-
Notifications
You must be signed in to change notification settings - Fork 27
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
5c217fc
commit 68a9d33
Showing
2 changed files
with
183 additions
and
5 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,130 @@ | ||
import numpy as np | ||
import emcee | ||
import tqdm | ||
import corner | ||
from scipy.stats import rice, norm, uniform | ||
from matplotlib import pyplot as plt | ||
|
||
from utilities.data_simulation.GenerateData import GenerateData | ||
|
||
class MCMC: | ||
""" | ||
Performs sampling of exponential data | ||
""" | ||
def __init__(self, | ||
data, | ||
b, | ||
data_scale=1e-5, | ||
parameter_scale=(1e-7, 1e-11, 1e-9), | ||
bounds=((0, 1), (0, 1), (0, 1)), | ||
priors=None, | ||
gaussian_noise=False, | ||
nwalkers=16, | ||
nsteps=10000, | ||
burn_in=2000, | ||
progress=True): | ||
""" | ||
Parameters | ||
---------- | ||
""" | ||
self.data = np.atleast_2d(np.asarray(data)) | ||
self.b = np.atleast_2d(np.asarray(b)).T | ||
self.data_scale = np.asarray(data_scale) | ||
self.parameter_scale = np.asarray(parameter_scale) | ||
self.bounds = np.asarray(bounds) | ||
self.priors = np.asarray(priors) | ||
self.nwalkers = nwalkers | ||
self.nsteps = nsteps | ||
self.burn_in = burn_in | ||
self.progress = progress | ||
if priors is None: | ||
self.prior = self.zero_prior | ||
else: | ||
self.prior = self.loglike_gauss_prior | ||
if gaussian_noise: | ||
self.likelihood = self.biexp_loglike_gauss | ||
else: | ||
self.likelihood = self.biexp_loglike_rice | ||
self.ndim = 3 | ||
self.chain = None | ||
self.means = None | ||
self.stds = None | ||
|
||
def accepted_dimensions(self): | ||
return (1, 1) | ||
|
||
def bounds_prior(self, params): | ||
return np.sum(uniform.logpdf(params, loc=self.bounds[:, 0], scale=self.bounds[:, 1] - self.bounds[:, 0]), 1) | ||
|
||
def loglike_gauss_prior(self, params): | ||
return np.sum(norm.logpdf(params, loc=self.priors[:, 0], scale=self.priors[:, 1]), 1) | ||
|
||
def zero_prior(self, params): | ||
return 0 | ||
|
||
def signal(self, f, D, D_star): | ||
return (f * np.exp(-self.b * D_star) + (1 - f) * np.exp(-self.b * D)).T | ||
|
||
def biexp_loglike_gauss(self, f, D, D_star): | ||
expected = self.signal(f, D, D_star) | ||
# check this! | ||
# print(f'likelihood {norm.logpdf(self.data, loc=expected/self.data_scale, scale=self.data_scale)}') | ||
return np.sum(norm.logpdf(self.data, loc=expected, scale=self.data_scale), 1) | ||
|
||
# def biexp_loglike_gauss_full(self, f, D, D_star): | ||
# expected = self.signal(f, D, D_star) | ||
# print(f'expected {expected}') | ||
# print(f'data {self.data}') | ||
# return norm.logpdf(self.data, loc=expected, scale=self.data_scale) | ||
|
||
def biexp_loglike_rice(self, f, D, D_star): | ||
expected = self.signal(f, D, D_star) | ||
# print(f'expected {expected}') | ||
return np.sum(rice.logpdf(self.data, b=expected/self.data_scale, scale=self.data_scale), 1) | ||
|
||
def posterior(self, params): | ||
params = np.atleast_2d(params) | ||
total = self.bounds_prior(params) | ||
# print(f'bounds params {total}') | ||
neginf = np.isneginf(total) | ||
# print(f'neginf {neginf}') | ||
f = params[~neginf, 0] | ||
D = params[~neginf, 1] | ||
D_star = params[~neginf, 2] | ||
prior = self.prior(params[~neginf, :]) | ||
# print(f'prior {prior}') | ||
likelihood = self.likelihood(f, D, D_star) | ||
# print(f'likelihood {likelihood}') | ||
total[~neginf] += prior + likelihood | ||
return total | ||
|
||
def sample(self, initial_pos): | ||
# f = initial_pos[0] | ||
# D = initial_pos[1] | ||
# D_star = initial_pos[2] | ||
# print(f'initial pos likelihood {self.biexp_loglike_gauss_full(f, D, D_star)}') | ||
print(f'initial pos likelihood {self.posterior(initial_pos)}') | ||
sampler = emcee.EnsembleSampler(self.nwalkers, 3, self.posterior, vectorize=True) | ||
pos = initial_pos + self.parameter_scale * np.random.randn(self.nwalkers, self.ndim) | ||
# print(f'pos {pos}') | ||
# print(f'nsteps {self.nsteps}') | ||
sampler.run_mcmc(pos, self.nsteps, progress=True) | ||
self.chain = sampler.get_chain(discard=self.burn_in, flat=True) | ||
self.means = np.mean(self.chain, 0) | ||
self.stds = np.std(self.chain, 0) | ||
print(f'final pos likelihood {self.posterior(self.means)}') | ||
# print(f'final pos likelihood {self.biexp_loglike_gauss_full(self.means[0], self.means[1], self.means[2])}') | ||
# print(f'chain {self.chain}') | ||
return self.means, self.stds | ||
|
||
def plot(self, truths=None, labels=('f', 'D', 'D*'), overplot=None): | ||
if truths is None: | ||
truths = self.means | ||
# print(f'chain size {self.chain.shape}') | ||
fig = corner.corner(self.chain, labels=labels, truths=truths) | ||
fig.suptitle("Sampling of the IVIM data", fontsize=16) | ||
if overplot is not None: | ||
corner.overplot_lines(fig, overplot, color='r') | ||
plt.show() | ||
|
58 changes: 53 additions & 5 deletions
58
tests/IVIMmodels/unit_tests/simple_test_run_of_algorithm.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,33 +1,81 @@ | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
import os | ||
from pathlib import Path | ||
#from src.standardized.ETP_SRI_LinearFitting import ETP_SRI_LinearFitting | ||
from src.standardized.IAR_LU_biexp import IAR_LU_biexp | ||
# from src.standardized.IAR_LU_biexp import IAR_LU_biexp | ||
#from src.standardized.IAR_LU_segmented_2step import IAR_LU_segmented_2step | ||
from src.standardized.PvH_KB_NKI_IVIMfit import PvH_KB_NKI_IVIMfit | ||
#from src.standardized.PV_MUMC_biexp import PV_MUMC_biexp | ||
from src.original.ETP_SRI.Sampling import MCMC | ||
|
||
## Simple test code... | ||
# Used to just do a test run of an algorithm during development | ||
def dev_test_run(model, **kwargs): | ||
bvalues = np.array([0, 50, 100, 150, 200, 500, 800]) | ||
bvalues = np.array([0, 20, 50, 75, 100, 150, 200, 300, 400, 500, 800, 1000, 1500]) | ||
|
||
def ivim_model(b, S0=1, f=0.1, Dstar=0.01, D=0.001): | ||
def ivim_model(b, f=0.1, Dstar=0.01, D=0.001, S0=1): | ||
# print(f'S0 {S0}') | ||
# print(f'Dstar {f*np.exp(-b*Dstar)}') | ||
# print(f'D {(1-f)*np.exp(-b*D)}') | ||
# print(f'sum {f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D)}') | ||
# print(f'S0 {(f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D))}') | ||
return S0*(f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D)) | ||
|
||
signals = ivim_model(bvalues) | ||
# TODO: add S0 fitting! | ||
true_f = 0.4 | ||
true_Dstar = 0.01 | ||
true_D = 0.001 | ||
truth = [true_f, true_D, true_Dstar] | ||
signals_noiseless = ivim_model(bvalues, true_f, true_Dstar, true_D) | ||
print(f'noiselss {signals_noiseless}') | ||
signals = signals_noiseless + np.abs(1e-1 * (np.random.randn(len(bvalues)) + 1j * np.random.randn(len(bvalues))) / np.sqrt(2)) | ||
|
||
#model = ETP_SRI_LinearFitting(thresholds=[200]) | ||
if kwargs: | ||
results = model.osipi_fit(signals, bvalues, **kwargs) | ||
else: | ||
results = model.osipi_fit(signals, bvalues) | ||
print(results) | ||
# print(results) # f, D*, D | ||
results_reordered = np.asarray([results[0], results[2], results[1]]) | ||
print(truth) | ||
print(results_reordered) | ||
#test = model.osipi_simple_bias_and_RMSE_test(SNR=20, bvalues=bvalues, f=0.1, Dstar=0.03, D=0.001, noise_realizations=10) | ||
signal_results = ivim_model(bvalues, results[0], results[1], results[2]) | ||
|
||
|
||
|
||
mcmc = MCMC(signals, bvalues, gaussian_noise=False, data_scale=1e-2) #, priors=((0.07, 1e-1), (0.0135, 1e-1), (0.001, 1e-1))) | ||
means, stds = mcmc.sample(truth) | ||
print(f'means {means} stds {stds}') | ||
print(f'expected {results_reordered}') | ||
print(f'truth {truth}') | ||
|
||
signal_means = ivim_model(bvalues, means[0], means[2], means[1]) | ||
plt.plot(bvalues, signals_noiseless, 'g', label='Noiseless Signal') | ||
plt.plot(bvalues, signals, '.g', label='Noisy Signal') | ||
plt.plot(bvalues, signal_results, 'r', label='Results Signal') | ||
plt.plot(bvalues, signal_means, 'b', label='Means Signal') | ||
plt.legend() | ||
# plt.show() | ||
|
||
mcmc.plot(overplot=truth) | ||
|
||
|
||
#model1 = ETP_SRI_LinearFitting(thresholds=[200]) | ||
#model2 = IAR_LU_biexp() | ||
model2 = PvH_KB_NKI_IVIMfit() | ||
|
||
#dev_test_run(model1, linear_fit_option=True) | ||
dev_test_run(model2) | ||
|
||
|
||
def run_sampling(): | ||
bvalues = np.array([0, 50, 100, 150, 200, 500, 800]) | ||
|
||
def ivim_model(b, S0=1, f=0.1, Dstar=0.01, D=0.001): | ||
return S0*(f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D)) | ||
|
||
signals = ivim_model(bvalues) | ||
|
||
|