def assert_equal(a, b):
if convert:
a = aslarray(a)
b = aslarray(b)
if check_axes and a.axes != b.axes:
raise AssertionError("axes differ:\n%s\n\nvs\n\n%s" % (a.axes.info, b.axes.info))
equal = test_func(a, b)
if not equal.all():
notequal = ~equal
raise AssertionError("\ngot:\n\n%s\n\nexpected:\n\n%s" % (a[notequal], b[notequal]))
def assert_equal(a, b):
if convert:
a = aslarray(a)
b = aslarray(b)
if check_axes and a.axes != b.axes:
raise AssertionError("axes differ:\n%s\n\nvs\n\n%s" %
(a.axes.info, b.axes.info))
equal = test_func(a, b)
if not equal.all():
notequal = ~equal
raise AssertionError("\ngot:\n\n%s\n\nexpected:\n\n%s" %
(a[notequal], b[notequal]))
def test_aslarray(self):
with open_excel(visible=False) as wb:
sheet = wb[0]
arr1 = ndtest([Axis(2), Axis(3)])
# no header so that we have an uniform dtype for the whole sheet
sheet['A1'] = arr1
res1 = aslarray(sheet['A1:C2'])
assert res1.equals(arr1)
assert res1.dtype == arr1.dtype
def test_aslarray(self):
with open_excel(visible=False) as wb:
sheet = wb[0]
arr1 = ndtest([Axis(2), Axis(3)])
# no header so that we have an uniform dtype for the whole sheet
sheet['A1'] = arr1
res1 = aslarray(sheet['A1:C2'])
assert res1.equals(arr1)
assert res1.dtype == arr1.dtype
def test_aslarray(self):
with open_excel(visible=False) as wb:
sheet = wb[0]
arr1 = ndrange((2, 3))
# no header so that we have an uniform dtype for the whole sheet
sheet['A1'] = arr1
res1 = aslarray(sheet['A1:C2'])
assert larray_equal(res1, arr1)
assert res1.dtype == arr1.dtype
def set_data(self,
data,
changes=None,
current_filter=None,
bg_gradient=None,
bg_value=None):
# ------------------- set changes -------------------
if changes is None:
changes = {}
assert isinstance(changes, dict)
self.changes = changes
self._changes2D = {}
# -------------------- set data ---------------------
if data is None:
data = np.empty((0, 0), dtype=np.int8)
la_data = la.aslarray(data)
if la_data.dtype.names is None:
dtn = la_data.dtype.name
if dtn not in SUPPORTED_FORMATS and not dtn.startswith('str') \
and not dtn.startswith('unicode'):
QMessageBox.critical(
self.dialog, "Error",
"{} arrays are currently not supported".format(dtn))
return
# for complex numbers, shading will be based on absolute value
# but for all other types it will be the real part
# TODO: there are a lot more complex dtypes than this. Is there a way to get them all in one shot?
if la_data.dtype in (np.complex64, np.complex128):
self.color_func = np.abs
else:
# XXX: this is a no-op (it returns the array itself) for most types (I think all non complex types)
# => use an explicit nop?
# def nop(v):
# return v
# self.color_func = nop
self.color_func = np.real
self.la_data = la_data
# ------------ set bg gradient and value ------------
self.bg_gradient = bg_gradient
self.bg_value = bg_value
# ------ set current filter and data to display -----
if current_filter is None:
current_filter = {}
assert isinstance(current_filter, dict)
self.current_filter = current_filter
self._set_labels_and_data_to_display()
# ------------------- reset model -------------------
self.reset()
def _set_labels_and_data_to_display(self):
la_data = self.filtered_data
if np.isscalar(la_data):
la_data = la.aslarray(la_data)
ndim, shape, axes = la_data.ndim, la_data.shape, la_data.axes
# get 2D shape + xlabels + ylabels
if ndim == 0:
self.xlabels = [[], []]
self.ylabels = [[]]
shape_2D = (1, 1)
elif ndim == 1:
self.xlabels = [axes.display_names, axes.labels[-1]]
self.ylabels = [[]]
shape_2D = (1, ) + shape
else:
self.xlabels = [axes.display_names, axes.labels[-1]]
otherlabels = axes.labels[:-1]
prod = Product(otherlabels)
self.ylabels = [_LazyNone(len(prod) + 1)] + [
_LazyDimLabels(prod, i) for i in range(len(otherlabels))
]
shape_2D = (np.prod(shape[:-1]), shape[-1])
# set data (reshape to a 2D array if not)
self._data2D = la_data.data.reshape(shape_2D)
self.total_rows, self.total_cols = shape_2D
size = self.total_rows * self.total_cols
self.reset_minmax()
# Use paging when the total size, number of rows or number of
# columns is too large
if size > LARGE_SIZE:
self.rows_loaded = min(self.ROWS_TO_LOAD, self.total_rows)
self.cols_loaded = min(self.COLS_TO_LOAD, self.total_cols)
else:
if self.total_rows > LARGE_NROWS:
self.rows_loaded = self.ROWS_TO_LOAD
else:
self.rows_loaded = self.total_rows
if self.total_cols > LARGE_COLS:
self.cols_loaded = self.COLS_TO_LOAD
else:
self.cols_loaded = self.total_cols
self._set_local_changes()