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)r=r<rCappendr)r6r>join)r Zhtmlscoltrr?Zlabelrrr#r6Zs"   zImages._repr_html_N)r7r8r9__doc__r@rBr6rrrr#r;Dsr;cOs|s tdg}|D]J}t|ts6tdt|q|sH||jq|r|j|vrtdq|dd}t |dd}t |dkr|d St j |d d d }t jd d d,tt|dd|D}Wdn1s0Yt||d j|d j|dS)aCombine images together, overlaying later images onto earlier ones. Parameters ---------- imgs : iterable of Image The images to combine. how : str, optional The compositing operator to combine pixels. Default is `'over'`. zNo images passed inExpected `Image`, got: `{0}`z,The stacked images must have the same shape.rCNhowr rrFZouter)copyrFignoredivideinvalidcSsg|] }|jqSr)r).0r?rrr# zstack..coordsdimsrC) ValueError isinstancer TypeErrorr)typerEshapegetr lenxrZalignrerrstatetzreduceZfliprUrV)ZimgskwargsZshapesr?rCopoutrrr#rns$     :rc CsFtrt|tjrddlm}nt|tjs4tdntj}|durF|n||}|jtksjt |jtj r|jj dkr| d}t |}tt|}tt|dd}||d||d}}n.tj||d\}}|dd |ddd }|} | t| d } |||| |j} |dur6| St|tj| S) aeReturn a numpy array after histogram equalization. For use in `shade`. Parameters ---------- data : ndarray mask : ndarray, optional Boolean array of missing points. Where True, the output will be `NaN`. nbins : int, optional Number of bins to use. Note that this argument is ignored for integer arrays, which bin by the integer values directly. Notes ----- This function is adapted from the implementation in scikit-image [1]_. References ---------- .. [1] http://scikit-image.org/docs/stable/api/skimage.exposure.html#equalize-hist r)interpzdata must be an ndarrayNuZi8r)Zbins)rrXndarray _cuda_utilsrfrrYdtypebool issubdtypeZintegerkindastypeZbincountZravelaranger]intZnonzeroZ histogramZcumsumfloatreshaper[wherenan) rmaskZnbinsrfZdata2ZhistZ bin_centersidxZ bin_edgesZcdfrdrrr#eq_hists&    rycCstt|tj|SN)rZlog1prurvdmrrr#rSr~cCst|tj|dS)NgUUUUUU?rrurvr{rrr#r~rScCst|tj|Srzrr{rrr#r~rS)logZcbrtZlinearrycCs.t|r |S|tvrt|Std|dS)Nz!Unknown interpolation method: {0})callable_interpolate_lookuprWr))rKrrr#_normalize_interpolate_hows rcCs6tr$t|jtjr$ddlm}m}nddlm}tj}|j dkrHt dt |} t |} t| t jrn| } n| } t| jtjr| } | tj} n | jjdkr| dk} n t| } | rttj|jjtjd|j|j|j|d S|durt | | } n&tj!|| jd d} || | g|R| | 8} tj"d d d x| | | } |durt#| | tj$} t | t%| f}n,|d krt d| d|d|dgd}Wdn1s0Yt|t&rt'|}t|t'rt!t't(t)t*|\}}}t+|d|dt,|}|| ||ddtj-}|| ||ddtj-}|| ||ddtj-}t#t| d|tj-}t.||||g}nRt|t/st|t0r^t*|}t1||d}t+|d|dt,|}tj2| j|dtj-d }tj2| j|dtj-d }tj2| j|dtj-d }|| ||dddtj-}t.||||g}nt3|r| |d|d|d}trt|tjrt4|}||dd}t#t|d|tj-|dddddf<nt5d6t7||8tj9| j}tr"t|tjr"t4|}t||j|j|dS)Nr)masked_clip_2drfrrizagg must be 2Drgr)r[rl)rUrVattrsrCrlrM)rPrOry+span is not (yet) valid to use with eq_hist)leftrrightT)byteszVExpected `cmap` of `matplotlib.colors.Colormap`, `list`, `str`, or `tuple`; got: '{0}'rT):rrXrrjrkrrf _cpu_utilsrndimrWrrdaArraycomputerLrnrlZbool_rpZint8roisnanallrzerosr[uint32rUrVrnanminarrayr_rurvnanmaxrlistzipmapr Zlinspacer]uint8dstackr0tuplerqfullrrrYr)rZviewrt)aggcmaprKalphaspan min_alpharCrrfZ interpolaterrrwoffsetZ masked_dataZrspanZgspanZbspanrgr4aZrgbacolorZaspanZ scaled_dataimgrrr# _interpolates             :     0 rc% str*t|jtjr*ddlm}m} tj} nddlm} t j}t j} |j dksTt d|j |j d} t| stt j|jddt jd |j ddt|j d|j|j df|j d|j|j dfg|d Sdurt d ttstt| tt| kr"t d tt| fd d| D} t| t| \} }}|j|j df|j dd}t|gd}t|tjr|}|}|durt |n|}t j dd\|dkr||8}n|dkr|| 7}|j!j"dkr |dur d||dk<Wdn1s 0Yt#|dd}d|t $|<t j dddR|%| |&t j'}|%||&t j'}|%||&t j'}Wdn1s0Yt $|}t j(|dd}t j dddR|%| |&t j'}|%||&t j'}|%||&t j'}Wdn1s80Yt j(|dddk}t )|||}t )|||}t )|||}t#|dd}t $|}|dur*t |} |j!j"dkr| dkr||dkB}t *|s||dk+} t )||t j,}t-||| |}!t |!.t /|!.g}"n|dkr.)rrirrM)rPrg)axisrNryrrr)4rrXrrjrkrfrrrrrrWZindexesrVr]rrr[rrrUdictrr)rZ transposerrrrrLrr_rlrorrdotrprsumrurminrvritemrrrrtr)%rrrKrrrrCcolor_baselinerfrrZcatsZcolorsZrsZgsbsZagg_trZ color_dataZbaselineZ color_totalrrr4Z color_maskZ cmask_sumZr2Zg2b2Zmissing_colorstotalrwrZa_scaledZ norm_spanrvaluesrrr# _colorize.s  &       , 6 6        "  rZ lightblueZdarkbluer(c Cst|tjstd|dur"|jn|}d|kr:dkrTnnd|krRdksdntd|||jdkrt|||||||S|jdkrt ||||||||StddS) aConvert a DataArray to an image by choosing an RGBA pixel color for each value. Requires a DataArray with a single data dimension, here called the "value", indexed using either 2D or 3D coordinates. For a DataArray with 2D coordinates, the RGB channels are computed from the values by interpolated lookup into the given colormap ``cmap``. The A channel is then set to the given fixed ``alpha`` value for all non-zero values, and to zero for all zero values. DataArrays with 3D coordinates are expected to contain values distributed over different categories that are indexed by the additional coordinate. Such an array would reduce to the 2D-coordinate case if collapsed across the categories (e.g. if one did ``aggc.sum(dim='cat')`` for a categorical dimension ``cat``). The RGB channels for the uncollapsed, 3D case are computed by averaging the colors in the provided ``color_key`` (with one color per category), weighted by the array's value for that category. The A channel is then computed from the array's total value collapsed across all categories at that location, ranging from the specified ``min_alpha`` to the maximum alpha value (255). Parameters ---------- agg : DataArray cmap : list of colors or matplotlib.colors.Colormap, optional The colormap to use for 2D agg arrays. Can be either a list of colors (specified either by name, RGBA hexcode, or as a tuple of ``(red, green, blue)`` values.), or a matplotlib colormap object. Default is ``["lightblue", "darkblue"]``. color_key : dict or iterable The colors to use for a 3D (categorical) agg array. Can be either a ``dict`` mapping from field name to colors, or an iterable of colors in the same order as the record fields, and including at least that many distinct colors. how : str or callable, optional The interpolation method to use, for the ``cmap`` of a 2D DataArray or the alpha channel of a 3D DataArray. Valid strings are 'eq_hist' [default], 'cbrt' (cube root), 'log' (logarithmic), and 'linear'. Callables take 2 arguments - a 2-dimensional array of magnitudes at each pixel, and a boolean mask array indicating missingness. They should return a numeric array of the same shape, with ``NaN`` values where the mask was True. alpha : int, optional Value between 0 - 255 representing the alpha value to use for colormapped pixels that contain data (i.e. non-NaN values). Also used as the maximum alpha value when alpha is indicating data value, such as for single colors or categorical plots. Regardless of this value, ``NaN`` values are set to be fully transparent when doing colormapping. min_alpha : float, optional The minimum alpha value to use for non-empty pixels when alpha is indicating data value, in [0, 255]. Use a higher value to avoid undersaturation, i.e. poorly visible low-value datapoints, at the expense of the overall dynamic range. span : list of min-max range, optional Min and max data values to use for colormap/alpha interpolation, when wishing to override autoranging. name : string name, optional Optional string name to give to the Image object to return, to label results for display. color_baseline : float or None Baseline for calculating how categorical data mixes to determine the color of a pixel. The color for each category is weighted by how far that category's value is above this baseline value, out of the total sum across all categories' values. A value of zero is appropriate for counts and for other physical quantities for which zero is a meaningful reference; each category then contributes to the final color in proportion to how much each category contributes to the final sum. However, if values can be negative or if they are on an interval scale where values e.g. twice as far from zero are not twice as high (such as temperature in Farenheit), then you will need to provide a suitable baseline value for use in calculating color mixing. A value of None (the default) means to take the minimum across the entire aggregate array, which is safe but may not weight the colors as you expect; any categories with values near this baseline will contribute almost nothing to the final color. As a special case, if the only data present in a pixel is at the baseline level, the color will be an evenly weighted average of all such categories with data (to avoid the color being undefined in this case). z!agg must be instance of DataArrayNrrz7min_alpha ({}) and alpha ({}) must be between 0 and 255rirz!agg must use 2D or 3D coordinates) rXr^ DataArrayrYrCrWr)rrr) rrrrKrrrrCrrrr#rsX .  rcCsvt|tstdt||dur*|jn|}|dur:|Stt|d dd}t |j |}t||j |j |dS)aReturn a new image, with the background set to `color`. Parameters ----------------- img : Image color : color name or tuple, optional The background color. Can be specified either by name, hexcode, or as a tuple of ``(red, green, blue)`` values. rJN)rrrrT)rXrrYr)rZrCrrr rr rrUrV)rrrCZ backgroundrrrr#r s  rrcirclec sttjstdttt}|dur6jn|}durvt|trT|dkr\t d|dkrhSt ||n\tt j rj dkrjdjdkrjdddkst djdkrȈnd|dur|rd nd }jd}|djdd\}} |d| dfjt jt jfv} | r@t jnd|rVt||n&| rht||nt||jt jkfd d tjdkr} n$t fd dtjdD} j| jj|dS)asSpread pixels in an image. Spreading expands each pixel a certain number of pixels on all sides according to a given shape, merging pixels using a specified compositing operator. This can be useful to make sparse plots more visible. Parameters ---------- img : Image or other DataArray px : int, optional Number of pixels to spread on all sides shape : str, optional The shape to spread by. Options are 'circle' [default] or 'square'. how : str, optional The name of the compositing operator to use when combining pixels. Default of None uses 'over' operator for Image objects and 'add' operator otherwise. mask : ndarray, shape (M, M), optional The mask to spread over. If provided, this mask is used instead of generating one based on `px` and `shape`. Must be a square array with odd dimensions. Pixels are spread from the center of the mask to locations where the mask is True. name : string name, optional Optional string name to give to the Image object to return, to label results for display. z#Expected `xr.DataArray`, got: `{0}`Nrz``px`` must be an integer >= 0rirz@mask must be a square 2 dimensional ndarray with odd dimensions.rmr addcs<tj|jd}|j||  fS)Nr)rrrlrrL)Zlayerbuf)extra fill_valuekernelrw padded_shaperr# apply_kernel\szspread..apply_kernelcs&g|]}dddd|fqSrzr)rQcategory)rrrr#rRdszspread..r)rXr^rrYr)rZrrCrrrW _mask_lookuprrjrr[rlrpfloat32float64rv_build_spread_kernel_build_float_kernel_build_int_kernelrr]rrange __class__rVrU) rpxr[rKrwrCis_imagewMN float_typerdr)rrrrrrwrr#r!sJ        rcs0t|ddt|dtfdd}|S)7Build a spreading kernel for a given composite operatorFr_arrc s|j\}}t|D]}t|D]}|||f}tD]}tD]} ||| frBrt|dkrt|||| |f} n<r|||| |fdkr|} n||||| |f} | |||| |f<qBq6qqdSr&)r[r r"rwrdrryxelr?jresult ignore_zeros mask_sizercrr# stencilledos        z%_build_int_kernel..stencilledrr r)rKrrrrrr#rjs   rcs.t|ddt|dtfdd}|S)rFrrc s|j\}}t|D]}t|D]}|||f}tD]}tD]~} ||| frBt|rr|||| |f} n:t|||| |fr|} n||||| |f} | |||| |f<qBq6qqdSrz)r[rrrrrrcrr#rs        z'_build_float_kernel..stencilledr)rKrrrrr#rs   rcs6t|ddt|rdndtfdd}|S)rTrrDrc s|j\}}|jd}t|D]}t|D]}|||f}oJt|d?d@} o\t| } | sf| r(t|D]} t|D]x} || | frz|dkr|| || |f} || || |fdkr|} n||| || |f} | || || |f<qzqnq(qdS)Nrr)r[rrrrr)r"rwrdrrrrrrZ process_imageZ process_arrayr?rrrrcrr#rs"        z$_build_spread_kernel..kernelr)rKrrrrr#rs  rcCs&t|}d|d}tj||fddS)z9Produce a square mask with sides of length ``2 * px + 1``rirrmr)rrrZones)rrrrr# _square_masks rcCsJtj| |ddd}tt|d|dddfd|dkddS) z8Produce a circular mask with a diameter of ``2 * r + 1``rZi4rriN?TF)rrqruZsqrt)rrrrr# _circle_masksr)Zsquarerrrc Cst|t}d|krdks(ntdt|tr:|dkrBtd|jtjtjfv}d}td|dD]} | }|rt |j | d} n`t |j dkrt |j || d} n>|rtt|n|dk} tj| ddd} t | j d| d} | |krf|d}qqf|dkrt|||||d S|Sd S) aXSpread pixels in an image dynamically based on the image density. Spreading expands each pixel a certain number of pixels on all sides according to a given shape, merging pixels using a specified compositing operator. This can be useful to make sparse plots more visible. Dynamic spreading determines how many pixels to spread based on a density heuristic. Spreading starts at 1 pixel, and stops when the fraction of adjacent non-empty pixels reaches the specified threshold, or the max_px is reached, whichever comes first. Parameters ---------- img : Image threshold : float, optional A tuning parameter in [0, 1], with higher values giving more spreading. max_px : int, optional Maximum number of pixels to spread on all sides. shape : str, optional The shape to spread by. Options are 'circle' [default] or 'square'. how : str, optional The name of the compositing operator to use when combining pixels. Default of None uses 'over' operator for Image objects and 'add' operator otherwise. rrzthreshold must be in [0, 1]zmax_px must be >= 0rir)rrlF)r[rKrCN)rXrrWrrrlrrrr _rgb_densityrr]r[_array_densityZ logical_notrrr) rZ thresholdZmax_pxr[rKrCrrZpx_rZdensityZmaskedZ flat_maskrrr#rs,  rT)ZnopythonZnogilcachec Cs |j\}}d}}td|D]}td|D]}|||f} |rHt| rT|s*| dkr*|d7}d} ttd||t||d|D]^} ttd||t||d|D]6} |rt|| | fr|s|| | fdkr| d7} qq| dkr*|d7}q*q|r||StjS)zCompute a density heuristic of an array. The density is a number in [0, 1], and indicates the normalized mean number of non-empty pixels that have neighbors in the given px radius. rr)r[rrrmaxrinf) r"rrrrcnt has_neighborsrrr neighborsr?rrrr#rs&  &&  rc Cs|j\}}d}}td|D]}td|D]}|||fd?d@r*|d7}d}ttd||t||d|D]H} ttd||t||d|D] } || | fd?d@r|d7}qqp|dkr*|d7}q*q|r||StjS)zCompute a density heuristic of an image. The density is a number in [0, 1], and indicates the normalized mean number of non-empty pixels that have neighbors in the given px radius. rrrr)r[rrrrr) r"rrrrrrrrr?rrrr#rs &&  r)Nre)NN)rrNNN)rrrNN)r)r)>Z __future__rrrcollections.abcr ImportError collectionsrr.rZnumpyrZnumbanbZtoolzr`Zxarrayr^Z dask.arrayrrZ PIL.Imager Zdatashader.colorsr r Zdatashader.compositer r rZdatashader.utilsrrrr Exception__all__rrobjectr;rryrrrrrrrZmemoizerrrrrrrZjitrrrrrr#sl        &* .ex  g  I    6