l0jh pdZddlZddlmZddlmZmZddlm Z m Z m Z m Z  ddl mZn(#e$r ZddlmZejeZYdZ[ndZ[wwxYw d.d e ed e ed e e d e e d e e f dZd/de e de e fdZ d0de de e dede fdZd1de e de ejfdZ d2de dedefdZ d3de de dedefd Zd1de de e fd!Z d4dedede e fd"Zd5d#Zd1d$Zd/d%Zej fd&Z!d6d(Z"d)Z#d*Z$d+ej%dddfd,Z&d-Z'dS)7zD grouping.py ------------- Functions for grouping values and rows. N)util)logtol) ArrayLikeIntegerNDArrayOptional)cKDTree) exceptions merge_tex merge_norm digits_vertex digits_norm digits_uvct|jdkrdS|d}|d}|d}|d}|tjtj}t |drPt|jdkr8tj t|jt}d||j<n-tj t|jt}|jd |zzg}|s||j j rp|j jd kr`|j jTt|j jt|jkr%||j jd |zz|jd }|s=tj||jjkr||d |zztj|tj}t/||d \} } tj t|jtj} | | |<tj|d| } || | dS)a Removes duplicate vertices, grouped by position and optionally texture coordinate and normal. Parameters ------------- mesh : Trimesh object Mesh to merge vertices on merge_tex : bool If True textured meshes with UV coordinates will have vertices merged regardless of UV coordinates merge_norm : bool If True, meshes with vertex normals will have vertices merged ignoring different normals digits_vertex : None or int Number of digits to consider for vertex position digits_norm : int Number of digits to consider for unit normals digits_uv : int Number of digits to consider for UV coordinates rNFfacesdtypeT texturevertex_normals) keep_order)maskinverse)lenverticesrdecimal_to_digitsrmergehasattrrnpzerosboolonesvisualdefinedkinduvappend_cacheshape column_stackroundastypeint64 unique_rowsnonzeroupdate_vertices) meshr rrrr referencedstackednormalsuirrs S/home/wildlama/miniconda3/envs/lam/lib/python3.11/site-packages/trimesh/grouping.pymerge_verticesr<sY< 4=Q    .sy99 tW=#dj//A"5"5Xc$-00=== !% 4:WS//t<<< }M 123G  9 K  9 K  ) ) KN &    3t}#5#5 5 5 t{~Y7888k*+G 4"(7++t}/BBBw"k/2333og&&,,..55bh??G wz*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]] frNrrc0g|]\}}|||zSrD).0r:jorders r; zgroup..s* W W WTQeAQK W W Wr=)r# asanyarrayargsortrr)greaterabsdiffrzeror+r3r&rr% concatenatezip) valuesr>r?originalnondupedupe_idxdupe_okdupe_lengroupsrGs @r;grouprXqsY*}V$$H     E e_F|C*RVBGFOO44ch?? *ss +yBJw//2Q677Hgc(mm4000Gwr~x#f++&?@@AAHg1   x7* *G   x7* *G W W W WS'1BHWDU-V-V W W WF Mr=Tdatadigits allow_intreturnct|dkr tjgtjSt ||}t|jdkr|S|r$t|jdkr |jddkrt tjd|jdz }|| }}d|dz zdz }||kr|| krtj t|tj}|j |dzz tj} t| D]"\} } tj|| | |zz|#|Stjtj|jj|jdzf} tj|| d } d | jd <| S) a We turn our array into integers based on the precision given by digits and then put them in a hashable format. Parameters --------- data : (n, m) array Input data digits : int or None How many digits to add to hash if data is floating point If None, tol.merge will be used Returns --------- hashable : (n,) May return as a `np.void` or a `np.uint64` rrrZrrr@)outrBF WRITEABLE)rr#arrayuint64 float_to_intr-intfloorminmaxr$Tr0 enumerate bitwise_xorrvoiditemsizeascontiguousarrayviewreshapeflags)rYrZr[as_int precisiond_mind_max thresholdhashablebitbangoffsetcolumnrresults r; hashable_rowsr|s* 4yyA~~x"),,,,$v . . .F 6<A S&&!++ Q10D0Dfl1o!56677 zz||VZZ\\u9q=)Q.  9  )!3!3xF 29===Hx9q=199")DDG#,G"4"4 W WxFY4F)GXVVVVVO Hbgv|4v|AFG H HE  !& ) ) . .u 5 5 = =b A AF %FL Mr=c<tj|}|jtjkr|S|jjdvs |jdkr|tjS|jjdkr|tj}|tj tj }n0t|ttjfstdtj|d|zzdz tjS)a+ Given a numpy array of float/bool/int, return as integers. Parameters ------------- data : (n, d) float, int, or bool Input data digits : float or int Precision for float conversion Returns ------------- as_int : (n, d) int Data as integers iubrrANz4Digits must be `None` or `int`, not `{type(digits)}`rgư>)r#rIrr1r)sizer0float64rr rr! isinstancereinteger TypeErrorr/)rYrZs r;rdrds" =  D zRX E ! !TY!^^{{28$$$ C  {{2:&& ~' 22 bj 1 2 2PNOOO 8TBJ&$. / / 6 6rx @ @@r=F return_indexreturn_inversec&tj|dd\}}}|}|s |s||S||g}|r||||r-||||S)aL Returns the same as np.unique, but ordered as per the first occurrence of the unique value in data. Examples --------- In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1] In [2]: np.unique(a) Out[2]: array([0, 1, 2, 3, 4]) In [3]: trimesh.grouping.unique_ordered(a) Out[3]: array([0, 3, 4, 1, 2]) Trr)r#uniquerJr+)rYrrrindexrrGr{s r;unique_orderedrs, Yt$tTTTFE7 MMOOE e}Um_F$ eEl###0 emmoog./// Mr=rQ minlength return_countscPtj|}t|jdks|jjdkrt d tj||}n;#t$r.tj dtj |||cYSwxYw| t}tj|d}|f}|r#tj|dz |}||fz }|r||} || fz }t|dkr|dS|S)ai 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#rIrr-rr) ValueErrorbincountrrwarningrr0r%wherecumsum) rQrrrcounts unique_binrretr unique_countss r;unique_bincountrAsPB]6 " "F 6<A!2c!9!95666  Vy999     3444y >       t$$JXj ! !! $F )C9Z((1,f5 z v   3xx1}}1v Js A$$5BBc| tj}nd| z}tj|}tjt |t }d|d<tj|dd|ddz |k|dd<||S)aW 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]) NrrTrrrB)rr!r#rIr$rr%rL)rYrZepsilonrs r; merge_runsrs2~)&/ =  D 8CIIT * * *DDGvd122hcrc*++g5DH :r=c$tj|}t||}tj|dd\}}}|s |s||S||g}|r|||r||t |S)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#rIrdrr+tuple) rYrrrZrr_junkrrr{s r; unique_floatrs =  D $ ' 'FYvDQUVVVE67 >F|6l^F f g ==r=ct||}|rt|ddddStj|ddddS)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)rYrZrrowss r;r2r2sa, f - - -DPddKKKABBOO 9TT B B B122 FFr=c|tj|}tj|}tj|td}|D]<}tj||}|ddk}||||<=|S)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) NF)rsubokraxis)r#rrI zeros_liker%equalsum)rYrr{valuetesttest_oks r;unique_value_in_rowrsX~4 =  D ]4t5 9 9 9F((xe$$(((""a'w-w Mr=cNt||}|t|S|}||}|dd|ddk}tjdtj|ddz}tjtj|t|gf|k}tj || d|tj |z}||} |dkr| dS| S)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^NrrBr)rBr) r|rXrJr#r+r3rMrOrtilerparange) rY require_countrZrwrGduperTstart_okrW groups_idxs r; group_rowsr#s"<T&111HX     EH ABB<8CRC= (DyBJt,,Q/!344Hwr~x#h--&ABBCC}TH WXh'//88- H H29LLFvJ!!"%%% r=ctj|tj}tj|tj}|d|jfg|jdz}|d|jfg|jdz}||||jd|jdS)a 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) array containing rows in both a and b rrrB)r#rIr1rorr-ravelrp)ab operationavbvs r; boolean_rowsr_s& arx(((A arx(((A "ag!'!*, - - 3 3 5 5B "ag!'!*, - - 3 3 5 5B 9R   ! !!' * * 2 22qwqz B BBr=-C6?c tj|tj}t|}|rt j|}t j|}t||\}}t j|}||fS)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#rIrfloatrvector_hemispherevector_to_sphericalgroup_distancespherical_to_vector)vectorsangleinclude_negative sphericalanglesrW new_vectorss r; group_vectorsrzs{.mG2:666G %LLE2(11(11I#Iu55NFF*622K  r=ctj|tj}tjt |t }t |}g}g}t|D]\}}||rtj| ||tj }d||<| tj ||d| |tj||fS)a Find groups of points which have neighbours closer than radius, where no two points in a group are farther than distance apart. Parameters --------- points : (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#rIrr$rr%r rjrbquery_ball_pointr1r+median) rQdistanceconsumedtreerrWrrrXs r;rrs(]6 4 4 4FxF 4000H 6??DF F!&)) u E?  ..uh??rxPPP biu A666777 e 8F  V ##r=cddlm}t|}||d}||}|S)aJ Find clusters of points which have neighbours closer than radius Parameters --------- points : (n, d) float Points of dimension d radius : float Max distance between points in a cluster Returns ---------- groups : (m,) sequence of int Indices of points in a cluster r)graphndarray)r output_type)rrr query_pairsconnected_components)pointsradiusrrpairsrWs r;clustersrsS" 6??D   v9  = =E  ' ' . .F Mr=rcjt||}tjt|djd<dd|dd|ddk}tjt|dzt t|d<|dd<ddddz }tj||k||k}|r#tj||dd}fd t|D} |r|d |dkr| S|rt|d s| St| dkr(t| d t|kr| St| d ko| d d d k} t| d ko!| ddt|dz k} | r;| r9tj | d| d | d <| n|d |dz} | |ks| |kr| Stj tjd dtjd d} | r| | d <n| r| | d<n| | | S) aZ Find the indices in an array of contiguous blocks of equal values. Parameters ------------ data : (n,) array Data to find blocks on min_len : int The minimum length group to be returned max_len : int The maximum length group to be retuurned wrap : bool Combine blocks on both ends of 1D array digits : None or int If dealing with floats how many digits to consider only_nonzero : bool Only return blocks of non- zero values Returns --------- blocks : (m) sequence of (*,) int Indices referencing data r^FrarNrBrrcLg|] \}}|||dz!S)rrD)rEr:okrinfls r;rHzblocks..s8 R R R2r RfT!WtAE{*+ R R Rr=r) rdr#rrrqr$re logical_andrjr%r+pop)rYr>r?wraprZ only_nonzeror3infl_leninfl_okblocksfirstlastcombined new_blockrrs @@r;rrs2 V , , ,DYs4yy ! !F %FLQRRjabbT#2#Y./G 8CLL1$C 0 0 0D4yyDHD2JABBx$ss)#HnX0(g2EFFG;.$tCRCy/::S R R R R79K9K R R RF /) 7d2h  M  T!W  M v;;!  F1I#d)) ; ;MF a5F1IaLA$56{{QD6":b>c$ii!m#D  )T ) &*fQi88F1I JJLLLL {Xb\1H'!!X%7%7   $r(DH--rya$q'/J/JI )%q  )&r  i((( Mr=ctj||f}||}||}tjt|d}d|d<|dd|ddk|dd<||S)a{ 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%TrrNrB)r#lexsortr$r)rWrYrGrs r; group_minrHsu& Jf~ & &E E]F ;D HS[[& ) )EE!Hqrr fSbSk)E!""I ;r=)NNNNN)NN)NT)N)FF)rFF)FFN)NF)rF)(__doc__numpyr#rr constantsrrtypedrrr r scipy.spatialr BaseExceptionEr ExceptionWrapperr%r<rXr|r1rdrrrrr2rr intersect1drrrrinfrrrDr=r;rs888888888888-%%%%%%%---)j)!,,GGGGGG -!%!%'+%)#' V5V5~V5V5G$ V5 '" V5  V5V5V5V5r668G,6hw>O6666tJN?? ?%g.?BF? ????D&A&Ax0&AGBH