a `mN@s ddlmZmZmZddlZddlZddlmZddlZ ddl Z ddl Z ddlmZddlmZddlmZddlZzddlmZWneyedZYn0z ddlZWneydZYn0zddlmZWneyedZYn0Gdd d eZe j d d d Z!e j d d d d Z"e#d de$de j%&DZ'Gddde(Z)Gddde(Z*ddZ+ddZ,ddZ-ddZ.ddZ/ddZ0dGd d!Z1d"d#Z2dHd%d&Z3dId)d*Z4d+d,Z5dJd0d1Z6d2d3Z7d4d5Z8d6d7Z9ed8d9d:d;d<Z:d=d>Z;d?d@ZdEdFZ?dS)K)absolute_importdivisionprint_functionN)getmro)memoize) DataArray) RaggedDtype) GeometryDtypec@seZdZdZdS)VisibleDeprecationWarningzVisible deprecation warning. By default, python will not show deprecation warnings, so this class can be used when a very visible warning is helpful, for example because the usage is most likely a user bug. N)__name__ __module__ __qualname____doc__rr/lib/python3.9/site-packages/datashader/utils.pyr "sr T)nopythonnogil)rrZparallelcCsg|] }t|qSr)int).0xrrr /rz([0-9]+)\.([0-9]+)\.([0-9]+)c@s0eZdZdZddZddZddZdd Zd S) ExprzBase class for expression-like objects. Implements hashing and equality checks. Subclasses should implement an ``inputs`` attribute/property, containing a tuple of everything that fully defines that expression. cCstt||fSN)hashtype_hashable_inputsselfrrr__hash__;sz Expr.__hash__cCs t|t|uo||kSr)rrrotherrrr__eq__>sz Expr.__eq__cCs ||k Srrr rrr__ne__Bsz Expr.__ne__cCs\g}|jD]H}t|ttfr,|t|q t|tjrH||q ||q t|S)zt Return a version of the inputs tuple that is suitable for hashing and equality comparisons ) Zinputs isinstancelistsetappendtuplenpZndarraytobytes)rresultZiprrrrEs   zExpr._hashable_inputsN)r r r rrr"r#rrrrrr4s rc@s*eZdZdZddZd ddZddZdS) DispatcherzSimple single dispatch.cCs i|_dSr)_lookuprrrr__init__XszDispatcher.__init__NcsF|durfddSttr8D]}||q$n |j<|S)z6Register dispatch of `func` on arguments of type `typ`Ncs |Sr)register)frtyprr^rz%Dispatcher.register..)r$r(r/r-)rr2functrr1rr/[s  zDispatcher.registercOs~|j}t|}||vr0|||g|Ri|St|ddD]*}||vr@|||g|Ri|Sq@td|dS)NzNo dispatch for {0} type)r-rr TypeErrorformat)rheadrestkwargsZlkr2clsrrr__call__fs zDispatcher.__call__)N)r r r rr.r/r=rrrrr,Vs r,cCs$tj|}t|tjo"|tjjvS)zCheck if a datashape is numeric and real. Example ------- >>> isrealfloat('int32') False >>> isrealfloat('float64') True >>> isrealfloat('string') False >>> isrealfloat('complex64') False ) datashape predicateslaunderr$UnittypesetsZfloatingZdtrrr isrealfloatts rDcCs$tj|}t|tjo"|tjjvS)zCheck if a datashape is numeric and real. Example ------- >>> isreal('int32') True >>> isreal('float64') True >>> isreal('string') False >>> isreal('complex64') False )r>r?r@r$rArBrealrCrrrisreals rFcCsftt|j}|||d|||}t|}tj|dd}||@}t |d|j ddS)aWnansum where all-NaN values remain NaNs. Note: In NumPy <=1.9 NaN is returned for slices that are all NaN, while later versions return 0. This function emulates the older behavior, which allows using NaN as a missing value indicator. Parameters ---------- array: Array to sum over axis: Axis to sum over raxis) r%rangendimremoveinsertZ transposer)Zisnanallwheresum)arrayrHTZ missing_valsZ all_emptyZ set_to_zerorrrnansum_missings      rRc Csp|jdd\}}|jdd\}}||j}||j}|d|d|d}|d|d|d}||fS)ziCalculate the resolution of xarray.DataArray raster and return it as the two-tuple (xres, yres). Nrr6)shapeZdimsvalues) rasterhwZydimZxdimZxcoordsZycoordsZxresZyresrrrcalc_ress  rZc Cs.|ddkr|n|}|ddkr0|n|}tj}}tj }}t|dd|gd|d |ggdg} ddt|ft|dft|t|ffD]V\} } t| t| | dg\} } }| |kr| }| |kr| }| |kr| }| |kr| }q|dd|dd}}||||||||fS)aCalculate the bounding box of a raster, and return it in a four-element tuple: (xmin, ymin, xmax, ymax). This calculation assumes the raster is uniformly sampled (equivalent to a flat-earth assumption, for geographic data) so that an affine transform (using the "Augmented Matrix" approach) suffices: https://en.wikipedia.org/wiki/Affine_transformation#Augmented_matrix Parameters ---------- xs : numpy.array 1D NumPy array of floats representing the x-values of a raster. This likely originated from an xarray.DataArray or xarray.Dataset object (xr.open_rasterio). ys : numpy.array 1D NumPy array of floats representing the y-values of a raster. This likely originated from an xarray.DataArray or xarray.Dataset object (xr.open_rasterio). res : tuple Two-tuple (int, int) which includes x and y resolutions (aka "grid/cell sizes"), respectively. rr6)r[r[?)rrr\@)maxminr)infrPlendot)xsysresZxboundZyboundZxminZyminZxmaxZymaxZAbZx_Zy_ry_xpadypadrrr calc_bboxs   0   rjc Cst|}t|d}||}}||}||||||}}t|||t|||} } | | krz| | fS| | fS)ai Transform continuous start and end coordinates into array indices. Parameters ---------- start : float coordinate of the lower bound. end : float coordinate of the upper bound. coords : numpy.ndarray coordinate values along the axis. res : tuple Resolution along an axis (aka "grid/cell sizes") r])raabsr_r^r) startendcoordsresizeZhalfZvminZvmaxspanZsidxZeidxrrr get_indicess "rqcCs|durt|}|j}|dur*||d}t|dtjrFtddnd}t|dtjrftddnd}|jdkr|d|kr|dddddf}|d|kr|ddd}nJ|d|kr|dddddddf}|d|kr|dddddf}|S)a  Reorients the array to a canonical orientation depending on whether the x and y-resolution values are positive or negative. Parameters ---------- raster : DataArray xarray DataArray to be reoriented res : tuple Two-tuple (int, int) which includes x and y resolutions (aka "grid/cell sizes"), respectively. layer : int Index of the raster layer to be reoriented (optional) Returns ------- array : numpy.ndarray Reoriented 2d NumPy ndarray Nr6rnsrT)rZdatar$r)Z timedelta64rJ)rWreZlayerrPZr0zeroZr1zerorrr orient_arrays   "("rucCs||\}}\}}||t|} ||t|} t| dt| d} } || || }}|| || }}t|||} t|||}|ddkr| ddd} |ddkr|ddd}| |fS)a Computes DataArray coordinates at bin centers Parameters ---------- width : int Number of coordinates along the x-axis height : int Number of coordinates along the y-axis x_range : tuple Left and right edge of the coordinates y_range : tuple Bottom and top edges of the coordinates res : tuple Two-tuple (int, int) which includes x and y resolutions (aka "grid/cell sizes"), respectively. Used to determine coordinate orientation. 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Options mean, sum, max, min, median, std, var) rUrr)Z concatenaterIZnanmeanZnansumnanmaxnanminZ nanmedianZnanstdZnanvar ValueError) aggregater}howrdrcconcatrrrdownsample_aggregateGs(, rlinearcCst|j}t|j}|dkr4|j|jdk}|dkrXt|||dddf}n2tjdt||tj ||j d|dddf}t |||fS)zHelper function similar to np.linspace which return values from aggregate min value to aggregate max value in either linear or log space. rrN)basenumdtype) r)rrVrrtr_rwZlogspaceZlog1perr)rrrZmax_valZmin_valvalsrrrsummarize_aggregate_valuesas     rcsgfdd}|S)z& simple arg caching decorator cs,rd|kr$||fdd<dS)Nrr6r)argsr0Zlastrrrg|szhold.._r)r0rgrrrholdvsr.png.cCsXddlm}tj|s"t||r0|||}|tj||||rT|SdS)zPGiven a datashader Image object, saves it to a disk file in the requested formatr)set_backgroundN) Zdatashader.transfer_functionsrospathexistsmkdirZto_pilZsavejoin)ZimgfilenameZfmtZ_returnZ export_pathZ backgroundrrrr export_images    rcCsvt|ttfrt|}t|ttfr0t|}tjd}||d}ttd|tjd|tj}||fS)a Projects the given (longitude, latitude) values into Web Mercator coordinates (meters East of Greenwich and meters North of the Equator). 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The pandas dataframe eventually contains two columns ('x' and 'y') with the data. Each time series is separated by a row of NaNs. Discussion at: https://github.com/bokeh/datashader/issues/286#issuecomment-334619499 x_values: 1D numpy array with the values to be plotted on the x axis (eg: time) y_values: 2D numpy array with the sequences to be plotted of shape (num sequences X length of each sequence) rr6rTN)rrf)r)ZzerosrUnanZtiler DataFrameZflatten)Zx_valuesZy_valuesrrfrrr!dataframe_from_multiple_sequencess  rc Csgd}|j|jd|ftjddf}|\}}}t||||dkrZgd}|jdd|f}|jdks|tj}tj|j|dd}|t |j dd|j d}t j ||j d} t|j dk} | s|j d } |jddd fd | | <| S) zkHelper for ``datashader.utils.mesh()``. Both arguments are assumed to be Pandas DataFrame objects. )rr6rsrNrs)rrsr6int64rG)r)rVrr)rZcrossitemrZtakeZreshapeZprodrUrrrrarepeat) vertices simplicesZwindingZ first_triabcZ vertex_idxsrreZverts_have_weightsZ weight_colrrr_pd_meshs $   " rcCsPt||}t|j|j}ttt|d|d}tj ||d}|S)ziHelper for ``datashader.utils.mesh()``. Both arguments are assumed to be Dask DataFrame objects. r) chunksize) rrr^Z npartitionsrr)ZceilrarZ from_pandas)rrreZapprox_npartitionsrrrr_dd_mesh0s rcCs|jjddksJd|jjdddd}|sBJdt|jdksh|jjddkshJd t|t j rt|t j rt ||St ||S) zMerge vertices and simplices into a triangular mesh, suitable to be passed into the ``Canvas.trimesh()`` method via the ``mesh`` keyword-argument. Both arguments are assumed to be Dask DataFrame objects. r6rzFAt least three vertex columns are required for the triangle definitionNcSst|tjSr)r)Z issubdtypeZintegerrCrrrr3Mrzmesh..z^Simplices must be integral. You may consider casting simplices to integers with ".astype(int)"rszMIf no vertex weight column is provided, a triangle weight column is required.) rVrUrZilocmaprMrarr$rrrr)rrZsimplices_all_intsrrrmeshBs & r)NN)rz)rr)rTrr)@Z __future__rrrrreinspectrZnumbanbZnumpyr)ZpandasrZtoolzrZxarrayrZdask.dataframeZ dataframerr>Zdatashader.datatypesr ImportErrorrr ExceptionZspatialpandas.geometryr UserWarningr ZjitZngjitZngjit_parallelr(match __version__groupsZ numba_versionrrr,rDrFrRrZrjrqruryrrrrrrrrrrrrrrrrrsj           " ( #&   )