From 2331347a6b2c385ba547fc542549c3e870b4d917 Mon Sep 17 00:00:00 2001
From: abaucher <achille.baucher@inria.fr>
Date: Thu, 27 Jan 2022 19:26:35 +0100
Subject: [PATCH] Go to module version
---
LimitsToGrowth03.ipynb | 49 +--
World2.ipynb | 2 +-
World3.ipynb | 14 +-
pydynamo/__init__.py | 26 +-
pydynamo/{ => core}/delays.py | 0
pydynamo/{ => core}/dynamo_converter.py | 0
pydynamo/{ => core}/parse_dynamo_functions.py | 0
pydynamo/{ => core}/parse_equations.py | 0
pydynamo/{ => core}/parse_system.py | 0
pydynamo/{ => core}/plot_system.py | 0
pydynamo/core/psdsystem.py | 81 ++++
pydynamo/{ => core}/specials.py | 0
pydynamo/{ => core}/system.py | 0
pydynamo/world2/#plot_uutils.py# | 4 +
pydynamo/world2/.#plot_uutils.py | 1 +
pydynamo/world2/__init__.py | 6 +
.../world2/code_pydynamo_w2.py | 0
.../world2/definitions_w2.json | 0
{world3 => pydynamo/world3}/__init__.py | 26 +-
.../code/limits_to_growth_DYNAMO_code.py | 0
.../code/limits_to_growth_pydynamo_code.py | 0
.../code/limits_to_growth_pydynamo_code_72.py | 0
.../world3}/code/world3_DYNAMO_code.py | 0
.../world3}/code/world3_pydynamo_code.py | 0
pydynamo/world3/code_pydynamo_w3.py | 406 ++++++++++++++++++
pydynamo/world3/definitions_w3.json | 1 +
.../world3}/images/standart_run.png | Bin
.../world3}/images/variable_graph.png | Bin
.../world3}/infos/get_definitions.py | 0
.../world3}/infos/get_sectors.py | 0
.../world3}/infos/translated_defs.md | 0
.../world3}/infos/variable_definitions.json | 0
{world3 => pydynamo/world3}/plot_utils.py | 0
33 files changed, 566 insertions(+), 50 deletions(-)
rename pydynamo/{ => core}/delays.py (100%)
rename pydynamo/{ => core}/dynamo_converter.py (100%)
rename pydynamo/{ => core}/parse_dynamo_functions.py (100%)
rename pydynamo/{ => core}/parse_equations.py (100%)
rename pydynamo/{ => core}/parse_system.py (100%)
rename pydynamo/{ => core}/plot_system.py (100%)
create mode 100644 pydynamo/core/psdsystem.py
rename pydynamo/{ => core}/specials.py (100%)
rename pydynamo/{ => core}/system.py (100%)
create mode 100644 pydynamo/world2/#plot_uutils.py#
create mode 120000 pydynamo/world2/.#plot_uutils.py
create mode 100644 pydynamo/world2/__init__.py
rename world2/pydynamo_w2.py => pydynamo/world2/code_pydynamo_w2.py (100%)
rename world2/definitions.json => pydynamo/world2/definitions_w2.json (100%)
rename {world3 => pydynamo/world3}/__init__.py (57%)
rename {world3 => pydynamo/world3}/code/limits_to_growth_DYNAMO_code.py (100%)
rename {world3 => pydynamo/world3}/code/limits_to_growth_pydynamo_code.py (100%)
rename {world3 => pydynamo/world3}/code/limits_to_growth_pydynamo_code_72.py (100%)
rename {world3 => pydynamo/world3}/code/world3_DYNAMO_code.py (100%)
rename {world3 => pydynamo/world3}/code/world3_pydynamo_code.py (100%)
create mode 100644 pydynamo/world3/code_pydynamo_w3.py
create mode 100644 pydynamo/world3/definitions_w3.json
rename {world3 => pydynamo/world3}/images/standart_run.png (100%)
rename {world3 => pydynamo/world3}/images/variable_graph.png (100%)
rename {world3 => pydynamo/world3}/infos/get_definitions.py (100%)
rename {world3 => pydynamo/world3}/infos/get_sectors.py (100%)
rename {world3 => pydynamo/world3}/infos/translated_defs.md (100%)
rename {world3 => pydynamo/world3}/infos/variable_definitions.json (100%)
rename {world3 => pydynamo/world3}/plot_utils.py (100%)
diff --git a/LimitsToGrowth03.ipynb b/LimitsToGrowth03.ipynb
index 7b22437b..7bf876d4 100644
--- a/LimitsToGrowth03.ipynb
+++ b/LimitsToGrowth03.ipynb
@@ -26,8 +26,7 @@
}
],
"source": [
- "from world3 import get_w3, plot_world_03, var_color\n",
- "import pydynamo as dno\n",
+ "from pydynamo import get_w3, plot_world_03, var_color, plot_tabhl, show_pyvis\n",
"s = get_w3()\n",
"s.run(N=200, dt=1)\n",
"plot_world_03(s, \"World3 scenario 1: Business as usual\")"
@@ -40,7 +39,7 @@
"outputs": [],
"source": [
"# Show the graph of direct relations (interactive with pyvis)\n",
- "dno.plot_system.show_pyvis(s, notebook=False, options=[], colors=var_color).show('tmp_sectormap.html')"
+ "show_pyvis(s, notebook=False, options=[], colors=var_color).show('tmp_sectormap.html')"
]
},
{
@@ -57,7 +56,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -70,7 +69,7 @@
],
"source": [
"# Get the non linear formula of some variable\n",
- "dno.plot_system.plot_tabhl(s, 'dcph')"
+ "plot_tabhl(s, 'dcph')"
]
},
{
@@ -81,7 +80,7 @@
{
"data": {
"text/plain": [
- "'industrial capital depreciation rate [dollars/year].'"
+ "'industrial capital depreciation'"
]
},
"execution_count": 4,
@@ -206,20 +205,19 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
- "data": {
- "image/png": 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2XUxm74VrFU6noXtDvh76NY08GvHExifYeHGjDa2shShHoFBUD0aT5M0/I3ltxXEGtm7AD5O7Ud+jYsND8zl17RTPbn6WZt7N+E/f/+CoqdhIo5rMPZ0a4+umq9RbAYCfix9Lbl9Ca9/WPLflOVadX2UjC2shcUfBsxG4Vky4sCIoR6Co82TkGpj63X6+2hbFxJ5NWPBAR1x1lVNfuZp5lWkbp+Hm6MZnAz/DXeduI2trFi46LRN7NOGvk/GciE2rVFpeTl58eduXdKjfgdnbZrPy3EobWVnLiDtapW8DoByBoo4TfS2LMfN38NfJeOaMaMNrI9qi1VSu+SYrL4vpf00nMy+T+QPnE+AWYCNrayYPdW+Cm07LF1sq91YA4OroymcDP6NTg068tO0lVpxbYQMLaxF52WZpCeUIFIqqYdf5JEZ+uo3Y1Gy+ntiFCT2bVjpNKSWv7niVU9dO8W6fd2nl28oGltZsvFwdub9rMCuPxFoVuOZGuDq68unAT+kS2IWXt73M72d/r7yRtYX4SJBG5QgUiqpg2e6LPLBoN75uOv6Y3qvCwnHF+erYV6y9sJanOz5Nn0Z9bJJmbeDRXs3KFbjmRrg4uPDpgE/pFtiNV7e/ym9nfrNJujWeaugoBjs7AiHEECHEKSHEWSHErBKOBwshNgkhDgohjgghhtnTHoUiz2ji1T+O8dJvx+jVwo/fnuhJUz83m6S9JXoLHx/4mKFNhzKxbd0KwhLg5czoDo1YvjeaxIxcm6Tp7ODMxwM+pkdQD17d8So/n/7ZJunWaOKOgpMn+DSp0mzt5giEEFrgM2Ao0Aa4TwhRXEHpZeBHKWUH4F5gvr3sUSiSM/U8vHgP3+y8yOQ+zfjq4c5Waepbw/mU87yw9QVCfEN4vcfrN9UwUWuZ3LcZeqOJpdsv2CxNZwdn5vWfR++g3ry+8/WbX7U0v6O4iu8fe74RdAHOSinPSyn1wA/AncXOkUC+jq8XUH7hEoXCCk5fTefOz7az70Iy748N48VhrSvdKZxPmj6NpzY9hZPWiXn95+Hi4GKTdGsbzf3dGdI2gG92XiA9J89m6Tppnfio/0d0CejCq9tf5cDVAzZLu0ZhMkLcMWjQrsqztqcjCAIK69RetuwrzBzgASHEZWAV8GRJCQkhJgsh9gkh9iUkJNjDVsVNzMYTVxk9fwdZeiM/TOnG3R1tFwvYaDIy8++ZxGTE8GG/Dwl0D7RZ2rWRqX2bk5Zj4Ps9l2yark6r44N+HxDkHsSMTTOITiufBHatIPEM5GVCww5VnnV1dxbfByyVUjYChgHfCiGus0lKuVBK2UlK2cnf37/KjVTUTqSUfL75HI99s4+mfm6sfLInEcE+Ns1j3sF5bI/ZzotdXySiQYRN066NhDX2puet9Vi0NYpcg/WBa6zBy8mLzwZ+hkTyxF9PkJqbatP0q53YQ+bPm8wRxACNC203suwrzKPAjwBSyp2AM1D7BNoVNY6cPCPPLD/Ev9ec5I72DflxSncCvWzbZPO/8/9jybEljGs1jrEtx9o07drM431vJT49l98OFP+7V55gz2A+6vcR0enRPLf5OQwmg83zqDauHARHN/BrUeVZ29MR7AVaCCGaCiF0mDuDi88OuQQMBBBCtMbsCFTbj6JSxKXmMO6Lnfx+6ArP39aSj+8Nx0WntWkex5OO89qO1+jYoCMvdH7BpmnXdnreWo/QIC+++Ps8xgpIVN+ITgGdeK37a+yO281XR2+i+MdXDkJge9DY9l61Brs5AimlAZgOrAVOYB4ddFwI8YYQYqTltOeASUKIw8D3wAQppe3vHEWd4VB0CiM/3caZ+AwWPtiR6QMqHkOgNBKyEpjx1wx8nX3NGkLam09DqDKYA9c0Jyoxk7XH4+ySx6hbRzG06VAWHF5AZFKkXfKoUowGcwyCamgWAjv3EUgpV0kpW0opm0sp37bse1VKucKyHiml7CmlDJNShksp19nTHsXNze8HY7jni53oHDT8Oq0Ht7W1vbRDam4qk9dPJl2fzrz+86jnUs/medwM3NY2gGZ+bny++Rz2erZ7qetL+Dr7MmfHHEyyfCEzaxyJp8CQfXM6AoWiKjCaJHNXn+Tp5YcIb+zNH0/0JCTA88YXlhMpJc9veZ6LaRf5eMDHtK7X2uZ53CxoNYIpfZtxNCaV7WeT7JKHl5MXz3R6hhPXTrAmao1d8qgyrhw0fypHoFCUn/ScPCZ/s48FW85xf9dgvnu0K/XcneyS19qLa9kVu4uZnWfSNbCrXfK4mRjVIYgGnk58vsV+AeqHNR1Gc6/mLDm+xG5vHlXClYOg8wDf6oljrRyBotZyITGT0fN3sPl0Am/e2Za3R7VD52CfWzorL4v/7PsPrXxaqRFCVuLkoOWxXs3YfjaJw9EpdslDIzQ82OZBTl47yd64vXbJo0q4cggCw0BTPVWycgSKWsnmU/GM/HQbCRm5fPtIFx7s3sSusg5fHfuKuMw4ZnedjbYaRnXUVu7rGoynswMLbCBRXRp3NL8DX2dfvo782m552BVjnllaomF4tZmgHIGiViGl5Ist53hk6V4aeruwcnovetxq36kn0enRLD22lGFNh9GxQUe75nWz4e7kwMM9mrDmeBznEjLskoeT1omxLcey9fJWrmZetUsediX+BBhzq61/AJQjUNQisvVGZvxwiHdWn2Rou0B+ndaDxr6uds/3vb3vodVoebbjs3bP62ZkQo8mODloWLjFNhLVJTGi+QgkkjUXamGnccw+82dQ9c1MV45AUSu4nJzF3Qt2sPLIFf51eys+vb9DpcNJWsP2mO1sit7E5PaTaeDWwO753YzUc3diXKfG/HrwMrGp2XbJ4xbPW2hXrx3/O/8/u6RvVy7vA1c/8Kl8YKSKohyBosZjjiS2nUtJWXz1cCee6H9rlcg85xnzmLtnLsEewTzU5iG753cz81jvZpgkfLU1ym55DGs2jBPXTnA+1X5vHnbh8l5o1LnKpacLoxyBosYipeSbnRd4YNFufFwd+X16TwaEVN1T+X9P/pcLaReY2XkmOq2uyvK9GWns68rIsIb8d88lUrL0dsljSJMhCARrL6y1S/p2ITvZHKO4UadqNUM5AkWNJNdgZNYvR3n1j+P0a+XPb0/0pLm/e5Xln5CVwOeHP6d3UG/6Nu5bZfnezEzp24wsvZFvdl60S/r+rv6E+oWy7fI2u6RvF2L2mz8bda5WM5QjUNQ44tNyuG/hLpbvi+bJAbey8MFONoskZi0fHfiIXGMuL3RRgnK2IiTAk4Eh9VmyPYosvX1UQ3sF9eJo4lGSc5Ltkr7NubwPENXaUQzKEShqGAcvJXPHJ9s4GZfO5+MjeO62VmhsFEnMWg4nHGbFuRU81OYhbvG8pUrzvtl5vF9zkrPy+HGvfQLL9ArqhUSy48oOu6Rvcy7vhfptwMmjWs1QjkBRY/hxXzTjvtiFs6OWX6f1YGho1Uf7MkkT7+x+B38Xfya3n1zl+d/sdGriS+cmPny5NYo8o+2F4tr6tcXX2ZetMVttnrbNkdL8RlDN/QOgHIGiBpBnNDFnxXFm/nyELk19WTHdPqJx1vD72d85nnScZzs9i5ujW7XYcLPzeL/mxKRks/Kw7UOUa4SGHg17sCNmR81XJE06Bzkp1d4/AMoRKKqZxIxcHli0m6U7LvBYr6YsndgZb9fqGaGTpk9j3oF5dKjfgeFNh1eLDXWB/q3q06qBBwu2nMNkh8A1vYJ6kZybzPHE4zZP26Zctmgj1QZHIIR4VwjhKYRwFEJsFEIkCCEeqArjFDc3h6NTGPHJNg5fTuHDcWG8fEcbHLTV92zy+aHPSc5JZnaX2VUyT6GuIoTg8X7NOX01g79Oxts8/R4NeyAQbIup4aOHoneDkyf4taxuS6x6I7hNSpkG3AFcAG4F/mVPoxQ3Pz/ujWbsFzvRagS/PN6Duzo0qlZ7ziaf5fuT33N3y7tVnIEq4I72gTTycWH+5rM2l4/2cfYxDyOt6Y7g0i5o3LXaFEcLY40F+fP4hwM/SSlT7WiP4iZHbzDx0m9HmfnLEbo08WXl9F60behVrTZJKZm7Zy5ujm482eHJarWlruCg1TC5TzMOXEph7wXbD/Xs1cg8jPRazjWbp20Tsq5BwgkI7lbdlgDWOYI/hRAngY7ARiGEP5BjX7MUNyMXkzK554udLNt9ial9m/P1I13wcav+GbsbLm1gd9xupneYjo+zT3WbU2cY27Ex9dx0fL7Z9oFregf1rtnDSKN3mz+Du1evHRZu6AiklLOAHkAnKWUekAXcaW/DFDcPUkp+2hfNsHlbOZ+QwfzxEcwaGoK2iucHlES2IZv39r5HC58WKuBMFeOi0zKxZxM2nUrgRGyaTdNuU68Nvs6+Nbd56OIO0OogqGbImlvTWewKTAM+t+xqCFT/wFdFrSAlS88T/z3Av34+QmgjL9Y83Ydh1TA/oDSWHFtCbGYss7vMxkFjfzVTRVEe7NYEN53W5oFravww0ku7zPEHHJ2r2xLAuqahJYAe81sBQAzwlt0sUtw0bD+byJCPtrI+8iqzhoaw7LFuNPR2qW6zCojJiGHxscUMaTKEzgHVP4SvLuLl6sj4brew8vAVLiVl2TTt3kG9Sc5N5ljiMZumW2nyss0ximtIsxBY5wiaSynfBfIApJRZQPW/0ytqLLkGI2//L5Lxi3bj6qTlt2k9mdq3eY1oCirMf/b9B43Q8Fyn56rblDrNo72a4qDR8OVW28pH9wzqiYNw4K9Lf9k03UoTsx9MebXOEeiFEC6ABBBCNAdy7WqVotZy+mo6oz7bwZdbo3igWzD/e7I37YKqd1RQSeyK3cX6i+t5LPQxAtwCqtucOk0DT2dGRwTx475oEtJtV7V4OXnROaAzGy5tsPkQ1Upxaaf5s3GX6rWjENY4gteANUBjIcQyYCMw065WKWodUkq+3nGBEZ9sIz4th68e7sRbo0Jx0dW8QO95pjzm7p5LkHsQD7d9uLrNUQCT+zRDbzSxdIdtA9cMumUQF9MucjbF9iOTKszFnWahOVff6rakAGtGDa0HRgMTgO8xjx7abF+zFLWJ+PQcJi7dy2srjtOjeT3WPN2Hga1rbljH/574L+dSzzGz80yctE7VbY4CaObvztB2AXyz8yLpOXk2S3dA8AAEgg2XNtgszUphzDMPHa1BzUJg3aihPkBbIB1IA9pY9ikUbIi8ytCPtrLzXBJv3NmWxRM64+9RcyvXq5lXmX9oPr2DetO/cf/qNkdRiMf73kp6joHv91yyWZp+Ln50qN+BDRdriCO4chD0GdC0ZlWh1oyXKywn4Qx0AfYDA+xikaJWkK038tb/Ilm2+xKtAz354d5wWjSoXk11a3h/3/sYTAalJ1QDCW3kRdemvny94yKP9mpms8EFg24ZxLt73+VS2iWCPYNtkmaFifrb/Nmkd/XaUQxrmoZGFFoGA+2AWhL+R2EPjl5OZfgnW1m2+xKT+zTj9yd61AonsCt2F2surOGx0Mdo7Nm4us1RlMDEnk2ISclmw4mrNktzYPBAANZfXG+zNCtM1N/QoB241atuS4ogytubLsyPUcellG0qmun+/fvrOzg4LMLsVMqluJSUlHRLYGDNmZBUl5BSkp5jID3HgEYj8HV1xMmx5nUGl4SUkoTsBAD8XfzV20ANRUrJ1bRcHDQCPxs2MSZmJ2KSJvxd/RHVNfpdSki7DDp3cLGflImzszONGjXC0bFoeFchxH4pZYmTgW/YNCSE+ATL0FHMlXY4cKAyhjo4OCwKCAho7e/vn6zRaMrliSIjI29p3VqpQ1Y1WXoDl5Ozcckz0tBVR6CXc7VKRpeXhKwEZJYk2DMYD13Nf3upy/il5xCXmkPTBh442+hBIyU3hZj0GII9g3HXudskzXKTmwFJueDTFFy87ZKFlJKkpCQuX75M06ZNrb7Omj6CfYXWDcD3Usrt1iQuhBgCzAO0wCIp5VzLoXb5TiAhIcEnLi6uIYCzs3NWixYtbDt+TFEpTFISn5ZDQroeB62gST03PF2qNpB8ZdEb9SRkJ+Ch81BOoBbg66ojPi2XxIxcGvm42iRNT50ncZo4ruVcq0ZHkG7+dLJf/kII6tWrR0JCQrmuu6EjkFJ+XUGDtMBnwGDgMrBXCLFCShkJaDQajczKynK6evVqYEhIyElHR0ejXq9XYi81iPScPK6k5JBrMOLjqiPQ2xmHGqCdXl7iMuMQCDVxrJbgoNXg7epISlYeAZ4mm7x5aoQGH2cfErMSyTXmVs+wYX0GOLqAnTWtKtLsWapFQoij/NMkVOQQIKWU7W+QdhfgrJTyvCW9HzCrlkbmn5CQkODv5+cX7+joaATQ6XSGctqvsAN6g5ErKTmk5eTh5KClqZ8bHs616y0gn3R9Oun6dOq71kenrX7Ja4V11HNz4lqmnuQsPf4ethFm83X2JSk7icTsRILcg2ySptWYjKDPBDf/qs3XSspyTXdUMu0gILrQ9mWga+ETcnJynAAiIyNDpJQEBgZe8fX1vU6PNi4uzi8xMdEfwGSqgUqCNwlGkyQxI5f49FwEEODljJ+7E5pa2rFqkiZiM2PRaXXUc6lZozQUZeOi0+Kmc+BaZh5+7k426dx31Dji7eRNSm4KgW6BaEQVvt3qMwEJTjWzabLUkpBSXixrsVH+Ijc31ykkJORUs2bNzl+6dKmJwWC4rncoICAgsV27difatWt3wsHBvq9VTZo0ITEx8br9UkoGDBhAWpptdNP79evHvn37rtu/dOlSpk+fbnU6mzdv5o47KuezTVKSlJHL3HkLmPHUk3g5O9KqgQf1PZzRCMGcOXMICgri1VdfBWDOnDm8//77lcoz3/YdO+wXOCQxO5FzZ88xpt8YPD08rzuu1+vp06cPBoN6Ea2J+LrpyDUYycw12ixNT50nUkoy8zJtlqZV5KYBwjxiqAZizczibkKIvUKIDCGEXghhFEJYUxvGAIUHazey7CvA0dFR7+XllaLRaKSLi4veyckpJzs7u0ZOS121ahVhYWF4el5foZSG0Wi7G9geSClJydJz+mo6MSnZOGoFXi6OBNdzxdGh6K3xzDPP8MYbb9g0/4o4Amsr7VxjLonZiYSGhHLk8JESz9HpdAwcOJDly5eXywZF1eDl4ohWI7iWpbdZmq6OrmiEhnR9us3StIrcdLMTqKF9bNY8Xn8K3Av8hDkgzUNASyuu2wu0EEI0xewA7gXuL3yCt7d3ypyVJxpfTD/jJ6UURn2Oq1aX2Lis10BTXi6uW8sOm9ymoSevjWhb5jmjRo0iOjqanJwcZsyYweTJk8s8f9myZUXOKe16d3d3pkyZwoYNG/jss8/466+/WLlyJdnZ2fTo0YMvvvii4DX322+/5bHHHsNgMLB48WK6dCmqRpiQkMDUqVO5dMk85f6jjz6iZ8+epdq4Z88eZsyYQU5ODi4uLixZsoRWrVqxdOlSVqxYQVZWFufOnWPUqFG89PrbxKfn8sN337Bk/of4+vjQITwMZ2fr2mMjIyPp168fly5d4umnn+app54C4LvvvuPjjz9Gr9fTtWtX5s+fj1arZc2aNbz44osYjUb8/Pz46quvWLBgAVqtlu+++45PPvmElJQU3nrrLfR6PfXq1WPZsmU0aNCAOXPmcO7cOc6fP09wcDAff/xxieWyZcsWZsyYAZiF5b5e8TUtGrco83uMGjWK2bNnM378eKu+t6Lq0GgE3q46rmXqMRht12ns6uhKtiHbBhZaiUEPhhzwrDkic8Wxqp1FSnlWCKGVUhqBJUKIg8DsG1xjEEJMB9ZiHj66WEp5XAjxxq5du1wBfHx80oQQJoM+2xVA46DLraqJPosXL8bX15fs7Gw6d+7MmDFjqFev9Hbk7du388UXX9zw+szMTLp27cp//vMfANq0aVPQpPLggw/y559/MmLECACysrI4dOgQf//9N4888gjHjhUNoDFjxgyeeeYZevXqxaVLl7j99ts5ceJEqTaGhISwdetWHBwc2LBhAy+++CK//PILAIcOHWL/gQNkGQWdw0IZPPZhXJycWPjRvzmwfx/e3t7079+fDh06WFV+J0+eZNOmTaSnp9OqVSsef/xxzp49y/Lly9m+fTuOjo5MmzaNZcuWMXToUCZNmsTff/9N06ZNuXbtGr6+vkydOhV3d3eef/55AJKTk9m1axdCCBYtWsS7775bUI6RkZFs27YNFxcX7r///hLL5f333+ezzz6jbce2nIk7Q7BfMI7asju527Vrx969e636zoqqx9dNR1JGLslZeTbTsHLWOpOoN08wq5J+glxLA4qT9a0JVY01jiBLCKEDDgkh3gVisXI2sJRyFbCq2L5XDx8+/BCYhznNe6hH6TVbCURGRnZs06bCk5oL+Pjjj/ntt98AiI6O5syZM2U6gmvXruHh8U9HT2nXa7VaxowZU3Depk2bePfdd8nKyuLatWu0bdu2wBHcd999APTp04e0tDRSUlKK5LlhwwYiIwsGWZGWlkZGRgbu7iW3M6ampvLwww9z5swZhBDk5ZlVHI0mEz169yMuW0Oe0cStLVthSk8gMSaFAf37Ub9+fQDGjRvH6dOnrSk+hg8fjpOTE05OTtSvX5+rV6+yceNG9u/fT+fO5mhf2dnZ1K9fn127dtGnT5+CCS6+viU/GV2+fJlx48YRGxuLXq8vMiFm5MiRuLi4lFkuPXv25Jlnn2HwqMEMv3M4/laM0NBqteh0OtLT04v8voqagYujFledA8mZevzcdTbpNHZxMN9HOYYcXB1tM0+hTHLTQeMIDjUjLGVJWOMIHsRc8U8HnsHc7j+mzCtqOJs3b2bDhg3s3LkTV1dX+vXrR05OTpnXODg4YDKZ0Gg0ZV7v7OyMVmvu787JyWHatGns27ePxo0bM2fOnCL5FL+pi2+bTCZ27dpldXPNK6+8Qv/+/fntt9+IioqiX7/+XLqWRWxqDgahxclBQyMfFzxdnXDWCnIq8adycvrn6Uyr1WIwGJBS8vDDD/POO+8UOXflypVWpfnkk0/y7LPPMnLkSDZv3sycOXMKjrm5uRWsl1Yus2bNomO/jqxevZp7h97LurXrCAkJuWG+ubm5VpexourxdXPkcnI2WXojbk6VHyyS7wiyDdn2dwRSmh2BsxfU4NF31jzZd8Q8byBNSvm6lPJZKWUNivJQflJTU/Hx8cHV1ZWTJ0+ya9euG17TqlUrzp8/X67r8yt9Pz8/MjIy+Pnnn4scz++k3LZtG15eXnh5FY3kddttt/HJJ58UbB86dOiG3yswsCHXMnP54LOFGEwm0rPzcHNywNtVRzN/9yLzAbp27cqWLVtISkoiLy+Pn3766YblUBYDBw7k559/Jj4+HjC/RV28eJFu3brx999/ExUVVbAfwMPDg/T0fzrtUlNTCQoyj+/++uvS5zGWVi5HThyh4a0NmfXCLLp07sLJkydvaHNSUhJ+fn7X6bIoag5eLjq0QpCcaZtOY0etIw4ah6rpJ8jLAmkE55rbLATWOYIRwGkhxLdCiDuEELV+9u+QIUMwGAy0bt2aWbNm0a1btxteM3z4cDZv3lyu6729vZk0aRLt2rXj9ttvL2gyycfZ2ZkOHTowdepUvvrqq+uu//jjj9m3bx/t27enTZs2LFiwoFT7MnINPDz1Kf71wix6dO2MwWhAqxGEBHri46rDoQRJ38DAQObMmUP37t3p2bMnldVwatOmDW+99Ra33XYb7du3Z/DgwcTGxuLv78/ChQsZPXo0YWFhjBs3DoARI0bw22+/ER4eztatW5kzZw5jx46lY8eO+Pn5lZpPSeVikibe+/A97up9F4O6DcLR0ZGhQ4ded+2VK1cYNmxYwfamTZsYPnx4pb63wr5oNQJPF0dSs/MwmWwTctLFwaVqHEGOpX+ghkubWKU+KoRwBIYC44BewHop5WMVzfTw4cMXwsLCrh+sbwW26iMoL7GxsTz00EOsX18DpGwt6A1GkrPySM7SozeY0AiBt4sjPm46XHVamylszpkzp0inbk0kLjOOpOwkmng1wc3R7brj7u7uZGRkXLd/9OjRzJ07l5YtrRkIp6gu0nPyiErM5BZfV7xcKz9DPCErgfiseEJ8Q9Bq7KigG3/SPGTUr2rvrxMnTlz3YFcp9VEAKWWeEGI1ZskJF2AUUGFHUBsJDAxk0qRJpKWllWsuga0xGE2k5eSRkpVHRq55TL27kwMNPJ3xdHa0WTCPwri7u7Nw4ULS0tJsPpfAFmQbsknKTsLb2fs6J3Du3DnGjBlDgwbXh87U6/WMGjVKOYFagLuTAw5aDclZeTZxBIX7CewmQmfQgyEbPBraJ30bcsM3AiFE/ptAP2Az8COwTkpZ4emYtfGNoDoxmkyk5RhIzcojPdfcKatz0ODjqsPH1RGdQ+2ICWAPpJREpUahN+m51ftWHOws6KWoPq6kZJOUqad1gEel5xQYTAZOXTtFfdf6+LvaSf8nMwFSL4N/a3Cs2sEI9ngjeAhYDkyRUuZW3kSFNZhMkrScPFKz80jPMWCSEketBj93HV4ujrg42q7ppzaTlJNEtiGbRh6NlBO4yfF2dSQxI5fUnDzquVVuToGDxgGdVmfffoKcNNDqwKFGiiUUwRoZ6vuqwhCFudknPcdAWs4/lb+DRoOvm7nyt2W7/81AriGX+Kx4PHQeeOpq9qgMReVxcdTi5KAlJavyjgDMzUN20xwyGc3DRt38avSw0XzUI1Q1ozcYScs2kJqTR1auEYn5yd/HVYeXiwNuTg6q8i8BKSUxmTFohIZA90BVRnUAIQTero5cTcshz2jCsZLNQy4OLqTmpqI36m0vUZ6bDkjz/IFaQM1UQKoCSpudWxqxsbEFKp+VUfyUUpKtN3A1LYczV9P598dfMP3J6RhNEn8PJ26t705IgAdBPi64OzteV8EtWLCAb775psw8SlMHvXDhAu3atbtu/+bNm/Hy8ioyrNLeLF26lCtXrtzwvNJUWj/98lNefvZlAtwCcNRU/RyAYcOGXTcT3FrKe+8VpqxyK62sCttambyril27djFp0qQSlXjzo+OlZedZnd6hQ4dYtWrVdfvzJ5PZpXkoNxWEFnTXj2Czlg8//JDg4OAS1Yj//PPPAukaW1BnHUF5+eCDD5g0aVKFrpVSkpFj4EpKNqfi0jkTn8HVtBzzcE9XR3xcdbRs4EGAlzOuurLfAKZOncpDDz1U0a9RKr179y7xz2IvrHUEJZFryCVNn4aj1hEvXfU8ca1atQpvb+8qz7ci5WYPW+2prLt69WqGDBlS4jFnBw1ODlpSbeAInLXOaISGrLys645VSppcSshONWsLVVDLyGAwlKn4O3z4cFauXElW1vW2V4RSrRRCHBVCHCltsUnuNYCHHnqI33//vWB7/Pjx/PHHH9ed98svv5R4c+7Zs4fu3bvToUMHevTowalTpwBYvHgJI+4cRb+Bg2nS7FaefOZZkjL1ODtq2fLnj4wZ0IX77hjAkf17rhvyaTKZaNGiRUHcUZPJxK233kpCQkKRp/1z584xZMgQOnbsSO/evUucSbt//37CwsIICwvjs88+s6pM7r33Xv73v/8VbE+YMIGff/4Zo9HIv/71Lzp37kz79u0LRPhMJhPTpk0jJCSEwYMHM2zYsIJZ1Pv376dv37507NiR22+/ndjYWH7++Wf27dvH+PHjCQ8PJzs7mzfeeIPOnTvTrl07Jk+eTOHRbN9++y3h4eG0a9eO3bt3m5uE0ODm6IYQgoSEBMaMGUPnzp3p3Lkz27eXHFK7JFuKl9G//vWvgrem4k+kd9xxR8Gkwvy4FbNmzSpSrvm/T0ZGBgMHDiQiIoLQ0NAS7ymA9957r6A8X3vtNcD85ta6dWsmTZpE27Ztue2228jOzi6x3ErCZDIxYcIEXn755SK2FkZKWfBdQ0NDC2a5l/VbNmnShBdeeIGIiAh++uknvvzySzp37kxYWBhjxowpqJQmTJjA448/Trdu3WjWrBmbN2/mkUceoXXr1kyYMAEwO5IJEyYU5P/hhx8W2LZx40YGDRoEmCcADhkyhBYtWjBz5kyEEHi5OLB+3Xq6detOREQEY8eOLZgjsnfvXnr06EFYWBhdunQhNTWVV199leXLlxMeHs7y5csL/rMRERE8MOwBjp04VvB7jxw5kgEDBjBw4EAyMzN55JFH6NKlCx06dCjxN/zrr78YNWpUwfb69eu5a9RIkEbWbT9QkE9hG0u71/v168fTTz9Np06dmDdvXom/bT5CCPr168eff/5Z5nlWI6UscQFusSzvWpZQyzIXmFvaddYshw4duiCl3Cel3Cd/m3ZVftEv3dol49O+Ui4eVvay6gV5I9zc3KSUUm7evFneeeedUkopU1JSZJMmTWReXl6Rc8+fPy8jIiIKtjdt2iSHDx8upZQyNTW14Pw1a9bKO0aOklEJGfLNDz6TQcG3yJ0nLsrTMUmyUeNgGXXhorxy5Yps3LixjI+Pl7m5ubJHjx7yiSeeuM6+OXPmyA8//FBKKeXatWvl6NGjpZRSvvbaa/K9996TUko5YMAAefr0aSmllLt27ZL9+/e/7pzQ0FC5ZcsWKaWUzz//vGzbtu11eRX+PlJK+euvv8qHHnpISillbm6ubNSokczKypJffPGFfPPNN6WUUubk5MiOHTvK8+fPy59++kkOHTpUGo1GGRsbK729veVPP/0k9Xq97N69u4yPj5dSSvnDDz/IiRMnSiml7Nu3r9y7d29BnklJSQXrDzzwgFyxYkXBeY899piUUsotW7bIkNYh8ljCMfnZws8Kyu2+++6TW7dulVJKefHiRRkSEnLddyzLltLKaMmSJUV+m+HDh8tNmzZJKaW85ZZbZEJCgjxw4IDs06dPwTmtW7eWly5dknl5eTI1NVVKKWVCQoJs3ry5NJlMUsp/7r21a9fKSZMmSZPJJI1Goxw+fLjcsmWLjIqKklqtVh48eFBKKeXYsWPlt99+W2K5FaZv375y586d8t5775VvvfVWwf58Wwvn/fPPP8tBgwZJg8Eg4+LiZOPGjeWVK1dK/S3z0/n3v/9dkG5iYmLB+ksvvSQ//vhjKaWUDz/8sBw3bpw0mUzy999/lx4eHvLIkSPSaDTKiIgIefDgQblv3z45aNCgguuTk5MLyqpfv34F5d+0aVOZkpIis7OzZXBwsLx06ZK8GBMrI7p0l5euXpNSSjl37lz5+uuvy9zcXNm0aVO5Z88eKeU//83iv2Ph/+yPK3+Ug4YPkgajQS5ZskQGBQUV3IuzZ88uKPfk5GTZokULmZGRUaTMTSaTbNWqVcF9dd9998kV//1KJhzdJHv37lVwfr6NUpZ9rz/++ONF0i9ue2G+++47OX369BKPRUZGXrcP2CdLqZNL7SyWlihkQojBUsrC2sSzhBAHgFm2cUXVS9++fZk2bRoJCQn88ssvjBkzhuJR0PJlEkoiISmZp8Y/yJmzZ5BSYDDkkZ1nFscaNHAgXVo1RiME7dq24XL0JRITE+nXr19BeqUpfj7yyCPceeedPP300yxevJiJEycWOZ6RkcGOHTsYO3Zswb7c3KKje1NSUkhJSaFPnz6AWQZ79erVNyyToUOHMmPGDHJzc1mzZg19+vTBxcWFdevWceTIkYInxNTUVM6cOcO2bdsYO3YsGo2GgIAA+vfvD8CpU6c4duwYgwcPBsxPgYGBgSXmaY1Ka6funUhJS4FsioiFWaPSWpotFS2jfDp06EB8fDxXrlwhISEBHx8fGjduTF5eHi+++CJ///03Go2GmJgYrl69SkBAQMG169atY926dQXS3xkZGZw5c4bg4GCaNm1KeHg4AB07duTChQtW2TNlyhTuueceXnrppTLP27ZtG/fddx9arZYGDRrQt29f9u7dW+pvmU++PAjAsWPHePnll0lJSSEjI4Pbb7+94NiIESMQQhAaGkqDBg0IDQ0FoG3btly4cIG+ffty/vx5nnzySYYPH85tt91WUCb562DWr8rX4GrTpg0XL14kOTmZ82dOcfuAvugcNOj1erp3786pU6cIDAwskHIpbeJnYZVeiSQ7N7ugn2Dw4MEF6rjr1q1jxYoVBW/gOTk5XLp0qcj4fCEEDz74IN999x0TJ05k586dfPPuv1izdT+RkScK4ofk2whl3+uFy/dG1K9fv8LNq8WxZtSQEEL0lFJut2z0wJZ9C6M+i77xSf9w0Q4Tyh566CG+++47fvjhB5YsWXLdcRcXlwIBOSkleoORXIOJs/EZPDdzNu069+CDL78jLT6Gu0cOISTAg92uOtxdXQri/eYrdFpL48aNadCgAX/99Rd79uxh2bJlRY6bTCa8vb1vKERXEZydnenXrx9r165l+fLl3HvvvYD5u3/yySdF/vBAqX0LUkratm3Lzp07y8zPGpVWo8lITEYMAkGAe0CR60tTI7399tu5evUqnTp1YsaMGSXaUlaHb77ibGE7S2Ls2LH8/PPPxMXFFfyRly1bRkJCAvv378fR0ZEmTZpcd72UktmzZzNlypQi+y9cuHCdumtpzUDF6dGjB5s2beK5556zi6JqYRXYCRMm8PvvvxMWFsbSpUsLms3gH3VajUZT5LtoNBoMBgM+Pj4cPnyYtWvXsmDBAn788UcWL17M6tWrefbZZ69LB4r+h/r2H8ibH39Jm0APtJaoX0ePHrXqOxRW6T13/hx9+vUhy5B13feTUvLLL7/QqlWrItdPnDiRgwcP0rBhQ1atWsXEiRMZMWIEzs7OjB19Jw4akI5uDB48mO+//77ItTe61wvnfyPyA1DZAmsq9EeB+UKIC0KIC8B84BGb5F5DmDBhAh999BFgfuooTosWLYiKukBsSjanr2YQnZxNrsEISPJyMghv1YxWAR7879cfEFwvJ12Y8ih+PvbYYzzwwAOMHTu2QNo6H09PT5o2bVpwvZSSw4cPFznH29sbb29vtm3bBnCdMymLcePGsWTJErZu3VrQN3L77bfz+eefF8Q5OH36NJmZmfTs2ZNffvkFk8nE1atXCyqEVq1akZCQUFD55uXlcfz4caCo8qg1Kq1Xs66ya/sufLx9qOdTNG5EaWqka9eu5dChQyxatKhUW8oqoyZNmnDo0CFMJhPR0dHs2bOn1LL64Ycf+Pnnnwve0FJTU6lfvz6Ojo5s2rSJixevD/N9++23s3jx4oK245iYmALl1tIorthanEcffZRhw4Zxzz33lPng0bt3b5YvX47RaCQhIYG///6bLl26lPpblkR6ejqBgYHk5eWV694CSExMxGQyMWbMGN566y0OHDiAlJIjR44UvAmVRrdu3di/ZxcXz58jPcdAZmYmp0+fplWrVsTGxhYEGkpPT8dgMJSpcvvtN98iECV2GN9+++188sknBW34Bw8eBGDJkiVFOqAbNmxIw4YNeeutt5g47k5A0K13f7Zv387Zs2ah5nwbb3Svl4fTp0+XOAqwItzQEUgp90spw4AwIExKGS6lPGCT3GsIDRo0oHXr1kWaX/QGE9cy9Vy6lsWlNCMBjW/hwLGT6BzMs3vdnRy4tb4Hr7w4m9deeYkOHTpY9cRfHsXPkSNHkpGRcV2zUD7Lli3jq6++IiwsjLZt25bYmbVkyRKeeOIJwsPDi3TA3ojbbruNLVu2MGjQIHQ68xjrxx57jDZt2hAREUG7du2YMmUKBoOBMWPG0KhRI9q0acMDDzxAREQEXl5e6HQ6fv75Z1544QXCwsIIDw8viFE8YcIEpk6dSnh4OE5OTmWqtGocNQzsPpC3Z77NksXXv7FZo9Jali2llVHPnj1p2rQpbdq04amnniIiIqLEsmrbti3p6ekEBQUVNH2NHz+effv2ERoayjfffFNiXITbbruN+++/n+7duxMaGsrdd99dZiVfvNxKe0t49tln6dChAw8++GCRN5rC3HXXXbRv356wsDAGDBjAu+++S0BAQKm/ZUm8+eabdO3alZ49e1oV96EwMTEx9OvXj/DwcB544AHeeecd9u/fT4cOHW44J8Tf35+lS5cw68nH6N45gu7du3Py5El0Oh3Lly/nySefJCwsjMGDB5OTk0P//v2JjIws6CyeOXMms2fPLvjPCiHINmRf9/945ZVXyMvLo3379rRt25ZXXnmlVJvGjx9P48aNaR3sD86e+DcIYOnSpdx33320b9++wMYbKRKXxYIFC4rc2zZVzi2t8yB/ARoAXwGrLdttgEdvdF1ZS5HO4nIux48fL7FzpDJkZGTIps2ayQtX4mX0tUx5IjZVHo5Oloejk+XxmFR5MSlTfvv9j3L2iy/aPO+y2Lt3r+zVq5fd8yneWVwR0tPTpZTmDsRmzZrJ2NhYW5gm9Ua9PJF0Qp5NPiuNJqNN0iyLqKioEjvU6xL2+i1vxJtvvim///57q8+/mJgpj8ekFnTCV5TknGR5LOGYzMrLqnAaTzzxhFz0+adSxhyQMutapewpTGmdxXFxcXLAgAGlXmezzuJCLAWWAPm9T6cxaw9dL6BfC5BSojeYyM4zkq03smHDBl54ehoPPDaNVKMj2ixzIBc/NyfcnR1wctAghOCBe8eyKCO1yuycO3cun3/+eblfuSuCTqfj2LFjDBs2rMJzCe644w5SUlLQ6/W88sorRTpFK4qUkisZVzBJE0HuQVUTX1Zhl9/SGvKHu1qLh4sDKdl6svOMuOoqLpLg5mBul8/MyyxQJS0PHTt2xM3Njf+88hSYsm0Wm/jDDz9kwYIFRULf5nPp0qWCeN62wBr10b1Sys5CiIPSMnpICHFIShle0UyrQ300NVtPYoaeHL0Ro+U7CyFwdtTg4qjFRafF1VGLsxJzqzFcy7lGbEYsAW4B1HMpPZ60om5iMJqIjE2jvqczAZ6V6xg/k3wGnVbHLZ63VCwBkwmuHjNLSvhUMA0bYg/10UwhRD3MsQgQQnQDqu7R2EaYTOYJf96ujrjotGYBK0dtwageRc0i15BLXGYcbo5u+DqXHOxeUbdx0Gpw1TmQkWOASj6Euzm6kZqbikmaKvbmmZtqDknpWjvvVWscwbPACqC5EGI74A/cbVer7ICPmw4fNxsLSynsgkmaiM6IRiM0BLkHqTc0Ram4O2lJSNdjNMlKBWVyc3QjOSeZHENOxQLaZ10DjSPYK8iNnbFGhvqAEKIv0AoQwCkppfVCHwpFOYnLjCPXkEuwZzCOWhVUXlE6bk4OxKfnkplrKBCkq1A6jv/0E5TbERgNFslp/1ohOV0SpToCIcToUg61FEIgpfzVTjYp6jCpuakk5yRTz6UeHjU84Lei+nGziDRm6ivnCBw0Djg5OJGZl4k/5YxYln0NkODqU+H8q5uyGsNGlLFUTIO5BvHxxx/TunVrxo8ff92xgwcP8uijjwJm2YZBgwYVjEGuDKXJVy9duhR/f38ee6zqwkB/9NFHVikXliRYBqVLXVcGvVHPlYwruDi6UN+1fpnn9ujRo0J5lCbFbS1llVtpZZVva2Xzrip++OEH3n77bbulf+7cOcLDw0uUxNbr9fTp08fqWfgajcBVpzX3E1QSN0c3sgxZmGTJcy9KRErISgJHV/NSSynVEUgpJ5ax1PqZxfPnz2f9+vUlDs/8v//7P5566ingn9mEhw4dKpcOSHkZN24cixYtslv6xbHWEVQVJmkiOt2sNtLIvdENO+zyJ4NVNRUpN3vYWl0y0JXFYDDQvHnzUqVRdDodAwcOLNdDl7uTA9l5RgzGclTgJaXj6G6OF1Ke+AT6TDDkgGvtHtVmVfe4EGK4EGKmEOLV/MXehtmTqVOncv78eYYOHVpE/hbM09KPHDlCWFgY8fHxPPDAA+zdu5fw8HDOnTvHxo0b6dChA6GhoTzyyCMFQm+l7V+zZg0hISFERETw66/WtaZ169atQIoB/gk4UposblZWFvfccw9t2rThrrvuomvXrgUBStatW3edFO7HH3/MlStX6N+/f4Go2OOPP06nTp1o27ZtgRxyPu+++y6hoaF06dKlYMp8YayRwy7NlvwyatmqJSP6jGDeq/MYfae5VbL4W0e7du0KxNfynyZLk8y+cOECvXv3JiIigoiIiBIr49JktTdv3ky/fv24++67CQkJYfz48UgpSyy3ksjOzmbo0KF8+eWXRWwtTE5ODhMnTiQ0NJQOHTqwadMmoOzf0t3dneeee46wsDB27txZppzxM888Q6dOnWjdujV79+5l9OjRtGjRomCsfmZmJsOHDycsLIx27doVVLzSPOGT8PDwUqXQS5P9Lk2Svbi8840YNWpUuebPuDmZW7iz9JVzjq4O5if6jLwM6y/KSjLHHHCpvc1CgFUzixcA3wDRwGvAUeCrG11X1lJ4ZvHL216+eu/Ke9OtXe755R45YfWEMpe5u+eWOuMun8LSvIX566+/CiSfpSw66zY7O1s2atRInjp1Skop5YMPPig//PDDG+4/ffq0NJlMcuzYsSXO4C0+e/CDDz6Qr776qpRSyitXrsiWLVtKKUuXxX3vvffk5MmTpZRSHj16VGq1Wrl3716ZkJAge/fuXaIUbvHvny+NazAYZN++feXhw4cLzsuXNP76668L7LdGDrswpdmSnZ0tGwY1lP/b9T8ZkxZTpIwK5yGllG3btpVRUVFSyn/klEuTzM7MzJTZ2dlSSilPnz4tO3bsKKUsOnO4NFntTZs2SU9PTxkdHS2NRqPs1q1bgcx1afdN/rGoqCg5cOBA+fXXXxfsz7e1cN7vv/9+gQz2iRMnZOPGjWV2dnapv6WUUgJy+fLl1/1mUl4vZzxz5kwppZQfffSRDAwMlFeuXJE5OTkyKChIJiYmyp9//rlA3ltKswS7lFLu379fPvjgg1LK0qXQS5P9LizvvH79+oLzi8s7Fy+X4hgMBunn51fisZIwGk3ySHSKvJJS8ZnB+ZxPOS/PJp+1MuM8KWMOSZl8sdL52pryziy25o2gh5TyISBZSvk60B1oaRevVAMoS3L61KlTNG3alJYtzV//4Ycf5u+//y51/8mTJ2natCktWrQwz05+4AGrbLjnnnsKxKh+/PFH7r7bPFp33bp1zJ07l/DwcPr161cgi7tt27YChdB27drRvn17wBzyLzIykp49exIeHs7XX39dovhZfj4RERF06NCB48ePF5F1zpeBvu+++65T7ywshx0eHs6UKVMKAr4UpjRbDh07RGDjQFqHtCbAPcDqMspn6NChbNq0idzcXFavXl0gmZ2Xl8ekSZMIDQ1l7NixRb5PPuvWreObb74hPDycrl27kpSUxJkzZwDo0qULjRo1QqPREB4ebrUM9J133snEiRNvGEVu27ZtBd81JCSEW265hdOnT5f6W4JZfbPwLNNNmzbRtWtXQkND+euvv4q8RY4cORKA0NBQ2rZtS2BgIE5OTjRr1ozo6GhCQ0NZv349L7zwAlu3bi3QE1qzZg1Dhw4FzFLo+WFRC0uhb9iwgenTpxMeHs7IkSMLZL9TU1MZO3Ys7dq145lnniliT2F55xuh1WrR6XQ31F3KR6MRuOi0ZOVWvrnM3dGdHEMOeSYrBkZmJwOmWt8sBNbNI8hvMMsSQjQEkoCSReUrwJs93yyXDHVFZxZbS2HJ6eoiKCiIevXqceTIEZYvX14gNCVLkcUtDSlliVK4xYmKiuL9999n7969+Pj4MGHChOtkoEtah9LlsI1GIx07dgTMlVLnzp2vs8VgMvC/rf9DCEEjj+v7BayRgS5NMvvDDz+kQYMGHD58GJPJVKIksyxFVnvz5s2lyh/fiJ49e7JmzRruv/9+m89/cHZ2LlChvZGc8Y1koFu2bMmBAwdYtWoVL7/8MgMHDuTVV19l3bp1/PLLL0DpUuilyX5Pnz69QN75woUL9OvXr+BYeeSVwTxIozwy2q46Ldcy9ZikrNQkUQ+dB/FZ8WToM/BxLqO5R0rITABHl1rdSZyPNW8EfwohvIH3gAPABaDsmqUW07p16xLbwcEsq3zhwoWC499++y19+/YtdX9ISAgXLlzg3LlzADeskAszbtw43n33XVJTUwueCkuTxe3Zsyc//vgjAJGRkQW67N26dStRCheKyhmnpaXh5uaGl5cXV69evS4wS3778fLlywuCa+RTmhy2Vqvl0KFDHDp0iDfeeOM6W9Iz0tlyYAvBtwYTdzmOS1GXriujJk2acOCAWej2wIEDREVFlVpWxSWzU1NTCQwMRKPR8O2335bYuVqarHZZ3EgG+o033sDHx4cnnniizHR69+5dULGePn2aS5cu0apVq1J/y+JUVs74ypUruLq68sADD/Cvf/2LAwcOkJqaisFgoF69f55wS5JCL032u7C889KlS8tlT2GSkpLw8/PD0dH64aBuOi0mKcmpZD+Bk9YJB43DjfsJctPBkAtu9Wvt3IHCWCND/aaUMkVK+Qvm0JUhUsrS9VgLIYQYIoQ4JYQ4K4QoNaJZYmKi9759+zqmp6dXu2sNCQkhNTW1xD+7s7MzS5YsYezYsYSGhqLRaJg6dWqZ+xcuXMjw4cOJiIigfv2yh0QW5u677+aHH37gnnvuKdhXmixufoS1Nm3a8PLLL9O2bVu8vLwscr3XS+ECTJ48mSFDhtC/f3/CwsLo0KEDISEh3H///QVRlfJJTk6mffv2zJs377rOdbBODru4LV26deHEyRM092vOlwu/LLGMxowZUxDB6dNPPy1oeitOSZLZ06ZN4+uvvyYsLIyTJ0+W+ERamqx2WRQut9KYN28e2dnZzJw5s9Rzpk2bhslkIjQ0lHHjxrF06VKcnJxK/S2LUxk5YzAHcenSpQvh4eG8/vrrvPzyy6xfv74gVnA+JUmhlyb7XVze2VquXLnCsGHDCrYrIq/saukwzqykIxBC4KHzIEOfUfYw0sx40DiAi3el8qsxlNZ5kL8ATwDehbZ9gGlWXKcFzgHNAB1wGGgji3UW5+XlHTh+/Hj6sWPHMtLS0iJlNchQF+eDDz6QX375pd3zyaesuKTWYDAYCjpGz549K5s0aSJzc3NtZZ7Nic+Ml8cSjsn4zPjrjtlCErs2U52/5aOPPip37txZZJ89pdBL6yy+6667CgZelIcTV1LlhcSMG594A1JzUuWxhGMyQ19KWvpss9x0WtXIc1cEe3QWT5JSphRyHMnAJCuu6wKclVKel1LqgR+AO4ufdPny5aCAgIA4jUZjfdQUO/P4448XaVe1Ny4uLqxevbrCE8qysrLo1asXYWFh3HXXXcyfP7/gybimkZabRnxWPF5OXvi5+FW3OTWO6vwtFy1aRLdu3Qq2586dy5gxY3jnnXdsmk/+hLIGDRpcd0yv1zNq1KhS3/7KwtXJgUy9sVwBmErCzdENIQTp+lKaADMTAHFTdBLnY40M9VGgvcWjIITQAkeklG1vcN3dwBAp5WOW7QeBrlLK6fky1Onp6a6xsbGBLVu2PHfixIlWjRo1ivbw8Lhutk5cXJxfYmKiP4DJZHItPJJCUXvIMeQQlRqFk9aJJl5NVHwBhU1JysglJiWbkAAPdA7aG19QBhdSL5BnyqOFT4uiB4x5cPW4WWXUO7hSedgTe8hQrwGWCyG+sGxPseyrDCaj0SguX77cuGnTpiX3ABYiICAgMSAgIBHMo4YqmbeiGjCYDFxKv4RGaGjs0Vg5AYXNyQ9Ok6k3VtoReOg8iMuMI8eQg7NDodFLGfGABHfr+/uqmoq8EVnjCF4AJgOPW7bXA9ZoIcQAjQttN7LsAzh29erVdtnZ2c6nTp1qBWAwGBzPnTt3a/Pmzc+W9FagqL3ky0cYTAaaeDZRiqIKu+DsqEEjBFl6Iz6VHHbiqfMkLjOONH3aP47AaICsRPMsYofKBcKxF1JKkpKSyjX0FqxoGipyshC+QCMp5RErznXAHNZyIGYHsBe4X0p5fP/+/fUdHBwWAe2wjFxKTEwM8PT0TNbpdLllpZuUlHRLfoBwRe0gJSeFLEMWPs4+FQoFqFBYS0J6LhKo71H5Pr7E7ESklPi7WiaY5qRAThp4BEINfphxdnamUaNG1w2/rVTTkBBiMzDScu5+IF4IsUNK+UxZ10kpDUKI6cBazCOIFkspjwsh3sDcez2yhHyel1LuKyvdTp06yXztFUXNZ/6h+Xx+5HOmhU/j8dDHb3yBQlEJfl91giXbL3D09dtwqmTz0LITy5i7Zy4rRq2gqc4bProNmveHe76xjbE1CGsaar2klGnAaOAbKWVXzE/5N0RKuUpK2VJK2VxK+bZl36tSyhUlnNvvRk5AUbv4/ezvfH74c+5sfidT20+tbnMUdYCwxt7ojSZOxlonT1EWA4PN1dyGixtg24fmSWR9Sp8bUpuxxhE4CCECgXuAP+1sj+ImYeeVnby+43W6BXbjtR6vqXCTiiohrLE3AIcvp1Q6rQC3AML8w1h//n+wawGE3QsBNT+eREWwxhG8gbl555yUcq8Qohlwxr5mKWozZ5LP8OzmZ2nq3ZQP+n2Ao6bmtqcqbi4aejnj567jcHSqTdIbfMtgTqSeI8pBA/1ftEmaNRFrJCZ+klK2l1I+btk+L6Ucc6PrFHWTKxlXmLphKq4OrswfOF+Fm1RUKUIIwhp52+SNAGCYR0s0UrKiRY8aPW+gstzQEQghWgohNgohjlm22wshXra/aYraRmJ2IpPXTybbkM3ngz8nwC2guk1S1EHCGntzLiGD9BwrpKTLQkr8N8+lR66BlaYUjCb7RYWrbqxpGvoSmA3kAViGjt5rT6MUtY90fTqPb3ic+Kx45g+cT0ufmzZkhaKG076RF1LC0ZhKNg9F/g7nN3NnsxFczU5gd9xum9hXE7HGEbhKKfcU21f5SNGKm4YcQw7TN07nbMpZPuz3IeH1w6vbJEUdJqyRN0Dl+glyM2DtSxAQSv++c/DQebDi3HWDHW8arHEEiUKI5kC+1tDdwPUhqBR1kjxTHs9veZ6D8Qd5p9c79AzqeeOLFAo74uOm45Z6rhyOTql4In+/C2kxMOw/OOncGNpkKBsvbiRDX454xrUIaxzBE8AXQIgQIgZ4GlCDwhUYTUZe3f4qWy5v4eVuLzOk6ZDqNkmhAKBDY2/2X0qumBJpzH7Y8QmEPwDBXQG489Y7yTHmsCpqlY0trRlYM2rovJRyEOAPhAB9gV72NkxRszGajLy24zX+PP8nT3Z4knta3XPjixSKKqJL03okpOdyIamcsmV5OfD7NHAPgNvfLtgd6hdKiG8IP5z6odIy1zWRUh2BEMJTCDFbCPGpEGIwkAU8DJzFPLlMUUfJdwJ/nPuDx8MeZ3L7ydVtkkJRhC5NfQHYE5VUvgu3/BsSTsLIj4tEHxNCcF/IfZxJPsO+qzefAEJZbwTfAq2Ao5gD0WwCxgJ3SSmvCzCjqBsYTUZe3fEqf5z7g2lh05gWPq26TVIorqO5vxv13HTsjrpm/UUx+2H7R+YmoRaDrzs8rOkwvJy8+P7kzReyvSzRuWZSylAAIcQizB3EwVLKnCqxTFHjyDHk8MLfL/BX9F9mEbkwJSKnqJkIIejS1Jc91jqC3Az4dcp1TUKFcXZwZvSto/km8hviMuNuqnkyZb0RFMzGkFIagcvKCdRd0vRpTFk/hU3Rm5jVZZZyAooaT5emvlxOziYmJfvGJ6+eCUln4a4FZQakv6fVPZikiR9P/Wg7Q2sAZTmCMCFEmmVJB9rnrwsh0qrKQEX1E58Vz4Q1EziSeIR3+7zL+Nbjq9skheKG5PcT7L3RW8Hh5XBoGfT5FzTrW+apjTwaMSB4AD+c+uGmGkpaqiOQUmqllJ6WxUNK6VBo3bMqjVRUH1GpUTy46kFi0mOYP3C+GiKqqDWEBHji4exQdj9B4ln48xkI7gF9X7Aq3Umhk0jXp7P81HIbWVr9qMCxilLZFbuL8avGk2PMYcmQJXRv2L26TVIorEarEXRu4lv6yCF9Fvw0ARycYMwi0FoTuRfa+rWlR8MefBP5DTmGm6O1XDkCRYn8eOpHpq6fSgPXBiwbtow29dpUt0kKRbnp2tSXcwmZxKcVq7ClhJUz4OoxGL0QvILKle6k0Elcy7nGL2d+saG11YdyBIoiGEwG3tn9Dm/uepOeQT35dui3NPJoVN1mKRQVoncLc7zhv88kFj2w63M4+iMMeKnEoaI3olNAJyLqR7Dk2BJyjWWGWa8VKEegKCA1N5XpG6fz35P/5eE2D/Nx/49x17lXt1kKRYVpHeiBv4cTm0/F/7MzaiusexlC7oBez1U47SfCn+Bq1lV+OPmDDSytXpQjUABwPOk44/4cx+643bze43We7/w8Wk3lgn8rFNWNEIK+Lf3ZeiYRo0maJSR+mwK+zWDU56CpeBXYJbALPYN6svDIQlJzbRMRrbpQjqCOI6Xk59M/89CqhzBKI18P+ZrRLUZXt1kKhc3o29Kf1Ow8c9SyQ9+ZVUWHvw/OlR/8+EzEM6Tr0/nq2FeVN7QaUY6gDpNtyObl7S/z+s7X6RTQiR/v+JH2/u2r2yyFwqb0buGHRsDmk/GweyE0jICmZc8XsJZWvq0Y3mw4yyKXEZtRe9X5lSOoo5xJPsP4VeNZeW4lU8OmMn/gfHycfarbLIXC5ni76ghv7M3VyC2QeAo6TQQhbJb+kx2eRCM0vLfvPZulWdUoR1DHkFKy7MQy7v3zXq5lX+PzQZ/zRPgTqj9AcVPTt2V9eiX9gsnJE9ratumzoXtDJrWfxPqL69kes92maVcVyhHUIRKzE5m2cRpz98ylW8Nu/DLyFxVRTFEnGNzIwFDNHs41ugucbD8SbkLbCTTxbML/7f6/WjmcVDmCOsKGixsYs2IMe+P28lLXl/h0wKfUc6lX3WYpFFVC6+gfEQIW68s/Z8AadFods7vO5lL6JRYdXWSXPOyJcgQ3OfFZ8Tyz6Rme2fwMDVwb8MPwH7g35F6EDdtIFYoaTU4aYv9iTnv14pcoBzJyDXbJpkfDHtzR7A4WHVlEZFKkXfKwF8oR3KTkGnNZdHQRI34bwdaYrTwd8TTLhi/jVp9bq9s0haJq2b0AclIw9X4OvcHEXyfjb3xNBZnVZRa+zr68tO0l9Ea93fKxNcoR3GRIKdl4aSOjfh/FvAPz6BrYlV9H/sqjoY/iqHGsbvMUiqolOxl2fAqthtM6oi/1PZxYdcR+wzy9nLyY02MOZ1PO8tmhz+yWj62xTm5PUSuITIrkg/0fsDt2N829mvPF4C/o0bBHdZulUFQf2+dBbir0n41GIxjSLoDle6PJzDXg5mSf6q93o96MaTGGJceW0DWgKz2Cav5/UL0R3AREpUbx3ObnGPfnOE5eO8nsLrP5eeTPygko6jbJF2DnfGh/LwSEAjC0XSC5BhObTyXYNesXurxAc+/mzN42m/gs+zVF2Qq7OgIhxBAhxCkhxFkhxKwSjj8rhIgUQhwRQmwUQtxiT3tuNmIzYnl1+6uM+mMUW2O2MqX9FFaPXs39re/HQaNe9hR1nPWvgUYLA18t2NWlqS9+7k6sOBxj16xdHFz4T9//kG3IZubfM8kz5d34omrEbo5ACKEFPgOGAm2A+4QQxUXtDwKdpJTtgZ+Bd+1lz81EVGoUr2x/hWG/DuPP839yf8j9rB69mukdpuOh86hu8xSK6ufiToj8HXrOKBJrQKsRjApvyMYT8SRl2He8fzPvZrzS7RX2X93Pv/f82655VRZ7PjZ2Ac5KKc8DCCF+AO4ECsZVSSk3FTp/F/CAHe2p9UQmRbLo6CI2XNyATqvjnlb3MLHdRALcAqrbNIWi5mA0mIPRezSEHk9ed/iezo1ZtC2K3w7G8FjvZnY1ZUTzEZxJPsOS40to5tWM+1vfb9f8Koo9HUEQEF1o+zLQtYzzHwVWl3RACDEZmAwQHBxsK/tqBSZpYnvMdr478R07ruzAw9GDx0IfY3zr8WpCmEJRErvmQ9wRuOcb0Lldd7hlAw/CG3uzfG80j/Zqavc5NTMiZhCVFsW/9/6bJp5NamTncY3oLBZCPAB0AkpUbZJSLpRSdpJSdvL3969a46qJdH0630V+x8jfRzJt4zROXTvFjIgZrL17LU9FPKWcgEJREskXYNP/Qath0HpkqaeN69yYM/EZHL5s/zgCWo2Wub3ncqv3rTy35TlOXjtp9zzLiz0dQQzQuNB2I8u+IgghBgEvASOllLVPpMPGnEk+w9u73mbQT4P4995/4+Xkxdzec1l/93oeC31M9QEoFKUhJfz5jLmDeNj7ZSqM3tE+EBdHLcv3Rpd6ji1xc3Tj0wGf4uboxpT1Uzifer5K8rUWezYN7QVaCCGaYnYA9wJFGsiEEB2AL4AhUsqaP8bKTqTp01h9fjW/nf2N40nHcdQ4MrTpUO4PuZ+2fm2r2zyFonawfR6c+8vsBG4QjN7D2ZFhoYGsOBTD7GEheDrbf7JloHsgi25bxMNrHmbSukl8PeTrGhMPXEgp7Ze4EMOAjwAtsFhK+bYQ4g1gn5RyhRBiAxAK5E/1uySlLP19DujUqZPct2+f3WyuKkzSxO7Y3fx29jc2XtyI3qSnpU9L7rr1LoY3G65iAygU5eH8Zvj2LmhzJ9y9xKp4A8diUrnjk228NKw1k/rYt9O4MKeuneKRtY/gqfNk8e2LCXQPrJJ8hRD7pZSdSjxmT0dgD2qzI5BScjjhMGsvrGXdhXXEZ8fjqfNkWNNh3NXiLlr7tlZicApFeYk9DEuGg1cjeGw9OFnffHrvwp1cSspiy8z+OGqrrsv0aMJRpqyfgpvOjYWDF9LUq6nd81SOoBqRUhKZFMmaC2tYe2EtsZmx6DQ6egX1YmjTofQP7o+T1qm6zVQoaidxR+GbUeDoAo+svWGTUHE2nrjKo1/vY9694dwZXr5rK8vJayeZsn4KAAsGLaB1vdZ2zU85girGaDJyOOEwm6I3sfHSRqLTo3HQONCjYQ+GNBlC/8b9cdfZPjiGQlGnuLANvr/fHGjmoT/Ar0W5kzCZJIM+3IKbzoEV03tW+Rv5hdQLTFo/iQx9Bh/0+4DuDbvbLS/lCKqArLwsdsbuZNOlTfx9+W+Sc5Nx0DjQNaArtze5nQHBA/By8qpuMxWK2o/JBDs/hQ1zwLcZPPgbeDe+4WWlsWz3RV767RjfPdqVXi38bGenlcRlxvH4hseJSo1iVpdZ3Btyr13yUY7ATlxOv8yu2F1sjt7Mrthd5Bpz8dB50DuoN/2D+9OrYS/15K9Q2JL4k7DyKYjeDa1HwJ3zwdmzUknm5BkZ8P5m/D2d+X1aj2rpp8vQZzBr6yy2XN7CuFbjeKHLCzaXjS/LEShlsnKQlJ3Enrg97I7dza7YXcRkmKdFBLkHMbblWPo17kdEgwil+69Q2JqMeNj2Iez50twUNOpzCLvPqtFBN8LZUcuMQS144ZejrI+8ym1tq16yxV3nzrz+85h3YB5Lji/hxLUTvNvnXYLcq6bfQr0RlEFmXib74vaxK3YXu+N2cyb5DAAejh50CuhE18CudAvsRjOvZmq0j0JhD5LOwd6vYP8SMORA2P0waA6421ZhwGA0MfjDv9FpNaye0RuNpvr+z2surOH1Ha8jhODNHm8y8JaBNklXNQ1Zid6o53DCYXPFH7ubY4nHMEojTlonOtTvQNfArnQN6Erreq2VzLNCYS/0WXBmLRz4xjxBTOMAbe+CvrPAz36hVlcevsKT3x/kw3Fh3NWheid6RadF8/zfzxOZFMnoFqN5vtPzlVYVUI6gDOIy41h/cT1bL2/lYPxBcow5aIWWtn5t6RpgfuIPqx+mhngqFPYk65q50j+xEs6sg7wss3pop4kQ8RB42L+5xmSSjJq/nbjUHDY+1xePKphtXBZ5xjw+PfQpS48vxd/Fnzk95tArqFeF01OOAHMw96jUKEJ8QwDYG7eX+Yfms++qOa3mXs3p3rA7XQO70rFBR6Xpo1DYk5xUuHIQLmyHsxvM60hw8zeLxbUdBbf0NOsGVSGHo1MYNX87E3s05dURxcOnVA9HE47yyvZXOJd6jtldZldYylp1FgOLjy7m88Ofs/2+7cw7MI/lp5ZT36U+T3V4isG3DKaJV5PqNlGhuDnJy4GrxyDmAMTsNy9J5v42hAYadYZ+s+HWgdCwQ5VX/oUJa+zN/V2CWbojirs7NqJNw8qNSLIFof6hLB+xnEVHFzEw2Db9BcWpM28EWy9vZdrGafRs2JPtV7bzUJuHeLLDkzg7ONvBSoWijmEyQlqMuXP32jm4FmVZP29e8kM1utWHRp0gKAIaRkBQR3DxrlbTi5OSpWfAf7bQ2NeVX6Z2x6EKpSfsiXojAEL9zMGrt1/ZzuBbBvN8p+fVSB+FojyYTJB2uWgFn7+eHAVG/T/nOjibJ3v5tYCQYZZKPwI8g2wy5NOeeLvqeH1kW578/iCf/HWWZwa3rG6T7E6dcQTezt4F69PDpysnoFCUhpSQHgfxkRB/wrJEQsIpyMv857zClX3L26Fec/O2b3PwCARN7X2SHhHWkE0n4/nkrzP0aelHx1t8q9sku1JnHAGYQ8bFZsTSzLvqJGcVihqP0QDRu+DUanM7fnwk5KT8c9ytPtRvbR69498K6t1qrvBreWV/I16/sy17L17j6eWH+HN6b7xcb96JonWmj0ChqFUYDeYJVEY9GHILreeAwfJpzC20buUxY64lPUua2SnmZh19BmidzJ21DdpA/TbgH2J2AG5Vr79TUzhwKZlxX+ykW7N6LJnQuVb3F6g+AoXCXkgJedmQm26uTHPTIDej9G19BugzLUtW0e28rH8qbWmyjX0OzuYK3sGyaHXmfQ6WT68guKU7NOkNzQeY5RsUBUQE+/D2qFBm/nKEt1ed4LURN2fEQOUIFIriSAlZSZByEVIuQUo0pEab9W6yksyTn3JSQZ9uruSl0YpEhTlgis4ddG7/LO71/1l3cLFU2M6FKm6nfyrtgkq8hAq9pGNaxxrfMVsbuKdzY07GpbN4exS3+Loyoaf9g8hUNcoRKOoeJhNkxpsr+JSL5ko+5VLRSj8vq+g1Tp7mStvVD3yamIc86tzNlbuT5VPnUcK2Zd3RVVXKtZgXh4VwOTmLOSsjcdU5cE/niste10SUI1DcfJiM5lEvRSp4y5Iaba7sjblFr3HxAa/G5hEwtw4y69t7B5v3eQfXuLHuiqrFQavhk/s7MOmb/bzw6xEcHUS16xHZEuUIFLWPvBzz5KXUy4WWwk/0l/+ZwJSPq5+5Qm/QFloNBa9g87Z3sLnSL0ecW0XdxMlByxcPdOSRpXt5ZvlhkjL0PNb75hiBqByBomZhMkHGVXNlnpZfyceYn+RTL5sdQGbC9de5NzBX6g07QJs7C1Xyweag5jq3qv8uipsOF52WJRM788zyQ7z1vxPEpeYwe1hrtNUoW20LlCNQVC05qcWe5C8XerqPhrTY65/mde7mytwzCALDzOv5i2eQeXFUUiGKqsHZUcun90fwxsrjLNoWxYm4NObd2wE/99qrUKzmEShshz7T3DafdqWEJ3rLuj696DUaB7PcsFcj81DGggq+0T/7nL1VR6uiRvLjvmhe+f0YPq463h8bVi0xj61FzSNQVA5Drrm5Jj0O0mPNT+3psf9sp8eZl9zU66919TNX5vWaQ9M+RZ/mvRqZm3SqUW1SoagM93RqTNuGnjz5/UEe+Go393cNZvbQkGqPZVBe1BtBXSUv29zWnpFgHkqZEW/5TDDvz0z4Z1928vXXaxzNEgMeAZYlsOinV2OzA3B0qfrvplBUMTl5Rv6z7hSLtkVRz03Hs4NbcU+nRjVqJrIKTHOzk5dtrqwLlpRi28mQlVio0k+4vokmHydPc3AQ9/qFPuuDZ2Chyr6hebjlTawzo1BUhMPRKbz1v0j2XkimZQN3nh3citvaNKjWGMj5KEdQU5HSLCeQm17KkmaRKSi0LyetaAWfk2JOozQ0DuY2djc/c8VeUiXv7m/+dPNXna4KRSWRUrLmWBxz15zkYlIWzfzdmNy7GaM6BOHsWH3NoMoRVAaT0TzLNC/7n099VrF9WaUcy7R8Zlu0ZCyfuekWeYJ0MBlubIPQgrOneaaqs6f5adzF2/zpbPksshTap3NXHa0KRTVgMJpYdSyOL7ac4/iVNLxcHLkzvCFjOzamXZBnlUvhK0cAkHAa9i+Bnk+bJXevHofMRLN2TPa1fyrs4pV+4WAb1qJ1MreNO7qCzvWfdUdX83h2J4+ii87d3CRTfH/+4uCsKnOFopYipWTHuSR+2BvN2uNx6A0mmvm7Mbh1Awa1aUBEsE+VzENQjgBg1+ewZlahHQJcfcG1nuXJ2c1SWbv8U3E7OBfb5/JPZV64cnd0+Wefgwto1WAshUJxPanZefx55AprjsWx63wSeUaJr5uO7s3r0aWJL52b+BIS4GGXPgXlCPLZvdAcT7XdGPPEJIfaOwFEoVDUbtJz8vj7dCIbT1xl1/kkrqSa+/o8nB1oE+hJ60BP2gR60qahJ7fWd690/0K1OQIhxBBgHqAFFkkp5xY77gR8A3QEkoBxUsoLZaV5U3UWKxQKhYXLyVnsvXCNfReSiYxN42RsOtl5ZolzIaCRjwvP39aKO8ODKpR+tUwoE0Jogc+AwcBlYK8QYoWUMrLQaY8CyVLKW4UQ9wL/BsbZyyaFQqGoqTTycaWRj2uBqqnRJLmYlMmJ2HROX00nKjHTbjIW9mzM7gKclVKeBxBC/ADcCRR2BHcCcyzrPwOfCiGErG3tVQqFQmFjtBpBM393mvm7M5xAu+ZlzxlBQUB0oe3Lln0lniOlNACpQL3iCQkhJgsh9gkh9iUklKA8qVAoFIoKUyumhkopF0opO0kpO/n7+1e3OQqFQnFTYU9HEAMUjufWyLKvxHOEEA6AF+ZOY4VCoVBUEfZ0BHuBFkKIpkIIHXAvsKLYOSuAhy3rdwN/qf4BhUKhqFrs1lkspTQIIaYDazEPH10spTwuhHgD2CelXAF8BXwrhDgLXMPsLBQKhUJRhdh1CqyUchWwqti+Vwut5wBj7WmDQqFQKMqmVnQWKxQKhcJ+KEegUCgUdZxapzUkhEgALlbwcj8g0Ybm2IqaahfUXNuUXeVD2VU+bka7bpFSljj+vtY5gsoghNhXmtZGdVJT7YKaa5uyq3wou8pHXbNLNQ0pFApFHUc5AoVCoajj1DVHsLC6DSiFmmoX1FzblF3lQ9lVPuqUXXWqj0ChUCgU11PX3ggUCoVCUQzlCBQKhaKOU+sdgRBisRAiXghxrNC+MCHETiHEUSHESiGEZ6Fjs4UQZ4UQp4QQtxfaP8Sy76wQYlbxfOxplxBisBBiv2X/fiHEgELXbLbYdciy1K9Cu5oIIbIL5b2g0DUdLeefFUJ8LISoVLTtcto1vpBNh4QQJiFEuOWYrcursRBikxAiUghxXAgxw7LfVwixXghxxvLpY9kvLOVxVghxRAgRUSithy3nnxFCPFxannaya7zFnqNCiB1CiLBCaV2w7D8khKhUHNgK2NVPCJFa6Pd6tVBaNvtPVsCufxWy6ZgQwiiE8LUcs1l53cC2sZZtkxCiU7FrbFuPSSlr9QL0ASKAY4X27QX6WtYfAd60rLcBDgNOQFPgHGZBPK1lvRmgs5zTpgrt6gA0tKy3A2IKXbMZ6FRN5dWk8HnF0tkDdAMEsBoYWlV2FbsuFDhnx/IKBCIs6x7Aact99C4wy7J/FvBvy/owS3kIS/nstuz3Bc5bPn0s6z5VaFeP/PyAofl2WbYvAH7VVF79gD9LSMem/8ny2lXs2hGYlZFtXl43sK010Kr4PY0d6jGbfJHqXihWYWGOdJbfEd4YiLSszwZmFzpvLdDdsqwttL/Iefa2q9g1ArMSq5Nlu8hNUMXlVeS8YjfuyULb9wFfVFN5/R/wdqFtm5dXsfz+wByH+xQQWKg8TlnWvwDuK3T+KcvxImVU/Dx721XsXB+KPmxcwIYVWznLqx8lOwK7/CcrWF7/BSZVRXkVtq3QdpF7unhZYIN6rNY3DZXCcczxkMGsbpofIKe08JnWhNW0p12FGQMckFLmFtq3xPIa+kplm2AqYFdTIcRBIcQWIURvy74gzGWUT3WW1zjg+2L77FJeQogmmN/edgMNpJSxlkNxQAPLepXfY1baVZhHMb+15COBdcLcLDnZFjaV067uQojDQojVQoi2ln01oryEEK7AEOCXQrvtUl4l2FYaNr/HblZH8AgwTQixH/Orlr6a7cmnTLssf4J/A1MK7R4vpQwFeluWB6vQrlggWErZAXgW+K8o1N9SBdyovLoCWVLKY4V226W8hBDumCuDp6WUaYWPSfPjV7WMwy6vXUKI/pgdwQuFdveSUkZgbjJ6QgjRpwrtOoBZAycM+AT4vbJ528iufEYA26WU1wrts3l53cg2e3NTOgIp5Ukp5W1Syo6YnxbPWQ6VFj7TmrCa9rQLIUQj4DfgISnluULXxFg+0zG/onapKruklLlSyiTL+n7L/paYy6ZRoSSqvLws3EuxtwF7lJcQwhHzH3SZlPJXy+6rQohAy/FAIN6yv8rusXLahRCiPbAIuDP/d4UiZRaP+R6sVJmVxy4pZZqUMsOyvgpwFEL4UQPKy0JZ95hNyqsM20rD9veYvdq5qnLh+rbl+pZPDfAN8Ihluy1FO1nOY+5gcbCsN+WfTpa2VWiXtyXP0cWud8DSFgk4Aj8DU6vQLn9Aa1lvZrmpfC3bxTuLh1WVXYX2xQDN7Flelu/3DfBRsf3vUbST8V3L+nCKdhbvsez3BaIwt8/7WNZ9q9CuYOAs0KPY+W6AR6H1HcCQKrQrgH/6gboAlyxp2PQ/WV67LNtemPvr3OxVXmXZVuj4Zor2Edi8HqvUH7cmLJi9dSyQh7lN7FFgBuae99PA3PwbzXL+S5ifLE9RaKQL5tEepy3HXqpKu4CXgUzgUKGlvuVG2w8cwdxePg9LxVxFdo2x5HsI8yv8iELpdAKOWcrr08JlXEW/Yz9gV7E07FFevTA3Fxwp9NsMA+oBG4EzwAb+cZAC+MxSLkeL/YEfwVwZnwUmVrFdi4DkQufus+xvhrnCOGwps0rd+xWwa7ol38PALgo5Kmz4nyyvXZZrJgA/FEvHpuV1A9vuwvxfyAWuUrQj2Kb1mJKYUCgUijrOTdlHoFAoFArrUY5AoVAo6jjKESgUCkUdRzkChUKhqOMoR6BQKBR1HOUIFIpSEELUK6RAGSeEiLGsZwgh5le3fQqFrVDDRxUKKxBCzAEypJTvV7ctCoWtUW8ECkU5sWjo/2lZnyOE+FoIsVUIcVEIMVoI8a5Fr36NRTogP37DFotQ2dp8WQOFoiagHIFCUXmaAwOAkcB3wCZpFr7LBoZbnMEnwN3SrJu0GHi7uoxVKIrjUN0GKBQ3AaullHlCiKOYNV/WWPYfxayf1ApzwKH1FlVsLWY5DYWiRqAcgUJReXIBpJQmIUSe/KfjzYT5PyaA41LK7tVloEJRFqppSKGwP6cAfyFEdzBLDhcKwKJQVDvKESgUdkZKqQfuBv4thDiMWV2yR7UapVAUQg0fVSgUijqOeiNQKBSKOo5yBAqFQlHHUY5AoVAo6jjKESgUCkUdRzkChUKhqOMoR6BQKBR1HOUIFAqFoo7z/7qLCbNzFdUoAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "ename": "NameError",
+ "evalue": "name 'dno' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-9-cc8fe2bbf192>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# PROBLEM ! food doesn't show any rebound\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_system\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_system\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'ly'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'al'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'dno' is not defined"
+ ]
}
],
"source": [
@@ -229,22 +227,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"dno.plot_system.plot_system(s, {'ler', 'ldr'}, rescale=True)"
]
diff --git a/World2.ipynb b/World2.ipynb
index 7e68dc86..90312990 100644
--- a/World2.ipynb
+++ b/World2.ipynb
@@ -26,7 +26,7 @@
}
],
"source": [
- "from world2 import get_w2, plot_w2\n",
+ "from pydynamo import get_w2, plot_w2\n",
"w2 = get_w2()\n",
"w2.run(400, 0.5)\n",
"plot_w2(w2, title='Standart World2 scenario')"
diff --git a/World3.ipynb b/World3.ipynb
index 7a29df3b..dc6c74a8 100644
--- a/World3.ipynb
+++ b/World3.ipynb
@@ -26,7 +26,7 @@
}
],
"source": [
- "from world3 import get_w3, plot_world_03, var_color\n",
+ "from pydynamo import get_w3, plot_world_03, var_color, plot_tabhl\n",
"s = get_w3()\n",
"s.run(N=200, dt=1)\n",
"plot_world_03(s, \"World3 scenario 1: Business as usual\")"
@@ -34,12 +34,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -52,21 +52,21 @@
],
"source": [
"# Get the non linear formula of some variable\n",
- "dno.plot_system.plot_tabhl(s, 'dcph')"
+ "plot_tabhl(s, 'dcph')"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'industrial capital depreciation rate [dollars/year].'"
+ "'industrial capital depreciation'"
]
},
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/pydynamo/__init__.py b/pydynamo/__init__.py
index a2fa651b..883a5751 100644
--- a/pydynamo/__init__.py
+++ b/pydynamo/__init__.py
@@ -2,8 +2,24 @@
__version__ = "0.1"
-import pydynamo.parse_system
-import pydynamo.system
-import pydynamo.plot_system
-import pydynamo.dynamo_converter
-import pydynamo.psdsystem
+from pydynamo.core import parse_system
+# import pydynamo.core.system
+from pydynamo.core.plot_system import plot_system, show_pyvis, plot_tabhl
+# import pydynamo.core.dynamo_converter
+from pydynamo.core import psdsystem
+from .world3 import w3_code, w3_defs, plot_world_03, plot_world_with_scales, var_color
+from .world2 import w2_code, w2_defs, scales_w2
+
+
+def get_w3():
+ w3 = parse_system.system_from_lines(w3_code)
+ w3.add_comments(w3_defs)
+ return w3
+
+def get_w2():
+ w2 = parse_system.system_from_lines(w2_code)
+ w2.add_comments(w2_defs)
+ return w2
+
+def plot_w2(w2, title=''):
+ plot_system(w2, scales_w2, scales=scales_w2, title=title)
diff --git a/pydynamo/delays.py b/pydynamo/core/delays.py
similarity index 100%
rename from pydynamo/delays.py
rename to pydynamo/core/delays.py
diff --git a/pydynamo/dynamo_converter.py b/pydynamo/core/dynamo_converter.py
similarity index 100%
rename from pydynamo/dynamo_converter.py
rename to pydynamo/core/dynamo_converter.py
diff --git a/pydynamo/parse_dynamo_functions.py b/pydynamo/core/parse_dynamo_functions.py
similarity index 100%
rename from pydynamo/parse_dynamo_functions.py
rename to pydynamo/core/parse_dynamo_functions.py
diff --git a/pydynamo/parse_equations.py b/pydynamo/core/parse_equations.py
similarity index 100%
rename from pydynamo/parse_equations.py
rename to pydynamo/core/parse_equations.py
diff --git a/pydynamo/parse_system.py b/pydynamo/core/parse_system.py
similarity index 100%
rename from pydynamo/parse_system.py
rename to pydynamo/core/parse_system.py
diff --git a/pydynamo/plot_system.py b/pydynamo/core/plot_system.py
similarity index 100%
rename from pydynamo/plot_system.py
rename to pydynamo/core/plot_system.py
diff --git a/pydynamo/core/psdsystem.py b/pydynamo/core/psdsystem.py
new file mode 100644
index 00000000..2d97191c
--- /dev/null
+++ b/pydynamo/core/psdsystem.py
@@ -0,0 +1,81 @@
+import networkx as nx
+import numpy as np
+import re
+from .system import System
+
+class PsdSystem(System):
+ def __init__(self, model):
+ self.model = model
+ self.caracs = {}
+ self.comments = {}
+ for n in self.model.components._dependencies:
+ try :
+ doc = getattr(self.model.components, n).__doc__
+ car = {}
+ coms = []
+ for l in doc.split('\n'):
+ if ':' in l:
+ key, val = l.split(':')
+ car[key.strip()] = val.strip()
+ else:
+ coms.append(l)
+ car['Comment'] = re.sub(' *', ' ', ' '.join(coms))
+ self.caracs[n] = car
+ try:
+ self.comments[n] = f"{car['Real Name']} [{car['Units']}]: {car['Comment']}"
+ except Exception as e:
+ if any(i in n for i in {'smooth', 'integ', 'delay'}):
+ self.comments[n] = ''
+ else:
+ print(f'Counlnt find comment for var {n}', e)
+ raise e
+ except Exception as e:
+ if 'active' not in n:
+ print(f'Problem for car of {n}')
+ raise e
+
+ self.df_run = None
+
+ def get_influence_graph(self):
+ G = nx.Graph()
+ for var, deps in self.model.components._dependencies.items():
+ for dep, i in deps.items():
+ if i is not None:
+ G.add_edge(dep, var)
+ return G
+
+ def get_tabhl_args(self, name):
+ # print(self.caracs)
+ if self.caracs[name]['Type'] == 'lookup':
+ try:
+ x, y = np.array(eval(self.caracs[name]['Original Eqn'])).T
+ ylabel = self.caracs[name]['Units']
+ return x, y, ylabel, '', ''
+ except Exception as e:
+ print('no such !')
+ raise e
+ return None, None, '', '', ''
+
+ def get_vars(self):
+ return list(self.model.components._dependencies.keys())
+
+ def get_time(self):
+ return np.arange(self.model.time.initial_time(), self.model.time.final_time()+self.model.time.time_step()/2, self.model.time.time_step())
+
+ def get_tabhl_arg(self, name):
+ pass
+
+ def get_var(self, name):
+ if self.df_run is None:
+ return self.get_time()*0
+ else:
+ return self.df_run[name]
+
+ def run(self):
+ self.df_run = self.model.run()
+
+ def get_eq(self, name):
+ try:
+ return self.caracs[name]['Original Eqn']
+ except:
+ return ""
diff --git a/pydynamo/specials.py b/pydynamo/core/specials.py
similarity index 100%
rename from pydynamo/specials.py
rename to pydynamo/core/specials.py
diff --git a/pydynamo/system.py b/pydynamo/core/system.py
similarity index 100%
rename from pydynamo/system.py
rename to pydynamo/core/system.py
diff --git a/pydynamo/world2/#plot_uutils.py# b/pydynamo/world2/#plot_uutils.py#
new file mode 100644
index 00000000..03622aa5
--- /dev/null
+++ b/pydynamo/world2/#plot_uutils.py#
@@ -0,0 +1,4 @@
+scales_w2={'nr': 1e12, 'ql':2, 'ci': 20e9,'p':8e9,'polr':40}
+
+def plot_w2(w2, title=''):
+ dno.plot_system.plot_system(w2, scales, scales=scales, title=title)
\ No newline at end of file
diff --git a/pydynamo/world2/.#plot_uutils.py b/pydynamo/world2/.#plot_uutils.py
new file mode 120000
index 00000000..081815ff
--- /dev/null
+++ b/pydynamo/world2/.#plot_uutils.py
@@ -0,0 +1 @@
+achille@larressingle.16481:1643298493
\ No newline at end of file
diff --git a/pydynamo/world2/__init__.py b/pydynamo/world2/__init__.py
new file mode 100644
index 00000000..05f49e50
--- /dev/null
+++ b/pydynamo/world2/__init__.py
@@ -0,0 +1,6 @@
+import json
+import os
+
+w2_defs = json.load(open(os.path.join(os.path.dirname(__file__),'definitions_w2.json')))
+w2_code = open(os.path.join(os.path.dirname(__file__),'code_pydynamo_w2.py')).readlines()
+scales_w2={'nr': 1e12, 'ql':2, 'ci': 20e9,'p':8e9,'polr':40}
diff --git a/world2/pydynamo_w2.py b/pydynamo/world2/code_pydynamo_w2.py
similarity index 100%
rename from world2/pydynamo_w2.py
rename to pydynamo/world2/code_pydynamo_w2.py
diff --git a/world2/definitions.json b/pydynamo/world2/definitions_w2.json
similarity index 100%
rename from world2/definitions.json
rename to pydynamo/world2/definitions_w2.json
diff --git a/world3/__init__.py b/pydynamo/world3/__init__.py
similarity index 57%
rename from world3/__init__.py
rename to pydynamo/world3/__init__.py
index 725d1381..b4dc7842 100644
--- a/world3/__init__.py
+++ b/pydynamo/world3/__init__.py
@@ -1,15 +1,30 @@
__version__ = "0.1"
-import pydynamo as dno
-from world3.plot_utils import plot_world_with_scales, plot_03_state, plot_03_life, plot_03_indices, plot_world_03
-from world3.infos import get_sectors, get_definitions
-from world3.infos.get_sectors import sector_color
+from .plot_utils import plot_world_with_scales, plot_world_03
+from .infos import get_sectors, get_definitions
+from .infos.get_sectors import sector_color
+import os
+import json
+code_file = os.path.join(os.path.dirname(__file__),'code_pydynamo_w3.py')
+
+w3_defs = json.load(open(os.path.join(os.path.dirname(__file__),'definitions_w3.json')))
+w3_code = open(code_file).readlines()
+
+var_sector, var_subsector = get_sectors.get_sectors(code_file)
+sector_vars = {sector: [] for sector in var_sector.values()}
+for var in var_sector:
+ sector_vars[var_sector[var]].append(var)
+
+var_color = {v: sector_color[sector] for v, sector in var_sector.items()}
+
+
+"""
code_file = 'world3/code/limits_to_growth_pydynamo_code.py'
code_file_72 = 'world3/code/limits_to_growth_pydynamo_code_72.py'
# Get every informations
-var_sector, var_subsector = get_sectors.get_sectors(code_file)
+
sector_vars = {sector: [] for sector in var_sector.values()}
for var in var_sector:
sector_vars[var_sector[var]].append(var)
@@ -27,3 +42,4 @@ def get_w3_72():
s = dno.parse_system.system_from_file(code_file_72)
s.add_comments(definitions)
return s
+"""
diff --git a/world3/code/limits_to_growth_DYNAMO_code.py b/pydynamo/world3/code/limits_to_growth_DYNAMO_code.py
similarity index 100%
rename from world3/code/limits_to_growth_DYNAMO_code.py
rename to pydynamo/world3/code/limits_to_growth_DYNAMO_code.py
diff --git a/world3/code/limits_to_growth_pydynamo_code.py b/pydynamo/world3/code/limits_to_growth_pydynamo_code.py
similarity index 100%
rename from world3/code/limits_to_growth_pydynamo_code.py
rename to pydynamo/world3/code/limits_to_growth_pydynamo_code.py
diff --git a/world3/code/limits_to_growth_pydynamo_code_72.py b/pydynamo/world3/code/limits_to_growth_pydynamo_code_72.py
similarity index 100%
rename from world3/code/limits_to_growth_pydynamo_code_72.py
rename to pydynamo/world3/code/limits_to_growth_pydynamo_code_72.py
diff --git a/world3/code/world3_DYNAMO_code.py b/pydynamo/world3/code/world3_DYNAMO_code.py
similarity index 100%
rename from world3/code/world3_DYNAMO_code.py
rename to pydynamo/world3/code/world3_DYNAMO_code.py
diff --git a/world3/code/world3_pydynamo_code.py b/pydynamo/world3/code/world3_pydynamo_code.py
similarity index 100%
rename from world3/code/world3_pydynamo_code.py
rename to pydynamo/world3/code/world3_pydynamo_code.py
diff --git a/pydynamo/world3/code_pydynamo_w3.py b/pydynamo/world3/code_pydynamo_w3.py
new file mode 100644
index 00000000..7083253e
--- /dev/null
+++ b/pydynamo/world3/code_pydynamo_w3.py
@@ -0,0 +1,406 @@
+# Population sector
+pop.k = p1.k + p2.k + p3.k + p4.k
+p1.k = p1.j + dt*(b.j - d1.j - mat1.j)
+p1.i = p1i
+p1i = 65e7
+d1.k = p1.k * m1.k
+m1.k = tabhl(m1t, le.k, 20, 80, 10)
+m1t = [0.0567, 0.0366, 0.0243, 0.0155, 0.0082, 0.0023, 0.001]
+mat1.k = p1.k*(1 - m1.k)/15
+p2.k = p2.j + dt*(mat1.j - d2.j - mat2.j)
+p2.i = p2i
+p2i = 70e7
+d2.k = p2.k * m2.k
+m2.k = tabhl(m2t, le.k, 20, 80, 10)
+m2t = [0.0266, 0.0171, 0.0110, 0.0065, 0.0040, 0.0016, 0.0008]
+mat2.k = p2.k*(1 - m2.k)/30
+p3.k = p3.j + dt*(mat2.j-d3.j-mat3.j)
+p3.i = p3i
+p3i = 19e7
+d3.k = p3.k * m3.k
+m3.k = tabhl(m3t, le.k, 20, 80, 10)
+m3t = [0.0562, 0.0373, 0.0252, 0.0171, 0.0118, 0.0083, 0.006]
+mat3.k = p3.k*(1 - m3.k)/20
+p4.k = p4.j + dt*(mat3.j - d4.j)
+p4.i = p4i
+p4i = 6e7
+d4.k = p4.k * m4.krct
+m4.k = tabhl(m4t, le.k, 20, 80, 10)
+m4t = [0.13, 0.11, 0.09, 0.07, 0.06, 0.05, 0.04]
+
+# Death rate subsector
+d.k = d1.j + d2.j + d3.j + d4.j
+cdr.k = 1000*d.k/pop.k # death rate
+le.k = dynamo_len*lmf.k*lmhs.k*lmp.k*lmc.k
+dynamo_len = 28
+lmf.k = tabhl(lmft, fpc.k/sfpc, 0, 5, 1)
+lmft = [0, 1, 1.43, 1.5, 1.5, 1.5]
+hsapc.k = tabhl(hsapct, sopc.k, 0, 2000, 250)
+hsapct = [0, 20, 50, 95, 140, 175, 200, 220, 230]
+ehspc.k = smooth(hsapc.j, hsid)
+hsid = 20
+lmhs.k = clip(lmhs2.k, lmhs1.k, time.k, 1940)
+lmhs1.k = tabhl(lmhs1t, ehspc.k, 0, 100, 20)
+lmhs1t = [1, 1.1, 1.4, 1.6, 1.7, 1.8]
+lmhs2.k = tabhl(lmhs2t, ehspc.k, 0, 100, 20)
+lmhs2t = [1, 1.5, 1.9, 2, 2, 2]
+fpu.k = tabhl(fput, pop.k, 0, 16e9, 2e9)
+fput = [0, 0.2, 0.4, 0.5, 0.58, 0.65, 0.72, 0.78, 0.80]
+cmi.k = tabhl(cmit, iopc.k, 0, 1600, 200)
+cmit = [0.5, 0.05, -0.1, -0.08, -0.02, 0.05, 0.1, 0.15, 0.2]
+lmc.k = 1 - (cmi.k*fpu.k)
+lmp.k = tabhl(lmpt, ppolx.k, 0, 100, 10)
+lmpt = [1, 0.99, 0.97, 0.95, 0.90, 0.85, 0.75, 0.65, 0.55, 0.40, 0.20]
+
+# Birth rate subsector
+b.k = clip(d.k, (tf.k*p2.k*0.5/rlt), time.k, pet)
+cbr.k = 1000*b.k/pop.k # birth rate
+rlt = 30
+pet = 4000
+cbr.k = 1000*b.j/pop.k
+tf.k = min(mtf.k, (mtf.k*(1-fce.k) + dtf.k*fce.k))
+mtf.k = mtfn * fm.k
+mtfn = 12
+fm.k = tabhl(fmt, le.k, 0, 80, 10)
+fmt = [0, 0.2, 0.4, 0.6, 0.7, 0.75, 0.79, 0.84, 0.87]
+dtf.k = dcfs.k*cmple.k
+cmple.k = tabhl(cmplet, ple.k, 0, 80, 10)
+cmplet = [3, 2.1, 1.6, 1.4, 1.3, 1.2, 1.1, 1.05, 1]
+ple.k = dlinf3(le.k, lpd)
+lpd = 20
+dcfs.k = clip(2.0, dcfsn*frsn.k*sfsn.k, time.k, zpgt)
+zpgt = 4000
+dcfsn = 3.8
+sfsn.k = tabhl(sfsnt, diopc.k, 0, 800, 200)
+sfsnt = [1.25, 1, 0.9, 0.8, 0.75]
+diopc.k = dlinf3(iopc.k, sad)
+sad = 20
+frsn.k = tabhl(frsnt, fie.k, -0.2, 0.2, 0.1)
+frsnt = [0.5, 0.6, 0.7, 0.85, 1]
+frsn.i = 0.82
+fie.k = (iopc.k - aiopc.k)/aiopc.k
+aiopc.k = smooth(iopc.j, ieat)
+ieat = 3
+nfc.k = (mtf.k / dtf.k) - 1
+fce.k = clip(1.0, tabhl(fcet, fcfpc.k, 0, 3, 0.5), time.k, fcest)
+fcest = 4000
+fcet = [0.75, 0.85, 0.9, 0.95, 0.98, 0.99, 1]
+fcfpc.k = dlinf3(fcapc.k, hsid)
+fcapc.k = fsafc.k*sopc.k
+fsafc.k = tabhl(fsafct, nfc.k, 0, 10, 2)
+fsafct = [0, 0.005, 0.015, 0.025, 0.03, 0.035]
+
+# Capital sector
+## Industrial subsector
+iopc.k = io.k/pop.k
+# ATTENTION: from next line a smdynamo_all difference is induced
+io.k = ic.k*(1-fcaor.k)*cuf.k/icor.k
+icor.k = clip(icor2.k, icor1, time.k, pyear)
+icor1 = 3
+icor2.k = icormrrct.k*icormlyt.k*icormpt.k
+icormrrct.k = tabhl(icormrrctt, nruf.j,0, 1, 0.1) # industrial capital output ratio multiplier from resource conservation technology
+icormrrctt = [3.75, 3.6, 3.47, 3.36, 3.25, 3.16, 3.1, 3.06, 3.02, 3.01, 3] # industrial capital output ratio multiplier from resource table
+icormlyt.k = tabhl(icormlytt, lymt.j, 1, 2, 0.2) # industrial capital output ratio multiplier from land yield technology
+# TRUC BIZARE !!(1,0.8)-(2,2)],
+icormlytt = [1, 1.05, 1.12, 1.25, 1.35, 1.5] # industrial capital output ratio multiplier table
+icormpt.k = tabhl(icormptt, ppgf.j, 0, 1, 0.1) # industrial capital output ratio multiplier from pollution technology
+icormptt = [1.25, 1.2, 1.15, 1.11, 1.08, 1.05, 1.03, 1.02, 1.01, 1, 1] # industrial capital output ratio multiplier from pollution table
+ic.k = ic.j + dt*(icir.j-icdr.j)
+ic.i = ici
+ici = 2.1e11
+icdr.k = ic.k/alic.k
+alic.k = clip(alic2, alic1, time.k, pyear)
+alic1 = 14
+alic2 = 14
+icir.k = io.k*fioai.k
+fioai.k = 1- fioaa.k - fioas.k - fioac.k
+fioac.k = clip(fioacv.k, fioacc.k, time.k, iet)
+iet = 4000
+fioacc.k = clip(fioac2, fioac1, time.k, pyear)
+fioac1 = 0.43
+fioac2 = 0.43
+fioacv.k = tabhl(fioacvt, iopc.k/iopcd, 0, 2, 0.2)
+fioacvt = [0.3, 0.32, 0.34, 0.36, 0.38, 0.43, 0.73, 0.77, 0.81, 0.82, 0.83]
+iopcd = 400
+
+## Service subsector
+isopc.k = clip(isopc2.k, isopc1.k, time.k, pyear)
+isopc1.k = tabhl(isopc1t, iopc.k, 0, 1600, 200)
+isopc1t = [40, 300, 640, 1000, 1220, 1450, 1650, 1800, 2000]
+isopc2.k = tabhl(isopc2t, iopc.k, 0, 1600, 200)
+isopc2t = [40, 300, 640, 1000, 1220, 1450, 1650, 1800, 2000]
+fioas.k = clip(fioas2.k, fioas1.k, time.k, pyear)
+fioas1.k = tabhl(fioas1t, sopc.k/isopc.k, 0, 2, 0.5)
+fioas1t = [0.3, 0.2, 0.1, 0.05, 0]
+fioas2.k = tabhl(fioas2t, sopc.k/isopc.k, 0, 2, 0.5)
+fioas2t = [0.3, 0.2, 0.1, 0.05, 0]
+scir.k = io.k*fioas.k
+sc.k = sc.j + dt*(scir.j-scdr.j)
+sc.i = sci
+sci = 1.44e11
+scdr.k = sc.k/alsc.k
+alsc.k = clip(alsc2, alsc1, time.k, pyear)
+alsc1 = 20
+alsc2 = 20
+so.k = (sc.k*cuf.k)/scor.k
+sopc.k = so.k/pop.k
+scor.k = clip(scor2, scor1, time.k, pyear)
+scor1 = 1
+scor2 = 1
+
+## Job subsector
+j.k = pjis.k + pjas.k +pjss.k
+pjis.k = ic.k*jpicu.k
+jpicu.k = tabhl(jpicut, iopc.k, 50, 800, 150)*1e-3
+jpicut = [0.37, 0.18, 0.12, 0.09, 0.07, 0.06]
+pjss.k = sc.k*jpscu.k
+jpscu.k = tabhl(jpscut, sopc.k, 50, 800, 150)*1e-3
+jpscut = [1.1, 0.6, 0.35, 0.2, 0.15, 0.15]
+pjas.k = jph.k*al.k
+jph.k = tabhl(jpht, aiph.k, 2, 30, 4)
+jpht = [2, 0.5, 0.4, 0.3, 0.27, 0.24, 0.2, 0.2]
+lf.k = (p2.k + p3.k)*lfpf
+lfpf = 0.75
+luf.k = j.k/lf.k
+lufd.k = smooth(luf.j, lufdt)
+lufdt = 2
+cuf.k = tabhl(cuft, lufd.k, 1, 11, 2)
+cuf.i = 1
+cuft = [1, 0.9, 0.7, 0.3, 0.1, 0.1]
+
+# Agricultural sector
+## Loop1: food from investment in land development
+lfc.k = al.k/palt
+palt = 3.2e9
+al.k = al.j + dt*(ldr.j - ler.j - lrui.j)
+al.i = ali
+ali = 0.9e9
+pal.k = pal.j + dt*(-ldr.j)
+pal.i = pali
+pali = 2.3e9
+f.k = ly.k*al.k*lfh*(1-pl)
+lfh = 0.7
+pl = 0.1
+fpc.k = f.k/pop.k
+ifpc.k = clip(ifpc2.k, ifpc1.k, time.k, pyear)
+ifpc1.k = tabhl(ifpc1t, iopc.k, 0, 1600, 200)
+ifpc1t = [230, 480, 690, 850, 970, 1070, 1150, 1210, 1250]
+ifpc2.k = tabhl(ifpc2t, iopc.k, 0, 1600, 200)
+ifpc2t = [230, 480, 690, 850, 970, 1070, 1150, 1210, 1250]
+tai.k = io.k*fioaa.k
+fioaa.k = clip(fioaa2.k, fioaa1.k, time.k, pyear)
+fioaa1.k = tabhl(fioaa1t, fpc.k/ifpc.k, 0, 2.5, 0.5)
+fioaa1t = [0.4, 0.2, 0.1, 0.025, 0, 0]
+fioaa2.k = tabhl(fioaa2t, fpc.k/ifpc.k, 0, 2.5, 0.5)
+fioaa2t = [0.4, 0.2, 0.1, 0.025, 0, 0]
+ldr.k = tai.k*fiald.k/dcph.k
+dcph.k = tabhl(dcpht, pal.k/palt, 0, 1, 0.1)
+dcpht = [1e5, 7400, 5200, 3500, 2400, 1500, 750, 300, 150, 75, 50]
+
+## Loop2: food from investment in agricultural inputs
+cai.k = tai.k * (1 - fiald.k)
+ai.k = smooth(cai.j, alai.k)
+ai.i = 5e9
+alai.k = clip(alai2, alai1, time.k, pyear)
+alai1 = 2
+alai2 = 2
+aiph.k = ai.k*(1 - falm.k)/al.k
+lymc.k = tabhl(lymct, aiph.k, 0, 1000, 40)
+lymct = [1, 3, 4.5, 5.0, 5.3, 5.6, 5.9, 6.1, 6.35, 6.6, 6.9, 7.2, 7.4, 7.6, 7.8, 8, 8.2, 8.4, 8.6, 8.8, 9, 9.2, 9.4, 9.6, 9.8, 10]
+ly.k = lymt.k*lfert.k*lymc.k*lymap.k
+lymt.k = clip(lyf2.k, lyf1, time.k, pyear) # land yield multiplier from technology
+lyf1 = 1
+lyf2.k = smooth(lyt.k, tdd)
+lyt.k = lyt.j + dt*(lytcr.k) # land yield technology
+lyt.i = 1
+lytcr.k = clip(lyt.j*lytcrm.j,0, time.k, pyear)# land yield technology change rate
+lytcrm.k = tabhl(lytcrmt, drf - fr.k, 0, 1, 1)# land yield technology change rate multiplier
+lytcrmt = [0, 0] # land yield technology change rate multiplier table
+drf = 2 # desired food ratio
+lymap.k = clip(lymap2.k, lymap1.k, time.k, appyear)
+appyear = 4000 # air pollution policy implementation time
+lymap1.k = tabhl(lymap1t, io.k/io70, 0, 30, 10)
+lymap1t = [1, 1, 0.7, 0.4]
+lymap2.k = tabhl(lymap2t, io.k/io70, 0, 30, 10)
+lymap2t = [1, 1, 0.98, 0.95] # Seen in wrld3+03.mdl
+io70 = 7.9e11
+
+## Loop 1 & 2: the investment allocation decision
+fiald.k = tabhl(fialdt, mpld.k/mpai.k, 0, 2, 0.25)
+fialdt = [0, 0.05, 0.15, 0.30, 0.50, 0.70, 0.85, 0.95, 1]
+mpld.k = ly.k/(dcph.k*sd)
+sd = 0.07
+mpai.k = alai.k*ly.k*mlymc.k/lymc.k
+mlymc.k = tabhl(mlymct, aiph.k, 0, 600, 40)
+mlymct = [0.075, 0.03, 0.015, 0.011, 0.009, 0.008, 0.007, 0.006, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005]
+
+## Loop 3: land erosion and urban-industrial use
+dynamo_all.k = alln*llmy.k
+alln = 1000
+llmy.k = clip(llmy2.k, llmy1.k, time.k, llmytm) # CHANGER !!!
+llmy1.k = tabhl(llmy1t, ly.k/ilf, 0, 9, 1)
+llmy1t = [1.2, 1, 0.63, 0.36, 0.16, 0.055, 0.04, 0.025, 0.015, 0.01]
+llmy2.k = tabhl(llmy2t, ly.k/ilf, 0, 9, 1)
+llmy2t = [1.2, 1, 0.63, 0.36, 0.29, 0.26, 0.24, 0.22, 0.21, 0.2] # WRLD3+03 !
+ler.k = al.k/dynamo_all.k
+uilpc.k = tabhl(uilpct, iopc.k, 0, 1600, 200)
+uilpct = [0.005, 0.008, 0.015, 0.025, 0.04, 0.055, 0.07, 0.08, 0.09]
+uilr.k = uilpc.k*pop.k
+lrui.k = max(0, (uilr.k - uil.k)/uildt)
+uildt = 10
+uil.k = uil.j + dt*(lrui.j)
+uil.i = uili
+uili = 8.2e6
+
+## Loop 4: land fertility degradation
+lfert.k = lfert.j + dt*(lfr.j-lfd.j)
+lfert.i = lferti
+lferti = 600
+lfdr.k = tabhl(lfdrt, ppolx.k, 0, 30, 10)
+lfdrt = [0, 0.1, 0.3, 0.5]
+lfd.k = lfert.k*lfdr.k
+
+## Loop 5: land fertility regeneration
+lfr.k = (ilf-lfert.k)/lfrt.k
+ilf = 600
+lfrt.k = tabhl(lfrtt, falm.k, 0, 0.10, 0.02)
+lfrtt = [20.0, 13.0, 8.0, 4.0, 2.0, 2.0]
+
+## Loop 6: dDiscontinuing land maintinance
+falm.k = tabhl(falmt, pfr.k, 0, 4, 1)
+falmt = [0.0, 0.04, 0.07, 0.09, 0.1]
+fr.k = fpc.k/sfpc
+sfpc = 230
+pfr.k = smooth(fr.j, fspd)
+pfr.i = 1
+fspd = 2
+
+# Nonrenewable resource sector
+nr.k = nr.j + dt*(-nrur.j)
+nr.i = nri
+nri = 1e12
+nrur.k = pop.k * pcrum.k * nruf.k
+nruf.k = clip(nruf2.k, nruf1, time.k, pyear)
+nruf1 = 1
+nruf2.k = smooth(rct.k, tdd)
+rct.k = rct.j - dt*(rctcr.k)# resource conservation technology
+rct.i = 1
+rctcr.k = clip(rct.j*rtcm.j, 0, time.k, pyear) # resource technology change rate
+rtcm.k = tabhl(rtcmt,1-nrur.j/drur, -1, 0, 1) # resource technology change rate multiplier
+drur = 4.8e09 # desired resource use rate
+rtcmt = [0, 0]# resource technology change mult table
+pcrum.k = tabhl(pcrumt, iopc.k, 0, 1600, 200)
+pcrumt = [0, 0.85, 2.6, 3.4, 3.8, 4.1, 4.4, 4.7, 5]
+nrfr.k = nr.k/nri
+fcaor.k = clip(fcaor2.k, fcaor1.k, time.k, fcaortm)
+fcaor1.k = tabhl(fcaor1t, nrfr.k, 0, 1, 0.1)
+fcaor1t = [1.0, 0.9, 0.7, 0.5, 0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05]
+fcaor2.k = tabhl(fcaor2t, nrfr.k, 0, 1, 0.1)
+fcaor2t = [1.0, 0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
+
+
+# Persistent pollution sector
+ppgr.k = (ppgio.k + ppgao.k)*ppgf.k
+ppgf.k = clip(ppgf2.k, ppgf1, time.k, pyear)
+ppgf1 = 1
+# Added for 03
+ppgf2.k = smooth(ppt.k, tdd)
+tdd = 20 # technology development delay
+ppt.k = ppt.j + dt*pptcr.k # persistent pollution technology
+ppti = 1 # Initial persistent pollution technology
+ppt.i = ppti
+pptcr.k = clip(ppt.j*pptcm.j, 0, time.k, pyear) # persistent pollution technology change rate
+pptcm.k = tabhl(pptcmt, 1 - ppolx.k/dppolx, -1, 0, 1)# persistent pollution technology change multiplier
+pptcmt = [0, 0] # persistent pollution technology change mult table
+dppolx = 1.2 # desired persistent pollution index
+# industrial capital output ratio multiplier from persistent pollution technology
+# is not implemented in Vensim while on the schemas
+ppgio.k = pcrum.k * pop.k * frpm*imef*imti
+frpm = 0.02
+imef = 0.1
+imti = 10
+ppgao.k = aiph.k*al.k*fipm*amti
+fipm = 0.001
+amti = 1
+ppapr.k = delay3(ppgr.j, pptd) # Delay3i ??
+pptd = 20
+ppol.k = ppol.j + dt*(ppapr.j-ppasr.j)
+ppol.i = ppoli
+ppoli = 2.5e7
+ppolx.k = ppol.k/ppol70
+ppol70 = 1.36e8
+ppasr.k = ppol.k/(ahl.k*1.4)
+ahlm.k = tabhl(ahlmt, ppolx.k, 1, 1001, 250)
+ahlmt = [1.0, 11.0, 21.0, 31.0, 41.0]
+ahl.k = ahl70*ahlm.k
+ahl70 = 1.5
+
+# Supplementary equations sector
+# foa.k = 0.22*f.k/(0.22*f.k + so.k + io.k)
+# foi.k = io.k/(0.22*f.k + so.k + io.k)
+# fos.k = so.k/(0.22*f.k + so.k + io.k)
+# World3 03 supplementary equations sector
+cio.k = io.k*fioacc.k # consumed industrial output
+ciopc.k = cio.k/pop.k # consumed industrial output per capita
+fao.k = pof * f.k/(pof*f.k + so.k + io.k) # fraction of output in agriculture
+fos.k = so.k/(pof*f.k + so.k + io.k) # fraction of output in services
+foi.k = io.k/(pof*f.k + so.k + io.k) # fraction of output in industry
+llmytm = 4000 # land life policy implementation time
+pof = 0.22 # price of food
+fcaortm = 4000# fraction of industrial capital allocated to obtaining resources switch time
+plinid.k = ppgio.k*ppgf.k/io.k # persistent pollution intensity industry
+resint.k = nrur.k/io.k # resource use intensity
+thousand = 1000 # THOUSAND
+
+# World3 03 indicators sector
+ugdp = 1 # GDP pc unit
+uai = 1 # unit agricultural input
+up = 1 # unit population
+ablgha.k = ppgr.k*hpup/hpgha # "Absorption Land (GHA)"
+ei.k = tabhl(eit, gdppc.k/ugdp, 0, 7000, 1000) # Education Index
+eit = [0, 0.81, 0.88, 0.92, 0.95, 0.98, 0.99, 1] # Education Index LOOKUP
+gdpi.k = log(gdppc.k/rlgdp, 10)/log(rhgdp/rlgdp, 10) # GDP index
+gdppc.k = tabhl(gdppct, iopc.k/ugdp, 0, 1000, 200) # GDP per capita
+gdppct = [120, 600, 1200, 1800, 2500, 3200] # GDP per capita LOOKUP
+hpgha = 1e9 # ha per gha
+hpup = 4 # ha per unit of pollution
+hef.k = (algha.k + ulgha.k + ablgha.k)/tl # Human Ecological Footprint
+hwi.k = (lei.k + ei.k + gdpi.k)/3 # Human Welfare Index
+lei.k = tabhl(leit, le.k/oy, 25, 85, 10) # Life Expectancy Index
+leit = [0, 0.16, 0.33, 0.5, 0.67, 0.84, 1]# Life Expectancy Index LOOKUP
+oy = 1 # one year
+rhgdp = 9508 # Ref Hi GDP
+rlgdp = 24 # Ref Lo GDP
+tl = 1.91 # Total land
+ulgha.k = uil.k/hpgha # "Urban Land (GHA)"
+algha.k = al.k/hpgha # "Arable Land in Gigahectares (GHA)"
+
+# Control card for simulation
+# Some have been removed because not implemented this way in pydynamo
+# SPEC dt = 0.5
+# SPEC length = 2100
+pyear = 1975
+# N time = 1900
+initial_time = 1900
+# A pltper.k = step(plp, plit)
+# C plp = 5
+# C plit = 1900
+# C prp = 0
+# A prtper.k = step(prp, prit) + step(-prp, prtt)
+# C prit = 1900
+# C prtt = 2100
+
+# Take care to keep the last line !
+# Parameter and structural changes for limits to growth
+
+
+# Additional lines for limits to growth code
+# Capital sector
+## Industrial subsector
+# icir.k = clip(icir2.k, io.k*fioai.k, time.k, icet)
+# helpicir2.k = min(icdr.j, io.k* fioai.k) # Icir2 value if certain strategy is chosen
+# icir2.k = clip(helpicir2.k, io.k* fioai.k, diopc.k-diop.k, 0) # Icir value after icet
+# icet=4000 # Industrial capital economy time
+# diop.k = sample(iopc.k, dist.k, 0) # Desired industrial ouptput p
+# dist.k = step(4000, disi+1905)+disi # Desired industrial stop time
+# disi=4000 # Desired industrial stop interval
diff --git a/pydynamo/world3/definitions_w3.json b/pydynamo/world3/definitions_w3.json
new file mode 100644
index 00000000..196b568d
--- /dev/null
+++ b/pydynamo/world3/definitions_w3.json
@@ -0,0 +1 @@
+{"pop": "population", "p1": "Population 0 To 14", "p1i": "initial population 0 to 14", "d1": "deaths 0 to 14", "m1": "mortality 0 to 14", "m1t": "mortality 0 to 14 table", "mat1": "maturation 14 to 15", "p2": "Population 15 To 44", "p2i": "initial population 15 to 44", "d2": "deaths 15 to 44", "m2": "mortality 15 to 44", "m2t": "mortality 15 to 44 table", "mat2": "maturation 44 to 45", "p3": "Population 45 To 64", "p3i": "initial population 54 to 64", "d3": "deaths 45 to 64", "m3": "mortality 45 to 64", "m3t": "mortality 45 to 64 table", "mat3": "maturation 64 to 65", "p4": "Population 65 Plus", "p4i": "initial population 65 plus", "d4": "deaths 65 plus", "m4": "mortality 65 plus", "m4t": "mortality 65 plus table", "d": "deaths", "cdr": "death rate", "le": "life expectancy", "dynamo_len": "life expectancy normal", "lmf": "lifetime multiplier from food", "lmft": "lifetime multiplier from food table", "hsapc": "health services per capita", "hsapct": "health services per capita table", "ehspc": "effective health services per capita", "hsid": "health services impact delay", "lmhs": "lifetime multiplier from health services", "lmhs1": "lifetime multiplier from health services 1", "lmhs1t": "lifetime multiplier from health services 1 table", "lmhs2": "lifetime multiplier from health services 2", "lmhs2t": "lifetime multiplier from health services 2 table", "fpu": "fraction of population urban", "fput": "fraction of population urban table", "cmi": "crowding multiplier from industry", "cmit": "crowding multiplier from industry table", "lmc": "lifetime multiplier from crowding", "lmp": "lifetime multiplier from persistent pollution", "lmpt": "lifetime multiplier from persistent pollution table", "b": "births", "cbr": "birth rate", "rlt": "reproductive lifetime", "pet": "population equilibrium time", "tf": "total fertility", "mtf": "maximum total fertility", "mtfn": "maximum total fertility normal", "fm": "fecundity multiplier", "fmt": "fecundity multiplier table", "dtf": "desired total fertility", "cmple": "completed multiplier from perceived lifetime", "cmplet": "completed multiplier from perceived lifetime table", "ple": "perceived life expectancy", "lpd": "lifetime perception delay", "dcfs": "desired completed family size", "zpgt": "zero population growth time", "dcfsn": "desired completed family size normal", "sfsn": "social family size normal", "sfsnt": "social family size normal table", "diopc": "delayed industrial output per capita", "sad": "social adjustment delay", "frsn": "family response to social norm", "frsnt": "family response to social norm table", "fie": "family income expectation", "aiopc": "average industrial output per capita", "ieat": "income expectation averaging time", "nfc": "need for fertility control", "fce": "fertility control effectiveness", "fcest": "fertility control effectiveness time", "fcet": "fertility control effectiveness table", "fcfpc": "fertility control facilities per capita", "fcapc": "fertility control allocation per capita", "fsafc": "fraction services allocated to fertility control", "fsafct": "fraction services allocated to fertility control table", "iopc": "industrial output per capita", "io": "industrial output", "icor": "industrial capital output ratio", "icor1": "industrial capital output ratio 1", "icor2": "industrial capital output ratio 2", "icormrrct": "industrial capital output ratio multiplier from resource conservation technology", "icormrrctt": "industrial capital output ratio multiplier from resource table", "icormlyt": "industrial capital output ratio multiplier from land yield technology", "icormlytt": "industrial capital output ratio multiplier table", "icormpt": "industrial capital output ratio multiplier from pollution technology", "icormptt": "industrial capital output ratio multiplier from pollution table", "ic": "Industrial Capital", "ici": "initial industrial capital", "icdr": "industrial capital depreciation", "alic": "average life of industrial capital", "alic1": "average life of industrial capital 1", "alic2": "average life of industrial capital 2", "icir": "industrial capital investment", "fioai": "fraction of industrial output allocated to investment", "fioac": "fraction of industrial output allocated to consumption", "iet": "industrial equilibrium time", "fioacc": "fraction of industrial output allocated to consumption constant", "fioac1": "fraction of industrial output allocated to consumption constant 1", "fioac2": "fraction of industrial output allocated to consumption constant 2", "fioacv": "fraction of industrial output allocated to consumption variable", "fioacvt": "fraction of industrial output allocated to consumption variable table", "iopcd": "industrial output per capita desired", "isopc": "indicated services output per capita", "isopc1": "indicated services output per capita 1", "isopc1t": "indicated services output per capita table 1", "isopc2": "indicated services output per capita 2", "isopc2t": "indicated services output per capita table 2", "fioas": "fraction of industrial output allocated to services", "fioas1": "fraction of industrial output allocated to services 1", "fioas1t": "fraction of industrial output allocated to services table 1", "fioas2": "fraction of industrial output allocated to services 2", "fioas2t": "fraction of industrial output allocated to services table 2", "scir": "service capital investment", "sc": "Service Capital", "sci": "initial service capital", "scdr": "service capital depreciation", "alsc": "average life of service capital", "alsc1": "average life of service capital 1", "alsc2": "average life of service capital 2", "so": "service output", "sopc": "service output per capita", "scor": "service capital output ratio", "scor1": "service capital output ratio 1", "scor2": "service capital output ratio 2", "j": "jobs", "pjis": "potential jobs industrial sector", "jpicu": "jobs per industrial capital unit", "jpicut": "jobs per industrial capital unit table", "pjss": "potential jobs service sector", "jpscu": "jobs per service capital unit", "jpscut": "jobs per service capital unit table", "pjas": "potential jobs agricultural sector", "jph": "jobs per hectare", "jpht": "jobs per hectare table", "lf": "labor force", "lfpf": "labor force participation fraction", "luf": "labor utilization fraction", "lufd": "Delayed Labor Utilization Fraction", "lufdt": "labor utilization fraction delay time", "cuf": "capacity utilization fraction", "cuft": "capacity utilization fraction table", "lfc": "land fr cult", "palt": "potentially arable land total", "al": "Arable Land", "ali": "initial arable land", "pal": "Potentially Arable Land", "pali": "initial potentially arable land", "f": "food", "lfh": "land fraction harvested", "pl": "processing loss", "fpc": "food per capita", "ifpc": "indicated food per capita", "ifpc1": "indicated food per capita 1", "ifpc1t": "indicated food per capita table 1", "ifpc2": "indicated food per capita 2", "ifpc2t": "indicated food per capita table 2", "tai": "total agricultural investment", "fioaa": "fraction of industrial output allocated to agriculture", "fioaa1": "fraction of industrial output allocated to agriculture 1", "fioaa1t": "fraction industrial output allocated to agriculture table 1", "fioaa2": "fraction of industrial output allocated to agriculture 2", "fioaa2t": "fraction industrial output allocated to agriculture table 2", "ldr": "land development rate", "dcph": "development cost per hectare", "dcpht": "development cost per hectare table", "cai": "current agricultural inputs", "ai": "Agricultural Inputs", "alai": "average life agricultural inputs", "alai1": "average life of agricultural inputs 1", "alai2": "average life of agricultural inputs 2", "aiph": "agricultural input per hectare", "lymc": "land yield multiplier from capital", "lymct": "land yield multiplier from capital table", "ly": "land yield", "lymt": "land yield multiplier from technology", "lyf1": "land yield factor 1", "lyf2": "land yield factor 2", "lyt": "Land Yield Technology", "lytcr": "land yield technology change rate", "lytcrm": "land yield technology change rate multiplier", "lytcrmt": "land yield technology change rate multiplier table", "drf": "desired food ratio", "lymap": "land yield multiplier from air pollution", "appyear": "air pollution policy implementation time", "lymap1": "land yield multipler from air pollution 1", "lymap1t": "land yield multipler from air pollution table 1", "lymap2": "land yield multiplier from air pollution 2", "lymap2t": "land yield multipler from air pollution table 2", "io70": "IND OUT IN 1970", "fiald": "fraction of agricultural inputs allocated to land development", "fialdt": "fraction of agricultural inputs allocated to land development table", "mpld": "marginal productivity of land development", "sd": "social discount", "mpai": "marginal productivity of agricultural inputs", "mlymc": "marginal land yield multiplier from capital", "mlymct": "marginal land yield multiplier from capital table", "dynamo_all": "average life of land", "alln": "average life of land normal", "llmy": "land life multiplier from land yield", "llmy1": "land life multiplier from land yield 1", "llmy1t": "land life multiplier from land yield table 1", "llmy2": "land life multiplier from land yield 2", "llmy2t": "land life multiplier from land yield table 2", "ler": "land erosion rate", "uilpc": "urban and industrial land required per capita", "uilpct": "urban and industrial land required per capita table", "uilr": "urban and industrial land required", "lrui": "land removal for urban and industrial use", "uildt": "urban and industrial land development time", "uil": "Urban and Industrial Land", "uili": "initial urban and industrial land", "lfert": "Land Fertility", "lferti": "initial land fertility", "lfdr": "land fertility degredation rate", "lfdrt": "land fertility degredation rate table", "lfd": "land fertility degredation", "lfr": "land fertility regeneration", "ilf": "inherent land fertility", "lfrt": "land fertility regeneration time", "lfrtt": "land fertility regeneration time table", "falm": "fraction of agricultural inputs for land maintenance", "falmt": "fraction of agricultural inputs for land maintenance table", "fr": "food ratio", "sfpc": "subsistence food per capita", "pfr": "Perceived Food Ratio", "fspd": "food shortage perception delay", "nr": "Nonrenewable Resources", "nri": "initial nonrenewable resources", "nrur": "resource usage rate", "nruf": "resource use factor", "nruf1": "resource use factor 1", "nruf2": "resource use fact 2", "rct": "Resource Conservation Technology", "rctcr": "resource technology change rate", "rtcm": "resource technology change rate multiplier", "drur": "desired resource use rate", "rtcmt": "resource technology change mult table", "pcrum": "per capita resource use multiplier", "pcrumt": "per capita resource use mult table", "nrfr": "fraction of resources remaining", "fcaor": "fraction of industrial capital allocated to obtaining resources", "fcaor1": "fraction of capital allocated to obtaining resources 1", "fcaor1t": "fraction of capital allocated to obtaining resources 1 table", "fcaor2": "fraction of capital allocated to obtaining resources 2", "fcaor2t": "fraction of capital allocated to obtaining resources 2 table", "ppgr": "persistent pollution generation rate", "ppgf": "persistent pollution generation factor", "ppgf1": "persistent pollution generation factor 1", "ppgf2": "persistent pollution generation factor 2", "tdd": "technology development delay", "ppt": "Persistent Pollution Technology", "ppti": "Initial persistent pollution technology", "pptcr": "persistent pollution technology change rate", "pptcm": "persistent pollution technology change multiplier", "pptcmt": "persistent pollution technology change mult table", "dppolx": "desired persistent pollution index", "ppgio": "persistent pollution generation industry", "frpm": "fraction of resources from persistent materials", "imef": "industrial material emissions factor", "imti": "industrial material toxicity index", "ppgao": "persistent pollution generation agriculture", "fipm": "fraction of agricultural inputs from persistent materials", "amti": "agricultural material toxicity index", "ppapr": "persistent pollution appearance rate", "pptd": "persistent pollution transmission delay", "ppol": "Persistent Pollution", "ppoli": "initial persistent pollution", "ppolx": "persistent pollution index", "ppol70": "persistent pollution in 1970", "ppasr": "persistent pollution assimilation rate", "ahlm": "assimilation half life multiplier", "ahlmt": "assimilation half life mult table", "ahl": "assimilation half life", "ahl70": "assimilation half life in 1970", "cio": "consumed industrial output", "ciopc": "consumed industrial output per capita", "fao": "fraction of output in agriculture", "fos": "fraction of output in services", "foi": "fraction of output in industry", "llmytm": "land life policy implementation time", "pof": "PRICE OF FOOD", "fcaortm": "fraction of industrial capital allocated to obtaining resources switch time", "plinid": "persistent pollution intensity industry", "resint": "resource use intensity", "thousand": "THOUSAND", "ugdp": "GDP pc unit", "uai": "unit agricultural input", "up": "unit population", "ablgha": "\"Absorption Land (GHA)\"", "ei": "Education Index", "eit": "Education Index LOOKUP", "gdpi": "GDP Index", "gdppc": "GDP per capita", "gdppct": "GDP per capita LOOKUP", "hpgha": "ha per Gha", "hpup": "ha per unit of pollution", "hef": "Human Ecological Footprint", "hwi": "Human Welfare Index", "lei": "Life Expectancy Index", "leit": "Life Expectancy Index LOOKUP", "oy": "one year", "rhgdp": "Ref Hi GDP", "rlgdp": "Ref Lo GDP", "tl": "Total Land", "ulgha": "\"Urban Land (GHA)\"", "algha": "\"Arable Land in Gigahectares (GHA)\"", "pyear": "POLICY YEAR", "initial_time": ""}
\ No newline at end of file
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diff --git a/world3/infos/get_definitions.py b/pydynamo/world3/infos/get_definitions.py
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