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MultiNorm class#29876
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5e0266a MultiNorm class
trygvrad6985111 updates based on feedback from review, @oscargus, @anntzer
trygvradf42d65b Apply suggestions from code review
trygvrad73713e7 Updates based on feedback from @anntzer
trygvrad78b173e change MultiNorm.n_intput to n_variables
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272 changes: 263 additions & 9 deletionslib/matplotlib/colors.py
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| Original file line number | Diff line number | Diff line change | ||||
|---|---|---|---|---|---|---|
| @@ -1420,10 +1420,10 @@ | ||||||
| combination_mode: str, 'sRGB_add' or 'sRGB_sub' | ||||||
| Describe how colormaps are combined in sRGB space | ||||||
| - If 'sRGB_add': Mixing produces brighter colors | ||||||
| ``sRGB = sum(colors)`` | ||||||
| - If 'sRGB_sub': Mixing produces darker colors | ||||||
| ``sRGB = 1 - sum(1 - colors)`` | ||||||
| name : str, optional | ||||||
| The name of the colormap family. | ||||||
| """ | ||||||
| @@ -1595,15 +1595,15 @@ | ||||||
| Parameters | ||||||
| ---------- | ||||||
| bad: :mpltype:`color`, default: None | ||||||
| If Matplotlib color, the bad value is set accordingly in the copy | ||||||
| under:tuple of :mpltype:`color`, default: None | ||||||
| If tuple, the ``under`` value of each component is set with the values | ||||||
| from the tuple. | ||||||
| over:tuple of :mpltype:`color`, default: None | ||||||
| If tuple, the ``over`` value of each component is set with the values | ||||||
| from the tuple. | ||||||
| Returns | ||||||
| @@ -2320,6 +2320,11 @@ | ||||||
| self._scale = None | ||||||
| self.callbacks = cbook.CallbackRegistry(signals=["changed"]) | ||||||
| @property | ||||||
| def n_variables(self): | ||||||
| # To be overridden by subclasses with multiple inputs | ||||||
| return 1 | ||||||
| @property | ||||||
| def vmin(self): | ||||||
| return self._vmin | ||||||
| @@ -3219,6 +3224,224 @@ | ||||||
| return value | ||||||
| class MultiNorm(Normalize): | ||||||
| """ | ||||||
| A mixin class which contains multiple scalar norms | ||||||
| """ | ||||||
| def __init__(self, norms, vmin=None, vmax=None, clip=False): | ||||||
| """ | ||||||
| Parameters | ||||||
| ---------- | ||||||
| norms : List of strings or `Normalize` objects | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more.
Suggested change
| ||||||
| The constituent norms. The list must have a minimum length of 2. | ||||||
| vmin, vmax : float, None, or list of float or None | ||||||
| Limits of the constituent norms. | ||||||
| If a list, each value is assigned to each of the constituent | ||||||
| norms. Single values are repeated to form a list of appropriate size. | ||||||
| clip : bool or list of bools, default: False | ||||||
| Determines the behavior for mapping values outside the range | ||||||
| ``[vmin, vmax]`` for the constituent norms. | ||||||
| If a list, each value is assigned to each of the constituent | ||||||
| norms. Single values are repeated to form a list of appropriate size. | ||||||
| """ | ||||||
| if isinstance(norms, str) or not np.iterable(norms): | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. This is equivalent, I think? Suggested change
| ||||||
| raise ValueError("A MultiNorm must be assigned multiple norms") | ||||||
| norms = [*norms] | ||||||
| for i, n in enumerate(norms): | ||||||
| if n is None: | ||||||
| norms[i] = Normalize() | ||||||
| elif isinstance(n, str): | ||||||
| scale_cls = _get_scale_cls_from_str(n) | ||||||
| norms[i] = mpl.colorizer._auto_norm_from_scale(scale_cls)() | ||||||
trygvrad marked this conversation as resolved. Show resolvedHide resolvedUh oh!There was an error while loading.Please reload this page. | ||||||
| elif not isinstance(n, Normalize): | ||||||
| raise ValueError( | ||||||
| "MultiNorm must be assigned multiple norms, where each norm " | ||||||
| f"is of type `None` `str`, or `Normalize`, not {type(n)}") | ||||||
| # Convert the list of norms to a tuple to make it immutable. | ||||||
| # If there is a use case for swapping a single norm, we can add support for | ||||||
| # that later | ||||||
| self._norms = tuple(norms) | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Does this not need a check that the length matches | ||||||
| self.callbacks = cbook.CallbackRegistry(signals=["changed"]) | ||||||
| self.vmin = vmin | ||||||
| self.vmax = vmax | ||||||
| self.clip = clip | ||||||
| for n in self._norms: | ||||||
| n.callbacks.connect('changed', self._changed) | ||||||
| @property | ||||||
| def n_variables(self): | ||||||
| return len(self._norms) | ||||||
| @property | ||||||
| def norms(self): | ||||||
| return self._norms | ||||||
| @property | ||||||
| def vmin(self): | ||||||
| return tuple(n.vmin for n in self._norms) | ||||||
| @vmin.setter | ||||||
| def vmin(self, value): | ||||||
| value = np.broadcast_to(value, self.n_variables) | ||||||
| with self.callbacks.blocked(): | ||||||
| for i, v in enumerate(value): | ||||||
| if v is not None: | ||||||
| self.norms[i].vmin = v | ||||||
| self._changed() | ||||||
| @property | ||||||
| def vmax(self): | ||||||
| return tuple(n.vmax for n in self._norms) | ||||||
| @vmax.setter | ||||||
| def vmax(self, value): | ||||||
| value = np.broadcast_to(value, self.n_variables) | ||||||
| with self.callbacks.blocked(): | ||||||
| for i, v in enumerate(value): | ||||||
| if v is not None: | ||||||
| self.norms[i].vmax = v | ||||||
| self._changed() | ||||||
| @property | ||||||
| def clip(self): | ||||||
| return tuple(n.clip for n in self._norms) | ||||||
| @clip.setter | ||||||
| def clip(self, value): | ||||||
| value = np.broadcast_to(value, self.n_variables) | ||||||
| with self.callbacks.blocked(): | ||||||
| for i, v in enumerate(value): | ||||||
| if v is not None: | ||||||
| self.norms[i].clip = v | ||||||
| self._changed() | ||||||
| def _changed(self): | ||||||
| """ | ||||||
| Call this whenever the norm is changed to notify all the | ||||||
| callback listeners to the 'changed' signal. | ||||||
| """ | ||||||
| self.callbacks.process('changed') | ||||||
| def __call__(self, value, clip=None): | ||||||
| """ | ||||||
| Normalize the data and return the normalized data. | ||||||
| Each variate in the input is assigned to the constituent norm. | ||||||
| Parameters | ||||||
| ---------- | ||||||
| value | ||||||
| Data to normalize. Must be of length `n_variables` or have a data type with | ||||||
| `n_variables` fields. | ||||||
| clip : list of bools or bool, optional | ||||||
| See the description of the parameter *clip* in Normalize. | ||||||
| If ``None``, defaults to ``self.clip`` (which defaults to | ||||||
| ``False``). | ||||||
| Returns | ||||||
| ------- | ||||||
| Data | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Single returns should be the type, not a name. | ||||||
| Normalized input values as a list of length `n_variables` | ||||||
| Notes | ||||||
| ----- | ||||||
| If not already initialized, ``self.vmin`` and ``self.vmax`` are | ||||||
| initialized using ``self.autoscale_None(value)``. | ||||||
| """ | ||||||
| if clip is None: | ||||||
| clip = self.clip | ||||||
| elif not np.iterable(clip): | ||||||
| clip = [clip]*self.n_variables | ||||||
| value = self._iterable_variates_in_data(value, self.n_variables) | ||||||
| result = [n(v, clip=c) for n, v, c in zip(self.norms, value, clip)] | ||||||
| return result | ||||||
| def inverse(self, value): | ||||||
| """ | ||||||
| Map the normalized value (i.e., index in the colormap) back to image data value. | ||||||
| Parameters | ||||||
| ---------- | ||||||
| value | ||||||
| Normalized value. Must be of length `n_variables` or have a data type with | ||||||
| `n_variables` fields. | ||||||
| """ | ||||||
| value = self._iterable_variates_in_data(value, self.n_variables) | ||||||
| result = [n.inverse(v) for n, v in zip(self.norms, value)] | ||||||
| return result | ||||||
| def autoscale(self, A): | ||||||
| """ | ||||||
| For each constituent norm, Set *vmin*, *vmax* to min, max of the corresponding | ||||||
| variate in *A*. | ||||||
| """ | ||||||
| with self.callbacks.blocked(): | ||||||
| # Pause callbacks while we are updating so we only get | ||||||
| # a single update signal at the end | ||||||
| A = self._iterable_variates_in_data(A, self.n_variables) | ||||||
| for n, a in zip(self.norms, A): | ||||||
| n.autoscale(a) | ||||||
| self._changed() | ||||||
| def autoscale_None(self, A): | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Seems to be missing a test? | ||||||
| """ | ||||||
| If *vmin* or *vmax* are not set on any constituent norm, | ||||||
| use the min/max of the corresponding variate in *A* to set them. | ||||||
| Parameters | ||||||
| ---------- | ||||||
| A | ||||||
| Data, must be of length `n_variables` or be an np.ndarray type with | ||||||
| `n_variables` fields. | ||||||
Comment on lines +3404 to +3405 There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. This seems to be the only docstring for a data parameter that mentions | ||||||
| """ | ||||||
| with self.callbacks.blocked(): | ||||||
| A = self._iterable_variates_in_data(A, self.n_variables) | ||||||
| for n, a in zip(self.norms, A): | ||||||
| n.autoscale_None(a) | ||||||
| self._changed() | ||||||
| def scaled(self): | ||||||
| """Return whether both *vmin* and *vmax* are set on all constituent norms""" | ||||||
| return all([(n.vmin is not None and n.vmax is not None) for n in self.norms]) | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. A bit too many parentheses, not that it really matters. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. I'm changing this to: returnall([n.scaledforninself.norms]) which should be more readable There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. This wasn't changed? | ||||||
| @staticmethod | ||||||
| def _iterable_variates_in_data(data, n_variables): | ||||||
| """ | ||||||
| Provides an iterable over the variates contained in the data. | ||||||
| An input array with `n_variables` fields is returned as a list of length n | ||||||
| referencing slices of the original array. | ||||||
| Parameters | ||||||
| ---------- | ||||||
| data : np.ndarray, tuple or list | ||||||
| The input array. It must either be an array with n_variables fields or have | ||||||
| a length (n_variables) | ||||||
| Returns | ||||||
| ------- | ||||||
| list of np.ndarray | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Dedent. | ||||||
| """ | ||||||
| if isinstance(data, np.ndarray) and data.dtype.fields is not None: | ||||||
| data = [data[descriptor[0]] for descriptor in data.dtype.descr] | ||||||
| if len(data) != n_variables: | ||||||
| raise ValueError("The input to this `MultiNorm` must be of shape " | ||||||
| f"({n_variables}, ...), or have a data type with " | ||||||
| f"{n_variables} fields.") | ||||||
| return data | ||||||
| def rgb_to_hsv(arr): | ||||||
| """ | ||||||
| Convert an array of float RGB values (in the range [0, 1]) to HSV values. | ||||||
| @@ -3856,3 +4079,34 @@ | ||||||
| norm = BoundaryNorm(levels, ncolors=n_data_colors) | ||||||
| return cmap, norm | ||||||
| def _get_scale_cls_from_str(scale_as_str): | ||||||
| """ | ||||||
| Returns the scale class from a string. | ||||||
| Used in the creation of norms from a string to ensure a reasonable error | ||||||
| in the case where an invalid string is used. This would normally use | ||||||
| `_api.check_getitem()`, which would produce the error | ||||||
| > 'not_a_norm' is not a valid value for norm; supported values are | ||||||
trygvrad marked this conversation as resolved. Show resolvedHide resolvedUh oh!There was an error while loading.Please reload this page. | ||||||
| > 'linear', 'log', 'symlog', 'asinh', 'logit', 'function', 'functionlog' | ||||||
| which is misleading because the norm keyword also accepts `Normalize` objects. | ||||||
| Parameters | ||||||
| ---------- | ||||||
| scale_as_str : string | ||||||
| A string corresponding to a scale | ||||||
| Returns | ||||||
| ------- | ||||||
| A subclass of ScaleBase. | ||||||
| """ | ||||||
| try: | ||||||
| scale_cls = scale._scale_mapping[scale_as_str] | ||||||
| except KeyError: | ||||||
| raise ValueError( | ||||||
| "Invalid norm str name; the following values are " | ||||||
| f"supported: {', '.join(scale._scale_mapping)}" | ||||||
| ) from None | ||||||
| return scale_cls | ||||||
30 changes: 30 additions & 0 deletionslib/matplotlib/colors.pyi
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