### Navigation

- index
- modules |
- next |
- previous |
- home|
- examples|
- tutorials|
- pyplot|
- docs »
- The Matplotlib API »

# scale

## `matplotlib.scale`

class `matplotlib.scale.``InvertedLog10Transform`(shorthand_name=None)

  Bases: `matplotlib.scale.InvertedLogTransformBase`

  Creates a new `TransformNode`.

  shorthand_name - a string representing the “name” of this
    transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
  `base` = 10.0
  `inverted`()

class `matplotlib.scale.``InvertedLog2Transform`(shorthand_name=None)

  Bases: `matplotlib.scale.InvertedLogTransformBase`

  Creates a new `TransformNode`.

  shorthand_name - a string representing the “name” of this
    transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
  `base` = 2.0
  `inverted`()

class `matplotlib.scale.``InvertedLogTransform`(base)

  Bases: `matplotlib.scale.InvertedLogTransformBase`

  `inverted`()

class `matplotlib.scale.``InvertedLogTransformBase`(shorthand_name=None)

  Bases: `matplotlib.transforms.Transform`

  Creates a new `TransformNode`.

  shorthand_name - a string representing the “name” of this
    transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
  `has_inverse` = True
  `input_dims` = 1
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

class `matplotlib.scale.``InvertedNaturalLogTransform`(shorthand_name=None)

  Bases: `matplotlib.scale.InvertedLogTransformBase`

  Creates a new `TransformNode`.

  shorthand_name - a string representing the “name” of this
    transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
  `base` = 2.718281828459045
  `inverted`()

class `matplotlib.scale.``InvertedSymmetricalLogTransform`(base, linthresh, linscale)

  Bases: `matplotlib.transforms.Transform`

  `has_inverse` = True
  `input_dims` = 1
  `inverted`()
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

class `matplotlib.scale.``LinearScale`(axis, **kwargs)

  Bases: `matplotlib.scale.ScaleBase`

  The default linear scale.

  `get_transform`()

    The transform for linear scaling is just the `IdentityTransform`.

  `name` = ‘linear’
  `set_default_locators_and_formatters`(axis)

    Set the locators and formatters to reasonable defaults for linear scaling.

class `matplotlib.scale.``Log10Transform`(nonpos)

  Bases: `matplotlib.scale.LogTransformBase`

  `base` = 10.0
  `inverted`()

class `matplotlib.scale.``Log2Transform`(nonpos)

  Bases: `matplotlib.scale.LogTransformBase`

  `base` = 2.0
  `inverted`()

class `matplotlib.scale.``LogScale`(axis, **kwargs)

  Bases: `matplotlib.scale.ScaleBase`

  A standard logarithmic scale. Care is taken so non-positive values are not plotted.

  For computational efficiency (to push as much as possible to Numpy C code in the common cases), this scale provides different transforms depending on the base of the logarithm:

  > - base 10 (`Log10Transform`)
  > - base 2 (`Log2Transform`)
  > - base e (`NaturalLogTransform`)
  > - arbitrary base (`LogTransform`)

  basex/basey:
    The base of the logarithm
  nonposx/nonposy: [‘mask’ | ‘clip’ ]
    non-positive values in x or y can be masked as invalid, or clipped to a very small positive number
  subsx/subsy:

    Where to place the subticks between each major tick. Should be a sequence of integers. 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.

  class `InvertedLog10Transform`(shorthand_name=None)

    Bases: `matplotlib.scale.InvertedLogTransformBase`

    Creates a new `TransformNode`.

    shorthand_name - a string representing the “name” of this
      transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
    `base` = 10.0
    `inverted`()
  class `InvertedLog2Transform`(shorthand_name=None)

    Bases: `matplotlib.scale.InvertedLogTransformBase`

    Creates a new `TransformNode`.

    shorthand_name - a string representing the “name” of this
      transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
    `base` = 2.0
    `inverted`()
  class `InvertedLogTransform`(base)

    Bases: `matplotlib.scale.InvertedLogTransformBase`

    `inverted`()
  class `InvertedNaturalLogTransform`(shorthand_name=None)

    Bases: `matplotlib.scale.InvertedLogTransformBase`

    Creates a new `TransformNode`.

    shorthand_name - a string representing the “name” of this
      transform. The name carries no significance other than to improve the readability of `str(transform)` when DEBUG=True.
    `base` = 2.718281828459045
    `inverted`()
  class `Log10Transform`(nonpos)

    Bases: `matplotlib.scale.LogTransformBase`

    `base` = 10.0
    `inverted`()
  class `Log2Transform`(nonpos)

    Bases: `matplotlib.scale.LogTransformBase`

    `base` = 2.0
    `inverted`()
  class `LogTransform`(base, nonpos)

    Bases: `matplotlib.scale.LogTransformBase`

    `inverted`()
  class `LogTransformBase`(nonpos)

    Bases: `matplotlib.transforms.Transform`

    `has_inverse` = True
    `input_dims` = 1
    `is_separable` = True
    `output_dims` = 1
    `transform_non_affine`(a)
  class `NaturalLogTransform`(nonpos)

    Bases: `matplotlib.scale.LogTransformBase`

    `base` = 2.718281828459045
    `inverted`()
  `get_transform`()

    Return a `Transform` instance appropriate for the given logarithm base.

  `limit_range_for_scale`(vmin, vmax, minpos)

    Limit the domain to positive values.

  `name` = ‘log’
  `set_default_locators_and_formatters`(axis)

    Set the locators and formatters to specialized versions for log scaling.

class `matplotlib.scale.``LogTransform`(base, nonpos)

  Bases: `matplotlib.scale.LogTransformBase`

  `inverted`()

class `matplotlib.scale.``LogTransformBase`(nonpos)

  Bases: `matplotlib.transforms.Transform`

  `has_inverse` = True
  `input_dims` = 1
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

class `matplotlib.scale.``LogisticTransform`(nonpos=’mask’)

  Bases: `matplotlib.transforms.Transform`

  `has_inverse` = True
  `input_dims` = 1
  `inverted`()
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

    logistic transform (base 10)

class `matplotlib.scale.``LogitScale`(axis, nonpos=’mask’)

  Bases: `matplotlib.scale.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[.

  nonpos: [‘mask’ | ‘clip’ ]
    values beyond ]0, 1[ can be masked as invalid, or clipped to a number very close to 0 or 1
  `get_transform`()

    Return a `LogitTransform` instance.

  `limit_range_for_scale`(vmin, vmax, minpos)

    Limit the domain to values between 0 and 1 (excluded).

  `name` = ‘logit’
  `set_default_locators_and_formatters`(axis)

class `matplotlib.scale.``LogitTransform`(nonpos)

  Bases: `matplotlib.transforms.Transform`

  `has_inverse` = True
  `input_dims` = 1
  `inverted`()
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

    logit transform (base 10), masked or clipped

class `matplotlib.scale.``NaturalLogTransform`(nonpos)

  Bases: `matplotlib.scale.LogTransformBase`

  `base` = 2.718281828459045
  `inverted`()

class `matplotlib.scale.``ScaleBase`

  Bases: `object`

  The base class for all scales.

  Scales are separable transformations, working on a single dimension.

  Any subclasses will want to override:

  > - `name`
  > - `get_transform()`
  > - `set_default_locators_and_formatters()`

  And optionally:
    - `limit_range_for_scale()`
  `get_transform`()

    Return the `Transform` object associated with this scale.

  `limit_range_for_scale`(vmin, vmax, minpos)

    Returns the range vmin, vmax, possibly limited to the domain supported by this scale.

    minpos should be the minimum positive value in the data.
      This is used by log scales to determine a minimum value.
  `set_default_locators_and_formatters`(axis)

    Set the `Locator` and `Formatter` objects on the given axis to match this scale.

class `matplotlib.scale.``SymmetricalLogScale`(axis, **kwargs)

  Bases: `matplotlib.scale.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).

  basex/basey:
    The base of the logarithm
  linthreshx/linthreshy:
    The range (-x, x) within which the plot is linear (to avoid having the plot go to infinity around zero).
  subsx/subsy:

    Where to place the subticks between each major tick. Should be a sequence of integers. 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.

  linscalex/linscaley:
    This allows the linear range (-linthresh to 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.
  class `InvertedSymmetricalLogTransform`(base, linthresh, linscale)

    Bases: `matplotlib.transforms.Transform`

    `has_inverse` = True
    `input_dims` = 1
    `inverted`()
    `is_separable` = True
    `output_dims` = 1
    `transform_non_affine`(a)
  class `SymmetricalLogTransform`(base, linthresh, linscale)

    Bases: `matplotlib.transforms.Transform`

    `has_inverse` = True
    `input_dims` = 1
    `inverted`()
    `is_separable` = True
    `output_dims` = 1
    `transform_non_affine`(a)
  `get_transform`()

    Return a `SymmetricalLogTransform` instance.

  `name` = ‘symlog’
  `set_default_locators_and_formatters`(axis)

    Set the locators and formatters to specialized versions for symmetrical log scaling.

class `matplotlib.scale.``SymmetricalLogTransform`(base, linthresh, linscale)

  Bases: `matplotlib.transforms.Transform`

  `has_inverse` = True
  `input_dims` = 1
  `inverted`()
  `is_separable` = True
  `output_dims` = 1
  `transform_non_affine`(a)

`matplotlib.scale.``get_scale_docs`()

  Helper function for generating docstrings related to scales.

`matplotlib.scale.``get_scale_names`()

`matplotlib.scale.``register_scale`(scale_class)

  Register a new kind of scale.

  scale_class must be a subclass of `ScaleBase`.

`matplotlib.scale.``scale_factory`(scale, axis, **kwargs)

  Return a scale class by name.

  ACCEPTS: [ linear | log | logit | symlog ]