SSB-43: adding matrix inversion

.gitattributes

@@ -40,6 +40,7 @@ CAL/CalibrationProcessing/lib/__init__.py -text
 CAL/CalibrationProcessing/lib/process_holography_dataset.py -text
 CAL/CalibrationProcessing/lib/processing/__init__.py -text
 CAL/CalibrationProcessing/lib/processing/averaging.py -text
+CAL/CalibrationProcessing/lib/processing/matrices.py -text
 CAL/CalibrationProcessing/test/CMakeLists.txt -text
 CAL/CalibrationProcessing/test/profiling_processing.py -text
 CAL/CalibrationProcessing/test/t_processing.py -text

CAL/CalibrationCommon/test/t_holography_dataset_class.py

@@ -48,6 +48,7 @@ class TestHolographyDatasetClass(unittest.TestCase):
             read_dict = HolographyDataset._read_grouped_data(h5file, '/test')
             self.assertDictEqual(test_dict, read_dict)
 
+
 if __name__ == '__main__':
     logging.basicConfig(format='%(name)s : %(message)s')
     unittest.main()

CAL/CalibrationProcessing/lib/CMakeLists.txt

@@ -20,6 +20,7 @@
 python_install(
     __init__.py
     processing/averaging.py
+    processing/matrices.py
     processing/__init__.py
     DESTINATION lofar/calibration)
 

CAL/CalibrationProcessing/lib/processing/__init__.py

-from .averaging import *
\ No newline at end of file
+from .averaging import *
+from .matrices import *
\ No newline at end of file

CAL/CalibrationProcessing/lib/processing/matrices.py

+import logging
+
+import numpy
+
+logger = logging.getLogger(__name__)
+
+
+def extract_crosscorrelation_matrices_from_HDS_datatable(datatable):
+    new_shape = (2, 2, datatable.shape[0])
+    new_array = numpy.zeros(shape=new_shape, dtype=numpy.complex)
+    new_array[0, 0, :] = datatable['XX']
+    new_array[0, 1, :] = datatable['XY']
+    new_array[1, 0, :] = datatable['YX']
+    new_array[1, 1, :] = datatable['YY']
+
+    return new_array
+
+
+def invert_crosscorrelation_matrices(cross_correlation_matrices):
+    """
+    Invert the matrices along the last axis
+    :param cross_correlation_matrices: matrices to invert
+    :type cross_correlation_matrices: numpy.ndarray
+    :return:
+    """
+    assert cross_correlation_matrices.ndim >= 3
+
+    return numpy.linalg.inv(cross_correlation_matrices)
+

CAL/CalibrationProcessing/test/t_processing.py

 import logging
 import unittest
 
-
 from lofar.calibration.common.datacontainers import HolographyDataset
 from lofar.calibration.processing import average_dataset_by_sample, average_values_by_sample
-from glob import glob
+from lofar.calibration.common.datacontainers.holography_dataset_definitions import HDS_data_sample_type
+from lofar.calibration.processing.matrices import extract_crosscorrelation_matrices_from_HDS_datatable
 logger = logging.getLogger('t_processing')
 import numpy
 
@@ -13,7 +13,6 @@ import numpy
 SAMPLE_DATASET = '/data/test/HolographyObservation/CS001HBA0.hdf5'
 
 
-
 class TestProcessing(unittest.TestCase):
     def setUp(self):
         pass
@@ -54,7 +53,8 @@ class TestProcessing(unittest.TestCase):
 
     def test_average_datatable_by_sample(self):
         holography_dataset = HolographyDataset.load_from_file(SAMPLE_DATASET)
-        averaged_data = average_dataset_by_sample(SAMPLE_DATASET, 2)
+        averaged_data = average_dataset_by_sample(SAMPLE_DATASET, 2).derived_data[
+            'PRE_NORMALIZATION_AVERAGE_TIME']
         self.assert_recursive_dict_have_the_same_keys(holography_dataset.data, averaged_data)
 
     def assert_recursive_dict_have_the_same_keys(self, expected, actual):
@@ -77,7 +77,21 @@ class TestProcessing(unittest.TestCase):
             else:
                 self.assertTrue(False, 'Expected key {} is missing in the actual dict'.format(key))
 
+    def test_matrix_inversion(self):
+        test_data = numpy.zeros(3, dtype=HDS_data_sample_type)
+        numpy.random.seed(3)
+        test_data['XX'] = numpy.random.randn(3) + 1.j*numpy.random.randn(3)
+        test_data['XY'] = numpy.random.randn(3) + 1.j * numpy.random.randn(3)
+        test_data['YX'] = numpy.random.randn(3) + 1.j * numpy.random.randn(3)
+        test_data['YY'] = numpy.random.randn(3) + 1.j * numpy.random.randn(3)
+
+        matrices_from_data = extract_crosscorrelation_matrices_from_HDS_datatable(test_data)
+
+        numpy.testing.assert_almost_equal(test_data['XX'], matrices_from_data[0, 0, :])
+        numpy.testing.assert_almost_equal(test_data['XY'], matrices_from_data[0, 1, :])
+        numpy.testing.assert_almost_equal(test_data['YX'], matrices_from_data[1, 0, :])
+        numpy.testing.assert_almost_equal(test_data['YY'], matrices_from_data[1, 1, :])
 
 if __name__ == '__main__':
     logging.basicConfig(format='%(name)s : %(message)s')
-    unittest.main()
\ No newline at end of file
+    unittest.main()