dwdatareader_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gi9NZer60aAw"
   },
   "source": [
    "# Example of [dwdatareader](https://pypi.org/project/dwdatareader/) inside a Jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_vuv0f36eW8"
   },
   "source": [
    "## Examples of reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EKoGE-xwx4dJ",
    "outputId": "f964d655-74f1-476c-9ab2-7e05bb9e73f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15.4 4020031\n"
     ]
    }
   ],
   "source": [
    "import dwdatareader as dw\n",
    "print(dw.__version__, dw.getVersion())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UiZalcFRIrIZ"
   },
   "source": [
    "### Option 1: Read entire file into Pandas Dataframe\n",
    "Warning: May be *huge*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nXjcOowCzzNv",
    "outputId": "2e57ae27-4886-44b9-e72c-aed42811882b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003-10-09 21:27:46.812004+00:00 100.0 Hz 95.8 s\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPSvel</th>\n",
       "      <th>Math 0</th>\n",
       "      <th>X absolute</th>\n",
       "      <th>Y absolute</th>\n",
       "      <th>Velocity</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Used sattelites</th>\n",
       "      <th>CNT 0</th>\n",
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       "  </thead>\n",
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       "      <th>0.00</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>0.02</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.744722</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.993056</td>\n",
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       "      <th>0.04</th>\n",
       "      <td>89.263916</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.241389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>1891.475879</td>\n",
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       "    <tr>\n",
       "      <th>95.76</th>\n",
       "      <td>-0.079346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.475879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95.77</th>\n",
       "      <td>-0.073242</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>11385 rows × 8 columns</p>\n",
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      "text/plain": [
       "          GPSvel  Math 0  X absolute  Y absolute  Velocity  Direction  \\\n",
       "0.00   89.257812     0.0         NaN         NaN       NaN        NaN   \n",
       "0.01   89.263916     0.0         NaN         NaN       NaN        NaN   \n",
       "0.02   89.270020     0.0         NaN         NaN       NaN        NaN   \n",
       "0.03   89.282227     0.0         NaN         NaN       NaN        NaN   \n",
       "0.04   89.263916     0.0         NaN         NaN       NaN        NaN   \n",
       "...          ...     ...         ...         ...       ...        ...   \n",
       "95.75  -0.079346     0.0         NaN         NaN       NaN        NaN   \n",
       "95.76  -0.079346     0.0         NaN         NaN       NaN        NaN   \n",
       "95.77  -0.073242     0.0         NaN         NaN       NaN        NaN   \n",
       "95.78  -0.085449     0.0         NaN         NaN       NaN        NaN   \n",
       "95.79  -0.091553     0.0         NaN         NaN       NaN        NaN   \n",
       "\n",
       "       Used sattelites        CNT 0  \n",
       "0.00               NaN     0.248333  \n",
       "0.01               NaN     0.496389  \n",
       "0.02               NaN     0.744722  \n",
       "0.03               NaN     0.993056  \n",
       "0.04               NaN     1.241389  \n",
       "...                ...          ...  \n",
       "95.75              NaN  1891.475879  \n",
       "95.76              NaN  1891.475879  \n",
       "95.77              NaN  1891.475879  \n",
       "95.78              NaN  1891.475879  \n",
       "95.79              NaN  1891.475879  \n",
       "\n",
       "[11385 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with dw.open('Example_Drive01.d7d') as d7d:\n",
    "    print(d7d.info)\n",
    "    channels = [channel for channel in d7d\n",
    "                if not 'CAN' in d7d[channel].channel_index] # workaround CAN bug\n",
    "    df = d7d.dataframe(channels) # all channels by default\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframe includes uneven time intervals and a lot of NaN due to the different sampling rates for some channels.\n",
    "The following is one way to regularize the dataframe using the index from a single channel using Pandas mehods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>GPSvel</th>\n",
       "      <th>Math 0</th>\n",
       "      <th>X absolute</th>\n",
       "      <th>Y absolute</th>\n",
       "      <th>Velocity</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Used sattelites</th>\n",
       "      <th>CNT 0</th>\n",
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       "      <td>8.0</td>\n",
       "      <td>1891.475879</td>\n",
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       "    <tr>\n",
       "      <th>95.76</th>\n",
       "      <td>-0.079346</td>\n",
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       "      <td>497.7526</td>\n",
       "      <td>2728.7605</td>\n",
       "      <td>0.583380</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>1891.475879</td>\n",
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       "      <th>95.77</th>\n",
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       "      <td>8.0</td>\n",
       "      <td>1891.475879</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>1891.475879</td>\n",
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      "text/plain": [
       "          GPSvel  Math 0  X absolute  Y absolute   Velocity   Direction  \\\n",
       "0.00   89.257812     0.0    497.3444   2728.4121  85.179039  233.500000   \n",
       "0.01   89.263916     0.0    497.3444   2728.4121  85.179039  233.500000   \n",
       "0.02   89.270020     0.0    497.3444   2728.4121  85.179039  233.500000   \n",
       "0.03   89.282227     0.0    497.3444   2728.4121  85.179039  233.500000   \n",
       "0.04   89.263916     0.0    497.3444   2728.4121  85.179039  233.500000   \n",
       "...          ...     ...         ...         ...        ...         ...   \n",
       "95.75  -0.079346     0.0    497.7526   2728.7605   0.583380  204.199997   \n",
       "95.76  -0.079346     0.0    497.7526   2728.7605   0.583380  204.199997   \n",
       "95.77  -0.073242     0.0    497.7526   2728.7605   0.583380  204.199997   \n",
       "95.78  -0.085449     0.0    497.7526   2728.7605   0.583380  204.199997   \n",
       "95.79  -0.091553     0.0    497.7526   2728.7605   0.583380  204.199997   \n",
       "\n",
       "       Used sattelites        CNT 0  \n",
       "0.00               8.0     0.248333  \n",
       "0.01               8.0     0.496389  \n",
       "0.02               8.0     0.744722  \n",
       "0.03               8.0     0.993056  \n",
       "0.04               8.0     1.241389  \n",
       "...                ...          ...  \n",
       "95.75              8.0  1891.475879  \n",
       "95.76              8.0  1891.475879  \n",
       "95.77              8.0  1891.475879  \n",
       "95.78              8.0  1891.475879  \n",
       "95.79              8.0  1891.475879  \n",
       "\n",
       "[9580 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newIndex = df.index[df['GPSvel'].notna()]\n",
    "df2 = df.bfill().ffill().reindex(index=newIndex)\n",
    "df2"
   ]
  },
  {
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    "outputId": "0d0e3a6a-3d4f-4ee8-d0a0-7b337704b28a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a channel\n",
    "df['GPSvel'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZliDhQ6OIdzF"
   },
   "source": [
    "### Option 2: Operate on chunks of a specific channel to minimize memory requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CaWrEkp03GGo",
    "outputId": "0a6c00e6-178d-4913-fab7-90bdd582a2f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: GPSvel (kph) v\n",
      "9580 total samples\n",
      "0.0 s, 2000 samples, mean velocity 71.3724578857422 kph\n",
      "20.0 s, 2000 samples, mean velocity 75.65996704101562 kph\n",
      "40.0 s, 2000 samples, mean velocity 76.49486999511718 kph\n",
      "60.0 s, 2000 samples, mean velocity 80.88096313476562 kph\n",
      "80.0 s, 1580 samples, mean velocity 45.3124150143394 kph\n"
     ]
    }
   ],
   "source": [
    "with dw.open('Example_Drive01.d7d') as d7d:\n",
    "    ch = d7d['GPSvel']\n",
    "    print('Channel:', ch)\n",
    "    print(ch.number_of_samples, 'total samples')\n",
    "    for ds in ch.series_generator(chunk_size=2000):\n",
    "        print(ds.index[0], 's,', len(ds), 'samples, mean velocity', ds.mean(), ch.unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "iAi-t9oGFjTN",
    "outputId": "1cf9dc74-f2d9-4b95-97cc-ecdc15453f2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='kph'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the last chunk\n",
    "ds.plot(ylabel=ch.unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BMqOF3N1F2sT"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "dwdatareader example.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}