:vj&i dZddlZddlmZddlmZmZddlm Z m Z m Z m Z m Z  ddlmZ d/d edzd edzd e dzd e dzd e dzf dZd0de dzde dzfdZ d1de de dzdede fdZd2de dzde ej0fdZ d3de dedefdZ d4de de dedefdZd2de de dzfd Z d5dedede dzfd!Zd6d"Zd2d#Zd0d$Z ejBfd%e d&e de ej0fd'Z"d7d(Z#de d)e de$e ejJe ffd*Z&d+Z'd,ejPdddfd-Z)d.Z*y#e$r#ZddlmZej&eZYdZ[!dZ[wwxYw)8zD grouping.py ------------- Functions for grouping values and rows. N)util)logtol) ArrayLikeIntegerNDArrayNumberSequence)cKDTree) exceptions merge_tex merge_norm digits_vertex digits_norm digits_uvcXt|jdk(ry|d}|d}|d}|d}|#tjtj }t |drVt|jdkDr>tjt|jt}d||j<n.tjt|jt}|jd |zzg}|s|jjr|jjd k(rv|jj`t|jjt|jk(r+|j!|jjd |zz|j"d }|sCtj$||jj$k(r|j!|d |zztj&|j)j+tj,}t/||d \} } tjt|jtj,} | | |<tj0|d| } |j3| | y)a Removes duplicate vertices, grouped by position and optionally texture coordinate and normal. Parameters ------------- mesh : Trimesh object Mesh to merge vertices on merge_tex : bool If True textured meshes with UV coordinates will have vertices merged regardless of UV coordinates merge_norm : bool If True, meshes with vertex normals will have vertices merged ignoring different normals digits_vertex : None or int Number of digits to consider for vertex position digits_norm : int Number of digits to consider for unit normals digits_uv : int Number of digits to consider for UV coordinates rNFfacesdtypeT texturevertex_normals) keep_order)maskinverse)lenverticesrdecimal_to_digitsrmergehasattrrnpzerosboolonesvisualdefinedkinduvappend_cacheshape column_stackroundastypeint64 unique_rowsnonzeroupdate_vertices) meshrrrrr referencedstackednormalsuirrs a/workspace/moltex-experiments/.pixi/envs/default/lib/python3.12/site-packages/trimesh/grouping.pymerge_verticesr=s< 4==Q    ..syy9 tW#djj/A"5XXc$--0= !% 4::WWS/t< }}M 123G  KK   KK   ) KKNN &   3t}}#5 5 t{{~~Y78kk*+G "((7+t}}/B/BBw"k/23oog&,,.55bhh?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]] frNrr)r$ asanyarrayargsortrr*greaterabsdiffrzeror,r4r'rr& concatenatezip) valuesr?r@originalordernondupedupe_idxdupe_okdupe_lenr;jgroupss r<grouprUqsS*}}V$H    E e_F||C**RVVBGGFO4chh? *s +yyBJJw/2Q67Hggc(m40Gwwr~~x#f+&?@AHg1   x7* *G   x7* *G-0'1BHWDU-V W-VTQeAQ -VF W MXsE%datadigits allow_intreturnct|dk(r%tjgtjSt ||}t|j dk(r|S|rt|j dk(r|j ddkrt tjd|j dz }|j|j}}d|dz zdz }||kr|| kDrtjt|tj}|j|dzzjtj} t| D]#\} } tj|| | |zz|%|Stjtj |jj"|j dzf} tj$|j'| j)d } d | j*d <| 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` rrrWrrr@)outrCF WRITEABLE)rr$arrayuint64 float_to_intr.intfloorminmaxr%Tr1 enumerate bitwise_xorrvoiditemsizeascontiguousarrayviewreshapeflags)rVrWrXas_int precisiond_mind_max thresholdhashablebitbangoffsetcolumnrresults r< hashable_rowsrys* 4yA~xx")),,$v .F 6<<A S&!+ Q10Dfll1o!567 zz|VZZ\u9q=)Q.  9 )!3xxF 299=Hxx9q=199"))DG#,G"4xFY4F)GXV#5O HHbggv||44v||AFG HE  ! !& ) . .u 5 = =b AF %FLL Mr>ctj|}|jtjk(r|S|jjdvs|j dk(r|j tjS|jjdk7r|j tj}|$tjtj}n8t|ttjfstdt!|dtj"|d|zzdz j tjS)a+ Given a numpy array of float/bool/int, return as integers. Parameters ------------- data : (n, d) float, int, or bool Input data digits : float or int Precision for float conversion Returns ------------- as_int : (n, d) int Data as integers iubrrBz%Digits must be `None` or `int`, not ``rgư>)r$rDrr2r*sizer1float64rr!rr" isinstancerbinteger TypeErrortyper0)rVrWs r<raras" == D zzRXX E !TYY!^{{288$$ C {{2::& ~'' 2 bjj 1 2?V ~QOPP 88TBJ&$. / 6 6rxx @@r>F return_indexreturn_inversectj|dd\}}}|j}|s|s||S||g}|r|j|||r"|j|j||S)aL Returns the same as np.unique, but ordered as per the first occurrence of the unique value in data. Examples --------- In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1] In [2]: np.unique(a) Out[2]: array([0, 1, 2, 3, 4]) In [3]: trimesh.grouping.unique_ordered(a) Out[3]: array([0, 3, 4, 1, 2]) Trr)r$uniquerEr,)rVrrrindexrrNrxs r<unique_orderedrsx, YYt$tTFE7 MMOE e}Um_F eEl# emmog./ Mr>rL minlength return_countsc6tj|}t|jdk7s|jj dk7r t d tj||}|jt}tj|d}|f}|r!tj|dz |}||fz }|r ||} || fz }t|dk(r|dS|S#t$r0tjdtj|||cYSwxYw)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$rDrr.rr* ValueErrorbincountrrwarningrr1r&wherecumsum) rLrrrcounts unique_binrretr unique_countss r<unique_bincountrAsB]]6 "F 6<<A!2!2c!9566  Vy9t$JXXj !! $F )C99Z(1,f5 zv   3x1}1v J9   34yy >   sC6DDc|tj}nd| z}tj|}tjt |t }d|d<tj|dd|ddz |kD|dd||S)aW Merge duplicate sequential values. This differs from unique_ordered in that values can occur in multiple places in the sequence, but only consecutive repeats are removed Parameters ----------- data: (n,) float or int Returns -------- merged: (m,) float or int Examples --------- In [1]: a Out[1]: array([-1, -1, -1, 0, 0, 1, 1, 2, 0, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 9]) In [2]: trimesh.grouping.merge_runs(a) Out[2]: array([-1, 0, 1, 2, 0, 3, 4, 5, 6, 7, 8, 9]) NrrTrrrC)rr"r$rDr%rr&rG)rVrWepsilonrs r< merge_runsrs|2~))&/ == D 88CIT *DDGvvd12hcr*+g5DH :r>ctj|}t||}tj|dd\}}}|s|s||S||g}|r|j ||r|j |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$rDrarr,tuple) rVrrrWro_junkrrrxs r< unique_floatrsz == D $ 'FYYvDQUVE67 >F|6l^F f g =r>cxt||}|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)ryrr$r)rVrWrrowss r<r3r3sG, f -DddKABOO 99TT B12 FFr>c |tj|}tj|}tj|td}|D]5}tj ||}|j ddk(}||||<7|S)a For a 2D array of integers find the position of a value in each row which only occurs once. If there are more than one value per row which occur once, the last one is returned. Parameters ---------- data : (n, d) int Data to check values unique : (m,) int List of unique values contained in data. Generated from np.unique if not passed Returns --------- result : (n, d) bool With one or zero True values per row. Examples ------------------------------------- In [0]: r = np.array([[-1, 1, 1], [-1, 1, -1], [-1, 1, 1], [-1, 1, -1], [-1, 1, -1]], dtype=np.int8) In [1]: unique_value_in_row(r) Out[1]: array([[ True, False, False], [False, True, False], [ True, False, False], [False, True, False], [False, True, False]], dtype=bool) In [2]: unique_value_in_row(r).sum(axis=1) Out[2]: array([1, 1, 1, 1, 1]) In [3]: r[unique_value_in_row(r)] Out[3]: array([-1, 1, -1, 1, 1], dtype=int8) F)rsubokraxis)r$rrD zeros_liker&equalsum)rVrrxvaluetesttest_oks r<unique_value_in_rowrszX~4 == D ]]4t5 9Fxxe$((("a'w-w Mr>ct||}| t|S|j}||}|dd|ddk7}tjdtj |ddz}tj tj|t|gf|k(}tj||jd|tj|z}||} |dk(r| jdS| S)a Returns index groups of duplicate rows, for example: [[1,2], [3,4], [1,2]] will return [[0,2], [1]] Note that using require_count allows numpy advanced indexing to be used in place of looping and checking hashes and is ~10x faster. Parameters ---------- data : (n, m) array Data to group require_count : None or int Only return groups of a specified length, eg: require_count = 2 [[1,2], [3,4], [1,2]] will return [[0,2]] digits : None or int If data is floating point how many decimals to consider, or calculated from tol.merge Returns ---------- groups : sequence (*,) int Indices from in indicating identical rows. r[NrrCr)rCr) ryrUrEr$r,r4rHrJrtilermarange) rV require_countrWrtrNduperPstart_okrT groups_idxs r< group_rowsr#s<T&1HX    EH AB<8CR= (DyyBJJt,Q/!34Hwwr~~x#h-&ABC}TH WWXh'//8- H299LFvJ!!"%% r>abctj|tj}tj|tj}|jd|jfg|j dzj }|jd|jfg|j dzj }|||j|jjd|j dS)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) int64 Array containing requested rows in both a and b rrrC)r$rDr2rlrr.ravelrm)rr operationavbvs r< boolean_rowsr_s, arxx(A arxx(A "agg!''!*, - 3 3 5B "agg!''!*, - 3 3 5B R  ! !!'' * 2 22qwwqz BBr>c tj|tj}t|}|rt j |}t j |}t||\}}t j|}||fS)a Group vectors based on an angle tolerance, with the option to include negative vectors. Parameters ----------- vectors : (n,3) float Direction vector angle : float Group vectors closer than this angle in radians include_negative : bool If True consider the same: [0,0,1] and [0,0,-1] Returns ------------ new_vectors : (m,3) float Direction vector groups : (m,) sequence of int Indices of source vectors r) r$rDr~floatrvector_hemispherevector_to_sphericalgroup_distancespherical_to_vector)vectorsangleinclude_negative sphericalanglesrT new_vectorss r< group_vectorsr}so.mmG2::6G %LE((1((1I#Iu5NFF**62K  r>distancec tj|tj}tjt |t }t |}g}g}t|D]\}}||r tj|j||tj}|||}d||<|jtj||d|j|tj||fS)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$rDr~r%rr&r rgr_query_ball_pointr2r,median) rLrconsumedtreerrTrrrUs r<rrs,]]6 4FxxF 40H 6?DF F!&) u E? ..uh?rxxPx&' biiu A67 e* 88F V ##r>cpddlm}t|}|j|d}|j |}|S)aJ Find clusters of points which have neighbours closer than radius Parameters --------- points : (n, d) float Points of dimension d radius : float Max distance between points in a cluster Returns ---------- groups : (m,) sequence of int Indices of points in a cluster r)graphndarray)r output_type)rrr query_pairsconnected_components)pointsradiusrrpairsrTs r<clustersrs<" 6?D   v9  =E  ' ' .F Mr>rc t||}tjt|}d|jd<|dd|dd|ddk7}tj t|dzt }t||d<||dd|dd|ddz } tj| |k\| |k} |rtj| ||dd} t| D cgc]\} } | s ||| || dz} } } |rD|d |dk7r| S|rt|d s| St| dk(rt| d t|k(r| St| d kDxr | d d d k(}t| d kDxr| ddt|dz k(}|r3|r1tj| d| d | d <| j| S| d | dz}||ks||kDr| Stjtj|d |dtj|d |d}|r|| d <| S|r|| d<| S| j|| Scc} } w) aZ Find the indices in an array of contiguous blocks of equal values. Parameters ------------ data : (n,) array Data to find blocks on min_len : int The minimum length group to be returned max_len : int The maximum length group to be retuurned wrap : bool Combine blocks on both ends of 1D array digits : None or int If dealing with floats how many digits to consider only_nonzero : bool Only return blocks of non- zero values Returns --------- blocks : (m) sequence of (*,) int Indices referencing data r[Fr^rNrCrrr) rar$rrrnr%rb logical_andrgr&r,pop)rVr?r@wraprW only_nonzerorr4inflinfl_leninfl_okr;okblocksfirstlastcombined new_blocks r<rrs2 V ,DYYs4y !F %FLLQRjabT#2Y./G 88CL1$C 0D4yDHD2JABx$s)#HnnX0(g2EFG..$tCRy/::C79K R9K2rfT!WtAE{+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$Ictj||f}||}||}tjt|d}d|d<|dd|ddk7|dd||S)a{ Given a list of groups find the minimum element of data within each group Parameters ----------- groups : (n,) sequence of (q,) int Indexes of each group corresponding to each element in data data : (m,) The data that groups indexes reference Returns ----------- minimums : (n,) Minimum value of data per group r&TrrNrC)r$lexsortr%r)rTrVrNrs r< group_minrNsk& JJf~ &E E]F ;D HHS[& )EE!Hqr fSbk)E!"I ;r>)NNNNN)NN)NT)N)FF)rFF)FFN)NF)g-C6?F)+__doc__numpyr$rr constantsrrtypedrrr r r scipy.spatialr BaseExceptionEr ExceptionWrapperr&r=rUryr2rarrrrr3rr intersect1drrrr~rrinfrrr>r<rsf@@-%""$("& $ V5d{V5t V5T> V5 4 V5 ~ V5r67T>67T>6tGK? ?$t^??C? ?D&Aw~&A9J&ATIN) )#')AE)\  D DDD DN#Y#$#P !  dN 8GD4n9z+-..CCC RXXC< F($ ($!'($ 72::  ()($V:BFFtRWcL}-)j))!,G -sD((E-E  E