From 1a2db0c6733e24a36a686b6f591003def507e51e Mon Sep 17 00:00:00 2001
From: stevehadd <stevehadd@yahoo.com>
Date: Mon, 11 Jul 2022 17:09:49 +0100
Subject: [PATCH] #4 #15 added code for radiation emulation (FF,CNN)  and
 rotors (RNN) to demonstrate neural networks.

---
 03_algorithm_selection.ipynb | 1866 +++++++++++++++-------------------
 1 file changed, 846 insertions(+), 1020 deletions(-)

diff --git a/03_algorithm_selection.ipynb b/03_algorithm_selection.ipynb
index d719b25..63ca6c4 100644
--- a/03_algorithm_selection.ipynb
+++ b/03_algorithm_selection.ipynb
@@ -143,12 +143,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 26,
    "id": "e73c9103-8a88-4bf5-a6fd-48a05597b9ca",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pathlib"
+    "import pathlib\n",
+    "import os\n",
+    "import functools\n",
+    "import math\n",
+    "import datetime"
    ]
   },
   {
@@ -189,350 +193,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "0c78392c-694f-49a1-9347-35983ad1c0d7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>32.700001</td>\n",
-       "      <td>-118.666664</td>\n",
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-       "      <td>1966</td>\n",
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-       "      <td>US NAVY SHIPS OF OPPORTUNITY</td>\n",
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-       "      <th>4</th>\n",
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-       "      <td>32.933334</td>\n",
-       "      <td>-117.916664</td>\n",
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-       "      <td>UNITED STATES</td>\n",
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-       "      <td>US036605</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
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-       "      <td>-58.681999</td>\n",
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-       "      <td>2014</td>\n",
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-       "      <td>914.778015</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686052</td>\n",
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-       "    <tr>\n",
-       "      <th>18999</th>\n",
-       "      <td>18999</td>\n",
-       "      <td>LIBERIA</td>\n",
-       "      <td>-13.661000</td>\n",
-       "      <td>-174.035004</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
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-       "      <td>SCRIPPS INSTITUTION OF OCEANOGRAPHY; LA JOLLA; CA</td>\n",
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-       "      <td>LR000315</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>985.260437</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686050</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2253127 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Unnamed: 0        country        lat         lon      date  year  \\\n",
-       "0               0  UNITED STATES  32.966667 -117.633331  19660412  1966   \n",
-       "1               1  UNITED STATES  33.016666 -118.116669  19660413  1966   \n",
-       "2               2  UNITED STATES  33.066666 -118.466667  19660414  1966   \n",
-       "3               3  UNITED STATES  32.700001 -118.666664  19660414  1966   \n",
-       "4               4  UNITED STATES  32.933334 -117.916664  19660414  1966   \n",
-       "...           ...            ...        ...         ...       ...   ...   \n",
-       "18995       18995  UNITED STATES -58.394001  -63.181000  20141231  2014   \n",
-       "18996       18996  UNITED STATES -58.500999  -63.125000  20141231  2014   \n",
-       "18997       18997  UNITED STATES -58.598000  -63.064999  20141231  2014   \n",
-       "18998       18998  UNITED STATES -58.681999  -63.015999  20141231  2014   \n",
-       "18999       18999        LIBERIA -13.661000 -174.035004  20141231  2014   \n",
-       "\n",
-       "       month  day                                          institute  \\\n",
-       "0          4   12                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "1          4   13                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "2          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "3          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "4          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "...      ...  ...                                                ...   \n",
-       "18995     12   31                                                  0   \n",
-       "18996     12   31                                                  0   \n",
-       "18997     12   31                                                  0   \n",
-       "18998     12   31                                                  0   \n",
-       "18999     12   31  SCRIPPS INSTITUTION OF OCEANOGRAPHY; LA JOLLA; CA   \n",
-       "\n",
-       "                                                platform cruise_number  \\\n",
-       "0                                              KEARSARGE      US044120   \n",
-       "1                                              KEARSARGE      US044120   \n",
-       "2                                              KEARSARGE      US044120   \n",
-       "3                                              KEARSARGE      US044120   \n",
-       "4                                              KEARSARGE      US044120   \n",
-       "...                                                  ...           ...   \n",
-       "18995  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18996  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18997  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18998  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18999              E. R. WILHELMSHAVEN (Call sign ELZY3)      LR000315   \n",
-       "\n",
-       "                      instrument      model manufacturer   max_depth  \\\n",
-       "0             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.892670   \n",
-       "1             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.852051   \n",
-       "2             XBT: T4 (SIPPICAN)         T4     SIPPICAN   70.602089   \n",
-       "3             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.907410   \n",
-       "4             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.811493   \n",
-       "...                          ...        ...          ...         ...   \n",
-       "18995  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  899.722412   \n",
-       "18996  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  929.809082   \n",
-       "18997  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  908.195984   \n",
-       "18998  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  914.778015   \n",
-       "18999  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  985.260437   \n",
-       "\n",
-       "       imeta_applied        id  \n",
-       "0                  1   2052528  \n",
-       "1                  1   2052529  \n",
-       "2                  1   2052530  \n",
-       "3                  1   2052531  \n",
-       "4                  1   2052532  \n",
-       "...              ...       ...  \n",
-       "18995              0  16686048  \n",
-       "18996              0  16686049  \n",
-       "18997              0  16686051  \n",
-       "18998              0  16686052  \n",
-       "18999              0  16686050  \n",
-       "\n",
-       "[2253127 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_data_loc = pathlib.Path('/project/informatics_lab/xbt')\n",
     "xbt_fname_template = 'xbt_{year}.csv'\n",
@@ -552,350 +216,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f954082e-ae95-45f3-aaf3-93881f5e4d06",
    "metadata": {},
-   "outputs": [
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-       "      <td>13</td>\n",
-       "      <td>US NAVY SHIPS OF OPPORTUNITY</td>\n",
-       "      <td>KEARSARGE</td>\n",
-       "      <td>US044120</td>\n",
-       "      <td>XBT: T4 (SIPPICAN)</td>\n",
-       "      <td>T4</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>466.852051</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2052529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>33.066666</td>\n",
-       "      <td>-118.466667</td>\n",
-       "      <td>19660414</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>US NAVY SHIPS OF OPPORTUNITY</td>\n",
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-       "      <td>XBT: T4 (SIPPICAN)</td>\n",
-       "      <td>T4</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>70.602089</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2052530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>32.700001</td>\n",
-       "      <td>-118.666664</td>\n",
-       "      <td>19660414</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>US NAVY SHIPS OF OPPORTUNITY</td>\n",
-       "      <td>KEARSARGE</td>\n",
-       "      <td>US044120</td>\n",
-       "      <td>XBT: T4 (SIPPICAN)</td>\n",
-       "      <td>T4</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>466.907410</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2052531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>32.933334</td>\n",
-       "      <td>-117.916664</td>\n",
-       "      <td>19660414</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>US NAVY SHIPS OF OPPORTUNITY</td>\n",
-       "      <td>KEARSARGE</td>\n",
-       "      <td>US044120</td>\n",
-       "      <td>XBT: T4 (SIPPICAN)</td>\n",
-       "      <td>T4</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>466.811493</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2052532</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18995</th>\n",
-       "      <td>18995</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>-58.394001</td>\n",
-       "      <td>-63.181000</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>LAURENCE M. GOULD (R/V; call sign WCX7445; bui...</td>\n",
-       "      <td>US036605</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>899.722412</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686048</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18996</th>\n",
-       "      <td>18996</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>-58.500999</td>\n",
-       "      <td>-63.125000</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>LAURENCE M. GOULD (R/V; call sign WCX7445; bui...</td>\n",
-       "      <td>US036605</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>929.809082</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18997</th>\n",
-       "      <td>18997</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>-58.598000</td>\n",
-       "      <td>-63.064999</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>LAURENCE M. GOULD (R/V; call sign WCX7445; bui...</td>\n",
-       "      <td>US036605</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>908.195984</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18998</th>\n",
-       "      <td>18998</td>\n",
-       "      <td>UNITED STATES</td>\n",
-       "      <td>-58.681999</td>\n",
-       "      <td>-63.015999</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>LAURENCE M. GOULD (R/V; call sign WCX7445; bui...</td>\n",
-       "      <td>US036605</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>914.778015</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18999</th>\n",
-       "      <td>18999</td>\n",
-       "      <td>LIBERIA</td>\n",
-       "      <td>-13.661000</td>\n",
-       "      <td>-174.035004</td>\n",
-       "      <td>20141231</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>SCRIPPS INSTITUTION OF OCEANOGRAPHY; LA JOLLA; CA</td>\n",
-       "      <td>E. R. WILHELMSHAVEN (Call sign ELZY3)</td>\n",
-       "      <td>LR000315</td>\n",
-       "      <td>XBT: DEEP BLUE (SIPPICAN)</td>\n",
-       "      <td>DEEP BLUE</td>\n",
-       "      <td>SIPPICAN</td>\n",
-       "      <td>985.260437</td>\n",
-       "      <td>0</td>\n",
-       "      <td>16686050</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2230877 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Unnamed: 0        country        lat         lon      date  year  \\\n",
-       "0               0  UNITED STATES  32.966667 -117.633331  19660412  1966   \n",
-       "1               1  UNITED STATES  33.016666 -118.116669  19660413  1966   \n",
-       "2               2  UNITED STATES  33.066666 -118.466667  19660414  1966   \n",
-       "3               3  UNITED STATES  32.700001 -118.666664  19660414  1966   \n",
-       "4               4  UNITED STATES  32.933334 -117.916664  19660414  1966   \n",
-       "...           ...            ...        ...         ...       ...   ...   \n",
-       "18995       18995  UNITED STATES -58.394001  -63.181000  20141231  2014   \n",
-       "18996       18996  UNITED STATES -58.500999  -63.125000  20141231  2014   \n",
-       "18997       18997  UNITED STATES -58.598000  -63.064999  20141231  2014   \n",
-       "18998       18998  UNITED STATES -58.681999  -63.015999  20141231  2014   \n",
-       "18999       18999        LIBERIA -13.661000 -174.035004  20141231  2014   \n",
-       "\n",
-       "       month  day                                          institute  \\\n",
-       "0          4   12                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "1          4   13                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "2          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "3          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "4          4   14                       US NAVY SHIPS OF OPPORTUNITY   \n",
-       "...      ...  ...                                                ...   \n",
-       "18995     12   31                                                  0   \n",
-       "18996     12   31                                                  0   \n",
-       "18997     12   31                                                  0   \n",
-       "18998     12   31                                                  0   \n",
-       "18999     12   31  SCRIPPS INSTITUTION OF OCEANOGRAPHY; LA JOLLA; CA   \n",
-       "\n",
-       "                                                platform cruise_number  \\\n",
-       "0                                              KEARSARGE      US044120   \n",
-       "1                                              KEARSARGE      US044120   \n",
-       "2                                              KEARSARGE      US044120   \n",
-       "3                                              KEARSARGE      US044120   \n",
-       "4                                              KEARSARGE      US044120   \n",
-       "...                                                  ...           ...   \n",
-       "18995  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18996  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18997  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18998  LAURENCE M. GOULD (R/V; call sign WCX7445; bui...      US036605   \n",
-       "18999              E. R. WILHELMSHAVEN (Call sign ELZY3)      LR000315   \n",
-       "\n",
-       "                      instrument      model manufacturer   max_depth  \\\n",
-       "0             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.892670   \n",
-       "1             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.852051   \n",
-       "2             XBT: T4 (SIPPICAN)         T4     SIPPICAN   70.602089   \n",
-       "3             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.907410   \n",
-       "4             XBT: T4 (SIPPICAN)         T4     SIPPICAN  466.811493   \n",
-       "...                          ...        ...          ...         ...   \n",
-       "18995  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  899.722412   \n",
-       "18996  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  929.809082   \n",
-       "18997  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  908.195984   \n",
-       "18998  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  914.778015   \n",
-       "18999  XBT: DEEP BLUE (SIPPICAN)  DEEP BLUE     SIPPICAN  985.260437   \n",
-       "\n",
-       "       imeta_applied        id  \n",
-       "0                  1   2052528  \n",
-       "1                  1   2052529  \n",
-       "2                  1   2052530  \n",
-       "3                  1   2052531  \n",
-       "4                  1   2052532  \n",
-       "...              ...       ...  \n",
-       "18995              0  16686048  \n",
-       "18996              0  16686049  \n",
-       "18997              0  16686051  \n",
-       "18998              0  16686052  \n",
-       "18999              0  16686050  \n",
-       "\n",
-       "[2230877 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_df = xbt_df[~((xbt_df['max_depth'] < 0) | (xbt_df['max_depth'].isna()))]\n",
     "xbt_df"
@@ -903,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "d17d54f6-ebcd-4622-86cf-34493d6d38f9",
    "metadata": {},
    "outputs": [],
@@ -913,48 +237,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "3f69d970-ae1b-4e37-ad7c-3e4aad3e990a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['XBT: T4 (SIPPICAN)', 'XBT: T7 (SIPPICAN)', 'XBT: DEEP BLUE (SIPPICAN)',\n",
-       "       'XBT: T4 (TSK - TSURUMI SEIKI Co.)', 'XBT: T10 (SIPPICAN)',\n",
-       "       'XBT: T6 (TSK - TSURUMI SEIKI Co.)', 'XBT: T7 (UNKNOWN BRAND)',\n",
-       "       'XBT: T7 (TSK - TSURUMI SEIKI Co.)', 'XBT: T4 (UNKNOWN BRAND)',\n",
-       "       'XBT: T5 (SIPPICAN)', 'XBT: T6 (SIPPICAN)',\n",
-       "       'XBT: T5 (TSK - TSURUMI SEIKI Co.)'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_df[target_feature].value_counts().index[:12]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "96275645-9a21-4419-81f3-664342fa6c41",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2157559, 17)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_df = xbt_df[~(xbt_df[target_feature].str.contains('UNKNOWN'))]\n",
     "xbt_df.shape"
@@ -962,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "6d1551f1-9a1d-44d0-8f36-f905cec21ea1",
    "metadata": {},
    "outputs": [],
@@ -972,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "90e363bf-6d3e-451c-bd78-289a5d6416ff",
    "metadata": {},
    "outputs": [],
@@ -983,49 +279,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "622ffbc5-c1e6-471e-a6e4-59a9fab213d5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1119188, 17)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_labelled.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "0142f623-20f9-421e-8ffa-252e3f982877",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1034887, 17)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xbt_unlabelled.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "0b6a597d-462a-4a2c-9c23-d3304ced7d7b",
    "metadata": {},
    "outputs": [],
@@ -1035,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "5af060a4-ab32-48b0-8b20-372a76433306",
    "metadata": {},
    "outputs": [],
@@ -1059,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "34125b8d-4869-4de5-820e-6f184255d94b",
    "metadata": {},
    "outputs": [],
@@ -1079,62 +353,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "159abc6e-13bf-4abb-b8ae-ce047fb0b8c7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ccp_alpha': 0.0,\n",
-       " 'class_weight': None,\n",
-       " 'criterion': 'gini',\n",
-       " 'max_depth': None,\n",
-       " 'max_features': None,\n",
-       " 'max_leaf_nodes': None,\n",
-       " 'min_impurity_decrease': 0.0,\n",
-       " 'min_impurity_split': None,\n",
-       " 'min_samples_leaf': 1,\n",
-       " 'min_samples_split': 2,\n",
-       " 'min_weight_fraction_leaf': 0.0,\n",
-       " 'random_state': None,\n",
-       " 'splitter': 'best'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sklearn.tree.DecisionTreeClassifier().get_params()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "9383a5ba-3ba4-4bdd-beab-977eefefe045",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.6 s, sys: 27.4 ms, total: 2.63 s\n",
-      "Wall time: 2.62 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeClassifier(max_depth=5, min_samples_leaf=2, min_samples_split=5)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "dt_clf = sklearn.tree.DecisionTreeClassifier(\n",
@@ -1163,23 +395,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "8fab12db-3995-48ee-b65e-16dbba75a3c1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 4608x4608 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig1 = matplotlib.pyplot.figure(figsize=(64,64))\n",
     "_ = sklearn.tree.plot_tree(dt_clf)\n",
@@ -1189,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "4db514ea-cf80-46ed-8a01-6cd4d5ad779e",
    "metadata": {},
    "outputs": [],
@@ -1204,30 +423,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "4f647f39-580e-4e8e-89ff-f59ece58a388",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 30.6 s, sys: 407 ms, total: 31 s\n",
-      "Wall time: 31 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RandomForestClassifier(max_depth=5, min_samples_leaf=2, min_samples_split=5,\n",
-       "                       n_estimators=20)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "rf_clf = sklearn.ensemble.RandomForestClassifier(\n",
@@ -1238,36 +437,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "2ea625a1-726b-4c13-a1e7-afd9b2f7b77e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "param value bootstrap=True\n",
-      "param value ccp_alpha=0.0\n",
-      "param value class_weight=None\n",
-      "param value criterion=gini\n",
-      "param value max_depth=5\n",
-      "param value max_features=auto\n",
-      "param value max_leaf_nodes=None\n",
-      "param value max_samples=None\n",
-      "param value min_impurity_decrease=0.0\n",
-      "param value min_impurity_split=None\n",
-      "param value min_samples_leaf=2\n",
-      "param value min_samples_split=5\n",
-      "param value min_weight_fraction_leaf=0.0\n",
-      "param value n_estimators=20\n",
-      "param value n_jobs=None\n",
-      "param value oob_score=False\n",
-      "param value random_state=None\n",
-      "param value verbose=0\n",
-      "param value warm_start=False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for p1,param_val in rf_clf.get_params().items():\n",
     "    print(f'param value {p1}={param_val}')"
@@ -1275,67 +448,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "29be4895-11b6-427f-8dc7-2ee20bdc7ed9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=2127632118),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1480289059),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=2031674859),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1303177278),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=400739472),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1951930984),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1334859393),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=510631549),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1914128362),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1625161630),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=43277808),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=2086797042),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=321459882),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=191584623),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=41992307),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1975767733),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1922022949),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=857753517),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1428610293),\n",
-       " DecisionTreeClassifier(max_depth=5, max_features='auto', min_samples_leaf=2,\n",
-       "                        min_samples_split=5, random_state=1654474062)]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rf_clf.estimators_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "32c0e37b-5926-4e3d-86ce-2b2808637d99",
    "metadata": {},
    "outputs": [],
@@ -1345,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "2939c278-7680-4686-b055-808ddfb5313c",
    "metadata": {},
    "outputs": [],
@@ -1358,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "ee1ae1e4-f7cd-491b-a7c7-54c7f254f885",
    "metadata": {},
    "outputs": [],
@@ -1369,31 +492,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "5cfc8863-e1c6-406f-ac14-6298924dd1cb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'frequency of different labels for random forest predictions.'}, ylabel='None'>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig1 = matplotlib.pyplot.figure(figsize=(16,8))\n",
     "ax1 = fig1.add_subplot(1,2,1, title='frequency of different labels for decision tree predictions.')\n",
@@ -1425,21 +527,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "1f49b813-464f-4999-9ede-3d205fdf67c6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/h01/shaddad/.conda/envs/ml-weather-tutorial-tf/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/home/h01/shaddad/.conda/envs/ml-weather-tutorial-tf/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "prec_dt, recall_dt, f1_dt, support_dt = sklearn.metrics.precision_recall_fscore_support(y_test, y_pred_dt, average=None)\n",
     "prec_rf, recall_rf, f1_rf, support_rf = sklearn.metrics.precision_recall_fscore_support(y_test, y_pred_rf, average=None)\n",
@@ -1458,33 +549,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "0c18243b-cf55-4c24-884f-fb19141d4146",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='classes'>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x2160 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig1 = matplotlib.pyplot.figure(figsize=(20,30))\n",
     "ax1 = fig1.add_subplot(2,2,1)\n",
@@ -1569,12 +637,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "6b38fd4b-ef1d-4929-bc87-373030a9bac1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow \n",
+    "import keras\n",
+    "import tensorflow.keras\n",
     "import tensorflow.keras.layers \n",
     "import tensorflow.keras.layers \n",
     "import tensorflow.keras.layers \n",
@@ -1582,9 +652,18 @@
     "import code"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7a80c7d2-de48-4eac-ac14-e81907c9c0b4",
+   "metadata": {},
+   "source": [
+    "## Define inputs\n",
+    "Specify the hyperparametersfor the pipeline and the location of the input data"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "id": "2d47a88f-7c9e-4148-8125-f0fb9e288531",
    "metadata": {},
    "outputs": [],
@@ -1598,48 +677,517 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fb16076-85ab-49e9-9a4e-98d437da5d9a",
+   "cell_type": "markdown",
+   "id": "2121ef05-b41c-4c83-a950-86b99a8e662c",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# prepreprroces"
+    "Set up the hyperparameters for training the neural network."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "a5fb8e27-4066-4572-941a-75451ee538a7",
+   "execution_count": 63,
+   "id": "0fb16076-85ab-49e9-9a4e-98d437da5d9a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train test split??"
+    "wl='sw'\n",
+    "target='nflx'\n",
+    "nsamps = '50.0K'\n",
+    "scale_data = True\n",
+    "if wl=='sw':\n",
+    "  model = 'sw_260'\n",
+    "  model_ref = 'sw_ga7'\n",
+    "elif wl=='lw':\n",
+    "  model = 'lw_300'\n",
+    "  model_ref = 'lw_ga7'\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d1d0098-fdf0-41d9-b876-5c638d7ac330",
+   "cell_type": "markdown",
+   "id": "71dec71a-599e-41a2-b200-9cd6f6157970",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# construct FF architecture"
+    "Construct the paths to file names that contain the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1825e462-ad13-40c9-88da-c95a9e9ab329",
+   "execution_count": 65,
+   "id": "1612a73b-7e99-43e9-a09b-72d2d78184df",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root dir: /project/informatics_lab/data_science_cop/socrates_emulation\n"
+     ]
+    }
+   ],
    "source": [
-    "# train NN"
+    "fnext='train'\n",
+    "fn_meta = model+'_meta_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_levs = model+'_dat_levs_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_lays = model+'_dat_lays_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_surf = model+'_dat_surf_'+nsamps+'_'+fnext+'.npz'\n",
+    "if target=='nflx':\n",
+    "  fn_trg = model+'_trg_levs_'+nsamps+'_'+fnext+'.npz'\n",
+    "if target=='ndiv':\n",
+    "  fn_trg = model+'_trg_lays_'+nsamps+'_'+fnext+'.npz'\n",
+    " \n",
+    "fnext='test'\n",
+    "fn_meta_test = model+'_meta_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_levs_test = model+'_dat_levs_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_lays_test = model+'_dat_lays_'+nsamps+'_'+fnext+'.npz'\n",
+    "fn_dat_surf_test = model+'_dat_surf_'+nsamps+'_'+fnext+'.npz'\n",
+    "if target=='nflx':\n",
+    "  fn_trg_test = model+'_trg_levs_'+nsamps+'_'+fnext+'.npz'\n",
+    "  fn_trg_ref = model_ref+'_trg_levs_'+nsamps+'_'+fnext+'.npz'\n",
+    "if target=='ndiv':\n",
+    "  fn_trg_test = model+'_trg_lays_'+nsamps+'_'+fnext+'.npz'\n",
+    "  fn_trg_ref = model_ref+'_trg_lays_'+nsamps+'_'+fnext+'.npz'\n",
+    "\n",
+    "print('root dir:',data_dir)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "id": "95ecd226-2fc8-4ad2-b90c-817c1fefc9c0",
+   "metadata": {},
+   "source": [
+    "### Loading and preparing the  training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "43a1a64f-dd58-4400-9b51-896f17f22bd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading sw_260_dat_lays_50.0K_train.npz\n",
+      "loading sw_260_dat_surf_50.0K_train.npz\n",
+      "loading sw_260_trg_levs_50.0K_train.npz\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('loading',fn_dat_lays)\n",
+    "with numpy.load(data_dir / fn_dat_lays) as npzfile:\n",
+    "    dat_lays = npzfile['dat_lays']\n",
+    "    \n",
+    "print('loading',fn_dat_surf)\n",
+    "with numpy.load(data_dir / fn_dat_surf) as npzfile:\n",
+    "    dat_surf = npzfile['dat_surf']\n",
+    "    \n",
+    "print('loading',fn_trg)\n",
+    "with numpy.load(data_dir / fn_trg) as npzfile:\n",
+    "    if target=='nflx':\n",
+    "         trg = npzfile['trg_levs']\n",
+    "    elif target=='ndiv':\n",
+    "         trg = npzfile['trg_lays']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "463356e2-60f7-40cf-aec5-23eb74a05b95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsamps = trg.shape[0]\n",
+    "nlays = dat_lays.shape[1]\n",
+    "nlay_feats = dat_lays.shape[2]\n",
+    "nsurf_feats = dat_surf.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "09586ce4-57c3-4b9b-9d67-a8b2aa1e8ea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000, 70, 35)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat_lays.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "a5fb8e27-4066-4572-941a-75451ee538a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removing constant layer features: 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "if scale_data: # normalize by range\n",
+    "  scaler_lays = []\n",
+    "  use_lays = []\n",
+    "  for ic in range(nlay_feats):\n",
+    "    min0 = numpy.min(dat_lays[:,:,ic])\n",
+    "    range0 = numpy.max(dat_lays[:,:,ic]) - min0\n",
+    "    if range0 > 0.:\n",
+    "      dat_lays[:,:,ic] = (dat_lays[:,:,ic] - min0)/range0\n",
+    "      scaler_lays.append([min0, range0])\n",
+    "      use_lays.append(ic)\n",
+    "  if len(use_lays)<nlay_feats:\n",
+    "    print('removing constant layer features:', nlay_feats-len(use_lays))\n",
+    "    dat_lays = dat_lays[:,:,use_lays]\n",
+    "    nlay_feats = len(use_lays)\n",
+    "      \n",
+    "\n",
+    "  scaler_surf = []\n",
+    "  use_surf = []\n",
+    "  for ic in range(nsurf_feats):\n",
+    "    min0 = numpy.min(dat_surf[:,ic])\n",
+    "    range0 = numpy.max(dat_surf[:,ic]) - min0\n",
+    "    if range0 > 0.:\n",
+    "      dat_surf[:,ic] = (dat_surf[:,ic] - min0)/range0\n",
+    "      scaler_surf.append([min0, range0])\n",
+    "      use_surf.append(ic)\n",
+    "  if len(use_surf)<nsurf_feats:\n",
+    "    print('removing constant surf features:', nsurf_feats-len(use_surf))\n",
+    "    dat_surf = dat_surf[:,:,use_surf]\n",
+    "    nsurf_feats = len(use_surf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "ef5a3c17-2dba-433c-8b90-98c1a532e9a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ntrg_samps = trg.shape[0]\n",
+    "\n",
+    "if target=='nflx' or target=='ndiv':\n",
+    "  nouts=1\n",
+    "  ntrg_levs = trg.shape[1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "86089c56-6f6e-44c0-98ee-96f635548d57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_model_mlp(nlays, nlay_feats):\n",
+    "    profile_input = tensorflow.keras.layers.Input(shape=(nlays, nlay_feats), name='profile_input')\n",
+    "    surf_input = tensorflow.keras.layers.Input(shape=(nsurf_feats,), name='surf_input')\n",
+    "    flat_profs = tensorflow.keras.layers.Flatten()(profile_input)\n",
+    "    raw_in = tensorflow.keras.layers.concatenate([flat_profs, surf_input])\n",
+    "    raw_size = (nlays*nlay_feats)+nsurf_feats\n",
+    "    prof_size = nlays*nlay_feats\n",
+    "\n",
+    "    x = tensorflow.keras.layers.Dense(512, use_bias=False, activation='relu')(raw_in)\n",
+    "    x = tensorflow.keras.layers.Dense(512, use_bias=False, activation='relu')(x)\n",
+    "    x = tensorflow.keras.layers.Dense(256, use_bias=False, activation='relu')(x)\n",
+    "    x = tensorflow.keras.layers.Dense(256, use_bias=False, activation='relu')(x)\n",
+    "    x = tensorflow.keras.layers.Dense(128, use_bias=False, activation='relu')(x)\n",
+    "    x = tensorflow.keras.layers.Dense(128, use_bias=False, activation='relu')(x)\n",
+    "\n",
+    "    main_output = tensorflow.keras.layers.Dense(ntrg_levs, use_bias=True, activation='linear', name='main_output')(x)\n",
+    "    model = tensorflow.keras.models.Model(inputs=[profile_input, surf_input], outputs=[main_output])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "74e0d400-b092-40a5-bc2e-4b05354af9ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_model_cnn(nlays, nlay_feats):\n",
+    "    profile_input = tensorflow.keras.layers.Input(shape=(nlays, nlay_feats), name='profile_input')\n",
+    "    surf_input = tensorflow.keras.layers.Input(shape=(nsurf_feats,), name='surf_input')\n",
+    "    flat_profs = tensorflow.keras.layers.Flatten()(profile_input)\n",
+    "    raw_in = tensorflow.keras.layers.concatenate([flat_profs, surf_input])\n",
+    "    raw_size = (nlays*nlay_feats)+nsurf_feats\n",
+    "    prof_size = nlays*nlay_feats\n",
+    "\n",
+    "    out = tensorflow.keras.layers.ZeroPadding1D(padding=1)(profile_input)\n",
+    "    out = tensorflow.keras.layers.Conv1D(32, 3, strides=1, activation='relu', use_bias=False, kernel_initializer='glorot_uniform', bias_initializer='zeros')(out)\n",
+    "    ident = out\n",
+    "    out = tensorflow.keras.layers.ZeroPadding1D(padding=1)(out)\n",
+    "    out = tensorflow.keras.layers.Conv1D(32, 3, strides=1, activation='relu', use_bias=False, kernel_initializer='glorot_uniform', bias_initializer='zeros')(out)\n",
+    "    out = tensorflow.keras.layers.ZeroPadding1D(padding=1)(out)\n",
+    "    out = tensorflow.keras.layers.Conv1D(32, 3, strides=1, activation='relu', use_bias=False, kernel_initializer='glorot_uniform', bias_initializer='zeros')(out)\n",
+    "    x = tensorflow.keras.layers.add([out, ident])\n",
+    "    out = tensorflow.keras.layers.Flatten()(x)\n",
+    "    out = tensorflow.keras.layers.Dense(prof_size, use_bias=False, activation='relu')(out)\n",
+    "\n",
+    "    out = tensorflow.keras.layers.concatenate([out, surf_input])\n",
+    "    x = tensorflow.keras.layers.add([out, raw_in])\n",
+    "    x = tensorflow.keras.layers.Dense(1024, use_bias=False, activation='relu')(x)\n",
+    "    x = tensorflow.keras.layers.Dense(1024, use_bias=False, activation='relu')(x)\n",
+    "\n",
+    "    main_output = tensorflow.keras.layers.Dense(ntrg_levs, use_bias=True, activation='linear', name='main_output')(x)\n",
+    "    model = tensorflow.keras.models.Model(inputs=[profile_input, surf_input], outputs=[main_output])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "7d1d0098-fdf0-41d9-b876-5c638d7ac330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dict = {'mlp': {'build_func': build_model_mlp,},\n",
+    "              'cnn_1d': {'build_func': build_model_cnn,},\n",
+    "             }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "eaef9962-ab06-4985-98c3-415b553c59ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "building and training model mlp\n",
+      "building and training model cnn_1d\n",
+      "CPU times: user 4min 39s, sys: 26.7 s, total: 5min 6s\n",
+      "Wall time: 1min 15s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "for model_name in model_dict.keys():\n",
+    "    print(f'building and training model {model_name}')\n",
+    "    model_dict[model_name]['model_object'] = model_dict[model_name]['build_func'](nlays=nlays, nlay_feats=nlay_feats)\n",
+    "    model_dict[model_name]['model_object'].compile(loss='mean_absolute_error',\n",
+    "                                                   optimizer='adam')\n",
+    "    model_dict[model_name]['model_object'].fit([dat_lays, dat_surf], trg, epochs=1, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "1825e462-ad13-40c9-88da-c95a9e9ab329",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66db5a77-b8ce-4d8a-b6fd-2925d06a1552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for model_name, selected_model in model_dict.items():\n",
+    "  predictions[model_name] = selected_model['model_object'].predict([dat_lays_test, dat_surf_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c59a213f-78a2-42d0-b7af-e095b2c80337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics_dict = {}\n",
+    "for model_name in model_dict.keys():\n",
+    "    metrics_dict['me_p'] = np.zeros(ntrg_levs)\n",
+    "    metrics_dict['me_ctl'] = np.zeros(ntrg_levs)\n",
+    "    metrics_dict['mae_p'] = np.zeros(ntrg_levs)\n",
+    "    metrics_dict['mae_ctl'] = np.zeros(ntrg_levs)\n",
+    "    for ilev in range(ntrg_levs):\n",
+    "      metrics_dict['me_p'][ilev] = np.mean(predictions[model_name][:,ilev] - trg_test[:,ilev])\n",
+    "      metrics_dict['me_ctl'][ilev] = np.mean(trg_ref[:,ilev] - trg_test[:,ilev])\n",
+    "      metrics_dict['mae_p'][ilev] = np.mean(np.abs(predictions[model_name][:,ilev] - trg_test[:,ilev]))\n",
+    "      metrics_dict['mae_ctl'][ilev] = np.mean(np.abs(trg_ref[:,ilev] - trg_test[:,ilev]))    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b203ea6-1c7a-4297-9bed-fc6e4fc5c50a",
+   "metadata": {},
+   "source": [
+    "### Visualise metrics\n",
+    "\n",
+    "Display the perfromance metrics for our trained algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "759c8a63-1080-4d69-8450-ca35c6dac757",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yax = numpy.arange(1,len(me_p[1:])+1)[::-1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9de013f-997f-4faf-a8fd-7558213d7d75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig1 = maplotlib.pyplot.figure('compare_NN_results', figsize=(16,6))\n",
+    "for ix1,model_name in enumerate(model_dict.keys()):\n",
+    "    ax1 = fig1.add_subplot(2,4,(4*ix1) + 1,title=f'results for {model_name}')\n",
+    "    ax1.plot(me_p[1:],yax, '-r', label='ME emu')\n",
+    "    ax1.set_xlabel('level')\n",
+    "    ax1.set_ylabel('flux / flux div. difference')\n",
+    "    ax1.legend(loc='upper right')\n",
+    "  \n",
+    "    ax1 = fig1.add_subplot(2,4,(4*ix1) + 2, title=f'results for {model_name}')\n",
+    "    ax1.plot(mae_p[1:],yax, '--r', label='MAE emu')\n",
+    "    ax1.set_xlabel('level')\n",
+    "    ax1.set_ylabel('flux / flux div. difference')\n",
+    "    ax1.legend(loc='upper right')\n",
+    "    \n",
+    "    ax1 = fig1.add_subplot(2,4, (4*ix1) + 3, title=f'results for {model_name}')\n",
+    "    plt.plot(me_ctl[1:],yax, '-c', label='ME ga7')\n",
+    "    ax1.set_xlabel('level')\n",
+    "    ax1.set_ylabel('flux / flux div. difference')\n",
+    "    ax1.legend(loc='upper right')\n",
+    "    \n",
+    "    ax1 = fig1.add_subplot(2,4, (4*ix1) + 4, title=f'results for {model_name}')\n",
+    "    ax1.plot(mae_ctl[1:],yax, '--c', label='MAE ga7')\n",
+    "    ax1.set_xlabel('level')\n",
+    "    ax1.set_ylabel('flux / flux div. difference')\n",
+    "    ax1.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b53155c-0d44-4ba1-b17a-b8bb40915548",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cb134b5-0533-4e2e-874d-b5fffa4199d7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bd95c22-4f47-4adb-a9d3-93cc839118b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ae66ab5-d1a9-4852-8735-86c2bb1fd491",
+   "metadata": {},
+   "source": [
+    "### Testing the output\n",
+    "\n",
+    "Next we load in the test data and do inferenceto check the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "02735791-0a41-4674-9d09-182bcee24f2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading sw_260_dat_lays_50.0K_test.npz\n",
+      "loading sw_260_dat_surf_50.0K_test.npz\n",
+      "loading sw_260_trg_levs_50.0K_test.npz\n",
+      "loading sw_ga7_trg_levs_50.0K_test.npz\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('loading',fn_dat_lays_test)\n",
+    "with numpy.load(data_dir / fn_dat_lays_test) as npzfile:\n",
+    "    dat_lays_test = npzfile['dat_lays']\n",
+    "\n",
+    "print('loading',fn_dat_surf_test)\n",
+    "with numpy.load(data_dir / fn_dat_surf_test) as npzfile:\n",
+    "    dat_surf_test = npzfile['dat_surf']\n",
+    "\n",
+    "print('loading',fn_trg_test)\n",
+    "with numpy.load(data_dir / fn_trg_test)as npzfile:\n",
+    "    if target=='nflx':\n",
+    "        trg_test = npzfile['trg_levs']\n",
+    "    elif target=='ndiv':\n",
+    "         trg_test = npzfile['trg_lays']\n",
+    "\n",
+    "print('loading',fn_trg_ref)\n",
+    "with numpy.load(data_dir / fn_trg_ref) as npzfile:\n",
+    "    if target=='nflx':\n",
+    "         trg_ref = npzfile['trg_levs_ref']\n",
+    "    elif target=='ndiv':\n",
+    "         trg_ref = npzfile['trg_lays_ref']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "5a33eeab-1ea6-4fb2-b328-e32a70f667cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale test data\n",
+    "if scale_data: # normalize by range\n",
+    "  dat_lays_test = dat_lays_test[:,:,use_lays]\n",
+    "  for ic in range(nlay_feats):\n",
+    "    dat_lays_test[:,:,ic] = (dat_lays_test[:,:,ic] - scaler_lays[ic][0])/scaler_lays[ic][1]\n",
+    "\n",
+    "  dat_surf_test = dat_surf_test[:,use_surf]\n",
+    "  for ic in range(nsurf_feats):\n",
+    "    dat_surf_test[:,ic] = (dat_surf_test[:,ic] - scaler_surf[ic][0])/scaler_surf[ic][1]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b07442e-a144-4926-8982-3ad350d93608",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "650c0064-36d6-4366-9cb4-a905173eb0de",
    "metadata": {},
    "outputs": [],
@@ -1675,71 +1223,349 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "c06fbf53-7998-4e67-8743-4f735e03df59",
+   "metadata": {},
+   "source": [
+    "### Example - Recurrent Neural Network"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "bcb8c5c2-6998-41a5-873d-4a3e2ca2280a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# load rotors data"
+    "try:\n",
+    "    falklands_data_dir = os.environ['OPMET_ROTORS_DATA_ROOT']\n",
+    "except KeyError:\n",
+    "    falklands_data_dir = '/project/informatics_lab/data_science_cop/ML_challenges/2021_opmet_challenge'\n",
+    "falklands_data_dir = pathlib.Path(falklands_data_dir) /  'Rotors'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "cd6b7e50-4595-47ce-b11a-70da4ac4ecec",
+   "execution_count": 19,
+   "id": "c50bc3b8-b01f-4caf-87fb-95c23c0188ed",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# clean, preprocess, split based on year"
+    "falklands_data_fname = 'new_training.csv'\n",
+    "falklands_data_path = falklands_data_dir / falklands_data_fname\n",
+    "falklands_df = pandas.read_csv(falklands_data_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0bc6c527-fbf5-411f-a81b-ee170251c87f",
+   "execution_count": 13,
+   "id": "f6424020-429b-43d6-813b-e2f266b087cd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create LSTM"
+    "temp_feature_names = [f'air_temp_{i1}' for i1 in range(1,23)]\n",
+    "humidity_feature_names = [f'sh_{i1}' for i1 in range(1,23)]\n",
+    "wind_direction_feature_names = [f'winddir_{i1}' for i1 in range(1,23)]\n",
+    "wind_speed_feature_names = [f'windspd_{i1}' for i1 in range(1,23)]\n",
+    "target_feature_name = 'rotors_present'\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e4a81caf-830e-407e-92aa-60cb1e19be61",
+   "execution_count": 20,
+   "id": "c1acb91b-84dd-4c50-a163-d402bd085287",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train LSTM"
+    "falklands_df = falklands_df.rename({'Rotors 1 is true': target_feature_name},axis=1)\n",
+    "falklands_df.loc[falklands_df[falklands_df[target_feature_name].isna()].index, target_feature_name] = 0\n",
+    "falklands_df['DTG'] = pandas.to_datetime(falklands_df['DTG'])\n",
+    "falklands_df = falklands_df.drop_duplicates(subset=['DTG'])\n",
+    "falklands_df = falklands_df[~falklands_df['DTG'].isnull()]\n",
+    "falklands_df = falklands_df[(falklands_df['wind_speed_obs'] >= 0.0) &\n",
+    "                            (falklands_df['air_temp_obs'] >= 0.0) &\n",
+    "                            (falklands_df['wind_direction_obs'] >= 0.0) &\n",
+    "                            (falklands_df['dewpoint_obs'] >= 0.0) \n",
+    "                           ]\n",
+    "falklands_df = falklands_df.drop_duplicates(subset='DTG')\n",
+    "falklands_df[target_feature_name]  = falklands_df[target_feature_name] .astype(bool)\n",
+    "falklands_df['time'] = pandas.to_datetime(falklands_df['DTG'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7d676600-4a91-497c-ab80-8ad1c4218138",
+   "execution_count": 10,
+   "id": "3c2188b0-59b8-40cc-b07a-efab21d83d7a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# evaluate (compare to previous notebook results)"
+    "def get_v_wind(wind_dir_name, wind_speed_name, row1):\n",
+    "    return math.cos(math.radians(row1[wind_dir_name])) * row1[wind_speed_name]\n",
+    "\n",
+    "def get_u_wind(wind_dir_name, wind_speed_name, row1):\n",
+    "    return math.sin(math.radians(row1[wind_dir_name])) * row1[wind_speed_name]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5ecb5a89-391c-43ea-80b7-a24dd0920c9b",
+   "execution_count": 23,
+   "id": "04b57a26-4c4a-433d-b3c4-b1faa1425173",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 16.2 s, sys: 2.07 s, total: 18.3 s\n",
+      "Wall time: 18.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "u_feature_template = 'u_wind_{level_ix}'\n",
+    "v_feature_template = 'v_wind_{level_ix}'\n",
+    "u_wind_feature_names = []\n",
+    "v_wind_features_names = []\n",
+    "for wsn1, wdn1 in zip(wind_speed_feature_names, wind_direction_feature_names):\n",
+    "    level_ix = int( wsn1.split('_')[1])\n",
+    "    u_feature = u_feature_template.format(level_ix=level_ix)\n",
+    "    u_wind_feature_names += [u_feature]\n",
+    "    falklands_df[u_feature] = falklands_df.apply(functools.partial(get_u_wind, wdn1, wsn1), axis='columns')\n",
+    "    v_feature = v_feature_template.format(level_ix=level_ix)\n",
+    "    v_wind_features_names += [v_feature]\n",
+    "    falklands_df[v_feature] = falklands_df.apply(functools.partial(get_v_wind, wdn1, wsn1), axis='columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e8f69909-cb92-49e5-a991-72643d61f2e5",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "rotors_train_df = falklands_df[falklands_df['time'] < datetime.datetime(2020,1,1,0,0)]\n",
+    "rotors_test_df = falklands_df[falklands_df['time'] > datetime.datetime(2020,1,1,0,0)]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "265004d5-67ab-4d1e-ae67-ebbaa5de8409",
+   "execution_count": 28,
+   "id": "403889d1-adb2-49b3-858c-c089407fb23f",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def preproc_input(data_subset, pp_dict):\n",
+    "    return numpy.concatenate([scaler1.transform(data_subset[[if1]]) for if1,scaler1 in pp_dict.items()],axis=1)\n",
+    "\n",
+    "def preproc_target(data_subset, enc1):\n",
+    "     return enc1.transform(data_subset[[target_feature_name]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f078f090-a21e-4e3f-b036-d1c50405dcda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/h01/shaddad/.conda/envs/ml-weather-tutorial-tf/lib/python3.8/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LabelEncoder()"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_feature_names = temp_feature_names + humidity_feature_names + u_wind_feature_names + v_wind_features_names\n",
+    "preproc_dict = {}\n",
+    "for if1 in input_feature_names:\n",
+    "    scaler1 = sklearn.preprocessing.StandardScaler()\n",
+    "    scaler1.fit(rotors_train_df[[if1]])\n",
+    "    preproc_dict[if1] = scaler1\n",
+    "    \n",
+    "target_encoder = sklearn.preprocessing.LabelEncoder()\n",
+    "target_encoder.fit(rotors_train_df[[target_feature_name]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8bb9d77f-3e82-4e58-b5f5-0009003fb881",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/h01/shaddad/.conda/envs/ml-weather-tutorial-tf/lib/python3.8/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train_rotors = preproc_input(rotors_train_df, preproc_dict)\n",
+    "y_train_rotors = preproc_target(rotors_train_df, target_encoder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "caa42d00-5000-4496-ae18-e13e2fa4d9b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/h01/shaddad/.conda/envs/ml-weather-tutorial-tf/lib/python3.8/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_test_rotors = preproc_input(rotors_test_df, preproc_dict)\n",
+    "y_test_rotors = preproc_target(rotors_test_df, target_encoder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0b32b8af-8c64-42a4-8021-be7a4dd9bd21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initial_learning_rate=2e-5\n",
+    "drop_out_rate=0.2\n",
+    "n_epochs=50\n",
+    "batch_size=100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "15cb9821-8811-4591-92fe-e0d37ce0db8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_nodes = 300\n",
+    "n_layers = 4\n",
+    "inputs_shape=X_train_rotors.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "4558c59e-be48-401d-a6ad-07d0a22190f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_ff_nn(n_layers, input_shape):\n",
+    "    model = tensorflow.keras.models.Sequential()\n",
+    "    model.add(tensorflow.keras.layers.Dropout(drop_out_rate, input_shape=(input_shape,)))\n",
+    "    for i in numpy.arange(0,n_layers):\n",
+    "        model.add(tensorflow.keras.layers.Dense(n_nodes, activation='relu', kernel_constraint=tensorflow.keras.constraints.max_norm(3)))\n",
+    "        model.add(tensorflow.keras.layers.Dropout(drop_out_rate))\n",
+    "    model.add(tensorflow.keras.layers.Dense(2, activation='softmax'))             # This is the output layer \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "d41e5dc2-a4c1-444f-94ac-28460f5e8841",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 55s, sys: 7.7 s, total: 2min 2s\n",
+      "Wall time: 39.9 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "rotors_ff_model = build_ff_nn(n_layers=n_layers, input_shape=inputs_shape)\n",
+    "opt = tensorflow.optimizers.Adam(learning_rate=initial_learning_rate)  \n",
+    "rotors_ff_model.compile(optimizer=opt, loss='mse', metrics=[tensorflow.keras.metrics.RootMeanSquaredError()])\n",
+    "rotors_history=rotors_ff_model.fit(X_train_rotors, \n",
+    "                                   y_train_rotors, \n",
+    "                                   validation_data=(X_test_rotors, \n",
+    "                                                    y_test_rotors), \n",
+    "                                   epochs=n_epochs, \n",
+    "                                   batch_size=batch_size, \n",
+    "                                   shuffle=True,\n",
+    "                                   verbose=False,\n",
+    "                                  )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0bc6c527-fbf5-411f-a81b-ee170251c87f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_LSTM_model(input_shape):\n",
+    "    model = tensorflow.keras.Sequential()\n",
+    "\n",
+    "    model.add(tensorflow.keras.layers.Input(shape=(1,input_shape,)))\n",
+    "    # Add a LSTM layer with 128 internal units.\n",
+    "    model.add(tensorflow.keras.layers.LSTM(64,))\n",
+    "\n",
+    "    # Add a Dense layer with 10 units.\n",
+    "    model.add(tensorflow.keras.layers.Dense(10))\n",
+    "    \n",
+    "    # add output layer\n",
+    "    model.add(tensorflow.keras.layers.Dense(2, activation='softmax'))             # This is the output layer \n",
+    "\n",
+    "    return model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "e4a81caf-830e-407e-92aa-60cb1e19be61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 51s, sys: 7.87 s, total: 1min 59s\n",
+      "Wall time: 38.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time \n",
+    "rotors_rnn_model = build_LSTM_model(input_shape=inputs_shape)\n",
+    "opt = tensorflow.optimizers.Adam(learning_rate=initial_learning_rate)  \n",
+    "rotors_rnn_model.compile(optimizer=opt, loss='mse', metrics=[tensorflow.keras.metrics.RootMeanSquaredError()])\n",
+    "rotors_history_rnn = rotors_ff_model.fit(X_train_rotors, \n",
+    "                                         y_train_rotors, \n",
+    "                                         validation_data=(X_test_rotors, \n",
+    "                                                          y_test_rotors), \n",
+    "                                         epochs=n_epochs, \n",
+    "                                         batch_size=batch_size, \n",
+    "                                         shuffle=True,         \n",
+    "                                         verbose=False,\n",
+    "                                  )"
+   ]
   },
   {
    "cell_type": "markdown",