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The class includes helper methods that simplifies transforming to and from normalized values. Parameters ---------- data : DataArray DataArray to normalize. width : Sequence of three numbers, optional Normalize the data to these (min, default, max) values. The default is None. rU_datar _data_unique_data_unique_index_data_unique_inverser{_data_is_numericz!tuple[float, float, float] | None_width)r_r`rarbrcrdNFr)rrrargcCs||_|s|nd|_tdj}t|dkr,|n|j|r>|n|}tj|dd\}}||_ t d|j |_ ||_ |dkr|dnt||_dS)NrT)Zreturn_inverserF)r_rdrrrFrrr4r(r`rrTrarbrrc)rrrrarZ to_uniqueZ data_uniqueZdata_unique_inverser r r!__init__zs  z_Normalize.__init__rIrc CsDtjdddd*d|jd|jd|jW5QRSQRXdS)NrpTr)Z precisionsuppress thresholdz<_Normalize(data, width=z)> z -> )r4Z printoptionsrdr`_values_uniquerr r r!__repr__sz_Normalize.__repr__r,cCs t|jSri)r0r`rir r r!__len__sz_Normalize.__len__cCs |j|Sri)r`)rkeyr r r! __getitem__sz_Normalize.__getitem__cCs|jSri)r_rir r r!rsz_Normalize.datacCs|jS)a+ Check if data is numeric. Examples -------- >>> a = xr.DataArray(["b", "a", "a", "b", "c"]) >>> _Normalize(a).data_is_numeric False >>> a = xr.DataArray([0.5, 0, 0, 0.5, 2, 3]) >>> _Normalize(a).data_is_numeric True )rcrir r r!data_is_numericsz_Normalize.data_is_numeric)rergcCsdSrir rrer r r! _calc_widthssz_Normalize._calc_widthsrcCsdSrir ror r r!rpsznp.ndarray | DataArraycCsj|jdkr|S|j\}}}t|t|}|dkrD|d|}n"|t||}||||}|S)zH Normalize the values so they're inbetween self._width. Nr)rdr4r%r$)rreZxminZxdefaultZxmaxZdiff_maxy_minyr]rr r r!rps  )rdrgcCsdSrir rrdr r r!_indexes_centeredsz_Normalize._indexes_centeredcCsdSrir rqr r r!rrscCs |ddS)zv Offset indexes to make sure being in the center of self.levels. ["a", "b", "c"] -> [1, 3, 5] r,r+r rqr r r!rrscCsJ|jdkrdS|jr|j}n$||j}|jj||jjd}||S)a9 Return a normalized number array for the unique levels. Examples -------- >>> a = xr.DataArray(["b", "a", "a", "b", "c"]) >>> _Normalize(a).values array([3, 1, 1, 3, 5]) Dimensions without coordinates: dim_0 >>> _Normalize(a, width=(18, 36, 72)).values array([45., 18., 18., 45., 72.]) Dimensions without coordinates: dim_0 >>> a = xr.DataArray([0.5, 0, 0, 0.5, 2, 3]) >>> _Normalize(a).values array([0.5, 0. , 0. , 0.5, 2. , 3. ]) Dimensions without coordinates: dim_0 >>> _Normalize(a, width=(18, 36, 72)).values array([27., 18., 18., 27., 54., 72.]) Dimensions without coordinates: dim_0 >>> _Normalize(a * 0, width=(18, 36, 72)).values array([36., 36., 36., 36., 36., 36.]) Dimensions without coordinates: dim_0 N)r)rrnrrrbcopyZreshaperrp)rrrr r r!r6s#  z_Normalize.valuesznp.ndarray | NonecCs2|jdkrdS|jr|j}n ||j}||S)a Return unique values. Examples -------- >>> a = xr.DataArray(["b", "a", "a", "b", "c"]) >>> _Normalize(a)._values_unique array([1, 3, 5]) >>> _Normalize(a, width=(18, 36, 72))._values_unique array([18., 45., 72.]) >>> a = xr.DataArray([0.5, 0, 0, 0.5, 2, 3]) >>> _Normalize(a)._values_unique array([0. , 0.5, 2. , 3. ]) >>> _Normalize(a, width=(18, 36, 72))._values_unique array([18., 27., 54., 72.]) N)rrnr`rrrarprrr r r!rh s   z_Normalize._values_uniquecCs|jr d}n ||j}|S)z Return ticks for plt.colorbar if the data is not numeric. Examples -------- >>> a = xr.DataArray(["b", "a", "a", "b", "c"]) >>> _Normalize(a).ticks array([1, 3, 5]) N)rnrrrartr r r!ticks*s  z_Normalize.tickscCst|jt|jddS)a  Return discrete levels that will evenly bound self.values. ["a", "b", "c"] -> [0, 2, 4, 6] Examples -------- >>> a = xr.DataArray(["b", "a", "a", "b", "c"]) >>> _Normalize(a).levels array([0, 2, 4, 6]) r+r,)r4r0rar%rir r r!r;=s z_Normalize.levelsrTcCs*|jdkrtdttt|j|jS)Nzself.data can't be None.)rhrJrrZr]rr`rir r r!_lookupMs z_Normalize._lookupcCs|jj|ddS)NZnearest)method)rvZ sort_indexZreindexrrqr r r! _lookup_arrTsz_Normalize._lookup_arrrcs&t}ddddfdd }||S)a Return a FuncFormatter that maps self.values elements back to the original value as a string. Useful with plt.colorbar. Examples -------- >>> a = xr.DataArray([0.5, 0, 0, 0.5, 2, 3]) >>> aa = _Normalize(a, width=(0, 0.5, 1)) >>> aa._lookup 0.000000 0.0 0.166667 0.5 0.666667 2.0 1.000000 3.0 dtype: float64 >>> aa.format(1) '3.0' Nr None | Anyrdposcs|gdSrrxrzrir r!_funcpsz _Normalize.format.._func)N)r"r)rrr}r rir!r[sz_Normalize.formatz Callable[[Any, None | Any], Any]csddddfdd }|S)a Return a lambda function that maps self.values elements back to the original value as a numpy array. 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Examples -------- >>> a = xr.DataArray([0.5, 0, 0, 0.5, 2, 3]) >>> aa = _Normalize(a, width=(0, 0.5, 1)) >>> aa._lookup 0.000000 0.0 0.166667 0.5 0.666667 2.0 1.000000 3.0 dtype: float64 >>> aa.func([0.16, 1]) array([0.5, 3. ]) Nrryrzcs |Srir|rzrir r!r}sz_Normalize.func.._func)Nr )rr}r rir!r usz_Normalize.func)NF)r __module__ __qualname____doc____annotations__ __slots__rerjrkrmpropertyrrnr rprrr6rhrur;rvrxrr r r r r!r^ZsV  .r^z None | boolz str | Noneztuple[bool, bool]) hueplt_norm sizeplt_normrGrH plotfunc_namergcCs|dkr dS|r"|jdkr"td|dkr>|jdk r:d}nd}|r^|jdkr^|jdkr^td|dkr|s~|jdk r~|jdks|jdk rd}nd}||fS)Nhist)FFz*Cannot create a colorbar when hue is None.TFz7Cannot create a legend when hue and markersize is None.)rKeyErrorrn)rrrGrHrr r r!_determine_guides, r)rrplotfunccCst|tr|n|g}gg}}|df|dffD]\}}|jdk r.gg} } |D]*} t| |d|jd\} } | | 7} | | 7} qNtj| dd\}}t|}||} t | |} t | | t |j\} } || 7}|| 7}q.|j ||dd}t ||S) Nr2rr)r2r T)Z return_indexr)Z framealpha)rFrGrr<r r4r(r-tolistrUr>rr@rA)rrrZ legend_axrr4r5Z huesizepltr1hdlZlblpZhdl_Zlbl_rr9r@r r r! _add_legends*        r) NNNNFNNTNF)FN)NNNN)r)NNNNNN)r)rNF)NNNNF)NNN)[ __future__rrrrwrinspectrtypingrrrrr r r r Znumpyr4ZpandasrZxarray.core.indexesr Zxarray.core.optionsrZxarray.core.pycompatrZxarray.core.utilsrrrrrKZmatplotlib.axesrrDrZmatplotlib.tickerrZ numpy.typingrZxarray.core.dataarrayrZxarray.core.datasetrZxarray.core.typesrrrrrrrVrXZ_LINEWIDTH_RANGEr"r*rAr1rbrzryr}rrrrrrrrrrrrrrrrrrr rr<r>rArQrSr^rrr r r r!s   (             6' !B(/  /  -%"1  4H  DS,:!