20b0467f89
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
111 lines
4.4 KiB
Python
111 lines
4.4 KiB
Python
import numpy as np
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import numpy.testing as npt
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import pytest
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from retry import retry
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from caimira.calculator.models.monte_carlo import sampleable
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@retry(tries=10)
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@pytest.mark.parametrize(
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"mean, std",[
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[1., 0.5],
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]
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)
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def test_normal(mean, std):
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# Test that the sample has approximately the right mean,
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# std deviation and distribution function.
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sample_size = 2000000
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samples = sampleable.Normal(mean, std).generate_samples(sample_size)
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histogram, bins = np.histogram(samples,bins=100, density=True)
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selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
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(bins[1:]+bins[:-1])/2,histogram) if b>=0.25 and b<=1.75])
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exact_dist = 1/(np.sqrt(2*np.pi)*std) * np.exp(
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-((np.array(selected_bins)-mean)/std)**2/2)
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assert len(samples) == sample_size
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npt.assert_allclose([samples.mean(), samples.std()], [mean, std], rtol=0.01)
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npt.assert_allclose(selected_histogram, exact_dist, rtol=0.02)
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@pytest.mark.parametrize(
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"mean_gaussian, std_gaussian",[
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[-0.6872121723362303, 0.10498338229297108],
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]
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)
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def test_lognormal(mean_gaussian, std_gaussian):
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# Test that the sample has approximately the right mean,
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# std deviation and distribution function.
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sample_size = 2000000
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samples = sampleable.LogNormal(mean_gaussian, std_gaussian
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).generate_samples(sample_size)
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histogram, bins = np.histogram(samples,bins=50, density=True)
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selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
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(bins[1:]+bins[:-1])/2,histogram) if b>=0.4 and b<=0.6])
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selected_bins = np.array(selected_bins)
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exact_dist = ( 1/(selected_bins*np.sqrt(2*np.pi)*std_gaussian) *
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np.exp(-((np.log(selected_bins)-mean_gaussian)/std_gaussian)**2/2) )
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exact_mean = np.exp(mean_gaussian + std_gaussian**2/2)
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exact_std = np.sqrt( (np.exp(std_gaussian**2)-1) *
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np.exp(2*mean_gaussian + std_gaussian**2) )
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assert len(samples) == sample_size
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npt.assert_allclose([samples.mean(), samples.std()],
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[exact_mean, exact_std], rtol=0.01)
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npt.assert_allclose(selected_histogram, exact_dist, rtol=0.03)
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@pytest.mark.parametrize(
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"use_kernel",
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[False, True],
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)
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def test_custom(use_kernel):
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# Test that the sample has approximately the right distribution
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# function, with both Custom and CustomKernel method. The latter
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# is less accurate for smooth functions.
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# the distribution function is an inverted parabola, with maximum 0.15,
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# which is 0 at the bounds (0,10) (normalized)
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norm = 500/3.
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function = lambda x: (-(5 - x)**2 + 25)/norm
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max_function = 0.15
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sample_size = 2000000
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if use_kernel:
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variable = np.linspace(0.1,9.9,100)
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frequencies = function(variable)
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samples = sampleable.CustomKernel(variable, frequencies,
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kernel_bandwidth=0.1
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).generate_samples(sample_size)
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else:
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samples = sampleable.Custom((0, 10), function, max_function
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).generate_samples(sample_size)
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histogram, bins = np.histogram(samples, bins=100, density=True)
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selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
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(bins[1:]+bins[:-1])/2,histogram) if b>=1 and b<=9])
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correct_dist = function(np.array(selected_bins))
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assert len(samples) == sample_size
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npt.assert_allclose(selected_histogram, correct_dist, rtol=0.05)
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def test_logcustomkernel():
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# Test that the sample has approximately the right distribution
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# function, for the LogCustomKernel.
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# the distribution function is an inverted parabola vs. the log of
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# the variable (normalized)
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norm = 500/3.
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function = lambda x: (-(5 - x)**2 + 25)/norm
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sample_size = 2000000
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log_variable = np.linspace(0.1,9.9,100)
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frequencies = function(log_variable)
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samples = sampleable.LogCustomKernel(log_variable, frequencies,
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kernel_bandwidth=0.1
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).generate_samples(sample_size)
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histogram, bins = np.histogram(np.log10(samples), bins=100, density=True)
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selected_bins,selected_histogram = zip(*[(b,h) for b,h in zip(
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(bins[1:]+bins[:-1])/2,histogram) if b>=1 and b<=9])
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correct_dist = function(np.array(selected_bins))
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assert len(samples) == sample_size
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npt.assert_allclose(selected_histogram, correct_dist, rtol=0.05)
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