ó î&]\c@`suddlmZmZmZddlZddlZddlZddlZddlZddl m Z ddl m Z ddl Z ddlZejdd„Zddd„Zdd„Zeddd „Zd „Zd efd „ƒYZd „Zeeeeed„Zy/e je dddddgƒZd„Z Wne!k rZd„Z nXdefd„ƒYZ"dS(i(tdivisiontprint_functiontabsolute_importN(t namedtuple(tPoolcC`s_tj|dtƒ|}|dk r7|j|ƒ}nt|tjƒs[tj|ƒ}n|S(s"Return an array of all value. tdtypeN(tnptonestbooltNonetastypet isinstancetndarraytasarray(tshapetvaluettypecodetout((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt _valarrays  c `s(|dkr3|dkr'tdƒ‚qNtj}n|dk rNtdƒ‚ntj|Œ}t‡fd†|Dƒƒ}tjg|D]}|jj^q†ƒ}t tj |dƒd|d|ƒ}tj |ˆ||Œƒ|dk r$t‡fd†|Dƒƒ}tj |ˆ||Œƒn|S( s¼ np.where(cond, x, fillvalue) always evaluates x even where cond is False. This one only evaluates f(arr1[cond], arr2[cond], ...). For example, >>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]) >>> def f(a, b): return a*b >>> _lazywhere(a > 2, (a, b), f, np.nan) array([ nan, nan, 21., 32.]) Notice it assumes that all `arrays` are of the same shape, or can be broadcasted together. s%One of (fillvalue, f2) must be given.s)Only one of (fillvalue, f2) can be given.c3`s!|]}tjˆ|ƒVqdS(N(Rtextract(t.0tarr(tcond(s/lib/python2.7/site-packages/scipy/_lib/_util.pys 4siRRc3`s"|]}tjˆ|ƒVqdS(N(RR(RR(R(s/lib/python2.7/site-packages/scipy/_lib/_util.pys 9sN( R t ValueErrorRtnantbroadcast_arraysttuplet mintypecodeRtcharRRtplace( Rtarraystft fillvaluetf2ttemptattcodeR((Rs/lib/python2.7/site-packages/scipy/_lib/_util.pyt _lazywheres    (% c `sþtj|Œ}tjg|D]}|jj^qƒ}ttj|dƒd|d|ƒ}x›tt|ƒƒD]‡}||||}‰tj ˆt kƒr¥qontjˆ|dƒ\‰} t ‡fd†|Dƒƒ} tj |ˆ|| ŒƒqoW|S(s? Mimic `np.select(condlist, choicelist)`. Notice it assumes that all `arrays` are of the same shape, or can be broadcasted together. All functions in `choicelist` must accept array arguments in the order given in `arrays` and must return an array of the same shape as broadcasted `arrays`. Examples -------- >>> x = np.arange(6) >>> np.select([x <3, x > 3], [x**2, x**3], default=0) array([ 0, 1, 4, 0, 64, 125]) >>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,)) array([ 0., 1., 4., 0., 64., 125.]) >>> a = -np.ones_like(x) >>> _lazyselect([x < 3, x > 3], ... [lambda x, a: x**2, lambda x, a: a * x**3], ... (x, a), default=np.nan) array([ 0., 1., 4., nan, -64., -125.]) iRRc3`s!|]}tjˆ|ƒVqdS(N(RR(RR(R(s/lib/python2.7/site-packages/scipy/_lib/_util.pys bs( RRRRRRRtrangetlentalltFalseRR( tcondlistt choicelistRtdefaultR#R$Rtindextfunct_R"((Rs/lib/python2.7/site-packages/scipy/_lib/_util.pyt _lazyselect?s(%tCcC`sîtj|ƒ}|dkr'|j}nt|dƒsB|f}ntjtj|ƒ|j }tj ||dtj ƒ}|j dd|}|dkr©||}n||||d!d }tj |||d|ƒ}|jdƒ|S(sôAllocate a new ndarray with aligned memory. Primary use case for this currently is working around a f2py issue in Numpy 1.9.1, where dtype.alignment is such that np.zeros() does not necessarily create arrays aligned up to it. t__len__itdataiiÿÿÿÿtorderN(RRR t alignmentthasattrt functoolstreducetoperatortmultitemsizetemptytuint8t__array_interface__R tfill(RRR4taligntsizetbuftoffsetR3((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt_aligned_zerosgs      cC`s6|jdk r2|j|jjdkr2|jƒS|S(sÅReturn an array equivalent to the input array. If the input array is a view of a much larger array, copy its contents to a newly allocated array. Otherwise, return the input unchanged. iN(tbaseR RAtcopy(tarray((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt _prune_arrays( tDeprecatedImportcB`s)eZdZd„Zd„Zd„ZRS(s2 Deprecated import, with redirection + warning. Examples -------- Suppose you previously had in some module:: from foo import spam If this has to be deprecated, do:: spam = DeprecatedImport("foo.spam", "baz") to redirect users to use "baz" module instead. cC`s6||_||_t|jƒtj|j|_dS(N(t _old_namet _new_namet __import__tsystmodulest_mod(tselftold_module_nametnew_module_name((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt__init__s   cC`s t|jƒS(N(tdirRO(RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt__dir__£scC`s0tjd|j|jftƒt|j|ƒS(Ns'Module %s is deprecated, use %s instead(twarningstwarnRJRKtDeprecationWarningtgetattrRO(RPtname((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt __getattr__¦s (t__name__t __module__t__doc__RSRUR[(((s/lib/python2.7/site-packages/scipy/_lib/_util.pyRI‹s  cC`s€|dks|tjkr(tjjjSt|tjtjfƒrStjj |ƒSt|tjj ƒrl|St d|ƒ‚dS(s6Turn seed into a np.random.RandomState instance If seed is None (or np.random), return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. s=%r cannot be used to seed a numpy.random.RandomState instanceN( R Rtrandomtmtrandt_randR tnumberstIntegraltintegert RandomStateR(tseed((s/lib/python2.7/site-packages/scipy/_lib/_util.pytcheck_random_state®s  c C`s÷|s<ddl}|jj|ƒr<d}t|ƒ‚q<n|sftjj|ƒrftdƒ‚qfn|rutjntj}||ƒ}|sº|j tj dƒkrºtdƒ‚qºn|rótj |j tj ƒsó||dtj ƒ}qón|S(sB Helper function for scipy argument validation. Many scipy linear algebra functions do support arbitrary array-like input arguments. Examples of commonly unsupported inputs include matrices containing inf/nan, sparse matrix representations, and matrices with complicated elements. Parameters ---------- a : array_like The array-like input. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default: True sparse_ok : bool, optional True if scipy sparse matrices are allowed. objects_ok : bool, optional True if arrays with dype('O') are allowed. mask_ok : bool, optional True if masked arrays are allowed. as_inexact : bool, optional True to convert the input array to a np.inexact dtype. Returns ------- ret : ndarray The converted validated array. iNsxSparse matrices are not supported by this function. Perhaps one of the scipy.sparse.linalg functions would work instead.smasked arrays are not supportedtOsobject arrays are not supportedR( t scipy.sparsetsparsetissparseRRtmat isMaskedArraytasarray_chkfiniteR Rt issubdtypetinexacttfloat_( R#t check_finitet sparse_okt objects_oktmask_okt as_inexacttscipytmsgttoarray((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt_asarray_validatedÁs"#  tArgSpectargstvarargstkeywordstdefaultscC`sNtj|ƒ}g|jjƒD]$}|jtjjkr|j^q}g|jjƒD]$}|jtjjkrY|j^qY}|r“|dnd}g|jjƒD]$}|jtjj kr©|j^q©}|rã|dnd}g|jjƒD]6}|jtjjkrù|j |j k rù|j ^qùp8d}t ||||ƒS(söinspect.getargspec replacement using inspect.signature. inspect.getargspec is deprecated in python 3. This is a replacement based on the (new in python 3.3) `inspect.signature`. Parameters ---------- func : callable A callable to inspect Returns ------- argspec : ArgSpec(args, varargs, varkw, defaults) This is similar to the result of inspect.getargspec(func) under python 2.x. NOTE: if the first argument of `func` is self, it is *not*, I repeat *not* included in argspec.args. This is done for consistency between inspect.getargspec() under python 2.x, and inspect.signature() under python 3.x. iN(tinspectt signaturet parameterstvaluestkindt ParametertPOSITIONAL_OR_KEYWORDRZtVAR_POSITIONALR t VAR_KEYWORDR,R<R{(R.tsigtpR|R}tvarkwR((s/lib/python2.7/site-packages/scipy/_lib/_util.pytgetargspec_no_selfs$$$!cC`s9tj|ƒ}|jddkr5|jjdƒn|S(sDinspect.getargspec replacement for compatibility with python 3.x. inspect.getargspec is deprecated in python 3. This wraps it, and *removes* `self` from the argument list of `func`, if present. This is done for forward compatibility with python 3. Parameters ---------- func : callable A callable to inspect Returns ------- argspec : ArgSpec(args, varargs, varkw, defaults) This is similar to the result of inspect.getargspec(func) under python 2.x. NOTE: if the first argument of `func` is self, it is *not*, I repeat *not* included in argspec.args. This is done for consistency between inspect.getargspec() under python 2.x, and inspect.signature() under python 3.x. iRP(R€t getargspecR|tpop(R.targspec((s/lib/python2.7/site-packages/scipy/_lib/_util.pyRŒ;st MapWrappercB`sYeZdZdd„Zd„Zd„Zd„Zd„Zd„Zd„Z d „Z RS( sv Parallelisation wrapper for working with map-like callables, such as `multiprocessing.Pool.map`. Parameters ---------- pool : int or map-like callable If `pool` is an integer, then it specifies the number of threads to use for parallelization. If ``int(pool) == 1``, then no parallel processing is used and the map builtin is used. If ``pool == -1``, then the pool will utilise all available CPUs. If `pool` is a map-like callable that follows the same calling sequence as the built-in map function, then this callable is used for parallelisation. icC`sâd|_t|_t|_t|ƒr?||_|j|_nŸt|ƒdkrxtƒ|_|jj|_t |_nft|ƒdkrnQt|ƒdkrÒtdt|ƒƒ|_|jj|_t |_n t dƒ‚dS(Niÿÿÿÿit processessUNumber of workers specified must be -1, an int >= 1, or an object with a 'map' method( R tpooltmapt_mapfuncR)t _own_pooltcallabletintRtTruet RuntimeError(RPR’((s/lib/python2.7/site-packages/scipy/_lib/_util.pyRSgs"        cC`s|S(N((RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt __enter__scC`s|jƒdS(N(tclose(RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt__del__„scC`s|jr|jjƒndS(N(R•R’t terminate(RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyR‡s cC`s|jr|jjƒndS(N(R•R’tjoin(RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyRž‹s cC`s|jr|jjƒndS(N(R•R’R›(RP((s/lib/python2.7/site-packages/scipy/_lib/_util.pyR›s cC`sF|jrB|dkr2|jjƒ|jjƒqB|jjƒndS(N(R•R R’R›RžR(RPtexc_typet exc_valuet traceback((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt__exit__“s    cC`s8y|j||ƒSWntk r3tdƒ‚nXdS(Ns;The map-like callable must be of the form f(func, iterable)(R”t TypeError(RPR.titerable((s/lib/python2.7/site-packages/scipy/_lib/_util.pyt__call__›s ( R\R]R^RSRšRœRRžR›R¢R¥(((s/lib/python2.7/site-packages/scipy/_lib/_util.pyRWs       (#t __future__RRRR7R9RMRVRbt collectionsRtmultiprocessingRR€tnumpyRRR RR%R0tfloatRDRHtobjectRIRgR˜R)RzRR{RŒtAttributeErrorR(((s/lib/python2.7/site-packages/scipy/_lib/_util.pyts4        $ ( #   C +