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adyork/bats_niskin_subset_example.ipynb

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bats_niskin_subset_example.ipynb
{
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"metadata": {
"colab": {
"name": "BATS_niskin_subset_example.ipynb",
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"<a href=\"https://colab.research.google.com/gist/adyork/2bc800067156b54fab855c389bd1a163/bats_niskin_subset_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QUliR1El8x17"
},
"source": [
"# BATS Niksin Data example\n",
"\n",
"Challenge: Use the ERDDAP api to pull data in csv format from BCO-DMO and make a plot.\n",
"\n",
"# Overview\n",
"\n",
"This notebook loads a csv file into a pandas data frame. The table is subset to columns for cruise, cast, timestamp,depth, and temperature. It then creates a plot of depth (y-axis) vs. temperature (x-axis). The y axis is inverted. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C4Jm5Zcq8vc7"
},
"source": [
"# Resources\n",
"* BCO-DMO ERDDAP Documentation https://erddap.bco-dmo.org/erddap/tabledap/documentation.html\n",
"* See workshop materials for more on ERDDAP: https://k-rns.github.io/workshop_data_reuse/"
]
},
{
"cell_type": "markdown",
"source": [
"Dataset Landing Page (see metadata and file access)\n",
" https://www.bco-dmo.org/dataset/3782\n",
"* in the url below we are asking for .csvp instead of csv so that the units are loaded into the header not as the first row of the data table where we want the values.\n"
],
"metadata": {
"id": "FOmY0PBnFss0"
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{
"cell_type": "code",
"metadata": {
"id": "K0UJ0cLI6xyz"
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"source": [
"# This link is a subset of the dataset just for cruise 314 cast 005\n",
"niskin_csv = \"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_3782.csv?cruise_type%2Ccruise_type_text%2Ccruise_number%2Ccast_number%2Cdecy%2Ctime%2Clatitude%2Clongitude%2Cfilename%2CId%2Cdepth%2Cniskin_number%2Cp1%2Cp2%2Cp3%2Cp4%2Cp5%2Cp6%2Cp7%2Cp8%2Cp9%2Cp10%2Cp11%2Cp12%2Cp13%2Cp14%2Cp15%2CChl%2CPhae%2Cp18%2Cp19%2Cp20%2Cp21%2CTemp%2CCTD_S%2CSal1%2CSigTh%2CO2%2COxFix%2CAnom1%2CCO2%2CAlk%2CNO31%2CNO21%2CPO41%2CSi1%2CPOC%2CPON%2CTOC%2CTN%2CBact%2CPOP%2CTDP%2CSRP%2CBSi%2CLSi%2CPro%2CSyn%2CPiceu%2CNaneu%2CNO3%2CNO2%2CPO4%2CSi%2CPres&cruise_number=%220314%22&cast_number=%22005%22&decy%3E=2015&decy%3C2016\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#load data table into a pandas data frame\n",
"import pandas as pd\n",
"df_niskin = pd.read_csv(niskin_csv,skiprows=[1])\n",
"df_niskin.head() #show first few rows of the table\n"
],
"metadata": {
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"height": 386
},
"id": "I1eRaDL8ExdB",
"outputId": "5daefe4b-d777-437d-ae32-60f7174bade9"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" cruise_type cruise_type_text cruise_number cast_number decy \\\n",
"0 1 BATS_core 314 5 2015.36071 \n",
"1 1 BATS_core 314 5 2015.36071 \n",
"2 1 BATS_core 314 5 2015.36071 \n",
"3 1 BATS_core 314 5 2015.36071 \n",
"4 1 BATS_core 314 5 2015.36071 \n",
"\n",
" time latitude longitude filename Id \\\n",
"0 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400501 \n",
"1 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400502 \n",
"2 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400503 \n",
"3 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400504 \n",
"4 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400505 \n",
"\n",
" ... LSi Pro Syn Piceu Naneu NO3 NO2 PO4 Si Pres \n",
"0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"1 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"3 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
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"[5 rows x 65 columns]"
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"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"print(\"Number of rows in this data table: \")\n",
"len(df_niskin)"
],
"metadata": {
"id": "pNO3truv94xQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(\"10 randomly sampled rows from the table\")\n",
"df_niskin.sample(10) "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 647
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"id": "hIxKwhB1-ckf",
"outputId": "6479f29b-aaae-4b64-b642-dbf76a417f39"
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"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"10 randomly sampled rows from the table\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" cruise_type cruise_type_text cruise_number cast_number decy \\\n",
"14 1 BATS_core 314 5 2015.36071 \n",
"5 1 BATS_core 314 5 2015.36071 \n",
"18 1 BATS_core 314 5 2015.36071 \n",
"19 1 BATS_core 314 5 2015.36071 \n",
"15 1 BATS_core 314 5 2015.36071 \n",
"2 1 BATS_core 314 5 2015.36071 \n",
"7 1 BATS_core 314 5 2015.36071 \n",
"16 1 BATS_core 314 5 2015.36071 \n",
"12 1 BATS_core 314 5 2015.36071 \n",
"13 1 BATS_core 314 5 2015.36071 \n",
"\n",
" time latitude longitude filename Id \\\n",
"14 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400515 \n",
"5 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400506 \n",
"18 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400519 \n",
"19 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400520 \n",
"15 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400517 \n",
"2 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400503 \n",
"7 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400508 \n",
"16 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400516 \n",
"12 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400513 \n",
"13 2015-05-12T15:51:00Z 31.67 -64.163 bats_bottle.txt 1031400514 \n",
"\n",
" ... LSi Pro Syn Piceu Naneu NO3 NO2 PO4 Si Pres \n",
"14 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"5 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"18 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"19 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"15 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"7 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"16 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"13 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"[10 rows x 65 columns]"
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" <td>bats_bottle.txt</td>\n",
" <td>1031400519</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400520</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400517</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400503</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400508</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400516</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400513</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>BATS_core</td>\n",
" <td>314</td>\n",
" <td>5</td>\n",
" <td>2015.36071</td>\n",
" <td>2015-05-12T15:51:00Z</td>\n",
" <td>31.67</td>\n",
" <td>-64.163</td>\n",
" <td>bats_bottle.txt</td>\n",
" <td>1031400514</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 65 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ab2b75b-c85b-4f5b-a95e-3532c376910f')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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"\n",
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" 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",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
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" const docLink = document.createElement('div');\n",
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" element.appendChild(docLink);\n",
" }\n",
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" "
]
},
"metadata": {},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"source": [
"#we just want to work with a few of these coluns so let's subset.\n",
"cols = ['cruise_number','cast_number','time','depth','Temp']\n",
"df_niskin = df_niskin.loc[:,cols]\n",
"df_niskin.head() #show first few rows"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "unzcUAbQF5xE",
"outputId": "46ade898-943f-4869-ee78-81569687e631"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" cruise_number cast_number time depth Temp\n",
"0 314 5 2015-05-12T15:51:00Z 599.6 15.602\n",
"1 314 5 2015-05-12T15:51:00Z 700.9 13.884\n",
"2 314 5 2015-05-12T15:51:00Z 800.4 11.668\n",
"3 314 5 2015-05-12T15:51:00Z 899.9 9.213\n",
"4 314 5 2015-05-12T15:51:00Z 1000.0 7.368"
],
"text/html": [
"\n",
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" <th></th>\n",
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" <th>cast_number</th>\n",
" <th>time</th>\n",
" <th>depth</th>\n",
" <th>Temp</th>\n",
" </tr>\n",
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" <td>2015-05-12T15:51:00Z</td>\n",
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" <td>7.368</td>\n",
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"</table>\n",
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" [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",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
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" element.appendChild(docLink);\n",
" }\n",
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]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"# Plotting a basic scatter plot\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"df_niskin.plot(\n",
" x='Temp', y='depth', \n",
" kind='line', \n",
" marker='o', c='orange',\n",
" title='Depth vs Temperture\\nCruise 314 Cast 005 Year 2015',\n",
" xlabel='Temp (C)', ylabel='Depth (m)', #units from the dataset metadata page\n",
" figsize=(5,7),\n",
" legend=None\n",
")\n",
"\n",
"#invert depth axis\n",
"ax = plt.gca()\n",
"ax.invert_yaxis()\n",
"\n",
"#show it!\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 473
},
"id": "4T1JXMJ9KKTC",
"outputId": "0dc74ac2-ae17-48b8-81e3-fd6e7185edc7"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 360x504 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
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