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----------
name: str or list of str
The variable names (one variable per array)
dims: list with length 1 or list of lists with length 1
The dimension of the arrays. Only 1D-Arrays are allowed
is_unstructured: bool or list of bool
True if the corresponding array is unstructured.
Returns
-------
%(Plotter.check_data.returns)s
""""""
if isinstance(name, six.string_types) or not is_iterable(name):
name = [name]
dims = [dims]
is_unstructured = [is_unstructured]
N = len(name)
if N != 1:
return [False] * N, [
'Number of provided names (%i) must equal 1!' % (N)] * N
elif len(dims) != 1:
return [False], [
'Number of provided dimension lists (%i) must equal 1!' % (
len(dims))]
elif len(is_unstructured) != 1:
return [False], [
('Number of provided unstructured information (%i) must '
'equal 1!') % (len(is_unstructured))]
if name[0] != 0 and not name[0]:
return [False], ['At least one variable name must be provided!']
# unstructured arrays have only 1 dimension
dimlen = cls.allowed_dims
if is_unstructured[0]:
dimlen -= 1
# Check that the array is two-dimensional
#
# if more than one array name is provided, the dimensions should be
# one les than dimlen to have a 2D array
if (not isstring(name[0]) and not is_iterable(name[0])
and len(name[0]) != 1 and len(dims[0]) != dimlen - 1):
return [False], ['Only one name is allowed per array!']
# otherwise the number of dimensions must equal dimlen
if len(dims[0]) != dimlen:
return [False], [
'An array with dimension %i is required, not %i' % (
dimlen, len(dims[0]))]
return [True], ['']"
815,"def check_data(cls, name, dims, is_unstructured):
""""""
A validation method for the data shape
Parameters
----------
name: list of str with length 2
The variable names (one for the first, two for the second array)
dims: list with length 2 of lists with length 1
The dimension of the arrays. Only 2D-Arrays are allowed (or 1-D if
an array is unstructured)
is_unstructured: bool or list of bool
True if the corresponding array is unstructured.
Returns
-------
%(Plotter.check_data.returns)s
""""""
if isinstance(name, six.string_types) or not is_iterable(name):
name = [name]
dims = [dims]
is_unstructured = [is_unstructured]
msg = ('Two arrays are required (one for the scalar and '
'one for the vector field)')
if len(name) < 2:
return [None], [msg]
elif len(name) > 2:
return [False], [msg]
valid1, msg1 = Simple2DBase.check_data(name[:1], dims[0:1],
is_unstructured[:1])
valid2, msg2 = BaseVectorPlotter.check_data(name[1:], dims[1:],
is_unstructured[1:])
return valid1 + valid2, msg1 + msg2"
816,"def record_diff(old, new):
""""""Return a JSON-compatible structure capable turn the `new` record back
into the `old` record. The parameters must be structures compatible with
json.dumps *or* strings compatible with json.loads. Note that by design,
`old == record_patch(new, record_diff(old, new))`""""""
old, new = _norm_json_params(old, new)
return json_delta.diff(new, old, verbose=False)"
817,"def record_patch(rec, diff):
""""""Return the JSON-compatible structure that results from applying the
changes in `diff` to the record `rec`. The parameters must be structures
compatible with json.dumps *or* strings compatible with json.loads. Note
that by design, `old == record_patch(new, record_diff(old, new))`""""""
rec, diff = _norm_json_params(rec, diff)
return json_delta.patch(rec, diff, in_place=False)"
818,"def append_diff_hist(diff, diff_hist=list()):
""""""Given a diff as generated by record_diff, append a diff record to the
list of diff_hist records.""""""
diff, diff_hist = _norm_json_params(diff, diff_hist)
if not diff_hist: