def get_area(ds_lda):
'''get the area of lda file'''
attrs = ds_lda.attrs
i = attrs['WEST-EAST_GRID_DIMENSION']
j = attrs['SOUTH-NORTH_GRID_DIMENSION']
# calculate attrs for area definition
shape = (j, i)
radius = (i * attrs['DX'] / 2, j * attrs['DY'] / 2)
# create area as same as WRF
area_id = 'wrf_circle'
proj_dict = {
'proj': wrf_projs[attrs['MAP_PROJ']],
'lat_0': attrs['MOAD_CEN_LAT'],
'lon_0': attrs['STAND_LON'],
'lat_1': attrs['TRUELAT1'],
'lat_2': attrs['TRUELAT2'],
'a': 6370000,
'b': 6370000
}
center = (0, 0)
wrf_area = AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius,
shape=shape)
return wrf_area
def get_area(hrit_files,
central_lat,
central_lon,
elevation,
time,
save_path,
fov,
shape,
proj,
projection_parameters,
composite=None,
fov_deg=True):
"""Shows in Jupyter Notebook results of pictures seen from sat
Parameters
Array of saved on disc files.
:param save_path:
:param composite:
:param time:
:param elevation:
:param central_lon:
:param central_lat:
:param hrit_files:
"""
# TO DO: Add local earth radius
from satpy.scene import Scene
import math
from pyresample.geometry import AreaDefinition, SwathDefinition, create_area_def
from pyresample import create_area_def
if composite is None:
composite = 'realistic_colors'
if fov_deg == True:
fov = [fov[0] * math.pi / 180, fov[1] * math.pi / 180]
area_def = []
for i in range(0, len(central_lon)):
altitude = elevation[i]
rad = satellite_info(6371228, elevation[i], fov[0], fov[1])[5] / 2
lat_0, lon_0 = central_lat[i], central_lon[i]
lat_1, lat_2 = central_lat[i], central_lon[i]
center = (central_lat[i], central_lon[i])
radius = (rad, rad)
area_id = 'wrf_circle'
proj_dict = {'proj': proj, 'lat_0': lat_0, 'lon_0': lon_0, \
'lat_1': lat_1, 'lat_2': lat_2, \
'a': 6370000, 'b': 6370000, 'h': altitude, 'azi': projection_parameters[0],
'tilt': projection_parameters[1]}
area_def.append(
AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius=radius,
shape=shape))
# area_def.append(AreaDefinition.create_area_def(area_id, proj_dict, center, radius=radius, shape=shape))
files = return_files(time, hrit_files)
return (area_def, files, composite)
def get_info(self, ):
'''
Read basic info from geo file generated by WPS
If want to run this on your laptop and care about the space,
you can use ncks to subset the nc file (we just need attrs)
ncks -F -d Time,1,,1000 -v Times geo_em.d01.nc geo_em.d01_subset.nc
ref: https://fabienmaussion.info/2018/01/06/wrf-projection/
'''
self.geo = xr.open_dataset(data_path + 'geo_em.'+domain+'.nc')
attrs = self.geo.attrs
i = attrs['i_parent_end']
j = attrs['j_parent_end']
# calculate attrs for area definition
shape = (j, i)
radius = (i*attrs['DX']/2, j*attrs['DY']/2)
# create area as same as WRF
area_id = 'wrf_circle'
proj_dict = {'proj': wrf_projs[attrs['MAP_PROJ']],
'lat_0': attrs['MOAD_CEN_LAT'],
'lon_0': attrs['STAND_LON'],
'lat_1': attrs['TRUELAT1'],
'lat_2': attrs['TRUELAT2'],
'a': 6370000,
'b': 6370000}
center = (0, 0)
self.area_def = AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius,
shape=shape)
def get_info(self, ):
# read basic info from geo file generrated by WPS
self.geo = xr.open_dataset(wps_path + 'geo_em.' + domain + '.nc')
attrs = self.geo.attrs
# read original attrs
self.lat_1 = attrs['TRUELAT1']
self.lat_2 = attrs['TRUELAT2']
self.lat_0 = attrs['MOAD_CEN_LAT']
self.lon_0 = attrs['STAND_LON']
self.cenlat = attrs['CEN_LAT']
self.cenlon = attrs['CEN_LON']
self.i = attrs['WEST-EAST_GRID_DIMENSION']
self.j = attrs['SOUTH-NORTH_GRID_DIMENSION']
self.dx = attrs['DX']
self.dy = attrs['DY']
self.mminlu = attrs['MMINLU']
self.map = attrs['MAP_PROJ']
self.eta = attrs['BOTTOM-TOP_GRID_DIMENSION']
# calculate attrs for area definition
shape = (self.j, self.i)
center = (0, 0)
radius = (self.i * self.dx / 2, self.j * self.dy / 2)
# create area as same as WRF
area_id = 'wrf_circle'
proj_dict = {'proj': 'lcc', 'lat_0': self.lat_0, 'lon_0': self.lon_0, \
'lat_1': self.lat_1, 'lat_2': self.lat_2, \
'a':6370000, 'b':6370000}
self.area_def = AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius,
shape=shape)
def get_info(self, wrf_path, fname, vnames):
# self.wrf = xr.open_dataset(wrf_path+fname)._file_obj.ds
self.ds = Dataset(wrf_path+fname)
self.xrds = xr.open_dataset(wrf_path+fname)
# check vname and read all of them
self.dv = {}
if isinstance(vnames, str):
self.dv.update({vnames: getvar(self.ds, vnames, timeidx=ALL_TIMES)})
if vnames != 'Times' and 'lon' not in self.dv:
self.dv.update({'lat': latlon_coords(self.dv[vnames])[0]})
self.dv.update({'lon': latlon_coords(self.dv[vnames])[1]})
elif isinstance(vnames, list):
for index, v in enumerate(vnames):
self.dv.update({v: getvar(self.ds, v, timeidx=ALL_TIMES)})
if v != 'Times' and 'lon' not in self.dv:
self.dv.update({'lat': latlon_coords(self.dv[v])[0]})
self.dv.update({'lon': latlon_coords(self.dv[v])[1]})
# get proj
attrs = self.xrds.attrs
i = attrs['WEST-EAST_GRID_DIMENSION']
j = attrs['SOUTH-NORTH_GRID_DIMENSION']
# calculate attrs for area definition
shape = (j, i)
radius = (i*attrs['DX']/2, j*attrs['DY']/2)
# create area as same as WRF
area_id = 'wrf_circle'
proj_dict = {'proj': wrf_projs[attrs['MAP_PROJ']],
'lat_0': attrs['MOAD_CEN_LAT'],
'lon_0': attrs['STAND_LON'],
'lat_1': attrs['TRUELAT1'],
'lat_2': attrs['TRUELAT2'],
'a': 6370000,
'b': 6370000}
center = (0, 0)
self.area_def = AreaDefinition.from_circle(area_id, proj_dict,
center, radius,
shape=shape)
def get_info(self, wps_path, domain):
# read basic info from geo file generrated by WPS
self.geo = xr.open_dataset(wps_path + 'geo_em.'+domain+'.nc')
attrs = self.geo.attrs
i = attrs['i_parent_end']
j = attrs['j_parent_end']
# calculate attrs for area definition
shape = (j, i)
radius = (i*attrs['DX']/2, j*attrs['DY']/2)
# create area as same as WRF
area_id = 'wrf_circle'
proj_dict = {'proj': wrf_projs[attrs['MAP_PROJ']],
'lat_0': attrs['MOAD_CEN_LAT'],
'lon_0': attrs['STAND_LON'],
'lat_1': attrs['TRUELAT1'],
'lat_2': attrs['TRUELAT2'],
'a': 6370000,
'b': 6370000}
center = (0, 0)
self.area_def = AreaDefinition.from_circle(area_id, proj_dict,
center, radius,
shape=shape)
def show_sat_perspective3(hrit_files,
central_lat,
central_lon,
elevation,
time,
save_path,
fov,
shape,
proj,
projection_parameters,
composite=None,
fov_deg=True):
"""Shows in Jupyter Notebook results of pictures seen from sat
Parameters
Array of saved on disc files.
:param save_path:
:param composite:
:param time:
:param elevation:
:param central_lon:
:param central_lat:
:param hrit_files:
"""
# TO DO: Add local earth radius
from satpy.scene import Scene
import math
from pyresample.geometry import AreaDefinition, SwathDefinition, create_area_def
from pyresample import create_area_def
if composite is None:
composite = 'realistic_colors'
if fov_deg == True:
fov = [fov[0] * math.pi / 180, fov[1] * math.pi / 180]
# lla = mat.find_sourounding_list(earth_rads, latitudes, longitudes, elevations, fov)
area_def = []
for i in range(0, len(central_lon)):
altitude = elevation[i]
rad = satellite_info(6371228, elevation[i], fov[0], fov[1])[5] / 2
lat_0, lon_0 = central_lat[i], central_lon[i]
lat_1, lat_2 = central_lat[i], central_lon[i]
center = (central_lat[i], central_lon[i])
radius = (rad, rad)
area_id = 'wrf_circle'
proj_dict = {'proj': proj, 'lat_0': lat_0, 'lon_0': lon_0, \
'lat_1': lat_1, 'lat_2': lat_2, \
'a': 6370000, 'b': 6370000, 'h': altitude, 'azi': projection_parameters[0],
'tilt': projection_parameters[1]}
area_def.append(
AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius=radius,
shape=shape))
# area_def.append(AreaDefinition.create_area_def(area_id, proj_dict, center, radius=radius, shape=shape))
files = return_files(time, hrit_files)
scn = Scene(filenames=files)
scn.load([composite])
new_scn = scn
for i, area_def in enumerate(area_def, start=0):
local_scn = scn.resample(area_def, radius_of_influence=50000)
local_scn.show(composite)
path = save_path + proj + str(projection_parameters[0]) + '_' + str(
projection_parameters[1]) + '_' + str(shape[0]) + '_' + str(
composite) + '_' + '_{date:%Y-%m-%d_%H_%M_%S}'.format(
date=scn.start_time) + '/' + str(i) + '.png'
local_scn.save_dataset(composite,
path,
writer='simple_image',
num_threads=8)
# if save_path:
# if (isinstance(load_photo[0], float)):
# photo_type = str(load_photo[0])
# else:
# photo_type = load_photo[0]
# name = photo_path + photo_type + pro_name + '_{date:%Y-%m-%d_%H_%M_%S}.png'.format(date=scn.start_time)
# plt.savefig(name, dpi=dpi)
# if not save_path:
# plt.show()
return ()
def show_sat_perspective2(hrit_files,
central_lat,
central_lon,
elevation,
time,
save_path,
fov,
shape,
composite=None,
fov_deg=True):
"""Shows in Jupyter Notebook results of pictures seen from sat
Parameters
Array of saved on disc files.
:param save_path:
:param composite:
:param time:
:param elevation:
:param central_lon:
:param central_lat:
:param hrit_files:
"""
# TO DO: Add local earth radius
import datetime as dt
from satpy.scene import Scene
import math
from satpy.resample import get_area_def
from datetime import datetime
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
import cartopy.feature as cfeature
from skyfield.api import Topos, load
import pyproj
import numpy as np
from astropy import units as u
from astropy.coordinates import Angle
# %matplotlib inline
import matplotlib.pyplot as plt
from pyresample.geometry import AreaDefinition, SwathDefinition, create_area_def
if composite is None:
composite = 'realistic_colors'
if fov_deg == True:
fov = [fov[0] * math.pi / 180, fov[1] * math.pi / 180]
area_def = []
for i in range(0, len(central_lon)):
area_id = 'ease_sh'
rad = satellite_info(6371228, elevation[i], fov[0], fov[1])[5] / 2
lat_0, lon_0 = central_lat[i], central_lon[i]
lat_1, lat_2 = central_lat[i], central_lon[i]
center = (central_lat[i], central_lon[i])
radius = (rad, rad)
area_id = 'wrf_circle'
proj_dict = {'proj': 'lcc', 'lat_0': lat_0, 'lon_0': lon_0, \
'lat_1': lat_1, 'lat_2': lat_2, \
'a': 6370000, 'b': 6370000}
area_def.append(
AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius,
shape=shape))
files = return_files(time, hrit_files)
scn = Scene(filenames=files)
scn.load([composite])
new_scn = scn
for i, area_def in enumerate(area_def, start=0):
local_scn = scn.resample(area_def, radius_of_influence=50000)
local_scn.show(composite)
path = save_path + str(shape[0]) + '_' + str(composite) + '_' + str(
i) + '_{date:%Y-%m-%d_%H_%M_%S}.png'.format(date=scn.start_time)
local_scn.save_dataset(composite,
path,
writer='simple_image',
num_threads=8)
# if save_path:
# if (isinstance(load_photo[0], float)):
# photo_type = str(load_photo[0])
# else:
# photo_type = load_photo[0]
# name = photo_path + photo_type + pro_name + '_{date:%Y-%m-%d_%H_%M_%S}.png'.format(date=scn.start_time)
# plt.savefig(name, dpi=dpi)
# if not save_path:
# plt.show()
return ()
def calculate_and_project(hrit_files,
sat_positions,
time,
save_path,
fov,
shape,
proj,
nadir_proj=True,
composite=None,
fov_deg=True,
save_data_path=None,
save_photos=True):
"""Shows in Jupyter Notebook results of pictures seen from sat
Parameters
Array of saved on disc files.
:param save_path:
:param composite:
:param time:
:param elevation:
:param central_lon:
:param central_lat:
:param hrit_files:
"""
# TO DO: Add local earth radius
from satpy.scene import Scene
from wutsat.fun import mat_fun
import math
from pyresample.geometry import AreaDefinition, SwathDefinition, create_area_def
# from pyresample import create_area_def
import os
if composite is None:
composite = 'realistic_colors'
if fov_deg == True:
fov = [fov[0] * math.pi / 180, fov[1] * math.pi / 180]
if nadir_proj:
nadir_proj = [0, 0]
central_lat, central_lon, elevation = mat_fun.find_sourounding_list(
earth_radius=sat_positions[3],
lat=sat_positions[0],
lon=sat_positions[1],
alt=sat_positions[2],
fov=fov)
#print(len(central_lat))
area_def = []
for i in range(0, len(central_lon)):
altitude = elevation[i]
rad = satellite_info(6371228, elevation[i], fov[0], fov[1])[5] / 2
lat_0, lon_0 = central_lat[i], central_lon[i]
lat_1, lat_2 = central_lat[i], central_lon[i]
center = (central_lat[i], central_lon[i])
radius = (rad, rad)
area_id = 'wrf_circle'
proj_dict = {'proj': proj, 'lat_0': lat_0, 'lon_0': lon_0, \
'lat_1': lat_1, 'lat_2': lat_2, \
'a': 6370000, 'b': 6370000, 'h': altitude, 'azi': nadir_proj[0],
'tilt': nadir_proj[1]}
area_def.append(
AreaDefinition.from_circle(area_id,
proj_dict,
center,
radius=radius,
shape=shape))
# area_def.append(AreaDefinition.create_area_def(area_id, proj_dict, center, radius=radius, shape=shape))
files = return_files(time, hrit_files)
if save_photos:
scn = Scene(filenames=files)
scn.load([composite])
for i, area in enumerate(area_def, start=0):
local_scn = scn.resample(area, radius_of_influence=50000)
local_scn.show(composite)
path = save_path + '/' + str(i) + '.png'
local_scn.save_dataset(composite,
path,
writer='simple_image',
num_threads=8)
sat_data = [area_def, files, [central_lat, central_lon, elevation]]
if save_data_path:
mat_fun.rwdata(save_data_path, 'sat_data.pkl', 'w', sat_data)
return ()