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H<$L*IL$HM IH" H$HD$`H$PHD$hD$xD4AVHcHH$ H$HHD$p 1HAHD$HHL$8D$HT$@Ht$`D0HIHHH$HL$hH$HH$Lff.HcT$LL$PHD$HL$xLH|$0Lt$@LD$8LT$`B$)HHA,<LE,Lt$hII9Ic Lt$XHA<AD9D$xDHL$pAHcHDDADEAeHD$HA ˃D$DIHD$PEH$E2D4HHHL SGAH HHsJH8SH561AZA[HD$(HD$ H$Ht H/H$Ht H/H$Ht H/uH\$ C8=HD$(H;3tHtHøC82H=b>1{KHĨH[]A\A]A^A_HH$oL*&LLD$LD$HPL8LAf)$fDAHL$XDHcHDE*H$EENE9McIcHL$XH$LHILIc3ML$LLHVHHH?HDHI)GfH}1LXLLH?LMLHI)HH)I|;IH߃HA9t&HcH9~H)HI|IHA9uL$AIE9QH$l$D$wH|$pIH;HHff.|$uHt$F8HD$HtH;tHƸF8ZMtL;-ԻtAE8H$Ht H/H$Ht H/H$Ht H/hHD$ H;mtHƸF8qHD$(H;I@HƸF8*Ht$(HH$HH H^fAHL$pAAMcIˉE+ADEAtaL$x)HcH|$0HH$D,4>HHPHH$HPHH$>LļHD$HA H$E+IHD$PD$A<A`H舼H{HndZVPcHH$2L(pOL$xE*A)HL$0EIcHH$D, E90EE9$HL$@HT$8H,葾H HD$ H;TL$L菻"AH=~91F\HD$E1BH=W91pFf)$wH=sHH5OIMHmAH=8FHD$1HmuHѺHD$AD$ImuL诺ff.@E18ÿHD$HLLL$|LL$HjDLHL$XHL$#LFHL$Ht$ HH$HHpHcImLH=+MI Ht$HH$HHH跹zMHt$HH$HH2H苹% H\$(HH$HHH_ H\$ HH$HHH3Hm LD$D$AHD$I(aLTH$H$HHjL "B]A\L辸HD$AD$I.L蔸HHD$Bhf)$]H=HLD$H5MLD$IM[LD$HH=WLD$ LLD$II)ImuLE1C}HmLHL$ƷL$7B H=H LL$H5 TLLL$IMpI)uLuI)CVLL$UL$AwLL$hLD$IYH=nLL$$KLL$IHC]HLL$L$YLϻCҶp-H=+1g$p*H=+1Q$p*H=+1;$p-H=+1%$p*H=+1$p-H=+1#p*H=z+1#H+AH=4:ADAWfHAVAUATUSHHfHnHrH)$~-HDŽ$flH$HDŽ$)$HLAHMHHtHzHHH$HWH$H4HIH=j1PAPL$ bH YHAH HCHt(HHHtHH$>DHHHHH@HH$HH$HH$L$`1H;DŽ$LHH$RAWL1L$6ZYzfo$`fDo$pfDo$fDo$fDo$fH~fDo$)$0fo$fo$D)$@fo$fo$D)$Pfo$fo$ D)$`fo$)$D)$pD)$)$)$)$)$)$)$)$ D)$0D)$@D)$PD)$`D)$p)$)$)$)$)$)$)$fք$H H$HGLgMtVI_IIILIIIu +IHYff.H$c Ń(fo$ HHH5!)$fo$0)$fo$@)$fo$P)$ fo$`)$0fo$p)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$)$fo$)$fo$)$H9pXH4H HHCH5HHHIHHHM0HID$L$HH$ײL$HLT$H$'L$LT$HIL@H7HIF HI9BLH$8L$HHDŽ$0L$8*L$H$I.I*hH$ H$H$8H$H$HH$HD$xH$HD$XH$H$H$IHH?HH觱IH$H;-$MHH5fH9p!HMHHHCH5HHHTIHHHMIH fL$)$0HDŽ$@L$8[L$HH$HL$H(H$8H=)Ht$HHAHL$@I.L$ I(c H$HHQH $HHx HHLH$H$H$`HH$HQHHHfo$`H$hLL$)$fo$pH$)$fo$)$fo$)$fo$)$fo$)$fo$)$ fo$)$0fo$)$@fo$)$Pfo$)$`fo$)$pfo$ )$IHrH*H5H9peL5ʿMIIFLD$LH5HHLD$IMI.vfLT$ LD$)$0HDŽ$@L$8LD$LT$ HHVHHt$HLD$ H=LT$HHCHL$@|LD$ LT$I(MI*H+"HHH$0HD$HD$L$0H0HVMHHHHcH$8LT$L$HHH$1H$fo$`$fo$p$fo$$fo$$fo$$fo$$ fo$$0fo$$@fo$$Pfo$$`fo$$pfo$$fo$ $fo$$fo$D$fo$D$ fo$D$0fo$D$@fo$D$Pfo$D$`fo$D$pfo$$fo$ $fo$0$fo$@$fo$P$HĠLT$HD1M~ff.@L9uH$vH$E1Ld$1D$H6L$HD$H$Ht$xHH$H$KD$H$H|$0$1E1 'gL$E1H|$ D$L$ff.Ip?LLN It{I9A~HHHH?HLHH)HLH)H~AIH$\$HЉff.@f/v Af(ȉ̓L9u\$ILLI9\LcLH\$0McILD$L\$ ID$HHc>HًHI9'H\$XL)L$HH\$xN;A,8\$HcD$ I9'H\$XL)L$HH\$xN;A,8\$ 9ЉfH$NL‹T$ *H$f*D$IRI;fH$*D$ ;HH?HLIHHIQIH)H$HH?HLIHHH)H$D9DHLt$`NL)ɉ$E1HLLL$hMI͉D$(H$f(LT$pMHLL)IHH)IH$1DH$Lh} H$IH9D$hH$ILT$Ht$ |$LD$PHD$@HD$8HD$@LD$PT$HH#dHHL$`LMIID9l$(Ic$LhtH\$H9t;H9!Ht$XH)H$HHt$xH,4H9D$p'LhHL$pLHHH?HLHH)HQH $H)HIHH9D$hLHL$hHHH?HLHH)HQH)H$HIHHHXHH5L *AH R,H-H8S1菩^_HDŽ$H$Ht H/^H$Ht H/=H$Ht H/uH$F8H=l"E1T/HL[]A\A]A^A_HL$L$LH$H$LLD$H$LD$H$_HH$ʣH$sGWHH HICf)$`uH=Ѿ7HHaH=!E1.HHH?HHH)D$(1L$(LL$@Mf(щl$HLՃHcILHL$0HHcHL$H9I9!Ht$XL)H$HHt$xH,4H9KH$}H$HH)HTH $IHH9D$@H$}!H\$@H$H)LTH$MJT$(|$t$  HD$8T$(LII9l$HMHL$XHD$H$HD$H$H9$L$H$HIHPHH$HH4$uH$F8H$HtH;5tHƸF8.MtL;tAB8H$Ht H/H$Ht H/H$Ht H/H$H;HƸF8H$HH$HHH蹠@LPLLH?LMLHI)H|$@H)H$MT:MJHxH $HHH?HLHHH)HH)HTIHfDD$(IMLL$h$Lt$`f(LT$pI9fDHHPHH$HPHH$XHL$轟L$L謟LL$蛟L$lHL$膟L$x7nDdQHH$`(F<2GLcGWHH IHˡHH$H;/H=HձH5ֱ3H0HHL$E1衞H=)L$HDŽ$覣IrG:AH(E1DH=wL$^)L$H$E1HH$HELLT$ LD$ LT$ LD$iI*tLH=E1(E1[Ƅ$AHDŽ$HDŽ$I(t=I*tML$fDDH=E1(E1H$2LL$[L$I*uLIH$HH$HHHL$L$H$HH$HHMHL$L$8LHD$̜HD$HHD$赜HD$LHD$螜LT$HD$I*,LwMjIZfInIEfInHflI*),$t4)$0H$0HImH$tIL fo$$)$0LHLT$LT$@H=&H$HH$HHHDŽ$E1E1H$HH$HH/Ho"H$H$HHjL $tAXAYH=tH%H5&/HHHDŽ$AI.iL\AI(uLؚIHuH輚H=.HHDŽ$AE1>A0H=H^LD$H5Z%/LD$IMI(uLFH=jU%E1H=fLD$.LD$IA!2LD$IML$A8E1AAkH A`H詙PH@`HtZHHtNHHtDHVH9Cu"HH+IH[H.HHu HHH5P H8ݙp[9H=A1p<H=+1pf9H=1~p<H= 1hp<H= 1RHHShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingbase class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type__int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)Incompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))Interpreter change detected - this module can only be loaded into one interpreter per process.value too large to convert to intunbound method %.200S() needs an argument__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__qualname__ must be set to a string object__name__ must be set to a string objectFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)cannot fit '%.200s' into an index-sized integerCannot copy memoryview slice with indirect dimensions (axis %d)Acquisition count is %d (line %d)invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'%.200s() takes %.8s %zd positional argument%.1s (%zd given) while calling a Python objectNULL result without error in PyObject_Callscipy.cluster._hierarchy.Heap.sift_upscipy.cluster._hierarchy.Heap.sift_downscipy.cluster._hierarchy.Heap.swapscipy.cluster._hierarchy.LinkageUnionFind.mergescipy.cluster._hierarchy.Heap.remove_minscipy.cluster._hierarchy.LinkageUnionFind.findscipy.cluster._hierarchy.find_min_distscipy.cluster._hierarchy.LinkageUnionFind.__reduce_cython__scipy.cluster._hierarchy.Heap.__reduce_cython__scipy.cluster._hierarchy.Heap.change_value../scipy/cluster/_structures.pxi'%.200s' object is not subscriptablescipy.cluster._hierarchy.labelscipy.cluster._hierarchy.get_max_dist_for_each_clusterscipy.cluster._hierarchy.get_max_Rfield_for_each_clusterscipy.cluster._hierarchy.cluster_monocritscipy.cluster._hierarchy.prelistscipy.cluster._hierarchy.inconsistentscipy.cluster._hierarchy.__pyx_unpickle_LinkageUnionFind__set_statecannot pass None into a C function argument that is declared 'not None'scipy.cluster._hierarchy.LinkageUnionFind.__setstate_cython__scipy.cluster._hierarchy.calculate_cluster_sizesscipy.cluster._hierarchy.__pyx_unpickle_Heap__set_statescipy.cluster._hierarchy.Heap.__setstate_cython____pyx_unpickle_LinkageUnionFindscipy.cluster._hierarchy.__pyx_unpickle_LinkageUnionFindscipy.cluster._hierarchy.__pyx_unpickle_Heapscipy.cluster._hierarchy.mst_single_linkagescipy.cluster._hierarchy.nn_chainscipy.cluster._hierarchy.Heap.__init__FromPyStructUtility.__pyx_convert__from_py___pyx_t_5scipy_7cluster_10_hierarchy_Pairscipy.cluster._hierarchy.Heap.get_minscipy.cluster._hierarchy.cluster_inscipy.cluster._hierarchy.LinkageUnionFind.__init__get_max_Rfield_for_each_clusterscipy.cluster._hierarchy.cluster_maxclust_monocritscipy.cluster._hierarchy.leadersscipy.cluster._hierarchy.fast_linkagescipy.cluster._hierarchy.cluster_maxclust_distscipy.cluster._hierarchy.cluster_distscipy.cluster._hierarchy.cophenetic_distancesscipy.cluster._hierarchy.linkageUnable to initialize pickling for %.200sModule '_hierarchy' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtime../../tmp/build-env-vm5wde73/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd_cython_3_2_4.cython_function_or_method_cython_3_2_4._common_types_metatypescipy.cluster._hierarchy.__pyx_scope_struct____Pyx_CFunc_4d3ae5__5scipy_7cluster_10_hierarchy_double__lParendou__etc_to_py_4d_xi_4d_yi_4d_xy_6size_x_6size_y_6size_iscipy.cluster._hierarchy.LinkageUnionFindStructure for fast cluster labeling in unsorted dendrogram.Binary heap. Heap stores values and keys. Values are passed explicitly, whereas keys are assigned implicitly to natural numbers (from 0 to n - 1). The supported operations (all have O(log n) time complexity): * Return the current minimum value and the corresponding key. * Remove the current minimum value. * Change the value of the given key. Note that the key must be still in the heap. The heap is stored as an array, where children of parent i have indices 2 * i + 1 and 2 * i + 2. All public methods are based on `sift_down` and `sift_up` methods, which restore the heap property by moving an element down or up in the heap. needs an argument%.200s() %stakes no keyword argumentstakes exactly one argument%.200s() %s (%zd given)_cython_3_2_4takes no argumentsBad call flags for CyFunctionpicklePickleError__loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requiredkeywords must be stringsdecompresszlib__pyx_capi__functionexactlyremove_minMemoryview is not initializedscipy/cluster/_hierarchy.pyxname '%U' is not defined__reduce_cython__change_valueprelistget_max_dist_for_each_clusterinconsistent__setstate_cython__tupleExpected %s, got %.200scalculate_cluster_sizes__pyx_state__pyx_unpickle_Heapmst_single_linkagenn_chain__init__get_mincluster_incluster_maxclust_monocritcluster_monocritleadersfast_linkagecluster_maxclust_distcluster_distcophenetic_distancesbuiltinscython_runtime__builtins__does not matchnumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncscipy._cyutilitymemoryviewnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointernumpy.import_arrayinit scipy.cluster._hierarchy__reduce____module____dictoffset____vectorcalloffset____weaklistoffset__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutinescipy.cluster._hierarchy.Heapconst intconst doublexXMWG֖llbls d؎=1;s3O*A@9R^RKYR?!?a[-`9xjǭ[ #%\rϦrl)fۦYy93,\oNӳJA)ah0JJaZ)5oXLLOiz.Ye>S*LS[,t^)#nͧT͒ldRXWTR֘iJ*4Ll#iGJfoTXyKʶMfەbiߚo9l2N} I|ۜW*f+YZ@X{Xgl'# +W޿a#m7#G"\S" % lOV^Sy6B3w.J1jDdAƗ&Ycc]RDK,Eڂ޽@6bsyyzM}Bg%Jlr9.Vt[v^L$jEN =q$fzh:|ԢYk>E^bzвb'u)}Lh:_8ڵ] ;;ŭMꬤpcqw=>k: T,-N(9E%D.r3:3XAh+}AHҜE],ļ(H1Cbuޤ"RtX#1 b5u2cCd}P@S&"&#%&Ꮋo]l M8m9t ]`3fܵFؠ rf5]wK^94\wTcdbko4/7F3#.E{ ~5Lsgc4q~r{=ۇڑk02d3[L52X+q?-oE=b3:M^_}L  Ua+?uՍ7贊 `ۄIGa!r` Dnzzƽ8rwAk˽%?;4* #! &k V>'=NyAt$iM(uRN㮻moGmpq}V]uCj*Eaؼ/Z%zS0O֟m2,4 e? .'ii}~ F9JɁR#1s$6(!%'T6[1$/ ~B;p!EW[֡I|k 6R'q۔k++QV5:*8%kcThnɕ s$>)}Q 33,aqʰ6 :cĄ׏Wޕ$9Ak.-,{uT ? cβ@Mu^MaKJ\B.IiNϠ_8_NΕ7CǖmWG\]~+j!YXv~LEZc"bD/A2|(@?aD -XSV=Xy BE^ʑc"L gNFp տQc_W꣣sHCˬtM\BvC_̔m_;kfľ0FBwcW]׵};viI{EsjNʢa@*!-/} '"  "$                   EP >#10Vmgcs- 3TT7:+QQ QPPS G : -  c'W'J'='0' '_=P=@=0= ===uhXH @B  __pyx_fatalerror Perform a pre-order traversal on the linkage tree and get a list of ids of the leaves. Parameters ---------- Z : ndarray The linkage matrix. members : ndarray The array to store the result. Note that this input array will be modified in-place. n : int The number of observations. Perform hierarchy clustering using MST algorithm for single linkage. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. Perform hierarchy clustering using nearest-neighbor chain algorithm. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. Perform hierarchy clustering. It implements "Generic Clustering Algorithm" from [1]. The worst case time complexity is O(N^3), but the best case time complexity is O(N^2) and it usually works quite close to the best case. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. References ---------- .. [1] Daniel Mullner, "Modern hierarchical, agglomerative clustering algorithms", :arXiv:`1109.2378v1`. Perform hierarchy clustering. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. Find the leader (root) of each flat cluster. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The flat clusters assignment returned by `fcluster` or `fclusterdata`. L, M : ndarray `L` and `M` store the result (i.e., these inputs are modified in-place). The leader of flat cluster ``L[i]`` is node ``M[i]``. nc : int The number of flat clusters. n : int The number of observations. Returns ------- err_node : int Found that `T` is invalid when examining node `err_node`. `-1` indicates success. Calculate the inconsistency statistics. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray A (n - 1) x 4 matrix to store the result (hence this input array is modified in-place). The inconsistency statistics ``R[i]`` are calculated over `d` levels below cluster ``i``. ``R[i, 0]`` is the mean of distances. ``R[i, 1]`` is the standard deviation of distances. ``R[i, 2]`` is the number of clusters included. ``R[i, 3]`` is the inconsistency coefficient. .. math:: \frac{\mathtt{Z[i,2]}-\mathtt{R[i,0]}} {R[i,1]} n : int The number of observations. d : int The number of levels included in calculation below a node. Get the maximum inconsistency coefficient for each non-singleton cluster. Parameters ---------- Z : ndarray The linkage matrix. MD : ndarray The array to store the result (hence this input array gets modified in-place). n : int The number of observations. Get the maximum statistic for each non-singleton cluster. For the i'th non-singleton cluster, max_rfs[i] = max{R[j, rf] j is a descendent of i}. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray The R matrix. max_rfs : ndarray The array to store the result. Note that this input arrays gets modified in-place. n : int The number of observations. rf : int Indicate which column of `R` is used. Calculate the cophenetic distances between each observation Parameters ---------- Z : ndarray The linkage matrix. d : ndarray The condensed matrix to store the cophenetic distances. n : int The number of observations. Form flat clusters by monocrit criterion. Parameters ---------- Z : ndarray The linkage matrix. MC : ndarray The monotonic criterion array. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when the MC values are less than or equal to `cutoff`. n : int The number of observations. Form flat clusters by maxclust_monocrit criterion. Parameters ---------- Z : ndarray The linkage matrix. MC : ndarray The monotonic criterion array. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. n : int The number of observations. max_nc : int The maximum number of clusters. Form flat clusters by maxclust criterion. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. n : int The number of observations. mc : int The maximum number of clusters. Form flat clusters by inconsistent criterion. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray The inconsistent matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when the inconsistent values are less than or or equal to `cutoff`. n : int The number of observations. Form flat clusters by distance criterion. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when distances are less than or equal to `cutoff`. n : int The number of observations. Calculate the size of each cluster. The result is the fourth column of the linkage matrix. Parameters ---------- Z : ndarray The linkage matrix. 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