test_openipdm.ipynb

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       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openipdm.ssm_kr import SSM_KR\n",
    "import numpy as np\n",
    "# create an instance of the model\n",
    "det_model = SSM_KR()\n",
    "\n",
    "# define inspectors errors (ID, bias, standard deviation)\n",
    "inspector_std= np.array([[ 0. ,  0. ,  2. ],\n",
    "                        [ 1. ,  0. ,  4. ],\n",
    "                        [ 2. ,  3. ,  4. ],\n",
    "                        [ 3. , -3. ,  4. ],\n",
    "                        [ 4. ,  3. ,  1.5],\n",
    "                        [ 5. , -3. ,  1.5]])\n",
    "\n",
    "# define the actions (0: do nothing, 1: perventive maintenance, 2: routine maintenance, 3: repairs, 4: replace)\n",
    "actions = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32)\n",
    "\n",
    "# define which inspeptors performed the inspection over time\n",
    "inspectors = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32)\n",
    "\n",
    "# define the total number of years\n",
    "total_years = np.array([2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028])\n",
    "\n",
    "# define the inspections performed over time\n",
    "y = np.array([[  np.nan, 88.75,   np.nan, 86.25,   np.nan, 85.  ,   np.nan, 85.  ,   np.nan,\n",
    "                    np.nan,   np.nan,   np.nan,   np.nan]])\n",
    "\n",
    "# run the model\n",
    "df_cond, df_speed = det_model.ssm_kr_predict(y=y, total_years=total_years, inspector_std=inspector_std, inspector=inspectors, Actions=actions)\n",
    "\n",
    "# plot the results\n",
    "plot_alt = det_model.plot_results(df_cond, df_speed)\n",
    "\n",
    "plot_alt\n"
   ]
  }
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