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Compatible parameters are passed to the inferred strategy function while inapplicable ones are ignored. This allows you, for example, to customise the min and max values, control the length or contents of strings, or exclude non-finite numbers. This is particularly useful when kwargs are passed through from :func:`arrays` which allow a variety of numeric dtypes, as it seamlessly handles the ``width`` or representable bounds for you. rdN)elementsc hdj|sJtj|D]q}dd|d|d||}|||jdr|||kDrt |dz|jdsY|||kset |d z|j j Dcic]\}}||vs||vs||c}}|Scc}}w) zDUpdate default args to the strategy with user-supplied keyword args.>rrrtrszdtype z requires =z to be %s min_)zat leastmax_)zat most) issupersetset intersection startswithrupdateitems)argskwkeymsgkvrdkwargss r7 compat_kwzfrom_dtype..compat_kws5@@DDDv;++B/ >C5):cU!F3K?*RPSWKXC#w">>&)bgs .C)# *=>>^^F+3&+0E)# *<==  > FLLNKDAqa4i171a4KL Ls C# C# bfwidth @rrsrtrurvrwrxryc)rrrrzr{)Sarq)rrc|dddk7S)Nrs r7zfrom_dtype..safornurrarb)rsrtiUrpc|dddk7S)Nrrrs r7rzfrom_dtype..safnrnr)zutf-8)codecrrrRrO[rFllNaTlOzNo strategy inference for r)&rmr$rjrdlocalsrnamesfieldsrFsttuplessubdtyper?kindbooleansfloatsr rminitemsizecomplex_numbersbinaryfilterintegersNP_FIXED_UNICODE characterstextstrjustsplit sampled_fromTIME_RESOLUTIONSbuildsrh from_typeobjectrmap)rdrprqrrrsrtrurvrwrxryrzr{rrrlsubssubtypeshaperresultroverflowreselemsrs` @r7rFrF~s6rxx(%x~~/ Rtq!1CkkJd 5;1&1JJyy$ ~~!guv66  zzS)+ s   wz*CENN0BB,GH   !   s ##   $c!enn*>)TFYzHEFMM %  s  aA4F.G!.K L"r" s U^^+a/0VyH9ST UV s NN'aA-5  & 8!#F: P9ZhOPWW $  s  V #!#W!=F: I9ZZHI z ! %)) ''%))//#.r23B78C //"23C E !KK)4rwwu~EEKK i8E5::uc2 s ||F## :5'BCC ::ejj !!KS Ks Q5Q5Q5Q;c6eZdZfdZdZdddZdZxZS) ArrayStrategyct|t||_||_t t j||_||_ ||_ ||_ |jdv|_ y)N)rV)super__init__tuplerfillr\rjprod array_sizerdelement_strategyuniquer_check_elements)selfrrrdrr __class__s r7rzArrayStrategy.__init__s[ 5\  bggen- 0 $zz;rnc d|jd|jd|jd|jd|jd S)NzArrayStrategy(z, shape=z, dtype=z, fill=z , unique=))rrrdrr)rs r7__repr__zArrayStrategy.__repr__sJT225Xdjj\JZZN'$))i a Q rnFrc |||< |j xr|||k7xr||k(}|r|r |jn |j}|jjdk(r[ dt|cxkxr,tj|jjknc}|rtd |d |d ||d td|d|d|jd||dt||d y#t$r9}td|dt|dt|d|jd|d}~wwxYw#t $r8}td|dt|dt|d|j|d}~wwxYw#t $rd }YwxYw)NzCould not add element=z of rdz to array of z- - possible mismatch of time units in dtypes?z%Internal error when checking element=rrFzGenerated subnormal float z from strategy z resulted in zx, probably as a result of NumPy being built with flush-to-zero compiler options. Consider passing allow_subnormal=False.zGenerated array element z from cannot be represented as dtype z - instead it becomes z (type zc). Consider using a more precise strategy, for example passing the `width` argument to `floats()`.) TypeErrorrr4rhrdr Exceptionrrrrabsrjfinfotiny) rvalridxrerr elem_changedstrategy is_subnormals r7 set_elementzArrayStrategy.set_elements  F3K //UC6#;4FU3RU:L $(tyyd.C.CHzz#%)#$s3x#K"((4::2F2K2K#KL  )4SE#*M&+A11"*3'|D((, ~5K#;/fSk):(=>!! ) % !(t3c34M<<""OQ    &7wd3c36m<<"$   "!)$)L)sAC*D/AE3* D,34D''D,/ E083E++E03 FFcfd|jvr+tj|j|jS|jjdvxr|jj dk(}tj|j |rtn |j}|jjr|jrntj|j|j |j d}t|j|D]\}}|j!|||nSt#t%|D].}|j!|j|j||0n tj&|j d}t)}t+j,|d|j t/d|j zt1dt3j4|j  5} | D]} |j7d|j d z }||s| j98|j!|j|j|||jr,|||vr| j9|j;||d ||< ddd|j=rtjd |rtn |j} |j!|j|j| dd |r| j?|j} | d} |jr& tj@| } | stEd | tjF||| |rW|j?|j}||k7}|j=r%tEd||d|jd||d|}|jI|jjK}|jLJ|S#1swYnxYw#tB$rd } YwxYw)Nr)rdr)rrr)rrdT)rqrrrg? )rqrr average_sizerbFrz,Cannot fill unique array with non-NaN value zArray elements rz - instead they become zk. Use a more precise strategy, e.g. without trailing null bytes, as this will be an error future versions.)'rrjzerosrdrrrrris_emptyrrlistsr enumeratedrawrrangelenfullrcumanyrmaxmathsqrt draw_integerrejectaddanyastypeisnanrrputmaskreshapecopybase)rdataunsized_string_dtyperrrr needs_fillseenr}_ one_element fill_valueis_nanoutmismatchs r7do_drawzArrayStrategy.do_drawJs  ?88$**DJJ? ? JJOO . K4::3F3F!3K //3GTZZ  99   {{))!__!__  &dii&673DAq$$Q23s6{+RA$$TYYt/D/D%EvqQR$7J5D!$//)DIIdoo67  *! *A))!T__q-@AA%a= ) $$TYYt/D/D%EvqQ{{!!9,$OO-$+$)JqM * *2~~!hh-A6tzz   499!5{AD Q'"-"4"4TZZ"@K(^ ;;'!#*!5"-J:.Y 6:{; -- +Cf}H||~%%fX&6%9: $ ~-D8}'(UU F +002{{""" S * *^%'!&'sB&PP"P" P0/P0)rf __module__ __qualname__rrrr __classcell__)rs@r7rrs< 5:4lvrnrc|(|s |jstj}|S|}|St||r|dnd|S)Nz.fillr)has_reusable_valuesrnothingr*)r}rrrls r7fill_forrsM | 55::= 0, a tuple of such integers, or a strategy that generates such values. * ``elements`` is a strategy for generating values to put in the array. If it is None a suitable value will be inferred based on the dtype, which may give any legal value (including eg NaN for floats). If a mapping, it will be passed as ``**kwargs`` to ``from_dtype()`` * ``fill`` is a strategy that may be used to generate a single background value for the array. If None, a suitable default will be inferred based on the other arguments. If set to :func:`~hypothesis.strategies.nothing` then filling behaviour will be disabled entirely and every element will be generated independently. * ``unique`` specifies if the elements of the array should all be distinct from one another. Note that in this case multiple NaN values may still be allowed. If fill is also set, the only valid values for it to return are NaN values (anything for which :obj:`numpy:numpy.isnan` returns True. So e.g. for complex numbers ``nan+1j`` is also a valid fill). Note that if ``unique`` is set to ``True`` the generated values must be hashable. Arrays of specified ``dtype`` and ``shape`` are generated for example like this: .. code-block:: pycon >>> import numpy as np >>> arrays(np.int8, (2, 3)).example() array([[-8, 6, 3], [-6, 4, 6]], dtype=int8) >>> arrays(np.float, 3, elements=st.floats(0, 1)).example() array([ 0.88974794, 0.77387938, 0.1977879 ]) Array values are generated in two parts: 1. Some subset of the coordinates of the array are populated with a value drawn from the elements strategy (or its inferred form). 2. If any coordinates were not assigned in the previous step, a single value is drawn from the ``fill`` strategy and is assigned to all remaining places. You can set :func:`fill=nothing() ` to disable this behaviour and draw a value for every element. If ``fill=None``, then it will attempt to infer the correct behaviour automatically. If ``unique`` is ``True``, no filling will occur by default. Otherwise, if it looks safe to reuse the values of elements across multiple coordinates (this will be the case for any inferred strategy, and for most of the builtins, but is not the case for mutable values or strategies built with flatmap, map, composite, etc) then it will use the elements strategy as the fill, else it will default to having no fill. Having a fill helps Hypothesis craft high quality examples, but its main importance is when the array generated is large: Hypothesis is primarily designed around testing small examples. If you have arrays with hundreds or more elements, having a fill value is essential if you want your tests to run in reasonable time. UnionTypec3XK|]"}ttj|$ywr[)rFrjrd)r^ds r7r`zarrays..2sLJrxx{3Ls(*c$t|SNrr?)r%r}rrrs r7rzarrays..;sfQtFSrnc$t|Sr'r()rSrdr}rrs r7rzarrays..?sfUAtFSrnrrz[{}]c"t|S)N)rrrr()r%rrrs r7rzarrays..Ns6!5tF#Srnr}force_has_reusable_valuesFTc3<K|]}t|tywr[rgr\r^rSs r7r`zarrays..]s .1Jq#  .zDArray shape must be integer in each dimension, provided shape was {})r}rr)"rhr4typesrr rone_of__args__rgSearchStrategyflatmaprmrjrdrrrrrrformatrFr*r%r(packmapped_strategyr+r\rrallrr)rdrr}rr unwrappeds````` r7r?r?sR E{wuk68>rnc tjttt t t ttS)z@Return a strategy that can return any non-flexible scalar dtype.) rr1rArHrMrErCrDrKrrnr7rJrJes? 99! rnstratcHttfd}|S)NcN|i|jtjSr[)rrjrd)rrr;s r7innerz%defines_dtype_strategy..innerts$d%f%))"((33rn)r+r#)r;r>s` r7defines_dtype_strategyr?ss+ U^44 Lrnznp.dtype[np.bool_]c,tjdS)z%Return a strategy for boolean dtypes.?)rrrrnr7rArA|s 773<rnc d}t||vd||mt|tr|f}t|dttfd|Dd|td|Drt |Dchc]}|dz c}}t j |}d|vr|dz }|d k(r=|jd |zj|jd |zjzS|j||zjScc}w) N)rAz)Unknown endianness: was {}, must be in {}z+Dtype must have at least one possible size.c3&K|]}|v ywr[r)r^rS valid_sizess r7r`z dtype_factory..s0Q[ 0sz7Invalid sizes: was {} must be an item or sequence in {}c3<K|]}t|tywr[r-r.s r7r`z dtype_factory..s1az!S!1r/rz{}rArCrD) rrgr\r8sortedrrrr5)rsizesrF endianness valid_endianrSr;s ` r7 dtype_factoryrLs'Ll"3   eS !HEuKL 0%0 0 E    151 1E2qAF23E OOE "E 4  Syy#*,,- 3::M:M0NNN 99j4'// 00 3s0 DrA)rJrJrIrznp.dtype[np.uint8]cyr[rrJrIs r7rMrM /2rnrznp.dtype[np.uint16]cyr[rrNs r7rMrM 03rnrznp.dtype[np.uint32]cyr[rrNs r7rMrMrQrnrznp.dtype[np.uint64]cyr[rrNs r7rMrMrQrnrrrrrNz!np.dtype[np.unsignedinteger[Any]]cyr[rrNs r7rMrMs >Arnctd|d|S)azReturn a strategy for unsigned integer dtypes. endianness may be ``<`` for little-endian, ``>`` for big-endian, ``=`` for native byte order, or ``?`` to allow either byte order. This argument only applies to dtypes of more than one byte. sizes must be a collection of integer sizes in bits. The default (8, 16, 32, 64) covers the full range of sizes. rrTrLrNs r7rMrMs( e_j AArnznp.dtype[np.int8]cyr[rrNs r7rHrHs .1rnznp.dtype[np.int16]cyr[rrNs r7rHrHrOrnznp.dtype[np.int32]cyr[rrNs r7rHrHrOrnznp.dtype[np.int64]cyr[rrNs r7rHrHrOrnznp.dtype[np.signedinteger[Any]]cyr[rrNs r7rHrHs ?z RRrnznp.dtype[np.str_]c ftdd||tdtt||dzd|S)a'Return a strategy for generating unicode string dtypes, of various lengths and byteorder. While Hypothesis' string strategies can generate empty strings, string dtypes with length 0 indicate that size is still to be determined, so the minimum length for string dtypes is 1. rrbrNrr{s r7rLrLrrnct}|D]9^}}t|ts|j|ry|j |;y)NFT)rrgrrr)lsr title_and_namers r7#_no_title_is_name_of_a_titled_fieldrsK 5D ( ne ,  0 KK ' ( rn)rqrrallow_subarrayssubtype_strategyrc tdd||tjddjdjtj dz}tj |tj||jd}tj||}|r%|tj||tdd z}tj|||d d f jtS) zqReturn a strategy for generating array (compound) dtypes, with members drawn from the given subtype strategy.sizerzf{}rb)rqc|d|dk7SNrrbr)rVs r7rzarray_dtypes..sber!unrnra)max_dimsmax_sidecBt|dtr|dS|ddS)Nrrgrr%s r7rzarray_dtypes..#j1s3ad1arncBt|dtr|dS|ddSrrrs r7rzarray_dtypes..rrn)r}rqrr unique_by) rrrrr5rr1rrrrr)rrqrrr field_names name_titlesr}s r7r>r>s8X.++a%))%,,7"''1:MMK)) +{+223LMK*,;@P)QHBII )<Q+O   88 A @   f 01 2rnr) max_leaves max_itemsizerrcZtj|d|jfdS)a@Return the most-general dtype strategy. Elements drawn from this strategy may be simple (from the subtype_strategy), or several such values drawn from :func:`array_dtypes` with ``allow_subarrays=True``. Subdtypes in an array dtype may be nested to any depth, subject to the max_leaves argument. ct|dS)NT)r)r>)r_s r7rznested_dtypes..s ,q$7rn)rc.duxs|jkSr[)r)r%rs r7rznested_dtypes.. s|t+Iqzz\/Irn)r recursiver)rrrs `r7rIrI s- <<7  f IJ Krnct|i|Sr[)_valid_tuple_axesrrs r7r r #s d -f --rnz Return a strategy for generating permissible tuple-values for the ``axis`` argument for a numpy sequential function (e.g. :func:`numpy:numpy.sum`), given an array of the specified dimensionality. z ct|i|Sr[)_mutually_broadcastable_shapesrs r7rr2s )4 :6 ::rna **Use with Generalised Universal Function signatures** A :doc:`universal function ` (or ufunc for short) is a function that operates on ndarrays in an element-by-element fashion, supporting array broadcasting, type casting, and several other standard features. A :doc:`generalised ufunc ` operates on sub-arrays rather than elements, based on the "signature" of the function. Compare e.g. :obj:`numpy.add() ` (ufunc) to :obj:`numpy.matmul() ` (gufunc). To generate shapes for a gufunc, you can pass the ``signature`` argument instead of ``num_shapes``. This must be a gufunc signature string; which you can write by hand or access as e.g. ``np.matmul.signature`` on generalised ufuncs. In this case, the ``side`` arguments are applied to the 'core dimensions' as well, ignoring any frozen dimensions. ``base_shape`` and the ``dims`` arguments are applied to the 'loop dimensions', and if necessary, the dimensionality of each shape is silently capped to respect the 32-dimension limit. The generated ``result_shape`` is the real result shape of applying the gufunc to arrays of the generated ``input_shapes``, even where this is different to broadcasting the loop dimensions. gufunc-compatible shapes shrink their loop dimensions as above, towards omitting optional core dimensions, and smaller-size core dimensions. .. code-block:: pycon >>> # np.matmul.signature == "(m?,n),(n,p?)->(m?,p?)" >>> for _ in range(3): ... mutually_broadcastable_shapes(signature=np.matmul.signature).example() BroadcastableShapes(input_shapes=((2,), (2,)), result_shape=()) BroadcastableShapes(input_shapes=((3, 4, 2), (1, 2)), result_shape=(3, 4)) BroadcastableShapes(input_shapes=((4, 2), (1, 2, 3)), result_shape=(4, 3)) .)min_dimsr allow_newaxisrrrallow_ellipsiscyr[rrrrrrs r7r@r@`srn)rrrrcyr[rrs r7r@r@ms DGrn)rrcyr[rrs r7r@r@xs FIrn)rrrcyr[rrs r7r@r@s25rnctt|dttd|Dd|dtt|dtt|dtt |d|t |kDr |std|d t |d d d t|d|C|r'ttt ||dzt}nYtt |t}n?tt |d|t |kDr |std|d t |d d d t|dtdd||t|||||dS)aReturn a strategy for :doc:`basic indexes ` of arrays with the specified shape, which may include dimensions of size zero. It generates tuples containing some mix of integers, :obj:`python:slice` objects, ``...`` (an ``Ellipsis``), and ``None``. When a length-one tuple would be generated, this strategy may instead return the element which will index the first axis, e.g. ``5`` instead of ``(5,)``. * ``shape`` is the shape of the array that will be indexed, as a tuple of positive integers. This must be at least two-dimensional for a tuple to be a valid index; for one-dimensional arrays use :func:`~hypothesis.strategies.slices` instead. * ``min_dims`` is the minimum dimensionality of the resulting array from use of the generated index. When ``min_dims == 0``, scalars and zero-dimensional arrays are both allowed. * ``max_dims`` is the maximum dimensionality of the resulting array, defaulting to ``len(shape) if not allow_newaxis else max(len(shape), min_dims) + 2``. * ``allow_newaxis`` specifies whether ``None`` is allowed in the index. * ``allow_ellipsis`` specifies whether ``...`` is allowed in the index. rc3JK|]}t|txr|dk\ywrNr-r]s r7r`z basic_indices..s# 9aJq#  )16 ) 9!#shape=z3, but all dimensions must be non-negative integers.rrrz min_dims=z is larger than len(shape)=zZ, but allow_newaxis=False makes it impossible for an indexing operation to add dimensions.z 2021-09-15F)since has_codemodrarz max_dims=dimsrT)rrrrallow_fewer_indices_than_dims)r$rrr8boolr\rr,rrrrrrrs r7r@r@sWDueW% 95 99 5(EFt^%56t]O4sHj)#e*]z!>> from numpy import array >>> x = array([-0, -1, -2, -3, -4]) >>> ind = (array([[4, 0], [0, 1]]),) # a tuple containing a 2D integer-array >>> x[ind] # the resulting array is commensurate with the indexing array(s) array([[-4, 0], [0, -1]]) Note that this strategy does not accommodate all variations of so-called 'advanced indexing', as prescribed by NumPy's nomenclature. Combinations of basic and advanced indexes are too complex to usefully define in a standard strategy; we leave application-specific strategies to the user. Advanced-boolean indexing can be defined as ``arrays(shape=..., dtype=bool)``, and is similarly left to the user. rc3JK|]}t|txr|dkDywrr-r]s r7r`z(integer_array_indices..%s#BqjC(2QU2Brrz* must be a non-empty tuple of integers > 0rzdtype=z must be an integer dtypec Xt|tjr| nd|dz S)Nrrb)rdrr})r?rr) index_shaperrdsigneds r7 array_forz(integer_array_indices..array_for.s.[[&$aB  rnc>tjfdDS)Nc30K|] }|ywr[r)r^rrrs r7r`z:integer_array_indices....6s'W +t(D'Ws)rr)rrrs`r7rz'integer_array_indices..6sBII'WQV'WXrn) r$rrr8r*rj issubdtypeinteger signedintegerr4)rrrdrrs` `@@r7rGrGs`ueW% B#BEBB 5(<=<0 eRZZ(VUH4M*N]]5""2"2 3F    X rnct|dd}|r|t|ttdttfvrRt |dk(sJt |dtr*|djtjk(sJt}|S|d}|S)Nr2rr#rbr) r4rhr0rr rrgr __bound__rjgenericr)rd dtype_argss r7 _unpack_dtyper:s B/Jd5k'%fh*OQV)WW:!### jmW -a=**bjj8 88E LqME Lrnc.t|dkr|rt|dnt}nt|dk(sJt|d}|tur tSt |t t dttfvr|Stj|S)Nrbrrar#) rrrrJrhr4r0rr rjrd)rrds r7_dtype_from_argsrHs 4yA~+/ d1g&C4yA~~d1g& | e VX>F F 88E?rnthingc tjtjtjtjtj tj tjg}tjtjtjtjtj tj tjjtjg}|tjk(rAtjtttt!t#S|t$k(rtj|tj&tj(|tj*tj&tj,tj.tj*St1|t2rJt5|tj6r0tj|}|j8dvr t;|SdSt=|}||}t?|}|t@k(rtC|dksJ|rtj,|dn|}tjtj(|tj&tj*tj*tj&tj*|tj*tj&tj*||tj*S|tjDjFtj.tHfvr!tK|}tM|tOdSy)zCalled by st.from_type to try to infer a strategy for thing using numpy. 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