Python matplotlib.cbook.simple_linear_interpolation() Examples

The following are 15 code examples of matplotlib.cbook.simple_linear_interpolation(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module matplotlib.cbook , or try the search function .
Example #1
Source File: polar.py    From Computable with MIT License 6 votes vote down vote up 
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()
Example #2
Source File: path.py    From Computable with MIT License 6 votes vote down vote up 
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)
Example #3
Source File: polar.py    From matplotlib-4-abaqus with MIT License 6 votes vote down vote up 
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()
Example #4
Source File: path.py    From matplotlib-4-abaqus with MIT License 6 votes vote down vote up 
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)
Example #5
Source File: polar.py    From neural-network-animation with MIT License 6 votes vote down vote up 
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()
Example #6
Source File: path.py    From neural-network-animation with MIT License 6 votes vote down vote up 
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)
Example #7
Source File: polar.py    From ImageFusion with MIT License 6 votes vote down vote up 
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()
Example #8
Source File: mlab.py    From Computable with MIT License 5 votes vote down vote up 
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
Example #9
Source File: mlab.py    From matplotlib-4-abaqus with MIT License 5 votes vote down vote up 
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
Example #10
Source File: mlab.py    From neural-network-animation with MIT License 5 votes vote down vote up 
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
Example #11
Source File: mlab.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
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
Example #12
Source File: mlab.py    From python3_ios with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
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
Example #13
Source File: mlab.py    From ImageFusion with MIT License 5 votes vote down vote up 
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
Example #14
Source File: mlab.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
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
Example #15
Source File: mlab.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
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