import numpy as np import experiment_loader import matplotlib from fitensemble import belt matplotlib.rcParams.update({'font.size': 20}) alpha = 0.2 num_grid = 500 phi = np.linspace(-180,180,num_grid) O = np.ones(num_grid) colors = ["b","g","r","y"] simulation_data = {} ff = "amber99" # Load FF data for comparison, not used in actual figure phi1, psi1, ass_raw, state_ind = experiment_loader.load_rama(ff, 1) J = scalar_couplings.J3_HN_HA(phi) predictions, measurements, uncertainties = experiment_loader.load(ff, keys=[("JC", 2, "J3_HN_HA")]) yi = measurements.iloc[0] oi = uncertainties.iloc[0] factor = 1.0 regularization_strength = 0.2 model = belt.MaxEntBELT(predictions.values, measurements.values, factor * uncertainties.values, regularization_strength) model.sample(5000) #obs = belt. ai = model.mcmc.trace("alpha")[:]
import sys import ALA3 ff = "amber96" effective_counts = 1000.0 num_bins = 0 num_states = num_bins ** 2 prior = "BW%d" % num_bins directory = "%s/%s" % (ALA3.data_dir, ff) out_dir = directory + "/models-%s/" % prior pymc_filename = out_dir + "/model.h5" predictions, measurements, uncertainties = experiment_loader.load(ff, stride=ALA3.stride) phi, psi, ass_raw, state_ind = experiment_loader.load_rama(ff, ALA3.stride) num_frames, num_measurements = predictions.shape if num_bins != 0: assignments = schwalbe_couplings.assign_grid(phi, psi, num_bins)[2] else: assignments = ass_raw prior_state_pops = np.bincount(assignments).astype("float") prior_state_pops /= prior_state_pops.sum() prior_state_pops *= effective_counts ALA3.bw_num_samples = 10000 model = bayesian_weighting.BayesianWeighting( predictions.values, measurements.values, uncertainties.values, assignments, prior_state_pops=prior_state_pops )
from fitensemble import lvbp import pandas as pd import numpy as np import matplotlib.pyplot as plt import ALA3 import experiment_loader ff = "amber99" prior = "maxent" regularization_strength = ALA3.regularization_strength_dict[prior][ff] #regularization_strength = 5.0 data_directory = "/%s/%s/" % (ALA3.data_dir, ff) model_directory = "/%s/%s/models-%s/" % (ALA3.data_dir, ff, prior) #model_directory = "/%s/%s/models-all-expt-%s/" % (ALA3.data_dir, ff, prior) predictions, measurements, uncertainties = experiment_loader.load(data_directory) phi, psi, ass_raw, state_ind = experiment_loader.load_rama(data_directory, 1) lvbp_model = lvbp.LVBP.load(model_directory + "/reg-%d-BB0.h5" % regularization_strength) #p = np.loadtxt(model_directory + "reg-%d-frame-populations.dat" % regularization_strength) a = lvbp_model.mcmc.trace("alpha")[:] plot(a[:,0])
import numpy as np from fitensemble import bayesian_weighting, belt import experiment_loader import ALA3 prior = "BW" ff = "amber96" stride = 1000 regularization_strength = 10.0 thin = 400 factor = 50 steps = 1000000 predictions_framewise, measurements, uncertainties = experiment_loader.load(ff, stride=stride) phi, psi, ass_raw0, state_ind0 = experiment_loader.load_rama(ff, stride) num_states = len(phi) assignments = np.arange(num_states) prior_pops = np.ones(num_states) predictions = pd.DataFrame(bayesian_weighting.framewise_to_statewise(predictions_framewise, assignments), columns=predictions_framewise.columns) model = bayesian_weighting.MaxentBayesianWeighting(predictions.values, measurements.values, uncertainties.values, assignments, regularization_strength) model.sample(steps * factor, thin=thin * factor) model2 = belt.MaxEntBELT(predictions.values, measurements.values, uncertainties.values, regularization_strength) model2.sample(steps, thin=thin) pi = model.mcmc.trace("matrix_populations")[:, 0]