Base knife-edge scan analysis implementation

Hi @lleguy @mercadil,

This is an example of a base implementation, specifically the knife edge scan analysis from Laurent's tb.knife_edge. This is agnostic from xarray data format, thus this can be easily used on other means, such as on a Karabo device.

I also have found out that there could be problems with using bisect especially with repetitive elements. I use slicing instead to find the valid range.

src/toolbox_scs/base/tests/test_knife_edge.py

 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