From da7643a165da6c3c374bcc7e62cd2539d1fec30e Mon Sep 17 00:00:00 2001 From: Ludovic Schorpp <51512737+LudovicSchorpp@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:57:48 +0100 Subject: [PATCH] update revision --- notebooks/application/10keq_test.npy | Bin 0 -> 848 bytes notebooks/application/10keq_test2.npy | Bin 0 -> 2288 bytes notebooks/application/Bumberg_3D.ipynb | 1674 ++++++++++++++++- .../application/analysis_3D_results.ipynb | 227 +++ 4 files changed, 1826 insertions(+), 75 deletions(-) create mode 100644 notebooks/application/10keq_test.npy create mode 100644 notebooks/application/10keq_test2.npy create mode 100644 notebooks/application/analysis_3D_results.ipynb diff --git a/notebooks/application/10keq_test.npy b/notebooks/application/10keq_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..792f864b52501bec59d72bd5b26e0f5838cb2acb GIT binary patch literal 848 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$-209AHK%}XoP^&-|;9@>_$^P=L8vDfm^Y|`yDewQNIr)yZf-AM^|klKg@60D=@#U@TW@a9wDV8Wgw6H#Yks|MIKjxgzf^^Xwn{F`W=Ix4*x1O-G4owY@5Pm2UD90sF@m@2n=hcyBj#mNn}K7D4+q znf0$L+=}e0ZLSmt&%R<8G(*2+deu9-0=sD<{oPmX1da7_`IB_)<1cU8wRu{-{R!_b zi68}s{l64mn|C+-w3~9%>41-p!~T=v!F{F|D(%-=ue$Pk>7jjJS2&+|_~@72lcwht zUEL4%Jq*6)cX+Rn{mM0hcdkyZvtRJ@&-0o^YWs`$gO1nw{II+Bk@vjjCbj*m-t#W* zd0A?|XyP1i+lUAIMIjZ951sEKP*0>d&{nULDy84=yCqqS77KClB~XM zUsKm_rl2wtd%GO#{}!oD_PT8cHSSE7uusxx=UDjuy;`Z+^9(sN3ewDra zBBSG`e}3#!Fc9s(T>R1Q7JF{vq~dS;KF{|%b8)|wz1&)DO^31?`>e&sXL{<&*cZn5 zZx7(VX_p@47I=NGw7sgq{>+dqCHCi4Zt_^azqoIa6k&f;_XzNT#s zo2{T{f9GF%4BPZt`$feE`KC_1v2U5@E*6%U$97(2UD-3a9_u06#ciDyP_UGbW@zO5-woa*#!%e&2H&tbBOIX+g E04xW6%>V!Z literal 0 HcmV?d00001 diff --git a/notebooks/application/10keq_test2.npy b/notebooks/application/10keq_test2.npy new file mode 100644 index 0000000000000000000000000000000000000000..8726b1c3dfe839c015a122548252c6a8b90ec522 GIT binary patch literal 2288 zcmbV|`9IYAAH{8BkTOLo6jwA;ref^d`z1!X6)~-Z$d>FThVfv`*h*5g-_%W!q(q|S zcHbYRqL>*=a-*9in%iQTOxAL5-+$qI9_N?yJU^VrF((*FPA8nLJbaK2gOtE z45$vFHdML+H8dvvP<&wYzL@ynkpJ+BKM=<+9O9x>=zOWF5W?7Ssa*L?(co|o(u(Rl8n!k z7oz}XjrIC@@?gF^BI0nhHPFQ2s$2J_!-(=c!Y6D!k@>5em%Zm7STFVFy^}}R6Al(j z)kLQfVZ&n9ikthYaNaO?Z6voIRjU7G6c8)He$}>b^zWO3h@5k=et&;=P%PJh*L5%{aBx8IqSxS4Q-SaTFzLe&G^l=)_09 zT|biu3l@>D{T4fpUkub01!#)!w-_z^Wt?$rtW%p68)6N2RqI95?KzM#SZ8);v;*lbeN*-y-q-cD9s^7ykR8S>qxUm-TK=$-{yC`=AwOZ7fpBntx z^JQ1x2tK0$)eyJo8w~KD&3bwKUjghrc5mQ7zY3V29=XW;7b7g;2~ua)Ps|D=k7VYR&L_Wt5?dPaB|n?9|RZHr*MN9TSaJFujG^Rk003b zxbL$XsTi?k>n#1&Swe~gPscZMVFj-uV|=+I%q!Y$J?YhrTZJRewX+V;$JaUG>B9!Q z3c63wcpnbjHPtwv(uBD?_K3@^2C>6}5rJbC1LQ4#U#nq|3yy<_&Ix!1MA9MJ+|L!ywN?}s#&MjQvWH({4@R92 z&xM6TzI0W-4RQEju>P?3QY{(+u6r=8AF9QG%JQ0QoUNMC?KLSJw7eAmaJJd2ho;3w`z5DC5xN)FV za;xJ;&wK39?Dk3gq66g#dF|f&-s3ME(&{c>Gw^Z!eAtY}hp9tl6HfgaghH{V#g_Q5 zNFqJ^$SNt+O_`hGmEg(X9OyOhr67{0_8G8x?uu3AEVL%iC% zj1;gh@H}N(x1QK(tadZPv;z%wbc|+cnG(^Lr3#xZ*}z)(V251x46Z6)0vbx4c%JLG zXXEt~c(}H@EYi7AdZD zptr}W)1Ydq%zOnq8B)xG3ImRR!g?nCCE>|Z>{R_Ydb;!@{zE`fsM0rsnsY6JH7_$^ zfqPeCk$5GM_qx(U&G;+kUTz9X@>xmPx3Svj;XH8fIWk$MA;r-TR-YevD8(0jf&;JA zN-j2W);(YDoauznc_%7I&c)qB~;~8PBmsv`TC5_t%Ko@LOwX@*21p`j^bGG9Ack zujdXbQVDX;;p@xj)hH@qt^-qz_(`fIyltOfxji@?_}j01Eip0!5mW8=H`+b8hUsqE-fjwyHB5G2(n$oiNrNq! zMTSK#!A9&i-S~oGq$j6P5h4nCRWiB-v`A=MWshuA^imJQ?m(i&dMd?FLGVrI|wpwh3|Zgyl1Vy?{R!1W(apd4*Y$$!rS&cnE+ zcVQt>GxtRJ;dT>B!*dQ3=O_eOx2_~oKM7hsj}<)Nbz#F|k*}_}5k*=5zRf7%H6|(k zy;x4q6m+vU9n;FphF>Zv%H(B+#L2ix`hNNqT&`|$e!I3IVIJgH6zw7ap;0rw@2d#C zj((hv)Lc~K{8FNBr3hhjPr{Zq+rd@sh0?&}9ME*F557rf5-QuGF{O02%^ zF@v3PZ3^i&8Q{+pZ!A1(KsXkp>UJC%K!KXuNOw;d5bO}Wsf08x#FIn4ckBO&e_L+c zzv0;cN-vKXsoz?UX^}Po$_y(oFCES!y+{Vjv#RbJU(TQ>u)?Fjwi}brG_Ysh&7k7E pOj4G29GJTa3Yh*MQQFq&9Xs9%@qLFeqeY{i(YL-0F0G{u_z!;VF2w)< literal 0 HcmV?d00001 diff --git a/notebooks/application/Bumberg_3D.ipynb b/notebooks/application/Bumberg_3D.ipynb index 794463f..fceb6ac 100644 --- a/notebooks/application/Bumberg_3D.ipynb +++ b/notebooks/application/Bumberg_3D.ipynb @@ -16,8 +16,8 @@ "from geone import imgplot3d\n", "import pyvista as pv\n", "\n", - "sys.path.append(\"../../../prog/geostat/EROS/\")\n", - "sys.path.append(\"../\")\n", + "sys.path.append(\"../../../../Thèse/prog et code/geostat/EROS/\")\n", + "sys.path.append(\"../../\")\n", "import EROS" ] }, @@ -71,14 +71,6 @@ " ew = pickle.load(f)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0f47dbd", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 5, @@ -160,7 +152,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -583,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "id": "5cd904d8", "metadata": { "scrolled": true @@ -595,69 +587,636 @@ "text": [ "sims 0\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 2.53 s\n", - "Discretization : time elapsed 1.88 s\n", - "Assign facies : time elapsed 1.34 s\n", + "Compute surfaces : time elapsed 2.48 s\n", + "Discretization : time elapsed 1.39 s\n", + "Assign facies : time elapsed 0.96 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 2.18 s\n", - "Discretization : time elapsed 2.02 s\n", - "Assign facies : time elapsed 1.63 s\n", + "Compute surfaces : time elapsed 1.98 s\n", + "Discretization : time elapsed 1.57 s\n", + "Assign facies : time elapsed 1.55 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.1 s\n", - "Discretization : time elapsed 1.4 s\n", - "Assign facies : time elapsed 1.13 s\n", + "Compute surfaces : time elapsed 1.23 s\n", + "Discretization : time elapsed 0.96 s\n", + "Assign facies : time elapsed 0.64 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 2.73 s\n", - "Discretization : time elapsed 4.26 s\n", - "Assign facies : time elapsed 4.3 s\n", + "Compute surfaces : time elapsed 2.57 s\n", + "Discretization : time elapsed 3.08 s\n", + "Assign facies : time elapsed 3.13 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 0.46 s\n", - "Discretization : time elapsed 0.68 s\n", - "Assign facies : time elapsed 0.26 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.58 s\n", + "Assign facies : time elapsed 0.25 s\n", "sims 1\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.88 s\n", - "Discretization : time elapsed 1.88 s\n", - "Assign facies : time elapsed 1.38 s\n", + "Compute surfaces : time elapsed 2.1 s\n", + "Discretization : time elapsed 1.42 s\n", + "Assign facies : time elapsed 1.11 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 2.46 s\n", - "Discretization : time elapsed 1.75 s\n", - "Assign facies : time elapsed 1.65 s\n", + "Compute surfaces : time elapsed 2.24 s\n", + "Discretization : time elapsed 1.62 s\n", + "Assign facies : time elapsed 1.51 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.56 s\n", - "Discretization : time elapsed 1.52 s\n", - "Assign facies : time elapsed 0.78 s\n", + "Compute surfaces : time elapsed 1.72 s\n", + "Discretization : time elapsed 1.05 s\n", + "Assign facies : time elapsed 0.86 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.94 s\n", - "Discretization : time elapsed 3.72 s\n", - "Assign facies : time elapsed 3.72 s\n", + "Compute surfaces : time elapsed 2.32 s\n", + "Discretization : time elapsed 2.97 s\n", + "Assign facies : time elapsed 3.37 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 0.44 s\n", - "Discretization : time elapsed 0.67 s\n", - "Assign facies : time elapsed 0.21 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.57 s\n", + "Assign facies : time elapsed 0.24 s\n", "sims 2\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 2.41 s\n", - "Discretization : time elapsed 1.95 s\n", - "Assign facies : time elapsed 1.5 s\n", + "Compute surfaces : time elapsed 2.48 s\n", + "Discretization : time elapsed 1.42 s\n", + "Assign facies : time elapsed 1.04 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.14 s\n", + "Discretization : time elapsed 1.38 s\n", + "Assign facies : time elapsed 1.31 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.85 s\n", + "Discretization : time elapsed 0.98 s\n", + "Assign facies : time elapsed 0.68 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.11 s\n", + "Discretization : time elapsed 2.68 s\n", + "Assign facies : time elapsed 2.97 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.58 s\n", + "Assign facies : time elapsed 0.25 s\n", + "sims 3\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.23 s\n", + "Discretization : time elapsed 1.38 s\n", + "Assign facies : time elapsed 1.01 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.91 s\n", + "Discretization : time elapsed 1.53 s\n", + "Assign facies : time elapsed 1.38 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 0.9 s\n", + "Assign facies : time elapsed 0.62 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.08 s\n", + "Discretization : time elapsed 2.79 s\n", + "Assign facies : time elapsed 3.01 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.56 s\n", + "Assign facies : time elapsed 0.27 s\n", + "sims 4\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.1 s\n", + "Discretization : time elapsed 1.43 s\n", + "Assign facies : time elapsed 1.06 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.14 s\n", + "Discretization : time elapsed 1.39 s\n", + "Assign facies : time elapsed 1.38 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.6 s\n", + "Discretization : time elapsed 0.88 s\n", + "Assign facies : time elapsed 0.58 s\n", "setup phase : time elapsed 0.0 s\n", "Compute surfaces : time elapsed 1.96 s\n", - "Discretization : time elapsed 1.86 s\n", - "Assign facies : time elapsed 1.76 s\n", + "Discretization : time elapsed 2.54 s\n", + "Assign facies : time elapsed 2.74 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.27 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.58 s\n", + "Assign facies : time elapsed 0.35 s\n", + "sims 5\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.48 s\n", + "Discretization : time elapsed 1.44 s\n", + "Assign facies : time elapsed 1.02 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.26 s\n", + "Discretization : time elapsed 1.43 s\n", + "Assign facies : time elapsed 1.39 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 1.01 s\n", + "Assign facies : time elapsed 0.82 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.2 s\n", + "Discretization : time elapsed 2.86 s\n", + "Assign facies : time elapsed 3.1 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.48 s\n", + "Assign facies : time elapsed 0.19 s\n", + "sims 6\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.09 s\n", + "Discretization : time elapsed 1.43 s\n", + "Assign facies : time elapsed 1.06 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.01 s\n", + "Discretization : time elapsed 1.49 s\n", + "Assign facies : time elapsed 1.42 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.34 s\n", + "Discretization : time elapsed 0.92 s\n", + "Assign facies : time elapsed 0.7 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.32 s\n", + "Discretization : time elapsed 2.81 s\n", + "Assign facies : time elapsed 3.04 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.52 s\n", + "Assign facies : time elapsed 0.2 s\n", + "sims 7\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.97 s\n", + "Discretization : time elapsed 1.47 s\n", + "Assign facies : time elapsed 1.17 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.12 s\n", + "Discretization : time elapsed 1.5 s\n", + "Assign facies : time elapsed 1.34 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.35 s\n", + "Discretization : time elapsed 0.93 s\n", + "Assign facies : time elapsed 0.68 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.95 s\n", + "Discretization : time elapsed 2.93 s\n", + "Assign facies : time elapsed 3.14 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.48 s\n", + "Assign facies : time elapsed 0.2 s\n", + "sims 8\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.45 s\n", + "Discretization : time elapsed 1.45 s\n", + "Assign facies : time elapsed 1.19 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.26 s\n", + "Discretization : time elapsed 1.53 s\n", + "Assign facies : time elapsed 1.62 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.97 s\n", + "Discretization : time elapsed 1.05 s\n", + "Assign facies : time elapsed 0.77 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.44 s\n", + "Discretization : time elapsed 2.63 s\n", + "Assign facies : time elapsed 2.75 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.49 s\n", + "Assign facies : time elapsed 0.22 s\n", + "sims 9\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.1 s\n", "Discretization : time elapsed 1.39 s\n", - "Assign facies : time elapsed 1.1 s\n", + "Assign facies : time elapsed 1.08 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.38 s\n", + "Discretization : time elapsed 1.39 s\n", + "Assign facies : time elapsed 1.4 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 0.89 s\n", + "Assign facies : time elapsed 0.6 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.7 s\n", + "Discretization : time elapsed 2.95 s\n", + "Assign facies : time elapsed 3.27 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.58 s\n", + "Assign facies : time elapsed 0.31 s\n", + "sims 10\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.34 s\n", + "Discretization : time elapsed 1.44 s\n", + "Assign facies : time elapsed 1.04 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.26 s\n", + "Discretization : time elapsed 1.37 s\n", + "Assign facies : time elapsed 1.31 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.23 s\n", + "Discretization : time elapsed 0.99 s\n", + "Assign facies : time elapsed 0.8 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.96 s\n", + "Discretization : time elapsed 2.86 s\n", + "Assign facies : time elapsed 3.0 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.5 s\n", + "Assign facies : time elapsed 0.24 s\n", + "sims 11\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.22 s\n", + "Discretization : time elapsed 1.31 s\n", + "Assign facies : time elapsed 0.98 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.14 s\n", + "Discretization : time elapsed 1.49 s\n", + "Assign facies : time elapsed 1.45 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.72 s\n", + "Discretization : time elapsed 1.02 s\n", + "Assign facies : time elapsed 0.84 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.08 s\n", + "Discretization : time elapsed 2.71 s\n", + "Assign facies : time elapsed 2.92 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.46 s\n", + "Assign facies : time elapsed 0.18 s\n", + "sims 12\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.35 s\n", + "Discretization : time elapsed 1.57 s\n", + "Assign facies : time elapsed 1.32 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.4 s\n", + "Discretization : time elapsed 1.41 s\n", + "Assign facies : time elapsed 1.28 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.72 s\n", + "Discretization : time elapsed 1.08 s\n", + "Assign facies : time elapsed 0.86 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.2 s\n", + "Discretization : time elapsed 2.68 s\n", + "Assign facies : time elapsed 2.9 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.73 s\n", + "Discretization : time elapsed 0.57 s\n", + "Assign facies : time elapsed 0.24 s\n", + "sims 13\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.47 s\n", + "Discretization : time elapsed 1.51 s\n", + "Assign facies : time elapsed 1.23 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.66 s\n", + "Discretization : time elapsed 1.47 s\n", + "Assign facies : time elapsed 1.39 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 1.02 s\n", + "Assign facies : time elapsed 0.78 s\n", "setup phase : time elapsed 0.0 s\n", - "Compute surfaces : time elapsed 1.61 s\n", - "Discretization : time elapsed 4.01 s\n", - "Assign facies : time elapsed 4.08 s\n", + "Compute surfaces : time elapsed 1.94 s\n", + "Discretization : time elapsed 2.74 s\n", + "Assign facies : time elapsed 2.95 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.47 s\n", + "Discretization : time elapsed 0.53 s\n", + "Assign facies : time elapsed 0.26 s\n", + "sims 14\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.44 s\n", + "Discretization : time elapsed 1.38 s\n", + "Assign facies : time elapsed 1.02 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.48 s\n", + "Discretization : time elapsed 1.4 s\n", + "Assign facies : time elapsed 1.35 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.58 s\n", + "Discretization : time elapsed 0.98 s\n", + "Assign facies : time elapsed 0.8 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.07 s\n", + "Discretization : time elapsed 2.47 s\n", + "Assign facies : time elapsed 2.53 s\n", "setup phase : time elapsed 0.0 s\n", "Compute surfaces : time elapsed 0.35 s\n", - "Discretization : time elapsed 0.75 s\n", + "Discretization : time elapsed 0.53 s\n", + "Assign facies : time elapsed 0.28 s\n", + "sims 15\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.57 s\n", + "Discretization : time elapsed 1.49 s\n", + "Assign facies : time elapsed 1.02 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.12 s\n", + "Discretization : time elapsed 1.33 s\n", + "Assign facies : time elapsed 1.31 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 0.98 s\n", + "Assign facies : time elapsed 0.73 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.93 s\n", + "Discretization : time elapsed 3.05 s\n", + "Assign facies : time elapsed 3.28 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.48 s\n", + "Discretization : time elapsed 0.57 s\n", + "Assign facies : time elapsed 0.28 s\n", + "sims 16\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.98 s\n", + "Discretization : time elapsed 1.34 s\n", + "Assign facies : time elapsed 0.97 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.65 s\n", + "Discretization : time elapsed 1.5 s\n", + "Assign facies : time elapsed 1.52 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.59 s\n", + "Discretization : time elapsed 0.96 s\n", + "Assign facies : time elapsed 0.67 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.34 s\n", + "Discretization : time elapsed 2.8 s\n", + "Assign facies : time elapsed 2.97 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.49 s\n", + "Discretization : time elapsed 0.61 s\n", + "Assign facies : time elapsed 0.3 s\n", + "sims 17\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.48 s\n", + "Discretization : time elapsed 1.38 s\n", + "Assign facies : time elapsed 1.1 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.28 s\n", + "Discretization : time elapsed 1.52 s\n", + "Assign facies : time elapsed 1.53 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.72 s\n", + "Discretization : time elapsed 1.04 s\n", + "Assign facies : time elapsed 0.81 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.07 s\n", + "Discretization : time elapsed 2.69 s\n", + "Assign facies : time elapsed 2.87 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.66 s\n", "Assign facies : time elapsed 0.34 s\n", - "CPU times: total: 3min 30s\n", - "Wall time: 1min 20s\n" + "sims 18\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.98 s\n", + "Discretization : time elapsed 1.35 s\n", + "Assign facies : time elapsed 1.0 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.39 s\n", + "Discretization : time elapsed 1.44 s\n", + "Assign facies : time elapsed 1.28 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 1.03 s\n", + "Assign facies : time elapsed 0.81 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.33 s\n", + "Discretization : time elapsed 2.56 s\n", + "Assign facies : time elapsed 2.71 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.51 s\n", + "Assign facies : time elapsed 0.31 s\n", + "sims 19\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.23 s\n", + "Discretization : time elapsed 1.41 s\n", + "Assign facies : time elapsed 1.07 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.52 s\n", + "Discretization : time elapsed 1.32 s\n", + "Assign facies : time elapsed 1.27 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.6 s\n", + "Discretization : time elapsed 1.07 s\n", + "Assign facies : time elapsed 0.84 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.72 s\n", + "Discretization : time elapsed 3.17 s\n", + "Assign facies : time elapsed 3.45 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.47 s\n", + "Assign facies : time elapsed 0.2 s\n", + "sims 20\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.58 s\n", + "Discretization : time elapsed 1.47 s\n", + "Assign facies : time elapsed 1.15 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.89 s\n", + "Discretization : time elapsed 1.51 s\n", + "Assign facies : time elapsed 1.51 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.48 s\n", + "Discretization : time elapsed 0.95 s\n", + "Assign facies : time elapsed 0.73 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.57 s\n", + "Discretization : time elapsed 3.14 s\n", + "Assign facies : time elapsed 3.41 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.49 s\n", + "Discretization : time elapsed 0.5 s\n", + "Assign facies : time elapsed 0.26 s\n", + "sims 21\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.1 s\n", + "Discretization : time elapsed 1.43 s\n", + "Assign facies : time elapsed 1.04 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.25 s\n", + "Discretization : time elapsed 1.48 s\n", + "Assign facies : time elapsed 1.41 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.48 s\n", + "Discretization : time elapsed 1.02 s\n", + "Assign facies : time elapsed 0.74 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.08 s\n", + "Discretization : time elapsed 2.59 s\n", + "Assign facies : time elapsed 2.74 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.51 s\n", + "Assign facies : time elapsed 0.2 s\n", + "sims 22\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.34 s\n", + "Discretization : time elapsed 1.4 s\n", + "Assign facies : time elapsed 1.0 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.39 s\n", + "Discretization : time elapsed 1.35 s\n", + "Assign facies : time elapsed 1.27 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.6 s\n", + "Discretization : time elapsed 1.01 s\n", + "Assign facies : time elapsed 0.84 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.45 s\n", + "Discretization : time elapsed 2.75 s\n", + "Assign facies : time elapsed 2.83 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.56 s\n", + "Assign facies : time elapsed 0.32 s\n", + "sims 23\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.33 s\n", + "Discretization : time elapsed 1.44 s\n", + "Assign facies : time elapsed 1.07 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.63 s\n", + "Discretization : time elapsed 1.55 s\n", + "Assign facies : time elapsed 1.5 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.22 s\n", + "Discretization : time elapsed 0.92 s\n", + "Assign facies : time elapsed 0.7 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.19 s\n", + "Discretization : time elapsed 2.6 s\n", + "Assign facies : time elapsed 2.86 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.48 s\n", + "Discretization : time elapsed 0.51 s\n", + "Assign facies : time elapsed 0.2 s\n", + "sims 24\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.7 s\n", + "Discretization : time elapsed 1.5 s\n", + "Assign facies : time elapsed 1.3 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.63 s\n", + "Discretization : time elapsed 1.44 s\n", + "Assign facies : time elapsed 1.3 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.35 s\n", + "Discretization : time elapsed 1.07 s\n", + "Assign facies : time elapsed 0.82 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.83 s\n", + "Discretization : time elapsed 2.5 s\n", + "Assign facies : time elapsed 2.62 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.38 s\n", + "Discretization : time elapsed 0.5 s\n", + "Assign facies : time elapsed 0.18 s\n", + "sims 25\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.59 s\n", + "Discretization : time elapsed 1.45 s\n", + "Assign facies : time elapsed 1.06 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.01 s\n", + "Discretization : time elapsed 1.37 s\n", + "Assign facies : time elapsed 1.37 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 1.06 s\n", + "Assign facies : time elapsed 0.89 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.44 s\n", + "Discretization : time elapsed 2.98 s\n", + "Assign facies : time elapsed 3.35 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.49 s\n", + "Discretization : time elapsed 0.49 s\n", + "Assign facies : time elapsed 0.19 s\n", + "sims 26\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.1 s\n", + "Discretization : time elapsed 1.37 s\n", + "Assign facies : time elapsed 1.09 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.51 s\n", + "Discretization : time elapsed 1.53 s\n", + "Assign facies : time elapsed 1.41 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.22 s\n", + "Discretization : time elapsed 0.9 s\n", + "Assign facies : time elapsed 0.67 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.2 s\n", + "Discretization : time elapsed 2.75 s\n", + "Assign facies : time elapsed 2.94 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.56 s\n", + "Assign facies : time elapsed 0.23 s\n", + "sims 27\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.59 s\n", + "Discretization : time elapsed 1.46 s\n", + "Assign facies : time elapsed 1.07 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.14 s\n", + "Discretization : time elapsed 1.39 s\n", + "Assign facies : time elapsed 1.26 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.47 s\n", + "Discretization : time elapsed 0.97 s\n", + "Assign facies : time elapsed 0.72 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.69 s\n", + "Discretization : time elapsed 3.05 s\n", + "Assign facies : time elapsed 3.24 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.48 s\n", + "Discretization : time elapsed 0.53 s\n", + "Assign facies : time elapsed 0.28 s\n", + "sims 28\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.11 s\n", + "Discretization : time elapsed 1.39 s\n", + "Assign facies : time elapsed 1.02 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.01 s\n", + "Discretization : time elapsed 1.46 s\n", + "Assign facies : time elapsed 1.43 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.35 s\n", + "Discretization : time elapsed 0.87 s\n", + "Assign facies : time elapsed 0.64 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.19 s\n", + "Discretization : time elapsed 2.93 s\n", + "Assign facies : time elapsed 3.17 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.48 s\n", + "Discretization : time elapsed 0.54 s\n", + "Assign facies : time elapsed 0.23 s\n", + "sims 29\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.73 s\n", + "Discretization : time elapsed 1.49 s\n", + "Assign facies : time elapsed 1.16 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.27 s\n", + "Discretization : time elapsed 1.56 s\n", + "Assign facies : time elapsed 1.56 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 1.23 s\n", + "Discretization : time elapsed 0.96 s\n", + "Assign facies : time elapsed 0.67 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 2.09 s\n", + "Discretization : time elapsed 2.68 s\n", + "Assign facies : time elapsed 2.84 s\n", + "setup phase : time elapsed 0.0 s\n", + "Compute surfaces : time elapsed 0.36 s\n", + "Discretization : time elapsed 0.55 s\n", + "Assign facies : time elapsed 0.24 s\n", + "CPU times: total: 1h 14min 53s\n", + "Wall time: 11min 22s\n" ] } ], @@ -668,19 +1227,19 @@ " top=top, bot=bot,\n", " alt=alts, covs=covs,\n", " dim=(nx, ny, nz), spa=(sx, sy, sz), ori=(ox, oy, oz),\n", - " nreal=3, seed=1, verbose=1)" + " nreal=30, seed=20, verbose=1)" ] }, { "cell_type": "code", "execution_count": 17, - "id": "367ce84c", + "id": "1efcb7b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "6.333333333333333" + "100" ] }, "execution_count": 17, @@ -689,77 +1248,1042 @@ } ], "source": [ - "19 / 3" + "nz" ] }, { "cell_type": "code", "execution_count": 18, - "id": "0884c960", + "id": "3fe83d00", "metadata": {}, "outputs": [], "source": [ - "arr_plot = sims[0].copy().astype(float)\n", - "arr_plot[sims[0] == -99] = np.nan" + "import flopy\n", + "\n", + "workspace = \"working\"\n", + "name = \"EROS_eq_perm\"\n", + "# Create the Flopy simulation object\n", + "sim = flopy.mf6.MFSimulation(\n", + " sim_name=name, exe_name=\"../../../../Thèse/prog et code/modfow/exe/win64/mf6.exe\", version=\"mf6\", sim_ws=workspace\n", + ")\n", + "\"../\"\n", + "# Create the Flopy temporal discretization object\n", + "tdis = flopy.mf6.modflow.mftdis.ModflowTdis(\n", + " sim, pname=\"tdis\", time_units=\"DAYS\", nper=1, perioddata=[(1.0, 1, 1.0)]\n", + ")\n", + "\n", + "# Create the Flopy groundwater flow (gwf) model object\n", + "model_nam_file = f\"{name}.nam\"\n", + "gwf = flopy.mf6.ModflowGwf(sim, modelname=name, model_nam_file=model_nam_file)\n", + "\n", + "# Create the Flopy iterative model solver (ims) Package object\n", + "ims = flopy.mf6.modflow.mfims.ModflowIms(sim, pname=\"ims\", complexity=\"complex\", outer_dvclose=5e-2, inner_dvclose=5e-3)\n", + "\n", + "botm = np.linspace(oz / nz, z1 - sz, nz)\n", + "delrow = sx\n", + "delcol = sy\n", + "dis = flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(\n", + " gwf,\n", + " pname=\"dis\",\n", + " nlay=nz,\n", + " nrow=ny,\n", + " ncol=nx,\n", + " delr=delrow,\n", + " delc=delcol,\n", + " top=z1,\n", + " botm=botm[::-1],\n", + ")\n", + "\n", + "headfile = f\"{name}.hds\"\n", + "head_filerecord = [headfile]\n", + "budgetfile = f\"{name}.cbb\"\n", + "budget_filerecord = [budgetfile]\n", + "saverecord = [(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")]\n", + "printrecord = [(\"BUDGET\", \"ALL\")]\n", + "oc = flopy.mf6.modflow.mfgwfoc.ModflowGwfoc(\n", + " gwf,\n", + " pname=\"oc\",\n", + " saverecord=saverecord,\n", + " head_filerecord=head_filerecord,\n", + " budget_filerecord=budget_filerecord,\n", + " printrecord=printrecord,\n", + ")" ] }, { "cell_type": "code", "execution_count": 19, - "id": "54980897", + "id": "18b2ac5f", + "metadata": {}, + "outputs": [], + "source": [ + "dic_facies_k = {3: 1e-1,\n", + " 1: 1e-4, \n", + " 2: 1e-2, \n", + " 4: 1e-5}\n", + "\n", + "K = sims[0].copy().astype(float)\n", + "K[sims[0] == -99] = 1e-30\n", + "\n", + "for key, value in dic_facies_k.items():\n", + " K[K == key] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fa00284f", + "metadata": {}, + "outputs": [], + "source": [ + "npf = flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf(\n", + " gwf, pname=\"npf\", icelltype=0, k= K,\n", + " save_flows=True, save_specific_discharge=True \n", + ")\n", + "\n", + " # write the changes to the npf file\n", + "# npf.write() " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "afc4c63c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ims...\n", + " writing model EROS_eq_perm...\n", + " writing model name file...\n", + " writing package dis...\n", + " writing package oc...\n", + " writing package npf...\n", + " writing package chd...\n", + " writing package ic...\n" + ] + } + ], + "source": [ + "# BCs\n", + "chd_rec = []\n", + "L = sim.get_model().dis.delc[0] * sim.get_model().dis.nrow.array\n", + "\n", + "# face x-z\n", + "for i in range(nx):\n", + " for iz in range(nz):\n", + " chd_rec.append(((iz, 0, i), L))\n", + " chd_rec.append(((iz, ny-1, i), 0))\n", + "\n", + "# face y-z\n", + "grad_y = np.linspace(L, 0, ny)\n", + "for i in range(1, ny-1):\n", + " for iz in range(1, nz-1):\n", + " chd_rec.append(((iz, i, 0), grad_y[i]))\n", + " chd_rec.append(((iz, i, nx-1), grad_y[i]))\n", + "\n", + "# face x-y\n", + "for i in range(1, nx-1):\n", + " for j in range(1, ny-1):\n", + " chd_rec.append(((0, j, i), grad_y[j]))\n", + " chd_rec.append(((nz-1, j, i), grad_y[j]))\n", + "\n", + "chd = flopy.mf6.modflow.mfgwfchd.ModflowGwfchd(\n", + " gwf,\n", + " pname=\"chd\",\n", + " maxbound=len(chd_rec),\n", + " stress_period_data=chd_rec,\n", + " save_flows=True,\n", + " )\n", + "\n", + "# chd.write()\n", + "\n", + "# initial conditions\n", + "a = np.ones((ny, nx)) * grad_y\n", + "start = np.zeros((nz, ny, nx))\n", + "start[:, :, :] = a.T\n", + "\n", + "ic = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=start)\n", + "# ic.write()\n", + "\n", + "# run the model\n", + "sim.write_simulation()\n", + "# sim.run_simulation()\n", + "\n", + "# get specific discharge\n", + "# spd = flopy.utils.CellBudgetFile(os.path.join(workspace, f\"{name}.cbb\"), precision=\"double\").get_data(text=\"DATA-SPDIS\")\n", + "\n", + "# get mean discharge in x, y, z\n", + "# qx2 = spd[0][\"qx\"].mean()\n", + "# qy2 = spd[0][\"qy\"].mean() \n", + "# qz2 = spd[0][\"qz\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "eff10d6f", + "metadata": {}, + "outputs": [], + "source": [ + "import flopy" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c0039edd", + "metadata": {}, + "outputs": [], + "source": [ + "import parallel" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "03d17558", + "metadata": {}, + "outputs": [], + "source": [ + "l_keq = parallel.parallel_compute(sims, dic_facies_k, 10, nx, ny, nz)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e8eeca18", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1., 2., 3., 4.])" + "[array([[ 7.92343704e-03, -6.34194675e-05, 8.12040695e-06],\n", + " [-6.57385998e-05, 5.74296857e-03, 1.97575915e-05],\n", + " [ 5.26381948e-07, 3.25082574e-05, 3.61533933e-04]]),\n", + " array([[ 7.98663524e-03, -2.39591434e-04, -1.62800581e-06],\n", + " [-2.40670950e-04, 5.51300511e-03, -9.75949768e-07],\n", + " [ 1.96435608e-06, -1.11765704e-05, 2.55115295e-04]]),\n", + " array([[ 1.35014401e-02, -6.13096617e-05, -4.74496784e-05],\n", + " [-6.24432874e-05, 1.07492494e-02, 1.46839463e-05],\n", + " [-3.88383964e-05, 1.41010535e-05, 4.92078525e-04]]),\n", + " array([[ 5.86175760e-03, -8.88224016e-05, 5.16178465e-06],\n", + " [-8.71836561e-05, 4.28594115e-03, 2.64811150e-05],\n", + " [ 7.56698962e-06, 2.61977528e-05, 2.81251117e-04]]),\n", + " array([[ 7.68138602e-03, 5.52246586e-04, -3.79999198e-06],\n", + " [ 5.52126231e-04, 5.51477432e-03, -1.85392950e-05],\n", + " [-3.58530199e-06, -1.85634887e-05, 3.56998416e-04]]),\n", + " array([[ 1.16328732e-02, 4.75318736e-06, -2.89733408e-06],\n", + " [ 3.77199459e-06, 9.52494994e-03, 3.37271220e-05],\n", + " [-4.91557353e-06, 3.23520597e-05, 4.80884731e-04]]),\n", + " array([[6.37026578e-03, 1.76952034e-04, 1.71217214e-05],\n", + " [1.76749468e-04, 4.44454133e-03, 1.45622970e-05],\n", + " [1.70639862e-05, 1.31183307e-05, 2.84849033e-04]]),\n", + " array([[ 9.27650773e-03, 4.74558004e-05, 1.15053310e-05],\n", + " [ 4.96399913e-05, 6.59641283e-03, -4.07538706e-05],\n", + " [ 1.09825893e-05, -3.87293814e-05, 3.40025933e-04]]),\n", + " array([[ 8.20214212e-03, -8.75958599e-05, -1.04714137e-06],\n", + " [-8.71805946e-05, 6.02330649e-03, -1.12335828e-05],\n", + " [-9.65355850e-07, -1.13040365e-05, 2.88794795e-04]]),\n", + " array([[ 9.24005477e-03, 4.01024129e-05, -2.89374306e-05],\n", + " [ 4.35473878e-05, 6.53075162e-03, 3.61176060e-06],\n", + " [-2.85991543e-05, 3.80803935e-06, 3.23198086e-04]]),\n", + " array([[ 6.53786576e-03, 4.44290115e-04, -6.27521955e-06],\n", + " [ 4.42249063e-04, 4.72480268e-03, -8.08274019e-06],\n", + " [-2.86516075e-06, -9.63856153e-06, 2.82599943e-04]]),\n", + " array([[ 1.27564607e-02, -1.25870411e-04, 2.47619026e-05],\n", + " [-1.29101475e-04, 1.01041006e-02, 3.84135161e-06],\n", + " [ 2.47886662e-05, 3.96981475e-06, 4.26827054e-04]]),\n", + " array([[ 1.28967272e-02, -3.74683864e-04, -3.68993823e-05],\n", + " [-3.75555717e-04, 1.14474396e-02, 1.66831131e-05],\n", + " [-3.27076093e-05, 1.20492598e-05, 5.59479254e-04]]),\n", + " array([[ 6.03052675e-03, 2.53813679e-04, 1.07828732e-05],\n", + " [ 2.53195295e-04, 4.26681408e-03, -9.46906822e-06],\n", + " [ 4.71790523e-06, -9.23751084e-06, 2.64933137e-04]]),\n", + " array([[ 8.37744426e-03, -6.08060789e-05, 1.75919151e-05],\n", + " [-6.25054753e-05, 6.44386529e-03, -3.55469178e-06],\n", + " [ 1.93819359e-05, -3.11552136e-06, 3.30193400e-04]]),\n", + " array([[ 6.71755538e-03, -2.41941956e-04, 4.57504820e-06],\n", + " [-2.45363902e-04, 4.68949233e-03, -2.52947259e-05],\n", + " [-5.01057092e-06, -2.16996381e-05, 2.86967427e-04]]),\n", + " array([[ 9.18005141e-03, 3.38566634e-04, 1.13368447e-05],\n", + " [ 3.40378801e-04, 5.57416631e-03, -2.45001474e-05],\n", + " [ 1.12041247e-05, -2.40178254e-05, 3.13453479e-04]]),\n", + " array([[ 8.42025640e-03, 1.82245551e-04, -1.64934572e-05],\n", + " [ 1.83476231e-04, 6.21330068e-03, -1.26648270e-05],\n", + " [-1.66647608e-05, -1.22300803e-05, 2.61288138e-04]]),\n", + " array([[ 1.01512262e-02, -1.51145792e-04, -1.73041234e-05],\n", + " [-1.47654720e-04, 7.66866214e-03, -1.62707870e-05],\n", + " [-1.69088394e-05, -1.68891039e-05, 3.46778116e-04]]),\n", + " array([[ 7.14006498e-03, 1.04312655e-04, -1.66643362e-05],\n", + " [ 1.01996863e-04, 5.71923044e-03, 1.56058110e-05],\n", + " [-1.81568765e-05, 1.63526506e-05, 2.96949569e-04]]),\n", + " array([[ 6.63292230e-03, -4.10179763e-05, -2.43875749e-06],\n", + " [-4.02255393e-05, 5.32151807e-03, 5.50351254e-06],\n", + " [-2.68405518e-06, 5.48071625e-06, 2.01989642e-04]]),\n", + " array([[ 8.14463911e-03, -1.75592087e-04, 1.02354685e-06],\n", + " [-1.81601516e-04, 5.45297534e-03, -1.37533299e-05],\n", + " [ 2.48339480e-06, -2.12013661e-05, 3.27930536e-04]]),\n", + " array([[ 8.00385833e-03, 2.61583586e-04, -6.45858243e-06],\n", + " [ 2.56706543e-04, 5.39647915e-03, 4.44801765e-05],\n", + " [-6.40139894e-06, 3.73308813e-05, 3.07855357e-04]]),\n", + " array([[ 1.30557142e-02, 1.75499684e-04, 3.28164600e-06],\n", + " [ 1.74422604e-04, 1.12097250e-02, -5.62332484e-05],\n", + " [ 6.57884606e-06, -5.91816579e-05, 5.18519621e-04]]),\n", + " array([[ 7.78537267e-03, -6.57991096e-05, 2.94293350e-06],\n", + " [-6.33144657e-05, 5.88934363e-03, -5.60258039e-06],\n", + " [ 2.83825532e-06, -5.35430558e-06, 2.45809575e-04]]),\n", + " array([[ 1.09941106e-02, -1.38074977e-04, -5.41608722e-07],\n", + " [-1.37994485e-04, 8.91767897e-03, 3.69304542e-05],\n", + " [ 1.94876687e-07, 3.66423310e-05, 4.08833941e-04]]),\n", + " array([[ 1.14619877e-02, -6.15677048e-04, 6.41504736e-05],\n", + " [-6.18812277e-04, 1.01489308e-02, 1.97791692e-05],\n", + " [ 6.54358010e-05, 2.33402290e-05, 4.55171648e-04]]),\n", + " array([[8.52348108e-03, 5.71697396e-04, 1.62689753e-05],\n", + " [5.71206900e-04, 5.84389102e-03, 1.91790292e-06],\n", + " [1.87117604e-05, 7.12149473e-07, 2.61992836e-04]]),\n", + " array([[ 9.38510903e-03, -1.26587492e-04, 8.83978724e-06],\n", + " [-1.26382804e-04, 6.46825868e-03, -1.73583189e-06],\n", + " [ 8.95048928e-06, -1.94308983e-06, 3.38675076e-04]]),\n", + " array([[ 6.29522484e-03, 2.55490639e-05, -6.25249706e-06],\n", + " [ 2.56261912e-05, 4.23576318e-03, 1.20965165e-05],\n", + " [-3.55966828e-06, 1.33199075e-05, 2.84541459e-04]])]" ] }, - "execution_count": 19, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.unique(arr_plot)" + "l_keq" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b27a843a", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_k_eq(k, model_ws=\"working\", modelname=\"EROS_eq_perm\", k22=None, k33=None, angle1=None, angle2=None, angle3=None):\n", + "\n", + " # model_ws = \"working\"\n", + " # modelname = \"EROS_eq_perm\"\n", + "\n", + " sim = flopy.mf6.MFSimulation.load(sim_ws=model_ws, sim_name=modelname, verbosity_level=0,\n", + " exe_name=\"../../../../Thèse/prog et code/modfow/exe/win64/mf6.exe\") # load the simulation\n", + "\n", + " gwf = sim.get_model()\n", + "\n", + " if angle1 is not None or angle2 is not None or angle3 is not None:\n", + " xt3d = True\n", + " else:\n", + " xt3d = False\n", + "\n", + " npf = flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf(\n", + " gwf, pname=\"npf\", icelltype=0, k=k, k22=k22, k33=k33, \n", + " angle1=angle1, angle2=angle2, angle3=angle3, xt3doptions=xt3d, \n", + " save_flows=True, save_specific_discharge=True \n", + " )\n", + "\n", + " # write the changes to the npf file\n", + " npf.write() \n", + "\n", + " ### computing discharge\n", + "\n", + " ## Y direction\n", + "\n", + " # BCs\n", + " chd_rec = []\n", + " L = sim.get_model().dis.delc[0] * sim.get_model().dis.nrow.array\n", + "\n", + " # face x-z\n", + " for i in range(nx):\n", + " for iz in range(nz):\n", + " chd_rec.append(((iz, 0, i), L))\n", + " chd_rec.append(((iz, ny-1, i), 0))\n", + "\n", + " # face y-z\n", + " grad_y = np.linspace(L, 0, ny)\n", + " for i in range(1, ny-1):\n", + " for iz in range(1, nz-1):\n", + " chd_rec.append(((iz, i, 0), grad_y[i]))\n", + " chd_rec.append(((iz, i, nx-1), grad_y[i]))\n", + "\n", + " # face x-y\n", + " for i in range(1, nx-1):\n", + " for j in range(1, ny-1):\n", + " chd_rec.append(((0, j, i), grad_y[j]))\n", + " chd_rec.append(((nz-1, j, i), grad_y[j]))\n", + "\n", + " chd = flopy.mf6.modflow.mfgwfchd.ModflowGwfchd(\n", + " gwf,\n", + " pname=\"chd\",\n", + " maxbound=len(chd_rec),\n", + " stress_period_data=chd_rec,\n", + " save_flows=True,\n", + " )\n", + "\n", + " chd.write()\n", + "\n", + " # initial conditions\n", + " a = np.ones((ny, nx)) * grad_y\n", + " start = np.zeros((nz, ny, nx))\n", + " start[:, :, :] = a.T\n", + "\n", + " ic = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=start)\n", + " ic.write()\n", + "\n", + " # run the model\n", + " sim.run_simulation()\n", + "\n", + " # get specific discharge\n", + " spd = flopy.utils.CellBudgetFile(os.path.join(model_ws, f\"{modelname}.cbb\"), precision=\"double\").get_data(text=\"DATA-SPDIS\")\n", + "\n", + " # get mean discharge in x, y, z\n", + " qx2 = spd[0][\"qx\"].mean()\n", + " qy2 = spd[0][\"qy\"].mean() \n", + " qz2 = spd[0][\"qz\"].mean()\n", + "\n", + " # print(qx2, qy2, qz2)\n", + " \n", + " ## X direction\n", + "\n", + " # BCs\n", + " chd_rec = []\n", + " L = sim.get_model().dis.delr[0] * sim.get_model().dis.ncol.array\n", + "\n", + " # face y-z\n", + " for i in range(ny):\n", + " for iz in range(nz):\n", + " chd_rec.append(((iz, i, 0), 0))\n", + " chd_rec.append(((iz, i, nx-1), L))\n", + "\n", + " # face x-z\n", + " grad_x = np.linspace(0, L, nx)\n", + " for i in range(1, nx-1):\n", + " for iz in range(1, nz-1):\n", + " chd_rec.append(((iz, 0, i), grad_x[i]))\n", + " chd_rec.append(((iz, ny-1, i), grad_x[i]))\n", + "\n", + " # face x-y\n", + " for i in range(1, ny-1):\n", + " for j in range(1, nx-1):\n", + " chd_rec.append(((0, i, j), grad_x[j]))\n", + " chd_rec.append(((nz-1, i, j), grad_x[j]))\n", + "\n", + " chd = flopy.mf6.modflow.mfgwfchd.ModflowGwfchd(\n", + " gwf,\n", + " pname=\"chd\",\n", + " maxbound=len(chd_rec),\n", + " stress_period_data=chd_rec,\n", + " save_flows=True,\n", + " )\n", + "\n", + " chd.write()\n", + "\n", + " # initial conditions\n", + " a = np.ones((ny, nx)) * grad_x\n", + " start = np.zeros((nz, ny, nx))\n", + " start[:, :, :] = a\n", + "\n", + " ic = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=start)\n", + " ic.write()\n", + "\n", + " # run the model\n", + " sim.run_simulation()\n", + "\n", + " # get specific discharge\n", + " spd = flopy.utils.CellBudgetFile(os.path.join(model_ws, f\"{modelname}.cbb\"), precision=\"double\").get_data(text=\"DATA-SPDIS\")\n", + "\n", + " # get mean discharge in x, y, z\n", + " qx1 = spd[0][\"qx\"].mean()\n", + " qy1 = spd[0][\"qy\"].mean()\n", + " qz1 = spd[0][\"qz\"].mean()\n", + "\n", + " ## Z direction\n", + "\n", + " L = 14\n", + "\n", + " # BCs\n", + " chd_rec = []\n", + "\n", + " # face x-y\n", + " for i in range(nx):\n", + " for j in range(ny):\n", + " chd_rec.append(((0, j, i), L))\n", + " chd_rec.append(((nz-1, j, i), 0))\n", + "\n", + " # face y-z\n", + " grad_z = np.linspace(L, 0, nz)\n", + " for i in range(1, ny-1):\n", + " for iz in range(1, nz-1):\n", + " chd_rec.append(((iz, i, 0), grad_z[iz]))\n", + " chd_rec.append(((iz, i, nx-1), grad_z[iz]))\n", + "\n", + " # face x-z\n", + " for i in range(1, nx-1):\n", + " for j in range(1, nz-1):\n", + " chd_rec.append(((j, 0, i), grad_z[j]))\n", + " chd_rec.append(((j, ny-1, i), grad_z[j])) \n", + "\n", + " chd = flopy.mf6.modflow.mfgwfchd.ModflowGwfchd(\n", + " gwf,\n", + " pname=\"chd\",\n", + " maxbound=len(chd_rec),\n", + " stress_period_data=chd_rec,\n", + " save_flows=True,\n", + " )\n", + "\n", + " chd.write()\n", + "\n", + " # initial conditions\n", + " a = np.ones((ny, nz)) * grad_z\n", + " start = np.zeros((nz, ny, nx))\n", + "\n", + " for i in range(nx):\n", + " start[:, :, i] = a.T\n", + "\n", + " ic = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=start)\n", + " ic.write()\n", + "\n", + " # run the model\n", + " sim.run_simulation(silent=True)\n", + "\n", + " # get specific discharge\n", + " spd = flopy.utils.CellBudgetFile(os.path.join(model_ws, f\"{modelname}.cbb\"), precision=\"double\").get_data(text=\"DATA-SPDIS\")\n", + "\n", + " # get mean discharge \n", + " qx3 = spd[0][\"qx\"].mean()\n", + " qy3 = spd[0][\"qy\"].mean()\n", + " qz3 = spd[0][\"qz\"].mean()\n", + "\n", + " # return tensor\n", + " K = np.array([[qx1, qx2, qx3], [qy1, qy2, qy3], [qz1, qz2, qz3]])\n", + "\n", + " return K" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "584c2855", + "metadata": {}, + "outputs": [], + "source": [ + "dic_facies_k = {3: 1e-1,\n", + " 1: 1e-4, \n", + " 2: 1e-2, \n", + " 4: 1e-5}\n", + "\n", + "# l_keq = []\n", + "# for isim in range(10):\n", + "# print(isim)\n", + "# K = sims[isim].copy().astype(float)\n", + "# K[sims[isim] == -99] = 1e-30\n", + "\n", + "# for key, value in dic_facies_k.items():\n", + "# K[K == key] = value\n", + "\n", + "# K_eq = compute_k_eq(K)\n", + "# l_keq.append(-K_eq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8ab5bcb", + "metadata": {}, + "outputs": [], + "source": [ + "# parallelize moddel runs\n", + "import multiprocessing\n", + "import shutil\n", + "\n", + "N_workers = 5\n", + "\n", + "\n", + "# for i in range(N_workers):\n", + "# shutil.copytree(\"working\", f\"working_{i}\") \n", + " \n", + "def run_model(i):\n", + " # create folder for each worker - copy \"working\" folder\n", + " shutil.copytree(\"working\", f\"working_{i}\")\n", + "\n", + " K = sims[i].copy().astype(float)\n", + " K[sims[i] == -99] = 1e-30\n", + "\n", + " for key, value in dic_facies_k.items():\n", + " K[K == key] = value\n", + "\n", + " K_eq = compute_k_eq(K, model_ws=f\"working_{i}\")\n", + " \n", + " shutil.rmtree(f\"working_{i}\")\n", + "\n", + " return -K_eq\n", + "\n", + "\n", + "with multiprocessing.Pool(N_workers) as pool:\n", + " l_keq = pool.map(run_model, range(5))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cf2de6a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fba51a9e", + "metadata": {}, + "outputs": [], + "source": [ + "# np.save(\"10keq_test2.npy\", l_keq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5cb236f", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "operands could not be broadcast together with shapes (30,3,3) (10,3,3) ", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[29], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m l_keq1 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m10keq_test.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m l_keq_tot \u001b[38;5;241m=\u001b[39m \u001b[43ml_keq\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43ml_keq1\u001b[49m\n", + "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (30,3,3) (10,3,3) " + ] + } + ], + "source": [ + "# l_keq1 = np.load(\"10keq_test.npy\")\n", + "# l_keq2 = np.load(\"10keq_test2.npy\")" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "53ee9d59", + "execution_count": 33, + "id": "0771b8d6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1., 2., 3., 4.])" + "40" ] }, - "execution_count": 31, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.unique(arr_plot)" + "len(l_keq)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "38f2fc88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kxx == [0.00792344 0.00798664 0.01350144 0.00586176 0.00768139 0.01163287\n", + " 0.00637027 0.00927651 0.00820214 0.00924005 0.00653787 0.01275646\n", + " 0.01289673 0.00603053 0.00837744 0.00671756 0.00918005 0.00842026\n", + " 0.01015123 0.00714006 0.00663292 0.00814464 0.00800386 0.01305571\n", + " 0.00778537 0.01099411 0.01146199 0.00852348 0.00938511 0.00629522\n", + " 0.00701411 0.01062553 0.00664278 0.00771208 0.00746521 0.00798857\n", + " 0.00898213 0.00694871 0.00722411 0.01249961]\n", + "Kyy == [0.00574297 0.00551301 0.01074925 0.00428594 0.00551477 0.00952495\n", + " 0.00444454 0.00659641 0.00602331 0.00653075 0.0047248 0.0101041\n", + " 0.01144744 0.00426681 0.00644387 0.00468949 0.00557417 0.0062133\n", + " 0.00766866 0.00571923 0.00532152 0.00545298 0.00539648 0.01120973\n", + " 0.00588934 0.00891768 0.01014893 0.00584389 0.00646826 0.00423576\n", + " 0.00479571 0.0077396 0.00446174 0.00630208 0.00534968 0.00556162\n", + " 0.00656107 0.00505479 0.0060398 0.00826548]\n", + "Kzz == [0.00036153 0.00025512 0.00049208 0.00028125 0.000357 0.00048088\n", + " 0.00028485 0.00034003 0.00028879 0.0003232 0.0002826 0.00042683\n", + " 0.00055948 0.00026493 0.00033019 0.00028697 0.00031345 0.00026129\n", + " 0.00034678 0.00029695 0.00020199 0.00032793 0.00030786 0.00051852\n", + " 0.00024581 0.00040883 0.00045517 0.00026199 0.00033868 0.00028454\n", + " 0.00025128 0.00035231 0.00021655 0.00028597 0.00026044 0.00031223\n", + " 0.00040816 0.00022985 0.00030326 0.00036769]\n" + ] + } + ], + "source": [ + "keq_all = np.array(l_keq)\n", + "\n", + "print(\"Kxx == \", keq_all[:, 0, 0])\n", + "print(\"Kyy == \", keq_all[:, 1, 1])\n", + "print(\"Kzz == \", keq_all[:, 2, 2])" ] }, { "cell_type": "code", "execution_count": 35, + "id": "a917fd00", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MEDIAN\n", + "[[ 8.07424872e-03 -4.52766000e-05 3.11228975e-06]\n", + " [-4.58926394e-05 5.86661733e-03 -1.28385349e-06]\n", + " [ 2.22387544e-06 -1.63178138e-06 3.10041790e-04]]\n", + "\n", + "MEAN\n", + "[[ 8.73174816e-03 4.72680470e-06 1.09698089e-06]\n", + " [ 3.90222493e-06 6.51984782e-03 -3.66815973e-08]\n", + " [ 1.58573888e-06 -5.10040394e-07 3.29331471e-04]]\n" + ] + } + ], + "source": [ + "print(\"MEDIAN\")\n", + "print(np.median(keq_all, axis=0))\n", + "\n", + "print(\"\\nMEAN\")\n", + "print(np.mean(keq_all, axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "06243873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([2., 2., 6., 9., 6., 7., 2., 2., 2., 2.]),\n", + " array([-3.6946709 , -3.65042541, -3.60617992, -3.56193443, -3.51768895,\n", + " -3.47344346, -3.42919797, -3.38495248, -3.34070699, -3.2964615 ,\n", + " -3.25221601]),\n", + " )" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.log10(keq_all[:, 0, 0]), bins=10, alpha=0.6)\n", + "plt.hist(np.log10(keq_all[:, 1, 1]), bins=10, alpha=0.6)\n", + "plt.hist(np.log10(keq_all[:, 2, 2]), bins=10, alpha=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "0134dcd1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 6), dpi=100)\n", + "plt.subplots_adjust(hspace=0.3, wspace=0.4)\n", + "\n", + "for i in range(3):\n", + " for j in range(3):\n", + " plt.subplot(3, 3, i*3+j+1)\n", + " plt.hist(keq_all[:, i, j], bins=10, alpha=0.5, color=\"red\")\n", + " \n", + " if j == 0:\n", + " plt.ylabel(\"Frequency\")\n", + " if i == 2:\n", + " plt.xlabel(\"K [m/s]\")\n", + "# plot legend on the last subplot\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "26dc976b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading simulation...\n", + " loading simulation name file...\n", + " loading tdis package...\n", + " loading model gwf6...\n", + " loading package dis...\n", + " loading package npf...\n", + " loading package chd...\n", + " loading package ic...\n", + " loading package oc...\n", + " loading solution package eros_eq_perm...\n", + "WARNING: Package with type npf already exists. Replacing existing package.\n", + "WARNING: Package with name chd already exists. Replacing existing package.\n", + "WARNING: Package with type ic already exists. Replacing existing package.\n", + "FloPy is using the following executable to run the model: ..\\..\\..\\..\\prog et code\\modfow\\exe\\win64\\mf6.exe\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.4.4 02/13/2024\n", + "\n", + " MODFLOW 6 compiled Feb 19 2024 14:20:00 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software has been approved for release by the U.S. Geological \n", + "Survey (USGS). Although the software has been subjected to rigorous \n", + "review, the USGS reserves the right to update the software as needed \n", + "pursuant to further analysis and review. No warranty, expressed or \n", + "implied, is made by the USGS or the U.S. Government as to the \n", + "functionality of the software and related material nor shall the \n", + "fact of release constitute any such warranty. Furthermore, the \n", + "software is released on condition that neither the USGS nor the U.S. \n", + "Government shall be held liable for any damages resulting from its \n", + "authorized or unauthorized use. Also refer to the USGS Water \n", + "Resources Software User Rights Notice for complete use, copyright, \n", + "and distribution information.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 9:51:30\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 9:57:02\n", + " Elapsed run time: 5 Minutes, 32.501 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "WARNING: Package with name chd already exists. Replacing existing package.\n", + "WARNING: Package with type ic already exists. Replacing existing package.\n", + "FloPy is using the following executable to run the model: ..\\..\\..\\..\\prog et code\\modfow\\exe\\win64\\mf6.exe\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.4.4 02/13/2024\n", + "\n", + " MODFLOW 6 compiled Feb 19 2024 14:20:00 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software has been approved for release by the U.S. Geological \n", + "Survey (USGS). Although the software has been subjected to rigorous \n", + "review, the USGS reserves the right to update the software as needed \n", + "pursuant to further analysis and review. No warranty, expressed or \n", + "implied, is made by the USGS or the U.S. Government as to the \n", + "functionality of the software and related material nor shall the \n", + "fact of release constitute any such warranty. Furthermore, the \n", + "software is released on condition that neither the USGS nor the U.S. \n", + "Government shall be held liable for any damages resulting from its \n", + "authorized or unauthorized use. Also refer to the USGS Water \n", + "Resources Software User Rights Notice for complete use, copyright, \n", + "and distribution information.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 9:57:46\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 10:03:47\n", + " Elapsed run time: 6 Minutes, 1.756 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "WARNING: Package with name chd already exists. Replacing existing package.\n", + "WARNING: Package with type ic already exists. Replacing existing package.\n", + "FloPy is using the following executable to run the model: ..\\..\\..\\..\\prog et code\\modfow\\exe\\win64\\mf6.exe\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.4.4 02/13/2024\n", + "\n", + " MODFLOW 6 compiled Feb 19 2024 14:20:00 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software has been approved for release by the U.S. Geological \n", + "Survey (USGS). Although the software has been subjected to rigorous \n", + "review, the USGS reserves the right to update the software as needed \n", + "pursuant to further analysis and review. No warranty, expressed or \n", + "implied, is made by the USGS or the U.S. Government as to the \n", + "functionality of the software and related material nor shall the \n", + "fact of release constitute any such warranty. Furthermore, the \n", + "software is released on condition that neither the USGS nor the U.S. \n", + "Government shall be held liable for any damages resulting from its \n", + "authorized or unauthorized use. Also refer to the USGS Water \n", + "Resources Software User Rights Notice for complete use, copyright, \n", + "and distribution information.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 10:04:42\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/12/02 10:07:56\n", + " Elapsed run time: 3 Minutes, 13.995 Seconds\n", + " \n", + " Normal termination of simulation.\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-7.01410789e-03, 1.49095554e-04, 6.35725847e-05],\n", + " [ 1.53869742e-04, -4.79571373e-03, -7.40974506e-05],\n", + " [ 1.17582930e-05, -1.17852262e-05, -1.34263178e-03]])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "K_eq = compute_k_eq(K)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "2c1989b6", + "metadata": {}, + "outputs": [], + "source": [ + "K_eq = np.array([[-7.01410789e-03, 1.49095554e-04, 6.35725847e-05],\n", + " [ 1.53869742e-04, -4.79571373e-03, -7.40974506e-05],\n", + " [ 1.17582930e-05, -1.17852262e-05, -1.34263178e-03]])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "c77d622e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(-K_eq)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0884c960", + "metadata": {}, + "outputs": [], + "source": [ + "arr_plot = sims[0].copy().astype(float)\n", + "arr_plot[sims[0] == -99] = np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "id": "35ecf428", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\emmal\\Anaconda3\\envs\\bebou\\Lib\\site-packages\\trame\\ui\\__init__.py:1: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('trame.ui')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + " __import__(\"pkg_resources\").declare_namespace(__name__)\n", + "c:\\Users\\emmal\\Anaconda3\\envs\\bebou\\Lib\\site-packages\\pkg_resources\\__init__.py:2349: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('trame')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + " declare_namespace(parent)\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c451c911365b42009671583bcc55ea82", + "model_id": "39cda4d49b3e40a58b619d165a370632", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Widget(value=\"