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Go over Anastasia's polarization tests and clean up repo a bit
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Abe Levitan committed
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@@ -1,29 +0,0 @@
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import numpy as numpy
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import torch as t
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def polarize_probe_linear(probe, polar_angle):
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"""
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Applies a linear polarizer to the probe
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Parameters:
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----------
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probe: t.Tensor
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A (...)x2x1xMxL tensor representing the probe
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polar_angle: float
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The angle between the fast-axis of the linear polarizer and the x-axis
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"""
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polarizer = t.tensor([[(math.cos(theta)) ** 2, math.sin(2 * theta) / 2], [math.sin(2 * theta) / 2, (math.sin(theta)) ** 2]])
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return t.tensordot(polarizer, probe, dims=([-3], [-4]))
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def apply_jones_matrix(probe, jones_matrix):
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"""
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Applies the given Jones matrix to the probe
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Parameters:
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----------
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probe: t.Tensor
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A (...)x2x1xMxL tensor representing the probe
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jones_matrix: t.tensor
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2x2
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"""
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@@ -1,5 +1,8 @@
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import numpy as numpy
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import torch as t
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# Abe - again, we don't need math here. replace with torch-native functions
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# in all the definitions here. We have to use torch here if we want to be
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# able to calculate derivatives w.r.t. the polarizer angle, say.
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import math
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from math import sin
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from math import cos
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@@ -10,8 +13,10 @@ __all__ = ['apply_linear_polarizer',
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'apply_quarter_wave_plate',
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'apply_circular_polarizer',
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'apply_jones_matrix']
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print()
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# Abe -
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def apply_linear_polarizer(probe, polarizer, multiple_modes=True, transpose=True):
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"""
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Applies a linear polarizer to the probe
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@@ -33,6 +38,9 @@ def apply_linear_polarizer(probe, polarizer, multiple_modes=True, transpose=True
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# polarizer = t.tensor([polarizer])
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if len(polarizer,shape) == 0:
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polarizer = t.tensor([polarizer])
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# Abe - Something feels overly complicated about this. Let's take a look
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# at it together and do some simplification
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pol_cos = lambda idx: cos(math.radians(polarizer[idx]))
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pol_sin = lambda idx: sin(math.radians(polarizer[idx]))
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jones_matrices = t.stack(([t.tensor([[(pol_cos(idx)) ** 2, pol_sin(idx) * pol_cos(idx)], [pol_sin(idx) * pol_cos(idx), (pol_sin(idx)) ** 2]]).to(dtype=t.cfloat) for idx in range(len(polarizer))]))
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@@ -1,48 +0,0 @@
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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 copy import copy
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import h5py
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import pathlib
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from CDTools.datasets import CDataset, Ptycho2DDataset, PolarizedPtycho2DDataset
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from CDTools.tools import data as cdtdata, initializers, polarization, interactions
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from CDTools.tools import plotting
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from torch.utils import data as torchdata
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from matplotlib import pyplot as plt
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from matplotlib.widgets import Slider
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from matplotlib import ticker
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from CDTools.models import FancyPtycho, PolarizedFancyPtycho
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from torch.utils import data as torchdata
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dataset = PolarizedPtycho2DDataset.from_cxi('test_ptycho.cxi')
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model = PolarizedFancyPtycho.from_dataset(dataset)
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# print('probe', model.probe.shape)
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# print('object', model.obj.shape)
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# print('transl', dataset.translations.shape)
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# print('polarizer', dataset.polarizer.shape)
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# print('analyzer', dataset.analyzer.shape)
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exit_waves = model.interaction(*dataset[[0]][0])
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print(exit_waves.shape)
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# sim_patterns = model.forward(*dataset[:][0])
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for loss in model.Adam_optimize(10, dataset, batch_size=1, lr=0.002, schedule=True):
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# And we liveplot the updates to the model as they happen
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print(model.report())
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# probe = t.rand(5, 2, 10, 12)
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# obj = t.rand(2, 2, 10, 12)
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# polarizer = t.rand(7)
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# analyzer = t.rand(7)
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# pol_probes = polarization.apply_linear_polarizer(probe, polarizer)
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# exit_waves = interactions.ptycho_2D_sinc(
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# probe, obj, translations,
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# shift_probe=True, multiple_modes=True, polarized=True)
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# analyzed_exit_waves = polarization.apply_linear_polarizer(exit_waves, analyzer)
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# print(analyzed_exit_waves.shape)
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@@ -1,246 +0,0 @@
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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 copy import copy
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import h5py
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import pathlib
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from CDTools.datasets import CDataset, Ptycho2DDataset, PolarizedPtycho2DDataset
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from CDTools.tools import data as cdtdata, initializers, polarization, interactions
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from CDTools.tools.polarization import apply_jones_matrix as jones
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from CDTools.tools import plotting
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from torch.utils import data as torchdata
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from matplotlib import pyplot as plt
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from matplotlib.widgets import Slider
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from matplotlib import ticker
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from CDTools.models import FancyPtycho, PolarizedFancyPtycho
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from torch.utils import data as torchdata
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from math import cos as cos, sin as sin
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import math
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dataset = PolarizedPtycho2DDataset.from_cxi('test_ptycho.cxi')
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model = PolarizedFancyPtycho.from_dataset(dataset)
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def test_apply_jones_matrix(multiple_modes=False, multiple_patterns=False, diff_jones_across_pixels=False, transpose=True, mode=0):
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angle = 87
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angle_2 = angle - 45
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def polarizer(angle):
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theta = math.radians(angle)
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polarizer = t.tensor([[(cos(theta)) ** 2, sin(2 * theta) / 2], [sin(2 * theta) / 2, sin(theta) ** 2]]).to(dtype=t.cfloat)
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return polarizer
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exponent = t.exp(-1j * math.pi / 4 * t.ones(2, 2))
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theta2 = math.radians(angle_2)
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quarter_plate = t.tensor([[(cos(theta2))**2 + 1j * (sin(theta2))**2, (1 - 1j) * sin(theta2) * cos(theta2)],
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[(1 - 1j) * sin(theta2) * cos(theta2), (sin(theta2))**2 + 1j * (cos(theta2))**2]])
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def build_from_quarters(jones1, jones2, jones3, jones4):
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x = t.cat((t.stack((jones1, jones1), dim=-1), t.stack((jones2, jones2), dim=-1)), dim=-1)
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y = t.cat((t.stack((jones3, jones3), dim=-1), t.stack((jones4, jones4), dim=-1)), dim=-1)
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x = t.stack((x, x), dim=-2)
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y = t.stack((y, y), dim=-2)
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return t.cat((x, y), dim=-2).to(dtype=t.cfloat)
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jones_plate = t.matmul(quarter_plate, polarizer(angle))
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jones0 = polarizer(0)
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jones90 = polarizer(90)
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jones45 = polarizer(45)
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'''
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after applying the polarizer and the quarter_plate, the probe should get circularly polarized
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probe: no multiple modes, 1 diffr pattern
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2xMxL
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jones_matrix: same jones matrix applied to all the pixels
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2x2
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'''
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if not multiple_modes and not multiple_patterns and not diff_jones_across_pixels:
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probe = t.rand(2, 4, 4, dtype=t.cfloat)
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out = jones(jones(probe, polarizer(angle), multiple_modes=False, transpose=transpose), quarter_plate, multiple_modes=False, transpose=transpose, mode=0)
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print('expected shape:(2, 3, 4)')
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print('actual:', out.shape)
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print('simulated:', out)
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'''probe: no multiple modes, 1 diffr pattern
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2xMxL = 2x4x4
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jones_matrix: jones matrices differ from pixel to pixel
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2x2xMxL = 2x2x4x4
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4 quarters:
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1: [:, :, :-2, :-2] - circular_polarizer,
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2: [:, :, :-2, -2:] - 0
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3: [:, :, -2:, :-2] - 90
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4: [:, :, -2:, -2:] - 45
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'''
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if not multiple_modes and not multiple_patterns and diff_jones_across_pixels:
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jones_matr = build_from_quarters(jones_plate, jones0, jones90, jones45)
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probe = t.ones(2, 4, 4).to(dtype=t.cfloat)
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print('jones:', jones_matr)
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# print('jones:', jones_matr)
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out = jones(probe, jones_matr, multiple_modes=False, transpose=transpose)
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print('expected shape:(2, 4, 4)')
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print('simulated:', out_simulated.shape)
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print('simulated:', out)
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'''
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probe: no multiple modes, multiple diffr patterns
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Nx2xMxL = 3x2x4x4
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jones_matrix: same jones matrix applied to all the pixels
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Nx2x2 = 3x2x2
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3 different matrices for each probe:
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1: 0
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2: 45
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3: 90
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'''
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if not multiple_modes and multiple_patterns and not diff_jones_across_pixels:
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probe = t.ones(2, 4, 4, dtype=t.cfloat)
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probe = t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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jones_matr = t.stack(([polarizer(angle) for angle in [0, 45, 90]]), dim=0)
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print('probe shape:', probe.shape, 'jones shape', jones_matr.shape)
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out = jones(probe, jones_matr, multiple_modes=False, transpose=transpose)
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print('expected shape: (3, 2, 4, 4)')
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print('actual:', out.shape)
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print('simulated:', out)
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'''
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probe: no multiple modes, multiple diffr pattern
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Nx2xMxL = 3x2x4x4
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jones_matrix: jones matrices differ from pixel to pixel
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Nx2x2xMxL = 3x2x2x4x4
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jones matrix for the 1st pattern:
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1: quat plate, 2: 90, 3: 0, 4: 45
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jones matrix for the 2nd pattern:
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1: 0, 2: 45, 3: 90, 4: quat plate
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jones matrix for the 3rd pattern:
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1: 90, 2: 0, 3: 45, 4: quat plate
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'''
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if not multiple_modes and multiple_patterns and diff_jones_across_pixels:
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jones_m = [build_from_quarters(jones_plate, jones90, jones0, jones45),
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build_from_quarters(jones0, jones45, jones90, jones_plate),
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build_from_quarters(jones90, jones0, jones45, jones_plate)]
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jones_matr = t.stack(([i for i in jones_m]), dim=0)
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probe = t.ones(2, 4, 4, dtype=t.cfloat)
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probe = t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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out = jones(probe, jones_matr, multiple_modes=False, transpose=transpose)
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print('expected shape: (3, 2, 4, 4)')
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print('actual shape:', out.shape)
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print('simulated:', out)
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'''
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probe: multiple modes, 1 diffr pattern
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Px2xMxL = 2x2x3x4
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jones_matrix: same jones matrix applied to all the pixels
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2x2 - quarter plate
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Nx2xMxL = 3x2x4x4
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'''
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if multiple_modes and not multiple_patterns and not diff_jones_across_pixels:
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probe = t.rand(2, 2, 3, 4, dtype=t.cfloat)
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out = jones(jones(probe, polarizer(angle), multiple_modes=True, transpose=transpose), quarter_plate, multiple_modes=True, transpose=transpose)
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print('expected shape: (2, 2, 3, 4)')
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print('actual:', out.shape)
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print('simulated:', out)
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'''
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probe: multiple modes, 1 diffr pattern
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Px2xMxL = 3x2x4x4
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jones_matrix: jones matrices differ from pixel to pixel
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2xMxL = 2x4x4
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4 quarters:
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1: [:, :, :-2, :-2] - circular_polarizer,
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2: [:, :, :-2, -2:] - 0
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3: [:, :, -2:, :-2] - 90
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4: [:, :, -2:, -2:] - 45
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'''
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if multiple_modes and not multiple_patterns and diff_jones_across_pixels:
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jones_matr = build_from_quarters(jones_plate, jones0, jones90, jones45)
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probe = t.ones(2, 4, 4, dtype=t.cfloat)
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probe = t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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out = jones(probe, jones_matr, multiple_modes=True, transpose=transpose)
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print('expected shape: (3, 2, 4, 4)')
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print('actual shape:', out.shape)
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print('simulated:', out)
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'''
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probe: multiple modes, multiple diffr patterns
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NxPx2xMxL = 3x2x2x3x4
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jones_matrix: same jones matrix applied to all the pixels (although differs from pattern to pattern)
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Nx2x2 = 3x2x2
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3 different matrices for each probe in one mode:
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1: 0
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2: 45
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3: 90
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'''
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if multiple_modes and multiple_patterns and not diff_jones_across_pixels:
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probe = t.ones(2, 3, 4, dtype=t.cfloat)
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# 1st mode
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probe_mode1 = t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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# 2nd mode
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probe_mode2 = 10 * t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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probe = t.stack((probe_mode1, probe_mode2), dim=1)
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jones_matr = t.stack(([polarizer(angle) for angle in [0, 45, 90]]), dim=0)
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print('probe shape:', probe.shape, 'jones shape', jones_matr.shape)
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out = jones(probe, jones_matr, multiple_modes=True, transpose=transpose)
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print('expected shape: (3, 2, 2, 3, 4)')
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print('actual:', out.shape)
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print('simulated (patterns in one mode):', out[:, mode, :, :, :])
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'''
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probe: multiple modes, multiple diffr pattern
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NxPx2xMxL = 3x4x2x4x4
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jones_matrix: jones matrices differ from pixel to pixel
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NxPx2x2xMxL = 3x2x2x2x4x4
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(differs across the patterns in each mode)
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jones matrix for the 1st pattern:
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1: quat plate, 2: 90, 3: 0, 4: 45
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jones matrix for the 2nd pattern:
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1: 0, 2: 45, 3: 90, 4: quat plate
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jones matrix for the 3rd pattern:
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1: 90, 2: 0, 3: 45, 4: quat plate
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'''
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if multiple_modes and multiple_patterns and diff_jones_across_pixels:
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jones_m = [build_from_quarters(jones_plate, jones90, jones0, jones45),
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build_from_quarters(jones0, jones45, jones90, jones_plate),
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build_from_quarters(jones90, jones0, jones45, jones_plate)]
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jones_matr = t.stack(([i for i in jones_m]), dim=0)
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probe = t.ones(2, 4, 4, dtype=t.cfloat)
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# one mode
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probe_mode = t.stack(([probe * (i + 1) for i in range(3)]), dim=0)
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probe = t.stack(([probe_mode * (10 ** i) for i in range(4)]), dim=1)
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print('probe:', probe.shape)
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print('jones:', jones_matr.shape)
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out = jones(probe, jones_matr, multiple_modes=True, transpose=transpose)
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print('expected shape: (3, 2, 2, 4, 4)')
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print('actual shape:', out.shape)
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print('simulated patterns in one mode:', out[:, mode, :, :, :])
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return None
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probe = t.rand(2, 3, 4, dtype=t.cfloat)
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angle = 57
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angle_2 = angle - 45
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theta = math.radians(angle)
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polarizer = t.tensor([[(cos(theta)) ** 2, sin(2 * theta) / 2], [sin(2 * theta) / 2, sin(theta) ** 2]]).to(dtype=t.cfloat)
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exponent = t.exp(-1j * math.pi / 4 * t.ones(2, 2))
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theta2 = math.radians(angle_2)
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quarter_plate = t.tensor([[(cos(theta2))**2 + 1j * (sin(theta2))**2, (1 - 1j) * sin(theta2) * cos(theta2)],
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[(1 - 1j) * sin(theta2) * cos(theta2), (sin(theta2))**2 + 1j * (cos(theta2))**2]])
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# pol = t.matmul(polarizer, probe)
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# pl = t.matmul(quarter_plate, pol)
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# print(pol)
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# print(pl)
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# pol = pol.unsqueeze(-1).unsqueeze(-1)
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# print(polarization.apply_jones_matrix(polarization.apply_jones_matrix(probe, polarizer), quarter_plate, multiple_modes=False))
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test_apply_jones_matrix(multiple_modes=True, multiple_patterns=True, diff_jones_across_pixels=True, mode=2)
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@@ -1,23 +1,33 @@
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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 copy import copy
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import h5py
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import pathlib
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from CDTools.datasets import CDataset, Ptycho2DDataset, PolarizedPtycho2DDataset
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from CDTools.tools import data as cdtdata, initializers, polarization, interactions
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from CDTools.tools.polarization import apply_jones_matrix as jones
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from CDTools.tools import plotting
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from torch.utils import data as torchdata
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from matplotlib import pyplot as plt
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from matplotlib.widgets import Slider
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from matplotlib import ticker
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from CDTools.models import FancyPtycho, PolarizedFancyPtycho
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from torch.utils import data as torchdata
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# Abe - I removed all the imports that didn't need to be here.
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# Abe - A few issues. First, you could just write "from math import cos, sin"
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# Second, we already have numpy imported, so better to use np.cos and np.sin
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from math import cos as cos, sin as sin
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# numpy also has np.pi and np.deg2rad
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import math
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# Further comments:
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#
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# 1) You should also introduce an assert statement for the output shape
|
||||
# checking, e.g. assert out.shape == t.Size(<shape>). See example in the
|
||||
# first test.
|
||||
#
|
||||
# 2) Instead of using "assert a and b and c and d...", use separate assert
|
||||
# statements for each condition. This is both more readable, and it allows the
|
||||
# testing system to pinpoint exactly which of the assert statements failed.
|
||||
#
|
||||
# 3) USE SPACES INSTEAD OF TABS!!! 4 spaces per indent is the convention for
|
||||
# this project.
|
||||
#
|
||||
# 4) Docstrings should go after the function definition, rather than before
|
||||
#
|
||||
# 5) Still missing cases where Jones matrix has multiple entries (N) but probe
|
||||
# doesn't. Low priority.
|
||||
|
||||
|
||||
angle = 87
|
||||
@@ -27,6 +37,7 @@ def polarizer(angle):
|
||||
theta = math.radians(angle)
|
||||
polarizer = t.tensor([[(cos(theta)) ** 2, sin(2 * theta) / 2], [sin(2 * theta) / 2, sin(theta) ** 2]]).to(dtype=t.cfloat)
|
||||
return polarizer
|
||||
|
||||
exponent = t.exp(-1j * math.pi / 4 * t.ones(2, 2))
|
||||
theta2 = math.radians(angle_2)
|
||||
quarter_plate = t.tensor([[(cos(theta2))**2 + 1j * (sin(theta2))**2, (1 - 1j) * sin(theta2) * cos(theta2)],
|
||||
@@ -54,7 +65,7 @@ jones_matrix: same jones matrix applied to all the pixels
|
||||
'''
|
||||
transpose = True
|
||||
def test_apply_jones_matrix_no_modes_no_mult_patterns_one_jones_matr():
|
||||
probe = t.rand(2, 4, 4, dtype=t.cfloat)
|
||||
probe = t.rand(2, 3, 4, dtype=t.cfloat)
|
||||
print(polarizer)
|
||||
out = jones(jones(probe, polarizer(angle), multiple_modes=False, transpose=transpose),
|
||||
quarter_plate, multiple_modes=False, transpose=transpose)
|
||||
@@ -62,6 +73,8 @@ def test_apply_jones_matrix_no_modes_no_mult_patterns_one_jones_matr():
|
||||
print('actual:', out.shape)
|
||||
print('simulated:', out)
|
||||
|
||||
assert out.shape == t.Size((2,3,4))
|
||||
|
||||
assert np.allclose(np.real(out[0]), np.imag(out[1]))
|
||||
|
||||
|
||||
@@ -75,6 +88,7 @@ jones_matrix: jones matrices differ from pixel to pixel
|
||||
3: [:, :, -2:, :-2] - 90
|
||||
4: [:, :, -2:, -2:] - 45
|
||||
'''
|
||||
|
||||
def test_apply_jones_matrix_no_modes_no_mult_patterns_diff_jones_matr():
|
||||
jones_matr = build_from_quarters(jones_plate, jones0, jones90, jones45)
|
||||
probe = t.ones(2, 4, 4).to(dtype=t.cfloat)
|
||||
@@ -88,12 +102,13 @@ def test_apply_jones_matrix_no_modes_no_mult_patterns_diff_jones_matr():
|
||||
print('simulated:', out.shape)
|
||||
print('simulated:', out)
|
||||
|
||||
assert (np.allclose(np.real(out[0, :-2, :-2]), np.imag(out[1, :-2, :-2]))
|
||||
and t.allclose(out[0, :-2, -2:], t.ones(2, 2, dtype=t.cfloat))
|
||||
and t.allclose(out[1, :-2, -2:], t.zeros(2, 2, dtype=t.cfloat))
|
||||
and t.allclose(out[0, -2:, :-2], t.zeros(2, 2, dtype=t.cfloat))
|
||||
and t.allclose(out[1, -2:, :-2], t.ones(2, 2, dtype=t.cfloat))
|
||||
and t.allclose(out[0, -2:, -2:], out[0, -2:, -2:]))
|
||||
# Example of using multiple asserts
|
||||
assert np.allclose(np.real(out[0, :-2, :-2]), np.imag(out[1, :-2, :-2]))
|
||||
assert t.allclose(out[0, :-2, -2:], t.ones(2, 2, dtype=t.cfloat))
|
||||
assert t.allclose(out[1, :-2, -2:], t.zeros(2, 2, dtype=t.cfloat))
|
||||
assert t.allclose(out[0, -2:, :-2], t.zeros(2, 2, dtype=t.cfloat))
|
||||
assert t.allclose(out[1, -2:, :-2], t.ones(2, 2, dtype=t.cfloat))
|
||||
assert t.allclose(out[0, -2:, -2:], out[0, -2:, -2:])
|
||||
|
||||
|
||||
'''
|
||||
@@ -320,19 +335,7 @@ def test_apply_jones_matrix_mult_modes_mult_patterns_diff_jones_matr():
|
||||
and np.allclose(np.real(out[2, 3, 0, -2:, -2:]), np.real(out[2, 3, 0, -2:, -2:])))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
'''
|
||||
I REALIZED THAT ANOTHER POSSIBLE SISTUATION WE HAVEN'T CONSIDERED BEFORE IS WHEN THE JONES MATRIX DOESN'T DIFFER ACROSS THE PATTERNS
|
||||
IN OTHER WORDS, PROBE.SHAPE CONTAINS N (NUM OF PATTERNS) BUT JONES_MATRIX.SHAPE DOESN'T
|
||||
|
||||
|
||||
NOW, WE'LL TEST THE CASES WHEN THE PROBE DOESN'T DIFFER FROM PATTERN TO PATTERN
|
||||
'''
|
||||
|
||||
|
||||
'''
|
||||
'''
|
||||
probe: no multiple modes, multiple diffr patterns
|
||||
Nx2xMxL = 3x2x4x4
|
||||
jones_matrix: same jones matrix applied to all the pixels
|
||||
@@ -278,7 +278,7 @@ def test_near_field():
|
||||
assert z.grad != 0
|
||||
assert spacing.grad[0] != 0
|
||||
assert wavelength.grad !=0
|
||||
|
||||
|
||||
|
||||
def test_generalized_near_field():
|
||||
|
||||
|
||||
Reference in new issue
Block a user