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vaisala_to_geojson_and_shapefile.py
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vaisala_to_geojson_and_shapefile.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jul 10 20:14:59 2020
@author: Weather Radar Team
"""
from datetime import datetime
import wradlib as wrl
import numpy as np
import matplotlib.pyplot as plt
import warnings, os, math, geojsoncontour, geojson, gdal, subprocess, json, glob
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)
def getUniqueID(i):
switcher = {
5:1,
10:2,
15:3,
20:4,
25:5,
30:6,
35:7,
40:8,
45:9,
50:10,
55:11,
60:12,
65:13
}
return switcher.get(i,np.nan)
def searchFile(path,time,scanName):
searchTime=time.strftime("%Y%m%d%H%M")[2:-1]
results = glob.glob('{}/*{}*'.format(path,searchTime))
radarFiles=[]
sweepNumbers=[]
for file in results:
f = wrl.util.get_wradlib_data_file(file)
raw=wrl.io.read_iris(f)
ppiVolType=raw['product_hdr']['product_configuration']['product_name']
if str(ppiVolType)==scanName:
sweepNumber=raw['product_hdr']['product_configuration']['product_specific_info']['sweep_number']
radarFiles.append(file)
sweepNumbers.append(sweepNumber)
return radarFiles,sweepNumbers
def extractVAISALA(radarFiles,sweepNumbers,time,scanName,moment):
f = wrl.util.get_wradlib_data_file(radarFiles[0])
raw=wrl.io.read_iris(f)
# ekstrak lokasi radar
radarLon=float(raw['product_hdr']['product_end']['longitude'])
radarLat=float(raw['product_hdr']['product_end']['latitude'])
radarAlt=float(raw['product_hdr']['product_end']['ground_height'])
radarLat -= 360 # radarLat=radarLat-360
sitecoords=(radarLon,radarLat,radarAlt)
# mempersiapkan container data
res=250. # resolusi data yang diinginkan dalam meter
resCoords=res/111229. # resolusi data dalam derajat
rmax=250000./111229. # range maksimum
lonMax,lonMin=radarLon+(rmax),radarLon-(rmax)
latMax,latMin=radarLat+(rmax),radarLat-(rmax)
nGrid=int(np.floor((lonMax-lonMin)/resCoords)) # jumlah grid
lonGrid=np.linspace(lonMin,lonMax,nGrid) # grid longitude
latGrid=np.linspace(latMin,latMax,nGrid) # grid latitude
dataContainer = np.zeros((len(lonGrid),len(latGrid))) # penampung data
allElevation=[]
for file,sweep in zip(radarFiles,sweepNumbers):
f = wrl.util.get_wradlib_data_file(file)
raw=wrl.io.read_iris(f)
timeEnd=raw['product_hdr']['product_end']['ingest_time']
# ekstrak azimuth data
missing_ray = None
x = raw['data'][sweep]['sweep_data'][moment]
az_start = x['azi_start'].copy()
az_stop = x['azi_stop'].copy()
ixmissing = np.array([], dtype="i4")
if missing_ray is not None:
ismissing1 = (az_start == missing_ray)
ismissing2 = (az_stop == missing_ray)
ismissing = (ismissing1 & ismissing2)
ixmissing = np.where(ismissing)[0]
if len(ixmissing) > 0:
# beamwidth = data["ingest_header"]["task_configuration"]
# ["task_misc_info"]["horizontal_beam_width"]
nrays = raw["nrays"]
# Interpolate az_start
f = interpolate.interp1d(np.arange(nrays)[~ismissing],
az_start[~ismissing])
az_start[ixmissing] = f(np.arange(nrays)[ismissing])
# Interpolate az_start
f = interpolate.interp1d(np.arange(nrays)[~ismissing],
az_stop[~ismissing])
az_stop[ixmissing] = f(np.arange(nrays)[ismissing])
az_stop[az_stop < az_start] += 360.
az = (az_start + az_stop) / 2.
az[az > 360.] -= 360.
az_start[az_start > 360.] -= 360.
az_stop[az_stop > 360.] -= 360.
rollby = -np.argmin(az)
az = np.roll(az, rollby, axis=0)
az_start = np.roll(az_start, rollby, axis=0)
az_stop = np.roll(az_stop, rollby, axis=0)
assert np.all(np.diff(az) > 0), "List of azimuth angles " \
"is not strictly increasing."
# ekstrak range data
range_info = raw['ingest_header']['task_configuration']['task_range_info']
first_bin = range_info['range_first_bin']
# last_bin = range_info['range_last_bin']
range_step = range_info['step_output_bins']
# nbins = data["nbins"]
# We assume that range_info['range_first_bin'] specifies
# the midpoint of the first bin
# If, however, range_info['range_first_bin'] is zero,
# we have to treat it differently
# ATTENTION: The resulting ranges are not fully consistent
# with range_info['range_last_bin']
if first_bin > 0.:
r = np.arange(raw["nbins"]) * range_step + first_bin
else:
r = np.arange(raw["nbins"]) * range_step + range_step/2.
# divide by 1e2 to get from cm to m according to spec
r = r / 1e2
# ekstrak elevation data
elevation=float('{0:.1f}'.format(raw['data'][sweep]['ingest_data_hdrs']['DB_DBZ']["fixed_angle"]))
allElevation.append(elevation)
print('Extracting radar data : SWEEP-{0} at Elevation Angle {1:.1f} deg ...'.format(sweep,elevation))
# ekstrak radar data
data = raw['data'][sweep]['sweep_data'][moment]['data']
# transformasi dari koordinat bola ke koordinat kartesian
rangeMesh, azimuthMesh =np.meshgrid(r,az) # meshgrid azimuth dan range
lonlatalt = wrl.georef.polar.spherical_to_proj(
rangeMesh, azimuthMesh, elevation, sitecoords
)
x, y = lonlatalt[:, :, 0], lonlatalt[:, :, 1]
# proses regriding ke data container yang sudah dibuat sebelumnya
lonMesh, latMesh=np.meshgrid(lonGrid,latGrid)
gridLatLon = np.vstack((lonMesh.ravel(), latMesh.ravel())).transpose()
xy=np.concatenate([x.ravel()[:,None],y.ravel()[:,None]], axis=1)
radius=r[np.size(r)-1]
center=[x.mean(),y.mean()]
gridded = wrl.comp.togrid(
xy, gridLatLon,
radius, center, data.ravel(),
wrl.ipol.Linear
)
griddedData = np.ma.masked_invalid(gridded).reshape((len(lonGrid), len(latGrid)))
dataContainer=np.dstack((dataContainer,griddedData))
return dataContainer,lonMesh,latMesh,timeEnd
def writeGeoJSON(dataContainer,lonMesh,latMesh,fileGeoJSON):
print('\nWriting geojson : {}'.format(fileGeoJSON))
cmaxData=np.nanmax(dataContainer[:,:,:],axis=2)
cmaxData[cmaxData<0]=np.nan;cmaxData[cmaxData>100]=np.nan
clevsZ = [5,10,15,20,25,30,35,40,45,50,55,60,65,70]
colors=['#07FEF6','#0096FF','#0002FE','#01FE03','#00C703','#009902','#FFFE00','#FFC801','#FF7707','#FB0103','#C90002','#980001','#FF00FF','#9800FE']
contourf=plt.contourf(lonMesh,latMesh,cmaxData,clevsZ,colors=colors,alpha=0.5)
geojson=geojsoncontour.contourf_to_geojson(
contourf=contourf,
min_angle_deg=3.0,
ndigits=3,
stroke_width=0.0,
fill_opacity=0.1)
# Add attribute to JSON file
dataDict=json.loads(geojson)
for i in range(len(dataDict['features'])):
properties=dataDict['features'][i]['properties']
valueInt=int(float(properties['title'][0:4]))
properties['value']=valueInt
properties['id']=getUniqueID(valueInt)
with open(fileGeoJSON,'w') as fp:
json.dump(dataDict,fp)
plt.close()
print('Finished writing geojson file')
def writeShapefile(fileGeoJSON,fileShapefile):
print('\nWriting shapefile : {}'.format(fileShapefile))
f=open(fileGeoJSON)
data=json.load(f)
f.close()
with open('data.geojson', 'w') as f:
geojson.dump(data, f)
args = ['ogr2ogr', '-f', 'ESRI Shapefile', fileShapefile, 'data.geojson']
subprocess.Popen(args)
print('Finished writing shapefile')
def main():
site='AMQ'
pathVAISALA='D:/project_webprogramming/wxradarexplore/radarDataExtraction/data/AMQ'
time=datetime(2020,6,20,12,10)
scanName='RAW_PPIVOLA '
moment='DB_DBZ'
radarFiles,sweepNumbers=searchFile(pathVAISALA,time,scanName)
dataContainer,lonMesh,latMesh,timeEnd=extractVAISALA(radarFiles,sweepNumbers,time,scanName,moment)
path='D:/project_webprogramming/wxradarexplore/radarDataConversion'
fileGeoJSON='{}/{}{}.json'.format(path,site,timeEnd.strftime("%Y%m%d%H%M"))
fileShapefile='{}/{}{}.shp'.format(path,site,timeEnd.strftime("%Y%m%d%H%M"))
writeGeoJSON(dataContainer,lonMesh,latMesh,fileGeoJSON)
writeShapefile(fileGeoJSON,fileShapefile)
main()