o Nrf@sUddlmZddlZddlZddlZddlmZmZddlmZddl m Z ddl m Z ddl ZddlmZddlmZmZmZmZmZmZdd lmZdd lmZdd lmZdd lmZdd l m!Z!m"Z"m#Z#GdddZ$dTddZ%GdddZ&ddZ'dUddZ(ddZ)ddZ*ddZ+ddZ,d d!Z-d"d#Z.dd$d%d&d'd(Z/ iZ0d)e1d*<e Z2d+d,Z3 e3d-Z4e3d.Z5e3d/Z6e3d0Z7e3d1Z8e3d2Z9e3d3Z:e3d4Z;e3d5Ze3d8Z?e3d9Z@e3d:ZAe3d;ZBe3d<ZCe3d=ZDe3d>ZEe3d?ZFe3d@ZGe3dAZHe3dBZIe3dCZJe3dDZKe3dEZLe3dEZMe3dFZNe3dGZOe3dHZPe3dIZQe3dJZRe3dKZSe3dLZTe3dMZUe3dNZVe3dOZWe3dPZXe3dQZYe3dRZZe3dSZ[dS)V) annotationsN)chainproduct)Integral)getitem)Lock)array_creation_dispatch)Arrayasarraybroadcast_shapes broadcast_tonormalize_chunksslices_from_chunks)arange) asarray_safe)tokenize)HighLevelGraph) derived_fromrandom_state_datatypenamec@s6eZdZdZddZddZeddZedd Ze e j j d d d`ddZ e e j j d d d`ddZe e j j d d d`ddZe e j j d d     daddZe e j j d d dbddZe e j j d d d`ddZe e j j d d dbddZe e j j d d d`dd Ze e j j d d dcd"d#Ze e j j d d d`d$d%Ze e j j d d d d e jd&d fd'd(Ze e j j d d dcd)d*Ze e j j d d dcd+d,Ze e j j d d dcd-d.Ze e j j d d d`d/d0Ze e j j d d d`d1d2Ze e j j d d  ddd4d5Ze e j j d d d`d6d7Ze e j j d d d`d8d9Z e e j j d d d`d:d;Z!e e j j d d dcdd?Z#e e j j d d d@dAZ$e e j j d d dbdBdCZ%e e j j d d d`dDdEZ&e e j j d d d e j'd d fdFdGZ e e j j d d dbdHdIZ(e e j j d d d`dJdKZ)e e j j d d d`dLdMZ*e e j j d d d`dNdOZ+e e j j d d d`dPdQZ,e e j j d d d`dRdSZ-e e j j d d d`dTdUZ.e e j j d d dcdVdWZ/e e j j d d d`dXdYZ0e e j j d d d`dZd[Z1e e j j d d d`d\d]Z2e e j j d d d`d^d_Z3d S)e Generatora Container for the BitGenerators. ``Generator`` exposes a number of methods for generating random numbers drawn from a variety of probability distributions and serves as a replacement for ``RandomState``. The main difference between the two is that ``Generator`` relies on an additional ``BitGenerator`` to manage state and generate the random bits, which are then transformed into random values from useful distributions. The default ``BitGenerator`` used by ``Generator`` is ``PCG64``. The ``BitGenerator`` can be changed by passing an instantiated ``BitGenerator`` to ``Generator``. The function :func:`dask.array.random.default_rng` is the recommended way to instantiate a ``Generator``. .. warning:: No Compatibility Guarantee. ``Generator`` does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change. Parameters ---------- bit_generator : BitGenerator BitGenerator to use as the core generator. Notes ----- In addition to the distribution-specific arguments, each ``Generator`` method takes a keyword argument `size` that defaults to ``None``. If `size` is ``None``, then a single value is generated and returned. If `size` is an integer, then a 1-D array filled with generated values is returned. If `size` is a tuple, then an array with that shape is filled and returned. The Python stdlib module `random` contains pseudo-random number generator with a number of methods that are similar to the ones available in ``Generator``. It uses Mersenne Twister, and this bit generator can be accessed using ``MT19937``. ``Generator``, besides being Dask-aware, has the advantage that it provides a much larger number of probability distributions to choose from. All ``Generator`` methods are identical to ``np.random.Generator`` except that they also take a `chunks=` keyword argument. ``Generator`` does not guarantee parity in the generated numbers with any third party library. In particular, numbers generated by `Dask` and `NumPy` will differ even if they use the same seed. Examples -------- >>> from numpy.random import PCG64 >>> from dask.array.random import Generator >>> rng = Generator(PCG64()) >>> rng.standard_normal().compute() # doctest: +SKIP array(0.44595957) # random See Also -------- default_rng : Recommended constructor for `Generator`. np.random.Generator cCs ||_dSN)_bit_generator)self bit_generatorrZ/var/www/html/software/conda/envs/catlas/lib/python3.10/site-packages/dask/array/random.py__init__^s zGenerator.__init__cCs"|jj}|d|jjjd7}|S)N()) __class____name__r)r_strrrr__str__aszGenerator.__str__cCst|jddSN.r)rrsplitrrrr _backend_namefszGenerator._backend_namecCs t|jSr) importlib import_moduler(r'rrr_backendls zGenerator._backendZ skipblocksNautocKt|d||f||d|SNbetasizechunks _wrap_funcrabr3r4kwargsrrrr1rzGenerator.betacKr/Nbinomialr2r5rnpr3r4r:rrrr=vr;zGenerator.binomialcKt|d|f||d|SN chisquarer2r5rdfr3r4r:rrrrCzzGenerator.chisquareTrc st|||\}}}} tt|} t|jt| } dt| ||} t| ggdd|DR} fddt| | | D}tj | || d}t || ||dS)Nda.random.choice-%scs|] }tt|VqdSrrangelen.0Zbdrrr z#Generator.choice..c s(i|]\}}}|t||fqSr) _choice_rng)rMkbitgenr3r8axisr@replaceshufflerr sz$Generator.choice.. dependenciesmeta) _choice_validate_paramslistr_spawn_bitgensrrKrziprfrom_collectionsr )rr8r3rUr@rTrVr4r[rYsizesbitgensnamekeysdskgraphrrSrchoice~s*  zGenerator.choice?cKrAN exponentialr2r5rscaler3r4r:rrrrjzGenerator.exponentialcKr/Nfr2r5rdfnumdfdenr3r4r:rrrror;z Generator.fcKr/Ngammar2r5rshaperlr3r4r:rrrrtzGenerator.gammacKrAN geometricr2r5rr@r3r4r:rrrryrFzGenerator.geometriccKr/Ngumbelr2r5rlocrlr3r4r:rrrr}rwzGenerator.gumbelcKt|d|||f||d|SNhypergeometricr2r5rZngoodZnbadnsampler3r4r:rrrrzGenerator.hypergeometricFcKs t|d|f|||||d|S)Nintegers)highr3dtypeendpointr4r5)rlowrr3rrr4r:rrrrs  zGenerator.integerscKr/Nlaplacer2r5r~rrrrrwzGenerator.laplacecKr/Nlogisticr2r5r~rrrrrwzGenerator.logisticcKr/N lognormalr2r5rmeansigmar3r4r:rrrrrwzGenerator.lognormalcKrAN logseriesr2r5rzrrrrrFzGenerator.logseriescK&t|d||f||t|ffd|SN multinomialr3r4 extra_chunksr6rKrr?Zpvalsr3r4r:rrrr zGenerator.multinomial marginalscKt|d||f|||d|S)Nmultivariate_hypergeometric)r3methodr4r5)rcolorsrr3rr4r:rrrrsz%Generator.multivariate_hypergeometriccKr/Nnegative_binomialr2r5r>rrrrrwzGenerator.negative_binomialcKr/Nnoncentral_chisquarer2r5rrEnoncr3r4r:rrrrrwzGenerator.noncentral_chisquarecKrN noncentral_fr2r5rrqrrrr3r4r:rrrr zGenerator.noncentral_fcKr/Nnormalr2r5r~rrrr%rwzGenerator.normalcKrANparetor2r5rr8r3r4r:rrrr+rFzGenerator.paretocCs\ddlm}|jdkrtdt|tjrt|dd}|jt |}t |j ||||S)Nr shuffle_slicecupyz`Generator.permutation` not supported for cupy-backed Generator objects. Use the 'numpy' array backend to call `dask.array.random.default_rng`, or pass in `numpy.random.PCG64()`.r.r4) dask.array.slicingrr(NotImplementedError isinstancenumbersNumberrr+rK_shufflerrxrindexrrr permutation/s      zGenerator.permutationcKrANpoissonr2r5rZlamr3r4r:rrrrBrFzGenerator.poissoncKrANpowerr2r5rrrrrFrFzGenerator.powercKst|df||||d|S)Nrandom)r3routr4r5)rr3rrr4r:rrrrJszGenerator.randomcKrANrayleighr2r5rkrrrrPrFzGenerator.rayleighcKt|df||d|SNstandard_cauchyr2r5rr3r4r:rrrrTzGenerator.standard_cauchycKrNstandard_exponentialr2r5rrrrrXzGenerator.standard_exponentialcKrANstandard_gammar2r5rrvr3r4r:rrrr^rmzGenerator.standard_gammacKrNstandard_normalr2r5rrrrrdrzGenerator.standard_normalcKrAN standard_tr2r5rDrrrrhrFzGenerator.standard_tcKrN triangularr2r5rleftmoderightr3r4r:rrrrlrzGenerator.triangularcKr/Nuniformr2r5rrrr3r4r:rrrrrrwzGenerator.uniformcKr/Nvonmisesr2r5rmukappar3r4r:rrrrxrwzGenerator.vonmisescKr/Nwaldr2r5rrrlr3r4r:rrrr~r;zGenerator.waldcKrANweibullr2r5rrrrrrFzGenerator.weibullcKrANzipfr2r5rrrrrrFzGenerator.zipfNr.)NTNrTr.rhNr.r{rhNr.)Nrr.)4r! __module__ __qualname____doc__rr#propertyr(r+rnprrr1r=rCrgrjrortryr}rZint64rrrrrrrrrrrrrrrfloat64rrrrrrrrrrrrrrrrrs@      $                                  rcCsBt|dr t|St|tr|St|drt|jStt|S)af Construct a new Generator with the default BitGenerator (PCG64). Parameters ---------- seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional A seed to initialize the `BitGenerator`. If None, then fresh, unpredictable entropy will be pulled from the OS. If an ``int`` or ``array_like[ints]`` is passed, then it will be passed to `SeedSequence` to derive the initial `BitGenerator` state. One may also pass in a `SeedSequence` instance. Additionally, when passed a `BitGenerator`, it will be wrapped by `Generator`. If passed a `Generator`, it will be returned unaltered. Returns ------- Generator The initialized generator object. Notes ----- If ``seed`` is not a `BitGenerator` or a `Generator`, a new `BitGenerator` is instantiated. This function does not manage a default global instance. Examples -------- ``default_rng`` is the recommended constructor for the random number class ``Generator``. Here are several ways we can construct a random number generator using ``default_rng`` and the ``Generator`` class. Here we use ``default_rng`` to generate a random float: >>> import dask.array as da >>> rng = da.random.default_rng(12345) >>> print(rng) Generator(PCG64) >>> rfloat = rng.random().compute() >>> rfloat array(0.86999885) >>> type(rfloat) Here we use ``default_rng`` to generate 3 random integers between 0 (inclusive) and 10 (exclusive): >>> import dask.array as da >>> rng = da.random.default_rng(12345) >>> rints = rng.integers(low=0, high=10, size=3).compute() >>> rints array([2, 8, 7]) >>> type(rints[0]) Here we specify a seed so that we have reproducible results: >>> import dask.array as da >>> rng = da.random.default_rng(seed=42) >>> print(rng) Generator(PCG64) >>> arr1 = rng.random((3, 3)).compute() >>> arr1 array([[0.91674416, 0.91098667, 0.8765925 ], [0.30931841, 0.95465607, 0.17509458], [0.99662814, 0.75203348, 0.15038118]]) If we exit and restart our Python interpreter, we'll see that we generate the same random numbers again: >>> import dask.array as da >>> rng = da.random.default_rng(seed=42) >>> arr2 = rng.random((3, 3)).compute() >>> arr2 array([[0.91674416, 0.91098667, 0.8765925 ], [0.30931841, 0.95465607, 0.17509458], [0.99662814, 0.75203348, 0.15038118]]) See Also -------- np.random.default_rng Zcapsuler)hasattrrrrrZdefault_bit_generator)seedrrr default_rngs R   rc@sReZdZdZd^ddZeddZd_ddZee j j d d d`d d Z ee j j d d d`ddZ ee j j d d d`ddZeeee j j d d daddZWdn1sZwYee j j d d dbddZee j j d d d`ddZee j j d d dbddZee j j d d d`ddZee j j d d dcdd Zee j j d d d`d!d"Zee j j d d dcd#d$Zee j j d d dcd%d&Zee j j d d dcd'd(Zee j j d d d`d)d*Zee j j d d d`d+d,Zee j j d d d`d-d.Zee j j d d d`d/d0Zee j j d d d`d1d2Z ee j j d d dcd3d4Z!ee j j d d d`d5d6Z"ee j j d d d7d8Z#ee j j d d dbd9d:Z$ee j j d d d`d;d<Z%ee j j d d ddd>d?Z&ee j j d d ded@dAZ'ee j j d d d`dBdCZ(e(Z ee j j d d dbdDdEZ)ee j j d d d`dFdGZ*ee j j d d d`dHdIZ+ee j j d d d`dJdKZ,ee j j d d d`dLdMZ-ee j j d d d`dNdOZ.ee j j d d d`dPdQZ/ee j j d d d`dRdSZ0ee j j d d dcdTdUZ1ee j j d d d`dVdWZ2ee j j d d d`dXdYZ3ee j j d d d`dZd[Z4ee j j d d d`d\d]Z5dS)f RandomStatea Mersenne Twister pseudo-random number generator This object contains state to deterministically generate pseudo-random numbers from a variety of probability distributions. It is identical to ``np.random.RandomState`` except that all functions also take a ``chunks=`` keyword argument. Parameters ---------- seed: Number Object to pass to RandomState to serve as deterministic seed RandomState: Callable[seed] -> RandomState A callable that, when provided with a ``seed`` keyword provides an object that operates identically to ``np.random.RandomState`` (the default). This might also be a function that returns a ``mkl_random``, or ``cupy.random.RandomState`` object. Examples -------- >>> import dask.array as da >>> state = da.random.RandomState(1234) # a seed >>> x = state.normal(10, 0.1, size=3, chunks=(2,)) >>> x.compute() array([10.01867852, 10.04812289, 9.89649746]) See Also -------- np.random.RandomState NcCs,tj||_|durtj|_dS||_dSr)rrr _numpy_stater _RandomState)rrrrrrr s  zRandomState.__init__cCst|jdd}t|Sr$)rrr&r)r*)rr(rrrr+s zRandomState._backendcCs|j|dSr)rr)rrrrrrszRandomState.seedr,r-r.cKr/r0r5r7rrrr1r;zRandomState.betacKr/r<r5r>rrrr=r;zRandomState.binomialcKrArBr5rDrrrrC"rFzRandomState.chisquareTcst||d|\}}}}}tt|} tt| |j} dt| ||} t| ggdd|DR} fddt| | | D} tj | | |d}t || ||dS) NrrGcsrHrrIrLrrrrN;rOz%RandomState.choice..c s$i|]\}}}|t||fqSr) _choice_rs)rMrQstater3r8r@rUrrrW<sz&RandomState.choice..rXrZ) r\r]rrrKrrr_rr`r )rr8r3rUr@r4rTr[rYra state_datarcrdrerfrrrrg(s.    zRandomState.choicerhcKrArir5rkrrrrjFrmzRandomState.exponentialcKr/rnr5rprrrroLr;z 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