From 79cf3f721f6d267ba6e6c8f4cb433f56ebf5efbd Mon Sep 17 00:00:00 2001
From: Cammille Carinan <cammille.carinan@xfel.eu>
Date: Tue, 18 May 2021 11:03:20 +0200
Subject: [PATCH] Rename methods, fix and test finite_array
---
src/toolbox_scs/base/knife_edge.py | 25 ++++----
src/toolbox_scs/base/tests/test_knife_edge.py | 57 +++++++++++++++----
2 files changed, 57 insertions(+), 25 deletions(-)
diff --git a/src/toolbox_scs/base/knife_edge.py b/src/toolbox_scs/base/knife_edge.py
index c2dfc35..3ccac7e 100644
--- a/src/toolbox_scs/base/knife_edge.py
+++ b/src/toolbox_scs/base/knife_edge.py
@@ -6,7 +6,9 @@ from scipy.optimize import curve_fit
def knife_edge(positions, intensities, axisRange=None, p0=None):
popt, pcov = knife_edge_base(positions, intensities,
axisRange=axisRange, p0=p0)
- width, std = popt[1], pcov[1, 1]**0.5
+ width, std = 0, 0
+ if popt is not None and pcov is not None:
+ width, std = popt[1], pcov[1, 1]**0.5
return width, std
@@ -36,7 +38,7 @@ def prepare_arrays(positions: np.ndarray, intensities: np.ndarray,
axisRange=None):
# Slice
if axisRange is not None:
- slice_ = range_slice(positions, *axisRange)
+ slice_ = range_mask(positions, *axisRange)
positions = positions[slice_]
intensities = intensities[slice_]
@@ -46,33 +48,28 @@ def prepare_arrays(positions: np.ndarray, intensities: np.ndarray,
intensities = intensities.flatten()
assert positions.shape == intensities.shape
- # Clean both arrays by only getting finite values and sorting
- positions, intensities = finite_arrays(positions, wrt=intensities)
- intensities, positions = sort_arrays(intensities, wrt=positions)
+ # Clean both arrays by only getting finite values
+ positions, intensities = finite_array(positions, wrt=intensities)
+ intensities, positions = finite_array(intensities, wrt=positions)
return positions, intensities
-def sort_arrays(array, *, wrt=None):
- index = np.argsort(wrt)
- return array[index], wrt[index]
-
-
-def finite_arrays(array, *, wrt=None):
+def finite_array(array, *, wrt):
index = np.isfinite(wrt)
return array[index], wrt[index]
-def range_slice(array, minimum=None, maximum=None):
+def range_mask(array, minimum=None, maximum=None):
default = np.ones(array.shape, dtype=np.bool)
min_slice, max_slice = default, default
if minimum is not None:
- if minimum > array[-1]:
+ if minimum > np.nanmax(array):
raise ValueError('The range minimum is too large.')
min_slice = array >= minimum
if maximum is not None:
- if maximum < array[0]:
+ if maximum < np.nanmin(array):
raise ValueError('The range maximum is too small.')
max_slice = array <= maximum
diff --git a/src/toolbox_scs/base/tests/test_knife_edge.py b/src/toolbox_scs/base/tests/test_knife_edge.py
index 9f20a73..4699597 100644
--- a/src/toolbox_scs/base/tests/test_knife_edge.py
+++ b/src/toolbox_scs/base/tests/test_knife_edge.py
@@ -1,45 +1,80 @@
import numpy as np
import pytest
-from ..knife_edge import range_slice
+from ..knife_edge import prepare_arrays, range_mask
-def test_range_slice():
+def test_range_mask():
arr = np.array([1, 2, 3, 4, 5])
# Exact
- slice_ = range_slice(arr, minimum=2, maximum=4)
+ slice_ = range_mask(arr, minimum=2, maximum=4)
np.testing.assert_array_equal(slice_, [False, True, True, True, False])
# Range exceeds the closest values
- slice_ = range_slice(arr, minimum=1.75, maximum=4.25)
+ slice_ = range_mask(arr, minimum=1.75, maximum=4.25)
np.testing.assert_array_equal(slice_, [False, True, True, True, False])
# Range misses the closest values
- slice_ = range_slice(arr, minimum=2.25, maximum=3.75)
+ slice_ = range_mask(arr, minimum=2.25, maximum=3.75)
np.testing.assert_array_equal(slice_, [False, False, True, False, False])
# Equidistant
- slice_ = range_slice(arr, minimum=2.5, maximum=4.5)
+ slice_ = range_mask(arr, minimum=2.5, maximum=4.5)
np.testing.assert_array_equal(slice_, [False, False, True, True, False])
# Out of bounds, valid minimum
- slice_ = range_slice(arr, minimum=0)
+ slice_ = range_mask(arr, minimum=0)
np.testing.assert_array_equal(slice_, [True, True, True, True, True])
# Out of bounds, invalid minimum
with pytest.raises(ValueError):
- range_slice(arr, minimum=6)
+ range_mask(arr, minimum=6)
# Out of bounds, valid maximum
- slice_ = range_slice(arr, maximum=6)
+ slice_ = range_mask(arr, maximum=6)
np.testing.assert_array_equal(slice_, [True, True, True, True, True])
# Out of bounds, invalid minimum
with pytest.raises(ValueError):
- range_slice(arr, maximum=0)
+ range_mask(arr, maximum=0)
# with NaNs
arr = np.array([1, np.nan, 3, np.nan, 5])
- slice_ = range_slice(arr, minimum=3)
+ slice_ = range_mask(arr, minimum=3)
np.testing.assert_array_equal(slice_, [False, False, True, False, True])
+
+
+def test_prepare_arrays():
+ # Setup test values
+ trains, pulses = 5, 10
+ size = trains * pulses
+ motor = np.arange(trains)
+ signal = np.random.randint(100, size=(trains, pulses))
+
+ # Test finite motor and signal values
+ positions, intensities = prepare_arrays(motor, signal)
+ assert positions.shape == (size,)
+ assert intensities.shape == (size,)
+
+ # Test finite motors and signals with NaNs
+ signal_nan = with_values(signal, value=np.nan, num=20)
+ positions, intensities = prepare_arrays(motor, signal_nan)
+ assert positions.shape == (size-20,)
+ assert np.isfinite(positions).all()
+ assert intensities.shape == (size-20,)
+ assert np.isfinite(intensities).all()
+
+ # Test finite signals and motors with NaNs
+ motor_nan = with_values(motor, value=np.nan, num=3)
+ positions, intensities = prepare_arrays(motor_nan, signal)
+ assert positions.shape == ((trains-3) * pulses,)
+ assert np.isfinite(positions).all()
+ assert intensities.shape == ((trains-3) * pulses,)
+ assert np.isfinite(intensities).all()
+
+
+def with_values(array, value, num=5):
+ copy = array.astype(np.float)
+ copy.ravel()[np.random.choice(copy.size, num, replace=False)] = value
+ return copy
--
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