100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
import pytest
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import numpy as np
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import unittest.mock
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import cristallina.analysis
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__author__ = "Alexander Steppke"
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@unittest.mock.patch("jungfrau_utils.file_adapter.locate_gain_file", lambda path, **kwargs: "tests/data/gains.h5")
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@unittest.mock.patch("jungfrau_utils.file_adapter.locate_pedestal_file", lambda path, **kwargs: "tests/data/JF16T03V01.res.h5")
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def test_image_calculations():
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res = cristallina.analysis.perform_image_calculations(['tests/data/p20841/raw/run0185/data/acq0001.*.h5'])
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# these values are only correct when using the specific gain and pedestal files included in the test data
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# they do not correspond to the gain and pedestal files used in the actual analysis (otherwise we need to include several here as test data)
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intensity = [1712858.6,
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693994.06,
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1766390.0,
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1055504.9,
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1516520.9,
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461969.06,
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3148285.5,
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934917.5,
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1866691.6,
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798191.2,
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2250207.0,
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453842.6]
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assert np.allclose(res["JF16T03V01_intensity"], intensity)
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def test_joblib_memory():
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""" We need joblib for fast caching of intermediate results in all cases. So we check
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if the basic function caching to disk works.
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"""
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def calc_example(x):
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return x**2
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calc_cached = cristallina.analysis.memory.cache(calc_example)
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assert calc_cached(8) == 64
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assert calc_cached.check_call_in_cache(8) == True
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def test_minimal_2d_gaussian():
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image = np.array([[0,0,0,0,0],
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[0,0,0,0,0],
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[0,0,1,0,0],
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[0,0,0,0,0],
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[0,0,0,0,0],
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])
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center_x, center_y, result = cristallina.analysis.fit_2d_gaussian(image)
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assert np.allclose(center_x, 2.0, rtol=1e-04)
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assert np.allclose(center_y, 2.0, rtol=1e-04)
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def test_2d_gaussian():
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# define normalized 2D gaussian
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def gauss2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
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return (1 / (2 * np.pi * sx * sy) * np.exp(-((x - mx) ** 2 / (2 * sx**2.0) + (y - my) ** 2 / (2 * sy**2))))
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x = np.arange(0, 150, 1)
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y = np.arange(0, 100, 1)
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x, y = np.meshgrid(x, y)
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z = gauss2d(x, y, mx=40, my=50, sx=20, sy=40)
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center_x, center_y, result = cristallina.analysis.fit_2d_gaussian(z)
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assert np.allclose(center_x, 40, rtol=1e-04)
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assert np.allclose(center_y, 50, rtol=1e-04)
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def test_2d_gaussian_rotated():
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# define normalized 2D gaussian
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def gauss2d_rotated(x=0, y=0, center_x=0, center_y=0, sx=1, sy=1, rotation=0):
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sr = np.sin(rotation)
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cr = np.cos(rotation)
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center_x_rot = center_x * cr - center_y * sr
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center_y_rot = center_x * sr + center_y * cr
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x_rot = x * cr - y * sr
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y_rot = x * sr + y * cr
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return (1 / (2 * np.pi * sx * sy) * np.exp(-((x_rot - center_x_rot) ** 2 / (2 * sx**2.0) + (y_rot - center_y_rot) ** 2 / (2 * sy**2))))
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x = np.arange(0, 150, 1)
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y = np.arange(0, 100, 1)
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x, y = np.meshgrid(x, y)
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z = 100*gauss2d_rotated(x, y, center_x=40, center_y=50, sx=10, sy=20, rotation=0.5)
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center_x, center_y, result = cristallina.analysis.fit_2d_gaussian_rotated(z, vary_rotation=True, plot=False)
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assert np.allclose(center_x, 40, rtol=1e-04)
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assert np.allclose(center_y, 50, rtol=1e-04)
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assert np.allclose(result.params['rotation'].value, 0.5, rtol=1e-02) |