From 92f04fb0ead397bfd1eb4154009d1c9c6eb58bbf Mon Sep 17 00:00:00 2001 From: fb0023 <142605787+fb0023@users.noreply.github.com> Date: Fri, 16 Aug 2024 12:51:02 -0500 Subject: [PATCH 01/47] r3 updates for ecco darwin user notebook --- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 52 ++++++++++++++----- 1 file changed, 38 insertions(+), 14 deletions(-) diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 64ccd48c..6c360626 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -2,13 +2,17 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: Air-Sea CO₂ Flux, ECCO-Darwin Model v5\n", + "title: Utilizing the Air-Sea CO₂ Flux ECCO-Darwin Model to Visualize CO₂ Exchange and Dynamics Between the Atmosphere and the Ocean\n", "description: Global, monthly average air-sea CO₂ flux at ~1/3° resolution from 2020 to 2022\n", - "author: Siddharth Chaudhary, Vishal Gaur\n", - "date: 29 August 2023\n", + "author: Siddharth Chaudhary, Vishal Gaur, Farnaz Bayat\n", + "date: 18 September, 2024\n", "execute:\n", " freeze: true\n", "---" @@ -19,12 +23,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run this notebook\n", + "## Access this notebook\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdai8otCdrVQzJgev8mjDhzKyCg7jcrB3UeTXNHoCiaMKrkaQ/viewform) and providing the required information. \n", + "\n", "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", + "Access the [**Air-Sea CO₂ Flux, ECCO-Darwin Model**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Summary and Application\n", + "- **Spatial Coverage**: Global\n", + "- **Spatial resolution**: 1/3°\n", + "- **Temporal extent**: January 2020 - December 2022\n", + "- **Temporal resolution**: Monthly\n", + "- **Unit**: Millimoles of CO₂ per meter squared per second\n", + "- **Utility**: Climate Research, Oceanography, Carbon Stock Monitoring\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main)", - " " + "For more, visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) dataset landing page. " ] }, { @@ -34,10 +52,12 @@ "source": [ "## Approach\n", "\n", - "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 Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint.\n", - "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", - "4. After the visualization, we will perform zonal statistics for a given polygon." + "During this notebook, you will:\n", + "\n", + "1. Identify available dates and temporal frequency of observations for a given collection using the GHGC API `/stac` endpoint\n", + "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint\n", + "3. Using `folium.plugins.DualMap`, you will visualize two tiles (side-by-side), allowing comparison of time points \n", + "4. After the visualization, you will perform zonal statistics for a given polygon" ] }, { @@ -46,9 +66,13 @@ "source": [ "## About the Data\n", "\n", - "The ocean is a major sink for atmospheric carbon dioxide (CO2), largely due to the presence of phytoplankton that use the CO₂ to grow. Studies have shown that global ocean CO₂ uptake has increased over recent decades, however there is uncertainty in the various mechanisms that affect ocean CO₂ flux and storage and how the ocean carbon sink will respond to future climate change. Because CO₂ fluxes can vary significantly across space and time, combined with deficiencies in ocean and atmosphere CO₂ observations, there is a need for models that can thoroughly represent these processes. Ocean biogeochemical models (OBMs) have the ability to resolve the physical and biogeochemical mechanisms contributing to spatial and temporal variations in air-sea CO₂ fluxes but previous OBMs do not integrate observations to improve model accuracy and have not been able to operate on the seasonal and multi-decadal timescales needed to adequately characterize these processes. The ECCO-Darwin model is an OBM that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project. A pilot study using ECCO-Darwin was completed by [Brix et al. (2015)](https://doi.org/10.1016/j.ocemod.2015.07.008) however an improved version of the model was developed by [Carroll et al. (2020)](https://doi.org/10.1029/2019MS001888) in which issues present in the first model were addressed using data assimilation and adjustments were made to initial conditions and biogeochemical parameters. The updated ECCO-Darwin model was compared with interpolation-based products to estimate surface ocean partial pressure (pCO2) and air-sea CO₂ flux. This dataset contains the gridded global, monthly mean air-sea CO₂ fluxes from version 5 of the ECCO-Darwin model. The data are available at ~1/3° horizontal resolution at the equator (~18 km at high latitudes) from January 2020 through December 2022.\n", + "The ocean is a major sink for atmospheric carbon dioxide (CO₂), largely due to the presence of phytoplankton that use the CO₂ to grow. Studies have shown that global ocean CO₂ uptake has increased over recent decades, however there is uncertainty in the various mechanisms that affect ocean CO₂ flux and storage and how the ocean carbon sink will respond to future climate change. \n", + "\n", + "Because CO₂ fluxes can vary significantly across space and time, combined with deficiencies in ocean and atmosphere CO₂ observations, there is a need for models that can thoroughly represent these processes. Ocean biogeochemical models (OBMs) have the ability to resolve the physical and biogeochemical mechanisms contributing to spatial and temporal variations in air-sea CO₂ fluxes but previous OBMs do not integrate observations to improve model accuracy and have not been able to operate on the seasonal and multi-decadal timescales needed to adequately characterize these processes. \n", + "\n", + "The ECCO-Darwin model is an OBM that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project. A pilot study using ECCO-Darwin was completed by [Brix et al. (2015)](https://doi.org/10.1016/j.ocemod.2015.07.008) however an improved version of the model was developed by [Carroll et al. (2020)](https://doi.org/10.1029/2019MS001888) in which issues present in the first model were addressed using data assimilation and adjustments were made to initial conditions and biogeochemical parameters. The updated ECCO-Darwin model was compared with interpolation-based products to estimate surface ocean partial pressure (pCO2) and air-sea CO₂ flux. This dataset contains the gridded global, monthly mean air-sea CO₂ fluxes from version 5 of the ECCO-Darwin model. \n", "\n", - "For more information regarding this dataset, please visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) data overview page." + "The data available in the US GHG Center hub are available at ~1/3° horizontal resolution at the equator (~18 km at high latitudes) from January 2020 through December 2022. For more information regarding this dataset, please visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) data overview page." ] }, { From f095a3439a69385cae477859cd8fecd748db0ffe Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 30 Aug 2024 10:22:27 -0500 Subject: [PATCH 02/47] Add transformation notebook --- .../vulcan-ffco2-yeargrid-v4.ipynb | 464 ++++++++++++++++++ 1 file changed, 464 insertions(+) create mode 100644 cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb new file mode 100644 index 00000000..3a0547a8 --- /dev/null +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -0,0 +1,464 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "c2cb82e3-0dc2-43c8-b63f-f59fdec6bd16", + "metadata": {}, + "source": [ + "---\n", + "title: Vulcan Fossil Fuel CO₂ Emissions v4.0\n", + "description: Documentation of data transformation & Validation\n", + "author: Paridhi Parajuli \n", + "date: August 20, 2023\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "456658d4-e4a8-472e-b69c-74754c3016ac", + "metadata": {}, + "source": [ + "This script was used to transform the VULCAN dataset provided in Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center with the calaulation of validation statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb4cc6fa-0591-4a20-9abf-f926c0f33941", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray\n", + "import pandas as pd\n", + "import boto3\n", + "import glob\n", + "import s3fs\n", + "import tempfile\n", + "from datetime import datetime\n", + "import os\n", + "import boto3\n", + "from pyproj import CRS\n", + "import numpy as np\n", + "\n", + "import rasterio\n", + "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", + "from rasterio.enums import Resampling\n", + "from rio_cogeo.cogeo import cog_translate\n", + "from rio_cogeo.profiles import cog_profiles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "892cc2b7-3339-4789-b42f-7f744c285e5a", + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"data_acquisition_method\": \"s3\",\n", + " \"raw_data_bucket\" : \"gsfc-ghg-store\",\n", + " \"raw_data_prefix\": \"Vulcan/v4.0/grid.1km.mn\",\n", + " \"cog_data_bucket\": \"ghgc-data-store-develop\",\n", + " \"cog_data_prefix\": \"transformed_cogs/VULCAN_v4\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbadf144-5485-4a47-a4e9-c37b717c0dd8", + "metadata": {}, + "outputs": [], + "source": [ + "session = boto3.session.Session()\n", + "s3_client = session.client(\"s3\")\n", + "\n", + "raw_data_bucket = config[\"raw_data_bucket\"]\n", + "raw_data_prefix= config[\"raw_data_prefix\"]\n", + "\n", + "cog_data_bucket = config['cog_data_bucket']\n", + "cog_data_prefix= config[\"cog_data_prefix\"]\n", + "\n", + "date_fmt=config['date_fmt']\n", + "\n", + "fs = s3fs.S3FileSystem()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "031b52b7-6716-4ae5-9fcb-5b82d301b7af", + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_s3_keys(bucket, model_name, ext):\n", + " \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n", + " keys = []\n", + "\n", + " kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n", + " while True:\n", + " resp = s3_client.list_objects_v2(**kwargs)\n", + " for obj in resp[\"Contents\"]:\n", + " if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n", + " keys.append(obj[\"Key\"])\n", + "\n", + " try:\n", + " kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n", + " except KeyError:\n", + " break\n", + "\n", + " return keys\n", + "\n", + "keys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdaa47f3-26e6-4fe3-85fd-034646d61d1f", + "metadata": {}, + "outputs": [], + "source": [ + "len(keys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe873e16-86b5-4904-aacb-a2c0b24bd786", + "metadata": {}, + "outputs": [], + "source": [ + "# To calculate the validation stats\n", + "overall= pd.DataFrame(columns=[\"data\",\"min\",\"max\",\"mean\",\"std\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ac0b8c3-ebc0-43d6-8cb1-473d4accde32", + "metadata": {}, + "outputs": [], + "source": [ + "# Step 1: Reproject the data \n", + "# Define the source and target CRS\n", + "# Also calculate raw - monthly validation stats\n", + "os.makedirs(\"reproj\", exist_ok=True)\n", + "src_crs = CRS.from_wkt('PROJCS[\"unknown\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Lambert_Conformal_Conic_2SP\"],PARAMETER[\"latitude_of_origin\",40],PARAMETER[\"central_meridian\",-97],PARAMETER[\"standard_parallel_1\",33],PARAMETER[\"standard_parallel_2\",45],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]')\n", + "dst_crs = CRS.from_epsg(4326) # WGS 84\n", + "df = pd.DataFrame(columns=['filename', 'min(raw)', 'max(raw)', 'mean(raw)', 'std(raw)'])\n", + "overall_raw= []\n", + "for key in keys:\n", + " url = f\"s3://{raw_data_bucket}/{key}\"\n", + " with rasterio.open(url) as src:\n", + " filename_elements = key.split(\"/\")[-1].split(\".\")[:-1]\n", + " output_tif = \"_\".join(filename_elements) + \".tif\"\n", + " data = src.read(1) # Read the first band\n", + " overall_raw.append(data)\n", + " \n", + " # Calculate statistics while ignoring NaN values\n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " std_val = np.nanstd(data) \n", + " stats = [output_tif, min_val, max_val, mean_val, std_val]\n", + " df.loc[len(df)] = stats\n", + " \n", + " transform, width, height = calculate_default_transform(\n", + " src.crs, dst_crs, src.width, src.height, *src.bounds)\n", + " kwargs = src.meta.copy()\n", + " kwargs.update({\n", + " 'crs': dst_crs,\n", + " 'transform': transform,\n", + " 'width': width,\n", + " 'height': height,\n", + " 'nodata': -9999\n", + " })\n", + "\n", + " with rasterio.open(f\"reproj/{output_tif}\", 'w', **kwargs) as dst:\n", + " for i in range(1, src.count + 1):\n", + " reproject(\n", + " source=rasterio.band(src, i),\n", + " destination=rasterio.band(dst, i),\n", + " src_transform=src.transform,\n", + " src_crs=src.crs,\n", + " dst_transform=transform,\n", + " dst_crs=dst_crs,\n", + " resampling=Resampling.nearest)\n", + " print(f\"Done for {output_tif}\")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e33b27b9-906b-4f58-aaf9-5fd97a6778c7", + "metadata": {}, + "outputs": [], + "source": [ + "# overall validation of raw data\n", + "overall_raw= np.array(overall_raw)\n", + "nan_min = np.nanmin(overall_raw)\n", + "nan_max = np.nanmax(overall_raw)\n", + "nan_mean = np.nanmean(overall_raw)\n", + "nan_std = np.nanstd(overall_raw)\n", + "overall.loc[len(overall)] = [\"raw\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db56a809-0f19-4e8a-b2f6-c0923069b8dc", + "metadata": {}, + "outputs": [], + "source": [ + "# validation for reprojected data - yearly calculation\n", + "overall_reproj = []\n", + "files = glob.glob(\"reproj/*.tif\")\n", + "df1 = pd.DataFrame(columns=['filename', 'min(reprojected)', 'max(reprojected)', 'mean(reprojected)', 'std(reprojected)'])\n", + "for file in files:\n", + " with rasterio.open(file) as src:\n", + " filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n", + " output_tif = \"_\".join(filename_elements) + \".tif\"\n", + " data = src.read(1) \n", + " data = np.ma.masked_equal(data, -9999)\n", + " overall_reproj.append(data)\n", + " \n", + " # Calculate statistics while ignoring NaN values\n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " std_val = np.nanstd(data) \n", + " stats = [output_tif, min_val, max_val, mean_val, std_val]\n", + " df1.loc[len(df1)] = stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1e53c3c-53d7-46a2-bff0-9bdde717be84", + "metadata": {}, + "outputs": [], + "source": [ + "# overall validation of reprojected data\n", + "overall_reproj= np.array(overall_reproj)\n", + "overall_reproj = np.ma.masked_equal(overall_reproj, -9999)\n", + "nan_min = np.nanmin(overall_reproj)\n", + "nan_max = np.nanmax(overall_reproj)\n", + "nan_mean = np.nanmean(overall_reproj)\n", + "nan_std = np.nanstd(overall_reproj)\n", + "overall.loc[len(overall)] = [\"reprojected\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1a2be30-69b5-4300-b2a0-50f7c4ba6ff4", + "metadata": {}, + "outputs": [], + "source": [ + "# Step 2: Replace nan and 0 values with -9999\n", + "os.makedirs(\"reproj2\", exist_ok=True)\n", + "files = glob.glob(\"reproj/*.tif\")\n", + "for file in files:\n", + " filename = file.split('/')[-1]\n", + " xda = xarray.open_dataarray(file).sel(band=1)\n", + " \n", + " data = xda.where(xda != 0, -9999) # Replace 0 with -9999\n", + " #data = data.where(data != -3.4e+38, -9999) # Replace -3.4e+38 with -9999\n", + " data = data.fillna(-9999) # Ensure all NaNs are replaced with -9999\n", + " data_array = data.values\n", + "\n", + " # Open the source raster to get metadata\n", + " with rasterio.open(file) as src:\n", + " meta = src.meta\n", + " meta.update({\n", + " 'nodata': -9999,\n", + " 'dtype': 'float32',\n", + " 'driver': 'COG'\n", + " })\n", + " with rasterio.open(f\"reproj2/{filename}\", 'w', **meta) as dst:\n", + " dst.write(data_array, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e806d44f-34a3-4acc-a3df-3afb4b92e007", + "metadata": {}, + "outputs": [], + "source": [ + "# validation for reprojected data (non zero) - monthly calculation\n", + "overall_reproj2=[]\n", + "files = glob.glob(\"reproj/*.tif\")\n", + "df11 = pd.DataFrame(columns=['filename', 'min(reproj_nonzero)', 'max(reproj_nonzero)', 'mean(reproj_nonzero)', 'std(reproj_nonzero)'])\n", + "for file in files:\n", + " with rasterio.open(file) as src:\n", + " filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n", + " output_tif = \"_\".join(filename_elements) + \".tif\"\n", + " data = src.read(1) \n", + " data = np.ma.masked_where((data == -9999) | (data == 0), data)\n", + " overall_reproj2.append(data)\n", + "\n", + " \n", + " # Calculate statistics while ignoring NaN values\n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " std_val = np.nanstd(data) \n", + " stats = [output_tif, min_val, max_val, mean_val, std_val]\n", + " df11.loc[len(df11)] = stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d82e2627-78e6-43a2-9c34-86cd3e043121", + "metadata": {}, + "outputs": [], + "source": [ + "# validation for reprojected data (non zero) - overall calculation\n", + "overall_reproj2= np.array(overall_reproj2)\n", + "overall_reproj2 = np.ma.masked_where((overall_reproj2 == -9999) | (overall_reproj2 == 0), overall_reproj2)\n", + "nan_min = np.nanmin(overall_reproj2)\n", + "nan_max = np.nanmax(overall_reproj2)\n", + "nan_mean = np.nanmean(overall_reproj2)\n", + "nan_std = np.nanstd(overall_reproj2)\n", + "overall.loc[len(overall)] = [\"reprojected_non_zero\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cf75b9f-c330-4d31-985c-ce78c3f77896", + "metadata": {}, + "outputs": [], + "source": [ + "# Step 3: To put overviews\n", + "COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n", + "OVERVIEW_LEVELS = 9\n", + "OVERVIEW_RESAMPLING = 'average'\n", + "\n", + "for file in glob.glob(\"reproj2/*.tif\"):\n", + " output_path = f\"output/{file.split(\"/\")[-1]}\"\n", + " \n", + " # Create a temporary file to hold the COG\n", + " with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file: \n", + " # Create COG with overviews and nodata value\n", + " cog_translate(\n", + " file,\n", + " temp_file.name,\n", + " cog_profiles.get(\"deflate\"),\n", + " overview_level=OVERVIEW_LEVELS,\n", + " overview_resampling=OVERVIEW_RESAMPLING,\n", + " nodata=-9999\n", + " )\n", + " # Move the temporary file to the desired local path\n", + " os.rename(temp_file.name, output_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4da3a099-bf4c-4210-87a4-ee5934732303", + "metadata": {}, + "outputs": [], + "source": [ + "# validation for final data with overviews - overall calculation\n", + "overall_final=[]\n", + "files = glob.glob(\"output/*.tif\")\n", + "df2 = pd.DataFrame(columns=['filename', 'min(transformed)', 'max(transformed)', 'mean(transformed)', 'std(transformed)'])\n", + "for file in files:\n", + " with rasterio.open(file) as src:\n", + " filename_elements = file.split(\"/\")[-1].split(\".\")[:-1]\n", + " output_tif = \"_\".join(filename_elements) + \".tif\"\n", + " data = src.read(1) # Read the first band\n", + " \n", + " # Mask -9999 values and NaNs for statistics calculation\n", + " data = np.ma.masked_where((data == -9999) | np.isnan(data), data)\n", + " overall_final.append(data)\n", + " \n", + " # Calculate statistics while ignoring NaN values\n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " total = np.nansum(data) \n", + " stats = [output_tif, min_val, max_val, mean_val, std_val]\n", + " df2.loc[len(df2)] = stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad2f7d75-a883-4071-8541-0635fc217f62", + "metadata": {}, + "outputs": [], + "source": [ + "# validation for final data (with overviews) - overall calculation\n", + "overall_final= np.array(overall_final)\n", + "overall_final = np.ma.masked_where((overall_final == -9999) | np.isnan(overall_final), overall_final)\n", + "nan_min = np.nanmin(overall_final)\n", + "nan_max = np.nanmax(overall_final)\n", + "nan_mean = np.nanmean(overall_final)\n", + "nan_std = np.nanstd(overall_final)\n", + "overall.loc[len(overall)] = [\"Transformed\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15d13943-8760-4e48-b6b5-29a880b85c39", + "metadata": {}, + "outputs": [], + "source": [ + "pd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85faf9bf-d4b0-4150-81dc-dfb5035771e8", + "metadata": {}, + "outputs": [], + "source": [ + "overall" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6722588b-b2f0-4556-be2d-ad86b2403334", + "metadata": {}, + "outputs": [], + "source": [ + "# Save to json\n", + "overall.to_json(\"overall_stats.json\")\n", + "pd.merge(pd.merge(df,df1, on='filename', how='inner'), pd.merge(df11,df2, on='filename', how='inner'), how='inner',on='filename' ).to_json(\"yearly_stats.json\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", 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add report qmd --- ...o2-yeargrid-v4_Processing and Verification Report.qmd | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd diff --git a/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd new file mode 100644 index 00000000..185e10f6 --- /dev/null +++ b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: Vulcan Fossil Fuel CO₂ Emissions v4.0 +description: "The Vulcan version 4.0 data product represents total carbon dioxide (CO₂) emissions resulting from the combustion of fossil fuel (FF) for the contiguous United States and District of Columbia. Referred to as FFCO₂, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Included is the estimated total annual FFCO2 for the United States, as well as the estimated total annual FFCO₂ per sector in units of metric tonnes of carbon per 1 km x 1 km grid cell per year (tonne C/km2/year). The Vulcan Project is a multiagency (NASA, DOE, NOAA, NIST) funded effort under the North American Carbon Program (NACP) to quantify North American fossil fuel carbon dioxide (FFCO₂) emissions at space and time scales much finer than has been achieved in the past. The purpose is to aid in quantification of the North American carbon budget, to support inverse estimation of carbon sources and sinks, and to support the demands posed by higher resolution FFCO₂ observations (in situ and remotely sensed). The detail and scope of the Vulcan FFCO₂ inventory has also made it a valuable tool for policymakers, demographers, social scientists and the public at large. Learn more at the Vulcan website: https://vulcan.rc.nau.edu/index.html" +--- + + +

This browser does not support PDFs. Please download the PDF to view it: Download PDF.

+ +
\ No newline at end of file From b2f82eb90289e1753a244c06c9e4787fe891aa81 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 30 Aug 2024 10:38:14 -0500 Subject: [PATCH 05/47] added vulcan transformation and report links --- datatransformationcode.qmd | 1 + processingreport.qmd | 1 + 2 files changed, 2 insertions(+) diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index a9fe5624..ae52014a 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -19,6 +19,7 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [ODIAC Fossil Fuel CO₂ Emissions](cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb) 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) +5. [Vulcan Fossil Fuel CO₂ Emissions v4.0](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) diff --git a/processingreport.qmd b/processingreport.qmd index eeca9a33..e162ae64 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -18,6 +18,7 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [ODIAC Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/odiac-ffco2-monthgrid-v2023_Processing and Verification Report.qmd) 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) +5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) From 3c4a0fa461686eff290861e51957e096d5eac2de Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 30 Aug 2024 10:48:21 -0500 Subject: [PATCH 06/47] minor update --- _quarto.yml | 6 ++++-- datatransformationcode.qmd | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index d3799c55..4a5482e8 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -83,7 +83,8 @@ website: - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb - - cog_transformation/epa-ch4emission-grid-v2express.ipynb + - cog_transformation/epa-ch4emission-grid-v2express.ipynb + - cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb @@ -108,7 +109,8 @@ website: - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/odiac-ffco2-monthgrid-v2023_Processing and Verification Report.qmd - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd - - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd + - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd + - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index ae52014a..8336d085 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -19,7 +19,7 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [ODIAC Fossil Fuel CO₂ Emissions](cog_transformation/odiac-ffco2-monthgrid-v2023.ipynb) 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) -5. [Vulcan Fossil Fuel CO₂ Emissions v4.0](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) +5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) From 7161cfc88cdfe9c25835e633c9b3ea6d691a1d5e Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Fri, 30 Aug 2024 13:19:32 -0500 Subject: [PATCH 07/47] vulcan data usage and flow diagram added --- .../vulcan-ffco2-yeargrid-v4_Data_Flow.png | Bin 0 -> 327587 bytes .../vulcan-ffco2-yeargrid-v4_Data_Flow.qmd | 3 + datausage.qmd | 2 + ...lcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 2734 +++++++++++++++++ 4 files changed, 2739 insertions(+) create mode 100644 data_workflow/media/vulcan-ffco2-yeargrid-v4_Data_Flow.png create mode 100644 data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd create mode 100644 user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb diff --git a/data_workflow/media/vulcan-ffco2-yeargrid-v4_Data_Flow.png b/data_workflow/media/vulcan-ffco2-yeargrid-v4_Data_Flow.png new file mode 100644 index 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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", + "\n", + "\n", + "# Define the first map layer with the CO2 Flux data for December 2022\n", + "map_layer_2021 = TileLayer(\n", + " tiles=_2021_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " attr=\"GHG\", # Set the attribution \n", + " name='2021 Total CO2 Fossil Fuel Emissions', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", + " opacity=0.8, # Adjust the transparency of the layer\n", + ")\n", + "# Add the first layer to the Dual Map \n", + "map_layer_2021.add_to(map_.m1)\n", + "\n", + "map_layer_2010 = TileLayer(\n", + " tiles=_2010_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " attr=\"GHG\", # Set the attribution \n", + " name='2010 Total CO2 Fossil Fuel Emissions', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", + " opacity=0.8, # Adjust the transparency of the layer\n", + ")\n", + "# Add the first layer to the Dual Map \n", + "map_layer_2010.add_to(map_.m2)\n", + "\n", + "map_" + ] + }, + { + "cell_type": "markdown", + "id": "3a00b691", + "metadata": {}, + "source": [ + "# Calculating Zonal Statistics\n", + "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8f1c5e47", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a polygon for the area of interest (aoi)\n", + "texas_aoi = {\n", + " \"type\": \"Feature\", # Create a feature object\n", + " \"properties\": {},\n", + " \"geometry\": { # Set the bounding coordinates for the polygon\n", + " \"coordinates\": [\n", + " [\n", + " [-95, 29], # South-east bounding coordinate\n", + " [-95, 33], # North-east bounding coordinate\n", + " [-104,33], # North-west bounding coordinate\n", + " [-104,29], # South-west bounding coordinate\n", + " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", + " ]\n", + " ],\n", + " \"type\": \"Polygon\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "65d66484", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new map to display the generated polygon\n", + "# We'll plug in the coordinates for a location\n", + "# Central to the study area and a reasonable zoom level\n", + "aoi_map = Map(\n", + "\n", + " # Base map is set to OpenStreetMap\n", + " tiles=\"OpenStreetMap\",\n", + "\n", + " # Define the spatial properties for the map\n", + " location=[\n", + " 30,-100\n", + " ],\n", + "\n", + " # Set the zoom value\n", + " zoom_start=6,\n", + ")\n", + "\n", + "# Insert the polygon to the map\n", + "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", + "\n", + "# Visualize the map\n", + "aoi_map" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7b0cf1d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 items\n" + ] + } + ], + "source": [ + "# Check total number of items available within the collection\n", + "items = requests.get(\n", + " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", + ").json()[\"features\"]\n", + "\n", + "# Print the total number of items (granules) found\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "960fc2da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'vulcan-ffco2-yeargrid-v4-updated-2021',\n", + " 'bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/vulcan-ffco2-yeargrid-v4-updated'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/vulcan-ffco2-yeargrid-v4-updated'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://dev.ghg.center/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://dev.ghg.center/api/stac/collections/vulcan-ffco2-yeargrid-v4-updated/items/vulcan-ffco2-yeargrid-v4-updated-2021'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://dev.ghg.center/api/raster/collections/vulcan-ffco2-yeargrid-v4-updated/items/vulcan-ffco2-yeargrid-v4-updated-2021/map?assets=total-co2&rescale=0%2C150&colormap_name=rdylbu_r',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'air-co2': {'href': 's3://ghgc-data-store-develop/transformed_cogs/vulcan-co2-yeargrid-v4-added-overview/AIR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Airport CO₂ Emissions',\n", + " 'proj:bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [2649, 5831],\n", + " 'description': 'Annual estimates of carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from airport for the conterminous United States and the State',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 318726.1875,\n", + " 'min': 0.11889950931072235,\n", + " 'count': 11,\n", + " 'buckets': [14659, 40, 6, 2, 4, 0, 2, 0, 0, 1]},\n", + " 'statistics': {'mean': 1190.7966562457523,\n", + " 'stddev': 5906.230747537605,\n", + " 'maximum': 318726.1875,\n", + " 'minimum': 0.11889950931072235,\n", + " 'valid_percent': 3.083506571888412}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-128.22654896758996, 22.857766529124284],\n", + " [-65.30917199495289, 22.857766529124284],\n", + " [-65.30917199495289, 51.44087947724907],\n", + " [-128.22654896758996, 51.44087947724907],\n", + " [-128.22654896758996, 22.857766529124284]]]},\n", + " 'proj:projjson': {'id': {'code': 4326, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',\n", + " 'coordinate_system': {'axis': [{'name': 'Geodetic latitude',\n", + " 'unit': 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longitude',\n", + " 'unit': 'degree',\n", + " 'direction': 'east',\n", + " 'abbreviation': 'Lon'}],\n", + " 'subtype': 'ellipsoidal'}},\n", + " 'proj:transform': [0.01079015211329739,\n", + " 0.0,\n", + " -128.22654896758996,\n", + " 0.0,\n", + " -0.01079015211329739,\n", + " 51.44087947724907,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'total-co2': {'href': 's3://ghgc-data-store-develop/transformed_cogs/vulcan-co2-yeargrid-v4-added-overview/TOT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total of all sectors CO₂ Emissions',\n", + " 'proj:bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [2649, 5831],\n", + " 'description': 'Annual estimates of carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from all sectors for the conterminous United States and the State.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 272530.15625,\n", + " 'min': 1.7858106104995386e-07,\n", + " 'count': 11,\n", + " 'buckets': [227843, 81, 36, 7, 3, 6, 1, 4, 1, 1]},\n", + " 'statistics': {'mean': 162.91311194255712,\n", + " 'stddev': 2080.549384731812,\n", + " 'maximum': 272530.15625,\n", + " 'minimum': 1.7858106104995386e-07,\n", + " 'valid_percent': 47.7767485917382}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-128.22654896758996, 22.857766529124284],\n", + " [-65.30917199495289, 22.857766529124284],\n", + " [-65.30917199495289, 51.44087947724907],\n", + " [-128.22654896758996, 51.44087947724907],\n", + " [-128.22654896758996, 22.857766529124284]]]},\n", + " 'proj:projjson': {'id': {'code': 4326, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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': [0.01079015211329739,\n", + " 0.0,\n", + " -128.22654896758996,\n", + " 0.0,\n", + " -0.01079015211329739,\n", + " 51.44087947724907,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://dev.ghg.center/api/raster/collections/vulcan-ffco2-yeargrid-v4-updated/items/vulcan-ffco2-yeargrid-v4-updated-2021/preview.png?assets=total-co2&rescale=0%2C150&colormap_name=rdylbu_r',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-128.22654896758996, 22.857766529124284],\n", + " [-65.30917199495289, 22.857766529124284],\n", + " [-65.30917199495289, 51.44087947724907],\n", + " [-128.22654896758996, 51.44087947724907],\n", + " [-128.22654896758996, 22.857766529124284]]]},\n", + " 'collection': 'vulcan-ffco2-yeargrid-v4-updated',\n", + " 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2021-01-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine the first item in the collection\n", + "items[0]" + ] + }, + { + "cell_type": "markdown", + "id": "04498e7b", + "metadata": {}, + "source": [ + "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8c7dcae7", + "metadata": {}, + "outputs": [], + "source": [ + "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", + "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", + "\n", + "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", + "def generate_stats(item, geojson):\n", + "\n", + " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", + " result = requests.post(\n", + "\n", + " # Raster API Endpoint for computing statistics\n", + " f\"{RASTER_API_URL}/cog/statistics\",\n", + "\n", + " # Pass the URL to the item, asset name, and raster identifier as parameters\n", + " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", + "\n", + " # Send the GeoJSON object (polygon) along with the request\n", + " json=geojson,\n", + "\n", + " # Return the response in JSON format\n", + " ).json()\n", + "\n", + " # Print the result\n", + " print(result)\n", + "\n", + " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", + " return {\n", + " **result[\"properties\"],\n", + " \"datetime\": item[\"properties\"][\"start_datetime\"][:10],\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d3666ccb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-01-01\n" + ] + } + ], + "source": [ + "# Generate a for loop that iterates over all the existing items in the collection\n", + "for item in items:\n", + "\n", + " # The loop will then retrieve the information for the start datetime of each item in the list\n", + " print(item[\"properties\"][\"start_datetime\"][:10])\n", + "\n", + " # Exit the loop after printing the start datetime for the first item in the collection\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "ca7e79ef", + "metadata": {}, + "source": [ + "With the function above we can generate the statistics for the AOI." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fce6ec4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.7858106104995386e-07, 'max': 3768974.0, 'mean': 261.6907958984375, 'count': 296120.0625, 'sum': 77491896.0, 'std': 11522.940944047226, 'median': 7.39058256149292, 'majority': 0.19903208315372467, 'minority': 1.7858106104995386e-07, 'unique': 242084.0, 'histogram': [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2.0], [1.7858106104995386e-07, 376897.40625, 753794.8125, 1130692.25, 1507589.625, 1884487.0, 2261384.5, 2638281.75, 3015179.25, 3392076.75, 3768974.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.20036202669143677, 'percentile_98': 1811.0848388671875}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.6228801769102574e-07, 'max': 3548930.5, 'mean': 254.9629364013672, 'count': 296120.0625, 'sum': 75499640.0, 'std': 10912.768301398139, 'median': 6.836727142333984, 'majority': 0.18084532022476196, 'minority': 1.6228801769102574e-07, 'unique': 242118.0, 'histogram': [[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0], [1.6228801769102574e-07, 354893.0625, 709786.125, 1064679.25, 1419572.25, 1774465.25, 2129358.5, 2484251.5, 2839144.5, 3194037.5, 3548930.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.1822243481874466, 'percentile_98': 1713.1064453125}}}}\n", 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7.44655704498291, 'majority': 0.5057953596115112, 'minority': 1.802979596732257e-07, 'unique': 242922.0, 'histogram': [[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [1.802979596732257e-07, 405096.25, 810192.5, 1215288.75, 1620385.0, 2025481.25, 2430577.5, 2835673.75, 3240770.0, 3645866.25, 4050962.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.20326974987983704, 'percentile_98': 1775.7847900390625}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.7791849415971228e-07, 'max': 3638032.0, 'mean': 267.9085693359375, 'count': 296120.0625, 'sum': 79333104.0, 'std': 12285.331741552607, 'median': 7.373904228210449, 'majority': 0.49914538860321045, 'minority': 1.7791849415971228e-07, 'unique': 242963.0, 'histogram': [[296494.0, 15.0, 6.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [1.7791849415971228e-07, 363803.1875, 727606.375, 1091409.5, 1455212.75, 1819016.0, 2182819.0, 2546622.25, 2910425.5, 3274228.75, 3638032.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.20115220546722412, 'percentile_98': 1766.38818359375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 2.130747986939241e-07, 'max': 4168191.0, 'mean': 272.1916809082031, 'count': 296120.0625, 'sum': 80601416.0, 'std': 13148.255549691754, 'median': 8.486613273620605, 'majority': 0.5976722836494446, 'minority': 2.130747986939241e-07, 'unique': 242958.0, 'histogram': [[296498.0, 15.0, 3.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 1.0], [2.130747986939241e-07, 416819.09375, 833638.1875, 1250457.25, 1667276.375, 2084095.5, 2500914.5, 2917733.75, 3334552.75, 3751371.75, 4168191.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.24038611352443695, 'percentile_98': 1771.21240234375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 2.0937106626206514e-07, 'max': 4368415.0, 'mean': 266.218017578125, 'count': 296120.0625, 'sum': 78832496.0, 'std': 13696.474874945012, 'median': 8.353293418884277, 'majority': 0.5873664617538452, 'minority': 2.0937106626206514e-07, 'unique': 242943.0, 'histogram': [[296501.0, 12.0, 3.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 1.0], [2.0937106626206514e-07, 436841.5, 873683.0, 1310524.5, 1747366.0, 2184207.5, 2621049.0, 3057890.5, 3494732.0, 3931573.5, 4368415.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.23638056218624115, 'percentile_98': 1761.578857421875}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.949561152514434e-07, 'max': 4275952.5, 'mean': 260.8044128417969, 'count': 296120.0625, 'sum': 77229416.0, 'std': 13665.846479453807, 'median': 7.816947937011719, 'majority': 0.5478244423866272, 'minority': 1.949561152514434e-07, 'unique': 243001.0, 'histogram': [[296501.0, 11.0, 4.0, 0.0, 0.0, 1.0, 0.0, 2.0, 0.0, 1.0], [1.949561152514434e-07, 427595.25, 855190.5, 1282785.75, 1710381.0, 2137976.25, 2565571.5, 2993166.75, 3420762.0, 3848357.25, 4275952.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.22091242671012878, 'percentile_98': 1687.38818359375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.8786006705795444e-07, 'max': 3873315.0, 'mean': 251.08050537109375, 'count': 296120.0625, 'sum': 74349976.0, 'std': 12656.74018063103, 'median': 7.482449054718018, 'majority': 0.5272803902626038, 'minority': 1.8786006705795444e-07, 'unique': 242919.0, 'histogram': [[296500.0, 12.0, 3.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0], [1.8786006705795444e-07, 387331.5, 774663.0, 1161994.5, 1549326.0, 1936657.5, 2323989.0, 2711320.5, 3098652.0, 3485983.5, 3873315.0]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.212051123380661, 'percentile_98': 1615.6910400390625}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.9114715144041838e-07, 'max': 4798060.5, 'mean': 255.62869262695312, 'count': 296120.0625, 'sum': 75696784.0, 'std': 14444.495145210165, 'median': 7.578545093536377, 'majority': 0.5363049507141113, 'minority': 1.9114715144041838e-07, 'unique': 242933.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0], [1.9114715144041838e-07, 479806.0625, 959612.125, 1439418.25, 1919224.25, 2399030.25, 2878836.5, 3358642.5, 3838448.5, 4318254.5, 4798060.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.21587365865707397, 'percentile_98': 1612.6146240234375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.898683592571615e-07, 'max': 4486994.5, 'mean': 243.4835662841797, 'count': 296120.0625, 'sum': 72100368.0, 'std': 13255.782134600735, 'median': 7.461791038513184, 'majority': 0.5328000783920288, 'minority': 1.898683592571615e-07, 'unique': 239140.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0], [1.898683592571615e-07, 448699.4375, 897398.875, 1346098.25, 1794797.75, 2243497.25, 2692196.5, 3140896.0, 3589595.5, 4038295.0, 4486994.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.2142595797777176, 'percentile_98': 1595.547119140625}}}}\n", + "CPU times: user 119 ms, sys: 18.7 ms, total: 138 ms\n", + "Wall time: 14.5 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# %%time = Wall time (execution time) for running the code below\n", + "\n", + "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the polygon\n", + "stats = [generate_stats(item, texas_aoi) for item in items]" + ] + }, + { + "cell_type": "markdown", + "id": "c3307f70", + "metadata": {}, + "source": [ + "Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ee0e9a7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 1.7858106104995386e-07,\n", + " 'max': 3768974.0,\n", + " 'mean': 261.6907958984375,\n", + " 'count': 296120.0625,\n", + " 'sum': 77491896.0,\n", + " 'std': 11522.940944047226,\n", + " 'median': 7.39058256149292,\n", + " 'majority': 0.19903208315372467,\n", + " 'minority': 1.7858106104995386e-07,\n", + " 'unique': 242084.0,\n", + " 'histogram': [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2.0],\n", + " [1.7858106104995386e-07,\n", + " 376897.40625,\n", + " 753794.8125,\n", + " 1130692.25,\n", + " 1507589.625,\n", + " 1884487.0,\n", + " 2261384.5,\n", + " 2638281.75,\n", + " 3015179.25,\n", + " 3392076.75,\n", + " 3768974.0]],\n", + " 'valid_percent': 95.72,\n", + " 'masked_pixels': 13265.0,\n", + " 'valid_pixels': 296520.0,\n", + " 'percentile_2': 0.20036202669143677,\n", + " 'percentile_98': 1811.0848388671875}},\n", + " 'datetime': '2021-01-01'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the stats for the first item in the collection\n", + "stats[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d6b249ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-01-011.785811e-073768974.0261.690796296120.062577491896.011522.9409447.3905830.1990321.785811e-07242084.0[[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0...95.7213265.0296520.00.2003621811.0848392021-01-01
12020-01-011.622880e-073548930.5254.962936296120.062575499640.010912.7683016.8367270.1808451.622880e-07242118.0[[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.1822241713.1064452020-01-01
22019-01-011.877873e-073369803.0276.884796296120.062581991144.011799.9111867.8145980.5271551.877873e-07242967.0[[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.2122441882.4636232019-01-01
32018-01-011.853067e-073868559.5278.522980296120.062582476240.012222.4167827.7118600.5199301.853067e-07242928.0[[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0...95.7213265.0296520.00.2089771912.6325682018-01-01
42017-01-011.802980e-074050962.5271.169403296120.062580298704.013290.4723777.4465570.5057951.802980e-07242922.0[[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0...95.7213265.0296520.00.2032701775.7847902017-01-01
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count sum \\\n", + "0 2021-01-01 1.785811e-07 3768974.0 261.690796 296120.0625 77491896.0 \n", + "1 2020-01-01 1.622880e-07 3548930.5 254.962936 296120.0625 75499640.0 \n", + "2 2019-01-01 1.877873e-07 3369803.0 276.884796 296120.0625 81991144.0 \n", + "3 2018-01-01 1.853067e-07 3868559.5 278.522980 296120.0625 82476240.0 \n", + "4 2017-01-01 1.802980e-07 4050962.5 271.169403 296120.0625 80298704.0 \n", + "\n", + " std median majority minority unique \\\n", + "0 11522.940944 7.390583 0.199032 1.785811e-07 242084.0 \n", + "1 10912.768301 6.836727 0.180845 1.622880e-07 242118.0 \n", + "2 11799.911186 7.814598 0.527155 1.877873e-07 242967.0 \n", + "3 12222.416782 7.711860 0.519930 1.853067e-07 242928.0 \n", + "4 13290.472377 7.446557 0.505795 1.802980e-07 242922.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0... 95.72 \n", + "1 [[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0... 95.72 \n", + "2 [[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0... 95.72 \n", + "3 [[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0... 95.72 \n", + "4 [[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0... 95.72 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 13265.0 296520.0 0.200362 1811.084839 2021-01-01 \n", + "1 13265.0 296520.0 0.182224 1713.106445 2020-01-01 \n", + "2 13265.0 296520.0 0.212244 1882.463623 2019-01-01 \n", + "3 13265.0 296520.0 0.208977 1912.632568 2018-01-01 \n", + "4 13265.0 296520.0 0.203270 1775.784790 2017-01-01 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", + "def clean_stats(stats_json) -> pd.DataFrame:\n", + "\n", + " # Normalize the JSON data\n", + " df = pd.json_normalize(stats_json)\n", + "\n", + " # Replace the naming \"statistics.b1\" in the columns\n", + " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", + "\n", + " # Set the datetime format\n", + " df[\"date\"] = pd.to_datetime(df[\"datetime\"])\n", + "\n", + " # Return the cleaned format\n", + " return df\n", + "\n", + "# Apply the generated function on the stats data\n", + "df = clean_stats(stats)\n", + "\n", + "# Display the stats for the first 5 granules in the collection in the table\n", + "# Change the value in the parenthesis to show more or a smaller number of rows in the table\n", + "df.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "b5459c6d", + "metadata": {}, + "source": [ + "## Visualizing the Data as a Time Series\n", + "We can now explore the total fossil fuel emission time series (2010 -2021) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ec61db4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xzv2g6b/uda6FCxdakqwff/zRse9Br+/4DB061JJkXb169YHHXrx40cqZM6dVs2bNWPvLlStn1a1bN87x+/fvv+/r6X7TfzVt2jTO98uy7NPRSbIGDRr0wLx3i4iIsMqWLWsVKVIk1jSGyb1W/fr1LX9/f+vy5cvxHpOQ6TUbNmxolSlT5r7HAAAAJAbTfwEAACTBqlWr5Onpqd69e8fa379/f1mWpdWrVz/wHBkzZnT8e1RUlC5evKjixYsrMDDwgVPL3M/HH3+stWvXxtok6fz589q1a5c6duyorFmzOo6vWLGiGjRooFWrVjn2BQYGatu2bY4pgu5251P9oaGhunHjRpKzJvVeEmPJkiWqWbOmgoKC9O+//zq2+vXrKzo6Wj/++GOizhceHq4sWbIk6NirV69K0gOPz5Ili8LDwyXZRxPVqFFDtWvXVtmyZR0LeT+Ir69vnO/V2rVrNXr06DjHdu7cOdb0OY899pgsy1Lnzp0d+zw9PfXII4/Emo7oXlLqtbJq1SpVrVo11ifZM2fOrK5du+rkyZM6cOBArOM7deokb29vx9c1a9aUJEd+Z79m161bp8jISPXt21ceHv/736pXXnlF/v7+caYp8/HxUadOnZJ93SVLlqhMmTIqXbp0rNdz3bp1JUkbN250HNuwYUO9+uqrevfdd/Xss8/K19dXM2fOjJPrTv7o6GhdvHhRmTNnVqlSpWL9HgoMDNTZs2e1Y8eOZN9DQmTOnFnS/36GpNi/M++MYqtdu7aOHz9+3ync7uW/57p165b+/fdfPf7445IU577v9/qOz8WLF5UhQwbHfcQnJiZG7dq105UrV/TRRx/FeuzmzZvy8fGJ8xxfX1/H44mVEud87bXXdODAAU2dOlUZMvxvMozkXGvUqFFat26dRo8ercDAwERn+q87v/sBAACcJV2XKj/++KOaNWumvHnzymazOYbKJ4ZlWRo/frxKliwpHx8f5cuXL9Yc2gAAIG06deqU8ubNG+fN8jvTnJw6deqB57h586aGDRvmWOcje/bsypEjh65cuZLoNwj/q2rVqqpfv36s7b+ZSpUqFec5ZcqU0b///qvr169LksaOHat9+/apQIECqlq1qt55551Yb64XKVJEr7/+uj755BNlz55djRo10scff5ys3Im5l8Q4evSoQkJClCNHjljbnXP9/fffiTqfv79/rDd67+fO6+NBx1+9etVxbOnSpbV27Vr98MMPWr9+vd59990EFT+enp5xvlf169fXQw89FOfYggULxvr6TuFw91RCAQEBunz58n2vm1KvlVOnTsX7Wr3z+P3uKSgoSJIc+Z39mo3v58nb21tFixaNky9fvnyxSp+kOnr0qPbv3x/n9VyyZElJcV/P48ePV9asWbVr1y59+OGHypkzZ6zHY2JiNGnSJJUoUSLW76E9e/bE+t4MHDhQmTNnVtWqVVWiRAn17NlTmzdvTvb9xOfatWuSYheSmzdvVv369R3rQeXIkcOxNk1i/xwvXbqkPn36KFeuXMqYMaNy5MihIkWKxDnXg17fydWrVy+FhITok08+UaVKlWI9ljFjxljr5Nxx69Ytx+OJldBzhoWF6cKFC47t0qVL9zzfuHHjNHv2bL333ntq0qSJU/J/9dVXGjp0qDp37qzu3bsn/ObiYVlWgtdgAQAASIh0Xapcv35dlSpV0scff5zkc/Tp00effPKJxo8fr0OHDmnlypWOOWIBAADup1evXnr//ffVpk0bLV68WGvWrNHatWuVLVu2JC2g7kxt2rTR8ePH9dFHHylv3rwaN26cypUrF2sEzoQJE7Rnzx699dZbunnzpnr37q1y5crp7NmzBpPbP23/XzExMWrQoME9R3GsXbtWLVu2TNT5S5curbCwsFiLRMfnTgGwZ8+eeI85deqUwsPDVbZsWUlS/vz5HevpeHh4yLKsWCMhnMHT0zPB+627Fqq/m6u8VuK7p//mN/maTcob4PcSExOjChUqxPt6/u+i4JJ93Yo7RcvevXvjnG/UqFF6/fXXVatWLS1YsEChoaFau3atypUrF+v3UJkyZXT48GEtWrRITzzxhL7++ms98cQTGj58uFPu62779u2TJBUvXlyS9Mcff6hevXr6999/NXHiRH3//fdau3atY52MxP7ObNOmjWbPnq1u3brpm2++0Zo1axQSEhLnXAl5fd9LtmzZdPv27fsWqiNGjNC0adM0evToe67RlCdPHp0/fz7O/jv77qx3kxh58uTRhQsX4vxc333OPn36KE+ePI7t2WefjXOuefPmaeDAgerWrZuGDh2a5Gv919q1a9W+fXs1bdpUM2bMSPT93cvly5eTtS4LAADA3dL1QvXBwcEKDg6O9/GIiAgNGTJECxcu1JUrV1S+fHmNGTNGderUkSQdPHhQ06dP1759+xyfULvz6SYAAJC2FSpUSOvWrYs1wkCSDh065Hj8jvg+Ibt06VJ16NBBEyZMcOy7deuWrly5kmKZJenw4cNxHjt06JCyZ88uPz8/x748efKoR48e6tGjh/7++29VqVJF77//fqy/P1WoUEEVKlTQ0KFDtWXLFtWoUUMzZszQyJEjU+Qe/isoKCjO9yoyMjLOm5DFihXTtWvXkjTK5V6aNWumhQsXasGCBRo8ePB9jy1ZsqRKliyp5cuXa8qUKfecBuyzzz6TJD311FOx9t++fdvx5mJSFo5OTSnxWilUqFC8r9U7jyeFs16z//15Klq0qGN/ZGSkTpw44bTX292KFSum3bt3q169eg/89P3169fVqVMnlS1bVtWrV9fYsWPVokULPfroo45jli5dqieffFKffvpprOdeuXIlzhvRfn5+eu655/Tcc88pMjJSzz77rN5//30NHjzYMaWTM0RHR+vLL79UpkyZHK/9b7/9VhEREVq5cmWsUUn/ne4soS5fvqz169drxIgRGjZsmGP/0aNH73l8Ql7fdytdurQk6cSJE6pYsWKcxz/++GO988476tu3rwYOHHjPczz00EPauHGjwsPDYy1Wv23bNsfjifXQQw/pk08+0cGDBx1F7r3O+eabb+rFF190PH5n5NcdK1asUJcuXfTss8/G+yHFhF7rv/tbtGihRx55RIsXL441lVhynDhxIs4oIAAAgORI1yNVHuS1117T1q1btWjRIu3Zs0etW7dW48aNHX/Z/vbbb1W0aFF99913KlKkiAoXLqwuXbrEOzQaAACkHU2aNFF0dLSmTp0aa/+kSZNks9livdnm5+d3z6LE09Mzzid4P/roozgjLZwlT548euihhzR//vxYefbt26c1a9Y4pm6Jjo6OM5VOzpw5lTdvXsdULuHh4bp9+3asYypUqCAPD497TveSEooVKxZnWqxZs2bF+f61adNGW7duVWhoaJxzXLlyJc59PEirVq1UoUIFvf/++9q6dWucx69evaohQ4Y4vh42bJguX76sbt26xcn222+/acyYMSpfvnysETMxMTF6+eWXdfXqVUfp4opS8rXSpEkTbd++Pdb3+Pr165o1a5YKFy4c603ahHD2a7Z+/fry9vbWhx9+GOvn+NNPP1VYWJiaNm2a6HMmRJs2bXTu3DnNnj07zmM3b950TOEn2afsOn36tObPn6+JEyeqcOHC6tChQ6z7vdfvoSVLlujcuXOx9l28eDHW197e3ipbtqwsy1JUVJQzbk2S/TXVu3dvHTx4UL1793aUCXdGIv03a1hYmObOnZvoa9zrXJI0efLkOFke9PqOT7Vq1SRJv/76a5zHvvrqK/Xu3Vvt2rXTxIkT4z1Hq1atFB0drVmzZjn2RUREaO7cuXrsscfiTNeXEE8//bS8vLw0bdo0xz7LsjRjxgzly5dP1atXlySVLVs21jSCDz/8sOP4H3/8UW3btlWtWrX0xRdfxDuSLqHXkuwfWGzatKkKFy6s7777zmkju8LCwvTHH3/EuhYAAEBypeuRKvdz+vRpzZ07V6dPn3YMSx4wYIBCQkI0d+5cjRo1SsePH9epU6e0ZMkSffbZZ4qOjla/fv3UqlUrbdiwwfAdAACAlNSsWTM9+eSTGjJkiE6ePKlKlSppzZo1WrFihfr27atixYo5jn344Ye1bt06TZw4UXnz5lWRIkX02GOP6amnntLnn3+ugIAAlS1bVlu3btW6deuULVu2FMs9btw4BQcHq1q1aurcubNu3rypjz76SAEBAXrnnXck2UuB/Pnzq1WrVqpUqZIyZ86sdevWaceOHY5RNRs2bNBrr72m1q1bq2TJkrp9+7Y+//xzeXp6Jno6raTq0qWLunXrppYtW6pBgwbavXu3QkND43y6/o033tDKlSv11FNPqWPHjnr44Yd1/fp17d27V0uXLtXJkycTNTWMl5eXvvnmG9WvX1+1atVSmzZtVKNGDXl5eWn//v368ssvFRQU5Fhnr127dtqxY4emTJmiAwcOqF27dgoKCtLOnTs1Z84cZcuWTUuXLpWXl5ck+xu5L7/8ss6ePas1a9bEGj10P7dv39aCBQvu+ViLFi0SfJ7ESMnXyqBBg7Rw4UIFBwerd+/eypo1q+bPn68TJ07o66+/TvSUaM5+zebIkUODBw/WiBEj1LhxYzVv3lyHDx/WtGnT9Oijj8b6lL8zvfTSS1q8eLG6deumjRs3qkaNGoqOjtahQ4e0ePFihYaG6pFHHtGGDRs0bdo0DR8+XFWqVJEkzZ07V3Xq1NHbb7+tsWPHSrKPkHr33XfVqVMnVa9eXXv37tUXX3wRa/SNZF/0Pnfu3KpRo4Zy5cqlgwcPaurUqWratGmsEVg2m021a9fWpk2bHngvYWFhjtfsjRs3dOzYMX3zzTf6448/1LZtW7333nuxru/t7a1mzZrp1Vdf1bVr1zR79mzlzJnznlNk3Y+/v79q1aqlsWPHKioqSvny5dOaNWt04sSJWMcl5PUdn6JFi6p8+fJat26dXn75Zcf+7du3q3379sqWLZvq1aunL774Itbzqlev7vjeP/bYY2r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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure size: 20 representing the width, 10 representing the height\n", + "fig = plt.figure(figsize=(20, 10))\n", + "\n", + "plt.plot(\n", + " df[\"datetime\"], # X-axis: sorted datetime\n", + " df[\"max\"], # Y-axis: maximum CO₂\n", + " color=\"red\", # Line color\n", + " linestyle=\"-\", # Line style\n", + " linewidth=0.5, # Line width\n", + " label=\"CO₂ emissions\", # Legend label\n", + ")\n", + "\n", + "# Display legend\n", + "plt.legend()\n", + "\n", + "# Insert label for the X-axis\n", + "plt.xlabel(\"Years\")\n", + "\n", + "# Insert label for the Y-axis\n", + "plt.ylabel(\"tC/km²/year\")\n", + "plt.xticks(rotation = 90)\n", + "\n", + "# Insert title for the plot\n", + "plt.title(\"Total Fossil Fuel CO₂ Emissions for Texas, Dallas (2010-2021)\")\n", + "\n", + "# Add data citation\n", + "plt.text(\n", + " df[\"datetime\"].iloc[0], # X-coordinate of the text\n", + " df[\"max\"].min(), # Y-coordinate of the text\n", + "\n", + "\n", + "\n", + "\n", + " # Text to be displayed\n", + " \"Source: https://doi.org/10.3334/ORNLDAAC/1741\", \n", + " fontsize=12, # Font size\n", + " horizontalalignment=\"left\", # Horizontal alignment\n", + " verticalalignment=\"top\", # Vertical alignment\n", + " color=\"blue\", # Text color\n", + ")\n", + "\n", + "\n", + "# Plot the time series\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a76ec4e6", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset.\n", + "\n", + "1. Install and import the necessary libraries\n", + "2. Fetch the collection from STAC collections using the appropriate endpoints\n", + "3. Count the number of existing granules within the collection\n", + "4. Map and compare the total fossil fuel CO₂ emissions for two distinctive months/years\n", + "5. Generate zonal statistics for the area of interest (AOI)\n", + "6. Visualizing the Data as a Time Series\n", + "\n", + "\n", + "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5f568fa1e6fd12f87aa849f1631d21334e58406e Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Fri, 30 Aug 2024 13:33:56 -0500 Subject: [PATCH 08/47] navigation bar updated --- _quarto.yml | 6 +- datausage.qmd | 2 +- ...lcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 1429 +---------------- workflow.qmd | 1 + 4 files changed, 12 insertions(+), 1426 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index 4a5482e8..35903109 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -56,7 +56,8 @@ website: - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb - - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb + - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb + - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -138,7 +139,8 @@ website: - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.qmd - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd - - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd + - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd + - data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd diff --git a/datausage.qmd b/datausage.qmd index 6912efd5..988c0a7b 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -20,7 +20,7 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. 4. U.S. Gridded Anthropogenic Methane Emissions Inventory - [Beginner level notebook](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset. -5. Vulcan Fossil Fuel CO₂ Emissions, Version 4 +5. Vulcan Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset. ## Natural Greenhouse Gas Emissions and Sinks diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 1db7291e..3b647938 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -313,708 +313,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "19177b0a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'vulcan-ffco2-yeargrid-v4-updated-2021',\n", - " 'bbox': [-128.22654896758996,\n", - " 22.857766529124284,\n", - " -65.30917199495289,\n", - " 51.44087947724907],\n", - " 'type': 'Feature',\n", - 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"{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.898683592571615e-07, 'max': 4486994.5, 'mean': 243.4835662841797, 'count': 296120.0625, 'sum': 72100368.0, 'std': 13255.782134600735, 'median': 7.461791038513184, 'majority': 0.5328000783920288, 'minority': 1.898683592571615e-07, 'unique': 239140.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0], [1.898683592571615e-07, 448699.4375, 897398.875, 1346098.25, 1794797.75, 2243497.25, 2692196.5, 3140896.0, 3589595.5, 4038295.0, 4486994.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.2142595797777176, 'percentile_98': 1595.547119140625}}}}\n", - "CPU times: user 119 ms, sys: 18.7 ms, total: 138 ms\n", - "Wall time: 14.5 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", diff --git a/workflow.qmd b/workflow.qmd index 53c59e11..e164d45b 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -16,6 +16,7 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [ODIAC Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/odiac-ffco2-monthgrid-v2023_Data_Flow.qmd) 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) +5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) ## Natural Greenhouse Gas Sources Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd) From ea84ef27844fd7bb43e26b2f64cb0e61c7c567f8 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Tue, 3 Sep 2024 10:29:37 -0500 Subject: [PATCH 09/47] change date --- cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb index 3a0547a8..eed02068 100644 --- a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -3,13 +3,17 @@ { "cell_type": "raw", "id": "c2cb82e3-0dc2-43c8-b63f-f59fdec6bd16", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", "title: Vulcan Fossil Fuel CO₂ Emissions v4.0\n", "description: Documentation of data transformation & Validation\n", "author: Paridhi Parajuli \n", - "date: August 20, 2023\n", + "date: August 20, 2024\n", "execute:\n", " freeze: true\n", "---" From 9646aa970fcd1fbbf36c9182915175433a1039b6 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Tue, 3 Sep 2024 10:32:41 -0500 Subject: [PATCH 10/47] add transformation notebook --- .../gra2pes-co2-monthgrid-v1.ipynb | 217 ++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 cog_transformation/gra2pes-co2-monthgrid-v1.ipynb diff --git a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb new file mode 100644 index 00000000..d40a7b4d --- /dev/null +++ b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "0190df52-e0dc-4c6e-9a94-c290c7b86ef9", + "metadata": {}, + "source": [ + "---\n", + "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", + "description: Documentation of data transformation & Validation\n", + "author: Paridhi Parajuli \n", + "date: September 03, 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "6477da5d-92e7-4d79-b49c-2c29a11ec1a1", + "metadata": {}, + "source": [ + "This script was used to transform the GRA2PES dataset to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca65368b-5280-4685-948e-d92fb20c9318", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import os\n", + "import glob\n", + "from datetime import datetime\n", + "import boto3\n", + "import s3fs\n", + "import tempfile\n", + "import numpy as np\n", + "\n", + "import rasterio\n", + "from rasterio.enums import Resampling\n", + "from rio_cogeo.cogeo import cog_translate\n", + "from rio_cogeo.profiles import cog_profiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d8ad9e2-e18b-4ca8-b68e-fadbe14d112a", + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"data_acquisition_method\": \"s3\",\n", + " \"raw_data_bucket\" : \"gsfc-ghg-store\",\n", + " \"raw_data_prefix\": \"GRA2PES/monthly_subset_regrid/2021\", \n", + " \"cog_data_bucket\": \"ghgc-data-store-develop\",\n", + " \"cog_data_prefix\": \"transformed_cogs/GRAAPES\",\n", + " \"date_fmt\" :\"%Y%m\",\n", + " \"transformation\": {}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7a394ef-d598-4769-aeb5-d22447b2d5f0", + "metadata": {}, + "outputs": [], + "source": [ + "session = boto3.session.Session()\n", + "s3_client = session.client(\"s3\")\n", + "\n", + "raw_data_bucket = config[\"raw_data_bucket\"]\n", + "raw_data_prefix= config[\"raw_data_prefix\"]\n", + "\n", + "cog_data_bucket = config['cog_data_bucket']\n", + "cog_data_prefix= config[\"cog_data_prefix\"]\n", + "\n", + "date_fmt=config['date_fmt']\n", + "\n", + "fs = s3fs.S3FileSystem()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04d90bf6-cd92-466e-9b23-23b927810de6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_s3_keys(bucket, model_name, ext):\n", + " \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n", + " keys = []\n", + "\n", + " kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n", + " while True:\n", + " resp = s3_client.list_objects_v2(**kwargs)\n", + " for obj in resp[\"Contents\"]:\n", + " if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n", + " keys.append(obj[\"Key\"])\n", + "\n", + " try:\n", + " kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n", + " except KeyError:\n", + " break\n", + "\n", + " return keys\n", + "\n", + "keys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".nc4\")\n", + "\n", + "def download_s3_objects(bucket, keys, download_dir):\n", + " \"\"\"Download all S3 objects listed in keys to the specified local directory.\"\"\"\n", + " if not os.path.exists(download_dir):\n", + " os.makedirs(download_dir)\n", + "\n", + " for key in keys:\n", + " local_filename = os.path.join(download_dir, os.path.basename(key))\n", + " try:\n", + " s3_client.download_file(bucket, key, local_filename)\n", + " print(f\"Downloaded {key} to {local_filename}\")\n", + " except (NoCredentialsError, PartialCredentialsError) as e:\n", + " print(f\"Credentials error: {e}\")\n", + " except Exception as e:\n", + " print(f\"Failed to download {key}: {e}\")\n", + "\n", + "#download_s3_objects(raw_data_bucket, keys, \"data\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b74c98b4-7059-4f47-b6b2-ae9be7bc62dc", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_date_from_key(key):\n", + " # Split the key to isolate the part that contains the date\n", + " parts = key.split('_')\n", + " for part in parts:\n", + " # Check if the part is numeric and has the length of 6 (YYYYMM format)\n", + " if part.isdigit() and len(part) == 6:\n", + " return part\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c273250-65f3-4e28-905c-d27ef4e373e3", + "metadata": {}, + "outputs": [], + "source": [ + "COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n", + "OVERVIEW_LEVELS = 4 \n", + "OVERVIEW_RESAMPLING = 'average'\n", + "\n", + "for key in glob.glob(\"data/*.nc4\"):\n", + " xds= xr.open_dataset(key)\n", + " xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n", + " \n", + " for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n", + " yearmonth = extract_date_from_key(key)\n", + " filename = f\"output_final/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n", + " data = getattr(xds,var)\n", + " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", + " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", + " \n", + " # Create a temporary file to hold the COG\n", + " with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:\n", + " data.rio.to_raster(f\"temp_{yearmonth}_{var}.tif\", **COG_PROFILE, nodata=-9999)\n", + " # Create COG with overviews and nodata value\n", + " cog_translate(\n", + " f\"temp_{yearmonth}_{var}.tif\",\n", + " temp_file.name,\n", + " cog_profiles.get(\"deflate\"),\n", + " overview_level=OVERVIEW_LEVELS,\n", + " overview_resampling=OVERVIEW_RESAMPLING,\n", + " nodata=-9999\n", + " )\n", + " \n", + " # Move the temporary file to the desired local path\n", + " os.rename(temp_file.name, filename)\n", + " \n", + " if os.path.exists(f\"temp_{yearmonth}_{var}.tif\"):\n", + " os.remove(f\"temp_{yearmonth}_{var}.tif\")\n", + " del data\n", + " print(f\"Done for: {filename}\")\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 591ffc7781a6bfe14a9c55a5aaf56bde9ffd0101 Mon Sep 17 00:00:00 2001 From: fb0023 <142605787+fb0023@users.noreply.github.com> Date: Tue, 3 Sep 2024 12:01:44 -0500 Subject: [PATCH 11/47] updated table of contents --- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 149 ++++++++++++++---- 1 file changed, 117 insertions(+), 32 deletions(-) diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 6c360626..51c9efd2 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -23,13 +23,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Access this notebook\n", + "## Access this Notebook\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdai8otCdrVQzJgev8mjDhzKyCg7jcrB3UeTXNHoCiaMKrkaQ/viewform) and providing the required information. \n", "\n", "\n", "Access the [**Air-Sea CO₂ Flux, ECCO-Darwin Model**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Content\n", + "- [Data Summary and Application](#data-summary-and-application)\n", + "- [Approach](#approach)\n", + "- [About the Data](#about-the-data)\n", + "- [Install the Required Libraries](#install-the-required-libraries) \n", + "- [Query the STAC API](#query-the-stac-api)\n", + "- [Explore Changes in CO₂ Levels](#explore-changes-in-co-levels)\n", + "- [Visualize CO₂ Flux Emissions](#visualize-co-flux-emissions)\n", + "- [Calculate Zonal Statistics](#calculate-zonal-statistics)\n", + "- [Time-Series Analysis](#time-series-analysis)\n", + "- [Summary](#summary)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -42,7 +59,7 @@ "- **Unit**: Millimoles of CO₂ per meter squared per second\n", "- **Utility**: Climate Research, Oceanography, Carbon Stock Monitoring\n", "\n", - "For more, visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) dataset landing page. " + "For more, visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) data overview page. " ] }, { @@ -57,7 +74,7 @@ "1. Identify available dates and temporal frequency of observations for a given collection using the GHGC API `/stac` endpoint\n", "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint\n", "3. Using `folium.plugins.DualMap`, you will visualize two tiles (side-by-side), allowing comparison of time points \n", - "4. After the visualization, you will perform zonal statistics for a given polygon" + "4. After the visualization, you will perform zonal statistics for a given location" ] }, { @@ -66,11 +83,11 @@ "source": [ "## About the Data\n", "\n", - "The ocean is a major sink for atmospheric carbon dioxide (CO₂), largely due to the presence of phytoplankton that use the CO₂ to grow. Studies have shown that global ocean CO₂ uptake has increased over recent decades, however there is uncertainty in the various mechanisms that affect ocean CO₂ flux and storage and how the ocean carbon sink will respond to future climate change. \n", + "The ocean is a major sink for atmospheric carbon dioxide (CO₂), largely due to the presence of phytoplankton that use the CO₂ to grow. Studies have shown that global ocean CO₂ uptake has increased over recent decades, however, there is uncertainty in the various mechanisms that affect ocean CO₂ flux and storage and how the ocean carbon sink will respond to future climate change.\n", "\n", - "Because CO₂ fluxes can vary significantly across space and time, combined with deficiencies in ocean and atmosphere CO₂ observations, there is a need for models that can thoroughly represent these processes. Ocean biogeochemical models (OBMs) have the ability to resolve the physical and biogeochemical mechanisms contributing to spatial and temporal variations in air-sea CO₂ fluxes but previous OBMs do not integrate observations to improve model accuracy and have not been able to operate on the seasonal and multi-decadal timescales needed to adequately characterize these processes. \n", + "Because CO₂ fluxes can vary significantly across space and time, combined with deficiencies in ocean and atmosphere CO₂ observations, there is a need for models that can thoroughly represent these processes. Ocean biogeochemical models (OBMs) can resolve the physical and biogeochemical mechanisms contributing to spatial and temporal variations in air-sea CO₂ fluxes but previous OBMs do not integrate observations to improve model accuracy and have not been able to operate on the seasonal and multi-decadal timescales needed to adequately characterize these processes.\n", "\n", - "The ECCO-Darwin model is an OBM that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project. A pilot study using ECCO-Darwin was completed by [Brix et al. (2015)](https://doi.org/10.1016/j.ocemod.2015.07.008) however an improved version of the model was developed by [Carroll et al. (2020)](https://doi.org/10.1029/2019MS001888) in which issues present in the first model were addressed using data assimilation and adjustments were made to initial conditions and biogeochemical parameters. The updated ECCO-Darwin model was compared with interpolation-based products to estimate surface ocean partial pressure (pCO2) and air-sea CO₂ flux. This dataset contains the gridded global, monthly mean air-sea CO₂ fluxes from version 5 of the ECCO-Darwin model. \n", + "The ECCO-Darwin model is an OBM that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project. A pilot study using ECCO-Darwin was completed by [Brix et al. (2015)](https://doi.org/10.1016/j.ocemod.2015.07.008) however, an improved version of the model was developed by [Carroll et al. (2020)](https://doi.org/10.1029/2019MS001888) in which issues present in the first model were addressed using data assimilation and adjustments were made to initial conditions and biogeochemical parameters. The updated ECCO-Darwin model was compared with interpolation-based products to estimate surface ocean partial pressure (pCO2) and air-sea CO₂ flux. This dataset contains the gridded global, monthly mean air-sea CO₂ fluxes from version 5 of the ECCO-Darwin model.\n", "\n", "The data available in the US GHG Center hub are available at ~1/3° horizontal resolution at the equator (~18 km at high latitudes) from January 2020 through December 2022. For more information regarding this dataset, please visit the [Air-Sea CO₂ Flux ECCO-Darwin Model](https://earth.gov/ghgcenter/data-catalog/eccodarwin-co2flux-monthgrid-v5) data overview page." ] @@ -80,9 +97,9 @@ "metadata": {}, "source": [ "# Install the Required Libraries\n", - "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:\n", + "Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install the libraries by running the following command line:\n", "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + "%pip install requests folium rasterstats pystac_client pandas matplotlib tabulate --quiet" ] }, { @@ -90,8 +107,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Querying the STAC API\n", - "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. " + "## Query the STAC API\n", + "First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook." ] }, { @@ -108,7 +125,8 @@ "from pystac_client import Client \n", "import branca \n", "import pandas as pd\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from tabulate import tabulate" ] }, { @@ -118,17 +136,36 @@ "outputs": [], "source": [ "# Provide the STAC and RASTER API endpoints\n", - "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", + "# The endpoint refers to a location within the API that executes a request on a data collection nesting on the server.\n", "\n", - "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "# The STAC API is a catalog of all the existing data collections stored in the GHG Center.\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac/\"\n", + "\n", + "# The RASTER API is used to fetch collections for visualization.\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/docs\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**STAC API Collection Names**\n", "\n", - "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection name as a variable. \n", "\n", - "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable \n", - "# Name of the collection for ECCO Darwin CO₂ flux monthly emissions\n", - "collection_name = \"eccodarwin-co2flux-monthgrid-v5\"" + "Here is a list of the collection names in the GHG Center STAC API:\n", + "- Air-Sea CO₂ Flux ECCO-Darwin Model: [eccodarwin-co2flux-monthgrid-v5](https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5) (**Used in this notebook!**)\n", + "- EMIT Methane Point Source Plume Complexes: [emit-ch4plume-v1](https://earth.gov/ghgcenter/api/stac/collections/emit-ch4plume-v1)\n", + "- U.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension: [epa-ch4emission-yeargrid-v2express](https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express)\n", + "- GOSAT-based Top-down Total and Natural Methane Emissions v1: [gosat-based-ch4budget-yeargrid-v1](https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1)\n", + "- Wetland Methane Emissions, LPJ-EOSIM Model v2 (Daily): [lpjeosim-wetlandch4-daygrid-v2](https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2)\n", + "- Wetland Methane Emissions, LPJ-EOSIM Model (Monthly): [lpjeosim-wetlandch4-monthgrid-v2](https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2)\n", + "- MiCASA Land Carbon Flux v1 (Daily): [micasa-carbonflux-daygrid-v1](https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1)\n", + "- OCO-2 MIP Top-down CO₂ Budgets v1: [oco2-mip-co2budget-yeargrid-v1](https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1)\n", + "- OCO-2 GEOS Column CO₂ Concentrations v10r: [oco2geos-co2-daygrid-v10r](https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r)\n", + "- ODIAC Fossil Fuel CO₂ Emissions v2023: [odiac-ffco2-monthgrid-v2023](https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023)\n", + "- SEDAC Gridded World Population Density v4.11: [sedac-popdensity-yeargrid5yr-v4.11](https://earth.gov/ghgcenter/api/stac/collections/sedac-popdensity-yeargrid5yr-v4.11)\n", + "- TM5-4DVar Isotopic CH4 Inverse Fluxes: [tm54dvar-ch4flux-monthgrid-v1](https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1)" ] }, { @@ -137,12 +174,45 @@ "metadata": {}, "outputs": [], "source": [ + "# The collection name is used to fetch the dataset from the STAC API. First, you must define the collection name as a variable. \n", + "# The collection name in the STAC API\n", + "collection_name = \"eccodarwin-co2flux-monthgrid-v5\"\n", + "\n", "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", "\n", - "# Print the properties of the collection to the console\n", - "collection" + "# Print the properties of the collection in a table\n", + "# Adjust display settings\n", + "pd.set_option('display.max_colwidth', None) # Set maximum column width to None to prevent cutting off text\n", + "\n", + "# Extract the relevant information about the collection\n", + "collection_info = {\n", + " \"Title\": collection.get(\"title\", \"N/A\"),\n", + " \"Description\": collection.get(\"description\", \"N/A\"),\n", + " \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"),\n", + " \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"),\n", + "}\n", + "\n", + "# Convert the extracted information into a DataFrame format\n", + "properties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n", + "\n", + "# Display the collection properties in a table\n", + "collection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n", + " .set_table_styles([\n", + " {\n", + " 'selector': 'th.col0, td.col0', # Select the first column\n", + " 'props': [('min-width', '200px'), # Set a minimum width\n", + " ('text-align', 'left')] # Align text to the left\n", + " },\n", + " {\n", + " 'selector': 'td.col1', # Select the second column\n", + " 'props': [('text-align', 'left')] # Align text to the left\n", + " }\n", + "])\n", + "\n", + "# Print the collection summary table\n", + "collection_summary" ] }, { @@ -150,7 +220,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 2020 to December 2022. By looking at the `dashboard:time density`, we observe that the data is periodic with monthly time density." + "Next, you will examine the contents of the `collection` under the `temporal` variable. You’ll see that the data is available from January 2020 to December 2022. Looking at the `dashboard:time density`, you can observe that the data is periodic with monthly time density." ] }, { @@ -217,7 +287,22 @@ "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", "\n", "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" + "print(f\"Found {len(items)} observations\")\n", + "\n", + "# Sort the items based on their date-time attribute\n", + "items_sorted = sorted(items, key=lambda x: x[\"properties\"][\"start_datetime\"])\n", + "\n", + "# Create an empty list\n", + "table_data = []\n", + "# Extract the ID and date-time information for each granule and add them to the list\n", + "for item in items_sorted:\n", + " table_data.append([item['id'], item['properties']['start_datetime']])\n", + "\n", + "# Define the table headers\n", + "headers = [\"Item ID\", \"Start Date-Time\"]\n", + "\n", + "# Print the table using tabulate\n", + "print(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))" ] }, { @@ -227,8 +312,8 @@ "outputs": [], "source": [ "# Examine the first item in the collection\n", - "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", - "items[0]" + "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] refers to the first item (granule) in the list/collection\n", + "items_sorted[0]" ] }, { @@ -236,9 +321,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exploring Changes in CO₂ Levels Using the Raster API\n", + "## Explore Changes in CO₂ Levels\n", "\n", - "In this notebook, we will explore the global changes of CO₂ flux over time in urban regions. We will visualize the outputs on a map using `folium`." + "You will now explore changes in CO₂ flux at a given location and time. You will visualize the outputs on a map using `folium`." ] }, { @@ -247,11 +332,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)\n", + "# Now we create a dictionary where the start datetime values for each granule are queried more explicitly by year and month (e.g., 2020-02)\n", "items = {item[\"properties\"][\"start_datetime\"]: item for item in items}\n", "\n", "# Next, we need to specify the asset name for this collection.\n", - "# The asset name is referring to the raster band containing the pixel values for the parameter of interest.\n", + "# The asset name refers to the raster band containing the pixel values for the parameter of interest.\n", "# For the case of the Air-Sea CO2 Flux ECCO-Darwin collection, the parameter of interest is “co2”.\n", "asset_name = \"co2\"" ] @@ -288,16 +373,16 @@ "outputs": [], "source": [ "# Choose a color map for displaying the first observation (event)\n", - "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", + "# Please refer to matplotlib library if you'd prefer to choose a different color ramp.\n", "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"magma\"\n", "\n", "# Make a GET request to retrieve information for the December 2022 tile which is the 1st item in the collection\n", "# To retrieve the first item in the collection we use \"0\" in the \"(items.keys())[0]\" statement \n", - "# If you want to select another item (granule) in the list (collection), you can refer to the Data Browser in the U.S. Greenhouse Gas Center website \n", + "# If you want to select another item (granule) in the list (collection), you can refer to the Data Browser on the U.S. Greenhouse Gas Center website \n", "# URL to the Air-Sea CO2 Flux ECCO-Darwin collection in the US GHG Center: https://dljsq618eotzp.cloudfront.net/browseui/#eccodarwin-co2flux-monthgrid-v5/\n", "\n", - "# A GET request is made for the December 2022 tile\n", + "# A GET request is made for the December 2022 tile.\n", "december_2022_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", From 47f8b94bf0ba8efdc53ffbd62579922c58aa37b5 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Wed, 4 Sep 2024 10:41:38 -0500 Subject: [PATCH 12/47] desc change --- cog_transformation/gra2pes-co2-monthgrid-v1.ipynb | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb index d40a7b4d..9cef16b8 100644 --- a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb +++ b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb @@ -3,15 +3,19 @@ { "cell_type": "raw", "id": "0190df52-e0dc-4c6e-9a94-c290c7b86ef9", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", - "description: Documentation of data transformation & Validation\n", - "author: Paridhi Parajuli \n", - "date: September 03, 2024\n", + "description: Monthly Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 30 August 2024\n", "execute:\n", - " freeze: true\n", + " freeze: true\n", "---" ] }, From de56fc98c20ac17eaa52e6ce91f42857e48f263a Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Wed, 4 Sep 2024 10:48:40 -0500 Subject: [PATCH 13/47] minor change --- cog_transformation/gra2pes-co2-monthgrid-v1.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb index 9cef16b8..4fa089f9 100644 --- a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb +++ b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb @@ -130,7 +130,7 @@ " except Exception as e:\n", " print(f\"Failed to download {key}: {e}\")\n", "\n", - "#download_s3_objects(raw_data_bucket, keys, \"data\")\n", + "download_s3_objects(raw_data_bucket, keys, \"data\")\n", "\n" ] }, @@ -168,7 +168,7 @@ " \n", " for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n", " yearmonth = extract_date_from_key(key)\n", - " filename = f\"output_final/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n", + " filename = f\"output/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n", " data = getattr(xds,var)\n", " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", From 519b64365f783197f8b234f60edee0f363399277 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Wed, 4 Sep 2024 13:29:05 -0500 Subject: [PATCH 14/47] add multiplication --- cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb index eed02068..744a81f1 100644 --- a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -275,6 +275,8 @@ " #data = data.where(data != -3.4e+38, -9999) # Replace -3.4e+38 with -9999\n", " data = data.fillna(-9999) # Ensure all NaNs are replaced with -9999\n", " data_array = data.values\n", + " # Multiply data\n", + " data_array = data_array *( 44/12)\n", "\n", " # Open the source raster to get metadata\n", " with rasterio.open(file) as src:\n", @@ -383,6 +385,8 @@ " \n", " # Mask -9999 values and NaNs for statistics calculation\n", " data = np.ma.masked_where((data == -9999) | np.isnan(data), data)\n", + " # Multiply data - undo the multiplication done during transformation\n", + " data = data *( 12/44)\n", " overall_final.append(data)\n", " \n", " # Calculate statistics while ignoring NaN values\n", From 0b216326e55b9cbf88855310ea4a3ccc88f527aa Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Wed, 4 Sep 2024 13:33:05 -0500 Subject: [PATCH 15/47] add stats --- .../vulcan-ffco2-yeargridv4/overall_stats.json | 1 + .../vulcan-ffco2-yeargridv4/yearly_stats.json | 1 + 2 files changed, 2 insertions(+) create mode 100644 generating_statistics_for_validation/vulcan-ffco2-yeargridv4/overall_stats.json create mode 100644 generating_statistics_for_validation/vulcan-ffco2-yeargridv4/yearly_stats.json diff --git a/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/overall_stats.json b/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/overall_stats.json new file mode 100644 index 00000000..323afe8d --- /dev/null +++ b/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/overall_stats.json @@ -0,0 +1 @@ +{"data":{"0":"raw","1":"reprojected","2":"reprojected_non_zero","3":"Transformed"},"min":{"0":0.0,"1":0.0,"2":0.0,"3":0.0},"max":{"0":6222153.0,"1":6222153.0,"2":6222153.0,"3":6222153.5},"mean":{"0":19.2182331085,"1":19.1889705658,"2":81.4993591309,"3":81.4993667603},"std":{"0":3572.6171875,"1":3555.7956542969,"2":7327.5859375,"3":7327.5864257812}} \ No newline at end of file diff --git a/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/yearly_stats.json b/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/yearly_stats.json new file mode 100644 index 00000000..1c5162e9 --- /dev/null +++ b/generating_statistics_for_validation/vulcan-ffco2-yeargridv4/yearly_stats.json @@ -0,0 +1 @@ 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\ No newline at end of file From d06ab81bc15635ac2843c2e2528ab6da470bc645 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 10:29:39 -0500 Subject: [PATCH 16/47] change mult order --- cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb index 744a81f1..4f3c2c35 100644 --- a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -270,13 +270,15 @@ "for file in files:\n", " filename = file.split('/')[-1]\n", " xda = xarray.open_dataarray(file).sel(band=1)\n", + "\n", + " # Multiply data\n", + " data = data *( 44/12)\n", " \n", " data = xda.where(xda != 0, -9999) # Replace 0 with -9999\n", " #data = data.where(data != -3.4e+38, -9999) # Replace -3.4e+38 with -9999\n", " data = data.fillna(-9999) # Ensure all NaNs are replaced with -9999\n", " data_array = data.values\n", - " # Multiply data\n", - " data_array = data_array *( 44/12)\n", + " \n", "\n", " # Open the source raster to get metadata\n", " with rasterio.open(file) as src:\n", From 468561ae230fc4bdf54d82c9757102f8afbfb4f2 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 13:35:05 -0500 Subject: [PATCH 17/47] change endpoints to prod --- .../vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 3b647938..25fb6f22 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -100,12 +100,12 @@ "outputs": [], "source": [ "# Provide STAC and RASTER API endpoints\n", - "STAC_API_URL = \"https://dev.ghg.center/api/stac\"\n", - "RASTER_API_URL = \"https://dev.ghg.center/api/raster\"\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", "\n", "# Please use the collection name similar to the one used in the STAC collection.\n", "# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \n", - "collection_name = \"vulcan-ffco2-yeargrid-v4-updated\"" + "collection_name = \"vulcan-ffco2-yeargrid-v4\"" ] }, { From f60de4ba764041f39f2111be57e760ad395c712f Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 13:38:59 -0500 Subject: [PATCH 18/47] add desc --- ...can-ffco2-yeargrid-v4_Processing and Verification Report.qmd | 2 +- .../vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd index 185e10f6..6c1d0d11 100644 --- a/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd @@ -1,6 +1,6 @@ --- title: Vulcan Fossil Fuel CO₂ Emissions v4.0 -description: "The Vulcan version 4.0 data product represents total carbon dioxide (CO₂) emissions resulting from the combustion of fossil fuel (FF) for the contiguous United States and District of Columbia. Referred to as FFCO₂, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Included is the estimated total annual FFCO2 for the United States, as well as the estimated total annual FFCO₂ per sector in units of metric tonnes of carbon per 1 km x 1 km grid cell per year (tonne C/km2/year). The Vulcan Project is a multiagency (NASA, DOE, NOAA, NIST) funded effort under the North American Carbon Program (NACP) to quantify North American fossil fuel carbon dioxide (FFCO₂) emissions at space and time scales much finer than has been achieved in the past. The purpose is to aid in quantification of the North American carbon budget, to support inverse estimation of carbon sources and sinks, and to support the demands posed by higher resolution FFCO₂ observations (in situ and remotely sensed). The detail and scope of the Vulcan FFCO₂ inventory has also made it a valuable tool for policymakers, demographers, social scientists and the public at large. Learn more at the Vulcan website: https://vulcan.rc.nau.edu/index.html" +description: "The Vulcan version 4.0 data product represents total carbon dioxide (CO2) emissions resulting from the combustion of fossil fuel (ff) for the contiguous United States and District of Columbia. Referred to as ffCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual ffCO2 for the United States, as well as the estimated total annual ffCO2 per sector." --- diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 25fb6f22..5e337248 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -50,7 +50,7 @@ "source": [ "## About the Data\n", "\n", - "The Vulcan version 4.0 data product represents total carbon dioxide (CO₂) emissions resulting from the combustion of fossil fuel (FF) for the contiguous United States and District of Columbia. Referred to as FFCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual FFCO2 for the United States, as well as the estimated total annual FFCO2 per sector.\n", + "The Vulcan version 4.0 data product represents total carbon dioxide (CO2) emissions resulting from the combustion of fossil fuel (ff) for the contiguous United States and District of Columbia. Referred to as ffCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual ffCO2 for the United States, as well as the estimated total annual ffCO2 per sector.\n", "\n", "For more information regarding this dataset, please visit the [Vulcan Fossil Fuel CO₂ Emissions, Version 4](https://earth.gov/ghgcenter/data-catalog/vulcan-ffco2-yeargrid-v4) data overview page." ] From 9f21a4a6887a75908f677de4dcabb3cadaf52f32 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 13:54:59 -0500 Subject: [PATCH 19/47] rename --- .../gra2pes-ghg-monthgrid-v1.ipynb | 221 ++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb diff --git a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb new file mode 100644 index 00000000..4fa089f9 --- /dev/null +++ b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "0190df52-e0dc-4c6e-9a94-c290c7b86ef9", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", + "description: Monthly Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 30 August 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "6477da5d-92e7-4d79-b49c-2c29a11ec1a1", + "metadata": {}, + "source": [ + "This script was used to transform the GRA2PES dataset to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca65368b-5280-4685-948e-d92fb20c9318", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import os\n", + "import glob\n", + "from datetime import datetime\n", + "import boto3\n", + "import s3fs\n", + "import tempfile\n", + "import numpy as np\n", + "\n", + "import rasterio\n", + "from rasterio.enums import Resampling\n", + "from rio_cogeo.cogeo import cog_translate\n", + "from rio_cogeo.profiles import cog_profiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d8ad9e2-e18b-4ca8-b68e-fadbe14d112a", + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"data_acquisition_method\": \"s3\",\n", + " \"raw_data_bucket\" : \"gsfc-ghg-store\",\n", + " \"raw_data_prefix\": \"GRA2PES/monthly_subset_regrid/2021\", \n", + " \"cog_data_bucket\": \"ghgc-data-store-develop\",\n", + " \"cog_data_prefix\": \"transformed_cogs/GRAAPES\",\n", + " \"date_fmt\" :\"%Y%m\",\n", + " \"transformation\": {}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7a394ef-d598-4769-aeb5-d22447b2d5f0", + "metadata": {}, + "outputs": [], + "source": [ + "session = boto3.session.Session()\n", + "s3_client = session.client(\"s3\")\n", + "\n", + "raw_data_bucket = config[\"raw_data_bucket\"]\n", + "raw_data_prefix= config[\"raw_data_prefix\"]\n", + "\n", + "cog_data_bucket = config['cog_data_bucket']\n", + "cog_data_prefix= config[\"cog_data_prefix\"]\n", + "\n", + "date_fmt=config['date_fmt']\n", + "\n", + "fs = s3fs.S3FileSystem()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04d90bf6-cd92-466e-9b23-23b927810de6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_s3_keys(bucket, model_name, ext):\n", + " \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n", + " keys = []\n", + "\n", + " kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n", + " while True:\n", + " resp = s3_client.list_objects_v2(**kwargs)\n", + " for obj in resp[\"Contents\"]:\n", + " if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n", + " keys.append(obj[\"Key\"])\n", + "\n", + " try:\n", + " kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n", + " except KeyError:\n", + " break\n", + "\n", + " return keys\n", + "\n", + "keys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".nc4\")\n", + "\n", + "def download_s3_objects(bucket, keys, download_dir):\n", + " \"\"\"Download all S3 objects listed in keys to the specified local directory.\"\"\"\n", + " if not os.path.exists(download_dir):\n", + " os.makedirs(download_dir)\n", + "\n", + " for key in keys:\n", + " local_filename = os.path.join(download_dir, os.path.basename(key))\n", + " try:\n", + " s3_client.download_file(bucket, key, local_filename)\n", + " print(f\"Downloaded {key} to {local_filename}\")\n", + " except (NoCredentialsError, PartialCredentialsError) as e:\n", + " print(f\"Credentials error: {e}\")\n", + " except Exception as e:\n", + " print(f\"Failed to download {key}: {e}\")\n", + "\n", + "download_s3_objects(raw_data_bucket, keys, \"data\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b74c98b4-7059-4f47-b6b2-ae9be7bc62dc", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_date_from_key(key):\n", + " # Split the key to isolate the part that contains the date\n", + " parts = key.split('_')\n", + " for part in parts:\n", + " # Check if the part is numeric and has the length of 6 (YYYYMM format)\n", + " if part.isdigit() and len(part) == 6:\n", + " return part\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c273250-65f3-4e28-905c-d27ef4e373e3", + "metadata": {}, + "outputs": [], + "source": [ + "COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n", + "OVERVIEW_LEVELS = 4 \n", + "OVERVIEW_RESAMPLING = 'average'\n", + "\n", + "for key in glob.glob(\"data/*.nc4\"):\n", + " xds= xr.open_dataset(key)\n", + " xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n", + " \n", + " for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n", + " yearmonth = extract_date_from_key(key)\n", + " filename = f\"output/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n", + " data = getattr(xds,var)\n", + " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", + " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", + " \n", + " # Create a temporary file to hold the COG\n", + " with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:\n", + " data.rio.to_raster(f\"temp_{yearmonth}_{var}.tif\", **COG_PROFILE, nodata=-9999)\n", + " # Create COG with overviews and nodata value\n", + " cog_translate(\n", + " f\"temp_{yearmonth}_{var}.tif\",\n", + " temp_file.name,\n", + " cog_profiles.get(\"deflate\"),\n", + " overview_level=OVERVIEW_LEVELS,\n", + " overview_resampling=OVERVIEW_RESAMPLING,\n", + " nodata=-9999\n", + " )\n", + " \n", + " # Move the temporary file to the desired local path\n", + " os.rename(temp_file.name, filename)\n", + " \n", + " if os.path.exists(f\"temp_{yearmonth}_{var}.tif\"):\n", + " os.remove(f\"temp_{yearmonth}_{var}.tif\")\n", + " del data\n", + " print(f\"Done for: {filename}\")\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 66552cb0e197ccdb287111a19fa3be1a0e11a3bc Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 13:56:03 -0500 Subject: [PATCH 20/47] remove prev --- .../gra2pes-co2-monthgrid-v1.ipynb | 221 ------------------ 1 file changed, 221 deletions(-) delete mode 100644 cog_transformation/gra2pes-co2-monthgrid-v1.ipynb diff --git a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb b/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb deleted file mode 100644 index 4fa089f9..00000000 --- a/cog_transformation/gra2pes-co2-monthgrid-v1.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "id": "0190df52-e0dc-4c6e-9a94-c290c7b86ef9", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "---\n", - "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", - "description: Monthly Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", - "author: Siddharth Chaudhary, Paridhi Parajuli\n", - "date: 30 August 2024\n", - "execute:\n", - " freeze: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "6477da5d-92e7-4d79-b49c-2c29a11ec1a1", - "metadata": {}, - "source": [ - "This script was used to transform the GRA2PES dataset to Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca65368b-5280-4685-948e-d92fb20c9318", - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import os\n", - "import glob\n", - "from datetime import datetime\n", - "import boto3\n", - "import s3fs\n", - "import tempfile\n", - "import numpy as np\n", - "\n", - "import rasterio\n", - "from rasterio.enums import Resampling\n", - "from rio_cogeo.cogeo import cog_translate\n", - "from rio_cogeo.profiles import cog_profiles\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d8ad9e2-e18b-4ca8-b68e-fadbe14d112a", - "metadata": {}, - "outputs": [], - "source": [ - "config = {\n", - " \"data_acquisition_method\": \"s3\",\n", - " \"raw_data_bucket\" : \"gsfc-ghg-store\",\n", - " \"raw_data_prefix\": \"GRA2PES/monthly_subset_regrid/2021\", \n", - " \"cog_data_bucket\": \"ghgc-data-store-develop\",\n", - " \"cog_data_prefix\": \"transformed_cogs/GRAAPES\",\n", - " \"date_fmt\" :\"%Y%m\",\n", - " \"transformation\": {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7a394ef-d598-4769-aeb5-d22447b2d5f0", - "metadata": {}, - "outputs": [], - "source": [ - "session = boto3.session.Session()\n", - "s3_client = session.client(\"s3\")\n", - "\n", - "raw_data_bucket = config[\"raw_data_bucket\"]\n", - "raw_data_prefix= config[\"raw_data_prefix\"]\n", - "\n", - "cog_data_bucket = config['cog_data_bucket']\n", - "cog_data_prefix= config[\"cog_data_prefix\"]\n", - "\n", - "date_fmt=config['date_fmt']\n", - "\n", - "fs = s3fs.S3FileSystem()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04d90bf6-cd92-466e-9b23-23b927810de6", - "metadata": {}, - "outputs": [], - "source": [ - "def get_all_s3_keys(bucket, model_name, ext):\n", - " \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n", - " keys = []\n", - "\n", - " kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n", - " while True:\n", - " resp = s3_client.list_objects_v2(**kwargs)\n", - " for obj in resp[\"Contents\"]:\n", - " if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n", - " keys.append(obj[\"Key\"])\n", - "\n", - " try:\n", - " kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n", - " except KeyError:\n", - " break\n", - "\n", - " return keys\n", - "\n", - "keys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".nc4\")\n", - "\n", - "def download_s3_objects(bucket, keys, download_dir):\n", - " \"\"\"Download all S3 objects listed in keys to the specified local directory.\"\"\"\n", - " if not os.path.exists(download_dir):\n", - " os.makedirs(download_dir)\n", - "\n", - " for key in keys:\n", - " local_filename = os.path.join(download_dir, os.path.basename(key))\n", - " try:\n", - " s3_client.download_file(bucket, key, local_filename)\n", - " print(f\"Downloaded {key} to {local_filename}\")\n", - " except (NoCredentialsError, PartialCredentialsError) as e:\n", - " print(f\"Credentials error: {e}\")\n", - " except Exception as e:\n", - " print(f\"Failed to download {key}: {e}\")\n", - "\n", - "download_s3_objects(raw_data_bucket, keys, \"data\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b74c98b4-7059-4f47-b6b2-ae9be7bc62dc", - "metadata": {}, - "outputs": [], - "source": [ - "def extract_date_from_key(key):\n", - " # Split the key to isolate the part that contains the date\n", - " parts = key.split('_')\n", - " for part in parts:\n", - " # Check if the part is numeric and has the length of 6 (YYYYMM format)\n", - " if part.isdigit() and len(part) == 6:\n", - " return part\n", - " return None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c273250-65f3-4e28-905c-d27ef4e373e3", - "metadata": {}, - "outputs": [], - "source": [ - "COG_PROFILE = {\"driver\": \"COG\", \"compress\": \"DEFLATE\"}\n", - "OVERVIEW_LEVELS = 4 \n", - "OVERVIEW_RESAMPLING = 'average'\n", - "\n", - "for key in glob.glob(\"data/*.nc4\"):\n", - " xds= xr.open_dataset(key)\n", - " xds = xds.assign_coords(lon=(((xds.lon + 180) % 360) - 180)).sortby(\"lon\")\n", - " \n", - " for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n", - " yearmonth = extract_date_from_key(key)\n", - " filename = f\"output/GRA2PESv1.0_total_{(\"-\").join(var.split('_'))}_{yearmonth}.tif\"\n", - " data = getattr(xds,var)\n", - " data.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", - " data.rio.write_crs(\"epsg:4326\", inplace=True)\n", - " \n", - " # Create a temporary file to hold the COG\n", - " with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as temp_file:\n", - " data.rio.to_raster(f\"temp_{yearmonth}_{var}.tif\", **COG_PROFILE, nodata=-9999)\n", - " # Create COG with overviews and nodata value\n", - " cog_translate(\n", - " f\"temp_{yearmonth}_{var}.tif\",\n", - " temp_file.name,\n", - " cog_profiles.get(\"deflate\"),\n", - " overview_level=OVERVIEW_LEVELS,\n", - " overview_resampling=OVERVIEW_RESAMPLING,\n", - " nodata=-9999\n", - " )\n", - " \n", - " # Move the temporary file to the desired local path\n", - " os.rename(temp_file.name, filename)\n", - " \n", - " if os.path.exists(f\"temp_{yearmonth}_{var}.tif\"):\n", - " os.remove(f\"temp_{yearmonth}_{var}.tif\")\n", - " del data\n", - " print(f\"Done for: {filename}\")\n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 23ba8a4c03eda2610e0b0e661e1170e05a0c5807 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 14:02:19 -0500 Subject: [PATCH 21/47] add links --- ...a2pes-ghg-monthgrid-v1_User_Notebook.ipynb | 527 ++++++++++++++++++ 1 file changed, 527 insertions(+) create mode 100644 user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb diff --git a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb new file mode 100644 index 00000000..fd1cc5c0 --- /dev/null +++ b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -0,0 +1,527 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "257bb7c7-a122-491f-b05f-5e5293762d9e", + "metadata": {}, + "source": [ + "---\n", + "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", + "description: Documentation of data usage\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 30 August 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "3c3d7d0a", + "metadata": {}, + "source": [ + "## Run this notebook\n", + "\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", + "\n", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fgra2pes-ghg-monthgrid-v1_User_Notebook_User_Notebook.ipynb&branch=main)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "8ad57cc0", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "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 Vulcan Fossil Fuel CO₂ Emissions Data product.\n", + "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", + "4. After the visualization, we will perform zonal statistics for a given polygon.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "c2ffe091", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "The Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future.\n", + "\n", + "For more information regarding this dataset, please visit the [GRA2PES Greenhouse Gas and Air Quality Species, Version 1](https://earth.gov/ghgcenter/data-catalog/gra2pes-ghg-monthgrid-v1) data overview page." + ] + }, + { + "cell_type": "markdown", + "id": "ce2f8197", + "metadata": {}, + "source": [ + "# Install the Required Libraries\n", + "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:\n", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "503e5661-b99b-421b-b4ba-361bb531e5fa", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import folium\n", + "import folium.plugins\n", + "from folium import Map, TileLayer \n", + "from pystac_client import Client \n", + "import branca \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "9904b2c6", + "metadata": {}, + "source": [ + "## Querying the STAC API\n", + "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a7602b2d-2650-42cb-9ae3-09eb3fe7891b", + "metadata": {}, + "outputs": [], + "source": [ + "# Provide STAC and RASTER API endpoints\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "\n", + "# Please use the collection name similar to the one used in the STAC collection.\n", + "# Name of the collection for Vulcan Fossil Fuel CO₂ Emissions, Version 4. \n", + "collection_name = \"gra2pes-ghg-monthgrid-v1\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "941eae92-6ec0-46a4-ab8f-739973b59e6f", + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch the collection from STAC collections using the appropriate endpoint\n", + "# the 'requests' library allows a HTTP request possible\n", + "collection_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n" + ] + }, + { + "cell_type": "markdown", + "id": "132947f6", + "metadata": {}, + "source": [ + "Examining the contents of our `collection` under the `temporal` variable, we see that the data is available from January 2010 to December 2021. By looking at the `dashboard:time density`, we observe that the data is periodic with year time density." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b56d6c87-11b9-48ca-9723-8304984a644b", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a function that would search for the above data collection in the STAC API\n", + "def 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" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "eaaf1487-2e71-490b-a968-e57ee332a919", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 items\n" + ] + } + ], + "source": [ + "# Apply the above function and check the total number of items available within the collection\n", + "number_of_items = get_item_count(collection_name)\n", + "items_graapes = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", + "print(f\"Found {len(items_vulcan)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "01619ee2-728c-4abc-9edf-a2c85d87128d", + "metadata": {}, + "outputs": [], + "source": [ + "# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\n", + "items = {item[\"properties\"][\"start_datetime\"][:7]: item for item in items_graapes} \n", + "# rh = Heterotrophic Respiration\n", + "asset_name = \"co2\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f9deb11d-4c33-4f59-8532-7829602e3113", + "metadata": {}, + "outputs": [], + "source": [ + "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\"]}" + ] + }, + { + "cell_type": "markdown", + "id": "8b38ebba", + "metadata": {}, + "source": [ + "Now, we will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. We will do this twice, once for 2021-01 and again for 2021-05, so that we can visualize each event independently." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "27b31984-ac8e-473f-ba2d-eb309c9708c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1&item=gra2pes-co2-monthgrid-v1-202101&assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n", + " 'center': [-97.94829999999851, 35.20137600000065, 0]}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "color_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", + "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", + "\n", + "# To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM']\" statement\n", + "# For example, you can change the current statement \"items['2003-12']\" to \"items['2016-10']\" \n", + "_202101_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{items['2021-01']['collection']}/items/{items['2021-01']['id']}/tilejson.json?collection={items['2021-01']['collection']}&item={items['2021-01']['id']}\"\n", + "\n", + " f\"&assets={asset_name}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + " f\"&rescale=0,150\", \n", + ").json()\n", + "_202101_tile" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "80f5933d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://dev.ghg.center/api/raster/collections/gra2pes-co2-monthgrid-v1/items/gra2pes-co2-monthgrid-v1-202105/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=gra2pes-co2-monthgrid-v1&item=gra2pes-co2-monthgrid-v1-202105&assets=co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-137.3143, 18.173376, -58.58229999999702, 52.229376000001295],\n", + " 'center': [-97.94829999999851, 35.20137600000065, 0]}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_202105_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{items['2021-05']['collection']}/items/{items['2021-05']['id']}/tilejson.json?collection={items['2021-05']['collection']}&item={items['2021-05']['id']}\"\n", + "\n", + " f\"&assets={asset_name}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + " f\"&rescale=0,150\", \n", + ").json()\n", + "_202105_tile" + ] + }, + { + "cell_type": "markdown", + "id": "1c9874c7", + "metadata": {}, + "source": [ + "## Visualizing Total Fossil Fuel CO₂ Emissions" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6cf3c0f1-8d39-4cdd-96c7-96e6229c1584", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", + "\n", + "\n", + "# Define the first map layer with the CO2 Flux data for December 2022\n", + "map_layer_202101 = TileLayer(\n", + " tiles=_202101_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " attr=\"GHG\", # Set the attribution \n", + " name='2021-01 Total CO2 Fossil Fuel Emissions', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", + " opacity=0.8, # Adjust the transparency of the layer\n", + ")\n", + "# Add the first layer to the Dual Map \n", + "map_layer_202101.add_to(map_.m1)\n", + "\n", + "map_layer_202105 = TileLayer(\n", + " tiles=_202105_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " attr=\"GHG\", # Set the attribution \n", + " name='2021-05 Total CO2 Emissions', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", + " opacity=0.8, # Adjust the transparency of the layer\n", + ")\n", + "# Add the first layer to the Dual Map \n", + "map_layer_2021.add_to(map_.m2)\n", + "\n", + "map_" + ] + }, + { + "cell_type": "markdown", + "id": "f3beb616-7e62-457b-9435-92a8f2d4ac60", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we have successfully explored, analyzed, and visualized the STAC collection for GRA2PES greenhouse gases Emissions, Version 1 dataset.\n", + "\n", + "1. Install and import the necessary libraries\n", + "2. Fetch the collection from STAC collections using the appropriate endpoints\n", + "3. Count the number of existing granules within the collection\n", + "4. Map and compare the total CO₂ emissions for two distinctive months/years\n", + "\n", + "\n", + "If you have any questions regarding this user notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d0920e0-c050-4ea5-9e18-88b46ec61cc5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ceca8be62de92f3e8ee5deda6167ed4b26f5c872 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Fri, 6 Sep 2024 16:26:35 -0500 Subject: [PATCH 22/47] add report --- ...-v1_Processing and Verification Report.qmd | 9 +++++++++ ...-v1_Processing and Verification Report.pdf | Bin 0 -> 1321786 bytes 2 files changed, 9 insertions(+) create mode 100644 processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.pdf diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd new file mode 100644 index 00000000..454c15c9 --- /dev/null +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: TM5-4DVar Isotopic CH₄ Inverse Fluxes +description: "The Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." +--- + + +

This browser does not support PDFs. Please download the PDF to view it: Download PDF.

+ +
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generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb create mode 100644 generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/monthly_stats.json create mode 100644 generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/overall_stats.json diff --git a/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb new file mode 100644 index 00000000..03811fd3 --- /dev/null +++ b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "9874cc9f-323e-490b-af48-e63f243d3b21", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import os\n", + "import glob\n", + "from datetime import datetime\n", + "import boto3\n", + "import s3fs\n", + "import tempfile\n", + "import numpy as np\n", + "import pandas as pd\n", + "import re\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7022892e-93b7-4074-962e-97b536db379f", + "metadata": {}, + "outputs": [], + "source": [ + "raw_files = glob.glob(\"data/*.nc4\")\n", + "output_files= glob.glob(\"output_final2/*.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "edf355ac-148a-448e-a031-e2bd6b30c32d", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_date_from_key(key):\n", + " # Split the key to isolate the part that contains the date\n", + " parts = key.split('_')\n", + " for part in parts:\n", + " # Check if the part is numeric and has the length of 6 (YYYYMM format)\n", + " if part.isdigit() and len(part) == 6:\n", + " return part\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a406a81-ef93-4736-93ad-2801d06dcc9a", + "metadata": {}, + "outputs": [], + "source": [ + "overall_raw= []\n", + "raw= pd.DataFrame(columns=['filename','min_raw','max_raw','mean_raw','std_raw'])\n", + "for file in raw_files:\n", + " xds= xr.open_dataset(file)\n", + " year_month = extract_date_from_key(file)\n", + " for var in [\"PM25-PRI\",\"CO2\",\"CO\",\"NOX\",\"SOX\"]:\n", + " data = getattr(xds,var)\n", + " overall_raw.append(data)\n", + " data = np.ma.masked_where((data == -9999), data)\n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " std_val = np.nanstd(data)\n", + " stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n", + " raw.loc[len(raw)] = stats" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7db9dbd8-0a91-427b-bfa6-bb38576dca57", + "metadata": {}, + "outputs": [], + "source": [ + "overall_cog=[]\n", + "cog= pd.DataFrame(columns=['filename','min_cog','max_cog','mean_cog','std_cog'])\n", + "for file in output_files:\n", + " data= xr.open_dataarray(file)\n", + " \n", + " year_month = file[:-4][-6:]\n", + " var = file.split(\"_\")[-2]\n", + " overall_cog.append(data)\n", + " data = np.ma.masked_where((data == -9999), data)\n", + " \n", + " \n", + " min_val = np.nanmin(data)\n", + " max_val = np.nanmax(data)\n", + " mean_val = np.nanmean(data)\n", + " std_val = np.nanstd(data)\n", + " stats = [f\"{var}_{year_month}\", min_val, max_val, mean_val, std_val]\n", + " cog.loc[len(cog)] = stats" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "23d1e778-d977-44d6-8eed-7df31e88e94b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['overall_raw', 0.0, 110011.766, 5.1753755, 172.26357]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# validation for reprojected data (non zero) - overall calculation\n", + "overall_raw= np.array(overall_raw)\n", + "overall_raw= np.ma.masked_where((overall_raw == -9999) , overall_raw)\n", + "nan_min = np.nanmin(overall_raw)\n", + "nan_max = np.nanmax(overall_raw)\n", + "nan_mean = np.nanmean(overall_raw)\n", + "nan_std = np.nanstd(overall_raw)\n", + "[\"overall_raw\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4c17a38a-4d0f-4f0e-a95d-67fac2128552", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['overall_cog', 0.0, 110011.766, 5.1753297, 172.27177]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overall_cog= np.array(overall_cog)\n", + "nan_min = np.nanmin(overall_cog)\n", + "nan_max = np.nanmax(overall_cog)\n", + "nan_mean = np.nanmean(overall_cog)\n", + "nan_std = np.nanstd(overall_cog)\n", + "[\"overall_cog\",nan_min,nan_max,nan_mean,nan_std]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "72623f8a-46ea-405c-9835-f006ea8699f6", + "metadata": {}, + "outputs": [], + "source": [ + "pd.merge(cog, raw, on='filename', how='inner').to_json(\"monthly_stats.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1b9c7790-37d6-4654-b260-c6656ac0af81", + "metadata": {}, + "outputs": [], + "source": [ + "keys = [\"overall_raw\", \"nan_min\", \"nan_max\", \"nan_mean\", \"nan_std\"]\n", + "values = [\"overall_cog\", 0.0, 110011.766, 5.1753297, 172.27177]\n", + "\n", + "# Create a dictionary from the lists\n", + "data_dict = dict(zip(keys, values))\n", + "\n", + "# Save the dictionary as a JSON file\n", + "with open(\"overall_stats.json\", \"w\") as json_file:\n", + " json.dump(data_dict, json_file, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c145b50f-25ff-49f0-b27e-9c5e9dae1b42", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/monthly_stats.json b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/monthly_stats.json new file mode 100644 index 00000000..2765cf36 --- /dev/null +++ b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/monthly_stats.json @@ -0,0 +1 @@ +{"filename":{"0":"CO2_202103","1":"PM25-PRI_202109","2":"NOX_202101","3":"PM25-PRI_202108","4":"CO2_202102","5":"CO_202109","6":"NOX_202102","7":"NOX_202103","8":"CO_202108","9":"CO2_202101","10":"CO2_202111","11":"CO2_202105","12":"NOX_202107","13":"NOX_202106","14":"NOX_202112","15":"CO2_202104","16":"CO2_202110","17":"CO2_202106","18":"CO2_202112","19":"NOX_202104","20":"NOX_202110","21":"SOX_202108","22":"SOX_202109","23":"NOX_202111","24":"NOX_202105","25":"CO2_202107","26":"CO_202103","27":"NOX_202108","28":"SOX_202104","29":"SOX_202110","30":"SOX_202111","31":"SOX_202105","32":"NOX_202109","33":"CO_202102","34":"PM25-PRI_202101","35":"CO2_202109","36":"PM25-PRI_202103","37":"SOX_202107","38":"SOX_202106","39":"SOX_202112","40":"CO_202101","41":"PM25-PRI_202102","42":"CO2_202108","43":"PM25-PRI_202112","44":"PM25-PRI_202106","45":"CO_202105","46":"CO_202111","47":"SOX_202102","48":"SOX_202103","49":"CO_202110","50":"CO_202104","51":"PM25-PRI_202107","52":"PM25-PRI_202105","53":"PM25-PRI_202111","54":"CO_202112","55":"CO_202106","56":"SOX_202101","57":"CO_202107","58":"PM25-PRI_202110","59":"PM25-PRI_202104"},"min_cog":{"0":0.0,"1":0.0,"2":0.0,"3":0.0,"4":0.0,"5":0.0,"6":0.0,"7":0.0,"8":0.0,"9":0.0,"10":0.0,"11":0.0,"12":0.0,"13":0.0,"14":0.0,"15":0.0,"16":0.0,"17":0.0,"18":0.0,"19":0.0,"20":0.0,"21":0.0,"22":0.0,"23":0.0,"24":0.0,"25":0.0,"26":0.0,"27":0.0,"28":0.0,"29":0.0,"30":0.0,"31":0.0,"32":0.0,"33":0.0,"34":0.0,"35":0.0,"36":0.0,"37":0.0,"38":0.0,"39":0.0,"40":0.0,"41":0.0,"42":0.0,"43":0.0,"44":0.0,"45":0.0,"46":0.0,"47":0.0,"48":0.0,"49":0.0,"50":0.0,"51":0.0,"52":0.0,"53":0.0,"54":0.0,"55":0.0,"56":0.0,"57":0.0,"58":0.0,"59":0.0},"max_cog":{"0":99142.828125,"1":60.1593589783,"2":140.4214324951,"3":61.5887756348,"4":97219.625,"5":397.3135986328,"6":130.5366210938,"7":105.1165237427,"8":415.3591918945,"9":91096.8828125,"10":88533.5546875,"11":62123.078125,"12":140.8819885254,"13":86.5910568237,"14":128.4561920166,"15":57396.87890625,"16":88998.8359375,"17":79892.359375,"18":73563.390625,"19":105.8392486572,"20":114.8524780273,"21":300.5194702148,"22":220.6692810059,"23":100.5299224854,"24":86.0685272217,"25":110011.765625,"26":412.7490234375,"27":110.732711792,"28":171.8090820312,"29":130.2423553467,"30":137.5035400391,"31":127.7108459473,"32":144.7532043457,"33":351.2598571777,"34":53.7161483765,"35":88965.75,"36":52.3354797363,"37":311.7534790039,"38":283.9587402344,"39":126.0853271484,"40":399.1550292969,"41":47.4615211487,"42":98845.890625,"43":58.1464614868,"44":59.0023841858,"45":405.7946777344,"46":394.1158447266,"47":348.0729675293,"48":178.0572814941,"49":426.3120727539,"50":384.8923339844,"51":61.7827987671,"52":60.6416397095,"53":57.6702461243,"54":394.0494995117,"55":393.0791625977,"56":183.6835021973,"57":413.178314209,"58":60.0170593262,"59":57.0394020081},"mean_cog":{"0":25.6467037201,"1":0.0116616879,"2":0.0681837499,"3":0.0144166574,"4":25.283706665,"5":0.1346394122,"6":0.0634888634,"7":0.069521524,"8":0.1573881805,"9":27.1259441376,"10":24.9467983246,"11":23.7026958466,"12":0.0725220144,"13":0.0687953159,"14":0.0695117414,"15":23.7975788116,"16":24.4442462921,"17":25.7730770111,"18":26.8717327118,"19":0.0661727414,"20":0.0671616197,"21":0.0184791461,"22":0.0167266242,"23":0.0673919544,"24":0.0655936003,"25":27.6484832764,"26":0.1506196856,"27":0.0732667074,"28":0.0155894095,"29":0.016421834,"30":0.0159227867,"31":0.0159961246,"32":0.0671358481,"33":0.1421051621,"34":0.013114511,"35":24.785697937,"36":0.0139651289,"37":0.018189935,"38":0.0169686675,"39":0.0166603811,"40":0.157701984,"41":0.0117344875,"42":27.550535202,"43":0.0128116012,"44":0.0138489679,"45":0.1381697953,"46":0.1438717544,"47":0.016828509,"48":0.0167361256,"49":0.1384300739,"50":0.1413106024,"51":0.0143202366,"52":0.012700703,"53":0.012601018,"54":0.1603042036,"55":0.1486292779,"56":0.0177297629,"57":0.1564442813,"58":0.012293742,"59":0.0128600961},"std_cog":{"0":354.3450927734,"1":0.0710811466,"2":0.5531046391,"3":0.074371919,"4":393.1003723145,"5":0.9946398139,"6":0.5418539047,"7":0.5088012815,"8":1.0618803501,"9":406.438659668,"10":342.8004150391,"11":338.4582214355,"12":0.5533254743,"13":0.5018032193,"14":0.5253078341,"15":319.9563598633,"16":353.5364685059,"17":407.8274230957,"18":360.8631896973,"19":0.4679081738,"20":0.5034154654,"21":0.6297003031,"22":0.5386172533,"23":0.4962346256,"24":0.4614534974,"25":459.3640441895,"26":1.0559206009,"27":0.5573871732,"28":0.4734382629,"29":0.491617173,"30":0.4692336023,"31":0.4754177034,"32":0.518550992,"33":0.9343034029,"34":0.070362255,"35":391.080871582,"36":0.0697423965,"37":0.6301494837,"38":0.5720852613,"39":0.4733521044,"40":1.0383332968,"41":0.063369073,"42":457.025390625,"43":0.0735483393,"44":0.0717824697,"45":1.0111931562,"46":1.0130162239,"47":0.5711205006,"48":0.5164202452,"49":1.0481258631,"50":0.9873959422,"51":0.0751705989,"52":0.0723561049,"53":0.0701987371,"54":1.0439089537,"55":1.0241541862,"56":0.5332579613,"57":1.0615743399,"58":0.0715863854,"5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\ No newline at end of file diff --git a/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/overall_stats.json b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/overall_stats.json new file mode 100644 index 00000000..32ee0e43 --- /dev/null +++ b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/overall_stats.json @@ -0,0 +1,7 @@ +{ + "overall_raw": "overall_cog", + "nan_min": 0.0, + "nan_max": 110011.766, + "nan_mean": 5.1753297, + "nan_std": 172.27177 +} \ No newline at end of file From e2d0cb6f246208ebe4f58a420e1bd336a3505876 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Mon, 9 Sep 2024 13:11:48 -0500 Subject: [PATCH 25/47] add links --- data_usage.qmd | 7 ++++--- datatransformationcode.qmd | 1 + datausage.qmd | 2 ++ processingreport.qmd | 1 + workflow.qmd | 1 + 5 files changed, 9 insertions(+), 3 deletions(-) diff --git a/data_usage.qmd b/data_usage.qmd index c2d4defc..4eabfd6b 100644 --- a/data_usage.qmd +++ b/data_usage.qmd @@ -21,11 +21,12 @@ On this site, you can find the technical documentation for the services the cent 3. [EMIT Methane Point Source Plume Complexes](user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) 5. [GOSAT-based Top-down Total and Natural Methane Emissions](user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb) -6. [Wetland Methane Emissions, LPJ-wsl Model](user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb) - +6. [Wetland Methane Emissions, LPJ-wsl Model](user_data_notebooks/lpjwsl-wetlandch4-grid-v1_User_Notebook.ipynb) 7. [OCO-2 MIP Top-Down CO₂ Budgets](user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb) 8. [OCO-2 GEOS Column CO₂ Concentrations](user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb) 9. [ODIAC Fossil Fuel CO₂ Emissions](user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb) 10. [SEDAC Gridded World Population Density](user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb) 11. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) -12. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) \ No newline at end of file +12. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) +13. [Vulcan Fossil Fuel CO₂ Emissions](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) +13. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) \ No newline at end of file diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index 8336d085..aa5b650e 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -20,6 +20,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) 5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) +6. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) diff --git a/datausage.qmd b/datausage.qmd index 988c0a7b..e3f55cc3 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -22,6 +22,8 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset. 5. Vulcan Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset. +5. GRA2PES + - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. ## Natural Greenhouse Gas Emissions and Sinks 1. Air-Sea CO₂ Flux, ECCO-Darwin Model v5 diff --git a/processingreport.qmd b/processingreport.qmd index e162ae64..eead5a26 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -19,6 +19,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) +6. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES) Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) diff --git a/workflow.qmd b/workflow.qmd index e164d45b..14aaa5d8 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -17,6 +17,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) +5. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES) Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) ## Natural Greenhouse Gas Sources Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd) From 7fbf92748302ce9a8b0e01db17a06ea38f86a177 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Mon, 9 Sep 2024 13:13:09 -0500 Subject: [PATCH 26/47] minor change --- data_usage.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data_usage.qmd b/data_usage.qmd index 4eabfd6b..bf975909 100644 --- a/data_usage.qmd +++ b/data_usage.qmd @@ -29,4 +29,4 @@ On this site, you can find the technical documentation for the services the cent 11. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) 12. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) 13. [Vulcan Fossil Fuel CO₂ Emissions](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) -13. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) \ No newline at end of file +13. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) \ No newline at end of file From 67181ac89fca1c0250703ffeda6245bfc2c197af Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Mon, 9 Sep 2024 13:20:37 -0500 Subject: [PATCH 27/47] add additional links --- _quarto.yml | 4 ++++ data_usage.qmd | 2 +- data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd | 2 +- datatransformationcode.qmd | 2 +- processingreport.qmd | 2 +- workflow.qmd | 2 +- 6 files changed, 9 insertions(+), 5 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index 35903109..c012a158 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -58,6 +58,7 @@ website: - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb + - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -86,6 +87,7 @@ website: - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb - cog_transformation/epa-ch4emission-grid-v2express.ipynb - cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb + - cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb @@ -112,6 +114,7 @@ website: - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd + - processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd @@ -141,6 +144,7 @@ website: - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd - data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd + - data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd diff --git a/data_usage.qmd b/data_usage.qmd index bf975909..cbc98c75 100644 --- a/data_usage.qmd +++ b/data_usage.qmd @@ -29,4 +29,4 @@ On this site, you can find the technical documentation for the services the cent 11. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) 12. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) 13. [Vulcan Fossil Fuel CO₂ Emissions](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) -13. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) \ No newline at end of file +13. [Greenhouse Gas And Air Pollutants Emissions System](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb) \ No newline at end of file diff --git a/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd b/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd index 43327b20..2411d7a3 100644 --- a/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd +++ b/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd @@ -1,3 +1,3 @@ -# Greenhouse Gas And Air Pollutants Emissions System (GRA2PES) +# Greenhouse Gas And Air Pollutants Emissions System ![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/gra2pes-ghg-monthgrid-v1_Data_Flow.png) \ No newline at end of file diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index aa5b650e..d12ae9ac 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -20,7 +20,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) 5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) -6. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) +6. [Greenhouse Gas And Air Pollutants Emissions System](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) diff --git a/processingreport.qmd b/processingreport.qmd index eead5a26..1eefa04d 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -19,7 +19,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) -6. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES) Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) +6. [Greenhouse Gas And Air Pollutants Emissions System Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) diff --git a/workflow.qmd b/workflow.qmd index 14aaa5d8..17da9878 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -17,7 +17,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) -5. [Greenhouse Gas And Air Pollutants Emissions System (GRA2PES) Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) +5. [Greenhouse Gas And Air Pollutants Emissions System Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) ## Natural Greenhouse Gas Sources Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd) From be5e939e5c711f1ee2fc6d61db0d8c71107c1c00 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Mon, 9 Sep 2024 14:06:44 -0500 Subject: [PATCH 28/47] minor change --- ...2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd index 454c15c9..2f9c302f 100644 --- a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -1,5 +1,5 @@ --- -title: TM5-4DVar Isotopic CH₄ Inverse Fluxes +title: Greenhouse Gas And Air Pollutants Emissions System description: "The Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." --- From 19edc6ae09c42451b4285e337cc8ce651307c66e Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Mon, 9 Sep 2024 16:04:16 -0500 Subject: [PATCH 29/47] rename endpoints --- ...arbonflux-monthgrid-v3_User_Notebook.ipynb | 24 +- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 24 +- .../emit-ch4plume-v1_User_Notebook.ipynb | 1024 +---------------- ...mission-grid-v2express_User_Notebook.ipynb | 22 +- ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 20 +- ...sim-wetlandch4-grid-v2_User_Notebook.ipynb | 788 +------------ ...etlandch4-monthgrid-v2_User_Notebook.ipynb | 743 +----------- ...-carbonflux-daygrid-v1_User_Notebook.ipynb | 912 +-------------- .../noaa-insitu_User_Notebook.ipynb | 66 +- .../oco2-mip-National-co2budget.ipynb | 42 +- ...-co2budget-yeargrid-v1_User_Notebook.ipynb | 767 +----------- ...2geos-co2-daygrid-v10r_User_Notebook.ipynb | 24 +- ...-ffco2-monthgrid-v2022_User_Notebook.ipynb | 26 +- ...-ffco2-monthgrid-v2023_User_Notebook.ipynb | 22 +- ...sity-yeargrid5yr-v4.11_User_Notebook.ipynb | 20 +- ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 34 +- ...lcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 747 +----------- 17 files changed, 340 insertions(+), 4965 deletions(-) diff --git a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb index 62e89826..82ef52fe 100644 --- a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb +++ b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fcasagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fcasagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "1. Identify available dates and temporal frequency of observations for a given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, visualize two tiles (side-by-side), allowing time point comparison.\n", "4. After the visualization, perform zonal statistics for a given polygon.\n", " " @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +232,7 @@ "# To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM']\" statement\n", "# For example, you can change the current statement \"items['2003-12']\" to \"items['2016-10']\" \n", "december_2003_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2003-12']['collection']}&item={items['2003-12']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2003-12']['collection']}/items/{items['2003-12']['id']}/tilejson.json?\"\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", @@ -248,7 +248,7 @@ "source": [ "# Now we apply the same process used in the previous step for the December 2017 tile\n", "december_2017_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2017-12']['collection']}&item={items['2017-12']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2017-12']['collection']}/items/{items['2017-12']['id']}/tilejson.json?\"\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", @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -534,7 +534,7 @@ "source": [ "# Fetch the third granule in the collection and set the color scheme and rescale values. \n", "october_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\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", @@ -614,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 64ccd48c..d5b3afa7 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "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 Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon." ] @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +277,7 @@ "december_2022_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[0]]['collection']}/items/{items[list(items.keys())[0]]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -309,7 +309,7 @@ "april_2021_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[20]]['collection']}&item={items[list(items.keys())[20]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[20]]['collection']}/items/{items[list(items.keys())[20]]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -494,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -735,7 +735,7 @@ "September2022_co2_flux = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[3]['collection']}&item={items[3]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[3]['collection']}/items/{items[3]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -870,7 +870,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb index e6277461..add388a4 100644 --- a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb +++ b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb @@ -25,7 +25,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Femit-ch4plume-v1_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Femit-ch4plume-v1_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -37,7 +37,7 @@ "## Approach\n", "\n", "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 Earth Surface Mineral Dust Source Investigation (EMIT) methane emission plumes data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.Map`, visualize the plumes.\n", "4. After the visualization, perform zonal statistics for a given polygon.\n", "\n", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -115,805 +115,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'emit-ch4plume-v1',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-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/emit-ch4plume-v1'}],\n", - " 'title': 'CH4 plumes emissions',\n", - " 'assets': None,\n", - " 'extent': {'spatial': {'bbox': [[-121.90662384033203,\n", - " -46.6751594543457,\n", - " 151.46371459960938,\n", - " 51.3333740234375]]},\n", - " 'temporal': {'interval': [['2022-08-10T06:49:57+00:00',\n", - " '2024-04-23T06:09:58+00:00']]}},\n", - " 'license': 'CC0-1.0',\n", - " 'keywords': None,\n", - " 'providers': None,\n", - " 'summaries': {'datetime': ['2022-08-10T06:49:57Z',\n", - " '2022-08-10T06:50:21Z',\n", - " '2022-08-10T06:51:32Z',\n", - " '2022-08-11T04:26:30Z',\n", - " '2022-08-14T05:14:12Z',\n", - " '2022-08-15T04:28:26Z',\n", - " '2022-08-15T04:28:38Z',\n", - " '2022-08-15T07:46:45Z',\n", - " '2022-08-15T14:08:23Z',\n", - " '2022-08-16T03:44:09Z',\n", - " '2022-08-16T10:10:35Z',\n", - " '2022-08-16T10:10:58Z',\n", - " '2022-08-16T11:45:05Z',\n", - " '2022-08-17T04:32:35Z',\n", - " '2022-08-17T09:20:38Z',\n", - " '2022-08-18T03:42:31Z',\n", - " '2022-08-18T07:01:05Z',\n", - " '2022-08-18T08:35:06Z',\n", - " '2022-08-18T11:44:40Z',\n", - " '2022-08-19T09:22:31Z',\n", - 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" 'roles': ['data', 'layer'],\n", - " 'title': 'CH4 Plumes Emissions',\n", - " 'description': 'Methane plumes from point source emitters.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': None,\n", - " 'dashboard:is_periodic': False,\n", - " 'dashboard:time_density': 'day'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -961,17 +165,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 1266 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check total number of items available\n", "number_of_items = get_item_count(collection_name)\n", @@ -1009,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1041,36 +237,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20230418T200118_000829&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-775.518310546875%2C2180.226318359375'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-104.76285251117253,\n", - " 39.85322425220504,\n", - " -104.74658553556483,\n", - " 39.86515336765068],\n", - " 'center': [-104.75471902336868, 39.85918880992786, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.\n", "item_id = \"EMIT_L2B_CH4PLM_001_20230418T200118_000829\"\n", "color_map = \"magma\"\n", "methane_plume_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={plume_complexes[item_id]['collection']}&item={plume_complexes[item_id]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{plume_complexes[item_id]['collection']}/items/{plume_complexes[item_id]['id']}/tilejson.json?\"\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", @@ -1088,172 +263,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Set a colormap for the granule\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)\n", @@ -1305,12 +317,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Plume AOI \n", - "plumes_coordinates = items[item_id][\"geometry\"][\"coordinates\"]\n", + "plumes_coordinates = plume_complexes[item_id][\"geometry\"][\"coordinates\"]\n", "methane_plume_aoi = {\n", " \"type\": \"Feature\",\n", " \"properties\": {},\n", @@ -1369,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1451,7 +463,7 @@ "outputs": [], "source": [ "plume_tile_2 = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[0]['collection']}&item={items[0]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[0]['collection']}/items/{items[0]['id']}/tilejson.json?\"\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", @@ -1521,7 +533,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb index ae104f3b..79e839c9 100644 --- a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb +++ b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fepa-ch4emission-grid-v2express_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fepa-ch4emission-grid-v2express_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "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.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon.\n", "\n", @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -252,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +292,7 @@ "january_2018_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2018-01']['collection']}/items/{items['2018-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -320,7 +320,7 @@ "january_2012_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2012-01']['collection']}/items/{items['2012-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -399,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -491,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -650,7 +650,7 @@ "tile_2016 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", diff --git a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index d079032e..92a8cc29 100644 --- a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fgosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fgosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "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.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon.\n", "\n", @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -196,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ "source": [ "color_map = \"rainbow\" # please select the color ramp from matplotlib library.\n", "january_2019_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2019-01-01']['collection']}&item={items['2019-01-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?\"\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", @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -454,7 +454,7 @@ "outputs": [], "source": [ "tile_2016 = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[0]['collection']}&item={items[0]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[0]['collection']}/items/{items[0]['id']}/tilejson.json?\"\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", diff --git a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb index e4e980bc..18ec6ea1 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb @@ -29,7 +29,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Flpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Flpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -41,7 +41,7 @@ "## Approach\n", "\n", "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-EOSIM Model data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, visualize two tiles (side-by-side), allowing time point comparison.\n", "4. After the visualization, perform zonal statistics for a given polygon.\n", " " @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,62 +122,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'lpjeosim-wetlandch4-daygrid-v2',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'}],\n", - " 'title': 'Wetland Methane Emissions, LPJ-EOSIM Model v2',\n", - " 'assets': None,\n", - " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", - " 'temporal': {'interval': [['1990-01-01T00:00:00+00:00',\n", - " '2024-02-27T00:00:00+00:00']]}},\n", - " 'license': 'CC0 1.0',\n", - " 'keywords': None,\n", - " 'providers': [{'url': None,\n", - " 'name': 'NASA',\n", - " 'roles': None,\n", - " 'description': None}],\n", - " 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-02-27T00:00:00Z']},\n", - " 'description': 'Global, daily estimates of methane (CH4) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated 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 have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions 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. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers.',\n", - " 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", - " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per day. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n", - " 'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n", - " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per day. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n", - " 'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n", - " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per day. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': None,\n", - " 'dashboard:is_periodic': True,\n", - " 'dashboard:time_density': 'day'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -254,17 +201,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 800 items\n" - ] - } - ], + "outputs": [], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -335,27 +274,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2&item=lpjeosim-wetlandch4-daygrid-v2-20240101day&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -368,7 +289,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date1]['collection']}&item={items[date1]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -388,27 +309,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2&item=lpjeosim-wetlandch4-daygrid-v2-20240130day&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Make a GET request to retrieve information for date mentioned below\n", "date2 = '2024-01-30'\n", @@ -416,7 +319,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year, month and date of the observed parameter, you can modify the date2 variable above\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date2]['collection']}&item={items[date2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -444,146 +347,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For this study we are going to compare the CH₄ Emissions in date1 and date2 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -655,110 +421,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -791,17 +456,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 800 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -873,18 +530,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 14.7 s, sys: 1.85 s, total: 16.5 s\n", - "Wall time: 6min 9s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -895,47 +543,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.0,\n", - " 'max': 0.0031482954509556293,\n", - " 'mean': 0.00022546476167109277,\n", - " 'count': 144.0,\n", - " 'sum': 0.03246692568063736,\n", - " 'std': 0.0005144286193682974,\n", - " 'median': 5.088370380690321e-05,\n", - " 'majority': 0.0,\n", - " 'minority': 0.0,\n", - " 'unique': 144.0,\n", - " 'histogram': [[119.0, 15.0, 2.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 2.0],\n", - " [0.0,\n", - " 0.000314829550916329,\n", - " 0.000629659101832658,\n", - " 0.0009444886236451566,\n", - " 0.001259318203665316,\n", - " 0.0015741477254778147,\n", - " 0.0018889772472903132,\n", - " 0.002203806769102812,\n", - " 0.002518636407330632,\n", - " 0.002833465812727809,\n", - " 0.0031482954509556293]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 144.0,\n", - " 'percentile_2': 1.4471482018052484e-07,\n", - " 'percentile_98': 0.002311925734393298}},\n", - " 'datetime': '2024-02-27T00:00:00+00:00'}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -943,202 +553,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02024-02-27T00:00:00+00:000.00.0031480.000225144.00.0324670.0005140.0000510.00.0144.0[[119.0, 15.0, 2.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0...100.00.0144.01.447148e-070.0023122024-02-27 00:00:00+00:00
12024-02-26T00:00:00+00:000.00.0031090.000225144.00.0324030.0005160.0000500.00.0144.0[[119.0, 15.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 0...100.00.0144.01.357729e-070.0023202024-02-26 00:00:00+00:00
22024-02-25T00:00:00+00:000.00.0031860.000230144.00.0330820.0005340.0000500.00.0144.0[[119.0, 14.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1...100.00.0144.01.308106e-070.0024062024-02-25 00:00:00+00:00
32024-02-24T00:00:00+00:000.00.0033230.000238144.00.0343000.0005590.0000500.00.0144.0[[120.0, 13.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1...100.00.0144.01.325920e-070.0025232024-02-24 00:00:00+00:00
42024-02-23T00:00:00+00:000.00.0033200.000242144.00.0347900.0005660.0000500.00.0144.0[[120.0, 13.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1...100.00.0144.01.359002e-070.0025472024-02-23 00:00:00+00:00
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum \\\n", - "0 2024-02-27T00:00:00+00:00 0.0 0.003148 0.000225 144.0 0.032467 \n", - "1 2024-02-26T00:00:00+00:00 0.0 0.003109 0.000225 144.0 0.032403 \n", - "2 2024-02-25T00:00:00+00:00 0.0 0.003186 0.000230 144.0 0.033082 \n", - "3 2024-02-24T00:00:00+00:00 0.0 0.003323 0.000238 144.0 0.034300 \n", - "4 2024-02-23T00:00:00+00:00 0.0 0.003320 0.000242 144.0 0.034790 \n", - "\n", - " std median majority minority unique \\\n", - "0 0.000514 0.000051 0.0 0.0 144.0 \n", - "1 0.000516 0.000050 0.0 0.0 144.0 \n", - "2 0.000534 0.000050 0.0 0.0 144.0 \n", - "3 0.000559 0.000050 0.0 0.0 144.0 \n", - "4 0.000566 0.000050 0.0 0.0 144.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[119.0, 15.0, 2.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0... 100.0 \n", - "1 [[119.0, 15.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 0... 100.0 \n", - "2 [[119.0, 14.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1... 100.0 \n", - "3 [[120.0, 13.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1... 100.0 \n", - "4 [[120.0, 13.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 0.0 144.0 1.447148e-07 0.002312 \n", - "1 0.0 144.0 1.357729e-07 0.002320 \n", - "2 0.0 144.0 1.308106e-07 0.002406 \n", - "3 0.0 144.0 1.325920e-07 0.002523 \n", - "4 0.0 144.0 1.359002e-07 0.002547 \n", - "\n", - " date \n", - "0 2024-02-27 00:00:00+00:00 \n", - "1 2024-02-26 00:00:00+00:00 \n", - "2 2024-02-25 00:00:00+00:00 \n", - "3 2024-02-24 00:00:00+00:00 \n", - "4 2024-02-23 00:00:00+00:00 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -1174,30 +591,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Daily CH4 emission Values for Texas, January 2022- March 2024')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -1235,17 +631,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-02-25T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# The 2024-02-25 observation is the 3rd item in the list\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -1255,33 +643,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-daygrid-v2&item=lpjeosim-wetlandch4-daygrid-v2-20240225day&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# A GET request is made for the 3rd item in the collection\n", "observed_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}&assets={asset_name}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n", "\n", " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", @@ -1298,97 +668,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the CH4 variability for the Texas region for Observed tile timeframe\n", "aoi_map_bbox = Map(\n", @@ -1453,7 +735,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb index 66f7c922..37b28ec3 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb @@ -29,7 +29,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Flpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Flpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -41,7 +41,7 @@ "## Approach\n", "\n", "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-EOSIM Model data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, visualize two tiles (side-by-side), allowing time point comparison.\n", "4. After the visualization, perform zonal statistics for a given polygon.\n", " " @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -201,17 +201,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 409 items\n" - ] - } - ], + "outputs": [], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -245,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -282,27 +274,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-monthgrid-v2&item=lpjeosim-wetlandch4-monthgrid-v2-199001&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -315,7 +289,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify month mentioned above.\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[month1]['collection']}&item={items[month1]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[month1]['collection']}/items/{items[month1]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -335,27 +309,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-monthgrid-v2&item=lpjeosim-wetlandch4-monthgrid-v2-199008&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Make a GET request to retrieve information for date mentioned below\n", "month2 = '1990-08'\n", @@ -363,7 +319,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year and month of the observed parameter, you can modify the month mentioned above.\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[month2]['collection']}&item={items[month2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[month2]['collection']}/items/{items[month2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -391,146 +347,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For this study we are going to compare the CH₄ Emissions for month1 and month2 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -576,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -602,110 +421,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -738,17 +456,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 409 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -778,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -820,18 +530,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.41 s, sys: 935 ms, total: 8.35 s\n", - "Wall time: 2min 50s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -842,47 +543,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.0,\n", - " 'max': 0.0871574655175209,\n", - " 'mean': 0.0054928527937995065,\n", - " 'count': 144.0,\n", - " 'sum': 0.7909708023071289,\n", - " 'std': 0.01329405556717722,\n", - " 'median': 0.0009731901809573174,\n", - " 'majority': 0.0,\n", - " 'minority': 0.0,\n", - " 'unique': 144.0,\n", - " 'histogram': [[125.0, 10.0, 0.0, 2.0, 2.0, 0.0, 2.0, 2.0, 0.0, 1.0],\n", - " [0.0,\n", - " 0.00871574692428112,\n", - " 0.01743149384856224,\n", - " 0.02614723891019821,\n", - " 0.03486298769712448,\n", - " 0.04357873275876045,\n", - " 0.05229447782039642,\n", - " 0.06101022660732269,\n", - " 0.06972597539424896,\n", - " 0.07844171673059464,\n", - " 0.0871574655175209]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 144.0,\n", - " 'percentile_2': 4.348626885075646e-06,\n", - " 'percentile_98': 0.058114333748817304}},\n", - " 'datetime': '2024-01-01T00:00:00+00:00'}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -890,202 +553,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02024-01-01T00:00:00+00:000.00.0871570.005493144.00.7909710.0132940.0009730.00.0144.0[[125.0, 10.0, 0.0, 2.0, 2.0, 0.0, 2.0, 2.0, 0...100.00.0144.00.0000040.0581142024-01-01 00:00:00+00:00
12023-12-01T00:00:00+00:000.00.0857190.006267144.00.9024170.0140870.0014100.00.0144.0[[121.0, 13.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2...100.00.0144.00.0000080.0653562023-12-01 00:00:00+00:00
22023-11-01T00:00:00+00:000.00.1013380.008064144.01.1611710.0157500.0023960.00.0144.0[[113.0, 17.0, 6.0, 2.0, 1.0, 0.0, 3.0, 0.0, 0...100.00.0144.00.0000140.0647722023-11-01 00:00:00+00:00
32023-10-01T00:00:00+00:000.00.1325460.011134144.01.6033350.0219180.0032950.00.0144.0[[115.0, 15.0, 5.0, 2.0, 1.0, 0.0, 2.0, 2.0, 0...100.00.0144.00.0000270.0954682023-10-01 00:00:00+00:00
42023-09-01T00:00:00+00:000.00.0846670.009330144.01.3434610.0153270.0037790.00.0144.0[[98.0, 26.0, 8.0, 3.0, 1.0, 1.0, 2.0, 3.0, 0....100.00.0144.00.0000420.0627352023-09-01 00:00:00+00:00
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum \\\n", - "0 2024-01-01T00:00:00+00:00 0.0 0.087157 0.005493 144.0 0.790971 \n", - "1 2023-12-01T00:00:00+00:00 0.0 0.085719 0.006267 144.0 0.902417 \n", - "2 2023-11-01T00:00:00+00:00 0.0 0.101338 0.008064 144.0 1.161171 \n", - "3 2023-10-01T00:00:00+00:00 0.0 0.132546 0.011134 144.0 1.603335 \n", - "4 2023-09-01T00:00:00+00:00 0.0 0.084667 0.009330 144.0 1.343461 \n", - "\n", - " std median majority minority unique \\\n", - "0 0.013294 0.000973 0.0 0.0 144.0 \n", - "1 0.014087 0.001410 0.0 0.0 144.0 \n", - "2 0.015750 0.002396 0.0 0.0 144.0 \n", - "3 0.021918 0.003295 0.0 0.0 144.0 \n", - "4 0.015327 0.003779 0.0 0.0 144.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[125.0, 10.0, 0.0, 2.0, 2.0, 0.0, 2.0, 2.0, 0... 100.0 \n", - "1 [[121.0, 13.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2... 100.0 \n", - "2 [[113.0, 17.0, 6.0, 2.0, 1.0, 0.0, 3.0, 0.0, 0... 100.0 \n", - "3 [[115.0, 15.0, 5.0, 2.0, 1.0, 0.0, 2.0, 2.0, 0... 100.0 \n", - "4 [[98.0, 26.0, 8.0, 3.0, 1.0, 1.0, 2.0, 3.0, 0.... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 0.0 144.0 0.000004 0.058114 \n", - "1 0.0 144.0 0.000008 0.065356 \n", - "2 0.0 144.0 0.000014 0.064772 \n", - "3 0.0 144.0 0.000027 0.095468 \n", - "4 0.0 144.0 0.000042 0.062735 \n", - "\n", - " date \n", - "0 2024-01-01 00:00:00+00:00 \n", - "1 2023-12-01 00:00:00+00:00 \n", - "2 2023-11-01 00:00:00+00:00 \n", - "3 2023-10-01 00:00:00+00:00 \n", - "4 2023-09-01 00:00:00+00:00 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -1121,30 +591,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Monthly CH4 emission Values for Texas, 1990-2024')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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GdbxQpbY280fiuyVU2X136eefG59udRVSuphp/2XlAd1eCFW8MFOFUAUul/S7wiOPPKLbbrtNI0eO1IABA2QYhj788ENddtll2rBhg6699tp0rBMAAAAAnC1W+y9CFSC7OKFSJZJd7b8MQ8oxefxuXl7sjWEnhSpOG1RfXx9+H3fpIq1b1/hywcDM6QPsE4UqwUocq4JKL4QqTqpUSSVEJlSBByT9rvDEE0/o6aef1nnnnddw2kknnaSePXvqzjvvJFQBAAAAkJ1o/wVAsrdSxeysJrsG1ScjPz/6bdfVBcKZTG8aO7VSJTK069w5+uPNLe2/EoUqwQ14K0MVJ/0+U5HJ522k7dulsrLkvic/n1AFrpd0+6/Vq1erf//+jU7v37+/Vq9ebcmiAAAAAMB1qFQBIIVveserxmiqZI64j3XZTLaIitX+K3h/ZXrTOLhx7bRB9VVV4WFaly7RAwe3tP+S4s/kKCwMPKesEq8Vnlvur1hzdDIhlVCFShV4QNKhSteuXfW3v/2t0enTp0/X3nvvbcmiAAAAAMB1mKkCQAo/qr6kJBC4poMV7b8yecS700OVYCjhtEqVyFClc+foLb6cVM3QFHaFKlbfTroYRuD1hVAFyKik3xXuuusunXnmmfrPf/6jAQMGyOfzaf78+Xrvvfeihi0AAAAAkBXiVaosXWr7cgBkSGilSmmptGOH1KKF9beTzGa/XTNVkpEoVMn0prGT23+Fhipt20qtWjW+nJsqL+IpLLR2Az5WqBLc6I/2Pu4ktbWBNTolMCNUQZZKulLlT3/6kz7++GO1bdtWM2fO1IwZM9S2bVstWrRIp5xySjrWCAAAAADOx0wVAFJ4pUowVEmHRJUqoS2VqFRJnlMH1VdVhVem+HzSpEmNL+eVUKWggEqVUNXVgdeVTIeOQdu3B9aTDCdV2gApSuldoW/fvpo2bZrVawEAAAAA96L9FwApeqVKOiQKVQwj8WXz8jJ3xHisUCV4mh2brqH3UaTQSpVo7bUiryfeXBArRbb/kqTu3Rtfzk0zVeIpKgr8zFZxe6hSU+O8UCXZSpWyMum339KzHsAmpkKVbdu2qXnz5g3/H0/wcgAAAACQVRhUD0AKbHoGN+HTOVPFqvZf2VypUl8fewh9aKgSryVUbm7867FatFAlGq/MVCkpsTaYTNT+y+m8EKqkM2wGbGLq3bdVq1ZavXq12rVrp5YtW8oXJX03DEM+n091XkjBAQAAACBZsUKV4uL0baoCcJ7qame0/wqGAsHNdSe2/4q2MWxnqBIvmDI7UyUnx95QJXKmSixeaf9VVmZPqOKWSpVg+y+nBGbbt0u77prc99hV1QWkkalQ5f3331fr1q0lSR988EFaFwQAAAAArhRrpgqbB0B2qanZuemdzlAl0WZ/cJO4pCR++69MbbzHem20c1B9ba11oYpdImeqxOKV9l9WP4di/T7dEqo4rVJlx47kK1Wk+K33ABcwFaocccQRUf8fAAAAAPD/xZqpAiC7VFdLwdbomaxUKSwMbMCXlDizUiWW2tpAUOGEShW/P/Gg+pwce8OLbGv/lY7nULRAr6DAXaHKpk2ZXklAKu2/AA/ISfYbZs+erfnz5zd8/eSTT2r//ffX0KFDtXnzZksXBwAAAKSkvl5aty7Tq0C2idX+C0B2CZ2pUlqavvZ/iUKVoqKdm8TxZqo4rZqhtnZnEJRufn/s+zB43/j98Vt7BWeq2CWZUMVpv9tU2DV/o7DQHTNVnNj+K5VQxe4wErBY0qHKjTfe2DCs/ssvv9R1112nIUOG6Mcff9R1111n+QIBAACApH33nTRuXKZXgWxDqAJACp+pYvWQ7VBm23/Fu6yV1QxWtfOprQ20UrSjvZGVM1XsYjZUcWJglgo7QxU3Vao4pf1XqqFKq1bSli2WLwewi6n2X6GWL1+uHj16SJJee+01nXDCCbrvvvv02WefaciQIZYvEAAAAEhaVZVz/thE9gjdSI1UVGR+IwyAu0VWqmSy/Vdwk9iOmSqJ2mSZFQxV7DgS36qZKnaGF9XVUps2iS9HpUpy3BSq2FXJZUaqoUrr1oEWZmYey4ADJV2pUlBQoIr/X7r67rvvatCgQZKk1q1bN1SwAAAAABlVU+OcPzaRXWINXm7ZkiMygWwRGrCme1C9mZkqUqCSIifKFpCVlSo1NbGD5WTYGarEuw+DoYSZmSpOrFRhpkpyCgrc0f4rNLR1gu3bA7+jZAVDFcClkj6E4Pe//72uu+46DRgwQIsWLdL06dMlSd9//7123313yxcIAAAAJK2mhkoVOEswVGnfPtMrAZBudlaqxNvsD52pEouVlSpWhyqZbv8VDMkTVaowUyW9qFQJF68qNhNqa1NbD6EKXC7pSpW//OUvysvL06uvvqqnn35aHTp0kCS99dZbGjx4sOULBAAAAJJWXe2NozPhHVSqANkjcqZKpgbVm9kkdmKlit/vjPZfoeuJN6jeqZUqzFRJjltCFadVqqSKUAUul3SlSqdOnfTGG280Ov3RRx+1ZEEAAABAk1GpAqchVAGyh12VKmYG1Qfbf8Xi1EoVu2ZGJGqhZhjOnKliZlOd9l/RxWrT6ab2XwUFgcemE6S6jjZtpG+/tXYtgI2SrlT57LPP9OWXXzZ8/frrr+vkk0/WLbfcoho3vPgAAADA+5ipAqchVAGyh1MqVcy2/3JapYpT2n+ZvQztv9KruDh9z6FQbqlUcVr7r1RRqQKXSzpUufTSS/X9999Lkn788UedddZZKikp0d///nfddNNNli8QAAAASBrtv+A0hCpA9gitVEnnxnai1lVubf9l56B6M+2/3Dqo3ivtv3Jy7KnKcEuoEnx9iVVx4xaEKnC5pEOV77//Xvvvv78k6e9//7sOP/xw/fWvf9WUKVP02muvWb0+AAAAIHm0/4LTEKoA2cOuI8kNI7DhHAvtvxJLVIXi85lr/+XEUMUr7b8kewIEt7X/cjtCFbhc0qGKYRiq//9vFu+++66GDBkiSerYsaM2bNhg7eoAAACAVND+C3arq4u/uUmoAmSPyJkX6doQTnS9bq9Usav9V7wWasHLJBpU79SZKl6oVLGLWypVnNT+q74+/mefeJo3l7ZutXY9gI2SfuQfdNBBuueeezR16lTNnTtXxx13nCRp+fLlKi8vt3yBAAAAQNKqq6lUgb0qKwNHVsdCqAJkj5qaxBv1djA7U8WJlSp2tf9ipgqC3BKqOKlSpbLS3GMxGp/PnrZuQJokHaqMHz9en332mUaOHKmxY8eqa9eukqRXX31V/fv3t3yBAAAAcJjt253/RxCVKrBbRQWhCoCARG257OKGSpVonyecNlPFae2/zIZ2XpmpYhc3hSpmKpXs4Pc7J+ABbJbgnaOxPn366Msvv2x0+oMPPqjceOWQAAAA8IYxY6Trr5e6dMn0SmKjUgV2q6wMbALGUlwcCF4AuNuTT0qXXRa/HVSkdB2IkOh67Z6pUlvrzfZfThtUL5lrKeelmSp2cMtMlWD7Lycc4GSm0gvwKMsOnSgqKlK+E8pbAQAAkF4VFc7fHKZSBXZLVKlix5BdAOllGNJ99wVC1FS+125m2n9lulIl2mujk9p/GYa5ShUnVoR4qf2Xz2dNcFVfH/v92E2VKk6pDjFT6RWPVb9XIANMPfJbt26t77//Xm3btlWrVq3ki/MHwaZNmyxbHAAAAByoqirxka+ZRqgCuyUKVQC434oV0q+/BjZey8rMf19hYXpa9lgxqN6pM1VatrTnAA6z7b/iVSbZPVPFrLw85x8EYzZsLCkJ/CzJPO+iqa2NXZnkplClsNAZB2uYqfSKp0ULadu2wPMdcBlTocqjjz6qZs2aSQrMVAEAAEAWq652fqhC+y/YzUyo4oRWHQBSN39+YPMv2ffA0lJpxw7rQxWr2n9lslJFCvwcoRvEfr+z2n85baaKWW5o/1VXZ66VXvA5lM5QxW3tv5ygqZUqrVtLmzYRqsCVTD3yhw8fHvX/AQAAkIWoVAEaSzRTBYD7zZ8vHXlk6qFK69bpWVcsmRhUn+zrYLBSJnRjtrY2EFLb1f6rqCj2+T6fu0MVp7f/SjZUaSovhCpOav/V1EqVYKiy557WrQmwScpx4rp167Ru3TrVR7xx9OnTp8mLAmz1r39J/ftLbdpkeiUAALiDW0IV5v3BTmYqVYqKAs+deBt4AJxr1Sqpd+/kWwQFWxdZLVH7HzMzVaxu/9W8eXLfE6yUiQxV7KpUqa1NXP1gGIHgJBanzlSx8nebLk4KVZwajkVKRyvBVDV1UH2bNoFQBXChpB/5n376qYYPH65vvvlGRkSpqc/nU53TX7CBSE8/Le2yC6EKAABmuSFUcVJrBGQHM6FKy5bSli1S+/Z2rAiAlTZtklq12hmOJsOqDeFkmalUsXIjOZUj6KO1H7N7UH2igzAStVlz6kwVKlUaixequEXweRYM88zcf+liVfsvwIWSfuRfcMEF6tatm55//nmVl5fHHVoPOJ5hSJ98Im3cmOmVAADgHm6YqeKko/iQHSorpbZt41+GUAVwrwULpAEDpK1bEwcVkfskmQxV7Hy/TjVUiaxIsbv9V1M2hSXnVji4ZaaKmfu/rMyeUMUNe5zV1YGfIT8/8PNkMlSxov3Xjz9atx7ARkm/cyxfvlwzZsxQ165d07EewF6rVgWCFZJxAADMc0OlipP6TSM7JFOpAsB95s+Xzj5beu8951SqmBlUX1MT2PC3Y7PY6koVu9p/xdvUN4zE952TQxWnV6r4/eYrVbZvb/rteaFSJdiOLtpzx25WVKp88ol16wFsFKcpZHQDBw7UF198kY61APZbvFg6+mgqVQAASIYbQhXaf8FuhCqAt335pdSrV2rtv9I1UyURny+wAWtFNYYZqYQq+fmZb/+V6L4xE6rYFV7U18ef7xKKmSqNeSFUCT4eg5UqmdTU1xbaf8HFkn7kP/fccxo+fLi++uor9erVS/kRL0YnnniiZYsD0u6TT6RjjpH+979MrwQAAPdwQ6hSX5/ZdgjIPmZClbIya460BWCvqqrABmZubuLh79GqR9JVqWK2+qSpLXrM8upMlUTsnKlSXW2+valb2n+ZDVXWrm367XkhVAm+xjglVGlq+y8OcoZLJR2qLFiwQPPnz9dbb73V6DwG1cN1/vtf6eKLpUWLMr0SAADco6bG+aEKYLfKysAmYDxuOGoYQGOffiodfHDg/xPNKYkWLJSWZvZobCdXqmS6/ZeZ+yZRmzU7239VVQWCPTPc0P6LSpXURavysltT23+1aEEFL1wr6fZfo0aN0rBhw7R69WrV19eH/SNQgasYRuANvLycZBwAgGTYPfgWcAMzlSpO6H8OIHnz50u//33g/xO1/6qpaVxJkI5KlUQb/aHcGqrY8XqZaFPYTDWQU0MVNwT5TgtVknleZUrwMZmX5/5KFTurvACLJR2qbNy4Uddee63Ky8vTsR7APsuWSXvtFfjjNx2l2AAAeFUq/eQBrzMTqrihFQuAxj7+eGelSqL2X9FmeqUjVDG7GS3Zd3S+VaFKXV3gfnZL+y87Z6okW6ni9Pccp4UqbuC09l92BLaAAyUdqpx66qn64IMP0rEWwF6ffCIddJD5PrQAACDwx29JiXtCFTcccQhvMFup4vSjhgGEq68PvOcFn99m2n9FVqqkY1B9MmGA2ypVfD77KvusGFRv59H2VVXJzVRx+ntOXZ25x6aVoUpTK5OcwgvtvwAXS/qR361bN40ZM0bz589X7969Gw2qHzVqlGWLA9Lqk0+kESMyvQoAANylujrQ/9gNoUpwA5s/9mCHysrERw+74ahhAOHWrZN2223n14mqNe2qVElmM9PpoUq0o+19PnsOjDBzPzpppkp1tbdmqvj9zqpUccPBOF5q/xVkGO4KtAClEKo899xzKisr09y5czV37tyw83w+H6EK3OO776Ru3TK9CgAA3KWqKhCqxGt94hTBo1wJVWAHwwhsrMVDpQrgPpWV4VVoidp/2TVTJZk2RnYdTW5VpYqdEm0K5+Qk3rh2avsvN7znOK39l88XCMgSvZ9nkpPaf1nx2tKsmbR9e+C/gIsk/chfvnx5OtYB2Cv4wSL45h18M3J7b00AANKtulpq2dI9lSq1teY3H4B0o1IFcJ/ITexE7b/sqlRJ5qABq44mTySVUCXTLYwS3Y+5uYFgLR6nDqp3w3uO2VDFqhZ6ifZ9CgsDj2M3fHaMFaosWSLtsUfg83q6+f1ScXHTrqNNG2nTJkIVuE7K0WtNTY2+++47+Z3+Ag1E8/33UvfuO79u3TrwIg4AAOILVqq4IVTJ9EYNsouZthVuOGoYQLjITexE7b+iVaoUFSXemE9WMgcF2tn+K9nwJtOVKomOtDczL4WZKqkzG6rk5FjTmstMqOKGamwp9nNnxgzphx/sWYMVgS37cXCppEOViooKjRgxQiUlJerZs6dWrlwpKTBL5c9//rPlCwTSIjikPqh1a2njxsytBwAAtwgO63VDz2kn9JpG9jDznMj05iGA5EULVeJtukarVElHKyGzm5k5OYE12RGqmGmDGCnTr4uJAiczYbhTZ6q4Icg3G6pYxWylihvEqlSprrbvOWVF+y9CFbhU0u/sY8aM0RdffKE5c+aoKOSF/KijjtL06dMtXRyQNosXh4cqwXJDAAAQXzJtJzKNShU4jRtasQAIV1UV3t4mUfuvaJUq6WB2M7OwMNB6zKmtrqOFKnYeuJEonDJT7eHUmSpueM9xWqhSUODsSpXQ+yteqGLX45FKFWSxpOPEmTNnavr06Tr00EPlCylx79Gjh5YtW2bp4oC0WbZM2muvnV+3aUOlCgAAZlRX27NZZIVMH/0KRHLDUcMAwiXb/itapYpkfVBgtv1XYWFgCLQdlSqpyPR7tZmZKol+d3a3/yorM3dZt7T/svOx6fb2X6Fzi2IdPFRT475KlZ9/tmY9gI2SrlRZv3692rVr1+j0HTt2hIUsgKPl5IT3vSYZBwDAHDdUqtTVBd7rYx3BB2SKG44aBhCusjL59l92HHxgdk5KUVGgUoVQJTozM1US3XdOHVTvhiDf73dWpYobQpXg60usNrd2tv+yYl4T7fjhUkmHKgcffLDefPPNhq+DQcqzzz6rfv36WbcyIF2ilZdSqQIAgDluCFWCf3BmeqMG2cPsEehu2OACEC7yfS8/P/6ma+iR5KGsPgg1si1ZLHZWqqTyM2Z6/pmZ9l9OClWSCe3cEOQ7sf2Xk2eqhFbC0f4LyKik31Xvv/9+DR48WF9//bX8fr8ee+wx/fe//9XChQs1d+7cdKwRsNa2bVLz5uGn8SIOAIA5bglVCgoyv1GD7GF2c9MNG1wAwlVVSa1a7fw6UXBgV6VKRYVUUpL4csFQxU0zVewUrG6NxWyo4tSZKk4P8pMJVXy+QHgV7/eViBcqVcyEKm5r/8V+HFwo6Vei/v3768MPP1RFRYX22msv/fvf/1Z5ebkWLlyovn37pmONgLW2bpVatAg/jUoVAADMqapy/kyV4FF8DKqHXSorzW1uUqkCuE+00DResBKrUkWydq5KMqGKk9t/RXuvtru1fLzby81NvOlv90yVbA1ViosD77dN4YVQJbT9V6ZnqlhRqdKqlbR5szXrAWyU0rtq79699cILL1i9FsAe27Y1DlXo4QgAgDnV1eYHpGZKaPsvKlVgh4oKc5UqmT4iG0Dyom1ixwtHYlWqFBaGb4g21Y4d5kKV4EyVtm2tuV2r5eUF7mOnclr7L6/NVEkmVCktDTyWS0tTvz2vtf+qqIh+GTvbfzU1sOWzEVyqCTVzgEtt3dq4/VeiYYMAACDATe2/qFSBXcweMU77L8B9IgfVJxKrUqW4OPoGaKpo/2WPvDznhSpmgzk725KlKpVQpSm8UKnitfZfgEsRqiD7RGv/BcBaNTXSL79kehUA0iEYqgT7WjtR8Cg+p2/UwDvMbm664ahhAOGiHUwQr11UrEoVK1oXhUo2VIm38ZnJ93Snv1c7baZKdbX5kM/uNmqpqKszvylPqNI4VPFC+y/ApQhVkH2itf8CYK2lS6Wnnsr0KgCkQ3BzyclVnrT/gt3MzlShUgVwn2Tbf8WqVCkpyUylSrD9V7yN60wGvpGhSn29s8IAM6GKU2equIHf76xKFTe0/wqdqRKrUsWu57NVlSo+n7UzpwAbEKog+0Rr/wXAWjU1zv4wCiB1wT/mioqc2wOd9l+wWzIzVahU8R42grwt2qD6eOysVDEzW8JMpUomq0UibzvRprfdzAyqd+pMFTdIpv1XWRmVKk5r/2XFTBXJ+WEWEEWTQ5Xly5fLzx+rcJNY7b+Kiqz9kAtkM0IVwLtCK1WcGqoEN7SoVIFdmKmSvXbskC64INOrQDol2/4r3kyVTLb/ireRnJvb9MA31XAx8r3a7hkNidbtxEH1ZmequAEzVZLjxFDFihCUg07gQk0OVbp3764ffvjBirUA9ogVqrRpI23aZP96AC+qriZUAbzKDaEKlSqwGzNVsldVlbRhQ6ZXgXSKNqg+3kZ8rEqVTLX/Kiw01/6rqe+XyWyOx7vtyE3vTFeCOW2mitMqeZrKiaGKU/6O/de/pO++Cz8tsv1XrJkqbmv/xUEncCHTj/xTTz016ul1dXUaNWqUmjVrJkmaMWOGNSsD0iXWTJXWrQOhSocO9q8J8BoqVQDvclOoQqUK7FJZaW5mn9MHMiN5NTXWbpTDeWJVqtTXBzbTIwXnekXKVKWKmZkqVmxoxqrQSSTyAIjQTe9gEJ3OypVE81ucNlNFctbMmaZKNlRZv75pt2dmpopTKlWWLAncN9277zzNy5UqfD6Cy5iuVJk5c6Y2bdqkFi1ahP2TpLKysrCvAUeLNVOlTRtp40b71wN4EaEK4F3BthNODlWCR/FRqQK7mJ2pwpGY3lNT0/Qjp+Fs0cKCoqLYn3Wrq2MPqndq+y8rquhSDVUiN1NDN2ljbRrbyWntv7zGiZUqTglVduwIHBQcymmhilWVKlTywoVMP/L/+te/6sYbb9Tw4cN1QUjP2GnTpunee+9Vjx490rJAwHK//Sb9/8qqMK1bE6oAVqmuds6HUQDWqq52V6UKAS/sQPuv7EWlSnaIrAwoLIw9MDxepYrV7b/MhLlmBtVnslIlXvsvJxy9npfnrEH1XpNMJVK2hSoVFY1DlerqwP0gxT54yDDs+6xh1aB6DjqBC5muVDnrrLM0f/58TZo0SX/605+0efPmdK4LSJ9YZdrMVAGsQ6UK4F1Oav9VVydF+0xK+y/YrbKSQfXZqraWSpVsFO89MFalitXtv8xuRptp/+WkShWnhSpOm6niNX6/sypVCgqc83fsjh2Bg4JDhT7PYn3OtfN5Y1Wo4oTnOpCkpAbVd+7cWXPnzlWvXr2033776e2335bPS70ckd1o/wVYh1AF8K5gmb8TQpUlS6SHHmp8Ou2/YDezlSo5OZkfugxr0f7L+6I9Z4uKYh/NHitcsHpQvdnXkmBVjRsrVdzS/svumSpeYnf7L8OIfpBtkJMqVVJp/1VXFzjfrs+/ie5Ps6jkhQsl/cjPycnRXXfdpZdfflmXX3656njQw21iBYHBQfUAmq66mlAF8DKfzxmhyo4d0TeoqFSB3cy24YH30P4rOwWDimjq66NvEltdqWJWsBWZW2aqmKlUmTQptTWmgpkq6WV3qJLowHC3hSqRz4/q6kCA67a9Wip54UIpx4kDBgzQ0qVL9dlnn2mvvfayck1AZkSrVHn8cenHHzOzHsDNqFQBvM8JoUpFRfTNzGDrFSpVYBezlSrwnmClChVI2SWV98BMhyqJ2n9lslIl9AAIM6HKhAmprTEVTmv/5bXXGrtDlUSc1P6roqJx+69gNbYU/eChmprA5xG3ff6lUgUu1KQarbKyMu23334qjDaEDXCieB9AWrVqXKny979L69end01u9+9/Z3oFcKKaGucc4QMgPZwcqgSHBFOpAruYnakC76mpCfyNwece74p2ZHu89l+xWN3+y6yiosB/E7X/cmKlSqz2Xz//nNoaU5Gby6D6dEomVLHjOeSkSpW8vOTbfwUH2bsxVHHbmpH1TE8TOuCAA0zNT/nss8+atCAgreIdRRh5NGtVlbRoES/s8dTUSI8+Kg0alOmVwGmoVAG8zwmhyo4d0Y/6Df7B6ffzPg570P4rewU/7+zYsXPzGt4Xr/1XLJmuVEnU/itTlSqRf4cHZ7fFW9fGjYH70orX3USVH06bqeK1ucbJhCp23M+Fhc75Ozba64zZUMVtVR+0/4ILmQ5VTj755Ib/NwxD999/vy677DK1bt06HesC0mPrVqlFC3OXXbxYataMI1zj2bHDOR844CzMVAG8zwmhSrz2X8FNJP5Agx38/sSbbvCmmprAxlZFRaCdMLwn1qD6ZN8DS0qc2/7LqYPqo4UqwcqwDRukjh1TX68U2KBPFFI4aaZKXZ01Q8GdpK7OWe+fTmr/Fe2xGdn+K9ZMFTsH1VuB9l9wIdOvXHfccUfY1w8//LCuvvpq7bnnnpYvCkibZEKVefOkI49kMyae7dsJnRBdTQ0figCvc3KoEtzYqavjfQr28NqRwzCvpibQRjjdff6RGbE2DFNp/1VcbF3rIsMw/7pjdqaKW9p/Bb9ev77poUpdXfwKnuD6nDJTJXRD3Sv8fvOVKlL632+taIWXTokqVdw8U8Vta0bW81jEDSSwbZvUvHn8ywQ/OC9aJPXvz2ZMPFSqIJZU/6gC4B5OCFUStf9iUD3s4rXBwTAvGKpkYlYG0q+2Nvpn2ky3/6qqMt/6Kicn8H7olkoVvz9+pUowzNqwIbV1hjJTZWhmpopd7b+qqrzXZjCZ9l8If57l5DT+/BGsVLErGLIq5KL9F1yIUAXZJVGlSllZYIOmri7wgl5WRqgSz44d3D+IzotHUQEI54RQJVH7LwbVwy5UqmSvmhqpZUsqVbwqVngR7z0wVshq5RHw8WaFRlNYmHimihMrVaK9j1dXB+7/9etTX2vobZkJVZzS/iv4s3tJsqFKthzEEOvnTPR3tpsH1Tu5QgiIglAF2SVRqNKmTWDo3dKlUp8+HOGaCKEKYqFSBfA+p4Qq0f7opFIFgF1o/+VtsSoDUmn/JVkXwKYSqqS7UiVWVU8ikbcd2f4rWqVKhw7WVaokav+Vn5/4MnaFKlSqZI/Kyp2Bbuhn3UR/Z9P+C7CN6Zkqjz/+eNjXfr9fU6ZMUdu2bcNOHzVqlDUrA9Jh61Zpl11in9+6tbRpU2CeymGHBf6f0CA22n8hFkIVwJtC//BNdUPJShUVgaPxIgVfg/gDDUC60f7L22JtYqfS/kuy7ij7ZEOVoiJ7ZqqYbUkWKjKQSDSoPhiqWFGpYqb91+DBiQMTu2aqEKoEJDNTKBVOqIYJ/YwbWjGX6O/sYPuvdevSv0Yr0f4r8Drz22/m50Aj40yHKo8++mjY1+3bt9fUqVPDTvP5fIQqcLZt26S99op9frBSZcECafhwafZsXtjjoVIFsdD+C/Cm0Oe2EypVduyIvqkUXGe0AZ4AYCXaf3lbZWXsSpVMvgfGev+LJVH7L6tmqlixGVhbG2jDLcVu/7X77tZUqphp/2UmxLBzporX/saqq0v8OwgVnE2UzOPfjXbsCIQqubmBfaxgqBL5mI02U4X2X+70ySfS009LkydneiUwyfQr1/Lly9O5DsAeidp/tW4dCFW2bQtcLj/fumGCXkSlCmIJHkGT7qOIANgr9AjJTG8oSYGj+KIdFUulCgC7UKnibcm2/0p0hHsm23/FqwbI5EwVKfx+Mdv+a9my1G4rlJn2X2YwUyV1yVaqlJYmHyq6UfBnzM8P7E+Vl+88L97rSLBSxW2ff/nMHnhdnzUr8FqSw7QON+C3hOxiZqbKokVS166Br3lhj49KFcRTUEDoBnhN6OZSqq1PrFRfH1hH5HuR379zsCzvUwDSiUoVb0u2/VeiTfpMtf8qLY2/SWfF371Wtf810/6rWTNr/s4w0/7LDGaqpC74mc2sYKiSKie09jIjWKnSvHmgJVQskQFLcKaKXVUfVt2ftP8KHNBdXy99+mmmVwKTTIcq77//vnr06KFt27Y1Om/r1q3q2bOn/vOf/1i6OMBywQqUWFq3lmbODMxTkWgbkgihCuIpLCRUAbwm9I/5nBxn/GFaUhK9qtTnY1A97GHVphzcKRiqUKniTaGzDELFqlSxqwVusqFKsJ1WLFa1/7IqVAm+psZq/1VYaE3Vj5n2X2Y4eaaKz2dP4JOqVCtVvC44U6V588A+llm0/3KvqirplFOkN97I9EpgkulQZfz48br44ovVvHnzRue1aNFCl156aaO5K4DjbN0aeFOKpU0b6ccfd4YqHOEa344d3jtSBtahUgXwHiceIRnsrR0N7+Owgxd73MO82loqVbwsXvuvaJUqiYKFTLX/ijdXVMp8+69QZtp/WfWaa1X7LyfPVMnNdfZmtZ2hihMOBjIrWKnSrFn8SpVIdrf/suo1jS4xgb9njjgi0D0HrmA6VPniiy80ePDgmOcPGjRIn1KiBKerrY3/Qa9Nm0Drr/btA19zhGt8wTd6pxs3Tlq3LtOryD6EKoD32HUEbjJKSmIfIZ7J9/H6emn5cunNN6Unn3T2UaJoGieGjbBPcKYKoYo3xRpUH6v9V6L3SavC/mRDleeei3++0ypVErX/ClaqNPW9NRvafzm9AiDZUKWsLPXXWzdVlgZnqiRbqVJT47zP6mbQ/ivwflNaKu2yi7R6daZXAxNMhypr165VfpwEPy8vT+vXr7dkUUDaJDoyoWVL6bHHdn5N+6/43BKqfP+9tHlzpleRfQhVAO9x4uZxvFAlU0e9bd8uHXCA9Mgj0sqV0quvBk6DNznxeQH7MKje25IdVJ8oWIhXXZmMZEOVRJxaqRIthApuGrdq1fS/8axs/+XUQfVO36y2s1Il9LHldKEzVZJt/2VXqGJl5Y/Twz87BNtNDhkivfVWplcDE0yHKh06dNCXX34Z8/ylS5dq1113tWRRQMbk5ARewIIoQYxvx47AjBqnv/lVVVnzxwuSU1AQ/Y9NAO7lxM3jWDNVJPs2OSL99pt0yCHSE09Il18u7bknr4de5sTnBezDoHpvS7b9V6INTStDFSsPbst0pUro5mxoS6547b922UXasCG12wu9La/PVHFD+69kfgfZFqokav8VGWzYGapYWfnD3tvOyshjjpFmz870amCC6VBlyJAhuv3221UV5YNDZWWl7rjjDh1//PGWLg6wXLL9HqlUiW/HjsAfkU6/j6qqov/Rg/RiUD3gPU7cPC4udt4R4pFHkhYWEqp4mROfF7APg+q9Ldag+ljtvxIFC/GqK5ORTZUqsUKVtm2lpnZLyYaZKk6vAHBipYpdIVk8TRlU78ZQxekVVXaorAy83wSrX/nbwfFMP/pvvfVWzZgxQ926ddPIkSPVvXt3+Xw+ffPNN3ryySdVV1ensWPHpnOtgP0YcBtfTU3gyImaGmdvJhCqZAbtvwDvifxDLZMDP+vqAn/0WrVBZaXITY9YbWLgDYQq2S34OdhNA5BhXqznd05O9N+5nZUqVoYqma5UCT34MXJQfeTf49XVgUDFaZUqTp2p4vTNar8/+VBl1arUbstsqFJQELhsMuuyWnCmSrNm8UOVyAOHrQo3zbAqlJScH/7ZITTEP/xwad486aijMrsmxGX63aO8vFwLFizQ5ZdfrjFjxsj4/x8gfD6fjjnmGD311FMqLy9P20KBJqupSf4Fn0H1iQU/cDgZ7b8yg1AF8B4nbR4Hj+BzYqgSuakW64hmeEMqzwvDSL6CGs5k5wYW7BdrUH0siYZEOzVUcWulytq1Tb8tN4Uqqc5UcfJmtRMrVYJ/x2byM2/oTBWntv+y6vkj0f5LCn+/Of54aeJEQhWHS+rR37lzZ82aNUubN2/W//73PxmGob333lutWrVK1/oA62zbFnhDSgbtvxJzw8Y5lSqZ4YbHBoDkVFUl/16aLsENJas2qKwUuclO+y9vSzZUCbaJyeQRsLCOlZtKcJ5kn9/V1fa0/woexW4VKzY0UzmIMRqzoUpZmfTf/zbttqw60p6ZKqkzjMD9Z1ZpqbR9e2q3lWyokkmhM1Wc3P7LqkoVp1dU2SHY/kuS9t1X+vrrzK4HCaX06a9Vq1Y6+OCDrV4LkF5btwaGqieDtDwxNwRPhCqZwaB6wHuqqqR27XZ+7fNl7oj7YKhSUtL0I1WtFq1ShddD70olVEn2yFw4G1VH3pXs89utlSqZbv8Vykz7r8JCqXVra2aqWBGKMlPFPnZWqmRSsCI7tDNItJaDkZ/F3TpTxWuP01SEtv/y+aQuXQKt7jp2zOiyEFsScTDgcqmEKm4IDDLNDfdRZaXzjmL2suCHPQbVA97jpAqM0FDFae2/Iu+noiLCfS9LdtOVg3YA94g1qD4WM5UqVvxdkuh2kmVV+y87K1WsmKliVaVZcGM73bw4UyVZbdtKGzem9r1uClWiVaNFW3/kfoydLSmtrNT02uM0FZHtJgcMkBYsyNx6kBChCrJHKu2/+KM3PsNwxgeORKhUsVddXeC544bHBoDkRB79lsmwINgWITJUqa/P/FHjVKpkl1QqVfh8CbhDOipVrDoQwMr3Oitel5Jt4xRL6EZtvFClRQtpy5am3ZaV7Yvs4PaZKp9+2vQgrEULafPm1L7XTaFKtIqTaK8vkaGKW9t/sffWOMTv359QxeEIVZA9qFRJDzfcR4Qq9gp+kHPCh1EA1nJSBUasmSq1tZkfGu2kih6kXyqVKk7Z4AIQX7KD6hNVkDhxDpiU+del0CoPs+2/rAiVrGxfZIdUKlUy/bsN9a9/NX1ORFN+724KVaTGP2u0KpTIMMKtlSpOepxmSn19eDDdtav0ww+ZWw8SSjpU2ZFq70Ig01KdqeL0wCBTgm+gTvnAEU9tLaGKnYIf5Nzw2ACQHCeGKpGVKnYeoRdLtIoeQhXvolIFUmCjjs89TWMYTa88sJrVlSpObFkpOet1yWz7L6tuy22hSrI/u5N+t9XV1oSKqbZbc1uoEilaaBsZPNo569DKUNJJj1On8PkCQTz78I6VdKhSXl6uCy+8UPPnz0/HeoD0SbVShRf26IJtV9xQqVJU5MwjwryKUAXwLieFKsFe05EbVHYeoRdLZHuOwkLCfS+jUiW7BTf2SkuduVnuJj/+KN1wQ6ZXES7ZTWwqVVITukkeulEb7SBHK0MVt1WqpDK3xkntv6wKVdq0SW2uiltDleDzw0z7Lztb4Frd/sspj1MnOfhg6ZNPMr0KxJB0qPLyyy9r69atGjhwoLp166Y///nP+vXXX9OxNsBaqcxUcUNgkCmhoYqTPnBEMozAHy9sZtkn+IcO7W4A74kMCzJdqRKcqRL6B7oTQpXITTheD72NQfWQAq9FHE3aND/+6MxgKt6ckMij5c3MVHFiqJLpo8RDN1R9vp0bw9EOcgwNVZp68JyVm8J2DKoPvW/MctJmtVWhSseO0qpVyX+fW0OV0tLA+4uZ9l92srr9F5+NGuvfX1q4MNOrQAxJhyonnHCCXnvtNf3666+6/PLL9fLLL6tz5846/vjjNWPGDPl5EsCpUqlUyfSHSycLhioFBc4Onvx+qVkzQhU7UakCeFdkWJDpUCU4U8WJ7b+YqZI9Umn/5ZQNLjRdcIOTSpWmW7HCXZ/ZY234xwv2ndr+K9Mb77E2VBO1/9pll6YNPndb+69UOGlPo6bGmlBl992ln39O/vvcGqo0bx44SNhs+y+7WBlKOulx6iQHHSQtXpzpVSCGlAfVt2nTRtdee62++OILPfLII3r33Xd12mmnabfddtPtt9+uCid+UEB2SyVUsbN00m3c0v6rqkpq2dKZR4R5FaEK4F1Oav8VDFUij1J1YqUKM1W8jUoVSDuPJEbqli933mf2eBuU0Vo72lWpYvXfqZl+XYoXqkT+rRm6Md62bdNCFSvbf9mxd5DKhrmTgnwqVVLTrFkgVDHT/stODKpPv2BFvp1hGUxLOVRZs2aNxo0bp3333VejR4/Waaedpvfee0+PPvqo/vGPf+jkk0+2cJmABbZuTb79F2ILrVRx0geOSMFQxU1Hvbld8Ogxpz82ACTPSaFK8H0oJyf8Dw0nhCrMVMkuDKqHRPsvKyxf7q7nRrTA3EylSlM3lc1uDCfDio33poQKsUKVWDNOg7fVtq20fn3qt2vlkfZO5cVQxeuVKvX14c+n5s2l336L/hk38jli90wV2n+lX7du0g8/ZHoViCLpR/+MGTM0efJkvf322+rRo4euvPJKnXvuuWrZsmXDZfbff38dcMABVq4TaLpg73VYw02VKq1aSZs3Z3ol2YNKFcC7Iv94ckKlSiQntP9ipkp2YVA9JNp/WeG33wKVHG4R7T0w0XtQZMvKVOzYEf39rykyvaEZrSIleHq8dTW1/ZfbBtWnwknvOdXV1rxOer1SJfIzbrD9V35+9JkqtP/ytv79pQULAuEKHCXpSpULLrhAu+22mz788EN9/vnnGjlyZFigIkl77rmnxo4da9UaAevQzss6bhlUX1UV+BDCZpZ9gqEKm4iA9zkxVHFKpQqhSvagUgUSlSpeFe/vx1jtv+K9B1nR/ivW+19TZPp1KTQ8Cd0UjhW2BDW1UsVNM1Xq6wPVucnK9O82lFWVKi1aSFu2JP99bglVgnstQcH2X2ZmqtiJ9l/WiReGBUMVOE7Sj/7Vq1erJMEbeHFxse64446UFwWkBT0IrbVjh1RWFviQtn17plcTW1WVu4548wLafwHZo6go8EdeJsQ6UjfahpZh2HtghZPapCH9qFTJXqF/X1Cp0jSVlYHP7G4KoGO1/4pXqWLFAWnpCFUy/bqUbPuvICsqVdzS/mvLlkBb62R5sf1Xqp/pkglVMrnHEdllJdj+K3jg4v9j77zDJLmqs//2zOzs5JkNszknaZUjQjJKFpIAGRFsEFEmGWOBMWADBuzPNg7gQLLBGGyDjDGSwGCEQCBkhISEBIqrnDbnMDt5Zyf398d10dXVFe6tuuFU9fk9jx7tTuiu7e6quve8532Pn6CoYjv+S9f549ot55q4eVxp4+4Y40iJKsOBzXLw7366eGYFw9QHx44B8+eLBRr1+K+WFnYp2YTjvximfmhpAQ4fdvPcUbGewYKWt0mzWTRhp0p9MTmp9vmq98JBkZiaqoi4bW3AoUNujyfP7NwJrFkDPPus6yORJ0wwT3KqlErZ9yVFdKpEiSdJx6VjpkpenCp9feLfq4prwcyPLlEFEOeRatNMnpwqwfivgweFYyUs/svVuavz/HF9DXLN8ePxDTqeOyuNsMoYQ+rT39PTg1LChapcLqNUKmGGysWaYYJwUV0vniV1fJx24dy7ObFTyR4sqjBM/eA6/ivMiRgsaLkQVYLOBRZVio/KOpNS1zCTDf/1hp0q2di5E1i7lp6oEreHCIv/kpnrlXVfQtGpkvXfFFUYTrq2LlyYzamSp/ivtKIKpWJ1Q4M+UcV773t75X8nT6JKMP5rZEQu/ss7Fxsa0kfGycLxX/rw3JpRnH8+8ItfAC95ib1jYhKR+vT/9Kc/NX0cDGOW2VkWVXTj3ejz4lRh7MHxXwxTXIL3UpeiSrkcvlEM2ueTokNMECyqhUXEMPULO1WKg19U4Zkq2dixQzhVKJHUhR12bR8ZERHJJqHoVJmZyVZcTXtdnDs3236jHpwqlIR8HTOFPLxIpHoQVbxB9ZOTtee+X1SZmamsjb1zyuScQZ2PX+9ro6TY+hNOAJ57jkUVYkjdPS6++GLTx8EwZrGxuK03vBv98eO0C+cc/2WfyUnxmnNnNsMUj2AnKpVZIaVSpRsv2MWXNOTWBMEiTVg3M1O/UOoaZrIRdKqwqJKenTuBSy8Vf7Y9ByuKpCJX2D1QxhlJMf4ra5d4UuyZzPO7uC7maabK0aPAggXqv0dJVNG5blyxAtizBzjzTPnfyYuoEjZTxRNVgvFP/uYhf2ORt9YwKapMTem7FtX72igp/stFkxiTSCpJfnBwEPfffz8OHz6M2dnZqu9de+21Wg6MYbQyPCwyCBl9eKLK8HA+nCoc/2WPyUmx8HO9GGX0c+ONwOte5/ooik/WwoRNqIgqbW1iM9LeXrkGebjahPiLZvUeacBUw5+H4sDxX/rYuRNYvbrSdZ10H7zjDuCSS8xG2yQ53tMK5lTjv7LcK3WKKjYFtbzFf61fr/57lO453hwUHaxcKRxuKmQVVWZmgP/+b+Caa9SeV5XgTJW4+C9/85DfKW3jfdc9U4XK59QFSfFfwZg3hgTKn/5bbrkFb3zjG3Hs2DF0dnZWzVoplUosqjA0GRpiUUU3nqhC/eI+Pq5/08HE43XIuOo2mZ3l990Ek5PAv/0biyqmKZeB178e+Pa3XR+JHFRirbw4ifb28EH1FO5TLO4zHvXejVkkOP5LH55Q4F3Pkwr0//RPwAteYDaNIElUcXUPHBurbh7QgTd/IS06RJW092q/W1WVeon/KuI9Z8UK4Gc/U/udrKLK2Bhwww12RJV58yp/7+ysOFWCM5v89Ri/6GLD/aXT6UXBneiSJGck9bpbnaJ81/nDP/xDvO1tb8PIyAgGBwcxMDDwq//6+/tNHCPDZIdFFf14ogp1NwLHf9nHW8y5es3vvRf49KfdPHeRGRws5oaMGtPT4rXOC5ScKl6HeLCww3Z5hhqUuoaZbLBTRT+trXL3lbEx89f2pDiW4D1Q1vVAMf4rK7biv8Jmpc6bBwwMpHvePMV/FWGmik5WrhQzVVTIKqqMj9tZ9wZnqjQ2is9+2HnmL7aHxX+ZJE9OL+ok3W/qfeYMUZRFlX379uG9730v2qjdxBkmjqEh/d089Y4nVlBXzP0dZtwlbAfX0UV793JRwwQsqthhaoqG8yOM6WmxQfOTB1GFyiaExX3Gg8pnkskOO1X0MDIiOrEBcV+RGWRtQ1RRjf86elSu6O05K9ISLLhSwJaoEvY8vb1CcEhDnpwq/f3A/Pnqv1dUIb+7W70RSYeoYmOdHpyp4hEV/+WdOy7iv/IiSlKH479yibKocuWVV+LBBx80cSwMYw6eqaIfb4DknDn5cKrw0HR7uBZV9u+nUeQtGoODvJCzweQk3WvqxERtcYmSqOIV4ajGfzGMR1G7huuRyclKQamohUsb7NwJrFkj/uzFfyVx7JgdUSVpUL1/f3HkiCjwJyH7b4yiiE4Vz1Wa1AQXvMcDwLJl6o4Fjzx12qcVgKjFf+lqMknzOHkRVYIzVTySnCpBUYWdKvmB479yidSn/3vf+96v/nzVVVfhgx/8IJ566imceuqpmBO4IF199dV6j5BhdDA0BCxf7vooiklzM+2LuyeqeFECcd1mjB7CNjs2OXCARpG3aLBTJRsTE2LTEXR6BKHsVAm7hqYd0puVoGumtZV+/Be7JRkPdqoUB5mB6kwyO3YAa9eKP1OK/5KZqeI/1iNH5Jwq/jlgaSiiqOJdF5OKtGH7jM2bgaefBi67TP1566HTnoqQ763ddJ+3XrOn7DHkRVQJuz4kzVTxn4c23vc8Ob2oI+NU4bUjOaQ+/a985Strvvbxj3+85mulUgkzFC7WDBNkeFgsthj9UFfMPcWfSjd1PUDBqeJS1CkqAwO8kMvCl78MnHgicPnl8T9H2akSVlzyMp5tE4xFSIr/onyfYvJNmoICta5hJj2u1zxFYefOyl6NmlNFJf5L1qniv2eloeiiSlzRO0xUOflk4Oab0z1vWLRp0aDiovPeO53nrRf9JnPeAflyquiI/8rToPp65/jx+Hg/3s+QRCr+a3Z2Vuo/FlQYsmRZeJZK3F0aR14G1cvmMzPZcV1gOHyYBTQTsFMlG6Oj4r8kKDtVwuK/XBG8r/sLVMGCC5XOLp6pUkzSuGCpFLiY7Lhe8xSFHTsq8V+UZqrIDKrn+C+BS1FlxQpgz550z1suAw3Kqfj2mZlJL/5QEfLDBIHZ2Wyvv+p7n2dRpaFBXDc4/quYcPxXLlG+en3ta1/DRMhFZHJyEl/72te0HBTDaCdL7BNHNMRD/eIejP9izOM6/qupid9rE/BMlWwcPy5XQJmaoitUj4/TcYEFC0r+AhVVpwo3aBSTNGtMKgUuJjssquhh1y5g1Srx5zw5VcLiv1hUSUcWUcVrWijyfXZwEOjpSfe7VOK/vPfOvy7L6hRauVJtno6sqBI1N9bmoPrgOd7ZCfT3x8d/+YUrjv/KF0kiPvW6W52iLKq89a1vxdDQUM3XR0ZG8Na3vlXLQTGMdpLyCeNgUSWevAyq5/gve7guMPA5a4bBwWJvVk0zPi4X9TE5SdepQmkuVXCAZzD+y6VThc+T+oKdKvWN6zVPUfBft2UaoWZmxL3SdIGJcvxX2r2tKXSIKlNTyXFCUc1bixcLt3pR6euTm9cTBjVRxS8qZnHgAOacKlHxtrZElbB5XV1d4nMQ51TxX0s5/itfJNUsucZBEmVRpVwuoxQSX7B37150d3drOSiG0U4WUYUV4VpmZio2Xeqvj6f4c/yXPVwWGEZH0w/9ZOIZGAD4Pp8eFacKiyrJxM1UCcZL2N6EhA0RBThOtKikFVV4Y1wMWFTJTvC6KOPi8L5vw6kSt4d0Ff9FMbJqcjJbcTWLUwUATjoJeOop9efNy3356NH0ogoVId+EqJLGqZLFWWFLVAmjszNcVHE5U4Xjv/TB8V+5RPrTf+aZZ6JUKqFUKuGyyy5Dk+/EmZmZwY4dO/CSl7zEyEEyTGZYVNGLP+PT1ZBiWby4Go7/sofL+K8DB4Bly4CtW908f5HJEjtgmk99CvjDP3R9FPHIiip5G1TviriZKq7jv6Ji0rzNLXf0FYu08V8UClxMdiYngY6Oyt+9aDcuMskzOAjMm1f5e0uLaOSI49gx8X9q8V9JRTGPtrbiNXvpcKp47qM0osrJJwNPPglcemn6Y6BMVqcKBSHfL6p4azYdThUVUSXr9dmWqBIm9sk4Vfznh634L17X6oHjv3KJ9NXkla98JQBgy5YtuPLKK9HhWzw2NzdjzZo1+M3f/E3tB8gwWuCZKnoJG5xGlZkZ8R5y/Jc9XHZtHjgALF3KoooJKEZNeHznO/RFFdn4L8+pUi7TG2xOeVB9cKaKy/ivqNdp7lzxPd58Fgt2qtQ3wTWPJ/B2dbk7pryxYwewdm3l7zIuDu9+6npQfTD+SxZ/UbkoZF3/e/fqLE6Vm29Wf16day2Trpe+PmDBgnS/S0XI99ZnOp0q3d1CmFUhy3s+Pi4+oy7W6V1d4t8afL2C8V/e/YedKvmC479yifSn/8/+7M8AAGvWrME111yDFiqbaoaRIYuowopwLceOVXfl5QGO/7KHf3Hsxd3YWnTu3y+cKox+qBX4/Rw96voIklFxqgA0O78oDapPmqni3+BRcap44n7e7p9MPGnERipdw0x2goXk9nYWVVTZubNWVEkSKo4ds1NgStpDpj2G1taK26YoTE7WDtZWIWv818qVarM1PPIS/9XXB2zcmO53qcV/zcxUiypZivK29ycTE+JzbnqdHvbv6uwMj/1zGf/Fzkx9JDkdWVQhiXIQ52//9m+jpaUFk5OT2Lt3L3bv3l31nyr//M//jLVr16KlpQVnn3027r777sif/c53voPLL78cvb296Orqwvnnn4/bbrtN+TmZOiXtDZcvXrXkyaniwU4VN9g+fzynCqMfqpvOcrlYoorfvk8NavFfUTNVgOp7PjWnClMseFB9fRMmqhStWG6aHTuANWsqf5d1qnR1uRdV/Pea2Vn5OSdZB9VTXJPpiP+KE1W8f3OUqOK9Fy5fG5MF/iwzVagI+SZmqnjYet/Hx4U7xkVdoasr/Bzj+K9ikOSM5NmMJFEWVZ5//nlceOGFaG1txerVq7F27VqsXbsWa9aswVp/h4kEN910E973vvfhYx/7GB555BFceOGFeOlLXxopzvzsZz/D5ZdfjltvvRUPPfQQLr30Urz85S/HI488ovrPYOqNLBcfdqrUkkdRhWequKG52e58CHaq1B/j4/koYMnGf3nnC8W5KnHFJduL/LCZKlFFOCpOFRZViknamSoUClxMdsLiv/JwT6LEwEB1sVjGXT42JgqbrgfV+xkYqJ4NE0fWQfUU0SmqBDvf/c0RcbMbFy0CjhxRe17KTmw/WWeqUBDyw0SV6ensokpvr/r7nhZPVDG9nouaqRL22Q+KKt55yPFf+cJlhDqTGuVP/1ve8hY0NTXh+9//PpYuXYpShpvQpz/9abz97W/HO97xDgDAZz/7Wdx222344he/iE984hM1P//Zz3626u9/8zd/g5tvvhm33HILzjzzzNDnmJiYwITvgjc8PJz6eJk6xXYxJg/kUVTh+C83eKKK7s/L2Bjw6KPA+edXf51FFTNQ7ooZHaU7g8TP7Ky8U8Ub1kqNOAdGcI6JaYLxX3H59HPm2BXV414nFveLBztV6puo+C9GnuA5JCM4HDtGw6ni58gR+aJ3VlGF4npnasqcU8X73pw54h4bFaPpDatftCj9cVDl6FF50S4INVGlsVGvU2XZMpFWYON9tyGqRAlNnZ3RThXvWuhfj9uK/2Knij6Sru0Ur/11jrKosmXLFjz00EM48cQTMz3x5OQkHnroIfzxH/9x1devuOIK3HvvvVKPMTs7i5GREcyfPz/yZz7xiU/gL/7iLzIdK1MAslx8bMeG5IG8iipczLKPKafKY48B//APwLe/Xf314WGx4GT0cvx4JaqCmngxMiIEC+qL+jgnhZ/JSfEZpiiqJM0KsSmqBOO/4kQV2zGEca8TxfeVyQYPqq9v2KmSnTBRJWnNbtOpoiKq9PbK/WyW+K+ZGfmYMZvocKpMTUWLKlNT4rMR51Q56SQhqlx6afrjoEqW2SNU7jneezdnjn5RZf9+4PTTsx9jEjZEleAa1yMq/svfBOwi/oudKvag3OhYpyjfjU866ST09fVlfuK+vj7MzMxg8eLFVV9fvHgxDh48KPUYn/rUp3Ds2DG89rWvjfyZj3zkIxgaGvrVf3vSDC9j6huO/6olT6KKV/zl+C83eB3sutm5U2yawqCQqVw0BgeBnh6a3dWjo+L/1IvVsveSqSnRgZmn+C8XonUw/ivus2nbccozVeqLtPFf1K6lTDrYqZIdyk6VpIx7PyqiShanitfoQg2TM1Vk479OPhl46qn0x6ADU/uPLA1NVO45pmaqeKKKDWyIKlG1Ftn4L5tOFaoiL8NYQvnT/7d/+7f40Ic+hDvvvBNHjx7F8PBw1X+qBOPDyuWyVKTYDTfcgD//8z/HTTfdhEUxNr+5c+eiq6ur6j+GUYJKZwcl8iSqeLBTxQ3NzWYWnTt3ikJ/1GM3N7MYqhO/qELtepgXUUV2kz05KUQViv8eyqJKHLYdpzxTpb5gp0p9MzlZXQBmp4o6wXNIdqYKxfgvFadKWlFF5f5nE12iSpjz2H/NjBNVVq4EIubzWqGhgWZTFzVRxf/5Z1GllqhaS1z8l6uZKgCtBIOiw681OZR9Wi9+8YsBAJdddlnV1z0xZEbyYr1w4UI0NjbWuFIOHz5c414JctNNN+Htb387vvWtb/3qeBjGGOxUqSWvogrPVLGPqfivnTuBF78YeP554JRTxNdGRyufS6/Iy8Pe9MCiij3YqSJHcKaKn+CGg4pThcX9YsJOlfomzKnS3+/uePJI8BySWTseO2Yn/mt2Vr7ge+SIfPxQXGRlEkUVVbwGiLj4LyBeVElTcNQpgjQ0mOncz/qYVJzmUfFfWeOjbIsqy5ebj/8KO8fb28PXG/79mX+mio21BkURkWEsonz1+ulPf6rliZubm3H22Wfj9ttvx6te9apfff3222/HK17xisjfu+GGG/C2t70NN9xwA6666iotx8IwsVAsIromL6KK/ybP8V9uMCWq7N8PvPrVIgLME1UOHKgMqfeKl+xO1MPAgBiOSVFkHhkR73NRRBXKThVKYkFU3jRQu8Gz7VSJKviwU6WYsFOlvgkO525vBzhuWo2pqeqiqkxh3JZTRQVb8V9FFVV0xH8BYlj54cNuhtU3NgohTjfeOjwtjY00zhXvvZs7V69TZfFiQHKEgDLBWZIunSoNDcCpp9Z+3S+e2I7/MgG1+Z0ME4OyqHLxxRdre/IPfOADePOb34xzzjkH559/Pr785S9j9+7deNe73gVAzEPZt28fvva1rwEQgsq1116Lz33uc3jhC1/4K5dLa2sruru7tR0XUzBmZrIPqqdWRHRNXkQV/6KCO4TdYEpUmZ0Vi8rvfa/ytQMHgKVLxZ/5/dYLdafKwoXFeb+pO1WixALbr79Kzr1tp4q34Q7CokoxSetUoXYtZdLBg+r1oLpXGxsT6xJK55GKqJJlnVqPoops/BdQGVYvK6roLN42NJgRVY4eFWvdtFBxR4bNVJmezi6qmGqe8Rw+ftHXW6e7EFUA4F//tfZrpVKloSgoqpie8aVb/PDOoayfCYaxRCqf3eDgIP793/8dTz/9NEqlEk466SS87W1vUxY2rrnmGhw9ehQf//jHceDAAZxyyim49dZbsXr1agDAgQMHsNuXifmlL30J09PTePe73413v/vdv/r6b//2b+P6669P809h6oHxcXHjTguLKrXkRVTxFzo4/ssNJgbVe90rJ54I/O3fVr6+f3+tU4XRw+AgsGEDXVFlwQLaxWpvwyjz2tnYrKWFUvxXuRwdhREW/2XbqRI1qJ6vS8UjrVOFQoGLyU7SoPpyWaxPli+3f2xF5tgxYNUq89d2mWgbrwg4MiJmHsiQpRBpUlQplcS/JU3UlE5RJfjvk43/AirD6i+9VO55dcd/mRBV+vqyiSpU7jmmBtWbwmsODMaTtbSYF1XSnuP+85CKmKaC91ml+plgmADKd8sHH3wQ69evx2c+8xn09/ejr68Pn/70p7F+/Xo8/PDDygdw3XXXYefOnZiYmMBDDz2Eiy666Fffu/7663HnnXf+6u933nknyuVyzX8sqDCxHD+eTVShWER0TR5FFY7/coOJQfWHDwubd7BwwU4Vc1B2qoyM0BdVVMR9yvFflESVOFzHf0U5ekxvwhk3cPxXfZPkVNmzB/i/FAZGI5Tiv/wuRBWxJG0x36SokqX4TiX+y3OqyKB7HoQ3U0U3fX1irZsWKu5Ik6KKidc+KnHBtPM4LuI2Cu/a4z8P87jWyOMx60B2bhLPsCGHsqjy/ve/H1dffTV27tyJ73znO/if//kf7NixA7/xG7+B973vfQYOkWEykmaz64edKrX4B4IDla4mavjf+3q9QdtmdrZ6Q2ki/mvnTmDNGvHnOXMqj89OFXMMDAhRheL10Iv/olysVomqohz/FVXIoHS+hW02qAyq5/ivYsKD6uubycnqAnCw4ePnP6d33ywCtgbVy4gktu+BWbrYk8hSfNchqkxN1c7Y8b4nK6qsWgXs3Sv3nLOz2Yek+zE1UyWrU4XKPcekqLJ4sWi804krUSVLA6vfaWaj/qG7yE9FALRN1nQdxhmpnCof/vCH0eS7+TQ1NeFDH/oQHnzwQa0HxzBaYKeKfoKvKcVCK5BdUGPUmZys3uiYFlU2bgSef178mUUVcwwOigGZFK+HeRBVvIWyTBcdZadKMFfag8r51tIiPg/Bog4VpwqLKsWEnSqMv/AedKrcey/NNXLeGR8X90oK51Hae2DaCLB6dKqoxH+VSvKv7fS0XlGFZ6rEY1JUWbZM7AV1EieqmFz3phFVwsQNKu+7ClSi6mwj24Cne4YNkxllUaWrq6tqzonHnj170CmbIcowNskqqlAVDFwSzLJvbqb5GrGoYp/ghsq0qOLlJgPA8HAly5pKkbcoDA6KjlCKhUAv/ovy++3dh9rakmc7UXaqRHWjUTnfWlvFZzVY1KHkVKHwOjF6YacK4yfoVNm6NR+RuXmkuZnGmmTuXDGkvqtL7feoxn9lcaoExZA0z501/kuFMFdMFnimSjxRooqO90BGVJGNWPJw6VTRcY7b2LfpLvJT3GvaQLZmyfFf5FAWVa655hq8/e1vx0033YQ9e/Zg7969uPHGG/GOd7wDr3/9600cI8NkQ4dThaJgQAmqwhOLKvYJiiomBtXv3AmsXi3+HMxN9hZ2VIq8RcFzIFFc6ObFqdLSUr2JjIKyUyUKKudbW1u4qELFqcIzVYpJmkIixWspowd/LKkXl8udpfopl+lE27S0iNk5WYreKqSZtyBLFsG3XM72Wffu1VFOFe+9np3VN8R6ejqbEBSE6kwVU2KPKp6o4j8em06VsM9WHHmcqeI/B/O41uD4LyZnKEvC//AP/4BSqYRrr70W0//3YZ8zZw5+7/d+D5/85Ce1HyDDZCbrBcp2Mcbj+HFxI9S50DOFfwNJCRZV7BPsHmtuFkUFnezbV4n52rwZ+Id/qP0ZKkXeokFRZD5+XMx7oVys9sT91tbqDuYwPKfK4KCVQ9MClfPNE1WCgobtTSXPVKk/VAuJeSx0MHL4PwsPPACcdx5w993ujieveMX9uGKr6fNItqu9pUXM8OjtVXt8qvFfrq5NsvFfMsh2c+uO/zI1U6W/X8Tw5p2JidrGl+lpfaLKAw/E/0xeRJUs8V/+z34eXbFUXFW24fiv3KLsVGlubsbnPvc5DAwMYMuWLXjkkUfQ39+Pz3zmM5iry4bJMDpRGRAchisXxle+Avzwh/afNw2U479Y8beLjfgvf4daR4dYeAYXn1SKvEXDlcgcR7lM3wHg3Ydk4r8oO1WiFvK2z7eouA4q8V9xM1X4usQA+Sx0MOrcey9wwQWujyKftLQk3y9NCwATE3L7iLlzhVNFVVQB0kW5mBRVslybshb7vPczzD1iav2Zl/iv2Vm9x+mKsLk77FSpJcugej95bODI4zHrQDZdx5QbjkmNsqji0dbWhlNPPRWnnXYa2kzd1BlGB3kdVD80JPJ58wA7VRgP06JK2OazqQnYvbviXgFYVDEF1YUudQeAJ/DKxH95ThWK/x4qM1WiCkpRThXbYmCUU4W6+MfYg0WV+uDBB4Gzz+bO0jiinAIy90vTgrnsPsKL/1IVVdKuXag6VbJm/XuxPzqcKrKYiP+iELNFleBcVkCfqNLbCxw+HP8zeRJVVM9xjv/KN7LNwHl8TwuOtNz9tre9TernvvKVr6Q+GIYxQl4H1Y+O0u1ICW4OKTtVWFSxS1j8l05R5fBhYPHi6q9t2AD87GfA0qWVr7Goog//JpniQq5Uou8AUIn/8pwqFIXqKFyIKmEdfG1twMCAe6dK1BBd6uIfY49SiYeNFp3Z2YrAyu91NFFr9dbW5PuK6TWJiqiSJv7LE45U9yq6hliH4bKg2dAgzpWkQfU60R3/xV3k6ugSVWSi17KKKt7nheJMFQ+O/8onsvcCrzbJKVFkkL6DXH/99Vi9ejXOPPNMlHlhyOSJ8XFg/vz0v++qiDgyQvOGMjtbK6pQHlTf3e36KOoL04Pqd+0C1qyp/trJJwO33gq88pWVr7Gooo+REaCzU/yZ4kwVgH6xWiX+i7JTJQovhs8WUQWltjbg0CH3g+rD4i0A+p9ThmH08cwzYu4bE0+cqBJ1v/RqEab3aLJFrrTxX21toniqOitDVzRQGFkKmjocWVGiiqn3Wnf8l4mZKrKzffKKLlFFhqyiine9Mr2em5lR/1xGpTmYXv/qrg1TbOCzgWwjONW6Wx0jfaa+613vwo033ojt27fjbW97G970pjdhfpZCNcPYIq8zVUZHk4tvLgiznFOO//K7GkolsdAt8sLUNabjv3burBVVTjoJ+KM/An7v9ypfY1FFH4ODlQ0/xZkqAP1itSfuy8SZTE+LayzFa2pUwaSjQ9yzbBEVfdLaKqIzFy6s/rptMbBcDn+tqDuqGIZRJ6qg5J+nwvFf0USJKnEzVaamxPqyKE4VVUyKKlmcKrqKq7bjv6jPVBkYyNYgSp00AkIU3uckSjjJi6ii61zKo0DB8V/x5PE9LTjSlcV//ud/xoEDB/DhD38Yt9xyC1auXInXvva1uO2229i5wtAmr/FfIyNAf7/9500ibCFPNf4rKKhxod08YfFfOhedUaJKfz/PVDHF4CDQ0yP+TG0h5znnqM+qUIn/AuiIRB/8IHD99ck/50JUUY3/ovC55e6yYsL7ICZIqQTcc09FVOHPSDRRzW9xgoO3F7EhqsjsIVtaROFV1XGSVlSZnTXX2U8hesd2/Bf1mSp9fbXNInklTGCentb3eV66FDh4MPr7eRFV0hA2U8VG/JfupgEK1yAXqMZ/MWRQateeO3cuXv/61+P222/HU089hZNPPhnXXXcdVq9ejVGbm2mGUSGvg+onJ2kUgYKEiSqUnSosqtjFhVOlsxNYuZJFFVMMDNAVVY4fF4V0ipsbP961SCb+C9Afm5eWvXuBH/8Y+PrX4ze9zc20BtUHRRUvp9013K3OMMUj7LxuawO2b6+e9caEk2amincPoOJUmTsXWLBA3QnvxX+pYvJ+lqVLXMc9rlQKj+RSfa9li8m6479MzFQ5erSYThXvPdIZ/7VsGbB/f/T3dYkqppu50pzj5XJtTLvpa6SJaDpqe01bcPxXbkl9BymVSiiVSiiXy5jVrcYzjE5ku4yi4AtXNXlyqgQ3QzJDL5lsmBZV9u2rFk88/vAPK3M/ABZVdBJ0qlA610dGhEuCeqyS36ly+HDyz+t2eKXl+HHgm98Efvd344tLtsWCuJkqg4M8vJFhGLe0twOrVrk+inyQZqaKTaeKbPyXavQXkN6pYpIsXeKU4r+8ZpuwtYIf3fFfJmaq9PcL0a5oeJ9/6qKKX/i04VQ5fjz9OnZionofbvoaqdvpBXD8VxJcmySHkqw4MTGBG264AZdffjlOOOEEPP744/j85z+P3bt3o6Ojw9QxMkw2ss5UqVe1PIoopwrFi7uqU2VgAPjJT8wfV5EJxn/p7riPWnj/wR9UF3ZZVNEH5Zkqo6NCTKPuVPHuQ3mL/5qdFZuzf/s34Kc/pSNWxM1UCXOqMIwpdMaWMPkjqojc1laJ/mLiSTNTxZZTRXYPmVZUkXWv2oTCvldH/JdsU1ce4r/6+/U4VSg4dv3kRVQJc6qYjNV64gng1FPVf6+pSVwb/Wt10/Ffup1eAMd/JUHhGs1UIX0GXHfddbjxxhuxatUqvPWtb8WNN96IBUVUzJnikdeZKh5RA29dkff4r7jNyw9+APz858Bll5k/tqJi0qmishlgUUUfg4PAihXiz01NtF7X0VHhVKE+U8XrPpItoOh2eGWlsRH4j/+Iz6m2eZ8aG6u4p/xExX8xjCkmJrI17jD5Jqoge8opwMUXV/7uFbZYgKslrVOFWvxXWqdKmvgvk/fbLEVYXccV5VSZnpYXsmWbU0wMqtddEO7vL6bzzZSocvfd0d/XJaqY5NFHgdNPV/+9OXPEtdEvqthwqpgQVepRNOD4r9wifQb8y7/8C1atWoW1a9firrvuwl133RX6c9/5zne0HRzDaCHPokpXFzA8DHR3u3n+MIoc//WjH9ESsPLI5KT43HrojDE6cgRYtEjuZ1lU0QflmSr++C/Kooo//ktGVDERH5GVpqaKuOaaY8fCYwA5/ouxjY0CC0OXYCOJxzvfWf13by/BokotcaJKX1/474yN0Yv/SjNIPK2oYpK0r6lO116YWOnFfwUd8VHINqeYmKlC1alCbY/rF1V0vQc2nCqm15hbtohYa1XmzBHNZkWI/6pHpwrHf+UW6avXtddeixK1CzHDyJB1porLIuKCBWI4HXVRJU9OlahC++ys6ML2CwKMOpOT1YtNnR33YUPqo6BW/M8zwZkqlF5XL/6LygySKDxLd9qhtHnAZqxE3KD6oSF2qjD2YFGlvokSVYJ4ayH+rNSSxqlic1C9zL5gwwbgXe9Sf3zvnqWC6Xtt2nkGsudCEl5CQ7Du5MV/yYoqKk4VnUVhUzNVvBjePBP87JpwqixYEC3GAvlwquzYIb/f9dPUVOtUyWv8F6W9pi04/iu3SJ8B119/vcHDYBiDZL35uVCDvRuUJ6qsW2f3+eM4dqzW4t7cTNMVEBTU4uK/HnkEeMELRI4pk57ggDyd58+uXekWmUw2gjNVKHXHePFfDQ30sqL9eNeiiQl6+ek6sRVX6XUpB2ltFcUMdqowtmBRpb6RLSRTu3dSIs1MFa/By/RAY9nzu60NOO009cdvbxf/FhPHlJa08wyCTVW68QqJup0qJuK/dIsqAwN6nCquCb7Wnqii0+WUtAalLqrMzoaLijIUJf6rXgfVc/xXblEaVM8wuWR2Vixw0uJCDfYKhZ6oQok8DaoPLrzj4r9+9CPgJS+xc1xFJlhg0FlgVXGqFImhIfVORt3P73VqUuuO8a6V1PEPqi+qqJIUr6iTOKcK4NapQlncY/TDokp9w6JKdrI4VUyL+KbP7zTu1bB9mE7SFjSDTVW6UY3/knWqmIj/0u0MGB01+57bImxfrtupkgR1UWXHjvTNtF78l21RRXf8V70Oquf4r9zCogrDJOHiwuVF2lAUVcIWdlTjv4Jd03HxX/fdB5x/vp3jKjImO9XqVVT57nfFf67wdyFRE1VGRsS1kjqzs2LDWOT4r44OcX+wwbFj4RuPOXPE6+xSVDGxwWTowqJKfaMa/8XUEieqRK3ZTQsLHrJxLGlJ41QxXWDP4lTRce+NEspU479cOVVMzcQrQgy/LVFl7tzoawd1USXtkHpAfI6DM1U4/is/cPxXbmFRhWGScHHh8oYvUxRVwmJXqA6qDxIVJTA0JBZ2XAjLTlinmq7O7cOHa6Pn6oHjx4X1nwLUFnJ+p0oeNpyynZN5pKNDvTiUlmPHwh1KpZK4lruM/5It+DDFgEUVOpTLwD332H1OdqpkJ4tTxTRUnSomHbphTpWbb07+PZ1NVWH7BtX4r7j1ll/00N0IYSL+SxcmXDQq2BJVli0DDhwI/x51UWXLFuCMM9L9blj8l+koLVOD6intNW3hNeAlwesJcrCowjBJmFb4w/C6rxcujB+25oLjx2s3MlSdKkGiut5+8hPgxS+2fzxFRFenWhgTE/VZvBobcyuq+MUKags5v6iSh9ilUikfxwmoZ1zbdqpEdeq2tbl1qiRtuKkJk0w2WFShw+go8Pd/b/c52amSnTQzVWyKKjJxLGlJ41Qx7dIJc6r8zu8k70epxX9FnXPbtgHvf3/l7ybiv3SLKrrWja6vQ2GiytiYGVFl//7w71EXVZ54Ajj55HS/GyaqmG44M+VUqcf4L1mo7cUZFlUYJhEXRTDK8V9jY7UbjDw5VcJEldtuA6680v7xFBHTgyrz4EbQDTtVovFcfYx+wgT0ONrb7Ykq4+PR1xnXokpSwafIbqV6hEUVOhw7Zm+ukwc7VbKTxqliK/6LqlPFtKgSXOeNjQHf+U7875lsqgLU47+i7rVDQ8Ddd1f+bmJQvc6CcNbZsH5crz/inCo634M4UUX1nLYtqmRpIAyL/zKNiUH11Paa1ODXhxwsqjAMRSjHf0U5VShuFoNiWFjXW7lcO6sjL53kFAnrVKtHIUQnY2NAf7+b556Zqd7MUVvIeQJ0nog7Hyhde44fV+vQtelUAaJfx7a28IKLrdc2acMdl/XN5A8WVejAoko+STNTxZZTZWzMvKhCzakSTGiYnAQuvBD4/vfjf890U5Vq/FeUK2NiAnjssYqYpTu+SPdMlaEhoKdHz2PNnUvPqWIq/itKVFHdN9gUVfr7gXnz0v9+mFPFNHmL//rCF+xFFZuC1xPkYFGFYSjixX9RHGoc5lTJc/zXM88AmzdX/h43zN4VBw4A99/v+ijkMN2pVo+4dKoMDQHd3ZW/UxRV8uZUiSvuq8YSmCTsWh+HbVElitZWdqow9mBRhQ6jo/bPrclJuWu269gdysTFf7keVB82R1InaWJubDtVjh0T8ww7O4GDB6N/T9f6P2qNpBr/FXWvnZgQ69pHHhF/N+FU0Smq9PcD8+freazmZppOFdW42SSSRBWVfUOcqKJ7T/TYY+mH1ANuRJW8xX/dfbe7RkVdsKhCDhZVGIYi3g2fYod/2GI2z/Ffwegvm8OWZXnoIeD2210fhRxhnWouu+8pdf6nZWwMGBx089yDg9VdU96mlgp+ZxTF66UqU1N0REnqTpUoouK/bH0+4qLJAHEfYlGlOLCoQgd2quSTqHMoLn7Z71QxeW1XvQ/awIZTJSiqtLcDr3kN8N//Hf17OmeqhL3vqvFfcU6VCy8EfvlL8XfqM1V0iiqumzpsOVUWLQIOHQr/nk5RRffrmWVIPSDOkdHR/DtVTDbwHT9Or2FZFWoNjgyLKkzBKZf1LLZtF+s8pwpFwl7TvDhVwuK/7r0XuOiiyt9tzgWQpa+PnnsmCtODKlXIi9iXxPHj+vKUVRkcrI4d8Da1lPCuR3kR0OLuJ8GuZ5f/JtWZKpREFZsbyiBJediuixqMXlhUoQOLKvkkzTlky9Wpax+pE9uD6r3ne8lLgB/+MPr3dDlVorrUdTpVXvQi4IEHxN91F4V1z1Qpmqji/4y0tZkRVTo7oxskVUWVYI0jKKrovOds2ZLNqeJipooJp4rJ+K/jx+k1z6rC6wlysKjCFJukjlFZXA2qd/HcSYRtLvJSvA6L/xofr96ctLfTu9nmSVQxGf+lei5QjHJLg2oMk04GBqpFFcrdMaWS3u5AXQQ/t0nxX97541rASuNUsXXtjCtyLV3qVlRJWnfwTJViwaIKHVzEf8m6Czn+Kxo+h9QwHXsa5lTp6BDvUW8vsGdP+O/pmqkyZ060qKLDqTI+DixeLOJtAf3xX7pnqmSds+GnXmaqxMWcqZ4/wfWmSafKkSPCZZMWVzNV8hT/NTaWf6cKiyrkYFGFKTYUbdsyeIPqgXwUYChe3IMDtgG5IjvF+K8jR+h/BjzCugdddfkVRVTxNiEuBNagU4WyqEK1aBVWdIt6L/3nj+uOQlUxz5bLL+k8+OpXo4ucNs6hJKcKx38ViywF4bh4I0YddqrkExOdzrqg5lIB7DtVRkcrz/ea1wDf+lb47+lyqkfFzHqNJrLiTZxTZe5cIRAdOVJf8V9UZ6roFlXizttjx9Rc2EFMiSqy87niKMqg+nqM/1JZC1KL4mZYVGEKzvh4fkUVz6myYAFw9Kjb40mCYvzXxETtex8ssh8/XlsM4fivbFCKSiiKqAIIsc/2InDHDuBf/xU47bTK1ygv5KgWq4PXmbjj9G+qXG9+qcZ/pXXD2XL+8KD6+iKLqNLYaK4bsx6hLKpQFf2pkGXdWG/CpKuZKgBw+eXAj38c/ns647/CIm9V47/iZqrMnQu84AXA/febif/SKaoMDBQr/itKVLElrJbL2SKV/et0na/nM88AmzdnewyO/0qGqlNF5frpOsmAqYFFFabY5NWp4rem5kFUoRj/FVboCM5U2bsXWLmy+meoxn8FZ8HkCR35wmnEmiKJKvPmiY2VDcplIaa85z3AF74AnHVW5XuUF3KuN4tRBO9D3iYyDL+rxXVMA9VB9V4UiSq2BMGkInse3KeMPCyq0OHYMfuvJztVGNu4mqkCiM/6ypXA9u21v6cr/qupKVzk8AqtOmaqzJ0LnHeeGFavO76I+kyVeoj/ikOHCOvtR3XuO558EjjllGyPwfFfyaSdqXLTTWbXFyqN4LyeIAeLKkyxCXMi5IGxsUqH8MKF9EUVik6VsEJHMOd2925g1arqn6EY/3X8eH6KcFEzd7J+PtJs1ooiqpTLdkWVt75VZE1/73vA+vXV36MU/xWM+KMqqgQXym1t0V1SlJwqqvFftkQVfxSJCrY+u+xUqS+yiCqUrqdFwLs22HQusKhSbGx8llSvAzZElbCZKh6/9mvAww/X/p7O+K+wIm2pJP7T5VQ57TTgscf0d9qbmKlSlPiv4F7OK55PT9sTVXSicz03MgJ0d2d7jDlzxH2Q47+iSetU+Y//MNvgqlKz5PUEOVhUYYpNXp0qQKU4zU6VdMgUOsJEFYrxX64XwSqEbUB1iCppBNKiiCqAXVGlvx/4oz8K3+BQKgIGN/pUHQDBz26enCoq8V+2rp1pC0q2XFZJ9x6qMXVMOlhUocOxY+JeaXM9yvFfxcWWOBfXaBGGjfivqJkqgDjesDWMzvivuCKtDqdKS0tl70p9psrAQPZiu4frpo6o986mU0UnOl9P2c91HC6cKnmL/xofTyeqDA+bXVuo1Cx57UgOFlWYYqNrporLGRGURJWoDYbJm19asogq1JwqWbJfKaCjOJzmXC6aqNLfb/55gu6PIJRmqvhjEgH3m8Uogp/dOFHFVFZzGlSbEkza9f0E33dZbH122alSX3D8Fx2OHRNrZpv3fXaqmKVUqi1Qz87a2ZdNTdkpTqruO2ZnzRagw5wqflElag1jOv7LI6tTZXy88vsbNohZFpRnquh8v10361AQVXReO6iJKi5mqphyqphYG5XL4jVOI6oMDZm9h3P8V67JeaWOYRLQ5VSxPQTR/3yURJWo4gGVweR+0ooq1OK/pqby1b1jKv6r3p0q8+fbcaoMDcV3xFHqjgkW16k6AIL3obiuVL9TxXVns2r8ly3y7lSh6qhi0sFOFTqMjoo1s837gIqowk6VcOL2EGH3dVUXZVqOHbPzPKpOFdPEDaoHokUVnfFfpp0q3u+fdx5w3320Z6roxHXygU1RJWwNrbueQ01UmTNHz+OoYMKpYmptNDUF9PSkF1VMrtc4/ivXsKjCFJs8x395LFggBpVTwD/rhToyhY4jR8TMGj/U4r/6+8VnIC9ExX9lXXSyU8WOqDI4KBacUVASUEdGgM7Oyt+pOgBU4r+oOVUoXu/ZqcJQIkshkZ0qeqHsVKEYk5sHwu6XtvYitp6HmkM+bFC9/54b51TRIarMmWPWqeL//Re8QKx7Kc9UoeqsSINNUSVszp8uN5WHblEl6/nj3z/YwsSgelMJKMePizVCmuutaacKx3/lGhZVmGKT10H1/gUUJadKnkQq2fc+uFiltrnp6wN6e10fRTbYqZKecll8RqmIKpTIc/yXrFPFtahC8Xqfd6cKVUcVk560RS/eGOtlYgLo6qIpqnBnaTpaWmoL+GEOEhOJAmNjZmeXeBTFqUIt/kvGqbJ+vXCCU43/MuGsoBr/pbswH7afT9uU48d/v9e579Bx/nivYREG1ZtoOBkbE820qtfb2VnRyGdyvcbxX7mGRRWm2FAtCsURzAru6RGFTgpQ7VwOI6mwFbVQDetscUmYm4YypuK/6tWp4n2Obc1UybuoQvH9DgqCUUNegVqnCsd/1ZLFqWKjgC3jVKH4OWXsw04V/bS20oz/ch3nSJWkonGUU8Vf5NftDPCwFf9FrZkrWNAMDqrPS/yXjFOlVAIuuICuqKJb2HPdfGTTqRKWPKFDVPFDMf4LsDtTJU/xX8ePpxNVRkbEvcq0U4Xjv3ILiyqMXsbGaHXb6BxUb2uuSrAjyNRmIQ1Ui2xhJIkqUQ4Qapubvr58iSph54mO4nC9OlU8IdOmU2XePPPPo4M8xX/JDqpnp0oyaZ0qtuK//ENww6D6OWXsw04V/di+77NTJRtJXc6trbXvZ1DsMHUesVNFoOJUoSSqRM0xmpio3k9861vxswRV0TlTpb9f75rc9boy7L0rl8Xr1aC5LBk2I1W3qKLzfqNLVGlq0v9axmHCqWIq/iutU2VoSPyf47+YCFhUYfTymc+IxQkVdBWFbG6GgoVCShTJqRI2pB6Ij+ZxAcd/CdIMAy6CqOIJmbYcVAMD+XWqUI1VUon/ouRUSXO9tzGgNe2m2Fb8V7BgE4Tq55SxD2+M9WPbCea/ZsfBoko4SWs7GaeKSVGFmlPFRoNf0KkSLJq6jv+SFW+iYhmDjQ+6Y8J1NkP294t4Ml24buoIEw5KpdqUDh3ojv8ql8W54HfUUHOqNDXZjf4CzMxUMRX/dfy4iAhV3VsND4v/c/wXEwGLKoxebrtNxBVRQddMFZsbX91dFDopglPF636IElUaGuy5kmTIU/yXN/8jCKVB9T/+MfDVr2Y7Fpt4he1Syc6Q+LzHf1EsVqvEf/mdKq7/PWmiPNrbzYvS1J0qPKiekYXjv/RjW7Tk+K9syMygShpUb2qPZjP+S/a+qSuBIY6k19N1/BeQbT2sy1EThc74r4EB/aIKtZkqphrgwprR0tZYmpvF+jHYNENNVJkzx270F5Cv+K+0QvnQkLhOc/wXEwGLKow+hofFQHVqokoRnCpUNt55c6qEvffehjtKVAHsFK9lyVP8V1S3CqVB9fv20ZlRJINfyLQh9uVJVCli/Je/65lCEU71WmjDUUXdqZJUJOSZKowHO1X0w/Ff+SKNU6WI8V+yTpXgfBMTJO05bcR/6S7SBjG5z9Md/6VTVKEY/xXXaJSFMKfKyEh6UWVysvZ6pVtUyXr+zJkTLcyY2kOaiv8y5VRJUxccGgIWLDC7XuP4r1zDogqjjzvvBF77Wlqiiq6OHttOlaCoQmVYfRGcKl7XW5yoQsmp4sV/2SoIZiFqQ0VpUH1fX74KmrbnWuRJVMnLoPrgZzcuP52SUyUNNkQVdqowRYFKw0yRsH0f8F+z44ia71DvyIgqwffTVvwXRadK2vufCkmvZ9R+xFb8F3U4/iuaqana9zau0SgLup0qpkUVHaJklKii0z0VJE9OlbTNwUNDosHV5B6C479yDYsqjD5+/GPgda8TCwAq5NWpErzhL1ggisGuoTq4OIyojZq3Qdu9G1ixwv5xqTI8LD4PeZgPErWhojSoPm+iStCqbFrwy9Og+rzGfyUNqvc7VSj+e+KwJapQdqrMzsYPCeWZKowHdxvqwyvsuIj/kikAe/ExTDXsVFFzqtgQVdKKvcF5E2mZMyffoorOArbuQfWu47+AWpdQmHCqg6iZKmnm1toQVYDsA+ajZqqYmlECmJupYuqansWpQiX+ixtyyMGiCqOPrVuBTZvMKeFpyONMlbD4rwULRLSaa/IW/xXlVBkfj+8IoBT/BYjjyYOoEmVdpuRUOXqU/uvoxy9kdnaaL1gPD6fbcLgguDmiWqwOG1QvE/9FYfOrig1RJe2miEoBm6qjirGPyUJHveGJrbbXSuWyXCGMO0vDSTtTpWiD6vPkVDGNTqeKi/2cblGlSPFfYZhyqrS358upooOomSomz2mO/9JDnhqXmRpYVGH0sH07sHYtvWJ0Hp0qYTf8oKjyxBPmC1dhFCX+K2nDTSn+yzun8iCqxMV/ZV10slNFdKsNDJh9vqQue0qMjFQXF6htbjzCBtXLxH9R3PwmEbaJ1U3az6it+K8kKMzKYfSQtTO7sZGG0FcEvFkTVEVLjv8KJ238V5EG1VN0qnivp8p+SFcNIO/xX5RnqlBcJ5uM/wpzqhRdVAlzqphca5iI/zJ1vGmF8uFhWvFfDDlyUjlhyHP77cAVV7g+ilp0XaBcD6r3iyqHDwOXXw489pid4/FTBKdKa6uIOIrL/aViqyyXKxuavIgqYa8rO1XS4xeGbYgqeSLYHUVtc+ORdlB9Xp0qssUh21CZS0Wt+YRJz8RENje0647wIuEVm6k6Fjn+K5y08V+2nCo24r8oOlW8PVDSjDA/uhrS4kQVSk1vUeicqTIwoDf+i2JTR2urmXVRVPwXVVFFx2fbVfyXbhHU1DrZvx9Teb1tOFWOHFG7tvNeghQsqjB6+MlPgEsvFX+m1F07O6sn39X2oPoop8rsLPCudwEve5mZro4kkpwqlBa7cU6V558HVq6M/t2whZgL/Bu6PIgqpuO/dDhVvMfKC34hc948WjOrXBNcUFLtUFaJ/+JB9eag4lRhikOa+5IfKg0cRcBV/Jcs/F6Hk0ZUCXYbmxLMbcV/qTpV0hSFVfB3idsQcYIUwamiS1TJKtwH0Sn4AOKxVM69sDpBa6ueWk2QvA2q14GL+C8TThVTeNd01Tk+pgfVP/qoOAcWLZL/HUo1N4ZFFUYD09Ni0dXTI/7e2yvUVgrouuBQcap87nPAZZcB553nRlSJc6pQ67iME1Weew5YtSr6d6l0W/f1ifMJoFso8BMV/+VqUH3YZ5Jq4T0Kv5Bpw6mSp86X4PWdaofy+Hh151hccc3vVHHZUZj23klZVKHiVGGKQ1ZRhdq6Kc94xd+83ePrnTQzVWwNqrcV/6Vyr/di7kziFwVURBxd68fFi4GlS5OPjSo6j5F64fRznwO+/OVsj2FKVMmbU0UHLuK/TAyqN4XnVImLYQ5jZETswU3UAmdngT/+Y+Dv/k7/YzPWYFGFyc7994sivwclUUUXrgfVL1wI/PSnwH33AdddZy5/NIk4pwq1IZxRRfjWVuDZZ+NFFRtzAWQ4ckS890B+RBVK8V9BZmfVu1Nc4xcy5883K6rkqdsoDGqbGz/BYkNU8YGKUyVtdyRlUYWdKsWByjWcnSp08IrNVMV1Jpy0M1WKFP+lIkbYcI74j0fl+XQJAJdeClx1Vfj38iBE65ypQplyGfj2t4EtW7I9jilRJaxwnlaUjBJVdH4edYiSc+aEC8F5i/8yhbenVhVVZmfFnszEteerXwVe+tJoITmKPDVB1gEsqjDZ+fGPq+epFFFUoTCo/uBB4ItfFBdRV6JKnFOFWl60v+Pbj4xThUr8V19fvkSVuPgvF4PqgwwOinOJeueXH5tOlcHBiuMwD4TFf+WlmBb1GQw6VVz9e5KiHqOgIkiHYasYwxsd8/zWb7k+AgE7Vejgn6lCfa3EVEgb/+W/P5k6j6L2ES6xHccVV4R2sZZualJr/imV7DtbdEdsUeXhh4Hzzwf278/2OK2tZhq6whxDaWPho0SVUonWnnLOHFGkD8LxXwLv3qEqqgBmaoF9fcCNN4qGaVUofe4YFlUYDTzwAHDOOZW/UxJVdBU3XIsqc+eKOSALFoi/t7aq3wx0kORUoTb8Luz9b2kBtm7NR/zXkSPFiP9yNVMlSF9f5RzKC8FB9SZnquRNVAkuKPMkqkQRdKq4uqb6P3cqmHaqTE+n72jk+K/isH276yMQ6BBV6qGj2QZeTBHHf+WLNKJKuSwKph4mC4bURHLbokrU87lab0XFG0URbPibnTX/nuqK/5qaoifq+fnqV4G3vCW7cGXKqaKTKFFFJ7qK5N3dtV/j+C+B1xzc3p5OVNH9Gn70o8Bf/VV+Xj8mEhZVmOw0NlZfDCiJKrpuUDa7CWdmwhcX/tfYlVMlbjFBzakShXf8nlgRBpVu67w5VUzGf6XtLvLjfz3zgn9QKjtVKoR1RlGNdwq7D0Vt6v2dsS5FojhXYhymBeksQ3qpfj4Ydfr63KyBguiI/2KhTw8c/5VP0sxUCVJP13YXokrYPdfVPrSpSV1U8V8PJibUfj8NukSVgQER+0uRiQnRoHjyycDatcCOHekfK++iCjXhNQzTDRx5eA2AaqeK6l5F933mwAER+e8foaBCXl7zOoFFFUY/lEQVXVCbF+JqMQtEX8SpOVWijrO1FVi5srrLLQjHf6UjKv7LZce9n6NH8+1UMT1TRVZUoTAoNGyjn6cFZpTg73equBxUT9WpkqWgZMOpMjOTr89hXhkbM3stlIXjv+jA8V/5JM1MlSD1dB5Rcaq0tbkTVcL2GVEEm1NsiSo6Ctj9/XRFle99D7j6avHn004DHn88+XfK5fD1Ud5FlTxQT9fIOLzGZZX4L2+/pnsP0d8PrF6d/vc5/osULKow2Qg7oYsoqlC7GbkUVaKgJjxF0dISH/0F0I3/ovaeBzEZ/6WDvDtVTJ/3g4PCDZMEha7QkRGgs7P26xQL2irH5HequBSv0s5UMR1NGRaPKYuNz+3ERD433HmiXBbXQZNRiLLwoHo6uIr/4sJGNpLOIZn5FNT2aCbJ4tZM+3xhooqrfWia+C///sOGqKJrpkp/v9ya3AU33AC87nXiz6eeCjz2WPLvRMWZsagSnVKiC3bFVqMiqnj7cN17CNsCOWMUFlWYbIQVURcuFMVL10R1RKSBmmBAUVTJU/xXkqhCKf7Lc1bkofvSZPyXDvIoqvgX8KYHIg4MyDlVKMymyFJcp0DUvcnvVHFJ2vivhgazn1HqThUbBZt6Z3JSFKyoOFWyvN/1VAw2jXdt4Dk1+UKmSJl0TynCeSQ7l+LYsXT35rREDaoP7kNtzCoB1OO/XDlVdIkqFJ0qBw6Ic9Y7tpNPBp58Mvn3ol57FlXMfy5N3hfz1FjgXaNUZqoMDYk5NbprgVlFFYpNhHUMiypMNsbGai8IFApuQHTXfBqo/Js8KIoq1OK/oli1CnjlK+N/hkr8l7/AmhdRJcqpQiHjPI/xX4C9hZNs/BeFAkaUqJKXxX1jY/ji3HS3mixp478A86IKZaeK7IZbVzxIPeJthCk4VbKKu+xU0UdU8dckebnfUEZHkZLCmiQrMjFngJ75girIOlVsNYSkmani35vaiG8quqjyzW8Cb3hD5e+y6Q4uRBX/WsvvBFelCKJK3q+ROlGZqeIXVXS+hllFFV5/kIJFFSYblK1rWYpCQbIUY8bHgUcflftZ2QukqyzbOPLiVOntBV7xivifMT0XIA15EFWiZqq4dqp451UenSo2yZuoEhb/RY2o7k2K13A/aeO/TJOlcGrjcyu7MeZh2ukZGwMWLy6GqELhWloU/PsRW8WG6en0RTpGQFVUsV2wSjM42Qayg+p1NjLGoRr/xTNV9HPggBhO70cm+jXqtfeilUzgdyRkacrJu6hiMv4rj44Jlfiv4WEhquhuzPLHezO5h0UVJhu2bcgqHD+u78aXxfL3+OPA3/+93M+Oj8sVstipYhYqThU/eRBVKMZ/+cW+o0dpblBUkI2ISIOKqOJaQB0ZyUf8V9SMDYrXcD9p478Asxss6vFfshtu23MfisTx48Dy5XRElSziLkdV6cNFkxeVuMY8o1qknJqqLcCaEFV0NubJoBJHYxNZp4qt6MusTpU8zVQZGDCzZ8m6jwhbH550EvDUU/G/58Kp4o/zztIE4RdVTHx+opoSdcFrjWpURBWqThWGFCyqMNkIi/8CaBTdZAUKGbIs2PfuBbZtk/vZqOHLQWRt4jahNncmCxRElZkZ0e3kkRdRJWxRaHrOQhz+183rKjUpTJimq0tcJ0wgO6ieQhxi1OaIWsdUlLifB1GFqlOFcvyXbMEm2D3LyDM2RkdUySru8vBYffhdI7buA7a684uMagPc7t21Ma4mRJWo/a0pqDpVZGeqsFOlgs74LxOD6rMmS4StD087TTSRxhH12qu+pyr4o8l0iSp5dKqwK7Ya1ZkqXV3p9xBR8/90iCocAUYGFlWYbERdEBYsEF3hLtFZFMoiGOzbB+zYIfezsjd8XQs2FZIu3JTiv6ans1mJKcR/BTuUKAppQUx32qQhTIzKU/RO8LybN8/cgGbZaCUKi/MsTgqbRN2HVLqkXEA1/qtITpW8XIOoMTYGrFhBQ1QpWvzX+HgxHMe2Cg0sqmRHdb3+iU8Av//71V8zJarYXGPk3ali61zIg1OF+kyVrOuPsPXhqacCjz0W/3tRr32pBPzBH6Q/njj8TZKmRBUdcW95jv/KY2E/7UyVNHWu3/zN8K9nFVWorR/rHBZVmGxExX/19gKHD9s/Hj+6Z6pkcar09sp1l8s6VVyQdMOnFP+VtZOEglMlOP8jL06VqM9IlkVXWNyDLFGiCvXXMop588wVE8tluQ5fCgu5qHOc2uI+6jiL7FQxGfdXFKdKnoRdalATVbKs2agNqr/xRuBLX3J9FPkhTSGZ2j0qTzz9tLiGn3Za9ddNrElsx1vLFvlsu3GjHDt5if/K80yV4WEzNYHm5mzrj7D14fr1yakcca/9VVelP544/E2SOkSVsOZBHU0ycftnHXD8V3UtwWb817PPhn89q3BPITWC+RUsqjDZiFJZe3uBI0fsH48fKjNV9u0DLrwQ2L49+WcpzwlI6gyn5FQ5fjzb4sT1YHUgv6KKiU61LFF+3us2O1uJU8vDaxmFSaeKLBTiHXVe300SJU7kQVRJu9j3Z1jrhrpTJWqGThCeqZKesTHhhqbQ1Z11zUZBoPYzMgLce6/ro8gO1fgvLoKkp1wGPv5x4P/9v9rvFSH+S8ap4kKQm50Nn3eRl/iv4H7OVHyTH10zVUolM9eyuXOz7XGnpipRix5h/+ZnnwUOHqz83Zbw5ke3UwWofU90iCp5jv+iFrschX9fk0ZUSbP3PXYM2L8//HqQ1alSpNj9AsCiCpONqEUnBVFF50yVLBeugQHg7LPl5qpk7Xo0SVIcDCWnysQEzeiaIHEbpCNHxHnkkQchIC7+K8uiK0sB3Xvd/EPY8/BaAuGfj/nz3YsqFApDURtjakXKqPtQkeO/TMYnZnWq2Ij/4pkqZqEU/ZfVEU3NqTI6Ctx/v+ujyA7V+C8ugqSjuVmIffPmiY74IEWI/5JxqrhoJok6l/IS/5XnmSqm0LH+CNvTLV4MHDok/jw+LmKPvv/9yvddiSreejRLE0Rcs6WOJhmO/zKPf1+jsgcbHk7vVPHOh7BrO4sqhYJFFSYbcfFfrkUVKvFfpZLYBMg6VaiKKkmFDEoXd9nCVhw2Oi/e+lbRARFG0KlCwR2QhCn7sg6niv/1zIuoEtb1btKpIvuZpyBcRH0mqBWr63FQvUlRJcsmxFb8F89UMYvtYmcSWdYKFK6lfkZHhXC/f7/rI8kHqoVkCi7oPNLaCvzpnwJ/8ifh3zcV/0XNqWL7mOLIS/yXq5kqlMTyIFnjv6Lwz1X58z8H3vzm6ugjF6JKcFB92hpL3LVbR5yr6ZmkHP9Vva9RSVfJ4lTxnFrDw7Xf45kqhYJFFSYb1OO/XDtVPPVeJmsUyD701CRJncuU4r9kC1tx2Oi82LlTZESHERRV8mCvNRn/ldWpcvSoiIzxf406YUKmqZkqKkIkhYVc1GeCWrE6a/xXqeSmCyyLG8C/idVNlnukrUH1sjNV8nANogglUSXruUmt0DE6Clx+OXDffa6PRI3g+2DrHsVOFTu0tgLnngssWxb+/XpxqlAWVWw5VTZtAtaskf95dqrUkjX+K4rTThOiyi9/KYrJ73sfsHVr5ftFif8KwvFfZh5XN8F9jez6bWgI6OpKd/8+eFDUHsLmKrNTpVCwqMJkg3L8l06bdNqbkRc5tGyZmK2SRN6dKlQ68HQ4VWwwMBAtqgTjv/KAqU6bLAJplFOFUuE9ijAh05RTxR+PlgQFUSXq+k5NVMka/+Vq0cxOlXTIboypfU7zhHddpHAdyorJSI40jI4CV1yRP1El2Ehj6x6fRlShsk7OE298I/DHfxz9/SKIKlSdKlEFU1eiyhVXAGedJf/zLpwqumaqmMLU+uPUU4EHHgA++lHg05+uFW9cOVV0DqoPIw+iCrW1hgvSxhpPT4t7d5rGrIMHgY0bw0WVycna2UQqsKhCChZVmGxQdqpQmKmybx+wYoW4mcko4ip5n7a7l5OKbNScKnmI/5qYAJ56Kvx7QadKHoiL/8ryedXhVMlj/FeUU8W1qEJhIRfnVKH03maN/3IVF0N1psr0tCigpcGWU4Xjv8ziXRfnzRPXLZdkXSdQE4ZGR4UjIKrZgyqjo9V7EVv3gTTxX67vnXnk4ovF+R5FEeK/ZJ0qttMMZGequCiYy5BXp8rMjHgcE5iK/+rtBX72M+C97xUxkkB1Mws7VaIxFZ/tQc0VK4Nux1fWeXxp47+iRJVSqVjxsXUOiypMNqJmqlDoBNcd/5XmwrV3L7B8ufizTJeASt5nqWT3BpnUtUWpAy9LEd6jXDYvXK1ZU22N9jM4KDI880TWrosodDhV8hj/xU6VaKJEcwr3Hj9Rn922NjlRxVXxfXZW3LPS4B8MqpusGxAqThVqn9M84a1F5s83E4VoE2qD6r2iQ1NTvj6fwWIzZacKiyr6YaeKGeL2QK2t1cdry6miSrAYrmN/mISOmSpRSSA6MLmu/OEPgVe8ovL3deuAHTvEn9mpEk2e479M1Up0H3NwT626n0iz/zh0SIgqYTNVssLrCVKwqMJkg1K+axDdg+qzOFUAYOVKYM+e+J9Xif9qbbVbGE56PSld3HUsTkxvymdmxHNEvWYmu5RMMTMTXYjNMhuinp0qwXPO1HmfR1ElL/FfUU4VmfivPA42NulUyYIN96GKUyXqPN6+Hfjud7UeVqGgIqrMzhbPqVIui3/TWWcBjzzi+mjkCe5FbN3jOf4rG7qKcaacKvU+UyVu3+cq/ksVF04VHfFfJmesmpqpAgCnn1799xNOqAyrz7NTJa4xNg+iiqn4Lx3roCh0X9fTzlTJwuHDYq5ymFMl6/NTqrsxLKowGTHZSZEVCoPq/U4VmWH1Kjd82fgYXeRpUL2OTiT/QswEw8PCidLSUvs+ekPRikSWIkeW+Uh+USWPThVbm/rBwfh4DT8UCoFTU+ExUNREFZVB9WGbE2r/Hhmoiio20DFTZft2EaHBhOOtRVyLKlmjJAB6ThWP888H7r3X9VHI4zL+S8WdS2mdTIGpKT2FeBPRjrb3tzJOleDn3CSlUvye1IVYkQYXM1V0xBaZFFWyxH+pFtGDoopt4c3vnM5y/sT9m3WJKiZfG1PxXzMzZhIqAP3royyxxmmZnhZr1TBRJSsU9uLMr2BRhclG3AVKh/01CzotvmkvXPv2VUSVdetEscTP4KCIJfJQdarYFFXyNKhex6LZtKjiOQNOOAF47rnq7z3yCHDmmeae2wVZXs8s85HyHP+lUxhOYmBAzalCtTBETYRQif+amqrdnOTVqWLq2ml7lpgqOmaqjIyIyAAmHG8t4lpUUZmBF4WtTfHzz6v9/HnnAb/8pZljMUGYU4Xjv+ija59WhPgvak6VpibR4BX1fMEiMztVKlAXVbKsk1X3Y5s20XGqeAPH0zI9HZ7GkAeniqm1RlSDmw5MO1WA5D2FjvelszN6pkoWeD1BChZVmGyUy9ERRa43vBScKocOAYsXiz+HOVU+9SngNa+p3DRUFqXsVIlmYiL7Rs10t7VXxN68uXZY/UMPAWefbe65XZBFVNHhVPEvpvMiqth2qqgMqnfdHRO1GKU2qF4l/itMVKEmEsnATpXkn4u7Bo2OsqgSh3ddnDfP7RpTZQZeFLaGx77yleIaL0t3tyioUhcxPcJmqlCM/8qjSG4SyqKK7agtmT2dzWNqbIwXVYJQFVVcOFVKJfqiStrrkGptZdkyYP9+8WcXoorO2kRUbSEPooqp+K+sQlUcJkQV/2e3tTX5fcuSHOKtnzo7eaZKHcCiCmOO3l7gyBF3z697pkrchf3IkfAMdP+w37Vrq50q5TJw333A298O/MVfqB8TO1WiGR/Ph1Nl3jwhqjz9dPX3Hn5YZJqHQbXIkWQJzzK8WodTJelrFIm6hmWZTxNF3maqREFtALhK/FdYFEoeRRVTg+pNZjfrQnbdkeRUOXxY73EVCS9yyXXjjo6il6lCR5C9e4E77lD7nVWrkucAUiFYbLYlrqvGV3ERpBrKooptp0pDQ/K6jpJTJQjV+C8XThUd6xSqThXVCCX/a0H1MyKLyVmOk5PmnSomGjhMOlVMx3/JuAOHhkSTSRq8Ok+YU0XH3FzKqRF1CIsqjDmKJKokbYS2bQP+7d/iHyMY9+K5Ed74RuDgQeAnP1E7JmpOFUqbxTzFf514IvDMM7XfC5tvQcEhEIU3IyaKLJFAOgbVJ32NIlFCZpSVOAt5E1Wiig/URIgoQTBsMR+Wz5/HzmZTThUdMyxMI1v0ivucjo66XTvlBdeiSl7iv8bGRMzobbfF/1zwmnrBBaLxJw8Es/Ipx3/l7XpuEp2iiu79h21RRYagI8skjY1iXS/7fHlxquiMBjcJ1ZkqadZhnvPRtHBgGpOiSl7jv6an8xv/1daWPMcqi6hy8KBIq+nqqt2z67i/UK4J1SEsqjDmWLTIvaiia+GUJBiMjAAPPFC9KQ17fn+H+Y03Aq97nfjzZz8L/M3fqBWiqDlVKBX/dCyaTUfYeEXs4HDK4eHoSBHKYkB/vyhyRZE1/iuLU2VsrLpbivLr6CdKyOzpUYtykUF1UD0VATUINVEl6j4UFklQFKeKqWunji5d004/2WOMuwaNjIjPAcUB5hTwruWuRRUd8V82BtUfOgS88IXAzp3xn/+gAHz++fkRVfIU/0X13ukCyk6VmZnw+QkuoexUoSqquHCq6KAo8V9AZVh9Xl77KPIsquQx/kv3MYc5VUyKKocOAUuWhDdC6riWU2pmZlhUYQzi2qmi80KftGAfHRX/7d1b+dr+/ZUh9R4LFoiB2bOzwKOPAmecIb7e3i6EFZXcRheiCjtV9OF3BvgXDnFD6imLASZFlaxOlYMHq10YlF9HP1FC5rx5YiaPTsbG5F9jCt0xcTNVKIkQKhtQKk6VqGGcsmTpgozDZJFBF/7Izzji3GajoyIu9OhRvcdWNLq6zORUy6Lj82jDqXLwoNjYb9oUP7A++O/ZtAl47jmzx6YLV/Ffqs1blNbJFKAsqlCE8kyViQmaokpwDWWys14nRYn/AirD6vMuqpRK0aJK1vuN6fMnj/Ffuo85uKcONrWGkZTCEYe39grbx+m4llNucKxDWFRhzOFaVNGZvZ7UTTgyAlx4oXCreOzdWyuqrFsn5qr8/OfAi15UfYynnho+lyUKF/FfSU4VKhd3HRs1U3MBPPyiyvr1IkIOiB9ST1kMGBig61TZtw9YuLD6a1RfRz82nSqA/DWTcgEjL4PqgdrXm4pTJWt0pqm5Jzo2IVRmssQJAiMj4p7Aw+rD8dwWJmZLqaAj/suGU8Xb2F95ZXwEWLCIVyrR69SPInhtsBX/pepW4vivalhUUSMYc2eSNE4VigXzsCYPKuuAOCjHf+XRqZK1WQgQr1vY9UrH/Ybjv2rRfcy2Z6p4a68wdDlV6uG+lxNYVGHSk7SZjRJVymWxWHDZYaibkRHg0kurRZV9+4AVK6p/ziue33BDJfrLj8pCLzijRSfHjtVu9GWcKlQ2izoWJ1lmgMjgF1X8w+rjhtS3tNgV0lTo74+Pj3LpVNm7N5+iStQ519Oj36miAoUCRtT9h9qg+qkpecckFaeKjtklJooWeXCqyBLXFDE6KtYKPKw+GZfFMR3xXw0NduK/liwBLrkEuPPO6J+zOa9BN2EzVWzc41WvSZSajyjAoooaNue8NDUVY6ZKQ4Nb8T0tlOO/VD+DnuvRpaii4/WMElU4/kv/4wL6j9nFTJUoUUXXTBVeT5CBRRUmPUk3AE9U2b4d+PSngZe/HLj6auAVrwB+53eAV77S7PHpXkTFbd5HRoDzzgOefLLytX37ap0q69eLbo1t20TnRhZMOlW++EXg1lurv5YUc0Dp4j4xocepYlJUGRgIF1XiHB+trXTFAJn4r7TOHx1OlQULqr9G9XX0E7XomjfPjFNFFteW43I5P/FfcccapChOFcBMEcNm9ElaZP/dcZ+JsTFgzRp2qkShIqTccgtwzz1mjkNHkaZUMi8MeRv7jg5RSIsqpuVZtAwKQrYci6qvGaXmIwqwqFJL3D1ENl5SBzLxX/61INX4ryB5EViKFP/lNSm6il5raRFxqpRFFdNOL47/qt3byIoqKtH8fmw4VajU3RgWVZgMJF0Q2tuFoPK3fwucdBLw3/8NfO974r8bbqBfHFFhdFQUOmdmKgu2qPiv668HLrss+3OaFFX6+6vnwwDi39UQc8mgtFkcH9czU8Vk/NfwcOVG7YkqSXEilMUAqjNVmprERpSdKtGoFP4B95bjONGUmqiShH+DH+ZUcfHvSbNptkGei75hxBV3Fi9mp4oss7PR33v2WSGqm0BH/Bdgvsjn39hfcIGInw0jz+eXy/gvVVGl3ooge/ZEf0+XqGKqC9s2lJxMnlMlbq/u34dSjf/KK0WK/wJE/WB21o27tKND3AcpiyozM2YFJ5PxX6acKrqPOSjqycxUyeJUiUvw0DVTpQj3vYLAogqTnrGx5AvC/fcDX/oS8JKX2F9s2bxxj4yIGIgNG4CtW8XXwpwqvb3iInvNNdmf06SoMjioXoiIE1xsk4f4L79ItWCB+FzEDakHaIsBSTNVsryeqsNgg7S0VIsq1OZuRGHLqaJq53e9kIsrxFATVeLuQ0GhMcypktf4LxPkwamia92xaBE7VcIIdkV2d4v1VxQDA+au9Triv2xw6JBYewJirsqPfxz+c2FFPNdza2Q5dqz6mmVrraQqQFMqmtviBS8Q69swdIkqeY14CiKT8W8LGadKUFTJg1MlD/NUgGLFfwHAypXxAqtJ2tvFfZCyqGL6+mVKeDbpVDEtlpuO/wo2I/vfY3aqFA5CVVAmdwQ3Maq4KBZlIe6G54kq555bmasS1k1dKgH/8R/A6tXZj4eaqEIJXYPqbW9uHnggekg9QFtUMTlTJWsnTEtLdfxXXjbgUZ9j3U4VfxSdDK5FlTiRjdo5Evc56+ysLghTcaroiP8qleIdBGnQMfPB5HmvswuTnSrhBIXm+fOjC7aAWVFFl1PFNP775xlniOaNMMKKeFmKbzYJFi9sNk6oujzz8HrqolwW18XPfCb8+7pEFd3MzLhpFEvqnLYpCDQ1ib2grKiSl/gvanzjG8CWLbVfz9pMFkeWdWXa9eGmTcIt4oI8OFVMYyr+y/SgepMz52RE7OHh9KKK/3odjG+XaUxPgkUVUrCowqQnq8q6YIHIuCwCnqhyzjnAgw/G/+yrXqXnOU2KKqOj+X5vdCzuOzrMxn8FWbkSuPnm6CH1AL2CsZ+kwrwLkcoj6FTJC1GRe7qdKoOD6qKKy4VcnpwqcXR1iQW7BxWnio74LxPne3AYNTVUiw1xLoAFC4C+Pj3HVSSCXbIuRZU8xmU1NAjXSphgF/bvobzm8BMsNtuK/1Kl3pwqk5PARReJfVFYIwhVUUVHwSsNSUU+m81AjY3Jg+rz6FShxq23ipjKMEyJaLZnqgBijqyrz0cenCqm4fivWmScKmln9AaF+WATXdbGdMB9gyNTBYsqTHqyLjpNFg1s53Z6Mzw2bQKee85Ol5NJUQVIt3in0v2fNP9FBpmbrU42bxZzbPyOiiCUCxxJebCuRZW41zVvdHUJS7IuVEUV1zNViiKqUHaqZF3sr1hRO5crK7rs8qZEKtXji4tEbGzU7/QpAqpOlcFBc/fMPMwQCFuTvfjFwE9+Uvv1PIsqwX+nreNWXfPWW2epV4B93/uAz32u9vuURRUXEZgyGf+2aGoS65O4AnpwH5qXaC1KPPKI/SbGrDNV0pwbJ5zg7n7Z3i6cKlnjOvMsqnD8Vy1p6zwy9/2+vupmzs7O6iY6jv8qHCyqMOnJqrIuXGhuIeFioV4qiUJ+QwOwf39lMKgpWlvpLL6poWNh7w3VM8HUlFgs+DnppHiXCpCfAkcYLrOiFy8210njAt3xZYODavZm190x4+PRG/08bG48wpwqYaKKi5kqWZ0qa9YAO3fqOJoKOpwBnZ3mHIiqxxd8/wF3g1zzQrBLdt48d06VPDA0VCuYr18P7N5d+7OuRZWpKX33Fapz0+ot/su7l1xxBfCLX9Q2g1AVVVzN74pbJ9tuWmtsFOdkXIOa6eY+E1Bp/gPE/SwqGcLkcWaJVUq7Ply9On2MUlY6Osw6VfJQ3Ob4r1pkROzgeSi7/z14sLoO2NVV61RhUaVQsKjCpIdy/JeJLqO4Qof/e5s3A//7v7VD6nVjejGbpgjOxSA5wgod558PfOQj8b+XZ1HFZef1xz/u5nnzguqsCteiSlzWNCVRJSkmIbjIDovPyNJRmBYd8V9r1wI7dug5Hg8dm5CgO0gnqscX5jgLPgalAhAFKDlVdGFy3XToUG2DT9RMLteiyte+Btxyi57H4vgvGngF2FIJeO97gX/8x+rvUxVVKDpVdDQ7qNDUlHz/yaOoYguZe/djjwGXXpqvuO2068PGRuBf/kX/8cjgOVVMiSp5qH1w/FctaepcskkNQVEluPfQETHpei/OVMGiCpOeehNVZIsb55wDfPe7Iv7EJKYXs8uWCccNUL+FHVMLpbC4pfb2+CH1QL5FFZdcdpnrI0iHrfNOdXHHM1XkCCto+gnawaOcKnkcVE/ZqWJKVNHhVBkdrURUBEU3Rn2mSmMj/Xumyet8cGMPCHcPRVFlcFDfrDAbx53mfatXpwoAvPSlwD33VF/zWFSpJq7IZ9s909SU/Hx5E1VMdtWn4eGHgcsvz5eokiUe9pxz9B6LLKYH1QP06yQc/1VLmvgv2f1vkqiiY6YKO1VIwaIKk56si06TM1VsL4j9N9NzzwV+/GPzThVTcw28Tc7y5cC+feJreRtAqGtxY2qRNDgoChuqUBVVbHfQ6YD6Atgmqos7yjNVdEejZeHgQRE9F0WwaE5lUL2OmSqrV+sXVYroVAmKKiMjFVFl0SIhzDEVVJwq5TLde6Yt8iSqjIykOzfDIvPmzDEvRqcZYFtvRRD/vaRUAt70JtF05mFTVHn6aeDv/k7uZ13Ff8U5Vfr7xfXOFo2NxRNVJiZozcF65BHgkktqHatU1rBh5HG/p2tQ/cqV6fbuFDAZ/2XSqaLrmKena2PXk0SVMBE2i1OFZ6oUGhZVmPRkvSCYnKmioyiUltWrxY3btKhiioEB4aLwiyouX88iojoY3INqgWhgQG6hmQeLdF7Q2fGqKkK7thzHzVShhKpThdKg+qyvr4mBu+Pj2QsilESV7u5wp4q38V+8GDh8WN/xFYFg9Mj8+eECgfezCxbQvGfaIkzYjSqEjo7Wfn5tziZJK6qEOS1trDXSOOfqNf7L45JLgLvvrvzdpqhy4ICIH5Mp0lF0quzbZ3dfWTSnSrlMT1TZv1+kQgRFFMp77rj4Xap0dIhrTVZR5YMfBFat0nNMtjG1bzPpVNF5zGHnVNL9eHhYND8Fj0nmHh7c/5mYqeI6NYKpgkUVJj31Fv8VRVDJLpXExsF0/JcpPBdFUFTJQxEzLxRNVJHtoKPcfUWNpNeqp0dfVEqa+C+qM1UoEdYl7idsUD0Fp4qOmSqmyFos7eigHf/ld6osXsxOlSDBtV3UfBBAfH3JEjP3zJmZ+AHOVAi7BpVK4efR7GxtJ2cenCpRexHTwkoaUaWe478A0em9d2/l7zZFlbExcT+94w65n6U2U8W2qFIkp4rXXU5JVPGaaMKuUzqiTk1RLufj3ufH+xxTfU1t0NCQv0H1OuO/oupYcXvtoSHR/ORH1h2SFP81M5P9dXOdGsFUkbOrIkOKrEOW4jbDWbE5qN5fBPG46aZ0RXMKeAV/v6jiaoPhmpYWMxuGehVV0mCqeFUq0RZ5JifjN3/z5ukTVVTjv1x3xyQVYqi8r0nxX8FFNiWnio7rfXu7KA5QorPT3DHpGFQ/MlLZ+HP8Vy3Bz2ZcPMTAALB0qZl7ps6il8nif5Kwm0SeRRXT9wF2qiQTJtAvXiw+l4BdUeX4ceAd7wD+8z+Tf9ZV/FecU8VzNdiiqSn5850XUcVbR9kUVUolIVRH8dRTwEknhX+PsqiSR6iLKuWy+SYAU4+fl/ivNHWsMKeKrJARPIdNuOQ5/osULKow6ck6ZKmxMX7BkQWbnbZhokreujj8ePFf/kH1eXOq6Fo8LFgQPwQ3Ld5rrEoRRBXVQkea3HIZKA00DyNpAahTlM5j/FcenCpJ8V9UnSq6rvdr1gC7dmV/HJ3kaVA9x3/VonKtGhgQMbMmujP97xNljh7N1vCQB1ElLLbMBseOpXOq1FMRJOxectFFlQgwnV3OSev+sTHghBOEkB287ob9LDtViuNU8dZRNteObW3x186HHwbOOkv8ObimZlFFLx0d4tprcjZslrpD3ubW+slT/JfqviaseVy2qTBY6wjGPevAdYMjU0WOK7+Mc3R08phSzm0uiPOyuZbFi/9qba0sCPPmVNHVoTh/vpmIuqI5VWRnqqTZgJkS9Ki+lh5J/26dThVV16Fry3HSTBUqs3sOHRJugyhknCoNDfadN7qaEtauBXbsyP44OqE0UyUq/oudKtGofDa9tYwJ8lL0iopqSeqi9rB5n5ydTe9UCXsvOP7LPVGiys9+Zv9YvGvHb/0W8N//Hf+zLgfVU3KqFEVUceFUaW2Nnyv3yCPAmWeKP8+fX93Al5f7S15ob6f9ek5M5FdUMelUaWzU61QJWzvGrRPCmsfT7n+DM1V04HovzlTBogqTHleLThlMxX+FbUL9RZAiEFbwz5NTZWamNhc8LaacKkUTVWSdKnEbxihMdZZRfS09kiKYdM5USRP/xTNVkglznvgJdi1H/bxtUUVX/NeaNcDOndkfx0NHkZSSqBI1qN5r0li0iJ0qQaLWdmHnSFpHqAw6130mz++oc0b2PLB5n0wb9eFqL5JWVKmnztKwvcP69cDWrW6Opa0NePWrgf/5n/ifdTmoPqoQb3seSBGdKjZfw7a2+Ndm+3Zg3Trx5+CMWRZV9NLaWjsbgxKUZv2okieniuo1Pew+IOMOCRPJOP6r8LCowqRHR2HL1OAsEwviqIt7WPxXnvEXIjwhSfb1NPV+qqBzcRJc6OoibQctVSFAVlTp6FAXVerVqZLUkT1vnr74L9VreUODuehGGfIS/yWDv+gZ5lRxga6uOZ2iiq7CM/X4L3+xvqWFdkShC8I2xlHFK1kHZRry4FCOm0cme/+gfp8E8iWquHAeuiTsfC2VxOevv9+uq9RbU7W3A7298S5Kl/FfUWtk2w5clZkq1D/T1JwqmtknywAAlN5JREFU3rXZe09diCpUHN02KJXi5xu6Js+iynPPibW+CWzEf8Vdu8JSHGSEjMOHa6Ofk5xraZA5lvvv1/ucTCQsqjDZyDo7RGdh0I+uTls/URcv16KK7sWsv+DvdcrKFrYpqOY6C66m4r/Sdrm6dghEwU4V/SSdczqdKkC+5kDlYVB9mmNIcrbYRMeGe/VqtZkqU1PAd78b/j1dmdOUnCphm6w8FOtdElbsDEaneJgWVah3Eh89KmbKhBFce0d1m+ZB2HP1XuThM+CaqHXMi14E/Pzn9o/Fu3Zce238wHqXg+rDCm+zs25ElaTXwBO0Z2bMdavrgJpT5fnngY0bK38PE1VMrwPKZRprZVtcdZXZx5eN1Awjz6LKtm3Ahg1mHlt3/FdUXTDqPEgb/3XwYK2oYuL6LVMT+sAH9D8vE0qOqihMITHlBDDRZRR1IXUpqpgYtu2Pplq+XOT4yuaYNze7F1V0O1VMxH8BxeoSki1etbeLzYIK9exUibuG6RSk87axykMc4ciIcCKoQMWpogvVaJA9e4CvfCX8e7qKXB0ddJwqpVLtuec6TnR6Wv0abZOwtYgLUcX1+yTDwYPR3bk9PdX3j6i5JNTvk0C+nCr1RtS92sRclaR1jH9NddFFwN13y/2sTaKKZIcPC3eNTVTiv6jPhKDmVHnkkcqQesCNU4VCE6RN/vRPzT5+lnrM5GQ+RZWtW0WcoylsOFVaW6Pft7TxXwcOxDujdEWmyZzDu3blb4+fU1hUYdySJ1El6kLqcmOVlNmaBn/817JlwL598s4fCkM4dTobTH0+s9zgKIoxsqIbO1Xkse1UyRNJn4mwYrVtDh1SjxuIcqpQPOdNsGNH9PVhdFRP4XTuXHP3KB1ummCH6ty5dq9T99wDfPaz9p5PlbC1nRclFCTt7DIZdHYSmzq/w7olPYKifNQ61tZ9cno63Sy82Vng+98HTjut9ntZOodlYFElmai14UknAU8+6e5YGhqEGBv12XYlqkSxf79ocrPJunXh55Wf1lbxGlIvCntOFZvRsXH784cfrgypB9yIKmnWQlNTxWr80UmWtVpenSq33QZceaW5x9cpqkRd0+PmWIU1bMg4VR54oPr8lnncNMjsdY8eVa+7MKlgUYVxy8KF5kQV3Z3MFOO/TAwJ9C+Oly+viCr16FQxFf9VRGQKQ2lEFXaqhKPTqZK3on3SxtjbQLskrqDpx1/4K5pTBRD3xuDckCh27Ije3IRlG6fF1Oe9XFZ/7ODPB4spixYBR45kPzZZhoZElx1Vwu7vUU6VoSFzw2nzUFDPk6jif34VQfyLXwSuuAJYtar2eyYFVCAfnwHXRK3fGhrE+sbmGizYHBZ0awV/lpIbdt8++6LK6acDl1wS/zPefD1d8ZymoOZUeeYZ4IQTKn9fsADo66v83ZaoouqsMFFbKQpr14oorDRQd3pF8dOfApdeau7xdcZ/RV3T29qi6xJpnSoPP1ztRJN5XBPMzIh/t6nEFaYKFlUYtwQXErqol0H1JkQVP56oIvt62u6qDUO3qMI3I32wU0WepE29zuN37epQJekzYSIWURVZUcV/ThSxC3DtWvlh9Tt3Rl8fXEX82GZmprpjf/Fi4XqyxegobVEFqBWiotaR5bK5WVE6479MOSryJKp4cYkqa9pt24Af/Qi47rrw75s+dhZVkolbx1xwgd7PvVfgjyJYEI5z+6YRyE3iQlRRgXpRmNpMlXK5Ov4n2GBq49rS3Ky+TjYxr7YonHOOcCikIY9OFe98Mln/cu1UCfudpMit4WGxV4lz3urcz8Tdp7z9FDcHW4FFFSY9OgpxeYr/qhenih9Vp0pHh3uboc4ifFOTvi4JD+qbD5Ok+XyMj7NThakmSXygIKrIxn91dVWcHNS7PdOwZo28qLJjR/QMjCKfD3FrqUWLRJ6+LUZGaIsqYa/VihXA3r3xP6tbONYZ/6WzG9NPkqjiLyhTEFU6O8V/MjOPZmeB974X+Kd/ihbOTDf5sKiSTFwR9uKL9TbMJBXggvfX4Dngx6WgEnat2r9fxDFThfrahZJTpVyuLbgGG/ioxn9Rc3BR4txz60tUufdeIYybxMZMlfZ2vfFfd90l7m1heHVEnaJK3NrWm4/IoooVWFRh0qFrg2pKVDl+XH93e9TFXefmWpU4e7EOFi4UHaCyBa2ODvdDbqkvToaGsuW8U3MVzMzIdwOnjf+i6FT5vd8Dnn9e3/EEsbl5odSRqQMKgpmsU8VfRCyiU0VFVBkejo5rKqpTJW5DB7hxqhw8aO/5dLByJbBnT/T3TcRA6Sx66Swc+Dl0SM2pEnZ+2XIfq4oq//iPwNVXi+tLFC0tZsV1XXOeikzcPe3008U6Shcy55F/rRMX/+Vynd3VVXtc1J0qeZmpQsGpEnYMwUItx3/lj6VL06+dqNctwjA9TwXQ23AS9dlVdaokxX/95CfAZZeFf89b39jaz4yOisYsFlWswKIKkw5d3eNe0V43JmIf4pwqrrrVdDtVgtb5hgbxWqo4VVyLKjYHEaZhcDC6GzuPqPx72tvVPx8247/uuAP4+c+Tf3dmBtiyBfjDPwQ+/3kz0S22bPZFnONBwakiK6r4nSqzs+GWcZsFHt3PpRL/FUcenCppxMmuLiG0R7FokV1RZWRErHOoifceYa9x1DrS+1kTIqtOh7Ipp0rc2pRi/JesqLJ7tyhcvPOd8T9n+thnZqojfJha4mK0mpqAl71M33OpipNx8V8uOfdc4MEHq7925Ii4zlGFugOfklPl2LHktYzMz2SF47/0EzefIw7qomQYjzwCnHGG2efQ7VSJiv9SmamS5FR59tnqeUl+vBmTOmdExnHsmJg3x6KKFVhUYdKhS2U1NbPCROd11IXUZRFft6gS5bqpd6eKzgLT4GA2pwo1+vvVRBXKg+offxx46qnk3x0YAE48Efjud8XfX/lK/eKwbEdY1s9mHorVqlAQVfr65IogXV1yndm2ityPPRa9IUjDqlXArl3JP3f8uHjfyuXwf6vOzq40xQQZ0rxH3d0VUS2MxYvtxn+Njoo4rTihhxph6z1/kc9EcV2nS8GUUwWIXgsH78VRoorMUFYdDA/Liyof/SjwyU8mr/MpzPhj7KF6HsXFf7nkBS8A7r+/+msm50PpgHr8l3fPt7lfj3KqyKxlbLzfHP+lnzPPFGKDKnlzqhw6JBp+TH9GGxvNx3/FOVXCxOK4NdHBg+J1iVqb+J0qNvbdo6PA6tU8G9gShO/QDGl0qazNzXY2bDqIu5C6is/RLaqEuQ5aW4XKXa9OFdmipywDA9lFFUpdxAMDQhyVgfqg+oEBucWHVzBvaADe8x7g1a8Gbr9d7/HJbF50nG+2OmZsQkFUCQ4cj8LrXIrD5n3yu98FXvEKfY8n+17s3i0W/1EbfZ3in4n7VNqikt+pFHZdtx3/NTICbNxId65K3L3P/72BgcpaxoSootOlYGJ2WxLBNWuUqGJrbSvrVLn7brHeOPnk5Mc0Hf/F0CKNUyUq/sslZ5wBPPyw66NQg3qnvbeuoOBUiVtz29zbcfyXftLOVaHu9Ary4x8DV1xh/nl0ro3SDKoHatdAcYPq77gD+PVfj34sr55kM/5r9Wp2qliCRRUmHTpVVhOLCBOPGXchdYVuUSWs4L98ObBtW35EFd2L5vnz9d6QsjpVTOTDZ6G/36yoYtOp0t8vJ6ocPSrmQXmsWxef6Z8GmSKyjk5LWx0zNqEgqsgiI9qaOOc/9KHwr993H3D++XqfS4YdO0RUWNQ1QucmRHZugwppj88vqoTdu2REN52MjtIWVaIIup79axkKM5bi0NmN6TExkRzr6F8nux66LiOqzMwAf/mXwJ//udxjUn/fGb3ojP9yOWfOK/J556epuYI6oV4U9pwqFGaqRK0VTKxL4kizTub4r3jOOac2uk+GvDlVbr/dnqjiclB9GHHxX3HzVAA3M1VYVLEGiypMOoo6NDYOkxENaTHhVAkTVfbulbN5ppmZoRvdi5MFC/RaJ7OKKtQKBaZFFcpOFY+kQclpkBGTdHRaFjH+y/U5EpcjH0TWqaJTJCqXgU99qjbqbvducb2XcdioIJNd7xdVoro7dX1OTRQv0hal/aJKWPym7eLesWPA+vU0h9XHub+C12C/69b19SAJE06Vw4eFy0mWY8foiyrXXy9cdLLrDY7/qi/SxH+FrZ+OHXPfjb9qldh3AcD+/cCyZW6PJ45SSZxnlEUVSk6VqPrJggV2i5/NzRz/pZv589PtyfImqhw5ImKuTKOz4WRqKrzRRHUOTlRqTbksrtkrVkT/ru2ZKqOjYk9HMeaygLCowqRDp6hSKtGKM4qiHpwqYfFfy5fLF3YoOFV0F+F1L3SLKKrIzlRJstmGMT5uz6kyOJhOVFm+HNi3T+vhWXOqFDH+y/U54o8eSkLWqaJTVBkdBTZtAv7936u/fvPNeqO/PNasSR5Wv3On+LmoDU6RnSre/JK4weK2KJdFAY+iUyWuSzYoqpiO/9KJCafKwYNyooq39qbuVBkeBm64Afjd35V/TI7/qi9URZXu7vD1k6ogaQJ/jND+/WKNSZWWFnF+Ui4KU3OqhN3HFi7UP5sxDo7/MkPUdSWOvIkquqJPZZ7HdDSqal0iyqmyfbtoSIrDxUyVrq581FgLAIsqTDqod42aIM7y5wob8V/Llsm/1xREFRNOFRZVolGZqdLYCMzOqj2+qeiDqNdRRjgNiiomZl7YcqoUMf7L9Tly8CCwZIncz8o4VXSLKkNDwCWXAI8/Xt2p+L//C7z4xfqex0NFVIlys1Ffc6QVVfyD6sOcKoC4btqcubF0KU2nSlxBJ8+iigkXdF8f0Nsb/zP+9VqcqGLDLZUkqnz1q2J+mUoxx+T7Pj2d3tFXKqmvg/KKTadd3Hk0M1Prto+K1Tx0yL2o4h9Wv28fbVGltVXsa/LgVBkfZ6eKB8d/mSFNBBj1mUR+stz7VLGREKMqqkQ5VZKivwA3M1WK1jRJGBZVmHTovCAsXKh3ITE7a2YhH3YhtXlzCcOWU0W2M6WjQz3eSTe6nSomZqrIdrGHQa1ApBL/lQabThXZ60ZQVDHB7Gxy5J4up0qeNkky0VqtrW7PEZWijD/+KQrdHddDQ0KQe/nLge9/X3zNK4qY+CysWwds3Rr/M96aIir+i7pTRUf8l1dYDtLbK+IWbLFkCU2nSty1atWq/IoqJkQzGVHFH39E3amybx9wwglqj2ky/itLXJqJJgyKlMt2O2TjCnAqxeDDh+1E28Rx0knAk0+KP7Ookh2/U8XWfJqoeK0od7gnqkxO2nktOf7LDGmG1efJqWLzHNLp4o3aN6aZqRJ2/77zTuDSS+N/18RMlbj9sLdOcTkjrI5gUYVJh84LwoIFei2v4+NmCkNhF1LXG1FbM1Xq3amic6ZKmBtIBWoFIpX4rzTYdqrIEBxUD+gv4MgsgnTNVMlTJ4vM+e36HFF1qsjEf+n89wwNCYfEG94A/Nd/ia/deitw1VX6nsPPWWcBDz0U/zPe5z0q/quoTpXgTJWw9cTSpXZFju7uSiQZJeI+A2EzVUwNqp+cTB4Cr4Ipp0qS8O8XVVwPmk4SVcLuuUmYjP/KsvafM0e9mJlHbBcKk0QV2WIwBVGlqUkIUrOzQlShPFOltVXcLygXhV3MVIlawyc5VWzVFTj+ywxnnQU8/LDa79i8/2YVum2eQ7biv8L2HFGvU1RqzchIcoOpiZkqce+ndy0pley63esUFlWYdOgWVXQ6AUx1Xoct2KM6S21hI/6rtVV0XshAYVA99ZkqWeOWXBeMgwwOiiKcKUx1bQVfR5WFZpiQtGJFZbCoLXQ4VdJ+Hl1ltMqc367PEd2iiu5/jyeqLFggHnvfPuCWW4Df+A19z+Gnpye+SO+3qEfFf+mMqTMh/pt0qtgWVah2tcV1m/tj1ACzTpWomLa0mCgcyIoq/vuHy/d9akrc56Ouh2kcsSbvA1lFlXpwqtjuao8TVVT2hRREFUA4s559lv5MFU9UyYNTZXbWbboEkG9RhZ0qyaRJ7LAlVKSJ4A6iu8YSh434r46O8DVHlCMnKv5LZv3kj/+yNVOlo0PswXhYvXFYVGHSoVO4MCGqmLjph22EiiaqREVTBQcaR0Fh7ozuxYnu+C8gW/HCdcE4SLlsdpMiE/eUhmD3v7f4aGlJPqdmZmqz3YOd0jbQ5VRJcy13lQsv41ySeQ9NohL/5S+oRglVujuu/ULoW94CfPnL4l5mMtJu2TIh3oThzVMBovONdRZDqDpVogbV2xJVTF1rdaGytjMtqugsepkYVH/kiJpTJQlbInqUqDo9rT4c12T8V5bPQL3Ef1ETVcKOJWwdQ0VU8eaq2MrfT0se4r+i5ue4IKqgaltU8YQmFXimihyLFol9gCw2RZWsaw2bThWda6OoNUxUk1XUdTeqziWzRvL2HjprqHFrdu9aYnteU53CogqTDsozVdipkp6hIVHkyTO6BxEGO2BdQ01UySvBjh2vE3b+/HRChW5RRWaBpmumSppr+Zw5buzEMjN2XJ8jKk4VGUzFfwFisOKNNwK//uv6Hj+M888H7rsv/Hs7dgBr14o/RxVVdUJJVPFHD0Q5IGyJKv4iaJqCi2mS1nb+AqnJ+C/dRS+XThWZe53NZpmGBn0CDsd/ucV2ATbNTJXOztqCGjVRhTp5iP/y7meuHNZ+KDlVeKaKGVTnqtgSKnSsNWw7VUzvM6PWHFHrzSinigyegKOzSSyuwdFrJtAdY8+EwqIKkw7KM1VMiSpRM1WKJKpQsEZnRfcQtVKJxkLcw3XBOCuuHA5JeJ3N8+cnLz7COkN0iyqyVuKssw/SXi9t2LLDkFnQux5Ur3vOkKn4L0BsKP7sz4DXvlbf44cRJ6r4nSqqQyPTQGlQvf88d+1U8TeJLFkixEFKJF2rFi0SBVFArNW8uSe6zx/dzTQmnCrT08lzX2RFlbyuOajGf7FTxQxx4l/UtSMsFsUfR+mSNWuA7dtdH0UyeYj/ouRUiWpk8hpKKMd/8UwVOVRFlclJe6JKPTpVZmbEXkeFqHtGlvjOhgb99Y+k97RUYqeKJVhUYdJBPf7LlKgS5lRxOag+zaKo6NgejmkbSgWONGKTihD41FPqj58Wv1Ml7no0PR2+ONMpqszOyokqOvJx02a7ZunWyUIeZqoA6gv4OHR3XPtFFUAMrF+xQt/jh3HiicAzz4R/z+9UiRoaqRNKThU/UU0atgQOfzFn6VJ6okpSkdZ/DfZfP/PgVHEhUMuKKiZjtMIIrivSNrVQjf/imSpmSDOoPuocoBCDWCqJNd6CBa6PJJ68xH9R2StHrRW8zxxlUYXjv+RYu1ZtxqatQfU6RAqbThVd1+Fjx9TPKdX4L1f3DJn1BIsqVlAMqGWY/6NeB9VTm6lCYeEfxLWrw+YNX4ZvfhN47DFg82Zg/frsx9baqr8gmJY00VFeN1bS723fDrznPSKayAYDA0JQmZ2Nd6p4PxdEZze5rU0VkD7+y1UhMA8zVdJcA2dno4WYuXP1ujeCoooNGhrE4j9M9N61C1i1Svw5r/FfOs7ZqCYNW53t/udfssSOO0aFsTGgtzf6+56ocu651V+nLqo0NrqJUvQKykkivmmROul6OTKSLpbWtFMl7Qyqeor/oiKqqDhVKHHOOfQ/K3mI//Kusbb3zGHXtqT9j82ZKqqfLb8DlIkmak5HFJOTdkQVHXFaeWxcHR5Wr9WZiP8C9KefyIoqjz6q7zmZUNipwqRDp6gSNZg2LTbjv1yLKlmYmREdwkUjzUDTJLIUPX7wA+DFLxY30h/8AHjVq7IdC4UufA/P3aGCTNF03z7gHe8A/uM/7GVbe5FNSfFfUVn1OlwjHjaL3mmvlzZz9v1Qn6mSxmZeKsV3qpmM/7LJmWcCjzxS+3V/ZKON+C8Tn12TThVb+J/fVuSYCknXqpUrgd27xZ/9m1bdcYC61326BWrZa1BPjxBVkrqPTV9Pkxphjh5N161PdaZKvcR/2Y4KSjOoPiiqpLl/m+TKK8VsFcrkIf7LFQ0NtXvHpLmflJ0qjByq98xy2c51R8dag1rjqgxpGjPi4r+Cr2G5LC+U6G48lhF5khI4GC2wU4VJh0y3sCy6O0dM2VPDbkauiyBZeOop4Pd+D7jnHvH36Wk981Q8ASLvs1n8eB2daToTjx0DLrhA34aDmqiiOjsiSVTp6wOuvRb48pdFkcwW/f3CTVQuA889F/1zR4+m71CVZXDQXtE7S/wX1ZkqLs+Ro0fju+nD6OwUn7+oDkDdxUFXzQDeXJUXvjD6Z2zEf5kgq6hSLifHiZbLZjtt/c+/dCm9IckyosoPf1i7ljHhVPGcVTrQPYx1cFDuvuw5tpKKeKavp8HrkTd3zSsyZRFV/Mc9OQk88QRw1lnZjhfgQfUyuHCqRBWXjh8PbwAKxn/199OK2zr/fNdHkExrq7j/sahSiyfoB9cGUffx5mZxvdu0yfyxsahiDmqzWD10xH/l0amStOcJrjmA6DV92H1GZoadKWSaxHhQvRUItWMwuYNi9BRgrjsqyqnicqZKFg4dAh56qOJWGRoSXVtZsdFpbJssKv/kpN6bLSVR5fBh9QJykqhyyy3CpbJhQ7ZjU0V2UH1fX/Smu71dzfIdhcq5mNWKnHaBTFlUcTmo/uBBYPFitd/p7BTXl6iihO7ZALa64oKcdx7wy19Wf21goPqzHnZ9yEPkRFpxEqjcM+PORdn5F1nwN4lQjP+SnakSvH7mIf5L57U0yk0ZpKFBXAuoiSrB6JS0okrwuvnII8CHPpT+OP3wTJVk8hj/deiQPXd0UfDe4zwUWm0XulWTOBYsEHGoVOO/mHyjo4Ejj06V4eF4p0pHR+2+Q2VQvcpr4q27dBG1nvA/B89UsQKLKkw6qAoqgP1B9Xl1qhw6BLzpTcA3viH+HixupUU1SzQPZFX5dZ4vlESVffvUB1zLOFWWLct2XLL43xf/oPo08V+AvmH1KvFMOjLB03w+XQ2ql3FJuuwEPnhQFKRV6OoSC944pwqVcz4L3d3is+1nyxYxa8qjtbW2CGHqnq6T2dn07syuLrHpA6LPRRtxXP4mkUWLhGhOiaTPQVubuD54ArmH7vPHRPyXTqfKkSNqbkpqokpw5lEWUcXfib1li/hPR0xnmsG3HvUS/2V7qHWaQfXB9dPhwyyqqOK9ruxUqaW1tXa+X9x626aowk6V+kNHM1wRnSphcxZV4r9kYqk9Ojr0vn5Re/HJycrzeGtjxigsqjDp0N3toXOwcD0Nqs/CoUPA614H3HmneD9lIyOSKKqoQkXlp1Rg3bcPWL5c7XeSPh824rXC8Apx8+blS1SZN8/NoFXKM1VcWu+3bgVWr1b7HU9UiZupUpTN74oVlXNkeBj4y78Efv/3K99vbKx973TOcKOIX1SJwoao4neq6C7060B2bWdaVMmDU0XFQSojqpi8/pgSVYIdoY8+Clx6KfDMM+mP1YPjv5LJg1Ml6AA8fFjdaVrvsKgSTZhTJW5tyqIKYxIda40iOlXCRBWV+C+V16SzU+9+JmovPjpa/TwU4+gKBosqDA10Fq1tDqrP80yVQ4dEoea000T33uAgO1WioDTki5KosnevuqiS5FRJW0BJi7fQ8BZFSQWkONHHlVPFdCxQGJTjv1xyxx3AJZeo/Y4X/xXlVImL//r859W7nl0urr25KuUycN11QlRJElFN3dOpbDKoiCrU40xlPgeNjcKp4V/LzJ2rt0tP97pP97VUNv7LI+l9z6tTJcju3cBrXlMbQZgGjv9Khpqowk4VM+Ql/mtqyr7wE+ZUicOmqOLKbc64Q0ezTL07Vby5wX5U5kybEFXCzuMsblomFSyqMOnQHf+lc4iSSadKcMGuciE1SZrikJf9/8Y3Av/1Xxz/FQelIV+URJU0UUcyokrYQFETzJ1b6RiVvabZcKqoDKp35aKiLqq4iKgcHxefp7iOqDBknCpR5/ztt6s5lWZn3cZ3eqLKl74EnHoq8Gu/lvw7JpwqlLo0ZUQV1RknzzwDfPrTascRFAu84Z1UkCnSLl8OPPVUtVOlsVHvv0O3+BS2Sc+CiqjS1iZEKOqiStY1gXfdC5vrlIaJifQF2nqJ/zI13zKKpPivsH1hV1d1JCWLKuq0tgpXWNr4S1u4KAarzlRZuFD8vA1nbqlEO8o971AcVq9j30a9sS0MnaJK2Dmj8pp0dek9v6PEUd2OaiYRFlWYdOi+UcgUFWSx6VQB3C9K0na9eREZZ54pYhGOHuX4ryg4/iuc6Wn1AdJJoorNbjL/ayl7TevvjxYfXThVVqwQMWy2oTxTxRV33QVcfLH67yU5VeLcU8eP184picO1u/KEE0Tk5O23Ax/8YPjPBM9FE/f0sE2UK8JmzQRRcaqUy2Ig9333qR1HUCygdN8D5Iq0K1cCjz+uZy0TxbFjej+PugVqlZkq8+aJe5ZLUSUYzRE8N/v7sztVtm0Ts5tWrRKOFR2kXftz/JcZ0sR/BQVXFlXUaW3NR/SXC1El6FRJampZsEAco+q+Ki0qtRzXDTl5Q1VQs4GO+K88OlV0xn+FoTJTpbNT7/oxLv7Lv66j1EhWUFhUYdQxcWPNg6jiqjM7CVV7sUe5LLqLSiURV3PzzexUiYJa/FeeB44liSo2SVMsihtI3dsrClpZGRqSPxf9MypsQnmmCuCmQ+wHPwCuukr995KcKnHxX+PjaqKKimBngoYGMcvrS18Sf5bBROdmZ2f6+9RnPgM895y+Y+nqEoXjuE5fFVHlf/4HOPdc9WtbUHBbulS4EangrVniWLkSeOwxs6LK7Kz8Z1cG3fNrVJwqFESVJKeKjtmFW7YAZ5wh1rutrdmLXVnuL/XiVKEkqsgei4ogyQjyIqo0Nrp3qkQ5pjwWLKDbXa5SOGZo1kJ0rDXq3akShuuZKjJOFWpNUgWERRVGHRM3Vp2iiqmFfFsbnc5WP2lFFT9veANw2216RJX2dnoLiaykjf/SXXwB4gusNpmcTNdNRVFUmZoSi02P5uZ0HR3BwbhpUXWq7N2b7nmyHCv1+C/blMvAs88KJ4YqMk6VqHNe1aniWlQBgA9/OL5wFWza0O0MAMRmI839fHoa+NSngIceqnwt63W+q0sIJnHFlI4Ouevm2BjwhS9Eu4DiCDpVVCPHKLBypYj/0rGWsYXuQfUq8/HyIKoA2Ru5Hn1UiCoAcPbZ1eevberJqWKiwS2KNDNVgkxPV68FmWTmzLH7Pqeludn+2jG4P0/qfqcsqtiO88s7KqKKrSYwHfu2enGqqDRzuZypIhv/xaKKcVhUYdQxkW+uW1QxsXBauFBYw6mRRlQJuo3WrhVZ97riv1wVzU0tTNra0glXJlxTuvPh03LggOhkViVuoVku27WXe8WiwcHqz/78+dHD32WOL+vnUCULdeHC9O6YiYn010oWVap57jlg06Z0n1/PqRIlqjQ3RxfhxsfV7p0URJUkyuXqc8iUUyWNqHLbbcBFF4mZJR5Zr/NdXSLCT0cx5ZOfBN7/fnGOqH4Wg9GL1JwqMqxcKc4Vk04V3fco3U4VGUePR15Elaw89RSwebP4s665KmnhQfVmiFuTzMzIiSUcb6ROqUR/TQGIQrBrp0pSg0hPj/pMviyofN5tn895h6JTRUcDB9U9WBxJc/Ci4r9kP+8qr8nFFwO/8RtyPyuDbPwXpcSVgsKiCqMOdVFFZUOpCsXOzTSiSn9/7eDP731PDHnNisuFxOSkuUVzmkK5iQ5rKuzbl+7zEudUSeom0Y1XLPLmC3nMnx/uTJqeTh7G2dOjNjg8DBVxKYs7JksxmPpMFdvFkbTRX4BY0Pf1RUdoxA28zKNTJYlgPI6J62jawu1//ifwl39ZHf81OpptTdTVBezfnz3iaNcu4Mkn9W3YVCLHqLB8uThfTIkq4+P61xi6nSoq9PQkiyqm3bE2RBX/2vDcc4EHHtD7+Cpw/JcZ0jZ6uDz/ikIenIHNze5nqiTVTxobgQsvNH9cHip7B9vOs7wTVwvxN1HYdMfpaODIo1MlSVQPW3OozIxVSfBZtAhYvVruZ2WIi//yX2vSJq4w0rCowqhjovu+q4tmtFaQiy4C7r5b/FmmwGqDNKLKoUNCIPKzYIGeQqRLUYXazd5EhzUVTIgqfX3ZB9Kq4IkqQZExSlQJEyOD6BpWb4Ms1/I8zFSxOVflzjvTDakHkp0qceRtpooMwWsEFafKkSPiM7V+ffXvHjuWzWXS3S3nVEmK1rz3XuDVr05/HEEoNpEk0dwsNqymirmHDwOLF+t9TJfz+ubNE00FVJ0qwWjONBw9Wn3fzrrfyHpfqZf4r7QRsWlJex719Ij7IscbpScPogoFp4rMWuYLXzB7TGnh80ONqFrI448DH/1o5e826xY61hp5dKokkTVy1OVrEiWqBPclHP9lHBZVGHVMOFU6O/U5VUxy0UXAXXeJP6tE9JgkraiiuzDg4VJUMXljSyM4mThXqLBvn5jnoUpwk+Hn6FG7Q0JVRRWZAcAuRJW0188sn0/q8V82u4FHRsTCNu2Gs6NDFHXSDHudmSmmqKISmZGGNKLK178OvPnNlb97MYxZr/Ne/FeSUyXJObJ9O7BuXeXvjY3ZzoElS/IX/wUA73ynucc+eFD/2qmxUV/818SE2nXEc/S4FFWC54//3JRpZEjCG1LvJ4tgmLVju17ivwC7jtG0axJPWDxyRHQRM+pcf73rI0gmD04V23D8lzk6O8NrIYcPizhKD5uiig5XHrXmVR2E7QdUmidciio8U4UMLKow6lCP/zLJunXAtm3iz6Oj2eM6dMCiSgWTN/s0w8s5/quWuLiqo0fdOFVk479kRB8doopqIWLlynTD6rPGf7kQVWQ7l00XAv3cfjtw+eXpf7+xURxvmq5e1XtnHkSVtjZ6TpVyGfjhD4GXvET8fcUKcQ0EsjdYtLWJa0tWUWXHDjEfzSPrusrlfLQsfOQj5h7bxNpJ57VUtTFh3jxxv4krlpm+lgYje/3nZtY1wZw5Iurr9NOrv55lrkrW8z1uRpZtpqcre5q8k8WpMjgozm0WVdIRTD6gCAWnCjVRheO/zNHREb7GHBqqjo+17VTJ2sBhIgLVNVkjR03NcpZBdqYKiyrGYVGFUae/X7/VNy+iSqkkLkx9fbWRBa5II6qY6Lb0cO1UMXWzTzPkq8jxX3v3pp/BE7WQpxT/FfZeyzhVFi8WnUhpmZlRnwm1YoUbUcVVt62M6NTaak9UufVW4GUvy/YYXV3qTpVyWQgkRXSq+Iv5ppwqKvepBx8EzjyzIuidcALw7LOV48tynS+VxH0zqVCbJKoEC/7d3flYV8kwO5ut6z1uLpEKYdGpWdE5qF7mHuVn3jzx2Y2759gUqIHqNW0wukuVlhYhnlASVSg5VX7xC+ATn3B9FHrIKqocPsyiSpFpbrZf+AxzquRVmGCnihpRtZDBQdF4590D8hb/lTSfJI+ErXHyHv/Fg+qtw6IKo87WrcCGDXofc+5cdReAK7y5KiMjHP8VdTxR8U6mmZgwd2NLM+SLWleSTkw4tVzFf8k6VWQKVl4+d1rSiLVpRZUsn09XM1VksVkI3L8fWLUq22N0dqo7VaamgN7e4okqaXLIVVHpTJucFDnnb31r5Wt+USXroHpAiGpZnSrlcvVGUEVw0zG7wiQqg0DD0LXGNBX/petaeuSI2j20u1t89uKw4VTx4/8M9/dna7RoaRGOsuBrcsopwBNPpHtMHU4VKqLKz3/urglKN1njv0zMS2LowE6VWlQKxzxTRY04UeXEE4GdO8XfJyfTRf+mQedaIy/INOSENd2oxn+5Ojdk47/mzxf3OcYYLKow6mzdCmzc6Poo3HHxxcDPfpZvp4pJUcVmhnIQk06VNNZJU11JLl9jk7iK/1KZqZJ0fF7XY1rSFL1dOVUoL85bWtSvi2kol/UUo9M4VY4fFwUhlX/n4CB9UcWGUyUqmsHj+HHgK18BrrkGeNWrgLPPFhthj6BTJWuDRVdXNqfK9HTt51BFVIkqFAej2FyR5VoF6BMGTMV/6XSq9PbK/3xDQ7LjtKXFXdNT1jXB3Lnh7oOmJnHtTvO663CqUIn/uv9+c01QttepUWuSpOIYO1XqA56pUotKkZ3jv9SIElWGhoAXvKASAZa3+K+8oWN9noRrp0pU/Jf/WlOP771lWFRh1Nm/X2zudaNjAZ41IkKGE04AnnmG1kwV1U2R7eK1LUw7VaJElfHx8JgVU/FfOmJMXD5/1DlKZaZKlCtJxknT3Z1NVElT9GZRpRZbThVd7qo0TpU03VFUmgHiCIoqLpwqN98sNr1f/CLwgx8Av//71d9fs6bSaaijUNLdnc2psmePmK0UfEwVUSXs+ZcupTGsPmv0CGVRRWf3qGr8FwC8853x308zT04XWdcELS21Q+o9Tj0VeOQR9ccsSvxXuWyn8cAWUWuSqan4eyuLKvUBBacKtUholflOHP+lRpxT5dxz3YkqlPdtJhgeVt/zzM6qxXC7nqkStp6YmrLngGIAsKjCpMWEcKGjUGyjk6JUEsWKvXtpFKfSOFWCg0GLgsnFSZR7AQD++7+B66+v/bqpriTX3cNZIzmicDVTJShkRJ1TMgWrrNEeQ0PqM6sWLRLFPlWyfD5dzlSRwZaosm8fsGxZ9sdJ41TxuqNU7sd5uPYHCxGTk+qCUxJJosqhQ8CLXxw9z6GxUWy8gOxFVkDOqeLF1IQRHFLvPaasqBIVZ7pkSXzkmC2oOFVMxDy4nKkCAO94R/z3TTYqJc0Q0yGqBOepeFx1lRBMVSlK/NfzzwObNpl7fNvNP1Edu0n7Qn/8F4sqxcWFUyXoSqPmVFGJxeT4LzXiRBVXTpU8xn81NFTW2mkYGUmOOA2iKiC6dKqo7MVdN+QWHOI7a4Yc1G+qWTfesrzoRcAPf5jfmSpFxeSNbeFCUbAI4/nnw4tXpuK/entFdrorsgypB0SRJmyRND5u114+d654ztlZsdhMYmBAXfBQJU38V2NjusVSkZ0qaQbV//Snwompwv79ekQVGadK8D32uqOKtlAOOlUA/YXdpEK2TL5+S4s4h3QUSmRmqsS9BmGiisqgeupOFSqiigl0XktVZ6q4JsnxnVVUufpq4Nd/Pfx7558P3Hef+mMWJf7r3nuBCy4w89gu7klR51HStcNzqgwN0WiUY8zQ0WF/zx68Z1MbVK8iqnD8lxpRjTvDw8DmzaIhC+D4rySyro/SuPNV15suZ6qozjct2n6RECyqMGps3w6sX2/u8bOe7LZElYsuAn7yExoL8LY2NVElOMy2SJhcnKxZIwpXYTz/fHjkkymrt2tRZd++bKJKlBBo+2avWmhTtQSnIe0gcduiSh4G1auKzT/4AfD442q/o0tUSXKqzJ1bW4hLs5DPw7U/6FRxwaFDyV3LGzeKa7+OQfUf/GC2Lunt24F166q/phL/Rd2pcuRItllAlEUV3fFfKjNVXBNV8PC6L4PzzlR54QujRZnGRnHOqX6+ixL/9fOfmxNVbBYKPaKKb0ldxz09FQdgHu6PTDre8x7REGkb/2eKolOF47/MELUHmZmpzPQCOP4riazrI9n4L7/jQ/U8dT1TRXY90dUV79BnMsGiCqPG888DGzaYeeyOjuyRRrZElVNOEc9DQVRRdaoE50eYwoUabnJQ/bJl0V3su3eHiyqmFtCLFuVbVGlvD7dF24ZioS2tqJLmNc0a/0V5cZ7mve3rUz+vdIoqcU6VsI5Cz6nS0JC/7rM4wpwqtpGJIvSG1esYhHn22XJuuaj5FlFOlayD6uPmuNjk298WroO06LjWm9o46+wetbUG1kWUqOJ1+ZrOBU8TAaYj/ouCU2XfPjGPzQQuCrBpnSoU7jeMeRob3Ytm1EQVlXlZ1JNKqJH0WfNivCcn7c2+yGP8V9b1kWz8l99ZlHTPCKZtmKw9JaES/xU3G5jJDIsqjBpbt4ruTBN0dclHVURh66bf0ABceGE+RRUTg1aDqHS/6MTkoPpSKVwoKpfFzTdKVDEV/3X4sP7HlSXrZjxqE2t7wxPnZmhsrF2oqM6uSENaUWXFioqdXJas8V+2u21VXtO0oorqeaVLVPnAB6Kz/4Hwf4/nVJHtPpqddV9UkIFCkcvrJozDL6rYKpQsWRIexxW2cVR1qlCN/zp+HNizJ9vaU4eoIuNeSkMeCx26SBJVTHPllcBtt6n9Tt6cKtu3iy59P/391VGmupugXEQFxYkqcftCr0BGfdYYk3+oDarn+C93eE5njv+KJ2sDn6xTRUVUCSY1uJxVqZIawaKKUZyvIP75n/8Za9euRUtLC84++2zcfffdkT974MABvOENb8AJJ5yAhoYGvO9977N3oIzApFNFhy3N5k3/3/+9Nm7DBaqiysGD5kWVqAFtpjHdLdDRUfsZ7esTRdUwEYnjv8IJc6W56IJqaRHFhbD3aP786qHQU1PJRVaPLPFFg4Pp5rasWCFm3aiQt5kqKqJpmiLqkSPq55Wu62lvb/znK+zf4zlVZAeS6xiobgMK8V8yeKKKzdc1yjkSVhTV4VRZsCB6lpgtbr4ZeMUrsj2GLlHFxNopj4UOXSSJKqYdzz094r6i8tnIer7bvHeWy8Af/ZFYGzz0UOXrv/iFmCkDiHNDtrAqiyunSphYJbMvHBjIFjPHMDJMTSXPzrOJqqjCThV9bNokhtVz/Fc8WZtO0jhVkhqlXDQVRhHWpBHVQMeiilGciio33XQT3ve+9+FjH/sYHnnkEVx44YV46Utfit27d4f+/MTEBHp7e/Gxj30Mp8d1dDLm0NWVG4Yup4otUWXBAhpdvxSdKq5EFZNOFUAsgp5/vvprzz8f3UFrqoPZtaiStWM3rBO9r8/+gN2WFnFNC4vDmz9fCC4e/f3yA3N7euSLmUGyOFVURZUsn08XM1VU4nfSDKoH1M+rmRk7m+Swza/nVJEtnqf9bNnGplMlS9F2/nwhgtqMbwgTVaIckSqRgFHF7YYG94Mtv/lN4Jprsj2GLlFlyZJsjxGGLqeKyffJ1GO7dqoAwKWXAj/9qfzPZxVVbO4bvvUt4KyzgM9/Hvi7v6t8/d57gV/7NfFnE+v1PMV/AaIoZcKFxjCUUYki5Piv7PiL3S5ElaxrjZkZ+46MrE0nNpwqLgkTeKIaCSg0SRUYp6LKpz/9abz97W/HO97xDmzevBmf/exnsXLlSnzxi18M/fk1a9bgc5/7HK699lp056EwUFRMbQg6O/MlqlBBtVhQdFHF5OLE60z244kqYefF5KSZgqtrUSWr1TWsaHr0qLxooYuWFlGgDOtQDIoqMjMWPHp6wuPgZLApquTNqaIiqqQZVJ9mwWmr4BznVOnulrt35lFUMRlZFnXvVBUbbYoOa9aIGFY/O3fWzlMB1F43qi6mAwdEISeNe8+PDlHFlMtXl1NldJRGJK0KcaLKwYN2PpO/8RvA978v//NUz5Ugg4PAl74EfPCDYn2wYAGwZYv43qOPAqedJv4c5sDOigtRpaGhOude5Vh6elhUYeqPefPkO9ddDuMuCn7XhCunSpa1hunG1TCy7jWj1hhBVEQVak6V4OsTtUZZtUrMAGaM4ExUmZycxEMPPYQrrrii6utXXHEF7r33Xm3PMzExgeHh4ar/mJSYXiTnzalChaiNRBRFFlVML/q8RZCfrVvjI/FMFASDBf+8QUVUmTs32qmyYEH1a3z0qLyTprs7vaiS9hq2cqWYO6BCligCF6KKJyLIoFpEnZwU9zeVDcf0tNxwcR3EzVQpmlPFH/9lct0R1Q1/+LB8gW3JEnFPtcUFFwA//3n118KG1HvI3n/iNp6Nje7iqf7rv4A3vjH741CP/9JxLXXh9sxKnKiyc6edNcHGjWIdJyuOmpqVp5s/+RPg//2/SsHuwx8GPvnJSjHIi5s0sV6n1NUus6ZiUYUxRUMD3XjHNWvEdVYGl3Mj8oz/vuJfg3v7+IkJe07nrGsNmwKQR9b1p4n4L0pOlbD4ryhRZcOG2qYsRhvOro59fX2YmZnB4sAGZfHixTiocSjmJz7xCXR3d//qv5UrV2p77Lpj2zZg/Xpzj8+iih1siCquBg2bvuGHiSr++C9bHcsuI1lUCttRhMXSuCgINTWJ45B1qsgeX5b4LyCdEBc1wNoULjp1VJ0qKkXUNJ+/w4fNX0s9wuK/VGeqDA7mQ1Txb6JMDnaNElVU7pEnnJDtXFeltVVc+/0zZ+JEFVniuu8XLRKfdduUy2KIeKD5KhX1EP9l8h6q6xiDREVz2BRVAODUU4HHH5f72TwUF++/X3zeL7648rXVq8W94utfB844o/L1osR/RSEj8Mybx6IKYwbViG6brF0rL6ow6rS1Vb/3wZmZ5XK+4r9cuJWCQtBf/IXa75uK/6LkVJEVVRYtstsEVmc4XxWWAsWjcrlc87UsfOQjH8HQ0NCv/tuj2snLVEjqyM8Kiyp2sFG8Luqg+rCOcC9yI2z4ehHZvz/bkHqAjlMFEAtEE6JKWqdKWrHMdje5q5kqsoUa1Zkq3vursv4wOWMsSD05VfyY7Arv7Ay/T6k4VU44wX4U0CWXAHfdVfn7jh3AunXhPyt7PYlzqixZUjvHxQaPPCIiiryO+izUQ/zXkSMiGtQEJoaZAzScKoCIALvlFjvPBZhvivnGN8SA+iB//MfABz4gHG8eUdfBLMgMh7eFzLEsWiQi0hhGN/7COoU5rH5WrwZ27XJ9FMUlWAsJiiq9vWIfkaf4L9tOlaCocv31auksRY//CjuWKFGF2vWnYDgTVRYuXIjGxsYaV8rhw4dr3CtZmDt3Lrq6uqr+Y1ISN5BbB7pEFSrdUVSZnTUfWVPUQfUe3obYvzEOi3xycQP75jfNPv7evdlFlbDZFS5FlaRB9TMzwHe/Kwa+ypAl/isvFG2mSl+f2OA0N8sXX12LKkWdqQJUrp0unCqqooqp44viJS8RDg6PXbtEVnIYpZLcBjROvFq61K4LzuNrXwN++7f1PJYOUcXU+iIPTpWiiyoXXAA89JCd57LB6Gj4umbdOiGs+EWVenCqJIkqf/mXwMkn2zkepr5oba04S10lDESh477IRBO8tgbX4Js2CYekTVElb04Vf8Pg9LSIuVaZATY9LRdznef4L9mZKoC7JJk6wJmo0tzcjLPPPhu333571ddvv/12XOBf7DF0sOFUyToskZ0qydgo9BfVqQJUZ+j7i29hkU8mF9BRReOPftTs8+7bl11UOflk4Iknqr/mKg9exqny+c8Dr3ylfAE9S/xXlvMzzXD2tBRtpsqRI+Lzt2iR+HOQO+6oFS5siiph8V9Fdqp41zDTTpWs8V/r1lUXKG1w4onA009X/j45GX3fi/o3BomLNHLlVNm2rTJMOyuUi0e6XIamRRUTrx8VUaW52W7nqel1eFxR5cMfrr4PFF1UoXQsTP3hOVVszuBTgZrQUySSnCq2RZWsDRyunSr79om1kon52Hl1qkTFf0WJQjxXxRhO478+8IEP4N/+7d/wla98BU8//TTe//73Y/fu3XjXu94FQER3XXvttVW/s2XLFmzZsgWjo6M4cuQItmzZgqeeesrF4dcfpgtInZ3ZL5SULOcUKZftLKCK7FTxz1Xxu7eyRD6lobe3tvg7OQls3272tdchqsybV/taUXSqHD0qFh933AH8zu/IP2baz0LWTdeKFeL9sUFRZ6qEnVcA8K//Ctx5Z/XXdEThyRLnVJGdqZInUcXDpFOloyO7U2XOHDH82Salkojt2Lkz+X4u62KKw5VTRWfhmbKoomtGWl5FlbAEAW8/YHtNIPM+5CFCQ0VIKLqows12jEs8p0pS97srZNePjDoyokp/f37iv1zPVNm1S7xWJj6vqjNVvGNyLZaG7cWPHYtuqmBRxRhORZVrrrkGn/3sZ/Hxj38cZ5xxBn72s5/h1ltvxerVqwEABw4cwO7du6t+58wzz8SZZ56Jhx56CN/4xjdw5pln4mUve5mLw68/ymWzmwmeqWKe4A3dFEV2qpxwAvDss+LPcaLK7KzZYaZhxd89e8R5GlYU1sW+fXqyp4OuCtlhcrqJcqq0t4tjet/7gM99Tu3alzb+a3g4vMAky8qV4jNgA+ozVdKIKr290UO59+wB7ruv+mv79rFTxRTe+ebCqaIiqrjCiwDr748vPMt+NuJYutSNU0UnWUUBG2uLrHhuOxOYElWiXFbeWsCmqBIlqPtRyXJ3icr608R6nVIUM+8LGZd4ThWTDSJZWLuW56qYIrjGHBysXoN76S/NzXaOJ2vCgAunit/Ju3MncNJJ5kUVmfgvT8hQ2ZeaQDX+a8MGUbtitON8UP11112HnTt3YmJiAg899BAuuuiiX33v+uuvx52BztByuVzz386dO+0edD2iEruSlqiuURXqdfEcZv8LQyXWJAuuRJXJSfOLE1mniulNZVgBYNcuIRCYFFUOHBBFtqyccgrw5JPVX3PRAdrdHf4+lUrAo48CL3sZsGaN2mOmjf/KWvResULMvJEhq0hOfaZKmAgRR5JTpbOzNrLOZBEzSJxTRXbQ8NCQHVFdB17HuIuZKnHWeSpcdhnwv/8rnIlr10b/nA5RZfFi+06VmRm9TQlZRYFDh0QMmil0OFVMuj1NiSpR/24Xosq6dcCOHfE/MzgY7mzNM0V3qlA6Fqb+oO5UWbMm+brHEWHpCJup4l+Dt7WJZri8xH9RcKqcdpp7p4rfHeLiNfET9p7GiSobN7JTxRDORRUmJ2zfDqxfb/Y5dEQg1Kuo0tZWGYQXR9FFlbhceF2sWyey3oHqOUNBd4LprqSwjvpdu4BzzgnvtNeFrnPsjDOALVsqf3cVqfFnfxb93H/1V8D/xVEq0daWbhBc1qK3iqiStdBAfaZKqaR2P/EEkjBRZWqq8rz+f7ON641HWFHT65BqaJDroI5baFPDaxQw6VSJmxVCPeKnu1ucD889Z15UMTWkPA7drlodooqNtVNaymVxfssMZE2D7fi05mZx3mdxbqqybp3Y68Rx+LC4R1BH5fpVdFGlXveFDA08pwpVUWXtWuEAiOPAgXBHPxNPUvwXIBo18zKo3vVMFU9UkU21UdkD+h0fSU2x/p91LaqE3evj9nqLF1fmAjNaYVGFkcPfkU8ZG44airS3hxdxjx8HfvM3Kzf1oosqNpg7VzhigGrhJOhUMb2AjnKqnHOOWaeKLk4/vVpUcdUJdfnl0d97y1vSFc3TFmSD1nBVVESVrIUG6jNVVPGK92FipRd5d9ppwGOPmXn+JKLiv1ReD5siUFa8e5pJcXr9+opAnkfOPx/4xjfiRRWZvHTT0a5pSIo1UyWrKHDwIG1R5bHHxD3VFC5m0qxZY/dzuXZtcsf2kSP0owEBtfWUKVGFipDhuujF1Dd+pwqVc8KPjFPlm98UtQRGjTBRJbjH+8Y37Iltra3R9/E9e4C3vS0+McbFtdQf/3X4sKhFyjYKxc0WiWN2Nn5OCiWnShhxoopqwyEjTU5214xz/B35Jsm6gaJYHLBBlFNlcFDcKF/3OvF9W6JKlMijk+lp4IYbzD5HFI2NtQXlYOST6QV0lKhy7rlmnSq6WLOm0h1lI7YtD2SN/1q6VD4bOWux2r/QtYXJ7NpSSfwXdV6tWiWK2N5clYkJu5/ZsKLmzIzbAYkm8e5pJq+jYQ4M3bFTJnnJS4Af/lB02EchM6heVmC1uRE7elRvZ+zcubTjv7Jyyy3Ay19u7vFNiCqzs/Hr9Usu0ft8Scg6VXSIKpSKGkV3qtTrvpChgX8tQ9GpsmoVEJhfXMNttwFXXmnneIpE8NoaNkPMpkjf3h59rd+/XzSzvPKVwNNPh/+Ma6dKuawWr21qVqvfqULpXueRlEqQNk2DiSUnO0fGObacKpQ2GnkiSsQYGQEuvBD4oz8CXv96URy0IarIznjJwp49wD/8g9nniGLtWuAXv6gusoTNVLHtVOnrAzZvNudU0dmR4RUuZ2fNZsHniayiypw54jPX35/8s1mL1S6KFKqfvzTHuGCB+Dz62b0bWL0aeOELxXkPiM51HbOFZNFR1MzT/dWGUwUQnxF/dJo3WycPnHmmcFDFfQ5l4r9kYuFkxBmd6L4nNDZmGzJOPf7r3nuBCy4w9/hZRakwku53X/iC3udLIsylGESXqAKYvR6r3PtMFFgoFpoYxgWtrbQH1Tc3V9IXwnj+ebH+5cY3daildsTVZkZHRSPD178uakbf/Gbtz7icqeKt31QibUdGzIkq1J0qcdea9euTG0gYZVhUYeTYt89uASkt9dqN1N4e7lTxiiWXXAK85z3Al75EozAwMAD8z/9ke4ytW8Viz0Wh8IQTgB/8oFpotB3/FVb8BcSG35SocuiQ3q4aL8uXRRVBVlEFAF72MuDWW5N/Lo8546rxjrLXBv/PhRVfd+8W3XwLF1bOuf37geXL5Y8lK2HxX0HyJJok4Ykqph1/y5eL9Y3H4cM07pEyNDQI51Scs0ZmAyqz8YybP2MCavcEyvFfhw6J16qpydxzmHCqUHuPZWIxdIkqTU3mnJ6qbrusgmMYLKowjIC6U8Uj6tr3jW8Ab3iD3WMpCv7h59TxorKWLgW++13gy1+uPXYXThUvFcFrbFERVYaHzcxlox7/lbS/37hR1M8YrbCowiSzc6e4MdiIxJgzJ75jggknqtNsdLRSLLn8cuDhh2lEWDz/PPCZz2R7jG3bxKbNxfyQTZtqRZWWFrGR9DBdDAxuhL0ojaC4oxPdESjesPo8dYfLkuZapkNUueoq8dlMQkfXnO0ivqnFq/86CdT+u7z4L0A4xA4fFoX4Zcv0H0sUSUXNqAhIj6SoHWp4/x7T3Z3BzYXOTnQbJAl7upwqS5cKYcEWumeqZMW02JZlgOyttwox3ST1IKoAybPCdA2qN+nmplC8nZwU/0aGqXc8pwqF8zKK+fPD943lMnDPPcCLXmT9kAoBNadKHH53w5w5onl0YKD6ZyYm3DlVdu0SjikV13Qap4rMvpbSoPowkmbCbNggGpMZrbCowiTzqU8BH/ygnefq6sqPqk+JJKeKx8aNNAprR48C999fLUKosm2byHh1obZv2gQ88UR8JJ5tq/fBg0LwMDmETHcEyumnA48+SrO4khWV3FePoSHxe1lYuFBcQ5NcDVSHZsZhaqZKkqjnnVtAZa7K/v20RJWk4rkpG7wpbDlV8i6qJCEzqJ6qU0XnTJWsmN44Z5lD96Mfifk6JjElqlBrpkiaL3DkiB5RJSlyJwtUircU9hoM4xob8+GyEjWs/qGHgLPOys+cOWr4RRUqTvKo63JwqHvYHnZ83N1MlV27xOe0s1NNVFFxqsiuc/zNF6oJChTYsIGdKgbgqyQTz6FDYnbFWWfZeb6uLru53UUhzqmS1IHqgv5+4OSTK/MJ0rBjh3DfuLgxLF8uNq3r10f/jO2NrdfFYRLdESgnnyzEqaKKKqqOocHB7E4VQMT93XVX/M/oiP+yXTQxVdg8cqS6uBd0nZXLlU2lK1ElKf4rqXtLhwvKJt49zbZThfrsDFVk1lQUnSpFvCfE0dGRTlSZmBDnyLx5+o/JT704VdatCy8ueugS9ovuVHEFCzkMNfLgVPGimINw9Fc2WlsrDa/UIxGDczi6u2v3sC6cKl78l1fjUImrVB1ULxvXFnSqUH5fw1i61G6TVJ3AogoTz+c+B7z//faej50q6YhyqoyM0BRVjh4FXv3q5MJvHOPjwKmnurEwNjQA73tfbVHav6Gz0ZXU3FwptAZFFRNdMbrjv1pbxftYxPivsAVpEroK31dfDdx8c/zPUB2aGUeajiCZ86Cvr7r72D+XKPj7p5wihEAXokpcUTPJkZA3UcW7p5nuAlu3TrgePYrmVJHZgMo4VWxvwqjFf5mmvT1dTMhddwEXX6z/eILUk6hiY4Br2vdbBgrNVCxuMIzAVpRpFsKcKjMzwOOPA6ed5uSQCoH/OqgjicAkwftGmPvdtVNFtXFU1aHf2Sn27XHRWQC9QfWq91u+PxuBRRUmmqEhMe/goovsPaeKrS/IzEz9XigoOlUaGqIHcR49KlwmDz2U7rG9QqfLXMi/+qvwr3vHZmMB3dtbKf76FxymhuOZ6OLu6RHvIbXiSlbSxH/pWpxt2iS67+MEBR2iH/WZKv5uojiCop7/vAoWdxsbxbXtyBG7G6SkwcZJ8V95FFWOHat2CZmgubm6Y7xoogqQvC6SWScsWWLXqaLDSacLG/EOaZ0q3/8+8PKX6z+eIC0tyZGSqvT10bvvr10bL6rouueZPJ8od8Tbplyu330hQ4M8OFXWrKl1qtx5J3DppXz+6GJwkIaoElWbkYn/cuVUmZ4WcyyTZggGUR1U39kp6hxJa09qg+rTrEv8LipGCyyqMNH88z8D111n94aaJf7r+HE6m3DbxM1UcZWjH3VMgBBVFi8WN8s03Y9e4cufVzo9ndxdYBrPeQHYWUBHiSr+Tnud6I7/AsRclTvuoFdcyUqa+C9A3/X2zDOFKB4FpaKlLFNTasNvZbur40SV3bsrQ+o9TjtNnG+UNptFE1W87k4b+DeZFAu9ppFxtPb01A4trRdsRMKlcS6Uy8CzzwoR3TT14lRZuzY6/kvnGtNknF6ateecOeZmvNjGX2CiUPBi6hv/TBWqosrKlcDevdVf+/rXgde/3s3xFBFd8c5ZiVpby8R/uXKqzMyI+29Tk9rvpnGqHDqUfJ5SH1Qvw/r1dly5dQSLKkw45TLwv/8LXHWV3efNIqrksUioC4pOFb/gEcTbTJ93nhhYr8q2bcKl4lEuu+mgCOJfhNiI//IXf/ftq8QR+b+uExPZoWecIeY2Uejg0Uma+C+dXH018L3vRX9fh5PKhaig8pyyhcDgTJVFi4RwCwjxJCiqnH+++7i64AajaDNVsgzuVsU/nHpmRn3jRp2kLjaZ5gtKAqJtbIgqaZwqY2PA/Pl23pt6EVWSmoF0DKkHzDpV0qz749braXA1lDno6KQ+x4ApPl5UEOVB9V7EksfAgPhv7Vp3x1Q0qMR/RV3rg06VsEYtF3UWvyvEj8w9Jo1T5eBBNacKtXvM1JTcHsZl0ktBYVGFCWd4WHRS2d5Is1MlHXFOFZeiSlSRwOvYufhiYTFWZevWypD4xYtFAdRFB0UQvzvBdvyXvxjoLwpT5/TTxZBd1y4j3aSJ/9LJC18oBqpHQXmDF4XscEIPFaeKv1gWdKoEc3zPPx+48EK1Y9FNMJKoiDNVbIkqwWH1RSRuA0p19ppuSqV0xd69e4EVK/Qfj580TpX+fiGq2MCEqJK3NbvOaMAlS8zNKErTEa9bVHFFsABXz812DC3y8Fn07o9f/zrw5je7PZaiQSX+K6o2E+ZUoTBTpbFR7NH8ax0vUi+JtE6VpPOUmlNl40bgf/5H/DkojsX9jrfvGRkR87PTRvIzAFhUYaI4eFDvQGpZ2KmSjiinistiSVKRoFQCzjkHePBB9cfetq0iqng3hokJGqKKtwixYfX2xJNgociUU8UEixeL3N6ikTb+SxeNjSJ/9sknw7+v43rpqhtVFtmF99Gj1Qv23t6KKBkW/zV/PvDXf63vONMQdI0lxX9R7AqPw2b8V9FFlba2+PNANibUVkTQ5KSYdaObuXPTzQVJMyBVlTRF7byLKlSJEqh1iirU4r90iiou1wXBjvt63hcytJidpd08tnChKF6Xy8LlfvXVro+oOJTLdOK/oq71wRpK2B7WlVNl27bqNViSM9/DZPwXpZkqn/wk8JWviMhv2WZqz6nyve8Br3iFSCY6+2zjh1pkWFRhwnEpqqQdsF3Pi2eKM1VkNmnNzUJcUS10RIkqrm9s/kWIzfivwUHh9vB/XbdTxeTC4ZvfNPO4LlGN/5K17Krwl38JvPe9wNNP137PhpNKJzMz6gPLZQuB09PVs1r8M4l27xZ509QIOlWSRJU9e2j+O6Kw6VTZtEncQ4KdekUh6bMh23yxZInYcJomKHLqIq0wEBYBqJs0n/eBARZVTLBuXfhclcOH9cV/mXQTu47/crkWD4oq1KJZGIYqa9eKYfX33ScaHlXmFzLReLNWKTlVwq715XJ1Ok3YujHNXJOsRIkqMkkQqg06aeK/KIgqzc3A9dcDH/iAEEpk7v9LlwI/+IGI4P/BD4AXv9j4YRYdFlWYcFyJKp2d7FRJQ5RTxVTHpwyym7RzzwUeeEDtsf0iwoYNoiBGLf7LxmvviSrBTloTg+pN5soXMa9fNf7LRDzTsmXAN74BvOc9wBNPVH9Ph+hn831L8/lLWwicP18UdgG6ETVBp0qSy9PVPT0tUfc0E6xZI4oJhw+bn53hgqQN6MBAtSgfxdKl5iKL/JhyVaW9HtgQJNM6VWTeNx3MnatfVKF6348aVq/TqRIs/uvEtVPFpZDBThWGSYe3DvrXfwV+53dcH01x8K6t1GeqBLHpFo+jsVEMVPfXOJLijtOS1/gvQKyZv/AF4Lrr5ESVhgbgueeAv/orbjzQBIsqTDh5jf+q1wtDc3P4IC+XRN24JyerO2AuuQS46670z7Nhg+hioOBUCboTTBcNFiwQBaigqGIi/uvgwWIWHE2hei0zNfNi8WLgxhtFXuojj1S+nrdiw759Is5MhbRF1MZG9fkttgku5JMK59SjJ4K0t4tiv42mAG+4sc6iKSVkPhsyLjCTcyD89PfTElWmpsx/DtM4VWzGfzU06I11CjrtKLFunSjiBDlyJB/XBxZVaBwLw+SJNWvEHqG/X1wDGT1411bq8V/BegWVpoemJjHXLo1TRRXPqZJ0/6Q6qH7zZuAf/1E4zWTIUw0gB7CowoRjsis9DtVC5Pe+B/zwh2Kzl7cioQ1cZhtH3biDkRXnnivsh7IEMzK96DNqThUbeN0SQVHFxILj0KF8dbq7pqFBrTBvsouptxe46SbgYx8Dbr5ZfC1vRfZ9+9SHRcvMVJmZiX4dKFxToggu5CkK61loahJrAVtxXI2NwP79+SiaqhLX1RdscojD5BwIP9ScKjagPlMF0LuepDzjae3acFHFhOhqYo2eJv6rs7OYogrvCxlGjrVrRac7D6jXS2enqFtQif9KmndLjaYmcZ/0R7CaFlXy6FTxePGLgZe9zPVR1CUsqjDhuHKqqAwSffRRkSH40EPA5ZcD3/52fS+eqQ2NjioSBDfTc+eKY5ctCPrnqXiUyzRubC6Gk5fLtaJKqaT/8+BKaK0XTDlVPObPB777XeBHPwL+4i/oOzGCmHKqxBUm9+6lO4ck7HoXdc7Lzsyghs2C2Jo1Qtwv4jUubgO6c6copsiwdKkQnkxDaaZKmgJ1GtLOVLEV/6Wbvj66osqKFeLaH+TIETHMWRcLFoj7j25cO1VcpgawqMIw6Vi2TKwVeEC9Xrxra5rrsqnjsRWtq4PGRlGPDDrz06baxNHZKRqNVGeqUG2+Y6zCogoTDuUNDyAWyh/4APClLwF/8ifA978vYqTOPtv1kdHCpX0zTlQJFkw2bxbZjjKEiSpLlghhwfWNTXWOhi527zY/SJfjv8xiwxre3Ax88YvifHn2WbPPpRtTokpfX3ihrLVVvEamzysVGhsrBSOVbuC8Dan3sLkJ3bgRuOeeYjpV4jagW7eKCE0ZVq0SnyXTUHKqBBsWTJEHp4pOKDtVGhuFgzGI7iG9S5aYcX65FlUoOVUoRbMw9UtjI73GxyCNjcDjj7ubw1pUvGtrcBC86+PxQ/mz2dRUuwaTmamS5vX2GmiS7p9z5lRElbylPjDGYFGFCadclsvYNoHMRfCDHwQ+/GERawOIzfI73iEKI/UKhZu1H1mnCgCcdBLw1FNyj7t1a62osnEj8OSTNEQV206VOXPE4iK4cdTtVuH4L7OYdqr4+d3fFU4/HdhajNsWVXp7gQcfpCWqtLRUnJxhTpWoe0BeRZVy2V6X8caNwAMPFFdUidqAhjUpRLFwof5ZXWFQmqliS1Rpa0s3UyWvThXKogog1q8jI2afY+lSMzOKJibUC6M6RZVDhyp7M9uwU4WhSFub+yQFGSjM/CgaOq+tOgg7nsnJ8PqJ35HhijBRRSb+K821v6FBCCoq8V8M83+wqMLQI6lId/PNooB8xRV2jievzMy4E8aAaItp2GZ682bg6aflHnfbttrO2o0bRYeN60VrW5v9xVNvb/hzejmuuuD4L3VU5qrs2GG3gK+je9PmQPc0XdktLckzVaJElUWLhKhio6Aqy9y5laJwPThV2tvtOlUmJ+tPVFFxqpiIlQzDlVMl7N9mS1RJcy2dmHC/5knL0aN6o7R0c9ppwGOPVX9Nd+OSKacKkK5DV9fa9amngJNP1vNYqrCowlCktZU/h/WKd22l0vgadq0fHQ1fa6vOOTbB/PnARRdVf01GVBkeFsevSmenWvwXw/wfLKowtbguxscxNgb84z8Cf/3Xro+EPq7zO6M6/cK6UDdvlneqHDggOvz8bNwIPPGEe6eKt2ianbV3DvX2iizcIIsWicGquqAwsyZvqCxIn30WOOEEs8ejG9sLS9VNSWtrcmf6kSPhXbW9vWJeV56cKnPmCGEgSJ5FFVuFiFWrRNd/HmfPJBG3AVUVDUol80KqKQdGkqjyilfUfm3XLlrXAJfoLApRd6qcfnq1m9NEbrpJUUUVnaLKk08K97kLOP6LoUhbG415Gox9/PFfFIgSVcLWvqYGwquwbBnw7ndXf01mpsrIiBBIVOnslIv/YqcKE4Bo5ZxxCoUOsqibz+7dYm6K6+I5RYLFjtHRdDcUXcyfL7rAg4Rtpjs71TZ0wc39+vVisCiVor/NQZ29veFFsd5eO1EtTDTd3fJxcCMj6bpqXEJ9YZk1/mtgwF2MSRj+f8/x47XXuygRL6+iis1CRGMj8Pa323ku28TlT6vOiVi82Px9ZXpaXFt0E3c9GBsDbr1V/N+PLadKvUFdVDnjjGpR5cgR/S42U/FfadApqpiK75OBnSoMRVpbWVSpVzo6xDXRxJomDe3ttdf6Y8fCRRUXkeYyyMxUYacKYxkWVZhaDh50OzshKjYKAPbvr3UpMIK2tuqCQFTngS2amsKHfYYNqgeqhzBHMTkZvjBpaxPzFqiIbWNj9hbQixaFF310O1UYdXp65Lp8ZmfpWMNVCBYwTDEyku5alkVUWbRIdKhTel/88V/j47XCbVRXWZQbhzo2nSoA8Pd/b++5bBL1uZiZUR+wuWqVaG7JI3HXg74+0czz4IPVX3fdnFJUqIsqy5aJOV4ehw/rv4ZScqqEFdrS4Hotw04VhiLsVKlfOjvFvaSnx/WRCIK1IiA6/su/dnR9bfczd264K99PFqeKzEwVT1Sh8powzmFRhanFtagSF5mzf3941BEjboiURBWPoOsoajbC+vXA9u3xj7V9O7BmTfj3Nm6kIaqUSnaj1179auAd76j9uk6nSlhXPJOMbJfPvn3AihWmj0Y/tkSVNEPqATlRJS7+i1rsjz/+K+ycjLPq53Hhb3OmSpFpbg7vqkvjYCqqqHL0KPBrvwb84hd2jyktU1PqglhWdM7UGRqiPRS5VBIRrl5j0OHD+p0qnZ3u8+o9dLlOd+2KXqPbgJ0qDEXYqVK/dHSIJA0q97uGhtr7eJRTxb+niBpmT5Xh4XSiymWXJQvx/vsllVg3xjksqjC1UBBVogZsh83TYARtbdUOHwqiysKFoljhZ2pKFHmCnHRS8lyVb3wDuPrq8O9t2ECj8N/cLGKDbG3k5s4Nfy6dTpVDh9xeE/KKbPzXM8/kb54KYM8CnVZUaW1NHlQ/NBRuEV+xInzGgkv8RWFZp0qeF/xtbVwQ00WYqKYypN7DtKhi8vOaJKpceSXwwAOVr0U5YykwOBjenGKSuXMrom5WbM6dS8vGjeIcAczEf5kQustltwL6U0+5m6cCsKjC0ITXMvWLJ6pQcaqEEeVU8TcGmpgrloWktWLaSO0//dPke2hDg/nZgkzuIL6iZZzgWlSJ695ip0o0QadK2sgcnaxZA+zcKfezSaLK+LjoIr344vDvv+MdNArT3d3ic+q6K0mnU+XQIZGlz6ghG//17LPAiScaPxztFMGpAoQvoLu6gPe8R/05TeKP/5KdqTIwYGbotw3YqaKPsA0oRVHFZDNIkqiyenX1QFnbs4j8zogk+vvtn9ey19OicPrpwJYt4s8mnCommJwMb1qyhcsh9QDHfzE0YadK/ZIHUUXGqTIxQaNxVZa08V8MkxIWVZhaXHelx8V/sVMlmjCniusbioqosnlzvKhy443A614X3UFw3nk0ioc9PaII7HoBrVNUOXiQRZU0yMZ/PfssDUFQFVuD6k2LKnnBH/8l61TJ65B6QBQ1uYnCHGlElRUrxGfKFCaHXCeJKgsXijXLrl3ia7t32x1SH2yMiWNgwL5TpWjX0yROP70yrN6UqOIXynXg2qH+1FPAySe7e/6gqELZbcbUD2edBZx5puujYFzQ2irWNVTiv8KQmalCzamS5CZJO6hehZkZ+o5bxhr8SWBqcV1AjRNVbM6qyBsUZ6oERZU4u+a8edEF6HIZuOEG4A1v0HhwhvBEFddW764uOZeEDK6F1rwiK6rs2mW3eKeLojhV8oL/3yM7UyXPospb3kJvrk2R2LEDWLtW7Xf8wp4JTA4wb2mJjgPs6xPP+8IXVuaq2L4ut7dXN8bEETWbziS6rqfT0/bnwaRh82bg6afFn00MqgfEuurQIX2P53qP1NcnxElXBBs9ZmbyOU+MKRZnnSVEWqb+aGgQ9QB2quglKX7ahlMlrLmNqVtYVGFqSTvcSRdxogovjqOhOFMlKKokbfhKpfD4i7vvBs49l9YNPYqeHhrxXzrPFddCa16RnakyM5OPIlMQWzNVDhxI51hI6gL+6U/dRpWo4v/3hC3mw4TUPXtYmGBEJFBQDEk7eNTk3JOjR82JBUlOlQULgPPPB+67T3xt1y67505Hh1i3yeAi/kuXq8KFyyYNzc2V+5up13vJErG+0oVLUWV21v0ezd/oMTbGBS+GYdzT2UlbVMnjTJWwJjI/JteSHuPj+ahLMVZgUYUJx+XCOEpUKZfzPXTXNBSdKsuXiyxRj6Qu1LVrK9Ebfr74ReC66/QfnwmoiCo64Zkq6ZCZqXLsmHtXU1psOVXS5sQ3NETfM2ZngU98Avjwh7Mdm038LoEwp8r8+bWRf3l2qjD66O6uXldlGbLZ2hrt+MiKaadKkqiycSPw/PPia+xUqUaXU8Xke6ybBQuE+wIwE/OxdKloGtBFlnV/Q0O2+zkFAd+/JtmyBTjjDJdHwzAMI67JlOK/5swR+yqPqPsGZadKcE0bxEbdgkUVxgeLKgw9okSVkRFaNyVqBJ0qFAbVNzVVO0+S8tLDhtXv3CkWAHnJ1u/uphH/BQhxVIcQyaJKOmScKs8/D2zaZOVwtGNrpooJvvEN4KqraMxhksVf1Jyers2K37hRXD/9BXMWVRhANCw8/njl72ndX4AonJqaq+JqpoqXv10qVRwZR46YiXyKQsWpkueZKnkSVc44ozJXxQSUnCodHfKiXhiuh9QD1aLK/fcDL3iB2+NhGIbp6KDlVAle66Piv+bOrZ7jSMmpkhRxPjWVrhlPBRZVGB8sqjDVTEyYvwgl0dkpBIEg+/fzkPo4wpwqrgfVe3iF/aTNdJio8oUvAO9+t7lj0w0lp0pSJ4cs1DpU8oI/PiSKvA6pB+w4VUzk7x8/Dnz1q8Dv/Z7exzVNUvxOqQS86EXAPfdUvjY0xM0IDPD2twNf+lLl79u2qQ+p91i1SgxxN4ErpwpQcWifdRbw8MPVX7OBqlPFtiAcN5NGhTyJKqefLhwPptDtVMkqqsiKemG4HlIPVK9JHnwQOOcct8fDMAxDUVTxX+uj4r/8UKsDJMV/2YBnqjA+WFRhqjl82H1HepRTJUtnZT1AcaYKIIZWHj0q/pyUcRkUVbZuFcWf884ze4w66ekRcREURJXeXnFOM3TJu6hieqaKCZfUP/6jiBN03UCgisyQ8De9Cfja16q/5jrnnnHPsmVClNu+Xfx961a6ooopB4bsTJDzzwd+/nP7543qTBXbThVdAkDeRJV77jG3ltbtVMmy7s8qqlBxqnhrkrzM7mEYptisWiVqW1QIXuujnCpAZR1EzakSJ6rYGhUQFsPM1C0sqjDVHDwoFvkuiRJV2KkSD8WZKkD1sPqkzfTChZX86nIZ+OAHgX/4B9NHqBevG4VC/Fdvb+2MBYYWeRZVgkKuCfbtE7OZdHHkCHDXXcCrX63vMW0hE7+zfr24Vx4/nm1uBlM83vMe4POfF3+mLKqYKrg3NsqdEy94AXDLLfbXwipOFS+uzCb+tVwW+vryI6osWCDOlUWLzDz+okV6G19cOlWOHDH3OsniOVUGB9mhyTAMDb7+df2O+ywEr/Vx4oAnUOTJqTI8bOf6z/FfjA8WVZhqKIgqHR3Rogo7VaIJFjjTDnfWjYqo4lEuA9/8JnDuucC6dSaPTj+eqELBqXLGGcCXv1w9kE6VsTG2t5pkaIiWLVyFZcvEddkkukWVG24QLpU8ujdkO+1f+Urg5ptFsc51kYuhwwteADzzjIhX3bo1/b3VpKhiazPsZ2pKFGM9urpEUdbmkHpArahdLpsZnB6HLlElT04VQHwOTF1HdUdouhJVbHUGJ+G9nhz9xTAME07Y/KykPRE1p0pUAzZgr5bJogrjg0UVphoKokpDQ3g34YED7FSJI+hUoYJ/Iy4zhHbVKuCJJ4QY8Ed/ZPro9NPRIRYnFJwqL30p8KpXia58zwGkCg+pz0ZDQ3REFpVCRFqWL8+fqLJ9O7B5s77Hs4lM/BcAvOY1QpTmIfVMkLe+Fbj+erFWSFt8XbjQnAOyXLYveIYV+c87z76oouJUcXHv0BVVdfSo+AzlhdNPz4847Sr+i8q9xhNV7r9fNGUxDMMw1bS3V1/r49YTnkM+T04Vm6IKN50y/weLKkw1FEQVQDgsgsUjjv+KR2VDbhNVp8pJJwFvfjPw//4fDaeNKg0N4mZPwakCAK94BfDXfy0KrU8+qf77u3fbLy4ViRNOEBFfYeTdfbdsmRA9TKJbVNm9m0bxJw2yTpV588TPPvBAfv+tjBle9Srgu9/NJlzk0eUVR9i65B3vAC66yO5xZI1fMk1Dgx4xx8U8mCy85jXAi15k7vFLJX0imSunCoUh9UBFVHn4YeCss1wfDcMwDD1UrvWeeEHNqUJBVOGZKowPFlWYag4doiGqbNwo4in8xA3SYoQzgqJTZflyYO9e8eehoeRoj7POEh1mF19s/thMsXAhLUHo9NOBm24C3vc+9d/dsUMIY0w6zjgD2LIl/Ht5nqcC6BtcHIduUWVqita5qYLMTBWPN74R+NSnWFRhqmlqAq68UszbykoeZ/aEFa+jnCqrVtk5Jg/ZxhgXbh6PUin7+563a/AZZwAnnmju8efPF0KTDlyJKhSG1AMVUSWLE49hGKbIBK/1ceuJ7m4Rh8pOlVo4/ovxwaIKU83BgzSifk48EXj6addHkS9aWoRqTo2mJmBmRvxZphhwwQXAv/6r+eMyybJl9Lp5Fy1Kd/PfuZNFlSycfnq8qGKyWGOa5uboaDNdDAwI50Va/Jn1eY9bk43/AkThfGSERRWmlne9C/iDP8j2GIsX648Am5kxP8x1zpzaGWNU4qhki9pZIp6ysmSJaL5i9KErVg1wF/9FyamyZw+nGjAMw0Shcq3v6aHpVImbqXLgAIsqjHVYVGGqmZigcdHcvFkMVPXIezHMBtSK+EHq6T38/OddH0E0qu8DiyrZ2LQJeO658O/l3aliiyzXNr+7Y2AgX7EzQbz4r5mZ5CHVc+YA//IvwNq1do6NyQ9dXULszYKJYfWDg2IDb5KwOVB9fTQGpwdzzqNwGZ+la1g9U0Gn4zOLU6WzM72ocvAgjbkzTU3AfffxPBWGYZgo0sR/UXOqxDl7Dx60I6yzqML4YFGFocnmzdVOlZERUQhg5JAputlk4ULRDVovnHqq6yMIp71dPSKOSsEpr3hOrTAxqwjRag0NFScaRfyiSt4FwuZm0WUvu5B/9atpNEkwxcOEqLJ/v/nC7MqVopPdj8ysNxvIxn+5FIeziir11Fwji06nyrFjIgo4DWmdKrOzYh1AobGrqUk4g1lUYRiGCaejo7LWmJwUTVhRUJ2pEne/6e/PlnAgCw+qZ3wQqroyjI9gVuKBA2znVoHa/Jk1a4Bt28xHezDxpMnuLpVobJbzzLJltd3RMzNiMdvU5OaYdLF4sbk4mOHh7GK6PxYx7yKWdx7yQp5xjQlR5Uc/Ai67TO9jBqEsqsyZU4kqjMNWwSCMrKKKy+gyqixdqk9UmZ1Nv85OK6ps2wasX5/uOXXT1CT+/VQbmxiGYVzjd8Um1Yt6emjOVAGimzTKZfONxQ0N4rWj9powzmBRhalAbbPjH4i5f78oTDJyUHsv16wR3WOmoz2YeFRFlenp/Bf9KRA2rP6hh4BzznFxNHoJi9PRhY4h9UGnSt7jsMplIRLxQp5xiQlR5c47gUsu0fuYQaJEFQozVWTJc/wXO19rWbJEX/xXlgaYtKLKY48Bp52W/nl10tQk1lvNza6PhGEYhib+a/3oaHxkJFWnimvmzBGNf7wXY/4PFlWYCgcP2hnsJMuqVZXNLztV1KAoqjz0UL7nGRQBVVFlzx5gxQpzx1MvnHEG8Oij1V+77TYxTDzvLFsmxA8T6BDTW1uLE//lwU4VxjUrVtSKE1k4dEi4L0wXQ8OO26XzIw0uRZWsAkDeBCwb9PYChw/reaws8WpFEFV6eoBXvcr1UTAMw9AlKKrE1YuozlQBwpsIZmbspKLMmSNeO2qvCeMMFlWYCtREFf9cFXaqqDEyQlNU4Q5Ft6iKKkUpQrvmtNNqRZVf/AI47zw3x6MTk04VHQU4v1Nl7978i4SlEjtVGPe0tIhNti5uuQV4+cv1PV4Uy5fXisBZIpNsMDBQ+3dXIlBDQ7bCPZWoNUp0d4s1u2uam9Od008+CZx8sv7jScOSJcAf/7Hro2AYhqGL/1qfFP/V3S3ivyg6VfyJNh5HjohGBdM0NYn7Nje4Mf8HiypMBWqiyoknVkQVdqrIUy4L9byz0/WRVFi+HHjmGd5Mu2b+/NoCTRwsquihs7O6aDI0JIbJxg0HzAsmnSqDg9kjA/2iShHi7MpldqowxeOHPwRe+lLzz9PcDExNmX8eXRw6VNt579KpAohCRlphhdo+gwINDbWFobRkif9K+7vHj/P9iGEYJi/4r/VJ8V89PXSdKsG9NWBvjcFOFSYAiypMBWqbnc2bRSEeEJ3QLKok43WPUov/amoCFi1iUcU1aZwqeZ9BQQW/3fqOO8wPZLaFSaeKLlHl+PFs3dXUGB/nhTzjnrAuwTSMjorH6erK/lgyZCk822b3buCXv6x2ELgWVRYvFmJPGorgFqSM7fvc8DCtBi6GYRgmGW8dlORU6eoSosrUFL2mNO/Y/NiqZXpOFd6LMf8HiypMhdWrgY0bXR9FhWXLKsU6as4LqrS1iRskNVEFEI4HFlXcwvFf7jj1VODxx8WfizJPBRDndF+fmcfWIap4M1X6+uxYwk3jxX9xZzDjGq+DMSu2r4dz51bca7OztESWoAtkzx5RIHjoocrXXMZ/AdmG1bOoYo7ZWeF6sckTT4i1DcMwDJM/kpwqTU1iTglAa60EiGiy4eHqr9l0qrCowvhgUYWp8PKXA+vWuT6KClkiBuqV9nZgbIyuqMKD6t0yb56aqJK3Ab6UOeMMYMsWcU0rkgMoa8Z+HDrjv4oiEHrxX7yQZ1zT2yvyq7Ny883A1VdnfxxZVqwQxX1AzzVGJ62tQjT12LMHeMMbgHvuqXxtYsJttnkWUYWjfM0xNiYaq2zy2GPA6afbfU6GYRgmG96+LcmpQpnubndOlTlzxFqM8jw+xiosqjC0MdkFXUQ8p8rICD1nz0c/Cpx0kuujqG9URZVymV5nSl7xRJWtW2k5AimjU1TZsaM4QhY7VRgK6BBVpqbEYyxbpueYZFi5UogVAL3B6f6YSEAc52/9logAo0IWUWVmhosQYTQ1ZZ/1c+xYfMexDKoNEo8+Cpx2WrbnZBiGYdyQ5FShjEtRRcc9mykULKowtNm8GXjgAXoCAVUoO1U2bizGYO48483ckWFykt8vnaxYIQpkt90GXHGF66PRize3RDc6FvvesRXFqdLQIP497FRhXLNwYXZR5e67gYsu0nM8sgRFlYUL7T5/HO3tojjusWcPsGmTEIZ1DTPPSpio8uCDIpaMSUdPj2giyIKOdb/qnKTdu8X5xDAMw+SLcpmdKmmZM4dFFaYKFlUY2mzeLIY6c1yAHJRnqjD5Yu9e3izrpFQSBfGf/AS49FLXR6MXk8Pqszql/E6VIogqzc2i+MZOFcY1vb3ZncQ33wy84hV6jkcWT+AGxPFTdqqMjQmh5eSTgSefdHdcfpYurb3ef+ADwOc/H/97LAZHM29edlFFh1PFa8ySwZtHxG5mhmGYfNHWJu7Jss1rFOP4u7pqZ6p4aybT+GfNMAxYVGGos3mzKELajIbIM5SdKky+KNLcDyps2iSi+Yp2bi5bBuzb5/oowvEG1e/fX4z7SEuLKL5xcZJxjY74r+3bxTrPJpTjv4JOFY8XvUi4eig4SINztJ59VryHd90V74Tdt4+H1Eehw6miQ1Tp6BBrFBl27SpGowLDMEy90dEh34Tr/Sw1liwRbkkXzJnjdrYdQw4WVRjarFsHPPUUO1Vk8TbkRSzcMnYpSlwSJc4+u3jRX4BZp0pWPKfK7Gwxsvw9UYWdKoxrdA2qt93pvnSpGJgO0BNV/E6V6enKNevXfg34+c9FxNa8ee6Oz6NUqggr//EfwFveAlxzDXDTTdG/s3cviypR9PRkj0/T0Uy1cqV8kYqH1DMMw+QTb60hE//V3S3vYLTJWWeJeXP+yEpbjpo5c3gfxlTBogpDm6YmYP36YnQY26CtjZ0qTDzNzaLbNQkWVfTz+tcDf/iHro9CP5SdKt5MlaJElMydy04VhgYLF2aP/3JBU1NlE05xpoonqhw4UGkoWrAA6O8X/82f7+74PBYvBg4dEvEX998PvPCFwJveBHz969FFDRZVoqES/7VxI7B1q9zP8pB6hmGYfOKJKjLxX93donZAjYaGiosXEPUvW0JHUxPvw5gqWFRh6LN5MztVZPGcKlNTNG+AjHvmz5friGRRRT8NDcVwSwQx4VSZmNBzDWtpEZ/lxYuzPxYF2KnCUCFrZNHYmGgEcQnFmSpezMaePdVzzdasAR55hIaosmaNiH+64w7gssuEaN3aCrzgBcDPfhb+O3v2sKgSha5B9VlFlQ0b5EWVJ54Qs34YhmGYfOE1cBw/nryf6OmhKyD89m8LtywgGj1sDKkHhFOF6mvCOIFFFYY+n/2s6J5ikvGcKgwTxfz5ots1if5+sZBimCRMOFWGhvR8/lpagGeeKY5AyDNVGCpkdX8dOODOhdzWJsQLavFffqdKUFR50YuA732PRvzXmjVCrP7P/wTe/ObK19/9buALXwj/HXaqRBMV/7VrF/CZz8g9hkyMSxIbNgDPP2/v+RiGYRj7eE6Vclk0/MXR3U13fsjataI5ZnQUOHjQnqjCThUmAIsqDH1WrEi+4DOCqCGnDOMhK6oAxYlMYsxiYojh4KAeUaW1VQxSXrs2+2NRYO5cUXxjpwpDgSz51fv3u3Mhe8PqJydpFQvinCoXXgj88Id0nCqPPgoMD1cLJUuXCsFq27ba39m/n6N8o4iK/9q2DbjlFrnH0BH/JRtDduyYe5cZwzAMkw7//LYkurtpCwiveQ3wrW/ZFVV4pgoTgCvVDFMk2KnCJDFvXrKoMjnJ8XGMW3SJKt6genaqMAwtXBbZPVGFGkGnil+wWLVKXBOpiCr/8i+imBHkve8FPv/52q9PT4tCBFNLlFPl0CHg4YflxEubsxSffBI45RQ7z8UwDMPoxe9USaKnh1bzSZBXvxr4znfsiyq8D2N8sKjCMEWCnSpMEjJOlT17RAGHYVTI0rUeRJeo4m0EiuRU4ZkqDBWyNHK4FFVWrKApqvidKvv21cZlXXghjfivpUvFfLBXvar2e2edJdyBjDxRDpHDh8VrvX178mPocKp4x5K0Rty6lWOZGYZh8opKwgB1p0p7uxBTfv5zjv9inMGiCsMUCXaqMEnIiCo8pJ5RZf788E7btOgSVRobgc7OYg2qHx3ljm+GBr29wJEj6X6XnSq1+J0qExO1m/bPfhY480zrh1VDQwNw333REVCLFgmXhcfEBLtf42htDV+7HzoEvPSlwq2ShC5RZcOG8Pg2P7t3c+MNwzBMXvGcKjIx35Rnqni85S3AjTeyU4VxBosqDFMk2ttFxjXPoGGiYFGFMYHuYfW6RBUAOPHE4lwTW1rE5obnHTEU6O0VQ0LT4FpUeeYZPUVonSR1jy5cSOdatmFD9Pde9CLgnnsqf9+/H1i+3Pwx5ZWo6/nhw/Kiiq74r40bk4fV79oFrF6d/bkYhmEY+/gbOJLYsAF405vMHk9WLrhA7PV6e+08X1MTJwYwVRBZmTMMo4XWVtE1aitXmckfsqIKb5gZFZYvF4UzXegUVf7pn/Q8DgXmzuWFPEOHhQvTO1WGh4WLzAWLFwNPPSWOnxJeoWNyMt9utKCoEpwPw8jR1yci3556KvlndTpVtm6N/xmXgijDMAyTjY4OkS7Q1JT8s62twPnnmz+mLJRKwIMP2ls3sVOFCcCiCsMUicZGt4UKhj4yMU2HDtmz0DLFYPlyuk6V887T8zgUaGnhhTxDhyzxX4A7x1VDgyhCL1jg5vmjaG4Wgsq+ffl2dpxwQvVclb17WVRJw8yMuN5PTSXPLBsf1xPRIuNUmZ0V+w2GYRgmf3R0iL0+NbduFmzujVhUYQKwqMIwRWNkhJ0qTDQylt+jR+kVmxjaLFtG16lSJFpa2KnC0CGrqOKSZcvo3ec8kWnPHhFRlldKpeq1BosqcgSFE+/zsGqV3AwgHSLlvHnxjTdJ4g7DMAxDG09U4XpROnhQPROARRWGKRrj43yTZKKR2XRPTfFQWUYNyk6VIjF3Li/kGTqknakyMuLeUbtyJT1RxSPvogoAvPCFwC9+If7MokoybW3A8eOVv5fLFQHj7LOT56rodH3FPdbAgBBeGIZhmHzS3l48p4pN1q4V92WG+T9YVGGYotHayqIKwzB2WbwYOHhQ3+ONjvJiPwx2qjCUiJqpkjQD4sAB9zMZVq6kN1PFowiiyoUXVuaq5D3OzAY9PdUOkdHRivB41lnJoopOB0l3t2hsCGP3bp65xzAMk2e8uHiuF6Vj40bgxS92fRQMIVhUYZii0d7ON0kmGxzvwKgyZ44oxHzlK/oe09W8BcrwTBWGEgsWiLhIP7OzwMtfHv97FAZdv/OdwAUXuD2GKIogqpx5ZkUImJzUM++jyPT0VAsZhw6JZgUAOOUU4PHH7R3Lxo3Rw+p37RJxZAzDMEx+mZriehHDaIJFFYYpGu3t7mM1GNqUSmIAahgsqDBp+cpXxHDij35UFFYZ/cydy04Vhg5z5gDT09Vf270b2L5ddEFGQUFUWbeObkGBwuuTlTlzxP+nptweR16YN69aVDl8GFi0SPx57lwhTEXhjwrTwYYN0cPqd+1ipwrDMEzeaWvjRACG0QSLKgxTNNra6BYKGBr09ABDQ+HfGxvjRRaTjoYG4G//FlizBrj2Wi6mmYCdKgx1nnlGzFp59tnon9m/H1i61N4x5Y2pqYookWfOOAN44AEx1JWJJxj/5XeqAOJ8OXAg/He/8Q3goov0HcuGDdFOFY7/YhiGyT8dHVwvYhhNsKjCMEWD47+YJObPB/r7w7939Cjd4b1MPnjnO4WwkpQBz6jDM1UY6jzzjIj/ShJV8u7EMEVLCzAx4foo9HDhhcA3v8nvtQxxThUgeq7KwADw1a8C73+/vmOJi//avZvjvxiGYfJORwc3UTKMJlhUYZiiwU4VJgkWVRjTbNwo5gKkYWICaG7WezxFoatLdH8zDBWamqpdac88A7ziFexUSUt7u3AtFIEXvhD49reBFStcHwl94maqANGiyp/8CfBnf6bX2RS3Rjx+nIV9hmGYvMNOFYbRBosqDFM0eKYKk8T8+dUxE36OHhXfZ5gsrFolOlrTMDRUnKKibtrbgY98xPVRMEyFBQuqC7B79wKXXhovqhw7xpv5KDo68j+k3qOzU7gtWFRJJhj/dfhwtahy2mnAo49W/87994smhAsvtHKIDMMwTEFgpwrDaINFFYYpGq95TXE25IwZ4roQ+/vZqcJkZ9Wq9E6VwUEWVRgmLyxcCBw5Uv21zk4xn4tRp729WGu4iy7iuCgZgvFfhw5Vx3+1tQmXiMf0NPCxjwGf/KSZ4+nqqp29d/w4z/RiGIYpAuxUYRht8ORAhikal13m+ggY6syfH52XffQoDyFlsrNiBYsqDFMP9PZWRJXBQaC7W/y5VAJmZ4GGQP9WuWz18HJHkZwqAPA3f8OFeBmCTpXx8drX7bLLxLwiQAgeb3ubEDVN4M1VOfvsytf27CnWZ5NhGKZeYVGFYbTBogrDMEy9kTRT5ayz7B4PUzzmzk0/bJlFFYbJD35R5dlngRNPFH/2IgDXrKn+eY73i+fyy4Hly10fhT54/oYcwZkqYXzgA+I/QIiTpZK549mwoVZU2bWLm24YhmGKwJ/9WXXEJMMwqeH4L4ZhmHqDB9UzlAh2rrOowjD5obcX6OsTf37mmYqocsIJ4XNV9u8Hli2zd3x547zzeAZJPTJnjoj0ksWkoAIAmzYBTz9d/bXdu1lUYRiGKQJLlpi/jzBMncCiCsMwTL0xb168qMKD6hkdyLhVfvQj4O1vr/4aiyoMkx/8M1VkRJUDB1hUYZgwvAaDyUmgudntsZx1FvCLX1R/bdcuno/DMAzDMAzjg0UVhmGYeqO7OzpmgqNZGF2sXAns3Rv9/ePHgb/7u9qfYVGFYfKDP/7ruefELAZAiCvsVGEYdQ4frh5S74KmJiGgbN9e+RrHfzEMwzAMw1TBogrDMEy90dgoBgiHUS7XDhZmmDSsXCniQqL45CeBP/gDMShxfLzydRZVGCY/+OO/JiYqw7Wjzn8WVRgmHC+K5fBhGln3r3kN8K1vVf7e389OZoZhGIZhGB9cOWMYhmEYRj/eoOownnsOeOop4OqrgVNOAZ58svI9FlUYJj+0tQFjY8DUlOhu92hoqJ2XBAhRZelSe8fHMHmhVBINL4cOuXeqAMCllwI//Wn11ziDn2EYhmEY5lewqMIwDMNUCCuCMUwaVq4E9uyp/Xq5DHzoQ8Df/70o0Jx+OrBlS+X7LKowTP7Yvh1Yv776a+3twOho9ddYVGGYcLq7geFhIapQcKp4EWDbtgEzM+xiZhiGYRiGCcCrI4ZhmHqkVKoVUGZnedPM6CPKqfLNbwLnnQesWSP+fvrpwKOPVr4/OiqKsQzD5Af/kHqPTZuEK83P+DjQ2mrvuBgmL/T0AAMDNGaqeLz2tSIC7OBBFkMZhmEYhmECcPWMYRimHunoAI4dq/4aOwQYnSxeLAoxQW68EXj/+yt/X7cO2LGj+mc4YoRh8sXTT9eKKiecUD2svr+fhXuGiaKnR6zDqDhVAOCSS4A77+Qh9QzDMAzDMCHwzoZhGKYemT9fFLj8HD0KLFjg5niY4hE1U2FysjLM2v9zHD3HMPmkuxv45S9rRZUTT6wWVf72b6sFVYZhKsybJ0QVSk6VpiYhptxxh3CfMgzDMAzDML+CRRWGYZh6hEUVxhZ+seTAgfAIkbih9gzD0Ka3VzhVFi6s/vqmTRVRZf9+EQV2ySXWD49hcoEX/zU0JIRKKrz2tcA//RM7VRiGYRiGYQKwqMIwDFOPsKjC2KCnRxSIPB5+GDjrrNqfCw6rZxgmP/T2intHMLavqwsYGRF//qu/Av7kT+wfG8PkBS/+C6AVgXnxxWLmHosqDMMwDMMwVbCowjAMU4/Mnw/09VV/rb9ffJ1hdBF0oESJKmecIYbVT0wAzc3WDo9hGA309or5KVFs3SqKxWefbe2QGCZ3ePFf1GhqAr7yFWDZMtdHwjAMwzAMQwoWVRiGYeqRtWtrh4OzU4XRzcqV1aLKo48Cp51W+3OnnAI88YRwtfT0WDs8hmE0sGQJcPLJ4d9bsQJ417uAP/1Tu8fEMHmjp0c0t1ByqXi8/OVAY6Pro2AYhmEYhiFFk+sDYBiGYRywYQPwta9Vf41FFUY3q1YBu3ZV/n78ONDWVvtz7e3A2Jjo0mVRhWHyxcteBlx5Zfj3TjgBmJwENm+2e0wMkzd6eoBt29gxzDAMwzAMkxNYVGEYhqlHliwBDh6s/hqLKoxuVq0C7r5b/LmvL/7z1dEB7NnDogrD5I2GBvFfGG95S/T3GIapMG8e8OyzwBVXuD4ShmEYhmEYRgLe5TAMw9QjpRJQLld/7ehR7pBk9LJypRBKAOCRR8LnqXicdhrws5+xqMIwRWLePKC72/VRMAx9Ojv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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -1182,17 +631,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-01T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# The 2023-11-01 observation is the 3rd item in the list\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -1202,33 +643,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=lpjeosim-wetlandch4-monthgrid-v2&item=lpjeosim-wetlandch4-monthgrid-v2-202311&assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# A GET request is made for the 3rd item in the collection\n", "observed_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}&assets={asset_name}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?&assets={asset_name}\"\n", "\n", " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", @@ -1245,97 +668,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the CH4 variability for the Texas region for the time in previous cell.\n", "aoi_map_bbox = Map(\n", @@ -1400,7 +735,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb index 46251617..f9a19152 100644 --- a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fmicasa-carbonflux-daygrid-v1_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fmicasa-carbonflux-daygrid-v1_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "1. Identify available dates and temporal frequency of observations for a given collection using the GHGC API `/stac` endpoint. The collection processed in this notebook is the Land-Atmosphere Carbon Flux data product\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint\n", "3. Using `folium.plugins.DualMap`, visualize two tiles (side-by-side), allowing time point comparison\n", "4. After the visualization, perform zonal statistics for a given polygon" ] @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -195,17 +195,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 800 items\n" - ] - } - ], + "outputs": [], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -275,27 +267,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20230101&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.32319876551628113%2C5.9415082931518555'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", - " 'center': [-2.842170943040401e-14, 0.0, 0]}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -308,7 +282,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date1]['collection']}&item={items[date1]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[date1]['collection']}/items/{items[date1]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -328,27 +302,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20230131&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.32319876551628113%2C5.9415082931518555'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", - " 'center': [-2.842170943040401e-14, 0.0, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Make a GET request to retrieve information for the date mentioned below\n", "date2 = '2023-01-31'\n", @@ -356,7 +312,7 @@ "\n", " # Pass the collection name, collection date, and its ID\n", " # To change the year, month and date of the observed parameter, you can modify the date mentioned above.\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[date2]['collection']}&item={items[date2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[date2]['collection']}/items/{items[date2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -384,255 +340,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For this study we are going to compare the Rh level for date1 and date2 over the State of Texas \n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -731,110 +441,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -867,17 +476,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 800 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -944,17 +545,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-12-31T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -989,47 +582,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.11864250898361206,\n", - " 'max': 1.3311004638671875,\n", - " 'mean': 0.7455709838867187,\n", - " 'count': 150.0,\n", - " 'sum': 111.83564758300781,\n", - " 'std': 0.2550486573615515,\n", - " 'median': 0.7395486831665039,\n", - " 'majority': 0.11864250898361206,\n", - " 'minority': 0.11864250898361206,\n", - " 'unique': 150.0,\n", - " 'histogram': [[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 18.0, 7.0, 6.0],\n", - " [0.11864250898361206,\n", - " 0.23988831043243408,\n", - " 0.3611341118812561,\n", - " 0.48237988352775574,\n", - " 0.6036257147789001,\n", - " 0.7248715162277222,\n", - " 0.8461172580718994,\n", - " 0.9673630595207214,\n", - " 1.0886088609695435,\n", - " 1.2098547220230103,\n", - " 1.3311004638671875]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 150.0,\n", - " 'percentile_2': 0.24085583359003068,\n", - " 'percentile_98': 1.2310137295722965}},\n", - " 'datetime': '2023-12-31'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -1044,195 +599,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02023-12-310.1186431.3311000.745571150.0111.8356480.2550490.7395490.1186430.118643150.0[[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 18.0...100.00.0150.00.2408561.2310142023-12-31
12023-12-300.1185601.3297130.744604150.0111.6906360.2548050.7385410.1185600.118560150.0[[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 18.0...100.00.0150.00.2406621.2292372023-12-30
22023-12-290.1184701.3282490.743593150.0111.5389790.2545470.7374900.1184700.118470150.0[[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 19.0...100.00.0150.00.2404561.2273792023-12-29
32023-12-280.1183731.3267060.742537150.0111.3805390.2542770.7366330.1183730.118373150.0[[3.0, 4.0, 17.0, 23.0, 23.0, 29.0, 20.0, 19.0...100.00.0150.00.2402381.2254392023-12-28
42023-12-270.1182681.3250840.741434150.0111.2151260.2539920.7357550.1182680.118268150.0[[3.0, 4.0, 17.0, 23.0, 23.0, 29.0, 20.0, 19.0...100.00.0150.00.2400071.2234152023-12-27
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum std \\\n", - "0 2023-12-31 0.118643 1.331100 0.745571 150.0 111.835648 0.255049 \n", - "1 2023-12-30 0.118560 1.329713 0.744604 150.0 111.690636 0.254805 \n", - "2 2023-12-29 0.118470 1.328249 0.743593 150.0 111.538979 0.254547 \n", - "3 2023-12-28 0.118373 1.326706 0.742537 150.0 111.380539 0.254277 \n", - "4 2023-12-27 0.118268 1.325084 0.741434 150.0 111.215126 0.253992 \n", - "\n", - " median majority minority unique \\\n", - "0 0.739549 0.118643 0.118643 150.0 \n", - "1 0.738541 0.118560 0.118560 150.0 \n", - "2 0.737490 0.118470 0.118470 150.0 \n", - "3 0.736633 0.118373 0.118373 150.0 \n", - "4 0.735755 0.118268 0.118268 150.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 18.0... 100.0 \n", - "1 [[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 18.0... 100.0 \n", - "2 [[3.0, 4.0, 17.0, 22.0, 24.0, 29.0, 20.0, 19.0... 100.0 \n", - "3 [[3.0, 4.0, 17.0, 23.0, 23.0, 29.0, 20.0, 19.0... 100.0 \n", - "4 [[3.0, 4.0, 17.0, 23.0, 23.0, 29.0, 20.0, 19.0... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 0.0 150.0 0.240856 1.231014 2023-12-31 \n", - "1 0.0 150.0 0.240662 1.229237 2023-12-30 \n", - "2 0.0 150.0 0.240456 1.227379 2023-12-29 \n", - "3 0.0 150.0 0.240238 1.225439 2023-12-28 \n", - "4 0.0 150.0 0.240007 1.223415 2023-12-27 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -1268,30 +637,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)')" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -1329,17 +677,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-12-29T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# Fetch the third item in the list as the observation item.\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -1349,33 +689,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=micasa-carbonflux-daygrid-v1&item=micasa-carbonflux-daygrid-v1-20231229&assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.32319876551628113%2C5.9415082931518555'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", - " 'center': [-2.842170943040401e-14, 0.0, 0]}" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# A GET request is made for the observed tile\n", "observed_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -1395,177 +717,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the Rh level for the Dallas, Texas area for the observed tile timeframe.\n", "aoi_map_bbox = Map(\n", @@ -1651,7 +805,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb index 4b78de5c..031424d9 100644 --- a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb +++ b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fnoaa-insitu_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fnoaa-insitu_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,32 +176,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_70808/2606016741.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "site_to_filter = 'ABP'\n", "filtered_df = combined_df_co2[combined_df_co2['site_code'] == site_to_filter]\n", @@ -224,32 +201,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/7b/5rrvrjx51l54jchgs0tqps0c0000gn/T/ipykernel_70808/1635934907.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "site_to_filter = 'ABP'\n", "filtered_df = combined_df_ch4[combined_df_ch4['site_code'] == site_to_filter]\n", @@ -304,7 +258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/oco2-mip-National-co2budget.ipynb b/user_data_notebooks/oco2-mip-National-co2budget.ipynb index f37f71d5..b25a4de8 100644 --- a/user_data_notebooks/oco2-mip-National-co2budget.ipynb +++ b/user_data_notebooks/oco2-mip-National-co2budget.ipynb @@ -15,7 +15,7 @@ "---" ] }, - { + { "attachments": {}, "cell_type": "markdown", "metadata": {}, @@ -24,7 +24,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2-mip-National-co2budget.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2-mip-National-co2budget.ipynb&branch=main)\n", " " ] }, @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "8ff420ae-1455-4a3b-8dfe-1da8abf16049", "metadata": { "tags": [] @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 2, "id": "10194dc3-274b-419c-96ab-b2bc51e6d19d", "metadata": { "tags": [] @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 3, "id": "7f380763-4b59-4f53-9993-803c37995a3b", "metadata": { "tags": [] @@ -303,7 +303,7 @@ "4 0.49 0.19 " ] }, - "execution_count": 132, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 4, "id": "dc8b03d3-2ff7-43db-bf7d-0054c3a7f2a8", "metadata": { "tags": [] @@ -520,7 +520,7 @@ "1236 -0.38 0.91 " ] }, - "execution_count": 133, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 5, "id": "f7be864d-4d01-442f-b3cb-a1aefff616ae", "metadata": { "tags": [] @@ -560,13 +560,13 @@ "(-0.30000000000000004, 6.3)" ] }, - "execution_count": 134, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 6, "id": "3f956003-05f7-4394-9764-56545ecc9ec9", "metadata": { "tags": [] @@ -624,13 +624,13 @@ "Text(0, 0.5, 'CO$_2$ Flux (TgCO$_2$ year$^{-1}$)')" ] }, - "execution_count": 135, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -679,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 7, "id": "d6cb1421-a44c-4e54-9bb1-fda73bc3ff27", "metadata": {}, "outputs": [ @@ -689,13 +689,13 @@ "Text(0.5, 0, 'dC_loss (TgCO$_2$ year$^{-1}$)')" ] }, - "execution_count": 136, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -775,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 8, "id": "65fb149a-a252-4018-876c-3245f1197a03", "metadata": { "tags": [] @@ -787,13 +787,13 @@ "Text(-0.18000000000000005, 2.6, 'Fractional error reduction: 0.91')" ] }, - "execution_count": 137, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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q164NoPDj9Pz827dvIycnB23atNHMMzQ0RPPmzXH16tUi13uRZ5eLj49HdHQ0Ro8ejTFjxmjmK5VKTaPYq1evolGjRvludWvVqlWxXutZYWFh+V6jNK5evYqxY8fmm9emTRusWbMm37xn/8ZmZmaoVq1avr/xn3/++dLXKqztjhCiyDY91atXx+7duzFhwgR888030NPTw+DBg9G0aVPNe49ICkIIZObopgdOE0N9ydq1jRs3DuvXr8eRI0fQrVu3Eq8vhMCIESNQu3ZtBAYG5rtjo0mTJhg6dChEEQOUHz9+HI6Ojli2bJlmXs2aNdGlS5eS70gJMVgUwczMDF5eXkU+96y7d++iW7duGD9+PBYtWgRra2ucOXMGo0eP1jQeNDExKXNNhX3oF/amMjQ0zPdYJpNpGtf9/fffGDRoEBYsWIDOnTtDoVBgx44dWLFiRbHrUKvV6NmzJ7766qsCzzk6Omr+//njVNj8vPoL26/n5xW1vRdtP2+/N2zYgBYtWuRbLu/LsKh/mM/S09MrsNzzDUOl+BsDxTsWL/obF4eDgwPOnz+fb96jR4+Qk5MDe3v7Itfr1KkTbt++jYSEBBgYGMDS0hIODg6FNmwmKq3MHBXqzT2mk9e+srAzTI2k+Wp0d3fH+PHjMWvWLHTp0qXEt3KGhYXh6tWr2L59e5HrFhWCHBwcEBsbi9OnT6Nt27Ylrr0s2MZCAsHBwVAqlVixYgVatmyJ2rVr4/79+/mWadiw4Qt/RRoZGb20j/x69eoVuJZ99uxZ1K5du9i/GAMDA+Hm5obZs2ejWbNmqFWrFu7evVusdfM0bdoUly9fhru7O7y8vPJNxf3yz+Pl5QUjI6N8+5WTk4Pg4GDUrVu3RNsqjL29PWrUqIGIiIgCteZ9GdarVw///PMPMjMzNes9f120evXqSE1NRXp6umbe831cSPE3rlu3bqF/YymOxbNatWqF8PDwfG09fv/9d8jlcvj6+r50fVtbW1haWuLEiROIi4vD22+/LWl9RFXFnDlzEBkZiW3btpV43bxb0b29vTXz4uLiYG5urpnWrVtX6Lr9+/fH4MGD4efnB0dHR/Tp0wdr165FSkpK6XakBHjGQgI1a9aEUqnEt99+i549eyIwMBDfffddvmVmzZqFBg0a4IMPPsD48eNhZGSEgIAA9O/fH7a2tnB3d8f58+dx584dmJubw9rausDrTJ8+Ha+99hoWLVqEgQMH4ty5c1i7dm2Rb6zCeHl5ISoqCjt27MBrr72Gw4cPY//+/SXa34kTJ2LDhg0YPHgwZs6cCVtbW9y6dQs7duzAhg0bSnRa3MzMDBMmTMDMmTNhbW0NV1dXLFu2DBkZGRg9enSJ6irK/PnzMXnyZFhYWKBr167IyspCcHAwHj16hGnTpmHIkCGYPXs2Ro8ejTlz5uDOnTv4+uuv822jRYsWMDU1xWeffYYPP/wQQUFBBRrNzps3Dx06dEDNmjUxaNAgKJVK/Pbbb5pGvO7u7jh9+jQGDRoEuVwOW1vbArXOnDkTAwYMQNOmTdGhQwf88ssv2LdvH/74448S7XOHDh3Qp08fTJo0qdDnO3XqhHr16mHYsGFYvnw5kpKSMGPGDIwZM0ZzWSwmJgYdOnTA1q1b0bx5cwDApk2bULduXVSvXh3nzp3DlClTMHXq1HwffFFRUUhKSkJUVBRUKpUmgHl5eRV6+YzoeSaG+riysLPOXltK1atXx4wZMzB37lwMHDiwVNt49qyEjY2N5t9Uu3btiuzyQF9fH5s2bcIXX3yBEydO4O+//8bixYvx1VdfISgoKN/ZZcm9sAVGFSPV7abPN7IUQoiVK1cKR0dHYWJiIjp37iy2bt1a4FbRkydPitatWwu5XC4sLS1F586dNc9fv35dtGzZUpiYmBTrdlNDQ0Ph6uoqli9fnq+OwhoINmrUSMybN0/zeObMmcLGxkaYm5uLgQMHilWrVuVrZFec201v3Lgh+vTpIywtLYWJiYmoU6eO+Oijj4RarS7yOBW2P0LkNhb68MMPha2t7QtvN31+veflNd7Ma3D5rG3btonGjRsLIyMjYWVlJdq2bSv27dunef7cuXOiUaNGwsjISDRu3Fjs3bu3wLb2798vvLy8hLGxsejRo4f4/vvvC9xuunfvXs3r2Nrair59++Z7jYYNGwq5XF7m203zGmjlUSgUYtOmTZrHbm5u+f7mhbl7967o3r27MDExEdbW1mLSpEniyZMnmufzjmdAQIBm3ieffCLs7e2FoaGhqFWrllixYoXmb56nsNunn98OUZ6q3Hgz77M4NTVV2Nvbi1WrVpWo8WZISIgAILZv3/7S1xCi8M+GZyUlJQlbW1sxd+7cIpeRovEmgwUREenMqxAshBBi7dq1wtbWVowcObLYwUKtVos6deoIX19foVKpXvoaLwsWQgjRoEEDMX369CKf510hREREOpScnFygvVVhl7LHjh2LVatWYfv27QUakmdmZhbYhrm5Oby8vLBp0yZ07NgRbdq0waxZs1C3bl3k5OTg9OnTiI+PL/LSs7+/P8LCwtCnTx/UrFkTT548wdatW3H58mV8++23Zdrnl2GwICIiKqWTJ0+iSZMm+eYNHz68wHKGhoZYtGgRhgwZUuC5GzduFNiGn58fTp48iZYtWyIkJARffvklJk6ciAcPHsDMzAyNGjXCqlWrMGrUqELrat68Oc6cOYPx48fj/v37MDc3R/369XHgwAH4+fmVYY9fTvb09MkrISUlBQqFAsnJySXqt4GIiLTjyZMniIyMhIeHR6mH6SbpvOjvUdzvUN5uSkRERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkREpHOv0H0EFZoUfwcGCyIi0pm8AfUyMjJ0XAkB//0dnh/osCTYjwUREemMvr4+LC0tERcXBwAwNTWVbNhyKj4hBDIyMhAXFwdLS8sSjfn0PAYLIiLSKQcHBwDQhAvSHUtLS83fo7QYLIiISKdkMhkcHR1hZ2eHnJwcXZfzyjI0NCzTmYo8DBZERFQh6OvrS/LFRrrFxptEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJFNpgsWSJUvw2muvoVq1arCzs0Pv3r1x/fp1XZdFREREz6g0weLUqVOYOHEi/v77bxw/fhxKpRKdOnVCenq6rksjIiKip2RCCKHrIkojPj4ednZ2OHXqFNq2bVusdVJSUqBQKJCcnAwLCwstV0hERFR1FPc71KAca5JUcnIyAMDa2rrIZbKyspCVlaV5nJKSovW6iIiIXmWV5lLIs4QQmDZtGl5//XX4+PgUudySJUugUCg0k4uLSzlWSURE9OqplJdCJk6ciMOHD+PMmTNwdnYucrnCzli4uLjwUggREVEJVdlLIR9++CEOHTqE06dPvzBUAIBcLodcLi8wf+DAgTA0NMS2bdvw2Wef4e7du/Dx8cGkSZMwfvx4AMCYMWOQk5ODzZs3AwB++OEHfPXVV7h+/Tq8vLwwZ84cjBgxAgAwbNgwmJqawt/fHwCwbt06+Pv7459//oGLiwuWL1+OQYMGAQAGDBgAR0dHrFmzBgCwatUqbN++HUFBQbCzs4O/vz/69OkDAOjVqxe8vb2xbNkyAMDSpUtx+PBh/PXXX7CwsMBPP/2Evn37QqlUonPnzmjevDkWLVoEAJg/fz4CAwNx/PhxyOVy7N69G0OGDEFaWhr8/PzQqVMnzJ49GwDw6aefIjw8HL/++isA4NChQxg5ciQSExPRqlUr9OvXD9OnTwcATJs2DVFRUdizZw8AYNeuXZg6dSpiYmLQpEkTjB49GpMmTQIAfPDBB0hOTsa2bdsAAD/++CPmzZuHiIgI1K1bF9OmTcOYMWMAAKNGjdIcZwDYsGEDVq5ciatXr8LT0xMLFizAsGHDAABDhw6FQqHAunXrAABr167Fxo0bERoaiho1amDVqlUYMGAAAKBfv35wdXXFypUrAQArVqzAnj17cO7cOdjY2GDTpk14++23AQA9evSAj48Pli5dCgBYvHgxfv/9d5w6dQrm5ub4+eef0b9/f2RlZaFjx45o06YN5s+fDwD4/PPPERQUhGPHjsHAwAD79u3Du+++i5SUFLzxxhvo3r07Pv30UwDAxx9/jOvXr+PgwYMAgP3792PcuHGIi4tD8+bNMXjwYEydOhUAMGXKFMTGxmLXrl0AgB07dmDmzJmIjo5Go0aNMG7cOHzwwQcAgHHjxiEjIwM//vgjAGDz5s344osvcOvWLXh7e+OTTz7RHOcRI0bA0NAQGzZsAAB89913WLt2LcLDw+Hm5oYvv/wSQ4cOBQAMHjwYNjY2WLt2LQDgm2++wdatWxEcHAxHR0esXbsW77zzDgCgT58+qFmzJr7++msAwLJly3Dw4EEEBgbCysoKW7ZsQe/evaFWq9GtWzc0adIEixcvBgAsXLgQJ0+exIkTJ2BiYoKdO3di4MCByMzMRPv27dGuXTvMnTsXADB79myEhobiyJEj0NPTw4EDBzB8+HA8evQIbdq0Qa9evfDxxx8DAGbMmIHbt29j//79AIC9e/di0qRJiI2NRbNmzfDee+9h8uTJAIBJkyYhMTER27dvBwB+RvAzgp8Rz3xGrFixAsVRac5YCCHw4YcfYv/+/Th58iRq1apV4m2w8SYREVHpVLkzFhMnTsTPP/+MgwcPolq1anjw4AEAQKFQwMTERMfVEREREVCJzljIZLJC52/atElzuvFleMaCiIiodKrcGYtKkn+IiIheaZXydlMiIiKqmBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkKlWwOH36NHr27AknJyfIZDIcOHBA1yURERHRMypVsEhPT0ejRo2wdu1aXZdCREREhTDQdQEl0bVrV3Tt2rXYy2dlZSErK0vzOCUlRRtlERER0VOV6oxFSS1ZsgQKhUIzubi46LokIiKiKq1KB4tZs2YhOTlZM0VHR+u6JCIioiqtUl0KKSm5XA65XK7rMoiIiF4ZVfqMBREREZUvBgsiIiKSTKW6FJKWloZbt25pHkdGRiIsLAzW1tZwdXXVYWVEREQEVLJgERwcjDfffFPzeNq0aQCA4cOHY/PmzTqqioiIiPJUqmDRrl07CCF0XQYREREVgW0siIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBFRpZSWpcSdhHRdl0HPqVQdZBFVdqFRj/Dz+Sh42ZljZBsPGBkw2xOVRsjdR5i47SIepDxBfScL9Pd1Rq/GNWBlZqTr0l55MvEKdWWZkpIChUKB5ORkWFhY6LocekWo1QInb8Thu1MRCIpM0sz3tq+GJe80QFNXKx1WR1S5CCGw+ewdLD58FUp1/q8vI309vFXPDv19XfBGLVsY6DO4S6m436EMFkRakq1U42BYDL4/HYGbcWkAAEN9GTrVd8C524lISs+GTAYMa+mGmZ29Uc3YUMcVE1Vs6VlKfLL3X/z6bywAoHtDR3zapQ7+vPoQu0Pu4fL9FM2ydtXk6NvUGf2bOaNmdXNdlVylMFgUgsGCykPKkxxsPx+FHwIj8TAlCwBgLjfA0BauGNnGAw4KYySlZ2Px4avYe/EeAMDBwhgLe9VHp/oOuiydqMK6FZeK8T9dxK24NBjoyfBZt7oY2cYdMplMs8yV+ynYHRKNA6ExeJSRo5nf1NUS/Zu5oEdDRwb4MmCwKASDBWnTg+Qn2BQYiW3no5CWpQQA2FvIMaqNBwa3cIVFIR9oZ24m4LP9lxCVlAEA6FLfAQt61Ye9hXG51k5Ukf3yz318svdfZGSrYG8hx7qhTeHrZl3k8tlKNU5ce4jdwfdw8kY8VE8vmRgb6qGrjyP6+zqjpacN9PRkRW6DCtJqsOjXrx+aNWuGTz/9NN/85cuXIygoCLt37y55xeWAwYK04cbDVHx/OgIHw2KQo8r951TLzhxj23qiV+MaL22gmZmtwjcnbuL70xFQqQWqyQ3wSdc6GNLclR989ErLVqrx5ZGr2Hz2DgCgdU0bfDO4CWzN5cXeRlzKE+wPjcHukHu49fSSJAA4W5ngnabO6OfrDBdrU6lLr5K0GiyqV6+OEydOoEGDBvnmX7p0CW+99RYePnxY8orLAYMFSUUIgfORSfj+dAROXIvTzG/uYY3xfp5oV9uuxKHgyv0UzNr3L/65lwwAaOZmhSV9G6CWfTVJayeqDGKTMzFx20VcjHoMAJj4Zk1M6+gN/VKGbSEEwqIfY3fIPfwSdh+pT88qAkArTxv0b+aMrj6OMDHSl6L8KkmrwcLExARhYWHw9vbON//atWto0qQJMjMzS15xOWCwoLJSqQWOXX4A/9MR+Cf6MQBAJsu9hDG2rSealPEOD5VaYOu5O1h+7DoyslUw1JdhQjsvfNCuJowN+YFHr4bAWwmYvD0UienZqGZsgFUDGuOtevaSbf9JjgrHLj/A7uB7CLydgLxvQXO5AXo0dET/Zs5o6mqVr/0GaTlYvPbaa+jZsyfmzp2bb/78+fPxyy+/ICQkpOQVlwMGCyqtJzkq7A65h//9FYG7ibntIeQGeujn64z33/CEh62ZpK8X8zgTcw+E48+nZ0M8q5thSZ8GaOFpI+nrEFUkarXA+lO3seL361ALoJ6jBb571xeuNtq7VHHvUQb2XYzBnpB7mrZOQO6/uX6+zninqTPbPD2l1WBx6NAhvPPOOxgyZAjat28PAPjzzz+xfft27N69G7179y514drEYEEl9Sg9G1vP3cXWc3eQmJ4NALA0NcR7Ld3wXmv3El3rLSkhBI5ceoB5hy4jIS337pJBr7lgVte6UJiyZTtVLckZOZi2K0wTpgc2c8GCXvXL7UydWi0QdCcJu4Pv4cilWGTmqAAAejKgbe3q6O/rgrfq2UFu8OqeOdT6XSGHDx/Gl19+ibCwMJiYmKBhw4aYN28e/Pz8Sl20tjFYUHFFJ2Xgf39FYFfwPc0HjLOVCd5/3QMDXnOBqVH5dVqbnJGDpUevYXtQFADA1lyO+W/XQ/cGjjxVS1VCeEwyJmwLQXRSJowM9LCoV30MfM1VZ/WkZSlx5N9Y7A6JxoU7jzTzLU0N0auRE/o3c0F9J4tX7t8fbzctBIMFvcyle8nwP30bRy7FIq9TP58aFhjbtia6+TjotCe/oMgkzNr3L27H546N0L6OHRb19kENSxOd1URUVjsvROHzg5eRrVTD1doU64Y2hU8Nha7L0ohMSMeekGjsDYnBg5Qnmvl1HKqhfzMX9G7sBBstnrmsSBgsCsFgQYURQuD0zQT4n7qNs7cTNfPb1q6OcW090bqmTYX5ZZKlVGFdwG2sO3kLOSoBUyN9zOjkjeGt3UvdWp5IF57kqPD5gXDsDsntJO6tunZY0b9xhb3Mp1ILnLmVgN3B0fj9ykNkK9UAAAM9GTrUze1G3M+7OgyrcDfikgcLa2tr3LhxA7a2trCyenFr2aSkpCKf0yUGC3pWjkqNX/+9D/9TEbj2IBUAoK8nQ8+GjhjbtibqOVXc98jNh6mYte8Sgu/mnqZt5KzAkr4NK3TNRHnuJqZjwk8XcSU2BXoyYHonb0zwq1lp+m1JzsjBoX9y+8b49+nt4UDuZcq+TWugv69zlbxNXPJgsWXLFgwaNAhyuRybN29+YbAYPnx4ySsuBwwWBOReP90RFIUfzkTifnLuqU1TI30Mbu6KUa97VJpLC2q1wPYLUVh65BpSs5TQ15NhzBuemNKhFu/Fpwrr+JWHmLYrDKlPlLAxM8I3g5ugjZetrssqtWsPUrAn+B72h8ZoGngDQCMXS/T3dUbPRk5QmFTMszAlxUshhWCweLXFpT7B5sA7+Onvu0h5kts5jq25HCPbuOPdFm4V9hTsyzxMeYIFv1zGkUsPAACu1qZY3McHb9SqruPKiP6jVKmx4vgNrD95G0Du+B3rhvrCQVE1buXMUakRcC0Ou0PuIeBanGbkVbmBHjrXd0A/X2e08bKt1JcstRos9PX1ERsbCzs7u3zzExMTYWdnB5VKVfKKywGDxavpdnwaNpyOwL6LMchW5V4X9bQ1w9i2nujdpEaV6Xjq+JWH+PxAuKaBWd8mNTCnRz1YmxnpuDJ61cWnZmHy9lCci8htwzSyjTtmda370u7uK6uEtCwcCI3B7uB7uP4wVTPfSWGMvk+7EXeXuO+b8qDVYKGnp4cHDx4UCBb3799HzZo12fMmVQjBd5LgfzoCf1x9qOlZz9fNCmPbeqJjXftKcz23JFKf5GDF7zew5dwdCAFYmRri8x710KdJjQrTAJVeLcF3kjDx54t4mJIFUyN9LOvXED0aOum6rHIhhMClmGTsDr6Hg2ExmjOlANDc3Rr9mjmjewNHmMnL7/b1stBKsPjmm28AAFOnTsWiRYtgbv7fGPcqlQqnT5/GnTt3EBoaWobStYfBoupTqwWOX32I709HIOTuf/efd6xnj3FtPdHMvegREauS0KhHmLXvkqZR6hu1bLG4dwOt9mBI9CwhBH4IvIMlR65CqRbwsjPHd+82hZdd1WvUWBxPclQ4fuUhdofcw1834zU/dkyN9NGtQe6Iq809rCv0DwCtBAsPDw8AwN27d+Hs7Ax9/f9OIRsZGcHd3R0LFy5EixYtylC69jBYVF1PclTYHxqDDX9FIOJpPw9G+nro27QG3n/DE1525i/ZQtWTo1Lj+9MRWPPnTWQr1TA21MNHb9XG6Nc9qvQtcaR7aVlKfLLnXxy+FAsA6NnICUv7Nqg0v8y1LTY5E/suxmB3cDTuJP7XjbibjSn6NXXGO77OcKqAjci1einkzTffxL59+2BlVbYBl8obg0XVk5yRg5/O38WmwDuabq+rGRtgWEs3jGjtDjv28Y/IhHTM3n9J00dHXUcLLO3bAI1cLHVbGFVJNx6mYvxPIYiIT4ehvgyzu9XF8NbuFfqXuK4IIRB89xF2B0fj8L+xSM/ObZ8okwGve9min68zOtd3qDDtwMr1rhCVSoVLly7Bzc2tQocNBouqI+ZxJjb+FYkdF6KQ8fQfo5PCGKNe98Cg5q4w5y+jfIQQ2BNyD4uPXMXjjBzoyYARrT0wvVNt/ookyRwMi8Gney8hM0cFBwtj/N/QpvB1q7jfCRVJepYSv4U/wO7gaJyP/K8vKAtjA/R82o14I2eFTgOaVoPFRx99hAYNGmD06NFQqVRo27Ytzp07B1NTU/z6669o165dWWrXGgaLyu/K/RR8f/o2fvk3Fqqnt3PVcaiGcX6e6NHQiaf4XyIhLQtf/HoFB8LuAwBqWJpgUe/6aF9HuiGp6dWTrVRj8eEr2HLuLoDcX9trBjV+Zbq6ltrdxHTsDbmHvRdjEPP4v5shatubo5+vM/o0cUb1auV/bLUaLGrUqIGDBw+iWbNmOHDgACZOnIiAgABs3boVAQEBCAwMLFPx2sJgUTkJIXD2diK+O3Ubf91M0MxvXdMG4/xqom0tW55mLaGT1+Mw50A47j3K/dDq3tAR83rWg101Xjqikrn/OBMfbLuIsOjHAIAP23vho7dqV+r+GioKtTr3s293SDSOhj9A1tNuxPX1ZHjTuzr6+bqgfR27crttV6vBwtjYGLdu3YKzszPGjh0LU1NTrF69GpGRkWjUqBFSUlLKVLy2MFhULkqVGkfCH8D/1G1cvp/7ntKTAd0aOGJc25po4FxxBiqqjDKylVj9x038768IqEXuKdfPutXFgGYuVfJWXJLeXzfjMXl7KB5l5EBhYohVAxvx7JeWJGfm4Nd/72N38D1NiAMAGzMj9GpcA/2bOaOuo3a/17QaLNzc3LBhwwZ06NABHh4eWLduHXr06IHLly/j9ddfx6NHj16+ER1gsKgcMrKV2HUhGv87E6n5RW1sqIeBzVzw/huecLHmLZNSCo9Jxqf7/kV4TG54a+5hjSV9G6Bm9VfvThoqHrVaYG3ALaz64waEyB0BeP1QX/7bLCc3H6ZiT8g97AuNQXxqlma+Tw0L9Pd1Qa/GTrA0lb5jPK0Gi/nz52P16tVwdHRERkYGbty4Ablcjh9++AEbNmzAuXPnylS8tjBYVGwJaVnYevYOtv59F48zcgAA1mZGGN7KHcNaubEHSS1SqtTYfPYOVvx+A5k5Khjp62FSey+M96tZZXtHpNJ5nJGNj3aG4eT1eADA4OYumNezfoW5c+FVolSpcepGPHYH38Of1x4iR5X7dW6kr4eO9ezRv5kz2nnbvWQrxaf1u0L27NmD6Oho9O/fH87OzgByByqztLREr169Sle1ljFYVEx3EtKx4a8I7Am5p7mG6GZjivff8ES/ps4cUKscRSdlYM6BcJy6kfulUcvOHEvfaQBft1ejYzF6sUv3kjH+pxDEPM6E3EAPX/T2Qf9mLrouiwAkpWfndiMecg9XY5+efXS3xq7xrSR7DQ5CVggGi4olNOoRvj8dgaOXH2h6oWvkrMA4v5roXN+Bjb90RAiBQ//cx8JfrmhGa3y3pSs+7lIHFsaVc6A2KhshBLYHRWP+ocvIVqnhZmOKdUObor4T2zlVROExydgTcg/N3K0k7T5d8mDxzTffYOzYsTA2NtZ07V2UyZMnl6zacsJgoXtqtUDA9Tj4n45A0DP3ar/pXR3j/GqiRQXv0vZV8jgjG18euYpdwfcAAHbV5FjYqz66+DjquDIqT5nZKsw5EI69F3PfB2/VtceKAY2qzFDgVHySBwsPDw8EBwfDxsZG07V3oRuUyRAREVHyissBg4XuZClVOBh2HxtOR+BmXBoAwFBfhl6Na2BsW0/Utn81xw+oDM7eTsBn+y5puh7uVM8eC3rVh6Oi4nU5TNK6k5CO8T+F4NqDVOjJgJmd62BcW0/eNfSK4qWQQjBYlL+UJznYfj4KPwRG4mFKbutlc7kBhrZwxcg2HnBQsN+EyuBJjgprT9zCd6duQ6kWMJcb4OMu3hjawo2XrKqoY5cfYMauf5CapYStuRG+HdwUrWra6Los0qHifoeWqrn3woULkZGRUWB+ZmYmFi5cWJpNFtu6devg4eEBY2Nj+Pr64q+//tLq61HpJKRlYcmRq2i95ASW/HYND1OyYG8hx6dd6+DsrPaY1a0uQ0UlYmyojxmdvXF48hto4mqJtCwl5h68jH7fncX1pyOoUtWgVKmx5LerGPdjCFKzlGjmZoXDk99gqKBiK9UZC319fcTGxsLOLv9tLImJibCzs4NKpZKswGft3LkTw4YNw7p169CmTRv4+/vjf//7H65cuQJXV9eXrs8zFtqnVgtsvxCFr367hpQnSgC5dxaMaeuJXo2dIDfgHR6VnUotsO38XSw7eh1pWUoY6Mkw3q8mJrX34i2HlVxc6hN8+HOoZqyK0a974NOuddhVPgHQ8qUQPT09PHz4ENWrV883/8SJExg4cCDi4+NLXnExtGjRAk2bNsX69es18+rWrYvevXtjyZIlL12fwUK7rsam4LP9lxAa9RgAUN/JAtM61sab3na8JlsFxSZnYu7Byzh+5SEAwMPWDF/2acBftpVUUGQSJv18EXGpWTAz0sfy/o3QrQEb6tJ/ivsdWqJhDa2srCCTySCTyVC7du18rfdVKhXS0tIwfvz40lf9AtnZ2QgJCcGnn36ab36nTp1w9uzZQtfJyspCVtZ/vZJV1K7GK7u8rqE3nomESi1gZqSP6Z288V4rNxjwl06V5agwwYb3muFo+APMPRiOyIR0DN7wNwY0c8Zn3epqpec/kp4QAhvPRGLJb9egUgvUtjfH+nd92fMqlVqJgsXq1ashhMCoUaOwYMECKBT/3cNsZGQEd3d3tGolXWccz0pISIBKpYK9ff5+6O3t7fHgwYNC11myZAkWLFhQYP7AgQNhaMhbpaQQn5qF6w9T8SQn9/KXXTU5nO0tcCBMDwd0WxqVI1u1Go/j0nDvUSbW7gW+n6OH2vbV2I6mglOq1bhyPwVxT7uFdrAwhqGjBaYG8AwjFZSTk1Os5UoULIYPHw4g99bT1q1b6+TL+fk+DoQQRfZ7MGvWLEybNk3zOCUlBS4uLti5cycvhZTR/ceZmHfoMv698hAWAOpZmWBhLw6//aoLvpOET/ddwq24NMQBqOddHYt6+XAMiQro+oNUjP8pBEhIRw19Geb2qId3W7qxHxkqUt6lkJcpUbDI4+fnp/n/zMzMAilGG1/atra20NfXL3B2Ii4ursBZjDxyuRxyefmPWV+V5Y0psfL4DWRkq2CgJ8OYtp6Y3L4Wu94mNHO3xuHJr8P/VATWnriFk9fj0WnVaUzvVBsjWrvz0lgFsT/0Hj7bF47MHBWcFMb4v6FN0cTVStdlURVRqn/lGRkZmDRpEuzs7GBubg4rK6t8kzYYGRnB19cXx48fzzf/+PHjaN26tVZek/K7GPUIPdcG4ovDV5GRrdLchvZJlzoMFaQhN9DH5A618NtHb6C5hzUyc1T44vBV9F4XiPCYZF2X90rLUqow58AlTN35DzJzVHijli1+nfwGQwVJqlRnLGbOnImAgACsW7cO7733Hv7v//4PMTEx8Pf3x9KlS6WuUWPatGkYNmwYmjVrhlatWuH7779HVFSU1hqMUq7kzBwsO3oNPwdFQQjA0tQQs7rWQX9fF97tQUWqWd0cO8a0xK7gaHx55CrCY1LQ6/8CMfp1D3z0Vi2YGpXq44dK6d6jDEzcdhH/3MsNd5M71MKUDrXYwRlJrlS3m7q6umLr1q1o164dLCwscPHiRXh5eeHHH3/E9u3bceTIEW3UCiC3g6xly5YhNjYWPj4+WLVqFdq2bVusdXm7acnkDUa16NerSEjLbdz1TlNnfNatDmzMeYmJii8u9QkW/nIFv/4bCwBwsTbBF70bwK929ZesSVI4dSMeU3aE4nFGDixNDbFqYGO8KeFw2vRq0Go/Fubm5rh8+TLc3Nzg7OyMffv2oXnz5oiMjESDBg2QlpZWpuK1hcGi+CIT0vH5gXCcuZUAAKhZ3Qxf9GYfBVQ2J649xOcHLiPmcSYAoHdjJ8zpUQ+2DKpaoVYLfHPiJtb8eRNCAA1qKLBuaFM2pqVS0WqX3p6enrhz5w4AoF69eti1axcA4JdffoGlpWVpNkkVRJZShTV/3ETn1adx5lYC5AZ6mNGpNo5MYZe+VHbt69jj96ltMaqNB/RkwIGw+3hr5SnsDo7GKzRsUbl4lJ6NkZsvYPUfuaFiSAtX7B7fiqGCtK5UZyxWrVoFfX19TJ48GQEBAejevTtUKhWUSiVWrlyJKVOmaKPWMuMZixc7eysBcw6EIyIhHQDwRi1bfNHbB242ZjqujKqif6If49N9l3A1NrfjutY1bbC4TwN42PL9Vlb/RD/GB9suIuZxJowN9bC4dwO84+us67KokivX0U2joqIQHByMmjVrolGjRmXdnNYwWBQuIS0Liw9fxf7QGABA9WpyzO1RDz0aOvKedtKqHJUaG89EYtXxG8hSqgEAtuZyuFqbwMXaFK7WpnCxNoWLlSlcbUzhYGHMxoYvIITAtvNRWPjLFWSr1HC3McX6d31R15Gfd1R2Wg0WW7duxcCBAwv0EZGdnY0dO3bgvffeK3nF5YDBIj+1WmBncDSW/nYNyZk5kMmAYS3dML2TNxQm7JmUys/dxHR8fvAyTt948ThDhvoy1LDMDR2a4GGVF0BMoDAxfGXDcEa2ErP3h2t+IHSub4/l/RvBwpj/lkkaWg0WuhrdtKwYLP5z7UEKPtt3CRefDhhWz9ECX/ZtgMYuljqti15tjzOyEZ2UiaikDEQlZSD6UQaik3Kne48yoVS/+OOqmrHBf2HDxhQuVv+d+ahhZVJlR9eNiE/DhJ8u4vrDVOjryfBJF2+MecPzlQ1ZpB1aGYQsT1HdaN+7d69Y3X2S7mRkK7Hmj5v43zMDhk3r5I3hHDCMKgBLUyNYmhqhgXPBzxGVWuBByhNEJT4NG4+eho+kDEQlZSIhLQupT5S4fD8Fl+8XHHBQJssdC8PF6unlFWsTuD5zuaW6ubxS9styNDwWM3b/i7QsJWzN5Vg7pAlaerKhNelOiYJFkyZNNKObdujQAQYG/62uUqkQGRmJLl26SF4kSeOPKw8x79B/t/p1qe+AeW/Xg6PCRMeVEb2cvl7uZZAaliaF3qGUka3EvUeZT4NGXujI1ISQjGwVYpOfIDb5CYLuJBVYX26gB2crE03QeLZ9h4u1CapVsEsKOSo1lh29hg1/RQIAmrtbY+2QJrCz4MBvpFslCha9e/cGAISFhaFz584wN/9vWN280U3feecdSQuksrv/OBMLfrmMY5cfAgBqWOYOGNahLgcMo6rD1MgAte2robZ9tQLPCSGQmJ6tCR3RT0NH3uWW+48zkaVU43Z8Om7Hpxe6fStTw//CxnPtOxwtjWFYjmf84lKeYNLPoZqANLatJ2Z29i7XGoiKUqo2Flu2bMGgQYMq3QBfr1obi7wBw1Ydv4H0pwOGvf+GJyZ38GJ3ykTPyFGpEfv4Sb62HVFJGbj39PGjjBcPF62vJ4Ojwjhf+468sx+u1qawNjOSrL3D3xGJmPRzKBLSsmAuN8DX/Ruii4+jJNsmehGtNt6Mjo6GTCaDs3PufdFBQUH4+eefUa9ePYwdO7b0VWvZqxQswqIf47N9l3DlaR8Bvm5WWNzHB3UcqvZ+E2lD6pMczRmOe/nadmQg+lEmsp/eKlsUUyN9uFqbwvmZO1jyQoezlWmxBvETQuD70xFYduw6VGoBb/tqWP9uU3hWN3/pukRS0GqweOONNzB27FgMGzYMDx48QO3ateHj44MbN25g8uTJmDt3bpmK15ZXIVgkZ+Zg+bFr2HY+d8AwhUnugGEDmnHAMCJtUKsF4tOyimzb8SDlCV72KVu9mhwuz5zhcH6mjYeDhTHSs5WYufsfzeXMPk1qYHEfH555pHKl1WBhZWWFv//+G97e3vjmm2+wc+dOBAYG4vfff8f48eMRERFRpuK1pSoHCyEEfvk3Fot+vYL41NwBw/o2rYHZ3epywDAiHcpSqhDzKPO/th2PMnPvbHl65iP1ifKF6xvqy2BsoI/ULCWM9PUwt2c9DG3hyltJqdxp9XbTnJwcTfuKP/74A2+//TYAoE6dOoiNjS3NJqkM7iSk4/OD4fjrZu6AYZ7VzfBFbx+0rmmr48qISG6gD8/q5kVeskjOyCnQtiOv746Yx5nIUQnkqJSoYWmCdUObohH7mqEKrlTBon79+vjuu+/QvXt3HD9+HIsWLQIA3L9/HzY2vH+6vGQpVfA/FYG1AbeQrVTDyEAPk970wjg/zyrbERBRVaMwNUQDU8UL++54mPIEdRyq8dIHVQqlepd+9dVX6NOnD5YvX47hw4drxgc5dOgQmjdvLmmBVLizt58OGBb/34Bhi3r5wJ0DOBFVGc/23UFUWZR6EDKVSoWUlBRYWVlp5t25cwempqYFuvquKKpCG4vEtCwsPnIV+y7mjgdgay7H3J710JMDhhERkRZptY0FkDteiJWVFZYuXYrx48fD0tIS7u7upd0cvYRaLbArOBpLnhkw7N0WbpjRmQOGERFRxVHmYdMtLCwQFhYGT09PqWrSmsp6xuL6g1TM3n8JwXcfAcgdMGxxHx80cbV6yZpERETS0PoZizxlzCX0AhnZSqz58yY2/hUJpVrA1Egf0zrWxojW7hwwjIiIKiQ2Ma6gTlx7iM8P/DdgWOf69pjXsz6c2IiLiIgqsBL97O3QoQP27duXb96VK1fg5uYGAEhISKgUl0QqstjkTIz/MQSjNgcj5nEmalia4H/vNYP/sGYMFUREVOGVqI2Fnp4e9PT0MHv2bCxYsKDA8w8fPoSTkxNUKpWkRUqlIrexUKrU2HLuLlb+fh3p2Sro68nw/usemPJWLd67TkREOqe1Nhbr16/HzJkz8e+//+LHH3/MN3Q6lU5Y9GPM3n8Jl+/nDhjW1NUSX/ZtwAHDiIio0ilxC8BevXrh3LlzuHLlClq1alVhxwWpDFKe5GDuwXD0WReIy/dToDAxxJK+DbBnfGuGCiIiqpRKdWtB3bp1ERQUBBcXF7z22mv4448/pK6rShNC4Jd/7qPDilPYeu4uhAD6NqmBP6f7YXBzV45CSkRElVap71lUKBQ4fPgwxowZg27dumHVqlVS1lVl3U1Mx3s/BOHD7aGIT82Cp60Zfn6/BVYObAxbjkJKRESVXInaWDzfZbRMJsPSpUvRpEkTjB49GidOnJC0uKokS6nC908HDMt6OmDYxHZeGN+OA4YREVHVUaJgUdQNJAMHDoS3tzd69+4tRU1Vzt8RiZi9/xJuPx0w7HUvWyzq7QMPDhhGRERVTImCRUBAAKytrQt9rnHjxggJCcHhw4clKawqSEzLwpdHrmHvxXsAcgcM+7xHXbzdyIkDhhERUZVU5rFCKpPy6sdCrRbYHZI7YNjjjNwBw4a2cMXMznU4YBgREVVK5TZWCOV342HugGEX7uQOGFb36YBhTTlgGBERvQIYLCSSma3CNyduYsPpCA4YRkRErywGCwkEXIvD5wfDce9R7oBhnerZY97b9VGDY3sQEdErhsGiDB4kP8GCXy7jt/AHAAAnhTEW9PJBx3r2Oq6MiIhINxgsyiAu9QmOXX4AfT0ZRr/ugSkdasFMzkNKRESvLn4LlkFDZ0vM61kfzT2sUdeRY3sQERExWJTR8Nbuui6BiIiowuDtCkRERCQZBgsiIiKSDIMFERERSabSBIvFixejdevWMDU1haWlpa7LISIiokJUmmCRnZ2N/v37Y8KECbouhYiIiIpQae4KWbBgAQBg8+bNui2EiIiIilRpgkVpZGVlISsrS/M4JSVFh9UQERFVfZXmUkhpLFmyBAqFQjO5uLjouiQiIqIqTafBYv78+ZDJZC+cgoODS739WbNmITk5WTNFR0dLWD0RERE9T6eXQiZNmoRBgwa9cBl3d/dSb18ul0Mul5d6fSIiIioZnQYLW1tb2Nra6rIEIiIiklClabwZFRWFpKQkREVFQaVSISwsDADg5eUFc3Nz3RZHREREACpRsJg7dy62bNmiedykSRMAQEBAANq1a6ejqoiIiOhZMiGE0HUR5SUlJQUKhQLJycmwsOAw50RERMVV3O/QKn27KREREZUvBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMpUiWNy5cwejR4+Gh4cHTExMULNmTcybNw/Z2dm6Lo2IiIieYaDrAorj2rVrUKvV8Pf3h5eXF8LDwzFmzBikp6fj66+/1nV5RERE9JRMCCF0XURpLF++HOvXr0dERESx10lJSYFCoUCXLl1gaGiIbdu24bPPPsPdu3fh4+ODSZMmYfz48QCAMWPGICcnB5s3bwYA/PDDD/jqq69w/fp1eHl5Yc6cORgxYgQAYNiwYTA1NYW/vz8AYN26dfD398c///wDFxcXLF++HIMGDQIADBgwAI6OjlizZg0AYNWqVdi+fTuCgoJgZ2cHf39/9OnTBwDQq1cveHt7Y9myZQCApUuX4vDhw/jrr79gYWGBn376CX379oVSqUTnzp3RvHlzLFq0CAAwf/58BAYG4vjx45DL5di9ezeGDBmCtLQ0+Pn5oVOnTpg9ezYA4NNPP0V4eDh+/fVXAMChQ4cwcuRIJCYmolWrVujXrx+mT58OAJg2bRqioqKwZ88eAMCuXbswdepUxMTEoEmTJhg9ejQmTZoEAPjggw+QnJyMbdu2AQB+/PFHzJs3DxEREahbty6mTZuGMWPGAABGjRqlOc4AsGHDBqxcuRJXr16Fp6cnFixYgGHDhgEAhg4dCoVCgXXr1gEA1q5di40bNyI0NBQ1atTAqlWrMGDAAABAv3794OrqipUrVwIAVqxYgT179uDcuXOwsbHBpk2b8PbbbwMAevToAR8fHyxduhQAsHjxYvz+++84deoUzM3N8fPPP6N///7IyspCx44d0aZNG8yfPx8A8PnnnyMoKAjHjh2DgYEB9u3bh3fffRcpKSl444030L17d3z66acAgI8//hjXr1/HwYMHAQD79+/HuHHjEBcXh+bNm2Pw4MGYOnUqAGDKlCmIjY3Frl27AAA7duzAzJkzER0djUaNGmHcuHH44IMPAADjxo1DRkYGfvzxRwDA5s2b8cUXX+DWrVvw9vbGJ598ojnOI0aMgKGhITZs2AAA+O6777B27VqEh4fDzc0NX375JYYOHQoAGDx4MGxsbLB27VoAwDfffIOtW7ciODgYjo6OWLt2Ld555x0AQJ8+fVCzZk1N4F+2bBkOHjyIwMBAWFlZYcuWLejduzfUajW6deuGJk2aYPHixQCAhQsX4uTJkzhx4gRMTEywc+dODBw4EJmZmWjfvj3atWuHuXPnAgBmz56N0NBQHDlyBHp6ejhw4ACGDx+OR48eoU2bNujVqxc+/vhjAMCMGTNw+/Zt7N+/HwCwd+9eTJo0CbGxsWjWrBnee+89TJ48GQAwadIkJCYmYvv27QDAzwh+RvAz4pnPiBUrVmDlypVITk6GhYUFilJpg8WcOXNw9OhRBAcHF7lMVlYWsrKyNI9TUlLg4uLy0oNCRERE+eX9OH/Zd2ilaGPxvNu3b+Pbb7/V/HIoypIlS6BQKDSTi4tLOVVIRET0atJpsJg/fz5kMtkLp+fPSNy/fx9dunRB//798f77779w+7NmzUJycrJmio6O1ubuEBERvfJ0eikkISEBCQkJL1zG3d0dxsbGAHJDxZtvvokWLVpg8+bN0NMrWS4q7mkcIiIiyq+436E6vSvE1tYWtra2xVo2JiYGb775Jnx9fbFp06YShwoiIiLSvkpxu+n9+/fRrl07uLq64uuvv0Z8fLzmOQcHBx1WRkRERM+qFMHi999/x61bt3Dr1i04Ozvne66S3tRCRERUJVWK6wkjRoyAEKLQiYiIiCqOShEsiIiIqHJgsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkqk0weLtt9+Gq6srjI2N4ejoiGHDhuH+/fu6LouIiIieUWmCxZtvvoldu3bh+vXr2Lt3L27fvo1+/frpuiwiIiJ6hkwIIXRdRGkcOnQIvXv3RlZWFgwNDYu1TkpKChQKBZKTk2FhYaHlComIiKqO4n6HGpRjTZJJSkrCtm3b0Lp16xeGiqysLGRlZWkeJycnA8g9OERERFR8ed+dLz0fISqRjz/+WJiamgoAomXLliIhIeGFy8+bN08A4MSJEydOnDhJNEVHR7/wu1enl0Lmz5+PBQsWvHCZCxcuoFmzZgCAhIQEJCUl4e7du1iwYAEUCgV+/fVXyGSyQtd9/oyFWq1GUlISbGxsilynpFJSUuDi4oLo6GheXnkGj0vReGwKx+NSNB6bwvG4FE0bx0YIgdTUVDg5OUFPr+gmmjoNFgkJCUhISHjhMu7u7jA2Ni4w/969e3BxccHZs2fRqlUrbZX4Umy3UTgel6Lx2BSOx6VoPDaF43Epmi6PjU7bWNja2sLW1rZU6+bloWfPSBAREZFuVYrGm0FBQQgKCsLrr78OKysrREREYO7cuahZs6ZOz1YQERFRfpWiHwsTExPs27cPHTp0gLe3N0aNGgUfHx+cOnUKcrlcp7XJ5XLMmzdP53VUNDwuReOxKRyPS9F4bArH41I0XR6bStuPBREREVU8leKMBREREVUODBZEREQkGQYLIiIikgyDBREREUnmlQ8WS5YswWuvvYZq1arBzs4OvXv3xvXr1/MtI4TA/Pnz4eTkBBMTE7Rr1w6XL1/Ot8z333+Pdu3awcLCAjKZDI8fPy7wWu7u7pDJZPmmTz/9VJu7VybleWwA4PDhw2jRogVMTExga2uLvn37amvXyqS8jsvJkycLvF/ypgsXLmh7N0ulPN8zN27cQK9evWBrawsLCwu0adMGAQEB2ty9UivP43Lx4kV07NgRlpaWsLGxwdixY5GWlqbN3Ss1KY5LUlISPvzwQ3h7e8PU1BSurq6YPHmyZmyoPI8ePcKwYcOgUCigUCgwbNiwIj+LqGxe+WBx6tQpTJw4EX///TeOHz8OpVKJTp06IT09XbPMsmXLsHLlSqxduxYXLlyAg4MDOnbsiNTUVM0yGRkZ6NKlCz777LMXvt7ChQsRGxurmebMmaO1fSur8jw2e/fuxbBhwzBy5Ej8888/CAwMxJAhQ7S6f6VVXseldevW+d4rsbGxeP/99+Hu7q7p5r6iKc/3TPfu3aFUKnHixAmEhISgcePG6NGjBx48eKDVfSyN8jou9+/fx1tvvQUvLy+cP38eR48exeXLlzFixAht72KpSHFc7t+/j/v37+Prr7/GpUuXsHnzZhw9ehSjR4/O91pDhgxBWFgYjh49iqNHjyIsLAzDhg0r1/19ZZRtWLCqJy4uTgAQp06dEkIIoVarhYODg1i6dKlmmSdPngiFQiG+++67AusHBAQIAOLRo0cFnnNzcxOrVq3SVulap61jk5OTI2rUqCH+97//abV+bdHme+ZZ2dnZws7OTixcuFDS+rVJW8cmPj5eABCnT5/WzEtJSREAxB9//KGdnZGQto6Lv7+/sLOzEyqVSjMvNDRUABA3b97Uzs5IqKzHJc+uXbuEkZGRyMnJEUIIceXKFQFA/P3335plzp07JwCIa9euaWlvSsfPz09MmjRJTJkyRVhaWgo7Ozvh7+8v0tLSxIgRI4S5ubnw9PQUR44c0axz+fJl0bVrV2FmZibs7OzEu+++K+Lj4zXP//bbb6JNmzZCoVAIa2tr0b17d3Hr1i3N85GRkQKA2Lt3r2jXrp0wMTERDRs2FGfPni3VPrzyZyyel3f6zNraGgAQGRmJBw8eoFOnTppl5HI5/Pz8cPbs2RJv/6uvvoKNjQ0aN26MxYsXIzs7W5rCy4G2js3FixcRExMDPT09NGnSBI6OjujatWuB08AVlbbfM3kOHTqEhISECvvrszDaOjY2NjaoW7cutm7divT0dCiVSvj7+8Pe3h6+vr7S7oQWaOu4ZGVlwcjIKN8AUSYmJgCAM2fOSFG6Vkl1XPLGxzAwyO1c+ty5c1AoFGjRooVmmZYtW0KhUJTp36S2bNmyBba2tggKCsKHH36ICRMmoH///mjdujUuXryIzp07Y9iwYcjIyEBsbCz8/PzQuHFjBAcH4+jRo3j48CEGDBig2V56ejqmTZuGCxcu4M8//4Senh769OkDtVqd73Vnz56NGTNmICwsDLVr18bgwYOhVCpLvgOlz1VVj1qtFj179hSvv/66Zl5gYKAAIGJiYvItO2bMGNGpU6cC23jRr8+VK1eKkydPin/++Uds2LBB2NraitGjR0u+H9qgzWOzfft2AUC4urqKPXv2iODgYDF48GBhY2MjEhMTtbI/UtH2e+ZZXbt2FV27dpWk7vKg7WNz79494evrK2QymdDX1xdOTk4iNDRU6t2QnDaPS3h4uDAwMBDLli0TWVlZIikpSfTt21cAEF9++aVW9kcqUhwXIYRISEgQrq6uYvbs2Zp5ixcvFrVq1SqwbK1atSrccfHz88t3DJRKpTAzMxPDhg3TzIuNjRUAxLlz58Tnn39e4FhER0cLAOL69euFvkbemaFLly4JIf47Y/HsWePLly8LAOLq1asl3geesXjGpEmT8O+//2L79u0Fnnt+mHUhRImHXp86dSr8/PzQsGFDvP/++/juu++wceNGJCYmlqnu8qDNY5OXmmfPno133nkHvr6+2LRpE2QyGXbv3l22wrVM2++ZPPfu3cOxY8cKXDeuyLR5bIQQ+OCDD2BnZ4e//voLQUFB6NWrF3r06IHY2Ngy165N2jwu9evXx5YtW7BixQqYmprCwcEBnp6esLe3h76+fplr1yYpjktKSgq6d++OevXqYd68eS/cxou2o2sNGzbU/L++vj5sbGzQoEEDzTx7e3sAQFxcHEJCQhAQEABzc3PNVKdOHQDA7du3Nf8dMmQIPD09YWFhAQ8PDwBAVFRUka/r6OioeY2SYrB46sMPP8ShQ4cQEBAAZ2dnzXwHBwcAKNAgLC4uTvPHLa2WLVsCAG7dulWm7Wibto9N3hu4Xr16mnlyuRyenp4F3vgVSXm+ZzZt2gQbGxu8/fbbpS+4HGn72Jw4cQK//vorduzYgTZt2qBp06ZYt24dTExMsGXLFml2QgvK4z0zZMgQPHjwADExMUhMTMT8+fMRHx+v+TKpiKQ4LqmpqejSpQvMzc2xf/9+GBoa5tvOw4cPC7xufHx8mT/HteHZ2oHcUPTsvLwwpFaroVar0bNnT4SFheWbbt68ibZt2wIAevbsicTERGzYsAHnz5/H+fPnAaDApfiiXqOkXvlgIYTApEmTsG/fPpw4caLAPz4PDw84ODjg+PHjmnnZ2dk4deoUWrduXabXDg0NBfDfF2tFU17HxtfXF3K5PN9tZjk5Obhz5w7c3NzKviMSK+/3jBACmzZtwnvvvVfgA6eiKa9jk5GRAQD52hLkPS7NB6G26eJzxt7eHubm5ti5cyeMjY3RsWPHMu2DNkh1XFJSUtCpUycYGRnh0KFDMDY2zredVq1aITk5GUFBQZp558+fR3Jycpk/x3WtadOmuHz5Mtzd3eHl5ZVvMjMzQ2JiIq5evYo5c+agQ4cOqFu3Lh49eqTdokp88aSKmTBhglAoFOLkyZMiNjZWM2VkZGiWWbp0qVAoFGLfvn3i0qVLYvDgwcLR0VGkpKRolomNjRWhoaFiw4YNmtbqoaGhmjYCZ8+eFStXrhShoaEiIiJC7Ny5Uzg5OYm333673Pe5uMrr2AghxJQpU0SNGjXEsWPHxLVr18To0aOFnZ2dSEpKKtd9Lo7yPC5CCPHHH38IAOLKlSvlto+lVV7HJj4+XtjY2Ii+ffuKsLAwcf36dTFjxgxhaGgowsLCyn2/X6Y83zPffvutCAkJEdevXxdr164VJiYmYs2aNeW6v8UlxXFJSUkRLVq0EA0aNBC3bt3Ktx2lUqnZTpcuXUTDhg3FuXPnxLlz50SDBg1Ejx49yn2fX8bPz09MmTIl37zC7igEIPbv3y9iYmJE9erVRb9+/cT58+fF7du3xbFjx8TIkSOFUqkUKpVK2NjYiHfffVfcvHlT/Pnnn+K1117TrC/Ef20snm2j9OjRIwFABAQElHgfXvlgAaDQadOmTZpl1Gq1mDdvnnBwcBByuVy0bdtW0+glz7x58164nZCQENGiRQuhUCiEsbGx8Pb2FvPmzRPp6enluLclU17HRojcWymnT58u7OzsRLVq1cRbb70lwsPDy2lPS6Y8j4sQQgwePFi0bt26HPas7Mrz2Fy4cEF06tRJWFtbi2rVqomWLVvmuwWvIinP4zJs2DBhbW0tjIyMRMOGDcXWrVvLaS9LTorjkteQtbApMjJSs1xiYqIYOnSoqFatmqhWrZoYOnToSxtM60JJg4UQQty4cUP06dNHWFpaChMTE1GnTh3x0UcfCbVaLYQQ4vjx46Ju3bpCLpeLhg0bipMnT2o1WHDYdCIiIpLMK9/GgoiIiKTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBRJITQuCtt95C586dCzy3bt06KBSKCj1yLRGVHoMFEUlOJpNh06ZNOH/+PPz9/TXzIyMj8cknn2DNmjVwdXWV9DVzcnIk3R4RlQ6DBRFphYuLC9asWYMZM2YgMjISQgiMHj0aHTp0QPPmzdGtWzeYm5vD3t4ew4YNQ0JCgmbdo0eP4vXXX4elpSVsbGzQo0cP3L59W/P8nTt3IJPJsGvXLrRr1w7Gxsb46aefdLGbRPQcDkJGRFrVu3dvPH78GO+88w4WLVqECxcuoFmzZhgzZgzee+89ZGZm4pNPPoFSqcSJEycAAHv37oVMJkODBg2Qnp6OuXPn4s6dOwgLC4Oenh7u3LkDDw8PuLu7Y8WKFWjSpAnkcjmcnJx0vLdExGBBRFoVFxcHHx8fJCYmYs+ePQgNDcX58+dx7NgxzTL37t2Di4sLrl+/jtq1axfYRnx8POzs7HDp0iX4+PhogsXq1asxZcqU8twdInoJXgohIq2ys7PD2LFjUbduXfTp0wchISEICAiAubm5ZqpTpw4AaC533L59G0OGDIGnpycsLCzg4eEBAAUafDZr1qx8d4aIXspA1wUQUdVnYGAAA4Pcjxu1Wo2ePXviq6++KrCco6MjAKBnz55wcXHBhg0b4OTkBLVaDR8fH2RnZ+db3szMTPvFE1GJMFgQUblq2rQp9u7dC3d3d03YeFZiYiKuXr0Kf39/vPHGGwCAM2fOlHeZRFRKvBRCROVq4sSJSEpKwuDBgxEUFISIiAj8/vvvGDVqFFQqFaysrGBjY4Pvv/8et27dwokTJzBt2jRdl01ExcRgQUTlysnJCYGBgVCpVOjcuTN8fHwwZcoUKBQK6OnpQU9PDzt27EBISAh8fHwwdepULF++XNdlE1Ex8a4QIiIikgzPWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCSZ/wf9Y1FSoMy7hwAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -846,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb index 2202315e..32d957ed 100644 --- a/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -34,7 +34,7 @@ "## Approach\n", "\n", "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 OCO-2 MIP Top-Down CO₂ Budgets data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon." ] @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -177,17 +177,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 6 items\n" - ] - } - ], + "outputs": [], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -201,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -243,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -263,27 +255,9 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=oco2-mip-co2budget-yeargrid-v1&item=oco2-mip-co2budget-yeargrid-v1-2020&assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -297,7 +271,7 @@ "co2_flux_1 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[0]]['collection']}/items/{items[list(items.keys())[0]]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -317,27 +291,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=oco2-mip-co2budget-yeargrid-v1&item=oco2-mip-co2budget-yeargrid-v1-2019&assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Make a GET request to retrieve information for the 2019 tile which is the 2st item in the collection\n", "# To retrieve the second item in the collection we use \"1\" in the \"(items.keys())[1]\" statement\n", @@ -346,7 +302,7 @@ "co2_flux_2 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[1]]['collection']}&item={items[list(items.keys())[1]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[1]]['collection']}/items/{items[list(items.keys())[1]]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -373,146 +329,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For this study we are going to compare the CO2 budget in 2020 and 2019 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -561,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -586,110 +405,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -717,17 +435,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 6 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -740,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -757,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -794,17 +504,9 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-01-01T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -825,24 +527,9 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.03978176414966583, 'max': 7348.5, 'mean': 710.6574534496499, 'count': 36.0, 'sum': 25583.668324187398, 'std': 1398.048920955156, 'median': 92.79795837402344, 'majority': 0.03978176414966583, 'minority': 0.03978176414966583, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.03978176414966583, 734.8858035877347, 1469.7318254113197, 2204.5778472349048, 2939.42386905849, 3674.269890882075, 4409.11591270566, 5143.961934529245, 5878.80795635283, 6613.653978176415, 7348.5]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.7717349633574486, 'percentile_98': 4147.699072265612}}}}\n", - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.09139306098222733, 'max': 8174.17724609375, 'mean': 795.2997507835842, 'count': 36.0, 'sum': 28630.79102820903, 'std': 1557.5783137149344, 'median': 108.24344635009766, 'majority': 0.09139306098222733, 'minority': 0.09139306098222733, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.09139306098222733, 817.499978364259, 1634.9085636675359, 2452.317148970813, 3269.7257342740895, 4087.134319577366, 4904.542904880644, 5721.95149018392, 6539.360075487197, 7356.768660790473, 8174.17724609375]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.9499634824693204, 'percentile_98': 4609.885083007798}}}}\n", - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.09151247888803482, 'max': 8392.841796875, 'mean': 816.3306965242243, 'count': 36.0, 'sum': 29387.905074872077, 'std': 1599.1110746714692, 'median': 110.89262771606445, 'majority': 0.09151247888803482, 'minority': 0.09151247888803482, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.09151247888803482, 839.3665409184993, 1678.6415693581105, 2517.916597797722, 3357.191626237333, 4196.466654676944, 5035.741683116556, 5875.016711556166, 6714.291739995778, 7553.566768435389, 8392.841796875]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.9720504634082319, 'percentile_98': 4733.363476562485}}}}\n", - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0910222977399826, 'max': 8138.96484375, 'mean': 791.9455965670446, 'count': 36.0, 'sum': 28510.041476413608, 'std': 1550.8237787859084, 'median': 107.77805709838867, 'majority': 0.0910222977399826, 'minority': 0.0910222977399826, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0910222977399826, 813.9784044429659, 1627.865786588192, 2441.7531687334176, 3255.640550878644, 4069.52793302387, 4883.415315169095, 5697.302697314321, 6511.190079459548, 7325.077461604774, 8138.96484375]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.9726840510964394, 'percentile_98': 4589.991333007798}}}}\n", - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08842560648918152, 'max': 8243.259765625, 'mean': 801.7388236944875, 'count': 36.0, 'sum': 28862.597653001547, 'std': 1570.4892862189242, 'median': 108.75741958618164, 'majority': 0.08842560648918152, 'minority': 0.08842560648918152, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08842560648918152, 824.4055596083402, 1648.7226936101913, 2473.0398276120422, 3297.3569616138934, 4121.674095615745, 4945.991229617595, 5770.308363619447, 6594.625497621298, 7418.942631623148, 8243.259765625]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.987190940976143, 'percentile_98': 4649.222045898423}}}}\n", - "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08379923552274704, 'max': 8397.8779296875, 'mean': 816.0609874193453, 'count': 36.0, 'sum': 29378.19554709643, 'std': 1599.6065142777418, 'median': 110.1262092590332, 'majority': 0.08379923552274704, 'minority': 0.08379923552274704, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08379923552274704, 839.8632122807205, 1679.6426253259183, 2519.4220383711163, 3359.201451416314, 4198.980864461511, 5038.76027750671, 5878.539690551907, 6718.319103597105, 7558.0985166423025, 8397.8779296875]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.9802812077105046, 'percentile_98': 4736.898437499985}}}}\n", - "CPU times: user 115 ms, sys: 2.32 ms, total: 117 ms\n", - "Wall time: 1.35 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -853,47 +540,9 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 0.03978176414966583,\n", - " 'max': 7348.5,\n", - " 'mean': 710.6574534496499,\n", - " 'count': 36.0,\n", - " 'sum': 25583.668324187398,\n", - " 'std': 1398.048920955156,\n", - " 'median': 92.79795837402344,\n", - " 'majority': 0.03978176414966583,\n", - " 'minority': 0.03978176414966583,\n", - " 'unique': 36.0,\n", - " 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", - " [0.03978176414966583,\n", - " 734.8858035877347,\n", - " 1469.7318254113197,\n", - " 2204.5778472349048,\n", - " 2939.42386905849,\n", - " 3674.269890882075,\n", - " 4409.11591270566,\n", - " 5143.961934529245,\n", - " 5878.80795635283,\n", - " 6613.653978176415,\n", - " 7348.5]],\n", - " 'valid_percent': 100.0,\n", - " 'masked_pixels': 0.0,\n", - " 'valid_pixels': 36.0,\n", - " 'percentile_2': 0.7717349633574486,\n", - " 'percentile_98': 4147.699072265612}},\n", - " 'datetime': '2020-01-01T00:00:00+00:00'}" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -908,202 +557,9 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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42016-01-01T00:00:00+00:000.0884268243.259766801.73882436.028862.5976531570.489286108.7574200.0884260.08842636.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.9871914649.2220462016-01-01 00:00:00+00:00
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count \\\n", - "0 2020-01-01T00:00:00+00:00 0.039782 7348.500000 710.657453 36.0 \n", - "1 2019-01-01T00:00:00+00:00 0.091393 8174.177246 795.299751 36.0 \n", - "2 2018-01-01T00:00:00+00:00 0.091512 8392.841797 816.330697 36.0 \n", - "3 2017-01-01T00:00:00+00:00 0.091022 8138.964844 791.945597 36.0 \n", - "4 2016-01-01T00:00:00+00:00 0.088426 8243.259766 801.738824 36.0 \n", - "\n", - " sum std median majority minority unique \\\n", - "0 25583.668324 1398.048921 92.797958 0.039782 0.039782 36.0 \n", - "1 28630.791028 1557.578314 108.243446 0.091393 0.091393 36.0 \n", - "2 29387.905075 1599.111075 110.892628 0.091512 0.091512 36.0 \n", - "3 28510.041476 1550.823779 107.778057 0.091022 0.091022 36.0 \n", - "4 28862.597653 1570.489286 108.757420 0.088426 0.088426 36.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", - "1 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", - "2 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", - "3 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", - "4 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", - "0 0.0 36.0 0.771735 4147.699072 \n", - "1 0.0 36.0 0.949963 4609.885083 \n", - "2 0.0 36.0 0.972050 4733.363477 \n", - "3 0.0 36.0 0.972684 4589.991333 \n", - "4 0.0 36.0 0.987191 4649.222046 \n", - "\n", - " date \n", - "0 2020-01-01 00:00:00+00:00 \n", - "1 2019-01-01 00:00:00+00:00 \n", - "2 2018-01-01 00:00:00+00:00 \n", - "3 2017-01-01 00:00:00+00:00 \n", - "4 2016-01-01 00:00:00+00:00 " - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -1138,30 +594,9 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'CO2 emission Values for Texas, Dallas (2015-2020)')" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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y5cvH64SQzKvXK1WqFPf3bNmyKWfOnKpQoYL8/f3jtpcqVUqSdOrUqQc+dtGiRZU1a1b16dNHU6dO1V9//ZVgn2eeeUaS1KJFCy1cuFBnz5596PlIZsfLtm3b1Lx5c2XMmDFuu7Ozs9q2baszZ84k6MK513Vxz70r7B+WXzJfhw4dOmjfvn1xo8euXLmiZcuWxRtjFR0drREjRqh06dJyc3OTi4uL3NzcdPToUR08ePCR52QLW19LW573pFi3bp0kc4Tdf1WuXFmlSpXSr7/+Gm971qxZ9fzzzz/RY0rm+ZYtW1YVKlSId7716tWTk5NTvJ/dokWL6ssvv9SSJUsUHBysmjVrxo21+6+pU6fqqaeekoeHh1xcXOTq6qpff/013mtUuXJlXb9+Xa1atdKPP/6YoOMpuRn3jWeTzO6Q1q1bK1euXHJ2dparq6sCAwMl6bF+ntauXas6derIx8cn7niDBg3SlStXdPHiRUlShQoV5Obmpi5duuibb75JMG7wYc6dO5fouLP7DRw4UHPmzNG4cePivc/c86D3vUfd9iD169fX8OHDFRwcrFq1aumdd97Rxo0b5eTkpEGDBsXtFxsbG+9nLCYmJtHjPer993HyG4ahTp06aePGjfr2228T7S590H0T254zZ07FxsYqNDT0gVkAAACQPCgEAQAApAPZs2eXl5eXTpw4YdP++fPnl6SH7n/vtsS+DLx27ZpeeOEFnT17Vj///LMKFy78yMe8cOGCDMOQn5+fXF1d4/3ZunVrgi+5s2XLluAYbm5uCba7ublJ0kPHNfn4+GjDhg2qUKGCPvroI5UpU0b+/v4aPHiwoqKiJEm1atXSkiVLFB0drXbt2ilv3rwqW7as5s2b98DjXrt2TYZhxI38+q97xar71/S5f52Pe+s53blz54GPc0+HDh2UIUOGuLWS5syZo8jIyLgxapLUs2dPDRw4UE2bNtWyZcu0bds27dixQ+XLl7fpMWxh62tpy/OeFPeeywc93/c/14nt9zguXLigffv2JTjXTJkyyTCMBD+7jRo1kp+fn+7evauePXvK2dk53u2ffvqp3n77bVWpUkU//PCDtm7dqh07dqh+/frxXqO2bdvq66+/1qlTp/Tyyy8rZ86cqlKlyiPHNT6ue8XIez+7N2/eVM2aNbVt2zYNHz5c69ev144dO7Ro0SJJtv3M/tf27dtVt25dSdKXX36p33//XTt27FD//v3jHa9IkSL65ZdflDNnTr3zzjsqUqSIihQpogkTJjzyMe7cuSMPD4+H7vPxxx9r+PDhCgkJ0bvvvhvvtnv/fd7/sySZxRcnJydlyZLlkTlsUbBgQT377LPaunVr3LaOHTvG+xmrXbt2gvs96v3X19dXd+/eTXQ9patXryb63moYht544w3Nnj1bM2fO1IsvvpjgmNKDn5fEjnnvdUiu9x0AAAA8mMujdwEAAIC9c3Z2Vu3atbVy5UqdOXNGefPmfej+zz33nFxcXLRkyRK99dZbie5zb0HxF154Id72a9euqU6dOjpx4oR+/fXXRNeMSEz27Nnl5OSkjRs3xhU//iuxbckpICBA8+fPl2EY2rdvn2bOnKmhQ4fK09NTffv2lSS9+OKLevHFFxUREaGtW7dq5MiRat26tQoWLKhq1aolOGbWrFmVIUMGnT9/PsFt99Ysyp49e7KdQ968eVW3bl3NnTtXn3zyiWbMmKGiRYuqVq1acfvMnj1b7dq104gRI+Ld9/Lly4/8AtvDw0MREREJtt9f6EjKa2nL826re19Gnz9/PsHP+Llz5xI814/TuZGY7Nmzy9PTU19//fUDb/+vt956Szdu3FCZMmXUrVs31axZU1mzZo27ffbs2QoKCtKUKVPi3S+xdZM6dOigDh066NatW/rtt980ePBgBQcH68iRIypQoEAynN2/li5dKkkKCgqSZHbvnDt3TuvXr4/rApIUt/5RUs2fP1+urq5avnx5vGLNvfea/6pZs6Zq1qypmJgY7dy5UxMnTlT37t3l5+enli1bPvAxsmfPHrcGU2I+/vhjDRkyREOGDNFHH32U4PYiRYrI09NT+/fvT3Db/v37VbRo0UcWmpLCMIx43TxDhgyJV5zKlClTvP1tef+9tzbQ/v37VaVKlbjtoaGhunz5ssqWLZsgwxtvvKEZM2Zo+vTpia5Zdu8++/fvV8OGDePdtn///gTHlP5dCys53wMBAACQODqCAAAA0ol+/frJMAx17tw50QXVo6KitGzZMklSrly51LFjR61evVoLFixIsO+RI0c0evRolSlTRk2bNo3bfu9LyL///ltr1qxRxYoVbc4XHBwswzB09uxZPf300wn+3L+weUpxcnJS+fLlNW7cOGXJkkW7d+9OsI+7u7sCAwM1evRoSdKePXsSPZa3t7eqVKmiRYsWxbvqPTY2VrNnz1bevHlVvHjxZM3fqVMnXbt2TYMGDdLevXvVoUOHeAUPJyenBMWZn376yaZRdwULFtTFixd14cKFuG2RkZFavXp1vP0e57W05Xl/lHtj3mbPnh1v+44dO3Tw4MFEuyeSQ3BwsI4fPy5fX99Ez7dgwYJx+3711VeaPXu2Jk2apKVLl+r69evq0KFDvOMl9hrt27dPW7ZseWAGb29vNWjQQP3791dkZKT+/PPPZD3Hn3/+WV999ZWqV6+uZ599Ni6nlLBI+8UXXzzWYzg5OcnFxSVeh9SdO3c0a9asB97H2dlZVapU0eeffy5Jj/y5KVmypI4fP57obcOGDdOQIUM0YMAADR48ONF9XFxc1LhxYy1atCheYe706dNat26dmjVr9tDHT4oTJ07o999/V9WqVeO2FSxYMN7PVokSJeJus/X9t379+vLw8NDMmTPjbZ85c6acnJzivaff+3/GjBkz9MUXXyT4Wb0nT548qly5smbPnh1vXN3WrVt1+PDhRJ+Xv//+W76+vvLz87Pl6QAAAMAToCMIAAAgnahWrZqmTJmirl27qlKlSnr77bdVpkwZRUVFac+ePZo2bZrKli2rxo0bSzLHUx0+fFht2rTRb7/9psaNG8vd3V1bt27V2LFjlSlTJv3www9xX9reuXNH9erV0549ezR+/HhFR0fHG2mUI0cOFSlS5IH5atSooS5duqhDhw7auXOnatWqJW9vb50/f16bNm1SQECA3n777RR5bpYvX67JkyeradOmKly4sAzD0KJFi3T9+vW4jqdBgwbpzJkzql27tvLmzavr169rwoQJ8dZESczIkSP1wgsv6LnnntOHH34oNzc3TZ48WQcOHNC8efOSrSvlniZNmih79uz63//+J2dnZ7Vv3z7e7cHBwZo5c6ZKliypcuXKadeuXfrf//73yC4xSXr11Vc1aNAgtWzZUr169dLdu3f12WefJVinxNbX0pbnPSlKlCihLl26aOLEicqQIYMaNGigkydPauDAgcqXL5969OiR5GPaonv37vrhhx9Uq1Yt9ejRQ+XKlVNsbKxOnz6tNWvW6IMPPlCVKlW0f/9+devWTe3bt4/7Qn369Olq3ry5xo8fr+7du0syX6Nhw4Zp8ODBCgwM1OHDhzV06FAVKlRI0dHRcY/buXNneXp6qkaNGsqdO7dCQ0M1cuRI+fj4xK1pJSmuEHXy5MlHnktsbGzcf7cRERE6ffq0Vq5cqYULF6pUqVJauHBh3L7Vq1dX1qxZ9dZbb2nw4MFydXXVnDlz9McffzzW89ioUSN9+umnat26tbp06aIrV65o7NixCQpNU6dO1dq1a9WoUSPlz59fd+/ejevGqlOnzkMfIygoSEOHDtXt27fl5eUVt/2TTz7RoEGDVL9+fTVq1Cjee5ekeMWYjz/+WM8884yCg4PVt29f3b17V4MGDVL27Nn1wQcfxLvfzp0745738PBwGYah77//XpK57ti9rq06deqoVq1aKleunDJnzqz9+/drzJgxcnJy0rBhwx753CXl/TdbtmwaMGCABg4cqGzZsqlu3brasWOHhgwZojfeeEOlS5eOu1+3bt00ffp0dezYUQEBAfGO6e7uHq/YNHr0aL3wwgt65ZVX1LVrV128eFF9+/ZV2bJlEy0gbd26VYGBgcn+HggAAIBEGAAAAEhX9u7da7Rv397Inz+/4ebmZnh7exsVK1Y0Bg0aZFy8eDHevpGRkcbnn39uVKlSxciYMaPh7u5ulChRwujdu7dx+fLlePueOHHCkPTAP+3bt7cp39dff21UqVLF8Pb2Njw9PY0iRYoY7dq1M3bu3Bm3T2BgoFGmTJkE9y1QoIDRqFGjBNslGe+8807c32fMmGFIMk6cOGEYhmEcOnTIaNWqlVGkSBHD09PT8PHxMSpXrmzMnDkz7j7Lly83GjRoYOTJk8dwc3MzcubMaTRs2NDYuHFjgudgxowZ8R5/48aNxvPPPx93TlWrVjWWLVsWb597mXbs2BFv+7p16wxJxrp16x753N3To0cPQ5LRsGHDBLddu3bN6NSpk5EzZ07Dy8vLePbZZ42NGzcagYGBRmBg4CPPZcWKFUaFChUMT09Po3DhwsakSZOMwYMHG4l9tHjUa2nL8/4g9x7z0qVL8bbHxMQYo0ePNooXL264uroa2bNnN9q0aWP8888/8fZ70M+QLRK7782bN40BAwYYJUqUMNzc3AwfHx8jICDA6NGjhxEaGmrcvHnTKFmypFG6dGnj1q1b8e77zjvvGK6ursa2bdsMwzCMiIgI48MPPzTy5MljeHh4GE899ZSxZMkSo3379kaBAgXi7vfNN98Yzz33nOHn52e4ubkZ/v7+RosWLYx9+/bFO3727NmNqlWrPvK82rdvH++/WU9PTyN//vxG48aNja+//tqIiIhIcJ/Nmzcb1apVM7y8vIwcOXIYb7zxhrF79+4EPzuJ/Yzc/zNnGObPTIkSJQx3d3ejcOHCxsiRI43p06fH++91y5YtxksvvWQUKFDAcHd3N3x9fY3AwEBj6dKljzzHY8eOGU5OTsbChQsTZHnY+9f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bN82ft1OnpDFjpPz5rU4EAACA1HbsmFn82bxZql5datNGKlbM6lRAmmAvdQMKQTaylxcUAAA4qFOnpOHDpTt3zAJQyZJWJ0J6cvCg2RlUtaq5BhXj4gAAABzb1avSwoXm+Lc8eaS2baUaNaQMGaxOBqQp9lI3YDQcAABAWnb+vDRihHThgtS/v1S+vNWJkB6VKiX9+KP03XeMiwMAAHBUkZHSypXSvHlSdLTUooU5LtjT0+pkAJ4QHUE2spfKHgAAcBBXrkijR0tHjkgffSRVrmx1IsB044Y5Lu6ff8yfUcbFAQAA2C/DkHbulL791hxD3aCB1KqVlDOn1ckAu2AvdQMKQTaylxcUAADYubAw6dNPpR07zK6LwECrEwGJuzcurlo1qWdPxsUBAADYk9OnpdmzpXXrpEqVzNFvZcpYnQqwO/ZSN6AQZCN7eUEBAICdunVLmjjR/CDWo4c5dsvJyepUwMMZhjk7fto0syhUt67ViQAAAPAg4eHSDz9IixZJvr5m8ScoSHJ2tjoZYLfspW5AIchG9vKCAgAAO3P3rvTFF+YirO+8IzVtSgEI9ufeuLgzZ8xxcfnyWZ0IAAAAkrnWzy+/SHPmmL+zNW8uvfSS5O1tdTLAIdhL3cDF6gAAAADpUlSUNGOGtGCB1KmTtHo1V+LBfmXKJI0ZI/31l9S1q1Sjhjkuzs3N6mQAAADp0x9/mOv+7N8vvfCCNGqUlCeP1akAWIRCEAAAQGqKiZHmzpVmzpRee80sALnwKxkcROnS0tKlZoGzQQOpXz+pTh2rUwEAAKQP586ZnzXWrJHKljVHv1WowMQBAIyGs5W9tHgBAIA0KjbWnMU9ZYo5iqFzZ8nd3epUQMq5cUP6+GPp/HlzXFzevFYnAgAAcDy3b0tLlkjffSd5eZkXm9Wty8VmQCqxl7oB7wgAAAApyTCkFSukCRPMkQzLlpkf0ABHlymTNHas9Oef0ttvSzVrSt27My4OAADgScXGShs2SLNmSZcumeuMzpwp+fhYnQxAGkUhCAAAIKWsXSv9739StWrS999LafjqICDFlCljjoubP98cF/fRR1Lt2lanAgAAsD8HD5rFn+3bpaAgadAgqWBBq1MBsAOMhrORvbR4AQCANGDLFmnkSPML8F69pGzZrE4EpA3h4ea4uNBQxsUBAADY4tIl84Kan36SihSR2rWTKldm3R8gjbCXugEdQQAAAMllzx5p+HApf37pyy8lPz+rEwFpS+bM0iefSAcOSG+9JQUGSu+/z7g4AACA/7p7V1q+3CwAOTtLLVtKP/7IGqMAHhuFIAAAgCf111/SsGHmTO7x46V8+axOBKRtZcua62XNm2eOi+vfX3r+eatTAQAAWMcwpM2bzdFvp09LjRtLX3wh+fpanQyAA2A0nI3spcULAACkouPHzQKQk5M0YIA5qgFA0oSHS0OGSBcvmuPi8uSxOhEAAEDqOX5cmj1b2rRJqlpVattWKl7c6lQAbGQvdQM6ggAAAJLqn3/MEXA3bpgFoNKlrU4E2K/MmaVPP5X275e6dJGee84cF+fqanUyAACAlHHtmvTdd+a4N39/s/gzcKCUIYPVyQA4KApBAAAAtrpwQRo5UjpzxhxlVbGi1YkAxxEQYM7CnzPn33Fxzz1ndSoAAIDkERUlrVolzZ0rRUZKLVpI338veXpanQxAOsBoOBvZS4sXAABIAVevSmPGSAcPSn37StWqWZ0IcGxhYea4uCtXpFGjzCtlAQAA7I1hSLt2Sd9+Kx0+bF7s0qqV5OdndTIAycRe6gZ0BAEAADxIeLg0bpy0davUu7f5hTSAlOfjY/63t2+f1Lmz9PzzUrdujIsDAAD24Z9/zHV/1q6VnnrKHH9btqzVqQCkY3QE2cheKnsAACAZ3L4tTZok/fKL1L27eeWek5PVqYD0yTDML1JmzjRn5wcFWZ0IAAAgoRs3pB9+kBYtkrJlk9q0McfcOjtbnQxACrKXugGFIBvZywsKAACeQESENG2auWjr229LL73Egq1AWhEWJg0ebI5qZFwcAABIC2JizIvH5swxf1d5+WWpWTMpY0arkwFIJfZSN2A0HAAAQFSUObd77lypQwdp9Wqu3APSGh8fafx46Y8/pDfekOrUkd57j3FxAAAg9e3bJ82aZf5eUru2NGKElDev1akA4IG4xBUAAKRfMTHm1Xv160uxsdKqVeYIB4pAQNpVvrz0009S9uzm2MYNG6xOBAAA0oPQUOnTT6W6daWvv5ZatTIvIOvThyIQgDSP0XA2spcWLwAAYAPDkBYvliZPlpo0MRdv9fCwOhWApLp+3RwXd/26OS4ud26rEwEAAEdy+7Y5Nvq778zPC6+9ZhaC6EgG8P/spW7AaDgAAJB+GIbZ9TNunDnC4ccfJW9vq1MBeFxZskgTJkh790qdOplfzLz7ruTCxxwAAPCYYmOl334zR79duCA1bWp2AGXJYnUyAHhsfEICAADpw/r10pgxUuXK5hV9Pj5WJwKQXCpUkJYvN7+wqV9fGjRIqlXL6lQAAMCeHDpk/i6xfbv5e8SAAVKhQlanAoBkwWg4G9lLixcAALjPtm3m4q0lS0q9e0u+vlYnApCSrl+XBg6Ubtwwx8XlymV1IgAAkFZdvizNn2+uP1iokNS2rVS1quTkZHUyAHbCXuoGdAQBAADHtHevFBJirhkydSprhwDpRZYs0sSJ5ntAhw5mh9A77zAuDgAAmCIizE7iBQvMv7dsKS1ZIrm7WxoLAFISn4YAAIBjOXRIGjbMXPvnk0+k/PmtTgTAChUqmFf3fvut1KCBOS6uZk2rUwEAACsYhrR1q/l7walTUqNG0uTJUvbsVicDgFTBaDgb2UuLFwAA6dbff0vDh0sxMeY872LFrE4EIK24ds0sBN28KY0cybg4AADSi7//lmbPln77zRz51ratVKKE1akAOBB7qRvQEQQAAOzbmTPmGkBXr5oFoLJlrU4EIK3JmtUcF7d7tzkurkEDqWtXxsUBAOCIrl+XvvvOHPeWO7dZ/BkwQMqQwepkAGAZPvkAAAD7dPGiuRD8yZNS//5SpUpWJwKQ1j31lDkubuZMsxg0eLD07LNWpwIAAE8qKkpavVqaO1e6e1dq0cIsBnl5WZ0MANIERsPZyF5avAAAcHjXrkljx0r79kl9+vAlLoDHc/WqOS7u9m1zXJyfn9WJAABAUhiG2e377bfmOqH160utWjECFkCqspe6AR1BAADAPty4IU2YIG3aJH34obkekJOT1akA2Kts2aRJk6Rdu6T27c1Fo99+m3FxAACkdf/8I82ZI61dK1WoIL3xhhQQYHUqAEjTGI4JAADStjt3pE8+kZo1k8qXl1aulOrUoQgEIHlUqiStWGGOjmnQQPr9d6sTAQCA+928aXb+vPiiORb6qafMzwVjxlAEAgAbMBrORvbS4gUAgMOIjJS++kr64QfpzTel5s1Z4BVAyrp6VRo40FxbYORIKWdOqxMBAJB+xcSYXT+zZ0vXr5sXhjVrJmXKZHUyAIhjL3UD5h4AAIC0JTpamjXL/NO+vbnoK6OaAKSGbNmkzz+Xdu6U2rWTgoOlt97iPQgAgNR04IDZ/bNnj1S7tjkSOl8+q1MBgF3jsloAAJA2xMZK8+dL9epJERHSqlVmIYgvYAGktqefNsfFeXiY4+I2b7Y6EQAAji00VBo3zvws8OWX0quvSmvWSH37UgQCgGTAaDgb2UuLFwAAdscwpKVLzUXbGzUyx8B5elqdCgBMV65IAwZIUVHSiBGMiwMAILncuSP9+KO0cKF58UXr1mYhyNXV6mQAYDN7qRtwiS0AALCGYUg//yx98okUGCgtXixlzGh1KgCIz9dXmjLFHBfXtq25SPWbb0rOzlYnAwDA/sTGShs3muv+nD9v/n/166+lLFmsTgYADo1CEAAASH0bN0qjR0sVK0oLFvDBD0Da9/TT0sqV0vTp5ri4jz+WqlWzOhUAAPbhyBFzDdCtW6VnnzVHvhUpYnUqAEg3GA1nI3tp8QIAIE3bsUMKCZGKFpX69JFy5LA6EQAk3ZUrUv/+UkyMOS6O9zIAABK6csW86GvZMqlAAaldO/MiCicnq5MBQLKxl7oBHUEAACDl7d8vDRtmrq0xebLk7291IgB4fL6+0tSp0vbtUps2UtOmUpcujIsDACAiQlqxQpo3zxwF3bKlOQLaw8PqZACQrlEIAgAAKefIEbMA5OEhjRkjFSxodSIASD6VK5tfdt0bFzdsmFSlitWpAABIXYYhbdsmffut9PffUnCw9PnndMwCQBrCaDgb2UuLFwAAacLJk9Lw4eYVgQMHSsWLW50IAFLW5cvmuDjDMEdg8uUXAMDRnTghzZ4tbdhgXgjRtq1UsqTVqQAgVdlL3YCOIAAAkHzOnTPXy7h4URowQCpXzupEAJA6smeXvvjCvCL6tdekZs2kzp0ZFwcAcCxhYdJ330lLlkh+fmbxp39/KUMGq5MBAB6CQhAAAHhyly9Lo0ZJx49LH30kPfOM1YkAwBpVqkgrV0pffik1bGiOi6tc2epUAAA8vqgoac0aae5c6c4dqXlzaeFCycvL6mQAABsxGs5G9tLiBQBAqrp+XfrkE2n3bqlPH6lWLasTAUDacW9cnGSOi8ue3do8AADYyjCkvXvNdX/++kuqW1dq3VrKndvqZACQpthL3YCOIAAAkHQ3b0qffWbOA//gA2noUMnJyepUAJC23BsXt3Wr+eXZyy9Lb7zBuDgAQNp19qw0Z470yy/mmOfXX5fKl7c6FQDgCTHAEwAA2O7uXWncOKlpU6lMGWnVKvPqQIpAAPBgVaua4+JiY81xcTt2WJ0IAIB/3bwpzZolvfii1LevWfhZsUIaO5YiEAA4CEbD2cheWrwAAEgRkZHS11+bC8N27iy1aMGCsADwOC5d+ndR7ZAQydfX6kQAgPQoJkZat06aPVu6elVq1sz8w3deAJAk9lI3YDQcAAB4sJgY88PhN99IbdtKq1dLLvz6AACPLUcOado0acsWqVUrc8HtN96guA4ASB1//ml2/+zaJT3/vDniOX9+q1MBAFIYnzYAAEBCsbHSwoXm2Ldbt8yRRh06UAQCgORSrZr53hodLTVowLg4AEDKuXhRmjBBql/fXLvu5ZelNWukfv0oAgFAOsFoOBvZS4sXAABPxDCk5culzz4zPyi+/bbk5WV1KgBwbBcvSh99JLm6SsOHMy4OAPDk7tyRli2TFiyQ3NzMLtT69c1/BwAkG3upG3BZLwAAMAtAv/5qLgj77LPSokVSpkxWpwKA9CFnTumrr/4dF9eihdSxI+PiAABJExsr/f679O230rlzUpMm5v9fsma1OhkAwGIUggAASO9+/10aNUoqV06aO1fKls3qRACQPt0bFzd1qtSwoRQSIlWqZHUqAEBadveutHWr9PPP0rZt5kVdffpIRYtanQwAkIYwGs5G9tLiBQCAzXbtMr9kLFhQ6tvXvCIdAJA2XLxort3g7m6Oi6NIDwCQpNu3zQ7S9eulPXvMsaJVqkjPPy8984zk5GR1QgBIV+ylbkBHEAAA6c2ff0rDhplfKk6cKOXJY3UiAMD9cuaUpk83uzZffVVq2VLq0IFxcQCQ3ty8af6/YMMG6Y8/zAsEqlc3x74NHiy58NUeAODR6Aiykb1U9gAAeKBjx8wCkLOzNGCAVLiw1YkAALaIjjbHxS1fLo0YIT31lNWJAAApJTxc2rTJLPzs3y95eUk1akiBgVL58ubv8gCANMNe6gYUgmxkLy8oAAAJnD5tjhW6dcssAJUqZXUiAMDjuHDBHBfn6flvZycAwL5dvy5t3GgWfv76S8qU6d/CT0AAnaAAkMbZS92A/lEAABxVaKh55fj581L//lKFClYnAgA8CT8/6euvzSvFX31VatVKev11viQEAHty5cq/hZ9Dh6QsWaRnnzXfz0uX5j0dAJAiKAQBAOBorlyRxowxP1j26ydVrWp1IgBAcnr2WWnlSmnyZKlRIykkhHFxAJBWXbok/fabtH69dPSo5Osr1awpvfmmVKKE5ORkdUIAQDrAaDgb2UuLFwAgHQsLk8aNk7Zvl3r3loKCrE4EAEhpoaHSRx9J3t7S0KFS1qxWJwKA9C001Oz22bBB+vtvKUcOc8xbYKBUtCiFHwBwMPZSN6AQZCN7eUEBAOnQrVvSpEnSr79KPXpI9evzARMA0puNG6WPP5Zee01q357RQgCQWs6e/bfwc/q0OcbzXuGnUCF+LwcAB2cvdQMKQTaylxcUAJCO3L0rTZsmLV0qde0qvfQSHzQBID2LjpY+/1xatcpcI65iRasTAYDjOX3638LP2bOSv/+/hZ8CBaxOBwBIZfZSN6AQZCN7eUEBAOlAVJQ0c6Y0f77UsaPUsqXk7Gx1KgBAWhEaaq4RlzGjNGyYuRA5ACDpDEM6efLfwk9oqJQv37+Fn7x5rU4IALCYvdQNXKwOAAAAbBQTI82bJ339tdS6tXnFt6ur1akAAGlNrlzSjBnm4uTNm0tt2kjt2jEuDgAexTCk48f/LfxcuiQVLGgWfUaMkHLntjohAACPhUIQAABpXWystHixNHmy1LSptHKl5O5udSoAQFpXq5b5/4zPP5eCg80vMStUsDoVAKQdhiEdOSKtX2+utXblilSkiBQUJI0dK+XMaXVCAACSBaPhbGQvLV4AAAdiGOYXeOPHS3XqSO++K3l5WZ0KAGCPzp83x8VlziwNHcq4OADpk2FIf/1ldvts3Chdvy6VKGF2/NSsKWXPbnVCAICdsZe6AR1BAACkRevWSWPGSFWrSt9/b35xBwDA48qd21xfbsMGc1xcu3ZS27aSk5PVyQAg5cTGSgcOmO99mzZJN25IpUqZhZ/Jk6WsWa1OCABAqqAQBABAWrJ1qzm6p3RpafZsydfX6kQAAEcSGChVry5NmvTvuLjy5a1OBQDJIyZG2rfPLPz8/rt065ZUtqz53teuneTjY3VCAAAswWg4G9lLixcAwE7t2SOFhEh58pije3LlsjoRAMDRnT8v9e1rjokbOpQvSAHYn+hoae9es/CzebN0965Urpy5xk/16lKmTFYnBAA4OHupG9ARBACAlQ4elIYNMz+kjhsn5ctndSIAQHqRO7f0zTfmF6gvvyy1by+1acO4OABpV1SUtHu3tH692UkfFSVVrGh2/Lz1luTtbXVCAADSJDqCbGQvlT0AgJ34+2+zAGQY0oABUtGiVicCAKRnUVHSxInSr79KI0eaV9QDgNUiI6UdO8yC9fbt5ui3SpXMwk/VqpKnp9UJAQDpnL3UDSgE2cheXlAAQBp35ow0fLgUHm4WgEqXtjoRAAD/OnfOHFGaLZs0ZAjj4gCkrogIads2s/Czc6e57emnzcJP5cqSh4e1+QAAuI+91A0YDQcAQGq4cMG8wvqff6T+/aWnnrI6EQAACfn7m+Pi1q83x8W9/rr02muMiwOQMu7cMUe8bdgg7dolOTubBZ/nn5f69JHc3KxOCACAQ6AjyEb2UtkDAKQxV69K//uf9Oef5oLc1atbnQgAANtERUmffSatW2dezBAQYHUiAPbu1i1pyxaz8LN7t1noqVpVCgoyL5RydbU6IQAASWIvdQMKQTaylxcUAJBGhIdL48ebH3R79TKvagQAwB6dPWtezJAjhzkujs9DAGx144b0++9m4WffPnO0W/Xq5qi3ChUkFwbVAADsm73UDSgE2cheXlAAgMVu35Y+/1z6+Wfp/felhg0ZpwMAcAzr1kkhIVKHDlLr1vz/DUBCYWHSpk1m4efAAcnbW6pRwyz8lCtnjn4DAMCB2EvdgEsvAABIDhER0pdfSosXS2+/La1aJWXIYHUqAACSz3PPmV/ofvaZ1LixNGqUVLas1akAWOnaNWnjRrPw89dfZsfgs89KbdqY7w/8PgwAQJpAR5CN7KWyBwBIZdHR5qLac+b8e4U0VzoCABzdmTPmuDg/P2nwYMbFAenFlSvSb7+ZhZ/Dh6UsWaSaNc2On9Kl6RQEAKQ79lI3sPTSjOjoaA0YMECFChWSp6enChcurKFDhyo2NlaSFBUVpT59+iggIEDe3t7y9/dXu3btdO7cuXjHiYiI0Hvvvafs2bPL29tbTZo00ZkzZ+Ltc+3aNbVt21Y+Pj7y8fFR27Ztdf369dQ6VQCAo4mNlebOlerVk2JipNWrpbZtKQIBANKHvHml2bPNEagvvWT+P5FrDAHHc/Gi9P330rvvSvXrm6OPL140O+BXrJDmzZO6dpXKlKEIBABAGmZpR1BISIjGjRunb775RmXKlNHOnTvVoUMHDR8+XO+//77CwsLUvHlzde7cWeXLl9e1a9fUvXt3RUdHa+fOnXHHefvtt7Vs2TLNnDlTvr6++uCDD3T16lXt2rVLzv//hVyDBg105swZTZs2TZLUpUsXFSxYUMuWLbMpq71U9gAAKcwwpCVLzHWAGjeW3nzTXPQWAID0KjJSmjDB7BIYNcr8QhiAfTp/3uz22bBBOnFCypnT7PYJDJSKFKHYAwDAfeylbmBpISg4OFh+fn6aPn163LaXX35ZXl5emjVrVqL32bFjhypXrqxTp04pf/78CgsLU44cOTRr1iy9+uqrkqRz584pX758WrFiherVq6eDBw+qdOnS2rp1q6pUqSJJ2rp1q6pVq6ZDhw6pRIkSj8xqLy8oACCFGIbZ9TNunLlGwrvvShkzWp0KAIC04964uFy5zHFxmTJZnQjAo5w582/h559/pNy5pVq1zMJPwYIUfgAAeAR7qRu4WPngzz77rKZOnaojR46oePHi+uOPP7Rp0yaNHz/+gfcJCwuTk5OTsmTJIknatWuXoqKiVLdu3bh9/P39VbZsWW3evFn16tXTli1b5OPjE1cEkqSqVavKx8dHmzdvTrQQFBERoYiIiLi/h4eHP/kJAwDs04YN0pgx0tNPSwsWmLPQAQBAfPfGxf3yi9S0qdS5s/Tqq3yRDKQlp079W/g5d07Kk8cs+gwYIOXPb3U6AACQQiwtBPXp00dhYWEqWbKknJ2dFRMTo5CQELVq1SrR/e/evau+ffuqdevWcdW10NBQubm5KWvWrPH29fPzU2hoaNw+OXPmTHC8nDlzxu1zv5EjR+rjjz9+ktMDANi77dulESOk4sWlb76Rsme3OhEAAGlfnTpmR8G4cdKLL5rj4kqXtjoVkP4Yhjne7V7h58IFs9gTGCgNHWoWgQAAQLpgaSFowYIFmj17tubOnasyZcpo79696t69u/z9/dW+fft4+0ZFRally5aKjY3V5MmTH3lswzDk9J8rz5wSuQrt/n3+q1+/furZs2fc38PDw5UvXz5bTw0AYM/++EMaPtwcbTNlijkiAwAA2M7NTerTxxw11bev5O8vDRrEuDggJRmGdOzYv4Wfy5elQoWkoCCzIJsrl9UJAQCARSwtBPXq1Ut9+/ZVy5YtJUkBAQE6deqURo4cGa8QFBUVpRYtWujEiRNau3ZtvFl7uXLlUmRkpK5duxavK+jixYuqXr163D4XLlxI8PiXLl2Sn59fotnc3d3l7u6eLOcJALAThw6ZBSBPT2nsWKlAAasTAQBg3/Llk+bMkX7+2RwX16WL1KIF4+KA5GAY0uHD0vr10saN0tWrUrFiZsfPp59KOXJYnRAAAKQRlhaCbt++rQwZMsTb5uzsrNjY2Li/3ysCHT16VOvWrZOvr2+8/StVqiRXV1f9/PPPatGihSTp/PnzOnDggMaMGSNJqlatmsLCwrR9+3ZVrlxZkrRt2zaFhYXFFYsAAOnYiRNmASgqyrxauXhxqxMBAOBYXngh/ri40aOlUqWsTgXYl9hY6a+/zG6fTZuksDCpRAmz8PPZZ9J935cAAADcY2khqHHjxgoJCVH+/PlVpkwZ7dmzR59++qk6duwoSYqOjlbz5s21e/duLV++XDExMXFr+mTLlk1ubm7y8fFRp06d9MEHH8jX11fZsmXThx9+qICAANWpU0eSVKpUKdWvX1+dO3fWF198IUn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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -1207,17 +642,9 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2018-01-01T00:00:00+00:00\n" - ] - } - ], + "outputs": [], "source": [ "# The 2018-01-01 observation is the 3rd item in the list.\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -1226,33 +653,15 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://earth.gov/ghgcenter/api/raster/stac/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=oco2-mip-co2budget-yeargrid-v1&item=oco2-mip-co2budget-yeargrid-v1-2018&assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", - " 'center': [0.0, 0.0, 0]}" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# A GET request is made for the 2018-01-01 tile\n", "co2_flux_3 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -1272,97 +681,9 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the 2018-01-01 tile\n", "aoi_map_bbox = Map(\n", @@ -1432,7 +753,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb index d82181e8..80ab5ca9 100644 --- a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb +++ b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2geos-co2-daygrid-v10r_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Foco2geos-co2-daygrid-v10r_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "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 OCO-2 GEOS Column CO₂ Concentrations data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json` endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, visualize two tiles (side-by-side), allowing time point comparison.\n", "4. After the visualization, perform zonal statistics for a given polygon.\n", "\n", @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ "source": [ "color_map = \"magma\"\n", "oco2_1 = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[0]]['collection']}/items/{items[list(items.keys())[0]]['id']}/tilejson.json?\"\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", @@ -245,7 +245,7 @@ "outputs": [], "source": [ "oco2_2 = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[1]]['collection']}&item={items[list(items.keys())[1]]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[1]]['collection']}/items/{items[list(items.keys())[1]]['id']}/tilejson.json?\"\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", @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +497,7 @@ "outputs": [], "source": [ "oco2_3 = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\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", @@ -568,7 +568,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb index 6c5b53fc..4decea51 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb @@ -27,9 +27,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fodiac-ffco2-monthgrid-v2022_User_Notebook.ipynb&branch=main)", - + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fodiac-ffco2-monthgrid-v2022_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -41,7 +39,7 @@ "## Approach\n", "\n", " 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.\n", - " 2. Pass the STAC item into raster API `/stac/tilejson.json` endpoint\n", + " 2. Pass the STAC item into raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint\n", " 3. We'll visualize two tiles (side-by-side) allowing for comparison of each of the time points using `folium.plugins.DualMap`\n", " 4. After the visualization, we'll perform zonal statistics for a given polygon.\n", " " @@ -98,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -257,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -289,7 +287,7 @@ "january_2020_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2020-01']['collection']}&item={items['2020-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -318,7 +316,7 @@ "january_2000_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2000-01']['collection']}&item={items['2000-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -391,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -483,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -664,7 +662,7 @@ "october_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -756,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb index 90f0a7a9..8c969671 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb @@ -42,7 +42,7 @@ "## Approach\n", "\n", " 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 2023.\n", - " 2. Pass the STAC item into raster API `/stac/tilejson.json` endpoint\n", + " 2. Pass the STAC item into raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint\n", " 3. We'll visualize two tiles (side-by-side) allowing for comparison of each of the time points using `folium.plugins.DualMap`\n", " 4. After the visualization, we'll perform zonal statistics for a given polygon.\n", " " @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -290,7 +290,7 @@ "january_2020_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2020-01']['collection']}&item={items['2020-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -319,7 +319,7 @@ "january_2000_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2000-01']['collection']}&item={items['2000-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +665,7 @@ "october_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}//collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -757,7 +757,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb b/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb index 22a9fa9c..0b8df698 100644 --- a/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb +++ b/user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fsedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fsedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", " 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 SEDAC gridded population density.\n", - " 2. Pass the STAC item into raster API `/stac/tilejson.json` endpoint\n", + " 2. Pass the STAC item into raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint\n", " 3. We'll visualize two tiles (side-by-side) allowing for comparison of each of the time points using `folium.plugins.DualMap`\n", " 4. After the visualization, we'll perform zonal statistics for a given polygon.\n", " " @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ "january_2020_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2020-01']['collection']}&item={items['2020-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2020-01']['collection']}/items/{items['2020-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -303,7 +303,7 @@ "january_2000_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2000-01']['collection']}&item={items['2000-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2000-01']['collection']}/items/{items['2000-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -646,7 +646,7 @@ "january2010_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -738,7 +738,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb index 9e7b83d9..885e7dd8 100644 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb @@ -23,7 +23,7 @@ "\n", "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Ftm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb&branch=main)", + "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Ftm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb&branch=main)\n", " " ] }, @@ -35,7 +35,7 @@ "## Approach\n", "\n", "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 TM5-4DVar Isotopic CH₄ Inverse Fluxes Data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json `endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon.\n", "\n", @@ -65,12 +65,10 @@ ] }, { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Import the following libraries\n", "import requests\n", @@ -94,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -224,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +275,7 @@ "ch4_flux_1 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2016-12-01']['collection']}&item={items['2016-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -305,7 +303,7 @@ "ch4_flux_2 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['1999-12-01']['collection']}&item={items['1999-12-01']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -386,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +670,7 @@ "ch4_flux_3 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -765,7 +763,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 5e337248..0ea45402 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -36,7 +36,7 @@ "## Approach\n", "\n", "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 Vulcan Fossil Fuel CO₂ Emissions Data product.\n", - "2. Pass the STAC item into the raster API `/stac/tilejson.json `endpoint.\n", + "2. Pass the STAC item into the raster API `/collections/{collection_id}/items/{item_id}/tilejson.json` endpoint.\n", "3. Using `folium.plugins.DualMap`, we will visualize two tiles (side-by-side), allowing us to compare time points. \n", "4. After the visualization, we will perform zonal statistics for a given polygon.\n", "\n", @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "503e5661-b99b-421b-b4ba-361bb531e5fa", "metadata": {}, "outputs": [], @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "id": "a7602b2d-2650-42cb-9ae3-09eb3fe7891b", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "941eae92-6ec0-46a4-ab8f-739973b59e6f", "metadata": {}, "outputs": [], @@ -130,139 +130,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "79b48141-ccc5-42ac-bc8f-1b480398265c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'vulcan-ffco2-yeargrid-v4-updated',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://dev.ghg.center/api/stac/collections/vulcan-ffco2-yeargrid-v4-updated/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://dev.ghg.center/api/stac/'},\n", - " {'rel': 'root',\n", - " 'type': 'application/json',\n", - " 'href': 'https://dev.ghg.center/api/stac/'},\n", - " {'rel': 'self',\n", - " 'type': 'application/json',\n", - " 'href': 'https://dev.ghg.center/api/stac/collections/vulcan-ffco2-yeargrid-v4-updated'}],\n", - " 'title': 'Vulcan Fossil Fuel CO₂ Emissions v4.0',\n", - " 'extent': {'spatial': {'bbox': [[-128.22654896758996,\n", - " 22.857766529124284,\n", - " -65.30917199495289,\n", - " 51.44087947724907]]},\n", - " 'temporal': {'interval': [['2010-01-01 00:00:00+00',\n", - " '2021-12-31 00:00:00+00']]}},\n", - " 'license': 'CC-BY-NC-4.0',\n", - " 'renders': {'air-co2': {'assets': ['air-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'cmt-co2': {'assets': ['cmt-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'cmv-co2': {'assets': ['cmv-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'com-co2': {'assets': ['com-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'elc-co2': {'assets': ['elc-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'ind-co2': {'assets': ['ind-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'nrd-co2': {'assets': ['nrd-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'onr-co2': {'assets': ['onr-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'res-co2': {'assets': ['res-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'rrd-co2': {'assets': ['rrd-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'dashboard': {'assets': ['total-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'},\n", - " 'total-co2': {'assets': ['total-co2'],\n", - " 'rescale': [[0, 150]],\n", - " 'colormap_name': 'rdylbu_r'}},\n", - " 'providers': [{'url': 'https://vulcan.rc.nau.edu/',\n", - " 'name': 'North American Carbon Program',\n", - " 'roles': ['producer', 'licensor']}],\n", - " 'summaries': {'datetime': ['2010-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n", - " 'description': 'The Vulcan version 4.0 data product represents total carbon dioxide (CO₂) emissions resulting from the combustion of fossil fuel (FF) for the contiguous United States and District of Columbia. Referred to as FFCO₂, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Included is the estimated total annual FFCO2 for the United States, as well as the estimated total annual FFCO₂ per sector in units of metric tonnes of carbon per 1 km x 1 km grid cell per year (tonne C/km2/year). The Vulcan Project is a multiagency (NASA, DOE, NOAA, NIST) funded effort under the North American Carbon Program (NACP) to quantify North American fossil fuel carbon dioxide (FFCO₂) emissions at space and time scales much finer than has been achieved in the past. The purpose is to aid in quantification of the North American carbon budget, to support inverse estimation of carbon sources and sinks, and to support the demands posed by higher resolution FFCO₂ observations (in situ and remotely sensed). The detail and scope of the Vulcan FFCO₂ inventory has also made it a valuable tool for policymakers, demographers, social scientists and the public at large. Learn more at the Vulcan website: https://vulcan.rc.nau.edu/index.html',\n", - " 'item_assets': {'air-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Airport Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from airports.'},\n", - " 'cmt-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Cement Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from cement production.'},\n", - " 'cmv-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Commercial Marine Vessel Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from commercial marine vessels.'},\n", - " 'com-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Commercial Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from commercial sources.'},\n", - " 'elc-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Power Plant Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from power plants.'},\n", - " 'ind-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Industrial Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from industrial fossil fuel combustion.'},\n", - " 'nrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Non-Road Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from non-road engines, equipment and vehicles.'},\n", - " 'onr-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'On-Road Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from on-road engines, equipment and vehicles.'},\n", - " 'res-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Residential Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions from residential sources.'},\n", - " 'rrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Railroad Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual FFCO₂ emissions coming from railroads.'},\n", - " 'total-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'Total Fossil Fuel CO₂ Emissions',\n", - " 'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (FFCO₂) across all sectors.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", - " 'dashboard:is_periodic': True,\n", - " 'dashboard:time_density': 'year'}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "collection_vulcan" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "b56d6c87-11b9-48ca-9723-8304984a644b", "metadata": {}, "outputs": [], @@ -292,18 +170,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "eaaf1487-2e71-490b-a968-e57ee332a919", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 12 items\n" - ] - } - ], + "outputs": [], "source": [ "# Apply the above function and check the total number of items available within the collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -325,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "id": "01619ee2-728c-4abc-9edf-a2c85d87128d", "metadata": {}, "outputs": [], @@ -338,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "f9deb11d-4c33-4f59-8532-7829602e3113", "metadata": {}, "outputs": [], @@ -356,31 +226,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "27b31984-ac8e-473f-ba2d-eb309c9708c1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://dev.ghg.center/api/raster/collections/vulcan-ffco2-yeargrid-v4-updated/items/vulcan-ffco2-yeargrid-v4-updated-2021/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=vulcan-ffco2-yeargrid-v4-updated&item=vulcan-ffco2-yeargrid-v4-updated-2021&assets=total-co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-128.22654896758996,\n", - " 22.857766529124284,\n", - " -65.30917199495289,\n", - " 51.44087947724907],\n", - " 'center': [-96.76786048127143, 37.14932300318668, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "color_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", @@ -388,8 +237,7 @@ "# To change the year and month of the observed parameter, you can modify the \"items['YYYY-MM']\" statement\n", "# For example, you can change the current statement \"items['2003-12']\" to \"items['2016-10']\" \n", "_2021_tile = requests.get(\n", - " f\"{RASTER_API_URL}/collections/{items['2021']['collection']}/items/{items['2021']['id']}/tilejson.json?collection={items['2021']['collection']}&item={items['2021']['id']}\"\n", - "\n", + " f\"{RASTER_API_URL}/collections/{items['2021']['collection']}/items/{items['2021']['id']}/tilejson.json?\"\n", " f\"&assets={asset_name}\"\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", " f\"&rescale=0,150\", \n", @@ -399,34 +247,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "80f5933d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tilejson': '2.2.0',\n", - " 'version': '1.0.0',\n", - " 'scheme': 'xyz',\n", - " 'tiles': ['https://dev.ghg.center/api/raster/collections/vulcan-ffco2-yeargrid-v4-updated/items/vulcan-ffco2-yeargrid-v4-updated-2010/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?collection=vulcan-ffco2-yeargrid-v4-updated&item=vulcan-ffco2-yeargrid-v4-updated-2010&assets=total-co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-128.22654896758996,\n", - " 22.857766529124284,\n", - " -65.30917199495289,\n", - " 51.44087947724907],\n", - " 'center': [-96.76786048127143, 37.14932300318668, 0]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "_2010_tile = requests.get(\n", - " f\"{RASTER_API_URL}/collections/{items['2010']['collection']}/items/{items['2010']['id']}/tilejson.json?collection={items['2010']['collection']}&item={items['2010']['id']}\"\n", + " f\"{RASTER_API_URL}/collections/{items['2010']['collection']}/items/{items['2010']['id']}/tilejson.json?\"\n", "\n", " f\"&assets={asset_name}\"\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", @@ -445,159 +272,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "6cf3c0f1-8d39-4cdd-96c7-96e6229c1584", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", "\n", @@ -637,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "id": "8f1c5e47", "metadata": {}, "outputs": [], @@ -663,116 +341,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "65d66484", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -800,18 +372,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "7b0cf1d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 12 items\n" - ] - } - ], + "outputs": [], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -843,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 39, "id": "8c7dcae7", "metadata": {}, "outputs": [], @@ -881,18 +445,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d3666ccb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2021-01-01\n" - ] - } - ], + "outputs": [], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -936,48 +492,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ee0e9a7e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'statistics': {'b1': {'min': 1.7858106104995386e-07,\n", - " 'max': 3768974.0,\n", - " 'mean': 261.6907958984375,\n", - " 'count': 296120.0625,\n", - " 'sum': 77491896.0,\n", - " 'std': 11522.940944047226,\n", - " 'median': 7.39058256149292,\n", - " 'majority': 0.19903208315372467,\n", - " 'minority': 1.7858106104995386e-07,\n", - " 'unique': 242084.0,\n", - " 'histogram': [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2.0],\n", - " [1.7858106104995386e-07,\n", - " 376897.40625,\n", - " 753794.8125,\n", - " 1130692.25,\n", - " 1507589.625,\n", - " 1884487.0,\n", - " 2261384.5,\n", - " 2638281.75,\n", - " 3015179.25,\n", - " 3392076.75,\n", - " 3768974.0]],\n", - " 'valid_percent': 95.72,\n", - " 'masked_pixels': 13265.0,\n", - " 'valid_pixels': 296520.0,\n", - " 'percentile_2': 0.20036202669143677,\n", - " 'percentile_98': 1811.0848388671875}},\n", - " 'datetime': '2021-01-01'}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -985,196 +503,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "d6b249ee", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-01-011.785811e-073768974.0261.690796296120.062577491896.011522.9409447.3905830.1990321.785811e-07242084.0[[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0...95.7213265.0296520.00.2003621811.0848392021-01-01
12020-01-011.622880e-073548930.5254.962936296120.062575499640.010912.7683016.8367270.1808451.622880e-07242118.0[[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.1822241713.1064452020-01-01
22019-01-011.877873e-073369803.0276.884796296120.062581991144.011799.9111867.8145980.5271551.877873e-07242967.0[[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.2122441882.4636232019-01-01
32018-01-011.853067e-073868559.5278.522980296120.062582476240.012222.4167827.7118600.5199301.853067e-07242928.0[[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0...95.7213265.0296520.00.2089771912.6325682018-01-01
42017-01-011.802980e-074050962.5271.169403296120.062580298704.013290.4723777.4465570.5057951.802980e-07242922.0[[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0...95.7213265.0296520.00.2032701775.7847902017-01-01
\n", - "
" - ], - "text/plain": [ - " datetime min max mean count sum \\\n", - "0 2021-01-01 1.785811e-07 3768974.0 261.690796 296120.0625 77491896.0 \n", - "1 2020-01-01 1.622880e-07 3548930.5 254.962936 296120.0625 75499640.0 \n", - "2 2019-01-01 1.877873e-07 3369803.0 276.884796 296120.0625 81991144.0 \n", - "3 2018-01-01 1.853067e-07 3868559.5 278.522980 296120.0625 82476240.0 \n", - "4 2017-01-01 1.802980e-07 4050962.5 271.169403 296120.0625 80298704.0 \n", - "\n", - " std median majority minority unique \\\n", - "0 11522.940944 7.390583 0.199032 1.785811e-07 242084.0 \n", - "1 10912.768301 6.836727 0.180845 1.622880e-07 242118.0 \n", - "2 11799.911186 7.814598 0.527155 1.877873e-07 242967.0 \n", - "3 12222.416782 7.711860 0.519930 1.853067e-07 242928.0 \n", - "4 13290.472377 7.446557 0.505795 1.802980e-07 242922.0 \n", - "\n", - " histogram valid_percent \\\n", - "0 [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0... 95.72 \n", - "1 [[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0... 95.72 \n", - "2 [[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0... 95.72 \n", - "3 [[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0... 95.72 \n", - "4 [[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0... 95.72 \n", - "\n", - " masked_pixels valid_pixels percentile_2 percentile_98 date \n", - "0 13265.0 296520.0 0.200362 1811.084839 2021-01-01 \n", - "1 13265.0 296520.0 0.182224 1713.106445 2020-01-01 \n", - "2 13265.0 296520.0 0.212244 1882.463623 2019-01-01 \n", - "3 13265.0 296520.0 0.208977 1912.632568 2018-01-01 \n", - "4 13265.0 296520.0 0.203270 1775.784790 2017-01-01 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -1210,21 +542,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "ec61db4a", "metadata": {}, - "outputs": [ - { - "data": { 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -1309,7 +630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.6" } }, "nbformat": 4, From 2862cd24b9a74a0ae32bcb455b1541c8331006d9 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Thu, 12 Sep 2024 10:37:58 -0500 Subject: [PATCH 30/47] minor change --- .../gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb index 03811fd3..a58895f0 100644 --- a/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb +++ b/generating_statistics_for_validation/gra2pes-ghg-monthgrid-v1/gra2pes-ghg-monthgrid-v1-generate-statistics.ipynb @@ -169,11 +169,12 @@ "metadata": {}, "outputs": [], "source": [ - "keys = [\"overall_raw\", \"nan_min\", \"nan_max\", \"nan_mean\", \"nan_std\"]\n", - "values = [\"overall_cog\", 0.0, 110011.766, 5.1753297, 172.27177]\n", "\n", - "# Create a dictionary from the lists\n", - "data_dict = dict(zip(keys, values))\n", + "keys = [\"data\", \"nan_min\", \"nan_max\", \"nan_mean\", \"nan_std\"]\n", + "values_set1 = [\"overall_raw\", 0.0, 110011.766, 5.1753297, 172.27177]\n", + "values_set2 = [\"overall_cog\", 0.0, 110011.766, 5.1753297, 172.27177]\n", + "\n", + "data_dict = {key: [val1, val2] for key, val1, val2 in zip(keys, values_set1, values_set2)}\n", "\n", "# Save the dictionary as a JSON file\n", "with open(\"overall_stats.json\", \"w\") as json_file:\n", From 4d1faf259648692fbf0bc0b562a2b096b7cab99b Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Thu, 12 Sep 2024 10:42:12 -0500 Subject: [PATCH 31/47] change category for gra2pes --- _quarto.yml | 8 ++++---- datatransformationcode.qmd | 2 +- datausage.qmd | 4 ++-- processingreport.qmd | 2 +- workflow.qmd | 3 ++- 5 files changed, 10 insertions(+), 9 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index c012a158..fbbd1888 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -58,7 +58,6 @@ website: - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb - user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb - user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb - - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -67,6 +66,7 @@ website: - user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb - user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb - user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb + - user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb - section: Large Emissions Events contents: - user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb @@ -87,13 +87,13 @@ website: - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb - cog_transformation/epa-ch4emission-grid-v2express.ipynb - cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb - - cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb - cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb - cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb - cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb + - cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb - section: Large Emissions Events contents: - cog_transformation/emit-ch4plume-v1.ipynb @@ -114,7 +114,6 @@ website: - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd - processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd - - processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd @@ -123,6 +122,7 @@ website: - processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd - processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd + - processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd - section: Large Emissions Events contents: - processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd @@ -144,7 +144,6 @@ website: - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd - data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd - data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd - - data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd - section: Natural Greenhouse Gas Sources Emissions and Sinks contents: - data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd @@ -153,6 +152,7 @@ website: - data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd - data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd - data_workflow/lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd + - data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd - section: Large Emissions Events contents: - data_workflow/emit-ch4plume-v1_Data_Flow.qmd diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index d12ae9ac..d25d80e8 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -20,13 +20,13 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory](cog_transformation/epa-ch4emission-grid-v2express.ipynb) 5. [Vulcan Fossil Fuel CO₂ Emissions](cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb) -6. [Greenhouse Gas And Air Pollutants Emissions System](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5](cog_transformation/eccodarwin-co2flux-monthgrid-v5.ipynb) 2. [GOSAT-based Top-down Total and Natural Methane Emissions](cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb) 3. [OCO-2 MIP Top-Down CO₂ Budgets](cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb) 4. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) +5. [Greenhouse Gas And Air Pollutants Emissions System](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes](cog_transformation/emit-ch4plume-v1.ipynb) diff --git a/datausage.qmd b/datausage.qmd index e3f55cc3..36b7bf00 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -22,8 +22,6 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset. 5. Vulcan Fossil Fuel CO₂ Emissions - [Beginner level notebook](user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Vulcan Fossil Fuel CO₂ Emissions, Version 4 dataset. -5. GRA2PES - - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. ## Natural Greenhouse Gas Emissions and Sinks 1. Air-Sea CO₂ Flux, ECCO-Darwin Model v5 @@ -39,6 +37,8 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. 6. Wetland Methane Emissions, LPJ-EOSIM model - [Beginner level notebook](user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset. +7. GRA2PES + - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. ## Large Emissions Events 1. EMIT Methane Point Source Plume Complexes diff --git a/processingreport.qmd b/processingreport.qmd index 1eefa04d..7a02d037 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -19,7 +19,6 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Processing and Verification Report](processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing and Verification Report.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Processing and Verification Report](processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd) -6. [Greenhouse Gas And Air Pollutants Emissions System Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) ## Natural Greenhouse Gas Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Processing and Verification Report](processing_and_verification_reports/eccodarwin-co2flux-monthgrid-v5_Processing and Verification Report.qmd) @@ -28,6 +27,7 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [OCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report](processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd) 5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report](processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd) +7. [Greenhouse Gas And Air Pollutants Emissions System Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Processing and Verification Report](processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd) diff --git a/workflow.qmd b/workflow.qmd index 17da9878..97876e77 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -17,7 +17,7 @@ Explore, analyze, and make a difference with the US GHG Center. 3. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 4. [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd) 5. [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd) -5. [Greenhouse Gas And Air Pollutants Emissions System Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) + ## Natural Greenhouse Gas Sources Emissions and Sinks 1. [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd) @@ -26,6 +26,7 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [OCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram](data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd) 5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram](data_workflow/lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd) +7. [Greenhouse Gas And Air Pollutants Emissions System Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Data Flow Diagram](data_workflow/emit-ch4plume-v1_Data_Flow.qmd) From 29c3a8586808c89bc27269e09f2b3676e8ca37dd Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Thu, 12 Sep 2024 10:51:24 -0500 Subject: [PATCH 32/47] update desc --- cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb | 2 +- ...2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb index 4fa089f9..690b6241 100644 --- a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb +++ b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -11,7 +11,7 @@ "source": [ "---\n", "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", - "description: Monthly Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", + "description: Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", "execute:\n", diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd index 2f9c302f..70486e6a 100644 --- a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -1,6 +1,6 @@ --- title: Greenhouse Gas And Air Pollutants Emissions System -description: "The Greenhouse gas And Air Pollutants Emissions System (GRA2PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 1 in the Scientific Details section below for a full sector list). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NOx, SOx, and PM2.5 with consistency in spatial and temporal distributions. Emissions by sectors are grouped into point and area sources, reported as column totals in units of metric tons per km2 per month. Spatial-temporal surrogates are developed to distribute CO2 emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA2PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." +description: "The Greenhouse gas And Air Pollutants Emissions System (GRA²PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 2 in the Scientific Details section below for a full sector list). Fossil fuel CO₂ (ffCO₂) emissions are developed along with those of air pollutants including CO, NOₓ, SO₂, and PM₂.₅ with consistency in spatial and temporal distributions. Emissions by sectors are reported as column totals in units of metric tons per km² per month. Spatial-temporal surrogates are developed to distribute CO₂ emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA²PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." --- From 7ebbbc11161a1ecbee599ffdc28ccd0a9be02178 Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Thu, 12 Sep 2024 10:58:33 -0500 Subject: [PATCH 33/47] change title --- cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb | 4 ++-- data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd | 2 +- datatransformationcode.qmd | 2 +- datausage.qmd | 2 +- ...hg-monthgrid-v1_Processing and Verification Report.qmd | 2 +- processingreport.qmd | 2 +- .../gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb | 8 ++++++-- workflow.qmd | 2 +- 8 files changed, 14 insertions(+), 10 deletions(-) diff --git a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb index 690b6241..26dd8464 100644 --- a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb +++ b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -10,8 +10,8 @@ }, "source": [ "---\n", - "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", - "description: Monthly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants over the contiguous United States\n", + "title: GRA²PES Greenhouse Gas and Air Quality Species\n", + "description:Monthly, 0.036 degree resolution emissions of fossil fuel carbon dioxide (ffCO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM₂.₅) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES).\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", "execute:\n", diff --git a/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd b/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd index 2411d7a3..064f9e4e 100644 --- a/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd +++ b/data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd @@ -1,3 +1,3 @@ -# Greenhouse Gas And Air Pollutants Emissions System +# GRA²PES Greenhouse Gas and Air Quality Species ![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/gra2pes-ghg-monthgrid-v1_Data_Flow.png) \ No newline at end of file diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index d25d80e8..a571d8fd 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -26,7 +26,7 @@ Explore, analyze, and make a difference with the US GHG Center. 2. [GOSAT-based Top-down Total and Natural Methane Emissions](cog_transformation/gosat-based-ch4budget-yeargrid-v1.ipynb) 3. [OCO-2 MIP Top-Down CO₂ Budgets](cog_transformation/oco2-mip-co2budget-yeargrid-v1.ipynb) 4. [TM5-4DVar Isotopic CH₄ Inverse Fluxes](cog_transformation/tm54dvar-ch4flux-monthgrid-v1.ipynb) -5. [Greenhouse Gas And Air Pollutants Emissions System](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) +5. [GRA²PES Greenhouse Gas and Air Quality Species](cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes](cog_transformation/emit-ch4plume-v1.ipynb) diff --git a/datausage.qmd b/datausage.qmd index 36b7bf00..172932a1 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -37,7 +37,7 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset. 6. Wetland Methane Emissions, LPJ-EOSIM model - [Beginner level notebook](user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Wetland Methane Emissions, LPJ-EOSIM model dataset. -7. GRA2PES +7. GRA²PES Greenhouse Gas and Air Quality Species - [Beginner level notebook](user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb.ipynb) to access, visualize, explore statistics, and create a time series of the GRA2PES, Version 1 dataset. ## Large Emissions Events diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd index 70486e6a..13d8d0d7 100644 --- a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -1,5 +1,5 @@ --- -title: Greenhouse Gas And Air Pollutants Emissions System +title: GRA²PES Greenhouse Gas and Air Quality Species description: "The Greenhouse gas And Air Pollutants Emissions System (GRA²PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 2 in the Scientific Details section below for a full sector list). Fossil fuel CO₂ (ffCO₂) emissions are developed along with those of air pollutants including CO, NOₓ, SO₂, and PM₂.₅ with consistency in spatial and temporal distributions. Emissions by sectors are reported as column totals in units of metric tons per km² per month. Spatial-temporal surrogates are developed to distribute CO₂ emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA²PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." --- diff --git a/processingreport.qmd b/processingreport.qmd index 7a02d037..3be2ec56 100644 --- a/processingreport.qmd +++ b/processingreport.qmd @@ -27,7 +27,7 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [OCO-2 MIP Top-Down CO₂ Budgets Processing and Verification Report](processing_and_verification_reports/oco2-mip-co2budget-yeargrid-v1_Processing and Verification Report.qmd) 5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Processing and Verification Report](processing_and_verification_reports/tm54dvar-ch4flux-monthgrid-v1_Processing and Verification Report.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM model Processing and Verification Report](processing_and_verification_reports/lpjeosim-wetlandch4-grid-v2_Processing and Verification Report.qmd) -7. [Greenhouse Gas And Air Pollutants Emissions System Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) +7. [GRA²PES Greenhouse Gas and Air Quality Species Processing and Verification Report](processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Processing and Verification Report](processing_and_verification_reports/emit-ch4plume-v1_Processing and Verification Report.qmd) diff --git a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb index fd1cc5c0..5989c0ad 100644 --- a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -3,10 +3,14 @@ { "cell_type": "raw", "id": "257bb7c7-a122-491f-b05f-5e5293762d9e", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: Greenhouse Gas And Air Pollutants Emissions System (GRA2PES)\n", + "title: GRA²PES Greenhouse Gas and Air Quality Species\n", "description: Documentation of data usage\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", diff --git a/workflow.qmd b/workflow.qmd index 97876e77..3caf1363 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -26,7 +26,7 @@ Explore, analyze, and make a difference with the US GHG Center. 4. [OCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram](data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd) 5. [TM5-4DVar Isotopic CH₄ Inverse Fluxes Data Flow Diagram](data_workflow/tm54dvar-ch4flux-monthgrid-v1_Data_Flow.qmd) 6. [Wetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram](data_workflow/lpjeosim-wetlandch4-grid-v2_Data_Flow.qmd) -7. [Greenhouse Gas And Air Pollutants Emissions System Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) +7. [GRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd) ## Large Emissions Events 1. [EMIT Methane Point Source Plume Complexes Data Flow Diagram](data_workflow/emit-ch4plume-v1_Data_Flow.qmd) From 5ff65464cf8e91750ed96603e670ebd581756d4e Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Thu, 12 Sep 2024 11:02:18 -0500 Subject: [PATCH 34/47] add space in desc --- cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb index 26dd8464..34235df4 100644 --- a/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb +++ b/cog_transformation/gra2pes-ghg-monthgrid-v1.ipynb @@ -11,7 +11,7 @@ "source": [ "---\n", "title: GRA²PES Greenhouse Gas and Air Quality Species\n", - "description:Monthly, 0.036 degree resolution emissions of fossil fuel carbon dioxide (ffCO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM₂.₅) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES).\n", + "description: Monthly, 0.036 degree resolution emissions of fossil fuel carbon dioxide (ffCO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM₂.₅) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES).\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", "execute:\n", From e3ba82cd2862e6f5644274e8d87497053ef4929c Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli <46867571+paridhi-parajuli@users.noreply.github.com> Date: Thu, 12 Sep 2024 16:34:51 -0500 Subject: [PATCH 35/47] Add documents for NIST NIST data usage and data flow diagram added --- _quarto.yml | 10 + .../influx-testbed-ghg-concentrations.ipynb | 265 ++++++++++++++++++ .../lam-testbed-ghg-concentrations.ipynb | 162 +++++++++++ .../nec-testbed-ghg-concentrations.ipynb | 196 +++++++++++++ ...x-testbed-ghg-concentrations_Data_Flow.qmd | 3 + ...m-testbed-ghg-concentrations_Data_Flow.qmd | 3 + ...x-testbed-ghg-concentrations_Data_Flow.png | Bin 0 -> 239379 bytes ...m-testbed-ghg-concentrations_Data_Flow.png | Bin 0 -> 241252 bytes ...c-testbed-ghg-concentrations_Data_Flow.png | Bin 0 -> 240699 bytes ...c-testbed-ghg-concentrations_Data_Flow.qmd | 3 + datatransformationcode.qmd | 3 + datausage.qmd | 3 + ...bed-ghg-concentrations_User_Notebook.ipynb | 206 ++++++++++++++ ...bed-ghg-concentrations_User_Notebook.ipynb | 205 ++++++++++++++ ...bed-ghg-concentrations_User_Notebook.ipynb | 207 ++++++++++++++ workflow.qmd | 3 + 16 files changed, 1269 insertions(+) create mode 100644 cog_transformation/influx-testbed-ghg-concentrations.ipynb create mode 100644 cog_transformation/lam-testbed-ghg-concentrations.ipynb create mode 100644 cog_transformation/nec-testbed-ghg-concentrations.ipynb create mode 100644 data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd create mode 100644 data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd create mode 100644 data_workflow/media/influx-testbed-ghg-concentrations_Data_Flow.png create mode 100644 data_workflow/media/lam-testbed-ghg-concentrations_Data_Flow.png create mode 100644 data_workflow/media/nec-testbed-ghg-concentrations_Data_Flow.png create mode 100644 data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd create mode 100644 user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb create mode 100644 user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb create mode 100644 user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb diff --git a/_quarto.yml b/_quarto.yml index fbbd1888..bc9667b6 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -74,6 +74,9 @@ website: contents: - user_data_notebooks/noaa-insitu_User_Notebook.ipynb - user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb + - user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb + - user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb + - user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb - section: Socioeconomic contents: - user_data_notebooks/sedac-popdensity-yeargrid5yr-v4.11_User_Notebook.ipynb @@ -101,6 +104,10 @@ website: contents: - cog_transformation/noaa-gggrn-concentrations.ipynb - cog_transformation/oco2geos-co2-daygrid-v10r.ipynb + - cog_transformation/influx-testbed-ghg-concentrations.ipynb + - cog_transformation/lam-testbed-ghg-concentrations.ipynb + - cog_transformation/nec-testbed-ghg-concentrations.ipynb + - section: Socioeconomic contents: - cog_transformation/sedac-popdensity-yeargrid5yr-v4.11.ipynb @@ -161,6 +168,9 @@ website: - data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd - data_workflow/noaa-gggrn-ch4-concentrations_Data_Flow.qmd - data_workflow/oco2geos-co2-daygrid-v10r_Data_Flow.qmd + - data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd + - data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd + - data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd - section: Socioeconomic contents: - data_workflow/sedac-popdensity-yeargrid5yr-v4.11_Data_Flow.qmd diff --git a/cog_transformation/influx-testbed-ghg-concentrations.ipynb b/cog_transformation/influx-testbed-ghg-concentrations.ipynb new file mode 100644 index 00000000..901d7bca --- /dev/null +++ b/cog_transformation/influx-testbed-ghg-concentrations.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "c5850703-19b9-43d9-9820-d441c17d6a9f", + "metadata": {}, + "source": [ + "---\n", + "title: NIST Flux GHG concentrations for Indianapolis Region\n", + "description: Documentation of data transformation \n", + "author: Paridhi Parajuli \n", + "date: August 28, 2023\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ba060420-788e-4a04-b1a6-9901f4573178", + "metadata": {}, + "source": [ + "This script was used to transform the NIST INFLUX dataset into meaningful csv files for ingestion to vector dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38300e-0d5f-4cfb-ae21-cab513fe6448", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import glob\n", + "import os\n", + "import zipfile\n", + "import wget\n", + "from collections import defaultdict\n", + "from io import StringIO\n", + "import re\n", + "import warnings\n", + "import warnings\n", + "from datetime import datetime, timedelta\n", + "# Ignore the FutureWarning\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a56f3d7c-3b90-4669-a52b-bfab7130d94d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "selected_level=\"level1\"\n", + "base_dir = \"data/\"\n", + "output_dir = \"output/\"\n", + "dat_file_pattern = f\"{base_dir}/*/*.dat\"\n", + "output_base_dataset_name = \"PSU_INFLUX_INSITU\" \n", + "constant_variables = [\"datetime\",\"latitude\",\"longitude\",\"level\",\"elevation_m\",\"intake_height_m\",\"Instr\"]\n", + "variables =[['CO2(ppm)'],['CH4(ppb)']] # exclude CO\n", + "metadata_link= \"UrbanTestBed-Metadata - INFLUX.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33681ce3-9cc6-4abc-b04b-8faf06c45392", + "metadata": {}, + "outputs": [], + "source": [ + "# Functions\n", + "def filter_dict(site_dict, selected_level):\n", + " return {key: [x for x in value if selected_level in x] for key, value in site_dict.items()}\n", + "\n", + "def flag_desired_level(df, desired_level):\n", + " df['is_max_height_data'] = df['level']== desired_level\n", + " return df\n", + "\n", + "def add_location(link, site_number):\n", + " meta= pd.read_csv(link)\n", + " location =meta[meta['Station Code']==f\"Site {site_number[-2:]}\"][['City','State']]#(get the actual site number)\n", + " return location['City'].item()+\",\"+location['State'].item()\n", + "\n", + "def convert_to_datetime(row):\n", + " year = int(row['Year'])\n", + " doy = int(row['DOY'])\n", + " hour = int(row['Hour'])\n", + " \n", + " # Create a datetime object for the start of the year\n", + " date = datetime(year, 1, 1) + timedelta(days=doy - 1)\n", + " # Add the hours\n", + " datetime_obj = date + timedelta(hours=hour)\n", + " # Format as yyyy-mm-ddThh:mm:ssZ\n", + " return datetime_obj.strftime('%Y-%m-%dT%H:%M:%SZ')\n", + "\n", + "def download_and_extract_zip_files(base_dir, levels):\n", + " \"\"\"\n", + " Download, extract, and delete zip files for the specified levels.\n", + "\n", + " Parameters:\n", + " base_dir (str): The base directory for storing the downloaded files.\n", + " levels (list): A list of levels to download and extract.\n", + " \"\"\"\n", + " # Ensure the base directory exists\n", + " os.makedirs(base_dir, exist_ok=True)\n", + "\n", + " # Loop through the levels and handle the download and extraction\n", + " for level in levels:\n", + " download_link = f\"https://www.datacommons.psu.edu/download/meteorology/influx/influx-tower-data/wmo-x2019-scale/level{level}.zip\"\n", + " fname = download_link.split(\"/\")[-1]\n", + " target_path = os.path.join(base_dir, fname)\n", + " \n", + " # Download the zip file\n", + " wget.download(download_link, target_path)\n", + " print(f\"Downloaded {download_link} to {target_path}\")\n", + "\n", + " # Extract the zip file\n", + " with zipfile.ZipFile(target_path, 'r') as zip_ref:\n", + " zip_ref.extractall(base_dir)\n", + " print(f\"Extracted {fname}\")\n", + "\n", + " # Delete the zip file after extraction\n", + " os.remove(target_path)\n", + "\n", + "def create_site_dict(pattern):\n", + " \"\"\"\n", + " Creates a dictionary where keys are site numbers extracted from file paths,\n", + " and values are lists of file paths corresponding to each site number.\n", + " \n", + " Args:\n", + " - pattern (str): Glob pattern to match files.\n", + " \n", + " Returns:\n", + " - dict: Dictionary mapping site numbers to lists of file paths.\n", + " \"\"\"\n", + " all_files = glob.glob(pattern)\n", + " site_dict = defaultdict(list)\n", + " \n", + " for file_path in all_files:\n", + " site_number = file_path.split('_')[-4]\n", + " site_dict[site_number].append(file_path)\n", + " \n", + " return dict(site_dict)\n", + "\n", + "def process_site_files(site_number, file_list):\n", + " \"\"\"\n", + " Process files for a given site number and save the combined DataFrame to CSV.\n", + " \n", + " Args:\n", + " - site_number (str): Site number to process.\n", + " - file_list (list): List of file paths corresponding to the site number.\n", + " \"\"\"\n", + " df = pd.DataFrame()\n", + " \n", + " for file_path in file_list:\n", + " with open(file_path, 'r') as file:\n", + " data = file.read()\n", + " \n", + " contents = data.split(\"\\nSite\")\n", + " lat = float((re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(1))\n", + " lat_hemisphere = (re.search(r'LATITUDE:\\s*([0-9.]+)\\s*[NS]', contents[0])).group(0)[-1]\n", + " \n", + " lon = float((re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(1))\n", + " lon_hemisphere = (re.search(r'LONGITUDE:\\s*([0-9.]+)\\s*[EW]', contents[0])).group(0)[-1]\n", + " \n", + " level= file_path.split(\"/\")[-2]\n", + " \n", + " elevation= re.search(r'ALTITUDE:\\s*([0-9.]+)\\s*m\\s*ASL', contents[0]).group(1)\n", + " intake_height= re.search(r'SAMPLING HEIGHT:\\s*([0-9.]+)\\s*m\\s*AGL', contents[0]).group(1)\n", + "\n", + " \n", + " data_io = StringIO(contents[1])\n", + " tmp_data = pd.read_csv(data_io, delim_whitespace=True)\n", + " tmp_data = tmp_data.reset_index().rename(columns={'index': 'Site'})\n", + " tmp= tmp_data.query(\"Flag==1\").copy()# 1 means no known problem, 0 is not recommemded, 9 is instrument issue (unrealistic)\n", + " #tmp['SiteCode'] = int(re.search(r'\\d+', site_number).group()) \n", + " tmp['latitude'] = lat\n", + " tmp['longitude'] = lon\n", + " tmp['level'] = int(re.search(r'\\d+', level).group())\n", + " tmp['elevation_m'] = elevation\n", + " tmp['intake_height_m']= intake_height\n", + "\n", + " if lat_hemisphere == 'S':\n", + " tmp['latitude'] = -1* tmp[\"latitude\"]\n", + " if lon_hemisphere == 'W':\n", + " tmp['longitude'] = -1* tmp[\"longitude\"]\n", + "\n", + " df = pd.concat([df, tmp], ignore_index=True)\n", + "\n", + " # Ensure the output directory exists\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " os.makedirs(output_dir+\"PSU_INFLUX_INSITU/\", exist_ok=True)\n", + " \n", + "\n", + " df['datetime'] = df[[\"Year\",\"DOY\",\"Hour\"]].apply(convert_to_datetime, axis=1)\n", + " df.reset_index(drop=True, inplace=True)\n", + " for v in variables:\n", + " tmp_file=df[constant_variables + v].copy()\n", + " tmp_file['unit'] = v[0][-4:-1] #CO2(ppm) get the unit only\n", + " \n", + " tmp_file.rename(columns={v[0]: 'value'}, inplace=True)\n", + " tmp_file['location']= add_location(metadata_link, site_number)\n", + " tmp_file = flag_desired_level(tmp_file, 1) # Flagging only level 1 data\n", + "\n", + " # Remove nan\n", + " tmp_file.dropna(subset=[\"value\"], inplace=True)\n", + "\n", + " #filter 0 values\n", + " tmp_file[tmp_file[\"value\"]!=0].to_csv(f\"{output_dir}/PSU_INFLUX_INSITU/NIST-FLUX-IN-{site_number}-{v[0][:-5]}-hourly-concentrations.csv\", index=False)\n", + " print(f\"CSV Created for Site {site_number}-{v[0][:-5]}!!!\")\n", + " return \n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f6b92fb-1b21-4fad-b0d1-c0a07ff388b0", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Download and extract zip files\n", + "levels_to_download = range(1, 5)\n", + "#download_and_extract_zip_files(base_dir=base_dir, levels=levels_to_download)\n", + "\n", + "# Create site dictionary\n", + "site_dict = create_site_dict(dat_file_pattern)\n", + "\n", + "# Comment if you want data from all levels\n", + "#site_dict = filter_dict(site_dict, selected_level)\n", + "\n", + "# Process each site's files\n", + "for site_number, file_list in site_dict.items():\n", + " print(f\"Processing Site Number: {site_number}, Total Files: {len(file_list)}\")\n", + " process_site_files(site_number, file_list)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cog_transformation/lam-testbed-ghg-concentrations.ipynb b/cog_transformation/lam-testbed-ghg-concentrations.ipynb new file mode 100644 index 00000000..13c13612 --- /dev/null +++ b/cog_transformation/lam-testbed-ghg-concentrations.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "2f231b28-78b7-47fa-9d37-571b0537c798", + "metadata": {}, + "source": [ + "---\n", + "title: NIST Urban Testbed GHG concentrations for Los Angeles Metropolitan Region\n", + "description: Documentation of data transformation \n", + "author: Paridhi Parajuli \n", + "date: August 28, 2023\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e1fc9d19-8d59-4cc4-a858-ba9d65ab324e", + "metadata": {}, + "source": [ + "This script was used to transform the NIST INFLUX dataset into meaningful csv files for ingestion to vector dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6185ff3-141c-443b-acc0-969aa7cfee6c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import glob\n", + "import os\n", + "import warnings\n", + "import warnings \n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdcbec12-f1e8-4401-a070-242b24351db0", + "metadata": {}, + "outputs": [], + "source": [ + "# download data from https://data.nist.gov/od/id/mds2-2388 into your desired_folder\n", + "source_dir = \"CA\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "308f7691-b6ce-4515-9e93-9f554c882051", + "metadata": {}, + "outputs": [], + "source": [ + "# Grouping the files for preparation\n", + "config_ca = pd.read_csv(\"LAM_sites-2.csv\") #metadata from providers\n", + "all_files= glob.glob(f\"{source_dir}/*.csv\")\n", + "all_files = [i.split(\"/\")[-1].split('.')[0] for i in glob.glob(f\"{source_dir}/*.csv\") ]\n", + "my_dict={}\n", + "for site in list(config_ca.SiteCode):\n", + " # for each site and variable, append into the dict\n", + " if (config_ca[config_ca[\"SiteCode\"]==site][\"Tower\"].item()) ==1 :\n", + "\n", + " co2_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"co2\" in f]\n", + " my_dict[f\"{site}-co2\"] = co2_files\n", + " # Find the files that do not have \"upwind\" or \"all\" and have \"ch4\"\n", + " ch4_files = [f for f in all_files if site in f and \"upwind\" not in f and \"all\" not in f and \"ch4\" in f]\n", + " my_dict[f\"{site}-ch4\"] = ch4_files\n", + " else:\n", + " co2_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"co2\" in f]\n", + " my_dict[f\"{site}-co2\"] = co2_upwind_files\n", + " \n", + " # Find the files that have \"upwind\" and \"ch4\"\n", + " ch4_upwind_files = [f for f in all_files if site in f and \"upwind\" in f and \"ch4\" in f]\n", + " my_dict[f\"{site}-ch4\"] = ch4_upwind_files\n", + "\n", + " if site in [\"IRV\",\"RAN\"]:\n", + " co2_files = [f for f in all_files if site in f and \"all\" in f and \"co2\" in f]\n", + " my_dict[f\"{site}-co2\"] = co2_files\n", + " ch4_files = [f for f in all_files if site in f and \"all\" in f and \"ch4\" in f]\n", + " my_dict[f\"{site}-ch4\"] = ch4_files\n", + " \n", + "del my_dict['USC2-co2']\n", + "del my_dict['USC2-ch4']\n", + "\n", + "for key in my_dict:\n", + " my_dict[key] = sorted(my_dict[key])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdcab4b2-28fe-4fa5-8e15-aa3f9852fd80", + "metadata": {}, + "outputs": [], + "source": [ + "# code to generate transformed data for CA\n", + "output_dir = \"output_LAM\"\n", + "os.makedirs(output_dir,exist_ok=True)\n", + "for key, value in my_dict.items():\n", + " df=pd.DataFrame()\n", + " variable = key.split(\"-\")[-1]\n", + " val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n", + " columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n", + " for file in value:\n", + " tmp = pd.read_csv(f\"CA/{file}.csv\")\n", + " tmp.dropna(subset=[val], inplace=True)\n", + " tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n", + " tmp= tmp[columns]\n", + " tmp.rename(columns={val: 'value'}, inplace=True)\n", + " tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n", + " tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n", + " tmp['location'] = config_ca[config_ca['SiteCode']==site][\"Location\"].item()\n", + " df = pd.concat([df, tmp], ignore_index=True)\n", + " \n", + " df['year']= df['datetime'].apply(lambda x: x[:4])\n", + " result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n", + " if result['max_height'].std() !=0:\n", + " print(f\"More than one max height for {file}\",result['max_height'].unique())\n", + " merged_df=pd.merge(df, result, on='year')\n", + " merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n", + " merged_df=merged_df.drop(columns=['year','max_height'])\n", + " merged_df.reset_index(drop=True, inplace=True)\n", + " merged_df.to_csv(f\"{output_dir}/NIST-testbed-LAM-{key}-hourly-concentrations.csv\", index=False)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4336997d-c52f-48a5-b44d-91f4480a1d5e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cog_transformation/nec-testbed-ghg-concentrations.ipynb b/cog_transformation/nec-testbed-ghg-concentrations.ipynb new file mode 100644 index 00000000..81424a31 --- /dev/null +++ b/cog_transformation/nec-testbed-ghg-concentrations.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "504c035a-53d2-41b7-b5a3-e5824319a5f6", + "metadata": {}, + "source": [ + "---\n", + "title: NIST Urban Testbed GHG concentrations for Northeast Corridor Region\n", + "description: Documentation of data transformation \n", + "author: Paridhi Parajuli \n", + "date: August 28, 2023\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e79030fa-6cb0-4612-b5f0-23d42c642b74", + "metadata": {}, + "source": [ + "This script was used to transform the NIST INFLUX dataset into meaningful csv files for ingestion to vector dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de4f10e4-c4d3-4255-af35-ad143adf8258", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import glob\n", + "import os\n", + "import warnings\n", + "import subprocess\n", + "import tarfile\n", + "import warnings \n", + "import requests\n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3c8e2bf-63bd-4da4-a8aa-4611490a12d7", + "metadata": {}, + "outputs": [], + "source": [ + "config = pd.read_csv(\"NEC_sites.csv\") #https://data.nist.gov/od/id/mds2-3012" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc5444c8-03ff-40f3-b880-1fe4b37c6859", + "metadata": {}, + "outputs": [], + "source": [ + "# Code to download the files into csv folder \n", + "sites = list(config.SiteCode)\n", + "for SiteCode in config.SiteCode[:2]:\n", + " print(SiteCode)\n", + " download_link = f\"https://data.nist.gov/od/ds/ark:/88434/mds2-3012/{SiteCode}.tgz\"\n", + " \n", + " # Check if the file exists on the server\n", + " response = requests.head(download_link)\n", + " if response.status_code != 404:\n", + " # File exists, proceed with download\n", + " result = subprocess.run([\"wget\", download_link, \"-O\", f\"{SiteCode}.tgz\"], \n", + " stdout=subprocess.DEVNULL,\n", + " stderr=subprocess.DEVNULL)\n", + "\n", + " # Check if wget succeeded\n", + " if result.returncode == 0:\n", + " # Ensure the file is not empty\n", + " if os.path.getsize(f\"{SiteCode}.tgz\") > 0:\n", + " # Extract the files\n", + " with tarfile.open(f\"{SiteCode}.tgz\", \"r:gz\") as tar:\n", + " tar.extractall()\n", + "\n", + " # Delete the .tgz file\n", + " os.remove(f\"{SiteCode}.tgz\")\n", + " else:\n", + " print(f\"File {SiteCode}.tgz is empty.\")\n", + " sites.remove(SiteCode)\n", + " os.remove(f\"{SiteCode}.tgz\") # Remove the empty file\n", + " else:\n", + " print(f\"Failed to download {SiteCode}.tgz.\")\n", + " sites.remove(SiteCode)\n", + " else:\n", + " print(f\"File {SiteCode}.tgz does not exist on the server.\")\n", + " sites.remove(SiteCode)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31572c98-45f9-41c1-aa02-0dde7a678638", + "metadata": {}, + "outputs": [], + "source": [ + "sites = list(config.SiteCode)\n", + "# These are not available\n", + "sites.remove('AWS')\n", + "sites.remove('BVA')\n", + "sites.remove('DNC')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e94566b8-7baf-4541-8ba2-76cc1ac8f180", + "metadata": {}, + "outputs": [], + "source": [ + "variables = ['ch4','co2']\n", + "output_dir =\"output_NEC\"\n", + "os.makedirs(output_dir,exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1732c3d5-19a0-4251-9462-34857fdb5892", + "metadata": {}, + "outputs": [], + "source": [ + "for site in sites:\n", + " for variable in variables:\n", + " df = pd.DataFrame()\n", + " files = glob.glob(f\"csv/{site}-*-{variable}-*.csv\")\n", + " val = f\"{variable}_ppm\" if variable == 'co2' else f\"{variable}_ppb\"\n", + " for file in files:\n", + " tmp = pd.read_csv(file)\n", + " tmp.dropna(subset=[val], inplace=True)\n", + " tmp.rename(columns={'datetime_UTC': 'datetime'}, inplace=True)\n", + " columns = [\"latitude\",\"longitude\",\"intake_height_m\",\"elevation_m\",\"datetime\",val ]\n", + " tmp= tmp[columns]\n", + " tmp.rename(columns={val: 'value'}, inplace=True)\n", + " tmp['datetime'] = pd.to_datetime(tmp['datetime'])\n", + " tmp['datetime'] = tmp['datetime'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n", + " tmp['location'] = config[config['SiteCode']==site][\"Location\"].item()\n", + " df = pd.concat([df, tmp], ignore_index=True)\n", + " \n", + " df['year']= df['datetime'].apply(lambda x: x[:4])\n", + " result = df.groupby(\"year\").agg(max_height= (\"intake_height_m\",\"max\"))\n", + " if result['max_height'].std() !=0:\n", + " print(f\"More than one max height for {file}\",result['max_height'].unique())\n", + " merged_df=pd.merge(df, result, on='year')\n", + " merged_df[\"is_max_height_data\"]= merged_df[\"max_height\"] == merged_df[\"intake_height_m\"]\n", + " merged_df=merged_df.drop(columns=['year','max_height'])\n", + " merged_df.reset_index(drop=True, inplace=True)\n", + " merged_df.to_csv(f\"{output_dir}/NIST-testbed-NEC-{site}-{variable}-hourly-concentrations.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ac10b20-6e1d-4cd7-a5cc-2e095bc26a04", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ec0cd8c-207d-4b03-ab9e-9f21ac2c02c5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd b/data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd new file mode 100644 index 00000000..8783ee29 --- /dev/null +++ b/data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd @@ -0,0 +1,3 @@ +# Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/influx-testbed-ghg-concentrations_Data_Flow.png) diff --git a/data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd b/data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd new file mode 100644 index 00000000..029796d7 --- /dev/null +++ b/data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd @@ -0,0 +1,3 @@ +# Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/lam-testbed-ghg-concentrations_Data_Flow.png) \ No newline at end of file diff --git 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b/data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd @@ -0,0 +1,3 @@ +# Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed + +![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/nec-testbed-ghg-concentrations_Data_Flow.png) diff --git a/datatransformationcode.qmd b/datatransformationcode.qmd index a571d8fd..aac9a894 100644 --- a/datatransformationcode.qmd +++ b/datatransformationcode.qmd @@ -34,6 +34,9 @@ Explore, analyze, and make a difference with the US GHG Center. ## Greenhouse Gas Concentrations 1. [Atmospheric Carbon Dioxide and Methane Concentrations from NOAA Global Monitoring Laboratory](cog_transformation/noaa-gggrn-concentrations.ipynb) 2. [OCO-2 GEOS Column CO₂ Concentrations](cog_transformation/oco2geos-co2-daygrid-v10r.ipynb) +3. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)](cog_transformation/influx-testbed-ghg-concentrations.ipynb) +4. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project](cog_transformation/lam-testbed-ghg-concentrations.ipynb) +5. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](cog_transformation/nec-testbed-ghg-concentrations.ipynb) ## Socioeconomic 1. [SEDAC Gridded World Population Density](cog_transformation/sedac-popdensity-yeargrid5yr-v4.11.ipynb) diff --git a/datausage.qmd b/datausage.qmd index 172932a1..df3711d5 100644 --- a/datausage.qmd +++ b/datausage.qmd @@ -49,6 +49,9 @@ Explore, analyze, and make a difference with the US GHG Center. - [Beginner level notebook](user_data_notebooks/noaa-insitu_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory dataset. 2. OCO-2 GEOS Column CO₂ Concentrations - [Beginner level notebook](user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb) to access, visualize, explore statistics, and create a time series of the OCO-2 GEOS Column CO₂ Concentrations dataset. +3. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)](user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook) +4. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project](user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook) +5. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook) ## Socioeconomic 1. SEDAC Gridded World Population Density diff --git a/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb new file mode 100644 index 00000000..7d8b25c4 --- /dev/null +++ b/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", + "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 12 September 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "1. Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "2. Visualize the time series data\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "NIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as site01ies. NIST's objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. site01ies to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\n", + "\n", + "For more information regarding this dataset, please visit the [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](https://earth.gov/ghgcenter/data-catalog/nec-testbed-ghg-concentrations) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the Required Libraries\n", + "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:\n", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objs as go\n", + "from io import StringIO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying the Feature Vector API\n", + "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "FEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to fetch CSV data for a station with a limit parameter\n", + "def get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n", + " # Use the ?f=csv and limit query to get more rows\n", + " url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n", + " print(url)\n", + " try:\n", + " response = requests.get(url)\n", + " \n", + " # Check if the response is successful\n", + " if response.status_code != 200:\n", + " print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n", + " return pd.DataFrame()\n", + "\n", + " # Check if the content type is CSV\n", + " content_type = response.headers.get('Content-Type')\n", + " if 'text/csv' not in content_type:\n", + " print(f\"Unexpected content type for {station_code}: {content_type}\")\n", + " print(\"Response content:\", response.text)\n", + " return pd.DataFrame()\n", + "\n", + " # Read the CSV content into a pandas DataFrame\n", + " csv_data = StringIO(response.text)\n", + " return pd.read_csv(csv_data)\n", + " \n", + " except requests.exceptions.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the CH₄ data for two NEC stations" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site01_ch4_hourly_concentrations/items?f=csv&elevation_m=256&limit=10000\n", + "https://earth.gov/ghgcenter/api/features/collections/public.nist_flux_in_site09_ch4_hourly_concentrations/items?f=csv&elevation_m=277&limit=10000\n" + ] + }, + { + "data": { + "image/png": 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kCRo3boyePXvC0dERP//8M9566y2oVCqMGTNGr+z58+fRr18/vP766xgyZAiWLVuGoUOHIiwsDI0bNwYApKenIzIyEvn5+drX+NVXX8HNzU1Sffr27YtTp07h7bffRmhoKG7evInExERcuXLFaCKSor6bdu3aha5duyIsLAzTpk2Dg4MDli9fjhdeeAEpKSl672m9evXg7e2tt33N+hMnTiA4OBi5ubkAYPB6NOfRsWPHCn19ISEhUCqVWLVqFYYMGWKyXGHfiVlZWfjmm2/Qv39/jBgxAvfv38e3336Lzp074/Dhw2jevHmR56Ixb7zxBlauXIl+/fph4sSJOHToEGbPno0zZ85g48aNemWlnAvTp0/H7Nmz8cYbb+CZZ55BVlYWjh49iuPHj+PFF18s9H2q0Eo72iP7ptuV7/PPPxdeXl7a7g0vv/yyiIyMFEIYtoKkpKQYbcHZtm2bwfKiuhF26tRJ707qO++8I+Ryubh79652mbHuNaNGjRLu7u56d3A0d5b+97//aZfl5uYKf39/vbvPq1atEg4ODgZ3G5cuXWqyu4Cu559/XoSFhRks9/X1Fc2aNSv0ubqM3Qkr+GdOy5YQQtSrV0907dq1yHLGWh0L/um2bPXu3Vs4OzuLCxcuaJddv35deHl5ifbt22uXmduyBUBs27bNaP1074L/97//FQD0umc+fvxYVK1atcjuhi1atBA+Pj6FltHdpp+fn2jSpIle95DNmzcLAOLjjz/WLhsyZIgAIGbOnGmwP93zY9euXQKAGDdunMH+NOf+pUuXhFwuN2h9+/PPP4Wjo6PecqnneWFddTTbWLp0qcE6qZ83U11mjLVsNW/eXPj5+YmMjAztst9//104ODiIwYMHa5dpzp/hw4frbbNPnz6iSpUq2seLFi0SAMStW7cM9l+Y2NhYAUD89ttvJsscP35cABATJkwQQghx9uxZo90O33rrLeHp6al9v8z5Xizs3C+KJS1bnTt31vuebdu2rZDJZHrdJfPz80VQUJDets15TcYY6y5uyWfs/fffL3Q/Gj4+Pnpd+YzRbdkSovCuWx07dhRNmzbVO+9VKpV47rnnxFNPPaVd1r17d1G7dm2D5z98+LDQ+lvSsmXs89m5c2eD/WvOsT179miX3bx5U7i4uIiJEydql2k+E4cOHdIr5+PjU2TL1p07dyRdp3RbtoQw/d2kUqnEU089ZXC+Zmdni1q1aokXX3xRu6xx48ZGW6hPnTql9922YcMGAUCsWrVKr5zmet+kSZNC656eni6qVasmAIgGDRqI0aNHizVr1uj9RtEw9Z2Yn58vcnNz9ZbduXNHVK9eXe+7rrBzseC19cSJEwKAeOONN/TKTZo0SQDQazWTei40a9bM4iEjFZn9jJIluxcTE4OcnBxs3rwZ9+/fx+bNmzFgwACjZX/44Qf4+PjgxRdfxO3bt7V/YWFh8PT0RFJSkuT9jhw5Ui8bU3h4OJRKJS5fvqxdpntH6v79+7h9+zbCw8ORnZ2Nv/76S297np6eGDhwoPaxs7MznnnmGfzzzz969W/YsCEaNGigV/8XXngBAIqsf0ZGBnx9fQ2WZ2VlwcvLS+IrVwsNDUViYqLB33fffWfyOTNmzDCZktfX19doa5UxmsxNBf8KZkBUKpXYvn07evfujdq1a2uXBwQEYMCAAdi7dy+ysrKkveACatWqhc6dOxdZLiYmBq6urli9erV22a+//orbt2/rHW9jzDkuR48exc2bN/HWW2/B1dVVu7x79+5o0KABtmzZYvCc0aNH6z0ODw/XO982bNgAmUyGadOmGTxXc+4rFAqoVCrExMTonZP+/v546qmnDM5JKed5UVxcXDBs2DCD5eZ83qRIS0vDiRMnMHToUFSuXFm7/Omnn8aLL76IrVu3GjzH2HuakZGhPc80LR+bNm3SS3ZQlPv37wNAoeeDZp1mX/Xq1UPz5s31WtuVSiUSEhLQo0cP7ftl7vei1HPfGl5//XW979k2bdpACIHXX39du0wul6NVq1YG35XW+q7XsOQzpttKXJhKlSrh0KFDVskumpmZiV27diEmJkb7Obh9+zYyMjLQuXNnnDt3DqmpqQCAnJwco4mkNK8vJyen2PXR0P183rt3D7dv30aHDh3wzz//GPRKaNSokbblCFC35NWvX1/vGG/duhXPPvustkVIU+61116TVBdnZ2ckJydb1BujoBMnTuDcuXMYMGAAMjIytO/5w4cP0bFjR+zZs0f7eZf6nnfr1g0hISGYNGkSFAoFLl++jPXr1+PDDz+Eo6NjkcemevXq+P333zF69GjcuXMHS5cuxYABA+Dn54dPPvkEQogiX5dcLoezszMAdc+azMxM5Ofno1WrVjh+/LhZ75GG5ntzwoQJessnTpwIAAafIynnQqVKlXDq1CmcO3fOojpVVOxGSJJVq1YNnTp1wpo1a5CdnQ2lUmmyO9y5c+dw7949+Pn5GV1/8+ZNyfutWbOm3mNNEKP7xX3q1Cl89NFH2LVrl8GP+oIXl6CgIINUur6+vvjjjz/06n/mzBlUq1bN4vob+4L19vbW/piTysPDw2iWLlNzm5w+fRopKSmF1ktqKuGqVasa3bejo/5Xx61bt5CdnW20m1PDhg2hUqlw9epVbVcEc9SqVUtSuUqVKqFHjx5Ys2YNPvnkEwDqLoSBgYHaINkUb29vyUGIJsg39lobNGhgMEWCq6urwXnk6+urd/5euHABNWrU0As0Cjp37hyEEHjqqaeMri/YVUvKeV6UwMBA7Q8AXeZ83qQo7D1t2LAhfv31Vzx8+FAv+19h3wve3t545ZVX8M033+CNN97A+++/j44dOyI6Ohr9+vUrNBufJpAq7HNqLCB75ZVX8MEHHyA1NRWBgYFITk7GzZs38corr2jLmPu9KPXct4aC76ePjw8AIDg42GC57rlrze96DXM/Y46OjggKCpK07blz52LIkCEIDg5GWFgYunXrhsGDB+vdJJLq/PnzEEJg6tSpmDp1qtEyN2/eRGBgINzc3LTd1XQ9evQIgGEXtuLYt28fpk2bhgMHDiA7O1tv3b1797THFjA87oDh99Ply5eNdgOX0q3VxcUFc+bMwcSJE1G9enU8++yzeOmllzB48GC9LrdSaX7kF9Zd7969e/D19ZX8nru6umLLli2IiYlB3759tfWeO3cuZs2aVWj3Z42AgAAsWbIEX375Jc6dO4dff/0Vc+bMwccff4yAgAC88cYbRW5j5cqVWLBgAf766y+96SUs/R64fPkyHBwcDDIi+/v7o1KlSno3rAFp58LMmTPRq1cv1KtXD02aNEGXLl0waNCgQrsyEoMtMtOAAQMwYsQIpKeno2vXrnr95nWpVCr4+fnptTLoMhXEGCOXy40u1wQzd+/eRYcOHeDt7Y2ZM2eiTp06cHV1xfHjxzF58mSDu9pFbU9T/6ZNm2LhwoVGyxb8AVJQlSpVjN7Fa9CgAU6cOIHHjx8b/RFbXJo++UuWLDEYG6Bx584dkz/YS4KpQE+pVBpdbs6PkMGDB+OHH37A/v370bRpU/z000946623ikx13aBBA/z222+4evVqkcfWXKbON3OpVCrIZDL88ssvRrdZ8AeBlPO8KMbee3M/b7ZS1Otzc3PDnj17kJSUhC1btmDbtm1Yt24dXnjhBWzfvt3k8zWfmz/++MNkymlNwNqoUSPtsldeeQVTpkzBDz/8gNjYWKxfvx4+Pj7o0qWLtoy534vW/AFeFFPvh7HlBb8rrfVdbykXFxfJ6exjYmIQHh6OjRs3Yvv27Zg3bx7mzJkDhUKhHbMrleZcnzRpkskWSM0P3YCAACQlJRnc7EpLSwMA1KhRw6x9m3LhwgV07NgRDRo0wMKFCxEcHAxnZ2ds3boVixYtsuh6WFyxsbHo0aMHfvzxR/z666+YOnUqZs+ejV27dqFFixZmbUtT/3nz5pn8fGq+CwMCArQti7qMveeNGzfGyZMncfr0ady5c0c7vvmdd95Bhw4dJNdPJpOhXr16qFevHrp3746nnnoKq1evLjLY+u677zB06FD07t0b7777Lvz8/CCXyzF79mxcuHBB8v5N1UkKKedC+/btceHCBWzatAnbt2/HN998g0WLFmHp0qWSAsqKisEWmaVPnz4YNWoUDh48qNdlpqA6depgx44daNeuXZE/GIo7YWNycjIyMjKgUCjQvn177fKLFy9avM06derg999/R8eOHS2qX4MGDbBhwwaD5T169MCBAwewYcMG9O/f3+L6mfL555/jhRdeQIMGDYyuz8/Px9WrV9GzZ0+r7rdatWpwd3fH2bNnDdb99ddfcHBw0AYxmhaIu3fv6gXrBe+yWaJLly6oVq0aVq9ejTZt2iA7O1tS0osePXpg7dq1+O677zBlypRCy2qSnpw9e9agxezs2bMGSVGkqFOnDn799VdkZmaabN2qU6cOhBCoVasW6tWrZ/Y+jLHk3Dbn8yZ1+7rvaUF//fUXqlatatGcVg4ODujYsSM6duyIhQsX4rPPPsOHH36IpKQkk3M6de3aFXK5HKtWrTKZJON///sfHB0d9QKpWrVq4ZlnnsG6deswduxYKBQK9O7dW68bkznfi2WFLV6TLT5jugICAvDWW2/hrbfews2bN9GyZUvMmjXLZLBl6jzWtIY5OTkVOUdY8+bN8c033+DMmTN6QfqhQ4e0663h559/Rm5uLn766Se9lgpLunNqhISEGO02ZuzzakqdOnUwceJETJw4EefOnUPz5s2xYMECk93hTb3nmgQq3t7ekt7zpKQkZGVl6SXJMPWey2Qyvd4XW7duhUqlsnj+t9q1a8PX11cb3Gn2YUxCQgJq164NhUKhV6Zg13JzvrNDQkKgUqlw7tw5vZuvN27cwN27dy3+HFWuXBnDhg3DsGHD8ODBA7Rv3x7Tp09nsFUIjtkis3h6emLJkiWYPn06evToYbJcTEwMlEqltjuXrvz8fNy9e1f72MPDQ++xuTR3Y3Tvvjx+/BhffvmlxduMiYlBamoqvv76a4N1OTk5ePjwYaHPb9u2Le7cuWPQNW306NEICAjAxIkTjU5UefPmTXz66acW1fnKlStYu3ZtoZManj59Go8ePdLLOGcNcrkcUVFR2LRpk173xhs3bmgnw9Zc7DQXyz179mjLPXz4ECtXrix2PRwdHdG/f3+sX78eK1asQNOmTSV1b+jXrx+aNm2KWbNm4cCBAwbr79+/r82W2apVK/j5+WHp0qV6XVR++eUXnDlzBt27dze73n379oUQQjtZpS7NeR0dHQ25XI4ZM2YY3HUWQiAjI8Ps/WoCGHM+f+Z83jw8PCR1KwwICEDz5s2xcuVKvbqcPHkS27dvR7du3STXTyMzM9NgmebHlbGuRRrBwcEYNmwYduzYgSVLlhisX7p0KXbt2oXXX3/doOvaK6+8goMHD2LZsmW4ffu2XhdCwLzvxbLCFq/JFp8xQN16XvB89PPzQ40aNQo9JzRZ6Qq+Fj8/P0REROC///2v3g9qjVu3bmn/36tXLzg5Oel9ToQQWLp0KQIDA632nWzs83nv3j0sX77c4m1269YNBw8exOHDh7XLbt26ZbI1U1d2dra2255GnTp14OXlVeh7buq7KSwsDHXq1MH8+fPx4MEDg+fpvuf9+vWDUqnUm5Q4NzcXy5cvR5s2bQrtxZCTk4OpU6ciICCgyBujhw4dMvqb4PDhw8jIyNDrbmnqO9HYcTt06JDB9cjUuWiM5nszLi5Ob7mmx44ln6OC1xlPT0/UrVu30GNJbNkiCxTWV1qjQ4cOGDVqFGbPno0TJ04gKioKTk5OOHfuHH744QfEx8drx3uFhYVhyZIl+PTTT1G3bl34+fkVOcZG13PPPQdfX18MGTIE48aNg0wmw6pVq4rVDWLQoEFYv349Ro8ejaSkJLRr1w5KpRJ//fUX1q9fj19//VWbjtuY7t27w9HRETt27MDIkSO1y319fbFx40Z069YNzZs3x8CBAxEWFgYAOH78ONauXYu2bdtaVOf9+/cjNTUVTZs21S7r2LEjFAoFmjVrBgBITEyEu7u7TVK0fvrpp0hMTMTzzz+Pt956C46Ojvjvf/+L3NxczJ07V1suKioKNWvWxOuvv453330Xcrkcy5YtQ7Vq1XDlypVi12Pw4MFYvHgxkpKStGnAi+Lk5ASFQoFOnTqhffv2iImJQbt27eDk5IRTp05hzZo18PX1xaxZs+Dk5IQ5c+Zg2LBh6NChA/r3769NSx0aGmqQel6KyMhIDBo0CIsXL8a5c+fQpUsXqFQqpKSkIDIyEmPHjkWdOnXw6aefYsqUKbh06RJ69+4NLy8vXLx4ERs3bsTIkSMxadIks/Zbp04dVKpUCUuXLoWXlxc8PDzQpk2bQscImPN5CwsLw7p16zBhwgS0bt0anp6eJm/SzJs3D127dkXbtm3x+uuva1O/+/j4YPr06Wa9LkA9tmDPnj3o3r07QkJCcPPmTXz55ZcICgoqMsX2okWL8Ndff+Gtt97Ctm3btC1Yv/76KzZt2oQOHTpgwYIFBs+LiYnBpEmTMGnSJFSuXNngjrg534tlhS1eky0+Y4D6pklQUBD69euHZs2awdPTEzt27MCRI0eMHk8NNzc3NGrUCOvWrUO9evVQuXJlNGnSBE2aNMEXX3yB559/Hk2bNsWIESNQu3Zt3LhxAwcOHMC1a9fw+++/A1CPoYyNjcW8efOQl5eH1q1b48cff0RKSgpWr16t14Xr8uXLWLVqFQB1shAA2ptwISEhhbbWR0VFwdnZGT169MCoUaPw4MEDfP311/Dz8zMaEErx3nvvYdWqVejSpQvGjx+vTf0eEhJS5BjQv//+Gx07dkRMTAwaNWoER0dHbNy4ETdu3NCbEqGgwr6bvvnmG3Tt2hWNGzfGsGHDEBgYiNTUVCQlJcHb2xs///wzAHWSl5dffhlTpkzBzZs3UbduXaxcuRKXLl3Ct99+q7e/mJgY1KhRA40aNUJWVhaWLVuGf/75B1u2bCkyedKqVauwevVq9OnTB2FhYXB2dsaZM2ewbNkyuLq64oMPPtCWNfWd+NJLL0GhUKBPnz7o3r07Ll68iKVLl6JRo0Z6QWVh52JBzZo1w5AhQ/DVV19pu38fPnwYK1euRO/evREZGVno6zKmUaNGiIiIQFhYGCpXroyjR49qp1KgQpRU2kMqm4yl5TXG1AS4X331lQgLCxNubm7Cy8tLNG3aVLz33nvi+vXr2jLp6emie/fuwsvLSwCGkxoX3LdmwsOkpCTtsn379olnn31WO3nre++9J3799VeDcqYmCS6Y6lcIdfrhOXPmiMaNGwsXFxfh6+srwsLCxIwZMwwmSTSmZ8+eomPHjkbXXb9+XbzzzjuiXr16wtXVVbi7u4uwsDAxa9YsvW0XZ1JjGEnJ26ZNGzFw4MAi6y6E5ZMad+7cWXh6egp3d3cRGRkp9u/fb/D8Y8eOiTZt2ghnZ2dRs2ZNsXDhwkInNTZVP1Mp3Rs3biwcHBwMJkAtyp07d8THH38smjZtKtzd3YWrq6to0qSJmDJlikhLS9Mru27dOtGiRQvtxI6FTbhakLH09/n5+WLevHmiQYMG2slWu3btqjfZqhDqNMXPP/+88PDwEB4eHqJBgwZizJgx4uzZs9oy5pznmzZtEo0aNRKOjo56qZYLO/ekft4ePHggBgwYICpVqiSAoic13rFjh2jXrp1wc3MT3t7eokePHiYnNS6Y0r3g+bNz507Rq1cvUaNGDeHs7Cxq1Kgh+vfvL/7++2+jr6mg3NxcsWjRIhEWFiY8PDyEu7u7aNmypYiLixOPHz82+bx27doZTbesS8r3YmHnflEsSf1e8HvW1Pts6pyW8pqMKewaU5zPmDG5ubni3XffFc2aNdNOvN6sWTODCX+NfU72798vwsLChLOzs0Hq7QsXLojBgwcLf39/4eTkJAIDA8VLL70kEhIS9LahVCrFZ599pp1QuXHjxuK7774zqKepSX11r4+F+emnn8TTTz8tXF1dRWhoqJgzZ45YtmyZ5O/XgmnYhRDijz/+EB06dDB7UuPbt2+LMWPGiAYNGggPDw/h4+Mj2rRpI9avX1/kPk19NwkhxG+//Saio6NFlSpVhIuLiwgJCRExMTFi586detvIyckRkyZNEv7+/sLFxUW0bt3a6HQKc+bMEQ0aNNBOFt+zZ89Cp38o+N68++67omXLlqJy5crC0dFRBAQEiJdfflkcP35cr6yp70SVSqU9N1xcXESLFi3E5s2bzToXTU1qPGPGDFGrVi3h5OQkgoODC53UuKCCx+XTTz8VzzzzjKhUqZJwc3MTDRo0ELNmzSr0O5GEkAlhxVGQRKSVkpKCiIgI/PXXX6WakELjxIkTaNmyJY4fP2618QH2qkWLFqhcuTJ27txZ2lUhIiKiCozBFpENde3aFUFBQUbHfpW0V199FSqVCuvXry/tqtjU0aNH0bp1a6xYsUJSl1ciIiIiW2GwRUTlwsmTJ3Hs2DEsWLAAt2/fxj///KM3ISoRERFRSWM2QiIqFxISEjBs2DDk5eVh7dq1DLSIiIio1LFli4iIiIiIyAbYskVERERERGQDDLaIiIiIiIhsgJMaS6BSqXD9+nV4eXlBJpOVdnWIiIiIiKiUCCFw//591KhRAw4OhbddMdiS4Pr16wgODi7tahARERERkZ24evUqgoKCCi3DYEsCLy8vAOo31Nvbu5RrA+Tl5WH79u2IioqCk5NTaVenwuPxsD88JvaHx8S+8HjYHx4T+8NjYl/s6XhkZWUhODhYGyMUhsGWBJqug97e3nYTbLm7u8Pb27vUTzbi8bBHPCb2h8fEvvB42B8eE/vDY2Jf7PF4SBlexAQZRERERERENsBgi4iIiIiIyAYYbBEREREREdkAx2wRERERUbmiVCqRl5dXrG3k5eXB0dERjx49glKptFLNyFIlfTycnJwgl8uLvR0GW0RERERUbjx48ADXrl2DEKJY2xFCwN/fH1evXuU8q3agpI+HTCZDUFAQPD09i7UdBltEREREVC4olUpcu3YN7u7uqFatWrF+lKtUKjx48ACenp5FTlxLtleSx0MIgVu3buHatWt46qmnitXCxWCLiIiIiMqFvLw8CCFQrVo1uLm5FWtbKpUKjx8/hqurK4MtO1DSx6NatWq4dOkS8vLyihVs8cwhIiIionKF3f6ouKx1DjHYIiIiIiIisgEGW0RERERERDbAYIuIiIiISIdSCSQnAwkJTkhOVj8uLUOHDkXv3r1LrwISZGRkwM/PD5cuXSrtqkiybds2NG/eHCqVyub7YrBFRERERPQvhQIIDQU6dnTAiBEe6NjRAaGh6uWlIT4+HitWrNA+joiIQGxsrEXb+uKLLxAaGgpXV1e0adMGhw8f1lv/1VdfISIiAt7e3pDJZLh7966k7c6aNQu9evVCaGgoAOD3339H//79ERwcDDc3NzRs2BDx8fF6z0lOToZMJjP4S09P1yuXmpqKgQMHolq1aggICECzZs1w9OhRi16/RpcuXeDk5ITVq1cXaztSMNgiIiIiIoI6oOrXD7h2TX95aqp6eWkEXD4+PqhUqVKxt7Nu3TpMmDAB06ZNw/Hjx9GsWTN07twZN2/e1JbJzs5Gly5d8MEHH0jebnZ2Nr799lu8/vrr2mXHjh2Dn58fvvvuO5w6dQoffvghpkyZgs8//9zg+WfPnkVaWpr2z8/PT7vuzp07aNeuHZycnLBlyxYcPHgQ8+bNg6+vr4XvwhNDhw7F4sWLi72dojDYqgA0TeFr16LUm8KJiIiISooQwMOH0v6ysoBx49TPMbYdABg/Xl1OyvbMmVM5ISEBTZs2hZubG6pUqYJOnTrh4cOHAPS7EQ4dOhS7d+9GfHy8tiVI03Xv5MmT6Nq1Kzw9PVG9enUMGjQIt2/f1u5j4cKFGDFiBIYNG4ZGjRph6dKlcHd3x7Jly7RlYmNj8f777+PZZ5+VXPetW7fCxcVF7znDhw9HfHw8OnTogNq1a2PgwIEYNmwYFEaiVT8/P/j7+2v/dNO6z5kzB8HBwVi+fDmeeeYZhISEICoqCnXq1NGWCQ0NxaefforBgwfD09MTISEh+Omnn3Dr1i306tULnp6eePrppw1aw3r06IGjR4/iwoULkl+rJRhslXOapvDISGDAAPW/pdkUTkRERFRSsrMBT09pfz4+6hYsU4RQt3j5+EjbXna2tDqmpaWhf//+GD58OM6cOYPk5GRER0dDGInW4uPj0bZtW4wYMULbEhQcHIy7d+/ihRdeQIsWLXD06FFs27YNN27cQExMDADg8ePHOHbsGDp16qTdloODAzp16oQDBw6Y9Z4WlJKSgrCwsCLL3bt3D5UrVzZY3rx5cwQEBODFF1/Evn379Nb99NNPaNWqFV5++WX4+/ujffv2+Prrrw22sWjRIrRr1w6//fYbunfvjkGDBmHw4MEYOHAgjh8/jjp16mDw4MF672nNmjVRvXp1pKSkWPCqpeOkxuWYpim84GdV0xSekABER5dO3YiIiIhIHWzl5+cjOjoaISEhAICmTZsaLevj4wNnZ2e4u7vD399fu/zzzz9HixYt8Nlnn2mXLVu2DMHBwfj777/h6ekJpVKJ6tWr622vevXq+Ouvv4pV/8uXL6NGjRqFltm/fz/WrVuHLVu2aJcFBARg6dKlaNWqFXJzc/HNN98gIiIChw4dQsuWLQEA//zzD5YsWYIJEybg/fffR0pKCmJjY+Hq6oohQ4Zot9WtWzeMGjUKAPDxxx9jyZIlaN26NV5++WUAwOTJk9G2bVvcuHFD732rUaMGLl++XKzXXxQGW+WUUqlu6jbVFC6TAbGxQK9eQDEmxSYiIiKyW+7uwIMH0sru2QN061Z0ua1bgfbtpe1bimbNmqFjx45o2rQpOnfujKioKPTr18+scUm///47kpKS4OnpabDuwoULaNasmeRtmSsnJweurq4m1588eRK9evXCtGnTEBUVpV1ev3591K9fX/v4ueeew4ULF7Bo0SKsWrUKAKBSqdCqVSt89tlnUKlUqFOnDi5cuIClS5fqBVtPP/209v+agFI3YNUsu3nzpl6w5ebmhmypTZAWYrBVTqWkGA7u1CUEcPWqulxERIlVi4iIiKjEyGSAh4e0slFRQFCQugeQsZvVMpl6fVSUdW9Uy+VyJCYmYv/+/di+fTv+85//4MMPP8ShQ4dQq1YtSdt48OABevTogTlz5hisCwgIgJOTE+RyOW7cuKG3rmBLjyWqVq2KO3fuGF13+vRpdOzYESNHjsRHH31U5LaeeeYZ7N27V6/ujRo10ivToEEDg7FfTk5O2v/LZDKTywqmes/MzES1atWKrFdxcMxWOZWWZt1yREREROWZXA5ospP/+9tcS/M4Ls42PYJkMhnatWuHGTNm4LfffoOzszM2btxotKyzszOUBbKdtWzZEqdOnUJoaCjq1q2r9+fh4QFnZ2eEhYVh586d2ueoVCrs3LkTbdu2LVbdW7RogdOnTxssP3XqFCIjIzFkyBDMmjVL0rZOnDiBgIAA7eN27drh7NmzemXOnTun7W5ZHI8ePcKFCxfQokWLYm+rMAy2yimd89Qq5YiIiIjKu+ho9Zj2wED95UFBthvrfujQIXz22Wc4evQorly5AoVCgVu3bqFhw4ZGy4eGhuLQoUO4dOkSbt++DZVKhTFjxiAzMxP9+/fHkSNHcOHCBfz6668YNmyYNjCbMGECvv76a6xcuRJnzpzBm2++iYcPH2LYsGHabaenp+PEiRM4f/48AODPP//EiRMnkJmZabL+nTt3xqlTp/Rat06ePInIyEhERUVhwoQJSE9PR3p6Om7duqUtExcXh02bNuH8+fM4efIkYmNjsWvXLowZM0Zb5p133sHBgwfx2Wef4fz58/jhhx/w9ddf65Wx1MGDB+Hi4lLsYLMoDLbKqfBw9RdDwTszGjIZEBysLkdEREREatHRwKVLwM6dKnz99UPs3KnCxYu2Syrm7e2NPXv2oFu3bqhXrx4++ugjLFiwAF27djVaftKkSZDL5WjUqBGqVauGK1euoEaNGti3bx+USiWioqLQtGlTxMbGolKlStpU6q+88grmz5+Pjz/+GM2bN8eJEyewbds2vaQZS5cuRYsWLTBixAgAQPv27dGiRQv89NNPJuvftGlTtGzZEuvXr9cuS0hIwK1bt/Ddd98hICBA+9e6dWttmcePH2PixIlo2rQpOnTogN9//x07duxAx44dtWVat26NjRs3Yu3atXj66acxf/58LFy4EK+99pplb7aOtWvX4rXXXoO71MF1FpIJY3klSU9WVhZ8fHxw7949eHt7l3Z1kJeXh61bt6Jbt256/VEL0mQjBPT7HmsCMGYjtA6px4NKDo+J/eExsS88HvaHx8Q6Hj16hIsXL6JWrVqFJm2QQqVSISsrC97e3npzP5GhLVu24N1338XJkydt9l5Z83jcvn0b9evXx9GjR02OiyvsXDInNuCZU46VRlM4EREREVUs3bt3x8iRI5Fa2ERlduTSpUv48ssvJScgKQ5mIyznoqPV6d0d/z3SDg7AxYtM905ERERE1hMbG1vaVZCsVatWaNWqVYnsiy1bFYBuYCWTMdAiIiIiIioJbNkiIiIiu6dUqueGTEtTZ9IND+fNQyKyfwy2iIiIyK4pFMD48cC1a0+WBQWp50Ti+GMismfsRkhERER2S5NZVzfQAoDUVPVyhaJ06kVEJAWDLSIiIrJLSqW6RcvYJDWaZbGx6nJERPaIwRYRERHZpZQUwxYtXUIAV6+qyxER2SMGW0RERGSX0tKsW46IqKQx2CIiIiK7FBBg3XJEUilVSiRfSkbC2QQkX0qGUlV6fVWHDh2K3r17l9r+pcjIyICfnx8uXbpU2lWRZOnSpejRo0eJ7IvBFhEREdml8HB11kGZzPh6mQwIDlaXI7IWxRkFQuND0XFVR4zYNgIdV3VEaHwoFGdKJxtLfHw8VqxYoX0cERFh8QTCX3zxBUJDQ+Hq6oo2bdrg8OHDeusvXLiAPn36oFq1avD29kZMTAxu3LhR5HZnzZqFXr16ITQ0FADw+++/o3///ggODoabmxsaNmyI+Ph4vecMHToUMpnM4K9x48baMqGhodrlcrkcvr6+kMvlGDNmjEWvX2P48OE4fvw4UkqgDzKDLSIiIrJLcrk6vbsxmgAsLo7zbZH1KM4o0G99P1zL0h8smJqVin7r+5VKwOXj44NKlSoVezvr1q3DhAkTMG3aNBw/fhzNmjVD586dcfPmTQDAw4cPERUVBZlMhl27dmHfvn14/PgxevToAZVKZXK72dnZ+Pbbb/H6669rlx07dgx+fn747rvvcOrUKXz44YeYMmUKPv/8c22Z+Ph4pKWlaf+uXr2KypUr4+WXX9aWOXLkiHZ9amoqNm7cCAB6ZSzh7OyMAQMGYPHixcXajhQMtioYU3cHiYiI7FF0NJCQALi76y8PClIv5zxbVBghBB4+fijpL+tRFsb9Mg4ChukvNcvG/zIeWY+yJG1PGEujaUJCQgKaNm0KNzc3VKlSBZ06dcLDhw8B6HcjHDp0KHbv3o34+Hhti4+m697JkyfRtWtXeHp6onr16hg0aBBu376t3cfChQsxYsQIDBs2DI0aNcLSpUvh7u6OZcuWAQD27duHS5cuYcWKFWjatCmaNm2KlStX4ujRo9i1a5fJum/duhUuLi549tlntcuGDx+O+Ph4dOjQAbVr18bAgQMxbNgwKHTmavDx8YG/v7/27+jRo7hz5w6GDRumLVOtWjW9Mr/++ivq1KmDDh06aMvIZDL897//xUsvvQR3d3c0bNgQBw4cwPnz5xEREQEPDw8899xzuHDhgl69e/TogZ9++gk5OTmSj5MlOKkxERER2bXoaGDDBmDNGvXjpCR110G2aFFRsvOy4Tnb0yrbEhC4dv8afOb4SCr/YMoDeDh7FFkuLS0N/fv3x9y5c9GnTx/cv38fKSkpRoO1+Ph4/P3332jSpAlmzpwJQB2Q3L17Fy+88ALeeOMNLFq0CDk5OZg8eTJiYmKwa9cuPH78GMeOHcOUKVO023JwcECnTp1w4MABAEBubi5kMhlcXFy0ZVxdXeHg4IC9e/eiU6dORuufkpKCsLCwIl/nvXv3ULlyZZPrv/32W3Tq1AkhISFG1z9+/Bjr16/HhAkTICvQevDJJ59g4cKFWLhwISZPnowBAwagdu3amDJlCmrWrInhw4dj7Nix+OWXX7TPadWqFfLz83Ho0CFEREQUWX9LMdgiIiIiu+eg0xfHhr+LiEpcWloa8vPzER0drQ00mjZtarSsj48PnJ2d4e7uDn9/f+3yzz//HC1atMBnn32mXbZs2TIEBwfj77//hqenJ5RKJapXr663verVq+Ovv/4CADz77LPw8PDA5MmT8dlnn0EIgffffx9KpRJphaT8vHz5MmrUqFHoa9y/fz/WrVuHLVu2GF1//fp1/PLLL1ijuaNixI8//oh79+5hyJAhBuuGDRuGmJgYAMDkyZPRtm1bTJ06FZ07dwYAjB8/Xq/FDADc3d3h4+ODy5cvF1r34mKwRURERETlkruTOx5MeSCp7J7Le9BtTbciy20dsBXtQ9pL2rcUzZo1Q8eOHdG0aVN07twZUVFR6NevH3x9fSU9H1AnpEhKSoKnp2Er3oULF9CsWbMit1GtWjX88MMPePPNN7F48WI4ODigf//+aNmyJRwcTI88ysnJgaurq8n1J0+eRK9evTBt2jRERUUZLbNy5UpUqlSp0KyLy5YtQ6dOnYwGdk8//bT2/5qAUjdgrV69Oh49eoSsrCx4e3trl7u5uSE7O9vkPq2BwRYRERERlUsymUxSVz4AiKoThSDvIKRmpRodtyWDDEHeQYiqEwW5g/X6sMrlciQmJmL//v3Yvn07/vOf/+DDDz/EoUOHUKtWLUnbePDgAXr06IE5c+YYrAsICICTkxPkcrlBZsEbN27otZBFRUXhwoULuH37NhwdHVGpUiX4+/ujdu3aJvddtWpV3Llzx+i606dPo2PHjhg5ciQ++ugjo2WEEFi2bBkGDRoEZ2dno2UuX76MnTt3YtWqVUbXOzk5af+v6WJobFnBRB+ZmZmoVq2aiVdmHUyQQUREREQVntxBjvgu6vSXMuiPCdI8jusSZ9VAS7t9mQzt2rXDjBkz8Ntvv8HZ2Vmbea8gZ2dnKJX68361bNkSp06dQmhoKOrWrav35+HhAWdnZ4SFhWHnzp3a56hUKuzcuRNt27Y12EfVqlVRqVIl7Nq1Czdv3kTPnj1N1r1FixY4ffq0wfJTp04hMjISQ4YMwaxZs0w+f/fu3Th//rxeNsOCli9fDj8/P5MtY5a4cOECHj16hBYtWlhtm8aUarA1e/ZstG7dGl5eXvDz80Pv3r1x9uxZvTLp6ekYNGgQ/P394eHhgZYtW2LDhg16ZTIzM/Haa6/B29sblSpVwuuvv44HD/SbjP/44w+Eh4fD1dUVwcHBmDt3rs1fnz1iNkIiIiIi46IbRiMhJgGB3oF6y4O8g5AQk4DohtZPf3no0CF89tlnOHr0KK5cuQKFQoFbt26hYcOGRsuHhobi0KFDuHTpEm7fvg2VSoUxY8YgMzMT/fv3x5EjR3DhwgX8+uuvGDZsmDYwmzBhAr7++musXLkSZ86cwZtvvomHDx/qjWVavnw5Dh48iAsXLuC7777Dyy+/jHfeeQf169c3Wf/OnTvj1KlTeq1bJ0+eRGRkJKKiojBhwgSkp6cjPT0dt27dMnj+t99+izZt2qBJkyZGt69SqbB8+XIMHjwYjo7W65SXkpKC2rVro06dOlbbpjGl2o1w9+7dGDNmDFq3bo38/Hx88MEHiIqKwunTp+HhoW7yHTx4MO7evYuffvoJVatWxZo1axATE4OjR49qI9HXXnsNaWlpSExMRF5eHoYNG4aRI0dqB9llZWUhKioKnTp1wtKlS/Hnn39i+PDhqFSpEkaOHFlqr5+IiIiI7Et0w2j0qt8Luy/txj+3/kHtarXRIbSDTVq0AMDb2xt79uxBXFwcsrKyEBISggULFqBr165Gy0+aNAlDhgxBo0aNkJOTg4sXLyI0NBT79u3D5MmTERUVhdzcXISEhKBLly7a8VavvPIKbt26hY8//hjp6elo3rw5tm3bppc04+zZs5gyZQoyMzMRGhqKDz/8EO+8806h9W/atClatmyJ9evXY9SoUQDUqexv3bqF7777Dt999522bEhIiDZVPaDOULhhwwaDCY917dixA1euXDFIcFFca9euxYgRI6y6TaOEHbl586YAIHbv3q1d5uHhIf73v//platcubL4+uuvhRBCnD59WgAQR44c0a7/5ZdfhEwmE6mpqUIIIb788kvh6+srcnNztWUmT54s6tevL6le9+7dEwDEvXv3LH5t1vT48WPx448/isePH0t+DqD+c3KyYcUqKEuOB9kWj4n94TGxL2XxeAwc+ORaVh6VxWNij3JycsTp06dFTk5OsbelVCrFnTt3hFKptELNyrfNmzeLhg0b2vS9subxOHnypPDz8xN37941Waawc8mc2MCuEmTcu3cPAPRy8D/33HNYt24dunfvjkqVKmH9+vV49OiRNh/+gQMHUKlSJbRq1Ur7nE6dOsHBwQGHDh1Cnz59cODAAbRv315v0F3nzp0xZ84c3LlzxyDbS25uLnJzc7WPs7KyAAB5eXnIy8uz+us2l6YO5tVFM0hQIC8v3+p1qsgsOx5kSzwm9ofHxL6UxeOhUsmhGf1QluotVVk8JvYoLy8PQgioVCqDZAjmEv/Oc6XZHpnWtWtX/P3337h69SqCg4Ntsg9rHo/U1FSsWLECXl5eJrelUqkghEBeXh7kBSb1M+dzajfBlkqlQmxsLNq1a6fXZ3P9+vV45ZVXUKVKFTg6OsLd3R0bN25E3bp1AajHdPn5+elty9HREZUrV0Z6erq2TMFsLpom0/T0dINga/bs2ZgxY4ZBHbdv3w73glPYl6LExEQzSvcCoD5Bt27dapsKVXDmHQ8qCTwm9ofHxL6UpeORmtoSgPpHXHm+jpWlY2KPHB0d4e/vjwcPHuDx48dW2eb9+/etsp3yTtPNT9NIYSvWOB7PPPMMgMLr+vjxY+Tk5GDPnj3Iz9dvqDAnXbzdBFtjxozByZMnsXfvXr3lU6dOxd27d7Fjxw5UrVoVP/74I2JiYpCSkmJywrfimjJlCiZMmKB9nJWVheDgYERFRenl5i8teXl5SExMxIsvvqiX1lIKmUyGbt2KnkOCpCvO8SDb4DGxPzwm9qUsHo/165/cWS6P17GyeEzs0aNHj3D16lV4enoWOveTFEII3L9/H15eXtrU4VR6Svp4PHr0CG5ubmjfvr3BuWROQGkXwdbYsWOxefNm7NmzB0FBQdrlFy5cwOeff46TJ0+icePGANQTv6WkpOCLL77A0qVL4e/vj5s3b+ptLz8/H5mZmdp5A/z9/Y3OK6BZV5CLiwtcXFwMljs5OdnVF6Bl9ZHZ1WsoT+zt/CAeE3vEY2JfytLx0J1TtazU2RJl6ZjYI6VSCZlMBgcHh0In4pVC071Msz0qXSV9PBwcHCCTyYx+Js35jJbqmSOEwNixY7Fx40bs2rXLoKufpomu4Bsql8u1b3jbtm1x9+5dHDt2TLt+165dUKlUaNOmjbbMnj179PpXJiYmon79+mbNzk1ERERE9k8zvofIUtY6h0o12BozZgy+++47rFmzBl5eXtoc/Dk5OQCABg0aoG7duhg1ahQOHz6MCxcuYMGCBUhMTETv3r0BAA0bNkSXLl0wYsQIHD58GPv27cPYsWPx6quvokaNGgCAAQMGwNnZGa+//jpOnTqFdevWIT4+Xq+rIBERERGVbZpEBtYar0UVl+YcKpgcw1yl2o1wyZIlAKDNLKixfPlyDB06FE5OTti6dSvef/999OjRAw8ePEDdunWxcuVKvf7aq1evxtixY9GxY0c4ODigb9++WLx4sXa9j48Ptm/fjjFjxiAsLAxVq1bFxx9/zDm2iIiIiMoRTTK1W7duwcnJqVjdzVQqFR4/foxHjx6xG6EdKMnjoVKpcOvWLbi7uxd7IuVSDbakNM899dRT2LBhQ6FlKleurJ3A2JSnn34aKSkpZtWPiIiIiMoOmUyGgIAAXLx4EZcvXy7WtoQQyMnJgZubGxNk2IGSPh4ODg6oWbNmsfdlFwkyqOTwu4KIiMoiXr9IKmdnZzz11FPF7kqYl5eHPXv2oH379kxaYgdK+ng4OztbpQWNwRYRERERlSsODg7FTv0ul8uRn58PV1dXBlt2oKweD3ZAJSIiIiIisgEGW0RERERERDbAYIuIiIiIiMgGGGwRERERERHZAIMtIiIiIiIiG2CwRUREREREZAMMtoiIiIiIiGyAwRYREREREZENMNgiIiIiIiKyAQZbFYxMVto1ICIiIiKqGBhsERERERER2QCDLSIiIiIiIhtgsEVERERERGQDDLaIiIiIiIhsgMFWBcMEGUREREREJYPBFhEREdk9IUq7BkRE5mOwRUREREREZAMMtoiIiIiIiGyAwRYREREREZENMNgiIiIiu8cET0RUFjHYIiIiIiIisgEGW0RERERERDbAYIuIiIiIiMgGGGwRERERERHZAIMtIiIiIiIiG2CwRUREREREZAMMtoiIiIiIiGyAwRYREREREZENMNgiIiIiIiKyAQZbRERERERENsBgq4KRyUq7BkREREREFQODrQqGwRYRERERUclgsEVERERERGQDDLaIiIiIiIhsgMEWERERERGRDTDYIiIiIiIisgEGW0RERERERDbAYIuIiIiIiMgGGGwRERERERHZAIMtIiIisnucJ5KIyiIGW0RERERERDbAYIuIiIjsnkr15P/JyYBSWWpVISKSjMEWERER2TWFQv2nERkJhIbqLyMiskcMtoiIiMhuKRRAv35Adrb+8tRU9XIGXERkzxhsERERkV1SKoHx4wEhDNdplsXGskshEdkvBltERERkl1JSgGvXTK8XArh6VV2OiMgeMdiqYJg6l4iIyoq0NOuWIyIqaQy2iIiIyC4FBFi3HBFRSWOwRURERHYpPBwICjLdK0MmA4KD1eWIiOwRgy0iIiKyS3I5EB9vfJ0mAIuLU5cjIrJHDLaIiIjIbkVHAwkJgLu7/vKgIPXy6OjSqRcRkRQMtoiIiMiuRUfrB1VJScDFiwy0iMj+OZZ2BahkMRshERGVRQ46t4cjIkqtGkREZmHLFhERERERkQ0w2CIiIiK7J0Rp14CIyHwMtoiIiIiIiGyAwRYREREREZENMNgiIiIiIiKyAQZbRERERERENsBgi4iIiIiIyAYYbBEREZHd4zyRRFQWMdgiIiIiIiKyAQZbRERERERENsBgq4JhNwwiIiqLVKon/09OBpTKUqsKEZFkDLaIiIjIrikU6j+NyEggNFR/GRGRPWKwRURERHZLoQD69QOys/WXp6aqlzPgIiJ7xmCLiIiI7JJSCYwfDwhhuE6zLDaWXQqJyH4x2CIiIiK7lJICXLtmer0QwNWr6nJERPaIwRYRERHZpbQ065YjIippDLaIiIjILgUEWLccEVFJY7BFREREdik8HAgKMj1tiUwGBAeryxER2SMGW0RERGSX5HIgPt74Ok0AFhenLkdEZI8YbBEREZHdio4GEhIAd3f95UFB6uXR0aVTLyIiKUo12Jo9ezZat24NLy8v+Pn5oXfv3jh79qxBuQMHDuCFF16Ah4cHvL290b59e+Tk5GjXZ2Zm4rXXXoO3tzcqVaqE119/HQ8ePNDbxh9//IHw8HC4uroiODgYc+fOtfnrIyIiouKLjtYPqpKSgIsXGWgRkf0r1WBr9+7dGDNmDA4ePIjExETk5eUhKioKDx8+1JY5cOAAunTpgqioKBw+fBhHjhzB2LFj4eDwpOqvvfYaTp06hcTERGzevBl79uzByJEjteuzsrIQFRWFkJAQHDt2DPPmzcP06dPx1VdflejrtQem+r0TERHZM53LPiIi2HWQiMoGx9Lc+bZt2/Qer1ixAn5+fjh27Bjat28PAHjnnXcwbtw4vP/++9py9evX1/7/zJkz2LZtG44cOYJWrVoBAP7zn/+gW7dumD9/PmrUqIHVq1fj8ePHWLZsGZydndG4cWOcOHECCxcu1AvKiIiIiIiIrKVUg62C7t27BwCoXLkyAODmzZs4dOgQXnvtNTz33HO4cOECGjRogFmzZuH5558HoG75qlSpkjbQAoBOnTrBwcEBhw4dQp8+fXDgwAG0b98ezs7O2jKdO3fGnDlzcOfOHfj6+urVIzc3F7m5udrHWVlZAIC8vDzk5eXZ5sWbQVMH8+ri9O+/Anl5+VavU0Vm2fEgW+IxsT88JvalLB4PlUoOTYecslRvqcriMSnveEzsiz0dD3PqYDfBlkqlQmxsLNq1a4cmTZoAAP755x8AwPTp0zF//nw0b94c//vf/9CxY0ecPHkSTz31FNLT0+Hn56e3LUdHR1SuXBnp6ekAgPT0dNSqVUuvTPXq1bXrCgZbs2fPxowZMwzquH37drgXHKFbihITE80o3QsAkJ+fj61bt9qmQhWceceDSgKPif3hMbEvZel4pKa2AFATAMr1dawsHZOKgsfEvtjD8cjOzpZc1m6CrTFjxuDkyZPYu3evdplKpQIAjBo1CsOGDQMAtGjRAjt37sSyZcswe/Zsm9RlypQpmDBhgvZxVlYWgoODERUVBW9vb5vs0xx5eXlITEzEiy++CCcnp6KfoMPR0RHdunWzUc0qpuIcD7INHhP7w2NiX8ri8UhIeDJIqzxex8riMSnveEzsiz0dD02vNynsItgaO3asNrFFUFCQdnnAv1PCN2rUSK98w4YNceXKFQCAv78/bt68qbc+Pz8fmZmZ8Pf315a5ceOGXhnNY00ZXS4uLnBxcTFY7uTkVOoHV5cl9ZHJZHb1GsoTezs/iMfEHvGY2JeydDx0E2SUlTpboiwdk4qCx8S+2MPxMGf/pZqNUAiBsWPHYuPGjdi1a5dBV7/Q0FDUqFHDIB3833//jZCQEABA27ZtcffuXRw7dky7fteuXVCpVGjTpo22zJ49e/T6VyYmJqJ+/foGXQiJiIjI/jCbLhGVRaUabI0ZMwbfffcd1qxZAy8vL6SnpyM9PV07h5ZMJsO7776LxYsXIyEhAefPn8fUqVPx119/4fXXXwegbuXq0qULRowYgcOHD2Pfvn0YO3YsXn31VdSoUQMAMGDAADg7O+P111/HqVOnsG7dOsTHx+t1FSQiIiIiIrKmUu1GuGTJEgBARESE3vLly5dj6NChAIDY2Fg8evQI77zzDjIzM9GsWTMkJiaiTp062vKrV6/G2LFj0bFjRzg4OKBv375YvHixdr2Pjw+2b9+OMWPGICwsDFWrVsXHH3/MtO9ERERERGQzpRpsCSEklXv//ff15tkqqHLlylizZk2h23j66aeRkpJiVv2IiIiIiIgsVardCImIiIiIiMorBltEREREREQ2wGCLiIiIiIjIBhhsERERERER2QCDLSIiIiIiIhtgsEVERERERGQDpZr6nYiIiIioolCqlEi5koK0+2kI8ApAeM1wyB3kpV0tsiEGW0RERERENqY4o8D4beNxLeuadlmQdxDiu8QjumF0KdaMbMmiboR5eXm4evUqzp49i8zMTGvXiYiIiIio3FCcUaDf+n56gRYApGalot/6flCcUZRSzcjWJAdb9+/fx5IlS9ChQwd4e3sjNDQUDRs2RLVq1RASEoIRI0bgyJEjtqwrERERVVBClHYNrEupBJKTgbVr1f8qlaVdI7IVpUqJ8dvGQ8DwJNYsi90WC6WKJ0F5JCnYWrhwIUJDQ7F8+XJ06tQJP/74I06cOIG///4bBw4cwLRp05Cfn4+oqCh06dIF586ds3W9iYiIiMokhQIIDQUiI4EBA9T/1q3riAMHAkq7amQDKVdSDFq0dAkIXM26ipQrKSVYKyopksZsHTlyBHv27EHjxo2Nrn/mmWcwfPhwLF26FMuXL0dKSgqeeuopq1aUrEMmK+0aEBERVVwKBdCvn2FL3fXrwJw5rdGypRIxMaVTN7KNtPtpVi1HZYukYGvt2rWSNubi4oLRo0cXq0JkWwy2iIiISodSCYwfb7xLpBAyAAITJ8rRty8gZ4K6ciPAS1qLpdRyVLYUa56tq1ev4urVq9aqCxEREVG5lZICXDPdmwyADNeuyZDC3mTlSnjNcAR5B0EG43e8ZZAh2DsY4TXDS7hmVBLMDrby8/MxdepU+Pj4IDQ0FKGhofDx8cFHH32EvLw8W9SRiIiIqMxLk9hLTGo5KhvkDnLEd4k3uk4TgMV1ieN8W+WU2cHW22+/ja+++gpz587Fb7/9ht9++w1z587Ft99+i3HjxtmijkRERERlXoDEXmJSy1HZEd0wGgkxCajiVkVveZB3EBJiEjjPVjlm9qTGa9aswffff4+uXbtqlz399NMIDg5G//79sWTJEqtWkIiIiKg8CA8HgoKA1FRTqewFgoKA8HAOsC6PohtGQ6lSIiZBnQElaUgSwmuGs0WrnDO7ZcvFxQWhoaEGy2vVqgVnZ2dr1ImIiIhIT3lI8CSXA/H/9iYr+HpkMnX0tWCBkskxyjHdwCoiNIKBVgVgdrA1duxYfPLJJ8jNzdUuy83NxaxZszB27FirVo6IiIioPImOBhISgMBA/eWBgcDkyUfQp085m72ZqIKT1I0wOlq/H+mOHTsQFBSEZs2aAQB+//13PH78GB07drR+DYmIiIjKkehooFcvwPHfX2GTJwPTp+fj11/TALQo1boRkXVJCrZ8fHz0Hvft21fvcXBwsPVqRERERFTO6XYVbNiQ82oRlVeSgq3ly5fbuh5ERERERETlitnZCDVu3ryJs2fPAgDq168PPz8/q1WKiIiIiIiorDM7QUZWVhYGDRqEwMBAdOjQAR06dEBgYCAGDhyIe/fu2aKOREREREREZY7ZwdaIESNw6NAhbN68GXfv3sXdu3exefNmHD16FKNGjbJFHYmIiIiIyjxhfII1KsfM7ka4efNm/Prrr3j++ee1yzp37oyvv/4aXbp0sWrliIiIiIiIyiqzg60qVaoYZCcE1BkLfX19rVIpIiIiorJIqQRSUoC0NCAgAAgPLzrTIBs7iMovs7sRfvTRR5gwYQLS09O1y9LT0/Huu+9i6tSpVq0cWV/BGeuJiIjIOhQKIDQUiIwEBgxQ/xsaql5ORBWT2S1bS5Yswfnz51GzZk3UrFkTAHDlyhW4uLjg1q1b+O9//6ste/z4cevVlIiIiMhOKRRAv36GrVSpqerlCQnqyYyJqGIxO9jq3bu3DapBREREVDYplcD48ca7Awqh7lUSGwv06sXJi4kqGrODrWnTptmiHkRERERlUkoKcO2a6fVCAFevqstFRJRYtYjIDlg8qfHRo0dx5swZAECjRo0QFhZmtUoRERERlRVpadYtR0Tlh9nB1rVr19C/f3/s27cPlSpVAgDcvXsXzz33HL7//nsEBQVZu45EREREdisgwLrliKj8MDsb4RtvvIG8vDycOXMGmZmZyMzMxJkzZ6BSqfDGG2/Yoo5EREREdis8HAgKMp3xVyYDgoPV5YioYjE72Nq9ezeWLFmC+vXra5fVr18f//nPf7Bnzx6rVo6sj6nfiYiIrEsuB+Lj1f8veJ3VPI6LY3IMoorI7GArODgYeXl5BsuVSiVq1KhhlUoRERERlSXR0er07oGB+suDgpj2nagiMzvYmjdvHt5++20cPXpUu+zo0aMYP3485s+fb9XKEREREZUV0dHApUv6yy5eZKBFVJGZnSBj6NChyM7ORps2beDoqH56fn4+HB0dMXz4cAwfPlxbNjMz03o1JSIiogqrrHSDL9hVsOBjpVKdAp6ZCSsmASOTsVG5ZnawFRcXZ4NqEBEREZVvCoV68uOCc3IdPQq89lrp1ImIbMvsYGvIkCG2qAcRERFRuaVQAP36qSc4LuiLL4D27WVwcSn5ehGRbUkas/Xw4UOzNmpueSIiIqLySqlUt2gZC7Q0Jk6UQ6ksuToRUcmQFGzVrVsX//d//4e0QjoYCyGQmJiIrl27YvHixVarIBEREVFZlZys/ivYdbCga9dkOH26SklUiYhKkKRuhMnJyfjggw8wffp0NGvWDK1atUKNGjXg6uqKO3fu4PTp0zhw4AAcHR0xZcoUjBo1ytb1JiIiIrI7CoX+48hIoHJlac+9c8fV+hUiolIlKdiqX78+NmzYgCtXruCHH35ASkoK9u/fj5ycHFStWhUtWrTA119/ja5du0LOGfuIiIioAtKMyypIanJmX99H1q0QEZU6sxJk1KxZExMnTsTEiRNtVR+ysbKSOpeIiKgskTIuqzBBQQKNGmVYt1JEVOrMzkZIREREZZtSCfz5ZxVkZckQHAyEhxvOB0XmSUkpelxWYRYsUPIYEJVDkhJkEBERUfmgUAB16zpi6tTnMXiwIyIjgdBQw7FGZB6pkxR7ehouGzMG6NOHk90SlUcMtoiIiCoIzZii1FT95amp6uX2HHBZ2j2vpAQESCv3/vuGy8LCrFsXIrIfDLaIiIgqAP0xRfoDeDWBTGwsONeThcLDgaAg02OjZTIgOBho2rRk60VEpYvBFhERUQVQ1JgiIYCrV9XlyHxyORAfb3ydJgCLi+PYOKKKxqIEGXfv3sXhw4dx8+ZNqFQqvXWDBw+2SsWIiIjIeqSOKZJajgxFRwMJCUDfvvrLg4LUgVZ0NLB1a6lUjeyEsPf+sGR1ZgdbP//8M1577TU8ePAA3t7ekOm0l8tkMgZbREREdkjqmCKp5ci46Gj9x0lJRWd75O9vovLL7G6EEydOxPDhw/HgwQPcvXsXd+7c0f5lSp21j4iIiEqU1DFF4eElW6+SoFQCycnA2rXqf0tyXFpEBLsOElVkZgdbqampGDduHNzd3W1RHyIiIrIB3TFFMpl+U0p5HlOkUKhT20dGAgMGgKnuiahEmR1sde7cGUePHrVFXYiIiMiGNGOKatTQXx4UpF5esAtcWadJdV8wMUhZSHVPROWD2WO2unfvjnfffRenT59G06ZN4eTkpLe+Z8+eVqscERERWVd0NNCtWz7c3NTX7+HDga++Kn8tWvqp7vUJoW7Ni40FevUqf6+diOyH2cHWiBEjAAAzZ840WCeTyaDkBB12zVRffSIiqjh0g4vatctnsGFOqvuIiBKrFhFVMGYHWwVTvRMREREB6taklBR1+viAgKKz8NmSvaa6501PoorFonm2iIiIiHQpFOpue7qtSUFB6qQc5o4FMxa0mRukMNU9EdkDsxNkAMDu3bvRo0cP1K1bF3Xr1kXPnj2RwinniYiIKiSFQj2Rb8Fue9euqZebk4jCVPbAy5fNq1NFTnVPRPbD7GDru+++Q6dOneDu7o5x48Zh3LhxcHNzQ8eOHbFmzRpb1JGsiN0XiIjImpRKYOTIwsuMHCltbqvCsgcmJZlXL/1U9/rrynOqeyKyL2YHW7NmzcLcuXOxbt06bbC1bt06/N///R8++eQTW9SRiIiI7FRyMpCRUXiZjAxg587CyxSVPbDgPqUEb5pU94GB+svLa6p7IrI/Zgdb//zzD3r06GGwvGfPnrh48aJVKkVERERlQ3KytHJFzWtVVPZAXeZMTBwdDVy69OTxmDHAxYsMtIioZJgdbAUHB2OnkdtTO3bsQHBwsFUqRUREROXL/fuFB1zmZgU0Z2Ji3a6CTz1Vsl0HpbbCUcUgYKTplso1s4OtiRMnYty4cXjzzTexatUqrFq1CqNHj0ZsbCwmTZpkizoSERGRnTJ3jqrYWOPBh7lZATVdC01tr7QUDP6ktMIZ6zpJROWD2anf33zzTfj7+2PBggVYv349AKBhw4ZYt24devXqZfUKEhERkf2KiACqVCl63BZQ+ETCmuyBqanSgw97m5hYk+CjIE0rXEIC4O5e8vUiotJjUer3Pn36YO/evcjIyEBGRgb27t3LQIuIiKgMKm6rilwOfPWVec8x1mVQN3uguUp6YmJjpCT4iI0F8vNLtFpEVMosCraIiIiINKKjgQ0bgKpVpZU31WVQkz3QXPYwMXFRCT40rXCnTpVcnYio9EnqRli5cmX8/fffqFq1Knx9fSErZLKmzMxMq1WOiIiISodSqQ4g0tLUwUx4eOGJJaKjgZdeAlxcTJeRydRdBQubSLhglsBq1YDbt423GEnZXkmR2rrGn0lEFYukYGvRokXw8vLS/r+wYIuIiIjKNoVC3SVOt6UmKEjdza9XL9NBmLOz6W1aOpHw0qXGx0HZ28TEUlvXKle2bT2IyL5ICraGDBmi/f/QoUNtVRciIiIqZQoF0Lev4fJr19TLCybD0ARhBVuk3NyAnBz9cnFx5s9vpelaOGgQkJ1t2fZ0sxWeO6d+bO0AragEH5pWuMaNrbtfIrJvZo/ZksvluHnzpsHyjIwMyO3h1hIRERFZRKkERo4svEzBrIOm5rvq3v3J/5OSijeRcHS0fuuWOdtTKNSp1zW++EL6hMjmKCzBh721whFRyTE72BIm0hbl5ubCubD+A0RERGTXkpOlpXDXZWq+KwedXxgREcUPMizZniYVe8HEFeZMiGwOUwk+goLUy6OjAZXKuvskIvsmeZ6txYsXAwBkMhm++eYbeHp6atcplUrs2bMHDRo0sH4NiYiIqETs2mXZ83Tnu7IXRaVil8nUAWKvXtZtbSrY2paU9GRcm0IBjBpl+Jxjx4DBg61XByKyH5JbthYtWoRFixZBCIGlS5dqHy9atAhLly5FdnY2li5datbOZ8+ejdatW8PLywt+fn7o3bs3zp49a7SsEAJdu3aFTCbDjz/+qLfuypUr6N69O9zd3eHn54d3330X+QUmskhOTkbLli3h4uKCunXrYsWKFWbVlYiIqLy7cqV4z7eH+a40pKZit3WAqGmF07Sy3b5tWGbJEmDjRiYfIyqPJLdsXbx4EQAQGRkJhUIBX1/fYu989+7dGDNmDFq3bo38/Hx88MEHiIqKwunTp+Hh4aFXNi4uzmgWRKVSie7du8Pf3x/79+9HWloaBg8eDCcnJ3z22Wfaunfv3h2jR4/G6tWrsXPnTrzxxhsICAhA586di/06iIiIyoOaNYv3fHuY70pDauBXEgFiYa1sGhMnyi2e1JmI7JfkYEsjKSnJajvftm2b3uMVK1bAz88Px44dQ/v27bXLT5w4gQULFuDo0aMIKPBNvn37dpw+fRo7duxA9erV0bx5c3zyySeYPHkypk+fDmdnZyxduhS1atXCggULAAANGzbE3r17sWjRogoXbDFrPxERmfLCC8C/9ynNYmy+q9K+3kgN/EoiQCyqlQ0Arl2T4fTpKujRw/b1odJjKvcBlV9mB1sAcO3aNfz000+4cuUKHj9+rLdu4cKFFlfm3r17ANSTKGtkZ2djwIAB+OKLL+Dv72/wnAMHDqBp06aoXr26dlnnzp3x5ptv4tSpU2jRogUOHDiATp066T2vc+fOiI2NNVqP3Nxc5Obmah9nZWUBAPLy8pCXl2fx67MWTR3Mq4sTAEAmE8jLyy+iLJnDsuNBtsRjYn94TOyL+jiorwtKpRJ5eeqsDe3aAZUrO/478a6xaEnzQ/HJOplMvWz+fCVUKqHdrkqlgma0gvnH3alAXQGVSm7W9p59FggMdMT164AQhq9FJhMIDASefTYf1j8t9et/9aoMUn5y3bnjys+IHbHF91a+8slvMB5r89jTdcScOpgdbO3cuRM9e/ZE7dq18ddff6FJkya4dOkShBBo2bKluZvTUqlUiI2NRbt27dCkSRPt8nfeeQfPPfccevXqZfR56enpeoEWAO3j9PT0QstkZWUhJycHbm5ueutmz56NGTNmGOxr+/btcHd3N//F2UhiYqIZpdXvX25uLrZu/dU2FargzDseVBJ4TOwPj4k9UV8X/v77b2zd+rd26YgRAZgzpzXUgZVukKIOqhwdVcjPf5JRokqVHLz++km4uKRh69Yn201Luw4gCACwVb3C7LrpPjc1tTmAEEnbUyqB06eroGVLf6Sm1jH6WoQAXnvtCH791bJ+hJp93LnjCl/fR2jUKEMn0YZ+/S9frgLg+SK36ev7iJ8RO2TNY3Lizgnt/83/XBBgH9eRbN1J/4pgdrA1ZcoUTJo0CTNmzICXlxc2bNgAPz8/vPbaa+jSpYu5m9MaM2YMTp48ib1792qX/fTTT9i1axd+++03i7driSlTpmDChAnax1lZWQgODkZUVBS8vb1LtC7G5OXlITExES+++CKcnJyKfoIOFxcXdOvWzUY1q5iKczzINnhM7A+PiX3RvStbr149dOtWV/u4WzegZUslJkyQIzX1yXOCgoAFC5RYv94BGzaolyUm5uP5550gl7cA0EIv9btcXkNnm5ZfdzTP3bhRbrDMmI0bZf/W/Ulw5eAg9FKua15Lnz7qepvL2D4CAwUWLlSiTx/9bmLdunVD587A0qXCZCsbIBAUJNCoUQY/I3bEFt9bD04/AC6r/8/fY+axp+uIptebFGYHW2fOnMHatWvVT3Z0RE5ODjw9PTFz5kz06tULb775prmbxNixY7F582bs2bMHQUFB2uW7du3ChQsXUKlSJb3yffv2RXh4OJKTk+Hv74/Dhw/rrb9x4wYAaLsd+vv7a5fplvH29jZo1QLUAYmLi4vBcicnp1I/uLosq4/Mrl5DeWJv5wfxmNgjHhP7I5fL4eSkn/s8Jgbo2xdw1PmVcOmSDHK5ozbQAoBOnZ4UUCjUSSA0du9+kvC4OMdc81zdebZMbU+hAF591TARhUqlH+AIIYOjoyMsqZapfVy/LsOrrzoazLOlPueBxYv1J2bWJ8OCBUrI5fyM2CNrHhNH+ZPPDI+zZezhM2LO/s2e1NjDw0M7TisgIAAXLlzQrrttLJ9pIYQQGDt2LDZu3Ihdu3ahVq1aeuvff/99/PHHHzhx4oT2D1CnoV++fDkAoG3btvjzzz9x8+ZN7fMSExPh7e2NRo0aacvs3LlTb9uJiYlo27atWfUlIiKqKArOPVXYXFSmJg/WXW9rUjL+aVy/btmkxkXN3QWo5+4yRjPhcdWqhuvefBMGLWJEVD6Y3bL17LPPYu/evWjYsCG6deuGiRMn4s8//4RCocCzzz5r1rbGjBmDNWvWYNOmTfDy8tKOsfLx8YGbmxv8/f2NJsWoWbOmNjCLiopCo0aNMGjQIMydOxfp6en46KOPMGbMGG3r1OjRo/H555/jvffew/Dhw7Fr1y6sX78eW7ZsMfflExERlSvFTY4mJcixxeTBBUnJ+Kdh6aTGUufuMiU6GnB1Bbp3118eFiZt/0RU9pjdsrVw4UK0adMGADBjxgx07NgR69atQ2hoKL799luztrVkyRLcu3cPERERCAgI0P6tW7dO8jbkcjk2b94MuVyOtm3bYuDAgRg8eDBmzpypLVOrVi1s2bIFiYmJaNasGRYsWIBvvvmmwqV9JyIiKq6CKd2lBDklMXmwufNlWTKpsSVzcumOYwNsG3ASkf0xq2VLqVTi2rVrePrppwGouxQuXbrU4p1bMteAseeEhIQUmdElIiKixBNtEBERlXf2MnmwpfNlmVMvS/YRGgrEx6tbtYio4jGrZUsulyMqKgp37tyxVX2IiIioDLGXyYPDw9VZBs1lTr00+zBnwubUVMvGhxFR+WB2N8ImTZrgn3/+sUVdiIiIqIyREoAEB6vLWSI5Wd0Vr6jOMHK5ugXJHObWS3cfUgMu3cQZBbsUElH5Z3aw9emnn2LSpEnYvHkz0tLSkJWVpfdHREREFYeUACQuTvpYpYItQJGR6q54ly8bL69UqgOytWuBypX1088XxZx6aWiyCgYGSn+O7vgwc1rFiKjsMzsboWYCtp49e0Km840hhIBMJoOSt22IiIgqFE0AMn688WQZUscraVLIF5Saany7mrm9dNcZS61uzIwZ5o2jUirVwVJamrrr4YULgGZKTnd3IDu76G2kpQFVqkjfJxGVfWYHW0lJSbaoBxEREZVh0dHqNOqaiZAjItQtTlJJmcNKlyYwK7hOypSfQUHAhx9Kr5uxoE53fJizs7RgKyAA+HeqUj3FTb9PZYcAD3ZFY3awVatWLQQHB+u1agHqlq2rhU0uQUREROWabpc83Ram5GT12KjCuuyZM0+WORMYGxMfb163RmNBXWrqk/87OqqDr9RU43WSydTrw8OBHTssqzMRlU1mj9mqVasWbt26ZbA8MzNTO9EwERERlQ3mjiGSWn7Llif/14y7Kiwjnzkp2M0JzIrDnNY2U+PWNI8tGR9G5Y8l0x5R2WZ2sKUZm1XQgwcP4OrqapVKke1wYC4REelSqZ4kmNBk/rOGnBz9x0WlQDcnBXtx5+ySmhlQalCXl2c6cUZQkHo559kiqpgkdyOcMGECAEAmk2Hq1Klwd3fXrlMqlTh06BCaN29u9QoSERGR7SxYAEyb9uRxUJDlk/AWFsAIob7hFxurHttVsJVHk0LeVFc8XcWds0uTGTAiovByUoM6lUr9b8FxawBw8SJbtIgqMsnB1m+//QZA3bL1559/wtnZWbvO2dkZzZo1w6RJk6xfQyIiIrKZgrO2aFqgEhLM31ZKSuHrdVOgFwx0NCnkjWUjlMn0AzBzAjNTpARSUoM63WCqYGDFQIt0qYRK+//kS8kIrxkOuQNPkvJMcrClyUI4bNgwxMfHw9vb22aVIiIiItuR2gJlLqktQabKabri9e2rvzwoCKhbF9AkRNYEZgXLmUNKICU1qHNykr5fduevuBRnFBi3bZz2ceTKSAR5ByG+SzyiG7KfaXll9pit5cuXM9AiIiIqw/buLfwXv6YFylxSW4IKK1ew+2JSkrorXkiI+fUxRZMZsCi6EzYXpBs0MYCioijOKNBvfT9k5mTqLU/NSkW/9f2gOFNI9hgq08wOth4+fIipU6fiueeeQ926dVG7dm29PyIiIrJvumnLramoAEYmA4KDpQU6GhERhl3xNFkCLZWTA2zaJK2sprXN01N/ue48W0SFUaqUGL9tvNE5tjTLYrfFQqmyUnYasitmz7P1xhtvYPfu3Rg0aBACAgKMZiYkIiIi+6RQAJMmWWeMyNq16lYqzRxahY1PKm4KdN2fG8VN/Z6Z+WRcmpREINHR6n3GxakfJyWpX7MmEQazeVNhUq6k4FqW6RNWQOBq1lWkXElBRGhEyVXMypQqJVKupCDtfhoCvAI4Hu1fZgdbv/zyC7Zs2YJ27drZoj5kY4yNiYgqLlMT9Bojlxsf26XbvXDAAPW/xjIYurnpp38PClIHK9ZIgV7c1O9FZUY0xkGnL1BRWQyJdKXdl3bCSi1njxRnFBi/bbxeUMnxaGpmdyP09fVF5cqVbVEXIiIishH9CXqLvvNWMNBSKtXB2r59hmWNzaH10ktP/q8Zd2WtuaaKm/od0M+MSGRLAV7STlip5eyNZjxawdY7jkdTMzvY+uSTT/Dxxx8jOzvbFvUhIiIiGyhu17uQEGDkSOPrNC1luhkMC7YEWTMFuiZLoDV6axS3lcwa2A2xfAuvGY4g7yDITNzkkEGGYO9ghNc0YzCjneB4tKKZ3Y1wwYIFuHDhAqpXr47Q0FA4Fch3evz4catVjoiIiKyjuEFFUUk1LM1gaAndObkKzsFlLmu0kpmD3fkrHrmDHPFd4tFvveEkcpoALK5LXJkc31RRxqMVh9nBVu/evW1QDSIiIrKl8hZUaLIEjh9vWYudTCY9BTxRcUU3jEZCTAJG/DxCL/17kHcQ4rrEldlxTRVhPFpxmR1sTZs2zRb1ICIiIhuSOkFvWRIdrU5w4WjmrxlLMiMWFjyytYqkiG4Yjey8bAzaOAgAkDQkqcxn7Cvv49GswewxWwBw9+5dfPPNN5gyZQoyM9XR+fHjx5Fqq4k7iIiIqFh0J+iVyawfbWnm0CpplowFCwqSnvadyJocZE9+ekeERpTpQAso3+PRrMXsYOuPP/5AvXr1MGfOHMyfPx93794FACgUCkyZMsXa9SMiIiIr0XS9q1HD+PpFiyzbrm5LUVmwYIF1A63y0lJIZC7NeDQABgFXWR+PZi1mB1sTJkzA0KFDce7cObi6umqXd+vWDXv27LFq5YiIiMi6oqOB8+fzja57++3Cs/zJZICzs+Fye2kpqlJFWrmJE43PIVYYBlRExmnGowV6B+otD/IOQkJMQpkdj2YtZgdbR44cwahRowyWBwYGIj093SqVIiIiItsx1fVOv6uh4XohgGee0V9mag6t0ghORoyQVo7zaxFZV3TDaFwaf0n7eHjz4bg4/mKFD7QAC4ItFxcXZGVlGSz/+++/Ua1aNatUiqxL9+7d48fm380jIqKKQ9PVMDDQ+PqCM7xYew6tksJh5kTWpdtVsLZv7QrddVCX2cFWz549MXPmTOTl5QEAZDIZrly5gsmTJ6Nv375WryAVj0IBhIY+eXz7tvqxomJP5k1ERIWIjjY9fis7u2TroqFSPfl/crLxG4dffy19e7duFbtKFtF9HURU/pkdbC1YsAAPHjyAn58fcnJy0KFDB9StWxdeXl6YNWuWLepIFlIo1BM+Fpx/JDVVvZwBFxERGaNUAu+8U9q1eEKhAH744cnjyEjjNw4zMqRvszQ64ygUwMCBhssLthYSUflh9jxbPj4+SExMxL59+/D777/jwYMHaNmyJTp16mSL+pGFlEr1RI/G+swLoe6LHxurnp+kLHb/ICIiy23caDwDhkKhbtWaNcuyiYJtQXPjsOD1THPjMCHBsu2a6iZpK6ZeBwAsXQpERsrg4lKydSKylX/u/IO1f65FgFdAmZ9LrLjMDrY02rVrh3bt2lmzLmRFKSmFXyiFeDJAOCKixKpFRESlTKEAXn3V+A+ffv2ASZOAefOKvx9rTfQr5cahuYKD1ZM8l5TCboBqTJwo1yYnISrrlp1YhmUnlgFQZyWM7xJfYZNlmN2NcNy4cVi8eLHB8s8//xyxlnzjkU2kpVm3HBERlX1F/egXAli4sGTrVBQpNw41qlSRFuTFxZnfq6M4wWNRN0AB4No1GU6flpi7nsgOKc4YH5+SmpWKfuv7mVxf3pkdbG3YsMFoi9Zzzz2HBEvb8snqAgKsW46IiMq+Jz/6TUcOZTljrZTU78OGlfx8YFIzH2ZkuBZdiMgOKVVKjN823ug6AfXdndhtsVCqyvAXjIXMDrYyMjLg4+NjsNzb2xu3b9+2SqWo+MLDi56YsqS7URARUekq770ZvLyA9esBT0/TZZo1K7n6aNy4Ia3c3bsctFXeiXI6O3bKlRRcyzLdfCsgcDXrKlKuVLwJ7swOturWrYtt27YZLP/ll19Qu3Ztq1SKik93YsqCNAGYJd0oiIio7CqLvRkKu3EI6F/HPvxQnUWxfXvb18scUrMk3r/vbNuKENlI2n1pd3KklitPzE6QMWHCBIwdOxa3bt3CCy+8AADYuXMnFixYgLi4OGvXj4pBMzFlwenPgoLUgVZJd6MgIqLSpen1kJoqIITxCEYuV88FZS834OPj1Yk7TCnY7TE11X4yKWo4SLy17eBgJ2862Q2lSomUKylIu59m15n9Aryk3cmRWq48MTvYGj58OHJzczFr1ix88sknAIDQ0FAsWbIEgwcPtnoFqXgKBlRVqwIXL7JFi4ioItL0ejAVvMhkwIQJ1slGaC2aG4eDBulPqCyXGx9fVlSQWBqTCkttaWvU6DaAOjatC5UdijMKjN82Xq97nr1m9guvGY4g7yCTXQllkCHIOwjhNSve+BWzuxECwJtvvolr167hxo0byMrKwj///MNAq4xwcWGgRURUkUVHA99/b3yQekICMHt24WOeSkN0NBAT8+TxokWWJ/L46CPDyZBtTXrLlm3rQWWH4owC/db3Mwhe7DWzn9xBjvguxsevyP5NyBPXJc4uW+VsrVgf62rVqsHT3r6RqVDWmveEiIjKrj59jDf/aCY0fvCghCskgW4gUr265dvJzlZ3rzc34CpOt8qbN6WVu3eP2QjpSWY/TRY/Xfac2c9Ua1uQdxASYhLsrjWupJgdbN24cQODBg1CjRo14OjoCLlcrvdHREREZZNSaTq5krlseXPPGok+xo+3Xpr7ogIxPz9p2/HxeVT8ylCZV54y+yUNScLF8RctCrSUKiWSLyVj7Z9rkXwp2e6CS6nMHrM1dOhQXLlyBVOnTkVAQABkbCops5RK9ZwraWnqC1d4OLsYEhFVZCkpQGZmadeiaE8SfVje4nTtmvr1RkRY9nzdQO3xY/VjXkPJGspTZr+I0AiLnmdsvFqgVyAGVhmIbuhmpdqVDLODrb179yIlJQXNmze3QXWopCgU6rt6uhmbgoLUdzSZpZCIqGKy53m4dIMq3UQfMpnlAZc5r1d3HzNnAl9//eRxVhYQGmr6GspuhGSOip7ZTzNerWA3yuv3r2PO/Tlo+VdLxDSNMfFs+2N2N8Lg4OByOyFbRSCTqQOtfv0MU+OmpqqXl/TAYSIisg9laR4uTZbCwED95dWqSd+G1NerUOgHV9OmmXcNlbofX192I6Qnmf00iSUKkkGGYO/gcpnZT8p4tYk7JpapLoVmB1txcXF4//33cenSJRtUh2xNCHWLlrF4WbMsNtZ6/diJiKjs0HTPK2yEgLPEeXdL4r5sdDSg+3Pks8/UQZCHR9HPDQpSv96iaG5QFpU0RPcaWpCU9zUoSKBRI4mzH1O5ppvZr2DAVd4z+xU1Xg0ArmVdKxPj1TTMDrZeeeUVJCcno06dOvDy8kLlypX1/si+5eYWPtmjEMDVq+p+7EREVLFouucBpgODNm2kbUtqsKVUAsnJwNq16n/NvdmnO06qWTN1MBgZWfTz4uOLHmOlVJq+QWmM5hpqrI5FJR5ZsEDJMV+kFd0wGgkxCQj01m+6Le+Z/crTeDUNs8dsxcXF2aAaVFKkXsTsud8+ERHZTnQ0MGkSsHCh4TXj3XeB69etty9T44eL66mnTK/z8AD+9z9p45NTUgq/QWkOTbfHUaOA27f1140apU7Hv3WrdfZF5UN0w2j0qt8Ljp+of64PbDoQK3qvKJctWhrlcbya2cHWkCFDbFEPKiFS75qVpX77RERkPQoFMH++8dac+fOB55+33n769TPcT2qq5duU0gL1ySfSE0FZ+8ZjdDTg5gZ0K5BMrUUL6+6H7JexsUiF0Q2sQiuFGgRaSpUSKVdSkHY/DQFeAQivGV6mgzHNeLXUrFST71WQd1CZGq9mdrAFAEqlEj/++CPOnDkDAGjcuDF69uzJebbKABeXwtPlymTS+7ETEVH5IqXb3LFjtt2Prcd6ORQxgEJ3WpQbN8zbtuYaaqwroQZ/KpG1GEuPHuQdhPgu8WW2m6FmvFq/9f0gg0wv4NI8XtBpQZkKKM0es3X+/Hk0bNgQgwcPhkKhgEKhwMCBA9G4cWNcuHDBFnUkK8rNBRYtMr5O0z8/Lo4XAyKiiqiobnNCANnZ0rZVWDIIa3bPM6awgE2lMr1OoVCncI+MBAYMAN55R/r1UPcaSmQLuoGHJj16wWQSqVmp6Le+HxRnym5qae14NS/98WqBXoGYHDoZfRr0KaWaWcbsYGvcuHGoU6cOrl69iuPHj+P48eO4cuUKatWqhXHjxtmijlQMBVPQ3rqlvnhMmgS4FpjOIyhI3Z+c82wREVVMJTVe11b7KSzA05g503h6dlPTokgd68xrKJUUKenRY7fFlnh6dGvvr+DrU4lC7pTYMbODrd27d2Pu3Ll6mQerVKmC//u//8Pu3butWjkqHs2Fo6DUVHW/+4YNnyxLSgIuXuRFgoioIrNkvK4lmQRtPS64sKDr7l3D+bCkdJ8suE3dRB7e3tKvocbqdvYs8P33Mvz5ZxVOvUJFKio9uoDA1ayrRaZHV6qUSL6UjLV/rkXypeRCg6WiyirOKBAaH2rwPEta2DStdqn39Qdwpj1Iw5xLc7Dxr41mb7M0mT1my8XFBffv3zdY/uDBAzhLnXyDbK6o/vAyGfDvkDsAQEREiVWNiIjslGY+KHO6+OmmWZeaSVCzH1Pjh4uSnKxuHQsIMD7GuLCughqxsUCvXupuglK6NerWMylJvV/Hf39FubhI725o7PWqu/c7AngeS5cKLF7Mm59kmtS05zv/2WkyYYY5472KKqsJjoy1tPVd3xcbYjZIHkMmdVLjvo37lplxW2a3bL300ksYOXIkDh06BCEEhBA4ePAgRo8ejZ49e9qijmQBKf3uH3GieiIi0iGXq1O+W0pqJkG5XB1gmErUVBTNmKrISPUYK3PrUXBOSVt2nzS31e/6dcOWNyJdUtOef5ryKULjQw1al8wZ71VU2R9O/WAyONIY+fNIyV0MOakxgMWLF6NOnTpo27YtXF1d4erqinbt2qFu3bqIL2rGPioxnCeLiIgsUa2a5c/VDZ6KSkTxzjvG15lqHbt0yfhyY4HVw4em961Lc600t1ujqSDPGN2gUEoAJYQ62oyNNX+CZ6oYNOnRZSj6zkTBAKqoliMBgRE/j8DOf3bicf7jIluZxmwdU2RwlJGTgVkps7T7T76UjNV/rEbcwTis/mO1XrdETmoMoFKlSti0aRPOnz+vTf3esGFD1K1b1+qVI8txniwiIrKEtW7W3bplfLmp+bU0FiwAYmL0lymVwKFDxssbC/A8PKTVUXOttKRbo26Qp/scUwFVaqr6dX/0UdHb1m15Yzd/Kkg3PXpRBARkkCF2Wyx61e8lqeUoMycTnVZ1QlX3qridfdtkOQGBW9kmPugFLD60GI2qNsI7298xun9Nt8TyOKmxWS1bWVlZUP37TVa3bl306NEDPXr0QO3atZGVlWWTCpJlNBcOU90xZDLDbIRERETWulmXk2O4TEoiiokTDZelpEhLOX/qlPrfosaOyWRAcPCT8V5yOWBu5xzd16D5v+b1FVZ+6VLp+2AvFTJFkx69kmulIsvqJswwp0WosEDLXBk5GXg54WWTgd61rGvot74fbj28VWSrXVmb1FhysLVx40a0atUKj4wM9MnJyUHr1q3x888/W7VyZLnCLhyaAKxRo5KrDxERlQ1F3ayTys3NcJmURBQFJwROTgZ+/FHaPjMz1f8WNXGxEIZzSkZHq1O3W5LrKy9P/a+U8dKmWvyMYS8VKkx0w2jMemGW5PJp99Ns0iIkpTujVBO3T8SiqEVGt6t5XG4nNV6yZAnee+89uLu7G6zz8PDA5MmT8fnnn1u1clQ8mgtHQZq5QPglTkREBVnSymOMsbFflrTUREZKr49mVhpLMhwC6utmWNiTx1K6/Onuz1otUQVb3ogAQBg5sR1k0jupBXgFmDXeS3K9CkmOYe52rmZdRVWPqupJjb0LTGrsXc4nNT558iQiCuk43L59e/z555/WqBNZUcHUsX5+T+YCsfRiRERE5Zupm3XmMNa6ZOubfI0bq/8tKvW7TGY6AYVui17HjtL2q3mONV6fTKa+OBdseSMqiqkASgYZgr2DtWng47vEF1q+tKXdT0N0w2hcGn9Ju6xxtcY499Y5tK3UtvQqZiHJwdadO3eQn59vcn1eXh7u3LljlUqR7bi68subiIiKZsk8T1WqFL7eWl0UTdHMe2Vu6ndTpNbXyUm/fHEEBqoDXc6zVT4Za52y5fY0AVVclzjIHeRQqpSo7FYZ49uMRxW3Ij6whajmXoy0pUXQdHXU7Sro4+pTproO6pIcbIWGhuLo0aMm1x89ehQhISFWqRTZTm7ukzt5trrYERFRxbRuXeHrdbso2vIa9OCBtHLGuv3p1kvK+Gfd/xe3C+aMGXtx7lw+Ay2ySEJMAjydPPWWBXkHISEmQZ1Q41QCAhYEIHJlJOIOxeF2jmUJMMa0HoMFUQusUWU9ui1wBVk7SC1JkoOt6OhofPjhh7hx44bBuvT0dHz00Ufo27evVStHxVcwBe2NG9Ln+iAiItJVVIAkJU25potiYGDRZS2hUKiTakhx7pzhsoK/6TT19dT/DavXgqX7vhQnUHJwYO8TMo9M5+SLbhiNwc0Gax8nDUnCxfEXEd0wGu8lvoeXE16WnKq9MF8c+QITtk8o9naM0bTAlSeSg633338fXl5eeOqpp/DWW28hPj4e8fHxePPNN1GvXj14enri/ffft2VdqQClUn1BMTU7vWYuk4I0c30wpSwREVmT1EAhOtr0JMXF2ee+ferrm5HEyUZ9/bW0iYOjo4GRI588TkoCzp9/8li310hx3LnDOVmoeHQTZkSERkDuIMcPp37AvP3zrLofS9PCy2XGvySCvYO1LXDljeRJjb28vLBv3z5MmTIF69at047PqlSpEgYOHIhZs2bBy8vLZhUlfQcOBGDMGEe9fulBQeruC9HRhc9lIoT6Ltzp0yVXXyIiKvsSEoBBg6TNeVWUTZuKvw1AP8hZutS85E/XrhlOHGyq9U434UdmJlCnzpPHWVlASIg6ICvO8HVfX4lRIpEJsgInsFKlxFtb3yql2jwhgwxB3kHwdPLEmYwzeuuShiRpk3eUR5KDLQDw8fHBl19+iS+++AK3b9+GEALVqlUzOLBkWxs3yjBnTmuD5ZoWq4QEdfrboub6kHrnj4iICFDfzFMogNWri7edwib/LY7bFtxst6SXR79+hkFdaiowbZr529LVqFFG8TZAVEDKlRSrTk5cHHFd4vDRTsP5FCJCI0q+MiVIenJ+HTKZDNWqVYOfnx8DrRKmVAITJmgif/33XvPFHxtbdCYmIiIiSxQ1YbCGbvr1gl3dpUxuXFIsSdduq7H6HK9ln5QqJZIvJWPtn2uRfCkZSpUV+oxaSVHp29Pu28eYkekR03Hw2kGDVi0AeC/xvVKoUcmxKNii0pOSAqSmylAw0NLQpLM1kseEiIioUHFxwOPHptcnJ6vnapTi55+f/D8yUj85k7W6EBaXu7vhxMG6gZRukFiGk6FRMRy4ewB1v6iLyJWRGKAYgMiVkQiND4XiTMlnGjN38uDkS8k4fUvamBE5bBvpH0k9YnLc2Lz988p1wMVgq4yR2t0hgz0RiIjITO+8ow5A3jPxuycyEti7V9q2cnL0H2u6ur/3njqoM0dUlHnlpZo4Ub81SaEAdGe5KRgk2po1kmyQ9Wz8ayPmXJqD1Pv63YVSs1LRb32/Ugm4CiosAItcGYlPUz6VtB2Zg217qm05t6XQ9fP3z0fihUS7ajW0FgZbZYzU7g5Su3kQERHpUiqBefNMB1xFPdcUIdR/Cxeav91nnlEHgdbk6qo/xkqTwbdgy54mSDSWJt7aRo6MwsaNHJ5hD5QqJSYkGk9vrglwYrfF2l1wcD7zfNGFjMhX5Vu5Jk+4yd2KbJUTEIj6Lspkq6G5rXr2xGo/yffs2WOtTVEhwsOBwEABmDjpZDIgOFjaXCdERFQxFWx1MsaSoGjWrKLLWNJ6I5cDzz5r/vMKs3r1k1atojL4Aup077aWkeGKV1+Vcy5MO5B8KdmgRUuXgMDVrKtIuZJSgrUqnFKlNKs+VVyr2LA2T+QL6YGcPbUaWotFwdb//vc/g7/+/ftj1apV1q4fFSCXAwsXqq9UMpn+VUGTqyQuTh1sBQWZTmErk6nv6hERUcWiUADBwUUnI7YkKIqPt6BCEoWGWm9bVQr8xiwqYYcQwP371tu/aeqLdmwsuxSWJsUZBV5e/7KksqWdgEI3QUbKlRQ8ePxA+nNLKMldnipPcll7bjW0lEXB1pgxYxAfH4/k5GQkJSUhKSkJDx8+RFJJ3PYh9OkjMHnyEdSoob88KEid9j06Wh2UmbroaT5bjRrZtp5ERGRfNF3lsrJss/3MTNts19oyM9Xvg6YFyZL077YihAxXr6oDQCp5ijMK9F3fF3dypU2YFuBlQTpLGzE38LudYx8p4Quyx1bD4rAo2Dp//jzatWsHlUqFjz/+GMuXL0eNGjWwbNkya9ePTGjbNg3nzz9plu3YUZ0hKlpn4u3oaHXwVZAmKLMk3S0REZVN+l3lSm9ckFxuutcFoO4KX1BhvTQKrpNys153qhSl0j6vh/YUAFYUSpUSQ34cIqmsDDIEewcjvGZ40YV12HLskT0FftagGzyqVCrsvrwbe+7swe7Lu8tUq5dFwVb16tWxePFifPzxx5g+fTrGjRuHx4XliiWb0M2gVL268fk5dIMvTbmCQRkREZV/pT23lSYwmjDhyWNjjGUqvHhRf94ujYQEIDBQf1lQENCzZ9H10UyVkpICPPec/c1xZY8BYHm385+dZnXDi+sSB7mD/Zw44TXD4ensafP9VHapbPN9APrB44n0E3hx9YtYeHkhXlz9Yqml37dEsRJk1K5dGytXrsTw4cPxniVpi8hqbt4E1q41nDiyIKVSfWFhX3AiooqlJFpKdFul3Nz012l6VcydazxIAoBJk4zfDFy5Eli/3nB5dDRw6ZL+MqnzgGmkpQH799vTdVHA09Nw/i+yvVV/SMs94O7kjoSYBEQ3tK8713IHOdrXbG/z/WTm2r6/sFwmx+2HT7o5PlbpN+qUpUQaVslG2Lx5c4wcOdIamyIL7dgBDBhQ9Jwgt28/KZOeXpI1JCKi0lQSLSW6rVI9ejz5f1KSfq8KY0ESALRpY/r6lZ1tfLmxFqnt26XUVi0gwP667D14AEyZUtq1qHiktmq9WOvFUgm0hISZtetWrmvRtuUy+2mhAwClUOLlBNNJSspSIg3JwVaLFi3QsmXLIv/MMXv2bLRu3RpeXl7w8/ND7969cfbsWe36zMxMvP3226hfvz7c3NxQs2ZNjBs3Dvfu3dPbzpUrV9C9e3e4u7vDz88P7777LvLz9dNMJicno2XLlnBxcUHdunWxYsUKs+pqj0zNxaGZE0ShMH3RSk0Fjh2zYeWIiMiuhIcXnqXWGnr1evL/2zpj7yMiDIMiY0GSZlxZcSQnA48eSSsbHKx+X+yry576AC1caDjnF9nW8zWfl1QuPKRsNTt6u3gXWUYpCg9Y7C0YA8pOIo2ic7/+q3fv3tr/CyEwe/ZsjB49GpUrW95vc/fu3RgzZgxat26N/Px8fPDBB4iKisLp06fh4eGB69ev4/r165g/fz4aNWqEy5cvY/To0bh+/ToS/s38oFQq0b17d/j7+2P//v1IS0vD4MGD4eTkhM8++wwAcPHiRXTv3h2jR4/G6tWrsXPnTrzxxhsICAhA586dLa5/aVIqgQkTjJ/4QqgvpoVdsCTcHCEionJEk6W2Xz/11CFCWD/q0k3PvmuX+c//66/ijytLTpZe9tVX1e+LZsyW/XQlVNflyy/VSTyoZLz9zNt4L/E9qGBkgGCBcvbKWDp3JwenYm/XnicVLu30+0WRHGxN051mHcCCBQswfvx41K5d2+Kdb9u2Te/xihUr4Ofnh2PHjqF9+/Zo0qQJNmzYoF1fp04dzJo1CwMHDkR+fj4cHR2xfft2nD59Gjt27ED16tXRvHlzfPLJJ5g8eTKmT58OZ2dnLF26FLVq1cKCBQsAAA0bNsTevXuxaNGiMhtsnT5dBamppi+UQpTuQGgiIrI/miy148apezhYm6nrjkIhLTHTHWnZtvUUDJCMJdIw5fvvgdmz7W3M1hMXLpR2DSoWZ0dnTHxuIubtn2eyTHX36nB2dC7BWhVfRk5GsbehEmZ8sEqYvWdhlBxslQRN98DCWsvu3bsHb29vODqqq37gwAE0bdoU1atX15bp3Lkz3nzzTZw6dQotWrTAgQMH0KlTJ73tdO7cGbEmbhfl5uYiNzdX+zjr3wlJ8vLykJcnfWI2W8nLy8OdO9adkdgeXldZpXnv+B7aDx4T+8NjYj969ABeeCEPvr7uJbbP8eMFunXLN9J1UP+Ou7e3EoD07krff5+PSZPk0E1l/9//CkhNbX/1KpCUlP/vmC27+kkEAAgOViIvz35/5JZHrfxbFbre3dm9WN9j+conw1zM3Y5SpTR4jkrn7kJeXp7e4/JOBhkCvQPxbMCzJX5tMWd/dvPNolKpEBsbi3bt2qFJkyZGy9y+fRuffPKJXjKO9PR0vUALgPZx+r8ZIEyVycrKQk5ODtwKpEyaPXs2ZsyYYbD/7du3w9295C5OhfH1rWLV7W3dutWq26uIEhMTS7sKVACPif3hMbEPf/5ZBYC08SnWcO2aDPPnH0LTpgXvsPfSe5STcwxVqjRFRkaBVIYm9O9vGJhlZACA9IDrl19OwNf3EUry/ZAqK+sAtm4tfqsESaMUSrx1+q1Cy1y5ewU/b/nZ4jFMf2T+of2/ub+9/rnwD7bm6D/nz9t/6m3v4jUz03GWYQICr1V+Db9u+7XE951tKmOPEXYTbI0ZMwYnT57E3r17ja7PyspC9+7d0ahRI0yfPt2mdZkyZQomaCYC+XffwcHBiIqKgrd30YMMbS0vLw9KZSJq1FDh+vWicpwUdsF5sq5bt25WrGHFkpeXh8TERLz44otwcip+v2gqPh4T+8NjYl8yMkr+7ndIyLPo1q3wcR+tWrVE69bAK6+Ys+WC1zgZYMb4kq5dm+P55wWWLhW4fh0mxrIJyGQF10kP6Cx16dJzmDbNDvs3llO7L+9Gxu+FB7dKKOHdxBsdQjpYtI+MPzOAK+r/S/7tdUL9T+06tdEtUv8513+7Dlx7sr3E7YnAbf2nB3oF4vr963Y97spcQd5BWNBpAfo06FMq+9f0epNCcrC1ePFivcf5+flYsWIFqlatqrd83LhxkneuMXbsWGzevBl79uxBUFCQwfr79++jS5cu8PLywsaNG/Uu1P7+/jh8+LBe+Rs3bmjXaf7VLNMt4+3tbdCqBQAuLi5wcXExWO7k5GQ3PxIOHw7Ao0dSvuSNlyl40di3zwnh4fY3qWNZYk/nB6nxmNgfHhP7kJFR8j/gg4MdUdShd3R0RN++5gRbpq6D0oKgatWAyEhHyOXA4sXq5CEGW5Kpt/fSS8DPP6uXLVoEvPOObQMtAHjwwAFOTlaZpYckuJVzS3I5S7/HHOVPfnqbuw25g9zgOXKdH24/n/8ZK/9YafC8AU0HYP7++WbW1DQZZKUauCW+lojI2pGlOqG0OcdOcrC1aNEivcf+/v5YtUp/8jeZTGZWsCWEwNtvv42NGzciOTkZtWrVMiiTlZWFzp07w8XFBT/99BNcXfXHKrVt2xazZs3CzZs34efnB0DdTcXb2xuNGjXSlinYVJuYmIi2bdtKrqs92bhRhjlzWhdrG5UrA1lZgKbLaWSkOiVwfLy0QcxERFQ2KRTAZ5+V7A94TYp1DaUSSLGDbM26Nxk1yUP699dPuR4UpJ4/7MCBJ8FWgZEJNlNS+yE1qYkWSishg7FMg7r6re9nNAiav38+Jj03qdDEH+ZIiElA3/V9rbItS3QI6VCqgZa5JAdbF82dkl2CMWPGYM2aNdi0aRO8vLy0Y6x8fHzg5uaGrKwsREVFITs7G9999x2ysrK0zXbVqlWDXC5HVFQUGjVqhEGDBmHu3LlIT0/HRx99hDFjxmhbp0aPHo3PP/8c7733HoYPH45du3Zh/fr12LJli9Vfk63pp3y37K7ajBnA9OmG6d8183MlJDDgIiIqjxQK9fd8SU//oUmxrqnD+PGFZy4sKR4e+o+jo9UTK2sCwaSkJwHZgQNPypXUvFyVKpXMfkgtvGY4gryDcC3LdDpnJwcnhNcsnXm2iprUuLDWpu9Pfm+1epTGhM66lColnFB2ekiUatv0kiVLcO/ePURERCAgIED7t27dOgDA8ePHcejQIfz555+oW7euXpmrV68CUDefbt68GXK5HG3btsXAgQMxePBgzJw5U7ufWrVqYcuWLUhMTESzZs2wYMECfPPNN2Uy7XtKCv5N+S4t0DJ2E+Trr41faDXLYmPtMwUuERFZTjNhsPq73vZd4HR9/716/5pgz1SgtX9/8Sc1NkdwsOEy3eumscmYgScTRNva9eu23wc9IXeQo3+T/oWW8XX1LVarSlEBk7mkpGTXTP5rLYozJXhHxIi9V43nd7BXklu2du3ahbFjx+LgwYMGSSLu3buH5557DkuWLEH79u0l77yoEy4iIkLSSRkSElJkRpeIiAj89ttvkutmr9LMmLfNVGtzYfNvCaFOhZuSor7IEBFR+ZCSUnrzL169qp5s+EmwZ9w336i7uJeUF16QVk6pBK5c0V8WHw/0tXFPqpo1bbt90qdUKbH25NpCy2TkZOBx/mO7mGtLcUaBD3Z+UOL77bfeyODGEpT2wL4nMS5IcstWXFwcRowYYTQbn4+PD0aNGmUwrousz5yuC1WrWn6H0JygjoiI7F9pf68nJxcd7JVkoOXgoD+JslKprqNuPq3kZHXX+tBQYP36J8tDQ0umjlKDQbKOlCsphXYhBNTp4YMWBZls3VGqlEi+lIy1f65F8qVkKFW26SqkOKNAv/X9cOeRBTOBF1NpZzUM8LTvSYwLkhxs/f777+jSpYvJ9VFRUTh27JhVKkWmhYcDgYECRaW1rVoVuHVLPajXEiXVH52IiEpGaX+v29tcqyoVEBOj7tqoUKgDqMhI4OzZJ2UiI4GXXzYMEq9dM5650JpkMv2kImR7afel3ZG4lX0L/db3Mwi4FGcUCI0PReTKSAxQDEDkykiExofqlbNGoKJUKTF+23jJ25JBhmBvI31my6hnAp4p7SqYRXKwdePGjULTHDo6OuLWLWkpM8lycjmwcKH6LolMZvpDdvu2yVUICjLdxVAmM8waRUREZV94OFClSuFljMyGYjWVK0srV62a7epgzMiRhY8jM8XWSUaEsI+MjRWJn4efWeVjt8VqW640LU0FW8ZSs1KNBmbFIaUFrqC4LnFW239pq/NlnVIfN2YOycFWYGAgTp48aXL9H3/8gYDSvm1WQfTpIzB58hHUqGHZ80319tQEYHFxnG+LiKgismUA4e9f+M0+QB0Mfvml7epQkBBARkbJZ2eUKjm5tGtApmiSTqRcScHj/McYvXm00ZYmzTLdwKy4pLbAaSTEJBjNICiXlc0fe7ezb1s9gLUlycFWt27dMHXqVDx69MhgXU5ODqZNm4aXXnrJqpUj09q2TcP58/kWPbdqVXUf9IINlUFBTPtORFRepaSoA4vCGLnEW01goDqpBGA64Bo2zPbd88oSe+t6Wd7dfHjT7Ods+msTAhcF4la26d5duoGZNZg7z5epVO1KUXZTTwsIqwawtiQ52Proo4+QmZmJevXqYe7cudi0aRM2bdqEOXPmoH79+sjMzMSHH35oy7pSAZa2PqWlqQOqp59+smzTJuDiRQZaRETlVWkmyNB0T9dMHBwYaLzcs8+W7Dxb9s7Xt7RrULFYMllx3KE43M4uZOyGDnNbpArStJJp5gOr6KwZwNqS5GCrevXq2L9/P5o0aYIpU6agT58+6NOnDz744AM0adIEe/fuRXVOdV4maHp76t5Z1EzaSERE5VNJ9PQ31WKl2z09Ohq4dMl4Oc1cYKR2p+QTzVVomiBGZqN56CwJ5oyRMh9YRbHpr02lXYUimTWpsWY+q9u3b+PQoUM4ePAgbt++ja1bt6JWrVq2qiNZmSb5hb32USciIuvTTMRb2JgpL6/i7cNUi1XBXhOmbu799VfJzwVWVNKQ0uRg1q80Ki65gxzxXeJtsu1g72CE1zTMPmZJqngp84FVFKv/XG33XQkt+hj7+vqidevWeOaZZ+DLNu5So7Tw3DJ2kSvs4ktERGWfXK47Zsr43bbi3jfVbbHq2NH85x85Urz9m8vREVi6tGT3aY6IiNKuQcUT3TAaE9pOsPp2X23yKuQO+j/ApKSKN8aSbITl1a3sW3bflZD3TMqoAwcCULeuY7G2oduytXev5cEbERGVDZoxU6ay2f7xR/G2r3szz5KRBXv2FG//5srPV4+L6ty5ZPcrBefZKh2KMwosOLDA6tv9/uT3Bi0wlqaKL+7Yr/LG3t8PBltl0MaNMsyZ0xqpqZY9X6lUX2x1L6o9eqgndOTAZCKi8i06GhZns7W1rKyS32dyMhAVVfL7LQrn2Sp5SpUSI38eaZNtG0vmYGmqeEvGfpWVNOmWsNZYOFthsFXGKJXAhAmaW4eW9f177TXg5ZfVd/R0XbumTrnLgIuIiCqSpk1LuwbGcZ6tkpV8KRkZOUXMj1AMm/7aBCFhwHxRqeKfC3oODmb8hNdMuFweVXGrYnQsnD1hsFXGpKQAqakyWBpoAcC6dabXCQHExrJLIRFRebZ3b/kYqGuN8cYREcBN86dXKhGcZ6tkJV9Ktun2V/+5Gioh/aCa6h63/9p+qCB9O+O3jTfailYePFY+Lu0qFInBVhlTEvOkXL3KrgtEROVZac65VZgqVcwPoIoTcHl6qoOt9HTLt2FLzEFWvtzKvoW/M/6WXN5U9zhzxyiV52Qa9x/ft3mQXFwMtsqYkpgnBbDfCzGRvbMkjS9RSSuJa4kl04sMH25e+cImSJbiwQNg0yb7bdnKsF2PNjIiIjTC5vu4++iu9v+m5vOSQWY0VbymC6K9j1H6//buPTyK6v4f+Ht3s7lnkxByIzcCCEZFQUQJGhu5Wi+AC2KxtVhvVVAJVOzXlp9IW/URRKEV2qqtV6AKhGqRighEo6AIohVFQEgIhCRAyJVcSHbn98e6gSXZSzYzc2Z236/nyROymZ3zGU42m8+ccz5HbUy2SFZ5eUBamgQoPBysVlJHFEj8LeNLpLZrrtHmlKIRI4BHHvH9+F69gIMH/W/PYHBMnd+1y/9zKMnd5s+kjPy++egV3kvRNuLC41y+Pj/hcn695PolnUrFO+Vl5iE6NFqR+Eh+TLZ0xmQCnntO2TvlRiMwcqSiTRAFHOcCZH/K+BKpzd2mwnLyZ3qfzQas6sZerdddB/Tr1/12nCTJMXVeq9MI9+0THUFwMRlNeGnCS4qdP9IciYEJAzu+XjN1DdIsrkOz6ZZ0rJm6BtYc6/lP72Cz29B0pkmxOPVGjRHJnmCypUO33CLh6qv9rPvuA7sd2LZNsdMTBRyb3eZ2AbIvZXyJ1LZ2rfIFMvyZRrh3r6Mybnf4uw3KueQotKGExkbREQQfa44Va6euRZgxTPZzz75qNoyGs396W3OsKJ1V2vH1bRffhpJZJR4TLQBYvnN5twpkBDKz0cxki+S3bp0Bn36aBiWnEnLNFpHvisuKPS5A9lbGl0hNhYXAtGnKDG35unWIu4q3NTXyxdIdF18spl1vojlTTAhrjhV/ueEvsp/32qxrOz127lTBzNhMt1MHz3XwVA/mzgaYdnu75m9kMtnSGTn22fIF12wR+c7XylBa3+WeAp/NBsyapdz5Cwq8H1NYCPTt2/X3RFTfy8gAQkPVb9cX/fuLjiB4nTsCJZfNJZvx/cnvO772N0no34s/GE4SJCzfuVx0GB4x2dIZOfbZ8jZdwmDgmi2i7vC1MhQrSJFoxcXOaXrK3Kw7csTz9wsLgSlT3E8VXLlS/pg8MRiAxYuB9evVbddXhw6JjiA4Fe4txCMfdKNSi4+Wfr4UC7ct7Pja3wJKM66YAZNBhYWXOqH1kT4mWzojx/Q+b/PoJYm71hN1R15mHtIt6d0u40uktnfeUa+tqirXr52jap7eg9QuCPHEE0Biorjpi940NIiOIPg4ix3VttbKfu5WW6vL184CSk6SDwsdbXYbth3dhp8O+Kns8elVZmym6BA8YrKlM2pN73vjDXXaIQoEJqMJS69f2uX3fCnjS6SGwkJgyRL12tuyxfXrs6Nq2lFTo+01yjExoiMILp6KHSmhu+3sr97fsb3I+gMaHY6lTphs6czIkYDRqPw+W7ybRtQ91hwr1kxdA0uYxeVxX8r4EilN6bVa3hQWajOpWbECSEoSHYV7AwaIjiC4eCt2pITuJFz/3vdv1ePTg7K6MtEheMRkS2e2bQPs9p6t2fJFnz6Knp4oIFlzrJg9YnbH11unb/WpjC+R0kSPKhUUaDOpOXHC8TlM/irfsjCbRUcQXFjESJ84jZBkpdadwdxcddohCjTnVrDK75vPqYOkCXLsRdUTzsIZvXuLjaMrlZXa3WcrK0t0BMGFRYxICUy2dEatNVsZGeq0Q0REynOO4Ih0/DiwXIMVmk+cAFpaREfRtVGjREcQXEamjxRa5a+otEhY23rGaYQkq7w8IC1N+TVbV12l6OmJiEhFiYmiI3DcLLz1VmDuXNGRnJWRoY3/m65ERwP5+aKjCC7bjm6DTRK3Qe7Oip14dNOjwtrXK04jJFmZTMBzzzl+ERgMyiVcf/+7YqcmIiKVpaWJbT8jw3GzEAAWLgTmzBEbj9OSJeL/b9zR6jqyQFZeL3i+LYDntj+HM+1nRIehK+32dtEheMRkS4duuUXCb3/7haJFLA5qe384Is3yZZ8UIrXl5QHx8eLaX7LEcbMQAB59FHjuOXGxOE2ZAlitjv8bLa7Zqq52FDYh9VQ2VooOATbJhuU7NTjfVsPeO/Ce6BA8YrKlU7m5FfjhB+Uy+exsxU5NREQqM5mACRPEtW/9sSDn6tXAokWej+3VS/l4AGDNGkdJepvN80bLImmxXH4gq26qFh0CAOBA9QEUlRZh1TeruI4rAISIDoC6z2YDvvkmAZs2KZcrDx6s2KmJAppBi7fIiSB+upzNBsyY4f24U6eUj8XpvvuAxx5Tr73u0mK5/EBWUlsiOgQAwIpvVnB0qxsGJ2r7j1YmWzpTWAg8/HAIysuvUbSd48cVPT0REals3z6x7RcXAydPio3hfNXVwMcfi47CPZu4Wg1Bad9JwS+SH9W11okOQVeuTLtSdAgecRqhjhQWOuaYq7FfSlWV8m0QBSKu2SItstmAjRvFtV9UBKxdK659T/bsER2Be0VFoiMILkYj/yzWox3lO0SH4BFHtnTCZgNmzXLOK1d+mlK1NqYtE2mOzW5DcVkxKhoqkBqTirzMPG5cTJpXVAQ0Nopr/7rrxLXtzdGjoiNwb+dO0REEl2Epw7CrYpfoMKibvq78WnQIHjHZ0oniYm2/IRAFg8K9hZj1/iwcrT/7Yky3pGPp9UthzbEKjIzIsy1bREegXWc0XGU7PFx0BMElOTpZdAjkh4rT2q4kw/FSnVC7IpFa1aCI9KJwbyGmvD3FJdECHPuyTHl7Cgr3FgIA7JK943tFpUWw2bnogsQrKxMdAfkjPV10BMHl77u4yagepcakig7BI45s6USqyj9HrIBEdJbNbsOs92dBQuf1WM7HCt4vgN1ux5LPlnR877rXruPIF2kC/2jXp6uuEh1BcGlobRAdAvlh4sCJokPwiCNbOjFyJKDmus2tW9Vri0jrisuKO41one9I/RHcuuZW1J+pd3n8/JEvIhF69xYdAfmjpkZ0BETad1nKZaJD8IjJlk4UFwN2u/fj5PLKK47qh0QEHKk74vdzzx354pRCEiUlRXQE5I/ERNERBJdzp4GTfnx65FPRIXjEZEsnRJR/LSjgHh9EAPB5+ec9er4ECUfqj6C4rFimiIi6h3+065PojaiDTau9VXQIFICYbOmEmqNaTkeOOEbUiIKdXCNSFQ3arphEgeubb0RHQN0VGQnk5YmOgkj78jK1/UJhsqUToqoDql0FkUiL5NpHq+p0FacSkhClpaIjoO7i/rpEgYEvZZ0QNd9e7SqIRFo0vM9wWc4ze+Ns9F3al8UySHX9+4uOgLqrsZGzS4h8UVRaJDoEj5hs6YSIZCshgVMYiACgqrFKtnOxOiGJ8Otfi46A/MHZJeqKQpToEMgPXxz7QnQIHjHZIreqq4F33hEdBZF4Gw9ulO1crE5IImzbJjoC8gdnl6irBS2iQyA/NLc1iw7BIyZbOnH8uJh2Z81iRUKi/dX7ZT2fszrhrPdn4Uz7GVnPTdSVDz8UHQF1V3o6Z5eozQ6WftejPpY+okPwiMmWToi6u3X0KOeME5kM8hTION+yL5Yh8qlIPLrpUUXOT+S0c6foCKi7xo4FTMr86iE3QgwhokMgP7S0aXtEksmWTowcCRgMYtouLxfTLpFWZMdnK3Zum2TDom2LmHCRoiIjRUdA3RXF5UOqMxvNokMgPzS1N4kOwSMmWzrx1FOAJIlp+8QJMe0SaUVqtPJDy89tf45TCkkxoirakv9OnxYdQfCJDo0WHQL5ISIkQnQIHjHZ0gGbDVi8WFz7iYni2ibSgqy4LMXbsEk2LN+5XPF2KDjV1IiOgLpr0yaumVbbgF4DRIdAfgg3hYsOwSMmWzpQXOzYb0OUtDRxbRNpQUJEgirtHDx1UJV2KPjs3Ss6AuourplW3z3D7hEdAvmBpd+px0Tus8FqSETAiSZ15tL278WdZ4noLO6zpS4l1+eScmqatT10z2RLB5KSxLU9bRqrIRF9WfGlKu38+nLuPEvKyMkRHQH5g/tsqSsvM0/zU9KoMxbIoB47I3DN/D//yTnjRFGh6pQF+/zY56q0Q8GHxRb0JzqaM0vUZjKacHnK5aLDoG4ySILKdfuIyZYOvPmmuLarq4GiInHtE2nB1RlXq9JOeT33WSBlHDggOgLqrqYm3uwU4Ytyba//oc7CQsJEh+ARky0d+Pprse0z2aJgNzhpsCrtqLU2jIJPW5voCKi77HZgOQuUqqqxpRFt4ItFb7Resp/Jlg7U14uOgCi4bS3dqko7iZHcZ4GUcdVVoiMgf3BEUl23F94uOgTyg12yiw7BIyZbOiA62eKccQp2mw5uUqWdNAv3WSBl3H236AjIH5IkOoLgsvPYTtEhkB9CjaGiQ/CIyZYOGAX30rZtYtsnEq3xjPIb3VlCLcjL5J0NUsaoUaIjIH/ExYmOILhofe0Pde10u7YrAIWIDoC8CxP82n/mGeD//T+WgKfgFRMWo3gbdmh7GgQRqc+g7SJruiZJEk42ncShmkMoqS3BoZpDSAhLQClKRYdG3RQaou2RLSZbOiA6yWlpAbZsAcaOFRsHkShj+4/Fl5XK7rXVeKYRxWXFyO+br2g7FJyKi0VHQP7o1Ut0BPrW1NaEkpqSjmSqpKYEh2p//FxzCKfbtD0iQr65ss+VokPwiMmWDlRViY4AeOMNJlsUvEb1HYVnPn1G8XZY+p2UUlEhOgLyR0qK6Ai0zWa34Wj9UbfJVNVp739ApcWkITs+G/3i+8EAA177+jUVIic5rbSuFB2CR0y2dKC9XXQEQF2d6AiIxDGoNJeHpd9JKampoiMgfwR7siVJEk41n3KZ6nduQnW47jDa7Z7/SLKEWdAvvh/6xfdDdly2y+esuCyEh4R3HLvifyuYbOlMeEg4osO1XfqdyZYOmM3i90jhvHEKZltL1Cn9nhCRoEo7FHxYVZa0qrmtGaW1pW5HpxrONHh8vtloRt+4vo7Rqbh+yI7PPptUxWcjPjze5xtmvOGlP0NShogOwSsmWzpgsQDV1WJj4F1RCmafl3+uSjvHTx9XpR0KPiaTNm7cUfccD4BfCTa7DccajrlNpioavc9xTY1O7Zjqd/7oVJ+YPjAZ5Vnczr0O9adXmPYXNjLZ0oHISPHJ1qBBYtsnEqmqUZ2FkyebTqrSDgUn0duIUPfp5UZnTXONazJ1zrS/w3WHccZ2xuPzo0Oj3U716xvXFxHmCFWug3sd6s/husOiQ/CKyZYOtLaKbd9oBGbMEBsDkUix4bGqtFNSW6JKO0SkD1qZ/tna3orDdYe7TKZKaktQ21Lr8fkhxhBkxmZ2mUxlx2cjISJBtbWxnuRl5sEII7fi0JE2m/aH65ls6UCI4F6aMwcI1fYWBkSKuiTpEmw7qvzu3vtP7le8DQpe6enAwYOio6DueOwxYOFC5duxS3ZUNFS4jE6dm0yV15dDguTxHElRSW6TqXRLOkKM2v+T02Q0wWw0o9Uu+C43+WxQb+1PvdL+Tz4hQp3Rc7cOHBDbPpFoceFxqrTDxdmkpFGjmGzpzXPPAX/6kzw3POta6lBSW9IxMnXu6FRpbSlabZ4TjEhzpNtkKjsuG1GhUT0PUgOYaOnL5Ismiw7BKyZbOtC7t9g3yHfeAZqbxSd9RKKU1paq0o5BEj+NhgLXpEnASy+JjoK6w2YDli8HCgq8H3vGdgZldWVup/qdaj7l8fkmgwkZsRluE6rEyERNTPUjOldWXJboELxisqUDpzz/flTFjBnAK6+IjoJIjB+qf1ClnVAT5+uScrZvFx0B+cN5s1WSJFSdrnKbTB2tPwq75HmtUe/I3m6TqQxLBswmswpXRCSPEEMI8jI1srDRAyZbOhATIzoCoLCQyRYFr1Mt6tzxaLG3qNIOBSeWfdeB0AYgvgSIKwHiDwHxJfgw6RAuXu6Y/tfc3uzx6REhEa77TJ031S8mTAN/UBDJZET6CNnK/iuJyZYOXHEF8OWXYmNo9vz7nShg2ew2lNaVqtKWJdSiSjsUnPbsER0BwdgGxB5xSaYQf+js11Gdt3/43g7gx+WcBhiQEZvRZTLVL74fkqOSOdWvh8IMYWiVuG5LD+blzRMdgk+YbOnApEnAiy+KjcGk/RsHRIooLitWrS1LGJMtUs5+FrtUgQREnXCfTMUeAYw2z6do6gXUZgM1/YCabCSF9sMbf3YkU5mxmZxurDTPRRdJQ/RyY0FosvX000+jsLAQ33//PSIiIjBy5Eg888wzGHTODrotLS34zW9+g3/9619obW3F+PHjsXz5ciQnJ3ccU1ZWhgceeABbt25FdHQ0pk+fjqeffhoh59RMLyoqwpw5c/Dtt98iIyMD8+bNw5133qnm5fpNC2u2JP7yoSBV0VChWltR5sCo5kXaVF8vOoIAYT7daaqfy79DT3t+fnsYUJPtklC5/LvVdV+/xIuBcf0VvB5yZQLgJR8mbSguK8a4AeNEh+GV0GTro48+wsyZMzF8+HC0t7fjd7/7HcaNG4fvvvsOUVGOPzpmz56N9957D6tXr0ZsbCwefPBBWK1WfPrppwAAm82GG2+8ESkpKdi2bRsqKirwy1/+EmazGU899RQAoKSkBDfeeCPuv/9+rFixAps3b8Y999yD1NRUjB8/Xtj1++rYMdERAHbu70dBKjUmVbW2WHKYlBQfD1RViY5CB4ztgOWo+9Gp6OOeny8ZgPo01wSqpt/ZrxtTAMnocziZmT28HuqWKHOU1zL4pA0t7fpY5yw02Xr//fddvn711VeRlJSEXbt24dprr0VdXR3+8Y9/YOXKlRg1ahQA4JVXXkFOTg4+++wzjBgxAh988AG+++47fPjhh0hOTsaQIUPwxz/+Eb/97W/xxBNPIDQ0FH/729+QnZ2NxYsXAwBycnLwySef4Pnnn9dFsiV6vRYAxMWJjoBIjLzMPBhhhB3K33HoH8/b16ScPn2A778XHYUWSEBktYepfmWAqd3zKVpif0ykukimarMAW5hs0dbUyHYq8kFyRLJqRZGoZz4p+0R0CD7R1Jqturo6AECvXr0AALt27UJbWxvGjBnTccyFF16IzMxMbN++HSNGjMD27dsxePBgl2mF48ePxwMPPIBvv/0WQ4cOxfbt213O4TymwM3GFa2trWhtPXtXo/7HuRdtbW1oE1DOqbTUBMD3u2BKuPBCO9raOK7eFefPhIifDeqa3H1yReoV2FGxQ5ZzefL+D+8H7M8RXyfiXXGFEVu2BMkC3JBmIK6062QqvgQIa/D8/PZQoC6r62SqJhtoiVflMgCgvp7vv2qKi4wDmODqQmVDpbD3lO60q5lky263o6CgAFdffTUuueQSAEBlZSVCQ0MRd96wSnJyMiorKzuOOTfRcn7f+T1Px9TX16O5uRkR5+3W+/TTT2PBggWdYvzggw8QGRnp/0X6qbLyWgDq/WLvSm3tCWzY8JnQGLRu06ZNokOg88jVJ7U1tbKcx5uaphps2LBBlbZE4etEnKioBADXiA5DHgYbEHPMfTIV48Nay4ZU98lUQx9A0kZiajTy/VdNF9gvwHZwUzo9MJ4xCnvPbGpq8vlYzSRbM2fOxJ49e/DJJ+KHBB977DHMmTOn4+v6+npkZGRg3LhxsFjUrxb20EPif+FnZCTihhtuEB2GJrW1tWHTpk0YO3YszGZuCKkFcvfJL/b8QoaovIsKjQrY1xlfJ+KNHw88/bSElhZ9VPBCeI37ZCquFDB5ubPcGuM+martC7RHeH6+Rlx8ce+A/b2gRWPax+D1ha+LDoN8cPdVd+OGq8W8Nuq7UXFIE8nWgw8+iPXr1+Pjjz9Genp6x+MpKSk4c+YMamtrXUa3qqqqkJKS0nHMjh2u03uqflwBfO4xVeetCq6qqoLFYuk0qgUAYWFhCAvrPN/abDYL+SOh3cvUcTUYjUaYzWKnMmqdqJ8Pck+OPjnTfgaN7Y0yReTZ8PThAf8zxNeJOGYzMGwY8GN9KfFCWoDYw+4TqvA6z8+3hXie6tfcC4BOEksPsrNNMJvF33QNFmazGWajGW12TnnWuqxeWcLeT7rTrtBkS5IkPPTQQ1i3bh2KioqQnZ3t8v1hw4bBbDZj8+bNmDx5MgBg3759KCsrQ25uLgAgNzcXTz75JI4fP46kpCQAjmkqFosFF110Uccx5w8zbtq0qeMcWqeFbQSOeym+RBSo/rLjL6q1FWaSb1E9UVdqa1VszGAHoivcJ1OWcu/naEz2MNUvDbD3/M+YiAigubnHp1FM796iIwg+EwZOwNrv14oOg7xIs6SJDsEnQpOtmTNnYuXKlXjnnXcQExPTscYqNjYWERERiI2Nxd133405c+agV69esFgseOihh5Cbm4sRI0YAAMaNG4eLLroId9xxBxYuXIjKykrMmzcPM2fO7Biduv/++/HCCy/g0UcfxV133YUtW7bg7bffxnvvvSfs2ruji8E31X33negIiMRQs9rR0fqjqrVFwalR7kHasDrPU/1CvJTQPhPleapfm/J7z4WFaTvZ+vE+MqnovmH3MdnSuAxLBvIy80SH4ROhydZf//pXAEB+fr7L46+88krHhsPPP/88jEYjJk+e7LKpsZPJZML69evxwAMPIDc3F1FRUZg+fTr+8Ic/dByTnZ2N9957D7Nnz8bSpUuRnp6Ol19+WRdl3wFtvAm06GMrAyLZRYdGq9ZWeZ0Pd/qJeiCqu7mL6cyPU/26SKbiDwERXsq22U1AXWbXyVRNP6CpN0RP9TNpfIZedbXoCILP6H6jER0ajcYz6kwhDwbhIeGy7ou15PolMBk1/uL9kfBphN6Eh4dj2bJlWLZsmdtjsrKyvFYjyc/Px+7du7sdoxZoYUNhLUxlJBLhjkvvwJvfvKlKW802DdxZoYCWknL+TAUJiK50n0xZjgIGL+/VpxPdJ1P1GbJM9VNSTIy2E5qEBNERBB+T0YTXJr2GyW9PFh1KwPjlpb/EtMHTUNFQgdSYVFza+1JMeHsCyurKkGHJQEp0Cgq/L/TpXPOumQdrjlXhiOWj7d+ABEAbo0rc1JiC1eh+oxGCELRD+Uo1IUb+Sib5NbQ2oKS2BIdqDqHmwhIg7Nxpf6WA2UuS3xbRxVS/c/59Rr3RXyU0eNlyS7Tz6nuRSqw5VrxlfQu3Fd4mOpSAkJOYg/y++S6PfXKXY5q+zW5D1pIsn881MGGgnKEpju/sOhAZqfKi5i5wgS4FK5PRhIzYDJTUlSjflkEfUyJIW9psbSirK0NJbQlKahxJlTO5Kqktwcmmk2cPTvrx41x2o2MEyt3o1OkkiJ7qpyQt3ND0ZIfy+6mTG7dceAt+3/f3eLL0SdGh6N4vL/ml2+8VlxWjvMH3afSp0alyhKQaJls6EB4uOgJtxEDkL0nq2UdtS60qcda01GHPnp7Hq8WP9nYDvv46E5WVBhiN4uPR04ddktBqOo7ToSU4HXoITaElaAo7hKYwx+eWsCOOyn8ehJxJQHhzNoz1/VB/uIupfrZQVX7Gtej0adERePbFF6IjCG6Xx14u+3qjYHT3+rux7mfruvxeRYMPm5D/KMYUg2sy9LU5O5MtHQjVwHugyQSsXSv+jw4tfthsRhw+fBnefdcEg0F8PHJ8AOJj6NlHCCRpAiRJprvxD8cAvWrkOZcHdls7Bg9WvBlBQgAMFR2EdplPu183FVcChDZ5fn5buKN6X1cjU7XZaG+1gEv99UkLfwMEM5PBhNcmvMbphD30w6kf3H4vNcb3kaobe9+om8IYTky2dKBG+b/xvNq5E5gyRXQUWmUC0Fd0EORC5ilPNpV+VRqAxERHQZpA+wDsOH78OJKTk2AyGYXHo/aHHe2ox1Gckg7hlFTi+GwvQbXd8blB8ryZoQEGxJnSkBjSD4kh2Y7P5mwkmfshyZyNuJAUmIy+/b/efz9w4oQqP9G6kJoKVPh+Y111o0aJjoBuufAWrJ26Fj9f+3O02DjC5Y/Y8Fi338vLzEOf6D441njM4zkMMMCarJ/CGE5MtnRA0ObYLjIzHR+i/2DR4ofdbsOBA/sxaNBAhISYhMcj4gMQH8O5H+3tbdiyZTPGjBmN0FBzz/pXsiHymUOqvM4MRkPAbiDe1mbDhg2f44YbboDZbBQdjuwkSUJ1c7VjndSP66YOnbNuqqyuDO12z0VW4sLj0C++H7Ljsl0/x2cjKzYLYSHybHr94IOynCZg3HAD8I9/iI7CveeeEx0BAY6CGetuW4efrvyp6FB06e4hd7v9nslowr2X34sFHy/weA4JEvae3otJmCRzdMpisqUDAwYARwXvdXrTTYCH6vtBra3Njg0b9uOGGwbAbNbX0HagamsD4uNbkZzc85sVRaXF8gTlAzM0cGeF3Gpqa0JpbWmXRSgO1RzyuidPqCkUfeP6dplMZcdlIz4iXvFrsNm0PYqjNoPBMWtDq8nWJZcAERGioyCnsf3Hcv8tP2X3yvb4/W9PfOvTefY07JEjHFUx2dKB668HiorExuBcx0MUbLqzcLenJPCFJpLNbkN5Q7nL6FRJbUlHMlXZWOn1HH1i+rgdneoT0wdGg9hRvQ8+ENq85kRFaXuPrf/7P9ER0Lnk2n+rT1QfHDvtecpcIMmwZCAvM8/t9212GzYe3OjbyQwyBaUiJls6kJ4uOgIgWt/bqBD5rTsLd3vKaAy86XVaIkkSalpqOiVTzs+Haw+jzd7m8RyWMIvHqX4RZm0PQzz+uOgItKWxUdvr19LSREdA57PmWLF26lo8vOFhlDeeLVeeFpOGK9OuxLrv13k9R1ZcVtAkWwYYsOT6JR6LWhSXFaPhjG8b3l0UdZFcoamGyZYOaOGX7e7doiMgEsPT3Ti5xUa4X0BMvmlpb/E41a++td7j80OMIW6n+vWL74f48HgYDDq8tfojLY/iiBIf77ipKXq6/vmio4E89X79UDdYc6yYOGgiisuKUdFQgdSYVORl5mFLyRafkq3s+GxsL9+uQqRiZVgysOT6JbDmeC5q0Z0ZJEbo76Ykky0dyMtzrDtp83zDVVFRUeLaJgoWN11wk+gQNM8u2XGs4ZjbZOpYg/e7xSnRKW6TqbSYNN2VFe6OSy8FSkpER6Etn30GLF0KTO7ZzDDZJSQ4tl0hbTIZTcjvm+/ymK/ThC9NvhQr96xUICptmHHFDNx68a3Iy8zz6fdpd2aQ1NnqehKaEEy2dMBkcrxB7tolLoaRI8W1TSTSlpItqrU1Mp0vNMCxibS7ZKq0thRnbGc8Pj86NNptMtU3ri8izZEqXYn2vPkmEBMDABJ0ufhBARUVgNXq2HZBrimFGRmOKoKzZ/s/YlZd7ShowoRLP3xZ1wkAdS09Sxjm5c3Dok8WoVVq7dF5lHLrxbd2SkQ9ycvMQ+/I3jjZdNLrsfEhyhcSkhuTLZ1o91wxWHEskEHB6tWvXlWtrf/s/w/uHua+PG6gaG1vxeG6wyipKemUTJXUlKCmxfPmgiaDCVlxWciOy+6UTGXHZaN3ZG9dT/VTUnQ0MHw48MUXABMuB+eaZDmTrSVLHAncLbcAxcXAkSPA5587kiebDXjpJe/naGx0PDc/X56YSHm+JltldWU9amdk+kiEhYahtVV7yZa3YhhdMRlNWH7DckxdM9XjcemWdFwUzTVbpBDR8+y3B/7UYqIu/a/qf6q1dbReY4tG/GSX7KhsrHQZnfqh+gd8eehLPPiXB1HeUO618mJSVJLb0al0SzpCjHz78teOHcDw4Xbs3Km/tQ9KGDLE8Tk1Ffjuu56fb8ECR6IFOEalnMnSHXecPeaLL4CvvvJ+rsOHex4PqedU8ylV2nln3ztIjkz2ugZVBG/FMNy59eJbMffYXCzatqjL7xtgwOIxi2E6pL+hXr5b6YToZOv0abHtE4kSG6Ze0Ypekb1Ua6un6lvrPU71a2lv8fj8SHOkx6l+0aEsgaqkbdvsWLNmPVatugmlpSZERDimux07Z8lbQgJgtwM1ngcadS/1x+UiV1wBbN7cs3OlpwO//71vx/mSbK1ZA0yf3rOYSD2+rtnKistCuCkcLTbPvyfd2XlsJxaNWYRJqyf59XylDIgb4LUYhicLxy7ElX2uxIwNM3Ci6ewws7PQxs0DbsaGQxvkCFVVTLZ0QvQ0wsREse0TiTJh0AR8evRTVdp6ePjDqrTjizZbG8rqyrpMpkpqSlDd7PkOkNFgRIYloyOZyorNQm1pLaz5VlzQ+wIkRSVxqp9gkZHA2rX2js3YbTbHtLWKCkcCkpcHXHzx2WRrwQLH9DetVe3rKWfF3969/T+H80d56VLf1lh9+aVv5/X1ONKG/L75+FPxn7weNyp7FExGE/748R/9akeChHEDxvn1XCXtvHdnj88x5eIpuCXnlk6VHk1GE9pEVorrASZbOiE62crKEts+kSiXJl+qWlv7a/ar1pYkSTh++ninJOpQrePzkfojsEt2j+dIiEg4OyIV57puKjM2E2aTuePYtrY2bKjbgKvSroLZbPZwVhLl3ClvAFBYCBw8ePbr+fMdicn8+Y7EKxBkZJwtr96TEbz09LPrtHzha1uBPqoYaPIy82CAweM0aQMMyMvMQ37ffCzatsjrLICuDEoYhL9/+feehCq7/vH9ERspz0yQrio96hmTLZ0QXaCiJ3f8iPTs0yPqjGoBQEmNvDW5G880uhahOCeZKqktQVNbk8fnh4eEO4pQdJFMZcdnwxJmkTVe0o7CQmDKlM7vPceOAX/4A9CrF3BKneUpinCORC1ZcnYkqrt7ij/xBDBw4NlRwO5UDbR7vo/R7eNIG4rLir2uR5UgobisGKP7jcYK6wpMfrv7ew5kx2fjQPUBf8OUXf/4/vjh4R9Eh6FZTLbIJ0y2iJTXv1f/bh3fbm/HkbojXSZTh2oOucx574oBBqRb0t2OTiVHJ/u8BoECh80GzJrV9U0+SXIkKk2e83TN62okKj8f+JP3GWAAgLlzHSN8/oqIAHwpJBcR4X8bpL6i0iKfjxvdbzSsOVasnboWD214CMcave8R6DQqexTWfLfGzyjlk5uei/9O+69sI1qBiskW+aS4mIt0KTipuffVjCtmuHwtSRJONp3smOp3fkGKsroy2CSbx3PGh8d7nOoXFhKm5CWRDhUXe16XJUlAi3/r+oWbNg24776uR6Ly8x1FQTwVpDIYgFWrgNtu61kc11wDrF/v23EU2Kw5VkwcNBHFZcUory/HjA0zPFYZTIhIQH7ffPx9l9hphBEhEdh29zahMegFky2dMJkcdxtF+fBDcW0TifR15deqtfW3XX/rNDp1us1zKdBQU6jHqX5x4XHqBE8Bo6JCdATKefVVIDS06++ZTMCLLwKTPczqeust4NZbex7Hm28CcXG+HUf64WuBjPPXI527RinCHOFxauGLN78Ik9GEHeU7ehJqj91+ye1C29cTJls6ERkJNDSIa1906XkiUd745g3V2pr1/qwuH0+LSXNJos79nBqTyql+JCtnKfRAc+217hMtJ6sVWLsWePhhoLz87OPp6Y5Kg74WwPBm927fj+OmxvqR3zcfCREJHqu1Okem3HFOLZz131k42nB2iDndko6l1y/tKK1us8t7B37ioInYeWwnyhvKvR8MYMn4JbK2H8iYbOmExSI22RJdoINIlNLaUtXasuZYOyVTWXFZCA8JVy0Gorw8R3JRXt71736DwbGO94TnJYGa8/77vh1ntQITJ3Yug9+dAhje+Dp6GMijjIHIZDThxZtf9GlkypNzpxaeX/7caVDvQS7JmN8xG0yYkzsHC8cuhM1uw192/AWzN872+rydlTsDqmKgkphs6cSgQa532dQWxmUdFKxUutEQbgzH2qlr1WmMyAOTyTGKM2VK5+85q/gtWwbMmeM+IdOizz/3fZTo/DL4cvN19DBQRxkDma8jU954K38+N3cuNpf4vwt3REgEnhr9FGZcMQOhIaEdbSZHJfv0/IoG3gnwFZMtnXjkEWDLFnHtx8SIa5tIpDBTGJpsypde4x1C0hKrFVizBpg61XW9cFra2el0JpMjITMYXBOu87/WCi2NEvkyepiefnYPMNIXX0amempM/zEIDwn3a58uABiTPQYFIwo6PZ4a41uG7+txBHCiv06MGweEC5xJlJsrrm0ikcLN6rzwfjb4Z6q0Q9QdzpGsrjgTsrQ018fT04E77lA2Ln9oaZTIOXoIdP4/Nhgc2de5e4CR/jhHpqYNnob8vvmyJlrO86+wrvD7+SutK7t8PC8zD+mWdBjQ9YvfAAMyLBnIy+SdAF8x2dIJkwlY0fGaUv+W4V13qd4kkSaoVRo9Ky5LlXaIfOHc1Li93fXx8nLH44WFjq+tVqC0FNi6FVi50vG5pAR4+WXPiZoIWhslcpespqU5HperGAcFLueUxbQY1x8is9Hs8XnD+wxHdHh0l98zGU1Yer3jTsD5CZfz6yXXL5E9eQxkTLZ0xFklSe1NDmNigFGj1G2TSCtGpI9QpR019/Mi8sTbpsYAUFBwdnqhc33TtGmOzyaTo+rfI4+oFLAPjEZtjhKdm6y+/no7/vjHT3DgQDsTLfKZNceKwwWHsXX6Vqy0rsTW6VvR/PtmDO8zvMvjh/cZjh33ei4bb82xYs3UNUizuCZx6ZZ0rJm6xud1Z+TANVs6Y7UCp061Y8iQOuzblwC4GeZ1Jz0dMJsddx599eqr2nyTIlLDXUPvwr++/Zfi7RSVFmHcgHGKt0PkjS+bGh854jjOUxGJhQsdnxcvBux2WUPsNrPnG/1COZPVtjYJGzZU8/2Wuq2rYho77t2BxpZG3PHvO3Cw5iD6x/fHG5PecDuidT411p0FC45s6ZDJBDzzzKcoKOjeu9e0aY47aDNn+nZ8WJhjJI132CiYjcoehehQ396ceuL1r19XvA0iX8hZlnzhQqC5GXj+eWDgwJ7F1RPZ2eLaJhIlOjwa6362Dv974H9Y97N1PidaTkqvOwsWTLZ0bOFCO1av9r1S4N13OxK1hx5yTKnwpqaGiRaRyWjCa5NeU7wdNffzIvJE7rLkoaGOaYddlZJXy9VXi2ubiIIbky2dmzIFOHnSsemxJwkJZ6d7hIYCv/mN5+PnzlV/bRiRVllzrLjzsjsVbSMihC840gZnWXJ3BS4MBiAjo/sFJ0Su/e3VS1zbRBTcmGwFgNBQ4JVXPB/z4ouu664WLnQkVOfPDTeZHI8759oTkcPfb/q721K4cri8z+WKnZuoOzyXJXd89qcseX6+uKRn924x7RIRMdkKEM5KhV3td+Ju3dXChUBTk2Mu/YMPOj43NTHRIupKaEgopl48VbHzJ0cnK3Zuou7ytIeWv2XJTSbgpZfkia+7Tp8W0y4REasRBhCrFZg40VEhqqLCMZ8+L8/z3UfnXHoi8m6FdQXe2fcOWtpbZD93YmSi7Ock6gl/3lN8OefatcB99wHV1fLF6k24OnuTExF1wmQrwDhLyBKRMqLMUYokW8dPH5f9nEQ9pcR7ijOJKypyfDjbWbDA/XOGDAG++sr/NlNS/H8uEVFPMNkiIvJRcVkxqpuVuR1/qvmUIucl0iKTCRg92vHhdOmlwMMPA+XlZx/r3RtYvhxoaQF++Uv/22PpdyIShckWEZGPyuvLvR/kJ6OBS2gpuHmatugcAfOXyEqIRBTcmGwREfnoRNMJxc6d3zdfsXMT6YW7aYvOcvRHj3b/nOdufUJEpDbeSiUi8pFSRSzMRjOTLSIPnOXo3e395cn5W58QEamJyRYRkY/SLGneD/JDu70dNrtNkXMTBQpnOfr0dNfHMzIc+0Oe/7inrU+IiNTCaYRERD7Ky8xDuiUdR+v9mMvkgQQJy3cuR8GIAlnPSxRoPK3revppecvUExHJgckWEZGPTEYTll6/FFPengIJkqznPnjqoKznIwpU7tZ1cesTItIiTiMkIuoGa44Va6auQbrFdc5SfGh8j87bv1f/Hj2fiIiItIcjW0RE3WTNsWLioIkoLitGRUMFUmNS8aeP/4TNJZv9Op/JYMKMK2bIHCURERGJxmSLiMgPJqPJpYLg3A/m+n2uOblzEBoSKkNUREREpCWcRkhEJIPhacO7/RyTwYS5I+di4diFCkREREREonFki4hIBovHLsZfd/7V63HPjH4GR+qPoH+v/phxxQyOaBEREQUwJltERDKICI3AxEET8c6+d9weM3HQRDx6zaMqRkVEREQicRohEZFM/v2zf2PioIldfm/ioIn498/+rW5AREREJBRHtoiIZPTvn/0bzWeaMffDuThQfQAXJFyARWMWISI0QnRoREREpDImW0REMosIjcALN7wgOgwiIiISjNMIiYiIiIiIFMBki4iIiIiISAFMtoiIiIiIiBTAZIuIiIiIiEgBTLaIiIiIiIgUwGSLiIiIiIhIAUy2iIiIiIiIFMBki4iIiIiISAFMtoiIiIiIiBTAZIuIiIiIiEgBTLaIiIiIiIgUwGSLiIiIiIhIAUy2iIiIiIiIFBAiOgA9kCQJAFBfXy84Eoe2tjY0NTWhvr4eZrNZdDhBj/2hPewT7WGfaAv7Q3vYJ9rDPtEWLfWHMydw5gieMNnyQUNDAwAgIyNDcCRERERERKQFDQ0NiI2N9XiMQfIlJQtydrsdx44dQ0xMDAwGg+hwUF9fj4yMDBw5cgQWi0V0OEGP/aE97BPtYZ9oC/tDe9gn2sM+0RYt9YckSWhoaECfPn1gNHpelcWRLR8YjUakp6eLDqMTi8Ui/IeNzmJ/aA/7RHvYJ9rC/tAe9on2sE+0RSv94W1Ey4kFMoiIiIiIiBTAZIuIiIiIiEgBTLZ0KCwsDPPnz0dYWJjoUAjsDy1in2gP+0Rb2B/awz7RHvaJtui1P1ggg4iIiIiISAEc2SIiIiIiIlIAky0iIiIiIiIFMNkiIiIiIiJSAJMtIiIiIiIiBTDZEuDpp5/G8OHDERMTg6SkJEyaNAn79u1zOaalpQUzZ85EQkICoqOjMXnyZFRVVbkc8/DDD2PYsGEICwvDkCFDOrVTWloKg8HQ6eOzzz5T8vJ0Sa0+ARy7jj/77LMYOHAgwsLCkJaWhieffFKpS9MltfrjiSee6PI1EhUVpeTl6ZKar5GNGzdixIgRiImJQWJiIiZPnozS0lKFrky/1OyTt99+G0OGDEFkZCSysrKwaNEipS5Lt+Toj6+//hrTpk1DRkYGIiIikJOTg6VLl3Zqq6ioCJdffjnCwsIwYMAAvPrqq0pfni6p1ScVFRW4/fbbMXDgQBiNRhQUFKhxebqkVp8UFhZi7NixSExMhMViQW5uLjZu3KjKNZ6PyZYAH330EWbOnInPPvsMmzZtQltbG8aNG4fTp093HDN79mz85z//werVq/HRRx/h2LFjsFqtnc5111134bbbbvPY3ocffoiKioqOj2HDhsl+TXqnZp/MmjULL7/8Mp599ll8//33ePfdd3HllVcqcl16pVZ/PPLIIy6vjYqKClx00UW49dZbFbs2vVKrT0pKSjBx4kSMGjUKX331FTZu3IiTJ092eZ5gp1af/Pe//8XPf/5z3H///dizZw+WL1+O559/Hi+88IJi16ZHcvTHrl27kJSUhDfffBPffvstfv/73+Oxxx5z+b8uKSnBjTfeiOuuuw5fffUVCgoKcM899wj7Q1LL1OqT1tZWJCYmYt68ebjssstUvUa9UatPPv74Y4wdOxYbNmzArl27cN111+Hmm2/G7t27Vb1eAIBEwh0/flwCIH300UeSJElSbW2tZDabpdWrV3ccs3fvXgmAtH379k7Pnz9/vnTZZZd1erykpEQCIO3evVup0AOWUn3y3XffSSEhIdL333+vWOyBSKn+ON9XX30lAZA+/vhj2WIPVEr1yerVq6WQkBDJZrN1PPbuu+9KBoNBOnPmjPwXEkCU6pNp06ZJU6ZMcXnsz3/+s5Seni7Z7XZ5LyKA9LQ/nGbMmCFdd911HV8/+uij0sUXX+xyzG233SaNHz9e5isIPEr1ybl+8pOfSLNmzZI17kCmRp84XXTRRdKCBQvkCbwbOLKlAXV1dQCAXr16AXBk7G1tbRgzZkzHMRdeeCEyMzOxffv2bp9/woQJSEpKwjXXXIN3331XnqADnFJ98p///Af9+vXD+vXrkZ2djb59++Kee+7BqVOn5L2AAKP0a8Tp5ZdfxsCBA5GXl9ezgIOAUn0ybNgwGI1GvPLKK7DZbKirq8Mbb7yBMWPGwGw2y3sRAUapPmltbUV4eLjLYxERETh69CgOHz4sQ+SBSa7+qKur6zgHAGzfvt3lHAAwfvz4Hv3uCxZK9Qn5T60+sdvtaGhoENJvTLYEs9vtKCgowNVXX41LLrkEAFBZWYnQ0FDExcW5HJucnIzKykqfzx0dHY3Fixdj9erVeO+993DNNddg0qRJTLi8ULJPDh06hMOHD2P16tV4/fXX8eqrr2LXrl2YMmWKnJcQUJTsj3O1tLRgxYoVuPvuu3sacsBTsk+ys7PxwQcf4He/+x3CwsIQFxeHo0eP4u2335bzEgKOkn0yfvx4FBYWYvPmzbDb7di/fz8WL14MwLFWhTqTqz+2bduGt956C/fdd1/HY5WVlUhOTu50jvr6ejQ3N8t7IQFEyT4h/6jZJ88++ywaGxsxdepU2eL3VYjqLZKLmTNnYs+ePfjkk09kP3fv3r0xZ86cjq+HDx+OY8eOYdGiRZgwYYLs7QUKJfvEbrejtbUVr7/+OgYOHAgA+Mc//oFhw4Zh3759GDRokOxt6p2S/XGudevWoaGhAdOnT1e0nUCgZJ9UVlbi3nvvxfTp0zFt2jQ0NDTg8ccfx5QpU7Bp0yYYDAbZ2wwESvbJvffei4MHD+Kmm25CW1sbLBYLZs2ahSeeeAJGI+/ZdkWO/tizZw8mTpyI+fPnY9y4cTJGF5zYJ9qjVp+sXLkSCxYswDvvvIOkpCS/2/IXf0sK9OCDD2L9+vXYunUr0tPTOx5PSUnBmTNnUFtb63J8VVUVUlJSetTmVVddhR9++KFH5whkSvdJamoqQkJCOhItAMjJyQEAlJWV9Sz4AKTma+Tll1/GTTfd1OmOMblSuk+WLVuG2NhYLFy4EEOHDsW1116LN998E5s3b8bnn38u12UEFKX7xGAw4JlnnkFjYyMOHz6MysrKjqI+/fr1k+UaAokc/fHdd99h9OjRuO+++zBv3jyX76WkpHSqKFlVVQWLxYKIiAh5LyZAKN0n1H1q9cm//vUv3HPPPXj77bc7Tb9VC5MtASRJwoMPPoh169Zhy5YtyM7Odvn+sGHDYDabsXnz5o7H9u3bh7KyMuTm5vao7a+++gqpqak9OkcgUqtPrr76arS3t+PgwYMdj+3fvx8AkJWV1cOrCBxqv0ZKSkqwdetWTiH0QK0+aWpq6jRaYjKZADhGhukstV8nJpMJaWlpCA0NxapVq5Cbm4vExMQeX0egkKs/vv32W1x33XWYPn16l9uC5ObmupwDADZt2tTjvw8CkVp9Qr5Ts09WrVqFX/3qV1i1ahVuvPFGZS7IF6qX5CDpgQcekGJjY6WioiKpoqKi46OpqanjmPvvv1/KzMyUtmzZIu3cuVPKzc2VcnNzXc5z4MABaffu3dKvf/1raeDAgdLu3bul3bt3S62trZIkSdKrr74qrVy5Utq7d6+0d+9e6cknn5SMRqP0z3/+U9Xr1QO1+sRms0mXX365dO2110pffvmltHPnTumqq66Sxo4dq+r1ap1a/eE0b948qU+fPlJ7e7sq16dHavXJ5s2bJYPBIC1YsEDav3+/tGvXLmn8+PFSVlaWS1ukXp+cOHFC+utf/yrt3btX2r17t/Twww9L4eHh0ueff67q9WqdHP3xzTffSImJidIvfvELl3McP36845hDhw5JkZGR0ty5c6W9e/dKy5Ytk0wmk/T++++rer16oFafSJLU8boZNmyYdPvtt0u7d++Wvv32W9WuVS/U6pMVK1ZIISEh0rJly1yOqa2tVfV6JUmSmGwJAKDLj1deeaXjmObmZmnGjBlSfHy8FBkZKd1yyy1SRUWFy3l+8pOfdHmekpISSZIcyVZOTo4UGRkpWSwW6corr3QppUlnqdUnkiRJ5eXlktVqlaKjo6Xk5GTpzjvvlKqrq1W6Un1Qsz9sNpuUnp4u/e53v1Pp6vRJzT5ZtWqVNHToUCkqKkpKTEyUJkyYIO3du1elK9UPtfrkxIkT0ogRI6SoqCgpMjJSGj16tPTZZ5+peKX6IEd/zJ8/v8tzZGVlubS1detWaciQIVJoaKjUr18/lzboLDX7xJdjSL0+cfd7bfr06epd7I8MkiRJ3ka/iIiIiIiIqHu4ZouIiIiIiEgBTLaIiIiIiIgUwGSLiIiIiIhIAUy2iIiIiIiIFMBki4iIiIiISAFMtoiIiIiIiBTAZIuIiIiIiEgBTLaIiIiIiIgUwGSLiIjoHHfeeScmTZokOgwiIgoAIaIDICIiUovBYPD4/fnz52Pp0qWQJEmliIiIKJAx2SIioqBRUVHR8e+33noLjz/+OPbt29fxWHR0NKKjo0WERkREAYjTCImIKGikpKR0fMTGxsJgMLg8Fh0d3WkaYX5+Ph566CEUFBQgPj4eycnJeOmll3D69Gn86le/QkxMDAYMGID//ve/Lm3t2bMHP/3pTxEdHY3k5GTccccdOHnypMpXTEREIjHZIiIi8uK1115D7969sWPHDjz00EN44IEHcOutt2LkyJH48ssvMW7cONxxxx1oamoCANTW1mLUqFEYOnQodu7ciffffx9VVVWYOnWq4CshIiI1MdkiIiLy4rLLLsO8efNwwQUX4LHHHkN4eDh69+6Ne++9FxdccAEef/xxVFdX43//+x8A4IUXXsDQoUPx1FNP4cILL8TQoUPxz3/+E1u3bsX+/fsFXw0REamFa7aIiIi8uPTSSzv+bTKZkJCQgMGDB3c8lpycDAA4fvw4AODrr7/G1q1bu1z/dfDgQQwcOFDhiImISAuYbBEREXlhNptdvjYYDC6POasc2u12AEBjYyNuvvlmPPPMM53OlZqaqmCkRESkJUy2iIiIZHb55Zdj7dq16Nu3L0JC+FZLRBSsuGaLiIhIZjNnzsSpU6cwbdo0fPHFFzh48CA2btyIX/3qV7DZbKLDIyIilTDZIiIiklmfPn3w6aefwmazYdy4cRg8eDAKCgoQFxcHo5FvvUREwcIgSZIkOggiIiIiIqJAw9trRERERERECmCyRUREREREpAAmW0RERERERApgskVERERERKQAJltEREREREQKYLJFRERERESkACZbRERERERECmCyRUREREREpAAmW0RERERERApgskVERERERKQAJltEREREREQK+P8BVbo3z9+5zQAAAABJRU5ErkJggg==", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get station name and elevation from metdata dataframe\n", + "# Fetch data for site01 (elevation 256) and site09 (elevation 277), using limit=10000\n", + "# ch4/co2 select the ghg \n", + "\n", + "site01_data = get_station_data_csv('site01', 'ch4', 'hourly', 256,limit=10000)\n", + "site09_data = get_station_data_csv('site09', 'ch4', 'hourly', 277,limit=10000)\n", + "\n", + "# Check if data was successfully retrieved before proceeding\n", + "if site01_data.empty or site09_data.empty:\n", + " print(\"No data available for one or both stations. Exiting.\")\n", + "else:\n", + " # Convert the 'datetime' column to datetime for plotting\n", + " site01_data['datetime'] = pd.to_datetime(site01_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + " site09_data['datetime'] = pd.to_datetime(site09_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + "\n", + " # Plot the data\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(site01_data['datetime'], site01_data['value'], label='site01 (256m)', color='blue', marker='o')\n", + " plt.plot(site09_data['datetime'], site09_data['value'], label='site09 (277m)', color='green', marker='o')\n", + "\n", + " plt.title('Methane (CH₄) Hourly Concentrations Over Time for site01 and site09 Stations')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('CH₄ Concentration (ppb)')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Show plot\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ams-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb new file mode 100644 index 00000000..457f63c3 --- /dev/null +++ b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb @@ -0,0 +1,205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", + "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 12 September 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "1. Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "2. Visualize the time series data\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "NIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as sciies. NIST's objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. sciies to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\n", + "\n", + "For more information regarding this dataset, please visit the [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](https://earth.gov/ghgcenter/data-catalog/nec-testbed-ghg-concentrations) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the Required Libraries\n", + "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:\n", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objs as go\n", + "from io import StringIO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying the Feature Vector API\n", + "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to fetch CSV data for a station with a limit parameter\n", + "def get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=100000):\n", + " # Use the ?f=csv and limit query to get more rows\n", + " url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n", + " print(url)\n", + " try:\n", + " response = requests.get(url)\n", + " \n", + " # Check if the response is successful\n", + " if response.status_code != 200:\n", + " print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n", + " return pd.DataFrame()\n", + "\n", + " # Check if the content type is CSV\n", + " content_type = response.headers.get('Content-Type')\n", + " if 'text/csv' not in content_type:\n", + " print(f\"Unexpected content type for {station_code}: {content_type}\")\n", + " print(\"Response content:\", response.text)\n", + " return pd.DataFrame()\n", + "\n", + " # Read the CSV content into a pandas DataFrame\n", + " csv_data = StringIO(response.text)\n", + " return pd.read_csv(csv_data)\n", + " \n", + " except requests.exceptions.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the CO₂ data for two NEC stations" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_sci_co2_hourly_concentrations/items?f=csv&elevation_m=489&limit=10000\n", + "https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_lam_com_co2_hourly_concentrations/items?f=csv&elevation_m=9&limit=10000\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get station name and elevation from metdata dataframe\n", + "# Fetch data for SCI (elevation 489) and COM (elevation 9), using limit=10000\n", + "# ch4/co2 select the ghg \n", + "sci_data = get_station_data_csv('sci', 'co2', 'hourly', 489, limit=10000)\n", + "com_data = get_station_data_csv('com', 'co2', 'hourly', 9, limit=10000)\n", + "\n", + "# Check if data was successfully retrieved before proceeding\n", + "if sci_data.empty or com_data.empty:\n", + " print(\"No data available for one or both stations. Exiting.\")\n", + "else:\n", + " # Convert the 'datetime' column to datetime for plotting\n", + " sci_data['datetime'] = pd.to_datetime(sci_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + " com_data['datetime'] = pd.to_datetime(com_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + "\n", + " # Plot the data\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(sci_data['datetime'], sci_data['value'], label='SCI (489m)', color='blue', marker='o')\n", + " plt.plot(com_data['datetime'], com_data['value'], label='COM (9m)', color='green', marker='o')\n", + "\n", + " plt.title('Carbon Dioxide (CO₂) Hourly Concentrations Over Time for SCI and COM Stations')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('CO₂ Concentration (ppm)')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Show plot\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ams-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb new file mode 100644 index 00000000..c8b78070 --- /dev/null +++ b/user_data_notebooks/nec-testbed-ghg-concentrations_User_Notebook.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", + "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "author: Siddharth Chaudhary, Paridhi Parajuli\n", + "date: 12 September 2024\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "1. Identify available dates and temporal frequency of observations for the given data. The collection processed in this notebook is the Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "2. Visualize the time series data\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "NIST is engaged in research to improve measurement of greenhouse gas emissions in areas containing multiple emission sources and sinks, such as UNYies. NIST's objective is to develop measurement tools supporting independent means to increase the accuracy of greenhouse gas emissions data at urban and regional geospatial scales. NIST has established three test beds in U.S. UNYies to develop and evaluate the performance of advanced measurement capabilities for emissions independent of their origin. Located in Indianapolis, Indiana, the Los Angeles air basin of California, and the U.S. Northeast corridor (beginning with the Baltimore/Washington D.C. region), the test beds have been selected for their varying meteorology, terrain and emissions characteristics. These test beds will serve as a means to independently diagnose the accuracy of emissions data obtained directly from emission or uptake sources.\n", + "\n", + "For more information regarding this dataset, please visit the [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](https://earth.gov/ghgcenter/data-catalog/nec-testbed-ghg-concentrations) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the Required Libraries\n", + "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:\n", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objs as go\n", + "from io import StringIO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying the Feature Vector API\n", + "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Feature Vector Application Programming Interface (API) where the items for this collection are stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FEATURE_API_URL=\"https://earth.gov/ghgcenter/api/features\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to fetch CSV data for a station with a limit parameter\n", + "def get_station_data_csv(station_code, gas_type, frequency, elevation_m, limit=10000):\n", + " # Use the ?f=csv and limit query to get more rows\n", + " url = f\"https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_{station_code}_{gas_type}_{frequency}_concentrations/items?f=csv&elevation_m={elevation_m}&limit={limit}\"\n", + " print(url)\n", + " try:\n", + " response = requests.get(url)\n", + " print(response)\n", + " # Check if the response is successful\n", + " if response.status_code != 200:\n", + " print(f\"Failed to fetch data for {station_code}. Status code: {response.status_code}\")\n", + " return pd.DataFrame()\n", + "\n", + " # Check if the content type is CSV\n", + " content_type = response.headers.get('Content-Type')\n", + " if 'text/csv' not in content_type:\n", + " print(f\"Unexpected content type for {station_code}: {content_type}\")\n", + " print(\"Response content:\", response.text)\n", + " return pd.DataFrame()\n", + "\n", + " # Read the CSV content into a pandas DataFrame\n", + " csv_data = StringIO(response.text)\n", + " return pd.read_csv(csv_data)\n", + " \n", + " except requests.exceptions.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the CH₄ data for two NEC stations" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_uny_ch4_hourly_concentrations/items?f=csv&elevation_m=483&limit=10000\n", + "\n", + "https://earth.gov/ghgcenter/api/features/collections/public.nist_testbed_nec_tmd_ch4_hourly_concentrations/items?f=csv&elevation_m=561&limit=10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get station name and elevation from metdata dataframe\n", + "# Fetch data for UNY (elevation 230) and TMD (elevation 489), using limit=10000\n", + "# ch4/co2 select the ghg \n", + "uny_data = get_station_data_csv('uny', 'ch4', 'hourly', 483, limit=10000)\n", + "tmd_data = get_station_data_csv('tmd', 'ch4', 'hourly', 561, limit=10000)\n", + "\n", + "# Check if data was successfully retrieved before proceeding\n", + "if uny_data.empty or tmd_data.empty:\n", + " print(\"No data available for one or both stations. Exiting.\")\n", + "else:\n", + " # Convert the 'datetime' column to datetime for plotting\n", + " uny_data['datetime'] = pd.to_datetime(uny_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + " tmd_data['datetime'] = pd.to_datetime(tmd_data['datetime'], format='%Y-%m-%dT%H:%M:%SZ')\n", + "\n", + " # Plot the data\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(uny_data['datetime'], uny_data['value'], label='UNY (230m)', color='blue', marker='o')\n", + " plt.plot(tmd_data['datetime'], tmd_data['value'], label='TMD (489m)', color='green', marker='o')\n", + "\n", + " plt.title('Methane (CH₄) Hourly Concentrations Over Time for UNY and TMD Stations')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('CH4 Concentration (ppb)')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Show plot\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ams-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workflow.qmd b/workflow.qmd index 3caf1363..a53f0705 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -35,6 +35,9 @@ Explore, analyze, and make a difference with the US GHG Center. 1. [Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Data Flow Diagram](data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd) 2. [Atmospheric Methane Concentrations from NOAA Global Monitoring Laboratory Data Flow Diagram](data_workflow/noaa-gggrn-ch4-concentrations_Data_Flow.qmd) 3. [OCO-2 GEOS Column CO₂ Concentrations Data Flow Diagram](data_workflow/oco2geos-co2-daygrid-v10r_Data_Flow.qmd) +4. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)](data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd) +5. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project](data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd) +6. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd) ## Socioeconomic 1. [SEDAC Gridded World Population Density Data Flow Diagram](data_workflow/sedac-popdensity-yeargrid5yr-v4.11_Data_Flow.qmd) From fa449c0a927dca6c103f1bbea216d646a57ac687 Mon Sep 17 00:00:00 2001 From: fb0023 <142605787+fb0023@users.noreply.github.com> Date: Fri, 13 Sep 2024 16:50:13 -0500 Subject: [PATCH 36/47] R3 Improvements to ecco darwin, emit, and epa gridded notebooks --- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 275 ++-- .../emit-ch4plume-v1_User_Notebook.ipynb | 1376 ++++------------- ...mission-grid-v2express_User_Notebook.ipynb | 329 ++-- 3 files changed, 663 insertions(+), 1317 deletions(-) diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 51c9efd2..9b6fddd6 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -12,7 +12,7 @@ "title: Utilizing the Air-Sea CO₂ Flux ECCO-Darwin Model to Visualize CO₂ Exchange and Dynamics Between the Atmosphere and the Ocean\n", "description: Global, monthly average air-sea CO₂ flux at ~1/3° resolution from 2020 to 2022\n", "author: Siddharth Chaudhary, Vishal Gaur, Farnaz Bayat\n", - "date: 18 September, 2024\n", + "date: 20 September, 2024\n", "execute:\n", " freeze: true\n", "---" @@ -27,21 +27,21 @@ "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdai8otCdrVQzJgev8mjDhzKyCg7jcrB3UeTXNHoCiaMKrkaQ/viewform) and providing the required information. \n", "\n", "\n", - "Access the [**Air-Sea CO₂ Flux, ECCO-Darwin Model**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Feccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub. " + "Access the [**Air-Sea CO₂ Flux, ECCO-Darwin Model**](https://us-ghg-center.github.io/ghgc-docs/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.html) notebook in the US GHG Center JupyterHub. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Table of Content\n", + "## Table of Contents\n", "- [Data Summary and Application](#data-summary-and-application)\n", "- [Approach](#approach)\n", "- [About the Data](#about-the-data)\n", - "- [Install the Required Libraries](#install-the-required-libraries) \n", + "- [Install the Required Libraries](#install-the-required-libraries)\n", "- [Query the STAC API](#query-the-stac-api)\n", - "- [Explore Changes in CO₂ Levels](#explore-changes-in-co-levels)\n", - "- [Visualize CO₂ Flux Emissions](#visualize-co-flux-emissions)\n", + "- [Visual Comparison Across Time Periods](#visual-comparison-across-time-periods)\n", + "- [Map Out Selected Tiles](#map-out-selected-tiles)\n", "- [Calculate Zonal Statistics](#calculate-zonal-statistics)\n", "- [Time-Series Analysis](#time-series-analysis)\n", "- [Summary](#summary)" @@ -52,7 +52,7 @@ "metadata": {}, "source": [ "## Data Summary and Application\n", - "- **Spatial Coverage**: Global\n", + "- **Spatial coverage**: Global\n", "- **Spatial resolution**: 1/3°\n", "- **Temporal extent**: January 2020 - December 2022\n", "- **Temporal resolution**: Monthly\n", @@ -97,20 +97,11 @@ "metadata": {}, "source": [ "# Install the Required Libraries\n", - "Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install the libraries by running the following command line:\n", + "Required libraries are pre-installed on the GHG Center Hub, except the `tabulate` library. If you need to run this notebook elsewhere, please install the libraries by running the following command line:\n", "\n", "%pip install requests folium rasterstats pystac_client pandas matplotlib tabulate --quiet" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Query the STAC API\n", - "First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook." - ] - }, { "cell_type": "code", "execution_count": null, @@ -129,9 +120,18 @@ "from tabulate import tabulate" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query the STAC API\n", + "First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center **Spatio Temporal Asset Catalog (STAC)** Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac/\"\n", "\n", "# The RASTER API is used to fetch collections for visualization.\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/docs\"" + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/\"" ] }, { @@ -151,21 +151,8 @@ "source": [ "**STAC API Collection Names**\n", "\n", - "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection name as a variable. \n", - "\n", - "Here is a list of the collection names in the GHG Center STAC API:\n", - "- Air-Sea CO₂ Flux ECCO-Darwin Model: [eccodarwin-co2flux-monthgrid-v5](https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5) (**Used in this notebook!**)\n", - "- EMIT Methane Point Source Plume Complexes: [emit-ch4plume-v1](https://earth.gov/ghgcenter/api/stac/collections/emit-ch4plume-v1)\n", - "- U.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension: [epa-ch4emission-yeargrid-v2express](https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express)\n", - "- GOSAT-based Top-down Total and Natural Methane Emissions v1: [gosat-based-ch4budget-yeargrid-v1](https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1)\n", - "- Wetland Methane Emissions, LPJ-EOSIM Model v2 (Daily): [lpjeosim-wetlandch4-daygrid-v2](https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2)\n", - "- Wetland Methane Emissions, LPJ-EOSIM Model (Monthly): [lpjeosim-wetlandch4-monthgrid-v2](https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2)\n", - "- MiCASA Land Carbon Flux v1 (Daily): [micasa-carbonflux-daygrid-v1](https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1)\n", - "- OCO-2 MIP Top-down CO₂ Budgets v1: [oco2-mip-co2budget-yeargrid-v1](https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1)\n", - "- OCO-2 GEOS Column CO₂ Concentrations v10r: [oco2geos-co2-daygrid-v10r](https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r)\n", - "- ODIAC Fossil Fuel CO₂ Emissions v2023: [odiac-ffco2-monthgrid-v2023](https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023)\n", - "- SEDAC Gridded World Population Density v4.11: [sedac-popdensity-yeargrid5yr-v4.11](https://earth.gov/ghgcenter/api/stac/collections/sedac-popdensity-yeargrid5yr-v4.11)\n", - "- TM5-4DVar Isotopic CH4 Inverse Fluxes: [tm54dvar-ch4flux-monthgrid-v1](https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1)" + "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection ID as a variable. \n", + "The collection ID, also known as the **collection name**, for the Air-Sea CO₂ Flux ECCO-Darwin Model dataset is [**eccodarwin-co2flux-monthgrid-v5**](https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5)" ] }, { @@ -184,29 +171,29 @@ "\n", "# Print the properties of the collection in a table\n", "# Adjust display settings\n", - "pd.set_option('display.max_colwidth', None) # Set maximum column width to None to prevent cutting off text\n", + "pd.set_option('display.max_colwidth', None) # Set maximum column width to \"None\" to prevent cutting off text\n", "\n", "# Extract the relevant information about the collection\n", "collection_info = {\n", - " \"Title\": collection.get(\"title\", \"N/A\"),\n", - " \"Description\": collection.get(\"description\", \"N/A\"),\n", - " \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"),\n", - " \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"),\n", + " \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n", + " \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n", + " \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n", + " \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n", "}\n", "\n", - "# Convert the extracted information into a DataFrame format\n", + "# Convert the derived information into a DataFrame format\n", "properties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n", "\n", - "# Display the collection properties in a table\n", + "# Display the properties in a table\n", "collection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n", " .set_table_styles([\n", " {\n", - " 'selector': 'th.col0, td.col0', # Select the first column\n", + " 'selector': 'th.col0, td.col0', # Select the first column\n", " 'props': [('min-width', '200px'), # Set a minimum width\n", " ('text-align', 'left')] # Align text to the left\n", " },\n", " {\n", - " 'selector': 'td.col1', # Select the second column\n", + " 'selector': 'td.col1', # Select the second column\n", " 'props': [('text-align', 'left')] # Align text to the left\n", " }\n", "])\n", @@ -225,11 +212,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# Create a function that would search for a data collection in the US GHG Center STAC API\n", + "# Create a function that would search for data collection in the US GHG Center STAC API\n", "\n", "# First, we need to define the function\n", "# The name of the function is \"get_item_count\" \n", @@ -239,10 +226,10 @@ " # Set a counter for the number of items existing in the collection \n", " count = 0 \n", "\n", - " # Define the path to retrieve the granules (items) of the collection of interest (Air-Sea CO2 Flux ECCO-Darwin model) in the STAC API\n", + " # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n", " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\" \n", "\n", - " # Run a while loop to make HTTP requests until there are no more URLs associated with the collection (Air-Sea CO2 Flux ECCO-Darwin model) in the STAC API\n", + " # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n", " while True:\n", "\n", " # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path \n", @@ -259,7 +246,7 @@ " # Increase the \"count\" by the number of items (granules) returned in the response\n", " count += int(stac[\"context\"].get(\"returned\", 0))\n", "\n", - " # Retrieve information about the next URL associated with the collection (Air-Sea CO2 Flux ECCO-Darwin model) in the STAC API (if applicable)\n", + " # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n", " next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n", "\n", " # Exit the loop if there are no other URLs\n", @@ -270,7 +257,7 @@ " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", " items_url = next[0][\"href\"]\n", "\n", - " # Return the information about the total number of granules found associated with the collection (Air-Sea CO2 Flux ECCO-Darwin model)\n", + " # Return the information about the total number of granules found associated with the collection\n", " return count" ] }, @@ -295,12 +282,16 @@ "# Create an empty list\n", "table_data = []\n", "# Extract the ID and date-time information for each granule and add them to the list\n", - "for item in items_sorted:\n", + "# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n", + "# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \n", + "for item in items_sorted[:5]:\n", " table_data.append([item['id'], item['properties']['start_datetime']])\n", "\n", "# Define the table headers\n", "headers = [\"Item ID\", \"Start Date-Time\"]\n", "\n", + "print(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n", + "\n", "# Print the table using tabulate\n", "print(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))" ] @@ -321,23 +312,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Explore Changes in CO₂ Levels\n", + "## Visual Comparison Across Time Periods\n", "\n", "You will now explore changes in CO₂ flux at a given location and time. You will visualize the outputs on a map using `folium`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# Now we create a dictionary where the start datetime values for each granule are queried more explicitly by year and month (e.g., 2020-02)\n", + "# Once again, apply the function created above \"get_item_count\" to the collection\n", + "# This step allows retrieving the number of granules “observations” in the collection.\n", + "number_of_items = get_item_count(collection_name)\n", + "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", + "\n", + "# Now you need to create a dictionary where the start datetime values for each granule are queried more explicitly by year and month (e.g., 2020-02)\n", "items = {item[\"properties\"][\"start_datetime\"]: item for item in items}\n", "\n", - "# Next, we need to specify the asset name for this collection.\n", + "# Next, you need to specify the asset name for this collection.\n", "# The asset name refers to the raster band containing the pixel values for the parameter of interest.\n", - "# For the case of the Air-Sea CO2 Flux ECCO-Darwin collection, the parameter of interest is “co2”.\n", + "# For the case of this collection, the parameter of interest is “co2”.\n", "asset_name = \"co2\"" ] }, @@ -345,12 +341,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." + "Below, you will enter the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +359,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. This step is done twice so that we can visualize two arbitrary events independently." + "Now, you will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. This step is done twice so that you can visualize two arbitrary events independently." ] }, { @@ -377,29 +373,42 @@ "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"magma\"\n", "\n", - "# Make a GET request to retrieve information for the December 2022 tile which is the 1st item in the collection\n", - "# To retrieve the first item in the collection we use \"0\" in the \"(items.keys())[0]\" statement \n", - "# If you want to select another item (granule) in the list (collection), you can refer to the Data Browser on the U.S. Greenhouse Gas Center website \n", + "\n", + "# You can retrieve the first observation of interest by defining the last 6 digits of its Item ID. \n", + "# The numbers indicate the year and month (YYYYMM) when the data was gathered.\n", + "# For example, the observation collected in December 2022 has the following item ID: eccodarwin-co2flux-monthgrid-v5-202212 \n", + "# To set the time, you will need to insert \"202212\" in the line below. \n", + "\n", + "\n", + "# Set the time\n", + "# If you want to select another time, you can refer to the Data Browser on the U.S. Greenhouse Gas Center website \n", "# URL to the Air-Sea CO2 Flux ECCO-Darwin collection in the US GHG Center: https://dljsq618eotzp.cloudfront.net/browseui/#eccodarwin-co2flux-monthgrid-v5/\n", + "observation_date_1 = '202212'\n", "\n", - "# A GET request is made for the December 2022 tile.\n", - "december_2022_tile = requests.get(\n", "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[0]]['collection']}&item={items[list(items.keys())[0]]['id']}\"\n", + "# Don't change anything here\n", + "observation_1 = f'eccodarwin-co2flux-monthgrid-v5-{observation_date_1}'\n", "\n", + "\n", + "# Make a GET request to retrieve information for the December 2022 tile\n", + "# A GET request is made for the December 2022 tile.\n", + "december_2022_tile = requests.get(\n", + " # Pass the collection name and item ID directly from the first item in the list\n", + " (f\"{RASTER_API_URL}/collections/{collection_name}/items/{observation_1}/WebMercatorQuad/tilejson.json?\"\n", + " \n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", - "\n", + " \n", " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", + " \n", " # Pass the minimum and maximum values for rescaling \n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " \n", "# Return the response in JSON format\n", ").json()\n", "\n", + "\n", "# Print the properties of the retrieved granule to the console\n", "december_2022_tile" ] @@ -410,28 +419,33 @@ "metadata": {}, "outputs": [], "source": [ - "# Make a GET request to retrieve information for the April 2021 tile which is the 21th item in the collection\n", - "# To retrieve the 21st item in the collection we use \"20\" in the \"(items.keys())[20]\" statement \n", - "# Keep in mind that a list starts from 0, therefore \"items[20]\" is referring to the 21st item in the list/collection\n", + "# You will repeat the same approach used in the previous step to retrieve the second observation of interest\n", + "observation_date_2 = '202104'\n", "\n", - "# A GET request is made for the April 2021 tile\n", - "april_2021_tile = requests.get(\n", "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[list(items.keys())[20]]['collection']}&item={items[list(items.keys())[20]]['id']}\"\n", + "# Don't change anything here\n", + "observation_2 = f'eccodarwin-co2flux-monthgrid-v5-{observation_date_2}'\n", "\n", + "\n", + "# Make a GET request to retrieve information for the December 2022 tile\n", + "# A GET request is made for the April 2021 tile.\n", + "april_2021_tile = requests.get(\n", + " # Pass the collection name and item ID directly from the first item in the list\n", + " (f\"{RASTER_API_URL}/collections/{collection_name}/items/{observation_2}/WebMercatorQuad/tilejson.json?\"\n", + " \n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", - "\n", + " \n", " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", - "\n", + " \n", + " # Pass the minimum and maximum values for rescaling \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " \n", "# Return the response in JSON format\n", ").json()\n", "\n", + "\n", "# Print the properties of the retrieved granule to the console\n", "april_2021_tile" ] @@ -441,7 +455,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizing CO₂ flux Emissions" + "## Map Out Selected Tiles\n", + "For this study, you are going to compare the CO₂ levels along the coast of California." ] }, { @@ -450,12 +465,11 @@ "metadata": {}, "outputs": [], "source": [ - "# For this study we are going to compare the CO2 level in 2021 and 2022 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", "\n", "# Set the initial zoom level and center of map for both tiles\n", "# 'folium.plugins' allows mapping side-by-side\n", - "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", + "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=7)\n", "\n", "\n", "# Define the first map layer with the CO2 Flux data for December 2022\n", @@ -535,13 +549,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Calculating Zonal Statistics\n", - "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon along the coast of California, United States. " + "# Calculate Zonal Statistics\n", + "To perform zonal statistics, you first need to create a polygon. In this use case, you are creating a polygon along the coast of California, United States." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -598,22 +612,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon. " + "Now that you created the polygon for the area of interest, you need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", "\n", - "# The function takes an item (granule) and a JSON (Coastal California polygon) as input parameters\n", + "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", "def generate_stats(item, geojson):\n", "\n", - " # A POST request is made to submit the data associated with the item of interest (specific observation) within the Coastal California boundaries to compute its statistics\n", + " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", " result = requests.post(\n", "\n", " # Raster API Endpoint for computing statistics\n", @@ -622,7 +636,7 @@ " # Pass the URL to the item, asset name, and raster identifier as parameters\n", " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", "\n", - " # Send the GeoJSON object (Coastal California polygon) along with the request\n", + " # Send the GeoJSON object (polygon) along with the request\n", " json=geojson,\n", "\n", " # Return the response in JSON format\n", @@ -642,7 +656,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we run the generated function in the previous step on a specific item (observation), we first check the total number of items available within the collection and retrieve the information regarding their start datetime." + "Before you run the generated function in the previous step on a specific item (observation), you need to check the total number of items available within the collection and retrieve the information regarding their start date-time." ] }, { @@ -651,7 +665,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Check total number of items available within the collection\n", + "# Check the total number of items available within the collection\n", "items = requests.get(\n", " f\"{STAC_API_URL}/collections/{collection_name}/items?limit=600\"\n", ").json()[\"features\"]\n", @@ -703,8 +717,12 @@ "%%time \n", "# %%time = Wall time (execution time) for running the code below\n", "\n", - "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the \"california_coast_aoi\" polygon\n", - "stats = [generate_stats(item, california_coast_aoi) for item in items]" + "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the aoi polygon\n", + "stats = {}\n", + "for item in items: \n", + " date = item[\"properties\"][\"start_datetime\"] # Get the associated date\n", + " year_month = date[:7].replace('-', '') # Convert datetime to year-month\n", + " stats[year_month] = generate_stats(item, california_coast_aoi)" ] }, { @@ -713,8 +731,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Print the stats for the first item in the collection\n", - "stats[0]" + "# Print the stats for the observation of interest\n", + "# For example, for printing the stats for the December 2022 tile, you can change the date inside the bracket \"['YYYYMM']\" to \"['202212']\n", + "stats['202212']" ] }, { @@ -732,9 +751,10 @@ "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", - "\n", + " pd.set_option('display.float_format', '{:.20f}'.format)\n", + " stats_json_ = [stats_json[datetime] for datetime in stats_json] \n", " # Normalize the JSON data \n", - " df = pd.json_normalize(stats_json)\n", + " df = pd.json_normalize(stats_json_)\n", "\n", " # Replace the naming \"statistics.b1\" in the columns\n", " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", @@ -758,8 +778,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizing the Data as a Time Series\n", - "We can now explore the fossil fuel emission time series (January 2020 -December 2022) available for the Coastal California area of the U.S. We can plot the data set using the code below:" + "## Time-Series Analysis\n", + "You can now explore the fossil fuel emission using this data collection (January 2020 -December 2022) for the Coastal California region. You can plot the data set using the code below:" ] }, { @@ -768,13 +788,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Sort the DataFrame by the datetime column so the plot is displaying the values from left to right (2020 -> 2022)\n", + "# Sort the DataFrame by the datetime column so the plot displays the values from left to right (2020 -> 2022)\n", "df_sorted = df.sort_values(by=\"datetime\")\n", "\n", "# Plot the timeseries analysis of the monthly air-sea CO₂ flux changes along the coast of California\n", "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", - "\n", "plt.plot(\n", " df_sorted[\"datetime\"], # X-axis: sorted datetime\n", " df_sorted[\"max\"], # Y-axis: maximum CO₂ value\n", @@ -820,18 +839,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looking at the plot above, we notice that CO₂ emission level increases particularly around 2022-09-01 for the defined area of interest. To take a closer look at monthly CO₂ flux variability across this region, we are going to retrieve and display data collected during the September 2022 observation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The 2022-09-01 observation is the 4th item in the list. \n", - "# Considering that a list starts with \"0\", we need to insert \"3\" in the \"items[3]\" statement\n", - "print(items[3][\"properties\"][\"start_datetime\"])" + "Next, you can take a closer look at the monthly CO₂ flux variability across this region for a particular day. For example, you can retrieve and display data collected during the September 2022 observation" ] }, { @@ -840,26 +848,35 @@ "metadata": {}, "outputs": [], "source": [ - "# A GET request is made for the September 2022 tile\n", - "September2022_co2_flux = requests.get(\n", + "# Choose the date of interest\n", + "date_of_interest = '202209'\n", + "\n", "\n", - " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[3]['collection']}&item={items[3]['id']}\"\n", + "# Don't change anything here\n", + "observation_of_interest = f'eccodarwin-co2flux-monthgrid-v5-{date_of_interest}'\n", "\n", + "\n", + "# Make a GET request to retrieve information for the september 2022 tile\n", + "# A GET request is made for the April 2021 tile.\n", + "september_2022_tile = requests.get(\n", + " # Pass the collection name and item ID directly from the first item in the list\n", + " (f\"{RASTER_API_URL}collections/{collection_name}/items/{observation_of_interest}/WebMercatorQuad/tilejson.json?\"\n", + " \n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", - "\n", + " \n", " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", - "\n", - " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n", - "\n", + " \n", + " # Pass the minimum and maximum values for rescaling \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"),\n", + " \n", "# Return the response in JSON format\n", ").json()\n", "\n", - "# Print the properties of the retrieved granule to the console \n", - "September2022_co2_flux" + "\n", + "# Print the properties of the retrieved granule to the console\n", + "september_2022_tile" ] }, { @@ -885,7 +902,7 @@ "\n", "# Define the map layer with the CO2 flux data for September 2022\n", "map_layer = TileLayer(\n", - " tiles=September2022_co2_flux[\"tiles\"][0], # Path to retrieve the tile\n", + " tiles=september_2022_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", " opacity = 0.7, # Adjust the transparency of the layer\n", ")\n", @@ -979,7 +996,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb index e6277461..0284d59f 100644 --- a/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb +++ b/user_data_notebooks/emit-ch4plume-v1_User_Notebook.ipynb @@ -2,17 +2,19 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: EMIT Methane Point Source Plume Complexes\n", - "description: >-\n", - " Daily aggregated, global point source methane emission plume estimates from\n", - " the EMIT instrument on the International Space Station (ISS)\n", - "authors: 'Siddharth Chaudhary, Vishal Gaur'\n", - "published: 1 August 2023\n", + "title: Utilizing NASA's EMIT Instrument to Monitor Methane Plumes from Point Source Emitters \n", + "description: Daily aggregated, global point source methane emission plume estimates from the EMIT instrument on the International Space Station (ISS)\n", + "authors: Siddharth Chaudhary, Vishal Gaur, Farnaz Bayat\n", + "date: 20 September, 2024\n", "execute:\n", - " freeze: true\n", + " freeze: true\n", "---" ] }, @@ -21,14 +23,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run this notebook\n", + "## Access this Notebook\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdai8otCdrVQzJgev8mjDhzKyCg7jcrB3UeTXNHoCiaMKrkaQ/viewform) and providing the required information. \n", "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Femit-ch4plume-v1_User_Notebook.ipynb&branch=main)", + "Access the [**EMIT Methane Point Source Plume Complexes**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Femit-ch4plume-v1_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub. \n", " " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "- [Data Summary and Application](#data-summary-and-application)\n", + "- [Approach](#approach)\n", + "- [About the Data](#about-the-data)\n", + "- [Install the Required Libraries](#install-the-required-libraries)\n", + "- [Query the STAC API](#query-the-stac-api)\n", + "- [Map Out Selected Tiles](#map-out-selected-tiles)\n", + "- [Calculate Zonal Statistics](#calculate-zonal-statistics)\n", + "- [Summary](#summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Summary and Application\n", + "- **Spatial coverage**: 52°N to 52°S latitude within target mask\n", + "- **Spatial resolution**: 60 m\n", + "- **Temporal extent**: August 1, 2022 - Ongoing\n", + "- **Temporal resolution**: Variable\n", + "- **Unit**: Parts per million meter (ppm-m)\n", + "- **Utility**: Methane Emissions, Plume Detection, Climate Monitoring \n", + "\n", + "For more, visit the [EMIT Methane Point Source Plume Complexes](https://earth.gov/ghgcenter/data-catalog/emit-ch4plume-v1) data overview page. " + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -50,7 +82,10 @@ "metadata": {}, "source": [ "## About the Data\n", - "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument builds upon NASA’s long history of developing advanced imaging spectrometers for new science and applications. EMIT launched to the International Space Station (ISS) on July 14, 2022. The data shows high-confidence research grade methane plumes from point source emitters - updated as they are identified - in keeping with Jet Propulsion Laboratory (JPL) Open Science and Open Data policy.\n", + "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument builds upon NASA’s long history of developing advanced imaging spectrometers for new science and applications. EMIT launched to the International Space Station (ISS) on July 14, 2022. The data shows high-confidence research grade methane plumes from point source emitters - updated as they are identified - in keeping with Jet Propulsion Laboratory (JPL) Open Science and Open Data policy. \n", + "\n", + "Large methane emissions, typically referred to as point source emissions, represent a significant proportion of total methane emissions from the production, transport, and processing of oil and natural gas, landfills, and other sources. By measuring the spectral fingerprint of methane, EMIT can map areas of high methane concentration over background levels in the atmosphere, identifying plume complexes, and estimating the methane enhancements.\n", + "\n", "For more information regarding this dataset, please visit the [EMIT Methane Point Source Plume Complexes](https://earth.gov/ghgcenter/data-catalog/emit-ch4plume-v1) data overview page." ] }, @@ -60,23 +95,14 @@ "metadata": {}, "source": [ "# Install the Required Libraries\n", - "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:\n", + "Required libraries are pre-installed on the GHG Center Hub, except the `tabulate` and `seaborn` libraries. If you need to run this notebook elsewhere, please install the libraries by running the following command line:\n", "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Querying the STAC API\n", - "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored." + "%pip install requests folium rasterstats pystac_client pandas matplotlib tabulate seaborn --quiet" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,18 +110,28 @@ "import requests\n", "import folium\n", "import folium.plugins\n", - "from folium import Map, TileLayer\n", - "from pystac_client import Client\n", - "import branca\n", + "from folium import Map, TileLayer \n", + "from pystac_client import Client \n", + "import branca \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "from tabulate import tabulate\n", "import branca.colormap as cm\n", "import seaborn as sns" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query the STAC API\n", + "First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center **Spatio Temporal Asset Catalog (STAC)** Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -103,824 +139,68 @@ "# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.\n", "\n", "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", - "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac/\"\n", "\n", "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**STAC API Collection Names**\n", "\n", - "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", - "# Name of the collection for methane emission plumes \n", - "collection_name = \"emit-ch4plume-v1\"" + "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection ID as a variable. \n", + "The collection ID, also known as the **collection name**, for the EMIT Methane Point Source Plume Complexes dataset is [**emit-ch4plume-v1**](https://earth.gov/ghgcenter/api/stac/collections/emit-ch4plume-v1)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'emit-ch4plume-v1',\n", - " 'type': 'Collection',\n", - " 'links': [{'rel': 'items',\n", - " 'type': 'application/geo+json',\n", - " 'href': 'https://ghg.center/api/stac/collections/emit-ch4plume-v1/items'},\n", - " {'rel': 'parent',\n", - " 'type': 'application/json',\n", - " 'href': 'https://ghg.center/api/stac/'},\n", - 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" '2024-03-22T21:45:09Z',\n", - " '2024-03-27T07:05:05Z',\n", - " '2024-03-27T10:07:08Z',\n", - " '2024-03-27T13:13:34Z',\n", - " '2024-03-28T06:17:14Z',\n", - " '2024-03-28T06:17:26Z',\n", - " '2024-03-28T10:53:28Z',\n", - " '2024-03-28T10:54:36Z',\n", - " '2024-03-28T12:25:08Z',\n", - " '2024-03-28T20:10:48Z',\n", - " '2024-03-31T11:37:53Z',\n", - " '2024-04-01T09:18:51Z',\n", - " '2024-04-01T09:19:15Z',\n", - " '2024-04-01T09:19:38Z',\n", - " '2024-04-03T07:46:26Z',\n", - " '2024-04-03T16:58:15Z',\n", - " '2024-04-04T06:53:36Z',\n", - " '2024-04-04T06:57:51Z',\n", - " '2024-04-04T08:28:37Z',\n", - " '2024-04-04T16:08:51Z',\n", - " '2024-04-05T07:41:39Z',\n", - " '2024-04-05T17:01:29Z',\n", - " '2024-04-08T05:22:54Z',\n", - " '2024-04-09T07:44:35Z',\n", - " '2024-04-13T07:53:28Z',\n", - " '2024-04-14T14:54:36Z',\n", - " '2024-04-15T06:21:41Z',\n", - " '2024-04-15T06:21:52Z',\n", - " '2024-04-15T09:25:56Z',\n", - " '2024-04-20T08:35:01Z',\n", - " '2024-04-20T10:14:48Z',\n", - " '2024-04-21T07:46:19Z',\n", - " '2024-04-21T18:42:22Z',\n", - " '2024-04-22T06:58:49Z',\n", - " '2024-04-23T03:04:34Z',\n", - " '2024-04-23T03:04:46Z',\n", - " '2024-04-23T06:09:58Z']},\n", - " 'description': 'Methane plumes from point source emitters',\n", - " 'item_assets': {'ch4-plume-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'layer'],\n", - " 'title': 'CH4 Plumes Emissions',\n", - " 'description': 'Methane plumes from point source emitters.'}},\n", - " 'stac_version': '1.0.0',\n", - " 'stac_extensions': None,\n", - " 'dashboard:is_periodic': False,\n", - " 'dashboard:time_density': 'day'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", + "# Name of the collection for methane emission plumes \n", + "collection_name = \"emit-ch4plume-v1\"\n", + "\n", + "\n", "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", "\n", - "# Print the properties of the collection to the console\n", - "collection" + "# Print the properties of the collection in a table\n", + "# Adjust display settings\n", + "pd.set_option('display.max_colwidth', None) # Set maximum column width to \"None\" to prevent cutting off text\n", + "\n", + "# Extract the relevant information about the collection\n", + "collection_info = {\n", + " \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n", + " \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n", + " \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n", + " \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n", + "}\n", + "\n", + "# Convert the derived information into a DataFrame format\n", + "properties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n", + "\n", + "# Display the properties in a table\n", + "collection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n", + " .set_table_styles([\n", + " {\n", + " 'selector': 'th.col0, td.col0', # Select the first column\n", + " 'props': [('min-width', '200px'), # Set a minimum width\n", + " ('text-align', 'left')] # Align text to the left\n", + " },\n", + " {\n", + " 'selector': 'td.col1', # Select the second column\n", + " 'props': [('text-align', 'left')] # Align text to the left\n", + " }\n", + "])\n", + "\n", + "# Print the collection summary table\n", + "collection_summary" ] }, { @@ -928,55 +208,93 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Examining the contents of our `collection` under the `temporal` variable, we note that data is available from August 2022 to May 2023. By looking at the `dashboard: time density`, we can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates)." + "Next, you will examine the contents of the `collection` under the `temporal` variable. You’ll see that the data is available since August 2022. Looking at the `dashboard: time density`, you can see that observations are conducted daily and non-periodically (i.e., there are plumes emissions for multiple places on the same dates)." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ + "# Create a function that would search for data collection in the US GHG Center STAC API\n", + "\n", + "# First, we need to define the function\n", + "# The name of the function is \"get_item_count\" \n", + "# The argument that will be passed to the defined function is \"collection_id\"\n", "def get_item_count(collection_id):\n", - " count = 0\n", - " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\"\n", "\n", + " # Set a counter for the number of items existing in the collection \n", + " count = 0 \n", + "\n", + " # Define the path to retrieve the granules (items) of the collection of interest in the STAC API\n", + " items_url = f\"{STAC_API_URL}/collections/{collection_id}/items\" \n", + "\n", + " # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API\n", " while True:\n", - " response = requests.get(items_url)\n", "\n", + " # Retrieve information about the granules by sending a \"get\" request to the STAC API using the defined collection path \n", + " response = requests.get(items_url) \n", + "\n", + " # If the items do not exist, print an error message and quit the loop\n", " if not response.ok:\n", " print(\"error getting items\")\n", " exit()\n", "\n", + " # Return the results of the HTTP response as JSON\n", " stac = response.json()\n", + "\n", + " # Increase the \"count\" by the number of items (granules) returned in the response\n", " count += int(stac[\"context\"].get(\"returned\", 0))\n", + "\n", + " # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)\n", " next = [link for link in stac[\"links\"] if link[\"rel\"] == \"next\"]\n", "\n", + " # Exit the loop if there are no other URLs\n", " if not next:\n", " break\n", + " \n", + " # Ensure the information gathered by other STAC API links associated with the collection are added to the original path\n", + " # \"href\" is the identifier for each of the tiles stored in the STAC API\n", " items_url = next[0][\"href\"]\n", "\n", + " # Return the information about the total number of granules found associated with the collection\n", " return count" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 1266 items\n" - ] - } - ], + "outputs": [], "source": [ - "# Check total number of items available\n", + "# Apply the function created above \"get_item_count\" to the collection\n", "number_of_items = get_item_count(collection_name)\n", + "\n", + "# Get the information about the number of granules found in the collection\n", "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", - "print(f\"Found {len(items)} items\")" + "\n", + "# Print the total number of items (granules) found\n", + "print(f\"Found {len(items)} observations\")\n", + "\n", + "# Sort the items based on their date-time attribute\n", + "items_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n", + "\n", + "# Create an empty list\n", + "table_data = []\n", + "# Extract the ID and date-time information for each granule and add them to the list\n", + "# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n", + "# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \n", + "for item in items_sorted[:5]:\n", + " table_data.append([item['id'], item['properties']['datetime']])\n", + "\n", + "# Define the table headers\n", + "headers = [\"Item ID\", \"Date-Time\"]\n", + "\n", + "print(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n", + "\n", + "# Print the table using tabulate\n", + "print(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))" ] }, { @@ -985,8 +303,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Examining the first item in the collection\n", - "items[0]" + "# Examine the first item in the collection\n", + "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] refers to the first item (granule) in the list/collection\n", + "items_sorted[0]" ] }, { @@ -994,36 +313,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Below, we are entering the minimum and maximum values to provide our upper and lower bounds in `rescale_values`." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Methane Emission Plumes (CH₄) using the Raster API\n", + "## Map Out Selected Tiles\n", "\n", - "In this notebook, we will explore global methane emission plumes from point sources. We will visualize the outputs on a map using `folium.`" + "You will now explore global methane emission plumes from point sources and visualize the results on a map using folium. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# To access the year value from each item more easily, this will let us query more explicitly by year and month (e.g., 2020-02)\n", + "# Once again, apply the function created above \"get_item_count\" to the Air-Sea CO2 Flux ECCO-Darwin collection\n", + "# This step allows retrieving the number of granules “observations” in the collection.\n", + "number_of_items = get_item_count(collection_name)\n", + "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", + "\n", + "\n", + "# Next, you need to create a dictionary where the \"id\" field of each item in the collection are queried more explicitly\n", "plume_complexes = {items[\"id\"]: items for items in items} \n", "\n", "\n", - "# Set the asset value to the appropriate parameter \n", + "# Next, you need to specify the asset name for this collection.\n", + "# The asset name refers to the raster band containing the pixel values for the parameter of interest.\n", + "# For the case of the EMIT Methane Point Source collection, the parameter of interest is “ch4-plume-emissions”.\n", "asset_name = \"ch4-plume-emissions\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, you will enter the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1036,231 +362,60 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we will pass the item id, collection name, and `rescaling_factor` to the `Raster API` endpoint. We will do this for only one item so that we can visualize the event. " + "Now, you will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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=emit-ch4plume-v1&item=EMIT_L2B_CH4PLM_001_20230418T200118_000829&assets=ch4-plume-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-775.518310546875%2C2180.226318359375'],\n", - " 'minzoom': 0,\n", - " 'maxzoom': 24,\n", - " 'bounds': [-104.76285251117253,\n", - " 39.85322425220504,\n", - " -104.74658553556483,\n", - " 39.86515336765068],\n", - " 'center': [-104.75471902336868, 39.85918880992786, 0]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Select the item ID which you want to visualize. Item ID is in the format yyyymmdd followed by the timestamp. This ID can be extracted from the COG name as well.\n", + "# To browse and select other tiles in the collection, please visit https://search.earthdata.nasa.gov/search/granules?p=C2748088093-LPCLOUD&pg[0][v]=f&pg[0][gsk]=-start_date&q=emit%20plume&tl=1694622854.77!3!!\n", + "\n", + "# You need to copy the entire granule nomenclature \n", "item_id = \"EMIT_L2B_CH4PLM_001_20230418T200118_000829\"\n", + "\n", + "# Choose a color map for displaying the first observation (event)\n", + "# Please refer to matplotlib library if you'd prefer to choose a different color ramp.\n", + "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"magma\"\n", + "\n", + "# Make a GET request to retrieve information for the selected tile defined in \"item_id\"\n", "methane_plume_tile = requests.get(\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={plume_complexes[item_id]['collection']}&item={plume_complexes[item_id]['id']}\"\n", + " \n", + " # Pass the collection name and item ID directly from the first item in the list\n", + " f\"{RASTER_API_URL}/collections/{plume_complexes[item_id]['collection']}/items/{plume_complexes[item_id]['id']}/tilejson.json?\"\n", + " \n", + " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", + " \n", + " # Pass the color formula and colormap for custom visualization\n", " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", + " \n", + " # Pass the minimum and maximum values for rescaling \n", " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", + " \n", + "# Return the response in JSON format\n", ").json()\n", + "\n", + "# Print the properties of the retrieved granule to the console\n", "methane_plume_tile" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing CH₄ Emission Plume\n" - ] - }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Set a colormap for the granule\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp (https://matplotlib.org/stable/users/explain/colors/colormaps.html)\n", "colormap = \"magma\" \n", "\n", "\n", - "#Defining the breaks in the colormap \n", + "# Defining the breaks in the colormap \n", "color_map = cm.LinearColormap(colors = ['#310597', '#4C02A1', '#6600A7', '#7E03A8', '#9511A1', '#AA2395', '#BC3587', '#CC4778', '#DA5A6A', '#E66C5C', '#F0804E', '#F89540','#FDAC33', '#FDC527', '#F8DF25'], vmin = 0, vmax = 1500 )\n", "\n", "\n", @@ -1268,17 +423,17 @@ "color_map.caption = 'ppm-m'\n", "\n", "# Set initial zoom and center of map for plume Layer\n", - "map_ = folium.Map(location=(methane_plume_tile[\"center\"][1], methane_plume_tile[\"center\"][0]), zoom_start=13, tiles=None, tooltip = 'test tool tip')\n", + "map_ = folium.Map(location=(methane_plume_tile[\"center\"][1], methane_plume_tile[\"center\"][0]), zoom_start=14, tiles=None, tooltip = 'test tool tip')\n", "folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n", "\n", "\n", "# Use the 'TileLayer' library to display the raster layer, add an appropriate caption, and adjust the transparency of the layer on the map\n", "map_layer = TileLayer(\n", - " tiles=methane_plume_tile[\"tiles\"][0],\n", + " tiles=methane_plume_tile[\"tiles\"][0], # Path to retrieve the tile\n", " name='Plume Complex Landfill',\n", - " overlay='True',\n", - " attr=\"GHG\",\n", - " opacity=1,\n", + " overlay='True', # The layer can be overlaid on the map\n", + " attr=\"GHG\", # Set the attribution \n", + " opacity=1, # Adjust the transparency of the layer\n", ")\n", "map_layer.add_to(map_)\n", "\n", @@ -1291,7 +446,7 @@ "\n", "\n", "# Visualizing the map\n", - "map_\n" + "map_" ] }, { @@ -1299,24 +454,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Calculating Zonal Statistics\n", - "To perform zonal statistics, first we need to create a polygon. In this use case we will create a polygon around the plume." + "# Calculate Zonal Statistics\n", + "To perform zonal statistics, you first need to create a polygon. In this use case, you are creating a polygon using the plume's retrieved coordinates." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "# Plume AOI \n", - "plumes_coordinates = items[item_id][\"geometry\"][\"coordinates\"]\n", + "# Access the coordinates of the plume feature (using the 'item_id')\n", + "plumes_coordinates = plume_complexes[item_id][\"geometry\"][\"coordinates\"]\n", + "\n", + "# Create a polygon for the area of interest (aoi)\n", "methane_plume_aoi = {\n", - " \"type\": \"Feature\",\n", + " \"type\": \"Feature\", # Create a feature object\n", " \"properties\": {},\n", - " \"geometry\": {\n", + " \"geometry\": { # The geometry of the feature\n", " \"coordinates\":\n", - " plumes_coordinates,\n", + " plumes_coordinates, # Use the plume's coordinates retrieved earlier\n", " \"type\": \"Polygon\",\n", " },\n", "}" @@ -1328,19 +485,32 @@ "metadata": {}, "outputs": [], "source": [ - "# We'll plug in the coordinates for a location\n", - "# central to the study area and a reasonable zoom level\n", - "region_name = \"Place_Holder\" # please put the name of the place you are trying to visualize\n", + "# Please put the name of the place you are trying to visualize. \n", + "# The granule that was selected by default is showuing plumes near Denver, United States. \n", + "region_name = \"Place_Holder\"\n", + "\n", + "\n", + "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", + " \n", + " # Base map is set to OpenStreetMap\n", " tiles=\"OpenStreetMap\",\n", + " \n", + " # Define the spatial properties for the map\n", " location=[\n", " plumes_coordinates[0][0][1],\n", " plumes_coordinates[0][0][0]\n", " ],\n", + " \n", + " # Set the center of the map\n", " zoom_start=12,\n", ")\n", "\n", + "# Insert the polygon to the map\n", "folium.GeoJson(methane_plume_aoi, name=region_name).add_to(aoi_map)\n", + "\n", + "\n", + "# Visualize the map\n", "aoi_map" ] }, @@ -1350,10 +520,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Check total number of items available\n", + "# Check the total number of items available within the collection\n", "items = requests.get(\n", " f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\"\n", ").json()[\"features\"]\n", + "\n", + "# Print the total number of items (granules) found\n", "print(f\"Found {len(items)} items\")" ] }, @@ -1363,24 +535,42 @@ "metadata": {}, "outputs": [], "source": [ - "# Explore the first item\n", + "# Examine the first item in the collection\n", + "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] refers to the first item (granule) in the list/collection\n", "items[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection\n", + "# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it\n", + "\n", + "# The function takes an item (granule) and a JSON (polygon) as input parameters\n", "def generate_stats(item, geojson):\n", + " \n", + " # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics\n", " result = requests.post(\n", + " \n", + " # Raster API Endpoint for computing statistics\n", " f\"{RASTER_API_URL}/cog/statistics\",\n", + " \n", + " # Pass the URL to the item, asset name, and raster identifier as parameters\n", " params={\"url\": item[\"assets\"][asset_name][\"href\"]},\n", + " \n", + " # Send the GeoJSON object (polygon) along with the request\n", " json=geojson,\n", + " \n", + " # Return the response in JSON format\n", " ).json()\n", + " \n", + " # Print the result\n", " print(result)\n", + " \n", + " # Return a dictionary containing the computed statistics along with the item's datetime information.\n", " return {\n", " **result[\"properties\"],\n", " \"item_id\": item[\"id\"][20:],\n", @@ -1393,8 +583,14 @@ "metadata": {}, "outputs": [], "source": [ + "# Generate a for loop that iterates over all the existing items in the collection \n", "for item in items:\n", - " print(item[\"id\"])\n", + " \n", + " # The loop will then retrieve the information for the start datetime of each item in the list\n", + " #print(item[\"id\"])\n", + " print(item[\"properties\"][\"datetime\"])\n", + " \n", + " # Exit the loop after printing the start datetime for the first item in the collection\n", " break" ] }, @@ -1413,6 +609,9 @@ "outputs": [], "source": [ "%%time\n", + "# %%time = Wall time (execution time) for running the code below\n", + "\n", + "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the aoi polygon\n", "stats = [generate_stats(item, methane_plume_aoi) for item in items]\n", "stats = [ stat for stat in stats if stat[\"statistics\"][\"b1\"][\"mean\"] != None]" ] @@ -1423,8 +622,7 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "stats" + "stats[0]" ] }, { @@ -1521,7 +719,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.5" }, "vscode": { "interpreter": { diff --git a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb index ae104f3b..56c562ba 100644 --- a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb +++ b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb @@ -2,13 +2,17 @@ "cells": [ { "cell_type": "raw", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: U.S. Gridded Anthropogenic Methane Emissions Inventory\n", + "title: Leveraging the U.S. Gridded Anthropogenic Methane Emissions Inventory for Monitoring Trends in Methane Emissions\n", "description: 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\n", - "authors: Siddharth Chaudhary, Vishal Gaur\n", - "published: 29 June 2023\n", + "authors: Siddharth Chaudhary, Vishal Gaur, Farnaz Bayat\n", + "published: 20 September, 2024\n", "execute:\n", " freeze: true\n", "---" @@ -19,12 +23,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run this notebook\n", + "## Access this Notebook\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdai8otCdrVQzJgev8mjDhzKyCg7jcrB3UeTXNHoCiaMKrkaQ/viewform) and providing the required information. \n", "\n", - "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.\n", + "Access the [**U.S. Gridded Anthropogenic Methane Emissions Inventory**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fepa-ch4emission-grid-v2express_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "- [Data Summary and Application](#data-summary-and-application)\n", + "- [Approach](#approach)\n", + "- [About the Data](#about-the-data)\n", + "- [Install the Required Libraries](#install-the-required-libraries)\n", + "- [Query the STAC API](#query-the-stac-api)\n", + "- [Visual Comparison Across Time Periods](#visual-comparison-across-time-periods)\n", + "- [Map Out Selected Tiles](#map-out-selected-tiles)\n", + "- [Calculate Zonal Statistics](#calculate-zonal-statistics)\n", + "- [Time-Series Analysis](#time-series-analysis)\n", + "- [Summary](#summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Summary and Application\n", + "- **Spatial coverage**: Contiguous United States\n", + "- **Spatial resolution**: 0.1° x 0.1°\n", + "- **Temporal extent**: 2012 - 2020\n", + "- **Temporal resolution**: Annual\n", + "- **Unit**: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)\n", + "- **Utility**: Methane Monitoring, Anthropogenic Emissions Analysis, Climate Research\n", "\n", - "[Launch in the US GHG Center JupyterHub (requires access)](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fepa-ch4emission-grid-v2express_User_Notebook.ipynb&branch=main)", - " " + "For more, visit the [U.S. Gridded Anthropogenic Methane Emissions Inventory](https://earth.gov/ghgcenter/data-catalog/epa-ch4emission-yeargrid-v2express) data overview page. " ] }, { @@ -49,7 +83,11 @@ "source": [ "## About the Data\n", "\n", - "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](https://www.epa.gov/ghgemissions/inventory-us-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.\n", + "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](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). \n", + "\n", + "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.\n", + "\n", + "The gridded dataset currently includes 34 data layers. The first data layer includes annual 2012-2020 gridded methane emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI (excluding Land Use, Land-Use Change and Forestry (LULUCF) sources, which are not included in the gridded GHGI). The next six data layers include annual 2012-2020 gridded methane fluxes from sources within the aggregate Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories. The remaining 27 data layers include annual 2012-2020 gridded methane emissions fluxes from individual emission sectors within each of the aggregate categories.\n", "\n", "For more information regarding this dataset, please visit the [U.S. Gridded Anthropogenic Methane Emissions Inventory](https://earth.gov/ghgcenter/data-catalog/epa-ch4emission-yeargrid-v2express) data overview page." ] @@ -60,18 +98,9 @@ "metadata": {}, "source": [ "# Install the Required Libraries\n", - "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:\n", + "Required libraries are pre-installed on the GHG Center Hub, except the `tabulate` library. If you need to run this notebook elsewhere, please install the libraries by running the following command line:\n", "\n", - "%pip install requests folium rasterstats pystac_client pandas matplotlib --quiet" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Querying the STAC API\n", - "First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored." + "%pip install requests folium rasterstats pystac_client pandas matplotlib tabulate --quiet" ] }, { @@ -84,16 +113,26 @@ "import requests\n", "import folium\n", "import folium.plugins\n", - "from folium import Map, TileLayer\n", - "from pystac_client import Client\n", - "import branca\n", + "from folium import Map, TileLayer \n", + "from pystac_client import Client \n", + "import branca \n", "import pandas as pd\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from tabulate import tabulate" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query the STAC API\n", + "First, you need to import the required libraries. Once imported, they allow better execution of a query in the GHG Center **Spatio Temporal Asset Catalog (STAC)** Application Programming Interface (API) where the granules for this collection are stored. You will learn the functionality of each library throughout the notebook." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -104,11 +143,17 @@ "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", "\n", "# The RASTER API is used to fetch collections for visualization\n", - "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**STAC API Collection Names**\n", "\n", - "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", - "# Name of the collection for gridded methane dataset \n", - "collection_name = \"epa-ch4emission-yeargrid-v2express\"" + "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection ID as a variable. \n", + "The collection ID, also known as the **collection name**, for the U.S. Gridded Anthropogenic Methane Emissions Inventory dataset is [**epa-ch4emission-yeargrid-v2express**](https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express)" ] }, { @@ -117,12 +162,45 @@ "metadata": {}, "outputs": [], "source": [ + "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", + "# Name of the collection for gridded methane dataset \n", + "collection_name = \"epa-ch4emission-yeargrid-v2express\"\n", + "\n", "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", "\n", - "# Print the properties of the collection to the console\n", - "collection" + "# Print the properties of the collection in a table\n", + "# Adjust display settings\n", + "pd.set_option('display.max_colwidth', None) # Set maximum column width to \"None\" to prevent cutting off text\n", + "\n", + "# Extract the relevant information about the collection\n", + "collection_info = {\n", + " \"Title\": collection.get(\"title\", \"N/A\"), # Extract the title of the collection \n", + " \"Description\": collection.get(\"description\", \"N/A\"), # Extract the dataset description\n", + " \"Temporal Extent\": collection.get(\"extent\", {}).get(\"temporal\", {}).get(\"interval\", \"N/A\"), # Extract the temporal coverage of the collection\n", + " \"Spatial Extent\": collection.get(\"extent\", {}).get(\"spatial\", {}).get(\"bbox\", \"N/A\"), # Extract the spatial coverage of the collection\n", + "}\n", + "\n", + "# Convert the derived information into a DataFrame format\n", + "properties_table = pd.DataFrame(list(collection_info.items()), columns=[\"Collection Summary\", \"\"])\n", + "\n", + "# Display the properties in a table\n", + "collection_summary = properties_table.style.set_properties(**{'text-align': 'left'}) \\\n", + " .set_table_styles([\n", + " {\n", + " 'selector': 'th.col0, td.col0', # Select the first column\n", + " 'props': [('min-width', '200px'), # Set a minimum width\n", + " ('text-align', 'left')] # Align text to the left\n", + " },\n", + " {\n", + " 'selector': 'td.col1', # Select the second column\n", + " 'props': [('text-align', 'left')] # Align text to the left\n", + " }\n", + "])\n", + "\n", + "# Print the collection summary table\n", + "collection_summary" ] }, { @@ -130,12 +208,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "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." + "Next, you will examine the contents of the `collection` under the `temporal` variable. You’ll see that the data is available from January 2012 to December 2020. By looking at the `dashboard:time density`, you can observe that the periodic frequency of these observations is yearly." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +275,26 @@ "items = requests.get(f\"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}\").json()[\"features\"]\n", "\n", "# Print the total number of items (granules) found\n", - "print(f\"Found {len(items)} items\")" + "print(f\"Found {len(items)} items\")\n", + "\n", + "# Sort the items based on their date-time attribute\n", + "items_sorted = sorted(items, key=lambda x: x[\"properties\"][\"datetime\"])\n", + "\n", + "# Create an empty list\n", + "table_data = []\n", + "# Extract the ID and date-time information for each granule and add them to the list\n", + "# By default, only the first 5 items in the collection are extracted to be displayed in the table. \n", + "# To see the date-time of all existing granules in this collection, remove \"5\" from \"item_sorted[:5]\" in the line below. \n", + "for item in items_sorted[:5]:\n", + " table_data.append([item['id'], item['properties']['datetime']])\n", + "\n", + "# Define the table headers\n", + "headers = [\"Item ID\", \"Start Date-Time\"]\n", + "\n", + "print(\"Below you see the first 5 items in the collection, along with their item IDs and corresponding Start Date-Time.\")\n", + "\n", + "# Print the table using tabulate\n", + "print(tabulate(table_data, headers=headers, tablefmt=\"fancy_grid\"))" ] }, { @@ -215,7 +312,7 @@ "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", - "items[0]" + "items_sorted[0]" ] }, { @@ -223,14 +320,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exploring Changes in Methane (CH4) Levels Using the Raster API\n", + "## Visual Comparison Across Time Periods\n", "\n", - "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`." + "In this notebook, you will explore the impacts of methane emissions and by examining changes over time in urban regions. You will visualize the outputs on a map using `folium`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -247,12 +344,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Below, we enter minimum and maximum values to provide our upper and lower bounds in `rescale_values`." + "Below, you will enter the minimum and maximum values to provide our upper and lower bounds in the `rescale_values`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -260,21 +357,12 @@ "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\"]}" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "items" - ] - }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we will pass the item id, collection name, asset name, and the `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." + "Now, you will pass the item id, collection name, asset name, and the `rescaling factor` to the `Raster API` endpoint. This step is done twice, once for January 2018 and again for January 2012, so that you can visualize each event independently." ] }, { @@ -288,11 +376,16 @@ "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", "color_map = \"rainbow\" \n", "\n", + "observation_date_1 = '2018'\n", + "\n", + "# Don't change anything here\n", + "observation_1 = f'epa-ch4emission-yeargrid-v2express-{observation_date_1}'\n", + "\n", "# Make a GET request to retrieve information for the 2018 tile \n", "january_2018_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2018-01']['collection']}&item={items['2018-01']['id']}\"\n", + " (f\"{RASTER_API_URL}/collections/{collection_name}/items/{observation_1}/WebMercatorQuad/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -301,7 +394,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -316,11 +409,17 @@ "metadata": {}, "outputs": [], "source": [ - "# Make a GET request to retrieve information for the 2012 tile \n", + "# You will repeat the same approach used in the previous step to retrieve the second observation of interest\n", + "observation_date_2 = '2012'\n", + "\n", + "# Don't change anything here\n", + "observation_2 = f'epa-ch4emission-yeargrid-v2express-{observation_date_2}'\n", + "\n", + "# Make a GET request to retrieve information for the 2018 tile \n", "january_2012_tile = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items['2012-01']['collection']}&item={items['2012-01']['id']}\"\n", + " (f\"{RASTER_API_URL}/collections/{collection_name}/items/{observation_2}/WebMercatorQuad/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -329,7 +428,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\", \n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -343,7 +442,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizing CH₄ emissions\n" + "## Map Out Selected Tiles" ] }, { @@ -355,12 +454,14 @@ "# Set initial zoom and center of map for CH₄ Layer\n", "# Centre of map [latitude,longitude]\n", "# 'folium.plugins' allows mapping side-by-side\n", - "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", + "map_ = folium.plugins.DualMap(location=(38.9, -80.0), zoom_start=8)\n", "\n", "# Define the first map layer (January 2018)\n", "map_layer_2018 = TileLayer(\n", " tiles=january_2018_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", + " name='January 2018', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", " opacity=0.7, # Adjust the transparency of the layer\n", ")\n", "\n", @@ -371,12 +472,20 @@ "map_layer_2012 = TileLayer(\n", " tiles=january_2012_tile[\"tiles\"][0], # Path to retrieve the tile\n", " attr=\"GHG\", # Set the attribution\n", + " name='January 2012', # Title for the layer\n", + " overlay=True, # The layer can be overlaid on the map\n", " opacity=0.7, # Adjust the transparency of the layer\n", ")\n", "\n", "# Add the second layer to the Dual Map\n", "map_layer_2012.add_to(map_.m2)\n", "\n", + "# Display data markers (titles) on both maps\n", + "folium.Marker((40, 5.0), tooltip=\"both\").add_to(map_)\n", + "\n", + "# Add a layer control to switch between map layers\n", + "folium.LayerControl(collapsed=False).add_to(map_)\n", + "\n", "# Visualize the Dual Map\n", "map_" ] @@ -386,40 +495,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Calculating Zonal Statistics" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA)." + "# Calculate Zonal Statistics\n", + "To perform zonal statistics, you first need to create a polygon. In this use case, you are creating a polygon for the Pittsburgh, Pennsylvania region." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# Texas, USA\n", - "texas_aoi = {\n", + "Pittsburgh_aoi = {\n", " \"type\": \"Feature\", # Create a feature object\n", " \"properties\": {},\n", " \"geometry\": { # Set the bounding coordinates for the polygon\n", " \"coordinates\": [\n", " [\n", - " # [13.686159004559698, -21.700046934333145],\n", - " # [13.686159004559698, -23.241974326585833],\n", - " # [14.753560168039911, -23.241974326585833],\n", - " # [14.753560168039911, -21.700046934333145],\n", - " # [13.686159004559698, -21.700046934333145],\n", - " [-95, 29], # South-east bounding coordinate\n", - " [-95, 33], # North-east bounding coordinate\n", - " [-104,33], # North-west bounding coordinate\n", - " [-104,29], # South-west bounding coordinate\n", - " [-95, 29] # South-east bounding coordinate (closing the polygon)\n", + " [-79.4, 39.9], # South-east bounding coordinate\n", + " [-79.4, 40.9], # North-east bounding coordinate\n", + " [-80.5, 40.9], # North-west bounding coordinate\n", + " [-80.5, 39.9], # South-west bounding coordinate\n", + " [-79.4, 39.9] # South-east bounding coordinate (closing the polygon)\n", " ]\n", " ],\n", " \"type\": \"Polygon\",\n", @@ -443,15 +540,15 @@ "\n", " # Define the spatial properties for the map\n", " location=[\n", - " 30,-100\n", + " 39.9,-79.4\n", " ],\n", "\n", " # Set the zoom value\n", - " zoom_start=6,\n", + " zoom_start=9,\n", ")\n", "\n", "# Insert the polygon to the map\n", - "folium.GeoJson(texas_aoi, name=\"Texas, USA\").add_to(aoi_map)\n", + "folium.GeoJson(Pittsburgh_aoi, name=\"Pittsburgh, USA\").add_to(aoi_map)\n", "\n", "# Visualize the map\n", "aoi_map" @@ -486,12 +583,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon." + "Now that you created the polygon for the area of interest, you need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -527,7 +624,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With the function above we can generate the statistics for the AOI." + "With the function above you can generate the statistics for the AOI." ] }, { @@ -540,7 +637,11 @@ "# %%time = Wall time (execution time) for running the code below\n", "\n", "# Generate statistics using the created function \"generate_stats\" within the bounding box defined by the polygon\n", - "stats = [generate_stats(item, texas_aoi) for item in items]" + "stats = {}\n", + "for item in items: \n", + " date = item[\"properties\"][\"datetime\"] # Get the associated date\n", + " year = date[:4] # Convert datetime to year\n", + " stats[year] = generate_stats(item, Pittsburgh_aoi)" ] }, { @@ -549,8 +650,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Print the stats for the first item in the collection\n", - "stats[0]" + "# Print the stats for the observation of interest\n", + "# For example, for printing the stats for the 2016 tile, you can change the date inside the bracket \"['YYYY']\" to \"['2016']\n", + "stats['2016']" ] }, { @@ -568,9 +670,10 @@ "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", - "\n", + " pd.set_option('display.float_format', '{:.20f}'.format)\n", + " stats_json_ = [stats_json[datetime] for datetime in stats_json] \n", " # Normalize the JSON data\n", - " df = pd.json_normalize(stats_json)\n", + " df = pd.json_normalize(stats_json_)\n", "\n", " # Replace the naming \"statistics.b1\" in the columns\n", " df.columns = [col.replace(\"statistics.b1.\", \"\") for col in df.columns]\n", @@ -594,8 +697,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizing the Data as a Time Series\n", - "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:" + "## Time-Series Analysis\n", + "You can now explore the gridded methane emission (Domestic Wastewater Treatment & Discharge (5D)) time series (January 2000 -December 2021) available for the Pittsburgh Pennsylvania area of the U.S. You can plot the data set using the code below:" ] }, { @@ -604,10 +707,15 @@ "metadata": {}, "outputs": [], "source": [ + "# Ensure 'datetime' column is in datetime format\n", + "df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + "# Sort the DataFrame by the datetime column so the plot displays the values from left to right (2020 -> 2022)\n", + "df_sorted = df.sort_values(by=\"datetime\")\n", + "\n", "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", "\n", - "\n", "plt.plot(\n", " df[\"date\"], # X-axis: sorted date\n", " df[\"max\"], # Y-axis: maximum CH4 emission\n", @@ -627,7 +735,24 @@ "plt.ylabel(\"CH4 emissions Molecules CH₄/cm²/s\")\n", "\n", "# Insert title for the plot\n", - "plt.title(\"CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)\")" + "plt.title(\"CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Pittsburgh, PA (2012-2021)\")\n", + "\n", + "\n", + "# Add data citation\n", + "plt.text(\n", + " df_sorted[\"datetime\"].iloc[0], # X-coordinate of the text \n", + " df_sorted[\"max\"].min(), # Y-coordinate of the text \n", + "\n", + " # Text to be displayed\n", + " \"Source: EPA Gridded Anthropogenic Methane Emissions Inventory\", \n", + " fontsize=12, # Font size\n", + " horizontalalignment=\"left\", # Horizontal alignment\n", + " verticalalignment=\"bottom\", # Vertical alignment\n", + " color=\"blue\", # Text color\n", + ")\n", + "\n", + "# Plot the time series\n", + "plt.show()" ] }, { @@ -646,11 +771,17 @@ "metadata": {}, "outputs": [], "source": [ - "# A GET request is made for the 2016 tile\n", + "# You will repeat the same approach used in the previous step to retrieve the second observation of interest\n", + "observation_date_3 = '2016'\n", + "\n", + "# Don't change anything here\n", + "observation_3 = f'epa-ch4emission-yeargrid-v2express-{observation_date_3}'\n", + "\n", + "# Make a GET request to retrieve information for the 2018 tile \n", "tile_2016 = requests.get(\n", "\n", " # Pass the collection name, the item number in the list, and its ID\n", - " f\"{RASTER_API_URL}/stac/tilejson.json?collection={items[2]['collection']}&item={items[2]['id']}\"\n", + " (f\"{RASTER_API_URL}/collections/{collection_name}/items/{observation_3}/WebMercatorQuad/tilejson.json?\"\n", "\n", " # Pass the asset name\n", " f\"&assets={asset_name}\"\n", @@ -659,7 +790,7 @@ " f\"&color_formula=gamma+r+1.05&colormap_name={color_map}\"\n", "\n", " # Pass the minimum and maximum values for rescaling\n", - " f\"&rescale={rescale_values['min']},{rescale_values['max']}\",\n", + " f\"&rescale={rescale_values['min']},{rescale_values['max']}\"), \n", "\n", "# Return the response in JSON format\n", ").json()\n", @@ -682,11 +813,11 @@ "\n", " # Set the center of the map\n", " location=[\n", - " 30,-100\n", + " 39.9,-79.4\n", " ],\n", "\n", " # Set the zoom value\n", - " zoom_start=8,\n", + " zoom_start=9,\n", ")\n", "\n", "# Define the map layer\n", @@ -742,7 +873,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.5" }, "vscode": { "interpreter": { From c1dc0415dfe2274111671028f8cdc21522331c36 Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Mon, 16 Sep 2024 09:53:42 -0500 Subject: [PATCH 37/47] processing reports --- _quarto.yml | 3 +++ ...tions_Processing and Verification Report.qmd | 9 +++++++++ ...tions_Processing and Verification Report.qmd | 9 +++++++++ ...tions_Processing and Verification Report.qmd | 9 +++++++++ ...tions_Processing and Verification Report.pdf | Bin 0 -> 56812 bytes ...tions_Processing and Verification Report.pdf | Bin 0 -> 56013 bytes ...tions_Processing and Verification Report.pdf | Bin 0 -> 56255 bytes processingreport.qmd | 3 +++ 8 files changed, 33 insertions(+) create mode 100644 processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd create mode 100644 processing_and_verification_reports/reports/influx-testbed-ghg-concentrations_Processing and Verification Report.pdf create mode 100644 processing_and_verification_reports/reports/lam-testbed-ghg-concentrations_Processing and Verification Report.pdf create mode 100644 processing_and_verification_reports/reports/nec-testbed-ghg-concentrations_Processing and Verification Report.pdf diff --git a/_quarto.yml b/_quarto.yml index bc9667b6..f922c8ad 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -138,6 +138,9 @@ website: - processing_and_verification_reports/noaa-gggrn-co2-concentrations_Processing and Verification Report.qmd - processing_and_verification_reports/noaa-gggrn-ch4-concentrations_Processing and Verification Report.qmd - processing_and_verification_reports/oco2geos-co2-daygrid-v10r_Processing and Verification Report.qmd + - processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd + - processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd + - processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd - section: Socioeconomic contents: - processing_and_verification_reports/sedac-popdensity-yeargrid5yr-v4.11_Processing and Verification Report.qmd diff --git a/processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd b/processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd new file mode 100644 index 00000000..284f56b0 --- /dev/null +++ b/processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) +description: "Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at Indianapolis Flux Experiment (INFLUX) tower sites" +--- + + +

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+ +
\ No newline at end of file diff --git a/processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd b/processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd new file mode 100644 index 00000000..17db63a5 --- /dev/null +++ b/processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project +description: "Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower and rooftop sites in California’s South Coast Air Basin " +--- + + +

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+ +
\ No newline at end of file diff --git a/processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd b/processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd new file mode 100644 index 00000000..5e458213 --- /dev/null +++ b/processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd @@ -0,0 +1,9 @@ +--- +title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed +description: "Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S." +--- + + +

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+ +
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[Atmospheric Carbon Dioxide Concentrations from NOAA Global Monitoring Laboratory Processing and Verification Report](processing_and_verification_reports/noaa-gggrn-co2-concentrations_Processing and Verification Report.qmd) 2. [Atmospheric Methane Concentrations from NOAA Global Monitoring Laboratory Processing and Verification Report](processing_and_verification_reports/noaa-gggrn-ch4-concentrations_Processing and Verification Report.qmd) 3. [OCO-2 GEOS Column CO₂ Concentrations Processing and Verification Report](processing_and_verification_reports/oco2geos-co2-daygrid-v10r_Processing and Verification Report.qmd) +3. [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)](processing_and_verification_reports/influx-testbed-ghg-concentrations_Processing and Verification Report.qmd) +4. [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project](processing_and_verification_reports/lam-testbed-ghg-concentrations_Processing and Verification Report.qmd) +5. [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed](processing_and_verification_reports/nec-testbed-ghg-concentrations_Processing and Verification Report.qmd) ## Socioeconomic 1. [SEDAC Gridded World Population Density Processing and Verification Report](processing_and_verification_reports/sedac-popdensity-yeargrid5yr-v4.11_Processing and Verification Report.qmd) From 055691d0a2f26122d2078a10da14e6d077358fa0 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Mon, 16 Sep 2024 22:31:06 -0500 Subject: [PATCH 38/47] outputs added to user notebooks --- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 1157 ++++++++++++++++- 1 file changed, 1119 insertions(+), 38 deletions(-) diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index d5b3afa7..26802440 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -72,9 +72,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -109,9 +118,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'eccodarwin-co2flux-monthgrid-v5',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'}],\n", + " 'title': 'Air-Sea CO₂ Flux, ECCO-Darwin Model v5',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2020-01-01T00:00:00+00:00',\n", + " '2022-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'co2': {'assets': ['co2'],\n", + " 'nodata': 'nan',\n", + " 'rescale': [[-0.0007, 0.0002]],\n", + " 'colormap_name': 'bwr'},\n", + " 'dashboard': {'assets': ['co2'],\n", + " 'nodata': 'nan',\n", + " 'rescale': [[-0.0007, 0.0002]],\n", + " 'colormap_name': 'bwr'}},\n", + " 'summaries': {'datetime': ['2020-01-01T00:00:00Z', '2022-12-31T00:00:00Z']},\n", + " 'description': 'Global, monthly average air-sea CO₂ flux (negative into ocean) at ~1/3° resolution from 2020 to 2022. Data are in units of millimoles of CO₂ per meter squared per second (mmol m²/s). Derived using the ECCO-Darwin model v5, which is an ocean biogeochemical model that assimilates Estimating the Circulation and Climate of the Ocean (ECCO) consortium ocean circulation estimates and biogeochemical processes from the Massachusetts Institute of Technology (MIT) Darwin Project.',\n", + " 'item_assets': {'co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Air-Sea CO₂ Flux',\n", + " 'description': 'Monthly mean air-sea CO₂ flux (negative into ocean).'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -182,9 +239,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 36 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the Air-Sea CO2 Flux ECCO-Darwin collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -198,9 +263,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'eccodarwin-co2flux-monthgrid-v5-202212',\n", + " 'bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/map?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202212.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Air-Sea CO₂ Flux',\n", + " 'proj:bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [721.0, 1440.0],\n", + " 'description': 'Monthly mean air-sea CO₂ flux (negative into ocean).',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 1e+20,\n", + " 'min': -0.0560546528687938,\n", + " 'count': 11.0,\n", + " 'buckets': [338606.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 186706.0]},\n", + " 'statistics': {'mean': 3.554192556042885e+19,\n", + " 'stddev': 4.786401658343999e+19,\n", + " 'maximum': 1e+20,\n", + " 'minimum': -0.0560546528687938,\n", + " 'valid_percent': 0.0001903630604288499}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.125, -90.124826629681],\n", + " [179.875, -90.124826629681],\n", + " [179.875, 89.875173370319],\n", + " [-180.125, 89.875173370319],\n", + " [-180.125, -90.124826629681]]]},\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': [0.25,\n", + " 0.0,\n", + " -180.125,\n", + " 0.0,\n", + " -0.24965325936199723,\n", + " 89.875173370319,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/preview.png?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.125, -90.124826629681],\n", + " [179.875, -90.124826629681],\n", + " [179.875, 89.875173370319],\n", + " [-180.125, 89.875173370319],\n", + " [-180.125, -90.124826629681]]]},\n", + " 'collection': 'eccodarwin-co2flux-monthgrid-v5',\n", + " 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2022-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -259,9 +424,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0007%2C0.0007'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'center': [-0.125, -0.1248266296809959, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -297,9 +480,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202104/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0007%2C0.0007'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'center': [-0.125, -0.1248266296809959, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the April 2021 tile which is the 21th item in the collection\n", "# To retrieve the 21st item in the collection we use \"20\" in the \"(items.keys())[20]\" statement \n", @@ -337,9 +538,236 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the CO2 level in 2021 and 2022 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -432,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -457,9 +885,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -494,7 +1029,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -538,9 +1073,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 36 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -553,9 +1096,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'eccodarwin-co2flux-monthgrid-v5-202212',\n", + " 'bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/map?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2': {'href': 's3://ghgc-data-store/eccodarwin-co2flux-monthgrid-v5/ECCO-Darwin_CO2_flux_202212.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Air-Sea CO₂ Flux',\n", + " 'proj:bbox': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [721.0, 1440.0],\n", + " 'description': 'Monthly mean air-sea CO₂ flux (negative into ocean).',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 1e+20,\n", + " 'min': -0.0560546528687938,\n", + " 'count': 11.0,\n", + " 'buckets': [338606.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 186706.0]},\n", + " 'statistics': {'mean': 3.554192556042885e+19,\n", + " 'stddev': 4.786401658343999e+19,\n", + " 'maximum': 1e+20,\n", + " 'minimum': -0.0560546528687938,\n", + " 'valid_percent': 0.0001903630604288499}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.125, -90.124826629681],\n", + " [179.875, -90.124826629681],\n", + " [179.875, 89.875173370319],\n", + " [-180.125, 89.875173370319],\n", + " [-180.125, -90.124826629681]]]},\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': [0.25,\n", + " 0.0,\n", + " -180.125,\n", + " 0.0,\n", + " -0.24965325936199723,\n", + " 89.875173370319,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/preview.png?assets=co2&nodata=nan&rescale=-0.0007%2C0.0002&colormap_name=bwr',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.125, -90.124826629681],\n", + " [179.875, -90.124826629681],\n", + " [179.875, 89.875173370319],\n", + " [-180.125, 89.875173370319],\n", + " [-180.125, -90.124826629681]]]},\n", + " 'collection': 'eccodarwin-co2flux-monthgrid-v5',\n", + " 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2022-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -563,9 +1206,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-12-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Generate a for loop that iterates over all the existing items in the collection \n", "for item in items:\n", @@ -587,9 +1238,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 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"cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': -0.00010515466364758998,\n", + " 'max': -2.6339156087043894e-05,\n", + " 'mean': -6.713517054587462e-05,\n", + " 'count': 86.62000274658203,\n", + " 'sum': -0.005815248657075913,\n", + " 'std': 1.9970110657389885e-05,\n", + " 'median': -6.633380711657132e-05,\n", + " 'majority': -0.00010515466364758998,\n", + " 'minority': -0.00010515466364758998,\n", + " 'unique': 115.0,\n", + " 'histogram': [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, 8.0, 7.0],\n", + " [-0.00010515466364758998,\n", + " -9.727311289153537e-05,\n", + " -8.939156213548077e-05,\n", + " -8.151001137942615e-05,\n", + " -7.362846062337154e-05,\n", + " -6.574690986731694e-05,\n", + " -5.786535911126232e-05,\n", + " -4.9983808355207714e-05,\n", + " -4.2102257599153106e-05,\n", + " -3.422070684309849e-05,\n", + " 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" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2022-12-01T00:00:00+00:00 -0.000105 -0.000026 -0.000067 86.620003 \n", + "1 2022-11-01T00:00:00+00:00 -0.000053 0.000010 -0.000030 86.620003 \n", + "2 2022-10-01T00:00:00+00:00 -0.000034 0.000047 -0.000002 86.620003 \n", + "3 2022-09-01T00:00:00+00:00 -0.000047 0.000050 -0.000007 86.620003 \n", + "4 2022-08-01T00:00:00+00:00 -0.000091 -0.000002 -0.000038 86.620003 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 -0.005815 0.000020 -0.000066 -0.000105 -0.000105 115.0 \n", + "1 -0.002556 0.000014 -0.000032 -0.000053 -0.000053 115.0 \n", + "2 -0.000151 0.000018 -0.000003 -0.000034 -0.000034 115.0 \n", + "3 -0.000597 0.000021 -0.000004 -0.000047 -0.000047 115.0 \n", + "4 -0.003311 0.000021 -0.000033 -0.000091 -0.000091 115.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[9.0, 8.0, 9.0, 8.0, 22.0, 10.0, 18.0, 16.0, ... 31.94 \n", + "1 [[6.0, 21.0, 17.0, 16.0, 13.0, 15.0, 11.0, 9.0... 31.94 \n", + "2 [[10.0, 15.0, 16.0, 13.0, 14.0, 15.0, 14.0, 12... 31.94 \n", + "3 [[6.0, 13.0, 20.0, 14.0, 17.0, 12.0, 24.0, 5.0... 31.94 \n", + "4 [[2.0, 10.0, 4.0, 8.0, 11.0, 15.0, 21.0, 20.0,... 31.94 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 245.0 115.0 -0.000104 -0.000032 \n", + "1 245.0 115.0 -0.000051 -0.000001 \n", + "2 245.0 115.0 -0.000032 0.000034 \n", + "3 245.0 115.0 -0.000045 0.000027 \n", + "4 245.0 115.0 -0.000081 -0.000007 \n", + "\n", + " date \n", + "0 2022-12-01 00:00:00+00:00 \n", + "1 2022-11-01 00:00:00+00:00 \n", + "2 2022-10-01 00:00:00+00:00 \n", + "3 2022-09-01 00:00:00+00:00 \n", + "4 2022-08-01 00:00:00+00:00 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -655,9 +1582,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Sort the DataFrame by the datetime column so the plot is displaying the values from left to right (2020 -> 2022)\n", "df_sorted = df.sort_values(by=\"datetime\")\n", @@ -716,9 +1654,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-09-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# The 2022-09-01 observation is the 4th item in the list. \n", "# Considering that a list starts with \"0\", we need to insert \"3\" in the \"items[3]\" statement\n", @@ -727,9 +1673,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202209/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=-0.0007%2C0.0007'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.125, -90.124826629681, 179.875, 89.875173370319],\n", + " 'center': [-0.125, -0.1248266296809959, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the September 2022 tile\n", "September2022_co2_flux = requests.get(\n", @@ -755,9 +1719,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the September 2022 tile\n", "aoi_map_bbox = Map(\n", From c9a4817deb9731c6df6ca7cdc8b6b92283ee64dc Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Tue, 17 Sep 2024 08:32:16 -0500 Subject: [PATCH 39/47] add output to remaining usernotebooks --- ...arbonflux-monthgrid-v3_User_Notebook.ipynb | 1827 +- ...mission-grid-v2express_User_Notebook.ipynb | 22981 +++++++++++++++- ...-ch4budget-yeargrid-v1_User_Notebook.ipynb | 3978 ++- ...sim-wetlandch4-grid-v2_User_Notebook.ipynb | 1205 +- ...etlandch4-monthgrid-v2_User_Notebook.ipynb | 1219 +- ...-carbonflux-daygrid-v1_User_Notebook.ipynb | 2766 +- .../noaa-insitu_User_Notebook.ipynb | 453 +- ...-co2budget-yeargrid-v1_User_Notebook.ipynb | 4249 ++- ...2geos-co2-daygrid-v10r_User_Notebook.ipynb | 1740 +- ...-ffco2-monthgrid-v2022_User_Notebook.ipynb | 1000 +- ...-ffco2-monthgrid-v2023_User_Notebook.ipynb | 1002 +- ...r-ch4flux-monthgrid-v1_User_Notebook.ipynb | 1483 +- ...lcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 2179 +- 13 files changed, 45637 insertions(+), 445 deletions(-) diff --git a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb index 82ef52fe..8ae72cef 100644 --- a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb +++ b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb @@ -73,9 +73,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import folium\n", @@ -104,9 +113,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'casagfed-carbonflux-monthgrid-v3',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'}],\n", + " 'title': 'CASA-GFED3 Land Carbon Flux v3',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2003-01-01T00:00:00+00:00',\n", + " '2017-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'rh': {'assets': ['rh'],\n", + " 'rescale': [[0, 0.3]],\n", + " 'colormap_name': 'purd'},\n", + " 'nee': {'assets': ['nee'],\n", + " 'rescale': [[-0.1, 0.1]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'npp': {'assets': ['npp'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n", + " 'fire': {'assets': ['fire'], 'rescale': [[0, 0.3]], 'colormap_name': 'purd'},\n", + " 'fuel': {'assets': ['fuel'],\n", + " 'rescale': [[0, 0.03]],\n", + " 'colormap_name': 'bupu'}},\n", + " 'summaries': {'datetime': ['2003-01-01T00:00:00Z', '2017-12-31T00:00:00Z']},\n", + " 'description': \"This dataset presents a variety of carbon flux parameters derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting model output provides monthly, global data at 0.5 degree resolution from January 2003 through December 2017. It includes the following carbon flux variables expressed in units of kilograms of carbon per square meter per month (kg Carbon/m2/mon): net primary production (NPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL). This product and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in a number of atmospheric carbon dioxide (CO₂) transport studies, and through the support of NASA's Carbon Monitoring System (CMS), it helps characterize, quantify, understand and predict the evolution of global carbon sources and sinks. The source dataset can be found at https://doi.org/10.5067/03147VMJE8J9. As of April 2024, this dataset has been replaced by an updated version in the US GHG Center titled MiCASA Land Carbon Flux v1 (STAC ids: micasa-carbonflux-daygrid-v1 and micasa-carbonflux-monthgrid-v1).\",\n", + " 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Heterotrophic Respiration (Rh)',\n", + " 'description': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.'},\n", + " 'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Ecosystem Exchange (NEE)',\n", + " 'description': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.'},\n", + " 'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Primary Production (NPP)',\n", + " 'description': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.'},\n", + " 'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fire Emissions (FIRE)',\n", + " 'description': 'Model-estimated flux of carbon to the atmosphere from wildfires.'},\n", + " 'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Wood Fuel Emissions (FUEL)',\n", + " 'description': 'Model-estimated flux of carbon to the atmosphere from wood burned for fuel.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from STAC collections using the appropriate endpoint\n", "# the 'requests' library allows a HTTP request possible\n", @@ -153,9 +229,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 180 items\n" + ] + } + ], "source": [ "# Apply the above function and check the total number of items available within the collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -165,9 +249,312 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'casagfed-carbonflux-monthgrid-v3-201712',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],\n", + " 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',\n", + " 'type': 'image/tiff; 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application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Wood Fuel Emissions (FUEL)',\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': 'Fuel emissions',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 0.020759999752044678,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [257568.0,\n", + " 1150.0,\n", + " 284.0,\n", + " 115.0,\n", + " 47.0,\n", + " 21.0,\n", + " 5.0,\n", + " 6.0,\n", + " 3.0,\n", + " 1.0]},\n", + " 'statistics': {'mean': 5.057307134848088e-05,\n", + " 'stddev': 0.0003876804548781365,\n", + " 'maximum': 0.020759999752044678,\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': 'casagfed-carbonflux-monthgrid-v3',\n", + " 'properties': {'end_datetime': '2017-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2017-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -222,9 +609,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-200312/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "color_map = \"purd\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", @@ -242,9 +647,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Now we apply the same process used in the previous step for the December 2017 tile\n", "december_2017_tile = requests.get(\n", @@ -266,9 +689,269 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the RH level in 2003 and 2017 over the State of Texas \n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -339,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -365,9 +1048,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# We will plug in the coordinates for a location inside the the polygon and a zoom level\n", "aoi_map = Map(\n", @@ -384,9 +1174,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 180 items\n" + ] + } + ], "source": [ "# Check the total number of items available for this location\n", "items = requests.get(\n", @@ -397,9 +1195,312 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'casagfed-carbonflux-monthgrid-v3-201712',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712'}],\n", + " 'assets': {'rh': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_Rh_Flux_Monthly_x720_y360_201712.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Heterotrophic Respiration (Rh)',\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': 'Model-estimated heterotrophic respiration (Rh), which is the flux of carbon from the soil to the atmosphere.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 0.6039900183677673,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [249101.0,\n", + " 7375.0,\n", + " 2429.0,\n", + " 252.0,\n", + " 32.0,\n", + " 5.0,\n", + " 2.0,\n", + " 2.0,\n", + " 0.0,\n", + " 2.0]},\n", + " 'statistics': {'mean': 0.006758838426321745,\n", + " 'stddev': 0.022668374702334404,\n", + " 'maximum': 0.6039900183677673,\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", + " 'nee': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NEE_Flux_Monthly_x720_y360_201712.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Ecosystem Exchange (NEE)',\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': 'Model-estimated net ecosystem exchange (NEE), which is the net carbon flux to the atmosphere.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 0.48997998237609863,\n", + " 'min': -0.11027999967336655,\n", + " 'count': 11.0,\n", + " 'buckets': [663.0,\n", + " 234393.0,\n", + " 23809.0,\n", + " 282.0,\n", + " 37.0,\n", + " 10.0,\n", + " 4.0,\n", + " 0.0,\n", + " 0.0,\n", + " 2.0]},\n", + " 'statistics': {'mean': 0.0015448036137968302,\n", + " 'stddev': 0.00977976992726326,\n", + " 'maximum': 0.48997998237609863,\n", + " 'minimum': -0.11027999967336655,\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", + " 'npp': {'href': 's3://ghgc-data-store/casagfed-carbonflux-monthgrid-v3/GEOSCarb_CASAGFED3v3_NPP_Flux_Monthly_x720_y360_201712.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'npp',\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': 'Model-estimated net primary production (NPP), which is the amount of carbon available from plants.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 0.23635999858379364,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [244636.0,\n", + " 3051.0,\n", + " 1928.0,\n", + " 2634.0,\n", + " 4088.0,\n", + " 2211.0,\n", + " 428.0,\n", + " 156.0,\n", + " 59.0,\n", + " 9.0]},\n", + " 'statistics': {'mean': 0.005214035045355558,\n", + " 'stddev': 0.021809572353959084,\n", + " 'maximum': 0.23635999858379364,\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': 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"execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017-12-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Identify the start Date Time of the first observation in the collection\n", "for item in items:\n", @@ -447,9 +1556,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.024049999192357063, 'max': 0.04072999954223633, 'mean': 0.03167419135570526, 'count': 6.300000190734863, 'sum': 0.1995474100112915, 'std': 0.005142866815977996, 'median': 0.02964000031352043, 'majority': 0.024049999192357063, 'minority': 0.024049999192357063, 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6.300000190734863,\n", + " 'sum': 0.1995474100112915,\n", + " 'std': 0.005142866815977996,\n", + " 'median': 0.02964000031352043,\n", + " 'majority': 0.024049999192357063,\n", + " 'minority': 0.024049999192357063,\n", + " 'unique': 12.0,\n", + " 'histogram': [[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0, 1.0],\n", + " [0.024049999192357063,\n", + " 0.025717999786138535,\n", + " 0.027385998517274857,\n", + " 0.029053999111056328,\n", + " 0.0307219997048378,\n", + " 0.03238999843597412,\n", + " 0.03405799716711044,\n", + " 0.035725999623537064,\n", + " 0.037393998354673386,\n", + " 0.039062000811100006,\n", + " 0.04072999954223633]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 12.0,\n", + " 'percentile_2': 0.024049999192357063,\n", + " 'percentile_98': 0.04072999954223633}},\n", + " 'start_datetime': '2017-12-01T00:00:00+00:00'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Generate stats for the first item in the collection\n", "stats[0]" @@ -467,9 +1803,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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start_datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02017-12-01T00:00:00+00:000.024050.040730.0316746.30.1995470.0051430.029640.024050.0240512.0[[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0,...100.00.012.00.024050.040732017-12-01 00:00:00+00:00
12017-11-01T00:00:00+00:000.023070.057290.0385776.30.2430360.0106360.035680.023070.0230712.0[[2.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2.0, 2.0, 1.0,...100.00.012.00.023070.057292017-11-01 00:00:00+00:00
22017-10-01T00:00:00+00:000.026720.058090.0392826.30.2474770.0100320.034120.026720.0267212.0[[1.0, 2.0, 3.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0,...100.00.012.00.026720.058092017-10-01 00:00:00+00:00
32017-09-01T00:00:00+00:000.016290.043600.0233106.30.1468520.0065230.021220.016290.0162912.0[[3.0, 2.0, 1.0, 1.0, 2.0, 0.0, 1.0, 0.0, 0.0,...100.00.012.00.016290.041222017-09-01 00:00:00+00:00
42017-08-01T00:00:00+00:000.033090.064350.0454206.30.2861440.0104100.042470.033090.0330912.0[[1.0, 4.0, 0.0, 2.0, 0.0, 2.0, 0.0, 2.0, 0.0,...100.00.012.00.033090.064352017-08-01 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " start_datetime min max mean count sum \\\n", + "0 2017-12-01T00:00:00+00:00 0.02405 0.04073 0.031674 6.3 0.199547 \n", + "1 2017-11-01T00:00:00+00:00 0.02307 0.05729 0.038577 6.3 0.243036 \n", + "2 2017-10-01T00:00:00+00:00 0.02672 0.05809 0.039282 6.3 0.247477 \n", + "3 2017-09-01T00:00:00+00:00 0.01629 0.04360 0.023310 6.3 0.146852 \n", + "4 2017-08-01T00:00:00+00:00 0.03309 0.06435 0.045420 6.3 0.286144 \n", + "\n", + " std median majority minority unique \\\n", + "0 0.005143 0.02964 0.02405 0.02405 12.0 \n", + "1 0.010636 0.03568 0.02307 0.02307 12.0 \n", + "2 0.010032 0.03412 0.02672 0.02672 12.0 \n", + "3 0.006523 0.02122 0.01629 0.01629 12.0 \n", + "4 0.010410 0.04247 0.03309 0.03309 12.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[1.0, 2.0, 0.0, 1.0, 1.0, 1.0, 0.0, 4.0, 1.0,... 100.0 \n", + "1 [[2.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2.0, 2.0, 1.0,... 100.0 \n", + "2 [[1.0, 2.0, 3.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0,... 100.0 \n", + "3 [[3.0, 2.0, 1.0, 1.0, 2.0, 0.0, 1.0, 0.0, 0.0,... 100.0 \n", + "4 [[1.0, 4.0, 0.0, 2.0, 0.0, 2.0, 0.0, 2.0, 0.0,... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 0.0 12.0 0.02405 0.04073 \n", + "1 0.0 12.0 0.02307 0.05729 \n", + "2 0.0 12.0 0.02672 0.05809 \n", + "3 0.0 12.0 0.01629 0.04122 \n", + "4 0.0 12.0 0.03309 0.06435 \n", + "\n", + " date \n", + "0 2017-12-01 00:00:00+00:00 \n", + "1 2017-11-01 00:00:00+00:00 \n", + "2 2017-10-01 00:00:00+00:00 \n", + "3 2017-09-01 00:00:00+00:00 \n", + "4 2017-08-01 00:00:00+00:00 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table.\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -494,9 +2023,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(20, 10)) #determine the width and height of the plot using the 'matplotlib' library\n", "\n", @@ -517,9 +2067,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017-10-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Now let's examine the Rh level for the 3rd item in the collection for Dallas, Texas area\n", "# Keep in mind that a list starts from 0, 1, 2,... therefore items[2] is referring to the third item in the list/collection\n", @@ -528,9 +2086,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201710/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C0.6039900183677673'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the third granule in the collection and set the color scheme and rescale values. \n", "october_tile = requests.get(\n", @@ -544,9 +2120,184 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Map the Rh level for the Dallas, Texas area for the October, 2017 timeframe\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb index 79e839c9..5b57f5d7 100644 --- a/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb +++ b/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.ipynb @@ -76,9 +76,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -113,9 +122,355 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'epa-ch4emission-yeargrid-v2express',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/epa-ch4emission-yeargrid-v2express'}],\n", + " 'title': 'U.S. Gridded Anthropogenic Methane Emissions Inventory v2 Express Extension',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2012-01-01T00:00:00+00:00',\n", + " '2020-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'dashboard': {'assets': ['total-methane'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'dwtd-waste': {'assets': ['dwtd-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'iwtd-waste': {'assets': ['iwtd-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'post-meter': {'assets': ['post-meter'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'refining-ps': {'assets': ['refining-ps'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-other': {'assets': ['total-other'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-waste': {'assets': ['total-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'surface-coal': {'assets': ['surface-coal'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'transport-ps': {'assets': ['transport-ps'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'abn-ong-other': {'assets': ['abn-ong-other'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'field-burning': {'assets': ['field-burning'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'production-ps': {'assets': ['production-ps'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-methane': {'assets': ['total-methane'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'exploration-ps': {'assets': ['exploration-ps'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'processing-ngs': {'assets': ['processing-ngs'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'production-ngs': {'assets': ['production-ngs'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'exploration-ngs': {'assets': ['exploration-ngs'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'composting-waste': {'assets': ['composting-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'distribution-ngs': {'assets': ['distribution-ngs'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'rice-cultivation': {'assets': ['rice-cultivation'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-coal-mines': {'assets': ['total-coal-mines'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'underground-coal': {'assets': ['underground-coal'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'manure-management': {'assets': ['manure-management'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-agriculture': {'assets': ['total-agriculture'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'msw-landfill-waste': {'assets': ['msw-landfill-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'abn-underground-coal': {'assets': ['abn-underground-coal'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'enteric-fermentation': {'assets': ['enteric-fermentation'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'petro-production-other': {'assets': ['petro-production-other'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'mobile-combustion-other': {'assets': ['mobile-combustion-other'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-petroleum-systems': {'assets': ['total-petroleum-systems'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'transmission-storage-ngs': {'assets': ['transmission-storage-ngs'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'industrial-landfill-waste': {'assets': ['industrial-landfill-waste'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'total-natural-gas-systems': {'assets': ['total-natural-gas-systems'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'ferroalloy-production-other': {'assets': ['ferroalloy-production-other'],\n", + " 'nodata': -9999,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'},\n", + " 'stationary-combustion-other': {'assets': ['stationary-combustion-other'],\n", + " 'maxzoom': 5,\n", + " 'minzoom': 0,\n", + " 'rescale': [[0, 20]],\n", + " 'colormap_name': 'epa-ghgi-ch4'}},\n", + " 'summaries': {'datetime': ['2012-01-01T00:00:00Z', '2020-01-01T00:00:00Z']},\n", + " 'description': \"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) from 2012 - 2020, 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 dataset contains methane emissions provided as fluxes, in units of megagrams of methane per square kilometer per year (Mg CH₄/km²/yr). It contains 34 data layers including a 'Total' layer with emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI; 6 aggregate layers with emission fluxes from Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories; and 27 layers representing methane emission fluxes from individual sector categories (i.e. the individual layers that make up each of the aggregate layers and the 'Total' layer). The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Mg/km²/yr for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends. The source data and addition information can be found at https://doi.org/10.5281/zenodo.8367082\",\n", + " 'item_assets': {'dwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Waste - Domestic Wastewater Treatment & Discharge (annual)',\n", + " 'description': 'Annual methane emissions from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},\n", + " 'iwtd-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Waste - Industrial Wastewater Treatment & Discharge (annual)',\n", + " 'description': 'Annual methane emissions from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category).'},\n", + " 'post-meter': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Natural Gas - Post Meter (annual)',\n", + " 'description': 'Annual methane emissions downstream of residential, commercial, industrial natural gas distribution meters (i.e., “Post Meter”) (inventory Energy 1B2b sub-category).'},\n", + " 'refining-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Petroleum - Refining (annual)',\n", + " 'description': 'Annual methane emissions from Petroleum Refining (inventory Energy 1B2a sub-category).'},\n", + " 'total-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Other (annual)',\n", + " 'description': 'Total annual methane emission fluxes from ‘Other’ remaining sources (sum of inventory categories 1A (Energy Combustion), 2B8 & 2C2 (Petrochemical & Ferroalloy Production) and 1B2a & 1B2b (Abandoned Oil & Gas Well Emissions)).'},\n", + " 'total-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Waste (annual)',\n", + " 'description': 'Total annual methane emission fluxes from Waste (sum of inventory Waste categories: Municipal Solid Waste (MSW) and Industrial Landfills (5A1), Composting (5B1), Domestic and Industrial Wastewater Treatment and Discharge (5D)).'},\n", + " 'surface-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Coal Mining - Surface Mining (annual)',\n", + " 'description': 'Annual methane emissions from active Surface Coal Mining (inventory Energy 1B1a sub-category).'},\n", + " 'transport-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Petroleum - Transportation (annual)',\n", + " 'description': 'Annual methane emissions from Petroleum Transportation (inventory Energy 1B2a sub-category).'},\n", + " 'abn-ong-other': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Other - Abandoned Oil and Gas Wells (annual)',\n", + " 'description': 'Annual methane emissions from Abandoned Oil and Gas Wells (inventory Energy 1B2a and 1B2b sub-categories).'},\n", + " 'field-burning': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Agriculture - Field Burning (annual)',\n", + " 'description': 'Annual methane emissions from field burning of agricultural residues (inventory Agriculture category 3F).'},\n", + " 'production-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Petroleum - Production (annual)',\n", + " 'description': 'Annual methane emissions from Petroleum Production (inventory Energy 1B2a sub-category).'},\n", + " 'total-methane': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Methane (annual)',\n", + " 'description': 'Total annual methane emission fluxes from all Agriculture, Energy, Waste, and ‘Other’ sources included in this dataset.'},\n", + " 'exploration-ps': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Petroleum - Exploration (annual)',\n", + " 'description': 'Annual methane emissions from Petroleum Exploration (inventory Energy 1B2a sub-category).'},\n", + " 'processing-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Natural Gas - Processing (annual)',\n", + " 'description': 'Annual methane emissions from Natural Gas Processing (inventory Energy 1B2b sub-category).'},\n", + " 'production-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Natural Gas - Production (annual)',\n", + " 'description': 'Annual methane emissions from Natural Gas Production (inventory Energy 1B2b sub-category).'},\n", + " 'exploration-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Natural Gas - Exploration (annual)',\n", + " 'description': 'Annual methane emissions from Natural Gas Exploration (inventory Energy 1B2b sub-category).'},\n", + " 'composting-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Waste - Composting (annual)',\n", + " 'description': 'Annual methane emissions from Composting (inventory Waste category 5B1).'},\n", + " 'distribution-ngs': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Natural Gas - Distribution (annual)',\n", + " 'description': 'Annual methane emissions from Natural Gas Distribution (inventory Energy 1B2b sub-category).'},\n", + " 'rice-cultivation': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Agriculture - Rice Cultivation (annual)',\n", + " 'description': 'Annual methane emissions from rice cultivation (inventory Agriculture category 3C).'},\n", + " 'total-coal-mines': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Coal Mines (annual)',\n", + " 'description': 'Total annual methane emission fluxes from Coal Mines (sum of inventory 1B1a sub-categories which includes Underground Coal Mining, Surface Coal Mining and Abandoned Underground Coal Mines).'},\n", + " 'underground-coal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Coal Mining - Underground Mining (annual)',\n", + " 'description': 'Annual methane emissions from active Underground Coal Mining (inventory Energy 1B1a sub-category).'},\n", + " 'manure-management': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Agriculture - Manure Management (annual)',\n", + " 'description': 'Annual methane emissions from livestock manure management (inventory Agriculture category 3B).'},\n", + " 'total-agriculture': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Agriculture (annual)',\n", + " 'description': 'Total annual methane emission fluxes from Agriculture sources (sum of inventory categories: Enteric Fermentation (3A), Manure Management (3B), Rice Cultivation (3C), Field Burning of Agricultural Residues (3F)).'},\n", + " 'msw-landfill-waste': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Waste - Municipal Solid Waste (MSW) Landfills 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"execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -186,9 +541,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 9 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -209,9 +572,1995 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'epa-ch4emission-yeargrid-v2express-2020',\n", + " 'bbox': [-129.99999694387628,\n", + " 19.99999923487448,\n", + " -60.00000305612369,\n", + " 55.00000076512553],\n", + " 'type': 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['https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-129.99999694387628,\n", + " 19.99999923487448,\n", + " -60.00000305612369,\n", + " 55.00000076512553],\n", + " 'center': [-94.99999999999999, 37.5, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -312,9 +20566,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': 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Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Set initial zoom and center of map for CH₄ Layer\n", "# Centre of map [latitude,longitude]\n", @@ -429,9 +20853,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -459,9 +20990,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 9 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -474,9 +21013,1995 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'epa-ch4emission-yeargrid-v2express-2020',\n", + " 'bbox': [-129.99999694387628,\n", + " 19.99999923487448,\n", + " -60.00000305612369,\n", + " 55.00000076512553],\n", + " 'type': 'Feature',\n", + " 'links': 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time) for running the code below\n", @@ -545,9 +23079,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.23683340847492218,\n", + " 'max': 22.17084503173828,\n", + " 'mean': 9.193570137023926,\n", + " 'count': 3.0,\n", + " 'sum': 27.580711364746094,\n", + " 'std': 9.39498967501574,\n", + " 'median': 5.173033714294434,\n", + " 'majority': 0.23683340847492218,\n", + " 'minority': 0.23683340847492218,\n", + " 'unique': 3.0,\n", + " 'histogram': [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", + " [0.23683340847492218,\n", + " 2.430234432220459,\n", + " 4.623635768890381,\n", + " 6.8170366287231445,\n", + " 9.010437965393066,\n", + " 11.203839302062988,\n", + " 13.397239685058594,\n", + " 15.590641021728516,\n", + " 17.784042358398438,\n", + " 19.97744369506836,\n", + " 22.17084503173828]],\n", + " 'valid_percent': 0.08,\n", + " 'masked_pixels': 3728.0,\n", + " 'valid_pixels': 3.0,\n", + " 'percentile_2': 0.23683340847492218,\n", + " 'percentile_98': 22.17084503173828}},\n", + " 'datetime': '2020-01-01T00:00:00+00:00'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -562,9 +23134,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02020-01-01T00:00:00+00:000.23683322.1708459.1935703.027.5807119.3949905.1730340.2368330.2368333.0[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.083728.03.00.23683322.1708452020-01-01 00:00:00+00:00
12019-01-01T00:00:00+00:000.31141029.15218512.0885193.036.26555612.3533636.8019610.3114100.3114103.0[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.083728.03.00.31141029.1521852019-01-01 00:00:00+00:00
22018-01-01T00:00:00+00:000.34180231.99733413.2683143.039.80494313.5590067.4658080.3418020.3418023.0[[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.083728.03.00.34180231.9973342018-01-01 00:00:00+00:00
32017-01-01T00:00:00+00:004.55588540.59160219.6906394.078.76255814.6790137.3376334.5558854.5558854.0[[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,...0.113727.04.04.55588540.5916022017-01-01 00:00:00+00:00
42016-01-01T00:00:00+00:004.96953634.22099317.1919295.085.95964111.04068613.5149354.9695364.9695365.0[[2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,...0.133726.05.04.96953634.2209932016-01-01 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2020-01-01T00:00:00+00:00 0.236833 22.170845 9.193570 3.0 \n", + "1 2019-01-01T00:00:00+00:00 0.311410 29.152185 12.088519 3.0 \n", + "2 2018-01-01T00:00:00+00:00 0.341802 31.997334 13.268314 3.0 \n", + "3 2017-01-01T00:00:00+00:00 4.555885 40.591602 19.690639 4.0 \n", + "4 2016-01-01T00:00:00+00:00 4.969536 34.220993 17.191929 5.0 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 27.580711 9.394990 5.173034 0.236833 0.236833 3.0 \n", + "1 36.265556 12.353363 6.801961 0.311410 0.311410 3.0 \n", + "2 39.804943 13.559006 7.465808 0.341802 0.341802 3.0 \n", + "3 78.762558 14.679013 7.337633 4.555885 4.555885 4.0 \n", + "4 85.959641 11.040686 13.514935 4.969536 4.969536 5.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", + "1 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", + "2 [[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.08 \n", + "3 [[2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,... 0.11 \n", + "4 [[2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,... 0.13 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 3728.0 3.0 0.236833 22.170845 \n", + "1 3728.0 3.0 0.311410 29.152185 \n", + "2 3728.0 3.0 0.341802 31.997334 \n", + "3 3727.0 4.0 4.555885 40.591602 \n", + "4 3726.0 5.0 4.969536 34.220993 \n", + "\n", + " date \n", + "0 2020-01-01 00:00:00+00:00 \n", + "1 2019-01-01 00:00:00+00:00 \n", + "2 2018-01-01 00:00:00+00:00 \n", + "3 2017-01-01 00:00:00+00:00 \n", + "4 2016-01-01 00:00:00+00:00 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -600,9 +23365,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'CH4 gridded methane emission from Domestic Wastewater Treatment & Discharge (5D) for Texas, Dallas (2012-202)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -632,9 +23418,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2018-01-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Print the properties for the 3rd item in the collection\n", "print(items[2][\"properties\"][\"datetime\"])" @@ -642,9 +23436,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/epa-ch4emission-yeargrid-v2express/items/epa-ch4emission-yeargrid-v2express-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=surface-coal&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-9999.0%2C569.109130859375'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-129.99999694387628,\n", + " 19.99999923487448,\n", + " -60.00000305612369,\n", + " 55.00000076512553],\n", + " 'center': [-94.99999999999999, 37.5, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the 2016 tile\n", "tile_2016 = requests.get(\n", @@ -670,9 +23485,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the 2016 tile\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb index 92a8cc29..ab47a874 100644 --- a/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gosat-based-ch4budget-yeargrid-v1_User_Notebook.ipynb @@ -76,9 +76,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -109,9 +118,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'gosat-based-ch4budget-yeargrid-v1',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'}],\n", + " 'title': 'GOSAT-based Top-down Total and Natural Methane Emissions v1',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2019-01-01T00:00:00+00:00',\n", + " '2019-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'dashboard': {'assets': ['post-total'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.3]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'post-total': {'assets': ['post-total'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.3]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'prior-total': {'assets': ['prior-total'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.3]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'post-wetland': {'assets': ['post-wetland'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.1]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'prior-wetland': {'assets': ['prior-wetland'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.1]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'post-wetland-uncertainty': {'assets': ['post-wetland-uncertainty'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.05]],\n", + " 'colormap_name': 'purd'},\n", + " 'prior-wetland-uncertainty': {'assets': ['prior-wetland-uncertainty'],\n", + " 'nodata': 9.96921e+36,\n", + " 'rescale': [[0, 0.05]],\n", + " 'colormap_name': 'purd'}},\n", + " 'summaries': {'datetime': ['2019-01-01T00:00:00Z']},\n", + " 'description': \"As part of the global stock take (GST), countries are asked to provide a record of their greenhouse gas (GHG) emissions to inform decisions on how to reduce GHG emissions. The NASA Carbon Monitoring System Flux (CMS-Flux) team has used remote sensing observations from Japan's Greenhouse gases Observing SATellite (GOSAT) to produce modeled total methane (CH₄) emissions and uncertainties on a 1 degree by 1 degree resolution grid for the year 2019. The GOSAT data is used in the model to inform total emission estimates, as well as wetland (the primary natural source of methane), and various human-related sources such as fossil fuel extraction, transport, agriculture, waste, and fires. A prior GHG emission estimate (and assocated uncertainty) is provided for each layer, which is the emissions estimate without GOSAT data. The posterior GHG emission layers are informed by GOSAT total column methane data. An advanced mathematical approach is used with a global chemistry transport model to quantify annual CH₄ emissions and uncertainties. These estimates are expressed in teragrams of CH₄ per year (Tg/yr). The source data can be found at https://doi.org/10.5281/zenodo.8306874 and more information can also be found on the CEOS website https://ceos.org/gst/methane.html\",\n", + " 'item_assets': {'post-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Posterior Total Methane Emissions',\n", + " 'description': 'Estimated total methane emissions per grid cell informed by GOSAT satellite total column methane data.'},\n", + " 'prior-total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Prior Total Methane Emissions',\n", + " 'description': 'Total methane emissions per grid cell estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n", + " 'post-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Wetland Posterior Methane Emissions',\n", + " 'description': 'Estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n", + " 'prior-wetland': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Wetland Prior Methane Emissions',\n", + " 'description': 'Methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'},\n", + " 'post-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Uncertainty - Wetland Posterior Methane Emissions',\n", + " 'description': 'Uncertainty in estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.'},\n", + " 'prior-wetland-uncertainty': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Uncertainty - Wetland Prior Methane Emissions',\n", + " 'description': 'Uncertainty in methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': False,\n", + " 'dashboard:time_density': 'year'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetching the collection from STAC collections using appropriate endpoint.\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", @@ -156,9 +253,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 items\n" + ] + } + ], "source": [ "# Check total number of items available\n", "number_of_items = get_item_count(collection_name)\n", @@ -168,9 +273,1668 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',\n", + " 'bbox': [-180.5, -90.5, 179.5, 89.5],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/gosat-based-ch4budget-yeargrid-v1'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 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[-180.5, -90.5]]]},\n", + " 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n", + " 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2019-01-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examining the first item in the collection\n", "items[0]" @@ -217,9 +1981,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['2019-01-01'])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "items.keys()" ] @@ -234,9 +2009,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=prior-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=0.0%2C2.121816635131836'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.5, -90.5, 179.5, 89.5],\n", + " 'center': [-0.5, -0.5, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "color_map = \"rainbow\" # please select the color ramp from matplotlib library.\n", "january_2019_tile = requests.get(\n", @@ -258,9 +2051,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Set initial zoom and center of map for CH₄ Layer\n", "# Centre of map [latitude,longitude]\n", @@ -334,9 +2221,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# We will plug in the coordinates for a location inside the the polygon and a zoom level\n", "\n", @@ -354,9 +2348,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 items\n" + ] + } + ], "source": [ "# Check total number of items available\n", "items = requests.get(\n", @@ -367,9 +2369,1668 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'gosat-based-ch4budget-yeargrid-v1-2019',\n", + " 'bbox': [-180.5, -90.5, 179.5, 89.5],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 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0.0, -1.0, 89.5, 0.0, 0.0, 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/gosat-based-ch4budget-yeargrid-v1/items/gosat-based-ch4budget-yeargrid-v1-2019/preview.png?assets=post-total&nodata=9.96921e%2B36&rescale=0%2C0.3&colormap_name=spectral_r',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.5, -90.5],\n", + " [179.5, -90.5],\n", + " [179.5, 89.5],\n", + " [-180.5, 89.5],\n", + " [-180.5, -90.5]]]},\n", + " 'collection': 'gosat-based-ch4budget-yeargrid-v1',\n", + " 'properties': {'end_datetime': '2019-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2019-01-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Explore the first item\n", "items[0]" @@ -404,9 +4065,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.87 ms, sys: 1.29 ms, total: 4.17 ms\n", + "Wall time: 844 ms\n" + ] + } + ], "source": [ "%%time\n", "stats = [generate_stats(item, texas_aoi) for item in items]" @@ -414,18 +4084,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.0002109968481818214,\n", + " 'max': 0.2379249483346939,\n", + " 'mean': 0.06609854102134705,\n", + " 'count': 36.0,\n", + " 'sum': 2.379547357559204,\n", + " 'std': 0.05244022027065174,\n", + " 'median': 0.06046847254037857,\n", + " 'majority': 0.0002109968481818214,\n", + " 'minority': 0.0002109968481818214,\n", + " 'unique': 50.0,\n", + " 'histogram': [[14.0, 7.0, 7.0, 10.0, 5.0, 3.0, 0.0, 1.0, 2.0, 1.0],\n", + " [0.0002109968481818214,\n", + " 0.023982390761375427,\n", + " 0.04775378480553627,\n", + " 0.07152518630027771,\n", + " 0.09529657661914825,\n", + " 0.1190679669380188,\n", + " 0.14283937215805054,\n", + " 0.16661076247692108,\n", + " 0.19038215279579163,\n", + " 0.21415354311466217,\n", + " 0.2379249483346939]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 50.0,\n", + " 'percentile_2': 0.002050149254500866,\n", + " 'percentile_98': 0.2056385725736618}},\n", + " 'datetime': '2019-01-01'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stats[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Use bbox initial zoom and map\n", "# Set up a map located w/in event bounds\n", diff --git a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb index 18ec6ea1..e29792d6 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-grid-v2_User_Notebook.ipynb @@ -81,9 +81,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -122,9 +131,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-daygrid-v2',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'}],\n", + " 'title': '(Daily) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", + " 'temporal': {'interval': [['1990-01-01 00:00:00+00',\n", + " '2024-05-31 00:00:00+00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'}},\n", + " 'providers': [{'name': 'NASA'}],\n", + " 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n", + " 'description': 'Global, daily estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated 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 have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions 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. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E_LL.001.',\n", + " 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n", + " 'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n", + " 'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'day'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -201,9 +271,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 800 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -218,9 +296,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-daygrid-v2-20240531',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ERA5_001_20240531.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. 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Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 3.8867296048294975e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [61178, 819, 185, 124, 78, 46, 22, 7, 0, 1]},\n", + " 'statistics': {'mean': 4.0174325630166816e-11,\n", + " 'stddev': 1.493077090568075e-10,\n", + " 'maximum': 3.8867296048294975e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09722222222222}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'lpjeosim-wetlandch4-daygrid-v2',\n", + " 'properties': {'datetime': '2024-05-31T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -274,9 +541,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -309,9 +594,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240130/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for date mentioned below\n", "date2 = '2024-01-30'\n", @@ -347,9 +650,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the CH₄ Emissions in date1 and date2 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -421,9 +873,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -456,9 +1015,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 800 items\n" + ] + } + ], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -471,9 +1038,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-daygrid-v2-20240531',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ERA5_001_20240531.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. 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Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 5.284403581384822e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [61618, 503, 152, 101, 53, 21, 5, 6, 0, 1]},\n", + " 'statistics': {'mean': 4.2160033887186084e-11,\n", + " 'stddev': 1.6741675825683113e-10,\n", + " 'maximum': 5.284403581384822e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09722222222222}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001/LPJ_EOSIM_L2_DCH4E_LL_001_20240531/LPJ_EOSIM_L2_DCH4E_LL_ensemble_mean_001_20240531.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Daily) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 3.8867296048294975e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [61178, 819, 185, 124, 78, 46, 22, 7, 0, 1]},\n", + " 'statistics': {'mean': 4.0174325630166816e-11,\n", + " 'stddev': 1.493077090568075e-10,\n", + " 'maximum': 3.8867296048294975e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09722222222222}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240531/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'lpjeosim-wetlandch4-daygrid-v2',\n", + " 'properties': {'datetime': '2024-05-31T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -530,9 +1286,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.37 s, sys: 2.02 s, total: 10.4 s\n", + "Wall time: 7min 25s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -543,9 +1308,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.0,\n", + " 'max': 1.4686858673673697e-10,\n", + " 'mean': 1.0534791788918696e-11,\n", + " 'count': 144.0,\n", + " 'sum': 1.5170100731154434e-09,\n", + " 'std': 2.322361621616834e-11,\n", + " 'median': 1.983341876149458e-12,\n", + " 'majority': 0.0,\n", + " 'minority': 0.0,\n", + " 'unique': 144.0,\n", + " 'histogram': [[119.0, 13.0, 4.0, 1.0, 1.0, 2.0, 1.0, 1.0, 0.0, 2.0],\n", + " [0.0,\n", + " 1.4686859367563088e-11,\n", + " 2.9373718735126175e-11,\n", + " 4.406057810268926e-11,\n", + " 5.874743747025235e-11,\n", + " 7.343430030726239e-11,\n", + " 8.812115620537853e-11,\n", + " 1.0280801210349466e-10,\n", + " 1.174948749405047e-10,\n", + " 1.3218173777751474e-10,\n", + " 1.4686858673673697e-10]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 144.0,\n", + " 'percentile_2': 9.560425300275503e-15,\n", + " 'percentile_98': 1.1029595986533636e-10}},\n", + " 'datetime': '2024-05-31T00:00:00+00:00'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -553,9 +1356,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02024-05-31T00:00:00+00:000.01.468686e-101.053479e-11144.01.517010e-092.322362e-111.983342e-120.00.0144.0[[119.0, 13.0, 4.0, 1.0, 1.0, 2.0, 1.0, 1.0, 0...100.00.0144.09.560425e-151.102960e-102024-05-31 00:00:00+00:00
12024-05-30T00:00:00+00:000.01.516797e-101.016293e-11144.01.463462e-092.298489e-111.655782e-120.00.0144.0[[122.0, 10.0, 4.0, 2.0, 2.0, 0.0, 2.0, 0.0, 0...100.00.0144.01.129274e-141.010287e-102024-05-30 00:00:00+00:00
22024-05-29T00:00:00+00:000.01.539605e-109.235067e-12144.01.329850e-092.194413e-111.510770e-120.00.0144.0[[122.0, 12.0, 4.0, 2.0, 0.0, 0.0, 2.0, 0.0, 1...100.00.0144.01.167209e-141.002319e-102024-05-29 00:00:00+00:00
32024-05-28T00:00:00+00:000.01.215138e-108.224680e-12144.01.184354e-091.798892e-111.588729e-120.00.0144.0[[119.0, 14.0, 3.0, 3.0, 1.0, 0.0, 2.0, 0.0, 1...100.00.0144.03.582269e-158.398084e-112024-05-28 00:00:00+00:00
42024-05-27T00:00:00+00:000.01.252385e-108.540256e-12144.01.229797e-091.867466e-111.676098e-120.00.0144.0[[119.0, 14.0, 3.0, 3.0, 1.0, 0.0, 1.0, 1.0, 1...100.00.0144.03.533827e-158.782612e-112024-05-27 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2024-05-31T00:00:00+00:00 0.0 1.468686e-10 1.053479e-11 144.0 \n", + "1 2024-05-30T00:00:00+00:00 0.0 1.516797e-10 1.016293e-11 144.0 \n", + "2 2024-05-29T00:00:00+00:00 0.0 1.539605e-10 9.235067e-12 144.0 \n", + "3 2024-05-28T00:00:00+00:00 0.0 1.215138e-10 8.224680e-12 144.0 \n", + "4 2024-05-27T00:00:00+00:00 0.0 1.252385e-10 8.540256e-12 144.0 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 1.517010e-09 2.322362e-11 1.983342e-12 0.0 0.0 144.0 \n", + "1 1.463462e-09 2.298489e-11 1.655782e-12 0.0 0.0 144.0 \n", + "2 1.329850e-09 2.194413e-11 1.510770e-12 0.0 0.0 144.0 \n", + "3 1.184354e-09 1.798892e-11 1.588729e-12 0.0 0.0 144.0 \n", + "4 1.229797e-09 1.867466e-11 1.676098e-12 0.0 0.0 144.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[119.0, 13.0, 4.0, 1.0, 1.0, 2.0, 1.0, 1.0, 0... 100.0 \n", + "1 [[122.0, 10.0, 4.0, 2.0, 2.0, 0.0, 2.0, 0.0, 0... 100.0 \n", + "2 [[122.0, 12.0, 4.0, 2.0, 0.0, 0.0, 2.0, 0.0, 1... 100.0 \n", + "3 [[119.0, 14.0, 3.0, 3.0, 1.0, 0.0, 2.0, 0.0, 1... 100.0 \n", + "4 [[119.0, 14.0, 3.0, 3.0, 1.0, 0.0, 1.0, 1.0, 1... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 0.0 144.0 9.560425e-15 1.102960e-10 \n", + "1 0.0 144.0 1.129274e-14 1.010287e-10 \n", + "2 0.0 144.0 1.167209e-14 1.002319e-10 \n", + "3 0.0 144.0 3.582269e-15 8.398084e-11 \n", + "4 0.0 144.0 3.533827e-15 8.782612e-11 \n", + "\n", + " date \n", + "0 2024-05-31 00:00:00+00:00 \n", + "1 2024-05-30 00:00:00+00:00 \n", + "2 2024-05-29 00:00:00+00:00 \n", + "3 2024-05-28 00:00:00+00:00 \n", + "4 2024-05-27 00:00:00+00:00 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -591,9 +1587,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Daily CH4 emission Values for Texas, January 2022- March 2024')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -631,9 +1648,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-29T00:00:00+00:00\n" + ] + } + ], "source": [ "# The 2024-02-25 observation is the 3rd item in the list\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -643,9 +1668,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-daygrid-v2/items/lpjeosim-wetlandch4-daygrid-v2-20240529/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the 3rd item in the collection\n", "observed_tile = requests.get(\n", @@ -668,9 +1711,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the CH4 variability for the Texas region for Observed tile timeframe\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb index 37b28ec3..c860659e 100644 --- a/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb +++ b/user_data_notebooks/lpjeosim-wetlandch4-monthgrid-v2_User_Notebook.ipynb @@ -81,9 +81,18 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -98,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -122,9 +131,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-monthgrid-v2',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'}],\n", + " 'title': '(Monthly) Wetland Methane Emissions, LPJ-EOSIM Model v2',\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", + " 'temporal': {'interval': [['1990-01-01 00:00:00+00',\n", + " '2024-05-31 00:00:00+00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'dashboard': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'era5-ch4-wetlands-emissions': {'assets': ['era5-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'merra2-ch4-wetlands-emissions': {'assets': ['merra2-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'},\n", + " 'ensemble-mean-ch4-wetlands-emissions': {'assets': ['ensemble-mean-ch4-wetlands-emissions'],\n", + " 'rescale': [[0, 3e-09]],\n", + " 'colormap_name': 'magma'}},\n", + " 'providers': [{'name': 'NASA'}],\n", + " 'summaries': {'datetime': ['1990-01-01T00:00:00Z', '2024-05-31T00:00:00Z']},\n", + " 'description': 'Global, monthly estimates of methane (CH₄) emissions from terrestrial wetlands at 0.5 x 0.5 degree spatial resolution using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Methane emissions from vegetated 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 have thus far been underrepresented. The LPJ-EOSIM model estimates wetland methane emissions 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. Input climate forcing data comes from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data and ECMWF Re-Analysis data (ERA5). An ensemble layer provides the result of the mean of the MERRA-2 and ERA5 layers. The source data can be found at https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E.001 and https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E_LL.001.',\n", + " 'item_assets': {'era5-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.'},\n", + " 'merra2-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.'},\n", + " 'ensemble-mean-ch4-wetlands-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n", + " 'description': 'Methane emissions from wetlands in units of kilograms of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -144,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -201,9 +271,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 413 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -218,9 +296,199 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-monthgrid-v2-202405',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ERA5_001_202405.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. 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"execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -274,9 +542,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-199001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -309,9 +595,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-199008/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for date mentioned below\n", "month2 = '1990-08'\n", @@ -347,9 +651,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the CH₄ Emissions for month1 and month2 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -395,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -421,9 +874,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -456,9 +1016,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 413 items\n" + ] + } + ], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -471,9 +1039,199 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'lpjeosim-wetlandch4-monthgrid-v2-202405',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/map?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'era5-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ERA5_001_202405.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, ERA5 LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. ECMWF Re-Analysis (ERA5) as input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 3.2603939548181415e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [61251, 731, 214, 98, 77, 34, 28, 25, 3, 2]},\n", + " 'statistics': {'mean': 3.2184362818990856e-11,\n", + " 'stddev': 1.3179453937864483e-10,\n", + " 'maximum': 3.2603939548181415e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09837962962963}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'merra2-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_MERRA2_001_202405.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, MERRA-2 LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 3.9324570266785486e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [61424, 584, 191, 119, 55, 42, 23, 13, 6, 3]},\n", + " 'statistics': {'mean': 3.452568789087992e-11,\n", + " 'stddev': 1.504910798913e-10,\n", + " 'maximum': 3.9324570266785486e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09722222222222}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'ensemble-mean-ch4-wetlands-emissions': {'href': 's3://lp-prod-protected/LPJ_EOSIM_L2_MCH4E_LL.001/LPJ_EOSIM_L2_MCH4E_LL_001_202405/LPJ_EOSIM_L2_MCH4E_LL_ensemble_mean_001_202405.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': '(Monthly) Wetland Methane Emissions, Ensemble Mean LPJ-EOSIM Model v2',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [360, 720],\n", + " 'description': 'Methane emissions from wetlands in units of grams of methane per meter squared per second. Ensemble of multiple climate forcing data sources input to LPJ-EOSIM model.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -99999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 2.8522617601112188e-09,\n", + " 'min': 0.0,\n", + " 'count': 11,\n", + " 'buckets': [60942, 894, 247, 132, 89, 60, 38, 31, 17, 10]},\n", + " 'statistics': {'mean': 3.335580191462005e-11,\n", + " 'stddev': 1.37447713656579e-10,\n", + " 'maximum': 2.8522617601112188e-09,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 24.09722222222222}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202405/preview.png?assets=ensemble-mean-ch4-wetlands-emissions&rescale=0%2C3e-09&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'lpjeosim-wetlandch4-monthgrid-v2',\n", + " 'properties': {'end_datetime': '2024-05-31T00:00:00+00:00',\n", + " 'start_datetime': '2024-05-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -488,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -530,9 +1288,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.82 s, sys: 784 ms, total: 4.6 s\n", + "Wall time: 3min 55s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -543,9 +1310,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.0,\n", + " 'max': 1.1635833963019593e-10,\n", + " 'mean': 8.54119119181318e-12,\n", + " 'count': 144.0,\n", + " 'sum': 1.2299314899877345e-09,\n", + " 'std': 1.8527584068163276e-11,\n", + " 'median': 1.7298481440677915e-12,\n", + " 'majority': 0.0,\n", + " 'minority': 0.0,\n", + " 'unique': 144.0,\n", + " 'histogram': [[118.0, 13.0, 5.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 2.0],\n", + " [0.0,\n", + " 1.1635833789547245e-11,\n", + " 2.327166757909449e-11,\n", + " 3.490750050128e-11,\n", + " 4.654333515818898e-11,\n", + " 5.8179169815097964e-11,\n", + " 6.981500100256e-11,\n", + " 8.145083912891593e-11,\n", + " 9.308667031637796e-11,\n", + " 1.0472250150383999e-10,\n", + " 1.1635833963019593e-10]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 144.0,\n", + " 'percentile_2': 3.998818403548555e-15,\n", + " 'percentile_98': 8.663564310396055e-11}},\n", + " 'datetime': '2024-05-01T00:00:00+00:00'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -553,9 +1358,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02024-05-01T00:00:00+00:000.01.163583e-108.541191e-12144.01.229931e-091.852758e-111.729848e-120.00.0144.0[[118.0, 13.0, 5.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0...100.00.0144.03.998818e-158.663564e-112024-05-01 00:00:00+00:00
12024-04-01T00:00:00+00:000.06.556285e-114.380293e-12144.06.307622e-101.021497e-118.170421e-130.00.0144.0[[122.0, 10.0, 4.0, 2.0, 1.0, 1.0, 1.0, 1.0, 0...100.00.0144.01.996469e-154.622102e-112024-04-01 00:00:00+00:00
22024-03-01T00:00:00+00:000.04.621008e-113.080667e-12144.04.436161e-107.204783e-126.172089e-130.00.0144.0[[123.0, 11.0, 3.0, 1.0, 1.0, 0.0, 3.0, 0.0, 0...100.00.0144.01.931019e-153.196650e-112024-03-01 00:00:00+00:00
32024-02-01T00:00:00+00:000.04.091915e-113.047132e-12144.04.387870e-107.153040e-125.414505e-130.00.0144.0[[120.0, 13.0, 2.0, 2.0, 1.0, 0.0, 2.0, 1.0, 0...100.00.0144.01.629149e-153.886856e-112024-02-01 00:00:00+00:00
42024-01-01T00:00:00+00:000.03.254087e-112.050796e-12144.02.953147e-104.963431e-123.348319e-130.00.0144.0[[125.0, 10.0, 0.0, 2.0, 2.0, 0.0, 2.0, 2.0, 0...100.00.0144.01.420968e-152.498179e-112024-01-01 00:00:00+00:00
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" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2024-05-01T00:00:00+00:00 0.0 1.163583e-10 8.541191e-12 144.0 \n", + "1 2024-04-01T00:00:00+00:00 0.0 6.556285e-11 4.380293e-12 144.0 \n", + "2 2024-03-01T00:00:00+00:00 0.0 4.621008e-11 3.080667e-12 144.0 \n", + "3 2024-02-01T00:00:00+00:00 0.0 4.091915e-11 3.047132e-12 144.0 \n", + "4 2024-01-01T00:00:00+00:00 0.0 3.254087e-11 2.050796e-12 144.0 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 1.229931e-09 1.852758e-11 1.729848e-12 0.0 0.0 144.0 \n", + "1 6.307622e-10 1.021497e-11 8.170421e-13 0.0 0.0 144.0 \n", + "2 4.436161e-10 7.204783e-12 6.172089e-13 0.0 0.0 144.0 \n", + "3 4.387870e-10 7.153040e-12 5.414505e-13 0.0 0.0 144.0 \n", + "4 2.953147e-10 4.963431e-12 3.348319e-13 0.0 0.0 144.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[118.0, 13.0, 5.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0... 100.0 \n", + "1 [[122.0, 10.0, 4.0, 2.0, 1.0, 1.0, 1.0, 1.0, 0... 100.0 \n", + "2 [[123.0, 11.0, 3.0, 1.0, 1.0, 0.0, 3.0, 0.0, 0... 100.0 \n", + "3 [[120.0, 13.0, 2.0, 2.0, 1.0, 0.0, 2.0, 1.0, 0... 100.0 \n", + "4 [[125.0, 10.0, 0.0, 2.0, 2.0, 0.0, 2.0, 2.0, 0... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 0.0 144.0 3.998818e-15 8.663564e-11 \n", + "1 0.0 144.0 1.996469e-15 4.622102e-11 \n", + "2 0.0 144.0 1.931019e-15 3.196650e-11 \n", + "3 0.0 144.0 1.629149e-15 3.886856e-11 \n", + "4 0.0 144.0 1.420968e-15 2.498179e-11 \n", + "\n", + " date \n", + "0 2024-05-01 00:00:00+00:00 \n", + "1 2024-04-01 00:00:00+00:00 \n", + "2 2024-03-01 00:00:00+00:00 \n", + "3 2024-02-01 00:00:00+00:00 \n", + "4 2024-01-01 00:00:00+00:00 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -591,9 +1589,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Monthly CH4 emission Values for Texas, 1990-2024')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -631,9 +1650,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-03-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# The 2023-11-01 observation is the 3rd item in the list\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -643,9 +1670,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/lpjeosim-wetlandch4-monthgrid-v2/items/lpjeosim-wetlandch4-monthgrid-v2-202403/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ensemble-mean-ch4-wetlands-emissions&color_formula=gamma+r+1.05&colormap_name=magma&rescale=0.0%2C0.0003'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the 3rd item in the collection\n", "observed_tile = requests.get(\n", @@ -668,9 +1713,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the CH4 variability for the Texas region for the time in previous cell.\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb index f9a19152..bc6aec8f 100644 --- a/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/micasa-carbonflux-daygrid-v1_User_Notebook.ipynb @@ -74,9 +74,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -117,9 +126,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'micasa-carbonflux-daygrid-v1',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/micasa-carbonflux-daygrid-v1'}],\n", + " 'title': '(Daily) MiCASA Land Carbon Flux v1',\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 179.99999999999994, 90]]},\n", + " 'temporal': {'interval': [['2001-01-01 00:00:00+00',\n", + " '2023-12-31 00:00:00+00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'rh': {'assets': ['rh'],\n", + " 'rescale': [[0, 8]],\n", + " 'colormap_name': 'purd'},\n", + " 'nbe': {'assets': ['nbe'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n", + " 'nee': {'assets': ['nee'],\n", + " 'rescale': [[-4, 4]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'npp': {'assets': ['npp'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n", + " 'atmc': {'assets': ['atmc'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n", + " 'fire': {'assets': ['fire'], 'rescale': [[0, 8]], 'colormap_name': 'purd'},\n", + " 'fuel': {'assets': ['fuel'], 'rescale': [[0, 0.5]], 'colormap_name': 'purd'},\n", + " 'dashboard': {'assets': ['npp'],\n", + " 'rescale': [[0, 8]],\n", + " 'colormap_name': 'purd'}},\n", + " 'providers': [{'name': 'NASA'}],\n", + " 'summaries': {'datetime': ['2001-01-01T00:00:00Z', '2023-12-31T00:00:00Z']},\n", + " 'description': \"This product provides estimated daily carbon flux to the atmosphere from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE) derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. All model calculations are driven by analyzed meteorological data from NASA's Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily data at 0.1 degree resolution starting from January 2001. The carbon flux variables are expressed in units of kilograms of carbon per square meter per day. MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA's Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks.\",\n", + " 'item_assets': {'rh': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Heterotrophic respiration (Rh), MiCASA Model v1',\n", + " 'description': 'Heterotrophic respiration (carbon flux from the soil to the atmosphere) in units of grams of carbon per square meter per day.'},\n", + " 'nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Biosphere Exchange (net carbon flux from the ecosystem), MiCASA Model v1',\n", + " 'description': 'Net Biosphere Exchange (net carbon flux from the ecosystem) in units of grams of carbon per square meter per day.'},\n", + " 'nee': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Ecosystem Exchange (NEE), MiCASA Model v1',\n", + " 'description': 'Net Ecosystem Exchange (net carbon flux to the atmosphere) in units of grams of carbon per square meter per day.'},\n", + " 'npp': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Net Primary Production (NPP), MiCASA Model v1',\n", + " 'description': 'Net Primary Production (carbon available from plants) in units of grams of carbon per square meter per day.'},\n", + " 'atmc': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Atmospheric Correction (ATMC), MiCASA Model v1',\n", + " 'description': 'A correction to account for long-term historical changes in the uptake of CO₂ from the atmosphere to the biosphere in units of grams of carbon per square meter per day.'},\n", + " 'fire': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fire emissions (FIRE), MiCASA Model v1',\n", + " 'description': 'Fire emissions (flux of carbon to the atmosphere from wildfires) in units of grams of carbon per square meter per day.'},\n", + " 'fuel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Wood fuel emissions (FUEL), MiCASA Model v1',\n", + " 'description': 'Wood fuel emissions (flux of carbon to the atmosphere from wood burned for fuel) in units of grams of carbon per square meter per day.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'day'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -195,9 +283,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 800 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -211,9 +307,471 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 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wood burned for fuel) in units of grams of carbon per square meter per day.',\n", + " 'raster:bands': [{'unit': 'g C m-2 day-1',\n", + " 'scale': 1.0,\n", + " 'nodata': 'nan',\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 0.6249907612800598,\n", + " 'min': -0.021494677290320396,\n", + " 'count': 11,\n", + " 'buckets': [518619, 4684, 688, 188, 65, 24, 6, 3, 7, 4]},\n", + " 'statistics': {'mean': 0.0015495388070121408,\n", + " 'stddev': 0.010684158697696962,\n", + " 'maximum': 0.6249907612800598,\n", + " 'minimum': -0.021494677290320396,\n", + " 'valid_percent': 100.0}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.0, -90.0],\n", + " [179.99999999999994, -90.0],\n", + " [179.99999999999994, 90.0],\n", + " [-180.0, 90.0],\n", + " [-180.0, -90.0]]]},\n", + " 'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},\n", + " 'name': 'WGS 84',\n", + " 'type': 'GeographicCRS',\n", + " 'datum': 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'https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231231/preview.png?assets=npp&rescale=0%2C8&colormap_name=purd',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [179.99999999999994, -90],\n", + " [179.99999999999994, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'micasa-carbonflux-daygrid-v1',\n", + " 'properties': {'datetime': '2023-12-31T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -267,9 +825,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20230101/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color for displaying the tiles\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -302,9 +878,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20230131/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the date mentioned below\n", "date2 = '2023-01-31'\n", @@ -340,9 +934,269 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the Rh level for date1 and date2 over the State of Texas \n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -441,9 +1295,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "aoi_map = Map(\n", @@ -476,9 +1437,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 800 items\n" + ] + } + ], "source": [ "# Check the total number of items available within the collection\n", "items = requests.get(\n", @@ -491,9 +1460,471 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'micasa-carbonflux-daygrid-v1-20231231',\n", + " 'bbox': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 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'properties': {'datetime': '2023-12-31T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -545,9 +1976,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-31T00:00:00+00:00\n" + ] + } + ], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -569,9 +2008,818 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": 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0.7456724643707275,\n", + " 'count': 147.99998474121094,\n", + " 'sum': 110.35951232910156,\n", + " 'std': 0.18667982445867687,\n", + " 'median': 0.7525566220283508,\n", + " 'majority': 0.1918513923883438,\n", + " 'minority': 0.1918513923883438,\n", + " 'unique': 165.0,\n", + " 'histogram': [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0, 15.0, 6.0],\n", + " [0.1918513923883438,\n", + " 0.28862321376800537,\n", + " 0.3853950500488281,\n", + " 0.4821668267250061,\n", + " 0.5789386630058289,\n", + " 0.6757104992866516,\n", + " 0.7724822759628296,\n", + " 0.8692541122436523,\n", + " 0.9660259485244751,\n", + " 1.0627977848052979,\n", + " 1.1595696210861206]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 165.0,\n", + " 'percentile_2': 0.27398860454559326,\n", + " 'percentile_98': 1.098567008972168}},\n", + " 'datetime': '2023-12-31'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -599,9 +2885,195 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02023-12-310.1918511.1595700.745672147.999985110.3595120.1866800.7525570.1918510.191851165.0[[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0,...100.00.0165.00.2739891.0985672023-12-31
12023-12-300.1779441.0757710.690748147.999985102.2306520.1731710.7009130.1779440.177944165.0[[3.0, 2.0, 6.0, 15.0, 27.0, 33.0, 27.0, 31.0,...100.00.0165.00.2582741.0227742023-12-30
22023-12-290.1727471.0489480.666380147.99998598.6242450.1683300.6756440.1727470.172747165.0[[3.0, 2.0, 6.0, 18.0, 27.0, 34.0, 26.0, 29.0,...100.00.0165.00.2477350.9904782023-12-29
32023-12-280.1756741.0702120.672139147.99998599.4764860.1702830.6755610.1756740.175674165.0[[3.0, 2.0, 8.0, 18.0, 25.0, 35.0, 29.0, 29.0,...100.00.0165.00.2495451.0069132023-12-28
42023-12-270.1936301.1708220.728575147.999985107.8290710.1864870.7290160.1936300.193630165.0[[3.0, 2.0, 9.0, 18.0, 26.0, 38.0, 28.0, 23.0,...100.00.0165.00.2725051.0978462023-12-27
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count sum std \\\n", + "0 2023-12-31 0.191851 1.159570 0.745672 147.999985 110.359512 0.186680 \n", + "1 2023-12-30 0.177944 1.075771 0.690748 147.999985 102.230652 0.173171 \n", + "2 2023-12-29 0.172747 1.048948 0.666380 147.999985 98.624245 0.168330 \n", + "3 2023-12-28 0.175674 1.070212 0.672139 147.999985 99.476486 0.170283 \n", + "4 2023-12-27 0.193630 1.170822 0.728575 147.999985 107.829071 0.186487 \n", + "\n", + " median majority minority unique \\\n", + "0 0.752557 0.191851 0.191851 165.0 \n", + "1 0.700913 0.177944 0.177944 165.0 \n", + "2 0.675644 0.172747 0.172747 165.0 \n", + "3 0.675561 0.175674 0.175674 165.0 \n", + "4 0.729016 0.193630 0.193630 165.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[3.0, 2.0, 5.0, 16.0, 26.0, 31.0, 30.0, 31.0,... 100.0 \n", + "1 [[3.0, 2.0, 6.0, 15.0, 27.0, 33.0, 27.0, 31.0,... 100.0 \n", + "2 [[3.0, 2.0, 6.0, 18.0, 27.0, 34.0, 26.0, 29.0,... 100.0 \n", + "3 [[3.0, 2.0, 8.0, 18.0, 25.0, 35.0, 29.0, 29.0,... 100.0 \n", + "4 [[3.0, 2.0, 9.0, 18.0, 26.0, 38.0, 28.0, 23.0,... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 0.0 165.0 0.273989 1.098567 2023-12-31 \n", + "1 0.0 165.0 0.258274 1.022774 2023-12-30 \n", + "2 0.0 165.0 0.247735 0.990478 2023-12-29 \n", + "3 0.0 165.0 0.249545 1.006913 2023-12-28 \n", + "4 0.0 165.0 0.272505 1.097846 2023-12-27 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -637,9 +3109,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (October 2021 to January 2024)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Determine the width and height of the plot using the 'matplotlib' library\n", "# Figure size: 20 representing the width, 10 representing the height\n", @@ -677,9 +3170,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-29T00:00:00+00:00\n" + ] + } + ], "source": [ "# Fetch the third item in the list as the observation item.\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -689,9 +3190,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/micasa-carbonflux-daygrid-v1/items/micasa-carbonflux-daygrid-v1-20231229/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh&color_formula=gamma+r+1.05&colormap_name=purd&rescale=-0.35656991600990295%2C7.2141876220703125'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 179.99999999999994, 90.0],\n", + " 'center': [-2.842170943040401e-14, 0.0, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the observed tile\n", "observed_tile = requests.get(\n", @@ -717,9 +3236,184 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the Rh level for the Dallas, Texas area for the observed tile timeframe.\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb index 031424d9..1b4469d8 100644 --- a/user_data_notebooks/noaa-insitu_User_Notebook.ipynb +++ b/user_data_notebooks/noaa-insitu_User_Notebook.ipynb @@ -71,9 +71,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -130,9 +139,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/850940753.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n" + ] + } + ], "source": [ "for file_info in file_list_ch4:\n", " if file_info[\"name\"].endswith(\"txt\"):\n", @@ -153,9 +351,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n", + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1028171191.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(StringIO(\"\\n\".join(Lines[index:])), delim_whitespace=True)\n" + ] + } + ], "source": [ "for file_info in file_list_co2:\n", " if file_info[\"name\"].endswith(\"txt\"):\n", @@ -176,9 +563,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/2606016741.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "site_to_filter = 'ABP'\n", "filtered_df = combined_df_co2[combined_df_co2['site_code'] == site_to_filter]\n", @@ -201,9 +611,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/c2/vxj2w9ms5899ncnjj83x65y00000gp/T/ipykernel_28429/1635934907.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " filtered_df['datetime'] = pd.to_datetime(filtered_df['datetime'])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "site_to_filter = 'ABP'\n", "filtered_df = combined_df_ch4[combined_df_ch4['site_code'] == site_to_filter]\n", diff --git a/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb b/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb index 32d957ed..86bde16e 100644 --- a/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/oco2-mip-co2budget-yeargrid-v1_User_Notebook.ipynb @@ -70,9 +70,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import folium\n", @@ -105,9 +114,234 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2-mip-co2budget-yeargrid-v1',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'}],\n", + " 'title': 'OCO-2 MIP Top-down CO₂ Budgets v1',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2015-01-01T00:00:00+00:00',\n", + " '2020-01-01T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'ff': {'assets': ['ff'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 450]],\n", + " 'colormap_name': 'purd'},\n", + " 'crop': {'assets': ['crop'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-100, 100]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'wood': {'assets': ['wood'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-100, 100]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'river': {'assets': ['river'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-50, 50]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'ff-std': {'assets': ['ff-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 100]],\n", + " 'colormap_name': 'purd'},\n", + " 'crop-std': {'assets': ['crop-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 50]],\n", + " 'colormap_name': 'purd'},\n", + " 'wood-std': {'assets': ['wood-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 50]],\n", + " 'colormap_name': 'purd'},\n", + " 'dashboard': {'assets': ['lnlgis-nbe'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-1200, 1200]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'river-std': {'assets': ['river-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 60]],\n", + " 'colormap_name': 'purd'},\n", + " 'lnlgis-nbe': {'assets': ['lnlgis-nbe'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-1200, 1200]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'lnlgis-nce': {'assets': ['lnlgis-nce'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-1200, 1200]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'lnlgis-dc-loss': {'assets': ['lnlgis-dc-loss'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-600, 600]],\n", + " 'colormap_name': 'coolwarm'},\n", + " 'lnlgis-nbe-std': {'assets': ['lnlgis-nbe-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 1000]],\n", + " 'colormap_name': 'purd'},\n", + " 'lnlgis-nce-std': {'assets': ['lnlgis-nce-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 1200]],\n", + " 'colormap_name': 'purd'},\n", + " 'lnlgis-dc-loss-std': {'assets': ['lnlgis-dc-loss-std'],\n", + " 'nodata': 0,\n", + " 'rescale': [[0, 800]],\n", + " 'colormap_name': 'purd'}},\n", + " 'summaries': {'datetime': ['2015-01-01T00:00:00+00:00',\n", + " '2020-01-01T00:00:00+00:00']},\n", + " 'description': 'This dataset contains top-down annual net land-atmosphere fluxes of CO₂ on a 1-degree grid for a six-year period (2015-2020) from an ensamble of atmospheric CO₂ inversions, as well as annual bottom-up estimates of fossil fuel emissions and lateral carbon fluxes due to crop trade, wood trade, river export and cement production. Annual changes (loss) in terrestrial carbon stocks are obtained by combining top-down and bottom-up estimates. The standard deviation for each variable is provided (std). IS layers assimilates in situ CO₂ mole fraction measurements from an international observational network. LNLG layers assimilates CO₂ measurements from the ACOS v10 algorithm which uses land nadir and land glint total column dry-air mole fractions observed by the OCO-2 satellite. LNLGIS layers assimilates both in situ and ACOS v10 OCO-2 land nadir and glint retrievals together. OG layers assimilates CO₂ measurements from the ACOS v10 algorithm using OCO-2 ocean glint retrievals. LNLGOGIS assimilates all sources together (in situ (IS), OCO-2 ACOS v10 land nadir and land glint (LNLG), OCO-2 ACOS v10 ocean glint (OG)). Data is provided in units of grams of carbon dioxide per square meter per year (g CO₂/m2/yr). The source dataset is titled Pilot top-down CO₂ budget constrained by the v10 OCO-2 MIP Version 1 and can be found at https://doi.org/10.48588/npf6-sw92',\n", + " 'item_assets': {'ff': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel and Cement Emissions',\n", + " 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere.'},\n", + " 'crop': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral Crop Flux',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture.'},\n", + " 'wood': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral Wood Flux',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage.'},\n", + " 'river': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral River Flux',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle.'},\n", + " 'ff-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel and Cement Emissions std',\n", + " 'description': 'The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere.'},\n", + " 'is-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Net Biosphere Exchange',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances).'},\n", + " 'is-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Terrestrial Net Carbon Exchange',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'crop-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral Crop Flux std',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture.'},\n", + " 'lnlg-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Net Biosphere Exchange',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlg-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Terrestrial Net Carbon Exchange',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'wood-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral Wood Flux std',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage.'},\n", + " 'river-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Lateral River Flux std',\n", + " 'description': 'The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle.'},\n", + " 'is-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Net Terrestrial Carbon Stock Loss',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'is-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Net Biosphere Exchange std',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'is-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Terrestrial Net Carbon Exchange std',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'lnlgis-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Net Biosphere Exchange',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlgis-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Terrestrial Net Carbon Exchange',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'lnlg-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Net Terrestrial Carbon Stock Loss',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlg-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Net Biosphere Exchange std',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlg-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Terrestrial Net Carbon Exchange std',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'lnlgogis-nbe': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Net Biosphere Exchange',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlgogis-nce': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Terrestrial Net Carbon Exchange',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'is-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'In Situ Net Terrestrial Carbon Stock Loss std',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlgis-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Net Terrestrial Carbon Stock Loss',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlgis-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Net Biosphere Exchange std',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlgis-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Terrestrial Net Carbon Exchange std',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'lnlg-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir and Land Glint Net Terrestrial Carbon Stock Loss std',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlgogis-dc-loss': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Net Terrestrial Carbon Stock Loss',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlgogis-nbe-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Net Biosphere Exchange',\n", + " 'description': 'Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances.'},\n", + " 'lnlgogis-nce-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Terrestrial Net Carbon Exchange std',\n", + " 'description': 'Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions.'},\n", + " 'lnlgis-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint and In Situ Net Terrestrial Carbon Stock Loss std',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'},\n", + " 'lnlgogis-dc-loss-std': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Land Nadir, Land Glint, Ocean Glint and In Situ Net Terrestrial Carbon Stock Loss std',\n", + " 'description': 'Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.).'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'year'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -177,9 +411,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 6 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -193,9 +435,1634 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2-mip-co2budget-yeargrid-v1-2020',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020/map?assets=lnlgis-nbe&nodata=0&rescale=-1200%2C1200&colormap_name=coolwarm',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'ff': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_FF_CO2_Budget_grid_v1_2020.tif',\n", + " 'type': 'image/tiff; 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"execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -291,9 +2176,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2019/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the 2019 tile which is the 2st item in the collection\n", "# To retrieve the second item in the collection we use \"1\" in the \"(items.keys())[1]\" statement\n", @@ -329,9 +2232,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare the CO2 budget in 2020 and 2019 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -405,9 +2457,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -435,9 +2594,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 6 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -450,9 +2617,1634 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2-mip-co2budget-yeargrid-v1-2020',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020/map?assets=lnlgis-nbe&nodata=0&rescale=-1200%2C1200&colormap_name=coolwarm',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'ff': {'href': 's3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_FF_CO2_Budget_grid_v1_2020.tif',\n", + " 'type': 'image/tiff; 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'Geodetic longitude',\n", + " 'unit': 'degree',\n", + " 'direction': 'east',\n", + " 'abbreviation': 'Lon'}],\n", + " 'subtype': 'ellipsoidal'}},\n", + " 'proj:transform': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020/preview.png?assets=lnlgis-nbe&nodata=0&rescale=-1200%2C1200&colormap_name=coolwarm',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'oco2-mip-co2budget-yeargrid-v1',\n", + " 'properties': {'end_datetime': '2020-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2020-01-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -504,9 +4296,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-01-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -527,9 +4327,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.03978176414966583, 'max': 7348.5, 'mean': 710.6574534496499, 'count': 36.0, 'sum': 25583.668324187398, 'std': 1398.048920955156, 'median': 88.17879486083984, 'majority': 0.03978176414966583, 'minority': 0.03978176414966583, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.03978176414966583, 734.8858035877347, 1469.7318254113197, 2204.5778472349048, 2939.42386905849, 3674.269890882075, 4409.11591270566, 5143.961934529245, 5878.80795635283, 6613.653978176415, 7348.5]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.03978176414966583, 'percentile_98': 7348.5}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.09139306098222733, 'max': 8174.17724609375, 'mean': 795.2997507835842, 'count': 36.0, 'sum': 28630.79102820903, 'std': 1557.5783137149344, 'median': 102.49484252929688, 'majority': 0.09139306098222733, 'minority': 0.09139306098222733, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.09139306098222733, 817.499978364259, 1634.9085636675359, 2452.317148970813, 3269.7257342740895, 4087.134319577366, 4904.542904880644, 5721.95149018392, 6539.360075487197, 7356.768660790473, 8174.17724609375]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.09139306098222733, 'percentile_98': 8174.17724609375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.09151247888803482, 'max': 8392.841796875, 'mean': 816.3306965242243, 'count': 36.0, 'sum': 29387.905074872077, 'std': 1599.1110746714692, 'median': 105.02102661132812, 'majority': 0.09151247888803482, 'minority': 0.09151247888803482, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.09151247888803482, 839.3665409184993, 1678.6415693581105, 2517.916597797722, 3357.191626237333, 4196.466654676944, 5035.741683116556, 5875.016711556166, 6714.291739995778, 7553.566768435389, 8392.841796875]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.09151247888803482, 'percentile_98': 8392.841796875}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0910222977399826, 'max': 8138.96484375, 'mean': 791.9455965670446, 'count': 36.0, 'sum': 28510.041476413608, 'std': 1550.8237787859084, 'median': 102.0539779663086, 'majority': 0.0910222977399826, 'minority': 0.0910222977399826, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0910222977399826, 813.9784044429659, 1627.865786588192, 2441.7531687334176, 3255.640550878644, 4069.52793302387, 4883.415315169095, 5697.302697314321, 6511.190079459548, 7325.077461604774, 8138.96484375]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0910222977399826, 'percentile_98': 8138.96484375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08842560648918152, 'max': 8243.259765625, 'mean': 801.7388236944875, 'count': 36.0, 'sum': 28862.597653001547, 'std': 1570.4892862189242, 'median': 103.01327514648438, 'majority': 0.08842560648918152, 'minority': 0.08842560648918152, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08842560648918152, 824.4055596083402, 1648.7226936101913, 2473.0398276120422, 3297.3569616138934, 4121.674095615745, 4945.991229617595, 5770.308363619447, 6594.625497621298, 7418.942631623148, 8243.259765625]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.08842560648918152, 'percentile_98': 8243.259765625}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.08379923552274704, 'max': 8397.8779296875, 'mean': 816.0609874193453, 'count': 36.0, 'sum': 29378.19554709643, 'std': 1599.6065142777418, 'median': 104.36005401611328, 'majority': 0.08379923552274704, 'minority': 0.08379923552274704, 'unique': 36.0, 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.08379923552274704, 839.8632122807205, 1679.6426253259183, 2519.4220383711163, 3359.201451416314, 4198.980864461511, 5038.76027750671, 5878.539690551907, 6718.319103597105, 7558.0985166423025, 8397.8779296875]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.08379923552274704, 'percentile_98': 8397.8779296875}}}}\n", + "CPU times: user 23.3 ms, sys: 6.84 ms, total: 30.2 ms\n", + "Wall time: 3.97 s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -540,9 +4355,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.03978176414966583,\n", + " 'max': 7348.5,\n", + " 'mean': 710.6574534496499,\n", + " 'count': 36.0,\n", + " 'sum': 25583.668324187398,\n", + " 'std': 1398.048920955156,\n", + " 'median': 88.17879486083984,\n", + " 'majority': 0.03978176414966583,\n", + " 'minority': 0.03978176414966583,\n", + " 'unique': 36.0,\n", + " 'histogram': [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", + " [0.03978176414966583,\n", + " 734.8858035877347,\n", + " 1469.7318254113197,\n", + " 2204.5778472349048,\n", + " 2939.42386905849,\n", + " 3674.269890882075,\n", + " 4409.11591270566,\n", + " 5143.961934529245,\n", + " 5878.80795635283,\n", + " 6613.653978176415,\n", + " 7348.5]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 36.0,\n", + " 'percentile_2': 0.03978176414966583,\n", + " 'percentile_98': 7348.5}},\n", + " 'datetime': '2020-01-01T00:00:00+00:00'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -557,9 +4410,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02020-01-01T00:00:00+00:000.0397827348.500000710.65745336.025583.6683241398.04892188.1787950.0397820.03978236.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.0397827348.5000002020-01-01 00:00:00+00:00
12019-01-01T00:00:00+00:000.0913938174.177246795.29975136.028630.7910281557.578314102.4948430.0913930.09139336.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.0913938174.1772462019-01-01 00:00:00+00:00
22018-01-01T00:00:00+00:000.0915128392.841797816.33069736.029387.9050751599.111075105.0210270.0915120.09151236.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.0915128392.8417972018-01-01 00:00:00+00:00
32017-01-01T00:00:00+00:000.0910228138.964844791.94559736.028510.0414761550.823779102.0539780.0910220.09102236.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.0910228138.9648442017-01-01 00:00:00+00:00
42016-01-01T00:00:00+00:000.0884268243.259766801.73882436.028862.5976531570.489286103.0132750.0884260.08842636.0[[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0...100.00.036.00.0884268243.2597662016-01-01 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2020-01-01T00:00:00+00:00 0.039782 7348.500000 710.657453 36.0 \n", + "1 2019-01-01T00:00:00+00:00 0.091393 8174.177246 795.299751 36.0 \n", + "2 2018-01-01T00:00:00+00:00 0.091512 8392.841797 816.330697 36.0 \n", + "3 2017-01-01T00:00:00+00:00 0.091022 8138.964844 791.945597 36.0 \n", + "4 2016-01-01T00:00:00+00:00 0.088426 8243.259766 801.738824 36.0 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 25583.668324 1398.048921 88.178795 0.039782 0.039782 36.0 \n", + "1 28630.791028 1557.578314 102.494843 0.091393 0.091393 36.0 \n", + "2 29387.905075 1599.111075 105.021027 0.091512 0.091512 36.0 \n", + "3 28510.041476 1550.823779 102.053978 0.091022 0.091022 36.0 \n", + "4 28862.597653 1570.489286 103.013275 0.088426 0.088426 36.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", + "1 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", + "2 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", + "3 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", + "4 [[28.0, 2.0, 1.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 0.0 36.0 0.039782 7348.500000 \n", + "1 0.0 36.0 0.091393 8174.177246 \n", + "2 0.0 36.0 0.091512 8392.841797 \n", + "3 0.0 36.0 0.091022 8138.964844 \n", + "4 0.0 36.0 0.088426 8243.259766 \n", + "\n", + " date \n", + "0 2020-01-01 00:00:00+00:00 \n", + "1 2019-01-01 00:00:00+00:00 \n", + "2 2018-01-01 00:00:00+00:00 \n", + "3 2017-01-01 00:00:00+00:00 \n", + "4 2016-01-01 00:00:00+00:00 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -594,9 +4640,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -642,9 +4699,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2018-01-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# The 2018-01-01 observation is the 3rd item in the list.\n", "# Considering that a list starts with \"0\", we need to insert \"2\" in the \"items[2]\" statement\n", @@ -653,9 +4718,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2018/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0%2C450'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the 2018-01-01 tile\n", "co2_flux_3 = requests.get(\n", @@ -681,9 +4764,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the 2018-01-01 tile\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb index 80ab5ca9..2d4edf58 100644 --- a/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb +++ b/user_data_notebooks/oco2geos-co2-daygrid-v10r_User_Notebook.ipynb @@ -67,9 +67,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -108,9 +117,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2geos-co2-daygrid-v10r',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'}],\n", + " 'title': 'OCO-2 GEOS Column CO₂ Concentrations v10r',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2015-01-01T00:00:00+00:00',\n", + " '2022-02-28T00:00:00+00:00']]}},\n", + " 'license': 'CC0-1.0',\n", + " 'renders': {'xco2': {'assets': ['xco2'],\n", + " 'nodata': 0,\n", + " 'rescale': [[412, 422]],\n", + " 'colormap_name': 'magma'},\n", + " 'dashboard': {'assets': ['xco2'],\n", + " 'nodata': 0,\n", + " 'rescale': [[412, 422]],\n", + " 'colormap_name': 'magma'}},\n", + " 'summaries': {'datetime': ['2015-01-01T00:00:00Z', '2022-02-28T00:00:00Z']},\n", + " 'description': 'This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from Orbiting Carbon Observatory-2 (OCO-2) satellite observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date. The source data can be found at https://doi.org/10.5067/Y9M4NM9MPCGH',\n", + " 'item_assets': {'xco2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n", + " 'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.'},\n", + " 'xco2prec': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n", + " 'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'day'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetching the collection from STAC collections using appropriate endpoint.\n", "collection = requests.get(f\"{STAC_API_URL}/collections/{collection_name}\").json()\n", @@ -155,9 +216,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2615 items\n" + ] + } + ], "source": [ "# Check total number of items available\n", "number_of_items = get_item_count(collection_name)\n", @@ -167,9 +236,164 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2geos-co2-daygrid-v10r-20220228',\n", + " 'bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/map?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'xco2': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2_L3CO2_day_B10206Ar_20220228.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n", + " 'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [361.0, 576.0],\n", + " 'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 423.60419320175424,\n", + " 'min': 411.7429234611336,\n", + " 'count': 11.0,\n", + " 'buckets': [37851.0,\n", + " 30550.0,\n", + " 19173.0,\n", + " 11220.0,\n", + " 15304.0,\n", + " 31151.0,\n", + " 45205.0,\n", + " 15819.0,\n", + " 1524.0,\n", + " 139.0]},\n", + " 'statistics': {'mean': 416.40504944204235,\n", + " 'stddev': 2.967704894550985,\n", + " 'maximum': 423.60419320175424,\n", + " 'minimum': 411.7429234611336,\n", + " 'valid_percent': 0.00048091720529393656}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\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': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n", + " 'xco2prec': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2PREC_L3CO2_day_B10206Ar_20220228.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n", + " 'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [361.0, 576.0],\n", + " 'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 1.0,\n", + " 'min': 0.09999999999999999,\n", + " 'count': 11.0,\n", + " 'buckets': [73789.0,\n", + " 19836.0,\n", + " 7943.0,\n", + " 4684.0,\n", + " 3634.0,\n", + " 3060.0,\n", + " 3094.0,\n", + " 3093.0,\n", + " 3814.0,\n", + " 84989.0]},\n", + " 'statistics': {'mean': 0.5499856972588942,\n", + " 'stddev': 0.4024318718400779,\n", + " 'maximum': 1.0,\n", + " 'minimum': 0.09999999999999999,\n", + " 'valid_percent': 0.00048091720529393656}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\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': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/preview.png?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\n", + " 'collection': 'oco2geos-co2-daygrid-v10r',\n", + " 'properties': {'datetime': '2022-02-28T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examining the first item in the collection\n", "items[0]" @@ -224,9 +448,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'center': [-0.3125, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "color_map = \"magma\"\n", "oco2_1 = requests.get(\n", @@ -240,9 +482,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220227/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'center': [-0.3125, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "oco2_2 = requests.get(\n", " f\"{RASTER_API_URL}/collections/{items[list(items.keys())[1]]['collection']}/items/{items[list(items.keys())[1]]['id']}/tilejson.json?\"\n", @@ -263,9 +523,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Set initial zoom and center of map for XCO₂ Layer\n", "# Centre of map [latitude,longitude]\n", @@ -333,9 +742,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# We will plug in the coordinates for a location inside the the polygon and a zoom level\n", "aoi_map = Map(\n", @@ -352,9 +868,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 600 items\n" + ] + } + ], "source": [ "# Check total number of items available\n", "items = requests.get(\n", @@ -365,9 +889,164 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'oco2geos-co2-daygrid-v10r-20220228',\n", + " 'bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/map?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'xco2': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2_L3CO2_day_B10206Ar_20220228.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ (XCO₂)',\n", + " 'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [361.0, 576.0],\n", + " 'description': 'Daily dry air column-averaged mole fractions of carbon dioxide created from data assimilations of OCO-2 satellite retrievals.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 423.60419320175424,\n", + " 'min': 411.7429234611336,\n", + " 'count': 11.0,\n", + " 'buckets': [37851.0,\n", + " 30550.0,\n", + " 19173.0,\n", + " 11220.0,\n", + " 15304.0,\n", + " 31151.0,\n", + " 45205.0,\n", + " 15819.0,\n", + " 1524.0,\n", + " 139.0]},\n", + " 'statistics': {'mean': 416.40504944204235,\n", + " 'stddev': 2.967704894550985,\n", + " 'maximum': 423.60419320175424,\n", + " 'minimum': 411.7429234611336,\n", + " 'valid_percent': 0.00048091720529393656}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\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': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n", + " 'xco2prec': {'href': 's3://ghgc-data-store/oco2geos-co2-daygrid-v10r/oco2_GEOS_XCO2PREC_L3CO2_day_B10206Ar_20220228.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Average Dry-Air Column CO₂ Precision (XCO₂PREC)',\n", + " 'proj:bbox': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [361.0, 576.0],\n", + " 'description': 'Random errors for daily dry air column-averaged mole fractions of carbon dioxide calculated using a posteriori diagnostics.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 1.0,\n", + " 'min': 0.09999999999999999,\n", + " 'count': 11.0,\n", + " 'buckets': [73789.0,\n", + " 19836.0,\n", + " 7943.0,\n", + " 4684.0,\n", + " 3634.0,\n", + " 3060.0,\n", + " 3094.0,\n", + " 3093.0,\n", + " 3814.0,\n", + " 84989.0]},\n", + " 'statistics': {'mean': 0.5499856972588942,\n", + " 'stddev': 0.4024318718400779,\n", + " 'maximum': 1.0,\n", + " 'minimum': 0.09999999999999999,\n", + " 'valid_percent': 0.00048091720529393656}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\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': [0.625, 0.0, -180.3125, 0.0, -0.5, 90.25, 0.0, 0.0, 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220228/preview.png?assets=xco2&nodata=0&rescale=412%2C422&colormap_name=magma',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180.3125, -90.25],\n", + " [179.6875, -90.25],\n", + " [179.6875, 90.25],\n", + " [-180.3125, 90.25],\n", + " [-180.3125, -90.25]]]},\n", + " 'collection': 'oco2geos-co2-daygrid-v10r',\n", + " 'properties': {'datetime': '2022-02-28T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Explore the first item\n", "items[0]" @@ -395,9 +1074,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-02-28T00:00:00+00:00\n" + ] + } + ], "source": [ "for item in items:\n", " print(item[\"properties\"][\"datetime\"])\n", @@ -414,9 +1101,618 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 418.2305856375024, 'max': 419.41294693970116, 'mean': 418.84807499634246, 'count': 115.20000457763672, 'sum': 48251.30015691298, 'std': 0.25036812587510676, 'median': 418.7834456388373, 'majority': 418.2305856375024, 'minority': 418.2305856375024, 'unique': 135.0, 'histogram': [[3.0, 4.0, 6.0, 22.0, 41.0, 12.0, 10.0, 15.0, 9.0, 13.0], [418.2305856375024, 418.34882176772226, 418.46705789794214, 418.585294028162, 418.7035301583819, 418.8217662886018, 418.9400024188216, 419.0582385490415, 419.1764746792614, 419.2947108094813, 419.41294693970116]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 135.0, 'percentile_2': 418.3893688605167, 'percentile_98': 419.3659042357467}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 418.6847545497585, 'max': 420.117827161448, 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12022-02-27T00:00:00+00:00418.684755420.117827419.509759115.20000548327.5261870.331645419.469568418.684755418.684755135.0[[4.0, 4.0, 9.0, 11.0, 29.0, 16.0, 23.0, 10.0,...100.00.0135.0418.811342420.0901602022-02-27 00:00:00+00:00
22022-02-26T00:00:00+00:00417.776690419.349191418.780854115.20000548243.5563080.284348418.774780417.776690417.776690135.0[[1.0, 3.0, 4.0, 7.0, 7.0, 32.0, 32.0, 21.0, 1...100.00.0135.0418.041596419.3299432022-02-26 00:00:00+00:00
32022-02-25T00:00:00+00:00417.941315419.452983418.702861115.20000548234.5715000.327788418.689477417.941315417.941315135.0[[6.0, 3.0, 13.0, 23.0, 21.0, 27.0, 13.0, 11.0...100.00.0135.0418.072064419.3596692022-02-25 00:00:00+00:00
42022-02-24T00:00:00+00:00417.856048419.575805418.788470115.20000548244.4336120.300634418.767628417.856048417.856048135.0[[3.0, 4.0, 5.0, 8.0, 30.0, 39.0, 22.0, 9.0, 9...100.00.0135.0418.033764419.4185462022-02-24 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count \\\n", + "0 2022-02-28T00:00:00+00:00 418.230586 419.412947 418.848075 115.200005 \n", + "1 2022-02-27T00:00:00+00:00 418.684755 420.117827 419.509759 115.200005 \n", + "2 2022-02-26T00:00:00+00:00 417.776690 419.349191 418.780854 115.200005 \n", + "3 2022-02-25T00:00:00+00:00 417.941315 419.452983 418.702861 115.200005 \n", + "4 2022-02-24T00:00:00+00:00 417.856048 419.575805 418.788470 115.200005 \n", + "\n", + " sum std median majority minority unique \\\n", + "0 48251.300157 0.250368 418.783446 418.230586 418.230586 135.0 \n", + "1 48327.526187 0.331645 419.469568 418.684755 418.684755 135.0 \n", + "2 48243.556308 0.284348 418.774780 417.776690 417.776690 135.0 \n", + "3 48234.571500 0.327788 418.689477 417.941315 417.941315 135.0 \n", + "4 48244.433612 0.300634 418.767628 417.856048 417.856048 135.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[3.0, 4.0, 6.0, 22.0, 41.0, 12.0, 10.0, 15.0,... 100.0 \n", + "1 [[4.0, 4.0, 9.0, 11.0, 29.0, 16.0, 23.0, 10.0,... 100.0 \n", + "2 [[1.0, 3.0, 4.0, 7.0, 7.0, 32.0, 32.0, 21.0, 1... 100.0 \n", + "3 [[6.0, 3.0, 13.0, 23.0, 21.0, 27.0, 13.0, 11.0... 100.0 \n", + "4 [[3.0, 4.0, 5.0, 8.0, 30.0, 39.0, 22.0, 9.0, 9... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 \\\n", + "0 0.0 135.0 418.389369 419.365904 \n", + "1 0.0 135.0 418.811342 420.090160 \n", + "2 0.0 135.0 418.041596 419.329943 \n", + "3 0.0 135.0 418.072064 419.359669 \n", + "4 0.0 135.0 418.033764 419.418546 \n", + "\n", + " date \n", + "0 2022-02-28 00:00:00+00:00 \n", + "1 2022-02-27 00:00:00+00:00 \n", + "2 2022-02-26 00:00:00+00:00 \n", + "3 2022-02-25 00:00:00+00:00 \n", + "4 2022-02-24 00:00:00+00:00 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def clean_stats(stats_json) -> pd.DataFrame:\n", " df = pd.json_normalize(stats_json)\n", @@ -459,9 +1986,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'CO₂ concentrations Values for Texas, Dallas (Jan 2015- Feb 2022)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(20, 10))\n", "\n", @@ -483,18 +2031,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-02-26T00:00:00+00:00\n" + ] + } + ], "source": [ "print(items[2][\"properties\"][\"datetime\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2geos-co2-daygrid-v10r/items/oco2geos-co2-daygrid-v10r-20220226/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=xco2&color_formula=gamma+r+1.05&colormap_name=magma&rescale=411.7429234611336%2C423.60419320175424'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.3125, -90.25, 179.6875, 90.25],\n", + " 'center': [-0.3125, 0.0, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "oco2_3 = requests.get(\n", " f\"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?\"\n", @@ -507,9 +2081,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Use bbox initial zoom and map\n", "# Set up a map located w/in event bounds\n", diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb index 4decea51..bbc12dbc 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2022_User_Notebook.ipynb @@ -79,9 +79,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -116,9 +125,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2022',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'}],\n", + " 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2022',\n", + " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", + " 'temporal': {'interval': [['2000-01-01T00:00:00+00:00',\n", + " '2021-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'dashboard': {'assets': ['co2-emissions'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-10, 60]],\n", + " 'colormap_name': 'jet'},\n", + " 'co2-emissions': {'assets': ['co2-emissions'],\n", + " 'nodata': 0,\n", + " 'rescale': [[-10, 60]],\n", + " 'colormap_name': 'jet'}},\n", + " 'providers': [{'url': 'https://www.nies.go.jp',\n", + " 'name': 'National Institute for Environmental Studies',\n", + " 'roles': ['producer', 'licensor']}],\n", + " 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n", + " 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2022 version containing monthly data from January 2000 to December 2021 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n", + " 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -189,9 +249,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 264 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -212,9 +280,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2022-202112',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/map?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2022/odiac2022_1km_excl_intl_202112.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [21600.0, 43200.0],\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 2497.01904296875,\n", + " 'min': -138.71914672851562,\n", + " 'count': 11.0,\n", + " 'buckets': [523457.0, 691.0, 95.0, 28.0, 11.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", + " 'statistics': {'mean': 0.9804128408432007,\n", + " 'stddev': 14.766693454324674,\n", + " 'maximum': 2497.01904296875,\n", + " 'minimum': -138.71914672851562,\n", + " 'valid_percent': 100.0}}],\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': [0.008333333333333333,\n", + " 0.0,\n", + " -180.0,\n", + " 0.0,\n", + " -0.008333333333333333,\n", + " 90.0,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/preview.png?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'odiac-ffco2-monthgrid-v2022',\n", + " 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2021-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -273,9 +441,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -307,9 +493,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-200001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the 2000 tile\n", "# 2000\n", @@ -344,9 +548,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", "\n", @@ -419,9 +772,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -449,9 +909,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 264 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -464,9 +932,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2022-202112',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/map?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2022/odiac2022_1km_excl_intl_202112.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [21600.0, 43200.0],\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 2497.01904296875,\n", + " 'min': -138.71914672851562,\n", + " 'count': 11.0,\n", + " 'buckets': [523457.0, 691.0, 95.0, 28.0, 11.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", + " 'statistics': {'mean': 0.9804128408432007,\n", + " 'stddev': 14.766693454324674,\n", + " 'maximum': 2497.01904296875,\n", + " 'minimum': -138.71914672851562,\n", + " 'valid_percent': 100.0}}],\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': [0.008333333333333333,\n", + " 0.0,\n", + " -180.0,\n", + " 0.0,\n", + " -0.008333333333333333,\n", + " 90.0,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202112/preview.png?assets=co2-emissions&nodata=0&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'odiac-ffco2-monthgrid-v2022',\n", + " 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2021-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -523,9 +1091,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.39 s, sys: 525 ms, total: 2.91 s\n", + "Wall time: 5min 22s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -537,9 +1114,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.0,\n", + " 'max': 404594.21875,\n", + " 'mean': 12.983534812927246,\n", + " 'count': 518400.0,\n", + " 'sum': 6730664.5,\n", + " 'std': 1073.4786327635964,\n", + " 'median': 0.0,\n", + " 'majority': 0.0,\n", + " 'minority': 0.7153176665306091,\n", + " 'unique': 160223.0,\n", + " 'histogram': [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n", + " [0.0,\n", + " 40459.421875,\n", + " 80918.84375,\n", + " 121378.265625,\n", + " 161837.6875,\n", + " 202297.109375,\n", + " 242756.53125,\n", + " 283215.9375,\n", + " 323675.375,\n", + " 364134.8125,\n", + " 404594.21875]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 518400.0,\n", + " 'percentile_2': 0.0,\n", + " 'percentile_98': 120.91593933105469}},\n", + " 'start_datetime': '2021-12'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -554,9 +1169,195 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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start_datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-120.0404594.2187512.983535518400.06730664.51073.4786330.00.00.715318160223.0[[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...100.00.0518400.00.0120.9159392021-12-01
12021-110.0379500.7187512.181822518400.06315056.51006.9004420.00.00.671284160209.0[[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...100.00.0518400.00.0113.4725272021-11-01
22021-100.0365564.1250011.742121518400.06087115.5969.9247070.00.00.647386160210.0[[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...100.00.0518400.00.0109.4328692021-10-01
32021-090.0369532.5312511.863684518400.06150133.5980.4529250.00.00.653934160213.0[[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...100.00.0518400.00.0110.5230562021-09-01
42021-080.0412252.3437513.224326518400.06855490.51093.7968850.00.00.728647160224.0[[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...100.00.0518400.00.0123.0584032021-08-01
\n", + "
" + ], + "text/plain": [ + " start_datetime min max mean count sum \\\n", + "0 2021-12 0.0 404594.21875 12.983535 518400.0 6730664.5 \n", + "1 2021-11 0.0 379500.71875 12.181822 518400.0 6315056.5 \n", + "2 2021-10 0.0 365564.12500 11.742121 518400.0 6087115.5 \n", + "3 2021-09 0.0 369532.53125 11.863684 518400.0 6150133.5 \n", + "4 2021-08 0.0 412252.34375 13.224326 518400.0 6855490.5 \n", + "\n", + " std median majority minority unique \\\n", + "0 1073.478633 0.0 0.0 0.715318 160223.0 \n", + "1 1006.900442 0.0 0.0 0.671284 160209.0 \n", + "2 969.924707 0.0 0.0 0.647386 160210.0 \n", + "3 980.452925 0.0 0.0 0.653934 160213.0 \n", + "4 1093.796885 0.0 0.0 0.728647 160224.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 100.0 \n", + "1 [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 100.0 \n", + "2 [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 100.0 \n", + "3 [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 100.0 \n", + "4 [[518384.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 0.0 518400.0 0.0 120.915939 2021-12-01 \n", + "1 0.0 518400.0 0.0 113.472527 2021-11-01 \n", + "2 0.0 518400.0 0.0 109.432869 2021-10-01 \n", + "3 0.0 518400.0 0.0 110.523056 2021-09-01 \n", + "4 0.0 518400.0 0.0 123.058403 2021-08-01 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -592,9 +1393,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -644,9 +1456,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-10-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Print the properties of the 3rd item in the collection\n", "print(items[2][\"properties\"][\"start_datetime\"])" @@ -654,9 +1474,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2022/items/odiac-ffco2-monthgrid-v2022-202110/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-138.71914672851562%2C2497.01904296875'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the October tile\n", "october_tile = requests.get(\n", @@ -682,9 +1520,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the October tile\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb index 8c969671..d25a55e1 100644 --- a/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb +++ b/user_data_notebooks/odiac-ffco2-monthgrid-v2023_User_Notebook.ipynb @@ -82,9 +82,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -119,9 +128,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2023',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'}],\n", + " 'title': 'ODIAC Fossil Fuel CO₂ Emissions v2023',\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", + " 'temporal': {'interval': [['2000-01-01 00:00:00+00',\n", + " '2022-12-31 00:00:00+00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'renders': {'dashboard': {'assets': ['co2-emissions'],\n", + " 'rescale': [[-10, 60]],\n", + " 'colormap_name': 'jet'},\n", + " 'co2-emissions': {'assets': ['co2-emissions'],\n", + " 'rescale': [[-10, 60]],\n", + " 'colormap_name': 'jet'}},\n", + " 'providers': [{'url': 'https://www.nies.go.jp',\n", + " 'name': 'National Institute for Environmental Studies',\n", + " 'roles': ['producer', 'licensor']}],\n", + " 'summaries': {'datetime': ['2000-01-01T00:00:00Z', '2022-12-31T00:00:00Z']},\n", + " 'description': 'The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2023 version containing monthly data from January 2000 to December 2022 that replaces all previous versions. The source dataset can be found at https://doi.org/10.17595/20170411.001',\n", + " 'item_assets': {'co2-emissions': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -192,9 +250,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 276 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -215,9 +281,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/map?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2023/odiac2023_1km_excl_intl_202212.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [21600, 43200],\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 31415.447265625,\n", + " 'min': -675.1028442382812,\n", + " 'count': 11,\n", + " 'buckets': [14870, 14, 1, 0, 0, 1, 0, 0, 0, 1]},\n", + " 'statistics': {'mean': 38.57990192785652,\n", + " 'stddev': 332.3921410156093,\n", + " 'maximum': 31415.447265625,\n", + " 'minimum': -675.1028442382812,\n", + " 'valid_percent': 2.8394699096679688}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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': [0.008333333333333333,\n", + " 0.0,\n", + " -180.0,\n", + " 0.0,\n", + " -0.008333333333333333,\n", + " 90.0,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/preview.png?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'odiac-ffco2-monthgrid-v2023',\n", + " 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2022-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -276,9 +444,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -310,9 +496,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_200001/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the 2000 tile\n", "# 2000\n", @@ -347,9 +551,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", "\n", @@ -422,9 +775,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -452,9 +912,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 276 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -467,9 +935,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/map?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'co2-emissions': {'href': 's3://ghgc-data-store/odiac-ffco2-monthgrid-v2023/odiac2023_1km_excl_intl_202212.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil Fuel CO₂ Emissions',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [21600, 43200],\n", + " 'description': 'Model-estimated monthly, 1 km resolution CO₂ emissions from fossil fuel combustion, cement production and gas flaring created using space-based nighttime light data and individual power plant emission/location profiles.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 31415.447265625,\n", + " 'min': -675.1028442382812,\n", + " 'count': 11,\n", + " 'buckets': [14870, 14, 1, 0, 0, 1, 0, 0, 0, 1]},\n", + " 'statistics': {'mean': 38.57990192785652,\n", + " 'stddev': 332.3921410156093,\n", + " 'maximum': 31415.447265625,\n", + " 'minimum': -675.1028442382812,\n", + " 'valid_percent': 2.8394699096679688}}],\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, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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': [0.008333333333333333,\n", + " 0.0,\n", + " -180.0,\n", + " 0.0,\n", + " -0.008333333333333333,\n", + " 90.0,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202212/preview.png?assets=co2-emissions&rescale=-10%2C60&colormap_name=jet',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-180, -90],\n", + " [180, -90],\n", + " [180, 90],\n", + " [-180, 90],\n", + " [-180, -90]]]},\n", + " 'collection': 'odiac-ffco2-monthgrid-v2023',\n", + " 'properties': {'end_datetime': '2022-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2022-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -526,9 +1096,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.5 s, sys: 543 ms, total: 3.04 s\n", + "Wall time: 5min 6s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -540,9 +1119,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 0.7438682317733765,\n", + " 'max': 420840.1875,\n", + " 'mean': 39.4478874206543,\n", + " 'count': 177466.0,\n", + " 'sum': 7000659.0,\n", + " 'std': 1908.1162831441904,\n", + " 'median': 3.2146987915039062,\n", + " 'majority': 64.48101043701172,\n", + " 'minority': 0.7438682317733765,\n", + " 'unique': 160218.0,\n", + " 'histogram': [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n", + " [0.7438682317733765,\n", + " 42084.6875,\n", + " 84168.6328125,\n", + " 126252.578125,\n", + " 168336.53125,\n", + " 210420.46875,\n", + " 252504.421875,\n", + " 294588.375,\n", + " 336672.3125,\n", + " 378756.25,\n", + " 420840.1875]],\n", + " 'valid_percent': 34.23,\n", + " 'masked_pixels': 340934.0,\n", + " 'valid_pixels': 177466.0,\n", + " 'percentile_2': 1.3178801536560059,\n", + " 'percentile_98': 220.57638549804688}},\n", + " 'start_datetime': '2022-12'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -557,9 +1174,195 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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42022-080.910771516733.5312548.361969177466.08582605.02342.8972453.93584378.9486690.910771160211.0[[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,...34.23340934.0177466.01.613806270.0672912022-08-01
\n", + "
" + ], + "text/plain": [ + " start_datetime min max mean count sum \\\n", + "0 2022-12 0.743868 420840.18750 39.447887 177466.0 7000659.0 \n", + "1 2022-11 0.608937 343526.96875 32.262497 177466.0 5725496.5 \n", + "2 2022-10 0.617746 348376.68750 32.727840 177466.0 5808079.0 \n", + "3 2022-09 0.737875 417608.00000 39.143070 177466.0 6946564.0 \n", + "4 2022-08 0.910771 516733.53125 48.361969 177466.0 8582605.0 \n", + "\n", + " std median majority minority unique \\\n", + "0 1908.116283 3.214699 64.481010 0.743868 160218.0 \n", + "1 1557.576964 2.635552 52.784710 0.608937 160204.0 \n", + "2 1579.566634 2.674915 53.548267 0.617746 160216.0 \n", + "3 1893.460654 3.190599 63.961494 0.737875 160221.0 \n", + "4 2342.897245 3.935843 78.948669 0.910771 160211.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 34.23 \n", + "1 [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 34.23 \n", + "2 [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 34.23 \n", + "3 [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 34.23 \n", + "4 [[177450.0, 9.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,... 34.23 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 340934.0 177466.0 1.317880 220.576385 2022-12-01 \n", + "1 340934.0 177466.0 1.081165 180.777283 2022-11-01 \n", + "2 340934.0 177466.0 1.097266 183.435852 2022-10-01 \n", + "3 340934.0 177466.0 1.308810 219.010132 2022-09-01 \n", + "4 340934.0 177466.0 1.613806 270.067291 2022-08-01 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -595,9 +1398,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -647,9 +1461,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-10-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Print the properties of the 3rd item in the collection\n", "print(items[2][\"properties\"][\"start_datetime\"])" @@ -657,9 +1479,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/odiac-ffco2-monthgrid-v2023/items/odiac-ffco2-monthgrid-v2023-odiac2023_1km_excl_intl_202210/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2-emissions&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-675.1028442382812%2C31415.447265625'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the October tile\n", "october_tile = requests.get(\n", @@ -685,9 +1525,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the October tile\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb index 885e7dd8..221daba3 100644 --- a/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/tm54dvar-ch4flux-monthgrid-v1_User_Notebook.ipynb @@ -66,9 +66,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Import the following libraries\n", "import requests\n", @@ -92,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -112,9 +121,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'tm54dvar-ch4flux-monthgrid-v1',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'}],\n", + " 'title': 'TM5-4DVar Isotopic CH4 Inverse Fluxes',\n", + " 'assets': None,\n", + " 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},\n", + " 'temporal': {'interval': [['1999-01-01T00:00:00+00:00',\n", + " '2016-12-31T00:00:00+00:00']]}},\n", + " 'license': 'CC-BY-4.0',\n", + " 'keywords': None,\n", + " 'providers': None,\n", + " 'summaries': {'datetime': ['1999-01-01T00:00:00Z', '2016-12-31T00:00:00Z']},\n", + " 'description': 'Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 1.',\n", + " 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total CH4 Emission',\n", + " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources'},\n", + " 'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil CH4 Emission',\n", + " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},\n", + " 'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Microbial CH4 Emission',\n", + " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.'},\n", + " 'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Pyrogenic CH4 Emission',\n", + " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': None,\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'month'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch the collection from the STAC API using the appropriate endpoint\n", "# The 'requests' library allows a HTTP request possible\n", @@ -134,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -185,9 +248,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 216 items\n" + ] + } + ], "source": [ "# Apply the function created above \"get_item_count\" to the data collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -201,9 +272,229 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612'}],\n", + " 'assets': {'total': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_total_201612.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total CH4 Emission',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [180.0, 360.0],\n", + " 'description': 'Total methane emission from microbial, fossil and pyrogenic sources',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 207.09559432166358,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [64446.0, 253.0, 61.0, 16.0, 14.0, 4.0, 3.0, 0.0, 2.0, 1.0]},\n", + " 'statistics': {'mean': 0.7699816366032659,\n", + " 'stddev': 3.8996905358416045,\n", + " 'maximum': 207.09559432166358,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 0.00154320987654321}}],\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': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", + " 'fossil': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_fossil_201612.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Fossil CH4 Emission',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [180.0, 360.0],\n", + " 'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 202.8189294183266,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [64633.0, 107.0, 35.0, 11.0, 8.0, 3.0, 1.0, 1.0, 0.0, 1.0]},\n", + " 'statistics': {'mean': 0.27127687553584495,\n", + " 'stddev': 2.731411670166909,\n", + " 'maximum': 202.8189294183266,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 0.00154320987654321}}],\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': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", + " 'microbial': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_microbial_201612.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Microbial CH4 Emission',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [180.0, 360.0],\n", + " 'description': 'Emission of methane from all microbial sources, such as wetlands, agriculture and termites.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 161.4604621003495,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [64610.0, 155.0, 22.0, 5.0, 2.0, 2.0, 2.0, 1.0, 0.0, 1.0]},\n", + " 'statistics': {'mean': 0.46611433673211145,\n", + " 'stddev': 2.2910210071489456,\n", + " 'maximum': 161.4604621003495,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 0.00154320987654321}}],\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': [1.0, 0.0, -180.0, 0.0, -1.0, 90.0, 0.0, 0.0, 1.0]},\n", + " 'pyrogenic': {'href': 's3://ghgc-data-store/tm54dvar-ch4flux-monthgrid-v1/methane_emis_pyrogenic_201612.tif',\n", + " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Pyrogenic CH4 Emission',\n", + " 'proj:bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'proj:epsg': 4326.0,\n", + " 'proj:shape': [180.0, 360.0],\n", + " 'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop burning.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float64',\n", + " 'histogram': {'max': 13.432528617097262,\n", + " 'min': 0.0,\n", + " 'count': 11.0,\n", + " 'buckets': [64440.0, 221.0, 78.0, 24.0, 18.0, 8.0, 3.0, 1.0, 1.0, 6.0]},\n", + " 'statistics': {'mean': 0.032590424335309266,\n", + " 'stddev': 0.28279054181617735,\n", + " 'maximum': 13.432528617097262,\n", + " 'minimum': 0.0,\n", + " 'valid_percent': 0.00154320987654321}}],\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': [1.0, 0.0, -180.0, 0.0, -1.0, 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': 'tm54dvar-ch4flux-monthgrid-v1',\n", + " 'properties': {'end_datetime': '2016-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2016-12-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': []}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -222,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -262,9 +553,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201612/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Choose a color map for displaying the first observation (event)\n", "# Please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", @@ -295,9 +604,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-199912/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Make a GET request to retrieve information for the 1999 tile\n", "ch4_flux_2 = requests.get(\n", @@ -331,9 +658,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California\n", "# To change the location, you can simply insert the latitude and longitude of the area of your interest in the \"location=(LAT, LONG)\" statement\n", @@ -384,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -409,9 +885,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -439,9 +1022,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 216 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -454,9 +1045,229 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'tm54dvar-ch4flux-monthgrid-v1-201612',\n", + " 'bbox': [-180.0, -90.0, 180.0, 90.0],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 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+ " 'stac_extensions': []}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -471,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -508,9 +1319,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016-12-01\n" + ] + } + ], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -532,9 +1351,234 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], 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156.53478735550414, 'std': 4.633345420525916, 'median': 2.5974040079993723, 'majority': 0.08095810131399311, 'minority': 0.08095810131399311, 'unique': 36.0, 'histogram': [[17.0, 8.0, 6.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.08095810131399311, 2.5551572536876024, 5.029356406061212, 7.5035555584348215, 9.97775471080843, 12.45195386318204, 14.926153015555649, 17.40035216792926, 19.874551320302867, 22.348750472676475, 24.822949625050086]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.08095810131399311, 'percentile_98': 24.822949625050086}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0809581013139931, 'max': 24.822949625050086, 'mean': 4.3481885376528915, 'count': 36.0, 'sum': 156.5347873555041, 'std': 4.633345420525916, 'median': 2.5974040079993728, 'majority': 0.0809581013139931, 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" 'sum': 325.42391340606196,\n", + " 'std': 11.97160706711745,\n", + " 'median': 3.662876577293575,\n", + " 'majority': 0.0464402866499578,\n", + " 'minority': 0.0464402866499578,\n", + " 'unique': 36.0,\n", + " 'histogram': [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 1.0],\n", + " [0.0464402866499578,\n", + " 5.003175128588197,\n", + " 9.959909970526436,\n", + " 14.916644812464675,\n", + " 19.873379654402914,\n", + " 24.830114496341153,\n", + " 29.786849338279392,\n", + " 34.74358418021763,\n", + " 39.700319022155874,\n", + " 44.65705386409412,\n", + " 49.61378870603235]],\n", + " 'valid_percent': 100.0,\n", + " 'masked_pixels': 0.0,\n", + " 'valid_pixels': 36.0,\n", + " 'percentile_2': 0.0464402866499578,\n", + " 'percentile_98': 49.61378870603235}},\n", + " 'datetime': '2016-12-01'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -562,9 +1644,195 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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02016-12-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-12-01
12016-11-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-11-01
22016-10-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-10-01
32016-09-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-09-01
42016-08-010.0464449.6137899.03955336.0325.42391311.9716073.6628770.046440.0464436.0[[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0...100.00.036.00.0464449.6137892016-08-01
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count sum std \\\n", + "0 2016-12-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", + "1 2016-11-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", + "2 2016-10-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", + "3 2016-09-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", + "4 2016-08-01 0.04644 49.613789 9.039553 36.0 325.423913 11.971607 \n", + "\n", + " median majority minority unique \\\n", + "0 3.662877 0.04644 0.04644 36.0 \n", + "1 3.662877 0.04644 0.04644 36.0 \n", + "2 3.662877 0.04644 0.04644 36.0 \n", + "3 3.662877 0.04644 0.04644 36.0 \n", + "4 3.662877 0.04644 0.04644 36.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", + "1 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", + "2 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", + "3 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", + "4 [[18.0, 9.0, 1.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0... 100.0 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 0.0 36.0 0.04644 49.613789 2016-12-01 \n", + "1 0.0 36.0 0.04644 49.613789 2016-11-01 \n", + "2 0.0 36.0 0.04644 49.613789 2016-10-01 \n", + "3 0.0 36.0 0.04644 49.613789 2016-09-01 \n", + "4 0.0 36.0 0.04644 49.613789 2016-08-01 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -600,9 +1868,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", @@ -652,9 +1931,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016-10-01T00:00:00+00:00\n" + ] + } + ], "source": [ "# Print the properties for the 3rd item in the collection\n", "print(items[2][\"properties\"][\"start_datetime\"])" @@ -662,9 +1949,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/tm54dvar-ch4flux-monthgrid-v1/items/tm54dvar-ch4flux-monthgrid-v1-201610/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C202.8189294183266'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-180.0, -90.0, 180.0, 90.0],\n", + " 'center': [0.0, 0.0, 0]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A GET request is made for the 3rd granule\n", "ch4_flux_3 = requests.get(\n", @@ -690,9 +1995,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the tile\n", "aoi_map_bbox = Map(\n", diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 0ea45402..228b47a8 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -68,10 +68,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "id": "503e5661-b99b-421b-b4ba-361bb531e5fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import requests\n", "import folium\n", @@ -94,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "id": "a7602b2d-2650-42cb-9ae3-09eb3fe7891b", "metadata": {}, "outputs": [], @@ -110,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "941eae92-6ec0-46a4-ab8f-739973b59e6f", "metadata": {}, "outputs": [], @@ -130,17 +139,139 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "79b48141-ccc5-42ac-bc8f-1b480398265c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'vulcan-ffco2-yeargrid-v4',\n", + " 'type': 'Collection',\n", + " 'links': [{'rel': 'items',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/items'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'}],\n", + " 'title': 'Vulcan Fossil Fuel CO₂ Emissions v4.0',\n", + " 'extent': {'spatial': {'bbox': [[-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907]]},\n", + " 'temporal': {'interval': [['2010-01-01 00:00:00+00',\n", + " '2021-12-31 00:00:00+00']]}},\n", + " 'license': 'CC-BY-NC-4.0',\n", + " 'renders': {'air-co2': {'assets': ['air-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'cmt-co2': {'assets': ['cmt-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'cmv-co2': {'assets': ['cmv-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'com-co2': {'assets': ['com-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'elc-co2': {'assets': ['elc-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'ind-co2': {'assets': ['ind-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'nrd-co2': {'assets': ['nrd-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'onr-co2': {'assets': ['onr-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'res-co2': {'assets': ['res-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'rrd-co2': {'assets': ['rrd-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'dashboard': {'assets': ['total-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'},\n", + " 'total-co2': {'assets': ['total-co2'],\n", + " 'rescale': [[0, 500]],\n", + " 'colormap_name': 'spectral_r'}},\n", + " 'providers': [{'url': 'https://vulcan.rc.nau.edu/',\n", + " 'name': 'North American Carbon Program',\n", + " 'roles': ['producer', 'licensor']}],\n", + " 'summaries': {'datetime': ['2010-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},\n", + " 'description': 'Annual (2010 - 2021), 1 km resolution estimates of carbon dioxide emissions from fossil fuels and cement production over the contiguous United States, version 4.0',\n", + " 'item_assets': {'air-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Airport Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.'},\n", + " 'cmt-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Cement Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from cement production.'},\n", + " 'cmv-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Commercial Marine Vessel Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from commercial marine vessels while maneuvering, hoteling, cruising and traveling within reduced speed zones at ports and shipping lanes. Includes only activity within 12 nautical miles (~22km) from the U.S. shoreline.'},\n", + " 'com-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Commercial Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from Commercial buildings.'},\n", + " 'elc-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Power Plant Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from power plants.'},\n", + " 'ind-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Industrial Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from Industrial buildings.'},\n", + " 'nrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Non-Road Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from off-road engines, equipment and vehicles including waterborne pleasure craft.'},\n", + " 'onr-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'On-Road Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from mobile vehicles on roads.'},\n", + " 'res-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Residential Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual ffCO₂ emissions from Residential buildings.'},\n", + " 'rrd-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Railroad Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual FFCO₂ emissions coming from railroads.'},\n", + " 'total-co2': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Fossil Fuel CO₂ Emissions',\n", + " 'description': 'Estimated total annual CO₂ emissions from fossil fuel combustion (ffCO₂) across all sectors..'}},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',\n", + " 'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],\n", + " 'dashboard:is_periodic': True,\n", + " 'dashboard:time_density': 'year'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "collection_vulcan" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "b56d6c87-11b9-48ca-9723-8304984a644b", "metadata": {}, "outputs": [], @@ -170,10 +301,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "eaaf1487-2e71-490b-a968-e57ee332a919", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 items\n" + ] + } + ], "source": [ "# Apply the above function and check the total number of items available within the collection\n", "number_of_items = get_item_count(collection_name)\n", @@ -183,10 +322,708 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "19177b0a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'vulcan-ffco2-yeargrid-v4-2021',\n", + " 'bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/map?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'air-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/AIR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n", + " 'type': 'image/tiff; 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'proj:projjson': {'id': {'code': 4326, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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': [0.01079015211329739,\n", + " 0.0,\n", + " -128.22654896758996,\n", + " 0.0,\n", + " -0.01079015211329739,\n", + " 51.44087947724907,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'rendered_preview': {'title': 'Rendered preview',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/preview.png?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n", + " 'rel': 'preview',\n", + " 'roles': ['overview'],\n", + " 'type': 'image/png'}},\n", + " 'geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-128.22654896758996, 22.857766529124284],\n", + " [-65.30917199495289, 22.857766529124284],\n", + " [-65.30917199495289, 51.44087947724907],\n", + " [-128.22654896758996, 51.44087947724907],\n", + " [-128.22654896758996, 22.857766529124284]]]},\n", + " 'collection': 'vulcan-ffco2-yeargrid-v4',\n", + " 'properties': {'end_datetime': '2021-12-31T00:00:00+00:00',\n", + " 'start_datetime': '2021-01-01T00:00:00+00:00'},\n", + " 'stac_version': '1.0.0',\n", + " 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", + " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection\n", @@ -195,7 +1032,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "id": "01619ee2-728c-4abc-9edf-a2c85d87128d", "metadata": {}, "outputs": [], @@ -208,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "id": "f9deb11d-4c33-4f59-8532-7829602e3113", "metadata": {}, "outputs": [], @@ -226,10 +1063,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "27b31984-ac8e-473f-ba2d-eb309c9708c1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=total-co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'center': [-96.76786048127143, 37.14932300318668, 0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "color_map = \"spectral_r\" # please refer to matplotlib library if you'd prefer choosing a different color ramp.\n", "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", @@ -247,10 +1105,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "80f5933d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2010/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=total-co2&color_formula=gamma+r+1.05&colormap_name=spectral_r&rescale=0%2C150'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'center': [-96.76786048127143, 37.14932300318668, 0]}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "_2010_tile = requests.get(\n", " f\"{RASTER_API_URL}/collections/{items['2010']['collection']}/items/{items['2010']['id']}/tilejson.json?\"\n", @@ -272,10 +1151,159 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6cf3c0f1-8d39-4cdd-96c7-96e6229c1584", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)\n", "\n", @@ -315,7 +1343,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 13, "id": "8f1c5e47", "metadata": {}, "outputs": [], @@ -341,10 +1369,117 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "65d66484", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new map to display the generated polygon\n", "# We'll plug in the coordinates for a location\n", @@ -372,10 +1507,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "7b0cf1d8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 items\n" + ] + } + ], "source": [ "# Check total number of items available within the collection\n", "items = requests.get(\n", @@ -388,10 +1531,708 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "960fc2da", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'vulcan-ffco2-yeargrid-v4-2021',\n", + " 'bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'type': 'Feature',\n", + " 'links': [{'rel': 'collection',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n", + " {'rel': 'parent',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4'},\n", + " {'rel': 'root',\n", + " 'type': 'application/json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/'},\n", + " {'rel': 'self',\n", + " 'type': 'application/geo+json',\n", + " 'href': 'https://earth.gov/ghgcenter/api/stac/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021'},\n", + " {'title': 'Map of Item',\n", + " 'href': 'https://earth.gov/ghgcenter/api/raster/collections/vulcan-ffco2-yeargrid-v4/items/vulcan-ffco2-yeargrid-v4-2021/map?assets=total-co2&rescale=0%2C500&colormap_name=spectral_r',\n", + " 'rel': 'preview',\n", + " 'type': 'text/html'}],\n", + " 'assets': {'air-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/AIR_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n", + " 'type': 'image/tiff; application=geotiff',\n", + " 'roles': ['data', 'layer'],\n", + " 'title': 'Total Airport CO₂ Emissions',\n", + " 'proj:bbox': [-128.22654896758996,\n", + " 22.857766529124284,\n", + " -65.30917199495289,\n", + " 51.44087947724907],\n", + " 'proj:epsg': 4326,\n", + " 'proj:wkt2': 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]',\n", + " 'proj:shape': [2649, 5831],\n", + " 'description': 'Estimated total annual ffCO₂ emissions from taxi, take-off, and landing up to 3000 ft.',\n", + " 'raster:bands': [{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 318726.1875,\n", + " 'min': 0.11889950931072235,\n", + " 'count': 11,\n", + " 'buckets': [14659, 40, 6, 2, 4, 0, 2, 0, 0, 1]},\n", + " 'statistics': {'mean': 1190.7966562457523,\n", + " 'stddev': 5906.230747537605,\n", + " 'maximum': 318726.1875,\n", + " 'minimum': 0.11889950931072235,\n", + " 'valid_percent': 3.083506571888412}}],\n", + " 'proj:geometry': {'type': 'Polygon',\n", + " 'coordinates': [[[-128.22654896758996, 22.857766529124284],\n", + " [-65.30917199495289, 22.857766529124284],\n", + " [-65.30917199495289, 51.44087947724907],\n", + " [-128.22654896758996, 51.44087947724907],\n", + " [-128.22654896758996, 22.857766529124284]]]},\n", + " 'proj:projjson': {'id': {'code': 4326, '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,\n", + " 'inverse_flattening': 298.257223563}},\n", + " '$schema': 'https://proj.org/schemas/v0.7/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': [0.01079015211329739,\n", + " 0.0,\n", + " -128.22654896758996,\n", + " 0.0,\n", + " -0.01079015211329739,\n", + " 51.44087947724907,\n", + " 0.0,\n", + " 0.0,\n", + " 1.0]},\n", + " 'cmt-co2': {'href': 's3://ghgc-data-store/vulcan-ffco2-yeargrid-v4/CMT_CO2_USA_mosaic_grid_1km_mn_2021.tif',\n", + " 'type': 'image/tiff; 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+ "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Examine the first item in the collection\n", "items[0]" @@ -407,7 +2248,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 17, "id": "8c7dcae7", "metadata": {}, "outputs": [], @@ -445,10 +2286,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "d3666ccb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-01-01\n" + ] + } + ], "source": [ "# Generate a for loop that iterates over all the existing items in the collection\n", "for item in items:\n", @@ -470,10 +2319,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "fce6ec4a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], 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'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.212051123380661, 'percentile_98': 1615.6910400390625}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.9114715144041838e-07, 'max': 4798060.5, 'mean': 255.62869262695312, 'count': 296120.0625, 'sum': 75696784.0, 'std': 14444.495145210165, 'median': 7.578545093536377, 'majority': 0.5363049507141113, 'minority': 1.9114715144041838e-07, 'unique': 242933.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0], [1.9114715144041838e-07, 479806.0625, 959612.125, 1439418.25, 1919224.25, 2399030.25, 2878836.5, 3358642.5, 3838448.5, 4318254.5, 4798060.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.21587365865707397, 'percentile_98': 1612.6146240234375}}}}\n", + "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 1.898683592571615e-07, 'max': 4486994.5, 'mean': 243.4835662841797, 'count': 296120.0625, 'sum': 72100368.0, 'std': 13255.782134600735, 'median': 7.461791038513184, 'majority': 0.5328000783920288, 'minority': 1.898683592571615e-07, 'unique': 239140.0, 'histogram': [[296505.0, 10.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0], [1.898683592571615e-07, 448699.4375, 897398.875, 1346098.25, 1794797.75, 2243497.25, 2692196.5, 3140896.0, 3589595.5, 4038295.0, 4486994.5]], 'valid_percent': 95.72, 'masked_pixels': 13265.0, 'valid_pixels': 296520.0, 'percentile_2': 0.2142595797777176, 'percentile_98': 1595.547119140625}}}}\n", + "CPU times: user 92.6 ms, sys: 21 ms, total: 114 ms\n", + "Wall time: 23.5 s\n" + ] + } + ], "source": [ "%%time\n", "# %%time = Wall time (execution time) for running the code below\n", @@ -492,10 +2362,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "ee0e9a7e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'statistics': {'b1': {'min': 1.7858106104995386e-07,\n", + " 'max': 3768974.0,\n", + " 'mean': 261.6907958984375,\n", + " 'count': 296120.0625,\n", + " 'sum': 77491896.0,\n", + " 'std': 11522.940944047226,\n", + " 'median': 7.39058256149292,\n", + " 'majority': 0.19903208315372467,\n", + " 'minority': 1.7858106104995386e-07,\n", + " 'unique': 242084.0,\n", + " 'histogram': [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2.0],\n", + " [1.7858106104995386e-07,\n", + " 376897.40625,\n", + " 753794.8125,\n", + " 1130692.25,\n", + " 1507589.625,\n", + " 1884487.0,\n", + " 2261384.5,\n", + " 2638281.75,\n", + " 3015179.25,\n", + " 3392076.75,\n", + " 3768974.0]],\n", + " 'valid_percent': 95.72,\n", + " 'masked_pixels': 13265.0,\n", + " 'valid_pixels': 296520.0,\n", + " 'percentile_2': 0.20036202669143677,\n", + " 'percentile_98': 1811.0848388671875}},\n", + " 'datetime': '2021-01-01'}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Print the stats for the first item in the collection\n", "stats[0]" @@ -503,10 +2411,196 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "d6b249ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02021-01-011.785811e-073768974.0261.690796296120.062577491896.011522.9409447.3905830.1990321.785811e-07242084.0[[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0...95.7213265.0296520.00.2003621811.0848392021-01-01
12020-01-011.622880e-073548930.5254.962936296120.062575499640.010912.7683016.8367270.1808451.622880e-07242118.0[[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.1822241713.1064452020-01-01
22019-01-011.877873e-073369803.0276.884796296120.062581991144.011799.9111867.8145980.5271551.877873e-07242967.0[[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0...95.7213265.0296520.00.2122441882.4636232019-01-01
32018-01-011.853067e-073868559.5278.522980296120.062582476240.012222.4167827.7118600.5199301.853067e-07242928.0[[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0...95.7213265.0296520.00.2089771912.6325682018-01-01
42017-01-011.802980e-074050962.5271.169403296120.062580298704.013290.4723777.4465570.5057951.802980e-07242922.0[[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0...95.7213265.0296520.00.2032701775.7847902017-01-01
\n", + "
" + ], + "text/plain": [ + " datetime min max mean count sum \\\n", + "0 2021-01-01 1.785811e-07 3768974.0 261.690796 296120.0625 77491896.0 \n", + "1 2020-01-01 1.622880e-07 3548930.5 254.962936 296120.0625 75499640.0 \n", + "2 2019-01-01 1.877873e-07 3369803.0 276.884796 296120.0625 81991144.0 \n", + "3 2018-01-01 1.853067e-07 3868559.5 278.522980 296120.0625 82476240.0 \n", + "4 2017-01-01 1.802980e-07 4050962.5 271.169403 296120.0625 80298704.0 \n", + "\n", + " std median majority minority unique \\\n", + "0 11522.940944 7.390583 0.199032 1.785811e-07 242084.0 \n", + "1 10912.768301 6.836727 0.180845 1.622880e-07 242118.0 \n", + "2 11799.911186 7.814598 0.527155 1.877873e-07 242967.0 \n", + "3 12222.416782 7.711860 0.519930 1.853067e-07 242928.0 \n", + "4 13290.472377 7.446557 0.505795 1.802980e-07 242922.0 \n", + "\n", + " histogram valid_percent \\\n", + "0 [[296498.0, 13.0, 6.0, 0.0, 1.0, 0.0, 0.0, 0.0... 95.72 \n", + "1 [[296495.0, 14.0, 6.0, 2.0, 1.0, 0.0, 0.0, 1.0... 95.72 \n", + "2 [[296495.0, 13.0, 6.0, 3.0, 0.0, 0.0, 0.0, 1.0... 95.72 \n", + "3 [[296497.0, 14.0, 6.0, 0.0, 0.0, 0.0, 1.0, 0.0... 95.72 \n", + "4 [[296499.0, 14.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0... 95.72 \n", + "\n", + " masked_pixels valid_pixels percentile_2 percentile_98 date \n", + "0 13265.0 296520.0 0.200362 1811.084839 2021-01-01 \n", + "1 13265.0 296520.0 0.182224 1713.106445 2020-01-01 \n", + "2 13265.0 296520.0 0.212244 1882.463623 2019-01-01 \n", + "3 13265.0 296520.0 0.208977 1912.632568 2018-01-01 \n", + "4 13265.0 296520.0 0.203270 1775.784790 2017-01-01 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a function that converts statistics in JSON format into a pandas DataFrame\n", "def clean_stats(stats_json) -> pd.DataFrame:\n", @@ -542,10 +2636,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "ec61db4a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Figure size: 20 representing the width, 10 representing the height\n", "fig = plt.figure(figsize=(20, 10))\n", From f04eafd8b7cedd3dbf6c73921c1e002658ed8557 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Tue, 17 Sep 2024 13:12:27 -0500 Subject: [PATCH 40/47] fix output window in usernotebooks --- ...arbonflux-monthgrid-v3_User_Notebook.ipynb | 323 ++++++++++-------- 1 file changed, 182 insertions(+), 141 deletions(-) diff --git a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb index 8ae72cef..13876511 100644 --- a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb +++ b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb @@ -71,10 +71,51 @@ "Please run the next cell to import the required libraries. " ] }, + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "!pip install ipywidgets" + ] + }, { "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "81a873d9ec5b4758b1517346d121b416", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output(layout=Layout(border_bottom='1px solid black', border_left='1px solid black', border_right='1px solid b…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display\n", + "import ipywidgets as widgets\n", + "\n", + "# Create a scrollable output widget with a fixed height\n", + "out = widgets.Output(layout={'border': '1px solid black', 'height': '300px', 'overflow_y': 'scroll'})\n", + "\n", + "display(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -93,12 +134,12 @@ "from pystac_client import Client \n", "import branca \n", "import pandas as pd\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -178,7 +219,7 @@ " 'dashboard:time_density': 'month'}" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -249,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -550,7 +591,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -625,7 +666,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -647,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -663,7 +704,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -689,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -722,7 +763,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_c2dadbdc85f53d2c611635e83d2fcf58 {\n", + " #map_2568d271cee858934c7bd78097d8bf6c {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -737,7 +778,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_56fc4f1da7363c98a80cf61b3aa30712 {\n", + " #map_aaa2c8843b2a25305b4c65014f14d94e {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -752,17 +793,17 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_c2dadbdc85f53d2c611635e83d2fcf58" ></div>\n", + " <div class="folium-map" id="map_2568d271cee858934c7bd78097d8bf6c" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_56fc4f1da7363c98a80cf61b3aa30712" ></div>\n", + " <div class="folium-map" id="map_aaa2c8843b2a25305b4c65014f14d94e" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_c2dadbdc85f53d2c611635e83d2fcf58 = L.map(\n", - " "map_c2dadbdc85f53d2c611635e83d2fcf58",\n", + " var map_2568d271cee858934c7bd78097d8bf6c = L.map(\n", + " "map_2568d271cee858934c7bd78097d8bf6c",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -776,31 +817,31 @@ "\n", " \n", " \n", - " var tile_layer_e04b604b8df320e279d8d59b71d9989c = L.tileLayer(\n", + " var tile_layer_cfa09e18609b5b286c3a0b927173dda9 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_e04b604b8df320e279d8d59b71d9989c.addTo(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " tile_layer_cfa09e18609b5b286c3a0b927173dda9.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", " \n", " \n", - " var tile_layer_77820cff3aab7b0993b9174a2e6047bc = L.tileLayer(\n", + " var tile_layer_728763e97e8fed65cc785de578d58220 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-200312/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_77820cff3aab7b0993b9174a2e6047bc.addTo(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " tile_layer_728763e97e8fed65cc785de578d58220.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", " \n", " \n", - " var marker_154298597f31c87adedf9df85f3b2952 = L.marker(\n", + " var marker_2ce7fdb69cd4669c47b849002853be1a = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " ).addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", " \n", " \n", - " marker_154298597f31c87adedf9df85f3b2952.bindTooltip(\n", + " marker_2ce7fdb69cd4669c47b849002853be1a.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -808,58 +849,58 @@ " );\n", " \n", " \n", - " var layer_control_ddff27a392bbc5f922ff30efc5123190_layers = {\n", + " var layer_control_f604ff7163aba74d2b0235581ccd2e37_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_e04b604b8df320e279d8d59b71d9989c,\n", + " "openstreetmap" : tile_layer_cfa09e18609b5b286c3a0b927173dda9,\n", " },\n", " overlays : {\n", - " "December 2003 RH Level" : tile_layer_77820cff3aab7b0993b9174a2e6047bc,\n", + " "December 2003 RH Level" : tile_layer_728763e97e8fed65cc785de578d58220,\n", " },\n", " };\n", - " let layer_control_ddff27a392bbc5f922ff30efc5123190 = L.control.layers(\n", - " layer_control_ddff27a392bbc5f922ff30efc5123190_layers.base_layers,\n", - " layer_control_ddff27a392bbc5f922ff30efc5123190_layers.overlays,\n", + " let layer_control_f604ff7163aba74d2b0235581ccd2e37 = L.control.layers(\n", + " layer_control_f604ff7163aba74d2b0235581ccd2e37_layers.base_layers,\n", + " layer_control_f604ff7163aba74d2b0235581ccd2e37_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " ).addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", "\n", " \n", " \n", - " var color_map_4a7087828047ef3316445a7e76843f5f = {};\n", + " var color_map_ce664534ccf357dce37f00fa6c6037ad = {};\n", "\n", " \n", - " color_map_4a7087828047ef3316445a7e76843f5f.color = d3.scale.threshold()\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 0.003006012024048096, 0.003607214428857715, 0.004208416833667335, 0.004809619238476954, 0.005410821643286573, 0.006012024048096192, 0.006613226452905812, 0.00721442885771543, 0.00781563126252505, 0.00841683366733467, 0.009018036072144289, 0.009619238476953907, 0.010220440881763526, 0.010821643286573146, 0.011422845691382766, 0.012024048096192385, 0.012625250501002003, 0.013226452905811623, 0.013827655310621242, 0.01442885771543086, 0.01503006012024048, 0.0156312625250501, 0.01623246492985972, 0.01683366733466934, 0.017434869739478956, 0.018036072144288578, 0.018637274549098193, 0.019238476953907815, 0.019839679358717437, 0.02044088176352705, 0.021042084168336674, 0.021643286573146292, 0.02224448897795591, 0.022845691382765532, 0.023446893787575147, 0.02404809619238477, 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'#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#f7f4f9ff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#cea4cfff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#e1237dff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff']);\n", " \n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.x = d3.scale.linear()\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.x = d3.scale.linear()\n", " .domain([0.0, 0.3])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.legend = L.control({position: 'topright'});\n", - " color_map_4a7087828047ef3316445a7e76843f5f.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_4a7087828047ef3316445a7e76843f5f.legend.addTo(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.legend = L.control({position: 'topright'});\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.legend.addTo(map_2568d271cee858934c7bd78097d8bf6c);\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.xAxis = d3.svg.axis()\n", - " .scale(color_map_4a7087828047ef3316445a7e76843f5f.x)\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.xAxis = d3.svg.axis()\n", + " .scale(color_map_ce664534ccf357dce37f00fa6c6037ad.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0.0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.g = color_map_4a7087828047ef3316445a7e76843f5f.svg.append("g")\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.g = color_map_ce664534ccf357dce37f00fa6c6037ad.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.g.selectAll("rect")\n", - " .data(color_map_4a7087828047ef3316445a7e76843f5f.color.range().map(function(d, i) {\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.g.selectAll("rect")\n", + " .data(color_map_ce664534ccf357dce37f00fa6c6037ad.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_4a7087828047ef3316445a7e76843f5f.x(color_map_4a7087828047ef3316445a7e76843f5f.color.domain()[i - 1]) : color_map_4a7087828047ef3316445a7e76843f5f.x.range()[0],\n", - " x1: i < color_map_4a7087828047ef3316445a7e76843f5f.color.domain().length ? color_map_4a7087828047ef3316445a7e76843f5f.x(color_map_4a7087828047ef3316445a7e76843f5f.color.domain()[i]) : color_map_4a7087828047ef3316445a7e76843f5f.x.range()[1],\n", + " x0: i ? color_map_ce664534ccf357dce37f00fa6c6037ad.x(color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain()[i - 1]) : color_map_ce664534ccf357dce37f00fa6c6037ad.x.range()[0],\n", + " x1: i < color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain().length ? color_map_ce664534ccf357dce37f00fa6c6037ad.x(color_map_ce664534ccf357dce37f00fa6c6037ad.color.domain()[i]) : color_map_ce664534ccf357dce37f00fa6c6037ad.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -869,13 +910,13 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_4a7087828047ef3316445a7e76843f5f.g.call(color_map_4a7087828047ef3316445a7e76843f5f.xAxis).append("text")\n", + " color_map_ce664534ccf357dce37f00fa6c6037ad.g.call(color_map_ce664534ccf357dce37f00fa6c6037ad.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (kg Carbon/m2/month)");\n", " \n", - " var map_56fc4f1da7363c98a80cf61b3aa30712 = L.map(\n", - " "map_56fc4f1da7363c98a80cf61b3aa30712",\n", + " var map_aaa2c8843b2a25305b4c65014f14d94e = L.map(\n", + " "map_aaa2c8843b2a25305b4c65014f14d94e",\n", " {\n", " center: [31.9, -99.9],\n", " crs: L.CRS.EPSG3857,\n", @@ -889,35 +930,35 @@ "\n", " \n", " \n", - " var tile_layer_4c601209fe169434c369a38150c9af1e = L.tileLayer(\n", + " var tile_layer_4a5503d199ee924e81f9c3e33f3b2c51 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_4c601209fe169434c369a38150c9af1e.addTo(map_56fc4f1da7363c98a80cf61b3aa30712);\n", + " tile_layer_4a5503d199ee924e81f9c3e33f3b2c51.addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", " \n", " \n", - " var tile_layer_d955c6211994742a17a973db47eaa658 = L.tileLayer(\n", + " var tile_layer_a87971af5b016f5cd8018cae05d47410 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201712/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_d955c6211994742a17a973db47eaa658.addTo(map_56fc4f1da7363c98a80cf61b3aa30712);\n", + " tile_layer_a87971af5b016f5cd8018cae05d47410.addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", " \n", " \n", - " map_c2dadbdc85f53d2c611635e83d2fcf58.sync(map_56fc4f1da7363c98a80cf61b3aa30712);\n", - " map_56fc4f1da7363c98a80cf61b3aa30712.sync(map_c2dadbdc85f53d2c611635e83d2fcf58);\n", + " map_2568d271cee858934c7bd78097d8bf6c.sync(map_aaa2c8843b2a25305b4c65014f14d94e);\n", + " map_aaa2c8843b2a25305b4c65014f14d94e.sync(map_2568d271cee858934c7bd78097d8bf6c);\n", " \n", " \n", - " var marker_2eda3a2b55af42f792c9d5b7ef0e4adc = L.marker(\n", + " var marker_10036912ba52403c93e7ba7e1c5dc55d = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_56fc4f1da7363c98a80cf61b3aa30712);\n", + " ).addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", " \n", " \n", - " marker_2eda3a2b55af42f792c9d5b7ef0e4adc.bindTooltip(\n", + " marker_10036912ba52403c93e7ba7e1c5dc55d.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -925,29 +966,29 @@ " );\n", " \n", " \n", - " var layer_control_fa49a42643aa4442a146e7fdeb260928_layers = {\n", + " var layer_control_44249bb3b87d48759ee835012bcd46af_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_4c601209fe169434c369a38150c9af1e,\n", + " "openstreetmap" : tile_layer_4a5503d199ee924e81f9c3e33f3b2c51,\n", " },\n", " overlays : {\n", - " "December 2017 RH Level" : tile_layer_d955c6211994742a17a973db47eaa658,\n", + " "December 2017 RH Level" : tile_layer_a87971af5b016f5cd8018cae05d47410,\n", " },\n", " };\n", - " let layer_control_fa49a42643aa4442a146e7fdeb260928 = L.control.layers(\n", - " layer_control_fa49a42643aa4442a146e7fdeb260928_layers.base_layers,\n", - " layer_control_fa49a42643aa4442a146e7fdeb260928_layers.overlays,\n", + " let layer_control_44249bb3b87d48759ee835012bcd46af = L.control.layers(\n", + " layer_control_44249bb3b87d48759ee835012bcd46af_layers.base_layers,\n", + " layer_control_44249bb3b87d48759ee835012bcd46af_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_56fc4f1da7363c98a80cf61b3aa30712);\n", + " ).addTo(map_aaa2c8843b2a25305b4c65014f14d94e);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1022,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1048,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1081,7 +1122,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_600ab2730c31d18e0f31aba4be5b8211 {\n", + " #map_25e8c40d7ccce990757e5b7c5672128d {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1095,14 +1136,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_600ab2730c31d18e0f31aba4be5b8211" ></div>\n", + " <div class="folium-map" id="map_25e8c40d7ccce990757e5b7c5672128d" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_600ab2730c31d18e0f31aba4be5b8211 = L.map(\n", - " "map_600ab2730c31d18e0f31aba4be5b8211",\n", + " var map_25e8c40d7ccce990757e5b7c5672128d = L.map(\n", + " "map_25e8c40d7ccce990757e5b7c5672128d",\n", " {\n", " center: [32.81, -96.93],\n", " crs: L.CRS.EPSG3857,\n", @@ -1116,44 +1157,44 @@ "\n", " \n", " \n", - " var tile_layer_692e59625c17fd6fe680d99007b98fbd = L.tileLayer(\n", + " var tile_layer_79f8737033b060dd64c24293e4a81038 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_692e59625c17fd6fe680d99007b98fbd.addTo(map_600ab2730c31d18e0f31aba4be5b8211);\n", + " tile_layer_79f8737033b060dd64c24293e4a81038.addTo(map_25e8c40d7ccce990757e5b7c5672128d);\n", " \n", " \n", "\n", - " function geo_json_9368d22e7dcb288b2c55c5d0deff15fc_onEachFeature(feature, layer) {\n", + " function geo_json_796b0e919f9eb123f8d4ce20105ef0cf_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_9368d22e7dcb288b2c55c5d0deff15fc = L.geoJson(null, {\n", - " onEachFeature: geo_json_9368d22e7dcb288b2c55c5d0deff15fc_onEachFeature,\n", + " var geo_json_796b0e919f9eb123f8d4ce20105ef0cf = L.geoJson(null, {\n", + " onEachFeature: geo_json_796b0e919f9eb123f8d4ce20105ef0cf_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_9368d22e7dcb288b2c55c5d0deff15fc_add (data) {\n", - " geo_json_9368d22e7dcb288b2c55c5d0deff15fc\n", + " function geo_json_796b0e919f9eb123f8d4ce20105ef0cf_add (data) {\n", + " geo_json_796b0e919f9eb123f8d4ce20105ef0cf\n", " .addData(data);\n", " }\n", - " geo_json_9368d22e7dcb288b2c55c5d0deff15fc_add({"geometry": {"coordinates": [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_9368d22e7dcb288b2c55c5d0deff15fc.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_796b0e919f9eb123f8d4ce20105ef0cf_add({"geometry": {"coordinates": [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_796b0e919f9eb123f8d4ce20105ef0cf.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_9368d22e7dcb288b2c55c5d0deff15fc.addTo(map_600ab2730c31d18e0f31aba4be5b8211);\n", + " geo_json_796b0e919f9eb123f8d4ce20105ef0cf.addTo(map_25e8c40d7ccce990757e5b7c5672128d);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1174,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1195,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1496,7 +1537,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1508,7 +1549,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1528,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1556,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1743,8 +1784,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.03646999970078468, 'max': 0.05527999997138977, 'mean': 0.04348228871822357, 'count': 6.300000190734863, 'sum': 0.2739384174346924, 'std': 0.005476967955864938, 'median': 0.04210999980568886, 'majority': 0.03646999970078468, 'minority': 0.03646999970078468, 'unique': 12.0, 'histogram': [[2.0, 0.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0, 0.0, 1.0], [0.03646999970078468, 0.03835099935531616, 0.04023199900984764, 0.04211299866437912, 0.0439939983189106, 0.04587499797344208, 0.047756001353263855, 0.049637001007795334, 0.05151800066232681, 0.05339900031685829, 0.05527999997138977]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.03646999970078468, 'percentile_98': 0.05527999997138977}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.017059998586773872, 'max': 0.023930000141263008, 'mean': 0.020088793709874153, 'count': 6.300000190734863, 'sum': 0.12655940651893616, 'std': 0.002199509794210104, 'median': 0.019680000841617584, 'majority': 0.017059998586773872, 'minority': 0.017059998586773872, 'unique': 12.0, 'histogram': [[2.0, 0.0, 1.0, 2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 2.0], [0.017059998586773872, 0.017746997997164726, 0.01843399927020073, 0.019120998680591583, 0.019807999953627586, 0.02049499936401844, 0.021182000637054443, 0.021869000047445297, 0.02255599945783615, 0.023243000730872154, 0.023930000141263008]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.017059998586773872, 'percentile_98': 0.023930000141263008}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-96.1, 32.28], [-96.1, 33.28], [-97.58, 33.28], [-97.58, 32.28], [-96.1, 32.28]]]}, 'properties': {'statistics': {'b1': {'min': 0.01664000004529953, 'max': 0.02370999939739704, 'mean': 0.019474824890494347, 'count': 6.300000190734863, 'sum': 0.12269140034914017, 'std': 0.0023577335642611815, 'median': 0.019190000370144844, 'majority': 0.01664000004529953, 'minority': 0.01664000004529953, 'unique': 12.0, 'histogram': [[2.0, 1.0, 0.0, 2.0, 1.0, 1.0, 3.0, 0.0, 0.0, 2.0], [0.01664000004529953, 0.017347000539302826, 0.018053999170660973, 0.01876099966466427, 0.019468000158667564, 0.02017499879002571, 0.020881999284029007, 0.021588999778032303, 0.0222960002720356, 0.023002998903393745, 0.02370999939739704]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 12.0, 'percentile_2': 0.01664000004529953, 'percentile_98': 0.02370999939739704}}}}\n", - "CPU times: user 1.94 s, sys: 562 ms, total: 2.5 s\n", - "Wall time: 1min 58s\n" + "CPU times: user 1.24 s, sys: 309 ms, total: 1.55 s\n", + "Wall time: 1min 22s\n" ] } ], @@ -1755,7 +1796,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1791,7 +1832,7 @@ " 'start_datetime': '2017-12-01T00:00:00+00:00'}" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1803,7 +1844,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1994,7 +2035,7 @@ "4 2017-08-01 00:00:00+00:00 " ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2023,7 +2064,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2032,7 +2073,7 @@ "Text(0.5, 1.0, 'Heterotrophic Respiration Values for Dallas, Texas (2003-2017)')" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, @@ -2067,7 +2108,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2086,7 +2127,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2102,7 +2143,7 @@ " 'center': [0.0, 0.0, 0]}" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2120,7 +2161,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2153,7 +2194,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_821b98326470216555339477cb099559 {\n", + " #map_b18e4e421e4320d93377eb2ec813b7cd {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -2168,14 +2209,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_821b98326470216555339477cb099559" ></div>\n", + " <div class="folium-map" id="map_b18e4e421e4320d93377eb2ec813b7cd" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_821b98326470216555339477cb099559 = L.map(\n", - " "map_821b98326470216555339477cb099559",\n", + " var map_b18e4e421e4320d93377eb2ec813b7cd = L.map(\n", + " "map_b18e4e421e4320d93377eb2ec813b7cd",\n", " {\n", " center: [32.8, -96.79],\n", " crs: L.CRS.EPSG3857,\n", @@ -2189,31 +2230,31 @@ "\n", " \n", " \n", - " var tile_layer_a81a794d76fffeb1e7d7410b30c06812 = L.tileLayer(\n", + " var tile_layer_80695508056a49c2fd3ce5e53159d901 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_a81a794d76fffeb1e7d7410b30c06812.addTo(map_821b98326470216555339477cb099559);\n", + " tile_layer_80695508056a49c2fd3ce5e53159d901.addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", " \n", " \n", - " var tile_layer_4a2da8fa8ca2a535f5ed97b373a65bc2 = L.tileLayer(\n", + " var tile_layer_f1948109cb609b88d65d39d9a1f05879 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/casagfed-carbonflux-monthgrid-v3/items/casagfed-carbonflux-monthgrid-v3-201710/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=rh\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=purd\\u0026rescale=0.0%2C0.6039900183677673",\n", " {"attribution": "GHG", "detectRetina": false, "legendEnabled": true, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_4a2da8fa8ca2a535f5ed97b373a65bc2.addTo(map_821b98326470216555339477cb099559);\n", + " tile_layer_f1948109cb609b88d65d39d9a1f05879.addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", " \n", " \n", - " var marker_84f19b6d9fcc70ceca52e1fd7878b185 = L.marker(\n", + " var marker_1c40c6f328d8f4f3102c588cb0f6b17a = L.marker(\n", " [40.0, 5.9],\n", " {}\n", - " ).addTo(map_821b98326470216555339477cb099559);\n", + " ).addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", " \n", " \n", - " marker_84f19b6d9fcc70ceca52e1fd7878b185.bindTooltip(\n", + " marker_1c40c6f328d8f4f3102c588cb0f6b17a.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -2221,58 +2262,58 @@ " );\n", " \n", " \n", - " var layer_control_f1cf4b125490221abf72f6384e55bab6_layers = {\n", + " var layer_control_da633cf7b0f52e140121490d7927a778_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_a81a794d76fffeb1e7d7410b30c06812,\n", + " "openstreetmap" : tile_layer_80695508056a49c2fd3ce5e53159d901,\n", " },\n", " overlays : {\n", - " "October 2017 RH Level" : tile_layer_4a2da8fa8ca2a535f5ed97b373a65bc2,\n", + " "October 2017 RH Level" : tile_layer_f1948109cb609b88d65d39d9a1f05879,\n", " },\n", " };\n", - " let layer_control_f1cf4b125490221abf72f6384e55bab6 = L.control.layers(\n", - " layer_control_f1cf4b125490221abf72f6384e55bab6_layers.base_layers,\n", - " layer_control_f1cf4b125490221abf72f6384e55bab6_layers.overlays,\n", + " let layer_control_da633cf7b0f52e140121490d7927a778 = L.control.layers(\n", + " layer_control_da633cf7b0f52e140121490d7927a778_layers.base_layers,\n", + " layer_control_da633cf7b0f52e140121490d7927a778_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_821b98326470216555339477cb099559);\n", + " ).addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", "\n", " \n", " \n", - " var color_map_84c02c59d4dc045111252471452872cf = {};\n", + " var color_map_bde09018ab691e4f64c5afe21a73beec = {};\n", "\n", " \n", - " color_map_84c02c59d4dc045111252471452872cf.color = d3.scale.threshold()\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.color = d3.scale.threshold()\n", " .domain([0.0, 0.0006012024048096192, 0.0012024048096192384, 0.0018036072144288575, 0.002404809619238477, 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'#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff', '#67001fff']);\n", " \n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.x = d3.scale.linear()\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.x = d3.scale.linear()\n", " .domain([0.0, 0.3])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.legend = L.control({position: 'topright'});\n", - " color_map_84c02c59d4dc045111252471452872cf.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_84c02c59d4dc045111252471452872cf.legend.addTo(map_821b98326470216555339477cb099559);\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.legend = L.control({position: 'topright'});\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.legend.addTo(map_b18e4e421e4320d93377eb2ec813b7cd);\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.xAxis = d3.svg.axis()\n", - " .scale(color_map_84c02c59d4dc045111252471452872cf.x)\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.xAxis = d3.svg.axis()\n", + " .scale(color_map_bde09018ab691e4f64c5afe21a73beec.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([0.0, 0.07, 0.15, 0.22, 0.3]);\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.g = color_map_84c02c59d4dc045111252471452872cf.svg.append("g")\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.g = color_map_bde09018ab691e4f64c5afe21a73beec.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.g.selectAll("rect")\n", - " .data(color_map_84c02c59d4dc045111252471452872cf.color.range().map(function(d, i) {\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.g.selectAll("rect")\n", + " .data(color_map_bde09018ab691e4f64c5afe21a73beec.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_84c02c59d4dc045111252471452872cf.x(color_map_84c02c59d4dc045111252471452872cf.color.domain()[i - 1]) : color_map_84c02c59d4dc045111252471452872cf.x.range()[0],\n", - " x1: i < color_map_84c02c59d4dc045111252471452872cf.color.domain().length ? color_map_84c02c59d4dc045111252471452872cf.x(color_map_84c02c59d4dc045111252471452872cf.color.domain()[i]) : color_map_84c02c59d4dc045111252471452872cf.x.range()[1],\n", + " x0: i ? color_map_bde09018ab691e4f64c5afe21a73beec.x(color_map_bde09018ab691e4f64c5afe21a73beec.color.domain()[i - 1]) : color_map_bde09018ab691e4f64c5afe21a73beec.x.range()[0],\n", + " x1: i < color_map_bde09018ab691e4f64c5afe21a73beec.color.domain().length ? color_map_bde09018ab691e4f64c5afe21a73beec.x(color_map_bde09018ab691e4f64c5afe21a73beec.color.domain()[i]) : color_map_bde09018ab691e4f64c5afe21a73beec.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -2282,7 +2323,7 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_84c02c59d4dc045111252471452872cf.g.call(color_map_84c02c59d4dc045111252471452872cf.xAxis).append("text")\n", + " color_map_bde09018ab691e4f64c5afe21a73beec.g.call(color_map_bde09018ab691e4f64c5afe21a73beec.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", " .text("Rh Values (kg Carbon/m2/month)");\n", @@ -2290,10 +2331,10 @@ "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } From 6f9ae504c19dc5be263e0c5fe937c3af54962c81 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Tue, 17 Sep 2024 13:12:27 -0500 Subject: [PATCH 41/47] fix output window in usernotebooks --- ...n-co2flux-monthgrid-v5_User_Notebook.ipynb | 211 +++++++++--------- 1 file changed, 101 insertions(+), 110 deletions(-) diff --git a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb index 26802440..c6a08a2f 100644 --- a/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb +++ b/user_data_notebooks/eccodarwin-co2flux-monthgrid-v5_User_Notebook.ipynb @@ -72,18 +72,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rrimal/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "# Import the following libraries\n", "import requests\n", @@ -98,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -164,7 +155,7 @@ " 'dashboard:time_density': 'month'}" ] }, - "execution_count": 3, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -188,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -263,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -361,7 +352,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 6, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -384,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -406,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -424,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -440,7 +431,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -496,7 +487,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 10, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -538,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -571,7 +562,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_7eb60a390570783876d1bce623409555 {\n", + " #map_1d84f425760589c5c57dfd3456c41c8c {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -585,7 +576,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_4733e6d16bcfcf3ceee335736640dae4 {\n", + " #map_2b8f4ebabc9915b6d1c296c332a5802a {\n", " position: absolute;\n", " width: 50.0%;\n", " height: 100.0%;\n", @@ -618,17 +609,17 @@ " #FF66CC 80%, #FF3399); height: 10px;"></div>\n", "</div>\n", " \n", - " <div class="folium-map" id="map_7eb60a390570783876d1bce623409555" ></div>\n", + " <div class="folium-map" id="map_1d84f425760589c5c57dfd3456c41c8c" ></div>\n", " \n", " \n", - " <div class="folium-map" id="map_4733e6d16bcfcf3ceee335736640dae4" ></div>\n", + " <div class="folium-map" id="map_2b8f4ebabc9915b6d1c296c332a5802a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_7eb60a390570783876d1bce623409555 = L.map(\n", - " "map_7eb60a390570783876d1bce623409555",\n", + " var map_1d84f425760589c5c57dfd3456c41c8c = L.map(\n", + " "map_1d84f425760589c5c57dfd3456c41c8c",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -642,31 +633,31 @@ "\n", " \n", " \n", - " var tile_layer_41b9122877bd303d413d61d69f007857 = L.tileLayer(\n", + " var tile_layer_f773cac2c5118d02914331befe7893dd = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_41b9122877bd303d413d61d69f007857.addTo(map_7eb60a390570783876d1bce623409555);\n", + " tile_layer_f773cac2c5118d02914331befe7893dd.addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", " \n", " \n", - " var tile_layer_fc26ad93c7c725b6a875a39a5da7919c = L.tileLayer(\n", + " var tile_layer_db1f3a71765c955789913dd105059d89 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202212/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_fc26ad93c7c725b6a875a39a5da7919c.addTo(map_7eb60a390570783876d1bce623409555);\n", + " tile_layer_db1f3a71765c955789913dd105059d89.addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", " \n", " \n", - " var marker_fe806cc9fd6dd61c0fda08935eb88f3a = L.marker(\n", + " var marker_a4b7208dfb15251816520d172570fa5c = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_7eb60a390570783876d1bce623409555);\n", + " ).addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", " \n", " \n", - " marker_fe806cc9fd6dd61c0fda08935eb88f3a.bindTooltip(\n", + " marker_a4b7208dfb15251816520d172570fa5c.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -674,24 +665,24 @@ " );\n", " \n", " \n", - " var layer_control_d8ab776be18c60f12213fd03d4a1675e_layers = {\n", + " var layer_control_4292915797c7d99b89b7677cac7fd245_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_41b9122877bd303d413d61d69f007857,\n", + " "openstreetmap" : tile_layer_f773cac2c5118d02914331befe7893dd,\n", " },\n", " overlays : {\n", - " "December 2022 CO2 Flux" : tile_layer_fc26ad93c7c725b6a875a39a5da7919c,\n", + " "December 2022 CO2 Flux" : tile_layer_db1f3a71765c955789913dd105059d89,\n", " },\n", " };\n", - " let layer_control_d8ab776be18c60f12213fd03d4a1675e = L.control.layers(\n", - " layer_control_d8ab776be18c60f12213fd03d4a1675e_layers.base_layers,\n", - " layer_control_d8ab776be18c60f12213fd03d4a1675e_layers.overlays,\n", + " let layer_control_4292915797c7d99b89b7677cac7fd245 = L.control.layers(\n", + " layer_control_4292915797c7d99b89b7677cac7fd245_layers.base_layers,\n", + " layer_control_4292915797c7d99b89b7677cac7fd245_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_7eb60a390570783876d1bce623409555);\n", + " ).addTo(map_1d84f425760589c5c57dfd3456c41c8c);\n", "\n", " \n", " \n", - " var map_4733e6d16bcfcf3ceee335736640dae4 = L.map(\n", - " "map_4733e6d16bcfcf3ceee335736640dae4",\n", + " var map_2b8f4ebabc9915b6d1c296c332a5802a = L.map(\n", + " "map_2b8f4ebabc9915b6d1c296c332a5802a",\n", " {\n", " center: [34.0, -118.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -705,35 +696,35 @@ "\n", " \n", " \n", - " var tile_layer_0a41a58858d4f2d9b74f1b6f70ff76dd = L.tileLayer(\n", + " var tile_layer_de846f26ee32feff54adeb0e6ae775c8 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_0a41a58858d4f2d9b74f1b6f70ff76dd.addTo(map_4733e6d16bcfcf3ceee335736640dae4);\n", + " tile_layer_de846f26ee32feff54adeb0e6ae775c8.addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", " \n", " \n", - " var tile_layer_709a5dfee2c42d24b84ba34e5b867e99 = L.tileLayer(\n", + " var tile_layer_c27e59e6968a77db4ae47767c742f632 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202104/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.8, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_709a5dfee2c42d24b84ba34e5b867e99.addTo(map_4733e6d16bcfcf3ceee335736640dae4);\n", + " tile_layer_c27e59e6968a77db4ae47767c742f632.addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", " \n", " \n", - " map_7eb60a390570783876d1bce623409555.sync(map_4733e6d16bcfcf3ceee335736640dae4);\n", - " map_4733e6d16bcfcf3ceee335736640dae4.sync(map_7eb60a390570783876d1bce623409555);\n", + " map_1d84f425760589c5c57dfd3456c41c8c.sync(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", + " map_2b8f4ebabc9915b6d1c296c332a5802a.sync(map_1d84f425760589c5c57dfd3456c41c8c);\n", " \n", " \n", - " var marker_950329728e5e45fdb5162837f98078c0 = L.marker(\n", + " var marker_ede3a5b5ad9c4ba9b53e1fb23748df93 = L.marker(\n", " [40.0, 5.0],\n", " {}\n", - " ).addTo(map_4733e6d16bcfcf3ceee335736640dae4);\n", + " ).addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", " \n", " \n", - " marker_950329728e5e45fdb5162837f98078c0.bindTooltip(\n", + " marker_ede3a5b5ad9c4ba9b53e1fb23748df93.bindTooltip(\n", " `<div>\n", " both\n", " </div>`,\n", @@ -741,29 +732,29 @@ " );\n", " \n", " \n", - " var layer_control_7703fa939bfd429cb7dcf2b04490ac6b_layers = {\n", + " var layer_control_c54cf0816afb496fb472f1dac46d88f4_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_0a41a58858d4f2d9b74f1b6f70ff76dd,\n", + " "openstreetmap" : tile_layer_de846f26ee32feff54adeb0e6ae775c8,\n", " },\n", " overlays : {\n", - " "April 2021 CO2 Flux" : tile_layer_709a5dfee2c42d24b84ba34e5b867e99,\n", + " "April 2021 CO2 Flux" : tile_layer_c27e59e6968a77db4ae47767c742f632,\n", " },\n", " };\n", - " let layer_control_7703fa939bfd429cb7dcf2b04490ac6b = L.control.layers(\n", - " layer_control_7703fa939bfd429cb7dcf2b04490ac6b_layers.base_layers,\n", - " layer_control_7703fa939bfd429cb7dcf2b04490ac6b_layers.overlays,\n", + " let layer_control_c54cf0816afb496fb472f1dac46d88f4 = L.control.layers(\n", + " layer_control_c54cf0816afb496fb472f1dac46d88f4_layers.base_layers,\n", + " layer_control_c54cf0816afb496fb472f1dac46d88f4_layers.overlays,\n", " {"autoZIndex": true, "collapsed": false, "position": "topright"}\n", - " ).addTo(map_4733e6d16bcfcf3ceee335736640dae4);\n", + " ).addTo(map_2b8f4ebabc9915b6d1c296c332a5802a);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -860,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -885,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -918,7 +909,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_951d6fbb996eb150c04b2a60cc4e1e59 {\n", + " #map_b026227a03ef1c8c8fdc2f21a0a0d90f {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -932,14 +923,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_951d6fbb996eb150c04b2a60cc4e1e59" ></div>\n", + " <div class="folium-map" id="map_b026227a03ef1c8c8fdc2f21a0a0d90f" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_951d6fbb996eb150c04b2a60cc4e1e59 = L.map(\n", - " "map_951d6fbb996eb150c04b2a60cc4e1e59",\n", + " var map_b026227a03ef1c8c8fdc2f21a0a0d90f = L.map(\n", + " "map_b026227a03ef1c8c8fdc2f21a0a0d90f",\n", " {\n", " center: [35.0, -120.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -953,44 +944,44 @@ "\n", " \n", " \n", - " var tile_layer_175e753d1abc1693a186ac66ca17292a = L.tileLayer(\n", + " var tile_layer_504c4687cdc33c3c47acf1f05ceefe5e = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_175e753d1abc1693a186ac66ca17292a.addTo(map_951d6fbb996eb150c04b2a60cc4e1e59);\n", + " tile_layer_504c4687cdc33c3c47acf1f05ceefe5e.addTo(map_b026227a03ef1c8c8fdc2f21a0a0d90f);\n", " \n", " \n", "\n", - " function geo_json_de83d4b25d1a1ee3193da1194ff00e3f_onEachFeature(feature, layer) {\n", + " function geo_json_69aa59710193d53dfbcdc75743e28c75_onEachFeature(feature, layer) {\n", " layer.on({\n", " });\n", " };\n", - " var geo_json_de83d4b25d1a1ee3193da1194ff00e3f = L.geoJson(null, {\n", - " onEachFeature: geo_json_de83d4b25d1a1ee3193da1194ff00e3f_onEachFeature,\n", + " var geo_json_69aa59710193d53dfbcdc75743e28c75 = L.geoJson(null, {\n", + " onEachFeature: geo_json_69aa59710193d53dfbcdc75743e28c75_onEachFeature,\n", " \n", " });\n", "\n", - " function geo_json_de83d4b25d1a1ee3193da1194ff00e3f_add (data) {\n", - " geo_json_de83d4b25d1a1ee3193da1194ff00e3f\n", + " function geo_json_69aa59710193d53dfbcdc75743e28c75_add (data) {\n", + " geo_json_69aa59710193d53dfbcdc75743e28c75\n", " .addData(data);\n", " }\n", - " geo_json_de83d4b25d1a1ee3193da1194ff00e3f_add({"geometry": {"coordinates": [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", - " geo_json_de83d4b25d1a1ee3193da1194ff00e3f.setStyle(function(feature) {return feature.properties.style;});\n", + " geo_json_69aa59710193d53dfbcdc75743e28c75_add({"geometry": {"coordinates": [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]], "type": "Polygon"}, "properties": {}, "type": "Feature"});\n", + " geo_json_69aa59710193d53dfbcdc75743e28c75.setStyle(function(feature) {return feature.properties.style;});\n", "\n", " \n", " \n", - " geo_json_de83d4b25d1a1ee3193da1194ff00e3f.addTo(map_951d6fbb996eb150c04b2a60cc4e1e59);\n", + " geo_json_69aa59710193d53dfbcdc75743e28c75.addTo(map_b026227a03ef1c8c8fdc2f21a0a0d90f);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1029,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1073,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1096,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1194,7 +1185,7 @@ " 'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}" ] }, - "execution_count": 16, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1206,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1238,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1281,8 +1272,8 @@ "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00011453414031127196, 'max': -2.5153009306526816e-05, 'mean': -5.606484279560035e-05, 'count': 86.62000274658203, 'sum': -0.004856336836941592, 'std': 1.3576859561140137e-05, 'median': -5.2092425995657114e-05, 'majority': -0.00011453414031127196, 'minority': -0.00011453414031127196, 'unique': 115.0, 'histogram': [[2.0, 2.0, 3.0, 1.0, 5.0, 18.0, 27.0, 39.0, 14.0, 4.0], [-0.00011453414031127196, -0.00010559602721079744, -9.665791411032294e-05, -8.771980100984842e-05, -7.87816879093739e-05, -6.984357480889939e-05, -6.090546170842488e-05, -5.196734860795037e-05, -4.302923550747585e-05, -3.409112240700133e-05, -2.5153009306526816e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -9.761120728455703e-05, 'percentile_98': -3.4258626892058585e-05}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00021918676108497462, 'max': -3.927832631416977e-05, 'mean': -0.00012144348425158578, 'count': 86.62000274658203, 'sum': -0.010519434939426852, 'std': 2.9769797386936854e-05, 'median': -0.00011626481094279005, 'majority': -0.00021918676108497462, 'minority': -0.00021918676108497462, 'unique': 115.0, 'histogram': [[5.0, 2.0, 5.0, 6.0, 12.0, 35.0, 28.0, 12.0, 8.0, 2.0], [-0.00021918676108497462, -0.00020119591760789414, -0.00018320507413081366, -0.00016521423065373315, -0.00014722338717665267, -0.0001292325436995722, -0.0001112417002224917, -9.32508567454112e-05, -7.526001326833072e-05, -5.726916979125024e-05, -3.927832631416977e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -0.0002064151684468108, 'percentile_98': -6.805256999965305e-05}}}}\n", "{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-124.19, 37.86], [-123.11, 37.86], [-119.96, 33.16], [-121.13, 33.16], [-124.19, 37.86]]]}, 'properties': {'statistics': {'b1': {'min': -0.00015412138841721786, 'max': -3.145271371838777e-05, 'mean': -9.593350082157177e-05, 'count': 86.62000274658203, 'sum': -0.008309760104653776, 'std': 2.9374048721393294e-05, 'median': -8.783572604921066e-05, 'majority': -0.00015412138841721786, 'minority': -0.00015412138841721786, 'unique': 115.0, 'histogram': [[10.0, 10.0, 11.0, 3.0, 15.0, 28.0, 20.0, 9.0, 5.0, 4.0], [-0.00015412138841721786, -0.00014185452094733484, -0.00012958765347745183, -0.00011732078600756883, -0.00010505391853768583, -9.278705106780281e-05, -8.05201835979198e-05, -6.825331612803679e-05, -5.5986448658153776e-05, -4.371958118827076e-05, -3.145271371838777e-05]], 'valid_percent': 31.94, 'masked_pixels': 245.0, 'valid_pixels': 115.0, 'percentile_2': -0.00015409053848538357, 'percentile_98': -4.834538500993245e-05}}}}\n", - "CPU times: user 438 ms, sys: 144 ms, total: 582 ms\n", - "Wall time: 22.5 s\n" + "CPU times: user 143 ms, sys: 35.5 ms, total: 178 ms\n", + "Wall time: 11.5 s\n" ] } ], @@ -1296,7 +1287,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1332,7 +1323,7 @@ " 'datetime': '2022-12-01T00:00:00+00:00'}" ] }, - "execution_count": 19, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1351,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1542,7 +1533,7 @@ "4 2022-08-01 00:00:00+00:00 " ] }, - "execution_count": 20, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1582,7 +1573,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1654,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1673,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1689,7 +1680,7 @@ " 'center': [-0.125, -0.1248266296809959, 0]}" ] }, - "execution_count": 23, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1719,7 +1710,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1752,7 +1743,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_f2268b55cc2dd075153bd773d263fd4c {\n", + " #map_b1804ab1fa95f7e34309234e1d555da9 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1789,14 +1780,14 @@ " September 2022\n", "</div>\n", " \n", - " <div class="folium-map" id="map_f2268b55cc2dd075153bd773d263fd4c" ></div>\n", + " <div class="folium-map" id="map_b1804ab1fa95f7e34309234e1d555da9" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_f2268b55cc2dd075153bd773d263fd4c = L.map(\n", - " "map_f2268b55cc2dd075153bd773d263fd4c",\n", + " var map_b1804ab1fa95f7e34309234e1d555da9 = L.map(\n", + " "map_b1804ab1fa95f7e34309234e1d555da9",\n", " {\n", " center: [34.0, -120.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1810,31 +1801,31 @@ "\n", " \n", " \n", - " var tile_layer_d6b4d2ca5c60a6480b85273ef007f72c = L.tileLayer(\n", + " var tile_layer_8d0bec0ea33a0290d6db1b68fc287faf = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_d6b4d2ca5c60a6480b85273ef007f72c.addTo(map_f2268b55cc2dd075153bd773d263fd4c);\n", + " tile_layer_8d0bec0ea33a0290d6db1b68fc287faf.addTo(map_b1804ab1fa95f7e34309234e1d555da9);\n", " \n", " \n", - " var tile_layer_0c1b5abdfb632ffe29797f2a6815c0fd = L.tileLayer(\n", + " var tile_layer_1e40c05ea4db66e2a66bd10b54db2229 = L.tileLayer(\n", " "https://earth.gov/ghgcenter/api/raster/collections/eccodarwin-co2flux-monthgrid-v5/items/eccodarwin-co2flux-monthgrid-v5-202209/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=co2\\u0026color_formula=gamma+r+1.05\\u0026colormap_name=magma\\u0026rescale=-0.0007%2C0.0007",\n", " {"attribution": "GHG", "detectRetina": false, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 0.7, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_0c1b5abdfb632ffe29797f2a6815c0fd.addTo(map_f2268b55cc2dd075153bd773d263fd4c);\n", + " tile_layer_1e40c05ea4db66e2a66bd10b54db2229.addTo(map_b1804ab1fa95f7e34309234e1d555da9);\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } From d024e18747a4a2557f19c6a85c12af49cfb53716 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Tue, 17 Sep 2024 14:24:26 -0500 Subject: [PATCH 42/47] fix cell output in user data notebooks --- styles.css | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/styles.css b/styles.css index 9be27fe2..01f214f2 100644 --- a/styles.css +++ b/styles.css @@ -2,6 +2,19 @@ margin: 10px; } + +/* Adjust height of the cell output in user notebooks*/ +.cell-output { + max-height: 500px; + overflow-y: auto; + overflow-x: auto; + background-color:#fefdfe; +} + + + + + /* .sidebar-item-text::before { content: '>' } */ From ca00715e36466dcb844cec3087e0f5bad5b759f3 Mon Sep 17 00:00:00 2001 From: Ram Sharan Rimal Date: Tue, 17 Sep 2024 14:27:53 -0500 Subject: [PATCH 43/47] remove ipython display --- ...arbonflux-monthgrid-v3_User_Notebook.ipynb | 41 ------------------- 1 file changed, 41 deletions(-) diff --git a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb index 13876511..25f83cd4 100644 --- a/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb +++ b/user_data_notebooks/casagfed-carbonflux-monthgrid-v3_User_Notebook.ipynb @@ -71,47 +71,6 @@ "Please run the next cell to import the required libraries. " ] }, - { - "cell_type": "raw", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "!pip install ipywidgets" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "81a873d9ec5b4758b1517346d121b416", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output(layout=Layout(border_bottom='1px solid black', border_left='1px solid black', border_right='1px solid b…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display\n", - "import ipywidgets as widgets\n", - "\n", - "# Create a scrollable output widget with a fixed height\n", - "out = widgets.Output(layout={'border': '1px solid black', 'height': '300px', 'overflow_y': 'scroll'})\n", - "\n", - "display(out)" - ] - }, { "cell_type": "code", "execution_count": 2, From 27f1f9bcf1640b3919bb244197ef0f9d7e47cc78 Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Tue, 17 Sep 2024 14:28:35 -0500 Subject: [PATCH 44/47] edits for r3 --- cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb | 2 +- data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd | 2 +- ...es-ghg-monthgrid-v1_Processing and Verification Report.qmd | 2 +- ...n-ffco2-yeargrid-v4_Processing and Verification Report.qmd | 4 ++-- .../gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb index 4f3c2c35..877e7a64 100644 --- a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -24,7 +24,7 @@ "id": "456658d4-e4a8-472e-b69c-74754c3016ac", "metadata": {}, "source": [ - "This script was used to transform the VULCAN dataset provided in Cloud Optimized GeoTIFF (COG) format for display in the Greenhouse Gas (GHG) Center with the calaulation of validation statistics." + "This script was used to transform the VULCAN dataset provided in GeoTIFF format for display in the Greenhouse Gas (GHG) Center with the calaulation of validation statistics." ] }, { diff --git a/data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd b/data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd index e0fcdef5..09159116 100644 --- a/data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd +++ b/data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd @@ -1,3 +1,3 @@ -# Vulcan Fossil Fuel CO₂ Emissions, Version 4 +# Vulcan Fossil Fuel CO₂ Emissions ![Data Flow Diagram Extending From Acquisition/Creation to User Delivery](./media/vulcan-ffco2-yeargrid-v4_Data_Flow.png) \ No newline at end of file diff --git a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd index 13d8d0d7..2a2bfc6e 100644 --- a/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/gra2pes-ghg-monthgrid-v1_Processing and Verification Report.qmd @@ -1,6 +1,6 @@ --- title: GRA²PES Greenhouse Gas and Air Quality Species -description: "The Greenhouse gas And Air Pollutants Emissions System (GRA²PES) dataset at the GHG Center is an aggregated, regridded, monthly high-resolution (0.036 x 0.036°) data product with emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc. (see Table 2 in the Scientific Details section below for a full sector list). Fossil fuel CO₂ (ffCO₂) emissions are developed along with those of air pollutants including CO, NOₓ, SO₂, and PM₂.₅ with consistency in spatial and temporal distributions. Emissions by sectors are reported as column totals in units of metric tons per km² per month. Spatial-temporal surrogates are developed to distribute CO₂ emissions to grid cells to keep consistency between greenhouse gases and air quality species. The current version of GRA²PES is for 2021. Long-term emissions and more greenhouse gas species (e.g., methane) are under development and will be added in the future." +description: "Monthly, 0.036 degree resolution emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM2.5) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES)." --- diff --git a/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd index 6c1d0d11..57369a6b 100644 --- a/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd +++ b/processing_and_verification_reports/vulcan-ffco2-yeargrid-v4_Processing and Verification Report.qmd @@ -1,6 +1,6 @@ --- -title: Vulcan Fossil Fuel CO₂ Emissions v4.0 -description: "The Vulcan version 4.0 data product represents total carbon dioxide (CO2) emissions resulting from the combustion of fossil fuel (ff) for the contiguous United States and District of Columbia. Referred to as ffCO2, the emissions from Vulcan are also categorized into 10 source sectors including; airports, cement production, commercial marine vessels, commercial, power plants, industrial, non-road, on-road, residential and railroads. Data are gridded annually on a 1-km grid for the years 2010 to 2021. These data are annual sums of hourly estimates. Shown is the estimated total annual ffCO2 for the United States, as well as the estimated total annual ffCO2 per sector." +title: Vulcan Fossil Fuel CO₂ Emissions +description: "Annual (2010 - 2021), 1 km resolution estimates of carbon dioxide emissions from fossil fuel combustion over the contiguous United States, version 4.0" --- diff --git a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb index 5989c0ad..484f5507 100644 --- a/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb +++ b/user_data_notebooks/gra2pes-ghg-monthgrid-v1_User_Notebook.ipynb @@ -11,7 +11,7 @@ "source": [ "---\n", "title: GRA²PES Greenhouse Gas and Air Quality Species\n", - "description: Documentation of data usage\n", + "description: Monthly, 0.036 degree resolution emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and particulate matter (PM2.5) emissions for the year 2021 over the Contiguous United States from the Greenhouse gas And Air Pollutants Emissions System (GRA²PES) \n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", "execute:\n", From 4121900749552c35b6a9a78494f22c29075ec3f9 Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Tue, 17 Sep 2024 15:29:49 -0500 Subject: [PATCH 45/47] edit navigation bar --- .../influx-testbed-ghg-concentrations_User_Notebook.ipynb | 4 ++-- .../lam-testbed-ghg-concentrations_User_Notebook.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb index 7d8b25c4..815dee4b 100644 --- a/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb +++ b/user_data_notebooks/influx-testbed-ghg-concentrations_User_Notebook.ipynb @@ -5,8 +5,8 @@ "metadata": {}, "source": [ "---\n", - "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", - "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "title: Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) \n", + "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at Indianapolis Flux Experiment (INFLUX) tower sites\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 12 September 2024\n", "execute:\n", diff --git a/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb index 457f63c3..6730fa70 100644 --- a/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb +++ b/user_data_notebooks/lam-testbed-ghg-concentrations_User_Notebook.ipynb @@ -5,8 +5,8 @@ "metadata": {}, "source": [ "---\n", - "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", - "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower sites in the Northeastern U.S.\n", + "title: Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n", + "description: Atmospheric concentrations of carbon dioxide (CO₂) and methane (CH₄) collected at NIST Urban Test Bed tower and rooftop sites in California’s South Coast Air Basin\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 12 September 2024\n", "execute:\n", From 8744212c5cf782ccc9e33be75ebe9b5d6f0e6aa3 Mon Sep 17 00:00:00 2001 From: siddharth0248 Date: Tue, 17 Sep 2024 16:04:23 -0500 Subject: [PATCH 46/47] title in transformation notebook --- .../influx-testbed-ghg-concentrations.ipynb | 8 ++++++-- cog_transformation/lam-testbed-ghg-concentrations.ipynb | 8 ++++++-- cog_transformation/nec-testbed-ghg-concentrations.ipynb | 8 ++++++-- cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb | 2 +- .../vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb | 2 +- 5 files changed, 20 insertions(+), 8 deletions(-) diff --git a/cog_transformation/influx-testbed-ghg-concentrations.ipynb b/cog_transformation/influx-testbed-ghg-concentrations.ipynb index 901d7bca..e87188ca 100644 --- a/cog_transformation/influx-testbed-ghg-concentrations.ipynb +++ b/cog_transformation/influx-testbed-ghg-concentrations.ipynb @@ -3,10 +3,14 @@ { "cell_type": "raw", "id": "c5850703-19b9-43d9-9820-d441c17d6a9f", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: NIST Flux GHG concentrations for Indianapolis Region\n", + "title: Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX)\n", "description: Documentation of data transformation \n", "author: Paridhi Parajuli \n", "date: August 28, 2023\n", diff --git a/cog_transformation/lam-testbed-ghg-concentrations.ipynb b/cog_transformation/lam-testbed-ghg-concentrations.ipynb index 13c13612..5a5e337a 100644 --- a/cog_transformation/lam-testbed-ghg-concentrations.ipynb +++ b/cog_transformation/lam-testbed-ghg-concentrations.ipynb @@ -3,10 +3,14 @@ { "cell_type": "raw", "id": "2f231b28-78b7-47fa-9d37-571b0537c798", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: NIST Urban Testbed GHG concentrations for Los Angeles Metropolitan Region\n", + "title: Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project\n", "description: Documentation of data transformation \n", "author: Paridhi Parajuli \n", "date: August 28, 2023\n", diff --git a/cog_transformation/nec-testbed-ghg-concentrations.ipynb b/cog_transformation/nec-testbed-ghg-concentrations.ipynb index 81424a31..7ca4afeb 100644 --- a/cog_transformation/nec-testbed-ghg-concentrations.ipynb +++ b/cog_transformation/nec-testbed-ghg-concentrations.ipynb @@ -3,10 +3,14 @@ { "cell_type": "raw", "id": "504c035a-53d2-41b7-b5a3-e5824319a5f6", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "raw" + } + }, "source": [ "---\n", - "title: NIST Urban Testbed GHG concentrations for Northeast Corridor Region\n", + "title: Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed\n", "description: Documentation of data transformation \n", "author: Paridhi Parajuli \n", "date: August 28, 2023\n", diff --git a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb index 877e7a64..d4d412cd 100644 --- a/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb +++ b/cog_transformation/vulcan-ffco2-yeargrid-v4.ipynb @@ -10,7 +10,7 @@ }, "source": [ "---\n", - "title: Vulcan Fossil Fuel CO₂ Emissions v4.0\n", + "title: Vulcan Fossil Fuel CO₂ Emissions\n", "description: Documentation of data transformation & Validation\n", "author: Paridhi Parajuli \n", "date: August 20, 2024\n", diff --git a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb index 5e337248..880b85bc 100644 --- a/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb +++ b/user_data_notebooks/vulcan-ffco2-yeargrid-v4_User_Notebook.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "---\n", - "title: Vulcan Fossil Fuel CO₂ Emissions, Version 4\n", + "title: Vulcan Fossil Fuel CO₂ Emissions\n", "description: Annual (2010 - 2021), 1 km resolution estimates of carbon dioxide emissions from fossil fuels and cement production over the contiguous United States, version 4.0.\n", "author: Siddharth Chaudhary, Paridhi Parajuli\n", "date: 30 August 2024\n", From 5c94610381fe006a65399f789746fe7f313bb04f Mon Sep 17 00:00:00 2001 From: Paridhi Parajuli Date: Tue, 17 Sep 2024 17:16:30 -0500 Subject: [PATCH 47/47] correct links --- ...ons_Processing 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