[英]python mask netcdf data using shapefile
I am using the following packages: 我使用以下包:
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
I have the following objects storing data: 我有以下对象存储数据:
print(precip_da)
Out[]:
<xarray.DataArray 'precip' (time: 13665, latitude: 200, longitude: 220)>
[601260000 values with dtype=float32]
Coordinates:
* longitude (longitude) float32 35.024994 35.074997 35.125 35.175003 ...
* latitude (latitude) float32 5.0249977 5.074997 5.125 5.174999 ...
* time (time) datetime64[ns] 1981-01-01 1981-01-02 1981-01-03 ...
Attributes:
standard_name: convective precipitation rate
long_name: Climate Hazards group InfraRed Precipitation with St...
units: mm/day
time_step: day
geostatial_lat_min: -50.0
geostatial_lat_max: 50.0
geostatial_lon_min: -180.0
geostatial_lon_max: 180.0
This looks as follows: 这看起来如下:
precip_da.mean(dim="time").plot()
I have my shapefile as a geopandas.GeoDataFrame
which represents a polygon. 我有我的shapefile作为
geopandas.GeoDataFrame
,它代表一个多边形。
awash = gpd.read_file(shp_dir)
awash
Out[]:
OID_ Name FolderPath SymbolID AltMode Base Clamped Extruded Snippet PopupInfo Shape_Leng Shape_Area geometry
0 0 Awash_Basin Awash_Basin.kml 0 0 0.0 -1 0 None None 30.180944 9.411263 POLYGON Z ((41.78939511000004 11.5539922500000...
Which looks as follows: 其外观如下:
awash.plot()
Plotted one on top of the other they look like this: 在另一个上面绘制一个,它们看起来像这样:
ax = awash.plot(alpha=0.2, color='black')
precip_da.mean(dim="time").plot(ax=ax,zorder=-1)
My question is, how do I mask the xarray.DataArray
by checking if the lat-lon points lie INSIDE the shapefile stored as a geopandas.GeoDataFrame
? 我的问题是,我怎么掩盖
xarray.DataArray
通过检查LAT-LON点位于INSIDE存储为shape文件geopandas.GeoDataFrame
?
I want to do something like the following: 我想做类似以下的事情:
masked_precip = precip_da.within(awash)
OR 要么
masked_precip = precip_da.loc[precip_da.isin(awash)]
I have thought about using the rasterio.mask
module but I don't know what format the input data needs to be. 我曾考虑使用
rasterio.mask
模块,但我不知道输入数据需要什么格式。 It sounds as if it does exactly the right thing: 听起来好像它做的正确:
" Creates a masked or filled array using input shapes. Pixels are masked or set to nodata outside the input shapes " “ 使用输入形状创建蒙版或填充数组。像素被屏蔽或设置为输入形状之外的节点数 ”
Reposted from GIS Stack Exchange here 这里是从GIS Stack Exchange转发而来的
This is the current working solution that I have taken from this gist . 这是我从这个要点中获取的当前工作解决方案。 This is Stephan Hoyer's answer to a github issue for the xarray project.
这是Stephan Hoyer对xarray项目的github问题的回答。
On top of the other packages above both affine
and rasterio
are required 除了上面的其他包之外,还需要
affine
和rasterio
from rasterio import features
from affine import Affine
def transform_from_latlon(lat, lon):
""" input 1D array of lat / lon and output an Affine transformation
"""
lat = np.asarray(lat)
lon = np.asarray(lon)
trans = Affine.translation(lon[0], lat[0])
scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])
return trans * scale
def rasterize(shapes, coords, latitude='latitude', longitude='longitude',
fill=np.nan, **kwargs):
"""Rasterize a list of (geometry, fill_value) tuples onto the given
xray coordinates. This only works for 1d latitude and longitude
arrays.
usage:
-----
1. read shapefile to geopandas.GeoDataFrame
`states = gpd.read_file(shp_dir+shp_file)`
2. encode the different shapefiles that capture those lat-lons as different
numbers i.e. 0.0, 1.0 ... and otherwise np.nan
`shapes = (zip(states.geometry, range(len(states))))`
3. Assign this to a new coord in your original xarray.DataArray
`ds['states'] = rasterize(shapes, ds.coords, longitude='X', latitude='Y')`
arguments:
---------
: **kwargs (dict): passed to `rasterio.rasterize` function
attrs:
-----
:transform (affine.Affine): how to translate from latlon to ...?
:raster (numpy.ndarray): use rasterio.features.rasterize fill the values
outside the .shp file with np.nan
:spatial_coords (dict): dictionary of {"X":xr.DataArray, "Y":xr.DataArray()}
with "X", "Y" as keys, and xr.DataArray as values
returns:
-------
:(xr.DataArray): DataArray with `values` of nan for points outside shapefile
and coords `Y` = latitude, 'X' = longitude.
"""
transform = transform_from_latlon(coords[latitude], coords[longitude])
out_shape = (len(coords[latitude]), len(coords[longitude]))
raster = features.rasterize(shapes, out_shape=out_shape,
fill=fill, transform=transform,
dtype=float, **kwargs)
spatial_coords = {latitude: coords[latitude], longitude: coords[longitude]}
return xr.DataArray(raster, coords=spatial_coords, dims=(latitude, longitude))
def add_shape_coord_from_data_array(xr_da, shp_path, coord_name):
""" Create a new coord for the xr_da indicating whether or not it
is inside the shapefile
Creates a new coord - "coord_name" which will have integer values
used to subset xr_da for plotting / analysis/
Usage:
-----
precip_da = add_shape_coord_from_data_array(precip_da, "awash.shp", "awash")
awash_da = precip_da.where(precip_da.awash==0, other=np.nan)
"""
# 1. read in shapefile
shp_gpd = gpd.read_file(shp_path)
# 2. create a list of tuples (shapely.geometry, id)
# this allows for many different polygons within a .shp file (e.g. States of US)
shapes = [(shape, n) for n, shape in enumerate(shp_gpd.geometry)]
# 3. create a new coord in the xr_da which will be set to the id in `shapes`
xr_da[coord_name] = rasterize(shapes, xr_da.coords,
longitude='longitude', latitude='latitude')
return xr_da
It can be implemented as follows: 它可以实现如下:
precip_da = add_shape_coord_from_data_array(precip_da, shp_dir, "awash")
awash_da = precip_da.where(precip_da.awash==0, other=np.nan)
awash_da.mean(dim="time").plot()
You should have a look at the following packages: 您应该看看以下包:
Both may get you to what you want. 两者都可以让你达到你想要的。
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