TiXi dZddlZddlmZddlmZmZddlm Z m Z m Z m Z m Z mZmZ ddlmZn(#e$r ZddlmZejeZYdZ[ndZ[wwxYw d1d e ed e ed e e d e e d e e f dZd2de e de e fdZ d3de de e dede fdZd4de e de ejfdZ d5de dedefdZ d6de de dedefd Zd4de de e fd!Z d7dedede e fd"Zd8d#Z d4d$Z!d2d%Z"ej#fd&e d'e de ejfd(Z$d9d*Z%de d+e dee ej&effd,Z'd-Z(d.ej)dddfd/Z*d0Z+dS):zD grouping.py ------------- Functions for grouping values and rows. N)util)logtol) ArrayLikeIntegerNDArrayNumberOptionalSequenceTuple)cKDTree) exceptions merge_tex merge_norm digits_vertex digits_norm digits_uvct|jdkrdS|d}|d}|d}|d}|tjtj}t |drPt|jdkr8tj t|jt}d||j<n-tj t|jt}|jd |zzg}|s||j j rp|j jd kr`|j jTt|j jt|jkr%||j jd |zz|jd }|s=tj||jjkr||d |zztj|tj}t/||d \} } tj t|jtj} | | |<tj|d| } || | dS)a Removes duplicate vertices, grouped by position and optionally texture coordinate and normal. Parameters ------------- mesh : Trimesh object Mesh to merge vertices on merge_tex : bool If True textured meshes with UV coordinates will have vertices merged regardless of UV coordinates merge_norm : bool If True, meshes with vertex normals will have vertices merged ignoring different normals digits_vertex : None or int Number of digits to consider for vertex position digits_norm : int Number of digits to consider for unit normals digits_uv : int Number of digits to consider for UV coordinates rNFfacesdtypeT texturevertex_normals) keep_order)maskinverse)lenverticesrdecimal_to_digitsrmergehasattrrnpzerosboolonesvisualdefinedkinduvappend_cacheshape column_stackroundastypeint64 unique_rowsnonzeroupdate_vertices) meshrrrrr referencedstackednormalsuir rs J/DATA/AppData/hermes/venv/lib/python3.11/site-packages/trimesh/grouping.pymerge_verticesr?sY< 4=Q    .sy99 tW=#dj//A"5"5Xc$-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|]\}}|||zSrG).0r=jorders r> zgroup..s* W W WTQeAQK W W Wr@)r& asanyarrayargsortrr,greaterabsdiffrzeror.r6r)r!r( concatenatezip) valuesrArBoriginalnondupedupe_idxdupe_okdupe_lengroupsrJs @r>groupr[qsY*}V$$H     E e_F|C*RVBGFOO44ch?? *ss +yBJw//2Q677Hgc(mm4000Gwr~x#f++&?@@AAHg1   x7* *G   x7* *G W W W WS'1BHWDU-V-V W W WF Mr@Tdatadigits allow_intreturnct|dkr tjgtjSt ||}t|jdkr|S|r$t|jdkr |jddkrt tjd|jdz }|| }}d|dz zdz }||kr|| krtj t|tj}|j |dzz tj} t| D]"\} } tj|| | |zz|#|Stjtj|jj|jdzf} tj|| d } d | jd <| S) a We turn our array into integers based on the precision given by digits and then put them in a hashable format. Parameters --------- data : (n, m) array Input data digits : int or None How many digits to add to hash if data is floating point If None, tol.merge will be used Returns --------- hashable : (n,) May return as a `np.void` or a `np.uint64` rrr]rrr@)outrEF WRITEABLE)r!r&arrayuint64 float_to_intr0intfloorminmaxr'Tr3 enumerate bitwise_xorrvoiditemsizeascontiguousarrayviewreshapeflags)r\r]r^as_int precisiond_mind_max thresholdhashablebitbangoffsetcolumnrresults r> hashable_rowsrs* 4yyA~~x"),,,,$v . . .F 6<A S&&!++ Q10D0Dfl1o!56677 zz||VZZ\\u9q=)Q.  9  )!3!3xF 29===Hx9q=199")DDG#,G"4"4 W WxFY4F)GXVVVVVO Hbgv|4v|AFG H HE  !& ) ) . .u 5 5 = =b A AF %FL Mr@c^tj|}|jtjkr|S|jjdvs |jdkr|tjS|jjdkr|tj}|tj tj }nAt|ttjfs tdt!|dtj|d|zzdz tjS) a+ Given a numpy array of float/bool/int, return as integers. Parameters ------------- data : (n, d) float, int, or bool Input data digits : float or int Precision for float conversion Returns ------------- as_int : (n, d) int Data as integers iubrrDNz%Digits must be `None` or `int`, not ``rgư>)r&rLrr4r,sizer3float64rr#rr$ isinstancerhinteger TypeErrortyper2)r\r]s r>rgrgs" =  D zRX E ! !TY!^^{{28$$$ C  {{2:&& ~' 22 bj 1 2 2QOV OOOPPP 8TBJ&$. / / 6 6rx @ @@r@F return_indexreturn_inversec&tj|dd\}}}|}|s |s||S||g}|r||||r-||||S)aL Returns the same as np.unique, but ordered as per the first occurrence of the unique value in data. Examples --------- In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1] In [2]: np.unique(a) Out[2]: array([0, 1, 2, 3, 4]) In [3]: trimesh.grouping.unique_ordered(a) Out[3]: array([0, 3, 4, 1, 2]) Trr)r&uniquerMr.)r\rrrindexr rJr~s r>unique_orderedrs, Yt$tTTTFE7 MMOOE e}Um_F$ eEl###0 emmoog./// Mr@rT 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&rLr!r0rr, ValueErrorbincountrrwarningrr3r(wherecumsum) rTrrrcounts 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]) NrrTrrrE)rr$r&rLr'r!r(rO)r\r]epsilonrs r> merge_runsrs2~)&/ =  D 8CIIT * * *DDGvd122hcrc*++g5DH :r@c$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&rLrgrr.tuple) r\rrr]ru_junkrr r~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 raTrrN)rrr&r)r\r]rrowss r>r5r5sa, 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&rrL zeros_liker(equalsum)r\rr~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. raNrrEr)rEr) rr[rMr&r.r6rPrRr!tilersarange) r\ require_countr]rzrJduperWstart_okrZ groups_idxs r> group_rowsr#s"<T&111HX     EH ABB<8CRC= (DyBJt,,Q/!344Hwr~x#h--&ABBCC}TH WXh'//88- H H29LLFvJ!!"%%% r@abctj|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) int64 Array containing requested rows in both a and b rrrE)r&rLr4rrrr0ravelrs)rr 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&rLrfloatrvector_hemispherevector_to_sphericalgroup_distancespherical_to_vector)vectorsangleinclude_negative sphericalanglesrZ new_vectorss r> group_vectorsr}s{.mG2:666G %LLE2(11(11I#Iu55NFF*622K  r@distancec8tj|tj}tjt |t }t |}g}g}t|D]\}}||rtj| ||tj }|||}d||<| tj ||d| |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&rLrr'r!r(rrmrequery_ball_pointr4r.median) rTrconsumedtreerrZrrr[s r>rrs,]6 4 4 4FxF 4000H 6??DF F!&)) u E?  ..uh??rxPPPx&' 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)pointsradiusrrpairsrZs 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 raFrdrNrErrcLg|] \}}|||dz!S)rrG)rHr=okrinfls r>rKzblocks..s8 R R R2r RfT!WtAE{*+ R R Rr@r) rgr&rr!rtr'rh logical_andrmr(r.pop)r\rArBwrapr] only_nonzeror6infl_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(TrrNrE)r&lexsortr'r!)rZr\rJrs r> group_minrNsu& 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 r r r scipy.spatialr BaseExceptionErExceptionWrapperr(r?r[rr4rgrrrrr5rr intersect1drrrrrinfrrrGr@r>rs@QQQQQQQQQQQQQQQQQQ-%%%%%%%---)j)!,,GGGGGG -!%!%'+%)#' V5V5~V5V5G$ V5 '" V5  V5V5V5V5r668G,6hw>O6666tJN?? ?%g.?BF? ????D&A&Ax0&AGBH