""" The Colorizer class which handles the data to color pipeline via a normalization and a colormap. .. admonition:: Provisional status of colorizer The ``colorizer`` module and classes in this file are considered provisional and may change at any time without a deprecation period. .. seealso:: :doc:`/gallery/color/colormap_reference` for a list of builtin colormaps. :ref:`colormap-manipulation` for examples of how to make colormaps. :ref:`colormaps` for an in-depth discussion of choosing colormaps. :ref:`colormapnorms` for more details about data normalization. """ import functools import numpy as np from numpy import ma from matplotlib import _api, colors, cbook, artist, scale import matplotlib as mpl mpl._docstring.interpd.register( colorizer_doc="""\ colorizer : `~matplotlib.colorizer.Colorizer` or None, default: None The Colorizer object used to map color to data. If None, a Colorizer object is created from a *norm* and *cmap*.""", ) class Colorizer: """ Data to color pipeline. This pipeline is accessible via `.Colorizer.to_rgba` and executed via the `.Colorizer.norm` and `.Colorizer.cmap` attributes. Parameters ---------- cmap: colorbar.Colorbar or str or None, default: None The colormap used to color data. norm: colors.Normalize or str or None, default: None The normalization used to normalize the data """ def __init__(self, cmap=None, norm=None): self._cmap = None self._set_cmap(cmap) self._id_norm = None self._norm = None self.norm = norm self.callbacks = cbook.CallbackRegistry(signals=["changed"]) self.colorbar = None def _scale_norm(self, norm, vmin, vmax, A): """ Helper for initial scaling. Used by public functions that create a ScalarMappable and support parameters *vmin*, *vmax* and *norm*. This makes sure that a *norm* will take precedence over *vmin*, *vmax*. Note that this method does not set the norm. """ if vmin is not None or vmax is not None: self.set_clim(vmin, vmax) if isinstance(norm, colors.Normalize): raise ValueError( "Passing a Normalize instance simultaneously with " "vmin/vmax is not supported. Please pass vmin/vmax " "as arguments to the norm object when creating it") # always resolve the autoscaling so we have concrete limits # rather than deferring to draw time. self.autoscale_None(A) @property def norm(self): return self._norm @norm.setter def norm(self, norm): norm = _ensure_norm(norm, n_components=self.cmap.n_variates) if norm is self.norm: # We aren't updating anything return in_init = self.norm is None # Remove the current callback and connect to the new one if not in_init: self.norm.callbacks.disconnect(self._id_norm) self._norm = norm self._id_norm = self.norm.callbacks.connect('changed', self.changed) if not in_init: self.changed() def to_rgba(self, x, alpha=None, bytes=False, norm=True): """ Return a normalized RGBA array corresponding to *x*. In the normal case, *x* is a 1D or 2D sequence of scalars, and the corresponding `~numpy.ndarray` of RGBA values will be returned, based on the norm and colormap set for this Colorizer. There is one special case, for handling images that are already RGB or RGBA, such as might have been read from an image file. If *x* is an `~numpy.ndarray` with 3 dimensions, and the last dimension is either 3 or 4, then it will be treated as an RGB or RGBA array, and no mapping will be done. The array can be `~numpy.uint8`, or it can be floats with values in the 0-1 range; otherwise a ValueError will be raised. Any NaNs or masked elements will be set to 0 alpha. If the last dimension is 3, the *alpha* kwarg (defaulting to 1) will be used to fill in the transparency. If the last dimension is 4, the *alpha* kwarg is ignored; it does not replace the preexisting alpha. A ValueError will be raised if the third dimension is other than 3 or 4. In either case, if *bytes* is *False* (default), the RGBA array will be floats in the 0-1 range; if it is *True*, the returned RGBA array will be `~numpy.uint8` in the 0 to 255 range. If norm is False, no normalization of the input data is performed, and it is assumed to be in the range (0-1). """ # First check for special case, image input: if isinstance(x, np.ndarray) and x.ndim == 3: return self._pass_image_data(x, alpha, bytes, norm) # Otherwise run norm -> colormap pipeline x = ma.asarray(x) if norm: x = self.norm(x) rgba = self.cmap(x, alpha=alpha, bytes=bytes) return rgba @staticmethod def _pass_image_data(x, alpha=None, bytes=False, norm=True): """ Helper function to pass ndarray of shape (...,3) or (..., 4) through `to_rgba()`, see `to_rgba()` for docstring. """ if x.shape[2] == 3: if alpha is None: alpha = 1 if x.dtype == np.uint8: alpha = np.uint8(alpha * 255) m, n = x.shape[:2] xx = np.empty(shape=(m, n, 4), dtype=x.dtype) xx[:, :, :3] = x xx[:, :, 3] = alpha elif x.shape[2] == 4: xx = x else: raise ValueError("Third dimension must be 3 or 4") if xx.dtype.kind == 'f': # If any of R, G, B, or A is nan, set to 0 if np.any(nans := np.isnan(x)): if x.shape[2] == 4: xx = xx.copy() xx[np.any(nans, axis=2), :] = 0 if norm and (xx.max() > 1 or xx.min() < 0): raise ValueError("Floating point image RGB values " "must be in the [0,1] range") if bytes: xx = (xx * 255).astype(np.uint8) elif xx.dtype == np.uint8: if not bytes: xx = xx.astype(np.float32) / 255 else: raise ValueError("Image RGB array must be uint8 or " "floating point; found %s" % xx.dtype) # Account for any masked entries in the original array # If any of R, G, B, or A are masked for an entry, we set alpha to 0 if np.ma.is_masked(x): xx[np.any(np.ma.getmaskarray(x), axis=2), 3] = 0 return xx def autoscale(self, A): """ Autoscale the scalar limits on the norm instance using the current array """ if A is None: raise TypeError('You must first set_array for mappable') # If the norm's limits are updated self.changed() will be called # through the callbacks attached to the norm self.norm.autoscale(A) def autoscale_None(self, A): """ Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None """ if A is None: raise TypeError('You must first set_array for mappable') # If the norm's limits are updated self.changed() will be called # through the callbacks attached to the norm self.norm.autoscale_None(A) def _set_cmap(self, cmap): """ Set the colormap for luminance data. Parameters ---------- cmap : `.Colormap` or str or None """ in_init = self._cmap is None cmap_obj = _ensure_cmap(cmap, accept_multivariate=True) if not in_init and self.norm.n_components != cmap_obj.n_variates: raise ValueError(f"The colormap {cmap} does not support " f"{self.norm.n_components} variates as required by " f"the {type(self.norm)} on this Colorizer") self._cmap = cmap_obj if not in_init: self.changed() # Things are not set up properly yet. @property def cmap(self): return self._cmap @cmap.setter def cmap(self, cmap): self._set_cmap(cmap) def set_clim(self, vmin=None, vmax=None): """ Set the norm limits for image scaling. Parameters ---------- vmin, vmax : float The limits. For scalar data, the limits may also be passed as a tuple (*vmin*, *vmax*) single positional argument. .. ACCEPTS: (vmin: float, vmax: float) """ if self.norm.n_components == 1: if vmax is None: try: vmin, vmax = vmin except (TypeError, ValueError): pass orig_vmin_vmax = self.norm.vmin, self.norm.vmax # Blocked context manager prevents callbacks from being triggered # until both vmin and vmax are updated with self.norm.callbacks.blocked(signal='changed'): # Since the @vmin/vmax.setter invokes colors._sanitize_extrema() # to sanitize the input, the input is not sanitized here if vmin is not None: self.norm.vmin = vmin if vmax is not None: self.norm.vmax = vmax # emit a update signal if the limits are changed if orig_vmin_vmax != (self.norm.vmin, self.norm.vmax): self.norm.callbacks.process('changed') def get_clim(self): """ Return the values (min, max) that are mapped to the colormap limits. """ return self.norm.vmin, self.norm.vmax def changed(self): """ Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal. """ self.callbacks.process('changed') self.stale = True @property def vmin(self): return self.get_clim()[0] @vmin.setter def vmin(self, vmin): self.set_clim(vmin=vmin) @property def vmax(self): return self.get_clim()[1] @vmax.setter def vmax(self, vmax): self.set_clim(vmax=vmax) @property def clip(self): return self.norm.clip @clip.setter def clip(self, clip): self.norm.clip = clip class _ColorizerInterface: """ Base class that contains the interface to `Colorizer` objects from a `ColorizingArtist` or `.cm.ScalarMappable`. Note: This class only contain functions that interface the .colorizer attribute. Other functions that as shared between `.ColorizingArtist` and `.cm.ScalarMappable` are not included. """ def _scale_norm(self, norm, vmin, vmax): self._colorizer._scale_norm(norm, vmin, vmax, self._A) def to_rgba(self, x, alpha=None, bytes=False, norm=True): """ Return a normalized RGBA array corresponding to *x*. In the normal case, *x* is a 1D or 2D sequence of scalars, and the corresponding `~numpy.ndarray` of RGBA values will be returned, based on the norm and colormap set for this Colorizer. There is one special case, for handling images that are already RGB or RGBA, such as might have been read from an image file. If *x* is an `~numpy.ndarray` with 3 dimensions, and the last dimension is either 3 or 4, then it will be treated as an RGB or RGBA array, and no mapping will be done. The array can be `~numpy.uint8`, or it can be floats with values in the 0-1 range; otherwise a ValueError will be raised. Any NaNs or masked elements will be set to 0 alpha. If the last dimension is 3, the *alpha* kwarg (defaulting to 1) will be used to fill in the transparency. If the last dimension is 4, the *alpha* kwarg is ignored; it does not replace the preexisting alpha. A ValueError will be raised if the third dimension is other than 3 or 4. In either case, if *bytes* is *False* (default), the RGBA array will be floats in the 0-1 range; if it is *True*, the returned RGBA array will be `~numpy.uint8` in the 0 to 255 range. If norm is False, no normalization of the input data is performed, and it is assumed to be in the range (0-1). """ return self._colorizer.to_rgba(x, alpha=alpha, bytes=bytes, norm=norm) def get_clim(self): """ Return the values (min, max) that are mapped to the colormap limits. """ return self._colorizer.get_clim() def set_clim(self, vmin=None, vmax=None): """ Set the norm limits for image scaling. Parameters ---------- vmin, vmax : float The limits. For scalar data, the limits may also be passed as a tuple (*vmin*, *vmax*) as a single positional argument. .. ACCEPTS: (vmin: float, vmax: float) """ # If the norm's limits are updated self.changed() will be called # through the callbacks attached to the norm self._colorizer.set_clim(vmin, vmax) def get_alpha(self): try: return super().get_alpha() except AttributeError: return 1 @property def cmap(self): return self._colorizer.cmap @cmap.setter def cmap(self, cmap): self._colorizer.cmap = cmap def get_cmap(self): """Return the `.Colormap` instance.""" return self._colorizer.cmap def set_cmap(self, cmap): """ Set the colormap for luminance data. Parameters ---------- cmap : `.Colormap` or str or None """ self.cmap = cmap @property def norm(self): return self._colorizer.norm @norm.setter def norm(self, norm): self._colorizer.norm = norm def set_norm(self, norm): """ Set the normalization instance. Parameters ---------- norm : `.Normalize` or str or None Notes ----- If there are any colorbars using the mappable for this norm, setting the norm of the mappable will reset the norm, locator, and formatters on the colorbar to default. """ self.norm = norm def autoscale(self): """ Autoscale the scalar limits on the norm instance using the current array """ self._colorizer.autoscale(self._A) def autoscale_None(self): """ Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None """ self._colorizer.autoscale_None(self._A) @property def colorbar(self): """ The last colorbar associated with this object. May be None """ return self._colorizer.colorbar @colorbar.setter def colorbar(self, colorbar): self._colorizer.colorbar = colorbar def _format_cursor_data_override(self, data): # This function overwrites Artist.format_cursor_data(). We cannot # implement cm.ScalarMappable.format_cursor_data() directly, because # most cm.ScalarMappable subclasses inherit from Artist first and from # cm.ScalarMappable second, so Artist.format_cursor_data would always # have precedence over cm.ScalarMappable.format_cursor_data. # Note if cm.ScalarMappable is depreciated, this functionality should be # implemented as format_cursor_data() on ColorizingArtist. if np.ma.getmask(data) or data is None: # NOTE: for multivariate data, if *any* of the fields are masked, # "[]" is returned here return "[]" if isinstance(self.norm, colors.MultiNorm): norms = self.norm.norms if isinstance(self.cmap, colors.BivarColormap): n_s = (self.cmap.N, self.cmap.M) else: # colors.MultivarColormap n_s = [part.N for part in self.cmap] else: # colors.Colormap norms = [self.norm] data = [data] n_s = [self.cmap.N] os = [f"{d:-#.{self._sig_digits_from_norm(no, d, n)}g}" for no, d, n in zip(norms, data, n_s)] return f"[{', '.join(os)}]" @staticmethod def _sig_digits_from_norm(norm, data, n): # Determines the number of significant digits # to use for a number given a norm, and n, where n is the # number of colors in the colormap. normed = norm(data) if np.isfinite(normed): if isinstance(norm, colors.BoundaryNorm): # not an invertible normalization mapping cur_idx = np.argmin(np.abs(norm.boundaries - data)) neigh_idx = max(0, cur_idx - 1) # use max diff to prevent delta == 0 delta = np.diff(norm.boundaries[neigh_idx:cur_idx + 2]).max() elif norm.vmin == norm.vmax: # singular norms, use delta of 10% of only value delta = np.abs(norm.vmin * .1) else: # Midpoints of neighboring color intervals. neighbors = norm.inverse((int(normed * n) + np.array([0, 1])) / n) delta = abs(neighbors - data).max() g_sig_digits = cbook._g_sig_digits(data, delta) else: g_sig_digits = 3 # Consistent with default below. return g_sig_digits class _ScalarMappable(_ColorizerInterface): """ A mixin class to map one or multiple sets of scalar data to RGBA. The ScalarMappable applies data normalization before returning RGBA colors from the given `~matplotlib.colors.Colormap`. """ # _ScalarMappable exists for compatibility with # code written before the introduction of the Colorizer # and ColorizingArtist classes. # _ScalarMappable can be depreciated so that ColorizingArtist # inherits directly from _ColorizerInterface. # in this case, the following changes should occur: # __init__() has its functionality moved to ColorizingArtist. # set_array(), get_array(), _get_colorizer() and # _check_exclusionary_keywords() are moved to ColorizingArtist. # changed() can be removed so long as colorbar.Colorbar # is changed to connect to the colorizer instead of the # ScalarMappable/ColorizingArtist, # otherwise changed() can be moved to ColorizingArtist. def __init__(self, norm=None, cmap=None, *, colorizer=None, **kwargs): """ Parameters ---------- norm : `.Normalize` (or subclass thereof) or str or None The normalizing object which scales data, typically into the interval ``[0, 1]``. If a `str`, a `.Normalize` subclass is dynamically generated based on the scale with the corresponding name. If *None*, *norm* defaults to a *colors.Normalize* object which initializes its scaling based on the first data processed. cmap : str or `~matplotlib.colors.Colormap` The colormap used to map normalized data values to RGBA colors. """ super().__init__(**kwargs) self._A = None self._colorizer = self._get_colorizer(colorizer=colorizer, norm=norm, cmap=cmap) self.colorbar = None self._id_colorizer = self._colorizer.callbacks.connect('changed', self.changed) self.callbacks = cbook.CallbackRegistry(signals=["changed"]) def set_array(self, A): """ Set the value array from array-like *A*. Parameters ---------- A : array-like or None The values that are mapped to colors. The base class `.ScalarMappable` does not make any assumptions on the dimensionality and shape of the value array *A*. """ if A is None: self._A = None return A = _ensure_multivariate_data(A, self.norm.n_components) A = cbook.safe_masked_invalid(A, copy=True) if not np.can_cast(A.dtype, float, "same_kind"): if A.dtype.fields is None: raise TypeError(f"Image data of dtype {A.dtype} cannot be " f"converted to float") else: for key in A.dtype.fields: if not np.can_cast(A[key].dtype, float, "same_kind"): raise TypeError(f"Image data of dtype {A.dtype} cannot be " f"converted to a sequence of floats") self._A = A if not self.norm.scaled(): self._colorizer.autoscale_None(A) def get_array(self): """ Return the array of values, that are mapped to colors. The base class `.ScalarMappable` does not make any assumptions on the dimensionality and shape of the array. """ return self._A def changed(self): """ Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal. """ self.callbacks.process('changed', self) self.stale = True @staticmethod def _check_exclusionary_keywords(colorizer, **kwargs): """ Raises a ValueError if any kwarg is not None while colorizer is not None """ if colorizer is not None: if any([val is not None for val in kwargs.values()]): raise ValueError("The `colorizer` keyword cannot be used simultaneously" " with any of the following keywords: " + ", ".join(f'`{key}`' for key in kwargs.keys())) @staticmethod def _get_colorizer(cmap, norm, colorizer): if isinstance(colorizer, Colorizer): _ScalarMappable._check_exclusionary_keywords( Colorizer, cmap=cmap, norm=norm ) return colorizer return Colorizer(cmap, norm) # The docstrings here must be generic enough to apply to all relevant methods. mpl._docstring.interpd.register( cmap_doc="""\ cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap` The Colormap instance or registered colormap name used to map scalar data to colors.""", multi_cmap_doc="""\ cmap : str, `~matplotlib.colors.Colormap`, `~matplotlib.colors.BivarColormap`\ or `~matplotlib.colors.MultivarColormap`, default: :rc:`image.cmap` The Colormap instance or registered colormap name used to map data values to colors. Multivariate data is only accepted if a multivariate colormap (`~matplotlib.colors.BivarColormap` or `~matplotlib.colors.MultivarColormap`) is used.""", norm_doc="""\ norm : str or `~matplotlib.colors.Normalize`, optional The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using *cmap*. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1. If given, this can be one of the following: - An instance of `.Normalize` or one of its subclasses (see :ref:`colormapnorms`). - A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a list of available scales, call `matplotlib.scale.get_scale_names()`. In that case, a suitable `.Normalize` subclass is dynamically generated and instantiated.""", multi_norm_doc="""\ norm : str, `~matplotlib.colors.Normalize` or list, optional The normalization method used to scale data to the [0, 1] range before mapping to colors using *cmap*. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1. This can be one of the following: - An instance of `.Normalize` or one of its subclasses (see :ref:`colormapnorms`). - A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a list of available scales, call `matplotlib.scale.get_scale_names()`. In this case, a suitable `.Normalize` subclass is dynamically generated and instantiated. - A list of scale names or `.Normalize` objects matching the number of variates in the colormap, for use with `~matplotlib.colors.BivarColormap` or `~matplotlib.colors.MultivarColormap`, i.e. ``["linear", "log"]``.""", vmin_vmax_doc="""\ vmin, vmax : float, optional When using scalar data and no explicit *norm*, *vmin* and *vmax* define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use *vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm* name together with *vmin*/*vmax* is acceptable).""", multi_vmin_vmax_doc="""\ vmin, vmax : float or list, optional When using scalar data and no explicit *norm*, *vmin* and *vmax* define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use *vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm* name together with *vmin*/*vmax* is acceptable). A list of values (vmin or vmax) can be used to define independent limits for each variate when using a `~matplotlib.colors.BivarColormap` or `~matplotlib.colors.MultivarColormap`.""", ) class ColorizingArtist(_ScalarMappable, artist.Artist): """ Base class for artists that make map data to color using a `.colorizer.Colorizer`. The `.colorizer.Colorizer` applies data normalization before returning RGBA colors from a `~matplotlib.colors.Colormap`. """ def __init__(self, colorizer, **kwargs): """ Parameters ---------- colorizer : `.colorizer.Colorizer` """ _api.check_isinstance(Colorizer, colorizer=colorizer) super().__init__(colorizer=colorizer, **kwargs) @property def colorizer(self): return self._colorizer @colorizer.setter def colorizer(self, cl): _api.check_isinstance(Colorizer, colorizer=cl) self._colorizer.callbacks.disconnect(self._id_colorizer) self._colorizer = cl self._id_colorizer = cl.callbacks.connect('changed', self.changed) def _set_colorizer_check_keywords(self, colorizer, **kwargs): """ Raises a ValueError if any kwarg is not None while colorizer is not None. """ self._check_exclusionary_keywords(colorizer, **kwargs) self.colorizer = colorizer def _auto_norm_from_scale(scale_cls): """ Automatically generate a norm class from *scale_cls*. This differs from `.colors.make_norm_from_scale` in the following points: - This function is not a class decorator, but directly returns a norm class (as if decorating `.Normalize`). - The scale is automatically constructed with ``nonpositive="mask"``, if it supports such a parameter, to work around the difference in defaults between standard scales (which use "clip") and norms (which use "mask"). Note that ``make_norm_from_scale`` caches the generated norm classes (not the instances) and reuses them for later calls. For example, ``type(_auto_norm_from_scale("log")) == LogNorm``. """ # Actually try to construct an instance, to verify whether # ``nonpositive="mask"`` is supported. try: norm = colors.make_norm_from_scale( functools.partial(scale_cls, nonpositive="mask"))( colors.Normalize)() except TypeError: norm = colors.make_norm_from_scale(scale_cls)( colors.Normalize)() return type(norm) def _ensure_norm(norm, n_components=1): if n_components == 1: _api.check_isinstance((colors.Norm, str, None), norm=norm) if norm is None: norm = colors.Normalize() elif isinstance(norm, str): scale_cls = _api.getitem_checked(scale._scale_mapping, norm=norm) return _auto_norm_from_scale(scale_cls)() return norm elif n_components > 1: if not np.iterable(norm): _api.check_isinstance((colors.MultiNorm, None, tuple), norm=norm) if norm is None: norm = colors.MultiNorm(['linear']*n_components) else: # iterable, i.e. multiple strings or Normalize objects norm = colors.MultiNorm(norm) if isinstance(norm, colors.MultiNorm) and norm.n_components == n_components: return norm raise ValueError( f"Invalid norm for multivariate colormap with {n_components} inputs") else: # n_components == 0 raise ValueError( "Invalid cmap. A colorizer object must have a cmap with `n_variates` >= 1") def _ensure_cmap(cmap, accept_multivariate=False): """ Ensure that we have a `.Colormap` object. For internal use to preserve type stability of errors. Parameters ---------- cmap : None, str, Colormap - if a `~matplotlib.colors.Colormap`, `~matplotlib.colors.MultivarColormap` or `~matplotlib.colors.BivarColormap`, return it - if a string, look it up in three corresponding databases when not found: raise an error based on the expected shape - if None, look up the default color map in mpl.colormaps accept_multivariate : bool, default False - if False, accept only Colormap, string in mpl.colormaps or None Returns ------- Colormap """ if accept_multivariate: types = (colors.Colormap, colors.BivarColormap, colors.MultivarColormap) mappings = (mpl.colormaps, mpl.multivar_colormaps, mpl.bivar_colormaps) else: types = (colors.Colormap, ) mappings = (mpl.colormaps, ) if isinstance(cmap, types): return cmap cmap_name = mpl._val_or_rc(cmap, "image.cmap") for mapping in mappings: if cmap_name in mapping: return mapping[cmap_name] # this error message is a variant of _api.check_in_list but gives # additional hints as to how to access multivariate colormaps raise ValueError(_api.list_suggestion_error_msg('cmap', cmap, mpl.colormaps) + "\nSee `matplotlib.bivar_colormaps()` and" " `matplotlib.multivar_colormaps()` for" " bivariate and multivariate colormaps") def _ensure_multivariate_data(data, n_components): """ Ensure that the data has dtype with n_components. Input data of shape (n_components, n, m) is converted to an array of shape (n, m) with data type np.dtype(f'{data.dtype}, ' * n_components) Complex data is returned as a view with dtype np.dtype('float64, float64') or np.dtype('float32, float32') If n_components is 1 and data is not of type np.ndarray (i.e. PIL.Image), the data is returned unchanged. If data is None, the function returns None Parameters ---------- n_components : int Number of variates in the data. data : np.ndarray, PIL.Image or None Returns ------- np.ndarray, PIL.Image or None """ if isinstance(data, np.ndarray): if len(data.dtype.descr) == n_components: # pass scalar data # and already formatted data return data elif data.dtype in [np.complex64, np.complex128]: if n_components != 2: raise ValueError("Invalid data entry for multivariate data. " "Complex numbers are incompatible with " f"{n_components} variates.") # pass complex data if data.dtype == np.complex128: dt = np.dtype('float64, float64') else: dt = np.dtype('float32, float32') reconstructed = np.ma.array(np.ma.getdata(data).view(dt)) if np.ma.is_masked(data): for descriptor in dt.descr: reconstructed[descriptor[0]][data.mask] = np.ma.masked return reconstructed if n_components > 1 and len(data) == n_components: # convert data from shape (n_components, n, m) # to (n, m) with a new dtype data = [np.ma.array(part, copy=False) for part in data] dt = np.dtype(', '.join([f'{part.dtype}' for part in data])) fields = [descriptor[0] for descriptor in dt.descr] reconstructed = np.ma.empty(data[0].shape, dtype=dt) for i, f in enumerate(fields): if data[i].shape != reconstructed.shape: raise ValueError("For multivariate data all variates must have same " f"shape, not {data[0].shape} and {data[i].shape}") reconstructed[f] = data[i] if np.ma.is_masked(data[i]): reconstructed[f][data[i].mask] = np.ma.masked return reconstructed if n_components == 1: # PIL.Image gets passed here return data elif n_components == 2: raise ValueError("Invalid data entry for multivariate data. The data" " must contain complex numbers, or have a first dimension 2," " or be of a dtype with 2 fields") else: raise ValueError("Invalid data entry for multivariate data. The shape" f" of the data must have a first dimension {n_components}" f" or be of a dtype with {n_components} fields")