drought/disaster/util/utils.py

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from geopandas import *
from pandas import *
from shapely.geometry import Point
import rasterio as rio
import rasterio.mask
from rasterio.warp import reproject, Resampling
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DATA_BASE_PATH = '/home/g214/data_from_chenhao/data_analyse/'
# DATA_BASE_PATH = r"D:\7. business\7.baoji\code\drought_analyse\data/"
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def get_buffer_data(gdf, crs={'init': 'epsg:4326', 'no_defs': True}, dis=1):
data = gdf
if not data.crs:
data.crs = crs
else:
crs = data.crs
data = data.to_crs(epsg=3857)
buffer = data.buffer(dis)
data.set_geometry(buffer, inplace=True)
data.to_crs(crs=crs, inplace=True)
return data
def read_xzqh(path):
shp_bjqh = GeoDataFrame.from_file(path)
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shp_bjqh = shp_bjqh[['geometry', 'county']]
# shp_bjqh.rename(columns={'县名称': 'county'}, inplace=True)
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shp_bjqh.to_crs(epsg=4326, inplace=True)
return shp_bjqh
def intersection(gdf1, gdf2):
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try:
data = geopandas.overlay(gdf1, gdf2, how='intersection')
data.crs = gdf1.crs
return data
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except Exception as e:
print(str(e))
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return None
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def intersection_xzqh(gdf):
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bjqh = read_xzqh(DATA_BASE_PATH + 'boundary/bj.shp')
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intersection_data = intersection(gdf, bjqh)
return intersection_data
def join(gdf1, gdf2):
return geopandas.sjoin(gdf1, gdf2, how="inner", op='intersects')
def agg_raster_by_gdf_value(path, gdf):
# crs = '+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs'
with rio.open(path) as src:
gdf = gdf.to_crs(epsg=3857)
sum = {}
for i in range(0, len(gdf)):
geo = gdf.iloc[i].geometry
try:
feature = [GeoSeries(geo).__geo_interface__['features'][0]['geometry']]
out_image, out_transform = rio.mask.mask(src, feature, crop=True, nodata=src.nodata)
res = np.nansum(np.where(out_image == src.nodata, np.nan, out_image))
if 'county' in gdf:
county = gdf.iloc[i].county
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print(county)
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if county in sum.keys():
sum[county] = sum.get(county) + res
else:
sum[county] = res
elif 'all' in sum.keys():
sum['all'] = sum.get('all') + res
else:
sum['all'] = res
# for band in out_image:
# for row in band:
# for column in row:
# if column != src.nodata:
# sum += column
# print(sum)
except Exception as e:
print(str(e))
return sum
def agg_raster_by_gdf_area(path, gdf):
# crs = '+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs'
with rio.open(path) as src:
gdf = gdf.to_crs(epsg=3857)
sum = {}
for i in range(0, len(gdf)):
geo = gdf.iloc[i].geometry
try:
feature = [GeoSeries(geo).__geo_interface__['features'][0]['geometry']]
out_image, out_transform = rio.mask.mask(src, feature, crop=True, nodata=src.nodata)
out_image = np.where(out_image == 1, 1, np.nan)
print(out_image)
res = np.nansum(out_image) * src.width * src.height
if 'county' in gdf:
county = gdf.iloc[i].county
if county in sum.keys():
sum[county] = sum.get(county) + res
else:
sum[county] = res
elif 'all' in sum.keys():
sum['all'] = sum.get('all') + res
else:
sum['all'] = res
except:
pass
return sum
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def agg_prevent_disater_by_gdf_area(path, gdf):
# crs = '+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs'
with rio.open(path) as src:
gdf = gdf.to_crs(epsg=3857)
sum = {}
for i in range(0, len(gdf)):
geo = gdf.iloc[i].geometry
try:
feature = [GeoSeries(geo).__geo_interface__['features'][0]['geometry']]
out_image, out_transform = rio.mask.mask(src, feature, crop=True, nodata=src.nodata)
out_image = np.where(out_image == 1, 1, np.nan)
print(out_image)
res = np.nansum(out_image) * src.width * src.height
name = str(gdf.iloc[i].name)
if name in sum.keys():
sum[name] = sum.get(name) + res
else:
sum[name] = res
except:
pass
return sum
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def agg_gdf_by_gdf_area(gdf1, gdf2):
intersection_data = intersection(gdf1, gdf2)
area = intersection_data.to_crs(epsg=3857).area
intersection_data['area'] = area
intersection_data = intersection_data[['name', 'area']]
# print(intersection_data.head())
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return intersection_data.groupby('name')['area'].sum()
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def agg_raster_by_gdf_area_and_aspect_slope(path, path_aspect, path_slope, gdf):
with rio.open(path) as src:
with rio.open(path_aspect) as src1:
with rio.open(path_slope) as src2:
gdf = gdf.to_crs(epsg=3857)
res_flat = 0
res_30 = 0
res_more_than_30 = 0
for i in range(0, len(gdf)):
geo = gdf.iloc[i].geometry
try:
feature = [GeoSeries(geo).__geo_interface__['features'][0]['geometry']]
out_image, out_transform = rio.mask.mask(src, feature, crop=True, nodata=src.nodata)
out_image_aspect, out_transform_aspect = rio.mask.mask(src1, feature, crop=True,
nodata=src1.nodata)
out_image_slope, _ = rio.mask.mask(src2, feature, crop=True, nodata=src2.nodata)
out_image_aspect_reproject = np.empty(shape=(out_image.shape[0], # same number of bands
round(out_image.shape[1]),
round(out_image.shape[2])))
out_image_slope_reproject = np.empty(shape=(out_image.shape[0], # same number of bands
round(out_image.shape[1]),
round(out_image.shape[2])))
reproject(
out_image_aspect, out_image_aspect_reproject,
src_transform=out_transform_aspect,
dst_transform=out_transform,
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src_crs='+proj=latlong',
dst_crs='+proj=latlong',
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resampling=Resampling.bilinear)
reproject(
out_image_slope, out_image_slope_reproject,
src_transform=out_transform_aspect,
dst_transform=out_transform,
# src_crs={'init': 'EPSG:3857'},
# dst_crs={'init': 'EPSG:3857'},
resampling=Resampling.bilinear)
out_image_slope_reproject = np.where(out_image_slope_reproject == src1.nodata, 1000, out_image_slope_reproject)
out_image_slope_reproject = out_image_slope_reproject.astype(int)
out_image_aspect_reproject = np.where(out_image_aspect_reproject == src2.nodata, 1000, out_image_aspect_reproject)
out_image_aspect_reproject = out_image_aspect_reproject.astype(int)
for i in range(0, out_image.shape[1]):
for j in range(0, out_image.shape[2]):
aspect = out_image_aspect_reproject[0, i, j]
slope = out_image_slope_reproject[0, i, j]
print(aspect, slope, out_image[0, i, j])
if out_image[0, i, j] > 0:
if slope <= 0:
print('slope')
if aspect < 0:
res_flat += 1
elif slope < 30:
res_30 += 1
else:
res_more_than_30 += 1
print(res_flat, res_30, res_more_than_30)
except Exception as e:
print(str(e))
return (res_flat, res_30, res_more_than_30)
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def merge_gdf_geo_to_geojson(gdf):
return gdf.to_json()