[AGIPD][TESTs]test_agipdlib AGIPDCtrl and get_bias_voltage for AGIPD1M and AGIPD500K

Based on https://git.xfel.eu/detectors/pycalibration/-/merge_requests/651

• Test AGIPDCtrl() methods to read conditions from Fast or slow data.
• differentiate between AGIPD1M and AGIPD500K sources and keys to read bias_voltage. (temporary unbreakable change.)

Description

Latest changes to agipdlib had a class AGIPDCtrl() created collecting all methods to read AGIPD parameter conditions from fast and slow data.

1. I have added tests for these methods
2. I have split some methods based on their functionality.
3. As bias_voltage is stored in different paths for AGIPD1M and AGIPD500K, a condition was added to select the right source.

• This is a temporary change and I think one should give the correct source and key through get_bias_voltage arguments.

4. All of the tests are based on EXtra-data mocked data. I have added mock_agipd1m_run, mock_agipd500K_run and mock_agipd1m_run_old at :

How Has This Been Tested?

Unit tests.

Relevant Documents (optional)

Types of changes

• Test (additional or refactored tests)

Checklist:

• My code follows the code style of this project.
• I have updated the documentation accordingly.
• I added tests where appropriate.

Reviewers

@calibration

src/cal_tools/agipdalgs.pyx

@@ -120,35 +120,18 @@ def histogram2d(cnp.ndarray[float, ndim=1] data_x, cnp.ndarray[float, ndim=1] da
     return ret, np.linspace(min_x, max_x, bins_x+1), np.linspace(min_y, max_y, bins_y+1)
 
 
-@boundscheck(False)
-@wraparound(False)
-def gain_choose(cnp.ndarray[cnp.uint8_t, ndim=3] a, cnp.ndarray[cnp.float32_t, ndim=4] choices):
-    """Specialised fast equivalent of np.choose(), to select data for a per-pixel gain stage"""
-    cdef int i, j, k
-    cdef cnp.uint8_t v
-    cdef cnp.ndarray[cnp.float32_t, ndim=3] out
-    out = np.zeros_like(a, dtype=np.float32)
-
-    assert (<object>choices).shape == (3,) + (<object>a).shape
-
-    with nogil:
-        for i in range(a.shape[0]):
-            for j in range(a.shape[1]):
-                for k in range(a.shape[2]):
-                    v = a[i, j, k]
-                    out[i, j, k] = choices[v, i, j, k]
-
-    return out
-
+ctypedef fused choices_t:
+    cnp.float32_t
+    cnp.int32_t
 
 @boundscheck(False)
 @wraparound(False)
-def gain_choose_int(cnp.ndarray[cnp.uint8_t, ndim=3] a, cnp.ndarray[cnp.int32_t, ndim=4] choices):
+def gain_choose(cnp.ndarray[cnp.uint8_t, ndim=3] a, cnp.ndarray[choices_t, ndim=4] choices):
     """Specialised fast equivalent of np.choose(), to select data for a per-pixel gain stage"""
     cdef int i, j, k
     cdef cnp.uint8_t v
-    cdef cnp.ndarray[cnp.int32_t, ndim=3] out
-    out = np.zeros_like(a, dtype=np.int32)
+    cdef cnp.ndarray[choices_t, ndim=3] out
+    out = np.zeros_like(a, dtype=(<object>choices).dtype)
 
     assert (<object>choices).shape == (3,) + (<object>a).shape