KhSrSSKrSSKrSSKJr SSKrSSKJr SSK J r \R"\RSS9r /SQr S.S jr\ "\5S.S j5rS rS/SS .S jjr\ "\5S0SS .Sjj5rS1SSSS.Sjjr"SS\5r"SS\5r"SS\5rSr\ "\5S5rSr\ "\5S5rSr\ "\5S5rSr\ "\5S5rS.S jr\ "\5S2S!j5r S3S"jr!\ "\!5S4S#j5r"S.SS$.S%jjr#\ "\#5S2SS$.S&jj5r$S2SS$.S'jjr%S.SS$.S(jjr&\ "\&5S2SS$.S)jj5r'S*r(\ "\(5S+5r)S3S,jr*\ "\*5S4S-j5r+g)5a~ Set operations for arrays based on sorting. Notes ----- For floating point arrays, inaccurate results may appear due to usual round-off and floating point comparison issues. Speed could be gained in some operations by an implementation of `numpy.sort`, that can provide directly the permutation vectors, thus avoiding calls to `numpy.argsort`. Original author: Robert Cimrman N) NamedTuple) overrides)_array_converternumpy)module) ediff1din1d intersect1disin setdiff1dsetxor1dunion1dunique unique_all unique_countsunique_inverse unique_valuesc XU4$N)aryto_endto_begins M/var/www/html/env/lib/python3.13/site-packages/numpy/lib/_arraysetops_impl.py_ediff1d_dispatcherr%s  ""c[U5nUSR5nURnUcUc USSUSS- $UcSnOV[R"U5n[R "X$SS9(d [ S5eUR5n[U5nUcSnOV[R"U5n[R "XSS9(d [ S5eUR5n[U5n[[U5S- S5n[R"XU-U-S 9nUS:aX(SU&US:aXXW-S&[R"USSUSSXXW-5 URU5$) a The differences between consecutive elements of an array. Parameters ---------- ary : array_like If necessary, will be flattened before the differences are taken. to_end : array_like, optional Number(s) to append at the end of the returned differences. to_begin : array_like, optional Number(s) to prepend at the beginning of the returned differences. Returns ------- ediff1d : ndarray The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``. See Also -------- diff, gradient Notes ----- When applied to masked arrays, this function drops the mask information if the `to_begin` and/or `to_end` parameters are used. Examples -------- >>> import numpy as np >>> x = np.array([1, 2, 4, 7, 0]) >>> np.ediff1d(x) array([ 1, 2, 3, -7]) >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99])) array([-99, 1, 2, ..., -7, 88, 99]) The returned array is always 1D. >>> y = [[1, 2, 4], [1, 6, 24]] >>> np.ediff1d(y) array([ 1, 2, -3, 5, 18]) rN same_kind)castingzSdtype of `to_begin` must be compatible with input `ary` under the `same_kind` rule.zQdtype of `to_end` must be compatible with input `ary` under the `same_kind` rule.)shape) rraveldtypenp asanyarraycan_cast TypeErrorlenmax empty_likesubtractwrap) rrrconv dtype_reql_beginl_endl_diffresults rrr)swZ C D q'--/C IFN12wSb!!==*{{8 DKL L>>#h- ~v&{{6kBKL LF SAq !F ]]3w&6&> ?F{#x qy$*w !KKABSb6'2B#CD 99V rc.[U5S:XaUS$U$)z4Unpacks one-element tuples for use as return values rr)r)xs r _unpack_tupler7s 1v{t r) equal_nancU4$rr)ar return_indexreturn_inverse return_countsaxisr8s r_unique_dispatcherr?s 5LrTc B^^^[R"U5nTc![XX#XPRSS9n[ U5$[R "UTS5nS/UR-nURSUT'URURsmmURTS[R"TSS[RS95n[R"U5n[URS5Vs/sHnSRUS9UR4PM n nURSS:aUR!U 5n O[R""[%U5U S9n UUU4S jn [XX#XWTS9nU "US54USS-n[ U5$![R Ra, [R RTUR5Sef=fs snf![&a*n Sn ['U RURS 95U eSn A ff=f) a Find the unique elements of an array. Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: * the indices of the input array that give the unique values * the indices of the unique array that reconstruct the input array * the number of times each unique value comes up in the input array Parameters ---------- ar : array_like Input array. Unless `axis` is specified, this will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of `ar` (along the specified axis, if provided, or in the flattened array) that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct `ar`. return_counts : bool, optional If True, also return the number of times each unique item appears in `ar`. axis : int or None, optional The axis to operate on. If None, `ar` will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1-D array with the dimension of the given axis, see the notes for more details. Object arrays or structured arrays that contain objects are not supported if the `axis` kwarg is used. The default is None. equal_nan : bool, optional If True, collapses multiple NaN values in the return array into one. .. versionadded:: 1.24 Returns ------- unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the original array. Only provided if `return_index` is True. unique_inverse : ndarray, optional The indices to reconstruct the original array from the unique array. Only provided if `return_inverse` is True. unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if `return_counts` is True. See Also -------- repeat : Repeat elements of an array. sort : Return a sorted copy of an array. Notes ----- When an axis is specified the subarrays indexed by the axis are sorted. This is done by making the specified axis the first dimension of the array (move the axis to the first dimension to keep the order of the other axes) and then flattening the subarrays in C order. The flattened subarrays are then viewed as a structured type with each element given a label, with the effect that we end up with a 1-D array of structured types that can be treated in the same way as any other 1-D array. The result is that the flattened subarrays are sorted in lexicographic order starting with the first element. .. versionchanged:: 1.21 Like np.sort, NaN will sort to the end of the values. For complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays. .. versionchanged:: 2.0 For multi-dimensional inputs, ``unique_inverse`` is reshaped such that the input can be reconstructed using ``np.take(unique, unique_inverse, axis=axis)``. The result is now not 1-dimensional when ``axis=None``. Note that in NumPy 2.0.0 a higher dimensional array was returned also when ``axis`` was not ``None``. This was reverted, but ``inverse.reshape(-1)`` can be used to ensure compatibility with both versions. Examples -------- >>> import numpy as np >>> np.unique([1, 1, 2, 2, 3, 3]) array([1, 2, 3]) >>> a = np.array([[1, 1], [2, 3]]) >>> np.unique(a) array([1, 2, 3]) Return the unique rows of a 2D array >>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]]) >>> np.unique(a, axis=0) array([[1, 0, 0], [2, 3, 4]]) Return the indices of the original array that give the unique values: >>> a = np.array(['a', 'b', 'b', 'c', 'a']) >>> u, indices = np.unique(a, return_index=True) >>> u array(['a', 'b', 'c'], dtype='>> indices array([0, 1, 3]) >>> a[indices] array(['a', 'b', 'c'], dtype='>> a = np.array([1, 2, 6, 4, 2, 3, 2]) >>> u, indices = np.unique(a, return_inverse=True) >>> u array([1, 2, 3, 4, 6]) >>> indices array([0, 1, 4, 3, 1, 2, 1]) >>> u[indices] array([1, 2, 6, 4, 2, 3, 2]) Reconstruct the input values from the unique values and counts: >>> a = np.array([1, 2, 6, 4, 2, 3, 2]) >>> values, counts = np.unique(a, return_counts=True) >>> values array([1, 2, 3, 4, 6]) >>> counts array([1, 3, 1, 1, 1]) >>> np.repeat(values, counts) array([1, 2, 2, 2, 3, 4, 6]) # original order not preserved Nr8 inverse_shaper>rrr$zf{i})iz;The axis argument to unique is not supported for dtype {dt})dtc>[U5nURT5nUR"U/TSSQ76n[R"UST5nU$)Nrr)r)viewreshaper%moveaxis)uniqnr> orig_dtype orig_shapes r reshape_uniqunique..reshape_uniqDsJ Iyy$||A/ 12/{{4D) r)r%r& _unique1dr"r7rI exceptions AxisErrorndimr$rHprodintpascontiguousarrayrangeformatrGemptyr)r()r:r;r<r=r>r8retrBrDr$ consolidatedemsgrNoutputrLrMs ` @@rrrsV r B |."+88$PS!!? [[T1 %C"''MM((1+M$ XXrxxJ JqM277:ab>#I JB  b !B5:288A;5G H5Gfmmam "(( +5GE H 8 88A;?775>L88CG59L |%!* "F6!9% '&* 4F   W == " "?mm%%dBGG4$>? I 8K bhh /0a78s0F;%G%#$G*G*A G"* H4%HHrAc[R"U5R5nU=(d UnU(aURU(aSOSS9nXn OUR 5 Un [R "U R [RS9n SU SS&U(aU R SS:aU RRS ;a[R"U S 5(aU RRS :Xa+[R"[R"U 5SS S 9n O[R"XS S S 9n U S:aU SU U SU S- :gU SU &SX'SXS-S&OU SSU SS :gU SS&X4n U(a U WU 4- n U(ac[R"U 5S- n [R "U R [RS9nXW'XcURU5OU4- n U(aS[R"[R "U 5U R"/4-5nU [R$"U54- n U $)z7 Find the unique elements of an array, ignoring shape. mergesort quicksortkindrCTNrrcfmMrcleft)sideF)r%r&flattenargsortsortrYr"boolr$rcisnan searchsortedcumsumrUrH concatenatenonzerosizediff)r:r;r<r=r8rBr>optional_indicespermauxmask aux_firstnanrZimaskinv_idxidxs rrPrPSs r  " " $B#5~zzl{ zLh   88CIIRWW -DD!HciilQ&399>>V+C HHSW   99>>S ??288C=$VLL??3BfEL ! Al#s+?   Jrc\rSrSr%\R \S'\R \S'\R \S'\R \S'Srg)UniqueAllResultivaluesindicesinverse_indicescountsrN__name__ __module__ __qualname____firstlineno__r%ndarray__annotations____static_attributes__rrrr|r|s* JJ ZZZZ JJrr|cR\rSrSr%\R \S'\R \S'Srg)UniqueCountsResultir}rrNrrrrrrs JJ JJrrcR\rSrSr%\R \S'\R \S'Srg)UniqueInverseResultir}rrNrrrrrrs JJZZrrcU4$rrr5s r_unique_all_dispatcherr 4Krc,[USSSSS9n[U6$)a  Find the unique elements of an array, and counts, inverse, and indices. This function is an Array API compatible alternative to:: np.unique(x, return_index=True, return_inverse=True, return_counts=True, equal_nan=False) but returns a namedtuple for easier access to each output. Parameters ---------- x : array_like Input array. It will be flattened if it is not already 1-D. Returns ------- out : namedtuple The result containing: * values - The unique elements of an input array. * indices - The first occurring indices for each unique element. * inverse_indices - The indices from the set of unique elements that reconstruct `x`. * counts - The corresponding counts for each unique element. See Also -------- unique : Find the unique elements of an array. Examples -------- >>> import numpy as np >>> x = [1, 1, 2] >>> uniq = np.unique_all(x) >>> uniq.values array([1, 2]) >>> uniq.indices array([0, 2]) >>> uniq.inverse_indices array([0, 0, 1]) >>> uniq.counts array([2, 1]) TFr;r<r=r8)rr|r6r3s rrrs+\  F F ##rcU4$rrr5s r_unique_counts_dispatcherrrrc,[USSSSS9n[U6$)a Find the unique elements and counts of an input array `x`. This function is an Array API compatible alternative to:: np.unique(x, return_counts=True, equal_nan=False) but returns a namedtuple for easier access to each output. Parameters ---------- x : array_like Input array. It will be flattened if it is not already 1-D. Returns ------- out : namedtuple The result containing: * values - The unique elements of an input array. * counts - The corresponding counts for each unique element. See Also -------- unique : Find the unique elements of an array. Examples -------- >>> import numpy as np >>> x = [1, 1, 2] >>> uniq = np.unique_counts(x) >>> uniq.values array([1, 2]) >>> uniq.counts array([2, 1]) FTr)rrrs rrrs+L  F v &&rcU4$rrr5s r_unique_inverse_dispatcherrrrc,[USSSSS9n[U6$)a Find the unique elements of `x` and indices to reconstruct `x`. This function is an Array API compatible alternative to:: np.unique(x, return_inverse=True, equal_nan=False) but returns a namedtuple for easier access to each output. Parameters ---------- x : array_like Input array. It will be flattened if it is not already 1-D. Returns ------- out : namedtuple The result containing: * values - The unique elements of an input array. * inverse_indices - The indices from the set of unique elements that reconstruct `x`. See Also -------- unique : Find the unique elements of an array. Examples -------- >>> import numpy as np >>> x = [1, 1, 2] >>> uniq = np.unique_inverse(x) >>> uniq.values array([1, 2]) >>> uniq.inverse_indices array([0, 0, 1]) FTr)rrrs rrrs+N  F  ''rcU4$rrr5s r_unique_values_dispatcherr9rrc[USSSSS9$)a Returns the unique elements of an input array `x`. This function is an Array API compatible alternative to:: np.unique(x, equal_nan=False) Parameters ---------- x : array_like Input array. It will be flattened if it is not already 1-D. Returns ------- out : ndarray The unique elements of an input array. See Also -------- unique : Find the unique elements of an array. Examples -------- >>> import numpy as np >>> np.unique_values([1, 1, 2]) array([1, 2]) Fr)rr5s rrr=s <    rcX4$rr)ar1ar2 assume_uniquereturn_indicess r_intersect1d_dispatcherrd :rcB[R"U5n[R"U5nU(d7U(a[USS9up[USS9upO7[U5n[U5nO UR5nUR5n[R"X45nU(a[R "USS9nXgnOUR 5 USSUSS:HnUSSUn U(a2WSSUn USSUUR- n U(d WU n WU n XU 4$U $)a Find the intersection of two arrays. Return the sorted, unique values that are in both of the input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. Will be flattened if not already 1D. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. If True but ``ar1`` or ``ar2`` are not unique, incorrect results and out-of-bounds indices could result. Default is False. return_indices : bool If True, the indices which correspond to the intersection of the two arrays are returned. The first instance of a value is used if there are multiple. Default is False. Returns ------- intersect1d : ndarray Sorted 1D array of common and unique elements. comm1 : ndarray The indices of the first occurrences of the common values in `ar1`. Only provided if `return_indices` is True. comm2 : ndarray The indices of the first occurrences of the common values in `ar2`. Only provided if `return_indices` is True. Examples -------- >>> import numpy as np >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1]) array([1, 3]) To intersect more than two arrays, use functools.reduce: >>> from functools import reduce >>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])) array([3]) To return the indices of the values common to the input arrays along with the intersected values: >>> x = np.array([1, 1, 2, 3, 4]) >>> y = np.array([2, 1, 4, 6]) >>> xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True) >>> x_ind, y_ind (array([0, 2, 4]), array([1, 0, 2])) >>> xy, x[x_ind], y[y_ind] (array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4])) T)r;r`rbrNr)r%r&rr#rorirjrq) rrrrind1ind2ruaux_sort_indicesrvint1d ar1_indices ar2_indicess rr r isp -- C -- C  s6ICs6IC+C+Ciikiik ..# $C::c <#  qr7c#2h D HTNE&s+D1 &qr*40388; {+K{+K;.. rcX4$rrrrrs r_setxor1d_dispatcherr :rcU(d[U5n[U5n[R"X4SS9nURS:XaU$UR 5 [R"S/USSUSS:gS/45nX4SSUSS-$)a[ Find the set exclusive-or of two arrays. Return the sorted, unique values that are in only one (not both) of the input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- setxor1d : ndarray Sorted 1D array of unique values that are in only one of the input arrays. Examples -------- >>> import numpy as np >>> a = np.array([1, 2, 3, 2, 4]) >>> b = np.array([2, 3, 5, 7, 5]) >>> np.setxor1d(a,b) array([1, 4, 5, 7]) Nr>rTrr)rr%rorqrj)rrrruflags rr r s> SkSk ..#$ /C xx1} HHJ >>D63qr7c#2h#6? @D ABx$s)# $$rrbcX4$rrrrrinvertrcs r_in1d_dispatcherrrrcL[R"S[SS9 [XX#US9$)a Test whether each element of a 1-D array is also present in a second array. .. deprecated:: 2.0 Use :func:`isin` instead of `in1d` for new code. Returns a boolean array the same length as `ar1` that is True where an element of `ar1` is in `ar2` and False otherwise. Parameters ---------- ar1 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of `ar1`. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. invert : bool, optional If True, the values in the returned array are inverted (that is, False where an element of `ar1` is in `ar2` and True otherwise). Default is False. ``np.in1d(a, b, invert=True)`` is equivalent to (but is faster than) ``np.invert(in1d(a, b))``. kind : {None, 'sort', 'table'}, optional The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations. * If 'sort', will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of `ar1` and `ar2`, not accounting for size of dtypes. * If 'table', will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of `ar1` plus the max-min value of `ar2`. `assume_unique` has no effect when the 'table' option is used. * If None, will automatically choose 'table' if the required memory allocation is less than or equal to 6 times the sum of the sizes of `ar1` and `ar2`, otherwise will use 'sort'. This is done to not use a large amount of memory by default, even though 'table' may be faster in most cases. If 'table' is chosen, `assume_unique` will have no effect. Returns ------- in1d : (M,) ndarray, bool The values `ar1[in1d]` are in `ar2`. See Also -------- isin : Version of this function that preserves the shape of ar1. Notes ----- `in1d` can be considered as an element-wise function version of the python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly equivalent to ``np.array([item in b for item in a])``. However, this idea fails if `ar2` is a set, or similar (non-sequence) container: As ``ar2`` is converted to an array, in those cases ``asarray(ar2)`` is an object array rather than the expected array of contained values. Using ``kind='table'`` tends to be faster than `kind='sort'` if the following relationship is true: ``log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927``, but may use greater memory. The default value for `kind` will be automatically selected based only on memory usage, so one may manually set ``kind='table'`` if memory constraints can be relaxed. Examples -------- >>> import numpy as np >>> test = np.array([0, 1, 2, 5, 0]) >>> states = [0, 2] >>> mask = np.in1d(test, states) >>> mask array([ True, False, True, False, True]) >>> test[mask] array([0, 2, 0]) >>> mask = np.in1d(test, states, invert=True) >>> mask array([False, True, False, True, False]) >>> test[mask] array([1, 5]) z,`in1d` is deprecated. Use `np.isin` instead.) stacklevelrb)warningswarnDeprecationWarning_in1drs rr r s+v MM6 =t <"UR@[S 9nUUU'U(aUS[7U5$UW$![,a URnGNf=f)Nrr>NrjtablezInvalid kind: 'z&'. Please use None, 'sort' or 'table'.c3R# UHoRRS;v M g7f))urDbN)r$rc).0r:s r _in1d..lsN:R 8:s%'>NrrrCrunsafe)r$outr!zYou have specified kind='table', but the range of values in `ar2` or `ar1` exceed the maximum integer of the datatype. Please set `kind` to None or 'sort'.zjThe 'table' method is only supported for boolean or integer arrays. Please select 'sort' or None for kind. g(\?T)r<r`rb)!r%asarrayr#r$objectrH ValueErrorallrq ones_likerk zeros_likeastypeuint8intminr*iinfooneszerosarrayrU OverflowErrorr+r, RuntimeError hasobjectr)rrorirYr")rrrrrc is_int_arraysuse_table_methodar2_minar2_max ar2_rangebelow_memory_constraintrange_safe_from_overflowoutgoing_arrayisin_helper_ar basic_mask in_range_ar1r$rcontains_objectrvarev_idxr:ordersarbool_arrrZs rrr^s> **S/   !C **S/   !C yyFkk"a  **dV#I JL LNC:NNM$@)@ 88q=||Ct44}}S55 99 **RXX&C 99 **RXX&CbffSk"bffSk"% #,qCHHsxx4G/H"H#,0C0G0G#G  % $!#c!>!#s$!?!#Qd!C01W}-!#)a-t!D01W}-.S^>7VH- .D ((2884 (CCJ9CH~7|A! "  "s(3S!!S;:S;cX4$rrelement test_elementsrrrcs r_isin_dispatcherrs  ##rcv[R"U5n[XUX4S9RUR5$)a Calculates ``element in test_elements``, broadcasting over `element` only. Returns a boolean array of the same shape as `element` that is True where an element of `element` is in `test_elements` and False otherwise. Parameters ---------- element : array_like Input array. test_elements : array_like The values against which to test each value of `element`. This argument is flattened if it is an array or array_like. See notes for behavior with non-array-like parameters. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. invert : bool, optional If True, the values in the returned array are inverted, as if calculating `element not in test_elements`. Default is False. ``np.isin(a, b, invert=True)`` is equivalent to (but faster than) ``np.invert(np.isin(a, b))``. kind : {None, 'sort', 'table'}, optional The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations. * If 'sort', will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of `element` and `test_elements`, not accounting for size of dtypes. * If 'table', will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of `element` plus the max-min value of `test_elements`. `assume_unique` has no effect when the 'table' option is used. * If None, will automatically choose 'table' if the required memory allocation is less than or equal to 6 times the sum of the sizes of `element` and `test_elements`, otherwise will use 'sort'. This is done to not use a large amount of memory by default, even though 'table' may be faster in most cases. If 'table' is chosen, `assume_unique` will have no effect. Returns ------- isin : ndarray, bool Has the same shape as `element`. The values `element[isin]` are in `test_elements`. Notes ----- `isin` is an element-wise function version of the python keyword `in`. ``isin(a, b)`` is roughly equivalent to ``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences. `element` and `test_elements` are converted to arrays if they are not already. If `test_elements` is a set (or other non-sequence collection) it will be converted to an object array with one element, rather than an array of the values contained in `test_elements`. This is a consequence of the `array` constructor's way of handling non-sequence collections. Converting the set to a list usually gives the desired behavior. Using ``kind='table'`` tends to be faster than `kind='sort'` if the following relationship is true: ``log10(len(test_elements)) > (log10(max(test_elements)-min(test_elements)) - 2.27) / 0.927``, but may use greater memory. The default value for `kind` will be automatically selected based only on memory usage, so one may manually set ``kind='table'`` if memory constraints can be relaxed. Examples -------- >>> import numpy as np >>> element = 2*np.arange(4).reshape((2, 2)) >>> element array([[0, 2], [4, 6]]) >>> test_elements = [1, 2, 4, 8] >>> mask = np.isin(element, test_elements) >>> mask array([[False, True], [ True, False]]) >>> element[mask] array([2, 4]) The indices of the matched values can be obtained with `nonzero`: >>> np.nonzero(mask) (array([0, 1]), array([1, 0])) The test can also be inverted: >>> mask = np.isin(element, test_elements, invert=True) >>> mask array([[ True, False], [False, True]]) >>> element[mask] array([0, 6]) Because of how `array` handles sets, the following does not work as expected: >>> test_set = {1, 2, 4, 8} >>> np.isin(element, test_set) array([[False, False], [False, False]]) Casting the set to a list gives the expected result: >>> np.isin(element, list(test_set)) array([[False, True], [ True, False]]) )rrrc)r%rrrHr"rs rr r s6hjj!G } ++277==+ABrcX4$rrrrs r_union1d_dispatcherrprrc@[[R"X4SS95$)a` Find the union of two arrays. Return the unique, sorted array of values that are in either of the two input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. They are flattened if they are not already 1D. Returns ------- union1d : ndarray Unique, sorted union of the input arrays. Examples -------- >>> import numpy as np >>> np.union1d([-1, 0, 1], [-2, 0, 2]) array([-2, -1, 0, 1, 2]) To find the union of more than two arrays, use functools.reduce: >>> from functools import reduce >>> reduce(np.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])) array([1, 2, 3, 4, 6]) Nr)rr%rors rrrts< "..#$7 88rcX4$rrrs r_setdiff1d_dispatcherrrrcU(a%[R"U5R5nO[U5n[U5nU[ XSSS9$)a Find the set difference of two arrays. Return the unique values in `ar1` that are not in `ar2`. Parameters ---------- ar1 : array_like Input array. ar2 : array_like Input comparison array. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- setdiff1d : ndarray 1D array of values in `ar1` that are not in `ar2`. The result is sorted when `assume_unique=False`, but otherwise only sorted if the input is sorted. Examples -------- >>> import numpy as np >>> a = np.array([1, 2, 3, 2, 4, 1]) >>> b = np.array([3, 4, 5, 6]) >>> np.setdiff1d(a, b) array([1, 2]) T)rr)r%rr#rrrs rr r sDBjjo##%SkSk uST$? @@r)NN)NNNN)FFFN)FFF)FFr)F),__doc__ functoolsrtypingrrr% numpy._corernumpy._core._multiarray_umathrpartialarray_function_dispatch__all__rrr7r?rrPr|rrrrrrrrrrrr rr rr rrr rrrr rrrrsj !:$++ %%g7 #,-W.Wt>B04CG +,27%)~!8<~!-~!B6;!+04D+`j  * /04$14$n23,'4,'^34-(5-(`23#4#N6: 01X2Xv-.(%/(%V )*`=d`=+`=FQtQh$!$ )*uBuB+uBp,-9.9@./%A0%Ar