diff --git a/pr-preview/pr-5/agenda.html b/pr-preview/pr-5/agenda.html new file mode 100644 index 0000000..f6a7f3b --- /dev/null +++ b/pr-preview/pr-5/agenda.html @@ -0,0 +1,552 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop – agenda + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Workshop Agenda

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The full-day workshop will be divided into the following sections:

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8:00 - 8:15
Welcome
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    +
  • Welcome message
    • +
  • Workshop Introduction
    • +
  • Team Introduction
    • +
  • Icebreaker
    • +
8:15 - 8:45
Overview of the U.S. Greenhouse Gas Center and the Science
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  • What is the US GHG Center?
    • +
  • Role of Methane and Carbon Dioxide in Climate Change
    • +
  • What is included in the Center and why these datasets
    • +
  • Introduction to the science use cases / demonstration areas that will be explored during the workshop
    • +
8:45 - 10:00
Introduction to the GHG Center Data Catalog
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  • Intro to the VEDA project and its relevance to Earth Science research and the US GHG Center
    • +
  • What is a STAC catalog?
    • +
  • Hands-on exercise: searching and accessing cloud-optimized datasets using the STAC catalog +
      +
    • Intro to JupyterHub and Jupyter Notebooks +
        +
      • Access to the JupyterHub environment
        • +
      • Creating and running a Jupyter Notebook in the hub
        • +
      • +
    • Using the pystac client library to read the US GHG STAC catalog +
        +
      • List all available datasets
        • +
      • Spatial and temporal search
        • +
      • Access assets and visualize them
        • +
      • +
    • Accessing the US GHG STAC catalog using QGIS on the browser +
        +
      • Establishing connection to the US GHG STAC catalog using the qgis-stac-plugin in QGIS in the GHG Center JupyterHub environment
        • +
      • Adding a dataset/asset from the catalog as layer in QGIS
        • +
      • +
    • +
10:00 - 10:15
BREAK
10:15 - 11:15
Interactive Session 1: EPA Gridded Methane Emissions Inventory
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  • Background and gridding methods overview/ example
    • +
  • Improving access and latency of human anthropogenic emissions
    • +
  • Hands-on exercise
    • +
11:15 - 12:15
Interactive Session 2: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes
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  • Description of data sets and analysis available via the U.S. GHG Center (e.g., gridded wetland emissions and ecosystem exchange estimates)
    • +
  • Hands-on exercise: Exploring annual U.S. wetland methane emissions and their - meteorological influences.
    • +
12:15 - 1:30
LUNCH BREAK
1:30 - 2:45
Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data
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  • Description of data sets and analysis available via the U.S. GHG Center (e.g., EMIT methane enhancements; plans for emission rates, CO2, etc.)
    • +
  • Hands-on exercise: Locating enhancement, understanding temporal relevance, exploring site with multiple observations.
    • +
2:45 - 3:15
Open Science in Action: Intro to GitHub & Communicating Science Discoveries via Web Dashboard
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  • How do VEDA, GitHub, and JupyterHub interoperate and why is this important?
    • +
  • Introduction to web dashboards for science communication +
      +
    • Hands-on exercise: creating a web dashboard to communicate science discoveries using the platform
      • +
    • +
3:15 - 3:30
Closing Remarks
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    +
  • Future Developments and Community Contributions
    • +
  • Engaging with the U.S. Greenhouse Gas Center
    • +
+

Throughout the workshop, facilitators will be available to provide support and guidance, ensuring participants are able to actively explore the workshop topics and apply their knowledge and skills to their own research projects.

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+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/chapters.html b/pr-preview/pr-5/chapters.html new file mode 100644 index 0000000..f7fce05 --- /dev/null +++ b/pr-preview/pr-5/chapters.html @@ -0,0 +1,464 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop – chapters + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Chapters

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The flow of the workshop

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+

Introduction

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  • Intro to the US GHG Center
    • +
  • The US GHG Center Data Catalog (STAC)
    • +
  • The US GHG Center JupyterHub environment
    • +
+
+
+

Science Use Cases

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    +
  • EPA Gridded Methane Emissions Inventory
    • +
  • Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes
    • +
  • Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data
    • +
+
+
+

Open Science in Action

+
    +
  • Intro to GitHub & Communicating Science Discoveries via Web Dashboard
    • +
+
+

ℹ️ Content for each chapter will be added as they are prepared, please check back.

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+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/ghg-center-at-ams-2024.html b/pr-preview/pr-5/ghg-center-at-ams-2024.html new file mode 100644 index 0000000..e785bad --- /dev/null +++ b/pr-preview/pr-5/ghg-center-at-ams-2024.html @@ -0,0 +1,517 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop - The US GHG Center at American Meteorological Society Annual Meeting 2024 + + + + + + + + + + + + + + + + + + + + + + + + + +
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The US GHG Center at American Meteorological Society Annual Meeting 2024

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(AMS 2024)

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Join members of the GHG Center team for several events at the 2024 AMS Annual Meeting at Baltimore, Maryland; January 28th - February 1st

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+

Short Course

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Advancing Open Science: An Interactive Workshop on Harnessing VEDA for Earth Science Research at the U.S. Greenhouse Gas Center

+

📅 Jan 28, 2024, 🕣 8:00 AM - 3:45 PM EST, 📍 Baltimore Convention Center

+

Learn how to effectively analyze and visualize Earth science data in the cloud using open-source tools and datasets [Jan 28, room ].

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+
+

Town Hall

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U.S. Greenhouse Gas Center: Listening Session.

+

📅 Jan 30, 2024, 🕣 12:25 PM - 1:15 PM EST, 📍 Holiday 5, Hilton Baltimore Inner Harbor

+

Come share your thoughts and see a demonstration of the US GHG Center. Upcoming developments and opportunities will be presented. [Jan 30, room Holiday 5 at Hilton Baltimore Inner Harbor].

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+
+

Presentations

+

Multiple presentations by GHG Center team members will occur throughout the week. Stop by to hear more from GHG Center team members!

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WhenRoomPresentation Title
Tues, Jan 30 4:45

Holiday 5

+

Hilton Baltimore Inner Harbor

8A.2 Stakeholder Engagement for the US Greenhouse Gas Center (Combley)
Mon, Jan 29 5:15

336

+

The Baltimore Convention Center

J4B.4 Developing a Cross-Institutional Open-Source Cyberinfrastructure to Explore, Analyze, and Communicate Greenhouse Gas Data and Information (Chaudhary)
Tues, Jan 30 5:15

Ballroom II

+

The Baltimore Convention Center

J8A.4 PPAP’s and NIST’s Urban Greenhouse Gas Measurements Program (Whetstone)
Wed, Jan 31 5:30

Holiday 5

+

Hilton Baltimore Inner Harbor

12A.5 NIST’s Urban Greenhouse Gas Measurements Program and Interactions with NOAA’s Air Resources Laboratory (Whetstone)
Mon, Jan 29 11:30

321/322

+

The Baltimore Convention Center

2B.4 Methane and Carbon Dioxide Point Source Measurements Across Six Continents from the EMIT Imaging Spectrometer on the International Space Station - and Contributions to the U.S. Greenhouse Gas Center (Green)
Mon, Jan 29 5:30

321/322

+

The Baltimore Convention Center

4B.5 Detectability of Anthropogenic Impacts on Terrestrial Carbon Storage through Space Based Greenhouse Gas Observations (Weir)
Thur, Feb 1 2:30

Holiday 4

+

Hilton Baltimore Inner Harbor

J15.4 NOAA Carbon Monitoring, Research and Innovation: Long-Standing Foundation to Support Climate Mitigation (Grubišić)
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+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/index.html b/pr-preview/pr-5/index.html new file mode 100644 index 0000000..0c23330 --- /dev/null +++ b/pr-preview/pr-5/index.html @@ -0,0 +1,476 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop - Utilizing Open Science Techniques for Exploring Greenhouse Gas Data + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Utilizing Open Science Techniques for Exploring Greenhouse Gas Data

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An Interactive Workshop on the U.S. Greenhouse Gas Center and VEDA Tools for use in Earth Science Research

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📅 Jan 28, 2024, 🕣 8:00 AM - 3:45 PM EST, 📍 Baltimore Convention Center

+

🔗 Session on the AMS Website

+
+

Description

+

This workshop provides a hands-on learning experience for researchers, educators, and students who are eager to harness NASA’s key greenhouse gas datasets, open-source tools, and accessible computing resources for visualizing, exploring, publishing, and communicating scientific results. Participants will learn about and utilize the new U.S. Greenhouse Gas Center (GHG Center) to examine curated, trusted data in a cloud computing environment. The activities will center around data and information aligned with three science use cases to ensure broad applicability. Participants will learn to access analysis workflows using cloud-optimized data, access data products via a standards-based catalog, configure data products for web-based visualizations, and communicate scientific discoveries through a web dashboard.

+

The workshop will provide an overview of the newly released U.S. Greenhouse Gas Center, a prototype system that aims to integrate federal and non-federal data, reflecting transparency in both data and methods, to address a variety of end user needs for greenhouse gas monitoring and to bring together actionable information from multiple sources. Examples of products that will be part of this training include methane concentration anomalies from space-based instruments (e.g., EMIT), data on natural emissions and fluxes available from the U.S. GHG Center, as well as gridding processes of anthropogenic methane emissions.

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Learning Outcomes

+

Upon completing this workshop, participants will be able to:

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    +
  1. Understand the scientific basis of the GHG products from the newly formed U.S. Greenhouse Gas Center.
    2. +
  2. Employ Python and Jupyter Notebooks for effective data analysis and visualization of Earth science data in the cloud.
    3. +
  3. Disseminate and share scientific findings to achieve a wider impact.
    4. +
  4. Understand basic principles for contributing to open-sourced software projects.
    5. +
+
+
+

Target Audience

+

The target audience for this workshop includes Earth Science researchers, educators, and students across all career stages and disciplines who are interested in utilizing GHG datasets and open-source tools for their research projects. The workshop will be beneficial for both experienced programmers seeking an overview of modern open tools and those just getting started with scientific programming.

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+
+

Code of Conduct

+

This workshop will be held following the Transform to Open Science Code of Conduct.

+ + +
+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html new file mode 100644 index 0000000..ddd1958 --- /dev/null +++ b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html @@ -0,0 +1,787 @@ + + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop - U.S. Gridded Anthropogenic Methane Emissions Inventory + + + + + + + + + + + + + + + + + + + + + + + + + +
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U.S. Gridded Anthropogenic Methane Emissions Inventory

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+ Spatially disaggregated 0.1°x 0.1° annual maps of U.S. anthropogenic methane emissions, consistent with the U.S. Inventory of Greenhouse Gas Emissions and Sinks +
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Author
+
+

Siddharth Chaudhary, Vishal Gaur

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+ +
+

Approach

+
    +
  1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the gridded methane emissions data product.
    2. +
  2. Pass the STAC item into the raster API /stac/tilejson.jsonendpoint.
    3. +
  3. Using folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.
    4. +
  4. After the visualization, we will perform zonal statistics for a given polygon.
    5. +
+
+
+

About the Data

+

The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions (for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI). This V2 Express Extension dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.

+
+
+

Installing the Required Libraries

+

Please run the next cell to install all the required libraries to run the notebook.

+
+
%pip install requests
+%pip install folium
+%pip install rasterstats
+%pip install pystac_client
+
+
+

Querying the STAC API

+
+
import requests
+from folium import Map, TileLayer
+from pystac_client import Client
+
+
+
# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+
+# Name of the collection for gridded methane dataset. 
+collection_name = "epa-ch4emission-yeargrid-v2"
+
+
+
# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
+

Examining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is yearly.

+
+
def get_item_count(collection_id):
+    count = 0
+    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+    while True:
+        response = requests.get(items_url)
+
+        if not response.ok:
+            print("error getting items")
+            exit()
+
+        stac = response.json()
+        count += int(stac["context"].get("returned", 0))
+        next = [link for link in stac["links"] if link["rel"] == "next"]
+
+        if not next:
+            break
+        items_url = next[0]["href"]
+
+    return count
+
+
+
# Check total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
+
+
# Examining the first item in the collection
+items[0]
+
+

This makes sense as there are 9 years between 2012 - 2020, meaning 9 records in total.

+

Below, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.

+
+
+

Exploring Changes in Methane (CH4) Levels Using the Raster API

+

In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.

+
+
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["datetime"][:7]: item for item in items} 
+asset_name = "surface-coal"
+
+
+
# Fetching the min and max values for a specific item
+rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
+
+
+
items
+
+

Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2018 and again for January 2012, so that we can visualize each event independently.

+
+
color_map = "rainbow" # please select the color ramp from matplotlib library.
+january_2018_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}"
+    f"&assets={asset_name}"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+january_2018_tile
+
+
+
january_2012_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}"
+    f"&assets={asset_name}"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+january_2012_tile
+
+
+
+

Visualizing CH₄ emissions

+
+
# We will import folium to map and folium.plugins to allow side-by-side mapping
+import folium
+import folium.plugins
+
+# Set initial zoom and center of map for CH₄ Layer
+# Centre of map [latitude,longitude]
+map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)
+
+# January 2018
+map_layer_2018 = TileLayer(
+    tiles=january_2018_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.7,
+)
+map_layer_2018.add_to(map_.m1)
+
+# January 2012
+map_layer_2012 = TileLayer(
+    tiles=january_2012_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.7,
+)
+map_layer_2012.add_to(map_.m2)
+
+# visualising the map
+map_
+
+
+
+
+
+

Calculating Zonal Statistics

+

To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA).

+
+
# Texas, USA
+texas_aoi = {
+    "type": "Feature",
+    "properties": {},
+    "geometry": {
+        "coordinates": [
+            [
+                # [13.686159004559698, -21.700046934333145],
+                # [13.686159004559698, -23.241974326585833],
+                # [14.753560168039911, -23.241974326585833],
+                # [14.753560168039911, -21.700046934333145],
+                # [13.686159004559698, -21.700046934333145],
+                [-95, 29],
+                [-95, 33],
+                [-104, 33],
+                [-104,29],
+                [-95, 29]
+            ]
+        ],
+        "type": "Polygon",
+    },
+}
+
+
+
# We will plug in the coordinates for a location inside the the polygon and a zoom level
+
+import folium
+
+aoi_map = Map(
+    tiles="OpenStreetMap",
+    location=[
+        30,-100
+    ],
+    zoom_start=6,
+)
+
+folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)
+aoi_map
+
+
+
# Check total number of items available
+items = requests.get(
+    f"{STAC_API_URL}/collections/{collection_name}/items?limit=300"
+).json()["features"]
+print(f"Found {len(items)} items")
+
+
+
# Explore the first item
+items[0]
+
+
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+def generate_stats(item, geojson):
+    result = requests.post(
+        f"{RASTER_API_URL}/cog/statistics",
+        params={"url": item["assets"][asset_name]["href"]},
+        json=geojson,
+    ).json()
+    return {
+        **result["properties"],
+        "datetime": item["properties"]["datetime"],
+    }
+
+

With the function above we can generate the statistics for the AOI.

+
+
%%time
+stats = [generate_stats(item, texas_aoi) for item in items]
+
+
+
stats[0]
+
+
+
import pandas as pd
+
+
+def clean_stats(stats_json) -> pd.DataFrame:
+    df = pd.json_normalize(stats_json)
+    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+    df["date"] = pd.to_datetime(df["datetime"])
+    return df
+
+
+df = clean_stats(stats)
+df.head(5)
+
+
+

Visualizing the Data as a Time Series

+

We can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:

+
+
import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(20, 10))
+
+
+plt.plot(
+    df["date"],
+    df["max"],
+    color="red",
+    linestyle="-",
+    linewidth=0.5,
+    label="Max monthly CO₂ emissions",
+)
+
+plt.legend()
+plt.xlabel("Years")
+plt.ylabel("CH4 emissions Molecules CH₄/cm²/s")
+plt.title("CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)")
+
+
+
print(items[2]["properties"]["datetime"])
+
+
+
tile_2016 = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}"
+    f"&assets={asset_name}"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}",
+).json()
+tile_2016
+
+
+
# Use bbox initial zoom and map
+# Set up a map located w/in event bounds
+import folium
+
+aoi_map_bbox = Map(
+    tiles="OpenStreetMap",
+    location=[
+        30,-100
+    ],
+    zoom_start=8,
+)
+
+map_layer = TileLayer(
+    tiles=tile_2016["tiles"][0],
+    attr="GHG", opacity = 0.5
+)
+
+map_layer.add_to(aoi_map_bbox)
+
+aoi_map_bbox
+
+
+
+

Summary

+

In this notebook we have successfully explored, analyzed, and visualized the STAC collection for gridded methane emissions.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/index.html b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/index.html new file mode 100644 index 0000000..7ba5989 --- /dev/null +++ b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/index.html @@ -0,0 +1,428 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop – index + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

EPA Gridded Methane Emissions Inventory

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+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html new file mode 100644 index 0000000..0c4985f --- /dev/null +++ b/pr-preview/pr-5/interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html @@ -0,0 +1,824 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop - About the Data + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

About the Data

+
+ + + +
+ + + + +
+ + +
+ +

The Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km.

+
+

Requirements

+
    +
  • NASA Earthdata Account
    • +
  • Set up Python Environment - See setup_instructions.md in the ‘/setup/’ folder
    • +
+
+
+

Learning Objectives

+
    +
  • How to use U.S.GHG Center STAC Catalogto access ‘ODIAC Fossil Fuel CO₂ Emissions’ and OCO-2 GEOS Column CO₂ Concentrations data.
    • +
  • How to use VEDA STAC Catalog to access NO₂ Emissions data.
    • +
  • How to visualize datasets using folium and perfom zonal statistics over South Africa.
    • +
  • How to plot time series plot for ODIAC and analyze the results.
    • +
+
+
+

Approach

+
    +
  1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2022.
    2. +
  2. Pass the STAC item into raster API /stac/tilejson.json endpoint
    3. +
  3. We’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap
    4. +
  4. After the visualization, we’ll perform zonal statistics for a given polygon.
    5. +
+
+
+

Setup

+

Import the required Python libraries.

+
+
# Import required libraries
+
+import earthaccess
+import warnings
+import requests
+import pandas as pd
+import geopandas
+import folium
+import folium.plugins
+import seaborn as sns
+import glob
+import numpy as np
+import matplotlib.pyplot as plt
+import branca.colormap as cm
+
+from folium import Map, TileLayer 
+from branca.element import Figure
+from pystac_client import Client 
+from pyproj import Geod
+from shapely import wkt
+from datetime import datetime
+from folium.plugins import MousePosition
+
+
+
+

Querying the STAC API

+

Search for ODIAC Fossil Fuel Co2 Emissions, OCO-2 and NO2 Emissions dataset

+
+
# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+STAC_API_URL_veda = "https://staging-stac.delta-backend.com"
+RASTER_API_URL_veda = "https://staging-raster.delta-backend.com"
+
+#Please use the collection name similar to the one used in STAC collection.
+# Name of the collection for ODIAC dataset. 
+collection_name_odiac = "odiac-ffco2-monthgrid-v2022"
+collection_name_oco = "oco2geos-co2-daygrid-v10r"
+collection_name_no2 = "no2-monthly"
+
+
+
# Fetching the collection from STAC collections using appropriate endpoint.
+collection_odiac = requests.get(f"{STAC_API_URL}/collections/{collection_name_odiac}").json()
+collection_odiac
+
+collection_oco2 = requests.get(f"{STAC_API_URL}/collections/{collection_name_oco}").json()
+collection_oco2
+
+collection_no2 = requests.get(f"{STAC_API_URL_veda}/collections/{collection_name_no2}").json()
+collection_no2
+
+

Examining the contents of our collection under summaries we see that the data is available from January 2000 to December 2021. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.

+
+
def get_item_count(STAC_API_URL, collection_id):
+    count = 0
+    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+    while True:
+        response = requests.get(items_url)
+
+        if not response.ok:
+            print("error getting items")
+            exit()
+
+        stac = response.json()
+        count += int(stac["context"].get("returned", 0))
+        next = [link for link in stac["links"] if link["rel"] == "next"]
+
+        if not next:
+            break
+        items_url = next[0]["href"]
+
+    return count
+
+
+
# Check total number of items available
+number_of_items_odiac = get_item_count(STAC_API_URL,collection_name_odiac)
+items_odiac = requests.get(f"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items_odiac}").json()["features"]
+print(f"Found {len(items_odiac)} odiac items")
+
+number_of_items_oco2 = get_item_count(STAC_API_URL,collection_name_oco)
+items_oco2 = requests.get(f"{STAC_API_URL}/collections/{collection_name_oco}/items?limit={number_of_items_oco2}").json()["features"]
+print(f"Found {len(items_oco2)} oco2 items")
+
+number_of_items_no2 = get_item_count(STAC_API_URL_veda,collection_name_no2)
+items_no2 = requests.get(f"{STAC_API_URL_veda}/collections/{collection_name_no2}/items?limit={number_of_items_no2}").json()["features"]
+print(f"Found {len(items_no2)} no2 items")
+
+

This makes sense as there are 22 years between 2000 - 2021, with 12 months per year, meaning 264 records in total.

+

Below, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values.

+
+
+

Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API

+

We will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.

+
+
# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items_odiac = {item["properties"]["start_datetime"][:7]: item for item in items_odiac} 
+asset_name = "co2-emissions"
+
+items_oco2 = {item["properties"]["datetime"][:10]: item for item in items_oco2} 
+asset_name1 = "xco2"
+
+items_no2 = {item["properties"]["start_datetime"][:10]: item for item in items_no2}
+
+
+
rescale_values_odiac = {"max":items_odiac[list(items_odiac.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items_odiac[list(items_odiac.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}
+rescale_values_oco2 = {'max':415 , 'min': 412}
+rescale_values_no2 = {'max': 9050373673124971, 'min': 0}
+
+

Now we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently.

+
+
+

Opening and Exploring Data Using the Raster API

+

In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using ‘folium’.

+
+
color_map = "rainbow" # please select the color ramp from matplotlib library.
+january_2020_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items_odiac['2020-01']['collection']}&item={items_odiac['2020-01']['id']}"
+    f"&assets={asset_name}"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values_odiac['min']},{rescale_values_odiac['max']}", 
+).json()
+january_2020_tile
+
+color_map1 = "magma"
+oco2_1 = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items_oco2['2020-01-20']['collection']}&item={items_oco2['2020-01-20']['id']}"
+    f"&assets={asset_name1}"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map1}"
+    f"&rescale={rescale_values_oco2['min']},{rescale_values_oco2['max']}", 
+).json()
+oco2_1
+
+no2_1 = requests.get(
+    f"{RASTER_API_URL_veda}/stac/tilejson.json?collection={items_no2['2020-01-01']['collection']}&item={items_no2['2020-01-01']['id']}"
+    f"&assets=cog_default"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map1}"
+    f"&rescale={rescale_values_no2['min']},{rescale_values_no2['max']}", 
+).json()
+no2_1
+
+
+
+

Define Spatial Region of Interest

+

For this example, our spatial region of interest (ROI) will be the a region in South Africa.

+
+
sa_aoi = {
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "properties": {},
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              25.519052777398997,
+              -24.8470086420499
+            ],
+            [
+              25.519052777398997,
+              -28.145634397543844
+            ],
+            [
+              30.29637465013832,
+              -28.145634397543844
+            ],
+            [
+              30.29637465013832,
+              -24.8470086420499
+            ],
+            [
+              25.519052777398997,
+              -24.8470086420499
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    }
+  ]
+}
+
+
+
+

Visualizing CO₂ emissions

+
+

+
+map_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)
+
+map_layer_2020 = TileLayer(
+    tiles=january_2020_tile["tiles"][0],
+    attr="GHG",
+    name ="ODIAC",
+    opacity=0.5,
+)
+map_layer_2020_2 = TileLayer(
+    tiles=no2_1["tiles"][0],
+    attr="GHG",
+    name = "NO2",
+    opacity=0.5,
+)
+sbs = folium.plugins.SideBySideLayers(layer_left=map_layer_2020, layer_right=map_layer_2020_2)
+map_layer_2020.add_to(map_)
+map_layer_2020_2.add_to(map_)
+folium.GeoJson(sa_aoi, name="louisiana, USA").add_to(map_)
+sbs.add_to(map_)
+MousePosition().add_to(map_)
+# visualising the map
+map_
+
+
+

+# Set initial zoom and center of map
+map_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)
+
+# December 2001
+map_layer_2020_odiac = TileLayer(
+    tiles=january_2020_tile["tiles"][0],
+    attr="GHG",
+    name ="ODIAC",
+    opacity=0.5,
+)
+map_layer_2020_odiac.add_to(map_)
+
+map_layer_2020_oco2 = TileLayer(
+    tiles=oco2_1["tiles"][0],
+    attr="GHG",
+    name = "OCO2",
+    opacity=0.5,
+)
+map_layer_2020_oco2.add_to(map_)
+
+map_layer_2020_no2 = TileLayer(
+    tiles=no2_1["tiles"][0],
+    attr="GHG",
+    name = "NO2",
+    opacity=0.5,
+)
+map_layer_2020_no2.add_to(map_)
+
+folium.GeoJson(sa_aoi, name="South Africa").add_to(map_)
+folium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)
+
+# visualising the map
+map_
+
+
+
+
+

Generate Statistics and Time Series Lineplots for CO2 Emission

+
+
# Check total number of items available
+items = requests.get(
+    f"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items}"
+).json()["features"]
+print(f"Found {len(items)} items")
+
+
+
# Explore one item to see what it contains
+items
+
+
+
# the bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+def generate_stats(item, geojson):
+    result = requests.post(
+        f"{RASTER_API_URL}/cog/statistics",
+        params={"url": item["assets"][asset_name]["href"]},
+        json=geojson,
+    ).json()['features']
+    return {
+        **result[0]["properties"],
+        "start_datetime": item["properties"]["start_datetime"][:7],
+    }
+
+

With the function above we can generate the statistics for the AOI.

+
+
%%time
+stats = [generate_stats(item,sa_aoi) for item in items]
+
+
+
stats[0]
+
+
+
def clean_stats(stats_json) -> pd.DataFrame:
+    df = pd.json_normalize(stats_json)
+    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+    df["date"] = pd.to_datetime(df["start_datetime"])
+    return df
+
+df = clean_stats(stats)
+df.head(5)
+
+
+
+

Visualizing the Data as a Time Series

+

We can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2021) for the Texas, Dallas area of USA. We can plot the data set using the code below:

+
+
import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(20, 10))
+
+
+plt.plot(
+    df["date"],
+    df["max"],
+    color="red",
+    linestyle="-",
+    linewidth=0.5,
+    label="Max monthly CO₂ emissions",
+)
+
+plt.legend()
+plt.xlabel("Years")
+plt.ylabel("CO2 emissions gC/m2/d")
+plt.title("CO2 emission Values for South Africa (2000-2021)")
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/index.html b/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/index.html new file mode 100644 index 0000000..30d321f --- /dev/null +++ b/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/index.html @@ -0,0 +1,428 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop – index + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html b/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html new file mode 100644 index 0000000..ac22bd7 --- /dev/null +++ b/pr-preview/pr-5/interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html @@ -0,0 +1,2898 @@ + + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop - Wetland Methane Emissions, LPJ-wsl Model + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Wetland Methane Emissions, LPJ-wsl Model

+
+ +
+
+ Global, daily and monthly 0.5 degree resolution methane emission estimates from wetlands, LPJ-wsl model +
+
+ + +
+ +
+
Author
+
+

Vishal Gaur, Siddharth Chaudhary

+
+
+ + + +
+ + +
+ +
+

Approach

+
    +
  1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Wetland Methane Emissions, LPJ-wsl Model data product.
    2. +
  2. Pass the STAC item into the raster API /stac/tilejson.jsonendpoint.
    3. +
  3. Using plugins from folium to visualize two tiles (side-by-side), allowing time point comparison.
    4. +
  4. After the visualization, perform zonal statistics for a given polygon.
    5. +
  5. Read monthly MERRA-2 data for different variables (precipitation rate, surface soil moisture)
    6. +
  6. Plot monthly time series for LPJ-wetland emission and different MERRA-2 dataset and analyse them.
    7. +
+
+
+

About the Data

+

Methane (CH₄) emissions from wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions has thus far been underrepresented. Using the Wald Schnee und Landschaft version (LPJ-wsl) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5 x 0.5 degree resolution by simulating wetland extent and using characteristics of these inundated areas, such as soil moisture, temperature, and carbon content, to estimate CH₄ quantities emitted into the atmosphere. Highlighted areas displayed in this dataset show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl Wetland Methane Emissions data product presented here consists of global daily and monthly model estimates of terrestrial wetland CH₄ emissions from 1980 - 2021. These data are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.

+
+
+

Flow of Workshop

+
    +
  1. Reading data +
      +
    1. Read LPJ dataset from STAC
      2. +
    2. Read subset of Merra-2 (few variables) data using credentials
      3. +
    3. Creating different region of interest
      4. +
    2. +
  2. Statistical Analysis +
      +
    1. Select the region and create time series of total emission in the region Note we will have line chart for two different years to show inter annual variability
      2. +
    2. Create dual folium map to show visual comparision
      3. +
    3. Plot monthly mean and climate mean using merra-2 data for same region of interest
      4. +
    4. Plot time series for the following data and time period to interpret the results: +
        +
      1. 2020, 2021 - LPJ monthly emissions
        2. +
      2. 2020, 2021 - Merra 2 T2M monthly anomaly
        3. +
      3. 2020, 2021 - Total precipitation rate
        4. +
      5. +
    3. +
+

Datasets to be used 1. Monthly LPJ Wetland CH4 Emissions 2. Monthly MERRA-2 Precipitation RateDataset: MERRA2_400.tavgM_2d_flx_Nx Variable: ‘PRECTOT’ https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary

+
    +

  1. Monthly MERRA-2 Surface Soil MoistureDataset: MERRA2_400.tavgM_2d_lnd_Nx Variable: ‘SFMC’ Long-term mean variable: ‘GWETTOP’ https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary

    2. +

  2. Monthly MERRA-2 T2MDataset: MERRA2_400.instM_2d_asm_Nx Variable: ‘T2M’ https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary

    3. +

  3. MERRA-2 Long-Term MeansMERRA2.tavgC_2d_ltm_Nx https://disc.gsfc.nasa.gov/datasets/M2TCNXLTM_1/summary

    4. +
+

Use case to be discussed: 1. Midwest floods in 2019 2. Pick events of interest

+
+
+

Installing the Required Libraries

+

Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:

+

pip install requests, folium, rasterstats, pystac_client, pandas, matplotlib, geopandas, seaborn

+
+

Querying the STAC API

+
+
import requests
+import folium
+import folium.plugins
+from folium import Map, TileLayer 
+from pystac_client import Client 
+import pandas as pd
+import matplotlib.pyplot as plt
+import branca.colormap as cm
+import geopandas
+from pyproj import Geod
+from shapely import wkt
+import seaborn as sns
+import glob
+import numpy as np
+from datetime import datetime
+import netCDF4 as nc
+
+
+
# Provide STAC and RASTER API endpoints
+STAC_API_URL = "http://ghg.center/api/stac"
+RASTER_API_URL = "https://ghg.center/api/raster"
+
+# Please use the collection name similar to the one used in STAC collection.
+
+# Name of the collection for wetland methane monthly emissions. 
+collection_name = "lpjwsl-wetlandch4-monthgrid-v1"
+
+
+
# Fetching the collection from STAC collections using appropriate endpoint.
+collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
+collection
+
+
{'id': 'lpjwsl-wetlandch4-monthgrid-v1',
+ 'type': 'Collection',
+ 'links': [{'rel': 'items',
+   'type': 'application/geo+json',
+   'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items'},
+  {'rel': 'parent',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/'},
+  {'rel': 'root',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/'},
+  {'rel': 'self',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'}],
+ 'title': 'Wetland Methane Emissions, LPJ-wsl Model (Monthly)',
+ 'assets': None,
+ 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},
+  'temporal': {'interval': [['1980-01-01T00:00:00+00:00',
+     '2021-12-01T00:00:00+00:00']]}},
+ 'license': 'CC-BY-4.0',
+ 'keywords': None,
+ 'providers': None,
+ 'summaries': {'datetime': ['1980-01-01T00:00:00Z', '2021-12-01T00:00:00Z']},
+ 'description': 'Wetland methane emissions produced by the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The LPJ-wsl model is regularly used in conjunction with NASA’s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations.',
+ 'item_assets': {'ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+   'roles': ['data', 'layer'],
+   'title': 'CH4 Wetland Emissions',
+   'description': 'Methane emissions from wetlands.'}},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': None,
+ 'dashboard:is_periodic': True,
+ 'dashboard:time_density': 'month'}
+
+
+

Examining the contents of our collection under summaries, we see that the data is available from January 1980 to may 2021. By looking at dashboard: time density, we can see that these observations are collected monthly.

+
+
def get_item_count(collection_id):
+    count = 0
+    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"
+
+    while True:
+        response = requests.get(items_url)
+
+        if not response.ok:
+            print("error getting items")
+            exit()
+
+        stac = response.json()
+        count += int(stac["context"].get("returned", 0))
+        next = [link for link in stac["links"] if link["rel"] == "next"]
+
+        if not next:
+            break
+        items_url = next[0]["href"]
+
+    return count
+
+
+
# Check total number of items available
+number_of_items = get_item_count(collection_name)
+items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+print(f"Found {len(items)} items")
+
+
Found 504 items
+
+
+
+
# Examining the first item in the collection
+items[0]
+
+
{'id': 'lpjwsl-wetlandch4-monthgrid-v1-202112',
+ 'bbox': [-180.0, -90.0, 180.0, 90.0],
+ 'type': 'Feature',
+ 'links': [{'rel': 'collection',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+  {'rel': 'parent',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},
+  {'rel': 'root',
+   'type': 'application/json',
+   'href': 'https://ghg.center/api/stac/'},
+  {'rel': 'self',
+   'type': 'application/geo+json',
+   'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items/lpjwsl-wetlandch4-monthgrid-v1-202112'}],
+ 'assets': {'ch4-wetlands-emissions': {'href': 's3://ghgc-data-store/lpjwsl-wetlandch4-monthgrid-v1/NASA_GSFC_ch4_wl_ch4_wetlands_v22_x720_y360_t12_202112.tif',
+   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
+   'roles': ['data', 'layer'],
+   'title': 'CH4 Wetland Emissions',
+   'proj:bbox': [-180.0, -90.0, 180.0, 90.0],
+   'proj:epsg': 4326.0,
+   'proj:shape': [360.0, 720.0],
+   'description': 'Methane emissions from wetlands.',
+   'raster:bands': [{'scale': 1.0,
+     'offset': 0.0,
+     'sampling': 'area',
+     'data_type': 'float32',
+     'histogram': {'max': 6.929981708526611,
+      'min': 0.0,
+      'count': 11.0,
+      'buckets': [258080.0,
+       575.0,
+       251.0,
+       124.0,
+       78.0,
+       41.0,
+       26.0,
+       16.0,
+       7.0,
+       2.0]},
+     'statistics': {'mean': 0.012271502055227757,
+      'stddev': 0.1378920078277588,
+      'maximum': 6.929981708526611,
+      'minimum': 0.0,
+      'valid_percent': 0.0003858024691358025}}],
+   'proj:geometry': {'type': 'Polygon',
+    'coordinates': [[[-180.0, -90.0],
+      [180.0, -90.0],
+      [180.0, 90.0],
+      [-180.0, 90.0],
+      [-180.0, -90.0]]]},
+   'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},
+    'name': 'WGS 84',
+    'type': 'GeographicCRS',
+    'datum': {'name': 'World Geodetic System 1984',
+     'type': 'GeodeticReferenceFrame',
+     'ellipsoid': {'name': 'WGS 84',
+      'semi_major_axis': 6378137.0,
+      'inverse_flattening': 298.257223563}},
+    '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',
+    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
+       'unit': 'degree',
+       'direction': 'north',
+       'abbreviation': 'Lat'},
+      {'name': 'Geodetic longitude',
+       'unit': 'degree',
+       'direction': 'east',
+       'abbreviation': 'Lon'}],
+     'subtype': 'ellipsoidal'}},
+   'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},
+ 'geometry': {'type': 'Polygon',
+  'coordinates': [[[-180, -90],
+    [180, -90],
+    [180, 90],
+    [-180, 90],
+    [-180, -90]]]},
+ 'collection': 'lpjwsl-wetlandch4-monthgrid-v1',
+ 'properties': {'datetime': '2021-12-01T00:00:00+00:00'},
+ 'stac_version': '1.0.0',
+ 'stac_extensions': []}
+
+
+

Below, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.

+
+
rescale_values = {'max': 2.0, 'min': 0.0}
+
+
+
+

Exploring Changes in Methane (CH4) Emission Levels Using the Raster API

+

In this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.

+
+
# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
+items = {item["properties"]["datetime"][:7]: item for item in items} 
+asset_name = 'ch4-wetlands-emissions'
+
+
+
# We create a area of interest (polygon) on which will be used later 
+
+aoi = [-95.9,-87.50,28.7,33.5]
+louisiana_aoi = {
+    "type": "Feature",
+    "properties": {},
+    "geometry": {
+        "coordinates": [
+            [
+                [aoi[0], aoi[2]],
+                [aoi[0], aoi[3]],
+                [aoi[1], aoi[3]],
+                [aoi[1],aoi[2]],
+                [aoi[0], aoi[2]]
+            ]
+        ],
+        "type": "Polygon",
+    },
+}
+
+

Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for may 2020 and again for may 2021, so we can visualize each event independently.

+
+
color_map = "magma" # select the color ramp from matplotlib library.
+may_2020_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2001-05']['collection']}&item={items['2020-05']['id']}"
+    "&assets=ch4-wetlands-emissions"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}"
+).json()
+may_2020_tile
+
+
{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202005&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C2.0'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
+
+
+
+
may_2021_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}"
+    "&assets=ch4-wetlands-emissions"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+may_2021_tile
+
+
{'tilejson': '2.2.0',
+ 'version': '1.0.0',
+ 'scheme': 'xyz',
+ 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202105&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C2.0'],
+ 'minzoom': 0,
+ 'maxzoom': 24,
+ 'bounds': [-180.0, -90.0, 180.0, 90.0],
+ 'center': [0.0, 0.0, 0]}
+
+
+
+
+

Visualizing CH₄ Emissions

+
+
# We will import folium to map and folium.plugins to allow side-by-side mapping
+# Set initial zoom and center of map for CH₄ Layer
+# Centre of map [latitude,longitude]
+
+from folium.plugins import MousePosition
+
+map_ = folium.Map(location=(30,-90), zoom_start=6)
+
+# May 2001
+map_layer_2001 = TileLayer(
+    tiles=may_2020_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.5,
+)
+
+
+# May 2021
+map_layer_2021 = TileLayer(
+    tiles=may_2021_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.5,
+)
+sbs = folium.plugins.SideBySideLayers(layer_left=map_layer_2001, layer_right=map_layer_2021)
+map_layer_2001.add_to(map_)
+map_layer_2021.add_to(map_)
+folium.GeoJson(louisiana_aoi, name="louisiana, USA").add_to(map_)
+sbs.add_to(map_)
+MousePosition().add_to(map_)
+# visualising the map
+map_
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
+
+
+
+
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
+
+
+
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
+
+def generate_stats(item, geojson):
+    result = requests.post(
+        f"{RASTER_API_URL}/cog/statistics",
+        params={"url": item["assets"]["ch4-wetlands-emissions"]["href"]},
+        json=geojson,
+    ).json()
+    return {
+        **result["properties"],
+        "datetime": item["properties"]["datetime"],
+    }
+
+

With the function above, we can generate the statistics for the area of interest.

+
+
# It will take around 5 minutes to run the statistics 
+
+stats = [generate_stats(item, louisiana_aoi) for item in items[:24]]
+stats
+
+
[{'statistics': {'b1': {'min': 0.0,
+    'max': 0.7171422839164734,
+    'mean': 0.15437047621783087,
+    'count': 170.0,
+    'sum': 26.24298095703125,
+    'std': 0.1835849342048151,
+    'median': 0.05701697617769241,
+    'majority': 0.0,
+    'minority': 0.00043509763781912625,
+    'unique': 123.0,
+    'histogram': [[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0, 3.0, 2.0],
+     [0.0,
+      0.07171422988176346,
+      0.14342845976352692,
+      0.21514268219470978,
+      0.28685691952705383,
+      0.3585711419582367,
+      0.43028536438941956,
+      0.5019996166229248,
+      0.5737138390541077,
+      0.6454280614852905,
+      0.7171422839164734]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 0.5802477025985717}},
+  'datetime': '2021-12-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 0.516049861907959,
+    'mean': 0.13532144883099725,
+    'count': 170.0,
+    'sum': 23.00464630126953,
+    'std': 0.15769760565780438,
+    'median': 0.05566539615392685,
+    'majority': 0.0,
+    'minority': 0.0002954523079097271,
+    'unique': 123.0,
+    'histogram': [[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0, 7.0, 7.0],
+     [0.0,
+      0.0516049861907959,
+      0.1032099723815918,
+      0.1548149585723877,
+      0.2064199447631836,
+      0.2580249309539795,
+      0.3096299171447754,
+      0.3612349033355713,
+      0.4128398895263672,
+      0.4644448757171631,
+      0.516049861907959]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 0.48761060655117044}},
+  'datetime': '2021-11-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 1.3371007442474365,
+    'mean': 0.27248965992647056,
+    'count': 170.0,
+    'sum': 46.3232421875,
+    'std': 0.32895022521723327,
+    'median': 0.08361007273197174,
+    'majority': 0.0,
+    'minority': 0.0006618715706281364,
+    'unique': 123.0,
+    'histogram': [[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0, 1.0, 2.0],
+     [0.0,
+      0.1337100714445114,
+      0.2674201428890228,
+      0.40113022923469543,
+      0.5348402857780457,
+      0.6685503721237183,
+      0.8022604584693909,
+      0.9359705448150635,
+      1.0696805715560913,
+      1.2033907175064087,
+      1.3371007442474365]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 1.060954008102417}},
+  'datetime': '2021-10-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 2.2005276679992676,
+    'mean': 0.45919602338005516,
+    'count': 170.0,
+    'sum': 78.06332397460938,
+    'std': 0.5457408213221397,
+    'median': 0.18731170892715454,
+    'majority': 0.0,
+    'minority': 0.002108836779370904,
+    'unique': 123.0,
+    'histogram': [[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0, 1.0, 2.0],
+     [0.0,
+      0.22005276381969452,
+      0.44010552763938904,
+      0.6601582765579224,
+      0.8802110552787781,
+      1.1002638339996338,
+      1.3203165531158447,
+      1.5403693914413452,
+      1.7604221105575562,
+      1.9804749488830566,
+      2.2005276679992676]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 1.6831329369544983}},
+  'datetime': '2021-09-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 2.2805004119873047,
+    'mean': 0.4716072531307445,
+    'count': 170.0,
+    'sum': 80.17323303222656,
+    'std': 0.5656065478170901,
+    'median': 0.19661885499954224,
+    'majority': 0.0,
+    'minority': 0.0015638105105608702,
+    'unique': 123.0,
+    'histogram': [[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0, 3.0, 1.0],
+     [0.0,
+      0.22805003821849823,
+      0.45610007643699646,
+      0.6841500997543335,
+      0.9122001528739929,
+      1.1402502059936523,
+      1.368300199508667,
+      1.5963503122329712,
+      1.8244003057479858,
+      2.05245041847229,
+      2.2805004119873047]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 1.8883533668518073}},
+  'datetime': '2021-08-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 3.072227716445923,
+    'mean': 0.6332277634564568,
+    'count': 170.0,
+    'sum': 107.64871978759766,
+    'std': 0.7631461866897622,
+    'median': 0.2839159667491913,
+    'majority': 0.0,
+    'minority': 0.0024779888335615396,
+    'unique': 123.0,
+    'histogram': [[91.0, 15.0, 12.0, 11.0, 13.0, 14.0, 3.0, 7.0, 3.0, 1.0],
+     [0.0,
+      0.30722278356552124,
+      0.6144455671310425,
+      0.921668291091919,
+      1.228891134262085,
+      1.5361138582229614,
+      1.843336582183838,
+      2.150559425354004,
+      2.45778226852417,
+      2.765004873275757,
+      3.072227716445923]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 2.494618062973023}},
+  'datetime': '2021-07-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 2.1353492736816406,
+    'mean': 0.5122916726505056,
+    'count': 170.0,
+    'sum': 87.08958435058594,
+    'std': 0.5816176132620369,
+    'median': 0.23781655728816986,
+    'majority': 0.0,
+    'minority': 0.002265247516334057,
+    'unique': 123.0,
+    'histogram': [[80.0, 19.0, 9.0, 9.0, 15.0, 17.0, 6.0, 7.0, 6.0, 2.0],
+     [0.0,
+      0.21353492140769958,
+      0.42706984281539917,
+      0.6406047940254211,
+      0.8541396856307983,
+      1.0676746368408203,
+      1.2812095880508423,
+      1.4947445392608643,
+      1.7082793712615967,
+      1.9218143224716187,
+      2.1353492736816406]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 1.7408480906486512}},
+  'datetime': '2021-06-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 1.7123756408691406,
+    'mean': 0.3947353587431066,
+    'count': 170.0,
+    'sum': 67.10501098632812,
+    'std': 0.4542843921906756,
+    'median': 0.16888344287872314,
+    'majority': 0.0,
+    'minority': 0.001768625108525157,
+    'unique': 123.0,
+    'histogram': [[87.0, 13.0, 9.0, 9.0, 19.0, 17.0, 3.0, 6.0, 5.0, 2.0],
+     [0.0,
+      0.17123755812644958,
+      0.34247511625289917,
+      0.5137127041816711,
+      0.6849502325057983,
+      0.8561878204345703,
+      1.0274254083633423,
+      1.1986629962921143,
+      1.3699004650115967,
+      1.5411380529403687,
+      1.7123756408691406]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 1.4123617601394653}},
+  'datetime': '2021-05-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 0.8859390616416931,
+    'mean': 0.24400625789866728,
+    'count': 170.0,
+    'sum': 41.48106384277344,
+    'std': 0.28321048400965054,
+    'median': 0.09922437369823456,
+    'majority': 0.0,
+    'minority': 0.0006858149426989257,
+    'unique': 123.0,
+    'histogram': [[84.0, 13.0, 10.0, 8.0, 5.0, 10.0, 11.0, 15.0, 7.0, 7.0],
+     [0.0,
+      0.08859390765428543,
+      0.17718781530857086,
+      0.2657817304134369,
+      0.3543756306171417,
+      0.44296953082084656,
+      0.5315634608268738,
+      0.6201573610305786,
+      0.7087512612342834,
+      0.7973451614379883,
+      0.8859390616416931]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 0.8462702941894532}},
+  'datetime': '2021-04-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 0.659404993057251,
+    'mean': 0.19500977011287915,
+    'count': 170.0,
+    'sum': 33.15166091918945,
+    'std': 0.22056241116653577,
+    'median': 0.087272509932518,
+    'majority': 0.0,
+    'minority': 0.0006441613077186048,
+    'unique': 123.0,
+    'histogram': [[79.0, 17.0, 7.0, 10.0, 5.0, 7.0, 12.0, 16.0, 4.0, 13.0],
+     [0.0,
+      0.0659404993057251,
+      0.1318809986114502,
+      0.1978214979171753,
+      0.2637619972229004,
+      0.3297024965286255,
+      0.3956429958343506,
+      0.4615834951400757,
+      0.5275239944458008,
+      0.5934644937515259,
+      0.659404993057251]],
+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
+    'valid_pixels': 170.0,
+    'percentile_2': 0.0,
+    'percentile_98': 0.6313274204730988}},
+  'datetime': '2021-03-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
+    'max': 0.4581904411315918,
+    'mean': 0.1083783093620749,
+    'count': 170.0,
+    'sum': 18.424312591552734,
+    'std': 0.13051518085377295,
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+  'datetime': '2020-10-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+  'datetime': '2020-09-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+  'datetime': '2020-08-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+  'datetime': '2020-07-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+  'datetime': '2020-06-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+    'percentile_2': 0.0,
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+  'datetime': '2020-05-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+  'datetime': '2020-04-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+    'histogram': [[86.0, 15.0, 9.0, 13.0, 14.0, 20.0, 9.0, 1.0, 2.0, 1.0],
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+    'masked_pixels': 0.0,
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+    'percentile_2': 0.0,
+    'percentile_98': 0.7838775122165684}},
+  'datetime': '2020-03-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+    'histogram': [[86.0, 15.0, 12.0, 8.0, 13.0, 10.0, 12.0, 7.0, 4.0, 3.0],
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+    'percentile_2': 0.0,
+    'percentile_98': 0.6206122100353242}},
+  'datetime': '2020-02-01T00:00:00+00:00'},
+ {'statistics': {'b1': {'min': 0.0,
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+    'median': 0.06703908741474152,
+    'majority': 0.0,
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+    'histogram': [[88.0, 12.0, 11.0, 9.0, 10.0, 13.0, 12.0, 8.0, 5.0, 2.0],
+     [0.0,
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+    'valid_percent': 100.0,
+    'masked_pixels': 0.0,
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+    'percentile_2': 0.0,
+    'percentile_98': 0.637993005514145}},
+  'datetime': '2020-01-01T00:00:00+00:00'}]
+
+
+
+
# Manipulating and cleaning the stats output from previous cell
+
+def clean_stats(stats_json) -> pd.DataFrame:
+    df = pd.json_normalize(stats_json)
+    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
+    df["date"] = pd.to_datetime(df["datetime"])
+    return df
+
+
+df = clean_stats(stats)
+df.head(5)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-12-01T00:00:00+00:000.00.7171420.154370170.026.2429810.1835850.0570170.00.000435123.0[[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0,...100.00.0170.00.00.5802482021-12-01 00:00:00+00:00
12021-11-01T00:00:00+00:000.00.5160500.135321170.023.0046460.1576980.0556650.00.000295123.0[[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0,...100.00.0170.00.00.4876112021-11-01 00:00:00+00:00
22021-10-01T00:00:00+00:000.01.3371010.272490170.046.3232420.3289500.0836100.00.000662123.0[[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0...100.00.0170.00.01.0609542021-10-01 00:00:00+00:00
32021-09-01T00:00:00+00:000.02.2005280.459196170.078.0633240.5457410.1873120.00.002109123.0[[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0...100.00.0170.00.01.6831332021-09-01 00:00:00+00:00
42021-08-01T00:00:00+00:000.02.2805000.471607170.080.1732330.5656070.1966190.00.001564123.0[[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0...100.00.0170.00.01.8883532021-08-01 00:00:00+00:00
+ +
+
+
+
+
# Filtering the stats for year 2020 and 2021
+
+df_2020_2021 = df[(df['date'].dt.year == 2020) | (df['date'].dt.year == 2021)]
+df_2020_2021['year'] = pd.to_datetime(df_2020_2021['datetime']).dt.year
+df_2020_2021['month'] = pd.to_datetime(df_2020_2021['datetime']).dt.month
+df_2020_2021
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98dateyearmonth
02021-12-01T00:00:00+00:000.00.7171420.154370170.026.2429810.1835850.0570170.00.000435123.0[[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0,...100.00.0170.00.00.5802482021-12-01 00:00:00+00:00202112
12021-11-01T00:00:00+00:000.00.5160500.135321170.023.0046460.1576980.0556650.00.000295123.0[[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0,...100.00.0170.00.00.4876112021-11-01 00:00:00+00:00202111
22021-10-01T00:00:00+00:000.01.3371010.272490170.046.3232420.3289500.0836100.00.000662123.0[[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0...100.00.0170.00.01.0609542021-10-01 00:00:00+00:00202110
32021-09-01T00:00:00+00:000.02.2005280.459196170.078.0633240.5457410.1873120.00.002109123.0[[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0...100.00.0170.00.01.6831332021-09-01 00:00:00+00:0020219
42021-08-01T00:00:00+00:000.02.2805000.471607170.080.1732330.5656070.1966190.00.001564123.0[[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0...100.00.0170.00.01.8883532021-08-01 00:00:00+00:0020218
52021-07-01T00:00:00+00:000.03.0722280.633228170.0107.6487200.7631460.2839160.00.002478123.0[[91.0, 15.0, 12.0, 11.0, 13.0, 14.0, 3.0, 7.0...100.00.0170.00.02.4946182021-07-01 00:00:00+00:0020217
62021-06-01T00:00:00+00:000.02.1353490.512292170.087.0895840.5816180.2378170.00.002265123.0[[80.0, 19.0, 9.0, 9.0, 15.0, 17.0, 6.0, 7.0, ...100.00.0170.00.01.7408482021-06-01 00:00:00+00:0020216
72021-05-01T00:00:00+00:000.01.7123760.394735170.067.1050110.4542840.1688830.00.001769123.0[[87.0, 13.0, 9.0, 9.0, 19.0, 17.0, 3.0, 6.0, ...100.00.0170.00.01.4123622021-05-01 00:00:00+00:0020215
82021-04-01T00:00:00+00:000.00.8859390.244006170.041.4810640.2832100.0992240.00.000686123.0[[84.0, 13.0, 10.0, 8.0, 5.0, 10.0, 11.0, 15.0...100.00.0170.00.00.8462702021-04-01 00:00:00+00:0020214
92021-03-01T00:00:00+00:000.00.6594050.195010170.033.1516610.2205620.0872730.00.000644123.0[[79.0, 17.0, 7.0, 10.0, 5.0, 7.0, 12.0, 16.0,...100.00.0170.00.00.6313272021-03-01 00:00:00+00:0020213
102021-02-01T00:00:00+00:000.00.4581900.108378170.018.4243130.1305150.0391950.00.000281123.0[[87.0, 15.0, 12.0, 9.0, 10.0, 9.0, 10.0, 8.0,...100.00.0170.00.00.3982762021-02-01 00:00:00+00:0020212
112021-01-01T00:00:00+00:000.00.6753560.153147170.026.0349290.1810560.0624690.00.000527123.0[[90.0, 12.0, 11.0, 8.0, 15.0, 16.0, 4.0, 7.0,...100.00.0170.00.00.5708202021-01-01 00:00:00+00:0020211
122020-12-01T00:00:00+00:000.00.6949240.145580170.024.7485580.1771340.0536350.00.000418123.0[[93.0, 13.0, 11.0, 11.0, 13.0, 13.0, 7.0, 5.0...100.00.0170.00.00.5663252020-12-01 00:00:00+00:00202012
132020-11-01T00:00:00+00:000.00.5358850.127838170.021.7324260.1534390.0497260.00.000282123.0[[86.0, 14.0, 14.0, 8.0, 13.0, 9.0, 8.0, 8.0, ...100.00.0170.00.00.4820692020-11-01 00:00:00+00:00202011
142020-10-01T00:00:00+00:000.01.2557410.257558170.043.7847980.3169340.0768580.00.000619123.0[[94.0, 13.0, 10.0, 10.0, 17.0, 9.0, 8.0, 4.0,...100.00.0170.00.01.0394352020-10-01 00:00:00+00:00202010
152020-09-01T00:00:00+00:000.01.9983480.431170170.073.2988890.5197150.1732440.00.001907123.0[[89.0, 16.0, 11.0, 11.0, 13.0, 8.0, 10.0, 8.0...100.00.0170.00.01.6112782020-09-01 00:00:00+00:0020209
162020-08-01T00:00:00+00:000.02.0058530.461982170.078.5369640.5712840.1652070.00.001270123.0[[91.0, 14.0, 11.0, 13.0, 6.0, 10.0, 6.0, 8.0,...100.00.0170.00.01.8289482020-08-01 00:00:00+00:0020208
172020-07-01T00:00:00+00:000.02.0708640.562290170.095.5893100.6425460.2530520.00.002213123.0[[81.0, 17.0, 9.0, 8.0, 8.0, 13.0, 10.0, 8.0, ...100.00.0170.00.01.9805022020-07-01 00:00:00+00:0020207
182020-06-01T00:00:00+00:000.01.6319790.394050170.066.9885410.4457750.1712480.00.001392123.0[[82.0, 18.0, 9.0, 9.0, 9.0, 18.0, 11.0, 9.0, ...100.00.0170.00.01.3640192020-06-01 00:00:00+00:0020206
192020-05-01T00:00:00+00:000.01.4179750.302493170.051.4238590.3509360.1216980.00.001449123.0[[88.0, 13.0, 10.0, 17.0, 12.0, 16.0, 7.0, 4.0...100.00.0170.00.01.0923352020-05-01 00:00:00+00:0020205
202020-04-01T00:00:00+00:000.00.9126240.209557170.035.6246570.2321420.1067520.00.001115123.0[[80.0, 17.0, 11.0, 12.0, 23.0, 9.0, 10.0, 5.0...100.00.0170.00.00.7237262020-04-01 00:00:00+00:0020204
212020-03-01T00:00:00+00:000.01.0447850.228748170.038.8872070.2577600.1043900.00.000984123.0[[86.0, 15.0, 9.0, 13.0, 14.0, 20.0, 9.0, 1.0,...100.00.0170.00.00.7838782020-03-01 00:00:00+00:0020203
222020-02-01T00:00:00+00:000.00.7303400.173006170.029.4110910.2041390.0657240.00.000559123.0[[86.0, 15.0, 12.0, 8.0, 13.0, 10.0, 12.0, 7.0...100.00.0170.00.00.6206122020-02-01 00:00:00+00:0020202
232020-01-01T00:00:00+00:000.00.7433940.179067170.030.4414480.2095150.0670390.00.000416123.0[[88.0, 12.0, 11.0, 9.0, 10.0, 13.0, 12.0, 8.0...100.00.0170.00.00.6379932020-01-01 00:00:00+00:0020201
+ +
+
+
+
+
+

Visualizing the Data as a Time Series

+

We can now explore the wetland methane emissions time series (January - December) for the year 2020,2021 available for the Louisiana area of the U.S. We can plot the data set using the code below:

+
+
items = {item["properties"]["datetime"][:7]: item for item in items} 
+fig = plt.figure(figsize=(20, 10))
+
+sns.lineplot(
+    df_2020_2021,
+    x = 'month', 
+    y = 'sum',
+    hue= 'year',
+    palette='flare'
+)
+
+# plt.legend()
+plt.xlabel("months")
+plt.ylabel("CH4 emissions g/m2")
+plt.title("CH4 emission Values for Louisiana for 2020 and 2021")
+
+
+colormap = cm.LinearColormap(colors = ['#2c115f','#721f81','#b73779','#f1605d','#feb078'], vmin = 0, vmax = 2 )
+colormap.caption = 'g CH₄/m²/day'
+
+may_2021_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}"
+    "&assets=ch4-wetlands-emissions"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+may_2021_tile
+june_2021_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-06']['collection']}&item={items['2021-06']['id']}"
+    "&assets=ch4-wetlands-emissions"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+june_2021_tile
+july_2021_tile = requests.get(
+    f"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-07']['collection']}&item={items['2021-07']['id']}"
+    "&assets=ch4-wetlands-emissions"
+    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
+    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
+).json()
+july_2021_tile
+
+map_ = folium.Map(location=(30,-90), zoom_start=5)
+
+# May 2001
+map_layer_202105 = TileLayer(
+    tiles=may_2020_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.5,
+    name='May'
+)
+map_layer_202105.add_to(map_)
+
+# May 2021
+map_layer_202106 = TileLayer(
+    tiles=june_2021_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.5,
+    name='June'
+)
+map_layer_202106.add_to(map_)
+
+map_layer_202107 = TileLayer(
+    tiles=july_2021_tile["tiles"][0],
+    attr="GHG",
+    opacity=0.5,
+    name='July'
+)
+map_layer_202107.add_to(map_)
+
+folium.GeoJson(louisiana_aoi, name="louisiana, USA").add_to(map_)
+folium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)
+
+svg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'
+map_.get_root().header.add_child(folium.Element(svg_style))
+map_.add_child(colormap)
+# visualising the map
+map_
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
+
+
+

+
+
+
+

MERRA2 visualization

+
+
merra_t2m_dir='merra_t2m_dir/'
+merra_soil_moisture_dir = 'merra_soil_moisture_dir/'
+merra_precip_rate_dir = 'merra_precip_rate_dir/'
+merra_t2m_clim_dir = 'merra_t2m_clim_dir/'
+savedir = 'saved_files'
+
+merra_precip_rate_clim_dir = merra_t2m_clim_dir 
+merra_soil_moisture_clim_dir = merra_t2m_clim_dir
+
+
+
params={
+    'MERRA-2 T2M':
+        {'var':'T2M',
+        'cmap':'Spectral_r',
+        'dir':merra_t2m_dir,
+        'nickname':'merra2_t2m',
+        'climdir':merra_t2m_clim_dir,
+        'climvar':'T2MMEAN'},
+    'MERRA-2 Surface Soil Moisture':
+        {'var':'GWETTOP',
+        'cmap':'Blues',
+        'dir':merra_soil_moisture_dir,
+        'nickname':'merra2_sm',
+        'climdir':merra_soil_moisture_clim_dir,
+        'climvar':'GWETTOP'},
+    'MERRA-2 Precipitation Rate':
+        {'var':'PRECTOT',
+        'cmap':'Spectral_r',
+        'dir':merra_precip_rate_dir,
+        'nickname':'merra2_pr',
+        'climdir':merra_precip_rate_clim_dir,
+        'climvar':'PRECTOT'}
+}
+
+boundaries={
+    'Global':[-180,180,-90,90],
+    'Louisiana': [-95.9,-87.50,28.7,33.5],
+    'CONUS':[-127.08,-63.87,23.55,49.19],   #   conus
+    'Florida':[-84.07,-79.14,24.85,30.5],
+    'Northeast':[-74.88,-69.81,40.48,42.88]
+}
+
+year=1991
+focus = 'Northeast'
+anomaly = 1
+param = ['MERRA-2 Precipitation Rate','MERRA-2 Surface Soil Moisture', 'MERRA-2 T2M' ]
+
+
+
def get_merra2_timeseries(year,focus,p,anomaly):
+    files = glob.glob(params[p]['dir']+'*.nc4')
+    if anomaly:
+        try:
+            clim_files = glob.glob(params[p]['climdir']+'*.nc4')
+        except:
+            print('Climatological mean files (climdir) not found for specified parameter.')
+            breakpoint()
+    month_labels = []
+    box_totals = []
+    month_field = []
+    dt = []
+    for i,f in enumerate(files):
+        data = nc.Dataset(f)
+        
+        #   Get bounding box
+        wlat = np.logical_and(
+            data['lat'][:] < boundaries[focus][3],
+            data['lat'][:] > boundaries[focus][2]
+        )
+        wlon = np.logical_and(
+            data['lon'][:] < boundaries[focus][1],
+            data['lon'][:] > boundaries[focus][0]
+        )
+
+        datestamp = f.split('.')[-2]
+        month = int(datestamp[-2::])
+
+        dt.append(datetime(year,month,1))
+        month_labels.append(datetime(year,month,1).strftime('%B'))
+
+        if anomaly:
+            #   Make sure you read the climatology for the right month (whichfile)
+            whichfile = [datetime(1991,month,1).strftime('%y%m')[1:] in f for f in clim_files]
+            climdata = nc.Dataset(np.array(clim_files)[whichfile][0])
+            
+            #   Calculate sum (emissions) or mean (met params) over your bounding box
+            if 'LPJ' in p:
+                clim_box_total = np.nansum(climdata[params[p]['climvar']][0,wlat,wlon])
+                now_box_total = np.nansum(data[params[p]['var']][0,wlat,wlon])
+            elif 'MERRA' in p:
+                clim_box_total = np.nanmean(climdata[params[p]['climvar']][0,wlat,wlon])
+                now_box_total = np.nanmean(data[params[p]['var']][0,wlat,wlon])
+
+            #   Replace fill values with NaN 
+            #   Otherwise differencing might give wild results? (Just be safe)
+            wfillclim = np.where(climdata[params[p]['climvar']][0,:,:] == climdata[params[p]['climvar']]._FillValue)
+            climfield = climdata[params[p]['climvar']][0,:,:]
+            climfield[wfillclim] = np.nan
+            wfillnow = np.where(data[params[p]['var']][0,:,:] == data[params[p]['var']]._FillValue)
+            nowfield = data[params[p]['var']][0,:,:]
+            nowfield[wfillnow] = np.nan
+
+            #   And finally, difference current month and long-term mean 
+            box_totals.append(now_box_total - clim_box_total)
+            month_field.append(nowfield - climfield)
+            climdata.close()
+        else:
+            if 'LPJ' in p:
+                box_totals.append(np.nansum(data[params[p]['var']][0,wlat,wlon]))
+            elif 'MERRA' in p:
+                box_totals.append(np.nanmean(data[params[p]['var']][0,wlat,wlon]))
+            #   Replace fill values with NaN (otherwise maps are hard to read) 
+            month_field.append(data[params[p]['var']][0,:,:])
+            wfill = np.where(month_field[-1] == data[params[p]['var']]._FillValue)
+            month_field[-1][wfill] = np.nan
+            #breakpoint()
+
+    #   Sort in case months are out of order
+    dti = np.argsort(dt)
+    month_labels = np.array(month_labels)[dti]
+    box_totals = np.array(box_totals)[dti]
+    month_field = np.array(month_field)[dti]
+
+    # print('mean ',np.nanmean(month_field))
+    # print('std ',np.nanstd(month_field))
+
+    data_return = {
+        'month_labels':month_labels,
+        'box_totals':box_totals,
+        'month_fields':month_field,
+        'units':data[params[p]['var']].units,
+        'lat':data['lat'][:],
+        'lon':data['lon'][:],
+        'mean':np.nanmean(month_field),
+        'std' : np.nanstd(month_field)
+    }
+    data.close()
+    return data_return
+
+
+
cmap = plt.get_cmap('gnuplot') 
+colors = cmap(np.linspace(0,1,len(param)))
+for i,p in enumerate(param):
+    
+    ts = get_merra2_timeseries(year,focus,p,anomaly)
+    print(f'Mean of variable {p} is {ts["mean"]}')
+    print(f'Standard deviation of variable {p} is {ts["std"]}')
+        
+    # if i == 0:
+    fig= plt.figure(figsize=(6,3))
+    ax = fig.add_subplot(1,1,1)
+
+    #breakpoint()
+    try:
+        ax.plot(
+            list(range(0,12)),
+            ts['box_totals'],
+            linestyle='-',
+            linewidth=2,
+            color=colors[i],
+            markersize=4,
+            marker='o',
+            label=p
+        )
+    except ValueError:
+        print('Double check that you have all twelve months of MERRA-2 data downloaded!')
+        print(params[p]['dir'])
+        breakpoint()
+
+    #   Construct plot title
+    title = '%s\n%s Mean Monthly %s'%(focus,year,p)
+    if anomaly:
+        title+=' Anomaly' 
+    if 'LPJ' in p:
+        title = title.replace('Mean','Total')
+    plt.title(title)
+    
+    plt.xticks(list(range(0,12)))
+    ax.set_xticklabels(ts['month_labels'],rotation=40,ha='right')
+
+
+    ax.legend(loc='best')
+    nickname = params[p]['nickname']
+    savename = '%s/box_summed_%s_%s_%s.png'% \
+        (savedir,nickname,year,focus)
+    if anomaly:
+        ax.plot(list(range(-1,13)),np.zeros(14),linewidth=0.4)
+        savename = savename.replace('.png','_Anomaly.png')
+    ax.set_xlim(-1,12)
+    ax.set_ylim(min(ts['box_totals']),max(ts['box_totals']))     #   manual per parameter
+    # print('Saving to '+savename)
+    plt.show()
+    plt.savefig('saved_files/'+savename.split('/')[-1],dpi=300,bbox_inches='tight')
+
+
Mean of variable MERRA-2 Precipitation Rate is -7.973682727424602e-07
+Standard deviation of variable MERRA-2 Precipitation Rate is 1.885925667011179e-05
+Mean of variable MERRA-2 Surface Soil Moisture is -0.00156168092507869
+Standard deviation of variable MERRA-2 Surface Soil Moisture is 0.0329766683280468
+Mean of variable MERRA-2 T2M is -0.08452282100915909
+Standard deviation of variable MERRA-2 T2M is 1.4482206106185913
+
+
+

+
+
+
<Figure size 640x480 with 0 Axes>
+
+
+

+
+
+
<Figure size 640x480 with 0 Axes>
+
+
+

+
+
+
<Figure size 640x480 with 0 Axes>
+
+
+
+
+
+

Summary

+

In this notebook, we have successfully explored, analyzed, and visualized the STAC collection for wetland methane emissions and MERRA-2 data.

+ + +
+
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Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data

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Spatio-Temporal Asset Catalog

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Introduction to US GHG Center

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Open Science in Action: Intro to GitHub & Communicating Science Discoveries via Web Dashboard

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Prerequisites

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  • Participants are expected to have a basic knowledge of the python programming language.

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  • Participants MUST obtain the following to be able to follow along the workshop:

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    1. US GHG Center JupyterHub Access
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🚨 Please make sure you have both of the accesses before you come to the workshop on Sunday.

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To Get US GHG Center JupyterHub access

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The US GHG Center notebook environment is available to authorized users on an as-need basis. You can request access by using our Hub Access Request form.

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To obtain Earthdata credentials

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Follow the tutorial on this link to register for Earthdata login: https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/earthdata-login.

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Make sure to note your username and password.

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+ + + + + \ No newline at end of file diff --git a/pr-preview/pr-5/robots.txt b/pr-preview/pr-5/robots.txt new file mode 100644 index 0000000..933eeba --- /dev/null +++ b/pr-preview/pr-5/robots.txt @@ -0,0 +1 @@ +Sitemap: https://us-ghg-center.github.io/ams-2024-workshop/sitemap.xml diff --git a/pr-preview/pr-5/search.json b/pr-preview/pr-5/search.json new file mode 100644 index 0000000..a21ed4f --- /dev/null +++ b/pr-preview/pr-5/search.json @@ -0,0 +1,415 @@ +[ + { + "objectID": "ghg-center-at-ams-2024.html", + "href": "ghg-center-at-ams-2024.html", + "title": "The US GHG Center at American Meteorological Society Annual Meeting 2024", + "section": "", + "text": "Join members of the GHG Center team for several events at the 2024 AMS Annual Meeting at Baltimore, Maryland; January 28th - February 1st" + }, + { + "objectID": "ghg-center-at-ams-2024.html#short-course", + "href": "ghg-center-at-ams-2024.html#short-course", + "title": "The US GHG Center at American Meteorological Society Annual Meeting 2024", + "section": "Short Course", + "text": "Short Course\nAdvancing Open Science: An Interactive Workshop on Harnessing VEDA for Earth Science Research at the U.S. Greenhouse Gas Center\n📅 Jan 28, 2024, 🕣 8:00 AM - 3:45 PM EST, 📍 Baltimore Convention Center\nLearn how to effectively analyze and visualize Earth science data in the cloud using open-source tools and datasets [Jan 28, room ]." + }, + { + "objectID": "ghg-center-at-ams-2024.html#town-hall", + "href": "ghg-center-at-ams-2024.html#town-hall", + "title": "The US GHG Center at American Meteorological Society Annual Meeting 2024", + "section": "Town Hall", + "text": "Town Hall\nU.S. Greenhouse Gas Center: Listening Session.\n📅 Jan 30, 2024, 🕣 12:25 PM - 1:15 PM EST, 📍 Holiday 5, Hilton Baltimore Inner Harbor\nCome share your thoughts and see a demonstration of the US GHG Center. Upcoming developments and opportunities will be presented. [Jan 30, room Holiday 5 at Hilton Baltimore Inner Harbor]." + }, + { + "objectID": "ghg-center-at-ams-2024.html#presentations", + "href": "ghg-center-at-ams-2024.html#presentations", + "title": "The US GHG Center at American Meteorological Society Annual Meeting 2024", + "section": "Presentations", + "text": "Presentations\nMultiple presentations by GHG Center team members will occur throughout the week. Stop by to hear more from GHG Center team members!\n\n\n\n\n\n\n\n\n\nWhen\nRoom\nPresentation Title\n\n\n\n\nTues, Jan 30 4:45\nHoliday 5\nHilton Baltimore Inner Harbor\n8A.2 Stakeholder Engagement for the US Greenhouse Gas Center (Combley)\n\n\nMon, Jan 29 5:15\n336\nThe Baltimore Convention Center\nJ4B.4 Developing a Cross-Institutional Open-Source Cyberinfrastructure to Explore, Analyze, and Communicate Greenhouse Gas Data and Information (Chaudhary)\n\n\nTues, Jan 30 5:15\nBallroom II\nThe Baltimore Convention Center\nJ8A.4 PPAP’s and NIST’s Urban Greenhouse Gas Measurements Program (Whetstone)\n\n\nWed, Jan 31 5:30\nHoliday 5\nHilton Baltimore Inner Harbor\n12A.5 NIST’s Urban Greenhouse Gas Measurements Program and Interactions with NOAA’s Air Resources Laboratory (Whetstone)\n\n\nMon, Jan 29 11:30\n321/322\nThe Baltimore Convention Center\n2B.4 Methane and Carbon Dioxide Point Source Measurements Across Six Continents from the EMIT Imaging Spectrometer on the International Space Station - and Contributions to the U.S. Greenhouse Gas Center (Green)\n\n\nMon, Jan 29 5:30\n321/322\nThe Baltimore Convention Center\n4B.5 Detectability of Anthropogenic Impacts on Terrestrial Carbon Storage through Space Based Greenhouse Gas Observations (Weir)\n\n\nThur, Feb 1 2:30\nHoliday 4\nHilton Baltimore Inner Harbor\nJ15.4 NOAA Carbon Monitoring, Research and Innovation: Long-Standing Foundation to Support Climate Mitigation (Grubišić)" + }, + { + "objectID": "intro-us-ghg-center/index.html", + "href": "intro-us-ghg-center/index.html", + "title": "Introduction to US GHG Center", + "section": "", + "text": "Introduction to US GHG Center" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/index.html", + "href": "interactive-session-1-epa-gridded-methane-emissions/index.html", + "title": "EPA Gridded Methane Emissions Inventory", + "section": "", + "text": "EPA Gridded Methane Emissions Inventory" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "", + "text": "Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the gridded methane emissions data product.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#approach", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#approach", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "", + "text": "Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the gridded methane emissions data product.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.\nAfter the visualization, we will perform zonal statistics for a given polygon." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#about-the-data", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#about-the-data", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "About the Data", + "text": "About the Data\nThe gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions (for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI). This V2 Express Extension dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#querying-the-stac-api", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#querying-the-stac-api", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "Querying the STAC API", + "text": "Querying the STAC API\n\nimport requests\nfrom folium import Map, TileLayer\nfrom pystac_client import Client\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"http://ghg.center/api/stac\"\nRASTER_API_URL = \"https://ghg.center/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n\n# Name of the collection for gridded methane dataset. \ncollection_name = \"epa-ch4emission-yeargrid-v2\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\nExamining the contents of our collection under the temporal variable, we see that the data is available from January 2012 to December 2020. By looking at the dashboard:time density, we observe that the periodic frequency of these observations is yearly.\n\ndef get_item_count(collection_id):\n count = 0\n items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n while True:\n response = requests.get(items_url)\n\n if not response.ok:\n print(\"error getting items\")\n exit()\n\n stac = response.json()\n count += int(stac[\"context\"].get(\"returned\", 0))\n next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n if not next:\n break\n items_url = next[0][\"href\"]\n\n return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\n\n# Examining the first item in the collection\nitems[0]\n\nThis makes sense as there are 9 years between 2012 - 2020, meaning 9 records in total.\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#exploring-changes-in-methane-ch4-levels-using-the-raster-api", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#exploring-changes-in-methane-ch4-levels-using-the-raster-api", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "Exploring Changes in Methane (CH4) Levels Using the Raster API", + "text": "Exploring Changes in Methane (CH4) Levels Using the Raster API\nIn this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:7]: item for item in items} \nasset_name = \"surface-coal\"\n\n\n# Fetching the min and max values for a specific item\nrescale_values = {\"max\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items[list(items.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\n\n\nitems\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2018 and again for January 2012, so that we can visualize each event independently.\n\ncolor_map = \"rainbow\" # please select the color ramp from matplotlib library.\njanuary_2018_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}\"\n f\"&assets={asset_name}\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njanuary_2018_tile\n\n\njanuary_2012_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}\"\n f\"&assets={asset_name}\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njanuary_2012_tile" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#visualizing-ch₄-emissions", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#visualizing-ch₄-emissions", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "Visualizing CH₄ emissions", + "text": "Visualizing CH₄ emissions\n\n# We will import folium to map and folium.plugins to allow side-by-side mapping\nimport folium\nimport folium.plugins\n\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\nmap_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n\n# January 2018\nmap_layer_2018 = TileLayer(\n tiles=january_2018_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.7,\n)\nmap_layer_2018.add_to(map_.m1)\n\n# January 2012\nmap_layer_2012 = TileLayer(\n tiles=january_2012_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.7,\n)\nmap_layer_2012.add_to(map_.m2)\n\n# visualising the map\nmap_" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#visualizing-the-data-as-a-time-series", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#visualizing-the-data-as-a-time-series", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "Visualizing the Data as a Time Series", + "text": "Visualizing the Data as a Time Series\nWe can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:\n\nimport matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n df[\"date\"],\n df[\"max\"],\n color=\"red\",\n linestyle=\"-\",\n linewidth=0.5,\n label=\"Max monthly CO₂ emissions\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"CH4 emissions Molecules CH₄/cm²/s\")\nplt.title(\"CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)\")\n\n\nprint(items[2][\"properties\"][\"datetime\"])\n\n\ntile_2016 = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n f\"&assets={asset_name}\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n).json()\ntile_2016\n\n\n# Use bbox initial zoom and map\n# Set up a map located w/in event bounds\nimport folium\n\naoi_map_bbox = Map(\n tiles=\"OpenStreetMap\",\n location=[\n 30,-100\n ],\n zoom_start=8,\n)\n\nmap_layer = TileLayer(\n tiles=tile_2016[\"tiles\"][0],\n attr=\"GHG\", opacity = 0.5\n)\n\nmap_layer.add_to(aoi_map_bbox)\n\naoi_map_bbox" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#summary", + "href": "interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html#summary", + "title": "U.S. Gridded Anthropogenic Methane Emissions Inventory", + "section": "Summary", + "text": "Summary\nIn this notebook we have successfully explored, analyzed, and visualized the STAC collection for gridded methane emissions." + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/index.html", + "href": "interactive-session-2-anthropogenic-ghg-emissions/index.html", + "title": "Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes", + "section": "", + "text": "Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes" + }, + { + "objectID": "intro-stac/index.html", + "href": "intro-stac/index.html", + "title": "Spatio-Temporal Asset Catalog", + "section": "", + "text": "Spatio-Temporal Asset Catalog" + }, + { + "objectID": "prerequisites.html", + "href": "prerequisites.html", + "title": "Prerequisites", + "section": "", + "text": "Participants are expected to have a basic knowledge of the python programming language.\nParticipants MUST obtain the following to be able to follow along the workshop:\n\nUS GHG Center JupyterHub Access\nEarthdata credentials\n\n\n\n🚨 Please make sure you have both of the accesses before you come to the workshop on Sunday.\n\n\n\nThe US GHG Center notebook environment is available to authorized users on an as-need basis. You can request access by using our Hub Access Request form.\n\nMake sure you have a GitHub Account. Take note of your GitHub username.\nFill out the request form and provide needed information. Make sure to use your organization/univerity email and use “AMS Workshop 2024” as the reason.\nWatch your email for notification of authorization and the invite to join the US GHG Center Hub Access GitHub Team.\nOnce you accept the invitation, you can go to hub.ghg.center and login using your GitHub credentials.\n\n\n\n\nFollow the tutorial on this link to register for Earthdata login: https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/earthdata-login.\nMake sure to note your username and password." + }, + { + "objectID": "prerequisites.html#to-get-us-ghg-center-jupyterhub-access", + "href": "prerequisites.html#to-get-us-ghg-center-jupyterhub-access", + "title": "Prerequisites", + "section": "", + "text": "The US GHG Center notebook environment is available to authorized users on an as-need basis. You can request access by using our Hub Access Request form.\n\nMake sure you have a GitHub Account. Take note of your GitHub username.\nFill out the request form and provide needed information. Make sure to use your organization/univerity email and use “AMS Workshop 2024” as the reason.\nWatch your email for notification of authorization and the invite to join the US GHG Center Hub Access GitHub Team.\nOnce you accept the invitation, you can go to hub.ghg.center and login using your GitHub credentials." + }, + { + "objectID": "prerequisites.html#to-obtain-earthdata-credentials", + "href": "prerequisites.html#to-obtain-earthdata-credentials", + "title": "Prerequisites", + "section": "", + "text": "Follow the tutorial on this link to register for Earthdata login: https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/earthdata-login.\nMake sure to note your username and password." + }, + { + "objectID": "agenda.html", + "href": "agenda.html", + "title": "Workshop Agenda", + "section": "", + "text": "Workshop Agenda\nThe full-day workshop will be divided into the following sections:\n\n\n\n\n\n\n\n\n8:00 - 8:15\nWelcome\n\nWelcome message\nWorkshop Introduction\nTeam Introduction\nIcebreaker\n\n\n\n8:15 - 8:45\nOverview of the U.S. Greenhouse Gas Center and the Science\n\nWhat is the US GHG Center?\nRole of Methane and Carbon Dioxide in Climate Change\nWhat is included in the Center and why these datasets\nIntroduction to the science use cases / demonstration areas that will be explored during the workshop\n\n\n\n8:45 - 10:00\nIntroduction to the GHG Center Data Catalog\n\nIntro to the VEDA project and its relevance to Earth Science research and the US GHG Center\nWhat is a STAC catalog?\nHands-on exercise: searching and accessing cloud-optimized datasets using the STAC catalog\n\nIntro to JupyterHub and Jupyter Notebooks\n\nAccess to the JupyterHub environment\nCreating and running a Jupyter Notebook in the hub\n\nUsing the pystac client library to read the US GHG STAC catalog\n\nList all available datasets\nSpatial and temporal search\nAccess assets and visualize them\n\nAccessing the US GHG STAC catalog using QGIS on the browser\n\nEstablishing connection to the US GHG STAC catalog using the qgis-stac-plugin in QGIS in the GHG Center JupyterHub environment\nAdding a dataset/asset from the catalog as layer in QGIS\n\n\n\n\n\n10:00 - 10:15\nBREAK\n\n\n10:15 - 11:15\nInteractive Session 1: EPA Gridded Methane Emissions Inventory\n\nBackground and gridding methods overview/ example\nImproving access and latency of human anthropogenic emissions\nHands-on exercise\n\n\n\n11:15 - 12:15\nInteractive Session 2: Complementing anthropogenic GHG emissions with natural GHG emissions and fluxes\n\nDescription of data sets and analysis available via the U.S. GHG Center (e.g., gridded wetland emissions and ecosystem exchange estimates)\nHands-on exercise: Exploring annual U.S. wetland methane emissions and their - meteorological influences.\n\n\n\n12:15 - 1:30\nLUNCH BREAK\n\n\n1:30 - 2:45\nIdentifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data\n\nDescription of data sets and analysis available via the U.S. GHG Center (e.g., EMIT methane enhancements; plans for emission rates, CO2, etc.)\nHands-on exercise: Locating enhancement, understanding temporal relevance, exploring site with multiple observations.\n\n\n\n2:45 - 3:15\nOpen Science in Action: Intro to GitHub & Communicating Science Discoveries via Web Dashboard\n\nHow do VEDA, GitHub, and JupyterHub interoperate and why is this important?\nIntroduction to web dashboards for science communication\n\nHands-on exercise: creating a web dashboard to communicate science discoveries using the platform\n\n\n\n\n3:15 - 3:30\nClosing Remarks\n\nFuture Developments and Community Contributions\nEngaging with the U.S. Greenhouse Gas Center\n\n\n\n\nThroughout the workshop, facilitators will be available to provide support and guidance, ensuring participants are able to actively explore the workshop topics and apply their knowledge and skills to their own research projects." + }, + { + "objectID": "open-science-in-action/index.html", + "href": "open-science-in-action/index.html", + "title": "Open Science in Action: Intro to GitHub & Communicating Science Discoveries via Web Dashboard", + "section": "", + "text": "Open Science in Action: Intro to GitHub & Communicating Science Discoveries via Web Dashboard" + }, + { + "objectID": "chapters.html", + "href": "chapters.html", + "title": "Chapters", + "section": "", + "text": "The flow of the workshop\n\n\n\nIntro to the US GHG Center\nThe US GHG Center Data Catalog (STAC)\nThe US GHG Center JupyterHub environment\n\n\n\n\n\nEPA Gridded Methane Emissions Inventory\nComplementing anthropogenic GHG emissions with natural GHG emissions and fluxes\nIdentifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data\n\n\n\n\n\nIntro to GitHub & Communicating Science Discoveries via Web Dashboard\n\n\nℹ️ Content for each chapter will be added as they are prepared, please check back." + }, + { + "objectID": "chapters.html#introduction", + "href": "chapters.html#introduction", + "title": "Chapters", + "section": "", + "text": "Intro to the US GHG Center\nThe US GHG Center Data Catalog (STAC)\nThe US GHG Center JupyterHub environment" + }, + { + "objectID": "chapters.html#science-use-cases", + "href": "chapters.html#science-use-cases", + "title": "Chapters", + "section": "", + "text": "EPA Gridded Methane Emissions Inventory\nComplementing anthropogenic GHG emissions with natural GHG emissions and fluxes\nIdentifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data" + }, + { + "objectID": "chapters.html#open-science-in-action", + "href": "chapters.html#open-science-in-action", + "title": "Chapters", + "section": "", + "text": "Intro to GitHub & Communicating Science Discoveries via Web Dashboard\n\n\nℹ️ Content for each chapter will be added as they are prepared, please check back." + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "", + "text": "Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Wetland Methane Emissions, LPJ-wsl Model data product.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing plugins from folium to visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.\nRead monthly MERRA-2 data for different variables (precipitation rate, surface soil moisture)\nPlot monthly time series for LPJ-wetland emission and different MERRA-2 dataset and analyse them." + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#approach", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#approach", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "", + "text": "Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the Wetland Methane Emissions, LPJ-wsl Model data product.\nPass the STAC item into the raster API /stac/tilejson.jsonendpoint.\nUsing plugins from folium to visualize two tiles (side-by-side), allowing time point comparison.\nAfter the visualization, perform zonal statistics for a given polygon.\nRead monthly MERRA-2 data for different variables (precipitation rate, surface soil moisture)\nPlot monthly time series for LPJ-wetland emission and different MERRA-2 dataset and analyse them." + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#about-the-data", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#about-the-data", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "About the Data", + "text": "About the Data\nMethane (CH₄) emissions from wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions has thus far been underrepresented. Using the Wald Schnee und Landschaft version (LPJ-wsl) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5 x 0.5 degree resolution by simulating wetland extent and using characteristics of these inundated areas, such as soil moisture, temperature, and carbon content, to estimate CH₄ quantities emitted into the atmosphere. Highlighted areas displayed in this dataset show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl Wetland Methane Emissions data product presented here consists of global daily and monthly model estimates of terrestrial wetland CH₄ emissions from 1980 - 2021. These data are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions." + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#flow-of-workshop", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#flow-of-workshop", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Flow of Workshop", + "text": "Flow of Workshop\n\nReading data\n\nRead LPJ dataset from STAC\nRead subset of Merra-2 (few variables) data using credentials\nCreating different region of interest\n\nStatistical Analysis\n\nSelect the region and create time series of total emission in the region Note we will have line chart for two different years to show inter annual variability\nCreate dual folium map to show visual comparision\nPlot monthly mean and climate mean using merra-2 data for same region of interest\nPlot time series for the following data and time period to interpret the results:\n\n2020, 2021 - LPJ monthly emissions\n2020, 2021 - Merra 2 T2M monthly anomaly\n2020, 2021 - Total precipitation rate\n\n\n\nDatasets to be used 1. Monthly LPJ Wetland CH4 Emissions 2. Monthly MERRA-2 Precipitation RateDataset: MERRA2_400.tavgM_2d_flx_Nx Variable: ‘PRECTOT’ https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary\n\nMonthly MERRA-2 Surface Soil MoistureDataset: MERRA2_400.tavgM_2d_lnd_Nx Variable: ‘SFMC’ Long-term mean variable: ‘GWETTOP’ https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary\nMonthly MERRA-2 T2MDataset: MERRA2_400.instM_2d_asm_Nx Variable: ‘T2M’ https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary\nMERRA-2 Long-Term MeansMERRA2.tavgC_2d_ltm_Nx https://disc.gsfc.nasa.gov/datasets/M2TCNXLTM_1/summary\n\nUse case to be discussed: 1. Midwest floods in 2019 2. Pick events of interest" + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#querying-the-stac-api", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#querying-the-stac-api", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Querying the STAC API", + "text": "Querying the STAC API\n\nimport requests\nimport folium\nimport folium.plugins\nfrom folium import Map, TileLayer \nfrom pystac_client import Client \nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport branca.colormap as cm\nimport geopandas\nfrom pyproj import Geod\nfrom shapely import wkt\nimport seaborn as sns\nimport glob\nimport numpy as np\nfrom datetime import datetime\nimport netCDF4 as nc\n\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"http://ghg.center/api/stac\"\nRASTER_API_URL = \"https://ghg.center/api/raster\"\n\n# Please use the collection name similar to the one used in STAC collection.\n\n# Name of the collection for wetland methane monthly emissions. \ncollection_name = \"lpjwsl-wetlandch4-monthgrid-v1\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\ncollection\n\n{'id': 'lpjwsl-wetlandch4-monthgrid-v1',\n 'type': 'Collection',\n 'links': [{'rel': 'items',\n 'type': 'application/geo+json',\n 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items'},\n {'rel': 'parent',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/'},\n {'rel': 'root',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/'},\n {'rel': 'self',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'}],\n 'title': 'Wetland Methane Emissions, LPJ-wsl Model (Monthly)',\n 'assets': None,\n 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n 'temporal': {'interval': [['1980-01-01T00:00:00+00:00',\n '2021-12-01T00:00:00+00:00']]}},\n 'license': 'CC-BY-4.0',\n 'keywords': None,\n 'providers': None,\n 'summaries': {'datetime': ['1980-01-01T00:00:00Z', '2021-12-01T00:00:00Z']},\n 'description': 'Wetland methane emissions produced by the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The LPJ-wsl model is regularly used in conjunction with NASA’s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations.',\n 'item_assets': {'ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n 'roles': ['data', 'layer'],\n 'title': 'CH4 Wetland Emissions',\n 'description': 'Methane emissions from wetlands.'}},\n 'stac_version': '1.0.0',\n 'stac_extensions': None,\n 'dashboard:is_periodic': True,\n 'dashboard:time_density': 'month'}\n\n\nExamining the contents of our collection under summaries, we see that the data is available from January 1980 to may 2021. By looking at dashboard: time density, we can see that these observations are collected monthly.\n\ndef get_item_count(collection_id):\n count = 0\n items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n while True:\n response = requests.get(items_url)\n\n if not response.ok:\n print(\"error getting items\")\n exit()\n\n stac = response.json()\n count += int(stac[\"context\"].get(\"returned\", 0))\n next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n if not next:\n break\n items_url = next[0][\"href\"]\n\n return count\n\n\n# Check total number of items available\nnumber_of_items = get_item_count(collection_name)\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\nFound 504 items\n\n\n\n# Examining the first item in the collection\nitems[0]\n\n{'id': 'lpjwsl-wetlandch4-monthgrid-v1-202112',\n 'bbox': [-180.0, -90.0, 180.0, 90.0],\n 'type': 'Feature',\n 'links': [{'rel': 'collection',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},\n {'rel': 'parent',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1'},\n {'rel': 'root',\n 'type': 'application/json',\n 'href': 'https://ghg.center/api/stac/'},\n {'rel': 'self',\n 'type': 'application/geo+json',\n 'href': 'https://ghg.center/api/stac/collections/lpjwsl-wetlandch4-monthgrid-v1/items/lpjwsl-wetlandch4-monthgrid-v1-202112'}],\n 'assets': {'ch4-wetlands-emissions': {'href': 's3://ghgc-data-store/lpjwsl-wetlandch4-monthgrid-v1/NASA_GSFC_ch4_wl_ch4_wetlands_v22_x720_y360_t12_202112.tif',\n 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n 'roles': ['data', 'layer'],\n 'title': 'CH4 Wetland Emissions',\n 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n 'proj:epsg': 4326.0,\n 'proj:shape': [360.0, 720.0],\n 'description': 'Methane emissions from wetlands.',\n 'raster:bands': [{'scale': 1.0,\n 'offset': 0.0,\n 'sampling': 'area',\n 'data_type': 'float32',\n 'histogram': {'max': 6.929981708526611,\n 'min': 0.0,\n 'count': 11.0,\n 'buckets': [258080.0,\n 575.0,\n 251.0,\n 124.0,\n 78.0,\n 41.0,\n 26.0,\n 16.0,\n 7.0,\n 2.0]},\n 'statistics': {'mean': 0.012271502055227757,\n 'stddev': 0.1378920078277588,\n 'maximum': 6.929981708526611,\n 'minimum': 0.0,\n 'valid_percent': 0.0003858024691358025}}],\n 'proj:geometry': {'type': 'Polygon',\n 'coordinates': [[[-180.0, -90.0],\n [180.0, -90.0],\n [180.0, 90.0],\n [-180.0, 90.0],\n [-180.0, -90.0]]]},\n 'proj:projjson': {'id': {'code': 4326.0, 'authority': 'EPSG'},\n 'name': 'WGS 84',\n 'type': 'GeographicCRS',\n 'datum': {'name': 'World Geodetic System 1984',\n 'type': 'GeodeticReferenceFrame',\n 'ellipsoid': {'name': 'WGS 84',\n 'semi_major_axis': 6378137.0,\n 'inverse_flattening': 298.257223563}},\n '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json',\n 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n 'unit': 'degree',\n 'direction': 'north',\n 'abbreviation': 'Lat'},\n {'name': 'Geodetic longitude',\n 'unit': 'degree',\n 'direction': 'east',\n 'abbreviation': 'Lon'}],\n 'subtype': 'ellipsoidal'}},\n 'proj:transform': [0.5, 0.0, -180.0, 0.0, -0.5, 90.0, 0.0, 0.0, 1.0]}},\n 'geometry': {'type': 'Polygon',\n 'coordinates': [[[-180, -90],\n [180, -90],\n [180, 90],\n [-180, 90],\n [-180, -90]]]},\n 'collection': 'lpjwsl-wetlandch4-monthgrid-v1',\n 'properties': {'datetime': '2021-12-01T00:00:00+00:00'},\n 'stac_version': '1.0.0',\n 'stac_extensions': []}\n\n\nBelow, we enter minimum and maximum values to provide our upper and lower bounds in rescale_values.\n\nrescale_values = {'max': 2.0, 'min': 0.0}" + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#exploring-changes-in-methane-ch4-emission-levels-using-the-raster-api", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#exploring-changes-in-methane-ch4-emission-levels-using-the-raster-api", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Exploring Changes in Methane (CH4) Emission Levels Using the Raster API", + "text": "Exploring Changes in Methane (CH4) Emission Levels Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using folium.\n\n# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\nitems = {item[\"properties\"][\"datetime\"][:7]: item for item in items} \nasset_name = 'ch4-wetlands-emissions'\n\n\n# We create a area of interest (polygon) on which will be used later \n\naoi = [-95.9,-87.50,28.7,33.5]\nlouisiana_aoi = {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [aoi[0], aoi[2]],\n [aoi[0], aoi[3]],\n [aoi[1], aoi[3]],\n [aoi[1],aoi[2]],\n [aoi[0], aoi[2]]\n ]\n ],\n \"type\": \"Polygon\",\n },\n}\n\nNow, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for may 2020 and again for may 2021, so we can visualize each event independently.\n\ncolor_map = \"magma\" # select the color ramp from matplotlib library.\nmay_2020_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2001-05']['collection']}&item={items['2020-05']['id']}\"\n \"&assets=ch4-wetlands-emissions\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\"\n).json()\nmay_2020_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202005&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C2.0'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}\n\n\n\nmay_2021_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}\"\n \"&assets=ch4-wetlands-emissions\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\nmay_2021_tile\n\n{'tilejson': '2.2.0',\n 'version': '1.0.0',\n 'scheme': 'xyz',\n 'tiles': ['https://ghg.center/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjwsl-wetlandch4-monthgrid-v1&item=lpjwsl-wetlandch4-monthgrid-v1-202105&assets=ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C2.0'],\n 'minzoom': 0,\n 'maxzoom': 24,\n 'bounds': [-180.0, -90.0, 180.0, 90.0],\n 'center': [0.0, 0.0, 0]}" + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#visualizing-ch₄-emissions", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#visualizing-ch₄-emissions", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Visualizing CH₄ Emissions", + "text": "Visualizing CH₄ Emissions\n\n# We will import folium to map and folium.plugins to allow side-by-side mapping\n# Set initial zoom and center of map for CH₄ Layer\n# Centre of map [latitude,longitude]\n\nfrom folium.plugins import MousePosition\n\nmap_ = folium.Map(location=(30,-90), zoom_start=6)\n\n# May 2001\nmap_layer_2001 = TileLayer(\n tiles=may_2020_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.5,\n)\n\n\n# May 2021\nmap_layer_2021 = TileLayer(\n tiles=may_2021_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.5,\n)\nsbs = folium.plugins.SideBySideLayers(layer_left=map_layer_2001, layer_right=map_layer_2021)\nmap_layer_2001.add_to(map_)\nmap_layer_2021.add_to(map_)\nfolium.GeoJson(louisiana_aoi, name=\"louisiana, USA\").add_to(map_)\nsbs.add_to(map_)\nMousePosition().add_to(map_)\n# visualising the map\nmap_\n\n\nMake this Notebook Trusted to load map: File -> Trust Notebook\n\n\n\nitems = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n\n\n# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n\ndef generate_stats(item, geojson):\n result = requests.post(\n f\"{RASTER_API_URL}/cog/statistics\",\n params={\"url\": item[\"assets\"][\"ch4-wetlands-emissions\"][\"href\"]},\n json=geojson,\n ).json()\n return {\n **result[\"properties\"],\n \"datetime\": item[\"properties\"][\"datetime\"],\n }\n\nWith the function above, we can generate the statistics for the area of interest.\n\n# It will take around 5 minutes to run the statistics \n\nstats = [generate_stats(item, louisiana_aoi) for item in items[:24]]\nstats\n\n[{'statistics': {'b1': {'min': 0.0,\n 'max': 0.7171422839164734,\n 'mean': 0.15437047621783087,\n 'count': 170.0,\n 'sum': 26.24298095703125,\n 'std': 0.1835849342048151,\n 'median': 0.05701697617769241,\n 'majority': 0.0,\n 'minority': 0.00043509763781912625,\n 'unique': 123.0,\n 'histogram': [[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0, 3.0, 2.0],\n [0.0,\n 0.07171422988176346,\n 0.14342845976352692,\n 0.21514268219470978,\n 0.28685691952705383,\n 0.3585711419582367,\n 0.43028536438941956,\n 0.5019996166229248,\n 0.5737138390541077,\n 0.6454280614852905,\n 0.7171422839164734]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.5802477025985717}},\n 'datetime': '2021-12-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.516049861907959,\n 'mean': 0.13532144883099725,\n 'count': 170.0,\n 'sum': 23.00464630126953,\n 'std': 0.15769760565780438,\n 'median': 0.05566539615392685,\n 'majority': 0.0,\n 'minority': 0.0002954523079097271,\n 'unique': 123.0,\n 'histogram': [[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0, 7.0, 7.0],\n [0.0,\n 0.0516049861907959,\n 0.1032099723815918,\n 0.1548149585723877,\n 0.2064199447631836,\n 0.2580249309539795,\n 0.3096299171447754,\n 0.3612349033355713,\n 0.4128398895263672,\n 0.4644448757171631,\n 0.516049861907959]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.48761060655117044}},\n 'datetime': '2021-11-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.3371007442474365,\n 'mean': 0.27248965992647056,\n 'count': 170.0,\n 'sum': 46.3232421875,\n 'std': 0.32895022521723327,\n 'median': 0.08361007273197174,\n 'majority': 0.0,\n 'minority': 0.0006618715706281364,\n 'unique': 123.0,\n 'histogram': [[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0, 1.0, 2.0],\n [0.0,\n 0.1337100714445114,\n 0.2674201428890228,\n 0.40113022923469543,\n 0.5348402857780457,\n 0.6685503721237183,\n 0.8022604584693909,\n 0.9359705448150635,\n 1.0696805715560913,\n 1.2033907175064087,\n 1.3371007442474365]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.060954008102417}},\n 'datetime': '2021-10-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 2.2005276679992676,\n 'mean': 0.45919602338005516,\n 'count': 170.0,\n 'sum': 78.06332397460938,\n 'std': 0.5457408213221397,\n 'median': 0.18731170892715454,\n 'majority': 0.0,\n 'minority': 0.002108836779370904,\n 'unique': 123.0,\n 'histogram': [[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0, 1.0, 2.0],\n [0.0,\n 0.22005276381969452,\n 0.44010552763938904,\n 0.6601582765579224,\n 0.8802110552787781,\n 1.1002638339996338,\n 1.3203165531158447,\n 1.5403693914413452,\n 1.7604221105575562,\n 1.9804749488830566,\n 2.2005276679992676]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.6831329369544983}},\n 'datetime': '2021-09-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 2.2805004119873047,\n 'mean': 0.4716072531307445,\n 'count': 170.0,\n 'sum': 80.17323303222656,\n 'std': 0.5656065478170901,\n 'median': 0.19661885499954224,\n 'majority': 0.0,\n 'minority': 0.0015638105105608702,\n 'unique': 123.0,\n 'histogram': [[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0, 3.0, 1.0],\n [0.0,\n 0.22805003821849823,\n 0.45610007643699646,\n 0.6841500997543335,\n 0.9122001528739929,\n 1.1402502059936523,\n 1.368300199508667,\n 1.5963503122329712,\n 1.8244003057479858,\n 2.05245041847229,\n 2.2805004119873047]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.8883533668518073}},\n 'datetime': '2021-08-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 3.072227716445923,\n 'mean': 0.6332277634564568,\n 'count': 170.0,\n 'sum': 107.64871978759766,\n 'std': 0.7631461866897622,\n 'median': 0.2839159667491913,\n 'majority': 0.0,\n 'minority': 0.0024779888335615396,\n 'unique': 123.0,\n 'histogram': [[91.0, 15.0, 12.0, 11.0, 13.0, 14.0, 3.0, 7.0, 3.0, 1.0],\n [0.0,\n 0.30722278356552124,\n 0.6144455671310425,\n 0.921668291091919,\n 1.228891134262085,\n 1.5361138582229614,\n 1.843336582183838,\n 2.150559425354004,\n 2.45778226852417,\n 2.765004873275757,\n 3.072227716445923]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 2.494618062973023}},\n 'datetime': '2021-07-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 2.1353492736816406,\n 'mean': 0.5122916726505056,\n 'count': 170.0,\n 'sum': 87.08958435058594,\n 'std': 0.5816176132620369,\n 'median': 0.23781655728816986,\n 'majority': 0.0,\n 'minority': 0.002265247516334057,\n 'unique': 123.0,\n 'histogram': [[80.0, 19.0, 9.0, 9.0, 15.0, 17.0, 6.0, 7.0, 6.0, 2.0],\n [0.0,\n 0.21353492140769958,\n 0.42706984281539917,\n 0.6406047940254211,\n 0.8541396856307983,\n 1.0676746368408203,\n 1.2812095880508423,\n 1.4947445392608643,\n 1.7082793712615967,\n 1.9218143224716187,\n 2.1353492736816406]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.7408480906486512}},\n 'datetime': '2021-06-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.7123756408691406,\n 'mean': 0.3947353587431066,\n 'count': 170.0,\n 'sum': 67.10501098632812,\n 'std': 0.4542843921906756,\n 'median': 0.16888344287872314,\n 'majority': 0.0,\n 'minority': 0.001768625108525157,\n 'unique': 123.0,\n 'histogram': [[87.0, 13.0, 9.0, 9.0, 19.0, 17.0, 3.0, 6.0, 5.0, 2.0],\n [0.0,\n 0.17123755812644958,\n 0.34247511625289917,\n 0.5137127041816711,\n 0.6849502325057983,\n 0.8561878204345703,\n 1.0274254083633423,\n 1.1986629962921143,\n 1.3699004650115967,\n 1.5411380529403687,\n 1.7123756408691406]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.4123617601394653}},\n 'datetime': '2021-05-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.8859390616416931,\n 'mean': 0.24400625789866728,\n 'count': 170.0,\n 'sum': 41.48106384277344,\n 'std': 0.28321048400965054,\n 'median': 0.09922437369823456,\n 'majority': 0.0,\n 'minority': 0.0006858149426989257,\n 'unique': 123.0,\n 'histogram': [[84.0, 13.0, 10.0, 8.0, 5.0, 10.0, 11.0, 15.0, 7.0, 7.0],\n [0.0,\n 0.08859390765428543,\n 0.17718781530857086,\n 0.2657817304134369,\n 0.3543756306171417,\n 0.44296953082084656,\n 0.5315634608268738,\n 0.6201573610305786,\n 0.7087512612342834,\n 0.7973451614379883,\n 0.8859390616416931]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.8462702941894532}},\n 'datetime': '2021-04-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.659404993057251,\n 'mean': 0.19500977011287915,\n 'count': 170.0,\n 'sum': 33.15166091918945,\n 'std': 0.22056241116653577,\n 'median': 0.087272509932518,\n 'majority': 0.0,\n 'minority': 0.0006441613077186048,\n 'unique': 123.0,\n 'histogram': [[79.0, 17.0, 7.0, 10.0, 5.0, 7.0, 12.0, 16.0, 4.0, 13.0],\n [0.0,\n 0.0659404993057251,\n 0.1318809986114502,\n 0.1978214979171753,\n 0.2637619972229004,\n 0.3297024965286255,\n 0.3956429958343506,\n 0.4615834951400757,\n 0.5275239944458008,\n 0.5934644937515259,\n 0.659404993057251]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.6313274204730988}},\n 'datetime': '2021-03-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.4581904411315918,\n 'mean': 0.1083783093620749,\n 'count': 170.0,\n 'sum': 18.424312591552734,\n 'std': 0.13051518085377295,\n 'median': 0.039194926619529724,\n 'majority': 0.0,\n 'minority': 0.00028075711452402174,\n 'unique': 123.0,\n 'histogram': [[87.0, 15.0, 12.0, 9.0, 10.0, 9.0, 10.0, 8.0, 7.0, 3.0],\n [0.0,\n 0.04581904411315918,\n 0.09163808822631836,\n 0.13745713233947754,\n 0.18327617645263672,\n 0.2290952205657959,\n 0.2749142646789551,\n 0.32073330879211426,\n 0.36655235290527344,\n 0.4123713970184326,\n 0.4581904411315918]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.398276316523552}},\n 'datetime': '2021-02-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.6753556728363037,\n 'mean': 0.1531466427971335,\n 'count': 170.0,\n 'sum': 26.034929275512695,\n 'std': 0.1810563542947181,\n 'median': 0.06246931105852127,\n 'majority': 0.0,\n 'minority': 0.0005269754328764975,\n 'unique': 123.0,\n 'histogram': [[90.0, 12.0, 11.0, 8.0, 15.0, 16.0, 4.0, 7.0, 5.0, 2.0],\n [0.0,\n 0.06753556430339813,\n 0.13507112860679626,\n 0.2026067078113556,\n 0.27014225721359253,\n 0.33767783641815186,\n 0.4052134156227112,\n 0.4727489650249481,\n 0.5402845144271851,\n 0.6078200936317444,\n 0.6753556728363037]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.5708203029632569}},\n 'datetime': '2021-01-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.6949237585067749,\n 'mean': 0.14557975320255054,\n 'count': 170.0,\n 'sum': 24.748558044433594,\n 'std': 0.17713435804121008,\n 'median': 0.05363526940345764,\n 'majority': 0.0,\n 'minority': 0.00041759133455343544,\n 'unique': 123.0,\n 'histogram': [[93.0, 13.0, 11.0, 11.0, 13.0, 13.0, 7.0, 5.0, 1.0, 3.0],\n [0.0,\n 0.06949237734079361,\n 0.13898475468158722,\n 0.20847712457180023,\n 0.27796950936317444,\n 0.34746187925338745,\n 0.41695424914360046,\n 0.4864466190338135,\n 0.5559390187263489,\n 0.6254313588142395,\n 0.6949237585067749]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.5663253521919251}},\n 'datetime': '2020-12-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 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123.0,\n 'histogram': [[94.0, 13.0, 10.0, 10.0, 17.0, 9.0, 8.0, 4.0, 3.0, 2.0],\n [0.0,\n 0.12557408213615417,\n 0.25114816427230835,\n 0.37672221660614014,\n 0.5022963285446167,\n 0.6278703808784485,\n 0.7534444332122803,\n 0.8790185451507568,\n 1.0045926570892334,\n 1.1301666498184204,\n 1.255740761756897]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.0394352316856386}},\n 'datetime': '2020-10-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.9983481168746948,\n 'mean': 0.43116993623621325,\n 'count': 170.0,\n 'sum': 73.29888916015625,\n 'std': 0.5197145296917448,\n 'median': 0.17324364185333252,\n 'majority': 0.0,\n 'minority': 0.0019067995017394423,\n 'unique': 123.0,\n 'histogram': [[89.0, 16.0, 11.0, 11.0, 13.0, 8.0, 10.0, 8.0, 2.0, 2.0],\n [0.0,\n 0.19983480870723724,\n 0.3996696174144745,\n 0.5995044112205505,\n 0.799339234828949,\n 0.9991740584373474,\n 1.199008822441101,\n 1.3988436460494995,\n 1.598678469657898,\n 1.7985132932662964,\n 1.9983481168746948]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.6112784051895146}},\n 'datetime': '2020-09-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 2.0058531761169434,\n 'mean': 0.4619821436264936,\n 'count': 170.0,\n 'sum': 78.5369644165039,\n 'std': 0.5712842682229454,\n 'median': 0.16520659625530243,\n 'majority': 0.0,\n 'minority': 0.001269881846383214,\n 'unique': 123.0,\n 'histogram': [[91.0, 14.0, 11.0, 13.0, 6.0, 10.0, 6.0, 8.0, 6.0, 5.0],\n [0.0,\n 0.20058532059192657,\n 0.40117064118385315,\n 0.6017559766769409,\n 0.8023412823677063,\n 1.0029265880584717,\n 1.2035119533538818,\n 1.4040971994400024,\n 1.6046825647354126,\n 1.8052678108215332,\n 2.0058531761169434]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.8289475870132446}},\n 'datetime': '2020-08-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 2.070863723754883,\n 'mean': 0.5622900570140166,\n 'count': 170.0,\n 'sum': 95.58930969238281,\n 'std': 0.6425463273519573,\n 'median': 0.2530515491962433,\n 'majority': 0.0,\n 'minority': 0.002212761202827096,\n 'unique': 123.0,\n 'histogram': [[81.0, 17.0, 9.0, 8.0, 8.0, 13.0, 10.0, 8.0, 10.0, 6.0],\n [0.0,\n 0.20708636939525604,\n 0.4141727387905121,\n 0.6212590932846069,\n 0.8283454775810242,\n 1.0354318618774414,\n 1.2425181865692139,\n 1.4496046304702759,\n 1.6566909551620483,\n 1.8637773990631104,\n 2.070863723754883]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.9805018997192385}},\n 'datetime': '2020-07-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.6319794654846191,\n 'mean': 0.39405023911420034,\n 'count': 170.0,\n 'sum': 66.98854064941406,\n 'std': 0.4457748225152684,\n 'median': 0.1712484061717987,\n 'majority': 0.0,\n 'minority': 0.0013915648451074958,\n 'unique': 123.0,\n 'histogram': [[82.0, 18.0, 9.0, 9.0, 9.0, 18.0, 11.0, 9.0, 3.0, 2.0],\n [0.0,\n 0.16319794952869415,\n 0.3263958990573883,\n 0.48959383368492126,\n 0.6527917981147766,\n 0.8159897327423096,\n 0.9791876673698425,\n 1.1423856019973755,\n 1.3055835962295532,\n 1.4687814712524414,\n 1.6319794654846191]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.3640193176269535}},\n 'datetime': '2020-06-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.4179749488830566,\n 'mean': 0.3024932861328125,\n 'count': 170.0,\n 'sum': 51.423858642578125,\n 'std': 0.35093550103567694,\n 'median': 0.12169831991195679,\n 'majority': 0.0,\n 'minority': 0.001449149684049189,\n 'unique': 123.0,\n 'histogram': [[88.0, 13.0, 10.0, 17.0, 12.0, 16.0, 7.0, 4.0, 0.0, 3.0],\n [0.0,\n 0.1417974978685379,\n 0.2835949957370758,\n 0.4253924787044525,\n 0.5671899914741516,\n 0.7089874744415283,\n 0.850784957408905,\n 0.9925824403762817,\n 1.1343799829483032,\n 1.2761774063110352,\n 1.4179749488830566]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 1.092334604263306}},\n 'datetime': '2020-05-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.9126242399215698,\n 'mean': 0.20955680398380055,\n 'count': 170.0,\n 'sum': 35.624656677246094,\n 'std': 0.23214241630129956,\n 'median': 0.10675151646137238,\n 'majority': 0.0,\n 'minority': 0.0011147678596898913,\n 'unique': 123.0,\n 'histogram': [[80.0, 17.0, 11.0, 12.0, 23.0, 9.0, 10.0, 5.0, 1.0, 2.0],\n [0.0,\n 0.09126242250204086,\n 0.18252484500408173,\n 0.273787260055542,\n 0.36504969000816345,\n 0.4563121199607849,\n 0.547574520111084,\n 0.6388369798660278,\n 0.7300993800163269,\n 0.8213618397712708,\n 0.9126242399215698]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.7237258589267731}},\n 'datetime': '2020-04-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 1.0447851419448853,\n 'mean': 0.22874827665441178,\n 'count': 170.0,\n 'sum': 38.88720703125,\n 'std': 0.25775953751223146,\n 'median': 0.10438993573188782,\n 'majority': 0.0,\n 'minority': 0.0009839008562266827,\n 'unique': 123.0,\n 'histogram': [[86.0, 15.0, 9.0, 13.0, 14.0, 20.0, 9.0, 1.0, 2.0, 1.0],\n [0.0,\n 0.10447851568460464,\n 0.2089570313692093,\n 0.31343555450439453,\n 0.4179140627384186,\n 0.5223925709724426,\n 0.6268711090087891,\n 0.7313495874404907,\n 0.8358281254768372,\n 0.9403066039085388,\n 1.0447851419448853]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.7838775122165684}},\n 'datetime': '2020-03-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.7303400635719299,\n 'mean': 0.1730064167695887,\n 'count': 170.0,\n 'sum': 29.411090850830078,\n 'std': 0.2041394445830063,\n 'median': 0.06572374701499939,\n 'majority': 0.0,\n 'minority': 0.0005588305648416281,\n 'unique': 123.0,\n 'histogram': [[86.0, 15.0, 12.0, 8.0, 13.0, 10.0, 12.0, 7.0, 4.0, 3.0],\n [0.0,\n 0.07303400337696075,\n 0.1460680067539215,\n 0.21910202503204346,\n 0.292136013507843,\n 0.36517003178596497,\n 0.4382040500640869,\n 0.5112380385398865,\n 0.584272027015686,\n 0.6573060750961304,\n 0.7303400635719299]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.6206122100353242}},\n 'datetime': '2020-02-01T00:00:00+00:00'},\n {'statistics': {'b1': {'min': 0.0,\n 'max': 0.7433937788009644,\n 'mean': 0.17906734242158778,\n 'count': 170.0,\n 'sum': 30.441448211669922,\n 'std': 0.20951516248017335,\n 'median': 0.06703908741474152,\n 'majority': 0.0,\n 'minority': 0.0004158392839599401,\n 'unique': 123.0,\n 'histogram': [[88.0, 12.0, 11.0, 9.0, 10.0, 13.0, 12.0, 8.0, 5.0, 2.0],\n [0.0,\n 0.0743393748998642,\n 0.1486787497997284,\n 0.22301813960075378,\n 0.2973574995994568,\n 0.3716968894004822,\n 0.44603627920150757,\n 0.520375669002533,\n 0.5947149991989136,\n 0.669054388999939,\n 0.7433937788009644]],\n 'valid_percent': 100.0,\n 'masked_pixels': 0.0,\n 'valid_pixels': 170.0,\n 'percentile_2': 0.0,\n 'percentile_98': 0.637993005514145}},\n 'datetime': '2020-01-01T00:00:00+00:00'}]\n\n\n\n# Manipulating and cleaning the stats output from previous cell\n\ndef clean_stats(stats_json) -> pd.DataFrame:\n df = pd.json_normalize(stats_json)\n df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n return df\n\n\ndf = clean_stats(stats)\ndf.head(5)\n\n\n\n\n\n\n\n\ndatetime\nmin\nmax\nmean\ncount\nsum\nstd\nmedian\nmajority\nminority\nunique\nhistogram\nvalid_percent\nmasked_pixels\nvalid_pixels\npercentile_2\npercentile_98\ndate\n\n\n\n\n0\n2021-12-01T00:00:00+00:00\n0.0\n0.717142\n0.154370\n170.0\n26.242981\n0.183585\n0.057017\n0.0\n0.000435\n123.0\n[[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0,...\n100.0\n0.0\n170.0\n0.0\n0.580248\n2021-12-01 00:00:00+00:00\n\n\n1\n2021-11-01T00:00:00+00:00\n0.0\n0.516050\n0.135321\n170.0\n23.004646\n0.157698\n0.055665\n0.0\n0.000295\n123.0\n[[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0,...\n100.0\n0.0\n170.0\n0.0\n0.487611\n2021-11-01 00:00:00+00:00\n\n\n2\n2021-10-01T00:00:00+00:00\n0.0\n1.337101\n0.272490\n170.0\n46.323242\n0.328950\n0.083610\n0.0\n0.000662\n123.0\n[[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0...\n100.0\n0.0\n170.0\n0.0\n1.060954\n2021-10-01 00:00:00+00:00\n\n\n3\n2021-09-01T00:00:00+00:00\n0.0\n2.200528\n0.459196\n170.0\n78.063324\n0.545741\n0.187312\n0.0\n0.002109\n123.0\n[[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0...\n100.0\n0.0\n170.0\n0.0\n1.683133\n2021-09-01 00:00:00+00:00\n\n\n4\n2021-08-01T00:00:00+00:00\n0.0\n2.280500\n0.471607\n170.0\n80.173233\n0.565607\n0.196619\n0.0\n0.001564\n123.0\n[[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0...\n100.0\n0.0\n170.0\n0.0\n1.888353\n2021-08-01 00:00:00+00:00\n\n\n\n\n\n\n\n\n# Filtering the stats for year 2020 and 2021\n\ndf_2020_2021 = df[(df['date'].dt.year == 2020) | (df['date'].dt.year == 2021)]\ndf_2020_2021['year'] = pd.to_datetime(df_2020_2021['datetime']).dt.year\ndf_2020_2021['month'] = pd.to_datetime(df_2020_2021['datetime']).dt.month\ndf_2020_2021\n\n\n\n\n\n\n\n\ndatetime\nmin\nmax\nmean\ncount\nsum\nstd\nmedian\nmajority\nminority\nunique\nhistogram\nvalid_percent\nmasked_pixels\nvalid_pixels\npercentile_2\npercentile_98\ndate\nyear\nmonth\n\n\n\n\n0\n2021-12-01T00:00:00+00:00\n0.0\n0.717142\n0.154370\n170.0\n26.242981\n0.183585\n0.057017\n0.0\n0.000435\n123.0\n[[92.0, 9.0, 13.0, 10.0, 14.0, 16.0, 7.0, 4.0,...\n100.0\n0.0\n170.0\n0.0\n0.580248\n2021-12-01 00:00:00+00:00\n2021\n12\n\n\n1\n2021-11-01T00:00:00+00:00\n0.0\n0.516050\n0.135321\n170.0\n23.004646\n0.157698\n0.055665\n0.0\n0.000295\n123.0\n[[84.0, 13.0, 12.0, 7.0, 9.0, 12.0, 11.0, 8.0,...\n100.0\n0.0\n170.0\n0.0\n0.487611\n2021-11-01 00:00:00+00:00\n2021\n11\n\n\n2\n2021-10-01T00:00:00+00:00\n0.0\n1.337101\n0.272490\n170.0\n46.323242\n0.328950\n0.083610\n0.0\n0.000662\n123.0\n[[94.0, 10.0, 13.0, 10.0, 18.0, 10.0, 7.0, 5.0...\n100.0\n0.0\n170.0\n0.0\n1.060954\n2021-10-01 00:00:00+00:00\n2021\n10\n\n\n3\n2021-09-01T00:00:00+00:00\n0.0\n2.200528\n0.459196\n170.0\n78.063324\n0.545741\n0.187312\n0.0\n0.002109\n123.0\n[[90.0, 15.0, 8.0, 16.0, 13.0, 11.0, 10.0, 4.0...\n100.0\n0.0\n170.0\n0.0\n1.683133\n2021-09-01 00:00:00+00:00\n2021\n9\n\n\n4\n2021-08-01T00:00:00+00:00\n0.0\n2.280500\n0.471607\n170.0\n80.173233\n0.565607\n0.196619\n0.0\n0.001564\n123.0\n[[89.0, 16.0, 11.0, 13.0, 14.0, 13.0, 5.0, 5.0...\n100.0\n0.0\n170.0\n0.0\n1.888353\n2021-08-01 00:00:00+00:00\n2021\n8\n\n\n5\n2021-07-01T00:00:00+00:00\n0.0\n3.072228\n0.633228\n170.0\n107.648720\n0.763146\n0.283916\n0.0\n0.002478\n123.0\n[[91.0, 15.0, 12.0, 11.0, 13.0, 14.0, 3.0, 7.0...\n100.0\n0.0\n170.0\n0.0\n2.494618\n2021-07-01 00:00:00+00:00\n2021\n7\n\n\n6\n2021-06-01T00:00:00+00:00\n0.0\n2.135349\n0.512292\n170.0\n87.089584\n0.581618\n0.237817\n0.0\n0.002265\n123.0\n[[80.0, 19.0, 9.0, 9.0, 15.0, 17.0, 6.0, 7.0, ...\n100.0\n0.0\n170.0\n0.0\n1.740848\n2021-06-01 00:00:00+00:00\n2021\n6\n\n\n7\n2021-05-01T00:00:00+00:00\n0.0\n1.712376\n0.394735\n170.0\n67.105011\n0.454284\n0.168883\n0.0\n0.001769\n123.0\n[[87.0, 13.0, 9.0, 9.0, 19.0, 17.0, 3.0, 6.0, ...\n100.0\n0.0\n170.0\n0.0\n1.412362\n2021-05-01 00:00:00+00:00\n2021\n5\n\n\n8\n2021-04-01T00:00:00+00:00\n0.0\n0.885939\n0.244006\n170.0\n41.481064\n0.283210\n0.099224\n0.0\n0.000686\n123.0\n[[84.0, 13.0, 10.0, 8.0, 5.0, 10.0, 11.0, 15.0...\n100.0\n0.0\n170.0\n0.0\n0.846270\n2021-04-01 00:00:00+00:00\n2021\n4\n\n\n9\n2021-03-01T00:00:00+00:00\n0.0\n0.659405\n0.195010\n170.0\n33.151661\n0.220562\n0.087273\n0.0\n0.000644\n123.0\n[[79.0, 17.0, 7.0, 10.0, 5.0, 7.0, 12.0, 16.0,...\n100.0\n0.0\n170.0\n0.0\n0.631327\n2021-03-01 00:00:00+00:00\n2021\n3\n\n\n10\n2021-02-01T00:00:00+00:00\n0.0\n0.458190\n0.108378\n170.0\n18.424313\n0.130515\n0.039195\n0.0\n0.000281\n123.0\n[[87.0, 15.0, 12.0, 9.0, 10.0, 9.0, 10.0, 8.0,...\n100.0\n0.0\n170.0\n0.0\n0.398276\n2021-02-01 00:00:00+00:00\n2021\n2\n\n\n11\n2021-01-01T00:00:00+00:00\n0.0\n0.675356\n0.153147\n170.0\n26.034929\n0.181056\n0.062469\n0.0\n0.000527\n123.0\n[[90.0, 12.0, 11.0, 8.0, 15.0, 16.0, 4.0, 7.0,...\n100.0\n0.0\n170.0\n0.0\n0.570820\n2021-01-01 00:00:00+00:00\n2021\n1\n\n\n12\n2020-12-01T00:00:00+00:00\n0.0\n0.694924\n0.145580\n170.0\n24.748558\n0.177134\n0.053635\n0.0\n0.000418\n123.0\n[[93.0, 13.0, 11.0, 11.0, 13.0, 13.0, 7.0, 5.0...\n100.0\n0.0\n170.0\n0.0\n0.566325\n2020-12-01 00:00:00+00:00\n2020\n12\n\n\n13\n2020-11-01T00:00:00+00:00\n0.0\n0.535885\n0.127838\n170.0\n21.732426\n0.153439\n0.049726\n0.0\n0.000282\n123.0\n[[86.0, 14.0, 14.0, 8.0, 13.0, 9.0, 8.0, 8.0, ...\n100.0\n0.0\n170.0\n0.0\n0.482069\n2020-11-01 00:00:00+00:00\n2020\n11\n\n\n14\n2020-10-01T00:00:00+00:00\n0.0\n1.255741\n0.257558\n170.0\n43.784798\n0.316934\n0.076858\n0.0\n0.000619\n123.0\n[[94.0, 13.0, 10.0, 10.0, 17.0, 9.0, 8.0, 4.0,...\n100.0\n0.0\n170.0\n0.0\n1.039435\n2020-10-01 00:00:00+00:00\n2020\n10\n\n\n15\n2020-09-01T00:00:00+00:00\n0.0\n1.998348\n0.431170\n170.0\n73.298889\n0.519715\n0.173244\n0.0\n0.001907\n123.0\n[[89.0, 16.0, 11.0, 11.0, 13.0, 8.0, 10.0, 8.0...\n100.0\n0.0\n170.0\n0.0\n1.611278\n2020-09-01 00:00:00+00:00\n2020\n9\n\n\n16\n2020-08-01T00:00:00+00:00\n0.0\n2.005853\n0.461982\n170.0\n78.536964\n0.571284\n0.165207\n0.0\n0.001270\n123.0\n[[91.0, 14.0, 11.0, 13.0, 6.0, 10.0, 6.0, 8.0,...\n100.0\n0.0\n170.0\n0.0\n1.828948\n2020-08-01 00:00:00+00:00\n2020\n8\n\n\n17\n2020-07-01T00:00:00+00:00\n0.0\n2.070864\n0.562290\n170.0\n95.589310\n0.642546\n0.253052\n0.0\n0.002213\n123.0\n[[81.0, 17.0, 9.0, 8.0, 8.0, 13.0, 10.0, 8.0, ...\n100.0\n0.0\n170.0\n0.0\n1.980502\n2020-07-01 00:00:00+00:00\n2020\n7\n\n\n18\n2020-06-01T00:00:00+00:00\n0.0\n1.631979\n0.394050\n170.0\n66.988541\n0.445775\n0.171248\n0.0\n0.001392\n123.0\n[[82.0, 18.0, 9.0, 9.0, 9.0, 18.0, 11.0, 9.0, ...\n100.0\n0.0\n170.0\n0.0\n1.364019\n2020-06-01 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00:00:00+00:00\n2020\n2\n\n\n23\n2020-01-01T00:00:00+00:00\n0.0\n0.743394\n0.179067\n170.0\n30.441448\n0.209515\n0.067039\n0.0\n0.000416\n123.0\n[[88.0, 12.0, 11.0, 9.0, 10.0, 13.0, 12.0, 8.0...\n100.0\n0.0\n170.0\n0.0\n0.637993\n2020-01-01 00:00:00+00:00\n2020\n1" + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#visualizing-the-data-as-a-time-series", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#visualizing-the-data-as-a-time-series", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Visualizing the Data as a Time Series", + "text": "Visualizing the Data as a Time Series\nWe can now explore the wetland methane emissions time series (January - December) for the year 2020,2021 available for the Louisiana area of the U.S. We can plot the data set using the code below:\n\nitems = {item[\"properties\"][\"datetime\"][:7]: item for item in items} \nfig = plt.figure(figsize=(20, 10))\n\nsns.lineplot(\n df_2020_2021,\n x = 'month', \n y = 'sum',\n hue= 'year',\n palette='flare'\n)\n\n# plt.legend()\nplt.xlabel(\"months\")\nplt.ylabel(\"CH4 emissions g/m2\")\nplt.title(\"CH4 emission Values for Louisiana for 2020 and 2021\")\n\n\ncolormap = cm.LinearColormap(colors = ['#2c115f','#721f81','#b73779','#f1605d','#feb078'], vmin = 0, vmax = 2 )\ncolormap.caption = 'g CH₄/m²/day'\n\nmay_2021_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}\"\n \"&assets=ch4-wetlands-emissions\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\nmay_2021_tile\njune_2021_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-06']['collection']}&item={items['2021-06']['id']}\"\n \"&assets=ch4-wetlands-emissions\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njune_2021_tile\njuly_2021_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2021-07']['collection']}&item={items['2021-07']['id']}\"\n \"&assets=ch4-wetlands-emissions\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n).json()\njuly_2021_tile\n\nmap_ = folium.Map(location=(30,-90), zoom_start=5)\n\n# May 2001\nmap_layer_202105 = TileLayer(\n tiles=may_2020_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.5,\n name='May'\n)\nmap_layer_202105.add_to(map_)\n\n# May 2021\nmap_layer_202106 = TileLayer(\n tiles=june_2021_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.5,\n name='June'\n)\nmap_layer_202106.add_to(map_)\n\nmap_layer_202107 = TileLayer(\n tiles=july_2021_tile[\"tiles\"][0],\n attr=\"GHG\",\n opacity=0.5,\n name='July'\n)\nmap_layer_202107.add_to(map_)\n\nfolium.GeoJson(louisiana_aoi, name=\"louisiana, USA\").add_to(map_)\nfolium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)\n\nsvg_style = '<style>svg#legend {font-size: 14px; background-color: white;}</style>'\nmap_.get_root().header.add_child(folium.Element(svg_style))\nmap_.add_child(colormap)\n# visualising the map\nmap_\n\nMake this Notebook Trusted to load map: File -> Trust Notebook\n\n\n\n\n\n\nMERRA2 visualization\n\nmerra_t2m_dir='merra_t2m_dir/'\nmerra_soil_moisture_dir = 'merra_soil_moisture_dir/'\nmerra_precip_rate_dir = 'merra_precip_rate_dir/'\nmerra_t2m_clim_dir = 'merra_t2m_clim_dir/'\nsavedir = 'saved_files'\n\nmerra_precip_rate_clim_dir = merra_t2m_clim_dir \nmerra_soil_moisture_clim_dir = merra_t2m_clim_dir\n\n\nparams={\n 'MERRA-2 T2M':\n {'var':'T2M',\n 'cmap':'Spectral_r',\n 'dir':merra_t2m_dir,\n 'nickname':'merra2_t2m',\n 'climdir':merra_t2m_clim_dir,\n 'climvar':'T2MMEAN'},\n 'MERRA-2 Surface Soil Moisture':\n {'var':'GWETTOP',\n 'cmap':'Blues',\n 'dir':merra_soil_moisture_dir,\n 'nickname':'merra2_sm',\n 'climdir':merra_soil_moisture_clim_dir,\n 'climvar':'GWETTOP'},\n 'MERRA-2 Precipitation Rate':\n {'var':'PRECTOT',\n 'cmap':'Spectral_r',\n 'dir':merra_precip_rate_dir,\n 'nickname':'merra2_pr',\n 'climdir':merra_precip_rate_clim_dir,\n 'climvar':'PRECTOT'}\n}\n\nboundaries={\n 'Global':[-180,180,-90,90],\n 'Louisiana': [-95.9,-87.50,28.7,33.5],\n 'CONUS':[-127.08,-63.87,23.55,49.19], # conus\n 'Florida':[-84.07,-79.14,24.85,30.5],\n 'Northeast':[-74.88,-69.81,40.48,42.88]\n}\n\nyear=1991\nfocus = 'Northeast'\nanomaly = 1\nparam = ['MERRA-2 Precipitation Rate','MERRA-2 Surface Soil Moisture', 'MERRA-2 T2M' ]\n\n\ndef get_merra2_timeseries(year,focus,p,anomaly):\n files = glob.glob(params[p]['dir']+'*.nc4')\n if anomaly:\n try:\n clim_files = glob.glob(params[p]['climdir']+'*.nc4')\n except:\n print('Climatological mean files (climdir) not found for specified parameter.')\n breakpoint()\n month_labels = []\n box_totals = []\n month_field = []\n dt = []\n for i,f in enumerate(files):\n data = nc.Dataset(f)\n \n # Get bounding box\n wlat = np.logical_and(\n data['lat'][:] < boundaries[focus][3],\n data['lat'][:] > boundaries[focus][2]\n )\n wlon = np.logical_and(\n data['lon'][:] < boundaries[focus][1],\n data['lon'][:] > boundaries[focus][0]\n )\n\n datestamp = f.split('.')[-2]\n month = int(datestamp[-2::])\n\n dt.append(datetime(year,month,1))\n month_labels.append(datetime(year,month,1).strftime('%B'))\n\n if anomaly:\n # Make sure you read the climatology for the right month (whichfile)\n whichfile = [datetime(1991,month,1).strftime('%y%m')[1:] in f for f in clim_files]\n climdata = nc.Dataset(np.array(clim_files)[whichfile][0])\n \n # Calculate sum (emissions) or mean (met params) over your bounding box\n if 'LPJ' in p:\n clim_box_total = np.nansum(climdata[params[p]['climvar']][0,wlat,wlon])\n now_box_total = np.nansum(data[params[p]['var']][0,wlat,wlon])\n elif 'MERRA' in p:\n clim_box_total = np.nanmean(climdata[params[p]['climvar']][0,wlat,wlon])\n now_box_total = np.nanmean(data[params[p]['var']][0,wlat,wlon])\n\n # Replace fill values with NaN \n # Otherwise differencing might give wild results? (Just be safe)\n wfillclim = np.where(climdata[params[p]['climvar']][0,:,:] == climdata[params[p]['climvar']]._FillValue)\n climfield = climdata[params[p]['climvar']][0,:,:]\n climfield[wfillclim] = np.nan\n wfillnow = np.where(data[params[p]['var']][0,:,:] == data[params[p]['var']]._FillValue)\n nowfield = data[params[p]['var']][0,:,:]\n nowfield[wfillnow] = np.nan\n\n # And finally, difference current month and long-term mean \n box_totals.append(now_box_total - clim_box_total)\n month_field.append(nowfield - climfield)\n climdata.close()\n else:\n if 'LPJ' in p:\n box_totals.append(np.nansum(data[params[p]['var']][0,wlat,wlon]))\n elif 'MERRA' in p:\n box_totals.append(np.nanmean(data[params[p]['var']][0,wlat,wlon]))\n # Replace fill values with NaN (otherwise maps are hard to read) \n month_field.append(data[params[p]['var']][0,:,:])\n wfill = np.where(month_field[-1] == data[params[p]['var']]._FillValue)\n month_field[-1][wfill] = np.nan\n #breakpoint()\n\n # Sort in case months are out of order\n dti = np.argsort(dt)\n month_labels = np.array(month_labels)[dti]\n box_totals = np.array(box_totals)[dti]\n month_field = np.array(month_field)[dti]\n\n # print('mean ',np.nanmean(month_field))\n # print('std ',np.nanstd(month_field))\n\n data_return = {\n 'month_labels':month_labels,\n 'box_totals':box_totals,\n 'month_fields':month_field,\n 'units':data[params[p]['var']].units,\n 'lat':data['lat'][:],\n 'lon':data['lon'][:],\n 'mean':np.nanmean(month_field),\n 'std' : np.nanstd(month_field)\n }\n data.close()\n return data_return \n\n\ncmap = plt.get_cmap('gnuplot') \ncolors = cmap(np.linspace(0,1,len(param)))\nfor i,p in enumerate(param):\n \n ts = get_merra2_timeseries(year,focus,p,anomaly)\n print(f'Mean of variable {p} is {ts[\"mean\"]}')\n print(f'Standard deviation of variable {p} is {ts[\"std\"]}')\n \n # if i == 0:\n fig= plt.figure(figsize=(6,3))\n ax = fig.add_subplot(1,1,1)\n\n #breakpoint()\n try:\n ax.plot(\n list(range(0,12)),\n ts['box_totals'],\n linestyle='-',\n linewidth=2,\n color=colors[i],\n markersize=4,\n marker='o',\n label=p\n )\n except ValueError:\n print('Double check that you have all twelve months of MERRA-2 data downloaded!')\n print(params[p]['dir'])\n breakpoint()\n\n # Construct plot title\n title = '%s\\n%s Mean Monthly %s'%(focus,year,p)\n if anomaly:\n title+=' Anomaly' \n if 'LPJ' in p:\n title = title.replace('Mean','Total')\n plt.title(title)\n \n plt.xticks(list(range(0,12)))\n ax.set_xticklabels(ts['month_labels'],rotation=40,ha='right')\n\n\n ax.legend(loc='best')\n nickname = params[p]['nickname']\n savename = '%s/box_summed_%s_%s_%s.png'% \\\n (savedir,nickname,year,focus)\n if anomaly:\n ax.plot(list(range(-1,13)),np.zeros(14),linewidth=0.4)\n savename = savename.replace('.png','_Anomaly.png')\n ax.set_xlim(-1,12)\n ax.set_ylim(min(ts['box_totals']),max(ts['box_totals'])) # manual per parameter\n # print('Saving to '+savename)\n plt.show()\n plt.savefig('saved_files/'+savename.split('/')[-1],dpi=300,bbox_inches='tight')\n\nMean of variable MERRA-2 Precipitation Rate is -7.973682727424602e-07\nStandard deviation of variable MERRA-2 Precipitation Rate is 1.885925667011179e-05\nMean of variable MERRA-2 Surface Soil Moisture is -0.00156168092507869\nStandard deviation of variable MERRA-2 Surface Soil Moisture is 0.0329766683280468\nMean of variable MERRA-2 T2M is -0.08452282100915909\nStandard deviation of variable MERRA-2 T2M is 1.4482206106185913\n\n\n\n\n\n<Figure size 640x480 with 0 Axes>\n\n\n\n\n\n<Figure size 640x480 with 0 Axes>\n\n\n\n\n\n<Figure size 640x480 with 0 Axes>" + }, + { + "objectID": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#summary", + "href": "interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html#summary", + "title": "Wetland Methane Emissions, LPJ-wsl Model", + "section": "Summary", + "text": "Summary\nIn this notebook, we have successfully explored, analyzed, and visualized the STAC collection for wetland methane emissions and MERRA-2 data." + }, + { + "objectID": "speakers.html", + "href": "speakers.html", + "title": "Speakers", + "section": "", + "text": "Argie Kavvada (HQ)\nShanna Combley (HQ)\nLesley Ott (GSFC)\nEmily Bell (GSFC)\n\n\n\n\n\nErin McDuffie\n\n\n\n\nTBD\n\n\n\nTBD\n\n\n\n\nKevin Bowman\nRob Green\n\n\n\n\n\nDeborah Smith\nSlesa Adhikari\nSiddharth Chaudhary" + }, + { + "objectID": "speakers.html#national-aeronautics-and-space-administration-nasa", + "href": "speakers.html#national-aeronautics-and-space-administration-nasa", + "title": "Speakers", + "section": "", + "text": "Argie Kavvada (HQ)\nShanna Combley (HQ)\nLesley Ott (GSFC)\nEmily Bell (GSFC)" + }, + { + "objectID": "speakers.html#environmental-protection-agency-epa", + "href": "speakers.html#environmental-protection-agency-epa", + "title": "Speakers", + "section": "", + "text": "Erin McDuffie" + }, + { + "objectID": "speakers.html#national-institute-for-standards-and-technology-nist", + "href": "speakers.html#national-institute-for-standards-and-technology-nist", + "title": "Speakers", + "section": "", + "text": "TBD" + }, + { + "objectID": "speakers.html#national-oceanic-and-atmospheric-administration-noaa", + "href": "speakers.html#national-oceanic-and-atmospheric-administration-noaa", + "title": "Speakers", + "section": "", + "text": "TBD" + }, + { + "objectID": "speakers.html#jet-propulsion-laboratory-jpl", + "href": "speakers.html#jet-propulsion-laboratory-jpl", + "title": "Speakers", + "section": "", + "text": "Kevin Bowman\nRob Green" + }, + { + "objectID": "speakers.html#university-of-alabama-in-huntsville-uah", + "href": "speakers.html#university-of-alabama-in-huntsville-uah", + "title": "Speakers", + "section": "", + "text": "Deborah Smith\nSlesa Adhikari\nSiddharth Chaudhary" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html", + "title": "About the Data", + "section": "", + "text": "The Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) is a high-spatial resolution global emission data product of CO₂ emissions from fossil fuel combustion (Oda and Maksyutov, 2011). ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. With the innovative emission modeling approach, ODIAC achieved the fine picture of global fossil fuel CO₂ emissions at a 1x1km." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#requirements", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#requirements", + "title": "About the Data", + "section": "Requirements", + "text": "Requirements\n\nNASA Earthdata Account\nSet up Python Environment - See setup_instructions.md in the ‘/setup/’ folder" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#learning-objectives", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#learning-objectives", + "title": "About the Data", + "section": "Learning Objectives", + "text": "Learning Objectives\n\nHow to use U.S.GHG Center STAC Catalogto access ‘ODIAC Fossil Fuel CO₂ Emissions’ and OCO-2 GEOS Column CO₂ Concentrations data.\nHow to use VEDA STAC Catalog to access NO₂ Emissions data.\nHow to visualize datasets using folium and perfom zonal statistics over South Africa.\nHow to plot time series plot for ODIAC and analyze the results." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#approach", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#approach", + "title": "About the Data", + "section": "Approach", + "text": "Approach\n\nIdentify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. Collection processed in this notebook is ODIAC CO₂ emissions version 2022.\nPass the STAC item into raster API /stac/tilejson.json endpoint\nWe’ll visualize two tiles (side-by-side) allowing for comparison of each of the time points using folium.plugins.DualMap\nAfter the visualization, we’ll perform zonal statistics for a given polygon." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#setup", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#setup", + "title": "About the Data", + "section": "Setup", + "text": "Setup\nImport the required Python libraries.\n\n# Import required libraries\n\nimport earthaccess\nimport warnings\nimport requests\nimport pandas as pd\nimport geopandas\nimport folium\nimport folium.plugins\nimport seaborn as sns\nimport glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport branca.colormap as cm\n\nfrom folium import Map, TileLayer \nfrom branca.element import Figure\nfrom pystac_client import Client \nfrom pyproj import Geod\nfrom shapely import wkt\nfrom datetime import datetime\nfrom folium.plugins import MousePosition" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#querying-the-stac-api", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#querying-the-stac-api", + "title": "About the Data", + "section": "Querying the STAC API", + "text": "Querying the STAC API\nSearch for ODIAC Fossil Fuel Co2 Emissions, OCO-2 and NO2 Emissions dataset\n\n# Provide STAC and RASTER API endpoints\nSTAC_API_URL = \"http://ghg.center/api/stac\"\nRASTER_API_URL = \"https://ghg.center/api/raster\"\n\nSTAC_API_URL_veda = \"https://staging-stac.delta-backend.com\"\nRASTER_API_URL_veda = \"https://staging-raster.delta-backend.com\"\n\n#Please use the collection name similar to the one used in STAC collection.\n# Name of the collection for ODIAC dataset. \ncollection_name_odiac = \"odiac-ffco2-monthgrid-v2022\"\ncollection_name_oco = \"oco2geos-co2-daygrid-v10r\"\ncollection_name_no2 = \"no2-monthly\"\n\n\n# Fetching the collection from STAC collections using appropriate endpoint.\ncollection_odiac = requests.get(f\"{STAC_API_URL}/collections/{collection_name_odiac}\").json()\ncollection_odiac\n\ncollection_oco2 = requests.get(f\"{STAC_API_URL}/collections/{collection_name_oco}\").json()\ncollection_oco2\n\ncollection_no2 = requests.get(f\"{STAC_API_URL_veda}/collections/{collection_name_no2}\").json()\ncollection_no2\n\nExamining the contents of our collection under summaries we see that the data is available from January 2000 to December 2021. By looking at the dashboard:time density we observe that the periodic frequency of these observations is monthly.\n\ndef get_item_count(STAC_API_URL, collection_id):\n count = 0\n items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n\n while True:\n response = requests.get(items_url)\n\n if not response.ok:\n print(\"error getting items\")\n exit()\n\n stac = response.json()\n count += int(stac[\"context\"].get(\"returned\", 0))\n next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n\n if not next:\n break\n items_url = next[0][\"href\"]\n\n return count\n\n\n# Check total number of items available\nnumber_of_items_odiac = get_item_count(STAC_API_URL,collection_name_odiac)\nitems_odiac = requests.get(f\"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items_odiac}\").json()[\"features\"]\nprint(f\"Found {len(items_odiac)} odiac items\")\n\nnumber_of_items_oco2 = get_item_count(STAC_API_URL,collection_name_oco)\nitems_oco2 = requests.get(f\"{STAC_API_URL}/collections/{collection_name_oco}/items?limit={number_of_items_oco2}\").json()[\"features\"]\nprint(f\"Found {len(items_oco2)} oco2 items\")\n\nnumber_of_items_no2 = get_item_count(STAC_API_URL_veda,collection_name_no2)\nitems_no2 = requests.get(f\"{STAC_API_URL_veda}/collections/{collection_name_no2}/items?limit={number_of_items_no2}\").json()[\"features\"]\nprint(f\"Found {len(items_no2)} no2 items\")\n\nThis makes sense as there are 22 years between 2000 - 2021, with 12 months per year, meaning 264 records in total.\nBelow, we are entering the minimum and maximum values to provide our upper and lower bounds in rescale_values." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#exploring-changes-in-carbon-dioxide-co₂-levels-using-the-raster-api", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#exploring-changes-in-carbon-dioxide-co₂-levels-using-the-raster-api", + "title": "About the Data", + "section": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API", + "text": "Exploring Changes in Carbon Dioxide (CO₂) levels using the Raster API\nWe will explore changes in fossil fuel emissions in urban egions. In this notebook, we’ll explore the impacts of these emissions and explore these changes over time. We’ll then visualize the outputs on a map using folium.\n\n# to access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)\nitems_odiac = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items_odiac} \nasset_name = \"co2-emissions\"\n\nitems_oco2 = {item[\"properties\"][\"datetime\"][:10]: item for item in items_oco2} \nasset_name1 = \"xco2\"\n\nitems_no2 = {item[\"properties\"][\"start_datetime\"][:10]: item for item in items_no2}\n\n\nrescale_values_odiac = {\"max\":items_odiac[list(items_odiac.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"max\"], \"min\":items_odiac[list(items_odiac.keys())[0]][\"assets\"][asset_name][\"raster:bands\"][0][\"histogram\"][\"min\"]}\nrescale_values_oco2 = {'max':415 , 'min': 412}\nrescale_values_no2 = {'max': 9050373673124971, 'min': 0}\n\nNow we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this twice, once for January 2020 and again for January 2000, so that we can visualize each event independently." + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#opening-and-exploring-data-using-the-raster-api", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#opening-and-exploring-data-using-the-raster-api", + "title": "About the Data", + "section": "Opening and Exploring Data Using the Raster API", + "text": "Opening and Exploring Data Using the Raster API\nIn this notebook, we will explore the temporal impacts of methane emissions. We will visualize the outputs on a map using ‘folium’.\n\ncolor_map = \"rainbow\" # please select the color ramp from matplotlib library.\njanuary_2020_tile = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items_odiac['2020-01']['collection']}&item={items_odiac['2020-01']['id']}\"\n f\"&assets={asset_name}\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n f\"&rescale={rescale_values_odiac['min']},{rescale_values_odiac['max']}\", \n).json()\njanuary_2020_tile\n\ncolor_map1 = \"magma\"\noco2_1 = requests.get(\n f\"{RASTER_API_URL}/stac/tilejson.json?collection={items_oco2['2020-01-20']['collection']}&item={items_oco2['2020-01-20']['id']}\"\n f\"&assets={asset_name1}\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map1}\"\n f\"&rescale={rescale_values_oco2['min']},{rescale_values_oco2['max']}\", \n).json()\noco2_1\n\nno2_1 = requests.get(\n f\"{RASTER_API_URL_veda}/stac/tilejson.json?collection={items_no2['2020-01-01']['collection']}&item={items_no2['2020-01-01']['id']}\"\n f\"&assets=cog_default\"\n f\"&color_formula=gamma+r+1.05&colormap_name={color_map1}\"\n f\"&rescale={rescale_values_no2['min']},{rescale_values_no2['max']}\", \n).json()\nno2_1" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#define-spatial-region-of-interest", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#define-spatial-region-of-interest", + "title": "About the Data", + "section": "Define Spatial Region of Interest", + "text": "Define Spatial Region of Interest\nFor this example, our spatial region of interest (ROI) will be the a region in South Africa.\n\nsa_aoi = {\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.519052777398997,\n -24.8470086420499\n ],\n [\n 25.519052777398997,\n -28.145634397543844\n ],\n [\n 30.29637465013832,\n -28.145634397543844\n ],\n [\n 30.29637465013832,\n -24.8470086420499\n ],\n [\n 25.519052777398997,\n -24.8470086420499\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-co₂-emissions", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-co₂-emissions", + "title": "About the Data", + "section": "Visualizing CO₂ emissions", + "text": "Visualizing CO₂ emissions\n\n\n\nmap_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)\n\nmap_layer_2020 = TileLayer(\n tiles=january_2020_tile[\"tiles\"][0],\n attr=\"GHG\",\n name =\"ODIAC\",\n opacity=0.5,\n)\nmap_layer_2020_2 = TileLayer(\n tiles=no2_1[\"tiles\"][0],\n attr=\"GHG\",\n name = \"NO2\",\n opacity=0.5,\n)\nsbs = folium.plugins.SideBySideLayers(layer_left=map_layer_2020, layer_right=map_layer_2020_2)\nmap_layer_2020.add_to(map_)\nmap_layer_2020_2.add_to(map_)\nfolium.GeoJson(sa_aoi, name=\"louisiana, USA\").add_to(map_)\nsbs.add_to(map_)\nMousePosition().add_to(map_)\n# visualising the map\nmap_\n\n\n\n# Set initial zoom and center of map\nmap_ = folium.Map(location=(-25.943840, 29.789560), zoom_start=7)\n\n# December 2001\nmap_layer_2020_odiac = TileLayer(\n tiles=january_2020_tile[\"tiles\"][0],\n attr=\"GHG\",\n name =\"ODIAC\",\n opacity=0.5,\n)\nmap_layer_2020_odiac.add_to(map_)\n\nmap_layer_2020_oco2 = TileLayer(\n tiles=oco2_1[\"tiles\"][0],\n attr=\"GHG\",\n name = \"OCO2\",\n opacity=0.5,\n)\nmap_layer_2020_oco2.add_to(map_)\n\nmap_layer_2020_no2 = TileLayer(\n tiles=no2_1[\"tiles\"][0],\n attr=\"GHG\",\n name = \"NO2\",\n opacity=0.5,\n)\nmap_layer_2020_no2.add_to(map_)\n\nfolium.GeoJson(sa_aoi, name=\"South Africa\").add_to(map_)\nfolium.LayerControl(collapsed=False,position='bottomleft').add_to(map_)\n\n# visualising the map\nmap_" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#generate-statistics-and-time-series-lineplots-for-co2-emission", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#generate-statistics-and-time-series-lineplots-for-co2-emission", + "title": "About the Data", + "section": "Generate Statistics and Time Series Lineplots for CO2 Emission", + "text": "Generate Statistics and Time Series Lineplots for CO2 Emission\n\n# Check total number of items available\nitems = requests.get(\n f\"{STAC_API_URL}/collections/{collection_name_odiac}/items?limit={number_of_items}\"\n).json()[\"features\"]\nprint(f\"Found {len(items)} items\")\n\n\n# Explore one item to see what it contains\nitems\n\n\n# the bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\ndef generate_stats(item, geojson):\n result = requests.post(\n f\"{RASTER_API_URL}/cog/statistics\",\n params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n json=geojson,\n ).json()['features']\n return {\n **result[0][\"properties\"],\n \"start_datetime\": item[\"properties\"][\"start_datetime\"][:7],\n }\n\nWith the function above we can generate the statistics for the AOI.\n\n%%time\nstats = [generate_stats(item,sa_aoi) for item in items]\n\n\nstats[0]\n\n\ndef clean_stats(stats_json) -> pd.DataFrame:\n df = pd.json_normalize(stats_json)\n df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n df[\"date\"] = pd.to_datetime(df[\"start_datetime\"])\n return df\n\ndf = clean_stats(stats)\ndf.head(5)" + }, + { + "objectID": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-the-data-as-a-time-series", + "href": "interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html#visualizing-the-data-as-a-time-series", + "title": "About the Data", + "section": "Visualizing the Data as a Time Series", + "text": "Visualizing the Data as a Time Series\nWe can now explore the ODIAC fossil fuel emission time series available (January 2000 -December 2021) for the Texas, Dallas area of USA. We can plot the data set using the code below:\n\nimport matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(20, 10))\n\n\nplt.plot(\n df[\"date\"],\n df[\"max\"],\n color=\"red\",\n linestyle=\"-\",\n linewidth=0.5,\n label=\"Max monthly CO₂ emissions\",\n)\n\nplt.legend()\nplt.xlabel(\"Years\")\nplt.ylabel(\"CO2 emissions gC/m2/d\")\nplt.title(\"CO2 emission Values for South Africa (2000-2021)\")" + }, + { + "objectID": "interactive-session-3-large-point-source-methane-emission-events/index.html", + "href": "interactive-session-3-large-point-source-methane-emission-events/index.html", + "title": "Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data", + "section": "", + "text": "Identifying and quantifying emissions from large point source methane emission events leveraging aircraft and satellite data" + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "Utilizing Open Science Techniques for Exploring Greenhouse Gas Data", + "section": "", + "text": "📅 Jan 28, 2024, 🕣 8:00 AM - 3:45 PM EST, 📍 Baltimore Convention Center\n🔗 Session on the AMS Website" + }, + { + "objectID": "index.html#description", + "href": "index.html#description", + "title": "Utilizing Open Science Techniques for Exploring Greenhouse Gas Data", + "section": "Description", + "text": "Description\nThis workshop provides a hands-on learning experience for researchers, educators, and students who are eager to harness NASA’s key greenhouse gas datasets, open-source tools, and accessible computing resources for visualizing, exploring, publishing, and communicating scientific results. Participants will learn about and utilize the new U.S. Greenhouse Gas Center (GHG Center) to examine curated, trusted data in a cloud computing environment. The activities will center around data and information aligned with three science use cases to ensure broad applicability. Participants will learn to access analysis workflows using cloud-optimized data, access data products via a standards-based catalog, configure data products for web-based visualizations, and communicate scientific discoveries through a web dashboard.\nThe workshop will provide an overview of the newly released U.S. Greenhouse Gas Center, a prototype system that aims to integrate federal and non-federal data, reflecting transparency in both data and methods, to address a variety of end user needs for greenhouse gas monitoring and to bring together actionable information from multiple sources. Examples of products that will be part of this training include methane concentration anomalies from space-based instruments (e.g., EMIT), data on natural emissions and fluxes available from the U.S. GHG Center, as well as gridding processes of anthropogenic methane emissions." + }, + { + "objectID": "index.html#learning-outcomes", + "href": "index.html#learning-outcomes", + "title": "Utilizing Open Science Techniques for Exploring Greenhouse Gas Data", + "section": "Learning Outcomes", + "text": "Learning Outcomes\nUpon completing this workshop, participants will be able to:\n\nUnderstand the scientific basis of the GHG products from the newly formed U.S. Greenhouse Gas Center.\nEmploy Python and Jupyter Notebooks for effective data analysis and visualization of Earth science data in the cloud.\nDisseminate and share scientific findings to achieve a wider impact.\nUnderstand basic principles for contributing to open-sourced software projects." + }, + { + "objectID": "index.html#target-audience", + "href": "index.html#target-audience", + "title": "Utilizing Open Science Techniques for Exploring Greenhouse Gas Data", + "section": "Target Audience", + "text": "Target Audience\nThe target audience for this workshop includes Earth Science researchers, educators, and students across all career stages and disciplines who are interested in utilizing GHG datasets and open-source tools for their research projects. 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this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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--- /dev/null +++ b/pr-preview/pr-5/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/pr-preview/pr-5/site_libs/quarto-html/tippy.css b/pr-preview/pr-5/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/pr-preview/pr-5/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/pr-preview/pr-5/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 0000000..3b21201 --- /dev/null +++ b/pr-preview/pr-5/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/pr-preview/pr-5/site_libs/quarto-search/autocomplete.umd.js b/pr-preview/pr-5/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..619c57c --- /dev/null +++ b/pr-preview/pr-5/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete 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a/pr-preview/pr-5/site_libs/quarto-search/quarto-search.js b/pr-preview/pr-5/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..f5d852d --- /dev/null +++ b/pr-preview/pr-5/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} diff --git a/pr-preview/pr-5/sitemap.xml b/pr-preview/pr-5/sitemap.xml new file mode 100644 index 0000000..06bf530 --- /dev/null +++ b/pr-preview/pr-5/sitemap.xml @@ -0,0 +1,39 @@ + + + + https://us-ghg-center.github.io/ams-2024-workshop/ghg-center-at-ams-2024.html + 2024-01-22T18:10:17.489Z + + + https://us-ghg-center.github.io/ams-2024-workshop/interactive-session-1-epa-gridded-methane-emissions/epa-ch4emission-grid-v2express_User_Notebook.html + 2024-01-22T18:10:15.765Z + + + https://us-ghg-center.github.io/ams-2024-workshop/prerequisites.html + 2024-01-22T18:10:13.037Z + + + https://us-ghg-center.github.io/ams-2024-workshop/agenda.html + 2024-01-22T18:10:12.281Z + + + https://us-ghg-center.github.io/ams-2024-workshop/chapters.html + 2024-01-22T18:10:13.293Z + + + https://us-ghg-center.github.io/ams-2024-workshop/interactive-session-2-anthropogenic-ghg-emissions/lpjwsl-wetlandch4-grid-v1_User_Notebook.html + 2024-01-22T18:10:14.889Z + + + https://us-ghg-center.github.io/ams-2024-workshop/speakers.html + 2024-01-22T18:10:15.353Z + + + https://us-ghg-center.github.io/ams-2024-workshop/interactive-session-1-epa-gridded-methane-emissions/odiac-ffco2-monthgrid-v2022_User_Notebook.html + 2024-01-22T18:10:16.189Z + + + https://us-ghg-center.github.io/ams-2024-workshop/index.html + 2024-01-22T18:10:17.165Z + + diff --git a/pr-preview/pr-5/speakers.html b/pr-preview/pr-5/speakers.html new file mode 100644 index 0000000..12f33fd --- /dev/null +++ b/pr-preview/pr-5/speakers.html @@ -0,0 +1,466 @@ + + + + + + + + + +U.S. Greenhouse Gas Center - AMS 2024 Workshop – speakers + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

Speakers

+
+

National Aeronautics and Space Administration (NASA):

+
    +
  • Argie Kavvada (HQ)
    • +
  • Shanna Combley (HQ)
    • +
  • Lesley Ott (GSFC)
    • +
  • Emily Bell (GSFC)
    • +
+
+
+

Environmental Protection Agency (EPA):

+
    +
  • Erin McDuffie
    • +
+
+
+

National Institute for Standards and Technology (NIST):

+

TBD

+
+
+

National Oceanic and Atmospheric Administration (NOAA):

+

TBD

+
+
+

Jet Propulsion Laboratory (JPL):

+
    +
  • Kevin Bowman
    • +
  • Rob Green
    • +
+
+
+

University of Alabama in Huntsville (UAH):

+
    +
  • Deborah Smith
    • +
  • Slesa Adhikari
    • +
  • Siddharth Chaudhary
    • +
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file