MotorData.ipynb

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        "<a href=\"https://colab.research.google.com/github/anya-tralshawala/MotorData/blob/main/MotorData.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "### **Motor Data**"
      ],
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        "id": "0buWaZMHdQhY"
      }
    },
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      "source": [
        "Only run first section if you need to..."
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              "              u_q    coolant  stator_winding       u_d  stator_tooth  \\\n",
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              "1330815 -2.100158  30.721319       45.132307  0.807309     41.734763   \n",
              "\n",
              "         motor_speed       i_d       i_q         pm  stator_yoke    ambient  \\\n",
              "0           0.002866  0.004419  0.000328  24.554214    18.316547  19.850691   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# reading csv file\n",
        "df = pd.read_csv(\"drive/MyDrive/measures_v2.csv\")\n",
        "df"
      ]
    },
    {
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              "              u_q    coolant  stator_winding       u_d  stator_tooth  \\\n",
              "0       -0.450682  18.805172       19.086670 -0.350055     18.293219   \n",
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              "\n",
              "         motor_speed       i_d       i_q         pm  stator_yoke    ambient  \\\n",
              "0           0.002866  0.004419  0.000328  24.554214    18.316547  19.850691   \n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>u_q</th>\n",
              "      <th>coolant</th>\n",
              "      <th>stator_winding</th>\n",
              "      <th>u_d</th>\n",
              "      <th>stator_tooth</th>\n",
              "      <th>motor_speed</th>\n",
              "      <th>i_d</th>\n",
              "      <th>i_q</th>\n",
              "      <th>pm</th>\n",
              "      <th>stator_yoke</th>\n",
              "      <th>ambient</th>\n",
              "      <th>torque</th>\n",
              "      <th>profile_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.450682</td>\n",
              "      <td>18.805172</td>\n",
              "      <td>19.086670</td>\n",
              "      <td>-0.350055</td>\n",
              "      <td>18.293219</td>\n",
              "      <td>0.002866</td>\n",
              "      <td>0.004419</td>\n",
              "      <td>0.000328</td>\n",
              "      <td>24.554214</td>\n",
              "      <td>18.316547</td>\n",
              "      <td>19.850691</td>\n",
              "      <td>1.871008e-01</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.325737</td>\n",
              "      <td>18.818571</td>\n",
              "      <td>19.092390</td>\n",
              "      <td>-0.305803</td>\n",
              "      <td>18.294807</td>\n",
              "      <td>0.000257</td>\n",
              "      <td>0.000606</td>\n",
              "      <td>-0.000785</td>\n",
              "      <td>24.538078</td>\n",
              "      <td>18.314955</td>\n",
              "      <td>19.850672</td>\n",
              "      <td>2.454175e-01</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.440864</td>\n",
              "      <td>18.828770</td>\n",
              "      <td>19.089380</td>\n",
              "      <td>-0.372503</td>\n",
              "      <td>18.294094</td>\n",
              "      <td>0.002355</td>\n",
              "      <td>0.001290</td>\n",
              "      <td>0.000386</td>\n",
              "      <td>24.544693</td>\n",
              "      <td>18.326307</td>\n",
              "      <td>19.850657</td>\n",
              "      <td>1.766153e-01</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.327026</td>\n",
              "      <td>18.835567</td>\n",
              "      <td>19.083031</td>\n",
              "      <td>-0.316199</td>\n",
              "      <td>18.292542</td>\n",
              "      <td>0.006105</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.002046</td>\n",
              "      <td>24.554018</td>\n",
              "      <td>18.330833</td>\n",
              "      <td>19.850647</td>\n",
              "      <td>2.383027e-01</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.471150</td>\n",
              "      <td>18.857033</td>\n",
              "      <td>19.082525</td>\n",
              "      <td>-0.332272</td>\n",
              "      <td>18.291428</td>\n",
              "      <td>0.003133</td>\n",
              "      <td>-0.064317</td>\n",
              "      <td>0.037184</td>\n",
              "      <td>24.565397</td>\n",
              "      <td>18.326662</td>\n",
              "      <td>19.850639</td>\n",
              "      <td>2.081967e-01</td>\n",
              "      <td>17</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330811</th>\n",
              "      <td>-2.152128</td>\n",
              "      <td>30.721298</td>\n",
              "      <td>45.274497</td>\n",
              "      <td>0.791801</td>\n",
              "      <td>41.934347</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>-2.000169</td>\n",
              "      <td>1.097528</td>\n",
              "      <td>62.147780</td>\n",
              "      <td>38.653720</td>\n",
              "      <td>23.989078</td>\n",
              "      <td>-8.116730e-60</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330812</th>\n",
              "      <td>-2.258684</td>\n",
              "      <td>30.721306</td>\n",
              "      <td>45.239017</td>\n",
              "      <td>0.778900</td>\n",
              "      <td>41.868923</td>\n",
              "      <td>0.002985</td>\n",
              "      <td>-2.000499</td>\n",
              "      <td>1.097569</td>\n",
              "      <td>62.142646</td>\n",
              "      <td>38.656328</td>\n",
              "      <td>23.970700</td>\n",
              "      <td>-5.815891e-60</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330813</th>\n",
              "      <td>-2.130312</td>\n",
              "      <td>30.721312</td>\n",
              "      <td>45.211576</td>\n",
              "      <td>0.804914</td>\n",
              "      <td>41.804819</td>\n",
              "      <td>0.002301</td>\n",
              "      <td>-1.999268</td>\n",
              "      <td>1.098765</td>\n",
              "      <td>62.138387</td>\n",
              "      <td>38.650923</td>\n",
              "      <td>23.977234</td>\n",
              "      <td>-4.167268e-60</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330814</th>\n",
              "      <td>-2.268498</td>\n",
              "      <td>30.721316</td>\n",
              "      <td>45.193508</td>\n",
              "      <td>0.763091</td>\n",
              "      <td>41.762220</td>\n",
              "      <td>0.005662</td>\n",
              "      <td>-2.000999</td>\n",
              "      <td>1.095696</td>\n",
              "      <td>62.133422</td>\n",
              "      <td>38.655686</td>\n",
              "      <td>24.001421</td>\n",
              "      <td>-2.985978e-60</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330815</th>\n",
              "      <td>-2.100158</td>\n",
              "      <td>30.721319</td>\n",
              "      <td>45.132307</td>\n",
              "      <td>0.807309</td>\n",
              "      <td>41.734763</td>\n",
              "      <td>0.004395</td>\n",
              "      <td>-2.000792</td>\n",
              "      <td>1.096487</td>\n",
              "      <td>62.131429</td>\n",
              "      <td>38.660370</td>\n",
              "      <td>24.027522</td>\n",
              "      <td>-2.139547e-60</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1330816 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77cd8d22-e0b8-4d59-b490-ee06483fe891')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-77cd8d22-e0b8-4d59-b490-ee06483fe891');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# # Create a boolean mask indicating which rows meet the specified criteria\n",
        "# mask = df['profile_id'].isin([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
        "#                               16, 17, 18, 19, 20])\n",
        "\n",
        "# # Use the mask to create a subset of the DataFrame\n",
        "# subset = df[mask]\n",
        "# subset\n",
        "subset = df\n",
        "subset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ujXne8oA_UtW"
      },
      "outputs": [],
      "source": [
        "# Create a sample DataFrame\n",
        "# Reset the index of the DataFrame\n",
        "subset = subset.reset_index(drop=True)\n",
        "\n",
        "# Create the boolean indexer based on the reset index\n",
        "subset['data'] = range(0, len(subset['profile_id']))\n",
        "\n",
        "df = pd.DataFrame({'motor_speed': subset['motor_speed'],\n",
        "                   'torque': subset['torque'],\n",
        "                   'u_d': subset['u_d'], \n",
        "                   'u_q': subset['u_q'], \n",
        "                   'i_d': subset['i_d'], \n",
        "                   'i_q': subset['i_q'], \n",
        "                   'ambient': subset['ambient'],\n",
        "                   'coolant': subset['coolant'], \n",
        "                   'pm': subset['pm'],\n",
        "                   'stator_winding': subset['stator_winding'],\t\n",
        "                   'stator_tooth': subset['stator_tooth'],\n",
        "                   'stator_yoke': subset['stator_yoke'],\n",
        "                   'profile_id': subset['profile_id'],\n",
        "                   'value': subset['profile_id'],\n",
        "                   'data': subset['data']})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CrvlwIMfZNXW"
      },
      "outputs": [],
      "source": [
        "# Create a sample DataFrame\n",
        "# df = pd.DataFrame({'value': [1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4],\n",
        "#                    'data': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})\n",
        "\n",
        "# Initialize a new time column with all values set to 0\n",
        "df['time'] = 0\n",
        "\n",
        "# Set the starting time to 0\n",
        "time = 0\n",
        "\n",
        "# Iterate through the rows of the DataFrame\n",
        "for i, row in df.iterrows():\n",
        "    # If the value in the 'value' column has changed from the previous row,\n",
        "    # reset the time to 0\n",
        "    if i > 0 and row['value'] != df.iloc[i-1]['value']:\n",
        "        time = 0\n",
        "    # Set the time for the current row\n",
        "    df.loc[i, 'time'] = time\n",
        "    # Increment the time by 0.5\n",
        "    time += 0.5\n",
        "\n",
        "# Print the resulting DataFrame\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_JF0wpqAQ8NJ"
      },
      "outputs": [],
      "source": [
        "# Save the DataFrame as a CSV file\n",
        "df.to_csv('data.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Manipulation and Visualizations**"
      ],
      "metadata": {
        "id": "0SJ0OXnm3fMH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run code from here..."
      ],
      "metadata": {
        "id": "0Csbv7g5ZVQH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TGFthYahfjQR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "d3fa9858-f7fb-45e0-ae25-1e277f4ec41a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         motor_speed        torque       u_d       u_q       i_d       i_q  \\\n",
              "0           0.002866  1.871008e-01 -0.350055 -0.450682  0.004419  0.000328   \n",
              "1           0.000257  2.454175e-01 -0.305803 -0.325737  0.000606 -0.000785   \n",
              "2           0.002355  1.766153e-01 -0.372503 -0.440864  0.001290  0.000386   \n",
              "3           0.006105  2.383027e-01 -0.316199 -0.327026  0.000026  0.002046   \n",
              "4           0.003133  2.081967e-01 -0.332272 -0.471150 -0.064317  0.037184   \n",
              "...              ...           ...       ...       ...       ...       ...   \n",
              "1330811     0.000380 -8.116730e-60  0.791801 -2.152128 -2.000169  1.097528   \n",
              "1330812     0.002985 -5.815891e-60  0.778900 -2.258684 -2.000499  1.097569   \n",
              "1330813     0.002301 -4.167268e-60  0.804914 -2.130312 -1.999268  1.098765   \n",
              "1330814     0.005662 -2.985978e-60  0.763091 -2.268498 -2.000999  1.095696   \n",
              "1330815     0.004395 -2.139547e-60  0.807309 -2.100158 -2.000792  1.096487   \n",
              "\n",
              "           ambient    coolant         pm  stator_winding  stator_tooth  \\\n",
              "0        19.850691  18.805172  24.554214       19.086670     18.293219   \n",
              "1        19.850672  18.818571  24.538078       19.092390     18.294807   \n",
              "2        19.850657  18.828770  24.544693       19.089380     18.294094   \n",
              "3        19.850647  18.835567  24.554018       19.083031     18.292542   \n",
              "4        19.850639  18.857033  24.565397       19.082525     18.291428   \n",
              "...            ...        ...        ...             ...           ...   \n",
              "1330811  23.989078  30.721298  62.147780       45.274497     41.934347   \n",
              "1330812  23.970700  30.721306  62.142646       45.239017     41.868923   \n",
              "1330813  23.977234  30.721312  62.138387       45.211576     41.804819   \n",
              "1330814  24.001421  30.721316  62.133422       45.193508     41.762220   \n",
              "1330815  24.027522  30.721319  62.131429       45.132307     41.734763   \n",
              "\n",
              "         stator_yoke  profile_id  value     data    time  \n",
              "0          18.316547          17     17        0     0.0  \n",
              "1          18.314955          17     17        1     0.5  \n",
              "2          18.326307          17     17        2     1.0  \n",
              "3          18.330833          17     17        3     1.5  \n",
              "4          18.326662          17     17        4     2.0  \n",
              "...              ...         ...    ...      ...     ...  \n",
              "1330811    38.653720          71     71  1330811  7325.5  \n",
              "1330812    38.656328          71     71  1330812  7326.0  \n",
              "1330813    38.650923          71     71  1330813  7326.5  \n",
              "1330814    38.655686          71     71  1330814  7327.0  \n",
              "1330815    38.660370          71     71  1330815  7327.5  \n",
              "\n",
              "[1330816 rows x 16 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>motor_speed</th>\n",
              "      <th>torque</th>\n",
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              "      <th>u_q</th>\n",
              "      <th>i_d</th>\n",
              "      <th>i_q</th>\n",
              "      <th>ambient</th>\n",
              "      <th>coolant</th>\n",
              "      <th>pm</th>\n",
              "      <th>stator_winding</th>\n",
              "      <th>stator_tooth</th>\n",
              "      <th>stator_yoke</th>\n",
              "      <th>profile_id</th>\n",
              "      <th>value</th>\n",
              "      <th>data</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.002866</td>\n",
              "      <td>1.871008e-01</td>\n",
              "      <td>-0.350055</td>\n",
              "      <td>-0.450682</td>\n",
              "      <td>0.004419</td>\n",
              "      <td>0.000328</td>\n",
              "      <td>19.850691</td>\n",
              "      <td>18.805172</td>\n",
              "      <td>24.554214</td>\n",
              "      <td>19.086670</td>\n",
              "      <td>18.293219</td>\n",
              "      <td>18.316547</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000257</td>\n",
              "      <td>2.454175e-01</td>\n",
              "      <td>-0.305803</td>\n",
              "      <td>-0.325737</td>\n",
              "      <td>0.000606</td>\n",
              "      <td>-0.000785</td>\n",
              "      <td>19.850672</td>\n",
              "      <td>18.818571</td>\n",
              "      <td>24.538078</td>\n",
              "      <td>19.092390</td>\n",
              "      <td>18.294807</td>\n",
              "      <td>18.314955</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.002355</td>\n",
              "      <td>1.766153e-01</td>\n",
              "      <td>-0.372503</td>\n",
              "      <td>-0.440864</td>\n",
              "      <td>0.001290</td>\n",
              "      <td>0.000386</td>\n",
              "      <td>19.850657</td>\n",
              "      <td>18.828770</td>\n",
              "      <td>24.544693</td>\n",
              "      <td>19.089380</td>\n",
              "      <td>18.294094</td>\n",
              "      <td>18.326307</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.006105</td>\n",
              "      <td>2.383027e-01</td>\n",
              "      <td>-0.316199</td>\n",
              "      <td>-0.327026</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.002046</td>\n",
              "      <td>19.850647</td>\n",
              "      <td>18.835567</td>\n",
              "      <td>24.554018</td>\n",
              "      <td>19.083031</td>\n",
              "      <td>18.292542</td>\n",
              "      <td>18.330833</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.003133</td>\n",
              "      <td>2.081967e-01</td>\n",
              "      <td>-0.332272</td>\n",
              "      <td>-0.471150</td>\n",
              "      <td>-0.064317</td>\n",
              "      <td>0.037184</td>\n",
              "      <td>19.850639</td>\n",
              "      <td>18.857033</td>\n",
              "      <td>24.565397</td>\n",
              "      <td>19.082525</td>\n",
              "      <td>18.291428</td>\n",
              "      <td>18.326662</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330811</th>\n",
              "      <td>0.000380</td>\n",
              "      <td>-8.116730e-60</td>\n",
              "      <td>0.791801</td>\n",
              "      <td>-2.152128</td>\n",
              "      <td>-2.000169</td>\n",
              "      <td>1.097528</td>\n",
              "      <td>23.989078</td>\n",
              "      <td>30.721298</td>\n",
              "      <td>62.147780</td>\n",
              "      <td>45.274497</td>\n",
              "      <td>41.934347</td>\n",
              "      <td>38.653720</td>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "      <td>1330811</td>\n",
              "      <td>7325.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330812</th>\n",
              "      <td>0.002985</td>\n",
              "      <td>-5.815891e-60</td>\n",
              "      <td>0.778900</td>\n",
              "      <td>-2.258684</td>\n",
              "      <td>-2.000499</td>\n",
              "      <td>1.097569</td>\n",
              "      <td>23.970700</td>\n",
              "      <td>30.721306</td>\n",
              "      <td>62.142646</td>\n",
              "      <td>45.239017</td>\n",
              "      <td>41.868923</td>\n",
              "      <td>38.656328</td>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "      <td>1330812</td>\n",
              "      <td>7326.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330813</th>\n",
              "      <td>0.002301</td>\n",
              "      <td>-4.167268e-60</td>\n",
              "      <td>0.804914</td>\n",
              "      <td>-2.130312</td>\n",
              "      <td>-1.999268</td>\n",
              "      <td>1.098765</td>\n",
              "      <td>23.977234</td>\n",
              "      <td>30.721312</td>\n",
              "      <td>62.138387</td>\n",
              "      <td>45.211576</td>\n",
              "      <td>41.804819</td>\n",
              "      <td>38.650923</td>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "      <td>1330813</td>\n",
              "      <td>7326.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330814</th>\n",
              "      <td>0.005662</td>\n",
              "      <td>-2.985978e-60</td>\n",
              "      <td>0.763091</td>\n",
              "      <td>-2.268498</td>\n",
              "      <td>-2.000999</td>\n",
              "      <td>1.095696</td>\n",
              "      <td>24.001421</td>\n",
              "      <td>30.721316</td>\n",
              "      <td>62.133422</td>\n",
              "      <td>45.193508</td>\n",
              "      <td>41.762220</td>\n",
              "      <td>38.655686</td>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "      <td>1330814</td>\n",
              "      <td>7327.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330815</th>\n",
              "      <td>0.004395</td>\n",
              "      <td>-2.139547e-60</td>\n",
              "      <td>0.807309</td>\n",
              "      <td>-2.100158</td>\n",
              "      <td>-2.000792</td>\n",
              "      <td>1.096487</td>\n",
              "      <td>24.027522</td>\n",
              "      <td>30.721319</td>\n",
              "      <td>62.131429</td>\n",
              "      <td>45.132307</td>\n",
              "      <td>41.734763</td>\n",
              "      <td>38.660370</td>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "      <td>1330815</td>\n",
              "      <td>7327.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1330816 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7327725f-2c9e-473e-ba12-c7c5d8f26b66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7327725f-2c9e-473e-ba12-c7c5d8f26b66');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"drive/MyDrive/data.csv\")\n",
        "\n",
        "# Cast all the values in the DataFrame to numbers\n",
        "# df = df.apply(pd.to_numeric)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t5zo8S_Ay_dV"
      },
      "outputs": [],
      "source": [
        "# #DO NOT RUN AS IS [will throw error]\n",
        "# # Map the values of A to different colors\n",
        "# colors = {2: 'red', 3: 'blue', 4: 'green'}\n",
        "\n",
        "# # Plot the values of A and color them based on the mapping\n",
        "# df.plot(x='time', y='torque', kind='scatter', color=[colors[x] \n",
        "#                                                      for x in df['value']])\n",
        "\n",
        "# # Show the plot\n",
        "# plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j3Coz09pfmv2"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ooOBnrW-dtp"
      },
      "outputs": [],
      "source": [
        "# # Create the scatter plot all profile_id's\n",
        "# sns.scatterplot(x='time', y='motor_speed', hue='profile_id', data=df)\n",
        "\n",
        "# # Show the plot\n",
        "# plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8WNRiww8gh8A"
      },
      "outputs": [],
      "source": [
        "# Create a boolean indexer that returns True for rows where profile_id == x\n",
        "mask = df['profile_id'] == 17\n",
        "df_filtered = df.loc[mask]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4dU_QDVXcQ0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "7128d0ef-0b81-4ebf-cee9-91e3973b2f7e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create the scatter plot u_d vs u_q\n",
        "sns.scatterplot(x='time', y='u_d', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='u_q', data=df_filtered)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hql82URFfs4q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2f63d19a-deea-4c6d-e053-7cd3eeb58132"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create the scatter plot i_d vs i_q\n",
        "sns.scatterplot(x='time', y='i_d', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='i_q', data=df_filtered)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4OgKDozsg8NQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "371cfcc5-d8a3-4006-c75e-b31204d276eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create the scatter plot motor_speed vs torque\n",
        "sns.scatterplot(x='time', y='motor_speed', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='torque', data=df_filtered)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZmMLocTCutO3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "aeb67328-d080-4336-f7ce-fc9846323b7b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create the scatter plot motor_speed vs torque\n",
        "sns.scatterplot(x='time', y='ambient', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='coolant', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='pm', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='stator_winding', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='stator_tooth', data=df_filtered)\n",
        "sns.scatterplot(x='time', y='stator_yoke', data=df_filtered)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create the figure and subplots\n",
        "fig, axs = plt.subplots(5, 1, sharex=True, figsize=(8, 6),\n",
        "                        gridspec_kw={'hspace': 0.5})\n",
        "# Plot the first data series on the first subplot\n",
        "axs[0].plot('time', 'u_d', data=df_filtered)\n",
        "axs[0].plot('time', 'u_q', data=df_filtered)\n",
        "\n",
        "# Customize the first subplot\n",
        "# axs[0].set_ylabel('u_d vs u_q')\n",
        "axs[0].set_title('u_d vs u_q')\n",
        "\n",
        "# Plot the second data series on the first subplot\n",
        "axs[1].plot('time', 'i_d', data=df_filtered)\n",
        "axs[1].plot('time', 'i_q', data=df_filtered)\n",
        "\n",
        "# Customize the second subplot\n",
        "# axs[1].set_ylabel('i_d vs i_q')\n",
        "axs[1].set_title('i_d vs i_q')\n",
        "\n",
        "# Plot the third data series on the first subplot\n",
        "axs[2].plot('time', 'motor_speed', data=df_filtered)\n",
        "\n",
        "# Customize the third subplot\n",
        "# axs[2].set_ylabel('torque vs motor_speed')\n",
        "axs[2].set_title('motor_speed vs time')\n",
        "\n",
        "# Plot the fourth data series on the first subplot\n",
        "axs[3].plot('time', 'torque', data=df_filtered)\n",
        "\n",
        "# Customize the fourth subplot\n",
        "# axs[3].set_ylabel('torque vs motor_speed')\n",
        "axs[3].set_title('torque vs time')\n",
        "\n",
        "# Plot the fifth data series on the first subplot\n",
        "axs[4].plot('time', 'ambient', data=df_filtered)\n",
        "axs[4].plot('time', 'coolant', data=df_filtered)\n",
        "axs[4].plot('time', 'pm', data=df_filtered)\n",
        "axs[4].plot('time', 'stator_winding', data=df_filtered)\n",
        "axs[4].plot('time', 'stator_tooth', data=df_filtered)\n",
        "axs[4].plot('time', 'stator_yoke', data=df_filtered)\n",
        "\n",
        "# Customize the fifth subplot\n",
        "# axs[4].set_ylabel('time vs other')\n",
        "axs[4].set_title('time vs other')\n",
        "\n",
        "# Show the plot\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "eCNzaUeg1mhq",
        "outputId": "df469465-b053-4355-82a5-2ae654b4e313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-42f549336841>:39: RuntimeWarning: Second argument 'pm' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
            "  axs[4].plot('time', 'pm', data=df_filtered)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}