From e1a97587f318265bf1165b60944e5f0c7cc57bbd Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 11 Oct 2025 18:57:19 +0300 Subject: [PATCH] Updated sill descriptions in documentation, updated spatial dependency index calculations --- CHANGELOG.rst | 5 +- README.md | 2 +- docs/build/doctrees/api/core/core.doctree | Bin 196508 -> 198366 bytes .../api/semivariogram/indicator.doctree | Bin 93191 -> 91592 bytes .../api/semivariogram/theoretical.doctree | Bin 198883 -> 200287 bytes docs/build/doctrees/environment.pickle | Bin 577332 -> 579507 bytes .../2-1-directional-semivariogram.doctree | Bin 76644 -> 76644 bytes .../core/data_models/blocks.html | 51 +- .../pyinterpolate/kriging/point/ordinary.html | 12 +- .../semivariogram/indicator/indicator.html | 22 +- .../classes/theoretical_variogram.html | 31 +- .../theoretical/spatial_dependency_index.html | 13 +- .../theoretical/theoretical.html | 14 +- docs/build/html/api/core/core.html | 6 +- .../html/api/semivariogram/indicator.html | 16 +- .../html/api/semivariogram/theoretical.html | 29 +- docs/build/html/searchindex.js | 2 +- docs/source/index.rst | 2 +- .../1-1-semivariogram-exploration.ipynb | 1327 +++++++++-------- .../functional/1-2-semivariogram-models.ipynb | 420 ++++-- .../1-3-spatial-dependency-index.ipynb | 18 +- .../2-1-directional-semivariogram.ipynb | 250 +--- .../3-4-directional-ordinary-kriging.ipynb | 94 +- .../4-1-semivariogram-regularization.ipynb | 119 +- .../4-2-poisson-kriging-centroid-based.ipynb | 286 ++-- .../4-3-poisson-kriging-area-to-area.ipynb | 270 ++-- ...sson-kriging-area-to-point-smoothing.ipynb | 83 +- pyproject.toml | 2 +- src/pyinterpolate/kriging/point/ordinary.py | 6 +- .../semivariogram/indicator/indicator.py | 22 +- .../classes/theoretical_variogram.py | 25 +- .../theoretical/spatial_dependency_index.py | 7 +- .../semivariogram/theoretical/theoretical.py | 8 +- .../theoretical/variogram_models/functions.py | 8 +- .../theoretical/variogram_models/models.py | 21 + src/pyinterpolate/transform/statistical.py | 3 + .../test_spatial_dependency_index.py | 5 +- ...mental-semivariogram-and-covariogram.ipynb | 301 ++-- ...mental-semivariogram-and-covariogram.ipynb | 44 +- .../a-1-3-experimental-variogram-class.ipynb | 200 ++- .../a-1-4-directional-variogram-class.ipynb | 38 +- .../a-1-5-variogram-point-cloud.ipynb | 78 +- .../1-1-semivariogram-exploration.ipynb | 9 +- 43 files changed, 1951 insertions(+), 1898 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index ad1f8139..8dabad68 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,8 +1,8 @@ Changes - from version >= 1.x ============================= -2025-0 ------- +2025-10-11 +---------- **version 1.1.0** @@ -12,6 +12,7 @@ Changes - from version >= 1.x * [docs] updated missing DOI in Indicator Variogram * [enhancement] representative point in `Blocks` is sampled from the largest `Polygon` when `MultiPolygon` is passed * [enhancement] `Blocks` object might be altered during CRS transformation or new object might be created (`inplace` parameter and copying mechanism) +* [bug] incosistent documenation - `sill` represents *partial sill*, not *total sill*, and documentation might be misleading 2025-07-17 ---------- diff --git a/README.md b/README.md index a7061afa..657ec65a 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # Pyinterpolate -**version 1.0.3** +**version 1.1.0** ![Logo](pyinterpolate-banner.png) diff --git a/docs/build/doctrees/api/core/core.doctree b/docs/build/doctrees/api/core/core.doctree index 1e3ba20f44e8dc8461b1a946a246d246e84afa18..0089fcee52d3b091a273ae44155dddf1a88ac8bc 100644 GIT binary patch delta 15612 zcmc&*dt6mj_Gc|0fP$&$RY6{Y;Jv)LJOnPnCCf@pm|oVD3W(y(L_!0x%V7#`h7mX`QyhQaL(F$t@Yh| zueJ8tYacfL+WGjhkh#0y?P-uI4o`#VpyZedlO|6ruM{t@!roq(4#^PRyS{wN_^SHR zl~d}B=BmP5r%bJCtPZ@Vaeyd%5MBtY-fW-OIID3$b%%x!C8A0jWV6Kw>MycywXMPT zzOYgCkMKM%GNwbM7&8`nVQW7v`WidDB1u|wL?lr}MkS`f5!I`-5VN3FWIPPfT1)57 z=vgD5#oegOfVGf2Mz;zZ6&cngE#GY(Ek#X9Q8=S%6aRC9qoj0a@(%6`>H8B&b%I~j|tThwI z#!Tp`%abyuPO7XfpESN|te7_k62;;B@EUlRf!oE2Iq;0uE~$suJ{L+cp9bc3+Hz=^ zQ!A=%)gAo{kQ1ksWjaG`JOfcf1FIuEnQ`Kgd2kz+#fe%MID<3&lVk5MiXMVfP$3fL zLmf;MkIsiooJ5n@wXZlmA5zd0r?pl`iQEMc0n@}S3m^+=Ar>!y+z?$w5UHpT2N%E{ zSP`eyPW9>LhS71t?FG95he?@nTI~0By9r>L9EQiyE+D{XcQ3YGssDDB{*NnUEKsmz zb$W>C@33o-fl})>vX{NH@mA$<{DzBcQH!@pj@r6-n=FeLO0(4zr=2Yvf&28eYcj9h zoW=jYQ&Q2%Hzlbmd{9gXF=J}<+vQz*}Q8$y-1~*(&KcRkN)#xczdH;RU>|X>nS{qJCP_n}G(ET{S6Aoct2(7*739`1*ICmR@lpbPV}p zhd3?r!Z@_WYt7qI!aDdSm13QA_!#ZvwnQ=R5h%8$#8cFaxQ2eE@>5^+B+AQS@Y+RC zZY%J$yL|1gcy7-X&n|*TacDfvds1NaHKhNBcrkM^3`hFw&PTiV!eW>KcyGL@T?*+~ z7cW+&f|K60))yQh<4K-c>xQjbZHM29F%2YcQM?Q*qsuWW)ZfGNNW8dq85HAM*{uC- z-xaOigUe{^!tLDb#4Yh+XDcS)i}3=wgA=xhZfhVB-%tR8wvc8rj%UR3kD#yi&ClJ1 zXC;sY#fywY7@^rZ--@31MfzHBkR+|Fiyb|i#N1U-hL@H6i%LF(h>Ud*r8OO}!9}S8 z!X-rTrRMT1c|pufmiOG|grCpNedrc$evvp?L1!}?3;Fv=&INk<%z zK!KNgN!d)RSp)enn`sO2L0#^!{s!Df*1~Lf!Y}tRU2cnCE|H|%y(YOtlX4F$xd(N* zkNM z;PNp*6HWY030g+CJYVZbU+apEwE2un&}t(}snr;6@gHCT&PkA~KO;e{{xTgBy-P?N zlnkWzxsQSiceKH@RmWs-yzb-R90lJ~n2grXe63>TM##YP3e=1Q1Ju-Jn1-DaC8&;x zQbg}WIjoL}V)ib|L=qC^IH-}VEZDP28Rlk@Qh^cL4Ym+5=1GXdA|;|gi5RX#6eP+l z34gCdP$P+GM|*sFt+~l9PLyF0de({Mo1rg`j~B5~IMh|$(Q}$}r*-V$ zGK*TAsJ}kk==s`cnp&MyT}t`VBkZ?PLxKE#Y`hpW0UY9;)FTi$j|85uv&nms!+Um6t!&6Fc@tdW!|gKR)=eaaI@|2x@&O!z*>;(52ife(N2K|As}7L);q`Vld1J|A ziR)Us#nzZ)vzu+6MN1sbIL-xDKt#mL2~{qet3)g=cGP^e>TscXgEk`jqfZ2N+Of!TSUpqoDERAvlUX9P$*^)zS78)zx8M)D&V2ajbotcDp2pTGJ3>;<%?mO4Hl4 zQ@b4IB~2xsJcnFTV_-}Q5#^uzy_f~-h`u-sui+?#=&q|Ey32wnofvO-i0q@35>C}2 z-EM*O+EFOShb@pYh-tnR5|eJXKw`-XlK0d*Pz<<5VcK*ROq(PoV|Y&DY!WT+!L7JY zht#A)@=~lH33%9{JwCm!@O}Vp{LrB_*E`{`Nd1s<3Tok#qi#uABLaeAlWbrQE(c< zwb7aV4H?D6aVbeUc0{sr>`Ug+h?S?{K1@zFJGPs6?8%l`&7?~bidpbJyAeB$W#Ry| zN1XVQlHhF=nNIl9nE}-B2jTuFB_Whw)&3KTa8xoMdbQ|^z>uY!HOZqh#hdg0gnFz= z*7jEvTr+ecWp1A7LDU@KsYw>CUqNmARWw}jGGH`na{Ra=S!=HKMd=%;YeO>a7j|4s z?X&v}JOk5lr)7-XX}jzMi$%-@!uxQtHg{5<7f#P*~3J}kC39Bs0@T_MD33d$4Qbgl{b-y z5T-gXI7JTQ;5j;xrZG>Jxf3m`>Lg+>(4M4^qc&A87p_%Yjm>cfq#*^ekRiYMfKk={CZBd(KDPx99JSfR(ksU1nUt} z$rA0+YXSnOj7mRba^KU*L1z8*Cu%73T{if9sxubZ*-p#4L}?E$>VXq^z59jLC}Xvl z^zok|Thz6~)Hc@cz4aoD0d%Fxn0)l#bQBw$%KHY9y!n?X-vyT_L(DS#?k<9|G|{{L z7dQZTcdDrQ6-MA#$r5{as)_>*ol-m9SRXT?E~eZZ$?uj**5qM^xG4y`YCG%ulY}+R z7z=k>bc~TYM0>!MAVhLI3>P~AQ*fhH(`mh-#v#rj#tIi=4_GfH7_j|dHd7~Pop@BcqhEXN`~M8DW5{<)^=D(e|X%6#Bs#<;?*Q_gIS`tx;>Tv zeyL=ACQfv~abz53S>uW2?^lz>4Q5&5jgEK;LdCqkXxF+v*cpS<#J>Y^1pPJd<`amm zLmIDs59?^TVI|#F%)ox)^C0ZjV|km&SemGvY%Bn$(ove&zO9KY48}Ku(VZqr!_aAs zl`F!q97m*asf^;PH?{|c0|!{DU^&G2p7=gYX2FqMbi#>gf;t=*hbp0ysO*JH;Sp|( z(ztdrKTXwc)+iE{g_tC3EKf?3s@)Ln@4aydJkK<@_V}rA@lYR(jp9>xL(t^u`IQsP z>*^Zid7Ka3$7u#ybvbXENm_$Fwed4$hY;f`s>wa<4fex&q?sUS^yYmUFBAriI!{HPE2#@)p z&~d9+6;-Qr`3>-S%$<_78(-8fD|lEm$mJi zGT325OL!G#VcOeC7`KhW!my5|5Yb(znLy^_(3VxE+?TQSpUJqm!Kaxxl({S~{ zDfoma&mmtXM-Qo|{(5!#Rau>$n~JTWeyZo;47PonX!(*Bb&Z;trj-&f=g^er;|rhhRVORzqJ2Q#dv-}LwJIAoFT74dimdO{v%pvv-V#j0XV62+PL z3BHvfw>5ajv}Qz;$AY(b%`(MVC;koJ$dH$#t6#hhzLQbIO2;%hd3e_|cI4 zXWoEk@F|{*WM%(4BgeHTXwU?B!HqatoF75pCKuTZrCq6gR~yJoB`%JJCOMC{fy~l3 z_#iK_Kn|rgf-D0fIHeza<&*&t7`gI;?oKhYgiAlt0(2J$$TD02_({3|l`yUv5$(fq z3G@=>cj|r+mgPM^9IprZbcATi4;)!4=W!_#=R8D`InPa!q;ejj4JgIIlq3_UG<1j9 zUy3Qe87N1J){)plrdEzDb|my9FezGo`w<#O1JH-sKg+QCnHZLJrNJ-fkxGMKEB5^k zM}_uhPG#_2vy{Q}AqE~}bSc{5uL4ECTk&<=kfjoZKQcWlxP{pwJ=?QnRD9|-JW7=* z$w9|Y+d*o*((Vru>`bR^yVA#7_8YR7XGA+Xf2WpGzI1yj+TcqmhBF3k*LB$YYcKD~ zfS;5KKGy1T*cE=@iBN@{P!su$ENNIKg|EquGIKCd4=~k%E!3SHL2qD3*pa0&2XdB5 zT6W$H;SNrh82Q#)CKT%S z+adal#(B6eTV8&{>uSdKcca{CUp8NW!ya$y7|t_4QB8d19kPd{>bO4^i_5VLPOEw3 zoW#=DuL7rFK#sgzyp%037rW%}}F4{Ayea^0RR*_%S0_KDAp6ufh{JB1di?72_z) z7)BRUd?7fN0MmtFIY}Zbzb8j}(%0iDzRsg(+C64b%avXJxmPc+ZcBZagz4;z7h8)CkXCWrXMD2n{40-Msvd5@?0I>g%ff`mwLPd@6DCv`tcHnEX3(Bx{;i* zZP76~H`lxftWcp7rBlgnRx5DJ6}aUVaD45!Bv%~YOKHSb9o!NNxV#7OUVPCqPd$jq z+ZG9vmspUnbcy7z_=y7Xp#&kmm_>CTE)VNt<2%Q9^*Q=phw-ij#=r;h7HmJzyj*;B z38H!a70boscO?w7^wZ?m>VC~^A*cLS`JTlpIV~rvlu?oVP8N!D26Fbob&=3vY)UFL z-jgHd&cr$#GLS39B7GKy(lb|xhl_?;G_4Z`iaoPvpSX9Rw2|==v3X&mNY_}VI18LCo?J9i{mLf{u#Z>X}EbN45l+G{P=;UkE&y~27O6SY( zVxE|}0K18mUJMjn-czp;5GYPAp`?ND=5>o;=Jt8AdVX@C1xxipawL16SicaH#S6)d>&8?U%y^W>E7JdLAaNE%?*TbyMSew z1)D_9GUC)*rY=J_l_B+(v&%4?TlgAJA8rzfY|c|zfr&&~I4jU8thXUgWd$bEqCAxq zJV2fOQF#D$uFq44#9=v{70{<}cP-U+Hk!1r z%a^CoZA^;NVj}wDI^yDwsCP4|j9v|9`l}c6dgc29fjys-fTg1Mdc0PQ71&jzMd6^H za_g5Bx4%~e#_O8c?p>}3g+kKCGl|qx}7mCLo#RgIG1X(s`(W5uuKk#h6%A(tg zjHmQ+8x%-O`z2oP9r6{53oK5)ILeTwn{6Qxgqv^kS#TY>nZ37g?wKeq8# z!8ZP?*f3O z6>P0n#m0HTjsi}i;GhrM4hyvPn^C~07HFKoe`JBi6q3WPs!tnBwiWm`EYoCmXU;f{ z(If9=i7BpcJ&4=YlqD z0lV>>7_t+e#2wt~q@6RS1(WDra99hKVqa6l0|xO@3(mw(74evOmZB4$ajCB~kI{hn zE6tx>>MPA-qWU>3z|Mv0E6vf#5c``kDUdqy$D6twv2YI`%ITw5qR69&vryyDHw``R zLPO82T@*k^6w0Fs^_54H&HPsnO2%O6bg;>#g3`I?u}m!ZqaKuO{-AV!p%Ij37m9uB z>56Ud3wU4XCY~=9npU_}XySxHbl#^&rS`k=Tby60P9z%&rNONuS){YEP)4NIKjFK8 zyXl3FB)h2T%4oE!P^QU^%%uuiErl`);oF5OV{h>b)MF5yD3sQzGjZtpv`|^6iPTca z))``wyhH4I86QU+B+*>H3YyCjjlt821YZ=N?j;-SGf08CXaO^6AC*0Vx zAC$gj6C5{A~F% z?!bBrBn~9CZ6Psv@F3~II!KnjgX~TJ1kE&aTMLyFucIN56YDKp=2$}pNf#veq`r}!R>Yo9pEVu$h-#(s-??1!S(Wts>O2KS?ER1=U*j=?_>orL!{^BBI)us ze}E4Ho}?FY`A@0oN|*n%NSgG^N)3DOu_ER2KNU$Y3p%C?6z4yo&*5d-5iC}n)ei?c zBc~o6i>2F#m`KNX790kJrN1x!^%0(gBqf0>jETiA?}R|X}LkFuel zIAY^*GOWdP8G0omPA^IoqEHuO6zoO{Qa;fmis!_I_PL|C201Etya)XA|~G3 zO=230)!uF*tt?hyVGeaBFVkDN^gw8(mu)YSUUs}#df9efSLJ1T-x%p-WM+OZ`+-KL zcK9FpNqX6jR42V+hj0EI4`GPga4-yVd$)gqHv`5o&56-&t>lo)&i+Zwn1)ILgyHxRs~z z5fvYB%YxsvXDEi$yOpao(6FfBx0qQXJqz8k;P?4iyo_7vnOto%wOr|Ho84^GAq$lr zE}J)tiQix;?su#Gtl2M)Hjn6Yl)D^p%gt5iVZ;2JTX~s@)GUsiqnzI)_%XLK4-@H8 zw=(F(+&NBTgVtMl=Xltyc8-7&Ij~>+Na)O5d>;D%_9^l296BfOT0KjoLGS+#*Px@s zjFz+L#1fSX=nPCE1~z7si0&m8v@Eqti9-8J8?@}igjO1Ky;~ae-6gU*UeBPFLDPzG zb)44GZ_0HghAG$clQiY}5>*}N3W8A`-z_V}rt0`SALBzl#)lLmRmYz`^&?{Nnio0- z$RF>ipD+espEvL#e$npt8gs1VU)S;fp_K5irTn^xeobu%o>YEcRsHnIRdxJ7#~$vz hSoT~jdo7+?9m{kUP#q$lZfEXyGbu!cN{pKL>J|eNxC4H(Azh8}bUEvz2*67*RCWPvr1LRsHO4dHQ~(}j z{=>e(=FI`MM`V*Cx!_DLxgecN<4vnz5^m85H*0;n4Sk!nz5`kxZthBHtDyjo8wy+X zE-4Xw;!;N12iJdUm6WZoDYM`bG^9wX!Q#Di(?UwGoKI6d)TN?i?wSRAJ( z?$ZkSbb2*7Mch!@a}CVKa;xpnbWGYY~z_jl-o|J1Tf5?KuE z>gnGyMkP|a*L!N#l+m89*sXbGbJkD&DxibJQO#nAq;J2*R7&3nfmt%9)9(3r{v==L zRagFk#^*^Du8Mw~5Pp+bi|PX$wtG1ZZbc_8-w4z2a=gmXJzj&qi)oBSPJ~4rY3Uy57+UQ=7^WqtdmG8?H~F zw0Al0{5HyE{Rd~o+c>nTG7!L5J4OthKd&7oID037t zb2&wKOakpa&Ii}Ff5AfBmq@qoh9vmh2NU)-T$4B!`$X?T;J^9QJ#I4`&!a!t3=4(% zElVOAVzf7`+2z|^iM^-~SG7K}(ziK2ayEm1tQ2eO-C zZjvg{GI{L!0xdI13N+IloUApCQulFPbw`q}&(chV%FTA}kLJ4GVR(AM{%|WGoukOGZYweJ5pF?YLchp@M2tHfSGCobKnbj64%?nq_WIk z?O=q&YAnkwijyh$EBFd`YcL--0_I~Im~@uekwoJUaK`!0K(oV!=Gp-$$FnvxZWcOi zMI!6oYsxXWT8;{`5@el$xgjdp)BN&fi?eu48US1 z)!u-e==32jnr?Bbq+Dz$U(|-&hafz}pOcH7l9Nqe$YK)(wB~ES&j}^eXg}lQ`6FDYFTaFTw zb@35!_`}q3Q_NDL?-VF31$lEGG3n#s#}Evg#OQ!!2qg+T}71 z?V#u}6Qd_h9Q9w;NxNLubGwLPB)V*|FC<202{*x-Om)X$4^;W*ILKFc>nPPw5HbQAW9B+yyEiIRQb&HVspQ{7)uhK zcX`SmkE6n0`Qp`y;GF-67p4wWc8;$U=Uvq0EZ?inx(KfEJ>$u<(6w1fGS{kWjzDK9 z6**l<6p28rhu6yt^;=m@zE+cytZXg#4IYL2Y_^1HkYa)yiayIGO;dZ%@vY?XWNLMu z>xWr>L(>}=>diW)YD1H?p$FsnwwrVT2Eq$AYr?rwTbpdR*8d`(PFs^{{zY)ZHf3%3 zCJ1a`s}F0~tZlumZB5O`GD^P0!BY1--?|PaOLFWB-zyzdcL@S+Jje^CDpp&hA56AJ zx+p%Np}lQ~w)HYNc}IV~42jT&W?wKCyd`bFU|;atSVgc>b<@BY;0}zspH$I^h@aA5k4ipMH)}cp#OS;ShZ?5?as; zQMeF(lhG*8wg?BFPF3w2K<3BPxxQz8tNOzixN1`kVb0O^!Pi z2Sicp7<9q?4Gq$SC}>5gqrvrmSmcNe_jC?blTrPQ6S7rNzUFY~0r6tplsukF&pZJA z;OYOjy?JS(oYH$>E4yN|S@N`Og4rQSbExLXG3kZ8k!H3@-bthP<8W%|aRH_~B44EH zjtHL&(>TA&22uKJ;Gj+k_&IJ#leP%o$kfKwU2_C|#>&{y_M}PI+WT4}h6DU+PRijp z$-~!QdQPUPI%AJW>Q>3IH0er*({Lx=ghA=LqCV!=%x#orF1!UJ($&?+kd&TD`*dCY zSV+gxRGnc_|6`i&DO*TKWc74U`7`F{I>Q*0o86~ra%#B z6igQNL(**%5sL%UeG}=y{QikZ2Ztx33Z^Y-x;gG;6tAX)ZjqJMe#raw!EY(ZyUw0c z;@HDe)t&KA<&)}+f0`~m?>wox+-2BPhrEj(&%zo!nyz}(SfB14nvJ0lS}$v*Ri90# zM{{rpqT6Utp2FNeji75X6~m)#dQ6WlrOZ5bka}J!qw$y4&hOTVZqu#P>8m`f3_UAY zv{xTupOQWw*B}Qm&#fvCEOpDC+?dC54U3LEH}6me#TDWn%y!Fz&9ZlI!QpP@5%;kO zJN>ej^QIevL9BETE9Dmv$fmvBa5TQhv*ZVnY{8^A#^CKB4*=bf~+44;IbDLNYq1CJVNXn#3D_hJJP&rr?bc|1{m zx0}N%81Kvsl>xi=#s>gj<}dsvx0;8p&wi^jRQ8)GQabys%J3}cn}Y9V=)AYeuaEQI zhqvK&+?^qw6Jp3n-uow&b;DTNwusx27BZ`ntDi;r3dQK*#+H>@EmSAYVRm*NgI!YJ ztPGv{F6p^l@MB>|AKMp?V|b?D(G5XKR;@Bs=G)Q_hhx`F<>AsU6)yVgYe< zi;9p;?dTR#V5ZN}FW9h#YOE@|jnB~8?Ve1P-Np-M?ddZ!X!=dkGE$pg;ux1{I{A3{ zq?~+wW}{9%;%SCPpgnPwO+)`xEkasWzxnf9yHwMMmBeImQL$!a3 zOKns9<$l}&8};b@%Y1<3*j>+yz|q-$1m?ol29PBn-m^m%5(|mT8xTazU+s{E^pYR) zQX6DBFc`A9Jm0sskL28bI!k$cprFY;#$?NJ0@D<~r*p?h z9Fy&PNQ4Di&B;;RoM5^{4||e(BzE>!-&W(5eLH<2rIwRyKsBQV7q{QDq_hkgX07i{>M;jIrtfu~5GisuTAxI|r39 za0Bbyoo#s)xg|%PkT>NRv((S0V<+Nn#1T2_h#baa*UxcSj!GMbR!Q}PY1?Ehz^TUU z1{(_X?1nyygiqUMCxJ9y&o0DJBc#Dp^#spFURW*42YPF!a7^I_jc8pXh}J1Y<~-#@ zTbo1Q&f@6VVL)1IgLM2c9E@M&P|s;NklIf)ni04^NA(yD+hMF`uN|b2);0hs_68Dz z45tOt*^mCvKrc4}^s)`8ymMa2q4pnf)6|ixJ>!B6=);fWSWL*ZdPZ9o>YA%O!(1gH zU1$JHv>UKqoQYn%HCJ}D-7UEdck~vjnSoiRI(haiqxu;#V}Qvar8;}ADknB) ztL`$soGXQnbV|ZEOEl>OYwTSoa-W8Bik^!h_*SlTnv1BCzXZOeX76t|XVK2Ny!v0{ z(ram>a)I;>+iGH@(qIftZf?X_8p#@ zW9|6&dfoBIJeAPC7ocX^_FjCJJDt#k_Acbkqk1hjoJ1Zpt}t$X6rZ&Wi-3P^wX~X( z_tzdA&Ob3w{L-Xk`a{*&MGp_-S0Ud>WLUlqrbRbImqe#0S>J-f@}-Ah#*~X~wWP2` z#-+eYzCvo&ob^n17hu2V>1xS>^_;AdYKm}IUSw=BlE^~!dHUCO93$%2cBAv90O5np z+nX=}_=9Q(zPJK6$g9ypvmH1$pP1n|T^fKbX!`S54Xb5TwF9|}&9LR54sKdW`807A z4gs%k7~jm--NK!kB;7(LsqWrBMbc#l(>{0sOW>%`l6>t#>h>ZQMauDQZ55iQx3miD z#e7>;WUvlZcr%LAFXHXooeo>Y-N@zzQhQMK8f;C`tFWaiKbjZ#%a1tWizHQkFx}AM z|1r~C-bEvSEaw&D!ktTx-qe2$M-(rOuW)w=u1eLUyRE=1J?<``|7_vLcAFaPAG%yL z>GFLxgOyT)b44)~yu>$;+WOll-#vu!uq4oyKob( z0Di4xqRvwes;bp_DyXtek=pfFsH!{ONNc^A2HdAIIk|^!aAIW}%6fFh}c20f@|GoR~Igk;HQ{gNU5PxIL$VqE7lQ|&UpS~jIFQfq`1 zsWO?#^_riHjlcaK-=W@DTspBan@VhLiu{S~5Ahtm1z|=0#Fn@P`{N&ljV87uymIbX`w_GD;Kw@j-X#Y#MSVSAJW)HAdOX!Ox-P5_h@o_#8-vD4^-L;9Bsif4#);Y zl2Nq{icm(|poq!{@2Zb641k~F&E?tD2$oH@<&k>1wpjnluTBZd^3+=3#8dq)+{k~D zU|k-`Mu%*egi@<8nF}VWBZ^hVGG{J7wByj)&DC*p=H<{gmQs2&e6+RZrxcJApRw1Xw%XO##y|52ck)2)TsX06; zou}r?CzYq>mNc5DKG~1o!}Bs1r3d!m-ry; z@(=GUQ3>Enr9~wGrtpuyxpGo^RTZXLOsl4DfhX0=7CSs4EolHxEOggY`R%I(bhmD% zkMj>>cvenxP>KjwHyxIzv}NfIpYY@2B4Xtad>xQB<&T^`4XyI~>ShE#;TFF9@IZ?C z5tK0~k>jA6pUp=N3%OaTO0X6!pcthmWr7mjSN(`j*bqzqgb}OGX$Nv2|>qH=zzdQFAbMAA|YOgM!-{brwVM>{9 zUbQUK8pMm@>E82Lh$UsJlVQln5nWiO|{AxR1 zAwAImuc(=6!z+|&KD3H5Y?CJgJ@To_a7*0$cvYj^@SB{q!)NcP?eO^-Gm z(&Q%AdsQUK|ZIWqACnaKQG`(#}qoAo$t4?F8s zuq}&}YEu2j^SSqY=bZ0+_uTi!KD503uH`q8tE?~K{F7HFEU`dva3~V&)uNH!-dt^9 z&@i+gheQ2Z|Gw_-(2y31YEk+d8qoHK1_$!Brh%aaJh|)|{eoI_U!OM6rR`akObhmR zE~LoU{9RgiWH{8Hs}0G_w{h0qUYSB{T!mC#@Jamn{A-oqfBqzq56`4E3`PdJ2ZMdu zaBwgZ?B5;Ifg(58hjK{Cp_R`2FrHoLjR(&&lBpGfJ#(pg&{} zEED3}sQT>{ji;XNB4)z|Js}PI5j+0faWSY&D@cunZBI;u9pm2>7n?cXcAdzKFjVM% z6-g_sm0@R3T^gS{oy{+OZ9}DaUPB2g#0#ldqyJsQ0N~ZD({TjwiN9u`#OkAnV`&^; z7Qk!3@FK6C(Zo-Zuu4vB?cJuze$UhDD|3irnV7~F(UyfZ$6p|hlUe9sTfaC-@`|*Ypl>Nf*at@zZ+xoN-w|8T&g>ri|wpZ;d^ftIm(o{xP2XA zN7(wz#G#1;B!#o5OyLc4<<_lamMHwITMyFPW9__>R5)=P(?pokB37aZlp`0l2AS{Q z8yQo4lE#d#xYy|0Y(ffOceC8OjTm<*a<;|6Lfl8}s#v z{xabv6@KV+kvw5*4b?kq(3MxwSjG&-s5Y>Y^DnkQ9HiEb!jXBna_ z!D$@K4MnGeNdZ%xChNk7v5sXrC2OeS=)>UTTA~u542pB{tzebQ8zz-{lgdXG2!k~^ zjiIS^{`b(-Qr)?7OA|%<#a}kzF%~wMz<;6~Yx$)^_luuBg2QaYDUZssgK)#5#*4=> zE?)PdoIP(itUJl^tdj?C7FeV?CR%*hj9-XXd?;bRG%?NOIAvm_IVQGkMkV{BQ;coK z26oW|wUFocCP?(wc_O}xQv>^a-AVYE0ARH^vm@| zG)W~Kdf5+f6M$H~4S76ytIaWg2Q}smHLR_aea;rYsXv2q7Cu>$gXQ)Grke4T-ptIO|avsRV+oMnr? zo+V%AyBpb4D&IR5kUqSX!@hP=CO04bQ#?O7k(BruwXx9cyh|L~i7ECO$<*W$?ae4p zeodt=eev`-L)JJ|Ip!8`H={CfUIx0WK|21+#eeldj;Lrsj7<`8Z=v|S1=H-TDlYFr zmuP*AZj`q9{^U+dMEEI6Me4q(>Yb0n&e#kQq<8zrwEds&_K#KKQk3|njBr=mDENvh z0_kXAU#jA_Stwq)n2Y;PWr}k@!b{>p3z9`_HIhVU8@)tUyTyzL#o|I6jv7As$x2wr zxRNqj#cVq=_{kF6Vja1Ag(|mZFYd_7f1{mO{%z+yc`E6mt{yfvCGqJz@61vb3&mVg z#dGz@lTuu)M;EJg8#8uRvNmUIfe3vvk9cTor1HNGAl=dFCee*a3rWkEv=FrtKovh) z;})YGu!%=^;}OT;5;D4kj2g&NesbKdf3zF#F!s7zKhuRzS7bgdK_m2tj<4zu_JxK< X_JpGJDeaMtqtWm>GDq~u9yH?{io6#B delta 3884 zcmdT`eN0=|75BNs&w(a{U}}t?{V-nv9AjX{%W47v8j=7B1)2tE%`gdOh6LDR2-_6e zqVz+w(X#A1JF66?kZ8)Lu1&Y7vox)(XxFM$mo06jZA428+tg0cRHfS@ZK($sE8bsq{?tMZKqX&TEO_L=c*wagJQ^XJiNUe3u$IQoP-JXyxNmH5bR^A5 zpW)D0c(mW++)esJeZp%n;&%>=9&`qS0=#$N;OKC#)I@?oXPE>V4)u*VM+YQ;06x$c z86*3VUg;r>(?h~~WPEsU=pb40cNT1E5w~<8Y@D)#Rnf8Vln=6T=SEna;z>(GtrVR6 zx9@9MyAhN-F^lsT{`{al^~68+8{`Mq*Mph+KYN!~%&ulnTd=YK+%LAkM7C*{%O&c? zKlzgdKgfoadmHw%2 zP+kt^DNq4^tf++SSXCa3f>KM0V>$TCPH2x^&j$}ETj=ChIehMPDGy#Z!4~W-1RvB2 zCyTMG5DW(X(xMY8@nRw5ey*;4A>J*|xdi2u8=O;!b1U+V7k+BNND*Y>Nh_=q((_6^ zB|4vj6D}yEY)-nsMnC&Ez_76kC`){&5VSibzq>q9L5vb&MnDE$^LduSijBdUVsLq~ zmRk$*9=ayu){TLt5@@noZ&@3O_DRId^*1dJ(L{dk%{*3M6w6IWmj_LRV8rGeV4*phs1z>6is=lUglmWv2Ic2IC@ zDcJ6mo=Z-@R+`{6Nu{jMhM&}d*U}R&WmtJUIu)HHVyHtsHk?WWzi~Us-FV0aMc|XP zPxnP{SbfvR+c$Zk8|S>>j!!~e=^Z)=BJk!fGzrcnrJ2THIXJ5Szp+^2e_XQGGo7IM zO0#nNohuE_8GUe)h_?)mSzn2_U;*n6~JGfnc2hV&N!oZ%5M|5_Xh|cN}8TXL$89eqC*u!4c13DuE()i0$>#*q_ zID)U&(6;L&yNfEHy|MwT{7?lS;-DY=>~AXXyW&^6N&8pQ{=LE|#6`4KLj(L5d#b_5 zKGSU;B*mw?jSv?xbu(?;EIaPl4CTyXmp~7ZCeto~gt&+wZ-zgxGF=&-;Up| zr++&K?XpGmNG;MU;%k5pkZ-4o^pfGD6b)76@V!t1-^U-_3oiHpW^98k5XH7_^g1|> zeT`to?yayLp2OL#(8SK^$?PNdXZ1{lC=Gkz*ErV*t?Uimrk@nA>NY}*gFUkiuCvR! zMTm~)^^yqjIEnF+?4?H$Uc4kCTFOI|L=h=YW=Yk;9#d>#2c!T~9enOc8D=&^CX91< ztQdDTL!ISI4ufH+Z4G^Sh19M~_`?8f<+jIb_{_PD*xCX^tjWQT=h*qBhc(vd;O@!i zc-zl4oZAUcv7k^nc!t>m0i4S5y#awAy^mZkC*TKIr*Cw>^x{GxQ zER8?<+A98VG<|I&r4_b^Q^Jm+P6w}i@-cj)og$zdW_R0p*NJj$>wvp5{v)!lUqxS_ z84O41zGE6rcR)>crvT7efQs4>#~R-|)W{bqdBu|wQm&qoLLi4W87A{(giM+j<{G-h} z1g1CDn*ihWvqd=41<$a3n%I7JEb6AkvR@M`5cc8WZkQ&OSS$&+)dUyR#G+ez;-xDC z$rEv91)ZF{>BOy3UGs5W6PTDP?DKiqSyKYVNIq5#TbrOvlwh)nN^q9$IZ|d;M7Vkf z;yL9A{;>wx0vu|F{JUQ!8=Z!)2uafL6`TpcPb|MlsxBl|7c~C-)0_CIqiM039(ao} kmKzHP;V&u5?}g2K*xnE3HA0?_g!3k&L+{1D-Va^yKNl|Er2qf` diff --git a/docs/build/doctrees/api/semivariogram/theoretical.doctree b/docs/build/doctrees/api/semivariogram/theoretical.doctree index fcd85233df63a633c7b50e58267339325f7bb1f5..2a3ecfa12c2e8b8c0c76ef54365579152e0ab3e7 100644 GIT binary patch delta 5949 zcmeHLdsLNG7Iz=~zKb9jNPxh=`2L5I5`U-L3?M)YoI0)LNW9_nCPDD zGax~0nU>EWMGJhilc}lXozMe@RGwF0g%sX0%}3i(P|qMuOSb)%LAthgRy(Mfgl;up z@OozA#yLUS`J#cKW|8qmkI}%NFZR`v=7fQoO^zVvAJGQhRpP5P7W;ylLymF14#?Ry zmxDTitR=lxMf7)Z4|@>5I4=k?xtrapq?64~woq=oV36-AO+NnydnKrO($=l_p-hP}SaAEanmEbDF$yu#x8BZ~QSv+gaC(11HRP)?5W^L-z z!=2PJ>h2K05Pr}7?oh^?*PexP{?ap6wVYhrU3>CTLN!;+Q<%xwi}mYQ9GF?gQ`T8w zK3}x%X;{R&)|J3wtz`Y4QEat4KN=RN74H99xtr>?w&-vQVBJFIhWouB;SX&WAy&K8 zR&E}!$*CXo6F+Qti+S@Wx{&X>>Bk$cn3|>mI)g6CeDEDV%bnDCyGseT9rn?w#&7(! zzIT7v#b?h)r}{pVL6W}F4cwg|8SCx=tC~#t>*^!&*9=MAH(#ojwSmNAO1ja zp8gL{hyisR8D8_DEpLW#nBxUj7)x+(Wm*KK1bQrH!944-Gfr({L-YEX+*1! zs4)>DMf4xMp|HQ2M%F71o;0lWC!XfiSPXtd?cIGL~#?3n7jy8M=QcECN}?T79uufeig@7?d#eVKJuz|1hqMB>Y5) zZ;OP#08G(eFoEg>Q?WjpR%9xHEkhhDGDX4~4zOatbusW1JgR#RhiWQlxWNpcs0D;= z8{#P2b>|IOHUfMx`3M`1=PXd6ifVPfk9$`fxZu(e;Eu!FSQH+Kh54!|TAjTZDAdbG zfRCeY4aSs2uz(GB$3YSlU{@T)CyMC$?)V-ROQ)}->cU`_et9H(2I}MFpX-Oghgk%E zKN_A@=8$29WWt+cD880zNJGDPSOdjaA5V)>j3?sZl2Ssrw;eN<=$&JL>f~HZNTey} zl4qG8_jnZiz|`;%Z8sCL3#^phVMeR(p__isK~ z{QTvMn~%Fwh+1F*JSCU>Y+y$k81$%AFgih{p7|hL2epbYwQ`hf+WZ|arxF{}h@q0a z)BVwMjhXP?bcls=T$c*NvG8dK*8i9e_cB<9?>+=4)a8V{?%)y43*9`+@S99HsjMKA zH^Kynz<*{@{?=e~HqqAzbAV&U6==$Vc36qGa^Qlpiab+sPc~Ro(R{EQv=Z}jY1}e0 zo#_s%DHmeZC1mLD$&XbIL%%%O3eTWz9YpG1<-x%ztjkG!3V7lET;37xhG|=&{f^7% zFDT`8lJWtA?m`)_Z`(l$d{@ZlyGqCF{hJ|;fuEkW8zPwMPq|kaA@mxSHiJR-$47{y zHsMPjf`Nb?duYSsyL-V!H?`6(P>nRExpxd0@x8sY)> zq?`|>A*cG%hUf{0AkYbSwh?X75eNn)nlKeu-%exd_JTV^V{;o=mHWt??=TDXIIuoU zYA0cSM3|#+>QUN6ChET&h8t7KAUF7_qu|D1g#P#AkWR-1w6qi12tw`dC3Eg38-bhK zp%5&1yPXPUf@A-|$MD6_KFY>pCQR2y--bW2rqk?v8H*cuSBf+ow3yiq-3M=6zL;(7 z=?ve=?$bvvVPB8Biv*cGbnE-Jv7}#jYhP7{;?#|$@3R(oR;Z{DoY=&Yd)_6z*a&azPPeI`>&lMZ;pBq~2y)H*O-Fxq3#6=J`f57~CSEoD1e!%{! zB+&*rgbnXAiz=F%<1w#}nTfvqL*@hR*x^sxeYY1Fu;D`%-xCdGRtZkt!M;?)S@=5E zZ)f3n%o`$c?MG}DG#~^(AhzseA$^BbK;F+C`njDf85AKiBhYey0ujyY-GQXaK#=s@ zH~9Q+_7tS)K`l&Y@=&{4;vpT~TA7ukjEEUY%Dssb4pKcEwwGO#r|*4Z@Ff$8h%NhA zErTo^c7RzGaRT3m9<9s*nR28~>h2;yuD<0Es|H+qjJaU=VRl4{#+eC1+nKSmjjppu z*VU`ykh`SY`jEh?N12p!C=bOO94?rw-)f_kRHqR9b%#y1XCw#}g$s_c069TUf|NeI zAf&*Moz9yr0CQG{qsmLs4*Uwe|J0?fc8pVHz8 zy*dbkJ6Mn-Lcx9e-y&2+6vq=B2{TQ9?-cu*L6QFAY4#8(v&nZ-P>1$D(QN!p2XPmX z>ka90=)BXXm_Zs}br>5u*l2YYf$)B*kpj-Jtx)Nx(HGCK*W929pS#4yy8YzJ4$sbl z!Lf9i*>i{aAFnCpwPxR|O7w7r-yksNk6&cKAl>USt7AbmQBmUFG%CvYfN{3H)K>M= z>qK5JL+KSk4%xeVa8R;&cWF71ErLJg`~(!4xI5F**!7(8Y_f*^!3-+^Pu{Xe}#jl z;s*1>c{kXY0h>m+%e#Yqym3+(Hh;?;GGs$9y_6*@QXI16(htPzhe%_v$(E&Aem03d0)bG+}g!bAVwzACQqmH J+rjCa;a@Mo0~i1R delta 4927 zcmc&&X;f5K61HyhUMng@jBE3;}g~{$TMu)@6z0ZfiL&ivBF)-ronN3oMKD79OQ^R%dl?Y{5=cZB!( zCDQu2DT$2up0;3o`)ih>T~0AW_2a)lmHvlxs=u#L{-CF9#u-o9eAoP{8TmL&KYD3Y zA^zQ!UfOiaI0oTb!^k8C5xgzU%wHeriQBTFKhI4!LO5SJ!B2CfF93+v&SdNZh~f2_ zW_~)O11MU8`i-6pFj%X~+7D{1t@r#0m0wSb77)5IG@l*$oB#Ps!c}>Y+YAT^ZjXdU7npQt;D^o2b=eWGYW2n)c zU8?jtF>@n>bZv?CVFnr6?Y!dvqxsQ%GyR=i;HL%6>jr8TdH&tVH`rpiGS6QtF9-v8 zkUwKH^H~M{+7~u|P_hX!ir3pRwVe4?po}5WPGX!Yo*Iq({9_?Jyf9cDP2%s3yu=yG zn=M{y28o3oa!S{>6s`uy)sl**gF2S1M?1^6kyz}>PdQ>BSC+KT!VrM*T2jegfQQyQ z%{;Nh{j!wt})b_R8}2qa)peEe_rO7V^qwe*L3fMOLP`Pfyle9a@xVAZl$ zC4e$V1R`Y=-}>mwQd%Y??TN?C25|7E)n?T}kT<(xk6!fchx}Go7Lt64K>b9CVIl9n z#;gd8CUmkU-R@(_WeA#Ygo7%b+)*4m+5+pu=`ee51Dek5$D9$UuR z6tB#ArULuo{NfnVk;Q1a$wIV#hlT@Mm#|=*xt*!#9SmW7^Wm>lvz*L$JqSjSd_;TW z=(NEYZUEo9D8MdtO`V{V_-)$_r#nM7pViX$F3Sh=oNxTF>1&p#zu*bO!8`h2QwZ|= zek|S1$k+X)tKP3WOk#@YpRTy_Ug)W>_lCbQHH8%S`O()EiLt(5gcNM{0W&1?+Ec!I zx1O*Nz@jg^7ajq11VLYPL#15dCt4{MEQI&-45R%-MHShLXK`ywAU5n`M*O^z1&E5= z#!zyJIt+`j`gI7Yp2OUi;Lcin>vpv)_GF@|rgEU+oM462e zk?{Q>2m`ZzEeJjb*-j(<#W3oLS3{r&m=R16h&#QZn;u~ji;3o4=yp6(V^bkswUK>kAkDg9OgYPX@#@P) zRjfH*=t8l6BzUQ!y^{l7saR#n`deTSsPjqxRUo$%=itjD;HbKQ0hu zQ5g;K;J~U;Fc@>6fWG?UqoEgr3ViNCIH^_=`j&e=sKm|LRFVq(G#gGSRb=(W>$wn) z7stQ>Scb3VP>5wh-N&`dDh$Yl<4}!X<-#Rp1z9HHmK=yv#ba7Ks2VNf$ghG_pL9s= zg>h8Ya#DKu;O?$$AbO66SKtX8TLTgLnelLNBKy)HCKl4f%H`+!cgJCy;P@}W=>AKh z`2)!mjLz6jGI>b~PINLWLqAHV6{vxvaE8lPyk{$&8-aL!FI6u9$7}^Z zeb7D{0uZcEeg}wFhTxX{^c{zg%erv!{YRqrKJp26frlP|L!kC0>*;WOG8&wE9s3ZY$4TwT(E6i305O`x4t{g=Q+4MfgpgV(j-Sh!G;3B zr)x<$I}9IEg9;K&Ysm(+eMk+Os+)}oHo-NHRTEDuKwcYohaEpuE z*w5t_nD-WIrUA6Gl7;dWZCx;DI~zvl2qn|SMd6=Kbe+x(JJ{27aO~d6QeDaR_msqC z09~jaXjki<-)79k^xsRw){QI{lF_n@nW=(XT9_Xk!Y6mJIj#-b@kTJr!sbS{O0xc2 z?STXh5M*LCyoy_P$!Js#x~;TJ`ahaUDK7@$Niv9hCU^Oz#Qu>PAytOcoA$7BkZfd9 zl8YIv+Q;m4UBIjRnORMzcSZ)cTbgvk#z+cHm!3#$d!O~diSM#Ykg2B}WU4_DmK*J1 z#a-Z?j{ccC?b3tEJ44@OK^1hO3i+Vs5OPd1HYd`#GV3VYq==ir9E|&b#lcv#w6eF9 zaipnmX~qeS9}-JI7?0<&|K^9?q#=lJ|0yEs1_1*O&^lha;UI0$RwSZ{24 z9lZ6&Z?bx3jEIU72CTiw0u&cy_hFik^pA}hF!*jue9zb0Go`c^EdrKv`%+#c;7p-36n(9(B^IRKo@1KLNn`wXOgF diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 79bcab66fe4f4ef8fcdf4cfa02424cfcab9cdf55..b1956989684fa8126e332fab2d70056b28d2bdb1 100644 GIT binary patch delta 104703 zcmd2^349bq)~9PGGm``efsllplY}E2NeK5%A~#{UypSsi$pC3`nS&b!6oudb!H(9d zfZ!si$R%RDQ1Cth&(-yIU60jW)&qA{l<&Q&>FJqF&xGzF*)P9ey1M>V{i^Fgc%GWtnu@BD!qVyn=SZ6U92se9a3;b%NGSzzAbC9feiIol z?H(5+RgX&&I!V`m)d}FPbbS`-Dp|*x!-^IcmM^ly0?I-1L@KE zWFTbDWOql~Ws_*3LE64MR+=>~Qp%nfFYTT%O8R$hjF3oEGDxCSKPEjQ9P3|FybS*v zoXPA`OJ!lv5~xsvRJc2VIFsnn9wbRf#*Q&>|{QQqJ*qh$DRaE4dgYicXA*s;NBg*rvi`d*|hh&-7s3?iKBM7nzu zNdlpLsZdP%N_i7=Cm%GlX(PS^2d9BaLYbLW)in(_H_U7hUzJnE_c2Sjiy9@vBT5G8 z-HFNJD2e42KfQHQZ1_VS$!U{P!|(HmPoLDo#Is9lAnP+8NxLU?AN-sW$Fm>d;Eb%S zC@HVWUs6@FsHA)myNiu@n55U+1nTW!xTDy+79TzrxjS)G?2ey{tISZQBg%9ZykalC~DJamBrBj=QFqBGp3 zf6A>r+a#A1HzmIF@g^YNS{$lML@5ak4X%=_MS1jQG=jGj^wYDoJ-%(0PSBmjLv&_0W&Zk z;u*6fpWxY(c=#J0nECh;&zRx(2cA8FhkxOLS&Sd>jM<8x@r+r5U+|3CfZy?qb^JDP zCnD?G2BQN`S%)?u$hvYEp0S?X9?w`ujl?t7JEQQ7^}iTAW8E(v&sfJx#xvH-Qt*uR zq^@|zx=#;0V;!Xzo?$<6h?#iGx=1#ju?{f+&scvLf@iEJ497Fp2S(u;Q~oh{#x#69 zo-u`I)`sIFZu2q%fyHI zu!ha%D@Xjm(qRm>ZZ@a%`$-WZ51E_IJ9aP_$I8&n=I;MI&o0Rtn+hW5o%@4b!p8Bo zATiApJ``_683eJMRXrFgReY5I7tw({5BEgystt2E!u^i+=yw2mznx#TeN1<_>YYlS zhY#VM?D_-~f?5XL7{XLE3B{R?X9V93yB6Cre^|ZL|5V_pdd7kz-=LXS4a!XXdgP z_8VxytU?yUz5{6=FJLk3KARop0k&<3|@Of_* zMcv+PetN`c7DF?+*_=ANfW=V7H=BpNQ^R7Y=9|qqrg|1bRfn{igDi&XzS+Eg`!g(t zD!6Vgt(mmol>FbimrHi6Pco6P$ zlw>VSmo}Fkm*$j*N)yZG!~fM%YUzAB`~Zk3TXK!`TUoxeuC#!DcRv(VR6b64OuD~( zwiHu2SIV!LD;X;03fWRY#hkEy`F%4h%NI2`XGtlQGo>??b0P=3pnBGSkf!X_L+G?a z#7fH#gM3#kMQXUdAZ)07{X8kRb_kt*m?VV_%FpI96^vFKA{J;=?1I=lS7C0cpgKyj z)`Sx0m9A(N)cLfOXCEac)pn6~E}czZe+Vk@Wz7tszjSorXoP9zLyV^(EV2e8U)DT8 z%Sz5GiL_3Qc;L-#SG8Cf&5WEEM}V-byi+ee22|(#aL& z(rqhWkY=xVh3zami@eZT(P07K&it+(GFJ7osd76g3t&Du9?#^wglD>U>*&u zVf95;@c#KhmW;+eK*>_flSzwNNe>5E5RM@M3Igd)`tKrRf+Ky>*lT-wRq|AjMdKLN zOwnDgD6J@3Vl6B$rbDbGQaX93ONCAcS@!P0WlO(rO6R)zL68OQX{Mlye~;z`eH~;$ z_cT*bA7>ZdDb5F3(4PYr1k#=6srH&>6;(^T)PGCC zAgp0nUU|i`a_bf7*L!sQ0x==TviAip8!O56rDu=@?Qf=__S=$pL4$%U=)PtOs^2!0 z7c?fwg6?mopp5!7UeNR)3wj`ML3DI`p$ji*UXUdn2wakMczb8A@?ww$Js7wkRQZrA zaZT7$+r?#2>YQK(*n@$Km3G~l%!%9(Wc>~`Q&5T&!-?D;WI=}m7lcHHUgdtG;9)w% z1-pVQnyqsM_QR6mNldI~p$9z5lu9;?=))bY>)}4Z(PFD#{v}E$UykF=bHYOmdUVGi z4j$cc9yIdcF&;d@0+&bUDu^wq1t_SlZXj=-SKwk#h0|IPTe0(B5H?TDBzX*u zq_fmjP<4<6u?GnLxubVKF7)J@u`bAx*aHgxC81{QtR_9_x)%j`*&@gi*@F=OB}yyq z=*hd+ok13a3(o;si#q=%4B#d053(d&p9)mcsqf|HdN|0E*mENPf1O zso)B%b%vEzlrQpH1cSO(qeW*6Ubu+GSE6v0Nn}e!`|)h216@E|ml4_G3NDkd^>`5% zEnwvx4!EiyvZVo>_KR%pj`MPnO|A)yha#Kp;!IX#(@~sPifnR-6F-s7-f(6nvgs90 zokTYOfw_$X9#&!@IQbCS3<75dB6IQ4V;7m%jUKhg++uWjMdr7n2g)CpqF>0}KXmQ* zBTw|b_#;pBfcO(nbW!*dPxKb}GffAM=OP=FalGYE8F3WkPZ@ET;!hcI7~xMD(aiIw zjA(rMQ%1Co{3#<^FOiubG%x%iBX)oOkZ}v1vA%`<1|BkIU>-bW+KGzCpE9CG)H;nd z6-Dx@CZtqKYj?(Fc`sR3LQ6P9OJQLzzof=q1}maXRyL&u&NMT-^#_VWzF}QMDZ;MJQ;Dg7kfX16sH*&uBoznaNt@Bb}`28)}2Yi zDzMW8H)JobX@CW3EC)`jN??il*o}8D7KXOBKy!A$Lq|MB;UO9iv3Q8XLjoR>@LTJ7)0eD3LR6PK*q($T%~aUi4iK_RM2S3#(z%nqn;bLW#FRVVV@mfdjTP z44%>~uBa+6EVDN_EontA(XNFEMbEZr;lq;-MbDhxA{>|ggsVj;4flK$x4sMMCspn@ z^Uir(E0@?gU)VpksUAM$UE@oyutM5lEh1W154%jYFS9O`don(bk}UUk;0(T}hrvbn zXEtT<9WB*YSGza0h$!`x(IRT+sdane$iShVt0KSYp)LHtgr>CpS4*8etE?#Ac`YIW zD&D1z_8vAAhiGm?`QX%?2wuME^;leKy$M>X;>m`~Q(#Zl{SA-IyiZc|2XuJ~_?Vt| zq5h#74LWX;lKozf!LTpL(c|!niY{a@5k4NS>@0bE2syxDP#%Ib;aGQ0!Aw0AJbBE= zicAZhf+<==_^~3BIE|j}RIpsZ^|XB=N%XQJwR&iqcs#4A6{*rvXU~44XikL|5q_)) zr>!{?z{>Zw0E!ji3f!ECz*eM}ma2HN;j$uZEhL~7aa=Yl0>%5;gL5!|ZbUYSO|}^^ zAA5(YS$RM&9g1@7XCf}qtYG5=Wmb@rkm>kpi{d!LDDzqXLOl5h!G(%CB*cL&>Fqe1 z(%3sQuDPm*@qIaXmR@Ko!lkCoZwymLz-Kwrm&fuHMxK7p$LM!X7zA>Aq)xAxta;a>%EE3yJ z_m$w5P53cIZ*$-?YJea%q7}q0c4>TK3GR|iBL>Wk+%q43m}}*piB?fdJuuFrqlE! zc~|AqS2kRxhdl%G+wm^@k{R2qJKA@%?jEf=Lw2d!qFs9FP?X~?Eh7B%w?Ao-;i;pp zR$+UO1G=a#4|SZ^V;)@t0u)a(tRw-SP4a=^HO-B2w`ya&td|9)F3d>uR$f7c?S#8au1ao}@K=j9v+C_Y5zdec5!t3v^L zGw6lvreuYKoFE+vixv^BN0v10#a^7NY&~4P^kToJTxDvhsHajWeXO?@5q=CFSFE5$ zL*CV!6T!>(US3f2`&li0#FGt|-DSHU0@~fnxWEW6ad2HI(X0#bex%9*<3cSB=~;V4 z7mApOOSHG+Kt{0k*4a+#P}iCBut^UO6>p_B?GrZz&%+H`MEL0w4$kG}G`2vvGjyzd zX^GdAg|l}+4|^ZK?eom`J}p&O*P?s1h-h8*qH-54sMZE~W#XCx|^Em@4*yW1ZNW9s96j z1|0L+h?y*s1xMVeku6>PY98O-l)&n#V+W0*ubisp^wme@VN0UoL;N13zOvnt0rl1A z>B11CSB%;FM*RL+n9iq>~9mB8uXIjNm-~xgdc-m#EHG!4n;d#Av}wLf4j`!AorW%+b)4Z z?~P!<;N9P)G-c4KrTU(OfTC7)T15Dn(zO7CN*8=c4_6)j72A}n!&)k;&eeljM6@DT z9nK|kbM=lMu2!E*Ys%G|S}Ll})fp`!0`^SJ<4zm41(v&7+-f!_f|u{@t|>#;SS?lY zWW(iZvF-fRm z>E%Pw>NQ$K_%SUjwa8F0E$n@*OKFx|rCoG3i(*+0X=nuA2K!Xo;DFxBD{XL}77>2h z;9e~ODc4GE0N+gE*8P1=ZBY1Qf}q+2@9M3)(gbg55#gr^&T5gN(gf@k#a3zp z_?irF0%QA2w?(`g+^5ofv>o{6`?O|~s;HX}y%U;{F%{*-g~~2Vm~5WAL_$1v%cEL* z@3J(d!4EfpV4ppf-v!UoH%&$G&Uy)3#Fh1|FFDhfXTf`-vRmFzS>pYrxQ-DTy)lO` znZ`7f>O*A&hz`<0YwIh5O^>y_c)+RDybR#ELdo@>i*Q`p!$cPwN{hY_xJ577V6^q< zR4eE$F+v6($#S(eB$!QVjum`1?2Yo}9H+j`Q?A&};Y`FO+RdO})}z0~3NF94P!9=9 zx(L0S8q4{?lQ2h%2tQ9EW^p1fj~U=rfUb=b`f!!mpogtr;sl?qcc*`Kt#cPz_uZPM%B?zOMD&Af#Jg8H- zM~jGnR0d@uJYwCdZ^#V{-z^3FR^)( z=z6-uzU!p!R!;}h>aTlW@|fT`B;co6dH4QSvrZmVb)>)6qEDU#Dg(mbnTSgq5U@#t zb{yMIuU$}zzxd_ziI=br&3HU*ak!Heg3b938tE%m!ROJw86*Yi;25=t@Y7cXCd025 z>22Jeh&4CKn=~uKAyt!nla_|`Yy`z5-@ru3Ciym@jnD@FaCYPM%!@5@ zOp>=*dL`+xEcBD^f@|a6ejQ}c2|ax6@ZR9r*rP>+A3L&(GwN?N*TvTKbiuW6=oLL| zJ((`}yqNo%mXHz!^;K(QlSg_{!**p4`~RK=4GmmOiB=L~2^ zR5uRka_yjaf8VGlk$#<;j$qH!wyhjiwJaC)@}XFk3rvJ;S=0@R+n(e?rQFV*E>`F# zy#?25eu{q<)Jbq)^LeifhNdmB!;j9wmQddntt5~iq{A1_M9Ac@#UyW=+6H#Sxc9WP zDZ4A3{m_!GE$vK4-Fte5c>H>D!bFQQ*15J(k^uUsqY5{+-K`N3>!Pf2rGS_@nK^MtW^u-w~}) zFI`G6oX@N3*ED(Q*_ADzXz&x+Bk9I$;a7*OUYp>S#Lakc;$bTu>fxYMy>RPT=f%%nsMGVt62aobNIScI=6ROa*LPZPns2bJrLOkwGO0putA~e zgcfXn_P3qr@BuFL99{n_9%^3u3!LRCj;$dQgs^=Futx_VYs(VxLuL^wQUG^I;13zyt#Z@L-h zYTTR%UOp(0YODRAw>RN!$Ul}9(6U^{r5^a@Ev^o=I@Dp?uFg$Wp-M|Tdh(~JLIo3X ziKZm@r?I$X!6|6aML{HeHC>2q%ER{HdDyB&M7bxIN|(5WbLnq5(qlo;_O_SaxILPa z)~Js(>7TDfHRbGKEfw~xucA9gwTSRzMp}roNag3?@~TQ;%Dzat__~FY_*b1I+E2$c zCGjs>7*HqiZ7m{NnZ&EhOP1QJstZf4E)s`(%`JY{N#v(ze2Dy23k&K*{zr?5fJ8Po zBC+zl2P9=s;|8@k5rGG_QY}^SWWzP6u^(n^tw9Zb<&pQRft@bNAN2BZM*iY6sPzLe zLArxkOvEJ)YGW>ge@v}CNEfdO!&SP-h?L^~n$Xn9%m|*4saiz%@sIO3sh4wflafA2 z<+`&>CtbTfh;K?)jh4E4b{=J5yIzZkR-|jshY6gnJ9W|(|B+93qgv{!PFI5#5q>N$ z*RJNwWUG5ewyGXEizP^fc=E=r8L21dQ-&D?;*p$E~ZBXa!NWh{gG?7NG0r*>Ua4}n7V{@;=T3+-(3%W*=}ZGbqzeM^jylE5Regt5R=EUov@eA7%Oy=`hF4GbhsTG zJT19eMEFtJQCeg)Raw5C$y+D5@gq5y+koXL_UqdLd(Pz>Kj4?Vx$$zqO>17}><__Ha88Q|KbGJ-PUGdAwW0#HodmyPTTxugekGI*uY=aOenK|3vu6J8HwiwE zc{+obAl;3QT0{iouem{ycMq>eG|Gw?*W~6z@bbM^)s&_6lYyI2kLAH1y>(jP71A2` zMdIoPuhp9g+S_oaMQHEi8)<0(&tXV01G!9uUo+snUE(TcYdyDwUBriN_yia?S~PDD zxfR6GPx+l@x*}Bge5SK)NqJFeZLxh}Nh$oa>pT!w3b!Vf zJo}kz)us^?-hUUvv15wbs*PT&LoW;?310T*T}@iSCaHAmdxEM9{+5<@^&~@4jk8RI zUq-xLz~Ft4&%O{`&&AK{31W9woM5f zqNVog-EN>35v{0gpNB2z@p?a_;jSp#tQ?R4n>_nSdiXN2Q4D}bb%IP zElxJ61BY41Ew8Mos-f|@B+P3DGE^^}aTDct_m#ddNDCM0eWAY=5v@3~r%aMZ_PKf) zgOR;^Qw6+EOLf&5yGDzMR%C2+7?Gbd>1Ax)^S&z}tF=^Dov{^KLU*~tyJtS zpKUiMf|u{l?7yyMqUy-Ki<2Lx+4Jq7)H|kJ`Z+=o=_IOxJV|p^6Okj^P z5v}IVDa#)3>f=Fu+2gI?i8!l8gdbD#Iw$jTx^o`O9>3^g%xBr-XDu~V*P0)-i11@Y zT7WF{ecf}Z>|xe1Rd)DwKe~G?>EJc+C&X&^qo5CVq^D;1&Z)Y9ups@YXe}aIQTI;I z6mq!L8m*VHvMYrlO$YuFTB@tAfJ3#2Xhp`hTqX43j4ja1SnSn8*QSi+YpJd}V{^5L zXx%QGCj06GpvzX2RTfs!kH(WuG}$JYIhi-XK?@V=WID8nXhro?{LcII zGN$Ht-m9g$>Wtl^MMOaLYtCt8<$L>#ir>lIo#sRY_B#)08T>riaQU5&wxZuj+s+oc z@jmpac8!p>e4zTc?ISIX>B)fNcm9=$_#gP47j@B~?si@Xo`+wwi11@ce&$@ZklUHs zMYFR6def2ae$RJ&w(31?v=$M5 zEN=^F82akH`ejjVJAY0l_idUJ5!lK6ix$7YQUT{s+!wy5{Qlrq-8zA5V7+o`X~^BK(-&WX@v?IhJ`k$U6I_;PV}YiCSvv*;N$7 zF;0sJKZc_P$U+|}Iy=)kwxr6=cHP5QDJrYz<|RTqZhy;lPW>pDkP3I#&X{o6?S>J0B;m1I=fM%hM?=}WD>f+|VsjyFF@iKdL4gGB< z=}fm)%M0Lf@ml?=-t@<6`DdIIrHcVMK{{7aT15OWc!Z}2j7K}X2Tl`e1=sgOM(UvO z_F7*GhiR$5x+)IVBBIn&hO*nNWmJ)_yNV3-dRAJXgSPR@e8)3Mz2n1lb0T>8-ZGTdYawT`OMh^5*b_86{cX7r!-w~6I-0O@h3^(OrxyOz zo3Kuc2tQ5OLfnlW;O5R+Q&j>h9W?VAlI7JI9@4>N$|_-8)8Xu}78=xQw9(?mc(UPI7<#PP zg`vy)-ruNen|vj%U4k}O20tsE@tMzOgTg>eke-a0iD=Z~RTuNtoJl(G)GU_3S{3BQi;g2tVee1qj2bM0;CBmEBrpudb-A zDuS2RZ)%{Q+#rN-N1#e4jaS?#_o%Q)y|lr1pWv-p zYO7AzHZ3Cb>7DL1HY=>L<(3zgu2^ZeUQ<|AQbE7HNq&dzQJpjv-t7Cbn@`dM&F$Q?*z}on_5KpQKvJU%gbr^(Tugd5`L)^--xU%t>AAk zyT3K_n@$qv*ZICK|6eT(s4LTXEh1W%M9UQw%M}9Gz_)t(0k>jOq8spHZL%w2+>6e62WW=3hLt-8hy*CHaI#x*w+VdZ--VJKTE zxfN|r1TWuPhO!LOLQYU3EQ5@-*T6F~E1kHMMA5#l%L}QMI@@y8R^LVGGA#|E-j<>k z5q{dTg-FbV^$>V-(q2_k1|N%pCyz_%$$2D+uGuDYDe2_$^KHU_rv0K`3lZvEZqXvb zk0EIRF6kfj!Vo_GKcbVchV4RjQ^JmEsjWI;N3@7&MZ%ONr1y0arnZdqu9n)W6ZV!C z5dnK^b7qs3?`<^|%gfg-niCP&@*1_cEuL(+mXV%nb{Xk1zC8K#?XGXJL?&r>T^4@g zvv$-T#02TJgfkJXHiuBwj{54NKz;2fBX}NqX%XSahVU>Lg5U?P$7| z+IsdHW$nnOMMNtSmT`xW!AGg3IthF94&T{&wU*kd6IQ83grA3{EudjAlcEI+nmix6 zLnn3f?h^Vn9pG=*Qg3zYBrPKRj8`o~opTtt=+mq0#U+BB0(i=j5>R{rO3JjBZXu zU_Z&Cr7E6mxcsEEt>`DwExYBHXJV4IJ4$yq`Z!8aASOshDUyl!A2>>bbWx!0DD@AX zhrU`w_%R|GoXZw+l&;fBn3|(>jh5Pab{oY}x>AdXRwV4iUSSY-lve8`Y{tF5Z#}Hg zQd@PxmTD2<$5Cnl4FgB%_C=(-*FKuNbu#z!KA~sR0e**;YO6DMhZYfj#;cZL&e;R~ zBzx7uimEc#4^q;^`{WPdKBJSyJMI%Qn$q~R77o;Dd{T>uR_w8gpY(T~gsJ&SpKGbD zI$@t^5fM=Dnsahk`TqPQK14SsBCwy-UQ1Ox*>L$ur(4lalFm}sJI0AtO>WYR+k6do z9Eb?gMT%h}{s%772yNB>euvOU^{eAUgXdt777>1IM}N*^3%N)6I>=J{2Etq|HTCQ% zifeS877?vT)|s#5&Gt9wAZzm1zPpSZT576J)@m&xT9K?>M}-O8kGxkiS($YFL&C_W zL-;*fYN}4w-C9JnB3Vn02|YMjFKH&L5B>g_?`HPrwbWFdtY@@{@H2e3fX%}FGd}5BunwHvvrWA_AQ7^Ej3lwtlnBg1niv6ImWDff4(*M z?V1x2*td?-(n&noaQW8zThX_scRw!lp_5q!QsjWfYr=_;)igT|P5#^pTls#@OKp2kj zZK?3H5XF1iX`QT1ecE?R?5kR;>{(-_yS=1Egdfw}LacRd`Xz`G+M!Gcp&74)w4;?L zg-}lD_c{sv%Sqpj+W*qRhB~3&Y7yaQWw&Jrr5|5UdU-AFMx|=yHDS7{Lu&;~Z=JF-^u=YDd}r#TUU-OmCo*2hza zT<+&f&AOkL_l?t?)ubm~_oBQ@`Bv?UK_fZ}K7QynEiLLvgQ65pCgOkKhaT0(fx07l zD0m_cXc6JZD&NP+Y#~SVZM}@Cd7^Jj zXN0(>Yna2d)LC8825S+~inL{%m0ipNy|kf^Ne`d(eT{9NmO87`Hd~7bKORyGi~%^e zZa*;7I;k99Kdr1Ng&&ZCm*|JAZ@~8_AyK?hFPSNC`aa)Ur-cM{9@l6Q(TeJ)tP1bb z%b1#P{%0-KRcCCs77+n8uQ|t>mG93t=iXp*A_Dv7by@~FPc~e>`94E)zXE?5U*lPE zhihf{V{HoYeK+527awS8L(hsU-uQb=#Q(q>|6OzS)xQ$_Yw#TWM~etQ#^Wc>V+%Rr z)~=eIB+!-KRLuuZ1UW(aM{!z2v?5uG4?ac*S!(Wgj+UCL_pXszMEEhXEudLAyeGpV z@Rjx&Yqh+J>vWLS z?<3z&;@+&Krs`zfs6|98k~REe`PGaEb&&P=$G$U{`?b_moveLYM6@DVPkthF<*T}9 zbdWXbQz5bGH19PnHB~3;Wi29Fku1f}{y_&>YA*IUEj3jq>pLwX0$Ts({9{(WKNp+3 zc+H6j>|(#8#YOUD!{uThHng6LO`U%e2JoKKtD7dj37^B!t;xPCaXmmzkbYBFCgOkK zXiw6|f%>xd_~3~cqeX-t6OzNpY$0#ER4-#{E_aER>U#DZWsbH`i-=ZaOmVrl>19mK z?{;dbt~z6NT12!WW0n7rEz?7K8GG*=-z@@%wNzJ~u?Mw?Xhp{U`46Elx8?8XWo*^A zzVFMusinH=jGfUUq7@l)ekUYz#{R39vCw}Cu}z)F^IEE_&e#uHMELQCTEOy$z-%}i z7Q4scCm8rjH!O4ybggv9ch}`YL{i7Ue4iM^g0LX{h|XF>w4&N69{4D|jH$Wd!?je` z)TYfm`S%Ew;d6)<5dqb%IUkyp@6QeA4q$U40=wZUT6&Nt8!k8eOl!K~r@ogrK`(_C zs9sPgI-skqX-F53aiWg%ppgfU@!$y)Wo~$fRYIh+dzZ1P9jfjgbQh{*BK`-i_-#fn#?)RqJg%j>CRJPVkQNcG$e7}af1sB!wKosn z(^6e^#@^8)q7@laT=75jGWPyAzORG*rlq>-jQv-Oh*o4wam7=5=(J<2zZKG(8uTO( z7Nk!UuSG;FGN!oVWA!o?_D|p6`pnf*U3DEBrA34vpQr^ae_&U9kzNwl{Y&W3RPBnj zFrZFip%xK7B;NLAJE0B!@f|fS#HQ=#s!J>AlrZ9Yv17At9-sI9`1wXHB&hSaL5m0< z9zE%6&bwyi`*YB_TiBckUcR>sBUb6;oo2pZ@Qe<{ z2;T>i9{236OICz*)i?oRFGsw4q9Yc>ZlK;p9{OmCPW>{@$P4#ft?W}Be zuGdQ?>>Q72O63+UJg8H-S&N8PR6b=1_?TYC)RuscXsNC`V+XZ}2&jC`xreNLZ|@LY zJMN@%r=~d(fj!K}w2W?^Y;X^=))`S#QIua^Tb2($Xi`-IzmA71vUeEUw8<01J27yW z#Jeywh&y<&lLxyn5X8GNFp9f*u!n>9U=SkynaB3>;9ef=1nzJP&6 ze3SOc&Nd{Qamij!%94uR^xFkJ=~roIFN&Fa4~XH&&W_> z$chp}dXyOQq{NUgC5Fr?F{Dt5A(u+b>hOvpy-FhTti+IKj3Mv!jBq7}%quZu9%I-N z^{fe$7`BBHD{SZRjAHXBiP$oD#zpRAShaN(|dni9zE!Jfhgl zN+Pzm62pd9VyFR13{^pip-w0<)Cr8Cj?^>dP-3Vhi-zCEd} z6X_cz%MU3jFSajt3E$p{baL4NW<}>UFe3^98c_()h(dry6aqA&5TFr-fOmZnjVQ#> zh(f^oy@*B>VrWDmKqCqP8c_()h(dry6oLZ|9-l`+(1=37O@xR>6k=#ZAwVMv z0UA*V(1=2SMic@xq7ZOfz>Fxw(1=2SMic@xq7a}Fh2SYeISbK>LKKZC1ZYGdKqCqP z8c_()h(dry6arlHfXBAAP9pNbXfTkzrom~3?>W?#vH?nzIX9LS)+}a}5AI1jbSANk z^EyU(9b>$X5njjm-o)sxV{F$kvNtiV>loE_jOjW?bRFZlj?rAlSgvCv*D;Rk7{zrW z4pVg^@>|E~trHI+=XH$TI!10CSNaYF_QOKrvGb7cVYN)EN z@k}nG<@b7DvVxx+|L z#1N|mZ9imrg#JB-j0{1i!bva0kZyEJEE!?JIHW#F>tjh8xzk9G$C4o!o{J@OF|>6d zw}-N;x|G|ON#E~}p@ukO$0RY1R0t_FDS^baPpd4cw3n8YgN8uM-=07QThMO-(eKjH z31k?#%ScxxkSYw%CBXGNjP#mBG9&_fB;@)6DzdS`X{A|-BtEpVqNKbAdn-K!c`)y9 ziDWFWq6XtLx&{u9GiPWA#3OSHS zN7X^jf25E!A%{k$lTOm1dF^OcSCUG08|jr@$>In!Cm=hP%kJluD}xvzRg{(0mY3A5K*K}_^ddt#pn(Fxy1x+AG8&9WXkZiKoNF5m@Cju=Zpi_7medwMH5=+0#AkpMrBmE47nA>5>d+seMdN3cWv*^tPZug{-sahnZwF*>9wYS>&n+w6B29 z=K^N%BBt~uk@Ws7(#wQ)*V%(UkVVp|&=*=00mxs>^H1ja+h&7q?CDFoko%4F$-bnl z0~%_`Ch~0Ev!w5Hqv(hINGI}uk$&HgjJBW+ha8o3NPp6U95B+t{-nPJ4Gy47>5l%u z^@E@w{Ykn74G^HK=|B4eJAhgT0Cy193?O|lyk`IzgW*>LNHvCY2a?4myvmtK!{db{ z`t?Av028JRBK72;k$yagOpCw)1H|s)H`$AB9Sr(5doXE_ImE$aDu#~_Cf8!vX$YB5 z4jF0n5HdId#~8Ts2){CwP8&j^>6syLCFJ;I2$_Om#!v_kLzf&%2882q1Xnx-SJ0ec zq)Xcc_C+P-<@PEZspuy|p-CXL4kKfdFssv2SiH2byvSbM;I!n*r_gzO(CzV{ME{H< zi!5h(@m)An5l@!TcBv#wI6+6Ik_n+W=0TAk_o7=;$zVYH3?=YY)Kt1YTHd7V}qF7q46a#?Jj_w|J0VnN9kK;;01qQ+v~G*>E=p>5*(Q7J~EHWIO~T z`jKG}Eb9j)9Ha&PNdkTJDzH)0Pmwfwau|#njr5~oWL-FpYanL?luP%(0|uVsz;M!m zzPW<*pu?uhSB+Rn#!&kRG8ryCGJ-6Jpyx=k4}!BJ$x#yu0{eVNE|~oLMv-NJTSt@Y zA-Hukxdnox9P%;*U+0j$5NydMchZ;FkYqY(F_dt2ES%kN1&KDJ+v1G6Qp#P}i9U1% znH7Nm++h`GW+Gib3{-tvh7e7k9z)FZ+7c2$-`W7Tx}`)G^il!n*^HY=7PX8e^Mx1a zqOs(JiAnBHE3Om<(D~!Y?SP*jM-D)+XFR!sp4|kuaNPtl+k`HYGnP6Fgjo8<1X2)= z0Ng?YlC-r7Ncv9sM-IK^OTvRW*ssLW{ zymc~p0r2bc$OQ&n_^b$O^WGFPS9p*Pv_XTQqXuO?;%<;nZKOXWvSOe$u7Yz` z%!#SwIyjG+Mh-&o{50|@1pB6w=O9=DCLDtHGsz(cPRt}vL9qHt@+t%quOhEO(D`a| z5`uTHChtPPZsXN!$Th-aH1%4t%z}<0+{VA@o@=3IkMZK;XF(@FEiH^z;JIgJ}iO z8c*^2yr}>x@)W<%utMn3PtnRkasq|og6yzL6lXnWk>C7Us6>zA3nQ)d)D<@jp5gPC`y&#hJAb4j9 zxfg;}rQ}5u`s&UsI-*hNOP80C)%4RM(A%5Q0qI*##tHlB(sEK4fxbLc!pX~;&d8!i zs=%*fMD53ltbxp|4(%%87WdPFO5kEYJy%IiL2&eXaukB1DwglFDzZ&@iC$k#?g_^U z0?0mu1g+l)1Z6D;>&)c7#0lD60}A~T7v8&;%!YHQQ7?L=7N&kL(Jo8Lfwm~c8P`8E zV|+=CBrJ}iUo3@^UZM+@k&O`iV;NZu0izIVmrQr9fSZ4LIY|=s(XW@2x^SGJz-_#Z zx3TjEl8QM7KqAy^46R%Z7GdN{(g^^lFt1|xI&v+XvjVoPV;7d+ zOlH$-*OMh7c(s$R`BvygzgrJCa+p@$z=HfuaL>^?>4Vn*pXWA_a^WbQUq^1Y;Is}(8&5CPfi@qdg`3GH2$*uj-U40lC|z+2 zXyj2WXEt5xgrWB+ea{K{a+EIG!ZHJGiL~8T($R4j&Oq?6c-YMi;=LH#C&zF`BJybo zq#eQ7Q912ljNyDlJPv1&{HP2tL62h$ryAlDJozaZV96)tm_t0p)1Q+O@p%rsw3Mt2;kgC?8$*XGQcEeVhpD%VwQua_mdHpK0uC%19|cgH!zd~Biz79 z4&=CjTn>yy0H-_RI1Wsd0anChjKRDI+CPt{Pjw-@BTUB_&U(ZdJozdaV3%KmF`Vg$ z*Yf1qGQg7OVhpD|;yj*QAOkFU0mg8KBNn;pzfeY4`eKaX)J80UGiaew8DPof7{hsu zSizI4WPl~tU<@ZXVl7WzE(0uiCB|^hBCdinZ2z?~!qV5tF>yVdLGlJSa1#eMxq&(k zINiV&4%EAW?Hp)u1GjPDPc8uXzk?$?5W%UExRV3BWq{q*J#tL^Gf&><2KIB{0XJ}f z1BVd6DU^7a0}sgnllU;k94!4fPk&TS7a!xmlL+8ENj$-UlQO_0JR`@%XL<4qZs0`@ zyy6C4<-lnKaIsK4!vKf)ri?I=Z_6?99hNS>=LX*Az=sInoJ;(O1E0zO6Y#kl6aU7O zzjOm%ao`_r;M-oH|Iot!awF$>mLCwn8JPGZ2Y!|TR>ptjnD`4%zTgIa=Rh0qh`9g( z&Y%b*0ys$%LpWgJ04(b>kzp9c`I#8b)7#4cOYVR%oSBJ{JUL1RSaP%+6JvOC90E8^ z6XQ9MBm*qJ6=OIn6CEi$y{n9{^zL#@?7@?Jxq;ps$aDi)9O&l;`g35A8yL)iVF=*N zOB~LDQ4ly7!V1lCr|0tYv2I`-2PV3KNgSBs25cOd?gnOX;3_w8H3w$7f$I=}Z|u%> zBlCEc0yj{|fnqmc=fGk&P{M&yH&DibN(68cCtlBi8W~`9T`I>M;xeATQbxp899ZiH z92~g84cy3qo87=h4s3PZJKVrd4(xUV zdpNMy4cyCt``p0&9C*+T9OS?eH*nNdw}%nI*`#=!XL(cx*lj&7$HXUi@>6c$X%0N& z2A<`>3vS>=4!q(9Ugf}P1RSi;GaPx-o#ibKyz2)3!hyfKfe$$Fu^ae=1E0Hrzj5G8 zH}Dk){^16`?d`HZh~T_bJjb*AAOo!SKgluiJWu|w8~B9-7u>+_9B2dc3@%ZCGqgW$ zAcO-J4uJg;LpjpcouwTII=F#I4n(x*-7lFC7t_ z=88RemOe7TYM&{`#4MiN&kgkFz#um;m;=Myz;F(Xas#6|aD@yw#4#Khj|k4D#R(jk zECcK=rpPhT#*?SJff*dQ$_-r2fmv?gIu6Wr1M@gg&rwwl^dw$z*0A`i~}p(z$!Rn{*N1R@GLjDfg3q+vm4mRfz1fuv|YS~ z16yT)-DkZV6Swo^1~+gU2ma&&(Es7c4tJKF9N6s!_HbaY8@QJP_ql=lIq)C?I9C@B za^Q#zu-ke_j)}*3@*{mv|JenN9C_TG(Sq{A5243XAD{kOb4xDxa zXE^XC0=Og~I^N>QyE4LV^gTHyzR#0CbORr8;8Qp783(>_1Aphh*KXh&4t(bZ{>g!V zL*U@I@jXX=a%VZuf&U_aivr>=9Jn9@>^}dHW8y`gOfuyB1~>z|V{!v#4urXZa0H_ zFKU46e`|U{Qs;izO7b5*$#lgY(l+$JUijfXP|mMSq2#E3ilE-UG#4@PeR*Nu3AuRmve=k}94jJvu@yLtQBPqxH_=Qp&;sQA3oA+RDjv z#y&EvbG8QoAx)AzZrb9Nbo;$POFJ)!PD3or8{37L*&F*OVn%Ol#6G~nJ+bkj5uVZE z(vgK0Q~{G0N_*`G;@W#*(rb%Z&W9ev+IFI-5hkyB}^p#skU= zP4tN6c(z-toIs}^AURPUY7p<0LPs0`q0ydLUTA`6G)KBnX2Du@ccJKv);mYi+=FD0 zbh?sND@{(L9~~qkI%j*7*R4sChvug=N%GL39!-)4E2MPwOd3gFJ_r>^^~5HIX1Jp8 zoa7Lh657Wznj1AJKU?nOy}eRsm%|{dryJ{g44#fnKMY-~mk*W{-_PTA`?}K6U#oWx zp@lD!Y}(-n36IEDOPK>Hp;>YYtvEu)gbs8?Ptzxkka3|yJfp+lp51n-zgLR1b17?q zVQ%!ZhsZFgzBUv)z#uP_WJb6lk%9j`WOP z0ZQxUbc8$Q%o8L<%2@_mXC8)fNBiKN`+4X|j!!bpd5N%EjCUo$Q3mc;XZ2Zr9CBal!kLhZO1G zYf?P>-DICs`fDSQIn9j&DQQyunq)ftQ6R6;qqy)+i67JiTdZ zI;z1{F7z-x^%$8JI@2>cR{C@;)1WKe==G1omYJ(PqvL4A6HtrmT-X^_clvK=->88~ z%g^#nrcdB?v)u_$`>CPVdPc|4k|!a{d>3|h-+wkll9&#``1rM)cbPS$#!owdJeFP70bijmec=#Je|5S*_ zZ}1R};8Hv|@Ng3z=HOu@9~9U$tL#Ll7WeI_a@R6QjXG-&yjU-zq!wo1)=C<;ojd@=!4IbF&$omicYO% zYi=C9_&gck$%Ufxz);9^`vEcnhv6vEm55`ooJc=ANOIjNSUGn}_F*zI&OJ`a7vmts z%bWWm$&v2@R~9(>;ET$=;XIzB7hfcKon6Hv7jhxaNz&1kxo954WKl?t`ywlt$W-ab z8cVcXKF%54SC5w>*FZC@O`+|dAfb_R9;8wcftDt<%Mh2Pk@MhOiRXF!W!NY28twWD zk%XQ!JK4~QX5C>4YrLYtP?VE;+e-`iOYe}`*$y-bCNlX8N-K(%R5v&c#rA~_^u*hS z7~y;R&fA8b5L|rQ&>Mo@?-+(du;?8_76kMiLpB62zhmeN!LRQau7F_VyM|s6EPmIJ zVsVBPl@?Z4H_)x`8WLjgQLD3Eby-D4&0@JQc%0gJ>Rm%gJY3TI2SZl~rvG3_OJR9q zJa$LrvyGwE`Nf6ROK_hlg{z_)ANaxWs|BvP_cuct1TX()NHXK|*lO6gIitXk&=_{X z@J%|s(A9{NQ@R;3*SKy*tWrfcV|NH{?`F*Oye6jc^KQlsogih=P@@&LlQz~3HC}@` zKg~B{&B63&bU>q3sJ?LlNj=V26Hqdp2#;)C2{43iG zVPiurC%mG{UIaVE@)wj8LKnsdz|Kze`Zvv;j2NR?g~mkO$U3#qn2F)qLSuI`=Bjlj z(XB&`F^$g@8vlrdVoz1G(m$^@_QD;nLWDWGv1^U-Lu@+1X-vo6tPIR`8h`8(&hDgh z-qRZ!s%i2*V`q1A=|1D{tQs#uo#ku#J3`J@_qZ)TRne8`vG$^h@}(7}wYZ}{Ge5rs zcBtm(H;BOQk0D7=YR-=#-5^-_V+fM9{l^egk!ODl=>x&Pe+=mdLE29ts4!Rk6w(8N z)jx&whu{bYKl>>p17Z>9Lq~!2sWM%!S;Lbd`JQ^|H}E0BqKh7raOKN>7IyZ z&PeYYTUY{{h*_5Fe+)^+{k7A74oTw|P5v^z*xL(Ub*+gvVYT+foABCK<4stDi}5C`R^J2@)_hKaDFuQx38q8{?n*FW zk9jr0lnlYQ2`pzqqA51b)EAVc!Zub`j!<`ryaLYdW9f%~4@smSwl!HmJ>bUIC7OC- z1r8*d(l9)mXzF5SELg|V>raFvC%7){;FgtHTUm_X)WgeJy>pXHQ}XHUYfRW99$#Y` z1Hm6_OnDGYUTeZ8SiaVT+#gwM8U(>tYfVETNO71(K~Uf@rN*&4PqyU{;3=%izNoeo zzF)r*%J`n{aF`PD&Yp0Xx?uRO!!&}EZ&_=~MdZ45rmh$|)|oOfJiN}7fg#=;Y^MQ( z)h)aseb&esZQ7)!riM{#Jc*(gULn7jOUf$o6;ZllqdB=T;x%$^039G0QZ0R~CFPZ+ zg++A6VnbA8v0#`g&^xNkX>{#RB+_y1VtYA!WWUB*Q(-N*s;bsrV68!@rmC>KdSOLX znYChpXfLYiQ4M0MYjBgRwE~hC+pP;rSM&+9di;U?vR5rDskXy=sx`G$fWN@=lMu9vLZ@}k9s<%{gabo*LEG~F`M(3ZYQ44vuZ z8gnB3ju?D{l~*0VRW?nti)hQpz95WSo(gcp(8!`C7DA{_6!N7m7^^t8e%j= z(EdM?j`Yf}$knt%v8gS+7;Z4rJ8R8p19(NPBdtXhl`Arf?eoxzTVSDj98T#1(8bQ+h2Q z*7AYwvN~_+six@+e__e09a}if%hFCHC#?zWCwf6l2VCnBHbHr$l?V}Cbl{B!+1kF zkIo}DJ8hR>7|f%Kh^>a&5)8>b*tJWE?FO$x^GogJI6AP@4aDZ8Qi7oezjiCJ?WfN| zG>N6&Pi#-(wI+T`@NO22I(bR9CmSt^hVG+7Z(Enahas9d*N{Md=}V`~HFU$U+7;e8 z*DxI8f0=9Ok73(+hMpJ>o@dC!aM3(N28OrKGYrJ=byxa-=NSfJyzhLL-k3k%FsB~^ zPu3d-Fn;r<oaS#gr17)|@=ilqFP`?3WQb)F zw;RT{!|LXJZVShTc3t~dxQv!p8$vs{xN0ZZMqzp%etq?=@a7HN(Y9L+lOb&U;8w#V zf!^~X4DdtxG`{wt@uOsV@ipT#I`6MwIy!u9JnWcWSXEO}SZb{G|t<|+F)GZ;Xi3Vu5o+C`qN=#0ys&CT z{*tN^cp+gCvO*vJ(b$c5)K6{W=x09~5K8#^Fne#a>NvHVNTGTDd6RtpeNro#56)VsZ8p=RUzFm>*-Y?y)gWBRmcDg z`>qa|gyE{yJpIqBL%L%8<<%ifkN@q8N3RJP#=G@I!8RSc_1$ZE;ak^)FvWakO-Qa0 zH}%j1t3wj_*&C}_mB+0ON#Q{98gRW%&>d^Ry*fe9t_|r8!G*OU$?^^Vw9d4CWZT=G zb$$N|tR_AAv?(r3cIn{Oc06V3&ei$8yuU-kzzbLBLORogr(r-j_mrtE-u~}TnZ|LQ z{=?QDbvDDDDHi+{$g%Ee6C>b}r%ip@v(7$Puw9E;6Is@_V5O^Ts<@$`kta=AJeMfg zN-%0=MgVc+~+Mo9B7M+;Yma zcM4|7Zf|A{GOxXv5x&6{KhoaJ8tlFHW)#6xDkj1_D-kcczK=N-!`u4sOegx7L2@Hx z`mhV)Pmec`hVb?YX4aV9Cve%e3Fh(qC6rj&cY?VyO`c#5G2$jEnmNIoDBs(66U_Gw zr#o*nGmG}bjpj6z`RR>jrb&?-cznR? zsGIoB6uaWvZZh}4{Eyyb&c^WDo6M|1Qf@Xg4(Hx%w!$5)x!H`b*W7orIf>ut=ndwK zWcF%Jr)$`66IB(KjVZ0DEzYbgt$+c8Rp+h^=2Ta8==6=|{>I0~LM7WLoBGi|PMN|R zpWA5uwO6p_$Zb2&pvipE5^`MRO1^Dnb!z3|5s?BC+aMtI-vN4@PdwP?dZii zOK-aIIPlYtR$MT5qo?aEsdV#ZOZ$$P*V8CwhSB=%mROp6izTtKL!-HSbjoda8kMDyoQ@cd1nDv<0! z@GjJP9vnDh?q$N=c03cT(Hx_{oiV3#Oc4lIprxbsv*t;}d|%^{v*tzS%hj?v8kMza zTQ;z{8kItOCN&;<&wNkE%XMGa%9l~&@D5WO|M9K)kI4V0cj(b48gIF19xu2wzj4<^ zbBsXG{%Q`RIZvBI+ zPft;-6)3bq+r~eSv)teI|7la;ZH{VOH`6k+D|b(}<~>2zEwglKUtU<|^5yeRp&gLEY>iVrxURa zBy{vjOBYxQu*C2@Lx}BmI%2sckzQP8F>&k;Vta(PUvBBfqmL5XGngffN1r3MH!w9h zO00&jK5*y%En@o=%}{sx#&SzcVr3Ps?G@*{KJ?_u^f`eE4EQ@oms+|+SJ^9FE&)u& ztOi>Oq`Y02h7`xtFw>Cu&z1lq9D z62-CcM%z@x`f^dzjkY2q6f~OMI(>bm#Z23+vZV1;yU|vIse?RH=dQBEaZyW+wi~Ep zmBlsd+hDXw^yn&!%Zs|rXgh${vMR(-vf2{OFFR<2_DNYS-@&6s+jE#Ni z_zOnc`$oETg(Z$Yyc)EMX~73Z+gBb^e{f6v#%McFBiC3mc)tG_Z9)i0Wi1*_7p$># z}ew%}?VOO9M_ zN#z;gETJt|AeOTW&0TKkZ03#!lNiS|nM;qO7eK3B=V3J!`K~MZfc3!7uzr?&rzIu? zmXu(nHFv!!u5pFK@@42{>-ai{ul9VJB-N zYDo#lw#qZ-Nw}1Y0Ms2FR|*~_t1GPd+-iv@SA}40J2z%a7L(A<%Noc0`j`pbc6F8EC6DaoTmT) delta 102510 zcmd6Q2Ygh;_CI&Fh5(_45|S*05?UaHF1V3)2fTb8|{v!|8-ONG#oa z7iniGb@k(^ytTxpB#nrb5)|98Cd#FcYt?r}6&0i}baXB(D$LE#OwTGPRknPbOkAzl z9pk6huI-9U&&%wbIe!`cD=!UhMF(1>-ZVK%il+meBo5fe(cE4nPAMMJQc9tNEKucL zBS^F|c3Are3qB10rOJwjXSFLAJjRoJ&Bh}>1Q>8!;+z~)HaF4f((u5z?8pN%;kZr5j2omdeLHx$=j2OQgHD>J2B=8mF9qc=fx|adb?tqX9%nx@v=VWoD#Rs!&V$ZFJ1UCjy9&n1n@2ws2u3Db{%1U5*^(4Rf;FxIh>-_TUn0Ap* zd7tZXkG*N<$JHdVsqO8&Dxar4wQD%nf1LVVu(kFPutj}k7^#TT8V!+JH*LcMY%VoSehQU<~m__*!ub3_Q z6|ca6IBUq)@sin&zwn9~3<;%WCc%hT%nn%aiuLtKykcEF3a?oIu8&u&7dOHy)=!(@ z73-$W@rrd&8(y*A*b=Xttjo2=OV-a4@rw1R4tT{nRA;k&ioiYfgFykdGi8n2l0+Km}-$y9Va5}8I$!Yih9Q}K%F*v)vwRB9Gp zF@;IPt1^Ua9$qmeaNred^F?@t%_=W`_S=a=3{KX1C6+BC%X%dYFtZjcu~_A!EQPID zVtJtWJC?$hEU_%Ua+Rg9HA^g$`u@OD*rFwt7F~a0DQs1E?g5^{mMyWg`Sxd)hr>gO zC3VqnEak)&F0stM`WH)LE0ESQfotW+`m#63f_`HCYNmZFve?zr;ej^Au`8iKX#qo_f>ClIv&kB&tG*H(fY%_y-X@8&7&86}o!U-A^{N{MCVFFb|%Qeye?Z=QPH;Kby= zQ);n#qvDiU+D+ssRGtz`p8}pj1p@G0JcUYBV(B?%2zw3{2{1ghkfl(WN-U0NSFjW+ zREg!iZKW)QI#pts@%KTN0=;rVa$eonSrV12#PZjr4_FEnti*CM>o=A{B?Gv()b4`~ zgNjySDM)O~QmAYtmMbp}U@26%63gT3C$JPMU5Vu%mmDmGiU;Mz6=AB#RcpRO**Ifx z1ghEHrHQVtO5W@y%4c)xDV@@4Dy!$LHEss;p`4kqJOZ^1Z)wMF**r5s$)4FW0<{jQ z9T@fWOnU@s9#UE&CED#iCgrUt-OLOOW{qGspGdPRnX}p{AI%z}Y?(DGaxGp6jN312sH2FHt%0aBXGo?1>SZnPq}}rC$1E<>k5K6-W9M;u;G# zw^8mKUQ4mhEi`PFUsR5#kFcN$dud&4?aR7l+FvMDtjGi^y;IK_-DpcI^c2l#mv`9P341( zsmkjM<|q*h(vFysB48m9Po#eyg1dSx-%Ix&0%i;3 zUYONPNmw>d>AieBO~Y(2waaoVaCn1NDgFp)p+wyBGQDyH9$dDn7iE+)w`5zeV_~BY zTscR%c-uMp(sOXf8T=R<5 zMfq+`w)8468>pOE7q7&wi%^!Y>J8V4O6zsUlo!^b9Q&2a>k5^gw`UPoCgZXB1nH=} zyuPsE<|3ChGbbafXue~9sk{S6n_WuT?X%77a;MU4Lo7LNPvn6HlQ9-97xj_HK{UG*lrDZ_m-*8mAv+)_n3pb3hZwz)8T^h} zJetF!xjdT3q9S=d#GZnfe-fggo9=1hWCi8JL*W#}+@IhD0e4r#c<&ta_EMBvW+gkF zVHS-xQx09Ub!cWmVLHs=OMOdrv4U^bk1q4?f|sm3`CJ@t#_eH>$lS*e1tk`D;sre( zWC4_6x}{+j z#8xmu6m;RP1g`Mw!Yl|UFCp3p6~6ZP6)=i9^7C@D(hF7pBLlU5bC|`m^_dWIp8dKh zmvcv$1+nFy;02+a)*+tV5#KR$uQSY&*>Y3pl1-CvkHXKKlzB^?qR($;Yg@sKRi64^ zJV*8wZ%O(JVoPVi3qoXTPAJOESPcGaexa|BbLER+mW^vIA+%RHxg~lbbJ0Hyvmmw> z7*a_3Wqv%yP3>1xd&P5q>c53@{IuM4S{vD*{*IMFf8f^bbFLr9hVP;_#xcIna#kS4HKtX)f3s*aQZrB7ENr=L&q^44rwtZ-(Bs%v@%4hxxV{`m21~3>`|oWr)5V z-#|m>i*KOq#Vh8~po79U(9nV48)!}(&-o4-4!C>=4TnL#gNEZ2-$BFCgYTfBb>|ys zXmj}n8X85ufrdtlZ=j)7;Tve!>-h#6c1ym2hW!Q>OFKb`ip_V>Q1SQ<8frw5%UGD3 z;nSd_l#d^5*4cmEaui_gipYYcwzSMbM|P=F8nC>nwBFU-sr5|0V}TmVFQ6KkO>0uO=IXcgvsCt%L4|-WV$ymsb zLlhuAm*gqPa9IG%>4o_YA9>wtzixEc8tw&TTKSb47PILl}R3J70^v(&E**BBwZs*4V|p%Rh4W8 zd~`K}s*;Tvi=Zl*DEnf7wpiVY&;bGV)QI6lIT?6V&VQAsZzV8^kUiTOZcvugCJmJA z-L(RN=^Z{WJw#Y!vi|Ef8LD=saBzJg3>QHqeNRKKL}`@(%nvUo9!+u8gsqN6x%qgO zrPO6j$jC}Bzy-H*=5CgV{J!F=x^=TZ3uonbsBQ>^k)ACys4X4WoOD%o?z3<#*NS3E zP8yU+dxr+l$|@t@w-K-($(Q?LB0 zHe6>cD&YY*!R_&P&~kXy)fp`rN5!Sc=sQS}aghdc*!ttue6tMFB zErDuHxEfc)BD6L6Rc~){TN8HBC8RZRR+%*cFoK<$xh1%M)Q1^Lz{wzVrRldFrAC3e zu}q|4wRNMAv8aS?LDMo2c!bqOl#dU(eU$CN5eN=IJ!S6G@qr+03m*hUghh}JvY7)} zl`{!cpl0Lp=V#`m=dbXbq_NTO;z&(D$K?fmSYCO?7Kr6@B0y*>)DaODA+fB;Qn2!a z85^$G6|o3yY?g?K;){mc*s!B*)o5(A&&mB1LObed+nP&HYWp(R^a7%eMc*+Nm9RHe z=F6DaK{P*Cra{e$y*vMv3<{9ccbXIO@KBq_*H)K#>H@~sGsC-;jGtlOwR4ll*+^SM_5n&O;=5XvPG8iFk z4%h99ScJAYm-Tirx6NUPT0`2LDx5CC30dy%=7?&8bWC%(bg@W7`t|`;7Zx!Vl`t?> zekN>1c^6)~z+%He zQS6+k=79^P*>2Y57E1Sv;GwNmd;MAHS}5g^R%9MRS{Sa}4%u1xeWh|s-VT3JD!&5i zwlH{_eL>LvRi%TE0w?2n@A^hmL!?8R2Iech!m9@6Um`4m^sY-HJbX3M-3IKSe@H9u zp2ZIKTMp8@%k>~Q`*hbp1dBw(*G4d3ghfaM!x{l= zvuaetf|u{VHsCW&5)s804Y&1WZ$E{!zE!)3801wdsF{2aMA0}y6RrJ%~OvV|y8k>axkSb`#0%dY{(@AtVONn@DW}d&$}GF?d6SMUYzPHY;WkoDP4`jPV`id_a?tzTGM;me=8&j3LVeF+`znzOb zVYN7{IeKM_w5cx;s@;(pi|QJ^l%21;w~Fu90l~?>QUj&w8+8LMiYt5=ibYriF~1u) zlvO!^DfQlr=0nvpI`CqL5I4P9?hxWrBEo8`&66T5su8c-&bUvqyeoq1{zpuH7X6F7zGyjU2EN?6jM zI*^YNVG+b4-vAV8rv#}v$17hSUV9SCzh96mBCIxESt2Y#YF$Mpm6adNDsyX45sT1P z*(f54FB)#E%)Y!5(khSC@`v@jYC`cVE?gb<3ai8A`?QVnZoTrWz30)&vT(vNtsqB! zAF3>mKo;(1V4tN4*$mJM!|?4yzugU8&48b{;mz=_a5L0hr`-(y&?~>%4CgD$B1AKs ztt^iq&A`6dQ&BSvFV_tEHksf9K)e}h)Dd)1q?a_Ogn#OlUTuWyj73lf#eb_4zL6Q$ z(MT{yF7rhGN4;)&X1EVlKwx%L1f(#YWB0K_Z zG+#R5?GoNl@vC0-aN8*MovDzWqv{-Z1wLBEJIAz2Dub#EFKc#=$$A?>?Hm&ri{PE( zqWYHQIFp^d1|0U=u?+N&EK2Hpi zItzbi;U5sD){y^XiN7GkH)m=nADy4g-RHYi@_TD`Y&g|` zw`SPa$wI1UIY&nSTeI+~Gp?RHq1GC^Q6W~wpU_m#`}In%s^`6oMNswhKOlgnDs1B& zF{r2zUfWD8Xr~@-0+l#k2~7OYnvy3BO6|AHy*TrH_@I=Dun1zm4{>0tdV*Qoo(?B# z;ospwu!r{>qn}j*Lcen-h~Ta7vO&D0K_-_s073lPAn$^PD8Vj> zSY3IyNg!4=fKQm-!k_=2Vs+&`_r7r(Jy=bDKOqpSgz&MliLeOSGlj<;-xof))vSmG zFF)AqZ>vbN`}Q)oXT`pE8FJ{VvPrlC-_GRSW{#*TNUvy)Tr)-5(N}oY#7q}q5kwWH zi16^OhPw^emvln5f!lou_#TYT*G+?bR$||_n{v02N-;W!6z78g1|lhAfoe+MW3Ye`Nx~UMAx{h&ObOt6&Z2X>wRsw z-~3}=IMsmp2m8)$MT+Xbbkq|ktvU>d0^jWAdipn1T4?$~o3_+87k}s#U)9v#D$62- zx%j2BJc5`D_7UQWTA^wdQt^YvycOEl6>Wv*+LnK{xh-(f)eqDbXeGiT$UxdcgonmJ z%04w$Q43%{7!>wWwgO*z<}ENqv<0Mdn)Z64Ug6a?7$?FaNE?h1;i1t6?8DX7)CTYo zY~BW|McaV>(7JptSgBWZwFPbwVG*PSmWlAtXaV*`?`moR_&WGa>=SPLcnfSb*N|%9 zU**1H$K01^G`qtdy(p-S@t6pUAdT^`2oH_MU|&72Ok?=__PVxpp!i^)P=_q}J>En? zS|&>4GZ@ld&8B%47H zXav*Z>u!i}cyQHg-+rkhzW~0>XG=~>PU_OWeJTIFp#Q;LK~M|;5!kR5Z}^VN*hSG} z`HJ<=TJSX~l|wAt?xlDLk>1)xDt8L;2Q6;Fn7Ql2KAWgE##P3ml8q7e>s@qovgcKT z7WD+37f{H<$>m=qhyy-hx-T&zEP~jICX9#wMx~GD>T$PG4{MFe0nMFX#0~_p)$Z;q z-M2!GYiff;q}NuW{vs?w0w_F}@?BHox?B+pUcT>q%=aUzS9pI2w&k%xhc89a-kS?WIY* z%PZq?z2K?C((@uLf>;y|o^S*BS`_Z8RKy~*MLDN;kan*SvmdVs=|xuEDn0xV4xd#0 zkLiMlDm?Z;$Y8I#e;wL-GqGRzBw8EwF3rb@v zY?Mf-yzi&F>8I~#FNLP+MMG_rk&H#qFK#x`;N8tn_dAVpXC)C)PZOeI)K><6SMIxACE-J|QG`Vh zBf6f$8q77;i=81a)Bi`w4tA_0bhwSd&S5g-oW=y?RQ{VpBBJ}+ezjZetqcpNr<42g zP(Ovh(W}UnV9)xxgTCGf*Xv*`oZ9OTY`(8nV}9sH)MW;s(k|O5`fw8bK2HB@?)Q){ z>1ssf(QD-neSBOQ^tHfO8}I`zb+DmAwToyGhJZZbS_E3FbHEEwf9u1;`eV8GpZ*9R zkKaUC1UX#w3&*r7&4Cj6Q%CN9wro(buTySRt*>nRsoe5LJa7ur)oCumq8icK^>Yu7 z)-Zi&b^oQ@4-pI&5!KhwseNsL2#X*?4cDz;mM(;$Q>Wj&%U8sLm+xlzF27eBdD7KW<9U#q5uD|AIHLYtac5m9{6aC>*`*U3Ygnksxh5`OBO zdw08p)giAo(flpZ9eU+gwc$R-LXTb1<>NvlxU7R63o~=z9V$Pg@=^t;z^kxebv@ob zGq(Usefl4hR6EeBybwMv&xOXte@k~#)okf9b;K=Qcx7~~vSjE@)L@Z*^d9H_0;(i` zg~8Vt{0D=}5a`^%rFN@yGvBg(54h+WpVaQm@v5jhm$L|dTee47HL7+}g$jo!K^*zn zj`^ACg$_TZBOMzB8{~tH@l5Idj3_^p{jC>7wKM*~0SIc5Zp4cNZlx>xu%^^ca(Ye? zv;kb?L=4(vfH28P5a`r3JS@;?QM7y&1mvf=ngX-(D(4Jxnk&$Da~>QnVV(0mvl5_k z<(wz|MsCpjrSKD}j`@E2HWZ`|b@)G+k$bClc_TMS6TwOVsOnpP#v-Wk_cwAAtKP`b zE>V(=4s?<@`rkUzyI$>E1oe_-46-oDfglXUYvHGOGl5xo#bdDHc58rJudR3-w<=IP ze->E2_Gw1#YVfh!pGT!wsOw|Y*Jz>k&tOGWvKla3I}1Ow5L|(RIn>(SswLB~;J?w|8Fy+y+Oh&JL==n18M>@dzJFWaze*Y;Vsc#2X z73N(L7D1+BHvlZCtjjVqy)eDNQK$}IU7(}?r31Ss29*DO)!#(~*T(LDA}p#=(GCoh z68Su@MYOnOYBfkI_sbG-z$eIvRawgvBf_E@!Rj|yO5$J*)dAMxF;WbDXK?wiNDUGZ zS6jXMi?GnwHMIx9ipM~gZOEeZJebkgQu^2?FLLDO)9;2z?iU*u>cDZ;WWX_ZX!*~D z&KChe8^?6UqT0s9QNyGdu237rf!MY|YDixlR{mv?_2J{NMubHWeOkq_tjci_O}*Kk zJKvGz^J`}{I>Qu696lO9qI_4}Eh4wJ3Oy>qA|!M=TxE5IgKKg{EO`0BcExYdVBN)I z5W4lDM@M>0>IXWSu-hnUc;ITnXC<)fu~ z+=%?41H$)5OI-pX{7poDZ3ur6VNs1k_|;U8zQs2d_jh0j?->YIbKn!EztdENMKyx8 zWrpP51Q@IXEck_haz89SKtxh)&FU+{B4p>R$T+g{{cWRaU-|60A{L?T>t>NjfiD{F z4WZ{NT+OJ$4WYlbxqtoO7GYg@UULa!nMgzWO0UjH3mJ=QbJx@*jO{uw&|1RS7Cr`w z2#X-bV>8FGDqS_7C5#tzfTgvB@tlaH+6r?-ghdeJaRb1D%Bs_(&f;DrRA5y3E~@Y=g#+Qn#yd6Jv=E z=uRvszlo6}BDXeli$z#eqn@e0W2p|Xw0y@cB9dx@b(aW>ka|{;vK|!5{oedH;Un;s2#X-r<6j)b8{{$8X(s3dp~BRsb>~Xafdf?}unN-!vWl<> z@{{v7054E)DorF!>6{$T@*b|V`#}BrP()a5yxtdK5yZIPfc^-Tb+sM> zXN_&J2QU`S-z;*#4~)|PF7x~nb&VKZu7zWHc@+N?fj}F@>mn?IxE41CMLKpG=}w-mKkG43Un>r{t^CJj$B8t7w)T$^VG*PiZxDO~UZW&5jn)_nwDtIg5w~3NL z2KjRx;1%Clezy0ih~(PfeI&vnh?&0u+DGrc%k#RcF;?8G`1vlWMPOHzfKQlS#a|*| zh19f)rWUNRg3V62)vSmGFW;X>rAHgR3mmbqz%kTO=*TFv(f^+Jyr`R^qY3}mETsfC zVVCfmu#*UjAWc}oNUayUBv{>m*ApH2nc47DSm{}|rSz`3B+hT_o~i@MUH6n<+L$B) zgSO=uFTx^-<+uSz(zA-xix2WEbbvLn6n^9?;0u;ZMI_Y*t3ZTBHG-wC13jPvEUmLa z+eIYR25XxLi;#V^A{!af%<@Htidcj;v#*Iv+I`V*F9N+);UZAgoeffAx4S=U@{Xu3 zY&)!dGU%d6OZtkhP7&T>EGjWYsOpnJKk5LXK3#oKsvl@Sz7HRTZ$(%Hu^<2CNLJ+} z!RLj==r}<)4Rl8Pa8Lu_6sDtASA<11!u9+8QYSt(_0|Q~#s{RzY6@66H#T*sZ|Ke}|Yh}7D^O%-8Ly};QPkbZuyqf-ZP4e7TJO8o*g z?luvrwSik9!lD{GsOk?rtOKt4^m~nMu?IzD)rRW<5f(u_BHlqOa%Wii{*y*@MflfP z`B+>Li_jj?8Ih697Y(;Zw67XHB0BRC$^8L`4@K2M+NZf0_P$6<`iif5MDH*bmGFqF z=Io)m8TOkF474`GehD9gA4OOMu^-=a9B+_6)I46$9|DEZ+6-$7e8TjGqD5F#BUpFr zmfSzlH9!YgQG28Tfg@925lOXsS8ov(L5B1jpjl8^`c@`sL(`t{9R8Z81GGa=lz(kL zO+;dC&}NCS2r@q17|>j8VMTJhBY#0|ezq+&KNEhIfL`9~`B3cbIv||3ue?XJRz!Yn z2%RD2hU}>!oJtiWlHdqgfun4Jd6*)7k{9yhNABihs5!xTRC^C}yqT%+3 z&Qzm6q$J(vc85L_)`c^g?$9S94e2Yr>JEL#So|NjLx1bQK+7HaBYX^g6JZg=c>Ka~ zyg}|zOPi=WbVk!1iU&SnxSjoSYUm6-LBB^%o8X&@=8o|ms zB#q!JX^V7#B^{Q=2l|c<5lOYdnkT{{$gp$+x*=57)eKLbvoECCrl#j-<}S>qV>c^?9WbgWaln**m8>svoam|^y4y*CwV{zgd>lZ z_ayg;$gd6I9uXGR*k!Yyllt;5dr=2izdk1o3+%FQiAbsq))^5N)d-gAcK@UUEG@VD znuw&@V0|aTB4p>R$Y*Bd2Xni*$5#=H&~EqpA|8`38g94yjcRneY4&lc9q&0ETZp<* zZ)p16?SN01Zd4n_;{U+!PSt^dmft-xd<=$(un1y326G&5kl&r711zm&$i*U(`t}!f zqP9qcMG)(81HgjHx@wKjcff}!9k%)KlQ`@HL*9d~TXX*ewXUGEZ+UK_wWL|9ZW zfUN?*+>l8-p7I?3JEQ}`t*6Q#wtiYfer*U3h_I+$2yvm3{fcE?F8m;~!?rAwzEnsW z`|aVruLH^6r^|bp?})&ljpRiU7S(9_=Dg~0$*=1G>#s->O+S0J{7--WDk82nVn2zn zs7Az8S2GrVo=@zD_THdcu4Xgf6Q+03ScFB$j#`nQ$Qmn{tI3_5idgXS{duTItrJAr z-B)$ouI8RpH5lOWbW{U`mY6MGN;eB2QSXwK*Wg?PlgLO!RMKyx;(Rt4| zi9XQ*)}#yNUmy8UL{e?A-WOp}jbPU8&;qxiqOK3oTm zm)xmaDx$L{e?AGDKK})VGTKWLCbvn~Xj#KU~Va zyoy+acD2Wd7#m+S+^%++0c!I&RH+?)k^w&52=}PJ;Ej$`6~1os{{8Mn-G@YVfu88A z{l3jkk*4&OUv;+cXDot(p#H*nRljd@LJtb%zI<^!d<32sVG+c5lyMYqkgNTL9;mcm zw)sp%SZ$5@M1(~(;-z}q=2n6}5q7ZpH1AodVW3B309Ij+?=?hN4bZ2YN5#2HR zV3R&8|Ml5XA~I{kHe7^7kb(Zjz~*WQXDg;=78IptW!~bj<>lvQ(D=`#NIs>>)rVs7 z=Tg(a?wch7fi{YnA}p#G#m3;C^B*|26*#gpm%>l2?fREghog9pJ``(zQU1p=?iPVS z8%380i)tJ+R1f{IKD@Nv0DDG6SZ%zX5@8Wi>nifFS^2@7bnYEi#3Hnley@nX;ERUa zNk3qyJ|~^d__x%J_n&V?^+7tIxgh>uk(TroUmgFxW-KbiK;N|L|9ZKSgMOYQ3qIB z3*zHMB-I9Mj0lTr1grmbk0~nB0oIA@<&Od6i%6;sR;~z(Y6R=pw^A2wx9`;fR`2i1 zzb3a;L{e?A?h#=TWDvgrHVZ22st*qM@XRdu*k5MOLK_@~N~d{Or3en;F{roLxqt)o z+Mnfr`S7TS{7@?mOLkadsbL{e?A{wczu8o^SXY^ja7 zW@#;h|0N=+Hdw!lun5^XD{_xn`N2GF?%P$wf|u{lLtP#EQm>2Y_OM^AP7nL;pFD?~ zdy6Uq&AnNB9lQr{3e&$#VJs@)V^{S$_;fueXn%NWO878L6k!p>gpA`*-XJ&oR(*JB z{Vd&b5n;8hNRbGOYQ#(Rw0G*mOKUOwei31{@w!)pMK$83I@>4p;kEmU)G=`N@kJ3~ zwedP8!lD}SQvK~O_2AWzj`>b%6NuO6BEo9p^{EJpAfx&XuwPKw&@Q*7t)OFsV_|*z z&#O}7KopI@D$K!95@At|TBmy5?e*a`_L@{T5U;i(!fI<>D-jmeh?nYk+x6k4wYoi8 zL|ARSMu@No*-I;OoLTw)elt3~{0Iv7`6^-&+V}1vVsL!XaQoi-EA+jq_Gf>_xrj}P z+hMtB8dOHUovX#xt4*XD_$N;XDxQRy!GW8_qd7d9%cFV9n#(aXw!5}3zE-4d8N+hR zmrlmw|G*c2l*1v0g0?UIQ1~$H6k!p>s@%_^yg|PBYx?lg^2J{f5msAmPKvOoM!ZyC z{J;9}((=W>77jv~kXk1ra zc$sigc5ZIrBHO&I+>FHqrL=QRV&TiVUG$;2wI*p4=qq&+fk0d9+KaHLUKH!`Uu1wE zti|8!D#|J>=)+zP#vgJ4VdP2rfQ+g|q62{(FM@(LkaiIk)!2Pir*o-3ytJIo0uf=g z@yZin5mNCg@)%k9!JJO++*HIOw9|Q;h~@T0gFBr?uBgJ?jI^Ah>@@i0k^D^fRXX(f zj)N8AFgD01cyxhB$9QxIBT0S@Bcpr~Ba?iXr;hUIMT{i*IUc=)5s_c! zsV6Wp$*=I#ZXUhKqth&M$_IJ!9FLym(K9^Si;-D=jHjOD(d!sl<)<(*%8z4Yke}wM zvl!KupU225AK}q?jOxhv!=%ud#MpJe)j{Oz;`ksh3CZ~!V@VF z;j%eI`)QB5WQY^dn++&_VgXBW34_lu_yU8kF!&mS|6p(#gKsgoiorDueuTim@IfZG z)B)0M7_5T8AtN?Oe?JOHA7Jni2A^PX2?B@gL`?7&S`TR3F}NRt9T+@_!9y54g27`L z?8e{;2pkL~eDsTkK>7-UuQB)!2A45t(+Gk@4BBJR5rZTQ7GWS`V9vrYhpwzgnmZAL zyMZBMvYCOQrVtf1h482;#7RvdU}_4HQ&R|`nnEnq)K#Zn65&-d5oa}pIAaR+ax(*5 zO(F7X3X#VYw!~)E1ZoP~LQQ=V<@8Nr^Qf8FI+()N+RU0sO<{YfDQq}3g)OM2uqo9P zwyBze#&!B6v6USCQ)D16e^CI zLJd+=s7h)IbxKX4ZmFppPm2E0fHYgfkla1jJH|BGBi=0nkSXn~cbE=zXq{Gu!zo}x=-PYW2c(+d}|2DqL` zKW$8!Fw*=O%{cCI;pvhUF#(^26{&`VzZ*@hg(+AXPc3JCiE#>^v!0ZHEk9{`dTp!xlTGKh9r=a2D&bW^uqXf z44H*-Omng=f<4tV$FYn`%}HZ=u{m*=8Lg}0+}lWbX@4BejU~Nkqi*C@vb`O>ryE&M zcgK=e4VgfaU>t(7vhM6Trkjtc+x8pJ0{$97^I@R;z@i& zUT$ViA$C}L7v$r8ug8<2I&jf}_m8yiU_hYYlP3o2Ru^qjy1sO_w(Y zN9o>PBnzU(y@^A5k*@4bW=El!gJ%-jGer8*^j4&SvU5lzwe%r2={cI%hh#*c5rmuC zwWpD$hMP#sMwoP&^V199TvMsbJds_{1FcCr^0I+`(3^420ExcnQTSN z4#Wbwqdl2nMg#9^OW$fw5^3uWWR4XHkiCN5(Sb}N&lu=u9Y}Tr4gf%Pwx`eE1o-Zy zAKgS!B5*ta>h0}mA8-iEqT&Ff9Xh4tT@yF^)Z^@jGtm4;}g}edsp$=KsO|WHMaaHbDAdf?|b0B#dqVa>s4v4-RM3z7_cQCmFqAP>R8xS2CLLP-^=1_7U zL{BY%_mmnhhU)Ab0p@1>FcNLSf!x(_5}h!NG^A^Wk!e<>K+U?-&xVl+((}ABGqXHT zwVg>C0F_niz2RhznQ^euEt91dbmj;`IU$Ain<{mq9Y>OFaQCT^WFJJiqsVrMSUuW~ zCNrcXbnR%eG78-YC~{f{*7wKIEp{@1+EW3GBfR)^sbmsdvqDW{$U?Zz9z$M+sEeKa zV#eoPlc~)iO`&VXl81rbY8=Uch!yqtIIw)@sbM_ej;;z6wE~LbxZ5T{%k)}CqUb{# zNn1ocmNMF?38Vpy-$XjoJ0_5u;rXv7ko^!T6Uk=~6;2|rK-7CO6nCC}JeeGT=#DAm zMTkaEC1)Uto<`1^u|QW-I_Ng3870%nLqLCYI(d)oyaSqG`^{vkbc%j|Gg)Ru?+I%5 zEX|z(!k*%S|7QlY!6`0y=1ejcu8+WuWp@79)u@i(xIJBan!y_ zCv&B@=~tp1q+CjBkie?B=5 z(I4~47Z82sAaBu^^FZKp3&=S5&6C)LBqtIbL|_`(5tz=I159@mulR z`?&P$7LoaI{mmls0z{8xl0y*9kjYMnJc>P0Iz#2fWJv@%oxr+XNBa3N(sElal#G>y zY{nO~c^q6bb^0KS*x@>P9cfP|WJCWtL*L9M|1={DS2KF@h!jIl<&fKfJ}Q^I22s5{ z=yzv$p;PVzb;&IN2wE>8anc)f=n_&KiOwz*KMo~ZoeGk%J6H|gz#0tUgzfpzmEPdh z_$Z%DgzKaNau-C$3&=@`oP|)*8#K9ytc2*PB67F1hYnau9*#sm8cJA#C5$V9624yn z)`^v{hs&6=43vHkXa3tV=xckpjH{NDMHYPA72B28-6F-(nk&dAE2F^oqUnTDQWN^j z3Mg?8?Q#pK+8%oJ7IGUz3}p~Fo(^<^3rBASfy?NTTR~v-=b<{(D{$Ve00~&L9`yB% zo(CdVLWeRA03{i_$wOVG>~2T`XxNK5+V?a=EE_y{~c3VP4oCB#DS-2hU*O3OBY)HrW} zLcj1zom&D@Gh3289Iin0X>|8+F7On1ii!EwCXx-;P=!+vLFp6e#1b+cuAvHD=tm_` z_N!QrCDb|^#5{fn$&vQcjxMszic=&gF@nD7g5I#7iwR9K53ZRu|NBnpwEKBMOYQ<~ z-p^$ndN;Ux`|0C%gL>_!-8Yj>5J7{+(vLQi`p!AvHOd$)>a(F#UWBPkPYT_9nfv;X zSAeNPPu?<2p_4B!hbzdw)k9#6R$>Z$b$J!fUgIIK>~)@$Q(n*WH+m@YCQi7+OK@?* z-Cn|GPEfprQcj>qz&W>kFDKmZA+SPscvA9Cp8ZfeSSsNUJj^MN;Vn36kRRuSCp-kk zVV@@@KgqM7@)8bm!m~)g`GkCk6UsaU#^5`Pw4 zXPoebm+&Pge2oO0g~;Dmo5}Y_;k?V3w1Ela73~*@VNy*VXyQ!DZ zj1ywLgg8!Ufdrh+$SpacwTHke*%nhck&zR5c8B(`RLCeSzZ2%;97ay!`NClYW%BlqHjn>++|e?Ls&%tr3dvj=$yEPDv1oGgDR&mZB*mq&6!s+TZ^6UKQ7 z<2hjx5^x?QPv(Sa9s(=lW=!D>NS?v7XL|@NJB`VY(;#^+&(FYoB(Qu3rf@zaFW}jk z9sWj}!_oN38>dG>yWKT=r!Q{Mc8JpWlQ;SeX3 zc?m~3;h2~30w=uWC7j@dQ(nR=obZ~L@Opc&zku`^q~NSfKFe=8?;)^iUG${nfAH*g zy@dBT;R7$>Lr(Yv2{@gTKjnnaJp{(_3r|XRe#!H{_ENs#gv&_4X`XzA6Rvs)jKeig zO8$Xo|Li6F!U?~53D-H{PbA>vPyUM&B=D)7jKWwNAj$KMaD^t!OR#c6q?b^O6QaC? zx}4C!OK8XmjlF~>oDkzBG)Drg>&AO2HhxQjm(Yq6+Ik6zoY28b=*S73y@W2Dkb(r9 zAIjZ0p{IwyWbN%qIpsb)zn_O9_veH`Ucz8b80IAm=Y&yS!e~ygdkJGXVS<-1krSpM z0q2bJR8DZ-?4huF&Ge+?Sv)_@OPI?E8D7GCPFUzAEaHU4UP2ZpH80_HPB_B}IR2jHl=I$OE^xxzUcx(^@V=Mu zPfqyAOZb=*E_n%`al#i~!k3)zwU_V>5}^NGMhZ@|zweXm+%)SNH9;~MG&|G(_-=voU)lyB9Mafa=9ia)bDyq93(goKXhf3recaY|df1?S&#A}4h45Lhibc~Wu`&rbFdx^hBy zFQEq~^!5_^a6&&Xp+8)q|2xP_8O(1P<|Pd0gi&6?Xil&r0q6SiSWcMWA+UN*@}%U+ zJbRj#Fr5=-x(OivEKW)D-ZGaHGQ5QOoUqVKSi}j7y@V`I$VCFq=jA+3DDV(iwTe6` zc`46c(Gm5ZJ#Y)Btn}WpiWAm&32Qmwb}wNAClq@LC7f`lmv9#+Z1EEA;e@S7!1=!H z+{P)}Jrq{a`#mZ70iOM!m#~Wy9`O<$<%Hc{!X8f8=OsMJ2~T+m2RY$ch@89{hd8Co zd&^NyIEDmVVUS9Q!KJ6vE$_a0H32$=3IWOTYBtZYa=%xIF-}0`P z@E#|8;3a&>37>cgpK`+INWeu2`Cpvym50FU`L!n{f5Wpcdk9YX3a4E4P~`79;YTmw zCrT2P}#i(>gF$cD|?UwHICi0&>enkVxua5z!3 z_+c_2;w?CSfF~gK(q|tgEhEl*lb=3JT1R~7PQoU@BV-^Q^9YHIxadnujrhPfIY7BG zKcW_xzW4p8jk@+s>ynh0v#=Dh{^`v+_$V2uT*$EE18@6MQzQQ2n;h7%oBH^B{y9C= zoR9o+l+N=R<99sNZI6@Gh<80H6legQ`8tWH52b(NCZl7y*>#h$WpTHLy;RPZJlS;3 z9@2+)*-dIieCbbfBJERO+D-KQZh-3xe_Grgpnc^}%SPJ2d}+fYzV=PtL?fSoTfXt7 z^-*Ta4C>GPsC3U0P?695XvzbN*q#6Or!HR1sQ-0SkI)JGppEa}OClmJ`_sNf+7*A= zfPFyw)|WOa>SuQneZS4Ffy$EX-p#tJF#gXkJ5D*57mwgy^CPpT8unJ7`rea`P1b^5 zeG(QhpMMhI__16@`-mUP(c*m@@>l=t0S8EY!!Bx3SIcM9ng^h$pUP#lkNBk=Exwsg zwf}Z!qx-oTw&R~ZNP0F4uX6Lis@VhT{WQ4(8;9GRU(_|3s_cX|A_N9%9F#0A3`8JG6&3OhM z?)wZV9`U8wqpa>Ex?-DMy_AQR#nQRY!nWY2&ydKdh+w(xo`rhU^yJch&ypcg_1sBx z;;@T8w5*$Q+cGOEP@VEw&3x=a{qor~?GTg@<;kG+4nw!ze+UGq?N9pyX$^d7!z1eY zCU2tm9|l&n{AvG1TC|(?2A%K>NuXEX#2tzwkkP1o2H=|PF_4YD*-{x9Os^b)Dm3w> zjg4sPn;b%;&JfnR&D~_!SSceDBU<<-N7Ya96cF!6rw5KgPquk!CytUy5i!2W(dwhI z{`Br1mT|uH5z5Y$Oa~L()G~VQI7v{(uk!1Rtpan~;y}>GOa1&9NFR3$q;KsAz5tET(M>x*?|y-di|F8+97;PJhg&*%X#JOOp-?n|5PoSp>!!&4ZX!{7r9{>0!B3|_M1DHwFZjB9xH3LWicy${FcVVy)gBqC8 z1%qr%w#BQT@G1(greQEL9m6M(xCw)AFt~)l4h*(nFavLq@#+-}c406dgU2ygi$M>( zWei@mfUC{(T$cG+3o`NKqCBW<@5`{O4~H=RIz@)@BNDyoAPa22?ixXwTlrB+7|v_x?f8lD^&w z;b`Q|f$GN63x`QdFFTycb9MvI0$A}!z=5fAhSc;hM%cWM+n$46{2a)w>n;{2vfig# zqWO&Pn&Nf|JQYTaJse!amCx74DxKH1_f`O>ZeA9to?>yLrv4yi;1vI+1scl(m zZ__M^X54FRNO!I?)uKCYGBu)a-el@3CD1Jqrg-}JXbAfEH^Gs;I8!TnOJ5Uwn&2T* z9lEoxsV}V=54V5#tED!z_A^D%(Q%NG7jNppGh$YmBg^g?V0x(Gh^-eR`D@4^v@2>b zdyy?EEiE%AvoI~K)Mc3OSWue!OzIbKZnB;`mkFQ7e#TKg1j?8CRq8dmWU{H5m8E{P z*PzN|Q@hTXvJ=T(OM-_R_&vNdJ*x;iD!ZkQWN!ku(4^_6<`In2OtN>Rb*Gr(YO!RJ zWFLe#WKTCG@f(In_H?>rim4k4BiXlMnKj*I=H=$Oi@H~`AE4h) zF}3524@&kIk#U!sP3<}5gk=8|DRXW%H5ts9eJO3Pd2%?hkELFQ z%9>6$wHZK1Qd4tss54zaO|3D$(;Xk8rkgN*jha$0Zga1x9mW&xH6>xZ=3Y}Lj1S&x z>W=Z(?)>`OO+7F@dOOQ6Te00VvnxY0bwKK9s7NA}-ZC|$)88^h<9)Txng(M${w$m= zgLvmzQxAUoh1AdB_CEafe&~p3*7(a2&G#cWvpU;^_Nm4sn z&tPmo$4@cWEPLb}$r#uLmAN(y2C%e{Hjn8vhb}cXbruv+yMVp7sb4RzrcP754q?l&3X z7TPV!&;>X;q6~u}+7o4H&l#WQisov7&owafR-%7I8Jb7qS%`8~JVBGkS{j!Pt817P z1B|wJFtmc`#SVr98@s=;Pu0@cS?U7lq6LdfW%rX!IvTE9;gJU24G9qS>~4s&;Gu~E zS922p{S8|JY4_s(Gey)(zq2A=+Xjv>kS>BeOv(hTbx zK+f5GgAI;lmwlISn2L7}+iqwNQQme#%jQfGYo)fv765za$bhrLY4bAE(GbuB+YL6j z@%(mH>QCDZ-67TfK0_ynrr&3XZ-M7CTn(}v={afX^9pjaiV7WRj{N-GeExa|oG$(T zK0{L@-bVl2Zs=%Y574uT))o=`mLgXiosec|T-Nq}!(a7)Z=3zB%n|zy?coUd)@#P- zvf}-Qj~heQg42dXI2F!lkDoUD*c52@T{6UbXcsRT{)GB&J#WNABz9>xcWygd?gCpn z`(4}uM)F2>t7KQ!{dr*S^airkmnf=ZP^l!?-xc9P7z^F~*AwdPc*Z>A2-R|hL`rbeHdLc$=sM`OfomZTW*okP#26(5n;8oy>UU%N0(=qheF2NNm}&lg*~i_|Ws~%x!ozI;ReV zYQ)j_h34p5NqPB>`I#Ba!qPhH&E5DtlT)X_J<;qQI)A;HnXS9mo7*6(vi0Ux7=NtfcAjp3yP27i1rYNtIZ?7t#jf0nuHImdrhjkXGQW4bc@WgH?na;`Z7@f`b*~L( z8$>s6Ft>o{)(z%v5FOlLPT}JDFaSC{7!MKaz81-Np@4o?Xp9AQ`m4|w2T^R1u{A_P zij3G%a*B+}5bY{5c7*6$k+G|EXxmaF+K}X>#!vlKfRxFbnk?*!hI;!U-EgEf|(c-UB{?3v}pL@el|2bc)b4r6CXCGRlCLo{Ir zlVsfvR+l|HSgk+a!9=OOlL^~vr?CaxIb$coWBE?@_@15Y@z-}6yYyx@xUXa%itQ3H z)Uh;^pMU3LS}vm(p|SVPW0F3-!`L3>n*E@$orTF2JCuIb-`L!UZ+y}H4;q_d_UjLF zK)!#_7%g%6%Hnnz$E4NS`j<&{jGAxV0wY+=JC>SI-Mjua4ynyLAeo{$&092ULt<*h$%!b^G}_pxHu#G0yWaWSH=zSxHFvU7$tjl8O))dZ zZW(~_EW3r7jzO)k3;KPrg}GOCN`Nyk zNhz^(uF16JZ0aQt{4bX!2I<8m7UnjcEU~n}^p_=;ju_X!gV(Ij9bDE0cUYJ`+j0l5 zOqo0VuRAPl@%~7cB^l%XE=xCz3tg7p7(eC0>d^}>OM8fZcUj^fYIdik3q+}RTAFc~ zzbmn{1OhX`kKAcVF&-EOX5#WLV^?}!GMee!*UhG~#&=n+x2LZju|&~T4_I54z4DwT zO_Gx7$;)t-{VvHGRd&>5O_Au=&spl1{b{xyC1vN2TTVA@!sZkix$_+`p8&g^UYMKD zoPK)vAH4FP{=>rbCGu^{G;Yh!rv4k0t0kTAq$Q4yf5&3xFzAqPtde?EbX|b{IX<^rd!^zbcEkxd=aQ-JU8fD7$^7? z1Mf@qd)Ly+h&R&YcPy2Hjm>dv>4mms zi!w77+2&^!6l7*+Vh1l=VaqJAWn|?Rz~nF2mRVQ;)5GPN*+tp5%mp@h|I(2KH`KJb z|K{Zu!TYcClWgM(7di5mWfnL(!ZXOC2!G8bYlX@RZm{LfD{$1!U&<$wP>cM`^qh>c z_)jf+>pQEkR#-WKnqjT-qXKG0)5chvl)0{1eyAs)Ui*fY%^PEVu~zND=>?9g%p8Xe z{V96+N`J#CMc=s~Bo zDYf5jjbQy0CZqPNZsKRFJj81-k0r>jDUI4-HFH}S1yeknYQehqhMN z(5IZ%*eH*?0#o6w#QqFj_>Yh^{FxWq${nl8!a#24q*f0AqQPy~vd~2|`pii%{ zCiD9ejP{$1p28Ya%UWwgp3%=}AI3ADUTck`Bi34L^I}IB?K9}?wbtQ0Im>8YOrKn9 z?Z+R>HriLpdJlGup2pN7gpc z^uhJk`n;&`jQ0QeWV{O*yr}C&yODms-rBOJ(Zq(RL{~$4@*~m^R{EZ!b2dPksDx%b zt?a8(oWNDr%vAR|dj57$lwD4%8Q#@|>()kmh>lwYT7v2cRK^eWmtV(PxF)iU1fVga z1+H9A$y|1EgH@`j_CUIFi#10|ryGu0I?&jA@Ve}td#vXz5nE67W#6o;LDTQEwuofI znk9A1#?k^tAkXoc(8#ftSemxNSc_i0&l*GCGSZWi&5g=h+z&OQzus@HV`M!xJ;l(X z?D+?*<4FB{G*`BMw#1yGg|LKT>uf6<^q_UL#76a0V`?%K0WYVwr04!=X$dYIFR1Lj pUDos-bm04LWlXJ~I&vdj^S-q)j)2SFx3(ZJVT~FYi;CtY{Xclg8O8tr diff --git a/docs/build/doctrees/usage/tutorials/functional/2-1-directional-semivariogram.doctree b/docs/build/doctrees/usage/tutorials/functional/2-1-directional-semivariogram.doctree index 804457743dec9aea772d5a9ca0363cc2b0d0b9f7..595fc5efecd6cfa9042877ba01714482ebe5f202 100644 GIT binary patch delta 162 zcmaEIjpfNTmJN~R3T8%WNyceL78XecCML!PCYF|_hQ>xImL^H&sU~Jg#+xh4pED9x X)llh7gsR6?d@Bf=wOvq|F_;4Yh2Ab9 delta 162 zcmaEIjpfNTmJN~R3MR=(mT8uzrskT=9??apED9x X)llh7gsR6?d@Bf=wOvq|F_;4YRQ)h< diff --git a/docs/build/html/_modules/pyinterpolate/core/data_models/blocks.html b/docs/build/html/_modules/pyinterpolate/core/data_models/blocks.html index 3a3aa37c..05bdcbb5 100644 --- a/docs/build/html/_modules/pyinterpolate/core/data_models/blocks.html +++ b/docs/build/html/_modules/pyinterpolate/core/data_models/blocks.html @@ -434,6 +434,7 @@

Source code for pyinterpolate.core.data_models.blocks

 @@ -665,7 +665,7 @@

Source code for pyinterpolate.kriging.point.ordinary

Parameters ---------- theoretical_model : TheoreticalVariogram - A trained theoretical variogram model. + Fitted theoretical variogram model. known_locations : numpy array The known locations: ``[x, y, value]``. @@ -674,7 +674,9 @@

Source code for pyinterpolate.kriging.point.ordinary

Point where you want to estimate value ``(x, y) <-> (lon, lat)``. sill : float - The sill (``c(0)``) of a dataset. + Partial sill, or sill when nugget is set to zero. Total sill is a sum + of partial sill and nugget. If given, then partial sill is fixed to + this value. neighbors_range : float, default=None The maximum distance where we search for neighbors. If ``None`` is diff --git a/docs/build/html/_modules/pyinterpolate/semivariogram/indicator/indicator.html b/docs/build/html/_modules/pyinterpolate/semivariogram/indicator/indicator.html index bcd1709e..d9cf7e18 100644 --- a/docs/build/html/_modules/pyinterpolate/semivariogram/indicator/indicator.html +++ b/docs/build/html/_modules/pyinterpolate/semivariogram/indicator/indicator.html @@ -810,7 +810,9 @@

Source code for pyinterpolate.semivariogram.indicator.indicator

``min_range`` and ``max_range``. sill : float, default = None - If given, then sill is fixed to this value. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. n_sill_values : int, default = 5 The last n experimental semivariance records for sill estimation. @@ -819,17 +821,13 @@

Source code for pyinterpolate.semivariogram.indicator.indicator

sill_from_variance : bool, default = False Estimate sill from the variance (semivariance at distance 0). - min_sill : float, default = 1 - The minimal fraction of the value chosen with the sill estimation - method. The value is: for ``sill_from_values`` - the mean of - the last ``n_sill_values`` number of experimental semivariances, - for ``sill_from_variance`` - the experimental variogram variance. - - max_sill : float, default = 5 - The maximum fraction of the value chosen with the sill estimation - method. The value is: for ``sill_from_values`` - the mean of - the last ``n_sill_values`` number of experimental semivariances, - for ``sill_from_variance`` - the experimental variogram variance. + min_sill : float, default = 0.5 + The minimal fraction of the variogram variance at lag 0 to + find partial sill, ``0 <= min_sill <= max_sill``. + + max_sill : float, default = 2 + The maximum fraction of the variogram variance at lag 0 to find + partial sill. number_of_sills : int, default = 16 How many equally spaced sill values are tested between diff --git a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/classes/theoretical_variogram.html b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/classes/theoretical_variogram.html index f42b5626..953b158f 100644 --- a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/classes/theoretical_variogram.html +++ b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/classes/theoretical_variogram.html @@ -7,7 +7,7 @@ - pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram — pyinterpolate 1.0.2 documentation + pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram — pyinterpolate 1.1.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.0.2 documentation

+

pyinterpolate 1.1.0 documentation

@@ -478,8 +478,9 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ The nugget parameter (bias at the zero distance). sill : float, default=0 - A value at which dissimilarity is close to its maximum if model is - bounded. Otherwise, it is usually close to the observations variance. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. rang : float, default=0 The semivariogram range is a distance at which spatial correlation @@ -649,8 +650,9 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ - 'spherical'. sill : float, default=0 - A value at which dissimilarity is close to its maximum if model is - bounded. Otherwise, it is usually close to observations variance. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. rang : float, default=0 The semivariogram range is a distance at which spatial correlation @@ -788,7 +790,9 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ ``min_range`` and ``max_range``. sill : float, default = None - If given, then sill is fixed to this value. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. n_sill_values : int, default = 5 The last n experimental semivariance records for sill estimation. @@ -797,13 +801,13 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ sill_from_variance : bool, default = False Estimate sill from the variance (semivariance at distance 0). - min_sill : float, default = 1 + min_sill : float, default = 0.5 The minimal fraction of the value chosen with the sill estimation method. The value is: for ``sill_from_values`` - the mean of the last ``n_sill_values`` number of experimental semivariances, for ``sill_from_variance`` - the experimental variogram variance. - max_sill : float, default = 5 + max_sill : float, default = 2 The maximum fraction of the value chosen with the sill estimation method. The value is: for ``sill_from_values`` - the mean of the last ``n_sill_values`` number of experimental semivariances, @@ -1370,6 +1374,9 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ nugget : float sill : float + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. rang : float @@ -1468,7 +1475,9 @@

Source code for pyinterpolate.semivariogram.theoretical.classes.theoretical_ Parameters ---------- sill : float, optional - Baseline sill. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. n_sill_values : int, default=5 Number of the last N experimental semivariances to use for sill diff --git a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/spatial_dependency_index.html b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/spatial_dependency_index.html index d14243ce..8b3e1e37 100644 --- a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/spatial_dependency_index.html +++ b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/spatial_dependency_index.html @@ -7,7 +7,7 @@ - pyinterpolate.semivariogram.theoretical.spatial_dependency_index — pyinterpolate 1.0.0 documentation + pyinterpolate.semivariogram.theoretical.spatial_dependency_index — pyinterpolate 1.1.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.0.0 documentation

+

pyinterpolate 1.1.0 documentation

@@ -437,7 +437,8 @@

Source code for pyinterpolate.semivariogram.theoretical.spatial_dependency_i Semivariogram nugget. sill : float - Semivariogram sill. + Partial sill, difference between total sill and nugget. If given, + then partial sill is fixed to this value. Returns ------- @@ -461,7 +462,9 @@

Source code for pyinterpolate.semivariogram.theoretical.spatial_dependency_i raise ValueError('Nugget cannot be set to 0 to ' 'calculate spatial dependence index') - ratio = (nugget / sill) * 100 + total_sill = sill + nugget + + ratio = (nugget / total_sill) * 100 if ratio < 25: spatial_dependency = 'strong' diff --git a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/theoretical.html b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/theoretical.html index f96533bb..86cb566c 100644 --- a/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/theoretical.html +++ b/docs/build/html/_modules/pyinterpolate/semivariogram/theoretical/theoretical.html @@ -7,7 +7,7 @@ - pyinterpolate.semivariogram.theoretical.theoretical — pyinterpolate 1.0.0 documentation + pyinterpolate.semivariogram.theoretical.theoretical — pyinterpolate 1.1.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.0.0 documentation

+

pyinterpolate 1.1.0 documentation

@@ -505,15 +505,17 @@

Source code for pyinterpolate.semivariogram.theoretical.theoretical

``min_range`` and ``max_range``. sill : float, default = None - If given, then sill is fixed to this value. + Partial sill, or sill when nugget is set to zero. Total sill is + a sum of partial sill and nugget. If given, then partial sill + is fixed to this value. min_sill : float, default = 0 The minimal fraction of the variogram variance at lag 0 to - find a sill, ``0 <= min_sill <= max_sill``. + find partial sill, ``0 <= min_sill <= max_sill``. max_sill : float, default = 1 The maximum fraction of the variogram variance at lag 0 to find - a sill. It *should be* lower or equal to 1. + partial sill. It *should be* lower or equal to 1. It is possible to set it above 1, but then warning is printed. number_of_sills : int, default = 16 diff --git a/docs/build/html/api/core/core.html b/docs/build/html/api/core/core.html index ec8658c5..4245bf09 100644 --- a/docs/build/html/api/core/core.html +++ b/docs/build/html/api/core/core.html @@ -807,7 +807,7 @@

Blocks#
-transform_crs(target_crs)[source]
+transform_crs(target_crs, inplace=True)[source]

Function transforms Blocks CRS

Parameters:
@@ -816,6 +816,10 @@

Blocks#pyproj.CRS.from_user_input(), such as an authority string (eg “EPSG:4326”) or a WKT string.

+
inplacebool, default = True

When set to True then transform object’s instance on the fly, +otherwise return modified object and do leave the old instance +unchanged.

+
diff --git a/docs/build/html/api/semivariogram/indicator.html b/docs/build/html/api/semivariogram/indicator.html index d098fb33..6bbff0bd 100644 --- a/docs/build/html/api/semivariogram/indicator.html +++ b/docs/build/html/api/semivariogram/indicator.html @@ -627,22 +627,20 @@

Indicator Semivariogramnumber_of_rangesint, default = 16

How many bins are tested between min_range and max_range.

-
sillfloat, default = None

If given, then sill is fixed to this value.

+
sillfloat, default = None

Partial sill, or sill when nugget is set to zero. Total sill is +a sum of partial sill and nugget. If given, then partial sill +is fixed to this value.

n_sill_valuesint, default = 5

The last n experimental semivariance records for sill estimation. (Used only when sill_from_variance is set to False).

sill_from_variancebool, default = False

Estimate sill from the variance (semivariance at distance 0).

-
min_sillfloat, default = 1

The minimal fraction of the value chosen with the sill estimation -method. The value is: for sill_from_values - the mean of -the last n_sill_values number of experimental semivariances, -for sill_from_variance - the experimental variogram variance.

+
min_sillfloat, default = 0.5

The minimal fraction of the variogram variance at lag 0 to +find partial sill, 0 <= min_sill <= max_sill.

-
max_sillfloat, default = 5

The maximum fraction of the value chosen with the sill estimation -method. The value is: for sill_from_values - the mean of -the last n_sill_values number of experimental semivariances, -for sill_from_variance - the experimental variogram variance.

+
max_sillfloat, default = 2

The maximum fraction of the variogram variance at lag 0 to find +partial sill.

number_of_sillsint, default = 16

How many equally spaced sill values are tested between min_sill and max_sill.

diff --git a/docs/build/html/api/semivariogram/theoretical.html b/docs/build/html/api/semivariogram/theoretical.html index 422701f8..033e6d9d 100644 --- a/docs/build/html/api/semivariogram/theoretical.html +++ b/docs/build/html/api/semivariogram/theoretical.html @@ -479,8 +479,9 @@

Theoretical Semivariogramfloat, default=0

The nugget parameter (bias at the zero distance).

-
sillfloat, default=0

A value at which dissimilarity is close to its maximum if model is -bounded. Otherwise, it is usually close to the observations variance.

+
sillfloat, default=0

Partial sill, or sill when nugget is set to zero. Total sill is +a sum of partial sill and nugget. If given, then partial sill +is fixed to this value.

rangfloat, default=0

The semivariogram range is a distance at which spatial correlation might be observed. It shouldn’t be set at a distance larger than @@ -617,19 +618,21 @@

Theoretical Semivariogramint, default = 16

How many equally spaced ranges are tested between min_range and max_range.

-
sillfloat, default = None

If given, then sill is fixed to this value.

+
sillfloat, default = None

Partial sill, or sill when nugget is set to zero. Total sill is +a sum of partial sill and nugget. If given, then partial sill +is fixed to this value.

n_sill_valuesint, default = 5

The last n experimental semivariance records for sill estimation. (Used only when sill_from_variance is set to False).

sill_from_variancebool, default = False

Estimate sill from the variance (semivariance at distance 0).

-
min_sillfloat, default = 1

The minimal fraction of the value chosen with the sill estimation +

min_sillfloat, default = 0.5

The minimal fraction of the value chosen with the sill estimation method. The value is: for sill_from_values - the mean of the last n_sill_values number of experimental semivariances, for sill_from_variance - the experimental variogram variance.

-
max_sillfloat, default = 5

The maximum fraction of the value chosen with the sill estimation +

max_sillfloat, default = 2

The maximum fraction of the value chosen with the sill estimation method. The value is: for sill_from_values - the mean of the last n_sill_values number of experimental semivariances, for sill_from_variance - the experimental variogram variance.

@@ -745,8 +748,9 @@

Theoretical Semivariogramfloat, default=0

A value at which dissimilarity is close to its maximum if model is -bounded. Otherwise, it is usually close to observations variance.

+
sillfloat, default=0

Partial sill, or sill when nugget is set to zero. Total sill is +a sum of partial sill and nugget. If given, then partial sill +is fixed to this value.

rangfloat, default=0

The semivariogram range is a distance at which spatial correlation exists. It shouldn’t be set at a distance larger than a half @@ -940,13 +944,15 @@

Theoretical Semivariogramint, default = 16

How many equally spaced ranges are tested between min_range and max_range.

-
sillfloat, default = None

If given, then sill is fixed to this value.

+
sillfloat, default = None

Partial sill, or sill when nugget is set to zero. Total sill is +a sum of partial sill and nugget. If given, then partial sill +is fixed to this value.

min_sillfloat, default = 0

The minimal fraction of the variogram variance at lag 0 to -find a sill, 0 <= min_sill <= max_sill.

+find partial sill, 0 <= min_sill <= max_sill.

max_sillfloat, default = 1

The maximum fraction of the variogram variance at lag 0 to find -a sill. It should be lower or equal to 1. +partial sill. It should be lower or equal to 1. It is possible to set it above 1, but then warning is printed.

number_of_sillsint, default = 16

How many equally spaced sill values are tested between @@ -993,7 +999,8 @@

Theoretical Semivariogram
nuggetfloat

Semivariogram nugget.

-
sillfloat

Semivariogram sill.

+
sillfloat

Partial sill, difference between total sill and nugget. If given, +then partial sill is fixed to this value.

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index b5492d4f..e179fe19 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. Example: Spatial Dependence over the same study extent but for different elements": [[33, "3.-Example:-Spatial-Dependence-over-the-same-study-extent-but-for-different-elements"]], "3. Fit semivariogram model": [[40, "3.-Fit-semivariogram-model"]], "3. Interpolate with directional Kriging": [[39, "3.-Interpolate-with-directional-Kriging"]], "3. Perform Kriging and validate outputs": [[37, "3.-Perform-Kriging-and-validate-outputs"]], "3. Predict values at unknown locations and evaluate output": [[36, "3.-Predict-values-at-unknown-locations-and-evaluate-output"]], "3. Prepare data for Poisson Kriging": [[42, "3.-Prepare-data-for-Poisson-Kriging"], [43, "3.-Prepare-data-for-Poisson-Kriging"]], "3. Regularize semivariogram": [[41, "3.-Regularize-semivariogram"]], "3. Smooth blocks": [[44, "3.-Smooth-blocks"]], "4. API": [[33, "4.-API"]], "4. Compare models": [[39, "4.-Compare-models"]], "4. Compare triangular vs elliptical neighbors selection methods": [[34, "4.-Compare-triangular-vs-elliptical-neighbors-selection-methods"]], "4. Experimental variogram from the point cloud": [[35, "4.-Experimental-variogram-from-the-point-cloud"]], "4. Export results": [[44, "4.-Export-results"]], "4. Filtering areas": [[42, "4.-Filtering-areas"], [43, "4.-Filtering-areas"]], "4. Prepare canvas": [[40, "4.-Prepare-canvas"]], "4. Remove outliers from the point cloud": [[38, "4.-Remove-outliers-from-the-point-cloud"]], "4. Visualize process": [[41, "4.-Visualize-process"]], "5. Evaluate": [[42, "5.-Evaluate"], [43, "5.-Evaluate"]], "5. Export semivariogram": [[41, "5.-Export-semivariogram"]], "5. Interpolate": [[40, "5.-Interpolate"]], "5. Is variogram point cloud a scatter plot?": [[35, "5.-Is-variogram-point-cloud-a-scatter-plot?"]], "5. Kriging Models based on different variograms": [[38, "5.-Kriging-Models-based-on-different-variograms"]], "API": [[0, null]], "Advanced": [[30, "advanced"]], "Aggregated Variogram": [[9, "aggregated-variogram"]], "Area-to-Point Poisson Kriging": [[44, null]], "Area-to-area Poisson Kriging": [[7, "area-to-area-poisson-kriging"], [43, null]], "Area-to-point Poisson Kriging": [[7, "area-to-point-poisson-kriging"]], "Author(s)": [[15, "author-s"]], "Beginner": [[30, "beginner"]], "Benchmarking Kriging": [[37, null]], "Bibliography": [[25, null]], "Block": [[4, "block"]], "Block and Poisson Kriging": [[7, null]], "Blocks": [[2, "blocks"]], "Blocks to points with Ordinary Kriging": [[40, null]], "Blog posts": [[28, "blog-posts"]], "Box plot": [[35, "Box-plot"]], "Case 1: West-East direction": [[34, "Case-1:-West-East-direction"]], "Case 2: North-South direction": [[34, "Case-2:-North-South-direction"]], "Case 3: Northwest-Southeast direction": [[34, "Case-3:-Northwest-Southeast-direction"]], "Case 4: Northeast-Southwest direction": [[34, "Case-4:-Northeast-Southwest-direction"]], "Case 5: Isotropic variogram - no leading direction": [[34, "Case-5:-Isotropic-variogram---no-leading-direction"]], "Centroid-based Poisson Kriging": [[7, "centroid-based-poisson-kriging"]], "Changelog": [[31, "Changelog"], [32, "Changelog"], [33, "Changelog"], [34, "Changelog"], [35, "Changelog"], [36, "Changelog"], [37, "Changelog"], [38, "Changelog"], [39, "Changelog"], [40, "Changelog"], [41, "Changelog"], [42, "Changelog"], [43, "Changelog"], [44, "Changelog"]], "Changes between version 0.x and 1.x": [[1, null]], "Chapter 1: Create random surface": [[32, "Chapter-1:-Create-random-surface"]], "Chapter 1: data preparation": [[31, "Chapter-1:-data-preparation"]], "Chapter 2: Calculate the experimental semivariogram": [[32, "Chapter-2:-Calculate-the-experimental-semivariogram"]], "Chapter 2: Experimental Variogram": [[31, "Chapter-2:-Experimental-Variogram"]], "Chapter 3: Fit variogram models": [[32, "Chapter-3:-Fit-variogram-models"]], "Chapter 3: Theoretical Variogram": [[31, "Chapter-3:-Theoretical-Variogram"]], "Chapter 4: Compare variogram models": [[32, "Chapter-4:-Compare-variogram-models"]], "Chapter 4: Fit semivariogram model automatically": [[31, "Chapter-4:-Fit-semivariogram-model-automatically"]], "Chapter 5: Exporting model": [[31, "Chapter-5:-Exporting-model"]], "Chapter 6: Importing fitted model": [[31, "Chapter-6:-Importing-fitted-model"]], "Check points statistics for each lag": [[35, "Check-points-statistics-for-each-lag"]], "Citation": [[24, "citation"], [26, null]], "Classes": [[1, "classes"], [1, "id2"]], "Community": [[14, null]], "Conda": [[27, "conda"]], "Contents": [[24, "contents"]], "Contributors": [[15, null], [15, "id1"]], "Core data structures": [[2, null]], "Cross-validation": [[5, "cross-validation"]], "Dataset": [[31, "Dataset"]], "Deconvolution": [[9, "deconvolution"]], "Development": [[18, null], [20, null]], "Deviation": [[9, "deviation"]], "Directional Ordinary Kriging": [[39, null]], "Directional Semivariogram": [[34, null]], "Directional Variogram": [[10, "directional-variogram"]], "Distance": [[4, null]], "Experimental Semivariance and Covariance": [[10, null]], "Experimental Variogram": [[10, "experimental-variogram"]], "Failing pylibtiff build - Linux": [[27, "failing-pylibtiff-build-linux"]], "Functions": [[1, "functions"], [1, "id1"]], "Functions and classes that are no longer supported": [[1, "functions-and-classes-that-are-no-longer-supported"]], "Important notice": [[24, "important-notice"]], "Including direction in experimental variogram": [[34, "Including-direction-in-experimental-variogram"]], "Indicator Kriging": [[8, "indicator-kriging"]], "Indicator Semivariogram": [[11, null]], "Installation": [[29, "installation"]], "Installation - additional topics": [[27, "installation-additional-topics"]], "Installation guidelines": [[27, "installation-guidelines"]], "Intermediate": [[30, "intermediate"]], "Introduction": [[24, "introduction"]], "Inverse Distance Weighting (IDW)": [[6, null]], "Known Bugs": [[19, null]], "Learning Materials": [[28, null]], "Maintainer(s)": [[15, "maintainer-s"]], "Manual setting": [[31, "Manual-setting"]], "Metrics": [[5, "metrics"]], "Models": [[31, "Models"]], "Models evaluation": [[5, null]], "More resources": [[35, "More-resources"]], "Network": [[16, null]], "New functions and classes": [[1, "new-functions-and-classes"]], "Ordinary Kriging": [[8, "ordinary-kriging"], [29, "ordinary-kriging"]], "Ordinary Kriging pipelines": [[3, "ordinary-kriging-pipelines"]], "Ordinary and Simple Kriging": [[36, null]], "Outliers and Kriging": [[38, null]], "Package structure": [[21, null]], "Pipelines": [[3, null]], "Point": [[4, "point"]], "Point Kriging": [[8, null]], "Point Support": [[2, "point-support"]], "Poisson Kriging Centroid-based approach": [[42, null]], "Poisson Kriging pipelines": [[3, "poisson-kriging-pipelines"]], "Prepare data": [[39, "Prepare-data"]], "Prerequisites": [[31, "Prerequisites"], [32, "Prerequisites"], [33, "Prerequisites"], [34, "Prerequisites"], [35, "Prerequisites"], [36, "Prerequisites"], [37, "Prerequisites"], [38, "Prerequisites"], [39, "Prerequisites"], [40, "Prerequisites"], [41, "Prerequisites"], [42, "Prerequisites"], [43, "Prerequisites"], [44, "Prerequisites"]], "Presentations & Workshops": [[28, "presentations-workshops"]], "Publications": [[28, "publications"]], "Pyinterpolate": [[24, null]], "Quickstart": [[29, null]], "Raster": [[13, "raster"]], "Requirements and dependencies (version >= 1)": [[22, null]], "Reviewers (JOSS)": [[15, "reviewers-joss"]], "Scatter plot": [[35, "Scatter-plot"]], "Semivariogram Deconvolution": [[9, null]], "Semivariogram Regularization": [[41, null]], "Semivariogram exploration": [[31, null]], "Semivariogram models": [[32, null]], "Setup": [[27, null]], "Simple Kriging": [[8, "simple-kriging"]], "Spatial Dependency Index": [[33, null]], "Table of contents": [[31, "Table-of-contents"], [32, "Table-of-contents"], [33, "Table-of-contents"], [34, "Table-of-contents"], [35, "Table-of-contents"], [36, "Table-of-contents"], [37, "Table-of-contents"], [38, "Table-of-contents"], [39, "Table-of-contents"], [40, "Table-of-contents"], [41, "Table-of-contents"], [42, "Table-of-contents"], [43, "Table-of-contents"], [44, "Table-of-contents"]], "Temporarily not available functions and classes": [[1, "temporarily-not-available-functions-and-classes"]], "Tests and contribution": [[23, null]], "The libspatialindex_c.so dependency error": [[27, "the-libspatialindex-c-so-dependency-error"]], "Theoretical Semivariogram": [[12, null]], "Tutorials": [[30, null]], "Universal Kriging": [[8, "universal-kriging"]], "Use Cases": [[17, null]], "Variogram Cloud": [[10, "variogram-cloud"]], "Variogram Points Cloud": [[35, null]], "Violin plot": [[35, "Violin-plot"]], "Visualization": [[13, null]], "What is the spatial dependency index?": [[33, "What-is-the-spatial-dependency-index?"]], "Working with Notebooks": [[27, "working-with-notebooks"]], "pip": [[27, "pip"]], "version 1.1.0": [[24, "version-1-1-0"]]}, "docnames": ["api/api", "api/changes", "api/core/core", "api/core/pipelines", "api/distance/distance", "api/evaluate/evaluate", "api/idw/idw", "api/kriging/block_kriging", "api/kriging/point_kriging", "api/semivariogram/deconvolution", "api/semivariogram/experimental", "api/semivariogram/indicator", "api/semivariogram/theoretical", "api/viz/raster", "community/community", "community/community/contributors", "community/community/forum", "community/community/use_cases", "contributor/development", "contributor/development/bugs", "contributor/development/development", "contributor/development/package", "contributor/development/requirements", "contributor/development/tests_and_contribution", "index", "science/bibliography", "science/citation", "setup/setup", "usage/learning_materials", "usage/quickstart", "usage/tutorials", "usage/tutorials/functional/1-1-semivariogram-exploration", "usage/tutorials/functional/1-2-semivariogram-models", "usage/tutorials/functional/1-3-spatial-dependency-index", "usage/tutorials/functional/2-1-directional-semivariogram", "usage/tutorials/functional/2-2-variogram-points-cloud", "usage/tutorials/functional/3-1-ordinary-and-simple-kriging", "usage/tutorials/functional/3-2-benchmark-kriging", "usage/tutorials/functional/3-3-outliers-and-kriging", "usage/tutorials/functional/3-4-directional-ordinary-kriging", "usage/tutorials/functional/3-5-blocks-to-points-ordinary-kriging", "usage/tutorials/functional/4-1-semivariogram-regularization", "usage/tutorials/functional/4-2-poisson-kriging-centroid-based", "usage/tutorials/functional/4-3-poisson-kriging-area-to-area", "usage/tutorials/functional/4-4-poisson-kriging-area-to-point-smoothing"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1}, "filenames": ["api/api.rst", "api/changes.rst", "api/core/core.rst", "api/core/pipelines.rst", "api/distance/distance.rst", "api/evaluate/evaluate.rst", "api/idw/idw.rst", "api/kriging/block_kriging.rst", "api/kriging/point_kriging.rst", "api/semivariogram/deconvolution.rst", "api/semivariogram/experimental.rst", "api/semivariogram/indicator.rst", "api/semivariogram/theoretical.rst", "api/viz/raster.rst", "community/community.rst", "community/community/contributors.rst", "community/community/forum.rst", "community/community/use_cases.rst", "contributor/development.rst", "contributor/development/bugs.rst", "contributor/development/development.rst", "contributor/development/package.rst", "contributor/development/requirements.rst", "contributor/development/tests_and_contribution.rst", "index.rst", "science/bibliography.rst", "science/citation.rst", "setup/setup.rst", "usage/learning_materials.rst", "usage/quickstart.rst", "usage/tutorials.rst", "usage/tutorials/functional/1-1-semivariogram-exploration.ipynb", "usage/tutorials/functional/1-2-semivariogram-models.ipynb", "usage/tutorials/functional/1-3-spatial-dependency-index.ipynb", "usage/tutorials/functional/2-1-directional-semivariogram.ipynb", "usage/tutorials/functional/2-2-variogram-points-cloud.ipynb", "usage/tutorials/functional/3-1-ordinary-and-simple-kriging.ipynb", "usage/tutorials/functional/3-2-benchmark-kriging.ipynb", "usage/tutorials/functional/3-3-outliers-and-kriging.ipynb", "usage/tutorials/functional/3-4-directional-ordinary-kriging.ipynb", "usage/tutorials/functional/3-5-blocks-to-points-ordinary-kriging.ipynb", "usage/tutorials/functional/4-1-semivariogram-regularization.ipynb", "usage/tutorials/functional/4-2-poisson-kriging-centroid-based.ipynb", "usage/tutorials/functional/4-3-poisson-kriging-area-to-area.ipynb", "usage/tutorials/functional/4-4-poisson-kriging-area-to-point-smoothing.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [2, 3, 5, 8, 9, 10, 11, 12, 13, 24, 26, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43], "0": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "00": [35, 36, 37, 38, 39, 40, 41, 42, 43], "000": 40, "00000": 34, "000000": [35, 36, 38, 42, 43], "000000000": 40, "000000e": 35, "000860949362576": [], "001": 9, "001038e": 35, "001294": 31, "001901e": 35, "002": 34, "002563": 36, "004792": [], "005": [], "006058": [], "0064564892549": 31, "006794571": 40, "007051": 42, "00739": 34, "007544": [], "00it": [], "01": [9, 31, 35, 36, 37, 38, 44], "01047660845177": 31, "011": 42, "012348e": 35, "014": 11, "015508": 42, "016640e": [], "016994": [], "018141e": 35, "018571": 38, "01876353608725": 31, "02": [31, 32, 35, 36, 37, 38, 39, 40, 41], "022": 42, "022283e": 35, "024": 43, "0240565312119": 31, "0241482853441": 31, "024150": 35, "024262": 35, "02497532421427": 31, "027444e": 35, "028": 43, "028333": 43, "02869": [24, 26], "03": [35, 40, 43], "030020e": 35, "030644": 36, "031608": 36, "03168070178782": 31, "032": 42, "032104e": 35, "032963": 38, "03326262442189": 36, "033615": 36, "034987": 36, "036257725481412": 37, "0363": 37, "037435": 36, "03801956055116": 31, "03804170525302": 31, "039": [36, 42], "039877": 40, "04": [33, 34, 35, 36, 39, 40], "04103246328214": 31, "041399": 42, "04241574410776": 31, "04267564264293": [], "045285": 44, "04638466313054": [], "0466929690614": 31, "04754473209462": 36, "0482965706882": 31, "04it": [], "05": [36, 37, 38, 39, 40, 41, 42, 43, 44], "050749295452192": 31, "051510": [], "051571": [], "051654": 44, "0526605973689": 31, "052784840121205": 31, "05298225928948": 31, "054": [], "0540709659597": 36, "054839": 40, "0553": 37, "055336": [], "056290": 38, "057807": [], "058427e": 43, "06": [35, 40, 41, 42, 43], "060410": [], "060664": [], "061101": 42, "061964": [], "062": [], "06410739650147": [], "06652524884377": 36, "067355202136": 31, "06802859611497": 31, "068647": [], "068681": 36, "07": [36, 37], "070109": [], "070950": [], "0711976064423": 31, "071219883873823": [], "071552e": 35, "071815847676675": [], "07274427357008": [], "07308420640933": 31, "073569e": 35, "074808717567237": 36, "075620504725634": 31, "077242": 40, "077293149680955": [], "079584": 38, "0798": [], "08": [11, 33], "082188e": [40, 41], "083003": 36, "084231": 35, "08535128528587": [], "0865256502935": 31, "08660014648092": 31, "087": 34, "089071e": 35, "08948321088144": 31, "089673": [], "09": [24, 33], "091": 43, "092138": 36, "092391": 36, "092456": 40, "093870": [], "097": 43, "097542e": 35, "09769269330142": [], "09777163016611": [], "09937611628263": 31, "09966521054352": 31, "09978787326568": 36, "1": [3, 4, 6, 8, 9, 10, 11, 12, 13, 18, 25, 27, 29, 33, 39], "10": [10, 11, 24, 26, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "100": [5, 12, 31, 32, 33, 36, 37, 38, 39, 40, 41, 42, 43], "1000": [13, 31, 34, 35, 36, 38], "10000": [31, 32, 34, 36, 37, 38], "100747e": [40, 41], "100923": 39, "101": [9, 25, 31, 41], "1016": 11, "101683": 36, "10191": 41, "102": [35, 40], "1021": 36, "1022": [33, 39], "103": [], "104": [31, 43], "105": 31, "1053": 39, "10599018471123": 31, "106": [38, 40], "107": [35, 40, 42], "107589e": 35, "108": [31, 36, 40], "1081": 39, "109": 40, "109080042476194": 31, "10_000": 35, "10k": [3, 8, 32], "11": [10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "110": [31, 36, 40, 43], "11000": 31, "110000": [], "1104528782418": 31, "111": 40, "1117": 36, "111953e": 43, "112": 40, "113": [], "114": 40, "1141": [33, 39], "114426": 38, "115": 42, "115109": [], "115241": 31, "11590986885119": 36, "116": 33, "116427": 36, "117": [33, 43], "118": 31, "119": [35, 43], "11955761301863": 31, "12": [10, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "120": [31, 42], "12000": 31, "121": [31, 42, 43], "121784": 36, "122": [], "122183": 44, "123": 31, "123151": [], "124": [31, 43], "125": 43, "1250": 35, "125000e": 35, "125214": [], "126": [31, 35, 42], "127": [], "127106": 36, "12765": 34, "1277277": [40, 41, 44], "1278": [], "128": [9, 25, 31, 36, 40, 41, 43], "12801853566801": 31, "12802692792883": [], "1285937": [41, 44], "128981e": 35, "129": [31, 40, 43], "12it": 38, "13": [31, 32, 35, 37, 38, 39, 40, 41, 42, 43], "130": [31, 40, 42], "13000": 31, "13003621033862": 31, "130648": 40, "131": [36, 40, 42, 43], "132": 43, "13262960e": 40, "132630e": [40, 41], "133": [31, 40, 42], "13305049768755": 36, "134": [42, 43], "135": [9, 10, 11, 13, 31, 34, 42], "135536": 35, "1359787": 43, "1359823": 43, "136": 31, "137": [31, 36, 37, 42], "138": 31, "138247": 42, "1382501": 42, "1383062": 43, "138748e": 35, "139": 31, "1390197": 42, "139443e": 35, "13it": 36, "14": [31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "140": [31, 42], "14000": 31, "140301": 31, "141": [31, 42], "1416415": [], "1416461": [], "1419423": [40, 41], "1419729": [40, 41], "142": 31, "1421335800296": 31, "1429778": [], "143": [31, 43], "144": [31, 43], "1442153": [40, 41], "144314": [], "1443204": [], "14443661433467": 31, "144901": [], "1449075": [], "145009": [], "146": [31, 42], "147": [35, 43], "147718818538": 31, "148": 31, "148492e": 35, "1486": 36, "148742": [], "14877246130683": 31, "14it": 36, "15": [31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], "150": 31, "1500": 36, "15000": 31, "1501": [12, 33], "1511": [12, 33], "1521668210121": 31, "15233925049904": 31, "153": 31, "154": [], "1540854": [], "1542867553994": 31, "154591287030115": 31, "154847": [], "155": [31, 40, 41], "155949": [40, 41], "156": [35, 40, 41], "1562065": [], "1562135": [], "157": [40, 41], "1572300": 43, "158": 31, "1580340": 43, "1580342": 43, "159": 36, "1592247": 42, "159227": 42, "16": [11, 12, 13, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "1600": [33, 39], "16000": 31, "1606435": 42, "161": 31, "16197426953403": [], "16227766": 4, "164": 43, "164396486557602": [], "165": 36, "165487": 40, "165517": 42, "165579885194006": 31, "165907": 38, "166": [40, 41], "1663131221094": 36, "166502": 42, "167": [34, 42], "1686399": [], "1686411": [], "169732478438334": 31, "16it": [36, 38, 39], "16th": 5, "17": [31, 34, 36, 38, 39, 40, 41, 42], "170": 31, "17000": 31, "170291e": 35, "1708410": 43, "1708426": 43, "1714888": [], "172": 31, "172201928710436": 31, "173": 31, "1731431": 43, "17391501187865": 31, "1745": 40, "1745084601238": 31, "174940e": 35, "175": 31, "176": [31, 36], "177": 31, "177585": [], "177906": [], "179": 31, "1797797": 42, "1797856": 42, "17it": 40, "18": [31, 32, 34, 35, 36, 38, 40, 42], "180": [9, 10, 11, 13, 31, 34], "18000": 31, "180223e": 35, "180609": 42, "1807281": 42, "181": 31, "181025": [33, 39], "181072": [33, 39], "181100": 39, "181140": 39, "181165": [33, 39], "181180": 39, "181220": 39, "181298": [33, 39], "181307": [33, 39], "181997e": 35, "1824360": 42, "1826198": 42, "182623": 42, "184891": [], "1854": [], "186": 31, "186224e": 35, "1866": 41, "187": [35, 43], "187153": 39, "187451": 43, "1875": 35, "18779541265431": 40, "187945e": 35, "188": 31, "188044": [], "1880445": [], "188693": 40, "1898429": [], "19": [24, 31, 32, 34, 38, 42], "19000": 31, "191": [], "1916957069014": 31, "192": [36, 40, 41], "1921343244303": [], "1929576": [], "193124": [], "193280": [], "193751": [40, 41], "1937530": [40, 41], "194045": [], "1940462": [], "195": 36, "1958207": [40, 41], "196": 31, "1963791": 42, "196758": 42, "1967596": 42, "1973": [], "1979": 5, "198695": 36, "199": [31, 33], "1994": [12, 33], "1995273": [], "19957574653137": 36, "1996": 25, "1998": [25, 32], "1999": 25, "1b7837": [31, 32], "1st": [10, 35], "2": [2, 4, 5, 6, 9, 10, 11, 12, 27, 29], "20": [31, 32, 34, 35, 37, 38, 39, 42, 43, 44], "200": [31, 32, 40], "2000": [31, 36], "20000": [29, 31, 41], "200000": [40, 42, 43], "2004": [5, 10], "20041395430178": 31, "2005": 10, "2006": 10, "2006358211872": 31, "200673": 36, "2008": [9, 11, 25, 41], "2009": [33, 39], "2010": [41, 42, 43], "201410": [], "2014119": [], "2019": 17, "202": [], "2020": 17, "2021": [17, 40], "2022": [17, 24, 26, 28], "2023": 17, "2025": [24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "202538": 35, "2028115200009779": 34, "2032": 40, "20324106610195": 31, "204688": 36, "205265e": 35, "20552245159655": 31, "2058": 40, "2058427": 43, "205904": 36, "206": 31, "207": [36, 40, 41], "2071877": 43, "2071878": 43, "2074073": [40, 41], "207411": [40, 41], "207836": [], "207836151123047": [], "2082188": [40, 41], "2093": 41, "209528": [40, 41], "2095343": [40, 41], "21": [31, 32, 34, 35, 38, 41], "210": 31, "2100746": [40, 41], "21014059512538": 31, "210327": 43, "211": 29, "21105": [24, 26], "2111953": 43, "2115688": [40, 41], "2115699": [40, 41], "211985": 43, "2119901": 43, "212348": 40, "212485": 39, "212962": 43, "213": [5, 43], "2132629": [40, 41], "213679": 42, "2140543123903": 31, "216": [31, 42], "217": 31, "217012": 36, "21707025705718": [], "217191e": 35, "217915e": 35, "2180": [31, 35, 36, 37, 38], "219": [31, 34], "219166": 42, "21926570120685": 31, "2194437759668": 31, "219750": [], "21it": [36, 39], "22": [25, 31, 32, 34, 38, 41, 43], "220": [31, 36], "220278345566044": 36, "2204635031941": 36, "221": 31, "22394168598046": 36, "225": [5, 9, 10, 11, 13, 34], "226": 31, "226161": 36, "226342": 43, "227": 38, "2272727272727275": 10, "228": 43, "228158": 43, "228405": 42, "2289": [], "23": [29, 31, 32, 33, 38, 39, 43], "23001": 42, "23011": 43, "23013": 43, "23015": [], "23031": 42, "230325e": 35, "2306": 36, "23070": 36, "231": [35, 36], "2326165739138": 31, "23283837095323": 36, "233": [], "236": 31, "23606798": 4, "2364": 36, "236450": [], "23657348059464": 31, "237": 36, "237449": 31, "237500": [], "237556": 36, "237674": 31, "237685": 31, "237878": 31, "238": 31, "238012": 31, "238419": 36, "238568e": 35, "239660": 39, "24": [10, 31, 32, 34, 35, 36, 39, 40, 41], "240": [], "241": 31, "242": [], "24264069": 4, "242891": 36, "243": [38, 40, 41], "243162": 36, "245": 38, "245371": 43, "245548": 35, "246": [], "246544791626": 31, "246919": [], "247": 36, "247968": [], "247976e": 35, "2485207100591715": 10, "249": 31, "249753": [], "24it": [36, 42], "25": [9, 10, 12, 25, 31, 32, 33, 34, 35, 36, 38, 39, 42, 43], "2500": [35, 36], "250000e": 35, "25001": [40, 41], "25007": [40, 41], "25009": 42, "250117139455426": 31, "25019": [40, 41], "250293e": 35, "250654": [], "251": [25, 31], "251532": 36, "25175378437421": 31, "252037": 36, "254017": [], "254551": 31, "254859": [], "254870": 31, "256": 36, "25603707133602": 31, "257": [33, 39], "258": [], "25974589970014": [], "2599999999999958": 10, "26": [31, 32, 33, 34, 35, 36], "26039701343071": 36, "261": [25, 31, 34], "261079471555718": 31, "26158958300948": 31, "262": 31, "262374": [], "262794": 41, "26318444803915": [], "2632": [], "264": 36, "264370168819994": 36, "264571e": 35, "266": 34, "2661009953751": 36, "267010": 35, "2682452745304": 36, "269": [31, 33, 39], "269811e": 35, "27": [31, 36], "270": [9, 10, 11, 13, 34], "270265": 36, "270738": 39, "271": 38, "272025": 42, "274131": [], "274181e": 35, "274414": [], "274566": 36, "274606e": 35, "275": [35, 36], "275000": 42, "275597": 31, "276": 35, "276866": 43, "277": 33, "277278e": [40, 41], "277757": 36, "278": [31, 36], "278133": 40, "278161": [], "278387e": 38, "27901638284231": 31, "279056": [], "27it": 36, "28": [31, 32, 42], "28018535668014": 31, "280913144602": 31, "281": [31, 36], "281077e": 35, "282": 31, "28228331690235": 31, "282328e": 35, "282639e": 35, "282839e": 35, "283208e": 35, "284266": [], "284539e": 35, "285938e": 41, "2861654512381": 31, "2869": [24, 26], "28852797444068": 31, "28949692320213": 36, "289906": [], "29": [31, 36, 43], "29020392031947": 31, "290276": 35, "291411": 40, "291578e": 35, "293621": 43, "295": 36, "295998e": 35, "296": 31, "296388": [], "297320": 42, "297386": 35, "29738612": 35, "298165129123845": 31, "299": 33, "29906598": 35, "299066": 35, "29it": 36, "2d": [11, 32], "2f": 33, "3": [4, 9, 10, 15, 27, 29], "30": [31, 36, 39, 43, 44], "300": 31, "3000": [31, 36], "300000": [40, 41], "300074": 43, "300318": [], "300443": 40, "300494": 34, "30055407864868": 40, "300589": 43, "3009": [], "301": [31, 43], "301750": 31, "302": 36, "3024215423431": [], "302529155804564": 31, "303": 43, "303271": 40, "303353": [], "304": 42, "304436": 42, "305187": [], "308": [31, 36], "308429e": 35, "309672e": 35, "30it": [], "31": [31, 34, 44], "310": 36, "3103": 39, "3108668325436": 31, "3125": 35, "312974e": 35, "313000": 36, "314089689802074": 36, "314496": 36, "315": [9, 10, 11, 13, 34], "316": 31, "316036601261": 31, "316194e": 35, "31636235955056": 40, "317351": 36, "31761394026825": 31, "318061": 43, "31822706193316": 31, "319": [], "319724": [], "32": [29, 31, 34, 36], "320": 35, "320333": [], "321571": 35, "321846": 42, "322511": 35, "323": 36, "3235398024696": 36, "32464307748702": 31, "325": 43, "325393": 31, "325632e": 35, "32608183289074": 31, "326242": 35, "326447": 36, "327": [31, 36], "327524e": 35, "328": 31, "32816550891573": 31, "32956877520513": 31, "32it": 36, "33": [31, 32, 34, 36], "330": 36, "33001": [40, 41], "33017": [], "33098793134502": 31, "33167274863581": 31, "33253400127717": 31, "332555": [], "333103e": 35, "333330": [33, 39], "333484": [33, 39], "333537": [33, 39], "333558": [33, 39], "333611": [33, 39], "333660": 39, "333700": 39, "333740": 39, "334": 31, "335": 31, "3351196859388": 31, "335979": 35, "335986": 38, "336218": 42, "336303": [], "3389": [], "339": 42, "3390411424663": 31, "34": [31, 33, 34], "34003": [], "34013": [], "34019": 42, "34025": [], "34029": 43, "340790e": 35, "3416": [], "342": 31, "3425": 38, "344": [], "344063e": 35, "344082": 40, "344179e": 35, "3447": 38, "3448": 38, "345": 31, "345097184986": [], "34523713634542": 31, "345417e": 35, "346": 31, "346216563217126": 36, "3476590353458": [], "348": 31, "348466": 36, "3491232940861": [], "34it": 40, "35": [31, 32, 34, 36, 42], "3500": 36, "3508257264928": 31, "351": 31, "351111": 31, "352372": 36, "355": [31, 34], "355742": [], "356085e": 35, "3561859868873": 31, "357": 36, "358": [31, 36], "358459": 36, "359": 43, "3594046318507935": 36, "359564": 36, "36": [31, 36, 40], "360": [9, 10, 11, 12, 13, 31, 34], "36013": 42, "36015": 43, "36025": 42, "36027": 42, "36043": 43, "36047": 43, "36055": [], "36061": [], "36063": 42, "36073": [], "36077": [], "36081": 43, "36083": 43, "36085": [42, 43], "36089": 43, "361": 35, "36103": [], "36107": 42, "36109": [], "36113": [], "36115": 42, "36121": [40, 41], "3618624554766": [], "362571": 40, "36305694482644": 31, "363393": 36, "364": 36, "364813": [], "365063": 43, "365733": 43, "367": [], "367675e": 35, "368489": 42, "369": [], "36902963659804": 31, "36955242757186": 40, "37": [31, 36], "371": [], "3715323497975": 31, "372719e": 38, "3744249472903": 31, "37451457470354": 31, "3750": 35, "375000e": 35, "37556735e": 36, "375669": 42, "377": 42, "37710889704957": 31, "378349": [], "3789": [], "3789476659944": 31, "379": [31, 40, 41], "38": [31, 36, 38], "3802492092461": 31, "381056": 42, "383": [40, 41], "383022": 34, "383062e": 43, "383446": [40, 41], "385": 36, "385000": [], "38515441300115": [], "386": 31, "386278": 35, "388009": 36, "388568567754305": 36, "389": [], "38it": [], "39": [31, 36, 38, 39, 42, 43], "390197e": 42, "3908": 35, "393": 31, "393895": 36, "39614083991057": 36, "396656": [], "3981": [], "399447": 40, "399982": [], "39it": [], "3rd": [10, 35, 42, 43], "4": [3, 4, 5, 8, 10, 13, 29, 36, 37], "40": [9, 25, 31, 35, 38, 41], "400": 29, "4000": [31, 36], "40000": [29, 34, 41], "400000": [34, 42, 43], "401": 31, "40103638563755": 36, "401124": 40, "401954e": 35, "402": 35, "402573": 36, "403": 31, "404376e": 35, "405": [], "40500030908063": [], "406723": 40, "407": 31, "40731524633007": 31, "407396": 35, "407681": [], "4082038923481": 31, "409025": 35, "41": [31, 38], "410529": [], "411": 36, "411124": [40, 41], "411182": 36, "412": 36, "4129328959": 40, "414": 42, "41547696e": 36, "416": 31, "416976": 43, "417": [], "418": [], "419": [31, 35], "41it": 38, "42": [31, 36], "420": [], "42003": 42, "42013": [], "42021": 42, "42027": 43, "42031": [], "42039": [2, 41, 43], "42049": [2, 41, 44], "42051": 43, "42059": 42, "42063": [], "42065": 42, "42071": 43, "42075": [], "42089": [], "42097": 42, "421": 31, "42103": 43, "42107": 43, "42109": 43, "421110": 43, "421124": [40, 41], "42115": [], "42117": 42, "42118925125396": 31, "42119": [], "42121": 43, "42131": [], "422445e": [], "4234809204555": 40, "4241649094325": 31, "424741737609907": [], "425975": 42, "426124": 44, "426904": [], "427": 31, "427998e": 35, "429": 36, "42906080741184": 31, "429779e": [], "43": [31, 33, 35, 40, 41], "430": 31, "431124": [40, 41, 44], "432": 42, "4326": [2, 31, 35, 36, 37, 38], "43414051411436": [], "436": [31, 35], "436124": 44, "4375": 35, "438": 43, "439457": 38, "43993793268754": 31, "44": 31, "440": 36, "44003": [], "441124": [40, 41, 44], "442153e": [40, 41], "442728": 42, "443458": 34, "443930e": 35, "444": 31, "444656398462776": 40, "444877856818": [], "446124": [41, 44], "446827": 36, "447563e": 35, "448797e": [], "449014": 43, "449076e": [], "449497": 42, "449596": [], "45": [9, 10, 11, 13, 31, 34], "4500": 36, "450000": [], "450174": [], "451": 31, "45175335007325": 31, "4547918981183": 31, "455192e": 35, "457": [40, 41], "458": [31, 43], "458219": 43, "458808": [], "46": 34, "46109182464066123": 31, "46215413444605": 37, "462801e": 35, "463536": 43, "464": [31, 35], "465": 36, "465729": [], "467": 36, "467516e": 35, "468": [], "468647": [], "469406229991364": 31, "47": [31, 36], "470": [31, 36], "471": [31, 36], "471335": 36, "471774": 39, "472": [], "472821": 42, "472968": [], "474": [], "475": 31, "4750877096409": 31, "47509500639893": 31, "475252": 44, "47529551999094": 31, "475344": 34, "4768161567573": [], "477": 31, "477588377276845": 36, "478527e": 35, "47898266": 35, "478983": 35, "4791021447186": [], "479106e": 35, "479607": 35, "48": [31, 33], "480": 31, "482": [36, 42], "483877": 43, "484712": 42, "485": 31, "48699687094353": 31, "487043e": 35, "4883": 35, "48893667070456": [], "489": 36, "48930987": 35, "489310": 35, "489574": [], "49": 31, "490": 35, "4906907968926": 31, "491": 31, "491185": 43, "494": 31, "4940": [], "495": [], "495489": [], "496168": [], "497719": [], "499": [31, 36], "499029": [], "49it": 39, "4f": 37, "5": [3, 8, 9, 10, 11, 12, 29, 32, 33, 36, 37, 39, 44], "50": [31, 34, 35, 36, 38, 42, 43], "500": [29, 34, 36, 37], "5000": [31, 35, 36], "500000e": 35, "50005": [], "50009": 42, "50011": 42, "500384": 42, "501718": [], "502": 31, "502338": [], "503272408247557": 40, "50350756874842": 31, "504": [40, 41], "504256444451812": 36, "505509671802704": 43, "506": [], "506538": 43, "5068": [40, 41], "507": [40, 41], "508": 31, "508218": [], "50844531675361": 36, "508784": [], "508918": 43, "509": 31, "51": 31, "510533": 36, "511493": [], "512": 36, "51207414267205": 31, "513": 31, "514": 35, "514141": 43, "515": 42, "5152950454789": 31, "516631312210944": 36, "51949179940027": [], "5196": 37, "52": [31, 36], "521": 31, "521794": 36, "522": 43, "522731": [], "523": 36, "5232513174528": 31, "5233842803291": 31, "52362092362816": 31, "524330": 36, "525000": 43, "526495": 35, "52649515": 35, "526820": 34, "526892863730268": [], "527178": 43, "52756082596147": 31, "52826399837278": 36, "528402": [], "528472": 36, "529001": 34, "529781": [], "52990402804862": 31, "529970": 42, "52it": 40, "53": 36, "532": [31, 40, 41], "532045": [], "532706465067776": 40, "533": [], "534367": [], "534590": 42, "534700": 42, "535": 31, "53561182896007": 36, "5362224280161": 31, "537": 43, "537041": 36, "538721218653905": 31, "539159": [40, 41], "539633": 36, "54": [], "540467": 36, "5406316105217": 31, "540854e": [], "541000": [], "541045": 31, "541137": 36, "541281": [], "541524": [], "541547": 36, "541688": [], "5419": 40, "542": 36, "542076": 36, "543": 42, "5434027777777798": 10, "54340278": 10, "543432e": 35, "54356381615929": [], "544131": 36, "545": 31, "545209": 31, "54535714285717": 40, "545416": 31, "546553": 36, "546930": 36, "5477": 36, "547783": [], "548": [31, 43], "548294": 31, "548391": 43, "548701": 36, "548872479116914": 36, "548982": 31, "549": 31, "54989393663284": 36, "55": [31, 36, 38], "5500": 36, "55011584792507": 31, "550405": [], "550448": [], "550631": 40, "550673": [40, 41], "551": 31, "55107310356686": 31, "551466": 31, "551653": [], "552705": [], "5541": [], "555": 31, "55548024563274": 31, "556": 35, "556471": [40, 41], "556828": [], "556830": [40, 41], "557971": [40, 41], "558": 31, "558079932090166": 36, "558488": 36, "559785533995694": 31, "56": [38, 40], "560": 31, "56007358684212": 31, "56033703031673": 31, "561": 31, "561271e": 35, "562": 31, "5625": 35, "563863": 36, "564067": [], "564156": [], "564830": [40, 41], "569363": 42, "57": 31, "571369": 35, "571504": 36, "5716": 35, "5719": 36, "571970": 35, "572": [], "572300e": 43, "572343": [], "573": 31, "573719": [], "5743674621095": 31, "574960469008332": 31, "575274": 42, "575340": [], "57574792919676": [], "577": 36, "577139": 43, "577791e": 35, "57793031445675": 31, "57846649760147": [], "57971156e": 40, "58": [12, 33, 35], "580230995312235": [], "580614e": 35, "5811036652226": 31, "58116437305063": 31, "5831565610547": 36, "584068e": [], "5850": 35, "585908": [], "58692115765933": 31, "588124": [], "588314e": 35, "58it": [], "59": [31, 41], "590": 41, "590461": [], "590651": [], "594676": 39, "595": 31, "596085": 36, "596802": 42, "59771789": 35, "597718": 35, "598170e": 35, "598187": [], "599": [40, 41, 42], "599394e": 35, "599397": [], "5aae61": 32, "6": [10, 29, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "60": 31, "6000": [31, 36], "60000": 29, "600235": [40, 41], "60025635e": 36, "600918422589984": 31, "602858e": 35, "603": [], "603778": 43, "604054": 42, "605129": 36, "60555128": 4, "606324": [], "606411": 36, "606436e": 42, "607": 39, "6073787035081": 31, "6083701575303": 31, "60853512852859": [], "61": 31, "610": 31, "610284": [], "61050146167261": 31, "6122447906015": 31, "61245695077403": 31, "612849": 35, "613": 42, "613496": 35, "61429501864575": 36, "614562": 39, "615233": [], "616031": 43, "616208": 36, "616235": 43, "616242e": 35, "61641918547662": 36, "618": 31, "619216": [], "61it": [], "62": 31, "6206": 36, "621525": [], "62223544944055": 31, "6235": 37, "6235188837861": 31, "624234": 35, "62423403": 35, "625": [10, 35, 44], "6250": 35, "625000e": 35, "626789": [], "629313": 44, "629350": 35, "629570728151343": [], "62it": 39, "63": 31, "630000": 42, "631": 31, "632457": [], "632906": [], "633": [], "636": 41, "63675547340748": 31, "637424": [40, 41], "637979e": 35, "638": [], "638052": 36, "6383701457184": 31, "638425": 40, "63it": 36, "64": [35, 36, 41], "640": [33, 39], "641255": [], "641322327860422": [], "6433994890514": 31, "643775": [], "649048e": 35, "64968180989848": 36, "6500": 36, "65000": 29, "650415": 36, "651": 31, "651028": [], "651128": 40, "65121077117155": [], "653": 31, "6546695631294": 36, "655343": [], "656": 42, "657804": [], "658729": [], "659": 34, "659009934605715": [], "65it": [], "66": 31, "6605413612897": 31, "661442": 36, "662526071583954": 31, "662744": 35, "664": [], "66432": 34, "665000": 43, "665082": 42, "665999914811835": 31, "667976041662143": [], "669672": [], "66it": [], "67": 31, "670733": [], "671": [40, 41], "671315": 31, "671378": 31, "671459e": 35, "6715648481398": 31, "671723": 40, "672": 31, "672267": [], "673435": [], "673959": 40, "6750": [], "675000": [], "67570949745885": 31, "6758": 37, "675932": 36, "676": [], "676038": [], "678002": [], "68": 33, "6829444343204": 36, "682969": [], "683110": [], "68704606120476": 36, "6875": 35, "687778": 44, "68872474483254": 36, "689": 36, "6890285994053": 31, "68985563827547": 31, "69": 31, "691185": 40, "69275424359088": 31, "693": [], "6938864138145": [], "693970": [], "695009": [], "6959364694": 36, "696": 36, "696596e": 35, "6972630338454": 31, "69875148762813": 36, "699327e": 35, "699529": 36, "7": [10, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "70": [24, 26, 31], "7000": [31, 36], "700000": 42, "700473": 34, "70216796984425": 31, "702323": 36, "702401": 42, "702517": [], "70264304761736": 31, "706": 31, "706056": [], "706218": 42, "706742": [], "707327": 35, "708155e": 35, "708283": 36, "708315": 31, "708844": 31, "709089": 35, "709962770143306": 36, "710706": 31, "712456": 38, "712477e": 35, "713751171886535": 31, "714": [], "714325": [], "714888e": [], "715758132458433": 31, "716043": 36, "71620705874227": 36, "717101": 39, "718654": [], "71946771": 35, "719468": 35, "720513": 34, "721": [39, 40, 41], "721316": [], "72150293483141": 31, "72152953542008": 31, "723": 39, "724090": 34, "725000": [], "726959": [], "727163279953": [], "727166": 42, "727680": 36, "727912692284356": [], "728": [40, 41], "72it": 39, "73": [31, 35], "731431e": 43, "731934": [], "73227416319057": 31, "73259814652391": 31, "735": 36, "73506526498": 36, "735107": 38, "736": 34, "736211": 36, "73656205645744": [], "73672": 36, "737": 31, "738": 42, "73934115929234": 31, "7399812761576": 36, "73998332422": [], "73it": 37, "74": [], "740012": 43, "740347": 36, "740584e": 38, "7406543587138": 31, "741397": [], "741451": 42, "742198e": 35, "742540088261137": [], "742790": 31, "744317": 44, "74437591": 35, "744376": 35, "744675e": 35, "745632": 43, "746723": 36, "746984e": 35, "7472710841447": 31, "74739593341536": 31, "748935": 34, "749322": 42, "74946968316414": 36, "75": [10, 12, 31, 33, 35, 36, 38, 40, 42, 43], "7500": [35, 36], "750000e": 35, "751082": [], "7517085053658": 31, "751882": 36, "752124": 35, "752529502587606": 31, "7540229326713": 31, "7550449009331": [], "755731938008125": 36, "756632": 42, "756648": 43, "7580926503687": 31, "7581649946207": 31, "75852817492543": 31, "7592363399267": 31, "76": 31, "760020": 38, "760625": 43, "7609479302331": 31, "762436350096": 31, "762a83": [31, 32], "764558": 36, "765": 25, "765146": 31, "766008": [40, 41], "767272": [], "767307": [], "767505": 35, "76750526": 35, "76811121400806": 31, "768622e": 35, "7692801878459": 31, "76966135344213": 31, "77": [25, 42], "770601587258": 31, "771429": [], "771562": [], "771834": [], "771916": [], "772718": 36, "773": 25, "7735": 37, "775000": 43, "775004": [], "777": 10, "777449": 36, "778474e": 35, "778854": 36, "778941": 35, "779787": [40, 41], "77it": [], "78": [31, 36, 40], "780406e": 35, "782004633917003": 31, "7822489110466222": 31, "782533": [], "78273230758606": 36, "786": 10, "78975639686188": 31, "79": 31, "79225949935216": 31, "793332": [], "793950": 43, "7954545454545454": 10, "7954880628821": 31, "7961845551719": 31, "796222": 42, "796418": 35, "79676323096689": 31, "7970611443549": [], "798": [31, 42], "798008": 42, "7995282117094": 31, "8": [10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "80": 31, "800": 31, "8000": [31, 36], "800424e": [], "801887": 36, "802603": [], "80283051543793": 31, "804": 43, "8044015563413": 31, "805222": 31, "8054": 36, "807282e": 42, "807347078599088": 42, "807629": 42, "807863": [], "808": 42, "8088419458209": 31, "80cdc1": 38, "81": [31, 33, 40], "8125": 35, "812849": [], "813": 31, "81365277536804": 31, "814415e": 35, "816": [40, 41], "817519": 42, "817863": 31, "818": 42, "818877": 43, "81901919": 39, "819578": [], "82": [31, 36, 40], "820307": 44, "821373": [], "822": [40, 41], "822367": [40, 41], "823045543266744": 31, "824114": 35, "824360e": 42, "824876": [], "825": 34, "825253": 38, "825540": 40, "82560538558577": [], "825975": [], "8261212464837": 31, "82818905258898": 31, "82842712": 4, "828618e": 35, "83": 40, "830241": 43, "830906": [], "83403385569056": [], "834552": [], "83518031738423": 31, "836839": 36, "838043": 39, "839033": [], "839407": 36, "84": [31, 36, 40], "842599": 36, "843530": [], "843791": 42, "844088230061459": [], "845891": [40, 41], "846": [40, 41], "847133389599": 36, "8472476840368": 31, "847620e": 35, "848001": 36, "849220": 35, "84922016": 35, "8495480710881": 31, "85": [33, 40, 41], "8500": 36, "85265986": 35, "852660": 35, "853": [], "853428": [], "85344518052113": 31, "853829": 40, "8539": [], "85390951880213": [], "8549895665524": 31, "8553986848418": 31, "8575410251533": 36, "8585979": 35, "858598": 35, "86": [31, 42], "860089474541724": [], "8608177528": [], "861": 34, "86159467872096": 31, "862": 36, "86215475": 35, "862155": 35, "863351e": 38, "8653795520153": 36, "866257": 38, "8667134434124": 31, "86828102822872": [], "869088e": 38, "872045": [], "872704": 42, "874119301205496": 31, "875": 44, "8750": 35, "875000e": 35, "875977e": 35, "876334": [], "877575e": 35, "877671": 43, "878133": 36, "87it": [], "88": 31, "883662": 43, "885612e": 35, "885706640114352": [], "8869047182717": 31, "888609052210118e": [], "888701": [], "8898": 36, "89": [29, 31, 36], "8906": [], "891937": 43, "8926871102454": [], "89394987189036": 36, "894660": [], "89534": 41, "895544254725": 31, "895574": 43, "896": 43, "8969420596828": 31, "897133": 35, "897284e": 35, "897535": 43, "898245e": 35, "898430e": [], "899": [], "9": [10, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "90": [9, 10, 11, 13, 31, 34, 36], "9000": [31, 36], "900000": [], "9001": 43, "9007": [], "9011": 42, "9013": 43, "901305021363": 31, "9017417512093": 31, "902991e": 35, "903": [], "904780": 35, "90478027": 35, "90493042664525": 31, "905790": 36, "906249": [], "90648819731166": 31, "906703056558335": 31, "908": 34, "908360": [], "909": [], "909108811707085": 36, "909623": 44, "91": [31, 38, 40], "910815": [], "91095667087166": [], "911": [], "911324": [], "911645": [], "911793": 43, "91307581152344": 31, "9184105895709": 31, "92045299212475": 31, "92082": [], "920820": [], "92120020199735": 31, "922": [], "92200000e": 40, "925": 41, "927": [], "927032": [], "927684": 35, "928": 43, "928547099740086": 31, "92881517059374": 31, "929577e": [], "92it": 43, "93": 43, "930238e": 35, "931": [], "931267": [], "932094": [], "933": [], "934470": 43, "934750": 31, "9352055777114": 36, "935704": [40, 41], "936": [40, 41], "936610e": 35, "937": [], "937369": [], "9375": 35, "939120": 35, "939216528212": 31, "93it": 38, "94": 34, "9402324430091": 31, "941210": [], "941316": 40, "942269e": 35, "944304526105059e": [], "944787": 43, "9454407598276": [], "9475766367128": 31, "9483172823313": 31, "94892418737072": 31, "949525193922875": [], "94it": [], "95": [12, 31, 33], "9500": 36, "950029": [], "9510457652993": 31, "951256": 36, "953": [], "953469": [], "954": 42, "9549217265119": 31, "957437e": 35, "958142": [], "958207e": [40, 41], "958282": 31, "958924": 40, "959002e": 35, "96": [31, 33, 42], "961": [40, 41], "96259104849804": 31, "96350436467912": 31, "963791e": 42, "964876543341": 31, "96538249296198": [], "965905495268544": 31, "966": 43, "967083": 36, "96775883309403": 36, "97": 43, "97000001181192": 31, "970139": 44, "971479": 38, "972404": [], "973063": [], "974452e": 35, "975": [34, 36], "97607277205168": 31, "976144e": 35, "976595": 40, "978": [40, 41], "978031": 43, "98": 40, "981213": [], "981414538699": 36, "981884": 35, "98333116163843": 31, "983900": 43, "985": [], "9874764849992061": 34, "987774": [], "989": [], "98971500146": 40, "98it": [], "99": 40, "9909": 31, "991219": [], "993": [], "995273e": [], "9970ab": 32, "99847007753777": 31, "99853574125973": [], "99it": [], "9f": 40, "A": [3, 7, 9, 10, 11, 12, 13, 31, 32, 33, 34, 42, 43], "AND": 41, "And": [32, 40], "As": [10, 17, 31, 34, 36], "At": [32, 39], "BUT": 33, "Be": 33, "But": [3, 24, 32, 34, 35, 40, 41, 42, 44], "By": [3, 31], "For": [32, 34, 35, 40], "If": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 23, 24, 31, 33, 34, 35, 36, 40, 41, 42, 43], "In": [10, 27, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "It": [2, 5, 6, 8, 9, 10, 12, 31, 32, 33, 34, 35, 36, 38, 41, 42, 43, 44], "Its": [6, 32], "No": [25, 33, 38, 40], "Not": [1, 33, 34], "OF": 27, "On": [33, 37], "One": 38, "Or": 41, "That": [5, 8, 31, 36, 37, 38, 41], "The": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 24, 29, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "Their": [42, 43], "Then": [2, 9, 24, 36, 38, 40, 42, 43, 44], "There": [24, 31, 33, 35, 38, 41], "These": 34, "To": [24, 31, 35, 37, 44], "With": [24, 27, 32, 35, 37, 38, 39], "_": [32, 36, 39, 40], "_automat": 31, "_experiment": 31, "_lag": [31, 32], "_linear_manu": 31, "_max_it": 9, "_model": 31, "_nestedsequ": [2, 4, 8], "_nugget": 32, "_rang": 32, "_sill": 32, "_supportsarrai": [2, 4, 8], "_weights_arrai": 1, "a6611a": 38, "a6dba0": 32, "ab": 40, "abl": [27, 40], "about": [2, 5, 24, 31, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44], "abov": [12, 31, 32, 35, 38, 42, 43], "abrupt": [42, 43], "absolut": [5, 9, 11, 12, 31, 35, 38, 40, 41], "academ": 10, "accept": 2, "access": [20, 24], "account": [10, 36, 43], "accur": [34, 42, 43], "accuraci": [5, 42, 43], "achiev": [40, 41], "across": [37, 39], "activ": 27, "actual": [31, 32, 36, 39, 42, 43], "ad": [6, 35], "adapt": [1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "addit": [22, 34, 35, 38], "administr": 41, "advanc": 21, "advantag": 33, "affect": [3, 7, 33, 34, 35, 37, 38, 44], "after": [9, 27, 32, 35, 38, 40, 41, 42, 43, 44], "again": [3, 41], "against": 12, "agenc": 17, "agg_dataset": 9, "agg_lag": 9, "aggreg": [0, 1, 2, 3, 24, 26, 28, 35, 40, 41, 44], "aggregared_data": 9, "aggregatedvariogram": 9, "agregowanych": 28, "ai": 44, "air": [28, 34], "air_pollut": 34, "algorithm": [3, 5, 7, 8, 9, 10, 13, 25, 31, 32, 33, 35, 36, 38, 41, 42, 43], "alia": [9, 10, 31], "alias": 31, "all": [1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 21, 23, 31, 32, 34, 35, 36, 37, 38, 39, 40, 43], "all_filt": 38, "allow": [3, 5, 7, 8, 13, 36], "allow_approx_solut": [8, 13, 36], "allow_approximate_solut": [3, 5, 8, 37], "allow_lsa": [7, 42], "allowed_model": 33, "almost": [31, 40], "alon": [5, 24], "along": [27, 31, 35, 41, 42, 43, 44], "alpha": [31, 34, 35, 36, 37, 38, 40, 44], "alreadi": 37, "also": [], "alter": 2, "alwai": [32, 33, 34, 36, 38, 40, 42, 43, 44], "america": [12, 33], "amplifi": [36, 38], "an": [2, 5, 7, 8, 9, 10, 11, 13, 31, 32, 33, 34, 35, 36, 38, 39, 40, 43], "analys": 33, "analysi": [8, 9, 11, 17, 24, 29, 31, 34, 35, 38, 40, 41, 42, 43], "analyz": [29, 40, 41, 42, 43], "angl": [2, 3, 8, 10, 34, 36], "angles_between_representative_point": 2, "angles_to_unknown_block": 1, "ani": [2, 4, 6, 8, 9, 10, 24, 31, 32, 33, 34, 40, 42, 43], "annot": 1, "anomal": 35, "anoth": [8, 33, 34, 35, 38, 40], "anyth": 2, "anywai": [3, 8], "apart": 33, "apcom": 5, "api": [1, 20, 21, 24, 34, 35], "append": [32, 36, 38, 40, 42, 43], "appli": [9, 31, 32, 33, 37], "applic": [5, 8, 10, 31, 36, 42], "approach": [30, 43], "appropri": 40, "approxim": [3, 5, 7, 8, 13, 31, 34, 36], "apt": 27, "ar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 23, 24, 25, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "arbitrari": 36, "area": [0, 1, 2, 3, 5, 8, 9, 10, 11, 13, 21, 24, 28, 30, 31, 32, 33, 34, 36, 40, 41], "area_geometri": 2, "area_index": 2, "area_to_area_pk": [7, 43, 44], "area_to_point_pk": 7, "area_valu": 2, "areal": [7, 9, 24, 40, 41, 42, 43, 44], "areal_centroid": 40, "areal_input": 40, "argument": [35, 42, 43], "armstrong": [25, 32], "around": [13, 31, 32, 35, 36, 38], "arr": 33, "arrai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 34, 35, 36, 39, 40, 42, 43], "arraylik": [8, 10], "arrow": 34, "art": 5, "artifici": 32, "as_cloud": [10, 31], "as_datafram": [10, 35], "asarrai": 32, "ascend": 38, "asid": 32, "assign": [2, 6, 10, 11, 31, 33, 37, 40, 41], "associ": 12, "assum": [5, 10, 36, 37, 38, 42, 43], "assumpt": [10, 35, 36, 41], "ata": [3, 43], "atp": 3, "attent": [42, 43], "attr": 31, "attribut": [2, 3, 5, 8, 9, 10, 11, 12], "attributeerror": [2, 4, 5, 9, 12], "attributesettofalsewarn": 10, "augment": 17, "author": [2, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "auto": 11, "autofit": [12, 31, 33, 39, 40], "autom": [11, 35], "automat": [40, 41], "avail": [3, 5, 8, 9, 10, 11, 12, 31, 32, 36], "averag": [5, 9, 10, 31, 32, 35, 36, 40, 41, 42, 43], "average_inblock_semivari": 9, "average_semivari": [10, 38], "avg_block_to_block_semivari": 9, "avg_inblock_semivari": 9, "avg_rms": 36, "avoid": [23, 36, 40], "awai": [5, 9, 11, 12], "awar": [31, 42], "ax": [10, 34, 37, 38, 39, 40, 42, 43, 44], "axi": [9, 10, 11, 13, 34, 35, 39, 40], "b": [10, 12, 33], "b2c": 17, "b2g": 17, "b_id": [42, 43], "back": 35, "backend": 3, "bad": 31, "balanc": 35, "balenoptera": 10, "banff": 10, "bar": [3, 5, 8], "bare": 32, "base": [0, 1, 3, 5, 8, 9, 10, 12, 13, 21, 24, 27, 30, 31, 32, 33, 34, 35, 36, 39, 40, 43, 44], "base1": 40, "base2": 40, "base3": 40, "base4": 40, "base5": 40, "base6": 40, "baselin": [9, 10, 11, 13, 32, 34, 37], "basic": [10, 21, 25, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "batch": 40, "becaus": [5, 27, 31, 32, 33, 35, 36, 38, 40, 42, 43, 44], "becom": [6, 31, 36], "been": [9, 12, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "befor": [5, 8, 9, 31, 32, 33, 35, 36, 38, 40, 41, 42, 43], "begin": [9, 10, 11, 13, 31, 34, 36, 38], "behav": [31, 32, 34, 40, 41], "behavior": [31, 32, 35, 36, 37, 41, 42, 43], "behind": [31, 40], "being": 31, "below": [9, 12, 31, 35, 38, 42, 43], "benchmark": [24, 30], "best": [31, 32, 33, 34, 36, 37, 38, 40], "bet": 31, "beta": 24, "better": [3, 5, 24, 31, 34, 35, 36, 37, 38, 41, 42, 43], "between": [2, 4, 7, 8, 9, 10, 11, 12, 24, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "bia": [5, 8, 11, 12, 24, 31, 36, 40, 42, 43], "bias": 38, "bias_experimental_model": 8, "bias_model": 8, "bias_valu": 8, "bibliographi": 24, "big": [8, 34, 37, 41, 42, 43], "bigger": [9, 11, 12, 41], "bin": [8, 9, 10, 11, 13, 34, 35, 38, 42, 43], "black": [31, 32, 34, 40, 44], "block": [0, 1, 3, 9, 10, 21, 24, 30, 41, 42, 43], "block_a": 2, "block_arr_to_dict": 1, "block_b": 2, "block_base_dist": 1, "block_coordin": 2, "block_data": [2, 41], "block_dataframe_to_dict": 1, "block_dist": 4, "block_id": [2, 7, 42, 43], "block_id_col_nam": 4, "block_index": 2, "block_pair": 2, "block_real_valu": 2, "block_representative_point": 2, "block_to_block_semivari": 9, "block_to_blocks_angl": 1, "block_valu": 2, "blockpk": 1, "blockpoissonkrig": 1, "blocks_dist": 2, "blocks_index": 2, "blocks_index_column": 2, "blocktoblockkrigingcomparison": 1, "blur": 32, "bonnin": 10, "book241284": 25, "bool": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31], "boolean": 36, "bore": 32, "born": 17, "both": [2, 31, 34, 35, 38, 40, 41], "bottom": [34, 35, 38], "bound": [12, 35], "boundari": 32, "box": [10, 38], "boxplot": [10, 38], "break": [1, 24], "breast": [40, 41, 42, 43, 44], "brew": 27, "bright": 34, "brighter": 34, "bring": 38, "buffer": [2, 4, 8, 13], "bug": [18, 24], "build": [9, 17, 37, 38, 39, 40], "build_experimental_variogram": [10, 31, 32, 33, 36, 37, 41], "build_mask_indic": 1, "build_theoretical_variogram": [12, 29, 31, 32, 36, 37, 38], "build_variogram_model": 31, "build_variogram_point_cloud": 1, "built": [32, 41], "byte": [2, 4, 8], "c": [10, 12, 25, 27, 29, 33, 38], "c2a5cf": 32, "cadmium": 33, "cageo": 11, "calc_block_to_block_dist": 4, "calc_pair_dist": 2, "calc_point_to_point_dist": 1, "calcul": [1, 2, 4, 5, 8, 9, 10, 11, 12, 21, 29, 31, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44], "calculate_angles_between_rep_point": 2, "calculate_angular_differ": 1, "calculate_angular_dist": 1, "calculate_average_p2b_semivari": 1, "calculate_avg_inblock_semivari": 9, "calculate_avg_semivariance_between_block": 9, "calculate_covari": 10, "calculate_deviation_decreas": 9, "calculate_deviation_ratio": 9, "calculate_distances_between_rep_point": 2, "calculate_experimental_variogram": 35, "calculate_model_error": 12, "calculate_point_support_dist": 2, "calculate_semivari": [10, 29], "calculate_spatial_dependence_index": [12, 33], "calculate_weighted_block_to_block_dist": 2, "call": [31, 32], "cambardella": [12, 33], "camera": 38, "can": [1, 2, 3, 5, 6, 7, 8, 10, 13, 24, 27, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "cancer": [40, 41, 42, 43, 44], "cancer_data": [2, 40, 41, 42, 43, 44], "cannot": [31, 32, 33, 36], "care": [24, 31, 32, 33, 40], "case": [5, 14, 27, 29, 31, 36, 37, 38, 40, 41, 42, 43, 44], "catastroph": 36, "catch": 34, "categor": 33, "caus": 4, "caution": 35, "cautiou": 33, "cb": 3, "cdist": 4, "cell": [2, 4, 31, 38, 41, 42, 43], "censu": [41, 42, 43], "center": [9, 10, 11, 13, 31, 34, 35], "central": [12, 33], "centroid": [0, 1, 2, 3, 21, 24, 30, 40, 41, 43, 44], "centroid_poisson_krig": [2, 7, 42, 43, 44], "centroidpoissonkriginginput": [1, 2], "challeng": 38, "chanc": [38, 42, 43], "chang": [10, 24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "changelog": 24, "channel": 40, "chaotic": 32, "check": [5, 12, 20, 31, 32, 34, 36, 38, 40, 41, 42, 43], "check_id": 1, "check_nugget": 1, "check_rang": 1, "check_sil": 1, "chemic": 33, "choic": [36, 38], "choos": [31, 32, 40], "choropleth": [9, 40, 44], "chosen": [11, 12, 31, 32, 36], "chosen_model": 31, "circl": [10, 31, 34], "circular": [9, 11, 12, 31, 32, 34], "circular_model": 31, "citi": [31, 33], "cividi": [32, 39], "clarif": [37, 38, 41, 42, 43], "clarifi": [42, 43], "clark": 5, "class": [2, 8, 9, 10, 11, 12, 21, 31, 32, 34, 35, 38, 41], "classic": [33, 36, 39], "clean": [10, 35, 38, 40], "clean_mask_indic": 1, "clearli": [39, 42, 43], "cli": 20, "clip": 5, "close": [5, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43], "closer": [33, 35, 37, 41, 42, 43], "closest": [1, 2, 3, 5, 6, 8, 9, 11, 12, 32, 33, 36, 37, 38, 41, 44], "closest_neighbor": 2, "cloud": [0, 1, 21, 30, 31, 40], "cloud_with": 38, "cloud_with_rem": 38, "cloud_without": 38, "cloud_without_rem": 38, "cluster": [3, 5, 7, 8, 13, 36], "clusterdetector": 1, "cmap": [31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "code": [2, 24, 29, 34], "col": [32, 33], "collect": 10, "color": [31, 32, 40, 42, 43, 44], "column": [2, 3, 4, 6, 31, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "com": 25, "come": [33, 35, 36, 42, 43], "command": 27, "commerc": 17, "commun": [16, 24], "compar": [5, 31, 33, 35, 36, 37, 38, 40, 41], "comparison": [8, 10, 31, 32, 33, 34, 38, 40], "complet": [1, 35], "complex": [2, 4, 8, 21, 41], "compound": 33, "comput": [3, 12, 25, 31, 40], "computation": 31, "concentr": [33, 34, 39], "concept": [34, 38, 42, 43, 44], "conda": 29, "condition": 32, "cone": 31, "configur": 27, "consid": [10, 12, 35, 38, 42, 43], "consider": 33, "consist": [34, 41], "constant": [32, 37, 40], "contain": 38, "contribut": 18, "contributor": 14, "control": [6, 31, 32, 34, 36, 37, 41], "conveni": [42, 43, 44], "convolve2d": 32, "coo_matrix": 32, "coordin": [2, 4, 5, 6, 8, 9, 10, 11, 13, 31, 32, 34, 36, 42, 43, 44], "copernicu": 38, "copi": [10, 33, 38, 44], "copper": 33, "core": [0, 9, 21, 22], "correct": [25, 31, 38], "correl": [10, 12, 24, 31, 32, 37], "could": [6, 8, 10, 12, 13, 24, 31, 36, 37, 38, 42, 43, 44], "count": [10, 34, 35, 36, 38, 40, 42, 43, 44], "counti": [24, 40, 41, 42, 43, 44], "countri": [24, 33], "covari": [0, 1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "covariance_fn": 1, "covariogram": 10, "cover": [1, 31, 36, 38], "coviari": [10, 31], "covid": 24, "cr": [2, 3, 31, 35, 36, 37, 38, 40, 44], "cran": 33, "creat": [4, 5, 9, 10, 11, 12, 13, 24, 25, 27, 31, 33, 37, 40, 41, 42, 43], "creation": [3, 5, 7, 8, 13], "crete": 32, "crime": 28, "cross": [0, 34], "cross_valid": 5, "csv": [9, 31, 33, 35, 36, 37, 38, 39], "cubic": [9, 11, 12, 31, 32], "cubic_model": 31, "current": 9, "current_deviation_decreas": 9, "current_ratio": 9, "curv": [12, 32, 33], "custom": [10, 17, 31], "custom_bin": [10, 11, 31], "custom_weight": [9, 10, 11, 31], "cv": 10, "cvc": 35, "d": [2, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "d9f0d3": 32, "d_": 37, "danger": 31, "danych": 28, "dark": 34, "darker": 34, "dash": 31, "dask": [1, 3], "data": [0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 17, 21, 24, 28, 29, 32, 33, 34, 35, 36, 37], "data_cr": [3, 44], "data_model": 2, "datafram": [2, 4, 10, 31, 36, 39, 42, 43], "dataset": [1, 2, 3, 5, 7, 8, 10, 13, 24, 26, 28, 32, 33, 34, 36, 38, 39, 40, 41, 42, 43, 44], "date": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "dcv": 9, "deal": [31, 35, 44], "debug": [9, 36], "decid": [2, 32, 33], "decis": [1, 24, 34, 38, 42, 43], "deconvolut": [0, 1, 2, 10, 21, 24, 25, 41, 42, 43, 44], "decreas": [6, 9, 31, 37, 41], "def": 32, "default": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 34, 36, 41, 42, 43], "defin": [9, 31, 32, 39, 40, 44], "define_whitening_matrix": 1, "definit": [32, 34], "degre": [3, 8, 9, 10, 11, 13, 31, 36], "deliv": 36, "dem": [29, 31, 35, 36, 37, 38], "dem_fil": 31, "dem_geometri": [31, 35, 36, 37, 38], "demand": 17, "denois": 43, "denomin": [10, 37, 40], "dens": [5, 9, 11, 12, 35, 40, 41, 42, 43], "densiti": [24, 35], "depend": [1, 12, 18, 30, 31, 32, 34, 35, 36, 41], "deriv": [8, 9, 11, 12, 31, 35, 36], "describ": [2, 9, 10, 11, 23, 31, 34, 35, 36, 38, 41, 42, 43], "descript": [12, 33], "design": 2, "desir": 42, "detail": [4, 34], "detect": [10, 38], "detector": 17, "determin": [38, 42, 43], "detrend": 8, "deutsch": [10, 25], "dev": [9, 22, 27], "develop": [22, 24, 37], "deviat": [0, 1, 10, 35, 38, 41, 42, 43], "deviation_direct": 9, "deviation_weight": [11, 12], "df": [31, 33, 34, 35, 36, 37, 38, 39], "dfc27d": 38, "dict": [2, 7, 8, 9, 10, 11, 12, 13, 31], "dict_represent": 31, "dictionari": [2, 8, 10, 11, 12, 13, 31], "did": 31, "didn": [9, 40], "differ": [5, 8, 9, 10, 12, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44], "digit": [31, 36, 38], "dim": 13, "dimens": [6, 13], "dimension": [6, 32], "diminish": 37, "dir_neighbors_selection_method": [31, 34, 39], "dir_var": 34, "dir_var_": 34, "dir_var_t": 34, "direct": [0, 1, 2, 8, 9, 11, 12, 13, 30, 31, 35, 36], "directional_covari": 1, "directional_covariogram": 1, "directional_point_cloud": 1, "directional_semivariance_cloud": 1, "directional_semivariogram": 1, "directional_variogram": 10, "directional_weighted_semivari": 1, "directionalvariogram": [10, 34, 39], "directli": [31, 33, 35, 36, 38, 42, 43], "directori": 23, "dirvar": 39, "disaggreg": 24, "disappoint": 32, "discord": [16, 40], "discret": 34, "diseas": [17, 24], "dispers": [35, 40, 42, 43], "dissimilar": [10, 12, 31], "distanc": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 21, 24, 31, 32, 34, 35, 36, 37, 38, 41], "distances_between_block": 9, "distances_between_point_support": 2, "distances_between_representative_point": 2, "distant": [5, 9, 10, 11, 12, 35, 36, 37, 38, 40, 41], "distinguish": 39, "distribut": [10, 27, 33, 35, 36, 39, 41, 42, 43, 44], "diverg": [42, 43], "divid": [9, 34, 36, 37, 38, 40, 41, 44], "divis": [9, 38, 42, 43], "do": [3, 5, 7, 8, 13, 24, 31, 34, 36, 40, 41, 42, 43], "document": [1, 20, 24], "doe": [5, 27, 31, 36, 38, 41], "doesn": [1, 3, 37, 38], "doi": [11, 24, 26], "domain": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "don": [3, 5, 7, 8, 13, 31, 32, 36, 37, 40], "done": [31, 40], "dordrecht": 10, "down": [35, 36, 38, 41], "downsid": 35, "downward": 41, "dozen": [36, 42, 43], "draw": [42, 43], "drawback": 37, "drift": 35, "driven": 36, "dropped_point": 2, "dt": 41, "dtype": [2, 4, 8, 35, 36, 38], "dubroca": 10, "due": [1, 2, 41], "duplic": 36, "durbec": 10, "dure": [1, 2, 8, 9, 41, 42, 43], "e": [2, 9, 10, 11, 12, 13, 17, 25, 31, 33, 34, 39, 40, 42, 43, 44], "e7d4e8": [31, 32], "e_": 5, "each": [2, 5, 6, 8, 9, 10, 11, 24, 29, 31, 33, 34, 36, 38, 40, 41, 42, 43, 44], "earlier": 35, "earth": 11, "easiest": 31, "easili": [31, 35], "east": [10, 36, 39], "eastern": 36, "ecolog": 10, "ecologi": [10, 41], "ecologist": [], "econom": 28, "edg": 34, "edgecolor": [40, 44], "edit": 10, "editor": 15, "edzer": [33, 39], "effect": [32, 34, 37], "effort": 10, "eg": 2, "element": 9, "eleph": 35, "elev": [31, 36, 38], "ellips": [9, 10, 11, 13, 34], "ellipt": [9, 10, 11, 13], "els": 40, "emphas": 37, "empir": [10, 12], "empirical_smv": 10, "empti": [31, 36, 39], "en": [5, 25], "encount": 27, "end": [39, 41, 44], "engin": [], "enthusiast": 34, "entir": 36, "enumer": 40, "env": 27, "environ": 27, "epidemiologi": [40, 41], "epsg": [2, 31, 35, 36, 37, 38], "equal": [5, 6, 9, 10, 11, 12, 13, 31, 34, 35, 36, 40, 41], "equat": [5, 10], "equidist": 9, "eras": 42, "err": [3, 39, 44], "err_to_nan": 7, "error": [1, 3, 4, 5, 7, 8, 9, 11, 12, 13, 29, 31, 32, 36, 37, 38, 39, 40, 41, 42, 43], "error_estim": [11, 12, 31], "errs_col": 40, "especi": [10, 38, 40, 41, 44], "essenti": [35, 36, 41], "est": [3, 44], "estim": [2, 3, 5, 6, 8, 9, 11, 12, 13, 31, 33, 34, 36, 37, 38, 41, 42, 43], "etc": [36, 40], "ethem": 15, "ethmtrgt": 15, "euclidean": 4, "eur": 25, "european": 17, "evalu": [0, 12, 21], "even": [31, 32, 36, 37, 39, 40, 42, 43], "event": 33, "everi": [10, 31, 33, 34], "everyth": 34, "exact": 36, "examin": 32, "exampl": [1, 2, 4, 9, 10, 12, 24, 31, 34, 35, 38, 39, 40, 42, 43, 44], "excel": [35, 38], "except": 41, "excit": 32, "exclud": 33, "exercis": 31, "exist": [12, 33, 38, 40], "exp_model": 40, "exp_semivar": 41, "exp_var": [33, 34, 35, 36, 37], "expect": [5, 8, 31, 41], "expected_valu": 8, "experi": [32, 36, 38, 41, 42, 43], "experimanet": 11, "experiment": [0, 1, 2, 8, 9, 11, 12, 21, 29, 33, 36, 38, 39, 40, 41, 42, 43, 44], "experimental_block_semivari": 9, "experimental_indicator_variogram": 11, "experimental_model": 11, "experimental_point_cloud": 10, "experimental_semivari": [10, 12, 35, 40], "experimental_semivariogram": 29, "experimental_variogram": [9, 11, 12, 29, 31, 32, 33, 36, 37, 38, 39, 40], "experimentalfeaturewarn": 1, "experimentalindicatorvariogram": 11, "experimentalvariogram": [2, 8, 9, 10, 12, 31, 34, 35, 36, 37, 38], "experimentalvariogrammodel": 1, "expert": 24, "explain": [31, 33], "explor": [30, 34, 35], "exponenti": [9, 11, 12, 31, 32], "exponential_model": 31, "export": [9, 12], "export_model": 9, "export_model_to_json": [9, 41], "extend": 33, "extent": [12, 34], "extern": [8, 17, 33], "extrem": [35, 37, 40], "ey": 32, "f": [12, 31, 33, 37, 40], "face": 1, "fact": [38, 43], "factor": [5, 10, 40], "fall": [2, 34, 42], "fals": [2, 3, 5, 7, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 37, 38, 39, 42, 43, 44], "familiar": 34, "far": 36, "fast": [34, 42], "faster": [6, 34, 37, 42], "fb": 5, "featur": [8, 34, 40], "fed": 36, "feel": 31, "few": [10, 31, 32, 35, 36, 40, 41, 42, 43], "fewer": 36, "field": [12, 33], "fig": [38, 39, 40], "figsiz": [31, 32, 34, 38, 39, 40, 42, 43, 44], "figur": [10, 31, 32, 35, 36, 38], "file": [1, 9, 12, 22, 27, 31, 40], "filenam": 2, "fill": [31, 36], "filter": [1, 3, 32, 38], "filter_block": [1, 3], "fin": 10, "final": [9, 10, 32, 36, 38, 44], "final_regularized_variogram": 9, "final_theoretical_model": 9, "find": [2, 11, 12, 24, 31, 33, 35, 38, 41], "fip": [2, 40, 41, 42, 43, 44], "first": [9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 38, 40, 41, 44], "fit": [3, 5, 7, 8, 9, 11, 12, 25, 29, 33, 35, 38, 41, 42, 43], "fit_bia": 8, "fit_transform": [9, 41], "fit_trend": 8, "fitted_regression_model": 8, "fitted_valu": 12, "five": [10, 31, 39, 42, 43], "fix": [10, 11, 12, 31], "flag": 33, "flat": 31, "flatten": 31, "float": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 34, 35, 41], "float64": [35, 36, 38], "flow": 29, "flu": 17, "fname": [9, 12], "focus": 31, "folder": 39, "follow": [10, 31, 33, 41, 42, 43, 44], "forc": [5, 31, 40], "forecast": [5, 11, 12, 31, 42, 43], "forecast_bia": [5, 42, 43], "forg": [27, 29], "forget": 41, "form": [9, 32, 36, 37], "format": 33, "fortran": 25, "four": [10, 33, 36, 37, 38], "frac": [5, 9, 33, 37], "fraction": [11, 12, 31, 34, 40], "frame": [2, 40], "frequenc": [39, 42, 43], "fresh": 27, "from": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 22, 24, 27, 28, 29, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 44], "from_dict": [12, 31], "from_ellips": 1, "from_ellipse_cloud": 1, "from_json": [12, 31, 42, 43, 44], "from_triangl": 1, "from_triangle_cloud": 1, "from_user_input": 2, "full": [5, 29, 31, 39], "fulli": 36, "function": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "further": [5, 9, 11, 12, 37, 38, 39], "futur": 1, "g": [2, 10, 31, 40, 42, 43, 44], "g_w": 10, "gain": 38, "gallach": 15, "gamma": 9, "gamma_": 9, "gamma_h": 9, "gamma_v": 9, "gaussian": [9, 11, 12, 31, 32], "gaussian_model": 31, "gb": 25, "gcc": 27, "gdf_pt": 40, "gener": [11, 28, 32, 35, 36, 37, 38, 41, 42, 43], "generate_logistic_map": 32, "geodatafram": [2, 3, 31, 35, 36, 37, 38, 39, 40, 42, 43, 44], "geograph": [9, 25, 31, 36, 39, 41], "geologi": [9, 25, 41], "geologist": 24, "geometri": [2, 3, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "geometry_col": [40, 41, 42, 43, 44], "geometry_column_nam": [2, 41, 42, 43, 44], "geometryarrai": 8, "geopackag": [40, 44], "geopanda": [1, 2, 3, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "geoscienc": 25, "geoseri": 8, "geostatist": [5, 10, 25, 28, 31, 32], "geostatystyk\u0105": 28, "get": [2, 3, 10, 24, 27, 28, 31, 32, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44], "get_aggregated_point_support_valu": 1, "get_areal_centroids_from_agg": 1, "get_areal_values_from_agg": 1, "get_blocks_valu": 2, "get_current_and_previous_lag": 1, "get_distances_between_known_block": 2, "get_distances_within_unknown": 1, "get_expected_valu": 8, "get_expected_values_map": 8, "get_indicator_map": 8, "get_lag": 1, "get_point_to_block_index": 2, "get_points_arrai": [2, 41], "get_study_max_rang": 1, "get_triangle_edg": 1, "get_weighted_dist": 2, "gi": [24, 25], "gist_earth": [31, 35, 36, 37, 38], "github": 24, "give": [38, 40, 43], "given": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 31, 34, 36, 40], "global": 36, "go": [34, 35, 36, 37, 38, 41], "goe": [35, 41], "good": [10, 31, 34, 36, 40, 41, 42, 43, 44], "goovaert": [9, 11, 25, 41], "gorz\u00f3w": 31, "gpd": [2, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "gpkg": [2, 29, 34, 40, 41, 42, 43, 44], "gradual": 34, "graph": 35, "great": 32, "greater": [3, 5, 8, 10, 11, 12, 13, 31, 33, 35, 36, 37, 38, 39, 40], "green": 34, "grid": [2, 33, 39, 40, 44], "group": [9, 10, 11, 12, 22, 23, 31, 35, 38, 42, 43], "groupbi": 36, "grow": 31, "gstat": [33, 39], "gt": [37, 39, 42, 43, 44], "guess": [32, 35, 37], "guid": 34, "guinet": 10, "h": [9, 10, 34], "ha": [1, 5, 8, 9, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "hack": 40, "half": [12, 34], "hand": [33, 37], "handi": 38, "handl": 1, "hashabl": [2, 7], "hasn": 9, "have": [2, 3, 5, 7, 8, 9, 11, 12, 13, 27, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "haven": 27, "he": 35, "head": [31, 33, 34, 36, 39, 40, 41, 42, 43, 44], "headach": 35, "health": 10, "heavili": [1, 2, 34, 35, 38, 42, 43], "height": 13, "help": 31, "here": [5, 23, 25, 31, 32, 35], "heterogen": 10, "hexagon": 42, "high": [21, 35, 37, 38, 40, 42, 43], "higher": [5, 35, 37], "hist": [39, 42, 43], "histogram": [39, 42, 43], "hope": 32, "horizont": [35, 38], "hotel": 17, "how": [3, 5, 8, 9, 11, 12, 24, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "howev": [41, 42, 43], "html": 3, "http": [3, 5, 11, 24, 25, 26, 33], "huge": [42, 43], "hugoledoux": 15, "hundr": [33, 36], "hyperparamet": [31, 37], "i": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 23, 24, 27, 28, 29, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "id": [2, 3, 7, 9, 40, 42, 43, 44], "idea": [3, 10, 31, 34, 36, 38, 40, 41, 42, 43, 44], "ideal": [42, 43], "idw": [0, 21], "idw_pow": 37, "idw_pr": 37, "idw_rms": 37, "idx": 40, "ignor": [10, 36], "iguzquiza": 25, "ik": 11, "ikei": 40, "iloc": 36, "imag": 32, "imagin": 36, "implement": [10, 31], "import": [2, 5, 10, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "importantli": [35, 42, 43], "impress": 38, "improv": 41, "imshow": 32, "in_cr": 31, "inaccur": 36, "inani": 15, "inblock": [2, 9], "inblock_semivari": [1, 9], "incid": [41, 42, 43], "includ": [9, 10, 11, 33, 44], "increas": [3, 8, 9, 31, 36, 41], "independ": 41, "index": [2, 4, 7, 11, 12, 30, 36, 37, 38, 41], "index_column_nam": [2, 40, 41, 42, 43, 44], "indexcolnotuniqueerror": 1, "indic": [0, 1, 2, 10, 12, 24, 28, 33, 35, 38, 40, 42, 43], "indicator_map": 8, "indicator_predict": 8, "indicator_variogram": 8, "indicatorkrig": 8, "indicatorvariogram": [1, 8], "indicatorvariogramdata": 11, "individu": 38, "industri": 41, "inf": [31, 33], "infect": [24, 40, 44], "influenc": [6, 10, 24, 33, 37, 38], "info": 9, "inform": [2, 33, 35, 36, 41, 42, 43], "infrastructur": 17, "inhabit": 40, "initi": [2, 8, 9, 11, 31, 32, 38, 41, 44], "initial_devi": 9, "initial_ratio": 32, "initial_regularized_model": 9, "inplac": [10, 34, 35, 36, 37, 38, 40], "input": [2, 8, 31, 36, 38], "insight": [35, 38, 42, 43], "inspect": [38, 40], "instal": [22, 31, 32], "instanc": [10, 31], "instead": [1, 2, 31, 32, 40, 42], "int": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 31, 32, 36, 37, 38], "intens": 31, "interest": [41, 42, 43], "intermedi": 21, "intern": [21, 35, 41], "interpol": [3, 5, 7, 8, 13, 21, 24, 26, 28, 29, 31, 34, 36, 37, 38, 42, 43], "interpolate_point": [1, 3, 38, 39, 40], "interpolate_points_dask": [1, 3], "interpolate_rast": 13, "interpolation_results_areal_to_point": 40, "interpret": 5, "interquartil": 35, "interv": [33, 34], "introduc": [1, 24, 36, 42], "invers": [0, 21, 24, 36, 37, 41], "inverse_distance_weight": [6, 37], "investig": 38, "invok": [10, 12, 35], "iowa": [12, 33], "ipykernel_75618": 39, "iqr": [10, 35, 40], "iqr_lower_limit": [10, 35, 40], "iqr_upper_limit": [10, 35, 40], "irregular": [9, 25, 41, 42, 43], "irregularli": 41, "is_covari": [10, 31], "is_fit": 9, "is_semivari": [10, 31], "is_transform": 9, "is_weighted_by_point_support": 7, "isin": [36, 37, 38], "iso": [10, 39], "isotrop": [10, 11, 12, 39], "issu": [1, 20, 24], "item": 37, "iter": [2, 6, 9, 12, 38, 41, 42, 43, 44], "its": [2, 12, 31, 32, 34, 37, 42], "j": [2, 9, 11, 12, 33], "join": [2, 16, 41], "joss": [24, 26, 28], "journal": [12, 24, 26, 33], "jp": 10, "json": [12, 31, 41, 42, 43, 44], "jump": 32, "june": 24, "just": [33, 40, 41], "k": 36, "karlen": [12, 33], "kei": [2, 10, 13], "kenohori": 15, "kernel": 35, "keyerror": [9, 12], "kilomet": 33, "kind": [5, 8, 10, 24, 35, 38, 39, 42, 43], "kluwer": 10, "know": [3, 5, 7, 8, 13, 24, 31, 32, 33, 36, 41, 42, 43], "knowledg": [32, 38], "known": [2, 3, 5, 6, 8, 18, 36, 37], "known_loc": [3, 6, 8, 29, 36, 37, 38, 39, 40], "known_point": [6, 8], "known_valu": 36, "konopka": [12, 33], "krige": [0, 1, 5, 9, 10, 11, 13, 21, 24, 25, 28, 30, 31, 33, 35, 41], "kriged_result": 39, "kriging_pr": 37, "kriging_rms": 37, "kriging_run": [], "kriging_typ": [3, 8], "krigingobject": 1, "kurtosi": [10, 35], "l": [10, 12, 33], "lack": 2, "lag": [5, 9, 10, 11, 12, 31, 32, 34, 36, 38, 40, 41], "lag_numb": 10, "lag_points_distribut": 5, "lakshaya": 15, "lakshayainani": 15, "lambda": 37, "lambda_": 37, "land": 38, "larg": [1, 3, 4, 5, 8, 12, 17, 24, 32, 35, 36, 37, 40, 41, 42, 43], "larger": [6, 12, 13, 31, 32, 34, 38], "largest": [2, 10, 38, 40], "last": [6, 11, 12, 24, 27, 31, 32, 33, 35, 40, 42, 43, 44], "lat": [2, 3, 8, 29, 40, 41, 42, 43], "lat_col": 31, "lat_col_nam": [2, 4], "later": [35, 38], "latitud": [2, 3, 4, 8, 31, 35, 36, 37, 38], "law": [10, 33], "layer": [2, 34, 40, 41, 42, 43, 44], "layer_nam": 2, "lead": [3, 5, 7, 8, 13, 32, 33, 39], "leak": 36, "learn": [9, 24, 32, 33, 34, 36, 37, 38, 39, 40, 41], "least": [25, 35], "leav": [31, 33, 34, 36, 41, 44], "left": [10, 35], "left_on": [42, 43], "legend": [31, 32, 34, 35, 36, 37, 38, 40, 42, 43, 44], "len": [6, 31, 36, 37, 38, 40], "length": [6, 32, 34], "less": [6, 10, 40, 41], "lesson": 32, "let": [10, 31, 32, 34, 36, 38, 39, 40, 42, 43], "leuangthong": 10, "level": [9, 10, 12, 21, 24, 33, 35, 38], "li": 31, "librari": [24, 27], "libspatialindex": 27, "libtiff": 27, "like": [2, 23, 31, 36, 40], "lim": 15, "limit": [5, 6, 8, 9, 12, 32, 33, 35, 36], "limit_deviation_ratio": 9, "line": [9, 10, 11, 13, 27, 31, 34, 35, 38], "linear": [8, 9, 11, 12, 25, 31, 32, 34, 36, 37, 38], "linear_model": 31, "link": 33, "list": [1, 2, 6, 8, 9, 10, 11, 12, 22, 31, 36, 38], "literatur": 31, "live": 40, "ll": 37, "load": [35, 36, 37, 38, 40], "loc": [35, 36, 37, 38], "local": 11, "locat": [3, 6, 7, 8, 10, 37, 42, 43], "log": [9, 33, 39], "log_process": 9, "logarithm": 33, "logist": 32, "logistic_map": 32, "lon": [2, 3, 8, 29, 40, 41, 42, 43], "lon_col": 31, "lon_col_nam": [2, 4], "long": [27, 34, 35, 41, 42, 43, 44], "longer": 34, "longitud": [2, 3, 4, 8, 31, 35, 36, 37, 38], "look": [3, 31, 32, 33, 34, 35, 36, 38, 42, 43], "lot": 35, "low": [32, 33, 35, 37, 38, 40, 41, 42, 43], "lower": [9, 10, 12, 31, 33, 35, 38, 42, 43], "lowest": [9, 10, 31, 32, 35, 38], "lowest_rms": 31, "lt": [36, 37, 38, 39, 40, 41, 42, 43, 44], "lunch": 41, "m": [4, 6, 10, 12, 25, 32, 33, 35], "machin": [24, 34, 36], "maco": 27, "mad": 40, "mae": [5, 11, 12, 31], "mai": [4, 8, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], "main": [10, 41], "mainten": 17, "major": [9, 10, 11, 13, 34], "make": [3, 24, 32, 33, 34, 36], "manag": [33, 41], "mani": [3, 8, 9, 11, 12, 25, 31, 34, 35, 36, 41, 42, 43, 44], "manual": [37, 38, 40], "map": [8, 9, 24, 31, 32, 34, 39, 40, 42, 43, 44], "marker": 34, "markers": [34, 44], "materi": 24, "mathemat": [9, 25, 41], "matplotlib": [31, 32, 34, 36, 38, 39, 40], "matrix": [3, 4, 5, 7, 8, 13, 32], "max": [10, 13, 35, 36, 38, 42, 43], "max_it": [9, 41], "max_nugget": [11, 12, 31, 33], "max_rang": [8, 9, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "max_sil": [11, 12, 31], "max_tick": [3, 8, 36, 39], "maxim": [9, 38, 41], "maximum": [2, 3, 5, 7, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 41], "maximum_point_rang": 41, "maximum_rang": [40, 41], "md": 23, "mean": [5, 6, 8, 9, 10, 11, 12, 24, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43], "mean_absolute_error": 5, "mean_dir_pr": 39, "mean_directional_result": 39, "mean_filt": 32, "mean_relative_differ": 1, "meaning": 41, "measur": [5, 8, 10, 24, 26, 28, 33, 35, 36, 42, 43], "mec": [31, 32], "median": [10, 35, 38, 40, 42, 43], "medic": 17, "mediterranean": 10, "memori": 4, "merg": [41, 42, 43], "messag": 27, "messi": 31, "meter": [31, 34, 36, 37, 38], "method": [1, 2, 3, 5, 8, 9, 10, 11, 12, 29, 31, 35, 36, 38, 40, 41, 42, 44], "metric": [0, 4, 31, 32, 35, 36, 37, 38], "metricstypeselectionerror": [1, 12], "meus": [33, 39], "meuse_fil": [33, 39], "meuse_grid": 39, "meuse_grid_fil": 39, "middl": [35, 38], "might": [1, 12, 24, 27, 31, 33, 34, 35, 36, 38, 40, 44], "mile": 33, "min": [10, 13, 35, 36, 38, 42, 43], "min_deviation_decreas": 9, "min_deviation_ratio": 9, "min_nugget": [11, 12, 31], "min_rang": [11, 12, 31], "min_sil": [11, 12, 31], "mine": 41, "minim": [9, 10, 11, 12, 31], "minimum": [3, 7, 9, 10, 11, 12, 32, 35], "minimum_deviation_decreas": 9, "minor": [9, 10, 11, 13, 24, 34, 38], "minu": 9, "mirror": 31, "mislead": [42, 43], "miss": [8, 29, 31, 36, 40, 43], "mix": 38, "ml": 44, "mode": [35, 39], "model": [0, 1, 3, 7, 8, 9, 10, 11, 12, 13, 21, 24, 25, 28, 30, 33, 34, 35, 37, 41], "model_error": 12, "model_from_dict": 31, "model_from_json": 31, "model_nam": [31, 39], "model_param": 12, "model_paramet": 12, "model_rms": 31, "model_typ": 12, "models_group": [9, 11, 12, 29, 31, 32, 33, 36, 37, 38, 39, 41], "moder": [12, 33], "modul": [17, 21], "moli\u0144ski": [15, 24, 26, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "monei": 31, "monestiez": 10, "monitor": [1, 9, 35, 38], "moorman": [12, 33], "more": [4, 5, 6, 8, 9, 12, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "moreov": [31, 32], "most": [1, 5, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43], "mostli": [31, 33, 36, 38], "mountain": 36, "move": [36, 41], "mrd": 9, "msg": 31, "much": [34, 36, 37, 42], "multimod": 35, "multipl": [1, 2, 5, 8, 9, 11, 12, 29, 31, 32, 33, 34, 36, 38, 40, 41, 42, 43], "multipli": [12, 40], "multipolygon": [2, 40, 41, 42, 43], "multivariateregress": 8, "must": [3, 4, 5, 6, 8, 9, 10, 11, 13, 31, 32, 33, 34, 36, 37, 40, 41, 42, 43], "mxn": [4, 6], "n": [3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 27, 32, 33, 34, 35, 36, 37, 38, 39, 40], "n_lag": 35, "n_sill_valu": [11, 12], "name": [2, 4, 5, 9, 11, 12, 27, 31, 32, 35, 36, 38, 39, 40], "nan": [7, 36, 42, 43, 44], "nanmean": 39, "ncol": [38, 39, 40], "ndarrai": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 32], "ndarray_pydant": 1, "ne": [9, 10, 11, 13, 34, 39], "ne_sw_direct": 34, "need": [10, 31, 32, 35, 36, 42, 44], "neg": [3, 5, 7, 12, 25, 32, 35, 42, 43], "neglig": [10, 31], "neighbor": [2, 3, 5, 6, 7, 8, 9, 10, 24, 31, 33, 36, 37, 40, 42, 43, 44], "neighborhood": [33, 36], "neighbors_numb": 36, "neighbors_rang": [3, 5, 7, 8, 36, 40, 42, 43], "neighbour": [2, 3, 6, 7], "netherland": 10, "network": 14, "never": 31, "new": [6, 10, 31, 35, 36, 40], "new_val": 32, "next": [31, 33, 36, 38, 41], "ningchuan": 25, "nn": 36, "no_closest_neighbor": 2, "no_neighbor": [3, 5, 6, 8, 29, 36, 37, 39, 40], "no_possible_neighbor": 2, "nois": [36, 38], "non": [10, 11, 31, 33, 35], "none": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31, 33, 36], "normal": [9, 31, 33, 35, 39, 41], "north": [10, 39], "northeastern": [10, 40, 41, 42, 43, 44], "northwestern": 10, "note": [2, 4, 5, 9, 10, 33, 34, 35, 36, 38, 42, 43], "notebook": [34, 40], "noth": 12, "novak": [12, 33], "now": [1, 24, 27, 31, 35, 36, 37, 38, 40, 41, 43], "np": [2, 8, 9, 10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44], "nrow": [38, 39, 40], "ns_direct": 34, "nthe": 31, "nugget": [9, 11, 12, 29, 31, 32, 33, 36, 38, 39, 40, 41], "number": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 32, 35, 36, 37, 38, 40, 42, 43, 44], "number_of_lag": 40, "number_of_neighbor": [3, 5, 7, 8, 13, 37, 42, 43, 44], "number_of_neighbour": 6, "number_of_nugget": [11, 12, 31], "number_of_rang": [11, 12, 31], "number_of_sil": [11, 12, 31], "number_of_threshold": 11, "number_of_tri": 36, "number_of_work": [3, 8], "numer": 33, "numpi": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "nw": [9, 10, 11, 13, 34, 39], "nw_se_direct": 34, "o": [1, 2, 10, 31, 32], "object": [2, 9, 10, 31, 33, 34, 35, 40, 41, 44], "oblig": 31, "observ": [2, 5, 6, 8, 10, 12, 24, 31, 32, 34, 35, 40, 41], "obtain": [37, 40], "occur": 41, "offici": 1, "ok": [5, 8], "ok_interpol": 36, "ol": [3, 5, 7, 8, 13, 31, 36], "old": 1, "omnidirect": [9, 10, 11, 13, 34], "omnidirectional_covari": 1, "omnidirectional_covariogram": 1, "omnidirectional_point_cloud": 1, "omnidirectional_semivari": 1, "omnidirectional_variogram": 1, "omnidirectional_variogram_cloud": 1, "onc": [6, 9, 34], "one": [8, 10, 13, 33, 34, 35, 36, 38, 40, 41, 42, 43], "ones": 32, "ongo": 40, "onli": [3, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 38, 40, 42, 43], "open": [24, 26], "oper": [1, 3, 9, 27, 41], "opinion": [32, 34], "opportun": 37, "opposit": 41, "opt_dev": 9, "optim": [1, 9, 11, 12, 17, 24, 31, 32, 33, 34, 41], "optimal_devi": 9, "optimal_model": 33, "option": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31, 33, 36], "orang": 35, "order": [2, 10, 33, 38], "ordinari": [0, 1, 5, 13, 21, 24, 25, 28, 30, 38, 42, 43, 44], "ordinary_krig": [1, 8, 29, 36, 37, 40, 42, 43, 44], "org": [3, 5, 11, 24, 26, 33], "origin": [9, 10, 11, 13, 34, 41], "oscil": [32, 41], "other": [1, 2, 4, 9, 10, 24, 28, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44], "other_block": 2, "otherwis": [11, 12, 13, 36], "our": [5, 10, 16, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44], "out_cr": 31, "outcom": [35, 38], "outlier": [10, 30, 34, 42, 43], "output": [8, 10, 31, 32, 34, 35, 40, 41, 42, 43, 44], "over": [5, 8, 10, 31, 32, 36, 38, 39, 40, 41, 42, 43, 44], "overcom": [24, 42, 43], "overestim": [5, 12, 42, 43], "overfit": 31, "overlap": 2, "overview": 21, "overwrit": [10, 12, 35], "overwritten": 12, "own": [32, 42], "p": [2, 5, 9, 10, 11, 12, 25, 31, 32, 33, 37, 40, 41], "p_": [5, 9], "packag": [1, 8, 18, 22, 24, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "page": 32, "pair": [2, 5, 9, 10, 11, 12, 31, 32, 35, 38, 41], "panda": [1, 2, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "param": 13, "paramet": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "parameter": 12, "parametr": 11, "pardo": 25, "parkin": [12, 33], "parse_kriging_input": 1, "parse_point_support_distances_arrai": 1, "part": [32, 35, 36, 38, 40, 41, 42, 43], "particular": [42, 43], "pass": [2, 11, 12, 31, 33, 36, 37, 40, 44], "path": 32, "pattern": [38, 40, 42, 43], "pcol1": 40, "pcol2": 40, "pd": [2, 4, 31, 33, 35, 36, 37, 38, 39, 42, 43, 44], "pdf": 33, "pebesma": [33, 39], "penal": [5, 41], "peopl": 24, "per": [5, 9, 35, 36, 38, 40, 44], "percent": [5, 33], "percentag": [5, 11, 12, 31, 33], "perf_count": 34, "perform": [2, 5, 7, 8, 9, 12, 31, 34, 36, 38, 39, 40, 41, 42, 43, 44], "phenomenon": 33, "physalu": 10, "pick": [34, 42, 43], "pictur": 34, "piec": [42, 43], "pipelin": [0, 1, 21, 31, 35], "pivot": 36, "pixel": [13, 32, 38], "place": [9, 10, 11, 13, 34, 38, 40], "plain": 36, "plane": 34, "plasma": 44, "pleas": 24, "plot": [8, 9, 10, 11, 12, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "plot_devi": 9, "plot_deviation_chang": 41, "plot_experimental_bias_model": 8, "plot_theoretical_bias_model": 8, "plot_trend_surfac": 8, "plot_variogram": [9, 41], "plot_weight": 9, "plot_weights_chang": 41, "plt": [31, 32, 34, 36, 38, 39, 40], "plu": [31, 35], "pm2": 34, "pm2_5": 34, "point": [0, 1, 3, 5, 6, 9, 10, 11, 12, 13, 21, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 39, 41, 42, 43], "point_cloud_filt": 38, "point_cloud_semivari": 1, "point_data": 29, "point_dist": [1, 4], "point_support": [2, 3, 7, 9, 41, 42, 43, 44], "point_support_blocks_index_nam": 2, "point_support_data": 2, "point_support_dataset": 9, "point_support_to_dict": 1, "point_support_tot": 2, "points_from_xi": [31, 35, 36, 37, 38, 39], "points_geometry_column": [2, 41, 42, 43, 44], "points_to_lon_lat": 1, "points_value_column": [2, 41, 42, 43, 44], "pointsupport": [1, 2, 3, 4, 7, 9, 21, 41, 42, 43, 44], "pointsupportdist": 2, "poisson": [0, 1, 10, 21, 24, 28, 30, 31, 40, 41], "poland": [31, 34], "pole": 10, "polish": 31, "pollut": [28, 34], "polygon": [2, 4, 7, 24, 40, 41, 42, 43], "polygon_id": [40, 41, 42, 43, 44], "polygon_lay": [40, 41, 42, 43, 44], "polygon_valu": [40, 41, 42, 43, 44], "polynomi": 32, "poorli": [40, 41, 42, 43], "pop": 2, "pop10": [2, 41, 42, 43, 44], "popul": [2, 10, 24, 35, 36, 37, 38, 40, 41, 42, 43, 44], "popular": 5, "population_lay": [41, 42, 43, 44], "posit": [5, 12, 32, 35, 40, 42, 43], "possibl": [8, 10, 12, 31, 32, 33, 34, 40], "possible_variogram": 10, "potenti": [3, 7], "power": [6, 8, 9, 11, 12, 31, 32, 36, 37, 42, 43], "power_model": 31, "pp": [5, 25], "practic": 33, "pre": [32, 40], "precis": 36, "pred": [39, 42, 43], "pred_col_nam": 40, "predefin": 31, "predict": [3, 5, 7, 8, 12, 17, 24, 29, 37, 38, 39, 42, 43, 44], "predicted_arrai": 5, "preds_col": 40, "prep_theo": 38, "prep_theo_no_out": 38, "prepar": [10, 11, 29, 33, 35], "prepare_pk_known_area": 1, "preprocess": 38, "presenc": [9, 25, 41], "present": [10, 32, 34, 38, 40], "preserv": 13, "previou": [32, 34, 43], "price": [17, 33], "primarili": 38, "print": [2, 4, 9, 10, 12, 29, 31, 33, 34, 36, 37, 40, 42, 43], "privaci": 24, "privat": 1, "probabl": [24, 32, 36, 38, 40, 41, 42, 43], "problem": [27, 31, 36, 41, 42, 43], "proc": 5, "proc_no_interpol": 38, "proc_raw_interpol": 38, "proce": [31, 35, 38, 40], "procedur": [9, 41], "process": [2, 3, 5, 8, 9, 10, 12, 21, 24, 31, 33, 36, 37, 38, 39, 40, 42, 43, 44], "process_mean": [8, 36], "produc": [38, 44], "product": [24, 31], "profil": 17, "profound": 1, "program": [11, 25, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "progress": [2, 3, 8], "progress_bar": [3, 8, 36, 38], "project": [2, 3, 17, 27, 31, 33], "pronounc": [38, 39], "properli": 27, "properti": [2, 5, 9, 12, 31, 33, 34, 35, 38, 42, 43], "proport": 37, "protect": [12, 24], "protect_from_overwrit": 12, "provid": [4, 5, 7, 9, 10, 12, 13, 24, 36, 39], "ps_block": [2, 3, 4], "ps_geometri": 2, "ps_layer_nam": 2, "ps_valu": 2, "pt": [11, 36, 41], "ptp": 33, "public": [10, 33], "publicznych": 28, "publish": 10, "purpl": 34, "purpos": [31, 32, 36], "put": 36, "pw": 37, "py": 39, "pyinterpol": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 21, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "pyplot": [31, 32, 34, 36, 38, 39, 40], "pyproj": 2, "pyproject": [22, 27], "python": [24, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "python3": 27, "pythonem": 28, "q": 40, "q1": [35, 38], "q2": 38, "q3": [35, 38], "qgi": 44, "qualiti": [28, 36], "quantil": 38, "quartil": [10, 35, 38, 42, 43], "question": [32, 40], "quick": 31, "quickli": 37, "quickstart": 24, "r": [12, 32, 33, 39], "r_df": 31, "radii": 34, "radiu": 41, "rais": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 31, 37], "raise_when_negative_error": [3, 7, 42, 43], "raise_when_negative_predict": [3, 7], "random": [33, 38, 42, 43], "randomli": [33, 36, 42, 43], "rang": [3, 5, 7, 8, 9, 11, 12, 13, 29, 31, 32, 34, 35, 36, 38, 40, 42, 43], "rare": [10, 36, 39], "raster": 0, "raster_dict": 13, "rate": [2, 10, 24, 28, 40, 41, 42, 43, 44], "rather": [31, 38, 41], "ratio": [3, 5, 9, 11, 12, 13, 33, 42, 43], "raw": [1, 38], "raw_interpol": 38, "raw_no_interpol": 38, "raw_theo": 38, "raw_theo_no_out": 38, "raw_variogram_filt": 38, "rawpoint": 1, "re": [24, 36, 37, 41, 44], "reach": 31, "read": [1, 7, 12, 29, 31, 35, 38], "read_block": 1, "read_csv": [1, 31, 33, 35, 36, 37, 38, 39], "read_fil": [2, 29, 34, 40, 41, 42, 43, 44], "read_txt": 1, "readi": 40, "real": [3, 5, 8, 31, 32, 35, 36, 38], "real_arrai": 5, "realist": 31, "realiz": [36, 42, 43], "realli": 36, "reason": [32, 33, 38], "recal": [10, 34], "recommed": 34, "recommend": [3, 5, 7, 8, 13, 34, 36], "record": [11, 12, 36], "rectangular": 42, "recurr": 32, "red": [33, 34, 39, 40, 44], "reduc": 33, "refactor": 1, "refer": [2, 5, 9, 11, 12, 24, 31, 37], "reference_input": 10, "reflect": 40, "reg": [3, 44], "reg_mod": 41, "reg_variogram": 9, "regard": 24, "region": [24, 42, 43, 44], "regress": 8, "regular": [1, 2, 3, 9, 21, 24, 30, 31, 40], "regular_grid_point": 40, "regularize_variogram": 9, "regularized_model": 9, "regularized_vari": 9, "regularized_variogram": [9, 41, 42, 43, 44], "rel": [5, 9, 34, 42, 43], "relat": [17, 31, 32, 33, 36, 37, 38, 41], "releas": [1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "reliabl": 43, "rememb": [31, 37], "remot": 44, "remov": [1, 2, 8, 10, 36], "remove_outli": [10, 35, 38, 40], "rental": 33, "rep_point": 2, "rep_points_column_nam": 2, "repeat": [42, 43], "repetit": 9, "report": [17, 24], "repres": [2, 4, 5, 6, 31, 32, 33, 34, 35, 38, 40, 42, 43], "represent": [2, 24, 32, 35, 42, 44], "representative_point": [40, 41, 42, 43], "representative_points_arrai": 2, "representative_points_column_nam": 2, "representative_points_from_centroid": 2, "representative_points_from_largest_area": 2, "representative_points_from_random_sampl": 2, "reproduc": 31, "reproject": [2, 31], "reproject_flat": 31, "reps_deviation_decreas": 9, "requir": [11, 12, 18, 27, 36, 37, 40, 41, 44], "research": 5, "reshap": 32, "resist": 31, "resolut": 24, "resourc": 25, "respect": 40, "respons": [8, 36], "result": [6, 7, 8, 9, 12, 13, 34, 36, 37, 38, 39, 40, 41, 42, 43], "retriev": [2, 36, 41], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 31, 32, 33, 35, 36, 44], "return_dist": 2, "return_param": 12, "rgeometri": 39, "rid": 3, "right": [10, 24, 35, 36], "right_on": [42, 43], "rise": [35, 40], "risk": [24, 40, 42, 44], "rmse": [3, 5, 9, 11, 12, 31, 32, 36, 40, 42, 43, 44], "role": 31, "room": 35, "root": [5, 9, 11, 12, 31, 32, 36, 37, 42, 43], "root_mean_squared_error": 5, "roughli": 38, "row": [2, 4, 6, 32, 35], "rtree": 27, "run": [24, 27, 31, 36, 38, 41, 42, 43], "runetimeerror": [8, 9, 10], "runtimewarn": 39, "rush": 31, "rx_": 32, "rxn": 32, "safe": [9, 11, 12, 31, 33, 41], "sagepub": 25, "same": [1, 2, 6, 9, 10, 11, 12, 13, 29, 31, 32, 34, 35, 36, 37, 38, 40, 43], "samivari": 9, "sampl": [2, 5, 8, 31, 33, 36, 37, 38, 42, 43], "sample_id": [42, 43], "satellit": 38, "satur": 38, "save": [9, 12, 31, 35, 40, 41], "scalabl": 42, "scale": [12, 17, 33], "scan": 36, "scatter": [10, 31, 32], "scatterplot": [10, 35], "scenario": [31, 36, 38, 40], "scienc": [11, 12, 33], "scientist": 24, "scipi": [4, 32, 35], "score": [35, 38], "scott": 15, "scottgallach": 15, "scratch": 32, "sdesabbata": 15, "sdi": 33, "se": [9, 10, 11, 13, 34, 39], "sea": 10, "sean": 15, "seanjunheng2": 15, "search": [3, 5, 7, 8, 31, 33, 36, 41, 42, 43], "search_radiu": 29, "second": [8, 10, 32, 34, 35, 36], "see": [4, 8, 9, 10, 11, 12, 31, 34, 35, 36, 38, 40, 41, 42, 43], "seem": 40, "seen": [33, 41], "select": [2, 3, 5, 8, 9, 10, 11, 13, 31, 36, 42, 43], "select_centroid_poisson_kriging_data": 1, "select_distances_between_block": 2, "select_neighbors_pk_centroid": 1, "select_neighbors_pk_centroid_with_angl": 1, "select_poisson_kriging_data": 1, "select_values_between_lag": 1, "select_values_in_rang": 1, "select_values_in_range_from_datafram": 1, "semi": [31, 34], "semi_major_axis_s": 34, "semi_model": 40, "semivar": 29, "semivari": [0, 2, 5, 9, 11, 12, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "semivariance_between_point_support": 9, "semivariance_fn": 1, "semivariogram": [0, 1, 2, 5, 7, 8, 10, 13, 21, 24, 25, 30, 35, 37, 38], "semivariogram_model": [3, 7, 13, 42, 43, 44], "semivariogramerrormodel": 1, "sens": [3, 44], "sensor": 38, "separ": [9, 11, 42, 43, 44], "sequenc": 32, "seri": 2, "serv": 37, "server": 16, "servic": [17, 38], "set": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43], "set_blocks_dataset": 1, "set_current_as_optim": 9, "set_index": [34, 40], "set_titl": [38, 39, 40], "set_xlabel": 38, "set_ylabel": 38, "setdifferencewarn": 1, "setup": 24, "seven": 32, "shape": [6, 31, 32, 38, 41, 42, 43, 44], "sharei": 40, "sharex": 40, "sharpli": 38, "shell": 20, "short": 34, "shorter": [34, 39], "shortest": 41, "should": [1, 2, 5, 6, 8, 9, 11, 12, 22, 27, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "shouldn": [5, 12, 34, 35, 38, 41], "show": [2, 3, 8, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43], "show_progress_bar": 8, "show_semivariogram": 9, "shp": 40, "side": 10, "sig": [7, 42, 43], "sign": [31, 38, 41], "signal": [32, 35], "signific": [37, 38, 41], "significantli": 34, "sill": [9, 11, 12, 29, 31, 32, 33, 36, 40], "sill_from_valu": [11, 12], "sill_from_vari": [11, 12], "similar": [10, 31, 32, 33, 34, 36, 38, 42], "simonmolinski": [15, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "simpl": [0, 5, 21, 24, 30, 32, 33, 37, 40], "simple_krig": [1, 8, 36], "simplest": 36, "simpli": [42, 43], "simplif": 42, "simplifi": [36, 37, 43], "simul": [32, 35, 36], "simultan": 34, "singl": [2, 9, 10, 11, 12, 13, 31, 32, 34, 36, 37, 42, 43], "singular": [3, 5, 7, 8, 13], "situat": 31, "size": [4, 9, 10, 11, 13, 32, 34, 40, 41, 42, 43, 44], "sk": [5, 8], "sk_interpol": 36, "sk_mean": 5, "skew": [10, 33, 35, 40], "skip": 40, "slice": 39, "slightli": [32, 38], "slow": [34, 41], "slower": 43, "slowest": [], "slowli": 41, "small": [9, 32, 34, 37, 40, 41, 42, 43], "smaller": [6, 9, 32, 34, 35, 38, 40, 41], "smallest": 34, "smape": [5, 11, 12, 31], "smooth": [3, 21, 40, 43], "smooth_area_to_point_pk": 1, "smooth_block": [1, 3, 44], "smooth_plot_data": 44, "smoother": 32, "smrd": 9, "so": [10, 24, 31, 36, 41, 42], "social": 24, "societi": [12, 33], "socio": 28, "softwar": [24, 26], "soil": [12, 33], "some": [1, 24, 25, 27, 31, 32, 33, 34, 38, 40, 41, 42], "someon": 40, "someth": 31, "sometim": [10, 31, 33, 34, 42, 43], "sophist": [35, 36], "sort": [36, 38], "sourc": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 24, 26, 37], "south": [10, 39], "southeastern": 10, "southwestern": 10, "space": [11, 12, 17, 31], "spars": [10, 32, 42, 43], "sparse_data": 32, "spatial": [1, 2, 4, 10, 12, 17, 24, 26, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44], "spatial_depend": 31, "spatial_dependency_level": 12, "spatial_dependency_ratio": [12, 33], "spatial_dependency_strength": [12, 33], "spatial_index": 31, "spatialindex": 27, "speak": 32, "speci": 41, "special": [31, 32, 34], "specialist": [], "specif": [5, 8, 10, 17, 31, 34, 35, 36, 38], "specifi": 2, "spectral_r": [40, 42, 43, 44], "sph": 40, "spheric": [8, 9, 11, 12, 29, 31, 32, 39, 40], "spherical_model": 31, "springer": [25, 32], "sqrt": [5, 36, 37, 42, 43], "squar": [5, 9, 11, 12, 25, 31, 32, 36, 37, 38, 42, 43], "squared_error": [42, 43], "src": 21, "stabil": 41, "stabl": 24, "stage": [24, 38], "standard": [10, 35, 38, 42, 43], "start": [31, 32, 33, 34, 35, 36, 41, 42, 43, 44], "stat": 38, "state": [32, 40], "statement": 43, "station_id": 34, "stationari": 31, "statist": [10, 24, 31, 36, 38, 42, 43], "statu": 31, "std": [10, 35, 36, 38, 42, 43], "step": [9, 23, 27, 30, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "step_siz": [8, 9, 10, 11, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "step_size_point": 41, "steroid": 35, "still": [31, 32, 35, 36, 39, 41], "stop": 9, "store": [2, 9, 11, 12, 31, 40, 41, 44], "store_dropped_point": 2, "store_model": 9, "str": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 31, 34, 40, 41], "straight": 34, "straightforward": 44, "strength": [12, 31, 33, 36], "string": [2, 10], "strong": [12, 31, 33], "stronger": 6, "structur": [0, 18, 32, 41], "studi": [5, 8, 12, 36, 41], "subplot": [11, 38, 39, 40], "subset": [10, 36], "substanti": 33, "subtract": 35, "sudo": 27, "suffici": 41, "suitabl": 38, "sum": 10, "sum_": [5, 9, 37], "summari": 40, "support": [0, 3, 4, 7, 9, 24, 35, 40, 41, 42, 43, 44], "suptitl": 40, "sure": [31, 34, 36, 42, 43], "surf_blur": 32, "surfac": [8, 21, 36], "sw": [9, 10, 11, 13, 34, 39], "swath": 24, "symmetr": [5, 9, 11, 12, 31], "symmetric_mean_absolute_percentage_error": 5, "symmetric_mean_relative_differ": 1, "system": [2, 8, 9, 10, 11, 13, 17, 27, 31, 32, 34, 36], "szymon": [15, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "t": [1, 2, 3, 5, 7, 8, 9, 12, 13, 23, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "t0e": 34, "t0t": 34, "tail": [35, 39], "take": [2, 3, 8, 10, 31, 32, 33, 34, 36, 38, 40, 41, 42, 43], "taken": 36, "target_cr": 2, "task": 38, "teach": 36, "technic": 32, "techniqu": [8, 24, 36, 37, 38, 42, 43, 44], "tell": [5, 31, 35, 38, 40, 42, 43], "temperatur": [10, 39], "tempor": 17, "tend": [10, 33], "termin": [9, 27], "test": [9, 10, 11, 12, 18, 21, 31, 32, 35, 36, 37, 38, 40, 42, 43], "test_sampl": 36, "test_undefin": 5, "text": 31, "th": [5, 37], "than": [3, 5, 6, 8, 9, 10, 11, 12, 13, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43], "thank": [25, 40], "thei": [2, 31, 36, 38, 43], "them": [3, 8, 9, 24, 25, 31, 32, 36, 38, 40, 41, 42, 43, 44], "theo_semi": 40, "theo_var": [12, 36, 37], "theoret": [0, 1, 3, 8, 9, 11, 21, 28, 29, 32, 33, 36, 39, 41, 44], "theoretical_block_model": 9, "theoretical_indicator_variogram": 11, "theoretical_model": [3, 5, 8, 9, 29, 36, 37, 38, 39, 40], "theoretical_semivariogram": 40, "theoretical_valu": 12, "theoretical_variogram_model": 12, "theoreticalindicatorvariogram": [1, 8, 11], "theoreticalmodelfunct": 1, "theoreticalsemivariogram": 40, "theoreticalvariogram": [2, 3, 5, 7, 8, 9, 12, 13, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "theoreticalvariogrammodel": [1, 12], "theori": 32, "theoriticalvariogram": 31, "therefor": 33, "thi": [1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "thing": [33, 35, 37], "third": [10, 38], "those": [1, 2, 3, 10, 27, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "three": [27, 31, 32, 34, 35], "threshold": [8, 11], "through": 41, "thu": [5, 32, 34, 35, 36, 38, 40, 41], "tick": 17, "time": [10, 31, 32, 34, 35, 36, 38, 40, 41, 42, 43], "titl": [31, 32, 34, 38], "to_cr": [35, 36, 37, 38], "to_dict": [12, 31], "to_fil": [40, 44], "to_json": [12, 31], "to_numpi": [31, 38, 39, 40, 42, 43], "to_tiff": 1, "tobiasz": 15, "tobiaszwojnar": 15, "tobler": [10, 33], "todo": [12, 20], "toler": [8, 9, 10, 11, 13, 31, 34, 36, 39], "toml": [22, 27], "too": [34, 35, 36, 38, 40, 41, 42, 43, 44], "tool": [21, 24, 35, 38], "top": [34, 35, 38, 40], "top_limit": 38, "total": [2, 33], "total_pop10": 41, "toward": [35, 40], "tqdm": [36, 37, 42, 43, 44], "trace": 31, "track": [9, 41], "tracker": 24, "train": [36, 37, 38, 42, 43], "train_without_outli": 38, "transform": [1, 2, 3, 9, 21, 24, 29, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44], "transform_blocks_to_numpi": 1, "transform_cr": 2, "transform_ps_to_dict": 1, "transit": [42, 43], "treat": [31, 34, 38], "trend": [8, 24, 31, 34, 35, 41], "trend_model": 8, "trend_valu": 8, "tri": 11, "triangl": 34, "triangle_mask": 1, "triangular": [], "tricki": [32, 35], "true": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 31, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "try": [42, 43], "tune": 43, "tupl": [2, 5, 8, 11, 12, 33], "turco": [12, 33], "turgut": 15, "tutori": [20, 21, 24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "two": [2, 5, 8, 9, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44], "txe": 34, "txt": 34, "type": [1, 3, 8, 9, 10, 11, 12, 31, 35, 38, 40], "u": [5, 10, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "u_": 9, "u_i": 10, "uk": 25, "unabl": 37, "unbias": 36, "uncertainti": [11, 29, 40], "uncertainty_col_nam": 40, "undefin": [5, 9, 31, 36], "undefinedsmapewarn": 5, "under": [35, 42, 43], "underestim": [5, 12, 42, 43], "underforecast": 5, "understand": [3, 31, 34, 35, 38, 41, 44], "understood": [42, 43], "undesir": 44, "unfortun": 37, "uniform": 38, "union": [2, 4, 8, 10, 12], "uniqu": [2, 4], "unique_block": 2, "unit": [3, 8, 9, 21, 25, 32, 34, 40, 41], "univers": [0, 24], "universalkrig": 8, "unknown": [3, 6, 7, 8, 12, 32, 37, 40], "unknown_block_index": [7, 42, 43], "unknown_loc": [3, 6, 8, 29, 36, 37, 38, 39, 40], "unknown_point": 29, "unnecessari": 40, "unreli": [38, 42, 43], "unsupport": 9, "untouch": 31, "up": [10, 31, 33, 35, 38], "updat": [1, 2, 9, 11, 12, 24], "upper": [33, 35, 38], "url": 28, "us": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 24, 27, 29, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "use_all_model": 8, "use_all_neighbors_in_rang": [3, 5, 8, 36, 39, 40], "use_point_support_cr": 2, "usecol": [33, 39], "useless": [32, 38], "user": [1, 2, 9, 12, 30, 34, 40], "usual": [5, 12, 29, 31, 32, 34, 36, 41], "util": 36, "v": [9, 12, 25, 33, 35], "v_": 9, "v_h": 9, "val": [32, 37, 41], "val_col_nam": 4, "valid": [0, 34, 35, 36, 38, 43], "validate_bin": 1, "validate_direct": 1, "validate_direction_and_toler": 1, "validate_krig": 5, "validate_plot_attributes_for_experimental_variogram": 1, "validate_plot_attributes_for_experimental_variogram_class": 1, "validate_point": 1, "validate_selected_error": 1, "validate_semivariance_weight": 1, "validate_theoretical_variogram": 1, "validate_toler": 1, "validate_weight": 1, "valu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 29, 31, 32, 33, 34, 35, 37, 38, 40, 42, 43, 44], "value1": 32, "value2": 32, "value_a": 2, "value_b": 2, "value_col": 34, "value_column_nam": [2, 40, 41, 42, 43, 44], "valueerror": [6, 7, 9, 12], "var": 39, "varfit": 25, "vari": [43, 44], "variabl": [12, 33, 34, 36, 37, 40, 41], "varianc": [3, 5, 7, 8, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 39, 42, 43], "variat": [33, 34], "variogram": [0, 1, 3, 5, 7, 8, 11, 12, 13, 21, 24, 25, 28, 29, 30, 33, 36, 37, 39, 40, 41, 44], "variogram_cloud": 40, "variogram_model_typ": [12, 31], "variogram_rang": 31, "variogram_weighting_method": [9, 41], "variogramcloud": [10, 35, 38, 40], "variogrammodelnotseterror": 1, "variogrampoint": [1, 10], "vc": [35, 40], "vc1000": 35, "ve": [35, 36, 43], "vector": 6, "verbos": [2, 3, 9, 12, 41, 42, 43, 44], "veri": [3, 8, 31, 32, 33, 34, 35, 37, 40, 42, 43, 44], "version": 18, "view": [5, 38], "vignett": 33, "violin": [10, 38, 40], "violinplot": [10, 38], "visibl": [35, 38], "visual": [0, 8, 31, 34, 35, 38, 40, 42, 43, 44], "visul": 38, "vital": 31, "viz": 21, "vmin": [31, 35, 36, 37, 38], "voila": 37, "vol": 25, "volum": [10, 40], "vv": 39, "w": [9, 10, 11, 13, 34, 39], "wa": [1, 2, 9, 10, 24, 25, 32, 34], "wai": [2, 10, 31, 32, 33, 35, 37, 40], "want": [2, 3, 5, 8, 34, 36, 40, 44], "warn": [5, 10, 11, 12, 31], "we": [3, 5, 7, 8, 10, 13, 24, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "we_direct": 34, "weak": [12, 32, 33, 34], "web": 33, "weight": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 24, 25, 31, 32, 36, 37, 38, 41], "weight_experimental_semivari": 1, "weighted_avg_point_support_semivari": 1, "weighted_block_to_block_dist": 2, "weighted_root_mean_squared_error": 5, "weightedblock2blocksemivari": 1, "weightedblock2pointsemivari": 1, "weighting_method": [5, 9], "weights_arrai": 1, "well": [5, 8, 31, 34, 36, 38, 41, 42, 43], "were": [4, 31], "weren": 2, "west": [10, 36, 39], "western": 36, "whale": 10, "what": [3, 5, 7, 8, 13, 31, 32, 34, 40, 41, 42, 43], "wheel": 27, "when": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 31, 32, 35, 36, 37, 38, 40, 41, 42, 43], "whenev": 33, "where": [2, 3, 4, 5, 6, 8, 9, 10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43], "which": [2, 8, 10, 12, 24, 27, 31, 32, 34, 37, 38, 40], "whisker": [35, 38], "white": [40, 44], "whole": [9, 31, 36], "why": [5, 8, 35, 36, 37, 38, 40, 41], "wide": [42, 43], "width": [8, 13, 34], "wielkopolski": 31, "wiki": 5, "wikipedia": 5, "wildli": [31, 36], "wise": 9, "with_stat": 38, "with_std": 38, "within": [2, 3, 5, 6, 8, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 41, 43], "without": [27, 31, 32, 33, 38, 40], "without_stat": 38, "without_std": 38, "wkt": 2, "wojnar": 15, "won": [23, 31, 32, 41], "word": [32, 38, 40], "work": [5, 11, 12, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "world": [32, 33, 36, 38], "worldwid": 24, "worsen": 36, "worst": [36, 42, 43], "would": [23, 40], "wrap": 32, "wrmse": 5, "wrong": [12, 31, 42, 43], "wronggeometrytypeerror": 1, "wzbogacani": 28, "x": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 21, 24, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "x1": 32, "x2": 32, "x_": 32, "x_i": 10, "xiao": 25, "xlabel": [31, 32, 36, 38], "xn": 32, "xyval": 32, "y": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "y1": 32, "y2": 32, "y_": 5, "ye": 35, "yellow": 34, "yet": [9, 12, 24, 27, 37], "yhat": [12, 31, 32, 36], "ylabel": [31, 32, 36, 38, 39, 42, 43], "you": [3, 5, 7, 8, 13, 23, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "your": [3, 5, 7, 8, 9, 13, 24, 27, 32, 34, 35, 37, 38, 40], "z": [10, 28, 35, 37, 38], "z_": 37, "z_i": 10, "z_lower_limit": [10, 35, 38], "z_upper_limit": [10, 35, 38], "z_w": 10, "zero": [9, 10, 12, 31, 35, 36, 37, 40, 41], "zhat": [7, 42, 43], "zinc": [33, 39], "zscore": [10, 35, 38], "zx11_2ts7tjfsny482gs54s80000gr": 39}, "titles": ["API", "Changes between version 0.x and 1.x", "Core data structures", "Pipelines", "Distance", "Models evaluation", "Inverse Distance Weighting (IDW)", "Block and Poisson Kriging", "Point Kriging", "Semivariogram Deconvolution", "Experimental Semivariance and Covariance", "Indicator Semivariogram", "Theoretical Semivariogram", "Visualization", "Community", "Contributors", "Network", "Use Cases", "Development", "Known Bugs", "Development", "Package structure", "Requirements and dependencies (version >= 1)", "Tests and contribution", "Pyinterpolate", "Bibliography", "Citation", "Setup", "Learning Materials", "Quickstart", "Tutorials", "Semivariogram exploration", "Semivariogram models", "Spatial Dependency Index", "Directional Semivariogram", "Variogram Points Cloud", "Ordinary and Simple Kriging", "Benchmarking Kriging", "Outliers and Kriging", "Directional Ordinary Kriging", "Blocks to points with Ordinary Kriging", "Semivariogram Regularization", "Poisson Kriging Centroid-based approach", "Area-to-area Poisson Kriging", "Area-to-Point Poisson Kriging"], "titleterms": {"": 15, "0": [1, 24], "1": [1, 22, 24, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "2": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "3": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "4": [31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44], "5": [31, 34, 35, 38, 40, 41, 42, 43], "6": 31, "The": 27, "addit": 27, "advanc": 30, "aggreg": 9, "analyz": [35, 38], "api": [0, 33], "approach": 42, "ar": 1, "area": [7, 42, 43, 44], "author": 15, "autom": [], "automat": 31, "avail": 1, "base": [7, 38, 42], "bechmark": 37, "beginn": 30, "benchmark": 37, "between": 1, "bibliographi": 25, "block": [2, 4, 7, 40, 44], "blog": 28, "box": 35, "bug": 19, "build": 27, "calcul": 32, "canva": 40, "case": [17, 34], "centroid": [7, 42], "chang": 1, "changelog": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "chapter": [31, 32], "check": 35, "citat": [24, 26], "class": 1, "cloud": [10, 35, 38], "commun": 14, "compar": [32, 34, 39], "conda": 27, "content": [24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "contribut": 23, "contributor": 15, "core": 2, "covari": 10, "creat": [32, 34, 35, 36, 38, 39], "cross": 5, "data": [2, 31, 38, 39, 40, 41, 42, 43, 44], "dataset": 31, "deconvolut": 9, "depend": [22, 27, 33], "detect": [35, 40], "develop": [18, 20], "deviat": 9, "differ": [33, 38], "direct": [10, 34, 39], "distanc": [4, 6], "distribut": 38, "do": 33, "each": 35, "east": 34, "element": 33, "ellipt": 34, "error": 27, "evalu": [5, 36, 42, 43], "exampl": 33, "experiment": [10, 31, 32, 34, 35], "explor": 31, "export": [31, 41, 44], "extent": 33, "fail": 27, "filter": [42, 43], "fit": [31, 32, 36, 40], "from": [35, 38], "function": 1, "guidelin": 27, "i": [33, 35], "idw": [6, 37], "import": [24, 31], "includ": 34, "index": 33, "indic": [8, 11], "instal": [27, 29], "intermedi": 30, "interpol": [39, 40], "introduct": [24, 37], "invers": 6, "isotrop": 34, "joss": 15, "known": 19, "krige": [3, 7, 8, 29, 36, 37, 38, 39, 40, 42, 43, 44], "lag": 35, "lead": 34, "learn": 28, "libspatialindex_c": 27, "linux": 27, "load": [42, 43, 44], "locat": 36, "longer": 1, "maintain": 15, "manual": 31, "materi": 28, "method": 34, "metric": 5, "model": [5, 31, 32, 36, 38, 39, 40, 42, 43, 44], "more": 35, "neighbor": 34, "network": 16, "new": 1, "north": 34, "northeast": 34, "northwest": 34, "notebook": 27, "notic": 24, "ordinari": [3, 8, 29, 36, 39, 40], "outlier": [35, 38, 40], "output": [36, 37], "over": 33, "packag": 21, "paramet": 41, "perform": 37, "pip": 27, "pipelin": 3, "plot": 35, "point": [2, 4, 7, 8, 35, 38, 40, 44], "poisson": [3, 7, 42, 43, 44], "post": 28, "potenti": 38, "predict": 36, "prepar": [31, 38, 39, 40, 41, 42, 43, 44], "prerequisit": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "present": 28, "process": [34, 41], "public": 28, "pyinterpol": 24, "pylibtiff": 27, "quickstart": 29, "random": 32, "raster": 13, "regular": [41, 42, 43, 44], "remov": [35, 38, 40], "requir": 22, "resourc": 35, "result": 44, "review": 15, "same": 33, "scatter": 35, "select": 34, "semivari": 10, "semivariogram": [9, 11, 12, 31, 32, 34, 36, 39, 40, 41, 42, 43, 44], "set": [31, 36, 41], "setup": 27, "simpl": [8, 36], "smooth": 44, "so": 27, "south": 34, "southeast": 34, "southwest": 34, "spatial": 33, "statist": 35, "structur": [2, 21], "studi": 33, "support": [1, 2], "surfac": 32, "tabl": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "temporarili": 1, "test": 23, "theoret": [12, 31], "tool": 37, "topic": 27, "triangular": 34, "tutori": 30, "univers": 8, "unknown": 36, "us": [17, 33], "v": 34, "valid": [5, 37], "valu": 36, "variogram": [9, 10, 31, 32, 34, 35, 38], "version": [1, 22, 24], "violin": 35, "visual": [13, 41], "we": 33, "weight": 6, "west": 34, "what": 33, "why": 33, "work": 27, "workshop": 28, "x": 1}}) \ No newline at end of file +Search.setIndex({"alltitles": {"1. Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. Example: Spatial Dependence over the same study extent but for different elements": [[33, "3.-Example:-Spatial-Dependence-over-the-same-study-extent-but-for-different-elements"]], "3. Fit semivariogram model": [[40, "3.-Fit-semivariogram-model"]], "3. Interpolate with directional Kriging": [[39, "3.-Interpolate-with-directional-Kriging"]], "3. Perform Kriging and validate outputs": [[37, "3.-Perform-Kriging-and-validate-outputs"]], "3. Predict values at unknown locations and evaluate output": [[36, "3.-Predict-values-at-unknown-locations-and-evaluate-output"]], "3. Prepare data for Poisson Kriging": [[42, "3.-Prepare-data-for-Poisson-Kriging"], [43, "3.-Prepare-data-for-Poisson-Kriging"]], "3. Regularize semivariogram": [[41, "3.-Regularize-semivariogram"]], "3. Smooth blocks": [[44, "3.-Smooth-blocks"]], "4. API": [[33, "4.-API"]], "4. Compare models": [[39, "4.-Compare-models"]], "4. Compare triangular vs elliptical neighbors selection methods": [[34, "4.-Compare-triangular-vs-elliptical-neighbors-selection-methods"]], "4. Experimental variogram from the point cloud": [[35, "4.-Experimental-variogram-from-the-point-cloud"]], "4. Export results": [[44, "4.-Export-results"]], "4. Filtering areas": [[42, "4.-Filtering-areas"], [43, "4.-Filtering-areas"]], "4. Prepare canvas": [[40, "4.-Prepare-canvas"]], "4. Remove outliers from the point cloud": [[38, "4.-Remove-outliers-from-the-point-cloud"]], "4. Visualize process": [[41, "4.-Visualize-process"]], "5. Evaluate": [[42, "5.-Evaluate"], [43, "5.-Evaluate"]], "5. Export semivariogram": [[41, "5.-Export-semivariogram"]], "5. Interpolate": [[40, "5.-Interpolate"]], "5. Is variogram point cloud a scatter plot?": [[35, "5.-Is-variogram-point-cloud-a-scatter-plot?"]], "5. Kriging Models based on different variograms": [[38, "5.-Kriging-Models-based-on-different-variograms"]], "API": [[0, null]], "Advanced": [[30, "advanced"]], "Aggregated Variogram": [[9, "aggregated-variogram"]], "Area-to-Point Poisson Kriging": [[44, null]], "Area-to-area Poisson Kriging": [[7, "area-to-area-poisson-kriging"], [43, null]], "Area-to-point Poisson Kriging": [[7, "area-to-point-poisson-kriging"]], "Author(s)": [[15, "author-s"]], "Beginner": [[30, "beginner"]], "Benchmarking Kriging": [[37, null]], "Bibliography": [[25, null]], "Block": [[4, "block"]], "Block and Poisson Kriging": [[7, null]], "Blocks": [[2, "blocks"]], "Blocks to points with Ordinary Kriging": [[40, null]], "Blog posts": [[28, "blog-posts"]], "Box plot": [[35, "Box-plot"]], "Case 1: West-East direction": [[34, "Case-1:-West-East-direction"]], "Case 2: North-South direction": [[34, "Case-2:-North-South-direction"]], "Case 3: Northwest-Southeast direction": [[34, "Case-3:-Northwest-Southeast-direction"]], "Case 4: Northeast-Southwest direction": [[34, "Case-4:-Northeast-Southwest-direction"]], "Case 5: Isotropic variogram - no leading direction": [[34, "Case-5:-Isotropic-variogram---no-leading-direction"]], "Centroid-based Poisson Kriging": [[7, "centroid-based-poisson-kriging"]], "Changelog": [[31, "Changelog"], [32, "Changelog"], [33, "Changelog"], [34, "Changelog"], [35, "Changelog"], [36, "Changelog"], [37, "Changelog"], [38, "Changelog"], [39, "Changelog"], [40, "Changelog"], [41, "Changelog"], [42, "Changelog"], [43, "Changelog"], [44, "Changelog"]], "Changes between version 0.x and 1.x": [[1, null]], "Chapter 1: Create random surface": [[32, "Chapter-1:-Create-random-surface"]], "Chapter 1: data preparation": [[31, "Chapter-1:-data-preparation"]], "Chapter 2: Calculate the experimental semivariogram": [[32, "Chapter-2:-Calculate-the-experimental-semivariogram"]], "Chapter 2: Experimental Variogram": [[31, "Chapter-2:-Experimental-Variogram"]], "Chapter 3: Fit variogram models": [[32, "Chapter-3:-Fit-variogram-models"]], "Chapter 3: Theoretical Variogram": [[31, "Chapter-3:-Theoretical-Variogram"]], "Chapter 4: Compare variogram models": [[32, "Chapter-4:-Compare-variogram-models"]], "Chapter 4: Fit semivariogram model automatically": [[31, "Chapter-4:-Fit-semivariogram-model-automatically"]], "Chapter 5: Exporting model": [[31, "Chapter-5:-Exporting-model"]], "Chapter 6: Importing fitted model": [[31, "Chapter-6:-Importing-fitted-model"]], "Check points statistics for each lag": [[35, "Check-points-statistics-for-each-lag"]], "Citation": [[24, "citation"], [26, null]], "Classes": [[1, "classes"], [1, "id2"]], "Community": [[14, null]], "Conda": [[27, "conda"]], "Contents": [[24, "contents"]], "Contributors": [[15, null], [15, "id1"]], "Core data structures": [[2, null]], "Cross-validation": [[5, "cross-validation"]], "Dataset": [[31, "Dataset"]], "Deconvolution": [[9, "deconvolution"]], "Development": [[18, null], [20, null]], "Deviation": [[9, "deviation"]], "Directional Ordinary Kriging": [[39, null]], "Directional Semivariogram": [[34, null]], "Directional Variogram": [[10, "directional-variogram"]], "Distance": [[4, null]], "Experimental Semivariance and Covariance": [[10, null]], "Experimental Variogram": [[10, "experimental-variogram"]], "Failing pylibtiff build - Linux": [[27, "failing-pylibtiff-build-linux"]], "Functions": [[1, "functions"], [1, "id1"]], "Functions and classes that are no longer supported": [[1, "functions-and-classes-that-are-no-longer-supported"]], "Important notice": [[24, "important-notice"]], "Including direction in experimental variogram": [[34, "Including-direction-in-experimental-variogram"]], "Indicator Kriging": [[8, "indicator-kriging"]], "Indicator Semivariogram": [[11, null]], "Installation": [[29, "installation"]], "Installation - additional topics": [[27, "installation-additional-topics"]], "Installation guidelines": [[27, "installation-guidelines"]], "Intermediate": [[30, "intermediate"]], "Introduction": [[24, "introduction"]], "Inverse Distance Weighting (IDW)": [[6, null]], "Known Bugs": [[19, null]], "Learning Materials": [[28, null]], "Maintainer(s)": [[15, "maintainer-s"]], "Manual setting": [[31, "Manual-setting"]], "Metrics": [[5, "metrics"]], "Models": [[31, "Models"]], "Models evaluation": [[5, null]], "More resources": [[35, "More-resources"]], "Network": [[16, null]], "New functions and classes": [[1, "new-functions-and-classes"]], "Ordinary Kriging": [[8, "ordinary-kriging"], [29, "ordinary-kriging"]], "Ordinary Kriging pipelines": [[3, "ordinary-kriging-pipelines"]], "Ordinary and Simple Kriging": [[36, null]], "Outliers and Kriging": [[38, null]], "Package structure": [[21, null]], "Pipelines": [[3, null]], "Point": [[4, "point"]], "Point Kriging": [[8, null]], "Point Support": [[2, "point-support"]], "Poisson Kriging Centroid-based approach": [[42, null]], "Poisson Kriging pipelines": [[3, "poisson-kriging-pipelines"]], "Prepare data": [[39, "Prepare-data"]], "Prerequisites": [[31, "Prerequisites"], [32, "Prerequisites"], [33, "Prerequisites"], [34, "Prerequisites"], [35, "Prerequisites"], [36, "Prerequisites"], [37, "Prerequisites"], [38, "Prerequisites"], [39, "Prerequisites"], [40, "Prerequisites"], [41, "Prerequisites"], [42, "Prerequisites"], [43, "Prerequisites"], [44, "Prerequisites"]], "Presentations & Workshops": [[28, "presentations-workshops"]], "Publications": [[28, "publications"]], "Pyinterpolate": [[24, null]], "Quickstart": [[29, null]], "Raster": [[13, "raster"]], "Requirements and dependencies (version >= 1)": [[22, null]], "Reviewers (JOSS)": [[15, "reviewers-joss"]], "Scatter plot": [[35, "Scatter-plot"]], "Semivariogram Deconvolution": [[9, null]], "Semivariogram Regularization": [[41, null]], "Semivariogram exploration": [[31, null]], "Semivariogram models": [[32, null]], "Setup": [[27, null]], "Simple Kriging": [[8, "simple-kriging"]], "Spatial Dependency Index": [[33, null]], "Table of contents": [[31, "Table-of-contents"], [32, "Table-of-contents"], [33, "Table-of-contents"], [34, "Table-of-contents"], [35, "Table-of-contents"], [36, "Table-of-contents"], [37, "Table-of-contents"], [38, "Table-of-contents"], [39, "Table-of-contents"], [40, "Table-of-contents"], [41, "Table-of-contents"], [42, "Table-of-contents"], [43, "Table-of-contents"], [44, "Table-of-contents"]], "Temporarily not available functions and classes": [[1, "temporarily-not-available-functions-and-classes"]], "Tests and contribution": [[23, null]], "The libspatialindex_c.so dependency error": [[27, "the-libspatialindex-c-so-dependency-error"]], "Theoretical Semivariogram": [[12, null]], "Tutorials": [[30, null]], "Universal Kriging": [[8, "universal-kriging"]], "Use Cases": [[17, null]], "Variogram Cloud": [[10, "variogram-cloud"]], "Variogram Points Cloud": [[35, null]], "Violin plot": [[35, "Violin-plot"]], "Visualization": [[13, null]], "What is the spatial dependency index?": [[33, "What-is-the-spatial-dependency-index?"]], "Working with Notebooks": [[27, "working-with-notebooks"]], "pip": [[27, "pip"]], "version 1.1.0": [[24, "version-1-1-0"]]}, "docnames": ["api/api", "api/changes", "api/core/core", "api/core/pipelines", "api/distance/distance", "api/evaluate/evaluate", "api/idw/idw", "api/kriging/block_kriging", "api/kriging/point_kriging", "api/semivariogram/deconvolution", "api/semivariogram/experimental", "api/semivariogram/indicator", "api/semivariogram/theoretical", "api/viz/raster", "community/community", "community/community/contributors", "community/community/forum", "community/community/use_cases", "contributor/development", "contributor/development/bugs", "contributor/development/development", "contributor/development/package", "contributor/development/requirements", "contributor/development/tests_and_contribution", "index", "science/bibliography", "science/citation", "setup/setup", "usage/learning_materials", "usage/quickstart", "usage/tutorials", "usage/tutorials/functional/1-1-semivariogram-exploration", "usage/tutorials/functional/1-2-semivariogram-models", "usage/tutorials/functional/1-3-spatial-dependency-index", "usage/tutorials/functional/2-1-directional-semivariogram", "usage/tutorials/functional/2-2-variogram-points-cloud", "usage/tutorials/functional/3-1-ordinary-and-simple-kriging", "usage/tutorials/functional/3-2-benchmark-kriging", "usage/tutorials/functional/3-3-outliers-and-kriging", "usage/tutorials/functional/3-4-directional-ordinary-kriging", "usage/tutorials/functional/3-5-blocks-to-points-ordinary-kriging", "usage/tutorials/functional/4-1-semivariogram-regularization", "usage/tutorials/functional/4-2-poisson-kriging-centroid-based", "usage/tutorials/functional/4-3-poisson-kriging-area-to-area", "usage/tutorials/functional/4-4-poisson-kriging-area-to-point-smoothing"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1}, "filenames": ["api/api.rst", "api/changes.rst", "api/core/core.rst", "api/core/pipelines.rst", "api/distance/distance.rst", "api/evaluate/evaluate.rst", "api/idw/idw.rst", "api/kriging/block_kriging.rst", "api/kriging/point_kriging.rst", "api/semivariogram/deconvolution.rst", "api/semivariogram/experimental.rst", "api/semivariogram/indicator.rst", "api/semivariogram/theoretical.rst", "api/viz/raster.rst", "community/community.rst", "community/community/contributors.rst", "community/community/forum.rst", "community/community/use_cases.rst", "contributor/development.rst", "contributor/development/bugs.rst", "contributor/development/development.rst", "contributor/development/package.rst", "contributor/development/requirements.rst", "contributor/development/tests_and_contribution.rst", "index.rst", "science/bibliography.rst", "science/citation.rst", "setup/setup.rst", "usage/learning_materials.rst", "usage/quickstart.rst", "usage/tutorials.rst", "usage/tutorials/functional/1-1-semivariogram-exploration.ipynb", "usage/tutorials/functional/1-2-semivariogram-models.ipynb", "usage/tutorials/functional/1-3-spatial-dependency-index.ipynb", "usage/tutorials/functional/2-1-directional-semivariogram.ipynb", "usage/tutorials/functional/2-2-variogram-points-cloud.ipynb", "usage/tutorials/functional/3-1-ordinary-and-simple-kriging.ipynb", "usage/tutorials/functional/3-2-benchmark-kriging.ipynb", "usage/tutorials/functional/3-3-outliers-and-kriging.ipynb", "usage/tutorials/functional/3-4-directional-ordinary-kriging.ipynb", "usage/tutorials/functional/3-5-blocks-to-points-ordinary-kriging.ipynb", "usage/tutorials/functional/4-1-semivariogram-regularization.ipynb", "usage/tutorials/functional/4-2-poisson-kriging-centroid-based.ipynb", "usage/tutorials/functional/4-3-poisson-kriging-area-to-area.ipynb", "usage/tutorials/functional/4-4-poisson-kriging-area-to-point-smoothing.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [2, 3, 5, 8, 9, 10, 11, 12, 13, 24, 26, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43], "0": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "00": [35, 36, 37, 38, 39, 40, 41, 42, 43], "000": 40, "00000": 34, "000000": [35, 36, 38, 42, 43], "000000000": 40, "000000e": 35, "000860949362576": [], "001": 9, "001038e": 35, "001294": 31, "001901e": 35, "002": 34, "002563": 36, "004792": [], "005": [], "006058": [], "0064564892549": 31, "006794571": 40, "007051": 42, "00739": 34, "007544": [], "00it": [], "01": [9, 31, 35, 36, 37, 38, 44], "01047660845177": 31, "011": 42, "012348e": 35, "014": 11, "015508": 42, "016640e": [], "016994": [], "018141e": 35, "018571": 38, "01876353608725": 31, "02": [31, 32, 35, 36, 37, 38, 39, 40, 41], "022": 42, "022283e": 35, "024": 43, "0240565312119": 31, "0241482853441": 31, "024150": 35, "024262": 35, "02497532421427": 31, "027444e": 35, "028": 43, "028333": 43, "02869": [24, 26], "03": [35, 40, 43], "030020e": 35, "030644": 36, "031608": 36, "03168070178782": 31, "032": 42, "032104e": 35, "032963": 38, "03326262442189": 36, "033615": 36, "034987": 36, "036257725481412": 37, "0363": 37, "037435": 36, "03801956055116": 31, "03804170525302": 31, "039": [36, 42], "039877": 40, "04": [33, 34, 35, 36, 39, 40], "04103246328214": 31, "041399": 42, "04241574410776": 31, "04267564264293": [], "045285": 44, "04638466313054": [], "0466929690614": 31, "04754473209462": 36, "0482965706882": 31, "04it": [], "05": [36, 37, 38, 39, 40, 41, 42, 43, 44], "050749295452192": 31, "051510": [], "051571": [], "051654": 44, "0526605973689": 31, "052784840121205": 31, "05298225928948": 31, "054": [], "0540709659597": 36, "054839": 40, "0553": 37, "055336": [], "056290": 38, "057807": [], "058427e": 43, "06": [35, 40, 41, 42, 43], "060410": [], "060664": [], "061101": 42, "061964": [], "062": [], "06410739650147": [], "06652524884377": 36, "067355202136": 31, "06802859611497": 31, "068647": [], "068681": 36, "07": [36, 37], "070109": [], "070950": [], "0711976064423": 31, "071219883873823": [], "071552e": 35, "071815847676675": [], "07274427357008": [], "07308420640933": 31, "073569e": 35, "074808717567237": 36, "075620504725634": 31, "077242": 40, "077293149680955": [], "079584": 38, "0798": [], "08": [11, 33], "082188e": [40, 41], "083003": 36, "084231": 35, "08535128528587": [], "0865256502935": 31, "08660014648092": 31, "087": 34, "089071e": 35, "08948321088144": 31, "089673": [], "09": [24, 33], "091": 43, "092138": 36, "092391": 36, "092456": 40, "093870": [], "097": 43, "097542e": 35, "09769269330142": [], "09777163016611": [], "09937611628263": 31, "09966521054352": 31, "09978787326568": 36, "1": [3, 4, 6, 8, 9, 10, 11, 12, 13, 18, 25, 27, 29, 33, 39], "10": [10, 11, 24, 26, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "100": [5, 12, 31, 32, 33, 36, 37, 38, 39, 40, 41, 42, 43], "1000": [13, 31, 34, 35, 36, 38], "10000": [31, 32, 34, 36, 37, 38], "100747e": [40, 41], "100923": 39, "101": [9, 25, 31, 41], "1016": 11, "101683": 36, "10191": 41, "102": [35, 40], "1021": 36, "1022": [33, 39], "103": [], "104": [31, 43], "105": 31, "1053": 39, "10599018471123": 31, "106": [38, 40], "107": [35, 40, 42], "107589e": 35, "108": [31, 36, 40], "1081": 39, "109": 40, "109080042476194": 31, "10_000": 35, "10k": [3, 8, 32], "11": [10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "110": [31, 36, 40, 43], "11000": 31, "110000": [], "1104528782418": 31, "111": 40, "1117": 36, "111953e": 43, "112": 40, "113": [], "114": 40, "1141": [33, 39], "114426": 38, "115": 42, "115109": [], "115241": 31, "11590986885119": 36, "116": 33, "116427": 36, "117": [33, 43], "118": 31, "119": [35, 43], "11955761301863": 31, "12": [10, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "120": [31, 42], "12000": 31, "121": [31, 42, 43], "121784": 36, "122": [], "122183": 44, "123": 31, "123151": [], "124": [31, 43], "125": 43, "1250": 35, "125000e": 35, "125214": [], "126": [31, 35, 42], "127": [], "127106": 36, "12765": 34, "1277277": [40, 41, 44], "1278": [], "128": [9, 25, 31, 36, 40, 41, 43], "12801853566801": 31, "12802692792883": [], "1285937": [41, 44], "128981e": 35, "129": [31, 40, 43], "12it": 38, "13": [31, 32, 35, 37, 38, 39, 40, 41, 42, 43], "130": [31, 40, 42], "13000": 31, "13003621033862": 31, "130648": 40, "131": [36, 40, 42, 43], "132": 43, "13262960e": 40, "132630e": [40, 41], "133": [31, 40, 42], "13305049768755": 36, "134": [42, 43], "135": [9, 10, 11, 13, 31, 34, 42], "135536": 35, "1359787": 43, "1359823": 43, "136": 31, "137": [31, 36, 37, 42], "138": 31, "138247": 42, "1382501": 42, "1383062": 43, "138748e": 35, "139": 31, "1390197": 42, "139443e": 35, "13it": 36, "14": [31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "140": [31, 42], "14000": 31, "140301": 31, "141": [31, 42], "1416415": [], "1416461": [], "1419423": [40, 41], "1419729": [40, 41], "142": 31, "1421335800296": 31, "1429778": [], "143": [31, 43], "144": [31, 43], "1442153": [40, 41], "144314": [], "1443204": [], "14443661433467": 31, "144901": [], "1449075": [], "145009": [], "146": [31, 42], "147": [35, 43], "147718818538": 31, "148": 31, "148492e": 35, "1486": 36, "148742": [], "14877246130683": 31, "14it": 36, "15": [31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], "150": 31, "1500": 36, "15000": 31, "1501": [12, 33], "1511": [12, 33], "1521668210121": 31, "15233925049904": 31, "153": 31, "154": [], "1540854": [], "1542867553994": 31, "154591287030115": 31, "154847": [], "155": [31, 40, 41], "155949": [40, 41], "156": [35, 40, 41], "1562065": [], "1562135": [], "157": [40, 41], "1572300": 43, "158": 31, "1580340": 43, "1580342": 43, "159": 36, "1592247": 42, "159227": 42, "16": [11, 12, 13, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "1600": [33, 39], "16000": 31, "1606435": 42, "161": 31, "16197426953403": [], "16227766": 4, "164": 43, "164396486557602": [], "165": 36, "165487": 40, "165517": 42, "165579885194006": 31, "165907": 38, "166": [40, 41], "1663131221094": 36, "166502": 42, "167": [34, 42], "1686399": [], "1686411": [], "169732478438334": 31, "16it": [36, 38, 39], "16th": 5, "17": [31, 34, 36, 38, 39, 40, 41, 42], "170": 31, "17000": 31, "170291e": 35, "1708410": 43, "1708426": 43, "1714888": [], "172": 31, "172201928710436": 31, "173": 31, "1731431": 43, "17391501187865": 31, "1745": 40, "1745084601238": 31, "174940e": 35, "175": 31, "176": [31, 36], "177": 31, "177585": [], "177906": [], "179": 31, "1797797": 42, "1797856": 42, "17it": 40, "18": [31, 32, 34, 35, 36, 38, 40, 42], "180": [9, 10, 11, 13, 31, 34], "18000": 31, "180223e": 35, "180609": 42, "1807281": 42, "181": 31, "181025": [33, 39], "181072": [33, 39], "181100": 39, "181140": 39, "181165": [33, 39], "181180": 39, "181220": 39, "181298": [33, 39], "181307": [33, 39], "181997e": 35, "1824360": 42, "1826198": 42, "182623": 42, "184891": [], "1854": [], "186": 31, "186224e": 35, "1866": 41, "187": [35, 43], "187153": 39, "187451": 43, "1875": 35, "18779541265431": 40, "187945e": 35, "188": 31, "188044": [], "1880445": [], "188693": 40, "1898429": [], "19": [24, 31, 32, 34, 38, 42], "19000": 31, "191": [], "1916957069014": 31, "192": [36, 40, 41], "1921343244303": [], "1929576": [], "193124": [], "193280": [], "193751": [40, 41], "1937530": [40, 41], "194045": [], "1940462": [], "195": 36, "1958207": [40, 41], "196": 31, "1963791": 42, "196758": 42, "1967596": 42, "1973": [], "1979": 5, "198695": 36, "199": [31, 33], "1994": [12, 33], "1995273": [], "19957574653137": 36, "1996": 25, "1998": [25, 32], "1999": 25, "1b7837": [31, 32], "1st": [10, 35], "2": [2, 4, 5, 6, 9, 10, 11, 12, 27, 29], "20": [31, 32, 34, 35, 37, 38, 39, 42, 43, 44], "200": [31, 32, 40], "2000": [31, 36], "20000": [29, 31, 41], "200000": [40, 42, 43], "2004": [5, 10], "20041395430178": 31, "2005": 10, "2006": 10, "2006358211872": 31, "200673": 36, "2008": [9, 11, 25, 41], "2009": [33, 39], "2010": [41, 42, 43], "201410": [], "2014119": [], "2019": 17, "202": [], "2020": 17, "2021": [17, 40], "2022": [17, 24, 26, 28], "2023": 17, "2025": [24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "202538": 35, "2028115200009779": 34, "2032": 40, "20324106610195": 31, "204688": 36, "205265e": 35, "20552245159655": 31, "2058": 40, "2058427": 43, "205904": 36, "206": 31, "207": [36, 40, 41], "2071877": 43, "2071878": 43, "2074073": [40, 41], "207411": [40, 41], "207836": [], "207836151123047": [], "2082188": [40, 41], "2093": 41, "209528": [40, 41], "2095343": [40, 41], "21": [31, 32, 34, 35, 38, 41], "210": 31, "2100746": [40, 41], "21014059512538": 31, "210327": 43, "211": 29, "21105": [24, 26], "2111953": 43, "2115688": [40, 41], "2115699": [40, 41], "211985": 43, "2119901": 43, "212348": 40, "212485": 39, "212962": 43, "213": [5, 43], "2132629": [40, 41], "213679": 42, "2140543123903": 31, "216": [31, 42], "217": 31, "217012": 36, "21707025705718": [], "217191e": 35, "217915e": 35, "2180": [31, 35, 36, 37, 38], "219": [31, 34], "219166": 42, "21926570120685": 31, "2194437759668": 31, "219750": [], "21it": [36, 39], "22": [25, 31, 32, 34, 38, 41, 43], "220": [31, 36], "220278345566044": 36, "2204635031941": 36, "221": 31, "22394168598046": 36, "225": [5, 9, 10, 11, 13, 34], "226": 31, "226161": 36, "226342": 43, "227": 38, "2272727272727275": 10, "228": 43, "228158": 43, "228405": 42, "2289": [], "23": [29, 31, 32, 33, 38, 39, 43], "23001": 42, "23011": 43, "23013": 43, "23015": [], "23031": 42, "230325e": 35, "2306": 36, "23070": 36, "231": [35, 36], "2326165739138": 31, "23283837095323": 36, "233": [], "236": 31, "23606798": 4, "2364": 36, "236450": [], "23657348059464": 31, "237": 36, "237449": 31, "237500": [], "237556": 36, "237674": 31, "237685": 31, "237878": 31, "238": 31, "238012": 31, "238419": 36, "238568e": 35, "239660": 39, "24": [10, 31, 32, 34, 35, 36, 39, 40, 41], "240": [], "241": 31, "242": [], "24264069": 4, "242891": 36, "243": [38, 40, 41], "243162": 36, "245": 38, "245371": 43, "245548": 35, "246": [], "246544791626": 31, "246919": [], "247": 36, "247968": [], "247976e": 35, "2485207100591715": 10, "249": 31, "249753": [], "24it": [36, 42], "25": [9, 10, 12, 25, 31, 32, 33, 34, 35, 36, 38, 39, 42, 43], "2500": [35, 36], "250000e": 35, "25001": [40, 41], "25007": [40, 41], "25009": 42, "250117139455426": 31, "25019": [40, 41], "250293e": 35, "250654": [], "251": [25, 31], "251532": 36, "25175378437421": 31, "252037": 36, "254017": [], "254551": 31, "254859": [], "254870": 31, "256": 36, "25603707133602": 31, "257": [33, 39], "258": [], "25974589970014": [], "2599999999999958": 10, "26": [31, 32, 33, 34, 35, 36], "26039701343071": 36, "261": [25, 31, 34], "261079471555718": 31, "26158958300948": 31, "262": 31, "262374": [], "262794": 41, "26318444803915": [], "2632": [], "264": 36, "264370168819994": 36, "264571e": 35, "266": 34, "2661009953751": 36, "267010": 35, "2682452745304": 36, "269": [31, 33, 39], "269811e": 35, "27": [31, 36], "270": [9, 10, 11, 13, 34], "270265": 36, "270738": 39, "271": 38, "272025": 42, "274131": [], "274181e": 35, "274414": [], "274566": 36, "274606e": 35, "275": [35, 36], "275000": 42, "275597": 31, "276": 35, "276866": 43, "277": 33, "277278e": [40, 41], "277757": 36, "278": [31, 36], "278133": 40, "278161": [], "278387e": 38, "27901638284231": 31, "279056": [], "27it": 36, "28": [31, 32, 42], "28018535668014": 31, "280913144602": 31, "281": [31, 36], "281077e": 35, "282": 31, "28228331690235": 31, "282328e": 35, "282639e": 35, "282839e": 35, "283208e": 35, "284266": [], "284539e": 35, "285938e": 41, "2861654512381": 31, "2869": [24, 26], "28852797444068": 31, "28949692320213": 36, "289906": [], "29": [31, 36, 43], "29020392031947": 31, "290276": 35, "291411": 40, "291578e": 35, "293621": 43, "295": 36, "295998e": 35, "296": 31, "296388": [], "297320": 42, "297386": 35, "29738612": 35, "298165129123845": 31, "299": 33, "29906598": 35, "299066": 35, "29it": 36, "2d": [11, 32], "2f": 33, "3": [4, 9, 10, 15, 27, 29], "30": [31, 36, 39, 43, 44], "300": 31, "3000": [31, 36], "300000": [40, 41], "300074": 43, "300318": [], "300443": 40, "300494": 34, "30055407864868": 40, "300589": 43, "3009": [], "301": [31, 43], "301750": 31, "302": 36, "3024215423431": [], "302529155804564": 31, "303": 43, "303271": 40, "303353": [], "304": 42, "304436": 42, "305187": [], "308": [31, 36], "308429e": 35, "309672e": 35, "30it": [], "31": [31, 34, 44], "310": 36, "3103": 39, "3108668325436": 31, "3125": 35, "312974e": 35, "313000": 36, "314089689802074": 36, "314496": 36, "315": [9, 10, 11, 13, 34], "316": 31, "316036601261": 31, "316194e": 35, "31636235955056": 40, "317351": 36, "31761394026825": 31, "318061": 43, "31822706193316": 31, "319": [], "319724": [], "32": [29, 31, 34, 36], "320": 35, "320333": [], "321571": 35, "321846": 42, "322511": 35, "323": 36, "3235398024696": 36, "32464307748702": 31, "325": 43, "325393": 31, "325632e": 35, "32608183289074": 31, "326242": 35, "326447": 36, "327": [31, 36], "327524e": 35, "328": 31, "32816550891573": 31, "32956877520513": 31, "32it": 36, "33": [31, 32, 34, 36], "330": 36, "33001": [40, 41], "33017": [], "33098793134502": 31, "33167274863581": 31, "33253400127717": 31, "332555": [], "333103e": 35, "333330": [33, 39], "333484": [33, 39], "333537": [33, 39], "333558": [33, 39], "333611": [33, 39], "333660": 39, "333700": 39, "333740": 39, "334": 31, "335": 31, "3351196859388": 31, "335979": 35, "335986": 38, "336218": 42, "336303": [], "3389": [], "339": 42, "3390411424663": 31, "34": [31, 33, 34], "34003": [], "34013": [], "34019": 42, "34025": [], "34029": 43, "340790e": 35, "3416": [], "342": 31, "3425": 38, "344": [], "344063e": 35, "344082": 40, "344179e": 35, "3447": 38, "3448": 38, "345": 31, "345097184986": [], "34523713634542": 31, "345417e": 35, "346": 31, "346216563217126": 36, "3476590353458": [], "348": 31, "348466": 36, "3491232940861": [], "34it": 40, "35": [31, 32, 34, 36, 42], "3500": 36, "3508257264928": 31, "351": 31, "351111": 31, "352372": 36, "355": [31, 34], "355742": [], "356085e": 35, "3561859868873": 31, "357": 36, "358": [31, 36], "358459": 36, "359": 43, "3594046318507935": 36, "359564": 36, "36": [31, 36, 40], "360": [9, 10, 11, 12, 13, 31, 34], "36013": 42, "36015": 43, "36025": 42, "36027": 42, "36043": 43, "36047": 43, "36055": [], "36061": [], "36063": 42, "36073": [], "36077": [], "36081": 43, "36083": 43, "36085": [42, 43], "36089": 43, "361": 35, "36103": [], "36107": 42, "36109": [], "36113": [], "36115": 42, "36121": [40, 41], "3618624554766": [], "362571": 40, "36305694482644": 31, "363393": 36, "364": 36, "364813": [], "365063": 43, "365733": 43, "367": [], "367675e": 35, "368489": 42, "369": [], "36902963659804": 31, "36955242757186": 40, "37": [31, 36], "371": [], "3715323497975": 31, "372719e": 38, "3744249472903": 31, "37451457470354": 31, "3750": 35, "375000e": 35, "37556735e": 36, "375669": 42, "377": 42, "37710889704957": 31, "378349": [], "3789": [], "3789476659944": 31, "379": [31, 40, 41], "38": [31, 36, 38], "3802492092461": 31, "381056": 42, "383": [40, 41], "383022": 34, "383062e": 43, "383446": [40, 41], "385": 36, "385000": [], "38515441300115": [], "386": 31, "386278": 35, "388009": 36, "388568567754305": 36, "389": [], "38it": [], "39": [31, 36, 38, 39, 42, 43], "390197e": 42, "3908": 35, "393": 31, "393895": 36, "39614083991057": 36, "396656": [], "3981": [], "399447": 40, "399982": [], "39it": [], "3rd": [10, 35, 42, 43], "4": [3, 4, 5, 8, 10, 13, 29, 36, 37], "40": [9, 25, 31, 35, 38, 41], "400": 29, "4000": [31, 36], "40000": [29, 34, 41], "400000": [34, 42, 43], "401": 31, "40103638563755": 36, "401124": 40, "401954e": 35, "402": 35, "402573": 36, "403": 31, "404376e": 35, "405": [], "40500030908063": [], "406723": 40, "407": 31, "40731524633007": 31, "407396": 35, "407681": [], "4082038923481": 31, "409025": 35, "41": [31, 38], "410529": [], "411": 36, "411124": [40, 41], "411182": 36, "412": 36, "4129328959": 40, "414": 42, "41547696e": 36, "416": 31, "416976": 43, "417": [], "418": [], "419": [31, 35], "41it": 38, "42": [31, 36], "420": [], "42003": 42, "42013": [], "42021": 42, "42027": 43, "42031": [], "42039": [2, 41, 43], "42049": [2, 41, 44], "42051": 43, "42059": 42, "42063": [], "42065": 42, "42071": 43, "42075": [], "42089": [], "42097": 42, "421": 31, "42103": 43, "42107": 43, "42109": 43, "421110": 43, "421124": [40, 41], "42115": [], "42117": 42, "42118925125396": 31, "42119": [], "42121": 43, "42131": [], "422445e": [], "4234809204555": 40, "4241649094325": 31, "424741737609907": [], "425975": 42, "426124": 44, "426904": [], "427": 31, "427998e": 35, "429": 36, "42906080741184": 31, "429779e": [], "43": [31, 33, 35, 40, 41], "430": 31, "431124": [40, 41, 44], "432": 42, "4326": [2, 31, 35, 36, 37, 38], "43414051411436": [], "436": [31, 35], "436124": 44, "4375": 35, "438": 43, "439457": 38, "43993793268754": 31, "44": 31, "440": 36, "44003": [], "441124": [40, 41, 44], "442153e": [40, 41], "442728": 42, "443458": 34, "443930e": 35, "444": 31, "444656398462776": 40, "444877856818": [], "446124": [41, 44], "446827": 36, "447563e": 35, "448797e": [], "449014": 43, "449076e": [], "449497": 42, "449596": [], "45": [9, 10, 11, 13, 31, 34], "4500": 36, "450000": [], "450174": [], "451": 31, "45175335007325": 31, "4547918981183": 31, "455192e": 35, "457": [40, 41], "458": [31, 43], "458219": 43, "458808": [], "46": 34, "46109182464066123": 31, "46215413444605": 37, "462801e": 35, "463536": 43, "464": [31, 35], "465": 36, "465729": [], "467": 36, "467516e": 35, "468": [], "468647": [], "469406229991364": 31, "47": [31, 36], "470": [31, 36], "471": [31, 36], "471335": 36, "471774": 39, "472": [], "472821": 42, "472968": [], "474": [], "475": 31, "4750877096409": 31, "47509500639893": 31, "475252": 44, "47529551999094": 31, "475344": 34, "4768161567573": [], "477": 31, "477588377276845": 36, "478527e": 35, "47898266": 35, "478983": 35, "4791021447186": [], "479106e": 35, "479607": 35, "48": [31, 33], "480": 31, "482": [36, 42], "483877": 43, "484712": 42, "485": 31, "48699687094353": 31, "487043e": 35, "4883": 35, "48893667070456": [], "489": 36, "48930987": 35, "489310": 35, "489574": [], "49": 31, "490": 35, "4906907968926": 31, "491": 31, "491185": 43, "494": 31, "4940": [], "495": [], "495489": [], "496168": [], "497719": [], "499": [31, 36], "499029": [], "49it": 39, "4f": 37, "5": [3, 8, 9, 10, 11, 12, 29, 32, 33, 36, 37, 39, 44], "50": [31, 34, 35, 36, 38, 42, 43], "500": [29, 34, 36, 37], "5000": [31, 35, 36], "500000e": 35, "50005": [], "50009": 42, "50011": 42, "500384": 42, "501718": [], "502": 31, "502338": [], "503272408247557": 40, "50350756874842": 31, "504": [40, 41], "504256444451812": 36, "505509671802704": 43, "506": [], "506538": 43, "5068": [40, 41], "507": [40, 41], "508": 31, "508218": [], "50844531675361": 36, "508784": [], "508918": 43, "509": 31, "51": 31, "510533": 36, "511493": [], "512": 36, "51207414267205": 31, "513": 31, "514": 35, "514141": 43, "515": 42, "5152950454789": 31, "516631312210944": 36, "51949179940027": [], "5196": 37, "52": [31, 36], "521": 31, "521794": 36, "522": 43, "522731": [], "523": 36, "5232513174528": 31, "5233842803291": 31, "52362092362816": 31, "524330": 36, "525000": 43, "526495": 35, "52649515": 35, "526820": 34, "526892863730268": [], "527178": 43, "52756082596147": 31, "52826399837278": 36, "528402": [], "528472": 36, "529001": 34, "529781": [], "52990402804862": 31, "529970": 42, "52it": 40, "53": 36, "532": [31, 40, 41], "532045": [], "532706465067776": 40, "533": [], "534367": [], "534590": 42, "534700": 42, "535": 31, "53561182896007": 36, "5362224280161": 31, "537": 43, "537041": 36, "538721218653905": 31, "539159": [40, 41], "539633": 36, "54": [], "540467": 36, "5406316105217": 31, "540854e": [], "541000": [], "541045": 31, "541137": 36, "541281": [], "541524": [], "541547": 36, "541688": [], "5419": 40, "542": 36, "542076": 36, "543": 42, "5434027777777798": 10, "54340278": 10, "543432e": 35, "54356381615929": [], "544131": 36, "545": 31, "545209": 31, "54535714285717": 40, "545416": 31, "546553": 36, "546930": 36, "5477": 36, "547783": [], "548": [31, 43], "548294": 31, "548391": 43, "548701": 36, "548872479116914": 36, "548982": 31, "549": 31, "54989393663284": 36, "55": [31, 36, 38], "5500": 36, "55011584792507": 31, "550405": [], "550448": [], "550631": 40, "550673": [40, 41], "551": 31, "55107310356686": 31, "551466": 31, "551653": [], "552705": [], "5541": [], "555": 31, "55548024563274": 31, "556": 35, "556471": [40, 41], "556828": [], "556830": [40, 41], "557971": [40, 41], "558": 31, "558079932090166": 36, "558488": 36, "559785533995694": 31, "56": [38, 40], "560": 31, "56007358684212": 31, "56033703031673": 31, "561": 31, "561271e": 35, "562": 31, "5625": 35, "563863": 36, "564067": [], "564156": [], "564830": [40, 41], "569363": 42, "57": 31, "571369": 35, "571504": 36, "5716": 35, "5719": 36, "571970": 35, "572": [], "572300e": 43, "572343": [], "573": 31, "573719": [], "5743674621095": 31, "574960469008332": 31, "575274": 42, "575340": [], "57574792919676": [], "577": 36, "577139": 43, "577791e": 35, "57793031445675": 31, "57846649760147": [], "57971156e": 40, "58": [12, 33, 35], "580230995312235": [], "580614e": 35, "5811036652226": 31, "58116437305063": 31, "5831565610547": 36, "584068e": [], "5850": 35, "585908": [], "58692115765933": 31, "588124": [], "588314e": 35, "58it": [], "59": [31, 41], "590": 41, "590461": [], "590651": [], "594676": 39, "595": 31, "596085": 36, "596802": 42, "59771789": 35, "597718": 35, "598170e": 35, "598187": [], "599": [40, 41, 42], "599394e": 35, "599397": [], "5aae61": 32, "6": [10, 29, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "60": 31, "6000": [31, 36], "60000": 29, "600235": [40, 41], "60025635e": 36, "600918422589984": 31, "602858e": 35, "603": [], "603778": 43, "604054": 42, "605129": 36, "60555128": 4, "606324": [], "606411": 36, "606436e": 42, "607": 39, "6073787035081": 31, "6083701575303": 31, "60853512852859": [], "61": 31, "610": 31, "610284": [], "61050146167261": 31, "6122447906015": 31, "61245695077403": 31, "612849": 35, "613": 42, "613496": 35, "61429501864575": 36, "614562": 39, "615233": [], "616031": 43, "616208": 36, "616235": 43, "616242e": 35, "61641918547662": 36, "618": 31, "619216": [], "61it": [], "62": 31, "6206": 36, "621525": [], "62223544944055": 31, "6235": 37, "6235188837861": 31, "624234": 35, "62423403": 35, "625": [10, 35, 44], "6250": 35, "625000e": 35, "626789": [], "629313": 44, "629350": 35, "629570728151343": [], "62it": 39, "63": 31, "630000": 42, "631": 31, "632457": [], "632906": [], "633": [], "636": 41, "63675547340748": 31, "637424": [40, 41], "637979e": 35, "638": [], "638052": 36, "6383701457184": 31, "638425": 40, "63it": 36, "64": [35, 36, 41], "640": [33, 39], "641255": [], "641322327860422": [], "6433994890514": 31, "643775": [], "649048e": 35, "64968180989848": 36, "6500": 36, "65000": 29, "650415": 36, "651": 31, "651028": [], "651128": 40, "65121077117155": [], "653": 31, "6546695631294": 36, "655343": [], "656": 42, "657804": [], "658729": [], "659": 34, "659009934605715": [], "65it": [], "66": 31, "6605413612897": 31, "661442": 36, "662526071583954": 31, "662744": 35, "664": [], "66432": 34, "665000": 43, "665082": 42, "665999914811835": 31, "667976041662143": [], "669672": [], "66it": [], "67": 31, "670733": [], "671": [40, 41], "671315": 31, "671378": 31, "671459e": 35, "6715648481398": 31, "671723": 40, "672": 31, "672267": [], "673435": [], "673959": 40, "6750": [], "675000": [], "67570949745885": 31, "6758": 37, "675932": 36, "676": [], "676038": [], "678002": [], "68": 33, "6829444343204": 36, "682969": [], "683110": [], "68704606120476": 36, "6875": 35, "687778": 44, "68872474483254": 36, "689": 36, "6890285994053": 31, "68985563827547": 31, "69": 31, "691185": 40, "69275424359088": 31, "693": [], "6938864138145": [], "693970": [], "695009": [], "6959364694": 36, "696": 36, "696596e": 35, "6972630338454": 31, "69875148762813": 36, "699327e": 35, "699529": 36, "7": [10, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "70": [24, 26, 31], "7000": [31, 36], "700000": 42, "700473": 34, "70216796984425": 31, "702323": 36, "702401": 42, "702517": [], "70264304761736": 31, "706": 31, "706056": [], "706218": 42, "706742": [], "707327": 35, "708155e": 35, "708283": 36, "708315": 31, "708844": 31, "709089": 35, "709962770143306": 36, "710706": 31, "712456": 38, "712477e": 35, "713751171886535": 31, "714": [], "714325": [], "714888e": [], "715758132458433": 31, "716043": 36, "71620705874227": 36, "717101": 39, "718654": [], "71946771": 35, "719468": 35, "720513": 34, "721": [39, 40, 41], "721316": [], "72150293483141": 31, "72152953542008": 31, "723": 39, "724090": 34, "725000": [], "726959": [], "727163279953": [], "727166": 42, "727680": 36, "727912692284356": [], "728": [40, 41], "72it": 39, "73": [31, 35], "731431e": 43, "731934": [], "73227416319057": 31, "73259814652391": 31, "735": 36, "73506526498": 36, "735107": 38, "736": 34, "736211": 36, "73656205645744": [], "73672": 36, "737": 31, "738": 42, "73934115929234": 31, "7399812761576": 36, "73998332422": [], "73it": 37, "74": [], "740012": 43, "740347": 36, "740584e": 38, "7406543587138": 31, "741397": [], "741451": 42, "742198e": 35, "742540088261137": [], "742790": 31, "744317": 44, "74437591": 35, "744376": 35, "744675e": 35, "745632": 43, "746723": 36, "746984e": 35, "7472710841447": 31, "74739593341536": 31, "748935": 34, "749322": 42, "74946968316414": 36, "75": [10, 12, 31, 33, 35, 36, 38, 40, 42, 43], "7500": [35, 36], "750000e": 35, "751082": [], "7517085053658": 31, "751882": 36, "752124": 35, "752529502587606": 31, "7540229326713": 31, "7550449009331": [], "755731938008125": 36, "756632": 42, "756648": 43, "7580926503687": 31, "7581649946207": 31, "75852817492543": 31, "7592363399267": 31, "76": 31, "760020": 38, "760625": 43, "7609479302331": 31, "762436350096": 31, "762a83": [31, 32], "764558": 36, "765": 25, "765146": 31, "766008": [40, 41], "767272": [], "767307": [], "767505": 35, "76750526": 35, "76811121400806": 31, "768622e": 35, "7692801878459": 31, "76966135344213": 31, "77": [25, 42], "770601587258": 31, "771429": [], "771562": [], "771834": [], "771916": [], "772718": 36, "773": 25, "7735": 37, "775000": 43, "775004": [], "777": 10, "777449": 36, "778474e": 35, "778854": 36, "778941": 35, "779787": [40, 41], "77it": [], "78": [31, 36, 40], "780406e": 35, "782004633917003": 31, "7822489110466222": 31, "782533": [], "78273230758606": 36, "786": 10, "78975639686188": 31, "79": 31, "79225949935216": 31, "793332": [], "793950": 43, "7954545454545454": 10, "7954880628821": 31, "7961845551719": 31, "796222": 42, "796418": 35, "79676323096689": 31, "7970611443549": [], "798": [31, 42], "798008": 42, "7995282117094": 31, "8": [10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "80": 31, "800": 31, "8000": [31, 36], "800424e": [], "801887": 36, "802603": [], "80283051543793": 31, "804": 43, "8044015563413": 31, "805222": 31, "8054": 36, "807282e": 42, "807347078599088": 42, "807629": 42, "807863": [], "808": 42, "8088419458209": 31, "80cdc1": 38, "81": [31, 33, 40], "8125": 35, "812849": [], "813": 31, "81365277536804": 31, "814415e": 35, "816": [40, 41], "817519": 42, "817863": 31, "818": 42, "818877": 43, "81901919": 39, "819578": [], "82": [31, 36, 40], "820307": 44, "821373": [], "822": [40, 41], "822367": [40, 41], "823045543266744": 31, "824114": 35, "824360e": 42, "824876": [], "825": 34, "825253": 38, "825540": 40, "82560538558577": [], "825975": [], "8261212464837": 31, "82818905258898": 31, "82842712": 4, "828618e": 35, "83": 40, "830241": 43, "830906": [], "83403385569056": [], "834552": [], "83518031738423": 31, "836839": 36, "838043": 39, "839033": [], "839407": 36, "84": [31, 36, 40], "842599": 36, "843530": [], "843791": 42, "844088230061459": [], "845891": [40, 41], "846": [40, 41], "847133389599": 36, "8472476840368": 31, "847620e": 35, "848001": 36, "849220": 35, "84922016": 35, "8495480710881": 31, "85": [33, 40, 41], "8500": 36, "85265986": 35, "852660": 35, "853": [], "853428": [], "85344518052113": 31, "853829": 40, "8539": [], "85390951880213": [], "8549895665524": 31, "8553986848418": 31, "8575410251533": 36, "8585979": 35, "858598": 35, "86": [31, 42], "860089474541724": [], "8608177528": [], "861": 34, "86159467872096": 31, "862": 36, "86215475": 35, "862155": 35, "863351e": 38, "8653795520153": 36, "866257": 38, "8667134434124": 31, "86828102822872": [], "869088e": 38, "872045": [], "872704": 42, "874119301205496": 31, "875": 44, "8750": 35, "875000e": 35, "875977e": 35, "876334": [], "877575e": 35, "877671": 43, "878133": 36, "87it": [], "88": 31, "883662": 43, "885612e": 35, "885706640114352": [], "8869047182717": 31, "888609052210118e": [], "888701": [], "8898": 36, "89": [29, 31, 36], "8906": [], "891937": 43, "8926871102454": [], "89394987189036": 36, "894660": [], "89534": 41, "895544254725": 31, "895574": 43, "896": 43, "8969420596828": 31, "897133": 35, "897284e": 35, "897535": 43, "898245e": 35, "898430e": [], "899": [], "9": [10, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "90": [9, 10, 11, 13, 31, 34, 36], "9000": [31, 36], "900000": [], "9001": 43, "9007": [], "9011": 42, "9013": 43, "901305021363": 31, "9017417512093": 31, "902991e": 35, "903": [], "904780": 35, "90478027": 35, "90493042664525": 31, "905790": 36, "906249": [], "90648819731166": 31, "906703056558335": 31, "908": 34, "908360": [], "909": [], "909108811707085": 36, "909623": 44, "91": [31, 38, 40], "910815": [], "91095667087166": [], "911": [], "911324": [], "911645": [], "911793": 43, "91307581152344": 31, "9184105895709": 31, "92045299212475": 31, "92082": [], "920820": [], "92120020199735": 31, "922": [], "92200000e": 40, "925": 41, "927": [], "927032": [], "927684": 35, "928": 43, "928547099740086": 31, "92881517059374": 31, "929577e": [], "92it": 43, "93": 43, "930238e": 35, "931": [], "931267": [], "932094": [], "933": [], "934470": 43, "934750": 31, "9352055777114": 36, "935704": [40, 41], "936": [40, 41], "936610e": 35, "937": [], "937369": [], "9375": 35, "939120": 35, "939216528212": 31, "93it": 38, "94": 34, "9402324430091": 31, "941210": [], "941316": 40, "942269e": 35, "944304526105059e": [], "944787": 43, "9454407598276": [], "9475766367128": 31, "9483172823313": 31, "94892418737072": 31, "949525193922875": [], "94it": [], "95": [12, 31, 33], "9500": 36, "950029": [], "9510457652993": 31, "951256": 36, "953": [], "953469": [], "954": 42, "9549217265119": 31, "957437e": 35, "958142": [], "958207e": [40, 41], "958282": 31, "958924": 40, "959002e": 35, "96": [31, 33, 42], "961": [40, 41], "96259104849804": 31, "96350436467912": 31, "963791e": 42, "964876543341": 31, "96538249296198": [], "965905495268544": 31, "966": 43, "967083": 36, "96775883309403": 36, "97": 43, "97000001181192": 31, "970139": 44, "971479": 38, "972404": [], "973063": [], "974452e": 35, "975": [34, 36], "97607277205168": 31, "976144e": 35, "976595": 40, "978": [40, 41], "978031": 43, "98": 40, "981213": [], "981414538699": 36, "981884": 35, "98333116163843": 31, "983900": 43, "985": [], "9874764849992061": 34, "987774": [], "989": [], "98971500146": 40, "98it": [], "99": 40, "9909": 31, "991219": [], "993": [], "995273e": [], "9970ab": 32, "99847007753777": 31, "99853574125973": [], "99it": [], "9f": 40, "A": [3, 7, 9, 10, 11, 12, 13, 31, 32, 33, 34, 42, 43], "AND": 41, "And": [32, 40], "As": [10, 17, 31, 34, 36], "At": [32, 39], "BUT": 33, "Be": 33, "But": [3, 24, 32, 34, 35, 40, 41, 42, 44], "By": [3, 31], "For": [32, 34, 35, 40], "If": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 23, 24, 31, 33, 34, 35, 36, 40, 41, 42, 43], "In": [10, 27, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "It": [2, 5, 6, 8, 9, 10, 12, 31, 32, 33, 34, 35, 36, 38, 41, 42, 43, 44], "Its": [6, 32], "No": [25, 33, 38, 40], "Not": [1, 33, 34], "OF": 27, "On": [33, 37], "One": 38, "Or": 41, "That": [5, 8, 31, 36, 37, 38, 41], "The": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 24, 29, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "Their": [42, 43], "Then": [2, 9, 24, 36, 38, 40, 42, 43, 44], "There": [24, 31, 33, 35, 38, 41], "These": 34, "To": [24, 31, 35, 37, 44], "With": [24, 27, 32, 35, 37, 38, 39], "_": [32, 36, 39, 40], "_automat": 31, "_experiment": 31, "_lag": [31, 32], "_linear_manu": 31, "_max_it": 9, "_model": 31, "_nestedsequ": [2, 4, 8], "_nugget": 32, "_rang": 32, "_sill": 32, "_supportsarrai": [2, 4, 8], "_weights_arrai": 1, "a6611a": 38, "a6dba0": 32, "ab": 40, "abl": [27, 40], "about": [2, 5, 24, 31, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44], "abov": [12, 31, 32, 35, 38, 42, 43], "abrupt": [42, 43], "absolut": [5, 9, 11, 12, 31, 35, 38, 40, 41], "academ": 10, "accept": 2, "access": [20, 24], "account": [10, 36, 43], "accur": [34, 42, 43], "accuraci": [5, 42, 43], "achiev": [40, 41], "across": [37, 39], "activ": 27, "actual": [31, 32, 36, 39, 42, 43], "ad": [6, 35], "adapt": [1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "addit": [22, 34, 35, 38], "administr": 41, "advanc": 21, "advantag": 33, "affect": [3, 7, 33, 34, 35, 37, 38, 44], "after": [9, 27, 32, 35, 38, 40, 41, 42, 43, 44], "again": [3, 41], "against": 12, "agenc": 17, "agg_dataset": 9, "agg_lag": 9, "aggreg": [0, 1, 2, 3, 24, 26, 28, 35, 40, 41, 44], "aggregared_data": 9, "aggregatedvariogram": 9, "agregowanych": 28, "ai": 44, "air": [28, 34], "air_pollut": 34, "algorithm": [3, 5, 7, 8, 9, 10, 13, 25, 31, 32, 33, 35, 36, 38, 41, 42, 43], "alia": [9, 10, 31], "alias": 31, "all": [1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 21, 23, 31, 32, 34, 35, 36, 37, 38, 39, 40, 43], "all_filt": 38, "allow": [3, 5, 7, 8, 13, 36], "allow_approx_solut": [8, 13, 36], "allow_approximate_solut": [3, 5, 8, 37], "allow_lsa": [7, 42], "allowed_model": 33, "almost": [31, 40], "alon": [5, 24], "along": [27, 31, 35, 41, 42, 43, 44], "alpha": [31, 34, 35, 36, 37, 38, 40, 44], "alreadi": 37, "also": [], "alter": 2, "alwai": [32, 33, 34, 36, 38, 40, 42, 43, 44], "america": [12, 33], "amplifi": [36, 38], "an": [2, 5, 7, 8, 9, 10, 11, 13, 31, 32, 33, 34, 35, 36, 38, 39, 40, 43], "analys": 33, "analysi": [8, 9, 11, 17, 24, 29, 31, 34, 35, 38, 40, 41, 42, 43], "analyz": [29, 40, 41, 42, 43], "angl": [2, 3, 8, 10, 34, 36], "angles_between_representative_point": 2, "angles_to_unknown_block": 1, "ani": [2, 4, 6, 8, 9, 10, 24, 31, 32, 33, 34, 40, 42, 43], "annot": 1, "anomal": 35, "anoth": [8, 33, 34, 35, 38, 40], "anyth": 2, "anywai": [3, 8], "apart": 33, "apcom": 5, "api": [1, 20, 21, 24, 34, 35], "append": [32, 36, 38, 40, 42, 43], "appli": [9, 31, 32, 33, 37], "applic": [5, 8, 10, 31, 36, 42], "approach": [30, 43], "appropri": 40, "approxim": [3, 5, 7, 8, 13, 31, 34, 36], "apt": 27, "ar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 23, 24, 25, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "arbitrari": 36, "area": [0, 1, 2, 3, 5, 8, 9, 10, 11, 13, 21, 24, 28, 30, 31, 32, 33, 34, 36, 40, 41], "area_geometri": 2, "area_index": 2, "area_to_area_pk": [7, 43, 44], "area_to_point_pk": 7, "area_valu": 2, "areal": [7, 9, 24, 40, 41, 42, 43, 44], "areal_centroid": 40, "areal_input": 40, "argument": [35, 42, 43], "armstrong": [25, 32], "around": [13, 31, 32, 35, 36, 38], "arr": 33, "arrai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 34, 35, 36, 39, 40, 42, 43], "arraylik": [8, 10], "arrow": 34, "art": 5, "artifici": 32, "as_cloud": [10, 31], "as_datafram": [10, 35], "asarrai": 32, "ascend": 38, "asid": 32, "assign": [2, 6, 10, 11, 31, 33, 37, 40, 41], "associ": 12, "assum": [5, 10, 36, 37, 38, 42, 43], "assumpt": [10, 35, 36, 41], "ata": [3, 43], "atp": 3, "attent": [42, 43], "attr": 31, "attribut": [2, 3, 5, 8, 9, 10, 11, 12], "attributeerror": [2, 4, 5, 9, 12], "attributesettofalsewarn": 10, "augment": 17, "author": [2, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "auto": 11, "autofit": [12, 31, 33, 39, 40], "autom": [11, 35], "automat": [40, 41], "avail": [3, 5, 8, 9, 10, 11, 12, 31, 32, 36], "averag": [5, 9, 10, 31, 32, 35, 36, 40, 41, 42, 43], "average_inblock_semivari": 9, "average_semivari": [10, 38], "avg_block_to_block_semivari": 9, "avg_inblock_semivari": 9, "avg_rms": 36, "avoid": [23, 36, 40], "awai": [5, 9, 11, 12], "awar": [31, 42], "ax": [10, 34, 37, 38, 39, 40, 42, 43, 44], "axi": [9, 10, 11, 13, 34, 35, 39, 40], "b": [10, 12, 33], "b2c": 17, "b2g": 17, "b_id": [42, 43], "back": 35, "backend": 3, "bad": 31, "balanc": 35, "balenoptera": 10, "banff": 10, "bar": [3, 5, 8], "bare": 32, "base": [0, 1, 3, 5, 8, 9, 10, 12, 13, 21, 24, 27, 30, 31, 32, 33, 34, 35, 36, 39, 40, 43, 44], "base1": 40, "base2": 40, "base3": 40, "base4": 40, "base5": 40, "base6": 40, "baselin": [9, 10, 11, 13, 32, 34, 37], "basic": [10, 21, 25, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "batch": 40, "becaus": [5, 27, 31, 32, 33, 35, 36, 38, 40, 42, 43, 44], "becom": [6, 31, 36], "been": [9, 12, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "befor": [5, 8, 9, 31, 32, 33, 35, 36, 38, 40, 41, 42, 43], "begin": [9, 10, 11, 13, 31, 34, 36, 38], "behav": [31, 32, 34, 40, 41], "behavior": [31, 32, 35, 36, 37, 41, 42, 43], "behind": [31, 40], "being": 31, "below": [9, 12, 31, 35, 38, 42, 43], "benchmark": [24, 30], "best": [31, 32, 33, 34, 36, 37, 38, 40], "bet": 31, "beta": 24, "better": [3, 5, 24, 31, 34, 35, 36, 37, 38, 41, 42, 43], "between": [2, 4, 7, 8, 9, 10, 11, 12, 24, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "bia": [5, 8, 11, 12, 24, 31, 36, 40, 42, 43], "bias": 38, "bias_experimental_model": 8, "bias_model": 8, "bias_valu": 8, "bibliographi": 24, "big": [8, 34, 37, 41, 42, 43], "bigger": [9, 11, 12, 41], "bin": [8, 9, 10, 11, 13, 34, 35, 38, 42, 43], "black": [31, 32, 34, 40, 44], "block": [0, 1, 3, 9, 10, 21, 24, 30, 41, 42, 43], "block_a": 2, "block_arr_to_dict": 1, "block_b": 2, "block_base_dist": 1, "block_coordin": 2, "block_data": [2, 41], "block_dataframe_to_dict": 1, "block_dist": 4, "block_id": [2, 7, 42, 43], "block_id_col_nam": 4, "block_index": 2, "block_pair": 2, "block_real_valu": 2, "block_representative_point": 2, "block_to_block_semivari": 9, "block_to_blocks_angl": 1, "block_valu": 2, "blockpk": 1, "blockpoissonkrig": 1, "blocks_dist": 2, "blocks_index": 2, "blocks_index_column": 2, "blocktoblockkrigingcomparison": 1, "blur": 32, "bonnin": 10, "book241284": 25, "bool": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31], "boolean": 36, "bore": 32, "born": 17, "both": [2, 31, 34, 35, 38, 40, 41], "bottom": [34, 35, 38], "bound": 35, "boundari": 32, "box": [10, 38], "boxplot": [10, 38], "break": [1, 24], "breast": [40, 41, 42, 43, 44], "brew": 27, "bright": 34, "brighter": 34, "bring": 38, "buffer": [2, 4, 8, 13], "bug": [18, 24], "build": [9, 17, 37, 38, 39, 40], "build_experimental_variogram": [10, 31, 32, 33, 36, 37, 41], "build_mask_indic": 1, "build_theoretical_variogram": [12, 29, 31, 32, 36, 37, 38], "build_variogram_model": 31, "build_variogram_point_cloud": 1, "built": [32, 41], "byte": [2, 4, 8], "c": [10, 12, 25, 27, 29, 33, 38], "c2a5cf": 32, "cadmium": 33, "cageo": 11, "calc_block_to_block_dist": 4, "calc_pair_dist": 2, "calc_point_to_point_dist": 1, "calcul": [1, 2, 4, 5, 8, 9, 10, 11, 12, 21, 29, 31, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44], "calculate_angles_between_rep_point": 2, "calculate_angular_differ": 1, "calculate_angular_dist": 1, "calculate_average_p2b_semivari": 1, "calculate_avg_inblock_semivari": 9, "calculate_avg_semivariance_between_block": 9, "calculate_covari": 10, "calculate_deviation_decreas": 9, "calculate_deviation_ratio": 9, "calculate_distances_between_rep_point": 2, "calculate_experimental_variogram": 35, "calculate_model_error": 12, "calculate_point_support_dist": 2, "calculate_semivari": [10, 29], "calculate_spatial_dependence_index": [12, 33], "calculate_weighted_block_to_block_dist": 2, "call": [31, 32], "cambardella": [12, 33], "camera": 38, "can": [1, 2, 3, 5, 6, 7, 8, 10, 13, 24, 27, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "cancer": [40, 41, 42, 43, 44], "cancer_data": [2, 40, 41, 42, 43, 44], "cannot": [31, 32, 33, 36], "care": [24, 31, 32, 33, 40], "case": [5, 14, 27, 29, 31, 36, 37, 38, 40, 41, 42, 43, 44], "catastroph": 36, "catch": 34, "categor": 33, "caus": 4, "caution": 35, "cautiou": 33, "cb": 3, "cdist": 4, "cell": [2, 4, 31, 38, 41, 42, 43], "censu": [41, 42, 43], "center": [9, 10, 11, 13, 31, 34, 35], "central": [12, 33], "centroid": [0, 1, 2, 3, 21, 24, 30, 40, 41, 43, 44], "centroid_poisson_krig": [2, 7, 42, 43, 44], "centroidpoissonkriginginput": [1, 2], "challeng": 38, "chanc": [38, 42, 43], "chang": [10, 24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "changelog": 24, "channel": 40, "chaotic": 32, "check": [5, 12, 20, 31, 32, 34, 36, 38, 40, 41, 42, 43], "check_id": 1, "check_nugget": 1, "check_rang": 1, "check_sil": 1, "chemic": 33, "choic": [36, 38], "choos": [31, 32, 40], "choropleth": [9, 40, 44], "chosen": [12, 31, 32, 36], "chosen_model": 31, "circl": [10, 31, 34], "circular": [9, 11, 12, 31, 32, 34], "circular_model": 31, "citi": [31, 33], "cividi": [32, 39], "clarif": [37, 38, 41, 42, 43], "clarifi": [42, 43], "clark": 5, "class": [2, 8, 9, 10, 11, 12, 21, 31, 32, 34, 35, 38, 41], "classic": [33, 36, 39], "clean": [10, 35, 38, 40], "clean_mask_indic": 1, "clearli": [39, 42, 43], "cli": 20, "clip": 5, "close": [5, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43], "closer": [33, 35, 37, 41, 42, 43], "closest": [1, 2, 3, 5, 6, 8, 9, 11, 12, 32, 33, 36, 37, 38, 41, 44], "closest_neighbor": 2, "cloud": [0, 1, 21, 30, 31, 40], "cloud_with": 38, "cloud_with_rem": 38, "cloud_without": 38, "cloud_without_rem": 38, "cluster": [3, 5, 7, 8, 13, 36], "clusterdetector": 1, "cmap": [31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "code": [2, 24, 29, 34], "col": [32, 33], "collect": 10, "color": [31, 32, 40, 42, 43, 44], "column": [2, 3, 4, 6, 31, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "com": 25, "come": [33, 35, 36, 42, 43], "command": 27, "commerc": 17, "commun": [16, 24], "compar": [5, 31, 33, 35, 36, 37, 38, 40, 41], "comparison": [8, 10, 31, 32, 33, 34, 38, 40], "complet": [1, 35], "complex": [2, 4, 8, 21, 41], "compound": 33, "comput": [3, 12, 25, 31, 40], "computation": 31, "concentr": [33, 34, 39], "concept": [34, 38, 42, 43, 44], "conda": 29, "condition": 32, "cone": 31, "configur": 27, "consid": [10, 12, 35, 38, 42, 43], "consider": 33, "consist": [34, 41], "constant": [32, 37, 40], "contain": 38, "contribut": 18, "contributor": 14, "control": [6, 31, 32, 34, 36, 37, 41], "conveni": [42, 43, 44], "convolve2d": 32, "coo_matrix": 32, "coordin": [2, 4, 5, 6, 8, 9, 10, 11, 13, 31, 32, 34, 36, 42, 43, 44], "copernicu": 38, "copi": [10, 33, 38, 44], "copper": 33, "core": [0, 9, 21, 22], "correct": [25, 31, 38], "correl": [10, 12, 24, 31, 32, 37], "could": [6, 8, 10, 12, 13, 24, 31, 36, 37, 38, 42, 43, 44], "count": [10, 34, 35, 36, 38, 40, 42, 43, 44], "counti": [24, 40, 41, 42, 43, 44], "countri": [24, 33], "covari": [0, 1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "covariance_fn": 1, "covariogram": 10, "cover": [1, 31, 36, 38], "coviari": [10, 31], "covid": 24, "cr": [2, 3, 31, 35, 36, 37, 38, 40, 44], "cran": 33, "creat": [4, 5, 9, 10, 11, 12, 13, 24, 25, 27, 31, 33, 37, 40, 41, 42, 43], "creation": [3, 5, 7, 8, 13], "crete": 32, "crime": 28, "cross": [0, 34], "cross_valid": 5, "csv": [9, 31, 33, 35, 36, 37, 38, 39], "cubic": [9, 11, 12, 31, 32], "cubic_model": 31, "current": 9, "current_deviation_decreas": 9, "current_ratio": 9, "curv": [12, 32, 33], "custom": [10, 17, 31], "custom_bin": [10, 11, 31], "custom_weight": [9, 10, 11, 31], "cv": 10, "cvc": 35, "d": [2, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "d9f0d3": 32, "d_": 37, "danger": 31, "danych": 28, "dark": 34, "darker": 34, "dash": 31, "dask": [1, 3], "data": [0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 17, 21, 24, 28, 29, 32, 33, 34, 35, 36, 37], "data_cr": [3, 44], "data_model": 2, "datafram": [2, 4, 10, 31, 36, 39, 42, 43], "dataset": [1, 2, 3, 5, 7, 8, 10, 13, 24, 26, 28, 32, 33, 34, 36, 38, 39, 40, 41, 42, 43, 44], "date": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "dcv": 9, "deal": [31, 35, 44], "debug": [9, 36], "decid": [2, 32, 33], "decis": [1, 24, 34, 38, 42, 43], "deconvolut": [0, 1, 2, 10, 21, 24, 25, 41, 42, 43, 44], "decreas": [6, 9, 31, 37, 41], "def": 32, "default": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 34, 36, 41, 42, 43], "defin": [9, 31, 32, 39, 40, 44], "define_whitening_matrix": 1, "definit": [32, 34], "degre": [3, 8, 9, 10, 11, 13, 31, 36], "deliv": 36, "dem": [29, 31, 35, 36, 37, 38], "dem_fil": 31, "dem_geometri": [31, 35, 36, 37, 38], "demand": 17, "denois": 43, "denomin": [10, 37, 40], "dens": [5, 9, 11, 12, 35, 40, 41, 42, 43], "densiti": [24, 35], "depend": [1, 12, 18, 30, 31, 32, 34, 35, 36, 41], "deriv": [8, 9, 11, 12, 31, 35, 36], "describ": [2, 9, 10, 11, 23, 31, 34, 35, 36, 38, 41, 42, 43], "descript": [12, 33], "design": 2, "desir": 42, "detail": [4, 34], "detect": [10, 38], "detector": 17, "determin": [38, 42, 43], "detrend": 8, "deutsch": [10, 25], "dev": [9, 22, 27], "develop": [22, 24, 37], "deviat": [0, 1, 10, 35, 38, 41, 42, 43], "deviation_direct": 9, "deviation_weight": [11, 12], "df": [31, 33, 34, 35, 36, 37, 38, 39], "dfc27d": 38, "dict": [2, 7, 8, 9, 10, 11, 12, 13, 31], "dict_represent": 31, "dictionari": [2, 8, 10, 11, 12, 13, 31], "did": 31, "didn": [9, 40], "differ": [5, 8, 9, 10, 12, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44], "digit": [31, 36, 38], "dim": 13, "dimens": [6, 13], "dimension": [6, 32], "diminish": 37, "dir_neighbors_selection_method": [31, 34, 39], "dir_var": 34, "dir_var_": 34, "dir_var_t": 34, "direct": [0, 1, 2, 8, 9, 11, 12, 13, 30, 31, 35, 36], "directional_covari": 1, "directional_covariogram": 1, "directional_point_cloud": 1, "directional_semivariance_cloud": 1, "directional_semivariogram": 1, "directional_variogram": 10, "directional_weighted_semivari": 1, "directionalvariogram": [10, 34, 39], "directli": [31, 33, 35, 36, 38, 42, 43], "directori": 23, "dirvar": 39, "disaggreg": 24, "disappoint": 32, "discord": [16, 40], "discret": 34, "diseas": [17, 24], "dispers": [35, 40, 42, 43], "dissimilar": [10, 12, 31], "distanc": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 21, 24, 31, 32, 34, 35, 36, 37, 38, 41], "distances_between_block": 9, "distances_between_point_support": 2, "distances_between_representative_point": 2, "distant": [5, 9, 10, 11, 12, 35, 36, 37, 38, 40, 41], "distinguish": 39, "distribut": [10, 27, 33, 35, 36, 39, 41, 42, 43, 44], "diverg": [42, 43], "divid": [9, 34, 36, 37, 38, 40, 41, 44], "divis": [9, 38, 42, 43], "do": [2, 3, 5, 7, 8, 13, 24, 31, 34, 36, 40, 41, 42, 43], "document": [1, 20, 24], "doe": [5, 27, 31, 36, 38, 41], "doesn": [1, 3, 37, 38], "doi": [11, 24, 26], "domain": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "don": [3, 5, 7, 8, 13, 31, 32, 36, 37, 40], "done": [31, 40], "dordrecht": 10, "down": [35, 36, 38, 41], "downsid": 35, "downward": 41, "dozen": [36, 42, 43], "draw": [42, 43], "drawback": 37, "drift": 35, "driven": 36, "dropped_point": 2, "dt": 41, "dtype": [2, 4, 8, 35, 36, 38], "dubroca": 10, "due": [1, 2, 41], "duplic": 36, "durbec": 10, "dure": [1, 2, 8, 9, 41, 42, 43], "e": [2, 9, 10, 11, 12, 13, 17, 25, 31, 33, 34, 39, 40, 42, 43, 44], "e7d4e8": [31, 32], "e_": 5, "each": [2, 5, 6, 8, 9, 10, 11, 24, 29, 31, 33, 34, 36, 38, 40, 41, 42, 43, 44], "earlier": 35, "earth": 11, "easiest": 31, "easili": [31, 35], "east": [10, 36, 39], "eastern": 36, "ecolog": 10, "ecologi": [10, 41], "ecologist": [], "econom": 28, "edg": 34, "edgecolor": [40, 44], "edit": 10, "editor": 15, "edzer": [33, 39], "effect": [32, 34, 37], "effort": 10, "eg": 2, "element": 9, "eleph": 35, "elev": [31, 36, 38], "ellips": [9, 10, 11, 13, 34], "ellipt": [9, 10, 11, 13], "els": 40, "emphas": 37, "empir": [10, 12], "empirical_smv": 10, "empti": [31, 36, 39], "en": [5, 25], "encount": 27, "end": [39, 41, 44], "engin": [], "enthusiast": 34, "entir": 36, "enumer": 40, "env": 27, "environ": 27, "epidemiologi": [40, 41], "epsg": [2, 31, 35, 36, 37, 38], "equal": [5, 6, 9, 10, 11, 12, 13, 31, 34, 35, 36, 40, 41], "equat": [5, 10], "equidist": 9, "eras": 42, "err": [3, 39, 44], "err_to_nan": 7, "error": [1, 3, 4, 5, 7, 8, 9, 11, 12, 13, 29, 31, 32, 36, 37, 38, 39, 40, 41, 42, 43], "error_estim": [11, 12, 31], "errs_col": 40, "especi": [10, 38, 40, 41, 44], "essenti": [35, 36, 41], "est": [3, 44], "estim": [2, 3, 5, 6, 8, 9, 11, 12, 13, 31, 33, 34, 36, 37, 38, 41, 42, 43], "etc": [36, 40], "ethem": 15, "ethmtrgt": 15, "euclidean": 4, "eur": 25, "european": 17, "evalu": [0, 12, 21], "even": [31, 32, 36, 37, 39, 40, 42, 43], "event": 33, "everi": [10, 31, 33, 34], "everyth": 34, "exact": 36, "examin": 32, "exampl": [1, 2, 4, 9, 10, 12, 24, 31, 34, 35, 38, 39, 40, 42, 43, 44], "excel": [35, 38], "except": 41, "excit": 32, "exclud": 33, "exercis": 31, "exist": [12, 33, 38, 40], "exp_model": 40, "exp_semivar": 41, "exp_var": [33, 34, 35, 36, 37], "expect": [5, 8, 31, 41], "expected_valu": 8, "experi": [32, 36, 38, 41, 42, 43], "experimanet": 11, "experiment": [0, 1, 2, 8, 9, 11, 12, 21, 29, 33, 36, 38, 39, 40, 41, 42, 43, 44], "experimental_block_semivari": 9, "experimental_indicator_variogram": 11, "experimental_model": 11, "experimental_point_cloud": 10, "experimental_semivari": [10, 12, 35, 40], "experimental_semivariogram": 29, "experimental_variogram": [9, 11, 12, 29, 31, 32, 33, 36, 37, 38, 39, 40], "experimentalfeaturewarn": 1, "experimentalindicatorvariogram": 11, "experimentalvariogram": [2, 8, 9, 10, 12, 31, 34, 35, 36, 37, 38], "experimentalvariogrammodel": 1, "expert": 24, "explain": [31, 33], "explor": [30, 34, 35], "exponenti": [9, 11, 12, 31, 32], "exponential_model": 31, "export": [9, 12], "export_model": 9, "export_model_to_json": [9, 41], "extend": 33, "extent": [12, 34], "extern": [8, 17, 33], "extrem": [35, 37, 40], "ey": 32, "f": [12, 31, 33, 37, 40], "face": 1, "fact": [38, 43], "factor": [5, 10, 40], "fall": [2, 34, 42], "fals": [2, 3, 5, 7, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 37, 38, 39, 42, 43, 44], "familiar": 34, "far": 36, "fast": [34, 42], "faster": [6, 34, 37, 42], "fb": 5, "featur": [8, 34, 40], "fed": 36, "feel": 31, "few": [10, 31, 32, 35, 36, 40, 41, 42, 43], "fewer": 36, "field": [12, 33], "fig": [38, 39, 40], "figsiz": [31, 32, 34, 38, 39, 40, 42, 43, 44], "figur": [10, 31, 32, 35, 36, 38], "file": [1, 9, 12, 22, 27, 31, 40], "filenam": 2, "fill": [31, 36], "filter": [1, 3, 32, 38], "filter_block": [1, 3], "fin": 10, "final": [9, 10, 32, 36, 38, 44], "final_regularized_variogram": 9, "final_theoretical_model": 9, "find": [2, 11, 12, 24, 31, 33, 35, 38, 41], "fip": [2, 40, 41, 42, 43, 44], "first": [9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 38, 40, 41, 44], "fit": [3, 5, 7, 8, 9, 11, 12, 25, 29, 33, 35, 38, 41, 42, 43], "fit_bia": 8, "fit_transform": [9, 41], "fit_trend": 8, "fitted_regression_model": 8, "fitted_valu": 12, "five": [10, 31, 39, 42, 43], "fix": [10, 11, 12, 31], "flag": 33, "flat": 31, "flatten": 31, "float": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 34, 35, 41], "float64": [35, 36, 38], "flow": 29, "flu": 17, "fly": 2, "fname": [9, 12], "focus": 31, "folder": 39, "follow": [10, 31, 33, 41, 42, 43, 44], "forc": [5, 31, 40], "forecast": [5, 11, 12, 31, 42, 43], "forecast_bia": [5, 42, 43], "forg": [27, 29], "forget": 41, "form": [9, 32, 36, 37], "format": 33, "fortran": 25, "four": [10, 33, 36, 37, 38], "frac": [5, 9, 33, 37], "fraction": [11, 12, 31, 34, 40], "frame": [2, 40], "frequenc": [39, 42, 43], "fresh": 27, "from": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 22, 24, 27, 28, 29, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 44], "from_dict": [12, 31], "from_ellips": 1, "from_ellipse_cloud": 1, "from_json": [12, 31, 42, 43, 44], "from_triangl": 1, "from_triangle_cloud": 1, "from_user_input": 2, "full": [5, 29, 31, 39], "fulli": 36, "function": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "further": [5, 9, 11, 12, 37, 38, 39], "futur": 1, "g": [2, 10, 31, 40, 42, 43, 44], "g_w": 10, "gain": 38, "gallach": 15, "gamma": 9, "gamma_": 9, "gamma_h": 9, "gamma_v": 9, "gaussian": [9, 11, 12, 31, 32], "gaussian_model": 31, "gb": 25, "gcc": 27, "gdf_pt": 40, "gener": [11, 28, 32, 35, 36, 37, 38, 41, 42, 43], "generate_logistic_map": 32, "geodatafram": [2, 3, 31, 35, 36, 37, 38, 39, 40, 42, 43, 44], "geograph": [9, 25, 31, 36, 39, 41], "geologi": [9, 25, 41], "geologist": 24, "geometri": [2, 3, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "geometry_col": [40, 41, 42, 43, 44], "geometry_column_nam": [2, 41, 42, 43, 44], "geometryarrai": 8, "geopackag": [40, 44], "geopanda": [1, 2, 3, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "geoscienc": 25, "geoseri": 8, "geostatist": [5, 10, 25, 28, 31, 32], "geostatystyk\u0105": 28, "get": [2, 3, 10, 24, 27, 28, 31, 32, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44], "get_aggregated_point_support_valu": 1, "get_areal_centroids_from_agg": 1, "get_areal_values_from_agg": 1, "get_blocks_valu": 2, "get_current_and_previous_lag": 1, "get_distances_between_known_block": 2, "get_distances_within_unknown": 1, "get_expected_valu": 8, "get_expected_values_map": 8, "get_indicator_map": 8, "get_lag": 1, "get_point_to_block_index": 2, "get_points_arrai": [2, 41], "get_study_max_rang": 1, "get_triangle_edg": 1, "get_weighted_dist": 2, "gi": [24, 25], "gist_earth": [31, 35, 36, 37, 38], "github": 24, "give": [38, 40, 43], "given": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 31, 34, 36, 40], "global": 36, "go": [34, 35, 36, 37, 38, 41], "goe": [35, 41], "good": [10, 31, 34, 36, 40, 41, 42, 43, 44], "goovaert": [9, 11, 25, 41], "gorz\u00f3w": 31, "gpd": [2, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "gpkg": [2, 29, 34, 40, 41, 42, 43, 44], "gradual": 34, "graph": 35, "great": 32, "greater": [3, 5, 8, 10, 11, 12, 13, 31, 33, 35, 36, 37, 38, 39, 40], "green": 34, "grid": [2, 33, 39, 40, 44], "group": [9, 10, 11, 12, 22, 23, 31, 35, 38, 42, 43], "groupbi": 36, "grow": 31, "gstat": [33, 39], "gt": [37, 39, 42, 43, 44], "guess": [32, 35, 37], "guid": 34, "guinet": 10, "h": [9, 10, 34], "ha": [1, 5, 8, 9, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "hack": 40, "half": [12, 34], "hand": [33, 37], "handi": 38, "handl": 1, "hashabl": [2, 7], "hasn": 9, "have": [2, 3, 5, 7, 8, 9, 11, 12, 13, 27, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "haven": 27, "he": 35, "head": [31, 33, 34, 36, 39, 40, 41, 42, 43, 44], "headach": 35, "health": 10, "heavili": [1, 2, 34, 35, 38, 42, 43], "height": 13, "help": 31, "here": [5, 23, 25, 31, 32, 35], "heterogen": 10, "hexagon": 42, "high": [21, 35, 37, 38, 40, 42, 43], "higher": [5, 35, 37], "hist": [39, 42, 43], "histogram": [39, 42, 43], "hope": 32, "horizont": [35, 38], "hotel": 17, "how": [3, 5, 8, 9, 11, 12, 24, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "howev": [41, 42, 43], "html": 3, "http": [3, 5, 11, 24, 25, 26, 33], "huge": [42, 43], "hugoledoux": 15, "hundr": [33, 36], "hyperparamet": [31, 37], "i": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 23, 24, 27, 28, 29, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "id": [2, 3, 7, 9, 40, 42, 43, 44], "idea": [3, 10, 31, 34, 36, 38, 40, 41, 42, 43, 44], "ideal": [42, 43], "idw": [0, 21], "idw_pow": 37, "idw_pr": 37, "idw_rms": 37, "idx": 40, "ignor": [10, 36], "iguzquiza": 25, "ik": 11, "ikei": 40, "iloc": 36, "imag": 32, "imagin": 36, "implement": [10, 31], "import": [2, 5, 10, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "importantli": [35, 42, 43], "impress": 38, "improv": 41, "imshow": 32, "in_cr": 31, "inaccur": 36, "inani": 15, "inblock": [2, 9], "inblock_semivari": [1, 9], "incid": [41, 42, 43], "includ": [9, 10, 11, 33, 44], "increas": [3, 8, 9, 31, 36, 41], "independ": 41, "index": [2, 4, 7, 11, 12, 30, 36, 37, 38, 41], "index_column_nam": [2, 40, 41, 42, 43, 44], "indexcolnotuniqueerror": 1, "indic": [0, 1, 2, 10, 12, 24, 28, 33, 35, 38, 40, 42, 43], "indicator_map": 8, "indicator_predict": 8, "indicator_variogram": 8, "indicatorkrig": 8, "indicatorvariogram": [1, 8], "indicatorvariogramdata": 11, "individu": 38, "industri": 41, "inf": [31, 33], "infect": [24, 40, 44], "influenc": [6, 10, 24, 33, 37, 38], "info": 9, "inform": [2, 33, 35, 36, 41, 42, 43], "infrastructur": 17, "inhabit": 40, "initi": [2, 8, 9, 11, 31, 32, 38, 41, 44], "initial_devi": 9, "initial_ratio": 32, "initial_regularized_model": 9, "inplac": [2, 10, 34, 35, 36, 37, 38, 40], "input": [2, 8, 31, 36, 38], "insight": [35, 38, 42, 43], "inspect": [38, 40], "instal": [22, 31, 32], "instanc": [2, 10, 31], "instead": [1, 2, 31, 32, 40, 42], "int": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 31, 32, 36, 37, 38], "intens": 31, "interest": [41, 42, 43], "intermedi": 21, "intern": [21, 35, 41], "interpol": [3, 5, 7, 8, 13, 21, 24, 26, 28, 29, 31, 34, 36, 37, 38, 42, 43], "interpolate_point": [1, 3, 38, 39, 40], "interpolate_points_dask": [1, 3], "interpolate_rast": 13, "interpolation_results_areal_to_point": 40, "interpret": 5, "interquartil": 35, "interv": [33, 34], "introduc": [1, 24, 36, 42], "invers": [0, 21, 24, 36, 37, 41], "inverse_distance_weight": [6, 37], "investig": 38, "invok": [10, 12, 35], "iowa": [12, 33], "ipykernel_75618": 39, "iqr": [10, 35, 40], "iqr_lower_limit": [10, 35, 40], "iqr_upper_limit": [10, 35, 40], "irregular": [9, 25, 41, 42, 43], "irregularli": 41, "is_covari": [10, 31], "is_fit": 9, "is_semivari": [10, 31], "is_transform": 9, "is_weighted_by_point_support": 7, "isin": [36, 37, 38], "iso": [10, 39], "isotrop": [10, 11, 12, 39], "issu": [1, 20, 24], "item": 37, "iter": [2, 6, 9, 12, 38, 41, 42, 43, 44], "its": [2, 12, 31, 32, 34, 37, 42], "j": [2, 9, 11, 12, 33], "join": [2, 16, 41], "joss": [24, 26, 28], "journal": [12, 24, 26, 33], "jp": 10, "json": [12, 31, 41, 42, 43, 44], "jump": 32, "june": 24, "just": [33, 40, 41], "k": 36, "karlen": [12, 33], "kei": [2, 10, 13], "kenohori": 15, "kernel": 35, "keyerror": [9, 12], "kilomet": 33, "kind": [5, 8, 10, 24, 35, 38, 39, 42, 43], "kluwer": 10, "know": [3, 5, 7, 8, 13, 24, 31, 32, 33, 36, 41, 42, 43], "knowledg": [32, 38], "known": [2, 3, 5, 6, 8, 18, 36, 37], "known_loc": [3, 6, 8, 29, 36, 37, 38, 39, 40], "known_point": [6, 8], "known_valu": 36, "konopka": [12, 33], "krige": [0, 1, 5, 9, 10, 11, 13, 21, 24, 25, 28, 30, 31, 33, 35, 41], "kriged_result": 39, "kriging_pr": 37, "kriging_rms": 37, "kriging_run": [], "kriging_typ": [3, 8], "krigingobject": 1, "kurtosi": [10, 35], "l": [10, 12, 33], "lack": 2, "lag": [5, 9, 10, 11, 12, 31, 32, 34, 36, 38, 40, 41], "lag_numb": 10, "lag_points_distribut": 5, "lakshaya": 15, "lakshayainani": 15, "lambda": 37, "lambda_": 37, "land": 38, "larg": [1, 3, 4, 5, 8, 12, 17, 24, 32, 35, 36, 37, 40, 41, 42, 43], "larger": [6, 12, 13, 31, 32, 34, 38], "largest": [2, 10, 38, 40], "last": [6, 11, 12, 24, 27, 31, 32, 33, 35, 40, 42, 43, 44], "lat": [2, 3, 8, 29, 40, 41, 42, 43], "lat_col": 31, "lat_col_nam": [2, 4], "later": [35, 38], "latitud": [2, 3, 4, 8, 31, 35, 36, 37, 38], "law": [10, 33], "layer": [2, 34, 40, 41, 42, 43, 44], "layer_nam": 2, "lead": [3, 5, 7, 8, 13, 32, 33, 39], "leak": 36, "learn": [9, 24, 32, 33, 34, 36, 37, 38, 39, 40, 41], "least": [25, 35], "leav": [2, 31, 33, 34, 36, 41, 44], "left": [10, 35], "left_on": [42, 43], "legend": [31, 32, 34, 35, 36, 37, 38, 40, 42, 43, 44], "len": [6, 31, 36, 37, 38, 40], "length": [6, 32, 34], "less": [6, 10, 40, 41], "lesson": 32, "let": [10, 31, 32, 34, 36, 38, 39, 40, 42, 43], "leuangthong": 10, "level": [9, 10, 12, 21, 24, 33, 35, 38], "li": 31, "librari": [24, 27], "libspatialindex": 27, "libtiff": 27, "like": [2, 23, 31, 36, 40], "lim": 15, "limit": [5, 6, 8, 9, 12, 32, 33, 35, 36], "limit_deviation_ratio": 9, "line": [9, 10, 11, 13, 27, 31, 34, 35, 38], "linear": [8, 9, 11, 12, 25, 31, 32, 34, 36, 37, 38], "linear_model": 31, "link": 33, "list": [1, 2, 6, 8, 9, 10, 11, 12, 22, 31, 36, 38], "literatur": 31, "live": 40, "ll": 37, "load": [35, 36, 37, 38, 40], "loc": [35, 36, 37, 38], "local": 11, "locat": [3, 6, 7, 8, 10, 37, 42, 43], "log": [9, 33, 39], "log_process": 9, "logarithm": 33, "logist": 32, "logistic_map": 32, "lon": [2, 3, 8, 29, 40, 41, 42, 43], "lon_col": 31, "lon_col_nam": [2, 4], "long": [27, 34, 35, 41, 42, 43, 44], "longer": 34, "longitud": [2, 3, 4, 8, 31, 35, 36, 37, 38], "look": [3, 31, 32, 33, 34, 35, 36, 38, 42, 43], "lot": 35, "low": [32, 33, 35, 37, 38, 40, 41, 42, 43], "lower": [9, 10, 12, 31, 33, 35, 38, 42, 43], "lowest": [9, 10, 31, 32, 35, 38], "lowest_rms": 31, "lt": [36, 37, 38, 39, 40, 41, 42, 43, 44], "lunch": 41, "m": [4, 6, 10, 12, 25, 32, 33, 35], "machin": [24, 34, 36], "maco": 27, "mad": 40, "mae": [5, 11, 12, 31], "mai": [4, 8, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], "main": [10, 41], "mainten": 17, "major": [9, 10, 11, 13, 34], "make": [3, 24, 32, 33, 34, 36], "manag": [33, 41], "mani": [3, 8, 9, 11, 12, 25, 31, 34, 35, 36, 41, 42, 43, 44], "manual": [37, 38, 40], "map": [8, 9, 24, 31, 32, 34, 39, 40, 42, 43, 44], "marker": 34, "markers": [34, 44], "materi": 24, "mathemat": [9, 25, 41], "matplotlib": [31, 32, 34, 36, 38, 39, 40], "matrix": [3, 4, 5, 7, 8, 13, 32], "max": [10, 13, 35, 36, 38, 42, 43], "max_it": [9, 41], "max_nugget": [11, 12, 31, 33], "max_rang": [8, 9, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "max_sil": [11, 12, 31], "max_tick": [3, 8, 36, 39], "maxim": [9, 38, 41], "maximum": [2, 3, 5, 7, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 41], "maximum_point_rang": 41, "maximum_rang": [40, 41], "md": 23, "mean": [5, 6, 8, 9, 10, 11, 12, 24, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43], "mean_absolute_error": 5, "mean_dir_pr": 39, "mean_directional_result": 39, "mean_filt": 32, "mean_relative_differ": 1, "meaning": 41, "measur": [5, 8, 10, 24, 26, 28, 33, 35, 36, 42, 43], "mec": [31, 32], "median": [10, 35, 38, 40, 42, 43], "medic": 17, "mediterranean": 10, "memori": 4, "merg": [41, 42, 43], "messag": 27, "messi": 31, "meter": [31, 34, 36, 37, 38], "method": [1, 2, 3, 5, 8, 9, 10, 11, 12, 29, 31, 35, 36, 38, 40, 41, 42, 44], "metric": [0, 4, 31, 32, 35, 36, 37, 38], "metricstypeselectionerror": [1, 12], "meus": [33, 39], "meuse_fil": [33, 39], "meuse_grid": 39, "meuse_grid_fil": 39, "middl": [35, 38], "might": [1, 12, 24, 27, 31, 33, 34, 35, 36, 38, 40, 44], "mile": 33, "min": [10, 13, 35, 36, 38, 42, 43], "min_deviation_decreas": 9, "min_deviation_ratio": 9, "min_nugget": [11, 12, 31], "min_rang": [11, 12, 31], "min_sil": [11, 12, 31], "mine": 41, "minim": [9, 10, 11, 12, 31], "minimum": [3, 7, 9, 10, 11, 12, 32, 35], "minimum_deviation_decreas": 9, "minor": [9, 10, 11, 13, 24, 34, 38], "minu": 9, "mirror": 31, "mislead": [42, 43], "miss": [8, 29, 31, 36, 40, 43], "mix": 38, "ml": 44, "mode": [35, 39], "model": [0, 1, 3, 7, 8, 9, 10, 11, 12, 13, 21, 24, 25, 28, 30, 33, 34, 35, 37, 41], "model_error": 12, "model_from_dict": 31, "model_from_json": 31, "model_nam": [31, 39], "model_param": 12, "model_paramet": 12, "model_rms": 31, "model_typ": 12, "models_group": [9, 11, 12, 29, 31, 32, 33, 36, 37, 38, 39, 41], "moder": [12, 33], "modifi": 2, "modul": [17, 21], "moli\u0144ski": [15, 24, 26, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "monei": 31, "monestiez": 10, "monitor": [1, 9, 35, 38], "moorman": [12, 33], "more": [4, 5, 6, 8, 9, 12, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "moreov": [31, 32], "most": [1, 5, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43], "mostli": [31, 33, 36, 38], "mountain": 36, "move": [36, 41], "mrd": 9, "msg": 31, "much": [34, 36, 37, 42], "multimod": 35, "multipl": [1, 2, 5, 8, 9, 11, 12, 29, 31, 32, 33, 34, 36, 38, 40, 41, 42, 43], "multipli": [12, 40], "multipolygon": [2, 40, 41, 42, 43], "multivariateregress": 8, "must": [3, 4, 5, 6, 8, 9, 10, 11, 13, 31, 32, 33, 34, 36, 37, 40, 41, 42, 43], "mxn": [4, 6], "n": [3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 27, 32, 33, 34, 35, 36, 37, 38, 39, 40], "n_lag": 35, "n_sill_valu": [11, 12], "name": [2, 4, 5, 9, 11, 12, 27, 31, 32, 35, 36, 38, 39, 40], "nan": [7, 36, 42, 43, 44], "nanmean": 39, "ncol": [38, 39, 40], "ndarrai": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 32], "ndarray_pydant": 1, "ne": [9, 10, 11, 13, 34, 39], "ne_sw_direct": 34, "need": [10, 31, 32, 35, 36, 42, 44], "neg": [3, 5, 7, 12, 25, 32, 35, 42, 43], "neglig": [10, 31], "neighbor": [2, 3, 5, 6, 7, 8, 9, 10, 24, 31, 33, 36, 37, 40, 42, 43, 44], "neighborhood": [33, 36], "neighbors_numb": 36, "neighbors_rang": [3, 5, 7, 8, 36, 40, 42, 43], "neighbour": [2, 3, 6, 7], "netherland": 10, "network": 14, "never": 31, "new": [6, 10, 31, 35, 36, 40], "new_val": 32, "next": [31, 33, 36, 38, 41], "ningchuan": 25, "nn": 36, "no_closest_neighbor": 2, "no_neighbor": [3, 5, 6, 8, 29, 36, 37, 39, 40], "no_possible_neighbor": 2, "nois": [36, 38], "non": [10, 11, 31, 33, 35], "none": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31, 33, 36], "normal": [9, 31, 33, 35, 39, 41], "north": [10, 39], "northeastern": [10, 40, 41, 42, 43, 44], "northwestern": 10, "note": [2, 4, 5, 9, 10, 33, 34, 35, 36, 38, 42, 43], "notebook": [34, 40], "noth": 12, "novak": [12, 33], "now": [1, 24, 27, 31, 35, 36, 37, 38, 40, 41, 43], "np": [2, 8, 9, 10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 42, 43, 44], "nrow": [38, 39, 40], "ns_direct": 34, "nthe": 31, "nugget": [9, 11, 12, 29, 31, 32, 33, 36, 38, 39, 40, 41], "number": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 32, 35, 36, 37, 38, 40, 42, 43, 44], "number_of_lag": 40, "number_of_neighbor": [3, 5, 7, 8, 13, 37, 42, 43, 44], "number_of_neighbour": 6, "number_of_nugget": [11, 12, 31], "number_of_rang": [11, 12, 31], "number_of_sil": [11, 12, 31], "number_of_threshold": 11, "number_of_tri": 36, "number_of_work": [3, 8], "numer": 33, "numpi": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "nw": [9, 10, 11, 13, 34, 39], "nw_se_direct": 34, "o": [1, 2, 10, 31, 32], "object": [2, 9, 10, 31, 33, 34, 35, 40, 41, 44], "oblig": 31, "observ": [2, 5, 6, 8, 10, 12, 24, 31, 32, 34, 35, 40, 41], "obtain": [37, 40], "occur": 41, "offici": 1, "ok": [5, 8], "ok_interpol": 36, "ol": [3, 5, 7, 8, 13, 31, 36], "old": [1, 2], "omnidirect": [9, 10, 11, 13, 34], "omnidirectional_covari": 1, "omnidirectional_covariogram": 1, "omnidirectional_point_cloud": 1, "omnidirectional_semivari": 1, "omnidirectional_variogram": 1, "omnidirectional_variogram_cloud": 1, "onc": [6, 9, 34], "one": [8, 10, 13, 33, 34, 35, 36, 38, 40, 41, 42, 43], "ones": 32, "ongo": 40, "onli": [3, 8, 9, 10, 11, 12, 31, 32, 34, 35, 36, 38, 40, 42, 43], "open": [24, 26], "oper": [1, 3, 9, 27, 41], "opinion": [32, 34], "opportun": 37, "opposit": 41, "opt_dev": 9, "optim": [1, 9, 11, 12, 17, 24, 31, 32, 33, 34, 41], "optimal_devi": 9, "optimal_model": 33, "option": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 31, 33, 36], "orang": 35, "order": [2, 10, 33, 38], "ordinari": [0, 1, 5, 13, 21, 24, 25, 28, 30, 38, 42, 43, 44], "ordinary_krig": [1, 8, 29, 36, 37, 40, 42, 43, 44], "org": [3, 5, 11, 24, 26, 33], "origin": [9, 10, 11, 13, 34, 41], "oscil": [32, 41], "other": [1, 2, 4, 9, 10, 24, 28, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44], "other_block": 2, "otherwis": [2, 11, 13, 36], "our": [5, 10, 16, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44], "out_cr": 31, "outcom": [35, 38], "outlier": [10, 30, 34, 42, 43], "output": [8, 10, 31, 32, 34, 35, 40, 41, 42, 43, 44], "over": [5, 8, 10, 31, 32, 36, 38, 39, 40, 41, 42, 43, 44], "overcom": [24, 42, 43], "overestim": [5, 12, 42, 43], "overfit": 31, "overlap": 2, "overview": 21, "overwrit": [10, 12, 35], "overwritten": 12, "own": [32, 42], "p": [2, 5, 9, 10, 11, 12, 25, 31, 32, 33, 37, 40, 41], "p_": [5, 9], "packag": [1, 8, 18, 22, 24, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "page": 32, "pair": [2, 5, 9, 10, 11, 12, 31, 32, 35, 38, 41], "panda": [1, 2, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "param": 13, "paramet": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44], "parameter": 12, "parametr": 11, "pardo": 25, "parkin": [12, 33], "parse_kriging_input": 1, "parse_point_support_distances_arrai": 1, "part": [32, 35, 36, 38, 40, 41, 42, 43], "partial": [11, 12], "particular": [42, 43], "pass": [2, 11, 12, 31, 33, 36, 37, 40, 44], "path": 32, "pattern": [38, 40, 42, 43], "pcol1": 40, "pcol2": 40, "pd": [2, 4, 31, 33, 35, 36, 37, 38, 39, 42, 43, 44], "pdf": 33, "pebesma": [33, 39], "penal": [5, 41], "peopl": 24, "per": [5, 9, 35, 36, 38, 40, 44], "percent": [5, 33], "percentag": [5, 11, 12, 31, 33], "perf_count": 34, "perform": [2, 5, 7, 8, 9, 12, 31, 34, 36, 38, 39, 40, 41, 42, 43, 44], "phenomenon": 33, "physalu": 10, "pick": [34, 42, 43], "pictur": 34, "piec": [42, 43], "pipelin": [0, 1, 21, 31, 35], "pivot": 36, "pixel": [13, 32, 38], "place": [9, 10, 11, 13, 34, 38, 40], "plain": 36, "plane": 34, "plasma": 44, "pleas": 24, "plot": [8, 9, 10, 11, 12, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "plot_devi": 9, "plot_deviation_chang": 41, "plot_experimental_bias_model": 8, "plot_theoretical_bias_model": 8, "plot_trend_surfac": 8, "plot_variogram": [9, 41], "plot_weight": 9, "plot_weights_chang": 41, "plt": [31, 32, 34, 36, 38, 39, 40], "plu": [31, 35], "pm2": 34, "pm2_5": 34, "point": [0, 1, 3, 5, 6, 9, 10, 11, 12, 13, 21, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 39, 41, 42, 43], "point_cloud_filt": 38, "point_cloud_semivari": 1, "point_data": 29, "point_dist": [1, 4], "point_support": [2, 3, 7, 9, 41, 42, 43, 44], "point_support_blocks_index_nam": 2, "point_support_data": 2, "point_support_dataset": 9, "point_support_to_dict": 1, "point_support_tot": 2, "points_from_xi": [31, 35, 36, 37, 38, 39], "points_geometry_column": [2, 41, 42, 43, 44], "points_to_lon_lat": 1, "points_value_column": [2, 41, 42, 43, 44], "pointsupport": [1, 2, 3, 4, 7, 9, 21, 41, 42, 43, 44], "pointsupportdist": 2, "poisson": [0, 1, 10, 21, 24, 28, 30, 31, 40, 41], "poland": [31, 34], "pole": 10, "polish": 31, "pollut": [28, 34], "polygon": [2, 4, 7, 24, 40, 41, 42, 43], "polygon_id": [40, 41, 42, 43, 44], "polygon_lay": [40, 41, 42, 43, 44], "polygon_valu": [40, 41, 42, 43, 44], "polynomi": 32, "poorli": [40, 41, 42, 43], "pop": 2, "pop10": [2, 41, 42, 43, 44], "popul": [2, 10, 24, 35, 36, 37, 38, 40, 41, 42, 43, 44], "popular": 5, "population_lay": [41, 42, 43, 44], "posit": [5, 12, 32, 35, 40, 42, 43], "possibl": [8, 10, 12, 31, 32, 33, 34, 40], "possible_variogram": 10, "potenti": [3, 7], "power": [6, 8, 9, 11, 12, 31, 32, 36, 37, 42, 43], "power_model": 31, "pp": [5, 25], "practic": 33, "pre": [32, 40], "precis": 36, "pred": [39, 42, 43], "pred_col_nam": 40, "predefin": 31, "predict": [3, 5, 7, 8, 12, 17, 24, 29, 37, 38, 39, 42, 43, 44], "predicted_arrai": 5, "preds_col": 40, "prep_theo": 38, "prep_theo_no_out": 38, "prepar": [10, 11, 29, 33, 35], "prepare_pk_known_area": 1, "preprocess": 38, "presenc": [9, 25, 41], "present": [10, 32, 34, 38, 40], "preserv": 13, "previou": [32, 34, 43], "price": [17, 33], "primarili": 38, "print": [2, 4, 9, 10, 12, 29, 31, 33, 34, 36, 37, 40, 42, 43], "privaci": 24, "privat": 1, "probabl": [24, 32, 36, 38, 40, 41, 42, 43], "problem": [27, 31, 36, 41, 42, 43], "proc": 5, "proc_no_interpol": 38, "proc_raw_interpol": 38, "proce": [31, 35, 38, 40], "procedur": [9, 41], "process": [2, 3, 5, 8, 9, 10, 12, 21, 24, 31, 33, 36, 37, 38, 39, 40, 42, 43, 44], "process_mean": [8, 36], "produc": [38, 44], "product": [24, 31], "profil": 17, "profound": 1, "program": [11, 25, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "progress": [2, 3, 8], "progress_bar": [3, 8, 36, 38], "project": [2, 3, 17, 27, 31, 33], "pronounc": [38, 39], "properli": 27, "properti": [2, 5, 9, 12, 31, 33, 34, 35, 38, 42, 43], "proport": 37, "protect": [12, 24], "protect_from_overwrit": 12, "provid": [4, 5, 7, 9, 10, 12, 13, 24, 36, 39], "ps_block": [2, 3, 4], "ps_geometri": 2, "ps_layer_nam": 2, "ps_valu": 2, "pt": [11, 36, 41], "ptp": 33, "public": [10, 33], "publicznych": 28, "publish": 10, "purpl": 34, "purpos": [31, 32, 36], "put": 36, "pw": 37, "py": 39, "pyinterpol": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 21, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "pyplot": [31, 32, 34, 36, 38, 39, 40], "pyproj": 2, "pyproject": [22, 27], "python": [24, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "python3": 27, "pythonem": 28, "q": 40, "q1": [35, 38], "q2": 38, "q3": [35, 38], "qgi": 44, "qualiti": [28, 36], "quantil": 38, "quartil": [10, 35, 38, 42, 43], "question": [32, 40], "quick": 31, "quickli": 37, "quickstart": 24, "r": [12, 32, 33, 39], "r_df": 31, "radii": 34, "radiu": 41, "rais": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 31, 37], "raise_when_negative_error": [3, 7, 42, 43], "raise_when_negative_predict": [3, 7], "random": [33, 38, 42, 43], "randomli": [33, 36, 42, 43], "rang": [3, 5, 7, 8, 9, 11, 12, 13, 29, 31, 32, 34, 35, 36, 38, 40, 42, 43], "rare": [10, 36, 39], "raster": 0, "raster_dict": 13, "rate": [2, 10, 24, 28, 40, 41, 42, 43, 44], "rather": [31, 38, 41], "ratio": [3, 5, 9, 11, 12, 13, 33, 42, 43], "raw": [1, 38], "raw_interpol": 38, "raw_no_interpol": 38, "raw_theo": 38, "raw_theo_no_out": 38, "raw_variogram_filt": 38, "rawpoint": 1, "re": [24, 36, 37, 41, 44], "reach": 31, "read": [1, 7, 12, 29, 31, 35, 38], "read_block": 1, "read_csv": [1, 31, 33, 35, 36, 37, 38, 39], "read_fil": [2, 29, 34, 40, 41, 42, 43, 44], "read_txt": 1, "readi": 40, "real": [3, 5, 8, 31, 32, 35, 36, 38], "real_arrai": 5, "realist": 31, "realiz": [36, 42, 43], "realli": 36, "reason": [32, 33, 38], "recal": [10, 34], "recommed": 34, "recommend": [3, 5, 7, 8, 13, 34, 36], "record": [11, 12, 36], "rectangular": 42, "recurr": 32, "red": [33, 34, 39, 40, 44], "reduc": 33, "refactor": 1, "refer": [2, 5, 9, 11, 12, 24, 31, 37], "reference_input": 10, "reflect": 40, "reg": [3, 44], "reg_mod": 41, "reg_variogram": 9, "regard": 24, "region": [24, 42, 43, 44], "regress": 8, "regular": [1, 2, 3, 9, 21, 24, 30, 31, 40], "regular_grid_point": 40, "regularize_variogram": 9, "regularized_model": 9, "regularized_vari": 9, "regularized_variogram": [9, 41, 42, 43, 44], "rel": [5, 9, 34, 42, 43], "relat": [17, 31, 32, 33, 36, 37, 38, 41], "releas": [1, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "reliabl": 43, "rememb": [31, 37], "remot": 44, "remov": [1, 2, 8, 10, 36], "remove_outli": [10, 35, 38, 40], "rental": 33, "rep_point": 2, "rep_points_column_nam": 2, "repeat": [42, 43], "repetit": 9, "report": [17, 24], "repres": [2, 4, 5, 6, 31, 32, 33, 34, 35, 38, 40, 42, 43], "represent": [2, 24, 32, 35, 42, 44], "representative_point": [40, 41, 42, 43], "representative_points_arrai": 2, "representative_points_column_nam": 2, "representative_points_from_centroid": 2, "representative_points_from_largest_area": 2, "representative_points_from_random_sampl": 2, "reproduc": 31, "reproject": [2, 31], "reproject_flat": 31, "reps_deviation_decreas": 9, "requir": [11, 12, 18, 27, 36, 37, 40, 41, 44], "research": 5, "reshap": 32, "resist": 31, "resolut": 24, "resourc": 25, "respect": 40, "respons": [8, 36], "result": [6, 7, 8, 9, 12, 13, 34, 36, 37, 38, 39, 40, 41, 42, 43], "retriev": [2, 36, 41], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 31, 32, 33, 35, 36, 44], "return_dist": 2, "return_param": 12, "rgeometri": 39, "rid": 3, "right": [10, 24, 35, 36], "right_on": [42, 43], "rise": [35, 40], "risk": [24, 40, 42, 44], "rmse": [3, 5, 9, 11, 12, 31, 32, 36, 40, 42, 43, 44], "role": 31, "room": 35, "root": [5, 9, 11, 12, 31, 32, 36, 37, 42, 43], "root_mean_squared_error": 5, "roughli": 38, "row": [2, 4, 6, 32, 35], "rtree": 27, "run": [24, 27, 31, 36, 38, 41, 42, 43], "runetimeerror": [8, 9, 10], "runtimewarn": 39, "rush": 31, "rx_": 32, "rxn": 32, "safe": [9, 11, 12, 31, 33, 41], "sagepub": 25, "same": [1, 2, 6, 9, 10, 11, 12, 13, 29, 31, 32, 34, 35, 36, 37, 38, 40, 43], "samivari": 9, "sampl": [2, 5, 8, 31, 33, 36, 37, 38, 42, 43], "sample_id": [42, 43], "satellit": 38, "satur": 38, "save": [9, 12, 31, 35, 40, 41], "scalabl": 42, "scale": [12, 17, 33], "scan": 36, "scatter": [10, 31, 32], "scatterplot": [10, 35], "scenario": [31, 36, 38, 40], "scienc": [11, 12, 33], "scientist": 24, "scipi": [4, 32, 35], "score": [35, 38], "scott": 15, "scottgallach": 15, "scratch": 32, "sdesabbata": 15, "sdi": 33, "se": [9, 10, 11, 13, 34, 39], "sea": 10, "sean": 15, "seanjunheng2": 15, "search": [3, 5, 7, 8, 31, 33, 36, 41, 42, 43], "search_radiu": 29, "second": [8, 10, 32, 34, 35, 36], "see": [4, 8, 9, 10, 11, 12, 31, 34, 35, 36, 38, 40, 41, 42, 43], "seem": 40, "seen": [33, 41], "select": [2, 3, 5, 8, 9, 10, 11, 13, 31, 36, 42, 43], "select_centroid_poisson_kriging_data": 1, "select_distances_between_block": 2, "select_neighbors_pk_centroid": 1, "select_neighbors_pk_centroid_with_angl": 1, "select_poisson_kriging_data": 1, "select_values_between_lag": 1, "select_values_in_rang": 1, "select_values_in_range_from_datafram": 1, "semi": [31, 34], "semi_major_axis_s": 34, "semi_model": 40, "semivar": 29, "semivari": [0, 2, 5, 9, 11, 12, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "semivariance_between_point_support": 9, "semivariance_fn": 1, "semivariogram": [0, 1, 2, 5, 7, 8, 10, 13, 21, 24, 25, 30, 35, 37, 38], "semivariogram_model": [3, 7, 13, 42, 43, 44], "semivariogramerrormodel": 1, "sens": [3, 44], "sensor": 38, "separ": [9, 11, 42, 43, 44], "sequenc": 32, "seri": 2, "serv": 37, "server": 16, "servic": [17, 38], "set": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43], "set_blocks_dataset": 1, "set_current_as_optim": 9, "set_index": [34, 40], "set_titl": [38, 39, 40], "set_xlabel": 38, "set_ylabel": 38, "setdifferencewarn": 1, "setup": 24, "seven": 32, "shape": [6, 31, 32, 38, 41, 42, 43, 44], "sharei": 40, "sharex": 40, "sharpli": 38, "shell": 20, "short": 34, "shorter": [34, 39], "shortest": 41, "should": [1, 2, 5, 6, 8, 9, 11, 12, 22, 27, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43], "shouldn": [5, 12, 34, 35, 38, 41], "show": [2, 3, 8, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43], "show_progress_bar": 8, "show_semivariogram": 9, "shp": 40, "side": 10, "sig": [7, 42, 43], "sign": [31, 38, 41], "signal": [32, 35], "signific": [37, 38, 41], "significantli": 34, "sill": [9, 11, 12, 29, 31, 32, 33, 36, 40], "sill_from_valu": 12, "sill_from_vari": [11, 12], "similar": [10, 31, 32, 33, 34, 36, 38, 42], "simonmolinski": [15, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "simpl": [0, 5, 21, 24, 30, 32, 33, 37, 40], "simple_krig": [1, 8, 36], "simplest": 36, "simpli": [42, 43], "simplif": 42, "simplifi": [36, 37, 43], "simul": [32, 35, 36], "simultan": 34, "singl": [2, 9, 10, 11, 12, 13, 31, 32, 34, 36, 37, 42, 43], "singular": [3, 5, 7, 8, 13], "situat": 31, "size": [4, 9, 10, 11, 13, 32, 34, 40, 41, 42, 43, 44], "sk": [5, 8], "sk_interpol": 36, "sk_mean": 5, "skew": [10, 33, 35, 40], "skip": 40, "slice": 39, "slightli": [32, 38], "slow": [34, 41], "slower": 43, "slowest": [], "slowli": 41, "small": [9, 32, 34, 37, 40, 41, 42, 43], "smaller": [6, 9, 32, 34, 35, 38, 40, 41], "smallest": 34, "smape": [5, 11, 12, 31], "smooth": [3, 21, 40, 43], "smooth_area_to_point_pk": 1, "smooth_block": [1, 3, 44], "smooth_plot_data": 44, "smoother": 32, "smrd": 9, "so": [10, 24, 31, 36, 41, 42], "social": 24, "societi": [12, 33], "socio": 28, "softwar": [24, 26], "soil": [12, 33], "some": [1, 24, 25, 27, 31, 32, 33, 34, 38, 40, 41, 42], "someon": 40, "someth": 31, "sometim": [10, 31, 33, 34, 42, 43], "sophist": [35, 36], "sort": [36, 38], "sourc": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 24, 26, 37], "south": [10, 39], "southeastern": 10, "southwestern": 10, "space": [11, 12, 17, 31], "spars": [10, 32, 42, 43], "sparse_data": 32, "spatial": [1, 2, 4, 10, 12, 17, 24, 26, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44], "spatial_depend": 31, "spatial_dependency_level": 12, "spatial_dependency_ratio": [12, 33], "spatial_dependency_strength": [12, 33], "spatial_index": 31, "spatialindex": 27, "speak": 32, "speci": 41, "special": [31, 32, 34], "specialist": [], "specif": [5, 8, 10, 17, 31, 34, 35, 36, 38], "specifi": 2, "spectral_r": [40, 42, 43, 44], "sph": 40, "spheric": [8, 9, 11, 12, 29, 31, 32, 39, 40], "spherical_model": 31, "springer": [25, 32], "sqrt": [5, 36, 37, 42, 43], "squar": [5, 9, 11, 12, 25, 31, 32, 36, 37, 38, 42, 43], "squared_error": [42, 43], "src": 21, "stabil": 41, "stabl": 24, "stage": [24, 38], "standard": [10, 35, 38, 42, 43], "start": [31, 32, 33, 34, 35, 36, 41, 42, 43, 44], "stat": 38, "state": [32, 40], "statement": 43, "station_id": 34, "stationari": 31, "statist": [10, 24, 31, 36, 38, 42, 43], "statu": 31, "std": [10, 35, 36, 38, 42, 43], "step": [9, 23, 27, 30, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "step_siz": [8, 9, 10, 11, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "step_size_point": 41, "steroid": 35, "still": [31, 32, 35, 36, 39, 41], "stop": 9, "store": [2, 9, 11, 12, 31, 40, 41, 44], "store_dropped_point": 2, "store_model": 9, "str": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 31, 34, 40, 41], "straight": 34, "straightforward": 44, "strength": [12, 31, 33, 36], "string": [2, 10], "strong": [12, 31, 33], "stronger": 6, "structur": [0, 18, 32, 41], "studi": [5, 8, 12, 36, 41], "subplot": [11, 38, 39, 40], "subset": [10, 36], "substanti": 33, "subtract": 35, "sudo": 27, "suffici": 41, "suitabl": 38, "sum": [10, 11, 12], "sum_": [5, 9, 37], "summari": 40, "support": [0, 3, 4, 7, 9, 24, 35, 40, 41, 42, 43, 44], "suptitl": 40, "sure": [31, 34, 36, 42, 43], "surf_blur": 32, "surfac": [8, 21, 36], "sw": [9, 10, 11, 13, 34, 39], "swath": 24, "symmetr": [5, 9, 11, 12, 31], "symmetric_mean_absolute_percentage_error": 5, "symmetric_mean_relative_differ": 1, "system": [2, 8, 9, 10, 11, 13, 17, 27, 31, 32, 34, 36], "szymon": [15, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "t": [1, 2, 3, 5, 7, 8, 9, 12, 13, 23, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "t0e": 34, "t0t": 34, "tail": [35, 39], "take": [2, 3, 8, 10, 31, 32, 33, 34, 36, 38, 40, 41, 42, 43], "taken": 36, "target_cr": 2, "task": 38, "teach": 36, "technic": 32, "techniqu": [8, 24, 36, 37, 38, 42, 43, 44], "tell": [5, 31, 35, 38, 40, 42, 43], "temperatur": [10, 39], "tempor": 17, "tend": [10, 33], "termin": [9, 27], "test": [9, 10, 11, 12, 18, 21, 31, 32, 35, 36, 37, 38, 40, 42, 43], "test_sampl": 36, "test_undefin": 5, "text": 31, "th": [5, 37], "than": [3, 5, 6, 8, 9, 10, 11, 12, 13, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43], "thank": [25, 40], "thei": [2, 31, 36, 38, 43], "them": [3, 8, 9, 24, 25, 31, 32, 36, 38, 40, 41, 42, 43, 44], "theo_semi": 40, "theo_var": [12, 36, 37], "theoret": [0, 1, 3, 8, 9, 11, 21, 28, 29, 32, 33, 36, 39, 41, 44], "theoretical_block_model": 9, "theoretical_indicator_variogram": 11, "theoretical_model": [3, 5, 8, 9, 29, 36, 37, 38, 39, 40], "theoretical_semivariogram": 40, "theoretical_valu": 12, "theoretical_variogram_model": 12, "theoreticalindicatorvariogram": [1, 8, 11], "theoreticalmodelfunct": 1, "theoreticalsemivariogram": 40, "theoreticalvariogram": [2, 3, 5, 7, 8, 9, 12, 13, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "theoreticalvariogrammodel": [1, 12], "theori": 32, "theoriticalvariogram": 31, "therefor": 33, "thi": [1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "thing": [33, 35, 37], "third": [10, 38], "those": [1, 2, 3, 10, 27, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "three": [27, 31, 32, 34, 35], "threshold": [8, 11], "through": 41, "thu": [5, 32, 34, 35, 36, 38, 40, 41], "tick": 17, "time": [10, 31, 32, 34, 35, 36, 38, 40, 41, 42, 43], "titl": [31, 32, 34, 38], "to_cr": [35, 36, 37, 38], "to_dict": [12, 31], "to_fil": [40, 44], "to_json": [12, 31], "to_numpi": [31, 38, 39, 40, 42, 43], "to_tiff": 1, "tobiasz": 15, "tobiaszwojnar": 15, "tobler": [10, 33], "todo": [12, 20], "toler": [8, 9, 10, 11, 13, 31, 34, 36, 39], "toml": [22, 27], "too": [34, 35, 36, 38, 40, 41, 42, 43, 44], "tool": [21, 24, 35, 38], "top": [34, 35, 38, 40], "top_limit": 38, "total": [2, 11, 12, 33], "total_pop10": 41, "toward": [35, 40], "tqdm": [36, 37, 42, 43, 44], "trace": 31, "track": [9, 41], "tracker": 24, "train": [36, 37, 38, 42, 43], "train_without_outli": 38, "transform": [1, 2, 3, 9, 21, 24, 29, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44], "transform_blocks_to_numpi": 1, "transform_cr": 2, "transform_ps_to_dict": 1, "transit": [42, 43], "treat": [31, 34, 38], "trend": [8, 24, 31, 34, 35, 41], "trend_model": 8, "trend_valu": 8, "tri": 11, "triangl": 34, "triangle_mask": 1, "triangular": [], "tricki": [32, 35], "true": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 31, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "try": [42, 43], "tune": 43, "tupl": [2, 5, 8, 11, 12, 33], "turco": [12, 33], "turgut": 15, "tutori": [20, 21, 24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "two": [2, 5, 8, 9, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44], "txe": 34, "txt": 34, "type": [1, 3, 8, 9, 10, 11, 12, 31, 35, 38, 40], "u": [5, 10, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "u_": 9, "u_i": 10, "uk": 25, "unabl": 37, "unbias": 36, "uncertainti": [11, 29, 40], "uncertainty_col_nam": 40, "unchang": 2, "undefin": [5, 9, 31, 36], "undefinedsmapewarn": 5, "under": [35, 42, 43], "underestim": [5, 12, 42, 43], "underforecast": 5, "understand": [3, 31, 34, 35, 38, 41, 44], "understood": [42, 43], "undesir": 44, "unfortun": 37, "uniform": 38, "union": [2, 4, 8, 10, 12], "uniqu": [2, 4], "unique_block": 2, "unit": [3, 8, 9, 21, 25, 32, 34, 40, 41], "univers": [0, 24], "universalkrig": 8, "unknown": [3, 6, 7, 8, 12, 32, 37, 40], "unknown_block_index": [7, 42, 43], "unknown_loc": [3, 6, 8, 29, 36, 37, 38, 39, 40], "unknown_point": 29, "unnecessari": 40, "unreli": [38, 42, 43], "unsupport": 9, "untouch": 31, "up": [10, 31, 33, 35, 38], "updat": [1, 2, 9, 11, 12, 24], "upper": [33, 35, 38], "url": 28, "us": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 24, 27, 29, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44], "use_all_model": 8, "use_all_neighbors_in_rang": [3, 5, 8, 36, 39, 40], "use_point_support_cr": 2, "usecol": [33, 39], "useless": [32, 38], "user": [1, 2, 9, 12, 30, 34, 40], "usual": [5, 29, 31, 32, 34, 36, 41], "util": 36, "v": [9, 12, 25, 33, 35], "v_": 9, "v_h": 9, "val": [32, 37, 41], "val_col_nam": 4, "valid": [0, 34, 35, 36, 38, 43], "validate_bin": 1, "validate_direct": 1, "validate_direction_and_toler": 1, "validate_krig": 5, "validate_plot_attributes_for_experimental_variogram": 1, "validate_plot_attributes_for_experimental_variogram_class": 1, "validate_point": 1, "validate_selected_error": 1, "validate_semivariance_weight": 1, "validate_theoretical_variogram": 1, "validate_toler": 1, "validate_weight": 1, "valu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 29, 31, 32, 33, 34, 35, 37, 38, 40, 42, 43, 44], "value1": 32, "value2": 32, "value_a": 2, "value_b": 2, "value_col": 34, "value_column_nam": [2, 40, 41, 42, 43, 44], "valueerror": [6, 7, 9, 12], "var": 39, "varfit": 25, "vari": [43, 44], "variabl": [12, 33, 34, 36, 37, 40, 41], "varianc": [3, 5, 7, 8, 10, 11, 12, 29, 31, 32, 33, 34, 35, 36, 39, 42, 43], "variat": [33, 34], "variogram": [0, 1, 3, 5, 7, 8, 11, 12, 13, 21, 24, 25, 28, 29, 30, 33, 36, 37, 39, 40, 41, 44], "variogram_cloud": 40, "variogram_model_typ": [12, 31], "variogram_rang": 31, "variogram_weighting_method": [9, 41], "variogramcloud": [10, 35, 38, 40], "variogrammodelnotseterror": 1, "variogrampoint": [1, 10], "vc": [35, 40], "vc1000": 35, "ve": [35, 36, 43], "vector": 6, "verbos": [2, 3, 9, 12, 41, 42, 43, 44], "veri": [3, 8, 31, 32, 33, 34, 35, 37, 40, 42, 43, 44], "version": 18, "view": [5, 38], "vignett": 33, "violin": [10, 38, 40], "violinplot": [10, 38], "visibl": [35, 38], "visual": [0, 8, 31, 34, 35, 38, 40, 42, 43, 44], "visul": 38, "vital": 31, "viz": 21, "vmin": [31, 35, 36, 37, 38], "voila": 37, "vol": 25, "volum": [10, 40], "vv": 39, "w": [9, 10, 11, 13, 34, 39], "wa": [1, 2, 9, 10, 24, 25, 32, 34], "wai": [2, 10, 31, 32, 33, 35, 37, 40], "want": [2, 3, 5, 8, 34, 36, 40, 44], "warn": [5, 10, 11, 12, 31], "we": [3, 5, 7, 8, 10, 13, 24, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "we_direct": 34, "weak": [12, 32, 33, 34], "web": 33, "weight": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 21, 24, 25, 31, 32, 36, 37, 38, 41], "weight_experimental_semivari": 1, "weighted_avg_point_support_semivari": 1, "weighted_block_to_block_dist": 2, "weighted_root_mean_squared_error": 5, "weightedblock2blocksemivari": 1, "weightedblock2pointsemivari": 1, "weighting_method": [5, 9], "weights_arrai": 1, "well": [5, 8, 31, 34, 36, 38, 41, 42, 43], "were": [4, 31], "weren": 2, "west": [10, 36, 39], "western": 36, "whale": 10, "what": [3, 5, 7, 8, 13, 31, 32, 34, 40, 41, 42, 43], "wheel": 27, "when": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 31, 32, 35, 36, 37, 38, 40, 41, 42, 43], "whenev": 33, "where": [2, 3, 4, 5, 6, 8, 9, 10, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43], "which": [2, 8, 10, 12, 24, 27, 31, 32, 34, 37, 38, 40], "whisker": [35, 38], "white": [40, 44], "whole": [9, 31, 36], "why": [5, 8, 35, 36, 37, 38, 40, 41], "wide": [42, 43], "width": [8, 13, 34], "wielkopolski": 31, "wiki": 5, "wikipedia": 5, "wildli": [31, 36], "wise": 9, "with_stat": 38, "with_std": 38, "within": [2, 3, 5, 6, 8, 9, 10, 11, 12, 31, 32, 33, 34, 35, 36, 41, 43], "without": [27, 31, 32, 33, 38, 40], "without_stat": 38, "without_std": 38, "wkt": 2, "wojnar": 15, "won": [23, 31, 32, 41], "word": [32, 38, 40], "work": [5, 11, 12, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], "world": [32, 33, 36, 38], "worldwid": 24, "worsen": 36, "worst": [36, 42, 43], "would": [23, 40], "wrap": 32, "wrmse": 5, "wrong": [12, 31, 42, 43], "wronggeometrytypeerror": 1, "wzbogacani": 28, "x": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 21, 24, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "x1": 32, "x2": 32, "x_": 32, "x_i": 10, "xiao": 25, "xlabel": [31, 32, 36, 38], "xn": 32, "xyval": 32, "y": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "y1": 32, "y2": 32, "y_": 5, "ye": 35, "yellow": 34, "yet": [9, 12, 24, 27, 37], "yhat": [12, 31, 32, 36], "ylabel": [31, 32, 36, 38, 39, 42, 43], "you": [3, 5, 7, 8, 13, 23, 24, 27, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "your": [3, 5, 7, 8, 9, 13, 24, 27, 32, 34, 35, 37, 38, 40], "z": [10, 28, 35, 37, 38], "z_": 37, "z_i": 10, "z_lower_limit": [10, 35, 38], "z_upper_limit": [10, 35, 38], "z_w": 10, "zero": [9, 10, 11, 12, 31, 35, 36, 37, 40, 41], "zhat": [7, 42, 43], "zinc": [33, 39], "zscore": [10, 35, 38], "zx11_2ts7tjfsny482gs54s80000gr": 39}, "titles": ["API", "Changes between version 0.x and 1.x", "Core data structures", "Pipelines", "Distance", "Models evaluation", "Inverse Distance Weighting (IDW)", "Block and Poisson Kriging", "Point Kriging", "Semivariogram Deconvolution", "Experimental Semivariance and Covariance", "Indicator Semivariogram", "Theoretical Semivariogram", "Visualization", "Community", "Contributors", "Network", "Use Cases", "Development", "Known Bugs", "Development", "Package structure", "Requirements and dependencies (version >= 1)", "Tests and contribution", "Pyinterpolate", "Bibliography", "Citation", "Setup", "Learning Materials", "Quickstart", "Tutorials", "Semivariogram exploration", "Semivariogram models", "Spatial Dependency Index", "Directional Semivariogram", "Variogram Points Cloud", "Ordinary and Simple Kriging", "Benchmarking Kriging", "Outliers and Kriging", "Directional Ordinary Kriging", "Blocks to points with Ordinary Kriging", "Semivariogram Regularization", "Poisson Kriging Centroid-based approach", "Area-to-area Poisson Kriging", "Area-to-Point Poisson Kriging"], "titleterms": {"": 15, "0": [1, 24], "1": [1, 22, 24, 31, 32, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44], "2": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "3": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "4": [31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44], "5": [31, 34, 35, 38, 40, 41, 42, 43], "6": 31, "The": 27, "addit": 27, "advanc": 30, "aggreg": 9, "analyz": [35, 38], "api": [0, 33], "approach": 42, "ar": 1, "area": [7, 42, 43, 44], "author": 15, "autom": [], "automat": 31, "avail": 1, "base": [7, 38, 42], "bechmark": 37, "beginn": 30, "benchmark": 37, "between": 1, "bibliographi": 25, "block": [2, 4, 7, 40, 44], "blog": 28, "box": 35, "bug": 19, "build": 27, "calcul": 32, "canva": 40, "case": [17, 34], "centroid": [7, 42], "chang": 1, "changelog": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "chapter": [31, 32], "check": 35, "citat": [24, 26], "class": 1, "cloud": [10, 35, 38], "commun": 14, "compar": [32, 34, 39], "conda": 27, "content": [24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "contribut": 23, "contributor": 15, "core": 2, "covari": 10, "creat": [32, 34, 35, 36, 38, 39], "cross": 5, "data": [2, 31, 38, 39, 40, 41, 42, 43, 44], "dataset": 31, "deconvolut": 9, "depend": [22, 27, 33], "detect": [35, 40], "develop": [18, 20], "deviat": 9, "differ": [33, 38], "direct": [10, 34, 39], "distanc": [4, 6], "distribut": 38, "do": 33, "each": 35, "east": 34, "element": 33, "ellipt": 34, "error": 27, "evalu": [5, 36, 42, 43], "exampl": 33, "experiment": [10, 31, 32, 34, 35], "explor": 31, "export": [31, 41, 44], "extent": 33, "fail": 27, "filter": [42, 43], "fit": [31, 32, 36, 40], "from": [35, 38], "function": 1, "guidelin": 27, "i": [33, 35], "idw": [6, 37], "import": [24, 31], "includ": 34, "index": 33, "indic": [8, 11], "instal": [27, 29], "intermedi": 30, "interpol": [39, 40], "introduct": [24, 37], "invers": 6, "isotrop": 34, "joss": 15, "known": 19, "krige": [3, 7, 8, 29, 36, 37, 38, 39, 40, 42, 43, 44], "lag": 35, "lead": 34, "learn": 28, "libspatialindex_c": 27, "linux": 27, "load": [42, 43, 44], "locat": 36, "longer": 1, "maintain": 15, "manual": 31, "materi": 28, "method": 34, "metric": 5, "model": [5, 31, 32, 36, 38, 39, 40, 42, 43, 44], "more": 35, "neighbor": 34, "network": 16, "new": 1, "north": 34, "northeast": 34, "northwest": 34, "notebook": 27, "notic": 24, "ordinari": [3, 8, 29, 36, 39, 40], "outlier": [35, 38, 40], "output": [36, 37], "over": 33, "packag": 21, "paramet": 41, "perform": 37, "pip": 27, "pipelin": 3, "plot": 35, "point": [2, 4, 7, 8, 35, 38, 40, 44], "poisson": [3, 7, 42, 43, 44], "post": 28, "potenti": 38, "predict": 36, "prepar": [31, 38, 39, 40, 41, 42, 43, 44], "prerequisit": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "present": 28, "process": [34, 41], "public": 28, "pyinterpol": 24, "pylibtiff": 27, "quickstart": 29, "random": 32, "raster": 13, "regular": [41, 42, 43, 44], "remov": [35, 38, 40], "requir": 22, "resourc": 35, "result": 44, "review": 15, "same": 33, "scatter": 35, "select": 34, "semivari": 10, "semivariogram": [9, 11, 12, 31, 32, 34, 36, 39, 40, 41, 42, 43, 44], "set": [31, 36, 41], "setup": 27, "simpl": [8, 36], "smooth": 44, "so": 27, "south": 34, "southeast": 34, "southwest": 34, "spatial": 33, "statist": 35, "structur": [2, 21], "studi": 33, "support": [1, 2], "surfac": 32, "tabl": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], "temporarili": 1, "test": 23, "theoret": [12, 31], "tool": 37, "topic": 27, "triangular": 34, "tutori": 30, "univers": 8, "unknown": 36, "us": [17, 33], "v": 34, "valid": [5, 37], "valu": 36, "variogram": [9, 10, 31, 32, 34, 35, 38], "version": [1, 22, 24], "violin": 35, "visual": [13, 41], "we": 33, "weight": 6, "west": 34, "what": 33, "why": 33, "work": 27, "workshop": 28, "x": 1}}) \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index d4ab7fc1..ea10eb2a 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,7 +14,7 @@ Pyinterpolate ----------------- .. note:: - The last documentation update: *2025-09-19* + The last documentation update: *2025-10-11* Important notice ................ diff --git a/docs/source/usage/tutorials/functional/1-1-semivariogram-exploration.ipynb b/docs/source/usage/tutorials/functional/1-1-semivariogram-exploration.ipynb index 271e08e2..d57bdb2d 100644 --- a/docs/source/usage/tutorials/functional/1-1-semivariogram-exploration.ipynb +++ b/docs/source/usage/tutorials/functional/1-1-semivariogram-exploration.ipynb @@ -76,15 +76,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "initial_id", "metadata": { "ExecuteTime": { - "end_time": "2025-02-15T14:41:51.533864Z", - "start_time": "2025-02-15T14:41:51.455694Z" + "end_time": "2025-09-16T08:49:33.687651Z", + "start_time": "2025-09-16T08:49:28.725226Z" } }, - "outputs": [], "source": [ "import geopandas as gpd\n", "import numpy as np\n", @@ -93,7 +91,9 @@ "import matplotlib.pyplot as plt\n", "\n", "from pyinterpolate import reproject_flat, ExperimentalVariogram, build_theoretical_variogram, TheoreticalVariogram" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", @@ -110,40 +110,51 @@ }, { "cell_type": "code", - "execution_count": 2, "id": "ac39949d6265b27f", "metadata": { - "ExecuteTime": { - "end_time": "2025-02-15T14:01:27.154118Z", - "start_time": "2025-02-15T14:01:27.146837Z" - }, "collapsed": false, "jupyter": { "outputs_hidden": false + }, + "ExecuteTime": { + "end_time": "2025-09-16T08:49:33.816215Z", + "start_time": "2025-09-16T08:49:33.813372Z" } }, - "outputs": [], "source": [ "DEM_FILE = '../data/dem.csv'" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, "id": "c731e3a33a69296", "metadata": { - "ExecuteTime": { - "end_time": "2025-02-15T14:01:27.254176Z", - "start_time": "2025-02-15T14:01:27.151404Z" - }, "collapsed": false, "jupyter": { "outputs_hidden": false + }, + "ExecuteTime": { + "end_time": "2025-09-16T08:49:33.862151Z", + "start_time": "2025-09-16T08:49:33.835747Z" } }, + "source": [ + "df = pd.read_csv(DEM_FILE)\n", + "df.head()" + ], "outputs": [ { "data": { + "text/plain": [ + " longitude latitude dem\n", + "0 15.115241 52.765146 91.275597\n", + "1 15.115241 52.742790 96.548294\n", + "2 15.115241 52.710706 51.254551\n", + "3 15.115241 52.708844 48.958282\n", + "4 15.115241 52.671378 16.817863" + ], "text/html": [ "
\n", "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
longitudelatitudedem
015.11524152.76514691.275597
115.11524152.74279096.548294
215.11524152.71070651.254551
315.11524152.70884448.958282
415.11524152.67137816.817863
\n
" + "text/plain": [ + " longitude latitude dem\n", + "0 15.115241 52.765146 91.275597\n", + "1 15.115241 52.742790 96.548294\n", + "2 15.115241 52.710706 51.254551\n", + "3 15.115241 52.708844 48.958282\n", + "4 15.115241 52.671378 16.817863" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
longitudelatitudedem
015.11524152.76514691.275597
115.11524152.74279096.548294
215.11524152.71070651.254551
315.11524152.70884448.958282
415.11524152.67137816.817863
\n", + "
" + ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "df = pd.read_csv(\n", - " 'data/dem.csv'\n", - ")\n", - "df.head()" - ] + "execution_count": 6 }, { "cell_type": "markdown", @@ -114,14 +179,12 @@ }, { "cell_type": "code", - "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2024-01-03T10:16:25.934970Z", - "start_time": "2024-01-03T10:16:25.858573Z" + "end_time": "2025-10-11T14:26:30.912908Z", + "start_time": "2025-10-11T14:26:30.868756Z" } }, - "outputs": [], "source": [ "# Populate geometry column and set CRS\n", "\n", @@ -130,17 +193,21 @@ "\n", "# Transform crs to metric values\n", "dem.to_crs(epsg=2180, inplace=True)" - ] + ], + "outputs": [], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2024-01-03T10:16:25.996778Z", - "start_time": "2024-01-03T10:16:25.928769Z" + "end_time": "2025-10-11T14:26:44.440762Z", + "start_time": "2025-10-11T14:26:44.428715Z" } }, + "source": [ + "dem.info()" + ], "outputs": [ { "name": "stdout", @@ -160,33 +227,33 @@ ] } ], - "source": [ - "dem.info()" - ] + "execution_count": 8 }, { "cell_type": "code", - "execution_count": 5, + "source": [ + "dem.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-10-11T14:26:45.884429Z", + "start_time": "2025-10-11T14:26:45.198155Z" + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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" + "text/plain": [ + "
" + ], + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "dem.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-03T10:16:26.735989Z", - "start_time": "2024-01-03T10:16:25.950292Z" - } - } + "execution_count": 9 }, { "cell_type": "markdown", @@ -219,8 +286,6 @@ }, { "cell_type": "code", - "execution_count": 6, - "outputs": [], "source": [ "exp_semi = calculate_semivariance(\n", " ds=dem[['geometry', 'dem']],\n", @@ -231,10 +296,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-03T10:16:30.207671Z", - "start_time": "2024-01-03T10:16:26.735729Z" + "end_time": "2025-10-11T14:26:51.997457Z", + "start_time": "2025-10-11T14:26:48.546159Z" } - } + }, + "outputs": [], + "execution_count": 10 }, { "cell_type": "markdown", @@ -247,17 +314,6 @@ }, { "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "plt.figure(figsize=(15, 6))\n", "plt.scatter(exp_semi[:, 0],\n", @@ -273,10 +329,23 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-03T10:16:30.478067Z", - "start_time": "2024-01-03T10:16:30.221869Z" + "end_time": "2025-10-11T14:26:52.259192Z", + "start_time": "2025-10-11T14:26:52.050485Z" } - } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 11 }, { "cell_type": "markdown", @@ -289,25 +358,6 @@ }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[8], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m exp_cov \u001B[38;5;241m=\u001B[39m \u001B[43mcalculate_covariance\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mds\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdem\u001B[49m\u001B[43m[\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mgeometry\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mdem\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43mstep_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m500\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mmax_range\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m10_000\u001B[39;49m\n\u001B[1;32m 5\u001B[0m \u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/Documents/GitHub/ptp-ref/src/pyinterpolate/semivariogram/experimental/experimental_covariogram.py:168\u001B[0m, in \u001B[0;36mcalculate_covariance\u001B[0;34m(ds, step_size, max_range, direction, tolerance, dir_neighbors_selection_method, custom_bins)\u001B[0m\n\u001B[1;32m 160\u001B[0m experimental_covariances \u001B[38;5;241m=\u001B[39m directional_covariance(\n\u001B[1;32m 161\u001B[0m ds\u001B[38;5;241m.\u001B[39mpoints,\n\u001B[1;32m 162\u001B[0m lags,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 165\u001B[0m dir_neighbors_selection_method\n\u001B[1;32m 166\u001B[0m )\n\u001B[1;32m 167\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m--> 168\u001B[0m experimental_covariances \u001B[38;5;241m=\u001B[39m \u001B[43momnidirectional_covariance\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 169\u001B[0m \u001B[43m \u001B[49m\u001B[43mds\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpoints\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlags\u001B[49m\n\u001B[1;32m 170\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 172\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m experimental_covariances\n", - "File \u001B[0;32m~/Documents/GitHub/ptp-ref/src/pyinterpolate/semivariogram/experimental/experimental_covariogram.py:256\u001B[0m, in \u001B[0;36momnidirectional_covariance\u001B[0;34m(points, lags)\u001B[0m\n\u001B[1;32m 242\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21momnidirectional_covariance\u001B[39m(points: np\u001B[38;5;241m.\u001B[39marray, lags: np\u001B[38;5;241m.\u001B[39marray) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m np\u001B[38;5;241m.\u001B[39marray:\n\u001B[1;32m 243\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Function calculates covariance from given points.\u001B[39;00m\n\u001B[1;32m 244\u001B[0m \n\u001B[1;32m 245\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 253\u001B[0m \u001B[38;5;124;03m covariances : numpy array\u001B[39;00m\n\u001B[1;32m 254\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 256\u001B[0m sorted_covariances \u001B[38;5;241m=\u001B[39m \u001B[43momnidirectional_variogram\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 257\u001B[0m \u001B[43m \u001B[49m\u001B[43mfn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcovariance_fn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 258\u001B[0m \u001B[43m \u001B[49m\u001B[43mpoints\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpoints\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 259\u001B[0m \u001B[43m \u001B[49m\u001B[43mlags\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlags\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 260\u001B[0m \u001B[43m \u001B[49m\u001B[43mcustom_weights\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\n\u001B[1;32m 261\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 263\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m sorted_covariances\n", - "File \u001B[0;32m~/Documents/GitHub/ptp-ref/src/pyinterpolate/semivariogram/experimental/functions/general.py:144\u001B[0m, in \u001B[0;36momnidirectional_variogram\u001B[0;34m(fn, points, lags, custom_weights)\u001B[0m\n\u001B[1;32m 141\u001B[0m threads\u001B[38;5;241m.\u001B[39mappend(thread)\n\u001B[1;32m 143\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m thread \u001B[38;5;129;01min\u001B[39;00m threads:\n\u001B[0;32m--> 144\u001B[0m \u001B[43mthread\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 146\u001B[0m omnidirectional_values \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(omnidirectional_values)\n\u001B[1;32m 147\u001B[0m sorted_omnidirectional_values \u001B[38;5;241m=\u001B[39m omnidirectional_values[\n\u001B[1;32m 148\u001B[0m omnidirectional_values[:, \u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39margsort()\n\u001B[1;32m 149\u001B[0m ]\n", - "File \u001B[0;32m~/miniconda3/envs/ptp-ref/lib/python3.9/threading.py:1060\u001B[0m, in \u001B[0;36mThread.join\u001B[0;34m(self, timeout)\u001B[0m\n\u001B[1;32m 1057\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot join current thread\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 1059\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1060\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait_for_tstate_lock\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1061\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1062\u001B[0m \u001B[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001B[39;00m\n\u001B[1;32m 1063\u001B[0m \u001B[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001B[39;00m\n\u001B[1;32m 1064\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_wait_for_tstate_lock(timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mmax\u001B[39m(timeout, \u001B[38;5;241m0\u001B[39m))\n", - "File \u001B[0;32m~/miniconda3/envs/ptp-ref/lib/python3.9/threading.py:1080\u001B[0m, in \u001B[0;36mThread._wait_for_tstate_lock\u001B[0;34m(self, block, timeout)\u001B[0m\n\u001B[1;32m 1077\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[1;32m 1079\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1080\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mlock\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43macquire\u001B[49m\u001B[43m(\u001B[49m\u001B[43mblock\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m 1081\u001B[0m lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[1;32m 1082\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_stop()\n", - "\u001B[0;31mKeyboardInterrupt\u001B[0m: " - ] - } - ], "source": [ "exp_cov = calculate_covariance(\n", " ds=dem[['geometry', 'dem']],\n", @@ -318,10 +368,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-03T14:45:08.736051Z", - "start_time": "2024-01-03T10:16:32.636776Z" + "end_time": "2025-10-11T14:26:55.250217Z", + "start_time": "2025-10-11T14:26:53.478622Z" } - } + }, + "outputs": [], + "execution_count": 12 }, { "cell_type": "markdown", @@ -334,17 +386,6 @@ }, { "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "plt.figure(figsize=(15, 6))\n", "plt.scatter(exp_cov[:, 0],\n", @@ -360,10 +401,23 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-29T08:41:10.057537731Z", - "start_time": "2023-12-29T08:41:09.826943446Z" + "end_time": "2025-10-11T14:26:56.715103Z", + "start_time": "2025-10-11T14:26:56.528841Z" } - } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 13 }, { "cell_type": "markdown", @@ -376,17 +430,6 @@ }, { "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "plt.figure(figsize=(15, 6))\n", "plt.scatter(exp_semi[:, 0],\n", @@ -402,37 +445,23 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-29T08:43:02.057896426Z", - "start_time": "2023-12-29T08:43:01.866454691Z" + "end_time": "2025-10-11T14:26:59.485780Z", + "start_time": "2025-10-11T14:26:59.321379Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Summary" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Next part" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Changelog" ], - "metadata": { - "collapsed": false - } + "execution_count": 14 } ], "metadata": { diff --git a/tutorials/api-examples/a-1-2-directional-experimental-semivariogram-and-covariogram.ipynb b/tutorials/api-examples/a-1-2-directional-experimental-semivariogram-and-covariogram.ipynb index e00e3c1b..2dc2973a 100644 --- a/tutorials/api-examples/a-1-2-directional-experimental-semivariogram-and-covariogram.ipynb +++ b/tutorials/api-examples/a-1-2-directional-experimental-semivariogram-and-covariogram.ipynb @@ -46,13 +46,13 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:42:39.310278Z", - "start_time": "2025-09-16T08:42:39.306924Z" + "end_time": "2025-10-11T14:27:19.360387Z", + "start_time": "2025-10-11T14:27:15.912294Z" } }, "id": "d6e309c05d3efec7", "outputs": [], - "execution_count": 5 + "execution_count": 1 }, { "cell_type": "markdown", @@ -78,13 +78,13 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:42:55.600229Z", - "start_time": "2025-09-16T08:42:55.557850Z" + "end_time": "2025-10-11T14:27:20.543848Z", + "start_time": "2025-10-11T14:27:19.866481Z" } }, "id": "84fc798cd350871a", "outputs": [], - "execution_count": 7 + "execution_count": 2 }, { "cell_type": "code", @@ -96,8 +96,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:42:59.626402Z", - "start_time": "2025-09-16T08:42:59.380299Z" + "end_time": "2025-10-11T14:27:21.800382Z", + "start_time": "2025-10-11T14:27:21.559386Z" } }, "id": "a24b9840fccb09a1", @@ -113,7 +113,7 @@ "output_type": "display_data" } ], - "execution_count": 8 + "execution_count": 3 }, { "cell_type": "markdown", @@ -201,13 +201,13 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:43:08.293315Z", - "start_time": "2025-09-16T08:43:08.289250Z" + "end_time": "2025-10-11T14:27:39.552549Z", + "start_time": "2025-10-11T14:27:39.548748Z" } }, "id": "9431e3fbd7ce0ea0", "outputs": [], - "execution_count": 9 + "execution_count": 4 }, { "cell_type": "markdown", @@ -248,8 +248,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:43:11.921870Z", - "start_time": "2025-09-16T08:43:11.677076Z" + "end_time": "2025-10-11T14:27:41.399919Z", + "start_time": "2025-10-11T14:27:41.088207Z" } }, "id": "6a4511ed4f42b026", @@ -265,7 +265,7 @@ "output_type": "display_data" } ], - "execution_count": 10 + "execution_count": 5 }, { "cell_type": "markdown", @@ -331,8 +331,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:43:19.063537Z", - "start_time": "2025-09-16T08:43:18.839601Z" + "end_time": "2025-10-11T14:27:44.110773Z", + "start_time": "2025-10-11T14:27:43.868994Z" } }, "id": "5c8d103196b3cf6e", @@ -348,7 +348,7 @@ "output_type": "display_data" } ], - "execution_count": 11 + "execution_count": 6 }, { "cell_type": "markdown", @@ -367,14 +367,6 @@ "collapsed": false }, "id": "bac62dee3d2533c6" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "dbfe02c8b7a768d9" } ], "metadata": { diff --git a/tutorials/api-examples/a-1-3-experimental-variogram-class.ipynb b/tutorials/api-examples/a-1-3-experimental-variogram-class.ipynb index 822b51f8..a304f23f 100644 --- a/tutorials/api-examples/a-1-3-experimental-variogram-class.ipynb +++ b/tutorials/api-examples/a-1-3-experimental-variogram-class.ipynb @@ -31,8 +31,6 @@ }, { "cell_type": "code", - "execution_count": 2, - "outputs": [], "source": [ "import geopandas as gpd\n", "from pyinterpolate import ExperimentalVariogram\n", @@ -41,43 +39,34 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:48:50.863763330Z", - "start_time": "2024-01-13T20:48:48.942047391Z" + "end_time": "2025-10-11T14:27:55.214111Z", + "start_time": "2025-10-11T14:27:52.952452Z" } }, - "id": "3481efecb9ba5d8a" + "id": "3481efecb9ba5d8a", + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 3, - "outputs": [], "source": [ "VALUE_COL = 'PM2.5'\n", - "df = gpd.read_file('data/air_pollution.gpkg', layer='pm2_5')\n", + "df = gpd.read_file('../data/air_pollution.gpkg', layer='pm2_5')\n", "df.set_index('station_id', inplace=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:48:51.197909807Z", - "start_time": "2024-01-13T20:48:50.866639206Z" + "end_time": "2025-10-11T14:28:00.140467Z", + "start_time": "2025-10-11T14:28:00.127840Z" } }, - "id": "1b46c2d0bc8d111c" + "id": "1b46c2d0bc8d111c", + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "df.plot(figsize=(10, 10), column=VALUE_COL, legend=True, markersize=50, alpha=0.7, marker=\"h\", cmap='Reds')\n", "plt.title('PM2.5 concentrations in Poland')\n", @@ -86,16 +75,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:48:51.649495088Z", - "start_time": "2024-01-13T20:48:51.204076029Z" + "end_time": "2025-10-11T14:28:04.339274Z", + "start_time": "2025-10-11T14:28:04.104837Z" } }, - "id": "ae18b4666eb87f39" + "id": "ae18b4666eb87f39", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 5, - "outputs": [], "source": [ "step_size = 60000 # meters\n", "max_range = 400000 # meters\n", @@ -108,15 +108,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:48:51.665744437Z", - "start_time": "2024-01-13T20:48:51.654328687Z" + "end_time": "2025-10-11T14:28:06.105317Z", + "start_time": "2025-10-11T14:28:06.090753Z" } }, - "id": "3c2ee6374fb15c64" + "id": "3c2ee6374fb15c64", + "outputs": [], + "execution_count": 5 }, { "cell_type": "code", - "execution_count": 6, + "source": [ + "print(evar)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-10-11T14:28:06.890041Z", + "start_time": "2025-10-11T14:28:06.885383Z" + } + }, + "id": "affb0a372d417b9e", "outputs": [ { "name": "stdout", @@ -135,47 +147,37 @@ ] } ], + "execution_count": 6 + }, + { + "cell_type": "code", "source": [ - "print(evar)" + "evar.plot()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:48:51.828406471Z", - "start_time": "2024-01-13T20:48:51.668284805Z" + "end_time": "2025-10-11T14:28:07.951255Z", + "start_time": "2025-10-11T14:28:07.810767Z" } }, - "id": "affb0a372d417b9e" - }, - { - "cell_type": "code", - "execution_count": 7, + "id": "db3d269dcd5bee77", "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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" + "text/plain": [ + "
" + ], + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "evar.plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-13T20:48:52.020614800Z", - "start_time": "2024-01-13T20:48:51.685355033Z" - } - }, - "id": "db3d269dcd5bee77" + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 11, - "outputs": [], "source": [ "step_size = 60000 # meters\n", "max_range = 600000 # meters\n", @@ -193,82 +195,76 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:49:31.416476834Z", - "start_time": "2024-01-13T20:49:31.132744361Z" + "end_time": "2025-10-11T14:28:10.081925Z", + "start_time": "2025-10-11T14:28:10.011665Z" } }, - "id": "14596c0eca656a4a" + "id": "14596c0eca656a4a", + "outputs": [], + "execution_count": 8 }, { "cell_type": "code", - "execution_count": 12, + "source": [ + "print(dir_exp_var)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-10-11T14:28:12.597410Z", + "start_time": "2025-10-11T14:28:12.592161Z" + } + }, + "id": "f2152dc53ae61024", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "+----------+--------------------+--------------------+\n", - "| lag | semivariance | covariance |\n", - "+----------+--------------------+--------------------+\n", - "| 60000.0 | 1.3887550757485296 | 4.046834073810726 |\n", - "| 120000.0 | 3.1782088761956047 | 3.4717090480446138 |\n", - "| 180000.0 | 3.080000917927103 | 4.096350345569313 |\n", - "| 240000.0 | 3.0723193854568707 | 3.6293534102518725 |\n", - "| 300000.0 | 3.0525240618326475 | 3.7611940630671885 |\n", - "| 360000.0 | 3.7324903276098107 | 2.810137101326647 |\n", - "| 420000.0 | 3.803229538949476 | 2.445211199569317 |\n", - "| 480000.0 | 4.873165488987691 | 1.2853425339567195 |\n", - "| 540000.0 | 4.068769433076887 | 1.443199100553518 |\n", - "+----------+--------------------+--------------------+\n" + "+----------+--------------------+---------------------+\n", + "| lag | semivariance | covariance |\n", + "+----------+--------------------+---------------------+\n", + "| 60000.0 | 2.7292675116500003 | 2.9833866466445254 |\n", + "| 120000.0 | 3.198221837823077 | 3.7229674546384204 |\n", + "| 180000.0 | 3.3543254060088894 | 3.5510311268463948 |\n", + "| 240000.0 | 4.547019175368236 | 2.1016007620441406 |\n", + "| 300000.0 | 4.388034806582664 | 1.614361951511518 |\n", + "| 360000.0 | 4.972648136478203 | 0.4260783473484782 |\n", + "| 420000.0 | 6.733431477934165 | -1.0438080568980284 |\n", + "| 480000.0 | 7.648841021924846 | -1.1761469856194273 |\n", + "| 540000.0 | 7.087442587929972 | -1.3113078294940659 |\n", + "+----------+--------------------+---------------------+\n" ] } ], + "execution_count": 9 + }, + { + "cell_type": "code", "source": [ - "print(dir_exp_var)" + "dir_exp_var.plot(variance=False)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-13T20:49:32.028482389Z", - "start_time": "2024-01-13T20:49:32.019582636Z" + "end_time": "2025-10-11T14:28:14.038109Z", + "start_time": "2025-10-11T14:28:13.927987Z" } }, - "id": "f2152dc53ae61024" - }, - { - "cell_type": "code", - "execution_count": 14, + "id": "679ad44999185b54", "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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" + "text/plain": [ + "
" + ], + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "dir_exp_var.plot(variance=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-13T20:50:03.028107906Z", - "start_time": "2024-01-13T20:50:02.748355984Z" - } - }, - "id": "679ad44999185b54" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "4888203a83ebe85c" + "execution_count": 10 } ], "metadata": { diff --git a/tutorials/api-examples/a-1-4-directional-variogram-class.ipynb b/tutorials/api-examples/a-1-4-directional-variogram-class.ipynb index cbe19c88..f9bd2982 100644 --- a/tutorials/api-examples/a-1-4-directional-variogram-class.ipynb +++ b/tutorials/api-examples/a-1-4-directional-variogram-class.ipynb @@ -46,8 +46,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:15.878419Z", - "start_time": "2025-09-16T08:48:13.697524Z" + "end_time": "2025-10-11T14:28:23.573292Z", + "start_time": "2025-10-11T14:28:21.326033Z" } }, "id": "507181193b81b39e", @@ -64,8 +64,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:16.047694Z", - "start_time": "2025-09-16T08:48:15.981731Z" + "end_time": "2025-10-11T14:28:23.793369Z", + "start_time": "2025-10-11T14:28:23.706154Z" } }, "id": "5e7ba6cbc0e37e81", @@ -82,8 +82,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:16.312175Z", - "start_time": "2025-09-16T08:48:16.065883Z" + "end_time": "2025-10-11T14:28:24.071699Z", + "start_time": "2025-10-11T14:28:23.815404Z" } }, "id": "908fe4c083cce45a", @@ -118,8 +118,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:16.535872Z", - "start_time": "2025-09-16T08:48:16.329615Z" + "end_time": "2025-10-11T14:28:26.664637Z", + "start_time": "2025-10-11T14:28:26.450951Z" } }, "id": "b87bade3a93eaf8", @@ -134,8 +134,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:23.642085Z", - "start_time": "2025-09-16T08:48:23.637077Z" + "end_time": "2025-10-11T14:28:26.784392Z", + "start_time": "2025-10-11T14:28:26.779576Z" } }, "id": "21a51c6f02ed22ae", @@ -161,8 +161,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:25.361248Z", - "start_time": "2025-09-16T08:48:25.117382Z" + "end_time": "2025-10-11T14:28:27.246658Z", + "start_time": "2025-10-11T14:28:26.974161Z" } }, "id": "7014b4d559b6e749", @@ -189,8 +189,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:28.742118Z", - "start_time": "2025-09-16T08:48:28.613210Z" + "end_time": "2025-10-11T14:28:27.381277Z", + "start_time": "2025-10-11T14:28:27.263226Z" } }, "id": "185308d10a0475e7", @@ -207,16 +207,6 @@ } ], "execution_count": 7 - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "c94cdc05b03f10b3" } ], "metadata": { diff --git a/tutorials/api-examples/a-1-5-variogram-point-cloud.ipynb b/tutorials/api-examples/a-1-5-variogram-point-cloud.ipynb index 0881955e..c8465240 100644 --- a/tutorials/api-examples/a-1-5-variogram-point-cloud.ipynb +++ b/tutorials/api-examples/a-1-5-variogram-point-cloud.ipynb @@ -46,8 +46,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:43.077292Z", - "start_time": "2025-09-16T08:48:41.013103Z" + "end_time": "2025-10-11T14:28:56.038568Z", + "start_time": "2025-10-11T14:28:53.743285Z" } }, "id": "2049cd3cc09613a7", @@ -72,8 +72,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:43.866614Z", - "start_time": "2025-09-16T08:48:43.183730Z" + "end_time": "2025-10-11T14:28:56.781275Z", + "start_time": "2025-10-11T14:28:56.139369Z" } }, "id": "48f45dde054d0b0a", @@ -110,8 +110,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:48:45.353886Z", - "start_time": "2025-09-16T08:48:45.350013Z" + "end_time": "2025-10-11T14:28:56.807771Z", + "start_time": "2025-10-11T14:28:56.804715Z" } }, "id": "2837a50972f51000", @@ -126,8 +126,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:08.149214Z", - "start_time": "2025-09-16T08:48:47.296259Z" + "end_time": "2025-10-11T14:29:21.005701Z", + "start_time": "2025-10-11T14:28:56.828745Z" } }, "id": "894d3040a85ef07f", @@ -142,8 +142,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:24.687613Z", - "start_time": "2025-09-16T08:49:09.508943Z" + "end_time": "2025-10-11T14:29:38.987992Z", + "start_time": "2025-10-11T14:29:22.516764Z" } }, "id": "9aeb481438e71994", @@ -190,8 +190,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:24.698149Z", - "start_time": "2025-09-16T08:49:24.694798Z" + "end_time": "2025-10-11T14:29:40.366558Z", + "start_time": "2025-10-11T14:29:40.364196Z" } }, "id": "b74017f63ade8086", @@ -206,8 +206,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:31.637440Z", - "start_time": "2025-09-16T08:49:28.082660Z" + "end_time": "2025-10-11T14:29:44.603973Z", + "start_time": "2025-10-11T14:29:41.918972Z" } }, "id": "315e2c6a06055736", @@ -222,8 +222,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:51.084538Z", - "start_time": "2025-09-16T08:49:33.619251Z" + "end_time": "2025-10-11T14:30:00.516876Z", + "start_time": "2025-10-11T14:29:46.371813Z" } }, "id": "a215725d8ec9ce1a", @@ -259,8 +259,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:49:54.334899Z", - "start_time": "2025-09-16T08:49:53.101416Z" + "end_time": "2025-10-11T14:30:03.392831Z", + "start_time": "2025-10-11T14:30:02.363850Z" } }, "id": "3dd25cf25c429097", @@ -296,8 +296,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:50:35.023696Z", - "start_time": "2025-09-16T08:49:56.512458Z" + "end_time": "2025-10-11T14:30:43.802067Z", + "start_time": "2025-10-11T14:30:05.204026Z" } }, "id": "70c0e0516f737ee9", @@ -349,8 +349,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:50:38.835046Z", - "start_time": "2025-09-16T08:50:36.590994Z" + "end_time": "2025-10-11T14:30:47.932873Z", + "start_time": "2025-10-11T14:30:45.619129Z" } }, "id": "ba0456bb78d0526e", @@ -371,8 +371,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:50:43.305944Z", - "start_time": "2025-09-16T08:50:40.594554Z" + "end_time": "2025-10-11T14:30:52.261892Z", + "start_time": "2025-10-11T14:30:49.710316Z" } }, "id": "daac787d92bbb315", @@ -390,8 +390,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:52:53.158644Z", - "start_time": "2025-09-16T08:50:45.412658Z" + "end_time": "2025-10-11T14:32:50.656176Z", + "start_time": "2025-10-11T14:30:54.447970Z" } }, "id": "8459bdedf019b50e", @@ -423,8 +423,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:52:55.057216Z", - "start_time": "2025-09-16T08:52:55.019743Z" + "end_time": "2025-10-11T14:32:52.594126Z", + "start_time": "2025-10-11T14:32:52.571254Z" } }, "id": "2ffc5235d9c3f0a3", @@ -442,8 +442,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:52:56.939408Z", - "start_time": "2025-09-16T08:52:56.841984Z" + "end_time": "2025-10-11T14:32:54.288308Z", + "start_time": "2025-10-11T14:32:54.190555Z" } }, "id": "fbfeef6d0f91a8ba", @@ -485,8 +485,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:53:18.585489Z", - "start_time": "2025-09-16T08:52:58.464075Z" + "end_time": "2025-10-11T14:33:18.357671Z", + "start_time": "2025-10-11T14:32:56.013204Z" } }, "id": "845ff8275c57dbb6", @@ -501,8 +501,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-09-16T08:53:19.956530Z", - "start_time": "2025-09-16T08:53:19.434980Z" + "end_time": "2025-10-11T14:33:19.735057Z", + "start_time": "2025-10-11T14:33:19.212904Z" } }, "id": "a2edc76e87a61742", @@ -529,16 +529,6 @@ } ], "execution_count": 17 - }, - { - "cell_type": "code", - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "e8861508c1ed576", - "execution_count": null } ], "metadata": { diff --git a/tutorials/functional/1-1-semivariogram-exploration.ipynb b/tutorials/functional/1-1-semivariogram-exploration.ipynb index 013aba54..d57bdb2d 100644 --- a/tutorials/functional/1-1-semivariogram-exploration.ipynb +++ b/tutorials/functional/1-1-semivariogram-exploration.ipynb @@ -544,7 +544,7 @@ "- **semivariance**: dissimilarity over a distance, and point cloud semivariances - non-averaged dissimilarities between point pairs,\n", "- **covariance**: similarity over a distance,\n", "- **variance**: non-spatial variance of all points.\n", - "- \n", + "\n", "The class has two useful methods:\n", "\n", "1. We can print object statistics with the `print()` function,\n", @@ -668,7 +668,7 @@ "- **Circles** represent semivariance,\n", "- **Plus signs** represent covariance,\n", "- **The dashed line** is a variance.\n", - "- \n", + "\n", "We see here that semivariance and covariance are mirrored. What does it mean? It is normal behavior, and we should expect it - semivariance and covariance have symmetrical trends (they differ in a sign). We can read it as:\n", "\n", "- **semivariances** show that the dissimilarity between point pairs over a distance increases,\n", @@ -696,7 +696,7 @@ "Semivariogram has three basic properties:\n", "\n", "- **nugget**: the initial value at a zero distance. In most cases, it is zero, but sometimes it represents a bias.\n", - "- **sill**: a distance where the semivariogram flattens and reaches approximately 95% of dissimilarity. Sometimes we cannot find a sill; for example, if differences grow exponentially, but in pyinterpolate it is usually set close to the variance of data,\n", + "- **sill**: a distance where the semivariogram flattens and reaches approximately 95% of dissimilarity. Sometimes we cannot find a sill; for example, if differences grow exponentially, but in pyinterpolate it is usually set close to the variance of data. In `pyinterpolate` we set **partial_sill** which is a difference between **total sill** and **nugget**.\n", "- **range**: is a distance where a variogram reaches its sill. Larger distances are negligible for interpolation.\n", "\n", "We are not forced to know all of them at the beginning. The package may easily derive them all with the class `TheoriticalVariogram.autofit()` method.\n", @@ -720,7 +720,7 @@ "\n", "We will set the following:\n", "\n", - "- **sill** to the experimental variogram variance, (but it might be set to the the last five lags average value of semivariances),\n", + "- **sill** (partial sill) to the experimental variogram variance, (but it might be set to the the last five lags average value of semivariances),\n", "- **nugget** to zero,\n", "- **range** to 8000 (we see in the experimental variogram plot that the semivariance *flattens* around this range, and it becomes close to the variance)." ] @@ -2079,6 +2079,7 @@ "\n", "| Date | Changes | Author |\n", "|------------|----------------------------------------------------|----------------------------------|\n", + "| 2025-10-11 | Updated **sill** descriptions | @SimonMolinsky (Szymon Moliński) |\n", "| 2025-09-16 | Removed `dir_neighbors_selection_method` parameter | @SimonMolinsky (Szymon Moliński) |\n", "| 2025-02-15 | Tutorial has been adapted to the 1.0 release | @SimonMolinsky (Szymon Moliński) |" ]

- Package doesn't read data files, data must be loaded into DataFrame and then passed into the Blocks object. """ +import copy from typing import Union, Hashable, Dict from numpy.typing import ArrayLike @@ -927,10 +928,9 @@

Source code for pyinterpolate.core.data_models.blocks

return df.loc[block_id, other_blocks]
- # TODO manage copying and inplace transformations
[docs] - def transform_crs(self, target_crs): + def transform_crs(self, target_crs, inplace=True): """Function transforms Blocks CRS Parameters @@ -940,23 +940,46 @@

Source code for pyinterpolate.core.data_models.blocks

:meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`, such as an authority string (eg "EPSG:4326") or a WKT string. + + inplace : bool, default = True + When set to `True` then transform object's instance on the fly, + otherwise return modified object and do leave the old instance + unchanged. """ # Transform core dataset - self.ds.to_crs(target_crs, inplace=True) + if inplace: + self.ds.to_crs(target_crs, inplace=True) - # representative points - self._get_representative_points() - self._points_to_floats() + # representative points + self._get_representative_points() + self._points_to_floats() - # distances - self.distances = self.calculate_distances_between_rep_points( - update=False - ) + # distances + self.distances = self.calculate_distances_between_rep_points( + update=False + ) + + # angles + self.angles = self.calculate_angles_between_rep_points( + update=False + ) + return None + else: + new_object = copy.deepcopy(self) + + new_object.ds.to_crs(target_crs, inplace=True) + new_object._get_representative_points() + new_object._points_to_floats() + + new_object.distances = new_object.calculate_distances_between_rep_points( + update=False + ) + + new_object.angles = new_object.calculate_angles_between_rep_points( + update=False + ) - # angles - self.angles = self.calculate_angles_between_rep_points( - update=False - )
+ return new_object
def _delete(self, block_index: Union[str, Hashable]): diff --git a/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html b/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html index 0ec8f293..0e76a61f 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html @@ -7,7 +7,7 @@ - pyinterpolate.kriging.point.ordinary — pyinterpolate 1.0.2 documentation + pyinterpolate.kriging.point.ordinary — pyinterpolate 1.1.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.0.2 documentation

+

pyinterpolate 1.1.0 documentation