Python matplotlib.scale() Examples

def xscale(*args, **kwargs):
    """
    Set the scaling of the *x*-axis.

    call signature::

      xscale(scale, **kwargs)

    The available scales are: %(scale)s

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    ax = gca()
    ax.set_xscale(*args, **kwargs)
    draw_if_interactive() 

def yscale(*args, **kwargs):
    """
    Set the scaling of the *y*-axis.

    call signature::

      yscale(scale, **kwargs)

    The available scales are: %(scale)s

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    ax = gca()
    ax.set_yscale(*args, **kwargs)
    draw_if_interactive() 

def xscale(*args, **kwargs):
    """
    Set the scaling of the *x*-axis.

    call signature::

      xscale(scale, **kwargs)

    The available scales are: %(scale)s

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    ax = gca()
    ax.set_xscale(*args, **kwargs)
    draw_if_interactive() 

def yscale(*args, **kwargs):
    """
    Set the scaling of the *y*-axis.

    call signature::

      yscale(scale, **kwargs)

    The available scales are: %(scale)s

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    ax = gca()
    ax.set_yscale(*args, **kwargs)
    draw_if_interactive() 

def set_xscale(self, value, **kwargs):
        """
        Call signature::

          set_xscale(value)

        Set the scaling of the x-axis: %(scale)s

        ACCEPTS: [%(scale)s]

        Different kwargs are accepted, depending on the scale:
        %(scale_docs)s
        """
        # If the scale is being set to log, clip nonposx to prevent headaches
        # around zero
        if value.lower() == 'log' and 'nonposx' not in kwargs.keys():
            kwargs['nonposx'] = 'clip'
        self.xaxis._set_scale(value, **kwargs)
        self.autoscale_view(scaley=False)
        self._update_transScale() 

def set_yscale(self, value, **kwargs):
        """
        Call signature::

          set_yscale(value)

        Set the scaling of the y-axis: %(scale)s

        ACCEPTS: [%(scale)s]

        Different kwargs are accepted, depending on the scale:
        %(scale_docs)s
        """
        # If the scale is being set to log, clip nonposy to prevent headaches
        # around zero
        if value.lower() == 'log' and 'nonposy' not in kwargs.keys():
            kwargs['nonposy'] = 'clip'
        self.yaxis._set_scale(value, **kwargs)
        self.autoscale_view(scalex=False)
        self._update_transScale() 

def xscale(*args, **kwargs):
    """
    Set the scaling of the *x*-axis.

    call signature::

      xscale(scale, **kwargs)

    The available scales are: %(scale)s

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    ax = gca()
    ax.set_xscale(*args, **kwargs)
    draw_if_interactive() 

def magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None,
                       sides=None, scale=None, hold=None, **kwargs):
    ax = gca()
    # allow callers to override the hold state by passing hold=True|False
    washold = ax.ishold()

    if hold is not None:
        ax.hold(hold)
    try:
        ret = ax.magnitude_spectrum(x, Fs=Fs, Fc=Fc, window=window,
                                    pad_to=pad_to, sides=sides, scale=scale,
                                    **kwargs)
        draw_if_interactive()
    finally:
        ax.hold(washold)

    return ret

# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 

def specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
             noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None,
             scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None,
             hold=None, **kwargs):
    ax = gca()
    # allow callers to override the hold state by passing hold=True|False
    washold = ax.ishold()

    if hold is not None:
        ax.hold(hold)
    try:
        ret = ax.specgram(x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend,
                          window=window, noverlap=noverlap, cmap=cmap,
                          xextent=xextent, pad_to=pad_to, sides=sides,
                          scale_by_freq=scale_by_freq, mode=mode, scale=scale,
                          vmin=vmin, vmax=vmax, **kwargs)
        draw_if_interactive()
    finally:
        ax.hold(washold)
    sci(ret[-1])
    return ret

# This function was autogenerated by boilerplate.py.  Do not edit as
# changes will be lost 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def specgram(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, cmap=None, xextent=None, pad_to=None,
        sides=None, scale_by_freq=None, mode=None, scale=None,
        vmin=None, vmax=None, *, data=None, **kwargs):
    __ret = gca().specgram(
        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
        scale=scale, vmin=vmin, vmax=vmax, **({"data": data} if data
        is not None else {}), **kwargs)
    sci(__ret[-1])
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def transform_non_affine(self, s):
        """
        Apply transformation to a Nx1 numpy array.

        Parameters
        ----------
        s : array
            Data to be transformed in display scale units.

        Return
        ------
        array or masked array
            Transformed data, in data value units.

        """
        T = self._T
        M = self._M
        W = self._W
        p = self._p
        # Calculate x
        return T * 10**(-(M-W)) * (10**(s-W) - (p**2)*10**(-(s-W)/p) + p**2 - 1) 

def set_xscale(self, value, **kwargs):
        """
        Call signature::

          set_xscale(value)

        Set the scaling of the x-axis: %(scale)s

        ACCEPTS: [%(scale)s]

        Different kwargs are accepted, depending on the scale:
        %(scale_docs)s
        """
        # If the scale is being set to log, clip nonposx to prevent headaches
        # around zero
        if value.lower() == 'log' and 'nonposx' not in kwargs.keys():
            kwargs['nonposx'] = 'clip'
        self.xaxis._set_scale(value, **kwargs)
        self.autoscale_view(scaley=False)
        self._update_transScale() 

def set_yscale(self, value, **kwargs):
        """
        Call signature::

          set_yscale(value)

        Set the scaling of the y-axis: %(scale)s

        ACCEPTS: [%(scale)s]

        Different kwargs are accepted, depending on the scale:
        %(scale_docs)s
        """
        # If the scale is being set to log, clip nonposy to prevent headaches
        # around zero
        if value.lower() == 'log' and 'nonposy' not in kwargs.keys():
            kwargs['nonposy'] = 'clip'
        self.yaxis._set_scale(value, **kwargs)
        self.autoscale_view(scalex=False)
        self._update_transScale() 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def specgram(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, cmap=None, xextent=None, pad_to=None,
        sides=None, scale_by_freq=None, mode=None, scale=None,
        vmin=None, vmax=None, *, data=None, **kwargs):
    __ret = gca().specgram(
        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
        scale=scale, vmin=vmin, vmax=vmax, **({"data": data} if data
        is not None else {}), **kwargs)
    sci(__ret[-1])
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def specgram(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, cmap=None, xextent=None, pad_to=None,
        sides=None, scale_by_freq=None, mode=None, scale=None,
        vmin=None, vmax=None, *, data=None, **kwargs):
    __ret = gca().specgram(
        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
        scale=scale, vmin=vmin, vmax=vmax, **({"data": data} if data
        is not None else {}), **kwargs)
    sci(__ret[-1])
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def xscale(*args, **kwargs):
    """
    Set the scaling of the x-axis.

    Call signature::

        xscale(scale, **kwargs)

    Parameters
    ----------
    scale : [%(scale)s]
        The scaling type.
    **kwargs
        Additional parameters depend on *scale*. See Notes.

    Notes
    -----
    This is the pyplot equivalent of calling `~.Axes.set_xscale` on the
    current axes.

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    gca().set_xscale(*args, **kwargs) 

def yscale(*args, **kwargs):
    """
    Set the scaling of the y-axis.

    Call signature::

        yscale(scale, **kwargs)

    Parameters
    ----------
    scale : [%(scale)s]
        The scaling type.
    **kwargs
        Additional parameters depend on *scale*. See Notes.

    Notes
    -----
    This is the pyplot equivalent of calling `~.Axes.set_yscale` on the
    current axes.

    Different keywords may be accepted, depending on the scale:

    %(scale_docs)s
    """
    gca().set_yscale(*args, **kwargs) 

def magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None,
                       sides=None, scale=None, hold=None, data=None, **kwargs):
    ax = gca()
    # Deprecated: allow callers to override the hold state
    # by passing hold=True|False
    washold = ax._hold

    if hold is not None:
        ax._hold = hold
        from matplotlib.cbook import mplDeprecation
        warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                      mplDeprecation)
    try:
        ret = ax.magnitude_spectrum(x, Fs=Fs, Fc=Fc, window=window,
                                    pad_to=pad_to, sides=sides, scale=scale,
                                    data=data, **kwargs)
    finally:
        ax._hold = washold

    return ret

# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def _set_lim_and_transforms(self):
        """
        set the *dataLim* and *viewLim*
        :class:`~matplotlib.transforms.Bbox` attributes and the
        *transScale*, *transData*, *transLimits* and *transAxes*
        transformations.

        .. note::

            This method is primarily used by rectilinear projections
            of the :class:`~matplotlib.axes.Axes` class, and is meant
            to be overridden by new kinds of projection axes that need
            different transformations and limits. (See
            :class:`~matplotlib.projections.polar.PolarAxes` for an
            example.

        """
        self.transAxes = mtransforms.BboxTransformTo(self.bbox)

        # Transforms the x and y axis separately by a scale factor.
        # It is assumed that this part will have non-linear components
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData) 

def get_data_ratio_log(self):
        """
        Returns the aspect ratio of the raw data in log scale.
        Will be used when both axis scales are in log.
        """
        xmin, xmax = self.get_xbound()
        ymin, ymax = self.get_ybound()

        xsize = max(math.fabs(math.log10(xmax) - math.log10(xmin)), 1e-30)
        ysize = max(math.fabs(math.log10(ymax) - math.log10(ymin)), 1e-30)

        return ysize / xsize 

def _set_lim_and_transforms(self):
        """
        set the *_xaxis_transform*, *_yaxis_transform*,
        *transScale*, *transData*, *transLimits* and *transAxes*
        transformations.

        .. note::

            This method is primarily used by rectilinear projections
            of the :class:`~matplotlib.axes.Axes` class, and is meant
            to be overridden by new kinds of projection axes that need
            different transformations and limits. (See
            :class:`~matplotlib.projections.polar.PolarAxes` for an
            example.

        """
        self.transAxes = mtransforms.BboxTransformTo(self.bbox)

        # Transforms the x and y axis separately by a scale factor.
        # It is assumed that this part will have non-linear components
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData) 

def get_data_ratio_log(self):
        """
        Returns the aspect ratio of the raw data in log scale.
        Will be used when both axis scales are in log.
        """
        xmin, xmax = self.get_xbound()
        ymin, ymax = self.get_ybound()

        xsize = max(abs(math.log10(xmax) - math.log10(xmin)), 1e-30)
        ysize = max(abs(math.log10(ymax) - math.log10(ymin)), 1e-30)

        return ysize / xsize 

def set_xscale(self, value, **kwargs):
        """
        Set the x-axis scale.

        Parameters
        ----------
        value : {"linear", "log", "symlog", "logit", ...}
            scaling strategy to apply

        Notes
        -----
        Different kwargs are accepted, depending on the scale. See
        the `~matplotlib.scale` module for more information.

        See also
        --------
        matplotlib.scale.LinearScale : linear transform

        matplotlib.scale.LogTransform : log transform

        matplotlib.scale.SymmetricalLogTransform : symlog transform

        matplotlib.scale.LogisticTransform : logit transform
        """
        g = self.get_shared_x_axes()
        for ax in g.get_siblings(self):
            ax.xaxis._set_scale(value, **kwargs)
            ax._update_transScale()
            ax.stale = True

        self.autoscale_view(scaley=False) 

def set_yscale(self, value, **kwargs):
        """
        Set the y-axis scale.

        Parameters
        ----------
        value : {"linear", "log", "symlog", "logit", ...}
            scaling strategy to apply

        Notes
        -----
        Different kwargs are accepted, depending on the scale. See
        the `~matplotlib.scale` module for more information.

        See also
        --------
        matplotlib.scale.LinearScale : linear transform

        matplotlib.scale.LogTransform : log transform

        matplotlib.scale.SymmetricalLogTransform : symlog transform

        matplotlib.scale.LogisticTransform : logit transform
        """
        g = self.get_shared_y_axes()
        for ax in g.get_siblings(self):
            ax.yaxis._set_scale(value, **kwargs)
            ax._update_transScale()
            ax.stale = True
        self.autoscale_view(scalex=False) 

def magnitude_spectrum(
        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None,
        scale=None, *, data=None, **kwargs):
    return gca().magnitude_spectrum(
        x=x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
        scale=scale, data=data, **kwargs)

# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def specgram(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, cmap=None, xextent=None, pad_to=None,
        sides=None, scale_by_freq=None, mode=None, scale=None,
        vmin=None, vmax=None, *, data=None, **kwargs):
    __ret = gca().specgram(
        x=x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
        scale=scale, vmin=vmin, vmax=vmax, data=data, **kwargs)
    sci(__ret[-1])
    return __ret

# Autogenerated by boilerplate.py.  Do not edit as changes will be lost. 

def _set_lim_and_transforms(self):
        """
        set the *_xaxis_transform*, *_yaxis_transform*,
        *transScale*, *transData*, *transLimits* and *transAxes*
        transformations.

        .. note::

            This method is primarily used by rectilinear projections
            of the :class:`~matplotlib.axes.Axes` class, and is meant
            to be overridden by new kinds of projection axes that need
            different transformations and limits. (See
            :class:`~matplotlib.projections.polar.PolarAxes` for an
            example.

        """
        self.transAxes = mtransforms.BboxTransformTo(self.bbox)

        # Transforms the x and y axis separately by a scale factor.
        # It is assumed that this part will have non-linear components
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData)