neulab/docprompting-conala huggingface dataset:
pandas.tseries.offsets.YearEnd.rollforward   YearEnd.rollforward()
 
Roll provided date forward to next offset only if not on offset.  Returns 
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.
pandas.tseries.offsets.YearEnd.rule_code   YearEnd.rule_code
pandas.UInt16Dtype   classpandas.UInt16Dtype[source]
 
An ExtensionDtype for uint16 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes       
None     Methods       
None
pandas.UInt32Dtype   classpandas.UInt32Dtype[source]
 
An ExtensionDtype for uint32 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes       
None     Methods       
None
pandas.UInt64Dtype   classpandas.UInt64Dtype[source]
 
An ExtensionDtype for uint64 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes       
None     Methods       
None
pandas.UInt64Index   classpandas.UInt64Index(data=None, dtype=None, copy=False, name=None)[source]
 
Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects. UInt64Index is a special case of Index with purely unsigned integer labels. .  Deprecated since version 1.4.0: In pandas v2.0 UInt64Index will be removed and NumericIndex used instead. UInt64Index will remain fully functional for the duration of pandas 1.x.   Parameters 
 
data:array-like (1-dimensional)


dtype:NumPy dtype (default: uint64)


copy:bool


Make a copy of input ndarray.  
name:object


Name to be stored in the index.      See also  Index

The base pandas Index type.  NumericIndex

Index of numpy int/uint/float data.    Notes An Index instance can only contain hashable objects. Attributes       
None     Methods       
None
pandas.UInt8Dtype   classpandas.UInt8Dtype[source]
 
An ExtensionDtype for uint8 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes       
None     Methods       
None
pandas.unique   pandas.unique(values)[source]
 
Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values.  Parameters 
 
values:1d array-like

  Returns 
 numpy.ndarray or ExtensionArray

The return can be:  Index : when the input is an Index Categorical : when the input is a Categorical dtype ndarray : when the input is a Series/ndarray  Return numpy.ndarray or ExtensionArray.      See also  Index.unique

Return unique values from an Index.  Series.unique

Return unique values of Series object.    Examples 
>>> pd.unique(pd.Series([2, 1, 3, 3]))
array([2, 1, 3])
  
>>> pd.unique(pd.Series([2] + [1] * 5))
array([2, 1])
  
>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
  
>>> pd.unique(
...     pd.Series(
...         [
...             pd.Timestamp("20160101", tz="US/Eastern"),
...             pd.Timestamp("20160101", tz="US/Eastern"),
...         ]
...     )
... )
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]
  
>>> pd.unique(
...     pd.Index(
...         [
...             pd.Timestamp("20160101", tz="US/Eastern"),
...             pd.Timestamp("20160101", tz="US/Eastern"),
...         ]
...     )
... )
DatetimeIndex(['2016-01-01 00:00:00-05:00'],
        dtype='datetime64[ns, US/Eastern]',
        freq=None)
  
>>> pd.unique(list("baabc"))
array(['b', 'a', 'c'], dtype=object)
  An unordered Categorical will return categories in the order of appearance. 
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']
  
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']
  An ordered Categorical preserves the category ordering. 
>>> pd.unique(
...     pd.Series(
...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
...     )
... )
['b', 'a', 'c']
Categories (3, object): ['a' < 'b' < 'c']
  An array of tuples 
>>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
pandas.util.hash_array   pandas.util.hash_array(vals, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]
 
Given a 1d array, return an array of deterministic integers.  Parameters 
 
vals:ndarray or ExtensionArray


encoding:str, default ‘utf8’


Encoding for data & key when strings.  
hash_key:str, default _default_hash_key


Hash_key for string key to encode.  
categorize:bool, default True


Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values.    Returns 
 ndarray[np.uint64, ndim=1]

Hashed values, same length as the vals.
pandas.util.hash_pandas_object   pandas.util.hash_pandas_object(obj, index=True, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]
 
Return a data hash of the Index/Series/DataFrame.  Parameters 
 
obj:Index, Series, or DataFrame


index:bool, default True


Include the index in the hash (if Series/DataFrame).  
encoding:str, default ‘utf8’


Encoding for data & key when strings.  
hash_key:str, default _default_hash_key


Hash_key for string key to encode.  
categorize:bool, default True


Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values.    Returns 
 Series of uint64, same length as the object
Plotting The following functions are contained in the pandas.plotting module.       
andrews_curves(frame, class_column[, ax, ...]) Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data.  
autocorrelation_plot(series[, ax]) Autocorrelation plot for time series.  
bootstrap_plot(series[, fig, size, samples]) Bootstrap plot on mean, median and mid-range statistics.  
boxplot(data[, column, by, ax, fontsize, ...]) Make a box plot from DataFrame columns.  
deregister_matplotlib_converters() Remove pandas formatters and converters.  
lag_plot(series[, lag, ax]) Lag plot for time series.  
parallel_coordinates(frame, class_column[, ...]) Parallel coordinates plotting.  
plot_params Stores pandas plotting options.  
radviz(frame, class_column[, ax, color, ...]) Plot a multidimensional dataset in 2D.  
register_matplotlib_converters() Register pandas formatters and converters with matplotlib.  
scatter_matrix(frame[, alpha, figsize, ax, ...]) Draw a matrix of scatter plots.  
table(ax, data[, rowLabels, colLabels]) Helper function to convert DataFrame and Series to matplotlib.table.
Resampling Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample().  Indexing, iteration       
Resampler.__iter__() Groupby iterator.  
Resampler.groups Dict {group name -> group labels}.  
Resampler.indices Dict {group name -> group indices}.  
Resampler.get_group(name[, obj]) Construct DataFrame from group with provided name.      Function application       
Resampler.apply([func]) Aggregate using one or more operations over the specified axis.  
Resampler.aggregate([func]) Aggregate using one or more operations over the specified axis.  
Resampler.transform(arg, *args, **kwargs) Call function producing a like-indexed Series on each group and return a Series with the transformed values.  
Resampler.pipe(func, *args, **kwargs) Apply a function func with arguments to this Resampler object and return the function's result.      Upsampling       
Resampler.ffill([limit]) Forward fill the values.  
Resampler.backfill([limit]) Backward fill the new missing values in the resampled data.  
Resampler.bfill([limit]) Backward fill the new missing values in the resampled data.  
Resampler.pad([limit]) Forward fill the values.  
Resampler.nearest([limit]) Resample by using the nearest value.  
Resampler.fillna(method[, limit]) Fill missing values introduced by upsampling.  
Resampler.asfreq([fill_value]) Return the values at the new freq, essentially a reindex.  
Resampler.interpolate([method, axis, limit, ...]) Interpolate values according to different methods.      Computations / descriptive stats       
Resampler.count() Compute count of group, excluding missing values.  
Resampler.nunique([_method]) Return number of unique elements in the group.  
Resampler.first([_method, min_count]) Compute first of group values.  
Resampler.last([_method, min_count]) Compute last of group values.  
Resampler.max([_method, min_count]) Compute max of group values.  
Resampler.mean([_method]) Compute mean of groups, excluding missing values.  
Resampler.median([_method]) Compute median of groups, excluding missing values.  
Resampler.min([_method, min_count]) Compute min of group values.  
Resampler.ohlc([_method]) Compute open, high, low and close values of a group, excluding missing values.  
Resampler.prod([_method, min_count]) Compute prod of group values.  
Resampler.size() Compute group sizes.  
Resampler.sem([_method]) Compute standard error of the mean of groups, excluding missing values.  
Resampler.std([ddof]) Compute standard deviation of groups, excluding missing values.  
Resampler.sum([_method, min_count]) Compute sum of group values.  
Resampler.var([ddof]) Compute variance of groups, excluding missing values.  
Resampler.quantile([q]) Return value at the given quantile.
skimage.color.combine_stains(stains, conv_matrix) [source]
 
Stain to RGB color space conversion.  Parameters 
 
stains(…, 3) array_like 

The image in stain color space. Final dimension denotes channels.  conv_matrix: ndarray

The stain separation matrix as described by G. Landini [1].    Returns 
 
out(…, 3) ndarray 

The image in RGB format. Same dimensions as input.    Raises 
 ValueError

If stains is not at least 2-D with shape (…, 3).     Notes Stain combination matrices available in the color module and their respective colorspace:  
rgb_from_hed: Hematoxylin + Eosin + DAB 
rgb_from_hdx: Hematoxylin + DAB 
rgb_from_fgx: Feulgen + Light Green 
rgb_from_bex: Giemsa stain : Methyl Blue + Eosin 
rgb_from_rbd: FastRed + FastBlue + DAB 
rgb_from_gdx: Methyl Green + DAB 
rgb_from_hax: Hematoxylin + AEC 
rgb_from_bro: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G 
rgb_from_bpx: Methyl Blue + Ponceau Fuchsin 
rgb_from_ahx: Alcian Blue + Hematoxylin 
rgb_from_hpx: Hematoxylin + PAS  References  
1  
https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html  
2  
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol. 23, no. 4, pp. 291–299, Aug. 2001.   Examples >>> from skimage import data
>>> from skimage.color import (separate_stains, combine_stains,
...                            hdx_from_rgb, rgb_from_hdx)
>>> ihc = data.immunohistochemistry()
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
>>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
skimage.color.convert_colorspace(arr, fromspace, tospace) [source]
 
Convert an image array to a new color space.  Valid color spaces are:

‘RGB’, ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’    Parameters 
 
arr(…, 3) array_like 

The image to convert. Final dimension denotes channels.  
fromspacestr 

The color space to convert from. Can be specified in lower case.  
tospacestr 

The color space to convert to. Can be specified in lower case.    Returns 
 
out(…, 3) ndarray 

The converted image. Same dimensions as input.    Raises 
 ValueError

If fromspace is not a valid color space  ValueError

If tospace is not a valid color space     Notes Conversion is performed through the “central” RGB color space, i.e. conversion from XYZ to HSV is implemented as XYZ -> RGB -> HSV instead of directly. Examples >>> from skimage import data
>>> img = data.astronaut()
>>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
skimage.color.deltaE_cie76(lab1, lab2) [source]
 
Euclidean distance between two points in Lab color space  Parameters 
 
lab1array_like 

reference color (Lab colorspace)  
lab2array_like 

comparison color (Lab colorspace)    Returns 
 
dEarray_like 

distance between colors lab1 and lab2     References  
1  
https://en.wikipedia.org/wiki/Color_difference  
2  
A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977).
skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source]
 
Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces.  Parameters 
 
lab1array_like 

reference color (Lab colorspace)  
lab2array_like 

comparison color (Lab colorspace)  
kLfloat (range), optional 

lightness scale factor, 1 for “acceptably close”; 2 for “imperceptible” see deltaE_cmc  
kCfloat (range), optional 

chroma scale factor, usually 1  
kHfloat (range), optional 

hue scale factor, usually 1    Returns 
 
deltaEarray_like 

The distance between lab1 and lab2     Notes CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (kL, kC, kH respectively). These default to 1. References  
1  
https://en.wikipedia.org/wiki/Color_difference  
2  
http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf DOI:10.1364/AO.33.008069  
3  
M. Melgosa, J. Quesada, and E. Hita, “Uniformity of some recent color metrics tested with an accurate color-difference tolerance dataset,” Appl. Opt. 33, 8069-8077 (1994).
skimage.color.deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015) [source]
 
Color difference according to CIEDE 94 standard Accommodates perceptual non-uniformities through the use of application specific scale factors (kH, kC, kL, k1, and k2).  Parameters 
 
lab1array_like 

reference color (Lab colorspace)  
lab2array_like 

comparison color (Lab colorspace)  
kHfloat, optional 

Hue scale  
kCfloat, optional 

Chroma scale  
kLfloat, optional 

Lightness scale  
k1float, optional 

first scale parameter  
k2float, optional 

second scale parameter    Returns 
 
dEarray_like 

color difference between lab1 and lab2     Notes deltaE_ciede94 is not symmetric with respect to lab1 and lab2. CIEDE94 defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently, the first color should be regarded as the “reference” color. kL, k1, k2 depend on the application and default to the values suggested for graphic arts   
Parameter Graphic Arts Textiles   
kL 1.000 2.000  
k1 0.045 0.048  
k2 0.015 0.014   References  
1  
https://en.wikipedia.org/wiki/Color_difference  
2  
http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
skimage.color.deltaE_cmc(lab1, lab2, kL=1, kC=1) [source]
 
Color difference from the CMC l:c standard. This color difference was developed by the Colour Measurement Committee (CMC) of the Society of Dyers and Colourists (United Kingdom). It is intended for use in the textile industry. The scale factors kL, kC set the weight given to differences in lightness and chroma relative to differences in hue. The usual values are kL=2, kC=1 for “acceptability” and kL=1, kC=1 for “imperceptibility”. Colors with dE > 1 are “different” for the given scale factors.  Parameters 
 
lab1array_like 

reference color (Lab colorspace)  
lab2array_like 

comparison color (Lab colorspace)    Returns 
 
dEarray_like 

distance between colors lab1 and lab2     Notes deltaE_cmc the defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1) References  
1  
https://en.wikipedia.org/wiki/Color_difference  
2  
http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html  
3  
F. J. J. Clarke, R. McDonald, and B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100, 128-132 (1984).
skimage.color.gray2rgb(image, alpha=None) [source]
 
Create an RGB representation of a gray-level image.  Parameters 
 
imagearray_like 

Input image.  
alphabool, optional 

Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.    Returns 
 
rgb(…, 3) ndarray 

RGB image. A new dimension of length 3 is added to input image.     Notes If the input is a 1-dimensional image of shape (M, ), the output will be shape (M, 3).
skimage.color.gray2rgba(image, alpha=None) [source]
 
Create a RGBA representation of a gray-level image.  Parameters 
 
imagearray_like 

Input image.  
alphaarray_like, optional 

Alpha channel of the output image. It may be a scalar or an array that can be broadcast to image. If not specified it is set to the maximum limit corresponding to the image dtype.    Returns 
 
rgbandarray 

RGBA image. A new dimension of length 4 is added to input image shape.
skimage.color.grey2rgb(image, alpha=None) [source]
 
Create an RGB representation of a gray-level image.  Parameters 
 
imagearray_like 

Input image.  
alphabool, optional 

Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.    Returns 
 
rgb(…, 3) ndarray 

RGB image. A new dimension of length 3 is added to input image.     Notes If the input is a 1-dimensional image of shape (M, ), the output will be shape (M, 3).
skimage.color.hed2rgb(hed) [source]
 
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.  Parameters 
 
hed(…, 3) array_like 

The image in the HED color space. Final dimension denotes channels.    Returns 
 
out(…, 3) ndarray 

The image in RGB. Same dimensions as input.    Raises 
 ValueError

If hed is not at least 2-D with shape (…, 3).     References  
1  
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.   Examples >>> from skimage import data
>>> from skimage.color import rgb2hed, hed2rgb
>>> ihc = data.immunohistochemistry()
>>> ihc_hed = rgb2hed(ihc)
>>> ihc_rgb = hed2rgb(ihc_hed)
skimage.color.hsv2rgb(hsv) [source]
 
HSV to RGB color space conversion.  Parameters 
 
hsv(…, 3) array_like 

The image in HSV format. Final dimension denotes channels.    Returns 
 
out(…, 3) ndarray 

The image in RGB format. Same dimensions as input.    Raises 
 ValueError

If hsv is not at least 2-D with shape (…, 3).     Notes Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1]. References  
1  
https://en.wikipedia.org/wiki/HSL_and_HSV   Examples >>> from skimage import data
>>> img = data.astronaut()
>>> img_hsv = rgb2hsv(img)
>>> img_rgb = hsv2rgb(img_hsv)
skimage.color.lab2lch(lab) [source]
 
CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace  Parameters 
 
lab(…, 3) array_like 

The N-D image in CIE-LAB format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied.    Returns 
 
out(…, 3) ndarray 

The image in LCH format, in a N-D array with same shape as input lab.    Raises 
 ValueError

If lch does not have at least 3 color channels (i.e. l, a, b).     Notes The Hue is expressed as an angle between (0, 2*pi) Examples >>> from skimage import data
>>> from skimage.color import rgb2lab, lab2lch
>>> img = data.astronaut()
>>> img_lab = rgb2lab(img)
>>> img_lch = lab2lch(img_lab)
skimage.color.lab2rgb(lab, illuminant='D65', observer='2') [source]
 
Lab to RGB color space conversion.  Parameters 
 
lab(…, 3) array_like 

The image in Lab format. Final dimension denotes channels.  
illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional 

The name of the illuminant (the function is NOT case sensitive).  
observer{“2”, “10”}, optional 

The aperture angle of the observer.    Returns 
 
out(…, 3) ndarray 

The image in RGB format. Same dimensions as input.    Raises 
 ValueError

If lab is not at least 2-D with shape (…, 3).     Notes This function uses lab2xyz and xyz2rgb. By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function get_xyz_coords for a list of supported illuminants. References  
1  
https://en.wikipedia.org/wiki/Standard_illuminant
skimage.color.lab2xyz(lab, illuminant='D65', observer='2') [source]
 
CIE-LAB to XYZcolor space conversion.  Parameters 
 
lab(…, 3) array_like 

The image in Lab format. Final dimension denotes channels.  
illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional 

The name of the illuminant (the function is NOT case sensitive).  
observer{“2”, “10”}, optional 

The aperture angle of the observer.    Returns 
 
out(…, 3) ndarray 

The image in XYZ format. Same dimensions as input.    Raises 
 ValueError

If lab is not at least 2-D with shape (…, 3).  ValueError

If either the illuminant or the observer angle are not supported or unknown.  UserWarning

If any of the pixels are invalid (Z < 0).     Notes By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883. See function ‘get_xyz_coords’ for a list of supported illuminants. References  
1  
http://www.easyrgb.com/index.php?X=MATH&H=07  
2  
https://en.wikipedia.org/wiki/Lab_color_space
skimage.color.label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay') [source]
 
Return an RGB image where color-coded labels are painted over the image.  Parameters 
 
labelarray, shape (M, N) 

Integer array of labels with the same shape as image.  
imagearray, shape (M, N, 3), optional 

Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring.  
colorslist, optional 

List of colors. If the number of labels exceeds the number of colors, then the colors are cycled.  
alphafloat [0, 1], optional 

Opacity of colorized labels. Ignored if image is None.  
bg_labelint, optional 

Label that’s treated as the background. If bg_label is specified, bg_color is None, and kind is overlay, background is not painted by any colors.  
bg_colorstr or array, optional 

Background color. Must be a name in color_dict or RGB float values between [0, 1].  
image_alphafloat [0, 1], optional 

Opacity of the image.  
kindstring, one of {‘overlay’, ‘avg’} 

The kind of color image desired. ‘overlay’ cycles over defined colors and overlays the colored labels over the original image. ‘avg’ replaces each labeled segment with its average color, for a stained-class or pastel painting appearance.    Returns 
 
resultarray of float, shape (M, N, 3) 

The result of blending a cycling colormap (colors) for each distinct value in label with the image, at a certain alpha value.
skimage.color.lch2lab(lch) [source]
 
CIE-LCH to CIE-LAB color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace  Parameters 
 
lch(…, 3) array_like 

The N-D image in CIE-LCH format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied.    Returns 
 
out(…, 3) ndarray 

The image in LAB format, with same shape as input lch.    Raises 
 ValueError

If lch does not have at least 3 color channels (i.e. l, c, h).     Examples >>> from skimage import data
>>> from skimage.color import rgb2lab, lch2lab
>>> img = data.astronaut()
>>> img_lab = rgb2lab(img)
>>> img_lch = lab2lch(img_lab)
>>> img_lab2 = lch2lab(img_lch)