MiXi SrSSKrSSKJr SSKJrJr SSKJ r J r J r J r J r JrJr SSKJr S/S \ \S \ \S \ \ S \ \ S \ \ 4 SjjrS0S\ \ S\ \ 4SjjrS1S\ S\ \ S\S\ 4SjjrS2S\ \ S\ \R44SjjrS3S\ S\S\4SjjrS4S\ S\ S\S\4SjjrS2S\ S\ \ 4S jjrS5S\S\S\ \ 4S!jjrS6S"jr S2S#jr!S0S$jr"\RF4S%\ S&\ S\ \R44S'jjr$S7S(jr%S\ S)\ S\\ \RL\44S*jr'S+r(S,\RRSSS4S-jr*S.r+g!\a#rSSKJr \R*"\5rSrCGN:SrCff=f)8zD grouping.py ------------- Functions for grouping values and rows. N)util)logtol) ArrayLikeIntegerNDArrayNumberOptionalSequenceTuple)cKDTree) exceptions merge_tex merge_norm digits_vertex digits_norm digits_uvc|[UR5S:XagUcSnUcSnUcSnUcSnUc$[R"[R 5n[ US5(aT[UR5S:a;[R"[UR5[S9nSX`R'O,[R"[UR5[S9nURS U--/nU(dURR(aURRS :XaxURRba[URR5[UR5:Xa+UR!URRS U--5 UR"S nU(dE[R$"U5URR$:XaUR!US U--5 [R&"U5R)5R+[R,5n[/XvSS 9up[R"[UR5[R,S9n XU'[R0"U5SU n UR3XS 9 g)ae Removes duplicate vertices, grouped by position and optionally texture coordinate and normal. Parameters ------------- mesh : Trimesh object Mesh to merge vertices on merge_tex : bool If True textured meshes with UV coordinates will have vertices merged regardless of UV coordinates merge_norm : bool If True, meshes with vertex normals will have vertices merged ignoring different normals digits_vertex : None or int Number of digits to consider for vertex position digits_norm : int Number of digits to consider for unit normals digits_uv : int Number of digits to consider for UV coordinates rNFfacesdtypeT texturevertex_normals) keep_order)maskinverse)lenverticesrdecimal_to_digitsrmergehasattrrnpzerosboolonesvisualdefinedkinduvappend_cacheshape column_stackroundastypeint64 unique_rowsnonzeroupdate_vertices) meshrrrrr referencedstackednormalsuir rs j/var/www/eduai.edurigo.com/storigo/production/storigo_env/lib/python3.13/site-packages/trimesh/grouping.pymerge_verticesr?s< 4==Q    ..syy9 tW#djj/A"5XXc$--0= !% ::WWS/t< }}M 123G  KK   KK   ) KKNN &   3t}}#5 5 t{{~~Y78kk*+G "((7+t}}/B/BBw"k/23oog&,,.55bhh?G w*t = min_length max_len : int The longest group allowed All groups will have len <= max_length Returns ---------- groups : sequence Contains indices to form groups IE [0,1,0,1] returns [[0,2], [1,3]] frNrr)r& asanyarrayargsortrr,greaterabsdiffrzeror.r6r)r!r( concatenatezip) valuesrArBoriginalordernondupedupe_idxdupe_okdupe_lenr=jgroupss r>grouprWqsC*}}V$H    E _F||C**RVVBGGFO4chh? *s +yyBJJw/2Q67Hggc(m40Gwwr~~x#f+&?@AHg1   7* *G   7* *G-01BHDU-V W-VTQe -VF W MXsE+datadigits allow_intreturnc[U5S:Xa#[R"/[RS9$[ XS9n[UR 5S:XaU$U(Ga[UR 5S:XGaUR SS::a[ [R"SUR S- 55nUR5UR5peSUS- -S- nXg:aXW*:a[R"[U5[RS9nURUS--R[R5n [U 5Hup[R"XX--US9 M! U$[R"[R URR"UR S-45n [R$"U5R'U 5R)S 5n S U R*S 'U $) ai We turn our array into integers based on the precision given by digits and then put them in a hashable format. Parameters --------- data : (n, m) array Input data digits : int or None How many digits to add to hash if data is floating point If None, tol.merge will be used Returns --------- hashable : (n,) May return as a `np.void` or a `np.uint64` rrrYrrr@)outrEF WRITEABLE)r!r&arrayuint64 float_to_intr0intfloorminmaxr'Tr3 enumerate bitwise_xorrvoiditemsizeascontiguousarrayviewreshapeflags)rXrYrZas_int precisiond_mind_max thresholdhashablebitbangoffsetcolumnrresults r> hashable_rowsr{s* 4yA~xx")),,$ .F 6<<A S&!+ Q10Dfll1o!567 zz|VZZ\u9q=)Q.   !3xxF 299=Hxx9q=199"))DG#,G"4xF4F)GXV#5O HHbggv||44v||AFG HE  ! !& ) . .u 5 = =b AF %FLL Mr@c[R"U5nUR[R:XaU$URRS;dUR S:XaUR [R5$URRS:waUR [R5nUc%[R"[R5nO=[U[[R45(d[S[!U5S35e[R""USU--S- 5R [R5$)z Given a numpy array of float/bool/int, return as integers. Parameters ------------- data : (n, d) float, int, or bool Input data digits : float or int Precision for float conversion Returns ------------- as_int : (n, d) int Data as integers iubrrDz%Digits must be `None` or `int`, not ``rgư>)r&rFrr4r,sizer3float64rr#rr$ isinstancerdinteger TypeErrortyper2)rXrYs r>rcrcs" == D zzRXX E !TYY!^{{288$$ C {{2::& ~'' 2 bjj 1 2 2?V ~QOPP 88TBJ&$. / 6 6rxx @@r@F return_indexreturn_inversec [R"USSS9up4nUR5nU(d U(dX6$X6/nU(aURXF5 U(a"URUR5U5 U$)a$ Returns the same as np.unique, but ordered as per the first occurrence of the unique value in data. Examples --------- In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1] In [2]: np.unique(a) Out[2]: array([0, 1, 2, 3, 4]) In [3]: trimesh.grouping.unique_ordered(a) Out[3]: array([0, 3, 4, 1, 2]) Trr)r&uniquerGr.)rXrrrindexr rPrzs r>unique_orderedrsp, YYt$tTF7 MMOE }m_F el# emmog./ Mr@rN minlength return_countscH[R"U5n[UR5S:wdURR S:wa [ S5e[R"XS9nUR[5n[R"U5SnU4nU(a![R"U5S- UnXx4- nU(a XFn Xy4- n[U5S:XaUS$U$![a. [R"S5 [R"XUS9s$f=f)a  For arrays of integers find unique values using bin counting. Roughly 10x faster for correct input than np.unique Parameters -------------- values : (n,) int Values to find unique members of minlength : int Maximum value that will occur in values (values.max()) return_inverse : bool If True, return an inverse such that unique[inverse] == values return_counts : bool If True, also return the number of times each unique item appears in values Returns ------------ unique : (m,) int Unique values in original array inverse : (n,) int, optional An array such that unique[inverse] == values Only returned if return_inverse is True counts : (m,) int, optional An array holding the counts of each unique item in values Only returned if return_counts is True rr=zinput must be 1D integers!)rzcasting failed, falling back!)rrr)r&rFr!r0rr, ValueErrorbincountrrwarningrr3r(wherecumsum) rNrrrcounts unique_binrretr unique_countss r>unique_bincountrAsB]]6 "F 6<<A!2!2c!9566  V9t$JXXj !! $F )C99Z(1,f5 z   3x1}1v J9   34yy    sC))5D! D!cUc[RnOSU*-n[R"U5n[R"[ U5[ S9nSUS'[R"USSUSS- 5U:USS&X$)a  Merge duplicate sequential values. This differs from unique_ordered in that values can occur in multiple places in the sequence, but only consecutive repeats are removed Parameters ----------- data: (n,) float or int Returns -------- merged: (m,) float or int Examples --------- In [1]: a Out[1]: array([-1, -1, -1, 0, 0, 1, 1, 2, 0, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 9]) In [2]: trimesh.grouping.merge_runs(a) Out[2]: array([-1, 0, 1, 2, 0, 3, 4, 5, 6, 7, 8, 9]) NrrTrrrE)rr$r&rFr'r!r(rI)rXrYepsilonrs r> merge_runsrsz2~))&/ == D 88CIT *DDGvvd12hcr*+g5DH :r@c[R"U5n[X5n[R"USSS9upVnU(d U(dX$X/nU(aUR U5 U(aUR U5 [ U5$)z Identical to the numpy.unique command, except evaluates floating point numbers, using a specified number of digits. If digits isn't specified, the library default TOL_MERGE will be used. Tr)r&rFrcrr.tuple) rXrrrYrq_junkrr rzs r> unique_floatrsr == D $ 'FYYvDQUVE7 >|l^F f g =r@cp[XS9nU(a[USSS9SS$[R"USSS9SS$)a Returns indices of unique rows. It will return the first occurrence of a row that is duplicated: [[1,2], [3,4], [1,2]] will return [0,1] Parameters --------- data : (n, m) array Floating point data digits : int or None How many digits to consider Returns -------- unique : (j,) int Index in data which is a unique row inverse : (n,) int Array to reconstruct original Example: data[unique][inverse] == data r]TrrN)r{rr&r)rXrYrrowss r>r5r5sE,  -DddKABOO 99TT B12 FFr@cUc[R"U5n[R"U5n[R"U[SS9nUH1n[R "X5nUR SS9S:HnXEX%'M3 U$)a, For a 2D array of integers find the position of a value in each row which only occurs once. If there are more than one value per row which occur once, the last one is returned. Parameters ---------- data : (n, d) int Data to check values unique : (m,) int List of unique values contained in data. Generated from np.unique if not passed Returns --------- result : (n, d) bool With one or zero True values per row. Examples ------------------------------------- In [0]: r = np.array([[-1, 1, 1], [-1, 1, -1], [-1, 1, 1], [-1, 1, -1], [-1, 1, -1]], dtype=np.int8) In [1]: unique_value_in_row(r) Out[1]: array([[ True, False, False], [False, True, False], [ True, False, False], [False, True, False], [False, True, False]], dtype=bool) In [2]: unique_value_in_row(r).sum(axis=1) Out[2]: array([1, 1, 1, 1, 1]) In [3]: r[unique_value_in_row(r)] Out[3]: array([-1, 1, -1, 1, 1], dtype=int8) F)rsubokraxis)r&rrF zeros_liker(equalsum)rXrrzvaluetesttest_oks r>unique_value_in_rowrstX~4 == D ]]4t5 9Fxx$((("a'- Mr@c[XS9nUc [U5$UR5nX4nUSSUSS:gn[R"S[R "U5SS-5n[R "[R"U[U5/455U:Hn[R"XgRS5U5[R"U5-nXHn US:XaU RS5$U $)a{ Returns index groups of duplicate rows, for example: [[1,2], [3,4], [1,2]] will return [[0,2], [1]] Note that using require_count allows numpy advanced indexing to be used in place of looping and checking hashes and is ~10x faster. Parameters ---------- data : (n, m) array Data to group require_count : None or int Only return groups of a specified length, eg: require_count = 2 [[1,2], [3,4], [1,2]] will return [[0,2]] digits : None or int If data is floating point how many decimals to consider, or calculated from tol.merge Returns ---------- groups : sequence (*,) int Indices from in indicating identical rows. r]NrrEr)rEr) r{rWrGr&r.r6rJrLr!tileroarange) rX require_countrYrvrPduperRstart_okrV groups_idxs r> group_rowsr#s<T1HX    EH AB<8CR= (DyyBJJt,Q/!34Hwwr~~x#h-&ABC}TH WWX'//8- H299LFJ!!"%% r@abc[R"U[RS9n[R"U[RS9nURSUR4/UR S-5R 5nURSUR4/UR S-5R 5nU"X45RUR5RSUR S5$)ac Find the rows in two arrays which occur in both rows. Parameters --------- a: (n, d) int Array with row vectors b: (m, d) int Array with row vectors operation : function Numpy boolean set operation function: -np.intersect1d -np.setdiff1d Returns -------- shared : (p, d) int64 Array containing requested rows in both a and b rrrE)r&rFr4rnrr0ravelro)rr operationavbvs r> boolean_rowsr_s, arxx(A arxx(A "agg!''!*, - 3 3 5B "agg!''!*, - 3 3 5B R  ! !!'' * 2 22qwwqz BBr@c[R"U[RS9n[U5nU(a[R "U5n[R "U5n[X15upE[R"U5nXe4$)a Group vectors based on an angle tolerance, with the option to include negative vectors. Parameters ----------- vectors : (n,3) float Direction vector angle : float Group vectors closer than this angle in radians include_negative : bool If True consider the same: [0,0,1] and [0,0,-1] Returns ------------ new_vectors : (m,3) float Direction vector groups : (m,) sequence of int Indices of source vectors r) r&rFrfloatrvector_hemispherevector_to_sphericalgroup_distancespherical_to_vector)vectorsangleinclude_negative sphericalanglesrV new_vectorss r> group_vectorsr}si.mmG2::6G %LE((1((1I#I5NF**62K  r@distancec[R"U[RS9n[R"[ U5[ S9n[ U5n/n/n[U5HupgX&(aM[R"URXq5[RS9nXU)nSX('UR[R"XSS95 URU5 M [R"U5U4$)a Find non-overlapping groups of points where no two points in a group are farther than 2*distance apart. Parameters --------- values : (n, d) float Points of dimension d distance : float Max distance between points in a cluster Returns ---------- unique : (m, d) float Median value of each group groups : (m) sequence of int Indexes of points that make up a group rTrr) r&rFrr'r!r(rriraquery_ball_pointr4r.median) rNrconsumedtreerrVrrrWs r>rrs,]]6 4FxxF 40H 6?DF F!&)  ? ..u?rxxP&' bii A67 e* 88F V ##r@cjSSKJn [U5nURUSS9nUR U5nU$)a Find clusters of points which have neighbours closer than radius Parameters --------- points : (n, d) float Points of dimension d radius : float Max distance between points in a cluster Returns ---------- groups : (m,) sequence of int Indices of points in a cluster r)graphndarray)r output_type)rrr query_pairsconnected_components)pointsradiusrrpairsrVs r>clustersrs<" 6?D   v9  =E  ' ' .F Mr@rc [XS9n[R"[U55nSURS'USSUSSUSS:gn[R "[U5S-[ S9n[U5US'XxSS&USSUSS- n [R"X:X:*5n U(a[R"XUSS5n [U 5V V s/sHupU (dMXhU XS-PM n n n U(GauUS US:waU $U(a[US 5(dU $[U 5S:Xa[U S 5[U5:XaU $[U 5S :=(a U S S S :Hn[U 5S :=(a U SS[U5S- :HnU(a9U(a2[R"U SU S 5U S 'U R5 U $U S U S-nUU:dUU:aU $[R"[R"US US5[R"US US55nU(aUU S 'U $U(aUU S'U $U RU5 U $s sn n f) a Find the indices in an array of contiguous blocks of equal values. Parameters ------------ data : (n,) array Data to find blocks on min_len : int The minimum length group to be returned max_len : int The maximum length group to be retuurned wrap : bool Combine blocks on both ends of 1D array digits : None or int If dealing with floats how many digits to consider only_nonzero : bool Only return blocks of non- zero values Returns --------- blocks : (m) sequence of (*,) int Indices referencing data r]Fr`rNrErrr) rcr&rr!rpr'rd logical_andrir(r.pop)rXrArBwraprY only_nonzerorr6inflinfl_leninfl_okr=okblocksfirstlastcombined new_blocks r>rrsu2  ,DYYs4y !F %FLLQRjabT#2Y./G 88CL1$C 0D4yDH2JABx$s)#HnnX0(2EFG..tCRy/::C79K R9Kr+f!WtE{+9KF R 7d2h M T!W M v;! F1I#d) ;MF a5F1IaLA$56{QD6":b>c$i!m#D T &*fQi8F1I JJL. M) {Xb\1H'!X%7   $r(DH-ryya$q'/JI%q  M&r M i( MgSs  I:(I:c[R"X45nXnXn[R"[U5S5nSUS'USSUSS:gUSS&X$)aG Given a list of groups find the minimum element of data within each group Parameters ----------- groups : (n,) sequence of (q,) int Indexes of each group corresponding to each element in data data : (m,) The data that groups indexes reference Returns ----------- minimums : (n,) Minimum value of data per group r(TrrNrE)r&lexsortr'r!)rVrXrPrs r> group_minrNsc& JJ~ &E ]F ;D HHS[& )EE!Hqr fSbk)E!"I ;r@)NNNNN)NN)NT)N)FF)rFF)FFN)NF)g-C6?F),__doc__numpyr&rr constantsrrtypedrrr r r r r scipy.spatialr BaseExceptionErExceptionWrapperr(r?rWr{r4rcrrrrr5rr intersect1drrrrrinfrrr@r>rspQQQ-%!%!%'+%)#' V5~V5V5G$ V5 '" V5  V5r68G,6hw>O6tJN? ?%g.?BF? ?D&Ax0&AGBHH