mirror of
https://github.com/cdtools-developers/cdtools.git
synced 2026-09-17 08:19:57 +02:00
Small changes to fix stuff
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+11
-10
@@ -451,16 +451,17 @@ class CDIModel(t.nn.Module):
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"""
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print('base models inspect: checking the object')
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a = self.obj.detach()
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def saveobj(a, filename):
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a = np.abs(a)
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plt.imshow(a)
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plt.savefig(filename)
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f = ['base_a.png', 'base_b.png', 'base_c.png', 'base_d.png']
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comp = [a[i, j, :, :] for i, j in zip([0, 0, 1, 1], [0, 1, 0, 1])]
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for i in range(4):
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saveobj(comp[i], f[i])
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#print('base models inspect: checking the object')
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#a = self.obj.detach()
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#def saveobj(a, filename):
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# a = np.abs(a)
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# plt.imshow(a)
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# plt.savefig(filename)
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#f = ['base_a.png', 'base_b.png', 'base_c.png', 'base_d.png']
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#comp = [a[i, j, :, :] for i, j in zip([0, 0, 1, 1], [0, 1, 0, 1])]
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#for i in range(4):
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# saveobj(comp[i], f[i])
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first_update = False
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if update and hasattr(self, 'figs') and self.figs:
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figs = self.figs
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@@ -63,7 +63,23 @@ class PolarizedFancyPtycho(FancyPtycho):
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def from_dataset(cls, dataset, probe_size=None, randomize_ang=0, padding=0, n_modes=1, dm_rank=None, translation_scale = 1, saturation=None, probe_support_radius=None, propagation_distance=None, restrict_obj=-1, scattering_mode=None, oversampling=1, auto_center=False, opt_for_fft=False, loss='amplitude mse', units='um', left_polarized=True):
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# When using this method, remember to pass through the inputs
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model = FancyPtycho.from_dataset(dataset, probe_size=None, randomize_ang=0, padding=0, n_modes=1, dm_rank=None, translation_scale = 1, saturation=None, probe_support_radius=None, propagation_distance=None, scattering_mode=None, oversampling=1, auto_center=False, opt_for_fft=False, loss='amplitude mse', units='um')
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model = FancyPtycho.from_dataset(
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dataset,
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probe_size=probe_size,
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randomize_ang=randomize_ang,
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padding=padding,
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n_modes=n_modes,
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dm_rank=dm_rank,
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translation_scale=translation_scale,
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saturation=saturation,
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probe_support_radius=probe_support_radius,
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propagation_distance=propagation_distance,
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scattering_mode=scattering_mode,
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oversampling=oversampling,
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auto_center=auto_center,
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opt_for_fft=opt_for_fft,
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loss=loss,
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units=units)
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# Mutate the class to its subclass
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@@ -79,9 +95,9 @@ class PolarizedFancyPtycho(FancyPtycho):
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probe_max = t.max(t.abs(probe))
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probe_stack = [0.01 * probe_max * t.rand(probe.shape, dtype=probe.dtype) for i in range(n_modes - 1)]
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probe = t.stack([probe, ] + probe_stack)
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print('probe', type(probe), probe.shape)
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#print('probe', type(probe), probe.shape)
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model.probe.data = probe
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print(model.probe.shape)
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#print(model.probe.shape)
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# obj = t.stack((model.obj.data, model.obj.data), dim=-3)
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# model.obj.data = t.stack((obj.data, obj.data), dim=-4)
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# obj = t.exp(1j * randomize_ang * (t.rand(obj_size)-0.5))
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@@ -90,14 +106,14 @@ class PolarizedFancyPtycho(FancyPtycho):
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# initialization (e.g. ((obj,0*obj),(0*obj,obj))
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obj = t.stack((obj, obj), dim=-3)
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obj = t.stack((obj, obj), dim=-4)
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print('object', type(obj), obj.shape)
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#print('object', type(obj), obj.shape)
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model.obj.data = obj
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print('polarized fancy ptycho from datset obj')
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#print('polarized fancy ptycho from datset obj')
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a = obj.detach()
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plt.imshow(np.real(a[0, 0, :, :]))
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plt.show()
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plt.imshow(np.real(a[0, 1, :, :]))
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plt.show()
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#plt.imshow(np.real(a[0, 0, :, :]))
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#plt.figure()
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#plt.imshow(np.real(a[0, 1, :, :]))
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#plt.show()
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# tensor vs tensor.data
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return model
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@@ -17,11 +17,11 @@ from matplotlib import ticker, patheffects
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__all__ = ['colorize', 'plot_amplitude', 'plot_phase',
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'plot_colorized', 'plot_translations', 'get_units_factor',
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'plot_nanomap', 'plot_real', 'plot_imag',
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'plot_nanomap_with_images',
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'polarized_plot_component_amplitudes',
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'polarized_plot_phase_ret',
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'polarized_plot_global_phases',
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'polarized_plot_ellipses']
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'plot_nanomap_with_images']#,
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#'polarized_plot_component_amplitudes',
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#'polarized_plot_phase_ret',
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#'polarized_plot_global_phases',
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#'polarized_plot_ellipses']
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def colorize(z):
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