mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2026-09-21 12:52:12 +02:00
Erik Fröjdh
8551281eb7
- renamed pedestal.hpp to bind_Pedestal.hpp for python bindings - variance is now always calculated in double and made private - cost correctness in a few places - removed push_fast and explicit updates of mean from Pedestal. If performance is needed use FastPedestal - Pedestal no longer caches std
205 lines
7.1 KiB
Python
205 lines
7.1 KiB
Python
import numpy as np
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import pytest
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from aare import Pedestal, Pedestal_d, Pedestal_f, Pedestal_i16
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@pytest.mark.parametrize(
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("dtype", "pedestal_type"),
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[(np.float64, Pedestal_d), (np.float32, Pedestal_f), (np.int16, Pedestal_i16)],
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)
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def test_pedestal_factory(dtype, pedestal_type):
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pedestal = Pedestal(rows=2, cols=3, n_samples=4, dtype=dtype)
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assert isinstance(pedestal, pedestal_type)
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assert pedestal.rows == 2
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assert pedestal.cols == 3
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assert pedestal.n_samples == 4
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def test_pedestal_factory_defaults_to_double():
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pedestal = Pedestal(2, 3)
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assert isinstance(pedestal, Pedestal_d)
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assert pedestal.n_samples == 1000
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def test_pedestal_factory_rejects_unbound_dtype():
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with pytest.raises(ValueError, match="Unsupported dtype for Pedestal"):
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Pedestal(2, 3, dtype=np.int32)
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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@pytest.mark.parametrize("name", ["rows", "cols", "n_samples"])
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def test_pedestal_factory_rejects_negative_parameters(dtype, name):
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parameters = dict(rows=1, cols=1, n_samples=10)
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parameters[name] = -1
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with pytest.raises(TypeError):
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Pedestal(**parameters, dtype=dtype)
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@pytest.mark.parametrize("pedestal_type", [Pedestal_d, Pedestal_f, Pedestal_i16])
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@pytest.mark.parametrize(
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"args", [(-1, 1), (1, -1), (-1, 1, 10), (1, -1, 10), (1, 1, -1)]
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)
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def test_pedestal_constructor_rejects_negative_parameters(pedestal_type, args):
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with pytest.raises(TypeError):
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pedestal_type(*args)
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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def test_pedestal_std_stays_finite_after_settling(dtype):
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pedestal = Pedestal(1, 1, n_samples=10, dtype=dtype)
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pedestal.push(np.array([[16382]], dtype=np.uint16))
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frame = np.array([[16383]], dtype=np.uint16)
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for _ in range(201):
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pedestal.push(frame)
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noise = pedestal.std().item()
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assert np.isfinite(noise)
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assert 0 <= noise < 1e-3
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@pytest.mark.parametrize(
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("pedestal_type", "expected_dtype"),
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[(Pedestal_d, np.float64), (Pedestal_f, np.float32), (Pedestal_i16, np.int16)],
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)
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def test_double_precision_moments(pedestal_type, expected_dtype):
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pedestal = pedestal_type(1, 1, 2)
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for value in [30000, 30003, 30002]:
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pedestal.push(np.array([[value]], dtype=np.uint16))
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assert pedestal.mean().dtype == expected_dtype
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assert pedestal.std().dtype == expected_dtype
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np.testing.assert_array_equal(
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pedestal.mean(), np.array([[30001.75]], dtype=expected_dtype)
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)
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np.testing.assert_allclose(
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pedestal.std(),
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np.array([[np.sqrt(1.1875)]], dtype=expected_dtype),
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rtol=1e-6,
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)
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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@pytest.mark.parametrize("method", ["push", "push_with_threshold"])
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@pytest.mark.parametrize("shape", [(2, 2), (3, 2)])
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def test_pedestal_rejects_mismatched_frame_shapes(dtype, method, shape):
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pedestal = Pedestal(2, 3, dtype=dtype)
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initial = np.full((2, 3), 7, dtype=np.uint16)
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pedestal.push(initial)
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frame = np.zeros(shape, dtype=np.uint16)
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args = (
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(np.full((2, 3), 10, dtype=dtype),)
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if method == "push_with_threshold"
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else ()
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)
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with pytest.raises(
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RuntimeError, match="Frame shape does not match pedestal shape"
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):
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getattr(pedestal, method)(frame, *args)
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np.testing.assert_array_equal(pedestal.mean(), initial)
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@pytest.mark.parametrize(
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("pedestal_type", "expected_dtype"),
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[(Pedestal_d, np.float64), (Pedestal_f, np.float32)],
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)
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def test_numpy_array_minus_pedestal(pedestal_type, expected_dtype):
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pedestal = pedestal_type(2, 3)
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pedestal.push(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.uint16))
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array = np.array([[12, 14, 16], [18, 20, 22]], dtype=np.uint16)
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result = array - pedestal
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np.testing.assert_array_equal(
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result, np.array([[10, 10, 10], [10, 10, 10]], dtype=expected_dtype)
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)
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assert result.dtype == expected_dtype
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def test_numpy_array_minus_pedestal_rejects_incompatible_shape():
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pedestal = Pedestal_d(2, 3)
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array = np.zeros((2, 2), dtype=np.float64)
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with pytest.raises(ValueError):
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array - pedestal
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def test_pedestal_exposes_mean_as_read_only_buffer():
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pedestal = Pedestal_d(2, 3)
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pedestal.push(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.uint16))
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mean = np.asarray(pedestal)
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np.testing.assert_array_equal(mean, pedestal.view())
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assert np.shares_memory(mean, pedestal.view())
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assert not mean.flags.writeable
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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@pytest.mark.parametrize("method", ["push", "push_with_threshold"])
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def test_pedestal_push_accepts_contiguous_frames(dtype, method):
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pedestal = Pedestal(2, 3, dtype=dtype)
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frame = np.arange(6, dtype=np.uint16).reshape(2, 3)
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args = (
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(np.full((2, 3), 10, dtype=dtype),)
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if method == "push_with_threshold"
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else ()
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)
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getattr(pedestal, method)(frame, *args)
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np.testing.assert_array_equal(pedestal.mean(), frame)
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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@pytest.mark.parametrize("input_name", ["push_frame", "threshold_frame", "threshold"])
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@pytest.mark.parametrize("layout", ["transpose", "slice", "reverse"])
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def test_pedestal_rejects_noncontiguous_inputs(dtype, input_name, layout):
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pedestal = Pedestal(2, 3, dtype=dtype)
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frame = np.zeros((2, 3), dtype=np.uint16)
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threshold = np.full((2, 3), 10, dtype=dtype)
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input_dtype = dtype if input_name == "threshold" else np.uint16
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if layout == "transpose":
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invalid = np.ones((3, 2), dtype=input_dtype).T
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elif layout == "slice":
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invalid = np.ones((2, 6), dtype=input_dtype)[:, ::2]
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else:
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invalid = np.ones((4, 6), dtype=input_dtype)[:2, :3][:, ::-1]
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assert not invalid.flags.c_contiguous
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with pytest.raises(TypeError):
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if input_name == "push_frame":
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pedestal.push(invalid)
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elif input_name == "threshold_frame":
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pedestal.push_with_threshold(invalid, threshold)
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else:
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pedestal.push_with_threshold(frame, invalid)
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np.testing.assert_array_equal(pedestal.mean(), np.zeros((2, 3)))
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@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16])
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@pytest.mark.parametrize("input_name", ["push_frame", "threshold_frame", "threshold"])
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@pytest.mark.parametrize("shape", [(), (6,), (2, 3, 2)])
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def test_pedestal_rejects_inputs_with_wrong_ndim(dtype, input_name, shape):
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pedestal = Pedestal(2, 3, dtype=dtype)
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frame = np.ones((2, 3), dtype=np.uint16)
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threshold = np.full((2, 3), 10, dtype=dtype)
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input_dtype = dtype if input_name == "threshold" else np.uint16
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invalid = np.ones(shape, dtype=input_dtype)
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name = "Threshold" if input_name == "threshold" else "Frame"
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with pytest.raises(ValueError, match=f"{name} must be 2-dimensional"):
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if input_name == "push_frame":
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pedestal.push(invalid)
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elif input_name == "threshold_frame":
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pedestal.push_with_threshold(invalid, threshold)
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else:
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pedestal.push_with_threshold(frame, invalid)
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np.testing.assert_array_equal(pedestal.mean(), np.zeros((2, 3)))
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