{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "24700c23",
   "metadata": {},
   "source": [
    "# Combine NS+OPS and extra_data\n",
    "\n",
    "This example illustrates how to:\n",
    "\n",
    "- Load data from two different instruments (NS and OPS)\n",
    "- Combine them on a common time base\n",
    "- Inspect and use the `extra_data` tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f8fc1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import aerosoltools as at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4638579d",
   "metadata": {},
   "source": [
    "## Load NS and OPS datasets\n",
    "\n",
    "A sample NanoScan (NS) and several OPS files are included in the test data directory. Here we utilize the very efficient `Load_data_from_folder` function, which can load and combine multiple files from the same instrument, by specifying a path and the loader function to use. In this example we also specify a (optional) search_word, which can be used to load only files containing this specific keyword in their name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49d55e32",
   "metadata": {},
   "outputs": [
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      },
      "text/plain": [
       "Loading files:   0%|          | 0/5 [00:00<?, ?file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "File load summary:\n",
      "\n",
      "+----------------------------------+---------+------------------------------------------------------------------------------------+\n",
      "|               File               | Status  |                                       Reason                                       |\n",
      "+----------------------------------+---------+------------------------------------------------------------------------------------+\n",
      "| Combine_example_OPS - file 1.csv | Loaded  |                                         -                                          |\n",
      "| Combine_example_OPS - file 2.csv | Loaded  |                                         -                                          |\n",
      "| Combine_example_OPS - file 3.csv | Loaded  |                                         -                                          |\n",
      "| Combine_example_OPS - file 4.csv | Loaded  |                                         -                                          |\n",
      "|      Combine_example_NS.csv      | Skipped | IndexError: too many indices for array: array is 0-dimensional, but 1 were indexed |\n",
      "+----------------------------------+---------+------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "ns_file = r\"..\\..\\tests\\data\\Combine_example_NS.csv\"\n",
    "ops_path = r\"..\\..\\tests\\data\"\n",
    "\n",
    "ns = at.Load_NS_file(ns_file, extra_data  =True)\n",
    "ops = at.Load_data_from_folder(ops_path, at.Load_OPS_file, search_word=\"Combine_example\",extra_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed98ead",
   "metadata": {},
   "source": [
    "The `Load_data_from_folder` function displays a progress bar so you can see how many files are loaded and the progress. In addition it prints a summary table, listing the files that were loaded, those that were skipped, and a reason as to why these were skipped. \n",
    "\n",
    "This enables you to load many files from a folder, which can include from subfolders, even if the folder contains many different files, where a simple search_word cannot distinguish them efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7ed6a7",
   "metadata": {},
   "source": [
    "## Inspect `extra_data` for each instrument\n",
    "\n",
    "Many loaders store auxiliary variables (e.g. flow, temperature, status flags)\n",
    "in the `.extra_data` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac7e9006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NS extra-data columns: ['Median (nm)', 'Mean (nm)', 'Geo Mean (nm)', 'Mode (nm)', 'GSD', 'Particle Density (g/cc)', 'Firmware Version', 'Status']\n",
      "OPS extra-data columns: ['Bin 1', 'Deadtime (s)', 'Temperature (C)', 'Humidity (%)', 'Ambient Pressure (kPa)', 'Alarms', 'Errors', 'Unnamed: 24']\n"
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      "text/plain": [
       "                     Median (nm)  Mean (nm)  Geo Mean (nm)  Mode (nm)     GSD  \\\n",
       "Datetime                                                                        \n",
       "2023-05-30 15:41:29      15.7036    25.3091        18.5497    15.3993  1.8865   \n",
       "2023-05-30 15:42:29      16.7808    29.9378        21.1565    15.3993  2.0303   \n",
       "2023-05-30 15:43:29      15.6058    21.0331        17.1038    15.3993  1.6626   \n",
       "2023-05-30 15:44:29      15.7465    21.2670        17.2874    15.3993  1.6668   \n",
       "2023-05-30 15:45:29      16.3273    24.3266        18.8023    15.3993  1.7967   \n",
       "\n",
       "                     Particle Density (g/cc)  Firmware Version  \\\n",
       "Datetime                                                         \n",
       "2023-05-30 15:41:29                      1.0               1.4   \n",
       "2023-05-30 15:42:29                      1.0               1.4   \n",
       "2023-05-30 15:43:29                      1.0               1.4   \n",
       "2023-05-30 15:44:29                      1.0               1.4   \n",
       "2023-05-30 15:45:29                      1.0               1.4   \n",
       "\n",
       "                                  Status  \n",
       "Datetime                                  \n",
       "2023-05-30 15:41:29  Filling reservoir;   \n",
       "2023-05-30 15:42:29            No errors  \n",
       "2023-05-30 15:43:29  Filling reservoir;   \n",
       "2023-05-30 15:44:29            No errors  \n",
       "2023-05-30 15:45:29            No errors  "
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      "text/plain": [
       "                     Bin 1  Deadtime (s)  Temperature (C)  Humidity (%)  \\\n",
       "Datetime                                                                  \n",
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       "\n",
       "                     Ambient Pressure (kPa)  Alarms  Errors  Unnamed: 24  \n",
       "Datetime                                                                  \n",
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       "2023-10-23 13:31:44                 100.733     NaN     NaN          NaN  \n",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "print(\"NS extra-data columns:\", list(ns.extra_data.columns))\n",
    "print(\"OPS extra-data columns:\", list(ops.extra_data.columns))\n",
    "\n",
    "# Show a small snippet if available\n",
    "display(ns.extra_data.head())\n",
    "display(ops.extra_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a32e416",
   "metadata": {},
   "source": [
    "## Combine NS and OPS on a common time grid\n",
    "\n",
    "The helper function `Combine_NS_OPS` aligns the two instruments in time and returns a combined dataset suitable for correlation analysis. If the timestamps between the two datasets does not match precisely e.g. differ a few seconds, then the matching can be relaxed via the `match` keyword. This can be set to either nearest, matching nearest datapoint within a tolerance time window. \n",
    "\n",
    "If the two datasets do not have the same sampling frequency e.g. one is per minute and the other is every second, the function can be called with `match = \"rebin\", rebin_freq = \"1min\"`, in which case the high frequency data is rebinned into 1 min times to match the low frequency data. A larger and common frequency can also be set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9d90f63",
   "metadata": {},
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-23 13:35:19</th>\n",
       "      <td>28912.922700</td>\n",
       "      <td>1746.8145</td>\n",
       "      <td>3937.4988</td>\n",
       "      <td>4386.7480</td>\n",
       "      <td>5792.1016</td>\n",
       "      <td>5432.0537</td>\n",
       "      <td>3771.3831</td>\n",
       "      <td>2020.9584</td>\n",
       "      <td>1083.6498</td>\n",
       "      <td>585.2220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 23:22:58</th>\n",
       "      <td>10894.390593</td>\n",
       "      <td>566.0240</td>\n",
       "      <td>1299.7166</td>\n",
       "      <td>1351.1873</td>\n",
       "      <td>2046.0566</td>\n",
       "      <td>2273.6438</td>\n",
       "      <td>1849.0562</td>\n",
       "      <td>1053.4200</td>\n",
       "      <td>357.7328</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.181798</td>\n",
       "      <td>1.857042</td>\n",
       "      <td>1.711253</td>\n",
       "      <td>1.462910</td>\n",
       "      <td>0.872719</td>\n",
       "      <td>0.421278</td>\n",
       "      <td>0.319729</td>\n",
       "      <td>0.257392</td>\n",
       "      <td>0.282528</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 23:23:58</th>\n",
       "      <td>10677.534091</td>\n",
       "      <td>521.8893</td>\n",
       "      <td>1213.6832</td>\n",
       "      <td>1371.0192</td>\n",
       "      <td>1979.4780</td>\n",
       "      <td>2195.3396</td>\n",
       "      <td>1825.7404</td>\n",
       "      <td>1096.6410</td>\n",
       "      <td>402.9259</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.891105</td>\n",
       "      <td>1.637886</td>\n",
       "      <td>1.498213</td>\n",
       "      <td>1.254038</td>\n",
       "      <td>0.832006</td>\n",
       "      <td>0.356717</td>\n",
       "      <td>0.293413</td>\n",
       "      <td>0.204987</td>\n",
       "      <td>0.275326</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 23:24:58</th>\n",
       "      <td>10860.292970</td>\n",
       "      <td>610.1611</td>\n",
       "      <td>1343.8060</td>\n",
       "      <td>1359.7363</td>\n",
       "      <td>2005.8888</td>\n",
       "      <td>2241.7649</td>\n",
       "      <td>1834.1871</td>\n",
       "      <td>1037.4493</td>\n",
       "      <td>314.7814</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.024242</td>\n",
       "      <td>3.527272</td>\n",
       "      <td>3.099999</td>\n",
       "      <td>2.460606</td>\n",
       "      <td>1.529293</td>\n",
       "      <td>0.713131</td>\n",
       "      <td>0.507071</td>\n",
       "      <td>0.369697</td>\n",
       "      <td>0.429293</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 23:25:58</th>\n",
       "      <td>10796.891972</td>\n",
       "      <td>544.4571</td>\n",
       "      <td>1282.0167</td>\n",
       "      <td>1390.1940</td>\n",
       "      <td>1961.6934</td>\n",
       "      <td>2197.8625</td>\n",
       "      <td>1852.8007</td>\n",
       "      <td>1115.0995</td>\n",
       "      <td>379.0328</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.777091</td>\n",
       "      <td>2.399495</td>\n",
       "      <td>2.224290</td>\n",
       "      <td>1.793328</td>\n",
       "      <td>1.043171</td>\n",
       "      <td>0.514537</td>\n",
       "      <td>0.387665</td>\n",
       "      <td>0.264820</td>\n",
       "      <td>0.301070</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 23:26:58</th>\n",
       "      <td>10888.894330</td>\n",
       "      <td>590.0825</td>\n",
       "      <td>1346.3096</td>\n",
       "      <td>1365.4722</td>\n",
       "      <td>2070.2341</td>\n",
       "      <td>2264.8035</td>\n",
       "      <td>1808.2617</td>\n",
       "      <td>1017.9924</td>\n",
       "      <td>371.5379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.640050</td>\n",
       "      <td>2.195177</td>\n",
       "      <td>2.032124</td>\n",
       "      <td>1.766408</td>\n",
       "      <td>1.069910</td>\n",
       "      <td>0.496205</td>\n",
       "      <td>0.362340</td>\n",
       "      <td>0.249612</td>\n",
       "      <td>0.264710</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4663 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Total_conc       11.5       15.4       20.5       27.4  \\\n",
       "Datetime                                                                        \n",
       "2023-10-23 13:31:19  28754.398451  1485.4313  3835.8508  4568.8809  5940.7231   \n",
       "2023-10-23 13:32:19  28443.038346  1638.4261  3983.9104  4449.2646  5711.5151   \n",
       "2023-10-23 13:33:19  28263.426000  1432.7457  3766.0723  4466.9683  5829.9810   \n",
       "2023-10-23 13:34:19  27502.510700  1694.3612  3835.4392  4182.8892  5507.8789   \n",
       "2023-10-23 13:35:19  28912.922700  1746.8145  3937.4988  4386.7480  5792.1016   \n",
       "...                           ...        ...        ...        ...        ...   \n",
       "2023-10-26 23:22:58  10894.390593   566.0240  1299.7166  1351.1873  2046.0566   \n",
       "2023-10-26 23:23:58  10677.534091   521.8893  1213.6832  1371.0192  1979.4780   \n",
       "2023-10-26 23:24:58  10860.292970   610.1611  1343.8060  1359.7363  2005.8888   \n",
       "2023-10-26 23:25:58  10796.891972   544.4571  1282.0167  1390.1940  1961.6934   \n",
       "2023-10-26 23:26:58  10888.894330   590.0825  1346.3096  1365.4722  2070.2341   \n",
       "\n",
       "                          36.5       48.7       64.9       86.6     115.5  \\\n",
       "Datetime                                                                    \n",
       "2023-10-23 13:31:19  5474.6567  3735.8103  1953.4159  1017.3690  560.3550   \n",
       "2023-10-23 13:32:19  5321.5229  3699.7014  1971.2294  1002.8712  513.6530   \n",
       "2023-10-23 13:33:19  5441.0298  3779.6973  2001.2233   994.9397  468.5747   \n",
       "2023-10-23 13:34:19  5217.7969  3656.2771  1933.9193   948.3063  440.3977   \n",
       "2023-10-23 13:35:19  5432.0537  3771.3831  2020.9584  1083.6498  585.2220   \n",
       "...                        ...        ...        ...        ...       ...   \n",
       "2023-10-26 23:22:58  2273.6438  1849.0562  1053.4200   357.7328    0.0000   \n",
       "2023-10-26 23:23:58  2195.3396  1825.7404  1096.6410   402.9259    0.0000   \n",
       "2023-10-26 23:24:58  2241.7649  1834.1871  1037.4493   314.7814    0.0000   \n",
       "2023-10-26 23:25:58  2197.8625  1852.8007  1115.0995   379.0328    0.0000   \n",
       "2023-10-26 23:26:58  2264.8035  1808.2617  1017.9924   371.5379    0.0000   \n",
       "\n",
       "                     ...    1561.5    1944.0    2420.5    3014.0    3752.5  \\\n",
       "Datetime             ...                                                     \n",
       "2023-10-23 13:31:19  ...  0.052996  0.035997  0.026998  0.024998  0.013999   \n",
       "2023-10-23 13:32:19  ...  0.547839  0.462708  0.430659  0.332509  0.221339   \n",
       "2023-10-23 13:33:19  ...  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2023-10-23 13:34:19  ...  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2023-10-23 13:35:19  ...  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "2023-10-26 23:22:58  ...  2.181798  1.857042  1.711253  1.462910  0.872719   \n",
       "2023-10-26 23:23:58  ...  1.891105  1.637886  1.498213  1.254038  0.832006   \n",
       "2023-10-26 23:24:58  ...  4.024242  3.527272  3.099999  2.460606  1.529293   \n",
       "2023-10-26 23:25:58  ...  2.777091  2.399495  2.224290  1.793328  1.043171   \n",
       "2023-10-26 23:26:58  ...  2.640050  2.195177  2.032124  1.766408  1.069910   \n",
       "\n",
       "                       4672.0    5816.5    7241.0    9015.5  All data  \n",
       "Datetime                                                               \n",
       "2023-10-23 13:31:19  0.006000  0.006000  0.002000  0.004000      True  \n",
       "2023-10-23 13:32:19  0.110169  0.081124  0.055084  0.055084      True  \n",
       "2023-10-23 13:33:19  0.000000  0.000000  0.000000  0.000000      True  \n",
       "2023-10-23 13:34:19  0.000000  0.000000  0.000000  0.000000      True  \n",
       "2023-10-23 13:35:19  0.000000  0.000000  0.000000  0.000000      True  \n",
       "...                       ...       ...       ...       ...       ...  \n",
       "2023-10-26 23:22:58  0.421278  0.319729  0.257392  0.282528      True  \n",
       "2023-10-26 23:23:58  0.356717  0.293413  0.204987  0.275326      True  \n",
       "2023-10-26 23:24:58  0.713131  0.507071  0.369697  0.429293      True  \n",
       "2023-10-26 23:25:58  0.514537  0.387665  0.264820  0.301070      True  \n",
       "2023-10-26 23:26:58  0.496205  0.362340  0.249612  0.264710      True  \n",
       "\n",
       "[4663 rows x 30 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = at.Combine_NS_OPS(ns, ops, match=\"nearest\",tolerance=\"30s\",)\n",
    "combined.data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd12495",
   "metadata": {},
   "source": [
    "The resulting variable is an aerosol2d class object, with the same functionalities as the raw loaded data. It is thus possible to plot particle data in the broad size range from 10 nm (Nanoscan lower limit) to 10 µm (OPS upper limit).\n",
    "\n",
    "The two datasets are combined by simply discarding the upper bins of the Nanoscan, while including all of the OPS bins, as the nanoscan is known to be uncertain at upper range of its particle size spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36e9f909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot_timeseries(y_3d=(1,0));\n",
    "# as some bins have concentrations of 0 cm-3 it is necessary to set the lower limit to 1 via y_3d = (1,0)\n",
    "# the upper limit of 0 indicates that the maximum value in the data should be used. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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