mirror of
https://github.com/cdtools-developers/cdtools.git
synced 2026-09-10 05:22:41 +02:00
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
from __future__ import division, print_function, absolute_import
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import numpy as np
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import torch as t
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from matplotlib import pyplot as plt
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import pickle
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import argparse
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from scipy import fftpack
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from CDTools.tools import cmath, plotting
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from CDTools.tools import image_processing as ip
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from CDTools.tools.analysis import *
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def calc_prtf(synth_obj, objects, basis, obj_slice=None):
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if obj_slice is None:
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obj_slice = np.s_[(objects[0].shape[0]//8)*3:(objects[0].shape[0]//8)*5,
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(objects[0].shape[1]//8)*3:(objects[0].shape[1]//8)*5]
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synth_obj = cmath.complex_to_torch(synth_obj[obj_slice])
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synth_fft = cmath.cabssq(cmath.fftshift(t.fft(synth_obj,2))).numpy()
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prtfs = []
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for obj in objects:
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obj = cmath.complex_to_torch(obj[obj_slice])
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single_fft = cmath.cabssq(cmath.fftshift(t.fft(obj,2))).numpy()
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di = np.linalg.norm(basis[:,0])
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dj = np.linalg.norm(basis[:,1])
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i_freqs = fftpack.fftshift(fftpack.fftfreq(single_fft.shape[0],d=di))
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j_freqs = fftpack.fftshift(fftpack.fftfreq(single_fft.shape[1],d=dj))
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Js,Is = np.meshgrid(j_freqs,i_freqs)
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Is = Is - np.mean(Is)
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Js = Js - np.mean(Js)
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Rs = np.sqrt(Is**2+Js**2)
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single_ints, bins = np.histogram(Rs,bins=100,weights=single_fft)
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synth_ints, bins = np.histogram(Rs,bins=100,weights=synth_fft)
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prtfs.append(synth_ints/single_ints)
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return bins[:-1], np.mean(prtfs,axis=0)
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def make_argparser():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('file', help='The reconstruction file to calculate metrics for')
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parser.add_argument('--use-probe', '-up', action='store_true', help='Use the probe instead of the object to align the reconstructions')
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return parser
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if __name__ == '__main__':
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args = make_argparser().parse_args()
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with open(args.file, 'rb') as f:
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dataset = pickle.load(f)
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synth_probe, synth_obj, aligned_objs = synthesize_reconstructions(
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dataset['probe'], dataset['obj'], args.use_probe)
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freqs, prtf = calc_prtf(synth_obj, aligned_objs, dataset['basis'])
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plotting.plot_phase(dataset['probe'][0][0],basis=1e6*dataset['basis'])
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plotting.plot_amplitude(dataset['probe'][0][0],basis=1e6*dataset['basis'])
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plotting.plot_colorized(dataset['probe'][0][0],basis=1e6*dataset['basis'])
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plotting.plot_phase(synth_probe[1],basis=1e6*dataset['basis'])
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plotting.plot_amplitude(synth_probe[1],basis=1e6*dataset['basis'])
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plotting.plot_colorized(synth_probe[1],basis=1e6*dataset['basis'])
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plotting.plot_amplitude(synth_obj,basis=1e6*dataset['basis'])
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plotting.plot_colorized(synth_obj,basis=1e6*dataset['basis'])
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plotting.plot_phase(synth_obj,basis=1e6*dataset['basis'])
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plt.figure()
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plt.show()
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exit()
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real_translations = dataset['basis'].dot(dataset['translation'][0].transpose())
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real_translations -= np.min(real_translations,axis=1)[:,None]
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plt.plot(real_translations[0]*1e6,real_translations[1]*1e6,'k.')
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plt.plot(real_translations[0]*1e6,real_translations[1]*1e6,'b-',linewidth=0.5)
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plt.figure()
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plt.plot(freqs*1e-6, prtf)
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plt.show()
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