使用shapefile的python掩码netcdf数据
使用shapefile的python掩码netcdf数据
提问于 2018-07-18 17:35:05
我正在使用以下包：
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd我有以下存储数据的对象：
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如下所示：
precip_da.mean(dim="time").plot()
我有我的shapefile作为一个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...如下所示：
awash.plot()
将一个绘制在另一个之上，它们看起来如下所示：
ax = awash.plot(alpha=0.2, color='black')
precip_da.mean(dim="time").plot(ax=ax,zorder=-1)
我的问题是，如何通过检查经度点是否位于存储为geopandas.GeoDataFrame的shapefile内来掩蔽xarray.DataArray
因此，我只需要落入该shapefile中的降水值(mm/day)。
我想做一些类似以下的事情：
masked_precip = precip_da.within(awash)或
masked_precip = precip_da.loc[precip_da.isin(awash)]编辑1
我考虑过使用rasterio.mask module，但我不知道输入数据需要什么格式。听起来它似乎做了一件正确的事情：
“使用输入形状创建掩码或填充数组。像素在输入形状之外被掩码或设置为无数据”
转载自GIS here
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-07-22 00:59:27
这是我从this gist获得的当前可用的解决方案。这是Stephan Hoyer对xarray项目的github issue的回答。
在上面的其他包之上，affine和rasterio都是必需的
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具体实现方法如下：
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()
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原文链接：
https://stackoverflow.com/questions/51398563
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