Python ma.isMaskedArray方法代码示例

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def set_verts(self, verts, closed=True):
        '''This allows one to delay initialization of the vertices.'''
        if np.ma.isMaskedArray(verts):
            verts = verts.astype(np.float_).filled(np.nan)
            # This is much faster than having Path do it one at a time.
        if closed:
            self._paths = []
            for xy in verts:
                if len(xy):
                    if np.ma.isMaskedArray(xy):
                        xy = np.ma.concatenate([xy, np.zeros((1, 2))])
                    else:
                        xy = np.asarray(xy)
                        xy = np.concatenate([xy, np.zeros((1, 2))])
                    codes = np.empty(xy.shape[0], dtype=mpath.Path.code_type)
                    codes[:] = mpath.Path.LINETO
                    codes[0] = mpath.Path.MOVETO
                    codes[-1] = mpath.Path.CLOSEPOLY
                    self._paths.append(mpath.Path(xy, codes))
                else:
                    self._paths.append(mpath.Path(xy))
        else:
            self._paths = [mpath.Path(xy) for xy in verts] 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def convert_mesh_to_paths(meshWidth, meshHeight, coordinates):
        """
        Converts a given mesh into a sequence of
        :class:`matplotlib.path.Path` objects for easier rendering by
        backends that do not directly support quadmeshes.

        This function is primarily of use to backend implementers.
        """
        Path = mpath.Path

        if ma.isMaskedArray(coordinates):
            c = coordinates.data
        else:
            c = coordinates

        points = np.concatenate((
                    c[0:-1, 0:-1],
                    c[0:-1, 1:],
                    c[1:, 1:],
                    c[1:, 0:-1],
                    c[0:-1, 0:-1]
                ), axis=2)
        points = points.reshape((meshWidth * meshHeight, 5, 2))
        return [Path(x) for x in points] 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def set_verts(self, verts, closed=True):
        '''This allows one to delay initialization of the vertices.'''
        if np.ma.isMaskedArray(verts):
            verts = verts.astype(np.float_).filled(np.nan)
            # This is much faster than having Path do it one at a time.
        if closed:
            self._paths = []
            for xy in verts:
                if len(xy):
                    if np.ma.isMaskedArray(xy):
                        xy = np.ma.concatenate([xy, xy[0:1]])
                    else:
                        xy = np.asarray(xy)
                        xy = np.concatenate([xy, xy[0:1]])
                    codes = np.empty(xy.shape[0], dtype=mpath.Path.code_type)
                    codes[:] = mpath.Path.LINETO
                    codes[0] = mpath.Path.MOVETO
                    codes[-1] = mpath.Path.CLOSEPOLY
                    self._paths.append(mpath.Path(xy, codes))
                else:
                    self._paths.append(mpath.Path(xy))
        else:
            self._paths = [mpath.Path(xy) for xy in verts] 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def update_datalim(self, xys, updatex=True, updatey=True):
        """
        Update the data lim bbox with seq of xy tups or equiv. 2-D array
        """
        # if no data is set currently, the bbox will ignore its
        # limits and set the bound to be the bounds of the xydata.
        # Otherwise, it will compute the bounds of it's current data
        # and the data in xydata

        if iterable(xys) and not len(xys):
            return
        if not ma.isMaskedArray(xys):
            xys = np.asarray(xys)
        self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits,
                                         updatex=updatex, updatey=updatey)
        self.ignore_existing_data_limits = False 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def __init__(self,vals,vals_dmin,vals_dmax,mask=ma.nomask):
        super(UncertContainer, self).__init__()
        
        # If input data already masked arrays extract unmasked data
        if ma.isMaskedArray(vals): 
            vals = vals.data
        if ma.isMaskedArray(vals_dmin):
            vals_dmin = vals_dmin.data
        if ma.isMaskedArray(vals_dmax):
            vals_dmax = vals_dmax.data
        
        # Adjust negative values
        ineg = np.where(vals_dmin <= 0.0)
        vals_dmin[ineg] = TOL*vals[ineg]

        # Calculate weight based on fractional uncertainty 
        diff = vals_dmax - vals_dmin
        diff_m = ma.masked_where(vals_dmax == vals_dmin,diff)        

        self.vals = ma.masked_where(vals == 0.0,vals)

        self.wt = (self.vals/diff_m)**2
        self.uncert = diff_m/self.vals

        self.wt.fill_value = np.inf
        self.uncert.fill_vaule = np.inf

        assert np.all(self.wt.mask == self.uncert.mask)
        
        # Mask data if uncertainty is not finite or if any of the inputs were
        # already masked

        mm = ma.mask_or(self.wt.mask,mask)
        
        self.vals.mask = mm
        self.wt.mask = mm
        self.uncert.mask = mm
        self.dmin = ma.array(vals_dmin,mask=mm,fill_value=np.inf)
        self.dmax = ma.array(vals_dmax,mask=mm,fill_value=np.inf)

        self.mask = ma.getmaskarray(self.vals) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def _check(points):
            if ma.isMaskedArray(points):
                warnings.warn("Bbox bounds are a masked array.")
            points = np.asarray(points)
            if (points[1, 0] - points[0, 0] == 0 or
                points[1, 1] - points[0, 1] == 0):
                warnings.warn("Singular Bbox.") 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def transform_affine(self, points):
            # The major speed trap here is just converting to the
            # points to an array in the first place.  If we can use
            # more arrays upstream, that should help here.
            if (not ma.isMaskedArray(points) and
                not isinstance(points, np.ndarray)):
                warnings.warn(
                    ('A non-numpy array of type %s was passed in for ' +
                     'transformation.  Please correct this.')
                    % type(points))
            return self._transform_affine(points) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def get_datalim(self, transData):
        transform = self.get_transform()
        transOffset = self.get_offset_transform()
        offsets = self._offsets
        paths = self.get_paths()

        if not transform.is_affine:
            paths = [transform.transform_path_non_affine(p) for p in paths]
            transform = transform.get_affine()
        if not transOffset.is_affine:
            offsets = transOffset.transform_non_affine(offsets)
            transOffset = transOffset.get_affine()

        offsets = np.asanyarray(offsets, np.float_)
        if np.ma.isMaskedArray(offsets):
            offsets = offsets.filled(np.nan)
            # get_path_collection_extents handles nan but not masked arrays
        offsets.shape = (-1, 2)                     # Make it Nx2

        if paths:
            result = mpath.get_path_collection_extents(
                transform.frozen(), paths, self.get_transforms(),
                offsets, transOffset.frozen())
            result = result.inverse_transformed(transData)
        else:
            result = transforms.Bbox([[0, 0], [0, 0]])
        return result 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def _prepare_points(self):
        """Point prep for drawing and hit testing"""

        transform = self.get_transform()
        transOffset = self.get_offset_transform()
        offsets = self._offsets
        paths = self.get_paths()

        if self.have_units():
            paths = []
            for path in self.get_paths():
                vertices = path.vertices
                xs, ys = vertices[:, 0], vertices[:, 1]
                xs = self.convert_xunits(xs)
                ys = self.convert_yunits(ys)
                paths.append(mpath.Path(zip(xs, ys), path.codes))

            if offsets.size > 0:
                xs = self.convert_xunits(offsets[:, 0])
                ys = self.convert_yunits(offsets[:, 1])
                offsets = zip(xs, ys)

        offsets = np.asanyarray(offsets, np.float_)
        offsets.shape = (-1, 2)             # Make it Nx2

        if not transform.is_affine:
            paths = [transform.transform_path_non_affine(path)
                     for path in paths]
            transform = transform.get_affine()
        if not transOffset.is_affine:
            offsets = transOffset.transform_non_affine(offsets)
            # This might have changed an ndarray into a masked array.
            transOffset = transOffset.get_affine()

        if np.ma.isMaskedArray(offsets):
            offsets = offsets.filled(np.nan)
            # Changing from a masked array to nan-filled ndarray
            # is probably most efficient at this point.

        return transform, transOffset, offsets, paths 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def convert_mesh_to_triangles(self, meshWidth, meshHeight, coordinates):
        """
        Converts a given mesh into a sequence of triangles, each point
        with its own color.  This is useful for experiments using
        `draw_qouraud_triangle`.
        """
        if ma.isMaskedArray(coordinates):
            p = coordinates.data
        else:
            p = coordinates

        p_a = p[:-1, :-1]
        p_b = p[:-1, 1:]
        p_c = p[1:, 1:]
        p_d = p[1:, :-1]
        p_center = (p_a + p_b + p_c + p_d) / 4.0

        triangles = np.concatenate((
                p_a, p_b, p_center,
                p_b, p_c, p_center,
                p_c, p_d, p_center,
                p_d, p_a, p_center,
            ), axis=2)
        triangles = triangles.reshape((meshWidth * meshHeight * 4, 3, 2))

        c = self.get_facecolor().reshape((meshHeight + 1, meshWidth + 1, 4))
        c_a = c[:-1, :-1]
        c_b = c[:-1, 1:]
        c_c = c[1:, 1:]
        c_d = c[1:, :-1]
        c_center = (c_a + c_b + c_c + c_d) / 4.0

        colors = np.concatenate((
                        c_a, c_b, c_center,
                        c_b, c_c, c_center,
                        c_c, c_d, c_center,
                        c_d, c_a, c_center,
                    ), axis=2)
        colors = colors.reshape((meshWidth * meshHeight * 4, 3, 4))

        return triangles, colors 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def is_string_like(obj):
    'Return True if *obj* looks like a string'
    if isinstance(obj, (str, unicode)):
        return True
    # numpy strings are subclass of str, ma strings are not
    if ma.isMaskedArray(obj):
        if obj.ndim == 0 and obj.dtype.kind in 'SU':
            return True
        else:
            return False
    try:
        obj + ''
    except:
        return False
    return True 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def get_datalim(self, transData):
        transform = self.get_transform()
        transOffset = self.get_offset_transform()
        offsets = self._offsets
        paths = self.get_paths()

        if not transform.is_affine:
            paths = [transform.transform_path_non_affine(p) for p in paths]
            transform = transform.get_affine()
        if not transOffset.is_affine:
            offsets = transOffset.transform_non_affine(offsets)
            transOffset = transOffset.get_affine()

        offsets = np.asanyarray(offsets, np.float_)
        if np.ma.isMaskedArray(offsets):
            offsets = offsets.filled(np.nan)
            # get_path_collection_extents handles nan but not masked arrays
        offsets.shape = (-1, 2)                     # Make it Nx2

        if len(paths) and len(offsets):
            result = mpath.get_path_collection_extents(
                transform.frozen(), paths, self.get_transforms(),
                offsets, transOffset.frozen())
            result = result.inverse_transformed(transData)
        else:
            result = transforms.Bbox.null()
        return result 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def _prepare_points(self):
        """Point prep for drawing and hit testing"""

        transform = self.get_transform()
        transOffset = self.get_offset_transform()
        offsets = self._offsets
        paths = self.get_paths()

        if self.have_units():
            paths = []
            for path in self.get_paths():
                vertices = path.vertices
                xs, ys = vertices[:, 0], vertices[:, 1]
                xs = self.convert_xunits(xs)
                ys = self.convert_yunits(ys)
                paths.append(mpath.Path(list(zip(xs, ys)), path.codes))

            if offsets.size > 0:
                xs = self.convert_xunits(offsets[:, 0])
                ys = self.convert_yunits(offsets[:, 1])
                offsets = list(zip(xs, ys))

        offsets = np.asanyarray(offsets, np.float_)
        offsets.shape = (-1, 2)             # Make it Nx2

        if not transform.is_affine:
            paths = [transform.transform_path_non_affine(path)
                     for path in paths]
            transform = transform.get_affine()
        if not transOffset.is_affine:
            offsets = transOffset.transform_non_affine(offsets)
            # This might have changed an ndarray into a masked array.
            transOffset = transOffset.get_affine()

        if np.ma.isMaskedArray(offsets):
            offsets = offsets.filled(np.nan)
            # Changing from a masked array to nan-filled ndarray
            # is probably most efficient at this point.

        return transform, transOffset, offsets, paths 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import isMaskedArray [as 别名]
def is_string_like(obj):
    'Return True if *obj* looks like a string'
    if isinstance(obj, six.string_types):
        return True
    # numpy strings are subclass of str, ma strings are not
    if ma.isMaskedArray(obj):
        if obj.ndim == 0 and obj.dtype.kind in 'SU':
            return True
        else:
            return False
    try:
        obj + ''
    except:
        return False
    return True