Remove LPD Mini component class (pending redesign)

docs/agipd_lpd_data.rst

@@ -27,10 +27,6 @@ DSSC and JUNGFRAU, pulling together the separate modules into a single array.
 
    The methods of this class are identical to those of :class:`LPD1M`, below.
 
-.. autoclass:: LPDMini
-
-   The methods of this class are identical to those of :class:`LPD1M:, below.
-
 .. autoclass:: LPD1M
 
    .. automethod:: get_array

docs/lpd_data.ipynb

@@ -292,14 +292,6 @@
    "source": [
     "LPD data may also be recorded in *parallel gain* mode, resulting in high-, medium- and low-gain frames for each pulse. To read this kind of data with the correct labels, use `LPD1M(run, parallel_gain=True)`. This will retrieve data with an extra gain dimension, labelled with 0, 1 and 2 for high-, medium- and low-gain respectively."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The LPD Mini consists of a single module.  \n",
-    "Its data can be accessed similarly using the `LPDMini` class."
-   ]
   }
  ],
  "metadata": {

extra_data/components.py

@@ -19,7 +19,6 @@
     'AGIPD500K',
     'DSSC1M',
     'LPD1M',
-    'LPDMini',
     'JUNGFRAU',
     'identify_multimod_detectors',
 ]
@@ -1430,8 +1429,30 @@ class DSSC1M(XtdfDetectorBase):
     module_shape = (128, 512)
 
 
-class LPDBase:
-    """Base LPD class supporting parallel gain mode."""
+@multimod_detectors
+class LPD1M(XtdfDetectorBase):
+    """An interface to LPD-1M data.
+
+    Parameters
+    ----------
+    data: DataCollection
+      A data collection, e.g. from :func:`.RunDirectory`.
+    modules: set of ints, optional
+      Detector module numbers to use. By default, all available modules
+      are used.
+    detector_name: str, optional
+      Name of a detector, e.g. 'FXE_DET_LPD1M-1'. This is only needed
+      if the dataset includes more than one LPD detector.
+    min_modules: int
+      Include trains where at least n modules have data. Default is 1.
+    parallel_gain: bool
+      Set to True to read this data as parallel gain data, where high, medium
+      and low gain data are stored sequentially within each train. This will
+      repeat the pulse & cell IDs from the first 1/3 of each train, and add gain
+      stage labels from 0 (high-gain) to 2 (low-gain).
+    """
+    _source_re = re.compile(r'(?P<detname>.+_LPD1M.*)/DET/(?P<modno>\d+)CH')
+    module_shape = (256, 256)
 
     def __init__(self, data: DataCollection, detector_name=None, modules=None,
                  *, min_modules=1, parallel_gain=False):
@@ -1515,56 +1536,6 @@ def _make_image_index(self, tids, inner_ids, inner_name='pulse'):
         )
 
 
-@multimod_detectors
-class LPD1M(LPDBase, XtdfDetectorBase):
-    """An interface to LPD-1M data.
-
-    Parameters
-    ----------
-    data: DataCollection
-      A data collection, e.g. from :func:`.RunDirectory`.
-    modules: set of ints, optional
-      Detector module numbers to use. By default, all available modules
-      are used.
-    detector_name: str, optional
-      Name of a detector, e.g. 'FXE_DET_LPD1M-1'. This is only needed
-      if the dataset includes more than one LPD detector.
-    min_modules: int
-      Include trains where at least n modules have data. Default is 1.
-    parallel_gain: bool
-      Set to True to read this data as parallel gain data, where high, medium
-      and low gain data are stored sequentially within each train. This will
-      repeat the pulse & cell IDs from the first 1/3 of each train, and add gain
-      stage labels from 0 (high-gain) to 2 (low-gain).
-    """
-    _source_re = re.compile(r'(?P<detname>.+_LPD1M.*)/DET/(?P<modno>\d+)CH')
-    module_shape = (256, 256)
-
-
-@multimod_detectors
-class LPDMini(LPDBase, XtdfDetectorBase):
-    """An interface to LPD-Mini data.
-
-    Parameters
-    ----------
-    data: DataCollection
-      A data collection, e.g. from :func:`.RunDirectory`.
-    modules: set of ints, optional
-      Detector module numbers to use. By default, all available modules
-      are used.
-    detector_name: str, optional
-      Name of a detector, e.g. 'FXE_DET_LPD_MINI'. This is only needed
-      if the dataset includes more than one LPD detector.
-    parallel_gain: bool
-      Set to True to read this data as parallel gain data, where high, medium
-      and low gain data are stored sequentially within each train. This will
-      repeat the pulse & cell IDs from the first 1/3 of each train, and add gain
-      stage labels from 0 (high-gain) to 2 (low-gain).
-    """
-    _source_re = re.compile(r'(?P<detname>.+_LPD_MINI.*)/DET/(?P<modno>\d+)CH')
-    module_shape = (256, 256)
-
-
 @multimod_detectors
 class JUNGFRAU(MultimodDetectorBase):
     """An interface to JUNGFRAU data.

extra_data/tests/make_examples.py

@@ -242,11 +242,6 @@ def make_fxe_run(dir_path, raw=True, format_version='0.5'):
                       frames_per_train=128)
         ], ntrains=480, chunksize=32, format_version=format_version)
 
-    path = osp.join(dir_path, f'{prefix}-R0450-LPDMINI00-S00000.h5')
-    write_file(path, [
-        LPDModule('FXE_DET_LPD_MINI/DET/0CH0', raw=raw, frames_per_train=128)
-        ], ntrains=480, chunksize=32, format_version=format_version)
-
     if not raw:
         return
     write_file(osp.join(dir_path, 'RAW-R0450-DA01-S00000.h5'), [
@@ -273,10 +268,6 @@ def make_lpd_parallelgain_run(dir_path, raw=True, format_version='0.5'):
                       frames_per_train=300)
         ], ntrains=100, chunksize=32, format_version=format_version)
 
-    path = osp.join(dir_path, f'{prefix}-R0450-LPDMINI00-S00000.h5')
-    write_file(path, [
-        LPDModule('FXE_DET_LPD_MINI/DET/0CH0', raw=raw, frames_per_train=300)
-    ], ntrains=100, chunksize=32, format_version=format_version)
 
 def make_lpd_run_mini_missed_train(dir_path):
     write_file(osp.join(dir_path, 'RAW-R0450-LPD00-S00000.h5'), [

extra_data/tests/test_components.py

@@ -7,7 +7,7 @@
 
 from extra_data.reader import RunDirectory, H5File, by_id, by_index
 from extra_data.components import (
-    AGIPD1M, DSSC1M, LPD1M, LPDMini, JUNGFRAU, identify_multimod_detectors,
+    AGIPD1M, DSSC1M, LPD1M, JUNGFRAU, identify_multimod_detectors,
 )
 
 
@@ -179,17 +179,6 @@ def test_get_array_lpd_parallelgain(mock_lpd_parallelgain_run):
     np.testing.assert_array_equal(arr.coords['gain'], np.arange(3))
     np.testing.assert_array_equal(arr.coords['pulse'], np.arange(100))
 
-    run = RunDirectory(mock_lpd_parallelgain_run)
-    det = LPDMini(run.select_trains(by_index[:2]), parallel_gain=True)
-    assert det.detector_name == 'FXE_DET_LPD_MINI'
-
-    arr = det.get_array('image.data')
-    assert arr.shape == (1, 2, 3, 100, 256, 256)
-    assert arr.dims == ('module', 'train', 'gain', 'pulse', 'slow_scan', 'fast_scan')
-    np.testing.assert_array_equal(arr.coords['gain'], np.arange(3))
-    np.testing.assert_array_equal(arr.coords['pulse'], np.arange(100))
-
-
 
 def test_get_array_lpd_parallelgain_select_pulses(mock_lpd_parallelgain_run):
     run = RunDirectory(mock_lpd_parallelgain_run)
@@ -206,20 +195,6 @@ def test_get_array_lpd_parallelgain_select_pulses(mock_lpd_parallelgain_run):
     assert arr.shape == (16, 2, 3, 5, 256, 256)
     np.testing.assert_array_equal(arr.coords['pulse'], np.arange(5))
 
-    run = RunDirectory(mock_lpd_parallelgain_run)
-    det = LPDMini(run.select_trains(by_index[:2]), parallel_gain=True)
-    assert det.detector_name == 'FXE_DET_LPD_MINI'
-
-    arr = det.get_array('image.data', pulses=np.s_[:5])
-    assert arr.shape == (1, 2, 3, 5, 256, 256)
-    assert arr.dims == ('module', 'train', 'gain', 'pulse', 'slow_scan', 'fast_scan')
-    np.testing.assert_array_equal(arr.coords['gain'], np.arange(3))
-    np.testing.assert_array_equal(arr.coords['pulse'], np.arange(5))
-
-    arr = det.get_array('image.data', pulses=by_id[:5])
-    assert arr.shape == (1, 2, 3, 5, 256, 256)
-    np.testing.assert_array_equal(arr.coords['pulse'], np.arange(5))
-
 
 def test_get_array_jungfrau(mock_jungfrau_run):
     run = RunDirectory(mock_jungfrau_run)
@@ -609,7 +584,7 @@ def test_write_selected_frames_proc(mock_spb_proc_run, tmp_path):
     test_file.unlink()
 
 def test_identify_multimod_detectors(mock_fxe_raw_run):
-    run = RunDirectory(mock_fxe_raw_run, include='*LPD[!MINI]*')
+    run = RunDirectory(mock_fxe_raw_run)
     name, cls = identify_multimod_detectors(run, single=True)
     assert name == 'FXE_DET_LPD1M-1'
     assert cls is LPD1M

extra_data/tests/test_reader_mockdata.py

@@ -161,7 +161,7 @@ def test_iterate_trains_require_all(mock_sa3_control_data):
 
 def test_read_fxe_raw_run(mock_fxe_raw_run):
     run = RunDirectory(mock_fxe_raw_run)
-    assert len(run.files) == 19  # 16 LPD 1M, 1 LPD Minimodules + 2 control data files
+    assert len(run.files) == 18  # 16 LPD 1M + 2 control data files
     assert run.train_ids == list(range(10000, 10480))
     run.info()  # Smoke test
 

extra_data/tests/test_streamer.py

@@ -11,7 +11,7 @@
 
 
 def test_merge_detector(mock_fxe_raw_run, mock_fxe_control_data, mock_spb_proc_run):
-    with RunDirectory(mock_fxe_raw_run, include="*LPD[!MINI]*") as run:
+    with RunDirectory(mock_fxe_raw_run) as run:
         for tid, data in _iter_trains(run, merge_detector=True):
             assert 'FXE_DET_LPD1M-1/DET/APPEND' in data
             assert 'FXE_DET_LPD1M-1/DET/0CH0:xtdf' not in data