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return self.get_xyz_2d(xcoord, x, ycoord, y, u, v)"
809,"def get_xyz_1d(self, xcoord, x, ycoord, y, u, v):
""""""Get closest x, y and z for the given `x` and `y` in `data` for
1d coords""""""
xclose = xcoord.indexes[xcoord.name].get_loc(x, method='nearest')
yclose = ycoord.indexes[ycoord.name].get_loc(y, method='nearest')
uval = u[yclose, xclose].values
vval = v[yclose, xclose].values
return xcoord[xclose].values, ycoord[yclose].values, uval, vval"
810,"def get_xyz_2d(self, xcoord, x, ycoord, y, u, v):
""""""Get closest x, y and z for the given `x` and `y` in `data` for
2d coords""""""
xy = xcoord.values.ravel() + 1j * ycoord.values.ravel()
dist = np.abs(xy - (x + 1j * y))
imin = np.nanargmin(dist)
xy_min = xy[imin]
return (xy_min.real, xy_min.imag, u.values.ravel()[imin],
v.values.ravel()[imin])"
811,"def hist2d(self, da, **kwargs):
""""""Make the two dimensional histogram
Parameters
----------
da: xarray.DataArray
The data source""""""
if self.value is None or self.value == 'counts':
normed = False
else:
normed = True
y = da.values
x = da.coords[da.dims[0]].values
counts, xedges, yedges = np.histogram2d(
x, y, normed=normed, **kwargs)
if self.value == 'counts':
counts = counts / counts.sum().astype(float)
return counts, xedges, yedges"
812,"def _statsmodels_bivariate_kde(self, x, y, bws, xsize, ysize, xyranges):
""""""Compute a bivariate kde using statsmodels.
This function is mainly motivated through
seaborn.distributions._statsmodels_bivariate_kde""""""
import statsmodels.nonparametric.api as smnp
for i, (coord, bw) in enumerate(zip([x, y], bws)):
if isinstance(bw, six.string_types):
bw_func = getattr(smnp.bandwidths, ""bw_"" + bw)
bws[i] = bw_func(coord)
kde = smnp.KDEMultivariate([x, y], ""cc"", bws)
x_support = np.linspace(xyranges[0][0], xyranges[0][1], xsize)
y_support = np.linspace(xyranges[1][0], xyranges[1][1], ysize)
xx, yy = np.meshgrid(x_support, y_support)
z = kde.pdf([xx.ravel(), yy.ravel()]).reshape(xx.shape)
return x_support, y_support, z"
813,"def check_data(cls, name, dims, is_unstructured=None):
""""""
A validation method for the data shape
Parameters
----------
name: str or list of str
The variable names (at maximum :attr:`allowed_vars` variables 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, optional
True if the corresponding array is unstructured. This keyword is
ignored
Returns
-------
%(Plotter.check_data.returns)s
""""""
if isinstance(name, six.string_types) or not is_iterable(name):
name = [name]
dims = [dims]
N = len(name)
if len(dims) != N:
return [False] * N, [
'Number of provided names (%i) and dimensions '
'%(i) are not the same' % (N, len(dims))] * N
checks = [True] * N
messages = [''] * N
for i, (n, d) in enumerate(zip(name, dims)):
if n != 0 and not n:
checks[i] = False
messages[i] = 'At least one variable name is required!'
elif ((not isstring(n) and is_iterable(n) and
len(n) > cls.allowed_vars) and
len(d) != (cls.allowed_dims - len(slist(n)))):
checks[i] = False
messages[i] = 'Only %i names are allowed per array!' % (
cls.allowed_vars)
elif len(d) != cls.allowed_dims:
checks[i] = False
messages[i] = 'Only %i-dimensional arrays are allowed!' % (
cls.allowed_dims)
return checks, messages"
814,"def check_data(cls, name, dims, is_unstructured):
""""""
A validation method for the data shape
Parameters