diff --git a/reciprocalspaceship/algorithms/scale_merged_intensities.py b/reciprocalspaceship/algorithms/scale_merged_intensities.py index 5fa2fe1a..8e9664d5 100644 --- a/reciprocalspaceship/algorithms/scale_merged_intensities.py +++ b/reciprocalspaceship/algorithms/scale_merged_intensities.py @@ -3,10 +3,11 @@ from scipy.special import logsumexp from scipy.stats import gamma,norm -def _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, centric, npoints=200): + +def _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, centric, npoints=100): """ Compute the mean and std deviation of the French-Wilson posterior using - Gauss-Legendre quadrature. + Chebyshev-Gauss quadrature. Parameters ---------- @@ -39,29 +40,45 @@ def _french_wilson_posterior_quad(Iobs, SigIobs, Sigma, centric, npoints=200): SigIobs = np.array(SigIobs, dtype=np.float64) Sigma = np.array(Sigma, dtype=np.float64) - half_window_size = 20. - Jmin = np.maximum(Iobs - half_window_size*SigIobs, 0.) - Jmax = Jmin + 2.*half_window_size*SigIobs - grid,weights = np.polynomial.legendre.leggauss(npoints) - - J = (Jmax - Jmin)[:,None] * grid / 2. + (Jmax + Jmin)[:,None] / 2. - log_prefactor = np.log(Jmax - Jmin) - np.log(2.) + #Integration window based on the normal, likelihood distribution + window_size = 20. #In standard devs + Jmin = Iobs - window_size*SigIobs/2. + Jmin = np.maximum(0., Jmin) + Jmax = Jmin + window_size*SigIobs - logweights = np.log(weights)[None,:] - logJ = np.log(J) + #Prior distribution paramters a = np.where(centric, 0.5, 1.) scale = np.where(centric, 2.*Sigma, Sigma) + + #Quadrature grid points + grid,weights = np.polynomial.chebyshev.chebgauss(npoints) + + #Change of variables for generalizing chebgauss + # Equivalent to: logweights = log(sqrt(1-grid**2.)*w) + logweights = (0.5*(np.log(1-grid) + np.log(1+grid)) + np.log(weights))[None,:] + J = (Jmax - Jmin)[:,None] * grid / 2. + (Jmax + Jmin)[:,None] / 2. + logJ = np.nan_to_num(np.log(J), -np.inf) + log_prefactor = np.log(Jmax - Jmin) - np.log(2.0) + + #Log prior (Wilson's prior for intensities) logP = gamma.logpdf(J, np.expand_dims(a, axis=-1), scale=scale[:,None]) + + #Log likelihood (Normal about Iobs) logL = norm.logpdf(J, loc=Iobs[:,None], scale=SigIobs[:,None]) - logZ = log_prefactor + logsumexp(logweights + logP + logL, axis=1) + + #Compute partition function + logZ = log_prefactor + logsumexp(logweights + logP + logL, axis=1) + + #Compute expected value and variance of intensity log_mean = log_prefactor + logsumexp(logweights + logJ + logP + logL - logZ[:,None], axis=1) mean = np.exp(log_mean) variance = np.exp(log_prefactor + logsumexp(logweights+2.*logJ+logP + logL - logZ[:,None], axis=1)) - mean**2 - logF = 0.5*logJ + #Compute expected value and variance of structure factor amplitude + logF = 0.5*logJ #Change variables log_mean_F = log_prefactor + logsumexp(logweights + logF + logP + logL - logZ[:,None], axis=1) mean_F = np.exp(log_mean_F) - variance_F = np.exp(log_prefactor + logsumexp(logweights+2.*logF+logP + logL - logZ[:,None], axis=1)) - mean_F**2 + variance_F = mean - mean_F**2 return mean, np.sqrt(variance), mean_F, np.sqrt(variance_F) def mean_intensity_by_miller_index(I, H, bandwidth): diff --git a/tests/algorithms/conftest.py b/tests/algorithms/conftest.py index d8582b6f..41711926 100644 --- a/tests/algorithms/conftest.py +++ b/tests/algorithms/conftest.py @@ -103,3 +103,22 @@ def data_fw1978_output(request): elif request.param == "acentric structurefactors": return data_fw1978_structurefactors(centric=False) +@pytest.fixture +def data_fw_cctbx(): + """ + Load French-Wilson test data from cctbx + """ + datapath = ["..", "data", "french_wilson", "fw_test_data.csv"] + inFN = abspath(join(dirname(__file__), *datapath)) + return pd.read_csv(inFN) + +@pytest.fixture +def data_fw_mcmc(): + """ + Load French-Wilson test data from PyMC3 + """ + datapath = ["..", "data", "french_wilson", "fw_mcmc_data.csv"] + inFN = abspath(join(dirname(__file__), *datapath)) + return pd.read_csv(inFN) + + diff --git a/tests/algorithms/test_scale_merged_intensities.py b/tests/algorithms/test_scale_merged_intensities.py index 2318786f..ffad91ba 100644 --- a/tests/algorithms/test_scale_merged_intensities.py +++ b/tests/algorithms/test_scale_merged_intensities.py @@ -189,4 +189,32 @@ def test_scale_merged_intensities_dropna(data_hewl_all, dropna): assert (scaled["FW-I"].to_numpy() >= 0.).all() - +@pytest.mark.parametrize("ref_method", ['mcmc', 'cctbx', 'mcmc vs cctbx']) +def test_fw_posterior_quad(ref_method, data_fw_cctbx, data_fw_mcmc): + """ + Test the _french_wilson_posterior_quad function directly against cctbx output. + The cctbx implementations can be found + - `acentric `_ + - `centric `_ + """ + if ref_method == 'cctbx': + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_cctbx.to_numpy(np.float64).T + Centric = Centric.astype(np.bool) + rtol = 0.06 + rs_J,rs_SigJ,rs_F,rs_SigF = _french_wilson_posterior_quad(I, SigI, Sigma, Centric) + elif ref_method == 'mcmc': + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_mcmc.to_numpy(np.float64).T + Centric = Centric.astype(np.bool) + rtol = 0.025 + rs_J,rs_SigJ,rs_F,rs_SigF = _french_wilson_posterior_quad(I, SigI, Sigma, Centric) + elif ref_method == 'mcmc vs cctbx': + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_mcmc.to_numpy(np.float64).T + Centric = Centric.astype(np.bool) + _,_,_,rs_J,rs_SigJ,rs_F,rs_SigF,_ = data_fw_cctbx.to_numpy(np.float64).T + rtol = 0.06 + + assert np.allclose(rs_J, J, rtol=rtol) + assert np.allclose(rs_SigJ, SigJ, rtol=rtol) + assert np.allclose(rs_F, F, rtol=rtol) + assert np.allclose(rs_SigF, SigF, rtol=rtol) + diff --git a/tests/data/french_wilson/diagnostics.py b/tests/data/french_wilson/diagnostics.py new file mode 100644 index 00000000..3f87aa6d --- /dev/null +++ b/tests/data/french_wilson/diagnostics.py @@ -0,0 +1,80 @@ +from reciprocalspaceship.algorithms.scale_merged_intensities import _french_wilson_posterior_quad +from matplotlib import pyplot as plt +import numpy as np +import pandas as pd + +data_fw_mcmc = pd.read_csv("fw_mcmc_data.csv") +I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_mcmc.to_numpy(np.float64).T + +Centric = Centric.astype(np.bool) +rs_J,rs_SigJ,rs_F,rs_SigF = _french_wilson_posterior_quad(I, SigI, Sigma, Centric) + +#All the plots +plt.plot(J, rs_J, 'k.', label='Acentric') +plt.plot(J[Centric], rs_J[Centric], 'r.', label='Centric') +plt.xlabel("MCMC J") +plt.ylabel("rs J") +plt.legend() +plt.savefig("J.png") + +plt.figure() +plt.plot(F, rs_F, 'k.', label='Acentric') +plt.plot(F[Centric], rs_F[Centric], 'r.', label='Centric') +plt.xlabel("MCMC F") +plt.ylabel("rs F") +plt.legend() +plt.savefig("F.png") + +plt.figure() +plt.plot(I/SigI, 100.*(rs_F - F)/F, 'k.', alpha=0.1, label='Acentric') +plt.plot((I/SigI)[Centric], (100.*(rs_F - F)/F)[Centric], 'r.', alpha=0.1, label='Centric') +plt.legend() +plt.xlabel('Iobs/SigIobs') +plt.ylabel('Percent Error (F)') +plt.savefig("Ferr.png") + +plt.figure() +plt.plot(I/SigI, 100.*(rs_J - J)/J, 'k.', alpha=0.1, label='Acentric') +plt.plot((I/SigI)[Centric], (100.*(rs_J - J)/J)[Centric], 'r.', alpha=0.1, label='Centric') +plt.legend() +plt.xlabel('Iobs/SigIobs') +plt.ylabel('Percent Error (J)') +plt.savefig("Jerr.png") + +plt.figure() +plt.hist((100.*(rs_J - J)/J)[~Centric], 100, color='k', label='Acentric', alpha=0.4) +plt.hist((100.*(rs_J - J)/J)[Centric], 100, color='r', label='Centric', alpha=0.4) +plt.legend() +plt.xlabel('Percent Error (J)') +plt.savefig("J_hist.png") + +plt.figure() +plt.hist((100.*(rs_F - F)/F)[~Centric], 100, color='k', label='Acentric', alpha=0.4) +plt.hist((100.*(rs_F - F)/F)[Centric], 100, color='r', label='Centric', alpha=0.4) +plt.legend() +plt.xlabel('Percent Error (F)') +plt.savefig("F_hist.png") + +plt.figure() +plt.hist((100.*(rs_SigF - SigF)/SigF)[~Centric], 100, color='k', label='Acentric', alpha=0.4) +plt.hist((100.*(rs_SigF - SigF)/SigF)[Centric], 100, color='r', label='Centric', alpha=0.4) +plt.legend() +plt.xlabel('Percent Error (SigF)') +plt.savefig("SigF_hist.png") + +plt.figure() +plt.hist((100.*(rs_SigJ - SigJ)/SigJ)[~Centric], 100, color='k', label='Acentric', alpha=0.4) +plt.hist((100.*(rs_SigJ - SigJ)/SigJ)[Centric], 100, color='r', label='Centric', alpha=0.4) +plt.legend() +plt.xlabel('Percent Error (SigJ)') +plt.savefig("SigJ_hist.png") + +plt.figure() +plt.plot((I/SigI)[~Centric], (100.*(rs_SigF - SigF)/SigF)[~Centric], 'k.', label='Acentric', alpha=0.1) +plt.plot((I/SigI)[Centric], (100.*(rs_SigF - SigF)/SigF)[Centric], 'r.', label='Centric', alpha=0.1) +plt.legend() +plt.ylabel('Percent Error (SigF)') +plt.xlabel('I/Sigma') +plt.savefig("SigF_hist.png") + +plt.show() diff --git a/tests/data/french_wilson/fw_mcmc_data.csv b/tests/data/french_wilson/fw_mcmc_data.csv new file mode 100644 index 00000000..45047286 --- /dev/null +++ b/tests/data/french_wilson/fw_mcmc_data.csv @@ -0,0 +1,2241 @@ +I,SigI,Sigma,FW-I,FW-SigI,FW-F,FW-SigF,CENTRIC +-3.0,1.1,1.1,0.2626791742365161,0.25084418357894356,0.4569540085498971,0.23210387395883084,False +-3.0,1.1,2.1,0.29364612589460337,0.2777197907827506,0.48382858198817974,0.24404103988040393,False +-3.0,1.1,3.1,0.3051086325463972,0.28693193275442,0.49347258460791465,0.24818025865645837,False +-3.0,1.1,4.1,0.3119409280797085,0.29283971518560054,0.4989644254406251,0.25094905902275033,False +-3.0,1.1,5.1,0.3154165227513831,0.296671213390763,0.5015922275219054,0.2526296895477706,False +-3.0,1.1,6.1,0.31921723747643227,0.2987212334185003,0.505155486831444,0.2530517172447769,False +-3.0,1.1,7.1,0.3212023251088959,0.30103694283296784,0.5065303527625683,0.2542229864491429,False 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b/tests/data/french_wilson/gen_fw_reference_data.py @@ -0,0 +1,39 @@ +#!/usr/bin/env cctbx.python + +from cctbx.french_wilson import fw_centric,fw_acentric +import pandas as pd +import numpy as np + +outFN = "fw_test_data.csv" + +Imin,Imax,Istep = -3, 10, 1.0 +SigImin,SigImax,SigIstep = 0.1, 10, 1.0 +Sigmamin,Sigmamax,Sigmastep = 0.1, 10., 1.0 + +I,SigI,Sigma = np.mgrid[Imin:Imax:Istep,SigImin:SigImax:SigIstep,Sigmamin:Sigmamax:Sigmastep].reshape((3, -1)) + +data = [] +for args in zip(I, SigI, Sigma): + i,sigi,sigma = args + result = fw_acentric(i, sigi, sigma, -4.) + if result != (-1., -1., -1., -1.): #These were rejected + data.append(args + result) + +data = np.vstack(data) +df = pd.DataFrame(data = data, columns=('I', 'SigI', 'Sigma', 'FW-I', 'FW-SigI', 'FW-F', 'FW-SigF')) +df['CENTRIC'] = False + +data = [] +for args in zip(I, SigI, Sigma): + i,sigi,sigma = args + result = fw_centric(i, sigi, sigma, -4.) + if result != (-1., -1., -1., -1.): #These were rejected + data.append(args + result) + +data = np.vstack(data) +_df = pd.DataFrame(data = data, columns=('I', 'SigI', 'Sigma', 'FW-I', 'FW-SigI', 'FW-F', 'FW-SigF')) +_df['CENTRIC'] = True +df = df.append(_df) + +df.to_csv(outFN, index=False) + diff --git a/tests/data/french_wilson/gen_mcmc_reference_data.py b/tests/data/french_wilson/gen_mcmc_reference_data.py new file mode 100755 index 00000000..cf343ed7 --- /dev/null +++ b/tests/data/french_wilson/gen_mcmc_reference_data.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python + +import numpy as np +import pymc3 as pm +import pandas as pd + + + +inFN = "fw_test_data.csv" +outFN = "fw_mcmc_data.csv" +nproc=7 +chain_length = 30_000 +burnin = 15_000 + +df = pd.read_csv(inFN) +I,SigI,Sigma,J,SigJ,F,SigF,Centric = df.to_numpy(np.float64).T +Centric = Centric.astype(np.bool) +df + +#Gamma prior params +a = np.where(Centric, 0.5, 1.) +scale = 1./np.where(Centric, 2.*Sigma, Sigma) + +with pm.Model() as model: + Wilson = pm.distributions.Gamma('Wilson', a, scale, shape=len(a)) + likelihood = pm.distributions.Normal('Likelihood', mu=Wilson, sigma=SigI, observed=I) + trace = pm.sample(draws=chain_length, tune=burnin, cores=nproc) + +samples = trace.get_values('Wilson') +mc_J = samples.mean(0) +mc_SigJ = samples.std(0) +mc_F = np.sqrt(samples).mean(0) +mc_SigF = np.sqrt(samples).std(0) + + +df['FW-I'] = mc_J +df['FW-SigI'] = mc_SigJ +df['FW-F'] = mc_F +df['FW-SigF'] = mc_SigF + +df.to_csv(outFN, index=False)