https://github.com/zmoon/epa-regions-python
EPA regions with GeoPandas / regionmask
https://github.com/zmoon/epa-regions-python
Last synced: about 1 year ago
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EPA regions with GeoPandas / regionmask
- Host: GitHub
- URL: https://github.com/zmoon/epa-regions-python
- Owner: zmoon
- License: unlicense
- Created: 2024-01-25T20:05:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-24T16:05:51.000Z (over 1 year ago)
- Last Synced: 2025-03-25T12:03:32.164Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 64.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# epa-regions-python
[EPA regions](https://www.epa.gov/aboutepa/regional-and-geographic-offices)
from [Natural Earth](https://www.naturalearthdata.com) data
with [GeoPandas](https://geopandas.org) / [regionmask](https://regionmask.readthedocs.io).
[](https://pypi.org/project/epa-regions/)
Code
```sh
python -m epa_regions -r 50m --states-only --save
```
## Installation
With `conda` (recommended):
```
conda activate ...
conda install -c conda-forge geopandas regionmask pooch pyogrio
pip install epa-regions
```
`pip install epa-regions` does not install any dependencies,
as it is expected that you will have installed them with `conda`.
* `geopandas`: needed if you want to use `epa_regions.get()`
* `pooch`: for downloading/caching the shapefiles for `epa_regions.get()`
* `pyogrio`: for faster reading of shapefiles
* `regionmask`: needed if you want to use `epa_regions.to_regionmask()`
Note that `epa_regions.look_up()` requires only `pandas`,
and you can access the region definitions
(region number, office, and state/territory constituents)
at `epa_regions.REGIONS` without any 3rd-party dependencies.
`python -m epa_regions` needs `matplotlib`.
## Usage
```python
import epa_regions
# GeoPandas GeoDataFrame
epa = epa_regions.get(resolution="50m")
# Convert to regionmask Regions for use with gridded data
epa = epa_regions.to_regionmask(epa)
```
### Point data
Code
```python
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import epa_regions
rng = np.random.default_rng(seed=123)
epa = epa_regions.get(resolution="50m")
# CONUS
lonmin, lonmax = -125, -66
latmin, latmax = 24, 50
n = 250
lon = rng.uniform(lonmin, lonmax, n)
lat = rng.uniform(latmin, latmax, n)
points = gpd.GeoDataFrame(
geometry=gpd.points_from_xy(lon, lat, crs="EPSG:4326")
)
fig, ax = plt.subplots(constrained_layout=True, figsize=(4, 2.5))
epa.plot(column="number", ax=ax, alpha=0.6)
points.sjoin(epa, predicate="within").plot(column="number", ax=ax, ec="0.3", lw=1)
ax.set(xlim=(lonmin, lonmax), ylim=(latmin, latmax))
ax.axis("off")
fig.savefig("points.png", dpi="figure", bbox_inches="tight")
```
### Gridded data
Code
```python
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import epa_regions
epa = epa_regions.to_regionmask(epa_regions.get(resolution="50m"))
# CONUS
lonmin, lonmax = -125, -66
latmin, latmax = 24, 50
ds = (
xr.tutorial.open_dataset("air_temperature")
.sel(lon=slice(lonmin + 360, lonmax + 360), lat=slice(latmax, latmin))
)
mask = epa.mask(ds.isel(time=0))
proj = ccrs.LambertConformal(central_longitude=-100)
tran = ccrs.PlateCarree()
fig = plt.figure(figsize=(6, 6), constrained_layout=True)
ax = fig.add_subplot(3, 1, (1, 2), projection=proj)
mask.plot.pcolormesh(
levels=np.arange(mask.min() - 0.5, mask.max() + 1),
ax=ax,
transform=ccrs.PlateCarree(),
cmap="tab10",
cbar_kwargs=dict(
orientation="horizontal",
fraction=0.075,
pad=0.05,
ticks=np.arange(mask.min(), mask.max() + 1),
format="R{x:.0f}",
label="EPA Region",
),
)
ax.add_feature(cfeature.STATES, linewidth=0.7, edgecolor="0.3")
ax.coastlines()
ax.set_extent([lonmin, lonmax - 2, latmin, latmax], crs=tran)
ax.set_title("")
ax = fig.add_subplot(3, 1, 3)
(dt,) = np.unique(ds.time.diff("time"))
window = pd.Timedelta("30D")
(
ds["air"].groupby(mask)
.mean()
.rolling(time=int(window / dt), center=True)
.mean()
.plot(
hue="mask",
ax=ax,
add_legend=False,
)
)
ax.set_xlabel("")
ax.text(
0.01,
0.97,
f"{window.total_seconds() / 86400:g}-day rolling mean",
ha="left",
va="top",
transform=ax.transAxes,
fontsize=11,
)
fig.savefig("gridded.png", dpi="figure", bbox_inches="tight")
```