Python cbook.simple_linear_interpolation方法代码示例

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import simple_linear_interpolation [as 别名]
def __init__(self, *args, **kwargs):
        """
        Create a new Polar Axes for a polar plot.

        The following optional kwargs are supported:

          - *resolution*: The number of points of interpolation between
            each pair of data points.  Set to 1 to disable
            interpolation.
        """
        self.resolution = kwargs.pop('resolution', 1)
        self._default_theta_offset = kwargs.pop('theta_offset', 0)
        self._default_theta_direction = kwargs.pop('theta_direction', 1)

        if self.resolution not in (None, 1):
            warnings.warn(
                """The resolution kwarg to Polar plots is now ignored.
If you need to interpolate data points, consider running
cbook.simple_linear_interpolation on the data before passing to matplotlib.""")
        Axes.__init__(self, *args, **kwargs)
        self.set_aspect('equal', adjustable='box', anchor='C')
        self.cla() 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import simple_linear_interpolation [as 别名]
def interpolated(self, steps):
        """
        Returns a new path resampled to length N x steps.  Does not
        currently handle interpolating curves.
        """
        if steps == 1:
            return self

        vertices = simple_linear_interpolation(self.vertices, steps)
        codes = self.codes
        if codes is not None:
            new_codes = Path.LINETO * np.ones(((len(codes) - 1) * steps + 1, ))
            new_codes[0::steps] = codes
        else:
            new_codes = None
        return Path(vertices, new_codes) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import simple_linear_interpolation [as 别名]
def __init__(self, *args, **kwargs):
        """
        Create a new Polar Axes for a polar plot.

        The following optional kwargs are supported:

          - *resolution*: The number of points of interpolation between
            each pair of data points.  Set to 1 to disable
            interpolation.
        """
        self.resolution = kwargs.pop('resolution', 1)
        self._default_theta_offset = kwargs.pop('theta_offset', 0)
        self._default_theta_direction = kwargs.pop('theta_direction', 1)
        self._default_rlabel_position = kwargs.pop('rlabel_position', 22.5)

        if self.resolution not in (None, 1):
            warnings.warn(
                """The resolution kwarg to Polar plots is now ignored.
If you need to interpolate data points, consider running
cbook.simple_linear_interpolation on the data before passing to matplotlib.""")
        Axes.__init__(self, *args, **kwargs)
        self.set_aspect('equal', adjustable='box', anchor='C')
        self.cla() 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import simple_linear_interpolation [as 别名]
def less_simple_linear_interpolation( x, y, xi, extrap=False ):
    """
    This function provides simple (but somewhat less so than
    :func:`cbook.simple_linear_interpolation`) linear interpolation.
    :func:`simple_linear_interpolation` will give a list of point
    between a start and an end, while this does true linear
    interpolation at an arbitrary set of points.

    This is very inefficient linear interpolation meant to be used
    only for a small number of points in relatively non-intensive use
    cases.  For real linear interpolation, use scipy.
    """
    if cbook.is_scalar(xi): xi = [xi]

    x = np.asarray(x)
    y = np.asarray(y)
    xi = np.asarray(xi)

    s = list(y.shape)
    s[0] = len(xi)
    yi = np.tile( np.nan, s )

    for ii,xx in enumerate(xi):
        bb = x == xx
        if np.any(bb):
            jj, = np.nonzero(bb)
            yi[ii] = y[jj[0]]
        elif xx<x[0]:
            if extrap:
                yi[ii] = y[0]
        elif xx>x[-1]:
            if extrap:
                yi[ii] = y[-1]
        else:
            jj, = np.nonzero(x<xx)
            jj = max(jj)

            yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj])

    return yi 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import simple_linear_interpolation [as 别名]
def less_simple_linear_interpolation(x, y, xi, extrap=False):
    """
    This function provides simple (but somewhat less so than
    :func:`cbook.simple_linear_interpolation`) linear interpolation.
    :func:`simple_linear_interpolation` will give a list of point
    between a start and an end, while this does true linear
    interpolation at an arbitrary set of points.

    This is very inefficient linear interpolation meant to be used
    only for a small number of points in relatively non-intensive use
    cases.  For real linear interpolation, use scipy.
    """
    x = np.asarray(x)
    y = np.asarray(y)
    xi = np.atleast_1d(xi)

    s = list(y.shape)
    s[0] = len(xi)
    yi = np.tile(np.nan, s)

    for ii, xx in enumerate(xi):
        bb = x == xx
        if np.any(bb):
            jj, = np.nonzero(bb)
            yi[ii] = y[jj[0]]
        elif xx < x[0]:
            if extrap:
                yi[ii] = y[0]
        elif xx > x[-1]:
            if extrap:
                yi[ii] = y[-1]
        else:
            jj, = np.nonzero(x < xx)
            jj = max(jj)

            yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj])

    return yi