|Le ^ddlmZddlmZddlmZddlZddlZddl Z ddl Z ddl ZddlmZddlmZddlmZmZddlmZmZ ddlZn #e$rdZYnwxYwgdZGd d ejZGd d Zd Z d2dZ!ddde!dZ"dZ#dZ$dZ%dZ&dZ'dZ(ddgedddddddf d Z)d3d!Z*d4d$Z+e j,d%Z-e j,d&Z.e j,d'Z/d(Z0d)Z1e0e1d*Z2d5d-Z3e j4d.d.d./d6d0Z5e j4d.d.d./d6d1Z6dS)7) annotations)IteratorBytesION) fromarray)rgbSets1to3)nansum_missingngjit)Imagestackshadeset_backgroundspread dynspreadc6eZdZdZdZdZd dZd dZdZd Z d S) r Flowerc|j}trtj|}|dkrtj|n|}t |dS)NrRGBA)datacupyasnumpynpflipudr)selforiginrarrs Flib/python3.11/site-packages/datashader/transfer_functions/__init__.pyto_pilz Image.to_pil sKy  &<%%D!'7!2!2biooof%%%pngct}|||||d|SNr)rr"saveseek)rformatrfps r! to_bytesiozImage.to_bytesio'sA YY F  V,,,   r#cN|S)z/Supports rich PNG display in a Jupyter notebook)r" _repr_png_)rs r!r-zImage._repr_png_-s{{}}'')))r#c@ddlm}ddlm}|}||ddt |jzdzd||  d z}|S) z0Supports rich HTML display in a Jupyter notebookrr) b64encoder$)r)z!zutf-8) iorbase64r/r"r'strborderr)getvaluedecode)rrr/bhs r! _repr_html_zImage._repr_html_1s $$$$$$ GII 1U+++ 3c$+6F6F FIY Y 3yy..55g>>?? @r#N)r)r$r) __name__ __module__ __qualname__ __slots____array_priority__r3r"r+r-r8rr#r!r r sjI F&&&& ***     r#r c$eZdZdZdZdZdZdS)Imagesz A list of HTML-representable objects to display in a table. Primarily intended for Image objects, but could be anything that has _repr_html_. cP|D]}t|dsJ||_d|_dS)z@Makes an HTML table from a list of HTML-representable arguments.r8N)hasattrimagesnum_cols)rrBis r!__init__zImages.__init__Hs< , ,A1]++ + + + +  r#c||_|S)zk Set the number of columns to use in the HTML table. Returns self for convenience. )rC)rns r!colsz Images.colsOs   r#cg}d}d}|jD]}t|dr|j|jnd}|d|zd|z|dz }|j%||jkrd}|d |zd |zd|zd zS) z@Supports rich display in a Jupyter notebook, using an HTML tablerz#nameNz"z

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" " " "  Mv--KLL L ::fd # #D VZZv66 7B 4yyA~~Aw 8TG 4 4 4D Hh 7 7 7==i %;%;d%;%;%;<<=============== T!W^$q',T J J JJs2E  EEctr(t|tjrddlm}t}nrs 28BHQ4J4J+K+Kr#cHtj|tj|dzS)NgUUUUUU?rrrrs r!rrsBHQ,B,BT,Jr#cBtj|tj|Srrrs r!rrsbhq"&!.D.Dr#)logcbrtlinearrct|r|S|tvr t|Std|)Nz!Unknown interpolation method: {0})callable_interpolate_lookuprer))rVs r!_normalize_interpolate_howrsI}}( # # #"3'' 8??DD E EEr#c|tdd}dd|zz }||z|z}|dkr/t|d||d|dz zz d}|df}|S)Nz3interpolator did not return a valid discrete_levelsg9/tg?rrr)remax)rspanrcmultiple lower_spans r!_rescale_discrete_levelsrs{NOOO A ac A 1$H!||a8T!WtAw->#??CC A Kr#c <tr(t|jtjr ddlm}m} nddlm}tj} |j dkrtdt|} |j} t| tj r| } n| } tj| jtjr#| } | tj} n+| jjdkr| dk} ntj| } | rKt1tj|jjtj|j|j|j| S|tj| | } n+tj || j d} || | g|R| | z} tj!d d 5| | | } d}t| tDtFfr| \} }|^tj$| | tj%}tj|tj&|f}|r|tO||}n2|d krtd| d|d|dz gd}dddn #1swxYwYt|tPrtE|}t|tFr*t|dtRrtE|}t|tDrtj tEtUtWtX|\}}}tj-|d|dt]|}tj/| | ||ddtj0}tj/| | ||ddtj0}tj/| | ||ddtj0}tj$tj| d|tj0}tj1||||g}nLt|tRst|tFr4tY|}tj2||dz}tj-|d|dt]|}tj3| j|dtj0 }tj3| j|dtj0 }tj3| j|dtj0 }tj/| | ||dddtj0}tj1||||g}nti|r| |dz |d|dz z }tr.t|tjrtj5|}||d}tj$tj|d|tj0|dddddf<n/tmd7tq||9tj:| j}tr.t|tjrtj5|}t1||j|j|S)Nr)masked_clip_2dryrragg must be 2Dur)rir~)rbrcattrsrJr}rZ)r]r\r+span is not (yet) valid to use with eq_hist)leftFrYrrightT)byteszVExpected `cmap` of `matplotlib.colors.Colormap`, `list`, `str`, or `tuple`; got: '{0}'ra);rrfrrrrry _cpu_utilsrndimrerdaArraycomputerYrr~bool_astypeint8kindisnanrr rriuint32rbrcrnanminarrayrnlisttuplerrnanmaxrrr2zipmaprlinspacerk nan_to_numuint8dstackrfullrrrgr)rhviewr)aggcmaprValphar min_alpharJrescale_discrete_levelsrry interpolaterrroffsetr masked_datarspangspanbspanrgr6argbacoloraspan scaled_dataimgs r! _interpolaters  38T\22777777777...... x1}})***-c22L 8D$!!||~~yy{{ }TZ**"u{{27## :?c ! !19DD8D>>D xxzz@RXCHN$&I///7:z(#)$@@@ @  |4;''$dj111!4tT)D)))) FND Xh 7 7 7;;|D$'' dT5M * * )$( !D/ <(D5$77K9[))29[+A+AAD& G?+F/FFi!!NOOO<DGd1g$5 6::D);;;;;;;;;;;;;;;,$!!Dzz$:d1gs#;#;Dzz$T htCS$,@'A'ABBue{47DGSYY77 M&&tU===E J J J Q QRTRZ [ [ M&&tU===E J J J Q QRTRZ [ [ M&&tU===E J J J Q QRTRZ [ [ HRXd^^Q . . 5 5bh ? ?y!Q1&& D#  T*T5"9"9TD  )U1W--{47DGSZZ88 GDJa 9 9 9 GDJa 9 9 9 GDJa 9 9 9 M&&tU#FFFU S S S Z Z[][c d dy!Q1&& $ Td1g~Q$q'(9:  4J{DL99 4,{33KtKt,,,+!6!65AAHHRRQQQ1W @@FtDzz@R@RTT T ))BI   & &tz 2 2C  3 -- l3 SZchT B B BBs*CJ88J<?J<c * tr,t|jtjr tj} n t j} |jdkstd|j|j d} t| stt j |j ddt j|j ddt|j d|j|j df|j d|j|j dfg|Std ttstt#| tt| kr0td td t| d fd | D} t%| t#| \} } }|j|j df|j ddz}|jgd}t|t(jr|}|}t1j5t1jdd|t j|n|}dddn #1swxYwYt jd5|dkr||z}n |dkr|| z }|jjdkr | d||dk<dddn #1swxYwYt?|d}d|t j |<t jdd5|!| |z "t j#}|!| |z "t j#}|!||z "t j#}dddn #1swxYwYt j |}t j$|d}t jdd5|!| |z "t j#}|!| |z "t j#}|!||z "t j#}dddn #1swxYwYt j$|ddk}t j%|||}t j%|||}t j%|||}t?|d}t j |}tM||||||||}t j'||||g(t j)|j } tr.t| tjrtj*| } t| |j ddt|j d|j|j df|j d|j|j dfg|S)Nrzagg must be 3Dr{rrr}rrcrbrJzXColor key must be provided, with at least as many colors as there are categorical fieldszInsufficient colors provided (z() for the categorical fields available ()c:g|]}t|Srrr_r color_keys r!r`z_colorize..t% . . .Ac)A, . . .r#)rrrrZAll-NaN slice encounteredr]r)axisr[)+rrfrrrrrreindexesrcrkr rrirdictrbrr transposerrrrYwarningscatch_warningsfilterwarningsrrnr~rr rdotrrsumr_interpolate_alpharrrr)!rrrVrrrrJcolor_baselinerrcatscolorsrsgsbsagg_tr color_databaseline color_totalrrr6 color_mask cmask_sumr2g2b2missing_colorstotalrrrs! ` r! _colorizerZs  38T\22  8q==)*** ;sx| $D t99ORXci!nBI>>>SXcrc] (1+sz#(1+'>?(1+sz#(1+'>?"BCCIMOOO O BCC C i & &/T9--..  9~~D !!;#i..;;.1$ii;;;<< </ . . . . . .FUCL))JBB CMSXb\OCHRaRL8 :E :   * *D$!!||~~J  " "WW*FGGG,:,B29Z(((WWWWWWWWWWWWWWW X & & &'' a<< ( "JJ \\ 8) #J   C ' 'N,F%&Jz!| $ '''''''''''''''!!444K!"Jrx~~ Hh 7 7 7>> ^^B   + 3 3BH = = ^^B   + 3 3BH = = ^^B   + 3 3BH = =>>>>>>>>>>>>>>>(4..Jz***I Hh 7 7 7==nnR  * 2 228 < <nnR  * 2 228 < <nnR  * 2 228 < <=============== VJQ///14N Q''A Q''A Q''A 4a ( ( (E 8E??D4c5$ KbccA Y1a| $ $ ) )") 4 4 < 4HH hu . . 1(0 %Ho $ & & Q Q  #H.Q R R R8,,1133RYx5H5H5M5M5O5OPI Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q # M'B0)LLI )  JKK K$dj111!4 ; s " "ry'7'71'<'<5A:&DHdUE2622Eud*T****2-c2256>4HH hu . . #{H3.s33QQ$q'8I4JANN i$ / / %!! I ##I..IfXy,*<*.rr#r}rrZrrrr)&rrfrrrrrrerrrrkeysrr empty_liker zeros_liker enumeraterYrrnr~rranyrrrrrrrirr rcrb)rrrrJr moduleragg_datar r r rrrrrr6rrrrDr value_maskrrrrrs ` r!_apply_discrete_colorkeyr,s%  38T\22  8q==)*** :i#>#>EFFFxH(BH%%&##%% >>  D . . . . . . .FUCL))JBB   X & & 3D$fl33A$fl33A$fl33A   4v|  4 4B   4v|  4 4B   4v|  4 4B$1] ZA:A:A:J-;,Bv}}Z(((H X & & &++ a<< ( "JJ \\ 8) #J   C ' 'N,F)*JzA~ & +++++++++++++++&'Jv||D!!" >>  !!!!!!!!!1_N ^R++A ^R++A ^R++A $E**11"(;;A ]]Aq!Q< ( ( - -fm < < D DQW M MF & 38T\22&f%% h    s 4J  J J  lightbluedarkbluerr(Fc t|tjstd||jn|}d|cxkrdkrnnd|cxkrdksnt d|d|d| r|dkrd } |jd kr@|(t|trt|||||St|||||||| S|jd krt||||||||| St d ) a$Convert 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. A dictionary ``color_key`` that specifies categories (values in ``agg``) and corresponding colors can be provided to support discrete coloring 2D aggregates, i.e aggregates with a single category per pixel, with no mixing. The A channel is set the given ``alpha`` value for all pixels in the categories specified in ``color_key``, and to zero otherwise. 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 mixed from separate values over all categories. They 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 categorical agg array. In 3D case, it 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. In 2D case, ``color_key`` must be a ``dict`` where all keys are categories, and values are corresponding colors. Number of categories does not necessarily equal to the number of unique values in the agg DataArray. 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. Note that ``min_alpha`` will not take any effect when doing discrete categorical coloring for 2D case as the aggregate can have only a single value to denote the category. span : list of min-max range, optional Min and max data values to use for 2D colormapping, and 3D 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 Fahrenheit), 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). rescale_discrete_levels : boolean, optional If ``how='eq_hist`` and there are only a few discrete values, then ``rescale_discrete_levels=True`` decreases the lower limit of the autoranged span so that the values are rendering towards the (more visible) top of the ``cmap`` range, thus avoiding washout of the lower values. Has no effect if ``how!=`eq_hist``. Default is False. z!agg must be instance of DataArrayNrrz min_alpha (z ) and alpha (z) must be between 0 and 255rFrrz!agg must use 2D or 3D coordinates) rfrl DataArrayrgrJrerrr,rr) rrrrVrrrrJr rs r!rrLsTV c2< ( (=;<<<|388D ) " " " "s " " " " "e):):):):s):):):):ayaauaaabbb(3)#3#3"' x1}}  Z 4%@%@ +Yt^  T3tY 799 9 QieT9dN022 2<===r#cddlm}t|ts/t dt |||jn|}||Stj t|dz dd}||j |}t||j |j|S)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. r)rWrUN)rrra)rdrWrfr rgr)rhrJrrrrrrbrc)rrrJrW backgroundrs r!rrs*))))) c5 ! !J6==d3iiHHIII|388D } #e**v-..33H==a@J 4* % %D cjsxd C C CCr#rcirclecB ttjs/tdt tt }|jn|}Ht|tr|dkrtd|dkrSt||ndttj r;j dkr0jdjdkrjddzdkstd||rd nd }jd}|dz jdd\}} |d zz| d zzfjtjtjfv} | r tjndt(r=tjt(j rt)jj_|rt/||n7| rt1||n$t3||jtjk fd  t7jdkr  } n9tj fd t;jdD} | jj | S)asSpread pixels in an image. Spreading expands each pixel a certain number of pixels on all sides according to a given shape, merging pixels using a specified compositing operator. This can be useful to make sparse plots more visible. Parameters ---------- img : Image or other DataArray px : int, optional Number of pixels to spread on all sides shape : str, optional The shape to spread by. Options are 'circle' [default] or 'square'. how : str, optional The name of the compositing operator to use when combining pixels. Default of None uses 'over' operator for Image objects and 'add' operator otherwise. mask : ndarray, shape (M, M), optional The mask to spread over. If provided, this mask is used instead of generating one based on `px` and `shape`. Must be a square array with odd dimensions. Pixels are spread from the center of the mask to locations where the mask is True. name : string name, optional Optional string name to give to the Image object to return, to label results for display. z#Expected `xr.DataArray`, got: `{0}`Nrz``px`` must be an integer >= 0rrz@mask must be a square 2 dimensional ndarray with odd dimensions.rrWaddctj|j}|j||  fS)Nr})rrr~rrY)layerbufextra fill_valuekernelr padded_shapes r! apply_kernelzspread..apply_kernel$sYglJekBBBuz4%%%5%<v-.33555r#c@g|]}dddd|fSrr)r_categoryr>rs r!r`zspread..,sF===$&c!!!AAAh,&788===r#r)!rfrlr1rgr)rhr rJintre _mask_lookuprrrrir~rfloat32float64rrrr_build_spread_kernel_build_float_kernel_build_int_kernelrrkrrange __class__rcrb)rpxrirVrrJis_imagewMN float_typervr>r:r;r<r=s` ` @@@@@r!rrs6 c2< ( (Q=DDT#YYOOPPP#u%%H|388D |"c"" ?b1ff=>> > 77JE"2&&rz**EtyA~~jmtz!},,A1Ba1G1G+,, , { +ffe 1 A FE 9RaR=DAq%KQuW-Lrz2:66J%,1J * 38T\22*<))C%c844 C$S!,,"339 +ABB666666666  39~~ql3i=====(-cil(;(;===>> ==38CJT= J JJr#ctddlm}m}||d||dztfd}|S)7Build a spreading kernel for a given composite operatorrrTvalidate_operatorFrK_arrc |j\}}t|D]}t|D]}|||f}t D]w}t D]e} ||| frY r|dkr|||z| |zf} n3 r|||z| |zfdkr|} n ||||z| |zf} | |||z| |zf<fxdSr&)rirH)r rrvrMrNyxelrDjresult ignore_zeros mask_sizerus r! stencilledz%_build_int_kernel..stencilled9s)y1q 7 7A1XX 7 7AYy)) 7 7A"9--771:7+CA),QUAE\):!-C#a!eQUl2CQ2F2F)+)+BAE1q5L0A)B)B06CAq1u -7 7 7 7 7r#rdrTrSr )rVr]r\rTrSr^rus `` @r!rGrG2syLKKKKKKKcE**** S6\ *B 777777 U7 r#cpddlm}m}||d||dztfd}|S)rQrrRFrTrUc |j\}}t|D]}t|D]}|||f}t D]}t D]}} ||| frqtj|r|||z| |zf} n?tj|||z| |zfr|} n ||||z| |zf} | |||z| |zf<~dSr)rirHrr) r rrvrMrNrWrXrYrDrZr[r]rus r!r^z'_build_float_kernel..stencilledSs)y1q 7 7A1XX 7 7AYy)) 7 7A"9--771:7!x||C),QUAE\):!##a!eQUl*;!.kernelnsYy1 JqMq ; ;A1XX ; ;AY ( Ds2ww"}.C %- E 4D  ;M ;"1XX ; ;!&q ; ;A#AqDz;#%q55-0QA->F#&q1ua!e|#4a#7#7-/FF-/RCAq1u 4E-F-FF4:AE1q5L 1 ; ; ; ;r#r_)rVrKrTrSr<rus ` @r!rErEfs|LKKKKKKKcD)))) S($>BB? @B ;;;;; U;* Mr#c`t|}d|zdz}tj||fdS)z9Produce a square mask with sides of length ``2 * px + 1``rrrr})rArones)rJrLs r! _square_maskris3 RB B A 7Aq6 ( ( ((r#ctj| |dzd}tjtj|dz|dddfdzz|dzkddS) z8Produce a circular mask with a diameter of ``2 * r + 1``ri4r}rN?TF)rrrsqrt)rrXs r! _circle_maskrns^ 1"a!e4(((A 8BGAqD1QQQW:q=011QsU:D% H HHr#)squarer4rlrc^t|t}d|cxkrdksntdt|tr|dkrtd|jt jt jfv}tr=t|j tj rtj |j |_ d}td|dzD]} | }|rt|j | dz} nt|jdkrt!|j || dz} n^|r&t jt j|n|dk} t j| dd} t!| j d| dz} | |kr|dz }n|dkrt)||||| S|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 >= 0rr)rr~F)rirVrJ)rfr rerAr~rrCrDrrrrrH _rgb_densityrkri_array_density logical_notrrr) r thresholdmax_pxrirVrJrKrOpx_rJdensitymasked flat_masks r!rrs4#u%%H    Q    6777 fc " "0fqjj.///rz2:66J * 38T\22*<)) CAvz""     B"38RT22GG ^^q $SXz2a4@@GG6@PR^BHSMM222saxFvAX>>>I$Y^UBqDAAG Y  AC E  Avvc3e4@@@@ r#T)nopythonnogilcachec J|j\}}dx}}td|D]}td|D]}|||f} |rtj| r|s| dkr|dz }d} tt d||z t ||zdz|D]m} tt d||z t ||zdz|D]5} |rtj|| | fr|s|| | fdkr| dz } 6n| dkr|dz }|r||z n tjS)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)rirHrrrrinf) r rOrJrMrNcnt has_neighborsrWrXrY neighborsrDrZs r!rrrrss 9DAqC- 1a[[ ' 'q! ' 'AQTB '28B<< ' 'Aq "3q!b&>>3q2vz13E3EFF++A"3q!b&>>3q2vz13E3EFF++'+QT0C0C+!++03AqD Q%NI+Q;;!Q&M '!$ /=  /r#c |j\}}dx}}td|D]}td|D]}|||fdz dzr|dz }d}ttd||z t||zdz|D]O} ttd||z t||zdz|D]} || | fdz dzr|dz }P|dkr|dz }|r||z n tjS)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. rrdrr)rirHrrrr~) r rJrMrNrrrWrXrrDrZs r!rqrqs8 9DAqC- 1a[[ ' 'q! ' 'AAqD R3& 'q "3q!b&>>3q2vz13E3EFF++A"3q!b&>>3q2vz13E3EFF++1IOs2+%NI+Q;;!Q&M '!$ /=  /r#)Nrw)NN)rr4NNN)rlrr4NN)r)7 __future__rcollections.abcrr0rrnumpyrnumbanbtoolzroxarrayrl dask.arrayrr PIL.Imagerdatashader.colorsrr datashader.utilsr r r Exception__all__r1r r?r rrrrrrr r,rrrmemoizerGrFrErirnrBrjitrrrqrr#r!rs""""""$$$$$$++++++++22222222KKKK DDD O N N"""""BL"""L&<&<&<&<&<&<&<&A>A>A>HDDDD,IKIKIKIKX 2 2 @)))III '&(( 7777tT...000/.00T...000/.000sA AA