Reading & Working with Geometry Files

Reading & Working with Geometry Files#

This notebooks demonstrates how to use the Grid.from_file() class method to load in geometry files such as:

  1. Shapefile

  2. GeoJSON

Highlighted is a workflow showcasing how to remap a variable from an unstructured grid to a Shapefile.

import warnings

import geocat.datafiles as geodf

import uxarray as ux

warnings.filterwarnings("ignore")

/home/docs/checkouts/readthedocs.org/user_builds/uxarray/conda/1454/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Downloading file 'registry.txt' from 'https://github.com/NCAR/geocat-datafiles/raw/main/registry.txt' to '/home/docs/.cache/geocat'.

Load a shapefile and plot#

  • This section demonstrates how to load a shapefile using uxarray’s Grid.from_file() function

  • The shapefile used in this example is the US national boundary file from the US Census Bureau. It is a 20m resolution shapefile that represents the national boundary of the United States.

  • The data plotted is subset to a specific bounding box, which is defined by the latitude and longitude bounds. The result is plotted using the matplotlib backend.

shp_filename = (
    "../../test/meshfiles/shp/cb_2018_us_nation_20m/cb_2018_us_nation_20m.shp"
)
uxds = ux.Grid.from_file(shp_filename)
lat_bounds = [-90, -70]
lon_bounds = [20, 55]
uxds = uxds.subset.bounding_box(lon_bounds, lat_bounds)
uxds.plot(
    title="US 20m Focus on Mainland (cb_2018_us_nation_20m.shp)",
    backend="matplotlib",
    width=500,
)

ERROR 1: PROJ: proj_create_from_database: Open of /home/docs/checkouts/readthedocs.org/user_builds/uxarray/conda/1454/share/proj failed

Transformed CRS: EPSG:4326
ⓘ

Load a Geojson file and plot#

  • This section demonstrates how to load a Geojson file using uxarray’s Grid.from_file() function

  • The Geojson file used in this example is a few buildings around downtown Chicago. The plot is shown using the “matplotlib” backend for bounds specific to the region.

geojson_filename = "../../test/meshfiles/geojson/sample_chicago_buildings.geojson"
uxgeojson = ux.Grid.from_file(geojson_filename)
lat_bounds = [41.6, 42.1]
lon_bounds = [-87.7, -87.5]
uxgeojson.subset.bounding_box(lon_bounds, lat_bounds).plot(backend="matplotlib")

Open NetCDF mesh file using the Grid.from_file() function#

  • Regular NetCDF files can also be opened using this function. Backend options available are:

    • xarray

    • geopandas (default for opening shapefile, geojson file and other file formats supported by geopandas read_file function)

  • In the following code, we load a NetCDF mesh file: scrip/outCSne8/outCSne8.nc and print out the grid contents.

nc_filename = "../../test/meshfiles/scrip/outCSne8/outCSne8.nc"
uxgrid = ux.Grid.from_file(nc_filename, backend="xarray")
uxgrid

<uxarray.Grid>
Original Grid Type: Scrip
Grid Dimensions:
  * n_node: 407
  * n_face: 384
  * n_max_face_nodes: 4
Grid Coordinates (Spherical):
  * node_lon: (407,)
  * node_lat: (407,)
  * face_lon: (384,)
  * face_lat: (384,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (384, 4)
Grid Descriptor Variables:

Remapping from Shapefile#

The following steps are needed for Remapping Global Relative Humidity Data on to a specific region defined by Shapefile using UXarray

  1. Read the shapefile (uxds)

shp_filename = (
    "../../test/meshfiles/shp/chicago_neighborhoods/chicago_neighborhoods.shp"
)
uxds = ux.Grid.from_file(shp_filename)
lat_bounds = [41, 43]
lon_bounds = [-89, -90]
uxds = uxds.subset.bounding_box(lon_bounds, lat_bounds)
uxds.plot(
    title="Chicago Neighborhoods",
    backend="bokeh",
)

Original CRS: None
Assigned CRS: EPSG:4326
ⓘ

  1. Initialize Input Data Files

    • The input datasets consist of NetCDF files that include global relative humidity data.

    • The datafiles variable points to two NetCDF files using the geodf.get function, specifying the paths:

      • The first file contains meteorological diagnostic data:
        netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc.

      • The second file provides the MPAS grid specification:
        netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc.

  2. Open the Datasets with UXarray

    • The ux.open_dataset() function is used to load these files, making them accessible as a UXarray Dataset.

    • uxds_source is the opened dataset that holds the meteorological data, such as relative humidity, structured over the MPAS grid.

datafiles = (
    geodf.get(
        "netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc"
    ),
    geodf.get("netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc"),
)

uxds_source = ux.open_dataset(datafiles[1], datafiles[0])

Downloading file 'netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc' from 'https://github.com/NCAR/geocat-datafiles/raw/main/netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc' to '/home/docs/.cache/geocat'.

Downloading file 'netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc' from 'https://github.com/NCAR/geocat-datafiles/raw/main/netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc' to '/home/docs/.cache/geocat'.

  1. Remap Relative Humidity Data

    • The relhum_200hPa variable is accessed from uxds_source to extract relative humidity data at 200 hPa pressure level.

    • Inverse Distance Weighted Remapping:

      • The data is remapped using UXarray’s inverse_distance_weighted method.

      • The remapping is done to “face centers,” adapting the data from its original grid to align with a new shape or structure.

  2. Plot the Remapped Data

    • The remapped data for Chicago neighborhoods is plotted using UXarray’s plotting utilities.

    • The plot uses the sequential_blue colormap and is rendered with the bokeh backend.

    • The title of the plot is “Chicago Neighborhoods Relative Humidity,” giving a clear representation of how relative humidity varies spatially.

chicago_relative_humidty = uxds_source["relhum_200hPa"].remap.inverse_distance_weighted(
    uxds, remap_to="face centers"
)

chicago_relative_humidty[0].plot(
    cmap="Blues",
    title="Chicago Neighborhoods Relative Humidty",
    backend="bokeh",
)
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