From 80f8618a02522e21b8386d41062c61850017403c Mon Sep 17 00:00:00 2001
From: hazemmahmoudnasa <136042713+hazemmahmoudnasa@users.noreply.github.com>
Date: Mon, 27 Jan 2025 11:00:07 -0500
Subject: [PATCH] Add files via upload

---
 TEMPO/tempo_geoxo_meeting_haqast.ipynb | 3684 ++++++++++++++++++++++++
 1 file changed, 3684 insertions(+)
 create mode 100644 TEMPO/tempo_geoxo_meeting_haqast.ipynb

diff --git a/TEMPO/tempo_geoxo_meeting_haqast.ipynb b/TEMPO/tempo_geoxo_meeting_haqast.ipynb
new file mode 100644
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Hands-on TEMPO Training\n",
+        "\n",
+        "---\n",
+        "    author: Barron H. Henderson\n",
+        "    date: 2025-01-27\n",
+        "    location: HAQAST Showcase 2025\n",
+        "---\n",
+        "\n",
+        "This training uses RSIG to easily and quickly access and analyze TEMPO data.\n",
+        "\n",
+        "Goals:\n",
+        "1. Install and import libraries\n",
+        "2. Explore and find data.\n",
+        "3. Compare TEMPO to observed NO2\n",
+        "4. Create a TEMPO map\n",
+        "5. Create a TEMPO Surface NO2 product\n",
+        "6. Adapt other tutorials"
+      ],
+      "metadata": {
+        "id": "Byl7HPnlKprW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Step 1: Install prerequisites\n",
+        "\n",
+        "* pandas is for tables\n",
+        "* xarray is for gridded data\n",
+        "* matplotlib is for plotting\n",
+        "* netcdf4 is for when RSIG returns NetCDF files\n",
+        "* pyproj is for coordinate projections\n",
+        "* pyrsig is for getting data\n",
+        "* pycno is for simple map overlays\n"
+      ],
+      "metadata": {
+        "id": "MoAOeuOHogdH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5PWqe1qdQ-Tt",
+        "outputId": "04cfdc48-b35c-4918-cf79-95bf089de20d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.0/250.0 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m26.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!python -m pip install -qq pandas xarray matplotlib netcdf4 pyproj pyrsig pycno"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now import the libraries.\n",
+        "\n",
+        "_If you get a `ModuleNotFoundError:`, try restarting the kernel._"
+      ],
+      "metadata": {
+        "id": "lGWqCZsy1fqF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import Libraries\n",
+        "import pyproj\n",
+        "import xarray as xr\n",
+        "import pyrsig\n",
+        "import pandas as pd\n",
+        "import pycno\n",
+        "import getpass\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "4fBuUtvjRBYz"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Step 2: Exploring Data\n",
+        "\n",
+        "* Import libraries\n",
+        "* Prepare a pyrsig object"
+      ],
+      "metadata": {
+        "id": "F9D5xp_uot3t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Choosing a Northeast domain for 2023 December 18th\n",
+        "# Dec 18th is in the public data.\n",
+        "locname = 'nyc'\n",
+        "bbox = (-74.8, 40.32, -71.43, 41.4)\n",
+        "bdate = '2023-12-18'"
+      ],
+      "metadata": {
+        "id": "qQz3ZPLzo0Mt"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "api = pyrsig.RsigApi(bdate=bdate, bbox=bbox, workdir=locname, gridfit=True)\n",
+        "# api_key = getpass.getpass('Enter TEMPO key (anonymous if unknown):')\n",
+        "api_key = 'anonymous'  # using public, so using anonymous\n",
+        "api.tempo_kw['api_key'] = api_key"
+      ],
+      "metadata": {
+        "id": "4PBjfm0YLQBJ"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# after the cell runs, click on the table button.\n",
+        "# Then use filters to find tempo data producs by names that start with tempo\n",
+        "descdf = api.descriptions()\n",
+        "descdf\n",
+        "# descdf.query('name.str.contains(\"tempo\")')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "-LqXE8LKpLOA",
+        "outputId": "f00b7e56-11cf-4a24-e7d3-bef91aedfcd8"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   name            label  \\\n",
+              "0           airnow.pm25      pm25(ug/m3)   \n",
+              "1           airnow.pm10      pm10(ug/m3)   \n",
+              "2          airnow.ozone       ozone(ppb)   \n",
+              "3             airnow.no          no(ppb)   \n",
+              "4            airnow.no2         no2(ppb)   \n",
+              "...                 ...              ...   \n",
+              "8227     nldas.pressure     pressure(Pa)   \n",
+              "8228     nldas.humidity  humidity(kg/kg)   \n",
+              "8229  nldas.temperature   temperature(K)   \n",
+              "8230         nldas.wind        wind(m/s)   \n",
+              "8231       hysplit.pm25      pm25(ug/m3)   \n",
+              "\n",
+              "                                            description         bbox_str  \\\n",
+              "0     UTC hourly mean surface measured particulate m...   -157 21 -51 59   \n",
+              "1     UTC hourly mean surface measured particulate m...   -157 21 -51 59   \n",
+              "2     UTC hourly mean surface measured ozone concent...   -157 21 -51 64   \n",
+              "3     UTC hourly mean surface measured nitric oxide ...   -157 21 -51 64   \n",
+              "4     UTC hourly mean surface measured nitrogen diox...   -157 21 -51 64   \n",
+              "...                                                 ...              ...   \n",
+              "8227  Modeled North American Land Data Assimilation ...  -180 -90 180 90   \n",
+              "8228  Modeled North American Land Data Assimilation ...  -180 -90 180 90   \n",
+              "8229  Modeled North American Land Data Assimilation ...  -180 -90 180 90   \n",
+              "8230  Modeled North American Land Data Assimilation ...  -180 -90 180 90   \n",
+              "8231  HYSPLIT modeled hourly aerosol fine particulat...  -180 -90 180 90   \n",
+              "\n",
+              "             beginPosition timeResolution           endPosition   prefix  \n",
+              "0     2003-01-02T00:00:00Z           PT1H                   now   airnow  \n",
+              "1     2003-01-02T00:00:00Z           PT1H                   now   airnow  \n",
+              "2     2003-01-02T00:00:00Z           PT1H                   now   airnow  \n",
+              "3     2003-01-02T00:00:00Z           PT1H                   now   airnow  \n",
+              "4     2003-01-02T00:00:00Z           PT1H                   now   airnow  \n",
+              "...                    ...            ...                   ...      ...  \n",
+              "8227    20110824T00:00:00Z           PT1D                   now    nldas  \n",
+              "8228    20110824T00:00:00Z           PT1D                   now    nldas  \n",
+              "8229    20110824T00:00:00Z           PT1D                   now    nldas  \n",
+              "8230    20110824T00:00:00Z           PT1D                   now    nldas  \n",
+              "8231  2011-01-01T00:00:00Z           PT1H  2015-12-31T23:59:59Z  hysplit  \n",
+              "\n",
+              "[8232 rows x 8 columns]"
+            ],
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+              "  <div id=\"df-dff4157b-3b54-4891-93a8-67d9e986476b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "      <th></th>\n",
+              "      <th>name</th>\n",
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+              "      <td>airnow.pm25</td>\n",
+              "      <td>pm25(ug/m3)</td>\n",
+              "      <td>UTC hourly mean surface measured particulate m...</td>\n",
+              "      <td>-157 21 -51 59</td>\n",
+              "      <td>2003-01-02T00:00:00Z</td>\n",
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+              "      <td>airnow.pm10</td>\n",
+              "      <td>pm10(ug/m3)</td>\n",
+              "      <td>UTC hourly mean surface measured particulate m...</td>\n",
+              "      <td>-157 21 -51 59</td>\n",
+              "      <td>2003-01-02T00:00:00Z</td>\n",
+              "      <td>PT1H</td>\n",
+              "      <td>now</td>\n",
+              "      <td>airnow</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>airnow.ozone</td>\n",
+              "      <td>ozone(ppb)</td>\n",
+              "      <td>UTC hourly mean surface measured ozone concent...</td>\n",
+              "      <td>-157 21 -51 64</td>\n",
+              "      <td>2003-01-02T00:00:00Z</td>\n",
+              "      <td>PT1H</td>\n",
+              "      <td>now</td>\n",
+              "      <td>airnow</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>airnow.no</td>\n",
+              "      <td>no(ppb)</td>\n",
+              "      <td>UTC hourly mean surface measured nitric oxide ...</td>\n",
+              "      <td>-157 21 -51 64</td>\n",
+              "      <td>2003-01-02T00:00:00Z</td>\n",
+              "      <td>PT1H</td>\n",
+              "      <td>now</td>\n",
+              "      <td>airnow</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>airnow.no2</td>\n",
+              "      <td>no2(ppb)</td>\n",
+              "      <td>UTC hourly mean surface measured nitrogen diox...</td>\n",
+              "      <td>-157 21 -51 64</td>\n",
+              "      <td>2003-01-02T00:00:00Z</td>\n",
+              "      <td>PT1H</td>\n",
+              "      <td>now</td>\n",
+              "      <td>airnow</td>\n",
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+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>8227</th>\n",
+              "      <td>nldas.pressure</td>\n",
+              "      <td>pressure(Pa)</td>\n",
+              "      <td>Modeled North American Land Data Assimilation ...</td>\n",
+              "      <td>-180 -90 180 90</td>\n",
+              "      <td>20110824T00:00:00Z</td>\n",
+              "      <td>PT1D</td>\n",
+              "      <td>now</td>\n",
+              "      <td>nldas</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8228</th>\n",
+              "      <td>nldas.humidity</td>\n",
+              "      <td>humidity(kg/kg)</td>\n",
+              "      <td>Modeled North American Land Data Assimilation ...</td>\n",
+              "      <td>-180 -90 180 90</td>\n",
+              "      <td>20110824T00:00:00Z</td>\n",
+              "      <td>PT1D</td>\n",
+              "      <td>now</td>\n",
+              "      <td>nldas</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8229</th>\n",
+              "      <td>nldas.temperature</td>\n",
+              "      <td>temperature(K)</td>\n",
+              "      <td>Modeled North American Land Data Assimilation ...</td>\n",
+              "      <td>-180 -90 180 90</td>\n",
+              "      <td>20110824T00:00:00Z</td>\n",
+              "      <td>PT1D</td>\n",
+              "      <td>now</td>\n",
+              "      <td>nldas</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8230</th>\n",
+              "      <td>nldas.wind</td>\n",
+              "      <td>wind(m/s)</td>\n",
+              "      <td>Modeled North American Land Data Assimilation ...</td>\n",
+              "      <td>-180 -90 180 90</td>\n",
+              "      <td>20110824T00:00:00Z</td>\n",
+              "      <td>PT1D</td>\n",
+              "      <td>now</td>\n",
+              "      <td>nldas</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8231</th>\n",
+              "      <td>hysplit.pm25</td>\n",
+              "      <td>pm25(ug/m3)</td>\n",
+              "      <td>HYSPLIT modeled hourly aerosol fine particulat...</td>\n",
+              "      <td>-180 -90 180 90</td>\n",
+              "      <td>2011-01-01T00:00:00Z</td>\n",
+              "      <td>PT1H</td>\n",
+              "      <td>2015-12-31T23:59:59Z</td>\n",
+              "      <td>hysplit</td>\n",
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+              "</table>\n",
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+              "</div>\n",
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+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dff4157b-3b54-4891-93a8-67d9e986476b')\"\n",
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+              "    .colab-df-container {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "\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-dff4157b-3b54-4891-93a8-67d9e986476b');\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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+              "        dataTable['output_type'] = 'display_data';\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "        const charts = await google.colab.kernel.invokeFunction(\n",
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+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('descdf')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "descdf",
+              "summary": "{\n  \"name\": \"descdf\",\n  \"rows\": 8232,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8139,\n        \"samples\": [\n          \"cmaq.amad.conus.metdot3d.vwindc\",\n          \"cmaq.equates.conus.emis.PFE\",\n          \"cmaq.amad.conus.soilinp.rainfall17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4092,\n        \"samples\": [\n          \"AALJ(ug/m3)\",\n          \"plcl(Pa)\",\n          \"l3.cldo4.viewing_zenith_angle(deg)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4227,\n        \"samples\": [\n          \"Climatological effective O2O2 height\",\n          \"Independent uncertainty of sulfur dioxide effective temperature , -1=temperature fitting was requested, but cross section is zero in this wavelength range, -2=no temperature fitting was requested and output for effective temperature and common uncertainty of it is based on f-code, -3=spectral fitting was done, but no independent uncertainty could be retrieved, -4=temperature fitting was requested, but differential optical depth is too small to retrieve temperature, -5=spectral fitting was done without using the independent uncertainty input, -6=no independent uncertainty input was given, -9=spectral fitting was not successful\",\n          \"Hourly average scaled humidity-corrected particulate matter not more than 2.5 microns in diameter.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bbox_str\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"-125 20 -50 50\",\n          \"-125 20 -60 50\",\n          \"-157 21 -51 59\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"beginPosition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 160,\n        \"samples\": [\n          \"2004-10-06T00:00:00Z\",\n          \"2011-02-07T00:00:00Z\",\n          \"2005-09-09T00:00:00Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timeResolution\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"PT1Y\",\n          \"PT1D\",\n          \"PT1H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"endPosition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 135,\n        \"samples\": [\n          \"2080-12-31T23:59:59Z\",\n          \"2001-07-20T23:59:59Z\",\n          \"2006-08-22T23:59:59Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefix\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"buoy\",\n          \"omibehr\",\n          \"omibehrioapi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tempokey = 'tempo.l2.no2.vertical_column_troposphere'"
+      ],
+      "metadata": {
+        "id": "4aIIr1lmTsYb"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# By default, the pyrsig uses 'ascii' backend, but 'xdr' is faster;\n",
+        "# both look the same in python, but the files are very different.\n",
+        "# I'm using xdr here for speed\n",
+        "df = api.to_dataframe(tempokey, backend='xdr')\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 617
+        },
+        "id": "uxQm1l7vpkzT",
+        "outputId": "21c468c6-966b-466e-e082-6c62e02d102f"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Timestamp(UTC)  LONGITUDE(deg)  LATITUDE(deg)  \\\n",
+              "0    2023-12-18T15:11:00+0000      -73.316078      41.398060   \n",
+              "1    2023-12-18T15:11:00+0000      -72.758522      41.398186   \n",
+              "2    2023-12-18T15:11:00+0000      -72.820793      41.396824   \n",
+              "3    2023-12-18T15:11:00+0000      -72.883331      41.394588   \n",
+              "4    2023-12-18T15:11:00+0000      -72.946648      41.391434   \n",
+              "..                        ...             ...            ...   \n",
+              "890  2023-12-18T18:18:00+0000      -73.764481      40.330441   \n",
+              "891  2023-12-18T18:18:00+0000      -73.826035      40.327888   \n",
+              "892  2023-12-18T18:18:00+0000      -73.886749      40.326015   \n",
+              "893  2023-12-18T18:18:00+0000      -73.947411      40.323883   \n",
+              "894  2023-12-18T18:18:00+0000      -74.009056      40.321693   \n",
+              "\n",
+              "     no2_vertical_column_troposphere(molecules/cm2)  Longitude_SW(deg)  \\\n",
+              "0                                      3.093594e+15         -73.281212   \n",
+              "1                                      7.423907e+13         -72.723591   \n",
+              "2                                      4.233557e+14         -72.786112   \n",
+              "3                                      1.407843e+15         -72.848591   \n",
+              "4                                      3.048500e+15         -72.911537   \n",
+              "..                                              ...                ...   \n",
+              "890                                    5.616886e+15         -73.730804   \n",
+              "891                                    4.467097e+15         -73.792147   \n",
+              "892                                    8.049984e+15         -73.853292   \n",
+              "893                                    1.004033e+16         -73.913992   \n",
+              "894                                    8.188616e+15         -73.975153   \n",
+              "\n",
+              "     Longitude_SE(deg)  Longitude_NW(deg)  Longitude_NE(deg)  \\\n",
+              "0           -73.344002         -73.288191         -73.351015   \n",
+              "1           -72.786112         -72.730680         -72.793116   \n",
+              "2           -72.848591         -72.793116         -72.855520   \n",
+              "3           -72.911537         -72.855520         -72.918381   \n",
+              "4           -72.974806         -72.918381         -72.981564   \n",
+              "..                 ...                ...                ...   \n",
+              "890         -73.792147         -73.737051         -73.798367   \n",
+              "891         -73.853292         -73.798367         -73.859488   \n",
+              "892         -73.913992         -73.859488         -73.920162   \n",
+              "893         -73.975153         -73.920162         -73.981304   \n",
+              "894         -74.036465         -73.981304         -74.042583   \n",
+              "\n",
+              "     Latitude_SW(deg)  Latitude_SE(deg)  Latitude_NW(deg)  Latitude_NE(deg)  \n",
+              "0           41.410378         41.407287         41.388793         41.385610  \n",
+              "1           41.410442         41.408224         41.389041         41.386914  \n",
+              "2           41.408224         41.406334         41.386914         41.385095  \n",
+              "3           41.406334         41.403631         41.385095         41.382483  \n",
+              "4           41.403631         41.400612         41.382483         41.379558  \n",
+              "..                ...               ...               ...               ...  \n",
+              "890         40.341922         40.339574         40.321104         40.318759  \n",
+              "891         40.339574         40.337360         40.318759         40.316548  \n",
+              "892         40.337360         40.335357         40.316548         40.314547  \n",
+              "893         40.335357         40.333200         40.314547         40.312383  \n",
+              "894         40.333200         40.330881         40.312383         40.310077  \n",
+              "\n",
+              "[9569 rows x 12 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-51842783-a90b-44d9-ac3d-0078e05d9d86\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Timestamp(UTC)</th>\n",
+              "      <th>LONGITUDE(deg)</th>\n",
+              "      <th>LATITUDE(deg)</th>\n",
+              "      <th>no2_vertical_column_troposphere(molecules/cm2)</th>\n",
+              "      <th>Longitude_SW(deg)</th>\n",
+              "      <th>Longitude_SE(deg)</th>\n",
+              "      <th>Longitude_NW(deg)</th>\n",
+              "      <th>Longitude_NE(deg)</th>\n",
+              "      <th>Latitude_SW(deg)</th>\n",
+              "      <th>Latitude_SE(deg)</th>\n",
+              "      <th>Latitude_NW(deg)</th>\n",
+              "      <th>Latitude_NE(deg)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-73.316078</td>\n",
+              "      <td>41.398060</td>\n",
+              "      <td>3.093594e+15</td>\n",
+              "      <td>-73.281212</td>\n",
+              "      <td>-73.344002</td>\n",
+              "      <td>-73.288191</td>\n",
+              "      <td>-73.351015</td>\n",
+              "      <td>41.410378</td>\n",
+              "      <td>41.407287</td>\n",
+              "      <td>41.388793</td>\n",
+              "      <td>41.385610</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.758522</td>\n",
+              "      <td>41.398186</td>\n",
+              "      <td>7.423907e+13</td>\n",
+              "      <td>-72.723591</td>\n",
+              "      <td>-72.786112</td>\n",
+              "      <td>-72.730680</td>\n",
+              "      <td>-72.793116</td>\n",
+              "      <td>41.410442</td>\n",
+              "      <td>41.408224</td>\n",
+              "      <td>41.389041</td>\n",
+              "      <td>41.386914</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.820793</td>\n",
+              "      <td>41.396824</td>\n",
+              "      <td>4.233557e+14</td>\n",
+              "      <td>-72.786112</td>\n",
+              "      <td>-72.848591</td>\n",
+              "      <td>-72.793116</td>\n",
+              "      <td>-72.855520</td>\n",
+              "      <td>41.408224</td>\n",
+              "      <td>41.406334</td>\n",
+              "      <td>41.386914</td>\n",
+              "      <td>41.385095</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.883331</td>\n",
+              "      <td>41.394588</td>\n",
+              "      <td>1.407843e+15</td>\n",
+              "      <td>-72.848591</td>\n",
+              "      <td>-72.911537</td>\n",
+              "      <td>-72.855520</td>\n",
+              "      <td>-72.918381</td>\n",
+              "      <td>41.406334</td>\n",
+              "      <td>41.403631</td>\n",
+              "      <td>41.385095</td>\n",
+              "      <td>41.382483</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.946648</td>\n",
+              "      <td>41.391434</td>\n",
+              "      <td>3.048500e+15</td>\n",
+              "      <td>-72.911537</td>\n",
+              "      <td>-72.974806</td>\n",
+              "      <td>-72.918381</td>\n",
+              "      <td>-72.981564</td>\n",
+              "      <td>41.403631</td>\n",
+              "      <td>41.400612</td>\n",
+              "      <td>41.382483</td>\n",
+              "      <td>41.379558</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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>890</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.764481</td>\n",
+              "      <td>40.330441</td>\n",
+              "      <td>5.616886e+15</td>\n",
+              "      <td>-73.730804</td>\n",
+              "      <td>-73.792147</td>\n",
+              "      <td>-73.737051</td>\n",
+              "      <td>-73.798367</td>\n",
+              "      <td>40.341922</td>\n",
+              "      <td>40.339574</td>\n",
+              "      <td>40.321104</td>\n",
+              "      <td>40.318759</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>891</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.826035</td>\n",
+              "      <td>40.327888</td>\n",
+              "      <td>4.467097e+15</td>\n",
+              "      <td>-73.792147</td>\n",
+              "      <td>-73.853292</td>\n",
+              "      <td>-73.798367</td>\n",
+              "      <td>-73.859488</td>\n",
+              "      <td>40.339574</td>\n",
+              "      <td>40.337360</td>\n",
+              "      <td>40.318759</td>\n",
+              "      <td>40.316548</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>892</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.886749</td>\n",
+              "      <td>40.326015</td>\n",
+              "      <td>8.049984e+15</td>\n",
+              "      <td>-73.853292</td>\n",
+              "      <td>-73.913992</td>\n",
+              "      <td>-73.859488</td>\n",
+              "      <td>-73.920162</td>\n",
+              "      <td>40.337360</td>\n",
+              "      <td>40.335357</td>\n",
+              "      <td>40.316548</td>\n",
+              "      <td>40.314547</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>893</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.947411</td>\n",
+              "      <td>40.323883</td>\n",
+              "      <td>1.004033e+16</td>\n",
+              "      <td>-73.913992</td>\n",
+              "      <td>-73.975153</td>\n",
+              "      <td>-73.920162</td>\n",
+              "      <td>-73.981304</td>\n",
+              "      <td>40.335357</td>\n",
+              "      <td>40.333200</td>\n",
+              "      <td>40.314547</td>\n",
+              "      <td>40.312383</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>894</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-74.009056</td>\n",
+              "      <td>40.321693</td>\n",
+              "      <td>8.188616e+15</td>\n",
+              "      <td>-73.975153</td>\n",
+              "      <td>-74.036465</td>\n",
+              "      <td>-73.981304</td>\n",
+              "      <td>-74.042583</td>\n",
+              "      <td>40.333200</td>\n",
+              "      <td>40.330881</td>\n",
+              "      <td>40.312383</td>\n",
+              "      <td>40.310077</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>9569 rows × 12 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51842783-a90b-44d9-ac3d-0078e05d9d86')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-51842783-a90b-44d9-ac3d-0078e05d9d86 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-51842783-a90b-44d9-ac3d-0078e05d9d86');\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",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-50b281af-b934-44b6-b452-442f96ee6b4d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50b281af-b934-44b6-b452-442f96ee6b4d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-50b281af-b934-44b6-b452-442f96ee6b4d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_be207af6-e973-4e17-b933-69d66b3aab6e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_be207af6-e973-4e17-b933-69d66b3aab6e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9569,\n  \"fields\": [\n    {\n      \"column\": \"Timestamp(UTC)\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-18T15:11:00+0000\",\n        \"max\": \"2023-12-18T18:18:00+0000\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"2023-12-18T15:18:00+0000\",\n          \"2023-12-18T17:18:00+0000\",\n          \"2023-12-18T15:11:00+0000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LONGITUDE(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9623313458301086,\n        \"min\": -74.79998779296875,\n        \"max\": -71.43016815185547,\n        \"num_unique_values\": 9470,\n        \"samples\": [\n          -73.41744232177734,\n          -72.19168853759766,\n          -72.66265106201172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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\"column\": \"Longitude_SW(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9628157974779525,\n        \"min\": -74.76691436767578,\n        \"max\": -71.39491844177246,\n        \"num_unique_values\": 9534,\n        \"samples\": [\n          -72.00566673278809,\n          -73.90005111694336,\n          -73.70192527770996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_SE(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9622616464038662,\n        \"min\": -74.82794952392578,\n        \"max\": -71.45800971984863,\n        \"num_unique_values\": 9541,\n        \"samples\": [\n          -71.53350257873535,\n          -74.26959800720215,\n          -72.68786430358887\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NW(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9624111341460376,\n        \"min\": -74.77294731140137,\n        \"max\": -71.40229415893555,\n        \"num_unique_values\": 9534,\n        \"samples\": [\n          -72.012939453125,\n          -74.75227165222168,\n          -73.58502388000488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NE(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9618573857344905,\n        \"min\": -74.83389091491699,\n        \"max\": -71.46535682678223,\n        \"num_unique_values\": 9539,\n        \"samples\": [\n          -73.43927192687988,\n          -73.4745979309082,\n          -72.69461059570312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SW(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3132486958796694,\n        \"min\": 40.33146286010742,\n        \"max\": 41.412230491638184,\n        \"num_unique_values\": 9529,\n        \"samples\": [\n          40.49777412414551,\n          40.62481689453125,\n          40.445374488830566\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SE(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31320502702458586,\n        \"min\": 40.32929706573486,\n        \"max\": 41.40946960449219,\n        \"num_unique_values\": 9525,\n        \"samples\": [\n          41.04582977294922,\n          40.46824359893799,\n          40.979135513305664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NW(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31310926455698884,\n        \"min\": 40.31065368652344,\n        \"max\": 41.39064121246338,\n        \"num_unique_values\": 9527,\n        \"samples\": [\n          40.47683334350586,\n          40.33511447906494,\n          40.68054485321045\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NE(deg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3130670047292377,\n        \"min\": 40.30849075317383,\n        \"max\": 41.38836193084717,\n        \"num_unique_values\": 9530,\n        \"samples\": [\n          40.47423839569092,\n          40.60145854949951,\n          40.42215442657471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Do it again, but cleanup the keys and add time object\n",
+        "# Notice that the file is reused\n",
+        "df = api.to_dataframe(tempokey, unit_keys=False, parse_dates=True, backend='xdr')\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 634
+        },
+        "id": "_-XxhcdbR782",
+        "outputId": "e44a9d00-e991-4b93-805e-b2f185ec818b"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using cached: nyc/tempo.l2.no2.vertical_column_troposphere_2023-12-18T000000Z_2023-12-18T235959Z.xdr.gz\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    Timestamp  LONGITUDE   LATITUDE  \\\n",
+              "0    2023-12-18T15:11:00+0000 -73.316078  41.398060   \n",
+              "1    2023-12-18T15:11:00+0000 -72.758522  41.398186   \n",
+              "2    2023-12-18T15:11:00+0000 -72.820793  41.396824   \n",
+              "3    2023-12-18T15:11:00+0000 -72.883331  41.394588   \n",
+              "4    2023-12-18T15:11:00+0000 -72.946648  41.391434   \n",
+              "..                        ...        ...        ...   \n",
+              "890  2023-12-18T18:18:00+0000 -73.764481  40.330441   \n",
+              "891  2023-12-18T18:18:00+0000 -73.826035  40.327888   \n",
+              "892  2023-12-18T18:18:00+0000 -73.886749  40.326015   \n",
+              "893  2023-12-18T18:18:00+0000 -73.947411  40.323883   \n",
+              "894  2023-12-18T18:18:00+0000 -74.009056  40.321693   \n",
+              "\n",
+              "     no2_vertical_column_troposphere  Longitude_SW  Longitude_SE  \\\n",
+              "0                       3.093594e+15    -73.281212    -73.344002   \n",
+              "1                       7.423907e+13    -72.723591    -72.786112   \n",
+              "2                       4.233557e+14    -72.786112    -72.848591   \n",
+              "3                       1.407843e+15    -72.848591    -72.911537   \n",
+              "4                       3.048500e+15    -72.911537    -72.974806   \n",
+              "..                               ...           ...           ...   \n",
+              "890                     5.616886e+15    -73.730804    -73.792147   \n",
+              "891                     4.467097e+15    -73.792147    -73.853292   \n",
+              "892                     8.049984e+15    -73.853292    -73.913992   \n",
+              "893                     1.004033e+16    -73.913992    -73.975153   \n",
+              "894                     8.188616e+15    -73.975153    -74.036465   \n",
+              "\n",
+              "     Longitude_NW  Longitude_NE  Latitude_SW  Latitude_SE  Latitude_NW  \\\n",
+              "0      -73.288191    -73.351015    41.410378    41.407287    41.388793   \n",
+              "1      -72.730680    -72.793116    41.410442    41.408224    41.389041   \n",
+              "2      -72.793116    -72.855520    41.408224    41.406334    41.386914   \n",
+              "3      -72.855520    -72.918381    41.406334    41.403631    41.385095   \n",
+              "4      -72.918381    -72.981564    41.403631    41.400612    41.382483   \n",
+              "..            ...           ...          ...          ...          ...   \n",
+              "890    -73.737051    -73.798367    40.341922    40.339574    40.321104   \n",
+              "891    -73.798367    -73.859488    40.339574    40.337360    40.318759   \n",
+              "892    -73.859488    -73.920162    40.337360    40.335357    40.316548   \n",
+              "893    -73.920162    -73.981304    40.335357    40.333200    40.314547   \n",
+              "894    -73.981304    -74.042583    40.333200    40.330881    40.312383   \n",
+              "\n",
+              "     Latitude_NE                      time  \n",
+              "0      41.385610 2023-12-18 15:11:00+00:00  \n",
+              "1      41.386914 2023-12-18 15:11:00+00:00  \n",
+              "2      41.385095 2023-12-18 15:11:00+00:00  \n",
+              "3      41.382483 2023-12-18 15:11:00+00:00  \n",
+              "4      41.379558 2023-12-18 15:11:00+00:00  \n",
+              "..           ...                       ...  \n",
+              "890    40.318759 2023-12-18 18:18:00+00:00  \n",
+              "891    40.316548 2023-12-18 18:18:00+00:00  \n",
+              "892    40.314547 2023-12-18 18:18:00+00:00  \n",
+              "893    40.312383 2023-12-18 18:18:00+00:00  \n",
+              "894    40.310077 2023-12-18 18:18:00+00:00  \n",
+              "\n",
+              "[9569 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-58dcbf6c-ea71-45af-a4cc-ff51e99e8b0a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "\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>Timestamp</th>\n",
+              "      <th>LONGITUDE</th>\n",
+              "      <th>LATITUDE</th>\n",
+              "      <th>no2_vertical_column_troposphere</th>\n",
+              "      <th>Longitude_SW</th>\n",
+              "      <th>Longitude_SE</th>\n",
+              "      <th>Longitude_NW</th>\n",
+              "      <th>Longitude_NE</th>\n",
+              "      <th>Latitude_SW</th>\n",
+              "      <th>Latitude_SE</th>\n",
+              "      <th>Latitude_NW</th>\n",
+              "      <th>Latitude_NE</th>\n",
+              "      <th>time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-73.316078</td>\n",
+              "      <td>41.398060</td>\n",
+              "      <td>3.093594e+15</td>\n",
+              "      <td>-73.281212</td>\n",
+              "      <td>-73.344002</td>\n",
+              "      <td>-73.288191</td>\n",
+              "      <td>-73.351015</td>\n",
+              "      <td>41.410378</td>\n",
+              "      <td>41.407287</td>\n",
+              "      <td>41.388793</td>\n",
+              "      <td>41.385610</td>\n",
+              "      <td>2023-12-18 15:11:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.758522</td>\n",
+              "      <td>41.398186</td>\n",
+              "      <td>7.423907e+13</td>\n",
+              "      <td>-72.723591</td>\n",
+              "      <td>-72.786112</td>\n",
+              "      <td>-72.730680</td>\n",
+              "      <td>-72.793116</td>\n",
+              "      <td>41.410442</td>\n",
+              "      <td>41.408224</td>\n",
+              "      <td>41.389041</td>\n",
+              "      <td>41.386914</td>\n",
+              "      <td>2023-12-18 15:11:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.820793</td>\n",
+              "      <td>41.396824</td>\n",
+              "      <td>4.233557e+14</td>\n",
+              "      <td>-72.786112</td>\n",
+              "      <td>-72.848591</td>\n",
+              "      <td>-72.793116</td>\n",
+              "      <td>-72.855520</td>\n",
+              "      <td>41.408224</td>\n",
+              "      <td>41.406334</td>\n",
+              "      <td>41.386914</td>\n",
+              "      <td>41.385095</td>\n",
+              "      <td>2023-12-18 15:11:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.883331</td>\n",
+              "      <td>41.394588</td>\n",
+              "      <td>1.407843e+15</td>\n",
+              "      <td>-72.848591</td>\n",
+              "      <td>-72.911537</td>\n",
+              "      <td>-72.855520</td>\n",
+              "      <td>-72.918381</td>\n",
+              "      <td>41.406334</td>\n",
+              "      <td>41.403631</td>\n",
+              "      <td>41.385095</td>\n",
+              "      <td>41.382483</td>\n",
+              "      <td>2023-12-18 15:11:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2023-12-18T15:11:00+0000</td>\n",
+              "      <td>-72.946648</td>\n",
+              "      <td>41.391434</td>\n",
+              "      <td>3.048500e+15</td>\n",
+              "      <td>-72.911537</td>\n",
+              "      <td>-72.974806</td>\n",
+              "      <td>-72.918381</td>\n",
+              "      <td>-72.981564</td>\n",
+              "      <td>41.403631</td>\n",
+              "      <td>41.400612</td>\n",
+              "      <td>41.382483</td>\n",
+              "      <td>41.379558</td>\n",
+              "      <td>2023-12-18 15:11:00+00:00</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>890</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.764481</td>\n",
+              "      <td>40.330441</td>\n",
+              "      <td>5.616886e+15</td>\n",
+              "      <td>-73.730804</td>\n",
+              "      <td>-73.792147</td>\n",
+              "      <td>-73.737051</td>\n",
+              "      <td>-73.798367</td>\n",
+              "      <td>40.341922</td>\n",
+              "      <td>40.339574</td>\n",
+              "      <td>40.321104</td>\n",
+              "      <td>40.318759</td>\n",
+              "      <td>2023-12-18 18:18:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>891</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.826035</td>\n",
+              "      <td>40.327888</td>\n",
+              "      <td>4.467097e+15</td>\n",
+              "      <td>-73.792147</td>\n",
+              "      <td>-73.853292</td>\n",
+              "      <td>-73.798367</td>\n",
+              "      <td>-73.859488</td>\n",
+              "      <td>40.339574</td>\n",
+              "      <td>40.337360</td>\n",
+              "      <td>40.318759</td>\n",
+              "      <td>40.316548</td>\n",
+              "      <td>2023-12-18 18:18:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>892</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.886749</td>\n",
+              "      <td>40.326015</td>\n",
+              "      <td>8.049984e+15</td>\n",
+              "      <td>-73.853292</td>\n",
+              "      <td>-73.913992</td>\n",
+              "      <td>-73.859488</td>\n",
+              "      <td>-73.920162</td>\n",
+              "      <td>40.337360</td>\n",
+              "      <td>40.335357</td>\n",
+              "      <td>40.316548</td>\n",
+              "      <td>40.314547</td>\n",
+              "      <td>2023-12-18 18:18:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>893</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-73.947411</td>\n",
+              "      <td>40.323883</td>\n",
+              "      <td>1.004033e+16</td>\n",
+              "      <td>-73.913992</td>\n",
+              "      <td>-73.975153</td>\n",
+              "      <td>-73.920162</td>\n",
+              "      <td>-73.981304</td>\n",
+              "      <td>40.335357</td>\n",
+              "      <td>40.333200</td>\n",
+              "      <td>40.314547</td>\n",
+              "      <td>40.312383</td>\n",
+              "      <td>2023-12-18 18:18:00+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>894</th>\n",
+              "      <td>2023-12-18T18:18:00+0000</td>\n",
+              "      <td>-74.009056</td>\n",
+              "      <td>40.321693</td>\n",
+              "      <td>8.188616e+15</td>\n",
+              "      <td>-73.975153</td>\n",
+              "      <td>-74.036465</td>\n",
+              "      <td>-73.981304</td>\n",
+              "      <td>-74.042583</td>\n",
+              "      <td>40.333200</td>\n",
+              "      <td>40.330881</td>\n",
+              "      <td>40.312383</td>\n",
+              "      <td>40.310077</td>\n",
+              "      <td>2023-12-18 18:18:00+00:00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "<p>9569 rows × 13 columns</p>\n",
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+              "        document.querySelector('#df-58dcbf6c-ea71-45af-a4cc-ff51e99e8b0a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-58dcbf6c-ea71-45af-a4cc-ff51e99e8b0a');\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",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-118bb8e1-f57e-447b-afc8-4c42edd6f075\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-118bb8e1-f57e-447b-afc8-4c42edd6f075')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-118bb8e1-f57e-447b-afc8-4c42edd6f075 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_2b864527-443f-48bb-a805-49b6557ee958\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2b864527-443f-48bb-a805-49b6557ee958 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9569,\n  \"fields\": [\n    {\n      \"column\": \"Timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-18T15:11:00+0000\",\n        \"max\": \"2023-12-18T18:18:00+0000\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"2023-12-18T15:18:00+0000\",\n          \"2023-12-18T17:18:00+0000\",\n          \"2023-12-18T15:11:00+0000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LONGITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9623313458301086,\n        \"min\": -74.79998779296875,\n        \"max\": -71.43016815185547,\n        \"num_unique_values\": 9470,\n        \"samples\": [\n          -73.41744232177734,\n          -72.19168853759766,\n          -72.66265106201172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LATITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3131580814081236,\n        \"min\": 40.32004928588867,\n        \"max\": 41.39975357055664,\n        \"num_unique_values\": 9391,\n        \"samples\": [\n          40.893558502197266,\n          40.551429748535156,\n          40.59719467163086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no2_vertical_column_troposphere\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7695156439940069.0,\n        \"min\": 888413850391.0116,\n        \"max\": 6.73551706334208e+16,\n        \"num_unique_values\": 9569,\n        \"samples\": [\n          919182732960312.2,\n          3758866343646434.0,\n          3295114010611118.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_SW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9628157974779525,\n        \"min\": -74.76691436767578,\n        \"max\": -71.39491844177246,\n        \"num_unique_values\": 9534,\n        \"samples\": [\n          -72.00566673278809,\n          -73.90005111694336,\n          -73.70192527770996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_SE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9622616464038662,\n        \"min\": -74.82794952392578,\n        \"max\": -71.45800971984863,\n        \"num_unique_values\": 9541,\n        \"samples\": [\n          -71.53350257873535,\n          -74.26959800720215,\n          -72.68786430358887\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9624111341460376,\n        \"min\": -74.77294731140137,\n        \"max\": -71.40229415893555,\n        \"num_unique_values\": 9534,\n        \"samples\": [\n          -72.012939453125,\n          -74.75227165222168,\n          -73.58502388000488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9618573857344905,\n        \"min\": -74.83389091491699,\n        \"max\": -71.46535682678223,\n        \"num_unique_values\": 9539,\n        \"samples\": [\n          -73.43927192687988,\n          -73.4745979309082,\n          -72.69461059570312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3132486958796694,\n        \"min\": 40.33146286010742,\n        \"max\": 41.412230491638184,\n        \"num_unique_values\": 9529,\n        \"samples\": [\n          40.49777412414551,\n          40.62481689453125,\n          40.445374488830566\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31320502702458586,\n        \"min\": 40.32929706573486,\n        \"max\": 41.40946960449219,\n        \"num_unique_values\": 9525,\n        \"samples\": [\n          41.04582977294922,\n          40.46824359893799,\n          40.979135513305664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31310926455698884,\n        \"min\": 40.31065368652344,\n        \"max\": 41.39064121246338,\n        \"num_unique_values\": 9527,\n        \"samples\": [\n          40.47683334350586,\n          40.33511447906494,\n          40.68054485321045\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3130670047292377,\n        \"min\": 40.30849075317383,\n        \"max\": 41.38836193084717,\n        \"num_unique_values\": 9530,\n        \"samples\": [\n          40.47423839569092,\n          40.60145854949951,\n          40.42215442657471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-18 15:11:00+00:00\",\n        \"max\": \"2023-12-18 18:18:00+00:00\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"2023-12-18 15:18:00+00:00\",\n          \"2023-12-18 17:18:00+00:00\",\n          \"2023-12-18 15:11:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Step 3: Compare to observations\n",
+        "\n",
+        "* Make an hourly average product.\n",
+        "* Make a simple time-series plot\n",
+        "* Do the same with airnow to compare"
+      ],
+      "metadata": {
+        "id": "gLFnlchtqmUi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make an hourly average\n",
+        "hdf = df.groupby(pd.Grouper(key='time', freq='1h')).mean(numeric_only=True)\n",
+        "hdf"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "g42ta_2cRts2",
+        "outputId": "be352c47-5c26-4650-e489-c79fe25e003f"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                           LONGITUDE   LATITUDE  \\\n",
+              "time                                              \n",
+              "2023-12-18 15:00:00+00:00 -73.283330  40.849232   \n",
+              "2023-12-18 16:00:00+00:00 -73.187944  40.852178   \n",
+              "2023-12-18 17:00:00+00:00 -73.093278  40.857026   \n",
+              "2023-12-18 18:00:00+00:00 -73.111625  40.827774   \n",
+              "\n",
+              "                           no2_vertical_column_troposphere  Longitude_SW  \\\n",
+              "time                                                                       \n",
+              "2023-12-18 15:00:00+00:00                     4.230446e+15    -73.249018   \n",
+              "2023-12-18 16:00:00+00:00                     5.531938e+15    -73.153566   \n",
+              "2023-12-18 17:00:00+00:00                     6.386307e+15    -73.058850   \n",
+              "2023-12-18 18:00:00+00:00                     5.733243e+15    -73.077252   \n",
+              "\n",
+              "                           Longitude_SE  Longitude_NW  Longitude_NE  \\\n",
+              "time                                                                  \n",
+              "2023-12-18 15:00:00+00:00    -73.310997    -73.255643    -73.317594   \n",
+              "2023-12-18 16:00:00+00:00    -73.215645    -73.160231    -73.222283   \n",
+              "2023-12-18 17:00:00+00:00    -73.120997    -73.065552    -73.127672   \n",
+              "2023-12-18 18:00:00+00:00    -73.139305    -73.083933    -73.145958   \n",
+              "\n",
+              "                           Latitude_SW  Latitude_SE  Latitude_NW  Latitude_NE  \n",
+              "time                                                                           \n",
+              "2023-12-18 15:00:00+00:00    40.860911    40.858619    40.839853    40.837564  \n",
+              "2023-12-18 16:00:00+00:00    40.863865    40.861568    40.842798    40.840506  \n",
+              "2023-12-18 17:00:00+00:00    40.868752    40.866385    40.847680    40.845316  \n",
+              "2023-12-18 18:00:00+00:00    40.839493    40.837118    40.818434    40.816065  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b487890a-df50-4853-8b3f-f61c2fcdbf30\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        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>LONGITUDE</th>\n",
+              "      <th>LATITUDE</th>\n",
+              "      <th>no2_vertical_column_troposphere</th>\n",
+              "      <th>Longitude_SW</th>\n",
+              "      <th>Longitude_SE</th>\n",
+              "      <th>Longitude_NW</th>\n",
+              "      <th>Longitude_NE</th>\n",
+              "      <th>Latitude_SW</th>\n",
+              "      <th>Latitude_SE</th>\n",
+              "      <th>Latitude_NW</th>\n",
+              "      <th>Latitude_NE</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>time</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2023-12-18 15:00:00+00:00</th>\n",
+              "      <td>-73.283330</td>\n",
+              "      <td>40.849232</td>\n",
+              "      <td>4.230446e+15</td>\n",
+              "      <td>-73.249018</td>\n",
+              "      <td>-73.310997</td>\n",
+              "      <td>-73.255643</td>\n",
+              "      <td>-73.317594</td>\n",
+              "      <td>40.860911</td>\n",
+              "      <td>40.858619</td>\n",
+              "      <td>40.839853</td>\n",
+              "      <td>40.837564</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-12-18 16:00:00+00:00</th>\n",
+              "      <td>-73.187944</td>\n",
+              "      <td>40.852178</td>\n",
+              "      <td>5.531938e+15</td>\n",
+              "      <td>-73.153566</td>\n",
+              "      <td>-73.215645</td>\n",
+              "      <td>-73.160231</td>\n",
+              "      <td>-73.222283</td>\n",
+              "      <td>40.863865</td>\n",
+              "      <td>40.861568</td>\n",
+              "      <td>40.842798</td>\n",
+              "      <td>40.840506</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-12-18 17:00:00+00:00</th>\n",
+              "      <td>-73.093278</td>\n",
+              "      <td>40.857026</td>\n",
+              "      <td>6.386307e+15</td>\n",
+              "      <td>-73.058850</td>\n",
+              "      <td>-73.120997</td>\n",
+              "      <td>-73.065552</td>\n",
+              "      <td>-73.127672</td>\n",
+              "      <td>40.868752</td>\n",
+              "      <td>40.866385</td>\n",
+              "      <td>40.847680</td>\n",
+              "      <td>40.845316</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-12-18 18:00:00+00:00</th>\n",
+              "      <td>-73.111625</td>\n",
+              "      <td>40.827774</td>\n",
+              "      <td>5.733243e+15</td>\n",
+              "      <td>-73.077252</td>\n",
+              "      <td>-73.139305</td>\n",
+              "      <td>-73.083933</td>\n",
+              "      <td>-73.145958</td>\n",
+              "      <td>40.839493</td>\n",
+              "      <td>40.837118</td>\n",
+              "      <td>40.818434</td>\n",
+              "      <td>40.816065</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b487890a-df50-4853-8b3f-f61c2fcdbf30')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b487890a-df50-4853-8b3f-f61c2fcdbf30 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b487890a-df50-4853-8b3f-f61c2fcdbf30');\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",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-88350121-3916-4ced-ae2f-5372de6dceed\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88350121-3916-4ced-ae2f-5372de6dceed')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-88350121-3916-4ced-ae2f-5372de6dceed button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_eee2980e-505e-422b-9144-c00741f89623\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('hdf')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_eee2980e-505e-422b-9144-c00741f89623 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('hdf');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "hdf",
+              "summary": "{\n  \"name\": \"hdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-18 15:00:00+00:00\",\n        \"max\": \"2023-12-18 18:00:00+00:00\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"2023-12-18 16:00:00+00:00\",\n          \"2023-12-18 18:00:00+00:00\",\n          \"2023-12-18 15:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LONGITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08651785771221834,\n        \"min\": -73.28333016231586,\n        \"max\": -73.09327761305975,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -73.18794355635997,\n          -73.11162489523619,\n          -73.28333016231586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LATITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012924701636222218,\n        \"min\": 40.827774376476995,\n        \"max\": 40.857026431683416,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40.85217789246768,\n          40.827774376476995,\n          40.84923159104135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no2_vertical_column_troposphere\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 903556826183023.5,\n        \"min\": 4230445775723593.0,\n        \"max\": 6386306739516596.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5531938358397798.0,\n          5733242612378440.0,\n          4230445775723593.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_SW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0865603523863404,\n        \"min\": -73.2490179056249,\n        \"max\": -73.05884952663146,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -73.15356633495985,\n          -73.07725201809045,\n          -73.2490179056249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_SE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08650214148294234,\n        \"min\": -73.3109965950918,\n        \"max\": -73.12099655623288,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -73.21564542092338,\n          -73.13930475144159,\n          -73.3109965950918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08652825043252842,\n        \"min\": -73.25564295579775,\n        \"max\": -73.0655517578125,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -73.16023132624198,\n          -73.08393347769072,\n          -73.25564295579775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude_NE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08647006508661075,\n        \"min\": -73.31759398669496,\n        \"max\": -73.12767157407151,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -73.22228255656552,\n          -73.14595779732709,\n          -73.31759398669496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012919349094532273,\n        \"min\": 40.83949312565131,\n        \"max\": 40.86875217909665,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40.86386514724775,\n          40.83949312565131,\n          40.860911368517925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_SE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012938661085377655,\n        \"min\": 40.83711778054506,\n        \"max\": 40.866385003709304,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40.86156842000194,\n          40.83711778054506,\n          40.85861860492031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012914619478792692,\n        \"min\": 40.81843406503851,\n        \"max\": 40.84767989915671,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40.842798033947396,\n          40.81843406503851,\n          40.839852530491925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude_NE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012933149009098194,\n        \"min\": 40.81606458069442,\n        \"max\": 40.8453164594198,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40.84050619297778,\n          40.81606458069442,\n          40.83756448490286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot a data column selected from the names above\n",
+        "tempocol = 'no2_vertical_column_troposphere'\n",
+        "ax = hdf[tempocol].plot(ylim=(0, None), ylabel='NO2 [molec/cm2]')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465
+        },
+        "id": "TJUKlR4jTEgP",
+        "outputId": "85ef4e8c-3756-4c6d-e72e-fe8148cc72c9"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "airnowkey = 'airnow.no2'\n",
+        "adf = api.to_dataframe(airnowkey, unit_keys=False, parse_dates=True)\n",
+        "adf"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 597
+        },
+        "id": "6jxgC38o-ER-",
+        "outputId": "bbee77f1-8d46-4966-be87-0dd0c46cac7e"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    Timestamp  LONGITUDE  LATITUDE  STATION   no2  \\\n",
+              "0    2023-12-18T00:00:00-0000  -73.33690  41.11890        1   5.0   \n",
+              "1    2023-12-18T00:00:00-0000  -72.90269  41.30129        2  16.0   \n",
+              "2    2023-12-18T00:00:00-0000  -73.96610  40.85355        3  15.0   \n",
+              "3    2023-12-18T00:00:00-0000  -74.12608  40.67025        4  12.0   \n",
+              "4    2023-12-18T00:00:00-0000  -74.06657  40.73169        5  20.0   \n",
+              "..                        ...        ...       ...      ...   ...   \n",
+              "211  2023-12-18T23:00:00-0000  -74.06657  40.73169        5  18.0   \n",
+              "212  2023-12-18T23:00:00-0000  -74.42944  40.46218        6   3.0   \n",
+              "213  2023-12-18T23:00:00-0000  -74.67630  40.78763        7   2.0   \n",
+              "214  2023-12-18T23:00:00-0000  -74.20837  40.64144        8  12.0   \n",
+              "215  2023-12-18T23:00:00-0000  -73.13913  40.96102        9   4.0   \n",
+              "\n",
+              "                                        SITE_NAME                      time  \n",
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+              "4                              840340171002;42602 2023-12-18 00:00:00+00:00  \n",
+              "..                                            ...                       ...  \n",
+              "211                            840340171002;42602 2023-12-18 23:00:00+00:00  \n",
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+              "215                            840361030044;42602 2023-12-18 23:00:00+00:00  \n",
+              "\n",
+              "[216 rows x 7 columns]"
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+              "      <th></th>\n",
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+              "      <th>LONGITUDE</th>\n",
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+              "      <td>2023-12-18T00:00:00-0000</td>\n",
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+              "      <td>2023-12-18 00:00:00+00:00</td>\n",
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+              "    <tr>\n",
+              "      <th>211</th>\n",
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+              "      <td>-74.06657</td>\n",
+              "      <td>40.73169</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>215</th>\n",
+              "      <td>2023-12-18T23:00:00-0000</td>\n",
+              "      <td>-73.13913</td>\n",
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+              "      <td>2023-12-18 23:00:00+00:00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>216 rows × 7 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7bf8aca-b094-43e4-be5d-a84fc83f819e')\"\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e7bf8aca-b094-43e4-be5d-a84fc83f819e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e7bf8aca-b094-43e4-be5d-a84fc83f819e');\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",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "  .colab-df-quickchart {\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "  .colab-df-quickchart {\n",
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+              "    cursor: pointer;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
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+              "    border: 2px solid var(--fill-color);\n",
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+              "    animation:\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
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+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "</div>\n",
+              "\n",
+              "  <div id=\"id_2c95910f-9f6c-40b3-90bc-c658af07d05f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
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+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('adf')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2c95910f-9f6c-40b3-90bc-c658af07d05f button.colab-df-generate');\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('adf');\n",
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+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adf",
+              "summary": "{\n  \"name\": \"adf\",\n  \"rows\": 216,\n  \"fields\": [\n    {\n      \"column\": \"Timestamp\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2023-12-18T08:00:00-0000\",\n          \"2023-12-18T16:00:00-0000\",\n          \"2023-12-18T00:00:00-0000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LONGITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5733663103564617,\n        \"min\": -74.6763,\n        \"max\": -72.90269,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -74.20837,\n          -72.90269,\n          -74.42944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LATITUDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2434064633615991,\n        \"min\": 40.46218,\n        \"max\": 41.30129,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          40.64144,\n          41.30129,\n          40.46218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.074439851862342,\n        \"min\": 0.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          1.5,\n          1.0,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SITE_NAME\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"                          840340390004;42602\",\n          \"                          840090090027;42602\",\n          \"                          840340230011;42602\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-18 00:00:00+00:00\",\n        \"max\": \"2023-12-18 23:00:00+00:00\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"2023-12-18 08:00:00+00:00\",\n          \"2023-12-18 16:00:00+00:00\",\n          \"2023-12-18 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "airnowno2 = adf['no2'].groupby(adf['time']).median()\n",
+        "ax = hdf[tempocol].plot(ylabel='TEMPO NO2 [molec/cm2]', color='r', marker='+', ylim=(0, 9e15))\n",
+        "airnowno2.plot(ax=ax.twinx(), color='k', marker='o', ylim=(0, 7), ylabel='AirNow NO2 [ppb]')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "id": "7IlNuTl1-WeJ",
+        "outputId": "1dd48459-9d6d-44ed-9d42-905b50d2aad1"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='time', ylabel='AirNow NO2 [ppb]'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Step 4: Create a TEMPO custom map\n",
+        "\n",
+        "* Here we will request similar data, but pregridded.\n",
+        "* This is a custom L3 file on a CMAQ 12km grid."
+      ],
+      "metadata": {
+        "id": "KCYqgur8qt_N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "api.grid_kw"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8Lngy0Twq5Bz",
+        "outputId": "655df133-f6bf-42ea-a7ed-8d041d461890"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'GDNAM': '12US1',\n",
+              " 'GDTYP': 2,\n",
+              " 'NCOLS': 21,\n",
+              " 'NROWS': 17,\n",
+              " 'XORIG': 1848000.0,\n",
+              " 'YORIG': 252000.0,\n",
+              " 'XCELL': 12000.0,\n",
+              " 'YCELL': 12000.0,\n",
+              " 'P_ALP': 33.0,\n",
+              " 'P_BET': 45.0,\n",
+              " 'P_GAM': -97.0,\n",
+              " 'XCENT': -97.0,\n",
+              " 'YCENT': 40.0,\n",
+              " 'VGTYP': 7,\n",
+              " 'VGTOP': 5000.0,\n",
+              " 'NLAYS': 35,\n",
+              " 'earth_radius': 6370000.0,\n",
+              " 'g': 9.81,\n",
+              " 'R': 287.04,\n",
+              " 'A': 50.0,\n",
+              " 'T0': 290,\n",
+              " 'P0': 100000.0,\n",
+              " 'REGRID_AGGREGATE': 'None'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now retrieve a NetCDF file with IOAPI coordinates (like CMAQ)\n",
+        "ds = api.to_ioapi(tempokey)\n",
+        "ds"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 405
+        },
+        "id": "IR9zFmTQTl9t",
+        "outputId": "9534ee3e-577c-4291-bad0-0bc029dd9db6"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<xarray.Dataset> Size: 138kB\n",
+              "Dimensions:          (TSTEP: 24, VAR: 4, DATE-TIME: 2, LAY: 1, ROW: 17, COL: 21)\n",
+              "Coordinates:\n",
+              "  * TSTEP            (TSTEP) datetime64[ns] 192B 2023-12-18 ... 2023-12-18T23...\n",
+              "  * LAY              (LAY) float32 4B 0.9975\n",
+              "  * ROW              (ROW) float64 136B 0.5 1.5 2.5 3.5 ... 13.5 14.5 15.5 16.5\n",
+              "  * COL              (COL) float64 168B 0.5 1.5 2.5 3.5 ... 17.5 18.5 19.5 20.5\n",
+              "Dimensions without coordinates: VAR, DATE-TIME\n",
+              "Data variables:\n",
+              "    TFLAG            (TSTEP, VAR, DATE-TIME) int32 768B ...\n",
+              "    LONGITUDE        (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "    LATITUDE         (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "    COUNT            (TSTEP, LAY, ROW, COL) int32 34kB ...\n",
+              "    NO2_VERTICAL_CO  (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "Attributes: (12/34)\n",
+              "    IOAPI_VERSION:  1.0 1997349 (Dec. 15, 1997)\n",
+              "    EXEC_ID:        ????????????????                                         ...\n",
+              "    FTYPE:          1\n",
+              "    CDATE:          2025027\n",
+              "    CTIME:          1551\n",
+              "    WDATE:          2025027\n",
+              "    ...             ...\n",
+              "    GDNAM:          M_02_99BRACE    \n",
+              "    UPNAM:          XDRConvert      \n",
+              "    VAR-LIST:       LONGITUDE       LATITUDE        COUNT           NO2_VERTI...\n",
+              "    FILEDESC:       http://tempo.si.edu/,TEMPOSubset,XDRConvert              ...\n",
+              "    HISTORY:        XDRConvert\n",
+              "    crs_proj4:      +proj=lcc +lat_1=33.0 +lat_2=45.0 +lat_0=40.0 +lon_0=-97...."
+            ],
+            "text/html": [
+              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+              "<defs>\n",
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+              "</symbol>\n",
+              "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+              "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "</symbol>\n",
+              "</defs>\n",
+              "</svg>\n",
+              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+              " *\n",
+              " */\n",
+              "\n",
+              ":root {\n",
+              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+              "  --xr-background-color: var(--jp-layout-color0, white);\n",
+              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+              "}\n",
+              "\n",
+              "html[theme=\"dark\"],\n",
+              "html[data-theme=\"dark\"],\n",
+              "body[data-theme=\"dark\"],\n",
+              "body.vscode-dark {\n",
+              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+              "  --xr-border-color: #1f1f1f;\n",
+              "  --xr-disabled-color: #515151;\n",
+              "  --xr-background-color: #111111;\n",
+              "  --xr-background-color-row-even: #111111;\n",
+              "  --xr-background-color-row-odd: #313131;\n",
+              "}\n",
+              "\n",
+              ".xr-wrap {\n",
+              "  display: block !important;\n",
+              "  min-width: 300px;\n",
+              "  max-width: 700px;\n",
+              "}\n",
+              "\n",
+              ".xr-text-repr-fallback {\n",
+              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-header {\n",
+              "  padding-top: 6px;\n",
+              "  padding-bottom: 6px;\n",
+              "  margin-bottom: 4px;\n",
+              "  border-bottom: solid 1px var(--xr-border-color);\n",
+              "}\n",
+              "\n",
+              ".xr-header > div,\n",
+              ".xr-header > ul {\n",
+              "  display: inline;\n",
+              "  margin-top: 0;\n",
+              "  margin-bottom: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type,\n",
+              ".xr-array-name {\n",
+              "  margin-left: 2px;\n",
+              "  margin-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type {\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-sections {\n",
+              "  padding-left: 0 !important;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input {\n",
+              "  display: inline-block;\n",
+              "  opacity: 0;\n",
+              "  height: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input + label {\n",
+              "  color: var(--xr-disabled-color);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label {\n",
+              "  cursor: pointer;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:focus + label {\n",
+              "  border: 2px solid var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label:hover {\n",
+              "  color: var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary {\n",
+              "  grid-column: 1;\n",
+              "  color: var(--xr-font-color2);\n",
+              "  font-weight: 500;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary > span {\n",
+              "  display: inline-block;\n",
+              "  padding-left: 0.5em;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:disabled + label {\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in + label:before {\n",
+              "  display: inline-block;\n",
+              "  content: \"►\";\n",
+              "  font-size: 11px;\n",
+              "  width: 15px;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:disabled + label:before {\n",
+              "  color: var(--xr-disabled-color);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked + label:before {\n",
+              "  content: \"▼\";\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked + label > span {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary,\n",
+              ".xr-section-inline-details {\n",
+              "  padding-top: 4px;\n",
+              "  padding-bottom: 4px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-inline-details {\n",
+              "  grid-column: 2 / -1;\n",
+              "}\n",
+              "\n",
+              ".xr-section-details {\n",
+              "  display: none;\n",
+              "  grid-column: 1 / -1;\n",
+              "  margin-bottom: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-array-wrap {\n",
+              "  grid-column: 1 / -1;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 20px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-array-wrap > label {\n",
+              "  grid-column: 1;\n",
+              "  vertical-align: top;\n",
+              "}\n",
+              "\n",
+              ".xr-preview {\n",
+              "  color: var(--xr-font-color3);\n",
+              "}\n",
+              "\n",
+              ".xr-array-preview,\n",
+              ".xr-array-data {\n",
+              "  padding: 0 5px !important;\n",
+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-array-data,\n",
+              ".xr-array-in:checked ~ .xr-array-preview {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-array-in:checked ~ .xr-array-data,\n",
+              ".xr-array-preview {\n",
+              "  display: inline-block;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list {\n",
+              "  display: inline-block !important;\n",
+              "  list-style: none;\n",
+              "  padding: 0 !important;\n",
+              "  margin: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list li {\n",
+              "  display: inline-block;\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list:before {\n",
+              "  content: \"(\";\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list:after {\n",
+              "  content: \")\";\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list li:not(:last-child):after {\n",
+              "  content: \",\";\n",
+              "  padding-right: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-has-index {\n",
+              "  font-weight: bold;\n",
+              "}\n",
+              "\n",
+              ".xr-var-list,\n",
+              ".xr-var-item {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-var-item > div,\n",
+              ".xr-var-item label,\n",
+              ".xr-var-item > .xr-var-name span {\n",
+              "  background-color: var(--xr-background-color-row-even);\n",
+              "  margin-bottom: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-var-item > .xr-var-name:hover span {\n",
+              "  padding-right: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-list > li:nth-child(odd) > div,\n",
+              ".xr-var-list > li:nth-child(odd) > label,\n",
+              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+              "  background-color: var(--xr-background-color-row-odd);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name {\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dims {\n",
+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dtype {\n",
+              "  grid-column: 3;\n",
+              "  text-align: right;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-preview {\n",
+              "  grid-column: 4;\n",
+              "}\n",
+              "\n",
+              ".xr-index-preview {\n",
+              "  grid-column: 2 / 5;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name,\n",
+              ".xr-var-dims,\n",
+              ".xr-var-dtype,\n",
+              ".xr-preview,\n",
+              ".xr-attrs dt {\n",
+              "  white-space: nowrap;\n",
+              "  overflow: hidden;\n",
+              "  text-overflow: ellipsis;\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name:hover,\n",
+              ".xr-var-dims:hover,\n",
+              ".xr-var-dtype:hover,\n",
+              ".xr-attrs dt:hover {\n",
+              "  overflow: visible;\n",
+              "  width: auto;\n",
+              "  z-index: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  display: none;\n",
+              "  background-color: var(--xr-background-color) !important;\n",
+              "  padding-bottom: 5px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+              ".xr-var-data-in:checked ~ .xr-var-data,\n",
+              ".xr-index-data-in:checked ~ .xr-index-data {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > table {\n",
+              "  float: right;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name span,\n",
+              ".xr-var-data,\n",
+              ".xr-index-name div,\n",
+              ".xr-index-data,\n",
+              ".xr-attrs {\n",
+              "  padding-left: 25px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs,\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  grid-column: 1 / -1;\n",
+              "}\n",
+              "\n",
+              "dl.xr-attrs {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 125px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt,\n",
+              ".xr-attrs dd {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  float: left;\n",
+              "  padding-right: 10px;\n",
+              "  width: auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt {\n",
+              "  font-weight: normal;\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt:hover span {\n",
+              "  display: inline-block;\n",
+              "  background: var(--xr-background-color);\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dd {\n",
+              "  grid-column: 2;\n",
+              "  white-space: pre-wrap;\n",
+              "  word-break: break-all;\n",
+              "}\n",
+              "\n",
+              ".xr-icon-database,\n",
+              ".xr-icon-file-text2,\n",
+              ".xr-no-icon {\n",
+              "  display: inline-block;\n",
+              "  vertical-align: middle;\n",
+              "  width: 1em;\n",
+              "  height: 1.5em !important;\n",
+              "  stroke-width: 0;\n",
+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 138kB\n",
+              "Dimensions:          (TSTEP: 24, VAR: 4, DATE-TIME: 2, LAY: 1, ROW: 17, COL: 21)\n",
+              "Coordinates:\n",
+              "  * TSTEP            (TSTEP) datetime64[ns] 192B 2023-12-18 ... 2023-12-18T23...\n",
+              "  * LAY              (LAY) float32 4B 0.9975\n",
+              "  * ROW              (ROW) float64 136B 0.5 1.5 2.5 3.5 ... 13.5 14.5 15.5 16.5\n",
+              "  * COL              (COL) float64 168B 0.5 1.5 2.5 3.5 ... 17.5 18.5 19.5 20.5\n",
+              "Dimensions without coordinates: VAR, DATE-TIME\n",
+              "Data variables:\n",
+              "    TFLAG            (TSTEP, VAR, DATE-TIME) int32 768B ...\n",
+              "    LONGITUDE        (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "    LATITUDE         (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "    COUNT            (TSTEP, LAY, ROW, COL) int32 34kB ...\n",
+              "    NO2_VERTICAL_CO  (TSTEP, LAY, ROW, COL) float32 34kB ...\n",
+              "Attributes: (12/34)\n",
+              "    IOAPI_VERSION:  1.0 1997349 (Dec. 15, 1997)\n",
+              "    EXEC_ID:        ????????????????                                         ...\n",
+              "    FTYPE:          1\n",
+              "    CDATE:          2025027\n",
+              "    CTIME:          1551\n",
+              "    WDATE:          2025027\n",
+              "    ...             ...\n",
+              "    GDNAM:          M_02_99BRACE    \n",
+              "    UPNAM:          XDRConvert      \n",
+              "    VAR-LIST:       LONGITUDE       LATITUDE        COUNT           NO2_VERTI...\n",
+              "    FILEDESC:       http://tempo.si.edu/,TEMPOSubset,XDRConvert              ...\n",
+              "    HISTORY:        XDRConvert\n",
+              "    crs_proj4:      +proj=lcc +lat_1=33.0 +lat_2=45.0 +lat_0=40.0 +lon_0=-97....</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-68344747-a692-4781-a51e-fba1316174df' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68344747-a692-4781-a51e-fba1316174df' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>TSTEP</span>: 24</li><li><span>VAR</span>: 4</li><li><span>DATE-TIME</span>: 2</li><li><span class='xr-has-index'>LAY</span>: 1</li><li><span class='xr-has-index'>ROW</span>: 17</li><li><span class='xr-has-index'>COL</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ed422ba4-7c2b-46b2-9767-3fdc8eacaf85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed422ba4-7c2b-46b2-9767-3fdc8eacaf85' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TSTEP</span></div><div class='xr-var-dims'>(TSTEP)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-12-18 ... 2023-12-18T23:00:00</div><input id='attrs-546f0724-6f0f-4012-8d54-c51049b0f734' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-546f0724-6f0f-4012-8d54-c51049b0f734' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8abf3b5c-fff1-4398-90b0-0f0ebbd74603' class='xr-var-data-in' type='checkbox'><label for='data-8abf3b5c-fff1-4398-90b0-0f0ebbd74603' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-12-18T00:00:00.000000000&#x27;, &#x27;2023-12-18T01:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T02:00:00.000000000&#x27;, &#x27;2023-12-18T03:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T04:00:00.000000000&#x27;, &#x27;2023-12-18T05:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T06:00:00.000000000&#x27;, &#x27;2023-12-18T07:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T08:00:00.000000000&#x27;, &#x27;2023-12-18T09:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T10:00:00.000000000&#x27;, &#x27;2023-12-18T11:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T12:00:00.000000000&#x27;, &#x27;2023-12-18T13:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T14:00:00.000000000&#x27;, &#x27;2023-12-18T15:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T16:00:00.000000000&#x27;, &#x27;2023-12-18T17:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T18:00:00.000000000&#x27;, &#x27;2023-12-18T19:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T20:00:00.000000000&#x27;, &#x27;2023-12-18T21:00:00.000000000&#x27;,\n",
+              "       &#x27;2023-12-18T22:00:00.000000000&#x27;, &#x27;2023-12-18T23:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LAY</span></div><div class='xr-var-dims'>(LAY)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9975</div><input id='attrs-8476c79e-d50b-4727-9e3f-72e5f663b3d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8476c79e-d50b-4727-9e3f-72e5f663b3d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7380e21c-ec8d-4e58-93c2-28fd50255480' class='xr-var-data-in' type='checkbox'><label for='data-7380e21c-ec8d-4e58-93c2-28fd50255480' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.9975], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ROW</span></div><div class='xr-var-dims'>(ROW)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 3.5 ... 14.5 15.5 16.5</div><input id='attrs-44f36310-c66d-4742-87e4-440c058b4e10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44f36310-c66d-4742-87e4-440c058b4e10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9857587-e529-478c-b48e-d07c4864a8f6' class='xr-var-data-in' type='checkbox'><label for='data-c9857587-e529-478c-b48e-d07c4864a8f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,\n",
+              "       12.5, 13.5, 14.5, 15.5, 16.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>COL</span></div><div class='xr-var-dims'>(COL)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 3.5 ... 18.5 19.5 20.5</div><input id='attrs-512484a8-2154-4bbd-bd50-94ffd8b18a33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-512484a8-2154-4bbd-bd50-94ffd8b18a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b557074f-f398-4b81-91e0-5e851e5aa679' class='xr-var-data-in' type='checkbox'><label for='data-b557074f-f398-4b81-91e0-5e851e5aa679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,\n",
+              "       12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6139181a-eefe-4485-ae7c-1402c548c886' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6139181a-eefe-4485-ae7c-1402c548c886' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TFLAG</span></div><div class='xr-var-dims'>(TSTEP, VAR, DATE-TIME)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-113dad51-89de-40f8-ae5b-1741ee47db11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-113dad51-89de-40f8-ae5b-1741ee47db11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec2712a-4e60-40f5-9e68-1784d1ae687a' class='xr-var-data-in' type='checkbox'><label for='data-4ec2712a-4e60-40f5-9e68-1784d1ae687a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>&lt;YYYYDDD,HHMMSS&gt;</dd><dt><span>long_name :</span></dt><dd>TFLAG           </dd><dt><span>var_desc :</span></dt><dd>Timestep-valid flags:  (1) YYYYDDD or (2) HHMMSS                                </dd></dl></div><div class='xr-var-data'><pre>[192 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LONGITUDE</span></div><div class='xr-var-dims'>(TSTEP, LAY, ROW, COL)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd217962-fe78-4374-8234-210ef529a53a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd217962-fe78-4374-8234-210ef529a53a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9088c850-2ccf-4841-a0d1-d668e8d581db' class='xr-var-data-in' type='checkbox'><label for='data-9088c850-2ccf-4841-a0d1-d668e8d581db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>deg</dd><dt><span>long_name :</span></dt><dd>LONGITUDE       </dd><dt><span>var_desc :</span></dt><dd>Longitude at the center of each grid cell                                       </dd></dl></div><div class='xr-var-data'><pre>[8568 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LATITUDE</span></div><div class='xr-var-dims'>(TSTEP, LAY, ROW, COL)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd7d5a24-aaeb-4808-b90a-d806f90a1053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd7d5a24-aaeb-4808-b90a-d806f90a1053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc9a3aa4-13c5-4822-9a52-19c8e88ba31a' class='xr-var-data-in' type='checkbox'><label for='data-cc9a3aa4-13c5-4822-9a52-19c8e88ba31a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>deg</dd><dt><span>long_name :</span></dt><dd>LATITUDE        </dd><dt><span>var_desc :</span></dt><dd>Latitude at the center of each grid cell                                        </dd></dl></div><div class='xr-var-data'><pre>[8568 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COUNT</span></div><div class='xr-var-dims'>(TSTEP, LAY, ROW, COL)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3cb71d85-cc02-438a-8dc2-224b96a7f9f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cb71d85-cc02-438a-8dc2-224b96a7f9f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35063bdd-fa63-489c-ba89-c418ddc8b471' class='xr-var-data-in' type='checkbox'><label for='data-35063bdd-fa63-489c-ba89-c418ddc8b471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>COUNT           </dd><dt><span>var_desc :</span></dt><dd>Number of data points regridded into grid cell                                  </dd></dl></div><div class='xr-var-data'><pre>[8568 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO2_VERTICAL_CO</span></div><div class='xr-var-dims'>(TSTEP, LAY, ROW, COL)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b317c61d-74c1-43c2-be46-e1321a74a2dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b317c61d-74c1-43c2-be46-e1321a74a2dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48740b3b-e84b-4b5b-bfe3-3698a2d75478' class='xr-var-data-in' type='checkbox'><label for='data-48740b3b-e84b-4b5b-bfe3-3698a2d75478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>molecules/cm2</dd><dt><span>long_name :</span></dt><dd>NO2_VERTICAL_CO </dd><dt><span>var_desc :</span></dt><dd>NO2_VERTICAL_CO                                                                 </dd></dl></div><div class='xr-var-data'><pre>[8568 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ecf99ee2-cea6-4e11-9741-eda03d57dd5e' class='xr-section-summary-in' type='checkbox'  ><label for='section-ecf99ee2-cea6-4e11-9741-eda03d57dd5e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>TSTEP</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1f5c5d07-f0ad-4248-bc59-4115fa38d894' class='xr-index-data-in' type='checkbox'/><label for='index-1f5c5d07-f0ad-4248-bc59-4115fa38d894' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-12-18 00:00:00&#x27;, &#x27;2023-12-18 01:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 02:00:00&#x27;, &#x27;2023-12-18 03:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 04:00:00&#x27;, &#x27;2023-12-18 05:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 06:00:00&#x27;, &#x27;2023-12-18 07:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 08:00:00&#x27;, &#x27;2023-12-18 09:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 10:00:00&#x27;, &#x27;2023-12-18 11:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 12:00:00&#x27;, &#x27;2023-12-18 13:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 14:00:00&#x27;, &#x27;2023-12-18 15:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 16:00:00&#x27;, &#x27;2023-12-18 17:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 18:00:00&#x27;, &#x27;2023-12-18 19:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 20:00:00&#x27;, &#x27;2023-12-18 21:00:00&#x27;,\n",
+              "               &#x27;2023-12-18 22:00:00&#x27;, &#x27;2023-12-18 23:00:00&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;TSTEP&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>LAY</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-157d02c8-36a0-41f3-9532-e2c17cffbe03' class='xr-index-data-in' type='checkbox'/><label for='index-157d02c8-36a0-41f3-9532-e2c17cffbe03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.9975], dtype=&#x27;float32&#x27;, name=&#x27;LAY&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ROW</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c27f249d-18d1-45c5-85ef-4cc5e6b1bf66' class='xr-index-data-in' type='checkbox'/><label for='index-c27f249d-18d1-45c5-85ef-4cc5e6b1bf66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,\n",
+              "       12.5, 13.5, 14.5, 15.5, 16.5],\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;ROW&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>COL</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bd7da018-11ce-46a8-95c4-b8dba6853288' class='xr-index-data-in' type='checkbox'/><label for='index-bd7da018-11ce-46a8-95c4-b8dba6853288' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,\n",
+              "       12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5],\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;COL&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b6509d9-911b-48cc-aa9e-bdd8f2928cd2' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b6509d9-911b-48cc-aa9e-bdd8f2928cd2' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IOAPI_VERSION :</span></dt><dd>1.0 1997349 (Dec. 15, 1997)</dd><dt><span>EXEC_ID :</span></dt><dd>????????????????                                                                </dd><dt><span>FTYPE :</span></dt><dd>1</dd><dt><span>CDATE :</span></dt><dd>2025027</dd><dt><span>CTIME :</span></dt><dd>1551</dd><dt><span>WDATE :</span></dt><dd>2025027</dd><dt><span>WTIME :</span></dt><dd>1551</dd><dt><span>SDATE :</span></dt><dd>2023352</dd><dt><span>STIME :</span></dt><dd>0</dd><dt><span>TSTEP :</span></dt><dd>10000</dd><dt><span>NTHIK :</span></dt><dd>1</dd><dt><span>NCOLS :</span></dt><dd>21</dd><dt><span>NROWS :</span></dt><dd>17</dd><dt><span>NLAYS :</span></dt><dd>1</dd><dt><span>NVARS :</span></dt><dd>4</dd><dt><span>GDTYP :</span></dt><dd>2</dd><dt><span>P_ALP :</span></dt><dd>33.0</dd><dt><span>P_BET :</span></dt><dd>45.0</dd><dt><span>P_GAM :</span></dt><dd>-97.0</dd><dt><span>XCENT :</span></dt><dd>-97.0</dd><dt><span>YCENT :</span></dt><dd>40.0</dd><dt><span>XORIG :</span></dt><dd>1848000.0</dd><dt><span>YORIG :</span></dt><dd>252000.0</dd><dt><span>XCELL :</span></dt><dd>12000.0</dd><dt><span>YCELL :</span></dt><dd>12000.0</dd><dt><span>VGTYP :</span></dt><dd>2</dd><dt><span>VGTOP :</span></dt><dd>10000.0</dd><dt><span>VGLVLS :</span></dt><dd>[1.    0.995]</dd><dt><span>GDNAM :</span></dt><dd>M_02_99BRACE    </dd><dt><span>UPNAM :</span></dt><dd>XDRConvert      </dd><dt><span>VAR-LIST :</span></dt><dd>LONGITUDE       LATITUDE        COUNT           NO2_VERTICAL_CO </dd><dt><span>FILEDESC :</span></dt><dd>http://tempo.si.edu/,TEMPOSubset,XDRConvert                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     </dd><dt><span>HISTORY :</span></dt><dd>XDRConvert</dd><dt><span>crs_proj4 :</span></dt><dd>+proj=lcc +lat_1=33.0 +lat_2=45.0 +lat_0=40.0 +lon_0=-97.0 +R=6370000.0 +x_0=-1848000.0 +y_0=-252000.0 +to_meter=12000.0 +no_defs</dd></dl></div></li></ul></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Choose a column from above, notice that names are truncated, so they can be weird\n",
+        "tempoikey = 'NO2_VERTICAL_CO'"
+      ],
+      "metadata": {
+        "id": "KWE0ErgEm7Nh"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now plot a map\n",
+        "cno = pycno.cno(ds.crs_proj4)\n",
+        "qm = ds[tempoikey].where(lambda x: x>0).mean(('TSTEP', 'LAY')).plot()\n",
+        "cno.drawstates(resnum=1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 537
+        },
+        "id": "xxI2l-eebtpt",
+        "outputId": "52003d15-918e-416d-80b3-3d855f775657"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/pycno/__init__.py:538: UserWarning: Downloading: https://www.giss.nasa.gov/tools/panoply/overlays/MWDB_Coasts_NA_1.cnob to /root/.pycno/MWDB_Coasts_NA_1.cnob\n",
+            "  warnings.warn('Downloading: ' + url + ' to ' + str(datapatho))\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.LineCollection at 0x7c3269c03ad0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Step 5: Make a surface NO2 map\n",
+        "\n",
+        "* Download CMAQ EQUATES surface and columns\n",
+        "* Extract CMAQ to gridded TEMPO\n",
+        "* Calculate a transfer function [^1]\n",
+        "* Estimate surface NO2\n",
+        "* Average and make a plot\n",
+        "\n",
+        "\n",
+        "[1] No warranty expressed or implied!\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8mC94BZ78SS8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get a column and surface estimate form CMAQ\n",
+        "cmaqcolkey = 'cmaq.equates.conus.integrated.NO2_COLUMN'\n",
+        "qids = api.to_ioapi(cmaqcolkey, bdate='2018-12-21')\n",
+        "cmaqsfckey = 'cmaq.equates.conus.aconc.NO2'\n",
+        "qsds = api.to_ioapi(cmaqsfckey, bdate='2018-12-21')"
+      ],
+      "metadata": {
+        "id": "lbKsYrs9rEkc"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Align Grids\n",
+        "\n",
+        "To align the grids, we have to convert between lambert projections. This is a little complicated, but pyrsig gives you all the tools you need.\n",
+        "\n",
+        "1. get 2d x/y for TEMPO L3 cell centroids\n",
+        "2. get 2d x/y for TEMPO L3 cell centroids on CMAQ grid\n",
+        "3. store for later use\n",
+        "4. pretend the EQUATES data is 2023\n"
+      ],
+      "metadata": {
+        "id": "RuVstnmCBUOd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1. get 2d x/y for TEMPO L3 cell centroids\n",
+        "y, x = xr.broadcast(ds.ROW, ds.COL)\n",
+        "# 2. get 2d x/y for TEMPO L3 cell centroids on CMAQ grid\n",
+        "dstproj = pyproj.Proj(ds.crs_proj4)\n",
+        "srcproj = pyproj.Proj(qids.crs_proj4)\n",
+        "X, Y = srcproj(*dstproj(x.values, y.values, inverse=True))\n",
+        "# 3. store the result for later use\n",
+        "ds['CMAQX'] = ('COL',), X.mean(0)\n",
+        "ds['CMAQY'] = ('ROW',), Y.mean(1)\n",
+        "# 4. here we pretend that the CMAQ times align with the TEMPO times\n",
+        "ds['CMAQT'] = ('TSTEP',), qsds.TSTEP.values"
+      ],
+      "metadata": {
+        "id": "gyjjtVPSrxcZ"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Extract CMAQ to TEMPO custom L3\n",
+        "\n",
+        "* We'll extract data using the CMAQ coordinates\n",
+        "* And, add the data to the TEMPO dataset"
+      ],
+      "metadata": {
+        "id": "5H-O1DTPBqZ6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now we extract the CMAQ at the TEMPO locations\n",
+        "# all extractions will output time, y, x data\n",
+        "dims = ('TSTEP', 'ROW', 'COL')\n",
+        "# all extractions use the same coordinates\n",
+        "selopts = dict(TSTEP=ds['CMAQT'], COL=ds['CMAQX'], ROW=ds['CMAQY'], method='nearest')\n",
+        "# 1 atm is the surface\n",
+        "selopts['LAY'] = 1\n",
+        "# Get CMAQ surface NO2 (NO2), and tropospheric column (NO2_COLUMN)\n",
+        "ds['CMAQ_NO2_SFC'] = dims, qsds['NO2'].sel(**selopts).data, {'units': 'ppb'}\n",
+        "ds['CMAQ_NO2_COL'] = dims, qids['NO2_COLUMN'].sel(**selopts).data * 1e15, {'units': 'molec/cm**2'}"
+      ],
+      "metadata": {
+        "id": "jku1uNrX5oV0"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the transfer function\n",
+        "ds['CMAQ_SFC2COL'] = ds['CMAQ_NO2_SFC'] / ds['CMAQ_NO2_COL']\n",
+        "ds['CMAQ_SFC2COL'].attrs.update(units='1')\n",
+        "# Calculate the estimate surface NO2\n",
+        "ds['TEMPO_SFC'] = ds['NO2_VERTICAL_CO'] * ds['CMAQ_SFC2COL']\n",
+        "ds['TEMPO_SFC'].attrs.update(units='ppb')"
+      ],
+      "metadata": {
+        "id": "zrwjSAkHAop8"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Now Plot TEMPO-based Surface NO2"
+      ],
+      "metadata": {
+        "id": "gkpA9lEBgCmo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now plot the time average\n",
+        "pds = ds.where(ds['NO2_VERTICAL_CO'] > 0).mean(('TSTEP', 'LAY'), keep_attrs=True)\n",
+        "\n",
+        "# Controlling figure canvas use\n",
+        "gskw = dict(left=0.05, right=0.95, bottom=0.05, top=0.95)\n",
+        "fig, axx = plt.subplots(2, 3, figsize=(18, 8), gridspec_kw=gskw)\n",
+        "\n",
+        "# Put CMAQ on top row : columns 0, 1, and 2\n",
+        "qmsfc = pds['CMAQ_NO2_SFC'].plot(ax=axx[0, 0], cmap='viridis')\n",
+        "qmcol = pds['CMAQ_NO2_COL'].plot(ax=axx[0, 1], cmap='cividis')\n",
+        "pds['CMAQ_SFC2COL'].plot(ax=axx[0, 2], cmap='Reds')\n",
+        "# Put TEMPO on bottom row and use the same colorscales as CMAQ\n",
+        "pds['TEMPO_SFC'].plot(ax=axx[1, 0], norm=qmsfc.norm, cmap=qmsfc.cmap)\n",
+        "pds['NO2_VERTICAL_CO'].plot(ax=axx[1, 1], norm=qmcol.norm, cmap=qmcol.cmap)\n",
+        "# add state overlays (alternatively)\n",
+        "cno.drawstates(ax=axx, resnum=1)\n",
+        "# hide the unused axes\n",
+        "axx[1, 2].set(visible=False)\n",
+        "# add a reminder\n",
+        "_ = fig.text(0.7, 0.25, 'Don\\'t look too close.\\nRemember, CMAQ is from 2018 and TEMPO is from 2023')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 426
+        },
+        "id": "tUA9K2E238KE",
+        "outputId": "0343778f-94f9-4760-c576-d62a563b7c3e"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x800 with 11 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Step 6: Adapt other tutorials\n",
+        "\n",
+        "* Go to https://barronh.github.io/pyrsig\n",
+        "* Navigate to an example you like\n",
+        "* Copy and paste chunks of data\n",
+        "* Then, run them\n",
+        "\n",
+        "Below, I did that for the Pittsburg Pandora evaluation"
+      ],
+      "metadata": {
+        "id": "5d5SRmX-9--z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pyrsig\n",
+        "\n",
+        "\n",
+        "bbox = (-74.5, 40., -73.5, 41)\n",
+        "cmaqkey = 'cmaq.equates.conus.integrated.NO2_COLUMN'\n",
+        "cmaqcol = 'NO2_COLUMN'\n",
+        "datakey = 'pandora.L2_rnvs3p1_8.nitrogen_dioxide_vertical_column_amount'\n",
+        "datacol = 'nitrogen_dioxide_vertical_column_amount'\n",
+        "# Or use TropOMI or any other NO2 columns data\n",
+        "# datakey = 'tropomi.offl.no2.nitrogendioxide_tropospheric_column'\n",
+        "\n",
+        "# Get a CMAQ file from RSIG\n",
+        "api = pyrsig.RsigApi(\n",
+        "    bbox=bbox, bdate='2018-07-01T12', edate='2018-07-01T23:59:59'\n",
+        ")\n",
+        "ds = api.to_ioapi(cmaqkey)\n",
+        "\n",
+        "# pair_rsigcmaq will match the CMAQ bbox, bdate, and edate\n",
+        "df = pyrsig.cmaq.pair_rsigcmaq(ds, cmaqcol, datakey)\n",
+        "\n",
+        "pdf = df.groupby(['time']).mean(numeric_only=True)\n",
+        "z1 = pdf[datacol]\n",
+        "z2 = (pdf['CMAQ_' + cmaqcol] * 1e15)\n",
+        "ax = z1.plot(marker='+', linestyle='none', label='Pandora')\n",
+        "ax = z2.plot(ax=ax, color='green', label='CMAQ')\n",
+        "ax.legend()\n",
+        "ax.set(ylim=(0, None), ylabel='NO2 [molecules/cm2]', xlabel='Time [UTC]')\n",
+        "ax.figure.savefig('cmaq_pandora.png')"
+      ],
+      "metadata": {
+        "id": "DIdcouw5b7-l",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451
+        },
+        "outputId": "37fe80ff-49b4-461b-dc1b-c65896fc480e"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "ssw0sRfTFNkf"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Hopefully, we won't use this...\n",
+        "\n",
+        "I archived all the downloads and posted them. If a server is down, we'll just use the archived data"
+      ],
+      "metadata": {
+        "id": "B_RLy5Pt1Yx6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# in case of emergency:\n",
+        "# !wget -N --no-check-certificate https://gaftp.epa.gov/Air/aqmg/bhenders/tutorial_data/pyrsig_tutorial_2024-05.zip\n",
+        "# !unzip pyrsig_tutorial_2024-05.zip\n",
+        "# !mkdir nyc; cd nyc; unzip ../pyrsig_tutorial_2024-05.zip"
+      ],
+      "metadata": {
+        "id": "h6byZKfsU1DW"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import zipfile\n",
+        "# import glob\n",
+        "# paths = glob.glob('*.gz')\n",
+        "# with zipfile.ZipFile('pyrsig_tutorial_2024-05.zip', mode='w') as zf:\n",
+        "#   for p in paths:\n",
+        "#     zf.write(p)"
+      ],
+      "metadata": {
+        "id": "xGvw3UtOnSQ5"
+      },
+      "execution_count": 24,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file