diff options
Diffstat (limited to 'silx/io/test/test_nxdata.py')
| -rw-r--r-- | silx/io/test/test_nxdata.py | 271 |
1 files changed, 262 insertions, 9 deletions
diff --git a/silx/io/test/test_nxdata.py b/silx/io/test/test_nxdata.py index f891db9..f7ea8a4 100644 --- a/silx/io/test/test_nxdata.py +++ b/silx/io/test/test_nxdata.py @@ -1,6 +1,6 @@ # coding: utf-8 # /*########################################################################## -# Copyright (C) 2016-2017 European Synchrotron Radiation Facility +# Copyright (C) 2016-2018 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal @@ -25,7 +25,7 @@ __authors__ = ["P. Knobel"] __license__ = "MIT" -__date__ = "27/09/2016" +__date__ = "27/01/2018" try: import h5py @@ -36,6 +36,10 @@ import tempfile import unittest from .. import nxdata +from silx.third_party import six + +text_dtype = h5py.special_dtype(vlen=six.text_type) + @unittest.skipIf(h5py is None, "silx.io.nxdata tests depend on h5py") class TestNXdata(unittest.TestCase): @@ -71,9 +75,16 @@ class TestNXdata(unittest.TestCase): g1d0 = g1d.create_group("1D_spectrum") g1d0.attrs["NX_class"] = "NXdata" g1d0.attrs["signal"] = "count" + g1d0.attrs["auxiliary_signals"] = numpy.array(["count2", "count3"], + dtype=text_dtype) g1d0.attrs["axes"] = "energy_calib" - g1d0.attrs["uncertainties"] = b"energy_errors", + g1d0.attrs["uncertainties"] = numpy.array(["energy_errors", ], + dtype=text_dtype) g1d0.create_dataset("count", data=numpy.arange(10)) + g1d0.create_dataset("count2", data=0.5 * numpy.arange(10)) + d = g1d0.create_dataset("count3", data=0.4 * numpy.arange(10)) + d.attrs["long_name"] = "3rd counter" + g1d0.create_dataset("title", data="Title as dataset (like nexpy)") g1d0.create_dataset("energy_calib", data=(10, 5)) # 10 * idx + 5 g1d0.create_dataset("energy_errors", data=3.14 * numpy.random.rand(10)) @@ -86,7 +97,7 @@ class TestNXdata(unittest.TestCase): g1d2 = g1d.create_group("4D_spectra") g1d2.attrs["NX_class"] = "NXdata" g1d2.attrs["signal"] = "counts" - g1d2.attrs["axes"] = b"energy", + g1d2.attrs["axes"] = numpy.array(["energy", ], dtype=text_dtype) ds = g1d2.create_dataset("counts", data=numpy.arange(2 * 2 * 3 * 10).reshape((2, 2, 3, 10))) ds.attrs["interpretation"] = "spectrum" ds = g1d2.create_dataset("errors", data=4.5 * numpy.random.rand(2, 2, 3, 10)) @@ -103,8 +114,11 @@ class TestNXdata(unittest.TestCase): g2d0 = g2d.create_group("2D_regular_image") g2d0.attrs["NX_class"] = "NXdata" g2d0.attrs["signal"] = "image" - g2d0.attrs["axes"] = b"rows_calib", b"columns_coordinates" + g2d0.attrs["auxiliary_signals"] = "image2" + g2d0.attrs["axes"] = numpy.array(["rows_calib", "columns_coordinates"], + dtype=text_dtype) g2d0.create_dataset("image", data=numpy.arange(4 * 6).reshape((4, 6))) + g2d0.create_dataset("image2", data=numpy.arange(4 * 6).reshape((4, 6))) ds = g2d0.create_dataset("rows_calib", data=(10, 5)) ds.attrs["long_name"] = "Calibrated Y" g2d0.create_dataset("columns_coordinates", data=0.5 + 0.02 * numpy.arange(6)) @@ -112,7 +126,9 @@ class TestNXdata(unittest.TestCase): g2d1 = g2d.create_group("2D_irregular_data") g2d1.attrs["NX_class"] = "NXdata" g2d1.attrs["signal"] = "data" - g2d1.attrs["axes"] = b"rows_coordinates", b"columns_coordinates" + g2d1.attrs["title"] = "Title as group attr" + g2d1.attrs["axes"] = numpy.array(["rows_coordinates", "columns_coordinates"], + dtype=text_dtype) g2d1.create_dataset("data", data=numpy.arange(64 * 128).reshape((64, 128))) g2d1.create_dataset("rows_coordinates", data=numpy.arange(64) + numpy.random.rand(64)) g2d1.create_dataset("columns_coordinates", data=numpy.arange(128) + 2.5 * numpy.random.rand(128)) @@ -126,19 +142,33 @@ class TestNXdata(unittest.TestCase): g2d3 = g2d.create_group("5D_images") g2d3.attrs["NX_class"] = "NXdata" g2d3.attrs["signal"] = "images" - g2d3.attrs["axes"] = b"rows_coordinates", b"columns_coordinates" + g2d3.attrs["axes"] = numpy.array(["rows_coordinates", "columns_coordinates"], + dtype=text_dtype) ds = g2d3.create_dataset("images", data=numpy.arange(2 * 2 * 2 * 4 * 6).reshape((2, 2, 2, 4, 6))) ds.attrs["interpretation"] = "image" g2d3.create_dataset("rows_coordinates", data=5 + 10 * numpy.arange(4)) g2d3.create_dataset("columns_coordinates", data=0.5 + 0.02 * numpy.arange(6)) + g2d4 = g2d.create_group("RGBA_image") + g2d4.attrs["NX_class"] = "NXdata" + g2d4.attrs["signal"] = "image" + g2d4.attrs["axes"] = numpy.array(["rows_calib", "columns_coordinates"], + dtype=text_dtype) + rgba_image = numpy.linspace(0, 1, num=7*8*3).reshape((7, 8, 3)) + rgba_image[:, :, 1] = 1 - rgba_image[:, :, 1] # invert G channel to add some color + ds = g2d4.create_dataset("image", data=rgba_image) + ds.attrs["interpretation"] = "rgba-image" + ds = g2d4.create_dataset("rows_calib", data=(10, 5)) + ds.attrs["long_name"] = "Calibrated Y" + g2d4.create_dataset("columns_coordinates", data=0.5+0.02*numpy.arange(8)) + # SCATTER g = self.h5f.create_group("scatters") gd0 = g.create_group("x_y_scatter") gd0.attrs["NX_class"] = "NXdata" gd0.attrs["signal"] = "y" - gd0.attrs["axes"] = b"x", + gd0.attrs["axes"] = numpy.array(["x", ], dtype=text_dtype) gd0.create_dataset("y", data=numpy.random.rand(128) - 0.5) gd0.create_dataset("x", data=2 * numpy.random.rand(128)) gd0.create_dataset("x_errors", data=0.05 * numpy.random.rand(128)) @@ -147,7 +177,7 @@ class TestNXdata(unittest.TestCase): gd1 = g.create_group("x_y_value_scatter") gd1.attrs["NX_class"] = "NXdata" gd1.attrs["signal"] = "values" - gd1.attrs["axes"] = b"x", b"y" + gd1.attrs["axes"] = numpy.array(["x", "y"], dtype=text_dtype) gd1.create_dataset("values", data=3.14 * numpy.random.rand(128)) gd1.create_dataset("y", data=numpy.random.rand(128)) gd1.create_dataset("y_errors", data=0.02 * numpy.random.rand(128)) @@ -199,6 +229,7 @@ class TestNXdata(unittest.TestCase): def testSpectra(self): nxd = nxdata.NXdata(self.h5f["spectra/1D_spectrum"]) self.assertTrue(nxd.signal_is_1d) + self.assertTrue(nxd.is_curve) self.assertTrue(numpy.array_equal(numpy.array(nxd.signal), numpy.arange(10))) self.assertEqual(nxd.axes_names, ["energy_calib"]) @@ -208,9 +239,18 @@ class TestNXdata(unittest.TestCase): self.assertIsNone(nxd.errors) self.assertFalse(nxd.is_scatter or nxd.is_x_y_value_scatter) self.assertIsNone(nxd.interpretation) + self.assertEqual(nxd.title, "Title as dataset (like nexpy)") + + self.assertEqual(nxd.auxiliary_signals_dataset_names, + ["count2", "count3"]) + self.assertEqual(nxd.auxiliary_signals_names, + ["count2", "3rd counter"]) + self.assertAlmostEqual(nxd.auxiliary_signals[1][2], + 0.8) # numpy.arange(10) * 0.4 nxd = nxdata.NXdata(self.h5f["spectra/2D_spectra"]) self.assertTrue(nxd.signal_is_2d) + self.assertTrue(nxd.is_curve) self.assertEqual(nxd.axes_names, [None, None]) self.assertEqual(nxd.axes_dataset_names, [None, None]) self.assertEqual(nxd.axes, [None, None]) @@ -221,6 +261,7 @@ class TestNXdata(unittest.TestCase): nxd = nxdata.NXdata(self.h5f["spectra/4D_spectra"]) self.assertFalse(nxd.signal_is_0d or nxd.signal_is_1d or nxd.signal_is_2d or nxd.signal_is_3d) + self.assertTrue(nxd.is_curve) self.assertEqual(nxd.axes_names, [None, None, None, "Calibrated energy"]) self.assertEqual(nxd.axes_dataset_names, @@ -242,15 +283,19 @@ class TestNXdata(unittest.TestCase): def testImages(self): nxd = nxdata.NXdata(self.h5f["images/2D_regular_image"]) self.assertTrue(nxd.signal_is_2d) + self.assertTrue(nxd.is_image) self.assertEqual(nxd.axes_names, ["Calibrated Y", "columns_coordinates"]) self.assertEqual(list(nxd.axes_dataset_names), ["rows_calib", "columns_coordinates"]) self.assertIsNone(nxd.errors) self.assertFalse(nxd.is_scatter or nxd.is_x_y_value_scatter) self.assertIsNone(nxd.interpretation) + self.assertEqual(len(nxd.auxiliary_signals), 1) + self.assertEqual(nxd.auxiliary_signals_names, ["image2"]) nxd = nxdata.NXdata(self.h5f["images/2D_irregular_data"]) self.assertTrue(nxd.signal_is_2d) + self.assertTrue(nxd.is_image) self.assertEqual(nxd.axes_dataset_names, nxd.axes_names) self.assertEqual(list(nxd.axes_dataset_names), @@ -259,8 +304,10 @@ class TestNXdata(unittest.TestCase): self.assertIsNone(nxd.errors) self.assertFalse(nxd.is_scatter or nxd.is_x_y_value_scatter) self.assertIsNone(nxd.interpretation) + self.assertEqual(nxd.title, "Title as group attr") nxd = nxdata.NXdata(self.h5f["images/5D_images"]) + self.assertTrue(nxd.is_image) self.assertFalse(nxd.signal_is_0d or nxd.signal_is_1d or nxd.signal_is_2d or nxd.signal_is_3d) self.assertEqual(nxd.axes_names, @@ -271,6 +318,16 @@ class TestNXdata(unittest.TestCase): self.assertFalse(nxd.is_scatter or nxd.is_x_y_value_scatter) self.assertEqual(nxd.interpretation, "image") + nxd = nxdata.NXdata(self.h5f["images/RGBA_image"]) + self.assertTrue(nxd.is_image) + self.assertEqual(nxd.interpretation, "rgba-image") + self.assertTrue(nxd.signal_is_3d) + self.assertEqual(nxd.axes_names, ["Calibrated Y", + "columns_coordinates", + None]) + self.assertEqual(list(nxd.axes_dataset_names), + ["rows_calib", "columns_coordinates", None]) + def testScatters(self): nxd = nxdata.NXdata(self.h5f["scatters/x_y_scatter"]) self.assertTrue(nxd.signal_is_1d) @@ -301,10 +358,206 @@ class TestNXdata(unittest.TestCase): self.assertIsNone(nxd.interpretation) +@unittest.skipIf(h5py is None, "silx.io.nxdata tests depend on h5py") +class TestLegacyNXdata(unittest.TestCase): + def setUp(self): + tmp = tempfile.NamedTemporaryFile(prefix="nxdata_legacy_examples_", + suffix=".h5", delete=True) + tmp.file.close() + self.h5fname = tmp.name + self.h5f = h5py.File(tmp.name, "w") + + def tearDown(self): + self.h5f.close() + + def testSignalAttrOnDataset(self): + g = self.h5f.create_group("2D") + g.attrs["NX_class"] = "NXdata" + + ds0 = g.create_dataset("image0", + data=numpy.arange(4 * 6).reshape((4, 6))) + ds0.attrs["signal"] = 1 + ds0.attrs["long_name"] = "My first image" + + ds1 = g.create_dataset("image1", + data=numpy.arange(4 * 6).reshape((4, 6))) + ds1.attrs["signal"] = "2" + ds1.attrs["long_name"] = "My 2nd image" + + ds2 = g.create_dataset("image2", + data=numpy.arange(4 * 6).reshape((4, 6))) + ds2.attrs["signal"] = 3 + + nxd = nxdata.NXdata(self.h5f["2D"]) + + self.assertEqual(nxd.signal_dataset_name, "image0") + self.assertEqual(nxd.signal_name, "My first image") + self.assertEqual(nxd.signal.shape, + (4, 6)) + + self.assertEqual(len(nxd.auxiliary_signals), 2) + self.assertEqual(nxd.auxiliary_signals[1].shape, + (4, 6)) + + self.assertEqual(nxd.auxiliary_signals_dataset_names, + ["image1", "image2"]) + self.assertEqual(nxd.auxiliary_signals_names, + ["My 2nd image", "image2"]) + + def testAxesOnSignalDataset(self): + g = self.h5f.create_group("2D") + g.attrs["NX_class"] = "NXdata" + + ds0 = g.create_dataset("image0", + data=numpy.arange(4 * 6).reshape((4, 6))) + ds0.attrs["signal"] = 1 + ds0.attrs["axes"] = "yaxis:xaxis" + + ds1 = g.create_dataset("yaxis", + data=numpy.arange(4)) + ds2 = g.create_dataset("xaxis", + data=numpy.arange(6)) + + nxd = nxdata.NXdata(self.h5f["2D"]) + + self.assertEqual(nxd.axes_dataset_names, + ["yaxis", "xaxis"]) + self.assertTrue(numpy.array_equal(nxd.axes[0], + numpy.arange(4))) + self.assertTrue(numpy.array_equal(nxd.axes[1], + numpy.arange(6))) + + def testAxesOnAxesDatasets(self): + g = self.h5f.create_group("2D") + g.attrs["NX_class"] = "NXdata" + + ds0 = g.create_dataset("image0", + data=numpy.arange(4 * 6).reshape((4, 6))) + ds0.attrs["signal"] = 1 + ds1 = g.create_dataset("yaxis", + data=numpy.arange(4)) + ds1.attrs["axis"] = 0 + ds2 = g.create_dataset("xaxis", + data=numpy.arange(6)) + ds2.attrs["axis"] = "1" + + nxd = nxdata.NXdata(self.h5f["2D"]) + self.assertEqual(nxd.axes_dataset_names, + ["yaxis", "xaxis"]) + self.assertTrue(numpy.array_equal(nxd.axes[0], + numpy.arange(4))) + self.assertTrue(numpy.array_equal(nxd.axes[1], + numpy.arange(6))) + + +class TestSaveNXdata(unittest.TestCase): + def setUp(self): + tmp = tempfile.NamedTemporaryFile(prefix="nxdata", + suffix=".h5", delete=True) + tmp.file.close() + self.h5fname = tmp.name + + def testSimpleSave(self): + sig = numpy.array([0, 1, 2]) + a0 = numpy.array([2, 3, 4]) + a1 = numpy.array([3, 4, 5]) + nxdata.save_NXdata(filename=self.h5fname, + signal=sig, + axes=[a0, a1], + signal_name="sig", + axes_names=["a0", "a1"], + nxentry_name="a", + nxdata_name="mydata") + + h5f = h5py.File(self.h5fname, "r") + self.assertTrue(nxdata.is_valid_nxdata(h5f["a/mydata"])) + + nxd = nxdata.NXdata(h5f["/a/mydata"]) + self.assertTrue(numpy.array_equal(nxd.signal, + sig)) + self.assertTrue(numpy.array_equal(nxd.axes[0], + a0)) + + h5f.close() + + def testSimplestSave(self): + sig = numpy.array([0, 1, 2]) + nxdata.save_NXdata(filename=self.h5fname, + signal=sig) + + h5f = h5py.File(self.h5fname, "r") + + self.assertTrue(nxdata.is_valid_nxdata(h5f["/entry/data0"])) + + nxd = nxdata.NXdata(h5f["/entry/data0"]) + self.assertTrue(numpy.array_equal(nxd.signal, + sig)) + h5f.close() + + def testSaveDefaultAxesNames(self): + sig = numpy.array([0, 1, 2]) + a0 = numpy.array([2, 3, 4]) + a1 = numpy.array([3, 4, 5]) + nxdata.save_NXdata(filename=self.h5fname, + signal=sig, + axes=[a0, a1], + signal_name="sig", + axes_names=None, + axes_long_names=["a", "b"], + nxentry_name="a", + nxdata_name="mydata") + + h5f = h5py.File(self.h5fname, "r") + self.assertTrue(nxdata.is_valid_nxdata(h5f["a/mydata"])) + + nxd = nxdata.NXdata(h5f["/a/mydata"]) + self.assertTrue(numpy.array_equal(nxd.signal, + sig)) + self.assertTrue(numpy.array_equal(nxd.axes[0], + a0)) + self.assertEqual(nxd.axes_dataset_names, + [u"dim0", u"dim1"]) + self.assertEqual(nxd.axes_names, + [u"a", u"b"]) + + h5f.close() + + def testSaveToExistingEntry(self): + h5f = h5py.File(self.h5fname, "w") + g = h5f.create_group("myentry") + g.attrs["NX_class"] = "NXentry" + h5f.close() + + sig = numpy.array([0, 1, 2]) + a0 = numpy.array([2, 3, 4]) + a1 = numpy.array([3, 4, 5]) + nxdata.save_NXdata(filename=self.h5fname, + signal=sig, + axes=[a0, a1], + signal_name="sig", + axes_names=["a0", "a1"], + nxentry_name="myentry", + nxdata_name="toto") + + h5f = h5py.File(self.h5fname, "r") + self.assertTrue(nxdata.is_valid_nxdata(h5f["myentry/toto"])) + + nxd = nxdata.NXdata(h5f["myentry/toto"]) + self.assertTrue(numpy.array_equal(nxd.signal, + sig)) + self.assertTrue(numpy.array_equal(nxd.axes[0], + a0)) + h5f.close() + + def suite(): test_suite = unittest.TestSuite() test_suite.addTest( unittest.defaultTestLoader.loadTestsFromTestCase(TestNXdata)) + test_suite.addTest( + unittest.defaultTestLoader.loadTestsFromTestCase(TestLegacyNXdata)) + test_suite.addTest( + unittest.defaultTestLoader.loadTestsFromTestCase(TestSaveNXdata)) return test_suite |