def add_binned_data(self, key, data, flat=True):
''' add data to binned_data
key : string
data : PISA Map or (array, binning)-tuple
flat : bool
is the data already flattened (i.e. the binning dimesnions unrolled)
'''
# TODO: logic to not copy back and forth
if isinstance(data, Map):
flat_array = data.hist.ravel()
self.binned_data[key] = (SmartArray(flat_array), data.binning)
elif isinstance(data, Sequence) and len(data) == 2:
binning, array = data
assert isinstance(binning, MultiDimBinning)
if isinstance(array, SmartArray):
array = array.get('host')
if flat:
flat_array = array
else:
# first dimesnions must match
assert array.shape[:binning.num_dims] == binning.shape
#flat_shape = [-1] + [d for d in array.shape[binning.num_dims-1:-1]]
flat_shape = [binning.size, -1]
#print(flat_shape)
flat_array = array.reshape(flat_shape)
if not isinstance(flat_array, SmartArray):
flat_array = SmartArray(flat_array.astype(FTYPE))
self.binned_data[key] = (binning, flat_array)
else:
raise TypeError('unknown dataformat')
def test_astype(self):
a = SmartArray(np.int32([42, 8, -5]))
aa = a.astype(np.float64)
self.assertIsInstance(aa, SmartArray)
# verify that SmartArray.astype() operates like ndarray.astype()...
self.assertPreciseEqual(aa.get('host'), a.get('host').astype(np.float64))
# ...and that both actually yield the expected dtype.
self.assertPreciseEqual(aa.get('host').dtype.type, np.float64)
self.assertIs(aa.dtype.type, np.float64)
def test_astype(self):
a = SmartArray(np.int32([42, 8, -5]))
aa = a.astype(np.float64)
self.assertIsInstance(aa, SmartArray)
# verify that SmartArray.astype() operates like ndarray.astype()...
self.assertPreciseEqual(aa.get('host'),
a.get('host').astype(np.float64))
# ...and that both actually yield the expected dtype.
self.assertPreciseEqual(aa.get('host').dtype.type, np.float64)
self.assertIs(aa.dtype.type, np.float64)
