""" Scales define the distribution of data values on an axis, e.g. a log scaling. The mapping is implemented through `.Transform` subclasses. The following scales are built-in: .. _builtin_scales: ============= ===================== ================================ ================================= Name Class Transform Inverted transform ============= ===================== ================================ ================================= "asinh" `AsinhScale` `AsinhTransform` `InvertedAsinhTransform` "function" `FuncScale` `FuncTransform` `FuncTransform` "functionlog" `FuncScaleLog` `FuncTransform` + `LogTransform` `InvertedLogTransform` + `FuncTransform` "linear" `LinearScale` `.IdentityTransform` `.IdentityTransform` "log" `LogScale` `LogTransform` `InvertedLogTransform` "logit" `LogitScale` `LogitTransform` `LogisticTransform` "symlog" `SymmetricalLogScale` `SymmetricalLogTransform` `InvertedSymmetricalLogTransform` ============= ===================== ================================ ================================= A user will often only use the scale name, e.g. when setting the scale through `~.Axes.set_xscale`: ``ax.set_xscale("log")``. See also the :ref:`scales examples ` in the documentation. Custom scaling can be achieved through `FuncScale`, or by creating your own `ScaleBase` subclass and corresponding transforms (see :doc:`/gallery/scales/custom_scale`). Third parties can register their scales by name through `register_scale`. """ # noqa: E501 import inspect import textwrap from functools import wraps import numpy as np import matplotlib as mpl from matplotlib import _api, _docstring from matplotlib.ticker import ( NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter, NullLocator, LogLocator, AutoLocator, AutoMinorLocator, SymmetricalLogLocator, AsinhLocator, LogitLocator) from matplotlib.transforms import Transform, IdentityTransform class ScaleBase: """ The base class for all scales. Scales are separable transformations, working on a single dimension. Subclasses should override :attr:`!name` The scale's name. :meth:`get_transform` A method returning a `.Transform`, which converts data coordinates to scaled coordinates. This transform should be invertible, so that e.g. mouse positions can be converted back to data coordinates. :meth:`set_default_locators_and_formatters` A method that sets default locators and formatters for an `~.axis.Axis` that uses this scale. :meth:`limit_range_for_scale` An optional method that "fixes" the axis range to acceptable values, e.g. restricting log-scaled axes to positive values. """ def __init__(self, axis): r""" Construct a new scale. Notes ----- The following note is for scale implementers. For back-compatibility reasons, scales take an `~matplotlib.axis.Axis` object as the first argument. .. deprecated:: 3.11 The *axis* parameter is now optional, i.e. matplotlib is compatible with `.ScaleBase` subclasses that do not take an *axis* parameter. The *axis* parameter is pending-deprecated. It will be deprecated in matplotlib 3.13, and removed in matplotlib 3.15. 3rd-party scales are recommended to remove the *axis* parameter now if they can afford to restrict compatibility to matplotlib >= 3.11 already. Otherwise, they may keep the *axis* parameter and remove it in time for matplotlib 3.13. """ def get_transform(self): """ Return the `.Transform` object associated with this scale. """ raise NotImplementedError() def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters of *axis* to instances suitable for this scale. """ raise NotImplementedError() def limit_range_for_scale(self, vmin, vmax, minpos): """ Return the range *vmin*, *vmax*, restricted to the domain supported by this scale (if any). *minpos* should be the minimum positive value in the data. This is used by log scales to determine a minimum value. """ return vmin, vmax def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. Accepts a scalar or array-like ``val``. For a scalar, returns a Python ``bool``. For an array, returns a bool ndarray of the same shape. This is a generic implementation, and subclasses may implement more efficient solutions for their domain. """ arr = np.asarray(val) with np.errstate(invalid='ignore'): try: vmin, vmax = self.limit_range_for_scale(arr, arr, minpos=1e-300) except (TypeError, ValueError): result = np.zeros(arr.shape, dtype=bool) else: result = np.isfinite(arr) & (vmin == arr) & (vmax == arr) return bool(result) if arr.ndim == 0 else result def _make_axis_parameter_optional(init_func): """ Decorator to allow leaving out the *axis* parameter in scale constructors. This decorator ensures backward compatibility for scale classes that previously required an *axis* parameter. It allows constructors to be called with or without the *axis* parameter. For simplicity, this does not handle the case when *axis* is passed as a keyword. However, scanning GitHub, there's no evidence that that is used anywhere. Parameters ---------- init_func : callable The original __init__ method of a scale class. Returns ------- callable A wrapped version of *init_func* that handles the optional *axis*. Notes ----- If the wrapped constructor defines *axis* as its first argument, the parameter is preserved when present. Otherwise, the value `None` is injected as the first argument. Examples -------- >>> from matplotlib.scale import ScaleBase >>> class CustomScale(ScaleBase): ... @_make_axis_parameter_optional ... def __init__(self, axis, custom_param=1): ... self.custom_param = custom_param """ @wraps(init_func) def wrapper(self, *args, **kwargs): sig = inspect.signature(init_func) try: # Try old signature. sig.bind(self, *args, **kwargs) except TypeError: # Use the new signature and pass in an unused axis=None. init_func(self, None, *args, **kwargs) else: # Use the old signature. init_func(self, *args, **kwargs) return wrapper class LinearScale(ScaleBase): """ The default linear scale. """ name = 'linear' @_make_axis_parameter_optional def __init__(self, axis): # This method is present only to prevent inheritance of the base class' # constructor docstring, which would otherwise end up interpolated into # the docstring of Axis.set_scale. """ """ # noqa: D419 def set_default_locators_and_formatters(self, axis): # docstring inherited axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) axis.set_minor_formatter(NullFormatter()) # update the minor locator for x and y axis based on rcParams if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']): axis.set_minor_locator(AutoMinorLocator()) else: axis.set_minor_locator(NullLocator()) def get_transform(self): """ Return the transform for linear scaling, which is just the `~matplotlib.transforms.IdentityTransform`. """ return IdentityTransform() def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. This is True for all values, except +-inf and NaN. """ arr = np.asarray(val) result = np.isfinite(arr) return bool(result) if arr.ndim == 0 else result class FuncTransform(Transform): """ A simple transform that takes and arbitrary function for the forward and inverse transform. """ input_dims = output_dims = 1 def __init__(self, forward, inverse): """ Parameters ---------- forward : callable The forward function for the transform. This function must have an inverse and, for best behavior, be monotonic. It must have the signature:: def forward(values: array-like) -> array-like inverse : callable The inverse of the forward function. Signature as ``forward``. """ super().__init__() if callable(forward) and callable(inverse): self._forward = forward self._inverse = inverse else: raise ValueError('arguments to FuncTransform must be functions') def transform_non_affine(self, values): return self._forward(values) def inverted(self): return FuncTransform(self._inverse, self._forward) class FuncScale(ScaleBase): """ Provide an arbitrary scale with user-supplied function for the axis. """ name = 'function' @_make_axis_parameter_optional def __init__(self, axis, functions): """ Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and will be removed in an imminent release. It can already be left out because of special preprocessing, so that ``FuncScale(functions)`` is valid. functions : (callable, callable) two-tuple of the forward and inverse functions for the scale. The forward function must be monotonic. Both functions must have the signature:: def forward(values: array-like) -> array-like """ forward, inverse = functions transform = FuncTransform(forward, inverse) self._transform = transform def get_transform(self): """Return the `.FuncTransform` associated with this scale.""" return self._transform def set_default_locators_and_formatters(self, axis): # docstring inherited axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) axis.set_minor_formatter(NullFormatter()) # update the minor locator for x and y axis based on rcParams if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']): axis.set_minor_locator(AutoMinorLocator()) else: axis.set_minor_locator(NullLocator()) class LogTransform(Transform): input_dims = output_dims = 1 def __init__(self, base, nonpositive='clip'): super().__init__() if base <= 0 or base == 1: raise ValueError('The log base cannot be <= 0 or == 1') self.base = base self._clip = _api.getitem_checked( {"clip": True, "mask": False}, nonpositive=nonpositive) self._log_funcs = {np.e: np.log, 2: np.log2, 10: np.log10} def __str__(self): return "{}(base={}, nonpositive={!r})".format( type(self).__name__, self.base, "clip" if self._clip else "mask") def transform_non_affine(self, values): # Ignore invalid values due to nans being passed to the transform. with np.errstate(divide="ignore", invalid="ignore"): log_func = self._log_funcs.get(self.base) if log_func: out = log_func(values) else: out = np.log(values) / np.log(self.base) if self._clip: # SVG spec says that conforming viewers must support values up # to 3.4e38 (C float); however experiments suggest that # Inkscape (which uses cairo for rendering) runs into cairo's # 24-bit limit (which is apparently shared by Agg). # Ghostscript (used for pdf rendering appears to overflow even # earlier, with the max value around 2 ** 15 for the tests to # pass. On the other hand, in practice, we want to clip beyond # np.log10(np.nextafter(0, 1)) ~ -323 # so 1000 seems safe. out[values <= 0] = -1000 return out def inverted(self): return InvertedLogTransform(self.base) class InvertedLogTransform(Transform): input_dims = output_dims = 1 def __init__(self, base): super().__init__() self.base = base self._exp_funcs = {np.e: np.exp, 2: np.exp2} def __str__(self): return f"{type(self).__name__}(base={self.base})" def transform_non_affine(self, values): exp_func = self._exp_funcs.get(self.base) if exp_func: return exp_func(values) else: return np.exp(values * np.log(self.base)) def inverted(self): return LogTransform(self.base) class LogScale(ScaleBase): """ A standard logarithmic scale. Care is taken to only plot positive values. """ name = 'log' @_make_axis_parameter_optional def __init__(self, axis=None, *, base=10, subs=None, nonpositive="clip"): """ Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and about to be removed in the future. It can already now be left out because of special preprocessing, so that ``LogScale(base=2)`` is valid. base : float, default: 10 The base of the logarithm. nonpositive : {'clip', 'mask'}, default: 'clip' Determines the behavior for non-positive values. They can either be masked as invalid, or clipped to a very small positive number. subs : sequence of int, default: None Where to place the subticks between each major tick. For example, in a log10 scale, ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. """ self._transform = LogTransform(base, nonpositive) self.subs = subs base = property(lambda self: self._transform.base) def set_default_locators_and_formatters(self, axis): # docstring inherited axis.set_major_locator(LogLocator(self.base)) axis.set_major_formatter(LogFormatterSciNotation(self.base)) axis.set_minor_locator(LogLocator(self.base, self.subs)) axis.set_minor_formatter( LogFormatterSciNotation(self.base, labelOnlyBase=(self.subs is not None))) def get_transform(self): """Return the `.LogTransform` associated with this scale.""" return self._transform def limit_range_for_scale(self, vmin, vmax, minpos): """Limit the domain to positive values.""" if not np.isfinite(minpos): minpos = 1e-300 # Should rarely (if ever) have a visible effect. return (minpos if vmin <= 0 else vmin, minpos if vmax <= 0 else vmax) def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. This is True for value(s) > 0 except +inf and NaN. """ arr = np.asarray(val) with np.errstate(invalid='ignore'): result = np.isfinite(arr) & (arr > 0) return bool(result) if arr.ndim == 0 else result class FuncScaleLog(LogScale): """ Provide an arbitrary scale with user-supplied function for the axis and then put on a logarithmic axes. """ name = 'functionlog' @_make_axis_parameter_optional def __init__(self, axis, functions, base=10): """ Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and about to be removed in the future. It can already now be left out because of special preprocessing, so that ``FuncScaleLog(functions=(forward, inverse))`` is valid. functions : (callable, callable) two-tuple of the forward and inverse functions for the scale. The forward function must be monotonic. Both functions must have the signature:: def forward(values: array-like) -> array-like base : float, default: 10 Logarithmic base of the scale. """ forward, inverse = functions self.subs = None self._transform = FuncTransform(forward, inverse) + LogTransform(base) @property def base(self): return self._transform._b.base # Base of the LogTransform. def get_transform(self): """Return the `.Transform` associated with this scale.""" return self._transform class SymmetricalLogTransform(Transform): input_dims = output_dims = 1 def __init__(self, base, linthresh, linscale): super().__init__() if base <= 1.0: raise ValueError("'base' must be larger than 1") if linthresh <= 0.0: raise ValueError("'linthresh' must be positive") if linscale <= 0.0: raise ValueError("'linscale' must be positive") self.base = base self.linthresh = linthresh self.linscale = linscale def transform_non_affine(self, values): linscale_adj = self.linscale / (1.0 - 1.0 / self.base) log_base = np.log(self.base) abs_a = np.abs(values) inside = abs_a <= self.linthresh if np.all(inside): # Fast path: all values in linear region return values * linscale_adj with np.errstate(divide="ignore", invalid="ignore"): out = np.sign(values) * self.linthresh * ( linscale_adj - np.log(self.linthresh) / log_base + np.log(abs_a) / log_base) out[inside] = values[inside] * linscale_adj return out def inverted(self): return InvertedSymmetricalLogTransform(self.base, self.linthresh, self.linscale) class InvertedSymmetricalLogTransform(Transform): input_dims = output_dims = 1 def __init__(self, base, linthresh, linscale): super().__init__() if base <= 1.0: raise ValueError("'base' must be larger than 1") if linthresh <= 0.0: raise ValueError("'linthresh' must be positive") if linscale <= 0.0: raise ValueError("'linscale' must be positive") self.base = base self.linthresh = linthresh self.linscale = linscale @_api.deprecated("3.11", name="invlinthresh", obj_type="attribute", alternative=".inverted().transform(linthresh)") @property def invlinthresh(self): invlinthresh = self.inverted().transform(self.linthresh) return invlinthresh def transform_non_affine(self, values): linscale_adj = self.linscale / (1.0 - 1.0 / self.base) invlinthresh = self.inverted().transform(self.linthresh) abs_a = np.abs(values) inside = abs_a <= invlinthresh if np.all(inside): # Fast path: all values in linear region return values / linscale_adj with np.errstate(divide="ignore", invalid="ignore"): out = np.sign(values) * self.linthresh * np.exp( (abs_a / self.linthresh - linscale_adj) * np.log(self.base)) out[inside] = values[inside] / linscale_adj return out def inverted(self): return SymmetricalLogTransform(self.base, self.linthresh, self.linscale) class SymmetricalLogScale(ScaleBase): """ The symmetrical logarithmic scale is logarithmic in both the positive and negative directions from the origin. Since the values close to zero tend toward infinity, there is a need to have a range around zero that is linear. The parameter *linthresh* allows the user to specify the size of this range (-*linthresh*, *linthresh*). See :doc:`/gallery/scales/symlog_demo` for a detailed description. Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and about to be removed in the future. It can already now be left out because of special preprocessing, so that ``SymmetricalLocSacle(base=2)`` is valid. base : float, default: 10 The base of the logarithm. linthresh : float, default: 2 Defines the range ``(-x, x)``, within which the plot is linear. This avoids having the plot go to infinity around zero. subs : sequence of int Where to place the subticks between each major tick. For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. linscale : float, optional This allows the linear range ``(-linthresh, linthresh)`` to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when *linscale* == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range. """ name = 'symlog' @_make_axis_parameter_optional def __init__(self, axis=None, *, base=10, linthresh=2, subs=None, linscale=1): self._transform = SymmetricalLogTransform(base, linthresh, linscale) self.subs = subs base = property(lambda self: self._transform.base) linthresh = property(lambda self: self._transform.linthresh) linscale = property(lambda self: self._transform.linscale) def set_default_locators_and_formatters(self, axis): # docstring inherited axis.set_major_locator(SymmetricalLogLocator(self.get_transform())) axis.set_major_formatter(LogFormatterSciNotation(self.base)) axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(), self.subs)) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """Return the `.SymmetricalLogTransform` associated with this scale.""" return self._transform def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. This is True for all values, except +-inf and NaN. """ arr = np.asarray(val) result = np.isfinite(arr) return bool(result) if arr.ndim == 0 else result class AsinhTransform(Transform): """Inverse hyperbolic-sine transformation used by `.AsinhScale`""" input_dims = output_dims = 1 def __init__(self, linear_width): super().__init__() if linear_width <= 0.0: raise ValueError("Scale parameter 'linear_width' " + "must be strictly positive") self.linear_width = linear_width def transform_non_affine(self, values): return self.linear_width * np.arcsinh(values / self.linear_width) def inverted(self): return InvertedAsinhTransform(self.linear_width) class InvertedAsinhTransform(Transform): """Hyperbolic sine transformation used by `.AsinhScale`""" input_dims = output_dims = 1 def __init__(self, linear_width): super().__init__() self.linear_width = linear_width def transform_non_affine(self, values): return self.linear_width * np.sinh(values / self.linear_width) def inverted(self): return AsinhTransform(self.linear_width) class AsinhScale(ScaleBase): """ A quasi-logarithmic scale based on the inverse hyperbolic sine (asinh) For values close to zero, this is essentially a linear scale, but for large magnitude values (either positive or negative) it is asymptotically logarithmic. The transition between these linear and logarithmic regimes is smooth, and has no discontinuities in the function gradient in contrast to the `.SymmetricalLogScale` ("symlog") scale. Specifically, the transformation of an axis coordinate :math:`a` is :math:`a \\rightarrow a_0 \\sinh^{-1} (a / a_0)` where :math:`a_0` is the effective width of the linear region of the transformation. In that region, the transformation is :math:`a \\rightarrow a + \\mathcal{O}(a^3)`. For large values of :math:`a` the transformation behaves as :math:`a \\rightarrow a_0 \\, \\mathrm{sgn}(a) \\ln |a| + \\mathcal{O}(1)`. .. note:: This API is provisional and may be revised in the future based on early user feedback. """ name = 'asinh' auto_tick_multipliers = { 3: (2, ), 4: (2, ), 5: (2, ), 8: (2, 4), 10: (2, 5), 16: (2, 4, 8), 64: (4, 16), 1024: (256, 512) } @_make_axis_parameter_optional def __init__(self, axis=None, *, linear_width=1.0, base=10, subs='auto', **kwargs): """ Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and about to be removed in the future. It can already now be left out because of special preprocessing, so that ``AsinhScale()`` is valid. linear_width : float, default: 1 The scale parameter (elsewhere referred to as :math:`a_0`) defining the extent of the quasi-linear region, and the coordinate values beyond which the transformation becomes asymptotically logarithmic. base : int, default: 10 The number base used for rounding tick locations on a logarithmic scale. If this is less than one, then rounding is to the nearest integer multiple of powers of ten. subs : sequence of int Multiples of the number base used for minor ticks. If set to 'auto', this will use built-in defaults, e.g. (2, 5) for base=10. """ super().__init__(axis) self._transform = AsinhTransform(linear_width) self._base = int(base) if subs == 'auto': self._subs = self.auto_tick_multipliers.get(self._base) else: self._subs = subs linear_width = property(lambda self: self._transform.linear_width) def get_transform(self): return self._transform def set_default_locators_and_formatters(self, axis): axis.set(major_locator=AsinhLocator(self.linear_width, base=self._base), minor_locator=AsinhLocator(self.linear_width, base=self._base, subs=self._subs), minor_formatter=NullFormatter()) if self._base > 1: axis.set_major_formatter(LogFormatterSciNotation(self._base)) else: axis.set_major_formatter('{x:.3g}') def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. This is True for all values, except +-inf and NaN. """ arr = np.asarray(val) result = np.isfinite(arr) return bool(result) if arr.ndim == 0 else result class LogitTransform(Transform): input_dims = output_dims = 1 def __init__(self, nonpositive='mask'): super().__init__() _api.check_in_list(['mask', 'clip'], nonpositive=nonpositive) self._nonpositive = nonpositive self._clip = {"clip": True, "mask": False}[nonpositive] def transform_non_affine(self, values): """logit transform (base 10), masked or clipped""" with np.errstate(divide="ignore", invalid="ignore"): out = np.log10(values / (1 - values)) if self._clip: # See LogTransform for choice of clip value. out[values <= 0] = -1000 out[1 <= values] = 1000 return out def inverted(self): return LogisticTransform(self._nonpositive) def __str__(self): return f"{type(self).__name__}({self._nonpositive!r})" class LogisticTransform(Transform): input_dims = output_dims = 1 def __init__(self, nonpositive='mask'): super().__init__() self._nonpositive = nonpositive def transform_non_affine(self, values): """logistic transform (base 10)""" return 1.0 / (1 + 10**(-values)) def inverted(self): return LogitTransform(self._nonpositive) def __str__(self): return f"{type(self).__name__}({self._nonpositive!r})" class LogitScale(ScaleBase): """ Logit scale for data between zero and one, both excluded. This scale is similar to a log scale close to zero and to one, and almost linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[. """ name = 'logit' @_make_axis_parameter_optional def __init__(self, axis=None, nonpositive='mask', *, one_half=r"\frac{1}{2}", use_overline=False): r""" Parameters ---------- axis : `~matplotlib.axis.Axis` The axis for the scale. .. note:: This parameter is unused and about to be removed in the future. It can already now be left out because of special preprocessing, so that ``LogitScale()`` is valid. nonpositive : {'mask', 'clip'} Determines the behavior for values beyond the open interval ]0, 1[. They can either be masked as invalid, or clipped to a number very close to 0 or 1. use_overline : bool, default: False Indicate the usage of survival notation (\overline{x}) in place of standard notation (1-x) for probability close to one. one_half : str, default: r"\frac{1}{2}" The string used for ticks formatter to represent 1/2. """ self._transform = LogitTransform(nonpositive) self._use_overline = use_overline self._one_half = one_half def get_transform(self): """Return the `.LogitTransform` associated with this scale.""" return self._transform def set_default_locators_and_formatters(self, axis): # docstring inherited # ..., 0.01, 0.1, 0.5, 0.9, 0.99, ... axis.set_major_locator(LogitLocator()) axis.set_major_formatter( LogitFormatter( one_half=self._one_half, use_overline=self._use_overline ) ) axis.set_minor_locator(LogitLocator(minor=True)) axis.set_minor_formatter( LogitFormatter( minor=True, one_half=self._one_half, use_overline=self._use_overline ) ) def limit_range_for_scale(self, vmin, vmax, minpos): """ Limit the domain to values between 0 and 1 (excluded). """ if not np.isfinite(minpos): minpos = 1e-7 # Should rarely (if ever) have a visible effect. return (minpos if vmin <= 0 else vmin, 1 - minpos if vmax >= 1 else vmax) def val_in_range(self, val): """ Return whether the value(s) are within the valid range for this scale. This is True for value(s) which are between 0 and 1 (excluded). """ arr = np.asarray(val) with np.errstate(invalid='ignore'): result = (0 < arr) & (arr < 1) return bool(result) if arr.ndim == 0 else result _scale_mapping = { 'linear': LinearScale, 'log': LogScale, 'symlog': SymmetricalLogScale, 'asinh': AsinhScale, 'logit': LogitScale, 'function': FuncScale, 'functionlog': FuncScaleLog, } # caching of signature info # For backward compatibility, the built-in scales will keep the *axis* parameter # in their constructors until matplotlib 3.15, i.e. as long as the *axis* parameter # is still supported. _scale_has_axis_parameter = { 'linear': True, 'log': True, 'symlog': True, 'asinh': True, 'logit': True, 'function': True, 'functionlog': True, } def get_scale_names(): """Return the names of the available scales.""" return sorted(_scale_mapping) def scale_factory(scale, axis, **kwargs): """ Return a scale class by name. Parameters ---------- scale : {%(names)s} axis : `~matplotlib.axis.Axis` """ scale_cls = _api.getitem_checked(_scale_mapping, scale=scale) if _scale_has_axis_parameter[scale]: return scale_cls(axis, **kwargs) else: return scale_cls(**kwargs) if scale_factory.__doc__: scale_factory.__doc__ = scale_factory.__doc__ % { "names": ", ".join(map(repr, get_scale_names()))} def register_scale(scale_class): """ Register a new kind of scale. Parameters ---------- scale_class : subclass of `ScaleBase` The scale to register. """ _scale_mapping[scale_class.name] = scale_class # migration code to handle the *axis* parameter has_axis_parameter = "axis" in inspect.signature(scale_class).parameters _scale_has_axis_parameter[scale_class.name] = has_axis_parameter if has_axis_parameter: _api.warn_deprecated( "3.11", message=f"The scale {scale_class.__qualname__!r} uses an 'axis' parameter " "in the constructors. This parameter is pending-deprecated since " "matplotlib 3.11. It will be fully deprecated in 3.13 and removed " "in 3.15. Starting with 3.11, 'register_scale()' accepts scales " "without the *axis* parameter.", pending=True, ) def _get_scale_docs(): """ Helper function for generating docstrings related to scales. """ docs = [] for name, scale_class in _scale_mapping.items(): docstring = inspect.getdoc(scale_class.__init__) or "" docs.extend([ f" {name!r}", "", textwrap.indent(docstring, " " * 8), "" ]) return "\n".join(docs) _docstring.interpd.register( scale_type='{%s}' % ', '.join([repr(x) for x in get_scale_names()]), scale_docs=_get_scale_docs().rstrip(), )