bi0UddlmZddlZddlZddlZddlmZddlmZddlm Z ddl Z ddl m Z ddlmZddlmZdd lmZdd lmZmZmZdd lmZdd lmZdd lmZddlmZm Z m!Z!m"Z"GddZ#dPdZ$GddZ%dZ&dQdZ'dZ(dZ)dZ*dZ+dZ,dZ-dddddZ.Gd d!eZ/Gd"d#eZ0Gd$d%e0Z1 iZ2d&e3d'<e Z4d(Z5 e5d)Z6e5d*Z7e5d+Z8e5d,Z9e5d-Z:e5d.Z;e5d/Z<e5d0Z=e5d1Z>e5d2Z?e5d3Z@e5d4ZAe5d5ZBe5d6ZCe5d7ZDe5d8ZEe5d9ZFe5d:ZGe5d;ZHe5d<ZIe5d=ZJe5d>ZKe5d?ZLe5d@ZMe5dAZNe5dAZOe5dBZPe5dCZQe5dDZRe5dEZSe5dFZTe5dGZUe5dHZVe5dIZWe5dJZXe5dKZYe5dLZZe5dMZ[e5dNZ\e5dOZ]y)R) annotationsN)product)Integral)Lock)new_collection)Array)IO)array_creation_dispatch)asarraybroadcast_shapesnormalize_chunks)arange) asarray_safe)tokenize)cached_property derived_fromrandom_state_datatypenamec~ eZdZdZdZdZedZedZe e jjdd1d Z e e jjdd1d Ze e jjdd1d Ze e jjd d2d Ze e jjdd3dZe e jjdd1dZe e jjdd3dZe e jjdd1dZe e jjdd4dZe e jjdd1dZe e jjddde j.dd fdZe e jjdd4dZe e jjdd4dZe e jjdd4dZe e jjdd1dZe e jjdd1dZe e jjd d5dZe e jjdd1dZe e jjdd1dZ e e jjdd1dZ!e e jjdd4dZ"e e jjdd1d Z#e e jjdd!Z$e e jjdd3d"Z%e e jjdd1d#Z&e e jjdde jNdd fd$Z e e jjdd3d%Z(e e jjdd1d&Z)e e jjdd1d'Z*e e jjdd1d(Z+e e jjdd1d)Z,e e jjdd1d*Z-e e jjdd1d+Z.e e jjdd4d,Z/e e jjdd1d-Z0e e jjdd1d.Z1e e jjdd1d/Z2e e jjdd1d0Z3y)6 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 c||_yN)_bit_generator)self bit_generators X/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/array/_array_expr/random.py__init__zGenerator.__init__Xs +c|jj}|d|jjjzdzz }|S)N()) __class____name__r)r_strs r__str__zGenerator.__str__[s<~~&& d))33<<t|d||f||t|ffd|SN multinomialr8r9 extra_chunksr;lenrrEpvalsr8r9r?s rrzGenerator.multinomial>     u:-)    rc (t|d||f|||d|S)Nmultivariate_hypergeometric)r8methodr9r:)rcolorsr|r8rr9r?s rrz%Generator.multivariate_hypergeometrics7  )        rc &t|d||f||d|SNnegative_binomialr7r:rDs rrzGenerator.negative_binomial+ %q! 26v IO  rc &t|d||f||d|SNnoncentral_chisquarer7r:rrLnoncr8r9r?s rrzGenerator.noncentral_chisquare + ("d 9=f PV  rc (t|d|||f||d|SN noncentral_fr7r:rrbrcrr8r9r?s rrzGenerator.noncentral_f, .% ;? RX  rc &t|d||f||d|SNnormalr7r:rss rrzGenerator.normalrurc $t|d|f||d|SNparetor7r:rr=r8r9r?s rrzGenerator.pareto$!P$vPPPrcddlm}|jdk(r tdt |t j r t|d}|jjt|}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()`.r3r9) dask.array.slicingrr+NotImplementedError isinstancenumbersNumberrr/r_shufflerrxrindexs r permutationzGenerator.permutation sv4    '%+  a (q(A $$SV,$$e,Q&&rc $t|d|f||d|SNpoissonr7r:rlamr8r9r?s rrzGenerator.poisson3$ 3ST&SFSSrc $t|d|f||d|SNpowerr7r:rs rrzGenerator.power7$OfOOOrc &t|df||||d|S)Nrandom)r8routr9r:)rr8rrr9r?s rrzGenerator.random;s* ( !%UF NT  rc $t|d|f||d|SNrayleighr7r:r[s rrzGenerator.rayleighA$ EVVVvVVrc "t|df||d|SNstandard_cauchyr7r:rr8r9r?s rrzGenerator.standard_cauchyE$ 1VVVvVVrc "t|df||d|SNstandard_exponentialr7r:rs rrzGenerator.standard_exponentialI' ( /3F FL  rc $t|d|f||d|SNstandard_gammar7r:rrir8r9r?s rrzGenerator.standard_gammaO) "E 04V GM  rc "t|df||d|SNstandard_normalr7r:rs rrzGenerator.standard_normalUrrc $t|d|f||d|SN standard_tr7r:rKs rrzGenerator.standard_tY$ bUtFUfUUrc (t|d|||f||d|SN triangularr7r:rleftmoderightr8r9r?s rrzGenerator.triangular], ,dE 8@, ;; ;; "))%%!4R5R"))%%!4V5V"))%%!4U5U"))%%!4   5 6"))%%!4 5 "))%%!4W5W"))%%!4 5 "))%%!4T5T"))%%!4 5 "))%%!4  5  "))%%!4 hh 5 ,"))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4T5T"))%%!4  5  "))%%!4EK  5  "))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4Q5Q"))%%!4'5'$"))%%!4T5T"))%%!4P5P"))%%!4bjjd6 5 "))%%!4W5W"))%%!4W5W"))%%!4 5 "))%%!4 5 "))%%!4W5W"))%%!4V5V"))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4Y5Y"))%%!4R5R"))%%!4O5Orrct|dr t|St|tr|St|drt|jStt j |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 capsuler)hasattrrrrr default_bit_generator)seeds r default_rngr#|sZdtY D) $  '++,, ,BB4H IIrc eZdZdZd/dZedZd0dZee jjdd1dZ ee jjdd1d Z ee jjdd1d Zej e5ee jjdd2d Zdddee jjdd3d Zee jjdd1d Zee jjdd3dZee jjdd1dZee jjdd4dZee jjdd1dZee jjdd4dZee jjdd4dZee jjdd4dZee jjdd1dZee jjdd1dZee jjdd1dZee jjdd1dZee jjdd1dZ ee jjdd4dZ!ee jjdd1dZ"ee jjddZ#ee jjdd3dZ$ee jjdd1dZ%ee jjdd5dZ&ee jjdd6d Z'ee jjdd1d!Z(e(Z ee jjdd3d"Z)ee jjdd1d#Z*ee jjdd1d$Z+ee jjdd1d%Z,ee jjdd1d&Z-ee jjdd1d'Z.ee jjdd1d(Z/ee jjdd1d)Z0ee jjdd4d*Z1ee jjdd1d+Z2ee jjdd1d,Z3ee jjdd1d-Z4ee jjdd1d.Z5y#1swYxYw)7 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 Nctjj||_|tj|_y||_yr)rrr% _numpy_stater _RandomState)rr"r%s rrzRandomState.__init__s;II11$73>3F # / / LW rczt|jjdd}tj|Sr')rr(r)r-r.)rr+s rr/zRandomState._backends5!!2!2399#>qA &&}55rc:|jj|yr)r'r")rr"s rr"zRandomState.seeds t$rr0r1c &t|d||f||d|Sr5r:r<s rr6zRandomState.beta r@rc &t|d||f||d|SrBr:rDs rrCzRandomState.binomialrGrc $t|d|f||d|SrIr:rKs rrJzRandomState.chisquarerMrc t|||||d|\}}}}}}}}tt|j|||j||SNr)rOr RandomChoicerQr') rr=r8rRrFr9rSrUrVs rrWzRandomState.choices`(aw1fM "QVVVT43D3DgqQ rc $t|d|f||d|SrYr:r[s rrZzRandomState.exponential+r]rc &t|d||f||d|Sr_r:ras rr`z RandomState.f1rdrc &t|d||f||d|Srfr:rhs rrgzRandomState.gamma5rjrc $t|d|f||d|Srlr:rns rrmzRandomState.geometric;rorc &t|d||f||d|Srqr:rss rrrzRandomState.gumbel?rurc (t|d|||f||d|Srwr:rys rrxzRandomState.hypergeometricEr}rc &t|d||f||d|Srr:rss rrzRandomState.laplaceRrrc &t|d||f||d|Srr:rss rrzRandomState.logisticXrrc &t|d||f||d|Srr:rs rrzRandomState.lognormal^rrc $t|d|f||d|Srr:rns rrzRandomState.logseriesdrorc >t|d||f||t|ffd|Srrrs rrzRandomState.multinomialhrrc &t|d||f||d|Srr:rDs rrzRandomState.negative_binomialurrc &t|d||f||d|Srr:rs rrz RandomState.noncentral_chisquare{rrc (t|d|||f||d|Srr:rs rrzRandomState.noncentral_frrc &t|d||f||d|Srr:rss rrzRandomState.normalrurc $t|d|f||d|Srr:rs rrzRandomState.paretorrcddlm}t|tjr t |d}t j t|}|jj||||S)Nrrr3r) rrrrrrrrr'rTrs rrzRandomState.permutationsR4 a (q(A #a&! !!%(Q&&rc $t|d|f||d|Srr:rs rrzRandomState.poissonrrc $t|d|f||d|Srr:rs rrzRandomState.powerrrc (t|d||f|||d|S)Nrandint)r8r9rr:)rrrr8r9rr?s rrEzRandomState.randints, )S$ -1& QW  rc &t|d||f||d|S)Nrandom_integersr7r:rs rrGzRandomState.random_integerss+ #S$ 59& LR  rc "t|df||d|S)N random_sampler7r:rs rrIzRandomState.random_samples$Td6TVTTrc $t|d|f||d|Srr:r[s rrzRandomState.rayleighrrc "t|df||d|Srr:rs rrzRandomState.standard_cauchyrrc "t|df||d|Srr:rs rrz RandomState.standard_exponentialrrc $t|d|f||d|Srr:rs rrzRandomState.standard_gammarrc "t|df||d|Srr:rs rrzRandomState.standard_normalrrc $t|d|f||d|Srr:rKs rrzRandomState.standard_trrc "t|df||d|S)Ntomaxintr7r:rs rrQzRandomState.tomaxints$ OfOOOrc (t|d|||f||d|Srr:rs rrzRandomState.triangularrrc &t|d||f||d|Srr:rs rrzRandomState.uniformrrc &t|d||f||d|Srr:rs rrzRandomState.vonmisesrrc &t|d||f||d|Srr:rs rrzRandomState.waldrrc $t|d|f||d|Sr r:rs rr zRandomState.weibullr rc $t|d|f||d|Srr:rs rrzRandomState.zipfrr)NNrr)NTNr3rr)NNr3l)NNr3)6r#rrrrrr/r"rrrr%r6rCrJ contextlibsuppressAttributeErrorrWrZr`rgrmrrrxrrrrrrrrrrrrrrErGrIrrrrrrrQrrrrr rrrrr%r%s> 66 %"))''A6R7R"))''A6V7V"))''A6U7U   ^ , bii++ :  ; ("))''A6 7 "))''A6W7W"))''A6 7 "))''A6T7T"))''A6 7 "))''A6  7  "))''A6 7 "))''A6 7 "))''A6 7 "))''A6T7T"))''A6  7  "))''A6 7 "))''A6 7 "))''A6 7 "))''A6 7 "))''A6Q7Q"))''A6'7'"))''A6T7T"))''A6P7P"))''A6 7 "))''A6 7 "))''A6U7UF"))''A6W7W"))''A6W7W"))''A6 7 "))''A6 7 "))''A6W7W"))''A6V7V"))''A6P7P"))''A6 7 "))''A6 7 "))''A6 7 "))''A6Y7Y"))''A6R7R"))''A6O7Ous &WW!r%ct|jdd}tj|}|jj |Sr')rr)r-r.rr#)bitgen backend_name backend_libs r_rng_from_bitgenr`sEF#))#.q1L)),7K    ) )& 11rc|j}t|}||_t|}|j||S)N)rS)statetyper`rT)rrrS state_datarbs rrrsA$$J'D')M$M ] +E === &&rc|jj|}|Dcgc]}t||}}|Scc}wr) _seed_seqspawnrc)r] n_bitgensseedsr"bitgenss r_spawn_bitgensrks@    " "9 -E.34d|tF|D!4G4 N5s<ct|tjjr||}t |}t ||}||d|i|S)z$Apply random module method with seedr8)rrr SeedSequencer`getattr)rngfuncnamer]r8argsr?funcs r_apply_random_funcrs sJ&"))001V 6 "C 3 !D  +D +F ++rcb|tj}||}t||}||d|i|S)z"Apply RandomState method with seedr8)r r%rn)r%rprdr8rqr?rbrrs r _apply_randomrus>-99  #E 5( #D  +D +F ++rcFt|}|j||||||S)N)r8rRrFrSrT)r`rW)rdr=r8rRrFrSrTrbs r _choice_rngrws' Z (E <<gw< WWrcVtj|}|j||||S)N)r8rRrF)r r%rW)rdr=r8rRrFrbs r _choice_rsry"s) # / / ;E <<g< ;;rc8g}t|trt|trQ|jdk(r t d|j j jjddd}nrt|trW||j jdgn|} |j j jjdd| }n td|} |dkrtdt|}|j|j}|j}|j dk7r td t#|} |j%||j'd}|t|t(sKt+|| }t-j.|j1dd d s td t|}n|j|j}|j dk7r tdt#|| k7r td|j%||j'd}|d}|dk7r tdt3||t,j4}|st#|ddkDr d} t | ||||||||fS)Nrz3`choice` not supported for cupy-backed `Generator`.r0r)r8rFzUnknown generator classrza must be greater than 0za must be one dimensional)likegHz>)rtolatolzprobabilities do not sum to 1zp must be one dimensionalza and p must have the same sizez)axis must be 0 since a is one dimensionalrz]replace=False is not currently supported for dask.array.choice with multi-chunk output arrays)rrrr+rr/rr#rWr%array ValueErrorr rechunkri_metandimrappend __dask_keys__rrrisclosesumr r) rbr=r8rRrFrSr9rVrUdummy_plen_aerr_msgs rrOrO'sXL!X eY '""f,)I>>((446==abD=QD { +45=enn**A3/aG>>((446==abG=TD67 7 q578 8 AJ IIagg ww 66Q;89 9AA OO a  }!U#QQ'A::aeegqt!< !@AA A !''"A 66Q;89 9 q6U?>? ?A OO a  | qyDEE fd"** =F s6!9~)   "'** dGQfdL @@rr3rrc l|t|ttfs|f}tt |||||||Sr)rtuplelistrRandom)rorpr8r9rrqr?s rr;r;ks?  4% ?w sHdFL$G rceZdZgdZddiZedZedZedZ edZ edZ d Z ed Z y ) r)ro distributionr8r9rrqr?rrc$|jdS)Nr?operandr*s rr?z Random.kwargs||H%%rc|jd}|jd}|g}t|}t|||jj dt j S)Nr8r9rr~)rr r r?getrr)rr8r9shapess rr9z Random.chunkssZ||F#h'(  ++//'2::6  rctt|j}t|jt rst |jjt|}t|}|Dcgc]}|j}}t}t|jj}nvt|jtrQtt||jj}t|}t }|jj"}n t%dt||j&|j|j(|j*}|j,d|}|||||fScc}w)Nz:Unknown object type: Not a Generator and Not a RandomState-)rrr9rrorrkrrrrfrsrcr%rr'rur( TypeErrorr8rqr?r) rsizesrj bitgen_token_bitgen func_appliergentokennames r_infoz Random._infos!Wdkk*+ dhh *$TXX%<%>rc$|jdS)Nr)operandsr*s rrzRandomChoice._metas}}W%%rc t|j|j|jDcic]1\}}}|t||j ||j |jf3c}}}Scc}}}wr)rrrdrryrrRrF)rkrbr8s rrzRandomChoice._layersg#&d&8&8&:DOOTZZ"X  5$  E4::tT\\466J J   s6A/returnr) r#rrrr _funcnamerr9rrdrrrrrr0r0sm K$/I#I&&++??&& rr0c(eZdZiZedZddZy)rPcTt|jt|jSr)rkrrrr*s rrdz RandomChoiceGenerator.state_data sdkk3tzz?;;rct|j|j|jDcic]G\}}}|t||j ||j |j|j|jfIc}}}Scc}}}wr) rrrdrrwrrRrFrSrT)rrr]r8s rrzRandomChoiceGenerator._layers$'""$dootzz$   64          sA BNr)r#rrrrrdrrrrrPrPsI<< rrPzdict[str, RandomState]_cached_statescfd}ttj|_ttj|_|S)Nctj}t5 t|}dddt |i|S#t$rt xt|<}Y8wxYw#1swY=xYwr)r backend_cached_states_lockrKeyErrorr%rn)rqr?keyrbattrs rwrapperz_make_api..wrapper/sr%--  < <&s+ < $wud#T4V44 <.9m;s#e < < rs",4)7GG&)"QQbObOJ \J~WOWOt2' ,,X < AAJ $Fk Rk \" 2" J L 2*,&+f  Z  k " 8  &  cN' k " 8 +, I  Z  k " k "  & 12 !78( 8  8  & I '/* ? 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