使用shapefile的python掩码netcdf数据

Question

我使用以下包：

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

这看起来如下：

precip_da.mean(dim="time").plot()

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...

其外观如下：

awash.plot()

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)

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 ？

所以我只想要降低INSIDE那个shapefile的降水值（mm / day）。

我想做类似以下的事情：

masked_precip = precip_da.within(awash)

要么

masked_precip = precip_da.loc[precip_da.isin(awash)]

编辑1

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。 听起来好像它做的正确：

“ 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 ”

这里是从GIS Stack Exchange转发而来的

Answer 1

这是我从这个要点中获取的当前工作解决方案。 这是Stephan Hoyer对xarray项目的github问题的回答。

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

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()

Answer 2

您应该看看以下包：

• 萨利姆和感兴趣的地区的例子
• regionmask

两者都可以让你达到你想要的。