue\n",
+        "\n",
+        "            # then directly adjust the data\n",
+        "            idx_dummy = np.bitwise_and(X_dummy[:,1] > X_te[idx,1].min(), X_dummy[:,1] < X_te[idx,1].max())\n",
+        "            y_te_rescaled = y_te[idx] - np.median(y_te[idx]) + np.median(med[idx_dummy])\n",
+        "        else:\n",
+        "            # we need to adjust the data based on the adaptation dataset\n",
+        "\n",
+        "            # first, select the data point belonging to this particular site\n",
+        "            idx = np.where(np.bitwise_and(X_te[:,2] == sex, (df_te['site'] == site).to_numpy()))[0]\n",
+        "\n",
+        "            # load the adaptation data\n",
+        "            y_ad = load_2d(os.path.join(idp_dir, 'resp_ad.txt'))\n",
+        "            X_ad = load_2d(os.path.join(idp_dir, 'cov_bspline_ad.txt'))\n",
+        "            idx_a = np.where(np.bitwise_and(X_ad[:,2] == sex, (df_ad['site'] == site).to_numpy()))[0]\n",
+        "            if len(idx) < 2 or len(idx_a) < 2:\n",
+        "                print('Insufficent data for site', sid, site, 'skipping...')\n",
+        "                continue\n",
+        "\n",
+        "            # adjust and rescale the data\n",
+        "            y_te_rescaled, s2_rescaled = nm.blr.predict_and_adjust(nm.blr.hyp,\n",
+        "                                                                   X_ad[idx_a,:],\n",
+        "                                                                   np.squeeze(y_ad[idx_a]),\n",
+        "                                                                   Xs=None,\n",
+        "                                                                   ys=np.squeeze(y_te[idx]))\n",
+        "        # plot the (adjusted) data points\n",
+        "        plt.scatter(X_te[idx,1], y_te_rescaled, s=4, color=clr, alpha = 0.1)\n",
+        "\n",
+        "    # plot the median of the dummy data\n",
+        "    plt.plot(xx, med, clr)\n",
+        "\n",
+        "    # fill the gaps in between the centiles\n",
+        "    junk, pr_int25 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.25,0.75])\n",
+        "    junk, pr_int95 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.05,0.95])\n",
+        "    junk, pr_int99 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.01,0.99])\n",
+        "    plt.fill_between(xx, pr_int25[:,0], pr_int25[:,1], alpha = 0.1,color=clr)\n",
+        "    plt.fill_between(xx, pr_int95[:,0], pr_int95[:,1], alpha = 0.1,color=clr)\n",
+        "    plt.fill_between(xx, pr_int99[:,0], pr_int99[:,1], alpha = 0.1,color=clr)\n",
+        "\n",
+        "    # make the width of each centile proportional to the epistemic uncertainty\n",
+        "    junk, pr_int25l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.25,0.75])\n",
+        "    junk, pr_int95l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.05,0.95])\n",
+        "    junk, pr_int99l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.01,0.99])\n",
+        "    junk, pr_int25u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.25,0.75])\n",
+        "    junk, pr_int95u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.05,0.95])\n",
+        "    junk, pr_int99u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.01,0.99])\n",
+        "    plt.fill_between(xx, pr_int25l[:,0], pr_int25u[:,0], alpha = 0.3,color=clr)\n",
+        "    plt.fill_between(xx, pr_int95l[:,0], pr_int95u[:,0], alpha = 0.3,color=clr)\n",
+        "    plt.fill_between(xx, pr_int99l[:,0], pr_int99u[:,0], alpha = 0.3,color=clr)\n",
+        "    plt.fill_between(xx, pr_int25l[:,1], pr_int25u[:,1], alpha = 0.3,color=clr)\n",
+        "    plt.fill_between(xx, pr_int95l[:,1], pr_int95u[:,1], alpha = 0.3,color=clr)\n",
+        "    plt.fill_between(xx, pr_int99l[:,1], pr_int99u[:,1], alpha = 0.3,color=clr)\n",
+        "\n",
+        "    # plot actual centile lines\n",
+        "    plt.plot(xx, pr_int25[:,0],color=clr, linewidth=0.5)\n",
+        "    plt.plot(xx, pr_int25[:,1],color=clr, linewidth=0.5)\n",
+        "    plt.plot(xx, pr_int95[:,0],color=clr, linewidth=0.5)\n",
+        "    plt.plot(xx, pr_int95[:,1],color=clr, linewidth=0.5)\n",
+        "    plt.plot(xx, pr_int99[:,0],color=clr, linewidth=0.5)\n",
+        "    plt.plot(xx, pr_int99[:,1],color=clr, linewidth=0.5)\n",
+        "\n",
+        "    plt.xlabel('Age')\n",
+        "    plt.ylabel(idp)\n",
+        "    plt.title(idp)\n",
+        "    plt.xlim((0,90))\n",
+        "    plt.savefig(os.path.join(idp_dir, 'centiles_' + str(sex)),  bbox_inches='tight')\n",
+        "    plt.show()\n",
+        "\n",
+        "os.chdir(out_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "135dbebd-f563-4a2a-9f44-f96757fb4b0b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "135dbebd-f563-4a2a-9f44-f96757fb4b0b",
+        "outputId": "6aabc7ba-2896-4a35-932d-bd843f90b8e5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "centiles_1.png\t    Models\t      Rho_predict.txt\tSMSE_predict.txt  ys2_predict.txt\n",
+            "cov_bspline_ad.txt  pRho_predict.txt  RMSE_predict.txt\tyhat_dummy.pkl\t  Z_predict.txt\n",
+            "cov_bspline_te.txt  resp_ad.txt       sitenum_ad.txt\tyhat_predict.txt\n",
+            "EXPV_predict.txt    resp_te.txt       sitenum_te.txt\tys2_dummy.pkl\n"
+          ]
+        }
+      ],
+      "source": [
+        "# explore an example output folder of a single model (one ROI)\n",
+        "# think about what each of these output files represents.\n",
+        "# Hint: look at the variable names and comments in the code block above\n",
+        "! ls rh_MeanThickness_thickness/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "fe1cac10-01f1-42fd-a4b7-cf08ce0d64be",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fe1cac10-01f1-42fd-a4b7-cf08ce0d64be",
+        "outputId": "cd83ef21-0698-4183-9b90-a48a1f90f465"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    436     436   11115\n"
+          ]
+        }
+      ],
+      "source": [
+        "# check that the number of deviation scores matches the number of subjects in the test set\n",
+        "# there should be one deviation score per subject (one line per subject), so we can\n",
+        "# verify by counting the line numbers in the Z_predict.txt file\n",
+        "! cat rh_MeanThickness_thickness/Z_predict.txt | wc"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "88d2dbc0-e82f-4af5-91eb-dc8aa60f6ba7",
+      "metadata": {
+        "id": "88d2dbc0-e82f-4af5-91eb-dc8aa60f6ba7"
+      },
+      "source": [
+        "The deviation scores are output as a text file in separate folders. We want to summarize the deviation scores across all models estimates so we can organize them into a single file, and merge the deviation scores into the original data file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "e3fb0ced-ed44-487c-86b8-07b9fc04d64e",
+      "metadata": {
+        "id": "e3fb0ced-ed44-487c-86b8-07b9fc04d64e"
+      },
+      "outputs": [],
+      "source": [
+        "! mkdir deviation_scores"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "571f549e-9edd-4f8b-a6b3-d76cd23609f0",
+      "metadata": {
+        "id": "571f549e-9edd-4f8b-a6b3-d76cd23609f0"
+      },
+      "outputs": [],
+      "source": [
+        "! for i in *; do if [[ -e ${i}/Z_predict.txt ]]; then cp ${i}/Z_predict.txt deviation_scores/${i}_Z_predict.txt; fi; done"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "9f63da6c-91e8-4852-91a7-a4e8bc9d9f31",
+      "metadata": {
+        "id": "9f63da6c-91e8-4852-91a7-a4e8bc9d9f31"
+      },
+      "outputs": [],
+      "source": [
+        "z_dir = '/content/braincharts/models/' + model_name + '/deviation_scores/'\n",
+        "\n",
+        "filelist = [name for name in os.listdir(z_dir)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "8791195b-09a9-4251-8fd7-35e80a028d2f",
+      "metadata": {
+        "id": "8791195b-09a9-4251-8fd7-35e80a028d2f"
+      },
+      "outputs": [],
+      "source": [
+        "os.chdir(z_dir)\n",
+        "Z_df = pd.concat([pd.read_csv(item, names=[item[:-4]]) for item in filelist], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "f1054959-dd17-4c1b-b1db-56cf849ecae2",
+      "metadata": {
+        "id": "f1054959-dd17-4c1b-b1db-56cf849ecae2"
+      },
+      "outputs": [],
+      "source": [
+        "df_te.reset_index(inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "6ab00be4-d9c8-49aa-b407-f69946ca2d6c",
+      "metadata": {
+        "id": "6ab00be4-d9c8-49aa-b407-f69946ca2d6c"
+      },
+      "outputs": [],
+      "source": [
+        "Z_df['sub_id'] = df_te['sub_id']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "9f6185b6-d9d7-4651-bbab-2cfba6c46963",
+      "metadata": {
+        "id": "9f6185b6-d9d7-4651-bbab-2cfba6c46963"
+      },
+      "outputs": [],
+      "source": [
+        "df_te_Z = pd.merge(df_te, Z_df, on='sub_id', how='inner')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "ae932714-60c3-4a36-8b72-cd9086a25761",
+      "metadata": {
+        "id": "ae932714-60c3-4a36-8b72-cd9086a25761"
+      },
+      "outputs": [],
+      "source": [
+        "df_te_Z.to_csv('OpenNeuroTransfer_deviation_scores.csv', index=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "9c97bd6e",
+      "metadata": {
+        "id": "9c97bd6e"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "apply_normative_models.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "braincharts",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "f65f66140ab2d9a57fedc58a3b7e1d01f34d12111107cec87dc46b07c8179a15"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file

From 700e8271af69303142471ff2a8bd3190e7b7a275 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 18 Nov 2024 09:56:14 +0100
Subject: [PATCH 63/68] Move numba and nutpie deps to pyproject.toml

---
 .gitignore                |   6 +-
 doc/source/pages/FAQs.rst |   2 +-
 poetry.lock               | 303 +++++++++++++++++++++++++++++---------
 pyproject.toml            |  14 +-
 tests/test_test.py        |   0
 5 files changed, 245 insertions(+), 80 deletions(-)
 create mode 100644 tests/test_test.py

diff --git a/.gitignore b/.gitignore
index c877c0c6..ddd329e6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -92,4 +92,8 @@ build/*
 # CLI test folder
 tests/cli_test/*
 !tests/cli_test/test_cli.sh
-!tests/cli_test/split_data.py
\ No newline at end of file
+!tests/cli_test/split_data.py
+
+docs/autoapi/*
+docs/_build/*
+docs
\ No newline at end of file
diff --git a/doc/source/pages/FAQs.rst b/doc/source/pages/FAQs.rst
index a8879d36..efe98bd0 100644
--- a/doc/source/pages/FAQs.rst
+++ b/doc/source/pages/FAQs.rst
@@ -5,7 +5,7 @@ Frequently Asked Questions
 
 Most of the questions we recieve are about interpretation of normative modeling outputs. 
 
-The PCNtoolkit develoers have written a protocol for how to run a normative modeling analysis which should be helpful to you if you are just getting started. 
+The PCNtoolkit developers have written a protocol for how to run a normative modeling analysis which should be helpful to you if you are just getting started. 
 
 Rutherford, S., Kia, S. M., Wolfers, T., ... Beckmann, C. F., & Marquand, A. F. (2022). The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
 
diff --git a/poetry.lock b/poetry.lock
index 4607f4a6..9c91ef44 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -626,59 +626,61 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "fonttools"
-version = "4.54.1"
+version = "4.55.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "fonttools-4.54.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:41bb0b250c8132b2fcac148e2e9198e62ff06f3cc472065dff839327945c5882"},
-    {file = "fonttools-4.54.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7965af9b67dd546e52afcf2e38641b5be956d68c425bef2158e95af11d229f10"},
-    {file = "fonttools-4.54.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:278913a168f90d53378c20c23b80f4e599dca62fbffae4cc620c8eed476b723e"},
-    {file = "fonttools-4.54.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:0e88e3018ac809b9662615072dcd6b84dca4c2d991c6d66e1970a112503bba7e"},
-    {file = "fonttools-4.54.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:4aa4817f0031206e637d1e685251ac61be64d1adef111060df84fdcbc6ab6c44"},
-    {file = "fonttools-4.54.1-cp310-cp310-win32.whl", hash = "sha256:7e3b7d44e18c085fd8c16dcc6f1ad6c61b71ff463636fcb13df7b1b818bd0c02"},
-    {file = "fonttools-4.54.1-cp310-cp310-win_amd64.whl", hash = "sha256:dd9cc95b8d6e27d01e1e1f1fae8559ef3c02c76317da650a19047f249acd519d"},
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 name = "logical-unification"
 version = "0.4.6"
@@ -1398,6 +1430,40 @@ test = ["coverage (>=7.2)", "pytest", "pytest-cov", "pytest-doctestplus", "pytes
 typing = ["tox"]
 zstd = ["pyzstd (>=0.14.3)"]
 
+[[package]]
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+description = "compiling Python code using LLVM"
+optional = false
+python-versions = ">=3.9"
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+llvmlite = "==0.43.*"
+numpy = ">=1.22,<2.1"
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1443,6 +1509,45 @@ files = [
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+
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+stan = ["bridgestan (>=2.4.1)"]
+
 [[package]]
 name = "packaging"
 version = "24.2"
@@ -1772,6 +1877,60 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
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+[package.extras]
+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -1799,13 +1958,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pymc"
-version = "5.18.0"
+version = "5.18.2"
 description = "Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -1814,7 +1973,7 @@ cachetools = ">=4.2.1"
 cloudpickle = "*"
 numpy = ">=1.15.0"
 pandas = ">=0.24.0"
-pytensor = ">=2.25.1,<2.26"
+pytensor = ">=2.26.1,<2.27"
 rich = ">=13.7.1"
 scipy = ">=1.4.1"
 threadpoolctl = ">=3.1.0,<4.0.0"
@@ -1836,28 +1995,28 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytensor"
-version = "2.25.5"
+version = "2.26.3"
 description = "Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs."
 optional = false
 python-versions = "<3.13,>=3.10"
 files = [
-    {file = "pytensor-2.25.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:23ffac5ce99089abe3b6e1ae0c957f3af2a382209fb5894eb42acba0e01b96bf"},
-    {file = "pytensor-2.25.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29754160a84ade77b63e1690aad8306da3b8f6f8e35c8ac5d36d1b19066b5f8d"},
-    {file = "pytensor-2.25.5-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:51ea5083ec4760e6cfda48422d1b69e88d1b68e0f9a4aca41b0a89a4f38a1da7"},
-    {file = "pytensor-2.25.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:343000f352c12ec36f5307d5c9ff7d8aba6dfa651be8c26b824d75cbad29d776"},
-    {file = "pytensor-2.25.5-cp310-cp310-win_amd64.whl", hash = "sha256:50264e6365578ee15298f23578ddc00cec4f015c1cb77dafbb6c3216b11f689c"},
-    {file = "pytensor-2.25.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a97e769900718fea3a0efd2a4c452775786ca3bd7966c6510e89a1356e516d55"},
-    {file = "pytensor-2.25.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eb7f6a370a99095850949798ee0ada38ace5132172fcbb4272398e687de5b417"},
-    {file = "pytensor-2.25.5-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:930429bdf5cee35868a06a9d075f2fb40b3471708a61bea62cfafd21aaa0811e"},
-    {file = "pytensor-2.25.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:78686d9e26b26da2f0824f1f5a297f153b948a93c46e527d43d7337fe31e5afb"},
-    {file = "pytensor-2.25.5-cp311-cp311-win_amd64.whl", hash = "sha256:8a7b9dc112e3cef8e05641da9429a46fe1cd9d3b3bf11763c3500bc941c49fc7"},
-    {file = "pytensor-2.25.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f15cf74eff5e53854f9e74ac4991ae850e48641ccbb211200d214c599b76d691"},
-    {file = "pytensor-2.25.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c99f2aed904178c59fac57f5a760b547f7c1c14c20d31e41721fbebf9e47cf1"},
-    {file = "pytensor-2.25.5-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:0c973a8a682854eb47378db33cfb5bc63ba5a868b5749fe79b2846e6795ff46d"},
-    {file = "pytensor-2.25.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:16344b21bd70eb689c7db7ab91eec556012c08e9ad8a7cbeb42812d53f89a64e"},
-    {file = "pytensor-2.25.5-cp312-cp312-win_amd64.whl", hash = "sha256:5edaa549903a8be02e6dcf56110d5cf523b6249460c455138e36686928a6c5a7"},
-    {file = "pytensor-2.25.5-py2.py3-none-any.whl", hash = "sha256:92271932118e4d9e6528eeba1cdd0e9e8f2d05d8493eb394da2cd8cf09c8836e"},
-    {file = "pytensor-2.25.5.tar.gz", hash = "sha256:f8b9f7d637104b2fc7fdd9c50219cc639744b718572c034b7cb8fe8f5051da92"},
+    {file = "pytensor-2.26.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bcce400cb435309c00af2dba8eaa8825f651eb4e39571966c141bb616ff17b5b"},
+    {file = "pytensor-2.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89da00c829a6107f275894f75219d48c18916e48a0946b77d8a1bebd4fe995b6"},
+    {file = "pytensor-2.26.3-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:f95bc45f2f2644866b4e603d85edbdc984e609d52877e6ca9381caaa641a1b03"},
+    {file = "pytensor-2.26.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:acc6e799fba9cbf7e544c17e33c976d4e7230c87a322cc69c35b710ef720087b"},
+    {file = "pytensor-2.26.3-cp310-cp310-win_amd64.whl", hash = "sha256:8c63279b86001029a38e82facc3b1d4a8b24723c1fd7263e3590886f3b2c2923"},
+    {file = "pytensor-2.26.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8e41d3b68d6dfabb75c99cd6cb579c9fb9c95f02c40fb5561f39fa7a4b8f6fc6"},
+    {file = "pytensor-2.26.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab08350715554bb53be83d3be75283bb8890caf72e97b122b4fae76244950d3f"},
+    {file = "pytensor-2.26.3-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:6b6985fb9cca966231d654d4396b293f1e33dd78a4bbb274af9f18e19defd5c3"},
+    {file = "pytensor-2.26.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:06362f848cb576e378fd2ff00caf79365aa44c0fe2473d68be1de78013925518"},
+    {file = "pytensor-2.26.3-cp311-cp311-win_amd64.whl", hash = "sha256:ae418d65b752a8bac50c0a401a052854ff85c509a51c70d7c6539299a75c1ba3"},
+    {file = "pytensor-2.26.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:65b6b327cd8e39440b6c090d6c7ff85192f3db99f753b76cb5483fbf3212304c"},
+    {file = "pytensor-2.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4954fbe7b45077063166c83e100d36ad461df283c2222d0d15dc523ac312609"},
+    {file = "pytensor-2.26.3-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:131141f7acbbe80bfa48895269ffcf4d572a0e01ebee81ef6b4f6f1042d88a59"},
+    {file = "pytensor-2.26.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c124fb425bd5867c6b206cb0b8d458ab1f983b180a63581abf9d07369b90e792"},
+    {file = "pytensor-2.26.3-cp312-cp312-win_amd64.whl", hash = "sha256:cce401c6020991767a2fde1d9dc7dda30a8289fc002552bc604e53623c92b8e0"},
+    {file = "pytensor-2.26.3-py2.py3-none-any.whl", hash = "sha256:447ce88dd75a71dbb7158f9ccec0ffc7dc8fdb379f334ebf870003c977e4346f"},
+    {file = "pytensor-2.26.3.tar.gz", hash = "sha256:703cfdba1d66b84a1739f50abdd7c18d25f788a85f2d07ed9b5bc66c929fb2fb"},
 ]
 
 [package.dependencies]
@@ -2623,4 +2782,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "83eae095eaf77b411c3945404211e299478b3374ecc52808f5b289f703bb3646"
+content-hash = "5b075d3de7d6307785d93bb28f9af1d879f19bcacc1093929d6cf8a252c75d48"
diff --git a/pyproject.toml b/pyproject.toml
index d370c791..eb1a87e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,14 +8,16 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<3.13"
-bspline = "^0.1.1"              # Install with pip
-nibabel = "^5.3.1"              # Prefer Conda
-pymc = "^5.18.0"                # Prefer Conda
-scikit-learn = "^1.5.2"         # Prefer Conda
-seaborn = "^0.13.2"             # Prefer conda
-six = "^1.16.0"                 # Prefer conda
+bspline = "^0.1.1"              
+nibabel = "^5.3.1"             
+pymc = "^5.18.0"                
+scikit-learn = "^1.5.2"         
+seaborn = "^0.13.2"            
+six = "^1.16.0"                 
 scipy = "^1.12"
 matplotlib = "^3.8.1"
+numba = "^0.60.0"
+nutpie = "^0.13.2"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"
diff --git a/tests/test_test.py b/tests/test_test.py
new file mode 100644
index 00000000..e69de29b

From e9928203a503ac5539389be5ec415f5a38c3ae5d Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 18 Nov 2024 14:29:18 +0100
Subject: [PATCH 64/68] remove numba and nutpie from pyproject.toml and add
 numpy

---
 README.md        |   2 +-
 poetry.lock      | 159 +----------------------------------------------
 pyproject.toml   |  20 +++---
 tests/testHBR.py |   2 +-
 4 files changed, 14 insertions(+), 169 deletions(-)

diff --git a/README.md b/README.md
index f18f7801..a3699197 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ using the download here: https://www.anaconda.com/download
 
 #### Create environment 
 ```
-conda create  python==3.12
+conda create -n  python==3.12
 ```
 
 #### Activate environment
diff --git a/poetry.lock b/poetry.lock
index 9c91ef44..ca8e0876 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1123,36 +1123,6 @@ files = [
     {file = "kiwisolver-1.4.7.tar.gz", hash = "sha256:9893ff81bd7107f7b685d3017cc6583daadb4fc26e4a888350df530e41980a60"},
 ]
 
-[[package]]
-name = "llvmlite"
-version = "0.43.0"
-description = "lightweight wrapper around basic LLVM functionality"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
-    {file = "llvmlite-0.43.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d4fd101f571a31acb1559ae1af30f30b1dc4b3186669f92ad780e17c81e91bc"},
-    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d434ec7e2ce3cc8f452d1cd9a28591745de022f931d67be688a737320dfcead"},
-    {file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6912a87782acdff6eb8bf01675ed01d60ca1f2551f8176a300a886f09e836a6a"},
-    {file = "llvmlite-0.43.0-cp310-cp310-win_amd64.whl", hash = "sha256:14f0e4bf2fd2d9a75a3534111e8ebeb08eda2f33e9bdd6dfa13282afacdde0ed"},
-    {file = "llvmlite-0.43.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8d0618cb9bfe40ac38a9633f2493d4d4e9fcc2f438d39a4e854f39cc0f5f98"},
-    {file = "llvmlite-0.43.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e0a9a1a39d4bf3517f2af9d23d479b4175ead205c592ceeb8b89af48a327ea57"},
-    {file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c1da416ab53e4f7f3bc8d4eeba36d801cc1894b9fbfbf2022b29b6bad34a7df2"},
-    {file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:977525a1e5f4059316b183fb4fd34fa858c9eade31f165427a3977c95e3ee749"},
-    {file = "llvmlite-0.43.0-cp311-cp311-win_amd64.whl", hash = "sha256:d5bd550001d26450bd90777736c69d68c487d17bf371438f975229b2b8241a91"},
-    {file = "llvmlite-0.43.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f99b600aa7f65235a5a05d0b9a9f31150c390f31261f2a0ba678e26823ec38f7"},
-    {file = "llvmlite-0.43.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:35d80d61d0cda2d767f72de99450766250560399edc309da16937b93d3b676e7"},
-    {file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eccce86bba940bae0d8d48ed925f21dbb813519169246e2ab292b5092aba121f"},
-    {file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df6509e1507ca0760787a199d19439cc887bfd82226f5af746d6977bd9f66844"},
-    {file = "llvmlite-0.43.0-cp312-cp312-win_amd64.whl", hash = "sha256:7a2872ee80dcf6b5dbdc838763d26554c2a18aa833d31a2635bff16aafefb9c9"},
-    {file = "llvmlite-0.43.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9cd2a7376f7b3367019b664c21f0c61766219faa3b03731113ead75107f3b66c"},
-    {file = "llvmlite-0.43.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:18e9953c748b105668487b7c81a3e97b046d8abf95c4ddc0cd3c94f4e4651ae8"},
-    {file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74937acd22dc11b33946b67dca7680e6d103d6e90eeaaaf932603bec6fe7b03a"},
-    {file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc9efc739cc6ed760f795806f67889923f7274276f0eb45092a1473e40d9b867"},
-    {file = "llvmlite-0.43.0-cp39-cp39-win_amd64.whl", hash = "sha256:47e147cdda9037f94b399bf03bfd8a6b6b1f2f90be94a454e3386f006455a9b4"},
-    {file = "llvmlite-0.43.0.tar.gz", hash = "sha256:ae2b5b5c3ef67354824fb75517c8db5fbe93bc02cd9671f3c62271626bc041d5"},
-]
-
 [[package]]
 name = "logical-unification"
 version = "0.4.6"
@@ -1430,40 +1400,6 @@ test = ["coverage (>=7.2)", "pytest", "pytest-cov", "pytest-doctestplus", "pytes
 typing = ["tox"]
 zstd = ["pyzstd (>=0.14.3)"]
 
-[[package]]
-name = "numba"
-version = "0.60.0"
-description = "compiling Python code using LLVM"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "numba-0.60.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d761de835cd38fb400d2c26bb103a2726f548dc30368853121d66201672e651"},
-    {file = "numba-0.60.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:159e618ef213fba758837f9837fb402bbe65326e60ba0633dbe6c7f274d42c1b"},
-    {file = "numba-0.60.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1527dc578b95c7c4ff248792ec33d097ba6bef9eda466c948b68dfc995c25781"},
-    {file = "numba-0.60.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fe0b28abb8d70f8160798f4de9d486143200f34458d34c4a214114e445d7124e"},
-    {file = "numba-0.60.0-cp310-cp310-win_amd64.whl", hash = "sha256:19407ced081d7e2e4b8d8c36aa57b7452e0283871c296e12d798852bc7d7f198"},
-    {file = "numba-0.60.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a17b70fc9e380ee29c42717e8cc0bfaa5556c416d94f9aa96ba13acb41bdece8"},
-    {file = "numba-0.60.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3fb02b344a2a80efa6f677aa5c40cd5dd452e1b35f8d1c2af0dfd9ada9978e4b"},
-    {file = "numba-0.60.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5f4fde652ea604ea3c86508a3fb31556a6157b2c76c8b51b1d45eb40c8598703"},
-    {file = "numba-0.60.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4142d7ac0210cc86432b818338a2bc368dc773a2f5cf1e32ff7c5b378bd63ee8"},
-    {file = "numba-0.60.0-cp311-cp311-win_amd64.whl", hash = "sha256:cac02c041e9b5bc8cf8f2034ff6f0dbafccd1ae9590dc146b3a02a45e53af4e2"},
-    {file = "numba-0.60.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d7da4098db31182fc5ffe4bc42c6f24cd7d1cb8a14b59fd755bfee32e34b8404"},
-    {file = "numba-0.60.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:38d6ea4c1f56417076ecf8fc327c831ae793282e0ff51080c5094cb726507b1c"},
-    {file = "numba-0.60.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:62908d29fb6a3229c242e981ca27e32a6e606cc253fc9e8faeb0e48760de241e"},
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-    {file = "numba-0.60.0-cp312-cp312-win_amd64.whl", hash = "sha256:f75262e8fe7fa96db1dca93d53a194a38c46da28b112b8a4aca168f0df860347"},
-    {file = "numba-0.60.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:01ef4cd7d83abe087d644eaa3d95831b777aa21d441a23703d649e06b8e06b74"},
-    {file = "numba-0.60.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:819a3dfd4630d95fd574036f99e47212a1af41cbcb019bf8afac63ff56834449"},
-    {file = "numba-0.60.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b983bd6ad82fe868493012487f34eae8bf7dd94654951404114f23c3466d34b"},
-    {file = "numba-0.60.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c151748cd269ddeab66334bd754817ffc0cabd9433acb0f551697e5151917d25"},
-    {file = "numba-0.60.0-cp39-cp39-win_amd64.whl", hash = "sha256:3031547a015710140e8c87226b4cfe927cac199835e5bf7d4fe5cb64e814e3ab"},
-    {file = "numba-0.60.0.tar.gz", hash = "sha256:5df6158e5584eece5fc83294b949fd30b9f1125df7708862205217e068aabf16"},
-]
-
-[package.dependencies]
-llvmlite = "==0.43.*"
-numpy = ">=1.22,<2.1"
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1509,45 +1445,6 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
-[[package]]
-name = "nutpie"
-version = "0.13.2"
-description = "Sample Stan or PyMC models"
-optional = false
-python-versions = ">=3.10"
-files = [
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-    {file = "nutpie-0.13.2.tar.gz", hash = "sha256:f14282e2ac045c67a9b262a865b02a243178c55b541b236b21dfcb0c3678bcea"},
-]
-
-[package.dependencies]
-arviz = ">=0.15.0"
-pandas = ">=2.0"
-pyarrow = ">=12.0.0"
-xarray = ">=2023.6.0"
-
-[package.extras]
-all = ["bridgestan (>=2.4.1)", "jax (>=0.4.27)", "numba (>=0.59.1)", "pymc (>=5.15.0)"]
-pymc = ["numba (>=0.59.1)", "pymc (>=5.15.0)"]
-pymc-jax = ["jax (>=0.4.27)", "pymc (>=5.15.0)"]
-stan = ["bridgestan (>=2.4.1)"]
-
 [[package]]
 name = "packaging"
 version = "24.2"
@@ -1877,60 +1774,6 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
-[[package]]
-name = "pyarrow"
-version = "18.0.0"
-description = "Python library for Apache Arrow"
-optional = false
-python-versions = ">=3.9"
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-    {file = "pyarrow-18.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b30a927c6dff89ee702686596f27c25160dd6c99be5bcc1513a763ae5b1bfc03"},
-    {file = "pyarrow-18.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:8f40ec677e942374e3d7f2fad6a67a4c2811a8b975e8703c6fd26d3b168a90e2"},
-    {file = "pyarrow-18.0.0.tar.gz", hash = "sha256:a6aa027b1a9d2970cf328ccd6dbe4a996bc13c39fd427f502782f5bdb9ca20f5"},
-]
-
-[package.extras]
-test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
-
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -2782,4 +2625,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "5b075d3de7d6307785d93bb28f9af1d879f19bcacc1093929d6cf8a252c75d48"
+content-hash = "8e68c8be0ee075c1bd72557f1e774ccbdb8fb0635317d234a79487e0577d1061"
diff --git a/pyproject.toml b/pyproject.toml
index eb1a87e0..266a05a0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,16 +8,15 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<3.13"
-bspline = "^0.1.1"              
-nibabel = "^5.3.1"             
-pymc = "^5.18.0"                
-scikit-learn = "^1.5.2"         
-seaborn = "^0.13.2"            
-six = "^1.16.0"                 
+bspline = "^0.1.1"             
+nibabel = "^5.3.1"              
+pymc = "^5.18.0"              
+scikit-learn = "^1.5.2"      
+six = "^1.16.0"          
 scipy = "^1.12"
-matplotlib = "^3.8.1"
-numba = "^0.60.0"
-nutpie = "^0.13.2"
+matplotlib = "^3.9.2"
+seaborn = "^0.13.2"
+numpy = "^1.26"
 
 [tool.poetry.group.dev.dependencies]
 sphinx-tabs = "^3.4.7"
@@ -30,5 +29,8 @@ ipykernel = "^6.29.5"
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
+[requires-python]
+python_version = ">=3.10,<3.13"
+
 [tool.poetry.scripts]
 normative = "pcntoolkit.normative:entrypoint"
diff --git a/tests/testHBR.py b/tests/testHBR.py
index db875fa2..cd4e833e 100644
--- a/tests/testHBR.py
+++ b/tests/testHBR.py
@@ -61,7 +61,7 @@
     model_type=model_type,
     likelihood="SHASHb",
     linear_sigma="True",
-    random_slope_mu="False",
+    random_slope_mu="True",
     linear_epsilon="False",
     linear_delta="False",
     nuts_sampler="nutpie",

From 0c083b24fcd83ecc37b70448046eea90e40c7e19 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Mon, 18 Nov 2024 14:58:19 +0100
Subject: [PATCH 65/68] Update README.md

---
 README.md | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index a3699197..04ba245a 100644
--- a/README.md
+++ b/README.md
@@ -23,16 +23,17 @@ conda create -n  python==3.12
 source activate 
 ```
 
-#### Install torch 
-
-Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. 
-
-### Install nutpie using conda 
+### Install nutpie and numba from conda-forge
 
 ```
 conda install nutpie numba -c conda-forge
 ```
 
+#### Install torch from pytorch.org
+
+Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. 
+
+
 #### Install PCNtoolkit
 
 Using pip:
@@ -65,6 +66,13 @@ conda create -y python==3.12 numpy mkl blas --prefix=/shared/conda/
 ```
 conda activate /shared/conda/
 ```
+
+### Install nutpie using conda 
+
+```
+conda install nutpie numba -c conda-forge
+```
+
 #### install torch 
 
 Using the command that you get from the command builder here:
@@ -76,11 +84,6 @@ https://pytorch.org/get-started/locally/
 If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip.
 
 
-### Install nutpie using conda 
-
-```
-conda install nutpie numba -c conda-forge
-```
 
 #### Clone the repo
 

From 6a86683d90d4bc8591aa683350b54349aefab0a1 Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 19 Nov 2024 13:54:28 +0100
Subject: [PATCH 66/68] Replace doc notebook .rst files and update installation
 instructions

---
 README.md                                     |    2 -
 .../pages/BLR_normativemodel_protocol.rst     | 1533 ++++++++++++--
 .../BLR_normativemodel_protocol_15_1.png      |  Bin 13275 -> 0 bytes
 .../BLR_normativemodel_protocol_16_0.png      |  Bin 0 -> 19471 bytes
 .../BLR_normativemodel_protocol_93_2.png      |  Bin 28250 -> 0 bytes
 .../BLR_normativemodel_protocol_97_1.png      |  Bin 0 -> 41759 bytes
 .../HBR_NormativeModel_FCONdata_Tutorial.rst  | 1885 ++++++++++++++++-
 ..._NormativeModel_FCONdata_Tutorial_10_3.png |  Bin 0 -> 143042 bytes
 ...R_NormativeModel_FCONdata_Tutorial_9_3.png |  Bin 78758 -> 0 bytes
 .../pages/apply_normative_models_ct.rst       | 1644 ++++++--------
 .../apply_normative_models_ct_27_1.png        |  Bin 0 -> 67166 bytes
 .../apply_normative_models_ct_27_3.png        |  Bin 0 -> 62973 bytes
 .../apply_normative_models_ct_27_5.png        |  Bin 0 -> 59930 bytes
 doc/source/pages/installation.rst             |   15 +-
 .../pages/normative_modelling_walkthrough.rst |  654 ++++--
 .../normative_modelling_walkthrough_22_0.png  |  Bin 29709 -> 0 bytes
 .../normative_modelling_walkthrough_22_1.png  |  Bin 29125 -> 0 bytes
 .../normative_modelling_walkthrough_22_2.png  |  Bin 29391 -> 0 bytes
 .../normative_modelling_walkthrough_22_3.png  |  Bin 30697 -> 0 bytes
 .../normative_modelling_walkthrough_22_4.png  |  Bin 25341 -> 0 bytes
 .../normative_modelling_walkthrough_22_5.png  |  Bin 26189 -> 0 bytes
 .../normative_modelling_walkthrough_22_6.png  |  Bin 26741 -> 0 bytes
 .../normative_modelling_walkthrough_22_7.png  |  Bin 27756 -> 0 bytes
 .../normative_modelling_walkthrough_23_0.png  |  Bin 0 -> 59333 bytes
 .../normative_modelling_walkthrough_23_1.png  |  Bin 0 -> 57692 bytes
 .../normative_modelling_walkthrough_23_2.png  |  Bin 0 -> 60751 bytes
 .../normative_modelling_walkthrough_23_3.png  |  Bin 0 -> 60649 bytes
 .../normative_modelling_walkthrough_23_4.png  |  Bin 0 -> 50217 bytes
 .../normative_modelling_walkthrough_23_5.png  |  Bin 0 -> 51525 bytes
 .../normative_modelling_walkthrough_23_6.png  |  Bin 0 -> 53373 bytes
 .../normative_modelling_walkthrough_23_7.png  |  Bin 0 -> 54104 bytes
 .../normative_modelling_walkthrough_32_1.png  |  Bin 9492 -> 0 bytes
 .../normative_modelling_walkthrough_33_1.png  |  Bin 0 -> 20978 bytes
 doc/source/pages/other_predictive_models.rst  |  415 ++--
 .../other_predictive_models_16_1.png          |  Bin 16613 -> 0 bytes
 .../other_predictive_models_17_1.png          |  Bin 18179 -> 35783 bytes
 .../other_predictive_models_18_1.png          |  Bin 0 -> 39366 bytes
 .../other_predictive_models_32_3.png          |  Bin 39660 -> 0 bytes
 .../other_predictive_models_33_1.png          |  Bin 0 -> 69309 bytes
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 .../other_predictive_models_34_1.png          |  Bin 0 -> 66810 bytes
 .../other_predictive_models_50_4.png          |  Bin 70420 -> 0 bytes
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diff --git a/README.md b/README.md
index 04ba245a..eddeb9ad 100644
--- a/README.md
+++ b/README.md
@@ -83,8 +83,6 @@ https://pytorch.org/get-started/locally/
 
 If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip.
 
-
-
 #### Clone the repo
 
 ```
diff --git a/doc/source/pages/BLR_normativemodel_protocol.rst b/doc/source/pages/BLR_normativemodel_protocol.rst
index 479b5163..8e5e8975 100644
--- a/doc/source/pages/BLR_normativemodel_protocol.rst
+++ b/doc/source/pages/BLR_normativemodel_protocol.rst
@@ -1,27 +1,24 @@
-.. title:: BLR tutorial
+`Predictive Clinical Neuroscience Toolkit `__
+======================================================================================
 
-Bayesian Linear Regression
+The Normative Modeling Framework for Computational Psychiatry Protocol
 ======================================================================
 
-The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
+Using Bayesian Linear Regression and Multi-Site Cortical Thickness Data
+-----------------------------------------------------------------------
 
 Created by `Saige Rutherford `__
 
-Using Multi-Site Cortical Thickness Data
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/BLR_protocol/BLR_normativemodel_protocol.ipynb 
-
-
-.. figure:: ./blr_fig2.png
-   :height: 400px
-   :align: center
+.. container::
 
 Data Preparation
----------------------------------------------
+================
 
 Install necessary libraries & grab data files
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+---------------------------------------------
+
+Step 1.
+~~~~~~~
 
 Begin by cloning the GitHub repository using the following commands.
 This repository contains the necessary code and example data. Then
@@ -33,33 +30,28 @@ your computer).
 
     ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
 
-
-.. parsed-literal::
-
-    Cloning into 'PCNtoolkit-demo'...
-    remote: Enumerating objects: 855, done.
-    remote: Counting objects: 100% (855/855), done.
-    remote: Compressing objects: 100% (737/737), done.
-    remote: Total 855 (delta 278), reused 601 (delta 101), pack-reused 0
-    Receiving objects: 100% (855/855), 18.07 MiB | 13.53 MiB/s, done.
-    Resolving deltas: 100% (278/278), done.
-
-
 .. code:: ipython3
 
     import os
     
     # set this path to the git cloned PCNtoolkit-demo repository --> Uncomment whichever line you need for either running on your own computer or on Google Colab.
-    #os.chdir('/Users/PCNtoolkit-demo/tutorials/BLR_protocol') # if running on your own computer, use this line (change the path to match where you cloned the repository)
-    os.chdir('/content/PCNtoolkit-demo/tutorials/BLR_protocol') # if running on Google Colab, use this line
+    #wdir = '/PCNtoolkit-demo' # if running on your own computer, use this line (change the path to match where you cloned the repository)
+    wdir ='/content/PCNtoolkit-demo' # if running on Google Colab, use this line
+    
+    os.chdir(os.path.join(wdir,'tutorials','BLR_protocol'))
+
 
 .. code:: ipython3
 
+    ! pip install nutpie
+    ! pip install pcntoolkit
     ! pip install -r requirements.txt
 
-
 Prepare covariate data
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------
+
+Step 2.
+~~~~~~~
 
 The data set (downloaded in Step 1) includes a multi-site dataset from
 the `Human Connectome Project Young Adult
@@ -88,8 +80,8 @@ depending on the research question.
 .. code:: ipython3
 
     # if running in Google colab, remove the "data/" folder from the path
-    hcp = pd.read_csv('/content/PCNtoolkit-demo/data/HCP1200_age_gender.csv')
-    ixi = pd.read_csv('/content/PCNtoolkit-demo/data/IXI_age_gender.csv')
+    hcp = pd.read_csv(os.path.join(wdir,'data','HCP1200_age_gender.csv'))
+    ixi = pd.read_csv(os.path.join(wdir,'data','IXI_age_gender.csv'))
 
 .. code:: ipython3
 
@@ -98,8 +90,8 @@ depending on the research question.
 
 .. parsed-literal::
 
-    /usr/local/lib/python3.7/dist-packages/pandas/core/reshape/merge.py:1218: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation
-      UserWarning,
+    :1: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation.
+      cov = pd.merge(hcp, ixi, on=["participant_id", "age", "sex", "site"], how='outer')
 
 
 .. code:: ipython3
@@ -108,29 +100,348 @@ depending on the research question.
 
 .. code:: ipython3
 
-    sns.displot(cov, x="age", hue="site", multiple="stack", height=6)
+    sns.displot(cov, x="age", hue="site", multiple="stack", height=6);
 
 
 
+.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_16_0.png
 
-.. parsed-literal::
 
-    
+.. code:: ipython3
 
+    cov.groupby(['site']).describe()
 
 
 
-.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_15_1.png
 
+.. raw:: html
 
-.. code:: ipython3
+    
+      

+        

+    
+    
\n",
        "                        \n",
+       "                            value=\"1500\">\n",
        "                        \n",
        "                    58150000.0030.02511

+      
+        
+          
+          
+          
+        
+        
+          
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+          
+          
+          
+          
+          
+        
+      
+    
agesex
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
site
hcp1206.028.8374793.69053422.00000026.00000029.0000032.00000037.000001206.01.5439470.4982721.01.02.02.02.0
ixi590.049.47653116.72086419.98083534.02772150.6119163.41341586.31896590.01.5559320.4972831.01.02.02.02.0

+    
+        

+    
+      

+        
+    
+      
+    
+        
+      

+    
+    
+    

+      
+    
+    
+    
+      
+    

+    
+        

+      
 
-    cov.groupby(['site']).describe()
 
 
 
-Prepare brain data
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Preprare brain data
+-------------------
+
+Step 3.
+~~~~~~~
 
 Next, format and combine the MRI data using the following commands. The
 example data contains cortical thickness maps estimated by running
@@ -139,7 +450,7 @@ was reduced by using ROIs from the Desikan-Killiany atlas. Including the
 Euler number as a covariate is also recommended, as this is a proxy
 metric for data quality. The `Euler
 number `__ from
-each subjects recon-all output folder was extracted into a text file
+each subject’s recon-all output folder was extracted into a text file
 and is merged into the cortical thickness data frame. The Euler number
 is site-specific, thus, to use the same exclusion threshold across sites
 it is important to center the site by subtracting the site median from
@@ -147,7 +458,7 @@ all subjects at a site. Then take the square root and multiply by
 negative one and exclude any subjects with a square root above 10.
 
 Here is some psuedo-code (run from a terminal in the folder that has all
-subjects recon-all output folders) that was used to extract these ROIs:
+subject’s recon-all output folders) that was used to extract these ROIs:
 
 ``export SUBJECTS_DIR=/path/to/study/freesurfer_data/``
 
@@ -157,14 +468,14 @@ subjects recon-all output folders) that was used to extract these ROIs:
 
 .. code:: ipython3
 
-    hcpya = pd.read_csv('/content/PCNtoolkit-demo/data/HCP1200_aparc_thickness.csv')
-    ixi = pd.read_csv('/content/PCNtoolkit-demo/data/IXI_aparc_thickness.csv')
+    hcpya = pd.read_csv(os.path.join(wdir,'data','HCP1200_aparc_thickness.csv'))
+    ixi = pd.read_csv(os.path.join(wdir,'data','IXI_aparc_thickness.csv'))
 
 .. code:: ipython3
 
     brain_all = pd.merge(ixi, hcpya, how='outer')
 
-We extracted the euler number from each subjects recon-all output
+We extracted the euler number from each subject’s recon-all output
 folder into a text file and we now need to format and combine these into
 our brain dataframe.
 
@@ -173,12 +484,12 @@ recon-all.log for each subject. Run this from the terminal in the folder
 where your subjects recon-all output folders are located. This assumes
 that all of your subject IDs start with “sub-” prefix.
 
-:literal:`for i in sub-*; do if [[ -e ${i}/scripts/recon-all.log ]]; then cat ${i}/scripts/recon-all.log | grep -A 1 "Computing euler" > temp_log; lh_en=$(cat temp_log | head -2 | tail -1 | awk -F '=' '{print $2}' | awk -F ',' '{print $1}'); rh_en=$(cat temp_log | head -2 | tail -1 | awk -F '=' '{print $3}'); echo "${i}, ${lh_en}, ${rh_en}" >> euler.csv; echo ${i}; fi; done`
+:literal:`for i in sub-\*; do if [[ -e ${i}/scripts/recon-all.log ]]; then cat ${i}/scripts/recon-all.log | grep -A 1 "Computing euler" > temp_log; lh_en=`cat temp_log | head -2 | tail -1 | awk -F '=' '{print $2}' | awk -F ',' '{print $1}'\`; rh_en=`cat temp_log | head -2 | tail -1 | awk -F '=' '{print $3}'\`; echo "${i}, ${lh_en}, ${rh_en}" >> euler.csv; echo ${i}; fi; done`
 
 .. code:: ipython3
 
-    hcp_euler = pd.read_csv('/content/PCNtoolkit-demo/data/hcp-ya_euler.csv')
-    ixi_euler = pd.read_csv('/content/PCNtoolkit-demo/data/ixi_euler.csv')
+    hcp_euler = pd.read_csv(os.path.join(wdir,'data','hcp-ya_euler.csv'))
+    ixi_euler = pd.read_csv(os.path.join(wdir,'data','ixi_euler.csv'))
 
 .. code:: ipython3
 
@@ -221,7 +532,7 @@ inclusion is not too strict or too lenient.
 
 .. code:: ipython3
 
-    df_euler.groupby(by='site').median()
+    df_euler.groupby(by='site')[['lh_euler', 'rh_euler', 'avg_euler']].median()
 
 
 
@@ -229,9 +540,8 @@ inclusion is not too strict or too lenient.
 .. raw:: html
 
     
-      

-        

-          

+      

+        

     
     
-          
+        
+      

+    
+    
+    

+      
+    
+    
+    
+      
+    

+    
         

       

 
@@ -363,6 +807,13 @@ inclusion is not too strict or too lenient.
 
     df_euler['site_median'] = df_euler['site_median'].replace({'hcp':-43,'ixi':-56})
 
+
+.. parsed-literal::
+
+    :1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
+      df_euler['site_median'] = df_euler['site_median'].replace({'hcp':-43,'ixi':-56})
+
+
 .. code:: ipython3
 
     df_euler['avg_euler_centered'] = df_euler['avg_euler'] - df_euler['site_median']
@@ -383,18 +834,19 @@ inclusion is not too strict or too lenient.
 
     brain_good = brain.query('avg_euler_centered_neg_sqrt < 10')
 
-.. warning::
-    **CRITICAL STEP:** If possible, data should be visually inspected to
-    verify that the data inclusion is not too strict or too lenient.
-    Subjects above the Euler number threshold should be manually checked to
-    verify and justify their exclusion due to poor data quality. This is
-    just one approach for automated QC used by the developers of the
-    PCNtoolkit. Other approaches such as the ENIGMA QC pipeline or UK
-    Biobanks QC pipeline are also viable options for automated QC.
+**CRITICAL STEP:** If possible, data should be visually inspected to
+verify that the data inclusion is not too strict or too lenient.
+Subjects above the Euler number threshold should be manually checked to
+verify and justify their exclusion due to poor data quality. This is
+just one approach for automated QC used by the developers of the
+PCNtoolkit. Other approaches such as the ENIGMA QC pipeline or UK
+Biobank’s QC pipeline are also viable options for automated QC.
 
 Combine covariate & cortical thickness dataframes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-------------------------------------------------
 
+Step 4.
+~~~~~~~
 
 The normative modeling function requires the covariate predictors and
 brain features to be in separate text files. However, it is important to
@@ -407,7 +859,7 @@ their own dataframes, using the commands below.
 
 .. code:: ipython3
 
-    # make sure to use how="inner" so that we only include subjects that have data in both the covariate and the cortical thickness files 
+    # make sure to use how="inner" so that we only include subjects that have data in both the covariate and the cortical thickness files
     all_data = pd.merge(brain_good, cov, how='inner')
 
 .. code:: ipython3
@@ -433,13 +885,15 @@ their own dataframes, using the commands below.
 
     all_data_covariates = all_data[['age','sex','site']]
 
-.. warning::
-    **CRITICAL STEP:** ``roi_ids`` is a variable that represents which brain
-    areas will be modeled and can be used to select subsets of the data
-    frame if you do not wish to run models for the whole brain.
+**CRITICAL STEP:** ``roi_ids`` is a variable that represents which brain
+areas will be modeled and can be used to select subsets of the data
+frame if you do not wish to run models for the whole brain.
 
 Add variable to model site/scanner effects
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------------------------
+
+Step 5.
+~~~~~~~
 
 Currently, the different sites are coded in a single column (named
 ‘site’) and are represented as a string data type. However, the
@@ -451,17 +905,689 @@ variables (0=not in this site, 1=present in this site).
 
 .. code:: ipython3
 
-    all_data_covariates = pd.get_dummies(all_data_covariates, columns=['site'])
+    all_data_covariates = pd.get_dummies(all_data_covariates, columns=['site'], dtype=int)
 
 .. code:: ipython3
 
-    all_data['Average_Thickness'] = all_data[['lh_MeanThickness_thickness','rh_MeanThickness_thickness']].mean(axis=1)
+    all_data_covariates.head()
+
+
+
+
+.. raw:: html
+
+    
+      

+        

+    
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+      
+    
agesexsite_hcpsite_ixi
027.0110
127.0210
233.0110
327.0110
435.0210

+    

+        

+    
+      

+        
+    
+      
+    
+        
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+    
+    
+    

+      
+    
+    
+    
+      
+    

+    
+        

+      

+
+
+
+
+.. code:: ipython3
 
+    all_data_covariates
 
 
+
+
+.. raw:: html
+
+    
+      

+        

+    
+    
+      
+        
+          
+          
+          
+          
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+        
+      
+    
agesexsite_hcpsite_ixi
027.000000110
127.000000210
233.000000110
327.000000110
435.000000210
...............
168747.723477101
168850.395619101
168942.989733101
169046.220397101
169141.741273101

+    
1692 rows × 4 columns

+    

+        

+    
+      

+        
+    
+      
+    
+        
+      

+    
+    
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+      
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+    
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+    

+    
+      

+        
+        
+        
+      

+    
+        

+      

+
+
+
+
+.. code:: ipython3
+
+    all_data['Average_Thickness'] = all_data[['lh_MeanThickness_thickness','rh_MeanThickness_thickness']].mean(axis=1)
+
 Train/test split
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------
 
+Step 6.
+~~~~~~~
 
 In this example, we use 80% of the data for training and 20% for
 testing. Please carefully read the experimental design section on
@@ -501,11 +1627,12 @@ Verify that your train & test arrays are the same size
     Test response size is:  (339, 6)
 
 
-.. warning::
-    **CRITICAL STEP:** The model would not learn the site effects if all the
-    data from one site was only in the test set. Therefore, we stratify the
-    train/test split using the site variable.
+**CRITICAL STEP:** The model would not learn the site effects if all the
+data from one site was only in the test set. Therefore, we stratify the
+train/test split using the site variable.
 
+Step 7.
+~~~~~~~
 
 When the data were split into train and test sets, the row index was not
 reset. This means that the row index in the train and test data frames
@@ -540,9 +1667,11 @@ which sites to evaluate model performance for, as follows:
     # Create a list with sites names to use in evaluating per-site metrics
     site_names = ['hcp', 'ixi']
 
-
 Setup output directories
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------
+
+Step 8.
+-------
 
 Save each brain region to its own text file (organized in separate
 directories) using the following commands, because for each response
@@ -557,6 +1686,7 @@ variable, Y (e.g., brain region) we fit a separate normative model.
 
     X_train.to_csv('cov_tr.txt', sep = '\t', header=False, index = False)
 
+
 .. code:: ipython3
 
     y_train.to_csv('resp_tr.txt', sep = '\t', header=False, index = False)
@@ -580,12 +1710,25 @@ variable, Y (e.g., brain region) we fit a separate normative model.
 
 .. code:: ipython3
 
+    # Note: please change the path in the following to wdir (depending on whether you are running on colab or not)
+    
     ! for i in `cat /content/PCNtoolkit-demo/data/roi_dir_names`; do if [[ -e resp_tr_${i}.txt ]]; then cd ROI_models; mkdir ${i}; cd ../; cp resp_tr_${i}.txt ROI_models/${i}/resp_tr.txt; cp resp_te_${i}.txt ROI_models/${i}/resp_te.txt; cp cov_tr.txt ROI_models/${i}/cov_tr.txt; cp cov_te.txt ROI_models/${i}/cov_te.txt; fi; done
 
+
+.. parsed-literal::
+
+    mkdir: cannot create directory ‘lh_MeanThickness_thickness’: File exists
+    mkdir: cannot create directory ‘lh_bankssts_thickness’: File exists
+    mkdir: cannot create directory ‘lh_caudalanteriorcingulate_thickness’: File exists
+    mkdir: cannot create directory ‘lh_superiorfrontal_thickness’: File exists
+    mkdir: cannot create directory ‘rh_MeanThickness_thickness’: File exists
+    mkdir: cannot create directory ‘rh_superiorfrontal_thickness’: File exists
+
+
 .. code:: ipython3
 
     # clean up files
-    ! rm resp_*.txt 
+    ! rm resp_*.txt
 
 .. code:: ipython3
 
@@ -593,11 +1736,13 @@ variable, Y (e.g., brain region) we fit a separate normative model.
     ! rm cov_t*.txt
 
 Algorithm & Modeling
--------------------------------
+====================
 
 Basis expansion using B-Splines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-------------------------------
 
+Step 9.
+~~~~~~~
 
 Now, set up a B-spline basis set that allows us to perform nonlinear
 regression using a linear model, using the following commands. This
@@ -615,29 +1760,29 @@ al `__.
 .. code:: ipython3
 
     # set this path to wherever your ROI_models folder is located (where you copied all of the covariate & response text files to in Step 4)
-    data_dir = '/content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/'
+    data_dir = os.path.join(wdir,'tutorials','BLR_protocol','ROI_models')
     
     # Create a cubic B-spline basis (used for regression)
     xmin = 10#16 # xmin & xmax are the boundaries for ages of participants in the dataset
     xmax = 95#90
     B = create_bspline_basis(xmin, xmax)
-    # create the basis expansion for the covariates for each of the 
-    for roi in roi_ids: 
+    # create the basis expansion for the covariates for each of the
+    for roi in roi_ids:
         print('Creating basis expansion for ROI:', roi)
         roi_dir = os.path.join(data_dir, roi)
         os.chdir(roi_dir)
-        # create output dir 
+        # create output dir
         os.makedirs(os.path.join(roi_dir,'blr'), exist_ok=True)
         # load train & test covariate data matrices
         X_tr = np.loadtxt(os.path.join(roi_dir, 'cov_tr.txt'))
         X_te = np.loadtxt(os.path.join(roi_dir, 'cov_te.txt'))
-        # add intercept column 
+        # add intercept column
         X_tr = np.concatenate((X_tr, np.ones((X_tr.shape[0],1))), axis=1)
         X_te = np.concatenate((X_te, np.ones((X_te.shape[0],1))), axis=1)
         np.savetxt(os.path.join(roi_dir, 'cov_int_tr.txt'), X_tr)
         np.savetxt(os.path.join(roi_dir, 'cov_int_te.txt'), X_te)
-        
-        # create Bspline basis set 
+    
+        # create Bspline basis set
         Phi = np.array([B(i) for i in X_tr[:,0]])
         Phis = np.array([B(i) for i in X_te[:,0]])
         X_tr = np.concatenate((X_tr, Phi), axis=1)
@@ -657,8 +1802,10 @@ al `__.
 
 
 Estimate normative model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------
 
+Step 10.
+~~~~~~~~
 
 Set up a variable (``data_dir``) that specifies the path to the ROI
 directories that were created in Step 7. Initiate two empty pandas data
@@ -674,6 +1821,8 @@ these data frames will be saved as individual csv files.
     blr_metrics = pd.DataFrame(columns = ['ROI', 'MSLL', 'EV', 'SMSE', 'RMSE', 'Rho'])
     blr_site_metrics = pd.DataFrame(columns = ['ROI', 'site', 'MSLL', 'EV', 'SMSE', 'RMSE', 'Rho'])
 
+Step 11.
+~~~~~~~~
 
 Estimate the normative models using a for loop to iterate over brain
 regions. An important consideration is whether to re-scale or
@@ -693,70 +1842,69 @@ arguments that are worthy of commenting on:
 ::
 
    - alg = 'blr': specifies we should use Bayesian Linear Regression.  
-   - optimizer = 'powell': use Powell's derivative-free optimization method (faster in this case than L-BFGS) 
-   - savemodel = False: do not write out the final estimated model to disk 
+   - optimizer = 'powell': use Powell's derivative-free optimization method (faster in this case than L-BFGS)
+   - savemodel = False: do not write out the final estimated model to disk
    - saveoutput = False: return the outputs directly rather than writing them to disk
    - standardize = False: Do not standardize the covariates or response variables
 
-.. warning::
-    **CRITICAL STEP:** This code fragment will loop through each region of
-    interest in the ``roi_ids`` list (created in step 4) using Bayesian
-    Linear Regression and evaluate the model on the independent test set. In
-    principle, we could estimate the normative models on the whole data
-    matrix at once (e.g., with the response variables stored in a
-    ``n_subjects`` by ``n_brain_measures`` NumPy array or a text file
-    instead of saved out into separate directories). However, running the
-    models iteratively gives some extra flexibility in that it does not
-    require that the included subjects are the same for each of the brain
-    measures.
+**CRITICAL STEP:** This code fragment will loop through each region of
+interest in the ``roi_ids`` list (created in step 4) using Bayesian
+Linear Regression and evaluate the model on the independent test set. In
+principle, we could estimate the normative models on the whole data
+matrix at once (e.g., with the response variables stored in a
+``n_subjects`` by ``n_brain_measures`` NumPy array or a text file
+instead of saved out into separate directories). However, running the
+models iteratively gives some extra flexibility in that it does not
+require that the included subjects are the same for each of the brain
+measures.
 
 .. code:: ipython3
 
     # Loop through ROIs
-    for roi in roi_ids: 
+    for roi in roi_ids:
         print('Running ROI:', roi)
         roi_dir = os.path.join(data_dir, roi)
         os.chdir(roi_dir)
-         
-        # configure the covariates to use. Change *_bspline_* to *_int_* to 
+    
+        # configure the covariates to use. Change *_bspline_* to *_int_* to
         cov_file_tr = os.path.join(roi_dir, 'cov_bspline_tr.txt')
         cov_file_te = os.path.join(roi_dir, 'cov_bspline_te.txt')
-        
+    
         # load train & test response files
         resp_file_tr = os.path.join(roi_dir, 'resp_tr.txt')
-        resp_file_te = os.path.join(roi_dir, 'resp_te.txt') 
-        
+        resp_file_te = os.path.join(roi_dir, 'resp_te.txt')
+    
         # run a basic model
-        yhat_te, s2_te, nm, Z, metrics_te = estimate(cov_file_tr, 
-                                                     resp_file_tr, 
-                                                     testresp=resp_file_te, 
-                                                     testcov=cov_file_te, 
-                                                     alg = 'blr', 
-                                                     optimizer = 'powell', 
-                                                     savemodel = True, 
+        yhat_te, s2_te, nm, Z, metrics_te = estimate(cov_file_tr,
+                                                     resp_file_tr,
+                                                     testresp=resp_file_te,
+                                                     testcov=cov_file_te,
+                                                     alg = 'blr',
+                                                     optimizer = 'powell',
+                                                     savemodel = True,
                                                      saveoutput = False,
                                                      standardize = False)
         # save metrics
         blr_metrics.loc[len(blr_metrics)] = [roi, metrics_te['MSLL'][0], metrics_te['EXPV'][0], metrics_te['SMSE'][0], metrics_te['RMSE'][0], metrics_te['Rho'][0]]
-        
+    
         # Compute metrics per site in test set, save to pandas df
         # load true test data
         X_te = np.loadtxt(cov_file_te)
         y_te = np.loadtxt(resp_file_te)
         y_te = y_te[:, np.newaxis] # make sure it is a 2-d array
-        
+    
         # load training data (required to compute the MSLL)
         y_tr = np.loadtxt(resp_file_tr)
         y_tr = y_tr[:, np.newaxis]
-        
-        for num, site in enumerate(sites):     
+    
+        for num, site in enumerate(sites):
             y_mean_te_site = np.array([[np.mean(y_te[site])]])
             y_var_te_site = np.array([[np.var(y_te[site])]])
             yhat_mean_te_site = np.array([[np.mean(yhat_te[site])]])
             yhat_var_te_site = np.array([[np.var(yhat_te[site])]])
-            
+    
             metrics_te_site = evaluate(y_te[site], yhat_te[site], s2_te[site], y_mean_te_site, y_var_te_site)
-            
+    
             site_name = site_names[num]
             blr_site_metrics.loc[len(blr_site_metrics)] = [roi, site_names[num], metrics_te_site['MSLL'][0], metrics_te_site['EXPV'][0], metrics_te_site['SMSE'][0], metrics_te_site['RMSE'][0], metrics_te_site['Rho'][0]]
 
@@ -764,40 +1912,54 @@ arguments that are worthy of commenting on:
 .. parsed-literal::
 
     Running ROI: lh_MeanThickness_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_MeanThickness_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
     Using default hyperparameters
-    Optimization terminated successfully.
-             Current function value: -1162.792820
-             Iterations: 2
-             Function evaluations: 47
-    Saving model meta-data...
-    Evaluating the model ...
 
 
 .. parsed-literal::
 
-    /usr/local/lib/python3.7/dist-packages/pcntoolkit/model/bayesreg.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=1.15485e-18): result may not be accurate.
-      invAXt = linalg.solve(self.A, X.T, check_finite=False)
-    /usr/local/lib/python3.7/dist-packages/pcntoolkit/model/bayesreg.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=4.51813e-19): result may not be accurate.
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/bayesreg.py:196: LinAlgWarning: Ill-conditioned matrix (rcond=1.15485e-18): result may not be accurate.
       invAXt = linalg.solve(self.A, X.T, check_finite=False)
 
 
 .. parsed-literal::
 
+    Optimization terminated successfully.
+             Current function value: -1162.792820
+             Iterations: 2
+             Function evaluations: 43
+    Saving model meta-data...
+    Evaluating the model ...
     Running ROI: rh_MeanThickness_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/rh_MeanThickness_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
     Using default hyperparameters
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/bayesreg.py:196: LinAlgWarning: Ill-conditioned matrix (rcond=4.51813e-19): result may not be accurate.
+      invAXt = linalg.solve(self.A, X.T, check_finite=False)
+
+
+.. parsed-literal::
+
     Optimization terminated successfully.
              Current function value: -1187.621858
              Iterations: 2
-             Function evaluations: 47
+             Function evaluations: 43
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_bankssts_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_bankssts_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -805,10 +1967,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -578.945257
              Iterations: 2
-             Function evaluations: 46
+             Function evaluations: 42
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_caudalanteriorcingulate_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_caudalanteriorcingulate_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -816,10 +1980,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -235.509099
              Iterations: 3
-             Function evaluations: 75
+             Function evaluations: 69
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_superiorfrontal_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_superiorfrontal_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -827,10 +1993,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -716.547377
              Iterations: 3
-             Function evaluations: 91
+             Function evaluations: 84
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: rh_superiorfrontal_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/rh_superiorfrontal_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -838,17 +2006,19 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -730.639309
              Iterations: 2
-             Function evaluations: 45
+             Function evaluations: 41
     Saving model meta-data...
     Evaluating the model ...
 
 
 Evaluation & Interpretation
-----------------------------------------
+===========================
 
 Describe the normative model performance
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------------------------
 
+Step 12.
+~~~~~~~~
 
 In step 11, when we looped over each region of interest in the
 ``roi_ids`` list (created in step 4) and evaluated the normative model
@@ -917,7 +2087,9 @@ can organize them into a single file, and merge the deviation scores
 into the original data file.
 
 Visualize normative model outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+---------------------------------
+
+.. container::
 
 Figure 4A viz
 ~~~~~~~~~~~~~
@@ -937,29 +2109,21 @@ Figure 4A viz
     plt.figure(dpi=380)
     fig, axes = joypy.joyplot(blr_site_metrics, column=['EV'], overlap=2.5, by="site", ylim='own', fill=True, figsize=(8,8)
                               , legend=False, xlabels=True, ylabels=True, colormap=lambda x: color_gradient(x, start=(.08, .45, .8),stop=(.8, .34, .44))
-                              , alpha=0.6, linewidth=.5, linecolor='w', fade=True)
+                              , alpha=0.6, linewidth=.5, linecolor='w', fade=True);
     plt.title('Test Set Explained Variance', fontsize=18, color='black', alpha=1)
     plt.xlabel('Explained Variance', fontsize=14, color='black', alpha=1)
     plt.ylabel('Site', fontsize=14, color='black', alpha=1)
-    plt.show
-
+    plt.show()
 
 
 
 .. parsed-literal::
 
-    
+    

 
 
 
-
-.. parsed-literal::
-
-    

-
-
-
-.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_93_2.png
+.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_97_1.png
 
 
 The code used to create the visualizations shown in Figure 4 panels B-F,
@@ -967,7 +2131,8 @@ can be found in this
 `notebook `__.
 
 Post-Hoc analysis ideas
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+-----------------------
+
 
 The code for running SVM classification and classical case vs. control
 t-testing on the outputs of normative modeling can be found in this
diff --git a/doc/source/pages/BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_15_1.png b/doc/source/pages/BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_15_1.png
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z%Q{w2Ye`X0_M&~vLJ_={!|*X%y1PHewwi*g{MAo*C&LrtZ1_&_GOU`__
+======================================================================================
 
-Hierarchical Bayesian Regression
+Hierarchical Bayesian Regression Normative Modelling and Transfer onto unseen site.
 ===================================================================================
 
 This notebook will go through basic data preparation (training and
-testing set, `see Saige's
+testing set, `see Saige’s
 tutorial `__
 on Normative Modelling for more detail), the actual training of the
 models, and will finally describe how to transfer the trained models
 onto unseen sites.
 
 Created by `Saige Rutherford `__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Adapted/edited by Andre Marquand and Pierre Berthet.
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/HBR_FCON/HBR_NormativeModel_FCONdata_Tutorial.ipynb
-
+adapted/edited by Andre Marquand and Pierre Berthet
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. container::
 
 Step 0: Install necessary libraries & grab data files
 -----------------------------------------------------
 
 .. code:: ipython3
 
-    ! pip install pcntoolkit==0.26
+    !pip install pcntoolkit
+    !pip install nutpie
+
+
+.. parsed-literal::
+
+    Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+      Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+         \ 64.9 MB 15.9 MB/s 0:00:05
+    [?25h  Installing build dependencies ... [?25l[?25hdone
+      Getting requirements to build wheel ... [?25l[?25hdone
+      Preparing metadata (pyproject.toml) ... [?25l[?25hdone
+    Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
+      Downloading bspline-0.1.1.tar.gz (84 kB)
+         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.2/84.2 kB 2.3 MB/s eta 0:00:00
+    [?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
+    Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
+      Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
+    Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
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+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (4.54.1)
+    Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.4.7)
+    Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (24.2)
+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (3.2.0)
+    Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (2.8.2)
+    Requirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (6.4.5)
+    Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (4.12.2)
+    Requirement already satisfied: arviz>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.20.0)
+    Requirement already satisfied: cachetools>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (5.5.0)
+    Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.1.0)
+    Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.2.2)
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+    Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.5.2->pcntoolkit==0.31.0) (1.4.2)
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+    Requirement already satisfied: miniKanren in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.3)
+    Requirement already satisfied: cons in /usr/local/lib/python3.10/dist-packages (from pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.4.6)
+    Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.0.0)
+    Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.18.0)
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+    Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.0)
+    Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.3/8.3 MB 55.1 MB/s eta 0:00:00
+    [?25hBuilding wheels for collected packages: pcntoolkit, bspline
+      Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
+      Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
+      Stored in directory: /tmp/pip-ephem-wheel-cache-f502unec/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
+      Building wheel for bspline (setup.py) ... [?25l[?25hdone
+      Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=150d24f295ccda92c9789d421e52c3858d43c66874deec4a463a87b4e5533448
+      Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
+    Successfully built pcntoolkit bspline
+    Installing collected packages: bspline, matplotlib, pcntoolkit
+      Attempting uninstall: matplotlib
+        Found existing installation: matplotlib 3.8.0
+        Uninstalling matplotlib-3.8.0:
+          Successfully uninstalled matplotlib-3.8.0
+    Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
+    Collecting nutpie
+      Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
+    Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from nutpie) (17.0.0)
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+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (3.2.0)
+    Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0->nutpie) (1.16.0)
+    Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl (1.5 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 16.5 MB/s eta 0:00:00
+    [?25hInstalling collected packages: nutpie
+    Successfully installed nutpie-0.13.2
 
 
 For this tutorial we will use data from the `Functional Connectom
@@ -47,7 +151,7 @@ First we import the required package, and create a working directory.
 
 .. code:: ipython3
 
-    processing_dir = "HBR_demo/"    # replace with a path to your working directory
+    processing_dir = "HBR_demo"    # replace with desired working directory
     if not os.path.isdir(processing_dir):
         os.makedirs(processing_dir)
     os.chdir(processing_dir)
@@ -65,9 +169,13 @@ color coded by the various sites:
 
     fcon = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv')
     
+    # extract the ICBM site for transfer
     icbm = fcon.loc[fcon['site'] == 'ICBM']
     icbm['sitenum'] = 0
+    
+    # remove from the training set (also Pittsburgh because it only has 3 samples)
     fcon = fcon.loc[fcon['site'] != 'ICBM']
+    fcon = fcon.loc[fcon['site'] != 'Pittsburgh']
     
     sites = fcon['site'].unique()
     fcon['sitenum'] = 0
@@ -86,6 +194,390 @@ color coded by the various sites:
     ax.set_xlabel('age')
 
 
+
+.. parsed-literal::
+
+    :5: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame.
+    Try using .loc[row_indexer,col_indexer] = value instead
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      icbm['sitenum'] = 0
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+
+
+.. parsed-literal::
+
+    site AnnArbor_a 24
+    site AnnArbor_b 32
+    site Atlanta 28
+    site Baltimore 23
+    site Bangor 20
+    site Beijing_Zang 198
+    site Berlin_Margulies 26
+    site Cambridge_Buckner 198
+    site Cleveland 31
+    site Leiden_2180 12
+    site Leiden_2200 19
+    site Milwaukee_b 46
+    site Munchen 15
+    site NewYork_a 83
+    site NewYork_a_ADHD 25
+    site Newark 19
+    site Oulu 102
+    site Oxford 22
+    site PaloAlto 17
+    site Queensland 19
+    site SaintLouis 31
+
+
+
+
+.. parsed-literal::
+
+    Text(0.5, 0, 'age')
+
+
+
+
+.. image:: HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png
+
+
 Step 1: Prepare training and testing sets
 -----------------------------------------
 
@@ -122,15 +614,42 @@ then displayed.
     icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
     icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
 
+
+.. parsed-literal::
+
+    sample size check
+    0 AnnArbor_a 10 14
+    1 AnnArbor_b 19 13
+    2 Atlanta 12 16
+    3 Baltimore 12 11
+    4 Bangor 10 10
+    5 Beijing_Zang 91 107
+    6 Berlin_Margulies 9 17
+    7 Cambridge_Buckner 96 102
+    8 Cleveland 13 18
+    9 Leiden_2180 5 7
+    10 Leiden_2200 11 8
+    11 Milwaukee_b 18 28
+    12 Munchen 9 6
+    13 NewYork_a 38 45
+    14 NewYork_a_ADHD 15 10
+    15 Newark 9 10
+    16 Oulu 50 52
+    17 Oxford 9 13
+    18 PaloAlto 8 9
+    19 Queensland 10 9
+    20 SaintLouis 18 13
+
+
 Otherwise you can just load these pre defined subsets:
 
 .. code:: ipython3
 
     # Optional
-    # fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
-    # fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
-    # icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
-    # icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+    #fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+    #fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+    #icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+    #icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
 
 Step 2: Configure HBR inputs: covariates, measures and batch effects
 --------------------------------------------------------------------
@@ -160,7 +679,12 @@ testing set (``_test``).
 
     X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
     Y_train = fcon_tr[idps].to_numpy(dtype=float)
-    batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+    
+    # configure batch effects for site and sex
+    #batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+    
+    # or only site
+    batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
     
     with open('X_train.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_train), file)
@@ -172,7 +696,8 @@ testing set (``_test``).
     
     X_test = (fcon_te['age']/100).to_numpy(dtype=float)
     Y_test = fcon_te[idps].to_numpy(dtype=float)
-    batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
     
     with open('X_test.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_test), file)
@@ -186,6 +711,536 @@ testing set (``_test``).
         with open(filename, 'rb') as f:
             return pickle.load(f)
 
+.. code:: ipython3
+
+    batch_effects_test
+
+
+
+
+.. parsed-literal::
+
+    array([[ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
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+           [ 3],
+           [ 3],
+           [ 3],
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+           [ 3],
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+           [ 4],
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+           [ 4],
+           [ 5],
+           [ 5],
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+           [ 5],
+           [ 5],
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+           [ 5],
+           [ 5],
+           [ 5],
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+           [ 5],
+           [ 5],
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+           [ 6],
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+           [ 6],
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+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
+           [ 7],
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+           [ 7],
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+           [13],
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+           [14],
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+           [14],
+           [14],
+           [14],
+           [14],
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+           [14],
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+           [15],
+           [15],
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+           [15],
+           [15],
+           [15],
+           [15],
+           [15],
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+           [16],
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+           [16],
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+           [16],
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+           [16],
+           [16],
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+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [19],
+           [19],
+           [19],
+           [19],
+           [19],
+           [19],
+           [19],
+           [19],
+           [19],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20]])
+
+
+
 Step 3: Files and Folders grooming
 ----------------------------------
 
@@ -226,12 +1281,399 @@ and output files will be written and how they will be named.
                            respfile=respfile,
                            tsbefile=tsbefile,
                            trbefile=trbefile,
+                           inscaler='standardize',
+                           outscaler='standardize',
+                           linear_mu='True',
+                           random_intercept_mu='True',
+                           centered_intercept_mu='True',
                            alg='hbr',
                            log_path=log_dir,
                            binary=True,
-                           output_path=output_path, testcov= testcovfile_path,
+                           output_path=output_path,
+                           testcov= testcovfile_path,
                            testresp = testrespfile_path,
-                           outputsuffix=outputsuffix, savemodel=True)
+                           outputsuffix=outputsuffix,
+                           savemodel=True,
+                           nuts_sampler='nutpie')
+
+
+.. parsed-literal::
+
+    inscaler: standardize
+    outscaler: standardize
+    Processing data in /content/HBR_demo/Y_train.pkl
+    Estimating model  1 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    

+        
Sampler Progress

+        
Total Chains: 1

+        
Active Chains: 0

+        

+            Finished Chains:
+            1
+        

+        
Sampling for now

+        

+            Estimated Time to Completion:
+            now
+        

+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
ProgressDrawsDivergencesStep SizeGradients/Draw

+                            
+                            
+                        150000.3415

+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+.. parsed-literal::
+
+    Normal
+
+
+
+.. raw:: html
+
+    

+
+
+
+.. parsed-literal::
+
+    Estimating model  2 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    

+        
Sampler Progress

+        
Total Chains: 1

+        
Active Chains: 0

+        

+            Finished Chains:
+            1
+        

+        
Sampling for now

+        

+            Estimated Time to Completion:
+            now
+        

+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
ProgressDrawsDivergencesStep SizeGradients/Draw

+                            
+                            
+                        150000.3315

+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+.. parsed-literal::
+
+    Normal
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+.. parsed-literal::
+
+    Saving model meta-data...
+    Evaluating the model ...
+    Writing outputs ...
+
 
 Here some analyses can be done, there are also some error metrics that
 could be of interest. This is covered in step 6 and in `Saige’s
@@ -249,7 +1691,8 @@ training and testing set of covariates, measures and batch effects:
 
     X_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
     Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
-    batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
     
     with open('X_adaptation.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_adapt), file)
@@ -261,7 +1704,8 @@ training and testing set of covariates, measures and batch effects:
     # Test data (new dataset)
     X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
     Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
-    batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
     
     with open('X_test_txfr.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_test_txfr), file)
@@ -270,6 +1714,7 @@ training and testing set of covariates, measures and batch effects:
     with open('txbefile.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
 
+
 .. code:: ipython3
 
     respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
@@ -294,6 +1739,11 @@ and testing). That is basically the only difference.
                                                 respfile=respfile,
                                                 tsbefile=tsbefile,
                                                 trbefile=trbefile,
+                                                inscaler='standardize',
+                                                outscaler='standardize',
+                                                linear_mu='True',
+                                                random_intercept_mu='True',
+                                                centered_intercept_mu='True',
                                                 model_path = model_path,
                                                 alg='hbr',
                                                 log_path=log_dir,
@@ -302,7 +1752,375 @@ and testing). That is basically the only difference.
                                                 testcov= testcovfile_path,
                                                 testresp = testrespfile_path,
                                                 outputsuffix=outputsuffix,
-                                                savemodel=True)
+                                                savemodel=True,
+                                                nuts_sampler='nutpie')
+
+
+.. parsed-literal::
+
+    Loading data ...
+    Using HBR transform...
+    Transferring model  1 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    

+        
Sampler Progress

+        
Total Chains: 1

+        
Active Chains: 0

+        

+            Finished Chains:
+            1
+        

+        
Sampling for now

+        

+            Estimated Time to Completion:
+            now
+        

+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
ProgressDrawsDivergencesStep SizeGradients/Draw

+                            
+                            
+                        150020.477

+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+.. parsed-literal::
+
+    Using HBR transform...
+    Transferring model  2 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    

+        
Sampler Progress

+        
Total Chains: 1

+        
Active Chains: 0

+        

+            Finished Chains:
+            1
+        

+        
Sampling for now

+        

+            Estimated Time to Completion:
+            now
+        

+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
ProgressDrawsDivergencesStep SizeGradients/Draw

+                            
+                            
+                        150010.4015

+    

+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    

+
+
+
+.. parsed-literal::
+
+    Evaluating the model ...
+    Writing outputs ...
+
+
+.. code:: ipython3
+
+    output_path
+
+
+
+
+.. parsed-literal::
+
+    '/content/HBR_demo/Transfer/'
+
+
+
+.. code:: ipython3
+
+    EV = pd.read_pickle('EXPV_estimate.pkl')
+    print(EV)
+
+
+.. parsed-literal::
+
+              0
+    0  0.438215
+    1  0.439181
 
 
 And that is it, you now have models that benefited from prior knowledge
@@ -311,19 +2129,12 @@ about different scanner sites to learn on unseen sites.
 Step 6: Interpreting model performance
 --------------------------------------
 
-Output evaluation metrics definitions
-
-=============  ==============================================================
-Abbreviation    Full name
-=============  ==============================================================
-NM             Normative Model
-EV / EXPV      Explained Variance
-MSLL           Mean Standardized Log Loss
-SMSE           Standardized Mean Squared Error
-RMSE           Root Mean Squared Error between true/predicted responses
-Rho            Pearson orrelation between true/predicted responses
-pRho           Parametric p-value for this correlation
-Z              Z-score or deviation score
-yhat           predictive mean
-ys2            predictive variance
-=============  ==============================================================
+Output evaluation metrics definitions: \* yhat - predictive mean \* ys2
+- predictive variance \* nm - normative model \* Z - deviance scores \*
+Rho - Pearson correlation between true and predicted responses \* pRho -
+parametric p-value for this correlation \* RMSE - root mean squared
+error between true/predicted responses \* SMSE - standardised mean
+squared error \* EV - explained variance \* MSLL - mean standardized log
+loss \* See page 23 in
+http://www.gaussianprocess.org/gpml/chapters/RW2.pdf
+
diff --git a/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png b/doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png
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-            "Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (4.12.2)\n",
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-            "Requirement already satisfied: cachetools>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (5.5.0)\n",
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-            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.5.2->pcntoolkit==0.31.0) (1.4.2)\n",
-            "Requirement already satisfied: setuptools>=60.0.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (75.1.0)\n",
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-            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.0.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (2.18.0)\n",
-            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from h5netcdf>=1.0.2->arviz>=0.13.0->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.12.1)\n",
-            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=13.7.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.1.2)\n",
-            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.12.1)\n",
-            "Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.0)\n"
-          ]
-        }
-      ],
-      "source": [
-        "! pip install https://github.com/amarquand/PCNtoolkit/archive/dev.zip"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "ddd7b3cb-b018-4ed4-8b55-15728d8c5411",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ddd7b3cb-b018-4ed4-8b55-15728d8c5411",
-        "outputId": "d3d408d4-9c55-49d2-cae9-e3a500e168e3"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "fatal: destination path 'braincharts' already exists and is not an empty directory.\n"
-          ]
-        }
-      ],
-      "source": [
-        "! git clone https://github.com/predictive-clinical-neuroscience/braincharts.git"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "b1849f76-b17d-4286-bf57-50ff56e81bf8",
-      "metadata": {
-        "id": "b1849f76-b17d-4286-bf57-50ff56e81bf8"
-      },
-      "outputs": [],
-      "source": [
-        "# we need to be in the scripts folder when we import the libraries in the code block below,\n",
-        "# because there is a function called nm_utils that is in the scripts folder that we need to import\n",
-        "import os\n",
-        "\n",
-        "wdir = 'braincharts'\n",
-        "\n",
-        "os.chdir(wdir) #this path is setup for running on Google Colab. Change it to match your local path if running locally\n",
-        "root_dir=os.getcwd()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b2227bc7-e798-470a-99bc-33561ce4511b",
-      "metadata": {
-        "id": "b2227bc7-e798-470a-99bc-33561ce4511b"
-      },
-      "source": [
-        "Now we import the required libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "ff661cf2-7d80-46bb-bcfb-1650a93eed3d",
-      "metadata": {
-        "id": "ff661cf2-7d80-46bb-bcfb-1650a93eed3d"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import pickle\n",
-        "from matplotlib import pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "from pcntoolkit.normative import estimate, predict, evaluate\n",
-        "from pcntoolkit.util.utils import compute_MSLL, create_design_matrix\n",
-        "os.chdir(os.path.join(root_dir, 'scripts'))\n",
-        "from nm_utils import remove_bad_subjects, load_2d\n",
-        "os.chdir(root_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "78719463-28b2-4849-b970-cfbe2f07d214",
-      "metadata": {
-        "id": "78719463-28b2-4849-b970-cfbe2f07d214"
-      },
-      "source": [
-        "We need to unzip the models."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "3b1d4d4b-68ab-4bba-87f5-6062995805d0",
-      "metadata": {
-        "id": "3b1d4d4b-68ab-4bba-87f5-6062995805d0"
-      },
-      "outputs": [],
-      "source": [
-        "os.chdir(os.path.join(root_dir,'models'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "d4b7b2f4-c514-4d4f-a6b0-9461e1b20831",
-      "metadata": {
-        "id": "d4b7b2f4-c514-4d4f-a6b0-9461e1b20831",
-        "outputId": "87f49919-54d4-4b2f-a959-28060596ced9",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Archive:  lifespan_57K_82sites.zip\n",
-            "replace lifespan_57K_82sites/Right-Pallidum/Models/meta_data.md? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
-          ]
-        }
-      ],
-      "source": [
-        "# we will use the biggest sample as our training set (approx. N=57000 subjects from 82 sites)\n",
-        "# for more info on the other pretrained models available in this repository,\n",
-        "# please refer to the accompanying paper https://elifesciences.org/articles/72904\n",
-        "! unzip lifespan_57K_82sites.zip"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "802b1da6-04cc-4310-af81-f50d38c3e653",
-      "metadata": {
-        "id": "802b1da6-04cc-4310-af81-f50d38c3e653"
-      },
-      "source": [
-        "Next, we configure some basic variables, like where we want the analysis to be done and which model we want to use.\n",
-        "\n",
-        "**Note:** We maintain a list of site ids for each dataset, which describe the site names in the training and test data (`site_ids_tr` and `site_ids_te`), plus also the adaptation data . The training site ids are provided as a text file in the distribution and the test ids are extracted automatically from the pandas dataframe (see below). If you use additional data from the sites (e.g. later waves from ABCD), it may be necessary to adjust the site names to match the names in the training set. See the accompanying paper for more details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "f52e2a19-9b63-4f0f-97c1-387f1a1872a2",
-      "metadata": {
-        "id": "f52e2a19-9b63-4f0f-97c1-387f1a1872a2"
-      },
-      "outputs": [],
-      "source": [
-        "# which model do we wish to use?\n",
-        "model_name = 'lifespan_57K_82sites'\n",
-        "site_names = 'site_ids_ct_82sites.txt'\n",
-        "\n",
-        "\n",
-        "# where the data files live\n",
-        "data_dir = os.path.join(root_dir,'docs')\n",
-        "\n",
-        "# where the models live\n",
-        "out_dir = os.path.join(root_dir, 'models', model_name)\n",
-        "\n",
-        "# load a set of site ids from this model. This must match the training data\n",
-        "with open(os.path.join(root_dir,'docs', site_names)) as f:\n",
-        "    site_ids_tr = f.read().splitlines()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3aab54a5-2579-48d8-a81b-bbd34cea1213",
-      "metadata": {
-        "id": "3aab54a5-2579-48d8-a81b-bbd34cea1213"
-      },
-      "source": [
-        "### Load test data\n",
-        "\n",
-        "**Note:** For the purposes of this tutorial, we make predictions for a multi-site transfer dataset, derived from [OpenNeuro](https://openneuro.org/)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "262d429a-160b-4ba3-9ba4-9acc195bc644",
-      "metadata": {
-        "id": "262d429a-160b-4ba3-9ba4-9acc195bc644"
-      },
-      "outputs": [],
-      "source": [
-        "test_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_te.csv')\n",
-        "\n",
-        "df_te = pd.read_csv(test_data)\n",
-        "\n",
-        "# extract a list of unique site ids from the test set\n",
-        "site_ids_te =  sorted(set(df_te['site'].to_list()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c636509a-8b12-43f1-811c-08cb22640be2",
-      "metadata": {
-        "id": "c636509a-8b12-43f1-811c-08cb22640be2"
-      },
-      "source": [
-        "### (Optional) Load adaptation data\n",
-        "\n",
-        "If the data you wish to make predictions for is not derived from the same scanning sites as those in the trainig set, it is necessary to learn the site effect so that we can account for it in the predictions. In order to do this in an unbiased way, we use a separate dataset, which we refer to as 'adaptation' data. This must contain data for all the same sites as in the test dataset and we assume these are coded in the same way, based on a the 'sitenum' column in the dataframe."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "53551023-aff6-4934-ad2d-d77bc63c562d",
-      "metadata": {
-        "id": "53551023-aff6-4934-ad2d-d77bc63c562d"
-      },
-      "outputs": [],
-      "source": [
-        "adaptation_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_ad.csv')\n",
-        "\n",
-        "df_ad = pd.read_csv(adaptation_data)\n",
-        "\n",
-        "# extract a list of unique site ids from the test set\n",
-        "site_ids_ad =  sorted(set(df_ad['site'].to_list()))\n",
-        "\n",
-        "if not all(elem in site_ids_ad for elem in site_ids_te):\n",
-        "    print('Warning: some of the testing sites are not in the adaptation data')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4f73e30e-c693-44b8-98c6-52b71b577ea8",
-      "metadata": {
-        "id": "4f73e30e-c693-44b8-98c6-52b71b577ea8"
-      },
-      "source": [
-        "### Configure which models to fit\n",
-        "\n",
-        "Now, we configure which imaging derived phenotypes (IDPs) we would like to process. This is just a list of column names in the dataframe we have loaded above.\n",
-        "\n",
-        "We could load the whole set (i.e. all phenotypes for which we have models for ..."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "b48e104c-cbac-4ae2-8377-cd3ff80162fd",
-      "metadata": {
-        "id": "b48e104c-cbac-4ae2-8377-cd3ff80162fd"
-      },
-      "outputs": [],
-      "source": [
-        "# load the list of idps for left and right hemispheres, plus subcortical regions\n",
-        "with open(os.path.join(data_dir, 'phenotypes_ct_lh.txt')) as f:\n",
-        "    idp_ids_lh = f.read().splitlines()\n",
-        "with open(os.path.join(data_dir, 'phenotypes_ct_rh.txt')) as f:\n",
-        "    idp_ids_rh = f.read().splitlines()\n",
-        "with open(os.path.join(data_dir, 'phenotypes_sc.txt')) as f:\n",
-        "    idp_ids_sc = f.read().splitlines()\n",
-        "\n",
-        "# we choose here to process all idps\n",
-        "idp_ids = idp_ids_lh + idp_ids_rh #+ idp_ids_sc"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "280731ad-47d8-43e2-8cb5-4eccfd9f3f81",
-      "metadata": {
-        "id": "280731ad-47d8-43e2-8cb5-4eccfd9f3f81"
-      },
-      "source": [
-        "... or alternatively, we could just specify a list"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "8b74d75f-77a5-474a-9c9b-29aab1ce53a2",
-      "metadata": {
-        "id": "8b74d75f-77a5-474a-9c9b-29aab1ce53a2"
-      },
-      "outputs": [],
-      "source": [
-        "idp_ids = [ 'Left-Thalamus-Proper', 'Left-Lateral-Ventricle', 'rh_MeanThickness_thickness']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "56ee1f7f-8684-4f1c-b142-a68176407029",
-      "metadata": {
-        "id": "56ee1f7f-8684-4f1c-b142-a68176407029"
-      },
-      "source": [
-        "### Configure covariates\n",
-        "\n",
-        "Now, we configure some parameters to fit the model. First, we choose which columns of the pandas dataframe contain the covariates (age and sex). The site parameters are configured automatically later on by the `configure_design_matrix()` function, when we loop through the IDPs in the list\n",
-        "\n",
-        "The supplied coefficients are derived from a 'warped' Bayesian linear regression model, which uses a nonlinear warping function to model non-Gaussianity (`sinarcsinh`) plus a non-linear basis expansion (a cubic b-spline basis set with 5 knot points, which is the default value in the PCNtoolkit package). Since we are sticking with the default value, we do not need to specify any parameters for this, but we do need to specify the limits. We choose to pad the input by a few years either side of the input range. We will also set a couple of options that control the estimation of the model\n",
-        "\n",
-        "For further details about the likelihood warping approach, see the accompanying paper and [Fraza et al 2021](https://www.biorxiv.org/content/10.1101/2021.04.05.438429v1)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "62312b8e-4972-4238-abf9-87d9bb33cc10",
-      "metadata": {
-        "id": "62312b8e-4972-4238-abf9-87d9bb33cc10"
-      },
-      "outputs": [],
-      "source": [
-        "# which data columns do we wish to use as covariates?\n",
-        "cols_cov = ['age','sex']\n",
-        "\n",
-        "# limits for cubic B-spline basis\n",
-        "xmin = -5\n",
-        "xmax = 110\n",
-        "\n",
-        "# Absolute Z treshold above which a sample is considered to be an outlier (without fitting any model)\n",
-        "outlier_thresh = 7"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "42bc1072-e9ed-4f2a-9fdd-cbd626a61542",
-      "metadata": {
-        "id": "42bc1072-e9ed-4f2a-9fdd-cbd626a61542"
-      },
-      "source": [
-        "### Make predictions\n",
-        "\n",
-        "This will make predictions for each IDP separately. This is done by extracting a column from the dataframe (i.e. specifying the IDP as the response variable) and saving it as a numpy array. Then, we configure the covariates, which is a numpy data array having the number of rows equal to the number of datapoints in the test set. The columns are specified as follows:\n",
-        "\n",
-        "- A global intercept (column of ones)\n",
-        "- The covariate columns (here age and sex, coded as 0=female/1=male)\n",
-        "- Dummy coded columns for the sites in the training set (one column per site)\n",
-        "- Columns for the basis expansion (seven columns for the default parameterisation)\n",
-        "\n",
-        "Once these are saved as numpy arrays in ascii format (as here) or (alternatively) in pickle format, these are passed as inputs to the `predict()` method in the PCNtoolkit normative modelling framework. These are written in the same format to the location specified by `idp_dir`. At the end of this step, we have a set of predictions and Z-statistics for the test dataset that we can take forward to further analysis.\n",
-        "\n",
-        "Note that when we need to make predictions on new data, the procedure is more involved, since we need to prepare, process and store covariates, response variables and site ids for the adaptation data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "07b7471b-c334-464f-8273-b409b7acaac2",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "07b7471b-c334-464f-8273-b409b7acaac2",
-        "outputId": "a1de6a82-4430-4e11-de5f-b2874dd3c5d9"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Running IDP 0 Left-Thalamus-Proper :\n",
-            "Some sites missing from the training data. Adapting model\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Evaluating the model ...\n",
-            "Evaluations Writing outputs ...\n",
-            "Writing outputs ...\n",
-            "Running IDP 1 Left-Lateral-Ventricle :\n",
-            "Some sites missing from the training data. Adapting model\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Evaluating the model ...\n",
-            "Evaluations Writing outputs ...\n",
-            "Writing outputs ...\n",
-            "Running IDP 2 rh_MeanThickness_thickness :\n",
-            "Some sites missing from the training data. Adapting model\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Evaluating the model ...\n",
-            "Evaluations Writing outputs ...\n",
-            "Writing outputs ...\n"
-          ]
-        }
-      ],
-      "source": [
-        "for idp_num, idp in enumerate(idp_ids):\n",
-        "    print('Running IDP', idp_num, idp, ':')\n",
-        "    idp_dir = os.path.join(out_dir, idp)\n",
-        "    os.chdir(idp_dir)\n",
-        "\n",
-        "    # extract and save the response variables for the test set\n",
-        "    y_te = df_te[idp].to_numpy()\n",
-        "\n",
-        "    # save the variables\n",
-        "    resp_file_te = os.path.join(idp_dir, 'resp_te.txt')\n",
-        "    np.savetxt(resp_file_te, y_te)\n",
-        "\n",
-        "    # configure and save the design matrix\n",
-        "    cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')\n",
-        "    X_te = create_design_matrix(df_te[cols_cov],\n",
-        "                                site_ids = df_te['site'],\n",
-        "                                all_sites = site_ids_tr,\n",
-        "                                basis = 'bspline',\n",
-        "                                xmin = xmin,\n",
-        "                                xmax = xmax)\n",
-        "    np.savetxt(cov_file_te, X_te)\n",
-        "\n",
-        "    # check whether all sites in the test set are represented in the training set\n",
-        "    if all(elem in site_ids_tr for elem in site_ids_te):\n",
-        "        print('All sites are present in the training data')\n",
-        "\n",
-        "        # just make predictions\n",
-        "        yhat_te, s2_te, Z = predict(cov_file_te,\n",
-        "                                    alg='blr',\n",
-        "                                    respfile=resp_file_te,\n",
-        "                                    model_path=os.path.join(idp_dir,'Models'))\n",
-        "    else:\n",
-        "        print('Some sites missing from the training data. Adapting model')\n",
-        "\n",
-        "        # save the covariates for the adaptation data\n",
-        "        X_ad = create_design_matrix(df_ad[cols_cov],\n",
-        "                                    site_ids = df_ad['site'],\n",
-        "                                    all_sites = site_ids_tr,\n",
-        "                                    basis = 'bspline',\n",
-        "                                    xmin = xmin,\n",
-        "                                    xmax = xmax)\n",
-        "        cov_file_ad = os.path.join(idp_dir, 'cov_bspline_ad.txt')\n",
-        "        np.savetxt(cov_file_ad, X_ad)\n",
-        "\n",
-        "        # save the responses for the adaptation data\n",
-        "        resp_file_ad = os.path.join(idp_dir, 'resp_ad.txt')\n",
-        "        y_ad = df_ad[idp].to_numpy()\n",
-        "        np.savetxt(resp_file_ad, y_ad)\n",
-        "\n",
-        "        # save the site ids for the adaptation data\n",
-        "        sitenum_file_ad = os.path.join(idp_dir, 'sitenum_ad.txt')\n",
-        "        site_num_ad = df_ad['sitenum'].to_numpy(dtype=int)\n",
-        "        np.savetxt(sitenum_file_ad, site_num_ad)\n",
-        "\n",
-        "        # save the site ids for the test data\n",
-        "        sitenum_file_te = os.path.join(idp_dir, 'sitenum_te.txt')\n",
-        "        site_num_te = df_te['sitenum'].to_numpy(dtype=int)\n",
-        "        np.savetxt(sitenum_file_te, site_num_te)\n",
-        "\n",
-        "        yhat_te, s2_te, Z = predict(cov_file_te,\n",
-        "                                    alg = 'blr',\n",
-        "                                    respfile = resp_file_te,\n",
-        "                                    model_path = os.path.join(idp_dir,'Models'),\n",
-        "                                    adaptrespfile = resp_file_ad,\n",
-        "                                    adaptcovfile = cov_file_ad,\n",
-        "                                    adaptvargroupfile = sitenum_file_ad,\n",
-        "                                    testvargroupfile = sitenum_file_te)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "75210821-ccb8-4bd2-82f3-641708811b21",
-      "metadata": {
-        "id": "75210821-ccb8-4bd2-82f3-641708811b21"
-      },
-      "source": [
-        "### Preparing dummy data for plotting\n",
-        "\n",
-        "Now, we plot the centiles of variation estimated by the normative model.\n",
-        "\n",
-        "We do this by making use of a set of dummy covariates that span the whole range of the input space (for age) for a fixed value of the other covariates (e.g. sex) so that we can make predictions for these dummy data points, then plot them. We configure these dummy predictions using the same procedure as we used for the real data. We can use the same dummy data for all the IDPs we wish to plot"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "2d0743d8-28ca-4a14-8ef0-99bf40434b5b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2d0743d8-28ca-4a14-8ef0-99bf40434b5b",
-        "outputId": "e8957652-cfd0-4a4c-998b-463c356bbaed"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "configuring dummy data ...\n"
-          ]
-        }
-      ],
-      "source": [
-        "# which sex do we want to plot?\n",
-        "sex = 1 # 1 = male 0 = female\n",
-        "if sex == 1:\n",
-        "    clr = 'blue';\n",
-        "else:\n",
-        "    clr = 'red'\n",
-        "\n",
-        "# create dummy data for visualisation\n",
-        "print('configuring dummy data ...')\n",
-        "xx = np.arange(xmin, xmax, 0.5)\n",
-        "X0_dummy = np.zeros((len(xx), 2))\n",
-        "X0_dummy[:,0] = xx\n",
-        "X0_dummy[:,1] = sex\n",
-        "\n",
-        "# create the design matrix\n",
-        "X_dummy = create_design_matrix(X0_dummy, xmin=xmin, xmax=xmax, site_ids=None, all_sites=site_ids_tr)\n",
-        "\n",
-        "# save the dummy covariates\n",
-        "cov_file_dummy = os.path.join(out_dir,'cov_bspline_dummy_mean.txt')\n",
-        "np.savetxt(cov_file_dummy, X_dummy)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "126323a3-2270-4796-97c4-94629730ddf7",
-      "metadata": {
-        "id": "126323a3-2270-4796-97c4-94629730ddf7"
-      },
-      "source": [
-        "### Plotting the normative models\n",
-        "\n",
-        "Now we loop through the IDPs, plotting each one separately. The outputs of this step are a set of quantitative regression metrics for each IDP and a set of centile curves which we plot the test data against.\n",
-        "\n",
-        "This part of the code is relatively complex because we need to keep track of many quantities for the plotting. We also need to remember whether the data need to be warped or not. By default in PCNtoolkit, predictions in the form of `yhat, s2` are always in the warped (Gaussian) space. If we want predictions in the input (non-Gaussian) space, then we need to warp them with the inverse of the estimated warping function. This can be done using the function `nm.blr.warp.warp_predictions()`.\n",
-        "\n",
-        "**Note:** it is necessary to update the intercept for each of the sites. For purposes of visualisation, here we do this by adjusting the median of the data to match the dummy predictions, but note that all the quantitative metrics are estimated using the predictions that are adjusted properly using a learned offset (or adjusted using a hold-out adaptation set, as above). Note also that for the calibration data we require at least two data points of the same sex in each site to be able to estimate the variance. Of course, in a real example, you would want many more than just two since we need to get a reliable estimate of the variance for each site."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "cdd68cc6-212b-4149-b86a-24e842078e1a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "cdd68cc6-212b-4149-b86a-24e842078e1a",
-        "outputId": "43a33a06-cffb-4bab-9bd1-293cc8e65d48"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Running IDP 0 Left-Thalamus-Proper :\n",
-            "Making predictions with dummy covariates (for visualisation)\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Writing outputs ...\n",
-            "metrics: {'RMSE': array([0.55690777]), 'Rho': array([0.]), 'pRho': array([1.]), 'SMSE': array([0.]), 'EXPV': array([0.])}\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "
"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Running IDP 1 Left-Lateral-Ventricle :\n",
-            "Making predictions with dummy covariates (for visualisation)\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Writing outputs ...\n",
-            "metrics: {'RMSE': array([4205.49266088]), 'Rho': array([0.45898577]), 'pRho': array([5.62632393e-25]), 'SMSE': array([0.81397727]), 'EXPV': array([0.19814613])}\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "
"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Running IDP 2 rh_MeanThickness_thickness :\n",
-            "Making predictions with dummy covariates (for visualisation)\n",
-            "Loading data ...\n",
-            "Prediction by model  1 of 1\n",
-            "Writing outputs ...\n",
-            "metrics: {'RMSE': array([0.08652435]), 'Rho': array([0.77666469]), 'pRho': array([2.97430261e-103]), 'SMSE': array([0.40227749]), 'EXPV': array([0.59789079])}\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "
"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "sns.set(style='whitegrid')\n",
-        "\n",
-        "for idp_num, idp in enumerate(idp_ids):\n",
-        "    print('Running IDP', idp_num, idp, ':')\n",
-        "    idp_dir = os.path.join(out_dir, idp)\n",
-        "    os.chdir(idp_dir)\n",
-        "\n",
-        "    # load the true data points\n",
-        "    yhat_te = load_2d(os.path.join(idp_dir, 'yhat_predict.txt'))\n",
-        "    s2_te = load_2d(os.path.join(idp_dir, 'ys2_predict.txt'))\n",
-        "    y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))\n",
-        "\n",
-        "    # set up the covariates for the dummy data\n",
-        "    print('Making predictions with dummy covariates (for visualisation)')\n",
-        "    yhat, s2 = predict(cov_file_dummy,\n",
-        "                       alg = 'blr',\n",
-        "                       respfile = None,\n",
-        "                       model_path = os.path.join(idp_dir,'Models'),\n",
-        "                       outputsuffix = '_dummy')\n",
-        "\n",
-        "    # load the normative model\n",
-        "    with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:\n",
-        "        nm = pickle.load(handle)\n",
-        "\n",
-        "    # get the warp and warp parameters\n",
-        "    W = nm.blr.warp\n",
-        "    warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]\n",
-        "\n",
-        "    # first, we warp predictions for the true data and compute evaluation metrics\n",
-        "    med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]\n",
-        "    med_te = med_te[:, np.newaxis]\n",
-        "    print('metrics:', evaluate(y_te, med_te))\n",
-        "\n",
-        "    # then, we warp dummy predictions to create the plots\n",
-        "    med, pr_int = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param)\n",
-        "\n",
-        "    # extract the different variance components to visualise\n",
-        "    beta, junk1, junk2 = nm.blr._parse_hyps(nm.blr.hyp, X_dummy)\n",
-        "    s2n = 1/beta # variation (aleatoric uncertainty)\n",
-        "    s2s = s2-s2n # modelling uncertainty (epistemic uncertainty)\n",
-        "\n",
-        "    # plot the data points\n",
-        "    y_te_rescaled_all = np.zeros_like(y_te)\n",
-        "    for sid, site in enumerate(site_ids_te):\n",
-        "        # plot the true test data points\n",
-        "        if all(elem in site_ids_tr for elem in site_ids_te):\n",
-        "            # all data in the test set are present in the training set\n",
-        "\n",
-        "            # first, we select the data points belonging to this particular site\n",
-        "            idx = np.where(np.bitwise_and(X_te[:,2] == sex, X_te[:,sid+len(cols_cov)+1] !=0))[0]\n",
-        "            if len(idx) == 0:\n",
-        "                print('No data for site', sid, site, 'skipping...')\n",
-        "                continue\n",
-        "\n",
-        "            # then directly adjust the data\n",
-        "            idx_dummy = np.bitwise_and(X_dummy[:,1] > X_te[idx,1].min(), X_dummy[:,1] < X_te[idx,1].max())\n",
-        "            y_te_rescaled = y_te[idx] - np.median(y_te[idx]) + np.median(med[idx_dummy])\n",
-        "        else:\n",
-        "            # we need to adjust the data based on the adaptation dataset\n",
-        "\n",
-        "            # first, select the data point belonging to this particular site\n",
-        "            idx = np.where(np.bitwise_and(X_te[:,2] == sex, (df_te['site'] == site).to_numpy()))[0]\n",
-        "\n",
-        "            # load the adaptation data\n",
-        "            y_ad = load_2d(os.path.join(idp_dir, 'resp_ad.txt'))\n",
-        "            X_ad = load_2d(os.path.join(idp_dir, 'cov_bspline_ad.txt'))\n",
-        "            idx_a = np.where(np.bitwise_and(X_ad[:,2] == sex, (df_ad['site'] == site).to_numpy()))[0]\n",
-        "            if len(idx) < 2 or len(idx_a) < 2:\n",
-        "                print('Insufficent data for site', sid, site, 'skipping...')\n",
-        "                continue\n",
-        "\n",
-        "            # adjust and rescale the data\n",
-        "            y_te_rescaled, s2_rescaled = nm.blr.predict_and_adjust(nm.blr.hyp,\n",
-        "                                                                   X_ad[idx_a,:],\n",
-        "                                                                   np.squeeze(y_ad[idx_a]),\n",
-        "                                                                   Xs=None,\n",
-        "                                                                   ys=np.squeeze(y_te[idx]))\n",
-        "        # plot the (adjusted) data points\n",
-        "        plt.scatter(X_te[idx,1], y_te_rescaled, s=4, color=clr, alpha = 0.1)\n",
-        "\n",
-        "    # plot the median of the dummy data\n",
-        "    plt.plot(xx, med, clr)\n",
-        "\n",
-        "    # fill the gaps in between the centiles\n",
-        "    junk, pr_int25 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.25,0.75])\n",
-        "    junk, pr_int95 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.05,0.95])\n",
-        "    junk, pr_int99 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.01,0.99])\n",
-        "    plt.fill_between(xx, pr_int25[:,0], pr_int25[:,1], alpha = 0.1,color=clr)\n",
-        "    plt.fill_between(xx, pr_int95[:,0], pr_int95[:,1], alpha = 0.1,color=clr)\n",
-        "    plt.fill_between(xx, pr_int99[:,0], pr_int99[:,1], alpha = 0.1,color=clr)\n",
-        "\n",
-        "    # make the width of each centile proportional to the epistemic uncertainty\n",
-        "    junk, pr_int25l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.25,0.75])\n",
-        "    junk, pr_int95l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.05,0.95])\n",
-        "    junk, pr_int99l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.01,0.99])\n",
-        "    junk, pr_int25u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.25,0.75])\n",
-        "    junk, pr_int95u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.05,0.95])\n",
-        "    junk, pr_int99u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.01,0.99])\n",
-        "    plt.fill_between(xx, pr_int25l[:,0], pr_int25u[:,0], alpha = 0.3,color=clr)\n",
-        "    plt.fill_between(xx, pr_int95l[:,0], pr_int95u[:,0], alpha = 0.3,color=clr)\n",
-        "    plt.fill_between(xx, pr_int99l[:,0], pr_int99u[:,0], alpha = 0.3,color=clr)\n",
-        "    plt.fill_between(xx, pr_int25l[:,1], pr_int25u[:,1], alpha = 0.3,color=clr)\n",
-        "    plt.fill_between(xx, pr_int95l[:,1], pr_int95u[:,1], alpha = 0.3,color=clr)\n",
-        "    plt.fill_between(xx, pr_int99l[:,1], pr_int99u[:,1], alpha = 0.3,color=clr)\n",
-        "\n",
-        "    # plot actual centile lines\n",
-        "    plt.plot(xx, pr_int25[:,0],color=clr, linewidth=0.5)\n",
-        "    plt.plot(xx, pr_int25[:,1],color=clr, linewidth=0.5)\n",
-        "    plt.plot(xx, pr_int95[:,0],color=clr, linewidth=0.5)\n",
-        "    plt.plot(xx, pr_int95[:,1],color=clr, linewidth=0.5)\n",
-        "    plt.plot(xx, pr_int99[:,0],color=clr, linewidth=0.5)\n",
-        "    plt.plot(xx, pr_int99[:,1],color=clr, linewidth=0.5)\n",
-        "\n",
-        "    plt.xlabel('Age')\n",
-        "    plt.ylabel(idp)\n",
-        "    plt.title(idp)\n",
-        "    plt.xlim((0,90))\n",
-        "    plt.savefig(os.path.join(idp_dir, 'centiles_' + str(sex)),  bbox_inches='tight')\n",
-        "    plt.show()\n",
-        "\n",
-        "os.chdir(out_dir)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "135dbebd-f563-4a2a-9f44-f96757fb4b0b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "135dbebd-f563-4a2a-9f44-f96757fb4b0b",
-        "outputId": "6aabc7ba-2896-4a35-932d-bd843f90b8e5"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "centiles_1.png\t    Models\t      Rho_predict.txt\tSMSE_predict.txt  ys2_predict.txt\n",
-            "cov_bspline_ad.txt  pRho_predict.txt  RMSE_predict.txt\tyhat_dummy.pkl\t  Z_predict.txt\n",
-            "cov_bspline_te.txt  resp_ad.txt       sitenum_ad.txt\tyhat_predict.txt\n",
-            "EXPV_predict.txt    resp_te.txt       sitenum_te.txt\tys2_dummy.pkl\n"
-          ]
-        }
-      ],
-      "source": [
-        "# explore an example output folder of a single model (one ROI)\n",
-        "# think about what each of these output files represents.\n",
-        "# Hint: look at the variable names and comments in the code block above\n",
-        "! ls rh_MeanThickness_thickness/"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "id": "fe1cac10-01f1-42fd-a4b7-cf08ce0d64be",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fe1cac10-01f1-42fd-a4b7-cf08ce0d64be",
-        "outputId": "cd83ef21-0698-4183-9b90-a48a1f90f465"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    436     436   11115\n"
-          ]
-        }
-      ],
-      "source": [
-        "# check that the number of deviation scores matches the number of subjects in the test set\n",
-        "# there should be one deviation score per subject (one line per subject), so we can\n",
-        "# verify by counting the line numbers in the Z_predict.txt file\n",
-        "! cat rh_MeanThickness_thickness/Z_predict.txt | wc"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "88d2dbc0-e82f-4af5-91eb-dc8aa60f6ba7",
-      "metadata": {
-        "id": "88d2dbc0-e82f-4af5-91eb-dc8aa60f6ba7"
-      },
-      "source": [
-        "The deviation scores are output as a text file in separate folders. We want to summarize the deviation scores across all models estimates so we can organize them into a single file, and merge the deviation scores into the original data file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "id": "e3fb0ced-ed44-487c-86b8-07b9fc04d64e",
-      "metadata": {
-        "id": "e3fb0ced-ed44-487c-86b8-07b9fc04d64e"
-      },
-      "outputs": [],
-      "source": [
-        "! mkdir deviation_scores"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "id": "571f549e-9edd-4f8b-a6b3-d76cd23609f0",
-      "metadata": {
-        "id": "571f549e-9edd-4f8b-a6b3-d76cd23609f0"
-      },
-      "outputs": [],
-      "source": [
-        "! for i in *; do if [[ -e ${i}/Z_predict.txt ]]; then cp ${i}/Z_predict.txt deviation_scores/${i}_Z_predict.txt; fi; done"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "id": "9f63da6c-91e8-4852-91a7-a4e8bc9d9f31",
-      "metadata": {
-        "id": "9f63da6c-91e8-4852-91a7-a4e8bc9d9f31"
-      },
-      "outputs": [],
-      "source": [
-        "z_dir = '/content/braincharts/models/' + model_name + '/deviation_scores/'\n",
-        "\n",
-        "filelist = [name for name in os.listdir(z_dir)]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "id": "8791195b-09a9-4251-8fd7-35e80a028d2f",
-      "metadata": {
-        "id": "8791195b-09a9-4251-8fd7-35e80a028d2f"
-      },
-      "outputs": [],
-      "source": [
-        "os.chdir(z_dir)\n",
-        "Z_df = pd.concat([pd.read_csv(item, names=[item[:-4]]) for item in filelist], axis=1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "id": "f1054959-dd17-4c1b-b1db-56cf849ecae2",
-      "metadata": {
-        "id": "f1054959-dd17-4c1b-b1db-56cf849ecae2"
-      },
-      "outputs": [],
-      "source": [
-        "df_te.reset_index(inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "id": "6ab00be4-d9c8-49aa-b407-f69946ca2d6c",
-      "metadata": {
-        "id": "6ab00be4-d9c8-49aa-b407-f69946ca2d6c"
-      },
-      "outputs": [],
-      "source": [
-        "Z_df['sub_id'] = df_te['sub_id']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "id": "9f6185b6-d9d7-4651-bbab-2cfba6c46963",
-      "metadata": {
-        "id": "9f6185b6-d9d7-4651-bbab-2cfba6c46963"
-      },
-      "outputs": [],
-      "source": [
-        "df_te_Z = pd.merge(df_te, Z_df, on='sub_id', how='inner')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "id": "ae932714-60c3-4a36-8b72-cd9086a25761",
-      "metadata": {
-        "id": "ae932714-60c3-4a36-8b72-cd9086a25761"
-      },
-      "outputs": [],
-      "source": [
-        "df_te_Z.to_csv('OpenNeuroTransfer_deviation_scores.csv', index=False)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "id": "9c97bd6e",
-      "metadata": {
-        "id": "9c97bd6e"
-      },
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "apply_normative_models.ipynb",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "braincharts",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.7"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "f65f66140ab2d9a57fedc58a3b7e1d01f34d12111107cec87dc46b07c8179a15"
-      }
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
+Using lifespan models to make predictions on new data
+-----------------------------------------------------
+
+This notebook shows how to apply the coefficients from pre-estimated
+normative models to new data. This can be done in two different ways:
+(i) using a new set of data derived from the same sites used to estimate
+the model and (ii) on a completely different set of sites. In the latter
+case, we also need to estimate the site effect, which requires some
+calibration/adaptation data. As an illustrative example, we use a
+dataset derived from several `OpenNeuro
+datasets `__ and adapt the learned model to make
+predictions on these data.
+
+First, if necessary, we install PCNtoolkit (note: this tutorial requires
+at least version 0.27)
+
+.. code:: ipython3
+
+    !pip install pcntoolkit
+    !pip install nutpie
+
+.. code:: ipython3
+
+    ! git clone https://github.com/predictive-clinical-neuroscience/braincharts.git
+
+.. code:: ipython3
+
+    # we need to be in the scripts folder when we import the libraries in the code block below,
+    # because there is a function called nm_utils that is in the scripts folder that we need to import
+    import os
+    wdir = 'braincharts'
+    
+    os.chdir(wdir) #this path is setup for running on Google Colab. Change it to match your local path if running locally
+    root_dir = os.getcwd()
+
+Now we import the required libraries
+
+.. code:: ipython3
+
+    import numpy as np
+    import pandas as pd
+    import pickle
+    from matplotlib import pyplot as plt
+    import seaborn as sns
+    
+    from pcntoolkit.normative import estimate, predict, evaluate
+    from pcntoolkit.util.utils import compute_MSLL, create_design_matrix
+    os.chdir(os.path.join(root_dir, 'scripts'))
+    from nm_utils import remove_bad_subjects, load_2d
+    os.chdir(root_dir)
+
+We need to unzip the models.
+
+.. code:: ipython3
+
+    os.chdir(os.path.join(root_dir, 'models'))
+
+.. code:: ipython3
+
+    # we will use the biggest sample as our training set (approx. N=57000 subjects from 82 sites)
+    # for more info on the other pretrained models available in this repository,
+    # please refer to the accompanying paper https://elifesciences.org/articles/72904
+    ! unzip lifespan_57K_82sites.zip
+
+Next, we configure some basic variables, like where we want the analysis
+to be done and which model we want to use.
+
+**Note:** We maintain a list of site ids for each dataset, which
+describe the site names in the training and test data (``site_ids_tr``
+and ``site_ids_te``), plus also the adaptation data . The training site
+ids are provided as a text file in the distribution and the test ids are
+extracted automatically from the pandas dataframe (see below). If you
+use additional data from the sites (e.g. later waves from ABCD), it may
+be necessary to adjust the site names to match the names in the training
+set. See the accompanying paper for more details
+
+.. code:: ipython3
+
+    # which model do we wish to use?
+    model_name = 'lifespan_57K_82sites'
+    site_names = 'site_ids_ct_82sites.txt'
+    
+    
+    # where the data files live
+    data_dir = os.path.join(root_dir,'docs')
+    
+    # where the models live
+    out_dir = os.path.join(root_dir, 'models', model_name)
+    
+    # load a set of site ids from this model. This must match the training data
+    with open(os.path.join(root_dir,'docs', site_names)) as f:
+        site_ids_tr = f.read().splitlines()
+
+Load test data
+~~~~~~~~~~~~~~
+
+**Note:** For the purposes of this tutorial, we make predictions for a
+multi-site transfer dataset, derived from
+`OpenNeuro `__.
+
+.. code:: ipython3
+
+    test_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_te.csv')
+    
+    df_te = pd.read_csv(test_data)
+    
+    # extract a list of unique site ids from the test set
+    site_ids_te =  sorted(set(df_te['site'].to_list()))
+
+(Optional) Load adaptation data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If the data you wish to make predictions for is not derived from the
+same scanning sites as those in the trainig set, it is necessary to
+learn the site effect so that we can account for it in the predictions.
+In order to do this in an unbiased way, we use a separate dataset, which
+we refer to as ‘adaptation’ data. This must contain data for all the
+same sites as in the test dataset and we assume these are coded in the
+same way, based on a the ‘sitenum’ column in the dataframe.
+
+.. code:: ipython3
+
+    adaptation_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_ad.csv')
+    
+    df_ad = pd.read_csv(adaptation_data)
+    
+    # extract a list of unique site ids from the test set
+    site_ids_ad =  sorted(set(df_ad['site'].to_list()))
+    
+    if not all(elem in site_ids_ad for elem in site_ids_te):
+        print('Warning: some of the testing sites are not in the adaptation data')
+
+Configure which models to fit
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now, we configure which imaging derived phenotypes (IDPs) we would like
+to process. This is just a list of column names in the dataframe we have
+loaded above.
+
+We could load the whole set (i.e. all phenotypes for which we have
+models for …
+
+.. code:: ipython3
+
+    # load the list of idps for left and right hemispheres, plus subcortical regions
+    with open(os.path.join(data_dir, 'phenotypes_ct_lh.txt')) as f:
+        idp_ids_lh = f.read().splitlines()
+    with open(os.path.join(data_dir, 'phenotypes_ct_rh.txt')) as f:
+        idp_ids_rh = f.read().splitlines()
+    with open(os.path.join(data_dir, 'phenotypes_sc.txt')) as f:
+        idp_ids_sc = f.read().splitlines()
+    
+    # we choose here to process all idps
+    idp_ids = idp_ids_lh + idp_ids_rh #+ idp_ids_sc
+
+… or alternatively, we could just specify a list
+
+.. code:: ipython3
+
+    idp_ids = [ 'Left-Thalamus-Proper', 'Left-Lateral-Ventricle', 'rh_MeanThickness_thickness']
+
+Configure covariates
+~~~~~~~~~~~~~~~~~~~~
+
+Now, we configure some parameters to fit the model. First, we choose
+which columns of the pandas dataframe contain the covariates (age and
+sex). The site parameters are configured automatically later on by the
+``configure_design_matrix()`` function, when we loop through the IDPs in
+the list
+
+The supplied coefficients are derived from a ‘warped’ Bayesian linear
+regression model, which uses a nonlinear warping function to model
+non-Gaussianity (``sinarcsinh``) plus a non-linear basis expansion (a
+cubic b-spline basis set with 5 knot points, which is the default value
+in the PCNtoolkit package). Since we are sticking with the default
+value, we do not need to specify any parameters for this, but we do need
+to specify the limits. We choose to pad the input by a few years either
+side of the input range. We will also set a couple of options that
+control the estimation of the model
+
+For further details about the likelihood warping approach, see the
+accompanying paper and `Fraza et al
+2021 `__.
+
+.. code:: ipython3
+
+    # which data columns do we wish to use as covariates?
+    cols_cov = ['age','sex']
+    
+    # limits for cubic B-spline basis
+    xmin = -5
+    xmax = 110
+    
+    # Absolute Z treshold above which a sample is considered to be an outlier (without fitting any model)
+    outlier_thresh = 7
+
+Make predictions
+~~~~~~~~~~~~~~~~
+
+This will make predictions for each IDP separately. This is done by
+extracting a column from the dataframe (i.e. specifying the IDP as the
+response variable) and saving it as a numpy array. Then, we configure
+the covariates, which is a numpy data array having the number of rows
+equal to the number of datapoints in the test set. The columns are
+specified as follows:
+
+- A global intercept (column of ones)
+- The covariate columns (here age and sex, coded as 0=female/1=male)
+- Dummy coded columns for the sites in the training set (one column per
+  site)
+- Columns for the basis expansion (seven columns for the default
+  parameterisation)
+
+Once these are saved as numpy arrays in ascii format (as here) or
+(alternatively) in pickle format, these are passed as inputs to the
+``predict()`` method in the PCNtoolkit normative modelling framework.
+These are written in the same format to the location specified by
+``idp_dir``. At the end of this step, we have a set of predictions and
+Z-statistics for the test dataset that we can take forward to further
+analysis.
+
+Note that when we need to make predictions on new data, the procedure is
+more involved, since we need to prepare, process and store covariates,
+response variables and site ids for the adaptation data.
+
+.. code:: ipython3
+
+    for idp_num, idp in enumerate(idp_ids):
+        print('Running IDP', idp_num, idp, ':')
+        idp_dir = os.path.join(out_dir, idp)
+        os.chdir(idp_dir)
+    
+        # extract and save the response variables for the test set
+        y_te = df_te[idp].to_numpy()
+    
+        # save the variables
+        resp_file_te = os.path.join(idp_dir, 'resp_te.txt')
+        np.savetxt(resp_file_te, y_te)
+    
+        # configure and save the design matrix
+        cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')
+        X_te = create_design_matrix(df_te[cols_cov],
+                                    site_ids = df_te['site'],
+                                    all_sites = site_ids_tr,
+                                    basis = 'bspline',
+                                    xmin = xmin,
+                                    xmax = xmax)
+        np.savetxt(cov_file_te, X_te)
+    
+        # check whether all sites in the test set are represented in the training set
+        if all(elem in site_ids_tr for elem in site_ids_te):
+            print('All sites are present in the training data')
+    
+            # just make predictions
+            yhat_te, s2_te, Z = predict(cov_file_te,
+                                        alg='blr',
+                                        respfile=resp_file_te,
+                                        model_path=os.path.join(idp_dir,'Models'))
+        else:
+            print('Some sites missing from the training data. Adapting model')
+    
+            # save the covariates for the adaptation data
+            X_ad = create_design_matrix(df_ad[cols_cov],
+                                        site_ids = df_ad['site'],
+                                        all_sites = site_ids_tr,
+                                        basis = 'bspline',
+                                        xmin = xmin,
+                                        xmax = xmax)
+            cov_file_ad = os.path.join(idp_dir, 'cov_bspline_ad.txt')
+            np.savetxt(cov_file_ad, X_ad)
+    
+            # save the responses for the adaptation data
+            resp_file_ad = os.path.join(idp_dir, 'resp_ad.txt')
+            y_ad = df_ad[idp].to_numpy()
+            np.savetxt(resp_file_ad, y_ad)
+    
+            # save the site ids for the adaptation data
+            sitenum_file_ad = os.path.join(idp_dir, 'sitenum_ad.txt')
+            site_num_ad = df_ad['sitenum'].to_numpy(dtype=int)
+            np.savetxt(sitenum_file_ad, site_num_ad)
+    
+            # save the site ids for the test data
+            sitenum_file_te = os.path.join(idp_dir, 'sitenum_te.txt')
+            site_num_te = df_te['sitenum'].to_numpy(dtype=int)
+            np.savetxt(sitenum_file_te, site_num_te)
+    
+            yhat_te, s2_te, Z = predict(cov_file_te,
+                                        alg = 'blr',
+                                        respfile = resp_file_te,
+                                        model_path = os.path.join(idp_dir,'Models'),
+                                        adaptrespfile = resp_file_ad,
+                                        adaptcovfile = cov_file_ad,
+                                        adaptvargroupfile = sitenum_file_ad,
+                                        testvargroupfile = sitenum_file_te)
+
+
+.. parsed-literal::
+
+    Running IDP 0 Left-Thalamus-Proper :
+    Some sites missing from the training data. Adapting model
+    Loading data ...
+    Prediction by model  1 of 1
+    Evaluating the model ...
+    Evaluations Writing outputs ...
+    Writing outputs ...
+    Running IDP 1 Left-Lateral-Ventricle :
+    Some sites missing from the training data. Adapting model
+    Loading data ...
+    Prediction by model  1 of 1
+    Evaluating the model ...
+    Evaluations Writing outputs ...
+    Writing outputs ...
+    Running IDP 2 rh_MeanThickness_thickness :
+    Some sites missing from the training data. Adapting model
+    Loading data ...
+    Prediction by model  1 of 1
+    Evaluating the model ...
+    Evaluations Writing outputs ...
+    Writing outputs ...
+
+
+Preparing dummy data for plotting
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now, we plot the centiles of variation estimated by the normative model.
+
+We do this by making use of a set of dummy covariates that span the
+whole range of the input space (for age) for a fixed value of the other
+covariates (e.g. sex) so that we can make predictions for these dummy
+data points, then plot them. We configure these dummy predictions using
+the same procedure as we used for the real data. We can use the same
+dummy data for all the IDPs we wish to plot
+
+.. code:: ipython3
+
+    # which sex do we want to plot?
+    sex = 1 # 1 = male 0 = female
+    if sex == 1:
+        clr = 'blue';
+    else:
+        clr = 'red'
+    
+    # create dummy data for visualisation
+    print('configuring dummy data ...')
+    xx = np.arange(xmin, xmax, 0.5)
+    X0_dummy = np.zeros((len(xx), 2))
+    X0_dummy[:,0] = xx
+    X0_dummy[:,1] = sex
+    
+    # create the design matrix
+    X_dummy = create_design_matrix(X0_dummy, xmin=xmin, xmax=xmax, site_ids=None, all_sites=site_ids_tr)
+    
+    # save the dummy covariates
+    cov_file_dummy = os.path.join(out_dir,'cov_bspline_dummy_mean.txt')
+    np.savetxt(cov_file_dummy, X_dummy)
+
+
+.. parsed-literal::
+
+    configuring dummy data ...
+
+
+Plotting the normative models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now we loop through the IDPs, plotting each one separately. The outputs
+of this step are a set of quantitative regression metrics for each IDP
+and a set of centile curves which we plot the test data against.
+
+This part of the code is relatively complex because we need to keep
+track of many quantities for the plotting. We also need to remember
+whether the data need to be warped or not. By default in PCNtoolkit,
+predictions in the form of ``yhat, s2`` are always in the warped
+(Gaussian) space. If we want predictions in the input (non-Gaussian)
+space, then we need to warp them with the inverse of the estimated
+warping function. This can be done using the function
+``nm.blr.warp.warp_predictions()``.
+
+**Note:** it is necessary to update the intercept for each of the sites.
+For purposes of visualisation, here we do this by adjusting the median
+of the data to match the dummy predictions, but note that all the
+quantitative metrics are estimated using the predictions that are
+adjusted properly using a learned offset (or adjusted using a hold-out
+adaptation set, as above). Note also that for the calibration data we
+require at least two data points of the same sex in each site to be able
+to estimate the variance. Of course, in a real example, you would want
+many more than just two since we need to get a reliable estimate of the
+variance for each site.
+
+.. code:: ipython3
+
+    sns.set(style='whitegrid')
+    
+    for idp_num, idp in enumerate(idp_ids):
+        print('Running IDP', idp_num, idp, ':')
+        idp_dir = os.path.join(out_dir, idp)
+        os.chdir(idp_dir)
+    
+        # load the true data points
+        yhat_te = load_2d(os.path.join(idp_dir, 'yhat_predict.txt'))
+        s2_te = load_2d(os.path.join(idp_dir, 'ys2_predict.txt'))
+        y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))
+    
+        # set up the covariates for the dummy data
+        print('Making predictions with dummy covariates (for visualisation)')
+        yhat, s2 = predict(cov_file_dummy,
+                           alg = 'blr',
+                           respfile = None,
+                           model_path = os.path.join(idp_dir,'Models'),
+                           outputsuffix = '_dummy')
+    
+        # load the normative model
+        with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:
+            nm = pickle.load(handle)
+    
+        # get the warp and warp parameters
+        W = nm.blr.warp
+        warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]
+    
+        # first, we warp predictions for the true data and compute evaluation metrics
+        med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]
+        med_te = med_te[:, np.newaxis]
+        print('metrics:', evaluate(y_te, med_te))
+    
+        # then, we warp dummy predictions to create the plots
+        med, pr_int = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param)
+    
+        # extract the different variance components to visualise
+        beta, junk1, junk2 = nm.blr._parse_hyps(nm.blr.hyp, X_dummy)
+        s2n = 1/beta # variation (aleatoric uncertainty)
+        s2s = s2-s2n # modelling uncertainty (epistemic uncertainty)
+    
+        # plot the data points
+        y_te_rescaled_all = np.zeros_like(y_te)
+        for sid, site in enumerate(site_ids_te):
+            # plot the true test data points
+            if all(elem in site_ids_tr for elem in site_ids_te):
+                # all data in the test set are present in the training set
+    
+                # first, we select the data points belonging to this particular site
+                idx = np.where(np.bitwise_and(X_te[:,2] == sex, X_te[:,sid+len(cols_cov)+1] !=0))[0]
+                if len(idx) == 0:
+                    print('No data for site', sid, site, 'skipping...')
+                    continue
+    
+                # then directly adjust the data
+                idx_dummy = np.bitwise_and(X_dummy[:,1] > X_te[idx,1].min(), X_dummy[:,1] < X_te[idx,1].max())
+                y_te_rescaled = y_te[idx] - np.median(y_te[idx]) + np.median(med[idx_dummy])
+            else:
+                # we need to adjust the data based on the adaptation dataset
+    
+                # first, select the data point belonging to this particular site
+                idx = np.where(np.bitwise_and(X_te[:,2] == sex, (df_te['site'] == site).to_numpy()))[0]
+    
+                # load the adaptation data
+                y_ad = load_2d(os.path.join(idp_dir, 'resp_ad.txt'))
+                X_ad = load_2d(os.path.join(idp_dir, 'cov_bspline_ad.txt'))
+                idx_a = np.where(np.bitwise_and(X_ad[:,2] == sex, (df_ad['site'] == site).to_numpy()))[0]
+                if len(idx) < 2 or len(idx_a) < 2:
+                    print('Insufficent data for site', sid, site, 'skipping...')
+                    continue
+    
+                # adjust and rescale the data
+                y_te_rescaled, s2_rescaled = nm.blr.predict_and_adjust(nm.blr.hyp,
+                                                                       X_ad[idx_a,:],
+                                                                       np.squeeze(y_ad[idx_a]),
+                                                                       Xs=None,
+                                                                       ys=np.squeeze(y_te[idx]))
+            # plot the (adjusted) data points
+            plt.scatter(X_te[idx,1], y_te_rescaled, s=4, color=clr, alpha = 0.1)
+    
+        # plot the median of the dummy data
+        plt.plot(xx, med, clr)
+    
+        # fill the gaps in between the centiles
+        junk, pr_int25 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.25,0.75])
+        junk, pr_int95 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.05,0.95])
+        junk, pr_int99 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.01,0.99])
+        plt.fill_between(xx, pr_int25[:,0], pr_int25[:,1], alpha = 0.1,color=clr)
+        plt.fill_between(xx, pr_int95[:,0], pr_int95[:,1], alpha = 0.1,color=clr)
+        plt.fill_between(xx, pr_int99[:,0], pr_int99[:,1], alpha = 0.1,color=clr)
+    
+        # make the width of each centile proportional to the epistemic uncertainty
+        junk, pr_int25l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.25,0.75])
+        junk, pr_int95l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.05,0.95])
+        junk, pr_int99l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.01,0.99])
+        junk, pr_int25u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.25,0.75])
+        junk, pr_int95u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.05,0.95])
+        junk, pr_int99u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.01,0.99])
+        plt.fill_between(xx, pr_int25l[:,0], pr_int25u[:,0], alpha = 0.3,color=clr)
+        plt.fill_between(xx, pr_int95l[:,0], pr_int95u[:,0], alpha = 0.3,color=clr)
+        plt.fill_between(xx, pr_int99l[:,0], pr_int99u[:,0], alpha = 0.3,color=clr)
+        plt.fill_between(xx, pr_int25l[:,1], pr_int25u[:,1], alpha = 0.3,color=clr)
+        plt.fill_between(xx, pr_int95l[:,1], pr_int95u[:,1], alpha = 0.3,color=clr)
+        plt.fill_between(xx, pr_int99l[:,1], pr_int99u[:,1], alpha = 0.3,color=clr)
+    
+        # plot actual centile lines
+        plt.plot(xx, pr_int25[:,0],color=clr, linewidth=0.5)
+        plt.plot(xx, pr_int25[:,1],color=clr, linewidth=0.5)
+        plt.plot(xx, pr_int95[:,0],color=clr, linewidth=0.5)
+        plt.plot(xx, pr_int95[:,1],color=clr, linewidth=0.5)
+        plt.plot(xx, pr_int99[:,0],color=clr, linewidth=0.5)
+        plt.plot(xx, pr_int99[:,1],color=clr, linewidth=0.5)
+    
+        plt.xlabel('Age')
+        plt.ylabel(idp)
+        plt.title(idp)
+        plt.xlim((0,90))
+        plt.savefig(os.path.join(idp_dir, 'centiles_' + str(sex)),  bbox_inches='tight')
+        plt.show()
+    
+    os.chdir(out_dir)
+
+
+.. parsed-literal::
+
+    Running IDP 0 Left-Thalamus-Proper :
+    Making predictions with dummy covariates (for visualisation)
+    Loading data ...
+    Prediction by model  1 of 1
+    Writing outputs ...
+    metrics: {'RMSE': array([0.55690777]), 'Rho': array([0.]), 'pRho': array([1.]), 'SMSE': array([0.]), 'EXPV': array([0.])}
+
+
+
+.. image:: apply_normative_models_ct_files/apply_normative_models_ct_27_1.png
+
+
+.. parsed-literal::
+
+    Running IDP 1 Left-Lateral-Ventricle :
+    Making predictions with dummy covariates (for visualisation)
+    Loading data ...
+    Prediction by model  1 of 1
+    Writing outputs ...
+    metrics: {'RMSE': array([4205.49266088]), 'Rho': array([0.45898577]), 'pRho': array([5.62632393e-25]), 'SMSE': array([0.81397727]), 'EXPV': array([0.19814613])}
+
+
+
+.. image:: apply_normative_models_ct_files/apply_normative_models_ct_27_3.png
+
+
+.. parsed-literal::
+
+    Running IDP 2 rh_MeanThickness_thickness :
+    Making predictions with dummy covariates (for visualisation)
+    Loading data ...
+    Prediction by model  1 of 1
+    Writing outputs ...
+    metrics: {'RMSE': array([0.08652435]), 'Rho': array([0.77666469]), 'pRho': array([2.97430261e-103]), 'SMSE': array([0.40227749]), 'EXPV': array([0.59789079])}
+
+
+
+.. image:: apply_normative_models_ct_files/apply_normative_models_ct_27_5.png
+
+
+.. code:: ipython3
+
+    # explore an example output folder of a single model (one ROI)
+    # think about what each of these output files represents.
+    # Hint: look at the variable names and comments in the code block above
+    ! ls rh_MeanThickness_thickness/
+
+
+.. parsed-literal::
+
+    centiles_1.png	    Models	      Rho_predict.txt	SMSE_predict.txt  ys2_predict.txt
+    cov_bspline_ad.txt  pRho_predict.txt  RMSE_predict.txt	yhat_dummy.pkl	  Z_predict.txt
+    cov_bspline_te.txt  resp_ad.txt       sitenum_ad.txt	yhat_predict.txt
+    EXPV_predict.txt    resp_te.txt       sitenum_te.txt	ys2_dummy.pkl
+
+
+.. code:: ipython3
+
+    # check that the number of deviation scores matches the number of subjects in the test set
+    # there should be one deviation score per subject (one line per subject), so we can
+    # verify by counting the line numbers in the Z_predict.txt file
+    ! cat rh_MeanThickness_thickness/Z_predict.txt | wc
+
+
+.. parsed-literal::
+
+        436     436   11115
+
+
+The deviation scores are output as a text file in separate folders. We
+want to summarize the deviation scores across all models estimates so we
+can organize them into a single file, and merge the deviation scores
+into the original data file.
+
+.. code:: ipython3
+
+    ! mkdir deviation_scores
+
+.. code:: ipython3
+
+    ! for i in *; do if [[ -e ${i}/Z_predict.txt ]]; then cp ${i}/Z_predict.txt deviation_scores/${i}_Z_predict.txt; fi; done
+
+.. code:: ipython3
+
+    z_dir = os.path.join(root_dir, 'models', model_name, 'deviation_scores')
+    
+    filelist = [name for name in os.listdir(z_dir)]
+
+.. code:: ipython3
+
+    os.chdir(z_dir)
+    Z_df = pd.concat([pd.read_csv(item, names=[item[:-4]]) for item in filelist], axis=1)
+
+.. code:: ipython3
+
+    df_te.reset_index(inplace=True)
+
+.. code:: ipython3
+
+    Z_df['sub_id'] = df_te['sub_id']
+
+.. code:: ipython3
+
+    df_te_Z = pd.merge(df_te, Z_df, on='sub_id', how='inner')
+
+.. code:: ipython3
+
+    df_te_Z.to_csv('OpenNeuroTransfer_deviation_scores.csv', index=False)
diff --git a/doc/source/pages/apply_normative_models_ct_files/apply_normative_models_ct_27_1.png b/doc/source/pages/apply_normative_models_ct_files/apply_normative_models_ct_27_1.png
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diff --git a/doc/source/pages/installation.rst b/doc/source/pages/installation.rst
index ce1551ce..18e50880 100644
--- a/doc/source/pages/installation.rst
+++ b/doc/source/pages/installation.rst
@@ -20,13 +20,19 @@ Basic installation (on a local machine)
 
 	source activate 
 
-4. Install required conda packages
+4. Install torch using the torch instructions.	
+
+.. code-block:: bash
+
+	# Command found on the torch website: https://pytorch.org/get-started/locally/
+
+5. Install required conda packages
 	
 .. code-block:: bash
 
-	conda install pip pandas scipy
+	conda install numba nutpie -c conda-forge
 	
-5. Install PCNtoolkit (plus dependencies)
+6. Install PCNtoolkit (plus dependencies)
 	
 .. code-block:: bash
 
@@ -60,7 +66,8 @@ Alternative installation (on a shared resource)
 	
 .. code-block:: bash
 
-	conda install -y pandas scipy 
+	# Command found on the torch website: https://pytorch.org/get-started/locally/
+	conda install numba nutpie -c conda-forge
 
 	
 5. Install pip dependencies
diff --git a/doc/source/pages/normative_modelling_walkthrough.rst b/doc/source/pages/normative_modelling_walkthrough.rst
index f63ec8fa..61a297a7 100644
--- a/doc/source/pages/normative_modelling_walkthrough.rst
+++ b/doc/source/pages/normative_modelling_walkthrough.rst
@@ -1,6 +1,4 @@
-.. title:: GPR tutorial
-
-Gaussian Process Regression 
+**DEMO ON NORMATIVE MODELING**
 ==============================
 
 Created by
@@ -10,14 +8,9 @@ Mariam Zabihi `@m_zabihi `__
 Saige Rutherford `@being_saige `__
 
 Thomas Wolfers `@ThomasWolfers `__
-\______________________________________________________________________________\_
-
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/CPC_2020/normative_modelling_walkthrough.ipynb
-
+\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
 
-Background Story
+**Background Story**
 --------------------
 
 Morten and Ingrid are concerned about the health of their father,
@@ -40,13 +33,116 @@ IQ, age as well as the same sex as Nordan.
 Do your best to get as far as you can. However, you do not need to feel
 bad if you cannot complete everything during the tutorial.
 
-**Task 0:** Load data and install the pcntoolkit
-------------------------------------------------
+**Task 0:** Load data and install PCNtoolkit
+--------------------------------------------
 
 .. code:: ipython3
 
-    #install normative modeling
-    ! pip install pcntoolkit==0.26
+    !pip install pcntoolkit
+    !pip install nutpie
+
+
+.. parsed-literal::
+
+    Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+      Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+         \ 64.9 MB 16.9 MB/s 0:00:05
+    [?25h  Installing build dependencies ... [?25l[?25hdone
+      Getting requirements to build wheel ... [?25l[?25hdone
+      Preparing metadata (pyproject.toml) ... [?25l[?25hdone
+    Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
+      Downloading bspline-0.1.1.tar.gz (84 kB)
+         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.2/84.2 kB 2.0 MB/s eta 0:00:00
+    [?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
+    Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
+      Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
+    Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
+    Requirement already satisfied: numpy<2.0,>=1.26 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.26.4)
+    Requirement already satisfied: pymc<6.0.0,>=5.18.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.18.0)
+    Requirement already satisfied: scikit-learn<2.0.0,>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.5.2)
+    Requirement already satisfied: scipy<2.0,>=1.12 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.13.1)
+    Requirement already satisfied: seaborn<0.14.0,>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (0.13.2)
+    Requirement already satisfied: six<2.0.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.16.0)
+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (4.54.1)
+    Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.4.7)
+    Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (24.2)
+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (3.2.0)
+    Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (2.8.2)
+    Requirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (6.4.5)
+    Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (4.12.2)
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+    Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.0)
+    Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.3/8.3 MB 37.7 MB/s eta 0:00:00
+    [?25hBuilding wheels for collected packages: pcntoolkit, bspline
+      Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
+      Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
+      Stored in directory: /tmp/pip-ephem-wheel-cache-nl3hy35q/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
+      Building wheel for bspline (setup.py) ... [?25l[?25hdone
+      Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=1da013ad20b77d85515a6a2318eaf7cc4baaa7772eb1d26249f923ce8d779d7e
+      Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
+    Successfully built pcntoolkit bspline
+    Installing collected packages: bspline, matplotlib, pcntoolkit
+      Attempting uninstall: matplotlib
+        Found existing installation: matplotlib 3.8.0
+        Uninstalling matplotlib-3.8.0:
+          Successfully uninstalled matplotlib-3.8.0
+    Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
+    Collecting nutpie
+      Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
+    Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from nutpie) (17.0.0)
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+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (0.12.1)
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+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (3.2.0)
+    Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0->nutpie) (1.16.0)
+    Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl (1.5 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 9.5 MB/s eta 0:00:00
+    [?25hInstalling collected packages: nutpie
+    Successfully installed nutpie-0.13.2
 
 
 **Option 1:** Connect your Google Drive account, and load data from
@@ -77,24 +173,24 @@ them to Google Drive.
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
-        
+    
     # code by S. Rutherford
 
 
 .. parsed-literal::
 
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
+    --2024-11-19 12:28:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
+    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
+    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
     Length: 17484 (17K) [text/plain]
     Saving to: ‘camcan_demographics.csv’
     
-    camcan_demographics 100%[===================>]  17.07K  --.-KB/s    in 0.001s  
+    camcan_demographics 100%[===================>]  17.07K  --.-KB/s    in 0.003s  
     
-    2022-02-17 15:03:58 (12.9 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
+    2024-11-19 12:28:31 (5.14 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
+    --2024-11-19 12:28:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
     Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
     Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
@@ -103,20 +199,20 @@ them to Google Drive.
     
     camcan_demographics 100%[===================>]     332  --.-KB/s    in 0s      
     
-    2022-02-17 15:03:58 (15.5 MB/s) - ‘camcan_demographics_nordan.csv’ saved [332/332]
+    2024-11-19 12:28:32 (4.10 MB/s) - ‘camcan_demographics_nordan.csv’ saved [332/332]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
+    --2024-11-19 12:28:32--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
+    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
+    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
     Length: 188944 (185K) [text/plain]
     Saving to: ‘camcan_features.csv’
     
     camcan_features.csv 100%[===================>] 184.52K  --.-KB/s    in 0.05s   
     
-    2022-02-17 15:03:58 (3.88 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
+    2024-11-19 12:28:33 (3.58 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
+    --2024-11-19 12:28:33--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
     Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
     Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
@@ -125,7 +221,7 @@ them to Google Drive.
     
     camcan_features_nor 100%[===================>]   1.66K  --.-KB/s    in 0s      
     
-    2022-02-17 15:03:59 (25.3 MB/s) - ‘camcan_features_nordan.csv’ saved [1695/1695]
+    2024-11-19 12:28:33 (22.1 MB/s) - ‘camcan_features_nordan.csv’ saved [1695/1695]
     
 
 
@@ -160,7 +256,7 @@ between those two files.
     print(norm_demographics)
     print(norm_features)
     
-    # find overlap in terms of participants between norm_sample_features and 
+    # find overlap in terms of participants between norm_sample_features and
     # norm_sample_demographics
     
     norm_demographics_features = pd.concat([norm_demographics, norm_features],
@@ -189,33 +285,182 @@ between those two files.
     CC723395      86   FEMALE    0        145
     
     [707 rows x 4 columns]
-                  left_Hippocampal_tail  ...  right_Whole_hippocampus
-    participants                         ...                         
-    CC110033                 482.768229  ...              3531.764896
-    CC110037                 595.269259  ...              3835.426137
-    CC110045                 655.847194  ...              3681.494304
-    CC110056                 561.345626  ...              3461.373764
-    CC110062                 756.521166  ...              4782.407821
-    ...                             ...  ...                      ...
-    CC722542                 467.896808  ...              3284.108783
-    CC722651                 406.326167  ...              3210.272905
-    CC722891                 393.430481  ...              2423.675065
-    CC723197                 475.929914  ...              3043.146264
-    CC723395                 444.301617  ...              2988.001288
+                  left_Hippocampal_tail  left_subiculum    left_CA1  \
+    participants                                                      
+    CC110033                 482.768229      419.948094  666.496024   
+    CC110037                 595.269259      502.320315  698.157779   
+    CC110045                 655.847194      476.433625  654.215689   
+    CC110056                 561.345626      447.258970  611.114561   
+    CC110062                 756.521166      521.034681  716.391590   
+    ...                             ...             ...         ...   
+    CC722542                 467.896808      440.794061  688.130914   
+    CC722651                 406.326167      393.469843  613.794018   
+    CC722891                 393.430481      303.049578  444.772656   
+    CC723197                 475.929914      372.449778  525.739508   
+    CC723395                 444.301617      330.688394  565.359058   
+    
+                  left_hippocampal-fissure  left_presubiculum  left_parasubiculum  \
+    participants                                                                    
+    CC110033                    131.719049         285.535445           59.209377   
+    CC110037                    156.304335         367.678385           60.817591   
+    CC110045                    146.767569         346.347202           67.481121   
+    CC110056                    126.615335         327.528926           70.901227   
+    CC110062                    206.205818         384.356075           80.329689   
+    ...                                ...                ...                 ...   
+    CC722542                    184.300085         306.287030           72.629722   
+    CC722651                    224.292557         254.786917           50.006651   
+    CC722891                    158.987352         202.213773           46.418129   
+    CC723197                    172.558200         222.384434           40.304889   
+    CC723395                    206.235576         197.417773           34.240227   
+    
+                  left_molecular_layer_HP  left_GC-ML-DG    left_CA3    left_CA4  \
+    participants                                                                   
+    CC110033                   583.239022     313.144932  223.022262  266.801434   
+    CC110037                   619.053381     311.428298  192.949211  260.551999   
+    CC110045                   622.037178     322.315065  204.756048  275.428880   
+    CC110056                   597.467595     323.777115  233.160437  278.133998   
+    CC110062                   666.590397     364.130988  253.917949  311.079938   
+    ...                               ...            ...         ...         ...   
+    CC722542                   597.823380     322.236056  252.159707  275.293551   
+    CC722651                   558.060369     322.176631  261.160474  282.029715   
+    CC722891                   384.798819     204.562530  149.220194  184.259996   
+    CC723197                   467.847632     262.950594  215.411133  232.938256   
+    CC723395                   470.969863     269.963160  234.219152  241.325755   
+    
+                  ...  right_hippocampal-fissure  right_presubiculum  \
+    participants  ...                                                  
+    CC110033      ...                 133.101613          263.829445   
+    CC110037      ...                 148.099481          339.309772   
+    CC110045      ...                 159.687619          324.398659   
+    CC110056      ...                 123.262352          290.196432   
+    CC110062      ...                 136.785201          406.323486   
+    ...           ...                        ...                 ...   
+    CC722542      ...                 147.391931          273.150743   
+    CC722651      ...                 185.053756          232.752897   
+    CC722891      ...                 140.980648          211.807774   
+    CC723197      ...                 213.080235          258.567312   
+    CC723395      ...                 205.934342          210.039976   
+    
+                  right_parasubiculum  right_molecular_layer_HP  right_GC-ML-DG  \
+    participants                                                                  
+    CC110033                47.651798                586.026640      328.057551   
+    CC110037                59.693186                617.448302      312.116795   
+    CC110045                55.738550                609.208671      314.460832   
+    CC110056                67.410418                569.389816      310.290805   
+    CC110062                80.403248                767.955163      383.194510   
+    ...                           ...                       ...             ...   
+    CC722542                50.874375                572.634593      302.504826   
+    CC722651                44.493903                533.912687      308.141458   
+    CC722891                57.371362                387.195124      199.979009   
+    CC723197                50.846316                481.409074      267.190903   
+    CC723395                33.216529                493.194601      274.020748   
+    
+                   right_CA3   right_CA4  right_fimbria  right_HATA  \
+    participants                                                      
+    CC110033      283.392837  282.565685      87.127463   73.589184   
+    CC110037      212.605572  269.307660      99.657823   60.920924   
+    CC110045      237.869822  271.505300      69.436808   59.323542   
+    CC110056      218.809310  267.327199      60.505521   51.726283   
+    CC110062      268.227177  325.403040      92.215816   85.484454   
+    ...                  ...         ...            ...         ...   
+    CC722542      236.946562  261.352283      46.144212   43.966509   
+    CC722651      247.810543  267.203989      68.730322   59.699644   
+    CC722891      146.836915  187.083211      27.913196   38.629828   
+    CC723197      211.618157  244.176755      51.893458   65.474967   
+    CC723395      232.592060  240.980896      68.335159   62.081225   
+    
+                  right_Whole_hippocampus  
+    participants                           
+    CC110033                  3531.764896  
+    CC110037                  3835.426137  
+    CC110045                  3681.494304  
+    CC110056                  3461.373764  
+    CC110062                  4782.407821  
+    ...                               ...  
+    CC722542                  3284.108783  
+    CC722651                  3210.272905  
+    CC722891                  2423.675065  
+    CC723197                  3043.146264  
+    CC723395                  2988.001288  
     
     [651 rows x 26 columns]
-              age sex_name  sex  ...  right_fimbria  right_HATA  right_Whole_hippocampus
-    CC110033   24     MALE    1  ...      87.127463   73.589184              3531.764896
-    CC110037   18     MALE    1  ...      99.657823   60.920924              3835.426137
-    CC110045   24   FEMALE    0  ...      69.436808   59.323542              3681.494304
-    CC110056   22   FEMALE    0  ...      60.505521   51.726283              3461.373764
-    CC110062   20     MALE    1  ...      92.215816   85.484454              4782.407821
-    ...       ...      ...  ...  ...            ...         ...                      ...
-    CC722542   79     MALE    1  ...      46.144212   43.966509              3284.108783
-    CC722651   79   FEMALE    0  ...      68.730322   59.699644              3210.272905
-    CC722891   84   FEMALE    0  ...      27.913196   38.629828              2423.675065
-    CC723197   80   FEMALE    0  ...      51.893458   65.474967              3043.146264
-    CC723395   86   FEMALE    0  ...      68.335159   62.081225              2988.001288
+              age sex_name  sex  IQ_random  left_Hippocampal_tail  left_subiculum  \
+    CC110033   24     MALE    1         73             482.768229      419.948094   
+    CC110037   18     MALE    1        103             595.269259      502.320315   
+    CC110045   24   FEMALE    0        124             655.847194      476.433625   
+    CC110056   22   FEMALE    0        124             561.345626      447.258970   
+    CC110062   20     MALE    1        126             756.521166      521.034681   
+    ...       ...      ...  ...        ...                    ...             ...   
+    CC722542   79     MALE    1        116             467.896808      440.794061   
+    CC722651   79   FEMALE    0        128             406.326167      393.469843   
+    CC722891   84   FEMALE    0        129             393.430481      303.049578   
+    CC723197   80   FEMALE    0         96             475.929914      372.449778   
+    CC723395   86   FEMALE    0        145             444.301617      330.688394   
+    
+                left_CA1  left_hippocampal-fissure  left_presubiculum  \
+    CC110033  666.496024                131.719049         285.535445   
+    CC110037  698.157779                156.304335         367.678385   
+    CC110045  654.215689                146.767569         346.347202   
+    CC110056  611.114561                126.615335         327.528926   
+    CC110062  716.391590                206.205818         384.356075   
+    ...              ...                       ...                ...   
+    CC722542  688.130914                184.300085         306.287030   
+    CC722651  613.794018                224.292557         254.786917   
+    CC722891  444.772656                158.987352         202.213773   
+    CC723197  525.739508                172.558200         222.384434   
+    CC723395  565.359058                206.235576         197.417773   
+    
+              left_parasubiculum  ...  right_hippocampal-fissure  \
+    CC110033           59.209377  ...                 133.101613   
+    CC110037           60.817591  ...                 148.099481   
+    CC110045           67.481121  ...                 159.687619   
+    CC110056           70.901227  ...                 123.262352   
+    CC110062           80.329689  ...                 136.785201   
+    ...                      ...  ...                        ...   
+    CC722542           72.629722  ...                 147.391931   
+    CC722651           50.006651  ...                 185.053756   
+    CC722891           46.418129  ...                 140.980648   
+    CC723197           40.304889  ...                 213.080235   
+    CC723395           34.240227  ...                 205.934342   
+    
+              right_presubiculum  right_parasubiculum  right_molecular_layer_HP  \
+    CC110033          263.829445            47.651798                586.026640   
+    CC110037          339.309772            59.693186                617.448302   
+    CC110045          324.398659            55.738550                609.208671   
+    CC110056          290.196432            67.410418                569.389816   
+    CC110062          406.323486            80.403248                767.955163   
+    ...                      ...                  ...                       ...   
+    CC722542          273.150743            50.874375                572.634593   
+    CC722651          232.752897            44.493903                533.912687   
+    CC722891          211.807774            57.371362                387.195124   
+    CC723197          258.567312            50.846316                481.409074   
+    CC723395          210.039976            33.216529                493.194601   
+    
+              right_GC-ML-DG   right_CA3   right_CA4  right_fimbria  right_HATA  \
+    CC110033      328.057551  283.392837  282.565685      87.127463   73.589184   
+    CC110037      312.116795  212.605572  269.307660      99.657823   60.920924   
+    CC110045      314.460832  237.869822  271.505300      69.436808   59.323542   
+    CC110056      310.290805  218.809310  267.327199      60.505521   51.726283   
+    CC110062      383.194510  268.227177  325.403040      92.215816   85.484454   
+    ...                  ...         ...         ...            ...         ...   
+    CC722542      302.504826  236.946562  261.352283      46.144212   43.966509   
+    CC722651      308.141458  247.810543  267.203989      68.730322   59.699644   
+    CC722891      199.979009  146.836915  187.083211      27.913196   38.629828   
+    CC723197      267.190903  211.618157  244.176755      51.893458   65.474967   
+    CC723395      274.020748  232.592060  240.980896      68.335159   62.081225   
+    
+              right_Whole_hippocampus  
+    CC110033              3531.764896  
+    CC110037              3835.426137  
+    CC110045              3681.494304  
+    CC110056              3461.373764  
+    CC110062              4782.407821  
+    ...                           ...  
+    CC722542              3284.108783  
+    CC722651              3210.272905  
+    CC722891              2423.675065  
+    CC723197              3043.146264  
+    CC723395              2988.001288  
     
     [650 rows x 30 columns]
 
@@ -252,22 +497,22 @@ putative biomarkers that are not restricted to brain imaging.
 
     # perpare covariate_normsample for sex and age
     covariate_normsample = norm_demographics_features[['sex',
-                                                       'age']] 
+                                                       'age']]
     
     covariate_normsample.to_csv('covariate_normsample.txt',
                                 sep = ' ',
-                                header = False, 
+                                header = False,
                                 index = False)
     
     # perpare features_normsample for relevant hyppocampal subfields
-    features_normsample = norm_demographics_features[['left_CA1', 
+    features_normsample = norm_demographics_features[['left_CA1',
                                                      'left_CA3',
                                                      'right_CA1',
                                                      'right_CA3']]
     
-    features_normsample.to_csv('features_normsample.txt', 
-                               sep = ' ', 
-                               header = False, 
+    features_normsample.to_csv('features_normsample.txt',
+                               sep = ' ',
+                               header = False,
                                index = False)
     
     # code by T. Wolfers
@@ -278,7 +523,7 @@ putative biomarkers that are not restricted to brain imaging.
 Once you have prepared and saved all the necessary files. Look at the
 pcntoolkit for running normative modeling. Select an appropritate method
 set up the toolkit and run your analyses using 2-fold cross validation
-in the normsample. Change the output suffix from estimate to ’_2fold’.
+in the normsample. Change the output suffix from estimate to ’\_2fold’.
 
 HINT: You primarily need the estimate function.
 
@@ -296,7 +541,7 @@ you will have no doubt when it is correctly running.
     
     # run normative modeling using 2-fold cross-validation
     
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            cvfolds = 2,
                            alg = 'gpr',
@@ -307,55 +552,66 @@ you will have no doubt when it is correctly running.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Warning: Estimation of posterior distribution failed
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Optimization terminated successfully.
-             Current function value: 1856.502251
-             Iterations: 40
-             Function evaluations: 99
-             Gradient evaluations: 99
+             Current function value: 1925.145213
+             Iterations: 30
+             Function evaluations: 75
+             Gradient evaluations: 69
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Optimization terminated successfully.
-             Current function value: 1596.239263
-             Iterations: 42
-             Function evaluations: 93
-             Gradient evaluations: 93
-    Estimating model  3 of 4
-    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Optimization terminated successfully.
-             Current function value: 1862.316698
-             Iterations: 47
-             Function evaluations: 104
-             Gradient evaluations: 104
-    Estimating model  4 of 4
-    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1587.950935
-             Iterations: 30
-             Function evaluations: 64
-             Gradient evaluations: 64
-    Estimating model  1 of 4
+             Current function value: 1627.864114
+             Iterations: 41
+             Function evaluations: 102
+             Gradient evaluations: 102
+    Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1916.461484
-             Iterations: 44
-             Function evaluations: 94
-             Gradient evaluations: 87
-    Estimating model  2 of 4
+             Current function value: 1922.205071
+             Iterations: 30
+             Function evaluations: 73
+             Gradient evaluations: 67
+    Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -365,36 +621,48 @@ you will have no doubt when it is correctly running.
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1611.661888
-             Iterations: 34
-             Function evaluations: 85
-             Gradient evaluations: 85
-    Estimating model  3 of 4
-    Warning: Estimation of posterior distribution failed
+             Current function value: 1621.445961
+             Iterations: 78
+             Function evaluations: 181
+             Gradient evaluations: 181
+    Estimating model  1 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1912.665851
-             Iterations: 61
-             Function evaluations: 133
-             Gradient evaluations: 126
-    Estimating model  4 of 4
+             Current function value: 1844.061877
+             Iterations: 36
+             Function evaluations: 81
+             Gradient evaluations: 81
+    Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
+    Optimization terminated successfully.
+             Current function value: 1580.315780
+             Iterations: 37
+             Function evaluations: 79
+             Gradient evaluations: 79
+    Estimating model  3 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
+    Optimization terminated successfully.
+             Current function value: 1851.005493
+             Iterations: 32
+             Function evaluations: 68
+             Gradient evaluations: 68
+    Estimating model  4 of 4
+    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1619.045647
-             Iterations: 43
-             Function evaluations: 110
-             Gradient evaluations: 105
+             Current function value: 1584.089863
+             Iterations: 39
+             Function evaluations: 91
+             Gradient evaluations: 91
     Evaluating the model ...
     Writing outputs ...
 
@@ -423,13 +691,13 @@ model using the appropriate specifications.
                                       20, 30, 40, 50, 60, 70, 80]}
     covariate_forwardmodel = pd.DataFrame(data=covariate_forwardmodel)
     
-    covariate_forwardmodel.to_csv('covariate_forwardmodel.txt', 
-                               sep = ' ', 
-                               header = False, 
+    covariate_forwardmodel.to_csv('covariate_forwardmodel.txt',
+                               sep = ' ',
+                               header = False,
                                index = False)
     
     # estimate forward model
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            testcov = 'covariate_forwardmodel.txt',
                            cvfolds = None,
@@ -441,8 +709,22 @@ model using the appropriate specifications.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -451,8 +733,8 @@ model using the appropriate specifications.
     Optimization terminated successfully.
              Current function value: 3781.497401
              Iterations: 20
-             Function evaluations: 61
-             Gradient evaluations: 54
+             Function evaluations: 58
+             Gradient evaluations: 52
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -466,10 +748,22 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3201.761309
-             Iterations: 39
-             Function evaluations: 108
-             Gradient evaluations: 108
+             Iterations: 48
+             Function evaluations: 114
+             Gradient evaluations: 114
     Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -480,9 +774,9 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3771.310488
-             Iterations: 47
-             Function evaluations: 181
-             Gradient evaluations: 167
+             Iterations: 48
+             Function evaluations: 156
+             Gradient evaluations: 143
     Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -496,9 +790,9 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3200.837262
-             Iterations: 40
-             Function evaluations: 104
-             Gradient evaluations: 104
+             Iterations: 42
+             Function evaluations: 116
+             Gradient evaluations: 116
     Writing outputs ...
 
 
@@ -508,7 +802,8 @@ model using the appropriate specifications.
 Visualize the forward model of the normative model similar to the figure
 below.
 
-.. figure:: 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
+.. figure::
+   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    :alt: 1-s2.0-S245190221830329X-gr2.jpg
 
    1-s2.0-S245190221830329X-gr2.jpg
@@ -531,12 +826,12 @@ individual participants.
         S_hat=np.mean(S2,axis=0)
         n=S2.shape[0]
         CI[i,:]=z*np.power(S_hat/n,.5)
-      return CI 
+      return CI
     
     
     feature_names=['left_CA1','left_CA3','right_CA1','right_CA3']
     sex_covariates=[ 'Female','Male']
-    # Creating plots for Female and male 
+    # Creating plots for Female and male
     for i,sex in enumerate(sex_covariates):
     #forward model data
         forward_yhat = pd.read_csv('yhat_forward.txt', sep = ' ', header=None)
@@ -544,71 +839,71 @@ individual participants.
         yhat_forward=yhat_forward[7*i:7*(i+1)]
         x_forward=[20, 30, 40, 50, 60, 70, 80]
     
-    # Find the index of the data exclusively for one sex. Female:0, Male: 1   
+    # Find the index of the data exclusively for one sex. Female:0, Male: 1
         inx=np.where(covariate_normsample.sex==i)[0]
         x=covariate_normsample.values[inx,1]
     # actual data
         y = pd.read_csv('features_normsample.txt', sep = ' ', header=None)
         y=y.values[inx]
-    # confidence Interval yhat+ z *(std/n^.5)-->.95 % CI:z=1.96, 99% CI:z=2.58 
+    # confidence Interval yhat+ z *(std/n^.5)-->.95 % CI:z=1.96, 99% CI:z=2.58
         s2= pd.read_csv('ys2_2fold.txt', sep = ' ', header=None)
         s2=s2.values[inx]
     
         CI_95=confidence_interval(s2,x,1.96)
         CI_99=confidence_interval(s2,x,2.58)
     
-    # Creat a trejactroy for each point     
+    # Creat a trejactroy for each point
         for j,name in enumerate(feature_names):
              fig=plt.figure()
              ax=fig.add_subplot(111)
              ax.plot(x_forward,yhat_forward[:,j], linewidth=4, label='Normative trejactory')
     
     
-             ax.plot(x_forward,CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g', label='95% confidence interval')  
-             ax.plot(x_forward,-CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g') 
+             ax.plot(x_forward,CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g', label='95% confidence interval')
+             ax.plot(x_forward,-CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g')
     
-             ax.plot(x_forward,CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k', label='99% confidence interval')  
-             ax.plot(x_forward,-CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k') 
+             ax.plot(x_forward,CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k', label='99% confidence interval')
+             ax.plot(x_forward,-CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k')
     
              ax.scatter(x,y[:,j],c='r', label=name)
              plt.legend(loc='upper left')
              plt.title('Normative trejectory of' +name+' in '+sex+' cohort')
              plt.show()
              plt.close()
-            
+    
     # code by M. Zabihi
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_0.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_0.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_1.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_1.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_2.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_2.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_3.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_3.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_4.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_4.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_5.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_5.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_6.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_6.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_7.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_7.png
 
 
 **TASK 6:** Apply the normative model to Nordan’s data and the dementia patients.
@@ -626,25 +921,25 @@ individual participants.
     
     # create a covariate file for Nordan's as well as the patient's demograhpics
     covariate_nordan = demographics_nordan[['sex',
-                                            'age']] 
+                                            'age']]
     covariate_nordan.to_csv('covariate_nordan.txt',
                             sep = ' ',
-                            header = False, 
+                            header = False,
                             index = False)
     
     # create the corresponding feature file
-    features_nordan = features_nordan[['left_CA1', 
+    features_nordan = features_nordan[['left_CA1',
                                       'left_CA3',
                                       'right_CA1',
                                       'right_CA3']]
     
-    features_nordan.to_csv('features_nordan.txt', 
-                            sep = ' ', 
-                            header = False, 
+    features_nordan.to_csv('features_nordan.txt',
+                            sep = ' ',
+                            header = False,
                             index = False)
     
     # apply normative modeling
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            testcov = 'covariate_nordan.txt',
                            testresp = 'features_nordan.txt',
@@ -657,8 +952,22 @@ individual participants.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -667,8 +976,8 @@ individual participants.
     Optimization terminated successfully.
              Current function value: 3781.497401
              Iterations: 20
-             Function evaluations: 61
-             Gradient evaluations: 54
+             Function evaluations: 58
+             Gradient evaluations: 52
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -682,10 +991,22 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3201.761309
-             Iterations: 39
-             Function evaluations: 108
-             Gradient evaluations: 108
+             Iterations: 48
+             Function evaluations: 114
+             Gradient evaluations: 114
     Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -696,9 +1017,9 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3771.310488
-             Iterations: 47
-             Function evaluations: 181
-             Gradient evaluations: 167
+             Iterations: 48
+             Function evaluations: 156
+             Gradient evaluations: 143
     Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -712,9 +1033,9 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3200.837262
-             Iterations: 40
-             Function evaluations: 104
-             Gradient evaluations: 104
+             Iterations: 42
+             Function evaluations: 116
+             Gradient evaluations: 116
     Evaluating the model ...
     Writing outputs ...
 
@@ -761,21 +1082,21 @@ age 20. Do that for both sexes seperately.
     lengths = len(forward_yhat[hyppocampal_subfield])
     for entry in forward_yhat[hyppocampal_subfield]:
       if count > 0 and count < 7:
-        loop_percentage_change_female = calculate_percentage_change(entry, 
+        loop_percentage_change_female = calculate_percentage_change(entry,
                                                                     forward_yhat.iloc[0,
                                                                                       hyppocampal_subfield])
         percentage_change_female.append(loop_percentage_change_female)
-      elif count > 7: 
+      elif count > 7:
         loop_percentage_change_male = calculate_percentage_change(entry,
                                                                   forward_yhat.iloc[9,
                                                                                     hyppocampal_subfield])
         percentage_change_male.append(loop_percentage_change_male)
-      count = count + 1 
+      count = count + 1
     
-    names = ['30 compared to 20 years', 
-             '40 compared to 20 years', 
-             '50 compared to 20 years', 
-             '60 compared to 20 years', 
+    names = ['30 compared to 20 years',
+             '40 compared to 20 years',
+             '50 compared to 20 years',
+             '60 compared to 20 years',
              '70 compared to 20 years',
              '80 compared to 20 years']
     
@@ -803,5 +1124,6 @@ age 20. Do that for both sexes seperately.
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_32_1.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_33_1.png
+
 
diff --git a/doc/source/pages/normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_0.png b/doc/source/pages/normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_0.png
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diff --git a/doc/build/doctrees/pages/apply_normative_models.doctree b/doc/build/doctrees/pages/apply_normative_models.doctree
index 4a24e45cbf77eab1dbabeb9b3688647acd0ae4fd..a1c3942eb79b910bde91b4d9efb967d5df32580f 100644
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diff --git a/doc/build/doctrees/pages/glossary.doctree b/doc/build/doctrees/pages/glossary.doctree
index 6151007c555a1d92a0f92bcb2138ee268e316027..6cea70a6043cea330ed7796b09fe0da80766813f 100644
GIT binary patch
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diff --git a/doc/build/doctrees/pages/normative_modelling_walkthrough.doctree b/doc/build/doctrees/pages/normative_modelling_walkthrough.doctree
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diff --git a/doc/build/doctrees/pages/pcntoolkit_background.doctree b/doc/build/doctrees/pages/pcntoolkit_background.doctree
index 26cc2d52ab050e5f51fdf32477aa2b6ee7a98741..004776a986682a9c4692589911b01be4737a4aa4 100644
GIT binary patch
delta 6019
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diff --git a/doc/build/doctrees/pages/post_hoc_analysis.doctree b/doc/build/doctrees/pages/post_hoc_analysis.doctree
index 5ee952c3fc4f2168321bae24931da6e45b7261ff..bd824185adc1c8d344f424e4ac0129b64d7348fc 100644
GIT binary patch
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diff --git a/doc/build/doctrees/pages/visualizations.doctree b/doc/build/doctrees/pages/visualizations.doctree
index fcbac270870277c7ae86289eef0e9e3895162900..6181093c6b2aa9854357b7eb3ca11f8d1bd19d70 100644
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z6sE}Y_;L(#vNrxgYwHZ31}b-7x7p-f0+M!>QT7a5BzjrHxti
-
+
 
   
   
   bayesreg — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 
 
    Tutorials
    
 
 
    Other Useful Stuff
    
 
      
@@ -111,7 +118,9 @@ 
      Source code for bayesreg
       from scipy.linalg import LinAlgError
 
 
-
      [docs]class BLR:
+
      
+[docs]
+class BLR:
     """Bayesian linear regression
 
     Estimation and prediction of Bayesian linear regression models
@@ -253,7 +262,9 @@ 
      Source code for bayesreg
       
         return beta, alpha, gamma
 
-
      [docs]    def post(self, hyp, X, y, Xv=None):
+
      
+[docs]
+    def post(self, hyp, X, y, Xv=None):
         """ Generic function to compute posterior distribution.
 
             This function will save the posterior mean and precision matrix as
@@ -306,7 +317,10 @@ 
      Source code for bayesreg
               self.D = D
         self.hyp = hyp
      
 
-
      [docs]    def loglik(self, hyp, X, y, Xv=None):
+
+
      
+[docs]
+    def loglik(self, hyp, X, y, Xv=None):
         """ Function to compute compute log (marginal) likelihood """
 
         # hyperparameters (alpha not needed)
@@ -363,7 +377,10 @@ 
      Source code for bayesreg
               self.nlZ = nlZ
         return nlZ
      
 
-
      [docs]    def penalized_loglik(self, hyp, X, y, Xv=None, l=0.1, norm='L1'):
+
+
      
+[docs]
+    def penalized_loglik(self, hyp, X, y, Xv=None, l=0.1, norm='L1'):
         """ Function to compute the penalized log (marginal) likelihood 
 
             :param hyp: hyperparameter vector
@@ -382,7 +399,10 @@ 
      Source code for bayesreg
                   print("Requested penalty not recognized, choose between 'L1' or 'L2'.")
         return L
      
 
-
      [docs]    def dloglik(self, hyp, X, y, Xv=None):
+
+
      
+[docs]
+    def dloglik(self, hyp, X, y, Xv=None):
         """ Function to compute derivatives """
 
         # hyperparameters
@@ -484,8 +504,11 @@ 
      Source code for bayesreg
               self.dnlZ = dnlZ
         return dnlZ
      
 
+
     # model estimation (optimization)
-
      [docs]    def estimate(self, hyp0, X, y, **kwargs):
+
      
+[docs]
+    def estimate(self, hyp0, X, y, **kwargs):
         """ Function to estimate the model 
 
             :param hyp: hyperparameter vector
@@ -542,7 +565,10 @@ 
      Source code for bayesreg
       
         return self.hyp
      
 
-
      [docs]    def predict(self, hyp, X, y, Xs,
+
+
      
+[docs]
+    def predict(self, hyp, X, y, Xs,
                 var_groups_test=None,
                 var_covariates_test=None, **kwargs):
         """ Function to make predictions from the model
@@ -595,7 +621,10 @@ 
      Source code for bayesreg
       
         return ys, s2
      
 
-
      [docs]    def predict_and_adjust(self, hyp, X, y, Xs=None,
+
+
      
+[docs]
+    def predict_and_adjust(self, hyp, X, y, Xs=None,
                            ys=None,
                            var_groups_test=None,
                            var_groups_adapt=None, **kwargs):
@@ -687,7 +716,9 @@ 
      Source code for bayesreg
                           ys = ys - residuals_mu
                 s2_out = None
 
-        return ys_out, s2_out
      
+        return ys_out, s2_out
      
+
      
+
 
      
 
            
      
diff --git a/doc/build/html/_modules/fileio.html b/doc/build/html/_modules/fileio.html
index 54095935..99728826 100644
--- a/doc/build/html/_modules/fileio.html
+++ b/doc/build/html/_modules/fileio.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   fileio — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
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-        
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+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 
    
 
    Tutorials
    
 
 
    Other Useful Stuff
    
 
      
@@ -138,7 +145,9 @@ 
      Source code for fileio
       # ------------------------
 
 
-
      [docs]def predictive_interval(s2_forward,
+
      
+[docs]
+def predictive_interval(s2_forward,
                         cov_forward,
                         multiplicator):
     """
@@ -153,7 +162,10 @@ 
      Source code for fileio
           return PI
      
 
 
-
      [docs]def create_mask(data_array, mask, verbose=False):
+
+
      
+[docs]
+def create_mask(data_array, mask, verbose=False):
     """
     Create a mask from a data array or a nifti file
 
@@ -189,7 +201,10 @@ 
      Source code for fileio
           return maskvol
      
 
 
-
      [docs]def vol2vec(dat, mask, verbose=False):
+
+
      
+[docs]
+def vol2vec(dat, mask, verbose=False):
     """
     Vectorise a 3d image
 
@@ -224,7 +239,10 @@ 
      Source code for fileio
           return dat
      
 
 
-
      [docs]def file_type(filename):
+
+
      
+[docs]
+def file_type(filename):
     """
     Determine the file type of a file
 
@@ -250,7 +268,10 @@ 
      Source code for fileio
           return ftype
      
 
 
-
      [docs]def file_extension(filename):
+
+
      
+[docs]
+def file_extension(filename):
     """
     Determine the file extension of a file (e.g. .nii.gz)
 
@@ -282,7 +303,10 @@ 
      Source code for fileio
           return ext
      
 
 
-
      [docs]def file_stem(filename):
+
+
      
+[docs]
+def file_stem(filename):
     """
     Determine the file stem of a file (e.g. /path/to/file.nii.gz -> file)
 
@@ -297,12 +321,15 @@ 
      Source code for fileio
       
     return stm
      
 
+
 # --------------
 # nifti routines
 # --------------
 
 
-
      [docs]def load_nifti(datafile, mask=None, vol=False, verbose=False):
+
      
+[docs]
+def load_nifti(datafile, mask=None, vol=False, verbose=False):
     """
     Load a nifti file into a numpy array
 
@@ -330,7 +357,10 @@ 
      Source code for fileio
           return dat
      
 
 
-
      [docs]def save_nifti(data, filename, examplenii, mask, dtype=None):
+
+
      
+[docs]
+def save_nifti(data, filename, examplenii, mask, dtype=None):
     '''
     Write output to nifti
 
@@ -372,12 +402,15 @@ 
      Source code for fileio
       
     nib.save(array_img, filename)
      
 
+
 # --------------
 # cifti routines
 # --------------
 
 
-
      [docs]def load_cifti(filename, vol=False, mask=None, rmtmp=True):
+
      
+[docs]
+def load_cifti(filename, vol=False, mask=None, rmtmp=True):
     """
     Load a cifti file into a numpy array 
 
@@ -439,7 +472,10 @@ 
      Source code for fileio
           return out
      
 
 
-
      [docs]def save_cifti(data, filename, example, mask=None, vol=True, volatlas=None):
+
+
      
+[docs]
+def save_cifti(data, filename, example, mask=None, vol=True, volatlas=None):
     """
     Save a cifti file from a numpy array
 
@@ -535,12 +571,15 @@ 
      Source code for fileio
           for f in tmpfiles:
         os.remove(f)
      
 
+
 # --------------
 # ascii routines
 # --------------
 
 
-
      [docs]def load_pd(filename):
+
      
+[docs]
+def load_pd(filename):
     """
     Load a csv file into a pandas dataframe
 
@@ -558,7 +597,10 @@ 
      Source code for fileio
           return x
      
 
 
-
      [docs]def save_pd(data, filename):
+
+
      
+[docs]
+def save_pd(data, filename):
     """
     Save a pandas dataframe to a csv file
 
@@ -577,7 +619,10 @@ 
      Source code for fileio
                       na_rep='NaN')
      
 
 
-
      [docs]def load_ascii(filename):
+
+
      
+[docs]
+def load_ascii(filename):
     """
     Load an ascii file into a numpy array
 
@@ -593,7 +638,10 @@ 
      Source code for fileio
           return x
      
 
 
-
      [docs]def save_ascii(data, filename):
+
+
      
+[docs]
+def save_ascii(data, filename):
     """
     Save a numpy array to an ascii file
 
@@ -607,12 +655,15 @@ 
      Source code for fileio
           # based on pandas
     np.savetxt(filename, data)
      
 
+
 # ----------------
 # generic routines
 # ----------------
 
 
-
      [docs]def save(data, filename, example=None, mask=None, text=False, dtype=None):
+
      
+[docs]
+def save(data, filename, example=None, mask=None, text=False, dtype=None):
     """
     Save a numpy array to a file
 
@@ -639,7 +690,10 @@ 
      Source code for fileio
               data.to_pickle(filename, protocol=PICKLE_PROTOCOL)
      
 
 
-
      [docs]def load(filename, mask=None, text=False, vol=True):
+
+
      
+[docs]
+def load(filename, mask=None, text=False, vol=True):
     """
     Load a numpy array from a file
 
@@ -664,12 +718,15 @@ 
      Source code for fileio
               x = x.to_numpy()
     return x
      
 
+
 # -------------------
 # sorting routines for batched in normative parallel
 # -------------------
 
 
-
      [docs]def tryint(s):
+
      
+[docs]
+def tryint(s):
     """
     Try to convert a string to an integer
 
@@ -686,7 +743,10 @@ 
      Source code for fileio
               return s
      
 
 
-
      [docs]def alphanum_key(s):
+
+
      
+[docs]
+def alphanum_key(s):
     """
     Turn a string into a list of numbers
 
@@ -699,7 +759,10 @@ 
      Source code for fileio
           return [tryint(c) for c in re.split('([0-9]+)', s)]
      
 
 
-
      [docs]def sort_nicely(l):
+
+
      
+[docs]
+def sort_nicely(l):
     """
     Sort a list of strings in a natural way
 
@@ -711,6 +774,7 @@ 
      Source code for fileio
           """
 
     return sorted(l, key=alphanum_key)
      
+
 
      
 
            
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From 91767a3931be985b0cb11bb28a735515c2f3172a Mon Sep 17 00:00:00 2001
From: Stijn 
Date: Tue, 19 Nov 2024 15:35:49 +0100
Subject: [PATCH 67/68] Update docs

---
 doc/build/doctrees/environment.pickle         |  Bin 285780 -> 343429 bytes
 doc/build/doctrees/index.doctree              |  Bin 4031 -> 4109 bytes
 .../pages/BLR_normativemodel_protocol.doctree |  Bin 91997 -> 154464 bytes
 doc/build/doctrees/pages/FAQs.doctree         |  Bin 4535 -> 4615 bytes
 ...R_NormativeModel_FCONdata_Tutorial.doctree |  Bin 38767 -> 179379 bytes
 .../doctrees/pages/acknowledgements.doctree   |  Bin 5030 -> 5117 bytes
 .../pages/apply_normative_models.doctree      |  Bin 72552 -> 60279 bytes
 doc/build/doctrees/pages/citing.doctree       |  Bin 16090 -> 16168 bytes
 doc/build/doctrees/pages/glossary.doctree     |  Bin 8947 -> 9025 bytes
 doc/build/doctrees/pages/installation.doctree |  Bin 14406 -> 15660 bytes
 doc/build/doctrees/pages/modindex.doctree     |  Bin 394153 -> 410015 bytes
 .../normative_modelling_walkthrough.doctree   |  Bin 113777 -> 161333 bytes
 .../pages/other_predictive_models.doctree     |  Bin 93205 -> 112567 bytes
 .../pages/pcntoolkit_background.doctree       |  Bin 41905 -> 42095 bytes
 .../doctrees/pages/post_hoc_analysis.doctree  |  Bin 46105 -> 46310 bytes
 .../doctrees/pages/visualizations.doctree     |  Bin 19938834 -> 19938996 bytes
 doc/build/html/.buildinfo                     |    4 +-
 .../_images/other_predictive_models_17_1.png  |  Bin 18179 -> 35783 bytes
 .../_images/other_predictive_models_56_0.png  |  Bin 24851 -> 47682 bytes
 .../_images/other_predictive_models_73_0.png  |  Bin 26035 -> 51001 bytes
 doc/build/html/_modules/bayesreg.html         |   87 +-
 doc/build/html/_modules/fileio.html           |  140 +-
 doc/build/html/_modules/gp.html               |  167 +-
 doc/build/html/_modules/index.html            |   47 +-
 doc/build/html/_modules/normative.html        |  262 ++-
 .../html/_modules/normative_parallel.html     |  370 ++--
 doc/build/html/_modules/rfa.html              |   82 +-
 doc/build/html/_modules/trendsurf.html        |   75 +-
 .../pages/BLR_normativemodel_protocol.rst.txt | 1531 +++++++++++--
 doc/build/html/_sources/pages/FAQs.rst.txt    |    2 +-
 ...R_NormativeModel_FCONdata_Tutorial.rst.txt | 1885 ++++++++++++++++-
 .../pages/apply_normative_models.rst.txt      |  369 ++--
 .../html/_sources/pages/installation.rst.txt  |   15 +-
 .../normative_modelling_walkthrough.rst.txt   |  654 ++++--
 .../pages/other_predictive_models.rst.txt     |  415 ++--
 .../_sphinx_javascript_frameworks_compat.js   |   17 +-
 doc/build/html/_static/basic.css              |   68 +-
 doc/build/html/_static/css/badge_only.css     |    2 +-
 doc/build/html/_static/css/theme.css          |    2 +-
 doc/build/html/_static/doctools.js            |  137 +-
 .../html/_static/documentation_options.js     |    5 +-
 doc/build/html/_static/graphviz.css           |    7 -
 doc/build/html/_static/language_data.js       |    9 +-
 doc/build/html/_static/pygments.css           |    1 +
 doc/build/html/_static/searchtools.js         |  267 ++-
 doc/build/html/_static/sphinx_highlight.js    |   16 +-
 doc/build/html/genindex.html                  |   49 +-
 doc/build/html/index.html                     |   71 +-
 doc/build/html/objects.inv                    |  Bin 3947 -> 4330 bytes
 .../pages/BLR_normativemodel_protocol.html    | 1513 +++++++++++--
 doc/build/html/pages/FAQs.html                |   54 +-
 .../HBR_NormativeModel_FCONdata_Tutorial.html | 1816 +++++++++++++++-
 doc/build/html/pages/acknowledgements.html    |   48 +-
 .../html/pages/apply_normative_models.html    |  233 +-
 doc/build/html/pages/citing.html              |   48 +-
 doc/build/html/pages/glossary.html            |   50 +-
 doc/build/html/pages/installation.html        |   67 +-
 doc/build/html/pages/modindex.html            |  390 ++--
 .../normative_modelling_walkthrough.html      |  631 ++++--
 .../html/pages/other_predictive_models.html   |  386 ++--
 .../html/pages/pcntoolkit_background.html     |   72 +-
 doc/build/html/pages/post_hoc_analysis.html   |   67 +-
 doc/build/html/pages/visualizations.html      |   68 +-
 doc/build/html/py-modindex.html               |   47 +-
 doc/build/html/search.html                    |   47 +-
 doc/build/html/searchindex.js                 |    2 +-
 doc/source/conf.py                            |    1 -
 .../pages/BLR_normativemodel_protocol.rst     |    4 -
 ...HBR_NormativeModel_FCONdata_Tutorial.ipynb |  628 ++++++
 .../HBR_NormativeModel_FCONdata_Tutorial.rst  |    2 -
 doc/source/pages/apply_normative_models.ipynb |  793 +++++++
 doc/source/pages/apply_normative_models.rst   |  369 ++--
 .../pages/apply_normative_models_ct.rst       |  624 ------
 .../apply_normative_models_29_1.png           |  Bin 32916 -> 0 bytes
 .../apply_normative_models_29_3.png           |  Bin 31992 -> 0 bytes
 .../apply_normative_models_29_5.png           |  Bin 31880 -> 0 bytes
 .../apply_normative_models_ct_27_1.png        |  Bin
 .../apply_normative_models_ct_27_3.png        |  Bin
 .../apply_normative_models_ct_27_5.png        |  Bin
 .../normative_modelling_walkthrough.ipynb     |  648 ++++++
 .../pages/normative_modelling_walkthrough.rst |  348 ---
 poetry.lock                                   |   35 +-
 pyproject.toml                                |    2 +
 83 files changed, 11867 insertions(+), 3882 deletions(-)
 create mode 100644 doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.ipynb
 create mode 100644 doc/source/pages/apply_normative_models.ipynb
 delete mode 100644 doc/source/pages/apply_normative_models_ct.rst
 delete mode 100644 doc/source/pages/apply_normative_models_files/apply_normative_models_29_1.png
 delete mode 100644 doc/source/pages/apply_normative_models_files/apply_normative_models_29_3.png
 delete mode 100644 doc/source/pages/apply_normative_models_files/apply_normative_models_29_5.png
 rename doc/source/pages/{apply_normative_models_ct_files => apply_normative_models_files}/apply_normative_models_ct_27_1.png (100%)
 rename doc/source/pages/{apply_normative_models_ct_files => apply_normative_models_files}/apply_normative_models_ct_27_3.png (100%)
 rename doc/source/pages/{apply_normative_models_ct_files => apply_normative_models_files}/apply_normative_models_ct_27_5.png (100%)
 create mode 100644 doc/source/pages/normative_modelling_walkthrough.ipynb

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diff --git a/doc/build/doctrees/index.doctree b/doc/build/doctrees/index.doctree
index 6b3b646b4e6f25ee66dca996c9d4b2893e73e18a..de33cbad63e47ad3683c2a1e37bd53286925fd6a 100644
GIT binary patch
delta 205
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diff --git a/doc/build/doctrees/pages/FAQs.doctree b/doc/build/doctrees/pages/FAQs.doctree
index 28e269f6f31e9255d9ee445ec4ee84af295184b4..c71de8ed54a357fbde75e0e912564eeae2b2fb3c 100644
GIT binary patch
delta 207
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delta 124
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HcmV?d00001

diff --git a/doc/source/pages/other_predictive_models.rst b/doc/source/pages/other_predictive_models.rst
index c67b860c..2b841cad 100644
--- a/doc/source/pages/other_predictive_models.rst
+++ b/doc/source/pages/other_predictive_models.rst
@@ -1,24 +1,23 @@
-.. title:: Predictive modeling tutorial
-
-Predictive modeling using deviation scores
-=============================================
-
-The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
-
-Created by `Saige Rutherford `__
-
+.. code:: ipython3
 
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/BLR_protocol/other_predictive_models.ipynb
+    ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
 
 
-.. code:: ipython3
+.. parsed-literal::
 
-    ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
+    Cloning into 'PCNtoolkit-demo'...
+    remote: Enumerating objects: 1237, done.
+    remote: Counting objects: 100% (360/360), done.
+    remote: Compressing objects: 100% (185/185), done.
+    remote: Total 1237 (delta 200), reused 306 (delta 172), pack-reused 877 (from 1)
+    Receiving objects: 100% (1237/1237), 141.45 MiB | 10.83 MiB/s, done.
+    Resolving deltas: 100% (562/562), done.
+    Updating files: 100% (70/70), done.
 
 
 .. code:: ipython3
 
+    
     import os
 
 .. code:: ipython3
@@ -42,7 +41,7 @@ Created by `Saige Rutherford `__
     import numpy as np
     from matplotlib import pyplot as plt
     from scipy import stats, linalg
-    from sklearn import preprocessing, decomposition, linear_model, metrics 
+    from sklearn import preprocessing, decomposition, linear_model, metrics
     import warnings
 
 .. code:: ipython3
@@ -62,7 +61,7 @@ Created by `Saige Rutherford `__
     plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title
 
 Load Data
------------------------------
+=========
 
 .. code:: ipython3
 
@@ -85,7 +84,7 @@ Load Data
 
 
 Create Train/Test Splits
---------------------------------------
+========================
 
 .. code:: ipython3
 
@@ -117,7 +116,7 @@ Create Train/Test Splits
     test_mu_centered_ct = (test_data_ct - train_data_ct.mean(axis=0))
 
 Principal Component Regression (BBS)
---------------------------------------
+====================================
 
 .. code:: ipython3
 
@@ -131,7 +130,7 @@ Principal Component Regression (BBS)
 
 .. code:: ipython3
 
-    print(f'First PC explains {pca_model_z.explained_variance_ratio_[0]*100:.2f}% of the total variance.')
+    print(f'First PC explains {pca_model_z.explained_variance_ratio_[0]*100:.2f}% of the total variance.\nThis is an artifact of zero inflated data')
     plt.figure(figsize=(10, 7))
     plt.bar(range(1, 51), pca_model_z.explained_variance_ratio_[1:51])
     plt.title('Deviations model Variance Explained Ratio\nPCs 1-50', fontsize=25)
@@ -141,15 +140,16 @@ Principal Component Regression (BBS)
 .. parsed-literal::
 
     First PC explains 23.41% of the total variance.
+    This is an artifact of zero inflated data
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_16_1.png
+.. image:: other_predictive_models_files/other_predictive_models_17_1.png
 
 
 .. code:: ipython3
 
-    print(f'First PC explains {pca_model_ct.explained_variance_ratio_[0]*100:.2f}% of the total variance.')
+    print(f'First PC explains {pca_model_ct.explained_variance_ratio_[0]*100:.2f}% of the total variance.\nThis is an artifact of zero inflated data')
     plt.figure(figsize=(10, 7))
     plt.bar(range(1, 51), pca_model_ct.explained_variance_ratio_[1:51])
     plt.title('Cortical Thickness model Variance Explained Ratio\nPCs 1-50', fontsize=25)
@@ -159,10 +159,11 @@ Principal Component Regression (BBS)
 .. parsed-literal::
 
     First PC explains 24.28% of the total variance.
+    This is an artifact of zero inflated data
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_17_1.png
+.. image:: other_predictive_models_files/other_predictive_models_18_1.png
 
 
 .. code:: ipython3
@@ -176,7 +177,7 @@ Principal Component Regression (BBS)
     test_transformed_ct = pca_model_ct.transform(test_data_ct)
 
 Fit Linear Regression Model
---------------------------------------
+---------------------------
 
 .. code:: ipython3
 
@@ -184,14 +185,14 @@ Fit Linear Regression Model
     # we will check that this matches sklearn results later
     
     # fit ols model on dimension reduced train data
-    train_features_z = np.hstack([np.ones((train_transformed_z.shape[0], 1)), 
+    train_features_z = np.hstack([np.ones((train_transformed_z.shape[0], 1)),
                                 train_transformed_z])
-    train_features_inv_z = linalg.pinv2(train_features_z)
+    train_features_inv_z = linalg.pinv(train_features_z)
     train_betas_z = np.dot(train_features_inv_z, train_phen)
     train_pred_phen_z = np.dot(train_features_z, train_betas_z)
     
     # fit ols model on dimension reduced test data
-    test_features_z = np.hstack([np.ones((test_transformed_z.shape[0], 1)), 
+    test_features_z = np.hstack([np.ones((test_transformed_z.shape[0], 1)),
                                test_transformed_z])
     test_pred_phen_z = np.dot(test_features_z, train_betas_z)
 
@@ -201,14 +202,14 @@ Fit Linear Regression Model
     # we will check that this matches sklearn results later
     
     # fit ols model on dimension reduced train data
-    train_features_ct = np.hstack([np.ones((train_transformed_ct.shape[0], 1)), 
+    train_features_ct = np.hstack([np.ones((train_transformed_ct.shape[0], 1)),
                                 train_transformed_ct])
-    train_features_inv_ct = linalg.pinv2(train_features_ct)
+    train_features_inv_ct = linalg.pinv(train_features_ct)
     train_betas_ct = np.dot(train_features_inv_ct, train_phen)
     train_pred_phen_ct = np.dot(train_features_ct, train_betas_ct)
     
     # fit ols model on dimension reduced test data
-    test_features_ct = np.hstack([np.ones((test_transformed_ct.shape[0], 1)), 
+    test_features_ct = np.hstack([np.ones((test_transformed_ct.shape[0], 1)),
                                test_transformed_ct])
     test_pred_phen_ct = np.dot(test_features_ct, train_betas_ct)
 
@@ -216,34 +217,20 @@ Fit Linear Regression Model
 
     # OLS using sklearn
     
-    lr_model_z = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+    lr_model_z = linear_model.LinearRegression(fit_intercept=True)
     lr_model_z.fit(train_transformed_z, train_phen)
     train_pred_phen_lr_model_z = lr_model_z.predict(train_transformed_z)
     test_pred_phen_lr_model_z = lr_model_z.predict(test_transformed_z)
 
-
-.. parsed-literal::
-
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
-
-
 .. code:: ipython3
 
     # OLS using sklearn
     
-    lr_model_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+    lr_model_ct = linear_model.LinearRegression(fit_intercept=True)
     lr_model_ct.fit(train_transformed_ct, train_phen)
     train_pred_phen_lr_model_ct = lr_model_ct.predict(train_transformed_ct)
     test_pred_phen_lr_model_ct = lr_model_ct.predict(test_transformed_ct)
 
-
-.. parsed-literal::
-
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
-
-
 .. code:: ipython3
 
     # ensure matrix math predictions and sklearn predictions are accurate to 5 decimals
@@ -270,8 +257,8 @@ Fit Linear Regression Model
     Passed
 
 
-Mean Squared/Absolute Error of Predictions
------------------------------------------------
+Accuracy of Predictions
+-----------------------
 
 .. code:: ipython3
 
@@ -326,18 +313,18 @@ BBS Cross Validation
 
     def bbs(X, y, n_components, n_cv_splits, pred_summary_function, verbose=False):
         assert X.shape[0] == y.shape[0]
-        
+    
         fold_accs_train = []
         fold_accs_test = []
         np.random.seed(42)
         shuffled_idxs = np.random.choice(range(X.shape[0]), size=X.shape[0], replace=False)
         for fold_i, test_idxs in enumerate(np.array_split(shuffled_idxs, n_cv_splits)):
-            train_mask = np.ones(X.shape[0], np.bool)
+            train_mask = np.ones(X.shape[0], bool)
             train_mask[test_idxs] = 0
     
             # create train/text X, y
             train_X, test_X = X[train_mask, :], X[test_idxs, :]
-            train_y, test_y = y[train_mask], y[test_idxs]  
+            train_y, test_y = y[train_mask], y[test_idxs]
     
             # mean center columns using train data only
             train_X_mu = train_X.mean(axis=0)
@@ -356,7 +343,7 @@ BBS Cross Validation
             # fit OLS model
             if verbose:
                 print(f'CV Fold: {fold_i+1:<10} Fitting Linear Regression model...')
-            lr_model = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+            lr_model = linear_model.LinearRegression(fit_intercept=True)
             lr_model.fit(train_X, train_y)
     
             train_pred = lr_model.predict(train_X)
@@ -364,11 +351,11 @@ BBS Cross Validation
     
             fold_accs_train.append(pred_summary_function(train_y, train_pred))
             fold_accs_test.append(pred_summary_function(test_y, test_pred))
-            
+    
             if verbose:
-                print(f'CV Fold: {fold_i+1:<10} Train MAE: {round(fold_accs_train[-1], 3):<10} Test MAE: {round(fold_accs_test[-1], 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Train Accuracy: {round(fold_accs_train[-1], 3):<10} Test Accuracy: {round(fold_accs_test[-1], 3):<10}')
+    
     
-        
         plt.figure(figsize=(13, 7))
         plt.plot(range(1, len(fold_accs_train)+1), fold_accs_train, linestyle='-', marker='o', color='C0', label='Train CV Performance')
         plt.plot(range(1, len(fold_accs_test)+1), fold_accs_test, linestyle='-', marker='o', color='C1', label='Test CV Performance')
@@ -377,7 +364,7 @@ BBS Cross Validation
         plt.xlabel('CV Fold')
         plt.legend(fontsize=20)
         plt.show()
-        
+    
         return fold_accs_train, fold_accs_test
 
 .. code:: ipython3
@@ -389,23 +376,23 @@ BBS Cross Validation
 
     CV Fold: 1          Fitting PCA model...
     CV Fold: 1          Fitting Linear Regression model...
-    CV Fold: 1          Train MAE: 0.599      Test MAE: 0.619     
+    CV Fold: 1          Train Accuracy: 0.599      Test Accuracy: 0.619     
     CV Fold: 2          Fitting PCA model...
     CV Fold: 2          Fitting Linear Regression model...
-    CV Fold: 2          Train MAE: 0.572      Test MAE: 0.713     
+    CV Fold: 2          Train Accuracy: 0.572      Test Accuracy: 0.713     
     CV Fold: 3          Fitting PCA model...
     CV Fold: 3          Fitting Linear Regression model...
-    CV Fold: 3          Train MAE: 0.577      Test MAE: 0.687     
+    CV Fold: 3          Train Accuracy: 0.577      Test Accuracy: 0.687     
     CV Fold: 4          Fitting PCA model...
     CV Fold: 4          Fitting Linear Regression model...
-    CV Fold: 4          Train MAE: 0.604      Test MAE: 0.608     
+    CV Fold: 4          Train Accuracy: 0.604      Test Accuracy: 0.608     
     CV Fold: 5          Fitting PCA model...
     CV Fold: 5          Fitting Linear Regression model...
-    CV Fold: 5          Train MAE: 0.581      Test MAE: 0.687     
+    CV Fold: 5          Train Accuracy: 0.581      Test Accuracy: 0.687     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_32_3.png
+.. image:: other_predictive_models_files/other_predictive_models_33_1.png
 
 
 .. code:: ipython3
@@ -417,41 +404,43 @@ BBS Cross Validation
 
     CV Fold: 1          Fitting PCA model...
     CV Fold: 1          Fitting Linear Regression model...
-    CV Fold: 1          Train MAE: 0.622      Test MAE: 0.643     
+    CV Fold: 1          Train Accuracy: 0.622      Test Accuracy: 0.643     
     CV Fold: 2          Fitting PCA model...
     CV Fold: 2          Fitting Linear Regression model...
-    CV Fold: 2          Train MAE: 0.605      Test MAE: 0.723     
+    CV Fold: 2          Train Accuracy: 0.605      Test Accuracy: 0.723     
     CV Fold: 3          Fitting PCA model...
     CV Fold: 3          Fitting Linear Regression model...
-    CV Fold: 3          Train MAE: 0.604      Test MAE: 0.701     
+    CV Fold: 3          Train Accuracy: 0.604      Test Accuracy: 0.701     
     CV Fold: 4          Fitting PCA model...
     CV Fold: 4          Fitting Linear Regression model...
-    CV Fold: 4          Train MAE: 0.624      Test MAE: 0.646     
+    CV Fold: 4          Train Accuracy: 0.624      Test Accuracy: 0.646     
     CV Fold: 5          Fitting PCA model...
     CV Fold: 5          Fitting Linear Regression model...
-    CV Fold: 5          Train MAE: 0.614      Test MAE: 0.722     
+    CV Fold: 5          Train Accuracy: 0.614      Test Accuracy: 0.722     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_33_3.png
+.. image:: other_predictive_models_files/other_predictive_models_34_1.png
 
 
 Connectome Predictive Modelling
---------------------------------------
+===============================
 
 .. code:: ipython3
 
     # correlation train_brain with train_phenotype
     train_z_pheno_corr_p = [stats.pearsonr(train_data_z[:, i], train_phen) for i in range(train_data_z.shape[1])]  # train_pheno_corr_p: (259200, )
+    # there are some nan correlations if brain data is poorly cropped (ie: some columns are always 0)
 
 .. code:: ipython3
 
     # correlation train_brain with train_phenotype
     train_ct_pheno_corr_p = [stats.pearsonr(train_data_ct[:, i], train_phen) for i in range(train_data_ct.shape[1])]  # train_pheno_corr_p: (259200, )
+    # there are some nan correlations if brain data is poorly cropped (ie: some columns are always 0)
 
 .. code:: ipython3
 
-    # split into positive and negative correlations 
+    # split into positive and negative correlations
     # and keep edges with p values below threshold
     pval_threshold = 0.01
     
@@ -495,15 +484,13 @@ Connectome Predictive Modelling
 
 .. code:: ipython3
 
-    fit_pos_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum_z.reshape(-1, 1), train_phen)
-    fit_neg_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum_z.reshape(-1, 1), train_phen)
-
+    fit_pos_z = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum_z.reshape(-1, 1), train_phen)
+    fit_neg_z = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum_z.reshape(-1, 1), train_phen)
 
 .. code:: ipython3
 
-    fit_pos_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum_ct.reshape(-1, 1), train_phen)
-    fit_neg_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum_ct.reshape(-1, 1), train_phen)
-
+    fit_pos_ct = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum_ct.reshape(-1, 1), train_phen)
+    fit_neg_ct = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum_ct.reshape(-1, 1), train_phen)
 
 .. code:: ipython3
 
@@ -512,52 +499,48 @@ Connectome Predictive Modelling
     pos_error_ct = metrics.mean_absolute_error(train_phen, fit_pos_ct.predict(train_pos_edges_sum_ct.reshape(-1, 1)))
     neg_error_ct = metrics.mean_absolute_error(train_phen, fit_neg_ct.predict(train_neg_edges_sum_ct.reshape(-1, 1)))
     
-    print(f'Training Error (MAE) (Positive Z Features Model) = {pos_error_z:.3f}')
-    print(f'Training Error (MAE) (Negative Z Features Model) = {neg_error_z:.3f}')
-    print(f'Training Error (MAE) (Positive CT Features Model) = {pos_error_ct:.3f}')
-    print(f'Training Error (MAE) (Negative CT Features Model) = {neg_error_ct:.3f}')
+    print(f'Training Error (Positive Z Features Model) = {pos_error_z:.3f}')
+    print(f'Training Error (Negative Z Features Model) = {neg_error_z:.3f}')
+    print(f'Training Error (Positive CT Features Model) = {pos_error_ct:.3f}')
+    print(f'Training Error (Negative CT Features Model) = {neg_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Training Error (MAE) (Positive Z Features Model) = 0.631
-    Training Error (MAE) (Negative Z Features Model) = 0.666
-    Training Error (MAE) (Positive CT Features Model) = 0.662
-    Training Error (MAE) (Negative CT Features Model) = 0.665
+    Training Error (Positive Z Features Model) = 0.631
+    Training Error (Negative Z Features Model) = 0.666
+    Training Error (Positive CT Features Model) = 0.662
+    Training Error (Negative CT Features Model) = 0.665
 
 
 .. code:: ipython3
 
     # combine positive/negative edges in one linear regression model
-    fit_pos_neg_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T, train_phen)
-
-
+    fit_pos_neg_z = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T, train_phen)
 
 .. code:: ipython3
 
     # combine positive/negative edges in one linear regression model
-    fit_pos_neg_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T, train_phen)
-
-
+    fit_pos_neg_ct = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T, train_phen)
 
 .. code:: ipython3
 
     pos_neg_error_z = metrics.mean_absolute_error(train_phen, fit_pos_neg_z.predict(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T))
     pos_neg_error_ct = metrics.mean_absolute_error(train_phen, fit_pos_neg_ct.predict(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T))
     
-    print(f'Training Error (MAE) (Positive/Negative Z Features Model) = {pos_neg_error_z:.3f}')
-    print(f'Training Error (MAE) (Positive/Negative CT Features Model) = {pos_neg_error_ct:.3f}')
+    print(f'Training Error (Positive/Negative Z Features Model) = {pos_neg_error_z:.3f}')
+    print(f'Training Error (Positive/Negative CT Features Model) = {pos_neg_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Training Error (MAE) (Positive/Negative Z Features Model) = 0.620
-    Training Error (MAE) (Positive/Negative CT Features Model) = 0.642
+    Training Error (Positive/Negative Z Features Model) = 0.620
+    Training Error (Positive/Negative CT Features Model) = 0.642
 
 
 .. code:: ipython3
 
-    # evaluate out of sample performance 
+    # evaluate out of sample performance
     test_pos_edges_sum_z = test_data_z[:, keep_edges_pos_z].sum(1)
     test_neg_edges_sum_z = test_data_z[:, keep_edges_neg_z].sum(1)
     
@@ -572,44 +555,44 @@ Connectome Predictive Modelling
     neg_test_error_ct = metrics.mean_absolute_error(test_phen, fit_neg_ct.predict(test_neg_edges_sum_ct.reshape(-1, 1)))
     pos_neg_test_error_ct = metrics.mean_absolute_error(test_phen, fit_pos_neg_ct.predict(np.stack((test_pos_edges_sum_ct, test_neg_edges_sum_ct)).T))
     
-    print(f'Testing Error (MAE) (Positive Z Features Model) = {pos_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Negative Z Features Model) = {neg_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Positive/Negative Z Features Model) = {pos_neg_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Positive CT Features Model) = {pos_test_error_ct:.3f}')
-    print(f'Testing Error (MAE) (Negative CT Features Model) = {neg_test_error_ct:.3f}')
-    print(f'Testing Error (MAE) (Positive/Negative CT Features Model) = {pos_neg_test_error_ct:.3f}')
+    print(f'Testing Error (Positive Z Features Model) = {pos_test_error_z:.3f}')
+    print(f'Testing Error (Negative Z Features Model) = {neg_test_error_z:.3f}')
+    print(f'Testing Error (Positive/Negative Z Features Model) = {pos_neg_test_error_z:.3f}')
+    print(f'Testing Error (Positive CT Features Model) = {pos_test_error_ct:.3f}')
+    print(f'Testing Error (Negative CT Features Model) = {neg_test_error_ct:.3f}')
+    print(f'Testing Error (Positive/Negative CT Features Model) = {pos_neg_test_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Testing Error (MAE) (Positive Z Features Model) = 0.705
-    Testing Error (MAE) (Negative Z Features Model) = 0.696
-    Testing Error (MAE) (Positive/Negative Z Features Model) = 0.697
-    Testing Error (MAE) (Positive CT Features Model) = 0.710
-    Testing Error (MAE) (Negative CT Features Model) = 0.695
-    Testing Error (MAE) (Positive/Negative CT Features Model) = 0.701
+    Testing Error (Positive Z Features Model) = 0.705
+    Testing Error (Negative Z Features Model) = 0.696
+    Testing Error (Positive/Negative Z Features Model) = 0.697
+    Testing Error (Positive CT Features Model) = 0.710
+    Testing Error (Negative CT Features Model) = 0.695
+    Testing Error (Positive/Negative CT Features Model) = 0.701
 
 
 CPM Cross Validation
---------------------------------------
+--------------------
 
 .. code:: ipython3
 
     def cpm(X, y, p_threshold, n_cv_splits, pred_summary_function, verbose=False):
         assert X.shape[0] == y.shape[0]
-        
+    
         fold_accs_train = []
         fold_accs_test = []
         np.random.seed(42)
         shuffled_idxs = np.random.choice(range(X.shape[0]), size=X.shape[0], replace=False)
         for fold_i, test_idxs in enumerate(np.array_split(shuffled_idxs, n_cv_splits)):
-            train_mask = np.ones(X.shape[0], np.bool)
+            train_mask = np.ones(X.shape[0], bool)
             train_mask[test_idxs] = 0
     
             # create train/text X, y
             train_X, test_X = X[train_mask, :], X[test_idxs, :]
-            train_y, test_y = y[train_mask], y[test_idxs]  
-            
+            train_y, test_y = y[train_mask], y[test_idxs]
+    
             # create correlation matrix between train_X and train_y
             if verbose:
                 print(f'CV Fold: {fold_i+1:<10} Computing correlations between train_X and train_y...')
@@ -622,18 +605,18 @@ CPM Cross Validation
                 # create masks for edges below p-threshold and split pos/neg correlations
                 keep_edges_pos = (train_corrs > 0) & (train_pvals < p_threshold)
                 keep_edges_neg = (train_corrs < 0) & (train_pvals < p_threshold)
-            
+    
             # sum X entries with significant correlations with y
             train_pos_edges_sum = train_X[:, keep_edges_pos].sum(1)
             train_neg_edges_sum = train_X[:, keep_edges_neg].sum(1)
             test_pos_edges_sum = test_X[:, keep_edges_pos].sum(1)
             test_neg_edges_sum = test_X[:, keep_edges_neg].sum(1)
-            
+    
             # fit linear regression models based on summed values
-            fit_pos = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum.reshape(-1, 1), train_y)
-            fit_neg = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum.reshape(-1, 1), train_y)
-            fit_pos_neg = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum, train_neg_edges_sum)).T, train_y)
-            
+            fit_pos = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum.reshape(-1, 1), train_y)
+            fit_neg = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum.reshape(-1, 1), train_y)
+            fit_pos_neg = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum, train_neg_edges_sum)).T, train_y)
+    
             # compute train errors
             train_pos_error = pred_summary_function(train_y, fit_pos.predict(train_pos_edges_sum.reshape(-1, 1)))
             train_neg_error = pred_summary_function(train_y, fit_neg.predict(train_neg_edges_sum.reshape(-1, 1)))
@@ -646,27 +629,27 @@ CPM Cross Validation
     
             fold_accs_train.append((train_pos_error, train_neg_error, train_posneg_error))
             fold_accs_test.append((test_pos_error, test_neg_error, test_posneg_error))
-            
+    
             if verbose:
-                print(f'CV Fold: {fold_i+1:<10} Train Pos-Edges Model MAE: {round(train_pos_error, 3):<10} Train Neg-Edges Model Accuracy: {round(train_neg_error, 3):<10} Train Pos/Neg-Edges Model Accuracy: {round(train_posneg_error, 3):<10}')
-                print(f'CV Fold: {fold_i+1:<10} Test  Pos-Edges Model MAE: {round(test_pos_error, 3):<10} Test  Neg-Edges Model Accuracy: {round(test_neg_error, 3):<10} Test  Pos/Neg-Edges Model Accuracy: {round(test_posneg_error, 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Train Pos-Edges Model Accuracy: {round(train_pos_error, 3):<10} Train Neg-Edges Model Accuracy: {round(train_neg_error, 3):<10} Train Pos/Neg-Edges Model Accuracy: {round(train_posneg_error, 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Test  Pos-Edges Model Accuracy: {round(test_pos_error, 3):<10} Test  Neg-Edges Model Accuracy: {round(test_neg_error, 3):<10} Test  Pos/Neg-Edges Model Accuracy: {round(test_posneg_error, 3):<10}')
+    
     
-        
         plt.figure(figsize=(13, 7))
         plt.plot(range(1, len(fold_accs_train)+1), [x[0] for x in fold_accs_train], linestyle='--', marker='o', color='C0', label='Train Pos-Edges Model')
         plt.plot(range(1, len(fold_accs_train)+1), [x[1] for x in fold_accs_train], linestyle='--', marker='o', color='C1', label='Train Neg-Edges Model')
         plt.plot(range(1, len(fold_accs_train)+1), [x[2] for x in fold_accs_train], linestyle='--', marker='o', color='C2', label='Train Pos/Neg-Edges Model')
-        
+    
         plt.plot(range(1, len(fold_accs_test)+1), [x[0] for x in fold_accs_test], linestyle='-', marker='o', color='C0', label='Test  Pos-Edges Model')
         plt.plot(range(1, len(fold_accs_test)+1), [x[1] for x in fold_accs_test], linestyle='-', marker='o', color='C1', label='Test  Neg-Edges Model')
         plt.plot(range(1, len(fold_accs_test)+1), [x[2] for x in fold_accs_test], linestyle='-', marker='o', color='C2', label='Test  Pos/Neg-Edges Model')
-        
+    
         plt.title(pred_summary_function.__name__, fontsize=20)
         plt.xticks(range(1, len(fold_accs_test)+1))
         plt.xlabel('CV Fold')
         plt.legend(fontsize=10)
         plt.show()
-        
+    
         return fold_accs_train, fold_accs_test
 
 .. code:: ipython3
@@ -674,32 +657,27 @@ CPM Cross Validation
     fold_accs_train_z, fold_accs_test_z = cpm(hcp_z, gscores, p_threshold=0.01, n_cv_splits=5, pred_summary_function=metrics.mean_absolute_error, verbose=True)
 
 
-
 .. parsed-literal::
 
     CV Fold: 1          Computing correlations between train_X and train_y...
-    CV Fold: 1          Train Pos-Edges Model MAE: 0.652      Train Neg-Edges Model MAE: 0.673      Train Pos/Neg-Edges Model MAE: 0.644     
-    CV Fold: 1          Test  Pos-Edges Model MAE: 0.636      Test  Neg-Edges Model MAE: 0.671      Test  Pos/Neg-Edges Model MAE: 0.632     
+    CV Fold: 1          Train Pos-Edges Model Accuracy: 0.652      Train Neg-Edges Model Accuracy: 0.673      Train Pos/Neg-Edges Model Accuracy: 0.644     
+    CV Fold: 1          Test  Pos-Edges Model Accuracy: 0.636      Test  Neg-Edges Model Accuracy: 0.671      Test  Pos/Neg-Edges Model Accuracy: 0.632     
     CV Fold: 2          Computing correlations between train_X and train_y...
-    CV Fold: 2          Train Pos-Edges Model MAE: 0.648      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 2          Test  Pos-Edges Model MAE: 0.651      Test  Neg-Edges Model MAE: 0.659      Test  Pos/Neg-Edges Model MAE: 0.662     
+    CV Fold: 2          Train Pos-Edges Model Accuracy: 0.648      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 2          Test  Pos-Edges Model Accuracy: 0.651      Test  Neg-Edges Model Accuracy: 0.659      Test  Pos/Neg-Edges Model Accuracy: 0.662     
     CV Fold: 3          Computing correlations between train_X and train_y...
-    CV Fold: 3          Train Pos-Edges Model MAE: 0.644      Train Neg-Edges Model MAE: 0.662      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 3          Test  Pos-Edges Model MAE: 0.65       Test  Neg-Edges Model MAE: 0.708      Test  Pos/Neg-Edges Model MAE: 0.646     
+    CV Fold: 3          Train Pos-Edges Model Accuracy: 0.644      Train Neg-Edges Model Accuracy: 0.662      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 3          Test  Pos-Edges Model Accuracy: 0.65       Test  Neg-Edges Model Accuracy: 0.708      Test  Pos/Neg-Edges Model Accuracy: 0.646     
     CV Fold: 4          Computing correlations between train_X and train_y...
-    CV Fold: 4          Train Pos-Edges Model MAE: 0.653      Train Neg-Edges Model MAE: 0.676      Train Pos/Neg-Edges Model MAE: 0.648     
-    CV Fold: 4          Test  Pos-Edges Model MAE: 0.626      Test  Neg-Edges Model MAE: 0.659      Test  Pos/Neg-Edges Model MAE: 0.625     
+    CV Fold: 4          Train Pos-Edges Model Accuracy: 0.653      Train Neg-Edges Model Accuracy: 0.676      Train Pos/Neg-Edges Model Accuracy: 0.648     
+    CV Fold: 4          Test  Pos-Edges Model Accuracy: 0.626      Test  Neg-Edges Model Accuracy: 0.659      Test  Pos/Neg-Edges Model Accuracy: 0.625     
     CV Fold: 5          Computing correlations between train_X and train_y...
+    CV Fold: 5          Train Pos-Edges Model Accuracy: 0.631      Train Neg-Edges Model Accuracy: 0.666      Train Pos/Neg-Edges Model Accuracy: 0.62      
+    CV Fold: 5          Test  Pos-Edges Model Accuracy: 0.704      Test  Neg-Edges Model Accuracy: 0.696      Test  Pos/Neg-Edges Model Accuracy: 0.697     
 
 
-.. parsed-literal::
-
-    CV Fold: 5          Train Pos-Edges Model MAE: 0.631      Train Neg-Edges Model MAE: 0.666      Train Pos/Neg-Edges Model MAE: 0.62      
-    CV Fold: 5          Test  Pos-Edges Model MAE: 0.704      Test  Neg-Edges Model MAE: 0.696      Test  Pos/Neg-Edges Model MAE: 0.697     
-
 
-
-.. image:: other_predictive_models_files/other_predictive_models_50_4.png
+.. image:: other_predictive_models_files/other_predictive_models_51_1.png
 
 
 .. code:: ipython3
@@ -710,51 +688,61 @@ CPM Cross Validation
 .. parsed-literal::
 
     CV Fold: 1          Computing correlations between train_X and train_y...
-    CV Fold: 1          Train Pos-Edges Model MAE: 0.675      Train Neg-Edges Model MAE: 0.673      Train Pos/Neg-Edges Model MAE: 0.659     
-    CV Fold: 1          Test  Pos-Edges Model MAE: 0.659      Test  Neg-Edges Model MAE: 0.67       Test  Pos/Neg-Edges Model MAE: 0.653     
+    CV Fold: 1          Train Pos-Edges Model Accuracy: 0.675      Train Neg-Edges Model Accuracy: 0.673      Train Pos/Neg-Edges Model Accuracy: 0.659     
+    CV Fold: 1          Test  Pos-Edges Model Accuracy: 0.659      Test  Neg-Edges Model Accuracy: 0.67       Test  Pos/Neg-Edges Model Accuracy: 0.653     
     CV Fold: 2          Computing correlations between train_X and train_y...
-    CV Fold: 2          Train Pos-Edges Model MAE: 0.674      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 2          Test  Pos-Edges Model MAE: 0.661      Test  Neg-Edges Model MAE: 0.657      Test  Pos/Neg-Edges Model MAE: 0.668     
+    CV Fold: 2          Train Pos-Edges Model Accuracy: 0.674      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 2          Test  Pos-Edges Model Accuracy: 0.661      Test  Neg-Edges Model Accuracy: 0.657      Test  Pos/Neg-Edges Model Accuracy: 0.668     
     CV Fold: 3          Computing correlations between train_X and train_y...
-    CV Fold: 3          Train Pos-Edges Model MAE: 0.659      Train Neg-Edges Model MAE: 0.665      Train Pos/Neg-Edges Model MAE: 0.644     
-    CV Fold: 3          Test  Pos-Edges Model MAE: 0.699      Test  Neg-Edges Model MAE: 0.704      Test  Pos/Neg-Edges Model MAE: 0.684     
+    CV Fold: 3          Train Pos-Edges Model Accuracy: 0.659      Train Neg-Edges Model Accuracy: 0.665      Train Pos/Neg-Edges Model Accuracy: 0.644     
+    CV Fold: 3          Test  Pos-Edges Model Accuracy: 0.699      Test  Neg-Edges Model Accuracy: 0.704      Test  Pos/Neg-Edges Model Accuracy: 0.684     
     CV Fold: 4          Computing correlations between train_X and train_y...
-    CV Fold: 4          Train Pos-Edges Model MAE: 0.674      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.658     
-    CV Fold: 4          Test  Pos-Edges Model MAE: 0.653      Test  Neg-Edges Model MAE: 0.656      Test  Pos/Neg-Edges Model MAE: 0.638     
+    CV Fold: 4          Train Pos-Edges Model Accuracy: 0.674      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.658     
+    CV Fold: 4          Test  Pos-Edges Model Accuracy: 0.653      Test  Neg-Edges Model Accuracy: 0.656      Test  Pos/Neg-Edges Model Accuracy: 0.638     
     CV Fold: 5          Computing correlations between train_X and train_y...
-    CV Fold: 5          Train Pos-Edges Model MAE: 0.662      Train Neg-Edges Model MAE: 0.666      Train Pos/Neg-Edges Model MAE: 0.642     
-    CV Fold: 5          Test  Pos-Edges Model MAE: 0.709      Test  Neg-Edges Model MAE: 0.698      Test  Pos/Neg-Edges Model MAE: 0.708     
+    CV Fold: 5          Train Pos-Edges Model Accuracy: 0.662      Train Neg-Edges Model Accuracy: 0.666      Train Pos/Neg-Edges Model Accuracy: 0.642     
+    CV Fold: 5          Test  Pos-Edges Model Accuracy: 0.709      Test  Neg-Edges Model Accuracy: 0.698      Test  Pos/Neg-Edges Model Accuracy: 0.708     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_51_2.png
+.. image:: other_predictive_models_files/other_predictive_models_52_1.png
 
 
 Lasso (Linear Regression + L1 Regularization)
------------------------------------------------------
+=============================================
 
 .. code:: ipython3
 
-    # LassoCV uses coordinate descent to select hyperparameter alpha 
+    # LassoCV uses coordinate descent to select hyperparameter alpha
     alpha_grid = np.array([10**a for a in np.arange(-3, 3, 0.25)])
-    lassoCV_model_z = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, normalize=False, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
+    lassoCV_model_z = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
 
 
+.. parsed-literal::
+
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    .....................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.5s remaining:    0.7s
+    /usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07308221069854426, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15375414865695802, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10611096508367268, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    .[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.5s finished
+
 
 .. code:: ipython3
 
-    # LassoCV uses coordinate descent to select hyperparameter alpha 
+    # LassoCV uses coordinate descent to select hyperparameter alpha
     alpha_grid = np.array([10**a for a in np.arange(-3, 3, 0.25)])
-    lassoCV_model_ct = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, normalize=False, random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
+    lassoCV_model_ct = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True,  random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
 
 
 .. parsed-literal::
 
     [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
-    ...................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.3s remaining:    0.5s
-    .....[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.3s finished
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
+    ....................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.2s remaining:    0.3s
+    ....[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.3s finished
 
 
 .. code:: ipython3
@@ -770,7 +758,7 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_55_0.png
+.. image:: other_predictive_models_files/other_predictive_models_56_0.png
 
 
 .. code:: ipython3
@@ -786,22 +774,18 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_56_0.png
+.. image:: other_predictive_models_files/other_predictive_models_57_0.png
 
 
 .. code:: ipython3
 
     # based on cv results above, set alpha=100
-    lasso_model_z = linear_model.Lasso(alpha=lassoCV_model_z.alpha_, fit_intercept=True, normalize=False).fit(train_data_z, train_phen)
-
-
+    lasso_model_z = linear_model.Lasso(alpha=lassoCV_model_z.alpha_, fit_intercept=True).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
     # based on cv results above, set alpha=100
-    lasso_model_ct = linear_model.Lasso(alpha=lassoCV_model_ct.alpha_, fit_intercept=True, normalize=False).fit(train_data_ct, train_phen)
-
-
+    lasso_model_ct = linear_model.Lasso(alpha=lassoCV_model_ct.alpha_, fit_intercept=True).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -832,23 +816,23 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 Ridge (Linear Regression + L2 Regularization)
---------------------------------------------------------
+=============================================
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     with warnings.catch_warnings():
         # ignore matrix decomposition errors
         warnings.simplefilter("ignore")
-        ridgeCV_model_z = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, cv=5).fit(train_data_z, train_phen)
+        ridgeCV_model_z = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, cv=5).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     with warnings.catch_warnings():
         # ignore matrix decomposition errors
         warnings.simplefilter("ignore")
-        ridgeCV_model_ct = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, cv=5).fit(train_data_ct, train_phen)
+        ridgeCV_model_ct = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True,  cv=5).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -874,14 +858,11 @@ Ridge (Linear Regression + L2 Regularization)
 
 .. code:: ipython3
 
-    ridge_model_z = linear_model.Ridge(alpha=ridge_alpha_z, fit_intercept=True, normalize=False).fit(train_data_z, train_phen)
-
-
+    ridge_model_z = linear_model.Ridge(alpha=ridge_alpha_z, fit_intercept=True).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
-    ridge_model_ct = linear_model.Ridge(alpha=ridge_alpha_ct, fit_intercept=True, normalize=False).fit(train_data_ct, train_phen)
-
+    ridge_model_ct = linear_model.Ridge(alpha=ridge_alpha_ct, fit_intercept=True).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -912,21 +893,81 @@ Ridge (Linear Regression + L2 Regularization)
 
 
 Elastic Net (Linear Regression + L1/L2 Regularization)
-------------------------------------------------------------
+======================================================
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     elasticnetCV_model_z = linear_model.ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
 
 
+.. parsed-literal::
 
-.. code:: ipython3
-
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    ............................................................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21318694590257792, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17936527851907158, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    .../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8618322913218321, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.57867990423236, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.2273489799189, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4036642558553467, tolerance: 0.04401109832998077
+      model = cd_fast.enet_coordinate_descent_gram(
+    .................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.073063075099014, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.227358858718446, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.883650580549045, tolerance: 0.04401109832998077
+      model = cd_fast.enet_coordinate_descent_gram(
+    ....................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18805636326129616, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0544661418971657, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1788130249701112, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13839227040918445, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................................................................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15009167262110168, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20204581109658193, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.09891903798924773, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ......................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13078279402705562, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18265009272980137, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0877297694903234, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................................................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11087317503455552, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ...................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16051209546739642, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07594686106816084, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10611096508367268, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ...../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15375414865695802, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07308221069854426, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..[Parallel(n_jobs=5)]: Done  35 out of  35 | elapsed:    6.3s finished
+
+
+.. code:: ipython3
+
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     elasticnetCV_model_ct = linear_model.ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
 
 
+.. parsed-literal::
+
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................[Parallel(n_jobs=5)]: Done  35 out of  35 | elapsed:    1.8s finished
+
 
 .. code:: ipython3
 
@@ -958,7 +999,7 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_72_0.png
+.. image:: other_predictive_models_files/other_predictive_models_73_0.png
 
 
 .. code:: ipython3
@@ -975,12 +1016,12 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_73_0.png
+.. image:: other_predictive_models_files/other_predictive_models_74_0.png
 
 
 .. code:: ipython3
 
-    elasticnet_model_z = linear_model.ElasticNet(alpha=elasticnetCV_model_z.alpha_, l1_ratio=elasticnetCV_model_z.l1_ratio_, fit_intercept=True, normalize=False, random_state=42).fit(train_data_z, train_phen)
+    elasticnet_model_z = linear_model.ElasticNet(alpha=elasticnetCV_model_z.alpha_, l1_ratio=elasticnetCV_model_z.l1_ratio_, fit_intercept=True,  random_state=42).fit(train_data_z, train_phen)
     
     train_preds_en_model_z = elasticnet_model_z.predict(train_data_z)
     test_preds_en_model_z = elasticnet_model_z.predict(test_data_z)
@@ -988,7 +1029,7 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
     train_mae_z = metrics.mean_absolute_error(train_phen, train_preds_en_model_z)
     test_mae_z = metrics.mean_absolute_error(test_phen, test_preds_en_model_z)
     
-    elasticnet_model_ct = linear_model.ElasticNet(alpha=elasticnetCV_model_ct.alpha_, l1_ratio=elasticnetCV_model_ct.l1_ratio_, fit_intercept=True, normalize=False, random_state=42).fit(train_data_ct, train_phen)
+    elasticnet_model_ct = linear_model.ElasticNet(alpha=elasticnetCV_model_ct.alpha_, l1_ratio=elasticnetCV_model_ct.l1_ratio_, fit_intercept=True, random_state=42).fit(train_data_ct, train_phen)
     
     train_preds_en_model_ct = elasticnet_model_ct.predict(train_data_ct)
     test_preds_en_model_ct = elasticnet_model_ct.predict(test_data_ct)
@@ -1008,3 +1049,5 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
     Test MAE Z model: 0.680
     Train MAE CT model: 0.633
     Test MAE CT model: 0.692
+
+
diff --git a/doc/source/pages/other_predictive_models_files/other_predictive_models_16_1.png b/doc/source/pages/other_predictive_models_files/other_predictive_models_16_1.png
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diff --git a/doc/build/html/_modules/gp.html b/doc/build/html/_modules/gp.html
index 72e63591..1fff0f37 100644
--- a/doc/build/html/_modules/gp.html
+++ b/doc/build/html/_modules/gp.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   gp — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 
 
Tutorials

 
 
Other Useful Stuff

 
    
@@ -134,7 +141,9 @@ 
    Source code for gp
     # --------------------
 
 
-
    [docs]class CovBase(with_metaclass(ABCMeta)):
+
    
+[docs]
+class CovBase(with_metaclass(ABCMeta)):
     """ Base class for covariance functions.
 
         All covariance functions must define the following methods::
@@ -148,7 +157,9 @@ 
    Source code for gp
         def __init__(self, x=None):
         self.n_params = np.nan
 
-
    [docs]    def get_n_params(self):
+
    
+[docs]
+    def get_n_params(self):
         """ Report the number of parameters required """
 
         assert not np.isnan(self.n_params), \
@@ -156,17 +167,27 @@ 
    Source code for gp
     
         return self.n_params
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def cov(self, theta, x, z=None):
         """ Return the full covariance (or cross-covariance if z is given) """
    
 
-
    [docs]    @abstractmethod
+
+
    
+[docs]
+    @abstractmethod
     def dcov(self, theta, x, i):
         """ Return the derivative of the covariance function with respect to
-            the i-th hyperparameter """
    
+            the i-th hyperparameter """
    
+
    
+
 
 
-
    [docs]class CovLin(CovBase):
+
    
+[docs]
+class CovLin(CovBase):
     """ Linear covariance function (no hyperparameters)
     """
 
@@ -174,7 +195,9 @@ 
    Source code for gp
             self.n_params = 0
         self.first_call = False
 
-
    [docs]    def cov(self, theta, x, z=None):
+
    
+[docs]
+    def cov(self, theta, x, z=None):
         if not self.first_call and not theta and theta is not None:
             self.first_call = True
             if len(theta) > 0 and theta[0] is not None:
@@ -186,11 +209,18 @@ 
    Source code for gp
             K = x.dot(z.T)
         return K
    
 
-
    [docs]    def dcov(self, theta, x, i):
-        raise ValueError("Invalid covariance function parameter")
    
+
+
    
+[docs]
+    def dcov(self, theta, x, i):
+        raise ValueError("Invalid covariance function parameter")
    
+
    
 
 
-
    [docs]class CovSqExp(CovBase):
+
+
    
+[docs]
+class CovSqExp(CovBase):
     """ Ordinary squared exponential covariance function.
         The hyperparameters are::
 
@@ -202,7 +232,9 @@ 
    Source code for gp
         def __init__(self, x=None):
         self.n_params = 2
 
-
    [docs]    def cov(self, theta, x, z=None):
+
    
+[docs]
+    def cov(self, theta, x, z=None):
         self.ell = np.exp(theta[0])
         self.sf2 = np.exp(2*theta[1])
 
@@ -213,7 +245,10 @@ 
    Source code for gp
             K = self.sf2 * np.exp(-R/2)
         return K
    
 
-
    [docs]    def dcov(self, theta, x, i):
+
+
    
+[docs]
+    def dcov(self, theta, x, i):
         self.ell = np.exp(theta[0])
         self.sf2 = np.exp(2*theta[1])
 
@@ -226,10 +261,14 @@ 
    Source code for gp
                 dK = 2*self.sf2 * np.exp(-R/2)
             return dK
         else:
-            raise ValueError("Invalid covariance function parameter")
    
+            raise ValueError("Invalid covariance function parameter")
    
+
    
+
 
 
-
    [docs]class CovSqExpARD(CovBase):
+
    
+[docs]
+class CovSqExpARD(CovBase):
     """ Squared exponential covariance function with ARD
         The hyperparameters are::
 
@@ -247,7 +286,9 @@ 
    Source code for gp
                 self.D = x.shape[1]
         self.n_params = self.D + 1
 
-
    [docs]    def cov(self, theta, x, z=None):
+
    
+[docs]
+    def cov(self, theta, x, z=None):
         self.ell = np.exp(theta[0:self.D])
         self.sf2 = np.exp(2*theta[self.D])
 
@@ -259,7 +300,10 @@ 
    Source code for gp
             K = self.sf2*np.exp(-R/2)
         return K
    
 
-
    [docs]    def dcov(self, theta, x, i):
+
+
    
+[docs]
+    def dcov(self, theta, x, i):
         K = self.cov(theta, x)
         if i < self.D:    # return derivative of lengthscale parameter
             dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
@@ -268,10 +312,14 @@ 
    Source code for gp
                 dK = 2*K
             return dK
         else:
-            raise ValueError("Invalid covariance function parameter")
    
+            raise ValueError("Invalid covariance function parameter")
    
+
    
 
 
-
    [docs]class CovSum(CovBase):
+
+
    
+[docs]
+class CovSum(CovBase):
     """ Sum of covariance functions. These are passed in as a cell array and
         intialised automatically. For example::
 
@@ -303,7 +351,9 @@ 
    Source code for gp
             else:
             self.N, self.D = x.shape
 
-
    [docs]    def cov(self, theta, x, z=None):
+
    
+[docs]
+    def cov(self, theta, x, z=None):
         theta_offset = 0
         for ci, covfunc in enumerate(self.covfuncs):
             try:
@@ -320,7 +370,10 @@ 
    Source code for gp
                     K += covfunc.cov(theta_c, x, z)
         return K
    
 
-
    [docs]    def dcov(self, theta, x, i):
+
+
    
+[docs]
+    def dcov(self, theta, x, i):
         theta_offset = 0
         for covfunc in self.covfuncs:
             n_params_c = covfunc.get_n_params()
@@ -333,14 +386,18 @@ 
    Source code for gp
                         dK = covfunc.dcov(theta_c, x, i)
                 else:
                     dK += covfunc.dcov(theta_c, x, i)
-        return dK
    
+        return dK
    
+
    
+
 
 # -----------------------
 # Gaussian process models
 # -----------------------
 
 
-
    [docs]class GPR:
+
    
+[docs]
+class GPR:
     """Gaussian process regression
 
     Estimation and prediction of Gaussian process regression models
@@ -407,7 +464,9 @@ 
    Source code for gp
             else:
             return True
 
-
    [docs]    def post(self, hyp, covfunc, X, y):
+
    
+[docs]
+    def post(self, hyp, covfunc, X, y):
         """ Generic function to compute posterior distribution.
         """
 
@@ -435,7 +494,10 @@ 
    Source code for gp
             self.hyp = hyp
         self.covfunc = covfunc
    
 
-
    [docs]    def loglik(self, hyp, covfunc, X, y):
+
+
    
+[docs]
+    def loglik(self, hyp, covfunc, X, y):
         """ Function to compute compute log (marginal) likelihood
         """
 
@@ -475,7 +537,10 @@ 
    Source code for gp
     
         return self.nlZ
    
 
-
    [docs]    def dloglik(self, hyp, covfunc, X, y):
+
+
    
+[docs]
+    def dloglik(self, hyp, covfunc, X, y):
         """ Function to compute derivatives
         """
 
@@ -526,8 +591,11 @@ 
    Source code for gp
     
         return self.dnlZ
    
 
+
     # model estimation (optimization)
-
    [docs]    def estimate(self, hyp0, covfunc, X, y, optimizer='cg'):
+
    
+[docs]
+    def estimate(self, hyp0, covfunc, X, y, optimizer='cg'):
         """ Function to estimate the model
         """
         if len(X.shape) == 1:
@@ -556,7 +624,10 @@ 
    Source code for gp
     
         return self.hyp
    
 
-
    [docs]    def predict(self, hyp, X, y, Xs):
+
+
    
+[docs]
+    def predict(self, hyp, X, y, Xs):
         """ Function to make predictions from the model
         """
         if len(hyp.shape) > 1:  # force 1d hyperparameter array
@@ -591,7 +662,9 @@ 
    Source code for gp
             v = solve(self.L, Ks.T)
         ys2 = kss - v.T.dot(v) + sn2
 
-        return ymu, ys2
    
+        return ymu, ys2
    
+
    
+
 
    
 
            
    
diff --git a/doc/build/html/_modules/index.html b/doc/build/html/_modules/index.html
index 4cc795aa..087396d6 100644
--- a/doc/build/html/_modules/index.html
+++ b/doc/build/html/_modules/index.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   Overview: module code — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff

 
    
diff --git a/doc/build/html/_modules/normative.html b/doc/build/html/_modules/normative.html
index bb71c8e8..c143588b 100644
--- a/doc/build/html/_modules/normative.html
+++ b/doc/build/html/_modules/normative.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   normative — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff

 
    
@@ -116,25 +123,37 @@ 
    Source code for normative
     #  Written by A. Marquand
 # ------------------------------------------------------------------------------
 
-from __future__ import print_function
-from __future__ import division
+from __future__ import division, print_function
 
-import os
-import sys
-import numpy as np
 import argparse
-import pickle
 import glob
+import os
+import pickle
+import sys
+from pathlib import Path
 
+import numpy as np
 from sklearn.model_selection import KFold
-from pathlib import Path
+
+try:
+    import nutpie
+except ImportError:
+    # warnings.warn("Nutpie not installed. For fitting HBR models with the nutpie backend, install it with `conda install nutpie numba`")
+    pass
+
 
 try:  # run as a package if installed
     from pcntoolkit import configs
     from pcntoolkit.dataio import fileio
     from pcntoolkit.normative_model.norm_utils import norm_init
-    from pcntoolkit.util.utils import compute_pearsonr, CustomCV, explained_var
-    from pcntoolkit.util.utils import compute_MSLL, scaler, get_package_versions
+    from pcntoolkit.util.utils import (
+        CustomCV,
+        compute_MSLL,
+        compute_pearsonr,
+        explained_var,
+        get_package_versions,
+        scaler,
+    )
 except ImportError:
     pass
 
@@ -146,15 +165,22 @@ 
    Source code for normative
     
     import configs
     from dataio import fileio
-
-    from util.utils import compute_pearsonr, CustomCV, explained_var, compute_MSLL
-    from util.utils import scaler, get_package_versions
     from normative_model.norm_utils import norm_init
+    from util.utils import (
+        CustomCV,
+        compute_MSLL,
+        compute_pearsonr,
+        explained_var,
+        get_package_versions,
+        scaler,
+    )
 
 PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
 
-
    [docs]def load_response_vars(datafile, maskfile=None, vol=True):
+
    
+[docs]
+def load_response_vars(datafile, maskfile=None, vol=True):
     """
     Load response variables from file. This will load the data and mask it if
     necessary. If the data is in ascii format it will be converted into a numpy
@@ -181,7 +207,10 @@ 
    Source code for normative
         return Y, volmask
    
 
 
-
    [docs]def get_args(*args):
+
+
    
+[docs]
+def get_args(*args):
     """
     Parse command line arguments for normative modeling
 
@@ -197,74 +226,67 @@ 
    Source code for normative
         :returns configparam: Parameters controlling the estimation algorithm
     :returns kw_args: Additional keyword arguments
     """
-
+    args = args[0][0]
     # parse arguments
     parser = argparse.ArgumentParser(description="Normative Modeling")
-    parser.add_argument("responses")
-    parser.add_argument("-f", help="Function to call", dest="func",
-                        default="estimate")
+    parser.add_argument("respfile", help="Response variables for the normative model")
+    parser.add_argument("-f", help="Function to call", dest="func", default="estimate")
     parser.add_argument("-m", help="mask file", dest="maskfile", default=None)
-    parser.add_argument("-c", help="covariates file", dest="covfile",
-                        default=None)
-    parser.add_argument("-k", help="cross-validation folds", dest="cvfolds",
-                        default=None)
-    parser.add_argument("-t", help="covariates (test data)", dest="testcov",
-                        default=None)
-    parser.add_argument("-r", help="responses (test data)", dest="testresp",
-                        default=None)
+    parser.add_argument("-c", help="covariates file", dest="covfile", default=None)
+    parser.add_argument("-k", help="cross-validation folds", dest="cvfolds", default=None)
+    parser.add_argument("-t", help="covariates (test data)", dest="testcov", default=None)
+    parser.add_argument("-r", help="responses (test data)", dest="testresp", default=None)
     parser.add_argument("-a", help="algorithm", dest="alg", default="gpr")
-    parser.add_argument("-x", help="algorithm specific config options",
-                        dest="configparam", default=None)
-    # parser.add_argument('-s', action='store_false',
-    #                 help="Flag to skip standardization.", dest="standardize")
-    parser.add_argument("keyword_args", nargs=argparse.REMAINDER)
+    parser.add_argument("-x", help="algorithm specific config options", dest="configparam", default=None)
+    parsed_args, keyword_args = parser.parse_known_args(args)
 
-    args = parser.parse_args()
-
-    # Process required  arguemnts
+    # Process required arguments
     wdir = os.path.realpath(os.path.curdir)
-    respfile = os.path.join(wdir, args.responses)
-    if args.covfile is None:
+    respfile = os.path.join(wdir, parsed_args.respfile)
+    if parsed_args.covfile is None:
         raise ValueError("No covariates specified")
     else:
-        covfile = args.covfile
+        covfile = parsed_args.covfile
 
     # Process optional arguments
-    if args.maskfile is None:
+    if parsed_args.maskfile is None:
         maskfile = None
     else:
-        maskfile = os.path.join(wdir, args.maskfile)
-    if args.testcov is None and args.cvfolds is not None:
+        maskfile = os.path.join(wdir, parsed_args.maskfile)
+    if parsed_args.testcov is None and parsed_args.cvfolds is not None:
         testcov = None
         testresp = None
-        cvfolds = int(args.cvfolds)
+        cvfolds = int(parsed_args.cvfolds)
         print("Running under " + str(cvfolds) + " fold cross-validation.")
     else:
         print("Test covariates specified")
-        testcov = args.testcov
+        testcov = parsed_args.testcov
         cvfolds = None
-        if args.testresp is None:
+        if parsed_args.testresp is None:
             testresp = None
             print("No test response variables specified")
         else:
-            testresp = args.testresp
-        if args.cvfolds is not None:
+            testresp = parsed_args.testresp
+        if parsed_args.cvfolds is not None:
             print("Ignoring cross-valdation specification (test data given)")
 
     # Process addtional keyword arguments. These are always added as strings
     kw_args = {}
-    for kw in args.keyword_args:
+    for kw in keyword_args:
         kw_arg = kw.split('=')
 
         exec("kw_args.update({'" + kw_arg[0] + "' : " +
              "'" + str(kw_arg[1]) + "'" + "})")
 
     return respfile, maskfile, covfile, cvfolds, \
-        testcov, testresp, args.func, args.alg, \
-        args.configparam, kw_args
    
+        testcov, testresp, parsed_args.func, parsed_args.alg, \
+        parsed_args.configparam, kw_args
    
+
 
 
-
    [docs]def evaluate(Y, Yhat, S2=None, mY=None, sY=None, nlZ=None, nm=None, Xz_tr=None, alg=None,
+
    
+[docs]
+def evaluate(Y, Yhat, S2=None, mY=None, sY=None, nlZ=None, nm=None, Xz_tr=None, alg=None,
              metrics=['Rho', 'RMSE', 'SMSE', 'EXPV', 'MSLL']):
     ''' Compute error metrics
     This function will compute error metrics based on a set of predictions Yhat
@@ -349,7 +371,10 @@ 
    Source code for normative
         return results
    
 
 
-
    [docs]def save_results(respfile, Yhat, S2, maskvol, Z=None, Y=None, outputsuffix=None,
+
+
    
+[docs]
+def save_results(respfile, Yhat, S2, maskvol, Z=None, Y=None, outputsuffix=None,
                  results=None, save_path=''):
     """
     Writes the results of the normative model to disk.
@@ -406,7 +431,10 @@ 
    Source code for normative
                                 example=exfile, mask=maskvol)
    
 
 
-
    [docs]def estimate(covfile, respfile, **kwargs):
+
+
    
+[docs]
+def estimate(covfile, respfile, **kwargs):
     """ Estimate a normative model
 
     This will estimate a model in one of two settings according to 
@@ -474,7 +502,9 @@ 
    Source code for normative
         # '_' is in the outputsuffix to
     # avoid file name parsing problem.
     inscaler = kwargs.pop('inscaler', 'None')
+    print(f"inscaler: {inscaler}")
     outscaler = kwargs.pop('outscaler', 'None')
+    print(f"outscaler: {outscaler}")
     warp = kwargs.get('warp', None)
 
     # convert from strings if necessary
@@ -627,7 +657,8 @@ 
    Source code for normative
                         if warp is not None:
                         # TODO: Warping for scaled data
                         if outscaler is not None and outscaler != 'None':
-                            raise ValueError("outscaler not yet supported warping")
+                            raise ValueError(
+                                "outscaler not yet supported warping")
                         warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]
                         Ywarp[ts, nz[i]] = nm.blr.warp.f(
                             Y[ts, nz[i]], warp_param)
@@ -730,7 +761,10 @@ 
    Source code for normative
             return output
    
 
 
-
    [docs]def fit(covfile, respfile, **kwargs):
+
+
    
+[docs]
+def fit(covfile, respfile, **kwargs):
     """
     Fits a normative model to the data.
 
@@ -756,6 +790,8 @@ 
    Source code for normative
         outputsuffix = "_" + outputsuffix.replace("_", "")
     inscaler = kwargs.pop('inscaler', 'None')
     outscaler = kwargs.pop('outscaler', 'None')
+    print(f"inscaler: {inscaler}")
+    print(f"outscaler: {outscaler}")
 
     if savemodel and not os.path.isdir('Models'):
         os.mkdir('Models')
@@ -824,7 +860,10 @@ 
    Source code for normative
         return nm
    
 
 
-
    [docs]def predict(covfile, respfile, maskfile=None, **kwargs):
+
+
    
+[docs]
+def predict(covfile, respfile, maskfile=None, **kwargs):
     '''
     Make predictions on the basis of a pre-estimated normative model 
     If only the covariates are specified then only predicted mean and variance 
@@ -905,6 +944,10 @@ 
    Source code for normative
         X = fileio.load(covfile)
     if len(X.shape) == 1:
         X = X[:, np.newaxis]
+    if respfile is not None:
+        Y, maskvol = load_response_vars(respfile, maskfile)
+        if len(Y.shape) == 1:
+            Y = Y[:, np.newaxis]
 
     sample_num = X.shape[0]
     if models is not None:
@@ -922,9 +965,13 @@ 
    Source code for normative
             Xz = scaler_cov[fold].transform(X)
     else:
         Xz = X
+    if respfile is not None:
+        if outscaler in ['standardize', 'minmax', 'robminmax']:
+            Yz = scaler_resp[fold].transform(Y)
+        else:
+            Yz = Y
 
     # estimate the models for all variabels
-    # TODO Z-scores adaptation for SHASH HBR
     for i, m in enumerate(models):
         print("Prediction by model ", i+1, "of", feature_num)
         nm = norm_init(Xz)
@@ -947,6 +994,10 @@ 
    Source code for normative
             else:
             Yhat[:, i] = yhat.squeeze()
             S2[:, i] = s2.squeeze()
+        if respfile is not None:
+            if alg == 'hbr':
+                # Z scores for HBR must be computed independently for each model
+                Z[:, i] = nm.get_mcmc_zscores(Xz, Yz[:, i:i+1], **kwargs)
 
     if respfile is None:
         save_results(None, Yhat, S2, None, outputsuffix=outputsuffix)
@@ -954,7 +1005,6 @@ 
    Source code for normative
             return (Yhat, S2)
 
     else:
-        Y, maskvol = load_response_vars(respfile, maskfile)
         if models is not None and len(Y.shape) > 1:
             Y = Y[:, models]
             if meta_data:
@@ -986,7 +1036,9 @@ 
    Source code for normative
             else:
             warp = False
 
-        Z = (Y - Yhat) / np.sqrt(S2)
+        if alg != 'hbr':
+            # For HBR the Z scores are already computed
+            Z = (Y - Yhat) / np.sqrt(S2)
 
         print("Evaluating the model ...")
         if meta_data and not warp:
@@ -1008,7 +1060,10 @@ 
    Source code for normative
                 return (Yhat, S2, Z)
    
 
 
-
    [docs]def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
+
+
    
+[docs]
+def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
              **kwargs):
     '''
     Transfer learning on the basis of a pre-estimated normative model by using 
@@ -1044,14 +1099,14 @@ 
    Source code for normative
             return
     # testing should not be obligatory for HBR,
     # but should be for BLR (since it doesn't produce transfer models)
-    elif (not 'model_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print(f'{kwargs=}')
         print('InputError: Some general mandatory arguments are missing.')
         return
     # hbr has one additional mandatory arguments
     elif alg == 'hbr':
-        if (not 'output_path' in list(kwargs.keys())):
+        if ('output_path' not in list(kwargs.keys())):
             print('InputError: Some mandatory arguments for hbr are missing.')
             return
         else:
@@ -1063,7 +1118,7 @@ 
    Source code for normative
         # or (testresp==None)
     elif alg == 'blr':
         if (testcov == None) or \
-                (not 'tsbefile' in list(kwargs.keys())):
+                ('tsbefile' not in list(kwargs.keys())):
             print('InputError: Some mandatory arguments for blr are missing.')
             return
     # general arguments
@@ -1266,7 +1321,10 @@ 
    Source code for normative
             Path(done_path).touch()
    
 
 
-
    [docs]def extend(covfile, respfile, maskfile=None, **kwargs):
+
+
    
+[docs]
+def extend(covfile, respfile, maskfile=None, **kwargs):
     '''
     This function extends an existing HBR model with data from new sites/scanners.
 
@@ -1299,9 +1357,9 @@ 
    Source code for normative
         if alg != 'hbr':
         print('Model extention is only possible for HBR models.')
         return
-    elif (not 'model_path' in list(kwargs.keys())) or \
-        (not 'output_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+        ('output_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
@@ -1376,7 +1434,10 @@ 
    Source code for normative
                                      str(i) + outputsuffix + '.pkl'))
    
 
 
-
    [docs]def tune(covfile, respfile, maskfile=None, **kwargs):
+
+
    
+[docs]
+def tune(covfile, respfile, maskfile=None, **kwargs):
     '''
     This function tunes an existing HBR model with real data.
 
@@ -1410,9 +1471,9 @@ 
    Source code for normative
         if alg != 'hbr':
         print('Model extention is only possible for HBR models.')
         return
-    elif (not 'model_path' in list(kwargs.keys())) or \
-        (not 'output_path' in list(kwargs.keys())) or \
-            (not 'trbefile' in list(kwargs.keys())):
+    elif ('model_path' not in list(kwargs.keys())) or \
+        ('output_path' not in list(kwargs.keys())) or \
+            ('trbefile' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
@@ -1487,7 +1548,10 @@ 
    Source code for normative
                                      str(i) + outputsuffix + '.pkl'))
    
 
 
-
    [docs]def merge(covfile=None, respfile=None, **kwargs):
+
+
    
+[docs]
+def merge(covfile=None, respfile=None, **kwargs):
     '''
     This function extends an existing HBR model with data from new sites/scanners.
 
@@ -1518,9 +1582,9 @@ 
    Source code for normative
         if alg != 'hbr':
         print('Merging models is only possible for HBR models.')
         return
-    elif (not 'model_path1' in list(kwargs.keys())) or \
-        (not 'model_path2' in list(kwargs.keys())) or \
-            (not 'output_path' in list(kwargs.keys())):
+    elif ('model_path1' not in list(kwargs.keys())) or \
+        ('model_path2' not in list(kwargs.keys())) or \
+            ('output_path' not in list(kwargs.keys())):
         print('InputError: Some mandatory arguments are missing.')
         return
     else:
@@ -1591,7 +1655,10 @@ 
    Source code for normative
                                             str(i) + outputsuffix + '.pkl'))
    
 
 
-
    [docs]def main(*args):
+
+
    
+[docs]
+def main(*args):
     """ Parse arguments and estimate model
     """
 
@@ -1620,6 +1687,13 @@ 
    Source code for normative
         exec(func + '(' + all_args + ')')
    
 
 
+
    
+[docs]
+def entrypoint():
+    main(sys.argv[1:])
    
+
+
+
 # For running from the command line:
 if __name__ == "__main__":
     main(sys.argv[1:])
diff --git a/doc/build/html/_modules/normative_parallel.html b/doc/build/html/_modules/normative_parallel.html
index 1cb6ed98..bcd334f9 100644
--- a/doc/build/html/_modules/normative_parallel.html
+++ b/doc/build/html/_modules/normative_parallel.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   normative_parallel — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff

 
    
@@ -135,7 +142,8 @@ 
    Source code for normative_parallel
     import time
 import numpy as np
 import pandas as pd
-from subprocess import call, check_output
+from datetime import datetime
+from subprocess import run, check_output
 
 
 try:
@@ -157,7 +165,9 @@ 
    Source code for normative_parallel
     PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
 
-
    [docs]def execute_nm(processing_dir,
+
    
+[docs]
+def execute_nm(processing_dir,
                python_path,
                job_name,
                covfile_path,
@@ -192,6 +202,7 @@ 
    Source code for normative_parallel
         :param testrespfile_path: Full path to a .txt file that contains all test features
     :param log_path: Path for saving log files
     :param binary: If True uses binary format for response file otherwise it is text
+    :param cluster_spec: 'torque' for PBS Torque and 'slurm' for Slurm clusters. 
     :param interactive: If False (default) the user should manually 
                         rerun the failed jobs or collect the results.
                         If 'auto' the job status are checked until all 
@@ -211,10 +222,11 @@ 
    Source code for normative_parallel
         cv_folds = kwargs.get('cv_folds', None)
     testcovfile_path = kwargs.get('testcovfile_path', None)
     testrespfile_path = kwargs.get('testrespfile_path', None)
-    outputsuffix = kwargs.get('outputsuffix', 'estimate')
+    outputsuffix = kwargs.get('outputsuffix', '_estimate')
     cluster_spec = kwargs.pop('cluster_spec', 'torque')
     log_path = kwargs.get('log_path', None)
     binary = kwargs.pop('binary', False)
+    cores = kwargs.pop('n_cores_per_batch','1')
 
     split_nm(processing_dir,
              respfile_path,
@@ -234,6 +246,8 @@ 
    Source code for normative_parallel
     
     kwargs.update({'batch_size': str(batch_size)})
     job_ids = []
+    start_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
+
     for n in range(1, number_of_batches+1):
         kwargs.update({'job_id': str(n)})
         if testrespfile_path is not None:
@@ -266,9 +280,10 @@ 
    Source code for normative_parallel
                         job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration,
+                                     cores=cores)
                     job_ids.append(job_id)
-                elif cluster_spec == 'sbatch':
+                elif cluster_spec == 'slurm':
                     # update the response file
                     kwargs.update({'testrespfile_path':
                                    batch_testrespfile_path})
@@ -282,8 +297,10 @@ 
    Source code for normative_parallel
                                       memory=memory,
                                   duration=duration,
                                   **kwargs)
-                    sbatch_nm(job_path=batch_job_path,
-                              log_path=log_path)
+
+                    job_id = sbatch_nm(job_path=batch_job_path)
+                    job_ids.append(job_id)
+
                 elif cluster_spec == 'new':
                     # this part requires addition in different envioronment [
                     sbatchwrap_nm(processing_dir=batch_processing_dir,
@@ -310,9 +327,10 @@ 
    Source code for normative_parallel
                         job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration, 
+                                     cores=cores)
                     job_ids.append(job_id)
-                elif cluster_spec == 'sbatch':
+                elif cluster_spec == 'slurm':
                     sbatchwrap_nm(batch_processing_dir,
                                   python_path,
                                   normative_path,
@@ -323,8 +341,9 @@ 
    Source code for normative_parallel
                                       memory=memory,
                                   duration=duration,
                                   **kwargs)
-                    sbatch_nm(job_path=batch_job_path,
-                              log_path=log_path)
+
+                    job_id = sbatch_nm(job_path=batch_job_path)
+                    job_ids.append(job_id)
                 elif cluster_spec == 'new':
                     # this part requires addition in different envioronment [
                     bashwrap_nm(processing_dir=batch_processing_dir, func=func,
@@ -352,9 +371,10 @@ 
    Source code for normative_parallel
                         job_id = qsub_nm(job_path=batch_job_path,
                                      log_path=log_path,
                                      memory=memory,
-                                     duration=duration)
+                                     duration=duration,
+                                     cores=cores)
                     job_ids.append(job_id)
-                elif cluster_spec == 'sbatch':
+                elif cluster_spec == 'slurm':
                     sbatchwrap_nm(batch_processing_dir,
                                   python_path,
                                   normative_path,
@@ -365,8 +385,10 @@ 
    Source code for normative_parallel
                                       memory=memory,
                                   duration=duration,
                                   **kwargs)
-                    sbatch_nm(job_path=batch_job_path,
-                              log_path=log_path)
+
+                    job_id = sbatch_nm(job_path=batch_job_path)
+                    job_ids.append(job_id)
+
                 elif cluster_spec == 'new':
                     # this part requires addition in different envioronment [
                     bashwrap_nm(processing_dir=batch_processing_dir, func=func,
@@ -376,7 +398,7 @@ 
    Source code for normative_parallel
     
     if interactive:
 
-        check_jobs(job_ids, delay=60)
+        check_jobs(job_ids, cluster_spec, start_time, delay=60)
 
         success = False
         while (not success):
@@ -393,16 +415,33 @@ 
    Source code for normative_parallel
                     if interactive == 'query':
                     response = yes_or_no('Rerun the failed jobs?')
                     if response:
-                        rerun_nm(processing_dir, log_path=log_path, memory=memory,
-                                 duration=duration, binary=binary,
-                                 interactive=interactive)
+                        if cluster_spec == 'torque':
+                            rerun_nm(processing_dir, log_path=log_path, memory=memory,
+                                     duration=duration, binary=binary,
+                                     interactive=interactive, cores=cores)
+                        elif cluster_spec == 'slurm':
+                            sbatchrerun_nm(processing_dir,
+                                           memory=memory,
+                                           duration=duration,
+                                           binary=binary,
+                                           log_path=log_path,
+                                           interactive=interactive)
+
                     else:
                         success = True
                 else:
                     print('Reruning the failed jobs ...')
-                    rerun_nm(processing_dir, log_path=log_path, memory=memory,
-                             duration=duration, binary=binary,
-                             interactive=interactive)
+                    if cluster_spec == 'torque':
+                        rerun_nm(processing_dir, log_path=log_path, memory=memory,
+                                 duration=duration, binary=binary,
+                                 interactive=interactive, cores=cores)
+                    elif cluster_spec == 'slurm':
+                        sbatchrerun_nm(processing_dir,
+                                       memory=memory,
+                                       duration=duration,
+                                       binary=binary,
+                                       log_path=log_path,
+                                       interactive=interactive)
 
         if interactive == 'query':
             response = yes_or_no('Collect the results?')
@@ -425,10 +464,13 @@ 
    Source code for normative_parallel
                                      outputsuffix=outputsuffix)
    
 
 
+
 """routines that are environment independent"""
 
 
-
    [docs]def split_nm(processing_dir,
+
    
+[docs]
+def split_nm(processing_dir,
              respfile_path,
              batch_size,
              binary,
@@ -543,7 +585,10 @@ 
    Source code for normative_parallel
                                              protocol=PICKLE_PROTOCOL)
    
 
 
-
    [docs]def collect_nm(processing_dir,
+
+
    
+[docs]
+def collect_nm(processing_dir,
                job_name,
                func='estimate',
                collect=False,
@@ -581,19 +626,15 @@ 
    Source code for normative_parallel
         batch_fail = []
 
     if (func != 'fit' and func != 'extend' and func != 'merge' and func != 'tune'):
-        file_example = []
         # TODO: Collect_nm only depends on yhat, thus does not work when no
         # prediction is made (when test cov is not specified).
-        for batch in batches:
-            if file_example == []:
-                file_example = glob.glob(batch + 'yhat' + outputsuffix
+        files = glob.glob(processing_dir + 'batch_*/' + 'yhat' + outputsuffix
                                          + file_extentions)
-            else:
-                break
-        if binary is False:
-            file_example = fileio.load(file_example[0])
+        if len(files) > 0:
+            file_example = fileio.load(files[0])
         else:
-            file_example = pd.read_pickle(file_example[0])
+            raise ValueError(f"Missing output files (yhats at: {processing_dir + 'batch_*/' + 'yhat' + outputsuffix + file_extentions}")
+
         numsubjects = file_example.shape[0]
         try:
             # doesn't exist if size=1, and txt file
@@ -947,7 +988,10 @@ 
    Source code for normative_parallel
             return False
    
 
 
-
    [docs]def delete_nm(processing_dir,
+
+
    
+[docs]
+def delete_nm(processing_dir,
               binary=False):
     '''This function deletes all processing for normative modelling and just keeps the combined output.
 
@@ -971,11 +1015,14 @@ 
    Source code for normative_parallel
             os.remove(processing_dir + 'failed_batches' + file_extentions)
    
 
 
+
 # all routines below are envronment dependent and require adaptation in novel
 # environments -> copy those routines and adapt them in accrodance with your
 # environment
 
-
    [docs]def bashwrap_nm(processing_dir,
+
    
+[docs]
+def bashwrap_nm(processing_dir,
                 python_path,
                 normative_path,
                 job_name,
@@ -1036,7 +1083,7 @@ 
    Source code for normative_parallel
             job_call = [python_path + ' ' + normative_path + ' -c ' +
                     covfile_path + ' -f ' + func]
     else:
-        raise ValueError("""For 'estimate' function either testcov or cvfold
+        raise ValueError("""For 'estimate' function either testrespfile_path or cvfold
               must be specified.""")
 
     # add algorithm-specific parameters
@@ -1065,10 +1112,14 @@ 
    Source code for normative_parallel
         os.chmod(processing_dir + job_name, 0o770)
    
 
 
-
    [docs]def qsub_nm(job_path,
+
+
    
+[docs]
+def qsub_nm(job_path,
             log_path,
             memory,
-            duration):
+            duration,
+            cores):
     '''This function submits a job.sh scipt to the torque custer using the qsub command.
 
     Basic usage::
@@ -1088,10 +1139,10 @@ 
    Source code for normative_parallel
         # created qsub command
     if log_path is None:
         qsub_call = ['echo ' + job_path + ' | qsub -N ' + job_path + ' -l ' +
-                     'procs=1' + ',mem=' + memory + ',walltime=' + duration]
+                     'nodes=1:ppn='+ cores + ',mem=' + memory + ',walltime=' + duration]
     else:
         qsub_call = ['echo ' + job_path + ' | qsub -N ' + job_path +
-                     ' -l ' + 'procs=1' + ',mem=' + memory + ',walltime=' +
+                     ' -l ' + 'nodes=1:ppn='+ cores + ',mem=' + memory + ',walltime=' +
                      duration + ' -o ' + log_path + ' -e ' + log_path]
 
     # submits job to cluster
@@ -1102,10 +1153,15 @@ 
    Source code for normative_parallel
         return job_id
    
 
 
-
    [docs]def rerun_nm(processing_dir,
+
+
    
+[docs]
+def rerun_nm(processing_dir,
              log_path,
              memory,
              duration,
+             cluster_spec,
+             cores,
              binary=False,
              interactive=False):
     '''This function reruns all failed batched in processing_dir after collect_nm has identified the failed batches.
@@ -1133,7 +1189,8 @@ 
    Source code for normative_parallel
                 job_id = qsub_nm(job_path=jobpath,
                              log_path=log_path,
                              memory=memory,
-                             duration=duration)
+                             duration=duration,
+                             cores=cores)
             job_ids.append(job_id)
     else:
         file_extentions = '.txt'
@@ -1146,17 +1203,21 @@ 
    Source code for normative_parallel
                 job_id = qsub_nm(job_path=jobpath,
                              log_path=log_path,
                              memory=memory,
-                             duration=duration)
+                             duration=duration,
+                             cores=cores)
             job_ids.append(job_id)
 
     if interactive:
-        check_jobs(job_ids, delay=60)
    
+        check_jobs(job_ids, cluster_spec, delay=60)
    
+
 
 
 # COPY the rotines above here and aadapt those to your cluster
 # bashwarp_nm; qsub_nm; rerun_nm
 
-
    [docs]def sbatchwrap_nm(processing_dir,
+
    
+[docs]
+def sbatchwrap_nm(processing_dir,
                   python_path,
                   normative_path,
                   job_name,
@@ -1164,6 +1225,7 @@ 
    Source code for normative_parallel
                       respfile_path,
                   memory,
                   duration,
+                  log_path,
                   func='estimate',
                   **kwargs):
     '''This function wraps normative modelling into a bash script to run it
@@ -1202,14 +1264,15 @@ 
    Source code for normative_parallel
         output_changedir = ['cd ' + processing_dir + '\n']
 
     sbatch_init = '#!/bin/bash\n'
-    sbatch_jobname = '#SBATCH --job-name=' + processing_dir + '\n'
-    sbatch_account = '#SBATCH --account=p33_norment\n'
+    sbatch_jobname = '#SBATCH --job-name=' + job_name + '\n'
     sbatch_nodes = '#SBATCH --nodes=1\n'
     sbatch_tasks = '#SBATCH --ntasks=1\n'
     sbatch_time = '#SBATCH --time=' + str(duration) + '\n'
     sbatch_memory = '#SBATCH --mem-per-cpu=' + str(memory) + '\n'
-    sbatch_module = 'module purge\n'
-    sbatch_anaconda = 'module load anaconda3\n'
+    sbatch_log_out = '#SBATCH -o ' + log_path + '%x_%j.out' + '\n'
+    sbatch_log_error = '#SBATCH -e ' + log_path + '%x_%j.err' + '\n'
+    # sbatch_module = 'module purge\n'
+    # sbatch_anaconda = 'module load anaconda3\n'
     sbatch_exit = 'set -o errexit\n'
 
     # echo -n "This script is running on "
@@ -1217,12 +1280,13 @@ 
    Source code for normative_parallel
     
     bash_environment = [sbatch_init +
                         sbatch_jobname +
-                        sbatch_account +
                         sbatch_nodes +
                         sbatch_tasks +
                         sbatch_time +
-                        sbatch_module +
-                        sbatch_anaconda]
+                        sbatch_memory +
+                        sbatch_log_out +
+                        sbatch_log_error
+                        ]
 
     # creates call of function for normative modelling
     if (testrespfile_path is not None) and (testcovfile_path is not None):
@@ -1239,7 +1303,7 @@ 
    Source code for normative_parallel
             job_call = [python_path + ' ' + normative_path + ' -c ' +
                     covfile_path + ' -f ' + func]
     else:
-        raise ValueError("""For 'estimate' function either testcov or cvfold
+        raise ValueError("""For 'estimate' function either testrespfile_path or cv_folds
               must be specified.""")
 
     # add algorithm-specific parameters
@@ -1268,19 +1332,20 @@ 
    Source code for normative_parallel
         os.chmod(processing_dir + job_name, 0o770)
    
 
 
-
    [docs]def sbatch_nm(job_path,
-              log_path):
+
+
    
+[docs]
+def sbatch_nm(job_path):
     '''This function submits a job.sh scipt to the torque custer using the qsub
     command.
 
     Basic usage::
 
-        sbatch_nm(job_path, log_path)
+        sbatch_nm(job_path)
 
     :param job_path: Full path to the job.sh file
-    :param log_path: The logs are currently stored in the working dir
 
-    :outputs: Submission of the job to the (torque) cluster.
+    :outputs: Submission of the job to the slurm cluster.
 
     written by (primarily) T Wolfers, (adapted) S Rutherford.
     '''
@@ -1289,15 +1354,22 @@ 
    Source code for normative_parallel
         sbatch_call = ['sbatch ' + job_path]
 
     # submits job to cluster
-    call(sbatch_call, shell=True)
    
+    job_id = check_output(sbatch_call, shell=True).decode(
+        sys.stdout.encoding).replace("\n", "")
+
+    return job_id
    
 
 
-
    [docs]def sbatchrerun_nm(processing_dir,
+
+
    
+[docs]
+def sbatchrerun_nm(processing_dir,
                    memory,
                    duration,
                    new_memory=False,
                    new_duration=False,
                    binary=False,
+                   interactive=False,
                    **kwargs):
     '''This function reruns all failed batched in processing_dir after collect_nm has identified he failed batches.
 
@@ -1315,7 +1387,12 @@ 
    Source code for normative_parallel
     
      written by (primarily) T Wolfers, (adapted) S Rutherford.
     '''
-    log_path = kwargs.pop('log_path', None)
+
+    # log_path = kwargs.pop('log_path', None)
+
+    job_ids = []
+
+    start_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
 
     if binary:
         file_extentions = '.pkl'
@@ -1329,11 +1406,12 @@ 
    Source code for normative_parallel
                     with fileinput.FileInput(jobpath, inplace=True) as file:
                     for line in file:
                         print(line.replace(duration, new_duration), end='')
-                if new_memory != False:
-                    with fileinput.FileInput(jobpath, inplace=True) as file:
-                        for line in file:
-                            print(line.replace(memory, new_memory), end='')
-                sbatch_nm(jobpath, log_path)
+            if new_memory != False:
+                with fileinput.FileInput(jobpath, inplace=True) as file:
+                    for line in file:
+                        print(line.replace(memory, new_memory), end='')
+            job_id = sbatch_nm(jobpath)
+            job_ids.append(job_id)
 
     else:
         file_extentions = '.txt'
@@ -1347,73 +1425,124 @@ 
    Source code for normative_parallel
                     with fileinput.FileInput(jobpath, inplace=True) as file:
                     for line in file:
                         print(line.replace(duration, new_duration), end='')
-                if new_memory != False:
-                    with fileinput.FileInput(jobpath, inplace=True) as file:
-                        for line in file:
-                            print(line.replace(memory, new_memory), end='')
-                sbatch_nm(jobpath,
-                          log_path)
    
+            if new_memory != False:
+                with fileinput.FileInput(jobpath, inplace=True) as file:
+                    for line in file:
+                        print(line.replace(memory, new_memory), end='')
+            job_id = sbatch_nm(jobpath)
+            job_ids.append(job_id)
+
+    if interactive:
+        check_jobs(job_ids, cluster_spec='slurm',
+                   start_time=start_time, delay=60)
    
+
 
 
-
    [docs]def retrieve_jobs():
+
    
+[docs]
+def retrieve_jobs(cluster_spec, start_time=None):
     """
     A utility function to retrieve task status from the outputs of qstat.
 
+    :param cluster_spec: type of cluster, either 'torque' or 'slurm'.
+
     :return: a dictionary of jobs.
 
     """
 
-    output = check_output('qstat', shell=True).decode(sys.stdout.encoding)
-    output = output.split('\n')
-    jobs = dict()
-    for line in output[2:-1]:
-        (Job_ID, Job_Name, User, Wall_Time, Status, Queue) = line.split()
-        jobs[Job_ID] = dict()
-        jobs[Job_ID]['name'] = Job_Name
-        jobs[Job_ID]['walltime'] = Wall_Time
-        jobs[Job_ID]['status'] = Status
+    if cluster_spec == 'torque':
+
+        output = check_output('qstat', shell=True).decode(sys.stdout.encoding)
+        output = output.split('\n')
+        jobs = dict()
+        for line in output[2:-1]:
+            (Job_ID, Job_Name, User, Wall_Time, Status, Queue) = line.split()
+            jobs[Job_ID] = dict()
+            jobs[Job_ID]['name'] = Job_Name
+            jobs[Job_ID]['walltime'] = Wall_Time
+            jobs[Job_ID]['status'] = Status
+
+    elif cluster_spec == 'slurm':
+
+        end_time = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
+        cmd = ['sacct', '-n', '-X', '--parsable2', '--noheader',
+               '-S', start_time, '-E', end_time, '--format=JobName,State']
+        jobs = run(cmd, capture_output=True, text=True)
 
     return jobs
    
 
 
-
    [docs]def check_job_status(jobs):
+
+
    
+[docs]
+def check_job_status(jobs, cluster_spec, start_time=None):
     """
     A utility function to count the tasks with different status.
 
     :param jobs: List of job ids.
-    :return: returns the number of taks athat are queued, running, completed etc
+    :param cluster_spec: type of cluster, either 'torque' or 'slurm'.
+    :return returns the number of taks athat are queued, running, completed etc
 
     """
-    running_jobs = retrieve_jobs()
+    running_jobs = retrieve_jobs(cluster_spec, start_time)
 
     r = 0
     c = 0
     q = 0
     u = 0
-    for job in jobs:
-        try:
-            if running_jobs[job]['status'] == 'C':
+
+    if cluster_spec == 'torque':
+
+        for job in jobs:
+            try:
+                if running_jobs[job]['status'] == 'C':
+                    c += 1
+                elif running_jobs[job]['status'] == 'Q':
+                    q += 1
+                elif running_jobs[job]['status'] == 'R':
+                    r += 1
+                else:
+                    u += 1
+            except:  # probably meanwhile the job is finished.
                 c += 1
-            elif running_jobs[job]['status'] == 'Q':
-                q += 1
-            elif running_jobs[job]['status'] == 'R':
-                r += 1
-            else:
-                u += 1
-        except:  # probably meanwhile the job is finished.
-            c += 1
-            continue
+                continue
+
+        print('Total Jobs:%d, Queued:%d, Running:%d, Completed:%d, Unknown:%d'
+              % (len(jobs), q, r, c, u))
+
+    elif cluster_spec == 'slurm':
+
+        lines = running_jobs.stdout.strip().split('\n')
+
+        for line in lines:
+            if line:
+                parts = line.split('|')
+                if len(parts) >= 2:
+                    job_name, state = parts[0], parts[1]
+                    if state == 'PENDING':
+                        q += 1
+                    elif state == 'RUNNING':
+                        r += 1
+                    elif state == 'COMPLETED':
+                        c += 1
+                    elif state == 'FAILED':
+                        u += 1
+
+        print('Total Jobs:%d, Pending:%d, Running:%d, Completed:%d, Failed:%d'
+              % (len(jobs), q, r, c, u))
 
-    print('Total Jobs:%d, Queued:%d, Running:%d, Completed:%d, Unknown:%d'
-          % (len(jobs), q, r, c, u))
     return q, r, c, u
    
 
 
-
    [docs]def check_jobs(jobs, delay=60):
+
+
    
+[docs]
+def check_jobs(jobs, cluster_spec, start_time=None, delay=60):
     """
     A utility function for chacking the status of submitted jobs.
 
     :param jobs: list of job ids.
+    :param cluster_spec: type of cluster, either 'torque' or 'slurm'.
     :param delay: the delay (in sec) between two consequative checks, defaults to 60.
 
     """
@@ -1421,11 +1550,12 @@ 
    Source code for normative_parallel
         n = len(jobs)
 
     while (True):
-        q, r, c, u = check_job_status(jobs)
+        q, r, c, u = check_job_status(jobs, cluster_spec, start_time)
         if c == n:
             print('All jobs are completed!')
             break
         time.sleep(delay)
    
+
 
    
 
            
    
diff --git a/doc/build/html/_modules/rfa.html b/doc/build/html/_modules/rfa.html
index 986a44c9..0ea213e2 100644
--- a/doc/build/html/_modules/rfa.html
+++ b/doc/build/html/_modules/rfa.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   rfa — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff

 
    
@@ -110,7 +117,9 @@ 
    Source code for rfa
     import torch
 
 
-
    [docs]class GPRRFA:
+
    
+[docs]
+class GPRRFA:
     """Random Feature Approximation for Gaussian Process Regression
 
     Estimation and prediction of Bayesian linear regression models
@@ -191,11 +200,16 @@ 
    Source code for rfa
     
         return X, y, hyp
 
-
    [docs]    def get_n_params(self, X):
+
    
+[docs]
+    def get_n_params(self, X):
 
         return X.shape[1] + 2
    
 
-
    [docs]    def post(self, hyp, X, y):
+
+
    
+[docs]
+    def post(self, hyp, X, y):
         """ Generic function to compute posterior distribution.
 
             This function will save the posterior mean and precision matrix as
@@ -240,7 +254,10 @@ 
    Source code for rfa
             if hasattr(self, '_iterations'):
             self._iterations += 1
    
 
-
    [docs]    def loglik(self, hyp, X, y):
+
+
    
+[docs]
+    def loglik(self, hyp, X, y):
         """ Function to compute compute log (marginal) likelihood """
         X, y, hyp = self._numpy2torch(X, y, hyp)
 
@@ -275,14 +292,20 @@ 
    Source code for rfa
             self.nlZ = nlZ
         return nlZ
    
 
-
    [docs]    def dloglik(self, hyp, X, y):
+
+
    
+[docs]
+    def dloglik(self, hyp, X, y):
         """ Function to compute derivatives """
 
         print("derivatives not available")
 
         return
    
 
-
    [docs]    def estimate(self, hyp0, X, y, optimizer='lbfgs'):
+
+
    
+[docs]
+    def estimate(self, hyp0, X, y, optimizer='lbfgs'):
         """ Function to estimate the model """
 
         if type(hyp0) is torch.Tensor:
@@ -324,7 +347,10 @@ 
    Source code for rfa
     
         return self.hyp.detach().numpy()
    
 
-
    [docs]    def predict(self, hyp, X, y, Xs):
+
+
    
+[docs]
+    def predict(self, hyp, X, y, Xs):
         """ Function to make predictions from the model """
 
         X, y, hyp = self._numpy2torch(X, y, hyp)
@@ -347,7 +373,9 @@ 
    Source code for rfa
                 torch.sum(Phis * torch.t(torch.solve(torch.t(Phis), self.A)[0]), 1)
 
         # return output as numpy arrays
-        return ys.detach().numpy().squeeze(), s2.detach().numpy().squeeze()
    
+        return ys.detach().numpy().squeeze(), s2.detach().numpy().squeeze()
    
+
    
+
 
    
 
            
    
diff --git a/doc/build/html/_modules/trendsurf.html b/doc/build/html/_modules/trendsurf.html
index d01ca0b3..1470dd83 100644
--- a/doc/build/html/_modules/trendsurf.html
+++ b/doc/build/html/_modules/trendsurf.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   trendsurf — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff

 
    
@@ -135,7 +142,9 @@ 
    Source code for trendsurf
         from model.bayesreg import BLR
 
 
-
    [docs]def load_data(datafile, maskfile=None):
+
    
+[docs]
+def load_data(datafile, maskfile=None):
     """
     Load data from disk
 
@@ -179,7 +188,10 @@ 
    Source code for trendsurf
         return dat, world, mask
    
 
 
-
    [docs]def create_basis(X, basis, mask):
+
+
    
+[docs]
+def create_basis(X, basis, mask):
     """
     Create a basis set
 
@@ -223,7 +235,10 @@ 
    Source code for trendsurf
         return Phi
    
 
 
-
    [docs]def write_nii(data, filename, examplenii, mask):
+
+
    
+[docs]
+def write_nii(data, filename, examplenii, mask):
     """
     Write data to nifti file
 
@@ -251,7 +266,10 @@ 
    Source code for trendsurf
         nib.save(array_img, filename)
    
 
 
-
    [docs]def get_args(*args):
+
+
    
+[docs]
+def get_args(*args):
     """
     Parse command line arguments
 
@@ -293,7 +311,10 @@ 
    Source code for trendsurf
         return filename, maskfile, basis, args.a, args.o
    
 
 
-
    [docs]def estimate(filename, maskfile, basis, ard=False, outputall=False,
+
+
    
+[docs]
+def estimate(filename, maskfile, basis, ard=False, outputall=False,
              saveoutput=True, **kwargs):
     """ Estimate a trend surface model
 
@@ -406,13 +427,17 @@ 
    Source code for trendsurf
             return out
    
 
 
-
    [docs]def main(*args):
+
+
    
+[docs]
+def main(*args):
     np.seterr(invalid='ignore')
 
     filename, maskfile, basis, ard, outputall = get_args(args)
     estimate(filename, maskfile, basis, ard, outputall)
    
 
 
+
 # For running from the command line:
 if __name__ == "__main__":
     main(sys.argv[1:])
diff --git a/doc/build/html/_sources/pages/BLR_normativemodel_protocol.rst.txt b/doc/build/html/_sources/pages/BLR_normativemodel_protocol.rst.txt
index 479b5163..4c23523b 100644
--- a/doc/build/html/_sources/pages/BLR_normativemodel_protocol.rst.txt
+++ b/doc/build/html/_sources/pages/BLR_normativemodel_protocol.rst.txt
@@ -1,27 +1,22 @@
-.. title:: BLR tutorial
+`Predictive Clinical Neuroscience Toolkit `__
+======================================================================================
 
-Bayesian Linear Regression
+The Normative Modeling Framework for Computational Psychiatry Protocol
 ======================================================================
 
-The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
+Using Bayesian Linear Regression and Multi-Site Cortical Thickness Data
+-----------------------------------------------------------------------
 
 Created by `Saige Rutherford `__
 
-Using Multi-Site Cortical Thickness Data
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/BLR_protocol/BLR_normativemodel_protocol.ipynb 
-
-
-.. figure:: ./blr_fig2.png
-   :height: 400px
-   :align: center
-
 Data Preparation
----------------------------------------------
+================
 
 Install necessary libraries & grab data files
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+---------------------------------------------
+
+Step 1.
+~~~~~~~
 
 Begin by cloning the GitHub repository using the following commands.
 This repository contains the necessary code and example data. Then
@@ -33,33 +28,28 @@ your computer).
 
     ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
 
-
-.. parsed-literal::
-
-    Cloning into 'PCNtoolkit-demo'...
-    remote: Enumerating objects: 855, done.
-    remote: Counting objects: 100% (855/855), done.
-    remote: Compressing objects: 100% (737/737), done.
-    remote: Total 855 (delta 278), reused 601 (delta 101), pack-reused 0
-    Receiving objects: 100% (855/855), 18.07 MiB | 13.53 MiB/s, done.
-    Resolving deltas: 100% (278/278), done.
-
-
 .. code:: ipython3
 
     import os
     
     # set this path to the git cloned PCNtoolkit-demo repository --> Uncomment whichever line you need for either running on your own computer or on Google Colab.
-    #os.chdir('/Users/PCNtoolkit-demo/tutorials/BLR_protocol') # if running on your own computer, use this line (change the path to match where you cloned the repository)
-    os.chdir('/content/PCNtoolkit-demo/tutorials/BLR_protocol') # if running on Google Colab, use this line
+    #wdir = '/PCNtoolkit-demo' # if running on your own computer, use this line (change the path to match where you cloned the repository)
+    wdir ='/content/PCNtoolkit-demo' # if running on Google Colab, use this line
+    
+    os.chdir(os.path.join(wdir,'tutorials','BLR_protocol'))
+
 
 .. code:: ipython3
 
+    ! pip install nutpie
+    ! pip install pcntoolkit
     ! pip install -r requirements.txt
 
-
 Prepare covariate data
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------
+
+Step 2.
+~~~~~~~
 
 The data set (downloaded in Step 1) includes a multi-site dataset from
 the `Human Connectome Project Young Adult
@@ -88,8 +78,8 @@ depending on the research question.
 .. code:: ipython3
 
     # if running in Google colab, remove the "data/" folder from the path
-    hcp = pd.read_csv('/content/PCNtoolkit-demo/data/HCP1200_age_gender.csv')
-    ixi = pd.read_csv('/content/PCNtoolkit-demo/data/IXI_age_gender.csv')
+    hcp = pd.read_csv(os.path.join(wdir,'data','HCP1200_age_gender.csv'))
+    ixi = pd.read_csv(os.path.join(wdir,'data','IXI_age_gender.csv'))
 
 .. code:: ipython3
 
@@ -98,8 +88,8 @@ depending on the research question.
 
 .. parsed-literal::
 
-    /usr/local/lib/python3.7/dist-packages/pandas/core/reshape/merge.py:1218: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation
-      UserWarning,
+    :1: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation.
+      cov = pd.merge(hcp, ixi, on=["participant_id", "age", "sex", "site"], how='outer')
 
 
 .. code:: ipython3
@@ -108,29 +98,348 @@ depending on the research question.
 
 .. code:: ipython3
 
-    sns.displot(cov, x="age", hue="site", multiple="stack", height=6)
+    sns.displot(cov, x="age", hue="site", multiple="stack", height=6);
 
 
 
+.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_16_0.png
 
-.. parsed-literal::
 
-    
+.. code:: ipython3
 
+    cov.groupby(['site']).describe()
 
 
 
-.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_15_1.png
 
+.. raw:: html
+
+    
+      
    
+        
    
+    
+    
+      
+        
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
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+          
+          
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+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+      
+      
+        
+          
+          
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+          
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+          
+          
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+          
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+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+      
+    
    agesex
    countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
    site
    hcp1206.028.8374793.69053422.00000026.00000029.0000032.00000037.000001206.01.5439470.4982721.01.02.02.02.0
    ixi590.049.47653116.72086419.98083534.02772150.6119163.41341586.31896590.01.5559320.4972831.01.02.02.02.0
    
+    
    
+        
    
+    
+      
    
+        
+    
+      
+    
+        
+      
    
+    
+    
+    
    
+      
+    
+    
+    
+      
+    
    
+    
+        
    
+      
    
 
-.. code:: ipython3
 
-    cov.groupby(['site']).describe()
 
 
+Preprare brain data
+-------------------
 
-Prepare brain data
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Step 3.
+~~~~~~~
 
 Next, format and combine the MRI data using the following commands. The
 example data contains cortical thickness maps estimated by running
@@ -139,7 +448,7 @@ was reduced by using ROIs from the Desikan-Killiany atlas. Including the
 Euler number as a covariate is also recommended, as this is a proxy
 metric for data quality. The `Euler
 number `__ from
-each subjects recon-all output folder was extracted into a text file
+each subject’s recon-all output folder was extracted into a text file
 and is merged into the cortical thickness data frame. The Euler number
 is site-specific, thus, to use the same exclusion threshold across sites
 it is important to center the site by subtracting the site median from
@@ -147,7 +456,7 @@ all subjects at a site. Then take the square root and multiply by
 negative one and exclude any subjects with a square root above 10.
 
 Here is some psuedo-code (run from a terminal in the folder that has all
-subjects recon-all output folders) that was used to extract these ROIs:
+subject’s recon-all output folders) that was used to extract these ROIs:
 
 ``export SUBJECTS_DIR=/path/to/study/freesurfer_data/``
 
@@ -157,14 +466,14 @@ subjects recon-all output folders) that was used to extract these ROIs:
 
 .. code:: ipython3
 
-    hcpya = pd.read_csv('/content/PCNtoolkit-demo/data/HCP1200_aparc_thickness.csv')
-    ixi = pd.read_csv('/content/PCNtoolkit-demo/data/IXI_aparc_thickness.csv')
+    hcpya = pd.read_csv(os.path.join(wdir,'data','HCP1200_aparc_thickness.csv'))
+    ixi = pd.read_csv(os.path.join(wdir,'data','IXI_aparc_thickness.csv'))
 
 .. code:: ipython3
 
     brain_all = pd.merge(ixi, hcpya, how='outer')
 
-We extracted the euler number from each subjects recon-all output
+We extracted the euler number from each subject’s recon-all output
 folder into a text file and we now need to format and combine these into
 our brain dataframe.
 
@@ -173,12 +482,12 @@ recon-all.log for each subject. Run this from the terminal in the folder
 where your subjects recon-all output folders are located. This assumes
 that all of your subject IDs start with “sub-” prefix.
 
-:literal:`for i in sub-*; do if [[ -e ${i}/scripts/recon-all.log ]]; then cat ${i}/scripts/recon-all.log | grep -A 1 "Computing euler" > temp_log; lh_en=$(cat temp_log | head -2 | tail -1 | awk -F '=' '{print $2}' | awk -F ',' '{print $1}'); rh_en=$(cat temp_log | head -2 | tail -1 | awk -F '=' '{print $3}'); echo "${i}, ${lh_en}, ${rh_en}" >> euler.csv; echo ${i}; fi; done`
+:literal:`for i in sub-\*; do if [[ -e ${i}/scripts/recon-all.log ]]; then cat ${i}/scripts/recon-all.log | grep -A 1 "Computing euler" > temp_log; lh_en=`cat temp_log | head -2 | tail -1 | awk -F '=' '{print $2}' | awk -F ',' '{print $1}'\`; rh_en=`cat temp_log | head -2 | tail -1 | awk -F '=' '{print $3}'\`; echo "${i}, ${lh_en}, ${rh_en}" >> euler.csv; echo ${i}; fi; done`
 
 .. code:: ipython3
 
-    hcp_euler = pd.read_csv('/content/PCNtoolkit-demo/data/hcp-ya_euler.csv')
-    ixi_euler = pd.read_csv('/content/PCNtoolkit-demo/data/ixi_euler.csv')
+    hcp_euler = pd.read_csv(os.path.join(wdir,'data','hcp-ya_euler.csv'))
+    ixi_euler = pd.read_csv(os.path.join(wdir,'data','ixi_euler.csv'))
 
 .. code:: ipython3
 
@@ -221,7 +530,7 @@ inclusion is not too strict or too lenient.
 
 .. code:: ipython3
 
-    df_euler.groupby(by='site').median()
+    df_euler.groupby(by='site')[['lh_euler', 'rh_euler', 'avg_euler']].median()
 
 
 
@@ -229,9 +538,8 @@ inclusion is not too strict or too lenient.
 .. raw:: html
 
     
-      
    
-        
    
-          
    
+      
    
+        
    
     
     
-          
+        
+      
    
+    
+    
+    
    
+      
+    
+    
+    
+      
+    
    
+    
         
    
       
    
 
@@ -363,6 +805,13 @@ inclusion is not too strict or too lenient.
 
     df_euler['site_median'] = df_euler['site_median'].replace({'hcp':-43,'ixi':-56})
 
+
+.. parsed-literal::
+
+    :1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
+      df_euler['site_median'] = df_euler['site_median'].replace({'hcp':-43,'ixi':-56})
+
+
 .. code:: ipython3
 
     df_euler['avg_euler_centered'] = df_euler['avg_euler'] - df_euler['site_median']
@@ -383,18 +832,19 @@ inclusion is not too strict or too lenient.
 
     brain_good = brain.query('avg_euler_centered_neg_sqrt < 10')
 
-.. warning::
-    **CRITICAL STEP:** If possible, data should be visually inspected to
-    verify that the data inclusion is not too strict or too lenient.
-    Subjects above the Euler number threshold should be manually checked to
-    verify and justify their exclusion due to poor data quality. This is
-    just one approach for automated QC used by the developers of the
-    PCNtoolkit. Other approaches such as the ENIGMA QC pipeline or UK
-    Biobanks QC pipeline are also viable options for automated QC.
+**CRITICAL STEP:** If possible, data should be visually inspected to
+verify that the data inclusion is not too strict or too lenient.
+Subjects above the Euler number threshold should be manually checked to
+verify and justify their exclusion due to poor data quality. This is
+just one approach for automated QC used by the developers of the
+PCNtoolkit. Other approaches such as the ENIGMA QC pipeline or UK
+Biobank’s QC pipeline are also viable options for automated QC.
 
 Combine covariate & cortical thickness dataframes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-------------------------------------------------
 
+Step 4.
+~~~~~~~
 
 The normative modeling function requires the covariate predictors and
 brain features to be in separate text files. However, it is important to
@@ -407,7 +857,7 @@ their own dataframes, using the commands below.
 
 .. code:: ipython3
 
-    # make sure to use how="inner" so that we only include subjects that have data in both the covariate and the cortical thickness files 
+    # make sure to use how="inner" so that we only include subjects that have data in both the covariate and the cortical thickness files
     all_data = pd.merge(brain_good, cov, how='inner')
 
 .. code:: ipython3
@@ -433,13 +883,15 @@ their own dataframes, using the commands below.
 
     all_data_covariates = all_data[['age','sex','site']]
 
-.. warning::
-    **CRITICAL STEP:** ``roi_ids`` is a variable that represents which brain
-    areas will be modeled and can be used to select subsets of the data
-    frame if you do not wish to run models for the whole brain.
+**CRITICAL STEP:** ``roi_ids`` is a variable that represents which brain
+areas will be modeled and can be used to select subsets of the data
+frame if you do not wish to run models for the whole brain.
 
 Add variable to model site/scanner effects
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------------------------
+
+Step 5.
+~~~~~~~
 
 Currently, the different sites are coded in a single column (named
 ‘site’) and are represented as a string data type. However, the
@@ -451,17 +903,689 @@ variables (0=not in this site, 1=present in this site).
 
 .. code:: ipython3
 
-    all_data_covariates = pd.get_dummies(all_data_covariates, columns=['site'])
+    all_data_covariates = pd.get_dummies(all_data_covariates, columns=['site'], dtype=int)
 
 .. code:: ipython3
 
-    all_data['Average_Thickness'] = all_data[['lh_MeanThickness_thickness','rh_MeanThickness_thickness']].mean(axis=1)
+    all_data_covariates.head()
+
+
+
+
+.. raw:: html
+
+    
+      
    
+        
    
+    
+    
+      
+        
+          
+          
+          
+          
+          
+        
+      
+      
+        
+          
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+        
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+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+      
+    
    agesexsite_hcpsite_ixi
    027.0110
    127.0210
    233.0110
    327.0110
    435.0210
    
+    
    
+        
    
+    
+      
    
+        
+    
+      
+    
+        
+      
    
+    
+    
+    
    
+      
+    
+    
+    
+      
+    
    
+    
+        
    
+      
    
 
 
 
+
+.. code:: ipython3
+
+    all_data_covariates
+
+
+
+
+.. raw:: html
+
+    
+      
    
+        
    
+    
+    
+      
+        
+          
+          
+          
+          
+          
+        
+      
+      
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+        
+      
+    
    agesexsite_hcpsite_ixi
    027.000000110
    127.000000210
    233.000000110
    327.000000110
    435.000000210
    ...............
    168747.723477101
    168850.395619101
    168942.989733101
    169046.220397101
    169141.741273101
    
+    
    1692 rows × 4 columns
    
+    
    
+        
    
+    
+      
    
+        
+    
+      
+    
+        
+      
    
+    
+    
+    
    
+      
+    
+    
+    
+      
+    
    
+    
+      
    
+        
+        
+        
+      
    
+    
+        
    
+      
    
+
+
+
+
+.. code:: ipython3
+
+    all_data['Average_Thickness'] = all_data[['lh_MeanThickness_thickness','rh_MeanThickness_thickness']].mean(axis=1)
+
 Train/test split
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------
 
+Step 6.
+~~~~~~~
 
 In this example, we use 80% of the data for training and 20% for
 testing. Please carefully read the experimental design section on
@@ -501,11 +1625,12 @@ Verify that your train & test arrays are the same size
     Test response size is:  (339, 6)
 
 
-.. warning::
-    **CRITICAL STEP:** The model would not learn the site effects if all the
-    data from one site was only in the test set. Therefore, we stratify the
-    train/test split using the site variable.
+**CRITICAL STEP:** The model would not learn the site effects if all the
+data from one site was only in the test set. Therefore, we stratify the
+train/test split using the site variable.
 
+Step 7.
+~~~~~~~
 
 When the data were split into train and test sets, the row index was not
 reset. This means that the row index in the train and test data frames
@@ -540,9 +1665,11 @@ which sites to evaluate model performance for, as follows:
     # Create a list with sites names to use in evaluating per-site metrics
     site_names = ['hcp', 'ixi']
 
-
 Setup output directories
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------
+
+Step 8.
+-------
 
 Save each brain region to its own text file (organized in separate
 directories) using the following commands, because for each response
@@ -557,6 +1684,7 @@ variable, Y (e.g., brain region) we fit a separate normative model.
 
     X_train.to_csv('cov_tr.txt', sep = '\t', header=False, index = False)
 
+
 .. code:: ipython3
 
     y_train.to_csv('resp_tr.txt', sep = '\t', header=False, index = False)
@@ -580,12 +1708,25 @@ variable, Y (e.g., brain region) we fit a separate normative model.
 
 .. code:: ipython3
 
+    # Note: please change the path in the following to wdir (depending on whether you are running on colab or not)
+    
     ! for i in `cat /content/PCNtoolkit-demo/data/roi_dir_names`; do if [[ -e resp_tr_${i}.txt ]]; then cd ROI_models; mkdir ${i}; cd ../; cp resp_tr_${i}.txt ROI_models/${i}/resp_tr.txt; cp resp_te_${i}.txt ROI_models/${i}/resp_te.txt; cp cov_tr.txt ROI_models/${i}/cov_tr.txt; cp cov_te.txt ROI_models/${i}/cov_te.txt; fi; done
 
+
+.. parsed-literal::
+
+    mkdir: cannot create directory ‘lh_MeanThickness_thickness’: File exists
+    mkdir: cannot create directory ‘lh_bankssts_thickness’: File exists
+    mkdir: cannot create directory ‘lh_caudalanteriorcingulate_thickness’: File exists
+    mkdir: cannot create directory ‘lh_superiorfrontal_thickness’: File exists
+    mkdir: cannot create directory ‘rh_MeanThickness_thickness’: File exists
+    mkdir: cannot create directory ‘rh_superiorfrontal_thickness’: File exists
+
+
 .. code:: ipython3
 
     # clean up files
-    ! rm resp_*.txt 
+    ! rm resp_*.txt
 
 .. code:: ipython3
 
@@ -593,11 +1734,13 @@ variable, Y (e.g., brain region) we fit a separate normative model.
     ! rm cov_t*.txt
 
 Algorithm & Modeling
--------------------------------
+====================
 
 Basis expansion using B-Splines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-------------------------------
 
+Step 9.
+~~~~~~~
 
 Now, set up a B-spline basis set that allows us to perform nonlinear
 regression using a linear model, using the following commands. This
@@ -615,29 +1758,29 @@ al `__.
 .. code:: ipython3
 
     # set this path to wherever your ROI_models folder is located (where you copied all of the covariate & response text files to in Step 4)
-    data_dir = '/content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/'
+    data_dir = os.path.join(wdir,'tutorials','BLR_protocol','ROI_models')
     
     # Create a cubic B-spline basis (used for regression)
     xmin = 10#16 # xmin & xmax are the boundaries for ages of participants in the dataset
     xmax = 95#90
     B = create_bspline_basis(xmin, xmax)
-    # create the basis expansion for the covariates for each of the 
-    for roi in roi_ids: 
+    # create the basis expansion for the covariates for each of the
+    for roi in roi_ids:
         print('Creating basis expansion for ROI:', roi)
         roi_dir = os.path.join(data_dir, roi)
         os.chdir(roi_dir)
-        # create output dir 
+        # create output dir
         os.makedirs(os.path.join(roi_dir,'blr'), exist_ok=True)
         # load train & test covariate data matrices
         X_tr = np.loadtxt(os.path.join(roi_dir, 'cov_tr.txt'))
         X_te = np.loadtxt(os.path.join(roi_dir, 'cov_te.txt'))
-        # add intercept column 
+        # add intercept column
         X_tr = np.concatenate((X_tr, np.ones((X_tr.shape[0],1))), axis=1)
         X_te = np.concatenate((X_te, np.ones((X_te.shape[0],1))), axis=1)
         np.savetxt(os.path.join(roi_dir, 'cov_int_tr.txt'), X_tr)
         np.savetxt(os.path.join(roi_dir, 'cov_int_te.txt'), X_te)
-        
-        # create Bspline basis set 
+    
+        # create Bspline basis set
         Phi = np.array([B(i) for i in X_tr[:,0]])
         Phis = np.array([B(i) for i in X_te[:,0]])
         X_tr = np.concatenate((X_tr, Phi), axis=1)
@@ -657,8 +1800,10 @@ al `__.
 
 
 Estimate normative model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------
 
+Step 10.
+~~~~~~~~
 
 Set up a variable (``data_dir``) that specifies the path to the ROI
 directories that were created in Step 7. Initiate two empty pandas data
@@ -674,6 +1819,8 @@ these data frames will be saved as individual csv files.
     blr_metrics = pd.DataFrame(columns = ['ROI', 'MSLL', 'EV', 'SMSE', 'RMSE', 'Rho'])
     blr_site_metrics = pd.DataFrame(columns = ['ROI', 'site', 'MSLL', 'EV', 'SMSE', 'RMSE', 'Rho'])
 
+Step 11.
+~~~~~~~~
 
 Estimate the normative models using a for loop to iterate over brain
 regions. An important consideration is whether to re-scale or
@@ -693,70 +1840,69 @@ arguments that are worthy of commenting on:
 ::
 
    - alg = 'blr': specifies we should use Bayesian Linear Regression.  
-   - optimizer = 'powell': use Powell's derivative-free optimization method (faster in this case than L-BFGS) 
-   - savemodel = False: do not write out the final estimated model to disk 
+   - optimizer = 'powell': use Powell's derivative-free optimization method (faster in this case than L-BFGS)
+   - savemodel = False: do not write out the final estimated model to disk
    - saveoutput = False: return the outputs directly rather than writing them to disk
    - standardize = False: Do not standardize the covariates or response variables
 
-.. warning::
-    **CRITICAL STEP:** This code fragment will loop through each region of
-    interest in the ``roi_ids`` list (created in step 4) using Bayesian
-    Linear Regression and evaluate the model on the independent test set. In
-    principle, we could estimate the normative models on the whole data
-    matrix at once (e.g., with the response variables stored in a
-    ``n_subjects`` by ``n_brain_measures`` NumPy array or a text file
-    instead of saved out into separate directories). However, running the
-    models iteratively gives some extra flexibility in that it does not
-    require that the included subjects are the same for each of the brain
-    measures.
+**CRITICAL STEP:** This code fragment will loop through each region of
+interest in the ``roi_ids`` list (created in step 4) using Bayesian
+Linear Regression and evaluate the model on the independent test set. In
+principle, we could estimate the normative models on the whole data
+matrix at once (e.g., with the response variables stored in a
+``n_subjects`` by ``n_brain_measures`` NumPy array or a text file
+instead of saved out into separate directories). However, running the
+models iteratively gives some extra flexibility in that it does not
+require that the included subjects are the same for each of the brain
+measures.
 
 .. code:: ipython3
 
     # Loop through ROIs
-    for roi in roi_ids: 
+    for roi in roi_ids:
         print('Running ROI:', roi)
         roi_dir = os.path.join(data_dir, roi)
         os.chdir(roi_dir)
-         
-        # configure the covariates to use. Change *_bspline_* to *_int_* to 
+    
+        # configure the covariates to use. Change *_bspline_* to *_int_* to
         cov_file_tr = os.path.join(roi_dir, 'cov_bspline_tr.txt')
         cov_file_te = os.path.join(roi_dir, 'cov_bspline_te.txt')
-        
+    
         # load train & test response files
         resp_file_tr = os.path.join(roi_dir, 'resp_tr.txt')
-        resp_file_te = os.path.join(roi_dir, 'resp_te.txt') 
-        
+        resp_file_te = os.path.join(roi_dir, 'resp_te.txt')
+    
         # run a basic model
-        yhat_te, s2_te, nm, Z, metrics_te = estimate(cov_file_tr, 
-                                                     resp_file_tr, 
-                                                     testresp=resp_file_te, 
-                                                     testcov=cov_file_te, 
-                                                     alg = 'blr', 
-                                                     optimizer = 'powell', 
-                                                     savemodel = True, 
+        yhat_te, s2_te, nm, Z, metrics_te = estimate(cov_file_tr,
+                                                     resp_file_tr,
+                                                     testresp=resp_file_te,
+                                                     testcov=cov_file_te,
+                                                     alg = 'blr',
+                                                     optimizer = 'powell',
+                                                     savemodel = True,
                                                      saveoutput = False,
                                                      standardize = False)
         # save metrics
         blr_metrics.loc[len(blr_metrics)] = [roi, metrics_te['MSLL'][0], metrics_te['EXPV'][0], metrics_te['SMSE'][0], metrics_te['RMSE'][0], metrics_te['Rho'][0]]
-        
+    
         # Compute metrics per site in test set, save to pandas df
         # load true test data
         X_te = np.loadtxt(cov_file_te)
         y_te = np.loadtxt(resp_file_te)
         y_te = y_te[:, np.newaxis] # make sure it is a 2-d array
-        
+    
         # load training data (required to compute the MSLL)
         y_tr = np.loadtxt(resp_file_tr)
         y_tr = y_tr[:, np.newaxis]
-        
-        for num, site in enumerate(sites):     
+    
+        for num, site in enumerate(sites):
             y_mean_te_site = np.array([[np.mean(y_te[site])]])
             y_var_te_site = np.array([[np.var(y_te[site])]])
             yhat_mean_te_site = np.array([[np.mean(yhat_te[site])]])
             yhat_var_te_site = np.array([[np.var(yhat_te[site])]])
-            
+    
             metrics_te_site = evaluate(y_te[site], yhat_te[site], s2_te[site], y_mean_te_site, y_var_te_site)
-            
+    
             site_name = site_names[num]
             blr_site_metrics.loc[len(blr_site_metrics)] = [roi, site_names[num], metrics_te_site['MSLL'][0], metrics_te_site['EXPV'][0], metrics_te_site['SMSE'][0], metrics_te_site['RMSE'][0], metrics_te_site['Rho'][0]]
 
@@ -764,40 +1910,54 @@ arguments that are worthy of commenting on:
 .. parsed-literal::
 
     Running ROI: lh_MeanThickness_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_MeanThickness_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
     Using default hyperparameters
-    Optimization terminated successfully.
-             Current function value: -1162.792820
-             Iterations: 2
-             Function evaluations: 47
-    Saving model meta-data...
-    Evaluating the model ...
 
 
 .. parsed-literal::
 
-    /usr/local/lib/python3.7/dist-packages/pcntoolkit/model/bayesreg.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=1.15485e-18): result may not be accurate.
-      invAXt = linalg.solve(self.A, X.T, check_finite=False)
-    /usr/local/lib/python3.7/dist-packages/pcntoolkit/model/bayesreg.py:187: LinAlgWarning: Ill-conditioned matrix (rcond=4.51813e-19): result may not be accurate.
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/bayesreg.py:196: LinAlgWarning: Ill-conditioned matrix (rcond=1.15485e-18): result may not be accurate.
       invAXt = linalg.solve(self.A, X.T, check_finite=False)
 
 
 .. parsed-literal::
 
+    Optimization terminated successfully.
+             Current function value: -1162.792820
+             Iterations: 2
+             Function evaluations: 43
+    Saving model meta-data...
+    Evaluating the model ...
     Running ROI: rh_MeanThickness_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/rh_MeanThickness_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
     Using default hyperparameters
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/bayesreg.py:196: LinAlgWarning: Ill-conditioned matrix (rcond=4.51813e-19): result may not be accurate.
+      invAXt = linalg.solve(self.A, X.T, check_finite=False)
+
+
+.. parsed-literal::
+
     Optimization terminated successfully.
              Current function value: -1187.621858
              Iterations: 2
-             Function evaluations: 47
+             Function evaluations: 43
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_bankssts_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_bankssts_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -805,10 +1965,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -578.945257
              Iterations: 2
-             Function evaluations: 46
+             Function evaluations: 42
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_caudalanteriorcingulate_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_caudalanteriorcingulate_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -816,10 +1978,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -235.509099
              Iterations: 3
-             Function evaluations: 75
+             Function evaluations: 69
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: lh_superiorfrontal_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/lh_superiorfrontal_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -827,10 +1991,12 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -716.547377
              Iterations: 3
-             Function evaluations: 91
+             Function evaluations: 84
     Saving model meta-data...
     Evaluating the model ...
     Running ROI: rh_superiorfrontal_thickness
+    inscaler: None
+    outscaler: None
     Processing data in /content/PCNtoolkit-demo/tutorials/BLR_protocol/ROI_models/rh_superiorfrontal_thickness/resp_tr.txt
     Estimating model  1 of 1
     configuring BLR ( order 1 )
@@ -838,17 +2004,19 @@ arguments that are worthy of commenting on:
     Optimization terminated successfully.
              Current function value: -730.639309
              Iterations: 2
-             Function evaluations: 45
+             Function evaluations: 41
     Saving model meta-data...
     Evaluating the model ...
 
 
 Evaluation & Interpretation
-----------------------------------------
+===========================
 
 Describe the normative model performance
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------------------------
 
+Step 12.
+~~~~~~~~
 
 In step 11, when we looped over each region of interest in the
 ``roi_ids`` list (created in step 4) and evaluated the normative model
@@ -917,7 +2085,7 @@ can organize them into a single file, and merge the deviation scores
 into the original data file.
 
 Visualize normative model outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+---------------------------------
 
 Figure 4A viz
 ~~~~~~~~~~~~~
@@ -937,29 +2105,21 @@ Figure 4A viz
     plt.figure(dpi=380)
     fig, axes = joypy.joyplot(blr_site_metrics, column=['EV'], overlap=2.5, by="site", ylim='own', fill=True, figsize=(8,8)
                               , legend=False, xlabels=True, ylabels=True, colormap=lambda x: color_gradient(x, start=(.08, .45, .8),stop=(.8, .34, .44))
-                              , alpha=0.6, linewidth=.5, linecolor='w', fade=True)
+                              , alpha=0.6, linewidth=.5, linecolor='w', fade=True);
     plt.title('Test Set Explained Variance', fontsize=18, color='black', alpha=1)
     plt.xlabel('Explained Variance', fontsize=14, color='black', alpha=1)
     plt.ylabel('Site', fontsize=14, color='black', alpha=1)
-    plt.show
-
+    plt.show()
 
 
 
 .. parsed-literal::
 
-    
+    
    
 
 
 
-
-.. parsed-literal::
-
-    
    
-
-
-
-.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_93_2.png
+.. image:: BLR_normativemodel_protocol_files/BLR_normativemodel_protocol_97_1.png
 
 
 The code used to create the visualizations shown in Figure 4 panels B-F,
@@ -967,7 +2127,8 @@ can be found in this
 `notebook `__.
 
 Post-Hoc analysis ideas
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+-----------------------
+
 
 The code for running SVM classification and classical case vs. control
 t-testing on the outputs of normative modeling can be found in this
diff --git a/doc/build/html/_sources/pages/FAQs.rst.txt b/doc/build/html/_sources/pages/FAQs.rst.txt
index a8879d36..efe98bd0 100644
--- a/doc/build/html/_sources/pages/FAQs.rst.txt
+++ b/doc/build/html/_sources/pages/FAQs.rst.txt
@@ -5,7 +5,7 @@ Frequently Asked Questions
 
 Most of the questions we recieve are about interpretation of normative modeling outputs. 
 
-The PCNtoolkit develoers have written a protocol for how to run a normative modeling analysis which should be helpful to you if you are just getting started. 
+The PCNtoolkit developers have written a protocol for how to run a normative modeling analysis which should be helpful to you if you are just getting started. 
 
 Rutherford, S., Kia, S. M., Wolfers, T., ... Beckmann, C. F., & Marquand, A. F. (2022). The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
 
diff --git a/doc/build/html/_sources/pages/HBR_NormativeModel_FCONdata_Tutorial.rst.txt b/doc/build/html/_sources/pages/HBR_NormativeModel_FCONdata_Tutorial.rst.txt
index d4933e38..55d17eb8 100644
--- a/doc/build/html/_sources/pages/HBR_NormativeModel_FCONdata_Tutorial.rst.txt
+++ b/doc/build/html/_sources/pages/HBR_NormativeModel_FCONdata_Tutorial.rst.txt
@@ -1,30 +1,132 @@
-.. title:: HBR tutorial
+`Predictive Clinical Neuroscience Toolkit `__
+======================================================================================
 
-Hierarchical Bayesian Regression
+Hierarchical Bayesian Regression Normative Modelling and Transfer onto unseen site.
 ===================================================================================
 
 This notebook will go through basic data preparation (training and
-testing set, `see Saige's
+testing set, `see Saige’s
 tutorial `__
 on Normative Modelling for more detail), the actual training of the
 models, and will finally describe how to transfer the trained models
 onto unseen sites.
 
 Created by `Saige Rutherford `__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Adapted/edited by Andre Marquand and Pierre Berthet.
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/HBR_FCON/HBR_NormativeModel_FCONdata_Tutorial.ipynb
-
-
+adapted/edited by Andre Marquand and Pierre Berthet
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Step 0: Install necessary libraries & grab data files
 -----------------------------------------------------
 
 .. code:: ipython3
 
-    ! pip install pcntoolkit==0.26
+    !pip install pcntoolkit
+    !pip install nutpie
+
+
+.. parsed-literal::
+
+    Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+      Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+         \ 64.9 MB 15.9 MB/s 0:00:05
+    [?25h  Installing build dependencies ... [?25l[?25hdone
+      Getting requirements to build wheel ... [?25l[?25hdone
+      Preparing metadata (pyproject.toml) ... [?25l[?25hdone
+    Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
+      Downloading bspline-0.1.1.tar.gz (84 kB)
+         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.2/84.2 kB 2.3 MB/s eta 0:00:00
+    [?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
+    Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
+      Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
+    Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
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+    Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.3/8.3 MB 55.1 MB/s eta 0:00:00
+    [?25hBuilding wheels for collected packages: pcntoolkit, bspline
+      Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
+      Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
+      Stored in directory: /tmp/pip-ephem-wheel-cache-f502unec/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
+      Building wheel for bspline (setup.py) ... [?25l[?25hdone
+      Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=150d24f295ccda92c9789d421e52c3858d43c66874deec4a463a87b4e5533448
+      Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
+    Successfully built pcntoolkit bspline
+    Installing collected packages: bspline, matplotlib, pcntoolkit
+      Attempting uninstall: matplotlib
+        Found existing installation: matplotlib 3.8.0
+        Uninstalling matplotlib-3.8.0:
+          Successfully uninstalled matplotlib-3.8.0
+    Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
+    Collecting nutpie
+      Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
+    Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from nutpie) (17.0.0)
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+    Requirement already satisfied: setuptools>=60.0.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.15.0->nutpie) (75.1.0)
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+    Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from arviz>=0.15.0->nutpie) (1.26.4)
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+    Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2.8.2)
+    Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2024.2)
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+    Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from h5netcdf>=1.0.2->arviz>=0.15.0->nutpie) (3.12.1)
+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (4.54.1)
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+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (3.2.0)
+    Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0->nutpie) (1.16.0)
+    Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl (1.5 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 16.5 MB/s eta 0:00:00
+    [?25hInstalling collected packages: nutpie
+    Successfully installed nutpie-0.13.2
 
 
 For this tutorial we will use data from the `Functional Connectom
@@ -47,7 +149,7 @@ First we import the required package, and create a working directory.
 
 .. code:: ipython3
 
-    processing_dir = "HBR_demo/"    # replace with a path to your working directory
+    processing_dir = "HBR_demo"    # replace with desired working directory
     if not os.path.isdir(processing_dir):
         os.makedirs(processing_dir)
     os.chdir(processing_dir)
@@ -65,9 +167,13 @@ color coded by the various sites:
 
     fcon = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000.csv')
     
+    # extract the ICBM site for transfer
     icbm = fcon.loc[fcon['site'] == 'ICBM']
     icbm['sitenum'] = 0
+    
+    # remove from the training set (also Pittsburgh because it only has 3 samples)
     fcon = fcon.loc[fcon['site'] != 'ICBM']
+    fcon = fcon.loc[fcon['site'] != 'Pittsburgh']
     
     sites = fcon['site'].unique()
     fcon['sitenum'] = 0
@@ -86,6 +192,390 @@ color coded by the various sites:
     ax.set_xlabel('age')
 
 
+
+.. parsed-literal::
+
+    :5: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame.
+    Try using .loc[row_indexer,col_indexer] = value instead
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      icbm['sitenum'] = 0
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+    :18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+    You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+    A typical example is when you are setting values in a column of a DataFrame, like:
+    
+    df["col"][row_indexer] = value
+    
+    Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+    
+      fcon['sitenum'].loc[idx] = i
+    :18: SettingWithCopyWarning: 
+    A value is trying to be set on a copy of a slice from a DataFrame
+    
+    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+      fcon['sitenum'].loc[idx] = i
+
+
+.. parsed-literal::
+
+    site AnnArbor_a 24
+    site AnnArbor_b 32
+    site Atlanta 28
+    site Baltimore 23
+    site Bangor 20
+    site Beijing_Zang 198
+    site Berlin_Margulies 26
+    site Cambridge_Buckner 198
+    site Cleveland 31
+    site Leiden_2180 12
+    site Leiden_2200 19
+    site Milwaukee_b 46
+    site Munchen 15
+    site NewYork_a 83
+    site NewYork_a_ADHD 25
+    site Newark 19
+    site Oulu 102
+    site Oxford 22
+    site PaloAlto 17
+    site Queensland 19
+    site SaintLouis 31
+
+
+
+
+.. parsed-literal::
+
+    Text(0.5, 0, 'age')
+
+
+
+
+.. image:: HBR_NormativeModel_FCONdata_Tutorial_files/HBR_NormativeModel_FCONdata_Tutorial_10_3.png
+
+
 Step 1: Prepare training and testing sets
 -----------------------------------------
 
@@ -122,15 +612,42 @@ then displayed.
     icbm_tr.to_csv(processing_dir + '/fcon1000_icbm_tr.csv')
     icbm_te.to_csv(processing_dir + '/fcon1000_icbm_te.csv')
 
+
+.. parsed-literal::
+
+    sample size check
+    0 AnnArbor_a 10 14
+    1 AnnArbor_b 19 13
+    2 Atlanta 12 16
+    3 Baltimore 12 11
+    4 Bangor 10 10
+    5 Beijing_Zang 91 107
+    6 Berlin_Margulies 9 17
+    7 Cambridge_Buckner 96 102
+    8 Cleveland 13 18
+    9 Leiden_2180 5 7
+    10 Leiden_2200 11 8
+    11 Milwaukee_b 18 28
+    12 Munchen 9 6
+    13 NewYork_a 38 45
+    14 NewYork_a_ADHD 15 10
+    15 Newark 9 10
+    16 Oulu 50 52
+    17 Oxford 9 13
+    18 PaloAlto 8 9
+    19 Queensland 10 9
+    20 SaintLouis 18 13
+
+
 Otherwise you can just load these pre defined subsets:
 
 .. code:: ipython3
 
     # Optional
-    # fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
-    # fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
-    # icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
-    # icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
+    #fcon_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_tr.csv')
+    #fcon_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_te.csv')
+    #icbm_tr = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_tr.csv')
+    #icbm_te = pd.read_csv('https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/main/data/fcon1000_icbm_te.csv')
 
 Step 2: Configure HBR inputs: covariates, measures and batch effects
 --------------------------------------------------------------------
@@ -160,7 +677,12 @@ testing set (``_test``).
 
     X_train = (fcon_tr['age']/100).to_numpy(dtype=float)
     Y_train = fcon_tr[idps].to_numpy(dtype=float)
-    batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+    
+    # configure batch effects for site and sex
+    #batch_effects_train = fcon_tr[['sitenum','sex']].to_numpy(dtype=int)
+    
+    # or only site
+    batch_effects_train = fcon_tr[['sitenum']].to_numpy(dtype=int)
     
     with open('X_train.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_train), file)
@@ -172,7 +694,8 @@ testing set (``_test``).
     
     X_test = (fcon_te['age']/100).to_numpy(dtype=float)
     Y_test = fcon_te[idps].to_numpy(dtype=float)
-    batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_test = fcon_te[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_test = fcon_te[['sitenum']].to_numpy(dtype=int)
     
     with open('X_test.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_test), file)
@@ -186,6 +709,536 @@ testing set (``_test``).
         with open(filename, 'rb') as f:
             return pickle.load(f)
 
+.. code:: ipython3
+
+    batch_effects_test
+
+
+
+
+.. parsed-literal::
+
+    array([[ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 0],
+           [ 1],
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+           [ 1],
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+           [ 3],
+           [ 3],
+           [ 3],
+           [ 3],
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+           [ 5],
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+           [ 5],
+           [ 5],
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+           [ 5],
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+           [ 5],
+           [ 5],
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+           [ 6],
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+           [ 7],
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+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
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+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
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+           [16],
+           [16],
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+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [16],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [17],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [18],
+           [19],
+           [19],
+           [19],
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+           [19],
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+           [19],
+           [19],
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+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20],
+           [20]])
+
+
+
 Step 3: Files and Folders grooming
 ----------------------------------
 
@@ -226,12 +1279,399 @@ and output files will be written and how they will be named.
                            respfile=respfile,
                            tsbefile=tsbefile,
                            trbefile=trbefile,
+                           inscaler='standardize',
+                           outscaler='standardize',
+                           linear_mu='True',
+                           random_intercept_mu='True',
+                           centered_intercept_mu='True',
                            alg='hbr',
                            log_path=log_dir,
                            binary=True,
-                           output_path=output_path, testcov= testcovfile_path,
+                           output_path=output_path,
+                           testcov= testcovfile_path,
                            testresp = testrespfile_path,
-                           outputsuffix=outputsuffix, savemodel=True)
+                           outputsuffix=outputsuffix,
+                           savemodel=True,
+                           nuts_sampler='nutpie')
+
+
+.. parsed-literal::
+
+    inscaler: standardize
+    outscaler: standardize
+    Processing data in /content/HBR_demo/Y_train.pkl
+    Estimating model  1 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    
    
+        
    Sampler Progress
    
+        
    Total Chains: 1
    
+        
    Active Chains: 0
    
+        
    
+            Finished Chains:
+            1
+        
    
+        
    Sampling for now
    
+        
    
+            Estimated Time to Completion:
+            now
+        
    
+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
    ProgressDrawsDivergencesStep SizeGradients/Draw
    
+                            
+                            
+                        150000.3415
    
+    
    
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+.. parsed-literal::
+
+    Normal
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+.. parsed-literal::
+
+    Estimating model  2 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    
    
+        
    Sampler Progress
    
+        
    Total Chains: 1
    
+        
    Active Chains: 0
    
+        
    
+            Finished Chains:
+            1
+        
    
+        
    Sampling for now
    
+        
    
+            Estimated Time to Completion:
+            now
+        
    
+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
    ProgressDrawsDivergencesStep SizeGradients/Draw
    
+                            
+                            
+                        150000.3315
    
+    
    
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+.. parsed-literal::
+
+    Normal
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+.. parsed-literal::
+
+    Saving model meta-data...
+    Evaluating the model ...
+    Writing outputs ...
+
 
 Here some analyses can be done, there are also some error metrics that
 could be of interest. This is covered in step 6 and in `Saige’s
@@ -249,7 +1689,8 @@ training and testing set of covariates, measures and batch effects:
 
     X_adapt = (icbm_tr['age']/100).to_numpy(dtype=float)
     Y_adapt = icbm_tr[idps].to_numpy(dtype=float)
-    batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_adapt = icbm_tr[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_adapt = icbm_tr[['sitenum']].to_numpy(dtype=int)
     
     with open('X_adaptation.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_adapt), file)
@@ -261,7 +1702,8 @@ training and testing set of covariates, measures and batch effects:
     # Test data (new dataset)
     X_test_txfr = (icbm_te['age']/100).to_numpy(dtype=float)
     Y_test_txfr = icbm_te[idps].to_numpy(dtype=float)
-    batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+    #batch_effects_test_txfr = icbm_te[['sitenum','sex']].to_numpy(dtype=int)
+    batch_effects_test_txfr = icbm_te[['sitenum']].to_numpy(dtype=int)
     
     with open('X_test_txfr.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(X_test_txfr), file)
@@ -270,6 +1712,7 @@ training and testing set of covariates, measures and batch effects:
     with open('txbefile.pkl', 'wb') as file:
         pickle.dump(pd.DataFrame(batch_effects_test_txfr), file)
 
+
 .. code:: ipython3
 
     respfile = os.path.join(processing_dir, 'Y_adaptation.pkl')
@@ -294,6 +1737,11 @@ and testing). That is basically the only difference.
                                                 respfile=respfile,
                                                 tsbefile=tsbefile,
                                                 trbefile=trbefile,
+                                                inscaler='standardize',
+                                                outscaler='standardize',
+                                                linear_mu='True',
+                                                random_intercept_mu='True',
+                                                centered_intercept_mu='True',
                                                 model_path = model_path,
                                                 alg='hbr',
                                                 log_path=log_dir,
@@ -302,7 +1750,375 @@ and testing). That is basically the only difference.
                                                 testcov= testcovfile_path,
                                                 testresp = testrespfile_path,
                                                 outputsuffix=outputsuffix,
-                                                savemodel=True)
+                                                savemodel=True,
+                                                nuts_sampler='nutpie')
+
+
+.. parsed-literal::
+
+    Loading data ...
+    Using HBR transform...
+    Transferring model  1 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    
    
+        
    Sampler Progress
    
+        
    Total Chains: 1
    
+        
    Active Chains: 0
    
+        
    
+            Finished Chains:
+            1
+        
    
+        
    Sampling for now
    
+        
    
+            Estimated Time to Completion:
+            now
+        
    
+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
    ProgressDrawsDivergencesStep SizeGradients/Draw
    
+                            
+                            
+                        150020.477
    
+    
    
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+.. parsed-literal::
+
+    Using HBR transform...
+    Transferring model  2 of 2
+
+
+
+.. raw:: html
+
+    
+    
+
+
+
+
+.. raw:: html
+
+    
+    
    
+        
    Sampler Progress
    
+        
    Total Chains: 1
    
+        
    Active Chains: 0
    
+        
    
+            Finished Chains:
+            1
+        
    
+        
    Sampling for now
    
+        
    
+            Estimated Time to Completion:
+            now
+        
    
+    
+        
+        
+        
+            
+                
+                    
+                    
+                    
+                    
+                    
+                
+            
+            
+    
+                    
+                        
+                        
+                        
+                        
+                        
+                    
+    
+                
+            
+        
    ProgressDrawsDivergencesStep SizeGradients/Draw
    
+                            
+                            
+                        150010.4015
    
+    
    
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+.. raw:: html
+
+    
    
+
+
+
+.. parsed-literal::
+
+    Evaluating the model ...
+    Writing outputs ...
+
+
+.. code:: ipython3
+
+    output_path
+
+
+
+
+.. parsed-literal::
+
+    '/content/HBR_demo/Transfer/'
+
+
+
+.. code:: ipython3
+
+    EV = pd.read_pickle('EXPV_estimate.pkl')
+    print(EV)
+
+
+.. parsed-literal::
+
+              0
+    0  0.438215
+    1  0.439181
 
 
 And that is it, you now have models that benefited from prior knowledge
@@ -311,19 +2127,12 @@ about different scanner sites to learn on unseen sites.
 Step 6: Interpreting model performance
 --------------------------------------
 
-Output evaluation metrics definitions
-
-=============  ==============================================================
-Abbreviation    Full name
-=============  ==============================================================
-NM             Normative Model
-EV / EXPV      Explained Variance
-MSLL           Mean Standardized Log Loss
-SMSE           Standardized Mean Squared Error
-RMSE           Root Mean Squared Error between true/predicted responses
-Rho            Pearson orrelation between true/predicted responses
-pRho           Parametric p-value for this correlation
-Z              Z-score or deviation score
-yhat           predictive mean
-ys2            predictive variance
-=============  ==============================================================
+Output evaluation metrics definitions: \* yhat - predictive mean \* ys2
+- predictive variance \* nm - normative model \* Z - deviance scores \*
+Rho - Pearson correlation between true and predicted responses \* pRho -
+parametric p-value for this correlation \* RMSE - root mean squared
+error between true/predicted responses \* SMSE - standardised mean
+squared error \* EV - explained variance \* MSLL - mean standardized log
+loss \* See page 23 in
+http://www.gaussianprocess.org/gpml/chapters/RW2.pdf
+
diff --git a/doc/build/html/_sources/pages/apply_normative_models.rst.txt b/doc/build/html/_sources/pages/apply_normative_models.rst.txt
index 9a55d8b4..1dabeb43 100644
--- a/doc/build/html/_sources/pages/apply_normative_models.rst.txt
+++ b/doc/build/html/_sources/pages/apply_normative_models.rst.txt
@@ -1,17 +1,5 @@
-.. title:: Braincharts tutorial
-
-Braincharts: transfer
-===================================
-
-Code for transfering the models from `Charting Brain Growth and Aging at High Spatial Precision. `__ 
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/braincharts/blob/master/scripts/apply_normative_models_ct.ipynb 
-
-
-.. figure:: ./brainchart_fig1.png
-   :height: 400px
-   :align: center
+Using lifespan models to make predictions on new data
+-----------------------------------------------------
 
 This notebook shows how to apply the coefficients from pre-estimated
 normative models to new data. This can be done in two different ways:
@@ -24,34 +12,26 @@ datasets `__ and adapt the learned model to make
 predictions on these data.
 
 First, if necessary, we install PCNtoolkit (note: this tutorial requires
-at least version 0.20)
+at least version 0.27)
 
 .. code:: ipython3
 
-    !pip install pcntoolkit==0.26
+    !pip install pcntoolkit
+    !pip install nutpie
 
 .. code:: ipython3
 
     ! git clone https://github.com/predictive-clinical-neuroscience/braincharts.git
 
-
-.. parsed-literal::
-
-    Cloning into 'braincharts'...
-    remote: Enumerating objects: 1444, done.
-    remote: Counting objects: 100% (1444/1444), done.
-    remote: Compressing objects: 100% (1365/1365), done.
-    remote: Total 1444 (delta 153), reused 1342 (delta 75), pack-reused 0
-    Receiving objects: 100% (1444/1444), 57.99 MiB | 34.87 MiB/s, done.
-    Resolving deltas: 100% (153/153), done.
-
-
 .. code:: ipython3
 
     # we need to be in the scripts folder when we import the libraries in the code block below,
     # because there is a function called nm_utils that is in the scripts folder that we need to import
     import os
-    os.chdir('/content/braincharts/scripts/') #this path is setup for running on Google Colab. Change it to match your local path if running locally
+    wdir = 'braincharts'
+    
+    os.chdir(wdir) #this path is setup for running on Google Colab. Change it to match your local path if running locally
+    root_dir = os.getcwd()
 
 Now we import the required libraries
 
@@ -65,45 +45,34 @@ Now we import the required libraries
     
     from pcntoolkit.normative import estimate, predict, evaluate
     from pcntoolkit.util.utils import compute_MSLL, create_design_matrix
+    os.chdir(os.path.join(root_dir, 'scripts'))
     from nm_utils import remove_bad_subjects, load_2d
+    os.chdir(root_dir)
 
 We need to unzip the models.
 
 .. code:: ipython3
 
-    os.chdir('/content/braincharts/models/')
-
-.. code:: ipython3
-
-    ls
-
-
-.. parsed-literal::
-
-    lifespan_12K_57sites_mqc2_train.zip  lifespan_29K_82sites_train.zip
-    lifespan_12K_59sites_mqc_train.zip   lifespan_57K_82sites.zip
-    lifespan_23K_57sites_mqc2.zip        README.md
-
+    os.chdir(os.path.join(root_dir, 'models'))
 
 .. code:: ipython3
 
     # we will use the biggest sample as our training set (approx. N=57000 subjects from 82 sites)
-    # for more info on the other pretrained models available in this repository, 
+    # for more info on the other pretrained models available in this repository,
     # please refer to the accompanying paper https://elifesciences.org/articles/72904
     ! unzip lifespan_57K_82sites.zip
 
 Next, we configure some basic variables, like where we want the analysis
 to be done and which model we want to use.
 
-.. note::
-    We maintain a list of site ids for each dataset, which
-    describe the site names in the training and test data (``site_ids_tr``
-    and ``site_ids_te``), plus also the adaptation data . The training site
-    ids are provided as a text file in the distribution and the test ids are
-    extracted automatically from the pandas dataframe (see below). If you
-    use additional data from the sites (e.g. later waves from ABCD), it may
-    be necessary to adjust the site names to match the names in the training
-    set. See the accompanying paper for more details
+**Note:** We maintain a list of site ids for each dataset, which
+describe the site names in the training and test data (``site_ids_tr``
+and ``site_ids_te``), plus also the adaptation data . The training site
+ids are provided as a text file in the distribution and the test ids are
+extracted automatically from the pandas dataframe (see below). If you
+use additional data from the sites (e.g. later waves from ABCD), it may
+be necessary to adjust the site names to match the names in the training
+set. See the accompanying paper for more details
 
 .. code:: ipython3
 
@@ -111,91 +80,35 @@ to be done and which model we want to use.
     model_name = 'lifespan_57K_82sites'
     site_names = 'site_ids_ct_82sites.txt'
     
-    # where the analysis takes place
-    root_dir = '/content/braincharts'
+    
+    # where the data files live
+    data_dir = os.path.join(root_dir,'docs')
+    
+    # where the models live
     out_dir = os.path.join(root_dir, 'models', model_name)
     
     # load a set of site ids from this model. This must match the training data
     with open(os.path.join(root_dir,'docs', site_names)) as f:
         site_ids_tr = f.read().splitlines()
 
-Download test dataset
------------------------------------------------------
-
-As mentioned above, to demonstrate this tool we will use a test dataset
-derived from the FCON 1000 dataset. We provide a prepackaged
-training/test split of these data in the required format (also after
-removing sites with only a few data points),
-`here `__.
-you can get these data by running the following commmands:
-
-.. code:: ipython3
-
-    os.chdir(root_dir)
-    !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/braincharts/master/docs/OpenNeuroTransfer_ct_te.csv
-    !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/braincharts/master/docs/OpenNeuroTransfer_ct_tr.csv
-
-
-.. parsed-literal::
-
-    --2022-02-17 15:01:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/braincharts/master/docs/OpenNeuroTransfer_ct_te.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
-    HTTP request sent, awaiting response... 200 OK
-    Length: 628752 (614K) [text/plain]
-    Saving to: ‘OpenNeuroTransfer_te.csv’
-    
-    OpenNeuroTransfer_t 100%[===================>] 614.02K  --.-KB/s    in 0.03s   
-    
-    2022-02-17 15:01:31 (22.0 MB/s) - ‘OpenNeuroTransfer_te.csv’ saved [628752/628752]
-    
-    --2022-02-17 15:01:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/braincharts/master/docs/OpenNeuroTransfer_ct_tr.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
-    HTTP request sent, awaiting response... 200 OK
-    Length: 163753 (160K) [text/plain]
-    Saving to: ‘OpenNeuroTransfer_tr.csv’
-    
-    OpenNeuroTransfer_c 100%[===================>] 159.92K  --.-KB/s    in 0.03s   
-    
-    2022-02-17 15:01:32 (6.08 MB/s) - ‘OpenNeuroTransfer_ct_tr.csv’ saved [163753/163753]
-    
-
-
 Load test data
------------------------------------------------------
-
-Now we load the test data and remove some subjects that may have poor
-scan quality. This asssesment is based on the Freesurfer Euler
-characteristic as described in the papers below.
+~~~~~~~~~~~~~~
 
-.. note::
-    For the purposes of this tutorial, we make predictions for all
-    sites in the FCON 1000 dataset, but two of them were also included in
-    the training data (named ‘Baltimore’ and ‘NewYork_a’). In this case,
-    this will only slightly bias the accuracy, but in order to replicate the
-    results in the paper, it would be necessary to additionally remove these
-    sites from the test dataframe.
-
-**References** - `Kia et al
-2021 `__
-- `Rosen et al
-2018 `__
+**Note:** For the purposes of this tutorial, we make predictions for a
+multi-site transfer dataset, derived from
+`OpenNeuro `__.
 
 .. code:: ipython3
 
-    test_data = os.path.join(root_dir, 'OpenNeuroTransfer_ct_te.csv')
+    test_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_te.csv')
     
     df_te = pd.read_csv(test_data)
     
-    # remove some bad subjects, this requires having a column called "avg_en" that corresponds to the average Euler number extracted from Freesurfer
-    # df_te, bad_sub = remove_bad_subjects(df_te, df_te)
-    
     # extract a list of unique site ids from the test set
     site_ids_te =  sorted(set(df_te['site'].to_list()))
 
 (Optional) Load adaptation data
------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If the data you wish to make predictions for is not derived from the
 same scanning sites as those in the trainig set, it is necessary to
@@ -207,13 +120,10 @@ same way, based on a the ‘sitenum’ column in the dataframe.
 
 .. code:: ipython3
 
-    adaptation_data = os.path.join(root_dir, 'OpenNeuroTransfer_ct_tr.csv')
+    adaptation_data = os.path.join(data_dir, 'OpenNeuroTransfer_ct_ad.csv')
     
     df_ad = pd.read_csv(adaptation_data)
     
-    # remove some bad subjects, this requires having a column called "avg_en" that corresponds to the average Euler number extracted from Freesurfer
-    # df_ad, bad_sub = remove_bad_subjects(df_ad, df_ad)
-    
     # extract a list of unique site ids from the test set
     site_ids_ad =  sorted(set(df_ad['site'].to_list()))
     
@@ -221,7 +131,7 @@ same way, based on a the ‘sitenum’ column in the dataframe.
         print('Warning: some of the testing sites are not in the adaptation data')
 
 Configure which models to fit
------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Now, we configure which imaging derived phenotypes (IDPs) we would like
 to process. This is just a list of column names in the dataframe we have
@@ -233,15 +143,15 @@ models for …
 .. code:: ipython3
 
     # load the list of idps for left and right hemispheres, plus subcortical regions
-    with open(os.path.join(root_dir,'docs','phenotypes_ct_lh.txt')) as f:
+    with open(os.path.join(data_dir, 'phenotypes_ct_lh.txt')) as f:
         idp_ids_lh = f.read().splitlines()
-    with open(os.path.join(root_dir,'docs','phenotypes_ct_rh.txt')) as f:
+    with open(os.path.join(data_dir, 'phenotypes_ct_rh.txt')) as f:
         idp_ids_rh = f.read().splitlines()
-    with open(os.path.join(root_dir,'docs','phenotypes_sc.txt')) as f:
+    with open(os.path.join(data_dir, 'phenotypes_sc.txt')) as f:
         idp_ids_sc = f.read().splitlines()
     
     # we choose here to process all idps
-    idp_ids = idp_ids_lh + idp_ids_rh + idp_ids_sc
+    idp_ids = idp_ids_lh + idp_ids_rh #+ idp_ids_sc
 
 … or alternatively, we could just specify a list
 
@@ -250,7 +160,7 @@ models for …
     idp_ids = [ 'Left-Thalamus-Proper', 'Left-Lateral-Ventricle', 'rh_MeanThickness_thickness']
 
 Configure covariates
------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~
 
 Now, we configure some parameters to fit the model. First, we choose
 which columns of the pandas dataframe contain the covariates (age and
@@ -274,18 +184,18 @@ accompanying paper and `Fraza et al
 
 .. code:: ipython3
 
-    # which data columns do we wish to use as covariates? 
+    # which data columns do we wish to use as covariates?
     cols_cov = ['age','sex']
     
-    # limits for cubic B-spline basis 
-    xmin = -5 
+    # limits for cubic B-spline basis
+    xmin = -5
     xmax = 110
     
     # Absolute Z treshold above which a sample is considered to be an outlier (without fitting any model)
     outlier_thresh = 7
 
 Make predictions
------------------------------------------------------
+~~~~~~~~~~~~~~~~
 
 This will make predictions for each IDP separately. This is done by
 extracting a column from the dataframe (i.e. specifying the IDP as the
@@ -294,12 +204,12 @@ the covariates, which is a numpy data array having the number of rows
 equal to the number of datapoints in the test set. The columns are
 specified as follows:
 
--  A global intercept (column of ones)
--  The covariate columns (here age and sex, coded as 0=female/1=male)
--  Dummy coded columns for the sites in the training set (one column per
-   site)
--  Columns for the basis expansion (seven columns for the default
-   parameterisation)
+- A global intercept (column of ones)
+- The covariate columns (here age and sex, coded as 0=female/1=male)
+- Dummy coded columns for the sites in the training set (one column per
+  site)
+- Columns for the basis expansion (seven columns for the default
+  parameterisation)
 
 Once these are saved as numpy arrays in ascii format (as here) or
 (alternatively) in pickle format, these are passed as inputs to the
@@ -309,74 +219,74 @@ These are written in the same format to the location specified by
 Z-statistics for the test dataset that we can take forward to further
 analysis.
 
-When we need to make predictions on new data, the procedure is
+Note that when we need to make predictions on new data, the procedure is
 more involved, since we need to prepare, process and store covariates,
 response variables and site ids for the adaptation data.
 
 .. code:: ipython3
 
-    for idp_num, idp in enumerate(idp_ids): 
+    for idp_num, idp in enumerate(idp_ids):
         print('Running IDP', idp_num, idp, ':')
         idp_dir = os.path.join(out_dir, idp)
         os.chdir(idp_dir)
-        
+    
         # extract and save the response variables for the test set
         y_te = df_te[idp].to_numpy()
-        
+    
         # save the variables
-        resp_file_te = os.path.join(idp_dir, 'resp_te.txt') 
+        resp_file_te = os.path.join(idp_dir, 'resp_te.txt')
         np.savetxt(resp_file_te, y_te)
-            
+    
         # configure and save the design matrix
         cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')
-        X_te = create_design_matrix(df_te[cols_cov], 
+        X_te = create_design_matrix(df_te[cols_cov],
                                     site_ids = df_te['site'],
                                     all_sites = site_ids_tr,
-                                    basis = 'bspline', 
-                                    xmin = xmin, 
+                                    basis = 'bspline',
+                                    xmin = xmin,
                                     xmax = xmax)
         np.savetxt(cov_file_te, X_te)
-        
+    
         # check whether all sites in the test set are represented in the training set
         if all(elem in site_ids_tr for elem in site_ids_te):
             print('All sites are present in the training data')
-            
+    
             # just make predictions
-            yhat_te, s2_te, Z = predict(cov_file_te, 
-                                        alg='blr', 
-                                        respfile=resp_file_te, 
+            yhat_te, s2_te, Z = predict(cov_file_te,
+                                        alg='blr',
+                                        respfile=resp_file_te,
                                         model_path=os.path.join(idp_dir,'Models'))
         else:
             print('Some sites missing from the training data. Adapting model')
-            
+    
             # save the covariates for the adaptation data
-            X_ad = create_design_matrix(df_ad[cols_cov], 
+            X_ad = create_design_matrix(df_ad[cols_cov],
                                         site_ids = df_ad['site'],
                                         all_sites = site_ids_tr,
-                                        basis = 'bspline', 
-                                        xmin = xmin, 
+                                        basis = 'bspline',
+                                        xmin = xmin,
                                         xmax = xmax)
-            cov_file_ad = os.path.join(idp_dir, 'cov_bspline_ad.txt')          
+            cov_file_ad = os.path.join(idp_dir, 'cov_bspline_ad.txt')
             np.savetxt(cov_file_ad, X_ad)
-            
+    
             # save the responses for the adaptation data
-            resp_file_ad = os.path.join(idp_dir, 'resp_ad.txt') 
+            resp_file_ad = os.path.join(idp_dir, 'resp_ad.txt')
             y_ad = df_ad[idp].to_numpy()
             np.savetxt(resp_file_ad, y_ad)
-           
+    
             # save the site ids for the adaptation data
-            sitenum_file_ad = os.path.join(idp_dir, 'sitenum_ad.txt') 
+            sitenum_file_ad = os.path.join(idp_dir, 'sitenum_ad.txt')
             site_num_ad = df_ad['sitenum'].to_numpy(dtype=int)
             np.savetxt(sitenum_file_ad, site_num_ad)
-            
-            # save the site ids for the test data 
+    
+            # save the site ids for the test data
             sitenum_file_te = os.path.join(idp_dir, 'sitenum_te.txt')
             site_num_te = df_te['sitenum'].to_numpy(dtype=int)
             np.savetxt(sitenum_file_te, site_num_te)
-             
-            yhat_te, s2_te, Z = predict(cov_file_te, 
-                                        alg = 'blr', 
-                                        respfile = resp_file_te, 
+    
+            yhat_te, s2_te, Z = predict(cov_file_te,
+                                        alg = 'blr',
+                                        respfile = resp_file_te,
                                         model_path = os.path.join(idp_dir,'Models'),
                                         adaptrespfile = resp_file_ad,
                                         adaptcovfile = cov_file_ad,
@@ -409,16 +319,8 @@ response variables and site ids for the adaptation data.
     Writing outputs ...
 
 
-Evaluate the performance
------------------------------------------------------
-
-.. figure:: ./brainchart_fig3.png
-   :height: 400px
-   :align: center
-
-
 Preparing dummy data for plotting
------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Now, we plot the centiles of variation estimated by the normative model.
 
@@ -431,9 +333,9 @@ dummy data for all the IDPs we wish to plot
 
 .. code:: ipython3
 
-    # which sex do we want to plot? 
+    # which sex do we want to plot?
     sex = 1 # 1 = male 0 = female
-    if sex == 1: 
+    if sex == 1:
         clr = 'blue';
     else:
         clr = 'red'
@@ -459,7 +361,7 @@ dummy data for all the IDPs we wish to plot
 
 
 Plotting the normative models
------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Now we loop through the IDPs, plotting each one separately. The outputs
 of this step are a set of quantitative regression metrics for each IDP
@@ -474,83 +376,82 @@ space, then we need to warp them with the inverse of the estimated
 warping function. This can be done using the function
 ``nm.blr.warp.warp_predictions()``.
 
-.. note::
-    It is necessary to update the intercept for each of the sites.
-    For purposes of visualisation, here we do this by adjusting the median
-    of the data to match the dummy predictions, but note that all the
-    quantitative metrics are estimated using the predictions that are
-    adjusted properly using a learned offset (or adjusted using a hold-out
-    adaptation set, as above). Note also that for the calibration data we
-    require at least two data points of the same sex in each site to be able
-    to estimate the variance. Of course, in a real example, you would want
-    many more than just two since we need to get a reliable estimate of the
-    variance for each site.
+**Note:** it is necessary to update the intercept for each of the sites.
+For purposes of visualisation, here we do this by adjusting the median
+of the data to match the dummy predictions, but note that all the
+quantitative metrics are estimated using the predictions that are
+adjusted properly using a learned offset (or adjusted using a hold-out
+adaptation set, as above). Note also that for the calibration data we
+require at least two data points of the same sex in each site to be able
+to estimate the variance. Of course, in a real example, you would want
+many more than just two since we need to get a reliable estimate of the
+variance for each site.
 
 .. code:: ipython3
 
     sns.set(style='whitegrid')
     
-    for idp_num, idp in enumerate(idp_ids): 
+    for idp_num, idp in enumerate(idp_ids):
         print('Running IDP', idp_num, idp, ':')
         idp_dir = os.path.join(out_dir, idp)
         os.chdir(idp_dir)
-        
+    
         # load the true data points
         yhat_te = load_2d(os.path.join(idp_dir, 'yhat_predict.txt'))
         s2_te = load_2d(os.path.join(idp_dir, 'ys2_predict.txt'))
         y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))
-                
+    
         # set up the covariates for the dummy data
         print('Making predictions with dummy covariates (for visualisation)')
-        yhat, s2 = predict(cov_file_dummy, 
-                           alg = 'blr', 
-                           respfile = None, 
-                           model_path = os.path.join(idp_dir,'Models'), 
+        yhat, s2 = predict(cov_file_dummy,
+                           alg = 'blr',
+                           respfile = None,
+                           model_path = os.path.join(idp_dir,'Models'),
                            outputsuffix = '_dummy')
-        
+    
         # load the normative model
         with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:
-            nm = pickle.load(handle) 
-        
+            nm = pickle.load(handle)
+    
         # get the warp and warp parameters
         W = nm.blr.warp
-        warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1] 
-            
+        warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]
+    
         # first, we warp predictions for the true data and compute evaluation metrics
         med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]
         med_te = med_te[:, np.newaxis]
         print('metrics:', evaluate(y_te, med_te))
-        
+    
         # then, we warp dummy predictions to create the plots
         med, pr_int = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param)
-        
+    
         # extract the different variance components to visualise
         beta, junk1, junk2 = nm.blr._parse_hyps(nm.blr.hyp, X_dummy)
         s2n = 1/beta # variation (aleatoric uncertainty)
         s2s = s2-s2n # modelling uncertainty (epistemic uncertainty)
-        
+    
         # plot the data points
         y_te_rescaled_all = np.zeros_like(y_te)
         for sid, site in enumerate(site_ids_te):
-            # plot the true test data points 
+            # plot the true test data points
             if all(elem in site_ids_tr for elem in site_ids_te):
                 # all data in the test set are present in the training set
-                
+    
                 # first, we select the data points belonging to this particular site
                 idx = np.where(np.bitwise_and(X_te[:,2] == sex, X_te[:,sid+len(cols_cov)+1] !=0))[0]
                 if len(idx) == 0:
                     print('No data for site', sid, site, 'skipping...')
                     continue
-                
+    
                 # then directly adjust the data
                 idx_dummy = np.bitwise_and(X_dummy[:,1] > X_te[idx,1].min(), X_dummy[:,1] < X_te[idx,1].max())
                 y_te_rescaled = y_te[idx] - np.median(y_te[idx]) + np.median(med[idx_dummy])
             else:
-                # we need to adjust the data based on the adaptation dataset 
-                
+                # we need to adjust the data based on the adaptation dataset
+    
                 # first, select the data point belonging to this particular site
                 idx = np.where(np.bitwise_and(X_te[:,2] == sex, (df_te['site'] == site).to_numpy()))[0]
-                
+    
                 # load the adaptation data
                 y_ad = load_2d(os.path.join(idp_dir, 'resp_ad.txt'))
                 X_ad = load_2d(os.path.join(idp_dir, 'cov_bspline_ad.txt'))
@@ -558,19 +459,19 @@ warping function. This can be done using the function
                 if len(idx) < 2 or len(idx_a) < 2:
                     print('Insufficent data for site', sid, site, 'skipping...')
                     continue
-                
+    
                 # adjust and rescale the data
-                y_te_rescaled, s2_rescaled = nm.blr.predict_and_adjust(nm.blr.hyp, 
-                                                                       X_ad[idx_a,:], 
-                                                                       np.squeeze(y_ad[idx_a]), 
-                                                                       Xs=None, 
+                y_te_rescaled, s2_rescaled = nm.blr.predict_and_adjust(nm.blr.hyp,
+                                                                       X_ad[idx_a,:],
+                                                                       np.squeeze(y_ad[idx_a]),
+                                                                       Xs=None,
                                                                        ys=np.squeeze(y_te[idx]))
             # plot the (adjusted) data points
             plt.scatter(X_te[idx,1], y_te_rescaled, s=4, color=clr, alpha = 0.1)
-           
+    
         # plot the median of the dummy data
         plt.plot(xx, med, clr)
-        
+    
         # fill the gaps in between the centiles
         junk, pr_int25 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.25,0.75])
         junk, pr_int95 = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2), warp_param, percentiles=[0.05,0.95])
@@ -578,14 +479,14 @@ warping function. This can be done using the function
         plt.fill_between(xx, pr_int25[:,0], pr_int25[:,1], alpha = 0.1,color=clr)
         plt.fill_between(xx, pr_int95[:,0], pr_int95[:,1], alpha = 0.1,color=clr)
         plt.fill_between(xx, pr_int99[:,0], pr_int99[:,1], alpha = 0.1,color=clr)
-                
+    
         # make the width of each centile proportional to the epistemic uncertainty
         junk, pr_int25l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.25,0.75])
         junk, pr_int95l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.05,0.95])
         junk, pr_int99l = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2-0.5*s2s), warp_param, percentiles=[0.01,0.99])
         junk, pr_int25u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.25,0.75])
         junk, pr_int95u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.05,0.95])
-        junk, pr_int99u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.01,0.99])    
+        junk, pr_int99u = W.warp_predictions(np.squeeze(yhat), np.squeeze(s2+0.5*s2s), warp_param, percentiles=[0.01,0.99])
         plt.fill_between(xx, pr_int25l[:,0], pr_int25u[:,0], alpha = 0.3,color=clr)
         plt.fill_between(xx, pr_int95l[:,0], pr_int95u[:,0], alpha = 0.3,color=clr)
         plt.fill_between(xx, pr_int99l[:,0], pr_int99u[:,0], alpha = 0.3,color=clr)
@@ -600,14 +501,14 @@ warping function. This can be done using the function
         plt.plot(xx, pr_int95[:,1],color=clr, linewidth=0.5)
         plt.plot(xx, pr_int99[:,0],color=clr, linewidth=0.5)
         plt.plot(xx, pr_int99[:,1],color=clr, linewidth=0.5)
-        
+    
         plt.xlabel('Age')
-        plt.ylabel(idp) 
+        plt.ylabel(idp)
         plt.title(idp)
         plt.xlim((0,90))
         plt.savefig(os.path.join(idp_dir, 'centiles_' + str(sex)),  bbox_inches='tight')
         plt.show()
-        
+    
     os.chdir(out_dir)
 
 
@@ -622,7 +523,7 @@ warping function. This can be done using the function
 
 
 
-.. image:: apply_normative_models_files/apply_normative_models_29_1.png
+.. image:: apply_normative_models_files/apply_normative_models_ct_27_1.png
 
 
 .. parsed-literal::
@@ -636,7 +537,7 @@ warping function. This can be done using the function
 
 
 
-.. image:: apply_normative_models_files/apply_normative_models_29_3.png
+.. image:: apply_normative_models_files/apply_normative_models_ct_27_3.png
 
 
 .. parsed-literal::
@@ -650,24 +551,23 @@ warping function. This can be done using the function
 
 
 
-.. image:: apply_normative_models_files/apply_normative_models_29_5.png
+.. image:: apply_normative_models_files/apply_normative_models_ct_27_5.png
 
 
 .. code:: ipython3
 
     # explore an example output folder of a single model (one ROI)
-    # think about what each of these output files represents. 
+    # think about what each of these output files represents.
     # Hint: look at the variable names and comments in the code block above
     ! ls rh_MeanThickness_thickness/
 
 
 .. parsed-literal::
 
-    centiles_1.png	    MSLL_predict.txt  RMSE_predict.txt	yhat_predict.txt
-    cov_bspline_ad.txt  pRho_predict.txt  sitenum_ad.txt	ys2_dummy.pkl
-    cov_bspline_te.txt  resp_ad.txt       sitenum_te.txt	ys2_predict.txt
-    EXPV_predict.txt    resp_te.txt       SMSE_predict.txt	Z_predict.txt
-    Models		    Rho_predict.txt   yhat_dummy.pkl
+    centiles_1.png	    Models	      Rho_predict.txt	SMSE_predict.txt  ys2_predict.txt
+    cov_bspline_ad.txt  pRho_predict.txt  RMSE_predict.txt	yhat_dummy.pkl	  Z_predict.txt
+    cov_bspline_te.txt  resp_ad.txt       sitenum_ad.txt	yhat_predict.txt
+    EXPV_predict.txt    resp_te.txt       sitenum_te.txt	ys2_dummy.pkl
 
 
 .. code:: ipython3
@@ -698,7 +598,8 @@ into the original data file.
 
 .. code:: ipython3
 
-    z_dir = '/content/braincharts/models/lifespan_57K_82sites/deviation_scores/'
+    z_dir = os.path.join(root_dir, 'models', model_name, 'deviation_scores')
+    
     filelist = [name for name in os.listdir(z_dir)]
 
 .. code:: ipython3
diff --git a/doc/build/html/_sources/pages/installation.rst.txt b/doc/build/html/_sources/pages/installation.rst.txt
index ce1551ce..18e50880 100644
--- a/doc/build/html/_sources/pages/installation.rst.txt
+++ b/doc/build/html/_sources/pages/installation.rst.txt
@@ -20,13 +20,19 @@ Basic installation (on a local machine)
 
 	source activate 
 
-4. Install required conda packages
+4. Install torch using the torch instructions.	
+
+.. code-block:: bash
+
+	# Command found on the torch website: https://pytorch.org/get-started/locally/
+
+5. Install required conda packages
 	
 .. code-block:: bash
 
-	conda install pip pandas scipy
+	conda install numba nutpie -c conda-forge
 	
-5. Install PCNtoolkit (plus dependencies)
+6. Install PCNtoolkit (plus dependencies)
 	
 .. code-block:: bash
 
@@ -60,7 +66,8 @@ Alternative installation (on a shared resource)
 	
 .. code-block:: bash
 
-	conda install -y pandas scipy 
+	# Command found on the torch website: https://pytorch.org/get-started/locally/
+	conda install numba nutpie -c conda-forge
 
 	
 5. Install pip dependencies
diff --git a/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt b/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
index f63ec8fa..61a297a7 100644
--- a/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
+++ b/doc/build/html/_sources/pages/normative_modelling_walkthrough.rst.txt
@@ -1,6 +1,4 @@
-.. title:: GPR tutorial
-
-Gaussian Process Regression 
+**DEMO ON NORMATIVE MODELING**
 ==============================
 
 Created by
@@ -10,14 +8,9 @@ Mariam Zabihi `@m_zabihi `__
 Saige Rutherford `@being_saige `__
 
 Thomas Wolfers `@ThomasWolfers `__
-\______________________________________________________________________________\_
-
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/CPC_2020/normative_modelling_walkthrough.ipynb
-
+\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
 
-Background Story
+**Background Story**
 --------------------
 
 Morten and Ingrid are concerned about the health of their father,
@@ -40,13 +33,116 @@ IQ, age as well as the same sex as Nordan.
 Do your best to get as far as you can. However, you do not need to feel
 bad if you cannot complete everything during the tutorial.
 
-**Task 0:** Load data and install the pcntoolkit
-------------------------------------------------
+**Task 0:** Load data and install PCNtoolkit
+--------------------------------------------
 
 .. code:: ipython3
 
-    #install normative modeling
-    ! pip install pcntoolkit==0.26
+    !pip install pcntoolkit
+    !pip install nutpie
+
+
+.. parsed-literal::
+
+    Collecting https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+      Downloading https://github.com/amarquand/PCNtoolkit/archive/dev.zip
+         \ 64.9 MB 16.9 MB/s 0:00:05
+    [?25h  Installing build dependencies ... [?25l[?25hdone
+      Getting requirements to build wheel ... [?25l[?25hdone
+      Preparing metadata (pyproject.toml) ... [?25l[?25hdone
+    Collecting bspline<0.2.0,>=0.1.1 (from pcntoolkit==0.31.0)
+      Downloading bspline-0.1.1.tar.gz (84 kB)
+         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.2/84.2 kB 2.0 MB/s eta 0:00:00
+    [?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
+    Collecting matplotlib<4.0.0,>=3.9.2 (from pcntoolkit==0.31.0)
+      Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
+    Requirement already satisfied: nibabel<6.0.0,>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.3.2)
+    Requirement already satisfied: numpy<2.0,>=1.26 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.26.4)
+    Requirement already satisfied: pymc<6.0.0,>=5.18.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (5.18.0)
+    Requirement already satisfied: scikit-learn<2.0.0,>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.5.2)
+    Requirement already satisfied: scipy<2.0,>=1.12 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.13.1)
+    Requirement already satisfied: seaborn<0.14.0,>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (0.13.2)
+    Requirement already satisfied: six<2.0.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from pcntoolkit==0.31.0) (1.16.0)
+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (4.54.1)
+    Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (1.4.7)
+    Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (24.2)
+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (3.2.0)
+    Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.9.2->pcntoolkit==0.31.0) (2.8.2)
+    Requirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (6.4.5)
+    Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from nibabel<6.0.0,>=5.3.1->pcntoolkit==0.31.0) (4.12.2)
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+    Requirement already satisfied: cachetools>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (5.5.0)
+    Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.1.0)
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+    Requirement already satisfied: threadpoolctl<4.0.0,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (3.5.0)
+    Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.5.2->pcntoolkit==0.31.0) (1.4.2)
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+    Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (0.12.1)
+    Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from logical-unification->pytensor<2.26,>=2.25.1->pymc<6.0.0,>=5.18.0->pcntoolkit==0.31.0) (1.0.0)
+    Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.3/8.3 MB 37.7 MB/s eta 0:00:00
+    [?25hBuilding wheels for collected packages: pcntoolkit, bspline
+      Building wheel for pcntoolkit (pyproject.toml) ... [?25l[?25hdone
+      Created wheel for pcntoolkit: filename=pcntoolkit-0.31.0-py3-none-any.whl size=114835 sha256=40635c10c24ccf2c319ee965aaf1038272cd5578f14d9cb3dd14598ddab31d00
+      Stored in directory: /tmp/pip-ephem-wheel-cache-nl3hy35q/wheels/9e/c4/29/3bca3a5facf8ef69b8622461d8520d24a19d3745aefa093d1e
+      Building wheel for bspline (setup.py) ... [?25l[?25hdone
+      Created wheel for bspline: filename=bspline-0.1.1-py3-none-any.whl size=84482 sha256=1da013ad20b77d85515a6a2318eaf7cc4baaa7772eb1d26249f923ce8d779d7e
+      Stored in directory: /root/.cache/pip/wheels/3c/ab/0a/70927853a6d9166bc777922736063a6f99c43a327c802f9326
+    Successfully built pcntoolkit bspline
+    Installing collected packages: bspline, matplotlib, pcntoolkit
+      Attempting uninstall: matplotlib
+        Found existing installation: matplotlib 3.8.0
+        Uninstalling matplotlib-3.8.0:
+          Successfully uninstalled matplotlib-3.8.0
+    Successfully installed bspline-0.1.1 matplotlib-3.9.2 pcntoolkit-0.31.0
+    Collecting nutpie
+      Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
+    Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from nutpie) (17.0.0)
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+    Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0->nutpie) (2024.2)
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+    Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (1.3.1)
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+    Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (11.0.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5->arviz>=0.15.0->nutpie) (3.2.0)
+    Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0->nutpie) (1.16.0)
+    Downloading nutpie-0.13.2-cp310-cp310-manylinux_2_28_x86_64.whl (1.5 MB)
+       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 9.5 MB/s eta 0:00:00
+    [?25hInstalling collected packages: nutpie
+    Successfully installed nutpie-0.13.2
 
 
 **Option 1:** Connect your Google Drive account, and load data from
@@ -77,24 +173,24 @@ them to Google Drive.
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
     !wget -nc https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
-        
+    
     # code by S. Rutherford
 
 
 .. parsed-literal::
 
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
+    --2024-11-19 12:28:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics.csv
+    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
+    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
     Length: 17484 (17K) [text/plain]
     Saving to: ‘camcan_demographics.csv’
     
-    camcan_demographics 100%[===================>]  17.07K  --.-KB/s    in 0.001s  
+    camcan_demographics 100%[===================>]  17.07K  --.-KB/s    in 0.003s  
     
-    2022-02-17 15:03:58 (12.9 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
+    2024-11-19 12:28:31 (5.14 MB/s) - ‘camcan_demographics.csv’ saved [17484/17484]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
+    --2024-11-19 12:28:31--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_demographics_nordan.csv
     Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
     Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
@@ -103,20 +199,20 @@ them to Google Drive.
     
     camcan_demographics 100%[===================>]     332  --.-KB/s    in 0s      
     
-    2022-02-17 15:03:58 (15.5 MB/s) - ‘camcan_demographics_nordan.csv’ saved [332/332]
+    2024-11-19 12:28:32 (4.10 MB/s) - ‘camcan_demographics_nordan.csv’ saved [332/332]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
-    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
-    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
+    --2024-11-19 12:28:32--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features.csv
+    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
+    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
     Length: 188944 (185K) [text/plain]
     Saving to: ‘camcan_features.csv’
     
     camcan_features.csv 100%[===================>] 184.52K  --.-KB/s    in 0.05s   
     
-    2022-02-17 15:03:58 (3.88 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
+    2024-11-19 12:28:33 (3.58 MB/s) - ‘camcan_features.csv’ saved [188944/188944]
     
-    --2022-02-17 15:03:58--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
+    --2024-11-19 12:28:33--  https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/master/tutorials/CPC_2020/data/camcan_features_nordan.csv
     Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
     Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
     HTTP request sent, awaiting response... 200 OK
@@ -125,7 +221,7 @@ them to Google Drive.
     
     camcan_features_nor 100%[===================>]   1.66K  --.-KB/s    in 0s      
     
-    2022-02-17 15:03:59 (25.3 MB/s) - ‘camcan_features_nordan.csv’ saved [1695/1695]
+    2024-11-19 12:28:33 (22.1 MB/s) - ‘camcan_features_nordan.csv’ saved [1695/1695]
     
 
 
@@ -160,7 +256,7 @@ between those two files.
     print(norm_demographics)
     print(norm_features)
     
-    # find overlap in terms of participants between norm_sample_features and 
+    # find overlap in terms of participants between norm_sample_features and
     # norm_sample_demographics
     
     norm_demographics_features = pd.concat([norm_demographics, norm_features],
@@ -189,33 +285,182 @@ between those two files.
     CC723395      86   FEMALE    0        145
     
     [707 rows x 4 columns]
-                  left_Hippocampal_tail  ...  right_Whole_hippocampus
-    participants                         ...                         
-    CC110033                 482.768229  ...              3531.764896
-    CC110037                 595.269259  ...              3835.426137
-    CC110045                 655.847194  ...              3681.494304
-    CC110056                 561.345626  ...              3461.373764
-    CC110062                 756.521166  ...              4782.407821
-    ...                             ...  ...                      ...
-    CC722542                 467.896808  ...              3284.108783
-    CC722651                 406.326167  ...              3210.272905
-    CC722891                 393.430481  ...              2423.675065
-    CC723197                 475.929914  ...              3043.146264
-    CC723395                 444.301617  ...              2988.001288
+                  left_Hippocampal_tail  left_subiculum    left_CA1  \
+    participants                                                      
+    CC110033                 482.768229      419.948094  666.496024   
+    CC110037                 595.269259      502.320315  698.157779   
+    CC110045                 655.847194      476.433625  654.215689   
+    CC110056                 561.345626      447.258970  611.114561   
+    CC110062                 756.521166      521.034681  716.391590   
+    ...                             ...             ...         ...   
+    CC722542                 467.896808      440.794061  688.130914   
+    CC722651                 406.326167      393.469843  613.794018   
+    CC722891                 393.430481      303.049578  444.772656   
+    CC723197                 475.929914      372.449778  525.739508   
+    CC723395                 444.301617      330.688394  565.359058   
+    
+                  left_hippocampal-fissure  left_presubiculum  left_parasubiculum  \
+    participants                                                                    
+    CC110033                    131.719049         285.535445           59.209377   
+    CC110037                    156.304335         367.678385           60.817591   
+    CC110045                    146.767569         346.347202           67.481121   
+    CC110056                    126.615335         327.528926           70.901227   
+    CC110062                    206.205818         384.356075           80.329689   
+    ...                                ...                ...                 ...   
+    CC722542                    184.300085         306.287030           72.629722   
+    CC722651                    224.292557         254.786917           50.006651   
+    CC722891                    158.987352         202.213773           46.418129   
+    CC723197                    172.558200         222.384434           40.304889   
+    CC723395                    206.235576         197.417773           34.240227   
+    
+                  left_molecular_layer_HP  left_GC-ML-DG    left_CA3    left_CA4  \
+    participants                                                                   
+    CC110033                   583.239022     313.144932  223.022262  266.801434   
+    CC110037                   619.053381     311.428298  192.949211  260.551999   
+    CC110045                   622.037178     322.315065  204.756048  275.428880   
+    CC110056                   597.467595     323.777115  233.160437  278.133998   
+    CC110062                   666.590397     364.130988  253.917949  311.079938   
+    ...                               ...            ...         ...         ...   
+    CC722542                   597.823380     322.236056  252.159707  275.293551   
+    CC722651                   558.060369     322.176631  261.160474  282.029715   
+    CC722891                   384.798819     204.562530  149.220194  184.259996   
+    CC723197                   467.847632     262.950594  215.411133  232.938256   
+    CC723395                   470.969863     269.963160  234.219152  241.325755   
+    
+                  ...  right_hippocampal-fissure  right_presubiculum  \
+    participants  ...                                                  
+    CC110033      ...                 133.101613          263.829445   
+    CC110037      ...                 148.099481          339.309772   
+    CC110045      ...                 159.687619          324.398659   
+    CC110056      ...                 123.262352          290.196432   
+    CC110062      ...                 136.785201          406.323486   
+    ...           ...                        ...                 ...   
+    CC722542      ...                 147.391931          273.150743   
+    CC722651      ...                 185.053756          232.752897   
+    CC722891      ...                 140.980648          211.807774   
+    CC723197      ...                 213.080235          258.567312   
+    CC723395      ...                 205.934342          210.039976   
+    
+                  right_parasubiculum  right_molecular_layer_HP  right_GC-ML-DG  \
+    participants                                                                  
+    CC110033                47.651798                586.026640      328.057551   
+    CC110037                59.693186                617.448302      312.116795   
+    CC110045                55.738550                609.208671      314.460832   
+    CC110056                67.410418                569.389816      310.290805   
+    CC110062                80.403248                767.955163      383.194510   
+    ...                           ...                       ...             ...   
+    CC722542                50.874375                572.634593      302.504826   
+    CC722651                44.493903                533.912687      308.141458   
+    CC722891                57.371362                387.195124      199.979009   
+    CC723197                50.846316                481.409074      267.190903   
+    CC723395                33.216529                493.194601      274.020748   
+    
+                   right_CA3   right_CA4  right_fimbria  right_HATA  \
+    participants                                                      
+    CC110033      283.392837  282.565685      87.127463   73.589184   
+    CC110037      212.605572  269.307660      99.657823   60.920924   
+    CC110045      237.869822  271.505300      69.436808   59.323542   
+    CC110056      218.809310  267.327199      60.505521   51.726283   
+    CC110062      268.227177  325.403040      92.215816   85.484454   
+    ...                  ...         ...            ...         ...   
+    CC722542      236.946562  261.352283      46.144212   43.966509   
+    CC722651      247.810543  267.203989      68.730322   59.699644   
+    CC722891      146.836915  187.083211      27.913196   38.629828   
+    CC723197      211.618157  244.176755      51.893458   65.474967   
+    CC723395      232.592060  240.980896      68.335159   62.081225   
+    
+                  right_Whole_hippocampus  
+    participants                           
+    CC110033                  3531.764896  
+    CC110037                  3835.426137  
+    CC110045                  3681.494304  
+    CC110056                  3461.373764  
+    CC110062                  4782.407821  
+    ...                               ...  
+    CC722542                  3284.108783  
+    CC722651                  3210.272905  
+    CC722891                  2423.675065  
+    CC723197                  3043.146264  
+    CC723395                  2988.001288  
     
     [651 rows x 26 columns]
-              age sex_name  sex  ...  right_fimbria  right_HATA  right_Whole_hippocampus
-    CC110033   24     MALE    1  ...      87.127463   73.589184              3531.764896
-    CC110037   18     MALE    1  ...      99.657823   60.920924              3835.426137
-    CC110045   24   FEMALE    0  ...      69.436808   59.323542              3681.494304
-    CC110056   22   FEMALE    0  ...      60.505521   51.726283              3461.373764
-    CC110062   20     MALE    1  ...      92.215816   85.484454              4782.407821
-    ...       ...      ...  ...  ...            ...         ...                      ...
-    CC722542   79     MALE    1  ...      46.144212   43.966509              3284.108783
-    CC722651   79   FEMALE    0  ...      68.730322   59.699644              3210.272905
-    CC722891   84   FEMALE    0  ...      27.913196   38.629828              2423.675065
-    CC723197   80   FEMALE    0  ...      51.893458   65.474967              3043.146264
-    CC723395   86   FEMALE    0  ...      68.335159   62.081225              2988.001288
+              age sex_name  sex  IQ_random  left_Hippocampal_tail  left_subiculum  \
+    CC110033   24     MALE    1         73             482.768229      419.948094   
+    CC110037   18     MALE    1        103             595.269259      502.320315   
+    CC110045   24   FEMALE    0        124             655.847194      476.433625   
+    CC110056   22   FEMALE    0        124             561.345626      447.258970   
+    CC110062   20     MALE    1        126             756.521166      521.034681   
+    ...       ...      ...  ...        ...                    ...             ...   
+    CC722542   79     MALE    1        116             467.896808      440.794061   
+    CC722651   79   FEMALE    0        128             406.326167      393.469843   
+    CC722891   84   FEMALE    0        129             393.430481      303.049578   
+    CC723197   80   FEMALE    0         96             475.929914      372.449778   
+    CC723395   86   FEMALE    0        145             444.301617      330.688394   
+    
+                left_CA1  left_hippocampal-fissure  left_presubiculum  \
+    CC110033  666.496024                131.719049         285.535445   
+    CC110037  698.157779                156.304335         367.678385   
+    CC110045  654.215689                146.767569         346.347202   
+    CC110056  611.114561                126.615335         327.528926   
+    CC110062  716.391590                206.205818         384.356075   
+    ...              ...                       ...                ...   
+    CC722542  688.130914                184.300085         306.287030   
+    CC722651  613.794018                224.292557         254.786917   
+    CC722891  444.772656                158.987352         202.213773   
+    CC723197  525.739508                172.558200         222.384434   
+    CC723395  565.359058                206.235576         197.417773   
+    
+              left_parasubiculum  ...  right_hippocampal-fissure  \
+    CC110033           59.209377  ...                 133.101613   
+    CC110037           60.817591  ...                 148.099481   
+    CC110045           67.481121  ...                 159.687619   
+    CC110056           70.901227  ...                 123.262352   
+    CC110062           80.329689  ...                 136.785201   
+    ...                      ...  ...                        ...   
+    CC722542           72.629722  ...                 147.391931   
+    CC722651           50.006651  ...                 185.053756   
+    CC722891           46.418129  ...                 140.980648   
+    CC723197           40.304889  ...                 213.080235   
+    CC723395           34.240227  ...                 205.934342   
+    
+              right_presubiculum  right_parasubiculum  right_molecular_layer_HP  \
+    CC110033          263.829445            47.651798                586.026640   
+    CC110037          339.309772            59.693186                617.448302   
+    CC110045          324.398659            55.738550                609.208671   
+    CC110056          290.196432            67.410418                569.389816   
+    CC110062          406.323486            80.403248                767.955163   
+    ...                      ...                  ...                       ...   
+    CC722542          273.150743            50.874375                572.634593   
+    CC722651          232.752897            44.493903                533.912687   
+    CC722891          211.807774            57.371362                387.195124   
+    CC723197          258.567312            50.846316                481.409074   
+    CC723395          210.039976            33.216529                493.194601   
+    
+              right_GC-ML-DG   right_CA3   right_CA4  right_fimbria  right_HATA  \
+    CC110033      328.057551  283.392837  282.565685      87.127463   73.589184   
+    CC110037      312.116795  212.605572  269.307660      99.657823   60.920924   
+    CC110045      314.460832  237.869822  271.505300      69.436808   59.323542   
+    CC110056      310.290805  218.809310  267.327199      60.505521   51.726283   
+    CC110062      383.194510  268.227177  325.403040      92.215816   85.484454   
+    ...                  ...         ...         ...            ...         ...   
+    CC722542      302.504826  236.946562  261.352283      46.144212   43.966509   
+    CC722651      308.141458  247.810543  267.203989      68.730322   59.699644   
+    CC722891      199.979009  146.836915  187.083211      27.913196   38.629828   
+    CC723197      267.190903  211.618157  244.176755      51.893458   65.474967   
+    CC723395      274.020748  232.592060  240.980896      68.335159   62.081225   
+    
+              right_Whole_hippocampus  
+    CC110033              3531.764896  
+    CC110037              3835.426137  
+    CC110045              3681.494304  
+    CC110056              3461.373764  
+    CC110062              4782.407821  
+    ...                           ...  
+    CC722542              3284.108783  
+    CC722651              3210.272905  
+    CC722891              2423.675065  
+    CC723197              3043.146264  
+    CC723395              2988.001288  
     
     [650 rows x 30 columns]
 
@@ -252,22 +497,22 @@ putative biomarkers that are not restricted to brain imaging.
 
     # perpare covariate_normsample for sex and age
     covariate_normsample = norm_demographics_features[['sex',
-                                                       'age']] 
+                                                       'age']]
     
     covariate_normsample.to_csv('covariate_normsample.txt',
                                 sep = ' ',
-                                header = False, 
+                                header = False,
                                 index = False)
     
     # perpare features_normsample for relevant hyppocampal subfields
-    features_normsample = norm_demographics_features[['left_CA1', 
+    features_normsample = norm_demographics_features[['left_CA1',
                                                      'left_CA3',
                                                      'right_CA1',
                                                      'right_CA3']]
     
-    features_normsample.to_csv('features_normsample.txt', 
-                               sep = ' ', 
-                               header = False, 
+    features_normsample.to_csv('features_normsample.txt',
+                               sep = ' ',
+                               header = False,
                                index = False)
     
     # code by T. Wolfers
@@ -278,7 +523,7 @@ putative biomarkers that are not restricted to brain imaging.
 Once you have prepared and saved all the necessary files. Look at the
 pcntoolkit for running normative modeling. Select an appropritate method
 set up the toolkit and run your analyses using 2-fold cross validation
-in the normsample. Change the output suffix from estimate to ’_2fold’.
+in the normsample. Change the output suffix from estimate to ’\_2fold’.
 
 HINT: You primarily need the estimate function.
 
@@ -296,7 +541,7 @@ you will have no doubt when it is correctly running.
     
     # run normative modeling using 2-fold cross-validation
     
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            cvfolds = 2,
                            alg = 'gpr',
@@ -307,55 +552,66 @@ you will have no doubt when it is correctly running.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Warning: Estimation of posterior distribution failed
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Optimization terminated successfully.
-             Current function value: 1856.502251
-             Iterations: 40
-             Function evaluations: 99
-             Gradient evaluations: 99
+             Current function value: 1925.145213
+             Iterations: 30
+             Function evaluations: 75
+             Gradient evaluations: 69
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Optimization terminated successfully.
-             Current function value: 1596.239263
-             Iterations: 42
-             Function evaluations: 93
-             Gradient evaluations: 93
-    Estimating model  3 of 4
-    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
-    Optimization terminated successfully.
-             Current function value: 1862.316698
-             Iterations: 47
-             Function evaluations: 104
-             Gradient evaluations: 104
-    Estimating model  4 of 4
-    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1587.950935
-             Iterations: 30
-             Function evaluations: 64
-             Gradient evaluations: 64
-    Estimating model  1 of 4
+             Current function value: 1627.864114
+             Iterations: 41
+             Function evaluations: 102
+             Gradient evaluations: 102
+    Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1916.461484
-             Iterations: 44
-             Function evaluations: 94
-             Gradient evaluations: 87
-    Estimating model  2 of 4
+             Current function value: 1922.205071
+             Iterations: 30
+             Function evaluations: 73
+             Gradient evaluations: 67
+    Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -365,36 +621,48 @@ you will have no doubt when it is correctly running.
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1611.661888
-             Iterations: 34
-             Function evaluations: 85
-             Gradient evaluations: 85
-    Estimating model  3 of 4
-    Warning: Estimation of posterior distribution failed
+             Current function value: 1621.445961
+             Iterations: 78
+             Function evaluations: 181
+             Gradient evaluations: 181
+    Estimating model  1 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1912.665851
-             Iterations: 61
-             Function evaluations: 133
-             Gradient evaluations: 126
-    Estimating model  4 of 4
+             Current function value: 1844.061877
+             Iterations: 36
+             Function evaluations: 81
+             Gradient evaluations: 81
+    Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
+    Optimization terminated successfully.
+             Current function value: 1580.315780
+             Iterations: 37
+             Function evaluations: 79
+             Gradient evaluations: 79
+    Estimating model  3 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
+    Optimization terminated successfully.
+             Current function value: 1851.005493
+             Iterations: 32
+             Function evaluations: 68
+             Gradient evaluations: 68
+    Estimating model  4 of 4
+    Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
-             Current function value: 1619.045647
-             Iterations: 43
-             Function evaluations: 110
-             Gradient evaluations: 105
+             Current function value: 1584.089863
+             Iterations: 39
+             Function evaluations: 91
+             Gradient evaluations: 91
     Evaluating the model ...
     Writing outputs ...
 
@@ -423,13 +691,13 @@ model using the appropriate specifications.
                                       20, 30, 40, 50, 60, 70, 80]}
     covariate_forwardmodel = pd.DataFrame(data=covariate_forwardmodel)
     
-    covariate_forwardmodel.to_csv('covariate_forwardmodel.txt', 
-                               sep = ' ', 
-                               header = False, 
+    covariate_forwardmodel.to_csv('covariate_forwardmodel.txt',
+                               sep = ' ',
+                               header = False,
                                index = False)
     
     # estimate forward model
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            testcov = 'covariate_forwardmodel.txt',
                            cvfolds = None,
@@ -441,8 +709,22 @@ model using the appropriate specifications.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -451,8 +733,8 @@ model using the appropriate specifications.
     Optimization terminated successfully.
              Current function value: 3781.497401
              Iterations: 20
-             Function evaluations: 61
-             Gradient evaluations: 54
+             Function evaluations: 58
+             Gradient evaluations: 52
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -466,10 +748,22 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3201.761309
-             Iterations: 39
-             Function evaluations: 108
-             Gradient evaluations: 108
+             Iterations: 48
+             Function evaluations: 114
+             Gradient evaluations: 114
     Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -480,9 +774,9 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3771.310488
-             Iterations: 47
-             Function evaluations: 181
-             Gradient evaluations: 167
+             Iterations: 48
+             Function evaluations: 156
+             Gradient evaluations: 143
     Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -496,9 +790,9 @@ model using the appropriate specifications.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3200.837262
-             Iterations: 40
-             Function evaluations: 104
-             Gradient evaluations: 104
+             Iterations: 42
+             Function evaluations: 116
+             Gradient evaluations: 116
     Writing outputs ...
 
 
@@ -508,7 +802,8 @@ model using the appropriate specifications.
 Visualize the forward model of the normative model similar to the figure
 below.
 
-.. figure:: 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i9xqV7oxvhZeeL/AA7qV34R8U6rb3ej2kZNjqN05iuLiQsDt2SMWwMtnIBG0DODSjfqKN07M9QhvbO4lkgt7uGWSLHmIkgZkyMjIHTIq7mhNQAUAHU0AMeGKT/WRhvTcOlADfs4H+qlkT2DZH5NkUAIBdKRkxyj3+U/4fyoArfb73/nwm/OgD//1P0ljhRCXGS54Lnkn+lAHN2HgLTtO8c6h48jv717vULdbeSB5MwooC9B1/hHBJHXFK1tSeVLUj8e+OJPB+m/aNN0K6127WaOKS1tclolbPzOQp2jOMA4ySKG7bDlLlWg3xLrOnaboP8Aa/jzVF0vTZwsTWse4kmQcxuwG9uM5ChRgHOaL9xN2Wpn+GfhJ4L0DxPb+NfC3nWyG18qG2hlzAysv3+cscqcgFiOhFJR1ugjBbo7ODU9NuL2bTbfULWS7tQDPAkwMkYYZBZRyAR3IqrjumYN54V1258eWfimPxVcxaVBbNBLpQX5JWIbk84zyCTjOVFKzE03K/Q43WPDeva58cdOfVNEkufDGl2fnWrtABbwy7Ccg9AxkC8Hngcbealp3Ia9/XY7qO78B+DFe1jv9D0RbiRpmjM8Vv5jnGWwSOTge4xVaI091GBqHx3+FlvBcHS/GGn6zdxI7Ja6Y5u3eQA4X9wr4yeOelF+wr9jh/CXxT8Wf8JDfeJ/HVn4os9LktwkWl2nhfUruOMjbiQSRwFccMc7j1wQMVMXrqQnJO7Op1b456eumi68M+CfGGqSuyYSbw7qFlCUJ+95s0IQ9eADkk57VTfYpy00NGD43eB/lXUl1/SpSuSmoeHdQgAP1aAKw9xRe473RzmoftCeHIPH+maJDr2iW2hTQM13d6jObSRJMHAUTbOny4ABzk9MUr6ibfMdWPjB4Fn8Tab4X03WoNTn1RN0c9hKlxbp1ADOrEAnaenpzT5lsNyWx2caRRr5cSKiDI2qAAPwHSnsUvI8w8Ya74j1H4paF4L8LXlzpQi/4mGoz/Z90N1GAPl3DqAoZecDLAZ4qXe+hm9ZaHY2XjXwzrfiXUvBdvcNNqWnRiS5heI7CMgEBjwcblz7mnctNN2KWk/DDwnouu6n4i06K8hu9WbdJ5V08aL3OzYVxk9ck+2BxRy2FypGjDte8n07S/FgluLTBmtZmjnMWem4DEgz7tSBGX4p8fL4Hn0228RLbTNqk5t7c2+9HZsgElCGUD5hnLj71O9tBOVnY3hrkMPF9Y39p6mSAuo9y8e5cfUjHegq5atNRsL8FrG9gnA4PlSBsexx0phdFigYUALtb+6fyoA//9X9Iby9trKMSXMgBc7UCgs7t6Ko5P4UBcqCLUtS5uHaxtu0KMPOYf7Tg/J/uqc/7XagWrLtta29lCtvaQrDGuQFUfn9T65/WiwWK2saHo/iGybTda0+G8tmYP5Uq7lBHQ/Uf1NFrjdnozg9U+K9t4N8cXPhzxi+jaJoMdojadNJdL59w2FwscP3m6t8qpxjnqKlPUhN3scbqFt4m1/xoPGXwV8K6vZPcsz6jqGtRtYWl4u5CEVJv3+0lfmYQ4PykdKVtbktNvQPiH4p+PFhrGm+GLPWNLttV1hS0Nto+meYEXkc3VyxViCMnEKgDBJINOTYSlJaGZqfw/j1f4teHfCWtavr2tGGzF5q66zezXNrK23cwjQFYgMZXIjUZ7k4ATvzBd8x6JqPwl+B+oeGJhp/h7w1pFgzAnUtMgtoGUhu8wX14O7PXFN2ZckmrnR634f1eLwQNA+H+qR6dd29vFBZ3Ep3AIuAckA8lRjODyc+9O1loHLp7pxVj8MfH2v+JdRn+Inidp9Mm0wWASxmMa3BKgFinRcMCxOBubHGMgyotkRhJ7s9J8MeHrDwnoNn4f01pmtrKMxo0z7nbJzyfxJ7D6dKvyNErIu/2hZfbjp326D7WF84wCUeaE7NtPOM9+lA7q5zlvofjJ/HOoahqWs2l14ZmtVjt9OaIFlkwuScjpkMc5OcgYGAalLUnladx+q/DD4b62HXVvAXh673kEtLpsRbPY525BHqD3qrD5UzzLV/AGn+Adf03TNE1Dx0sfiC82rJpWoGKKxThVDjDCXaG6v0VTz6xZJ6EWUWds3gr4maSfM8P/F2e828GDxDpFvdIQO2+3Fu47ckt+NWXY4zx/dfGrRLCTXIPDvhPSp45Ee/1fTdTQPdoAAkbrcxRkDOODI+eMGpehEubc66D42+HLeGObxXoniLw2kiiT7Tf6Y8lqQeQ32q3MsODkEEuOO9O5SlbcveDrb4X3uv6j418Fa3peoXurjbPNZaglwp5ydoViFOQMigEknc6y80vS9RaB9Q061uTbOJoPOhV/KccBl3AkHHTGPzFFirIs9ucc5z/WmBVu9L0y+IkvLCCZx913jBYfRuopWCxW/sTyebDVb+3x1Uy+cv0xIGIH0IpisLnxFb8EWN+OvG63b8vmUn3yv4UAZP/CXy/wDQla7/AN+j/hQM/9b9D9R03ULbSb+50N459be2f7PcXhyPMIOAccKucfKoxQxNaaGP4G8Wvc6VJpni7XNLfxFpKM+qpDIoWAbjgt/CMAgNg4BJ5qU+4ovuZr/F2LX5ZLH4WeG7vxg8bGJ7+GVbbSonHUNdvkSEdxCshHene4XvsVdX0HxzfaXda38SfiG+k6VaxNPPpnhWNoPkAyQ13IDO/AIzGITn86Qbak/whm+EV5Ddy/DXSbe3uk2G9eSFvtjs2SDLM5Lyng/MWP8ASmmmCkmaHh/xX4ok8S6/p3iWwtFsLacLpn2ItNNImTy6oSV42n5guCSKL6gpO50bXerXOGtdEWLHR7yZUPPcLGH/ACJU0DZyfxDuviFaWdnDoekwasmoT/Zb6O2hKNFAwwSWZjwem7GBjNJ36EzcrEU3wg8DaT4KvPDt9faiuiK7385kucbWVQS+QvZV6Ht70cugnCKVjlfD+gP4h8f+H/Efgq41CfwZaac1mZRqUqGJkV18oqzBx0Q4HGADmptd3QkryViXxhp3iv4dWGveIdI8V3GuStdRXL2NzfSKbG0JbJwJMuSSi54GB0PNN6bCd4rc6K/vPFviTwNp/iT4U6xN9rvGR9t84YCPlXA8wHBDD8lOM8U9Wrot3cbo0bX4bCDxWnj06sX11rRbeVpYg0X3QpwqFPTA7YA4zT5eoctnc6T/AIqOPqumXPuWkh/TD0ytSjrXiweGNLm1nxFpc9tZ2yhpZ4pY5ETJwBgkNySPur3FK9gvYpXvi7w/r/hG5vrLXbnTLW9tJDDqLW8sSxAqf3gcgDI9iOlK90Ju6F+GDWcfg+wtLPxePEflhx9tLYZxuY4IzuXHI5ORjmmmEdih8Y/BGu+PPCw0bQdRht5I5vPkimXAnCjhd2CVOce3rRNXWgpxclobXgCw1zS/Bul6b4khtIb61gEMkdsf3SKpIQDHH3MA44z0oW2o4rSzIdc+F/w38SzNceIPAXh/ULiTrNcadE0h99+NxPv1p2Ksjgdf0XwP4E1+08PaF4+8XeEru8ge5jW1v3vLKONQfvRXYljQfIfuqvA9MGk3YiVoml4au/idrGlpr3g/4meH/FFmztH5eraI9m7MvUGW3cBT7mE+uO9Fxp82xqjx18RdM/d+IPg5qNwy8GbQdVtbyI+4E7W8n4FPxNFx3AfG/wAC2/7vxB/bfh64GR5Gr6PdQHPoH2GNz6bXINFxOSRs+HviR4D8VOkGheLdLurp13m0FyguVH+1ETvX8RRdDUlLY6XB9DTGf//X+4pfFPxI8axm28GeEm8N2EwIOs+I0KyhD/FDYxsJSx9ZWix3DDilqJ6lPw7+zl8NtIKT65preIrtDu36p+8iLZBz5GRGcEA5ZSQehFCjYlQS1On8F+AtE+Hg1abT9QuWh1Cf7U4uZAEgHPCgAADBPJyeOTximlYajysg8afETSvDmhXmpvaG9toUXzdwGGV22AhScupJ6gbP9qpbsEpJEnhPwv4Zn02PXLCyjjh1iKK5eKC3FrG4K5UNGoycA9HLAHmmrdASW6OqgggtolhtoY4ol4VUTao+mOlMdh0kiRDMjqn+8cUDIzdIfuCST02ocfnQBzms3/jA+J7DTbPQLWfw/dQyC/mnPzg88HDbQOnBBzk8CkyWaui2On6dp0Vp4esdPtrEZMa2rjy+TyQVHPPU0LyGrLYx4fh7okXi3UPGhsxJf6nb/ZZ45Zt0DIQoI2Fe4UA549utK2txcqvc6K3iazgS3tbGCKGNQqRxHaqjjgKFAH/6/WqWg7EhmkA+a1k+oKn+ZoGH2qPo6yr2IMZH8qAIb2DStYtZdN1CK2u7e5XZJBIAyyDvkenSgN9xDo2kvpLaH9gg/s9oDbm3CYjMR4K4GABzS02DRqxnweC/DmnaKNE0rRrO2hhjZLcNCspic5O75s5IY56/jRZWJcVsjnfA/h3UfDNhDoPi3xybjW7iaWW3EV62WiGOFjk4bHJzs43Y6AUlpuJK27Ot+y65b82+rxTL6XVuCx/4FGVH/jpp6laiG91qAEXGipMD3s7lST9RJsx+Zphc5DxE+t33jnRb9WsbbQreKWO+XUbAiUl9wYLIUKgEbRhWA4Od3SpauS1eVzstCsdC0/TktPDlvZQWKklI7QJ5YJOSfl4zzyfami9FsaFMCO6t7e8t5bS7gSaCdCkkcgyrqeCCD2OcUMLLqYGo/Dj4f6tpkei6p4K0S8soRiOG4sInWP3UFSVPuKEkhJJbEH/Cqvh3/wBCfZfrQM//0P0oGc8sfXrnB/GgDkPGWp+KNS0eew+Gc9u+rRSokkz7TFCo5K5Py7/YA4yc4yDUu72Jk7rQ2tP0iZ4Le48Qype3qqrOAMQRy45MceMDkcMctz1FNaDXmZrfD3w7/wAJnP47m8+S9nthayRySgwFAAN23HXaMY6d8d6Vri5Ve5k6H4d17wJofiCfTdWvvE15dStd2sVyvyRnBwgyw3HnnGAcAADrRsKzjdnSeG7nW9W0Ozv/ABDZtp19NHuntlIyhycZOSRkAHAPGQO2aaKjtqZvhrwpr+j+Kdc1nU/E76hY6g4NnaMn/HsM9Mk+mBx1xk80JO4opp3bOrxj/CmUV9QsYNTsLnTbrd5N3C8Em1ip2spU4I5BwetAMyvBng/SvAugxeHtGluZLaJ2kDXD73ZmPJJAAH4DFJKwkrG7TGM82PzDD5i+YBuKZ5A9cUXAfnHPpQBy7w+Ph8QFlS5sP+ESFrtaIj9952OO2Qc477dvbNTrcnVyKWtfFnwLoXiK78L69qBtLi0gWaSWaA+ScgEKuASWwQcBcdcHijmXUTmk7MzT8W/Dt346sfBOg6Rd3/2oK015bHbFErIHVwB95cEEnIAHTPSi+ugufU6HXPB15q3iHR9cg8R3lomlMWa2UsRNnscOox67g2R0xTa1Kcbu4up+BtG1TxPp/i++s0n1LS0KW8gd0CjJPK5IbBJIPHP4UWG43dzoPPcf623kH+0MMP05pjAXMGeZdh9Gyp/I0Ac7B4906fx7P8P10+9W7t7Rbk3DR5hxgHAPXocZ6buKXNfQnmu7G1PomkXUhmm02ATf89VTbIPo4+b9aLDsZevadr9jo15P4Rvrh9SjiZrW3upRJFJIOgJky3I/2gPpQ9hO9tCLw1dePDoVnP4m0zTTqDJm4igmMZQ5IH95SSoGfmA5x0FJX6gnK2pqf25HCMX+n31pz/HCXX8WjLKB9SKY7jf+El8O/wDQbsv+/i/40xn/0f0G8QR67caFfXaWzSzxW8j22mwS7TK+07Q8nUknHyjA9d1J7CexyXwI8CX/AIV8Pz6zr63kWq6zIZZ4LjjylVm2/L2Yg5Pfn2pRT3IpppanpRmLkrbqJCOrZwoqjQQQbjvnbzD16YA/D/HP4UAS4GMDj6UAL+FAHK6dqHj1/GurWOo6JaJ4egt1awuUceZLJ8o2nnv8/UDGB1BpXZMea4/wBrvirxBpM954t8NHRLuO6eOODJO+MAYbnnqSPQ4yOKEEW3udPTKCgAoA5m38A6Rb+Orjx/HdXpvrm2Fs0Rl/dBQAMgf8BHHTqcUrWdyeVXudNTKOX8EeBU8GNqrLrl9qX9qXZuj9qOfKzngepOeT3wKSQkrDfFfwy8H+NdSstV8Q6cZ57NSo2OUEik52vj7wzyPqaTjcTimdDZaZpunbfsFhb25WNIQY4gpEa8KuQOgxgDOKpaDSSMCx8V6vc+O73wlJ4UvIdOtLZZotTb/VSMQpKj5QO5HBLZU5GDST1EndnUUyg/lQAm0HggE/SgDD8V6/4f8ABWjTeI9Y82C2hKIWgQ7yWbAXCnnnnmjRCbSL2l3kWq6Zaarpl2z215ClxD5kfVHGRwMEcHnmgFrqWd9wn3oFYeqv/Q4/nQMpa9JqUujXsOj3K2WoPA62s1xGSkcmDtJwCMA49aGJ6rQr+DbfxHZ+GrG38WajBf6rGjLPcQ42tySv1+XAPHXmktNwSsrM0/sOm/8APlD/AN8imPlR/9L9JXlEeF5Lnoq9TQAwxvL80547RqePxPWgCbgDGBhenoKADuR3HX2oAKAOX8eSeP4rOx/4V/BYy3DXSC7F0QFEOOSM9Rnr39KTZMr9DqPf26UygoAKACgAoAKACgAoAKACgBOep54AJxzQAtAHL+ONE8Z6ydL/AOEQ8Sx6T9muhLeb13edH6Dg5xzwcA55IwKTTJkm7WOoPXpjPUfn+FMohu7W1v7d7W9tYriCTho5UDqw91NANXJEjSNVijRURFCqqjAAHReOABxgCjYVhtwbgW8ptBGZ9jeUJM7d+OM47ZxSew/Q5X4ax/ERNLupPiNNbteyXLPbpDszHF6EoMHnOBycdTSV+pMOa2p1TW8LncEAY/xKdp/MVRRH9lh/uT/9/wA0Af/T/SaOJIx8o+bue5oAdQADrQBy/g7wHa+Dr/Wr+21a+vDrNz9pZLh8rEcn7vvyRn0CiklbUlRS1OoplBQAUAFABQAUAFABQAUAFABQBy/h7SfG1n4q1y+17xBb3mjXTA6baohDwj34GOODydx546UknclJp3Z1FMoKACgAoAKACgAoAKAOY1zx9p2geLNG8JXFhey3GtZ8qaKLMaYOPmOfUHOOg5NJslys7WOnx70yj//U/SigAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAEKhiGKgsDwTyR680BYXBoA/9X9KKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoABQA7A9BQB//2Q==
+.. figure::
+   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
    :alt: 1-s2.0-S245190221830329X-gr2.jpg
 
    1-s2.0-S245190221830329X-gr2.jpg
@@ -531,12 +826,12 @@ individual participants.
         S_hat=np.mean(S2,axis=0)
         n=S2.shape[0]
         CI[i,:]=z*np.power(S_hat/n,.5)
-      return CI 
+      return CI
     
     
     feature_names=['left_CA1','left_CA3','right_CA1','right_CA3']
     sex_covariates=[ 'Female','Male']
-    # Creating plots for Female and male 
+    # Creating plots for Female and male
     for i,sex in enumerate(sex_covariates):
     #forward model data
         forward_yhat = pd.read_csv('yhat_forward.txt', sep = ' ', header=None)
@@ -544,71 +839,71 @@ individual participants.
         yhat_forward=yhat_forward[7*i:7*(i+1)]
         x_forward=[20, 30, 40, 50, 60, 70, 80]
     
-    # Find the index of the data exclusively for one sex. Female:0, Male: 1   
+    # Find the index of the data exclusively for one sex. Female:0, Male: 1
         inx=np.where(covariate_normsample.sex==i)[0]
         x=covariate_normsample.values[inx,1]
     # actual data
         y = pd.read_csv('features_normsample.txt', sep = ' ', header=None)
         y=y.values[inx]
-    # confidence Interval yhat+ z *(std/n^.5)-->.95 % CI:z=1.96, 99% CI:z=2.58 
+    # confidence Interval yhat+ z *(std/n^.5)-->.95 % CI:z=1.96, 99% CI:z=2.58
         s2= pd.read_csv('ys2_2fold.txt', sep = ' ', header=None)
         s2=s2.values[inx]
     
         CI_95=confidence_interval(s2,x,1.96)
         CI_99=confidence_interval(s2,x,2.58)
     
-    # Creat a trejactroy for each point     
+    # Creat a trejactroy for each point
         for j,name in enumerate(feature_names):
              fig=plt.figure()
              ax=fig.add_subplot(111)
              ax.plot(x_forward,yhat_forward[:,j], linewidth=4, label='Normative trejactory')
     
     
-             ax.plot(x_forward,CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g', label='95% confidence interval')  
-             ax.plot(x_forward,-CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g') 
+             ax.plot(x_forward,CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g', label='95% confidence interval')
+             ax.plot(x_forward,-CI_95[:,j]+yhat_forward[:,j], linewidth=2,linestyle='--',c='g')
     
-             ax.plot(x_forward,CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k', label='99% confidence interval')  
-             ax.plot(x_forward,-CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k') 
+             ax.plot(x_forward,CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k', label='99% confidence interval')
+             ax.plot(x_forward,-CI_99[:,j]+yhat_forward[:,j], linewidth=1,linestyle='--',c='k')
     
              ax.scatter(x,y[:,j],c='r', label=name)
              plt.legend(loc='upper left')
              plt.title('Normative trejectory of' +name+' in '+sex+' cohort')
              plt.show()
              plt.close()
-            
+    
     # code by M. Zabihi
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_0.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_0.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_1.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_1.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_2.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_2.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_3.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_3.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_4.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_4.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_5.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_5.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_6.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_6.png
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_22_7.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_23_7.png
 
 
 **TASK 6:** Apply the normative model to Nordan’s data and the dementia patients.
@@ -626,25 +921,25 @@ individual participants.
     
     # create a covariate file for Nordan's as well as the patient's demograhpics
     covariate_nordan = demographics_nordan[['sex',
-                                            'age']] 
+                                            'age']]
     covariate_nordan.to_csv('covariate_nordan.txt',
                             sep = ' ',
-                            header = False, 
+                            header = False,
                             index = False)
     
     # create the corresponding feature file
-    features_nordan = features_nordan[['left_CA1', 
+    features_nordan = features_nordan[['left_CA1',
                                       'left_CA3',
                                       'right_CA1',
                                       'right_CA3']]
     
-    features_nordan.to_csv('features_nordan.txt', 
-                            sep = ' ', 
-                            header = False, 
+    features_nordan.to_csv('features_nordan.txt',
+                            sep = ' ',
+                            header = False,
                             index = False)
     
     # apply normative modeling
-    pcn.normative.estimate(covfile = 'covariate_normsample.txt', 
+    pcn.normative.estimate(covfile = 'covariate_normsample.txt',
                            respfile = 'features_normsample.txt',
                            testcov = 'covariate_nordan.txt',
                            testresp = 'features_nordan.txt',
@@ -657,8 +952,22 @@ individual participants.
 
 .. parsed-literal::
 
+    inscaler: None
+    outscaler: None
     Processing data in features_normsample.txt
     Estimating model  1 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -667,8 +976,8 @@ individual participants.
     Optimization terminated successfully.
              Current function value: 3781.497401
              Iterations: 20
-             Function evaluations: 61
-             Gradient evaluations: 54
+             Function evaluations: 58
+             Gradient evaluations: 52
     Estimating model  2 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -682,10 +991,22 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3201.761309
-             Iterations: 39
-             Function evaluations: 108
-             Gradient evaluations: 108
+             Iterations: 48
+             Function evaluations: 114
+             Gradient evaluations: 114
     Estimating model  3 of 4
+
+
+.. parsed-literal::
+
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:147: RuntimeWarning: overflow encountered in exp
+      self.sf2 = np.exp(2*theta[self.D])
+    /usr/local/lib/python3.10/dist-packages/pcntoolkit/model/gp.py:160: RuntimeWarning: invalid value encountered in multiply
+      dK = K * squared_dist(x[:, i]/self.ell[i], x[:, i]/self.ell[i])
+
+
+.. parsed-literal::
+
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -696,9 +1017,9 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3771.310488
-             Iterations: 47
-             Function evaluations: 181
-             Gradient evaluations: 167
+             Iterations: 48
+             Function evaluations: 156
+             Gradient evaluations: 143
     Estimating model  4 of 4
     Warning: Estimation of posterior distribution failed
     Warning: Estimation of posterior distribution failed
@@ -712,9 +1033,9 @@ individual participants.
     Warning: Estimation of posterior distribution failed
     Optimization terminated successfully.
              Current function value: 3200.837262
-             Iterations: 40
-             Function evaluations: 104
-             Gradient evaluations: 104
+             Iterations: 42
+             Function evaluations: 116
+             Gradient evaluations: 116
     Evaluating the model ...
     Writing outputs ...
 
@@ -761,21 +1082,21 @@ age 20. Do that for both sexes seperately.
     lengths = len(forward_yhat[hyppocampal_subfield])
     for entry in forward_yhat[hyppocampal_subfield]:
       if count > 0 and count < 7:
-        loop_percentage_change_female = calculate_percentage_change(entry, 
+        loop_percentage_change_female = calculate_percentage_change(entry,
                                                                     forward_yhat.iloc[0,
                                                                                       hyppocampal_subfield])
         percentage_change_female.append(loop_percentage_change_female)
-      elif count > 7: 
+      elif count > 7:
         loop_percentage_change_male = calculate_percentage_change(entry,
                                                                   forward_yhat.iloc[9,
                                                                                     hyppocampal_subfield])
         percentage_change_male.append(loop_percentage_change_male)
-      count = count + 1 
+      count = count + 1
     
-    names = ['30 compared to 20 years', 
-             '40 compared to 20 years', 
-             '50 compared to 20 years', 
-             '60 compared to 20 years', 
+    names = ['30 compared to 20 years',
+             '40 compared to 20 years',
+             '50 compared to 20 years',
+             '60 compared to 20 years',
              '70 compared to 20 years',
              '80 compared to 20 years']
     
@@ -803,5 +1124,6 @@ age 20. Do that for both sexes seperately.
 
 
 
-.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_32_1.png
+.. image:: normative_modelling_walkthrough_files/normative_modelling_walkthrough_33_1.png
+
 
diff --git a/doc/build/html/_sources/pages/other_predictive_models.rst.txt b/doc/build/html/_sources/pages/other_predictive_models.rst.txt
index c67b860c..2b841cad 100644
--- a/doc/build/html/_sources/pages/other_predictive_models.rst.txt
+++ b/doc/build/html/_sources/pages/other_predictive_models.rst.txt
@@ -1,24 +1,23 @@
-.. title:: Predictive modeling tutorial
-
-Predictive modeling using deviation scores
-=============================================
-
-The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.
-
-Created by `Saige Rutherford `__
-
+.. code:: ipython3
 
-.. image:: https://colab.research.google.com/assets/colab-badge.svg 
-    :target: https://colab.research.google.com/github/predictive-clinical-neuroscience/PCNtoolkit-demo/blob/main/tutorials/BLR_protocol/other_predictive_models.ipynb
+    ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
 
 
-.. code:: ipython3
+.. parsed-literal::
 
-    ! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git
+    Cloning into 'PCNtoolkit-demo'...
+    remote: Enumerating objects: 1237, done.
+    remote: Counting objects: 100% (360/360), done.
+    remote: Compressing objects: 100% (185/185), done.
+    remote: Total 1237 (delta 200), reused 306 (delta 172), pack-reused 877 (from 1)
+    Receiving objects: 100% (1237/1237), 141.45 MiB | 10.83 MiB/s, done.
+    Resolving deltas: 100% (562/562), done.
+    Updating files: 100% (70/70), done.
 
 
 .. code:: ipython3
 
+    
     import os
 
 .. code:: ipython3
@@ -42,7 +41,7 @@ Created by `Saige Rutherford `__
     import numpy as np
     from matplotlib import pyplot as plt
     from scipy import stats, linalg
-    from sklearn import preprocessing, decomposition, linear_model, metrics 
+    from sklearn import preprocessing, decomposition, linear_model, metrics
     import warnings
 
 .. code:: ipython3
@@ -62,7 +61,7 @@ Created by `Saige Rutherford `__
     plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title
 
 Load Data
------------------------------
+=========
 
 .. code:: ipython3
 
@@ -85,7 +84,7 @@ Load Data
 
 
 Create Train/Test Splits
---------------------------------------
+========================
 
 .. code:: ipython3
 
@@ -117,7 +116,7 @@ Create Train/Test Splits
     test_mu_centered_ct = (test_data_ct - train_data_ct.mean(axis=0))
 
 Principal Component Regression (BBS)
---------------------------------------
+====================================
 
 .. code:: ipython3
 
@@ -131,7 +130,7 @@ Principal Component Regression (BBS)
 
 .. code:: ipython3
 
-    print(f'First PC explains {pca_model_z.explained_variance_ratio_[0]*100:.2f}% of the total variance.')
+    print(f'First PC explains {pca_model_z.explained_variance_ratio_[0]*100:.2f}% of the total variance.\nThis is an artifact of zero inflated data')
     plt.figure(figsize=(10, 7))
     plt.bar(range(1, 51), pca_model_z.explained_variance_ratio_[1:51])
     plt.title('Deviations model Variance Explained Ratio\nPCs 1-50', fontsize=25)
@@ -141,15 +140,16 @@ Principal Component Regression (BBS)
 .. parsed-literal::
 
     First PC explains 23.41% of the total variance.
+    This is an artifact of zero inflated data
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_16_1.png
+.. image:: other_predictive_models_files/other_predictive_models_17_1.png
 
 
 .. code:: ipython3
 
-    print(f'First PC explains {pca_model_ct.explained_variance_ratio_[0]*100:.2f}% of the total variance.')
+    print(f'First PC explains {pca_model_ct.explained_variance_ratio_[0]*100:.2f}% of the total variance.\nThis is an artifact of zero inflated data')
     plt.figure(figsize=(10, 7))
     plt.bar(range(1, 51), pca_model_ct.explained_variance_ratio_[1:51])
     plt.title('Cortical Thickness model Variance Explained Ratio\nPCs 1-50', fontsize=25)
@@ -159,10 +159,11 @@ Principal Component Regression (BBS)
 .. parsed-literal::
 
     First PC explains 24.28% of the total variance.
+    This is an artifact of zero inflated data
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_17_1.png
+.. image:: other_predictive_models_files/other_predictive_models_18_1.png
 
 
 .. code:: ipython3
@@ -176,7 +177,7 @@ Principal Component Regression (BBS)
     test_transformed_ct = pca_model_ct.transform(test_data_ct)
 
 Fit Linear Regression Model
---------------------------------------
+---------------------------
 
 .. code:: ipython3
 
@@ -184,14 +185,14 @@ Fit Linear Regression Model
     # we will check that this matches sklearn results later
     
     # fit ols model on dimension reduced train data
-    train_features_z = np.hstack([np.ones((train_transformed_z.shape[0], 1)), 
+    train_features_z = np.hstack([np.ones((train_transformed_z.shape[0], 1)),
                                 train_transformed_z])
-    train_features_inv_z = linalg.pinv2(train_features_z)
+    train_features_inv_z = linalg.pinv(train_features_z)
     train_betas_z = np.dot(train_features_inv_z, train_phen)
     train_pred_phen_z = np.dot(train_features_z, train_betas_z)
     
     # fit ols model on dimension reduced test data
-    test_features_z = np.hstack([np.ones((test_transformed_z.shape[0], 1)), 
+    test_features_z = np.hstack([np.ones((test_transformed_z.shape[0], 1)),
                                test_transformed_z])
     test_pred_phen_z = np.dot(test_features_z, train_betas_z)
 
@@ -201,14 +202,14 @@ Fit Linear Regression Model
     # we will check that this matches sklearn results later
     
     # fit ols model on dimension reduced train data
-    train_features_ct = np.hstack([np.ones((train_transformed_ct.shape[0], 1)), 
+    train_features_ct = np.hstack([np.ones((train_transformed_ct.shape[0], 1)),
                                 train_transformed_ct])
-    train_features_inv_ct = linalg.pinv2(train_features_ct)
+    train_features_inv_ct = linalg.pinv(train_features_ct)
     train_betas_ct = np.dot(train_features_inv_ct, train_phen)
     train_pred_phen_ct = np.dot(train_features_ct, train_betas_ct)
     
     # fit ols model on dimension reduced test data
-    test_features_ct = np.hstack([np.ones((test_transformed_ct.shape[0], 1)), 
+    test_features_ct = np.hstack([np.ones((test_transformed_ct.shape[0], 1)),
                                test_transformed_ct])
     test_pred_phen_ct = np.dot(test_features_ct, train_betas_ct)
 
@@ -216,34 +217,20 @@ Fit Linear Regression Model
 
     # OLS using sklearn
     
-    lr_model_z = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+    lr_model_z = linear_model.LinearRegression(fit_intercept=True)
     lr_model_z.fit(train_transformed_z, train_phen)
     train_pred_phen_lr_model_z = lr_model_z.predict(train_transformed_z)
     test_pred_phen_lr_model_z = lr_model_z.predict(test_transformed_z)
 
-
-.. parsed-literal::
-
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
-
-
 .. code:: ipython3
 
     # OLS using sklearn
     
-    lr_model_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+    lr_model_ct = linear_model.LinearRegression(fit_intercept=True)
     lr_model_ct.fit(train_transformed_ct, train_phen)
     train_pred_phen_lr_model_ct = lr_model_ct.predict(train_transformed_ct)
     test_pred_phen_lr_model_ct = lr_model_ct.predict(test_transformed_ct)
 
-
-.. parsed-literal::
-
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
-
-
 .. code:: ipython3
 
     # ensure matrix math predictions and sklearn predictions are accurate to 5 decimals
@@ -270,8 +257,8 @@ Fit Linear Regression Model
     Passed
 
 
-Mean Squared/Absolute Error of Predictions
------------------------------------------------
+Accuracy of Predictions
+-----------------------
 
 .. code:: ipython3
 
@@ -326,18 +313,18 @@ BBS Cross Validation
 
     def bbs(X, y, n_components, n_cv_splits, pred_summary_function, verbose=False):
         assert X.shape[0] == y.shape[0]
-        
+    
         fold_accs_train = []
         fold_accs_test = []
         np.random.seed(42)
         shuffled_idxs = np.random.choice(range(X.shape[0]), size=X.shape[0], replace=False)
         for fold_i, test_idxs in enumerate(np.array_split(shuffled_idxs, n_cv_splits)):
-            train_mask = np.ones(X.shape[0], np.bool)
+            train_mask = np.ones(X.shape[0], bool)
             train_mask[test_idxs] = 0
     
             # create train/text X, y
             train_X, test_X = X[train_mask, :], X[test_idxs, :]
-            train_y, test_y = y[train_mask], y[test_idxs]  
+            train_y, test_y = y[train_mask], y[test_idxs]
     
             # mean center columns using train data only
             train_X_mu = train_X.mean(axis=0)
@@ -356,7 +343,7 @@ BBS Cross Validation
             # fit OLS model
             if verbose:
                 print(f'CV Fold: {fold_i+1:<10} Fitting Linear Regression model...')
-            lr_model = linear_model.LinearRegression(fit_intercept=True, normalize=False)
+            lr_model = linear_model.LinearRegression(fit_intercept=True)
             lr_model.fit(train_X, train_y)
     
             train_pred = lr_model.predict(train_X)
@@ -364,11 +351,11 @@ BBS Cross Validation
     
             fold_accs_train.append(pred_summary_function(train_y, train_pred))
             fold_accs_test.append(pred_summary_function(test_y, test_pred))
-            
+    
             if verbose:
-                print(f'CV Fold: {fold_i+1:<10} Train MAE: {round(fold_accs_train[-1], 3):<10} Test MAE: {round(fold_accs_test[-1], 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Train Accuracy: {round(fold_accs_train[-1], 3):<10} Test Accuracy: {round(fold_accs_test[-1], 3):<10}')
+    
     
-        
         plt.figure(figsize=(13, 7))
         plt.plot(range(1, len(fold_accs_train)+1), fold_accs_train, linestyle='-', marker='o', color='C0', label='Train CV Performance')
         plt.plot(range(1, len(fold_accs_test)+1), fold_accs_test, linestyle='-', marker='o', color='C1', label='Test CV Performance')
@@ -377,7 +364,7 @@ BBS Cross Validation
         plt.xlabel('CV Fold')
         plt.legend(fontsize=20)
         plt.show()
-        
+    
         return fold_accs_train, fold_accs_test
 
 .. code:: ipython3
@@ -389,23 +376,23 @@ BBS Cross Validation
 
     CV Fold: 1          Fitting PCA model...
     CV Fold: 1          Fitting Linear Regression model...
-    CV Fold: 1          Train MAE: 0.599      Test MAE: 0.619     
+    CV Fold: 1          Train Accuracy: 0.599      Test Accuracy: 0.619     
     CV Fold: 2          Fitting PCA model...
     CV Fold: 2          Fitting Linear Regression model...
-    CV Fold: 2          Train MAE: 0.572      Test MAE: 0.713     
+    CV Fold: 2          Train Accuracy: 0.572      Test Accuracy: 0.713     
     CV Fold: 3          Fitting PCA model...
     CV Fold: 3          Fitting Linear Regression model...
-    CV Fold: 3          Train MAE: 0.577      Test MAE: 0.687     
+    CV Fold: 3          Train Accuracy: 0.577      Test Accuracy: 0.687     
     CV Fold: 4          Fitting PCA model...
     CV Fold: 4          Fitting Linear Regression model...
-    CV Fold: 4          Train MAE: 0.604      Test MAE: 0.608     
+    CV Fold: 4          Train Accuracy: 0.604      Test Accuracy: 0.608     
     CV Fold: 5          Fitting PCA model...
     CV Fold: 5          Fitting Linear Regression model...
-    CV Fold: 5          Train MAE: 0.581      Test MAE: 0.687     
+    CV Fold: 5          Train Accuracy: 0.581      Test Accuracy: 0.687     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_32_3.png
+.. image:: other_predictive_models_files/other_predictive_models_33_1.png
 
 
 .. code:: ipython3
@@ -417,41 +404,43 @@ BBS Cross Validation
 
     CV Fold: 1          Fitting PCA model...
     CV Fold: 1          Fitting Linear Regression model...
-    CV Fold: 1          Train MAE: 0.622      Test MAE: 0.643     
+    CV Fold: 1          Train Accuracy: 0.622      Test Accuracy: 0.643     
     CV Fold: 2          Fitting PCA model...
     CV Fold: 2          Fitting Linear Regression model...
-    CV Fold: 2          Train MAE: 0.605      Test MAE: 0.723     
+    CV Fold: 2          Train Accuracy: 0.605      Test Accuracy: 0.723     
     CV Fold: 3          Fitting PCA model...
     CV Fold: 3          Fitting Linear Regression model...
-    CV Fold: 3          Train MAE: 0.604      Test MAE: 0.701     
+    CV Fold: 3          Train Accuracy: 0.604      Test Accuracy: 0.701     
     CV Fold: 4          Fitting PCA model...
     CV Fold: 4          Fitting Linear Regression model...
-    CV Fold: 4          Train MAE: 0.624      Test MAE: 0.646     
+    CV Fold: 4          Train Accuracy: 0.624      Test Accuracy: 0.646     
     CV Fold: 5          Fitting PCA model...
     CV Fold: 5          Fitting Linear Regression model...
-    CV Fold: 5          Train MAE: 0.614      Test MAE: 0.722     
+    CV Fold: 5          Train Accuracy: 0.614      Test Accuracy: 0.722     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_33_3.png
+.. image:: other_predictive_models_files/other_predictive_models_34_1.png
 
 
 Connectome Predictive Modelling
---------------------------------------
+===============================
 
 .. code:: ipython3
 
     # correlation train_brain with train_phenotype
     train_z_pheno_corr_p = [stats.pearsonr(train_data_z[:, i], train_phen) for i in range(train_data_z.shape[1])]  # train_pheno_corr_p: (259200, )
+    # there are some nan correlations if brain data is poorly cropped (ie: some columns are always 0)
 
 .. code:: ipython3
 
     # correlation train_brain with train_phenotype
     train_ct_pheno_corr_p = [stats.pearsonr(train_data_ct[:, i], train_phen) for i in range(train_data_ct.shape[1])]  # train_pheno_corr_p: (259200, )
+    # there are some nan correlations if brain data is poorly cropped (ie: some columns are always 0)
 
 .. code:: ipython3
 
-    # split into positive and negative correlations 
+    # split into positive and negative correlations
     # and keep edges with p values below threshold
     pval_threshold = 0.01
     
@@ -495,15 +484,13 @@ Connectome Predictive Modelling
 
 .. code:: ipython3
 
-    fit_pos_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum_z.reshape(-1, 1), train_phen)
-    fit_neg_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum_z.reshape(-1, 1), train_phen)
-
+    fit_pos_z = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum_z.reshape(-1, 1), train_phen)
+    fit_neg_z = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum_z.reshape(-1, 1), train_phen)
 
 .. code:: ipython3
 
-    fit_pos_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum_ct.reshape(-1, 1), train_phen)
-    fit_neg_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum_ct.reshape(-1, 1), train_phen)
-
+    fit_pos_ct = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum_ct.reshape(-1, 1), train_phen)
+    fit_neg_ct = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum_ct.reshape(-1, 1), train_phen)
 
 .. code:: ipython3
 
@@ -512,52 +499,48 @@ Connectome Predictive Modelling
     pos_error_ct = metrics.mean_absolute_error(train_phen, fit_pos_ct.predict(train_pos_edges_sum_ct.reshape(-1, 1)))
     neg_error_ct = metrics.mean_absolute_error(train_phen, fit_neg_ct.predict(train_neg_edges_sum_ct.reshape(-1, 1)))
     
-    print(f'Training Error (MAE) (Positive Z Features Model) = {pos_error_z:.3f}')
-    print(f'Training Error (MAE) (Negative Z Features Model) = {neg_error_z:.3f}')
-    print(f'Training Error (MAE) (Positive CT Features Model) = {pos_error_ct:.3f}')
-    print(f'Training Error (MAE) (Negative CT Features Model) = {neg_error_ct:.3f}')
+    print(f'Training Error (Positive Z Features Model) = {pos_error_z:.3f}')
+    print(f'Training Error (Negative Z Features Model) = {neg_error_z:.3f}')
+    print(f'Training Error (Positive CT Features Model) = {pos_error_ct:.3f}')
+    print(f'Training Error (Negative CT Features Model) = {neg_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Training Error (MAE) (Positive Z Features Model) = 0.631
-    Training Error (MAE) (Negative Z Features Model) = 0.666
-    Training Error (MAE) (Positive CT Features Model) = 0.662
-    Training Error (MAE) (Negative CT Features Model) = 0.665
+    Training Error (Positive Z Features Model) = 0.631
+    Training Error (Negative Z Features Model) = 0.666
+    Training Error (Positive CT Features Model) = 0.662
+    Training Error (Negative CT Features Model) = 0.665
 
 
 .. code:: ipython3
 
     # combine positive/negative edges in one linear regression model
-    fit_pos_neg_z = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T, train_phen)
-
-
+    fit_pos_neg_z = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T, train_phen)
 
 .. code:: ipython3
 
     # combine positive/negative edges in one linear regression model
-    fit_pos_neg_ct = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T, train_phen)
-
-
+    fit_pos_neg_ct = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T, train_phen)
 
 .. code:: ipython3
 
     pos_neg_error_z = metrics.mean_absolute_error(train_phen, fit_pos_neg_z.predict(np.stack((train_pos_edges_sum_z, train_neg_edges_sum_z)).T))
     pos_neg_error_ct = metrics.mean_absolute_error(train_phen, fit_pos_neg_ct.predict(np.stack((train_pos_edges_sum_ct, train_neg_edges_sum_ct)).T))
     
-    print(f'Training Error (MAE) (Positive/Negative Z Features Model) = {pos_neg_error_z:.3f}')
-    print(f'Training Error (MAE) (Positive/Negative CT Features Model) = {pos_neg_error_ct:.3f}')
+    print(f'Training Error (Positive/Negative Z Features Model) = {pos_neg_error_z:.3f}')
+    print(f'Training Error (Positive/Negative CT Features Model) = {pos_neg_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Training Error (MAE) (Positive/Negative Z Features Model) = 0.620
-    Training Error (MAE) (Positive/Negative CT Features Model) = 0.642
+    Training Error (Positive/Negative Z Features Model) = 0.620
+    Training Error (Positive/Negative CT Features Model) = 0.642
 
 
 .. code:: ipython3
 
-    # evaluate out of sample performance 
+    # evaluate out of sample performance
     test_pos_edges_sum_z = test_data_z[:, keep_edges_pos_z].sum(1)
     test_neg_edges_sum_z = test_data_z[:, keep_edges_neg_z].sum(1)
     
@@ -572,44 +555,44 @@ Connectome Predictive Modelling
     neg_test_error_ct = metrics.mean_absolute_error(test_phen, fit_neg_ct.predict(test_neg_edges_sum_ct.reshape(-1, 1)))
     pos_neg_test_error_ct = metrics.mean_absolute_error(test_phen, fit_pos_neg_ct.predict(np.stack((test_pos_edges_sum_ct, test_neg_edges_sum_ct)).T))
     
-    print(f'Testing Error (MAE) (Positive Z Features Model) = {pos_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Negative Z Features Model) = {neg_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Positive/Negative Z Features Model) = {pos_neg_test_error_z:.3f}')
-    print(f'Testing Error (MAE) (Positive CT Features Model) = {pos_test_error_ct:.3f}')
-    print(f'Testing Error (MAE) (Negative CT Features Model) = {neg_test_error_ct:.3f}')
-    print(f'Testing Error (MAE) (Positive/Negative CT Features Model) = {pos_neg_test_error_ct:.3f}')
+    print(f'Testing Error (Positive Z Features Model) = {pos_test_error_z:.3f}')
+    print(f'Testing Error (Negative Z Features Model) = {neg_test_error_z:.3f}')
+    print(f'Testing Error (Positive/Negative Z Features Model) = {pos_neg_test_error_z:.3f}')
+    print(f'Testing Error (Positive CT Features Model) = {pos_test_error_ct:.3f}')
+    print(f'Testing Error (Negative CT Features Model) = {neg_test_error_ct:.3f}')
+    print(f'Testing Error (Positive/Negative CT Features Model) = {pos_neg_test_error_ct:.3f}')
 
 
 .. parsed-literal::
 
-    Testing Error (MAE) (Positive Z Features Model) = 0.705
-    Testing Error (MAE) (Negative Z Features Model) = 0.696
-    Testing Error (MAE) (Positive/Negative Z Features Model) = 0.697
-    Testing Error (MAE) (Positive CT Features Model) = 0.710
-    Testing Error (MAE) (Negative CT Features Model) = 0.695
-    Testing Error (MAE) (Positive/Negative CT Features Model) = 0.701
+    Testing Error (Positive Z Features Model) = 0.705
+    Testing Error (Negative Z Features Model) = 0.696
+    Testing Error (Positive/Negative Z Features Model) = 0.697
+    Testing Error (Positive CT Features Model) = 0.710
+    Testing Error (Negative CT Features Model) = 0.695
+    Testing Error (Positive/Negative CT Features Model) = 0.701
 
 
 CPM Cross Validation
---------------------------------------
+--------------------
 
 .. code:: ipython3
 
     def cpm(X, y, p_threshold, n_cv_splits, pred_summary_function, verbose=False):
         assert X.shape[0] == y.shape[0]
-        
+    
         fold_accs_train = []
         fold_accs_test = []
         np.random.seed(42)
         shuffled_idxs = np.random.choice(range(X.shape[0]), size=X.shape[0], replace=False)
         for fold_i, test_idxs in enumerate(np.array_split(shuffled_idxs, n_cv_splits)):
-            train_mask = np.ones(X.shape[0], np.bool)
+            train_mask = np.ones(X.shape[0], bool)
             train_mask[test_idxs] = 0
     
             # create train/text X, y
             train_X, test_X = X[train_mask, :], X[test_idxs, :]
-            train_y, test_y = y[train_mask], y[test_idxs]  
-            
+            train_y, test_y = y[train_mask], y[test_idxs]
+    
             # create correlation matrix between train_X and train_y
             if verbose:
                 print(f'CV Fold: {fold_i+1:<10} Computing correlations between train_X and train_y...')
@@ -622,18 +605,18 @@ CPM Cross Validation
                 # create masks for edges below p-threshold and split pos/neg correlations
                 keep_edges_pos = (train_corrs > 0) & (train_pvals < p_threshold)
                 keep_edges_neg = (train_corrs < 0) & (train_pvals < p_threshold)
-            
+    
             # sum X entries with significant correlations with y
             train_pos_edges_sum = train_X[:, keep_edges_pos].sum(1)
             train_neg_edges_sum = train_X[:, keep_edges_neg].sum(1)
             test_pos_edges_sum = test_X[:, keep_edges_pos].sum(1)
             test_neg_edges_sum = test_X[:, keep_edges_neg].sum(1)
-            
+    
             # fit linear regression models based on summed values
-            fit_pos = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_pos_edges_sum.reshape(-1, 1), train_y)
-            fit_neg = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(train_neg_edges_sum.reshape(-1, 1), train_y)
-            fit_pos_neg = linear_model.LinearRegression(fit_intercept=True, normalize=False).fit(np.stack((train_pos_edges_sum, train_neg_edges_sum)).T, train_y)
-            
+            fit_pos = linear_model.LinearRegression(fit_intercept=True).fit(train_pos_edges_sum.reshape(-1, 1), train_y)
+            fit_neg = linear_model.LinearRegression(fit_intercept=True).fit(train_neg_edges_sum.reshape(-1, 1), train_y)
+            fit_pos_neg = linear_model.LinearRegression(fit_intercept=True).fit(np.stack((train_pos_edges_sum, train_neg_edges_sum)).T, train_y)
+    
             # compute train errors
             train_pos_error = pred_summary_function(train_y, fit_pos.predict(train_pos_edges_sum.reshape(-1, 1)))
             train_neg_error = pred_summary_function(train_y, fit_neg.predict(train_neg_edges_sum.reshape(-1, 1)))
@@ -646,27 +629,27 @@ CPM Cross Validation
     
             fold_accs_train.append((train_pos_error, train_neg_error, train_posneg_error))
             fold_accs_test.append((test_pos_error, test_neg_error, test_posneg_error))
-            
+    
             if verbose:
-                print(f'CV Fold: {fold_i+1:<10} Train Pos-Edges Model MAE: {round(train_pos_error, 3):<10} Train Neg-Edges Model Accuracy: {round(train_neg_error, 3):<10} Train Pos/Neg-Edges Model Accuracy: {round(train_posneg_error, 3):<10}')
-                print(f'CV Fold: {fold_i+1:<10} Test  Pos-Edges Model MAE: {round(test_pos_error, 3):<10} Test  Neg-Edges Model Accuracy: {round(test_neg_error, 3):<10} Test  Pos/Neg-Edges Model Accuracy: {round(test_posneg_error, 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Train Pos-Edges Model Accuracy: {round(train_pos_error, 3):<10} Train Neg-Edges Model Accuracy: {round(train_neg_error, 3):<10} Train Pos/Neg-Edges Model Accuracy: {round(train_posneg_error, 3):<10}')
+                print(f'CV Fold: {fold_i+1:<10} Test  Pos-Edges Model Accuracy: {round(test_pos_error, 3):<10} Test  Neg-Edges Model Accuracy: {round(test_neg_error, 3):<10} Test  Pos/Neg-Edges Model Accuracy: {round(test_posneg_error, 3):<10}')
+    
     
-        
         plt.figure(figsize=(13, 7))
         plt.plot(range(1, len(fold_accs_train)+1), [x[0] for x in fold_accs_train], linestyle='--', marker='o', color='C0', label='Train Pos-Edges Model')
         plt.plot(range(1, len(fold_accs_train)+1), [x[1] for x in fold_accs_train], linestyle='--', marker='o', color='C1', label='Train Neg-Edges Model')
         plt.plot(range(1, len(fold_accs_train)+1), [x[2] for x in fold_accs_train], linestyle='--', marker='o', color='C2', label='Train Pos/Neg-Edges Model')
-        
+    
         plt.plot(range(1, len(fold_accs_test)+1), [x[0] for x in fold_accs_test], linestyle='-', marker='o', color='C0', label='Test  Pos-Edges Model')
         plt.plot(range(1, len(fold_accs_test)+1), [x[1] for x in fold_accs_test], linestyle='-', marker='o', color='C1', label='Test  Neg-Edges Model')
         plt.plot(range(1, len(fold_accs_test)+1), [x[2] for x in fold_accs_test], linestyle='-', marker='o', color='C2', label='Test  Pos/Neg-Edges Model')
-        
+    
         plt.title(pred_summary_function.__name__, fontsize=20)
         plt.xticks(range(1, len(fold_accs_test)+1))
         plt.xlabel('CV Fold')
         plt.legend(fontsize=10)
         plt.show()
-        
+    
         return fold_accs_train, fold_accs_test
 
 .. code:: ipython3
@@ -674,32 +657,27 @@ CPM Cross Validation
     fold_accs_train_z, fold_accs_test_z = cpm(hcp_z, gscores, p_threshold=0.01, n_cv_splits=5, pred_summary_function=metrics.mean_absolute_error, verbose=True)
 
 
-
 .. parsed-literal::
 
     CV Fold: 1          Computing correlations between train_X and train_y...
-    CV Fold: 1          Train Pos-Edges Model MAE: 0.652      Train Neg-Edges Model MAE: 0.673      Train Pos/Neg-Edges Model MAE: 0.644     
-    CV Fold: 1          Test  Pos-Edges Model MAE: 0.636      Test  Neg-Edges Model MAE: 0.671      Test  Pos/Neg-Edges Model MAE: 0.632     
+    CV Fold: 1          Train Pos-Edges Model Accuracy: 0.652      Train Neg-Edges Model Accuracy: 0.673      Train Pos/Neg-Edges Model Accuracy: 0.644     
+    CV Fold: 1          Test  Pos-Edges Model Accuracy: 0.636      Test  Neg-Edges Model Accuracy: 0.671      Test  Pos/Neg-Edges Model Accuracy: 0.632     
     CV Fold: 2          Computing correlations between train_X and train_y...
-    CV Fold: 2          Train Pos-Edges Model MAE: 0.648      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 2          Test  Pos-Edges Model MAE: 0.651      Test  Neg-Edges Model MAE: 0.659      Test  Pos/Neg-Edges Model MAE: 0.662     
+    CV Fold: 2          Train Pos-Edges Model Accuracy: 0.648      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 2          Test  Pos-Edges Model Accuracy: 0.651      Test  Neg-Edges Model Accuracy: 0.659      Test  Pos/Neg-Edges Model Accuracy: 0.662     
     CV Fold: 3          Computing correlations between train_X and train_y...
-    CV Fold: 3          Train Pos-Edges Model MAE: 0.644      Train Neg-Edges Model MAE: 0.662      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 3          Test  Pos-Edges Model MAE: 0.65       Test  Neg-Edges Model MAE: 0.708      Test  Pos/Neg-Edges Model MAE: 0.646     
+    CV Fold: 3          Train Pos-Edges Model Accuracy: 0.644      Train Neg-Edges Model Accuracy: 0.662      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 3          Test  Pos-Edges Model Accuracy: 0.65       Test  Neg-Edges Model Accuracy: 0.708      Test  Pos/Neg-Edges Model Accuracy: 0.646     
     CV Fold: 4          Computing correlations between train_X and train_y...
-    CV Fold: 4          Train Pos-Edges Model MAE: 0.653      Train Neg-Edges Model MAE: 0.676      Train Pos/Neg-Edges Model MAE: 0.648     
-    CV Fold: 4          Test  Pos-Edges Model MAE: 0.626      Test  Neg-Edges Model MAE: 0.659      Test  Pos/Neg-Edges Model MAE: 0.625     
+    CV Fold: 4          Train Pos-Edges Model Accuracy: 0.653      Train Neg-Edges Model Accuracy: 0.676      Train Pos/Neg-Edges Model Accuracy: 0.648     
+    CV Fold: 4          Test  Pos-Edges Model Accuracy: 0.626      Test  Neg-Edges Model Accuracy: 0.659      Test  Pos/Neg-Edges Model Accuracy: 0.625     
     CV Fold: 5          Computing correlations between train_X and train_y...
+    CV Fold: 5          Train Pos-Edges Model Accuracy: 0.631      Train Neg-Edges Model Accuracy: 0.666      Train Pos/Neg-Edges Model Accuracy: 0.62      
+    CV Fold: 5          Test  Pos-Edges Model Accuracy: 0.704      Test  Neg-Edges Model Accuracy: 0.696      Test  Pos/Neg-Edges Model Accuracy: 0.697     
 
 
-.. parsed-literal::
-
-    CV Fold: 5          Train Pos-Edges Model MAE: 0.631      Train Neg-Edges Model MAE: 0.666      Train Pos/Neg-Edges Model MAE: 0.62      
-    CV Fold: 5          Test  Pos-Edges Model MAE: 0.704      Test  Neg-Edges Model MAE: 0.696      Test  Pos/Neg-Edges Model MAE: 0.697     
-
 
-
-.. image:: other_predictive_models_files/other_predictive_models_50_4.png
+.. image:: other_predictive_models_files/other_predictive_models_51_1.png
 
 
 .. code:: ipython3
@@ -710,51 +688,61 @@ CPM Cross Validation
 .. parsed-literal::
 
     CV Fold: 1          Computing correlations between train_X and train_y...
-    CV Fold: 1          Train Pos-Edges Model MAE: 0.675      Train Neg-Edges Model MAE: 0.673      Train Pos/Neg-Edges Model MAE: 0.659     
-    CV Fold: 1          Test  Pos-Edges Model MAE: 0.659      Test  Neg-Edges Model MAE: 0.67       Test  Pos/Neg-Edges Model MAE: 0.653     
+    CV Fold: 1          Train Pos-Edges Model Accuracy: 0.675      Train Neg-Edges Model Accuracy: 0.673      Train Pos/Neg-Edges Model Accuracy: 0.659     
+    CV Fold: 1          Test  Pos-Edges Model Accuracy: 0.659      Test  Neg-Edges Model Accuracy: 0.67       Test  Pos/Neg-Edges Model Accuracy: 0.653     
     CV Fold: 2          Computing correlations between train_X and train_y...
-    CV Fold: 2          Train Pos-Edges Model MAE: 0.674      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.636     
-    CV Fold: 2          Test  Pos-Edges Model MAE: 0.661      Test  Neg-Edges Model MAE: 0.657      Test  Pos/Neg-Edges Model MAE: 0.668     
+    CV Fold: 2          Train Pos-Edges Model Accuracy: 0.674      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.636     
+    CV Fold: 2          Test  Pos-Edges Model Accuracy: 0.661      Test  Neg-Edges Model Accuracy: 0.657      Test  Pos/Neg-Edges Model Accuracy: 0.668     
     CV Fold: 3          Computing correlations between train_X and train_y...
-    CV Fold: 3          Train Pos-Edges Model MAE: 0.659      Train Neg-Edges Model MAE: 0.665      Train Pos/Neg-Edges Model MAE: 0.644     
-    CV Fold: 3          Test  Pos-Edges Model MAE: 0.699      Test  Neg-Edges Model MAE: 0.704      Test  Pos/Neg-Edges Model MAE: 0.684     
+    CV Fold: 3          Train Pos-Edges Model Accuracy: 0.659      Train Neg-Edges Model Accuracy: 0.665      Train Pos/Neg-Edges Model Accuracy: 0.644     
+    CV Fold: 3          Test  Pos-Edges Model Accuracy: 0.699      Test  Neg-Edges Model Accuracy: 0.704      Test  Pos/Neg-Edges Model Accuracy: 0.684     
     CV Fold: 4          Computing correlations between train_X and train_y...
-    CV Fold: 4          Train Pos-Edges Model MAE: 0.674      Train Neg-Edges Model MAE: 0.678      Train Pos/Neg-Edges Model MAE: 0.658     
-    CV Fold: 4          Test  Pos-Edges Model MAE: 0.653      Test  Neg-Edges Model MAE: 0.656      Test  Pos/Neg-Edges Model MAE: 0.638     
+    CV Fold: 4          Train Pos-Edges Model Accuracy: 0.674      Train Neg-Edges Model Accuracy: 0.678      Train Pos/Neg-Edges Model Accuracy: 0.658     
+    CV Fold: 4          Test  Pos-Edges Model Accuracy: 0.653      Test  Neg-Edges Model Accuracy: 0.656      Test  Pos/Neg-Edges Model Accuracy: 0.638     
     CV Fold: 5          Computing correlations between train_X and train_y...
-    CV Fold: 5          Train Pos-Edges Model MAE: 0.662      Train Neg-Edges Model MAE: 0.666      Train Pos/Neg-Edges Model MAE: 0.642     
-    CV Fold: 5          Test  Pos-Edges Model MAE: 0.709      Test  Neg-Edges Model MAE: 0.698      Test  Pos/Neg-Edges Model MAE: 0.708     
+    CV Fold: 5          Train Pos-Edges Model Accuracy: 0.662      Train Neg-Edges Model Accuracy: 0.666      Train Pos/Neg-Edges Model Accuracy: 0.642     
+    CV Fold: 5          Test  Pos-Edges Model Accuracy: 0.709      Test  Neg-Edges Model Accuracy: 0.698      Test  Pos/Neg-Edges Model Accuracy: 0.708     
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_51_2.png
+.. image:: other_predictive_models_files/other_predictive_models_52_1.png
 
 
 Lasso (Linear Regression + L1 Regularization)
------------------------------------------------------
+=============================================
 
 .. code:: ipython3
 
-    # LassoCV uses coordinate descent to select hyperparameter alpha 
+    # LassoCV uses coordinate descent to select hyperparameter alpha
     alpha_grid = np.array([10**a for a in np.arange(-3, 3, 0.25)])
-    lassoCV_model_z = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, normalize=False, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
+    lassoCV_model_z = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
 
 
+.. parsed-literal::
+
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    .....................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.5s remaining:    0.7s
+    /usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07308221069854426, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15375414865695802, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10611096508367268, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    .[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.5s finished
+
 
 .. code:: ipython3
 
-    # LassoCV uses coordinate descent to select hyperparameter alpha 
+    # LassoCV uses coordinate descent to select hyperparameter alpha
     alpha_grid = np.array([10**a for a in np.arange(-3, 3, 0.25)])
-    lassoCV_model_ct = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True, normalize=False, random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
+    lassoCV_model_ct = linear_model.LassoCV(cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, fit_intercept=True,  random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
 
 
 .. parsed-literal::
 
     [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
-    ...................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.3s remaining:    0.5s
-    .....[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.3s finished
-    /usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:155: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
-      FutureWarning,
+    ....................................................................................................................[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.2s remaining:    0.3s
+    ....[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.3s finished
 
 
 .. code:: ipython3
@@ -770,7 +758,7 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_55_0.png
+.. image:: other_predictive_models_files/other_predictive_models_56_0.png
 
 
 .. code:: ipython3
@@ -786,22 +774,18 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_56_0.png
+.. image:: other_predictive_models_files/other_predictive_models_57_0.png
 
 
 .. code:: ipython3
 
     # based on cv results above, set alpha=100
-    lasso_model_z = linear_model.Lasso(alpha=lassoCV_model_z.alpha_, fit_intercept=True, normalize=False).fit(train_data_z, train_phen)
-
-
+    lasso_model_z = linear_model.Lasso(alpha=lassoCV_model_z.alpha_, fit_intercept=True).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
     # based on cv results above, set alpha=100
-    lasso_model_ct = linear_model.Lasso(alpha=lassoCV_model_ct.alpha_, fit_intercept=True, normalize=False).fit(train_data_ct, train_phen)
-
-
+    lasso_model_ct = linear_model.Lasso(alpha=lassoCV_model_ct.alpha_, fit_intercept=True).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -832,23 +816,23 @@ Lasso (Linear Regression + L1 Regularization)
 
 
 Ridge (Linear Regression + L2 Regularization)
---------------------------------------------------------
+=============================================
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     with warnings.catch_warnings():
         # ignore matrix decomposition errors
         warnings.simplefilter("ignore")
-        ridgeCV_model_z = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, cv=5).fit(train_data_z, train_phen)
+        ridgeCV_model_z = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, cv=5).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     with warnings.catch_warnings():
         # ignore matrix decomposition errors
         warnings.simplefilter("ignore")
-        ridgeCV_model_ct = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, cv=5).fit(train_data_ct, train_phen)
+        ridgeCV_model_ct = linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True,  cv=5).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -874,14 +858,11 @@ Ridge (Linear Regression + L2 Regularization)
 
 .. code:: ipython3
 
-    ridge_model_z = linear_model.Ridge(alpha=ridge_alpha_z, fit_intercept=True, normalize=False).fit(train_data_z, train_phen)
-
-
+    ridge_model_z = linear_model.Ridge(alpha=ridge_alpha_z, fit_intercept=True).fit(train_data_z, train_phen)
 
 .. code:: ipython3
 
-    ridge_model_ct = linear_model.Ridge(alpha=ridge_alpha_ct, fit_intercept=True, normalize=False).fit(train_data_ct, train_phen)
-
+    ridge_model_ct = linear_model.Ridge(alpha=ridge_alpha_ct, fit_intercept=True).fit(train_data_ct, train_phen)
 
 .. code:: ipython3
 
@@ -912,21 +893,81 @@ Ridge (Linear Regression + L2 Regularization)
 
 
 Elastic Net (Linear Regression + L1/L2 Regularization)
-------------------------------------------------------------
+======================================================
 
 .. code:: ipython3
 
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     elasticnetCV_model_z = linear_model.ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, random_state=42, verbose=True, n_jobs=5).fit(train_data_z, train_phen)
 
 
+.. parsed-literal::
 
-.. code:: ipython3
-
-    # RidgeCV uses generalized cross validation to select hyperparameter alpha 
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    ............................................................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21318694590257792, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17936527851907158, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    .../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8618322913218321, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.57867990423236, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.2273489799189, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4036642558553467, tolerance: 0.04401109832998077
+      model = cd_fast.enet_coordinate_descent_gram(
+    .................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.073063075099014, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.227358858718446, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.883650580549045, tolerance: 0.04401109832998077
+      model = cd_fast.enet_coordinate_descent_gram(
+    ....................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18805636326129616, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0544661418971657, tolerance: 0.0423918944559644
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1788130249701112, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13839227040918445, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................................................................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15009167262110168, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ........................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20204581109658193, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.09891903798924773, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ......................................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13078279402705562, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18265009272980137, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0877297694903234, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................................................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11087317503455552, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ...................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16051209546739642, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..................................../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07594686106816084, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ............................................................./usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10611096508367268, tolerance: 0.04382929483334259
+      model = cd_fast.enet_coordinate_descent_gram(
+    ...../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15375414865695802, tolerance: 0.03970345334827422
+      model = cd_fast.enet_coordinate_descent_gram(
+    ../usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07308221069854426, tolerance: 0.04611195889050071
+      model = cd_fast.enet_coordinate_descent_gram(
+    ..[Parallel(n_jobs=5)]: Done  35 out of  35 | elapsed:    6.3s finished
+
+
+.. code:: ipython3
+
+    # RidgeCV uses generalized cross validation to select hyperparameter alpha
     elasticnetCV_model_ct = linear_model.ElasticNetCV(l1_ratio=[.1, .5, .7, .9, .95, .99, 1], cv=5, n_alphas=len(alpha_grid), alphas=alpha_grid, random_state=42, verbose=True, n_jobs=5).fit(train_data_ct, train_phen)
 
 
+.. parsed-literal::
+
+    [Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.
+    ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................[Parallel(n_jobs=5)]: Done  35 out of  35 | elapsed:    1.8s finished
+
 
 .. code:: ipython3
 
@@ -958,7 +999,7 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_72_0.png
+.. image:: other_predictive_models_files/other_predictive_models_73_0.png
 
 
 .. code:: ipython3
@@ -975,12 +1016,12 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
 
 
 
-.. image:: other_predictive_models_files/other_predictive_models_73_0.png
+.. image:: other_predictive_models_files/other_predictive_models_74_0.png
 
 
 .. code:: ipython3
 
-    elasticnet_model_z = linear_model.ElasticNet(alpha=elasticnetCV_model_z.alpha_, l1_ratio=elasticnetCV_model_z.l1_ratio_, fit_intercept=True, normalize=False, random_state=42).fit(train_data_z, train_phen)
+    elasticnet_model_z = linear_model.ElasticNet(alpha=elasticnetCV_model_z.alpha_, l1_ratio=elasticnetCV_model_z.l1_ratio_, fit_intercept=True,  random_state=42).fit(train_data_z, train_phen)
     
     train_preds_en_model_z = elasticnet_model_z.predict(train_data_z)
     test_preds_en_model_z = elasticnet_model_z.predict(test_data_z)
@@ -988,7 +1029,7 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
     train_mae_z = metrics.mean_absolute_error(train_phen, train_preds_en_model_z)
     test_mae_z = metrics.mean_absolute_error(test_phen, test_preds_en_model_z)
     
-    elasticnet_model_ct = linear_model.ElasticNet(alpha=elasticnetCV_model_ct.alpha_, l1_ratio=elasticnetCV_model_ct.l1_ratio_, fit_intercept=True, normalize=False, random_state=42).fit(train_data_ct, train_phen)
+    elasticnet_model_ct = linear_model.ElasticNet(alpha=elasticnetCV_model_ct.alpha_, l1_ratio=elasticnetCV_model_ct.l1_ratio_, fit_intercept=True, random_state=42).fit(train_data_ct, train_phen)
     
     train_preds_en_model_ct = elasticnet_model_ct.predict(train_data_ct)
     test_preds_en_model_ct = elasticnet_model_ct.predict(test_data_ct)
@@ -1008,3 +1049,5 @@ Elastic Net (Linear Regression + L1/L2 Regularization)
     Test MAE Z model: 0.680
     Train MAE CT model: 0.633
     Test MAE CT model: 0.692
+
+
diff --git a/doc/build/html/_static/_sphinx_javascript_frameworks_compat.js b/doc/build/html/_static/_sphinx_javascript_frameworks_compat.js
index 8549469d..81415803 100644
--- a/doc/build/html/_static/_sphinx_javascript_frameworks_compat.js
+++ b/doc/build/html/_static/_sphinx_javascript_frameworks_compat.js
@@ -1,20 +1,9 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
+/* Compatability shim for jQuery and underscores.js.
  *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
  */
 
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
 /**
  * small helper function to urldecode strings
  *
diff --git a/doc/build/html/_static/basic.css b/doc/build/html/_static/basic.css
index 08896771..7ebbd6d0 100644
--- a/doc/build/html/_static/basic.css
+++ b/doc/build/html/_static/basic.css
@@ -1,12 +1,5 @@
 /*
- * basic.css
- * ~~~~~~~~~
- *
  * Sphinx stylesheet -- basic theme.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 
 /* -- main layout ----------------------------------------------------------- */
@@ -115,15 +108,11 @@ img {
 /* -- search page ----------------------------------------------------------- */
 
 ul.search {
-    margin: 10px 0 0 20px;
-    padding: 0;
+    margin-top: 10px;
 }
 
 ul.search li {
-    padding: 5px 0 5px 20px;
-    background-image: url(file.png);
-    background-repeat: no-repeat;
-    background-position: 0 7px;
+    padding: 5px 0;
 }
 
 ul.search li a {
@@ -237,6 +226,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -324,17 +317,17 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
+
 nav.contents,
 aside.topic,
-
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
+
 nav.contents,
 aside.topic,
-
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -375,7 +368,6 @@ div.sidebar > :last-child,
 aside.sidebar > :last-child,
 nav.contents > :last-child,
 aside.topic > :last-child,
-
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -385,7 +377,6 @@ div.sidebar::after,
 aside.sidebar::after,
 nav.contents::after,
 aside.topic::after,
-
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -611,25 +602,6 @@ ul.simple p {
     margin-bottom: 0;
 }
 
-/* Docutils 0.17 and older (footnotes & citations) */
-dl.footnote > dt,
-dl.citation > dt {
-    float: left;
-    margin-right: 0.5em;
-}
-
-dl.footnote > dd,
-dl.citation > dd {
-    margin-bottom: 0em;
-}
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
-    content: "";
-    clear: both;
-}
-
-/* Docutils 0.18+ (footnotes & citations) */
 aside.footnote > span,
 div.citation > span {
     float: left;
@@ -654,8 +626,6 @@ div.citation > p:last-of-type:after {
     clear: both;
 }
 
-/* Footnotes & citations ends */
-
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;
@@ -668,10 +638,6 @@ dl.field-list > dt {
     padding-right: 5px;
 }
 
-dl.field-list > dt:after {
-    content: ":";
-}
-
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
@@ -697,6 +663,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -765,6 +741,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/doc/build/html/_static/css/badge_only.css b/doc/build/html/_static/css/badge_only.css
index c718cee4..88ba55b9 100644
--- a/doc/build/html/_static/css/badge_only.css
+++ b/doc/build/html/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/doc/build/html/_static/css/theme.css b/doc/build/html/_static/css/theme.css
index 19a446a0..0f14f106 100644
--- a/doc/build/html/_static/css/theme.css
+++ b/doc/build/html/_static/css/theme.css
@@ -1,4 +1,4 @@
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dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .property{display:inline-block;padding-right:8px;max-width:100%}html.writer-html4 .rst-content dl:not(.docutils) .k,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel,.rst-content .menuselection{font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .guilabel,.rst-content .menuselection{border:1px solid #7fbbe3;background:#e7f2fa}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/doc/build/html/_static/doctools.js b/doc/build/html/_static/doctools.js
index c3db08d1..0398ebb9 100644
--- a/doc/build/html/_static/doctools.js
+++ b/doc/build/html/_static/doctools.js
@@ -1,15 +1,15 @@
 /*
- * doctools.js
- * ~~~~~~~~~~~
- *
  * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 "use strict";
 
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
 const _ready = (callback) => {
   if (document.readyState !== "loading") {
     callback();
@@ -18,73 +18,11 @@ const _ready = (callback) => {
   }
 };
 
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
 /**
  * Small JavaScript module for the documentation.
  */
 const Documentation = {
   init: () => {
-    Documentation.highlightSearchWords();
     Documentation.initDomainIndexTable();
     Documentation.initOnKeyListeners();
   },
@@ -126,51 +64,6 @@ const Documentation = {
     Documentation.LOCALE = catalog.locale;
   },
 
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '
    ' +
-            '' +
-            Documentation.gettext("Hide Search Matches") +
-            "
    "
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-  },
-
   /**
    * helper function to focus on search bar
    */
@@ -210,15 +103,11 @@ const Documentation = {
     )
       return;
 
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
     document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
 
       if (!event.shiftKey) {
         switch (event.key) {
@@ -240,10 +129,6 @@ const Documentation = {
               event.preventDefault();
             }
             break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
 
diff --git a/doc/build/html/_static/documentation_options.js b/doc/build/html/_static/documentation_options.js
index 908cd41f..b86f7e34 100644
--- a/doc/build/html/_static/documentation_options.js
+++ b/doc/build/html/_static/documentation_options.js
@@ -1,5 +1,4 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+const DOCUMENTATION_OPTIONS = {
     VERSION: '0.20',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
@@ -10,5 +9,5 @@ var DOCUMENTATION_OPTIONS = {
     SOURCELINK_SUFFIX: '.txt',
     NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/doc/build/html/_static/graphviz.css b/doc/build/html/_static/graphviz.css
index 19e7afd3..30f3837b 100644
--- a/doc/build/html/_static/graphviz.css
+++ b/doc/build/html/_static/graphviz.css
@@ -1,12 +1,5 @@
 /*
- * graphviz.css
- * ~~~~~~~~~~~~
- *
  * Sphinx stylesheet -- graphviz extension.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 
 img.graphviz {
diff --git a/doc/build/html/_static/language_data.js b/doc/build/html/_static/language_data.js
index 2e22b06a..c7fe6c6f 100644
--- a/doc/build/html/_static/language_data.js
+++ b/doc/build/html/_static/language_data.js
@@ -1,19 +1,12 @@
 /*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
diff --git a/doc/build/html/_static/pygments.css b/doc/build/html/_static/pygments.css
index 691aeb82..0d49244e 100644
--- a/doc/build/html/_static/pygments.css
+++ b/doc/build/html/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/doc/build/html/_static/searchtools.js b/doc/build/html/_static/searchtools.js
index ac4d5861..2c774d17 100644
--- a/doc/build/html/_static/searchtools.js
+++ b/doc/build/html/_static/searchtools.js
@@ -1,12 +1,5 @@
 /*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
  * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 "use strict";
 
@@ -20,7 +13,7 @@ if (typeof Scorer === "undefined") {
     // and returns the new score.
     /*
     score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
+      const [docname, title, anchor, descr, score, filename, kind] = result
       return score
     },
     */
@@ -47,6 +40,14 @@ if (typeof Scorer === "undefined") {
   };
 }
 
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
 const _removeChildren = (element) => {
   while (element && element.lastChild) element.removeChild(element.lastChild);
 };
@@ -57,16 +58,20 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, highlightTerms, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
-  const [docName, title, anchor, descr] = item;
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
 
   let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
   let requestUrl;
   let linkUrl;
   if (docBuilder === "dirhtml") {
@@ -75,29 +80,35 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
   let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
+            Search.makeSearchSummary(data, searchTerms, anchor)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -109,28 +120,46 @@ const _finishSearch = (resultCount) => {
       "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
     );
   else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
 };
 const _displayNextItem = (
   results,
   resultCount,
+  searchTerms,
   highlightTerms,
-  searchTerms
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -154,15 +183,26 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
+    if (docContent) return docContent.textContent;
+
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
     );
     return "";
   },
@@ -215,6 +255,7 @@ const Search = {
     searchSummary.classList.add("search-summary");
     searchSummary.innerText = "";
     const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
     searchList.classList.add("search");
 
     const out = document.getElementById("search-results");
@@ -235,10 +276,7 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
+  _parseQuery: (query) => {
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -266,40 +304,98 @@ const Search = {
       }
     });
 
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
     // console.debug("SEARCH: searching for:");
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
     _removeChildren(document.getElementById("search-progress"));
 
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+            SearchResultKind.title,
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
     // lookup as object
     objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -313,14 +409,19 @@ const Search = {
       return acc;
     }, []);
 
-    results = results.reverse();
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
@@ -384,6 +485,7 @@ const Search = {
         descr,
         score,
         filenames[match[0]],
+        SearchResultKind.object,
       ]);
     };
     Object.keys(objects).forEach((prefix) =>
@@ -401,8 +503,8 @@ const Search = {
     // prepare search
     const terms = Search._index.terms;
     const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
     const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
     const titles = Search._index.titles;
 
     const scoreMap = new Map();
@@ -418,14 +520,18 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
       }
 
       // no match but word was a required one
@@ -448,9 +554,8 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
       });
     });
 
@@ -491,6 +596,7 @@ const Search = {
         null,
         score,
         filenames[file],
+        SearchResultKind.text,
       ]);
     }
     return results;
@@ -499,16 +605,15 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
+   * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
     if (text === "") return null;
 
+    const textLower = text.toLowerCase();
     const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
+      .map((k) => textLower.indexOf(k.toLowerCase()))
       .filter((i) => i > -1)
       .slice(-1)[0];
     const startWithContext = Math.max(actualStartPosition - 120, 0);
@@ -516,13 +621,9 @@ const Search = {
     const top = startWithContext === 0 ? "" : "...";
     const tail = startWithContext + 240 < text.length ? "..." : "";
 
-    let summary = document.createElement("div");
+    let summary = document.createElement("p");
     summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
 
     return summary;
   },
diff --git a/doc/build/html/_static/sphinx_highlight.js b/doc/build/html/_static/sphinx_highlight.js
index aae669d7..8a96c69a 100644
--- a/doc/build/html/_static/sphinx_highlight.js
+++ b/doc/build/html/_static/sphinx_highlight.js
@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/doc/build/html/genindex.html b/doc/build/html/genindex.html
index 1509fff9..d55d9018 100644
--- a/doc/build/html/genindex.html
+++ b/doc/build/html/genindex.html
@@ -1,28 +1,24 @@
+
+
 
-
+
 
   
   
   Index — Predictive Clinical Neuroscience Toolkit 0.20 documentation
-      
-      
-      
+      
+      
+      
       
-      
       
       
 
   
-  
-  
-        
-        
-        
-        
-        
-        
+      
+      
+      
+      
+      
     
     
      
@@ -62,13 +58,24 @@
 

 
Tutorials

 
 
Other Useful Stuff