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101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
from __future__ import division, print_function, absolute_import
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import pytest
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import numpy as np
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import torch as t
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from itertools import combinations
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from CDTools.tools import analysis, cmath, initializers
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def test_orthogonalize_probes():
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# The test strategy should be to define a few non-orthogonal probes
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# and orthogonalize them. Then we can test two features of the results:
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# 1) Are they orthogonal?
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# 2) Is the total intensity at each point the same as it was originally?
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probe_xs = np.arange(128) - 64
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probe_ys = np.arange(150) - 75
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probe_Ys, probe_Xs = np.meshgrid(probe_ys, probe_xs)
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probe_Rs = np.sqrt(probe_Xs**2 + probe_Ys**2)
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probes = np.array([10*np.exp(-probe_Rs**2 / (2 * 10**2)),
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3*np.exp(-probe_Rs**2 / (2 * 12**2)),
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1*np.exp(-probe_Rs**2 / (2 * 15**2))]).astype(np.complex64)
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ortho_probes = cmath.torch_to_complex(analysis.orthogonalize_probes(probes))
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for p1,p2 in combinations(ortho_probes,2):
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assert np.sum(np.conj(p1)*p2) / np.sum(np.abs(p1)**2) < 1e-6
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for probe in ortho_probes:
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print(np.sum(np.abs(probe)**2))
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for probe in probes:
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print(np.sum(np.abs(probe)**2))
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probe_intensity = np.sum(np.abs(probes)**2,axis=0)
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ortho_probe_intensity = np.sum(np.abs(ortho_probes)**2,axis=0)
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assert np.allclose(probe_intensity,ortho_probe_intensity)
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from matplotlib import pyplot as plt
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def test_standardize():
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# Start by making a probe and object that should meet the standardization
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# conditions
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probe = initializers.gaussian((230,240),(20,20),curvature=(0.01,0.01))
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probe = cmath.torch_to_complex(probe)
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probe = probe * np.sqrt(len(probe.ravel()) / np.sum(np.abs(probe)**2))
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probe = probe * np.exp(-1j * np.angle(np.sum(probe)))
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assert np.isclose(1, np.sum(np.abs(probe)**2)/ len(probe.ravel()))
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assert np.isclose(0,np.angle(np.sum(probe)))
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obj = 30 * np.random.rand(230,240) * np.exp(1j * (np.random.rand(230,240) - 0.5))
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obj_slice = np.s_[(obj.shape[0]//8)*3:(obj.shape[0]//8)*5,
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(obj.shape[1]//8)*3:(obj.shape[1]//8)*5]
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obj = obj * np.exp(-1j * np.angle(np.sum(obj[obj_slice])))
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assert np.isclose(0,np.angle(np.sum(obj[obj_slice])))
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# Then make a nonstandard version of them and standardize it
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# First, don't add a phase ramp and test
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test_probe = probe * 37.6 * np.exp(1j*0.35)
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test_obj = obj / 37.6 * np.exp(1j*1.43)
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s_probe, s_obj = analysis.standardize(test_probe, test_obj)
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assert np.allclose(probe, s_probe)
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assert np.allclose(obj, s_obj)
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# Test that it works on torch tensors
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s_probe, s_obj = analysis.standardize(cmath.complex_to_torch(test_probe).to(t.float32), cmath.complex_to_torch(test_obj).to(t.float32))
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s_probe = cmath.torch_to_complex(s_probe)
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s_obj = cmath.torch_to_complex(s_obj)
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assert np.allclose(probe, s_probe)
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assert np.allclose(obj, s_obj)
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# And ensure that standardization maps back to the standard versions
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phase_ramp_dir = (np.random.rand(2) - 0.5)
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probe_Xs, probe_Ys = np.mgrid[:probe.shape[0],:probe.shape[1]]
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phase_ramp = np.exp(1j*probe_Ys * phase_ramp_dir[1]+
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1j*probe_Xs * phase_ramp_dir[0])
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test_probe = test_probe * phase_ramp
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obj_Xs, obj_Ys = np.mgrid[:obj.shape[0],:obj.shape[1]]
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phase_ramp = np.exp(-1j*obj_Ys * phase_ramp_dir[1]+
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-1j*obj_Xs * phase_ramp_dir[0])
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test_obj = test_obj * phase_ramp
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s_probe, s_obj = analysis.standardize(test_probe, test_obj, correct_ramp=True)
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assert np.max(s_probe - probe) / np.max(np.abs(probe)) < 1e-4
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assert np.max(s_obj - obj) / np.max(np.abs(obj)) < 1e-4
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