/usr/lib/.build-id/8f/
/usr/lib/.build-id/8f
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Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/core/_multiarray_umath.cpython-39-x86_64-linux-gnu.so (7382112B)
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Please port your code to use NPY_STRING instead.%u%hd%hu%lu%lld%llustrictASCIIInvalid data-type for arraynumpy.dtypes.BoolDTypenumpy.dtypes.ByteDTypenumpy.dtypes.Int8DTypenumpy.dtypes.UByteDTypenumpy.dtypes.UInt8DTypenumpy.dtypes.ShortDTypenumpy.dtypes.Int16DTypenumpy.dtypes.UShortDTypenumpy.dtypes.UInt16DTypenumpy.dtypes.IntDTypenumpy.dtypes.Int32DTypenumpy.dtypes.UIntDTypenumpy.dtypes.UInt32DTypenumpy.dtypes.LongDTypenumpy.dtypes.Int64DTypenumpy.dtypes.ULongDTypenumpy.dtypes.UInt64DTypenumpy.dtypes.LongLongDTypenumpy.dtypes.ULongLongDTypenumpy.dtypes.Float16DTypenumpy.dtypes.Float32DTypenumpy.dtypes.Float64DTypenumpy.dtypes.LongDoubleDTypenumpy.dtypes.Complex64DTypenumpy.dtypes.Complex128DTypenumpy.dtypes.CLongDoubleDTypenumpy.dtypes.ObjectDTypenumpy.dtypes.BytesDTypenumpy.dtypes.StrDTypenumpy.dtypes.VoidDTypenumpy.dtypes.DateTime64DTypenumpy.dtypes.TimeDelta64DTypeBOOLUBYTEUSHORTUINTUINTPULONGULONGLONGHALFCFLOATCDOUBLECLONGDOUBLESTRINGUNICODEVOIDDATETIMETIMEDELTAGenericNumberInexactSignedIntegerUnsignedIntegerComplexFloatingFlexibleCharactertypeinfo䳵44t$Lɵl͵,ǵֱ\ȵȵt ԵԵҵӵյֵTOOOOAAGGGGAA<<<<<<<<?????? __?C?too many operands provided to einstein sum functionnot enough operands provided to einstein sum functionmore operands provided to einstein sum function than specified in the subscripts stringfewer operands provided to einstein sum function than specified in the subscripts stringeinstein sum subscripts string contains too many subscripts for operand %deinstein sum subscripts string contains a '.' that is not part of an ellipsis ('...') in operand %dinvalid subscript '%c' in einstein sum subscripts string, subscripts must be lettersoperand has more dimensions than subscripts given in einstein sum, but no '...' ellipsis provided to broadcast the extra dimensions.einstein sum subscript string has too many distinct labelseinstein sum subscript string does not contain proper '->' output specifiedeinstein sum subscripts string includes output subscript '%c' multiple timeseinstein sum subscripts string included output subscript '%c' which never appeared in an inputeinstein sum subscripts string contains too many subscripts in the outputeinstein sum subscripts string contains a '.' that is not part of an ellipsis ('...') in the outputoutput has more dimensions than subscripts given in einstein sum, but no '...' ellipsis provided to broadcast the extra dimensions.out parameter does not have the correct number of dimensions, has %d but should have %doutput had too few broadcast dimensionsdimensions in single operand for collapsing index '%c' don't match (%d != %d)dimensions in operand %d for collapsing index '%c' don't match (%d != %d)too many subscripts in einsuminvalid data type for einsum 膶(  RKD=6/(0ѶhѶ`ѶXѶPѶHѶ@Ѷ8Ѷ" H@80( 0'b'['T'M'F'?'8'999999990IhI`IXIPIHI@I8IXPY@Y0Y YYYXb cccbbbbq"rr rrqqq0쀷΀tV8bD&P杷НX쨷ר¨tdTD0TG7'밷аVƷ0ƷƷŷŷŷhŷ@ŷ IpIIIxIIIIIIIIIIIIIH@HIHIHHHIHHHHHHHIH0I(IH IHHHIHHHHHHHILHHHLH|HLHLHLHtHLHLHLHLHLHLHLHlHHHHXHHHHHH HHHHHHHHHGHHG|HGGGtHGGGGGGGlHGFHGHGGGHGGGGGGGHlOQOlPOLPO,QO QPLQPPlQP,PdOdOdOdOdOdO P ƒfpᄻ=G냻Q̂,O,O,O,O,O,Oz~xc|i^s^L}kunazfpN\oejNNNNNNx`yntd(wirt_pxDlPsbu$gp\fNNNNNN\lHbhg(]mchp^@ka f[}hrG^wlNlNlNlNlNlNgc}fnx&d{Fis_|kva@zfpv\t}NNNNNNm/noxc}iOs^~ku_azfp?\r O O O O O O^w ltraufo\Lv|i,q<_s\dnZ O O O O O Ol l wb+|gqk]};j{t_ye[oZ2o,N,N,N,N,N,N_}7iws^xdWnY ~#kcu`zfCp[%sLLLLLL^v~]lvb{=g}q\.}i t_-ydnmZ"vMMMMMMa|gr]3wblsXr}jtu`zeoU[*|lMlMlMlMlMlM%hWxRUOPV0QSNVQdTDOUPSMrKKKKKKhvEblX{ hIr]iwb)mXyyeo9[tKKKKKKY`*W RTzOUPRS2NVfQSN>UPRMvJJJJJJBe|!kau`XzfAp[{hrQ^wqcm1YD|lKlKlKlKlKlKiyj*u`we p[^xZhr^rv:czmXwJJJJJJiUPQS1NTyO RL=UPRMSNeQELUlJlJlJlJlJlJoPRMSNWQ7LTkOQLCS#NPKjKAKKIIIIIIJb#欻ⰻ9gⰻⰻⰻⰻⰻⰻⰻⰻⰻⰻⰻڶ9>ĺû»»ûûûûpûûûûûûûB»KƻλͻKƻ̻KƻKƻKƻ̻KƻKƻKƻKƻKƻKƻKƻ=˻ɻcѻлɻeлɻɻɻϻɻɻɻɻɻɻɻOϻindex %ld is out of bounds for axis %d with size %ldindex %ld is out of bounds for size %ld?iterator is too largeGetIterNext internal iterator error - unexpected itflags/ndim/nop combination (%04x/%d/%d)GetIterNext internal iterator error - unexpected itflags/ndim/nop combinationCannot retrieve a GetMultiIndex function for an iterator that doesn't track a multi-index.Cannot retrieve a GetMultiIndex function for an iterator that used DELAY_BUFALLOC before a Reset callGetGetMultiIndex internal iterator error - unexpected itflags/ndim/nop combination (%04x/%d/%d)GetGetMultiIndex internal iterator error - unexpected itflags/ndim/nop combination$+T+d++*4+D+*x+h++X+++Can't set fields in a non-void array scalar.Called NumPy timedelta repr on a non-datetime typeCalled NumPy datetime repr on a non-datetime typeThere are no type variables left in %scannot convert NaN to integer ratiocannot convert Infinity to integer ratioThe Python built-in `round` is deprecated for complex scalars, and will raise a `TypeError` in a future release. Use `np.round` or `scalar.round` instead.size must be non-negative and not greater than %dvoid: descr must be a `void` dtype that is not a subarray dtype (structured or unstructured). Got '%.100R'.NumPy scalar %R can only exported as a buffer without format.user-defined scalar %R registered for built-in dtype %S? This should be impossible.expected a readable buffer objectCalled NumPy timedelta str on a non-datetime typeNumPy datetime metadata is corruptedIn future, it will be an error for 'np.bool_' scalars to be interpreted as an indexCalled NumPy datetime str on a non-datetime typeinvalid index to scalar variable.can only be called with ndarray objectcannot byteswap a scalar in-placeCreating a NumPy object scalar. NumPy object scalars should never be created. If you see this message please inform the NumPy developers. Since this message should never be shown this will raise a TypeError in the future.can't index void scalar without fieldsunhashable type: 'writeable void-scalar'numerator of value (the value itself)array-scalars are immutableitem%%.%igError while formatting*%%+.%ig(%s%sj)'NaT'numpy.timedelta64(%S)numpy.timedelta64(%S,'%S')numpy.datetime64('%s')numpy.datetime64('%s','%S')|O:__round__|OO|O:object_|O:bool_O|O&:voidscalar buffer is readonly%ldwmanyfewToo %s arguments for %s__array_interface____ref|O&:__array__U:__format__numpy.core._multiarray_umathNONaT%lld %simagrealonly accepts 1 argument.|O&:byteswapO:dumpnumpy.core.arrayprint_void_scalar_repr0123456789ABCDEFnumpyVisibleDeprecationWarning%Sj-inf+nan+inf(%S%Sj)%%.%iLg%%+.%iLginvalid index (%d)Bcannot delete scalar field__getitem__(O)invalid indextolistconjsqueezetakesearchsortedviewflattenraveltraceargsortargmaxargminastyperesizeinplacendigitsnumpy.timedelta64numpy.datetime64numpy.voidnumpy.str_numpy.bytes_numpy.clongdoublenumpy.complex128numpy.complex64numpy.longdoublenumpy.float64numpy.float32numpy.float16numpy.ulonglongnumpy.uint64numpy.uint32numpy.uint16numpy.uint8numpy.longlongnumpy.int64numpy.int32numpy.int16numpy.int8numpy.bool_numpy.object_numpy.generic__class_getitem__bit_countas_integer_ratiois_integer__complex__numeratordenominatordenominator of value (1)integer value of flagsdtype objectbase objectitemset__array_wrap____sizeof____reduce__NumPy array scalar formatterndimnbytesflatArray protocol: Python sideArray protocol: struct__array_priority__Array priority.yearsmonthsweekshoursminutesmillisecondsmicrosecondsnanosecondspicosecondsfemtosecondsattosecondsgeneric time unitsnumpy.characternumpy.flexiblenumpy.complexfloatingnumpy.floatingnumpy.inexactnumpy.unsignedintegernumpy.signedintegernumpy.integernumpy.number!+X*p*********.0C7yAC-C6? ,eX?Kloop of ufunc does not support argument %d of type %s which has no callable %s methodIntegers to negative integer powers are not allowed.unorderable types for comparison?????scalar negativescalar absolutenumpy.exceptionsComplexWarningscalar powerscalar remainderscalar addscalar subtractscalar multiplyscalar dividescalar floor_dividescalar divmodTT༾ż44yo.$$$$$$ܿ"ܿ}nVԿԿ7$Ѿ<ѾTѾlѾоѾLҾҾlҾLҾҾԾMԾ8ԾԾ(վ־־־־,׾@ؾؾpؾ@ؾؾپAھ,ھپھ۾ ܾ۾۾pܾݾEݾ4ݾݾݾ޾ ޾0޾H޾ݾ޾ntHtH6YpY5u``8/})``NX@@@@@@OY5@@/S]pppppp#jD\=H l H c M   yU.``O/s}x xxS4 k((((((h$$h$*% %$$%%%%%t%\%B%"&&""""""$8,X,8,,,,,---t-\-D-,----x*x*x*x*x*x*t,444.554455555x5`5F5R484X2X2X2X2X2X2l4<=d=>===>>>>>p>R>2>=<::::::.=KKKKMlNNNlNxPPPtbb@@@@@@8HXh(H""H"l""""Z"!Z"!!!!!!!8"8"8"8"8"8"!0$@$P$`$# $x????0?h?>@>>>>>>>>>>>>>>>??????>OBCOB]B]B]B]B]B]B]B]B]B]B]B]B]B]BBBBBBB]BOFGOF]F]F]F]F]F]F]F]F]F]F]F]F]F]FFFFFFF]FOJKOJ]J]J]J]J]J]J]J]J]J]J]J]J]J]JJJJJJJ]J_NP_NmNmNmNmNmNmNmNmNmNmNmNmNmNmNNNNNNNmNoRToR}R}R}R}R}R}R}R}R}R}R}R}R}R}R R R R R R R}RV@XVVVVVVVVVVVVVVVPVPVPVPVPVPVVZp\ZZZZZZZZZZZZZZZZZZZZZZ^`^^^^^^^^^^^^^^^^^^^^^^2cd2c@c@c@c@c@c@c@c@c@c@c@c@c@c@cbbbbbb@c^gh^glglglglglglglglglglglglglglggggggglgkkkkpkkKj8lKj8l\lYjYjYjYjYjYjYjYjYjYjYjYjHlHlHlHlHlHl8ln(pn(pppnnnnnnnnnnnn8p8p8p8p8p8p(prHtrHttrrrrrrrrrrrrXtXtXtXtXtXtHtvhxvhxxvvvvvvvvvvvvxxxxxxxxxxxxhx{|{||{{{{{{{{{{{{|||||||7Ȁ7ȀFFFFFFFFFFFF؀؀؀؀؀؀ȀxxxxxxxׇׇЉJ؍J؍ YYYYYYYYYYYY؍((p888888(xxx6Ț6ȚEEEEEEEEEEEEؚؚؚؚؚؚȚjXjXyyyyyyyyyyyyhhhhhhX}}((((((ȦئTpTpwpfuuuupuupuu@@@@@@phh{000000xxŭŭŭŭŭŭŭŭ@@@@@@бббձձձձбձձбձձPPPPPPбηppppppȻȻۻX @@@ EEEE@EE@EE@``s((((((``````H0H0m0[55550550550 0@XXXXXXhhhhhhxxxxxxP$6$WEi@E@E@@EE@EE@h{000000@P`p0((-(-((--(--(#XE3X]X]XX]]X]]X@S@ucp p 8 8 8 8 8 8  h h h h h h #XE3X]X]XX]]X]]XPdPv"""(###(!-!(!-!(!(!-!-!(!-!-!""""""(! 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'vD?H%s?-lUȴ˿?Qo<l?-f{i?j~L˿bAAѿ{d?ſ !?x$1;p1͞?5Jf?n2NuͿ?؉j<УUdh?+1?̿;пԾ"3?"X?x]^</- ?N9(J? 房Ͽ?Ow҉ݿ?zCs nUHj<?sRV?KD|׿fx?@|?tU'>l?'>mݺr?e(o&G*,co??㔱k<"|`??(2׿[#ٲ?ȩ?V G#b5?\\?v/Ua5j*U? ve??tl؋2<? ֪d?Y*[Li?20x<?ӏGM?$c<9'I'?ZΘFMSA?pG(K|T>?1"z> 5?@j'2 U >?ܷ<^4y?,0 E?F ?Qƿy?xwPzU:c?W% Dֺͪ?4v>c=xM&w >?c,pb?W'`ۂp N Y?m+=J\?}(eOsо8.4>?̣K<hn?M"v?cƨb(j*+t?'b7pdsaO?4 3kz1=? xdw"e8> ?I' [<(1?7ax#s?.sZmVi?Hl(Y` C?V*D)WÙ ?Q4amǻ>*6rv> ? )<:6m_?20 G ?wH)22MB}Ŏ?; -ľ4^X> +x?J# tH?ȵj>/ e?G<3Ɓ?i|M.Z?c >?˪_2=غ>6Ծa>kCZLe)t>f*NP?q@8d<%H?ClAq>?Ɍ| U>CH R>Bj:R쾳D">>Ь#1M&>>혻Z8E^>.wr8?3S<1^?uRB>BBS >7ldԾq>vT9(d>DRai;F>ڔ"?,b{<'??5R5>01׾Ei;>=_0˪_>OEZ?p 0hr>XqR 05>0>. ?Vr ыn^NFe>R _+\ >S[Px[>@g;5>q?7eJ稾b >!l nJ z>I{Ua2GTn<$A>ܽ?Twu<9?9>b9npj}>=sƂmQ':W>"nSrJ?ݜ">بu24v=5<T!?8<@?FVX>QC:VX y3 P> @{Y )>+n-i9ߺ=,njӽ2Ppұ=cN)#?fqϰ *1:u^!> u^\="WE5%*=J_?**L=b:˽^%?tk u) ] = +Gr(|`׃>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>elementwise == comparison failed and returning scalar instead; this will raise an error in the future.elementwise != comparison failed and returning scalar instead; this will raise an error in the future.Void-arrays can only be compared for equality.Cannot compare structured or void to non-void arrays.Cannot compare structured arrays unless they have a common dtype. I.e. `np.result_type(arr1, arr2)` must be defined.Cannot compare structured dtypes with different number of fields. (unreachable error please report to NumPy devs)Cannot compare subarrays with different shapes. (unreachable error, please report to NumPy devs.)Cannot compare structured with unstructured void arrays. (unreachable error, please report to NumPy devs.)cannot compare unstructured voids of different length. Use bytes to compare. (This may return array of False in the future.)8Ѐ_discover_array_parameters|dtypecurrently it is only possible to register a DType for scalars deriving from `np.generic`, got '%S'.Can only map one python type to DType.Could not convert object to sequencesetting an array element with a sequence. The requested array has an inhomogeneous shape after %d dimensions. The detected shape was %R + inhomogeneous part.setting an array element with a sequence. The requested array would exceed the maximum number of dimension of %d._simple_strided_call() takes exactly one tuple with as many arrays as the method takes arguments (%d+%d).All inputs must be NumPy arrays.input dtype %S was not an exact instance of the bound DType class %S.All arrays must be one dimensional.All arrays must have the same length.method does not support unaligned input.cannot perform method call with the given dtypes._simple_strided_call(): requires dtypes to not require a cast (must match exactly with `_resolve_descriptors()`)._resolve_descriptors() takes exactly one tuple with as many elements as the method takes arguments (%d+%d).only output dtypes may be omitted (set to None).dtype tuple can only contain dtype instances or None.resolve_descriptors cast level did not match stored one. (set level is %d, got %d for method %s)resolve_descriptors cast level changed even though the cast is non-parametric where the only possible change should be from equivalent to no casting. (set level is %d, got %d for method %s)ArrayMethod inputs and outputs must be greater zero andnot exceed %d. (method: %s)ArrayMethod has invalid casting `%d`. (method: %s)ArrayMethod must provide all input and output DTypes. (method: %s)ArrayMethod provided object %R is not a DType.(method: %s)abstract DType %S are currently not supported.(method: %s)invalid slot number %d to ArrayMethod: %sCannot set casting to -1 (invalid) when not providing the default `resolve_descriptors` function. (method: %s)All input DTypes must be specified when using the default `resolve_descriptors` function. (method: %s)Must specify output DTypes or use custom `resolve_descriptors` when there are no inputs. (method: %s)must provide a `resolve_descriptors` function if any output DType is parametric. (method: %s)Must provide unaligned strided inner loop when using NPY_METH_SUPPORTS_UNALIGNED flag (in method: %s)Must not provide unaligned strided inner loop when not using NPY_METH_SUPPORTS_UNALIGNED flag (in method: %s)Must provide a strided inner loop function. (method: %s)ArrayMethod spec contained a non DType.whether the method supports unaligned inputs/outputs.call on 1-d inputs and pre-allocated outputs (single call)._simple_strided_call() outputiNNiOOnumpy._BoundArrayMethod_supports_unaligned_resolve_descriptorsResolve the given dtypes._simple_strided_callnumpy._ArrayMethod5(赸صȵXassignment destinationwhere maskinput array__qualname__OO:_ArrayFunctionDispatcher__name__numpy.core._internalnumpy.%s is not callable.__dict___implementationarray_function_errmsg_formattermaximum number (%d) of distinct argument types implementing __array_function__ exceededdispatcher for __array_function__ did not return an iterable`like` argument dispatching, but first argument is not positional in call to %S.The `like` argument must be an array-like that implements the `__array_function__` protocol.O:array__get_implementing_argsnumpy._ArrayFunctionDispatcherT{ZfZdZgmemory allocation failed%ds%dw%dxinvalid field namendarray is not C-contiguousndarray is not contiguousbuffer source arrayNULL view in getbuffer_dtype_from_pep3118dtypes with overlapping or out-of-order fields are not representable as buffers. Consider reordering the fields.':' is not an allowed character in buffer field namescannot expose native-only dtype '%c' in non-native byte order '%c' via buffer interfacecannot include dtype '%c' in a buffercannot include dtype '%s' in a bufferObject of type %S appears to be C subclassed NumPy array, void scalar, or allocated in a non-standard way.NumPy reserves the right to change the size of these structures. Projects are required to take this into account by either recompiling against a specific NumPy version or padding the struct and enforcing a maximum NumPy version.ndarray is not Fortran contiguous'%s' is not a valid PEP 3118 buffer format stringinternal error: numpy.core._internal._dtype_from_pep3118 did not return a valid dtype, got %sScsw'7Ym` 0 +sL8$data type not orderedOOOunsafe{s:s}invalid output shapeattempt to get %s of an empty sequenceoutput array does not match result of np.%s.array_clip: must set either max or minclip: Output array must have thesame shape as the input.attempting to conjugate non-numeric dtype; this will error in the future to match the behavior of np.conjugate?$@Y@@@@j@.AcAחA eA$@indices must be integral: the provided empty sequence was inferred as float. Wrap it with 'np.array(indices, dtype=np.intp)'Expected an input array of integer or boolean data typeExpected an input array of unsigned byte data type-count larger than number of elements0 should be passed as minlength instead of None; this will error in future.'minlength' must not be negative'list' argument must have no negative elementsThe weights and list don't have the same length.place: mask and data must be the same sizeCannot insert from an empty array!array of sample points is emptyfp and xp are not of the same length.too many dimensions passed to ravel_multi_indexinvalid dims: array size defined by dims is larger than the maximum possible size.only 'C' or 'F' order is permittedparameter %s must be a sequence of length %dcannot unravel if shape has zero entries (is empty).invalid entry in coordinates arraydimensions are too large; arrays and shapes with a total size greater than 'intp' are not supported.multiple indices are not supported for 0d arraysindex %ld is out of bounds for array with size %ldargument docstring of add_docstring should be a stralready has a different docstringCannot set a docstring for that object'order' must be either 'little' or 'big''order' must begin with 'l' or 'b'only int indices permittedbincount|minlength|weightsO|_monotonicityO!OO:placeinterp|right|leftfpinterp_complexOO&|OO&:ravel_multi_indexOO&|O&:unravel_indexadd_docstring%s method %s__doc__object %sO|O&s:packlittleO|O&Os:unpackbitorderinputvals()%s,newaxis,%ld(%S,)%s(%S)%sshapes are not alignedcan't convert offsetshapes %s and %s not aligned: %d (dim %d) != %d (dim %d)output array is not acceptable (must have the right datatype, number of dimensions, and be a C-Array)output array has wrong dimensionsConversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)only length-1 arrays can be converted to Python scalarsThe DTypes %S and %S do not have a common DType. For example they cannot be stored in a single array unless the dtype is `object`.The DType %S could not be promoted by %S. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: %Scannot write object arrays to a file in binary modeNot enough free space to write %ld bytesproblem writing element %ld to fileproblem writing separator to fileobj is NULL in PyArray_NewCopy%ld requested and %ld written08Bќќȝ``ttttttttttt즹\\lLLllOȹOȹȹOȹȹOȹȹOȹȹOȹȹ`̹P̹̹̹p̹̹̹ ͹͹̹ҹҹҹҹҹҹҹҹxҹҹhҹҹҹҹҹҹҹ ҹҹҹҹ|ҹӹֹӹӹֹhֹ`Թӹӹӹӹ ԹԹԹӹֹpչ casting from object to the parametric DType %S requires the specified output dtype instance. This may be a NumPy issue, since the correct instance should be discovered automatically, however.Impossible cast to string path requested._set_promotion_state() argument or NPY_PROMOTION_STATE must be a string._set_promotion_state() argument or NPY_PROMOTION_STATE must be 'weak', 'legacy', or 'weak_and_warn' but got '%.100S'dtype is NULL in PyArray_CastToTypebuiltin cast from %S to %S not found, this should not be possible.Internal NumPy error, within-DType cast missing for %S!Invalid or missing field %R, this should be impossible and indicates a NumPy bug.Cannot cast array data from %R to %R according to the rule %sCannot cast scalar from %R to %R according to the rule %sat least one type needed to promoteresult dtype changed due to the removal of value-based promotion from NumPy. Changed from %S to %S.no arrays or types available to calculate result typeThe dtype `%R` is not a valid dtype for concatenation since it is a subarray dtype (the subarray dimensions would be added as array dimensions).Not supported for this data-type.This function currently only supports native NumPy dtypes and old-style user dtypes, but the dtype was %S. (The function may need to be updated to support arbitraryuser dtypes.)A cast must have one input and one output.A cast where input and output DType (class) are identical must currently support unaligned data. (method: %s)A cast was already added for %S -> %S. (method: %s)numpy.core._ufunc_configNO_NEP50_WARNINGweakweak_and_warninvalid promotion state!No cast function available.object_to_any_castany_to_object_castvoid_to_any_castany_to_void_castO!O!:_get_castingimpl'no''safe''same_kind''unsafe''equiv'cannot cast dtype %S to %S.0-length sequence.legacy_cast_to_stringnumeric_castnumeric_copy_or_byteswapstring_to_string_castvoid_to_void_castobject_to_object_cast}}G@ߌ3ǭߌ3Gv;w0Bv;w0Binexact matches and case insensitive matches for clip mode are deprecated, please use one of 'clip', 'raise', or 'wrap' instead.NoneType copy mode not allowed.an integer is required for the axisinteger won't fit into a C intUsing `axis=32` (MAXDIMS) is deprecated. 32/MAXDIMS had the same meaning as `axis=None` which should be used instead. (Deprecated NumPy 1.23)integers are required for the axis tuple elementsmust be one of 'quick', 'heap', or 'stable'inexact matches and case insensitive matches for search side are deprecated, please use one of 'left' or 'right' instead.must be one of 'C', 'F', 'A', or 'K'must be one of 'clip', 'raise', or 'wrap'integer clipmode must be np.RAISE, np.WRAP, or np.CLIPlist of clipmodes has wrong length (%zd instead of %d)must be one of 'valid', 'same', or 'full'inexact matches and case insensitive matches for convolve/correlate mode are deprecated, please use one of 'valid', 'same', or 'full' instead.convolve/correlate mode not understoodinteger convolve/correlate mode must be 0, 1, or 2must be one of 'no', 'equiv', 'safe', 'same_kind', or 'unsafe'Maximum allowed dimension exceededPassing None into shape arguments as an alias for () is deprecated.expected a sequence of integers or a single integer.expected a sequence of integers or a single integer, got '%.100R'maximum supported dimension for an ndarray is %d, found %dexpected a sequence of integers or a single integerDType strings 'O4' and 'O8' are deprecated because they are platform specific. Use 'O' insteadwrapraiseoutput must be an array_CopyModeAxisErroriiOduplicate value in 'axis'not recognized%s %s (got %R)%s must be str, not %smust be 'introselect'select kindintroselectmust be 'left' or 'right'search sideclipmodean integer is requiredclipmode not understoodsamefullequivsame_kind0>>>>>HP>>>>>>>>>>`p>>>>>>>>>>>>>>   xx8xxxpxxxxxxxInconsistent object during array creation? Content of sequences changed (length inconsistent).Inconsistent object during array creation? Content of sequences changed (cache not consumed).PyArray_GetArrayParamsFromObject() C-API function is removed `PyArray_FromAny()` should be used at this time. New C-API may be exposed in the future (please do request this if it would help you).Unable to avoid copy while converting from an object implementing the `__array__` protocol. NumPy cannot ensure that no copy will be made.object __array__ method not producing an arrayPyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr.PyArray_FromDims: use PyArray_SimpleNew.cannot copy from array of size %ld into an array of size %ldnumber of dimensions must be within [0, %d]negative dimensions are not allowedarray is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.Setting __array_finalize__ = None to indicate no finalizationshould be done is deprecated. Instead, just inherit from ndarray or, if that is not possible, explicitly set to ndarray.__array_function__; this will raise a TypeError in the future. (Deprecated since NumPy 1.23)subtype is NULL in PyArray_NewFromDescrdescr is NULL in PyArray_NewFromDescrItem size %zd for PEP 3118 buffer format string %s does not match the dtype %c item size %d.A builtin ctypes object gave a PEP3118 format string that does not match its itemsize, so a best-guess will be made of the data type. Newer versions of python may behave correctly.For the given ctypes object, neither the item size computed from the PEP 3118 buffer format nor from converting the type to a np.dtype matched the actual size. This is a bug both in python and numpyPEP3118 dimensions do not satisfy 0 <= ndim <= NPY_MAXDIMSndim computed from the PEP 3118 buffer format is greater than 1, but shape is NULL.NumPy currently does not support importing buffers which include suboffsets as they are not compatible with the NumPymemory layout without a copy. Consider copying the original before trying to convert it to a NumPy array.Invalid __array_interface__ value, must be a dictMissing __array_interface__ typestr__array_interface__ typestr must be a stringMissing __array_interface__ shape__array_interface__ data must be a 2-tuple with (data pointer integer, read-only flag)first element of __array_interface__ data tuple must be an integer.__array_interface__ offset must be an integercannot coerce scalar to array with size > 1mismatch in length of strides and shapecannot create an OBJECT array from memory bufferitemsize cannot be zero in typeoffset must be non-negative and no greater than buffer length (%ld)cannot determine count if itemsize is 0buffer size must be a multiple of element sizebuffer is smaller than requested sizearange() not supported for inputs with DType %S.arange: scalar arguments expected instead of a tuple.arange: overflow while computing lengthsubtype is NULL in PyArray_Newdata type must provide an itemsizeMust specify length when using variable-size data-type.iterator too short: Expected %zd but iterator had only %zd items.Unable to read character files of that array typestring or file could not be read to its end due to unmatched data; this will raise a ValueError in the future.prototype is NULL in PyArray_NewLikeArrayUnable to avoid copy while creating an array from given array.object of too small depth for desired arrayobject too deep for desired arrayUnable to avoid copy while creating an array.WRITEBACKIFCOPY used for non-array input.Unable to avoid copy while creating a new array.no fill-function for data-type.Cannot create an object array from a stringstring size must be a multiple of element sizestring is smaller than requested sizedon't know how to read character strings with that array typendarray subclass __array_wrap__ method returned an object which was not an instance of an ndarray subclass__get__'context' must be NULLdestination arrayEmpty data-typenumpy.core._exceptions_ArrayMemoryErrornpy_ctypes_check%c%c%dinvalid __array_struct__typestrshape must be a tuplestrides must be a tuplePEP 3118 bufferarray interface objectarange: cannot compute lengthMaximum allowed size exceededcannot allocate array memoryCannot read into object arraycould not seek in filezero-valued itemsizecannot get datetime metadata from non-datetime typeAsked to create a datetime type with a non-datetime type numberCannot create a NumPy datetime other than NaT with generic unitsNumPy datetime metadata with corrupt unit valueThe NumPy PyArray_DatetimeStructToDatetime function has been removedThe NumPy PyArray_TimedeltaStructToTimedelta function has been removedCannot convert a NumPy datetime value other than NaT with generic unitsNumPy datetime metadata is corrupted with invalid base unitThe NumPy PyArray_DatetimeToDatetimeStruct function has been removedThe NumPy PyArray_TimedeltaToTimedeltaStruct function has been removedCan't use 'den' divisor with generic unitsdivisor (%d) is not a multiple of a lower-unit in datetime metadatadivisor (%d) is not a multiple of a lower-unit in datetime metadata "%s"Cannot convert from specific units to generic units in NumPy datetimes or timedeltasInteger overflow while computing the conversion factor between NumPy datetime units %s and %sCannot cast %s from metadata %S to %S according to the rule %sCannot get a common metadata divisor for Numpy datetime metadata %S and %S because they have incompatible nonlinear base time units.Integer overflow getting a common metadata divisor for NumPy datetime metadata %S and %S.Invalid datetime unit "%s" in metadataInvalid datetime unit in metadata string "%s"Invalid datetime metadata string "%s" at position %zdInvalid datetime metadata string "%s"Require tuple for tuple to NumPy datetime metadata conversion, not %RRequire tuple of size 2 to 4 for tuple to NumPy datetime metadata conversionWhen passing a 3-tuple as (unit, num, event), the event is ignored (since 1.7) - use (unit, num) insteadInvalid tuple values for tuple to NumPy datetime metadata conversionLoaded pickle file contains non-default event data for a datetime type, which has been ignored since 1.7When passing a 4-tuple as (unit, num, den, event), the event argument is ignored (since 1.7), so should be NoneInvalid object for specifying NumPy datetime metadataInvalid time (%d,%d,%d,%d) when converting to NumPy datetimeparsing timezone aware datetimes is deprecated; this will raise an error in the futureInvalid date (%ld,%d,%d) when converting to NumPy datetimeConverting an integer to a NumPy datetime requires a specified unitCould not convert object to NumPy datetimeCould not convert object to NumPy timedeltaconvert_pyobjects_to_datetimes requires that all the type_nums provided be datetime or timedeltaarange needs at least a stopping valuecannot use a datetime as a step in arangedatetime_arange was given a non-datetime dtypearange requires both a start and a stop for NumPy datetime64 rangesarange: cannot use NaT (not-a-time) datetime valuesfind_object_datetime_type needs a datetime or timedelta type numberdatetime.datetime_CAPI[%d%s]Invalid datetime typestr "%s"%d%syearmonthhourminutemicrosecondtzinfoutcoffsettotal_secondsfromutcdatetime.date objectdatetime.datetime objectNumPy timedelta64 scalardatetime.timedelta objectarange: step cannot be zerodatetime_caststimedelta_and_datetime_castdatetime_to_timedelta_castfsas8x8xu8xtuxwwvPvvwPuyzyyPz}h{~z}|(yhbztn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,確||$|lܽLܼtTúTúTúx,úººT$ºD<<  ;Failed to use '%s' to convert to a local timeCannot parse "%s" as unit '%s' using casting rule %sMonth out of range in datetime string "%s"Day out of range in datetime string "%s"Hours out of range in datetime string "%s"Minutes out of range in datetime string "%s"Seconds out of range in datetime string "%s"Timezone hours offset out of range in datetime string "%s"Timezone minutes offset out of range in datetime string "%s"Error parsing datetime string "%s" at position %zdCannot create a local timezone-based date string from a NumPy datetime without forcing 'unsafe' castingCannot create a string with unit precision '%s' from the NumPy datetime, which has data at unit precision '%s', requires 'unsafe' or 'same_kind' castingThe string provided for NumPy ISO datetime formatting was too short, with length %ldinput must have type NumPy datetimeCannot create a datetime string as units '%s' from a NumPy datetime with units '%s' according to the rule %sUnsupported timezone input string "%s"localtime_r%04ldO|OOO&:datetime_as_stringautolocalnaivearrtimezoneTdt$4DbackwardforwardmodifiedfollowingmodifiedprecedingNaT input in busday_offsetOO|O&O&O&O!O:busday_offsetOO|O&O&O!O:busday_countO|O&O&O!O:is_busdayweekmaskholidaysbegindatesenddatesoffsetsrollInvalid business day roll parameter "%s"the business day weekmask must have at least one valid business dayNon-business day date in busday_offsetCannot compute a business day count with a NaT (not-a-time) dateCannot supply both the weekmask/holidays and the busdaycal parameters to busday_offset()busday_offset: must provide a NumPy array for 'out'Cannot supply both the weekmask/holidays and the busdaycal parameters to busday_count()Cannot supply both the weekmask/holidays and the busdaycal parameters to is_busday()`hP y  y Invalid business day weekmask string "%s"A business day weekmask array must have length 7A business day weekmask array must have all 1's and 0'sCouldn't convert object into a business day weekmaskCannot safely convert provided holidays input into an array of datesholidays must be a provided as a one-dimensional arrayCannot construct a numpy.busdaycal with a weekmask of all zeros%(#(#(#(#(#(#$(#(#(#(#(#$$(#(#y$|O&O&:busdaycalnumpy.busdaycalendarnumpy.core._dtype_name_get__str____repr__%c%cthere are no fields definedDuplicate field names given._array_descrnumpy.core._dtype_ctypesdtype_from_ctypes_typeOi|O(iOOOOiiiO):__setstate__(iOOOOiii):__setstate__(iOOOOii):__setstate__(iOOOii):__setstate__(OOOii):__setstate__(NOO)O:set_typeDictdata type not understoodField name must be a strf%d_usefieldsformatstitlesoffset %ld cannot be negativefield names must be stringsmetadataOnO|O&O&O!:dtype_commastringdata type %R not understood|O&:newbyteorderduplicate field of name {!r}formatField named %R not found.Field index %zd out of range.alignnumpy.dtypesubdtypeisbuiltinisnativeisalignedstructhasobjectnumalignmentCannot delete dtype names attributemust replace all names at once with a sequence of length %ditem #%d of names is of type %s and not string../../numpy/core/src/multiarray/descriptor.c*** Reference count error detected: an attempt was made to deallocate the dtype %d (%c) *** can't handle version %d of numpy.dtype pickleendian is not 1-char string in Numpy dtype unpicklingendian is not a string in Numpy dtype unpicklinginconsistent fields and names in Numpy dtype unpicklingnon-tuple names in Numpy dtype unpicklingnon-dict fields in Numpy dtype unpicklingincorrect subarray in __setstate__incorrect subarray shape in __setstate__incorrect value for flags variable (overflow)Invalid datetime dtype (metadata, c_metadata): %R while trying to convert the given data type from a tuple objectTuple must have size 2, but has size %zdmismatch in size of old and new data-descriptordtypes of the form (old_dtype, new_dtype) containing the object dtype are not supportedinvalid itemsize in generic type tuplePassing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.invalid shape in fixed-type tuple: dimension smaller then zero.invalid shape in fixed-type tuple: dimension does not fit into a C int.invalid shape in fixed-type tuple: dtype size in bytes must fit into a C int. while trying to convert the given data type from a list objectField elements must be 2- or 3-tuples, got '%R'If a tuple, the first element of a field tuple must have two elements, not %zdFirst element of field tuple is neither a tuple nor strField titles must be non-empty stringsField elements must be tuples with at most 3 elements, got '%R'field %R occurs more than once while trying to convert the given data type from a dict object'names', 'formats', 'offsets', and 'titles' dict entries must have the same lengthNumPy dtype descriptor includes 'aligned' entry, but its value is neither True nor Falseoffset %ld for NumPy dtype with fields is not divisible by the field alignment %d with align=Truename already used as a name or titletitle already used as a name or title.Cannot create a NumPy dtype with overlapping object fieldsNumPy dtype descriptor requires %d bytes, cannot override to smaller itemsize of %dNumPy dtype descriptor requires alignment of %d bytes, which is not divisible into the specified itemsize %dCannot construct a dtype from an arrayCannot interpret '%R' as a data typeArray length must be >= 0, not %ld while trying to convert the given data type from its `.dtype` attribute.in the future the `.dtype` attribute of a given datatype object must be a valid dtype instance. `data_type.dtype` may need to be coerced using `np.dtype(data_type.dtype)`. (Deprecated NumPy 1.20)'%S' must not inherit np.dtype.__new__(). User DTypes should currently call `PyArrayDescr_Type.tp_new` from their new._commastring is not returning a list with len >= 1Expected at least one field namecannot use field titles in multi-field indexThere are no fields in dtype %S.Field key must be an integer field offset, single field name, or list of field names.-x,,),]]^l]4]]numpy_dltensorused_dltensor$stream__dlpack__iiNumPy only supports stream=None.Cannot export readonly array since signalling readonly is unsupported by DLPack.DLPack only supports strides which are a multiple of itemsize.DLPack only supports native byte order.DLPack only supports IEEE floating point types without padding (longdouble typically is not IEEE).DLPack only supports signed/unsigned integers, float and complex dtypes.maxdims of DLPack tensor is higher than the supported maxdims.Unsupported device in DLTensor.Unsupported lanes in DLTensor dtype.Unsupported dtype in DLTensor.h~~~H _promote_fieldsnumpy.dtypes_add_dtype_helperOsnumpy._DTypeMeta_abstract_legacy_parametric_is_numericStrings cannot have a negative size but a size of %ld was givenStrings too large to store inside array.Preliminary-API: Cannot subclass DType.DTypes can only be created using the NumPy API.Preliminary-API: Cannot __init__ DType class.byte-like to large to store inside array.A bytes-like object is required, not '%s'Preliminary-API: Flexible/Parametric legacy DType '%S' can only be instantiated using `np.dtype(...)`currently only the no-argument instantiation is supported; use `np.dtype` instead.string too large to store inside array.Invalid type promotion with void datatypes of different lengths. Use the `np.bytes_` datatype instead to pad the shorter value with trailing zero bytes.Internal NumPy error: `_promote_fields` did not return a valid descriptor object.invalid type promotion with subarray datatypes (shape mismatch).invalid type promotion with structured datatype(s).During creation/wrapping of legacy DType, the original class was not of PyArrayDescr_Type (it is replaced in this step). The extension creating a custom DType for type %S must be modified to ensure `Py_TYPE(descr) == &PyArrayDescr_Type` or that of an existing dtype (with the assumption it is just copied over and can be replaced).DType class corresponding to the scalar type and dtype of the same name. Please see `numpy.dtype` for the typical way to create dtype instances and :ref:`arrays.dtypes` for additional information.Preliminary NumPy API: The Type of NumPy DTypes (metaclass)scalar type corresponding to the DType.numpy float printing code is not re-entrant. Ping the devs to fix it. d'@ByPD?Gz?Cannot cast data from %S to %S.Cannot cast array data from %R to %R.Required internal cast from %R to %R was not done in a single step (a secondary cast must currently be between instances of the same DType class and such a cast must currently return the input descriptors unmodified).Can't cast from structure to non-structure, except if the structure only has a single field.structures must have the same sizeInternal NumPy error, casting %S to %S required wrapping, probably it incorrectly flagged support for unaligned data. (aligned passed to discovery is %d)Only bool and uint8 masks are supported.NumPy CastRawArrays cannot do a reductioninvalid subarray shapeInternal error, `get_clear_loop` not set for the DType '%S'Internal error, tried to fetch clear function for the user dtype '%S' without fields or subarray (legacy support).Passed in DType must be a valid (initialized) DTypeMeta instance!A custom DType must implement `__repr__` and `__str__` since the default inherited version (currently) fails.Not giving a type object is currently not supported, but is expected to be supported eventually. This would mean that e.g. indexing a NumPy array will return a 0-D array and not a scalar.DType %R appears already registered?invalid DType flags specified, only NPY_DT_PARAMETRIC, NPY_DT_ABSTRACT, and NPY_DT_NUMERIC are valid flags for user DTypes.Invalid slot with value %d passed in.PyArray_ArrFunc casting slot with value %d is disabled.Invalid PyArray_ArrFunc slot with value %d passed in.A DType must provide a getitem/setitem (there may be an exception here in the future if no scalar type is provided)A DType must provide an ensure_canonical implementation.Parametric DType must define a common-instance and descriptor discovery function!DType must at least provide a function to cast (or just copy) between its own instances!DType must provide a function to cast (or just copy) between its own instances!Instantiating %S did not return a dtype instance, this is invalid (especially without a custom `default_descr()`).Using legacy SETITEM with NULL array object is only supported for basic NumPy DTypes.ufunc object passed is not a ufunc!promoter must (currently) be a PyCapsule.The new DType API is currently in an exploratory phase and should NOT be used for production code. Expect modifications and crashes! To experiment with the new API you must set `NUMPY_EXPERIMENTAL_DTYPE_API=1` as an environment variable.Experimental DType API version %d requested, but NumPy is exporting version %d. Recompile your DType and/or upgrade NumPy to match.__associated_array_dtype__numpy._ufunc_promoterNUMPY_EXPERIMENTAL_DTYPE_APIexperimental_dtype_api_table4`hcPc8c ccbbbbbxb`bHb````````cfuture versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning. %s : %s %s : %s %s : %s %s : %s%s %s : %s %s : %s Cannot delete flags _warn_on_write attributecannot set '_warn_on_write' flag when 'writable' is Falsecannot clear '_warn_on_write', set writeable True to clear this private flagCannot delete flags writeable attributeCannot set flags on array scalars.Cannot delete flags aligned attributeCannot delete flags writebackifcopy attributeNeed a NumPy array to create a flags objectnumpy.core.multiarray.flagsobj (with WARN_ON_WRITE=True)TrueFalseUnknown flagflagsobjFORCCARRAYFARRAYFORTRANBEHAVEDc_contiguousf_contiguousfortranwritebackifcopyowndatawriteable_writeable_no_warn_warn_on_writefncforcbehavedcarrayfarraylu,vTv\xwluvvluLwwluwlulu4xxvw,u,uw,u,u,u,u,u,u,u,ux,u,u,u,u,u,u,u*wxCannot delete array real partCannot delete array datanot enough data for arrayCannot delete array stridesinvalid stridesCannot delete array shapeCannot delete array dtypeinvalid data-type for array_view_is_safeCannot delete array flat iteratorCannot convert complex type number %d to floatAssigning the 'data' attribute is an inherently unsafe operation and will be removed in the future.cannot set single-segment buffer for discontiguous arrayno memory handler found but OWNDATA flag setstrides must be same length as shape (%d)strides is not compatible with available memoryIncompatible shape for in-place modification. Use `.reshape()` to make a copy with the desired shape.Changing the dtype of a 0d array is only supported if the itemsize is unchangedChanging the dtype to a subarray type is only supported if the total itemsize is unchangedTo change to a dtype of a different size, the last axis must be contiguousWhen changing to a smaller dtype, its size must be a divisor of the size of original dtypeWhen changing to a larger dtype, its size must be a divisor of the total size in bytes of the last axis of the array.Cannot delete array imaginary partarray does not have imaginary part to set(cccii)(Hash) Error while computing builtin hash(Hash) names is not a tuple ???(Hash) fields is not a dict ???(Hash) names and fields inconsistent ???(Hash) key of dtype dict not a string ???(Hash) value of dtype dict not a dtype ???(Hash) Less than 2 items in dtype dict ???(Hash) First item in compound dtype tuple not a descr ???(Hash) Second item in compound dtype tuple not an int ???(Hash) Error while getting shape item of subarray dtype ???(Hash) Shape of subarray dtype neither a tuple or int ???PyArray_DescrHash argument must be a type descriptoroutput array does not match result of ndarray.takecannot do a non-empty take from an empty axes.put: first argument must be an arrayputmask: first argument must be an arrayputmask: mask and data must be the same sizeoperands could not be broadcast together with shape (%zd,) (%zd,)repeats may not contain negative values.choose: invalid shape for output array.type does not have compare functionPassing booleans as partition index is deprecated (warning added in NumPy 1.22)Partition index must be integerkth array must have dimension <= 1need sequence of keys with len > 0 in lexsortall keys need to be the same shapeitem %zd type does not have compare functioncompare not supported for typecould not parse sorter argumentsorter must only contain integersdiag requires an array of at least two dimensionsaxis1 and axis2 cannot be the sameCalling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(cond).nonzero()` if the old behavior was intended. If the context of this warning is of the form `arr[nonzero(cond)]`, just use `arr[cond]`.Calling nonzero on 0d arrays is deprecated, as it behaves surprisingly. Use `atleast_1d(arr).nonzero()` if the old behavior was intended.number of non-zero array elements changed during function execution.put: output arrayputmask: output arrayinvalid entry in choice arraysort arraynot a valid sort kindkth(=%zd) out of bounds (%zd)partition arraynot a valid partition kindsorter.size must equal a.sizeSorter index out of range.condition must be a 1-d array?too many boolean indicesunsupported iterator indexunderlying arrayUnsupported padding modenumpy.neigh_internal_iternumpy.broadcastresetnumiternumpy.flatitercoordseach index entry must be either a slice, an integer, Ellipsis, or newaxis../../numpy/core/src/multiarray/iterators.ccannot use Ellipsis or newaxes hereboolean index array should have 1 dimensionCannot delete iterator elementsError setting single item of array.boolean index array has too many valuesarray is not broadcastable to correct shapeNumpy IterAllButAxis requires an ndarrayshape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg %d with shape %S and arg %d with shape %S.broadcast dimensions too large.Need at least 0 and at most %d array objects.keyword arguments not accepted.l0khkgklen() of unsized objecttoo many indices for arrayno field of name %Scannot delete array elementsGetitem not returning arraynumpy.mapiterOut of bound index found. This was previously ignored when the indexing result contained no elements. In the future the index error will be raised. This error occurs either due to an empty slice, or if an array has zero elements even before indexing. (Use `warnings.simplefilter('error')` to turn this DeprecationWarning into an error and get more details on the invalid index.)an index can only have a single ellipsis ('...')arrays used as indices must be of integer (or boolean) typeonly integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indicestoo many indices for array: array is %d-dimensional, but %d were indexednumber of dimensions must be within [0, %d], indexing result would have %dboolean index did not match indexed array along dimension %d; dimension is %ld but corresponding boolean dimension is %ldNumPy boolean array indexing assignment requires a boolean indexNumPy boolean array indexing assignment requires a 0 or 1-dimensional input, input has %d dimensionsThe boolean mask assignment indexing array must have the same number of dimensions as the array being indexedNumPy boolean array indexing assignment cannot assign %ld input values to the %ld output values where the mask is truetoo many advanced (array) indices. This probably means you are indexing with too many booleans. (more than %d found)shape mismatch: indexing arrays could not be broadcast together with shapes %Sinternal error: failed to find output array strideswhen no subspace is given, the number of index arrays cannot be above %d, but %d index arrays foundadvanced indexing operation result is too largeshape mismatch: value array of shape %S could not be broadcast to indexing result of shape %S__array_ufunc__ requires at least 2 argumentsCannot specify output type twice.Type must be a sub-type of ndarray typetostring() is deprecated. Use tobytes() instead.Cannot specify order when the array has no fields.cannot set aligned flag of mis-aligned array to Truecannot set WRITEBACKIFCOPY flag to Truemaking a non-writeable array writeable is deprecated for arrays without a base which do not own their data.cannot set WRITEABLE flag to True of this arrayUnable to avoid copy while casting in never copy mode.Unable to convert %R to complexcan't handle version %d of numpy.ndarray pickleimpossible dimension while unpickling arrayobject pickle not returning listFailed to encode latin1 string when unpickling a Numpy array. pickle.load(a, encoding='latin1') is assumed.buffer size does not match array sizetypes argument to ndarray.__array_function__ must be iterableitemset must have at least one argumentcan only convert an array of size 1 to a Python scalarincorrect number of indices for arrayGetting an 'int' from os.dup() failedGetting a FILE* from a Python file object via _fdopen failed. If you built NumPy, you probably linked with the wrong debug/release runtimeobtaining file position failedself is NULL in PyArray_GetFieldtyped is NULL in PyArray_GetFieldnew type is larger than original typenew type plus offset is larger than original typeself is NULL in PyArray_SetFielddtype is NULL in PyArray_SetField|order|type|offset|out|axis2|axis1|O&:tostring|O&:tobytes:__copy__|mode|axisii:swapaxes|kind_newnames|OOO:setflags|sorter|side|iO&:roundFailed to allocate new tuple|iinvalid shape|O&O&:reshapeO|O&:repeatOO|O&:putkth'out' must be an array|iii:diagonal|O&O&O&:cumsum|O&O&O&:cumprod|O&:conjugateO|O&O&:compressO:choose|O&O&|copy|subok|castingargpartition$keepdims(iO!O!iO):__setstate__(O!O!iO):__setstate__pickle not returning string(N)ONcO:__deepcopy__deepcopyOOOO:__array_function__only accepts 1 argumentO:fillpicklePickleBuffernumpy.core.numeric_frombufferN(NONN)numpy.core._methods_amin_dump_amax_std_dumps_clip_var_any_sum_all_ptp_prod_meanO|ss:tofilenumpy.compatos_PathLikeos_fspathopenwbdupRawIOBasetellseeking file failedliseekcloseflush_getfield_is_safeoffset is negativeO&|i:getfieldOO&|i:setfieldarray to be byte-swappedkwargsconditionvaluesrepeatsrefcheckdecimalsuicsep__array_prepare____array_finalize____array_ufunc____reduce_ex____dlpack_device__PyArray_As1D: use PyArray_AsCArray.PyArray_As2D: use PyArray_AsCArray.This function has been removedC arrays of only 1-3 dimensions availableCannot find a common data type.dot: too many dimensions in resultdot not available for this typefastCopyAndTranspose and the underlying C function PyArray_CopyAndTranspose have been deprecated. Use the transpose method followed by a C-order copy instead, e.g. ``arr.T.copy()``first array argument cannot be emptysecond array argument cannot be emptyfunction not available for this data typedid not understand one of the typesdid not understand one of the types; 'None' not acceptedvectors have different lengthsargument a of putmask must be a numpy arraystring comparisons are only allowed for dtypes with a scalar type that is a subtype of str or bytes.string operation on non-string arrayresult array type does not match underlying function'args' must be a sequence of argumentscomparison of non-string arraysin non-unique mode `precision` must be suppliedif supplied, trim must be 'k', '.', '0' or '-' found `%100S`The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer insteadarange() requires stop to be specified.the subscripts for each operand must be a list or a tupleeach subscripts list may have only one ellipsiseach subscript must be either an integer or an ellipsissubscript is not within the valid range [0, 52)_reconstruct: First argument must be a sub-type of ndarrayndmin bigger than allowable number of dimensions NPY_MAXDIMS (=%d)NumPy was imported from a Python sub-interpreter but NumPy does not properly support sub-interpreters. This will likely work for most users but might cause hard to track down issues or subtle bugs. A common user of the rare sub-interpreter feature is wsgi which also allows single-interpreter mode. Improvements in the case of bugs are welcome, but is not on the NumPy roadmap, and full support may require significant effort to achieve.The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged.one or more objects not callablemust specify the einstein sum subscripts string and at least one operand, or at least one operand and its corresponding subscripts listmust specify the einstein sum subscripts string and at least one operandmust provide at least an operand and a subscripts list to einsumkeyword parameter out must be an array for einsum'%s' is an invalid keyword for einsumat least one array or dtype is requiredeither both or neither of x and y should be givenUnpickling a scalar with object dtype is deprecated. Object scalars should never be created. If this was a properly created pickle, please open a NumPy issue. In a best effort this returns the original object.Unpickling NPY_LIST_PICKLE (structured void) scalar requires an array. The pickle file may be corrupted?Pickled array is not compatible with requested scalar dtype. The pickle file may be corrupted?Failed to encode Numpy scalar data string to latin1, pickle.load(a, encoding='latin1') is assumed if unpickling.initialization string is too smallinitializing object must be a bytes object'offset' argument only permitted for binary filesInteger overflow in computing overlapneed at least one array to concatenatezero-dimensional arrays cannot be concatenatedall the input arrays must have same number of dimensions, but the array at index %d has %d dimension(s) and the array at index %d has %d dimension(s)all the input array dimensions except for the concatenation axis must match exactly, but along dimension %d, the array at index %d has size %d and the array at index %d has size %dOutput array has wrong dimensionalityOutput array is the wrong shapetotal number of elements too large to concatenateOutput array is the wrong sizeconcatenate() with `axis=None` will use same-kind casting by default in the future. Please use `casting='unsafe'` to retain the old behaviour. In the future this will be a TypeError.The first input argument needs to be a sequenceconcatenate() only takes `out` or `dtype` as an argument, but both were provided.could not initialize Py%sArrType_Typecannot load multiarray module.Insert vals sequentially into equivalent 1-d positions indicated by mask.Get the current NEP 50 promotion state.Set the NEP 50 promotion state. This is not thread-safe. The optional warnings can be safely silenced using the `np._no_nep50_warning()` context manager._set_numpy_warn_if_no_mem_policyChange the warn if no mem policy flag for testing.Give a warning on reload and big warning in sub-interpreters.mode must be 0, 1, or 2O:min_scalar_typeO:fastCopyAndTransposepromote_typesfrom_can_castcorrelate2correlatevdotinnerproductputmaskkeyslexsortO&O&O|Olen(args) must be < %dO!O&|O&O:copytoOOs#O&:compare_chararraysdragon4_scientific|min_digits|exp_digits|pad_left|trim|sign|unique|precisiondragon4_positional|pad_right|fractionalO&:datetime_dataO|O&nn$O:frombufferOO&|n$O:fromiters#|O&ns$O:fromstring|startarange$like|step|stopsubscripts list is too longprototypeempty_like|shapeemptyzerosO!O&O&:_reconstructcount_nonzero$ndmin$subok$order$copyOI:format_longfloatnot a longfloat|Oi:set_string_functionArgument must be callable.too many operandsinvalid keyword|y|xO!|O:scalarnormalize_axis_index|msg_prefixasfortranarrayascontiguousarrayO|O&nsl$O:fromfilerbasarrayasanyarrayOO|O:shares_memory_implmax_work must be an integerInvalid value for max_workTooHardErrorExceeded max_workError in computing overlapOutput array must be 1Dseqconcatenate$casting$dtypecurrent_allocatorDTypePromotionErrorNPY_NUMPY_2_BEHAVIOR_UFuncNoLoopErrorNUMPY_WARN_IF_NO_MEM_POLICYBoolDatetimeTimedeltaUByteUShortUIntULongULongLongHalfCFloatCDoubleCLongDoubleStringUnicodeVoidObject_ARRAY_API_UFUNC_APIerrortracemalloc_domain3.1__version____cpu_features____cpu_baseline____cpu_dispatch__DATETIMEUNITSALLOW_THREADSBUFSIZECLIPITEM_HASOBJECTLIST_PICKLEITEM_IS_POINTERNEEDS_INITNEEDS_PYAPIUSE_GETITEMUSE_SETITEMWRAPMAXDIMSMAY_SHARE_BOUNDSMAY_SHARE_EXACT_flagdictdstsrcselfothera1a2cmprstrip_get_implementing_args_get_ndarray_c_versionset_numeric_opsset_datetimeparse_functionnested_itersc_einsumresult_typeshares_memorymay_share_memory_vec_string_placeunpackbitsset_legacy_print_mode_get_experimental_dtype_api_load_from_filelikeseterrobjgeterrobj_get_promotion_state_set_promotion_state_get_sfloat_dtype_get_madvise_hugepage_set_madvise_hugepage_reload_guard_using_numpy2_behaviorfrom_dlpack  Iterator CreateCompatibleStrides may only be called if a multi-index is being trackedIterator CreateCompatibleStrides may only be called if DONT_NEGATE_STRIDES was used to prevent reverse iteration of an axisindex provided for an iterator view was out of boundscannot provide an iterator view when buffering is enabledaxis out of bounds in iterator GetStrideAxisArrayinternal error in iterator perm ------ BEGIN ITERATOR DUMP ------------- END ITERATOR DUMP -------Cannot call GotoMultiIndex on an iterator without requesting a multi-index in the constructorCannot call GotoMultiIndex on an iterator which is bufferedCannot call GotoMultiIndex on an iterator which has the flag EXTERNAL_LOOPIterator GotoMultiIndex called with an out-of-bounds multi-indexIterator GotoMultiIndex called with a multi-index outside the restricted iteration rangeCannot call GotoIndex on an iterator without requesting a C or Fortran index in the constructorCannot call GotoIndex on an iterator which is bufferedCannot call GotoIndex on an iterator which has the flag EXTERNAL_LOOPIterator GotoIndex called with an out-of-bounds indexIterator GotoIndex called with an index outside the restricted iteration range.Iterator reset failed due to a casting failure. This error is set as a Python error.Iterator RemoveAxis may only be called if a multi-index is being trackedIterator RemoveAxis may not be called on an index is being trackedIterator RemoveAxis may not be called on a buffered iteratoraxis out of bounds in iterator RemoveAxisCannot call ResetToIterIndexRange on an iterator without requesting ranged iteration support in the constructorOut-of-bounds range [%ld, %ld) passed to ResetToIterIndexRangeOut-of-bounds range passed to ResetToIterIndexRangeInvalid range [%ld, %ld) passed to ResetToIterIndexRangeInvalid range passed to ResetToIterIndexRangeCannot call GotoIterIndex on an iterator which has the flag EXTERNAL_LOOPIterator GotoIterIndex called with an iterindex outside the iteration range.Iterator flag EXTERNAL_LOOP cannot be used if an index or multi-index is being trackedIterator flag EXTERNAL_LOOP cannot be used with ranged iteration unless buffering is also enabled| Iterator Address: %p | ItFlags: IDENTPERM NEGPERM HASINDEX HASMULTIINDEX FORCEDORDER EXLOOP RANGE BUFFER GROWINNER ONEITERATION DELAYBUF NEEDSAPI REDUCE REUSE_REDUCE_LOOPS | NDim: %d | NOp: %d | MaskOp: %d | IterSize: %d | IterStart: %d | IterEnd: %d | IterIndex: %d | BufferData SizeOf: %d | AxisData SizeOf: %d || Perm: | DTypes: %d %p (nil) %i (op nil) | Flags[%d]: READ WRITE CAST BUFNEVER ALIGNED VIRTUAL WRITEMASKED | BufferData:| BufferSize: %d | Size: %d | BufIterEnd: %d | REDUCE Pos: %d | REDUCE OuterSize: %d | REDUCE OuterDim: %d | Strides: | Ptrs: | ReadTransferFn: | AxisData[%d]: | Shape: %d | Index: %d | Index Value: %d | Buffers: | WriteTransferData: | WriteTransferFn: | ReadTransferData: | REDUCE Outer Ptrs: | Fixed Strides: | OpItFlags:| Operand DTypes: | BaseOffsets: | InitDataPtrs: | Iterator SizeOf: %d | REDUCE Outer Strides: | Operands: | Index Stride: %d | InitIndex: %d out of memory automatically allocated output array specified with an inconsistent axis mapping; the axis mapping cannot include dimension %d which is too large for the iterator dimension of %d.automatically allocated output array specified with an inconsistent axis mapping; the axis mapping is missing an entry for dimension %d.Iterator automatic output has an array subtype which changed the dimensions of the outputinput operand has more dimensions than allowed by the axis remappingIterator input op_axes[%d][%d] (==%d) is not a valid axis of op[%d], which has %d dimensions output operand requires a reduction along dimension %d, but the reduction is not enabled. The dimension size of 1 does not match the expected output shape.output operand requires a reduction, but is flagged as write-only, not read-writeoutput operand requires a reduction, but is flagged as the ARRAYMASK operand which is not permitted to be the result of a reductionoperand was set up as a reduction along axis %d, but the length of the axis is %zd (it has to be 1)operands could not be broadcast together with shapes %Soperands could not be broadcast together with shapes %S and requested shape %Soperands could not be broadcast together with remapped shapes [original->remapped]: %Soperands could not be broadcast together with remapped shapes [original->remapped]: %S and requested shape %Snon-broadcastable operand with shape %S doesn't match the broadcast shape %Snon-broadcastable output operand with shape %S doesn't match the broadcast shape %Snon-broadcastable operand with shape %S [remapped to %S] doesn't match the broadcast shape %Snon-broadcastable output operand with shape %S [remapped to %S] doesn't match the broadcast shape %SCannot construct an iterator with more than %d operands (%d were requested)Using `oa_ndim == 0` when `op_axes` is NULL. Use `oa_ndim == -1` or the MultiNew iterator for NumPy <1.8 compatibilityIf 'op_axes' or 'itershape' is not NULL in the iterator constructor, 'oa_ndim' must be zero or greaterCannot construct an iterator with more than %d dimensions (%d were requested for op_axes)If 'oa_ndim' is zero or greater in the iterator constructor, then op_axes cannot be NULLThe 'op_axes' provided to the iterator constructor for operand %d contained invalid values %dThe 'op_axes' provided to the iterator constructor for operand %d contained duplicate value %dA per-operand flag was passed as a global flag to the iterator constructorIterator flags C_INDEX and F_INDEX cannot both be specifiedIterator flag RANGED cannot be used with the flag EXTERNAL_LOOP unless BUFFERED is also enabledA global iterator flag was passed as a per-operand flag to the iterator constructorOnly one of the iterator flags READWRITE, READONLY, and WRITEONLY may be specified for an operandNone of the iterator flags READWRITE, READONLY, or WRITEONLY were specified for an operandIf an iterator operand is writeable, must use the flag UPDATEIFCOPY instead of COPYThe iterator flag WRITEMASKED may only be used with READWRITE or WRITEONLYThe iterator flag WRITEMASKED may not be used together with ARRAYMASKThe iterator flag VIRTUAL should be be used together with READWRITEOnly one iterator operand may receive an ARRAYMASK flagIterator operand was NULL, but neither the ALLOCATE nor the VIRTUAL flag was specifiedAutomatic allocation was requested for an iterator operand, but it wasn't flagged for writingAutomatic allocation was requested for an iterator operand, and it was flagged as readable, but buffering without delayed allocation was enabledIterator operand flag VIRTUAL was specified, but the operand was not NULLoperand array with iterator write flag setIteration of zero-sized operands is not enabledIterator input operand has no dtype descrIterator operand or requested dtype holds references, but the NPY_ITER_REFS_OK flag was not enabledIterator inputs must be ndarraysAn iterator operand was flagged as WRITEMASKED, but no ARRAYMASK operand was given to supply the maskAn iterator operand was flagged as the ARRAYMASK, but no WRITEMASKED operands were given to use the maskIterator operand %d dtype could not be cast from %R to %R according to the rule %sIterator requested dtype could not be cast from %R to %R, the operand %d dtype, according to the rule %sIterator operand required copying or buffering, but neither copying nor buffering was enabledIterator reduction operand is WRITEMASKED, but also broadcasts to multiple mask values. There can be only one mask value per WRITEMASKED element.Iterator operand required buffering, to be contiguous as requested, but buffering is not enabled->pooo pjpapXp%psppp~~||zzxxvvttrr``nnlljjhhffddbbPP^^\\ZZXXVVTTRR@@NNLLJJHHFFDDBB00>><<::88664422 ..,,**((&&$$""Iterator global flags must be a list or tuple of stringsUnexpected iterator global flag "%s"op_flags must be a tuple or array of per-op flag-tuplesUnexpected per-op iterator flag "%s"Must provide at least one operandIterator operand is flagged as writeable, but is an object which cannot be written back to via WRITEBACKIFCOPYIterator construction used delayed buffer allocation, and no reset has been done yetIterator operand index %zd is out of boundsIterator operand %zd is not writeableIterator slice assignment only supports a step of 1Wrong size to assign to iterator sliceinvalid index type for iterator indexingIterator was already initializedIf op_axes is provided, at least one list of axes must be contained within itop_axes must be a tuple/list matching the number of opsEach entry of op_axes must be None or a tuple/listToo many dimensions in op_axesEach entry of op_axes must have the same size'op_axes' and 'itershape' must have the same number of entries equal to the iterator ndimIterator is not tracking a multi-indexIterator is in an invalid stateTemporary data has not been written back to one of the operands. Typically nditer is used as a context manager otherwise 'close' must be called before reading iteration results.operation on non-initialized iteratorIterator does not have an indexCannot delete nditer iterindexCannot delete nditer iterrangeCannot delete nditer multi_indexmulti_index must be set with a sequenceaxes must be a tuple of axis arraysaxes must have at least 2 entries for nested iterationEach item in axes must be a an integer tupleAn axis is used more than onceIterator slicing only supports a step of 1bufferedc_indexcopy_if_overlapcommon_dtypedelay_bufallocf_indexgrow_innergrowinnerrefs_okreduce_okzerosize_okallocatearraymaskcontigno_subtypeno_broadcastoverlap_assume_elementwisereadwriteupdateifcopyvirtualwriteonlywritemaskedToo many operandsIterator is past the endO|O&OOO&O&OO&i:nditerIterator: (nil)Iterator is invalidnpyiter_dealloci:remove_axisCannot delete nditer indexnnWrong number of indicesOO|O&OOO&O&iToo many axes givenAn axis is out of boundsop_flagsop_dtypesop_axesitershapebuffersizenumpy.nditeriterindexiterrangeitviewshas_delayed_bufallociterationneedsapihas_multi_indexhas_indexnopitersizefinishediternextremove_multi_indexenable_external_loopdebug_print__enter____exit__hhf8hghhggggghgggggggggggg@gjkmkkkkkkkkkkllkk4lkkkkTk only integer scalar arrays can be converted to a scalar index while converting array to boolThe truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()inplace matrix multiplication requires the first operand to have at least one and the second at least two dimensions.PyArray_SetNumericOps is deprecated. Use PyUFunc_ReplaceLoopBySignature to replace ufunc inner loop functions instead.PyArray_GetNumericOps is deprecated.{s, [(i), (i, i), (i)]}{s, [(i, i), (i, i), (i, i)]} in ndarray.__float__ in ndarray.__int__squarereciprocal_ones_likesqrtcbrtpositiveinvertleft_shiftright_shiftbitwise_andbitwise_orbitwise_xorlessless_equalnot_equalgreatergreater_equaltrue_dividelogical_orlogical_andfloorceilmaximumminimummatmul(Oi)reduceaccumulate@Concatenation operation is not implemented for NumPy arrays, use np.concatenate() instead. Please do not rely on this error; it may not be given on all Python implementations.axes don't match arrayrepeated axis in transposeresize only works on single-segment arraysnegative dimensions not allowedcannot resize this array: it does not own its datacannot resize an array that references or is referenced by another array in this way. Use the np.resize function.cannot resize an array that references or is referenced by another array in this way. Use the np.resize function or refcheck=Falsecannot allocate memory for arrayorder 'K' is not permitted for reshapingcan only specify one unknown dimensioncannot reshape array of size %zd into shape %Scannot select an axis to squeeze out which has size not equal to one obj is NULL in PyArray_CheckAnyScalarExactConverting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.Converting `np.complex` to a dtype is deprecated. The current result is `complex128` which is not strictly correct.Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.Converting `np.unsignedinteger` to a dtype is deprecated. The current result is `np.dtype(np.uint)` which is not strictly correct. Note that the result depends on the system. To ensure stable results you may want to use `np.uint64` or `np.uint32`.Converting `np.character` to a dtype is deprecated. The current result is `np.dtype(np.str_)` which is not strictly correct. Note that `np.character` is generally deprecated and 'S1' should be used.Converting `np.generic` to a dtype is deprecated. The current result is `np.dtype(np.void)` which is not strictly correct.PyArray_ScalarFromObject() is deprecated and scheduled for removal. If you are using this (undocumented) function, please notify the NumPy developers to look for solutions.(Deprecated in NumPy 1.23)Fields must be a dictionary_makenames_listd2d2d2d2d2d2d2d2d2d2d2d2d2d2d2d2d2d2222d2d2d2$7$7$7$7$7$7$7$7$7$7$7$7$7$7$7$7$7$77d77$7$7$77777777777777777778d8\8777_default_array_repr_default_array_strO:__format__Unable to configure default ndarray.__repr__Unable to configure default ndarray.__str___PyEval_EvalFrameDefaulttypeinforangednumpy.core.multiarray.typeinforangedInformation about a scalar numpy type with a rangenumpy.core.multiarray.typeinfoInformation about a scalar numpy typeThe character used to represent the typeThe numeric id assigned to the typeThe number of bits in the typeThe alignment of the type in bytesThe maximum value of this typeThe minimum value of this typeThe python type object this info is aboutthe cast will continue to be considered impossible.the previous definition will continue to be used.A cast from %R to %R was registered/modified using `%s` after the cast had been used. This registration will have (mostly) no effect: %s The most likely fix is to ensure that casts are the first thing initialized after dtype registration. Please contact the NumPy developers with any questions!cannot register aflexible data-typea required array function is missing.Failed to register dtype for %S: Legacy user dtypes using `NPY_ITEM_IS_POINTER` or `NPY_ITEM_REFCOUNT` are unsupported. It is possible to create such a dtype only if it is a structured dtype with names and fields hardcoded at registration time. Please contact the NumPy developers if this used to work but now fails.The ->f->fastputmask member of custom dtypes is ignored; setting it may be an error in the future. The custom dtype you are using must be revised, but results will not be affected.The ->f->fastclip member of custom dtypes is deprecated; setting it will be an error in the future. The custom dtype you are using must be changed to use PyUFunc_RegisterLoopForDescr to attach a custom loop to np.core.umath.clip, np.minimum, and np.maximumAt least one of the types provided to RegisterCanCast must be user-defined.missing typeobjectRegisterDataTypenumpy.dtype[%s]invalid type number.PyArray_RegisterCastFuncPyArray_RegisterCanCastlegacy_castlatin1file reader does not support subarray dtypes. You canput the dtype into a structured one using `np.dtype(('name', dtype))` to avoid this limitation.Text reading control character must be a single unicode character or None; but got: %.100RThe values for control characters '%s' and '%s' are incompatiblecontrol character '%s' cannot be a newline (`\r` or `\n`).internal error: dtype must be provided and be a NumPy dtypeencoding must be a unicode string.usecols must be an int or a sequence of ints but it contains at least one element of type '%s'If a structured dtype is used, the number of columns in `usecols` must match the effective number of fields. But %zd usecols were given and the number of fields is %zd.quotechar|c_byte_converters|byte_converters|filelike|encoding|converters|max_rows|skiplines|usecols|imaginary_unit|quote|comment|delimiter,"Input line %zd contained no data and will not be counted towards `max_rows=%zd`. This differs from the behaviour in NumPy <=1.22 which counted lines rather than rows. If desired, the previous behaviour can be achieved by using `itertools.islice`. Please see the 1.23 release notes for an example on how to do this. If you wish to ignore this warning, use `warnings.filterwarnings`. This warning is expected to be removed in the future and is given only once per `loadtxt` call.converters must be a dictionary mapping columns to converter functions or a single callable.keys of the converters dictionary must be integers; got %.100Rconverter specified for column %zd, which is invalid for the number of fields %zd.values of the converters dictionary must be callable, but the value associated with key %R is notthe number of columns changed from %zd to %zd at row %zd; use `usecols` to select a subset and avoid this errorthe dtype passed requires %zd columns but %zd were found at row %zd; use `usecols` to select a subset and avoid this errorarray is too big. Cannot read file as a single array; providing a maximum number of rows to read may help.invalid column index %zd at row %zd with %zd columnscould not convert string %.100R to %S at row %zd, column %zd.non-string returned while reading dataerror reading from object, expected an iterable.readloadtxt(): Parsing an integer via a float is deprecated. To avoid this warning, you can: * make sure the original data is stored as integers. * use the `converters=` keyword argument. If you only use NumPy 1.23 or later, `converters=float` will normally work. * Use `np.loadtxt(...).astype(np.int64)` parsing the file as floating point and then convert it. (On all NumPy versions.) (Deprecated NumPy 1.23)line too long to handle while reading file.too many columns found; cannot read file.Found an unquoted embedded newline within a single line of input. This is currently not supported.,, l , H `tdDhk,7could not broadcast %s from shape %S into shape %SNumPy internal error: name is NULL in %s() at argument %d.NumPy internal error: data is NULL in %s() at argument %d.NumPy internal error: positional argument `|` after keyword only `$` one to %s() at argument %d.NumPy internal error: non-required argument after required | or $ one to %s() at argument %d.NumPy internal error: non-kwarg marked with | or $ to %s() at argument %d.NumPy internal error: function %s() has %d arguments, but the maximum is currently limited to %d for easier parsing; it can be increased by modifying `_NPY_MAX_KWARGS`.integer argument expected, got floatPython int too large to convert to C int%s() takes %d positional arguments but %zd were given%s() takes from %d to %d positional arguments but %zd were givenargument for %s() given by name ('%S') and position (position %zd)converter cleanup of parameter %d to %s() not supported.%s() missing required positional argument %d%s() missing required argument '%S' (pos %d)%s() got an unexpected keyword argument '%S'Identity cache already includes the item.cannot convert longdouble infinity to integercannot convert longdouble NaN to integeroverflow encountered in conversion from python longCould not parse python long as longdouble: %s (%s)Could not parse long as longdouble: %sInternal Numpy error: call to PyUFuncOverride_GetOutObjects with non-dict kwds.0nfinityLength of environment variable '%s' is %d, only %d accepted%sYou cannot disable CPU feature '%s', since it is part of the baseline optimizations: (SSE SSE2 SSE3).%sYou cannot %s CPU features (%s), since they are not part of the dispatched optimizations (SSSE3 SSE41 POPCNT SSE42 AVX F16C FMA3 AVX2 AVX512F AVX512CD AVX512_KNL AVX512_KNM AVX512_SKX AVX512_CLX AVX512_CNL AVX512_ICL).%sYou cannot %s CPU features (%s), since they are not supported by your machine.NumPy was built with baseline optimizations: (SSE SSE2 SSE3) but your machine doesn't support: (%s).Both NPY_DISABLE_CPU_FEATURES and NPY_ENABLE_CPU_FEATURES environment variables cannot be set simultaneously.During parsing environment variable: 'NPY_ENABLE_CPU_FEATURES': During parsing environment variable: 'NPY_DISABLE_CPU_FEATURES': , SSE2SSSE3SSE41POPCNTSSE42F16CFMA3AVX2AVX512FAVX512CDAVX512_KNLAVX512_KNMAVX512_SKXAVX512_CLXAVX512_CNLAVX512_ICLenableNPY_ENABLE_CPU_FEATURESNPY_DISABLE_CPU_FEATURESSSEAVXdisableMMXXOPFMA4AVX512ERAVX512PFAVX5124FMAPSAVX5124VNNIWAVX512VPOPCNTDQAVX512VLAVX512BWAVX512DQAVX512VNNIAVX512IFMAAVX512VBMIAVX512VBMI2AVX512BITALGAVX512FP16AVX512_SPRVSXVSX2VSX3VSX4VXEVXE2NEONNEON_FP16NEON_VFPV4ASIMDFPHPASIMDHPASIMDDPASIMDFHM??On entry to %.*s parameter number %d had an illegal valuenoONOOiO(OO)_UFuncBinaryResolutionError_UFuncInputCastingError_UFuncOutputCastingErrorxfound a user loop for ufunc '%s' matching the type-tuple, but the inputs and/or outputs could not be cast according to the casting ruleufunc 'isnat' is only defined for np.datetime64 and np.timedelta64.ufunc '%s' output (typecode '%c') could not be coerced to provided output parameter (typecode '%c') according to the casting rule '%s'ufunc '%s' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule '%s'Only NumPy must call `ufunc->type_resolver()` explicitly. NumPy ensures that a type-tuple is normalized now to be a tuple only containing None or descriptors. If anything else is passed (you are seeing this message), the `type_resolver()` was called directly by a third party. This is unexpected, please inform the NumPy developers about it. Also note that `type_resolver` will be phased out, since it must be replaced.No loop matching the specified signature and casting was found for ufunc %sufunc %s is configured to use binary comparison type resolution but has the wrong number of inputs or outputsufunc %s is configured to use uniform operation type resolution but has no inputsThe numpy boolean negative, the `-` operator, is not supported, use the `~` operator or the logical_not function instead.numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.Internal NumPy error: new DType in signature not yet supported. (This should be unreachable code!)deciding which one of two abstract dtypes is a better match is not yet implemented. This will pick the better (or bail) in the future.Could not find a loop for the inputs: %S The two promoters %S and %S matched the input equally well. Promoters must be designed to be unambiguous. NOTE: This indicates an error in NumPy or an extending library and should be reported.Info must be a tuple: (tuple of DTypes or None, ArrayMethod or promoter)DType tuple length does not match ufunc number of operandsDType tuple may only contain None and DType classesSecond argument to info must be an ArrayMethod or promoterA loop/promoter has already been registered with '%s' for %RCalling python functions for promotion is not implemented.internal numpy array, logical ufunc was not a ufunc?! during ufunc promotion.test%s encountered in %sUFUNC_PYVALS%s must be a length 3 list.invalid error mask (%d)Warning: %s encountered in %s python callback specified for %s (in %s) but no function found.log specified for %s (in %s) but no object with write method found.buffer size (%d) is not in range (%ld - %ld) or not a multiple of 16python object must be callable or have a callable write methodcannot use legacy wrapping ArrayMethod without calling the ufunc itself. If this error is hit, the solution will be to port the legacy ufunc loop implementation to the new API.getting initial failed because it can only done for legacy ufunc loops when the ufunc is provided.cannot call %s without its ufunc as caller context.legacy_ufunc_wrapper_for_%slogical_xor_NoValue__call__reduceatouterarray_ufunc_errmsg_formatteroperand '%.200s' does not support ufuncs (__array_ufunc__=None)Internal Numpy error: unknown ufunc method '%s' in call to PyUFunc_CheckOverridereduction operation '%s' is not reorderable, so at most one axis may be specifiedoutput parameter for reduction operation %s has the wrong number of dimensions: Found %d but expected %d (must match the operand's when keepdims=True)output parameter for reduction operation %s has the wrong number of dimensions: Found %d but expected %dreduction operation '%s' does not have an identity, so to use a where mask one has to specify 'initial'zero-size array to reduction operation %s which has no identityO(OOi)numpy_1.24_ufunc_call_info_get_strided_loop$fixed_stridesOO|O:atfirst operand must be array%S %sresolve_dtypes$reduction$signaturefunction not supported|where|initial|keepdimstoo many values for 'axis'cannot accumulate on a scalarcannot reduceat on a scalaraxes should be a list.OutputInput$out$extobj$sig$where$axis$axesmatrixfirst(NN)'keepdims' must be a boolean'subok' must be a booleandivide by zerooverflowunderflowinvalid value?expect '('expect '->'expect ','expect ',' or ')'%s at position %d in "%s"unknown user-defined typeloop already registerednumpy.ufuncninnoutnargsntypesidentity_resolve_dtypes_and_context__array_prepare__ must return an ndarray or subclass thereof__array_prepare__ must return an ndarray or subclass thereof which is otherwise identical to its inputThe ufunc implementation for %s with the given dtype signature is not possible under the casting rule %sufunc call_info has already been filled/used!calling get_strided_loop with incompatible context_get_strided_loop(): fixed_strides tuple must contain Python ints or None_get_strided_loop(): fixed_strides must be a tuple or NoneCannot pass a new user DType instance to the `dtype` or `signature` arguments of ufuncs. Pass the DType class instead.The `dtype` and `signature` arguments to ufuncs only select the general DType and not details such as the byte order or time unit. You can avoid this error by using the scalar types `np.float64` or the dtype string notation.%s.at does not support ufunc with non-trivial signature: %s has signature %s.Only unary and binary ufuncs supported at this timeOnly single output ufuncs supported at this timesecond operand needed for ufuncsecond operand provided when ufunc is unaryThe ufunc %s has a custom masked-inner-loop-selector.NumPy assumes that this is NEVER used. If you do make use of this please notify the NumPy developers to discuss future solutions. (See NEP 41 and 43) NumPy will continue, but ignore the custom loop selector. This should only affect performance.the resolved dtypes are not compatible with %s.%s. Resolved (%R, %R, %R)_ufunc_doc_signature_formatterufunc %s has an invalid identityCannot provide `dtype` when a ufunc has no outputsa single item type tuple cannot contain None.The use of a length 1 tuple for the ufunc `signature` is deprecated. Use `dtype` or fill thetuple with `None`s.a type-tuple must be specified of length %d for ufunc '%s'Input DTypes to the signature must not be abstract. The behaviour may be defined in the future.a type-string for %s, %d typecode(s) before and %d after the -> signThe use of a length 1 string for the ufunc `signature` is deprecated. Use `dtype` attribute or pass a tuple with `None`s.the signature object to ufunc must be a string or a tuple.ufunc is not compatible with reduction operations.resolve_dtypes: The dtypes must be a tuple of `ufunc.nargs` length.All input dtypes must be provided (except the first one in reductions)Provided dtype must be a valid NumPy dtype, int, float, complex, or None.Reduction signature must end with None, instead pass the first DType in the signature.Output dtype must not be passed for reductions, pass the first input instead.Reduction not defined on ufunc with signature%s only supported for binary functions%s only supported for functions returning a single valueThe 'out' tuple must have exactly one entry per ufunc outputaccumulate does not allow multiple axesaccumulation currently only supports `object` dtype with referencesprovided out is the wrong size for the accumulation.reduceat does not allow multiple axesindex %ld out-of-bounds in %s.%s [0, %ld)reduceat currently only supports `object` dtype with referencesaxes should be a list with an entry for all %d inputs and outputs; entries for outputs can only be omitted if none of them has core axes.%s: operand %d has %d core dimensions, but %zd dimensions are specified by axes tuple.%s: axes item %d should be a tuple.%s: operand %d has %d core dimensions, but the axes item is a single integer.axes item %d has value %d repeated'out' must be a tuple of arrays'out' must be an array or a tuple with a single arraycannot specify both 'axis' and 'axes'cannot specify 'out' as both a positional and keyword argumentcannot specify both 'signature' and 'dtype'%s.outer() was passed a numpy matrix as %s argument. Special handling of matrix is deprecated and will result in an error in most cases. Please convert the matrix to a NumPy array to retain the old behaviour. You can use `matrix.A` to achieve this.maximum supported dimension for an ndarray is %d, but `%s.outer()` result would have %d.%s.outer() called with ndarray-subclass of type '%s' which modified its shape after a reshape. `outer()` relies on reshaping the inputs and is for example not supported for the 'np.matrix' class (the usage of matrix is generally discouraged). To work around this issue, please convert the inputs to numpy arrays.return arrays must be of ArrayTypeToo many operands when including where= parameter%s does not support keepdims: its signature %s requires %s %d to have %d core dimensions, but keepdims can only be used when all inputs have the same number of core dimensions and all outputs have no core dimensions.%s: axis can only be used with a single shared core dimension, not with the %d distinct ones implied by signature %s.%s: %s operand %d does not have enough dimensions (has %d, gufunc core with signature %s requires %d)%s: %s operand %d has a mismatch in its core dimension %d, with gufunc signature %s (size %zd is different from %zd)%s: Output operand %d has core dimension %d unspecified, with gufunc signature %stoo many dimensions for generalized ufunc %scannot specify both 'sig' and 'signature'method outer is not allowed in ufunc with non-trivial signatureouter product only supported for binary functionsexactly two arguments expectedThe `PyUFunc_GenericFunction()` C-API function has been disabled. Please use `PyObject_Call(ufunc, args, kwargs)`, which has identical behaviour but allows subclass and `__array_ufunc__` override handling and only returns the normal ufunc result.Error object must be a list of length 3expect dimension name or non-zero frozen size? must be used, name already seen with ?? cannot be used, name already seen without ?unexpected end of signature string',' must not be followed by ')'incomplete signature: not all arguments foundCannot construct a ufunc with more than %d operands (requested number were: inputs = %d and outputs = %d)PyUFunc_SetUsesArraysAsData() C-API function has been disabled. It was initially deprecated in NumPy 1.19.A non-compatible loop was already registered for ufunc %s and DTypes %S.unknown user defined struct dtypeuserloop for user dtype not found vuu$v,v(n?,m?)modfnextafterrad2degradianssignbitspacingeuler_gammaERR_IGNOREERR_WARNERR_CALLERR_RAISEERR_PRINTERR_LOGERR_DEFAULTSHIFT_DIVIDEBYZEROSHIFT_OVERFLOWSHIFT_UNDERFLOWSHIFT_INVALIDFPE_DIVIDEBYZEROFPE_OVERFLOWFPE_UNDERFLOWFPE_INVALIDFLOATING_POINT_SUPPORTUFUNC_PYVALS_NAMEUFUNC_BUFSIZE_DEFAULTPINFNINFPZERONZERONANdynamic ufunc based on a python functionCannot change docstring of ufunc with non-NULL docstringThis function used to be the numpy.ones_like, but now a specific function for that has been written for consistency with the other *_like functions. It is only used internally in a limited fashion now. See Also -------- ones_likeCalculate the absolute value element-wise. ``np.abs`` is a shorthand for this function. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- absolute : ndarray An ndarray containing the absolute value of each element in `x`. For complex input, ``a + ib``, the absolute value is :math:`\sqrt{ a^2 + b^2 }`. This is a scalar if `x` is a scalar. Examples -------- >>> x = np.array([-1.2, 1.2]) >>> np.absolute(x) array([ 1.2, 1.2]) >>> np.absolute(1.2 + 1j) 1.5620499351813308 Plot the function over ``[-10, 10]``: >>> import matplotlib.pyplot as plt >>> x = np.linspace(start=-10, stop=10, num=101) >>> plt.plot(x, np.absolute(x)) >>> plt.show() Plot the function over the complex plane: >>> xx = x + 1j * x[:, np.newaxis] >>> plt.imshow(np.abs(xx), extent=[-10, 10, -10, 10], cmap='gray') >>> plt.show() The `abs` function can be used as a shorthand for ``np.absolute`` on ndarrays. >>> x = np.array([-1.2, 1.2]) >>> abs(x) array([1.2, 1.2])Add arguments element-wise. Parameters ---------- x1, x2 : array_like The arrays to be added. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- add : ndarray or scalar The sum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. Notes ----- Equivalent to `x1` + `x2` in terms of array broadcasting. Examples -------- >>> np.add(1.0, 4.0) 5.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.add(x1, x2) array([[ 0., 2., 4.], [ 3., 5., 7.], [ 6., 8., 10.]]) The ``+`` operator can be used as a shorthand for ``np.add`` on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 + x2 array([[ 0., 2., 4.], [ 3., 5., 7.], [ 6., 8., 10.]])cannot add indexed loop to ufunc add with NPY_BYTENot a PyArrayMethodObject in ufunc add with NPY_BYTEcannot add indexed loop to ufunc add with NPY_UBYTENot a PyArrayMethodObject in ufunc add with NPY_UBYTEcannot add indexed loop to ufunc add with NPY_SHORTNot a PyArrayMethodObject in ufunc add with NPY_SHORTcannot add indexed loop to ufunc add with NPY_USHORTNot a PyArrayMethodObject in ufunc add with NPY_USHORTcannot add indexed loop to ufunc add with NPY_INTNot a PyArrayMethodObject in ufunc add with NPY_INTcannot add indexed loop to ufunc add with NPY_UINTNot a PyArrayMethodObject in ufunc add with NPY_UINTcannot add indexed loop to ufunc add with NPY_LONGNot a PyArrayMethodObject in ufunc add with NPY_LONGcannot add indexed loop to ufunc add with NPY_ULONGNot a PyArrayMethodObject in ufunc add with NPY_ULONGcannot add indexed loop to ufunc add with NPY_LONGLONGNot a PyArrayMethodObject in ufunc add with NPY_LONGLONGcannot add indexed loop to ufunc add with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc add with NPY_ULONGLONGcannot add indexed loop to ufunc add with NPY_HALFNot a PyArrayMethodObject in ufunc add with NPY_HALFcannot add indexed loop to ufunc add with NPY_FLOATNot a PyArrayMethodObject in ufunc add with NPY_FLOATcannot add indexed loop to ufunc add with NPY_DOUBLENot a PyArrayMethodObject in ufunc add with NPY_DOUBLEcannot add indexed loop to ufunc add with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc add with NPY_LONGDOUBLEcannot add indexed loop to ufunc add with NPY_CFLOATNot a PyArrayMethodObject in ufunc add with NPY_CFLOATcannot add indexed loop to ufunc add with NPY_CDOUBLENot a PyArrayMethodObject in ufunc add with NPY_CDOUBLEcannot add indexed loop to ufunc add with NPY_CLONGDOUBLENot a PyArrayMethodObject in ufunc add with NPY_CLONGDOUBLETrigonometric inverse cosine, element-wise. The inverse of `cos` so that, if ``y = cos(x)``, then ``x = arccos(y)``. Parameters ---------- x : array_like `x`-coordinate on the unit circle. For real arguments, the domain is [-1, 1]. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- angle : ndarray The angle of the ray intersecting the unit circle at the given `x`-coordinate in radians [0, pi]. This is a scalar if `x` is a scalar. See Also -------- cos, arctan, arcsin, emath.arccos Notes ----- `arccos` is a multivalued function: for each `x` there are infinitely many numbers `z` such that ``cos(z) = x``. The convention is to return the angle `z` whose real part lies in `[0, pi]`. For real-valued input data types, `arccos` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arccos` is a complex analytic function that has branch cuts ``[-inf, -1]`` and `[1, inf]` and is continuous from above on the former and from below on the latter. The inverse `cos` is also known as `acos` or cos^-1. References ---------- M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 79. https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- We expect the arccos of 1 to be 0, and of -1 to be pi: >>> np.arccos([1, -1]) array([ 0. , 3.14159265]) Plot arccos: >>> import matplotlib.pyplot as plt >>> x = np.linspace(-1, 1, num=100) >>> plt.plot(x, np.arccos(x)) >>> plt.axis('tight') >>> plt.show()Inverse hyperbolic cosine, element-wise. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- arccosh : ndarray Array of the same shape as `x`. This is a scalar if `x` is a scalar. See Also -------- cosh, arcsinh, sinh, arctanh, tanh Notes ----- `arccosh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `cosh(z) = x`. The convention is to return the `z` whose imaginary part lies in ``[-pi, pi]`` and the real part in ``[0, inf]``. For real-valued input data types, `arccosh` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arccosh` is a complex analytical function that has a branch cut `[-inf, 1]` and is continuous from above on it. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 86. https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arccosh Examples -------- >>> np.arccosh([np.e, 10.0]) array([ 1.65745445, 2.99322285]) >>> np.arccosh(1) 0.0Inverse sine, element-wise. Parameters ---------- x : array_like `y`-coordinate on the unit circle. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- angle : ndarray The inverse sine of each element in `x`, in radians and in the closed interval ``[-pi/2, pi/2]``. This is a scalar if `x` is a scalar. See Also -------- sin, cos, arccos, tan, arctan, arctan2, emath.arcsin Notes ----- `arcsin` is a multivalued function: for each `x` there are infinitely many numbers `z` such that :math:`sin(z) = x`. The convention is to return the angle `z` whose real part lies in [-pi/2, pi/2]. For real-valued input data types, *arcsin* always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arcsin` is a complex analytic function that has, by convention, the branch cuts [-inf, -1] and [1, inf] and is continuous from above on the former and from below on the latter. The inverse sine is also known as `asin` or sin^{-1}. References ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79ff. https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- >>> np.arcsin(1) # pi/2 1.5707963267948966 >>> np.arcsin(-1) # -pi/2 -1.5707963267948966 >>> np.arcsin(0) 0.0Inverse hyperbolic sine element-wise. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Array of the same shape as `x`. This is a scalar if `x` is a scalar. Notes ----- `arcsinh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `sinh(z) = x`. The convention is to return the `z` whose imaginary part lies in `[-pi/2, pi/2]`. For real-valued input data types, `arcsinh` always returns real output. For each value that cannot be expressed as a real number or infinity, it returns ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arccos` is a complex analytical function that has branch cuts `[1j, infj]` and `[-1j, -infj]` and is continuous from the right on the former and from the left on the latter. The inverse hyperbolic sine is also known as `asinh` or ``sinh^-1``. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 86. https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arcsinh Examples -------- >>> np.arcsinh(np.array([np.e, 10.0])) array([ 1.72538256, 2.99822295])Trigonometric inverse tangent, element-wise. The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``. Parameters ---------- x : array_like out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Out has the same shape as `x`. Its real part is in ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``). This is a scalar if `x` is a scalar. See Also -------- arctan2 : The "four quadrant" arctan of the angle formed by (`x`, `y`) and the positive `x`-axis. angle : Argument of complex values. Notes ----- `arctan` is a multi-valued function: for each `x` there are infinitely many numbers `z` such that tan(`z`) = `x`. The convention is to return the angle `z` whose real part lies in [-pi/2, pi/2]. For real-valued input data types, `arctan` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arctan` is a complex analytic function that has [``1j, infj``] and [``-1j, -infj``] as branch cuts, and is continuous from the left on the former and from the right on the latter. The inverse tangent is also known as `atan` or tan^{-1}. References ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79. https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- We expect the arctan of 0 to be 0, and of 1 to be pi/4: >>> np.arctan([0, 1]) array([ 0. , 0.78539816]) >>> np.pi/4 0.78539816339744828 Plot arctan: >>> import matplotlib.pyplot as plt >>> x = np.linspace(-10, 10) >>> plt.plot(x, np.arctan(x)) >>> plt.axis('tight') >>> plt.show()Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly. The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and passing through the point (`x2`, `x1`). (Note the role reversal: the "`y`-coordinate" is the first function parameter, the "`x`-coordinate" is the second.) By IEEE convention, this function is defined for `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see Notes for specific values). This function is not defined for complex-valued arguments; for the so-called argument of complex values, use `angle`. Parameters ---------- x1 : array_like, real-valued `y`-coordinates. x2 : array_like, real-valued `x`-coordinates. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- angle : ndarray Array of angles in radians, in the range ``[-pi, pi]``. This is a scalar if both `x1` and `x2` are scalars. See Also -------- arctan, tan, angle Notes ----- *arctan2* is identical to the `atan2` function of the underlying C library. The following special values are defined in the C standard: [1]_ ====== ====== ================ `x1` `x2` `arctan2(x1,x2)` ====== ====== ================ +/- 0 +0 +/- 0 +/- 0 -0 +/- pi > 0 +/-inf +0 / +pi < 0 +/-inf -0 / -pi +/-inf +inf +/- (pi/4) +/-inf -inf +/- (3*pi/4) ====== ====== ================ Note that +0 and -0 are distinct floating point numbers, as are +inf and -inf. References ---------- .. [1] ISO/IEC standard 9899:1999, "Programming language C." Examples -------- Consider four points in different quadrants: >>> x = np.array([-1, +1, +1, -1]) >>> y = np.array([-1, -1, +1, +1]) >>> np.arctan2(y, x) * 180 / np.pi array([-135., -45., 45., 135.]) Note the order of the parameters. `arctan2` is defined also when `x2` = 0 and at several other special points, obtaining values in the range ``[-pi, pi]``: >>> np.arctan2([1., -1.], [0., 0.]) array([ 1.57079633, -1.57079633]) >>> np.arctan2([0., 0., np.inf], [+0., -0., np.inf]) array([0. , 3.14159265, 0.78539816])Inverse hyperbolic tangent element-wise. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Array of the same shape as `x`. This is a scalar if `x` is a scalar. See Also -------- emath.arctanh Notes ----- `arctanh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that ``tanh(z) = x``. The convention is to return the `z` whose imaginary part lies in `[-pi/2, pi/2]`. For real-valued input data types, `arctanh` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `arctanh` is a complex analytical function that has branch cuts `[-1, -inf]` and `[1, inf]` and is continuous from above on the former and from below on the latter. The inverse hyperbolic tangent is also known as `atanh` or ``tanh^-1``. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 86. https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arctanh Examples -------- >>> np.arctanh([0, -0.5]) array([ 0. , -0.54930614])Compute the bit-wise AND of two arrays element-wise. Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``&``. Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_and bitwise_or bitwise_xor binary_repr : Return the binary representation of the input number as a string. Examples -------- The number 13 is represented by ``00001101``. Likewise, 17 is represented by ``00010001``. The bit-wise AND of 13 and 17 is therefore ``000000001``, or 1: >>> np.bitwise_and(13, 17) 1 >>> np.bitwise_and(14, 13) 12 >>> np.binary_repr(12) '1100' >>> np.bitwise_and([14,3], 13) array([12, 1]) >>> np.bitwise_and([11,7], [4,25]) array([0, 1]) >>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16])) array([ 2, 4, 16]) >>> np.bitwise_and([True, True], [False, True]) array([False, True]) The ``&`` operator can be used as a shorthand for ``np.bitwise_and`` on ndarrays. >>> x1 = np.array([2, 5, 255]) >>> x2 = np.array([3, 14, 16]) >>> x1 & x2 array([ 2, 4, 16])Compute the bit-wise OR of two arrays element-wise. Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``|``. Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_or bitwise_and bitwise_xor binary_repr : Return the binary representation of the input number as a string. Examples -------- The number 13 has the binary representation ``00001101``. Likewise, 16 is represented by ``00010000``. The bit-wise OR of 13 and 16 is then ``00011101``, or 29: >>> np.bitwise_or(13, 16) 29 >>> np.binary_repr(29) '11101' >>> np.bitwise_or(32, 2) 34 >>> np.bitwise_or([33, 4], 1) array([33, 5]) >>> np.bitwise_or([33, 4], [1, 2]) array([33, 6]) >>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4])) array([ 6, 5, 255]) >>> np.array([2, 5, 255]) | np.array([4, 4, 4]) array([ 6, 5, 255]) >>> np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32), ... np.array([4, 4, 4, 2147483647], dtype=np.int32)) array([ 6, 5, 255, 2147483647]) >>> np.bitwise_or([True, True], [False, True]) array([ True, True]) The ``|`` operator can be used as a shorthand for ``np.bitwise_or`` on ndarrays. >>> x1 = np.array([2, 5, 255]) >>> x2 = np.array([4, 4, 4]) >>> x1 | x2 array([ 6, 5, 255])Compute the bit-wise XOR of two arrays element-wise. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``^``. Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_xor bitwise_and bitwise_or binary_repr : Return the binary representation of the input number as a string. Examples -------- The number 13 is represented by ``00001101``. Likewise, 17 is represented by ``00010001``. The bit-wise XOR of 13 and 17 is therefore ``00011100``, or 28: >>> np.bitwise_xor(13, 17) 28 >>> np.binary_repr(28) '11100' >>> np.bitwise_xor(31, 5) 26 >>> np.bitwise_xor([31,3], 5) array([26, 6]) >>> np.bitwise_xor([31,3], [5,6]) array([26, 5]) >>> np.bitwise_xor([True, True], [False, True]) array([ True, False]) The ``^`` operator can be used as a shorthand for ``np.bitwise_xor`` on ndarrays. >>> x1 = np.array([True, True]) >>> x2 = np.array([False, True]) >>> x1 ^ x2 array([ True, False])Return the cube-root of an array, element-wise. .. versionadded:: 1.10.0 Parameters ---------- x : array_like The values whose cube-roots are required. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray An array of the same shape as `x`, containing the cube cube-root of each element in `x`. If `out` was provided, `y` is a reference to it. This is a scalar if `x` is a scalar. Examples -------- >>> np.cbrt([1,8,27]) array([ 1., 2., 3.])Return the ceiling of the input, element-wise. The ceil of the scalar `x` is the smallest integer `i`, such that ``i >= x``. It is often denoted as :math:`\lceil x \rceil`. Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The ceiling of each element in `x`, with `float` dtype. This is a scalar if `x` is a scalar. See Also -------- floor, trunc, rint, fix Examples -------- >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.ceil(a) array([-1., -1., -0., 1., 2., 2., 2.])Clip (limit) the values in an array. Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of ``[0, 1]`` is specified, values smaller than 0 become 0, and values larger than 1 become 1. Equivalent to but faster than ``np.minimum(np.maximum(a, a_min), a_max)``. Parameters ---------- a : array_like Array containing elements to clip. a_min : array_like Minimum value. a_max : array_like Maximum value. out : ndarray, optional The results will be placed in this array. It may be the input array for in-place clipping. `out` must be of the right shape to hold the output. Its type is preserved. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. See Also -------- numpy.clip : Wrapper that makes the ``a_min`` and ``a_max`` arguments optional, dispatching to one of `~numpy.core.umath.clip`, `~numpy.core.umath.minimum`, and `~numpy.core.umath.maximum`. Returns ------- clipped_array : ndarray An array with the elements of `a`, but where values < `a_min` are replaced with `a_min`, and those > `a_max` with `a_max`.Return the complex conjugate, element-wise. The complex conjugate of a complex number is obtained by changing the sign of its imaginary part. Parameters ---------- x : array_like Input value. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The complex conjugate of `x`, with same dtype as `y`. This is a scalar if `x` is a scalar. Notes ----- `conj` is an alias for `conjugate`: >>> np.conj is np.conjugate True Examples -------- >>> np.conjugate(1+2j) (1-2j) >>> x = np.eye(2) + 1j * np.eye(2) >>> np.conjugate(x) array([[ 1.-1.j, 0.-0.j], [ 0.-0.j, 1.-1.j]])Change the sign of x1 to that of x2, element-wise. If `x2` is a scalar, its sign will be copied to all elements of `x1`. Parameters ---------- x1 : array_like Values to change the sign of. x2 : array_like The sign of `x2` is copied to `x1`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar The values of `x1` with the sign of `x2`. This is a scalar if both `x1` and `x2` are scalars. Examples -------- >>> np.copysign(1.3, -1) -1.3 >>> 1/np.copysign(0, 1) inf >>> 1/np.copysign(0, -1) -inf >>> np.copysign([-1, 0, 1], -1.1) array([-1., -0., -1.]) >>> np.copysign([-1, 0, 1], np.arange(3)-1) array([-1., 0., 1.])Cosine element-wise. Parameters ---------- x : array_like Input array in radians. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding cosine values. This is a scalar if `x` is a scalar. Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples) References ---------- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972. Examples -------- >>> np.cos(np.array([0, np.pi/2, np.pi])) array([ 1.00000000e+00, 6.12303177e-17, -1.00000000e+00]) >>> >>> # Example of providing the optional output parameter >>> out1 = np.array([0], dtype='d') >>> out2 = np.cos([0.1], out1) >>> out2 is out1 True >>> >>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.cos(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File "", line 1, in ValueError: operands could not be broadcast together with shapes (3,3) (2,2)Hyperbolic cosine, element-wise. Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array of same shape as `x`. This is a scalar if `x` is a scalar. Examples -------- >>> np.cosh(0) 1.0 The hyperbolic cosine describes the shape of a hanging cable: >>> import matplotlib.pyplot as plt >>> x = np.linspace(-4, 4, 1000) >>> plt.plot(x, np.cosh(x)) >>> plt.show()Convert angles from degrees to radians. Parameters ---------- x : array_like Angles in degrees. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding angle in radians. This is a scalar if `x` is a scalar. See Also -------- rad2deg : Convert angles from radians to degrees. unwrap : Remove large jumps in angle by wrapping. Notes ----- .. versionadded:: 1.3.0 ``deg2rad(x)`` is ``x * pi / 180``. Examples -------- >>> np.deg2rad(180) 3.1415926535897931Convert angles from radians to degrees. Parameters ---------- x : array_like Input array in radians. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray of floats The corresponding degree values; if `out` was supplied this is a reference to it. This is a scalar if `x` is a scalar. See Also -------- rad2deg : equivalent function Examples -------- Convert a radian array to degrees >>> rad = np.arange(12.)*np.pi/6 >>> np.degrees(rad) array([ 0., 30., 60., 90., 120., 150., 180., 210., 240., 270., 300., 330.]) >>> out = np.zeros((rad.shape)) >>> r = np.degrees(rad, out) >>> np.all(r == out) TrueDivide arguments element-wise. Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The quotient ``x1/x2``, element-wise. This is a scalar if both `x1` and `x2` are scalars. See Also -------- seterr : Set whether to raise or warn on overflow, underflow and division by zero. Notes ----- Equivalent to ``x1`` / ``x2`` in terms of array-broadcasting. The ``true_divide(x1, x2)`` function is an alias for ``divide(x1, x2)``. Examples -------- >>> np.divide(2.0, 4.0) 0.5 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.divide(x1, x2) array([[nan, 1. , 1. ], [inf, 4. , 2.5], [inf, 7. , 4. ]]) The ``/`` operator can be used as a shorthand for ``np.divide`` on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = 2 * np.ones(3) >>> x1 / x2 array([[0. , 0.5, 1. ], [1.5, 2. , 2.5], [3. , 3.5, 4. ]])cannot add indexed loop to ufunc divide with NPY_HALFNot a PyArrayMethodObject in ufunc divide with NPY_HALFcannot add indexed loop to ufunc divide with NPY_FLOATNot a PyArrayMethodObject in ufunc divide with NPY_FLOATcannot add indexed loop to ufunc divide with NPY_DOUBLENot a PyArrayMethodObject in ufunc divide with NPY_DOUBLEcannot add indexed loop to ufunc divide with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc divide with NPY_LONGDOUBLEReturn element-wise quotient and remainder simultaneously. .. versionadded:: 1.13.0 ``np.divmod(x, y)`` is equivalent to ``(x // y, x % y)``, but faster because it avoids redundant work. It is used to implement the Python built-in function ``divmod`` on NumPy arrays. Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out1 : ndarray Element-wise quotient resulting from floor division. This is a scalar if both `x1` and `x2` are scalars. out2 : ndarray Element-wise remainder from floor division. This is a scalar if both `x1` and `x2` are scalars. See Also -------- floor_divide : Equivalent to Python's ``//`` operator. remainder : Equivalent to Python's ``%`` operator. modf : Equivalent to ``divmod(x, 1)`` for positive ``x`` with the return values switched. Examples -------- >>> np.divmod(np.arange(5), 3) (array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1])) The `divmod` function can be used as a shorthand for ``np.divmod`` on ndarrays. >>> x = np.arange(5) >>> divmod(x, 3) (array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))Return (x1 == x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- not_equal, greater_equal, less_equal, greater, less Examples -------- >>> np.equal([0, 1, 3], np.arange(3)) array([ True, True, False]) What is compared are values, not types. So an int (1) and an array of length one can evaluate as True: >>> np.equal(1, np.ones(1)) array([ True]) The ``==`` operator can be used as a shorthand for ``np.equal`` on ndarrays. >>> a = np.array([2, 4, 6]) >>> b = np.array([2, 4, 2]) >>> a == b array([ True, True, False])Calculate the exponential of all elements in the input array. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise exponential of `x`. This is a scalar if `x` is a scalar. See Also -------- expm1 : Calculate ``exp(x) - 1`` for all elements in the array. exp2 : Calculate ``2**x`` for all elements in the array. Notes ----- The irrational number ``e`` is also known as Euler's number. It is approximately 2.718281, and is the base of the natural logarithm, ``ln`` (this means that, if :math:`x = \ln y = \log_e y`, then :math:`e^x = y`. For real input, ``exp(x)`` is always positive. For complex arguments, ``x = a + ib``, we can write :math:`e^x = e^a e^{ib}`. The first term, :math:`e^a`, is already known (it is the real argument, described above). The second term, :math:`e^{ib}`, is :math:`\cos b + i \sin b`, a function with magnitude 1 and a periodic phase. References ---------- .. [1] Wikipedia, "Exponential function", https://en.wikipedia.org/wiki/Exponential_function .. [2] M. Abramovitz and I. A. Stegun, "Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables," Dover, 1964, p. 69, https://personal.math.ubc.ca/~cbm/aands/page_69.htm Examples -------- Plot the magnitude and phase of ``exp(x)`` in the complex plane: >>> import matplotlib.pyplot as plt >>> x = np.linspace(-2*np.pi, 2*np.pi, 100) >>> xx = x + 1j * x[:, np.newaxis] # a + ib over complex plane >>> out = np.exp(xx) >>> plt.subplot(121) >>> plt.imshow(np.abs(out), ... extent=[-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi], cmap='gray') >>> plt.title('Magnitude of exp(x)') >>> plt.subplot(122) >>> plt.imshow(np.angle(out), ... extent=[-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi], cmap='hsv') >>> plt.title('Phase (angle) of exp(x)') >>> plt.show()Calculate `2**p` for all `p` in the input array. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Element-wise 2 to the power `x`. This is a scalar if `x` is a scalar. See Also -------- power Notes ----- .. versionadded:: 1.3.0 Examples -------- >>> np.exp2([2, 3]) array([ 4., 8.])Calculate ``exp(x) - 1`` for all elements in the array. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Element-wise exponential minus one: ``out = exp(x) - 1``. This is a scalar if `x` is a scalar. See Also -------- log1p : ``log(1 + x)``, the inverse of expm1. Notes ----- This function provides greater precision than ``exp(x) - 1`` for small values of ``x``. Examples -------- The true value of ``exp(1e-10) - 1`` is ``1.00000000005e-10`` to about 32 significant digits. This example shows the superiority of expm1 in this case. >>> np.expm1(1e-10) 1.00000000005e-10 >>> np.exp(1e-10) - 1 1.000000082740371e-10Compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in `x`. Complex values are not handled, use `absolute` to find the absolute values of complex data. Parameters ---------- x : array_like The array of numbers for which the absolute values are required. If `x` is a scalar, the result `y` will also be a scalar. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The absolute values of `x`, the returned values are always floats. This is a scalar if `x` is a scalar. See Also -------- absolute : Absolute values including `complex` types. Examples -------- >>> np.fabs(-1) 1.0 >>> np.fabs([-1.2, 1.2]) array([ 1.2, 1.2])First array elements raised to powers from second array, element-wise. Raise each base in `x1` to the positionally-corresponding power in `x2`. `x1` and `x2` must be broadcastable to the same shape. This differs from the power function in that integers, float16, and float32 are promoted to floats with a minimum precision of float64 so that the result is always inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers. Negative values raised to a non-integral value will return ``nan``. To get complex results, cast the input to complex, or specify the ``dtype`` to be ``complex`` (see the example below). .. versionadded:: 1.12.0 Parameters ---------- x1 : array_like The bases. x2 : array_like The exponents. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The bases in `x1` raised to the exponents in `x2`. This is a scalar if both `x1` and `x2` are scalars. See Also -------- power : power function that preserves type Examples -------- Cube each element in a list. >>> x1 = range(6) >>> x1 [0, 1, 2, 3, 4, 5] >>> np.float_power(x1, 3) array([ 0., 1., 8., 27., 64., 125.]) Raise the bases to different exponents. >>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0] >>> np.float_power(x1, x2) array([ 0., 1., 8., 27., 16., 5.]) The effect of broadcasting. >>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]]) >>> x2 array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]]) >>> np.float_power(x1, x2) array([[ 0., 1., 8., 27., 16., 5.], [ 0., 1., 8., 27., 16., 5.]]) Negative values raised to a non-integral value will result in ``nan`` (and a warning will be generated). >>> x3 = np.array([-1, -4]) >>> with np.errstate(invalid='ignore'): ... p = np.float_power(x3, 1.5) ... >>> p array([nan, nan]) To get complex results, give the argument ``dtype=complex``. >>> np.float_power(x3, 1.5, dtype=complex) array([-1.83697020e-16-1.j, -1.46957616e-15-8.j])Return the floor of the input, element-wise. The floor of the scalar `x` is the largest integer `i`, such that `i <= x`. It is often denoted as :math:`\lfloor x \rfloor`. Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The floor of each element in `x`. This is a scalar if `x` is a scalar. See Also -------- ceil, trunc, rint, fix Notes ----- Some spreadsheet programs calculate the "floor-towards-zero", where ``floor(-2.5) == -2``. NumPy instead uses the definition of `floor` where `floor(-2.5) == -3`. The "floor-towards-zero" function is called ``fix`` in NumPy. Examples -------- >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.floor(a) array([-2., -2., -1., 0., 1., 1., 2.])Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python ``//`` operator and pairs with the Python ``%`` (`remainder`), function so that ``a = a % b + b * (a // b)`` up to roundoff. Parameters ---------- x1 : array_like Numerator. x2 : array_like Denominator. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray y = floor(`x1`/`x2`) This is a scalar if both `x1` and `x2` are scalars. See Also -------- remainder : Remainder complementary to floor_divide. divmod : Simultaneous floor division and remainder. divide : Standard division. floor : Round a number to the nearest integer toward minus infinity. ceil : Round a number to the nearest integer toward infinity. Examples -------- >>> np.floor_divide(7,3) 2 >>> np.floor_divide([1., 2., 3., 4.], 2.5) array([ 0., 0., 1., 1.]) The ``//`` operator can be used as a shorthand for ``np.floor_divide`` on ndarrays. >>> x1 = np.array([1., 2., 3., 4.]) >>> x1 // 2.5 array([0., 0., 1., 1.])cannot add indexed loop to ufunc floor_divide with NPY_HALFNot a PyArrayMethodObject in ufunc floor_divide with NPY_HALFcannot add indexed loop to ufunc floor_divide with NPY_FLOATNot a PyArrayMethodObject in ufunc floor_divide with NPY_FLOATcannot add indexed loop to ufunc floor_divide with NPY_DOUBLENot a PyArrayMethodObject in ufunc floor_divide with NPY_DOUBLEcannot add indexed loop to ufunc floor_divide with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc floor_divide with NPY_LONGDOUBLEcannot add indexed loop to ufunc floor_divide with NPY_BYTENot a PyArrayMethodObject in ufunc floor_divide with NPY_BYTEcannot add indexed loop to ufunc floor_divide with NPY_UBYTENot a PyArrayMethodObject in ufunc floor_divide with NPY_UBYTEcannot add indexed loop to ufunc floor_divide with NPY_SHORTNot a PyArrayMethodObject in ufunc floor_divide with NPY_SHORTcannot add indexed loop to ufunc floor_divide with NPY_USHORTNot a PyArrayMethodObject in ufunc floor_divide with NPY_USHORTcannot add indexed loop to ufunc floor_divide with NPY_INTNot a PyArrayMethodObject in ufunc floor_divide with NPY_INTcannot add indexed loop to ufunc floor_divide with NPY_UINTNot a PyArrayMethodObject in ufunc floor_divide with NPY_UINTcannot add indexed loop to ufunc floor_divide with NPY_LONGNot a PyArrayMethodObject in ufunc floor_divide with NPY_LONGcannot add indexed loop to ufunc floor_divide with NPY_ULONGNot a PyArrayMethodObject in ufunc floor_divide with NPY_ULONGcannot add indexed loop to ufunc floor_divide with NPY_LONGLONGNot a PyArrayMethodObject in ufunc floor_divide with NPY_LONGLONGcannot add indexed loop to ufunc floor_divide with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc floor_divide with NPY_ULONGLONGElement-wise maximum of array elements. Compare two arrays and return a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The maximum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. See Also -------- fmin : Element-wise minimum of two arrays, ignores NaNs. maximum : Element-wise maximum of two arrays, propagates NaNs. amax : The maximum value of an array along a given axis, propagates NaNs. nanmax : The maximum value of an array along a given axis, ignores NaNs. minimum, amin, nanmin Notes ----- .. versionadded:: 1.3.0 The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting. Examples -------- >>> np.fmax([2, 3, 4], [1, 5, 2]) array([ 2., 5., 4.]) >>> np.fmax(np.eye(2), [0.5, 2]) array([[ 1. , 2. ], [ 0.5, 2. ]]) >>> np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan]) array([ 0., 0., nan])cannot add indexed loop to ufunc fmax with NPY_HALFNot a PyArrayMethodObject in ufunc fmax with NPY_HALFcannot add indexed loop to ufunc fmax with NPY_FLOATNot a PyArrayMethodObject in ufunc fmax with NPY_FLOATcannot add indexed loop to ufunc fmax with NPY_DOUBLENot a PyArrayMethodObject in ufunc fmax with NPY_DOUBLEcannot add indexed loop to ufunc fmax with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc fmax with NPY_LONGDOUBLEcannot add indexed loop to ufunc fmax with NPY_BYTENot a PyArrayMethodObject in ufunc fmax with NPY_BYTEcannot add indexed loop to ufunc fmax with NPY_UBYTENot a PyArrayMethodObject in ufunc fmax with NPY_UBYTEcannot add indexed loop to ufunc fmax with NPY_SHORTNot a PyArrayMethodObject in ufunc fmax with NPY_SHORTcannot add indexed loop to ufunc fmax with NPY_USHORTNot a PyArrayMethodObject in ufunc fmax with NPY_USHORTcannot add indexed loop to ufunc fmax with NPY_INTNot a PyArrayMethodObject in ufunc fmax with NPY_INTcannot add indexed loop to ufunc fmax with NPY_UINTNot a PyArrayMethodObject in ufunc fmax with NPY_UINTcannot add indexed loop to ufunc fmax with NPY_LONGNot a PyArrayMethodObject in ufunc fmax with NPY_LONGcannot add indexed loop to ufunc fmax with NPY_ULONGNot a PyArrayMethodObject in ufunc fmax with NPY_ULONGcannot add indexed loop to ufunc fmax with NPY_LONGLONGNot a PyArrayMethodObject in ufunc fmax with NPY_LONGLONGcannot add indexed loop to ufunc fmax with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc fmax with NPY_ULONGLONGElement-wise minimum of array elements. Compare two arrays and return a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The minimum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. See Also -------- fmax : Element-wise maximum of two arrays, ignores NaNs. minimum : Element-wise minimum of two arrays, propagates NaNs. amin : The minimum value of an array along a given axis, propagates NaNs. nanmin : The minimum value of an array along a given axis, ignores NaNs. maximum, amax, nanmax Notes ----- .. versionadded:: 1.3.0 The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting. Examples -------- >>> np.fmin([2, 3, 4], [1, 5, 2]) array([1, 3, 2]) >>> np.fmin(np.eye(2), [0.5, 2]) array([[ 0.5, 0. ], [ 0. , 1. ]]) >>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan]) array([ 0., 0., nan])cannot add indexed loop to ufunc fmin with NPY_HALFNot a PyArrayMethodObject in ufunc fmin with NPY_HALFcannot add indexed loop to ufunc fmin with NPY_FLOATNot a PyArrayMethodObject in ufunc fmin with NPY_FLOATcannot add indexed loop to ufunc fmin with NPY_DOUBLENot a PyArrayMethodObject in ufunc fmin with NPY_DOUBLEcannot add indexed loop to ufunc fmin with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc fmin with NPY_LONGDOUBLEcannot add indexed loop to ufunc fmin with NPY_BYTENot a PyArrayMethodObject in ufunc fmin with NPY_BYTEcannot add indexed loop to ufunc fmin with NPY_UBYTENot a PyArrayMethodObject in ufunc fmin with NPY_UBYTEcannot add indexed loop to ufunc fmin with NPY_SHORTNot a PyArrayMethodObject in ufunc fmin with NPY_SHORTcannot add indexed loop to ufunc fmin with NPY_USHORTNot a PyArrayMethodObject in ufunc fmin with NPY_USHORTcannot add indexed loop to ufunc fmin with NPY_INTNot a PyArrayMethodObject in ufunc fmin with NPY_INTcannot add indexed loop to ufunc fmin with NPY_UINTNot a PyArrayMethodObject in ufunc fmin with NPY_UINTcannot add indexed loop to ufunc fmin with NPY_LONGNot a PyArrayMethodObject in ufunc fmin with NPY_LONGcannot add indexed loop to ufunc fmin with NPY_ULONGNot a PyArrayMethodObject in ufunc fmin with NPY_ULONGcannot add indexed loop to ufunc fmin with NPY_LONGLONGNot a PyArrayMethodObject in ufunc fmin with NPY_LONGLONGcannot add indexed loop to ufunc fmin with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc fmin with NPY_ULONGLONGReturns the element-wise remainder of division. This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend `x1`. It is equivalent to the Matlab(TM) ``rem`` function and should not be confused with the Python modulus operator ``x1 % x2``. Parameters ---------- x1 : array_like Dividend. x2 : array_like Divisor. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : array_like The remainder of the division of `x1` by `x2`. This is a scalar if both `x1` and `x2` are scalars. See Also -------- remainder : Equivalent to the Python ``%`` operator. divide Notes ----- The result of the modulo operation for negative dividend and divisors is bound by conventions. For `fmod`, the sign of result is the sign of the dividend, while for `remainder` the sign of the result is the sign of the divisor. The `fmod` function is equivalent to the Matlab(TM) ``rem`` function. Examples -------- >>> np.fmod([-3, -2, -1, 1, 2, 3], 2) array([-1, 0, -1, 1, 0, 1]) >>> np.remainder([-3, -2, -1, 1, 2, 3], 2) array([1, 0, 1, 1, 0, 1]) >>> np.fmod([5, 3], [2, 2.]) array([ 1., 1.]) >>> a = np.arange(-3, 3).reshape(3, 2) >>> a array([[-3, -2], [-1, 0], [ 1, 2]]) >>> np.fmod(a, [2,2]) array([[-1, 0], [-1, 0], [ 1, 0]])Decompose the elements of x into mantissa and twos exponent. Returns (`mantissa`, `exponent`), where ``x = mantissa * 2**exponent``. The mantissa lies in the open interval(-1, 1), while the twos exponent is a signed integer. Parameters ---------- x : array_like Array of numbers to be decomposed. out1 : ndarray, optional Output array for the mantissa. Must have the same shape as `x`. out2 : ndarray, optional Output array for the exponent. Must have the same shape as `x`. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- mantissa : ndarray Floating values between -1 and 1. This is a scalar if `x` is a scalar. exponent : ndarray Integer exponents of 2. This is a scalar if `x` is a scalar. See Also -------- ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`. Notes ----- Complex dtypes are not supported, they will raise a TypeError. Examples -------- >>> x = np.arange(9) >>> y1, y2 = np.frexp(x) >>> y1 array([ 0. , 0.5 , 0.5 , 0.75 , 0.5 , 0.625, 0.75 , 0.875, 0.5 ]) >>> y2 array([0, 1, 2, 2, 3, 3, 3, 3, 4]) >>> y1 * 2**y2 array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.])Returns the greatest common divisor of ``|x1|`` and ``|x2|`` Parameters ---------- x1, x2 : array_like, int Arrays of values. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). Returns ------- y : ndarray or scalar The greatest common divisor of the absolute value of the inputs This is a scalar if both `x1` and `x2` are scalars. See Also -------- lcm : The lowest common multiple Examples -------- >>> np.gcd(12, 20) 4 >>> np.gcd.reduce([15, 25, 35]) 5 >>> np.gcd(np.arange(6), 20) array([20, 1, 2, 1, 4, 5])Return the truth value of (x1 > x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- greater_equal, less, less_equal, equal, not_equal Examples -------- >>> np.greater([4,2],[2,2]) array([ True, False]) The ``>`` operator can be used as a shorthand for ``np.greater`` on ndarrays. >>> a = np.array([4, 2]) >>> b = np.array([2, 2]) >>> a > b array([ True, False])Return the truth value of (x1 >= x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : bool or ndarray of bool Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- greater, less, less_equal, equal, not_equal Examples -------- >>> np.greater_equal([4, 2, 1], [2, 2, 2]) array([ True, True, False]) The ``>=`` operator can be used as a shorthand for ``np.greater_equal`` on ndarrays. >>> a = np.array([4, 2, 1]) >>> b = np.array([2, 2, 2]) >>> a >= b array([ True, True, False])Compute the Heaviside step function. The Heaviside step function is defined as:: 0 if x1 < 0 heaviside(x1, x2) = x2 if x1 == 0 1 if x1 > 0 where `x2` is often taken to be 0.5, but 0 and 1 are also sometimes used. Parameters ---------- x1 : array_like Input values. x2 : array_like The value of the function when x1 is 0. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar The output array, element-wise Heaviside step function of `x1`. This is a scalar if both `x1` and `x2` are scalars. Notes ----- .. versionadded:: 1.13.0 References ---------- .. Wikipedia, "Heaviside step function", https://en.wikipedia.org/wiki/Heaviside_step_function Examples -------- >>> np.heaviside([-1.5, 0, 2.0], 0.5) array([ 0. , 0.5, 1. ]) >>> np.heaviside([-1.5, 0, 2.0], 1) array([ 0., 1., 1.])Given the "legs" of a right triangle, return its hypotenuse. Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument. (See Examples) Parameters ---------- x1, x2 : array_like Leg of the triangle(s). If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- z : ndarray The hypotenuse of the triangle(s). This is a scalar if both `x1` and `x2` are scalars. Examples -------- >>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3))) array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]]) Example showing broadcast of scalar_like argument: >>> np.hypot(3*np.ones((3, 3)), [4]) array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]])Compute bit-wise inversion, or bit-wise NOT, element-wise. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``~``. For signed integer inputs, the two's complement is returned. In a two's-complement system negative numbers are represented by the two's complement of the absolute value. This is the most common method of representing signed integers on computers [1]_. A N-bit two's-complement system can represent every integer in the range :math:`-2^{N-1}` to :math:`+2^{N-1}-1`. Parameters ---------- x : array_like Only integer and boolean types are handled. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Result. This is a scalar if `x` is a scalar. See Also -------- bitwise_and, bitwise_or, bitwise_xor logical_not binary_repr : Return the binary representation of the input number as a string. Notes ----- `bitwise_not` is an alias for `invert`: >>> np.bitwise_not is np.invert True References ---------- .. [1] Wikipedia, "Two's complement", https://en.wikipedia.org/wiki/Two's_complement Examples -------- We've seen that 13 is represented by ``00001101``. The invert or bit-wise NOT of 13 is then: >>> x = np.invert(np.array(13, dtype=np.uint8)) >>> x 242 >>> np.binary_repr(x, width=8) '11110010' The result depends on the bit-width: >>> x = np.invert(np.array(13, dtype=np.uint16)) >>> x 65522 >>> np.binary_repr(x, width=16) '1111111111110010' When using signed integer types the result is the two's complement of the result for the unsigned type: >>> np.invert(np.array([13], dtype=np.int8)) array([-14], dtype=int8) >>> np.binary_repr(-14, width=8) '11110010' Booleans are accepted as well: >>> np.invert(np.array([True, False])) array([False, True]) The ``~`` operator can be used as a shorthand for ``np.invert`` on ndarrays. >>> x1 = np.array([True, False]) >>> ~x1 array([False, True])Test element-wise for finiteness (not infinity and not Not a Number). The result is returned as a boolean array. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray, bool True where ``x`` is not positive infinity, negative infinity, or NaN; false otherwise. This is a scalar if `x` is a scalar. See Also -------- isinf, isneginf, isposinf, isnan Notes ----- Not a Number, positive infinity and negative infinity are considered to be non-finite. NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Errors result if the second argument is also supplied when `x` is a scalar input, or if first and second arguments have different shapes. Examples -------- >>> np.isfinite(1) True >>> np.isfinite(0) True >>> np.isfinite(np.nan) False >>> np.isfinite(np.inf) False >>> np.isfinite(np.NINF) False >>> np.isfinite([np.log(-1.),1.,np.log(0)]) array([False, True, False]) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isfinite(x, y) array([0, 1, 0]) >>> y array([0, 1, 0])Test element-wise for positive or negative infinity. Returns a boolean array of the same shape as `x`, True where ``x == +/-inf``, otherwise False. Parameters ---------- x : array_like Input values out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : bool (scalar) or boolean ndarray True where ``x`` is positive or negative infinity, false otherwise. This is a scalar if `x` is a scalar. See Also -------- isneginf, isposinf, isnan, isfinite Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is supplied when the first argument is a scalar, or if the first and second arguments have different shapes. Examples -------- >>> np.isinf(np.inf) True >>> np.isinf(np.nan) False >>> np.isinf(np.NINF) True >>> np.isinf([np.inf, -np.inf, 1.0, np.nan]) array([ True, True, False, False]) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isinf(x, y) array([1, 0, 1]) >>> y array([1, 0, 1])Test element-wise for NaN and return result as a boolean array. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or bool True where ``x`` is NaN, false otherwise. This is a scalar if `x` is a scalar. See Also -------- isinf, isneginf, isposinf, isfinite, isnat Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Examples -------- >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False])Test element-wise for NaT (not a time) and return result as a boolean array. .. versionadded:: 1.13.0 Parameters ---------- x : array_like Input array with datetime or timedelta data type. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or bool True where ``x`` is NaT, false otherwise. This is a scalar if `x` is a scalar. See Also -------- isnan, isinf, isneginf, isposinf, isfinite Examples -------- >>> np.isnat(np.datetime64("NaT")) True >>> np.isnat(np.datetime64("2016-01-01")) False >>> np.isnat(np.array(["NaT", "2016-01-01"], dtype="datetime64[ns]")) array([ True, False])Returns the lowest common multiple of ``|x1|`` and ``|x2|`` Parameters ---------- x1, x2 : array_like, int Arrays of values. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). Returns ------- y : ndarray or scalar The lowest common multiple of the absolute value of the inputs This is a scalar if both `x1` and `x2` are scalars. See Also -------- gcd : The greatest common divisor Examples -------- >>> np.lcm(12, 20) 60 >>> np.lcm.reduce([3, 12, 20]) 60 >>> np.lcm.reduce([40, 12, 20]) 120 >>> np.lcm(np.arange(6), 20) array([ 0, 20, 20, 60, 20, 20])Returns x1 * 2**x2, element-wise. The mantissas `x1` and twos exponents `x2` are used to construct floating point numbers ``x1 * 2**x2``. Parameters ---------- x1 : array_like Array of multipliers. x2 : array_like, int Array of twos exponents. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The result of ``x1 * 2**x2``. This is a scalar if both `x1` and `x2` are scalars. See Also -------- frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`. Notes ----- Complex dtypes are not supported, they will raise a TypeError. `ldexp` is useful as the inverse of `frexp`, if used by itself it is more clear to simply use the expression ``x1 * 2**x2``. Examples -------- >>> np.ldexp(5, np.arange(4)) array([ 5., 10., 20., 40.], dtype=float16) >>> x = np.arange(6) >>> np.ldexp(*np.frexp(x)) array([ 0., 1., 2., 3., 4., 5.])Shift the bits of an integer to the left. Bits are shifted to the left by appending `x2` 0s at the right of `x1`. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying `x1` by ``2**x2``. Parameters ---------- x1 : array_like of integer type Input values. x2 : array_like of integer type Number of zeros to append to `x1`. Has to be non-negative. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : array of integer type Return `x1` with bits shifted `x2` times to the left. This is a scalar if both `x1` and `x2` are scalars. See Also -------- right_shift : Shift the bits of an integer to the right. binary_repr : Return the binary representation of the input number as a string. Examples -------- >>> np.binary_repr(5) '101' >>> np.left_shift(5, 2) 20 >>> np.binary_repr(20) '10100' >>> np.left_shift(5, [1,2,3]) array([10, 20, 40]) Note that the dtype of the second argument may change the dtype of the result and can lead to unexpected results in some cases (see :ref:`Casting Rules `): >>> a = np.left_shift(np.uint8(255), 1) # Expect 254 >>> print(a, type(a)) # Unexpected result due to upcasting 510 >>> b = np.left_shift(np.uint8(255), np.uint8(1)) >>> print(b, type(b)) 254 The ``<<`` operator can be used as a shorthand for ``np.left_shift`` on ndarrays. >>> x1 = 5 >>> x2 = np.array([1, 2, 3]) >>> x1 << x2 array([10, 20, 40])Return the truth value of (x1 < x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- greater, less_equal, greater_equal, equal, not_equal Examples -------- >>> np.less([1, 2], [2, 2]) array([ True, False]) The ``<`` operator can be used as a shorthand for ``np.less`` on ndarrays. >>> a = np.array([1, 2]) >>> b = np.array([2, 2]) >>> a < b array([ True, False])Return the truth value of (x1 <= x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- greater, less, greater_equal, equal, not_equal Examples -------- >>> np.less_equal([4, 2, 1], [2, 2, 2]) array([False, True, True]) The ``<=`` operator can be used as a shorthand for ``np.less_equal`` on ndarrays. >>> a = np.array([4, 2, 1]) >>> b = np.array([2, 2, 2]) >>> a <= b array([False, True, True])Natural logarithm, element-wise. The natural logarithm `log` is the inverse of the exponential function, so that `log(exp(x)) = x`. The natural logarithm is logarithm in base `e`. Parameters ---------- x : array_like Input value. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The natural logarithm of `x`, element-wise. This is a scalar if `x` is a scalar. See Also -------- log10, log2, log1p, emath.log Notes ----- Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `exp(z) = x`. The convention is to return the `z` whose imaginary part lies in `(-pi, pi]`. For real-valued input data types, `log` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `log` is a complex analytical function that has a branch cut `[-inf, 0]` and is continuous from above on it. `log` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard. In the cases where the input has a negative real part and a very small negative complex part (approaching 0), the result is so close to `-pi` that it evaluates to exactly `-pi`. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 67. https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples -------- >>> np.log([1, np.e, np.e**2, 0]) array([ 0., 1., 2., -Inf])Return the base 10 logarithm of the input array, element-wise. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The logarithm to the base 10 of `x`, element-wise. NaNs are returned where x is negative. This is a scalar if `x` is a scalar. See Also -------- emath.log10 Notes ----- Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `10**z = x`. The convention is to return the `z` whose imaginary part lies in `(-pi, pi]`. For real-valued input data types, `log10` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `log10` is a complex analytical function that has a branch cut `[-inf, 0]` and is continuous from above on it. `log10` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard. In the cases where the input has a negative real part and a very small negative complex part (approaching 0), the result is so close to `-pi` that it evaluates to exactly `-pi`. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 67. https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples -------- >>> np.log10([1e-15, -3.]) array([-15., nan])Return the natural logarithm of one plus the input array, element-wise. Calculates ``log(1 + x)``. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray Natural logarithm of `1 + x`, element-wise. This is a scalar if `x` is a scalar. See Also -------- expm1 : ``exp(x) - 1``, the inverse of `log1p`. Notes ----- For real-valued input, `log1p` is accurate also for `x` so small that `1 + x == 1` in floating-point accuracy. Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `exp(z) = 1 + x`. The convention is to return the `z` whose imaginary part lies in `[-pi, pi]`. For real-valued input data types, `log1p` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `log1p` is a complex analytical function that has a branch cut `[-inf, -1]` and is continuous from above on it. `log1p` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard. References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", 10th printing, 1964, pp. 67. https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples -------- >>> np.log1p(1e-99) 1e-99 >>> np.log(1 + 1e-99) 0.0Base-2 logarithm of `x`. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray Base-2 logarithm of `x`. This is a scalar if `x` is a scalar. See Also -------- log, log10, log1p, emath.log2 Notes ----- .. versionadded:: 1.3.0 Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `2**z = x`. The convention is to return the `z` whose imaginary part lies in `(-pi, pi]`. For real-valued input data types, `log2` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag. For complex-valued input, `log2` is a complex analytical function that has a branch cut `[-inf, 0]` and is continuous from above on it. `log2` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard. In the cases where the input has a negative real part and a very small negative complex part (approaching 0), the result is so close to `-pi` that it evaluates to exactly `-pi`. Examples -------- >>> x = np.array([0, 1, 2, 2**4]) >>> np.log2(x) array([-Inf, 0., 1., 4.]) >>> xi = np.array([0+1.j, 1, 2+0.j, 4.j]) >>> np.log2(xi) array([ 0.+2.26618007j, 0.+0.j , 1.+0.j , 2.+2.26618007j])Logarithm of the sum of exponentiations of the inputs. Calculates ``log(exp(x1) + exp(x2))``. This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the logarithm of the calculated probability is stored. This function allows adding probabilities stored in such a fashion. Parameters ---------- x1, x2 : array_like Input values. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- result : ndarray Logarithm of ``exp(x1) + exp(x2)``. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logaddexp2: Logarithm of the sum of exponentiations of inputs in base 2. Notes ----- .. versionadded:: 1.3.0 Examples -------- >>> prob1 = np.log(1e-50) >>> prob2 = np.log(2.5e-50) >>> prob12 = np.logaddexp(prob1, prob2) >>> prob12 -113.87649168120691 >>> np.exp(prob12) 3.5000000000000057e-50Logarithm of the sum of exponentiations of the inputs in base-2. Calculates ``log2(2**x1 + 2**x2)``. This function is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the base-2 logarithm of the calculated probability can be used instead. This function allows adding probabilities stored in such a fashion. Parameters ---------- x1, x2 : array_like Input values. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- result : ndarray Base-2 logarithm of ``2**x1 + 2**x2``. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logaddexp: Logarithm of the sum of exponentiations of the inputs. Notes ----- .. versionadded:: 1.3.0 Examples -------- >>> prob1 = np.log2(1e-50) >>> prob2 = np.log2(2.5e-50) >>> prob12 = np.logaddexp2(prob1, prob2) >>> prob1, prob2, prob12 (-166.09640474436813, -164.77447664948076, -164.28904982231052) >>> 2**prob12 3.4999999999999914e-50Compute the truth value of x1 AND x2 element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or bool Boolean result of the logical AND operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_or, logical_not, logical_xor bitwise_and Examples -------- >>> np.logical_and(True, False) False >>> np.logical_and([True, False], [False, False]) array([False, False]) >>> x = np.arange(5) >>> np.logical_and(x>1, x<4) array([False, False, True, True, False]) The ``&`` operator can be used as a shorthand for ``np.logical_and`` on boolean ndarrays. >>> a = np.array([True, False]) >>> b = np.array([False, False]) >>> a & b array([False, False])Compute the truth value of NOT x element-wise. Parameters ---------- x : array_like Logical NOT is applied to the elements of `x`. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : bool or ndarray of bool Boolean result with the same shape as `x` of the NOT operation on elements of `x`. This is a scalar if `x` is a scalar. See Also -------- logical_and, logical_or, logical_xor Examples -------- >>> np.logical_not(3) False >>> np.logical_not([True, False, 0, 1]) array([False, True, True, False]) >>> x = np.arange(5) >>> np.logical_not(x<3) array([False, False, False, True, True])Compute the truth value of x1 OR x2 element-wise. Parameters ---------- x1, x2 : array_like Logical OR is applied to the elements of `x1` and `x2`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or bool Boolean result of the logical OR operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_and, logical_not, logical_xor bitwise_or Examples -------- >>> np.logical_or(True, False) True >>> np.logical_or([True, False], [False, False]) array([ True, False]) >>> x = np.arange(5) >>> np.logical_or(x < 1, x > 3) array([ True, False, False, False, True]) The ``|`` operator can be used as a shorthand for ``np.logical_or`` on boolean ndarrays. >>> a = np.array([True, False]) >>> b = np.array([False, False]) >>> a | b array([ True, False])Compute the truth value of x1 XOR x2, element-wise. Parameters ---------- x1, x2 : array_like Logical XOR is applied to the elements of `x1` and `x2`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : bool or ndarray of bool Boolean result of the logical XOR operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars. See Also -------- logical_and, logical_or, logical_not, bitwise_xor Examples -------- >>> np.logical_xor(True, False) True >>> np.logical_xor([True, True, False, False], [True, False, True, False]) array([False, True, True, False]) >>> x = np.arange(5) >>> np.logical_xor(x < 1, x > 3) array([ True, False, False, False, True]) Simple example showing support of broadcasting >>> np.logical_xor(0, np.eye(2)) array([[ True, False], [False, True]])Matrix product of two arrays. Parameters ---------- x1, x2 : array_like Input arrays, scalars not allowed. out : ndarray, optional A location into which the result is stored. If provided, it must have a shape that matches the signature `(n,k),(k,m)->(n,m)`. If not provided or None, a freshly-allocated array is returned. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. .. versionadded:: 1.16 Now handles ufunc kwargs Returns ------- y : ndarray The matrix product of the inputs. This is a scalar only when both x1, x2 are 1-d vectors. Raises ------ ValueError If the last dimension of `x1` is not the same size as the second-to-last dimension of `x2`. If a scalar value is passed in. See Also -------- vdot : Complex-conjugating dot product. tensordot : Sum products over arbitrary axes. einsum : Einstein summation convention. dot : alternative matrix product with different broadcasting rules. Notes ----- The behavior depends on the arguments in the following way. - If both arguments are 2-D they are multiplied like conventional matrices. - If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly. - If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed. - If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed. ``matmul`` differs from ``dot`` in two important ways: - Multiplication by scalars is not allowed, use ``*`` instead. - Stacks of matrices are broadcast together as if the matrices were elements, respecting the signature ``(n,k),(k,m)->(n,m)``: >>> a = np.ones([9, 5, 7, 4]) >>> c = np.ones([9, 5, 4, 3]) >>> np.dot(a, c).shape (9, 5, 7, 9, 5, 3) >>> np.matmul(a, c).shape (9, 5, 7, 3) >>> # n is 7, k is 4, m is 3 The matmul function implements the semantics of the ``@`` operator introduced in Python 3.5 following :pep:`465`. It uses an optimized BLAS library when possible (see `numpy.linalg`). Examples -------- For 2-D arrays it is the matrix product: >>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([[4, 1], ... [2, 2]]) >>> np.matmul(a, b) array([[4, 1], [2, 2]]) For 2-D mixed with 1-D, the result is the usual. >>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([1, 2]) >>> np.matmul(a, b) array([1, 2]) >>> np.matmul(b, a) array([1, 2]) Broadcasting is conventional for stacks of arrays >>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4)) >>> b = np.arange(2 * 2 * 4).reshape((2, 4, 2)) >>> np.matmul(a,b).shape (2, 2, 2) >>> np.matmul(a, b)[0, 1, 1] 98 >>> sum(a[0, 1, :] * b[0 , :, 1]) 98 Vector, vector returns the scalar inner product, but neither argument is complex-conjugated: >>> np.matmul([2j, 3j], [2j, 3j]) (-13+0j) Scalar multiplication raises an error. >>> np.matmul([1,2], 3) Traceback (most recent call last): ... ValueError: matmul: Input operand 1 does not have enough dimensions ... The ``@`` operator can be used as a shorthand for ``np.matmul`` on ndarrays. >>> x1 = np.array([2j, 3j]) >>> x2 = np.array([2j, 3j]) >>> x1 @ x2 (-13+0j) .. versionadded:: 1.10.0Element-wise maximum of array elements. Compare two arrays and return a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated. Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The maximum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. See Also -------- minimum : Element-wise minimum of two arrays, propagates NaNs. fmax : Element-wise maximum of two arrays, ignores NaNs. amax : The maximum value of an array along a given axis, propagates NaNs. nanmax : The maximum value of an array along a given axis, ignores NaNs. fmin, amin, nanmin Notes ----- The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither x1 nor x2 are nans, but it is faster and does proper broadcasting. Examples -------- >>> np.maximum([2, 3, 4], [1, 5, 2]) array([2, 5, 4]) >>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting array([[ 1. , 2. ], [ 0.5, 2. ]]) >>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan]) array([nan, nan, nan]) >>> np.maximum(np.Inf, 1) infcannot add indexed loop to ufunc maximum with NPY_HALFNot a PyArrayMethodObject in ufunc maximum with NPY_HALFcannot add indexed loop to ufunc maximum with NPY_FLOATNot a PyArrayMethodObject in ufunc maximum with NPY_FLOATcannot add indexed loop to ufunc maximum with NPY_DOUBLENot a PyArrayMethodObject in ufunc maximum with NPY_DOUBLEcannot add indexed loop to ufunc maximum with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc maximum with NPY_LONGDOUBLEcannot add indexed loop to ufunc maximum with NPY_BYTENot a PyArrayMethodObject in ufunc maximum with NPY_BYTEcannot add indexed loop to ufunc maximum with NPY_UBYTENot a PyArrayMethodObject in ufunc maximum with NPY_UBYTEcannot add indexed loop to ufunc maximum with NPY_SHORTNot a PyArrayMethodObject in ufunc maximum with NPY_SHORTcannot add indexed loop to ufunc maximum with NPY_USHORTNot a PyArrayMethodObject in ufunc maximum with NPY_USHORTcannot add indexed loop to ufunc maximum with NPY_INTNot a PyArrayMethodObject in ufunc maximum with NPY_INTcannot add indexed loop to ufunc maximum with NPY_UINTNot a PyArrayMethodObject in ufunc maximum with NPY_UINTcannot add indexed loop to ufunc maximum with NPY_LONGNot a PyArrayMethodObject in ufunc maximum with NPY_LONGcannot add indexed loop to ufunc maximum with NPY_ULONGNot a PyArrayMethodObject in ufunc maximum with NPY_ULONGcannot add indexed loop to ufunc maximum with NPY_LONGLONGNot a PyArrayMethodObject in ufunc maximum with NPY_LONGLONGcannot add indexed loop to ufunc maximum with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc maximum with NPY_ULONGLONGElement-wise minimum of array elements. Compare two arrays and return a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated. Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The minimum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. See Also -------- maximum : Element-wise maximum of two arrays, propagates NaNs. fmin : Element-wise minimum of two arrays, ignores NaNs. amin : The minimum value of an array along a given axis, propagates NaNs. nanmin : The minimum value of an array along a given axis, ignores NaNs. fmax, amax, nanmax Notes ----- The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting. Examples -------- >>> np.minimum([2, 3, 4], [1, 5, 2]) array([1, 3, 2]) >>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting array([[ 0.5, 0. ], [ 0. , 1. ]]) >>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan]) array([nan, nan, nan]) >>> np.minimum(-np.Inf, 1) -infcannot add indexed loop to ufunc minimum with NPY_HALFNot a PyArrayMethodObject in ufunc minimum with NPY_HALFcannot add indexed loop to ufunc minimum with NPY_FLOATNot a PyArrayMethodObject in ufunc minimum with NPY_FLOATcannot add indexed loop to ufunc minimum with NPY_DOUBLENot a PyArrayMethodObject in ufunc minimum with NPY_DOUBLEcannot add indexed loop to ufunc minimum with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc minimum with NPY_LONGDOUBLEcannot add indexed loop to ufunc minimum with NPY_BYTENot a PyArrayMethodObject in ufunc minimum with NPY_BYTEcannot add indexed loop to ufunc minimum with NPY_UBYTENot a PyArrayMethodObject in ufunc minimum with NPY_UBYTEcannot add indexed loop to ufunc minimum with NPY_SHORTNot a PyArrayMethodObject in ufunc minimum with NPY_SHORTcannot add indexed loop to ufunc minimum with NPY_USHORTNot a PyArrayMethodObject in ufunc minimum with NPY_USHORTcannot add indexed loop to ufunc minimum with NPY_INTNot a PyArrayMethodObject in ufunc minimum with NPY_INTcannot add indexed loop to ufunc minimum with NPY_UINTNot a PyArrayMethodObject in ufunc minimum with NPY_UINTcannot add indexed loop to ufunc minimum with NPY_LONGNot a PyArrayMethodObject in ufunc minimum with NPY_LONGcannot add indexed loop to ufunc minimum with NPY_ULONGNot a PyArrayMethodObject in ufunc minimum with NPY_ULONGcannot add indexed loop to ufunc minimum with NPY_LONGLONGNot a PyArrayMethodObject in ufunc minimum with NPY_LONGLONGcannot add indexed loop to ufunc minimum with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc minimum with NPY_ULONGLONGReturn the fractional and integral parts of an array, element-wise. The fractional and integral parts are negative if the given number is negative. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y1 : ndarray Fractional part of `x`. This is a scalar if `x` is a scalar. y2 : ndarray Integral part of `x`. This is a scalar if `x` is a scalar. Notes ----- For integer input the return values are floats. See Also -------- divmod : ``divmod(x, 1)`` is equivalent to ``modf`` with the return values switched, except it always has a positive remainder. Examples -------- >>> np.modf([0, 3.5]) (array([ 0. , 0.5]), array([ 0., 3.])) >>> np.modf(-0.5) (-0.5, -0)Multiply arguments element-wise. Parameters ---------- x1, x2 : array_like Input arrays to be multiplied. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The product of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. Notes ----- Equivalent to `x1` * `x2` in terms of array broadcasting. Examples -------- >>> np.multiply(2.0, 4.0) 8.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]]) The ``*`` operator can be used as a shorthand for ``np.multiply`` on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 * x2 array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]])cannot add indexed loop to ufunc multiply with NPY_BYTENot a PyArrayMethodObject in ufunc multiply with NPY_BYTEcannot add indexed loop to ufunc multiply with NPY_UBYTENot a PyArrayMethodObject in ufunc multiply with NPY_UBYTEcannot add indexed loop to ufunc multiply with NPY_SHORTNot a PyArrayMethodObject in ufunc multiply with NPY_SHORTcannot add indexed loop to ufunc multiply with NPY_USHORTNot a PyArrayMethodObject in ufunc multiply with NPY_USHORTcannot add indexed loop to ufunc multiply with NPY_INTNot a PyArrayMethodObject in ufunc multiply with NPY_INTcannot add indexed loop to ufunc multiply with NPY_UINTNot a PyArrayMethodObject in ufunc multiply with NPY_UINTcannot add indexed loop to ufunc multiply with NPY_LONGNot a PyArrayMethodObject in ufunc multiply with NPY_LONGcannot add indexed loop to ufunc multiply with NPY_ULONGNot a PyArrayMethodObject in ufunc multiply with NPY_ULONGcannot add indexed loop to ufunc multiply with NPY_LONGLONGNot a PyArrayMethodObject in ufunc multiply with NPY_LONGLONGcannot add indexed loop to ufunc multiply with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc multiply with NPY_ULONGLONGcannot add indexed loop to ufunc multiply with NPY_HALFNot a PyArrayMethodObject in ufunc multiply with NPY_HALFcannot add indexed loop to ufunc multiply with NPY_FLOATNot a PyArrayMethodObject in ufunc multiply with NPY_FLOATcannot add indexed loop to ufunc multiply with NPY_DOUBLENot a PyArrayMethodObject in ufunc multiply with NPY_DOUBLEcannot add indexed loop to ufunc multiply with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc multiply with NPY_LONGDOUBLEcannot add indexed loop to ufunc multiply with NPY_CFLOATNot a PyArrayMethodObject in ufunc multiply with NPY_CFLOATcannot add indexed loop to ufunc multiply with NPY_CDOUBLENot a PyArrayMethodObject in ufunc multiply with NPY_CDOUBLEcannot add indexed loop to ufunc multiply with NPY_CLONGDOUBLENot a PyArrayMethodObject in ufunc multiply with NPY_CLONGDOUBLENumerical negative, element-wise. Parameters ---------- x : array_like or scalar Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar Returned array or scalar: `y = -x`. This is a scalar if `x` is a scalar. Examples -------- >>> np.negative([1.,-1.]) array([-1., 1.]) The unary ``-`` operator can be used as a shorthand for ``np.negative`` on ndarrays. >>> x1 = np.array(([1., -1.])) >>> -x1 array([-1., 1.])Return the next floating-point value after x1 towards x2, element-wise. Parameters ---------- x1 : array_like Values to find the next representable value of. x2 : array_like The direction where to look for the next representable value of `x1`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar The next representable values of `x1` in the direction of `x2`. This is a scalar if both `x1` and `x2` are scalars. Examples -------- >>> eps = np.finfo(np.float64).eps >>> np.nextafter(1, 2) == eps + 1 True >>> np.nextafter([1, 2], [2, 1]) == [eps + 1, 2 - eps] array([ True, True])Return (x1 != x2) element-wise. Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars. See Also -------- equal, greater, greater_equal, less, less_equal Examples -------- >>> np.not_equal([1.,2.], [1., 3.]) array([False, True]) >>> np.not_equal([1, 2], [[1, 3],[1, 4]]) array([[False, True], [False, True]]) The ``!=`` operator can be used as a shorthand for ``np.not_equal`` on ndarrays. >>> a = np.array([1., 2.]) >>> b = np.array([1., 3.]) >>> a != b array([False, True])Numerical positive, element-wise. .. versionadded:: 1.13.0 Parameters ---------- x : array_like or scalar Input array. Returns ------- y : ndarray or scalar Returned array or scalar: `y = +x`. This is a scalar if `x` is a scalar. Notes ----- Equivalent to `x.copy()`, but only defined for types that support arithmetic. Examples -------- >>> x1 = np.array(([1., -1.])) >>> np.positive(x1) array([ 1., -1.]) The unary ``+`` operator can be used as a shorthand for ``np.positive`` on ndarrays. >>> x1 = np.array(([1., -1.])) >>> +x1 array([ 1., -1.])First array elements raised to powers from second array, element-wise. Raise each base in `x1` to the positionally-corresponding power in `x2`. `x1` and `x2` must be broadcastable to the same shape. An integer type raised to a negative integer power will raise a ``ValueError``. Negative values raised to a non-integral value will return ``nan``. To get complex results, cast the input to complex, or specify the ``dtype`` to be ``complex`` (see the example below). Parameters ---------- x1 : array_like The bases. x2 : array_like The exponents. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The bases in `x1` raised to the exponents in `x2`. This is a scalar if both `x1` and `x2` are scalars. See Also -------- float_power : power function that promotes integers to float Examples -------- Cube each element in an array. >>> x1 = np.arange(6) >>> x1 [0, 1, 2, 3, 4, 5] >>> np.power(x1, 3) array([ 0, 1, 8, 27, 64, 125]) Raise the bases to different exponents. >>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0] >>> np.power(x1, x2) array([ 0., 1., 8., 27., 16., 5.]) The effect of broadcasting. >>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]]) >>> x2 array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]]) >>> np.power(x1, x2) array([[ 0, 1, 8, 27, 16, 5], [ 0, 1, 8, 27, 16, 5]]) The ``**`` operator can be used as a shorthand for ``np.power`` on ndarrays. >>> x2 = np.array([1, 2, 3, 3, 2, 1]) >>> x1 = np.arange(6) >>> x1 ** x2 array([ 0, 1, 8, 27, 16, 5]) Negative values raised to a non-integral value will result in ``nan`` (and a warning will be generated). >>> x3 = np.array([-1.0, -4.0]) >>> with np.errstate(invalid='ignore'): ... p = np.power(x3, 1.5) ... >>> p array([nan, nan]) To get complex results, give the argument ``dtype=complex``. >>> np.power(x3, 1.5, dtype=complex) array([-1.83697020e-16-1.j, -1.46957616e-15-8.j])Convert angles from radians to degrees. Parameters ---------- x : array_like Angle in radians. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding angle in degrees. This is a scalar if `x` is a scalar. See Also -------- deg2rad : Convert angles from degrees to radians. unwrap : Remove large jumps in angle by wrapping. Notes ----- .. versionadded:: 1.3.0 rad2deg(x) is ``180 * x / pi``. Examples -------- >>> np.rad2deg(np.pi/2) 90.0Convert angles from degrees to radians. Parameters ---------- x : array_like Input array in degrees. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding radian values. This is a scalar if `x` is a scalar. See Also -------- deg2rad : equivalent function Examples -------- Convert a degree array to radians >>> deg = np.arange(12.) * 30. >>> np.radians(deg) array([ 0. , 0.52359878, 1.04719755, 1.57079633, 2.0943951 , 2.61799388, 3.14159265, 3.66519143, 4.1887902 , 4.71238898, 5.23598776, 5.75958653]) >>> out = np.zeros((deg.shape)) >>> ret = np.radians(deg, out) >>> ret is out TrueReturn the reciprocal of the argument, element-wise. Calculates ``1/x``. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray Return array. This is a scalar if `x` is a scalar. Notes ----- .. note:: This function is not designed to work with integers. For integer arguments with absolute value larger than 1 the result is always zero because of the way Python handles integer division. For integer zero the result is an overflow. Examples -------- >>> np.reciprocal(2.) 0.5 >>> np.reciprocal([1, 2., 3.33]) array([ 1. , 0.5 , 0.3003003])Returns the element-wise remainder of division. Computes the remainder complementary to the `floor_divide` function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor `x2`. The MATLAB function equivalent to ``np.remainder`` is ``mod``. .. warning:: This should not be confused with: * Python 3.7's `math.remainder` and C's ``remainder``, which computes the IEEE remainder, which are the complement to ``round(x1 / x2)``. * The MATLAB ``rem`` function and or the C ``%`` operator which is the complement to ``int(x1 / x2)``. Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The element-wise remainder of the quotient ``floor_divide(x1, x2)``. This is a scalar if both `x1` and `x2` are scalars. See Also -------- floor_divide : Equivalent of Python ``//`` operator. divmod : Simultaneous floor division and remainder. fmod : Equivalent of the MATLAB ``rem`` function. divide, floor Notes ----- Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of) integers. ``mod`` is an alias of ``remainder``. Examples -------- >>> np.remainder([4, 7], [2, 3]) array([0, 1]) >>> np.remainder(np.arange(7), 5) array([0, 1, 2, 3, 4, 0, 1]) The ``%`` operator can be used as a shorthand for ``np.remainder`` on ndarrays. >>> x1 = np.arange(7) >>> x1 % 5 array([0, 1, 2, 3, 4, 0, 1])Shift the bits of an integer to the right. Bits are shifted to the right `x2`. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing `x1` by ``2**x2``. Parameters ---------- x1 : array_like, int Input values. x2 : array_like, int Number of bits to remove at the right of `x1`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray, int Return `x1` with bits shifted `x2` times to the right. This is a scalar if both `x1` and `x2` are scalars. See Also -------- left_shift : Shift the bits of an integer to the left. binary_repr : Return the binary representation of the input number as a string. Examples -------- >>> np.binary_repr(10) '1010' >>> np.right_shift(10, 1) 5 >>> np.binary_repr(5) '101' >>> np.right_shift(10, [1,2,3]) array([5, 2, 1]) The ``>>`` operator can be used as a shorthand for ``np.right_shift`` on ndarrays. >>> x1 = 10 >>> x2 = np.array([1,2,3]) >>> x1 >> x2 array([5, 2, 1])Round elements of the array to the nearest integer. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Output array is same shape and type as `x`. This is a scalar if `x` is a scalar. See Also -------- fix, ceil, floor, trunc Notes ----- For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc. Examples -------- >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.rint(a) array([-2., -2., -0., 0., 2., 2., 2.])Returns an element-wise indication of the sign of a number. The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``. nan is returned for nan inputs. For complex inputs, the `sign` function returns ``sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j``. complex(nan, 0) is returned for complex nan inputs. Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The sign of `x`. This is a scalar if `x` is a scalar. Notes ----- There is more than one definition of sign in common use for complex numbers. The definition used here is equivalent to :math:`x/\sqrt{x*x}` which is different from a common alternative, :math:`x/|x|`. Examples -------- >>> np.sign([-5., 4.5]) array([-1., 1.]) >>> np.sign(0) 0 >>> np.sign(5-2j) (1+0j)Returns element-wise True where signbit is set (less than zero). Parameters ---------- x : array_like The input value(s). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- result : ndarray of bool Output array, or reference to `out` if that was supplied. This is a scalar if `x` is a scalar. Examples -------- >>> np.signbit(-1.2) True >>> np.signbit(np.array([1, -2.3, 2.1])) array([False, True, False])Trigonometric sine, element-wise. Parameters ---------- x : array_like Angle, in radians (:math:`2 \pi` rad equals 360 degrees). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : array_like The sine of each element of x. This is a scalar if `x` is a scalar. See Also -------- arcsin, sinh, cos Notes ----- The sine is one of the fundamental functions of trigonometry (the mathematical study of triangles). Consider a circle of radius 1 centered on the origin. A ray comes in from the :math:`+x` axis, makes an angle at the origin (measured counter-clockwise from that axis), and departs from the origin. The :math:`y` coordinate of the outgoing ray's intersection with the unit circle is the sine of that angle. It ranges from -1 for :math:`x=3\pi / 2` to +1 for :math:`\pi / 2.` The function has zeroes where the angle is a multiple of :math:`\pi`. Sines of angles between :math:`\pi` and :math:`2\pi` are negative. The numerous properties of the sine and related functions are included in any standard trigonometry text. Examples -------- Print sine of one angle: >>> np.sin(np.pi/2.) 1.0 Print sines of an array of angles given in degrees: >>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. ) array([ 0. , 0.5 , 0.70710678, 0.8660254 , 1. ]) Plot the sine function: >>> import matplotlib.pylab as plt >>> x = np.linspace(-np.pi, np.pi, 201) >>> plt.plot(x, np.sin(x)) >>> plt.xlabel('Angle [rad]') >>> plt.ylabel('sin(x)') >>> plt.axis('tight') >>> plt.show()Hyperbolic sine, element-wise. Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar. Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples) References ---------- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972, pg. 83. Examples -------- >>> np.sinh(0) 0.0 >>> np.sinh(np.pi*1j/2) 1j >>> np.sinh(np.pi*1j) # (exact value is 0) 1.2246063538223773e-016j >>> # Discrepancy due to vagaries of floating point arithmetic. >>> # Example of providing the optional output parameter >>> out1 = np.array([0], dtype='d') >>> out2 = np.sinh([0.1], out1) >>> out2 is out1 True >>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.sinh(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File "", line 1, in ValueError: operands could not be broadcast together with shapes (3,3) (2,2)Return the distance between x and the nearest adjacent number. Parameters ---------- x : array_like Values to find the spacing of. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar The spacing of values of `x`. This is a scalar if `x` is a scalar. Notes ----- It can be considered as a generalization of EPS: ``spacing(np.float64(1)) == np.finfo(np.float64).eps``, and there should not be any representable number between ``x + spacing(x)`` and x for any finite x. Spacing of +- inf and NaN is NaN. Examples -------- >>> np.spacing(1) == np.finfo(np.float64).eps TrueReturn the non-negative square-root of an array, element-wise. Parameters ---------- x : array_like The values whose square-roots are required. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray An array of the same shape as `x`, containing the positive square-root of each element in `x`. If any element in `x` is complex, a complex array is returned (and the square-roots of negative reals are calculated). If all of the elements in `x` are real, so is `y`, with negative elements returning ``nan``. If `out` was provided, `y` is a reference to it. This is a scalar if `x` is a scalar. See Also -------- emath.sqrt A version which returns complex numbers when given negative reals. Note that 0.0 and -0.0 are handled differently for complex inputs. Notes ----- *sqrt* has--consistent with common convention--as its branch cut the real "interval" [`-inf`, 0), and is continuous from above on it. A branch cut is a curve in the complex plane across which a given complex function fails to be continuous. Examples -------- >>> np.sqrt([1,4,9]) array([ 1., 2., 3.]) >>> np.sqrt([4, -1, -3+4J]) array([ 2.+0.j, 0.+1.j, 1.+2.j]) >>> np.sqrt([4, -1, np.inf]) array([ 2., nan, inf])Return the element-wise square of the input. Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- out : ndarray or scalar Element-wise `x*x`, of the same shape and dtype as `x`. This is a scalar if `x` is a scalar. See Also -------- numpy.linalg.matrix_power sqrt power Examples -------- >>> np.square([-1j, 1]) array([-1.-0.j, 1.+0.j])Subtract arguments, element-wise. Parameters ---------- x1, x2 : array_like The arrays to be subtracted from each other. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The difference of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars. Notes ----- Equivalent to ``x1 - x2`` in terms of array broadcasting. Examples -------- >>> np.subtract(1.0, 4.0) -3.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.subtract(x1, x2) array([[ 0., 0., 0.], [ 3., 3., 3.], [ 6., 6., 6.]]) The ``-`` operator can be used as a shorthand for ``np.subtract`` on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 - x2 array([[0., 0., 0.], [3., 3., 3.], [6., 6., 6.]])cannot add indexed loop to ufunc subtract with NPY_BYTENot a PyArrayMethodObject in ufunc subtract with NPY_BYTEcannot add indexed loop to ufunc subtract with NPY_UBYTENot a PyArrayMethodObject in ufunc subtract with NPY_UBYTEcannot add indexed loop to ufunc subtract with NPY_SHORTNot a PyArrayMethodObject in ufunc subtract with NPY_SHORTcannot add indexed loop to ufunc subtract with NPY_USHORTNot a PyArrayMethodObject in ufunc subtract with NPY_USHORTcannot add indexed loop to ufunc subtract with NPY_INTNot a PyArrayMethodObject in ufunc subtract with NPY_INTcannot add indexed loop to ufunc subtract with NPY_UINTNot a PyArrayMethodObject in ufunc subtract with NPY_UINTcannot add indexed loop to ufunc subtract with NPY_LONGNot a PyArrayMethodObject in ufunc subtract with NPY_LONGcannot add indexed loop to ufunc subtract with NPY_ULONGNot a PyArrayMethodObject in ufunc subtract with NPY_ULONGcannot add indexed loop to ufunc subtract with NPY_LONGLONGNot a PyArrayMethodObject in ufunc subtract with NPY_LONGLONGcannot add indexed loop to ufunc subtract with NPY_ULONGLONGNot a PyArrayMethodObject in ufunc subtract with NPY_ULONGLONGcannot add indexed loop to ufunc subtract with NPY_HALFNot a PyArrayMethodObject in ufunc subtract with NPY_HALFcannot add indexed loop to ufunc subtract with NPY_FLOATNot a PyArrayMethodObject in ufunc subtract with NPY_FLOATcannot add indexed loop to ufunc subtract with NPY_DOUBLENot a PyArrayMethodObject in ufunc subtract with NPY_DOUBLEcannot add indexed loop to ufunc subtract with NPY_LONGDOUBLENot a PyArrayMethodObject in ufunc subtract with NPY_LONGDOUBLEcannot add indexed loop to ufunc subtract with NPY_CFLOATNot a PyArrayMethodObject in ufunc subtract with NPY_CFLOATcannot add indexed loop to ufunc subtract with NPY_CDOUBLENot a PyArrayMethodObject in ufunc subtract with NPY_CDOUBLEcannot add indexed loop to ufunc subtract with NPY_CLONGDOUBLENot a PyArrayMethodObject in ufunc subtract with NPY_CLONGDOUBLECompute tangent element-wise. Equivalent to ``np.sin(x)/np.cos(x)`` element-wise. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding tangent values. This is a scalar if `x` is a scalar. Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples) References ---------- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972. Examples -------- >>> from math import pi >>> np.tan(np.array([-pi,pi/2,pi])) array([ 1.22460635e-16, 1.63317787e+16, -1.22460635e-16]) >>> >>> # Example of providing the optional output parameter illustrating >>> # that what is returned is a reference to said parameter >>> out1 = np.array([0], dtype='d') >>> out2 = np.cos([0.1], out1) >>> out2 is out1 True >>> >>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.cos(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File "", line 1, in ValueError: operands could not be broadcast together with shapes (3,3) (2,2)Compute hyperbolic tangent element-wise. Equivalent to ``np.sinh(x)/np.cosh(x)`` or ``-1j * np.tan(1j*x)``. Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray The corresponding hyperbolic tangent values. This is a scalar if `x` is a scalar. Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples) References ---------- .. [1] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972, pg. 83. https://personal.math.ubc.ca/~cbm/aands/page_83.htm .. [2] Wikipedia, "Hyperbolic function", https://en.wikipedia.org/wiki/Hyperbolic_function Examples -------- >>> np.tanh((0, np.pi*1j, np.pi*1j/2)) array([ 0. +0.00000000e+00j, 0. -1.22460635e-16j, 0. +1.63317787e+16j]) >>> # Example of providing the optional output parameter illustrating >>> # that what is returned is a reference to said parameter >>> out1 = np.array([0], dtype='d') >>> out2 = np.tanh([0.1], out1) >>> out2 is out1 True >>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.tanh(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File "", line 1, in ValueError: operands could not be broadcast together with shapes (3,3) (2,2)Return the truncated value of the input, element-wise. The truncated value of the scalar `x` is the nearest integer `i` which is closer to zero than `x` is. In short, the fractional part of the signed number `x` is discarded. Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs `. Returns ------- y : ndarray or scalar The truncated value of each element in `x`. This is a scalar if `x` is a scalar. See Also -------- ceil, floor, rint, fix Notes ----- .. versionadded:: 1.3.0 Examples -------- >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.trunc(a) array([-1., -1., -0., 0., 1., 1., 2.])cannot intern umath strings while initializing _multiarray_umath..eB5<;?[>r1?cܥL@9RFߑ?+eG?9B.?iW @ox?-DT! @ý.@5?q(7[?+eG?+eG?&{?&{?9B.?9B.?templated_string_comparison[EOEEkDCEgEsEgE[EEEEEEEEEE)FFFFAF5FMatching loop was not an ArrayMethod.Did not find the to-be-wrapped loop in the ufunc with given DTypes. 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products_contig_contig_outstride0_twolongdouble_sum_of_products_stride0_contig_outstride0_twolongdouble_sum_of_products_contig_stride0_outstride0_twocfloat_sum_of_products_onecfloat_sum_of_products_contig_onecfloat_sum_of_products_contig_outstride0_onecfloat_sum_of_products_outstride0_onecfloat_sum_of_products_twocfloat_sum_of_products_contig_twocfloat_sum_of_products_outstride0_twocfloat_sum_of_products_threecfloat_sum_of_products_contig_threecfloat_sum_of_products_outstride0_threecfloat_sum_of_products_anycfloat_sum_of_products_contig_anycfloat_sum_of_products_outstride0_anycdouble_sum_of_products_onecdouble_sum_of_products_contig_onecdouble_sum_of_products_contig_outstride0_onecdouble_sum_of_products_outstride0_onecdouble_sum_of_products_twocdouble_sum_of_products_contig_twocdouble_sum_of_products_outstride0_twocdouble_sum_of_products_threecdouble_sum_of_products_contig_threecdouble_sum_of_products_outstride0_threecdouble_sum_of_products_anycdouble_sum_of_products_contig_anycdouble_sum_of_products_outstride0_anyclongdouble_sum_of_products_oneclongdouble_sum_of_products_contig_oneclongdouble_sum_of_products_contig_outstride0_oneclongdouble_sum_of_products_outstride0_oneclongdouble_sum_of_products_twoclongdouble_sum_of_products_contig_twoclongdouble_sum_of_products_outstride0_twoclongdouble_sum_of_products_threeclongdouble_sum_of_products_contig_threeclongdouble_sum_of_products_outstride0_threeclongdouble_sum_of_products_anyclongdouble_sum_of_products_contig_anyclongdouble_sum_of_products_outstride0_anybool_sum_of_products_onebool_sum_of_products_contig_onebool_sum_of_products_outstride0_onebool_sum_of_products_twobool_sum_of_products_contig_twobool_sum_of_products_outstride0_twobool_sum_of_products_threebool_sum_of_products_contig_threebool_sum_of_products_outstride0_threebool_sum_of_products_anybool_sum_of_products_contig_anybool_sum_of_products_outstride0_anyhalf_sum_of_products_threehalf_sum_of_products_twohalf_sum_of_products_onehalf_sum_of_products_anyhalf_sum_of_products_contig_threehalf_sum_of_products_contig_twohalf_sum_of_products_contig_onehalf_sum_of_products_contig_anyhalf_sum_of_products_outstride0_threehalf_sum_of_products_outstride0_twohalf_sum_of_products_outstride0_onehalf_sum_of_products_outstride0_anyhalf_sum_of_products_contig_contig_outstride0_twohalf_sum_of_products_muladdhalf_sum_of_products_contig_stride0_outcontig_twohalf_sum_of_products_stride0_contig_outcontig_twohalf_sum_of_products_contig_stride0_outstride0_twoobject_sum_of_products_anyobject_sum_of_products_oneobject_sum_of_products_twoobject_sum_of_products_threeobject_sum_of_products_contig_anyobject_sum_of_products_contig_oneobject_sum_of_products_contig_twoobject_sum_of_products_contig_threeobject_sum_of_products_outstride0_anyobject_sum_of_products_outstride0_oneobject_sum_of_products_outstride0_twoobject_sum_of_products_outstride0_threefloat_sum_of_products_muladdfloat_sum_of_products_stride0_contig_outcontig_twofloat_sum_of_products_contig_stride0_outcontig_twodouble_sum_of_arrfloat_sum_of_arrfloat_sum_of_products_stride0_contig_outstride0_twofloat_sum_of_products_contig_stride0_outstride0_twofloat_sum_of_products_contig_contig_outstride0_twohalf_sum_of_products_contig_outstride0_onehalf_sum_of_products_stride0_contig_outstride0_two_unspecialized_table_outstride0_specialized_table_contig_outstride0_unary_specialization_table_binary_specialization_table_allcontig_specialized_table_aligned_strided_to_strided_size1_aligned_strided_to_strided_size1_srcstride0_aligned_strided_to_contig_size1_aligned_contig_to_strided_size1_aligned_strided_to_strided_size2_aligned_strided_to_strided_size2_srcstride0_aligned_swap_strided_to_strided_size2_aligned_swap_strided_to_strided_size2_srcstride0_aligned_strided_to_contig_size2_aligned_strided_to_contig_size2_srcstride0_aligned_swap_strided_to_contig_size2_aligned_swap_strided_to_contig_size2_srcstride0_aligned_contig_to_strided_size2_aligned_swap_contig_to_strided_size2_aligned_swap_contig_to_contig_size2_aligned_strided_to_strided_size4_aligned_strided_to_strided_size4_srcstride0_aligned_swap_pair_strided_to_strided_size4_aligned_swap_pair_strided_to_strided_size4_srcstride0_aligned_strided_to_contig_size4_aligned_strided_to_contig_size4_srcstride0_aligned_swap_pair_strided_to_contig_size4_aligned_swap_pair_strided_to_contig_size4_srcstride0_aligned_contig_to_strided_size4_aligned_swap_pair_contig_to_strided_size4_aligned_swap_pair_contig_to_contig_size4_aligned_strided_to_strided_size8_aligned_strided_to_strided_size8_srcstride0_aligned_swap_pair_strided_to_strided_size8_aligned_swap_pair_strided_to_strided_size8_srcstride0_aligned_strided_to_contig_size8_aligned_strided_to_contig_size8_srcstride0_aligned_swap_pair_strided_to_contig_size8_aligned_swap_pair_strided_to_contig_size8_srcstride0_aligned_contig_to_strided_size8_aligned_swap_pair_contig_to_strided_size8_aligned_swap_pair_contig_to_contig_size8_aligned_strided_to_strided_size16_aligned_strided_to_strided_size16_srcstride0_aligned_strided_to_contig_size16_aligned_strided_to_contig_size16_srcstride0_aligned_contig_to_strided_size16_aligned_cast_bool_to_bool_aligned_contig_cast_bool_to_bool_aligned_cast_bool_to_ushort_aligned_contig_cast_bool_to_ushort_aligned_cast_bool_to_uint_aligned_contig_cast_bool_to_uint_aligned_cast_bool_to_ulong_aligned_contig_cast_bool_to_ulong_aligned_cast_bool_to_ulonglong_aligned_contig_cast_bool_to_ulonglong_aligned_cast_bool_to_byte_aligned_contig_cast_bool_to_byte_aligned_cast_bool_to_short_aligned_contig_cast_bool_to_short_aligned_cast_bool_to_int_aligned_contig_cast_bool_to_int_aligned_cast_bool_to_long_aligned_contig_cast_bool_to_long_aligned_cast_bool_to_longlong_aligned_contig_cast_bool_to_longlong_aligned_cast_bool_to_float_aligned_contig_cast_bool_to_float_aligned_cast_bool_to_double_aligned_contig_cast_bool_to_double_aligned_cast_bool_to_longdouble_aligned_contig_cast_bool_to_longdouble_aligned_cast_bool_to_cfloat_aligned_contig_cast_bool_to_cfloat_aligned_cast_bool_to_cdouble_aligned_contig_cast_bool_to_cdouble_aligned_cast_bool_to_clongdouble_aligned_contig_cast_bool_to_clongdouble_aligned_cast_ubyte_to_ubyte_aligned_contig_cast_ubyte_to_ubyte_aligned_cast_ubyte_to_ushort_aligned_contig_cast_ubyte_to_ushort_aligned_cast_ubyte_to_uint_aligned_contig_cast_ubyte_to_uint_aligned_cast_ubyte_to_ulong_aligned_contig_cast_ubyte_to_ulong_aligned_cast_ubyte_to_ulonglong_aligned_contig_cast_ubyte_to_ulonglong_aligned_cast_ubyte_to_byte_aligned_contig_cast_ubyte_to_byte_aligned_cast_ubyte_to_short_aligned_contig_cast_ubyte_to_short_aligned_cast_ubyte_to_int_aligned_contig_cast_ubyte_to_int_aligned_cast_ubyte_to_long_aligned_contig_cast_ubyte_to_long_aligned_cast_ubyte_to_longlong_aligned_contig_cast_ubyte_to_longlong_aligned_cast_ubyte_to_float_aligned_contig_cast_ubyte_to_float_aligned_cast_ubyte_to_double_aligned_contig_cast_ubyte_to_double_aligned_cast_ubyte_to_longdouble_aligned_contig_cast_ubyte_to_longdouble_aligned_cast_ubyte_to_cfloat_aligned_contig_cast_ubyte_to_cfloat_aligned_cast_ubyte_to_cdouble_aligned_contig_cast_ubyte_to_cdouble_aligned_cast_ubyte_to_clongdouble_aligned_contig_cast_ubyte_to_clongdouble_aligned_cast_ushort_to_bool_aligned_contig_cast_ushort_to_bool_aligned_cast_ushort_to_ubyte_aligned_contig_cast_ushort_to_ubyte_aligned_cast_ushort_to_ushort_aligned_contig_cast_ushort_to_ushort_aligned_cast_ushort_to_uint_aligned_contig_cast_ushort_to_uint_aligned_cast_ushort_to_ulong_aligned_contig_cast_ushort_to_ulong_aligned_cast_ushort_to_ulonglong_aligned_contig_cast_ushort_to_ulonglong_aligned_cast_ushort_to_byte_aligned_contig_cast_ushort_to_byte_aligned_cast_ushort_to_short_aligned_contig_cast_ushort_to_short_aligned_cast_ushort_to_int_aligned_contig_cast_ushort_to_int_aligned_cast_ushort_to_long_aligned_contig_cast_ushort_to_long_aligned_cast_ushort_to_longlong_aligned_contig_cast_ushort_to_longlong_aligned_cast_ushort_to_float_aligned_contig_cast_ushort_to_float_aligned_cast_ushort_to_double_aligned_contig_cast_ushort_to_double_aligned_cast_ushort_to_longdouble_aligned_contig_cast_ushort_to_longdouble_aligned_cast_ushort_to_cfloat_aligned_contig_cast_ushort_to_cfloat_aligned_cast_ushort_to_cdouble_aligned_contig_cast_ushort_to_cdouble_aligned_cast_ushort_to_clongdouble_aligned_contig_cast_ushort_to_clongdouble_aligned_cast_uint_to_bool_aligned_contig_cast_uint_to_bool_aligned_cast_uint_to_ubyte_aligned_contig_cast_uint_to_ubyte_aligned_cast_uint_to_ushort_aligned_contig_cast_uint_to_ushort_aligned_cast_uint_to_uint_aligned_contig_cast_uint_to_uint_aligned_cast_uint_to_ulong_aligned_contig_cast_uint_to_ulong_aligned_cast_uint_to_ulonglong_aligned_contig_cast_uint_to_ulonglong_aligned_cast_uint_to_byte_aligned_contig_cast_uint_to_byte_aligned_cast_uint_to_short_aligned_contig_cast_uint_to_short_aligned_cast_uint_to_int_aligned_contig_cast_uint_to_int_aligned_cast_uint_to_long_aligned_contig_cast_uint_to_long_aligned_cast_uint_to_longlong_aligned_contig_cast_uint_to_longlong_aligned_cast_uint_to_float_aligned_contig_cast_uint_to_float_aligned_cast_uint_to_double_aligned_contig_cast_uint_to_double_aligned_cast_uint_to_longdouble_aligned_contig_cast_uint_to_longdouble_aligned_cast_uint_to_cfloat_aligned_contig_cast_uint_to_cfloat_aligned_cast_uint_to_cdouble_aligned_contig_cast_uint_to_cdouble_aligned_cast_uint_to_clongdouble_aligned_contig_cast_uint_to_clongdouble_aligned_cast_ulong_to_bool_aligned_contig_cast_ulong_to_bool_aligned_cast_ulong_to_ubyte_aligned_contig_cast_ulong_to_ubyte_aligned_cast_ulong_to_ushort_aligned_contig_cast_ulong_to_ushort_aligned_cast_ulong_to_uint_aligned_contig_cast_ulong_to_uint_aligned_cast_ulong_to_ulong_aligned_contig_cast_ulong_to_ulong_aligned_cast_ulong_to_ulonglong_aligned_contig_cast_ulong_to_ulonglong_aligned_cast_ulong_to_byte_aligned_contig_cast_ulong_to_byte_aligned_cast_ulong_to_short_aligned_contig_cast_ulong_to_short_aligned_cast_ulong_to_int_aligned_contig_cast_ulong_to_int_aligned_cast_ulong_to_long_aligned_contig_cast_ulong_to_long_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st_long_to_ubyte_aligned_contig_cast_long_to_ubyte_aligned_cast_long_to_ushort_aligned_contig_cast_long_to_ushort_aligned_cast_long_to_uint_aligned_contig_cast_long_to_uint_aligned_cast_long_to_ulong_aligned_contig_cast_long_to_ulong_aligned_cast_long_to_ulonglong_aligned_contig_cast_long_to_ulonglong_aligned_cast_long_to_byte_aligned_contig_cast_long_to_byte_aligned_cast_long_to_short_aligned_contig_cast_long_to_short_aligned_cast_long_to_int_aligned_contig_cast_long_to_int_aligned_cast_long_to_long_aligned_contig_cast_long_to_long_aligned_cast_long_to_longlong_aligned_contig_cast_long_to_longlong_aligned_cast_long_to_float_aligned_contig_cast_long_to_float_aligned_cast_long_to_double_aligned_contig_cast_long_to_double_aligned_cast_long_to_longdouble_aligned_contig_cast_long_to_longdouble_aligned_cast_long_to_cfloat_aligned_contig_cast_long_to_cfloat_aligned_cast_long_to_cdouble_aligned_contig_cast_long_to_cdouble_aligned_cast_long_to_clongdouble_aligned_contig_cast_long_to_clongdouble_aligned_cast_longlong_to_bool_aligned_contig_cast_longlong_to_bool_aligned_cast_longlong_to_ubyte_aligned_contig_cast_longlong_to_ubyte_aligned_cast_longlong_to_ushort_aligned_contig_cast_longlong_to_ushort_aligned_cast_longlong_to_uint_aligned_contig_cast_longlong_to_uint_aligned_cast_longlong_to_ulong_aligned_contig_cast_longlong_to_ulong_aligned_cast_longlong_to_ulonglong_aligned_contig_cast_longlong_to_ulonglong_aligned_cast_longlong_to_byte_aligned_contig_cast_longlong_to_byte_aligned_cast_longlong_to_short_aligned_contig_cast_longlong_to_short_aligned_cast_longlong_to_int_aligned_contig_cast_longlong_to_int_aligned_cast_longlong_to_long_aligned_contig_cast_longlong_to_long_aligned_cast_longlong_to_longlong_aligned_contig_cast_longlong_to_longlong_aligned_cast_longlong_to_float_aligned_contig_cast_longlong_to_float_aligned_cast_longlong_to_double_aligned_contig_cast_longlong_to_double_aligned_cast_longlong_to_longdouble_aligned_contig_cast_longlong_to_longdouble_aligned_cast_longlong_to_cfloat_aligned_contig_cast_longlong_to_cfloat_aligned_cast_longlong_to_cdouble_aligned_contig_cast_longlong_to_cdouble_aligned_cast_longlong_to_clongdouble_aligned_contig_cast_longlong_to_clongdouble_aligned_cast_float_to_bool_aligned_contig_cast_float_to_bool_aligned_cast_float_to_ubyte_aligned_contig_cast_float_to_ubyte_aligned_cast_float_to_ushort_aligned_contig_cast_float_to_ushort_aligned_cast_float_to_uint_aligned_contig_cast_float_to_uint_aligned_cast_float_to_ulong_aligned_contig_cast_float_to_ulong_aligned_cast_float_to_ulonglong_aligned_contig_cast_float_to_ulonglong_aligned_cast_float_to_byte_aligned_contig_cast_float_to_byte_aligned_cast_float_to_short_aligned_contig_cast_float_to_short_aligned_cast_float_to_int_aligned_contig_cast_float_to_int_aligned_cast_float_to_long_aligned_contig_cast_float_to_long_aligned_cast_float_to_longlong_aligned_contig_cast_float_to_longlong_aligned_cast_float_to_float_aligned_contig_cast_float_to_float_aligned_cast_float_to_double_aligned_contig_cast_float_to_double_aligned_cast_float_to_longdouble_aligned_contig_cast_float_to_longdouble_aligned_cast_float_to_cfloat_aligned_contig_cast_float_to_cfloat_aligned_cast_float_to_cdouble_aligned_contig_cast_float_to_cdouble_aligned_cast_float_to_clongdouble_aligned_contig_cast_float_to_clongdouble_aligned_cast_double_to_bool_aligned_contig_cast_double_to_bool_aligned_cast_double_to_ubyte_aligned_contig_cast_double_to_ubyte_aligned_cast_double_to_ushort_aligned_contig_cast_double_to_ushort_aligned_cast_double_to_uint_aligned_contig_cast_double_to_uint_aligned_cast_double_to_ulong_aligned_contig_cast_double_to_ulong_aligned_cast_double_to_ulonglong_aligned_contig_cast_double_to_ulonglong_aligned_cast_double_to_byte_aligned_contig_cast_double_to_byte_aligned_cast_double_to_short_aligned_contig_cast_double_to_short_aligned_cast_double_to_int_aligned_contig_cast_double_to_int_aligned_cast_double_to_long_aligned_contig_cast_double_to_long_aligned_cast_double_to_longlong_aligned_contig_cast_double_to_longlong_aligned_cast_double_to_float_aligned_contig_cast_double_to_float_aligned_cast_double_to_double_aligned_contig_cast_double_to_double_aligned_cast_double_to_longdouble_aligned_contig_cast_double_to_longdouble_aligned_cast_double_to_cfloat_aligned_contig_cast_double_to_cfloat_aligned_cast_double_to_cdouble_aligned_contig_cast_double_to_cdouble_aligned_cast_double_to_clongdouble_aligned_contig_cast_double_to_clongdouble_aligned_cast_longdouble_to_bool_aligned_contig_cast_longdouble_to_bool_aligned_cast_longdouble_to_ubyte_aligned_contig_cast_longdouble_to_ubyte_aligned_cast_longdouble_to_ushort_aligned_contig_cast_longdouble_to_ushort_aligned_cast_longdouble_to_uint_aligned_contig_cast_longdouble_to_uint_aligned_cast_longdouble_to_ulong_aligned_contig_cast_longdouble_to_ulong_aligned_cast_longdouble_to_ulonglong_aligned_contig_cast_longdouble_to_ulonglong_aligned_cast_longdouble_to_byte_aligned_contig_cast_longdouble_to_byte_aligned_cast_longdouble_to_short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