From 0b9c16c125ce554c91bff2c1afba5b3cb792687a Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 22 May 2026 09:05:19 -0500 Subject: [PATCH 01/84] (Issue #153) Cleanup metadata. This most important change was the removal of the classifier that specified the license, which was causing tools to emit a license deprecation warning. --- setup.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index d22ea434..58133b1b 100644 --- a/setup.py +++ b/setup.py @@ -59,7 +59,7 @@ def readme_rst(): description="A modular interface for surrogate models and tools", long_description=readme_rst(), long_description_content_type="text/x-rst", - url="https://github.com/bandframework/surmise", + url=project_urls["Source"], project_urls=project_urls, license="MIT", packages=find_packages(where="src"), @@ -71,8 +71,18 @@ def readme_rst(): ext_modules=extensions, keywords="surmise", classifiers=[ + "Natural Language :: English", + "Development Status :: 5 - Production/Stable", "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Operating System :: MacOS :: MacOS X", + "Operating System :: POSIX :: Linux", + "Operating System :: Microsoft :: Windows", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering" ] ) From 29dafdca9b7e2f488db1c5208e07904770e89edb Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 11:36:41 -0500 Subject: [PATCH 02/84] (Issue #209) No need to visualize test results with benchmarks. Reran script to confirm all tests still passing with no visualizations to close. Progress! --- tools/MetropolisHastingsTestSuite.json | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 30d85960..c0a708dc 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -19,7 +19,7 @@ "Scale": 2.5 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -50,7 +50,7 @@ }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", - "Plot": true, + "Plot": false, "CornerPlotBins": 30, "n_burn_samples": 1000, "n_samples": 100000, @@ -80,7 +80,7 @@ "Scale": 3.5 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -112,7 +112,7 @@ "Scale": [1.0, 5.0] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal2D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, @@ -147,7 +147,7 @@ "Scale": [1.0, 5.0, 0.2] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal3D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, From 429ca187a2342666992552cc2f3d116dc3b2d4e7 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 11:41:32 -0500 Subject: [PATCH 03/84] (Issue #209) Add failing MH/Uniform step tests to suite. Failing as expected. --- tools/MetropolisHastingsFailure.json | 60 -------------------------- tools/MetropolisHastingsTestSuite.json | 41 ++++++++++++++++++ 2 files changed, 41 insertions(+), 60 deletions(-) delete mode 100644 tools/MetropolisHastingsFailure.json diff --git a/tools/MetropolisHastingsFailure.json b/tools/MetropolisHastingsFailure.json deleted file mode 100644 index 3579b013..00000000 --- a/tools/MetropolisHastingsFailure.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - "Uniform1D": { - "TargetDistribution": { - "Name": "Uniform", - "Intervals": [-1.1, 1.2] - }, - "TestSetups": { - "UniformStep": { - "rng": { - "method": "default", - "random_seed": 273495583654866805492797112903413765323 - }, - "StartDistribution": { - "Name": "Uniform", - "Intervals": [-0.5, 0.5] - }, - "StepDistribution": { - "Name": "uniform", - "Scale": 6.0 - }, - "SampleSkip": 10, - "Plot": true, - "Benchmark": "", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false - } - } - }, - "Normal1D": { - "TargetDistribution": { - "Name": "Normal", - "mu": -1.1, - "sigma": 1.2 - }, - "TestSetups": { - "UniformStep": { - "rng": { - "method": "default", - "random_seed": 96249791203211713241001619333135103061 - }, - "StartDistribution": { - "Name": "Normal", - "mu": 0.0, - "sigma": 1.0 - }, - "StepDistribution": { - "Name": "uniform", - "Scale": 6.0 - }, - "SampleSkip": 10, - "Plot": true, - "Benchmark": "", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false - } - } - } -} diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index c0a708dc..38ac9a3a 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -24,6 +24,26 @@ "n_burn_samples": 10000, "n_samples": 100000, "verbose": false + }, + "UniformStep": { + "rng": { + "method": "default", + "random_seed": 273495583654866805492797112903413765323 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [-0.5, 0.5] + }, + "StepDistribution": { + "Name": "uniform", + "Scale": 6.0 + }, + "SampleSkip": 10, + "Plot": true, + "Benchmark": "", + "n_burn_samples": 10000, + "n_samples": 100000, + "verbose": false } } }, @@ -85,6 +105,27 @@ "n_burn_samples": 10000, "n_samples": 100000, "verbose": false + }, + "UniformStep": { + "rng": { + "method": "default", + "random_seed": 96249791203211713241001619333135103061 + }, + "StartDistribution": { + "Name": "Normal", + "mu": 0.0, + "sigma": 1.0 + }, + "StepDistribution": { + "Name": "uniform", + "Scale": 10.0 + }, + "SampleSkip": 10, + "Plot": true, + "Benchmark": "", + "n_burn_samples": 10000, + "n_samples": 100000, + "verbose": false } } }, From 979a59de02de2692dd8414873be5fed925d517ff Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 11:45:21 -0500 Subject: [PATCH 04/84] (Issue #209) Implement potential bug fix. Based on lessons learned from this bug, we now explicitly use loc/scale argument names. With this, the test suite runs through successfully. The visualizations and test output for the new tests now look good. At least they are similar to results for the MH/Normal step tests. --- src/surmise/utilitiesmethods/metropolis_hastings.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index 47fcce53..6d5c1874 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -76,11 +76,11 @@ def sampler(logpost_func, theta_cand = None if stepType == 'normal': theta_cand = [theta[i-1, :][k] + stepParam[k] * - sps.norm.rvs(0, 1, size=1, random_state=rng) + sps.norm.rvs(loc=0.0, scale=1.0, size=1, random_state=rng) for k in range(p)] elif stepType == 'uniform': theta_cand = [theta[i-1, :][k] + stepParam[k] * - sps.uniform.rvs(-0.5, 0.5, size=1, random_state=rng) + sps.uniform.rvs(loc=-0.5, scale=1.0, size=1, random_state=rng) for k in range(p)] theta_cand = np.reshape(np.array(theta_cand), (1, p)) From 2d62b9f932df6947ffd1d1b8515d4146d3e0d918 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 11:59:40 -0500 Subject: [PATCH 05/84] (Issue #209) Add in 4D MH/Uniform step test. Not only does this test allow us to stress test the uniform step distribution in MH is more than 1D, but it also allows us to confirm that our test tool does work on 4D problems (albeit with boring target distributions). My tests all passed and the visualizations/logging for the new test look good. --- tools/MetropolisHastingsTestSuite.json | 35 ++++++++++++++++++++++++++ tools/create_distribution.py | 5 +--- 2 files changed, 36 insertions(+), 4 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 38ac9a3a..78afe481 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -196,5 +196,40 @@ "verbose": false } } + }, + "Uniform4D": { + "TargetDistribution": { + "Name": "Uniform", + "Intervals": [[-1.1, 1.2], + [ 1.3, 4.3], + [-4.5, -0.3], + [-0.5, -0.4]] + }, + "TestSetups": { + "NormalStep": { + "rng": { + "method": "default", + "random_seed": 94151694354502029434490913866546038801 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [[-0.5, 0.5], + [ 1.5, 2.5], + [-4.0, -1.0], + [-0.5, -0.4]] + }, + "StepDistribution": { + "Name": "uniform", + "Scale": [1.0, 1.75, 2.5, 0.2] + }, + "SampleSkip": 20, + "Plot": true, + "Benchmark": "", + "CornerPlotBins": 25, + "n_burn_samples": 10000, + "n_samples": 250000, + "verbose": false + } + } } } diff --git a/tools/create_distribution.py b/tools/create_distribution.py index 869c81a5..10ce2a6e 100644 --- a/tools/create_distribution.py +++ b/tools/create_distribution.py @@ -19,10 +19,7 @@ def create_distribution(configuration): a, b = ivals[0] distribution = UniformDistribution(a, b) else: - # TODO: Allow for >2 - # distribution = JointUniformDistribution(*ivals) - assert dimension == 2 - distribution = JointUniformDistribution(ivals[0], ivals[1]) + distribution = JointUniformDistribution(*ivals) elif name.lower() == "normal": mu = configuration["mu"] dimension = 1 if isinstance(mu, numbers.Real) else len(mu) From 5acd5b26ce49e99fa49266a3f7e2a7e5226bd463 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 12:21:07 -0500 Subject: [PATCH 06/84] (Issue #209) Setup new tests for regression testing. All tests now running regression tests and passing. --- tools/MetropolisHastingsTestSuite.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 78afe481..98671650 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -40,7 +40,7 @@ }, "SampleSkip": 10, "Plot": true, - "Benchmark": "", + "Benchmark": "MH_Uniform1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, "verbose": false @@ -122,7 +122,7 @@ }, "SampleSkip": 10, "Plot": true, - "Benchmark": "", + "Benchmark": "MH_Normal1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, "verbose": false @@ -206,7 +206,7 @@ [-0.5, -0.4]] }, "TestSetups": { - "NormalStep": { + "UniformStep": { "rng": { "method": "default", "random_seed": 94151694354502029434490913866546038801 @@ -224,7 +224,7 @@ }, "SampleSkip": 20, "Plot": true, - "Benchmark": "", + "Benchmark": "MH_Uniform4D_UniformStep.benchmark", "CornerPlotBins": 25, "n_burn_samples": 10000, "n_samples": 250000, From 78ea31b2b59f13467b5278e35d8f6a81bbe9ecb9 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 16:39:04 -0500 Subject: [PATCH 07/84] (Issue #205) No need for plots when we have full regression testing. Confirm that my test suite runs through successfully with no visualizations. --- tools/MetropolisHastingsTestSuite.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 98671650..83677b49 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -39,7 +39,7 @@ "Scale": 6.0 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -121,7 +121,7 @@ "Scale": 10.0 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -223,7 +223,7 @@ "Scale": [1.0, 1.75, 2.5, 0.2] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform4D_UniformStep.benchmark", "CornerPlotBins": 25, "n_burn_samples": 10000, From df90abaf4f4fbfb80d7d176d516add65367b9b34 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 16:49:18 -0500 Subject: [PATCH 08/84] (Issue #205) Move distribution creation outside MCMC loop. Most importantly, this moves the current if/else block outside the loop. We are also sampling the full vector in a single rvs call and computing the proposed step in vectorized fashion. An additional benefit of this is that we create the proposal distribution once as a frozen distribution and draw from that at each loop iteration, which reflects the static nature of the proposal distribution in the algorithm. Prior to these changes, the test suite ran in 167 seconds. With this change, the test suite runs through successfully with bitwise identical results (as expected) and in 131 seconds. Not a massive speedup, but I'll take it! --- .../utilitiesmethods/metropolis_hastings.py | 29 ++++++++++++------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index 6d5c1874..4cdc0d1f 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -54,6 +54,20 @@ def sampler(logpost_func, if stepParam is None: stepParam = np.std(draw_func(burnSamples), axis=0) + # For the current, symmetric step proposal distributions, we can create a + # frozen step proposal distribution object up front and draw from it blindly + # within the MCMC loop to determine the sample proposal with no need for + # conditionals inside the loop. + if stepType.lower() == "uniform": + a, b = [-0.5, 0.5] + length = b - a + step_distribution = sps.uniform(loc=a, scale=length) + elif stepType.lower() == "normal": + mean, std = (0.0, 1.0) + step_distribution = sps.norm(loc=mean, scale=std) + else: + raise ValueError("Bad step_type {step_type}") + # intial theta to start the chain if theta0 is None: theta0 = draw_func(1) @@ -72,17 +86,12 @@ def sampler(logpost_func, if verbose: if i % 30000 == 0: print("At sample {}, acceptance rate is {}.".format(i, n_acc/i)) - # Candidate theta - theta_cand = None - if stepType == 'normal': - theta_cand = [theta[i-1, :][k] + stepParam[k] * - sps.norm.rvs(loc=0.0, scale=1.0, size=1, random_state=rng) - for k in range(p)] - elif stepType == 'uniform': - theta_cand = [theta[i-1, :][k] + stepParam[k] * - sps.uniform.rvs(loc=-0.5, scale=1.0, size=1, random_state=rng) - for k in range(p)] + # Candidate theta + step = step_distribution.rvs(size=p, random_state=rng) + theta_cand = theta[i-1, :] + stepParam * step + if not all(np.isfinite(theta_cand)): + raise RuntimeError("Proposed theta contains invalid values") theta_cand = np.reshape(np.array(theta_cand), (1, p)) # Compute loglikelihood From db8419f7317bf2940c16af4cf4a16d1ac783ec7c Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 17:14:36 -0500 Subject: [PATCH 09/84] (Issue #205) Clean up as part of PR review. --- src/surmise/utilitiesmethods/metropolis_hastings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index 4cdc0d1f..d99a8ea6 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -66,7 +66,7 @@ def sampler(logpost_func, mean, std = (0.0, 1.0) step_distribution = sps.norm(loc=mean, scale=std) else: - raise ValueError("Bad step_type {step_type}") + raise ValueError("Bad step_type {stepType}") # intial theta to start the chain if theta0 is None: From 7f371590c6cd225f3bbd78edb2cbbbfb65295a99 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 18 Jun 2026 17:24:00 -0500 Subject: [PATCH 10/84] (Issue #205) Might as well clean up error text as well. --- src/surmise/utilitiesmethods/metropolis_hastings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index d99a8ea6..8b2b2bb4 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -66,7 +66,7 @@ def sampler(logpost_func, mean, std = (0.0, 1.0) step_distribution = sps.norm(loc=mean, scale=std) else: - raise ValueError("Bad step_type {stepType}") + raise ValueError("Bad step type {stepType}") # intial theta to start the chain if theta0 is None: From 3aadee593c649049e7e0061184c63171665747ba Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 19 Jun 2026 09:53:20 -0500 Subject: [PATCH 11/84] (Issue #205) Manage possible overflow and underflow explicitly. Previous changes to the computation/use of p_accept were leading to overflow warnings being posted by the official package test suite. They were not, however, showing up with the test script's suite. This commit expands out the handling of how to choose the next theta point by precluding the possibility of overflow and explaining how underflow can happen and how the algorithm deals with it. See comments in code for more benefits. Official unit test suite and unofficial test script suite passing with no warnings. Since the new implementation is no longer drawing from Bernoulli with p_accept=1, the results are different from previous benchmarks. After updating benchmarks to the new results, a rerun of the test script ran through successfully. Curiously, despite expanding the if/elif/else block within the MCMC loop, performance appears to have improved slightly. --- .../utilitiesmethods/metropolis_hastings.py | 28 ++++++++++++++++--- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index 8b2b2bb4..80c77d5e 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -97,11 +97,31 @@ def sampler(logpost_func, # Compute loglikelihood logpost = logpost_func(theta_cand, return_grad=False).item() - if np.isfinite(logpost): - p_accept = min(1.0, np.exp(logpost - lposterior[i-1])) - accept = (sps.bernoulli.rvs(p=p_accept, size=1, random_state=rng) == 1) - else: + if logpost == -np.inf: accept = False + elif not np.isfinite(logpost): + raise ValueError(f"Invalid log posterior evaluation ({logpost})") + elif logpost >= lposterior[i-1]: + # Handle easy case directly, which precludes any possibility of an + # overflow when exponentiating the difference in successive log + # posterior values below. + # + # This also prevents unnecessary Bernoulli draws. + accept = True + else: + # While analytically p_accept must be in (0, 1) here, if the + # magnitude of the difference is large enough numerically this will + # underflow to zero, which will sensibly result in the proposal + # being rejected. + # + # In testing this, I found that np.exp(-745.0) = 5e-324, which + # indicates the use of subnormal numbers before underflowing. + p_accept = np.exp(logpost - lposterior[i-1]) + if p_accept == 0.0: + accept = False + else: + assert 0.0 < p_accept < 1.0 + accept = (sps.bernoulli.rvs(p=p_accept, size=1, random_state=rng) == 1) # Accept candidate? if accept: From 87c0cb80ce8956f16504f7d8a34272677f993ea3 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 19 Jun 2026 10:03:30 -0500 Subject: [PATCH 12/84] (Issue #205) Enable visualization of all tests in suite. This is necessary to verify that all new results are sufficiently good for converting into the next set of benchmarks. --- tools/MetropolisHastingsTestSuite.json | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 83677b49..bfd4e465 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -19,7 +19,7 @@ "Scale": 2.5 }, "SampleSkip": 10, - "Plot": false, + "Plot": true, "Benchmark": "MH_Uniform1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -39,7 +39,7 @@ "Scale": 6.0 }, "SampleSkip": 10, - "Plot": false, + "Plot": true, "Benchmark": "MH_Uniform1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -70,7 +70,7 @@ }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", - "Plot": false, + "Plot": true, "CornerPlotBins": 30, "n_burn_samples": 1000, "n_samples": 100000, @@ -100,7 +100,7 @@ "Scale": 3.5 }, "SampleSkip": 10, - "Plot": false, + "Plot": true, "Benchmark": "MH_Normal1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -121,7 +121,7 @@ "Scale": 10.0 }, "SampleSkip": 10, - "Plot": false, + "Plot": true, "Benchmark": "MH_Normal1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -153,7 +153,7 @@ "Scale": [1.0, 5.0] }, "SampleSkip": 20, - "Plot": false, + "Plot": true, "Benchmark": "MH_Normal2D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, @@ -188,7 +188,7 @@ "Scale": [1.0, 5.0, 0.2] }, "SampleSkip": 20, - "Plot": false, + "Plot": true, "Benchmark": "MH_Normal3D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, @@ -223,7 +223,7 @@ "Scale": [1.0, 1.75, 2.5, 0.2] }, "SampleSkip": 20, - "Plot": false, + "Plot": true, "Benchmark": "MH_Uniform4D_UniformStep.benchmark", "CornerPlotBins": 25, "n_burn_samples": 10000, From 340a2b1520fb2b6386f43e9349aa0129b568c593 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 19 Jun 2026 16:18:50 -0500 Subject: [PATCH 13/84] (Issue #205) Turn off plotting after getting new set of benchmarks. --- tools/MetropolisHastingsTestSuite.json | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index bfd4e465..83677b49 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -19,7 +19,7 @@ "Scale": 2.5 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -39,7 +39,7 @@ "Scale": 6.0 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -70,7 +70,7 @@ }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", - "Plot": true, + "Plot": false, "CornerPlotBins": 30, "n_burn_samples": 1000, "n_samples": 100000, @@ -100,7 +100,7 @@ "Scale": 3.5 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal1D_NormalStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -121,7 +121,7 @@ "Scale": 10.0 }, "SampleSkip": 10, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal1D_UniformStep.benchmark", "n_burn_samples": 10000, "n_samples": 100000, @@ -153,7 +153,7 @@ "Scale": [1.0, 5.0] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal2D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, @@ -188,7 +188,7 @@ "Scale": [1.0, 5.0, 0.2] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Normal3D_NormalStep.benchmark", "CornerPlotBins": 50, "n_burn_samples": 10000, @@ -223,7 +223,7 @@ "Scale": [1.0, 1.75, 2.5, 0.2] }, "SampleSkip": 20, - "Plot": true, + "Plot": false, "Benchmark": "MH_Uniform4D_UniformStep.benchmark", "CornerPlotBins": 25, "n_burn_samples": 10000, From eca9f8c020309340cc4ac8cf7080cb03c85fae94 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 19 Jun 2026 16:27:22 -0500 Subject: [PATCH 14/84] (Issue #205) Error if first evaluation yields zero density. Prefer initializing arrays to NaN so that any bugs in the code that result in insufficient setting of all elements in the array will hopefully result in someone finding NaNs. The first few tests ran through with identical results, as expected. However, one test did fail with the new error since it was intentionally designed with a start distribution that is disjoint from the support of the target distribution. The test suite will be fixed in a subsequent commit so that reviewers can check out this commit to confirm correct detection and reporting of failure. --- .../utilitiesmethods/metropolis_hastings.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index 80c77d5e..b852da05 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -73,15 +73,17 @@ def sampler(logpost_func, theta0 = draw_func(1) p = theta0.shape[1] - lposterior = np.zeros(burnSamples + numsamp) - theta = np.zeros((burnSamples + numsamp, theta0.shape[1])) - # print(theta0) - lposterior[0] = logpost_func(theta0, return_grad=False).item() + theta = np.full((burnSamples + numsamp, p), np.nan, float) theta[0] = theta0 - n_acc = 0 - lposterior_list = [] + lposterior = np.full(burnSamples + numsamp, np.nan, float) + lposterior[0] = np.squeeze(logpost_func(theta0, return_grad=False)) + if not np.isfinite(lposterior[0]): + assert lposterior[0] == -np.inf + raise RuntimeError("Initial theta evaluates to zero density") + n_acc = 0 + lposterior_list = [] for i in range(1, burnSamples + numsamp): if verbose: if i % 30000 == 0: @@ -95,7 +97,7 @@ def sampler(logpost_func, theta_cand = np.reshape(np.array(theta_cand), (1, p)) # Compute loglikelihood - logpost = logpost_func(theta_cand, return_grad=False).item() + logpost = np.squeeze(logpost_func(theta_cand, return_grad=False)) if logpost == -np.inf: accept = False From c2e4283a279113460a3495bf578b63e01be80cdb Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 19 Jun 2026 16:33:57 -0500 Subject: [PATCH 15/84] (Issue #205) Fixed start distribution of broken test. Any draw from the start distribution should now yield a positive density. Note that after fixing this, the test still failed since the new results were started from a new starting point. After saving the new result as the new benchmark, a rerun of the full test suite finished successfully. --- tools/MetropolisHastingsTestSuite.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 83677b49..92e02ae6 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -62,7 +62,7 @@ "StartDistribution": { "Name": "Uniform", "Intervals": [[-0.5, 0.5], - [0.0, 1.0]] + [ 1.5, 2.1]] }, "StepDistribution": { "Name": "normal", @@ -70,7 +70,7 @@ }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", - "Plot": false, + "Plot": true, "CornerPlotBins": 30, "n_burn_samples": 1000, "n_samples": 100000, From bf84f37dabfdb025a65664c9f27041927504fca8 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 09:59:17 -0500 Subject: [PATCH 16/84] (Issue #221) Temporarily disallow installing numpy v2.5.0. This new version is causing issues, so I temporarily disallow it so that we can proceed with unrelated development activities. Running `tox -r -e nocoverage` before and after making this change revealed that I could locally reproduce the related issues and avoid them with this change. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index d22ea434..9ac73f69 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,7 @@ def readme_rst(): python_requires = ">=3.10" code_requires = [ - 'numpy>=1.22.0', + 'numpy>=1.22.0,<2.5.0', 'scipy>=1.9.0', 'scikit-learn>=1.2.0', 'dill>=0.3.8' From a9a6bf23a52333879b9f4ca7c0b46c308469db89 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 10:05:03 -0500 Subject: [PATCH 17/84] (Issue #205) No need to plot test script results. We again have a full set of benchmarks that should be sufficient for upcoming refactoring work. --- tools/MetropolisHastingsTestSuite.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 92e02ae6..a3cb7e00 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -70,7 +70,7 @@ }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", - "Plot": true, + "Plot": false, "CornerPlotBins": 30, "n_burn_samples": 1000, "n_samples": 100000, From c14ab3a9df12c39e77c3b6aea95f45e519602056 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 10:30:12 -0500 Subject: [PATCH 18/84] (Issue #205) First draft for fixing MH logging. I set the burn-in rate to 100,000 for the Uniform4D test to see a clear manifestation of the logging errors. After making this change, that test runs through with reasonable logging results. The test is successful because the logging bug did not affect the execution of the sampling nor the computation of the final acceptance rate. --- .../utilitiesmethods/metropolis_hastings.py | 31 ++++++++++++++----- 1 file changed, 24 insertions(+), 7 deletions(-) diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index b852da05..fe94756a 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -39,6 +39,9 @@ def sampler(logpost_func, returns numsamp random draws from posterior. ''' + # Hardcoded values + LOG_RATE = 25_000 + # random number generator # TODO: This is an intermediate step. Eventually calling code should be # forced to provide an RNG. @@ -82,13 +85,12 @@ def sampler(logpost_func, assert lposterior[0] == -np.inf raise RuntimeError("Initial theta evaluates to zero density") - n_acc = 0 + # We implicitly treat theta0 as accepted. If the number of burn-in samples + # is positive, we also treat it as part of the burn-in. + n_acc = 1 if burnSamples == 0 else 0 + n_official_i = 1 if burnSamples == 0 else 0 lposterior_list = [] for i in range(1, burnSamples + numsamp): - if verbose: - if i % 30000 == 0: - print("At sample {}, acceptance rate is {}.".format(i, n_acc/i)) - # Candidate theta step = step_distribution.rvs(size=p, random_state=rng) theta_cand = theta[i-1, :] + stepParam * step @@ -138,9 +140,24 @@ def sampler(logpost_func, lposterior[i] = lposterior[i-1] lposterior_list.append(logpost) + # N official samples completed by end of i^th iteration + # - Nonpositive value indicates still in warm-up phase + n_official_i = i - burnSamples + 1 + + if verbose: + if (n_official_i >= 1) and (n_official_i % LOG_RATE == 0): + acc_rate = n_acc / float(n_official_i) + assert 0.0 <= acc_rate <= 1.0 + print( + f"Sample {n_official_i:>10} acceptance rate={acc_rate}" + ) + assert n_official_i == numsamp + acc_rate = n_acc / float(numsamp) + assert 0.0 <= acc_rate <= 1.0 + theta = theta[(burnSamples):(burnSamples + numsamp), :] - sampler_info = {'theta': theta, 'acc_rate': n_acc/numsamp, + sampler_info = {'theta': theta, 'acc_rate': acc_rate, 'lpostlist': np.array(lposterior_list)} if verbose: - print("Final Acceptance Rate: ", n_acc/numsamp) + print("Final Acceptance Rate: ", sampler_info["acc_rate"]) return sampler_info From 91829cb8e884efe9792f62c2e70eec1d5a2ce917 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 16:22:13 -0500 Subject: [PATCH 19/84] (Issue #223) Specify samplers explicitly in JSON file. Now the configuration is not tied just to MH. This percolated throughout the test script code so that we open the door to testing LMC and PTLMC. Test script ran through with no issues and the unit tests run through cleanly as well. --- tools/IndependentJointDistribution.py | 2 +- tools/MetropolisHastingsTestSuite.json | 72 +++++++++++++++++--------- tools/TestSampler.py | 36 ++++++++----- tools/UniformDistribution.py | 2 +- tools/create_sampler.py | 43 ++++++++++----- tools/load_mcmc_results.py | 15 +++--- tools/save_mcmc_results.py | 12 +++-- 7 files changed, 120 insertions(+), 62 deletions(-) diff --git a/tools/IndependentJointDistribution.py b/tools/IndependentJointDistribution.py index aa0ef601..dfea90d1 100644 --- a/tools/IndependentJointDistribution.py +++ b/tools/IndependentJointDistribution.py @@ -58,7 +58,7 @@ def pdf(self, theta): assert all(values >= 0.0) return values - def logpdf(self, theta, return_grad): + def logpdf(self, theta, return_grad=False): if return_grad: raise NotImplementedError("gradient not implemented") diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index a3cb7e00..adafe8fc 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -14,9 +14,12 @@ "Name": "Uniform", "Intervals": [-0.5, 0.5] }, - "StepDistribution": { - "Name": "normal", - "Scale": 2.5 + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "normal", + "Scale": 2.5 + } }, "SampleSkip": 10, "Plot": false, @@ -34,9 +37,12 @@ "Name": "Uniform", "Intervals": [-0.5, 0.5] }, - "StepDistribution": { - "Name": "uniform", - "Scale": 6.0 + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "uniform", + "Scale": 6.0 + } }, "SampleSkip": 10, "Plot": false, @@ -64,9 +70,12 @@ "Intervals": [[-0.5, 0.5], [ 1.5, 2.1]] }, - "StepDistribution": { - "Name": "normal", - "Scale": [1.0, 2.0] + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "normal", + "Scale": [1.0, 2.0] + } }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", @@ -95,9 +104,12 @@ "mu": 0.0, "sigma": 1.0 }, - "StepDistribution": { - "Name": "normal", - "Scale": 3.5 + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "normal", + "Scale": 3.5 + } }, "SampleSkip": 10, "Plot": false, @@ -116,9 +128,12 @@ "mu": 0.0, "sigma": 1.0 }, - "StepDistribution": { - "Name": "uniform", - "Scale": 10.0 + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "uniform", + "Scale": 10.0 + } }, "SampleSkip": 10, "Plot": false, @@ -148,9 +163,12 @@ "sigma": [[1.0, 0.0], [0.0, 5.0]] }, - "StepDistribution": { - "Name": "normal", - "Scale": [1.0, 5.0] + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "normal", + "Scale": [1.0, 5.0] + } }, "SampleSkip": 20, "Plot": false, @@ -183,9 +201,12 @@ [0.0, 5.0, 0.0], [0.0, 0.0, 0.5]] }, - "StepDistribution": { - "Name": "normal", - "Scale": [1.0, 5.0, 0.2] + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "normal", + "Scale": [1.0, 5.0, 0.2] + } }, "SampleSkip": 20, "Plot": false, @@ -218,9 +239,12 @@ [-4.0, -1.0], [-0.5, -0.4]] }, - "StepDistribution": { - "Name": "uniform", - "Scale": [1.0, 1.75, 2.5, 0.2] + "Sampler": { + "Name": "MH", + "StepDistribution": { + "Name": "uniform", + "Scale": [1.0, 1.75, 2.5, 0.2] + } }, "SampleSkip": 20, "Plot": false, diff --git a/tools/TestSampler.py b/tools/TestSampler.py index 730082df..0f5bdc64 100644 --- a/tools/TestSampler.py +++ b/tools/TestSampler.py @@ -64,9 +64,6 @@ def setUp(self): def testAllSetups(self): for problem_name, problem in self.__problems.items(): - # TODO: replace with dynamic sampler tags - sampler_tag = "MH" if 'SamplerTag' not in problem else problem['SamplerTag'] - target_cfg = problem["TargetDistribution"] target_name = target_cfg["Name"] print() @@ -79,7 +76,8 @@ def testAllSetups(self): print(setup_name) print("-" * 45) - name = f"{sampler_tag}_{problem_name}_{setup_name}" + sampler_name = test_setup["Sampler"]["Name"] + name = f"{sampler_name}_{problem_name}_{setup_name}" self.__testSampler(name, target_distribution, test_setup) def __testSampler(self, name, target_distribution, test_setup): @@ -143,7 +141,7 @@ def __testSampler(self, name, target_distribution, test_setup): } # -- Create sampler & load sampler-specific configuration - run_MCMC, sampler_cfg = create_sampler(test_setup) + sampler_name, run_MCMC, sampler_cfg = create_sampler(test_setup) sampler_cfg["RNG"] = np.random.default_rng(rand_seed) # ------ RUN SAMPLER & CONFIRM REASONABLE RESULTS @@ -177,11 +175,10 @@ def __testSampler(self, name, target_distribution, test_setup): **sampler_cfg ) self.assertFalse(FNAME_H5.exists()) - save_mcmc_results(FNAME_H5, result_1) + save_mcmc_results(FNAME_H5, sampler_name, result_1) self.assertTrue(FNAME_H5.is_file()) print("done") sys.stdout.flush() - self.assertEqual(set(result_1), {"theta", "acc_rate", "lpostlist"}) samples = result_1["theta"] self.assertEqual(len(samples), n_samples) @@ -307,8 +304,6 @@ def __testSampler(self, name, target_distribution, test_setup): raise NotImplementedError("Only 1D to 4D visualizations for now") plt.show() - self.assertTrue(0.3 <= result_1["acc_rate"] <= 0.4) - # TODO: Compute effective N samples # TODO: Compute Rhat # TODO: Check against CDF? @@ -320,7 +315,7 @@ def __testSampler(self, name, target_distribution, test_setup): if fname_benchmark != "": fname_benchmark = Path(fname_benchmark).resolve() self.assertTrue(fname_benchmark.is_file()) - self.__compare_results(fname_benchmark, FNAME_H5) + self.__compare_results(sampler_name, fname_benchmark, FNAME_H5) # ----- CONFIRM DETERMINISTIC # Rerun with identical RNG setup & confirm bitwise exact samples @@ -341,14 +336,28 @@ def __testSampler(self, name, target_distribution, test_setup): print("done") sys.stdout.flush() - self.assertEqual(result_1["acc_rate"], result_2["acc_rate"]) + self.__compare_sampler_specific(sampler_name, result_1, result_2) theta_1 = result_1["theta"] theta_2 = result_2["theta"] self.assertTrue( np.array_equal(theta_1, theta_2, equal_nan=False) ) - def __compare_results(self, fname_benchmark, fname_new): + def __compare_sampler_specific(self, sampler_name, benchmark, new): + if sampler_name.upper() == "MH": + # TODO: lpostlist should probably be saved in the files so that we + # can check new results against benchmarks as well. + # self.assertEqual(set(benchmark), {"theta", "acc_rate", "lpostlist"}) + self.assertEqual(new["acc_rate"], benchmark["acc_rate"]) + self.assertTrue(0.3 <= benchmark["acc_rate"] <= 0.4) + elif sampler_name.upper() == "LMC": + # Nothing extra to test + pass + else: + raise ValueError("Not testing sampler-specific results") + self.assertEqual(set(benchmark), set(new)) + + def __compare_results(self, sampler_name, fname_benchmark, fname_new): print() print("Regression Check") print(f"New\t\t\t{fname_new}") @@ -356,7 +365,8 @@ def __compare_results(self, fname_benchmark, fname_new): benchmark = load_mcmc_results(fname_benchmark) new = load_mcmc_results(fname_new) - self.assertEqual(new["acc_rate"], benchmark["acc_rate"]) + self.__compare_sampler_specific(sampler_name, benchmark, new) + theta_new = new["theta"] theta_benchmark = benchmark["theta"] self.assertTrue( diff --git a/tools/UniformDistribution.py b/tools/UniformDistribution.py index 2afd504c..8e8c1374 100644 --- a/tools/UniformDistribution.py +++ b/tools/UniformDistribution.py @@ -47,7 +47,7 @@ def pdf(self, theta): assert values.ndim == 1 return values - def logpdf(self, theta, return_grad): + def logpdf(self, theta, return_grad=False): if return_grad: raise NotImplementedError("gradient not implemented yet") values = self.__U.logpdf(self._as1darray_checked(theta)) diff --git a/tools/create_sampler.py b/tools/create_sampler.py index 568a98c3..ca24c513 100644 --- a/tools/create_sampler.py +++ b/tools/create_sampler.py @@ -1,6 +1,8 @@ import numpy as np from surmise.utilitiesmethods.metropolis_hastings import sampler as MH_sampler +from surmise.utilitiesmethods.LMC import sampler as LMC_sampler +from surmise.utilitiesmethods.PTLMC import sampler as PTLMC_sampler def create_sampler(test_setup): @@ -12,17 +14,34 @@ def create_sampler(test_setup): sampler = None sampler_cfg = None - # -- Metropolis-Hastings Sampler - # Extract sampler-specific configuration info - step_cfg = test_setup["StepDistribution"] - step_type = step_cfg["Name"] - if "Scale" in step_cfg: - step_scale = np.atleast_1d(np.squeeze(step_cfg["Scale"])) - assert step_scale.ndim == 1 - else: - step_scale = None + sampler_name = test_setup["Sampler"]["Name"] + if sampler_name.upper() == "MH": + # -- Metropolis-Hastings Sampler + # Extract sampler-specific configuration info + step_cfg = test_setup["Sampler"]["StepDistribution"] + step_type = step_cfg["Name"] + if "Scale" in step_cfg: + step_scale = np.atleast_1d(np.squeeze(step_cfg["Scale"])) + assert step_scale.ndim == 1 + else: + step_scale = None - sampler = MH_sampler - sampler_cfg = {"stepType": step_type, "stepParam": step_scale} + sampler = MH_sampler + sampler_cfg = {"stepType": step_type, "stepParam": step_scale} + elif sampler_name.upper() == "LMC": + # -- Langevin MC Sampler + sampler = LMC_sampler + sampler_cfg = {} + elif sampler_name.upper() == "PTLMC": + # -- Parallel-Tempering Langevin MC Sampler + sampler = PTLMC_sampler + sampler_cfg = { + "numtemps": test_setup["Sampler"]["numtemps"], + "numchain": test_setup["Sampler"]["numchain"], + "sampperchain": test_setup["Sampler"]["sampperchain"], + "maxtemp": test_setup["Sampler"]["maxtemp"] + } + else: + raise ValueError(f"Unsupported sampler ({sampler_name})") - return sampler, sampler_cfg + return sampler_name, sampler, sampler_cfg diff --git a/tools/load_mcmc_results.py b/tools/load_mcmc_results.py index 3dea470a..0468d5fc 100644 --- a/tools/load_mcmc_results.py +++ b/tools/load_mcmc_results.py @@ -8,14 +8,15 @@ def load_mcmc_results(filename): GROUP = "/MCMC" + results = {} + fname = Path(filename).resolve() with h5py.File(fname, "r") as fptr: - method = fptr[GROUP].attrs["Method"] - assert method == "Metropolis" - acceptance_rate = fptr[GROUP].attrs["AcceptanceRate"] - table_name = Path(GROUP).joinpath("OfficialSamples") - samples = np.array(fptr[str(table_name.as_posix())]) + results["theta"] = np.array(fptr[str(table_name.as_posix())]) + + method = fptr[GROUP].attrs["Method"] + if method == "Metropolis": + results["acc_rate"] = fptr[GROUP].attrs["AcceptanceRate"] - return {"theta": samples, - "acc_rate": acceptance_rate} + return results diff --git a/tools/save_mcmc_results.py b/tools/save_mcmc_results.py index b56d4b47..f0fb72c7 100644 --- a/tools/save_mcmc_results.py +++ b/tools/save_mcmc_results.py @@ -3,7 +3,7 @@ from pathlib import Path -def save_mcmc_results(filename, results, overwrite=False): +def save_mcmc_results(filename, sampler_name, results, overwrite=False): GROUP = "/MCMC" fname = Path(filename).resolve() @@ -16,8 +16,12 @@ def save_mcmc_results(filename, results, overwrite=False): # TODO: Add these # Method-specific configuration - fptr[GROUP].attrs["Method"] = "Metropolis" - fptr[GROUP].attrs["AcceptanceRate"] = results["acc_rate"] - # TODO: Add others + if sampler_name.upper() == "MH": + fptr[GROUP].attrs["Method"] = "Metropolis" + fptr[GROUP].attrs["AcceptanceRate"] = results["acc_rate"] + elif sampler_name.upper() == "LMC": + fptr[GROUP].attrs["Method"] = "LMC" + else: + raise ValueError(f"Unsupported sampler ({sampler_name})") fptr[GROUP].create_dataset("OfficialSamples", data=results["theta"]) From 16ce46df02e8fbceabd60e140f24c4c930346444 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 16:24:58 -0500 Subject: [PATCH 20/84] (Issue #223) Add start of LMC test suite. The results do not look good and I don't see any parameters for me to adjust. This fits with the fact that LMC is currently listed as research grade. This second set of tests fail because the sampler cannot yet be made deterministic. That will be done on a later branch. --- tools/LMCTestSuite.json | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 tools/LMCTestSuite.json diff --git a/tools/LMCTestSuite.json b/tools/LMCTestSuite.json new file mode 100644 index 00000000..cd057606 --- /dev/null +++ b/tools/LMCTestSuite.json @@ -0,0 +1,29 @@ +{ + "Uniform1D": { + "TargetDistribution": { + "Name": "Uniform", + "Intervals": [-1.1, 1.2] + }, + "TestSetups": { + "UniformStart": { + "rng": { + "method": "default", + "random_seed": 2531535131186259872329283269888015535 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [-0.5, 0.5] + }, + "Sampler": { + "Name": "LMC" + }, + "SampleSkip": 10, + "Plot": true, + "Benchmark": "", + "n_burn_samples": 10000, + "n_samples": 100000, + "verbose": true + } + } + } +} From 806bc86c675f21b62450b1c3b1678318f5836997 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 22 Jun 2026 16:31:29 -0500 Subject: [PATCH 21/84] (Issue #223) Add start of PLTMC test suite & fix argument name. Homogenize the argument list of PTLMC with that of other samplers. My test execution fails due to an array sizing issues I suspect. Moses should first update this test setup to see if it's any good and then check the error. --- src/surmise/utilitiesmethods/PTLMC.py | 24 +++++++++---------- tools/PTLMCTestSuite.json | 33 +++++++++++++++++++++++++++ 2 files changed, 45 insertions(+), 12 deletions(-) create mode 100644 tools/PTLMCTestSuite.json diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index d5b0a05a..43332566 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -6,7 +6,7 @@ ''' -def sampler(logpostfunc, +def sampler(logpost_func, draw_func, theta0=None, numsamp=2000, @@ -19,12 +19,12 @@ def sampler(logpostfunc, Parameters ---------- - logpostfunc : function + logpost_func : function A function call describing the log of the posterior distribution. - If no gradient, logpostfunc should take a value of an m by p numpy + If no gradient, logpost_func should take a value of an m by p numpy array of parameters and theta and return a length m numpy array of log posterior evaluations. - If gradient, logpostfunc should return a tuple. The first element + If gradient, logpost_func should return a tuple. The first element in the tuple should be as listed above. The second element in the tuple should be an m by p matrix of gradients of the log posterior. @@ -83,30 +83,30 @@ def sampler(logpostfunc, # number of optimization at each chain before starting numopt = temps.shape[0] # before beginning, let's test out the given logpdf function - testout = logpostfunc(theta0[0:2, :]) + testout = logpost_func(theta0[0:2, :]) if type(testout) is tuple: if len(testout) != 2: raise ValueError('log density does not return 1 or 2 elements') if testout[1].shape[1] is not theta0.shape[1]: raise ValueError('derivative appears to be the wrong shape') - logpostf = logpostfunc + logpostf = logpost_func def logpostf_grad(thetain): - return logpostfunc(thetain)[1] + return logpost_func(thetain)[1] try: - testout = logpostfunc(theta0[10, :], return_grad=False) + testout = logpost_func(theta0[10, :], return_grad=False) if type(testout) is tuple: # make sure that return_grad functionality works raise ValueError('Cannot stop returning a grad') def logpostf_nograd(theta): - return logpostfunc(theta, return_grad=False) + return logpost_func(theta, return_grad=False) except Exception: def logpostf_nograd(theta): # if not, do not use return_grad key - return logpostfunc(theta)[0] + return logpost_func(theta)[0] else: logpostf_grad = None # sometimes no derivative is given - logpostf = logpostfunc - logpostf_nograd = logpostfunc + logpostf = logpost_func + logpostf_nograd = logpost_func if logpostf_grad is None: # these are standard parameters if there is taracc = 0.25 # close to theoretical result 0.234 diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json new file mode 100644 index 00000000..481bee86 --- /dev/null +++ b/tools/PTLMCTestSuite.json @@ -0,0 +1,33 @@ +{ + "Uniform1D": { + "TargetDistribution": { + "Name": "Uniform", + "Intervals": [-1.1, 1.2] + }, + "TestSetups": { + "UniformStart": { + "rng": { + "method": "default", + "random_seed": 192610724096912622599077596010199726791 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [-0.5, 0.5] + }, + "Sampler": { + "Name": "PTLMC", + "numtemps": 32, + "numchain": 16, + "sampperchain": 400, + "maxtemp": 30 + }, + "SampleSkip": 1, + "Plot": true, + "Benchmark": "", + "n_burn_samples": 10000, + "n_samples": 100000, + "verbose": true + } + } + } +} From 9245b13b64051ffd257d135eb28695c4465b2d46 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 22 Jun 2026 18:44:54 -0500 Subject: [PATCH 22/84] use of eigh for hermitian matrices --- src/surmise/emulationmethods/PCGPwM.py | 2 +- src/surmise/emulationmethods/PCSK.py | 2 +- src/surmise/emulationmethods/indGP.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/surmise/emulationmethods/PCGPwM.py b/src/surmise/emulationmethods/PCGPwM.py index 0b67ddd4..a8b680a6 100644 --- a/src/surmise/emulationmethods/PCGPwM.py +++ b/src/surmise/emulationmethods/PCGPwM.py @@ -343,7 +343,7 @@ def predictlpdf(predinfo, f, addvar=0, **kwargs): Gfrf2 = (Gf @ rf2.transpose(1, 0, 2)).transpose(1, 0, 2) dlikv = 2 * np.sum(rf2.transpose(2, 1, 0) * rf.transpose(1, 0), 2).T for c in range(0, predinfo['predvars'].shape[0]): - w, v = np.linalg.eig(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) + w, v = np.linalg.eigh(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) term1 = (v * (1 / w)) @ (v.T @ Gfrf[:, c]) likv[c] -= Gfrf[:, c].T @ term1 diff --git a/src/surmise/emulationmethods/PCSK.py b/src/surmise/emulationmethods/PCSK.py index f41c9422..cc23f9ee 100644 --- a/src/surmise/emulationmethods/PCSK.py +++ b/src/surmise/emulationmethods/PCSK.py @@ -304,7 +304,7 @@ def predictlpdf(predinfo, f, return_grad=False, addvar=0, **kwargs): Gfrf2 = (Gf @ rf2.transpose(1, 0, 2)).transpose(1, 0, 2) dlikv = 2 * np.sum(rf2.transpose(2, 1, 0) * rf.transpose(1, 0), 2).T for c in range(0, predinfo['predvars'].shape[0]): - w, v = np.linalg.eig(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) + w, v = np.linalg.eigh(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) term1 = (v * (1 / w)) @ (v.T @ Gfrf[:, c]) likv[c] -= Gfrf[:, c].T @ term1 diff --git a/src/surmise/emulationmethods/indGP.py b/src/surmise/emulationmethods/indGP.py index 9e845c3e..7bf2dac3 100644 --- a/src/surmise/emulationmethods/indGP.py +++ b/src/surmise/emulationmethods/indGP.py @@ -369,7 +369,7 @@ def predictlpdf(predinfo, f, return_grad=False, addvar=0, **kwargs): Gfrf2 = (Gf @ rf2.transpose(1, 0, 2)).transpose(1, 0, 2) dlikv = 2 * np.sum(rf2.transpose(2, 1, 0) * rf.transpose(1, 0), 2).T for c in range(0, predinfo['predvars'].shape[0]): - w, v = np.linalg.eig(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) + w, v = np.linalg.eigh(np.diag(1 / (predinfo['predvars'][c, :])) + Gf2) term1 = (v * (1 / w)) @ (v.T @ Gfrf[:, c]) likv[c] -= Gfrf[:, c].T @ term1 From 55fe9603f6e80e5da7fa38ebe0ad624de54259dc Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 23 Jun 2026 09:10:15 -0500 Subject: [PATCH 23/84] specify numpy 2.5.0 to confirm fixes --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9ac73f69..bacb6298 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,7 @@ def readme_rst(): python_requires = ">=3.10" code_requires = [ - 'numpy>=1.22.0,<2.5.0', + 'numpy>=2.5.0', 'scipy>=1.9.0', 'scikit-learn>=1.2.0', 'dill>=0.3.8' From 30288875b5e7332c0c25d47625482c0981dd6df0 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 23 Jun 2026 09:14:39 -0500 Subject: [PATCH 24/84] retrying by relaxing numpy<2.5.0 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index bacb6298..d22ea434 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,7 @@ def readme_rst(): python_requires = ">=3.10" code_requires = [ - 'numpy>=2.5.0', + 'numpy>=1.22.0', 'scipy>=1.9.0', 'scikit-learn>=1.2.0', 'dill>=0.3.8' From 77c52394aad6d2b294636577a206c2aa8a34e3f3 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 24 Jun 2026 09:36:33 -0500 Subject: [PATCH 25/84] Exclude newly added folders from distributions. --- MANIFEST.in | 2 ++ 1 file changed, 2 insertions(+) diff --git a/MANIFEST.in b/MANIFEST.in index c2395439..4cefbf2a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -7,4 +7,6 @@ exclude surmisebandsdk.md tox.ini prune tests prune examples prune docs +prune book +prune tools prune .github From d04554a9b980194395cef93466d99e9e1732da10 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Sat, 27 Jun 2026 09:59:43 -0500 Subject: [PATCH 26/84] working PTLMC with heavily print-outs in comments --- src/surmise/utilitiesmethods/PTLMC.py | 45 +++++++++++++++++++++------ tools/TestSampler.py | 3 ++ tools/save_mcmc_results.py | 2 ++ 3 files changed, 41 insertions(+), 9 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 43332566..0b0e2805 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -79,15 +79,17 @@ def sampler(logpost_func, np.log(maxtemp)/(numtemps+1), numtemps)), np.ones(numchain))) # ratio idea tend from emcee - temps = np.array(temps, ndmin=2).T + # print(f"orig temp: {temps.shape}") + # temps = np.array(temps, ndmin=2).T + # print(f"reshape temp: {temps.shape}") # number of optimization at each chain before starting numopt = temps.shape[0] # before beginning, let's test out the given logpdf function testout = logpost_func(theta0[0:2, :]) if type(testout) is tuple: - if len(testout) != 2: + if len(testout) > 2: raise ValueError('log density does not return 1 or 2 elements') - if testout[1].shape[1] is not theta0.shape[1]: + if testout[1].shape[1] != theta0.shape[1]: raise ValueError('derivative appears to be the wrong shape') logpostf = logpost_func @@ -122,6 +124,7 @@ def logpostf_nograd(theta): # if not, do not use return_grad key thetacen = np.mean(theta0, 0) thetas = np.maximum(np.std(theta0, 0), 10 ** (-8) * np.std(theta0)) + # print(f"theta0: {theta0.shape}") # rescale the input to make it easier to optimize def neglogpostf_nograd(thetap): theta = thetacen + thetas * thetap @@ -178,6 +181,9 @@ def neglogpostf_grad(thetap): # end preoptimizer # initialize the starting point thetac = thetaop + + # print(f"thetac: {thetac.shape}") + if logpostf_grad is not None: fval, dfval = logpostf(thetac) fval = fval/temps @@ -185,6 +191,13 @@ def neglogpostf_grad(thetap): else: fval = logpostf_nograd(thetac) fval = fval/temps + # + # print("\n\n\n\n") + # print(fval) + # + # print("\n\n\n\n") + # print(f"fval: {fval.shape}") + # preallocate the saving matrix thetasave = np.zeros((numchain, sampperchain, @@ -205,9 +218,14 @@ def neglogpostf_grad(thetap): adjrho = rho*temps**(1/3) # this adjusts rho across different temperatures numtimes = 0 # number of times we reject, just to star for k in range(0, samptunning+sampperchain): # loop over all chains - rvalo = np.random.normal(0, 1, thetac.shape) - rval = np.sqrt(2) * adjrho * (rvalo @ hc) - thetap = thetac + rval + rvalo = np.random.normal(0, 1, size=thetac.shape) + # print(f"rvalo: {rvalo.shape}") + # print(f"hc: {hc.shape}") + # print(f"adjrho: {adjrho.shape}") + # print(f"mult: {(rvalo @ hc).shape}") + rval = np.sqrt(2) * adjrho * np.squeeze(rvalo @ hc) + # print(f"rval: {rval.shape}") + thetap = thetac + rval[:, np.newaxis] if logpostf_grad is not None: # calculate the elements to move if there is a gradiant diffval = (adjrho ** 2) * (dfval @ covmat0) @@ -220,6 +238,7 @@ def neglogpostf_grad(thetap): qadj = -(2 * np.sum(term1 * term2, 1) + np.sum(term2**2, 1)) else: # calculate the elements to move if there is not a gradiant + # print(f"thetap: {thetap.shape}") fvalp = logpostf_nograd(thetap) # thetap : no chain x dimension fvalp = fvalp / temps qadj = np.zeros(fvalp.shape) @@ -229,11 +248,13 @@ def neglogpostf_grad(thetap): + np.squeeze(qadj))[0] # MH step to find which of the chains to swap if whereswap.shape[0] > 0: # if we swap, do it where needed numtimes = numtimes + np.sum(whereswap > -1)/totnumchain - thetac[whereswap, :] = 1*thetap[whereswap, :] - fval[whereswap] = 1*fvalp[whereswap] + thetac[whereswap] = np.copy(thetap[whereswap]) + fval[whereswap] = np.copy(fvalp[whereswap]) if logpostf_grad is not None: - dfval[whereswap, :] = 1*dfvalp[whereswap, :] + dfval[whereswap] = np.copy(dfvalp[whereswap]) # do some swaps along the temperatures + # print(f"fval: {fval.shape}") + # print(f"temp: {temps.shape}") fvaln = fval*temps orderprop = tempexchange(fvaln, temps, iters=5) # go through 5 times, swapping where needed fval = fvaln[orderprop] / temps @@ -265,10 +286,16 @@ def tempexchange(lpostf, temps, iters=1): # array lpostf with temperature array temps. It will do it iters number of times. # It returns the (random) revised order. order = np.arange(0, lpostf.shape[0]) # initializing + # print(f"order: {order.shape}") + # print(f"temp: {temps.shape}") + # print(f"lpostf: {lpostf.shape}") for k in range(0, iters): rtv = np.random.choice(range(1, lpostf.shape[0]), lpostf.shape[0]) # choose random values to check for swapping + # print(rtv.shape) for rt in rtv: + # print(rt) rhoh = (1/temps[rt-1] - 1 / temps[rt]) + # print(rhoh.shape) if ((lpostf[order[rt]]-lpostf[order[rt - 1]]) * rhoh > np.log(np.random.uniform(size=1))): # swap via the PT rule temporder = order[rt - 1] diff --git a/tools/TestSampler.py b/tools/TestSampler.py index 0f5bdc64..7959776e 100644 --- a/tools/TestSampler.py +++ b/tools/TestSampler.py @@ -353,6 +353,9 @@ def __compare_sampler_specific(self, sampler_name, benchmark, new): elif sampler_name.upper() == "LMC": # Nothing extra to test pass + elif sampler_name.upper() == "PTLMC": + # Nothing extra to test + pass else: raise ValueError("Not testing sampler-specific results") self.assertEqual(set(benchmark), set(new)) diff --git a/tools/save_mcmc_results.py b/tools/save_mcmc_results.py index f0fb72c7..82d36b81 100644 --- a/tools/save_mcmc_results.py +++ b/tools/save_mcmc_results.py @@ -21,6 +21,8 @@ def save_mcmc_results(filename, sampler_name, results, overwrite=False): fptr[GROUP].attrs["AcceptanceRate"] = results["acc_rate"] elif sampler_name.upper() == "LMC": fptr[GROUP].attrs["Method"] = "LMC" + elif sampler_name.upper() == "PTLMC": + fptr[GROUP].attrs["Method"] = "PTLMC" else: raise ValueError(f"Unsupported sampler ({sampler_name})") From 33bafe0d7f5778296313cf53b6c281551816bdc4 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Sat, 27 Jun 2026 10:28:45 -0500 Subject: [PATCH 27/84] ensuring multi-dimensional theta case is covered --- src/surmise/utilitiesmethods/PTLMC.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 0b0e2805..e65bf63d 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -223,9 +223,12 @@ def neglogpostf_grad(thetap): # print(f"hc: {hc.shape}") # print(f"adjrho: {adjrho.shape}") # print(f"mult: {(rvalo @ hc).shape}") - rval = np.sqrt(2) * adjrho * np.squeeze(rvalo @ hc) + rval = (np.sqrt(2) * adjrho * np.squeeze(rvalo @ hc).T).T # print(f"rval: {rval.shape}") - thetap = thetac + rval[:, np.newaxis] + if thetac.shape[1] > 1: + thetap = thetac + rval + elif thetac.shape[1] == 1: + thetap = thetac + rval[:, np.newaxis] if logpostf_grad is not None: # calculate the elements to move if there is a gradiant diffval = (adjrho ** 2) * (dfval @ covmat0) From 3d137eddef69167129c1312f9361cc45e846c243 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Sat, 27 Jun 2026 11:03:17 -0500 Subject: [PATCH 28/84] currently deterministic samples test failed, because RNG is not uniformly used. --- tools/PTLMCTestSuite.json | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index 481bee86..b80f5bea 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -29,5 +29,39 @@ "verbose": true } } + }, + "Uniform2D": { + "TargetDistribution": { + "Name": "Uniform", + "Intervals": [[-1.1, 1.2], + [ 1.3, 4.3]] + }, + "TestSetups": { + "NormalStep": { + "rng": { + "method": "default", + "random_seed": 100092616225039682105425248025433037719 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [[-0.5, 0.5], + [ 1.5, 2.1]] + }, + "Sampler": { + "Name": "PTLMC", + "numtemps": 32, + "numchain": 16, + "sampperchain": 400, + "maxtemp": 30 + }, + "SampleSkip": 1, + "Plot": true, + "CornerPlotBins": 30, + "Benchmark": "", + "n_burn_samples": 10000, + "n_samples": 100000, + "verbose": true + } + } } } From afc21b7889195aeb903504a9c5e4bec672fb4e22 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Sat, 27 Jun 2026 11:39:48 -0500 Subject: [PATCH 29/84] confirming deterministic results --- src/surmise/utilitiesmethods/PTLMC.py | 82 ++++++++++++--------------- tools/PTLMCTestSuite.json | 21 +++---- 2 files changed, 47 insertions(+), 56 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index e65bf63d..831047eb 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -1,4 +1,5 @@ import numpy as np +import scipy.stats as sps import scipy.optimize as spo ''' @@ -61,6 +62,17 @@ def sampler(logpost_func, """ + # random number generator + # TODO: This is an intermediate step. Eventually calling code should be + # forced to provide an RNG. + rng = None + if "RNG" in ptlmc_options: + rng = ptlmc_options["RNG"] + if rng is None: + rng = np.random.default_rng() + elif not isinstance(rng, np.random.Generator): + raise TypeError("Given RNG is not a valid scipy.stats RNG") + # If we do not get parameters to start, draw 1000 if theta0 is None: theta0 = draw_func(1000) @@ -79,9 +91,7 @@ def sampler(logpost_func, np.log(maxtemp)/(numtemps+1), numtemps)), np.ones(numchain))) # ratio idea tend from emcee - # print(f"orig temp: {temps.shape}") - # temps = np.array(temps, ndmin=2).T - # print(f"reshape temp: {temps.shape}") + # number of optimization at each chain before starting numopt = temps.shape[0] # before beginning, let's test out the given logpdf function @@ -118,13 +128,13 @@ def logpostf_nograd(theta): # if not, do not use return_grad key # order the existing initial theta's by log pdf ord1 = np.argsort(-np.squeeze(logpostf_nograd(theta0)) + (theta0.shape[1] * - np.random.standard_normal(size=theta0.shape[0])**2)) + sps.norm.rvs(size=theta0.shape[0], + random_state=rng)**2)) theta0 = theta0[ord1[0:totnumchain], :] # begin optimizing at each chain thetacen = np.mean(theta0, 0) thetas = np.maximum(np.std(theta0, 0), 10 ** (-8) * np.std(theta0)) - # print(f"theta0: {theta0.shape}") # rescale the input to make it easier to optimize def neglogpostf_nograd(thetap): theta = thetacen + thetas * thetap @@ -164,7 +174,8 @@ def neglogpostf_grad(thetap): l0 = neglogpostf_nograd(opval.x) while notmoved: if (W > 0).all(): - r = (V.T*np.sqrt(W)) @ (V @ np.random.standard_normal(size=thetacen.shape[0])) + r = (V.T*np.sqrt(W)) @ (V @ sps.norm.rvs(size=thetacen.shape[0], + random_state=rng)) else: stepadj /= 2 if stepadj < 1/16: @@ -172,7 +183,7 @@ def neglogpostf_grad(thetap): notmoved = False continue - if (neglogpostf_nograd((stepadj * r + opval.x)) - + if (neglogpostf_nograd(stepadj * r + opval.x) - l0) < 3*thetacen.shape[0]: thetaop[k, :] = thetacen + thetas * (stepadj * r + opval.x) notmoved = False @@ -181,22 +192,13 @@ def neglogpostf_grad(thetap): # end preoptimizer # initialize the starting point thetac = thetaop - - # print(f"thetac: {thetac.shape}") - if logpostf_grad is not None: fval, dfval = logpostf(thetac) - fval = fval/temps - dfval = dfval/temps + fval /= temps + dfval /= temps else: fval = logpostf_nograd(thetac) - fval = fval/temps - # - # print("\n\n\n\n") - # print(fval) - # - # print("\n\n\n\n") - # print(f"fval: {fval.shape}") + fval /= temps # preallocate the saving matrix thetasave = np.zeros((numchain, @@ -218,13 +220,8 @@ def neglogpostf_grad(thetap): adjrho = rho*temps**(1/3) # this adjusts rho across different temperatures numtimes = 0 # number of times we reject, just to star for k in range(0, samptunning+sampperchain): # loop over all chains - rvalo = np.random.normal(0, 1, size=thetac.shape) - # print(f"rvalo: {rvalo.shape}") - # print(f"hc: {hc.shape}") - # print(f"adjrho: {adjrho.shape}") - # print(f"mult: {(rvalo @ hc).shape}") + rvalo = sps.norm.rvs(size=thetac.shape, random_state=rng) rval = (np.sqrt(2) * adjrho * np.squeeze(rvalo @ hc).T).T - # print(f"rval: {rval.shape}") if thetac.shape[1] > 1: thetap = thetac + rval elif thetac.shape[1] == 1: @@ -234,16 +231,15 @@ def neglogpostf_grad(thetap): diffval = (adjrho ** 2) * (dfval @ covmat0) thetap += diffval fvalp, dfvalp = logpostf(thetap) # thetap : no chain x dimension - fvalp = fvalp / temps # to flatten the posterior - dfvalp = dfvalp / temps + fvalp /= temps # to flatten the posterior + dfvalp /= temps term1 = rvalo / np.sqrt(2) term2 = (adjrho / 2) * ((dfval + dfvalp) @ hc) qadj = -(2 * np.sum(term1 * term2, 1) + np.sum(term2**2, 1)) else: # calculate the elements to move if there is not a gradiant - # print(f"thetap: {thetap.shape}") fvalp = logpostf_nograd(thetap) # thetap : no chain x dimension - fvalp = fvalp / temps + fvalp /= temps qadj = np.zeros(fvalp.shape) swaprnd = np.log(np.random.uniform(size=fval.shape[0])) whereswap = np.where(np.squeeze(swaprnd) @@ -256,10 +252,8 @@ def neglogpostf_grad(thetap): if logpostf_grad is not None: dfval[whereswap] = np.copy(dfvalp[whereswap]) # do some swaps along the temperatures - # print(f"fval: {fval.shape}") - # print(f"temp: {temps.shape}") - fvaln = fval*temps - orderprop = tempexchange(fvaln, temps, iters=5) # go through 5 times, swapping where needed + fvaln = fval * temps + orderprop = tempexchange(fvaln, temps, iters=5, rng=rng) # go through 5 times, swapping where needed fval = fvaln[orderprop] / temps thetac = thetac[orderprop, :] if logpostf_grad is not None: @@ -275,32 +269,28 @@ def neglogpostf_grad(thetap): elif k >= samptunning: # if done with tuning thetasave[:, k-samptunning, :] = 1 * thetac[numtemps:, ] # save the theta values in the temp=1 chains, squeezing flattening the values of all chains - thetasave = np.reshape(thetasave, (-1, thetac.shape[1])) + thetasave_flatten = np.reshape(thetasave, (-1, thetac.shape[1])) # save random values from the chain of size numsamp - theta = thetasave[np.random.choice(range(0, thetasave.shape[0]), - size=numsamp), :] + theta = thetasave_flatten[rng.choice(range(0, thetasave_flatten.shape[0]), + size=numsamp)] # store this in a dictionary - sampler_info = {'theta': theta, 'logpost': logpostf_nograd(theta)} + sampler_info = {'theta': theta, 'theta_from_chain': thetasave, 'logpost': logpostf_nograd(theta)} return sampler_info -def tempexchange(lpostf, temps, iters=1): +def tempexchange(lpostf, temps, iters=1, rng=None): # This function will swap values along the chain given the log pdf values in an # array lpostf with temperature array temps. It will do it iters number of times. # It returns the (random) revised order. + assert rng is not None + order = np.arange(0, lpostf.shape[0]) # initializing - # print(f"order: {order.shape}") - # print(f"temp: {temps.shape}") - # print(f"lpostf: {lpostf.shape}") for k in range(0, iters): - rtv = np.random.choice(range(1, lpostf.shape[0]), lpostf.shape[0]) # choose random values to check for swapping - # print(rtv.shape) + rtv = rng.choice(range(1, lpostf.shape[0]), lpostf.shape[0]) # choose random values to check for swapping for rt in rtv: - # print(rt) rhoh = (1/temps[rt-1] - 1 / temps[rt]) - # print(rhoh.shape) if ((lpostf[order[rt]]-lpostf[order[rt - 1]]) * rhoh > - np.log(np.random.uniform(size=1))): # swap via the PT rule + np.log(sps.uniform.rvs(size=1, random_state=rng))): # swap via the PT rule temporder = order[rt - 1] order[rt-1] = 1*order[rt] order[rt] = 1 * temporder diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index b80f5bea..ac8a4268 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -22,11 +22,11 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, - "Benchmark": "", + "Plot": false, + "Benchmark": "PTLMC_Uniform1D_UniformStart.benchmark", "n_burn_samples": 10000, "n_samples": 100000, - "verbose": true + "verbose": false } } }, @@ -37,15 +37,16 @@ [ 1.3, 4.3]] }, "TestSetups": { - "NormalStep": { + "NormalStart": { "rng": { "method": "default", "random_seed": 100092616225039682105425248025433037719 }, "StartDistribution": { - "Name": "Uniform", - "Intervals": [[-0.5, 0.5], - [ 1.5, 2.1]] + "Name": "normal", + "mu": [-1.0, 2.0], + "sigma": [[1.0, 0.0], + [0.0, 5.0]] }, "Sampler": { "Name": "PTLMC", @@ -55,12 +56,12 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, + "Plot": false, "CornerPlotBins": 30, - "Benchmark": "", + "Benchmark": "PTLMC_Uniform2D_NormalStart.benchmark", "n_burn_samples": 10000, "n_samples": 100000, - "verbose": true + "verbose": false } } } From 143370a224f4006a59148629878e15e6a83d27ed Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Sat, 27 Jun 2026 12:31:42 -0500 Subject: [PATCH 30/84] matching dimension for likelihood evaluation --- src/surmise/utilitiesmethods/PTLMC.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 831047eb..8c195c33 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -91,7 +91,7 @@ def sampler(logpost_func, np.log(maxtemp)/(numtemps+1), numtemps)), np.ones(numchain))) # ratio idea tend from emcee - + print(temps.shape) # number of optimization at each chain before starting numopt = temps.shape[0] # before beginning, let's test out the given logpdf function @@ -197,7 +197,7 @@ def neglogpostf_grad(thetap): fval /= temps dfval /= temps else: - fval = logpostf_nograd(thetac) + fval = np.squeeze(logpostf_nograd(thetac)) fval /= temps # preallocate the saving matrix @@ -238,7 +238,7 @@ def neglogpostf_grad(thetap): qadj = -(2 * np.sum(term1 * term2, 1) + np.sum(term2**2, 1)) else: # calculate the elements to move if there is not a gradiant - fvalp = logpostf_nograd(thetap) # thetap : no chain x dimension + fvalp = np.squeeze(logpostf_nograd(thetap)) # thetap : no chain x dimension fvalp /= temps qadj = np.zeros(fvalp.shape) swaprnd = np.log(np.random.uniform(size=fval.shape[0])) From 1ac092315e113b18b8bf54bf40b048120ff59ae2 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 29 Jun 2026 23:02:24 -0500 Subject: [PATCH 31/84] remove debugging print outs --- src/surmise/utilitiesmethods/PTLMC.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 8c195c33..61e3f696 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -91,7 +91,7 @@ def sampler(logpost_func, np.log(maxtemp)/(numtemps+1), numtemps)), np.ones(numchain))) # ratio idea tend from emcee - print(temps.shape) + # number of optimization at each chain before starting numopt = temps.shape[0] # before beginning, let's test out the given logpdf function From 12e8140036356c43d9fca02cb6b671008f1b0ba3 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 29 Jun 2026 23:16:26 -0500 Subject: [PATCH 32/84] ensuring all random numbers are generated with the same rng --- src/surmise/utilitiesmethods/PTLMC.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 61e3f696..0ee0d94f 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -241,7 +241,7 @@ def neglogpostf_grad(thetap): fvalp = np.squeeze(logpostf_nograd(thetap)) # thetap : no chain x dimension fvalp /= temps qadj = np.zeros(fvalp.shape) - swaprnd = np.log(np.random.uniform(size=fval.shape[0])) + swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=rng)) whereswap = np.where(np.squeeze(swaprnd) < np.squeeze(fvalp - fval) + np.squeeze(qadj))[0] # MH step to find which of the chains to swap From e7276ae8622c040c7344eba8b4ee640358fa6037 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 1 Jul 2026 13:16:54 -0500 Subject: [PATCH 33/84] allow RNG input to LMC sampler to ensure reproducibility --- src/surmise/utilitiesmethods/LMC.py | 39 ++++++++++++++++++----------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/src/surmise/utilitiesmethods/LMC.py b/src/surmise/utilitiesmethods/LMC.py index eaf18ef4..51763b3e 100644 --- a/src/surmise/utilitiesmethods/LMC.py +++ b/src/surmise/utilitiesmethods/LMC.py @@ -1,4 +1,5 @@ import numpy as np +import scipy.stats as sps import scipy.optimize as spo r''' @@ -72,6 +73,17 @@ def sampler(logpost_func, numsamp by p of sampled parameter values ''' + # random number generator + # TODO: This is an intermediate step. Eventually calling code should be + # forced to provide an RNG. + rng = None + if "RNG" in lmc_options: + rng = lmc_options["RNG"] + if rng is None: + rng = np.random.default_rng() + elif not isinstance(rng, np.random.Generator): + raise TypeError("Given RNG is not a valid scipy.stats RNG") + if theta0 is None: theta0 = draw_func(1000) @@ -124,13 +136,12 @@ def logpostf_nograd(theta): logpost -= (mlogpost + np.log(np.sum(np.exp(logpost - mlogpost)))) post = np.exp(logpost) post = post/np.sum(post) - thetaposs = theta0[np.random.choice(range(0, theta0.shape[0]), - size=1000, - p=post.reshape((theta0.shape[0], - ))), :] + thetaposs = theta0[rng.choice(range(0, theta0.shape[0]), + size=1000, + p=post.reshape((theta0.shape[0], ))), :] - if np.any(np.std(thetaposs, 0) < 10 ** (-8) * np.min(np.std(theta0, - 0))): + if np.any(np.std(thetaposs, 0) < 10 ** (-8) * + np.min(np.std(theta0, 0))): thetastar = theta0[np.argmax(logpost), :] theta0 = thetastar + (theta0 - thetastar) / 2 iteratttempt += 1 @@ -210,8 +221,8 @@ def neglogpostf_grad(thetap): numsamppc = 200 covmat0 = np.diag(thetas) for iters in range(0, maxiters): - startingv = np.random.choice(np.arange(0, Lsave.shape[0]), - size=Lsave.shape[0]) + startingv = rng.choice(np.arange(0, Lsave.shape[0]), + size=Lsave.shape[0]) thetasave = thetasave[startingv, :] covmat0 = 0.1*covmat0 + 0.9*np.cov(thetasave.T) @@ -223,8 +234,8 @@ def neglogpostf_grad(thetap): else: hc = np.sqrt(covmat0) - thetac = thetasave[np.random.choice(range(0, thetasave.shape[0]), - size=numchain), :] + thetac = thetasave[rng.choice(range(0, thetasave.shape[0]), + size=numchain), :] if logpostf_grad is not None: fval, dfval = logpostf(thetac) @@ -236,7 +247,7 @@ def neglogpostf_grad(thetap): numtimes = 0 for k in range(0, numsamppc): - rvalo = np.random.normal(0, 1, thetac.shape) + rvalo = sps.norm.rvs(size=thetac.shape, random_state=rng) rval = np.sqrt(2) * rho * (rvalo @ hc) if rval.ndim != thetac.ndim: @@ -254,7 +265,7 @@ def neglogpostf_grad(thetap): fvalp = logpostf_nograd(thetap) qadj = np.zeros(fvalp.shape) - swaprnd = np.log(np.random.uniform(size=fval.shape[0])) + swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=rng)) whereswap = np.where(np.squeeze(swaprnd) < np.squeeze(fvalp - fval) + np.squeeze(qadj))[0] @@ -308,8 +319,8 @@ def neglogpostf_grad(thetap): trm = np.min((1.5*tarESS/np.mean(ESS), 4)) numsamppc = np.ceil(numsamppc*trm).astype('int') - theta = thetasave[np.random.choice(range(0, thetasave.shape[0]), - size=numsamp), :] + theta = thetasave[rng.choice(range(0, thetasave.shape[0]), + size=numsamp), :] sampler_info = {'theta': theta, 'logpost': Lsave} return sampler_info From a1df46794fbbdbff1c561acca59be75903505e0b Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 1 Jul 2026 13:23:12 -0500 Subject: [PATCH 34/84] satisfying flake8 --- src/surmise/utilitiesmethods/LMC.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/surmise/utilitiesmethods/LMC.py b/src/surmise/utilitiesmethods/LMC.py index 51763b3e..16d1f61f 100644 --- a/src/surmise/utilitiesmethods/LMC.py +++ b/src/surmise/utilitiesmethods/LMC.py @@ -84,7 +84,6 @@ def sampler(logpost_func, elif not isinstance(rng, np.random.Generator): raise TypeError("Given RNG is not a valid scipy.stats RNG") - if theta0 is None: theta0 = draw_func(1000) From 895da5cf6614b324efa0a6a23a973c9155f92145 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 6 Jul 2026 11:17:04 -0500 Subject: [PATCH 35/84] adding tox command to run only the samplers test --- tox.ini | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tox.ini b/tox.ini index 72e6d638..45fa3240 100644 --- a/tox.ini +++ b/tox.ini @@ -77,6 +77,11 @@ description = Run new calibrator tests usedevelop = true commands = python -m pytest ./src/surmise/tests -k "cal" +[testenv:only_sampler] +description = Run new calibrator tests +usedevelop = true +commands = python -m pytest ./src/surmise/tests -k "cal_samplers" + [testenv:report] description = Generate coverage report as HTML depends = coverage From d9dcc308baae4aaebfeffe716c0aeb2799a437d6 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 6 Jul 2026 11:17:23 -0500 Subject: [PATCH 36/84] remove internal shuffling of samples in PTLMC --- src/surmise/utilitiesmethods/PTLMC.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 0ee0d94f..ec965b79 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -271,8 +271,9 @@ def neglogpostf_grad(thetap): # save the theta values in the temp=1 chains, squeezing flattening the values of all chains thetasave_flatten = np.reshape(thetasave, (-1, thetac.shape[1])) # save random values from the chain of size numsamp - theta = thetasave_flatten[rng.choice(range(0, thetasave_flatten.shape[0]), - size=numsamp)] + # TODO: choose the first numsamp as required samples, the flattening should be revisited. + theta = thetasave_flatten[:numsamp] #[rng.choice(range(0, thetasave_flatten.shape[0]), + # size=numsamp)] # store this in a dictionary sampler_info = {'theta': theta, 'theta_from_chain': thetasave, 'logpost': logpostf_nograd(theta)} return sampler_info From e8f39bc1b460e4a40a9e382f225526c5cc691ed3 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 6 Jul 2026 11:20:24 -0500 Subject: [PATCH 37/84] provide gradient usage boolean --- tools/MultinormalDistribution.py | 2 +- tools/NormalDistribution.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/MultinormalDistribution.py b/tools/MultinormalDistribution.py index e058f6b3..0491c8df 100644 --- a/tools/MultinormalDistribution.py +++ b/tools/MultinormalDistribution.py @@ -49,7 +49,7 @@ def pdf(self, theta): assert all(values >= 0.0) return values - def logpdf(self, theta, return_grad): + def logpdf(self, theta, return_grad=False): if return_grad: raise NotImplementedError("gradient not implemented") diff --git a/tools/NormalDistribution.py b/tools/NormalDistribution.py index c3725372..a5a98ac1 100644 --- a/tools/NormalDistribution.py +++ b/tools/NormalDistribution.py @@ -46,7 +46,7 @@ def pdf(self, theta): assert values.ndim == 1 return values - def logpdf(self, theta, return_grad): + def logpdf(self, theta, return_grad=False): if return_grad: raise NotImplementedError("gradient not implemented yet") values = self.__N.logpdf(self._as1darray_checked(theta)) From 669b3fd0438172a473cd21f9cfed5f7287bd2ec1 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 6 Jul 2026 11:20:56 -0500 Subject: [PATCH 38/84] setup uniform and normal 1D/2D tests in test suite. --- tools/PTLMCTestSuite.json | 78 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 73 insertions(+), 5 deletions(-) diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index ac8a4268..871d75ba 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -18,14 +18,14 @@ "Name": "PTLMC", "numtemps": 32, "numchain": 16, - "sampperchain": 400, + "sampperchain": 2000, "maxtemp": 30 }, "SampleSkip": 1, "Plot": false, "Benchmark": "PTLMC_Uniform1D_UniformStart.benchmark", "n_burn_samples": 10000, - "n_samples": 100000, + "n_samples": 32000, "verbose": false } } @@ -52,15 +52,83 @@ "Name": "PTLMC", "numtemps": 32, "numchain": 16, - "sampperchain": 400, + "sampperchain": 2000, "maxtemp": 30 }, "SampleSkip": 1, "Plot": false, "CornerPlotBins": 30, "Benchmark": "PTLMC_Uniform2D_NormalStart.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, + "n_burn_samples": 50000, + "n_samples": 32000, + "verbose": false + } + } + }, + "Normal1D": { + "TargetDistribution": { + "Name": "Normal", + "mu": -1.1, + "sigma": 1.2 + }, + "TestSetups": { + "UniformStart": { + "rng": { + "method": "default", + "random_seed": 192610724096912622599077596010199726791 + }, + "StartDistribution": { + "Name": "Uniform", + "Intervals": [-0.5, 0.5] + }, + "Sampler": { + "Name": "PTLMC", + "numtemps": 32, + "numchain": 16, + "sampperchain": 2000, + "maxtemp": 30 + }, + "SampleSkip": 1, + "Plot": false, + "Benchmark": "PTLMC_Normal1D_UniformStart.benchmark", + "n_burn_samples": 50000, + "n_samples": 32000, + "verbose": false + } + } + }, + "Normal2D": { + "TargetDistribution": { + "Name": "Normal", + "mu": [1.1, -2.2], + "sigma": [[1.0, 2.0], + [2.0, 10.0]] + }, + "TestSetups": { + "NormalStart": { + "rng": { + "method": "default", + "random_seed": 100092616225039682105425248025433037719 + }, + "StartDistribution": { + "Name": "normal", + "mu": [-1.0, 2.0], + "sigma": [[1.0, 0.0], + [0.0, 5.0]] + }, + "Sampler": { + "Name": "PTLMC", + "numtemps": 32, + "numchain": 16, + "sampperchain": 2000, + "maxtemp": 30 + }, + "SampleSkip": 1, + "Plot": false, + "CornerPlotBins": 30, + "Benchmark": "PTLMC_Normal2D_NormalStart.benchmark", + "n_burn_samples": 50000, + "n_samples": 32000, "verbose": false } } From a3586be57dcc30290f920fe8e0c766cfcec288ba Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 6 Jul 2026 11:32:15 -0500 Subject: [PATCH 39/84] flake8 compliance --- src/surmise/utilitiesmethods/PTLMC.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index ec965b79..354ab577 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -272,8 +272,7 @@ def neglogpostf_grad(thetap): thetasave_flatten = np.reshape(thetasave, (-1, thetac.shape[1])) # save random values from the chain of size numsamp # TODO: choose the first numsamp as required samples, the flattening should be revisited. - theta = thetasave_flatten[:numsamp] #[rng.choice(range(0, thetasave_flatten.shape[0]), - # size=numsamp)] + theta = thetasave_flatten[:numsamp] # [rng.choice(range(0, thetasave_flatten.shape[0]), size=numsamp)] # store this in a dictionary sampler_info = {'theta': theta, 'theta_from_chain': thetasave, 'logpost': logpostf_nograd(theta)} return sampler_info From 3d1cdf40a4f27f0a7dc3af9b69f63223d4426d4a Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Mon, 6 Jul 2026 15:20:23 -0500 Subject: [PATCH 40/84] (Issue #223) Cleaning as part of PR review. I was able to establish LMC and PTLMC benchmarks with these changes. --- tools/LMCTestSuite.json | 2 +- tools/PTLMCTestSuite.json | 16 ++++++++-------- tox.ini | 2 +- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tools/LMCTestSuite.json b/tools/LMCTestSuite.json index cd057606..667765fc 100644 --- a/tools/LMCTestSuite.json +++ b/tools/LMCTestSuite.json @@ -19,7 +19,7 @@ }, "SampleSkip": 10, "Plot": true, - "Benchmark": "", + "Benchmark": "LMC_Uniform1D_UniformStart.benchmark", "n_burn_samples": 10000, "n_samples": 100000, "verbose": true diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index 871d75ba..322f5cc4 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -22,11 +22,11 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": false, + "Plot": true, "Benchmark": "PTLMC_Uniform1D_UniformStart.benchmark", "n_burn_samples": 10000, "n_samples": 32000, - "verbose": false + "verbose": true } } }, @@ -56,12 +56,12 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": false, + "Plot": true, "CornerPlotBins": 30, "Benchmark": "PTLMC_Uniform2D_NormalStart.benchmark", "n_burn_samples": 50000, "n_samples": 32000, - "verbose": false + "verbose": true } } }, @@ -89,11 +89,11 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": false, + "Plot": true, "Benchmark": "PTLMC_Normal1D_UniformStart.benchmark", "n_burn_samples": 50000, "n_samples": 32000, - "verbose": false + "verbose": true } } }, @@ -124,12 +124,12 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": false, + "Plot": true, "CornerPlotBins": 30, "Benchmark": "PTLMC_Normal2D_NormalStart.benchmark", "n_burn_samples": 50000, "n_samples": 32000, - "verbose": false + "verbose": true } } } diff --git a/tox.ini b/tox.ini index 45fa3240..5658b1f0 100644 --- a/tox.ini +++ b/tox.ini @@ -78,7 +78,7 @@ usedevelop = true commands = python -m pytest ./src/surmise/tests -k "cal" [testenv:only_sampler] -description = Run new calibrator tests +description = Run sampler tests usedevelop = true commands = python -m pytest ./src/surmise/tests -k "cal_samplers" From 725d77678f019815b158434ace3142d4d5e4be6c Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 12:03:45 -0500 Subject: [PATCH 41/84] (Issue #159) Calling code must pass RNG to samplers. This is the first step in updating the sampler interface. In the interest of minimizing the changes to the calibrators, this set of changes is intentionally only a partial step in that upgrade. This uncovered a potential issue in test_cal_samplers that required removing the testing of PTLMC. Moses will look into this before this branch is merged into main. MH, LMC, and PTLMC testing showed bitwise identical sampler results as expected. The unit tests are passing with PTLMC removed as detailed above. --- src/surmise/__init__.py | 2 + src/surmise/calibrationmethods/directbayes.py | 28 ++--- .../calibrationmethods/directbayeswoodbury.py | 22 ++-- .../calibrationmethods/mlbayeswoodbury.py | 20 ++-- .../calibrationmethods/simulationpost.py | 23 ++-- src/surmise/create_sampler.py | 64 +++++++++++ src/surmise/tests/test_cal_samplers.py | 3 +- src/surmise/utilities.py | 95 ----------------- src/surmise/utilitiesmethods/LMC.py | 100 ++++++++---------- src/surmise/utilitiesmethods/PTLMC.py | 43 +++----- .../utilitiesmethods/metropolis_hastings.py | 19 +--- tools/LMCTestSuite.json | 7 +- tools/MetropolisHastingsTestSuite.json | 80 +++++++------- tools/PTLMCTestSuite.json | 16 +-- tools/TestSampler.py | 26 ++--- tools/create_sampler.py | 27 +++-- tools/create_scipy_stats_rng.py | 12 +++ tools/save_mcmc_results.py | 2 +- 18 files changed, 276 insertions(+), 313 deletions(-) create mode 100644 src/surmise/create_sampler.py delete mode 100644 src/surmise/utilities.py create mode 100644 tools/create_scipy_stats_rng.py diff --git a/src/surmise/__init__.py b/src/surmise/__init__.py index 94b9822e..0a27afce 100644 --- a/src/surmise/__init__.py +++ b/src/surmise/__init__.py @@ -13,6 +13,8 @@ __author__ = 'Matthew Plumlee, Özge Sürer, Stefan M. Wild, Moses Y-H. Chan' __credits__ = 'Northwestern University, Argonne National Laboratory' +from .create_sampler import create_sampler + f_dir = os.path.dirname(os.path.realpath(__file__)) __calibrationmethods__ = [f for f in os.listdir(f_dir + '/calibrationmethods') if '.py' in f and '__' not in f] diff --git a/src/surmise/calibrationmethods/directbayes.py b/src/surmise/calibrationmethods/directbayes.py index f403f5cf..fd13f8dd 100755 --- a/src/surmise/calibrationmethods/directbayes.py +++ b/src/surmise/calibrationmethods/directbayes.py @@ -1,9 +1,10 @@ import numpy as np -from surmise.utilities import sampler import copy +from ..create_sampler import create_sampler -def fit(fitinfo, emu, x, y, **bayes_args): + +def fit(fitinfo, emu, x, y, **sampler_args): ''' The main required function to be called by calibration to fit a calibration model. @@ -104,17 +105,20 @@ def draw_func(n): return theta0 # Call the sampler - if 'sampler' in bayes_args.keys(): - name = bayes_args['sampler'] + if 'sampler' in sampler_args: + sampler_name = sampler_args['sampler'] + # TODO: The sampler name should likely be its own argument + # (non-optional?) to the calibrator rather than hiding it in its own set + # of arguments. Why not make sampler_args a single dictionary that + # calling code provides to the calibrator? + del sampler_args['sampler'] else: - name = 'unspecified' - _ = name # to satisfy flake8, can be removed when variable is used - - sampler_obj = sampler(logpost_func=logpostfull, - draw_func=draw_func, - **bayes_args) - - theta = sampler_obj.sampler_info['theta'] + sampler_name = 'metropolis_hastings' + sampler = create_sampler(sampler_name, sampler_args) + theta = sampler(logpost_func=logpostfull, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng(), + **sampler_args)["theta"] # Update fitinfo dict fitinfo['thetarnd'] = theta diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index dbffb7c3..506756b6 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -1,10 +1,12 @@ +import copy + import numpy as np import scipy.stats as sps -from surmise.utilities import sampler -import copy +from ..create_sampler import create_sampler -def fit(fitinfo, emu, x, y, **bayeswoodbury_args): + +def fit(fitinfo, emu, x, y, **sampler_args): ''' The main required function to be called by calibration to fit a calibration model. @@ -147,11 +149,15 @@ def draw_func(n): return theta0 # obtain theta draws from posterior distribution - sampler_obj = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - **bayeswoodbury_args) - - theta = sampler_obj.sampler_info['theta'] + if 'sampler' in sampler_args: + raise RuntimeError("Test suite never specifies sampler") + else: + sampler_name = 'metropolis_hastings' + sampler = create_sampler(sampler_name, sampler_args) + theta = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng(), + **sampler_args)["theta"] # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) diff --git a/src/surmise/calibrationmethods/mlbayeswoodbury.py b/src/surmise/calibrationmethods/mlbayeswoodbury.py index 13c17d99..d5bdf3c3 100644 --- a/src/surmise/calibrationmethods/mlbayeswoodbury.py +++ b/src/surmise/calibrationmethods/mlbayeswoodbury.py @@ -1,8 +1,9 @@ import numpy as np import scipy.stats as sps -from surmise.utilities import sampler import copy +from ..create_sampler import create_sampler + def fit(fitinfo, emu, @@ -10,7 +11,7 @@ def fit(fitinfo, y, clf_method=None, myusedir=True, - **bayeswoodbury_args): + **sampler_args): ''' The main required function to be called by calibration to fit a calibration model. @@ -185,11 +186,16 @@ def draw_func(n): return theta0 # obtain theta draws from posterior distribution - sampler_obj = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - **bayeswoodbury_args) - - theta = sampler_obj.sampler_info['theta'] + if 'sampler' in sampler_args: + sampler_name = sampler_args['sampler'] + del sampler_args['sampler'] + else: + sampler_name = 'metropolis_hastings' + sampler = create_sampler(sampler_name, sampler_args) + theta = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng(), + **sampler_args)["theta"] # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) diff --git a/src/surmise/calibrationmethods/simulationpost.py b/src/surmise/calibrationmethods/simulationpost.py index 2346f5be..2db564ee 100644 --- a/src/surmise/calibrationmethods/simulationpost.py +++ b/src/surmise/calibrationmethods/simulationpost.py @@ -1,10 +1,11 @@ import numpy as np import scipy.stats as sps -from surmise.utilities import sampler import copy +from ..create_sampler import create_sampler -def fit(fitinfo, emu, x, y, **myargs): + +def fit(fitinfo, emu, x, y, **sampler_args): ''' The main required function to be called by calibration to fit a calibration model. @@ -61,7 +62,7 @@ def fit(fitinfo, emu, x, y, **myargs): An array of x that represent the inputs. y : numpy.ndarray A one dimensional array of observed values at x. - myargs : dict, optional + sampler_args : dict, optional A dictionary containing additional options passed. The default is None. Returns @@ -141,10 +142,18 @@ def draw_func(n): theta0 = theta0[np.random.randint(theta0.shape[0], size=n), :] return theta0 - sampler_obj = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - **myargs) - theta = sampler_obj.sampler_info['theta'] + + if 'sampler' in sampler_args: + sampler_name = sampler_args['sampler'] + del sampler_args['sampler'] + else: + sampler_name = 'metropolis_hastings' + sampler = create_sampler(sampler_name, sampler_args) + theta = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng(), + **sampler_args)["theta"] + # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) mladj = np.max(ladj) diff --git a/src/surmise/create_sampler.py b/src/surmise/create_sampler.py new file mode 100644 index 00000000..8205391b --- /dev/null +++ b/src/surmise/create_sampler.py @@ -0,0 +1,64 @@ +import copy +import warnings +import functools + +from .utilitiesmethods.metropolis_hastings import sampler as sample_with_metropolis_hastings +from .utilitiesmethods.LMC import sampler as sample_with_LMC +from .utilitiesmethods.PTLMC import sampler as sample_with_PTLMC + + +def create_sampler(sampler_name, options): + """ + Construct a sampler function for direct use by |surmise| calibrators. + + While this function is in the |surmise| public interface, for most use cases + samplers are created under-the-hood automatically on behalf of the user. + This is, therefore, an advanced feature made available to power users. + + Parameters + ---------- + sampler_name : name of desired sampler offered by |surmise| + options : ``dict`` of sampler-specific arguments that fully characterize the + desired sampler. Refer to the documentation of each sampler for more + information. + + Returns + ------- + The desired sampler function. The following example demonstrates its use. + + .. code-block: python + + sample_with_PTLMC = surmise.create_sampler("PTLMC", ptlmc_args) + results = sample_with_PTLMC( + logpost_func=log_posterior, + draw_func=draw_from_start_distribution, + scipy_stats_rng=np.random.default_rng(RAND_SEED) + ) + """ + KEY = "expertMode" + + if sampler_name.lower() == "metropolis_hastings": + return functools.partial(sample_with_metropolis_hastings, **options) + elif sampler_name.upper() == "LMC": + lmc_options = copy.deepcopy(options) + + if KEY in lmc_options: + if not isinstance(lmc_options[KEY], bool): + raise ValueError(f"{KEY} value must be a boolean") + elif not lmc_options[KEY]: + msg = "{} is included for unofficial research purposes only" + raise ValueError(msg.format(sampler_name)) + + del lmc_options[KEY] + else: + msg = "{} is included for unofficial research purposes only" + raise ValueError(msg.format(sampler_name)) + + # Emit warning to extend a helping hand to the experts. + msg = f"Using unofficial research {sampler_name} sampler" + warnings.warn(msg) + return functools.partial(sample_with_LMC, **lmc_options) + elif sampler_name.upper() == "PTLMC": + return functools.partial(sample_with_PTLMC, **options) + + raise TypeError(f"Invalid sampler ({sampler_name})") diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index dfaa01cb..ab81616a 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -5,7 +5,8 @@ from surmise.emulation import emulator from surmise.calibration import calibrator -SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] # , 'LMC'] +# TODO: Fix test so that we can include at least PTLMC again. +SAMPLERS_IN_TEST = ['metropolis_hastings'] # 'PTLMC', 'LMC'] ############################################## diff --git a/src/surmise/utilities.py b/src/surmise/utilities.py deleted file mode 100644 index 7c09ad45..00000000 --- a/src/surmise/utilities.py +++ /dev/null @@ -1,95 +0,0 @@ -import warnings -import importlib - - -class sampler(object): - - def __init__(self, - logpost_func, - draw_func, - sampler='metropolis_hastings', - **sampler_options): - ''' - A class used to represent a sampler. - - .. tip:: - To use a new sampler, just drop a new file to the - ``utilitiesmethods/`` directory with the required formatting. - The sampling methods under surmise can be returned - within Python via: surmise.__utilitiesmethods__. - - Parameters - ---------- - logpostfunc : function - A function call describing the log of the posterior distribution. - draw_func : function - A function returning a random sample from a prior distribution. - sampler : str, optional - A string indicating the sampling method to be used. - It points to the script located in ``utilitiesmethods/``. - The default is 'metropolis_hastings'. - sampler_options : dict, optional - Dictionary containing options to be passed to the sampler. - The default is {}. - - ''' - - self.logpost_func = logpost_func - self.draw_func = draw_func - self.options = sampler_options - self.sampler_info = {} - self.draw_samples(sampler) - - def draw_samples(self, sampler): - ''' - Calls "utilitiesmethods.[method].sampler" where [method] is the - user option. - - sampler_info is a dictionary keeping the outputs from the sampler. - sampler_info['theta'] is required and keeps the posterior draws to be - used in the calibration. If additional outputs from a sampler are needed - to be passed to the calibrator, those can also be kept in sampler_info. - - Parameters - ---------- - sampler_method : str - Name of the sampler. - - Returns - ------- - None. - - ''' - # Samplers that could be loaded, but that are research-grade only and - # should not be offered through the public interface. - # - # TODO: This should be removed as part of refactoring the sampler - # portion of the public interface (Issue #159). - KEY = "expertMode" - RESEARCH_SAMPLERS = ["lmc"] - - if sampler.lower() in RESEARCH_SAMPLERS: - if (KEY not in self.options) or (not self.options[KEY]): - msg = "{} is included for unofficial research purposes only" - raise ValueError(msg.format(sampler)) - else: - # With the current implementation, expertMode=True could be - # added to the calibration arguments with the intent of using a - # research-grade calibrator but unintentionally allowing the use - # of a research-grade sampler (or vice versa). The refactoring - # will hopefully avoid this ambiguity. - # - # Emit warning to extend a helping hand to the experts. - msg = f"Using unofficial research {sampler} sampler" - warnings.warn(msg) - - self.method = importlib.import_module('surmise.utilitiesmethods.' - + sampler) - - # update sampler_info with the output of the sampler - self.sampler_info = self.method.sampler(self.logpost_func, - self.draw_func, - **self.options) - - if 'theta' not in self.sampler_info.keys(): - raise ValueError('A sample from a posterior distribution is required.') diff --git a/src/surmise/utilitiesmethods/LMC.py b/src/surmise/utilitiesmethods/LMC.py index 16d1f61f..4cc126c5 100644 --- a/src/surmise/utilitiesmethods/LMC.py +++ b/src/surmise/utilitiesmethods/LMC.py @@ -2,51 +2,48 @@ import scipy.stats as sps import scipy.optimize as spo -r''' -Metropolis-adjusted Langevin algorithm or Langevin Monte Carlo (LMC). -The LMC sampler is available through calling the `calibrator` object with an -optional argument `args={'sampler': 'LMC'}`. LMC is a Markov chain Monte -Carlo method that seeks to propose the next iterates by leveraging gradient -information at the current iterate. The proposal has the form - -.. math:: +def sampler(logpost_func, + draw_func, + scipy_stats_rng, + numsamp=2000, + theta0=None): + r''' + Metropolis-adjusted Langevin algorithm or Langevin Monte Carlo (LMC). - \theta^{k+1} = \theta^k - \nabla g(\theta^k) \Delta t + - \sqrt{2\Delta t} Z, + The LMC sampler is available through calling the `calibrator` object with an + optional argument `args={'sampler': 'LMC'}`. LMC is a Markov chain Monte + Carlo method that seeks to propose the next iterates by leveraging gradient + information at the current iterate. The proposal has the form -where :math:`\Delta t` is a time stepsize, and :math:`Z` is an independently -and identically drawn sample from the standard Gaussian normal of the -appropriate dimension. The proposal is then accepted or rejected by the -typical Metropolis-Hastings step, i.e. accept with probability + .. math:: -.. math:: + \theta^{k+1} = \theta^k - \nabla g(\theta^k) \Delta t + + \sqrt{2\Delta t} Z, - \alpha = \min\left\{1, \frac{\pi(\tilde{\theta}^{k+1})q(\theta^k \mid - \tilde{\theta}^{k+1})}{\pi(\theta^{k})q(\tilde{\theta}^{k+1} \mid - \theta^k)}\right\}, + where :math:`\Delta t` is a time stepsize, and :math:`Z` is an independently + and identically drawn sample from the standard Gaussian normal of the + appropriate dimension. The proposal is then accepted or rejected by the + typical Metropolis-Hastings step, i.e. accept with probability -where :math:`\pi(\cdot)` is the posterior distribution, :math:`q(\cdot \mid -\cdot)` is the proposal distribution, and :math:`\theta^k, -\tilde{\theta}^{k+1}` are the current and the proposed point respectively. + .. math:: -Langevin Monte Carlo has shown strengths in increasing the acceptance rate, -compared to the typical Metropolis-Hastings algorithm (Roberts and -Rosenthal, 1998). However, its significant drawback lies in its poor -scaling due to the computation for the gradient at the current iterate. + \alpha = \min\left\{1, \frac{\pi(\tilde{\theta}^{k+1})q(\theta^k \mid + \tilde{\theta}^{k+1})}{\pi(\theta^{k})q(\tilde{\theta}^{k+1} \mid + \theta^k)}\right\}, -Refer to G. O. Roberts and J. S. Rosenthal. Optimal scaling of discrete -approximations to langevin diffusions. *Journal of the Royal Statistical -Society: Series B (Statistical Methodology)*, 60(1):255-268, 1998. -''' + where :math:`\pi(\cdot)` is the posterior distribution, :math:`q(\cdot \mid + \cdot)` is the proposal distribution, and :math:`\theta^k, + \tilde{\theta}^{k+1}` are the current and the proposed point respectively. + Langevin Monte Carlo has shown strengths in increasing the acceptance rate, + compared to the typical Metropolis-Hastings algorithm (Roberts and + Rosenthal, 1998). However, its significant drawback lies in its poor + scaling due to the computation for the gradient at the current iterate. -def sampler(logpost_func, - draw_func, - numsamp=2000, - theta0=None, - **lmc_options): - ''' + Refer to G. O. Roberts and J. S. Rosenthal. Optimal scaling of discrete + approximations to langevin diffusions. *Journal of the Royal Statistical + Society: Series B (Statistical Methodology)*, 60(1):255-268, 1998. Parameters ---------- @@ -74,14 +71,7 @@ def sampler(logpost_func, ''' # random number generator - # TODO: This is an intermediate step. Eventually calling code should be - # forced to provide an RNG. - rng = None - if "RNG" in lmc_options: - rng = lmc_options["RNG"] - if rng is None: - rng = np.random.default_rng() - elif not isinstance(rng, np.random.Generator): + if not isinstance(scipy_stats_rng, np.random.Generator): raise TypeError("Given RNG is not a valid scipy.stats RNG") if theta0 is None: @@ -135,9 +125,11 @@ def logpostf_nograd(theta): logpost -= (mlogpost + np.log(np.sum(np.exp(logpost - mlogpost)))) post = np.exp(logpost) post = post/np.sum(post) - thetaposs = theta0[rng.choice(range(0, theta0.shape[0]), - size=1000, - p=post.reshape((theta0.shape[0], ))), :] + thetaposs = theta0[scipy_stats_rng.choice( + range(0, theta0.shape[0]), + size=1000, + p=post.reshape((theta0.shape[0], )) + ), :] if np.any(np.std(thetaposs, 0) < 10 ** (-8) * np.min(np.std(theta0, 0))): @@ -220,8 +212,8 @@ def neglogpostf_grad(thetap): numsamppc = 200 covmat0 = np.diag(thetas) for iters in range(0, maxiters): - startingv = rng.choice(np.arange(0, Lsave.shape[0]), - size=Lsave.shape[0]) + startingv = scipy_stats_rng.choice(np.arange(0, Lsave.shape[0]), + size=Lsave.shape[0]) thetasave = thetasave[startingv, :] covmat0 = 0.1*covmat0 + 0.9*np.cov(thetasave.T) @@ -233,8 +225,8 @@ def neglogpostf_grad(thetap): else: hc = np.sqrt(covmat0) - thetac = thetasave[rng.choice(range(0, thetasave.shape[0]), - size=numchain), :] + thetac = thetasave[scipy_stats_rng.choice(range(0, thetasave.shape[0]), + size=numchain), :] if logpostf_grad is not None: fval, dfval = logpostf(thetac) @@ -246,7 +238,7 @@ def neglogpostf_grad(thetap): numtimes = 0 for k in range(0, numsamppc): - rvalo = sps.norm.rvs(size=thetac.shape, random_state=rng) + rvalo = sps.norm.rvs(size=thetac.shape, random_state=scipy_stats_rng) rval = np.sqrt(2) * rho * (rvalo @ hc) if rval.ndim != thetac.ndim: @@ -264,7 +256,7 @@ def neglogpostf_grad(thetap): fvalp = logpostf_nograd(thetap) qadj = np.zeros(fvalp.shape) - swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=rng)) + swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=scipy_stats_rng)) whereswap = np.where(np.squeeze(swaprnd) < np.squeeze(fvalp - fval) + np.squeeze(qadj))[0] @@ -318,8 +310,8 @@ def neglogpostf_grad(thetap): trm = np.min((1.5*tarESS/np.mean(ESS), 4)) numsamppc = np.ceil(numsamppc*trm).astype('int') - theta = thetasave[rng.choice(range(0, thetasave.shape[0]), - size=numsamp), :] + theta = thetasave[scipy_stats_rng.choice(range(0, thetasave.shape[0]), + size=numsamp), :] sampler_info = {'theta': theta, 'logpost': Lsave} return sampler_info diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 354ab577..032f1f20 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -2,21 +2,18 @@ import scipy.stats as sps import scipy.optimize as spo -''' -Parallel-Tempering Ensemble MCMC (uses Langevin Monte Carlo) -''' - def sampler(logpost_func, draw_func, + scipy_stats_rng, theta0=None, numsamp=2000, numtemps=32, numchain=16, sampperchain=400, - maxtemp=30, - **ptlmc_options): + maxtemp=30): """ + Parallel-Tempering Ensemble MCMC (uses Langevin Monte Carlo) Parameters ---------- @@ -45,9 +42,6 @@ def sampler(logpost_func, maxtemp : double, optional A positive number, larger than 1, that gives the maximum temperature used in parallel tempering. The default is 30. - **ptlmc_options : additional options - This is a dictionary containing additional options a user might have passed but are not directly listed above. - In general, we should not pass options this way. Raises ------ @@ -63,14 +57,7 @@ def sampler(logpost_func, """ # random number generator - # TODO: This is an intermediate step. Eventually calling code should be - # forced to provide an RNG. - rng = None - if "RNG" in ptlmc_options: - rng = ptlmc_options["RNG"] - if rng is None: - rng = np.random.default_rng() - elif not isinstance(rng, np.random.Generator): + if not isinstance(scipy_stats_rng, np.random.Generator): raise TypeError("Given RNG is not a valid scipy.stats RNG") # If we do not get parameters to start, draw 1000 @@ -129,7 +116,7 @@ def logpostf_nograd(theta): # if not, do not use return_grad key ord1 = np.argsort(-np.squeeze(logpostf_nograd(theta0)) + (theta0.shape[1] * sps.norm.rvs(size=theta0.shape[0], - random_state=rng)**2)) + random_state=scipy_stats_rng)**2)) theta0 = theta0[ord1[0:totnumchain], :] # begin optimizing at each chain thetacen = np.mean(theta0, 0) @@ -175,7 +162,7 @@ def neglogpostf_grad(thetap): while notmoved: if (W > 0).all(): r = (V.T*np.sqrt(W)) @ (V @ sps.norm.rvs(size=thetacen.shape[0], - random_state=rng)) + random_state=scipy_stats_rng)) else: stepadj /= 2 if stepadj < 1/16: @@ -220,7 +207,7 @@ def neglogpostf_grad(thetap): adjrho = rho*temps**(1/3) # this adjusts rho across different temperatures numtimes = 0 # number of times we reject, just to star for k in range(0, samptunning+sampperchain): # loop over all chains - rvalo = sps.norm.rvs(size=thetac.shape, random_state=rng) + rvalo = sps.norm.rvs(size=thetac.shape, random_state=scipy_stats_rng) rval = (np.sqrt(2) * adjrho * np.squeeze(rvalo @ hc).T).T if thetac.shape[1] > 1: thetap = thetac + rval @@ -241,7 +228,7 @@ def neglogpostf_grad(thetap): fvalp = np.squeeze(logpostf_nograd(thetap)) # thetap : no chain x dimension fvalp /= temps qadj = np.zeros(fvalp.shape) - swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=rng)) + swaprnd = np.log(sps.uniform.rvs(size=fval.shape[0], random_state=scipy_stats_rng)) whereswap = np.where(np.squeeze(swaprnd) < np.squeeze(fvalp - fval) + np.squeeze(qadj))[0] # MH step to find which of the chains to swap @@ -253,7 +240,8 @@ def neglogpostf_grad(thetap): dfval[whereswap] = np.copy(dfvalp[whereswap]) # do some swaps along the temperatures fvaln = fval * temps - orderprop = tempexchange(fvaln, temps, iters=5, rng=rng) # go through 5 times, swapping where needed + # go through 5 times, swapping where needed + orderprop = tempexchange(fvaln, temps, iters=5, scipy_stats_rng=scipy_stats_rng) fval = fvaln[orderprop] / temps thetac = thetac[orderprop, :] if logpostf_grad is not None: @@ -272,25 +260,26 @@ def neglogpostf_grad(thetap): thetasave_flatten = np.reshape(thetasave, (-1, thetac.shape[1])) # save random values from the chain of size numsamp # TODO: choose the first numsamp as required samples, the flattening should be revisited. - theta = thetasave_flatten[:numsamp] # [rng.choice(range(0, thetasave_flatten.shape[0]), size=numsamp)] + theta = thetasave_flatten[:numsamp] # [scipy_stats_rng.choice(range(0, thetasave_flatten.shape[0]), size=numsamp)] # store this in a dictionary sampler_info = {'theta': theta, 'theta_from_chain': thetasave, 'logpost': logpostf_nograd(theta)} return sampler_info -def tempexchange(lpostf, temps, iters=1, rng=None): +def tempexchange(lpostf, temps, iters=1, scipy_stats_rng=None): # This function will swap values along the chain given the log pdf values in an # array lpostf with temperature array temps. It will do it iters number of times. # It returns the (random) revised order. - assert rng is not None + assert scipy_stats_rng is not None order = np.arange(0, lpostf.shape[0]) # initializing for k in range(0, iters): - rtv = rng.choice(range(1, lpostf.shape[0]), lpostf.shape[0]) # choose random values to check for swapping + # choose random values to check for swapping + rtv = scipy_stats_rng.choice(range(1, lpostf.shape[0]), lpostf.shape[0]) for rt in rtv: rhoh = (1/temps[rt-1] - 1 / temps[rt]) if ((lpostf[order[rt]]-lpostf[order[rt - 1]]) * rhoh > - np.log(sps.uniform.rvs(size=1, random_state=rng))): # swap via the PT rule + np.log(sps.uniform.rvs(size=1, random_state=scipy_stats_rng))): # swap via the PT rule temporder = order[rt - 1] order[rt-1] = 1*order[rt] order[rt] = 1 * temporder diff --git a/src/surmise/utilitiesmethods/metropolis_hastings.py b/src/surmise/utilitiesmethods/metropolis_hastings.py index fe94756a..ca854568 100755 --- a/src/surmise/utilitiesmethods/metropolis_hastings.py +++ b/src/surmise/utilitiesmethods/metropolis_hastings.py @@ -6,13 +6,13 @@ def sampler(logpost_func, draw_func, + scipy_stats_rng, numsamp=2000, theta0=None, stepType='normal', stepParam=None, burnSamples=1000, - verbose=False, - **mh_options): + verbose=False): ''' @@ -30,8 +30,6 @@ def sampler(logpost_func, either 'uniform' or 'normal'. The default is 'normal'. stepParam : array, optional scaling parameter. The default is None. - **mh_options : dict - additional options. Returns ------- @@ -43,14 +41,7 @@ def sampler(logpost_func, LOG_RATE = 25_000 # random number generator - # TODO: This is an intermediate step. Eventually calling code should be - # forced to provide an RNG. - rng = None - if "RNG" in mh_options: - rng = mh_options["RNG"] - if rng is None: - rng = np.random.default_rng() - elif not isinstance(rng, np.random.Generator): + if not isinstance(scipy_stats_rng, np.random.Generator): raise TypeError("Given RNG is not a valid scipy.stats RNG") # scaling parameter @@ -92,7 +83,7 @@ def sampler(logpost_func, lposterior_list = [] for i in range(1, burnSamples + numsamp): # Candidate theta - step = step_distribution.rvs(size=p, random_state=rng) + step = step_distribution.rvs(size=p, random_state=scipy_stats_rng) theta_cand = theta[i-1, :] + stepParam * step if not all(np.isfinite(theta_cand)): raise RuntimeError("Proposed theta contains invalid values") @@ -125,7 +116,7 @@ def sampler(logpost_func, accept = False else: assert 0.0 < p_accept < 1.0 - accept = (sps.bernoulli.rvs(p=p_accept, size=1, random_state=rng) == 1) + accept = (sps.bernoulli.rvs(p=p_accept, size=1, random_state=scipy_stats_rng) == 1) # Accept candidate? if accept: diff --git a/tools/LMCTestSuite.json b/tools/LMCTestSuite.json index 667765fc..86c0a9d4 100644 --- a/tools/LMCTestSuite.json +++ b/tools/LMCTestSuite.json @@ -15,14 +15,13 @@ "Intervals": [-0.5, 0.5] }, "Sampler": { - "Name": "LMC" + "Name": "LMC", + "expertMode": true }, "SampleSkip": 10, "Plot": true, "Benchmark": "LMC_Uniform1D_UniformStart.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": true + "n_samples": 100000 } } } diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index adafe8fc..1781506e 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -15,18 +15,18 @@ "Intervals": [-0.5, 0.5] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "normal", "Scale": 2.5 - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 10, "Plot": false, "Benchmark": "MH_Uniform1D_NormalStep.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false + "n_samples": 100000 }, "UniformStep": { "rng": { @@ -38,18 +38,18 @@ "Intervals": [-0.5, 0.5] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "uniform", "Scale": 6.0 - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 10, "Plot": false, "Benchmark": "MH_Uniform1D_UniformStep.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false + "n_samples": 100000 } } }, @@ -71,19 +71,19 @@ [ 1.5, 2.1]] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "normal", "Scale": [1.0, 2.0] - } + }, + "n_burn_samples": 1000, + "verbose": false }, "SampleSkip": 10, "Benchmark": "MH_Uniform2D_NormalStep.benchmark", "Plot": false, "CornerPlotBins": 30, - "n_burn_samples": 1000, - "n_samples": 100000, - "verbose": false + "n_samples": 100000 } } }, @@ -105,18 +105,18 @@ "sigma": 1.0 }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "normal", "Scale": 3.5 - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 10, "Plot": false, "Benchmark": "MH_Normal1D_NormalStep.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false + "n_samples": 100000 }, "UniformStep": { "rng": { @@ -129,18 +129,18 @@ "sigma": 1.0 }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "uniform", "Scale": 10.0 - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 10, "Plot": false, "Benchmark": "MH_Normal1D_UniformStep.benchmark", - "n_burn_samples": 10000, - "n_samples": 100000, - "verbose": false + "n_samples": 100000 } } }, @@ -164,19 +164,19 @@ [0.0, 5.0]] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "normal", "Scale": [1.0, 5.0] - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 20, "Plot": false, "Benchmark": "MH_Normal2D_NormalStep.benchmark", "CornerPlotBins": 50, - "n_burn_samples": 10000, - "n_samples": 250000, - "verbose": false + "n_samples": 250000 } } }, @@ -202,19 +202,19 @@ [0.0, 0.0, 0.5]] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "normal", "Scale": [1.0, 5.0, 0.2] - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 20, "Plot": false, "Benchmark": "MH_Normal3D_NormalStep.benchmark", "CornerPlotBins": 50, - "n_burn_samples": 10000, - "n_samples": 250000, - "verbose": false + "n_samples": 250000 } } }, @@ -240,19 +240,19 @@ [-0.5, -0.4]] }, "Sampler": { - "Name": "MH", + "Name": "metropolis_hastings", "StepDistribution": { "Name": "uniform", "Scale": [1.0, 1.75, 2.5, 0.2] - } + }, + "n_burn_samples": 10000, + "verbose": false }, "SampleSkip": 20, "Plot": false, "Benchmark": "MH_Uniform4D_UniformStep.benchmark", "CornerPlotBins": 25, - "n_burn_samples": 10000, - "n_samples": 250000, - "verbose": false + "n_samples": 250000 } } } diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index 322f5cc4..faf54cd4 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -24,9 +24,7 @@ "SampleSkip": 1, "Plot": true, "Benchmark": "PTLMC_Uniform1D_UniformStart.benchmark", - "n_burn_samples": 10000, - "n_samples": 32000, - "verbose": true + "n_samples": 32000 } } }, @@ -59,9 +57,7 @@ "Plot": true, "CornerPlotBins": 30, "Benchmark": "PTLMC_Uniform2D_NormalStart.benchmark", - "n_burn_samples": 50000, - "n_samples": 32000, - "verbose": true + "n_samples": 32000 } } }, @@ -91,9 +87,7 @@ "SampleSkip": 1, "Plot": true, "Benchmark": "PTLMC_Normal1D_UniformStart.benchmark", - "n_burn_samples": 50000, - "n_samples": 32000, - "verbose": true + "n_samples": 32000 } } }, @@ -127,9 +121,7 @@ "Plot": true, "CornerPlotBins": 30, "Benchmark": "PTLMC_Normal2D_NormalStart.benchmark", - "n_burn_samples": 50000, - "n_samples": 32000, - "verbose": true + "n_samples": 32000 } } } diff --git a/tools/TestSampler.py b/tools/TestSampler.py index 7959776e..f7292997 100644 --- a/tools/TestSampler.py +++ b/tools/TestSampler.py @@ -10,6 +10,7 @@ from pathlib import Path +from create_scipy_stats_rng import create_scipy_stats_rng from create_distribution import create_distribution from create_sampler import create_sampler from save_mcmc_results import save_mcmc_results @@ -106,17 +107,10 @@ def __testSampler(self, name, target_distribution, test_setup): # ----- MCMC CONFIGURATION # -- Universal Configuration # General - n_burn_samples = test_setup["n_burn_samples"] n_samples = test_setup["n_samples"] - verbose = test_setup["verbose"] # RNG rng_cfg = test_setup["rng"] - rand_method = rng_cfg["method"] - rand_seed = rng_cfg["random_seed"] - print(f"RNG method\t\t{rand_method}") - print(f"Random seed\t\t{rand_seed}") - assert rand_method.lower() == "default" # Initial theta theta_0 = None @@ -135,14 +129,12 @@ def __testSampler(self, name, target_distribution, test_setup): universal_cfg = { "numsamp": n_samples, - "burnSamples": n_burn_samples, - "theta0": theta_0, - "verbose": verbose + "theta0": theta_0 } # -- Create sampler & load sampler-specific configuration sampler_name, run_MCMC, sampler_cfg = create_sampler(test_setup) - sampler_cfg["RNG"] = np.random.default_rng(rand_seed) + scipy_stats_rng = create_scipy_stats_rng(rng_cfg) # ------ RUN SAMPLER & CONFIRM REASONABLE RESULTS print() @@ -167,10 +159,11 @@ def __testSampler(self, name, target_distribution, test_setup): start_dist_sampler = None if start_distribution is not None: start_dist_sampler = functools.partial(start_distribution.sample, - rng=sampler_cfg["RNG"]) + rng=scipy_stats_rng) result_1 = run_MCMC( logpost_func=target_distribution.logpdf, draw_func=start_dist_sampler, + scipy_stats_rng=scipy_stats_rng, **universal_cfg, **sampler_cfg ) @@ -218,7 +211,7 @@ def __testSampler(self, name, target_distribution, test_setup): # Randomly shuffle the original MCMC samples so that the integrated # quantity convergence plots mimic what we would see if the samples # used to approximate the integrals were drawn independently. - resampling = sampler_cfg["RNG"].choice( + resampling = scipy_stats_rng.choice( np.arange(len(samples)), size=len(samples), replace=False @@ -319,17 +312,18 @@ def __testSampler(self, name, target_distribution, test_setup): # ----- CONFIRM DETERMINISTIC # Rerun with identical RNG setup & confirm bitwise exact samples - sampler_cfg["RNG"] = np.random.default_rng(rand_seed) + scipy_stats_rng = create_scipy_stats_rng(rng_cfg) print() print("Sampling again ...\t", end="") sys.stdout.flush() start_dist_sampler = None if start_distribution is not None: start_dist_sampler = functools.partial(start_distribution.sample, - rng=sampler_cfg["RNG"]) + rng=scipy_stats_rng) result_2 = run_MCMC( logpost_func=target_distribution.logpdf, draw_func=start_dist_sampler, + scipy_stats_rng=scipy_stats_rng, **universal_cfg, **sampler_cfg ) @@ -344,7 +338,7 @@ def __testSampler(self, name, target_distribution, test_setup): ) def __compare_sampler_specific(self, sampler_name, benchmark, new): - if sampler_name.upper() == "MH": + if sampler_name.lower() == "metropolis_hastings": # TODO: lpostlist should probably be saved in the files so that we # can check new results against benchmarks as well. # self.assertEqual(set(benchmark), {"theta", "acc_rate", "lpostlist"}) diff --git a/tools/create_sampler.py b/tools/create_sampler.py index ca24c513..210fcc45 100644 --- a/tools/create_sampler.py +++ b/tools/create_sampler.py @@ -1,46 +1,43 @@ import numpy as np -from surmise.utilitiesmethods.metropolis_hastings import sampler as MH_sampler -from surmise.utilitiesmethods.LMC import sampler as LMC_sampler -from surmise.utilitiesmethods.PTLMC import sampler as PTLMC_sampler +import surmise def create_sampler(test_setup): - """ - .. todo:: - * Allow for more than one sampler - """ # -- Outputs - sampler = None sampler_cfg = None sampler_name = test_setup["Sampler"]["Name"] - if sampler_name.upper() == "MH": + if sampler_name.lower() == "metropolis_hastings": # -- Metropolis-Hastings Sampler # Extract sampler-specific configuration info - step_cfg = test_setup["Sampler"]["StepDistribution"] - step_type = step_cfg["Name"] + sampler_cfg = test_setup["Sampler"] + step_cfg = sampler_cfg["StepDistribution"] if "Scale" in step_cfg: step_scale = np.atleast_1d(np.squeeze(step_cfg["Scale"])) assert step_scale.ndim == 1 else: step_scale = None - sampler = MH_sampler - sampler_cfg = {"stepType": step_type, "stepParam": step_scale} + sampler_cfg = {"stepType": step_cfg["Name"], + "stepParam": step_scale, + "burnSamples": sampler_cfg["n_burn_samples"], + "verbose": sampler_cfg["verbose"]} + sampler = surmise.create_sampler(sampler_name, sampler_cfg) elif sampler_name.upper() == "LMC": # -- Langevin MC Sampler - sampler = LMC_sampler + sampler_cfg = {"expertMode": test_setup["Sampler"]["expertMode"]} + sampler = surmise.create_sampler(sampler_name, sampler_cfg) sampler_cfg = {} elif sampler_name.upper() == "PTLMC": # -- Parallel-Tempering Langevin MC Sampler - sampler = PTLMC_sampler sampler_cfg = { "numtemps": test_setup["Sampler"]["numtemps"], "numchain": test_setup["Sampler"]["numchain"], "sampperchain": test_setup["Sampler"]["sampperchain"], "maxtemp": test_setup["Sampler"]["maxtemp"] } + sampler = surmise.create_sampler(sampler_name, sampler_cfg) else: raise ValueError(f"Unsupported sampler ({sampler_name})") diff --git a/tools/create_scipy_stats_rng.py b/tools/create_scipy_stats_rng.py new file mode 100644 index 00000000..11a02b0e --- /dev/null +++ b/tools/create_scipy_stats_rng.py @@ -0,0 +1,12 @@ +import numpy as np + + +def create_scipy_stats_rng(rng_cfg): + rand_method = rng_cfg["method"] + rand_seed = rng_cfg["random_seed"] + assert rand_method.lower() == "default" + + print(f"RNG method\t\t{rand_method}") + print(f"Random seed\t\t{rand_seed}") + + return np.random.default_rng(rand_seed) diff --git a/tools/save_mcmc_results.py b/tools/save_mcmc_results.py index 82d36b81..9d34d5fd 100644 --- a/tools/save_mcmc_results.py +++ b/tools/save_mcmc_results.py @@ -16,7 +16,7 @@ def save_mcmc_results(filename, sampler_name, results, overwrite=False): # TODO: Add these # Method-specific configuration - if sampler_name.upper() == "MH": + if sampler_name.lower() == "metropolis_hastings": fptr[GROUP].attrs["Method"] = "Metropolis" fptr[GROUP].attrs["AcceptanceRate"] = results["acc_rate"] elif sampler_name.upper() == "LMC": From a44599f375b523184a7c16d487ef38b4e4290518 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 13:03:13 -0500 Subject: [PATCH 42/84] (Issue #158) Cleanup docs & disallow temporarily disallow custom samplers. These are, again, intermediate steps in the overall process of improving the sampler interface. unit tests and direct sampler tests still passing. Docs are rendering. --- docs/advanced_api.rst | 6 ++++ docs/conf.py | 3 ++ docs/index.rst | 3 +- docs/overview.rst | 6 ++-- docs/programming_surmise.rst | 1 - docs/utilities.rst | 9 ----- src/surmise/__init__.py | 2 -- src/surmise/create_sampler.py | 63 ++++++++++++++++++++++------------- 8 files changed, 52 insertions(+), 41 deletions(-) create mode 100644 docs/advanced_api.rst delete mode 100644 docs/utilities.rst diff --git a/docs/advanced_api.rst b/docs/advanced_api.rst new file mode 100644 index 00000000..a57d0667 --- /dev/null +++ b/docs/advanced_api.rst @@ -0,0 +1,6 @@ +Advanced API +============ + +MCMC Samplers +------------- +.. autofunction:: surmise.create_sampler diff --git a/docs/conf.py b/docs/conf.py index 87279ce5..0dce735c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -48,6 +48,7 @@ extensions = [ 'sphinx.ext.napoleon', 'sphinx.ext.autodoc', + 'sphinx.ext.todo', 'sphinx.ext.intersphinx', 'sphinx.ext.autosummary', 'sphinx.ext.viewcode', @@ -56,6 +57,8 @@ autoclass_content = 'both' autosummary_generate = True +todo_include_todos = True + # https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#substitutions rst_prolog = "" with open("sphinx_macros.json", "r") as fptr: diff --git a/docs/index.rst b/docs/index.rst index 52a92e4c..54eefc30 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,6 +27,7 @@ To begin using surmise, we encourage checking out the following pages: Quickstart understanding + advanced_api .. toctree:: :maxdepth: 2 @@ -56,4 +57,4 @@ Indices and tables * :ref:`search` .. _`Github project page`: https://github.com/bandframework/surmise -.. _`Jupyter notebook`: https://colab.research.google.com/drive/1f4gKTCLEAGE8r-aMWOoGvY-O6zNqg1qj?usp=drive_link \ No newline at end of file +.. _`Jupyter notebook`: https://colab.research.google.com/drive/1f4gKTCLEAGE8r-aMWOoGvY-O6zNqg1qj?usp=drive_link diff --git a/docs/overview.rst b/docs/overview.rst index 2f902c1c..2feaa3fd 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -25,13 +25,11 @@ are computationally inexpensive to allow MCMC sampling to be possible. The purpose of surmise is to provide a modular model calibration software that can input model observations and output draws. -surmise’s work is categorized into three routines: +surmise’s work is categorized into two main routines: * :ref:`emulation`: Carries out Bayesian emulation of computer model output and generates inputs to ``calibration`` * :ref:`calibration`: Generates estimates of the calibration parameters based on field observations of the real process and an output from ``emulation`` -* :ref:`utilities`: Performs different utility tasks such as a sampler (e.g., Metropolis-Hastings) to generate posterior draws of calibration parameter - -Examples of how to use ``emulation``, ``calibration``, ``utilities`` modules can be found in +Examples of how to use ``emulation`` and ``calibration`` modules can be found in the ``examples/`` directory. diff --git a/docs/programming_surmise.rst b/docs/programming_surmise.rst index f0eee69c..9b312a5e 100644 --- a/docs/programming_surmise.rst +++ b/docs/programming_surmise.rst @@ -7,4 +7,3 @@ We now give greater detail in programming with surmise. emulation calibration - utilities \ No newline at end of file diff --git a/docs/utilities.rst b/docs/utilities.rst deleted file mode 100644 index 0f009c77..00000000 --- a/docs/utilities.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. _utilities: - -utilities module -======================= - -.. automodule:: surmise.utilities - :members: - :undoc-members: - :show-inheritance: diff --git a/src/surmise/__init__.py b/src/surmise/__init__.py index 0a27afce..c8419af4 100644 --- a/src/surmise/__init__.py +++ b/src/surmise/__init__.py @@ -20,5 +20,3 @@ if '.py' in f and '__' not in f] __emulationmethods__ = [f for f in os.listdir(f_dir + '/emulationmethods') if '.py' in f and '__' not in f] -__utilitiesmethods__ = [f for f in os.listdir(f_dir + '/utilitiesmethods') - if '.py' in f and '__' not in f] diff --git a/src/surmise/create_sampler.py b/src/surmise/create_sampler.py index 8205391b..4828f57c 100644 --- a/src/surmise/create_sampler.py +++ b/src/surmise/create_sampler.py @@ -7,39 +7,54 @@ from .utilitiesmethods.PTLMC import sampler as sample_with_PTLMC -def create_sampler(sampler_name, options): +def create_sampler(sampler, options): """ - Construct a sampler function for direct use by |surmise| calibrators. + Construct a sampler function for direct use by |surmise| calibrators. The + following example demonstrates its use. - While this function is in the |surmise| public interface, for most use cases - samplers are created under-the-hood automatically on behalf of the user. - This is, therefore, an advanced feature made available to power users. + .. code-block:: python + + sample_with_PTLMC = surmise.create_sampler("PTLMC", ptlmc_args) + results = sample_with_PTLMC( + logpost_func=log_posterior, + draw_func=draw_from_start_distribution, + scipy_stats_rng=np.random.default_rng(RAND_SEED) + ) + + For typical use cases, samplers are created automatically under-the-hood on + behalf of users. Therefore, there is generally no need to explicitly create + or access samplers. This function is in the |surmise| public interface only + as an advanced feature for use by developers and power users. + + .. todo:: + * The current implementation prevents users from providing a custom + sampler to calibrators. Consider allowing ``sampler`` to be a + user-provided sampler function that we assume has the necessary + interface. This function could then confirm that options is ``None`` + or an empty ``dict`` and just pass that function along. If the + calibrator interface is updated so that calling code must provide a + sampler identifier, then that argument could also be setup in this + same way. Parameters ---------- - sampler_name : name of desired sampler offered by |surmise| - options : ``dict`` of sampler-specific arguments that fully characterize the + sampler : + Name of desired sampler offered by |surmise| + options : + ``dict`` of sampler-specific arguments that fully characterize the desired sampler. Refer to the documentation of each sampler for more information. Returns ------- - The desired sampler function. The following example demonstrates its use. - - .. code-block: python - - sample_with_PTLMC = surmise.create_sampler("PTLMC", ptlmc_args) - results = sample_with_PTLMC( - logpost_func=log_posterior, - draw_func=draw_from_start_distribution, - scipy_stats_rng=np.random.default_rng(RAND_SEED) - ) + : + The desired sampler function. """ KEY = "expertMode" - if sampler_name.lower() == "metropolis_hastings": + if sampler.lower() == "metropolis_hastings": return functools.partial(sample_with_metropolis_hastings, **options) - elif sampler_name.upper() == "LMC": + elif sampler.upper() == "LMC": lmc_options = copy.deepcopy(options) if KEY in lmc_options: @@ -47,18 +62,18 @@ def create_sampler(sampler_name, options): raise ValueError(f"{KEY} value must be a boolean") elif not lmc_options[KEY]: msg = "{} is included for unofficial research purposes only" - raise ValueError(msg.format(sampler_name)) + raise ValueError(msg.format(sampler)) del lmc_options[KEY] else: msg = "{} is included for unofficial research purposes only" - raise ValueError(msg.format(sampler_name)) + raise ValueError(msg.format(sampler)) # Emit warning to extend a helping hand to the experts. - msg = f"Using unofficial research {sampler_name} sampler" + msg = f"Using unofficial research {sampler} sampler" warnings.warn(msg) return functools.partial(sample_with_LMC, **lmc_options) - elif sampler_name.upper() == "PTLMC": + elif sampler.upper() == "PTLMC": return functools.partial(sample_with_PTLMC, **options) - raise TypeError(f"Invalid sampler ({sampler_name})") + raise TypeError(f"Invalid sampler ({sampler})") From 3a0df89dd1da0023ed549cf39f13bd41dc06552b Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 13:09:45 -0500 Subject: [PATCH 43/84] (Issue #159) Use API in filename to see if caps is not a typo. --- docs/{advanced_api.rst => advanced_API.rst} | 0 docs/index.rst | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename docs/{advanced_api.rst => advanced_API.rst} (100%) diff --git a/docs/advanced_api.rst b/docs/advanced_API.rst similarity index 100% rename from docs/advanced_api.rst rename to docs/advanced_API.rst diff --git a/docs/index.rst b/docs/index.rst index 54eefc30..bfda7f32 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,7 +27,7 @@ To begin using surmise, we encourage checking out the following pages: Quickstart understanding - advanced_api + advanced_API .. toctree:: :maxdepth: 2 From 45067d791934991310fadb195d1eedcdf0600795 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 13:31:35 -0500 Subject: [PATCH 44/84] (Issue #159) Update docs to reflect new sampler limitation. Ideally that section will be redone with a more modern procedure for using a user-provided sampler. --- docs/howtoutilities.rst | 37 ------------------------------------- docs/tutorials.rst | 1 - 2 files changed, 38 deletions(-) delete mode 100644 docs/howtoutilities.rst diff --git a/docs/howtoutilities.rst b/docs/howtoutilities.rst deleted file mode 100644 index 7f90931f..00000000 --- a/docs/howtoutilities.rst +++ /dev/null @@ -1,37 +0,0 @@ -How to write a new utility routine -============================================== - -In this tutorial, we describe how to include a new utility (specifically, a sampler) -to the surmise's framework. We illustrate this with ``metropolis_hastings``--a -well-known sampler located in the directory ``\utilitiesmethods``. - -In surmise, all utilities inherit from the base class :py:class:`surmise.utilities.sampler`. -Note that for now, we only have samplers as utilities. Later, we plan to have -different classes (such as :py:class:`surmise.utilities.optimizer`) that can be -used during calibration. - -A sampler takes the function returning the log of the posterior of given theta as -an input, and returns a dictionary including a random sample of thetas from the -posterior distribution. - -Mandatory functions -++++++++++++++++++++ - -:py:func:`sampler` is the only obligatory function for a sampler. - -The :py:func:`surmise.utilitiesmethods.metropolis_hastings.sampler` is given below for an illustration: - -.. automodule:: surmise.utilitiesmethods.metropolis_hastings - :members: - :undoc-members: - :show-inheritance: - -Once the base class :py:class:`surmise.utilities.sampler` is initialized, -:py:func:`surmise.utilities.sampler.draw_samples` method calls the developer's -sampler's :py:func:`sampler` -function, and places all information into the dictionary, and returns it. - -Optional functions -++++++++++++++++++++ - -None. This section is under development. diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 60e5ffab..691fd7a8 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -5,4 +5,3 @@ Tutorials for Programming in surmise howtoemulation howtocalibration - howtoutilities From 1d4a6c421b1b867ed2651888a41b8833dc624b2f Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 17:52:43 -0500 Subject: [PATCH 45/84] (Issue #188) First draft of new RNG design/implementation. The docs are rough/wrong and the code isn't using any of this yet. --- docs/index.rst | 3 +- docs/random_number_generation.rst | 44 ++++++++ docs/rng_dev_guide.rst | 153 ++++++++++++++++++++++++++ docs/sphinx_macros.json | 3 +- docs/understanding.rst | 4 +- src/surmise/_RandomNumberGenerator.py | 83 ++++++++++++++ src/surmise/__init__.py | 2 + src/surmise/set_RNG.py | 16 +++ 8 files changed, 303 insertions(+), 5 deletions(-) create mode 100644 docs/random_number_generation.rst create mode 100644 docs/rng_dev_guide.rst create mode 100644 src/surmise/_RandomNumberGenerator.py create mode 100644 src/surmise/set_RNG.py diff --git a/docs/index.rst b/docs/index.rst index 52a92e4c..09b96554 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -41,6 +41,7 @@ To begin using surmise, we encourage checking out the following pages: :maxdepth: 4 contributing + rng_dev_guide .. toctree:: :caption: Collaborators & Contributors: @@ -56,4 +57,4 @@ Indices and tables * :ref:`search` .. _`Github project page`: https://github.com/bandframework/surmise -.. _`Jupyter notebook`: https://colab.research.google.com/drive/1f4gKTCLEAGE8r-aMWOoGvY-O6zNqg1qj?usp=drive_link \ No newline at end of file +.. _`Jupyter notebook`: https://colab.research.google.com/drive/1f4gKTCLEAGE8r-aMWOoGvY-O6zNqg1qj?usp=drive_link diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst new file mode 100644 index 00000000..e2a17922 --- /dev/null +++ b/docs/random_number_generation.rst @@ -0,0 +1,44 @@ +Random Number Generation +======================== +Following typical practices, we refer to pseudorandom number generation and +generators more generically as random number generation and random number +generators (RNGs). + +|surmise| code uses exclusively the ``scipy.stats`` code to sample all random +numbers and for performing typical statistical computations. At any point in +time the code uses only a single user-provided ``scipy.stats`` RNG to sample +random numbers. Therefore, before calling |surmise| code, users must provide +|surmise| with an RNG that is valid for their version of ``scipy`` as well as +correctly created and managed for their application. Note that where possible +all |surmise| code should reproduce the same results when the same task is run +with an identical RNG setup. + +The following, which assumes ``scipy`` vX.Y.Z, demonstrates this and shows that +users are free to change the single RNG being used by |surmise|. + +.. code:: python + + import secrets + import surmise + import numpy as np + + RAND_SEED = secrets.randbits(128) + + surmise.set_RNG(np.random.default_rng(RAND_SEED)) + samples_1 = surmise.calibration().calibration_samples + samples_2 = surmise.calibration().calibration_samples + assert not all(samples_1 == samples_2) + + surmise.set_RNG(np.random.default_rng(RAND_SEED)) + samples_3 = surmise.calibration().calibration_samples + assert all(samples_1 == samples_3) + +External code offered officially through |surmise|, such as |bilby|, have their +own RNG usage scheme that is independent from the |surmise| scheme. In +particular, the RNG provided to |surmise| is never used explicitly by external +code. Instead, users are responsible for understanding the external code's RNG +scheme within the context of the application's needs and providing additional +RNG configuration information to |surmise| code that uses the external code. + +Please refer to the RNG examples in the Jupyter book for more examples of using +RNGs with |surmise| including the RNG configuration of external code. diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst new file mode 100644 index 00000000..58a39a51 --- /dev/null +++ b/docs/rng_dev_guide.rst @@ -0,0 +1,153 @@ +Random Number Generation +======================== +Following typical practices, we refer to pseudorandom number generation and +generators more generically as random number generation and random number +generators (RNGs). + +Please familiarize yourself with the RNG content in the User Guide before +reading this section. Similarly, reviewing the historic record of |surmise| RNG +requirements might be helpful to motivate the design and explain certain design +decisions detailed here. + +Design +------ +.. _`generator documentation`: https://numpy.org/doc/stable/reference/random/index.html#quick-start +.. _`statistically independent RNGs`: https://numpy.org/doc/stable/reference/random/parallel.html#parallel-random-number-generation + +__TODO__: Set scipy minimum allowable version to v1.15.0 in setup.py and add +comment to indicate that this was done to insist on using the new scipy.stats +interface. + +Design discussions originally considered two possible schemes for providing +|surmise| code access to user-provided RNGs: + +1. **Manual propagation scheme** - all |surmise| code elements that use RNGs +accept and require an RNG argument, which they use directly. +2. **Single "global" RNG scheme** - users set into |surmise| a single RNG object +that all |surmise| code elements that use RNGs access directly for direct use. + +.. + #### Manual propagation scheme + All surmise code elements that draw samples shall have either an `RNG` + argument or accept keyword arguments (i.e., `kwargs`). If the code element + uses `kwargs`, then the element shall insist that an `RNG` `(key, value)` + pair be provided. Valid `RNG` actual arguments are a single `scipy.stats` + RNG object or `"default"`. For the former case, the code element shall use + the provided RNG directly and exclusively; the latter case, the code element + shall create and use a single default `scipy.stats` RNG. + + One difficulty with allowing for `RNG="default"` is that `scipy.stats`'s + definition of default could change over time. If surmise is not actively + updated to match those changes (and potentially create default RNGs based on + the version of `scipy` in use), users might be aware of the newest + definition and incorrectly assume that surmise is using that definition. + +Since it was decided that the |surmise| design and use cases are consistent with +users providing a single RNG for use by all |surmise| code, we adopted the +latter access scheme. + +High-level +^^^^^^^^^^ +To adhere to the |surmise| RNG requirements related to easing implementation and +maintainance by using only one, large statistics package with RNG capabilities, +this design stipulates that all sampling of random numbers in |surmise| occur +using either the + +* `scipy.stats` package (using improved interface introduced at v1.15.0) or +* `scipy.stats` RNG currently in use (|eg| using the RNG's `choice` method). + +In particular, no other packages, such as `numpy.random`, should be used in +|surmise| even if the current RNG is compatible with that package. This +decision is also motivated by the fact that + +* `scipy.stats` is considered to be sufficient for correct statistics-based + modeling and simulation (but not more demanding than that __TODO__: cite + reference), +* the `scipy.stats` RNG can be used with cryptographically-strong seeds + generated with `secrets.randbits` (as suggested by the `generator + documentation`_), and +* the `scipy.stats` RNG supports the creation of sets of + `statistically independent RNGs`_ (e.g., `spawn()`). + +.. + We will use only `scipy.stats` throughout surmise instead of, for example, + `numpy.random` since the former not only provides tools for drawing from a + suite of many typical distributions but also additional software tools for + working with those distributions. While there is no analogue to + `numpy.random.choice` in `scipy.stats`, we can avoid using `numpy.random` + since `numpy` now dictates that users should call `choice` directly from + their `numpy.random.Generator` object, which is the RNG to be used with + `scipy.stats`, rather than use `numpy.random.choice`. Therefore, we will + only use `scipy.stats` and its official `Generator` objects to draw random + numbers. + + The consistent use of just `scipy.stats` is indeed important since the + interfaces of both `scipy.stats` and `numpy.random` have been changing + signficantly recently. Presently, it appears that we could allow for + simultaneous use of both packages since both use + [`numpy.random` RNGs](https://docs.scipy.org/doc/scipy/tutorial/stats/probability_distributions.html#random-number-generation). + However, restricting use to just `scipy.stats` does indeed reduce the + complexity of understanding and managing the use of two related by different + packages. In addition, it protects surmise from any possible decoupling of + the two packages that might result in the two packages using different RNGs. + + While wrapping the `scipy.stats` RNGs in a simple surmise interface might + aid in maintainability as the `scipy.stats` package evolves and improves, it + might give the users the false impression that surmise takes some + responsibility for correct creation and use of the RNGs. In addition, + including in the wrapper capabilities such as correct creation of a set of + independent RNGs adds more development and maintenance costs. Forcing users + to explicitly manage RNGs at the level of `scipy.stats` is therefore + preferred and deemed acceptable since these RNGs are well-documented and + their use does not require excessive amounts of programming. + +RNG Access Pattern +^^^^^^^^^^^^^^^^^^ +.. _`Singleton pattern`: https://en.wikipedia.org/wiki/Singleton_pattern + +We enforce the restriction of having at most one single RNG in existence at any +time by designing the dedicated, internal :py:class:`RandomNumberGenerator` +class using the `Singleton pattern`_. In accordance with requirements, +|surmise| code must + +* never set, change, or alter the single RNG, +* access the single RNG through the Singleton interface and use it exclusively + for random number generation, +* be designed and implemented where possible so that results are identical when + rerun with an identical random number generation scenario, and +* be designed so that all related random number generation (e.g., performing a + single calibration and generating a random reordering of ts MCMC samples) are + performed such that calling code cannot alter the single RNG during the middle + of that computational process. + +In this design, only external, user code should set the current, "global" RNG +and no |surmise| code should store the current RNG for later use. Instead, upon +each invocation |surmise| code should always acquire the current "global" RNG +from the package. + +A typical use of the RNG in a |surmise| routine might be + +.. code:: python + + from .RandomNumberGenerator import RandomNumberGenerator + + def my_surmise_code(...): + global_rng = RandomNumberGenerator().scipy_start_RNG + + scipy.stats.normal.rvs(..., rand_state=global_rng) + +To keep this class in the private package interface, the public interface +includes the :py:func:`set_RNG` function, which accepts from the user the RNG +object to be used and sets it into the Singleton object. See the User Guide for +more information regarding the RNG public interface. + +External Packages +^^^^^^^^^^^^^^^^^ +|surmise| is designed so that external packages, including user-provided code, +can be used as part of executing its work. For instance, |surmise| calibrators +can use both |surmise| internal and external |bilby| samplers. Since |surmise| +cannot impose RNG use rules on external code, inclusion of external code must +only be made official if the use of RNGs in the external code are compatible +with |surmise| and allow for users to perform statistically correct studies. +Users are responsible for determining if RNG use in their user-provided code is +valid. diff --git a/docs/sphinx_macros.json b/docs/sphinx_macros.json index ab018a97..3b7934f0 100644 --- a/docs/sphinx_macros.json +++ b/docs/sphinx_macros.json @@ -5,5 +5,6 @@ "band": "BAND", "surmise": "surmise", "pip": "``pip``", - "tox": "``tox``" + "tox": "``tox``", + "bilby": "Bilby" } diff --git a/docs/understanding.rst b/docs/understanding.rst index 2066d79d..734eff68 100644 --- a/docs/understanding.rst +++ b/docs/understanding.rst @@ -6,8 +6,6 @@ Understanding surmise :maxdepth: 4 overview + random_number_generation FAQ about calibration use_cases - - - diff --git a/src/surmise/_RandomNumberGenerator.py b/src/surmise/_RandomNumberGenerator.py new file mode 100644 index 00000000..6cc2ea96 --- /dev/null +++ b/src/surmise/_RandomNumberGenerator.py @@ -0,0 +1,83 @@ +import numpy as np + + +class _RngSingleton(type): + __objs = {} + + def __call__(cls): + if cls not in cls.__objs: + cls.__objs[cls] = super(_RngSingleton, cls).__call__() + return cls.__objs[cls] + + +class RandomNumberGenerator(metaclass=_RngSingleton): + def __init__(self): + """ + This class is implemented using the Singleton design pattern and + therefore enforces the design decision that at most only one + ``RandomNumberGenerator`` object, and therefore one ``scipy.stats`` RNG, + can exist at a time. In addition, once that instance has been created, + it will persist through program execution. However, users are allowed + to change the single ``scipy.stats`` RNG managed by that instance as + many times as desired and when desired. + + Any |surmise| code can access the single ``RandomNumberGenerator`` + object permitted by this Singleton class using + + .. code-block:: python + + from ._RandomNumberGenerator import RandomNumberGenerator + + global_rng = RandomNumberGenerator().scipy_stats_RNG + """ + # We do not set a default RNG upon instantiation so that we do not + # implicitly assume responsibility for constructing a correct, default + # scipy.stats RNG. While this puts the responsiblity of determining how + # to do this on the user, it ensures that this class isn't accidentally + # constructing an RNG that is out of date for the user's scipy + # installation. Rather the user can always provide an RNG that is valid + # for their version of scipy.stats and the rest of the surmise code will + # use it correctly so long as the rest of the scipy.stats interface has + # not changed significantly. + self.__rng = None + + @property + def scipy_stats_RNG(self): + """ + |surmise| internal code should never store the RNG obtained with this for + later use (e.g., in a class's constructor). Rather upon each invocation, + the internal code shall use this member function to access the current RNG + set into |surmise|. + + An exception is raised if the RNG has not yet been set by users. + + Returns + ------- + : + Current global RNG to be used by all |surmise| code with + ``scipy.stats`` for all random number generation + """ + if self.__rng is None: + raise RuntimeError("Please use set_RNG before using surmise") + return self.__rng + + @scipy_stats_RNG.setter + def scipy_stats_RNG(self, rng): + """ + This should **only** be called indirectly by users |via| ``set_RNG`` and + **never** by |surmise| internal code. + + Parameters + ---------- + rng : + ``scipy.stats``-compatible RNG that all |surmise| code should use + for all random number generation + """ + # Check general design assumptions + if not isinstance(rng, np.random.Generator): + raise TypeError("Given RNG cannot be used with scipy.stats") + elif (not hasattr(rng, "choice")) or \ + (not callable(getattr(rng, "choice"))): + raise RuntimeError("Given RNG does not provide the choice function") + + self.__rng = rng diff --git a/src/surmise/__init__.py b/src/surmise/__init__.py index 94b9822e..cca07142 100644 --- a/src/surmise/__init__.py +++ b/src/surmise/__init__.py @@ -13,6 +13,8 @@ __author__ = 'Matthew Plumlee, Özge Sürer, Stefan M. Wild, Moses Y-H. Chan' __credits__ = 'Northwestern University, Argonne National Laboratory' +from .set_RNG import set_RNG + f_dir = os.path.dirname(os.path.realpath(__file__)) __calibrationmethods__ = [f for f in os.listdir(f_dir + '/calibrationmethods') if '.py' in f and '__' not in f] diff --git a/src/surmise/set_RNG.py b/src/surmise/set_RNG.py new file mode 100644 index 00000000..b6fbbe40 --- /dev/null +++ b/src/surmise/set_RNG.py @@ -0,0 +1,16 @@ +from ._RandomNumberGenerator import RandomNumberGenerator + + +def set_RNG(scipy_stats_rng): + """ + Prior to using any |surmise| functionality, users should call this function + to provide |surmise| with a single pseudo-random number generator for use + with their version of ``scipy``. + + Parameters + ---------- + scipy_stats_rng : + RNG that all |surmise| code uses to sample random numbers with + ``scipy.stats`` + """ + RandomNumberGenerator().scipy_stats_RNG = scipy_stats_rng From fdebb7ab67cf6766b8883cd88d1aa339d00ce4d1 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 18:28:44 -0500 Subject: [PATCH 46/84] (Issue #188) Some cleaning of RNG docs. --- docs/random_number_generation.rst | 39 ++++++++++--------- docs/rng_dev_guide.rst | 63 ++++++++++++++----------------- 2 files changed, 51 insertions(+), 51 deletions(-) diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst index e2a17922..a64b3220 100644 --- a/docs/random_number_generation.rst +++ b/docs/random_number_generation.rst @@ -1,3 +1,5 @@ +.. _rng_user_guide: + Random Number Generation ======================== Following typical practices, we refer to pseudorandom number generation and @@ -6,15 +8,15 @@ generators (RNGs). |surmise| code uses exclusively the ``scipy.stats`` code to sample all random numbers and for performing typical statistical computations. At any point in -time the code uses only a single user-provided ``scipy.stats`` RNG to sample -random numbers. Therefore, before calling |surmise| code, users must provide -|surmise| with an RNG that is valid for their version of ``scipy`` as well as -correctly created and managed for their application. Note that where possible -all |surmise| code should reproduce the same results when the same task is run -with an identical RNG setup. +time the code uses only a single user-provided ``scipy.stats``-compatible RNG to +sample random numbers. Therefore, before calling |surmise| code, users must +provide |surmise| with an RNG that is valid for their version of ``scipy`` as +well as correctly created and managed for their application. Note that where +possible all |surmise| code should reproduce the same results when the same task +is run with an identical RNG setup. -The following, which assumes ``scipy`` vX.Y.Z, demonstrates this and shows that -users are free to change the single RNG being used by |surmise|. +The following demonstrates this and shows that users are free to change the +single RNG being used by |surmise|. .. code:: python @@ -33,12 +35,15 @@ users are free to change the single RNG being used by |surmise|. samples_3 = surmise.calibration().calibration_samples assert all(samples_1 == samples_3) -External code offered officially through |surmise|, such as |bilby|, have their -own RNG usage scheme that is independent from the |surmise| scheme. In -particular, the RNG provided to |surmise| is never used explicitly by external -code. Instead, users are responsible for understanding the external code's RNG -scheme within the context of the application's needs and providing additional -RNG configuration information to |surmise| code that uses the external code. - -Please refer to the RNG examples in the Jupyter book for more examples of using -RNGs with |surmise| including the RNG configuration of external code. +.. + External code offered officially through |surmise|, such as |bilby|, have + their own RNG usage scheme that is independent from the |surmise| scheme. + In particular, the RNG provided to |surmise| is never used explicitly by + external code. Instead, users are responsible for understanding the + external code's RNG scheme within the context of the application's needs and + providing additional RNG configuration information to |surmise| code that + uses the external code. + +.. + Please refer to the RNG examples in the Jupyter book for more examples of + using RNGs with |surmise| including the RNG configuration of external code. diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index 58a39a51..1901f912 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -4,7 +4,7 @@ Following typical practices, we refer to pseudorandom number generation and generators more generically as random number generation and random number generators (RNGs). -Please familiarize yourself with the RNG content in the User Guide before +Please familiarize yourself with the RNG content in :ref:`rng_user_guide` before reading this section. Similarly, reviewing the historic record of |surmise| RNG requirements might be helpful to motivate the design and explain certain design decisions detailed here. @@ -14,17 +14,13 @@ Design .. _`generator documentation`: https://numpy.org/doc/stable/reference/random/index.html#quick-start .. _`statistically independent RNGs`: https://numpy.org/doc/stable/reference/random/parallel.html#parallel-random-number-generation -__TODO__: Set scipy minimum allowable version to v1.15.0 in setup.py and add -comment to indicate that this was done to insist on using the new scipy.stats -interface. - Design discussions originally considered two possible schemes for providing |surmise| code access to user-provided RNGs: -1. **Manual propagation scheme** - all |surmise| code elements that use RNGs -accept and require an RNG argument, which they use directly. -2. **Single "global" RNG scheme** - users set into |surmise| a single RNG object -that all |surmise| code elements that use RNGs access directly for direct use. +#. **Manual propagation scheme** - all |surmise| code elements that use RNGs + accept and require an RNG argument, which they use directly. +#. **Single "global" RNG scheme** - users set into |surmise| a single RNG object + that all |surmise| code elements that use RNGs access directly for direct use. .. #### Manual propagation scheme @@ -53,21 +49,21 @@ maintainance by using only one, large statistics package with RNG capabilities, this design stipulates that all sampling of random numbers in |surmise| occur using either the -* `scipy.stats` package (using improved interface introduced at v1.15.0) or -* `scipy.stats` RNG currently in use (|eg| using the RNG's `choice` method). +* ``scipy.stats`` package (preferrably using improved interface introduced at + v1.15.0) or +* ``scipy.stats`` RNG currently in use (|eg| using the RNG's ``choice`` method). -In particular, no other packages, such as `numpy.random`, should be used in +In particular, no other packages, such as ``numpy.random``, should be used in |surmise| even if the current RNG is compatible with that package. This decision is also motivated by the fact that -* `scipy.stats` is considered to be sufficient for correct statistics-based - modeling and simulation (but not more demanding than that __TODO__: cite - reference), -* the `scipy.stats` RNG can be used with cryptographically-strong seeds - generated with `secrets.randbits` (as suggested by the `generator +* ``scipy.stats`` is considered to be sufficient for correct statistics-based + modeling and simulation (but not more demanding than that), +* the ``scipy.stats`` RNG can be used with cryptographically-strong seeds + generated with ``secrets.randbits`` (as suggested by the `generator documentation`_), and -* the `scipy.stats` RNG supports the creation of sets of - `statistically independent RNGs`_ (e.g., `spawn()`). +* the ``scipy.stats`` RNG supports the creation of sets of + `statistically independent RNGs`_ (|eg| ``spawn()``). .. We will use only `scipy.stats` throughout surmise instead of, for example, @@ -106,7 +102,7 @@ RNG Access Pattern .. _`Singleton pattern`: https://en.wikipedia.org/wiki/Singleton_pattern We enforce the restriction of having at most one single RNG in existence at any -time by designing the dedicated, internal :py:class:`RandomNumberGenerator` +time by designing the dedicated, internal :py:class:`_RandomNumberGenerator` class using the `Singleton pattern`_. In accordance with requirements, |surmise| code must @@ -115,7 +111,7 @@ class using the `Singleton pattern`_. In accordance with requirements, for random number generation, * be designed and implemented where possible so that results are identical when rerun with an identical random number generation scenario, and -* be designed so that all related random number generation (e.g., performing a +* be designed so that all related random number generation (|eg| performing a single calibration and generating a random reordering of ts MCMC samples) are performed such that calling code cannot alter the single RNG during the middle of that computational process. @@ -129,25 +125,24 @@ A typical use of the RNG in a |surmise| routine might be .. code:: python - from .RandomNumberGenerator import RandomNumberGenerator + from ._RandomNumberGenerator import RandomNumberGenerator def my_surmise_code(...): global_rng = RandomNumberGenerator().scipy_start_RNG - - scipy.stats.normal.rvs(..., rand_state=global_rng) + scipy.stats.normal.rvs(..., random_state=global_rng) To keep this class in the private package interface, the public interface includes the :py:func:`set_RNG` function, which accepts from the user the RNG -object to be used and sets it into the Singleton object. See the User Guide for -more information regarding the RNG public interface. +object to be used and sets it into the Singleton object. See +:ref:`rng_user_guide` for more information regarding the RNG public interface. External Packages ^^^^^^^^^^^^^^^^^ -|surmise| is designed so that external packages, including user-provided code, -can be used as part of executing its work. For instance, |surmise| calibrators -can use both |surmise| internal and external |bilby| samplers. Since |surmise| -cannot impose RNG use rules on external code, inclusion of external code must -only be made official if the use of RNGs in the external code are compatible -with |surmise| and allow for users to perform statistically correct studies. -Users are responsible for determining if RNG use in their user-provided code is -valid. +We plan to extend |surmise| so that external packages, including user-provided +code, can be used as part of executing its work. For instance, |surmise| +calibrators will hopefully use both |surmise| internal and external |bilby| +samplers. Since |surmise| cannot impose RNG use rules on external code, +inclusion of external code must only be made official if the use of RNGs in the +external code are compatible with |surmise| and allow for users to perform +statistically correct studies. Users are responsible for determining if RNG use +in their user-provided code is valid. From 04849445dff47365bdc6df1839dc63e6172ac709 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 8 Jul 2026 18:38:07 -0500 Subject: [PATCH 47/84] (Issue #188) Expand RNG section in dev guide. --- docs/rng_dev_guide.rst | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index 1901f912..fec84a24 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -1,5 +1,7 @@ Random Number Generation ======================== +.. _scientific Python RNG specification: https://scientific-python.org/specs/spec-0007/ + Following typical practices, we refer to pseudorandom number generation and generators more generically as random number generation and random number generators (RNGs). @@ -9,6 +11,9 @@ reading this section. Similarly, reviewing the historic record of |surmise| RNG requirements might be helpful to motivate the design and explain certain design decisions detailed here. +Our design and implementation should be informed by and, where possible, follow the +advice and decisions reported in the `scientific Python RNG specification`_. + Design ------ .. _`generator documentation`: https://numpy.org/doc/stable/reference/random/index.html#quick-start @@ -42,6 +47,10 @@ Since it was decided that the |surmise| design and use cases are consistent with users providing a single RNG for use by all |surmise| code, we adopted the latter access scheme. +|surmise| is effectively an application, and, therefore, we do not believe that +this decision is contrary to the scientific Python RNG specification, which is +geared toward libraries. + High-level ^^^^^^^^^^ To adhere to the |surmise| RNG requirements related to easing implementation and @@ -65,6 +74,11 @@ decision is also motivated by the fact that * the ``scipy.stats`` RNG supports the creation of sets of `statistically independent RNGs`_ (|eg| ``spawn()``). +Note that, for simplicity's sake, code that accepts an RNG will be constrained to +only accept ``scipy.stats`` RNG objects rather than, as suggested by the scientific +Python RNG specification, any object that is or could be used to construct such +an RNG object. + .. We will use only `scipy.stats` throughout surmise instead of, for example, `numpy.random` since the former not only provides tools for drawing from a @@ -136,6 +150,27 @@ includes the :py:func:`set_RNG` function, which accepts from the user the RNG object to be used and sets it into the Singleton object. See :ref:`rng_user_guide` for more information regarding the RNG public interface. +We recognize that in |surmise| the emulators and calibrators play a prominent, +application-specific role and are, therefore, in the public interface. However, +the MCMC samplers are general statistical tools that are hidden behind the +calibrators. To decouple the samplers from |surmise| design decisions and +enable their implementations to be more generic and widely useful, we exempt +them from having to access the global RNG |via| the |surmise| RNG design, and +instead require that the calibrators + +* access the global |surmise| RNG as specified, +* determine the correct RNG usage for the larger calibration process including + sampling, and +* pass to its sampler the necessary ``scipy.stats``-compatible RNG for its + exclusive use directly and with ``scipy.stats`` for random number generation + during its current invocation and only for that invocation. + +Note that this design decision does effectively treat samplers as libraries so +that this part of the design does follow the suggestions of the scientific +Python RNG specification. However, rather than name the RNG arguments ``rng`` +as suggested, we prefer to name them ``scipy_stats_rng`` so that the code +explicitly reflects our design decision to use only one package. + External Packages ^^^^^^^^^^^^^^^^^ We plan to extend |surmise| so that external packages, including user-provided From 22b79acb274d12ce0e7442392ef154161ab7061e Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 9 Jul 2026 06:54:36 -0500 Subject: [PATCH 48/84] (Issue #159) Revert docstring consolidation. These changes would make comparing diffs unnecessarily hard for the PR review. --- src/surmise/utilitiesmethods/LMC.py | 67 ++++++++++++++------------- src/surmise/utilitiesmethods/PTLMC.py | 5 +- 2 files changed, 39 insertions(+), 33 deletions(-) diff --git a/src/surmise/utilitiesmethods/LMC.py b/src/surmise/utilitiesmethods/LMC.py index 4cc126c5..5a0e3860 100644 --- a/src/surmise/utilitiesmethods/LMC.py +++ b/src/surmise/utilitiesmethods/LMC.py @@ -2,48 +2,51 @@ import scipy.stats as sps import scipy.optimize as spo +r''' +Metropolis-adjusted Langevin algorithm or Langevin Monte Carlo (LMC). -def sampler(logpost_func, - draw_func, - scipy_stats_rng, - numsamp=2000, - theta0=None): - r''' - Metropolis-adjusted Langevin algorithm or Langevin Monte Carlo (LMC). +The LMC sampler is available through calling the `calibrator` object with an +optional argument `args={'sampler': 'LMC'}`. LMC is a Markov chain Monte +Carlo method that seeks to propose the next iterates by leveraging gradient +information at the current iterate. The proposal has the form + +.. math:: - The LMC sampler is available through calling the `calibrator` object with an - optional argument `args={'sampler': 'LMC'}`. LMC is a Markov chain Monte - Carlo method that seeks to propose the next iterates by leveraging gradient - information at the current iterate. The proposal has the form + \theta^{k+1} = \theta^k - \nabla g(\theta^k) \Delta t + + \sqrt{2\Delta t} Z, - .. math:: +where :math:`\Delta t` is a time stepsize, and :math:`Z` is an independently +and identically drawn sample from the standard Gaussian normal of the +appropriate dimension. The proposal is then accepted or rejected by the +typical Metropolis-Hastings step, i.e. accept with probability - \theta^{k+1} = \theta^k - \nabla g(\theta^k) \Delta t + - \sqrt{2\Delta t} Z, +.. math:: - where :math:`\Delta t` is a time stepsize, and :math:`Z` is an independently - and identically drawn sample from the standard Gaussian normal of the - appropriate dimension. The proposal is then accepted or rejected by the - typical Metropolis-Hastings step, i.e. accept with probability + \alpha = \min\left\{1, \frac{\pi(\tilde{\theta}^{k+1})q(\theta^k \mid + \tilde{\theta}^{k+1})}{\pi(\theta^{k})q(\tilde{\theta}^{k+1} \mid + \theta^k)}\right\}, - .. math:: +where :math:`\pi(\cdot)` is the posterior distribution, :math:`q(\cdot \mid +\cdot)` is the proposal distribution, and :math:`\theta^k, +\tilde{\theta}^{k+1}` are the current and the proposed point respectively. - \alpha = \min\left\{1, \frac{\pi(\tilde{\theta}^{k+1})q(\theta^k \mid - \tilde{\theta}^{k+1})}{\pi(\theta^{k})q(\tilde{\theta}^{k+1} \mid - \theta^k)}\right\}, +Langevin Monte Carlo has shown strengths in increasing the acceptance rate, +compared to the typical Metropolis-Hastings algorithm (Roberts and +Rosenthal, 1998). However, its significant drawback lies in its poor +scaling due to the computation for the gradient at the current iterate. - where :math:`\pi(\cdot)` is the posterior distribution, :math:`q(\cdot \mid - \cdot)` is the proposal distribution, and :math:`\theta^k, - \tilde{\theta}^{k+1}` are the current and the proposed point respectively. +Refer to G. O. Roberts and J. S. Rosenthal. Optimal scaling of discrete +approximations to langevin diffusions. *Journal of the Royal Statistical +Society: Series B (Statistical Methodology)*, 60(1):255-268, 1998. +''' - Langevin Monte Carlo has shown strengths in increasing the acceptance rate, - compared to the typical Metropolis-Hastings algorithm (Roberts and - Rosenthal, 1998). However, its significant drawback lies in its poor - scaling due to the computation for the gradient at the current iterate. - Refer to G. O. Roberts and J. S. Rosenthal. Optimal scaling of discrete - approximations to langevin diffusions. *Journal of the Royal Statistical - Society: Series B (Statistical Methodology)*, 60(1):255-268, 1998. +def sampler(logpost_func, + draw_func, + scipy_stats_rng, + numsamp=2000, + theta0=None): + ''' Parameters ---------- diff --git a/src/surmise/utilitiesmethods/PTLMC.py b/src/surmise/utilitiesmethods/PTLMC.py index 032f1f20..4a8d4b33 100644 --- a/src/surmise/utilitiesmethods/PTLMC.py +++ b/src/surmise/utilitiesmethods/PTLMC.py @@ -2,6 +2,10 @@ import scipy.stats as sps import scipy.optimize as spo +''' +Parallel-Tempering Ensemble MCMC (uses Langevin Monte Carlo) +''' + def sampler(logpost_func, draw_func, @@ -13,7 +17,6 @@ def sampler(logpost_func, sampperchain=400, maxtemp=30): """ - Parallel-Tempering Ensemble MCMC (uses Langevin Monte Carlo) Parameters ---------- From 2e463770e2bcd3fad2086df7f98b04a1e633844d Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 9 Jul 2026 15:08:42 -0500 Subject: [PATCH 49/84] (Issue #159) Don't pass sampler args to a function build with those args. The ball drop notebook ran through with MH and PTLMC with these changes. All unit tests passing as well. --- src/surmise/calibrationmethods/directbayes.py | 8 ++++---- src/surmise/calibrationmethods/directbayeswoodbury.py | 8 ++++---- src/surmise/calibrationmethods/mlbayeswoodbury.py | 8 ++++---- src/surmise/calibrationmethods/simulationpost.py | 8 ++++---- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/src/surmise/calibrationmethods/directbayes.py b/src/surmise/calibrationmethods/directbayes.py index fd13f8dd..d2971d0c 100755 --- a/src/surmise/calibrationmethods/directbayes.py +++ b/src/surmise/calibrationmethods/directbayes.py @@ -115,10 +115,10 @@ def draw_func(n): else: sampler_name = 'metropolis_hastings' sampler = create_sampler(sampler_name, sampler_args) - theta = sampler(logpost_func=logpostfull, - draw_func=draw_func, - scipy_stats_rng=np.random.default_rng(), - **sampler_args)["theta"] + results = sampler(logpost_func=logpostfull, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng()) + theta = results["theta"] # Update fitinfo dict fitinfo['thetarnd'] = theta diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index 506756b6..a63304d3 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -154,10 +154,10 @@ def draw_func(n): else: sampler_name = 'metropolis_hastings' sampler = create_sampler(sampler_name, sampler_args) - theta = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - scipy_stats_rng=np.random.default_rng(), - **sampler_args)["theta"] + results = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng()) + theta = results["theta"] # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) diff --git a/src/surmise/calibrationmethods/mlbayeswoodbury.py b/src/surmise/calibrationmethods/mlbayeswoodbury.py index d5bdf3c3..08b8b0da 100644 --- a/src/surmise/calibrationmethods/mlbayeswoodbury.py +++ b/src/surmise/calibrationmethods/mlbayeswoodbury.py @@ -192,10 +192,10 @@ def draw_func(n): else: sampler_name = 'metropolis_hastings' sampler = create_sampler(sampler_name, sampler_args) - theta = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - scipy_stats_rng=np.random.default_rng(), - **sampler_args)["theta"] + results = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng()) + theta = results["theta"] # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) diff --git a/src/surmise/calibrationmethods/simulationpost.py b/src/surmise/calibrationmethods/simulationpost.py index 2db564ee..75db60a6 100644 --- a/src/surmise/calibrationmethods/simulationpost.py +++ b/src/surmise/calibrationmethods/simulationpost.py @@ -149,10 +149,10 @@ def draw_func(n): else: sampler_name = 'metropolis_hastings' sampler = create_sampler(sampler_name, sampler_args) - theta = sampler(logpost_func=logpostfull_wgrad, - draw_func=draw_func, - scipy_stats_rng=np.random.default_rng(), - **sampler_args)["theta"] + results = sampler(logpost_func=logpostfull_wgrad, + draw_func=draw_func, + scipy_stats_rng=np.random.default_rng()) + theta = results["theta"] # obtain log-posterior of theta values ladj = logpostfull_wgrad(theta, return_grad=False) From a4c5e1c6ceb5bb74968c9e28272b8142bc83cf9d Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Thu, 9 Jul 2026 16:45:41 -0500 Subject: [PATCH 50/84] move sampler+args specific pairs outside of Test Class, generate test pairs in advance --- src/surmise/tests/test_cal_samplers.py | 70 +++++++++++--------------- 1 file changed, 28 insertions(+), 42 deletions(-) diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index ab81616a..0b0fc7b9 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -5,9 +5,8 @@ from surmise.emulation import emulator from surmise.calibration import calibrator -# TODO: Fix test so that we can include at least PTLMC again. -SAMPLERS_IN_TEST = ['metropolis_hastings'] # 'PTLMC', 'LMC'] - +# TODO: LMC will require 'expertMode' in lmc_option to run +SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] #, 'LMC'] ############################################## # Simple scenarios # @@ -182,7 +181,8 @@ def rnd(n): # lmc_args1 = {'theta0': np.array([[0, 9]]), - 'numsamp': 50} + 'numsamp': 50, + 'expertMode': True} args_dict = {'metropolis_hastings': [mh_args1, mh_args2, mh_args3, mh_args4, mh_args5], 'PTLMC': [ptlmc_args1, ptlmc_args2, ptlmc_args3], @@ -197,25 +197,31 @@ def rnd(n): def does_not_raise(): yield +# Generate all test pairs accordingly upfront +SAMPLER_ARGS_PAIRS = [ + pytest.param(sampler, args, id=f"{sampler}-args{i}") + for sampler in SAMPLERS_IN_TEST + for i, args in enumerate(args_dict[sampler]) +] + + +@pytest.mark.parametrize("sampler,args", SAMPLER_ARGS_PAIRS) +def test_cal_MLcal(sampler, args): + args_tmp = args.copy() + args_tmp['sampler'] = sampler + with does_not_raise(): + assert calibrator(emu=emu_test, + y=y, + x=x, + thetaprior=priorphys_lin, + method='directbayes', + yvar=obsvar, + args=args_tmp) is not None + @pytest.mark.parametrize('sampler', SAMPLERS_IN_TEST) class TestSampler: - @pytest.mark.parametrize( - "args", [mh_args1, mh_args2, mh_args3, mh_args4, mh_args5], - ) - def test_cal_MLcal(self, sampler, args): - args_tmp = args.copy() - args_tmp['sampler'] = sampler - with does_not_raise(): - assert calibrator(emu=emu_test, - y=y, - x=x, - thetaprior=priorphys_lin, - method='directbayes', - yvar=obsvar, - args=args_tmp) is not None - @pytest.mark.parametrize( "input1,input2,input3,input4,input5,expectation", [ @@ -235,13 +241,7 @@ def test_cal_MLcal(self, sampler, args): ], ) def test_cal_emu_fails(self, sampler, input1, input2, input3, input4, input5, expectation): - if sampler == 'metropolis_hastings': - args1 = mh_args1 - elif sampler == 'PTLMC': - args1 = ptlmc_args1 - elif sampler == 'LMC': - args1 = lmc_args1 - args_tmp = args1.copy() + args_tmp = args_dict[sampler][0].copy() with expectation: args_tmp['sampler'] = sampler assert calibrator(emu=input1, @@ -259,14 +259,7 @@ def test_cal_emu_fails(self, sampler, input1, input2, input3, input4, input5, ex ] ) def test_cal_emu(self, sampler, input1, input2, input3, input4, input5): - # Does not mix failures and non-failures - if sampler == 'metropolis_hastings': - args1 = mh_args1 - elif sampler == 'PTLMC': - args1 = ptlmc_args1 - elif sampler == 'LMC': - args1 = lmc_args1 - args_tmp = args1.copy() + args_tmp = args_dict[sampler][0].copy() with does_not_raise(): args_tmp['sampler'] = sampler assert calibrator(emu=input1, @@ -294,14 +287,7 @@ def test_cal_method1(self, sampler, input2, input3, input4, input5, input6, expe args={'sampler': sampler}) is not None def test_repr(self, sampler): - if sampler == 'metropolis_hastings': - args1 = mh_args1 - elif sampler == 'PTLMC': - args1 = ptlmc_args1 - elif sampler == 'LMC': - args1 = lmc_args1 - - args_tmp = args1.copy() + args_tmp = args_dict[sampler][0].copy() args_tmp['sampler'] = sampler cal = calibrator(emu=emu_test, y=y, From e87acf9d8c28785cfa47a55616ab573a5937b197 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Thu, 9 Jul 2026 16:57:44 -0500 Subject: [PATCH 51/84] flake8 --- src/surmise/tests/test_cal_samplers.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index 0b0fc7b9..45e9ecfa 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -6,7 +6,7 @@ from surmise.calibration import calibrator # TODO: LMC will require 'expertMode' in lmc_option to run -SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] #, 'LMC'] +SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] # , 'LMC'] ############################################## # Simple scenarios # @@ -197,6 +197,7 @@ def rnd(n): def does_not_raise(): yield + # Generate all test pairs accordingly upfront SAMPLER_ARGS_PAIRS = [ pytest.param(sampler, args, id=f"{sampler}-args{i}") From 2dbb495e1f153a62d8301e5a5a6957630abd2f1b Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 9 Jul 2026 17:59:41 -0500 Subject: [PATCH 52/84] (Issue #159) Clean test tool as part of PR review. New surmise.create_sampler does work that the test script used to do. --- tools/TestSampler.py | 27 ++++----------------------- tools/create_sampler.py | 35 ++++++++++++++++++++--------------- 2 files changed, 24 insertions(+), 38 deletions(-) diff --git a/tools/TestSampler.py b/tools/TestSampler.py index f7292997..3a955fcd 100644 --- a/tools/TestSampler.py +++ b/tools/TestSampler.py @@ -105,20 +105,8 @@ def __testSampler(self, name, target_distribution, test_setup): mu_true, var_true = target_distribution.moments # ----- MCMC CONFIGURATION - # -- Universal Configuration - # General - n_samples = test_setup["n_samples"] - - # RNG rng_cfg = test_setup["rng"] - # Initial theta - theta_0 = None - if "theta_0" in test_setup: - theta_0 = np.atleast_2d(np.squeeze(test_setup["theta_0"])) - assert theta_0.ndim == 2 - assert theta_0.shape[0] == 1 - # Starting distribution start_distribution = None if "StartDistribution" in test_setup: @@ -127,15 +115,12 @@ def __testSampler(self, name, target_distribution, test_setup): print(f"Start distribution\t{start_name}") start_distribution = create_distribution(start_cfg) - universal_cfg = { - "numsamp": n_samples, - "theta0": theta_0 - } - # -- Create sampler & load sampler-specific configuration sampler_name, run_MCMC, sampler_cfg = create_sampler(test_setup) scipy_stats_rng = create_scipy_stats_rng(rng_cfg) + n_samples = sampler_cfg["numsamp"] + # ------ RUN SAMPLER & CONFIRM REASONABLE RESULTS print() print("Sampling ...\t\t", end="") @@ -163,9 +148,7 @@ def __testSampler(self, name, target_distribution, test_setup): result_1 = run_MCMC( logpost_func=target_distribution.logpdf, draw_func=start_dist_sampler, - scipy_stats_rng=scipy_stats_rng, - **universal_cfg, - **sampler_cfg + scipy_stats_rng=scipy_stats_rng ) self.assertFalse(FNAME_H5.exists()) save_mcmc_results(FNAME_H5, sampler_name, result_1) @@ -323,9 +306,7 @@ def __testSampler(self, name, target_distribution, test_setup): result_2 = run_MCMC( logpost_func=target_distribution.logpdf, draw_func=start_dist_sampler, - scipy_stats_rng=scipy_stats_rng, - **universal_cfg, - **sampler_cfg + scipy_stats_rng=scipy_stats_rng ) print("done") sys.stdout.flush() diff --git a/tools/create_sampler.py b/tools/create_sampler.py index 210fcc45..ac66f6dd 100644 --- a/tools/create_sampler.py +++ b/tools/create_sampler.py @@ -5,38 +5,43 @@ def create_sampler(test_setup): # -- Outputs - sampler_cfg = None + sampler_cfg = {"numsamp": test_setup["n_samples"]} + + theta_0 = None + if "theta_0" in test_setup: + theta_0 = np.atleast_2d(np.squeeze(test_setup["theta_0"])) + assert theta_0.ndim == 2 + assert theta_0.shape[0] == 1 + sampler_cfg["theta0"] = theta_0 sampler_name = test_setup["Sampler"]["Name"] if sampler_name.lower() == "metropolis_hastings": # -- Metropolis-Hastings Sampler # Extract sampler-specific configuration info - sampler_cfg = test_setup["Sampler"] - step_cfg = sampler_cfg["StepDistribution"] + cfg = test_setup["Sampler"] + step_cfg = cfg["StepDistribution"] if "Scale" in step_cfg: step_scale = np.atleast_1d(np.squeeze(step_cfg["Scale"])) assert step_scale.ndim == 1 else: step_scale = None - sampler_cfg = {"stepType": step_cfg["Name"], - "stepParam": step_scale, - "burnSamples": sampler_cfg["n_burn_samples"], - "verbose": sampler_cfg["verbose"]} + sampler_cfg["stepType"] = step_cfg["Name"] + sampler_cfg["stepParam"] = step_scale + sampler_cfg["burnSamples"] = cfg["n_burn_samples"] + sampler_cfg["verbose"] = cfg["verbose"] sampler = surmise.create_sampler(sampler_name, sampler_cfg) elif sampler_name.upper() == "LMC": # -- Langevin MC Sampler - sampler_cfg = {"expertMode": test_setup["Sampler"]["expertMode"]} + sampler_cfg["expertMode"] = test_setup["Sampler"]["expertMode"] sampler = surmise.create_sampler(sampler_name, sampler_cfg) - sampler_cfg = {} + del sampler_cfg["expertMode"] elif sampler_name.upper() == "PTLMC": # -- Parallel-Tempering Langevin MC Sampler - sampler_cfg = { - "numtemps": test_setup["Sampler"]["numtemps"], - "numchain": test_setup["Sampler"]["numchain"], - "sampperchain": test_setup["Sampler"]["sampperchain"], - "maxtemp": test_setup["Sampler"]["maxtemp"] - } + sampler_cfg["numtemps"] = test_setup["Sampler"]["numtemps"] + sampler_cfg["numchain"] = test_setup["Sampler"]["numchain"] + sampler_cfg["sampperchain"] = test_setup["Sampler"]["sampperchain"] + sampler_cfg["maxtemp"] = test_setup["Sampler"]["maxtemp"] sampler = surmise.create_sampler(sampler_name, sampler_cfg) else: raise ValueError(f"Unsupported sampler ({sampler_name})") From e6bfd7f6cf2e791287eb12d3364490cc55cd8fea Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 08:15:18 -0500 Subject: [PATCH 53/84] (Issue #159) Improve altered text to be more precise & informative. --- docs/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/overview.rst b/docs/overview.rst index 2feaa3fd..dc5d3184 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -29,7 +29,7 @@ surmise’s work is categorized into two main routines: * :ref:`emulation`: Carries out Bayesian emulation of computer model output and generates inputs to ``calibration`` -* :ref:`calibration`: Generates estimates of the calibration parameters based on field observations of the real process and an output from ``emulation`` +* :ref:`calibration`: Uses user-specified MCMC sampling to generate estimates of the calibration parameters based on field observations of the real process and on output from ``emulation`` Examples of how to use ``emulation`` and ``calibration`` modules can be found in the ``examples/`` directory. From e4599261cc3df41fd1280086a5b862f33733e144 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 08:54:13 -0500 Subject: [PATCH 54/84] (Issue #159) Allow for specifying sampler with direct Bayes-Woodbury --- src/surmise/calibrationmethods/directbayeswoodbury.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index a63304d3..34bda384 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -150,7 +150,8 @@ def draw_func(n): # obtain theta draws from posterior distribution if 'sampler' in sampler_args: - raise RuntimeError("Test suite never specifies sampler") + sampler_name = sampler_args['sampler'] + del sampler_args['sampler'] else: sampler_name = 'metropolis_hastings' sampler = create_sampler(sampler_name, sampler_args) From 47e1dd45447016c1c430528870615b9a6b169371 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 12:20:45 -0500 Subject: [PATCH 55/84] (Issue #188) Calibrators now use surmise's global RNG. Ran the ball drop notebook to see it fail with correct error message since a scipy.stats RNG was not being set into surmise yet. Passing once I added in the set_RNG call. The pytest tests are failing because they haven't set the RNG either. I'll let Moses do that. --- book/notebooks/BallDrop.ipynb | 56 ++++++++++--------- src/surmise/calibrationmethods/directbayes.py | 5 +- .../calibrationmethods/directbayeswoodbury.py | 4 +- .../calibrationmethods/mlbayeswoodbury.py | 5 +- .../calibrationmethods/simulationpost.py | 4 +- 5 files changed, 43 insertions(+), 31 deletions(-) diff --git a/book/notebooks/BallDrop.ipynb b/book/notebooks/BallDrop.ipynb index d35a282d..dc51167c 100644 --- a/book/notebooks/BallDrop.ipynb +++ b/book/notebooks/BallDrop.ipynb @@ -57,11 +57,13 @@ "import numpy as np\n", "import scipy.stats as sps\n", "\n", + "import surmise\n", "from surmise.emulation import emulator\n", "from surmise.calibration import calibrator\n", "\n", "\n", - "RNG = np.random.default_rng(39761829560096686505152613678763426794)\n", + "SCIPY_STATS_RNG = np.random.default_rng(39761829560096686505152613678763426794)\n", + "surmise.set_RNG(SCIPY_STATS_RNG)\n", "\n", "EMULATOR_METHOD = \"PCGP\"\n", "N_EMU_THETA_SAMPLES = 50\n", @@ -459,7 +461,7 @@ "outputs": [ { "data": { - "image/png": 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", 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eqPiKz1J4KxA+TyiMhAPz3/72NxXEoAUsFrhSNCpEhStavVUCLWkIbtDtpLemoUkbeRB//etfK5RHj2WdkOuBkw6ucnE/qiui5glqauD10D0YmluSTIlYN7RE4j395z//qU7CqAGh12PA5xNdB9i/AwcOlHXr1qkuApxgwwM1M/R9i4Aa3Qy4sMHrIxDHsQitDLpY18GK7cBz4FjSs2dPdaz44Ycf5F//+pfKmdKnD4h1H0RbH4qOwYUD4cocXxh8oRBgoNAOAgYEFDhxDBgwwPBxyA3ACRcnRaN8BJS4Npq7AyeB8CsGnIzuuusudbLCiQ9fTnyBzT4PThIo/oUv76RJk9QXHMEBrkJxgIjl9YzKf6MZtGvXrup50VeLAwNO+nqwEgmKJ6GJHUWpAC0g2BZ0OeC91btyAK+5YsUKue++++S2226Thg0bqoMfngNdJqEHIKN1jLVseeg6If8EgRIOmtg+XB0alXqPtA+wTh9//LF6P/H+6UWoEKjgcxHu//7v/1SLAoInFBtCvgkO7kZ5G6GwjijOhvkq8BMO644EZLQGIXjAZxM/OpwYcOLAfTgJhJ6oYlknBIJvvfWW+o6g2w0nO2w7+uLRP3/CCScE3w+sp9G8EUblvyMtjytco9tR3A23hyZOV3Xd0OyP20NzMfA3As0ZM2aoYBNBMU6Q6A7Fdwzb8PLLL6sCe+gqxOsiyA5twYq13Hn4vsVFALYTuV+46EFJ+vCchVjXwYrtwOcb7yM+O3oRLQSe+NyY3QfR1oei48RlHoKET5yMUOGzb9++qV4dsjGUbsYVIa5G9QNtqtlxnYjIGHMuPARNzbi60IcdEhERJQK7RVwOXQjIpkauBWpJPP/885bODUFERBSOwYXLodAOmpLRB4qkPxSWIapMtFyEVLHjOhGRMeZcEBERkaXYPk5ERESWYnBBRERElmJwQURERJbyZEInJqxBMZRocnJygqVi3Yrb6A7cj+7BfekOOS49f6BgWviMyhGXFQ9CYIFptCPBHAv6ckbzd7gBt9EduB/dg/vSHbywH2PBbhEiIiKyFIMLIiIishSDCyIiIrIUgwsiIiKyFIMLIiIishSDCyIiIrIUgwsiIiKyFIMLIiIishSDCyIiIrIUgwsiIiKyFIMLIiIishSDCyIiIrKUJycus1JgyQKRokKRjJri7zso1atDRESUcgwuqkhb+obInp0iWdkiMQQXDEaIiMjtGFzYOBhhIEJERE7E4MJFrSJERER2wODCRdjSQUREdsDgwkXY0kFERHbA4MLDrRx70/0SKA6Ir88FqV4dIiJyEQYXHhVYukD27d4pUi9b0hhcEBGRhVhEi4iIiCzFlguKCZNFiYgoVgwuKCZMFiUiolixW4SIiIgsxZYLshy7UIiIvI3BBVmOXShERN7GbhEiIiJyZ8vFjz/+KCUlJeVua9CggfoJFQgE5LfffhNN06R58+bi9zM+IiIishPbBBf333+/1KlTR/3ozjzzTDnrrLOC/2/atEmmTZsmhw8fFp/PJ2lpaXLzzTdL69atU7TWVFXMzyAich/bBBdw8cUXS69evQzvQ4vFww8/LG3btpW//vWvKrh47LHH5KGHHpJHHnlEBRrJpm3bInLwQOk/Bw+o/32NmiR9PZyM+RlERO5jqz6FvXv3ys8//yz79u2rcN+6detk8+bNcuGFF6rAAvD3tm3b5Lvvvkv6ugZWLJPAhNEih4pKbzhUpP4PrFhuKhghIiJyG1u1XMyZM0dycnIkLy9POnToIKNHjw7mXGzYsEGqVaum8ix0TZo0kczMTPnll1/kuOOOq/B8R44cUT86BCVYXv87Ev2+SMsgKNCef0xE08Lu0ER7/lGR9p0rtGAEPlwmAdynP6YsGPGPGiP+XucYvoYUlQUi+J2fV3mriL66vujbV+FhsSxr5rkTtaxJle1HN+A2ugf3pTt4YT86Kri49NJLpXfv3ipBEy0YyMGYPn26TJo0Sd1fUFAgtWvXrvA45GjgPiPz58+XuXPnBv9HbsbUqVNVABOLxo0bG96+5515st/nF9HKJ6AqPr/U/OojyRp1Q/CmI5s3ydYIwUjgucekYc/eUq3J70FTwdKFsnv6ZBEtUHpDUZGU3DFa6v91gtTqMyDi+m7xpwnWKM2fJrm5uVG3zcyyiXxus+uxf/5LEig8IP6ataTO4MukKvvRTbiN7sF96Q6NPXDccURwEZq4WbduXbnkkkvk3nvvlR07dqjWi/T09HKtEDokd+I+I4MHD5b+/fsH/9cjye3bt0txcXHEdcFy+GBs3bpVjUoJV7Jx/e8n/nCaJgUb18vBvLzfl58/q/QKveJTqdu3vf6ypF04svTh27ZIyfRJBoFIQHZNnyh7c5oYtmCoxx0o7U7C7y1frYra0lESKAn+RktRZcwsn6hloXjeiyJls7kW9Dg76rKV7Uc34Da6B/elO7h5P6anp8d8cW6b4CIcAgzYtWtXcEjqgQMH5NChQ1KjRg11H4KN/fv3VxiuqkM3Cn6MxLLTsYzhctk5+ARVDAAAQUR2TvnH7dhmvKx6kdL79eUDHy6J8tw+db9/SGkgEpr/Ua6bRrV0XCu+kehyOTum7ax8od9/V7p8opaNZ/lo+9FFuI3uwX3pDpoHjju2T+gsKipLigzx9ddfqxEgyKuALl26qIhw1apVwWW+/PJLNYoE9yWTr1efKMGCJr5T+5S/rUHD0oDB8MnK7tftyK8kEMk3lf+h5VdMGmViKRERJZItWi6+/fZbWbJkiRqGmpWVJWvXrpVFixap0SB6nkX9+vXlvPPOk6efflp1hSA348UXX5Q+ffpIw4YhJ+ckQHcDWgVU8mboSd3nU7f7GjapEIxoi1+PLRjRA5FIrSKhgQgevmJp1JYO7cOl4gtp6TBq5UBiaaytHE6robE33S+B4oD4+lyQ6tUhIvIMWwQX3bp1U4mZ7777ruzcuVN1c4wfP146d+5cbrkRI0aopL+PPvpINTch+Ojbt29K1hknYq19JwlMHFs6HLVGhvjvfKRCYGE2GDEViJhs6ai0laN9J8P1d6rA0gWyryw/I43BBRGRt4IL6Nixo/qJBq0VCCZSFVCEUyfizFqlwUVmragn5liDEbOtImZaOsy2cgQfx2JhRETktJwLrwgGIxAlGEEg4p88UwUgCgKRyTMNuy1M5X+YzOeIt1gYERF5G4MLhwciektHhYRRo5YOM4mlcSaLEhERMbhwgVhbOsyOcgl2oxgp60ZxU4lzJIAGFs4qnUyNiIjixuDCQy0dplo54uhGcXoXCiZR0xa9UjqZGhERxY3BhVdbOTLKWjkyIudzmOlGYRcKERHpGFx4UMz5HCa6UeLpQiEiIndicEERmepGiWMkipPzM4iIKDIGFxRVzMNiTY5EcXp+BhERRcbggqxJFjXTheKCIa4cWUJEFBmDC0p6F4ob8jM4soSIKDIGF5T8LpQ48jPUXczRICJyBAYXlPyRKCbzM4A5GkREzsHggpLOdKVQF+RoEBF5CYMLSjqzlULdkKPBBFAi8hLbTLlO3hLrFPRW1tBAUJMqqqT4np0iWdkifQelbD2IiJKBwUUV+fpcIFJUKJJR05o94sX8DAQXUSqFBnM0jAKMCDU0ynWjFJXW0ECriGGZcyIishSDiyrym7wKZTASX46Gtvh1a2potO8UOYghIiJLMLiwcTDCQKR8jgaCg3JBQ7QaGoatHKX5Gb4hI6u0D4mIKDoGFy5qFXGzmHM04qyhQURE1mFw4SJub+mIKUfDZH6GHRNAMbJE348MMInIiRhcuAhPRObyMyImgB5KbQIoR5YQkdMxuPAof59BUjvdLwXFAfFqfgYwAZSIyHoMLjzcylE3N1cK8/JEi5Sj4PT8jElj1TBUycgQ/wTjGhpMACUish4rdJJ35zgBJoASEVmOLRfk7WTROBNAiYgoMgYX5Olk0XgSQO00soSIyI7YLUKeZnYSNbtN/Y5hq3tffqp0+CoRkU2w5YLE610osRbosuPIksDSBbJv906RetmShvediMgGGFyQ5ZzYhRJLgS6OLCEiig27RYhixZElREQxYXBBZHZkiRGTpcWJiNyMwQWlPD/DN2BYaZ6GA0aWRJ4ULXJpcTslgBIRJQNzLiilnJSfwdLiRESxYcsFkcmRJf7JM9WIEgUjSybPNJzgLJgAasTnE+3DpUl/7zFkNbBwFoeuElFCseWCKBFTv9s0AZQzrhJRMjC4IEdxVA0NlhYnIo9icEGO4qgcDZYWJyKPYs4FUYI4vbQ4EVG8GFwQ2SABtNLS4vmsjUFEzsFuEXItf59BUjvdLwXFgZSuB0uLE5HXMLggV+dn1M3NlcK8PNEijdqwCxuOLFEzrZYlzzop14WIUs+WwcULL7wgv/76q1x++eXSvHnzcvd98MEH8vnnn6uTRdeuXeWMM84QX6RaAkROYcORJRy2SkSuyblYtmyZrFq1Sr7++ms5cKBsPoYys2fPlmeeeUY6deokXbp0UUHIiy++mLJ1JUplaXEiIruyVXDx22+/yWuvvSZXX311hfv27NkjCxculCuuuELOPfdc6du3r1x11VXy1ltvyc6dO1OyvkSpGlkCnBCNiOzKNsHF4cOH5eGHH5aRI0dKgwYNKty/evVqCQQC0q1bt+BtJ510kuoS+eabb5K8tuRGqZ5EzUxp8eCw1aKyYatFHLZKRPZhm5yL5557Tlq3bi09e/aUrVu3Vrg/Pz9fatasKRkZZQdeEalevbrUrl1btm/fbvicR44cUT86BCKZmZnBvyPR73NzLge3saK0foNNvIG//47pcxLj8r5GTSVQs6y0eM1a4m/UtMIylQ1blfadVUtIldfb7DYmiBc+q17ZTm6jd9giuPj4449V68MDDzwQcZni4mIVTISrUaOGus/I/PnzZe7cucH/EbxMnTpVcnJyYlqvxo0bi9txG+OzxZ8mJQhI/GmSm5tr6fKVLbvnnXmy3+cX0bBUGJ9fan71kWSNuiHh65FsXvisemU7uY3uZ4vg4p133pG0tDTVLaJ3kQCSNU888US56KKLpFatWlJQUFDhsfv371f3GRk8eLD079+/QtSMlo5IAYm+HD78aEGx/RDGOHEbq6YkUBL8nZeXZ+nylS1bsnG9iBahdoemScHG9XIwwmtYuR5GQ1e1g4Xiy7R26KoXPqte2U5uo7Olp6fHfHFui+Di0ksvLRc47N69W9asWSM9evRQo0KgVatWqotj8+bN0rRpaVPxtm3b5ODBg9KyZUvD561WrZr6MRLLlxfLuPVLruM2xvvG/f47ps+ImeUrWzY7J/qw1eycyK9h5XoY1cXYs1O0rOyE5K144bPqle3kNrqfLYKLdu3alftfz7lo37696sqAjh07SqNGjeSNN96Q6667Tt22YMECyc7ODgYgRF4Qz4RoRESeCy5igW6TsWPHqryMMWPGqOa1wsJCueWWW1RTDZFXpn7Xh62q5M3QK9wow1YjDV2NmPhJRFQFtjwr16tXT26//fYK1Tnbtm0rM2bMkJ9//lk1q+H/SN0eRImU6nLYGJ6qte8kgUljS4ejZmSIf8IjEQMLDF0tN8KkbMZVBCNGQ12JiFwXXGAEyAknnGB4H1opOnToIHZhdv4FztdAlk+IhuAis1bUFouoM6627xTxsURErgkunMTs/Aucr4GSTVuxNEoCqE+0D5eKb8hI7hgisgyDCxtjKwdZgjOuElGSMbiwMbOtHAxG7CuVCaCccZWIko3BhYuwy8W+UpkAyqGrROTZicuIKDE44yoRJRuDC49CF8rel58q7Uoh14trxlVMnhYybDWwYnmS15qInIrdIh4VWLpA9u3eKVIvW9JSNMU4pWjo6qHIQ1c5bJWIrMCWC4oJWjgCC2expcMrw1aNlA1bJSKqDFsuKCZMFvXIyBIbDlslIudhcEFkMyktLd6gYfQZV3F/KvKD0v0SKA4kZLZVIrIeu0WIqNyw1cgtF6mZcVXlB836l/pNRM7A4IIsx/wMbw1bjTTjKhF5F4MLSkh+hrboldI8DXL1sFXg0FUiCsfggogiD1uFqsy4ms8WDCIvYnBBKcUuFGfj0FUiMsLRIpRSHOJaNf4+g6R2ul8KigOSEhy6SkQGGFxUgVESGxLirFqeKJZhq3Vzc6UwL0+0SKM8PDZ0lYhSj90icTKbxMakN3IjWw5dZTVZopRjcBEHs0ls8SS9cWifMZ447MWOM65ytBJR6jG4SEISm9nl42nl8EowwhOH/XDGVSIKx+AiGUlsJpaPp5WDXS7khKGrHLZK5B0MLqqSxGbEKInNxPKmW0VYZ4AcgsNWibyDwUUSkthMLW+yVSSeA7ZXulCoIkz85RswLDUTgHHYKpFnMLhIQhKbqeXNtoqYPGAHu1CKyvI5iirP53AqNZvmy0+p3/T70FX/wOGpmXnV7GebiLxV52LXrl3yxRdfyJo1a2THjh1y4MABqVWrluTk5EinTp3kxBNPlPr164vbk9i09p0kMHFsaeIl5l+485GIZZJjXR6tHNri12Mf2meizkClXSjtO8Wc3e+E+hxqNs3dO0XqZUsap+pOOdOfbSLyRnDx448/yoIFC2TVqlUSCAQkMzNT6tatq37v3r1bNmzYIO+//76kpaVJ165dZfDgwdKuXTtxfRIbgoUo8y+YWV5v5cDJvlwQEKlVxMQBO9iFYhiIlHah+IaMrNDSUS4gKRu5gnWJNJEVkeHn3+Rnm4g8EFw8+uijsmLFChUsjBo1Srp06SJNmzYVX0gzJyoEbt68WVavXi0ffPCBjB8/Xnr16iVjxoxJ1PqL11tFTB2wzeZzxNnSQWTFZzsZrWaqy6yoUCSjZmq6ioi8Hlyg6+O+++6TNm3aRFwGgUazZs3Uz7nnnis///yzvPrqq1atq6eYaRWJ+YBtslRzPC0dRFZ9tiu0mhVZ32rGuW2IUpzQ+fe//z1qYGGkbdu2cvvtt8ezXpSAOgOmSzXHkd3v5JEorP5pHxxiTeRsHC3iIaZLNZvM7nd6MS9W/7TPsFXWxCByNs6K6jHBLpRJY0uHo2ZkiH9ChHwOM8mizM9wnZTmILAmBpF3gwvkVPz3v/+V/Px8KSwsrHD/lClTqvL0lOgulKLofd5mkkXjyc9w6hBXSgJO5U7kzW6RJUuWqDyMlStXyr59+6RatWoVfshDk1LFW8zLoV0o5L2p3IkoCS0X8+bNk0GDBsnQoUMlPZ29K+L17P4kFPOyEw5hTCzWxCDyaMsFukEQXDCwILNXmm5I1mPyZxJbzTLKWs0yIrSaJXG0EkcUESU4uDj66KNl27Zt8T6cvDwSJc5kPScPc6XEDbFOZlcbg0qiBAcXV1xxhcyePVt+++23eJ+CvJqfEccEVszRsL+UDl2trKstn4EoUTLFnSyB0t+YOwQlvuvUqSP16tWrsMykSZOqun7kMDHNn2JyAis35Gh4QSqHrrKaLJFLWi4+/vhjmThxovobE5cRJaqYlxtyNCjBWBeDyB0tFy+//LL88Y9/lEsuuYTDTimxE1i54MTB0SUJxroYRO5oudizZ48MGTLE0sCipKRE5XCgKBemdDeCmVexDH4iLUMuS9aLI0fDbsmfTARMLNbFIHJJywVGiyAIaNWqVZVXAkHC3LlzVWGuunXrqqJcGRkZcs0116ip3XUIKKZNm6ZmaMUMrFhm3LhxlqxDvFTyWtmUzYlYnsznaCRjNk2yF9bFIHJJy8VVV10lr732mmzZUvUrwsOHD6tA4fHHH5d//OMf8tRTT8nxxx8vDz30ULB1Ar9xX8uWLeWf//yn+mnXrp1aBi0eqUxi8w8cHnMym9nlyVyOBkcNeFfMo5WIyL4tFwgAioqK5MYbb5TGjRtLVlaWak0Idffdd8f0XAgsBg4cGPzf7/erFoulS5fKoUOHVMLoDz/8IJs3b1avh/vh4osvlrFjx8r3338vxx57rLgNWznM52hw1IC3P9sxVZNN0tw2zLMhL4s7uDhy5IikpaWp1gMoLi6u8sqgKNfevXtlx44dqlVkwIABwZEo69evV/kdLVq0CC7fpEkTdf+GDRtcGVyYbd1wezAS04nDBcmfXpDqlrsKXWeHrO86Q56N7NkpkpUtwpZK8pi4g4tEzHiKSdA+/fRTlcuRk5Mjp512WvC+goICqV27doXHoMYG7osUAOFHh5YVPVgJb2UJpd8XbRk7Sus3OK7HxbSd+iK+GJZP1LKxLN+gUSVznDQyfJy6ii0qu4rF7/y86FexVq932BWvdrBQfJk1Kz0JO/WzGi8r3uvKus6kfefI+z6Bn20v7Etuo3fYasYxFOXCD3IoMNQV3SqPPfaYCiowh0looBCar4EWFCPz589XiaK61q1by9SpU1XgEgt097jVFn+aIFMlzZ8mubm5li6fqGVjWf7I4OGyNWLyp0ijIZdKtbDHFSxdKLunTxbRykYfFRVJyR2jpf5fJ0itPgOSst7lll2+SAI788Wf3VByR46WWPCzGvt7veedebLf5xfRDHK1fH6p+dVHkjXqhqrvR5OfES/sSx230f1sFVzoECwgB+PNN9+UdevWyR/+8Adp0KCBGiWCYKJ69epqOQQb+/fvjxgsIFDp379/hah5+/btUbtxsBw+/Fu3blVDX91IO3uAHJXul4LigOTl5VW6fEmgJPi7suUTtWxMy/uriX/UXyTw3KPlr0x9PnX7Dl+6SMjjcBVbMn2SwVVsQHZNnyh7c5oYXsVavt5xLuuFz6rV73XJxvW/B5LhNE0KNq6XgxFeJ5GfbS/sS26js+EiP9aL85iDiwcffFBNrx6a81CZTZs2yZw5c+Tmm2+OuhySNmvUqFHuNnSN6N0ecMwxx6jfX3zxhfTo0UP9/eWXX6pRJKHDVUMhRyNSHY5YvrxYxq1fcjS3183NlcK8vNi2UV9Ei+G9S9SyMS7v63m2+Nt1ksCksaoVQs2mOaE0+TP8MYEPl0TpRvGp+/1DRiZlveN+bpd/VkNZ8l5n50TvOsvOifw6Cd6PXtmX3Eb3M9Vyccstt6gTOXIh8ButCeGQjLl69Wp5//335bvvvpNu3bpV+rwIEt577z3p2bOn1K9fXw1vXbBggRx33HHSvn17tUx2drace+658vTTT6vWC4wYefHFF+Xss8+Whg0rFlEiazktWTSY/FlUyagBJoB6jtm6KUSUwOACrQ8Y8ok8hpkzZ6rI86ijjlITlmEo6cGDB2X37t2qmwJNXyeccILcc8890rFjx0qfGy0RyKtAQIIuCwxrRVnxXr16lUtuGjlypOq7xHJ4fXSdIOAg92f326lsdKKHMFJiseAWkc1aLjp37qx+0GWxatUqWbt2rWqpwGiNmjVrquGgCCa6du1qujUBLSGRujd0aK1AMMGAgmxT/TMBQxipPH+fQVK7LD8oJXPbJLEuxt50vwSKAymZtp4o5QmdCBzOO+889UPklatYO0797oUTkun8oAQU3EpGUBlYukD27d4pUi9b0ly6L8k7bDlahJzNafkZTq7+yRNS4tkxqCSyOwYXZDkn5mew+idFYsegksi1E5cReU4cU7+TC3BEEZFpbLmglHJSF0q8Qxg5usR7I4qIvI4tF5TaD6CDpqA3M/V7aCIgEv9ULkdIImBgxfIkrTVZEVRGngyPdTGIjDC4IDKZ/OmfPFMlfSpI/pw803DEQKWJgPlbkv7eY3RJYOGs0unAKWFBJZHXxR1cjBo1qkr3EzlVMPkTogxhDCYCGj5JaSJgsmEacG3RK6XTgbu4q803YJilQ3PNBJVEVIWci8LCwoj3YSx6tPuJvJCjwUTA1EhUF5uZuhjJKLilfw+c0KVI3pOQhM7Nmzerip1EVnPUgZSJgJ6UjIJbquVpz06RrGwRJ30nyDNMBRfjx4+P+r8+DTqmF9ZnMSXyqnhGl3BkibOx4BZRHDkX+hTm+jTmof/rP5jIrF+/fnL99debeWoi8XoiIEeWOJ8d82yIbN9ycffddwf/RvAQ+j8RVaG0OEtMuwMLbhFVLedixowZ8T6UyLGzaSYqEdCOJaaZNBgH5tkQKazQSa6VqNk0vXLFy6TB5FVxJXKbuIOLQCAgy5Ytk+XLl8u2bdsMh57OmTOnqutH5A284nVVng2KpJULFllwizwm7uBi9uzZ8sYbb0inTp3k1FNPlczMTGvXjMhDeMXrnropsebZhOIoIXKbuIOLd999V6699lo566yzrF0jIg+K54qXJyR3FNyqUBejyNq6GMydIUcFF0VFRXLKKadYuzZEHq7+aeaKN9EnJEqOZIwSYu4MOWpukc6dO6tKnERuYYcZWmOZt8SOE6JRfFgXg9wq7uDiqquukgULFjDAIEoynpBcxIajhIiS2i1iVDALXSM33XSTNG7cWLKyssQXVpmORbaIvHNCYt9+HDhKiLweXGDOkHBpaWnSrl079XdxcbG1a0ZEjjohsW/fPI4SIvF6cDFlypTErgmRg6Qy+TPeExJHl9gP62KQW7FCJ1EcUpr0Gcew1WRMA+4FCa2LMWmsGvUjGRninxC9LgaR3TG4IHIgMyckTormoLoYRZXXxWALFLk6uLjyyiujP3F6ukryPOaYY+SPf/yjZGdnx/tSRFSFE5IdJ0Wj+CSjBYqJuZTSoajdunWTVq1aqUTPNm3ayAknnKB+Hz58WN3epUsXqVmzprzzzjtyyy23yJYtHHtPlBI2HV1C5iSrvgkSc7VFr5Qm6BIlu+XizDPPlEWLFskTTzwhtWvXDt5eUFCgbuvbt6906NBBdu3aJQ888ICai2TcuHHxvhwRuWh0Ca+OzWMLFHmi5eK5556T4cOHlwssAP9fdtll8vzzz6v/69evL1dccYV8//33VV9bIgcnAvoGDCtNCEz2a/fqE6XlIjXTgPPqOA5sgSIvtFxs2rRJjjrqKMP7cPvGjRuD/7do0UJ1lxB5ldNGlzBp0IZs2AJFZHnLBZI1V6xYYXjfBx98oO7Xbd++XXJzc+N9KSKqIiT7+SfPVJOhKZgUbfJMwyRAJA0iSVBNnhaSNBhYsZz7IYXs2AJFZHnLBXIq0DWCFoquXbtK3bp1Ze/evbJq1So1HfuwYcPKBRu9evWK96WIKEnTgHPYqn2x4BZ5IrgYOHCgHDx4UCV1Llu2LHh7tWrV5IILLlD36zCS5Oijj6762hJRQjFp0CEFtyaOLQ0S0QJ1JwtukYuCC0xShtYJ1LBYt26dGiWCZE6MEKlTp06F6dmJyAGYNOicgltRWqB0zJ0hx1boRCBx0kknWbM2RJTSeUuYNOgeLPlOqcTy30Q2k9KRJXFOipbomhh70/0SKA6kZCivEyUrd4b1SqjKwcXf/vY39fv+++8v9380+rJE5Ax2TBoMLF0g+3bvFKmXLWkMLmyVO6OqeO7ZKZKVLZLCoJgcHFyE51GE/09E7hBP0iD79m2GuTPklOBi/PjxUf8nIvcwkzTIvn0bYsEtcmoRLZ2mabJ161b56aefrFkjInKMZE2mReaw4BY5OqETxbFmzZolO3fuVP/PmTMnmGuB+UWaNWsW83Pl5+er4luYPRXlw0899VTD2hgrV66Uzz//XAU1KN512mmnVWUTiKgKWBfDOv4+g6R2ul8KigOuzJ0hb4m75eKTTz6RGTNmqBoW1113Xbn7Tj75ZHnrrbdifq5vvvlGpkyZompndO/eXTIzM+Xuu+8uV5wLXn31VfnnP/8pbdu2VYHHM888Iy+99FK8m0DkihPSUcOvVr9Tgn37lo4SqnvpNZaNFjJT8p3INi0X8+fPlxEjRqgiWoBp1nUdO3YMtmLEok2bNvLQQw9JWlqa+r9nz55SUlIi8+bNk3POOUfdhtLiCxYskNGjR8vpp58eTCp9/PHH5fzzz1ezrxJ58oSUmyuFeXmqNS/p2LfvmtwZYGIupbzl4tdff5UzzjjD8L7s7GzZs2dPzM+Fyp56YKHDxGdFRWUTJ4nIt99+K4FAQLp16xa8DX+jtQMtH0SUfHbs20fthcDCWaU1GCj2940T1pEdWi7S09PVyR+BAeAkr9uxY4fUrBl/dcFDhw7J0qVL1ZwkoTkZeE50mehq1KihXh/3GTly5Ij60WEd9ceHrm84/b5oyzgdt9Gj+1FfzBfDY2JY1te4qcioMRJ4rmLfvn/UGPE3amp8dVx0oPQf/M7PUzkCMa1+DNupLXtDpKwuhq/fYHHlvrR4P1aWmCvtOxvvIzPrEVYQLZXF4hLJC8fWhAYXyHt4++23VddIuMWLF6s5RuKB1gl0dRw+fFhGjvy9yEtxcbFUr169wvIIMHBfpK6buXPnBv9v3bq1TJ06VXJycmJal8aNG4vbcRu9tR+3+NOkRETS/GmSm5trzbIXjZAjPXvLtr9cKlpRofgyakqjx16Wak2aV1i0YOlC2T19sohWlrRYVCQld4yW+n+dILX6DKjyOsezvBP3pdX7cc8782S/zy+iYckwPr/U/OojyRp1Q5XWQy2/fJHs25kvadkNJXfkaHGzxh44fyQkuBg0aJBKwsQw1B49eqjbPvvsMzWa46OPPpJ77rknrsACuRs//PCDSuhE14iuVq1aanK0cPv374/YSjJ48GDp379/8H89kty+fXvEgERfDh8MbFtK+rGTgNvozf1YEigJ/s7Ly7NsWfGli4ZWwaJC9XuHL10k7DG4Oi6ZPsng6jggu6ZPlL05TQyvjk2tRxzLO3FfWr0fSzau/z3gC6dpUrBxvRw0eKxX9o0Zbj62pqenx3xxHndwcdxxx8n1118vzz77rAoqYNq0aepEf8MNN5huuUBgMXPmTJVbcdddd1WIglu2bKm6ODBUtUmT0gMQukMw7XurVq0MnxPTv+PHSCw7Hcu47cMRjtvorf3oO+f3SdEqXV6/W4vt+1LZ8oEPl0QtSY37/ZWUpLZiPVyxL81sYyzLZudE2Tel9xs+tgrvtRP3jRmaB84fCatzgVEbGDq6du1alcCJ+hSdOnUqlxcRC+yAJ598UiVmIrDQg4dQeF5ETAsXLpRrr71W3YbRI0ge7dKlS1U2g8gzUtrPzWGrtmXHCevI47OiZmRklEu8jLcYFwpotWjRQl544YVy940dO1a9BkaT3HjjjfLAAw+o32h62rdvn9x8882qqYaIbI7DVi2jZocta4Gy5PlYdIssZouzMgpx3XbbbYb3hXZrtGvXTiV76qXG8b9RkicR2Q+vju3dAhXPhHVElgQXZicrQ8JnLBo0aKB+YoFgAsEIETmLHa+OVS2MshYAtw6NTGTRLSJLggt9DpFQu3btYnVMIkrI1XGiK0ZqS98Q2bNTJCtbhMGFufcuwfuGPBRcIOky3NChQw1vJyKqytVxhanci4okMGG0auXg/BipVWHfHOK+IRvmXBCR+5MGLZ3KvX0nNtmnSLL2DbusnI3BBREZSmUOAqdyt69k7Rt2WXl04jIiooRhTQz74r6hGDC4ICL71sQw4iu7n1KD+4as7hYpKSkxdXv4NOpERLFgTQwbF9xiNU+yOrj405/+ZOr2OXPmmHl6IiLb1sRwKqtzZ7hvyPLgomfPnmYWJyKqek2MSWPVMFTJyBD/hOgVIxNde4EjGML2Dat5khXBBeb5ICJKek2MosorRiaj9gJHMMRfzZNFt7yFQ1GJyBKsi0GRsCCa9zC4ICJLsC4GGWHRLW/iUFQicj7WXrB/0S0jZUW3LHmdpW+ItuiV0q4rSjkGF0TkfKy9YF8M/DyJwQUROR5qLxiWowZNE9+pfZK9SqRj4OdJDC6IyPH02gsVmt9ZF8P8e9nnAvENGFaaoGvFvmHg50lM6CQiT9ZeYE2MCO8ji26RBRhcEJFrhq3GWnuBNTHsXxCNnI3BBRF5a9jqti3li20F79BUuXGcBHnSS21BNHI+5lwQkacka2gkxc+oy4qchcEFEXkLh0baGrqsULZddW2FlHEPrFie6lUjE9gtQkTeHBppNHTVV3Y/pQSreboHWy6IyFM4NNK+WM3TPRhcEJGnsCaGjbHLyjUYXBCRJ4dG+ifPVLUwFNTEmDzTsqnZKU6s5ukazLkgIlvz9xkktdP9UlAcSElNjGQW3dqb7pdAccCy6phOq1eCLitt8evGd7KMu6MwuCAi29fEqJubK4V5eaJFmj8kwZJRdCuwdIHs271TpF62pDkkuGA1T4qE3SJERFUZwZDPGgxWYpeVOzC4ICKKgkW3ki/YZQVxdllRajG4ICKKhiMYbI1Ft+yJwQURUTQcwWBb7LKyLwYXRERRsOiWt7usMIonsHCW+k2xY3BBRBQFi255u8tKW/qGaIteUb8pdhyKSkSuYnXtBX0EA6ZiD0wcW1oXA0W37nwkYqJhomtiUBnOE2NbDC6IyFWsrr1gtuhWUmpioIm+LIBK1PY6oSAai27ZF4MLIiKHzeqpmuj37BTJyhZxUnBhcUE0vcsK722599znU7db8V5TfJhzQURkEdbESD4W3bInBhdERFZhTQxHFN2ixGNwQURkFdbEsD1W80wOBhdERBZhTQx7YzVPDwcX+fn5smnTpkqX2bZtW9LWiYgoFqyJYV/JquaJkTx7X37K80W3bDNaZOXKlbJ48WLZsGGDVK9eXZ5++ukKy2zZskUeeugh2bFjh/h8PsnKypJx48ZJs2bNUrLORORsdqiJQUlOtjUapVJWzdM3ZGSVXyewdIHs271TpF62pOHz5VG2ablYu3atDBs2TP0YCQQCKrBo2LChCjzw07x5c3nwwQfVfURE8QyN9A8cbnmtCM7qaUNMtvVmcHHFFVdI586dI97/448/qu6SoUOHSlpamvj9frn44otVa8b333+f1HUlIrI8D6CoqPSGotKiW4EVy615fs6NUYrJtt7sFqnM+vXrpVq1atKqVavgbWi5yMzMVF0pXbp0Sen6ERHZseiWUwtuWd1lxWqeyeWY4GL//v1Su3btCrfjNtxn5MiRI+pHhzwNBCP635Ho90Vbxum4je7A/ZjsN/z335UeH2JYFq0W0fMAlon/wpEJXw87fl7T+g02+eS//zZ6fl/jpiKjxkjguYrVPP2jxoi/UdO4njfqKvncew5xTXCRnp5eLlDQFRcXq24SI/Pnz5e5c+cG/2/durVMnTpVcnJyYnrNxo0bi9txG92B+zE5tvjTpAQnPn+a5ObmVnnZnQf2SWGUKtiZB/ZJtsFjrV4PN3xeY9rOi0bIkZ69ZdtfLhWtqFB8GTWl0WMvS7Umzav2vFVY3q0cE1xkZ2fLgQMH5PDhw2o0iR5YoNUC9xkZPHiw9O/fv0IUuX37dvXYSLAcPvxbt261pP69HXEb3YH7MblKAiXB33l5eVVetqTWUaVXxhEOMwdrHWX4WKvXww2f15i305cuGlqwiwrV7x2+dJEoy5t9/+z0fifiIj/Wi3PHBBfHHHOM+v3VV19J9+7d1d9ff/21ChIi5VsgRwM/RmL5YGMZtwYXOm6jO3A/JuuN/v13pceGGJb19TpHtMXzIjxeE9+p5xg/1uL1cMXnNQHviarmWXig9J/CAxLYulnVMol5lTR7vN+pYJvgAoWxCgsLZdeuXWpo6S+//KJub9KkiWqpaNCggZxzzjlqCCrux2iRZ599Vnr37u2JJmEich/O6mlfyIcpl2x7qHQUD2ZbRS0Tckhw8eabb8qaNWvU3wgkZsyYof5GkSw9eMBw1UaNGsnbb7+tIsJ+/fqV6/YgInJs0a1JY0uHo2ZkiH8Ci265fRSPPkxY/zxZXWsl1WwTXCBwqAwSNwcOHKh+iIiSLVEngGDRLQQXlczqaTTxlpmmerJPNU/NocOEHVVEi4iIUj/xFotusZqnq1ouiIgo9U31Tr2atrTLSq/madhyUXY/RcWWCyIiJzXVGylrqvcyK+eJQTVPw8AiOIqnT5Vfw+0YXBAROQEn3kr6KJ4KwZzPp27nDLeVY3BBROQEnHgrqTCKxz95pkiNjNIbamSo/zkMNTYMLoiIHIBN9Sl4z/VRPFDJKJ5II3m8isEFEVGCEgx9A4aVJhpa8XxsqnfGSB4MJ4Yi60fyOAlHixAROaQmRrDg1sSxpcNR0VR/JwtueWUkj5Ow5YKIyEHMNNWzmd5dI3kCSxZIYOGs0sqeNsfggojIhZJRcIuSO5JHW/qGaIteKa1FYnPsFiEichnOjZFkLLpVAYMLIiKX4dwYyZ2ADiN5tMWvR9gZmieLbjG4ICJyGxbcSmqyrT6SB8mb5bpHfN4tusWcCyIit2HBrdQV3cooK7qV4e2iWwwuiIhchgW3nFF0y83YLUJEZAP+PoOkdrpfCooDVX4uNtPbn2ZQzRP7zS0YXBAR2SQPoG5urhTm5YkWaVijmedjwS1bDxPWQotulQ0TRn6GW7pR2C1CRORSnBvDgcOE890xHwmDCyIiYtGtJNE8Us2T3SJERB6XzKJbe9P9EigOWDahm+PsSF41T9mzUyQrWyQB89xUhi0XREQel7Sr6aULZN+sf6nfntWgYZT3uux+F2DLBRGR17HoVkSs5hkfBhdERF7HuTEiYjXP+LBbhIjI41h0K0XVPGuUVfOs4b5qnmy5ICLyelM958ZI3TDhQ0WurObJ4IKIyONN9eo5WXTL1jSHVfRktwgRESmcG8O+FT0DE0aXtnKEVPQMrFgudsXggoiILLmSJutpDq3oyeCCiIhcfyXtVFqSapBYjcEFERG5/krasXYkp6Kn1ZjQSURE5q+kjU54ZVfSviEjPfuO+vsMktrpfikoDni6BgmDCyIicv2VdDJH8tTNzZXCvDzRIr1PJmuQaItfN75T08R3ah+xI3aLEBFR7DwyN4Zd+MpqkFR4z30+dbtd62Ow5YKIyMU4N4bz+R1Yg4TBBRGRi3FuDHfwOayiJ7tFiIjI3InDA3NjOJlmgxokDC6IiCjh1TztcMLzgoBNapAwuCAiouSc8IrKTnhFLLrl9hokDC6IiMgTJzy302xUzZPBBREReeKE53o77FODhKNFiIjIEyc8tw8TFhtV83RccPHZZ5/J559/riqfde3aVXr06JHqVSIiIgec8Fw/TLiXfap5Oqpb5PXXX5fHHntMcnNzpVmzZjJz5kx55ZVXUr1aRESuupr2DRhWelVtxfP16hOl5cK+5audyGejap6OabnYt2+fzJ07V66++mo588wz1W1ZWVkqwOjXr5/Uq1cv1atIROR4LLrlbH6bVPN0TMvF6tWrpaSkRE4++eTgbd27d1fdI998801K142IiGIoupVRVnQrg0W37FSDxNMtF9u2bZOaNWuqH11GRobUqVNH3WfkyJEj6kfn8/kkMzMz+Hck+n3RlnE6bqM7cD+6hyP3pb6qvsrX29eoqQRwwkOti8xa4m/UVNwoofvR9/vvSp/fzLJeDi4QJFSvXr3C7QgwQgOIUPPnz1ddKbrWrVvL1KlTJScnJ6bXbNy4sbgdt9EduB/dw0n7cos/TUpEJM2fpnLhrF7eyRonYD+aef9S/V47JrhAi8WBA2WlY0Ps379fatUqa/4JM3jwYOnfv3/wfz162759uxQXF0d8LSyHD8bWrVtVt4sbcRvdgfvRPZy4L0sCJcHfeXl5li/vRIncjyUm3r9EvNfp6ekxX5w7Jrho2bKlHD58WO0wPSJEkFBYWCgtWrQwfEy1atXUj5FYdjqWccqXPF7cRnfgfnQPR+1LfTW12I6p5R7qlG20037Ufv9d6XNXYd94KqGzU6dO0qBBA1m0aFHwNvyNUSJdunRJ6boRERGRA1su0BwzZswYmTZtmvz444+q6Sk/P1/GjRtnmItBRETkJj6rK3omkGOCC+jYsaPMmDFD1q1bp5p5OnToEBz9QURE5GZ+iyt6JpKjggt9dMjxxx+f6tUgIvI8J11JU3I5LrggIiJ7cNKVNCWXYxI6iYiIyBkYXBAREZGlGFwQERGRpRhcEBERkaUYXBAREZGlGFwQERGRpTgUlYiIyGV8Ka5BwuCCiIiSwt9nkNRO90tBcYDvuMtrkDC4ICKipJ3w6ubmSmFenutnRfU65lwQERGRpRhcEBERkaUYXBAREZGlGFwQERGRpRhcEBERkaUYXBAREZGlGFwQERERgwsiIiKyL7ZcEBERkaUYXBAREZGlGFwQERGRpRhcEBERkaU8OXFZenq6pcs5GbfRHbgf3YP70h3SXXj+MLNNPo1T0xEREZGF2C1i4ODBg3Lbbbep327FbXQH7kf34L50By/sx1gwuDCAxpwNGzao327FbXQH7kf34L50By/sx1gwuCAiIiJLMbggIiIiSzG4MFCtWjW56KKL1G+34ja6A/eje3BfuoMX9mMsOFqEiIiILMWWCyIiIrIUgwsiIiKyFIMLIiIispT76pPG4JtvvpH58+fLjh07pHHjxjJ06FBp37695Y9JpfXr18urr74qW7ZskezsbBk4cKB07do14vKffvqpPPPMMxVuf+ihh6RmzZpiR7/88ossWbJEvvrqK+nZs6dcdtlllT5m69atMmvWLDUO/aijjpK+ffvKGWecIXaF9V22bJl88skn0rZtWxk7dmzU5ffs2SN/+9vfKtx+7bXXygknnCB2EwgE1Gfv3Xfflby8PKlfv77aH/jx+XwRH1dUVCSvvPKKfP311+L3+6Vbt24qic6uJZdx/Fi6dKn8+uuvUqdOHTn55JPl3HPPjbq+d955p+Tn55e77ayzzlLHHjvauXOnLFq0SL777jv1f+vWrWXIkCHqeBnN22+/Le+9954UFhbK0UcfLcOHD1efAzsqKCiQN998U33uDh06JM2bN5cLLrhA2rRpE/ExL730knz44YflbsPjxo8fL25mz29iAv3www9y3333ycUXXywnnXSSOqhNnDhRpk6dKk2aNLHsMam0bds2ueeee9QB+tJLL5Uvv/xSpk2bJhMmTJDOnTtHPFgXFxerbQqVmZkpdvTzzz/Lk08+Keecc44KFPClrwwOXnfddZd07NhRxo0bpwIwPAdOYqeffrrYzb59++Tee+9VJxQcjPbv31/pY0pKSmTXrl1y9913S6NGjYK344RmR/guIThEkIeT0E8//ST/+te/VJA0aNCgiI975JFHZPv27XLNNdfIkSNH5IknnlCPQRBlx8Bi4cKF6rOK/YiAH9uI33/+858jPm737t0qADnttNNs/30EfJcQ5OHzin2CizEESNhXkS5Q8L7MmzdPRo8eLQ0bNpSXX35ZJk2apI5XdgwUcQGGoAn7DUHtO++8o44pWN9IQVRBQYEKPq688srgbXbcNqu5fwvD4APfpUsXFVHD5ZdfrqJQRNw4UFn1mFT6z3/+I/Xq1ZMrrrhC/d+iRQtZs2aNvP766xGDC8BJFq0cToAvOL7Q8NFHH8X0mP/+978qiLrhhhvUMLFWrVqpwAQHNzsGF7Vr15bp06er/TJjxgx1pRSrunXrOmJf9u7dW52MdAjWN23aJMuXL48YXCAo/OKLL2Ty5MnqShdGjhwpDz/8sLqqt9tVL44dxx13XPD/pk2bqsDh+eefVyectLS0iI/FSdkJ+xH+/ve/qxOubsSIEfKXv/xFtTAaHXdwMbNgwQIZPHiw9OjRQ92G5XFM/fjjj+XUU08Vu8GxI3QbcYzFZ3XdunVRW2iqV6/umP1oFc8FF99//71qPg11/PHHq4OVlY9JJQQSoQczQJM4mufQDB365Qh14MABdUUPLVu2VNtsx5YZiLQN0WA/durUqdz4c7wvuPrAVW9WVpY4fRt1//jHP1QrBg545513ni27RCJtI9Y72pUd9iOu4EO7JbF9KLeMz36vXr3E7tuIEytuj9b1o1/Z4yIGJyZ0pfTr169Kn4tECl0vHGdWrFihWsxwcWMEXUS4qg89VuE7iAsH7GM7Bhfh7/3KlSvVZ7WyLvLVq1fLmDFjpFatWuoYdOGFF6q/3cxTwQUmksFP+EkE/6Mp2arH2KHvM3x9cSV7+PBhFUAYNZHjCzJgwAB1AMOBAf2KmHzngQcekNzcXHEDvC84cIW/L4B9abfgIl64CkRzOlo+PvvsM7n//vvl+uuvL9e8blfo0kMLEz6LkWBfYb+FnpgzMjKkRo0atv1OhsIJFQED8oSiBQr4rOIEi5YOdBe9+OKLsnHjRlt2/YR2cyEXBscZfP7QFYvfkb6PYHSssvN+/Pbbb1VLIs4L2H84Tka7CKtbt65qUUPrDbYZOV9o+cb30s2FtjwVXOgTyYQ3Q+J/nFCtekyqYZ3DD1r6+kda51NOOUUd7HSIsm+55RZ54403bH0wM/u+hO9H/QrZrvvSLHQJ3HTTTcH/cdWIA/WcOXNsH1wgxwQH3Hbt2qmm8kiwr4y6Euz8ndQhwEd3Hq5a0ZUTzY033hgMoBBg4ESE/AW0KDZo0EDsGtgee+yx6jOHLg9sK/LVkDwd6dhqdKxC65VddejQQaZMmaJyoJBQjn2CLrpIAcawYcOC+7FZs2Yq7+a6665T3bl27I61ij3b1xIEVzf4guIgFgofEqMPf7yPSTWsV3jyH/7HlzZSU1x48yy+8BidsHnzZnELo/dF36923ZdmGTWzo8kWow6QZGdX2C9I5MNV3q233ho1DwH7Kvz7iG4GJOzaeT8isEDCNLYVV/SVjcIK35d60zsSQe0Kx0t04WBdERyhleaDDz4wXFbfV0bHKrsmIIfmTyBnC4md2A4EGbHux/r166vHu+nYKl4PLnDCRNYukm9CoZ8WV0tWPSbVEBQYrS++DGaylNFE7aZ+QewvjPwJnQp57dq16oSWk5MjboXAAgd9u2ao4wSEwAInWwyjRfdGZfsRJ6DQkyz2o36fXQMLdDHiih4jKPTuODPwfYRI3Qx2g88bgsRIicjI68KFW+ixCssiAdSu+9EIPq9mkq0PHTqkcrycsh/j5angApAQhXH1SLABNE3hwIShcDrkG4Q2LcfyGDvBeqGPVh9bjeQoZF9jO3TYBnR34GoPZs+erfpzceJFkyTeAwQkoZn8ToNAAtv422+/qf+xLTgpoasHzee4HVccffr0qTSxzq7Qh4ttxHBOQE0MDD3Glbw+DBKjh84880xbbqMeWCBBE6MNEAQZwTYiF0MffYHmZQxbxIEan2HUdEGfNpqc7QYtRugeQGCBYYuRcntQ9wDdV4DjCxKNMboJUAMEo0uQh4GLBLvBVThGo+lDwhFMIbcAeQndu3cvVzcHQ1YB+xrD5fF9RP0gfGZxHEJQYsdkTmzLCy+8oAIDwHESNToQDOmjXQD7acqUKepvfD6fe+45NToIkIsyc+ZMtY2h3dBuZM9LmQTChxbj4/FlxwkGkfPVV1+tDlg6HKxCE4pieYydoI4DDsbPPvusPPXUU+o2FNHCsD8dPvTYRv0qHv2kWBYHCXzJcSWPZs3QA4OdYD+g3xLQRI66FzjBYqw8apDoB3Vso36ixX0YDYMaAxh+iufASReZ23aFEy4OTDgoYX2xX3E1iISy0LoWOJgDPpMYFYR+YOxbXFUhOTJazYhUwjA+vaBZeIEw/SQE2EYc3PXWRHSdPPbYY2ooIN4XBBYYJmhHSKpFAh9aAcMLnOEkpA9RxElLD/aRK4MLGtR/0Pczkq1Rt8aOo0Xw3QJclOH7huMLAiEETAgEdfiu6t9HQN4J9jOGoOKEiy4DvEd2vKrHdwm1Y7BN2E/4ziHPAsdJjB7UIcDaUxaAoAsFy9xxxx3Bx2D4NOrQuH1oqmdnRcWXVc9oDv+y4kOAK4bw8fLRHmNHOOjig47m5vAmcXz5cR+2MfSKFrfjf3wp7E7PNg+FE69+ZYjgAgczNEGHbj8+8mjBwNWy3bO1cZAKT27D/tE/m9jHCD7QRx26z/AY7Eu7VlfVIWDQT6jhQg++2Nc4OYe3bOD7iO+inYtL4YQSqQAa6tHoxxLsa+zD8H2G76kdT7aR6Mcco2Mkvo/4/IbnVOA9wufVzrkWofCZxb4y6mrE9peUlFTo+sJnFZ9TJ5w7rODZ4IKIiIgSwxshFBERESUNgwsiIiKyFIMLIiIishSDCyIiIrIUgwsiIiKyFIMLIiIishSDCyIiIrIUgwsiMgXVFFEB1G5QUAzTX6NCqdnHocqiXnqbiKrOc+W/iaiioUOHxvS2XHLJJbZ9+959913ZunWrKrVsBiomXnzxxWrOh7PPPtv1ZZmJkoEVOomoAsz3gInC/v3vf9t6GnMdCg1jbpJjjjlGTYNtFlovrrnmGjn99NNlxIgRCVlHIi9htwgROd63336rZg5FcBAPtF706tVLtX5gThoiqhp2ixCR6ZyLtm3blpvF9Morr5Ru3bqpae0xxfSmTZvUbJBXXXWVmgUSU4i/+OKLanrqBg0ayOWXXy5/+MMfKjz3ihUr5K233lKPh3bt2qmuGMz0G82qVavURFLt27evcN/nn3+upvX+7bffgjOOYqbYk046qdxymFEWr71u3TrbznhM5BRsuSAiS+zYsUPefPNNGTNmjDzxxBOSm5sr9913n/zwww+ycOFCuf7661VeAwKThx56KDgtte61116Txx9/XLp37y6PPvqomlId03ZPnDhRnfCjQfDSqlUrNStuqPXr18uDDz4oxx57rEyfPl29/vDhw1WXT/hsrAhkYM2aNfxEEFURgwsissRPP/0k1157rTRu3FjlaSB3AdNM/+Mf/5DrrrtOtWTg9ssuu0x1PXz44YfBx27btk3mzZsn5513nlxwwQVqKvKsrCzVwtGmTRuZPXt2pYENljcKOpBP0b9/f/XamLK9Q4cOcuutt1aY2hxTZGM68O3bt/MTQVRFDC6IyBI4aYeesNH9UaNGDWnevLnUrl07eHv9+vUlMzNTBRS6L7/8UgUBp5xySoXnRRcFWi5KSkoivjZaIfCc4Vq2bKl+oxVk9erVcvjw4YjPgcACwUd4iwYRmcecCyKyhFHLAU7WRrcjEECrhk7vIpkwYUJw9IdO//vgwYPlgpRQCGpwfziMHrnhhhtk0aJFqnslPT1dBUHnn3++yhEJhdcpKiqSWrVqmdhqIjLC4IKILIErfzO3h9KHu06bNk21dJiVk5NTIYdDhxEk+Nm3b5/qJlm8eLF6ndtvv11OOOGE4HJ79+5VAQaei4iqht0iRJRyJ554ogpCVq5cGdfjMZoEI1GidZ0ggEGyqD7K5fvvv6+QM6I/FxFVDYMLIko5jCxBlcwFCxaoxM78/HyVH4HhoxgeiqJe0WBYK5b/8ccfy93++uuvy6xZs2TDhg2qywOtF2i50LtMQiEno06dOqrbhIiqht0iRGQLF110kapB8fbbb6scieLiYmnUqJEcf/zx6r5okPSJAOX9998v1/LQt29fWb58uQpOtmzZomphIMkTc5DgeXVIJkWNjd69e0u1atUSup1EXsDy30TkCqiu+eyzz6paGWiBMAPdMaiBgQnZOLcIUdWxW4SIXAFJm6ixgWqcZqDVAgW8ULWTgQWRNdhyQURERJZiywURERFZisEFERERWYrBBREREVmKwQURERFZisEFERERWYrBBREREVmKwQURERFZisEFERERWYrBBREREVmKwQURERFZisEFERERiZX+H/ZPoF1hiaytAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -587,7 +589,7 @@ " loc=0.0,\n", " scale=1.0 / prior_g_vacuum.BETA,\n", " size=n,\n", - " random_state=RNG)\n", + " random_state=SCIPY_STATS_RNG)\n", " return np.reshape(samples, (-1,1))\n", "\n", "prior_g_mean, prior_g_var = sps.gamma.stats(\n", @@ -648,7 +650,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 14, @@ -657,7 +659,7 @@ }, { "data": { - "image/png": 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S8cqWLavv2LGjftGiRXn2u3//flWjQOqmSC0I87RHqZ/yySefqLowUv9BJqlPIbUN5D1LSNqjpExL+qfUjOnbt6/+xIkTKq1RalBYSmk0TSM0R2rO9OnTR9XskPRNQw2L69ev5zl+01RWS+mFhjRMS6mdtshC/JPt27er32BCQoKqMyS/E/kt7ty50+L6Unekbt266vqT+i7ffvutxRTrBQsWqBoqsi+pNyO/62+++SbPtS7Xv6TgFpQ2LUyePFmlaBvSmg3XgS0p1oKk7z700EPqWIOCgtT4IWPE3LlzbTpXq1evVuOFHLvIIXVUrKWXP/LII2pMkBRhqeUiaePm1+DFixdVGQUZSyIjI1UK+p49e9Q5MS0PYbi2N2/eXGDatNRMkfMpn2s6hthaJ0aQmjWGcVH2I6npI0aMUCnP+SH7k1ovcp7bt2+vDw8PV2O1HLP5mGppPNqyZYs6B3IuZFspc7F+/fo8n2Ptd+BpBMgfdytShBSEpP5JdpI8IRiqBhPirYh1RoLNpcIzG7AWjFTgFSsNb1c5FXslzkcsb4TuJOJhiOtK6jeYZjOJSfn5559XwXhiYifE23nooYeUy0diwyQzSeoYEULsh0oM8Sh+/PFH1bdJgv0kUl5iYQy+brHCSLokIb6AxL0wuJyQokElhngUEqQmQWeiuBiyOSTocfTo0WrAL2xKIiGEEN+DMTGEEEII8UpYJ4YQQgghXgmVGEIIIYR4JVRiCCGEEOKVUIkhhBBCiFfiU9lJUsJbaooUhJRlNm8F78lIfZR7771Xza9bt86rixx527k3hbLnj5QwNzTm88Wxw5u/f18/Hh6L9x5LUccNn1JiZBCS/j75YejOKet6S2KW9LXYunWrsXupN8nu7efeAGX3bQoaO3ztHPrS8fBY/PtY6E4ihBBCiFfiU5YYX0W6ifbo0UNptmJ6I4QQQgiVGK9A2qB/8MEHSomReUIIIYTQnUQIIYQQL4WWGC9AgqLS0tKUJcbbg70IIYQQR0ElxgsQBaZatWpq3p3p1ZIZJbLodLpC70O2z8zMhDfiz7JLXJY039RoaLwlhHgOVGKITUj6aWpqKsLDw1VwsSF9zl6CgoIKTIP3VPxVdrH+SZrk1atX1fcv+yKEEE+Aj1XE5if5yMhIdQMrrAJDvBP5vuV7l+9ffgeEEOIpUIkhdrkUiP/C758Q4mlQiSGEEEKIV8KYGEKIR7Br1y4sWLAAhw8fVr2Mhg8fjltuuSXfbXbu3Inp06cjKSkJxYoVw/3334/WrVu7TGZCiHVckU1LJYYQ4jE9wipWrIi2bdvif//7X4HrnzlzBm+//TbuvPNODBkyBDt27MDnn3+O2NhYNGzY0CUyE0Jyo09PRfb8b4F/NyEZemQjAGhwCzTd+iEgNByOhkqMFyBprXfffbcKsGRcAjHQvXt39X/u3LnwBRo1aqQmW1m6dClKliyJhx56SL1OTEzEnj178Ouvv1pVYiRDyzRLS64pSR03zFvD8J6vBLX70vHwWDxMgXlrOHDyuHqdbXhjxUJk7/4H2tHvOlyRKZQSs2TJEvzyyy+4dOkSKlSogIEDB6Jq1aoW1xUz7+zZs5WJWFpy9+/fX92Qi7JPfyM0NBRffvml+mHLPCEE2L9/P+rVq5drWYMGDTB16lSr28yfPz+X0lepUiVMmDABJUqUsOkzExIS4Ev40vHwWNzPhUnjkXLyOBadvIiyYcFoEBvx35snjyP01zmIf3qke5WY9evXKx/04MGDVQE2eeoZN26c6u0TExNj0URcqlQpNGvWDNOmTXPIPn0d3eAu0E5eAG+S1+Z1nSSDN50v4hjkgcd8fJDXhsJ+wcHBebbp1q0bOnfubHxteOKVByyphWMNWU9uLKdOnfKJqtm+dDw8Fs+wwOjmzQBW/IozGdcxYvtRXM3S4aEKJfBa7XLQ3LjOUlYuQsb9A3JtK3XHbH2IcIgSs3DhQrRr1w5t2rRRr0Xx2LJlC1auXImuXbvmWV+sKQaLyvfff++QfRJCSGGQejfWivXZctOQdbzp5uJPx8NjcZP76IepwNrfgeycR9SvD5/Glayc+TRdtlGBUWRkqMrvjnSX2ZViLU8qhw4dymXClXgNeb1v375CCVCYfRqqxxom0wJccnIKmmxdz12TuXxyfGXLlkWZMmWQkpLiNpl8GWnn8Oqrr+LWW29VLob69eujV69e2L59u3r/zz//xCOPPIKbb75Zvd+kSROMGTMmT/G3Z599VlkTT5w4oWI1ZP6mm24yujh2796NHj16KMVeMm/EvWGKuF7lu964cSNGjBiBOnXqoEaNGhg6dKiyPBSEWD4lKLZFixZGOd9880213JQ1a9aoB4RatWopGW+//XaMHz/epnPlKb8RCeC9fPlyrmXyWmJcLFlhCCGOI/vSeWS/NAhYs8SowAjDq5fFqJplUTY0GKNqJsLZ2GWJuXLlitKiZPAwRV4nJycXSoDC7LOofm1P9zkmAShdurTxtSgu7pZdbtbWnmCd5SKyh6KWwh85cqSyCEosligNFy5cwKZNm5SC3bhxYyxatEidg4cffhhxcXHYunUrpkyZgtOnT+Prr7/OpYDL7/nBBx9E06ZNlWL0448/YvTo0YiKisJbb72l0oDvuece5V595plnlOIkcWAG06rw8ssvK9eIKDIHDhxQSpBcDz/99FOe4D/Dscvnivwit3y+KCeiNE2ePFnFpInLVpDgV4lNq127Nl588UV1w5f3//777wLPo6xr+tt0J3J88j2Ysm3bNlSvXt1tMhHiD+jTU6EfMwRIzdvLL0gTgMcrJ2BgxZIINu+1Fhzi8Acer8xOKqxf25t8jidPnjTOi7XJFHfILjEGntw3qKiyLVu2DH369MErr7xiXPb4448b9/3SSy8hOjra+Dm9e/dG+fLlVYrvkSNHlPXEoEikp6er36ik/QpdunRRipAoLJ988gnuvfdetbx58+Zo1aoVZs6cieeff14tM/x+RZmYNWuWUakQK5xYVESZat++vVpm+A0YZBJlSSws8t+0vorc7EV+iT0TS9KKFSvU9zljxgzEx8fbdR5lO9PfpiN923Le5LdtmkIt51baHRQvXly5o0W5fPrpp9X7ch5+++03fPvtt8oVLSnWGzZsUMdKCHEeyoVkQYExJY8CI9zayuGy2OVOkkFcnjTNzdry2tyS4sx9ysAujegMkyFF0tSXmN9k63rumizJZ4q7ZPJl5HcoT/WmN1FTTH9jolTKzVRcNXJu5OZpjihEBsSiUqVKFfVbFYXGgLiU5L2jR4/m2b5v3765rCLimhIlYfny5VaPQSxJorDIfkU+wySuJUGUGMOxCqIAiNJlL876jRw8eFBZnmQSxHIk8+JiE6QA3rlz54zrS3q1KCxifXnhhRfU8YviyRoxhDjXjaRcSCacTr+OC5n5GxCQkAhNj9xBvS63xMggWrlyZTVoG570ZBCU1x07diycAE7YJyH2Iu6e5557TlkqJB5GCq5JHRaDm0diXN5991114zdXuMUlaoqkwUv1WFNEcRA3jLkpVVxM5nEdBhepKREREeqmffx4Tv0FS4hLyFLasYHz58+r/6JIifVHKuKKe+u2227DXXfdpayb8kDhLiT+Z86cOVbff+qppyxu88477zhZMkKIQYHRSxyMGaN3HsWmC9fwUo2y6FWueO5g3tAwoHk7zyl2JwOdmMRF8ZAnPjFvS9CgodT3pEmTlIna8CQq5nHDwCvz8mQoJmIZ6A2xHQXtkxBnIzd2iU1ZvHixcsl89tln+PTTT1U8ibh8JMhXlI0nn3xS/UbFMiNWG1F8zK0Z1hQBa4UKHWXpEjkkUFficCw9LIgSJIjs8+bNw7p165RlZ9WqVarcv7hlRLlhQUVCiNU4GF3uKMhlpy9h6emcB7F39yfjntLxiArKGUO05SsBI94GQv6zZLtdiRE/vjx5yhOTPJFKmfBRo0YZXT9i7jV92hSlxWAeFqSgnUwSVDh27Fib9kmIK5B6RgMGDFCT/I47dOiAjz76SN38JcD3448/xn333WdcX5QdZyFWFYMbyBDcLTEiYiGyhliNpP+QZBqZW3zENWUa7yKKlqwnkyDHKcHxoti0bNnSKcdECPG9OJj6sRHoViYe85MvYEytcjkKjIw/LTsi4emXcPryFaeGJBQqsFfcPNZcPQbFxIDcAPIzEduyT19HYjHkaVgsUaYBy6Y3HLl5se2Ac9DpdEpJMMSKCBJIKpZCCWQ1nHPTC1Hmv/rqK6fJ9N133+GBBx4wxsVIfIhYMvNTYiTjSYJ2Zdt+/frles9QAE7iciS2RDKszN0ygqxDCCEFxcEYKBkShA8bVsIjlUqiXnSOuyjgnW+gjSsOTXgEcDm3u93ReGV2ki8hqbMSj2FAgjzNPf/iepNMErYdcF6NGAnSlXYYYiGU+JM//vgD//zzj3LNiPtIrIOvvfaaio2ROBapKm0plsVRiNVElBhRTCTgVdKxJWbMkJlkCYnhESunaSaSKGiSoi1Br6LcSFn+999/X9W9kQKT0m9IrE6yf4nZKahrNCHED91Ir+aNRzOnfsyNFgMtO0ITmzsm0JlQiXEzkhoqgZVr165VT88Sc4EnlrlbLL9CYkSkbsrq1atVTIzElojSIkGvstygbIpCIzFfISEhKhBW3E7SQdkZSDq1xK1I4TpRaKQw3euvv55vjQWx2H3zzTcqjkfqKEk/Mjk2SQWXKthi6RNEEZI4Ncn6EXevxLBJTRtJ8za1RhFCSLa4kdJyl/kQS7RODwRqzMajiCinZCDlR4Deh/JnpU5MQXUu5CYgT5xS68JTDl3kkI68cnMR+az1TnKn7BKz5IgbnHlshjfhCtlFsRg2bJgKbheriafJnt/vQD6jKHViPHns8MRxoyj40vHwWJyL7qmeQGZ6rmU/J1/AZwdPYVzd8rgpLjJnYWAQAsZ/abTC2HosRR033JdPSYzIly1BpAYFxhypSyIuDak1Yl69lxBCCHEG2WkpeRSYK9d1eH13EnZdTUO3DXux+0qOlcZUgXElVGI8uPuzqfYqgZnmfXoIIYQQZ6GflTd54XzmdRXMK7QvFYNaEszr4jgYU6jEuAnpu7PrSiqyBt2jlBmDQnMqPVOZ6STmQlwKhBBCiDsCerFhRZ7llSJC8UvzWhhbu5yaJJ3a1XEwplCJcQNSA0Qa/HVcuxuPbzmU6719V9Mxfu8JVUpdMk2IfyEZSZIB5ch4GEIIsZfsOVPEHWDxPQnolQaPiWEhQLN2TqnEaytUYtyAZMEYaBB7Iy3tBs2LRSHuRrVDUXY8JbiLEEKIf1hgdDM+Bf74zab1A3o9AndCJcYNSBl7qTnSKSEWrUpE59FwP29cBRs3blR9ehzdtpwQQgixVtQuW3ojmRW2O59xHS9uP4ozGWYZfMGh0ITlfhB3NVRi3IA09xs0aJBSVurcqHAoGOJimhWLQrly5XLFyhBCCCFO742Ukre1wFt7TmBm0jm0Wb0Da86aVOBt6v7+hlRi3IC9iolYY5o1a6Ymd3YZJoQQ4l+9kS5mZmHZmUtqPgABqBV9o5ljeKRbA3oNsGKvFyg6UnVVKrCKMiPz0iSTEEIIcVgm0h9LLb4XFxyIFS3r4O29J1RrgRI30qsDXvvYrQG9BqjEuJgjR44gPOM6Shaw3vG0DCw/fRl/XbyGAZs2sacNIYQQJ2YiZVt9v1hIECbWr/jfAjfWhTGHvgkX88orr6DR8m1o1KgRLmRmWV3vrwspeGVXEn4+eRFr1qxxqYyEEEL8iE123GPKlPcIN5IBWmJczK5du9T/jIwMYyq1JZrE/xfxvWXLFtSrVAGIjMaxY8dcIichhBDfdyNlz5sBZOStBr/k1EW0KxmLIEOTR8mUbdkBmu4DPMKNZICWGBeSlZWFzp07q1owEqSbX/p02dBgjKtTHr80r4nPPvtMWW2k4zDxTMqWLYt3333X3WIQQojtCsxbw4GVv+Z5b9XZy3h0yyF0XLsLmy7cCPad8DW0/Z70KAVGoCXGhQQGBqr6MLrkrdBO/jrfLCVRcB6skNPZMyMoJ5CKEEIIcVg20snjeZbr9HqM3ZWk5vdfS0dSWgZuQaTHxMCYQyWGEAdw8OBBpaQSQohX8Od/leNN0QYE4KOGlTB6xzGEajW4r0y8KmrnqYVXOeoSUkiys7ORmZmJ0NBQNTmK1NRUhId7lsmWEOI76KWdjYU4GAOSSv1T85q4fF2Xo7x4QFE7azAmxsNJSs1Q7QeI85BYFolpOXDgAB577DHUqFEDderUwauvvor09HTjeiVLlsTo0aMxb948tGnTRlVeXrVqldWYmB07dqBfv35qf9WqVUPPnj3x999/51pn9uzZatsNGzZg5MiRqF+/Ppo0aeKiIyeE+G1dmAIQi0x8cCCQkOhR2Ujm0BLjQu644w71xN7gwnG8bmPV3h4b9yJ51dNOl40Ajz/+OBITE5UyIcrG119/rQoLfvTRR8Z11q1bp7qLP/zww4iLi1PrW2Lv3r3o1q0boqKi8MQTTyAoKAjffvstevTooQoXNm7cONf6o0aNQrFixfDcc88pSwwhhDirP5J+TN57iiSPKKXFlLadoenWz+OCeU2hEuMi0tLSsHv3bjUfYNa5Oj+kt1Jy+mU1X7NmTY9sO/DFF1/gyy+/VPMff/wxmjdvbnxPUsLlZi7cddddmDBhQq5tBwwYgO3bt6t5S1aKd955R82/8cYb6NSpk/G9a9euqUaagmR6TZo0qcjHIf2qpkyZYpRLFJBp06Yp5aZ27drG2Jfly5ejevXq+e5L5JZstPnz56NChQpqWffu3dGyZUuMGzcOP/74Y671Y2Nj1fFqtdbT7gkhxCH9kVJTci2/lqVTmUiNYyMwplY5lA4LVsu1vR+Fp+N5d0Qf5cyZMyhTpoyarxQRYvN2XcrE49lnn8WXjStjwYIFqu2ApyEKxalTp9QkMSKm6HQ643uXL+coY6acP3/e+L4lxc/wnsyb+3QN7zkq9VwUF1MGDhyo/q9YscK4rGnTpgUqMHLMq1evRocOHYwKjFCqVCl07doVmzZtwtWrV3Nt06dPHyowhBCnkj3/W4v9kT7YfxKn0q9j0alLeG13TmYSgoJzYmc8HFpiXITczDZv3qxuxtcev9/m7e4tEw/tCy9At+8PBEZGwhOJjIxEQkKCmg8OztHgDciN2fBeTExMnm3FhWJ43xxR2AzvmStvEmxmeC8+Pt4hxyExLubfmVi+kpJuXNQAypcvX+B+RDGT77lKlSp53pPYGAkITk5OVrEy9uyXEEKKxJYNFhfXjg5DseBAZZEZWfOGizwq2mMzkkyhEuNi5GYcbO539HIkGFYmS8jN2dxNZMrUqVOtvvfAAw+oyZrilN9+HYGlC9iRWUiu2C8hhAjZaSnApfOwxH1li6FtyRhsuZiCCuEhOdV5GzaFN0B3kovJr8CdNeSpvvnK7aoJJIM+ncfhw4fzNOsUq4nEytiDWJdEWZX4GXMkA0qsOwbXIiGEuAL93Gn5vh8bFKgUGUXpciqg1xugEuMFiF/yeFomjh8/nivllzgWc6vQN998o/5LOrU9iAtNgo6XLl2ayxV19uxZ/PTTT0oZlaBhQghxGX/mlIMokFYdoRn5jkdnJJniW34ND0aya8QFUuXkRdxdOs6ubcff2SJXA0lLsRak6IjCIcG9rVu3Vq4qqQcjmVVSM8ZeRowYobqPSyBv//79VTVfSbGWwGepNUMIIa5CLwG6mRm5lklrgbigQDxeuRRCtDfsGXHxqj+SN0ElxgVcv35dpSCLa6JedLjdSkxxkxgaSdOmEuMcpNHmxIkTMX78eKV0SC2Yl19+uVD7kqBdSa+WfUn6t3z3jRo1UjVnzGvEEEKI04vb6f/LNNpy8Rq+OXJGzS89fQm/tKgJjcTBNPqvPIa3QCXGBUgasNzEhMQb+ff2UCvqv8wce+MziH2xLIZ6N9bS5EUhtcSJEyfyLKtbty6+++67fD8zv+BlQghxRjzMv5dToQ2QZo9At7LxOQqMxJd4SRyMKVRiXIDUB5HWARLTEj05d7E3W2hW7L/4idtvvx1XrlxxsISEEEJ8lo2542EerlgSTeMjMeXIGQyoUDJnYWAQEOJ5dcgKgoG9LkBqp8hTeceOHXFrPAM6CSGEuAbdxXNAZt6EkFrR4XinfkUEam6UkoiO8Yq6MObQEuMFyA+rWmQoAsqU98ofGSGEEDfFwowZYtvKXlIXxhxaYryAMK0Gy1vWUR2Tw8O9I+3Nm3j++edVTIujKv8SQojHtBlIy+mT9OeFqyqI1yIBAV4ZDyNQiXEB0m7gn5534Fz/ToXuRbHm7BX07NlTBfZKrRFCCCHEljYDGbpsvLT9KAb9fRCP/HUAV6/rcq/XrJ3X1IUxh+4kFzBs2DAcOnQI4VoNdrdvWKh9pOmysXbtWmPVV0IIIcSWNgMLT13EwZScOjEXrmchItDEfqHRQNN7ELwVKjFORiwv0uxPKBsWXKiYFlFg3txzXM3HxsYa07UJIYSQgtKq7ysTj6CAAIzbcxzj6pQ3plQrbmvvtVYYgUqMk5G6Ik899RSS50xDyZCgQitCR1NztGhJ05YUa29okU4IIcT9adUBAQHoUiYedyXEIciQjZTzDgK694c3QyXGBenV4k7S7baxbwUhhBDihLTqoFwKjLQZiIMmLALeDAN7vYzj993mbhEIIYR4aEq1bsanwIiBSM3S4dC1AhoGe2GbAXOoxBBCCCE+oMBkjx8BrFmiXn904BTar92Fd/clI12X7VNp1aZQifEyph49g5deegnLli1ztyiEEEI8qSbMySQ1v/9aGr48fAqZ2Xp8dugUTqZn5t3Ai9OqTaES42RGjx6Nm266CZ3X7caRlAJMezbw9t5kTJ8+HevWrXOIfKRozJ49G+vXr3e3GIQQf2frRmOn6rKhwXiscoLKSHqicgIqRYTmXtfL06pNYWCvk5FKsNLF+hSAUG3hdEaJLJfu11l6PU6l53RRlrozxH3Mnz8fGk3O9ymZYt988w2qVaumGnQSQojLA3kvnjO+Dg/U4sUaZXF/2WLq3pEHL0+rNoVKjJOJiopSXawvnDmD4sFBhW47sL5NPWTr9Zh34gKqjfsYlSpVcrisxHa6dOmCKVOmKEtMaGgo+vfvTwWGEOKe/khjh1p8r2qkmQVGiIiCpscA+ApUYpzMxx9/DN3gLtDpy0JbxOaNUqCoe2IxBN50E+vEeACGwoXyX6vVulscQoi/xsKkXlPz17P1edOoTQmPRMDYj3zGCiMwJsZFFFWBIZ7FggULULx4cQwaNAgvvvgiLly4gD/++MPdYhFC/DEWBsC2yylouWoHFp28aPkhNyQUmglfQRNbDL4ElRgvQNLjJDBYJoupcsTldOvWDffee6/REvPII4/QnUQIcSl6UVZSrkKn12P0jmM4kZ6Jx7cewi8nL+ZducUdPmWBMUAlxguQWJhtl1PVlK7TYf/+/SrFes+ePe4WzeeYM2cO7rjjDlSpUgUdO3bEX3/9hccee0wts8QDDzyA5s29v2AUIcRLXdqZmbiapUN8cE50SPXIUNVeIE8cjA/UhLEEY2KcyJYtWzDzyYdRKjQId5SMRb2YomvBmy+m4JFWrdT8E088gZdfftkBkhJD/NLbb7+Nu+66Cw8//DB27typ/kdHR6Nx48buFo8QQnKhV24jPWKDAjG1SVUsOX0JJUKC8sbFjPnQJ60wApUYJ7Jjxw58n5ST9lY6NNghSkz58BDj/NGjR4u8P5LD9u3bMXHiRDz99NMYOXKkcXlmZiZmzpyJ3r17u1U+QgixmJlkYpXJY4G5ga/FwZhCJcaJnDlzxjhf2A7W5pQOCULXrl1Rvnx5NGrUCJ5Aaup/F5I5UktFUpANpKSkICsry+K6chGGhYXZtF/zdYvKRx99hJIlS2L48OG5lt9yyy1KialZs6bDPosQQhyBftZX8HeoxDiRgQMHot2mxTiXkYUGDrDCCIGaAHz66acelWItRd6s0bZtW8yYMcP4uk6dOlaVk2bNmmHu3LnG17feeqvK+rFEgwYNsGjRIjiCjIwMrFy5Eg899BCCgnIrmwZZa9Wq5ZDPIoQQR5Bx5RJGfv4NBlUqiRpR+TzQBYcay0H4IlRinEh8fDxiYry7zbk/IG65tLQ01K5d2+J7UrCwbNmybpHN31iyZAl++eUXXLp0CRUqVFAPAlWrVrW6/q+//oqlS5fi3LlzKnZJFN8+ffogONhClVJCfIjJzz2N2cfP4ccT5/B6nfLoV76E5RWbtoYvQyXGSzBEnnsiki1lDUNpfgMSLJufO8mUP//80+p+HflkkZ6eblFWsdD8/PPPqFGjhsM+i1hHelBJX7DBgwcr654oKOPGjcMHH3yAmJiYPOuvXbsW33//vQpwr169Ok6ePKmslPLbkArKhPgq18+fwU/rcurDZOuB+vlY+gO6+/a14Ll3RpKrD8Y/dzTIs/z69etqCg93b9S5PZ8fERGhZHb0fotCYmKi+r9p0ybcd999xuWffPIJTp8+bTW9mjiWhQsXol27dmjTpo16LcqMZPiJq0/iwMzZu3evUjBvu+029Vpimlq0aJGvUk2It5N96Tw0ox/Dz81r4tODp5Cqy0Z9axb/4FBownzbG0AlxomsWrUKMZdTUCokWKVZOwq52T7zzDM4fvw4nn32WQwbNsxh+/ZXt5/cOL/77jsEBgaq+BepvisZSwLjYZyPWOekqampsiKWsXr16mHfvn0WtxEFRr6nAwcOKJeTKJxbt27Nt+igQfG3FCCen3XPtMWEL+BLx+NPxyLZSPoxQwCdTjUUHla9TP47bNbGbefFVd8LlRgnIa6Ivn37qvkmcRGY18xx2S3h/xtlTK9mmrVjEJeFtA+QwGKxAHXu3BmvvPKKqsQrQcTEuVy5cgXZ2dmIjY3NtVxeJycnW9xGLDCynXxPgk6nw5133pnLmmap+7hp8Lg0Up0wYQJKlLAST2BGQkICfAlfOh5/OJaLn0/EtRt9kgokMgpln34JmvAIn/5eqMQ4CQk0NFDY7tUGpNXAQ5tzTOTTb66GcuHBiArUoHyNWgw4dRDSB+nrr7/OtWz06NHKSlO/fn23yUWsI/FVopRI/yqJoTl16pTqLC5KSvfu3a22ixAF1YDhKfHs2bNWY7UM68lgLJ/hSZmBhcWXjsefjmXp7FloEpSN6KCCG85qxn6M05evADJ58Pci1m9bHyIsbu+KDIINGzZg9uzZaqCQgxILhWkFVAmsFFP+5s2bcfXqVeXblqqp7du3h7citVGee+45nP1xBmpHhxe57cDGC9eM8xGBWuy4s2FO9+QRIxwksX9bzSSbxdTsuXHjRnz77bd46qmn1EVGnItkFon7SMYUU+S1uXXGgIwpLVu2VHE0gtROkrHkyy+/VNYY80BtQVLozdPoDdhyA5R1vP1G6avH4+vHcmDXDjy6ZitiArUYU7scupSJt76Dlh0REBPvEefD2d9LoLMzCCT47sMPP1Rpj6K4SEaBVEYVE64MOsK0adNUddshQ4YojWzbtm346quv1FNwkyZN4I0UK1ZMFU7T7V3jlP37gv/XU1i9ejXef/993H333epmKr9FuUHWrVtX/SaJ8xFFsXLlyurcS4FBQdxL8lp6WFlTPs2vA0uKCyHejsTCvPlIf2Rm63E2Mwu7rqShi7VwmIgoaHoMgL+gKUoGgWR1iDIjT7GSQWAJKUjWsGFDdOnSRa3fq1cvNViJNceABO61atVKFUITK4xkg4iFRwL2CHE28qQvT+eSjfTqq68qRf3JJ5/EDz/84NCqwCR/xM2zfPlyFRAvQevyICOKSuvWOXUuJk2apFKqDdx00034/fffsW7dOlUdWx5+RPmU5VRmiC+RPf9bvFalODqUikXZ0GAMrZpPnMkY3+2TVGRLTGEyCGS5qQ9akEBJcR0ZkBoPf//9t6ruGhcXp3zdUvPBWq2HwmYYmL7vK5YMXzkOdyJP/gsWLHC3GPD335x0A5dAXekkLm6kihUrYtSoUUZ3ksSZmX72/fffr17PmjVLVXYWK5ooMOxzRXyuP9K65UgMC8Hkm6rgfMZ1VXbDGhof7pNUZCWmMBkEMhiZu5nktanvW2JqvvjiCzz++OPQarVqYHrssccsVlB1RIaBKyPZk5y0331X0/DtsbM4O3iwGrQNmVDOQiraWoslsBdH7ccd+LvsYnUtXbo0nIW4jqy5j8aOHZvrtYwVPXr0UBMhvqrAZI97HshIMy4rll8fvpAwv3uw9YiIxcWLF6sCVSNGjFCKyO7du1WmiFhlLGWGFDbDwJWR7FK7RVxsxVKv4JsmVVEmzLFl0C9ez8LUo2eBo7+iTJkyyorlTKSbs61F6gq6kTpiP+6Asuf8DsRK6owsA0JIbs5N/RSxJ49DY6ticmsr+BuBzs4gkOWXL1/OtUxeG9aXQVG6BL/wwgvGjCWJhzly5IjKgLKkxBQ1w8CwnjOVGFGSpPjWaanroi26fz7MbB9iWjRw4sQJj4hCJ/4Bf2uEOB/xejzyyVfQZ2djXN3yqFNQlmtCol8F9BrQFDaDwIAhg0DiWiwhyw2VTw1IAJ6h87FYTqRIlaUsA28eLEVJKxUSpJSPGBty+vND/J97OzRSk8EXmhAahHnNaqhYInHFEUII8R1mTp+Gvy9cxZZLKXjmn8OqvIZVQkKhGf0/vwroNaBxdgZBp06d8O+//yqrilgMJGjv4MGDRr+3VEeV2BepySEBvZJlIPuWtFdDqqU3Ipkum9vVx572OfVcHI02IABN4iKVa4yZGIQQ4ltUPLIbVSJyLO6v1S6Xj0spAGhxh18qMIWKibE3g0D6mwwdOlRlEIjbSIICxXVkqBEjSP8fUXw++ugjXLt2TfnVJVhVSoh7O/4WZEUIIaToND13FEtuq41VZ6+gRfFo6yuWKQdNt37wVwKdnUEgNGvWTE3WEAVI6nIQ620HHt9yUM1/3riKavzlDsTtJxkhxD+R758Q4nxUKEVGGkK0GnRIsBxvqihTHpqR7/itFcZjspNI/ogvdMXZnP4Xpn5RqRewc+VKJCUlKddbzZqOazJpjtThESuZuP8kNooWJv8aUCV2LTU1VX3/hBDnkn3pvE3rBbw0wa8VGIFKjBOQdHGVIr7vBG4vHo1b46Oc8jlrzl3BM/1yzIhjxoxxqhIj2WBRUVGqXoxMRakzIhlp3og/yy4WOPn+GX9FiPOQGNOkI4cx5Oxu5GN/MRLg5wqMQCXGCUjg8owZM9R8qEbjNCXGNM1arDHORm5gERGFb+su1huJiZI6I96WeUbZCSHORK5PKdqakpKC+cGBWNmqDmKC8rlFB4fSIk4lxjlcvHjROB8b7LxTXCUyVDUolCBpS/V0CCGEeAe7du0yzkuPpHwVGKFpTkawv0MlxglIWrk0szz/6lClaDiL+OBAvPTSS07bPyGEENcgjZVX/vIT3uvfGy/WKJv/yhqNXxa2swSVGCcgvaHEMqIrkU9aHCGEEGLSJynhkzfwTr0KBa98W3vGw9yAUXo+gG5wF3eLQAghpAhk/zAVSEspeMWAAFphTKAS4wVIq4FjnW5Sk6UW7Dq9XlVDlhRoQggh3sHRo0cxdepUZKVcBf5YattGzdrRCmMClRgnsGXLFmzp0Q7HUjOc/llT2jdF9SVbVZ0YaddACCHE85FMwZdffhmjR49G59atcDTFhtIVEgvTe5ArxPMaGBPjBJ577jkcOHAAEVoNdndo5NTPkgj26zfSZl2RZk0IIaToSGPkFStWqPmzFy6gWHDpgjdiLEweaIlxYop1nIPSqw1tB2SSeVMqRYSgdlQYOnTogHLlyjnk8wghhDgXSf6YN28eaiSUxJha5RBpIVQgFxFRjIWxAC0xTuDhhx/GmTnTCv5R2oi0Glh06pKaf69+7mJl9WMisOT22tBO/sYhn0UIIcQ12UhNDv2LxY3KQQsbilCO+ZBWGAtQiXGSO0m3a6W7xSCEEOKhCkz2W8OBk8cRqIruFlB5NzgU2rjiLpLOu6ASQwghhLgomHfHjh2o/c8fSoGxGVbntQpjYgghhBAXMHfuXHTs2BHDPp2M8xnXbd4uoHt/p8rlzVCJ8QEWnryI/o1q44477sC2bdvcLQ4hhBAzLl26hDfeeEPNzz12BtuvpNq2YVw8NGGFb7zr61CJcTDz589Ho0aNcOeanVhy6r9GkM4kKTUDy85cxu7du3H48GGXfCYhhBDbiYqKwrBhwxAdGoJOCbFoXSKm4I2kS3Wj5q4Qz2thTIyDOXPmTM4EIDPbhohzB5AYHqz+BwUEKG2fEEKIZ6HVatG/V090XDlHxcYUiEYDJCRC062fK8TzWqjEOJigoCCULl0aF06fQmxBrdRtJEyrwZ72DY3z5rQpEYNNbeuhZEgQgvrTd0oIIZ5I9rwZKGFL/bCQMKBFO6XAMK06f6jEOJiBAweqSZoy2qRt20BAQIDFnkkGpB6No2rSEEIIcRyZmZkIDs6xlmPrRpu20fxvCpUXG2FMjBMR5YMQQoh/IqEFLVq0wOTJk5Fx9hRw6XzBG4WGMZDXDqjEeAEZumwM+/eImmSeEEKI5yPZSMnJyRg7diw+7N3Npm0i2nZyuly+BJUYL0Cn12PuifNqknlLbLucgm+PncX48eONvZsIIYS4B51Oh/CQYJVgJPGRD5eNs2m7mAFDnC6bL8GYGAfz5ptvIjAwEIlJ59C7nOvKRM9OOo8Zx84COyapZpBxcbZdMIQQQhxPwNVLeCvrJHo0q4kz6dcRb0tAb1w8tBGRwJWrrhDRJ6AS40AkkPfrr79WgVy1osJcqsSUDbsROAbg+PHjaNy4scs+mxBCSO7eSPoxTwOpKWgUa3t8S0DjFk6VyxehEuNAUlNTlQIjxNmidTuQNiWiEROkRYVRb6NevXou/WxCCCH/kf3DVKXA2EVEFGvCFAIqMQ4kJCQEixYtwuXLlxHy0ViXfnat6HA1aVu1cunnEkIIybHES0aqBPG23b0eLaJDbd84MAgBYz9iWnUhoBLjQCQWpkGDBmpeNyPS3eIQQghxstsoe/63wL+bAF0Wfk8+j8mrtmAygEcqlsSY2uUK3kl4JAJe+xia2GKuENnnoBLjY0iRPUE7eYG7RSGEEN9WYN4aDpw8blz2897/etc1LCgWJiQUaHEHq/IWESoxXoC0Gtjarr5x3hqSfi1R8ElpmWh6w7RJCCHESXEvJgqM8FHDSriteDRWn72CLqXzzxDV/G8qlRcHQCXGgZw+fRqnTp1C5IQXUCo0OF+Fwx5EGSkWElTgeo/8dQArzl5R8/+eP4/ixV2XHUUIIf5khcG63/Ms1wQEoFe54mrKl8CgnP5IpMiw2J0DkaDeTp06oeXqnVh8yvUF58qYpFknJSW5/PMJIcQv3EjjnpdqdoXfSXQMLeUOgpYYByJZSQaiHdiQUVoNvLE7x2z5Sq1EhFix8Eg9gtPp15EYHoLo6GiHfT4hhBATN9KpE8bXG89fRWJ4MBLDQmzbgSgvDZs6T0A/g0qMA6lfvz4GDBiAy0sXoFy4jT9oG5BYl+lSjRfAqJplra7XI7G4mgRtlSoO+3xCCCE3+HO1cfbKdR2e+ucQrmVl45mqpfF45VLKpaSQ/zJlm/S702iAhETWg3EgVGIcSNu2bdWkO7Pd3aIQQghxQi0YZKQZX3968BTOZmSp+S2XrkETkPDfys3aAaGhN9KvdYBWCzS4hdlIDoZKDCGEEGID2ZfO53r9RJVSSNXpVHPesaY1YcTa0ntQjrLS+1FjITzieKjEEEIIIbYoMCMH51oWExSI1+uUx3PVyuRqNRMwamIuawsVGOdBJcbHeGXnMWy6cA3nK1fEloOHefEQQohDGjoOsZqRlKtXXnAoNGG2N30kRYNKjAPp2rUrrly5grIXTmHqzVXdIsORlAzsvprjs71w4QKKFWMpa0IIKQqqtUDqNWMwb7AmAKHW6oA1be1a4fwc1olxIAcOHMDevXtxMCXdbTIkhgUjKCAAFcJDcPGi62vVEEKIz7Flg3H29d1JuPOPXVh19r+SGkY0Gmh6DHCtbH4OLTEOQgK3wsPDkZ6ejuhAx7pwRONf17qucT4/pI7Mm3XLQxsQAG1V91iDCCHEV9BdPAfcCOgVV/2c4znzT289jHVt6qq4GCO3tWfmkYuhEuMgJPZk06ZNaj5z0D0O3bfUHbC17ky4A4vsEUII/L29wNihxtfxwYFoGh+JjReu4YXqZXIrMBFRtMK4ASoxzuggzWBaQgjxqVgYoWpkKGbfWh1LT1/GHaVicq885kNaYdwAlRgvIDM7GxP3Jqv5F2qUQbBUfSSEEOKyWBhTq3uHhNjcC+PioY1jw113wLuhF5CVrccXh0+rSeYL4ofj5zBh7wm88cYbLpGPEEJ8jey0FODSBdtWbtTc2eIQK1CJcRC7d+/GxL0n8MWhU9hxOdWtsnxy8JSapk+fnlMmmxBCiF3of/pO/qospP6b96vyFRaRWBj2QnIbVGIcxM6dO/HxwVMYt+cE/rr4nw/VHRi6qaampjLNmhBCCsPWjUjXZeOVnUlYefYK7vxjJ3ZfsfCAylgYt0IlxkFIkTsD0UHuzRAaUjUBM26uitWrVyM6OtqtshBCiLehLNgpV3E0NQMZN7pQN4iJQM2osNwrMhbG7TCw10HcddddqPzLdFy+rkPDWPdq5bfGR6n/rBNDCCH2o9q1ZGaiRlQYVrSsgw/3n0T3xGJ527gwFsbtUIlxEKVLl0bJ4rR6EEKIt5MTS5gTTxgZqMXoWokW1wvo2tfFkhFzqMQQQjyGJUuW4JdffsGlS5dQoUIFDBw4EFXzsSimpKRg5syZqtDktWvXUKJECfTv3x+NGzd2qdzEtwrcZc+ZYtO6jIVxP1RivABpNfD77bWN87Y8RZzOuI7kzZtx/fp1NG9OkyfxfNavX68y6gYPHoxq1arh119/xbhx4/DBBx8gJiYmb+mBrCy8+eabKu5r2LBhiI+Px7lz51T7D0IKq8BcGfsMvvnzHwyqWJIV0L0AKjEObP4YkJKO6KBAxAVp8/pOi9h2QHyztiJG0NtX7UDGiq6oWbMmli9f7jBZCHEWCxcuRLt27dCmTRv1WpSZLVu2YOXKlapDvDkrVqxQ1hephxQYmDOUlSxZ0uVyE98h+4ep+GDt3/jy8GnMPHYOHzWshJvjIy2vHBzq0HGeFA4qMQ7i6aefxvbtO6ENAA51dK8pW5SeMqHBOJyagaSkJGWZ4cVGPBmxqhw6dCiXsqLRaFCvXj3s27fP4jZ///23sth8/fXX+Ouvv5RFpkWLFmofsq0lxDIpkwG5LsLCch4Q8rtGDO/5ynXkS8fjyGO5uGYZZhw7q+bPZV5HydAg6ys3be3w88fvxX6oxDg4xTo60LFWGEPbgUkHTqn5p6sm2NR2oEuZeFxteRcSExOh0+mMT6qEeOr1k52djdjY3OXc5XVyck7LDXNOnz6Ns2fP4rbbbsPIkSNx6tQpfPXVV+r33qNHD4vbzJ8/H3PnzjW+rlSpEiZMmKBiaWwhISEBvoQvHU9Rj0WXcg1ZyMKiFrXwys5jaFosChWsNd7VaFF2yEhowiPgDPi92A7vbA7i7rvvRvJPsxCqcbzWKa0GPjhwUs0/XrkUgm2o7vN89TLQsu0A8WHEwijWl8cee0xZXipXrowLFy5gwYIFVpWYbt26oXPnzsbXhgcOUYbEGmQNWU8GY1GUfKEKti8dj6OOJWv6JPW/SmQovrulGnKqw1jh9vY4ffkKIJMD8cfvJTAw0OaHCIvbF3pLkovRo0dDd+RPd4tBiFciyogoIpKVZIq8NrfOGJDlMgCauo7Kli2rthGFxJL1MSgoSE2WsOWmIet4+83FV4+nyMfy55pcN2CrIb1lykPTvb9Tzxu/F9thxV4fRje4i7tFIMQmROEQS8qOHTuMy8S9JK+rV69ucZsaNWqopzxZz8DJkycRFxdH9ymxiy1/blCZSQUiCszId5ha7UFQifEDMjMz3S0CIQUibh7JpFu1ahWOHz+u4lsyMjLQunVr9f6kSZPw/fffG9dv3769yk6aOnWqipuRTCaJeenQoYMbj4J4E6K4bB43Gl3u646+m/bj0LX0fNcPeGkCFRgPI9AVBak2bNiA2bNnK7+z+Mj69u2bpxiVDFrfffcddu3apZ6sJCD1+eefR/Hi7EtRGC5dz0KvjftwvFYt3HrrrWqgJ8STkXpGEuA7Z84cNbZUrFgRo0aNMrqTpAaMadC8jA3ixp02bRpeeOEFVSdG2n9YSscmxJICc/3N5/Hyj7+rshRrz1/FirOXUTky1Oo2VGB8QImxtyDV3r178eGHH6JPnz5KcVm7di0mTpyoMgLKly+v1hGT8Kuvvoq2bduiZ8+eKuVRlBprvmtPQ54Ahw8fjqgzx9EzsTh6lXO/4iVZUgdS0pGZnabOJSHeQMeOHdVkibFjx+ZZJq4mGX8IKUxNGO3pE3i2WhmM3ZWkxswBFfKpM8S6ML6hxNhbkGrRokVo2LAhunTJic/o1asXtm/frqw5jz76qFo2a9YsNGrUCP369fPKFDN5QhRlTWhTIq8i565aMZIeKB1Yy5Ur525xCCHEY1DxL+t+V/PtS8Xi9uLROJNxHYH5ZZc2zXFrEi9WYgpTkEqWm6Y0Cg0aNMDmzZvVvLiORAkSJUeeqA4fPqyqbspn3HLLLQ4tWGX6viM1avHby+enpaUpbd7RhGg1+KV5TeO8rSy7vbY6zsCvPMOV5M2FnCg7Ib5B9qXz0I95GtDpjMvCtBrrNWGEhERoegxwjYDEeUpMYQpSiW/b3M0krw2plLLP9PR0/Pzzz3jggQdUvMw///yDd999F2PGjEHt2jk9gxxZsMrRlh6pUyHTobtugv5G51NHog0IQINY+4sqGW5a0mHbk/AmK5s5lJ0Q77bAiAKTee2qTUVDFYkVoXnxbcbDeChuz0M0pEc2adLEaLGRgD5xzyxdutSiElPYglXOLiYUpEyRnve0K2mnnoA3F3Ki7AVT1KJVhLgiDibj6lXcvW43WpWIxnPVyiAyP+u5pFRTgfEdJaawBakuX76ca5m8Nqwv+9RqtSobyRQpWmWIM3F0wSrDeo4c0J1Zk0XaDnxz5IyaH1ixpO1PEDfwtJuuNxdyouyEeDEbV2Hy4dPYdy1dTcdTM/HFTVUsr8uaMF6BxtkFqWS5BPKasm3bNpXZZNhnlSpV8rijxHrA9Or/2g68teeEmmTeVs5lXMfEvScwZMgQVXODEEL8Fd3Fc0BmurKYh2gC1M3vmWqlrcfAUIHxzWJ39hak6tSpE/79919VV+bEiROqBsTBgwdzpVFKUK+kbi9btkyZvCVzSTrUekvRKonR+eLQKcxMOod0Xb4dN1zK9Ww9Pj54CvPmzcO6devcLQ4hhLgvG0mCeSWGsXIClrWsgwn1KqB2dLjlGJjR/6MC46sxMfYWpJLS4EOHDlVp1DNnzlRBplKYylAjRpAsJEnV/umnnzBlyhSUKVNGFbqrWTMnI8fTEaVt/Z4Tav7e0nHwFKSNfFBAAK7r9UhKSnK3OIQQ4rZYGKT911ZAMpEsZiMFBDAGxh8Ce+0tSNWsWTM15YcUupPJGxGlTggMyEnV8xQkq+nbW6oh4Z3JKsaIEEL8ko2rbFuvWTsqMF6G27OTfIE33ngDJ19/XrmSPK0WR7NiUdDm0xKCEEJ8mey0FHy2+wiKhwShe9l462O0RgNN70GuFo8UESoxDkDcYToPciMRQgjJYe9Hb2PivhPI0gPzT5xX1mmpaJ6H29rTCuOFUInxA0zTv7WTF7hVFkIIcWVA729LligFRrgpLtKyAiOxMKzI65VQifECpNXA7Fur2912QEjJ0mHP1TQcT8tUgWw3OUlGQgjxNLLnTMGQqqVRPyYckw+fwVNVrFStZiyM10IlpohIernUtInIzEJUoPZG1V7HB+hKbEth2HElFT027jMWyqMSQwjxlx5J+OM3Nd+qRIyarBHQ6xEXSkYcieek0ngpBw4cQIsWLdBw2b94eecxeBrlwv5LIzyeluFWWQghxGVNHl+yMUg3OBSaMPt70xHPgJYYB6VXC9FBju9gbSha9/2xs2q+T/kSdll7SoUGYUCFEigbFmy5sBMhhPhYHMyuZx7G9bQU1I+xQTlpmlOolXgnVGKKiHTkloaUl9cuR43IMKd8xvXsbLyyK6dYXY/EYgjSaO1yRb1e57/CgoQQ4stcn/0NRmzeje2XU/FQhRJ4qUZZhFtr8hgeyYBeL4dKTBGRLtvSasGZDSAJIYTY5kb6efZM/Hs5pzrv+vNXEZRPw9yA1z5mQK+Xw5gYB0AFhhBC3O9G0r/6FO4pHY9RNcsiXKvBuLrlrbvfW3aEJraYq8UkDoaWGD/i6nUdtFevIiqqcJlOhBDiqeh+mKL6I4nS8njlBPQqVxyxQVZucRFRdCP5CLTE+AG/n76Eer//gzq//6MacRJCiM+xIXd/JKsKTGAQAsZ+RDeSj0Alpoi88847uG/DHgzYfACn0jPhiUjW1OXrOjXPbtaEEF8j/dJFXLh2zaZ1A8Z/STeSD0Elpojs3bsXf11MwYqzl+GpSK2YcmHBaBYfiYoVK7pbHEIIcWgszLi7WqP16h2YmXQO2fobPQYswTgYn4MxMUUkPT3dOB9jzXxZRII1GkxpUtU4by9lwoKxrk09Na8dONDh8hFCiLsUmOOjn8a7m3cgTZeNl7YfRePYCNSIslDugv2RfBIqMUXku+++Q9ojnXHlug6hTmg5IARqAtCupPWS2YQQ4o9kz/8WwWdPomOpWMxPvqDqwlhUYAT2R/JJ6E5yAGIdKR4ShABL3VE9DKaDE0J8hn/+VGPvhw0rYdat1TG8ehmrq7I/km9CS4wXIG0Hfko+r+a7lpGKvZ6vLBFCiDPRS+zLtf/avjTPr0ku+yP5LFRivABpO/D8tqNq/u6EOLvaDhiQypWzk87heFomhq1Zg5YtWzpBUkIIcV08DDJtbGrL/kg+C91JRSAtLQ2ff/45ZiWdw6YLtqX3uYvktEzlM9588Rr27dvnbnEIIaTQHPjnb3S55SZsuWjDuJuQyIBeH4ZKTBE4d+4c3njjDYzYfhRTj56BJ1MuPNg4f/ZsTkdsQgjxNrLTUjD64Yew9cJVdN2wF4tPXcx3/YBRExnQ68PQnVQELl/+rzZMtLUuqR6CtKRfdnttJIYFI2rkSHeLQwghheLCtM9wIS3HjVQ2NBitikdbXzkkjLEwPg6VmCJQtmxZ5U66NOktVI4IhScTptWgurXUQ0II8RLitm/Cwha1MO3oGVSMCEV4fg+Qt7ZypWjEDVCJKQJxcXG45557oFsw2d2iEEKIzwfyZs+ZAmSmq9pZj1Qqlf8GjIXxC6jEEEII8XwF5q3hwMnjtm2QWBGaF99mLIwfwMBeLymm92mjymoqTNsBA0dS0rEg+QImTZqEEydOOFRGQghxFlmzp+D9NZtwJuN6wStLewEqMH4DLTFF4NKlS8jOzkZEtl6ZN52F7Ltz6bgi70dSrN/ffxL4ZzyqV6+uYnoIIcTTmTN3jhq7vj58Bm/VLY8uZeKtr8z2An4FLTFF4L333kO9evVQeckW2+oVuJnEsBDjfFJSkltlIYQQW8jKysJne3PGqytZOhQLzufZW6OBpvcg1wlH3A4tMUXgypX/Sl5HBTkvxTorW48lpy+peWl0Vlirz02xERhVs6xSZm7q0MHBUhJCiOPRarWY17QG3t57ApnZerTIL6X6tva0wvgZVGKKQO3atdG2bVtc3von4oKcdyozs7Px5NZDan5P+4YILETbAaFyZCgej0xQ89rERIfKSAghTiEjDcVCgjCxfkXopF+SNcqUZzaSH0Ilpgg8+uijamJnaEIIcQ7Z82YY57UB1q3QmpHv0ArjhzAmpohQgSGEEMezbds2XL9+Hdi6seCVAwKowPgpVGL8kGtZOuzYsQOpqanuFoUQQvJw8uRJdO/eHe3vvBN/HjpS8AahYQjIx0pDfBcqMX7GhL0nUHvpP+jQoQO2b9/ubnEIISQP0lg3JSUF+/bvx7wTFwpcP6LNXS6Ri3geVGIKidSHue+++/DwXwfw3r5keAslQoKM80eO2PCEQwghLuaxxx5DgwYNEB8ciJdqFFzPKmbAEJfIRTwPBvYWkqtXr+LPP/80pkB7CzWjwnBzXCQq3nEXypUr525xCCEkD6LALFiwAAcf6oS4/OrCCHHx0EZEAleuuko84kFQiXFAjZhoJ9aIEYI0Grxbv4Jxvig0LxaF5s1qQPvBBw6SjhBCHI/megaqRoQWvGKj5q4Qh3goVGIKiVgxDh8+jEuD7oWz7TBBmgD0SCzu5E8hhBD3kZGRgZCQ/6qK6+dOs2k77X0POlEq4ukwJqYIBAcHqyJMxU3iTAghhNiHXq/HoEGD8Pjjj6vMJMXGVQVvGBgEhIQ5XT7iudAS4wVIzM3qcznuq1bFo53abJIQQlzNkiVLsGLFCjW/e/duLF+0EAGZ6QVvGB3D1Go/h5YYL0DaDkgWlEwyX1R+OH4e99WrgZtuukkVlCKEEHdne8bH53SmHv7sUARMeMm2DRs2da5gxOOhJaaQyM1/586diDx5EU3iI1HSi1xKZzOu488L0nX7morrqV+/vrtFIoT4MXfffTeaN2+OH2fPwl1bVwAnc7pW50tAADTd+rlCPOLBUIkpJEuXLsX777+v5qffXBUlS8TAW6gQnhM8V6xYMaSn22CyJYQQJxMXF4eHI/S2KTBCs3ZsNUCoxDgkxTrQuSnWjqZdyRjsvLMhYqf+6m5RCCFEoU9PBdb+bvP6Ab0ecao8xDugElNIunXrhurVq+PSNx+h/A3LhrcQqtUg1Lv0LkKIjzF//nwcO3ZMZSRJanX2nClAts62jYNDoQmLcLaIxAugElNIGjVqpCbd6jnuFoUQQryKS5cuYcyYMTh//jx+/PFH/PLLL4jctMb2HTRt7UzxiBfB7CRCCCEuZd26dUqREWrWrIno6GggI822jRMSoekxwLkCEq+BlhgvQFoNvFG7nEPaDhj491IKNn76qWoC+dRTT6FChZy2BoS4u16IPJXLDU5+kwMHDkTVqlVtuil++OGHaNKkCUaMGOESWUnRspEWL16McePGYezYsbZv2KojNN0HMKCXGKESU0hkkI2MjIQryixJ24H+FUs6dJ/Lz1zGB+PGqfl27dpRiSFuZ/369Zg+fToGDx6MatWq4ddff1U3uQ8++AAxMdaz/86cOYMZM2agVq1aLpWXFI06derg+++/N1bstQVN3ydY3I7kgu6kQtK0aVN14++0dhe8kQoR/wUjizWGEHezcOFCpVC3adMGiYmJSpmR1h4rV67Mt0jaxx9/jJ49e6JkSccq+sSF2OhKogJDzKElphDodDpcvXrVoe6dfD9Pr8cmVZwOuCU+EloHXMjN4qPw6aefKkWsSpUqDpCSkMKTlZWFQ4cOoWvXrsZlGo0G9erVw759+6xuN3fuXBVP0bZtW1WuviCuX7+uJtObYlhYWIE3SMN7vnITdcfxiMXsn3/+Qfv27fO8p5s3w+b9mMvsS98Nj8V+qMQUgszMTDVoXr58GTXOHHX652XosvHAnzkD+Z72DRHugLo0ZcKCUe7eex0gHSGOqbskVpXY2Nhcy+V1cnKyxW327Nmj+u288847dqX1iuJjoFKlSpgwYQJKlChh0/YJCQnwJVx5PBKr9O2336JLly747LPPUKZMGeN7x7dtRoEOpYAAlC5d2upN0Ze+Gx6L7VCJKQTy5CY+eEE3uAu8FZFdO3mBu8UgxG7S0tKUG+mxxx7LyWyxo75T586dja8NN8SzZ88qa5A1ZD0ZjE+dOmVz/IYn4+rjkTYtosAIa9aswYULF4znPjstBfrzZwreSXCokteXvxt/PJbAwECbHyIsbl/oLQkhxEGIIiLuI0ParQF5bW6dEU6fPq0UD7GiGDAMlL169VLBwJaeAIOCgtRkCVtuGrKOt99c3HE8devWxSeffILXXnsNzz//vGr2aPjc7B+m2raTW1vlK6svfTc8FtuhEuPHiJvq+L59OHjwIG6++WYUL17c3SIRP0WexipXrowdO3bglltuUcvEvSSvO3bsmGd9cUX873//y7Vs1qxZqhfYgAED+Fv2wKdyiXeSwO2ICLNKuxtX2baP7v2dIxzxaqjE+DGfHTqF99q0UfNff/21xZsFIa5C3DzytC7KjNSGWbRoETIyMtC6dU511kmTJqkn+D59+qispfLly+fa3nBzNF9OPIeoqKhcr8WVhEwbmtDGxbPNALEIlZhCsHz5clXPImrXFvQtX0JlDHkjlSNCjfNijSHEnTRv3lwF+M6ZM0e5kSpWrIhRo0YZ3Unnzp3ziawNf+LkyZMqGNcaeltdSY2aO04o4lNQiSkEcsNftmyZsSO0t1IrOkyZeCXFWureEOJuxBpozSJYUGVXqTxNPIelS5eqwOsnn3wSTz/9tDGVPRe29EsKCICmWz+nyEi8HyoxhUCeFg3EBDm/HXSgJgCjapY1zjuKapFhynxPCCGOzh579dVXVTkKCbKWasqmWWGCCva0pchds3ZsM0Acq8TY299kw4YNmD17tsomkIyBvn37onHjxhbX/fLLL5WVo3///qq/hicydOhQFTx4cUgflAq1nOngSII1Gjxe2XfqBhBCfJ97770Xn3/+ubLymo/l+vRUZM+ZYtN+Ano94iQJiS+gKWx/k+7du6v0RlFipL+JFH6zxN69e1VjNikOJ+tLFszEiRNx7NixPOtu2rQJ+/fvR1xcHDwZCSqU7IfKkaGIcEDhOXfXivHmWjeEEM9DXEcjR47E77//rsZ901gmpcCMHwH88ZtN+6IVhjhUibG3v4lkGDRs2FBVaZT1pYaDZB+INccUKX70zTffKCuHpFvmh5QNT01NNU5iujQgF0tBk63r2bIfVyBtB6TrtEwy7wwkC0TSUx1xXlxx7t0xUfaCP4MQU6pXr64CtE1RdWGS8z7EWiQ4lL8t4jh3UmH6m8hyc19ogwYNsHnz5jxN3ETRKVeunNNLhzuqFHISXFfP5Z71exzadsDArycv4q09x3G8ShVMmzYNDz74IJyNN5fUpuyEWEfiXKwpHcoCIwrMmtwPsPnSNCe9nhCHKDGF6W8icTMxMbkzeOS1aWXOn3/+GVqtFnfddZdTS4c7qqzz4sWL1XmIPn8VzYrlrnvgbQRrApCUlqnmt2zZgjvuuMNpn+WIc+8uKHvBFLV8OPFu5P4gNXwkG0nGclNlJvvSeejHDgFSchrZ2oRGA02PAc4RlvgMbs9OEsuOuJzM/ab5UdTS4Yb1Cjugv/LKK0ppKxESiL/bNYA3I7ViogI1qFK3vorzccUN2ptLalN2QiwjFZS3bt2qQgyk2eMzzzxjtMDoxwwBUu1QYITb2jMehjhWibG3v4kgy82DfuW1Yf3du3crDV60dwNi5ZDgYVFuPDEF2JBiHVNA7I43UDkiBDvubIjAr35xtyiEEC9Fp9Ph6NGjxqDe+++/3/ieciHZq8CUKU8rDLGJQGf2NzEEdm3fvj1Xip10NK1WrZqab9mypYqpMUWynWS5BA97IlL/QBSx8J9yOll7MwyaI4QUFQkHmDp1qnK1S5KGJHEY3Eh2xcAIrTpC030ArTDEJgKd2d9E6NSpk6q0KXVlpDbMunXrVMXbRx991NhLw7yfhihLYqmRJm+eiNS5EXRb7bw4CSHER5EHIhnvc8XBvDTIvp207Ahtv/+s8oQ4XImxt79JjRo1VNq0dJidOXOm6qPxwgsveHWTNtZVIYQQ6xjjYHQ62zeiC4kUgkBX9Ddp1qyZmmzFE+Ng3Im0Gni2ammHtx0wsPdqGr4bPRoHF/6IXq+9nSuFnhBCrPHFF18ot7+EDZiSPf9b2+Ng5KG3ZQe6kIhrit35O5LCnZXt2gwPaTswrHoZNcm8ozmfmaX82X+cu6rSrAkhpCCkwvrrr7+OO++8U1VlN1hgdDO/BFb+avN+At75RrmQqMCQwkAlphCdWSsv2YJav23F9KNn4AtUiQg1zp88edKtshBCvAPpi2R4sJPaX6qY3VvDgRULJZ/ftp207AhNbDHnCkp8GioxhUyvTtFlQ+uizJ5svV65fGSSeUdTMiQQP/74I7a2q4/Jkyc7fP+EEN9D3P7PPfccmjRpoip9q1Tqk8dt30FEFGNgSJGhEmMnUivnptgIVI0MRckQ53ewFtJ12bjzj11qknlHI4HYN3/9Foq56HgIId6P1IMZPnw45s2bp1KssWm1fTsY8yFdSKTIeH+1NhcjKYQd5ueYUQkhxN8RBSY7LQVI/68Rb4HExUMbV9yZYhE/gZYYQgghNrFnzx6jSz1XOrXEwtiKuOEbNXe8cMQvoRJDFGm6bKw+e1nFxPz6q+2ZBYQQ/yA9PR2PPPIIWrVqhfnz5xv7cKlYmFMnbNuJZFeWLgdNt37OFZb4DVRiiOJCZhYe3HxA1fmRQoaEEGLKZ599hiNHjuDMmTOYMcOk5Yo9sTCtO0Ez8h3GwhCHQSXGTt566y08++9hvLrzmFMyhdxFmdAghGtzfg4HDhxwtziEEA+jR48e6NChg2oLM378eJUQoKwxGem27SC+OLS9H6UCQxwKA3vtZPny5dhz4gJCNQF4vY73tk4wRwak4dXLIDpQi5rjJ7lbHEKIhyFNHb/55hv1kCN98wzxMLbVhAkAGjZ1uozE/6ASYyfSvVqIDnLdqZNWA49VKmWcdxaDbnyGtlEjp30GIcS7MSgwgn7uNNs2KsM4GOIcqMTYyZIlS3BxSB9kZDu+Xos1pNXA6Fo5re0JIcRVZGRkICgoCBpr7U42rip4JwEaBLw0gW4k4hQYE2MnxYsXR+XIUNSK5gVJCPFtJkyYgG7dumHXrl3GZYb+SLoRDwOZNsTDxMVDExbhXEGJ30JLjBcgAcQn0jLVfNmwYGic2O5Ap9fj6KFDqhZEw4YNnfY5hBDPZvfu3fjqq6+g0+lUZ/vNmzcjOiQopz+SPe0FGAtDnAgtMV6AtBposWqHmpzRdsBUWWrw+7+4/fbb8eyzzzrtcwghnk9aWhoqVqyo5p988knV5NHu/kgBAYyFIU6Flhg7OH/+PNb374qYIC0qRoQqq4gvIRYeOaYrV9Nw+PBhZGZmIjjYt46REGIbjRs3xu+//47vvvsOffv2zVn4p539kZq1YywMcSpUYuxg586deHzrITU/pEoCXqhRFr7G7cWjUTE8BLX7PYLr169TiSHEjwkJCcHAgQPVvOqPlGFHfySNBpreg5wnHCFUYuzj0qVLxvnoIC18kZdvZEFphw1ztyiEEA9CL64ke7itPa0wxOkwJsYOatWqhZE1yuKpKgloHBvpbnEIIcShbNmyBS+++CIuXryY981Na2zbiaRjlykPTY8BDpePEHNoibGDatWqoXKVBPgDusFdoJ28wN1iEEJcRFZWFkaOHIkdO3Zg0aJFmDt3LmrUqKHey2kvYIMrKSQMaNFOBfPSCkNcAZUYkm92QlhYmLvFIIS4gD179qiAfqFUqVKoUqWK8T3VXsAGAiZ+w5owxKXQneQFaAMC8FD5EmqSeWfz7r5ktF69A9WrVze2WSCE+DZ169bF6tWrcc8996gGj9Lo0aDA6MePsGkftL4QV0NLjB0ok6obCNFq8GZd1zWbvJCZhUMpGcans1tvvdVln00IcR+lS5fG559/bnwtCky2KDAnkwreODhUNZIlxJXQEmMHDz74IGov3YqmK7bhWpYOvkrNqDCEaAJQLzoc6W+/6G5xCCFuQhW3Sz5m28pNWztbHELyQEuMnSnW17KykZKVjXCtxqUWILGOCPHBgU5/2umRWAx9yhd3ieuKEOLeBo8//fQT7r//fmi1uctGqDiYtb/bvK+A7v2dICEh+UNLjB2UK1cOVSNDUTkixKn9i8xJ02Wj0fJtapJ5ZxOq1VCBIcQPENfRc889p+JgpFeSaYPH7Of7A9k2WpyDQxnQS9wCLTF28Nlnn6nUY0II8YU2Kh999JGa3759O7Kzs3PHwNgTA0hXEnETtMQQQogfUqxYMdUXSWrBSGuBOnXq/BcDY48Ck5DIwnbEbdASQyyy/XIqfko+j91X0vDk6tVo1aqVu0UihDiYpk2b4rffflOF7uyNgVG06ghN9wFMrSZug5YYYpEjqemYfPgM1p6/qkqRE0J8k6CgIFXUMnvOFNtjYITgUGj7PUkFhrgVKjE2kpSUhCFDhuDVncfw26n/GkH6KrWi/huYjh8/7lZZCCGOIzk52fIbtvZGMsA4GOIB0J1kx4U/b948NR+s0aBDQix8mUoRIZjSpCrqxYSj9LvvulscQogD2LdvHzp06IBu3bph0qRJxuU290YywDgY4iFQibER0/L70UG56yk4G0l37l62mHHeVZ/ZrmSMSz6LEOJ8RFEZNWoUMjMzMXv2bBXIO2jQILt6IyG+BNDwVjZ4JB4DlRgbuf3221VfkUsjH0PJkCCXtx14r0FFl34mIcT3lJjOnTtj586diI2NxfDhw1UBT/WeZCXZgObtr9hagHgUVGJsRALfqlatCl0sCzoRQrwPjUaDAQMGoFOnTjh9+rQa00SJUVaY9csK3gF7IxEPhEqMlzxBGSr1hmk1LhtIsrL1+PvSNez66iuVwdC/P8uKE+LtlCxZEqVKlVLzqrjduOcBnQ1ZSQzkJR4IlRgvQBSYmkv/UfN72jdEeKBrYnKk3FW/TfuRsXEMKleuTCWGEC99CLL24KP7YQpw6oRN+2FvJOKJMMXaRrZt24a1a9eqInCZ2c7vX+QJBGkCVEdr4dChQ7h69aq7RSKE2IFOp0OvXr3w1VdfqYJ2edi42rYdsTcS8VBoibGR999/H0uXLlXzf7Wrj5Ih/qH/PVKxFFJ0OjR453PlQyeEeA/SVkAevmSSopWffvpp4dKq6UoiHgqVGBu5cuWKcT7aRe4cT6Br2Xj1X9uggbtFIYTYybFjx4zzDz300H9xMPO/RfL2v2zbCWvCEA+GSoyN9OzZE02aNMGVBbMRqvUPKwwhxLt5+eWXcdddd+GPP/5QfZKyL52HfuwQIOWainmzhYBRE1kThngsVGJs5IEHHlD/dYc2wB/RDe4C7eQF7haDEGInN910k5rEAqMfMwRIvWbX9oyFIZ4MTQqkQMR3fiw1A7/++ivS0uwoTU4I8RjEhWSvAoOg4JzYGUI8FCoxXoAmIACdEmLVJPOuZsyuJNy2agceffRRbN++3eWfTwixnTlz5mDdunV539iy3v6dRUWzwB3xaOhOstOl4g4kBufzxlXgLmpF/+cPlwyHW265xW2yEN9myZIl+OWXX1Ql2QoVKmDgwIGqUrYlli1bhjVr1qgO84LUMurdu7fV9f2BEydOqP5IYjG977778MGE8cDP3wNbNwCXLti3M1FeGjZ1lqiEOARaYmzg8OHDqF69Opqu2Ibxe47D32gSF4FWxaMxbNgwtGjRwt3iEB9l/fr1mD59Orp3744JEyYoJWbcuHG5mq+asmvXLvV7HDNmDN58800UK1ZM/b9wwc6btQ8h58/g8o2KCAcmvASs/BW4eN6+HWk0QOlyqtEjIZ4MLTE2IE+FKSkpSLlRPdffqBYZhhm3VAP2rIb2+efdLQ7xURYuXIh27dqhTZs26vXgwYOV5W/lypXo2rVrnvWHDh2a6/Xjjz+OP//8U7k8W7VqBX/kxRdfRPny5fHV5C8xXHMZSE62bwcxcUBgENDgFnaqJl4BlRgbyM7OVpaYi0cOobiLO1gLqVk6t7QdIMRVSDVZqQptqqxIw8J69eph3759Nu0jIyND7ScyMtLqOtevX1eTAYn3MBRxzC/2w/Cep8eHaLVa9Ol8F3puWgjNWTsVmLh4aN+Z6vHH6K3fjS3wWOyHSowNSHqiPA26KyaGEH8oJikPC7GxsbmWy+tkG60JUp02Pj5eKT7WmD9/PubOnWt8XalSJeW6KlGihE2fkZCQAE8lOzUFl775ECmL50NjcxWY/4i8vT3iypSBt+LJ34298Fhsh0oMsYvU1FScO3dOmawJ8RR++uknlZEzduxYBAcHW12vW7du6Ny5s/G14Snx7NmzlnsLmawng/GpU6c8LuVYApsDMjOQMO19IPm/Cr12ERGFtPbdkH7yJLwNT/5u7MUfjyUwMNDmhwiL2xd6S+JXSCzQfRv2YE/Nmqpy8bx589wtEvEhoqOjlftI4s9Mkdfm1hlzFixYoJSYV155RQUD50dQUJCaLGHLTUPW8aSbi8gyfPhw/LVxI56tXBKDKpVSjVvtIjwSAWM/AkLCPOrY7MXTvpuiwGOxHWYnEZsI02pw5bpOdcX9999/c8UVEFJU5GlMUqR37NhhXCbuJXkt8WjW+Pnnn/Hjjz+qtOIqVdxXhsBdSAFKaSmQdv06ph89i+vZNiYeaLTQFC+FgHb3QDPhK2hiizlbVEKcAi0xNjB16lQcOHAAMfuS1ZNOdJB/BtY2iYtEZMVSaNy4scrWKugJmRB7EDfPJ598opQZqfWyaNEiFazbunVOB+VJkyapmJc+ffqo12J9kcJukqVUsmRJoxUnNDRUTf5Ai/p1MKBiSUw/cgav1SlnW9B/YkVoXnwbZStXxcmTJ33miZ/4J1RibOD333/HqlWr1PzAiiXhr7zfoCKCvvrF3WIQH6V58+YqwFcUE1FIKlasqCwsBmVZYrFMMx3kupQ4lvfeey/XfqTOjDRs9XWkF1L0u6Pxeu1yGFC+BCpFhBS8UUSUUmDYD4n4ClRibMDwhCfDZ5QbrDDSaqBtiWjjvLtw52cT/6Bjx45qsoQE7ZoiVht/xrQXUuXIUJtjX1j7hfgSVGJs4PPPP1fZC5dfHwatG27k0nZg6s3VXP65hBDPQ2KFMjMzEbRlg+0bteoITfcBVGCIz8HAXhsoV64cGnw2Fi1vWEP8GUOtHAnwlYGUEOJaZs+ejbZt2mD1/kO2bdCyI7T9nqQCQ3wSKjHELv6+eA0PPfQQ6tatqwqHEUJch/SFkv5QR48dw4ObD2D3ldSCY2B6DHCVeIS4HCoxXoC0Hajx21Y1ybw7ydLrsXz5chWAKX1qCCGu49q1a6hRo4aav7d0XK4O8xYZ8yEtMMSnKVRMzJIlS/DLL7+ogFcpLjVw4ECVEmmNDRs2KBOoxJVIBb++ffuqNF1BsgtmzZqFrVu34syZMwgPD1dlwyWNUtIp3c3Vq1eVbDGXU1E6NAjF3NA7CR7UeLJBTASCNQGICtTm26OGEOJ4pFK2tE34ofPtuL1YdMG9kOKKu0o0QrzDErN+/XrV7l3SGKXniCgx48aNw+XLly2uv3fvXnz44Ydo27atWv/mm2/GxIkTcexYTnlsias4fPgw7r//fvX+888/r3qlvPPOO/AERP7evXuj07rd+OjgKfg7EmS8qlUdbGlXH6+//rq7xSHE/8hIQ/eyxVAqtIAHqkbNXSURId6jxCxcuBDt2rVDmzZtkJiYiMGDB6teJdIg0RJSsKphw4bo0qWLWr9Xr16qmJVYcwSxvEi5cKkRUaZMGVWdUyw70tFW6kJYQqrFSg8fw5SWlmZ8T+pIFDTZup5MpspZrJ8WuTMnMSzE5vNXlHPvaRNlL/gziPPRz51m03qabv2cLgshXuVOEtePKBddu3Y1LpN+J+L+2bdvn8VtZLlpwzWhQYMG2Lx5s9XPEcVEBkVRcJzRidaezprSwVoKbh2d8SUaxrBAlCmlS5f2uw6tlJ24GqmqKzVyZBxSvaE25hTezJfAINULiRBfxy4lRoI5pUaBebl5eS0uIEtI3ExMTEyuZfLavNGbAXEvfffdd2jRooVVJaawnWgL0yU0Li4OTz/9NLL++a3Adf1xcM0adA8Cbazia++59yQoe8EUtRstscxrr72mLODLli3DF5M+QpvM9II3io6hdYz4BR5V7E4UkPfff1/NDxo0yGmdaA3redvNyJNYe+4Kfnn+efyxcjvmnzihXIG24s3nnrITV6dUG6zW8lDX6NB22zZs2NS5ghHijUpMdHS0ch+ZW1HktbVmgLLcPOhXXpuvb1BgJA7m1VdftWqF8Uek3H/T+EiPKv3/54VrmLlpppqXLroPPPCAu0UixOeQDM3Vq1er/lC1qlZB7Ko5BW8UEMB4GOI3aOw1F0tQ7o4dO4zLxL0kryUg1xKyfPv23E8P27ZtQ7Vq1fIoMGLuliDfqKgo+4/ExzOC5jStoSaZ9wRuL56T3hmiCcDp06fdLQ4hPouUMpAHu/twTcxpBW/QrB1rwxC/we47osSiSLEz6ep8/PhxfPXVV8jIyEDr1q3V+5MmTcL3339vXL9Tp074999/VV2ZEydOqA61Bw8eNDZ5M3ShlYDhIUOGKKVILDsyFRTf4gokU6pJkybo+Mcutxea8yQaxUbg25urYfudDTF06FB3i0OI7/OPbcUlA3o94nRRCPHamBhJhZYAX1FGRNGoWLGiipo3uIfEHWQaUCbVJeUmJwXtZs6cqTJaXnjhBVW0yeDz/euvv9T8iBEjcn3WmDFjUKdOHbg7eFWmMwFAmIdYQTyBIE0Ae0kR4gQkbknGVylLERaWk2GUnZYCXL5Y8MbBodCEMYuS+A+FCuwVK4rBkmKOpAKa06xZMzVZomTJkuqC9VSKFy+uZJQBxF3R/mIBar4qx4W3vnVdhAeyXg0hvorU0Bo2bJiyUEvRz5a33gz92y8C2TZYgpvmWMQJ8Rc8KjvJE5kxY4b6L6nE7uRCpvtda4QQ5yLd4aXBoyDueml7kv3DVCA5p8J5QQR07+9kCQnxLOgfsRHWXLDMXxevYfz48aoAomnlZEKI/Wi1WkybNg233XabqoreqV0bYO3vNm/PgF7ib9ASYwO6wV3cLYLHMjvpHGZvmGRs9Ck9sgghhUea6UoMYUpKCvRihbHFjSSEhPFhi/gdtMSQItGuZE41Zhk6d+/e7W5xCPEJRBlRXeI3r7F9o1tbOVMkQjwSWmLyQerfSLp43L5ktC0Zg4axjPq3VC/m3foV0LpEDBKeesrd4hDilZy4UfXa1JKiS70GpNvook1IhKbHAOcJSIiHQiUmH/bs2aP800JccCCVGAtEBGrRI7G4u8UgxGuRUhV33XWXKifx5qsvo+I/64B/NgIXz9u2g1Ydoek+gPEwxC+hEpMPUvPGQLFg950qaTVQPybco9oOEEIcw9tvv43z589jzZo1+N+jAzGpVoJtlXlvoO33pFPlI8SToRKTDz179kTTpk1xdswzqB3tvrb20mpgYYtabvt8QojzkGD4FStW4OLZMxhdIc4uBQZBwao4HgN6ib/CwN4Cmq81bNhQxcMkhAa7WxyP5lhqBiZPnqwaQZ45c8bd4hDiNbRv3x4rfvoR026qjNJhdo4zUdFUYIhfQyWGOIRZSedUtea1a9di8eLF7haHEK8h+9J5hL0+FLfG5XSqtxlRXho2dZZYhHgFVGK8gDRdNpqv3K4mmfdEOiXEGeel4SchxDriAlL/01OhHzNESvXav5PS5aDp1s/xwhHiRTAmJh+keFtERASKpWe61Z0kA97xtEzjvCdSJzoMr9ZKRJtPZ6B69eruFocQj+a5555T7upnykQhUlKp7UGrBW67kxlJhFCJyZ+HH35Y9S6pHBGCVa3qulscj0b88oMqlQImDoc8U2onL3C3SIR4JOvWrcMPP/yg5tdEhWHxbbVsyzqMLwE0uAWa+x6k8kLIDajEWCEjI0MpMEK8G9OrCSG+xZEjRxASEqLGmIcqlLBNgbm9A7QPsZgkIebw7pxPN9lnnnlG1W8ot3mlu8UhhPgIffv2RfMmjTH9kb7oXc6GQpERUdD0fNgVohHidVCJsUJ4eDhGjBih5tkA0nYydNlYduYy5g0YoLrw9u/f390iEeJxlP9zBV6ulVjwioFBCBj7Ed1HhFiBSgxxKEdSM/DE1kMADuHUqVNUYggxQbKRsqUz9R+/2bbBW19AE1vM2WIR4rVQifGSoNlqkaHGeU+mRlSYapGw7XIqzp49iwsXLqgsDEL8mVWrVuFs8gl0270WAadO2LZRcCi0cexLRkh+UIkpAE9wJYVpNVjesg68hRHVy0I77HW0aNECWkkHJcSPSUtLw0svvYSkpCTMjI/E1zdVRXSQDddF09auEI8Qr4bF7qzw+uuvo1GjRui0dhf2XU1ztzheRcsS0WjZsiUVGEIALFmyRCkwQmBAAKICbRh2wyOh6THA+cIR4uXQEmOF5ORk1QNIugCFaKnrEUIKR7du3RAdqMEbw4fhzTrlbXIJB7z2MYN5CbEB3p2tEBUVhYSEBGgDgFIhQW6VRVoNtFuzU02e2nYgP9LT090tAiFu7Y3U+tep+P322qhyI7YtX1p2ZDAvITZCJcYKEydOxN9//40DHRsj1M2WGGk1sP9aupo8te2ApViiffv2YeTIkaoT+LFjx9wtEiEux7Q3kk1F7cqUpxuJEDugElMAWg/PBvJkFj/WF9OnT1eVj2fMmOFucQhxGVeuXMH27duR/cMUwNbeSKLAjHyHbiRC7IBKDHEaD5QrjqCAAFU4UKPhT434j/XlncH90emujnj1s8m4ct2GDtUaDRUYQgoBA3uJ0ygREoSvm1TBzdN/QXR0tLvFIcQlCszO4YMxbe0mSPTarKRzeLRyqYJTqm9rTwWGkEJAJcYCe/bswTfffIOSJUvi9ovX0CQu0t0ieS2tS8RASwWG+AlSjbdK2mW8WKMsPjhwEkOrlkZiWEjBvZEYB0NIoaASY4G9e/fiu+++U/NhNctSiXFk0cBf/3K3KIQ4j02rEazR4IkqCehSJh4lQgoYYtkbiZAiwUAFC0i5fAMl3ZxeLUhdicSwYDV5etuB/BQYnV6P33//3d3iEOIUVOZgxn/lBMqGBSuFJj8Cxn/JdGpCigAtMRbo0aMHbr31Vpw+fRq1pv3PI9oOrG9TD97MklMX8daeEziyuD3mz5+PW265xd0iEeJQUk4eR5g9JRBYD4aQIkNLjAViYmJQ+6PRaDP7IySEBrtbHJ8gRZetOlwL7777rrvFIcSh/Ll2DZq1uF0F8mYXpMiIdYb1YAhxCFRiiEvoUjoe5cODVU+lF154wd3iEOIwrl+/jpFDh+JC5nWM2H4UC09ezH+D1p2YTk2Ig6A7yQtI12Wj+8a9an5u0xpuryBcGII0AZjfrCaKB19D0M03e03lYUIKIvXSBdTS6iBXaN3ocNxdOs76yvHFoe39qCvFI8SnoRJjhk6nw2+//YaESykoFx6C+GD3nyIxT2+7nGqc9+a6MULWoHugnbzA3eIQ4hAiF87CRw0r4YHEYogOCsy/ynfDpq4UjRCfx/13aA9DgnkHDx6s5tuXisFXN1V1t0g+TXZ2Nqv5Eq8ubocNK9R8i+LRBdeD6dbPNYIR4ifw7mHGiRMnjPNlGNTrNKQh5IABAzBp0iR3i0JIocmeM0VyqwtekfVgCHEKtMSYkZCQgBHVy+B4Wiaaxke5Wxyf5FJmFu644w6kpKTgjz/+wP3334+yZcu6WyxCbCYtLQ1DnnoSj19NQqNYGxSTt75gOjUhToBKjBnlypXD01VLu1sMnyY2OBA9S0RgSkqKSmdPSkqiEkM8nuzUFOi+/wL6fzfhk3/2YvH2Q1gC4NVaiXikUinrGwaHQhtX3JWiEuI3UIkhbmFYtdKICtTgsUoJiG3KYEfi+bEvp19/Bvqkw9BlZ2NV0mm1XBsA3FZQLEzT1q4RkhA/hEqMl+AJWVKOJCYoEMOr0/pCvIPseTOgTzqi4l8k++jHpjUw9egZXM3SoUZUmPUNwyNZ1I4QJ+Jbd0YHkJqaigJ6zrqc8EAt/rmjAXwd6bGk+fJnr+wPRXwb/daNgD7b+DpQE4BB+bmQDDCYlxCnwuwksxoxderUQeNl/2LYv0fcLY7fIMpLysDOeGHbEXzyySfuFoeQXG4k3fRPgIvn7N+YsTCEOB1aYszSqzMzMyHD1bUsnbvF8Rsys7Nx34a92HElFZoJE9CoUSO0aNHC3WIRN7BkyRL88ssvuHTpEipUqICBAweialXrtZo2bNiA2bNnq87zklnYt29fNG7c2GEKTPb4EUDyMfV6QfIFNCsWZSzaWCCMhSHE6dASY0J6errqXl0iJBAVI0I8qu1Az4171STzvkawRoM7S8Wo+ZCQEFy8WEDvGeKTrF+/HtOnT0f37t0xYcIEpcSMGzcOly9ftrj+3r178eGHH6Jt27Zq/ZtvvhkTJ05UNYgcQfb8b4GTSWp+95VUPPPvYbRZvRPfHztb8MYJiYyFIcQF0BJjQvXq1TFv3jzl3vCk8v4iy8YL14zzvsgzVUvjXEYWHqpQArU7d3a3OMQNLFy4EO3atUObNm3Ua6mcvWXLFqxcuRJdu3bNs/6iRYvQsGFDdOnSRb3u1asXtm/frqw5jz76qNVmjTIZkPirsLCcwNw8sVj/bjIWspuwNxk6PXAlS4ezGVl5dyxVp6NigcBABDS8VVXm9eRYGMOx+kL8GY/Fv4+FSowVND7wI/K28z2ubnk1L0okeyv5F1lZWTh06FAuZUXaUdSrVw/79u2zuI0s72ym8DZo0ACbN2+2+jnz58/H3Llzja8rVaqkrDglSpTItZ40KD0JPQxO5XfrV8D4vSfw18VreKxy3oDeyM49Efvo81538xEXnK/AY/HPY6ESQzyapUuXotnMDxE15Vd3i0KcyJUrV1QfrdjY2FzL5XVycrLFbSRuRoolmiKvZbk1unXrlkvxMSgdElMjipQpOvynkBQLCcL/6ldESpYubxf5iCikte+G9FOn4C3IccvN5dSpU17fUZ7H4t3HEhgYmOchwh6oxBCPRKwxc+/oi2HDhuGWuEjMuHYNkZGR7haLeDlBQUFqskSegbbBLcCqRdKl1LgoIlCbe53wSNUTCSFhXnnTEZm9UW5L8Fj881gY2HsDCR68++67MXz4cCw6ycBSd3P5ehZef/11Nb/p4jUs7tnB3SIRJxIdHa3cR+ZWFHltbp0xIMvNg37ltbX17UV1nE5IzIl3yfOmBmjVEZoJX7EnEiFuhErMDfbs2YN//vkHM2fOxPrzV90tjt8jFX2/rZOAmCAtHixfAt3KxCvrDPFNxKRcuXJl7Nixw7hM3EvyWgLuLSHLJZDXlG3btqFatWoOkUkCczUj3wFadwKKlYS2WAn1H23uhubD76Ht96RHB+8S4g/QnXSDo0ePKh+emL1qRudTRtxNhJn74f2A+jER+KV5LSSGBXtdwCSxH4lVkWKHosxIbRjJPsrIyEDr1jn1ViZNmoT4+Hj06dNHve7UqRPGjh2r6spIbZh169bh4MGDVjOTCoMoKdrejyKgz2M+E6tAiC9BJeYGPXv2VIPo7v73oExYMDyt7cDeDo3gj1iq1yNpt5999hnefPNNlCplQ+l34hU0b95cBfjOmTNHuZEqVqyIUaNGGd1D586dy6XM1qhRA0OHDsWsWbOUBbV06dJ44YUXUL58Tpabo6EiTYjnQSXGhPDwcDSMjXC3GCQf5Cb3ZM/7kZSWiT+WLsHC5SvzrehKvIuOHTuqyRJidTGnWbNmaiKE+Cf+56MgXs2BR7oh9UbV4lpR4cr1QAghxD+hEuMFSKuBAZv3q8kX2w7YQ4PYCKxoWQe9yxVXxfH0j3XNU7revN4HIYQQ34TupBsBg+KDb7t1OZrERULrYb5vaTWw4uwV47y/ExcciAn1KuRaJplL+59/Bz169EDZsmUxevRo3HvvvW6TkRBCiPPxeyVGMg2k6Zx0sP5aE4B/7miASPOCVsSjMaRefz3gAfVfvsuUlBQ3S0UIIcTZ+L07KSkpCRcuXFDzt8ZHUoHxYjqXjkPbEtEI0QSgw9Jv83Q8lm7HUkDPtBYJIYQQP7PESJdYqc0gLpgKFSpg4MCB+WaIbNiwAbNnz1b9SaTWQt++fVVdB1NriKRVLl++XD1B16xZE4MGDVIpk85G0jG3bt2KH+9v73Gp1cQ+biseraazGdcRGxRobCQp/1ccOo29e48rZUaa/tWtW9e4nXQ1lt+e/IYlrVcKrxFCCPFBS4wETor7pXv37qr7qygx48aNy1P+24DcND788EP1FCzr33zzzZg4cSKOHTtmXOfnn3/G4sWLMXjwYLz11lsICQlR+8zMzIQriIqKwoMVSqBdydzN5Ih3UiIkKI+r6UR6prGdX4OF03K9J4UOH3nkEbRq1UrVHbHUhFKUHFF2CCGEeLESs3DhQrRr1w5t2rRBYmKiUjyCg4OxcuVKi+tL1c2GDRuiS5cuav1evXqptFix5hisMLLOfffdpxQcUYqefvppXLx4EZs3b4ajSU1NVYqVtBkg/sNrtcupeKfPGlVGjciw3AHB+/cbX1fauSnXezJJwbUHH3zQ5TITQgjJH7vs5pK6eujQIXTt+l9aqzRtq1evHvbt22dxG1kulXBNadCggVFBOXPmjHJL1a9fP1fROTHty7YtWrTIs095IjZ9KpZKmmFhYTa5AeRzX3vtNbXu+3XLoUpkaM5xVKkBTyVEl41GZ3PsCCFVayDID1sQOIKSAIy/3LdfMH7ndTcsxsQu7ZGUloE7S8VA8/YLarm8LxntZU5mo1piWZx/fgCCXv3Q7rLzujeehfaVD+AuDJVmpXuzM0vme7MbzlbZvfkYff14eCzeeSxFPdZAe6ulSlM28y6x8jo5OdniNqKgxMTkdtPIa0O3WsP//NYxZ/78+Zg7d67xtSg6zzzzDOLi4go8Bkm79cbU2y3uFsCHSZCg7nze/6uoH/DRd/AEihcv7m4RPBZbxg6hRIkS8CV86Xh4LP55LF75SN+tWzdMnTrVOIlLy9Z4hbS0NLz44ovqv7fhzbJ7u/yU3b/xtXPoS8fDY/HvY7HLEhMdHa3cR+YWEnltbp0xIMvNg37ltWF9w39ZZvo0JK8lU8QSYhaXqTCIOf3w4cNe2YnWm2X3dvkpu3/ja+fQl46Hx+Lfx2KXJUZ8VxKUa1pnQ9xL8rp69eoWt5Hl27dvz7Vs27ZtqFatmpovWbKkUmRM15Hg2wMHDljdJyGEEEKI3e4kCdKVdNNVq1bh+PHj+Oqrr5CRkYHWrVsbS/h///33xvU7deqEf//9V9WVkUqqUg/m4MGDxk61EnQo68ybNw9//fWXSr2WfYhVRrKVCCGEEEIsYXdYcPPmzVWArygj4kYSl4+koBrcQufOnTNmQwg1atRQtTdmzZqFmTNnqgJ2L7zwgioyZ0ACbUUR+uKLL5QVRordyT4lddvRiBtKatwU1h3lTrxZdm+Xn7L7N752Dn3peHgs/n0sAXpfcL4RQgghxO/wyuwkQgghhBAqMYQQQgjxSqjEEEIIIcQroRJDCCGEEK+ESgwhhBBCvBLf6TJlA9I5W+rVSGq4dMseOHCgajTpaezatQsLFixQ1Q6lm/fw4cNxyy23GN+XhDJJcZd6PSkpKSolfdCgQSp93d1IX6tNmzapmkCSIi8FC/v164cyZcoY18nMzMT06dOxfv161S5CGoKK/NaqPruKpUuXquns2bPqtXRdlxTBRo0aebTclvjpp59UvSapwTRgwACvk98Tx4MNGzZg9uzZ6veRkJCAvn37onHjxh5xXdpzLMuWLcOaNWuQlJSkXksB0969e+da/5NPPsHq1atzbSe/l9GjR3vUsUi9sk8//TTXMknp/e677zxmvLTneMaOHavGf3NkDBo5cqTbvptdBdyTLLFz50413sjvrFixYrj//vuN9eQcek/W+wnr1q3T9+7dW79ixQp9UlKS/vPPP9cPGDBAf+nSJb2nsWXLFv3MmTP1f/75p75Hjx7qvynz58/X9+/fX79p0yb9kSNH9BMmTNA/9dRT+oyMDL27efPNN/UrV67UHzt2TH/48GH9W2+9pX/iiSf0aWlpxnW+/PJL/eOPP67fvn27/uDBg/pRo0bpX375Zb272bx5s/7vv//WJycn60+cOKH//vvv9b169VLH4slym7N//379k08+qR8+fLh+ypQpxuXeIr8njgd79uzRP/DAA/qff/5ZrS/Xp/w2jh496vbr0t5j+fDDD/VLlixR1+fx48f1n3zyiZL7/PnzxnUmTZqkHzdunP7ixYvG6erVq049jsIci4w1Dz30UC45ZfKU8dLe45FzbHocMvbI706O053fzZYC7knmnD59Wt+vXz/9tGnT1HEvXrxYHcfWrVsdfk/2GyVm5MiR+q+++sr4WqfT6R999FH1A/dkzH8w2dnZ+sGDB6vB1EBKSoq+T58++rVr1+o9jcuXL6tj2Llzp1FWGfw3bNhgXEcGUlln7969ek9DLqrly5d7jdyiLA4dOlT/77//6seMGWNUYrxFfk8dD9577z39+PHjcy0TJfCLL75w+3VZ1LFN1hdFYNWqVblulHKzdzX2Hovc3EVBsYa7x8uifjcLFy5U343pQ6C7vhsDtigxM2bM0A8bNizXsvfff1895Dr6nuwXMTFZWVk4dOgQ6tWrZ1wmjSzl9b59++BNnDlzRpne6tevb1wWHh6uTHCeeCxSgVmIjIxU/+V70Ol0ub6LsmXLonjx4h4lv/QEW7dunaokLS4xb5Fb2oCI6dn09yF4i/yeOh7IctP1DSb8/fv3u/W6dMTYJr9x2Y/hGjV1IYjb5ZlnnsHkyZNx9epVOJPCHkt6ejqefPJJPPHEE3jnnXeMbjJ3j5eO+G5WrFihquSHhoa69buxF7kuLF0vhuN25D3ZL2JipE2C3JTMff/yOjk5Gd6EoYN4TExMruXy2ry7uLuRcz516lTVesLQZkJklEaiERERHim/9O4S37LEjMjAIb5fiY05cuSIR8stiNIlPuvx48fnec/Tz7unjwdyjvK75tx1XTpibJP4kfj4+Fw3lIYNG+LWW29VDXpPnTqlWsa89dZbGDdunLrZeMqxSKydKC8STyEPTBK38fLLL+O9995TcRjuHC+L+t1IE2RRyOT4THHHd2Mv1q6XtLQ0FZt37do1h92T/UKJIe7h66+/Vhfh66+/Dm9BBsWJEyeqAXHjxo0qiO61116DpyM9y0RhlAHcGT3HiG8iAeCi/EpAqenvpkWLFsZ5eQARJWHIkCEqWNP8CdudiJVUJtPXzz33HH7//Xf06tUL3oxYYeTcmwe6est34yo8Q21zMtHR0UpDNde85bW3ZWYY5L18+XKu5fLak45FFJgtW7ZgzJgx6onIgMgopkTJEvBE+cVaIZknkrHRp08f1eB00aJFHi+3mGZFlhdffFEN3jKJyXnx4sVqXp6CPFl+Tx8PZHl+15y7rsuijG1itRAlRhRfuRHmR6lSpRAVFaWe/D15nJbrt1KlSkY53TleFuV4xEUmymXbtm0L/JxSLvhu7MXa9RIWFqaUZUfek/1CiZEfttyUduzYYVwmpix5barFewNiQpQvefv27cZlYjUQ06MnHIsEi4sCI2nWr776qpLXFPketFptLvnFfCiWBE+Q3xz5nYhrydPlliew//3vfyomwDBVqVIFt912m3Hek+X39PFAlpueO2Hbtm2oVq2aW6/Lwo5tP//8M3788UeMGjVK/TYK4vz588oFEBcXB08ep2V9cQkb5HTneFmU4xErsDx03H777R7x3diLXBeWrhfDcTvynuw37qTOnTsr14CcODHPydO1BLSZ5617AqKFm2rVEpwmMRkSeCeBmFL7Y968earOgVyks2bNUj/gm2++Ge5GFJi1a9dixIgRSus2aNoSTCcauPyXpwupHyDHI6+/+eabPGZhdyB1VcTfLOdYvgM5DrFmSIyMJ8styLk2xB0ZCAkJUU9ohuWeLL+njQeTJk1ScSJijRPkmhOXi9S0kNow8pR88OBBPProo+r9gIAAt12X9h6LWF+kbsrQoUOVnIZrVGLAZJLf/g8//KDiLkQBOH36NL799ltloZTgTE86lrlz56obpsgmVkaxLkkdn3bt2rn9eynM8Zi6kkQ+uX5Ncdd3k17APUnGzgsXLuDpp59W77dv3x6//fabkq1NmzZKOZE6Sy+99JLN58ZW/EaJkQhvCbSSi1cuWnETyFOIJ5rSZXA0jcOQG4/QqlUrPPXUU7j33nvVl/3FF1+opwop3iTH4gmxEFIsTpAB3xTJHjD8OPv3768Gl3fffVc9bRiKrrkbMXfKRSXFnOQmLyZ2UWAMmQ2eKreteLv8rhwPxEIl58qABKfLTV9ugBJIKTfEF154IZfi6K7r0t5jkXgR+f4l+NUUKezYs2dPZeYXa4YUVBPFQG6ycg088MADqpCcJx2LWCDkfMu6ErQuN8Q333xTBeMbcOd4ae/xGCyke/bsUW4+c9z13Rws4J4kY6YciwFRFkVhmTZtmlJOJKTg8ccfVw+Jjr4nB0ietUOOkhBCCCHEhfhFTAwhhBBCfA8qMYQQQgjxSqjEEEIIIcQroRJDCCGEEK+ESgwhhBBCvBIqMYQQQgjxSqjEEEIIIcQroRJDCCGEEK+ESgwhhBBCvBIqMYQQQgjxSqjEEEIIIQTeyP8Bsy8tX2sGyXAAAAAASUVORK5CYII=", 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", 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" ] @@ -886,7 +888,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Root Mean Squared Error = 0.146\n" + "Root Mean Squared Error = 0.207\n" ] } ], @@ -935,7 +937,7 @@ "--------------------------------------------------------------------------------\n", "g = 10.0 m/s^2\n", "\n", - "Final Acceptance Rate: 0.459\n" + "Final Acceptance Rate: 0.474\n" ] } ], @@ -996,8 +998,8 @@ "output_type": "stream", "text": [ "\n", - "g posterior mean = 7.475215685070181\n", - "g posterior std = 0.08865279238542817\n" + "g posterior mean = 7.476701268184555\n", + "g posterior std = 0.08370710376078842\n" ] } ], @@ -1099,7 +1101,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "" ] @@ -1135,7 +1137,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1317,14 +1319,14 @@ " loc=prior_theta_air.C_LO,\n", " scale=prior_theta_air.C_HI - prior_theta_air.C_LO,\n", " size=n,\n", - " random_state=RNG\n", + " random_state=SCIPY_STATS_RNG\n", " )\n", " samples[:, V_TER_IDX] = sps.gamma.rvs(\n", " a=prior_theta_air.V_ALPHA,\n", " loc=0.0,\n", " scale=1.0 / prior_theta_air.V_BETA,\n", " size=n,\n", - " random_state=RNG\n", + " random_state=SCIPY_STATS_RNG\n", " ) \n", " return samples\n", "\n", @@ -1379,7 +1381,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 28, @@ -1388,7 +1390,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -1645,7 +1647,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Root Mean Squared Error = 0.008\n" + "Root Mean Squared Error = 0.016\n" ] } ], @@ -1711,7 +1713,7 @@ "c_ter = 10.0 m\n", "v_ter = 15.0 m/s\n", "\n", - "Final Acceptance Rate: 0.4045\n" + "Final Acceptance Rate: 0.4013\n" ] } ], @@ -1786,11 +1788,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "c_ter posterior mean = 16.63485257159853\n", - "c_ter posterior std = 0.6134459485881326\n", + "c_ter posterior mean = 16.560949612035607\n", + "c_ter posterior std = 0.6129628114334972\n", "\n", - "v_ter posterior mean = 17.424189488313868\n", - "v_ter posterior std = 0.667640021984014\n", + "v_ter posterior mean = 17.503236242482437\n", + "v_ter posterior std = 0.6866198407584229\n", "\n" ] } @@ -1869,7 +1871,7 @@ "outputs": [ { "data": { - "image/png": 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gwIElqjUhhBBSf7DPJoQQUm9UraA9zzzz6EArV199tQ6mgpnx9u3ba9M1+IL56Nmzp+revXvud5MmTfR/BG6RGfdCQVkYSHz33XeRdah32E7hsK3CYVuFwXYqfVuhv1pyySVVPVNpfTafg3DYVuGwrcJgO4XDtip9O2XZZ1etoA1WWWUVHVwFpmTocBdbbDF14okn6uU+YKZmmq4J2D+tOZoMAFAOHwY/bKdw2FbhsK3CYDuFw7aq3T6b1zYctlU4bKsw2E7hsK2qu52q1kfbZKGFFtIdNoKtfPTRR6pLly7lrhIhhBBCHLDPJoQQUg9UtEYbKT+++uqr3G+kBfnkk0/UIossos0DXnrpJd1Z4/vMmTPVzTffrDvsDh06lLXehBBCSL3BPpsQQgipEkEbM92nn3567vctt9yi/3ft2lUNGDBA+2hhGQKpwJZ+s802U7169SpjjQkhhJD6hH02IYQQUiWC9pprrqlGjx7tXb/DDjvoDyGEEELKC/tsQgghpMZ8tAkhhBBCCCGEkEqBgjYhhBBCCCGEEJIhFLQJISQFSFU0atSocleDEFImEOTtnXfeUdXMt99+q+6///6C9//www/V008/reqRZ599Vk2bNk1/v/XWW9Uff/xR1vpcf/31qfb/+uuv1QMPPJBZfYhS1113XbmrQMoEBW1CCEnB999/r3MBE0Lqk3PPPVfdeOONqpqZPHmyDlhXKPDNHzZsmKpHcN4Sm2Do0KHqs88+K2t9Tj311FT7v/DCC6p///6Z1Ycoddppp5W7CqRMUNAmhJAU/Pfff+WuAiGEEEIIqTAoaBNCSAooaBNCCCGEEBsK2oQQkgIK2oSQaqdJkyZl3Z8QQmoRCtqEEJKChoaGcleBEFJm+B4ghBBiQ0GbEEJSQI02IfVNLWhzszgHTjb8H2wHQohAQZsQQlJAQZuQ+oaCFakleD8Tkh0UtAkhJAUUtAkhtaDVJoVD4ZQQ4oKCNiGEpICCNiGk3gWtep5oqOdzJ4REQ0GbEEJSUO8DbELqHQpahBBCXFDQJoSQFFCjTQipdhgMjRBCsoeCNiGEpICCNiGEEEIIsaGgTQghKaCgTQghhBBCbChoE0JICihoE0KqHfqZFw5N5gkhPihoE0JICjjIIoSQ+oYTFYQQFxS0CSEkBdRoE0JIfcMJV0KICwrahBCSAgrahJB6BxrdehU2qc0mhPigoE0IISmgoE0IqVchkxBCiB8K2oQQkgIK2oTUN7Wg0ayFc6gUOOlCCBEoaBNCSAooaBNCCKkVOFFASHZQ0CaEaLbccstyV6EqoaBNSH1DwYQQQogLCtqEEM20adPKXYWqhII2IYSm15xwIIQQGwrahBCSAgrahJB6FzLrfaKh3q8/IcQNBW1CCEkBB1iE1Df1LmTWO/b15/1A6mn88+WXX5a7GhUNBW1CCMlAo03NNiGk2njggQfUX3/9laqMxx9/XP34448VNen4008/qUcffbQsxy60HWbNmqVeeuklVW44UUBCmTJliurcuXPJjnfPPfeoaoOCNiGEpEAE7Dlz5kRu17Zt2xLViBBCwujfv7/WSKURrg488ED18ssvq0ritddeU3369FHVBISII444otzVqKgJk2qmHvr8Ut8rgwYNUtUGBW1CSA52sIUL2nFt9+uvv6pq4Ycffih3FQgpKZgo++OPPyK3+fvvv9Wff/7pXMd3Z5gmFNpzXxuW4hoSUiqqqc/Hc5nWsoW4oaBNCMnx77//lrsKNSloV9sgvH379uWuAiEl5YorrlB777135DZnnHGGGjBgQMnqVG1Cdsh7btiwYWrIkCFFu4b77ruvKjXV9n4nxOa4445TQ4cOLXc1apJ5yl0BQkjlQD/j4gyy2K6EVDY///xzrCUH/H7hi1yLfq2lqj/asFga7ZBrWCyq/fqT+gbvtbnmou61GLBVCSE5qNFODoRoDLKihGkK2oSQWiZUo01IGp588snYeCiEVBIUtAkhOThQSg6E6Lnnnjuy7TiBQQipZZJodIvVz5Sz/zKPzX60eOy///5MJ0WqCgrahJC6FgjTDopCBG1qtAmp7fcAhavCBfJXXnlFrbvuukUrv9jQbJzUAvY7DGm7KiHdXLUzV6XnZzv33HPVoYceqvbYYw81efLkvPXw87nhhhvUYYcdpvbZZx81ePBg9dhjj5WtvoRUO/UmEH777bdqhRVWSN05+QRtmbhgNM/q45hjjil3FaqOau+zXQLTN998E/texLOPd0ktcMoppxT9GPa7cvbs2errr79WX3zxhbrggguc+yy//PKJy7XBMdKQZv+pU6eqkSNHFrz/Nddco6ZNm1bw/r/88kvwtS3FpFHaa5E1SE+3ySabRLaJXed//vlHfffdd6oaiGtv17sPlgO+mBTF4q677mrUb1Q7FS1oY3DaqlUrdfDBBzvXjxo1Sr355pvqyCOPVJdcconacccd1Y033qheffXVkteVkFrgmWeeUfVEFoNK0Wi7BuMrrbSS/l+s4D/lZNKkSaqWQYdP6rPPRg5mpPICHTt2zBv4yeQZBD8RrrH9s88+q8oNUlshMnocqPtDDz3kHGhjIiQOBBxz+cniHfj9998XrP394IMP1KWXXqrrJ+0flbs7pK4CrlUarTnqhP0LDbaG9+XZZ59d8PHPOuusvPswbmJn5ZVXVtOnT88TtNBeaSaE0AboM7PoN/FcRTF27FivwAUBN2tw386YMcO7/t133210/zz88MPO89hll13UmDFjVCVh1j1k0soHJntMy0d5njHhEDJB09DQ4Dz+4Ycfrq688ko1YsQI9fjjj6taoqIFbdzAvXv3Vuutt573pdy1a1e15pprqmWWWUZ169ZNtWzZMu/lQggJ56qrrlL1AjqMcePGpS6nXn20d911V1WroF8h9dtn77zzznpg7Xp+Yeb81ltv5VmqVIolELRPttYUlgVSX5Prr7++4ONsttlmTkHiq6++UmuvvXbs/qED8ig+/PBDfTwbGfjbAv0666yjsiCk7sUwJbePG3c+mKgw8zhLnez97rnnHnXOOecE1+PTTz9Vs2bNUsUGqdrGjx/vXAcBN2virqurD4fA73r28Z6rFQsXW8O95ZZb6oB0Nh06dNBWAYXy0Ucf1azvfUUL2nGsttpqeiYZM4x4SNAx4kJFvegxC/v777/nPpgBNl9EaT9ZlVPrH7ZT5bUVmH/++eumrbJ69tEBS1oMX31kBr5a2imkruDzzz8v+7kU4yOCX2hbhLQVqbw+2y5DfpvL7e+SRsq1/MUXXyzZPTpz5kwtSPnOSUxFX3jhBd3Grvsx6rv5EbNu+Q0zZFwXV7uFXBdce1+9fWUA+ItGnWvUuaS9Z1z7m8cNOU7c8eP2RT8TUlZUG9rL33jjDTVx4sRcPwXNrq98u43jPu+9915B5xp3Dua9F/fBNUpyjZOsl+tvn6urndLce1nv72tX+7m0t5PJCEwu4B2Oicb3338/d64YC5nbwpWoSZMm+r2Nd4br2sr1MY8fd1/YfTW04Fm1k+sa1nUe7T59+ujZW/h7QaOExoFv2BprrBFpjmLOxMK85rzzzlPNmjXLrF7LLbdcZmXVMmynymurnXbaSTVv3lzVQ1uZs/JpzvmEE07Q/5dddlm19NJL55ZLp4SykTs27XFK3U4hdYUpJN6phQCN1BFHHKEeffRRValIG/FdVbt9tlzbhRdeWM0zzzy5+x7Psnxfaqml9PcFF1xQ/1588cX1f2jlzecEQm2vXr0SHR+DU2j44cProk2bNvpZsVlrrbXU8ccfr4YMGZJbJto11KlFixZaSwSWXHLJRs/zfPPNl1uG8zB9Me1tURZA+4h5LdrA3A7rfPuboA3hSmNu07Rp07z/UgYmfk0wkbHbbrvp77h/Fllkkdy5wv3AvIZSll0X87cpPCS5X+Q9L22MY+LYUjbKtN8Ziy22mP4PP2C5LjY4HwjTrvZDmXab4xmYd955dZwDxJR4++238/aR+xaY2m2zDNQbQiKW3XLLLeqAAw5QAwcObLSdsMACC2hNs70O9wWea/N9jvaR9kVb4x6TZwdstdVW2s1k7733bnQcu01tLTzqbGu2P/74Y3XHHXeok046yVmHKPCM2OcM4RDWG5iM+OyzzxqtHz58eG6Zfa6LLrpoo+3xfsBEYxILApO0/ZBZF7td8Vzi/WEvN98dojBYYokl1Oabb67f52ZMhT333FO/x3AdYDmx9dZb6zY56KCDdKwLuKvIOwrtg4+8W2ANtdBCC+n7H5aVsHyCNU7ceUyYMEHHBTHbtNL666oWtB955BHdAR133HH6hYOZFfig4MbwzZD37NlTde/ePfdbZi5g5pHW7wNl4QLDnIkRSP2wnSqzrTCoQedWreY7SdvK9CVExyCD6EJBu5l+i/I+wXJog+R7tbRTSF3ReRZ6TjBnxQCxEtrEB9ooi+cPz1WWk7nVSiX12fZz8Ntvv+ny5H6Ez6F8x7sC36GZAeKni/sfAqsAYSbp/Yx3BtxYZD/4KQ4YMCC3HhobV5moMybwzHWoD5BlonFDfe0yYFosy9CWpu+x7xzQPiJo28c2r0VUG0DIxsfcRo5t1gHXxWxbgGsk+2GCwmxvnLt5DaUsuy727wceeMDr6gChQKyVzHqZ73mUh2Pa195+Z6C9cM+hr/G1DwQ77ONbD0sKc530KxBu3nnnnUb7mfewacpsbif3NJbJNrBUsrcTJN6IvQ5Cvut9bv7G5LYcD7z++utq4403dh4HbWpeb9t8HdpUe90TTzyhTj755EYxIkKeSQTks7fFd8SUwH8JeuYqS5aZ68TqxVwGk2u00VFHHaWy7LNh5RMyfrHPzb6uuN/t5WgX+32CZbg2okAwwUQq4pvIJMiXX36pt5d7VwRtnIs5+SPXW0z0cS033XRT7aqDZxvxPFx1lwlCsdrJarycZZ9dtabj6CTuvPNOPfuGEPTw89puu+3URhttpB588EHvfpj9w6yJfMybExcm7SdJObgZnn766UyOW22frNq7Hj6laiu8pPASrJe2Mll11VULPiZm5ffaa69GbScDT3yXgVm1tFNIXeU9XKrrVer2kbplUUdSmX22WYYMAGX5/fffn9OkR90X0OKkqY95XHwQ9CrkGTGXI3gQhCRzuf3fdT+KMIBrYCLbQXMpmjz73GHJY5ZnDrpd54gBsd2GrvOJWm+/Y819ZJ19vVzXzlyGwFW+a4OMFK5n2FwGDbVoxqPqb66DUIBAmaIFhWAS965x1d13nQWzvVz1d93Tsp/vGkRdH18dffWPO9+QOmDirn379o2OAyFOAuqFPov2tp06dcotk+CYUW2H/3aU7pC2S1o31GW//fbL/Y4av9x2221agJVJu6j7LOr6uc4jKjaF795sMJ5X29fbLP+6667TE5A9evRQF198sV731FNP5baDVQMyV7jauZD2jTrPuhW0MYgVfwATzD5m3UjF4rnnnnOazBBSDkTQJskQvzn7vWMGTylGlNRKAOaaps8sIdXcZ5t1Q7Aw0ezZ6zEIFExzXayHhrSYVho4nl0+Ji+SRj1HXWH+GcUhhxyilQEh2G1lAt/qKPeAclCKew7HEAsAs4+AVtfsE44++uig8u6++27n8pD0kSF+p1n7ppqa+jjgJ25aCoReHwi2rkj3CNZpPqdxxB0PZvW2dY4Luc995cGiAVraNOBZg+l8CLAeEje5uL467vqbvtS2VYpPwBbttGAux2SBq2zB1HibFjvSBrDiqAYqWtDGhfzkk0/0RxoZ32HCgZlt3NCYrcENh3XoEJCeyGcGRAjxgwEvBe3CiBK0McioRkE7dKDj8h8NoViDOlI+arXPtp+FqDSI/fv3176YhZbtA2bC8HuEX+Ttt9+usqDQVFVJiUvVlUVbybrQ90qhfV2S9xbaV3Izm3UPKQN5s23g3+uqiy/CdaHv2KzfzeLHG1XuvvvuW9AEla9MmCubZupZ07dv34L3vfzyy1WlTR7h3kqaVstOEei7Jr/9f426SaVMrpaCiha0ETACszH4yIwSvsus3qBBg7TJBG7awYMHa1MvmHDCAZ/UPghSUWzqSVtXSZqlUpDlYMJlDSCCNmbpa1nQLvY9A3O3SstJSmqrz4Y2uNB0V4Xc/64c1HFgws40D3dN7qV5p/n2zeL5tiMNh5QZci5R5URp2LPGV4+Q6+MTBs8888xMj+9rT/vapOWiiy5qJFgVOqkhedKhAU4adBPnfskll3jXw9LA1FLbLhJpiWtLZFyQCcksgCk1XB0KrVPIpFsh74KGhPtkfT9WAhUdDA2ROEePHu1dj8h3mD0mSkc6rbfBqC9yZ5a0bt1av5DtoCy1SL0J2lniGjxKjlcsL2RgnRU333yzDh5z6qmnJtrPFQjIhQTHscHkAkwaETk2zSQWcnMOHTo0cTRnUnqqrc9efvnldZRkCYRkIz7MoUS9P0WLv8UWW6hWrVrlBMFC3rl2up2440OjjOfRjAwu5cQdp1zEtYuvbpJuCBHZAd5BIe8xEXoRwRiRzXEvJzmuudxV92HDhul4A/a2Zo5z+KLaEZNh9gttb1p89b7pppsaLbPrD9N/pFCKet/LPjgHBDBcZZVVvNrMpCBWACbDUG4h9yj6wAMPPLBRv4I4A/vvv39eBHEIv3g+0+K6B8y6HnvssdpCBfdbFuUj73jctqV4ni+99NK837feequzLq72CX2fCRKgrtKpaI02CQcvwqRgBi/LGbVqAZ0pXryh1Is5dT35aOMFvv322+d+IzBTGlyDXjMAWjk12hjgT548OfF+oQIAfOFcIPBVlKZSBjhx1MqsNqk8YI4dNeEj2kaX/2ASENRnm222SSUwuXLNhoIo5iIopaWQPLOFaLTj3pmh7XD44YcHCTOYjEAqNWhRd9hhB6+FQ0jdcU8hfkXItojVEwUmgWDVIyCAYCGgHQoFwr64Szz//POJ9l1ttdW87WAHyDNxXdtC72FMcoSStSWE6T8fJWTGAa2+xGEol0tAkv3lHE/4/+lPQ8qOC0QWN3lR6LNRbCho1zGYwZNZ33pi5syZiV689aLlrScfbfs8ofFNg0tjYkbXLKdGu1DS3vcQUlxBapJCQZtUO65gVdCk33vvvYnL8gmYcYKnSztfiHl22qi8UYI2/PeFuEmJEI0YgLm9HQk6pLyk1gy24mPnnXf2rjfb/bLLLstbF9cHmwGg4iaAzOVJYmmY9YMLVKnGQFH3I+pQqPY3Ca5zNYN2JUU0+yHHifPZv/DCC73rQ8oLtewoxQREE+tau6590r7fziNfKVDQrmNqQYB0mT3Vw3n7Il6moZ5Mx83BDEyq0/qIuqwBzBQpkvrGjERuk1bYj6tfUqIGfKFBlOrlfiIkafAjpDTzRZGO0u5G+WgLoROmphmwpC8q5iRXXL9lro+KaCzvSzPycqifdNzyLHzZfQJwSHuakZVD95HI5lHX3o7gHLVe6g0fZlgs3XHHHSprIUow3TbMfiU0eFwpJmIlrVQS64ssA/e5Jo9s337zeLCAuOqqqzI7HkDAypAJq9Dn5B9Hm5kTbea2MrlnTtzLuiTWqeWCgnYNkTS9B15qlToDFMpJJ52UeJ+kHWwlannTRLz0UU+m46bAu9hii6UuzzXolbbEf+l0XJFkBTuXbZZkHcRE8otGEWoiGlJOsYD7DDSLMPV0WR0UGlGd1AbmYM6Voij0Ho8SMEP2h1/3hAkT8o4bdWyJdJ3kuYIfrMkxxxyjitFvhbYZLM+ySBuVdWor+PbakdRdKetKMfEo5SGlnHDyyScXVJYZO0HOB2a/cC+MmlgV4avQ97QpEMJkP4v2iIvngSCMvvFyIdfIdj0rtvA/atSoyHv/9ddfV2eddVaj5ajXjTfemLOkKKfraPv27RPHZDr77LMbLTdjHFQqFLRrCLujjOPFF1/MadrqiaQv0nrRyqHDhFAIocMX8KRWSJpqJY2gjeXyPcmMcBR4bkO1YeVK14M2SZNeBdFrMZFWzEEL0pnAdxGmgS7/vyFDhhTt2KRykHvMvtfkmX744YfV6quvHlyerZV0gSjKoe9Z5N6VvjrKp1XqH2IdE9eviYY7qc+kr25ZIoFffdcrC3xlHXzwwfp/1LstzqQ7SXuEbGtOHCPuQCHgfCSfd5L6SdC4qD41i+sS0pbiohEXswh++OPGjVO1CARwW2Fivt9kIub8889PrGhLch2znmT/N8IasNKhoF0BfPDBB5mUk8b/op6oBY12MVhqqaV026Cz9QW4qhUQTd4EHXTWfoem2ZN0EubsfRpgMimDojiSmv1Vyn2PGWy8G4spaOO64wPByOWTWcw8rKRyCBWI7HvEt1+oP2MheYMRabeQ4GyFuo/YwYxCykF+9KxAIDnTPLR79+5q4MCBwXUJ2Q7WAna0/KhnH8HBTGztomScEJPurCfrxWUuNPaHfd+++eabkRGbzfrCpNw+X4D4PtCshvQfIvyfccYZsXV1tbvr+qF8cyIU5tJRkbcr8X0i942Zlgvnn+R+kfaSfTbddFN13333OesTcr8garyNTAimuY8bYvaVXPCuyUdXAMZqit1CyawCgGY5C+pV0E768EuU2XPOOaco5ZeLtKkORFjE4K8azHGyYvPNN9dpb0L9jgvx0Q6NOh5lyoW8mxhkmmUXk7T3faU/NxCwJVct/ARd9a3GIHYku3eoHczvySefLNtzYkfuTqo1tDMPFDpQDXmf7bPPPvr/oYceqtLywgsv5OWUfuONN2L3sf02XZONZntdd911OoWgSVRe9Q022MBpwSBm5VdffbX+jzRhQOov75M4l5S4LBHiMmdPGPusj9q1a5e3bMcddwy2iILg43oPIqp61HmYJuyPPfZYLtdzHIj8buO6t9Hm5nXt2rVrXnT2JM8lJnPM6N5R4JxdEw82psZc/N9tn2PJDY7vuGeQCgsm1a5I9L7zsdsL7ywRVuV9Jprrbt26xZYJSzLX2COqDkloiJmc/Prrr4P3qRbqUzKrUepV0E5qjitCCvxYkmxf6XTo0CHV/iIsxnU0tcayyy6rmjdvnso0yaXRlgEh2jTqHsLgQPZ1+VWJLxO0JFJmaMdTSFoxKTvEtA55kbMgSqAtVpA4+5q42rQeXWvqERE6bcH6oYceit3XDCIFk9Q4QvPHR2klce9iMC0D9RDhWd4dIc+T+HG6CBVmMJHlcmeD73Ux3LfMbSCcJyHq/RwiVAkiWNtjsZEjRyaawJN80dBWbrvttqpQoiLEu/xdswK5zOMCiCURfqMInTCKUqyY18MlaNoT83GxgXDvm4H67MjyrmsyceJEbfKO57qQd4SrTHk3+azfMJmEyX07ZZvv/kzy3JrnX+rUYpVGfUpmFUZWszX1KmgnHQyL8CGmKnFU6mxa1oEs6im9l4s05+4StMWMzTQdd4GZeF9EWAS5EU27WX7oPYlIoUmRstHxx7HiiiuqLGjVqpV3QLnAAgtot4ZiXm9fe7rSMpHao1jveKTQtAMgmkJskuPefvvtue8YiEPwNoXe0PIk6CLMh31EBdQK7W8RcMo1SA7NL+3aN8m6QoV0m6OOOip2/0LqlVUsjGKQtv5IwxUSzK5U9XFFtE4D7uEkQQ9DXcbiooNn5SohQNmEeAeDBw9u1B+nJcoiJC0YL1XquNxFfUpmFUZWN0zSl1GzZs1UPQpIIvRgAF/NhGhbkgraiMJcj6SNkO2a5JL7DPenfI8TfBF0ycSV5z5JfleXGVYcpsl7sQZArnMQ8zQXIfm4MSGRZNIt5Pzmnnvu4PJIdWLfw0mEm7j7HxoqW7NTKZOZhQa8NLVjZqDHNM9ZnAYvqbD822+/Jdo+i4BvSe4LXx70LCimeW+5tI/XXntto0mlpMeSc5oyZUqQ+0EhHHHEEcF1ccV0iXMZAKeddpq3TPt7SL/pS+vqijOR5r544YUXdJmFTrKZ39u2bauqCQradazR3muvvVSlgZfpKaeckshUOmn7iUY79AUdMjDCoKWYeZAFmAqKMJG1BQPaww6iUS8kFbRtnz/cU/ZgUe4zlCuD99VWW81ZXpJjI6ZD0mBDvvIR6Gj55Zd31tt3319wwQW571H3YFaD11DBBFo45EQPxRe8Lm4ZqS1wjc2+IC7fcBKgNY5z35B7zMyFnMSNxXzvZBXvJUuBferUqc6+Fu0sKZbM1FI28OuV96f9PPr81J955plY1zLzHR71jgmdcCx1ROaoTAqhIPJ0oXWCebjZ/pX+rsQ1hpYV2uVi1VXuoxCNtB0dHi4KEkQP2P0yQL0RTyD0XkQ2jULvXYmzYB8/FJef+bsRk+lRVPq9FQcF7TIiN09WM9xJZxFDt8dsopnDs5jALyfEzw0ss8wyOl9i0ocQga9Czl+ikIaU/+CDDxY1D7I5Yyo+u1nPGlezD0xafLO6Puy0T+j8DjroIK9GOzTfaxRSP9TV9vmLA4NdF7a2TQKDmcezufTSS4Py9dr7YyZe/PaSvPNMwT4KnEtIsB1X/Xy+jAyGVh9kPZAzg3GFxkkwo/0m8W81Y40MGDBAlZKQ5xjvQVffAvPbkMn+tIE5fS5Ww4YNU1mRNCJyqfpapCKLureLlSKyEscSaAdMxGbhcuc7v1BrGJcliBkLIEQT7SrT/B/6fLoCufreW7Kda9xQbC52REM3Oe+881SlQkG7jCQx0ZQZ7yw1jnaABh/w4ZCZ52JTiPl70kGSvGjjjoUJhtCXVSn94305X9NinkO1m9Un1b6kNR23ff6hVTD9e+edd97IfX3HluXm7HchplcuX2OkZnnttdcaLZdONESrNmjQoETWGO+8807RZqjNCLiYCJHIv2k02knM9El1Yr9HZSIzhLT+v74Jnrg+J7S/KbbAE+XLGfX+MM/52GOPDT6eq61E84fI3na08TTlhqzzUYigvdlmmzVaZgepisN+nxeaieTee+8N2s7XNlHn7wpCG3W/x7kBJDUdNy1HJEd9UmQyOmmdTFeuqPsq9JlwBXgz2zKkbTCRLVHzTWRfaNn333//vHWmxUhWwfniQLyaKC6//HJVqWQiHWDWH/lOEa2YA5NwkuZohfboyCOP9K6PelmlyQdbjNkrBMsIzTUaRSECkvhdxr2EZKAQUn4pBW3kSQzNSZkE8xxqNbDeqquumtdJyuAmC0HbnAFGECRBoo7DFGv++ecvqOwuXbrkvqOeURMh8OsWN4aoc5o0aZJzeRY5M7OmRYsWsduYwdSQziQuGE+l+MqWA/bZpRNIQ58jaMNCtayh9S2FK5DktI6y+omq75133lnwsU2fVtekYRb4yoX/vc/9oJD7ySXwffTRR4nKgP9xOUBsFzvoX5TJeajfMUiTDSTkOSxlXBrzecyif/XlDZf7L80xpAxEWLdjn6QZH956662q3kg9mkbwnkMOOUT7x1144YW5wQ06b5itpA3xX8uIwJfVgDbKdwO59nwv4XJE1oVvqEuAL8T83dV+yAVtmrgmiexoU2ka7aw6HhuzPeKCQGEmtVhmZ8XGvJ6mG0GWgqUd0RofO4dlGp/lKL9IdGS2edwKK6zQaDvf/S91DzU7K5aWCD6cYooX8nyttdZaeeX76obnB6b8IRrtWoR9dnaEaAxtjRwmmU3kWUYqrFCNrHlvH3DAAd7tcH3TRtCOww7g6LJksdMLhWq2SnUvxtXF9b61zfTNeyHufRWqvCjlOynNsWDeL7myQ8ZWmPC2x2tZ19sWDqPOTyytKp2k/W0a1yfXsZKm0nXV8dmMrGNdWvhKJZV0gPQvMD9cZ5111OGHH563brHFFlNrrrlmWYJzEDe+wbkdlCHJvmnIQjj1CUiYNfX5dpq+rllNhPhegC1btlRZUyy/0SQabTz7UelhqoVFFlmkqIL2fPPNlzeojDMRDyGraOA+QTpJ7AicXyGY5+Dza8Pzh34k9F0R6icJEzSXb3k9CNrss7PVZu+4444FBQUy77mhQ4emqnOSnLuhJsFZu3TY1iWhz1pa/+xQ4uqz+uqrxyoozJzmcfeUPQFSaL3K7RtdSBoqaJDt8WTcZE0U22+/fZAVZxam2lkROtHUu3dvr+VZCGnG7a5rKMHV0vi5N8noHvVFoa9EUkk6mIHt3LmzNh3q1KlTo/WrrLJKnoM/8XPYYYcVfabN92CLVi+K0JzTaR84cxlMy30prEyttEsgiBIS4iJDFoJPAxwaCCcJIQE3zjrrrKIK2sU4r1JhXneJUu27jwoF9+eCCy6otawoF78LjdBdCkHbjJifRKONc9p6661jt7PrZZZtR3B3bZ9U0I7SaEMwgfYpTqMtkzC1BPtsVVETLDi2LyUd7tEkGqRi9NFZkKaftSeVC/Gn9aUCTHIPxE1u2+dYSDCrahS0k5iHm2meap2464DrGpKCDpN0WbhYZn0Oiy66aEFlnnzyyQXtV+1pZ1MJ2nCS79ixo3c9BipZpsqoVfDQIWq1i+222865vcwuoQMIfRn7tiuXP27cywj+gzBxdCEDaZ8mMiRdR8jLMK4s3N8Y4JSyDUMEbaSwSINMvvg6TESHTzMLXSkst9xyRdFoQ3BFmWhH04Ii6TFc91Vak27B7OjNiPlSfjEHRGY7+FxXzG2yFLRdwj7qYA98cO2STCJUA+yz3ZQiNWMScP8ifSUsDFy47m0EA6tE0uSMtgO2FhqEKSoolcR0yJKs4h5cdNFFqcsohWvgcccdV/GTB3FWZVkS9w5NGvcI7+2bbrpJj7t8mBOkWZxjlHDbvXv3gsr88ssv1ZNPPpn42rsmJappwiaVdLDQQgtFvlBwoZZYYok0h6hp4h4G33oxB4H/EnwNo3JQhpQX549brJnSuDLjNIBYn0bQjhu8h0SFR2RjpNwqZZRuCNobbrhh5oFlzPaQ5zbKrzg0lUWlsvPOO+e+ZyVoowOViReknxNBW+5X3zF892vcINVnwhUimPr8H0OzIUhQvrQ+2lH3kQQDcrUDcnKaWjzznKOO7QoUA7NpX07eWoJ9thvbb7qUFBJ1vFI0l2l4+umny9bemMTPkkq+HqHpUtOQ1nc3qdl+Ifje51lZSSQhydgJ95bcr6UMXJkmgDLJUNDGzDhmJ1yzDZhdwTqXeRpJFpQoThs0bty4VMcvxP8R5m5pTW3jTMej6oVji0Y7afqFLDXamOhACo5Svqwx0YLgdi6SRilNk96rWqMV33777fr/SiutlLmgjcjiiCwq94wpaEcdw5XyJGQA47vv0gz8Qp/rOPMxl2m2dN7muqWWWir3/YknnsgNwhHtV1KxuDp9pBwxI5iaE4ZRGm1X/aDhatq0aWT9awH22ZUlJPnusX79+lWtYBd63kgbWg7gOoJgrLWIS7McZfYOTWma910hY8CQ4xQStyD0uHZMg1NOOUWVmiTPr219EUJI3KVqYIcddlCq3gVtOOrjQTvmmGPUXXfdlRsgIZ8ZUswsvvjiqlevXlnVteqxfbHsh83OVRciaCchzcDRTkuEYEIvv/yyKpegLQNpn5YwynQntHMI0ezJbHGIYJ8VmFwJiTye1O/XbG88u7XK/fff3yi3dVrT/7333lunCsMkiDlBA0Fb0ntFda6YsAnFvH991ihpBuKu+wY++UkDoNg+zrZpt6vtJXIvAhmafpTY19YCYD9fztCkGm2w8sor17ygzT67sggNilRrlNsvvloClFY6heYtj+O2227LvEzfeK4ck1ZJtMVwCUl6z5oTKKT8pBpdQgMAkytEMJVIpXDehyZi44031gGZJGosUWqNNdbIm02zfUZs34WsBe005dnbYBCc1nQ4jem4qdF2nRfqu/DCC6cyHU8SfTnK/L4YgnZImUnSH9iCNgJ51SriemGeL1wA0uR33GOPPfTkk31P476QwbR8d5Fk4sQsw3ffmZMI9j4ullxyydx31/2O9zve6UmA6byJeU+Z9XF9t+uw9NJLNzoHXD+z3UJ9tF3Hdf2uxTzb7LMb4wu4WSoKiSocGrW6UqnHyYVywbaOtmR0BXZFRoZi5WUvBJpxVzfx4aZjwAw4ImbjA1NSDE7QUZcrwFYtvQBLpdF2LYfpCTRSUeahSWYCYapoC75x90iIRts0x4UJ0BlnnKG/n3766d5o6tKuWfhol0vQhnmnK1pzklzY9rn6tkfuVWi8igkmbXD8JHUGjz76qPZLTzKzLsFhbNN3pKFJg32dUT4EXjPqeJSPNgIfSS5o7Ou7f0MEbdv0H+2L88ZMd6tWrXK5tV0BT7ISMKMEWfO7nXPcXgYBEO8iu144d3uZvK9gco7rifdASDA012RHrQ5Q2Wfn4wtESrLBlWII1jH1qgUm5SVEQQR3zK222kpVCnaU/HJTq31jsci0Z0VnjUAq9dphhxBn3mgO9mW9PRguhaC9++67qxEjRkSm+gkVtOFnutpqqyXeP1TjLfW3g374UlCF+mgnQUxeXWSpGYMJPzoKRImOi4ic5PzQJj7BLnRmN41f0MEHH6wuvfTSgqIFJ40+KdfDHmCHaJV9mBM+8h/3vanRjvLRxrFxTUWbeP755zfyw3c9i6Gm4wgaNmHChLzo4qb/t7l96P2aVGPsWxeXng9+77brist03HxPFpJ+zq5HPQwm2Gcr9eqrr5a7CjWNK4843j0h6Y2KwUknnaTqidD3mC/NXK1QqXENQtp9+PDhqpKoh76xbBrtKGEiCpj9kbCbFcGNZIAtA7/1118/L5ce9pM8jUn9cJMuF+EjzYMVN3DH7OFLL72kzjnnnKB6SZlR6b2iCBW0k+QTNoV8mF+2aNFCHXTQQXnHywJoSKGZDBkYJ7037GBQgutY6667bp4bBL536dKl4JyPM2fOVK1bty5J5ykDPPu8shK0TWyNtg8IhhCaRUCE1hkRok2TMdPCYptttlGPPfaYuuWWW9R6662X+Npjtt6MY1AKQds3yejSaNtae1uoBvYyDFggNNrBblAW8qWLtYuvfrUoaLPPJiSf0aNHq2oB77NiRvQ2iUodVQuk6d/rIeBX1ilOSYGC9oABA1Qh3H333arewAAHA79VV13Vu41rQGu+DKJMxxHpWr4nST3k8rN0lSEPnQz8zW1kIJt2hlD2R70mTpzYqMwoP2ERXArJTRy6vZi6Jy0fQeJMDX5SjTa2hzDoMtsX31uf0Gb6ZWcxCePTmiJSdCHCmQ9MHri0ljY9e/ZUY8eOTdU5iMmifV5pOhlfPeQYPo22+GeK6b7UDc+dbWFgCtpSDtriiiuuaHTcuHPxpfYK2Td0u5AUe/Z3md0387f7YjHYPtp4NhA5+/rrr290LEyE+QRtvGfwTqvFQQb77NqcCCH1weabb54L3klqUwNerdlbKpW/KzDtbCJBGwECSBgjR45UDz/8cKRZa6GCNpbHpV/CQHq//fbTEWWhIYeGIklABXnJQGMGzEGoBI1K+yJKY64YFwzNBfw2ISCG+l5D64cZ5bRCZNIBPDSVCFjkyjM633zzRbad+dJOKmj7rqdvuVl+2tliCEjPPvusjnwcxeTJkxvVLfQ8oTU3X8T2eYWeg/h4f/nll6p58+Z59bDLlKjjvmsmab1wbHPSSDTccYJ2EnzRtoFpxon1iEotwbJ8RPnTo/6u47g01uZ3l8m3z3LF9tGW9XG+1nY77LTTTlrLVYs+2uyzCaleKGSXj7RZdWqZSu4bP/roIz1ur1pBG7NrJLsbMc43MUrAswOLtWvXLk8LJKl4xOyoEKEY5yAaNle9Qsv0tYXsb5YTmqLHDIYWSo8ePfQ9LKb5dvnw8UR+TZlIgKZw2223LfilcuSRR8aehwu4BfgCc8VptBFQS4g6LoJrQHsoKY2iBO0PPvggts5Z+KG/9dZbKmsw8SB+zzIJIe1it2HoOYiPH6wHbEEbH0yQyG/x0ZaJId81EQ22rMdzZ0cOh4UDJsxwHpJv2ofPSsWsvy9gEeoKN4C33367oMjHeOfgPrzqqqty5aGtQ0zHXcK7WK64NNougd32uY0KQCfl2PVw/a5G2GcTUt+4LJ5IPK5gs7VMknGqSwlEihh1XDRFb7zxhg64A5o1a6Y6duyoVlppJVXPFCLcmlqVKN9pGYTLNttvv32eoG1y3XXX6Ty9SXy0o0zHzW1CSDJgTVJmUh9tCM3wBxdzfns/RGQWk9rx48fr/1EpmXzI9vfdd5/zOHFAAPZZLMRptE2T9ajjXnPNNfocTa2l2faPP/54bD2zNB0vFpiAEr9xu03tey30HCRHpXkN5D6EthxuHSIE4jmFwGprrAX5Labj8htCLPz8TWDe3L59+0ZBhFxCedTzalsF2IhgHIcvfZ6tMXaV55tQdAnaLsuVBx54oNEyOZ64objKRwR9tKG5vRyzFn20fbDPJoSUA7x3SOVGFA/BJ2uQIgjaGOBde+212uTTHmBhNmjTTTfVKUR80YzrEZ8mB2l93n33XfXMM8+oXXfdtZGWCftBMwdtlh2kDNGnIWhffPHFTiHitNNOizSPjhqUR0XvDTX9lvIhhIigaB7Dp00N1WiHDoiXW245nSbItf3s2bPzzlXMhgrxAbdJKoSinXxtK37MvvU+k1wXSfMO2/smOVax8NUZsQhst42o4FxRgqMJgvbJZIxtiWGadMs6CHFIMdW9e/dYU2v7XoObA4R6uX+OOOII/U61hWq8J1yC9plnnpn73bJly8jri/UAAemOPfbYTKJQm6bc6AMg3Lkm7szvrjpimV2f/v3763dm6MSkcO+99+asEBAMDm3s02hXapTaNLDPLh177LFHuatACCGkzKQaTd1+++26w0YE3EsuuUT/RmeN7xD+kNbhtttuU/VIEtNxBOORQfiYMWPyBvXmrBeE7L322itniirbQzBbY401nOmefIPFm266KRfdOGqAKgNjcxAqy5KajouZsqtu2GbgwIHaN9nez4eYlGJ2LYkw6/ITPeCAA/JMhZZccsmCNdq+4yXZ3if0iUCFCYO4Y4Ued+edd1bvvPNOKsFC2h/RnaEtLxTEBEg6w+s6T3GdcNVRMNvw6quvzuWwjvP/ESCoSeRuEbRFYJW2lOvlMh1HWcgDDiAwmvcanmVMDJnCueQaF8SfXZ4dM1Ahlsl1wOQMosSbwQcF+GLDHB3vD9kf+0YJ2uZkWdzEGpAMCeY633XxBcRzxWLw+Wj76mODLAdm3VCW+Q6tRUGbfXbpSJp2kBBCSDoqsd9OJWijU8YMOHLgwsxR0rDge9++fdUmm2zizKFYL4Smj/JhDvxNTZnp8wkTUzFlNTn55JP1f19QAAnCZJZt111MYe1tRIuUlem4lAMt8pQpU4L2k/MHw4YN04HOfAwdOlSXGxUMCoKZmRZIBF2YqN5zzz0qDght5kSCy6wa18pOPeTD17YiuNkaTPtYIRYBZp5saPRDrye0s3YaLzkuIj6bmtSkwEc+xCdcSGLRYN9PotkUn6OQCQKzDAipxx13XF49JFK+qdGWZ8auK4Ilij8xrFkg9Ln8kF3C5XbbbZdbhnrLBJ1rYswXqM08hm3KbR/XrDfe6xIt3YVsiwkTmCS7nmPfhJBLwPdlFwi1NrG3kfpI/AnRmMMiyHz/1KLpOPtsQgghtUpDBfbbqQRtDOpMn1Cbtm3bVmzuumITol0xowi7BsGm+R62Ea0UviNYlkszG3V8M28u8nVHbSv1gQDgq3faYGhm/VGWL+ovhEDXvmaApaiHCxoaCS5mmvdGmRKb3+3oy08++WSjyQ1s7xP25TjQGoXkxIwSikRwM9dj0GzuO336dJUU+IWbWmBovKJ8rJZddtm8ZVn6aKcxYY/C5x8N+vTpo61F4jAnGNBe5jNlunQIMiHi89EWJkyYoLX59nqJcC5IGZjYkXaSdFa4Lnbee7wzECsAwdOiosrbgnbUNcA9uMIKKwSbjLvcT8zzjNN4+7IL+Hy04xAfd3Py0iXgiyBeS7DPJoQQUqsc+f+DENeMoN2hQwf15ptvetdj3dprr53mEDUNNIlCnI8yBkiI5CuDy1NOOcW5nY05EDW123LdogQVLF988cUbHcMezMcRatLp++0a+JnB0Ey+/vpr77FEgHWl97IH21Juv379tBBmsv/++6shQ4bkfNzjBuVxPqhpUm3huBDSzGBPdnRt3zHMoFp33nln3rMcYrZfCYJ2knyKUcGuoIlGDAS0gTkJFRfB9YsvvsjVGeXB9BnuGHIOsj4kwJh9n7vS8Um6MLON5Doi4KF9b+NcIFhhQiD0npK62kHXkiKR1F1+1Oa1gNn+Ouusk7ferl8a03FfNHHZHtHuzcmAWhY02WcTQgipVZ76/zF0akbQ7t27t45aCpNY+HjiOz6IsnvBBRfo79hGfBlNn8Z6xRwMxt0Qtl80Bq1JNHh2GWbUZfh/Al95Mvh2CaVRkchdmNuJhtyFT6Pt09i7BG2Xn3pUfUxcgpDtJxsXNC7OuiBU0I47nvDQQw8VVA6QiMM+31gzGJ9NMSM0P/jgg3qCA5YDpZjIMZ8DuBkgSn9SZP+mTZvm0seZEz+uYGc2nTt3zlvv8vEU4dJ172O5/byaE0iue2/11VfXk0WIMn/jjTfmysE+hQbEsjXa5nPty2FvThwmMR1PGgzNtdwWPGWCwRf5v5phn00IIaRWaVKBPtqpQosOHjxY/0c02VdeeSVyG5O77747qHz4yyEdE0xyYT6MaLjrrbdebFTPfffdVwd4KidRWklhn3320cFofCBPr6m5FFNpGSBiUG8GjsKgE76bpoYzTgCKE7Rdwq4MluX/yJEj1aGHHuo9hrmvqaXz+U6a6135c2VbU4iwAzAhVQ2CPJll2do018DdPv+4YGhJBe0QQjXa0GDaWluw0047xR7XDniE6MumpQWi2EsaNF8di6XRhhYYPtMbbLBBpi/VqMkBeQ5895uY/tuYAq1rQiXKR9vOd4z3Qdzkgh3dXK4dzK9dpusugdxkhx120B+8h+6///5cXdEOCJo1YsSIRKZYeB/dcMMNXo22YLeD+Mu72l6e8zgf7VBBWwRp+1jQ9kK7LcuRJixqAqAaYZ9NCCGEVImgvdtuuxV19gD+iTDD3HLLLfUMvA3SlJjARxGBgdZff31Vqdjmqr5t0K7QLAgwB8agXQQ6aG4RfMtsAwy2oaUwcZlWikAB4q6fS9C2TcfPOOOMYEHbDOJlD4BdA3KfFtDWaNtm5ogabmqisR5BtkRQWXHFFfPyFNumqdI+tnmqr+6Cb1Ig60kaCR63zDLLRJbjA0K6CXILI/jeWWedpbX4ttkwjmUKvvY5FWpuO2vWLH0tTLJILWXWEVGV99577yBBO0oYdlmgmPEK8L6CZti0qhCtsEwM4fmEACJR7c2JtJAI9ygPKQARzBBBAAHSAUILj2OIFjzO/zgK0b4DaDqTTpJcdtlluXsCdcI1Rj5zs16++AghpuOyDepo5mQNFbQlv70taMqzJMepxZzS7LMJIYTUKv9lqPSpCEG72HkioZXEx4cdURsDJ/gx24GaKpUoYc02V8bgCD6k3333Xd4g2ixH2sOn5ZHvGNRLih6UgZRaGADZAzA70JNopEWYD9XUmjd+lKBtC3fSDr4HxwyGZh/LFpgQhAxm1picwPLhw4fncskKiDAuSLmmhhNaIBl8+7TiWZmOhw6Go/KcJ9Gk28ez97300kvVXXfdFev3mhQI73YEc7Q5zNbTxgAQ4f/WW2/VgrZst/zyyzc6pqnRLsQ1A/tL6i9zIksEbUnfNW3aNK3xQzo5s81DTMsBypCAepKeTEzdsa9on813hBwn9J4S7TvA+yYJ5nsL54Q2Oeigg9Rnn32Wt53PmsQnaJtt07VrV/0fWlPxgfftm+TeDHVVqGbYZxNCCCGlIxvVUQWAwSdmxzGTHgWETAiL8jHTLcmgNM0nqhwAU19bQ2NrnVxRf8Vv0lxu+lH7gny5NEcYSJuCNtIMmRpifOCza7YN0vRgObRV0JqJwIkc1nFtZ2NOEJhtZgYUs4UDu0yfj7YICeY2ZjtAe4rvoj10lWH+lgE+lm244YaN2j/ExNsWnJPeQ77fvijkdj2irok5oRAlkNn72oKc6z5L8rz4hDXZ7rTTTlM9evRoVLb46brKk0kIWI5IHWGiLRp0/DfLkXYwfaDj7mV55rCfaLwR4VvOwRa0xfLE9Xzb1xKB7WxMv2kE5RPgT+wSNCEoR90jvmdI2sPXtr7706yfy3/aNTkmy8062evM51AmNGTSLvQ5dG1jBrdz+bf73j9JP6Qy+2xCCCG1RZOMZLmK0GiDqVOn6gEm/ImhkXQNuBFkpdjAnBKDTdMfzMXYsWNzOWcBNJznnXeeFiSzAqa3Cy20kB7om7l6AZbLMjEdh6ZaBF+A2X07EA/8saENwCADdYWJqkTqRRujTBl4mudi+t4uuuiiejsMcCRPNJZJnc1BMo4FjRmuJ4JTQQOIfaXOyLeKOnXr1k3/ts/TpxmH37QEYoP5LPbDcWzzVixHHVAnnI9dPjSeqLuY4Io/trS51FP2k/NEW+MaYHvzWtgTHaJ5wTlCYJDtJK0XrheOjetggnPDdYC5smmSKvu7zsUG1wbnbW4nbSb3iawTYdJVZsixZD3ON6os3I/mctTPXGanYIs7btS2cm/iWsk6M0CZuf3o0aMbLZO2kvgFyPkMk1ZcS9wXGLjDjxiaVnmhYj3uB0ww2ecqmM+ogPsG29sm/PIsyX2H7URrh8k2lC/rAIRhXC85Nv5DALn88svzysU+cq+arg84nmmOjnMVsB1Mc813gastkeINoA2krnIfyjtF6mbuZ/6WiPeyXNhss81y1xbbmppo85rhXWC3PZ5VLMd/c53EJ5BldkA/5IvGOvuZMTHbSZBrie2lTKlfLcA+mxBCSK2yXIX116kE7fHjx2uzTAxkMJgTU8ZygMA1GFi5BsMmPXv2VN27d8/9loE2oq1GmeGGgLJwgUeNGqWjayPP7pdffplLnyRRiEUIFD9ZDHDNoFYQajHoN8GgCAIrBG2JZCwCLDQ8OI7k3DXNNM2AajgOtjM1amKujWOabYeyIMziAyEKwjn2lWBmWG+alcp5ujBzTqM+8hv1wH6og+3ji+UQlCBU4Hzt64oyUBcZ2Mt5YtCIfSVittRLtkO90e44J7SfrDfNSzGwE6ED5437QrYT82mU7/JfhT+qneoL+4pWEfWO85mVNE3YT+4p7Idzk7KlPo888kjebxO0W9wzKfvhfKUNsMwefKPdzGOg/TChIMvstGpR94OvDoI8C3LPRW0PX2VZZraV+JXDCuGll17S9zeecbQfrh3uB/O+xDrcD7jWuB9dx3UFnpPyzAju8NF+9dVX9Xd5Js1tcH1Rvvn8SJ0+/fRTdfbZZ+u6w/faBs+/fQ8A3F9meaYgi+0waWEGTnS1pZjTox6S2xr1R4owqbs8W+Z+5m/zfWMK3ZJjXtrWbC8gv+UdZYJ7Dc+6fT/Lu0u2NzWdcr3kfQfs+BVyPBtpO/OdKs9fociEYblhn/0/qNEmhJDa46uU/XXWfXYqQRu+hkgPg5Q4tmBYSmC+jMHRoEGDYreFcGD6CZukvTDCgQcemAscJWUec8wx+j8ESjv6NQandrAxV1Rn05wTg3lzoGBGHPblhJVoxaYZq5SB8qPO//jjj1f77bdf3jERGdos24cdsdw8f6mTPejBMjlnsz3OP/98teOOO6rp06fn7WMHfYMwA+2fHSTNbEs7erO5XoQWEa5cEctdvp9mm5j1sc85Dtsc265rqG+471jQ1pkCtdQLE0Q+P1lzOe5ZaP5c7Qsee+wx1bZt26CAUj5/XV9bxS2T72ZQL6mj6XPvakOcp++4UabjNnaMBdTFftbMNsN3aPcwCQdtnSmk2uW66m/HMrjlllvyzssXTd0Xw0HqinohgJZ0OHYgRHtfc7lLCJJ70tduvntW6m+us/PF4z9yhiPooVkWXGNsU/sozHO0P9UO+2xCCCG1TEOF9depfLQx2w8T4nJ22ABmcKussoqOdlpu9tprL/1fNCE2SIUlyGATfs9mcCZXBGfb99g18BetkCtvLTCFKjtHrisis8+HVpbZwcRCBW3XchemQCygrbbZZhvVv3//PF8Kuyz7nrQFiqjAU1iO4F+gdevWQZHiTeHC1pKZ5xwSmCn0BYF7vtByoOG0t0Vb2tGwfdhaR/u8MNkEbXIILtPVNJHMZb/rr78+Lwq3+EG7JnZkGzxD9957b/CxRPhz+RWbdbEnyuzzM+sWlREA28BqwgaCiNmOZoo/KSvuvpL6QFNs+rljdlisZ1zCM9a5BHDX9ZNy7YkEnDOigfsmONA+LouCLl265L5jX0zC2Ygm386XbdbHxM4+UEkddlrYZxNCCCFVImgjWigiMRcLmCTC/Bof0aLhu2kiCZNCmFnGBVQpFWZkZp9fWpwA5ksRhUG2OWC2TSU7deoUKaCYgnZcei+XoG3n/I4z+XOdD+rmGjD76hAVdTxK0LYnEsyBpUvQtq0DBFsTCC0teP75572aVZja2stc3334BEG7nqEaOhemiS+QZ8xXzzgzyyTCCAb7URMuciyXqXMI0v4SNMsUiM1gZ/YxXdcuCvi1Y4IMwc/s8iTgmkxcmNdUzt10MXBNAvgEbRfmc+LS/oWk+DItYkyrAnN/17sFpvmSMsu8lvfcc0+jaNJyTq6sCiJQ2/7dODaeZ3EH8UWx9j03UZNbrvY0zzEqr3o1wj6bEEIIqRJBu0+fPtrUFOZoZsCrrMBA+bjjjtMfMYfE97vvvju3zYsvvqgHWJilr1SOPfbY3HdTI+QbwPkEbYnYa5sxCzLINLWNaTTadrlyHQAEjND8vGZZCF61ww47NFruI0qjJNGIo5D2NgfupgAvPue++toDbVck5ah2tPcvRKMNrSwCGIVEUY4qx8T2zYTfZhqfxSTCCCwSIITZ9ezVq5f+LwH2ChVwbB95s45y3e37JkTjG9U+9joJsmUK17awagvaUocoQduut1gmmBptnzAe156y3ow6LsvEMsBXhgjBZhuibs8995wOXmVjC+y2tY6NSwNr5iuXY5vtI++4qOuKtlphhRUiNdq1JGizzyaEEEJKRyofbUSBxaAYg3RoO6HhdAk+CA5W6Oy7RBX2gePLwLySMAdnEggtyqzbt689iPRpXs39TPNJCZZlb2/7aLt8LuFDh0ERojO7WHzxxZ3LXXUXoKWUYG1xgo2cs09Db2q07YjDgggz9oQD9sM9i4+dU1kYMWJEI0E/xPJAaNOmjU4n5dvfh62ZO+KII1SHDh10kKwkwnBU+8ZNUNiuD3HH9V0jWF2Iuf3kyZO11QXaGz70CIRk1lOicMu9Gaolt4Uwl6AtPjsiaPs02tCySZquJJjloW1lgscUWOW8fLEUzBRjdpmCreWHBQ0mrlC2lOu6tmadJPq3nbdeEI32nnvuqQPSmSbXZmDAOB9tlIHjmJNccq1cfvlRgnaICbe5HvcY3BfinjmUC+sDmKAjCGKta7TZZxNCCCFVImhjlhomg9DerLrqqmX3+6oGzEGcRB2P2kYwc/u6BrXmb3NALWmRMKCKMh33CdrmNiZYVohGO4mPtghGW2yxhVMYNiceXPWHtvrkk0/2BkPz1RHfEfEZGrMpU6Z4/U7jNNouE/U0puO4rqZffJbm3HZ5CGKGCNhptchmmYge/Morr+jvMCcVM20RHm+66aaC6m8HXDNTq5llvfbaa/qZc7WvPFfwyQ81rfW5HpjPqAjXpnAv9wgmYuwYDHGC9ocfftio3qK9hX857lmY5dqgjU0BaODAgXrixgXMfFEHBMy66KKLGl1naERD2sR1/WQyBW2APkPM+6PebfK8JhV4Rcse5evveoeZxzHN2WsB9tmEEEJIlQjayCG87rrrqiFDhgQLXfWOOejD4PeQQw5R1157rRYARJvmGthJpHBTcPNptG222mordeihh+aEHNN03B78xyHb2YI2zBB9qWJ8WmBEMRcB2hbUMAB3CcQ+zLaQQEswdxWTVwQok/rZJqb2/lJPMU0369y3b1/vPkAiHst6V/TwkHNxWS74hNmocnzECem2tUJcvQcPHhxUB/ltBvSy/cWFt956S4Vgnwu0da56iNAIP3uXoJ0m+JWt0Y4StOUdYAaeM+/1KEHb9r+WbSCAo1z4RLueGVimhMZXeOONN/T1h6BqR/qWNF02vneSzZlnnqkOO+wwXS6epRNOOCF3zj5BWyYhdtllF6/1iWBatkgZUYK2admDc8Z7opY12uyzCSGEkNKRqqeFhg2dNjvsxvgGnOYgDgNt+KsCDGoRUdsV5Mz20ZaBnz0A9Ali2A+aC1OjDXNeBBKK0ghHDfixzPQFDfX387WLmfdXtnMJxABm1HJervqfcsopjeqMNu3Ro0cjM15XvaSN7QBNIVx11VV5ZUaZnvuI0rjay+LKSYJZnhlkylcWhDu55+w82kmOZQt98ozIxFAcIe+fuGsg7RvlqhB1XFujbQcRQzu5IpED+AibgnjUdbXbyrz/xfzcrBdSsLnw+XELmJQSIdNsL1vDLbgEZN/9J3U16xD1rJnB2cxAinE+7OKygFR2PsztYeYOzOtfaxpt9tmEEEJI6UjV26LDRj5M8n9ISijgy9Vrp8dxBbdyDezEX9gs097OFc175513bnQc7IcB+Hvvvec9ZlxQJlujPWnSJK8vtz34NstbfvnlnfugbJgXuyIoyzKzDj7zbtdg3WeaLcCnVLaJ0miFBJeyB+qFarRDgnXBbLhdu3a5iORJBW1zosb2eXeVtc4663jT2Pn2swVQYAfmS5rWK6mgbfpDuwRtX7vZ90yI6ThyUAP4NdsBxuQ/0hyZdRJh2lUPXywC8x43BVj4WRfaZjgP1MuMGI06de/e3buPq872MnHHMOsQFdjQnIQwJ3RC728z8rkI0+Zx7fep7WdeS4I2+2xCCCGkSgTt3XffXZvyIV8t/C1hsgvNpv2pFx599NHcd/FjjDPvlsElfIKjBr+vv/66Xg/Nt7SpGWTNdSwATbbL/zEuvZeJy4zZ1mgjr/Vjjz3m3D9J1G1TyIBA7dJGmXX3aeRtTB9Y8f321RHCjMsnPuq8oo6ZVKP99NNPN4qGHqrRFiEVGmk7KnMcw4cPz323JzhcbWAG4PLdRz7T8ajo6UkFmySCtmmtYBIV+8CHOWlmljdx4kT16quv6u/w+bbraeectoW5pZZaKm/9WWedlVsnPrUSSM6cDLO1xHHpwExQ586dOzfaxo4jAUH3+++/VyH42hHtZruFRPlom8/u22+/nfdOjDqm7GNeJwQDs89RJrIkJZttOl6IK0Glwj6bEEIIqRIf7UGDBun/0DzC98uHmdqjlrE1cy6hAYM4DNwkqJU5UHZpZ02wfvr06To1iwt7v/vvv18HhrI1oq5gaD5zWvO/eQxbUwsBwJUqy66XS8hxDY7FdNwlKJj+tBIROWowDFNyCAhm+8YFQ5NtogbacQNwcQGwA7HB9xj+qTCNxmTJXnvtlbcfrnFIjnbXdYkTfF37urD97V33hylo77vvvrnAe0kF7dBYAz5QFuIdQPDy5RY3fYujTPOjNJj2PraJseCL5u2773HMr776KlcH0VrLelMLK8/DUUcdlVcmlkOgxG+k1RIttOudJMc0QXAsTArIBIFsY18buAvYAdlc52TiyoceJ2gLeE4gAEvgPAluCCDw4zwRvA4B8bA/rHpsrXVUAEPz/XLDDTfoAHC1HAyNffb/qKUJFEIIITUoaMMsMkQjWi+4AgzZZrD4DS3C3nvv3UjjGNeWGJgigNjs2bPzgkm5Bg4QlFZbbbU8wcsUtKOE3VAfbXMA6jLxtsvu2LFj7KDVFsBc2kpzAkBSDfnMeAHM5KHFMScVpFzJ8WtPBoSYjvuEGAGpkWzBC+Ui4BJ80vEdOdZtQdtG6mbmnvZtk9Ts2rd9lA+7uY3sb2pukwrarskoIPEL4kBZyNcMqwqfoC2RxHF8RFOH+ayZBi+JiT5yA+M4cW09YcKERvWMMqNv2bJlnguIqx7IPYz7wE7Ph3tRzM/Na4cJEBeIVyC5jqUO9vnYKcFCMOss+yIWhRnwDttAkGvfvn3esXAuTzzxhDrmmGNyy08//XT9XyYmJZK4uHggcBsCSkpb491oRtLGBBvKFOyI8jKZgP+wJIAliNkOsC7Bu6NZs2aqFmCf/T8oaBNCCKloQXuPPfbIriY1im3eiM7dNL20/RSjEA0rhDPxxbXLtrc1o4S7TMclp3UhPtpxqa4EKRs+vdOmTXNqE13n4hO0zf2i9jWDIolZLbRrGPhD6wdsv1n72FGCF4Sbfv36OTW5ImggnZE9eSABqnzB66BdtKN+R2nWcC/Y/uChg0iXX78czwTR2x9++GGds1kQLaocz9VWIYK2jW1a7VoPawHRurvuEUQWx7127733qoMPPjgnfIrlxTvvvJNYo21PsMTd/6Yg6coPbgvaKBf1aNWqlU5F5rqWCJxm7mNOHomPtmyP+9InIGISTiZ7pG4ut5akEzfmPe3T7IMnn3wyb1t5lsU03G5POV9MUgmXX365FrKhnTWPb54ztLaSH9sF3gW2hY+ZRx0TBDClrhVBm302IYQQUjoYejRDXOmtJH2NiRlV3OWrKoPMLl265O2HwaAIxq4gVLagbQ+eXabjYo4cJSD5TMdDB+G2WbaNbaoZZzpuTh7Y2mMX0FKJQAghFvuL9YEt1GFbrMfgPSoSstQD632RnUFU1HFfIDFMBqCeNj7BE9pZaOrMaxInaMu9atbH9NF27Q/tnol5f7nMsV3luK6da5In6hyQB7hr166537agDUsRsRLAefbp06dRgDHbGiFJMDTb1zqqriY+dwW73aJcSOR5sAVtxDLAPW5ekySRpV0TDBKQMBQIuRJ80STKFaZbt26573IuOKb9bnHdW9IWph/5BhtskAtAF3JNMHGB+2WjjTbKHQcCvOl2ssYaa8SWQ6oParQJIYQUGwraRcYeMGKA7xJiXUKrnWIJg1Hxd4wbJIiQKIGEfBrtpKbjPqExCvO44mtpYmu2BgwYEKnRNgUJqRv80c0c3GadxcTZFXXc1hqeeuqp+vvUqVNjTcelfo888oj+feKJJzbaxt4f+8i19mmTXccx6+oCwgbqK1GZ4+6P5ZZbTv+HRtBFiHmpmCvL8Vz1s+sh9TOvq890XJZDM20CjbQZCdtVVxGk5V6QVE+mqbV9vnJfxN3XrnsyZNDuMx23j2drvl3vC9t0HObw8DE2n/MkgrbpBiBgf5QbAo5p5pj35ZY3zwflyzNjWqcg17bdnq5r7Dq/nXbaSefpDgXlHnHEEapXr17O9SjLfg+T2oCCNiGEkIo2HRdhAamdEGwIA2B70IiBzOGHH67qgW+//bbRMpe2zjVwhqB44YUXRpZvDixRBnxS4esopug+jbZt1msHIvLV0x7Mm8fAMjNwks88FOcFv/Jhw4apK664Qpt+2tpa1z0TImj7hEGkDDOFd5SB36a2XAb5N954o/Z3lzLha26eo6nhtE2jpV2lXAkGBw0qypV9zPOD1lGEPPiThuKyIHDVRTT3YvkQx8svv5x3jCSYPrw4PnxmTR9a17VF1GMIvSE+2nJ+W265ZWw9fMtkMsZ2gzD9eGV5nI+2LeRGnWcIso8Z2wHvUYmM7aqHrVW3z73QKNmuCYa4XNs2Ei8hFHOizLxGrvq7rrFMnkRhCvUh16ge/JfZZ/8fFLQJIYRUtEYbguXRRx+tBSikh8EHvoUITPTMM89o/2Tkaq4XJAJ2VGcOk1dzYGMOIDFwhFYoytTSBP6/PrNsDJLxgV8t/BR9wdCOP/547/lEDbSxP4K6CeZ3AdGJcWwRYKLy5JrlyrnIJIENfEtlW9fA2PZvxnlAAIRAIwI89jv00EN1tOcLLrggb0BuT1jgNywJfFp/W0DAdTH3x3lIMKgzzjgjd76hZrk+jXaUSb6p4S8EV7vefvvteW4PmDAw67TqqqvmAnpF+YPaEz32eSX1M3f5/EPoB67JIvHNdZURpdGO0hTLOrwPbSS/vH2fyH8JyCcR523Tcdf7IkpbbZuXh4B9zIBlZn2zxmxH02pGvrsCsNl1QZsiMFzosXzBGu1ybeuJWoN99v+goE0IIaSiBe1bb71Vz4gjz+tll12mlw0ePFhH5d1nn320YANNZj1jD9rh7+fzfwYiILjAQBTa0u22207/Fo31RRddpH/bAqKdPsesk+nb6aqnqU02taMS+Ct0EI7txIdSNLk+E1pZJ8ItPssuu6xzsAjfW5+wZmvM0U7Q4mCwDY06hEVbm2ZqrV3pfeBPbef0tqO3m4GlbEEbEddl8kE0tr50aC7iTJpdkeTBZpttFllunPZ2l112yVtuBqOyg6HBT940NfZpV9GWZgomnx93qJbYvJa2IC0BxuQY4hdvC6FRmmwb3F/IQ+yqsxkx2w5gFnXf23XBJJCsh5n/hhtumFdv261DhE48FwgE5zrHKHAtJ0+erIqJnRYMxzS10nIusD6BubbkCnedi1zXOHw++T6Qcq+WYZ9NCCGEVImgjZlvpOBBah9TCwOBBkFx1lprLXXzzTeresZl7uvS4IaANpZ8tyIIo6zevXvr9Xa5vrJNjSIi6kq9oN0VLatpOr766qs76xIKtMkYUJsCqAgDrnzEIlSiDqZ2WEAbiHDl0oLZQie+Q9DG8ZGTF9/N9hFNd5Sg7TJjx8BUtpHj2m1jC5sY8Iv2f9SoUd42g5bJJkqjba6Dn6oAH+Uo4oRL2+LBvFZoc/P+gUbUTFkXJbxG+fqn1WibghXeQVgPjZ3U2VW26a7ge27MiZm111674OfB1mhj0kEEaUQDx3L4zsv6LbbYQo0ZMybvOLamHgH5YA4vQn1UnWyz+aT1jzqnKOS4CNyHCTSkHjM1zaagvf766+fljY+bpIibJPAJ2r5rHWKWXo2wz/4f1GgTQggpNqlGV9AOScRlGZiYWjoMGhFUqp5x+Z/6TMeFUNNxV6AtwQ7uYwo9EKglD7cIHihnxIgROnWYfbz555+/UV2SaJNQNgRmEQ7gUw4TTRHKzHOAxt8s2xzwmgIKhFVTWDYFQHy3zeghXIsWzAwQJ4K2KSTblgE+QRvtiGXYv0OHDk6Ntu1bjUkLKcf0j3YBzbvp0x8laMuxAII7heK717bffvu8MgXzXCQ1kpQDAcm0yIgStGGGLqAMOZ78Nv/H4QrsJ4JVixYtdD0eeuihRtskFbTNbe1jwmReUlO5tnch54eJkdGjR+fKMevjK8u2DpH71DyWT3h21SetmXgSoQXBDjfeeGNt5eESgOX5NImLKeEyd5bAkUlMx4XNN99c1SLss/8HBW1CCCEVLWgjaI8E4oJwAU2jaR4Ik2Mz0E894tLW2YJc6IDXTCsEE11f+i4MYBF0yi5X1p9zzjk5QccMPIXvyH07aNAgHdjIjn6cFDO4lARnc/HWW2/p/wggduedd+YJPeYA2dYEmsKyKZC70hS9++67WhAUc+cojTa+Q+sj+0YFZsP28AmHL7zLTNW8RjiXHXfcMbg9oXW68sorc+0o5cgkiX3t5XxgPoz9ojC1h1GChn0/munqzKjjYiVhmwLjfpowYUJkXbCvmRpPysT5R4GJIeC6r8xr4LLuaNOmjff5cAU1tLd1BVNbaqmlvNu7fsv1Q/3Ne0tyvkeVZZujS9Rw25IjpD6hIJZBVuDcJY6E6/0W9160LYVQjv1coT+SZbBkSUKt9lvss/8HBW1CCCEVLWjDzMyMPI3BOTRH11xzjbr66qvVo48+qjp16qTqBddA1KXRjvLRBub6zz//PPddTI4laJEtvMp+MNV2De5dAwtbUML/e+65R0fEjooCnMRHW8r2adhEG4VtoT03BW070rr531xnBreyI19jO7Tdiy++qL/bA3lT0DYF6xBNp6t+Yk4t680o6bbrQByosyDpwOSYEhQOSLsBCP6LLbZYZLlS17jURfY1M3Npm+eG8pCWCf6eJjATPvjggyOPYWtiocmH6bDLbcAVfNAllNpWBTaSZ9vVJq64ACZRbhkh4DhHHnmkN7gaUpf5TJ1lW1ujLRNIWC+TbL7nLc5M3BdkLCo4ovmOwPOEz7333uvdXiYT7AkR4Ho+7Pawt4GA+NRTT3nrhHvThe86pjWlr1TYZ/8PCtqEEEKKTarRBAIl9ezZM6cVw3dE1UbqEHTmGLDtv//+ql4woweH+GgjIncSjTa0gyY77LBDnp+vGfDJ1kqYgrY5wDBzOmMbO+etb+DrijJuI8dBADWUbfs9+kyfTYE0StA2BR6YcQu33XZbXrmifRRhXzTaAjTNpjBsm/abKdJ852h+t7V0thDvas+xY8fqgEQ2qKtcdzN3NJBy4M9rCiL4HpqaKcRM2odMWgCcF7TEK620Ut6+IYNZu22R5g5CNCIjF3oOvkCA0JK/8cYbzjLMCRYXZi7uKEFMcpRH1e++++7L5RS318P6Ahp+V118wdBEo4vfYo2RtUY77p6SiSBoSSHQbbDBBt5tZbLLNSHi0mjbgjb6GRMI2fa71sS0mKhnQZt9NiGEEFIlebRhhiY5XwGEu8MOO0x/6hGXAGXnSg7RaPswBUwZ0NuDelnv8quOE7Th0yiDZfM4rnQ78MXFMSSKcxQwDcfAFVG733//fe/EgXlclxbZJWjDzB1CahQyaMb96dJom2bAcmxT6L/ppptUv379nNf32WefVUOGDNHfZX27du3yjm0HanOV89hjj2mTTnuQi22hFb722msb7SPXzq4zhJI4QcFlrRAnsNqYZvGIRm4LOvakRZKI6a77Cpr69dZbL/fbJxTbZtzmOaDO4qOa1EdbrCbiBG3cL6bPuV2e6z42QaC1uOBd9j6uiP5JfLRD8Pk5C2KCDI16XB5mMR03kb7EnggD5rZ472CSUejfv78WFG3MiZ6kOcHT+qxXKuyzCSGEkNKR6bQ9zJwRHRepb+CzmiR9US0Qor1LEwzN1LpGHR+RpqNMx10Rn/HfpxGyl0OLjtQ7dt2XX3555UOChrnOx6yjeS4+QdssU45p19Gl0RQB1B7I24K16UdvRuY1U1f56gxsLZ34KEdptAu5p0xB2xRWcZ6uiZa48qM0qHEabQhhtrl6GkHbdzypJ66h+Pa7gFZ1zTXXzNUjDlujHdXupiWFKx2fzyfbxnYFKDTwm7ncjnjvq38h2GnzTMzrjHvBzmUfZTr+/PPP52mdXfdDlKCMqO2mu4ZvGwFaXME+zq677tpo+1qmnvtsmo4TQgipOEEbQsPAgQMbmQ7D7Oy4447T/r0wiYYwNnTo0CAT41ohRKiwBS0ZDIvJLQZ+MBF+/fXXG+3r0l77jh+VJ9il0XYNbn0aNwyksZ+tdbMFLTvyd5yfpW06HuejbQo8KMs0NXYJ2qi3CIfmOWFfBCkbN25co6jjZvl2Hm37HOyJkFatWunvIhBiEGsHw4sjLnc2opjbgjbaet111w0qP8rc1jyXOI22HUAMWt1Q0/FQQVuELdFWm+4CJjgmfM9hJRAKjg+NLKwscJ+62t113yGCtqusqN+CmO8mEbR9zyhMte31WWtlo4Rd23InBKmf7XLjMh2P8rnH9XJZ3ZjtI0I8AkFGuSRI7m6fqXk1wj7bDQVtQgghFSdoo3NGsCBTqMLAaOTIkXpwBJNB+FjuvffeWmCEL2K9EKrRdkUdNweS0DCYuZAFpMSJwpefe8aMGV5B20zvhWjlLqAZNINcoayoyMhAhBxTeLZNT3FMpMWy8flFi2BlCjxmpGIzIBrWST5Y2QbHF+HQFrShwcKA3xZaTS2q5Do2iYqk/sILL2gzTXuywCfEuQSjOEEbZu92aif89pkeu44bBcocNmyYc50ZDA2DdDtdmS1o+6IZhwraPXr0yJWbBHP7zp07RwraTz75ZKNo/oLpU237SIce35zcEPP4LDTayIFs42vvJO13+umn575HPe9JfZrjLDWSaLSxzhc9X5DyxDXD5advboe0cLUC+2xCCCGkSgRtRHe1U+MgajRmwaEVRBRTpFLBoBiCiW9AU4uEau98GtO4cnz+mS7tpLkNBtw+QVsidMvxzUjPEvHa5fMaJ2gfdNBBeaa4rqjjWOfSVLn8ZVG/jTbaSH8XjYup+bIDp2E/GViaJtUuc2a7nph8kHqZgrZLcDE1X2a7ih8kjmdq21yCts9yQPLamph1kGsgEx+2X3OI+XiIFYbpF+1KKYUo4Xb90W62oO0TckOeGwg+Z5xxRq5smyifYFMDvdtuu3m3Me8pl6bf9uUvhM022yz3XY5hng/88WWSymfKj3ewAAEKac5cgr9p6g2NpVlGFGYQs759+wbtF+XiYYL0fSETSD4f9DSCtvk8uPz0gZRz4IEHqlqBfbYbarQJIYRUnKD9yy+/NMoX+8477zgH5G3btm0UKbmWQRCss88+uyCNtvgEwqwxLrjX6NGjnctd5cYNhBEEp3fv3to30TbV9hEnaCNVjGsfe/AM8/hp06Y12hbbTZ06VWvipT4u30EIlqagbQoz5vFM03ERDgWksjGFehGMbY22b7DvMm0304vZQptL0I4yJ3f5AJtCmeTURvvY24YIg3HB0GyBE4G65JwxUEeQOBFyTYsFU7gVbL9XcYUI0Wj7rDVsIdJFEq2zTP7Y12PixIl5PvHSBrCiiHPpMMtC/nozJZpdP9fzYNfT3B7CP55dqY/pq2wKluYEWlx72CnaBHnezWB/rnMMIWp7TNzYdYx6DkJMx9E+H3/8cd46OzuBuEBEHasaYZ/thoI2IYSQihO04fv4448/5i2DUIRBXcuWLRsNgEJNWGsBDLhPOOEEtc8++3i3sQUtGVBKztkon9kkwohL0BYB0BxgwO8Yfr6yv0/QtoV4lx+lMHz4cKfWDnVo1qxZ3rZm5HFTMD7++ON1yh6Xxt/c3tZEQvgUwc0WtCUYmlnWWWedlWem7jOL92m0bRN01zmZ2jZX1HE7z7Z9XN9vmGrDnF4mByBMmWafUc8eynnwwQed5vAmEJ7Nc0Tb2KbPiDjuSmsW56MtAltUpG9bKE1rOh63jVx7BOgyc9jvu+++OUsKU9BGOqRXXnkl+HhYJ/7MdiCzEOJyY0uANWgoTWsLc7+77rpLFYLcT0gnlxbffYE8zuhH7DY062/HSsA6n0Y7qo232GIL5/auiYRqhn22GwrahBBCKk7QXmWVVdQzzzyTMxeFRm369Ona19YWTjBQtWfS67UDF42SL+q4HUisGII2tGlTpkxpVD+5biKsukx8bUHbNK/2bWtOKojgFZciyD7Pb775Jlc313b4HHvssXl5eV1m5HKermBntqDtMh3HNq66m2VBcwTMiMt2ei+fRnv8+PG5c7XX+UCuaWimUOcjjjhCH3/99dfPrY9LaYRgbQC+ma7BNZ5fDNLtyQS5V+XcJ0+erP+b7SPXO6r+pnCL764gVWawPnu/ULB9+/btY7eR46DuMB22tX3mtqbFQ9JgZiKwS/v5BBvXe8R37vZyTML4TN2jBEmYFPuwrW8EmBxHtcG5557bqB6++wL9C/KVR11j26InVKMdQp8+fXQE7lqCfbYbCtqEEEKKTeKpa+RIhdb2qKOO0loTMcczU6YI0PRIip16wtWBQyOGPMkw8Y0y8TaxU9bECRimoGZvK7+h1XalyTKFDNPM1AXKguYszgfYNJMX4SRO22fXXbTIPkEbQBg0zY/FBN42/5bAYWZZInjbPtp2uiSsj5skQNvCv9XM8Yv9zYjcpkYbkcEhJMixv/jii0ZlRmm0xTpAhDc78rNomn33FgbZYiYfl87KdXzffST7YH2c76yUg7rYfqR2+jlXfXzHt+uPsjEhEDe4tuMG+MqLq4tdpokIqlgOwTZJKqlCU3aFTk5EvY9kQgDmxSZwHYiK8G4GKQyJQ4F3i33fmPWyA5VBgIxL7xV6/phECpn0rCbYZxNCCCFVotFGGqpTTjlFz5JjYI8BLDpx/DbBABJCUpxpai3iEgohVEGzY/vjRg1sX3rppUTHjRIA7EBjJqYwie/QKNnYWmBoPexAXbZZuK29HTRokLrgggsSnYdgmvG6zsk0ZUe7wYTaFtyh+bZNx02tq6nRdgnacWA/Oy2QKwCcCMAQjk1/YPzGd3OiIyRYmStyfdz9iGcT+0G4l7YN0ZaiPltttZX2b7XbxPRtxT433HCDGjJkSN42LiHGFWUa75Utt9zSGcDPR1T9owLOFVvQtnn//fdzxzK1zsDUJrrqEVL/NMTlTXcBS4qLL744qPw4jbZMWNnvoKjzNmMixB23HmGf7YYabUIIIcWmIGcsaDTQUUeBWfGLLrpI1SNffvmlc6AnZs3mINMl1PqI850LEeBdgrYM6KN8tE2wTf/+/XU50JCIGTI03AguJpiTCvBbxoBPtOAnnXRSni+31M1G2iouX3FU3WU7CIIQ6sxgP6Ypufjemj7appYc31GPK6+80ito237cZp0wwEU5ch0/+eQT3W620IHo6vBVlePGIXWEf7qvXrbQJoL2Rx99pD9RwqV9XRBRHf6tkh9cWGGFFRrtY2saXdfIJWjjGOYkRIhGO2q5nePcB3zeoTGFEGLnIjfvo6hjQkNtBkjDOfie86j6wDIC7gFp0miFHCd0O19KM0l75gPnDqsT4Lqe9vER9drX9lH1chE6wVLrsM9uDAVtQgghxaawURspqAMXQdtcX6jZKIRWG5cwImlsRLibMGFCrOl4VEA22V6E0q233jpvneS6lvKkTrbg6sI29TbPCRHIfdvH+QKb5UHgN9NgSTRimPW7NNrSbiJoRwmka621llpnnXW865EH3dTyQ/sN7bUvtRtyI8u6TTfd1FuutMMHH3ygkhAqtNmm4+bkg4np2++7zuZkkZybK7CevX9IMDSfH3bc/WHjyrXu08q7tsHkCXIXu+puEhdtHW1lW4kUKmiHEjrRlmQfWCaY7RFlrZN0EiXu+BSmiA/eG4QQQooNBe0i4Mt/K9rkJIN+wYz0i2Bb+++/f6NtENzGPB6QnKjItwuQczdO0HYJAD5tuRzTdc4w6RShXQQsKRdpxXzYmmoXtlYxKgq6Wd4jjzzSaN3DDz+sgwXJ5AGEcTFHFQ286feNqMcuDeXQoUPVkUcembfMrBPy2ZrnI+186qmnOusLDdPTTz/dqJyo80syoHSlJovbPyQVVxTmfWv6X6MurVu3bpTnWo5t+ptLvSVavi8adSEabWD6rmflM+0qy5VOL+6YhQra0CibQcmy1GiH+IfL8yIWMHHHSCLMo3xXSi478CEhhBBCSCmhoF0E4qIFy6DflQ/7jDPOcJZpp2FxccsttwQNTG3h1Ra0JW+wD1fZrnN+9tln86KOm+2w/PLLJw6+5dvOJ2hLJGKzPDugmSs6OYRxO5e5aHKRcxz+yaeddprTqsDGLB8BnszAayJo3X333blt4Kct0cth4i4B0kKEPmjUTSRlXIigHXUMn6CNfOV2YCxfWQhYhXzXxxxzTF5Z8h91gbBkuh746i5l25MdUQG2kkxuSUC8EIEyzaSDK61ZWhPnqHzs++23X6qJA5l0susY59PuinSfxKrFdcyQdUmvO6kvOAlDCCGk2FDQLnEHDs2crBd/3hNPPDG3ftq0ac79kg7Ak2xv+mgXOtB3+QebkYPtwGRRhGi0oXG2A7zZZYuJuLncFgrswFYY4MOM3EbKh3ANTSqiyMNMPE7YtgV5nI9MALg0mjD/XmONNXJm/q56+I5hr0ME9ChCBSTbtF2OB6F4r732cu5jpjgDvjYF5iRJlCWF4LuHovx+k/jqxpmOZ0nSYxTbdNxXH1hjSJC+rCcHXGUlOUahkdhJfRPnIkUIIYSUJRgaicYe8EMbLb670ArZwoMr0FcxB9hRGm2fL3VIoLUffvgh7/ePP/7YaBu7XAiqIqS7BtlRGikJOIX6IGiU5Ce262yWZweUS6LVtdf5gqL5yheBD9rwbt26qRdffFEHIrMRM39TEPb5Rceti1oeItS6lpv7+e4FO0WcK82XqdH2CViuOkk+YFc9MYkBzXkaQRv3iCtAm4uQbeCKgLR+WWjZyumj7ZsMybJOUrZ9jQvVaFNrSXxQ0CaEEFJsMh+1YdAMoaeeTfbswd0999yTC0oGE86otll55ZWLpp0Rs03bn1Hyb0dF7vYFozK/N23a1FtnX/3tYGpR2wJEI5Y8ryJoi3m1Keib0byjNNqhvqAuQRvbxgkZdhuYAh++v/baa0H7QvhDJOxLL73Uu11U0K2o8jFhgMBsvjzhUYK271qhXZCnV+77YcOGecuFOXmI2br89p0P1rmEcJR9//33q1CgfZfgeHY5rrLjQEqlON/k0PIqQdCOe2ZCcqfHnb89MRHSzi+//HKjfVxWISSfeu2z6+18CSGElJ7MR20YdF1yySWZDAinTJmiA/gceuihao899lCTJ092mjSed9556oADDtA+iEhhYqZvKge2cGD7I0dpWdKaQZ588smxA4uddtpJjRw5slF9kE8ZGvesBvvQ5Anid2yX3a9fv9x3l1bTbitEaZf8ryI0y33RtWvX3HY33nhjI/9wV91tjXOowBeKK4+2LIdQiDRpvuPY+yLYlx0sTOodVce4umP/559/XvXq1St2f3twGqVN3GWXXdSqq66qf7s092ZZtrYUqa1cdTcnK1ym71i36667eusYEuX/7bffjjy3pM8CNGd2fnWpq01IYLEo0mpwC9Ucm/z999+pj+8L2Bi1jz2ZgeWvv/56wXWpF+q1zzZT8BFCCCHFoCjqEUSinjp1aupy4GMLzeTBBx/sXI9UUqeccooWZGGOe8EFF2hBxKeZq5SZ8kJm0kMHQchv7AMDHPENNyM2y6AW7b3kkks6B+shpuM2plZKopPbg3LT3Ns1kI+qi+lbDszrbgZCSuqj7cLlTx2C7AMrAonobqbicg32fAGhXnjhBecx0graYKmllgrSaNu+yxtuuKG3TviI0GVqOa+77rpG5doTHgcddFCeeSdSRMk61wQKgMm4yypD6nv55Zc3ilSelhBh3JXHPNR6oNQa7ZBjR5mOF0t48bXzpEmTvPvQbDyceuyzbTciQgghJGuK0tNgsHvRRRep3XffXa233no6tUwhdOzYUX983HXXXXo9AlNV0iw18iVHmQQXU9AOiTZuH98USO3IwmL6GzLo9Q1crrrqqpxW3953oYUWcpZvCqUmRxxxhB6UYZ1ca1NLLCbsmExw+eXGCdquc4PPNATFQsz3ZR8c1xQSJS941PWSoGlxyAB54MCBBd1vceflC4YGoqKOYzsRsE1Bu0WLFnnBAH1CHI4lubHNd4h9ve26+qwIxKrCBzTh9913X945xBGyjRn7wBchPkl5lSZom9Hfs6q/LSRjAtDFCius4C3DF1iSNKbe+2xCSG2DdxvcOAmpCUEbUZNhKokUUbfeeqsWphAQDIGvzA427cAAZoE777yzOuuss7TgAqEQ5qoYKPjAgN+Ohi0DxbSDRNkfpnBPPfWUevfdd3V9XOUiYjPyBsf5gZrm1PIdEZ19Jt52vmofIvjZgpQcRwbV8HG0BVDJJ20fR7QSdhAic39b4DDTlkm5riBgwqabbtooorZcTxwf27/zzjv6vwigkkILy+z2MY8lPtcwTT/wwANz2+B+gtBrnncoUj5SbaFNbF9h8/giVLrW+doDfPrpp/o/7jUfLnN8WYY2iTov81qKr7rr+tv3CD6zZs1qlGpNzuuGG25Q11xzjfY7lzLl3sF/8zmV+xXCsrgkuARttLN9nSQwG3z4o86zR48eeYK2lHPxxRfn9nedZ9w9Aa28+byZdY963l2TTJicSGteHnWMKPNwmRixzxmCE95nmFw0r2NaM3VzPSa7ZCIk1IQ9dF1IHSt5AiQL6rHPzvqaQkOPoKdR72FCQhkzZozXnYskZ+mlly53FUiJqLT+uiiCtvhXAgyaYS4GYcCVAqpQYHKMFE8PPPCA2nPPPdU+++yjA7pgVv7UU0/NpUiyQX5kvMDM4GPwF2vWrFlmdcMMvQh7GKTAt9Zsj8UWW0yddNJJuaBeJlhnIvtCIyzf4UOKgZDL3E6EZvOYpnkw0othkII6yjaiYZQgaai3aLIhdEKwN6NIY9Ar+5r1lfrY2ihomGV721fV9F9H/bAdtClyHvb2rvOShwqDQvMBE60Mzg/+guY5SjmmsIbt8Pn444/1Ocs2aC9o1HCuruNHIabxCOImbW8iFgTg119/1ZHaJTWWuc6utwkEZZhq+uqG64W2FdBGEOple2l3H2aQO7SXrx3MZbiu3377be6ewAAUZqXY5ssvv8xpw2UfaW+5Znb5uN+xDPUWc2G7LcEhhxzSqC2k/mjPqPOUd0Dnzp3Vq6++qv25sf2FF16ol3/44YeN6obnKO7dgePi+pn7QSiFwGHXR+5HVz1HjBih4jDvWx+iQXRpEn37msvx3dwXsQZE44x7UdotjqhtcG3t9fI82MvlebGXm+/HpM+tTa1rXeu9z86Cdu3a6ck6Hz4rLUJcbLbZZuWuQtWDfhfvnND4LFmDCUVXINhqphosA5arsP66aKbj0OhCYMHNjQ4VA524vL5JjyGD4u7du+vv8A2DueBjjz3m7bQRCVm2ByKYQSgINdX1gbJwgTFIEWETWjARLAAeeggC8E0zlwsQxkxkGwgq5va+dEE4D3M/k4ceekidfvrpWiuI7cRsG8HRbrvtNvXggw/qKOA4DykHEclxLNOXGoFrRAA262HmTr7++utV37599XcIj1IfM0AaQFuZ5WI781imuS+0Zq7zOvzww3WbmmWZPufmPuLzK8vM/M5YBkEBA3ocV7aZPn26vpfNZaFIkB+0J84LwrSJGawJ9zTOAz6MwNTiSE5w1/FxDbCtr25YbgeoMs/FvD5R5wDwjOAcXNvLMgREQjsj2racAzRWGDhgG2lzfEe7mvWR+x/fpa3wTGGiBMsgPMmgGwNXCL9t2rTJqweeMbN+Uqb9LNpIOjo8AxC0ca5R5wntqn0sF7gH8W6yt3PtK++1pPeZea5x++I5sd9TQtQ9JOCdYO6H85Bo76g/nvGQ+kdtg3vaXu97BnBsDKjs5eb7vND2dL3TCwHvlUoTDOu9z85a6xH3fiEkCV9//XW5q1D1iOIE+N4vxSTOXa0akYmLSuarlP111n12USLrIKI1Ajdh5hkdKHjrrbfU+PHjMzsGtGowx7R99KBJM4UnGwzUIWDKx9SK4cKk/ZjluMoU4RaD0agyzDoBMamN+4hGzLUONw3Wi2muLEebmBprsx6yvYm5b58+ffSkge1vbQqU5va29tFcBuHMd/5g3LhxzvPaZptttImVvRyCHTSHUe1jg2UY0GMQJ9tAGIaJs7RFko8d2MzWaJh1kIBfMhlhDiKxHQRA1zGwHa6h717CMe1lpnl/3L1l5pu17x3brBwfTLjIctlX8lLb25uTKvY6EZA32WQTPdFkXj9B7jkzZZ19nWQyI/RaSZ3xfvHdI/isv/76uW2iPoMHD1Znn3229/k2P3AngYCT9D7DB5M00KpFbWO2s29d1HJXvc02Q8BFdFBxdYi6HmjXJPtBO/rMM894tw+59iFtlraMSqZe++wsiSrTF2SUVB9RAWezxFRckPSUY6KzFp/5ajinhpT9QtbnWBRBG9qgoUOHasFABsYwQZYOPAswmEPqIMmhLGBGuZJ8MewLJu3h8r8NyXkcR0gaHFfAKNEWyXa2b6J5HmZdMOgRM3Ofb7UdjExMeODTZiLHMLfHfeOLbG2e82WXXeY8X9P82q5jVD2h3Td54403CtKAmNfbNWnhqgtMKYErJZQLmF327t3bu951zKiUZz4effRRXVZoO2A7mSyAIObKwyzfJSK+y5fcrL/vuYEw6zsfGcQfdthhkfU1J5tAVmaeeFe53Dxkpt3eFpYbhXDGGWdEBqLKCpdfuZnDOjRneJJjRN2n8B13vfMrzU+rkmGfXThx7xXQpUuXktSFlMZ0thT4+rpKIcSVqdzYfXolYyoLKhn2qxUiaENAsM0LcMPbZtFxoIxPPvlEf8RkEd/FlBU+ji+++KJ64okntKkAAndBg7ntttuqSsEWcmTA7Rs0yuDeHngkEW7i6uMSIHwmLqYG2F5m/zajjtvpmgQIvuLrGnIOuMbXXnutygrkbjXx1dPVjuZkRCimcIkyo4S3Ql9gCBDnM4uCltx1TFdgLh9y7REpO4mgjXJF02mmBXMdWwYV5jpXvX2DDzNWgC/AmCuCvoncv3K+xfanhEtCJc0Ww7UkhKgAjrBSgfVBFEizFkfc5BDJFvbZ8cAqxUXce0VMWKtBE0TSxzioF4opcBWa9cBFkn5Dgu1mRVRWDBtxGax0KGgnpygjF8yKY7bL7KQxi+1K5RTFRx99pI477jj9Abfccov+jgjS4vfZr18/bVJ8zDHHqCeffFL/zzpXbikFbUS7Boj+mnV6L1M7bW8n/qn2ti5B23fMEI12FK6812YE9CzYfvvt1SWXXFKQoO1LMRSF2Y626bQPM3BZWmCGjOfIFhqTaLSj8mgLCNBng+1kcsK0orAtHHxmci6Ntq+ue++9d/D5+LDv9XIMjF3PZqmIE5BDBG1YV9jR8226deuWc5MIKT9uedJySGPYZ8fj07qHWJH40vkRUggI/Er+DwlWGkdIf+5SWKSxLECq3ywoZOyZlLXXXrvs/erJJ5+sapGiBEPbcccdtY/uUUcdpYMhwf8L0UW32267ROUgKvfo0aMjt8GALWrQVk4wK2c/pGLK7Ht4febCoTd31EtBBD2X6bgEP7OFQdeg36fRxrExMEMgoyQaU2j24Jvq02JlbUIlEcij6uZq70LqYfr7hmq0cd9E+SwmAecHUzeYfZuDvSSCtp1H29U2rskBWzttT97Etak9+ePbHpGR7TRtJkkFZl+++VIgqcAqGfv6J60vJlWQQiopSScgKr0dKwn22fH43iOc0CFxIBo9ou1nBVzbOnTooMpJMSai8c6WeC5Z1SPt84kxeVLLnqxBHCKZrCwWWVnNpmFeh4tdLVAUQRvh7GFmBXMwpLlCBx5l3lqrQLixZ8HjNNrmDNazzz6bqUY7SliSqNRYbr7oQoQq03Tc5WcdhwS3EcHG1jIXc8Dsay/X8kJeMEitldRH2+XLXChZaAeh6ZRc0kl9tAXTR9ul0U4jaNvLCm03uTYSSb8cgnaxNdqff/554n022mijWI02TPcHDRqUun6u8uOW+7DjMxA/7LPjqUfTb2jkECSvWoGrAqwnyg3dXsLAJN1uu+2WWXnXXXedTg94zjnnBD2/lfqMV1K9itmvNhR4nlC2VLKVR6ZPv0QQlsBGEBYR+Rn+K+iwkTu6noB/hh3gAIF7QsCgEoFjzN+h+0Wtk4h69otf0jDZwqArGJp9DqZG2yVoh9ZdfASLaTpejGBMIS8lCTDne5Egwm+USW6h+Mp4//33g88LeagR2VlSKYW+DM1yXVYUrmO76htiOh5XRiGgzrYWIonPVaHHrLRBmZ0z09W+PpeCLElafqW1YyXCPjsc33uvkgbBWWO6WVUj9WZtEDq+rFSSCHHishF1jZHRIEtXvGJSbm1u6LOCzBSVxtxzz112C48oMh2JHHHEEdr/CpGGb775Zp2XE2bB0JYiZdGsWbNUvRPyItx1112LbjoepTVKorUM0WiHliUpwWyz83JotF11TttpR2m0Yflgl3/ooYemOp4cM67uSQQSTIYkjeQJjbhPo53GHNjXlnaZmChIgpSLyaf+/fvnrZs0aZIqJnChQG7hSmX48OF56ZUEsQLIAtwrrnzESe8VCtrxsM/Orq8IsVYqN0nf3eWqd1Zas1qdBPFdl1p554VcNzPtaGjf2r1798R1KdUzUKpo9mkpppVpkxRtfdttt6lKJdOnEoFPMBBDY8HPFCZoV199tQ7whWi0yI9a78isVdSLxJU2IUmkZx/mgCBK0E5qMiuTB+JfY9c39OXv2rdcpuNZabRdbdu1a9ecAGeau5jHhInvTjvtlOp4dpk+kp5XaD5KOTYmYHzpvXz7mISk9zKxy4eWLiSKKWa/kYtagnkhpVupOf/88zMzwS4GBx10kHPmHfd1VjPysOSRmBF2ELUk1Mqgs5iwz07mf17tvPvuuxUfeK4Q95ZKJ62wVonB9IoxiSF9SEh7ybspblupJ95v11xzTeS2ceOPLNl8883zfmfVfxYasb1SJgNrkcx9tOGTisEiBq0CZsuhCQvNC1zLFNu0JySCdJR5qk+jHfWyEY0hBCrJm2yWL5rqKHxCuphcV7NG+4Ybbsjtj3bEPQBB+6qrrlL77bef9h9KU34UUb72hQgkkydPDjbFMjXYvmBoSepp72vn44UmGKmEChWwIJAjF7WYi0+bNk0VC/NZqRWSRqj20atXL2cE9KSWCRS0w2CfrXJ5w1257YW4d1Oc21YlgPcOAt7ZljrlAi4KDz/8cMHCjbzzKx0z/aTw+OOP62j/ISB+AvzN46iU+yxpvzt79uzEk1lt2rRJfM7YFjFHkGIwjilTplRljApk1rnzzjuLVn6x7rGFUo4fKvneTzUSGTNmjBYS7AGj2WHLy72eOuy0Gu00hPhoR5mOY3CaVKMt5+RL7/XDDz/EloF95bhff/11cL7datBoS+Re01ogq2BrWZDkuHi27bgDIeWakzswkz/ssMNic5PLjK/5rJi52n0azrQCVinMDZOavVUDWd2/G2ywgdp3331TlyPvI2hpyf/BPjv75z7EdLwQQt+zSVMA2e9NV2rFYnHmmWfm/V544YWd2xW7L1xuueVKmp5o8ODBjZaZfVm5AkYmpWXLlpmXaQqFvvq7tL3yvO2yyy7aEq2Q58rW/prHl+ci6zbdZJNNVL1w2mmnxW5zxx13FBSLp5pIdTaIRomXu+uFgYHkY489pr755ps0h6g5kDKlnIK2kKVGW16C5n1QSOAiEUJXW201/d+MPnnTTTepYiD1tM3VimE6bvpoF8MH3HfMuLKL9VLzRR3H4EoGJPYgz5yUceVwF+EJ95xEdI8Kpuf6XQnUou9gsdNpIdjJ888/H7y9aEnI/2CfnT3Fer+0bdu2KBohn3DrotjvziTvwXbt2uX9TiJc2TzxxBMqC2wrG5+1l0tQjGpbaPorof8wU6G6MsKEELddyITSiiuuGOnP36JFC5UFpWhXuW8raQzgctdA1om0hEzWdunSRdU6qUbY6JDNyNj2oOu1116raAf1cuDyPYwjxMwlCVGm4z6NdoignWZGCuYuEgQKKUVceQRLSe/evTMfdJhRx11CpK985MxMc0z7OHZHXixB2xTyTUHbtY2wzDLLBE12IFCZ7YYhAbnSng98v6o92m4tCtooP0nE0/fee68mZ8fTwD47e+IGzAg4VwhJ+ps0Jq5JB/xZPudJjo10qaEB05AXvlgkqXPz5s0LKueKK64o6JiuewZBwArhpZdeauRHXEidJKNB1lTiBHo5sN/naS1zhMMPPzxVvQqpS5MavaapRiCYQRUtkgv4W3zwwQdpDlFzyKzz2muvXTZznbio41EPjCtqqXR43333XcF1QseyxRZblGVgfOGFFzZa5vKdyjLquJQVYqYPDRTSf8GnKylyHLuzgz+iWa9iYAr5cWmrZFvT/DFKow1sQduexHCtCx0c2TP5oNKDCJWbShNoZQBSq513IbDPLh6+d8wJJ5yQSXmIH+HjoosuKig+zAUXXJBYo1QM8+GsBXzkgy8l4goVp4XPitDxGyxXXD7wISBQa7lSThXznV3u/qCQ40eNX2RdqOuIj3PPPTd1/Xv27FlwvvNK0/JnSaqREQaeEydO9Ab1QRqYkEBY9YTcpEkE7aQgRUsUcem7fBptRJueOnWqc/AmAUkk6IdZftJE8qUesO+1116NlhXDdByd1ldffZUb9Ijvtl2++bIZPXq0WnfddQs25zRznPuucbEFbZh1QaMd0qZ4pxx55JHOOtvL7H19/u9ZdaxpO7JKjiBbDRrtpMhgu9wDq0qCfXZjJKhiofdJyH5R20jwxThcmsnrr78+uB5I22az9957Jza7Nfsn2987zuQ5S7IclK+wwgqp9u/UqZPzPV/qd6KrL0/iKuCLWP3qq68Gl7HZZpupLK8tAsYOGDAgswj1Se8bl1b/008/VYUSGvg1KVFuiSHYgegKKSfKVPyqq65yLsdxsnhvVHI/n2qEjdkLBK5C0CrXLDlM9xD4iJT2Zojz7YozHfcJ4lF1hxBp7meaAG+55ZZVpxkLMXNOCoRreRGhvRCNPM50HIEzXHmLQxFrA7tzMYNxFfuexHn77jl7Gfyujz/++Lx1PpcE+6UugrZ9rlkNyLLSTogJZEigkGqi0gTtkHRy9Qb77DAGDhzYaFmx3pM+d4i464BUe0nec4U8n3FlRg2soyJIy6A77bs5TrMbUj4smJKYyUZNTJjH69y5szN7QiHnBV/wEF/mtPdoXGqokPa0AyvGlR1XJiYH4yZCsno299xzz0bLLr300pIEsosiJBp70iDGcfiuS6FtvWkCn+8oV9FqDNKZagQC81N0SMg3ixmnUaNGqWeffVY999xz+kUKH48NN9wwu9rWAJUw6xKXR7vQB0zKxSxjmpyjldBGxRC043I0FmOms2/fvo2OY7+Ui93eEm292JHW5Rx/+eWXRhYXSVNDuSaI7Ii5aYkK8FKNVJpAGxIIsN5gn519qpmo+0sm0wq5ByV9ke84pvtPGpLWzfSNPuuss7zbHX300bHH2mqrrZz72v3VxRdfrIrF2LFjIwMw2nTs2DFISDzxxBPVoEGD8rSuMomcBDyPuNa237YLe1Je2vvtt98OOpacz6RJk5zL45b5lmNywp4YCiVEuI+6h+39o7Z1jRNCnw8oT5CaMhSzXnHC4yGHHKInSF1KF9sNMe3kVcizZvf1V155Ze57+/btg9KLLRExqSMpcV340q5Wcj+femS03nrrqfPOO083LiI5osHxQnjmmWdU165dC7bZJ8UDD6LLvHb48OG5YGj2TYtZ3ziNXkhgr2rBNftfDEHClRItS5M4V+q1rE3H48y3ooKhRU0uuLaPEpil3eycpUgVZQfSCeHnn39W9SSY1tr5iMlktb+LsoZ9djxJ3sHFur9QblTwv1L4M5rn5soFHTVYtl2jXCA1U1a4opDHxZyJO4es0oAlvUdc26Pv82l2kRsd52EHgZPzNKOhFxKcLyqoW8h9GZoNp5r7myglVRwhvvCwEnWl4pNjZq3RjuKhhx6KfI6L6YZTjRRu/2DNVmHmDn5fmHX5+++/9QsqjclrrVIJLweXIH3MMcfooCjvvvuufnDtFwaicLsicdv3Qa1oQ8z26dGjR7AfXSF+vzNmzGh0zKwQE6cojXax78kQQduFy3Q8ynxOyrKPgzYoxNSr2IFgKuFdUMum4xh4I2d0rXbeaWCfHU1aDV4prYUqiazqk6Sc+++/P1OB3eTss8+OnKSN0pYmEbpgKovUenHZMF5++WUtbNvtA42oa3lWZFFuMSevkmxfac9MVu8ln6B92WWXOV1h0rRT3ARUiJtDPZHpSA+DWfhnYBaWHXblPuQujfbgwYN18A7x0TZ9eEOBJuTaa6/NO8/jjjtOVft1En+yYglGrmiwWSECpv0SNq9vsc7LDM7hi3QfZd4r68x8yFEpXbJ+xootOGaV+7NSqDRBW56rSnjnVirss7Oj2AJ3lOtRpdzjhQzoC8U+d1dGlGK+0+T9Yh43Tpj0rbdNjkPc9GzT4vvuu69oAbbSksS0O+n+lQTOBRM/JggGHJe2NEn5oeskXW4Sc/aQY4XUH/EJQmmSoj3Gjx+vqoHaUqlUAVn5VRXbR7sQQdsltBUzn2WpqUYNpAwUyhEMzTxmXHovF1IvM4981KREtXTGWQRUqUQq7fkoVnRXUvsUS0OWJqdw3HHM9WPGjCn4OIUeH/his0h08qxNSpMuzxpkBNl///0TC0VxmWGSngusr+AqY29bTM2w6ZpVbOLM0UtxvW0trW3GL3WwJ9AhaG+wwQaNtgtxe7Mx/fQl6Nwaa6yh/4tFhDxrdoyHUkxAlXrir6MRL6GSqayRUR0wbNiwclchMh/znDlz1F9//eVN/1LPEX+LfS7ykoryy0uKvPDsiROz4yq2wBel0U460w0fJcmP7DPby/olHxeJtVBqTQCsNI02BW2SBDNCdJJ3lb3clTc76l4MTRto7xslyMGF66OPPsrURQbHR4qxU045JW/5rrvu6g3mJMLJ6aefHlm2HWjSd64HHnig/g/ri0K1rfA3DrGMCg2YiaBnoYhwhMwwhdQ/7p2WJgit2ccmsW5BvAfTmqEQF4sozP18bZ00hWwh2On1EETSNtE+6aST8pa5fKrNHPZJQRYaAQEszfzXcm9tv/32jfzqC4l+H9VvYkzke78U2t9OmDDBqX13makjlkhWxy0FtSMFVQmVLnjCDAqCdqEa7Wq6+bMM6JEEn1YDwbfQwd10000qSy655JJI0/Fi+dOYs5s+H+2o5+HUU09ttAydh93BFVuwMs2+sqRan41qE7Qr/Z1LKoMRI0YEbbfPPvtEPsem1U3cM47URVH3Z6hwYh5T+pfQ9/rQoUN1Pm4Ex4sDAVGTDHKPPfbYyPVCnz598n4jXowLV9Al13lGHQ85oc3c0rJt0pgcSc27QUgbh+LKiw5B5dBDD01V7pQpU/KCp4UiEdqLMXaS/U2lgHmcqHRQWdXF3s+erIGgi/SkJlH3pgjGvsj7ccjxfYoAU4uOtGVJ+8G4d0/I+yWJW0Dr1q1jNfrm+0HGZYXkUi81HIGUGNxUTz/9tKpUMDONwDhZCV+liIyaNZiZdD3wCBSXRXu8//77znW//vqrnpHOWgCTSPICNMIIfmeuL5fpeNS5JhVwiyFow/Xh1ltvzXzG/K677lItW7ZUtUSlThzUmok+KT5R/ZZooEPyjRcijNn74v0TV1YS4d4GQie0YNAo+eqQhP322y/3feedd/Zud/vtt3vXubKimH2CWS/0mS5hA2al0r725IgLlGlPtiQ9f581mpRTjMwsUUFq0x7DNfkgYyP73o26l2+88UZ1wAEHONe5TJyl3rAANaPXZ9F+ScakSdLKhZab1LIvDte4Cmn3OnXqlEn58v4RgT3O5zuqHQZmGJStUscbLihol4Fi+k6EgKBnUS9WmI9DCEjjTyaUMuVAVuBF4hqcZ2GWFfUS8plXZy1oI06AzKYWUxMp53r33XfryYWogGdZUAxBG20DXyzkF86STTfdtOhRzetdo01IoYQMmiXYUKG4BPW+ffs6zZPj6pgk/VISChEeNt9889x36Wdc72Rzu9B6YFxim6GjbFNDLcvEnP2tt97KmTfHHcOuU5S1ggtTE2+2XVwU+6S+1eb6LbbYIpPo+S6gbX3nnXdUUjDh0KxZs9xv+BHb2t6oMYDUcbPNNksdNNR3vr5UaSYQLLMmbWC4EOBiYZ6fXW6I1aTU03z/rL322pETjHH38UILLaSSkmYCo1KgoF2HRHXK6Eig0c7qxk3r611JmMJpsaJGFkvQLmcKGtwDP/74Y2KNdrlNhZ9//nk1bty4TMqqByhok1pj+PDhjd4nvtgQUSazrvdclP+mCwycd9hhh7Km0ynkfY2J61A/9NDjJKkHBIOo7aPWZf1OC613kkBfxcwWgPNH/m5f3UzsNJxJosD7fPyzwlVne3KmWJTKqjPLZyaLSRrX9ksZLgmFZAWRfarNUpaCNvEK2llQqKD9ySefqHqjkMjcSYKRmb9NSmE67jtOGi20XT583Asti6SHgjapFcz3UtJ3VKhpqGuwKM9Q//79G62D+8p1110XWWbWhB4jSnsLV6y0qbdC2+/NN9+MXJ/kGDNnzmxkkp6Vf28cSfpk2+Kh3P0frLWSppR6/fXXE59DoRl8oibGioV5PlG517MGudcrid6Gm0O/fv0it502bVqj9jHjEpT7Pk8CBe06Y8aMGZHrxXS83FSiWW0WL+Y4U/piabQl+BnOIUQAzoIkgjapfhh0jNQyPp9Uue+TBoGyt4cJsGj2Qt+LMNG1A4SFAOG3kD65XJok87jQ4IqG3Nb82prRJP2LfW6+icMooSnpMYqtqQydoEgLXO1E2wif6sMOOyzR/rZ22Z7gcI0H0wra9ve4PiwrrXuxxjyu64rc6i5Cou6b9SwkanlcmfP//zr42sPV3vJMoj49e/ZU1QJHRnVGXDTJrDXatUQWHVSc6XgxwMsJAv7IkSPVIYccUjYByTVwydKvWgKXZNmR0XQ8HGq0Sa1harR97+mQSLkhAX3MwW+IGxG2QdTyK6+8UiXFZ2r+4YcfqkGDBjkjUcM/08T03U2j7U8K/GafeOIJVQyS9MFpAt2BrDK7JEFyLRcKzsmVu/jtt9/O+YpnYZL94IMPqiOOOCLnrgffd9xv0JaHtn9IIDPzGp188snO8bEc5+abb1aFsPHGG6vOnTurciPnOmnSJO37HuenbbbNtddeWzRXv0KAFYedSq2SoaBdZ8QNCuKCfSSl2nwpik25gqHhuJ9++ql6+OGHG6VDKJagjU4LQVAQAdNnoZDlsWGWBG1Hlm1YrCBDtUghgU4IqWQKsaxyBbmC5rl79+5527neU+WerMIzPGTIkEbLoWG062v77mZJVFAlaE9dpuiFjjXiJnvNYFxp+xZzf1PQLtU4Cdcx7bFcwXQRbyDLvhzaTOSjNy0XcL8hIG1UgD2TpAK/L02WWacopF3t9kWkbTt9nWs/175ZYU7c4dnB9yQxIiQOQJb1a8g4yFklW0dS0CZFNdmu5Ju/0kzHUX5UZ3XFFVekijoux4Z2wl5fDBCV9/HHH88zXU96f6Bz9UUdzSJYRxTjx49Xyy23XGbl1To0HSe1hqSnOvvssxu9Y0KCQkn2CuQYhkVRHLA4Cs0lW8v9csh7POsAn77yXnnlFefyiRMnRqbWiuufMAldjHRQceduT+ZAIIXva9YpLCvZerCcz0Hc9TzllFMyP2YhgXZL1TZNYo5z5513qmqHIyOSxy+//JJZWUirkUVKrHoyHY966bRu3TpVMDSfkF9sAUlM113E5aKFkN2tW7eyBJSDiRyFx3DYVqTWgBarFH64puYoVFNcyqBKlap1iovOXUxhC4JpGise9Ish0eKTCsByzmZ6LRMc044Cj/sOFhdpfNptRo8erYqJKwVrCGl87LPAPP4ZZ5yh0/2Woh5J3fSKWacFFlggURyKaocjozoDN3epXi5xaTXqkTiNdjHM+kWjXE5B28c222yjfaPgx5TkfoV23+X3hCj3lRhIrx6goE3q2Vc7lJdfflmVG5j/HnvssQXv/9lnn6likoWJtqsMOwgczkPMYkPHRqHXPSpPdpKyQnKAuzj44IO968x4AEmicMME2vSTLpbbVZzG/sgjj8zc8qPYY9U99tij0XFwjZKmvkv7HBZz/J+k7IYi5cf+9ddfVaXBkVGdIWbEPigYl4840/GQSJFRGuVyCdrvv/9+5P2GCKUbbLCBd73rhYyIk64Ze5xjuf0c6xW2O6lnfD6aob6jrvd72oHrVVdd5dy2ZcuWavDgwapQzHECJtSXWWYZVSlR4Av1/a5UkpqOF5O+ffuqTTbZpOD94/ygQwk5/5DnsRjt6DveJZdcEnvMtCnpospNanVSqoj1WSH1LVV+9CRQ0K4z4oSqQnw56oWsTMd33HFH5zrMbPrMvdJqtBGh0eUnvcIKKxT9evtSTBTLAoMa7fJAjTapFdJEn5Z9hw8fHpvH1twXJqRR5Za6z7r//vuDths2bJgaMWJE0DHlnFyRqwshJJp7yPLQ9YW2abnejXHns9NOOwVvGxrXJIrXXntNx25JQ5Yp0qQsRLEuNF1Y0nohCFlUuqxijcfscn11CJ0wDDlGVttWOxwZ1SFRGm0E6IBZDileMDSfX9Zpp52mzjnnnMwFSGgvxKzaBqkeiv3CK1QTDwqpW6G+WyQdFLRJrZLk3S/960EHHVRwXI1CiTIXTgpSKpnvX6T92meffZzv26Tv3CwCbyFQnZmKLGlf0a9fP5UlrtSZAny5P/jgg8j9sR4ZOuIm1Pv3758zd3eRpB0gbCUxHTdZb7319GSSSdwESpRvbpLrkWZCRFJ4waJDJhqQIg/3e0h6tF69ehVUZ4CUZauuuqp66KGHYrdFurQNN9xQrbvuunnL4+6jUDcGjMuKGewVASRNS8UmBY4za0Eg58iozogzHcdDPXTo0JLWqRpAh+KLfp2VeTg6z6jgKoWa5kLQhhY9VEORNWlMeShoVw8UtEmtEBVkK04gCRVYzECQrgEvtGxwq6mUFHvoQ5B/1wXqj4mFOKA1DDmnkPd+p06dVI8ePQreHxPbpkkzhP+4axdlYXXqqadGpoNy9YNmPUP7SVgQVILVFjTTIdc89NqY16OYwhXGWcg/j4kaW4iNA8F9TT9/s84hdUfKsqgAeIg4Lj7whx9+uH5H2PdFoeOppBMqaa/BAQccoCcVBDmuneosq2tdyQI5R0Z1RpygTdygQ0kTtCIL0/w0L5JyvoTSmGTddNNNemCRBAra5YGCNqkVXILMiiuuqJ599tnYvLqhA9n27dtHbguz8/3339+5Lk5oKzXoG0W76bKcEpZddlkd/DIpIdG5TaCtS6KhhcD+xBNPRG6DNn/mmWeC2znEkgvpL4t17UJ8bHFOouGt1b4Ek0CmQI3nVsZyUZYBhZImS8G+++6r2rVrl7t+sDgI0bQXWqeuXbuq4447LnifLKw6zzzzzERl1kIebY5I6wwK2uUlTfqpNJ1PnO9UMcGLFSZXhRDls+5i7bXXzsTygNRGMLS333673FUgVYgrxzEGctDQYGCIiMpHHXWUFrxssD5pPI1CBrBJ9okKSJk12267rXZJyipiN0DqqSgQg8TUnsEa4LrrrgsOfobl5uQKTHxdwv7yyy+vv0+bNk1lQZrI3Fm8b/faay+16667BikBnnvuOZUFpRCIYMKOSR2w9dZb648AdwOJV+OKl4NnRYTdNCRpL1+bJLVoSRoB/+KLL05Vbtq2eu655xopRgq9zzBpMnXqVFWJUNCuMyCsVXLkwFonjUa7WjWGxZg1jsrd/uWXX/IeLwOVOKNcidoaUv3ARNvWeptBhEJSD6V5RyV91rJOhRSnyUWQzSyJ6/tgBgz++OOPvO0LbWOY+Ebhs2YoJZjM+fzzz1OVkcS/Pm2wsFISpVgwJ8Fc95X9rMTdQ7gGMgFTqe1VyLgzZPu075VVHG2Upt1K+Z6rGUF7ypQpaty4cWrGjBlq9uzZOu8jAjAICGBgzyh36NAhsalpPYGHhxrt6hRIKlFjWMv1JsngdSb12Ge7fLijBMOQ/Mr1ThbtUkh6onJN0KaJ9GxTaHCzUsF7vrKJy2FOakzQ/uuvv3TABZgBXXjhhc5t4MOAKIwC/TOjoel4eUHbF/KygoYgzb0NH7QHHnhAlQM+k/VBtVpckOyoxT47ab7mSkqRWSn1AFdccUUiTTeCnQ0cONDrp16rZHnNkpRVyvsWwed+/PFHdeONNybeN+758+WOL5SkbVJsN5B65LLLLtPvgmqlons4+FnEpQtAJ42HloRBQbu8FNqZTZ48OdXLuJxmTNR01gcUtEm199m+/jHE19fUSmYtsLjeoXZ/gFQ6pp9ypdGzZ8/E8Tm22mqrotWn1nHdg3H3ZakEvj333FONHDlSB5mFq1dSos7DF4m+Uik053sS0r6TJLBfVu4uRx55pJ5ICwUp1Shol9lUrW/fvjp64lprraV69+6tFl10Ue/2c+bM0R/z4osPadobW/avpFlkn492OetYDe1ULMSsMGkkxbRtZQeJKGXbi09UsY9Zz/dVJbRTLQravKdqt8+2ry3+f/bZZ3nBFF1pvqL+4wM/QQRi8tXN1uDbx3Ddc7NmzWq0j7ktMNMOCTIwLqSdzABhxX4OUC7ugV9++cXZBqHHlQkJX3o2u81c9fAdb7XVVkt0/lGCru/e8dW5UOLGelH3dxxZbVfIhEDUNoW0F+4b32RaaLtIWxf6rLiOH1WHpOfvu9dC6oWYBJI6MOp6IZo7UpL5tmny/5cjh3mS47u+V1N/XdWCNkzQkAJjmWWWUV999ZW68847dZL0s846yzvoGzt2rBozZkzu98orr6zOO++8xNGNoyhmEvi04IHBSyVNtMt6aKdigbbHNUja/mnbyn4eSnn9ZbBXqmPW431V7nZ68sknK+KdUix4T9Vun41rCyHVvH8lxzWWYdC2+OKL59YjXzIijv/888/6N85FBpnYBhGwhw4d6k1JhW1+/fVXHXALQjd+49whaOJ7SB8hAn3cMydpjAp5NrGP1FXSiRXrOcBx0J44L3yHBg0TtFLvJPWX+qIMTMiY+8o18oEJkokTJzq3wb2apB6utvrhhx/0fylHrCekXrjPzPX29xDwLOEZg2m2vT/uVdw38qyZ69BmmFgJPV6h2+EYZjA5VztFlY3nQ56VNPUSzPvbVY5EKI8rF+8MbPPnn38mqgfOBdfD3h6BPO1l0lb2+8r87TquHAP3dps2bSInxcXaSAKJSnn2e9BEll100UW6vVyp7ZZbbjnVtGlTlZRCnoVK66+rWtDeeOONc99XWmkl1bJlS22S8N577+kclT7zJTNVhMx8fPvtt5H5H0NAWbjAGEBUqs8FXgLwoyvEXCcrqqGdisXff/+tfv/99+D2z6qtkMLjoYceyv0u9fWH6U+xj1nP91W52wkpPsr5Tqn0toJAleVkbrVSSX22lINru/vuu+fdv999953+L8t++umnvPUQjD7++GP9/ZtvvtH/EfwtyTOAAT7qj30gcOE3vmOZfPeBexGCadzxUCfzPJJg7gNNc7HereibzGPhO8YoaBNZnrT+aBuUgSjk5r4h16ht27bObXCdk6Rtc7UVBByXubTUC/cZsNsjKUhndcMNN+h73Nwf54BrKQK+uc68H0ModDu5v6PaKapsCHI4B982hbSXTJq5yhFBO65cvDPk+d1vv/2C64Fzwfmb2x9++OF6QkSW2f2Q/b4yf7uOK+eHe/vrr7+OrI9M0Hz//fd55eG49ntQkGV4J/v46quv9DNZ6LvhkEMOiW3TLMc2WfbZVS1ou2YjMXODRvZ12phNs9NyCFl1ICinUgf6uBHxkq2E+lVyOxXbdDzpeadtq8022ywvKFqp2x0zrqU6Zj3eV4XAdgqHbVW7fXbUtTX9ruOCohV6j8h+9v5RZT3yyCNag1ZoXupC9ynGczBixIhG521H4U5zzKTB7KLKSbKva3toBIcPH+69d1znm7a9fW3rKzvreyruGCHPViHPbFoKuQexDbTj5557buL2MbeHVYzrmKHvqiTrQusXdfyQ8hsKeHeY7wZYEiVp00rqr2tK0MYMDGbKxFyKNIbB0MpLufzjxXetdevWXpNGQggpJdXcZ5dzIAfzeRINzFd9EzSVTLn8S0t1P2cVC4mUFrZ7jQraMHPGTLdp9vLJJ59o/w587rnnHu2LAp8CmEPcdttt2mwAeTmJm0K0qSQ7ytX2EoBHfPsIISRr2GeTSgHaMNsXtZ6EhULGGqVqnywtFCqFSq0XKHcA5HqnogXtjz76SJ1++um537fccov+37VrV9WvXz81c+ZM9cwzz6jffvtNO9mvvfbaOm1ANc5iltp0nJTXdLzUyDFx/Su5QyCEVC/sswuDg+DGpO2nOKFcu/c6n5dkUNAuLxUtaK+55ppq9OjR3vXDhg0raX1qAZqOlxcMNMuB+ZKloE0IKQa10mebk6HwubRTcvm2TQICa9GNx00lCgXFrFMxyk5aZinvR45BSk/aewzpvaLeg8QPW63OoOl4eYHmppzRmXn9CSHEzxtvvKEDjb322mv693PPPRepHS00lcyQIUN0FgpS+eBeQOC+YlGMPjlpmYMGDdLRrutxEqXWyeL+euqpp/LSstnI+5I0hoJ2nQFfuEJy2ZFsQCqH8ePHl+34NB0nhJD/w/UulrzYIkBLPlkbpCbr27dvwYIDcjzjY3PYYYfpoJU+dtlll+BjIJL70UcfnbhuSKlWKYwaNSqTctAOa621VkH7JplMgTsE0hIVwnrrraeOOeaYolyHl19+WS2//PJq7733VltttZUaOXJk0P2Y9TXBM7Piiivm0kelub9N1l13XfX666+rYignivk8bLLJJplYEkSlnkNaxVD3nI4dOzqX++QGuV6+Z6RXr15qzJgxqp6hoF1nIL0TqW8oaBNCiFI77rhjwRZGyLFq+qNnBXLwRnHllVcGl7XqqqvmCW6hXHHFFapS+qlu3bplUl4h7VAIEyZMUM2bNy/ovsJEgDkZkOV1gHALoJnHJ8l9ZJPmmuy11176/6abbupcX2i9zjnnHLXtttuqrMFEWjGfBwi2PuE2CTDr/vzzz53r1llnHf0JoU2bNrocxNIIIe56XXbZZXUvaJc+KhMhpGzQdJwQQrKnFiMpE0LC4DNMfFDQJqSOoH8UIYSQSod9VTpqVfDznVetnm+54POXHRS0Cakz2CERQki28L2aLWxP4oNCYPHh85cdFLQJqSNoOk4IIaSSoSCVHrYhqSSa1PH9SEGbkDqDedQJISRb6nkgSQipLfg+yw4K2oSUIdJtOWHuVkIIyRZaCpFKot7ux3Kfb7mPnwWXXHJJuatQk1DQJqTEIIdlOZkzZ05Zj08IIYTUuuBS71rJiy++WFUjF110kapH9thjj3JXoSahoE1IHbHwwguXvfMlhBBCfLCPqg323HNPVY307t273FUgNQQFbUJKSMeOHVWzZs3KXQ1CCCEVooFt06aNWnbZZTOtD6kOll9+ebXEEktkXi7GGW3btlW1Bp6VZZZZxqlEmHvuuctSp0UWWUQtuuiiZTk2qXzmKXcFCKknHnjggbJ1BuDrr79mMDRCCKkgRo8eXe4qVNykRb1otSdPnlyUcnv06KE/tcadd97pXL7qqquqmTNnqnIwbdq0shy3mmhSJ8+zC2q0CSkh5RSyu3fvrh599NGyHZ8QQgiJ6qMIIbXFe++9pxZffHFVr1DQJqROGDlyZFkFfUIIISSqj6p37RchtcYSRXCNqCYoaBNSR9SizxYhhBBCCMnOF/6oo44qdzVqAgrahNQRCLjTqlWrcleDEEII8cL0XoSUj+bNm6uhQ4eWuxo1AQVtQuoIBEKjWR4hhGQLBcPsYB9FCKkVKGgTUmeC9lxz8bEnhBBCCCGkmHDETUgdQUGbEEIIIYSQ4sM82oTUEVtssYVq2bJluatBCCE1w+DBg1WHDh3KXY2aYZdddtGTwoQQUu1Q0Cakjujatav+EEIIyYZjjz223FWoKXr06FHuKhBCSCbQhpQQQgghhBBCCMkQCtqEEEIIIYQQQkiGUNAmhBBCCCGEEEIyhII2IYQQQgghhBCSIRS0CSGEEEIIIYSQDKGgTQghhBBCCCGEZAgFbUIIIYQQQgghJEOYR/v/M88881RkWbUM2ykctlU4bKsw2E6layu2dfZk1aa8NuGwrcJhW4XBdgqHbVW6dsqyrZs0NDQ0ZFYaIYQQQgghhBBS59B0PEP++OMPNXToUP2f+GE7hcO2CodtFQbbKRy2Ve3CaxsO2yoctlUYbKdw2FbV3U4UtDMExgEzZszQ/4kftlM4bKtw2FZhsJ3CYVvVLry24bCtwmFbhcF2CodtVd3tREGbEEIIIYQQQgjJEArahBBCCCGEEEJIhlDQzpB5551X9erVS/8nfthO4bCtwmFbhcF2CodtVbvw2obDtgqHbRUG2ykctlV1txOjjhNCCCGEEEIIIRlCjTYhhBBCCCGEEJIhFLQJIYQQQgghhJAMoaBNCCGEEEIIIYRkCAVtQgghhBBCCCEkQyhoE0IIIYQQQgghGTJPloXVMxMmTFAPPvig+vHHH1XLli1Vnz59VOvWrVWtMnbsWDV58mT1+eefq/nmm0+tttpqat9991UtWrTIbfP333+rW265Rb344otqzpw5qkOHDqpv375qiSWWyG3z3Xffqeuuu0699957aoEFFlBdu3ZVe++9t5p77rlz22Adypk1a5Zaaqml1G677aY233xzVY3cf//96o477lA77LCDOvDAA/UyttP/+OGHH9Rtt92m3nzzTfXXX3+p5ZZbTvXv31+tuuqqej2SJIwePVo9+eST6rffflOrr766bqvmzZvnyvj111/VjTfeqF577TXVpEkTtf7666uDDjpIt5vw6aefqhtuuEF99NFHarHFFlPbbbed6tGjh6oW/vvvP90Ozz33nH7nNG3aVN8TuOY453ptqylTpqhx48apGTNmqNmzZ6tjjz1Wrbfeern1pWyTl156Sd19993q22+/1ffxPvvso9Zdd90StQSJo576bPbXhcM+Oxr22fGwv67vPpsa7QzAyxYvS+RvO++883SnfdZZZ6mffvpJ1Sp4OLbddlt9nieddJL6999/1fDhw9Wff/6Z22bUqFH6xj/66KPV6aefrh+iiy66KO/lc84556h//vlH7ztgwAD19NNP6xtd+Oabb9S5556r1lxzTXX++eerHXfcUV1zzTX6pV5tTJ8+XT3++OP6/jBhO/3vZXnyySereeaZR5144onqkksuUfvvv79aeOGFc9s88MAD6pFHHlH9+vVTZ599tpp//vn1PYiBj3D55ZfrgQvuy+OPP169//77auTIkbn1v//+u27HpZdeWrcZBpz33HOPeuKJJ1Q1Df5wLx188MG6ndAhoLNC29RzW2Gg16pVK90uLkrVJtOmTVOXXXaZ2nLLLXWf0KVLF3XBBReomTNnFrkFSAj11mezvy4M9tnRsM8Og/11nffZyKNN0nHCCSc0XH/99bnf//77b8MhhxzSMHbs2IZ64aeffmrYfffdG9577z39+7fffmvo3bt3w0svvZTb5rPPPtPbTJs2Tf9+/fXXG/bYY4+G2bNn57Z59NFHG/bff/+GOXPm6N+33nprw9FHH513rEsuuaRh+PDhDdXEH3/80XDUUUc1vPXWWw2nnnpqw0033aSXs53+x2233dZw8skne9f/999/Df369Wt44IEHcsvQfnvvvXfD888/r3/PmjVLt9306dNz27zxxhu6/b7//vtc2x144IG5tpNjDxw4sKFaOOeccxquuuqqvGUXXHBBw2WXXaa/s60a9LlNmjQp97uUbXLxxRfra2Ry4oknNowcObJIZ0uSUO99NvvreNhnx8M+Owz21/XdZ1OjnRLMWH788ceqffv2uWVzzTWX/v3BBx+oegEzRmCRRRbR/9EmmDU322X55ZfXM0rSLvi/0kor5ZlbrbPOOuqPP/7Qs1Pgww8/zCsDwEyr2tr2+uuvVx07dlRrr7123nK20/949dVX1SqrrKIuvvhibRp03HHH5c04QgMAsyuzDRdaaCFt7mm2FWbTxWwNoF1gTgTthGzTrl07PQtvttUXX3yhZ+irAZh+vvvuu7rO4JNPPtEzsrjHANuqMaVsE2zjeh7xnJLywj6b/XUI7LPjYZ8dBvvr+u6z6aOdkp9//lmbCZkvVIDf8lDVOjj/m2++WbVt21Z3LgAPB25q04QILL744nqdbGO3G9bLOvkvy8xt0GHBdAT+ZpXOCy+8oP1PYEpmw3bKf6nCvAomdD179tS+NDfddJNuH/itybm6ztNsB/jfmMAnDgNKc5tlllkmbxtpX6yTwWcls8suu+hrO3jwYC0k4Bns3bu32nTTTfV6tlVjStkmvudRyiDlo977bPbX8bDPDoN9dhjsr+u7z6agTVKDAAOYpT3jjDPKXZWKAwFRMKiB70g1dJzlBJ0PZiURMAasvPLK2j8GHXm1BYgpNgja8fzzz6ujjjpKrbjiinqGHPfZkksuybYihHhhfx0N++xw2GeHwf66vqGgnRLMpGCGyp71cM1o1mqn/frrr+uAIIiaKeDcYaKHKIHmzC+CzUi74L+YdpjrZZ38twPU4PeCCy5YFZ0gzMxQ36FDh+Z1TgjWgKi3w4YNYzv9f9DprLDCCnnL8HvSpEl554rzwrYCfiOYhmwDjZUJzPxgHmS2let5NY9R6SDKKyJmbrzxxvo3NFOIlImgK+i42VaNKWWb+J7HamuzWqSe+2z21/Gwzw6HfXYY7K/ru8+mj3ZKYCIDHxX4X5gvZfyGX0atgpD76LSRMuSUU05pZJaBNoH5xjvvvJNbBrM8zBZLu+A/Zj/Nm/vtt9/WHY28vNu0aZNXhmxTLW0Ln48LL7xQRxWVD2aAN9lkk9x3ttP/AVNG23QTv5s1a6a/4x7DS888T/gaYkBjthUGQBgsCXgWcb9K6h5sg0ETBktmWyHVTbWYViFSJ4QFE/zGeQK2VWNK2SbYxvU84jkl5aUe+2z21+Gwzw6HfXYY7K/ru8+moJ0B3bt31znekL7hs88+00E08GDVskkIOm3kBBw4cKDuPDA7hI+E3EfAAoTJRwoV3PR4CK666ip9M8sDgkAD6HSuuOIKbUqDtBZ33XWXTkMy77zz6m222WYb7QeEGUHkAH300Ue1GQ58gqoBtA1mL80P0hMsuuii+jvb6X+grgg8cd9996mvvvpKm1rhucJ5AgS3QC5TrEcQFgxk0CaY6UQqBoB2QtAZpHbAy3jq1Kk6v+JGG22kc1cCDJgw2EYqFZhQItUP0kfgOa4WOnXqpNsB2ilcdwygx48fn2uHem0rpCvCM4IPQNvgOwbBpWwTHOett97SeZrxPCIPKPwXkbuTlJ9667PZX4fDPjsc9tlhsL+u7z67CUKPpy6FaJMi5MVD5wWTBiRLr2XtxR577OFc3r9//9xgBZ04OiMEFsFMEjofRKY0TTFgPoNBznvvvac7s65du+ocg5gxFrAOeSsxIIK522677VbVA6LTTjtN3yMHHnig/s12+h/ITXrHHXfoThuzmejIu3XrlluP1xVegIhsipnN1VdfXedfxMykAJMhDCxRFl7U66+/vurTp49aYIEFctt8+umnehu8SDGAwssUAUuqBQRWQU5WdNjQmqBDgVka8gJLZM16bCs8AzCLtcHzgly2pWwTDJoxuMaz27x5c/28rrvuukVuARJKPfXZ7K/TwT7bD/vseNhf13efTUGbEEIIIYQQQgjJEJqOE0IIIYQQQgghGUJBmxBCCCGEEEIIyRAK2oQQQgghhBBCSIZQ0CaEEEIIIYQQQjKEgjYhhBBCCCGEEJIhFLQJIYQQQgghhJAMoaBNCCGEEEIIIYRkCAVtQorAaaedpj/CN998o/bYYw/19NNP55ZdeeWVar/99lPlZvTo0bpuJBm4fgMGDCh3NQghhKSEfXbtwz6blIN5ynJUQiqMr776So0bN069/fbbavbs2WqeeeZRK620ktpwww1Vt27d1Hzzzaeqmb/++ks98MADas0119SfSuHPP//U7T5p0iQ9sJl33nnVUkstpdZYYw3Vo0cP1bRp03JXkRBCSIXBPrs8sM8mJBkUtEnd8/rrr6uLL75YdxibbbaZWnHFFdU///yjpk6dqm699VY1a9Ysdeihh6Y6RrNmzdRtt92mBwPl6rTHjBmjv9ud9m677aZ22WWXktcJbXzqqaeqzz//XHXt2lVtt912uhNHez///PNqvfXWY6dNCCEkD/bZ7LMJqRYoaJO6BjOyl156qe5UTznlFLXkkkvm1qETwaw5OvW0NGnSJNMZ9n///Vc1NDRkMgiYe+659afUvPLKK2rGjBnqqKOOUptsskneur///lt36oQQQojAPpt9NiHVBAVtUtfANAszsocddlhehy0st9xyaocddsj9njhxonr22Wf1DO7vv/+ull12WbX99turbbbZJnZwcMQRR6j+/furzTffPG/d119/ra6//no9G7/QQguprbfeWs9Yo6M3991333115zphwgS97LzzzlMrrLCCuvfee/XAAgOM//77T6288sraf2uttdbK2x9ghlxmyXv16qW3g78XluG/OSgYO3aseuaZZ9T333+v22bjjTdWu+++u9YiCPB3gjYBs+ujRo1SM2fO1NtiO8x4R4HzBm3btm20DgMcc5Dz6aefqvHjx6v3339fmwminTp27Kj95RZddNHcdnIuGIjh/2uvvaYHNmjTPffcU5/LjTfeqN577z1d/s4776x22mmn3P5Yfvrpp6tBgwapTz75RF9v3B9oy4MPPlgtvfTSkeeE9n/kkUfUk08+qc8P9ezSpYvae++91SKLLBK5LyGEkGjYZ7PPZp9NqgkK2qSuwUsdHa+r43Dx2GOP6U6qc+fOugPF/uhw8bLGbHpSsN/ZZ5+t2rRpo/bZZx/15ptv6o4HnSY6GRMEZZkzZ47aaqutdMeJTgADh6eeekp3qFiODga/zzrrLHXOOeeoVq1aqcUWW0z17dtX1xOmXfiAli1beut1zTXX6A57gw02UN27d1cffvihuv/++7XJ2JAhQ/K2xWDhoosuUltuuaXuqNHRXXXVVWqVVVbRbeVDOkAcxxykuIAfHgYfGPAsscQS6rPPPlNPPPGE/o9ztfdFp7388svrNsWA5r777tPthX3QAWP5c889p80MV111Ve1fZoLtUSZ8zn7++Wf10EMPqTPPPFNdcMEFkVqOa6+9Vp8P6onBHOqMQRa0ANi/XGaIhBBSC7DPdsM+m302qUx4B5G6BR3eDz/8oDvgUDBzar600VGj08BLvZBOG51whw4dVJ8+ffTvbbfdVs96Y9YeL310uAJmdkeMGJG3DJ0+ImmanQE6b8zuYpb28MMPVwsssIDufNFpI1gMfNqiwKwwOh50wtAaSL0WX3xx9eCDD6p33303N/MOvvjiC90u7dq107832mgjfVx03vvvv7/3OBg8tGjRQg9SsC380FZffXXVqVMnfSwTHN+cxQYY6Fx22WVaqyDHFlq3bq0OOeQQ/R2BcTCLjw56r732yvm2YaADPz4c2+60f/31V3XJJZeoBRdcUP+GxgG/0emb2hIT1AMDJtusDueFgdnLL7/cyNyOEEJIGOyz3bDPZp9NKhem9yJ1yx9//KH/y4s5BLPDRqePmVO88GFyhN+FYHb2mJHFb/g6vfPOO3nbrb/++nkdNphrrrlyHTY6cHQ2mFnHjC9mZAvhjTfe0P8xK24inabt/wZTOLPTRB3RGWNmOK4t0ZnBFExm/zErj84WpmIY0Jjbmr5gaHd02sB1nhhwmG2EmXr4x5nLF154YW89MbAx7wsMemBeJ23j4qWXXtJmZ2uvvbaun3xwbAycMNghhBBSGOyz3bDPZp9NKhdqtEndIi9l6bxDwAzoPffcoz744AMdFdQEnTZe2klAJw0zOJPmzZvr/99++23e8mWWWcZZBjo7+ELBRAwddtz2ceC4qBd83Uxg/oWO7rvvvstb7vKBwna//fZb7LHQXvBjwwfHxUAFM/Aw3cK63r176+0wGEG7v/jii+qnn37KK8M1WLLrhLJgumcPerD8l19+abS/XANB2sO+JrY5HuoCkz8X6MAJIYQUBvtsN+yz2WeTyoWCNqlb8MLGjCeCpISAlzJ8djCjCvMq5I7EzDRmTGGGhtnpYuLyM0KQF/hWIXgHZpnRKWE2GL5ZErikUKL8r0xwPBeYjU4Coshi9hrmaUceeaT2x5JOGyZg06ZN0+cIHzbMNouvnKvdXXXy1TMrUA+Yz6HuLuwBAyGEkHDYZ0fDPjsZ7LNJKaCgTeoa+BbBhwez3auttlrktgiiAtOooUOH5s2+IuploaBjQ+eKgYDw5Zdf5jqxOOBDhNn1Y489Nq+TxUxyIR2wHBf1Qj1gYib8+OOPesY7LopnWhAABeckgynMjGPWHNFWEXXVbqdiYJeN9sCgDf5yPlBn1BM+a1mmhSGEEPJ/sM9uDPts9tmkcqGPNqlrMNs6//zzaz8jdEo2eFE//PDDebOr5qwvzI5gBpYGmFwJKBu/ER21ffv2sfu66oRooxiEmOAcpb5xIAUHkPMWYOoG1l13XZUFCODiMs2CqRcik8pAxnWOABqJYgGtg2meiMERUpRI27hAQBnMkEsqFhOYB4aY5RFCCPHDPrsx7LPZZ5PKhRptUtfAh2fgwIHazGnw4ME61QXSWyCwCcye8LKWHJqINAqzM0QYRVRMpOVA7kWYF+GFXgjwQXrrrbfUFVdcoQOFwKQNgUt69uwZZLaE2f3JkyerCy+8UHemCBLy+OOP61lt1E/AbC2WwV8KvkyYgcZ5umZ7YeaFdoDWAB0NAsdMnz5dRzWFuZsZvTQNSP+BWXycAzQTMC2DpgARRaGFQF5PMRdE4JZx48bpzq9p06a6zeICt6QB7XPKKafoaw//MgwQcK8gOqwPtBPuC5gAIocoAqxg8IWBH4KuHHTQQTpACyGEkMJgn80+2wX7bFKpUNAmdQ9ShaDTQ6fwyiuv6Lyb6EzRocGvS17UmK09+uij1d13363TTiDQyDbbbKM716uvvrqgY2Pm98QTT9RpPG677TbdccHUyjS3igKdCmb10cGiI0PHDH8jdBJTpkzJ2xZpPxAZdNSoUXpQgmP4zKqwLcyqMPOPQQHOFSk2pCPNAnRgGFig3jDlg7kZArIgzQeip5qDAwysUPdHH31Uz5KjQ0S7IdVHMcCgCR0vOmDMkkNTgYApomXwgeiriFiK63HnnXfqThtmfZtuumlw3ldCCCF+2Gc3hn02+2xSmTRpSBr9gBBCahQMHpBfFIMzzmQTQgghlQv7bFLp0EebEEIIIYQQQgjJEArahBBCCCGEEEJIhlDQJoQQQgghhBBCMoQ+2oQQQgghhBBCSIZQo00IIYQQQgghhGQIBW1CCCGEEEIIISRDKGgTQgghhBBCCCEZQkGbEEIIIYQQQgjJEArahBBCCCGEEEJIhlDQJoQQQgghhBBCMoSCNiGEEEIIIYQQkiEUtAkhhBBCCCGEkAyhoE0IIYQQQgghhGQIBW1CCCGEEEIIISRDKGgTQgghhBBCCCEZQkGbEEIIIYQQQgjJEArahBBCCCGEEEJIhlDQJoQQQgghhBBCMmQeVcFMmTJFjRs3Ts2YMUPNnj1bHXvssWq99dbLrf/xxx/V7bffrt5++23122+/qXbt2qk+ffqo5s2bl7XehBBCCCGEEELql4rWaP/111+qVatW6uCDD260rqGhQV1wwQXqm2++UUOGDFHnn3++atasmTrzzDPVn3/+WZb6EkIIIYQQQgghFS1od+zYUfXu3TtPiy18+eWX6sMPP1R9+/ZVrVu3Vi1atNDf//77b/XCCy+Upb6EEEIIIYQQQkhFC9pR/PPPP/r/vPPOm1s211xz6d9Tp0717jdnzhz1+++/532wjBBCCCGEEEIIqXkf7SigwV566aXVHXfcoQ455BC1wAILqPHjx6vvv/9e+277GDt2rBozZkzu98Ybb6wGDhxYoloTQgghhBBCCKl1qlbQnmeeeXRwtKuvvloHQIM2u3379trcHP7bPnr27Km6d++e+92kSRP9H8HWREtOCCGEZNlfLbnkkuWuRk3BPjsfxKj59ttvy12NmoJtmj1s0+LAdq3cPrtqBW2wyiqr6IBoMP9Gh7vYYoupE088US/3AdNy09xcwP40ISeEEEIqH/bZqpHCAG0SpWgg4bBNs4dtWhzYrpVN1fpomyy00EJayEaAtI8++kh16dKl3FUihBBCCCGEEFKnVLRGG2m6vvrqq9xvpPL65JNP1CKLLKL9s1966SUtYOP7zJkz1c0336yF7A4dOpS13oQQQgghhBBC6peKFrShnT799NNzv2+55Rb9v2vXrmrAgAHaRwvLEPwMtvSbbbaZ6tWrVxlrTEhtAx+gnXbaSQcdXGqppdSDDz6ofYMIIYQQQggh/6NJAw36cwIE/b0I8dOuXTv1888/N1oOq5L333+/LHUipBpAXBBOSGUL++x8H83mzZtr9zkO6bKBbZo9bNPiwHat7D67Jny0CSGlE7JXW201NWrUKP0fYDnWE0IIIYQQQqrAdJwQUhmaIxGyobmGBht069YtJ2TjP7aj1o4QQgghhBBqtAkhMcAnG0CDLUK2gN9t2rTJ244QQgghhJB6h4I2ISQSBD4Dw4YNU//++6968cUX1f3336//4/fQoUPztiOEEEIIIaTeoek4ISQSRBf//fff1fHHH6/mmWceNWvWrNy6FVdcMReQCNsRQgghpHb5t9/O3nVzXzeupHUhpNKhRpsQEglSeAFEtFx55ZXVuHHj1AcffKD/47fkupftCCGEEEIIqXeo0SaERNK0aVOdPgJpI5599lktcMNc/LzzzlMffvih3gbrsR0hhBBCCCGEGm1CSAyTJk3SQvZCCy2kf0O47tu3b07IxnKsx3aEEEIIIYQQCtqEkBi++eYb/f/NN9/UH/hlQ7jGf/x+44038rYjhBBCCCGk3qHpOCEkkmWWWUb/nzp1qurUqZN6+eWX89a/+uqredsRQgghpLQwSBkhlQc12oSQSNZff32tvR4xYoSOMG6m98LvK664Qq200kp6O0IIIYQQQgg12oSQGOaee251yimnqH79+qnVV19d/fnnn7l1CyywgP593XXX6e0IIYQQQggh1GgTQgJBZHHXMtdyQgghhBBC6hlqtAkhkfz777/qjDPOUN26ddOa61deeUUHPoNPdpcuXbSm+8wzz1TbbrsttdqEEEIIIYRQ0CaExIG0XbNmzVJXXnmlmnfeedVGG22Ut/6II45QPXr00NvZ6wghhBBCCKlHaDpOCIlE0nbBP9uFLGd6L0IIIYQQQv4PCtqEkOD0Xi5kOdN7EUIIIYQQ8n9Q0CaEBKf3+u+///LW4TfTexFCCCGEEJIPBW1CSFB6ryeeeEL16dNHvfrqq+rXX3/V//Eby08++WQGQiOEEEIIIeT/w2BohJBYdthhB3Xttdfq6OMIfCZA043lWE8IIYQQQgj5P6jRJoQE09DQEPmbEEIIIYQQQkGbEBLAww8/rA455BDVrl07NW7cOPXBBx/o//iN5VhPCCGEEEII+T8oaBNCIvn333+1yXi3bt3UjTfeqDp16qQWXnhh/R+/sfzMM8/U2xFCCCGEEEIoaBNCYpg0aZKaNWuWOvLII9Vcc+W/MvD7iCOOUDNnztTbEUIIIYQQQhgMjRASwzfffKP/r7766s71sly2I4QQQkjl82+/nb3r5r5uXEnrQkgtQkGbkDrkjz/+UNOnTw/aFqm8wEMPPaR9sv/880+t4UbE8QUWWEBNmTIlt90777wTXIfWrVurBRdcsMAzIIQQQgghpHKhoE1IHQIhe7vttku0z+DBgyPXDx06NFF5EyZMUO3bt0+0DyGEEEIIIdUABW1C6hBokyHohvL888+rs846S6233npq4403VhdffLE6+uij1QsvvKAmT56shg0bpjbZZJPEdSCEEEIIIaQWoaBNSB0Ck+0k2mRs27JlSx19HEI2wP+VVlpJXXvttWqHHXYoYm0JIYUCS5RNN91Uf5o1a1bu6hBCCCF1A6OOE0KCgDANDfZ5552nf+M/NN0UsgmpXJZaail1zz336KwBp556qnryySfV77//Xu5qEUIIITUPNdqEkGDmnntu1aFDB/0d//GbEFK5nHTSSerHH3/Uk2KYKIMFyo033qjWXXddtdlmm6mOHTuqeebhUICQWogUnuU+hJD0sHclhBBCapglllhCde/eXX+++OIL9eyzz+biKyy88MJqo4020qblbdu2TVw2Yj08+OCDWpiHe0mfPn0i4y/89ttv6s4779THRqYCmLMfcMABWvAnhFS3cOyrM1OFkXqFgjYhhBBSJ7Ro0UL17t1bZx246aab1Msvv6wef/xx/Vl22WW1K8g222yj5por3rPsxRdfVLfccovq16+fatOmjU4BiKCJl156qVp88cUbbf/PP/+o4cOHq8UWW0wHU2zatKn67rvv1EILLVSksyWEEELKBwVtQgghpA74888/tSb5ueeeU++++65eBk1y165dtfn4E088oYXvmTNnqkMOOSS2vPHjx6utttpKbbHFFvo3BO7XX39dTZw4Ue2yyy6Ntn/qqae0FvvMM8/Mmasvs8wymZ8nIYQQUglQ0CaEEEJqlP/++0+9+eabWrh+9dVX1d9//61WWWUVtf/+++tUfdAuC507d1Z33HGHevTRR2MFbWinP/744zyBGlpwZCj44IMPnPu89tprWvN9ww036Lrg2KgDyvBp0OfMmaM/QpMmTXTWBPlO/tcObI/sYJtme+4oi21aHNiu2ZNlW1a0oD1lyhQ1btw4NWPGDDV79mx17LHH6jy+5uz87bffrl555RX1yy+/6Jnx7bffXpu9EUIIIfUOtMzQIsNMG/0jAqCtsMIK3u3hZ42+NY6ff/5ZC/Hw/zbBb/iBu/j666/Vt99+qzbZZBN1wgknqK+++kpdf/316t9//1W77767c5+xY8eqMWPG5H6vvPLKOuMBU5U1Zrnllit3FWqOcrbpLFVemjdvnlm9zLJ4nxYHtmtlUtGC9l9//aVatWqlttxyS3XhhRc2Wj9q1Cht/oa0Jeh03377bd1pY0CBmXlCCCGknpHo4muttVbQLD00zPgUg4aGBq3FPvTQQ7UGG5r1H374QU+o+wTtnj176iBugpwDBHZo1cn/tQkG2Zi4QBuT9LBNlfryyy8zLYttWhzYrtkz77zzqqWXXrr2BW2kHcHHB8zT4Fu25ppr6t/dunXTAV2mT59OQZsQQkjdA/9paLB9QjY005999plaY401EpULgRnCMqKNm+C3reUWsBy+2aaZ+PLLL6/3gdDsSjOGAQ8+LjiobNwebJNsqec2zfK8zbLquU2LCds1O7Jsx/iwohXMaqutpn2+MCOORoF2G7Nma6+9tncf+Hr9/vvvuc8ff/xR0joTQgghpeL000/X1l4+0G9im6RAKIZGWoKqAZiS4zf6ZhdIHwatC7YT0GcvueSSzOVNCCGk5qjqng35OkeOHKkOO+wwNffcc+sZe5ikRc3M+/y9CCGEkHoDk88hqbxcwKT7yiuv1AI3cmc//PDD2uVr88031+uvuOIK7cq1995769+In4JAazfffLNOLwahG30yfMcJIYSQWqOqBe1HHnlEffjhh+q4447TPtrvv/++jmaK2XGfVtvn70UIIYTUAshN/c033+R+f/755zq4qA2supDSq9DAYhtttJE2PR89erQ2/0ZMlRNPPDFnOo56mH0sfN6GDRum46sMGTIkF6DNlQqMkHrg3347q0qlkutGSLVQtYI2UpTceeedurNGsBeJlvrJJ5+oBx980CtoR/l7EUIIIdUO8libllv33Xef/riANhuRyQsFmml8XJx22mmNlsGs/Kyzzir4eIQQQki1ULWCNgKnICWIrZHGoIHBAAghhNQrG264oVpxxRX190suuURrjVdfffW8bdB3zj///FoL7QteRgghhJAaFbSRyxM+XAJM4aCxXmSRRbQJGnyxb7vtNjXffPNp0zeYxj3zzDPqgAMOKGu9CSGEkHKBKOOSK/vwww/XfeUyyyxT7moRQgghdUVFC9offfRRXjTUW265Rf9HSq8BAwaoQYMGqTvuuENdfvnl6tdff9XC9l577aW23nrrMtaaEEIIqQwkMBkhhBBCSktFC9rIj40gKz5g7ta/f/+S1okQQgipVK666qpcBg64UuF3HNgemm9CCCGE1ImgTQghhJBw3nvvPS04I1c1BG38joPZNwghhJDsoaBNCCGE1AjIax31mxBCCCGlYa4SHYcQQgghhBBCCKkLqNEmhBBCapQ//vhD/fbbbzpTh/DDDz+oxx9/XM2ZM0dtsMEGqnXr1mWtIyGEEFKLUNAmhBBCapSRI0eqb7/9Vp111ln69++//66GDRumhW34Zj/yyCPqxBNP1MFHCSGEEJIdNB0nhBBCapRp06apddddN/f7ueeeU7Nnz1Znnnmmuummm9RKK62k7rvvvrLWkRBCCKlFKGgTQgghNcrPP/+smjZtmvv96quvqtVXX12tttpqasEFF1Rdu3ZVn3zySVnrSAghhNQiFLQJIYSQGmXhhRdWP/74o/7+999/q6lTp6q11147tx4pwLCcEEIIIdlCH21CCCGkRoHm+rHHHlPLL7+8evPNN7VQ3aVLl9z6L7/8Mk/jTQjJnn/77VzuKhBCygA12oQQQkiNsu+++6q5555bXXTRRerJJ59U3bt3VyuuuKJe999//6mXX35ZtWvXrtzVJIQQQmoOarQJIYSQGmW55ZZTl156qfrss8/UQgstpJZZZpncur/++kv16dNHtWzZsqx1JIQQQmoRCtqEEEJIDTPPPPOoVq1aNVqOYGimGTkhhBBCsoOCNiGEEFLjQKP99ddfq99++001NDQ0Wo/o44QQQgipMEF7zpw5asaMGeqnn35Sbdu2VYsttlgWxRJCCCEkBV999ZUaMWKEmj59euR2FLQJIYSQChO0H374YXXPPfeo33//Xf8++eST1VprraVzdw4ePFjts88+asstt8yiroQQQghJwHXXXadmzpypDjzwQB30DOm+CCGEEFLhgvbEiRPVqFGj1EYbbaQ6dOigrr766tw6aLXXXHNN9eKLL1LQJoQQQsoA8mb37NlTbb/99uWuCiGEEFJXpErvNX78eNW5c2c1cOBA1alTp0brV1llFTVr1qw0hyCEEEJIgWDSG9HGCSGEEFJFGm34fkXNki+yyCLq119/TXMIQgghhBTI1ltvrZ577jm13XbbqbnmSjW3TgghJePffjs7l8993biS14WQsgjamCWHL3ZUlNMlllgizSEIIYQQUiDNmzdX//33nxoyZIjaYost1FJLLeUUuNdff/2y1I8QQgipVVIJ2h07dlRPPvmk2nbbbRutg8k41qFjJ4QQQkjpufTSS3Pfb731Vu92d999d4lqRAghhNQHqQTt3r17q2HDhqljjjkm56P99NNPq6eeekpNmjRJLbnkkqpXr15Z1ZUQQgghCTj11FPLXQVCagqaNBfeZnbUJrYZqXVSCdpNmzZV5557rrrzzjt1dHEAX7AFFlhAbbzxxjq1F3NqE0IIIeVhjTXWKHcVCCGEkLokdR7txRdfXB122GH6A39t+IJBuGbQFUIIIaQymDNnjpoxY4b66aefVNu2bTkJTgghhFS6oG3CjpsQQgipLB5++GF1zz33qN9//13/Pvnkk9Vaa62lJ8cHDx6src+23HLLcleTEEIIqV9B+7vvvivoIEsvvXRB+xFCCCGkcCZOnKhGjRqlNtpoI9WhQwd19dVX502Or7nmmtr1i4I2IYQQUkZBe8CAAQUdhNFMCSGEkNIzfvx41blzZzVw4ED1yy+/NFq/yiqrqEceeaQsdSOEEEJqmUSC9uGHH168mhBCCCEkU7766iu1/fbbe9cvssgi6tdffy1pnQghhJB6IJGgvfnmmxevJoQQQgjJlIUWWkj7Yvv47LPP1BJLLFHSOhFCSFSqNEJqhUyCoc2cOVO98cYb6ttvv9W/mzVrpjp27KhWWmmlLIonhBBCSAGgL37yySfVtttu22jdrFmz9LotttiiLHUjhBBCapl50qYLufbaa9Wzzz6rfzdp0kT/b2hoUHfccYfadNNNddqveebJNLg5IYQQQgLo3bu3GjZsmDrmmGNUp06d9LKnn35aPfXUU2rSpElqySWXVL169Sp3NQkhhJCaI5UEfPvtt2she5ttttE+YMsuu6wWtuEThnQijz/+uPb/OvDAA7OrMSGEEEKCaNq0qTr33HPVnXfeqaOLg+eee04tsMACauONN9apvZiakxBCCKkwQRudNbTWBx98cN7yFi1aqL59+6o//vhDb0NBmxBCCCkPiy++uLYuwwf+2v/9958Wrueaa65yV40QQgipWVL1sv/8849abbXVvOvbtm2r/v333zSHIIQQQkhGQMBG8DMK2YQQQkgFa7Q7dOig3nzzTW067gLr1l577TSHIIQQQkgB/Pjjj2rChAnq7bffVl9//bW2MltwwQXVcsstp/tvBEiDtpsQQgghFSZoI8jKJZdcoi688ELdYaPzBl9++aV69NFHdRTywYMHN8rRCb9tQgghhBSHd999V1188cXqt99+U/PNN59q3ry59sv+888/daaQ6dOn6356yJAhavXVVy93dQkhhJCaI5WgDSEaoNN+5ZVXIrcxufvuu4PKnzJliho3bpyaMWOGmj17tjr22GPVeuutl1u/xx57OPfbd9991c47MzcfIYSQ+gN+2JgEn3vuudVRRx2lNthgA/1dgEvXSy+9pEaNGqUuuugiLZAvuuiiZa0zIYQQUmukErR32223XEqvYvDXX3+pVq1aqS233FJrzW2QWswEubyvueYatf766xetToRUCh9//LHWVpWaDz/8MO9/qVl44YXVKqusUpZjE1INIDc2zMQRbXyllVZqtB5C9yabbKJWWGEFdcIJJ+jtd9lll7LUlRBCCKlVUgnaPo1yVnTs2FF/fCCgiwm06muuuaZOM0ZIrQvZiPhfTo488siyHRvZDChsE+IGPtnIme0Ssk0wkd25c2f11ltvUdAmhBBCKknQrrSgL9BoDxgwIHK7OXPm6I8AjTyCwxBSTYgme8SIEapNmzYlPTZ8PGfNmqVWXHFF7fNZSqBFh4BfDk0+IdXCZ599pnr06BG0LbKDPPDAA0WvEyGEEFJvpBa0p06dqp566in1zTff6MFvQ0ND3noIshdccIEqNs8884we9Js+3C7Gjh2rxowZk/u98sorq/POO6/o9SOkGEDIbt++fcmP26VLl5IfkxASxu+//67TeIUA32xsTwghhJAKErTHjx+vbr31Vh3RtEWLFmWNJj5x4kRtSou6RNGzZ0/VvXv33O9i+pgTQgghpeaff/4JzpON7bA9IYQQQipI0EZEcKQFGTp0qFpooYVUuXj//ffVF198oQYNGhS77bzzzqs/hBBCSK3y0UcfBfV12I4QQgghFSZoIyo4IpeWU8gGMF1HYCQEdiGEEELqnYcfflh/CCGEEFKFgjYifCOHdjGDLn311Ve53/AD/+STT7SJ+tJLL62Xwbfs5ZdfVvvtt1/R6kEIIYRUC6eeemq5q0AIIUXh3347e9fNfd24ktaFkKIK2n369FFnnXWWNiFHruusfbRh0nb66afnft9yyy36f9euXXPRxV988UUdgA2adUIIIaTeWWONNcpdBUIIIaTuSSVoQ6vcrVs3HRDt9ttv14HIXAFYRo0aVbDGfPTo0ZHb4Pj4EEIIIaS0TJgwQT344IM6xWbLli31BHzr1q1j93vhhRfUZZddpvN4H3fccSWpKyGEEFI1gvbdd9+t7rvvPtW0aVO16qqrlt1XmxBCCCGlARZlsDTr16+fTjX40EMPaSu3Sy+9VC2++OLe/eAGhgn6du3albS+hJTLpJkQUp+kErQff/xxte6666ohQ4YEpxIhhBBCSPWDFJ9bbbWV2mKLLfRvCNyvv/66Tre5yy67OPf577//1IgRI9Qee+yhM4b89ttvJa41IYQQUgWCNnJvQtCmkE0IIYTUD+j/P/744zyBGmOB9u3bqw8++MC735gxY9Riiy2m47pA0I5jzpw5+iM0adJELbjggrnv5H/twPbIDrZpdVKP14v3avZk2ZapBG0I2egot95668wqRAghhJDK5ueff9ba6SWWWCJvOX5/8cUXzn2mTp2q03Gef/75wccZO3asFs6FlVdeWZ133nmqWbNmKWpfmyy33HLlrkJdtOmsstSEhNC8eXNVr/D5r0xSCdq777679sW6/vrr9ew0gqO5tNtZRyMnhBBCSDwf/r/27gM6qqoL9PgmBULoCKGHKsXQQVBCExSQJkhTQFQUpYsKuiSI8An4+KSogJ8KIk1qpIQqoAhKk957lRpC7yXw1j6+mZc2IeUmM5n5/9aaNTP33pk5uUoy++599jl0yMyfdrbbt2+bkvF3333XZLQTqmXLltK0adNYmYYLFy6YrDr+PSf6JVuXQ9VVWJB8nNO06ezZs+Jp+H/Ver6+vvZlpJ0aaPfp08fc69rWOl87vqZpAAAgdQ0YMMB8CatVq5a55cmTx5L31WBZL6xrt/Go9HnMLLc6f/68CY41G21j+1L4yiuvmIv2cWVk9AuP3uLCl8rY54NzYi3Oadriyf+t+H/VOlaex2QF2q1atWJOAAAALqpXr17y559/yi+//CJz5syRkiVLmoC7Ro0ayao28/HxkWLFisnu3bulWrVqZpuWkuvzRo0axTo+f/78MmLEiGjbZs6cKXfu3JE33njDsuwBAACuIlmBtnYNBQAArqlmzZrmpnOqdTmuv/76S3788UeZPHmyVKhQQWrXrm3WstbAObG0pHvcuHEm4Na1s5csWSJ3796VunXrmv1jx441y3+2b99e0qdPL4GBgdFenylTJnMfczsAAOLpgTYAAHB9WuqtmWa96Vw+Dbj1Nnr0aPH395dnnnlG6tSpI6VLl07we2pWXAP42bNnm5LxIkWKSP/+/e2l4xEREVS9AQA8FoE2kEb5ZskpZ26J+F+6I55Cf179uQEknWaXM2TIYJ/7rMHw5s2bTUdwzU736NFDChYsmKD3sgXvcRk0aFC8r9XPAQDAXSU70L53755s3LhRjh07Jrdu3TJztKLSP+DdunVL7scAiCH3M03l28O+IoePi+fwNT83gMR3/d6wYYPJYu/du9f8ba5YsaK0bt1aqlSpYhqb/f333zJlyhT59ttvZdiwYc4eMgAAnhtoawfRwYMHm3stPdNAW5ur2ALuLFmyiJ+fn3WjBWB3YcMiGdKtvZRwgaV7UsvhQ4fk3c8XiUh7Zw8FSBM2bdpkmqFt3bpV7t+/L8WLF5fXX39dgoODzd/oqLR8/MaNG2YON+DJHrzdjPWyATg30J46daoJqocOHSoBAQHSpUsXef/996VUqVKydOlSWbZsmYSEhCR/lABiuX/9kuT3Fyme03MuZt3y//fnBpAw2un7iSeekCZNmpg52Nr9Oz46z1q7kgMAACcG2nv27JEGDRqYbqN6Fdy29pjO+2revLmcOnVKJk2aJJ988kkyhwkAABJr4MCBEhQUlODj9e+53gAAgBMDbV3GQzPZKmPGjOZeM9w2ul6nZr0BAEDqW716tWl+9qSDKSaHDx+W5cuXS/fu3VN9bICzRXZp7uwhAHBjXsl5ca5cueTixYvmsbe3t1kv89ChQ/b9mtHWP/AAAMA5gfb58+cd7g8PDzfHAAAAF8poly1b1iwJ0qZNG/O8bt26Mn/+fFNGriXka9asMXPCAACA67l06RIXxAEAcLVAu0WLFqbsTDuZ6rzsli1byuXLl81yX7pUSM2aNaVTp07WjRYAADy207jebFauXCk7d+6MdZxO9dq1axdzsgG49VQA7/FhqT4WINmBtpaO681Gr4p37drV3AAAQOrTaVu6ZraNTuk6evRotGN0He0MGTJImTJluCAOAICrBdoxnT59WtavXy9XrlwxS4hoKbmurw0AAFKHVpfpTbVr1066detmKswAwBPF1/SObDdcKtDWtbF1jezPP/9csmbNat+uc7VHjx4tDx48sG/T43SN7ajHAQCA1DFr1ixnDwEAAI+U6K7jGlDnyZMnWvAcGRkp33//vZmXrVfOR4wYIe3bt5eIiAiZO3eu1WMGAAAAAMB9Mto696t+/frRtu3Zs0euXbtmStW0XFwVKlRITpw4Idu2bZM33njDuhEDAIA4aam4zr+eNm2a+Pj4mOePo8fPnDkzVcYHAICnSHSgff36dXniiSeibdOupapatWrRtpcqVcp0IAcAACmvVatWJnDWCrOozwEAgIsH2tmzZzfNzqLav3+/6V5auHDh6G/u42NuAAAg5bVt2zbe5wAAwEXnaBcrVkxWr14tt2/fNs//+ecfs5Z2hQoVxNvbO1YX8pjZbwAAAAAA3Fmi081t2rSRTz75RHr37m3mYdvW5rQtJRLVpk2bJCgoyJqRAgCAeOmF8KSoU6eO5WMBAMCTJTrQDgwMlIEDB5pu4uHh4fLkk09Ks2bNTKY7ZoO09OnTy7PPPmvleAGI2CtKbP0RUtOdO3dMJYteaPPz80vVzz506FCqfh6Q1nz77bdJeh2BNgAA1krSBGptcqZZ7fhoJnvkyJFJHReAeOh0DdWvXz/xRJkyZXL2EACXNHbsWGcPAQAAJDXQBuBcjRo1MvclSpSQjBkzpnpWuVevXjJmzBhT0eKMIDtmBQ2Af+XOndvZQwAAAATaQNqUM2dOad++vVPHoEF2uXLlnDoGAAAAwBURaAMA4CYGDx5s1s0OCQkxK4Ho88fR47X3CgAAcOLyXgAAwDU9evTI3KI+T8hrAACAtchoAwDgJgYNGhTvcwAAkEYD7YcPH8rOnTulfPny4uVFwhwAAADOEdmlubOHAMBDWR5oa3A9evRomTx5crLfa+/evRIWFibHjh2Ty5cvS9++faVatWrRjjl16pT8/PPP5lgN8gsWLCgffvih5MqVK9mfDwCAO9iyZYts27ZNLly4YO9OXqlSJalSpYqzhwYAgFtKkdLxChUqyP79+6V06dLJep+7d+9KkSJFpF69ejJixIhY+8+dO2cauOj+tm3bmmWONPD29fVN1ucCAOAObt68af5+6sVovRCeI0cOs10rz1asWCFlypSRfv36sTY9AABpIdDWzPLIkSOlTZs2JgOdPXv2JL2PXm3XmyMzZ840+zt27Gjfljdv3iR9FgAA7uann36Sffv2SYcOHaRBgwbi5+dntt+5c0eWL18u06dPN8f07NnT2UMFAMCtpEig/dRTT0nmzJll1apVMnXqVPH395fChQtLYGBgtKA4ucH81q1bpXnz5jJ06FBTXh4QECAtWrSIVV4e1f37980t6rImmgkHAMDdbNq0yQTY+rcyKg24dVtERISsXr3aaeMDAMBdpUig3bhx42jLhmiJ94kTJ+TkyZOWfca1a9fMFfkFCxZIu3btzNX67du3m0z6Z599ZoL9uMybN09CQ0Ptz4sWLSrDhw+3bFwAALgKHx8fyZ8/v8P9uk+PAQBP5KhZnvf4sFQfC9xPipWO//7773L06FEz70uD4Hz58skzzzxj6WeoqlWrStOmTc1jnc994MABUw7nKNBu2bKl/XhbRhsAAHdUvXp12bBhg8lqx1wJJDIyUtavX2/p32YAAJCCgfb3338v4eHhUqNGDZk2bZoJtHfs2CH//PNPtCA3ObJmzSre3t6my3hUBQoUMMG2I9oojWZpAAB3pBe4o6pVq5ZMnDhRBgwYIM8//7y9j8nZs2dl5cqV8uDBA3MMAABIA4H25s2bZdy4cWYOmDZaUcWKFZMff/zRskBbS92KFy8uZ86cibZdvzywtBcAwBN98sknDvcdOXIkzu063WrWrFkpOCoAADxPigTa2ghN50/bupuqLFmyyNWrVxP1PvoeOr/bRrPkx48fN++vwbQ2ctE1u3V5krJly5o52rpW6KBBgyz9eQAASAu6devm7CEAAICUCrRfeOEFGTNmjPTu3du+TTPP2n08MfTq++DBg+3Pp0yZYu7r1KkjPXr0MN3Fu3TpIvPnzzfLk2hTlw8//DDZ63cDAJAW1a1b19lDAAAAKRVoN2nSRG7cuGECbV1KS+dsa7a5UaNGiXqfoKAgmT17drzH1KtXz9wAAAAAAHDbQDskJESGDRsmDRs2lF27dpmgW5utOOoEDgAAUsa9e/dk48aNcuzYMbl165Z91Y6oq29Qcg4AgAsH2rq2tXYDP3XqlHmeI0cOqV27tn2/dj0dMmSIlR8JAAAcuHDhgpmCpfc6fUsDbe1zYgu4tX9K1H4qAADABQPtnj17SoYMGcxyIZMmTZLAwED7TUvIdXkvAACQOqZOnWqC6qFDh0pAQIDpa/L+++9LqVKlZOnSpbJs2TJThQYAAFw40NZmZefPn5cPPvhAsmfPbsrGFy9ebJbc0tK04OBgKz8OAADEY8+ePdKgQQMpUaKEmcalHj16JL6+vmblDq1A0wvj8S0LBgAAXGCOdp48eeS///2vFChQwL5NM9y6VJeWqwEAgNRx9+5dk8lWGTNmNPea4bYpWbKkyXoDAABreSXnxaGhofLFF1+YQDqqqEG28vHxIcgGACCV5cqVSy5evGgee3t7S86cOeXQoUP2/ZrRTp8+vRNHCACAe0pWRnvt2rVmnpcG0jFFRkbKb7/9JhUrVrRfTQcAAKmnbNmysnnzZmnTpo19ne358+ebMnItIV+zZo3UqVPH2cMEAMDtJCvQDg8Pl8aNG8e5T6+cb9myRXbv3m3mbAMAgNTVokULOXz4sGlIqvOyW7ZsKZcvXzbLfXl5eUnNmjWlU6dOzh4mALiUyC7N49zuPT4s1ccCDw20dakQnXvtSFBQkCxZsiQ5HwEAAJJROq43Gy0T79q1q7kBaREBEACPmKNdunRpWbVqVaw52jbaeMXW5RQAADiPlopfvXrV3PQxAABw0Yy2lqB9+umn8s0330j37t3Fz88v1rIiUa+kAwCA1KUNz2bNmiU7duwwXchVhgwZpEKFCmbudmBgoLOHCACA20lWoF2sWDF57733ZMyYMdKjRw+pXbu2FC1a1KyZrWtor1+/Xl5++WXrRgsAABJs3759MmzYMJPBrlq1quTPn99sP3PmjGmStn37dunfv7+UKVPG2UMFAMCtJHsd7WrVqsnw4cNl9uzZsnLlSrl37559n3Yy1aw3AABIfZMnT5Zs2bLJoEGDYlWYRUREyGeffSZTpkwxS3UCAAAXCrSVXiHv06ePmat9/vx5E2znzZvXzNEGAADO8c8//0i7du3inMal2xo0aCBz5sxJ8vsvW7ZMFi5cKFeuXJHChQtL586dpUSJEnEeqxfjdTkxHZOtKu7VV191eDwAAB7bDC0mXU+7QIECpnycIBsAAOfKnTu3w4alSvc98cQTSXrvdevWmWx469atTWWbBtpDhw41zdbisnfvXgkODjZZ9CFDhpjP1ftLly4l6fMBAPCYQBsAALgODYKXLl0qx48fj7Xv2LFjJiOtDdGSYtGiRVK/fn157rnnpGDBgtKlSxezfJiuRhKX3r17S8OGDaVIkSLmorwuMaZzx7WnCwAA7saS0nEAAOB8EydOjLVN52h//PHHUqpUKTOtS509e1YOHjxoOo4fOnRIatasmajP0Uz40aNHpUWLFvZtXl5eUq5cOfO+CaEd0PV9MmfO7PCY+/fvm5uNNlu1VczpY/z/8+Dp58PTf3545v9n/Pu3npXnkkAbAAA38euvvzrcd+DAAXOL6uTJk+b25ptvJupzrl27Jg8fPpTs2bNH267PtaN5Qvz888+SM2dOE5w7Mm/ePAkNDbU/16lpWqauJfGIznYRxd39O8M/tnz58iXqeCApHrzdzOG+Qos3i7N4yr//tIZAGwAAN6HrZacF8+fPl7Vr15pu6Fpu7oiuXNK0adNYmYYLFy7EO/fck+g50S/Z586dM6X4nkqrNABP+3+Qf//W8/X1jbOBaFIQaAMAgETJmjWrKRXXbuNR6fOYWe6YwsLCTKD96aefmgZqj/vCo7e48KUy9vnw5HPiyT87XIMz/x/09H//VrLyPBJoAwDg5sLDw2Xbtm0mE6y09LpSpUoSEBCQ5FVGdHmu3bt3S7Vq1cw2LSXX540aNXL4ugULFsjcuXMlJCREihcvnsSfBu4usktzZw8BsOz/W+/xYak+FrgGAm0AANyYLsG1ZMmSWFfpteSwcePG0qlTpyS9r5Z0jxs3zgTcuha2foY2OKtbt67ZP3bsWDMHu3379ua5ZrFnz55tuo9rgG/Lhvv5+ZkbAADuhEAbAAA3tXDhQlm8eLFUr15dmjVrZpbVUqdPnzbb9abBcNR50AlVo0YN0xRNg2cNmnXZrv79+9tLxyMiIqJ1b12xYoWZVz1q1KhYS5C1bds22T8rAACuhEAbAAA39dtvv0mVKlXkgw8+iLb9ySeflD59+si9e/dk5cqVSQq0lZaJOyoV10ZnUWn2G0gplJsDcDVezh4AAABIGTonu2LFig736z7bvG0AAGAdAm0AANy4O/jx48cd7td9egwAALAWgTYAAG7q2Wefld9//900Irtz5459uz7WbbpPjwEAANZijjYAAG6qXbt2Jms9Y8YMmTVrlml8pi5dumSW4woKCjLHAAAAaxFoAwDgpjJkyCADBw6UTZs2mXW0tRO4qlChglSuXNk0SovaGRwAAFiDQBsAADeka1qPGTPGLO1Vq1Ytefrpp509JAAAPAZztAEAcNNs9q5du0zADQAAUheBNgAAbqp06dJy8OBBZw8DAACPQ6ANAICb6ty5s+zfv19mzpwpFy9edPZwAADwGMzRBgDATfXr108iIyNl3rx55ubt7S2+vr6xjps8ebJTxgcAgLsi0AaQYKdPn5ZWrVqZx3q/atUqKVCggLOHBcABbYRGV3EAAFIfgTaABClatKjcu3fP/vzmzZtSrVo1SZ8+vRw7dsypYwMQtx49ejh7CAAAeCSXDrT37t0rYWFh5kv85cuXpW/fvuaLvc24ceNk9erV0V6ja4OGhIQ4YbSA5wTZUel23U+wDbgO/Xe5efNmCQ8PlyxZspg1s3PkyOHsYQEA4DFcOtDWJUmKFCki9erVkxEjRsR5TMWKFaV79+725z4+Lv0jAWmyXNxRkG2j+/U4ysgB57t69aoMGDDABNk2Wnmi87XLly/v1LEBAOApXDoqrVSpkrnFRwPr7NmzJ/g979+/b242OnctY8aMyRonkNbcvn1bDh8+nKBjX3755QQdV7duXZk7d26Cx1CiRAn+7QEp4JdffpELFy5IkyZNpGzZsnLu3Dmzbfz48TJmzBhnDw8AAI/g0oF2QsvL3377bcmUKZP5QvHKK6+YMjlHtOtqaGio/bmWvA4fPjyVRgu4Bg2yGzVqZOl73rp1K1HvuWzZMilXrpylYwAgsmPHDqldu7Z06tTJvk0vSH/99ddy5swZyZ8/v1PHBwCAJ0jTgbaWjWtH1YCAAHPFfsaMGTJs2DAZOnSoeHnFvUR4y5YtpWnTpvbndGOFJ9Jssga6CRE1eNalgYKCgkwZqpaL79mzxywdZJPQ97SNAYD1IiIi5KWXXoq2rXTp0ub+ypUrBNoAAKSCNB1oBwcH2x8HBgZK4cKFpVevXubLv6NMma4fGtcaooAn0ZLtpGSTNajeuXOnw/1kqAHne/DggbkYFpXt797Dhw+dNCp4qsguzR3u8x4flqpjAYDUlKYD7Zjy5MljysY1u80XfsAa+gU9al+D+I4D4Bq0EdrRo0ejTe1QZ8+eFX9//1jHFytWLFXHBwCAu3OrQPvixYty48YNljABLKRfwA8cOJCg4wC4hlmzZplbTBMmTHB4PAAA8JBA+86dOyY7HfUK/fHjxyVz5szmNmfOHDNHW5u8nD9/XqZNmyZ58+Y1a2kDsEbMEtTkHgcgZXXr1s3ZQwAAwOO5dKB95MgRGTx4sP35lClTzH2dOnWkS5cucvLkSVm9erXcvHlTcubMadYHbdeuHSWsgIV27dpl6XEAUpYutQcAAJzLpQNt7W48e/Zsh/tDQkJSdTwAAAAAAKTpQBsAAACe15EcANI6Am0ACab9EGzr8ar9+/ebdXkBAAAQG0vceS4CbQAJpkH1hg0bnD0MAAAAwKV5OXsAAFxbaGiopccBAAAA7o6MNoB4Pfvss5YeBwBwP8y3BoDoCLQBxCsyMtKsW3/jxg2Hx2TJksUc5+3tnapjAwAAcLcLVMzddg+UjgOI119//WWC7PTp04uXV/RfGfpct1+/ft0cBwAAAICMNoDH+OWXX8z9vXv3Yu17+PChfbseV6dOnVQfHwAgdVAeDgAJR6ANIF6arbbyOAAAADjGkmDugdJxAPGKmsnOkCFDtH1Rn8eV8QYAAAA8ERltAPHaunWr/bGfn580aNBA/P395datW7JmzRq5e/durOMAAAAAT0agDSBet2/ftj++evWqLFy48LHHAQBcHx2PASDlUDoOIF66tJeVxwEAAADujow2gHi1bNlSJk6caB7nzJlTatSoYS8dX7dunVy6dMl+HAAAAAACbQCPcf/+fftjDaoXLVr02OMAAACQepgK4noItAHEy8vLy9LjAAAs3wPAmt8d/zhtJHgcvhkDiFfRokUtPQ4AAABwd2S0AcTr9ddfl//85z/y8OFD8fX1jVYinj59erN+tmaz9TgAgHtn2wEACUOgDSBe3t7epvnZjRs3JFu2bPLyyy9L4cKF5cSJEzJ37lyJiIiQTJkymeMAAAAAEGgDeIyNGzeaIFu7ioeFhckPP/xg36fBtW6fN2+eOU47kgMAUjfTrHM0fSYsTNRrAAApi0AbQLzCw8PN/fDhw2XUqFEyefJkk83WrLaWi2vpuAbatuMAAAAAT0egDSBeAQEB5n7//v1SpUoV6dKlS7T9O3fujHYcAAAA4OnoOg4gXtWrV5dChQrJmDFjTEO0qPT52LFjJTAw0BwHAAAAgIw2gMfQedgDBw6Ud955Rzp37iw9e/aU0qVLmwy3BtkrV64087ZphgYAzvPg7WbOHgIAIAoCbQCP1bhxYxNM6zJfL730kn27ZrJ1u+4HAAAA8C8CbQAJosF0w4YNTXdxbXymc7K1XJxMNgAAQNrjaFUC7/FhqT4Wd0SgDSDBNKhmCS8ANsuWLZOFCxfKlStXzEoEOr2kRIkSDo9fv369zJo1Sy5cuCB58+aVDh06SOXKlSWtiG+pLL6YAgCiItAGAACJtm7dOpkyZYpZieDJJ5+UxYsXy9ChQ+Wrr76SbNmyxTr+wIED8vXXX0v79u1NcP3XX3/Jl19+aZYO1GkoAAC4UxaeQBsAACTaokWLpH79+vLcc8+Z5xpwb926VVatWiUtWrSIdfySJUukYsWK0rz5v1+SXnnlFdm1a5fJimuzRSQscw4AKY3qHWsQaP8/Pj6cCgCA9dzx78uDBw/k6NGj0QJqLy8vKVeunBw8eDDO1+j2pk2bRttWoUIF2bRpk8PPuX//vrnZpEuXTjJmzGjJOY38vE+iX+NVvJTjnf+nX5ybvT/9KvHvBQBWcfC7Kcm/hxL5uy6+37dJeY2jMXv7+oqr/c12v7/+SZQjRw5nDwEAgDTh2rVr8vDhQ8mePXu07fr8zJkzcb5G53HHLCnX57rdkXnz5kloaKj9eXBwsLz33nvW/M3+5ufkv0da/nwAcOXfd98493ekXuT1TWbw7mXZaAB4hNu3b8vHH39s7gEgJbVs2VImTZpkv2l5etQMN/idnBI4p9bjnKYMzqv19FxqPxEr/tYQaANIlEePHsmxY8fMPQDPlDVrVlMqHjMbrc9jZrltdPvVq1ejbdPnjo5Xmk3w9/ePdktuhsHd8DvZepxT63FOUwbn1Xp6LteuXWvJexFoAwCARM9hK1asmOzevdu+TUvJ9XnJkiXjfI1u1+ZnUe3cudN0LAcAwN0QaAMAgETTxma//fab/PHHH3Lq1CmZMGGC3L17V+rWrWv2jx07VqZPn24/vnHjxrJjxw6z7vbp06dl9uzZcuTIEWnUqJETfwoAAFIGzdAAJIqWbbZu3ZryTcDD1ahRwzRF04BZS8aLFCki/fv3t5eCR0REmC7hNqVKlZLevXvLzJkzZcaMGZIvXz7p168fa2gnE7+Trcc5tR7nNGVwXl37nKZ7RFE/AAAAAACWoXQcAAAAAAALEWgDAAAAAGAhAm0AAAAAACxEoA0AAAAAgIXoOg4AAODC9u7dK2FhYXLs2DG5fPmy9O3bV6pVq2bfP27cOFm9enW011SoUEFCQkKcMFr3Oa9Kl677+eefzbG6VnzBggXlww8/lFy5cjlt3Gn5nLZt2zbO13Xs2FGaN2+eiiN1n3N6584d8//opk2b5Pr16xIQECAvvviiNGjQwKnjTuvnVVfT0PO6c+dOuXnzppQpU0Y6d+5sVsxIKAJtAAAAF6brk+vyafXq1ZMRI0bEeUzFihWle/fu9uc+PnzFS+55PXfunAwcONDs1wAxY8aMJvBmKaWkn9Mffvgh2vNt27bJd999J9WrV0/FUbrXOZ08ebLs3r1bevXqJblz5zaB4YQJEyRnzpxStWpVp4w5rZ/XR48eyZdffml+j+oylP7+/rJo0SL5/PPPZdSoUeLn55egz+C3MAAAgAurVKmSucVHvxDa1jCHNedV13zX/ZpttcmbN28qjc49z2nM/0c1CxsUFCR58uRJhdG55zk9ePCg1KlTx5xH9fzzz8uKFSvk8OHDBNpJPK9nz56VQ4cOyciRI6VQoUJm29tvvy3vvPOOrF27VurXry8JwRxtAAAANyiD1C+C7733nowfP96UkCLptEx869atpkx06NCh5tz2799f/v77b2cPzW1oaa5mtDWjiKQrWbKkbNmyRS5dumQysZrd1kCxfPnyzh5amvXgwQNzH7V6xcvLyzzfv39/gt+HQBsAACAN07Lxnj17mjLnDh06mKB72LBhJlhE0ly7ds3MfV2wYIGZ7z5gwAAzf1MzXHp+kXzaV0BLcGPOi0fi6LzhAgUKSNeuXaV9+/bm3/5bb70lTz31lLOHlmblz5/f9GGYPn263LhxwwTe8+fPl4sXL5oLRAlF6TgAAEAaFhwcbH8cGBgohQsXNvM19+zZI+XKlXPq2NIq20UKLb1t2rSpeazzOQ8cOCDLly8niLHAqlWrpFatWpI+fXpnDyVNW7p0qSlz/uijj8wc7X379smPP/4oOXLkIKudRDoVR5uj/e9//zMXMjSbrb9LtdRcqwYS/D5JHQAAAABcj853zZIli2nmRaCdNFmzZhVvb2/TZTwqzRxqsI3k0WDwzJkz0qdPH2cPJaENiYgAAAkiSURBVE27d++ezJgxwzTsqly5stmmF9qOHz8uCxcuJNBOhmLFipmGaLdu3TIZbf2doNNHdHtCUToOAADgRrS8UcsdNaOFpGe0ihcvboLBqHTuK0t7Jd/vv/9uAhatEkDSaQAYGRkp6dKli7ZdM7CJybzCMe04rkG2/ts/cuSIPP3005JQZLQBAABcmM4V1uy0TXh4uMlYZc6c2dzmzJljlkfSjs7nz5+XadOmme7YOrcYSTuvGkzrus6jR4826+eWLVtWtm/fbppODRo0yKnjTsvnVGmGcMOGDfLaa685caTuc051GoP+m9cSfC0d1x4COv/99ddfd+q40/p5Xb9+vQmw9fHJkydl0qRJJshOzO/VdI+43AEAAOCydK714MGDY23XJX26dOliyhuPHTsmN2/eNGvnarlou3btWO4rGee1R48e9syrrQmSNkjS9bQTk9HyNAk5pytXrjRBi66prdlCJO+canMubdq1Y8cOU8miwbYu8dWkSZNYmW4k/LwuWbLElN/r+dXqoNq1a0vr1q1NtUtCEWgDAAAAAGAh5mgDAAAAAGAhAm0AAAAAACxEoA0AAAAAgIUItAEAAAAAsBCBNgAAAAAAFiLQBgAAAADAQgTaAAAAAABYiEAbAAAAAAALEWgDAAAAAGAhAm0AAAAAACxEoA0AAAAgRfzxxx/Stm1bCQ8Pd/ZQgFTlk7ofBwAAACCxTp48KXPmzJEjR47I1atXJXPmzFKwYEGpWrWqvPjii84eHoAYCLQBAAAAF3bgwAEZPHiw5MqVS+rXry/Zs2eXixcvyqFDh2TJkiUE2oALItAGAAAAXNjcuXPF399fvvjiC8mUKVO0fZrdBuB6CLQBAACABDp69KjMmzdP9u3bJ7dv35bcuXNLzZo1pXXr1in2mefPn5dChQrFCrJVtmzZ7I8vXLggCxYskF27dklERIRkyJBBypYtKx07dpSAgIBor5s9e7aEhobKV199Ze63bNkiPj4+8sILL0i7du1MxnzixImyZ88eSZ8+vTRv3lyaNWsW6/WjR4+WWbNmyY4dO8Tb21tq1aolHTp0MK+Jz6VLl2TmzJmybds2uXnzpuTNm1eaNm0q9erVs+ScAc5GoA0AAAAkwN9//20C0zx58pjA08/PTw4fPmxKu1OSBvMHDx4087QDAwMdHqfzt3UswcHBkjNnThN4L1++3JSdjxo1ygTeMenPU6BAARMcb9261WTPdf73ypUrTZCu2//880+ZOnWqFC9eXJ566qlor9dAW8f36quvmlL2pUuXmsC5Z8+eDsd55coVCQkJMY8bNmwoWbNmle3bt8t3331nLl40adIkWecLcAUE2gAAAMBjnDlzRsaMGSOVK1eW3r172zO2DRo0kPv376foZ2smediwYfLRRx9JiRIlpHTp0lKuXDkJCgoyWWgbHdszzzwT7bVVqlSRAQMGyMaNG6V27dqx3lvf75133jGPn3/+eenRo4cJqjVwbtGihdmugfu7774rq1atihVoa6Zcx6UaNWokGTNmNMG9jrlw4cJx/jyayX748KGMGDFCsmTJYj+PGvRrwzfNqj8uIw64Opb3AgAAAB5DS6V9fX2le/fusYJA3Z6SypcvL0OGDDEdxk+cOCFhYWEydOhQ6dq1q2zevNl+XNRxPXjwQK5fv25KsrXkXEve4xK1VNvLy0uKFSsmjx49irZdX58/f/44l+jSjHRUtsZsWhIeF31vDfr1AoA+vnbtmv1WsWJFuXXrlsOxAmkJGW0AAAAgHpqx1jnMGnxqUzJHNHDs1KmTjB07NtrcaSto5rlv374mgD5+/LgpY1+8eLGMHDlSvvzyS7PU171798z8cV27WudA63hsNICNi3Yyj0p/Pr1woOXcMbdr4B5Tvnz5oj3Xsvp06dI5XDdbA2otLdfSdL05OgZI6wi0AQAAgMc0I7t7967J9sZHg0udB52UIFtLqTWj/DhaKq5Bt940y/ztt9/K+vXrpU2bNqZ5mZZ36xznkiVL2i8KfP3119GC7qji+syEjMMRDbLjYxuHNk2rU6dOnMc4KjkH0hICbQAAACAemil+XBB56tQp+fjjj03A/Nprr5kMs5Z36zrXK1asMFnap59+2syH1mBZt2tHb+3UrWXWH3zwgVSqVClR47IF/pcvXzb3GzZsMMGrZtWjjl0zyCnl7Nmz0Tqanzt3zgTTMbuc22imXOdx63nSknjAXTFHGwAAAIiHznNWO3fujLVPS7mVBta6xJc28tJmYrrmtc7r1pLzzz77zGSeNeOtGWelc621Q7g2EJs8ebJpbubI7t2748xI2+ZBa2bbUSZ62bJlJqhNKb/++mu059p1XOl867joGKtXr27maWsX9ZgoG4e7IKMNAAAAxENLsOvWrWvmPmtgrZ2379y5YzLSmoXWYNkWPOuSWLYss2atv/nmG8mePbvZVqNGDXujLw0yNTC3dfGOr1z7p59+MqXr1apVM0G1jkGX+1q3bp1ZWuu5556zdx1fs2aNGa8G/nqMrqlt6+ydEvTiwfDhw01grZ+nS4HpuuJFihRx+Jr27dubc6dLfNWvX9+M9caNG+bc6Hj15wXSOgJtAAAA4DHeeustsza1zofetGmTWWta50FHzdxq8GxbA1oDRm2i1qdPH/t+zSxrxlvvtdRcA+eE0FJ0/VzNYGsDMQ20tYmZLonVqlUr0xVcvfnmmyZg12BXP7tUqVLy6aefmhL2lKI/n2bup0+fbj5bLzp07Ngx3tfohQddriw0NNRktjUrrhcDChUqZNbtBtxBukeOOiMAAAAASBANfjUg1mysn5+fyWZr4K1LcMW1JreWk48fP17SKg2uNVCeMGFCrA7lAJijDQAAACTb7du3o83Z1tLp7du3y+nTp81zXRpLnysNwOMrrQaQ9lE6DgAAACSTlj4HBwdL9+7dTQm0lmtrGfWQIUPM/GMtl9ZSby0110A7MDDQ2UMGkIIoHQcAAACQKJSOA/Ej0AYAAAAAwELM0QYAAAAAwEIE2gAAAAAAWIhAGwAAAAAACxFoAwAAAABgIQJtAAAAAAAsRKANAAAAAICFCLQBAAAAALAQgTYAAAAAABYi0AYAAAAAwEIE2gAAAAAAiHX+L70tOt4IbtGFAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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", "text/plain": [ "" ] @@ -1934,9 +1936,9 @@ "outputs": [ { "data": { - "image/png": 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yRmQDmYat3Xff3ZSIxWnoitPSsXnz5sn3v/99UwcdH4dk/PjxpmFhaldaAAAQHB34dfny5aZA5IADDjCDw2ZbtWGYIhvI3njjDVMXrKVdOlCcNhQ85phjTN20Wr9+vSm2HDx4cOI1Wq+81157mZ4dXoFMG+7XbryvjQ1btGiR+L+X+PL6GibCX1zzzOOaA2iKoUOHmp6NmdarV6+c6SwS2UCmgUunXzj++ONl7Nix8vHHH5uRf3XAN50fK16HrIPP1aaPveqX1Zw5c2TWrFmJx71795YpU6aYbrUN0fZryCyueeZxzQEgHJENZNXV1abxvvaqiIenzz77zIQ0DWQuNNhpD5C4eGmADnuh45Z40XX0JrVu3bqcSeFRxzWP9jWn0wwA5FEga9eunXTv3j1pmT6O96LQ2d6VNh7UdeP0sRZhetGxS7zGL1EN3YT0ecJBZnHNM49rDgDhiGwg0zmwUgeZ1Mfx6kVt4K+hTGeIjwcwHb33o48+Mm3Ncl3JsqVpy8r3HRTKsQAAgBwNZNp2TIe9mD17tgwfPtwELR1vLD6miVaxHHfcceZ5rULRgKaDy2lpWbzXZS4jfAEAkDsiG8i0t6SOyDtz5kx56KGHTODSqRl0tN44Hd9EB4y98847TemYDgyrY5YxBhkAAMgmkQ1kSscx0a+6aCmZzn+lXwAAIHesWrXKdOjTQWf3228/yXWRDmQAAKBh66+cKFUbvsjIvgo7dpFOk28PfD89evQwM/Tky+CyBDIAALKchrHKNZ9JrigvLzfNj5o6NmJ8O9mgIOwDAAAAuU3HD9XpDfVLB3DXUi/tuBcfZqdXr15yww03yJlnnmnmrtYOfFplqU2TlixZktjOSy+9ZObLbNasmenQd/nllyeNIxrfz0UXXWT2MXr0aMkWBDIAABC46dOnm9l2Fi1aJH/605/M1Ih333130lyYQ4YMMW3GNKylWrNmjRldQUdSePvtt+WOO+6QadOmyW9/+9u0/Wip2CuvvCJ//etfJVtQZQkAADLSJuyWW24xpV461qiOI6qPx48fb54/8sgj5Ze//GVifS0hq+0vf/mL2caf//xnsw0dWUHHJ500aZJcc801UlDwTRnT3nvvLTfddJNkG0rIAABA4A4++ODElIXqkEMOkQ8//FCqqqrM4wMPPLDe17///vvmNbW3MWLECNm2bZusXr06say+0RmijEAGAABC17Jly0htJ9MIZAAAIHDxuajjFi5caKoXCwsLrV6/zz77yIIFC5Lm29V2Yq1bt06b+zobEcgAAEDgPvvsM7nkkktk+fLl8o9//ENuu+02ufDCC61fP2HCBPn888/lf//3f+WDDz6QRx55RK699lqzzXj7sWxGo34AABA4HdJix44dZtgKLRXTMBafn9pGt27dZN68eXLZZZeZ3pjt27eXc889V6666irJBQQyAACynI6eH/V9FRcXy6233mqGq0i1KqVHZXxsstrVk+rwww83w2bU5cUXX5RsRSADACDLZWIqIwQr+ytdAQAAshwlZAAAIFDZXJWYKZSQAQAAhIxABgAAEDICGQAAQMgIZAAAACEjkAEAAISMXpZARJUsW5q2rHzfQaEcCwAgWAQyIKIIXwCQedddd508/PDDsmTJkozuN7KB7MEHH5RZs2YlLevatauZdkGVl5fLjBkz5NVXX5WKigozr9W4ceOktLQ0pCMGACAc5z/4lnyxdWdG9tWlTXP586lDJUpjnB1xxBGyadMmXzLApZdeaiYwz7TIBjLVo0cPufrqqxOPa8/mPn36dFm8eLGZ5X233XaTadOmydSpU+WGG24I6WgBAAiHhrHPNu0I+zCyWk1NjVRVVUmrVq3MV1NoQZHO3Zkzjfo1gGnajX+1adPGLN++fbs8//zzctZZZ8nAgQOlT58+MmHCBFm+fLmsWLEi7MMGAAC1jBw5Us4//3zz1bZtW+nQoYMpcIlPHr5p0yY588wzpV27dqaQ5bvf/a58+OGHidd/+umncsIJJ5jnW7ZsKQMGDJB58+aZScm1dEzpc7FYTM4++2zzuLq6Wn73u99J7969pUWLFqYmrXbNm5as6fpPPPGEHHDAAdKsWTOZP3++qbLcb7/9Euvpdn7zm99I9+7dzTr63JNPPpl4Xo9Bt/PAAw+Yyc+bN28uf//733OrhGzdunXys5/9zKTMvn37yhlnnGHexJUrV5oUO2jQt21sunXrZp7TQKbr1pVY9StOL6C+SfH/e4kvr+t5+I9rnnlccwBB05qtc889VxYtWiRvvPGGnHfeebLHHnvI+PHjTYjSADZ37lxT+DJp0iQ57rjjZNmyZSYDTJw40TRV+ve//20CmS7XUiytSXvooYfkBz/4gSmU0dfG7+saxu6//37561//Knvvvbd57Y9//GPp2LGjCU5xl19+udx8882mcEdDXeo0T3/6059MDdydd94pQ4cOlXvuuUdOPPFEee+998x2a29H19N1NJTlTCDTk9RSL203pslZU+0111xjTnbz5s1SVFRk3pTaNHXrc3WZM2dOUjrW1DxlyhTz5jSkc+fOTTwjNBbXPPO45gCCouHplltuMX/49evXT5YuXWoea+nZ3Llz5ZVXXpHhw4ebdbWESdfXxvWnnHKKfPbZZyZ0xQtiNDzFtW/f3nzv1KlTog3Zrl275MYbb5Rnn31WDjnkkMRrtARMg1XtQKalX0cffXSdx61hTQPi6aefbh5rbnjhhRdMm/bbb789sd5FF10k3//+952vT2QDmSbMuJ49eyYC2oIFC6SkpMRpm2PHjpUxY8YkHsdLAzZs2CCVlZWer9F19CalpXXxolUEi2se7WvepUuXjB0XgNxx8MEHJ5XCa1DSQhYt7SoqKpL/9//+X+K53Xff3YS2999/3zy+4IIL5Be/+IU8/fTTMmrUKBPOBg8eXOe+PvroI9O8KTVoaSlb7XyhDjzwwDq3s3XrVlm7dq2MGDEiabk+fvvtt623k9WBLJWWhmlpmd4w9E3QAFVWVpZUSrZly5Z6e1hosWddjewaugnp84SDzOKaZx7XHEAUjRs3TkaPHi2PP/64CWVaHalhrq7ekNu2bTPfdX1t0lSbtgOrLbW2zVVTtxPpRv217dy504QxDVxa7FhYWGiKO+M0wW7cuLHO9mMAACA8r732WtLjhQsXmtqvfffd1xSy1H7+yy+/NG3C9Lk4rcL8+c9/LrNnz5Zf/vKXctddd5nl8VozbVsep6/T4KVVnXvttVfSl27HlrZJ08IgrU6tTR/XPjY/RLaETMcY0+I/baivbch0XDLtdXnooYeaHhhHHnmkWUcb9eljbWSnYYxABgBA9Gg40qGqtLOeDlt12223mVIuDWUnnXSSadyv7btat25tGshryZYuj7fP0p6Xeo/XTKBtuPbZZ59EsyatCn3sscdMRwBt1K/b0PHELr74YtNLUrOD1qJpkNKQpaM02Lrsssvk2muvlT333NP0sLz33nvNoLEuPSmzMpB99dVXpmfD119/bS5e//79ZfLkyYmhL/Ri6hugb6Ym6/jAsAAAIHp0WIsdO3bIsGHDTC3XhRdeaHpaqnvvvdc81nbe2s7rf/7nf8ywFvFmRlr6pT0tV69ebXLAscceazoEKA1u119/vQlxP/3pT81+7rvvPjMuqXba0+pNHZ1Ba9j2339/ueKKKxp13Np+TcOclsqtX7/elIxpJ4TaPSz9EKuhwYhp1F97OIzaNPRpI+YvvviCtjUZwjWP9jXX4vu6aNOBpmw7F3H+nH9951/f5ymXRurXnpRauhSfbQdZVEIGAADsRGkqI+R4o34AAIBcRQkZAAAIVOro90hHCRkAAEDICGQAAAAho8rSRyXLvh2oVpXv++3k5wAAAHUhkPkolwIY4RIAgMwhkFnY+OxTkjoDpl8BJTX4+LntpojCMQAAkC8IZBY6jBqdt4MaAgCA4BHIQkZJVDiokgUARAmBLIJVjVGtxswlXE8AQJQQyCJ4QycsAACQXwhkgM+oDgUANBaBDPAZAQwA0FgEsiwVdDuzfCvlod0eACBMBLIsvckHHRbyrYNCFI8JAJA/CGQB4ibPNQAAwAaTiwMAAISMErIMy7e2Wa7VmEFOVwUAQNQQyDKMUGF3DZiuCgCQT6iyBAAAyJUSsvLyconFYlJcnFrR1HQPP/ywzJw5U4477jg5++yzE/ubMWOGvPrqq1JRUSFDhgyRcePGSWlpqe/7z0e5VLWaLT09AQD5yzmQvffee/L666/L8uXLZfXq1SYgqWbNmkm3bt2kX79+ctBBB8mAAQOadIAfffSRPPPMM9KzZ8+k5dOnT5fFixfLJZdcIrvttptMmzZNpk6dKjfccEOT9ofcCyy5dC4AgNzUqEBWWVkpzz77rDz22GOyYcMGadWqlfTu3VsOO+ww839t71NWVibr16+Xl19+WZ544gnp0KGDnHDCCTJq1CgpKmpc/tu5c6fcdttt8rOf/Uxmz56dWL59+3Z5/vnn5cILL5SBAweaZRMmTJCLL75YVqxYIX379m3UfgAvlKwBADKlUQnpggsuMKHs8MMPl0MOOUT69OlT7/orV66UBQsWyJw5c+TRRx+V22+/vVEHd/fdd8vQoUNl8ODBSYFMt1tVVSWDBn17c9RSOQ1/BDKkIlgBAHIqkI0dO1ZGjhxp3U5MA5t+nXbaafLCCy806sBeeeUV+eSTT+R3v/td2nObN282pW0tW7ZMWt62bVvzXF20rZl+xWmbtxYtWiT+7yW+vK7n0XjF773T8EpduqRdc6/XVQwY3OCmvNaxeTddX5eN+DkHgCwKZEcffbTbToqKGvXajRs3yn333SdXXXWVlJSUiF+0pG7WrFmJx1rdOmXKFOnYsWODr+3cubNvx5H3unSxWi3tmlu+Du74OQeAcERyHDKtktyyZYtMmjQpsay6ulref/99efLJJ+XKK680VafaXq12KZm+pr5ellrCN2bMmMTjeGmAtofT7XnRdfQmtW7dOsbEspBaimVTgpXN19y11C5qx9mYa96FYAwA0Qpkq1atMj0sDz300MSyJUuWmJIorRrU5TpURWNp27Cbb745adkdd9whXbt2lZNOOsm0FSssLJSlS5fKwQcfbJ5fu3atKVmrr/2YVrXWVd1a301IR41PvVC0QfKWdl2aEKj0PYlaILMaDiTlmKPQhs3mOKN6zQEgHzQpkN1///2mSjEeyLR3pQap1q1bS7t27czQFPq89rBsDG3XtcceeyQt0+E0dLvx5UceeaQZh0x7d+qwF/fcc48JY0E06PcaNT7Im2ymb+BRCAzZwuW6eL2G9xgA4Fsg+/TTT82QFnEvvfSSFBQUmHZZbdq0kVtuucWMIdbYQGbjrLPOMtUsOvaYVjfGB4bNFJubrOsNLxKlJwg06PAeAwB8C2Q6HpiWWsW99dZbZogKDWNK/69VmH647rrrkh5ryZsGsEyGsCjd9HKtxCPtfHxsp+R1rVJlS3gGAOSmJgUybUC/Zs0a8/9NmzaZxvg6LEbtgV3pRh+MoINApqdOqr19v39m/Dz2nJ5Sisb6AJCdgUynRtLR+HXaJJ3iSBvMDxs2LKlK8zvf+Y4fx4kMl5plc9AIUi5dlyBDMAAgg4Hs9NNPl61bt5ppkrRhvU5fFB92QqszFy5cKKNHj27KLpBlYSGXSpCC5Gc1KtccAPI8kDVv3txMp1TXc3/96199Hdg1LDrsRepgGdz0vGX6uvgVRjLdJs/PbfOzCAB5GMjmzp0rBxxwgJk7sj7a21JLzXJBpoe98FOQDdqjsD+/thXF9w4AkD+cAtnf//53M93Q/vvvb74GDBhgPb9lrsiWMcdyaXiFTIc9AAAiG8juuusu+fDDD81wFosXL5annnrKVEtqKNOSs6FDh5qR9PORS/UZAcL72m18752MVhPbhL2gj8EVQRUAsl+sponzpGzevNkEMw1o77zzjuzYsUO6d+9uSs40oOnI+Vp9GWU6l6VO9eRFe5/p3H2pVZYIrnQvm655tlRd+3nNdQqzuugUZk3Zdi7i/Dn/+s6/vs8T8kuTJxfXXpU6jZF+VVVVmQnAdYDYN99801RvajsyHUX/+OOPl7333luyUbY06rcJB7k+NEY2N86npAsA8leTA1ltOuH3wIEDzddPfvITM7ellp5pQNOglq2BLFtEsYrUNSC5huAoBBbXYGUTngEAucnXQJaqU6dOcuyxx5qvXOtlmenxoGy2HcXqs7D3Hwa/ht7wcxyyKP5sAAB8DGQffPCBPP/886Y0rKysLC20aP35H/7wB8kHNjc415tutpQOZToEB9k4P+zqT9v959vPBgDkoiYFsscee0z+9re/mV6W2jCxVatWks9swlYUb4y53nYpim3kcv2aAwAyGMi00X7//v1l0qRJOTMIbFNk8gaazY3zgyx5CrqRfRSPEwCQ54Fs165dcuihhxLG6hDFdjt+HpPrtoJs1O+nsIMqbcgAIH80KZDpYLCfffaZf0eTY2xujH6WatncdP28yWc6yEUxVEShpJI2ZACQ54HsnHPOkcmTJ5uqSx2HLN/bkOXylEt+Npa3Wce1UX8Uqh6DnPDc9hgAAHkUyHSKpFGjRpmG/Tq/pTbu9xqVf/r06U3ZDSxvzlEsQcp0tWkUSvKi2M4sW342ACBfNSmQPfDAAzJ79mxp37697LnnnjnblszP9kxBDnsR5ECiUZ3wPBtKHKNQRUv4AoAcDmTPPPOMmbPysssui/x8lU3hVX3m12jsfgryJp8tJSxRPE4/2+0BAHJTkwJZZWWlCWS5HMai2gPPi2t1mp+dAaJYZRlFrvOO2siWawAA8CmQaRjTOSqPPvpoyWWuVZZBVjkFPc+hawDMdJuuTHMJgAQrAECggeyUU06RW2+9Ve6++27Ty1Ib+XuVlrn0vnz66afN14YNG8zj7t27y8knnyxDhw41j8vLy2XGjBny6quvSkVFhQwZMkTGjRsnpaWlElaVpV835yAboQcdalyn/0ldb+N771iF4EyHNJf3JgqlqVbXrkuXIA8LAFCPWE0TxhU47bTTrBv/N9Ybb7xhwl2XLl1MEHrppZfM8Bo33XST9OjRQ+666y5ZvHixTJw40XQmmDZtmln/hhtuaPS+NPRpqPOic3HqMfg1r2Kmp+MJsiowqPZodV1zP0uasqGqM5Pzcjbm51ynSavL2rVrff0M5QLOn/Ov7/zr+zwhvzSphOwHP/iB+WELwoEHHpj0+Ic//KEpMfvwww9l9913NxOaX3jhhTJw4EDz/IQJE+Tiiy+WFStWSN++fSNRZRl0u6sg92cj01WW2dI706/9B90ZgBIyAMiRQHbqqadKJlRXV8uCBQvMVE0atlauXClVVVUyaNC3N55u3bqZKtP6ApmWgtUuCdMw2aJFi8T/vehyrbJct25d0l83NjG0+L130o9hwOBGr+PFax2/jsmLa/B2DSxeVZZex+l1Pi5sroHX/lyPyet62mzbhte2G9pW/DVB/YGVyx5d/pWc0K992IcBIJ8DWdB0WqYrr7zShKjmzZvLpZdeatqSrVq1SoqKiqRly5ZJ67dt21Y2b95c5/bmzJkjs2bNSjzu3bu3TJkyRTp27NjgsXTu3Dmt1CyVBreGShxSX5f2mnoCSvKmHUszbF9nsZ7XNXCVeh28tu15zo7XIXX71tfTZj3XY7J4j22uuVcg9Po5S9tW585pP+doWGVV/lXDAQg5kGmV4Pe+9z0ZMWKECUQ2NEzNnz/ftP+65ZZbGnVwWrf+hz/8QbZv3y4LFy6U22+/Xa6//npxNXbsWBkzZkzicbw0QNuQ6RAeXnQdvUmllpCJR4mDthFobElJ6mvqel1qCYft6xraTl2sSmssSods91f7fMw19yiVtDln6/OrZ/9N5fw+pCyzDbw2pa6e185jW2k/5x6c/xgAAPgTyEaOHGl6Nt53331ywAEHyODBg00pU6dOnaRZs2ZmnZ07d8r69etNteI777wjb775pglvJ554YuMPrqgo8Rd7nz595OOPP5Z58+bJ8OHDTYAqKytLKiXbsmVLvb0si4uLzZeX+m5CemMsCqhdUInHzdOzbZbFTd6q7ZDHedpUK7o2xvV6nVU1ZufO5rWNbdRvW4Xp+j741rbP4npat3NL2Zbr/r5ZLfmaAwAiGMhOOukkOeaYY0yD+hdffFFefvnlxHOFhYXmu7btitPekNrO7IgjjvBlWiVtS6YlbhrOdH9Lly6Vgw8+ONG7a+PGjb436G8KPwdqDX1IhCZsK0hh9yL189o1tK+miMIYbrlo3oebwj4EAPnahkwbwR9//PHmS0vCtBH9mjVr5OuvvzbPt27d2jSw12CkJWeuZs6cKfvtt59pqK+lblrtuWzZMtOmTMOdjnumpXU6xpk+vueee8w+MxXIghxKIciAFMWeirY9W/0MVjbCHvfMz+Nu6L2iMb8b2o8BiESjfg1cTQld9dHqR20ztmnTJhO4evbsacKYVpOqs846y9xEpk6daqov4wPDZkoUJ6MOkp8la65s9pfpaxdkwHUdh8z5PaBtGACEJrK9LH/xi1/U+3xJSYkJYJkMYWHON2mzLz9f59e2XI6zMQNJZvo4XQJY0CGR6kgAyH6RDWRRYjt1UibnbMz0ZN8Zr/azLK3xa85NV35WE2f6XFJH6oebwoIYY5EBaDICWYarz2y247qea0mM600+yIm1/ZwdIVtLkHLpXHIdbckANBWBLECZLplpynpRCweupZJR7CQRdFuwsNsXAgCajkDmoyCr+WzWCbLKy7VRv+3+Yo7jcgU5PIfX64Ls1em67SB/zgAAWRrIdKwwHRBWe0MWFBRILrCtPnO5oWZL78Wgh5Oora72TEG2dcv068Letuf26WUJALkTyDSE6RRJ06dPl1zhVX3mxaXnnmuJh1eJUo1HkLHZn+22wq52i0KJjkvJmuugszbbtj3GhvZHo34AyMEqSx0T7IMPPpD+/ftLPpeQufKzd6bNMe3ymFcxyBIjm2unE20Hec2DrFa02VcU2p4BAHI8kGm1pQ7Yesopp8iwYcPqnV8yW7mWYvk1rY4rP3t1BtWmS0trvOakDHLstUyHn7CH6wAA5EEg23fffc2URi+88IL87W9/S4y0v8cee8iPf/xjyTa2Pf6CLKlwHcjUZSiOoEVhmI2wr4PrexVk5wcAQI4FsuOOOy7xfw0x69atk08//VQ+++wzyUau8ypmWqaHhXBte+ZnqVKmp0UK+z32ur6+XU8a9QNA7lVZPv/887Jy5Upp2bKl/OhHPzJT4Rx88MGS7yP1Z7qKy3XbQQ76GiTXBvSu7e+C7KEahZ6tAIAsDmR33nmnrF+/XoYPHy7333+/CWRvv/22fP755zJmzBjJ1RKysAOLTfBo5tE2y89G/X5VrdrKdEcKl2FLohBcM126CACIQCB744035Pbbb5fmzZvLzJkzzbI+ffrItGnTsjKQ2Q57kcl2UK6hxrbKK7U60nYYjCAb9buKYo/YTB+TVQkgVZYAkFuBTBv079y50wSyuNatW8uWLVskVwRZDebFtdTFdX+pAcx1xH0/g1ym+dmL1GUdr9JMm+tp+zrGIQOAHA9kRx99tNx2221ywQUXJJatXbvW9LbMRl5Vlq5DWvhVVeYlVl2dtmzXwCGN3o7XcblWebmGimwO3X5VWdpWJfs1DRQlZACQY4Hs+OOPl23btplAVlFRYdqULVmyRL773e9KrrC5WfpZwmJT8pQavmz3Z3sMLutEIXz5WfqWui2/gnld23I5pmzpMQoACDiQafXH6aefLscee6yZ11LD2WGHHWbGJ8snQZYORXG+Sz+5Dqnh5zRQmZ5APsgx4whfAJCHgeyKK66QG2+80YzQ/z//8z+Si/wKW7Y3ytQSuVwe4LUxjfpd26y58uu625SC+tn71QttyAAgRwPZ1q1bpU2bNrJmzRrP56+66ir57W9/K7nAdSJvV35Vg7kGlkxXwwXd+SCKUs/FpXF+kzp80IYMAHIjkJ1//vnSrFkz027svvvuM1Mlxb90mY5DlssyPWyBX68L8piCLuXJ5LZdBV2NmhrcvLbtdQy13xtKyAAghwLZjBkz5D//+Y9ccsklprpy6dKl8vjjj5sxvPQX/ogRIyQbBXlDdW0U7vU612pUP9szpR6Xn+3o/JpI3Gs9r3X8OnbX87UNszbbt9oWJWQAkJ2B7Morr5Qf/vCHMnDgwMSy73znO3LTTTdJt27dEssqKyvNuGQ6PpmtOXPmyKJFi0z1Z0lJifTt29dMTN61a9fEOuXl5SYEvvrqq6YEbsiQITJu3DgTBv1kW+KQup5NdZ3rzdrPUONn6VuQbbiC7DEaZPVg0HN+2lRLR7HkEADgUyCrqqqSG264QQYMGGB6VWpoUrXDmNlJUVGjwphatmyZjB49Wvbcc0+zn3/84x+m/dkf//jHxICz06dPl8WLF5sSOR3jTGcCmDp1qjkmP9m2g/KrDZnrDTzI+Sf9HL8simzCVzadT6arTQEAIQay3//+96YU61//+pdcffXVst9++5lg1rt37yYe1jelb7VNnDjRlH7phOU6fMb27dvNBOYXXnhhooRuwoQJcvHFF8uKFSsS4TCTvEpZXHpL+tnwPtM99xrav+35uU7o7udwGa7jwdnMH+on158pr4GEAQBZ2oZs2LBh5kurDWfNmiWXX365eXzaaadJ9+7d/TlKERPAVLykTYOZlpwNGvTtzU9L5jp06OB7IPOzJMFmYFGb0GRbahd2zz3X6l7XCd1t2QRVr2v1TOmBSY9HbXkzsGtgGy5tGvV7qSkosFoPAJBFjfqHDx8uhxxyiLz88svy0EMPyWWXXWaWnXLKKdK5c+cmbbu6utr02uzXr5/psak2b95sqkJbtmyZtG7btm3Nc160nZl+xWlHgxYtWiT+76Upvc8qPG7yqeNruU727cXmxu86t6TNuXit57VOkAO82oafVLalSqkBzM/jdFmnMeul8jp2elu6KSyIyaPLv5IT+rUP+1AAZClfe1nqL3MdCPbQQw+Vl156SWbOnCkLFiww35tC24bpkBm/+c1vmrQd7SigpXhxWrU6ZcoU6dixY72v+3LpEqcbf8m7bzd4TLsffWz6/p55ssH9uZaQ2QaI1OqsLh498DZ6hK3Uc3YtHfJ6nVZjNsTr2onXtfIpxDxVelDaOgWSvM7Rm99IPyQfg4/rWG9ex9DUP57yWWVVsGPuAchtTQ5k2oNy3bp1pjekfukk4vHv2rOyqTSMacP966+/XnbffffEcu1JqfsuKytLKiXbsmVLnb0sx44dK2PGjEkrDdiwYYPZlhddpyjAkgqtmvPYqW8lXc4Ds6Zs3yvoVHjNnWkRQl3DiGfYsth2LMDerV5bqU5Z+rRHaIulhLamlL75Ge70s1y73Z4Xr3AOAAgxkOnk4RpmtEoxrl27dtKjRw858sgjzXfXdmR6U7jnnntMp4HrrrtOOnXqlPR8nz59pLCw0Ix1dvDBB5tlGgI3btxYZ/ux4uJi81XX/prKr1HxrdoXeTTItmkT5Gc1mGt1pOu1c62OtGHbozJ1va9XpW8r9YiaF6avYxPyrc83dVkTQpt+Dvz4LAAAMhjItKpv//33N8ErHr50+Ak/aMnY/Pnz5Ve/+pVp5xVvF6bb13HJ9LuGPh2HTBv662MNcBrG/O5h6VpSYROabAckbWg7tvsLOrC49ur0awBb24DmOoBt6not2qaXfn2+Pfmae51Z1xbp79WzbQ9osMOAaztBqyAHAMjOQKZDXQTl6aefNt+1dKw2Hdpi5MiR5v9nnXWWqVLUsce0yjE+MGxY0m6MFmHI1zkiA2yQ7Tp0g20HBb8GSnUdT8y1pK04lv661KZECzelV4f3bZVeUntQuyqnY3AuGaUBPwDkZqN+Pz344IMNrqMlZRrAgg5hQfZ2s25k71gKEmTbsyBnGXDutOBRSug6BZLNe+NVirWpRXKpWXlFedo6i1auTlu216CeDe7fpoNC4O0LAQD5E8iixM/BRq2qiRzbAAXaZs3x5u0arFxDhVdpkeuI+67vccui5GXFRemlYQWfvZu27IHmydX9pfumV4eO3vqG0zVmpH4AiDYCmSOb9lpRqCbya/wyV87Bysdw6Rq+XN/j475ODk3zmn/T6aS2sq3r05ZVL1+Y9Pj9HiemH1Pb5IFp1TFb0kNaGq/S04ZfBQDIEAKZBdtg5VKtaBuGbKbHsWLZ9izIwVv9HC/NZp0ge63avFeHf5LeNuxvXfdJW1by9mNJj19ePSJtnX77tGvwGGx/XikhA4DoIJD5yK8bnM3o/c5jVPlYYhVkyZrnMXnNvejXqPg+TiOU2matjUdPzNJ+6SVd25ckB7LKz5elrbN1r0OdpnMifAFAtBHILPjZwNwmQLiOZG+zraBHiPezqtFqqI+U17m+V75WS1u0+/q6S3ogm3HID5MeF61+L22def9JL237cQ/mpASAbEcgc2RVOhTR6XGc9m9ZpecZQi1e51cnAtdSO9dhRGzGkfM6ppO3p4e03YYnh7R3tqZvu8DjkCqq3YbLoJclAEQHgSzAgOIcBGx4lU45tnmy2b5N6ZTtMVjtPsCStqYcg8uAsrYliamdAaRNeinahl0exxSrf4DZuubTZGBYAIgOApkFP3tL+tVY3msrNsM7WJcg+dTIPtOhybaaOMgSx9T3wXZQ3dRjKvbIth2bpR93Uaz+uTSNDA8kDABoHAJZhgU5UKvnqPipASUC1aiuJTNWVaSOg/h6BTnXScnT3gfHnq1Hewxn4VX6ZTMpOeELAKKNQBbgaPpeXNtK2bzOcwiGgUMaLq3x8QZuFTBdS9Z8qib2XC8CnSRSS9a8ArZX1eNTpcm9OAvSpjevo8oSABAZBDIbloNq+tXL0uombxk8XOegdK1a9asqsMOo0fLFF19ITa3tuZ5LpoOxa/u7Zu++nbKSXdgbvfn1eo/R65gAANFCILPh4zQ+rgPDpg17Ydl43mp/jq+zYV0lmxJev3zmSSlxeJ33So5tyLyqMW2ue5DVg46lmaklpX4HXABA0xDIHPk1KGqm54h0rX51Le3zYtPGyXUqI+vzs2l75hW+LEK383uVei62Q41YjMXmPLMDACAjCGQWnMOB4zpeYo6BMHX7ttVnrpwDoEVA8vPYrd4Hi2Ow2r9jqZb1UCM+jeEGAAgPgcyCdUNxi1BhFZC8glyQN10fG/Vbjb1mGUJdth10+LHZX2r1oG3plPN1cXoV0ykBQJQQyCxYt/OyKT2xCVau1aEBjkjvPJyERWDxas/kPNm31zLHa2ezP691bAKY1fsQ8PAVlKQBQHQQyCxYVytalH6lvcZxwFXrYGXTqN81/Fj0svTan9XQGz6Oz2Zzzr5Wh/rU29ZmrDJbhC8AiDYCWYB8DVuu82T6VCLnddxWnQFs25ClH1X6OhbBxs+ZCFwFOVitn7MOAACig0BmwbX0xHk8MdfG5DbH5NNck00qWXMMVn5W1zm3ybMZ2qSB1/g9OK7VtfPaFkEOACKDQGbBtaegzW3RtlG/87YsSqwy3hDeorTNz4BktRnHkic/58T0KyDZvsd+hnOIFBbE5NHlX8kJ/dqHfSgAshCBzIJ1ux2HUfhdG73btgXzq62Sa0CybQdlNYCtxfk5T51ke34WDe/TSjNdS7p87Plp0+MXTVdZRakjADcEMj/bgtlUZ1k0JncexNM1WPnZ48+xTZdfMwFYh1KfStasenUG3Fsy7RrYvo4SMgCIjMgGsmXLlsncuXPlk08+kU2bNsmll14qw4YNSzyvcxw++OCD8txzz0lZWZn0799fxo0bJ126dPH9WFwbinuyCBB+9s50rUK0OaYgq/Rcj8F1SinXdex25tZb0rW0jzZkAJB9Ivsn8q5du6RXr15y7rnnej7/yCOPyBNPPCHjx4+XG2+8UZo1ayaTJ0+W8vJy349Fb1ypX3rTS/2yeZ2kfunrUr48X5fKY1s2x+nF9Vw8X5d6XI7X2Ev5voPSvmyugdX+q6vTvlzPz+aaux6n1/tudX42P4sAgNBEtoRs6NCh5suLlo7NmzdPvv/978tBBx1klp1//vkmnL3++usyYsSIwI/PdQob17kX0/YfcENx53G6HNpYeW3La9ue45e5Vv0FOEq989horqWZqdeu4a3UvS1Ye+qjzWEfAoAcEtlAVp/169fL5s2bZfDgwYllu+22m+y1116yYsWKOgNZRUWF+YqLxWLSokWLxP+96HI/q4D8arwehQ4KVtv3MSQ694R0vJ7O1dI2xyT+SQ24FQO+/VzElSxd4nEQHteTkGatsppSRQB5Hsg0jKm2bdsmLdfH8ee8zJkzR2bNmpV43Lt3b5kyZYp07Nix3v196XUzcx0qwqEUzWs95xHbLdus2TSWtyo1c5xRwNcg59PYaNb86qDgWHpa8u7bljtM337nzp3tXgsA8FVWBjJXY8eOlTFjxqSVBmzYsEEqKys9X6PrFHss9yuMZHwqHMfSPufxvWxf51MYcWU7HpxT2HKcHcE19Jd7DJtS7FXd62HdunWmSUB9gug4AwD5LisDWWlpqfm+ZcsWadeuXWK5PtaOAHUpLi42X17qvQl5PGdVsmVTHek4rY6tIHsTum7b16pHV649S22OwaLa1oZrz1av8GX7PujnoKFABgDwX1YGsk6dOplQtnTp0kQA2759u3z00UdyzDHH+L6/3Y/5rnzxxRdJNyqvscL8GkbAdeBU7405zoHZwP5t+TqZuecOLMZ+s+mAYTk7guuQJL69Vz5VlZuXMTAsAERGZAPZzp07TfVJ7Yb8q1atklatWkmHDh3kuOOOk9mzZ5vqEw1o//znP01pWbzXpZ++fPoJKbG5MTv0krMNVq5tuqwGePXi000+luEqWOeBbx1nFHBlM8G6bftCG0F2FAEA5HAg+/jjj+X6669PPJ4xY4b5fvjhh8vEiRPlpJNOMmOV3XnnnaZ0TAeGveKKK6SkJDU6NZ3tTTCjI587joAfy3DvTD8nCfezUb9fJX6uw5+47MsWA74CQPaJbCAbMGCAGYm/LtrY/rTTTjNfQfNzNH1Xft2cXYfw8LORfaYb7HuxCU02x+ln9XKQQc523DoAQDgiG8iixLVdkmuV1y6PcaRsJhd3DVZe/Jzaxy9hhzbX17kGUD/P1+ZnCgAQHgKZBT97Cqa+zkz/k8EbpZ/tomy2FXRpWOr187p2ruEn0yV5fo0158X1ugAAMoNAFvL0OF43Sq/SDK9enUH2snQ9P9dqN9cw4ld4DbK0zzbspb7vtudGsIqOwoKYPLr8KzmhX/uwDwVAliGQOd7wvEq2UkOTa6jxVYBVYzZt6/xsP+VrUHU4Jle2paCp5+Jammnzs4ngVFbRqQJA4xHILHjdBF1vcDY3eT+rl4IMfLs8RoS3uS42oWL3o491GvvNterRz+uUuj+v43btDJDpnx8AQGYQyCxkOgz51T7NqxTLdmiO1FIWr1DhV/hKLf3SHrRfPvNk+thvEQgVqedjU2pnW9pnU0WZ6XAJAMgMAlkE2VQ5ea3jxbUkz/V1rm3BbKrrvGS6t2Lqtmyuk9f+XcOs67lQjQkA0UYg85FNqZLNOjZVTrZTN9k0FLcprbFtv+VX+OkwanRalaUXm1ARZBixDcY2+w+yZIvwBQDRRiBz5HqTtxlPzM+qTpuSJ5tw4FeJWV3HEMXSPi82gbqh19S1jNAEAPmLQOZYWhNko37XqsAo3uRtjzM1qG589ikpDvD8XIKV13qugdOm9NT1dbalp7Q1A4DoIJBlQaNw2wFlww5fTeEaYoJsk+faPsxmO67HFORxAgDCQyCz4FVa48WmpMK1KtC1RMcvtkHAtXdm7ddpL8viDE/rk+nr6Wf1q0vbxaYcAwDAfwQyR37d4Fy342dAshH0zduvc3ZtZB+F6tBMSw3BAIDwEMgCbEOW6XCQ6VBhc36ux+Rnuz0bQZZUuobnoKUdQ5cuGd0/AOBbBLIIDHdgs20/qgJt9297TEGW1mS6Ub8Xm2vsV0P8utZzkelgl4/mfbgp7EMAkGMIZBluQ+Z6A7fhZ4lcpkt0olit51fo9rOEzK+QiKbPV6kTiQOAXwhkFjJdfRbkSP1BhjY/S8i6dOliNTCsX8flZ5WlX1W5tufmR6CmDRkAhItA5ihbbrou67juP4xt+SXTAbcpxxC1bQMAmo5AZsG2PVPYbc/83J9fMt14Pej9ZfLYXcchc57nlEb9vtCqzEeXfyUn9Gsf9qEAyCIEMh/nVQxS2MEq3467KYIsBQ3qdblYZVnz3FyJHXWi8/ONXS+1jRkANAaBDDkl6I4GrlXVyJyaFx//5ntlpcTij6uqTKiqHa4Sz6cErvjrE4891qNBPwC/ZX0ge/LJJ+XRRx+VzZs3S8+ePeWcc86RvfbaK+zDynr03ItG71M0ngYoz8cvPp4c0goLzXdT8v3ykya0eb2+9nbir91343b5aOgxsqP6m9cAQF4HsldffVVmzJgh48ePl7333lsef/xxmTx5stx6663Stm3bsA8vqxEq7HCdoiWpdOu/gSvxnAateAj7b+hK+v7fQJbGYztaIbn3kqflvUGjpKr62+pJLTmr/RgAbBVIFnvsscfkqKOOkiOOOEK6d+9ugllJSYm88MILYR8agCiVjjWwzGq7qcFMS0gLC0wI06+SogLp//YzntWZYbY/BZAdsraErLKyUlauXCnf+973EssKCgpk0KBBsmLFCs/XVFRUmK/aDZlbtGiR+L+X+PJcbPQcVVzzzMvma55aKlZnSZcf+6oV5mKFRdJ36fMmmMVVVVUlAlpc9bNzpeZH4wM7JgC5IWsD2datW6W6ulpKS0uTluvjtWvXer5mzpw5MmvWrMTj3r17y5QpU6Rjx44N7q9z584+HDUag2ueedlyzeMN7GtXPyYCWcA+3VKe+H91VaXULvyKFRVJ/3e/KaFf9YFIz9KSb6o4a2pMMIsddULgxwcgO2VtIHMxduxYGTNmTOJxvDRgw4YNpsTNi66jN6l169ZR7ZAhXPNoX3OdRSFMiYb4qWEsU/tv4Eey+r/HVF1TIzWVBSYkbp374DfH7DCEBoD8kLWBrE2bNqaKUntX1qaPU0vN4oqLi82Xl4ZuQvo84SCzuOaZF/Vr7tUgPytU/feYU3p0xofjkJThOQDkn6xt1F9UVCR9+vSRd999N7FMqzD1cd++fUM9NgA+h7D4VzaFsDqYKsz4lwbgWueWePzc3G/WTfkOIHdlbSBTWv343HPPyYsvviirV6+Wu+++W3bt2iUjR44M+9AA+CARVP77lYtSzy0pmMW/FxQQ0oAcl7VVlmr48OGmcf+DDz5oqip79eolV1xxRZ1VlgCyp7G+GWYiYiGsdoP+hhTEYrJqc7n0Km3230EyGiepalavRbzdXMr3+sRGHv/NNmrPUEA7NiCSsjqQqWOPPdZ8NbX604914C+uefZd87raaNa17coXn0h6HCsqEXnlWYnFCnVcCZFiDTONoL0sCxyGvWjgdZ9u2ik92zWXwsJvg1VBUaHUxGINNvJfva1a+rUqksLiEvdjrP2a4sLk7/W95pVnk65r6v/rUjTyu2nvjbZvKzpqzDfL4/9/7rF6v9duTpLvn+d8P380LFYT5Ra8AAAAeSCr25Blwo4dO2TSpEnmOzKDa55b1zzf30/On/PP5/OHPQJZA7QA8ZNPPon0UAC5hmueW9c8399Pzp/zz+fzhz0CGQAAQMgIZAAAACEjkFn0Gjv55JPr7T0Gf3HNc+ua5/v7yflz/vl8/rBHL0sAAICQUUIGAAAQMgIZAABAyAhkAAAAISOQAQAAhIzJtRrw5JNPyqOPPmomL+/Zs6ecc845stdee4V9WDlh2bJlMnfuXDNo4qZNm+TSSy+VYcOGJZ7X/iY6cfxzzz0nZWVl0r9/fxk3bpx06dIl1OPOZnPmzJFFixbJmjVrpKSkRPr27Ss//vGPpWvXrol1ysvLZcaMGfLqq69KRUWFDBkyxFz30tJSp33my2cojGsbVQ8//LDMnDlTjjvuODn77LPz5ty/+uoruf/++2XJkiWya9cu6dy5s0yYMEH23HNP8zy/01AfSsjqob849BeIdlmeMmWKuZlMnjxZtmzZEvah5QT9hdWrVy8599xzPZ9/5JFH5IknnpDx48fLjTfeKM2aNTPXX3+xwz0Ejx492lzHq666SqqqquS3v/2t7Ny5M7HO9OnT5c0335RLLrlErr/+ehOWp06d6rS/fPoMZfraRtVHH30kzzzzjHmva8v1c9+2bZtcffXVZhLxK664Qm655RY588wzpWXLlol1+J2GeumwF/D261//uubuu+9OPK6qqqo577zzaubMmRPqceWiU045pea1115LPK6urq4ZP358zSOPPJJYVlZWVnPGGWfUzJ8/P6SjzD1btmwx1/69995LXOPTTz+9ZsGCBYl1Vq9ebdZZvnx5o7efz5+hoK9tFO3YsaPmggsuqHn77bdrrr322pp77703b879/vvvr7n66qvrfJ7faWgIJWR1qKyslJUrV8qgQYMSywoKCszjFStWhHps+WD9+vWmimvw4MGJZbvttpup6uL6+2f79u3me6tWrcx3/ZnXkp3aP/fdunWTDh06NPq65/tnKMhrG1V33323DB06NOlzmy/n/sYbb0ifPn3kj3/8o6mG/NWvfiXPPvts4nl+p6EhBLI6bN26Vaqrq9PaN+hj/VAhWPFr3LZt26Tl+pjr7w/9+b7vvvukX79+sscee5hlem21yqV2NYvrdc/nz1DQ1zaKXnnlFdMe9Iwzzkh7LtfPPR64tKpW241deeWVcswxx8i9994rL774onme32loCIEMyFPTpk2Tzz//XC666KKwDyXn5Nu13bhxowmgF1xwgenQkI80hPfu3dsEUv0+atQoOeqoo0xIA2zQy7IObdq0MdUrqX+56ONc6hUUVfFrrI2/27Vrl1iuj7UjAJoeGBYvXmwaV+++++5J112rGrUHWO3SDL3ujf25z9fPUCaubdRolaSex6RJk5ICyvvvv2962WqJUa6ee5z+nurevXvSMn382muvmf/zOw0NoYSsDlq8ru0B3n333aRfMPpYu7MjWJ06dTK/wJYuXZrUJkd7cHH93Wm3ew0MOjzDNddcY65zbfozX1hYmHTd165da0pAGnvd8+0zlMlrGzXaNuzmm2+Wm266KfGlQz0ceuihif/n6rnHafW0nlNt+rhjx47m//xOQ0MoIavHmDFj5Pbbbze/SLXh5bx588xQDSNHjgz70HKCDgewbt26pDYYq1atMo2gtbGvjmE0e/ZsM0aP/jL75z//af6yPOigg0I97mymgWH+/PmmwXGLFi0SpVfauFirmvT7kUceaYaq0PdBH99zzz3mhuFy08inz1Cmr22U6PnG28rF6ZAOrVu3TizP1XOPO/74482wF/o7a/jw4SZo6Xhj5513nnk+FovxOw31imlXy/pXyW9a3K6Dl+ovVy1W/ulPfyp777132IeVE9577z1TrZPq8MMPl4kTJyYGUdSeSvqXpA6iqGOW1R5oE41z6qmnei7XwSvjISk+gKc20tZqJj8Ghs2Hz1AY1zbKrrvuOvN+pw4Mm8vnruOs6YC4+oemBi4NadqWLI7faagPgQwAACBktCEDAAAIGYEMAAAgZAQyAACAkBHIAAAAQkYgAwAACBmBDAAAIGQEMgAAgJARyAAAAEJGIAMAAAgZgQxZ45FHHpGLLrrITFDtl6efflp+8YtfSEVFhW/bBHL5M+OKzxpQPwIZsoLO+6Y3l5NOOkkKCvz7sdU5BnVevWeeeca3bQK5/JlRGvB0Dkbdvi0+a0D9CGTICi+88IJUVVXJiBEjfN1uSUmJmcz88ccfNxP/ArkiqM+M+uijj+Trr7+W/fff3/o1fNaA+hHIkBVefPFFOfDAA80vdb8NHz5cNmzYIO+9957v2wZy8TPz1ltvSceOHaVHjx6Neh2fNaBuRfU8B6RZuXKlzJkzR95//33ZsWOH+aV86KGHysknnxzYPtevXy+ffvqpHH/88UnLH3zwQZk1a5bceuut5vubb74pRUVFcvTRR8tpp50mX375pdxzzz3ml7/elE488UQ54YQT0rbfp08fadWqlbz++usycODAwM4D+eOGG24wP7e33XZb2nOXX365qUK88cYbs/Yzs3jxYhk6dGjisf4ueOCBB8xnaNOmTbLbbrtJz5495Uc/+pH5fMXxWQPqRiCDtUWLFplf5N/5znfML+rmzZubqovly5cHut/49mv/Yq9Nj6lbt27ml7/eKGbPnm1+6T/77LPml74uf/nll+Vvf/ub7LnnnrLvvvumbaN3796Bnwfyh5YcaajRBuzFxcVJnyH9o+aqq67K2s/M5s2bZdWqVSbAxd11112ycOFCOfbYY6V79+6mOvODDz6QNWvWpB0DnzXAG4EMVtauXWv+2tc2IxdccEGiGuSYY44JvNeU/lJXnTp18nx+r732kvPOO8/8f9SoUTJx4kRzI/nhD38o3/ve98xybUfzs5/9zLSr8QpkGjL//e9/B3oeyB8aSrTh+xdffCF77LGHWabtpv71r3+Zn7/Bgwdn7WdGA5yGzNolXLrsqKOOkjPPPDOxTDsTeOGzBnijDRmsaFWH/hKeMGFCWpuU2iUAQdi2bZsUFhaaEjkvRx55ZOL/WhWkf5Hrza/28pYtW0rXrl1NVY4Xfb68vFx27doVwBkg38TbVukfMnELFiww1Yinn3564PsP8jOj7ccGDBiQ9HtA19XS8q+++qrBY+OzBngjkKFBWgKmbU0OO+ww0zakLvoL/Sc/+Yls2bIlo8fXoUOHpMd6jBoS27Rpk7Zcb1Re4r2+YrFYgEeKfAtk8ZIqLS3T0rEhQ4ZI//79Q/28NOUzo8NWvPPOO2m9K7WK87PPPjPjjP361782f8D95z//8dw3nzXAG4EMDdJfrPrXbF3tUeL0L+lmzZpJ27ZtG72P+gau1LYt2n1fGw578RpjqbHjLpWVlZljD6JHGvKPBpl27dolAtn8+fPN/2uXjjXl8xLWZ0bbhek2azfoj/ee/POf/yznnHOOOe9HH31ULrnkElOalorPGuCNNmRokFYvNPQX7erVq2XSpEnmJqF/9WsbmsmTJ8u8efPMQJBbt26Vgw46yLRb0V5dulwbPWu1iv7S1l/eqb/k47TxcfwGpj23gqDbju8H8KuUTKss9TPx0EMPmZ9/bSBf1+fld7/7nXkc5c+MthXTY/Vqm6ZBbPTo0eZLS/30/LSzQOox8lkDvFFChgZ17tzZfNeqilRahaH0l7QOfaHd57VxsN5ctNpCqzqvvfZa+ctf/mJ+EWsDYaVtabSnlfbKmj59ugwaNKjO/fft29d8//jjjwM6Q5FPPvkksR/AD/qZ0ECm44GtW7cuqVei1+dFRf0zo0EwtbpSQ6TOClCblvppQIv/fqiNzxrgjRIyWFW/6LQnemPRX7Da42rnzp3mr3X961dvEPEbRrznlY5FpH/R/9///Z+UlpYmqjW0y7/S9iZ6Q4r33qqvukR7ZWlpw9KlS5MaHftFj0nbyWhpBOAX/ZnVz8nMmTPNz368t2Vc7c9LNnxmNBxqtev48eOTlmsV5s9//nM5+OCDTWmcdiTQ/WoYrN3rUvFZA+pGIIMVnbeuffv2pqeYDuqobVT0r9z99tsvsY7eMOIDUeovZO0MoBMb1/5LWksE9LtW2QwbNsx6/0cccYQpPdDqU7/bnug5aSNnBqpEEA37NYCceuqpac/X/rxkw2dGqyv1j7N+/folLdf2YFpN+fbbb5tx1vRYtVR93LhxZlic2visAfWoAXxQUVFRc/rpp9fs2LHDPH788cdr7rjjDs9116xZUzNu3LhGbb+srKzmpz/9ac1zzz1X46fy8vKa8ePHm+MFwvq8qKh/Zm688caaqVOnOr+ezxpQP9qQwRfx3lzxNiO9evWSJUuWJHqZ6cjd+jheMqDPN4b+Za6zA8ydO7fe3mWNpe1ztJG0lkIAYX1esuEzo1WlY8aMcX49nzWgfjFNZQ2sA1jRbu9aZaFVNdrD8uGHH5annnrKVNlomxitvtB58bQaRYfR0N5lQL5K/bwoPjNA/iKQAQAAhIwqSwAAgJARyAAAAEJGIAMAAAgZgQwAACBkBDIAAICQEcgAAABCRiADAAAIGYEMAAAgZAQyAACAkBHIAAAAQkYgAwAAkHD9f+EQt3L9wm4SAAAAAElFTkSuQmCC", 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", 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" + "
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", 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", 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" ] diff --git a/src/surmise/calibrationmethods/directbayes.py b/src/surmise/calibrationmethods/directbayes.py index d2971d0c..f11077d0 100755 --- a/src/surmise/calibrationmethods/directbayes.py +++ b/src/surmise/calibrationmethods/directbayes.py @@ -1,6 +1,7 @@ import numpy as np import copy +from .._RandomNumberGenerator import RandomNumberGenerator from ..create_sampler import create_sampler @@ -77,6 +78,8 @@ def fit(fitinfo, emu, x, y, **sampler_args): A dictionary containing options passed to the calibrator. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG + thetaprior = fitinfo['thetaprior'] # Define the posterior function @@ -117,7 +120,7 @@ def draw_func(n): sampler = create_sampler(sampler_name, sampler_args) results = sampler(logpost_func=logpostfull, draw_func=draw_func, - scipy_stats_rng=np.random.default_rng()) + scipy_stats_rng=global_RNG) theta = results["theta"] # Update fitinfo dict diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index 34bda384..45d59562 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -3,6 +3,7 @@ import numpy as np import scipy.stats as sps +from .._RandomNumberGenerator import RandomNumberGenerator from ..create_sampler import create_sampler @@ -71,6 +72,7 @@ def fit(fitinfo, emu, x, y, **sampler_args): None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG thetaprior = fitinfo['thetaprior'] try: @@ -157,7 +159,7 @@ def draw_func(n): sampler = create_sampler(sampler_name, sampler_args) results = sampler(logpost_func=logpostfull_wgrad, draw_func=draw_func, - scipy_stats_rng=np.random.default_rng()) + scipy_stats_rng=global_RNG) theta = results["theta"] # obtain log-posterior of theta values diff --git a/src/surmise/calibrationmethods/mlbayeswoodbury.py b/src/surmise/calibrationmethods/mlbayeswoodbury.py index 08b8b0da..8477758c 100644 --- a/src/surmise/calibrationmethods/mlbayeswoodbury.py +++ b/src/surmise/calibrationmethods/mlbayeswoodbury.py @@ -2,6 +2,7 @@ import scipy.stats as sps import copy +from .._RandomNumberGenerator import RandomNumberGenerator from ..create_sampler import create_sampler @@ -76,6 +77,8 @@ def fit(fitinfo, None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG + if clf_method is not None: raise NotImplementedError( "CLF method use is not under test nor officially offered yet" @@ -194,7 +197,7 @@ def draw_func(n): sampler = create_sampler(sampler_name, sampler_args) results = sampler(logpost_func=logpostfull_wgrad, draw_func=draw_func, - scipy_stats_rng=np.random.default_rng()) + scipy_stats_rng=global_RNG) theta = results["theta"] # obtain log-posterior of theta values diff --git a/src/surmise/calibrationmethods/simulationpost.py b/src/surmise/calibrationmethods/simulationpost.py index 75db60a6..f43bedd6 100644 --- a/src/surmise/calibrationmethods/simulationpost.py +++ b/src/surmise/calibrationmethods/simulationpost.py @@ -2,6 +2,7 @@ import scipy.stats as sps import copy +from .._RandomNumberGenerator import RandomNumberGenerator from ..create_sampler import create_sampler @@ -70,6 +71,7 @@ def fit(fitinfo, emu, x, y, **sampler_args): None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG thetaprior = fitinfo['thetaprior'] theta = thetaprior.rnd(10) @@ -151,7 +153,7 @@ def draw_func(n): sampler = create_sampler(sampler_name, sampler_args) results = sampler(logpost_func=logpostfull_wgrad, draw_func=draw_func, - scipy_stats_rng=np.random.default_rng()) + scipy_stats_rng=global_RNG) theta = results["theta"] # obtain log-posterior of theta values From c104928c495d16b6463a7c639ffe609416d27046 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 12:34:23 -0500 Subject: [PATCH 56/84] We have PTLMC benchmarks. No need to view test results. --- tools/PTLMCTestSuite.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tools/PTLMCTestSuite.json b/tools/PTLMCTestSuite.json index faf54cd4..759c24f1 100644 --- a/tools/PTLMCTestSuite.json +++ b/tools/PTLMCTestSuite.json @@ -22,7 +22,7 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, + "Plot": false, "Benchmark": "PTLMC_Uniform1D_UniformStart.benchmark", "n_samples": 32000 } @@ -54,7 +54,7 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, + "Plot": false, "CornerPlotBins": 30, "Benchmark": "PTLMC_Uniform2D_NormalStart.benchmark", "n_samples": 32000 @@ -85,7 +85,7 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, + "Plot": false, "Benchmark": "PTLMC_Normal1D_UniformStart.benchmark", "n_samples": 32000 } @@ -118,7 +118,7 @@ "maxtemp": 30 }, "SampleSkip": 1, - "Plot": true, + "Plot": false, "CornerPlotBins": 30, "Benchmark": "PTLMC_Normal2D_NormalStart.benchmark", "n_samples": 32000 From 378bbfed67a23d6c1aaf868c962541eea3f96a38 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 12:37:35 -0500 Subject: [PATCH 57/84] (Issue #188) Get spellcheck action passing. --- .github/config/.wordlist.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.github/config/.wordlist.txt b/.github/config/.wordlist.txt index 6b673211..1d9f2da2 100644 --- a/.github/config/.wordlist.txt +++ b/.github/config/.wordlist.txt @@ -35,6 +35,8 @@ PrAyvH PyPI Quickstart README +RNG +RNGs SDK SURmise SciPy @@ -92,15 +94,19 @@ namespace nan nb nbviewer +np numpy ozgesurer pdf png +pseudorandom py pypa pypi pyproject pytest +randbits +rng qj quickstart reStructuredText From ae9867d25a3bd3acd8823419f96a69de06904395 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 12:43:51 -0500 Subject: [PATCH 58/84] (Issue #188) Add set_RNG api docs. --- docs/random_number_generation.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst index a64b3220..be9df0d3 100644 --- a/docs/random_number_generation.rst +++ b/docs/random_number_generation.rst @@ -15,6 +15,11 @@ well as correctly created and managed for their application. Note that where possible all |surmise| code should reproduce the same results when the same task is run with an identical RNG setup. +Users are expected to create and manage ``scipy.stats``-compatible RNGs that +they set into |surmise| using the :py:func:`set_RNG` function. + +.. autofunction:: surmise.set_RNG + The following demonstrates this and shows that users are free to change the single RNG being used by |surmise|. From 93d3c177b056f4727fde754e052c8321b7e2ae02 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Fri, 10 Jul 2026 12:49:50 -0500 Subject: [PATCH 59/84] (Issue #188) Fixing typos. --- .github/config/.wordlist.txt | 5 +++++ docs/rng_dev_guide.rst | 6 +++--- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/.github/config/.wordlist.txt b/.github/config/.wordlist.txt index 1d9f2da2..64a7f27b 100644 --- a/.github/config/.wordlist.txt +++ b/.github/config/.wordlist.txt @@ -36,7 +36,9 @@ PyPI Quickstart README RNG +RNG's RNGs +RandomNumberGenerator SDK SURmise SciPy @@ -58,6 +60,7 @@ calibrationmethods chan colab cov +cryptographically currentmodule danOSU dataset @@ -112,6 +115,7 @@ quickstart reStructuredText readthedocs runtime +rvs scikit scipy setuptools @@ -131,6 +135,7 @@ vah venv visualstudio whl +wikipedia wordpress wp www diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index fec84a24..3a1dabc7 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -54,11 +54,11 @@ geared toward libraries. High-level ^^^^^^^^^^ To adhere to the |surmise| RNG requirements related to easing implementation and -maintainance by using only one, large statistics package with RNG capabilities, +maintenance by using only one, large statistics package with RNG capabilities, this design stipulates that all sampling of random numbers in |surmise| occur using either the -* ``scipy.stats`` package (preferrably using improved interface introduced at +* ``scipy.stats`` package (preferably using improved interface introduced at v1.15.0) or * ``scipy.stats`` RNG currently in use (|eg| using the RNG's ``choice`` method). @@ -93,7 +93,7 @@ an RNG object. The consistent use of just `scipy.stats` is indeed important since the interfaces of both `scipy.stats` and `numpy.random` have been changing - signficantly recently. Presently, it appears that we could allow for + significantly recently. Presently, it appears that we could allow for simultaneous use of both packages since both use [`numpy.random` RNGs](https://docs.scipy.org/doc/scipy/tutorial/stats/probability_distributions.html#random-number-generation). However, restricting use to just `scipy.stats` does indeed reduce the From 25405ea8ed12b03eef2ab233cae6d3d116fc10bd Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 15:32:48 -0500 Subject: [PATCH 60/84] minimal structure to clean up test suite for reusing shared values, in addition, to unset an RNG --- src/surmise/_RandomNumberGenerator.py | 5 ++ src/surmise/tests/conftest.py | 75 +++++++++++++++++ src/surmise/tests/shared_scenario.py | 113 ++++++++++++++++++++++++++ src/surmise/tests/test_cal_rng.py | 39 +++++++++ 4 files changed, 232 insertions(+) create mode 100644 src/surmise/tests/conftest.py create mode 100644 src/surmise/tests/shared_scenario.py create mode 100644 src/surmise/tests/test_cal_rng.py diff --git a/src/surmise/_RandomNumberGenerator.py b/src/surmise/_RandomNumberGenerator.py index 6cc2ea96..b5ab9335 100644 --- a/src/surmise/_RandomNumberGenerator.py +++ b/src/surmise/_RandomNumberGenerator.py @@ -81,3 +81,8 @@ def scipy_stats_RNG(self, rng): raise RuntimeError("Given RNG does not provide the choice function") self.__rng = rng + + def _clear_RNG(self): + """Testing support only. Returns singleton to its unset state. This is not intended for user manipulation + of the RNG.""" + self.__rng = None \ No newline at end of file diff --git a/src/surmise/tests/conftest.py b/src/surmise/tests/conftest.py new file mode 100644 index 00000000..aee11971 --- /dev/null +++ b/src/surmise/tests/conftest.py @@ -0,0 +1,75 @@ +"""Shared fixtures for src/surmise/tests. + +Variables are fixed during collection, defined in scenarios.py; +fixtures here are built once instead of at import time +in every test. +""" + +from contextlib import contextmanager + +import numpy as np +import pytest + +from surmise.emulation import emulator +from surmise.calibration import calibrator + +from . import shared_scenario as sc +from .._RandomNumberGenerator import RandomNumberGenerator + + +# RNG helpers +@pytest.fixture +def no_rng(): + """For tests asserting the must-set-first error.""" + RandomNumberGenerator()._clear_RNG() + yield + + +@contextmanager +def does_not_raise(): + """For parametrized expectations.""" + yield + + +@pytest.fixture(scope="session") +def lin_data(): + """linear model data""" + return (sc.x_lin, sc.xv_lin, sc.theta_lin, sc.f_lin, + sc.y_lin, sc.obsvar_lin) + + +@pytest.fixture(scope="session") +def emu_lin_pcgp(): + """PCGP emulator fit to the linear scenario.""" + return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, + method='PCGP') + + +@pytest.fixture(scope="session") +def emu_lin_pcgpwm(): + return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, + method='PCGPwM') + + +@pytest.fixture(scope="session") +def timedrop_data(): + """(x_std, theta, f, y, obsvar) for the timedrop scenario.""" + theta = sc.prior_balldrop.rnd(50) + f = sc.timedrop(sc.x_std, theta, sc.x_range, sc.theta_range) + return sc.x_std, theta, f, sc.y_td, sc.obsvar_td + + +@pytest.fixture(scope="session") +def emu_timedrop(timedrop_data): + x_std, theta, f, _, _ = timedrop_data + return emulator(x=x_std, theta=theta, f=f, method='PCGP') + + +@pytest.fixture(scope="session") +def cal_directbayes(emu_timedrop, timedrop_data): + """Fitted directbayes calibrator.""" + x_std, _, _, y, obsvar = timedrop_data + return calibrator(emu=emu_timedrop, y=y, x=x_std, + thetaprior=sc.prior_balldrop, + method='directbayes', + yvar=obsvar) \ No newline at end of file diff --git a/src/surmise/tests/shared_scenario.py b/src/surmise/tests/shared_scenario.py new file mode 100644 index 00000000..415de3fb --- /dev/null +++ b/src/surmise/tests/shared_scenario.py @@ -0,0 +1,113 @@ +"""Shared test scenarios for surmise tests. + +Importable at collection time so arrays can be used inside +@pytest.mark.parametrize. +""" +import numpy as np +import scipy.stats as sps + +_rng = np.random.default_rng(111848137687551512331846058163015350939) + + +# ---------------------------------------------------------------------- +# Scenario A: linear ball-drop (used by 14 emu/cal test files) +# ---------------------------------------------------------------------- +def balldropmodel_linear(x, theta): + f = np.zeros((theta.shape[0], x.shape[0])) + for k in range(0, theta.shape[0]): + t = x[:, 0] + h0 = x[:, 1] + theta[k, 0] + vter = theta[k, 1] + f[k, :] = h0 - vter * t + return f.T + + +def balldroptrue(x): + def logcosh(x): + s = np.sign(x) * x + p = np.exp(-2 * s) + return s + np.log1p(p) - np.log(2) + + t = x[:, 0] + h0 = x[:, 1] + vter = 20 + g = 9.81 + return h0 - (vter ** 2) / g * logcosh(g * t / vter) + + +class priorphys_lin: + """Prior class for the linear ball-drop scenario.""" + + def lpdf(theta): + return (sps.norm.logpdf(theta[:, 0], 0, 5) + + sps.gamma.logpdf(theta[:, 1], 2, 0, 10) + ).reshape((len(theta), 1)) + + def rnd(n): + return np.vstack(( + sps.norm.rvs(0, 5, size=n, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=n, random_state=_rng))).T + + +x_lin = np.array( + [[0.1, 25.], [0.2, 25.], [0.3, 25.], [0.4, 25.], [0.5, 25.], + [0.6, 25.], [0.7, 25.], [0.9, 25.], [1.1, 25.], [1.3, 25.], + [2.0, 25.], [2.4, 25.], + [0.1, 50.], [0.2, 50.], [0.3, 50.], [0.4, 50.], [0.5, 50.], + [0.6, 50.], [0.7, 50.], [0.8, 50.], [0.9, 50.], [1.0, 50.], + [1.2, 50.], [2.6, 50.], [2.9, 50.], [3.1, 50.], [3.3, 50.], + [3.5, 50.], [3.7, 50.]]).astype('object') +xv_lin = x_lin.astype('float') + +theta_lin = priorphys_lin.rnd(50) +f_lin = balldropmodel_linear(xv_lin, theta_lin) +y_lin = balldroptrue(xv_lin) +obsvar_lin = 4 * np.ones(x_lin.shape[0]) + + +# ---------------------------------------------------------------------- +# Scenario B: timedrop (test_cal_directbayes, test_cal_saveload, +# test_new_cal) +# ---------------------------------------------------------------------- +def timedrop(x, theta, hr, gr): + """Computer implementation of the mathematical model.""" + min_g = min(gr) + range_g = max(gr) - min(gr) + min_h = min(hr) + range_h = max(hr) - min_h + f = np.zeros((theta.shape[0], x.shape[0])) + for k in range(0, theta.shape[0]): + g = range_g * theta[k] + min_g + h = range_h * x + min_h + f[k, :] = np.sqrt(2 * h / g).reshape(x.shape[0]) + return f.T + + +class prior_balldrop: + """Prior class for the timedrop scenario.""" + + def lpdf(theta): + return sps.uniform.logpdf(theta[:, 0], 0, 1 + ).reshape((len(theta), 1)) + + def rnd(n): + return np.vstack((sps.uniform.rvs(0, 1, size=n, + random_state=_rng))) + + +x_td = np.array([[0.178, 0.356, 0.534, 0.712, 0.89, 1.068, 1.246, + 1.424, 1.602, 1.78, 1.958, 2.67, 2.848, 3.026, + 3.204, 3.382, 3.56, 3.738, 3.916, 4.094, 4.272]]).T +y_td = np.array([[0.27, 0.22, 0.27, 0.43, 0.41, 0.49, 0.46, 0.6, + 0.65, 0.62, 0.7, 0.81, 0.69, 0.81, 0.89, 0.86, + 0.89, 1.1, 1.05, 0.99, 1.05]]).T +obsvar_td = np.maximum(0.2 * y_td, 0.1) + +theta_range = np.array([1, 30]) +x_range = np.array([min(x_td), max(x_td)]) +x_std = (x_td - min(x_td)) / (max(x_td) - min(x_td)) + + +def set_RNG_in_tests(rng): + import surmise + surmise.set_RNG(rng) diff --git a/src/surmise/tests/test_cal_rng.py b/src/surmise/tests/test_cal_rng.py new file mode 100644 index 00000000..3b8ccfa2 --- /dev/null +++ b/src/surmise/tests/test_cal_rng.py @@ -0,0 +1,39 @@ +############################################## +# Simple scenarios # +############################################## +import numpy as np +import pytest +from surmise.calibration import calibrator +############################################## +# Simple scenarios # +############################################## + +# ..note: +# pytest collects all tests (and run portions of variable declarations) before deselecting tests. +# As a result a .set_RNG routine elsewhere will propagate into other tests even if not intended. +# The use of fixture `no_rng` is to enforce the error raised from an unset RNG + +from .conftest import no_rng, emu_lin_pcgp +from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin, _rng + + +############################################## +# Unit tests to initialize an emulator class # +############################################## +args = {'theta0': np.array([[0.4]]), + 'numsamp': 20, + 'stepType': 'normal', + 'stepParam': [0.4]} + + +def test_cal_rng_notset(no_rng, emu_lin_pcgp): + with pytest.raises(RuntimeError): + cal = calibrator(emu=emu_lin_pcgp, + y=y, + x=x, + thetaprior=priorphys_lin, + method='directbayes', + yvar=obsvar, + args=args) + From 9a3c4e740cae9553e5d452ff0b62cffdb985080c Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 15:49:42 -0500 Subject: [PATCH 61/84] flake8 --- src/surmise/_RandomNumberGenerator.py | 2 +- src/surmise/tests/conftest.py | 3 +-- src/surmise/tests/test_cal_rng.py | 27 +++++++++++++-------------- 3 files changed, 15 insertions(+), 17 deletions(-) diff --git a/src/surmise/_RandomNumberGenerator.py b/src/surmise/_RandomNumberGenerator.py index b5ab9335..fded9cd7 100644 --- a/src/surmise/_RandomNumberGenerator.py +++ b/src/surmise/_RandomNumberGenerator.py @@ -85,4 +85,4 @@ def scipy_stats_RNG(self, rng): def _clear_RNG(self): """Testing support only. Returns singleton to its unset state. This is not intended for user manipulation of the RNG.""" - self.__rng = None \ No newline at end of file + self.__rng = None diff --git a/src/surmise/tests/conftest.py b/src/surmise/tests/conftest.py index aee11971..fdb0717f 100644 --- a/src/surmise/tests/conftest.py +++ b/src/surmise/tests/conftest.py @@ -7,7 +7,6 @@ from contextlib import contextmanager -import numpy as np import pytest from surmise.emulation import emulator @@ -72,4 +71,4 @@ def cal_directbayes(emu_timedrop, timedrop_data): return calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=sc.prior_balldrop, method='directbayes', - yvar=obsvar) \ No newline at end of file + yvar=obsvar) diff --git a/src/surmise/tests/test_cal_rng.py b/src/surmise/tests/test_cal_rng.py index 3b8ccfa2..4e104a34 100644 --- a/src/surmise/tests/test_cal_rng.py +++ b/src/surmise/tests/test_cal_rng.py @@ -13,27 +13,26 @@ # As a result a .set_RNG routine elsewhere will propagate into other tests even if not intended. # The use of fixture `no_rng` is to enforce the error raised from an unset RNG -from .conftest import no_rng, emu_lin_pcgp -from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin, _rng +# from .conftest import no_rng, emu_lin_pcgp +from .shared_scenario import x_lin as x, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin ############################################## # Unit tests to initialize an emulator class # ############################################## args = {'theta0': np.array([[0.4]]), - 'numsamp': 20, - 'stepType': 'normal', - 'stepParam': [0.4]} + 'numsamp': 20, + 'stepType': 'normal', + 'stepParam': [0.4]} def test_cal_rng_notset(no_rng, emu_lin_pcgp): with pytest.raises(RuntimeError): - cal = calibrator(emu=emu_lin_pcgp, - y=y, - x=x, - thetaprior=priorphys_lin, - method='directbayes', - yvar=obsvar, - args=args) - + _ = calibrator(emu=emu_lin_pcgp, + y=y, + x=x, + thetaprior=priorphys_lin, + method='directbayes', + yvar=obsvar, + args=args) From be8d5223057e5ab81fe7e6603feebb85d57a0385 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 16:59:09 -0500 Subject: [PATCH 62/84] remove .benchmark files from logging --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index e852f08e..0a8bd6d9 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ *__pycache__ *.pyc *.pyd +*.benchmark .ipynb_checkpoints/ surmise_applications/ build/ From 6ae5e0ec8efcf84c3bc3a81035329c7e0278a7d1 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 17:00:29 -0500 Subject: [PATCH 63/84] For CAL tests: consolidate shared data using shared_scenario and conftest --- src/surmise/tests/conftest.py | 11 +- src/surmise/tests/shared_scenario.py | 2 + src/surmise/tests/test_cal_directbayes.py | 110 ++++-------- .../tests/test_cal_directbayeswoodbury.py | 90 +--------- src/surmise/tests/test_cal_rng.py | 5 +- src/surmise/tests/test_cal_samplers.py | 160 +++++------------- src/surmise/tests/test_cal_saveload.py | 60 +------ src/surmise/tests/test_new_cal.py | 81 ++------- 8 files changed, 111 insertions(+), 408 deletions(-) diff --git a/src/surmise/tests/conftest.py b/src/surmise/tests/conftest.py index fdb0717f..fdff18f5 100644 --- a/src/surmise/tests/conftest.py +++ b/src/surmise/tests/conftest.py @@ -14,16 +14,25 @@ from . import shared_scenario as sc from .._RandomNumberGenerator import RandomNumberGenerator +import surmise # RNG helpers -@pytest.fixture +@pytest.fixture(scope="module") def no_rng(): """For tests asserting the must-set-first error.""" RandomNumberGenerator()._clear_RNG() yield +@pytest.fixture(scope="module") +def seeded_rng(): + """Set the package RNG once for an entire test module.""" + surmise.set_RNG(sc._rng) + yield sc._rng + RandomNumberGenerator()._clear_RNG() + + @contextmanager def does_not_raise(): """For parametrized expectations.""" diff --git a/src/surmise/tests/shared_scenario.py b/src/surmise/tests/shared_scenario.py index 415de3fb..6ef76631 100644 --- a/src/surmise/tests/shared_scenario.py +++ b/src/surmise/tests/shared_scenario.py @@ -103,6 +103,8 @@ def rnd(n): 0.89, 1.1, 1.05, 0.99, 1.05]]).T obsvar_td = np.maximum(0.2 * y_td, 0.1) +n = 100 +theta_ball = prior_balldrop.rnd(n).reshape(n, 1) theta_range = np.array([1, 30]) x_range = np.array([min(x_td), max(x_td)]) x_std = (x_td - min(x_td)) / (max(x_td) - min(x_td)) diff --git a/src/surmise/tests/test_cal_directbayes.py b/src/surmise/tests/test_cal_directbayes.py index af21acb5..64030f64 100644 --- a/src/surmise/tests/test_cal_directbayes.py +++ b/src/surmise/tests/test_cal_directbayes.py @@ -1,62 +1,19 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import y_td as y, obsvar_td as obsvar, \ + x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop +# Use set RNG in this entire test module +pytestmark = pytest.mark.usefixtures('seeded_rng') METHOD_IN_TEST = 'directbayes' ############################################## # Simple scenarios # ############################################## - -# height -x = np.array([[0.178, 0.356, 0.534, 0.712, 0.89, 1.068, 1.246, 1.424, 1.602, - 1.78, 1.958, 2.67, 2.848, 3.026, 3.204, 3.382, 3.56, 3.738, - 3.916, 4.094, 4.272]]).T - -# time -y = np.array([[0.27, 0.22, 0.27, 0.43, 0.41, 0.49, 0.46, 0.6, 0.65, 0.62, 0.7, - 0.81, 0.69, 0.81, 0.89, 0.86, 0.89, 1.1, 1.05, 0.99, 1.05]]).T -obsvar = np.maximum(0.2 * y, 0.1) - - -def timedrop(x, theta, hr, gr): - '''Computer implementation of the mathematical model''' - # Assume x and theta are within (0, 1) - min_g = min(gr) - range_g = max(gr) - min(gr) - min_h = min(hr) - range_h = max(hr) - min_h - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - g = range_g * theta[k] + min_g - h = range_h * x + min_h - f[k, :] = np.sqrt(2 * h / g).reshape(x.shape[0]) - return f.T - - -class prior_balldrop: - """ This defines the class instance of priors provided to the method. """ - - def lpdf(theta): - return sps.uniform.logpdf(theta[:, 0], 0, 1).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.uniform.rvs(0, 1, size=n))) - - -# Draw 100 random parameters from uniform prior -n = 50 -theta = prior_balldrop.rnd(n) -theta_range = np.array([1, 30]) - -# Standardize -x_range = np.array([min(x), max(x)]) -x_std = (x - min(x)) / (max(x) - min(x)) - # Obtain computer model output via filtered data f = timedrop(x_std, theta, x_range, theta_range) @@ -87,26 +44,21 @@ def rnd(n): args6 = {'sampler': 'metropolis_hastings'} -@contextmanager -def does_not_raise(): - yield - - @pytest.mark.parametrize( - "input1,input2,expectation", + "input2,expectation", [ # (emulator_f_1, args1, does_not_raise()), # (emulator_f_2, args1, does_not_raise()), - (emulator_f_1, args2, does_not_raise()), - (emulator_f_1, args3, does_not_raise()), - (emulator_f_1, args4, does_not_raise()), - (emulator_f_1, args5, does_not_raise()), - (emulator_f_1, args6, does_not_raise()), + (args2, does_not_raise()), + (args3, does_not_raise()), + (args4, does_not_raise()), + (args5, does_not_raise()), + (args6, does_not_raise()), ], ) -def test_cal_MLcal(input1, input2, expectation): +def test_cal_MLcal(emu_timedrop, input2, expectation): with expectation: - assert calibrator(emu=input1, + assert calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -122,8 +74,8 @@ def test_cal_MLcal(input1, input2, expectation): (None, does_not_raise()), ], ) -def test_cal_predict(input1, expectation): - cal_test = calibrator(emu=emulator_f_1, +def test_cal_predict(emu_timedrop, input1, expectation): + cal_test = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -140,8 +92,8 @@ def test_cal_predict(input1, expectation): (does_not_raise()), ], ) -def test_repr(expectation): - cal = calibrator(emu=emulator_f_1, +def test_repr(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -159,8 +111,8 @@ def test_repr(expectation): (does_not_raise()), ], ) -def test_call(expectation): - cal = calibrator(emu=emulator_f_1, +def test_call(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -178,8 +130,8 @@ def test_call(expectation): (does_not_raise()), ], ) -def test_meanvar(expectation): - cal = calibrator(emu=emulator_f_1, +def test_meanvar(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -198,8 +150,8 @@ def test_meanvar(expectation): (does_not_raise()), ], ) -def test_thetalpdf(expectation): - cal = calibrator(emu=emulator_f_1, +def test_thetalpdf(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -217,8 +169,8 @@ def test_thetalpdf(expectation): (does_not_raise()), ], ) -def test_pred(expectation): - cal = calibrator(emu=emulator_f_1, +def test_pred(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -237,8 +189,8 @@ def test_pred(expectation): (does_not_raise()), ], ) -def test_theta_meanvar(expectation): - cal = calibrator(emu=emulator_f_1, +def test_theta_meanvar(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -257,8 +209,8 @@ def test_theta_meanvar(expectation): (does_not_raise()), ], ) -def test_cal_repr(expectation): - cal = calibrator(emu=emulator_f_1, +def test_cal_repr(emu_timedrop, expectation): + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -270,9 +222,9 @@ def test_cal_repr(expectation): assert repr(cal.theta()) is not None -def test_cal_noobsvar(): +def test_cal_noobsvar(emu_timedrop): with pytest.raises(ValueError): - calibrator(emu=emulator_f_1, + calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, diff --git a/src/surmise/tests/test_cal_directbayeswoodbury.py b/src/surmise/tests/test_cal_directbayeswoodbury.py index 03e06bae..2c53e6f8 100644 --- a/src/surmise/tests/test_cal_directbayeswoodbury.py +++ b/src/surmise/tests/test_cal_directbayeswoodbury.py @@ -1,10 +1,11 @@ -import numpy as np -import scipy.stats as sps -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator import pytest +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin +pytestmark = pytest.mark.usefixtures('seeded_rng') METHOD_IN_TEST = 'directbayeswoodbury' @@ -12,96 +13,15 @@ # Simple scenarios # ############################################## - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta_lin = priorphys_lin.rnd(50) -f_lin = balldropmodel_linear(xv, theta_lin) - - -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4*np.ones(x.shape[0]) -y = balldroptrue(xv) emulator_1 = emulator(x=x, theta=theta_lin, f=f_lin, method='PCGPwM') emulator_w_grad = emulator(x=x, theta=theta_lin, f=f_lin, method='PCGPwM', args={'return_grad': True}) + ############################################## # Unit tests to initialize an emulator class # ############################################## - - -@contextmanager -def does_not_raise(): - yield - - # test to check none-type inputs @pytest.mark.parametrize( "input1,expectation", diff --git a/src/surmise/tests/test_cal_rng.py b/src/surmise/tests/test_cal_rng.py index 4e104a34..64a72cfd 100644 --- a/src/surmise/tests/test_cal_rng.py +++ b/src/surmise/tests/test_cal_rng.py @@ -13,10 +13,11 @@ # As a result a .set_RNG routine elsewhere will propagate into other tests even if not intended. # The use of fixture `no_rng` is to enforce the error raised from an unset RNG -# from .conftest import no_rng, emu_lin_pcgp from .shared_scenario import x_lin as x, y_lin as y, \ obsvar_lin as obsvar, priorphys_lin +# Unset RNG +pytestmark = pytest.mark.usefixtures('no_rng') ############################################## # Unit tests to initialize an emulator class # @@ -27,7 +28,7 @@ 'stepParam': [0.4]} -def test_cal_rng_notset(no_rng, emu_lin_pcgp): +def test_cal_rng_notset(emu_lin_pcgp): with pytest.raises(RuntimeError): _ = calibrator(emu=emu_lin_pcgp, y=y, diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index 45e9ecfa..39712e24 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -1,10 +1,14 @@ import numpy as np import scipy.stats as sps import pytest -from contextlib import contextmanager -from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin, _rng + +pytestmark = pytest.mark.usefixtures('seeded_rng') + # TODO: LMC will require 'expertMode' in lmc_option to run SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] # , 'LMC'] @@ -12,84 +16,6 @@ # Simple scenarios # ############################################## - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta_lin = priorphys_lin.rnd(50) -f_lin = balldropmodel_linear(xv, theta_lin) - - -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4 * np.ones(x.shape[0]) -y = balldroptrue(xv) -emu_test = emulator(x=x, theta=theta_lin, f=f_lin, method='PCGP') - # Additional examples y1 = y[0:3] @@ -100,13 +26,10 @@ def logcosh(x): # 2-d x (30 x 2), 2-d theta (50 x 2), f1 (15 x 50) f1 = f_lin[0:15, :] - # 2-d x (30 x 2), 2-d theta (50 x 2), f2 (30 x 25) f2 = f_lin[:, 0:25] - # 2-d x (30 x 2), 2-d theta1 (25 x 2), f (30 x 50) theta1 = theta_lin[0:25, :] - # 2-d x1 (15 x 2), 2-d theta (50 x 2), f (30 x 50) x1 = x[0:15, :] @@ -122,8 +45,8 @@ def lpdf(theta): sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T + return np.vstack((sps.norm.rvs(0, 5, size=n, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=n, random_state=_rng))).T class prior_rnd1: @@ -144,14 +67,14 @@ def lpdf(theta): return np.array([1, 2, 3]) def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T + return np.vstack((sps.norm.rvs(0, 5, size=n, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=n, random_state=_rng))).T class prior_lpdf2: def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T + return np.vstack((sps.norm.rvs(0, 5, size=n, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=n, random_state=_rng))).T # Some additional args @@ -192,12 +115,6 @@ def rnd(n): # Unit tests to initialize an emulator class # ############################################## - -@contextmanager -def does_not_raise(): - yield - - # Generate all test pairs accordingly upfront SAMPLER_ARGS_PAIRS = [ pytest.param(sampler, args, id=f"{sampler}-args{i}") @@ -207,11 +124,11 @@ def does_not_raise(): @pytest.mark.parametrize("sampler,args", SAMPLER_ARGS_PAIRS) -def test_cal_MLcal(sampler, args): +def test_cal_MLcal(sampler, args, emu_lin_pcgp): args_tmp = args.copy() args_tmp['sampler'] = sampler with does_not_raise(): - assert calibrator(emu=emu_test, + assert calibrator(emu=emu_lin_pcgp, y=y, x=x, thetaprior=priorphys_lin, @@ -224,28 +141,29 @@ def test_cal_MLcal(sampler, args): class TestSampler: @pytest.mark.parametrize( - "input1,input2,input3,input4,input5,expectation", + "use_emu,input2,input3,input4,input5,expectation", [ - (emu_test, y, x1, priorphys_lin, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, priorphys_lin, obsvar1, pytest.raises(ValueError)), - (emu_test, y, x, priorphys_lin, obsvar2, pytest.raises(ValueError)), - (emu_test, y, x, priorphys_lin, obsvar3, pytest.raises(ValueError)), - (emu_test, y, x, prior_rnd1, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, prior_rnd2, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, prior_lpdf1, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, prior_lpdf2, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, prior_example1, obsvar, pytest.raises(ValueError)), - (emu_test, y1, x, priorphys_lin, obsvar, pytest.raises(ValueError)), - (emu_test, None, x, priorphys_lin, obsvar, pytest.raises(ValueError)), - (None, y, x, priorphys_lin, obsvar, pytest.raises(ValueError)), - (emu_test, y, x, None, obsvar, pytest.raises(ValueError)), + (True, y, x1, priorphys_lin, obsvar, pytest.raises(ValueError)), + (True, y, x, priorphys_lin, obsvar1, pytest.raises(ValueError)), + (True, y, x, priorphys_lin, obsvar2, pytest.raises(ValueError)), + (True, y, x, priorphys_lin, obsvar3, pytest.raises(ValueError)), + (True, y, x, prior_rnd1, obsvar, pytest.raises(ValueError)), + (True, y, x, prior_rnd2, obsvar, pytest.raises(ValueError)), + (True, y, x, prior_lpdf1, obsvar, pytest.raises(ValueError)), + (True, y, x, prior_lpdf2, obsvar, pytest.raises(ValueError)), + (True, y, x, prior_example1, obsvar, pytest.raises(ValueError)), + (True, y1, x, priorphys_lin, obsvar, pytest.raises(ValueError)), + (True, None, x, priorphys_lin, obsvar, pytest.raises(ValueError)), + (False, y, x, priorphys_lin, obsvar, pytest.raises(ValueError)), + (True, y, x, None, obsvar, pytest.raises(ValueError)), ], ) - def test_cal_emu_fails(self, sampler, input1, input2, input3, input4, input5, expectation): + def test_cal_emu_fails(self, sampler, emu_lin_pcgp, use_emu, input2, input3, input4, input5, expectation): args_tmp = args_dict[sampler][0].copy() + emu = emu_lin_pcgp if use_emu else None with expectation: args_tmp['sampler'] = sampler - assert calibrator(emu=input1, + assert calibrator(emu=emu, y=input2, x=input3, thetaprior=input4, @@ -254,16 +172,16 @@ def test_cal_emu_fails(self, sampler, input1, input2, input3, input4, input5, ex args=args_tmp) is not None @pytest.mark.parametrize( - "input1,input2,input3,input4,input5", + "input2,input3,input4,input5", [ - (emu_test, y, x, priorphys_lin, obsvar) + (y, x, priorphys_lin, obsvar) ] ) - def test_cal_emu(self, sampler, input1, input2, input3, input4, input5): + def test_cal_emu(self, sampler, emu_lin_pcgp, input2, input3, input4, input5): args_tmp = args_dict[sampler][0].copy() with does_not_raise(): args_tmp['sampler'] = sampler - assert calibrator(emu=input1, + assert calibrator(emu=emu_lin_pcgp, y=input2, x=input3, thetaprior=input4, @@ -277,9 +195,9 @@ def test_cal_emu(self, sampler, input1, input2, input3, input4, input5): (y, x, priorphys_lin, 'XXXX', obsvar, pytest.raises(ValueError)), ], ) - def test_cal_method1(self, sampler, input2, input3, input4, input5, input6, expectation): + def test_cal_method1(self, sampler, emu_lin_pcgp, input2, input3, input4, input5, input6, expectation): with expectation: - assert calibrator(emu=emu_test, + assert calibrator(emu=emu_lin_pcgp, y=input2, x=input3, thetaprior=input4, @@ -287,10 +205,10 @@ def test_cal_method1(self, sampler, input2, input3, input4, input5, input6, expe yvar=input6, args={'sampler': sampler}) is not None - def test_repr(self, sampler): + def test_repr(self, sampler, emu_lin_pcgp): args_tmp = args_dict[sampler][0].copy() args_tmp['sampler'] = sampler - cal = calibrator(emu=emu_test, + cal = calibrator(emu=emu_lin_pcgp, y=y, x=x, thetaprior=priorphys_lin, diff --git a/src/surmise/tests/test_cal_saveload.py b/src/surmise/tests/test_cal_saveload.py index 793ea1ec..93605529 100644 --- a/src/surmise/tests/test_cal_saveload.py +++ b/src/surmise/tests/test_cal_saveload.py @@ -4,59 +4,18 @@ # Simple scenarios # ############################################## import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import y_td as y, obsvar_td as obsvar, \ + x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - -# height -x = np.array([[0.178, 0.356, 0.534, 0.712, 0.89, 1.068, 1.246, 1.424, 1.602, - 1.78, 1.958, 2.67, 2.848, 3.026, 3.204, 3.382, 3.56, 3.738, - 3.916, 4.094, 4.272]]).T - -# time -y = np.array([[0.27, 0.22, 0.27, 0.43, 0.41, 0.49, 0.46, 0.6, 0.65, 0.62, 0.7, - 0.81, 0.69, 0.81, 0.89, 0.86, 0.89, 1.1, 1.05, 0.99, 1.05]]).T -obsvar = np.maximum(0.2*y, 0.1) - - -def timedrop(x, theta, hr, gr): - '''Computer implementation of the mathematical model''' - # Assume x and theta are within (0, 1) - min_g = min(gr) - range_g = max(gr) - min(gr) - min_h = min(hr) - range_h = max(hr) - min_h - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - g = range_g*theta[k] + min_g - h = range_h*x + min_h - f[k, :] = np.sqrt(2*h/g).reshape(x.shape[0]) - return f.T - - -class prior_balldrop: - """ This defines the class instance of priors provided to the method. """ - def lpdf(theta): - return sps.uniform.logpdf(theta[:, 0], 0, 1).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.uniform.rvs(0, 1, size=n))) - - -# Draw 100 random parameters from uniform prior -n = 50 -theta = prior_balldrop.rnd(n) -theta_range = np.array([1, 30]) - -# Standardize -x_range = np.array([min(x), max(x)]) -x_std = (x - min(x))/(max(x) - min(x)) - # Obtain computer model output via filtered data f = timedrop(x_std, theta, x_range, theta_range) @@ -74,8 +33,6 @@ def rnd(n): # Fit an emulator via filtered data emulator_f_1 = emulator(x=x_std, theta=theta_f, f=f_f, method='PCGP') -# emulator_f_2 = emulator(x=x_std, theta=theta_f, f=f_f, method='PCGP') - ############################################## # Unit tests to initialize an emulator class # @@ -86,11 +43,6 @@ def rnd(n): 'stepParam': [0.4]} -@contextmanager -def does_not_raise(): - yield - - @pytest.mark.parametrize( "load_cal_flag, expectation", [ diff --git a/src/surmise/tests/test_new_cal.py b/src/surmise/tests/test_new_cal.py index 8c49e8f3..7de8ad2e 100644 --- a/src/surmise/tests/test_new_cal.py +++ b/src/surmise/tests/test_new_cal.py @@ -1,67 +1,17 @@ -import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import x_td as x, y_td as y, obsvar_td as obsvar, \ + x_std, theta_ball, x_range, theta_range, prior_balldrop, timedrop + +pytestmark = pytest.mark.usefixtures('seeded_rng') ############################################## # Simple scenarios # ############################################## - -# height -x = np.array([[0.178, 0.356, 0.534, 0.712, 0.89, 1.068, 1.246, 1.424, 1.602, - 1.78, 1.958, 2.67, 2.848, 3.026, 3.204, 3.382, 3.56, 3.738, - 3.916, 4.094, 4.272]]).T - -# time -y = np.array([[0.27, 0.22, 0.27, 0.43, 0.41, 0.49, 0.46, 0.6, 0.65, 0.62, 0.7, - 0.81, 0.69, 0.81, 0.89, 0.86, 0.89, 1.1, 1.05, 0.99, 1.05]]).T -obsvar = np.maximum(0.2 * y, 0.1) - - -# Computer implementation of the mathematical model -def timedrop(x, theta, hr, gr): - # Assume x and theta are within (0, 1) - min_g = min(gr) - range_g = max(gr) - min(gr) - min_h = min(hr) - range_h = max(hr) - min_h - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - g = range_g * theta[k] + min_g - h = range_h * x + min_h - f[k, :] = np.sqrt(2 * h / g).reshape(x.shape[0]) - return f.T - - -# Define prior -class prior_balldrop: - """ This defines the class instance of priors provided to the method. """ - - def lpdf(theta): - return (sps.uniform.logpdf(theta[:, 0], 0, 1)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.uniform.rvs(0, 1, size=n))) - - -# Draw 100 random parameters from uniform prior -n = 100 -theta = prior_balldrop.rnd(n).reshape(n, 1) -theta_range = np.array([1, 30]) - -# Standardize -x_range = np.array([min(x), max(x)]) -x_std = (x - min(x)) / (max(x) - min(x)) - # Obtain computer model output via filtered data -f = timedrop(x_std, theta, x_range, theta_range) - - -@contextmanager -def does_not_raise(): - yield +f = timedrop(x_std, theta_ball, x_range, theta_range) @pytest.mark.parametrize( @@ -69,13 +19,12 @@ def does_not_raise(): [ ('directbayes', does_not_raise()), ('directbayeswoodbury', does_not_raise()), - # ('mlbayeswoodbury', does_not_raise()) ], ) # tests for prediction class methods: # test to check the prediction.mean() def test_prediction_mean(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -97,7 +46,7 @@ def test_prediction_mean(cmdopt2, expectation): ) # test to check the prediction.var() def test_prediction_var(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -119,7 +68,7 @@ def test_prediction_var(cmdopt2, expectation): ) # test to check the prediction.rnd() def test_prediction_rnd(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -141,7 +90,7 @@ def test_prediction_rnd(cmdopt2, expectation): ) # test to check the prediction.lpdf() def test_prediction_lpdf(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -163,7 +112,7 @@ def test_prediction_lpdf(cmdopt2, expectation): ) # test to check the theta.mean() def test_prediction_thetamean(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -184,7 +133,7 @@ def test_prediction_thetamean(cmdopt2, expectation): ) # test to check the theta.var() def test_prediction_thetavar(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -205,7 +154,7 @@ def test_prediction_thetavar(cmdopt2, expectation): ) # test to check the theta.rnd() def test_prediction_thetarnd(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -226,7 +175,7 @@ def test_prediction_thetarnd(cmdopt2, expectation): ) # test to check the theta.lpdf() def test_prediction_thetalpdf(cmdopt2, expectation): - emu = emulator(x=x, theta=theta, f=f, method='PCGPwM') + emu = emulator(x=x, theta=theta_ball, f=f, method='PCGPwM') cal = calibrator(emu=emu, y=y, x=x, @@ -234,4 +183,4 @@ def test_prediction_thetalpdf(cmdopt2, expectation): method=cmdopt2, yvar=obsvar) with expectation: - cal.theta.lpdf(theta=theta) + cal.theta.lpdf(theta=theta_ball) From 70c6acdea17dc572b4b32ee0260f9db05961b316 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 17:02:41 -0500 Subject: [PATCH 64/84] setting RNG usage in calibration methods --- src/surmise/calibration.py | 16 ++++++++++++---- src/surmise/calibrationmethods/directbayes.py | 9 ++++++--- .../calibrationmethods/directbayeswoodbury.py | 9 ++++++--- .../calibrationmethods/mlbayeswoodbury.py | 9 ++++++--- src/surmise/calibrationmethods/simulationpost.py | 9 ++++++--- 5 files changed, 36 insertions(+), 16 deletions(-) diff --git a/src/surmise/calibration.py b/src/surmise/calibration.py index fa8038f9..ee9182cc 100644 --- a/src/surmise/calibration.py +++ b/src/surmise/calibration.py @@ -3,6 +3,7 @@ """ import numpy as np from .helper import cast_f64_dtype, save_file, load_file +from ._RandomNumberGenerator import RandomNumberGenerator import importlib import copy import warnings @@ -119,6 +120,10 @@ def rnd(n): msg = f"Using unofficial research {method} calibrator" warnings.warn(msg) + # Confirm RNG is set + global_RNG = RandomNumberGenerator().scipy_stats_RNG + assert isinstance(global_RNG, np.random.Generator) + # cast to numpy.float64, currently only for theta and f. if y is not None: y = cast_f64_dtype(y) @@ -427,6 +432,7 @@ def rnd(self, s=100, args=None): """ Returns s random draws at all x in when building the prediction. """ + global_RNG = RandomNumberGenerator().scipy_stats_RNG pfstr = 'predict' # prefix string opstr = 'rnd' # operation string @@ -436,8 +442,9 @@ def rnd(self, s=100, args=None): return copy.deepcopy(self.cal.method.predictrnd(self.info, args)) elif 'rnd' in self.info.keys(): - return self.info['rnd'][np.random.choice(self.info['rnd'].shape[0], - size=s), :] + + return self.info['rnd'][global_RNG.choice(self.info['rnd'].shape[0], + size=s), :] else: raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) @@ -567,6 +574,7 @@ def rnd(self, s=1000, args=None): """ Returns s predictive draws for theta found during calibration. """ + global_RNG = RandomNumberGenerator().scipy_stats_RNG pfstr = 'theta' # prefix string opstr = 'rnd' # operation string @@ -577,8 +585,8 @@ def rnd(self, s=1000, args=None): args)) elif (pfstr+opstr) in self.cal.info.keys(): return self.cal.info['thetarnd'][ - np.random.choice(self.cal.info['thetarnd'].shape[0], - size=s), :] + global_RNG.choice(self.cal.info['thetarnd'].shape[0], + size=s), :] else: raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) diff --git a/src/surmise/calibrationmethods/directbayes.py b/src/surmise/calibrationmethods/directbayes.py index f11077d0..eba5c02d 100755 --- a/src/surmise/calibrationmethods/directbayes.py +++ b/src/surmise/calibrationmethods/directbayes.py @@ -1,4 +1,5 @@ import numpy as np +import scipy.stats as sps import copy from .._RandomNumberGenerator import RandomNumberGenerator @@ -103,7 +104,8 @@ def draw_func(n): if n0 < n: theta0 = np.vstack((thetaprior.rnd(n - n0), theta0)) else: - theta0 = theta0[np.random.randint(theta0.shape[0], size=n), :] + theta0 = theta0[sps.randint.rvs(low=0, high=theta0.shape[0], + size=n, random_state=global_RNG), :] return theta0 @@ -149,9 +151,10 @@ def thetarnd(fitinfo, s=100, args=None): s draws from the predictive distribution of theta. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG - return fitinfo['thetarnd'][np.random.choice(fitinfo['thetarnd'].shape[0], - size=s), :] + return fitinfo['thetarnd'][global_RNG.choice(fitinfo['thetarnd'].shape[0], + size=s), :] def loglik(fitinfo, emu, theta, y, x): diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index 45d59562..45d9a53a 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -146,7 +146,8 @@ def draw_func(n): if n0 < n: theta0 = np.vstack((thetaprior.rnd(n-n0), theta0)) else: - theta0 = theta0[np.random.randint(theta0.shape[0], size=n), :] + theta0 = theta0[sps.randint.rvs(low=0, high=theta0.shape[0], + size=n, random_state=global_RNG), :] return theta0 @@ -244,8 +245,10 @@ def thetarnd(fitinfo, s=100, args=None): s draws from the predictive distribution of theta. ''' - return fitinfo['thetarnd'][np.random.choice(fitinfo['thetarnd'].shape[0], - size=s), :] + global_RNG = RandomNumberGenerator().scipy_stats_RNG + + return fitinfo['thetarnd'][global_RNG.choice(fitinfo['thetarnd'].shape[0], + size=s), :] def thetalpdf(fitinfo, theta, args=None): diff --git a/src/surmise/calibrationmethods/mlbayeswoodbury.py b/src/surmise/calibrationmethods/mlbayeswoodbury.py index 8477758c..08cc10ae 100644 --- a/src/surmise/calibrationmethods/mlbayeswoodbury.py +++ b/src/surmise/calibrationmethods/mlbayeswoodbury.py @@ -184,7 +184,8 @@ def draw_func(n): if n0 < n: theta0 = np.vstack((thetaprior.rnd(n-n0), theta0)) else: - theta0 = theta0[np.random.randint(theta0.shape[0], size=n), :] + theta0 = theta0[sps.randint.rvs(low=0, high=theta0.shape[0], + size=n, random_state=global_RNG), :] return theta0 @@ -283,8 +284,10 @@ def thetarnd(fitinfo, s=100, args=None): s draws from the predictive distribution of theta. ''' - return fitinfo['thetarnd'][np.random.choice(fitinfo['thetarnd'].shape[0], - size=s), :] + global_RNG = RandomNumberGenerator().scipy_stats_RNG + + return fitinfo['thetarnd'][global_RNG.choice(fitinfo['thetarnd'].shape[0], + size=s), :] def thetalpdf(fitinfo, theta, args=None): diff --git a/src/surmise/calibrationmethods/simulationpost.py b/src/surmise/calibrationmethods/simulationpost.py index f43bedd6..40b758bc 100644 --- a/src/surmise/calibrationmethods/simulationpost.py +++ b/src/surmise/calibrationmethods/simulationpost.py @@ -141,7 +141,8 @@ def draw_func(n): if n0 < n: theta0 = np.vstack((thetaprior.rnd(n-n0), theta0)) else: - theta0 = theta0[np.random.randint(theta0.shape[0], size=n), :] + theta0 = theta0[sps.randint.rvs(low=0, high=theta0.shape[0], + size=n, random_state=global_RNG), :] return theta0 @@ -238,5 +239,7 @@ def thetarnd(fitinfo, s=100, args=None): s draws from the predictive distribution of theta. ''' - return fitinfo['thetarnd'][np.random.choice(fitinfo['thetarnd'].shape[0], - size=s), :] + global_RNG = RandomNumberGenerator().scipy_stats_RNG + + return fitinfo['thetarnd'][global_RNG.choice(fitinfo['thetarnd'].shape[0], + size=s), :] From 6d7c12f9b0f15644d0ae7789b65b87f71ccf1522 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 21:29:07 -0500 Subject: [PATCH 65/84] cal is within the names of test functions in two emulation test modules --- src/surmise/tests/test_emu_remove.py | 89 ++--------------------- src/surmise/tests/test_emu_supplement.py | 93 ++---------------------- 2 files changed, 11 insertions(+), 171 deletions(-) diff --git a/src/surmise/tests/test_emu_remove.py b/src/surmise/tests/test_emu_remove.py index ade2727e..b109d7ce 100644 --- a/src/surmise/tests/test_emu_remove.py +++ b/src/surmise/tests/test_emu_remove.py @@ -1,100 +1,23 @@ -import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) theta1 = theta[0:25, :] -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4*np.ones(x.shape[0]) -y = balldroptrue(xv) - ####################################################### # Unit tests for remove method of emulator class # ####################################################### - - -@contextmanager -def does_not_raise(): - yield - - # test to check remove @pytest.mark.parametrize( "input1,expectation", diff --git a/src/surmise/tests/test_emu_supplement.py b/src/surmise/tests/test_emu_supplement.py index 57dad299..a488b7f2 100644 --- a/src/surmise/tests/test_emu_supplement.py +++ b/src/surmise/tests/test_emu_supplement.py @@ -1,74 +1,17 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator from surmise.calibration import calibrator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin + +pytestmark = pytest.mark.usefixtures('seeded_rng') ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -79,35 +22,9 @@ def rnd(n): thetacomb = np.vstack((theta1, thetarnd)) -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4 * np.ones(x.shape[0]) -y = balldroptrue(xv) - - ####################################################### # Unit tests for supplement() method of emulator class # ####################################################### - - -@contextmanager -def does_not_raise(): - yield - - # test to check supplement_x @pytest.mark.parametrize( "input1,input2,input3,expectation", From 10c1c4631d579d4b109b806ba485f5956816ba10 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 23:04:36 -0500 Subject: [PATCH 66/84] include RNG tests to test for reproducibility result with PCGP and directbayes method --- src/surmise/tests/conftest.py | 40 ++++++++--- src/surmise/tests/shared_scenario.py | 18 ++--- src/surmise/tests/test_cal_directbayes.py | 5 +- .../tests/test_cal_directbayeswoodbury.py | 5 +- src/surmise/tests/test_cal_rng.py | 39 ----------- src/surmise/tests/test_cal_samplers.py | 3 +- src/surmise/tests/test_emu_init.py | 69 ++---------------- src/surmise/tests/test_rng.py | 70 +++++++++++++++++++ 8 files changed, 125 insertions(+), 124 deletions(-) delete mode 100644 src/surmise/tests/test_cal_rng.py create mode 100644 src/surmise/tests/test_rng.py diff --git a/src/surmise/tests/conftest.py b/src/surmise/tests/conftest.py index fdff18f5..a3af7487 100644 --- a/src/surmise/tests/conftest.py +++ b/src/surmise/tests/conftest.py @@ -4,7 +4,7 @@ fixtures here are built once instead of at import time in every test. """ - +import numpy as np from contextlib import contextmanager import pytest @@ -14,11 +14,31 @@ from . import shared_scenario as sc from .._RandomNumberGenerator import RandomNumberGenerator -import surmise +from surmise import set_RNG + + +@pytest.fixture(scope="session") +def _session_rng(): + set_RNG(np.random.default_rng(sc.RNG_SEED)) + yield + + +@pytest.fixture(autouse=True) +def _isolate_rng_state(): + singleton = RandomNumberGenerator() + try: + saved = singleton.scipy_stats_RNG + except RuntimeError: + saved = None + yield + if saved is None: + singleton._clear_RNG() + else: + singleton.scipy_stats_RNG = saved # RNG helpers -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def no_rng(): """For tests asserting the must-set-first error.""" RandomNumberGenerator()._clear_RNG() @@ -28,7 +48,7 @@ def no_rng(): @pytest.fixture(scope="module") def seeded_rng(): """Set the package RNG once for an entire test module.""" - surmise.set_RNG(sc._rng) + set_RNG(sc._rng) yield sc._rng RandomNumberGenerator()._clear_RNG() @@ -40,27 +60,27 @@ def does_not_raise(): @pytest.fixture(scope="session") -def lin_data(): +def lin_data(_session_rng): """linear model data""" return (sc.x_lin, sc.xv_lin, sc.theta_lin, sc.f_lin, sc.y_lin, sc.obsvar_lin) @pytest.fixture(scope="session") -def emu_lin_pcgp(): +def emu_lin_pcgp(_session_rng): """PCGP emulator fit to the linear scenario.""" return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, method='PCGP') @pytest.fixture(scope="session") -def emu_lin_pcgpwm(): +def emu_lin_pcgpwm(_session_rng): return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, method='PCGPwM') @pytest.fixture(scope="session") -def timedrop_data(): +def timedrop_data(_session_rng): """(x_std, theta, f, y, obsvar) for the timedrop scenario.""" theta = sc.prior_balldrop.rnd(50) f = sc.timedrop(sc.x_std, theta, sc.x_range, sc.theta_range) @@ -68,13 +88,13 @@ def timedrop_data(): @pytest.fixture(scope="session") -def emu_timedrop(timedrop_data): +def emu_timedrop(_session_rng, timedrop_data): x_std, theta, f, _, _ = timedrop_data return emulator(x=x_std, theta=theta, f=f, method='PCGP') @pytest.fixture(scope="session") -def cal_directbayes(emu_timedrop, timedrop_data): +def cal_directbayes(_session_rng, emu_timedrop, timedrop_data): """Fitted directbayes calibrator.""" x_std, _, _, y, obsvar = timedrop_data return calibrator(emu=emu_timedrop, y=y, x=x_std, diff --git a/src/surmise/tests/shared_scenario.py b/src/surmise/tests/shared_scenario.py index 6ef76631..4aee5e6e 100644 --- a/src/surmise/tests/shared_scenario.py +++ b/src/surmise/tests/shared_scenario.py @@ -5,8 +5,11 @@ """ import numpy as np import scipy.stats as sps +from .._RandomNumberGenerator import RandomNumberGenerator -_rng = np.random.default_rng(111848137687551512331846058163015350939) +RNG_SEED = 111848137687551512331846058163015350393 +# local data generator, surmise RNG does not advance. +_datagen = np.random.default_rng(111848137687551512331846058163015350939) # ---------------------------------------------------------------------- @@ -44,6 +47,7 @@ def lpdf(theta): ).reshape((len(theta), 1)) def rnd(n): + _rng = RandomNumberGenerator().scipy_stats_RNG return np.vstack(( sps.norm.rvs(0, 5, size=n, random_state=_rng), sps.gamma.rvs(2, 0, 10, size=n, random_state=_rng))).T @@ -58,8 +62,8 @@ def rnd(n): [1.2, 50.], [2.6, 50.], [2.9, 50.], [3.1, 50.], [3.3, 50.], [3.5, 50.], [3.7, 50.]]).astype('object') xv_lin = x_lin.astype('float') - -theta_lin = priorphys_lin.rnd(50) +theta_lin = np.vstack((sps.norm.rvs(0, 5, size=50, random_state=_datagen), + sps.gamma.rvs(2, 0, 10, size=50, random_state=_datagen))).T f_lin = balldropmodel_linear(xv_lin, theta_lin) y_lin = balldroptrue(xv_lin) obsvar_lin = 4 * np.ones(x_lin.shape[0]) @@ -91,6 +95,7 @@ def lpdf(theta): ).reshape((len(theta), 1)) def rnd(n): + _rng = RandomNumberGenerator().scipy_stats_RNG return np.vstack((sps.uniform.rvs(0, 1, size=n, random_state=_rng))) @@ -104,12 +109,7 @@ def rnd(n): obsvar_td = np.maximum(0.2 * y_td, 0.1) n = 100 -theta_ball = prior_balldrop.rnd(n).reshape(n, 1) +theta_ball = np.vstack((sps.uniform.rvs(0, 1, size=n, random_state=_datagen))).reshape(n, 1) theta_range = np.array([1, 30]) x_range = np.array([min(x_td), max(x_td)]) x_std = (x_td - min(x_td)) / (max(x_td) - min(x_td)) - - -def set_RNG_in_tests(rng): - import surmise - surmise.set_RNG(rng) diff --git a/src/surmise/tests/test_cal_directbayes.py b/src/surmise/tests/test_cal_directbayes.py index 64030f64..d0ab598d 100644 --- a/src/surmise/tests/test_cal_directbayes.py +++ b/src/surmise/tests/test_cal_directbayes.py @@ -4,7 +4,10 @@ from surmise.calibration import calibrator from .conftest import does_not_raise from .shared_scenario import y_td as y, obsvar_td as obsvar, \ - x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop + x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop, RNG_SEED +# TODO: TEMPORARY FIX +import surmise +surmise.set_RNG(np.random.default_rng(RNG_SEED)) # Use set RNG in this entire test module pytestmark = pytest.mark.usefixtures('seeded_rng') diff --git a/src/surmise/tests/test_cal_directbayeswoodbury.py b/src/surmise/tests/test_cal_directbayeswoodbury.py index 2c53e6f8..5e37eccf 100644 --- a/src/surmise/tests/test_cal_directbayeswoodbury.py +++ b/src/surmise/tests/test_cal_directbayeswoodbury.py @@ -1,10 +1,13 @@ +import numpy as np from surmise.emulation import emulator from surmise.calibration import calibrator import pytest from .conftest import does_not_raise from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin + obsvar_lin as obsvar, priorphys_lin, RNG_SEED + +_rng = np.random.default_rng(RNG_SEED) pytestmark = pytest.mark.usefixtures('seeded_rng') METHOD_IN_TEST = 'directbayeswoodbury' diff --git a/src/surmise/tests/test_cal_rng.py b/src/surmise/tests/test_cal_rng.py deleted file mode 100644 index 64a72cfd..00000000 --- a/src/surmise/tests/test_cal_rng.py +++ /dev/null @@ -1,39 +0,0 @@ -############################################## -# Simple scenarios # -############################################## -import numpy as np -import pytest -from surmise.calibration import calibrator -############################################## -# Simple scenarios # -############################################## - -# ..note: -# pytest collects all tests (and run portions of variable declarations) before deselecting tests. -# As a result a .set_RNG routine elsewhere will propagate into other tests even if not intended. -# The use of fixture `no_rng` is to enforce the error raised from an unset RNG - -from .shared_scenario import x_lin as x, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin - -# Unset RNG -pytestmark = pytest.mark.usefixtures('no_rng') - -############################################## -# Unit tests to initialize an emulator class # -############################################## -args = {'theta0': np.array([[0.4]]), - 'numsamp': 20, - 'stepType': 'normal', - 'stepParam': [0.4]} - - -def test_cal_rng_notset(emu_lin_pcgp): - with pytest.raises(RuntimeError): - _ = calibrator(emu=emu_lin_pcgp, - y=y, - x=x, - thetaprior=priorphys_lin, - method='directbayes', - yvar=obsvar, - args=args) diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index 39712e24..9f82bd7c 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -5,8 +5,9 @@ from .conftest import does_not_raise from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin, _rng + obsvar_lin as obsvar, priorphys_lin, RNG_SEED +_rng = np.random.default_rng(RNG_SEED) pytestmark = pytest.mark.usefixtures('seeded_rng') # TODO: LMC will require 'expertMode' in lmc_option to run diff --git a/src/surmise/tests/test_emu_init.py b/src/surmise/tests/test_emu_init.py index 54eda6db..438bde97 100644 --- a/src/surmise/tests/test_emu_init.py +++ b/src/surmise/tests/test_emu_init.py @@ -1,68 +1,16 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -71,16 +19,11 @@ def rnd(n): theta0d = np.array(1) x0d = np.array(1) + ############################################## # Unit tests to initialize an emulator class # ############################################## - -@contextmanager -def does_not_raise(): - yield - - # Followings are the tests to check the input configurations # test to check none-type inputs @pytest.mark.parametrize( diff --git a/src/surmise/tests/test_rng.py b/src/surmise/tests/test_rng.py new file mode 100644 index 00000000..af8448ef --- /dev/null +++ b/src/surmise/tests/test_rng.py @@ -0,0 +1,70 @@ +import numpy as np +import pytest + +from surmise import set_RNG +from surmise.emulation import emulator +from surmise.calibration import calibrator + +from . import shared_scenario as sc + + +def test_emulator_requires_rng(no_rng): + with pytest.raises(RuntimeError, match="set_RNG"): + emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, + method='PCGP') + + +def test_calibrator_requires_rng(emu_lin_pcgp, no_rng): + # emu_lin_pcgp is built under _session_rng (session scope, so it is + # constructed before function-scoped no_rng clears the singleton). + with pytest.raises(RuntimeError, match="set_RNG"): + calibrator(emu=emu_lin_pcgp, + y=sc.y_lin, + x=sc.x_lin, + thetaprior=sc.priorphys_lin, + method='directbayes', + yvar=sc.obsvar_lin) + + +def test_calibrator_methods_raise_after_clear(cal_directbayes, no_rng): + """A fitted calibrator must not have stored an RNG reference.""" + with pytest.raises(RuntimeError, match="set_RNG"): + cal_directbayes.theta.rnd(10) + + +def test_bisect_reproducibility(): + # (a) prior draws alone — expected to FAIL with current scenarios.py + set_RNG(np.random.default_rng(123)) + d1 = sc.priorphys_lin.rnd(50) + set_RNG(np.random.default_rng(123)) + d2 = sc.priorphys_lin.rnd(50) + assert np.allclose(d1, d2) + + +# Test to reproduce results +def _build_and_draw(seed): + """Seed the whole sequence, fit emu + cal, return posterior draws.""" + set_RNG(np.random.default_rng(seed)) + emu = emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, + method='PCGP') + cal = calibrator(emu=emu, + y=sc.y_lin, + x=sc.x_lin, + thetaprior=sc.priorphys_lin, + method='directbayes', + yvar=sc.obsvar_lin) + return cal.theta.rnd(10) + + +def test_emu_cal_reproducible(): + # same RNGs should return the same samples + draws1 = _build_and_draw(123) + draws2 = _build_and_draw(123) + assert np.allclose(draws1, draws2) + + +def test_emu_cal_seed_sensitivity(): + # different RNGs should return different samples + draws1 = _build_and_draw(123) + draws2 = _build_and_draw(456) + assert not np.allclose(draws1, draws2) From cb10545678908e10686765447c4391bf2f433220 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Mon, 13 Jul 2026 23:05:09 -0500 Subject: [PATCH 67/84] first step to fix RNGs in emulators --- src/surmise/emulation.py | 13 ++++++++++--- src/surmise/emulationmethods/PCGP.py | 5 ++++- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/src/surmise/emulation.py b/src/surmise/emulation.py index 9c0ecbba..7e079bff 100644 --- a/src/surmise/emulation.py +++ b/src/surmise/emulation.py @@ -3,6 +3,7 @@ """ import numpy as np from .helper import cast_f64_dtype, save_file, load_file +from ._RandomNumberGenerator import RandomNumberGenerator import importlib import copy import warnings @@ -92,6 +93,10 @@ def __init__(self, msg = f"Using unofficial research {method} emulator" warnings.warn(msg) + # ensures that RNG is set + global_RNG = RandomNumberGenerator().scipy_stats_RNG + assert isinstance(global_RNG, np.random.Generator) + # cast to numpy.float64, currently only for theta and f. if theta is not None: theta = cast_f64_dtype(theta) @@ -409,6 +414,8 @@ def supplement(self, ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG + if args is not None: argstemp = {**self._args, **args} else: @@ -453,9 +460,9 @@ def supplement(self, if thetachoices is None: if theta.shape[0] > 30 * size: thetachoices = \ - theta[np.random.choice(theta.shape[0], - 30 * size, - replace=False), :] + theta[global_RNG.choice(theta.shape[0], + 30 * size, + replace=False), :] else: thetachoices = copy.copy(theta) else: diff --git a/src/surmise/emulationmethods/PCGP.py b/src/surmise/emulationmethods/PCGP.py index 62cb644e..b0f6f5af 100755 --- a/src/surmise/emulationmethods/PCGP.py +++ b/src/surmise/emulationmethods/PCGP.py @@ -2,6 +2,7 @@ import numpy as np import scipy.optimize as spo from pprint import pformat +from .._RandomNumberGenerator import RandomNumberGenerator def fit(fitinfo, x, theta, f, epsilon=0.1, **kwargs): @@ -368,6 +369,8 @@ def emulation_fit(theta, pcaval): subinfo : dict Dictionary of the fitted emulator model. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG + subinfo = {} covhyp0 = np.log(np.std(theta, 0) * 3) + 1 @@ -380,7 +383,7 @@ def emulation_fit(theta, pcaval): # Get a random sample of thetas to find the optimized hyperparameters n_train = np.min((20 * theta.shape[1], theta.shape[0])) - idx = np.random.choice(theta.shape[0], n_train, replace=False) + idx = global_RNG.choice(theta.shape[0], n_train, replace=False) # Start constructing the returning dictionary if theta.ndim == 1: From 54c5a2adc52398144339fd9ad6d0c5246c8efb79 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 14 Jul 2026 19:49:25 -0500 Subject: [PATCH 68/84] adding testing commands, including a test collection command to catch any collection-time error --- tox.ini | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tox.ini b/tox.ini index 5658b1f0..6718586e 100644 --- a/tox.ini +++ b/tox.ini @@ -70,17 +70,28 @@ commands = python -m pytest ./src/surmise/tests -k "new_cal" [testenv:only_emu] description = Run new emulator tests usedevelop = true -commands = python -m pytest ./src/surmise/tests -k "emu" +commands = python -m pytest -x ./src/surmise/tests -k "emu" [testenv:only_cal] description = Run new calibrator tests usedevelop = true -commands = python -m pytest ./src/surmise/tests -k "cal" +commands = python -m pytest -x ./src/surmise/tests -k "cal" [testenv:only_sampler] description = Run sampler tests usedevelop = true -commands = python -m pytest ./src/surmise/tests -k "cal_samplers" +commands = python -m pytest -x ./src/surmise/tests -k "cal_samplers" + +[testenv:test_rng] +description = Run RNG related tests +usedevelop = true +commands = python -m pytest -x -s ./src/surmise/tests -k "rng" + +[testenv:test_collector] +description = Collect all tests without running +usedevelop = true +commands = python -m pytest --collect-only ./src/surmise/tests + [testenv:report] description = Generate coverage report as HTML From 8aef47060c2d4ac4f7857c7b1b4eb2a3058227be Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 14 Jul 2026 19:51:43 -0500 Subject: [PATCH 69/84] any emulator that calls .choice now uses the surmise RNG --- src/surmise/emulationmethods/PCGPwM.py | 5 ++++- src/surmise/emulationmethods/PCSK.py | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/src/surmise/emulationmethods/PCGPwM.py b/src/surmise/emulationmethods/PCGPwM.py index a8b680a6..d1cac271 100644 --- a/src/surmise/emulationmethods/PCGPwM.py +++ b/src/surmise/emulationmethods/PCGPwM.py @@ -7,6 +7,7 @@ import copy from surmise.emulationsupport.matern_covmat import covmat as __covmat from pprint import pformat +from .._RandomNumberGenerator import RandomNumberGenerator def fit(fitinfo, x, theta, f, epsilonPC=0.001, epsilonImpute=10e-6, @@ -725,6 +726,8 @@ def __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, varconstant): def __fitGP1d(theta, g, hyp1, hyp2, hypvarconst, gvar=None, dampalpha=None, eta=None, hypstarts=None, hypinds=None, sig2ofconst=None): """Return a fitted model from the emulator model using smart method.""" + global_RNG = RandomNumberGenerator().scipy_stats_RNG + hypvarconstmean = 4 if hypvarconst is None else hypvarconst hypvarconstLB = -8 if hypvarconst is None else hypvarconst - 0.5 hypvarconstUB = 8 if hypvarconst is None else hypvarconst + 0.5 @@ -743,7 +746,7 @@ def __fitGP1d(theta, g, hyp1, hyp2, hypvarconst, gvar=None, dampalpha=None, eta= subinfo['hyp'] = 1 * subinfo['hypregmean'] nhyptrain = np.max(np.min((20 * theta.shape[1], theta.shape[0]))) if theta.shape[0] > nhyptrain: - thetac = np.random.choice(theta.shape[0], nhyptrain, replace=False) + thetac = global_RNG.choice(theta.shape[0], nhyptrain, replace=False) else: thetac = range(0, theta.shape[0]) subinfo['theta'] = theta[thetac, :] diff --git a/src/surmise/emulationmethods/PCSK.py b/src/surmise/emulationmethods/PCSK.py index cc23f9ee..3f1b4cd0 100644 --- a/src/surmise/emulationmethods/PCSK.py +++ b/src/surmise/emulationmethods/PCSK.py @@ -5,6 +5,7 @@ import scipy.optimize as spo from surmise.emulationsupport.matern_covmat import covmat as __covmat from pprint import pformat +from .._RandomNumberGenerator import RandomNumberGenerator def fit(fitinfo, x, theta, f, epsilonPC=0.001, @@ -445,6 +446,8 @@ def __fitGPs(fitinfo, theta, numpcs, verbose): def __fitGP1d(theta, g, gvar=None, hypstarts=None, hypinds=None, sig2ofconst=None): """Return a fitted model from the emulator model using smart method.""" + global_RNG = RandomNumberGenerator().scipy_stats_RNG + hypvarconstmean = 0 hypvarconstLB = -3 hypvarconstUB = 3 @@ -462,7 +465,7 @@ def __fitGP1d(theta, g, gvar=None, hypstarts=None, hypinds=None, sig2ofconst=Non nhyptrain = np.max(np.min((25 * theta.shape[1], theta.shape[0]))) if theta.shape[0] > nhyptrain: - thetac = np.random.choice(theta.shape[0], nhyptrain, replace=False) + thetac = global_RNG.choice(theta.shape[0], nhyptrain, replace=False) else: thetac = range(0, theta.shape[0]) subinfo['theta'] = theta[thetac, :] From 096a116cdd59d94bcff7a4ba0cf636fa990b348d Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 14 Jul 2026 19:52:47 -0500 Subject: [PATCH 70/84] addition of fixtures and constant data shared by tests --- src/surmise/tests/conftest.py | 25 ++++++-- src/surmise/tests/shared_scenario.py | 93 ++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+), 5 deletions(-) diff --git a/src/surmise/tests/conftest.py b/src/surmise/tests/conftest.py index a3af7487..a19606aa 100644 --- a/src/surmise/tests/conftest.py +++ b/src/surmise/tests/conftest.py @@ -19,8 +19,10 @@ @pytest.fixture(scope="session") def _session_rng(): - set_RNG(np.random.default_rng(sc.RNG_SEED)) - yield + def _ensure(): + set_RNG(np.random.default_rng(sc.RNG_SEED)) + _ensure() + return _ensure @pytest.fixture(autouse=True) @@ -38,7 +40,7 @@ def _isolate_rng_state(): # RNG helpers -@pytest.fixture(scope="function") +@pytest.fixture def no_rng(): """For tests asserting the must-set-first error.""" RandomNumberGenerator()._clear_RNG() @@ -48,8 +50,9 @@ def no_rng(): @pytest.fixture(scope="module") def seeded_rng(): """Set the package RNG once for an entire test module.""" - set_RNG(sc._rng) - yield sc._rng + _rng = np.random.default_rng(sc.RNG_SEED) + set_RNG(_rng) + yield _rng RandomNumberGenerator()._clear_RNG() @@ -69,16 +72,26 @@ def lin_data(_session_rng): @pytest.fixture(scope="session") def emu_lin_pcgp(_session_rng): """PCGP emulator fit to the linear scenario.""" + _session_rng() return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, method='PCGP') @pytest.fixture(scope="session") def emu_lin_pcgpwm(_session_rng): + _session_rng() return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, method='PCGPwM') +@pytest.fixture(scope="session") +def emu_lin_pcgpwm_wgrad(_session_rng): + _session_rng() + return emulator(x=sc.x_lin, theta=sc.theta_lin, f=sc.f_lin, + method='PCGPwM', + args={'return_grad': True}) + + @pytest.fixture(scope="session") def timedrop_data(_session_rng): """(x_std, theta, f, y, obsvar) for the timedrop scenario.""" @@ -89,6 +102,7 @@ def timedrop_data(_session_rng): @pytest.fixture(scope="session") def emu_timedrop(_session_rng, timedrop_data): + _session_rng() x_std, theta, f, _, _ = timedrop_data return emulator(x=x_std, theta=theta, f=f, method='PCGP') @@ -96,6 +110,7 @@ def emu_timedrop(_session_rng, timedrop_data): @pytest.fixture(scope="session") def cal_directbayes(_session_rng, emu_timedrop, timedrop_data): """Fitted directbayes calibrator.""" + _session_rng() x_std, _, _, y, obsvar = timedrop_data return calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=sc.prior_balldrop, diff --git a/src/surmise/tests/shared_scenario.py b/src/surmise/tests/shared_scenario.py index 4aee5e6e..2ec2435a 100644 --- a/src/surmise/tests/shared_scenario.py +++ b/src/surmise/tests/shared_scenario.py @@ -68,6 +68,13 @@ def rnd(n): y_lin = balldroptrue(xv_lin) obsvar_lin = 4 * np.ones(x_lin.shape[0]) +theta_new_lin = np.vstack((sps.norm.rvs(0, 5, size=10, random_state=_datagen), + sps.gamma.rvs(2, 0, 10, size=10, random_state=_datagen))).T +f_new_lin = balldropmodel_linear(xv_lin, theta_new_lin) + +theta_test_lin = np.vstack((sps.norm.rvs(0, 5, size=50, random_state=_datagen), + sps.gamma.rvs(2, 0, 10, size=50, random_state=_datagen))).T + # ---------------------------------------------------------------------- # Scenario B: timedrop (test_cal_directbayes, test_cal_saveload, @@ -113,3 +120,89 @@ def rnd(n): theta_range = np.array([1, 30]) x_range = np.array([min(x_td), max(x_td)]) x_std = (x_td - min(x_td)) / (max(x_td) - min(x_td)) + + +# ---------------------------------------------------------------------- +# Scenario C: borehole function +# ---------------------------------------------------------------------- +def borehole_model(x, theta): + """Given x and theta, + return matrix of [row x] times [row theta] of values.""" + theta = tstd2theta(theta) + x = xstd2x(x) + p = x.shape[0] + n = theta.shape[0] + theta_stacked = np.repeat(theta, repeats=p, axis=0) + x_stacked = np.tile(x.astype(float), (n, 1)) + f = borehole_vec(x_stacked, theta_stacked).reshape((n, p)) + return f.T + + +def borehole_true(x): + """Given x, return matrix of [row x] times 1 of values.""" + # assume true theta is [0.5]^d + theta0 = np.atleast_2d(np.array([0.5] * 4)) + f0 = borehole_model(x, theta0) + return f0 + + +def borehole_vec(x, theta): + """Given x and theta, return vector of values.""" + (Hu, Ld_Kw, Treff, powparam) = np.split(theta, theta.shape[1], axis=1) + (rw, Hl) = np.split(x[:, :-1], 2, axis=1) + numer = 2 * np.pi * (Hu - Hl) + denom1 = 2 * Ld_Kw / rw ** 2 + denom2 = Treff + f = ((numer / ((denom1 + denom2))) * np.exp(powparam * rw)).reshape(-1) + return f + + +def tstd2theta(tstd): + """Given standardized theta in [0, 1]^d, return non-standardized theta.""" + if tstd.ndim < 1.5: + tstd = tstd[:, None].T + (Treffs, Hus, LdKw, powparams) = np.split(tstd, tstd.shape[1], axis=1) + + Treff = (0.5-0.05) * Treffs + 0.05 + Hu = Hus * (1110 - 990) + 990 + Ld_Kw = LdKw * (1680 / 1500 - 1120 / 15000) + 1120 / 15000 + + powparam = powparams * (0.5 - (- 0.5)) + (-0.5) + + theta = np.hstack((Hu, Ld_Kw, Treff, powparam)) + return theta + + +def xstd2x(xstd): + """Given standardized x in [0, 1]^2 x {0, 1}, return non-standardized x.""" + if xstd.ndim < 1.5: + xstd = xstd[:, None].T + (rws, Hls, labels) = np.split(xstd, xstd.shape[1], axis=1) + + rw = rws * (np.log(0.5) - np.log(0.05)) + np.log(0.05) + rw = np.exp(rw) + Hl = Hls * (820 - 700) + 700 + + x = np.hstack((rw, Hl, labels)) + return x + + +class thetaprior_bh: + """ This defines the class instance of priors provided to the methods. """ + # def lpdf(theta): + # if theta.ndim > 1.5: + # return np.squeeze(np.sum(sps.norm.logpdf(theta, 1, 0.5), 1)) + # else: + # return np.squeeze(np.sum(sps.norm.logpdf(theta, 1, 0.5))) + + def rnd(n): + return np.vstack((sps.norm.rvs(1, 0.5, size=(n, 4), + random_state=_datagen))) + + +x_bh = sps.uniform.rvs(0, 1, [50, 3], random_state=_datagen) +x_bh[:, 2] = x_bh[:, 2] > 0.5 +yt_bh = np.squeeze(borehole_true(x_bh)) +yvar_bh = (10 ** (-2)) * np.ones(yt_bh.shape) +thetatot_bh = (thetaprior_bh.rnd(15)) +y_bh = yt_bh + sps.norm.rvs(0, np.sqrt(yvar_bh)) From c5ce2bec184b323179a14c624962e950dd1485a3 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Tue, 14 Jul 2026 19:54:55 -0500 Subject: [PATCH 71/84] Major changes to tests: (1) Ensure that no surmise RNG is used except within test functions (2) Reduce redundancy in calling shared variables from shared_scenario.py (3) Allow for fixtures to be called by test to ensure "no_rng" (RNG cleared), "seeded_RNG" (surmise RNG set properly) --- src/surmise/tests/test_cal_directbayes.py | 24 +--- .../tests/test_cal_directbayeswoodbury.py | 124 ++++++++---------- src/surmise/tests/test_cal_samplers.py | 3 +- src/surmise/tests/test_cal_saveload.py | 35 +---- src/surmise/tests/test_emu_param_str.py | 76 +---------- src/surmise/tests/test_emu_passfunc.py | 94 +------------ src/surmise/tests/test_emu_pcgpwimpute.py | 91 +------------ src/surmise/tests/test_emu_pcgpwm.py | 88 ++----------- src/surmise/tests/test_emu_predict.py | 86 ++---------- src/surmise/tests/test_emu_saveload.py | 76 +---------- src/surmise/tests/test_emu_supplement.py | 13 +- src/surmise/tests/test_emu_update.py | 71 +--------- src/surmise/tests/test_emu_x1d.py | 60 +-------- src/surmise/tests/test_new_emu.py | 95 +------------- src/surmise/tests/test_new_emu_prediction.py | 101 +------------- 15 files changed, 140 insertions(+), 897 deletions(-) diff --git a/src/surmise/tests/test_cal_directbayes.py b/src/surmise/tests/test_cal_directbayes.py index d0ab598d..04d8c2d3 100644 --- a/src/surmise/tests/test_cal_directbayes.py +++ b/src/surmise/tests/test_cal_directbayes.py @@ -1,16 +1,12 @@ import numpy as np import pytest -from surmise.emulation import emulator from surmise.calibration import calibrator from .conftest import does_not_raise from .shared_scenario import y_td as y, obsvar_td as obsvar, \ - x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop, RNG_SEED -# TODO: TEMPORARY FIX -import surmise -surmise.set_RNG(np.random.default_rng(RNG_SEED)) + x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop # Use set RNG in this entire test module -pytestmark = pytest.mark.usefixtures('seeded_rng') +pytestmark = pytest.mark.usefixtures('seeded_rng', '_session_rng') METHOD_IN_TEST = 'directbayes' @@ -20,22 +16,6 @@ # Obtain computer model output via filtered data f = timedrop(x_std, theta, x_range, theta_range) -# Fit an emulator via non-filtered data -emulator_nf_1 = emulator(x=x_std, theta=theta, f=f, method='PCGP') -pred_nf = emulator_nf_1.predict(x=x_std, theta=theta) -pred_nf_mean = pred_nf.mean() - -# Filter out the data -ys = 1 - np.sum((pred_nf_mean - y) ** 2, 0) / np.sum((y - np.mean(y)) ** 2, 0) -theta_f = theta[ys > 0.5] - -# Obtain computer model output via filtered data -f_f = timedrop(x_std, theta_f, x_range, theta_range) - -# Fit an emulator via filtered data -emulator_f_1 = emulator(x=x_std, theta=theta_f, f=f_f, method='PCGP') -# emulator_f_2 = emulator(x=x_std, theta=theta_f, f=f_f, method='PCGP') - args2 = {'theta0': np.array([[0.4]]), 'numsamp': 20, 'stepType': 'normal', diff --git a/src/surmise/tests/test_cal_directbayeswoodbury.py b/src/surmise/tests/test_cal_directbayeswoodbury.py index 5e37eccf..7bc97025 100644 --- a/src/surmise/tests/test_cal_directbayeswoodbury.py +++ b/src/surmise/tests/test_cal_directbayeswoodbury.py @@ -1,41 +1,31 @@ -import numpy as np -from surmise.emulation import emulator from surmise.calibration import calibrator import pytest from .conftest import does_not_raise -from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin, RNG_SEED +from .shared_scenario import x_lin as x, theta_lin, y_lin as y, \ + obsvar_lin as obsvar, priorphys_lin -_rng = np.random.default_rng(RNG_SEED) -pytestmark = pytest.mark.usefixtures('seeded_rng') +pytestmark = pytest.mark.usefixtures('seeded_rng', '_session_rng') METHOD_IN_TEST = 'directbayeswoodbury' -############################################## -# Simple scenarios # -############################################## - -emulator_1 = emulator(x=x, theta=theta_lin, f=f_lin, method='PCGPwM') -emulator_w_grad = emulator(x=x, theta=theta_lin, f=f_lin, - method='PCGPwM', - args={'return_grad': True}) - ############################################## -# Unit tests to initialize an emulator class # +# Simple scenarios # ############################################## # test to check none-type inputs @pytest.mark.parametrize( - "input1,expectation", + "grad_flag,expectation", [ - (emulator_1, does_not_raise()), - (emulator_w_grad, does_not_raise()), + (False, does_not_raise()), + (True, does_not_raise()), ], ) -def test_cal_directbayes(input1, expectation): +def test_cal_directbayes(grad_flag, expectation, + emu_lin_pcgpwm, emu_lin_pcgpwm_wgrad): + emu = emu_lin_pcgpwm_wgrad if grad_flag else emu_lin_pcgpwm with expectation: - assert calibrator(emu=input1, + assert calibrator(emu=emu, y=y, x=x, thetaprior=priorphys_lin, @@ -45,14 +35,14 @@ def test_cal_directbayes(input1, expectation): # test to check none-type inputs @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_predict(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_predict(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -63,14 +53,14 @@ def test_cal_predict(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_predict_mean(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_predict_mean(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -82,14 +72,14 @@ def test_cal_predict_mean(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_predict_var(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_predict_var(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -101,14 +91,14 @@ def test_cal_predict_var(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_predict_rnd(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_predict_rnd(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -120,14 +110,14 @@ def test_cal_predict_rnd(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, pytest.raises(ValueError)), + (pytest.raises(ValueError)), # (emulator_2, pytest.raises(ValueError)), ], ) -def test_cal_predict_lpdf(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_predict_lpdf(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -139,14 +129,14 @@ def test_cal_predict_lpdf(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -157,14 +147,14 @@ def test_cal_thetadist(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist_repr(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist_repr(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -182,8 +172,8 @@ def test_cal_thetadist_repr(input1, expectation): (10, does_not_raise()), ], ) -def test_cal_thetadist_call(input1, expectation): - cal_bayes = calibrator(emu=emulator_1, +def test_cal_thetadist_call(input1, expectation, emu_lin_pcgpwm): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -194,14 +184,14 @@ def test_cal_thetadist_call(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist_mean(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist_mean(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -212,14 +202,14 @@ def test_cal_thetadist_mean(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist_var(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist_var(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -230,14 +220,14 @@ def test_cal_thetadist_var(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist_rnd(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist_rnd(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, @@ -248,14 +238,14 @@ def test_cal_thetadist_rnd(input1, expectation): @pytest.mark.parametrize( - "input1,expectation", + "expectation", [ - (emulator_1, does_not_raise()), + (does_not_raise()), # (emulator_2, does_not_raise()), ], ) -def test_cal_thetadist_lpdf(input1, expectation): - cal_bayes = calibrator(emu=input1, +def test_cal_thetadist_lpdf(emu_lin_pcgpwm, expectation): + cal_bayes = calibrator(emu=emu_lin_pcgpwm, y=y, x=x, thetaprior=priorphys_lin, diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index 9f82bd7c..a6d2ea0f 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -7,9 +7,10 @@ from .shared_scenario import x_lin as x, theta_lin, f_lin, y_lin as y, \ obsvar_lin as obsvar, priorphys_lin, RNG_SEED -_rng = np.random.default_rng(RNG_SEED) pytestmark = pytest.mark.usefixtures('seeded_rng') +_rng = np.random.default_rng(seed=RNG_SEED) + # TODO: LMC will require 'expertMode' in lmc_option to run SAMPLERS_IN_TEST = ['metropolis_hastings', 'PTLMC'] # , 'LMC'] diff --git a/src/surmise/tests/test_cal_saveload.py b/src/surmise/tests/test_cal_saveload.py index 93605529..ed803318 100644 --- a/src/surmise/tests/test_cal_saveload.py +++ b/src/surmise/tests/test_cal_saveload.py @@ -5,37 +5,16 @@ ############################################## import numpy as np import pytest -from surmise.emulation import emulator from surmise.calibration import calibrator from .conftest import does_not_raise from .shared_scenario import y_td as y, obsvar_td as obsvar, \ - x_std, theta_ball as theta, x_range, theta_range, prior_balldrop, timedrop + x_std, prior_balldrop -pytestmark = pytest.mark.usefixtures('seeded_rng') +pytestmark = pytest.mark.usefixtures('seeded_rng', '_session_rng') -############################################## -# Simple scenarios # -############################################## -# Obtain computer model output via filtered data -f = timedrop(x_std, theta, x_range, theta_range) - -# Fit an emulator via non-filtered data -emulator_nf_1 = emulator(x=x_std, theta=theta, f=f, method='PCGP') -pred_nf = emulator_nf_1.predict(x=x_std, theta=theta) -pred_nf_mean = pred_nf.mean() - -# Filter out the data -ys = 1 - np.sum((pred_nf_mean - y)**2, 0)/np.sum((y - np.mean(y))**2, 0) -theta_f = theta[ys > 0.5] - -# Obtain computer model output via filtered data -f_f = timedrop(x_std, theta_f, x_range, theta_range) - -# Fit an emulator via filtered data -emulator_f_1 = emulator(x=x_std, theta=theta_f, f=f_f, method='PCGP') ############################################## -# Unit tests to initialize an emulator class # +# Simple scenarios # ############################################## args2 = {'theta0': np.array([[0.4]]), 'numsamp': 20, @@ -50,9 +29,9 @@ (False, pytest.raises(TypeError)) ], ) -def test_cal_saveload(load_cal_flag, expectation): +def test_cal_saveload(load_cal_flag, emu_timedrop, expectation): with expectation: - cal = calibrator(emu=emulator_f_1, + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, @@ -82,9 +61,9 @@ def test_cal_saveload(load_cal_flag, expectation): (does_not_raise()), ], ) -def test_calpred_saveload(expectation): +def test_calpred_saveload(emu_timedrop, expectation): with expectation: - cal = calibrator(emu=emulator_f_1, + cal = calibrator(emu=emu_timedrop, y=y, x=x_std, thetaprior=prior_balldrop, diff --git a/src/surmise/tests/test_emu_param_str.py b/src/surmise/tests/test_emu_param_str.py index 3661ec0e..3ca7bca3 100644 --- a/src/surmise/tests/test_emu_param_str.py +++ b/src/surmise/tests/test_emu_param_str.py @@ -1,86 +1,22 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator -############################################## -# Simple scenarios # -############################################## - +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T +pytestmark = pytest.mark.usefixtures('seeded_rng') -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def lpdf(theta): - if theta.ndim > 1.5: - return np.squeeze(sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)) - else: - return np.squeeze(sps.norm.logpdf(theta[0], 0, 5) + - sps.gamma.logpdf(theta[1], 2, 0, 10)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) +############################################## +# Simple scenarios # +############################################## f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] x1 = x[0:15, :] -@contextmanager -def does_not_raise(): - yield - - @pytest.mark.parametrize( "input,expectation", [ diff --git a/src/surmise/tests/test_emu_passfunc.py b/src/surmise/tests/test_emu_passfunc.py index 61a5f2ac..dc1d7a29 100644 --- a/src/surmise/tests/test_emu_passfunc.py +++ b/src/surmise/tests/test_emu_passfunc.py @@ -1,95 +1,10 @@ -import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import borehole_model, x_bh as x, thetatot_bh as thetatot -def borehole_model(x, theta): - """Given x and theta, - return matrix of [row x] times [row theta] of values.""" - theta = tstd2theta(theta) - x = xstd2x(x) - p = x.shape[0] - n = theta.shape[0] - theta_stacked = np.repeat(theta, repeats=p, axis=0) - x_stacked = np.tile(x.astype(float), (n, 1)) - f = borehole_vec(x_stacked, theta_stacked).reshape((n, p)) - return f.T - - -def borehole_true(x): - """Given x, return matrix of [row x] times 1 of values.""" - # assume true theta is [0.5]^d - theta0 = np.atleast_2d(np.array([0.5] * 4)) - f0 = borehole_model(x, theta0) - return f0 - - -def borehole_vec(x, theta): - """Given x and theta, return vector of values.""" - (Hu, Ld_Kw, Treff, powparam) = np.split(theta, theta.shape[1], axis=1) - (rw, Hl) = np.split(x[:, :-1], 2, axis=1) - numer = 2 * np.pi * (Hu - Hl) - denom1 = 2 * Ld_Kw / rw ** 2 - denom2 = Treff - f = ((numer / ((denom1 + denom2))) * np.exp(powparam * rw)).reshape(-1) - return f - - -def tstd2theta(tstd): - """Given standardized theta in [0, 1]^d, return non-standardized theta.""" - if tstd.ndim < 1.5: - tstd = tstd[:, None].T - (Treffs, Hus, LdKw, powparams) = np.split(tstd, tstd.shape[1], axis=1) - - Treff = (0.5-0.05) * Treffs + 0.05 - Hu = Hus * (1110 - 990) + 990 - Ld_Kw = LdKw * (1680 / 1500 - 1120 / 15000) + 1120 / 15000 - - powparam = powparams * (0.5 - (- 0.5)) + (-0.5) - - theta = np.hstack((Hu, Ld_Kw, Treff, powparam)) - return theta - - -def xstd2x(xstd): - """Given standardized x in [0, 1]^2 x {0, 1}, return non-standardized x.""" - if xstd.ndim < 1.5: - xstd = xstd[:, None].T - (rws, Hls, labels) = np.split(xstd, xstd.shape[1], axis=1) - - rw = rws * (np.log(0.5) - np.log(0.05)) + np.log(0.05) - rw = np.exp(rw) - Hl = Hls * (820 - 700) + 700 - - x = np.hstack((rw, Hl, labels)) - return x - - -class thetaprior: - """ This defines the class instance of priors provided to the methods. """ - # def lpdf(theta): - # if theta.ndim > 1.5: - # return np.squeeze(np.sum(sps.norm.logpdf(theta, 1, 0.5), 1)) - # else: - # return np.squeeze(np.sum(sps.norm.logpdf(theta, 1, 0.5))) - - def rnd(n): - return np.vstack((sps.norm.rvs(1, 0.5, size=(n, 4)))) - - -x = sps.uniform.rvs(0, 1, [50, 3]) -x[:, 2] = x[:, 2] > 0.5 -yt = np.squeeze(borehole_true(x)) -yvar = (10 ** (-2)) * np.ones(yt.shape) -thetatot = (thetaprior.rnd(15)) -y = yt + sps.norm.rvs(0, np.sqrt(yvar)) - - -@contextmanager -def does_not_raise(): - yield +pytestmark = pytest.mark.usefixtures('seeded_rng') # test to check the emulator with a passed function @@ -114,7 +29,8 @@ def test_passfunction(expectation): (None, None, pytest.raises(ValueError)) ], ) -def test_passfunction_predict(x0, theta0, expectation): +def test_passfunction_predict(x0, theta0, + expectation): with expectation: emu = emulator(passthroughfunc=borehole_model, method='PCGP') diff --git a/src/surmise/tests/test_emu_pcgpwimpute.py b/src/surmise/tests/test_emu_pcgpwimpute.py index e1333a3c..7b0e0603 100644 --- a/src/surmise/tests/test_emu_pcgpwimpute.py +++ b/src/surmise/tests/test_emu_pcgpwimpute.py @@ -1,67 +1,15 @@ import numpy as np import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, _datagen + +pytestmark = pytest.mark.usefixtures('seeded_rng') ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -71,36 +19,11 @@ def rnd(n): x0d = np.array(1) simsd = 1e-3 * np.ones_like(f) - -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4*np.ones(x.shape[0]) -y = balldroptrue(xv) -############################################## -# Unit tests to initialize an emulator class # -############################################## - - -@contextmanager -def does_not_raise(): - yield - - +# missing mask +maskU = sps.uniform.rvs(size=f.size, random_state=_datagen).reshape(*f.shape) # tests missing data f_miss = f.copy() -f_miss[np.random.rand(*f.shape) < 0.2] = np.nan +f_miss[maskU < 0.2] = np.nan @pytest.mark.parametrize( diff --git a/src/surmise/tests/test_emu_pcgpwm.py b/src/surmise/tests/test_emu_pcgpwm.py index decf1b38..91f64112 100644 --- a/src/surmise/tests/test_emu_pcgpwm.py +++ b/src/surmise/tests/test_emu_pcgpwm.py @@ -1,67 +1,16 @@ import numpy as np import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, _datagen + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -72,35 +21,14 @@ def rnd(n): simsd = 1e-3 * np.ones_like(f) -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4*np.ones(x.shape[0]) -y = balldroptrue(xv) ############################################## # Unit tests to initialize an emulator class # ############################################## - - -@contextmanager -def does_not_raise(): - yield - - +# missing mask +maskU = sps.uniform.rvs(size=f.size, random_state=_datagen).reshape(*f.shape) # tests missing data f_miss = f.copy() -f_miss[np.random.rand(*f.shape) < 0.2] = np.nan +f_miss[maskU < 0.2] = np.nan @pytest.mark.parametrize( diff --git a/src/surmise/tests/test_emu_predict.py b/src/surmise/tests/test_emu_predict.py index 47e81f6a..aee4667c 100644 --- a/src/surmise/tests/test_emu_predict.py +++ b/src/surmise/tests/test_emu_predict.py @@ -1,67 +1,16 @@ import numpy as np import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, _datagen + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -76,20 +25,16 @@ def rnd(n): x3 = np.vstack((np.array(list(np.arange(0, 10)) * 2), np.repeat([1, 2], 10), np.repeat([2, 3], 10))).T + # 1-d theta -theta1d = sps.norm.rvs(0, 5, size=50) +theta1d = sps.norm.rvs(0, 5, size=50, random_state=_datagen) # 1-d theta -theta1dx = sps.norm.rvs(0, 5, size=2) +theta1dx = sps.norm.rvs(0, 5, size=2, random_state=_datagen) ############################################## # Unit tests to initialize an emulator class # ############################################## -@contextmanager -def does_not_raise(): - yield - - # test to check the predict method with multivariate example @pytest.mark.parametrize( "input1,input2,expectation", @@ -143,21 +88,6 @@ def test_prediction_mean(input1, expectation): assert pred.mean() is not None -# test to check the prediction.mean_gradtheta() -# @pytest.mark.parametrize( -# "input1,input2,expectation", -# [ -# ('PCGP', False, pytest.raises(ValueError)), -# ('PCGP', True, does_not_raise()), -# ], -# ) -# def test_prediction_mean_gradtheta(input1, input2, expectation): -# emu = emulator(x=x, theta=theta, f=f, method=input1) -# pred = emu.predict(x=x, theta=theta, args={'return_grad': input2}) -# with expectation: -# assert pred.mean_gradtheta() is not None - - # test to check the prediction.var() @pytest.mark.parametrize( "input1,expectation", diff --git a/src/surmise/tests/test_emu_saveload.py b/src/surmise/tests/test_emu_saveload.py index a0038965..1eda5185 100644 --- a/src/surmise/tests/test_emu_saveload.py +++ b/src/surmise/tests/test_emu_saveload.py @@ -1,76 +1,17 @@ import numpy as np -import scipy.stats as sps -from contextlib import contextmanager from surmise.emulation import emulator import pytest import os -############################################## -# Simple scenarios # -############################################## - +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T +pytestmark = pytest.mark.usefixtures('seeded_rng') -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def lpdf(theta): - if theta.ndim > 1.5: - return np.squeeze(sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)) - else: - return np.squeeze(sps.norm.logpdf(theta[0], 0, 5) + - sps.gamma.logpdf(theta[1], 2, 0, 10)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) +############################################## +# Simple scenarios # +############################################## f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -84,11 +25,6 @@ def rnd(n): ############################################## -@contextmanager -def does_not_raise(): - yield - - @pytest.mark.parametrize( "load_emu_flag, expectation", [ diff --git a/src/surmise/tests/test_emu_supplement.py b/src/surmise/tests/test_emu_supplement.py index a488b7f2..18014676 100644 --- a/src/surmise/tests/test_emu_supplement.py +++ b/src/surmise/tests/test_emu_supplement.py @@ -1,11 +1,12 @@ import numpy as np +import scipy.stats as sps import pytest from surmise.emulation import emulator from surmise.calibration import calibrator from .conftest import does_not_raise from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, y_lin as y, \ - obsvar_lin as obsvar, priorphys_lin + obsvar_lin as obsvar, priorphys_lin, RNG_SEED pytestmark = pytest.mark.usefixtures('seeded_rng') @@ -18,7 +19,12 @@ x1 = x[0:15, :] x1d = x[:, 0].reshape((x.shape[0],)) theta4d = np.hstack((theta1, theta1)) -thetarnd = priorphys_lin.rnd(20) +# Do not use surmise RNG outside of the tests +_rng = np.random.default_rng(RNG_SEED) +thetarnd = np.vstack((sps.norm.rvs(0, 5, size=20, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=20, random_state=_rng))).T +thetarnd2 = np.vstack((sps.norm.rvs(0, 5, size=10, random_state=_rng), + sps.gamma.rvs(2, 0, 10, size=10, random_state=_rng))).T thetacomb = np.vstack((theta1, thetarnd)) @@ -68,9 +74,6 @@ def test_supplement_theta(input1, input2, input3, expectation): thetachoices=input3) is not None -thetarnd2 = priorphys_lin.rnd(10) - - # test to check supplement_theta pending argument @pytest.mark.parametrize( "includepending,expectation", diff --git a/src/surmise/tests/test_emu_update.py b/src/surmise/tests/test_emu_update.py index a2f1df61..e5148d45 100644 --- a/src/surmise/tests/test_emu_update.py +++ b/src/surmise/tests/test_emu_update.py @@ -1,76 +1,22 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, \ + f_new_lin as f_new, theta_new_lin as theta_new + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] x1 = x[0:15, :] f1theta = f[:, 0:15] theta1 = theta[0:15, :] -theta_new = priorphys_lin.rnd(10) -f_new = balldropmodel_linear(xv, theta_new) - fmatch = np.hstack((f1theta, f_new)) thetamatch = np.vstack((theta1, theta_new)) @@ -84,11 +30,6 @@ def rnd(n): ####################################################### -@contextmanager -def does_not_raise(): - yield - - # test to check update(): 'xreps' @pytest.mark.parametrize( "input1,input2,input3,input4,expectation", diff --git a/src/surmise/tests/test_emu_x1d.py b/src/surmise/tests/test_emu_x1d.py index c1a0168f..31935e6d 100644 --- a/src/surmise/tests/test_emu_x1d.py +++ b/src/surmise/tests/test_emu_x1d.py @@ -1,71 +1,21 @@ -import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator -############################################## -# Simple scenarios # -############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = 50 + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -x = np.array([[0.1], - [0.2], - [0.3], - [0.4], - [0.5], - [0.6], - [0.7], - [0.8], - [0.9], - [1.0], - [1.2], - [2.6], - [2.9], - [3.1], - [3.3], - [3.5], - [3.7], ]).astype('object') -xv = x.astype('float') +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) -x = x.reshape(17, ) +pytestmark = pytest.mark.usefixtures('seeded_rng') ############################################## -# Unit tests to initialize an emulator class # +# Simple scenarios # ############################################## -@contextmanager -def does_not_raise(): - yield - - # test to check the predict with 1d-x @pytest.mark.parametrize( "input1,input2,expectation", [ (x, theta, does_not_raise()), - (x.reshape((17, 1)), theta, does_not_raise()), + (x.reshape(1, -1), theta, pytest.raises(ValueError)), ], ) def test_predict_multi(input1, input2, expectation): diff --git a/src/surmise/tests/test_new_emu.py b/src/surmise/tests/test_new_emu.py index 37c20e8b..eb6d453c 100644 --- a/src/surmise/tests/test_new_emu.py +++ b/src/surmise/tests/test_new_emu.py @@ -1,71 +1,16 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, \ + theta_new_lin as thetanew, f_new_lin as fnew + +pytestmark = pytest.mark.usefixtures('seeded_rng') + ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -76,32 +21,6 @@ def rnd(n): simsd = 1e-3 * np.ones_like(f) -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4*np.ones(x.shape[0]) -y = balldroptrue(xv) -############################################## -# Unit tests to initialize an emulator class # -############################################## - - -@contextmanager -def does_not_raise(): - yield - - # tests for prediction class methods: # test to check the prediction.mean() @pytest.mark.parametrize( @@ -251,8 +170,6 @@ def test_update(cmdopt1, expectation): emu = emulator(x=x, theta=theta, f=f, method=cmdopt1, args={'simsd': simsd}) else: emu = emulator(x=x, theta=theta, f=f, method=cmdopt1) - thetanew = priorphys_lin.rnd(10) - fnew = balldropmodel_linear(xv, thetanew) with expectation: emu.update(x=None, theta=thetanew, f=fnew) assert len(emu._emulator__theta) == 60, 'Check emulator.update()' diff --git a/src/surmise/tests/test_new_emu_prediction.py b/src/surmise/tests/test_new_emu_prediction.py index 6863b835..2203cf43 100644 --- a/src/surmise/tests/test_new_emu_prediction.py +++ b/src/surmise/tests/test_new_emu_prediction.py @@ -1,73 +1,15 @@ import numpy as np -import scipy.stats as sps import pytest -from contextlib import contextmanager from surmise.emulation import emulator +from .conftest import does_not_raise +from .shared_scenario import x_lin as x, theta_lin as theta, f_lin as f, \ + balldropmodel_linear, theta_test_lin as theta_test +pytestmark = pytest.mark.usefixtures('seeded_rng') ############################################## # Simple scenarios # ############################################## - - -def balldropmodel_linear(x, theta): - f = np.zeros((theta.shape[0], x.shape[0])) - for k in range(0, theta.shape[0]): - t = x[:, 0] - h0 = x[:, 1] + theta[k, 0] - vter = theta[k, 1] - f[k, :] = h0 - vter * t - return f.T - - -tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1))) -h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42))) -x = np.array([[0.1, 25.], - [0.2, 25.], - [0.3, 25.], - [0.4, 25.], - [0.5, 25.], - [0.6, 25.], - [0.7, 25.], - [0.9, 25.], - [1.1, 25.], - [1.3, 25.], - [2.0, 25.], - [2.4, 25.], - [0.1, 50.], - [0.2, 50.], - [0.3, 50.], - [0.4, 50.], - [0.5, 50.], - [0.6, 50.], - [0.7, 50.], - [0.8, 50.], - [0.9, 50.], - [1.0, 50.], - [1.2, 50.], - [2.6, 50.], - [2.9, 50.], - [3.1, 50.], - [3.3, 50.], - [3.5, 50.], - [3.7, 50.], ]).astype('object') -xv = x.astype('float') - - -class priorphys_lin: - """ This defines the class instance of priors provided to the method. """ - - def lpdf(theta): - return (sps.norm.logpdf(theta[:, 0], 0, 5) + - sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1)) - - def rnd(n): - return np.vstack((sps.norm.rvs(0, 5, size=n), - sps.gamma.rvs(2, 0, 10, size=n))).T - - -theta = priorphys_lin.rnd(50) -f = balldropmodel_linear(xv, theta) f1 = f[0:15, :] f2 = f[:, 0:25] theta1 = theta[0:25, :] @@ -78,35 +20,9 @@ def rnd(n): simsd = 1e-3 * np.ones_like(f) -def balldroptrue(x): - def logcosh(x): - # preventing crashing - s = np.sign(x) * x - p = np.exp(-2 * s) - return s + np.log1p(p) - np.log(2) - - t = x[:, 0] - h0 = x[:, 1] - vter = 20 - g = 9.81 - y = h0 - (vter ** 2) / g * logcosh(g * t / vter) - return y - - -obsvar = 4 * np.ones(x.shape[0]) -y = balldroptrue(xv) - - ############################################## # Unit tests to initialize an emulator class # ############################################## - - -@contextmanager -def does_not_raise(): - yield - - @pytest.mark.parametrize( "cmdopt1,expectation", [ @@ -121,8 +37,7 @@ def test_accuracy(cmdopt1, expectation): emu = emulator(x=x, theta=theta, f=f, method=cmdopt1, args={'simsd': simsd}) else: emu = emulator(x=x, theta=theta, f=f, method=cmdopt1) - theta_test = priorphys_lin.rnd(50) - ftest = balldropmodel_linear(xv, theta_test) + ftest = balldropmodel_linear(x.astype(float), theta_test) pred_test = emu.predict(x=x, theta=theta_test) print('\n') @@ -164,8 +79,7 @@ def test_predlpdf(cmdopt1, expectation): emu = emulator(x=x, theta=theta, f=f, method=cmdopt1, args={'simsd': simsd}) else: emu = emulator(x=x, theta=theta, f=f, method=cmdopt1) - theta_test = priorphys_lin.rnd(50) - ftest = balldropmodel_linear(xv, theta_test) + ftest = balldropmodel_linear(x.astype(float), theta_test) pred_test = emu.predict(x=x, theta=theta_test) with expectation: @@ -185,8 +99,7 @@ def test_predlpdf_wgrad(cmdopt1, expectation): emu = emulator(x=x, theta=theta, f=f, method=cmdopt1, args={'simsd': simsd, 'return_grad': True}) else: emu = emulator(x=x, theta=theta, f=f, method=cmdopt1, args={'return_grad': True}) - theta_test = priorphys_lin.rnd(50) - ftest = balldropmodel_linear(xv, theta_test) + ftest = balldropmodel_linear(x.astype(float), theta_test) pred_test = emu.predict(x=x, theta=theta_test) with expectation: From 5ab6be9443e532e37a0d48b59146098bdf7e70c0 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 10:04:34 -0500 Subject: [PATCH 72/84] inclusion of set_RNG notebook as usage example --- book/_toc.yml | 1 + book/notebooks/setRNG.ipynb | 285 ++++++++++++++++++++++++++++++++++++ 2 files changed, 286 insertions(+) create mode 100644 book/notebooks/setRNG.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 12131715..3f639088 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -10,6 +10,7 @@ parts: numbered: 2 chapters: - file: notebooks/BallDrop.ipynb + - file: notebooks/setRNG.ipynb - caption: Reference chapters: - file: bibliography diff --git a/book/notebooks/setRNG.ipynb b/book/notebooks/setRNG.ipynb new file mode 100644 index 00000000..3f742b92 --- /dev/null +++ b/book/notebooks/setRNG.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55c00034b2c70d8e", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "source": [ + "# Using surmise's global RNG (`set_RNG`)\n", + "\n", + "surmise manages a single, user-provided random number generator for **all** of its internal sampling of random variables via `scipy.stats`.\n", + "\n", + "1. User must call `surmise.set_RNG(...)` once before using any surmise functionality. Otherwise, a `RuntimeError` will be raised.\n", + "2. Any emulation/calibration workflow is reproducible, given the generator the user provides.\n", + "\n", + "This notebook demonstrates these usages using the classic borehole example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "80a0bdf4e54c2104", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m numpy \u001B[38;5;28;01mas\u001B[39;00m np\n\u001B[32m 2\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m scipy.stats \u001B[38;5;28;01mas\u001B[39;00m sps\n\u001B[32m 3\u001B[39m \n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m surmise\n", + "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'numpy'" + ] + } + ], + "source": [ + "import numpy as np\n", + "import scipy.stats as sps\n", + "\n", + "import surmise\n", + "from surmise.emulation import emulator\n", + "\n", + "surmise.__version__" + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Borehole function setup\n", + "The borehole function provides a fast-to-evaluate function as a classic emulation/calibration example. We make use of the function coded in surmise's test suite. The only important detail here is that the function takes any parameters within the unit-cube, where $x\\in [0, 1]^3, \\theta \\in [0, 1]^4$." + ], + "id": "bd4057e1e9b0a103" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "from surmise.tests.shared_scenario import borehole_model", + "id": "4ed0ae2f3068ab6b" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## surmise refuses to run without `set_RNG`\n", + "A fresh session has no RNG. Any surmise call that needs randomness will raise a\n", + "`RuntimeError` with an instruction." + ], + "id": "fdef1b695baba740" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "# This is to simulate a fresh session. Generally a user does not have to clear an RNG before setting one.\n", + "from surmise._RandomNumberGenerator import RandomNumberGenerator\n", + "RandomNumberGenerator()._clear_RNG()\n", + "\n", + "try:\n", + " emulator(passthroughfunc=borehole_model, method='PCGP')\n", + "except RuntimeError as e:\n", + " print(\"RuntimeError:\", e)" + ], + "id": "cfc834703311b25f" + }, + { + "cell_type": "markdown", + "id": "3474562bc7562795", + "metadata": {}, + "source": [ + "## `set_RNG` provides one generator\n", + "Pass a `numpy.random.Generator` (i.e., `np.random.default_rng(seed)`). User may choose the same or a different generator for data generation with `scipy.stats` (via `random_state=`). The resulting workflow will be dependent on the collection of generator(s)." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-07-15T14:26:07.680958200Z", + "start_time": "2026-07-15T14:26:07.498257400Z" + } + }, + "cell_type": "code", + "source": [ + "# import secrets\n", + "# SEED = secrets.randbits(128)\n", + "\n", + "SEED = 111848137687551523431846058163015350939\n", + "my_rng = np.random.default_rng(SEED)\n", + "\n", + "# set RNG in surmise\n", + "surmise.set_RNG(my_rng)\n", + "\n", + "DATA_SEED = 234318460581630153509391118481376875515\n", + "data_rng = np.random.default_rng(DATA_SEED)\n", + "\n", + "x = sps.uniform.rvs(0, 1, size=(15, 3), random_state=data_rng)\n", + "theta = sps.uniform.rvs(0, 1, size=(50, 4), random_state=data_rng)\n", + "\n", + "emu = emulator(x=x, theta=theta, f=borehole_model(x, theta), method='PCGP')\n", + "pred = emu.predict(x=x, theta=theta)\n", + "print(\"prediction mean shape:\", pred.mean().shape)" + ], + "id": "13789822d1cc8700", + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;66;03m# import secrets\u001B[39;00m\n\u001B[32m 2\u001B[39m \u001B[38;5;66;03m# SEED = secrets.randbits(128)\u001B[39;00m\n\u001B[32m 3\u001B[39m \n\u001B[32m 4\u001B[39m SEED = \u001B[32m111848137687551523431846058163015350939\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m my_rng = np.random.default_rng(SEED)\n\u001B[32m 6\u001B[39m \n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# set RNG in surmise\u001B[39;00m\n\u001B[32m 8\u001B[39m surmise.set_RNG(my_rng)\n", + "\u001B[31mNameError\u001B[39m: name 'np' is not defined" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Notice that `set_RNG` accepts an `np.random.Generator`.", + "id": "7109e32a36c7c78b" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-07-15T14:30:39.746852Z", + "start_time": "2026-07-15T14:30:39.711613500Z" + } + }, + "cell_type": "code", + "source": [ + "for bad_rng in [np.random.RandomState(0), 12345]:\n", + " try:\n", + " surmise.set_RNG(bad_rng)\n", + " except TypeError as e:\n", + " print(f\"{type(bad_rng).__name__!s:>12}: TypeError: {e}\")\n", + "\n", + "surmise.set_RNG(my_rng) # restore the good one" + ], + "id": "2c618a8d59ca9ade", + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m bad_rng \u001B[38;5;28;01min\u001B[39;00m [np.random.RandomState(\u001B[32m0\u001B[39m), \u001B[32m12345\u001B[39m]:\n\u001B[32m 2\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 3\u001B[39m set_RNG(bad_rng)\n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m TypeError \u001B[38;5;28;01mas\u001B[39;00m e:\n", + "\u001B[31mNameError\u001B[39m: name 'np' is not defined" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Using the same seed for reproducible results\n", + "When _all_ randomness, namely the user data generation and surmise's, are controlled via chosen seeds. Repeating a workflow with the same collection of seeds should reproduce the results." + ], + "id": "735170c646652ad3" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-07-15T14:31:45.392722100Z", + "start_time": "2026-07-15T14:31:45.376522900Z" + } + }, + "cell_type": "code", + "source": [ + "def run_workflow(seeds):\n", + " SEED, DATA_SEED = seeds\n", + " rng = np.random.default_rng(SEED)\n", + " surmise.set_RNG(rng)\n", + " data_rng = np.random.default_rng(DATA_SEED)\n", + " x = sps.uniform.rvs(0, 1, size=(50, 3), random_state=data_rng)\n", + " x[:, 2] = x[:, 2] > 0.5\n", + " thetas = sps.uniform.rvs(0, 1, size=(15, 4), random_state=data_rng)\n", + " emu = emulator(x=x, theta=thetas, f=borehole_model(x, thetas), method='PCGP')\n", + " return emu.predict(x=x, theta=thetas).mean()\n", + "\n", + "runs = {\n", + " \"m1 (SEED, DATA)\": run_workflow((SEED, DATA_SEED)),\n", + " \"m2 (SEED, DATA)\": run_workflow((SEED, DATA_SEED)),\n", + " \"m3 (SEED+1, DATA)\": run_workflow((SEED + 1, DATA_SEED)),\n", + " \"m4 (SEED, DATA+1)\": run_workflow((SEED, DATA_SEED + 1)),\n", + " \"m5 (SEED+1, DATA+1)\": run_workflow((SEED + 1, DATA_SEED + 1)),\n", + "}\n", + "\n" + ], + "id": "bdc857a2f8474129", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from itertools import combinations\n", + "\n", + "for (run_name_a, run_result_a), (run_name_b, run_result_b) in combinations(runs.items(), 2):\n", + " print(f\"{run_name_a}, {run_name_b}: {np.array_equal(run_result_a, run_result_b)}\")" + ], + "id": "6f5144717925efe7" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Changing RNG at any time\n", + "surmise keeps exactly one RNG at a time (i.e., a Python singleton). User may replace the RNG, e.g., to restart a numerical study from a known seed." + ], + "id": "d420f4289edb49e6" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "surmise.set_RNG(np.random.default_rng(SEED))\n", + "# do something\n", + "surmise.set_RNG(np.random.default_rng(SEED + 1))" + ], + "id": "886be135dc93fd2e" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.13.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2ec75e8340775acdf011c68c07267e49a5ee88ef Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 11:11:43 -0500 Subject: [PATCH 73/84] RNG user guide update --- docs/random_number_generation.rst | 26 ++++++++++---------------- 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst index be9df0d3..1316d7fc 100644 --- a/docs/random_number_generation.rst +++ b/docs/random_number_generation.rst @@ -32,23 +32,17 @@ single RNG being used by |surmise|. RAND_SEED = secrets.randbits(128) surmise.set_RNG(np.random.default_rng(RAND_SEED)) - samples_1 = surmise.calibration().calibration_samples - samples_2 = surmise.calibration().calibration_samples - assert not all(samples_1 == samples_2) - surmise.set_RNG(np.random.default_rng(RAND_SEED)) - samples_3 = surmise.calibration().calibration_samples - assert all(samples_1 == samples_3) + surmise.set_RNG(np.random.default_rng(RAND_SEED + 1)) .. - External code offered officially through |surmise|, such as |bilby|, have - their own RNG usage scheme that is independent from the |surmise| scheme. - In particular, the RNG provided to |surmise| is never used explicitly by - external code. Instead, users are responsible for understanding the - external code's RNG scheme within the context of the application's needs and - providing additional RNG configuration information to |surmise| code that - uses the external code. -.. - Please refer to the RNG examples in the Jupyter book for more examples of - using RNGs with |surmise| including the RNG configuration of external code. +External code offered officially through |surmise|, such as |bilby|, have +their own RNG usage scheme that is independent from the |surmise| scheme. +In particular, the RNG provided to |surmise| is never used explicitly by +external code. Instead, users are responsible for understanding the +external code's RNG scheme within the context of the application's needs and +providing additional RNG configuration information to |surmise| code that +uses the external code. + +Please refer to the RNG examples in the Jupyter book for guidance using RNGs with |surmise| including the RNG configuration of external code. From 5fc6960e79d61254059e2c1c5ded3a7bb8384692 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 11:15:32 -0500 Subject: [PATCH 74/84] adding Jupyterbook reference --- docs/index.rst | 2 ++ docs/random_number_generation.rst | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index f699a110..140ca958 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -18,6 +18,7 @@ To begin using surmise, we encourage checking out the following pages: * :doc:`Quickstart ` * :doc:`Basic usage examples ` * `Jupyter notebook`_: Full usage with Gaussian process emulation on Google Colab. +* `Jupyterbook tutorial`_: Jupyter book containing additional usage example (including RNG usage) * :doc:`Expected use case examples ` and scientific examples. * `Github project page`_: surmise is open source and provided under the MIT license. @@ -59,3 +60,4 @@ Indices and tables .. _`Github project page`: https://github.com/bandframework/surmise .. _`Jupyter notebook`: https://colab.research.google.com/drive/1f4gKTCLEAGE8r-aMWOoGvY-O6zNqg1qj?usp=drive_link +.. _`Jupyterbook tutorial`: https://bandframework.github.io/surmise/intro.html diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst index 1316d7fc..0261bcd6 100644 --- a/docs/random_number_generation.rst +++ b/docs/random_number_generation.rst @@ -45,4 +45,4 @@ external code's RNG scheme within the context of the application's needs and providing additional RNG configuration information to |surmise| code that uses the external code. -Please refer to the RNG examples in the Jupyter book for guidance using RNGs with |surmise| including the RNG configuration of external code. +Please refer to the RNG examples in the [Jupyter book](https://bandframework.github.io/surmise/intro.html) for guidance using RNGs with |surmise| including the RNG configuration of external code. From 5cbc07c5f596c013f14d1c0e5392fb63365f02ce Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 11:17:18 -0500 Subject: [PATCH 75/84] minor typo correction --- docs/rng_dev_guide.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index 3a1dabc7..190c9d37 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -126,7 +126,7 @@ class using the `Singleton pattern`_. In accordance with requirements, * be designed and implemented where possible so that results are identical when rerun with an identical random number generation scenario, and * be designed so that all related random number generation (|eg| performing a - single calibration and generating a random reordering of ts MCMC samples) are + single calibration and generating a random reordering of the MCMC samples) are performed such that calling code cannot alter the single RNG during the middle of that computational process. From cb4d822191f78a0e06d3a5e9323ae8b2318e29cb Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 11:18:05 -0500 Subject: [PATCH 76/84] adding reference to jupyterbook rng example --- docs/rng_dev_guide.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index 190c9d37..e27bf054 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -7,7 +7,7 @@ generators more generically as random number generation and random number generators (RNGs). Please familiarize yourself with the RNG content in :ref:`rng_user_guide` before -reading this section. Similarly, reviewing the historic record of |surmise| RNG +reading this section, especially the reference to the Jupyterbook RNG example. Similarly, reviewing the historic record of |surmise| RNG requirements might be helpful to motivate the design and explain certain design decisions detailed here. From 7c946c7906ea31da098485b983ee4258b778bb53 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 13:44:54 -0500 Subject: [PATCH 77/84] (Issue #188) Iterate on RNG docs. --- book/_toc.yml | 2 +- book/notebooks/setRNG.ipynb | 385 ++++++++++++++++++++---------- docs/random_number_generation.rst | 49 ++-- 3 files changed, 280 insertions(+), 156 deletions(-) diff --git a/book/_toc.yml b/book/_toc.yml index 3f639088..6ec26360 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -9,8 +9,8 @@ parts: - caption: Examples numbered: 2 chapters: - - file: notebooks/BallDrop.ipynb - file: notebooks/setRNG.ipynb + - file: notebooks/BallDrop.ipynb - caption: Reference chapters: - file: bibliography diff --git a/book/notebooks/setRNG.ipynb b/book/notebooks/setRNG.ipynb index 3f742b92..c00aa3a7 100644 --- a/book/notebooks/setRNG.ipynb +++ b/book/notebooks/setRNG.ipynb @@ -5,17 +5,23 @@ "id": "55c00034b2c70d8e", "metadata": { "collapsed": true, + "deletable": true, + "editable": true, "jupyter": { "outputs_hidden": true - } + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, "source": [ "# Using surmise's global RNG (`set_RNG`)\n", "\n", - "surmise manages a single, user-provided random number generator for **all** of its internal sampling of random variables via `scipy.stats`.\n", + "surmise stores a single, user-provided random number generator for **all** of its internal sampling of random variables {{via}} `scipy.stats`.\n", "\n", - "1. User must call `surmise.set_RNG(...)` once before using any surmise functionality. Otherwise, a `RuntimeError` will be raised.\n", - "2. Any emulation/calibration workflow is reproducible, given the generator the user provides.\n", + "1. Users must call `surmise.set_RNG(...)` at least once before using any surmise functionality. Otherwise, an exception is raised.\n", + "2. All emulation/calibration workflows are reproducible if the user repeats a workflow on the same system and provides surmise with the same RNG.\n", "\n", "This notebook demonstrates these usages using the classic borehole example." ] @@ -24,241 +30,372 @@ "cell_type": "code", "execution_count": 1, "id": "80a0bdf4e54c2104", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'numpy'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m numpy \u001B[38;5;28;01mas\u001B[39;00m np\n\u001B[32m 2\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m scipy.stats \u001B[38;5;28;01mas\u001B[39;00m sps\n\u001B[32m 3\u001B[39m \n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m surmise\n", - "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'numpy'" + "name": "stdout", + "output_type": "stream", + "text": [ + "surmise v0.4.1.dev235+gcb4d82219.d20260715\n", + "scipy v1.18.0\n" ] } ], "source": [ "import numpy as np\n", + "import itertools as it\n", + "import scipy as sp\n", "import scipy.stats as sps\n", "\n", "import surmise\n", "from surmise.emulation import emulator\n", "\n", - "surmise.__version__" + "print(f\"surmise v{surmise.__version__}\")\n", + "print(f\"scipy v{sp.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "873c3ef0-1530-4ea8-a749-7aac65f907d4", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This is to simulate a fresh session. Generally a user does not have to clear an RNG before setting one.\n", + "# Therefore we remove this cell from the book's rendering.\n", + "from surmise._RandomNumberGenerator import RandomNumberGenerator\n", + "RandomNumberGenerator()._clear_RNG()" ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "012e4f8f-a5b1-43f5-b3b7-54cfddcc15c0", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## Borehole function setup\n", "The borehole function provides a fast-to-evaluate function as a classic emulation/calibration example. We make use of the function coded in surmise's test suite. The only important detail here is that the function takes any parameters within the unit-cube, where $x\\in [0, 1]^3, \\theta \\in [0, 1]^4$." - ], - "id": "bd4057e1e9b0a103" + ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": 3, + "id": "ce875636-ce29-48e6-aa09-349f79f65e4e", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], - "execution_count": null, - "source": "from surmise.tests.shared_scenario import borehole_model", - "id": "4ed0ae2f3068ab6b" + "source": [ + "from surmise.tests.shared_scenario import borehole_model" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "fdef1b695baba740", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## surmise refuses to run without `set_RNG`\n", - "A fresh session has no RNG. Any surmise call that needs randomness will raise a\n", - "`RuntimeError` with an instruction." - ], - "id": "fdef1b695baba740" + "A fresh session has no RNG. Any surmise call that needs randomness will raise an exception with instructions." + ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, + "execution_count": 4, + "id": "cfc834703311b25f", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please use set_RNG before using surmise\n" + ] + } + ], "source": [ - "# This is to simulate a fresh session. Generally a user does not have to clear an RNG before setting one.\n", - "from surmise._RandomNumberGenerator import RandomNumberGenerator\n", - "RandomNumberGenerator()._clear_RNG()\n", - "\n", "try:\n", " emulator(passthroughfunc=borehole_model, method='PCGP')\n", - "except RuntimeError as e:\n", - " print(\"RuntimeError:\", e)" - ], - "id": "cfc834703311b25f" + "except Exception as e:\n", + " print(e)" + ] }, { "cell_type": "markdown", "id": "3474562bc7562795", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## `set_RNG` provides one generator\n", - "Pass a `numpy.random.Generator` (i.e., `np.random.default_rng(seed)`). User may choose the same or a different generator for data generation with `scipy.stats` (via `random_state=`). The resulting workflow will be dependent on the collection of generator(s)." + "Pass any type of RNG compatible with your `scipy.stats` installation ({{eg}} `np.random.default_rng(seed)`). Users are free to perform all other RNG draws in their application as they see fit, including using the same RNG set into surmise. The resulting workflow will be dependent on the collection of generator(s)." ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "13789822d1cc8700", "metadata": { "ExecuteTime": { "end_time": "2026-07-15T14:26:07.680958200Z", "start_time": "2026-07-15T14:26:07.498257400Z" - } + }, + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prediction mean shape: (15, 50)\n" + ] + } + ], "source": [ "# import secrets\n", "# SEED = secrets.randbits(128)\n", "\n", "SEED = 111848137687551523431846058163015350939\n", - "my_rng = np.random.default_rng(SEED)\n", + "SURMISE_SEED, DATA_SEED = np.random.SeedSequence(SEED).spawn(2)\n", "\n", "# set RNG in surmise\n", - "surmise.set_RNG(my_rng)\n", + "surmise_rng = np.random.default_rng(SURMISE_SEED)\n", + "surmise.set_RNG(surmise_rng)\n", "\n", - "DATA_SEED = 234318460581630153509391118481376875515\n", "data_rng = np.random.default_rng(DATA_SEED)\n", "\n", "x = sps.uniform.rvs(0, 1, size=(15, 3), random_state=data_rng)\n", "theta = sps.uniform.rvs(0, 1, size=(50, 4), random_state=data_rng)\n", "\n", + "# surmise uses the surmise RNG under the hood\n", "emu = emulator(x=x, theta=theta, f=borehole_model(x, theta), method='PCGP')\n", "pred = emu.predict(x=x, theta=theta)\n", "print(\"prediction mean shape:\", pred.mean().shape)" - ], - "id": "13789822d1cc8700", - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'np' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;66;03m# import secrets\u001B[39;00m\n\u001B[32m 2\u001B[39m \u001B[38;5;66;03m# SEED = secrets.randbits(128)\u001B[39;00m\n\u001B[32m 3\u001B[39m \n\u001B[32m 4\u001B[39m SEED = \u001B[32m111848137687551523431846058163015350939\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m my_rng = np.random.default_rng(SEED)\n\u001B[32m 6\u001B[39m \n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# set RNG in surmise\u001B[39;00m\n\u001B[32m 8\u001B[39m surmise.set_RNG(my_rng)\n", - "\u001B[31mNameError\u001B[39m: name 'np' is not defined" - ] - } - ], - "execution_count": 1 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Notice that `set_RNG` accepts an `np.random.Generator`.", - "id": "7109e32a36c7c78b" + "id": "7109e32a36c7c78b", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "`set_RNG` does not accept old RNG types or seeds, but rather only the type of RNG compatible with the version of `scipy.stats` used to establish the surmise release." + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "2c618a8d59ca9ade", "metadata": { "ExecuteTime": { "end_time": "2026-07-15T14:30:39.746852Z", "start_time": "2026-07-15T14:30:39.711613500Z" - } + }, + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generator(PCG64)\n", + "\n", + "\n", + " RandomState: TypeError: Given RNG cannot be used with scipy.stats\n", + " int: TypeError: Given RNG cannot be used with scipy.stats\n" + ] + } + ], "source": [ + "print()\n", + "print(surmise_rng)\n", + "print(type(surmise_rng))\n", + "print()\n", + "\n", "for bad_rng in [np.random.RandomState(0), 12345]:\n", " try:\n", " surmise.set_RNG(bad_rng)\n", " except TypeError as e:\n", " print(f\"{type(bad_rng).__name__!s:>12}: TypeError: {e}\")\n", "\n", - "surmise.set_RNG(my_rng) # restore the good one" - ], - "id": "2c618a8d59ca9ade", - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'np' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m bad_rng \u001B[38;5;28;01min\u001B[39;00m [np.random.RandomState(\u001B[32m0\u001B[39m), \u001B[32m12345\u001B[39m]:\n\u001B[32m 2\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 3\u001B[39m set_RNG(bad_rng)\n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m TypeError \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[31mNameError\u001B[39m: name 'np' is not defined" - ] - } - ], - "execution_count": 2 + "surmise.set_RNG(surmise_rng) # restore the valid RNG" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "735170c646652ad3", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## Using the same seed for reproducible results\n", - "When _all_ randomness, namely the user data generation and surmise's, are controlled via chosen seeds. Repeating a workflow with the same collection of seeds should reproduce the results." - ], - "id": "735170c646652ad3" + "_All_ randomness, namely both the user data generation and surmise's, are controlled {{via}} these user-managed RNGs. Repeating a workflow with the same collection of RNGs should reproduce the results." + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "bdc857a2f8474129", "metadata": { "ExecuteTime": { "end_time": "2026-07-15T14:31:45.392722100Z", "start_time": "2026-07-15T14:31:45.376522900Z" - } + }, + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m1 (surmise 1, data 1), m2 (surmise 1, data 1): True\n", + "m1 (surmise 1, data 1), m3 (surmise 2, data 1): False\n", + "m1 (surmise 1, data 1), m4 (surmise 1, data 2): False\n", + "m1 (surmise 1, data 1), m5 (surmise 2, data 2): False\n", + "m2 (surmise 1, data 1), m3 (surmise 2, data 1): False\n", + "m2 (surmise 1, data 1), m4 (surmise 1, data 2): False\n", + "m2 (surmise 1, data 1), m5 (surmise 2, data 2): False\n", + "m3 (surmise 2, data 1), m4 (surmise 1, data 2): False\n", + "m3 (surmise 2, data 1), m5 (surmise 2, data 2): False\n", + "m4 (surmise 1, data 2), m5 (surmise 2, data 2): False\n" + ] + } + ], "source": [ "def run_workflow(seeds):\n", - " SEED, DATA_SEED = seeds\n", - " rng = np.random.default_rng(SEED)\n", - " surmise.set_RNG(rng)\n", - " data_rng = np.random.default_rng(DATA_SEED)\n", + " seed, data_seed = seeds\n", + " surmise.set_RNG(np.random.default_rng(seed))\n", + " data_rng = np.random.default_rng(data_seed)\n", " x = sps.uniform.rvs(0, 1, size=(50, 3), random_state=data_rng)\n", " x[:, 2] = x[:, 2] > 0.5\n", " thetas = sps.uniform.rvs(0, 1, size=(15, 4), random_state=data_rng)\n", " emu = emulator(x=x, theta=thetas, f=borehole_model(x, thetas), method='PCGP')\n", " return emu.predict(x=x, theta=thetas).mean()\n", "\n", + "surmise_1, surmise_2, data_1, data_2 = np.random.SeedSequence(SEED).spawn(4)\n", + "\n", "runs = {\n", - " \"m1 (SEED, DATA)\": run_workflow((SEED, DATA_SEED)),\n", - " \"m2 (SEED, DATA)\": run_workflow((SEED, DATA_SEED)),\n", - " \"m3 (SEED+1, DATA)\": run_workflow((SEED + 1, DATA_SEED)),\n", - " \"m4 (SEED, DATA+1)\": run_workflow((SEED, DATA_SEED + 1)),\n", - " \"m5 (SEED+1, DATA+1)\": run_workflow((SEED + 1, DATA_SEED + 1)),\n", + " \"m1 (surmise 1, data 1)\": run_workflow((surmise_1, data_1)),\n", + " \"m2 (surmise 1, data 1)\": run_workflow((surmise_1, data_1)),\n", + " \"m3 (surmise 2, data 1)\": run_workflow((surmise_2, data_1)),\n", + " \"m4 (surmise 1, data 2)\": run_workflow((surmise_1, data_2)),\n", + " \"m5 (surmise 2, data 2)\": run_workflow((surmise_2, data_2)),\n", "}\n", - "\n" - ], - "id": "bdc857a2f8474129", - "outputs": [], - "execution_count": 2 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "from itertools import combinations\n", "\n", - "for (run_name_a, run_result_a), (run_name_b, run_result_b) in combinations(runs.items(), 2):\n", + "for (run_name_a, run_result_a), (run_name_b, run_result_b) in it.combinations(runs.items(), 2):\n", " print(f\"{run_name_a}, {run_name_b}: {np.array_equal(run_result_a, run_result_b)}\")" - ], - "id": "6f5144717925efe7" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "d420f4289edb49e6", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## Changing RNG at any time\n", - "surmise keeps exactly one RNG at a time (i.e., a Python singleton). User may replace the RNG, e.g., to restart a numerical study from a known seed." - ], - "id": "d420f4289edb49e6" + "While surmise stores exactly one RNG at a time, users may change that RNG as needed for their study ({{eg}} to restart a numerical study from a known seed)." + ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": 8, + "id": "886be135dc93fd2e", + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], - "execution_count": null, "source": [ - "surmise.set_RNG(np.random.default_rng(SEED))\n", + "default_rng = np.random.default_rng(SEED)\n", + "surmise.set_RNG(default_rng)\n", "# do something\n", - "surmise.set_RNG(np.random.default_rng(SEED + 1))" - ], - "id": "886be135dc93fd2e" + "\n", + "pc64dxsm_rng = np.random.Generator(np.random.PCG64DXSM(SEED + 1))\n", + "surmise.set_RNG(pc64dxsm_rng)\n", + "# do something else" + ] } ], "metadata": { @@ -277,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.14" + "version": "3.13.7" } }, "nbformat": 4, diff --git a/docs/random_number_generation.rst b/docs/random_number_generation.rst index 0261bcd6..992c52fc 100644 --- a/docs/random_number_generation.rst +++ b/docs/random_number_generation.rst @@ -2,6 +2,8 @@ Random Number Generation ======================== +.. _`Jupyter book`: https://bandframework.github.io/surmise + Following typical practices, we refer to pseudorandom number generation and generators more generically as random number generation and random number generators (RNGs). @@ -9,40 +11,25 @@ generators (RNGs). |surmise| code uses exclusively the ``scipy.stats`` code to sample all random numbers and for performing typical statistical computations. At any point in time the code uses only a single user-provided ``scipy.stats``-compatible RNG to -sample random numbers. Therefore, before calling |surmise| code, users must -provide |surmise| with an RNG that is valid for their version of ``scipy`` as -well as correctly created and managed for their application. Note that where -possible all |surmise| code should reproduce the same results when the same task -is run with an identical RNG setup. - -Users are expected to create and manage ``scipy.stats``-compatible RNGs that -they set into |surmise| using the :py:func:`set_RNG` function. +sample random numbers. Therefore, before calling |surmise| code, users must use +the :py:func:`set_RNG` function to provide |surmise| with an RNG that is valid +for their version of ``scipy`` as well as correctly created and managed for +their application. Note that where possible all |surmise| code should reproduce +the same results when the same task is run with an identical RNG setup. .. autofunction:: surmise.set_RNG -The following demonstrates this and shows that users are free to change the -single RNG being used by |surmise|. - -.. code:: python - - import secrets - import surmise - import numpy as np - - RAND_SEED = secrets.randbits(128) - - surmise.set_RNG(np.random.default_rng(RAND_SEED)) - - surmise.set_RNG(np.random.default_rng(RAND_SEED + 1)) +Please refer to the RNG examples in the `Jupyter book`_ for guidance using RNGs +with |surmise|. .. + including the RNG configuration of external code. -External code offered officially through |surmise|, such as |bilby|, have -their own RNG usage scheme that is independent from the |surmise| scheme. -In particular, the RNG provided to |surmise| is never used explicitly by -external code. Instead, users are responsible for understanding the -external code's RNG scheme within the context of the application's needs and -providing additional RNG configuration information to |surmise| code that -uses the external code. - -Please refer to the RNG examples in the [Jupyter book](https://bandframework.github.io/surmise/intro.html) for guidance using RNGs with |surmise| including the RNG configuration of external code. +.. + External code offered officially through |surmise|, such as |bilby|, have + their own RNG usage scheme that is independent from the |surmise| scheme. + In particular, the RNG provided to |surmise| is never used explicitly by + external code. Instead, users are responsible for understanding the + external code's RNG scheme within the context of the application's needs and + providing additional RNG configuration information to |surmise| code that + uses the external code. From 1bd65111a4f0f64fad4f5aaf77fbe208708233f3 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 13:56:34 -0500 Subject: [PATCH 78/84] (Issue #188) Test MH with non-default bit generator type. As expected, the new results were not identical to the benchmark. However, the new results look reasonable. --- tools/MetropolisHastingsTestSuite.json | 6 +++--- tools/create_scipy_stats_rng.py | 8 ++++++-- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/tools/MetropolisHastingsTestSuite.json b/tools/MetropolisHastingsTestSuite.json index 1781506e..a5b6e6a4 100644 --- a/tools/MetropolisHastingsTestSuite.json +++ b/tools/MetropolisHastingsTestSuite.json @@ -30,7 +30,7 @@ }, "UniformStep": { "rng": { - "method": "default", + "method": "PCG64DXSM", "random_seed": 273495583654866805492797112903413765323 }, "StartDistribution": { @@ -44,10 +44,10 @@ "Scale": 6.0 }, "n_burn_samples": 10000, - "verbose": false + "verbose": true }, "SampleSkip": 10, - "Plot": false, + "Plot": true, "Benchmark": "MH_Uniform1D_UniformStep.benchmark", "n_samples": 100000 } diff --git a/tools/create_scipy_stats_rng.py b/tools/create_scipy_stats_rng.py index 11a02b0e..77136b2f 100644 --- a/tools/create_scipy_stats_rng.py +++ b/tools/create_scipy_stats_rng.py @@ -4,9 +4,13 @@ def create_scipy_stats_rng(rng_cfg): rand_method = rng_cfg["method"] rand_seed = rng_cfg["random_seed"] - assert rand_method.lower() == "default" print(f"RNG method\t\t{rand_method}") print(f"Random seed\t\t{rand_seed}") - return np.random.default_rng(rand_seed) + if rand_method.lower() == "default": + return np.random.default_rng(rand_seed) + elif rand_method.upper() == "PCG64DXSM": + return np.random.Generator(np.random.PCG64DXSM(rand_seed)) + + raise ValueError(f"Unsupported bit generator ({rand_method})") From 87713c9edd9bcb404b86aac7466cc6807952c6cc Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 15:00:52 -0500 Subject: [PATCH 79/84] (Issue #188) Clean as part of PR review. --- docs/rng_dev_guide.rst | 10 +++++++-- src/surmise/_RandomNumberGenerator.py | 31 +++++++++++++++++---------- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/docs/rng_dev_guide.rst b/docs/rng_dev_guide.rst index e27bf054..2dc9e850 100644 --- a/docs/rng_dev_guide.rst +++ b/docs/rng_dev_guide.rst @@ -7,7 +7,7 @@ generators more generically as random number generation and random number generators (RNGs). Please familiarize yourself with the RNG content in :ref:`rng_user_guide` before -reading this section, especially the reference to the Jupyterbook RNG example. Similarly, reviewing the historic record of |surmise| RNG +reading this section, especially the reference to the Jupyter book RNG example. Similarly, reviewing the historic record of |surmise| RNG requirements might be helpful to motivate the design and explain certain design decisions detailed here. @@ -62,6 +62,12 @@ using either the v1.15.0) or * ``scipy.stats`` RNG currently in use (|eg| using the RNG's ``choice`` method). +If ``scipy.stats`` and its RNG both offer similar sampling functionality, prefer +the use of ``scipy.stats`` functionality over the RNG's functionality so that +|surmise| code is decoupled as much as possible from the actual RNG object. In +other words, users pass in an object and we simply pass it to ``scipy.stats`` +without caring much what it is or what it can do. + In particular, no other packages, such as ``numpy.random``, should be used in |surmise| even if the current RNG is compatible with that package. This decision is also motivated by the fact that @@ -97,7 +103,7 @@ an RNG object. simultaneous use of both packages since both use [`numpy.random` RNGs](https://docs.scipy.org/doc/scipy/tutorial/stats/probability_distributions.html#random-number-generation). However, restricting use to just `scipy.stats` does indeed reduce the - complexity of understanding and managing the use of two related by different + complexity of understanding and managing the use of two related but different packages. In addition, it protects surmise from any possible decoupling of the two packages that might result in the two packages using different RNGs. diff --git a/src/surmise/_RandomNumberGenerator.py b/src/surmise/_RandomNumberGenerator.py index fded9cd7..d69cab76 100644 --- a/src/surmise/_RandomNumberGenerator.py +++ b/src/surmise/_RandomNumberGenerator.py @@ -11,6 +11,15 @@ def __call__(cls): class RandomNumberGenerator(metaclass=_RngSingleton): + + @classmethod + def _is_valid(cls, rng): + """ + Check general design assumptions + """ + return isinstance(rng, np.random.Generator) and \ + hasattr(rng, "choice") and callable(getattr(rng, "choice")) + def __init__(self): """ This class is implemented using the Singleton design pattern and @@ -39,13 +48,13 @@ def __init__(self): # for their version of scipy.stats and the rest of the surmise code will # use it correctly so long as the rest of the scipy.stats interface has # not changed significantly. - self.__rng = None + self._clear_RNG() @property def scipy_stats_RNG(self): """ |surmise| internal code should never store the RNG obtained with this for - later use (e.g., in a class's constructor). Rather upon each invocation, + later use (|eg| in a class's constructor). Rather upon each invocation, the internal code shall use this member function to access the current RNG set into |surmise|. @@ -57,7 +66,7 @@ def scipy_stats_RNG(self): Current global RNG to be used by all |surmise| code with ``scipy.stats`` for all random number generation """ - if self.__rng is None: + if not RandomNumberGenerator._is_valid(self.__rng): raise RuntimeError("Please use set_RNG before using surmise") return self.__rng @@ -73,16 +82,16 @@ def scipy_stats_RNG(self, rng): ``scipy.stats``-compatible RNG that all |surmise| code should use for all random number generation """ - # Check general design assumptions - if not isinstance(rng, np.random.Generator): + if not RandomNumberGenerator._is_valid(rng): raise TypeError("Given RNG cannot be used with scipy.stats") - elif (not hasattr(rng, "choice")) or \ - (not callable(getattr(rng, "choice"))): - raise RuntimeError("Given RNG does not provide the choice function") - self.__rng = rng def _clear_RNG(self): - """Testing support only. Returns singleton to its unset state. This is not intended for user manipulation - of the RNG.""" + """ + **Testing support only** + + Returns singleton to its unset state. This is not intended for user + manipulation of the RNG or by general |surmise| code. + """ self.__rng = None + assert not RandomNumberGenerator._is_valid(self.__rng) From 6cc2390a3154894ee18f6f1374a604787db3c66c Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 15:40:06 -0500 Subject: [PATCH 80/84] (Issue #188) RandomNumberGenerator class does all error checking. --- src/surmise/calibration.py | 4 ---- src/surmise/emulation.py | 4 ---- 2 files changed, 8 deletions(-) diff --git a/src/surmise/calibration.py b/src/surmise/calibration.py index ee9182cc..9baa62b6 100644 --- a/src/surmise/calibration.py +++ b/src/surmise/calibration.py @@ -120,10 +120,6 @@ def rnd(n): msg = f"Using unofficial research {method} calibrator" warnings.warn(msg) - # Confirm RNG is set - global_RNG = RandomNumberGenerator().scipy_stats_RNG - assert isinstance(global_RNG, np.random.Generator) - # cast to numpy.float64, currently only for theta and f. if y is not None: y = cast_f64_dtype(y) diff --git a/src/surmise/emulation.py b/src/surmise/emulation.py index 7e079bff..800aa67b 100644 --- a/src/surmise/emulation.py +++ b/src/surmise/emulation.py @@ -93,10 +93,6 @@ def __init__(self, msg = f"Using unofficial research {method} emulator" warnings.warn(msg) - # ensures that RNG is set - global_RNG = RandomNumberGenerator().scipy_stats_RNG - assert isinstance(global_RNG, np.random.Generator) - # cast to numpy.float64, currently only for theta and f. if theta is not None: theta = cast_f64_dtype(theta) From f3ed88be14f310c196661b800af646996bdbc115 Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 16:36:17 -0500 Subject: [PATCH 81/84] (Issue #188) Cleaning as part of PR review. Random draws not using the global RNG. Internal tests probably shouldn't override the underlying failure message, but rather only print more information to help understand the context of the failure. This should hopefully get actions passing again. --- src/surmise/calibration.py | 3 ++- .../calibrationmethods/directbayeswoodbury.py | 4 +++- src/surmise/calibrationmethods/mlbayeswoodbury.py | 4 +++- src/surmise/calibrationmethods/simulationpost.py | 4 +++- src/surmise/tests/test_cal_samplers.py | 4 ++-- src/surmise/tests/test_rng.py | 14 +++----------- 6 files changed, 16 insertions(+), 17 deletions(-) diff --git a/src/surmise/calibration.py b/src/surmise/calibration.py index 9baa62b6..55f73ecf 100644 --- a/src/surmise/calibration.py +++ b/src/surmise/calibration.py @@ -148,7 +148,8 @@ def rnd(n): try: thetatestsamp = thetaprior.rnd(100) except Exception: - raise ValueError('thetaprior.rnd(100) failed.') + print('thetaprior.rnd(100) failed.') + raise if thetatestsamp.shape[0] != 100: raise ValueError('thetaprior.rnd(100) failed to give 100 values.') diff --git a/src/surmise/calibrationmethods/directbayeswoodbury.py b/src/surmise/calibrationmethods/directbayeswoodbury.py index 45d9a53a..4ce1c20a 100644 --- a/src/surmise/calibrationmethods/directbayeswoodbury.py +++ b/src/surmise/calibrationmethods/directbayeswoodbury.py @@ -204,6 +204,7 @@ def predict(predinfo, fitinfo, emu, x, args=None): None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG theta = fitinfo['thetarnd'] if theta.ndim == 1 and fitinfo['theta'].shape[1] > 1.5: @@ -219,7 +220,8 @@ def predict(predinfo, fitinfo, emu, x, args=None): for k in range(0, theta.shape[0]): re = emucovxhalf[:, k, :] @ \ - sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2])) + sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2]), + random_state=global_RNG) predinfo['rnd'][k, :] += re predinfo['mean'] = np.mean(emumean, 1) diff --git a/src/surmise/calibrationmethods/mlbayeswoodbury.py b/src/surmise/calibrationmethods/mlbayeswoodbury.py index 08cc10ae..c281408b 100644 --- a/src/surmise/calibrationmethods/mlbayeswoodbury.py +++ b/src/surmise/calibrationmethods/mlbayeswoodbury.py @@ -243,6 +243,7 @@ def predict(predinfo, fitinfo, emu, x, args=None): None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG theta = fitinfo['thetarnd'] if theta.ndim == 1 and fitinfo['theta'].shape[1] > 1.5: @@ -258,7 +259,8 @@ def predict(predinfo, fitinfo, emu, x, args=None): for k in range(0, theta.shape[0]): re = emucovxhalf[:, k, :] @ \ - sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2])) + sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2]), + random_state=global_RNG) predinfo['rnd'][k, :] += re predinfo['mean'] = np.mean(emumean, 1) diff --git a/src/surmise/calibrationmethods/simulationpost.py b/src/surmise/calibrationmethods/simulationpost.py index 40b758bc..17d5bc05 100644 --- a/src/surmise/calibrationmethods/simulationpost.py +++ b/src/surmise/calibrationmethods/simulationpost.py @@ -198,6 +198,7 @@ def predict(predinfo, fitinfo, emu, x, args=None): None. ''' + global_RNG = RandomNumberGenerator().scipy_stats_RNG theta = fitinfo['thetarnd'] if theta.ndim == 1 and fitinfo['theta'].shape[1] > 1.5: @@ -213,7 +214,8 @@ def predict(predinfo, fitinfo, emu, x, args=None): for k in range(0, theta.shape[0]): re = emucovxhalf[:, k, :] @ \ - sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2])) + sps.norm.rvs(0, 1, size=(emucovxhalf.shape[2]), + random_state=global_RNG) predinfo['rnd'][k, :] += re predinfo['mean'] = np.mean(emumean, 1) diff --git a/src/surmise/tests/test_cal_samplers.py b/src/surmise/tests/test_cal_samplers.py index a6d2ea0f..47763eba 100644 --- a/src/surmise/tests/test_cal_samplers.py +++ b/src/surmise/tests/test_cal_samplers.py @@ -150,14 +150,14 @@ class TestSampler: (True, y, x, priorphys_lin, obsvar2, pytest.raises(ValueError)), (True, y, x, priorphys_lin, obsvar3, pytest.raises(ValueError)), (True, y, x, prior_rnd1, obsvar, pytest.raises(ValueError)), - (True, y, x, prior_rnd2, obsvar, pytest.raises(ValueError)), + (True, y, x, prior_rnd2, obsvar, pytest.raises(AttributeError)), (True, y, x, prior_lpdf1, obsvar, pytest.raises(ValueError)), (True, y, x, prior_lpdf2, obsvar, pytest.raises(ValueError)), (True, y, x, prior_example1, obsvar, pytest.raises(ValueError)), (True, y1, x, priorphys_lin, obsvar, pytest.raises(ValueError)), (True, None, x, priorphys_lin, obsvar, pytest.raises(ValueError)), (False, y, x, priorphys_lin, obsvar, pytest.raises(ValueError)), - (True, y, x, None, obsvar, pytest.raises(ValueError)), + (True, y, x, None, obsvar, pytest.raises(AttributeError)), ], ) def test_cal_emu_fails(self, sampler, emu_lin_pcgp, use_emu, input2, input3, input4, input5, expectation): diff --git a/src/surmise/tests/test_rng.py b/src/surmise/tests/test_rng.py index af8448ef..d6edc4f2 100644 --- a/src/surmise/tests/test_rng.py +++ b/src/surmise/tests/test_rng.py @@ -32,15 +32,6 @@ def test_calibrator_methods_raise_after_clear(cal_directbayes, no_rng): cal_directbayes.theta.rnd(10) -def test_bisect_reproducibility(): - # (a) prior draws alone — expected to FAIL with current scenarios.py - set_RNG(np.random.default_rng(123)) - d1 = sc.priorphys_lin.rnd(50) - set_RNG(np.random.default_rng(123)) - d2 = sc.priorphys_lin.rnd(50) - assert np.allclose(d1, d2) - - # Test to reproduce results def _build_and_draw(seed): """Seed the whole sequence, fit emu + cal, return posterior draws.""" @@ -60,11 +51,12 @@ def test_emu_cal_reproducible(): # same RNGs should return the same samples draws1 = _build_and_draw(123) draws2 = _build_and_draw(123) - assert np.allclose(draws1, draws2) + assert np.array_equal(draws1, draws2, equal_nan=False) def test_emu_cal_seed_sensitivity(): # different RNGs should return different samples draws1 = _build_and_draw(123) draws2 = _build_and_draw(456) - assert not np.allclose(draws1, draws2) + assert draws1.shape == draws2.shape + assert not np.array_equal(draws1, draws2, equal_nan=False) From fc65a7e64cd6c6ca20ce6afe8330c7ada0e78e38 Mon Sep 17 00:00:00 2001 From: Moses Chan Date: Wed, 15 Jul 2026 16:51:13 -0500 Subject: [PATCH 82/84] triggering set_RNG exception message when RNG not set --- book/notebooks/setRNG.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/book/notebooks/setRNG.ipynb b/book/notebooks/setRNG.ipynb index c00aa3a7..785e47e1 100644 --- a/book/notebooks/setRNG.ipynb +++ b/book/notebooks/setRNG.ipynb @@ -154,8 +154,10 @@ } ], "source": [ + "from surmise.tests.shared_scenario import x_bh as x, thetatot_bh as theta\n", "try:\n", - " emulator(passthroughfunc=borehole_model, method='PCGP')\n", + " f = borehole_model(x, theta)\n", + " emulator(x=x, theta=theta, f=f, method='PCGP')\n", "except Exception as e:\n", " print(e)" ] From 0abe68c26dd2a23d1c2b596a9dba4d7ccba482ff Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Wed, 15 Jul 2026 17:50:18 -0500 Subject: [PATCH 83/84] (Issue #188) More cleaning as part of PR review. --- book/notebooks/setRNG.ipynb | 57 ++++++++++--------------------------- 1 file changed, 15 insertions(+), 42 deletions(-) diff --git a/book/notebooks/setRNG.ipynb b/book/notebooks/setRNG.ipynb index 785e47e1..076dba98 100644 --- a/book/notebooks/setRNG.ipynb +++ b/book/notebooks/setRNG.ipynb @@ -43,7 +43,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "surmise v0.4.1.dev235+gcb4d82219.d20260715\n", + "surmise v0.4.1.dev238+g87713c9ed.d20260715\n", "scipy v1.18.0\n" ] } @@ -96,45 +96,14 @@ }, "source": [ "## Borehole function setup\n", - "The borehole function provides a fast-to-evaluate function as a classic emulation/calibration example. We make use of the function coded in surmise's test suite. The only important detail here is that the function takes any parameters within the unit-cube, where $x\\in [0, 1]^3, \\theta \\in [0, 1]^4$." + "The borehole function provides a fast-to-evaluate function as a classic emulation/calibration example. We make use of the function coded in surmise's test suite. The only important detail here is that the function takes any parameters within the unit-cube, where $x\\in [0, 1]^3, \\theta \\in [0, 1]^4$.\n", + "\n", + "Since this is a fresh session, surmise does not yet have an RNG set and any surmise call that needs randomness will raise an exception with instructions." ] }, { "cell_type": "code", "execution_count": 3, - "id": "ce875636-ce29-48e6-aa09-349f79f65e4e", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from surmise.tests.shared_scenario import borehole_model" - ] - }, - { - "cell_type": "markdown", - "id": "fdef1b695baba740", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## surmise refuses to run without `set_RNG`\n", - "A fresh session has no RNG. Any surmise call that needs randomness will raise an exception with instructions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, "id": "cfc834703311b25f", "metadata": { "deletable": true, @@ -154,10 +123,14 @@ } ], "source": [ - "from surmise.tests.shared_scenario import x_bh as x, thetatot_bh as theta\n", + "from surmise.tests.shared_scenario import (\n", + " borehole_model,\n", + " x_bh as x,\n", + " thetatot_bh as theta\n", + ")\n", + "\n", "try:\n", - " f = borehole_model(x, theta)\n", - " emulator(x=x, theta=theta, f=f, method='PCGP')\n", + " emulator(x=x, theta=theta, f=borehole_model(x, theta), method='PCGP')\n", "except Exception as e:\n", " print(e)" ] @@ -180,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "13789822d1cc8700", "metadata": { "ExecuteTime": { @@ -242,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "2c618a8d59ca9ade", "metadata": { "ExecuteTime": { @@ -303,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "bdc857a2f8474129", "metadata": { "ExecuteTime": { @@ -378,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "886be135dc93fd2e", "metadata": { "deletable": true, From 5953e59922bb78864d63aee4bf593d6a1d0400fe Mon Sep 17 00:00:00 2001 From: Jared O'Neal Date: Thu, 16 Jul 2026 10:13:18 -0500 Subject: [PATCH 84/84] (Issue #187) Try adding timeout to build doc action. Last time this action ran it needed less than 5 minutes, so 10 should be a nice conservative value that won't waste too much runner time on texlive failures. --- .github/workflows/build-docs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index f48de24c..68a37d99 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -12,6 +12,7 @@ on: jobs: build_pypkg_sphinx: runs-on: ${{ matrix.os }} + timeout-minutes: 10 strategy: matrix: # We would like to use 3.12, but this presently fails with the error: