yohm/hello_oacis_salib.ipynb

## hello_oacis_salib.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from SALib.sample import saltelli\n",
    "from SALib.analyze import sobol\n",
    "from SALib.test_functions import Ishigami\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import oacis module\n",
    "\n",
    "import os,sys\n",
    "sys.path.append( os.environ['OACIS_ROOT'] )\n",
    "import oacis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model inputs\n",
    "problem = {\n",
    "    'num_vars': 3,\n",
    "    'names': ['x1', 'x2', 'x3'],\n",
    "    'bounds': [[-3.14159265359, 3.14159265359],\n",
    "               [-3.14159265359, 3.14159265359],\n",
    "               [-3.14159265359, 3.14159265359]]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.76100994, -2.53413626,  0.11658254],\n",
       "       [ 1.11060209, -2.53413626,  0.11658254],\n",
       "       [-1.76100994, -1.38058271,  0.11658254],\n",
       "       ...,\n",
       "       [-0.05215535,  0.7025632 ,  1.48182544],\n",
       "       [-0.05215535,  1.89906822, -2.29790322],\n",
       "       [-0.05215535,  1.89906822,  1.48182544]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define problems\n",
    "param_values = saltelli.sample(problem, 1000)\n",
    "param_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find ishigami simulator and create ParameterSets and Runs\n",
    "sim = oacis.Simulator.find_by_name('ishigami')\n",
    "host = oacis.Host.find_by_name('localhost')\n",
    "ps_array = []\n",
    "for param in param_values:\n",
    "    ps = sim.find_or_create_parameter_set( {\"x1\":param[0],\"x2\":param[1],\"x3\":param[2]} )\n",
    "    ps.find_or_create_runs_upto( 1, submitted_to=host )\n",
    "    ps_array.append(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wait until all PS are complete\n",
    "w = oacis.OacisWatcher()\n",
    "w.do_async(lambda: oacis.OacisWatcher.await_all_ps( ps_array ) )\n",
    "w.loop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.75105941,  6.87147587,  5.41107282, ...,  2.86940226,\n",
       "        6.15243593,  6.15243593])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output values\n",
    "Y = [ ps.average_result(\"y\")[0] for ps in ps_array ]\n",
    "Y = np.array(Y)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'S1': array([0.32335916, 0.44263043, 0.        ]),\n",
       " 'S1_conf': array([0.06448807, 0.07219383, 0.        ]),\n",
       " 'ST': array([0.56611852, 0.68848614, 0.        ]),\n",
       " 'ST_conf': array([0.08892856, 0.06029776, 0.        ]),\n",
       " 'S2': array([[        nan,  0.24403091, -0.00165458],\n",
       "        [        nan,         nan, -0.0093013 ],\n",
       "        [        nan,         nan,         nan]]),\n",
       " 'S2_conf': array([[       nan, 0.11048291, 0.07550143],\n",
       "        [       nan,        nan, 0.09667665],\n",
       "        [       nan,        nan,        nan]])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Si = sobol.analyze(problem, Y)\n",
    "Si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange( problem['num_vars'] )\n",
    "plt.xticks(x, problem['names'])\n",
    "plt.ylabel('Si')\n",
    "plt.bar(x, Si['S1'], yerr=Si['S1_conf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange( problem['num_vars'] )\n",
    "s2 = Si['S2']\n",
    "y = [ s2[0][1], s2[0][2], s2[1][2] ]\n",
    "e = Si['S2_conf']\n",
    "yerr = [ e[0][1], e[0][2], e[1][2] ]\n",
    "plt.xticks(x, ['x1-x2', 'x1-x3', 'x2-x3'])\n",
    "plt.ylabel('Sij')\n",
    "plt.bar(x, y, yerr=yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange( problem['num_vars'] )\n",
    "plt.xticks(x, problem['names'])\n",
    "plt.ylabel('ST')\n",
    "plt.bar(x, Si['ST'], yerr=Si['ST_conf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"from SALib.sample import saltelli\n",
	"from SALib.analyze import sobol\n",
	"from SALib.test_functions import Ishigami\n",
	"import numpy as np"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"# import oacis module\n",
	"\n",
	"import os,sys\n",
	"sys.path.append( os.environ['OACIS_ROOT'] )\n",
	"import oacis"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"# Define the model inputs\n",
	"problem = {\n",
	" 'num_vars': 3,\n",
	" 'names': ['x1', 'x2', 'x3'],\n",
	" 'bounds': [[-3.14159265359, 3.14159265359],\n",
	" [-3.14159265359, 3.14159265359],\n",
	" [-3.14159265359, 3.14159265359]]\n",
	"}"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([[-1.76100994, -2.53413626, 0.11658254],\n",
	" [ 1.11060209, -2.53413626, 0.11658254],\n",
	" [-1.76100994, -1.38058271, 0.11658254],\n",
	" ...,\n",
	" [-0.05215535, 0.7025632 , 1.48182544],\n",
	" [-0.05215535, 1.89906822, -2.29790322],\n",
	" [-0.05215535, 1.89906822, 1.48182544]])"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# define problems\n",
	"param_values = saltelli.sample(problem, 1000)\n",
	"param_values"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [],
	"source": [
	"# find ishigami simulator and create ParameterSets and Runs\n",
	"sim = oacis.Simulator.find_by_name('ishigami')\n",
	"host = oacis.Host.find_by_name('localhost')\n",
	"ps_array = []\n",
	"for param in param_values:\n",
	" ps = sim.find_or_create_parameter_set( {\"x1\":param[0],\"x2\":param[1],\"x3\":param[2]} )\n",
	" ps.find_or_create_runs_upto( 1, submitted_to=host )\n",
	" ps_array.append(ps)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": [
	"# wait until all PS are complete\n",
	"w = oacis.OacisWatcher()\n",
	"w.do_async(lambda: oacis.OacisWatcher.await_all_ps( ps_array ) )\n",
	"w.loop()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([-2.75105941, 6.87147587, 5.41107282, ..., 2.86940226,\n",
	" 6.15243593, 6.15243593])"
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# output values\n",
	"Y = [ ps.average_result(\"y\")[0] for ps in ps_array ]\n",
	"Y = np.array(Y)\n",
	"Y"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{'S1': array([0.32335916, 0.44263043, 0. ]),\n",
	" 'S1_conf': array([0.06448807, 0.07219383, 0. ]),\n",
	" 'ST': array([0.56611852, 0.68848614, 0. ]),\n",
	" 'ST_conf': array([0.08892856, 0.06029776, 0. ]),\n",
	" 'S2': array([[ nan, 0.24403091, -0.00165458],\n",
	" [ nan, nan, -0.0093013 ],\n",
	" [ nan, nan, nan]]),\n",
	" 'S2_conf': array([[ nan, 0.11048291, 0.07550143],\n",
	" [ nan, nan, 0.09667665],\n",
	" [ nan, nan, nan]])}"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"Si = sobol.analyze(problem, Y)\n",
	"Si"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<BarContainer object of 3 artists>"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"x = np.arange( problem['num_vars'] )\n",
	"plt.xticks(x, problem['names'])\n",
	"plt.ylabel('Si')\n",
	"plt.bar(x, Si['S1'], yerr=Si['S1_conf'])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<BarContainer object of 3 artists>"
	]
	},
	"execution_count": 11,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"x = np.arange( problem['num_vars'] )\n",
	"s2 = Si['S2']\n",
	"y = [ s2[0][1], s2[0][2], s2[1][2] ]\n",
	"e = Si['S2_conf']\n",
	"yerr = [ e[0][1], e[0][2], e[1][2] ]\n",
	"plt.xticks(x, ['x1-x2', 'x1-x3', 'x2-x3'])\n",
	"plt.ylabel('Sij')\n",
	"plt.bar(x, y, yerr=yerr)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<BarContainer object of 3 artists>"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"x = np.arange( problem['num_vars'] )\n",
	"plt.xticks(x, problem['names'])\n",
	"plt.ylabel('ST')\n",
	"plt.bar(x, Si['ST'], yerr=Si['ST_conf'])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.6.9"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 4
	}