[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/files.py

@@ -40,7 +40,7 @@ def sequence_trains(train_ids, trains_per_sequence=256):
             per sequence, 256 by default.
 
     Yields:
-        (int, array_like) Current sequence ID, train mask.
+        (int, slice) Current sequence ID, train mask.
     """
 
     num_trains = len(train_ids)
@@ -61,7 +61,7 @@ def sequence_pulses(train_ids, pulses_per_train=1, pulse_offsets=None,
             train. If scalar, it is assumed to be constant for all
             trains. If omitted, it is 1 by default.
         pulse_offsets (array_like, optional): Offsets for the first
-            pulse in each train, computed from pulses_per_train of
+            pulse in each train, computed from pulses_per_train if
             omitted.
         trains_per_sequence (int, optional): Number of trains
             per sequence, 256 by default.
@@ -156,7 +156,8 @@ class DataFile(h5py.File):
 
         return self
 
-    def create_index(self, train_ids, timestamp=None, flag=None, origin=None):
+    def create_index(self, train_ids, timestamps=None, flags=None,
+                     origins=None):
         """Create global INDEX datasets.
 
         These datasets are agnostic of any source and describe the
@@ -166,9 +167,9 @@ class DataFile(h5py.File):
             train_ids (array_like): Train IDs contained in this file.
             timestamps (array_like, optional): Timestamp of each train,
                 0 if omitted.
-            flag (array_like, optional): Whether the time server is the
+            flags (array_like, optional): Whether the time server is the
                 initial origin of each train, 1 if omitted.
-            origin (array_like, optional): Which source is the initial
+            origins (array_like, optional): Which source is the initial
                 origin of each train, -1 (time server) if omitted.
 
         Returns:
@@ -177,26 +178,27 @@ class DataFile(h5py.File):
 
         self.create_dataset('INDEX/trainId', data=train_ids, dtype=np.uint64)
 
-        if timestamp is None:
-            timestamp = np.zeros_like(train_ids, dtype=np.uint64)
-        elif len(timestamp) != len(train_ids):
-            raise ValueError('timestamp and train_ids must be same length')
+        if timestamps is None:
+            timestamps = np.zeros_like(train_ids, dtype=np.uint64)
+        elif len(timestamps) != len(train_ids):
+            raise ValueError('timestamps and train_ids must be same length')
 
-        self.create_dataset('INDEX/timestamp', data=timestamp, dtype=np.uint64)
+        self.create_dataset('INDEX/timestamp', data=timestamps,
+                            dtype=np.uint64)
 
-        if flag is None:
-            flag = np.ones_like(train_ids, dtype=np.int32)
-        elif len(flag) != len(train_ids):
-            raise ValueError('flag and train_ids must be same length')
+        if flags is None:
+            flags = np.ones_like(train_ids, dtype=np.int32)
+        elif len(flags) != len(train_ids):
+            raise ValueError('flags and train_ids must be same length')
 
-        self.create_dataset('INDEX/flag', data=flag, dtype=np.int32)
+        self.create_dataset('INDEX/flag', data=flags, dtype=np.int32)
 
-        if origin is None:
-            origin = np.full_like(train_ids, -1, dtype=np.int32)
-        elif len(origin) != len(train_ids):
-            raise ValueError('origin and train_ids must be same length')
+        if origins is None:
+            origins = np.full_like(train_ids, -1, dtype=np.int32)
+        elif len(origins) != len(train_ids):
+            raise ValueError('origins and train_ids must be same length')
 
-        self.create_dataset('INDEX/origin', data=origin, dtype=np.int32)
+        self.create_dataset('INDEX/origin', data=origins, dtype=np.int32)
 
     def create_control_source(self, source):
         """Create group for a control source ("slow data").
@@ -239,8 +241,8 @@ class DataFile(h5py.File):
     def create_metadata(self, like=None, *,
                         creation_date=None, update_date=None, proposal=0,
                         run=None, sequence=None, daq_library='1.x',
-                        karabo_framework='2.x', control_sources=[],
-                        instrument_sources=[]):
+                        karabo_framework='2.x', control_sources=(),
+                        instrument_sources=()):
         """Create METADATA datasets.
 
         Args:
@@ -392,7 +394,6 @@ class ControlSource(h5py.Group):
 
         self.create_dataset(f'{key}/value', data=values)
         self.create_dataset(f'{key}/timestamp', data=timestamps)
-        self.__nonempty_index = True
 
         if run_entry is None:
             run_entry = (values[0], timestamps[0])
@@ -441,11 +442,12 @@ class ControlSource(h5py.Group):
         self.__run_group.create_dataset(
             f'{key}/value', data=value, shape=shape, dtype=dtype)
         self.__run_group.create_dataset(
-            f'{key}/timestamp', data=value, shape=shape, dtype=np.uint64)
+            f'{key}/timestamp', data=timestamp, shape=shape, dtype=np.uint64)
 
     def create_index(self, num_trains):
         """Create source-specific INDEX datasets.
 
+
         Depending on whether this source has train-varying data or not,
         different count/first datasets are written.