o Nrf81@sddlmZddlZddlZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZddlmZddlmZddlmZdd lmZmZe eejfej eeejfej!e eejfej"e eejfej#e ej$j%ej$j#e eejfee ej$j%eeejd8d d Z&e ej$j%d9d dZ'eej$j%d:ddZ(e)de )de)de)de)dddZ*e)de )dddZ+e)de )de)de)dddZ,e)de )dddZ-ddZ.eej$j%d d!Z/eeejfd"d#Z0d$d%Z1d;d(d)Z2eeejfd*d+Z3d,d-Z4Gd.d/d/eZ5eejd0d1Z6Gd2d3d3e5Z7ed4d5e5d6d7Z8e89d5e7dS)<) annotationsN)chunk)Array) concatenate_lookup divide_lookup einsum_lookup empty_lookupnannumel_lookup numel_lookuppercentile_lookuptensordot_lookupto_cupy_dispatchto_numpy_dispatch)divide) ma_divide _percentile)CreationDispatchDaskBackendEntrypointlinearcCs t|||SNr)aqmethodr\/var/www/html/software/conda/envs/catlas/lib/python3.10/site-packages/dask/array/backends.py percentile#s rcCs`tjj||d}dd|D}tdd|Drtd|r.t|}t|dkr.|d|_|S) N)axiscSsg|] }t|dr|jqS) fill_value)hasattrr).0irrr +sz _concatenate..css|] }t|tjVqdSr) isinstancenpndarray)r frrr ,sz_concatenate..zADask doesn't support masked array's with non-scalar `fill_value`sr)r$ma concatenateany ValueErroruniquelenr)ZarraysroutZ fill_valuesrrr _concatenate(s   r0cs&zt|Wntytt| dttd|Ynw|\z t}tWnty;gd}Ynwz t}tWntyTgd}Ynw|j|j}|j|j}d}||krjd}n3t|D].}||krd}n|dkr||7<|dkr||7<qn|stdfddt|D} | } d} D]} | | 9} qd| f} fd d| D}fd dt|D} | }d} D]} | | 9} q| df}fd d| D}||  | }|| |}t j ||}| ||S) Nrr(TFzshape-mismatch for sumcg|]}|vr|qSrrr k)axes_arrr"fz_tensordot..cg|]}|qSrrr r)as_rrr"lcr2rrr3)axes_brrr"nr6cr8rrr9)bsrrr"tr;) iter TypeErrorlistranger.shapendimr,Z transposeZreshaper$r)dot)rbaxesnanbZndaZndbequalr4notinZ newaxes_aZN2rZ newshape_aZoldaZ newaxes_bZ newshape_bZoldbatZbtresr)r:r5r<r=r _tensordot8sr         rMcupycsddltjjtjjtjtt jt t jt t jfdd}ttjfdd}tjfdd}dS)Nrcj|fi|Sr)ZasnumpydatakwargsrNrr cupy_to_numpyz$register_cupy..cupy_to_numpycrOr)ZasarrayrPrSrr numpy_to_cupyrUz$register_cupy..numpy_to_cupycs(|dd|ddj|i|S)NZcastingorder)popeinsum)argsrRrSrr _cupy_einsums  z#register_cupy.._cupy_einsum)rNrregisterr%r*r tensordotr rr _numel_arrayliker _nannumelrr r$r)rTrVr[rrSr register_cupy|s   r`Zcupyxc spddlm}z ddlmmWnty!}ztd|d}~wwdfdd }t||t|tdS)Nr)spmatrixhstackvstackz>Stacking of sparse arrays requires at least CuPy version 8.0.0cs0|dkr|S|dkr|Sd|}t|)Nrr(zECan only concatenate cupy sparse matrices for axis in {0, 1}. Got %s)r,Lrmsgrbrr_concat_cupy_sparsesz+register_cupyx.._concat_cupy_sparser) Zcupyx.scipy.sparserarcrd ImportErrorrr\r _tensordot_scipy_sparse)raerhrrbrregister_cupyxs  rmsparsecCsHddl}t|j|jt|j|jt|jtt |jt dS)Nr) rnrr\ZCOOr*r r]r _numel_ndarrayr _nannumel_sparse)rnrrrregister_sparses rqscipycs:ddldfdd }tjj|tjjtdS)Nrcs8|dkr j|S|dkrj|Sd|}t|)Nrr(zFCan only concatenate scipy sparse matrices for axis in {0, 1}. Got %s)rnrdrcr,rerrrrr0s  z+register_scipy_sparse.._concatenateri)Z scipy.sparserr\rnrar rk)r0rrsrregister_scipy_sparses rtcCs|j|jkr dksJJt|dt|dkr#dks&JJ|d\}|d\}|dvr8|dvs:J|j||j|ksFJ|dkrS|dkrS|j|S|dkra|dkra|j|jS|dkrm|dkrm||S|dkrz|dkr|||jSdSdS)Nr1rr()rr()rCr.rBT)rrErFZa_axisZb_axisrrrrks ,     rkcKstjt|fi|S)z'Numel implementation for masked arrays.)rsumr$Z ones_likexrRrrr _numel_maskedsrycKt|fddi|S)zJNumel implementation for arrays that want to return numel of type ndarray.coerce_np_ndarrayT_numelrwrrrrosrocKrz)zKNumel implementation for arrays that want to return numel of the same type.r{Fr|rwrrrr^sr^r{boolc s&|j|dd}|dd|dtj}durBtj|d}|dur'|S|r5tjdt||dStj||dt|d Stt t fsLgt fd d D}|d urmt fd d t tD}nt fdd t tD}|rt tj||d|Stj||||d S)a  A reduction to count the number of elements. This has an additional kwarg in coerce_np_ndarray, which determines whether to ensure that the resulting array is a numpy.ndarray, or whether we allow it to be other array types via `np.full_like`. keepdimsFrNdtype)r)r()rBrr)rBrc3s|]}|VqdSrrr dim)rBrrr'sz_numel..Tc3s$|] }|vr |ndVqdS)r(NrrrrBrrr's c3s |] }|vr|VqdSrrrrrrr's)rBgetr$Zfloat64prodfullr.Z full_liker#tupler@mathrAZ broadcast_toarray)rxr{rRrrrZ new_shaperrrr}s,    r}cKstjt|fi|S)z;A reduction to count the number of elements, excluding nans)rrvr$isnanrwrrrr_%sr_cKs&t|fi|}t|dr|S|S)a  A reduction to count the number of elements in a sparse array, excluding nans. This will in general result in a dense matrix with an unpredictable fill value. So make it official and convert it to dense. https://github.com/dask/dask/issues/7169 todense)r_rr)rxrRnrrrrp+srpc@seZdZdZeddZeddZeddddd Zedddd d Z edddd d Z edddddZ eddddddZ dS)ArrayBackendEntrypointzjDask-Array version of ``DaskBackendEntrypoint`` See Also -------- NumpyBackendEntrypoint cCt)zReturn the backend-specific RandomState class For example, the 'numpy' backend simply returns ``numpy.random.RandomState``. NotImplementedErrorselfrrr RandomStateAz"ArrayBackendEntrypoint.RandomStatecCr)z$Return the default BitGenerator typerrrrrdefault_bit_generatorJsz,ArrayBackendEntrypoint.default_bit_generatorN)rmetacKr)ztCreate an array of ones Returns a new array having a specified shape and filled with ones. rrBrrrRrrronesOrzArrayBackendEntrypoint.onescKr)zvCreate an array of zeros Returns a new array having a specified shape and filled with zeros. rrrrrzerosXrzArrayBackendEntrypoint.zeroscKr)z`Create an empty array Returns an uninitialized array having a specified shape. rrrrremptyaszArrayBackendEntrypoint.emptycKr)zCreate a uniformly filled array Returns a new array having a specified shape and filled with fill_value. r)rBrrrrRrrrrirzArrayBackendEntrypoint.fullr(cKr)zCreate an ascending or descending array Returns evenly spaced values within the half-open interval ``[start, stop)`` as a one-dimensional array. r)startstopsteprrrRrrrarangerrzArrayBackendEntrypoint.arange)Nr() __name__ __module__ __qualname____doc__propertyrr staticmethodrrrrrrrrrr9s   rcKs|SrrrPrrrto_numpy_dispatch_from_numpy|rc@s>eZdZeddZed ddZeddZed d Zd S) NumpyBackendEntrypointcCstSr)r)clsrrrto_backend_dispatchrz*NumpyBackendEntrypoint.to_backend_dispatchrQrcKs(t|jtjr |S|j|fi|Sr)r#_metar$r%Z map_blocksr)rrQrRrrr to_backendsz!NumpyBackendEntrypoint.to_backendcCtjjSr)r$randomrrrrrrz"NumpyBackendEntrypoint.RandomStatecCrr)r$rZPCG64rrrrrrz,NumpyBackendEntrypoint.default_bit_generatorN)rQr) rrr classmethodrrrrrrrrrrs   rrnumpyarray_creation_dispatch) module_namedefaultZentrypoint_classname)rri)r1)r{r~): __future__rrrr$Z dask.arrayrZdask.array.corerZdask.array.dispatchrrrrr r r r r rZdask.array.numpy_compatrZ np_dividerZdask.array.percentilerZ dask.backendsrrr\objectr%r*r]rYrr)Z masked_arrayrr0rMZ register_lazyr`rmrqrtrkryror^r}r_rprrrrZregister_backendrrrrsz   0        C       (  C