From 73c2957723157373058a0ff9f356f4c41e900765 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Fri, 29 Jan 2021 12:41:09 -0500 Subject: [PATCH 01/12] added fw test data from cctbx --- tests/data/french_wilson/fw_test_data.csv | 2241 +++++++++++++++++++++ tests/data/gen_fw_reference_data.py | 39 + 2 files changed, 2280 insertions(+) create mode 100644 tests/data/french_wilson/fw_test_data.csv create mode 100755 tests/data/gen_fw_reference_data.py diff --git a/tests/data/french_wilson/fw_test_data.csv b/tests/data/french_wilson/fw_test_data.csv new file mode 100644 index 00000000..c4bdf903 --- /dev/null +++ b/tests/data/french_wilson/fw_test_data.csv @@ -0,0 +1,2241 @@ +,I,SigI,Sigma,FW-I,FW-SigI,FW-F,FW-SigF,CENTRIC +0,-3.0,1.1,1.1,0.2625,0.2518,0.4568992727664879,0.23226348674022632,False +1,-3.0,1.1,2.1,0.2930666666666667,0.27769047619047627,0.48343277453577954,0.24381400496423009,False 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+++ b/tests/data/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 = "french_wilson/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) + From e59fb0f4777945989f0ac01514976847850a9cce Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Fri, 29 Jan 2021 12:49:36 -0500 Subject: [PATCH 02/12] add fixture for french wilson cctbx test data --- tests/conftest.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/tests/conftest.py b/tests/conftest.py index 3ed2e0c8..0d5d5d37 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -61,6 +61,23 @@ def data_gemmi(): datapath = ["data", "fmodel", "9LYZ.mtz"] return load_dataset(datapath, as_gemmi=True) +@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_merged(): + """ + Load HEWL diffraction data from APS 24-ID-C + """ + datapath = ["data", "data_merged.mtz"] + return load_dataset(datapath) + @pytest.fixture def data_fmodel(): """ From 26161014f0c7107e5412424fa95e0545c857f0dd Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Fri, 29 Jan 2021 13:03:57 -0500 Subject: [PATCH 03/12] don't polute the csv with rangeindex --- tests/data/french_wilson/fw_test_data.csv | 4482 ++++++++++----------- tests/data/gen_fw_reference_data.py | 4 +- 2 files changed, 2243 insertions(+), 2243 deletions(-) diff --git a/tests/data/french_wilson/fw_test_data.csv b/tests/data/french_wilson/fw_test_data.csv index c4bdf903..bb43884a 100644 --- a/tests/data/french_wilson/fw_test_data.csv +++ b/tests/data/french_wilson/fw_test_data.csv @@ -1,2241 +1,2241 @@ -,I,SigI,Sigma,FW-I,FW-SigI,FW-F,FW-SigF,CENTRIC -0,-3.0,1.1,1.1,0.2625,0.2518,0.4568992727664879,0.23226348674022632,False 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da77202b..a9bbbf28 100755 --- a/tests/data/gen_fw_reference_data.py +++ b/tests/data/gen_fw_reference_data.py @@ -33,7 +33,7 @@ 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) + +df.to_csv(outFN, index=False) From f5a949aa1116bce51851b9564b9b1106a97f37c0 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Fri, 29 Jan 2021 13:19:10 -0500 Subject: [PATCH 04/12] move fw fixture into algorithms --- tests/algorithms/conftest.py | 9 ++++ tests/conftest.py | 82 ------------------------------------ 2 files changed, 9 insertions(+), 82 deletions(-) diff --git a/tests/algorithms/conftest.py b/tests/algorithms/conftest.py index d8582b6f..4ff3961d 100644 --- a/tests/algorithms/conftest.py +++ b/tests/algorithms/conftest.py @@ -103,3 +103,12 @@ 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) + diff --git a/tests/conftest.py b/tests/conftest.py index 0d5d5d37..a2350d66 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -61,85 +61,3 @@ def data_gemmi(): datapath = ["data", "fmodel", "9LYZ.mtz"] return load_dataset(datapath, as_gemmi=True) -@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_merged(): - """ - Load HEWL diffraction data from APS 24-ID-C - """ - datapath = ["data", "data_merged.mtz"] - return load_dataset(datapath) - -@pytest.fixture -def data_fmodel(): - """ - Load fmodel results for 9LYZ.mtz - """ - datapath = ["data", "fmodel", "9LYZ.mtz"] - return load_dataset(datapath) - -@pytest.fixture -def data_fmodel_P1(): - """ - Load fmodel results for 9LYZ.mtz - """ - datapath = ["data", "fmodel", "9LYZ_p1.mtz"] - return load_dataset(datapath) - -def get_mtz_by_spacegroup(): - """ - Get absolute paths to MTZ files generated by phenix.fmodel for - testing crystallographic symmetry operations. - """ - datadir = abspath(join(dirname(__file__) + '/data/fmodel/')) - files = [join(datadir, i) for i in listdir(datadir) if re.match(r'.*(? Date: Fri, 29 Jan 2021 13:19:36 -0500 Subject: [PATCH 05/12] first version testing fw against cctbx --- tests/algorithms/test_scale_merged_intensities.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/tests/algorithms/test_scale_merged_intensities.py b/tests/algorithms/test_scale_merged_intensities.py index 2318786f..cdcc38f2 100644 --- a/tests/algorithms/test_scale_merged_intensities.py +++ b/tests/algorithms/test_scale_merged_intensities.py @@ -189,4 +189,17 @@ def test_scale_merged_intensities_dropna(data_hewl_all, dropna): assert (scaled["FW-I"].to_numpy() >= 0.).all() - +def test_fw_posterior_quad(data_fw_cctbx): + """ + Test the _french_wilson_posterior_quad function directly against cctbx output. + The cctbx implementations can be found + - `acentric `_ + - `centric `_ + """ + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_cctbx.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) + assert np.allclose(rs_J, J, rtol=0.05) + assert np.allclose(rs_SigJ, SigJ, rtol=0.05) + assert np.allclose(rs_F, F, rtol=0.05) + assert np.allclose(rs_SigF, SigF, rtol=0.10) From fd2065f933235d74fd8716b0c3947bdaa591c33a Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Fri, 29 Jan 2021 16:42:28 -0500 Subject: [PATCH 06/12] made new fw reference data from pymc3 --- tests/data/french_wilson/fw_mcmc_data.csv | 2241 +++++++++++++++++ .../gen_fw_reference_data.py | 2 +- .../french_wilson/gen_mcmc_reference_data.py | 38 + 3 files changed, 2280 insertions(+), 1 deletion(-) create mode 100644 tests/data/french_wilson/fw_mcmc_data.csv rename tests/data/{ => french_wilson}/gen_fw_reference_data.py (96%) create mode 100755 tests/data/french_wilson/gen_mcmc_reference_data.py 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..6d8dfb49 --- /dev/null +++ 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SigImin,SigImax,SigIstep = 0.1, 10, 1.0 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..f4161c35 --- /dev/null +++ b/tests/data/french_wilson/gen_mcmc_reference_data.py @@ -0,0 +1,38 @@ +#!/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" + +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(3000, cores=7) + +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) From e9fb663addac85dbb76ba47095960ceb737e8471 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Tue, 2 Feb 2021 04:33:05 -0500 Subject: [PATCH 07/12] chebyshev quadrature & mcmc reference data --- .../algorithms/scale_merged_intensities.py | 45 +- tests/algorithms/conftest.py | 10 + .../test_scale_merged_intensities.py | 10 +- tests/data/french_wilson/fw_mcmc_data.csv | 4480 ++++++++--------- .../french_wilson/gen_mcmc_reference_data.py | 5 +- 5 files changed, 2292 insertions(+), 2258 deletions(-) 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 4ff3961d..41711926 100644 --- a/tests/algorithms/conftest.py +++ b/tests/algorithms/conftest.py @@ -112,3 +112,13 @@ def data_fw_cctbx(): 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 cdcc38f2..9ca77ad0 100644 --- a/tests/algorithms/test_scale_merged_intensities.py +++ b/tests/algorithms/test_scale_merged_intensities.py @@ -189,17 +189,21 @@ def test_scale_merged_intensities_dropna(data_hewl_all, dropna): assert (scaled["FW-I"].to_numpy() >= 0.).all() -def test_fw_posterior_quad(data_fw_cctbx): +@pytest.mark.parametrize("ref_method", ['mcmc', '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 `_ """ - I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_cctbx.to_numpy(np.float64).T + if ref_method == 'cctbx': + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_cctbx.to_numpy(np.float64).T + elif ref_method == 'mcmc': + I,SigI,Sigma,J,SigJ,F,SigF,Centric = data_fw_cctbx.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) assert np.allclose(rs_J, J, rtol=0.05) assert np.allclose(rs_SigJ, SigJ, rtol=0.05) assert np.allclose(rs_F, F, rtol=0.05) - assert np.allclose(rs_SigF, SigF, rtol=0.10) + assert np.allclose(rs_SigF, SigF, rtol=0.06) diff --git a/tests/data/french_wilson/fw_mcmc_data.csv b/tests/data/french_wilson/fw_mcmc_data.csv index 6d8dfb49..62ba1951 100644 --- a/tests/data/french_wilson/fw_mcmc_data.csv +++ b/tests/data/french_wilson/fw_mcmc_data.csv @@ -1,2241 +1,2241 @@ ,I,SigI,Sigma,FW-I,FW-SigI,FW-F,FW-SigF,CENTRIC -0,-3.0,1.1,1.1,0.25971052666975974,0.2501760625622006,0.45321473706841614,0.23303847058751342,False -1,-3.0,1.1,2.1,0.28943802669776947,0.27471786226975786,0.47989717383340724,0.24318044585138707,False -2,-3.0,1.1,3.1,0.30287434232844074,0.28509153536112636,0.49107291087394245,0.24843860113562813,False -3,-3.0,1.1,4.1,0.3123004682949697,0.2906257494349407,0.500126729907118,0.2493465907675203,False -4,-3.0,1.1,5.1,0.3175764800935327,0.2971248190558512,0.5039017789853978,0.2523082979786449,False -5,-3.0,1.1,6.1,0.32070985146911524,0.29932273134475995,0.50687811855175,0.25255578473389834,False -6,-3.0,1.1,7.1,0.32013162366172354,0.30130796795279047,0.5051884242352903,0.2547867337213339,False -7,-3.0,1.1,8.1,0.3235419950269571,0.30196625018155654,0.5081683695742942,0.25555215356392885,False -8,-3.0,1.1,9.1,0.32098951242916063,0.29650534605716394,0.507044754525765,0.2527748589893218,False -9,-3.0,2.1,1.1,0.5484077778302894,0.5146928051975825,0.6623092799313216,0.3312917076341388,False 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-1938,6.0,7.1,2.1,2.232464347984536,2.7144585662321945,1.2274173705600913,0.852004076534786,True -1939,6.0,7.1,3.1,2.877165044562849,3.252082074601724,1.4159733222339614,0.9339082370793099,True -1940,6.0,7.1,4.1,3.3514035300071257,3.6882414404119976,1.5357232974657375,0.9964725202573695,True -1941,6.0,7.1,5.1,3.698424834628292,3.8689519103700736,1.6315952219098004,1.0179989520964443,True -1942,6.0,7.1,6.1,3.9566348652874144,4.082481172594744,1.6946710657668043,1.0415012453858208,True -1943,6.0,7.1,7.1,4.1369681269462895,4.185311474496792,1.740016073754729,1.0532389045328086,True -1944,6.0,7.1,8.1,4.340488925222958,4.359419734474998,1.7852474902940378,1.0739554570008019,True -1945,6.0,7.1,9.1,4.45187625078064,4.396003732121947,1.8156363589607318,1.0748678350385676,True -1946,6.0,8.1,1.1,1.2083109798799425,1.6152843847356868,0.8857741529785628,0.6509340441204889,True -1947,6.0,8.1,2.1,2.170519141360337,2.6710192407536275,1.207426851909832,0.8441798023214351,True -1948,6.0,8.1,3.1,2.8706512768096824,3.3818623216208015,1.4041962443128415,0.9480950291333698,True -1949,6.0,8.1,4.1,3.395948968433258,3.820340919218889,1.5379104957008751,1.0152734979532896,True -1950,6.0,8.1,5.1,3.771823991930625,4.155433956938796,1.6287468500261586,1.057831598346594,True -1951,6.0,8.1,6.1,4.0475364475427655,4.324279993962449,1.7033410002011005,1.0705913714282658,True -1952,6.0,8.1,7.1,4.244100377949105,4.474793381574604,1.7456049530981121,1.0940583739766525,True -1953,6.0,8.1,8.1,4.506786147718365,4.699351459245845,1.800381212830226,1.1249060566137592,True -1954,6.0,8.1,9.1,4.587815775385439,4.807750129691934,1.8143286633405562,1.1384319376960137,True -1955,6.0,9.1,1.1,1.1905635542993207,1.6199504845618269,0.8770636155531484,0.6490939597409373,True -1956,6.0,9.1,2.1,2.1324555282096584,2.7259984118185523,1.189152663733559,0.8475679739968037,True -1957,6.0,9.1,3.1,2.842091995419568,3.493503128425535,1.3787040691691352,0.9701892006593412,True -1958,6.0,9.1,4.1,3.4119611473834763,3.99304757504335,1.5286028346094256,1.0369833756659281,True -1959,6.0,9.1,5.1,3.8256033142432404,4.361899312325902,1.6301589419494389,1.0808261369089436,True -1960,6.0,9.1,6.1,4.123635752227238,4.498070845609139,1.7057962947560361,1.1017689199754357,True -1961,6.0,9.1,7.1,4.337210366188808,4.772669908847836,1.7459924421256974,1.1352183746877753,True -1962,6.0,9.1,8.1,4.6216164401404,4.941556140513252,1.816652916925372,1.1495166895556261,True -1963,6.0,9.1,9.1,4.732084971439106,4.998262258646302,1.842123432387418,1.15700744651374,True -1964,7.0,0.1,0.1,6.949806068746635,0.09982692378400321,2.6361804752900757,0.01893595643029228,True -1965,7.0,0.1,1.1,6.993160823339378,0.10038837804505385,2.6443903802882827,0.01898262305684174,True -1966,7.0,0.1,2.1,6.996184101667391,0.09945027286131942,2.644963249856207,0.018801823780663616,True -1967,7.0,0.1,3.1,6.997375150279415,0.10047400294872165,2.6451870285334436,0.018993008130021943,True -1968,7.0,0.1,4.1,6.998048658407674,0.09919887471227169,2.645316064400736,0.018750408824134115,True -1969,7.0,0.1,5.1,6.997996653872486,0.09961042351108787,2.645305640176494,0.018834115931259852,True -1970,7.0,0.1,6.1,6.999255780939446,0.10041511392191974,2.645542584224361,0.01897935707451853,True -1971,7.0,0.1,7.1,6.998521856450484,0.10034792295610055,2.6454039672996426,0.01896592327540614,True -1972,7.0,0.1,8.1,6.999127628020067,0.09988494733843123,2.6455190818219965,0.018878976025640645,True -1973,7.0,0.1,9.1,6.998552207614888,0.09802360746720848,2.6454128079071872,0.018527908016729183,True -1974,7.0,1.1,0.1,0.8995555527529596,0.8004816401014586,0.833326518266748,0.45290447856737165,True -1975,7.0,1.1,1.1,6.345589059956792,1.1102597042547575,2.5091160374281714,0.22344075428963497,True -1976,7.0,1.1,2.1,6.598432725883869,1.1096424968019198,2.559392746985505,0.21895545794026441,True -1977,7.0,1.1,3.1,6.729845633928665,1.0948478235214294,2.5853433627459634,0.21411522746881145,True -1978,7.0,1.1,4.1,6.7522534600430575,1.1139432053883485,2.589409857678984,0.21727873572425802,True -1979,7.0,1.1,5.1,6.795273999682468,1.1016657212235,2.5979947833577204,0.21376881280599191,True -1980,7.0,1.1,6.1,6.812364709198131,1.1093054544363838,2.60115688296662,0.21528487962113627,True -1981,7.0,1.1,7.1,6.816846600411394,1.1057785220814835,2.602086201137627,0.21446212313721771,True -1982,7.0,1.1,8.1,6.84609403753028,1.1054750734841055,2.6077047959479995,0.21440553798854514,True -1983,7.0,1.1,9.1,6.826262945056534,1.1020742872238098,2.603956572566506,0.21371268854276618,True -1984,7.0,2.1,1.1,4.4570991742770225,2.129149661083083,2.0325406985956374,0.5708567971298865,True -1985,7.0,2.1,2.1,5.444180059081148,2.2266174076133898,2.26964035877557,0.5412140989463534,True -1986,7.0,2.1,3.1,5.850004291903687,2.1783828352102605,2.365601562712294,0.5039181862136377,True -1987,7.0,2.1,4.1,6.026784604077472,2.1873795762794126,2.4050181924919736,0.4926175980009138,True -1988,7.0,2.1,5.1,6.1102430529752185,2.219267317803824,2.4185226872966337,0.510872845242981,True -1989,7.0,2.1,6.1,6.216168687067523,2.2068485830179836,2.4444822295946773,0.49058670616246813,True -1990,7.0,2.1,7.1,6.269978214643671,2.1915314768171616,2.457662248285804,0.4794522791627901,True -1991,7.0,2.1,8.1,6.336728971820307,2.19404479143975,2.473145033839629,0.4693427462043703,True -1992,7.0,2.1,9.1,6.308939518484262,2.2196321692159864,2.4644910060115874,0.4849985564640353,True -1993,7.0,3.1,1.1,2.555327847988698,2.288128606237747,1.4029060235154145,0.766278367940039,True -1994,7.0,3.1,2.1,3.933036926884541,2.827379116271945,1.8113901875528826,0.8074048026372171,True -1995,7.0,3.1,3.1,4.526435165875723,2.984591637293022,1.9680563033617253,0.808201430754661,True -1996,7.0,3.1,4.1,4.884045818060435,3.0674701904218478,2.0567940617425844,0.808482532675211,True -1997,7.0,3.1,5.1,5.133251553756345,3.0863673207772964,2.126430018044307,0.7820146623410972,True -1998,7.0,3.1,6.1,5.247075807992838,3.1373308500807764,2.150784712550011,0.788163008681574,True -1999,7.0,3.1,7.1,5.448958134414802,3.1352839431853408,2.200386978395529,0.7792658594614402,True -2000,7.0,3.1,8.1,5.45225817338622,3.1759680447561673,2.1960441232898753,0.7935038638533529,True -2001,7.0,3.1,9.1,5.517316287187641,3.184367244813898,2.2103130463379532,0.7948789369305427,True -2002,7.0,4.1,1.1,1.803673522956988,2.050273015390919,1.1162807344112329,0.7467200579462978,True -2003,7.0,4.1,2.1,3.0924737260467596,2.841885458362724,1.53497646451306,0.8580914749825564,True -2004,7.0,4.1,3.1,3.773087048974649,3.229397297570011,1.7188885858642153,0.9047149155178088,True -2005,7.0,4.1,4.1,4.193989996973038,3.431691761381843,1.8274009150336978,0.9244435584214966,True -2006,7.0,4.1,5.1,4.524552554422879,3.472194032595676,1.9195346990085755,0.9164819112917176,True -2007,7.0,4.1,6.1,4.72394865901713,3.549698049565823,1.9696958227300596,0.9188291598206962,True -2008,7.0,4.1,7.1,4.888977376657405,3.603268832368914,2.0076997222315494,0.9263472361963903,True -2009,7.0,4.1,8.1,4.9272218284791816,3.6511839910450377,2.01746981126824,0.9257631387673969,True -2010,7.0,4.1,9.1,5.036573396949746,3.640076568744136,2.0469157719743745,0.920168038671407,True -2011,7.0,5.1,1.1,1.4832701292995631,1.8326482919751015,0.9951622195224938,0.7020842443287321,True -2012,7.0,5.1,2.1,2.6753635597484497,2.8401160136692085,1.384972792978525,0.8701803965027691,True -2013,7.0,5.1,3.1,3.4008728841979443,3.336808550554405,1.5841419009530997,0.9441225142123544,True -2014,7.0,5.1,4.1,3.840862959088019,3.570540026424824,1.7079484124555218,0.9611322382891545,True -2015,7.0,5.1,5.1,4.244960853217898,3.72563784913342,1.814122501152401,0.9766884887365446,True -2016,7.0,5.1,6.1,4.416304723039387,3.8630577149318492,1.847218036285673,1.0020430377285636,True -2017,7.0,5.1,7.1,4.6130129568335345,3.9401635393431653,1.9001446726666968,1.0012308324107346,True -2018,7.0,5.1,8.1,4.76551353532981,4.00690409775051,1.9374158004689335,1.005949079935492,True -2019,7.0,5.1,9.1,4.852730178341205,4.0941595248216025,1.954554482696315,1.0160939683478816,True -2020,7.0,6.1,1.1,1.3713039963121936,1.7479006724160542,0.9539453883810032,0.6791849470496335,True -2021,7.0,6.1,2.1,2.4853048611693844,2.8167150515452724,1.314826344755491,0.8697910923355673,True -2022,7.0,6.1,3.1,3.213089256871859,3.401797318010559,1.5159315731210743,0.9565776092333277,True -2023,7.0,6.1,4.1,3.758529750925904,3.738063444878806,1.661709960631987,0.9986239320496466,True -2024,7.0,6.1,5.1,4.005107417454764,3.9388044031930614,1.717955591511624,1.0265164407103882,True -2025,7.0,6.1,6.1,4.320582565032216,4.116426111654313,1.8011149454670086,1.0375777167266058,True -2026,7.0,6.1,7.1,4.51875772844569,4.231800292084008,1.8496825341463095,1.0475840068079567,True -2027,7.0,6.1,8.1,4.711563278193298,4.348673639524237,1.8928172457630912,1.0624528941723277,True -2028,7.0,6.1,9.1,4.809268412604393,4.362644269546108,1.917390315032226,1.0643696690647209,True -2029,7.0,7.1,1.1,1.2846542107885133,1.7131349441998271,0.9105894870508654,0.6748933225784385,True -2030,7.0,7.1,2.1,2.3307964416656497,2.831262944078903,1.2502335959106552,0.8761919865657042,True -2031,7.0,7.1,3.1,3.09871210579244,3.391056147093424,1.4808473868363088,0.9517368978307659,True -2032,7.0,7.1,4.1,3.6104288521710632,3.8186195018078197,1.6091862535091959,1.0104199392767546,True -2033,7.0,7.1,5.1,4.0239731541190435,4.137659943888645,1.7087616129379017,1.0507649138931598,True -2034,7.0,7.1,6.1,4.328423505189823,4.4084686370655035,1.7747019582481565,1.085751566694697,True -2035,7.0,7.1,7.1,4.548736090130206,4.457142481142243,1.837552883272783,1.0826520638257222,True -2036,7.0,7.1,8.1,4.723785320175596,4.602301567402678,1.8734384510118405,1.1018228943192039,True -2037,7.0,7.1,9.1,4.889178682911363,4.718263851408437,1.9092289550793953,1.115358005304949,True 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"fw_mcmc_data.csv" +nproc=4 +chain_length = 30_000 +burnin = 10_000 df = pd.read_csv(inFN) I,SigI,Sigma,J,SigJ,F,SigF,Centric = df.to_numpy(np.float64).T @@ -21,7 +24,7 @@ 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(3000, cores=7) + trace = pm.sample(draws=chain_length, tune=burnin, cores=nproc) samples = trace.get_values('Wilson') mc_J = samples.mean(0) From ee9f78f9d74e901218bbb63181f4474cbe119aba Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Tue, 2 Feb 2021 04:48:49 -0500 Subject: [PATCH 08/12] oops accidentally mangled this file --- tests/conftest.py | 65 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/tests/conftest.py b/tests/conftest.py index a2350d66..3ed2e0c8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -61,3 +61,68 @@ def data_gemmi(): datapath = ["data", "fmodel", "9LYZ.mtz"] return load_dataset(datapath, as_gemmi=True) +@pytest.fixture +def data_fmodel(): + """ + Load fmodel results for 9LYZ.mtz + """ + datapath = ["data", "fmodel", "9LYZ.mtz"] + return load_dataset(datapath) + +@pytest.fixture +def data_fmodel_P1(): + """ + Load fmodel results for 9LYZ.mtz + """ + datapath = ["data", "fmodel", "9LYZ_p1.mtz"] + return load_dataset(datapath) + +def get_mtz_by_spacegroup(): + """ + Get absolute paths to MTZ files generated by phenix.fmodel for + testing crystallographic symmetry operations. + """ + datadir = abspath(join(dirname(__file__) + '/data/fmodel/')) + files = [join(datadir, i) for i in listdir(datadir) if re.match(r'.*(? Date: Tue, 2 Feb 2021 05:44:30 -0500 Subject: [PATCH 09/12] unequivocally better than linear interpolation --- .../test_scale_merged_intensities.py | 27 +- tests/data/french_wilson/fw_mcmc_data.csv | 4482 ++++++++--------- .../french_wilson/gen_mcmc_reference_data.py | 6 +- 3 files changed, 2263 insertions(+), 2252 deletions(-) diff --git a/tests/algorithms/test_scale_merged_intensities.py b/tests/algorithms/test_scale_merged_intensities.py index 9ca77ad0..ffad91ba 100644 --- a/tests/algorithms/test_scale_merged_intensities.py +++ b/tests/algorithms/test_scale_merged_intensities.py @@ -189,7 +189,7 @@ 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']) +@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. @@ -199,11 +199,22 @@ def test_fw_posterior_quad(ref_method, data_fw_cctbx, data_fw_mcmc): """ 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_cctbx.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) - assert np.allclose(rs_J, J, rtol=0.05) - assert np.allclose(rs_SigJ, SigJ, rtol=0.05) - assert np.allclose(rs_F, F, rtol=0.05) - assert np.allclose(rs_SigF, SigF, rtol=0.06) + 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/fw_mcmc_data.csv b/tests/data/french_wilson/fw_mcmc_data.csv index 62ba1951..45047286 100644 --- a/tests/data/french_wilson/fw_mcmc_data.csv +++ b/tests/data/french_wilson/fw_mcmc_data.csv @@ -1,2241 +1,2241 @@ -,I,SigI,Sigma,FW-I,FW-SigI,FW-F,FW-SigF,CENTRIC -0,-3.0,1.1,1.1,0.26293930472226246,0.2507902149125766,0.45736519248781016,0.23185423313548578,False -1,-3.0,1.1,2.1,0.29313321054562314,0.27724417924571015,0.48340853794129257,0.24382246818349101,False -2,-3.0,1.1,3.1,0.3048989049614726,0.2858229351908296,0.4936674011957009,0.2473689591645333,False 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-1971,7.0,0.1,7.1,6.998955795082741,0.10022963281015201,2.645486129275703,0.018945577128195897,True -1972,7.0,0.1,8.1,6.998640005800985,0.0998254672311652,2.645426990290744,0.018869150536991806,True -1973,7.0,0.1,9.1,6.998393547785745,0.10011522034547032,2.6453800212204914,0.018923295515358096,True -1974,7.0,1.1,0.1,0.8977970935920142,0.8075346066189228,0.8299224039738086,0.45719350058190966,True -1975,7.0,1.1,1.1,6.35802840700162,1.1057721144533774,2.511673972774831,0.22253597796101215,True -1976,7.0,1.1,2.1,6.61734724183498,1.1105814976851796,2.563106602983551,0.2187047863610978,True -1977,7.0,1.1,3.1,6.7104846738509805,1.1121024557311925,2.5813263706593967,0.21734498381393405,True -1978,7.0,1.1,4.1,6.763808301617626,1.1081122574289395,2.591767079131562,0.21575845092961246,True -1979,7.0,1.1,5.1,6.791153705471265,1.1100118901278753,2.5970318508444024,0.2158223138854704,True -1980,7.0,1.1,6.1,6.805632462974852,1.1086874396929192,2.599862317561243,0.21528676851987663,True -1981,7.0,1.1,7.1,6.823731014983298,1.1080304288111944,2.603388641639483,0.21470583496012385,True -1982,7.0,1.1,8.1,6.833588660804767,1.1050830115280523,2.6053233998984924,0.2141930034947883,True -1983,7.0,1.1,9.1,6.842738187155524,1.1127050274535184,2.606981233853216,0.21537649243337872,True -1984,7.0,2.1,1.1,4.411534424127095,2.186059156960503,2.012326822597854,0.6017268343527556,True -1985,7.0,2.1,2.1,5.466286193465939,2.2077645220333912,2.277159189163356,0.5299360533827021,True -1986,7.0,2.1,3.1,5.845733805467048,2.1932572553252934,2.365268924349137,0.50123519526801,True -1987,7.0,2.1,4.1,6.038225095935568,2.1908891307976193,2.407739902980961,0.4909314163191184,True -1988,7.0,2.1,5.1,6.134755626786487,2.205174485267535,2.4274178318153408,0.4923396150743043,True -1989,7.0,2.1,6.1,6.213110654128313,2.2022045962654273,2.444193667791177,0.48890486646975795,True -1990,7.0,2.1,7.1,6.278889637950658,2.183135987595041,2.460045813433169,0.476512574609246,True -1991,7.0,2.1,8.1,6.325869285694302,2.192383286012186,2.469676539687079,0.4759906249219077,True -1992,7.0,2.1,9.1,6.354807737924611,2.1818077728627148,2.4765720949343764,0.47052991033201347,True -1993,7.0,3.1,1.1,2.4979078493509843,2.257896718201961,1.3828577926965984,0.7652530134075324,True -1994,7.0,3.1,2.1,3.8974590741519477,2.8254619387160322,1.8004506362476769,0.8098373790998185,True -1995,7.0,3.1,3.1,4.55217517600083,2.9737848035418133,1.9772357811089003,0.8016943569113589,True -1996,7.0,3.1,4.1,4.911252193324871,3.0459911055177615,2.068929003432809,0.7942197253149458,True -1997,7.0,3.1,5.1,5.133061702002368,3.0974634944096042,2.1234785436565393,0.789873772594512,True -1998,7.0,3.1,6.1,5.304589638892806,3.1039630728340653,2.1666263165215587,0.7812298275469218,True -1999,7.0,3.1,7.1,5.38597303576504,3.132719459427759,2.1846076539121433,0.7832384274493845,True -2000,7.0,3.1,8.1,5.531631182645843,3.120880380401808,2.2233615203654753,0.7670037368905379,True -2001,7.0,3.1,9.1,5.5471886350313175,3.1495396862332314,2.2248790486835617,0.7727236593765278,True -2002,7.0,4.1,1.1,1.8073894615317778,2.005004065687618,1.126490215477016,0.7337637603250261,True -2003,7.0,4.1,2.1,3.0614361309784495,2.8555569688574747,1.5215144192519154,0.8639618064399388,True -2004,7.0,4.1,3.1,3.7955182750656884,3.2091744730218172,1.7278531829228605,0.9000231404408139,True -2005,7.0,4.1,4.1,4.209299229179102,3.397408980384263,1.8347263372655824,0.9181930605940586,True -2006,7.0,4.1,5.1,4.490595247541842,3.5171093919998095,1.9060059217896739,0.9261407418122506,True -2007,7.0,4.1,6.1,4.688111609259522,3.566375437350559,1.958258853625105,0.9237607219721662,True -2008,7.0,4.1,7.1,4.869169945954668,3.609644101755879,2.00521015295137,0.9210332178892018,True -2009,7.0,4.1,8.1,4.937411627030918,3.657400140167398,2.0185815983662168,0.9288379609858335,True -2010,7.0,4.1,9.1,5.034679624773254,3.6764058019039187,2.042937191962736,0.9279478727107692,True -2011,7.0,5.1,1.1,1.5128559342874646,1.8598277249093451,1.0073553022282686,0.7057557859203422,True -2012,7.0,5.1,2.1,2.702832023490194,2.8536238264002978,1.3930964972074351,0.8729915079533394,True -2013,7.0,5.1,3.1,3.396592732731127,3.3174370998708897,1.5867862709239473,0.9373910940149236,True -2014,7.0,5.1,4.1,3.8642550822358905,3.6023463620730136,1.7085013770222004,0.9722541473036164,True -2015,7.0,5.1,5.1,4.189492722438333,3.740393642169549,1.7958081139864277,0.9821231797380766,True -2016,7.0,5.1,6.1,4.388907141304521,3.8620433179449796,1.8414712597924803,0.9989448136222924,True -2017,7.0,5.1,7.1,4.6059346653989675,3.9467113454472518,1.8996122178599057,0.9987032027366033,True -2018,7.0,5.1,8.1,4.715695147551204,4.018618059619471,1.9222186801869083,1.0103318727485993,True -2019,7.0,5.1,9.1,4.83861942018353,4.063935038875875,1.953115407601939,1.011908901419248,True -2020,7.0,6.1,1.1,1.3734549087370043,1.7552896923404808,0.9536001068153506,0.6812501339586815,True -2021,7.0,6.1,2.1,2.4686356594419636,2.799289326933064,1.31175396688262,0.8648336197267396,True -2022,7.0,6.1,3.1,3.218082945772766,3.3944806546128254,1.519406359285446,0.9536704153614592,True -2023,7.0,6.1,4.1,3.6851624727013355,3.76392953299389,1.6358855178610157,1.004510350944155,True -2024,7.0,6.1,5.1,4.066645415084739,3.968897861347333,1.7363092219941605,1.0256099164412895,True -2025,7.0,6.1,6.1,4.333826815983973,4.119809390016519,1.8039695015906514,1.0389999294103691,True -2026,7.0,6.1,7.1,4.524949201521108,4.2205238670816225,1.8494234890131025,1.0509909418295118,True -2027,7.0,6.1,8.1,4.679199500383203,4.318765794446613,1.8849784377375336,1.0611577590762817,True -2028,7.0,6.1,9.1,4.812430064456588,4.406701058500731,1.9144235666016591,1.0711734089758143,True -2029,7.0,7.1,1.1,1.2974094407619545,1.699546919287055,0.9210638954372219,0.6701124840532006,True -2030,7.0,7.1,2.1,2.363144767776515,2.8031258767085707,1.2703423207055515,0.8656645747637848,True -2031,7.0,7.1,3.1,3.1158208452606178,3.477602186756291,1.4771209307069184,0.9664029187291583,True -2032,7.0,7.1,4.1,3.6297438843946987,3.8775295400344967,1.6087536278463996,1.0206153287531192,True -2033,7.0,7.1,5.1,4.010408495355689,4.1508510184817045,1.7029596317594504,1.0537252905541843,True -2034,7.0,7.1,6.1,4.325779635848193,4.375711550006869,1.7776162543622307,1.0797500118432193,True -2035,7.0,7.1,7.1,4.5385221025742855,4.493110427024036,1.8290142661620001,1.0923501804595757,True -2036,7.0,7.1,8.1,4.724392250300992,4.617827156110251,1.8710535531072983,1.106142328864405,True -2037,7.0,7.1,9.1,4.8886863238594795,4.706150096651883,1.9095694425588234,1.1145541116989668,True -2038,7.0,8.1,1.1,1.2501728368817064,1.6730987960127788,0.9016570330507974,0.6612015060718632,True -2039,7.0,8.1,2.1,2.283820995603222,2.814174605320013,1.2380655983531852,0.8666109679536651,True -2040,7.0,8.1,3.1,3.0578611575765593,3.558537975691502,1.4503913882782256,0.97684491009827,True -2041,7.0,8.1,4.1,3.6103414730163643,4.025084379502148,1.588760027723156,1.0422010589735555,True -2042,7.0,8.1,5.1,4.028469653617575,4.351941781613687,1.690235860582148,1.0823919757739109,True -2043,7.0,8.1,6.1,4.351389791093336,4.554619147125493,1.7689378323875045,1.1055535881364968,True -2044,7.0,8.1,7.1,4.595444577061166,4.77018670220619,1.8212473906739306,1.1307088568789958,True -2045,7.0,8.1,8.1,4.826098984818278,4.878954340784108,1.8773447024652734,1.1409100985370628,True -2046,7.0,8.1,9.1,4.964226396486182,4.99469775859281,1.9071923760146567,1.1518869898378523,True -2047,7.0,9.1,1.1,1.2177059582502436,1.6445359218861735,0.8876638615096978,0.6555599341173267,True -2048,7.0,9.1,2.1,2.241326777851816,2.8185053163221014,1.2212965115915613,0.8658877575218186,True -2049,7.0,9.1,3.1,3.0209309488099434,3.6161585893547077,1.4317012476438167,0.9854757664727785,True -2050,7.0,9.1,4.1,3.6215260827664597,4.138944989500183,1.5844282017636628,1.0540936183387106,True -2051,7.0,9.1,5.1,4.0529412381429735,4.499550475052371,1.686582802609158,1.09926324785558,True -2052,7.0,9.1,6.1,4.4051631918754905,4.784551633924276,1.7663316997155436,1.1336822828532984,True -2053,7.0,9.1,7.1,4.6835820873533605,4.976707908902824,1.8307166593463005,1.154148430898422,True -2054,7.0,9.1,8.1,4.889545046702884,5.158305788519845,1.8737529081990265,1.1741358889492313,True -2055,7.0,9.1,9.1,5.072856930216672,5.278868876685914,1.9122495392614025,1.1900246341194871,True -2056,8.0,0.1,0.1,7.949692609099186,0.0994466913653746,2.819464768273384,0.017636030287637966,True -2057,8.0,0.1,1.1,7.994611000820332,0.09989064877821306,2.827419126674595,0.01766586920916517,True -2058,8.0,0.1,2.1,7.996702979660657,0.10037432388660862,2.827788526490242,0.01774905943140596,True -2059,8.0,0.1,3.1,7.997115623542065,0.09987745289755288,2.8278620427247545,0.01766043200349609,True -2060,8.0,0.1,4.1,7.997907174865722,0.09975280839689608,2.8280021418012633,0.017636916776879504,True -2061,8.0,0.1,5.1,7.998634153854209,0.09955977889770684,2.8281308887292513,0.017601990498333296,True -2062,8.0,0.1,6.1,7.998887728146814,0.09979254633414532,2.82817546241497,0.01764318396890178,True -2063,8.0,0.1,7.1,7.9989021893347525,0.10043955002709103,2.828177305749286,0.0177571557276595,True -2064,8.0,0.1,8.1,7.998958096896831,0.09984973877090378,2.828187842581158,0.01765287435389586,True -2065,8.0,0.1,9.1,7.998169938843396,0.09961053191234245,2.828048753100168,0.017612181351530993,True -2066,8.0,1.1,0.1,1.6533592767329928,1.0529900166664252,1.1974046097293396,0.46859521693234574,True -2067,8.0,1.1,1.1,7.366460186651836,1.104360565616577,2.7063136648364723,0.20573413467649465,True -2068,8.0,1.1,2.1,7.630021367359643,1.1020762259932833,2.7548933804081894,0.20145428747663544,True -2069,8.0,1.1,3.1,7.724652349462876,1.1050246522668727,2.772068359981035,0.20072208910582384,True -2070,8.0,1.1,4.1,7.770192853437352,1.1036046971566866,2.7803324777962533,0.19986036712872954,True 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a/tests/data/french_wilson/gen_mcmc_reference_data.py +++ b/tests/data/french_wilson/gen_mcmc_reference_data.py @@ -8,9 +8,9 @@ inFN = "fw_test_data.csv" outFN = "fw_mcmc_data.csv" -nproc=4 +nproc=7 chain_length = 30_000 -burnin = 10_000 +burnin = 15_000 df = pd.read_csv(inFN) I,SigI,Sigma,J,SigJ,F,SigF,Centric = df.to_numpy(np.float64).T @@ -38,4 +38,4 @@ df['FW-F'] = mc_F df['FW-SigF'] = mc_SigF -df.to_csv(outFN) +df.to_csv(outFN, index=False) From 42a82df85f4e8f2c8b59549d4d1544f492363002 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Tue, 2 Feb 2021 05:49:16 -0500 Subject: [PATCH 10/12] i hope this isn't clutter, but it has been useful --- tests/data/french_wilson/diagnostics.py | 73 +++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 tests/data/french_wilson/diagnostics.py diff --git a/tests/data/french_wilson/diagnostics.py b/tests/data/french_wilson/diagnostics.py new file mode 100644 index 00000000..84a4dfaa --- /dev/null +++ b/tests/data/french_wilson/diagnostics.py @@ -0,0 +1,73 @@ +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("cctbx 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("cctbx 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_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() From 7a1c6bd8ad1f5eb6d7275b146d0fb6457004aa2a Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Tue, 2 Feb 2021 06:04:50 -0500 Subject: [PATCH 11/12] updated labels --- tests/data/french_wilson/diagnostics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/data/french_wilson/diagnostics.py b/tests/data/french_wilson/diagnostics.py index 84a4dfaa..e5f090ad 100644 --- a/tests/data/french_wilson/diagnostics.py +++ b/tests/data/french_wilson/diagnostics.py @@ -12,7 +12,7 @@ #All the plots plt.plot(J, rs_J, 'k.', label='Acentric') plt.plot(J[Centric], rs_J[Centric], 'r.', label='Centric') -plt.xlabel("cctbx J") +plt.xlabel("MCMC J") plt.ylabel("rs J") plt.legend() plt.savefig("J.png") @@ -20,7 +20,7 @@ plt.figure() plt.plot(F, rs_F, 'k.', label='Acentric') plt.plot(F[Centric], rs_F[Centric], 'r.', label='Centric') -plt.xlabel("cctbx F") +plt.xlabel("MCMC F") plt.ylabel("rs F") plt.legend() plt.savefig("F.png") From 71f10b4237ddd9a610858e6c86a7f243f6dbffc7 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Tue, 2 Feb 2021 06:31:52 -0500 Subject: [PATCH 12/12] added a missing histogram --- tests/data/french_wilson/diagnostics.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tests/data/french_wilson/diagnostics.py b/tests/data/french_wilson/diagnostics.py index e5f090ad..3f87aa6d 100644 --- a/tests/data/french_wilson/diagnostics.py +++ b/tests/data/french_wilson/diagnostics.py @@ -48,6 +48,13 @@ 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)