我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.ma.array()。
def calculate_contour_dvh(mask, doseplane, maxdose, dd, id, structure): """Calculate the differential DVH for the given contour and dose plane.""" # Multiply the structure mask by the dose plane to get the dose mask mask = ma.array(doseplane * dd['dosegridscaling'] * 100, mask=~mask) # Calculate the differential dvh hist, edges = np.histogram(mask.compressed(), bins=maxdose, range=(0, maxdose)) # Calculate the volume for the contour for the given dose plane vol = sum(hist) * ((id['pixelspacing'][0]) * (id['pixelspacing'][1]) * (structure['thickness'])) return hist, vol # ========================== Test DVH Calculation =========================== #
def test_flatten_wflexible(self): # Test flatten standard & nested (w, x, _, _) = self.data test = merge_arrays((x, w), flatten=True) control = np.array([(1, 1, 2, 3.0), (2, 4, 5, 6.0)], dtype=[('f0', int), ('a', int), ('ba', float), ('bb', int)]) assert_equal(test, control) test = merge_arrays((x, w), flatten=False) controldtype = [('f0', int), ('f1', [('a', int), ('b', [('ba', float), ('bb', int)])])] control = np.array([(1., (1, (2, 3.0))), (2, (4, (5, 6.0)))], dtype=controldtype) assert_equal(test, control)
def test_defaults(self): # Test defaults: no exception raised if keys of defaults are not fields. (_, _, _, z) = self.data zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '|S3'), ('B', float), ('C', float)]) defaults = {'A': '???', 'B': -999., 'C': -9999., 'D': -99999.} test = stack_arrays((z, zz), defaults=defaults) control = ma.array([('A', 1, -9999.), ('B', 2, -9999.), ( 'a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '|S3'), ('B', float), ('C', float)], mask=[(0, 0, 1), (0, 0, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0)]) assert_equal(test, control) assert_equal(test.data, control.data) assert_equal(test.mask, control.mask)
def test_autoconversion(self): # Tests autoconversion adtype = [('A', int), ('B', bool), ('C', float)] a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype) bdtype = [('A', int), ('B', float), ('C', float)] b = ma.array([(4, 5, 6)], dtype=bdtype) control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)], dtype=bdtype) test = stack_arrays((a, b), autoconvert=True) assert_equal(test, control) assert_equal(test.mask, control.mask) try: test = stack_arrays((a, b), autoconvert=False) except TypeError: pass else: raise AssertionError
def test_join(self): a, b = self.a, self.b # Fixme, this test is broken #test = join_by(('a', 'b'), a, b) #control = np.array([(5, 55, 105, 100), (6, 56, 106, 101), # (7, 57, 107, 102), (8, 58, 108, 103), # (9, 59, 109, 104)], # dtype=[('a', int), ('b', int), # ('c', int), ('d', int)]) #assert_equal(test, control) # Hack to avoid pyflakes unused variable warnings join_by(('a', 'b'), a, b) np.array([(5, 55, 105, 100), (6, 56, 106, 101), (7, 57, 107, 102), (8, 58, 108, 103), (9, 59, 109, 104)], dtype=[('a', int), ('b', int), ('c', int), ('d', int)])
def test_leftouter_join(self): a, b = self.a, self.b test = join_by(('a', 'b'), a, b, 'leftouter') control = ma.array([(0, 50, 100, -1), (1, 51, 101, -1), (2, 52, 102, -1), (3, 53, 103, -1), (4, 54, 104, -1), (5, 55, 105, -1), (6, 56, 106, -1), (7, 57, 107, -1), (8, 58, 108, -1), (9, 59, 109, -1)], mask=[(0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1)], dtype=[('a', int), ('b', int), ('c', int), ('d', int)]) assert_equal(test, control)
def test_two_keys_two_vars(self): a = np.array(list(zip(np.tile([10, 11], 5), np.repeat(np.arange(5), 2), np.arange(50, 60), np.arange(10, 20))), dtype=[('k', int), ('a', int), ('b', int), ('c', int)]) b = np.array(list(zip(np.tile([10, 11], 5), np.repeat(np.arange(5), 2), np.arange(65, 75), np.arange(0, 10))), dtype=[('k', int), ('a', int), ('b', int), ('c', int)]) control = np.array([(10, 0, 50, 65, 10, 0), (11, 0, 51, 66, 11, 1), (10, 1, 52, 67, 12, 2), (11, 1, 53, 68, 13, 3), (10, 2, 54, 69, 14, 4), (11, 2, 55, 70, 15, 5), (10, 3, 56, 71, 16, 6), (11, 3, 57, 72, 17, 7), (10, 4, 58, 73, 18, 8), (11, 4, 59, 74, 19, 9)], dtype=[('k', int), ('a', int), ('b1', int), ('b2', int), ('c1', int), ('c2', int)]) test = join_by( ['a', 'k'], a, b, r1postfix='1', r2postfix='2', jointype='inner') assert_equal(test.dtype, control.dtype) assert_equal(test, control)
def test_format(self): a = np.array([(1, 2), (3, 4)]) c = BytesIO() # Sequence of formats np.savetxt(c, a, fmt=['%02d', '%3.1f']) c.seek(0) assert_equal(c.readlines(), [b'01 2.0\n', b'03 4.0\n']) # A single multiformat string c = BytesIO() np.savetxt(c, a, fmt='%02d : %3.1f') c.seek(0) lines = c.readlines() assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n']) # Specify delimiter, should be overiden c = BytesIO() np.savetxt(c, a, fmt='%02d : %3.1f', delimiter=',') c.seek(0) lines = c.readlines() assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n']) # Bad fmt, should raise a ValueError c = BytesIO() assert_raises(ValueError, np.savetxt, c, a, fmt=99)
def test_record(self): c = TextIO() c.write('1 2\n3 4') c.seek(0) x = np.loadtxt(c, dtype=[('x', np.int32), ('y', np.int32)]) a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) assert_array_equal(x, a) d = TextIO() d.write('M 64.0 75.0\nF 25.0 60.0') d.seek(0) mydescriptor = {'names': ('gender', 'age', 'weight'), 'formats': ('S1', 'i4', 'f4')} b = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)], dtype=mydescriptor) y = np.loadtxt(d, dtype=mydescriptor) assert_array_equal(y, b)
def test_dtype_with_object(self): # Test using an explicit dtype with an object data = """ 1; 2001-01-01 2; 2002-01-31 """ ndtype = [('idx', int), ('code', np.object)] func = lambda s: strptime(s.strip(), "%Y-%m-%d") converters = {1: func} test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) control = np.array( [(1, datetime(2001, 1, 1)), (2, datetime(2002, 1, 31))], dtype=ndtype) assert_equal(test, control) ndtype = [('nest', [('idx', int), ('code', np.object)])] try: test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) except NotImplementedError: pass else: errmsg = "Nested dtype involving objects should be supported." raise AssertionError(errmsg)
def test_usecols(self): # Test the selection of columns # Select 1 column control = np.array([[1, 2], [3, 4]], float) data = TextIO() np.savetxt(data, control) data.seek(0) test = np.ndfromtxt(data, dtype=float, usecols=(1,)) assert_equal(test, control[:, 1]) # control = np.array([[1, 2, 3], [3, 4, 5]], float) data = TextIO() np.savetxt(data, control) data.seek(0) test = np.ndfromtxt(data, dtype=float, usecols=(1, 2)) assert_equal(test, control[:, 1:]) # Testing with arrays instead of tuples. data.seek(0) test = np.ndfromtxt(data, dtype=float, usecols=np.array([1, 2])) assert_equal(test, control[:, 1:])
def test_withmissing(self): data = TextIO('A,B\n0,1\n2,N/A') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.mafromtxt(data, dtype=None, **kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', np.int), ('B', np.int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) # data.seek(0) test = np.mafromtxt(data, **kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', np.float), ('B', np.float)]) assert_equal(test, control) assert_equal(test.mask, control.mask)
def test_replace_space(self): # Test the 'replace_space' option txt = "A.A, B (B), C:C\n1, 2, 3.14" # Test default: replace ' ' by '_' and delete non-alphanum chars test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None) ctrl_dtype = [("AA", int), ("B_B", int), ("CC", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no replace, no delete test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None, replace_space='', deletechars='') ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no delete (spaces are replaced by _) test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None, deletechars='') ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl)
def test_replace_space_known_dtype(self): # Test the 'replace_space' (and related) options when dtype != None txt = "A.A, B (B), C:C\n1, 2, 3" # Test default: replace ' ' by '_' and delete non-alphanum chars test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int) ctrl_dtype = [("AA", int), ("B_B", int), ("CC", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no replace, no delete test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int, replace_space='', deletechars='') ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no delete (spaces are replaced by _) test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int, deletechars='') ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl)
def test_recfromtxt(self): # data = TextIO('A,B\n0,1\n2,3') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.recfromtxt(data, **kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', np.int), ('B', np.int)]) self.assertTrue(isinstance(test, np.recarray)) assert_equal(test, control) # data = TextIO('A,B\n0,1\n2,N/A') test = np.recfromtxt(data, dtype=None, usemask=True, **kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', np.int), ('B', np.int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) assert_equal(test.A, [0, 2])
def test_gzip_loadtxt(): # Thanks to another windows brokeness, we can't use # NamedTemporaryFile: a file created from this function cannot be # reopened by another open call. So we first put the gzipped string # of the test reference array, write it to a securely opened file, # which is then read from by the loadtxt function s = BytesIO() g = gzip.GzipFile(fileobj=s, mode='w') g.write(b'1 2 3\n') g.close() s.seek(0) with temppath(suffix='.gz') as name: with open(name, 'wb') as f: f.write(s.read()) res = np.loadtxt(name) s.close() assert_array_equal(res, [1, 2, 3])
def test_set_mask_fromfields(self): mbase = self.base.copy().view(mrecarray) nmask = np.array( [(0, 1, 0), (0, 1, 0), (1, 0, 1), (1, 0, 1), (0, 0, 0)], dtype=[('a', bool), ('b', bool), ('c', bool)]) mbase.mask = nmask assert_equal(mbase.a.mask, [0, 0, 1, 1, 0]) assert_equal(mbase.b.mask, [1, 1, 0, 0, 0]) assert_equal(mbase.c.mask, [0, 0, 1, 1, 0]) # Reinitalizes and redo mbase.mask = False mbase.fieldmask = nmask assert_equal(mbase.a.mask, [0, 0, 1, 1, 0]) assert_equal(mbase.b.mask, [1, 1, 0, 0, 0]) assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
def test_exotic_formats(self): # Test that 'exotic' formats are processed properly easy = mrecarray(1, dtype=[('i', int), ('s', '|S8'), ('f', float)]) easy[0] = masked assert_equal(easy.filled(1).item(), (1, asbytes('1'), 1.)) solo = mrecarray(1, dtype=[('f0', '<f8', (2, 2))]) solo[0] = masked assert_equal(solo.filled(1).item(), np.array((1,), dtype=solo.dtype).item()) mult = mrecarray(2, dtype="i4, (2,3)float, float") mult[0] = masked mult[1] = (1, 1, 1) mult.filled(0) assert_equal_records(mult.filled(0), np.array([(0, 0, 0), (1, 1, 1)], dtype=mult.dtype))
def __setstate__(self, state): """ Restore the internal state of the masked array. This is for pickling. ``state`` is typically the output of the ``__getstate__`` output, and is a 5-tuple: - class name - a tuple giving the shape of the data - a typecode for the data - a binary string for the data - a binary string for the mask. """ (ver, shp, typ, isf, raw, msk, flv) = state ndarray.__setstate__(self, (shp, typ, isf, raw)) mdtype = dtype([(k, bool_) for (k, _) in self.dtype.descr]) self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk)) self.fill_value = flv
def get_contour_mask(dd, id, dosegridpoints, contour): """Get the mask for the contour with respect to the dose plane.""" doselut = dd['lut'] c = matplotlib.path.Path(list(contour)) # def inpolygon(polygon, xp, yp): # return np.array( # [Point(x, y).intersects(polygon) for x, y in zip(xp, yp)], # dtype=np.bool) # p = Polygon(contour) # x, y = np.meshgrid(np.array(dd['lut'][0]), np.array(dd['lut'][1])) # mask = inpolygon(p, x.ravel(), y.ravel()) # return mask.reshape((len(doselut[1]), len(doselut[0]))) grid = c.contains_points(dosegridpoints) grid = grid.reshape((len(doselut[1]), len(doselut[0]))) return grid
def main(): # Read the example RT structure and RT dose files # The testdata was downloaded from the dicompyler website as testdata.zip # Obtain the structures and DVHs from the DICOM data rtssfile = 'testdata/rtss.dcm' rtdosefile = 'testdata/rtdose.dcm' RTss = dicomparser.DicomParser(rtssfile) #RTdose = dicomparser.DicomParser("testdata/rtdose.dcm") RTstructures = RTss.GetStructures() # Generate the calculated DVHs calcdvhs = {} for key, structure in RTstructures.iteritems(): calcdvhs[key] = dvhcalc.get_dvh(rtssfile, rtdosefile, key) if (key in calcdvhs) and (len(calcdvhs[key].counts) and calcdvhs[key].counts[0]!=0): print ('DVH found for ' + structure['name']) pl.plot(calcdvhs[key].counts * 100/calcdvhs[key].counts[0], color=dvhcalc.np.array(structure['color'], dtype=float) / 255, label=structure['name'], linestyle='dashed') #else: # print("%d: no DVH"%key) pl.xlabel('Distance (cm)') pl.ylabel('Percentage Volume') pl.legend(loc=7, borderaxespad=-5) pl.setp(pl.gca().get_legend().get_texts(), fontsize='x-small') pl.savefig('testdata/dvh.png', dpi = 75)
def setUp(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array([('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)]) w = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z)
def test_zip_descr(self): # Test zip_descr (w, x, y, z) = self.data # Std array test = zip_descr((x, x), flatten=True) assert_equal(test, np.dtype([('', int), ('', int)])) test = zip_descr((x, x), flatten=False) assert_equal(test, np.dtype([('', int), ('', int)])) # Std & flexible-dtype test = zip_descr((x, z), flatten=True) assert_equal(test, np.dtype([('', int), ('A', '|S3'), ('B', float)])) test = zip_descr((x, z), flatten=False) assert_equal(test, np.dtype([('', int), ('', [('A', '|S3'), ('B', float)])])) # Standard & nested dtype test = zip_descr((x, w), flatten=True) assert_equal(test, np.dtype([('', int), ('a', int), ('ba', float), ('bb', int)])) test = zip_descr((x, w), flatten=False) assert_equal(test, np.dtype([('', int), ('', [('a', int), ('b', [('ba', float), ('bb', int)])])]))
def test_drop_fields(self): # Test drop_fields a = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) # A basic field test = drop_fields(a, 'a') control = np.array([((2, 3.0),), ((5, 6.0),)], dtype=[('b', [('ba', float), ('bb', int)])]) assert_equal(test, control) # Another basic field (but nesting two fields) test = drop_fields(a, 'b') control = np.array([(1,), (4,)], dtype=[('a', int)]) assert_equal(test, control) # A nested sub-field test = drop_fields(a, ['ba', ]) control = np.array([(1, (3.0,)), (4, (6.0,))], dtype=[('a', int), ('b', [('bb', int)])]) assert_equal(test, control) # All the nested sub-field from a field: zap that field test = drop_fields(a, ['ba', 'bb']) control = np.array([(1,), (4,)], dtype=[('a', int)]) assert_equal(test, control) test = drop_fields(a, ['a', 'b']) assert_(test is None)
def test_rename_fields(self): # Test rename fields a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))], dtype=[('a', int), ('b', [('ba', float), ('bb', (float, 2))])]) test = rename_fields(a, {'a': 'A', 'bb': 'BB'}) newdtype = [('A', int), ('b', [('ba', float), ('BB', (float, 2))])] control = a.view(newdtype) assert_equal(test.dtype, newdtype) assert_equal(test, control)
def test_find_duplicates(self): # Test find_duplicates a = ma.array([(2, (2., 'B')), (1, (2., 'B')), (2, (2., 'B')), (1, (1., 'B')), (2, (2., 'B')), (2, (2., 'C'))], mask=[(0, (0, 0)), (0, (0, 0)), (0, (0, 0)), (0, (0, 0)), (1, (0, 0)), (0, (1, 0))], dtype=[('A', int), ('B', [('BA', float), ('BB', '|S1')])]) test = find_duplicates(a, ignoremask=False, return_index=True) control = [0, 2] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='A', return_index=True) control = [0, 1, 2, 3, 5] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='B', return_index=True) control = [0, 1, 2, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='BA', return_index=True) control = [0, 1, 2, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='BB', return_index=True) control = [0, 1, 2, 3, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]])
def test_simple_flexible(self): # Test recursive_fill_fields on flexible-array a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)]) b = np.zeros((3,), dtype=a.dtype) test = recursive_fill_fields(a, b) control = np.array([(1, 10.), (2, 20.), (0, 0.)], dtype=[('A', int), ('B', float)]) assert_equal(test, control)
def test_masked_flexible(self): # Test recursive_fill_fields on masked flexible-array a = ma.array([(1, 10.), (2, 20.)], mask=[(0, 1), (1, 0)], dtype=[('A', int), ('B', float)]) b = ma.zeros((3,), dtype=a.dtype) test = recursive_fill_fields(a, b) control = ma.array([(1, 10.), (2, 20.), (0, 0.)], mask=[(0, 1), (1, 0), (0, 0)], dtype=[('A', int), ('B', float)]) assert_equal(test, control)
def setUp(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array( [('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)]) w = np.array( [(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z)
def test_solo(self): # Test merge_arrays on a single array. (_, x, _, z) = self.data test = merge_arrays(x) control = np.array([(1,), (2,)], dtype=[('f0', int)]) assert_equal(test, control) test = merge_arrays((x,)) assert_equal(test, control) test = merge_arrays(z, flatten=False) assert_equal(test, z) test = merge_arrays(z, flatten=True) assert_equal(test, z)
def test_solo_w_flatten(self): # Test merge_arrays on a single array w & w/o flattening w = self.data[0] test = merge_arrays(w, flatten=False) assert_equal(test, w) test = merge_arrays(w, flatten=True) control = np.array([(1, 2, 3.0), (4, 5, 6.0)], dtype=[('a', int), ('ba', float), ('bb', int)]) assert_equal(test, control)
def test_flatten(self): # Test standard & flexible (_, x, _, z) = self.data test = merge_arrays((x, z), flatten=True) control = np.array([(1, 'A', 1.), (2, 'B', 2.)], dtype=[('f0', int), ('A', '|S3'), ('B', float)]) assert_equal(test, control) test = merge_arrays((x, z), flatten=False) control = np.array([(1, ('A', 1.)), (2, ('B', 2.))], dtype=[('f0', int), ('f1', [('A', '|S3'), ('B', float)])]) assert_equal(test, control)
def test_wmasked_arrays(self): # Test merge_arrays masked arrays (_, x, _, _) = self.data mx = ma.array([1, 2, 3], mask=[1, 0, 0]) test = merge_arrays((x, mx), usemask=True) control = ma.array([(1, 1), (2, 2), (-1, 3)], mask=[(0, 1), (0, 0), (1, 0)], dtype=[('f0', int), ('f1', int)]) assert_equal(test, control) test = merge_arrays((x, mx), usemask=True, asrecarray=True) assert_equal(test, control) assert_(isinstance(test, MaskedRecords))
def test_w_singlefield(self): # Test single field test = merge_arrays((np.array([1, 2]).view([('a', int)]), np.array([10., 20., 30.])),) control = ma.array([(1, 10.), (2, 20.), (-1, 30.)], mask=[(0, 0), (0, 0), (1, 0)], dtype=[('a', int), ('f1', float)]) assert_equal(test, control)
def test_w_shorter_flex(self): # Test merge_arrays w/ a shorter flexndarray. z = self.data[-1] # Fixme, this test looks incomplete and broken #test = merge_arrays((z, np.array([10, 20, 30]).view([('C', int)]))) #control = np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)], # dtype=[('A', '|S3'), ('B', float), ('C', int)]) #assert_equal(test, control) # Hack to avoid pyflakes warnings about unused variables merge_arrays((z, np.array([10, 20, 30]).view([('C', int)]))) np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)], dtype=[('A', '|S3'), ('B', float), ('C', int)])
def setUp(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array( [('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)]) w = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z)
def test_append_single(self): # Test simple case (_, x, _, _) = self.data test = append_fields(x, 'A', data=[10, 20, 30]) control = ma.array([(1, 10), (2, 20), (-1, 30)], mask=[(0, 0), (0, 0), (1, 0)], dtype=[('f0', int), ('A', int)],) assert_equal(test, control)
def test_append_double(self): # Test simple case (_, x, _, _) = self.data test = append_fields(x, ('A', 'B'), data=[[10, 20, 30], [100, 200]]) control = ma.array([(1, 10, 100), (2, 20, 200), (-1, 30, -1)], mask=[(0, 0, 0), (0, 0, 0), (1, 0, 1)], dtype=[('f0', int), ('A', int), ('B', int)],) assert_equal(test, control)
def test_append_on_flex(self): # Test append_fields on flexible type arrays z = self.data[-1] test = append_fields(z, 'C', data=[10, 20, 30]) control = ma.array([('A', 1., 10), ('B', 2., 20), (-1, -1., 30)], mask=[(0, 0, 0), (0, 0, 0), (1, 1, 0)], dtype=[('A', '|S3'), ('B', float), ('C', int)],) assert_equal(test, control)
def test_append_on_nested(self): # Test append_fields on nested fields w = self.data[0] test = append_fields(w, 'C', data=[10, 20, 30]) control = ma.array([(1, (2, 3.0), 10), (4, (5, 6.0), 20), (-1, (-1, -1.), 30)], mask=[( 0, (0, 0), 0), (0, (0, 0), 0), (1, (1, 1), 0)], dtype=[('a', int), ('b', [('ba', float), ('bb', int)]), ('C', int)],) assert_equal(test, control)
def test_unnamed_fields(self): # Tests combinations of arrays w/o named fields (_, x, y, _) = self.data test = stack_arrays((x, x), usemask=False) control = np.array([1, 2, 1, 2]) assert_equal(test, control) test = stack_arrays((x, y), usemask=False) control = np.array([1, 2, 10, 20, 30]) assert_equal(test, control) test = stack_arrays((y, x), usemask=False) control = np.array([10, 20, 30, 1, 2]) assert_equal(test, control)
def test_unnamed_and_named_fields(self): # Test combination of arrays w/ & w/o named fields (_, x, _, z) = self.data test = stack_arrays((x, z)) control = ma.array([(1, -1, -1), (2, -1, -1), (-1, 'A', 1), (-1, 'B', 2)], mask=[(0, 1, 1), (0, 1, 1), (1, 0, 0), (1, 0, 0)], dtype=[('f0', int), ('A', '|S3'), ('B', float)]) assert_equal(test, control) assert_equal(test.mask, control.mask) test = stack_arrays((z, x)) control = ma.array([('A', 1, -1), ('B', 2, -1), (-1, -1, 1), (-1, -1, 2), ], mask=[(0, 0, 1), (0, 0, 1), (1, 1, 0), (1, 1, 0)], dtype=[('A', '|S3'), ('B', float), ('f2', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) test = stack_arrays((z, z, x)) control = ma.array([('A', 1, -1), ('B', 2, -1), ('A', 1, -1), ('B', 2, -1), (-1, -1, 1), (-1, -1, 2), ], mask=[(0, 0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (1, 1, 0), (1, 1, 0)], dtype=[('A', '|S3'), ('B', float), ('f2', int)]) assert_equal(test, control)
