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objects.py
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############################################################
# Program is part of PyAPS #
# Copyright 2012, by the California Institute of Technology#
# Contact: [email protected] #
# Modified by A. Benoit and R. Jolivet 2019 #
# Ecole Normale Superieure, Paris #
# Contact: [email protected] #
############################################################
import os.path
import sys
import numpy as np
import scipy.integrate as intg
import scipy.interpolate as si
import matplotlib.pyplot as plt
from pyaps3 import utils, processor
##############Creating a class object for PyAPS use.
class PyAPS:
'''Class for dealing with Atmospheric phase corrections in radar/geo coordinates.
Operates on one weather model file and one Geo.rsc file at a time.'''
def __init__(self, gribfile, dem, lat, lon, inc=0.0, mask=None,
grib='era5', humidity='Q', Del='comb', model='ERA5', verb=False):
'''Initiates the data structure for atmos corrections in geocoded domain.
Args:
* gribfile (str) : path to downloaded grib file
* dem (np.array) : height in size of (length, width)
* lat (np.array) : latitude in size of (length, width)
* lon (np.array) : longitude in size of (length, width)
Kwargs:
* inc (number or np.array) : incidence angle (in size of (length, width) for np.array)
* mask (np.array) : mask of valid pixels in size of (length, width)
* grib (str) : grib name in ['ERA5', 'ERAINT', 'HRES', 'NARR', 'MERRA']
* humidity (str) : ['Q', 'R']
* Del (str) : ['comb', 'wet', 'dry']
* model (str) : ECMWF dataset name in ['era5', 'eraint', 'hres']
* verb (bool) : True or False
.. note::
For ISCE products, lat/lon can be read from lat/lon.rdr file
For ROIPAC products, lat, lon = utils.get_lat_lon('radar_16rlks.hgt.rsc')
'''
#--------- Check files exist and we have what's needed for computation
# check grib type and import module
grib = grib.upper()
if grib in ['ERA5','ERAINT','HRES']:
from pyaps3 import era
elif grib == 'NARR':
from pyaps3 import narr
elif grib == 'MERRA':
from pyaps3 import merra
else:
raise ValueError('PyAPS: Undefined grib file source: {}'.format(grib))
self.grib = grib
# Check Humidity variable
humidity = humidity.upper()
assert humidity in ('Q','R'), 'PyAPS: Undefined humidity.'
self.humidity = humidity
if self.grib in ('NARR','MERRA'):
assert self.humidity in ('Q'), 'PyAPS: Relative humidity not provided by NARR/MERRA.'
# Check the model for ECMWF
self.model = model
# Check grib file exists
assert os.path.isfile(gribfile), 'PyAPS: GRIB File does not exist.'
self.gfile = gribfile
# Get altitude, lon, lat, etc
self.dem = dem
self.lon = lon
self.lat = lat
self.inc = inc
if mask is None:
self.mask = np.ones(self.dem.shape)
else:
self.mask = mask
# Get size
self.ny, self.nx = self.dem.shape
assert self.lon.shape == (self.ny, self.nx), 'PyAPS: Longitude array size mismatch'
assert self.lat.shape == (self.ny, self.nx), 'PyAPS: Latitude array size mismatch'
assert self.mask.shape == (self.ny, self.nx), 'PyAPS: Mask array size mismatch'
# check incidence angle size and type
if isinstance(self.inc, np.ndarray):
if verb:
print('INFO: INCIDENCE ANGLE AS AN ARRAY')
assert self.inc.shape == (self.ny, self.nx), 'PyAPS: Incidence array size mismatch'
elif isinstance(self.inc, (int, float, np.float32, np.float64)):
if verb:
print('INFO: INCIDENCE ANGLE AS A NUMBER: {} DEG'.format(self.inc))
else:
raise ValueError('PyAPS: unrecognized incidence data type: {}'.format(type(self.inc)))
#--------- Initialize variables
self.dict = processor.initconst()
# Get some scales
if grib in ('ERA5','ERAINT','HRES'):
self.hgtscale = ((self.dict['maxAlt']-self.dict['minAlt'])/self.dict['nhgt'])/0.703
self.bufspc = 1.2
elif grib in ('NARR'):
self.hgtscale = ((self.dict['maxAlt']-self.dict['minAlt'])/self.dict['nhgt'])/0.3
self.bufspc = 1.2
elif grib in ('MERRA'):
self.hgtscale = ((self.dict['maxAlt']-self.dict['minAlt'])/self.dict['nhgt'])/0.5
self.bufspc = 1.0
# Problems in isce when lon and lat arrays have weird numbers
self.lon[self.lon < 0.] += 360.0
self.minlon = np.nanmin(self.lon*self.mask) - self.bufspc
self.maxlon = np.nanmax(self.lon*self.mask) + self.bufspc
self.minlat = np.nanmin(self.lat*self.mask) - self.bufspc
self.maxlat = np.nanmax(self.lat*self.mask) + self.bufspc
if verb:
print('INFO: AREA COVERAGE IN SNWE: ({:.2f}, {:.2f}, {:.2f}, {:.2f})'.format(
self.maxlat, self.minlat, self.minlon, self.maxlon))
#--------- Extract infos from gribfiles
if self.grib in ('ERA'):
assert False, 'Need to modify get_era to fit with the new standards'
[lvls,latlist,lonlist,gph,tmp,vpr] = era.get_era(self.gfile,
self.minlat,
self.maxlat,
self.minlon,
self.maxlon,
self.dict,
humidity=self.humidity,
verbose=verb)
elif self.grib in ('ERA5','ERAINT','HRES'):
[lvls,latlist,lonlist,gph,tmp,vpr] = era.get_ecmwf(self.model,
self.gfile,
self.minlat,
self.maxlat,
self.minlon,
self.maxlon,
self.dict,
humidity=self.humidity,
verbose=verb)
elif self.grib in ('NARR'):
assert False, 'Need to modify get_narr to fit with the new standards'
[lvls,latlist,lonlist,gph,tmp,vpr] = narr.get_narr(self.gfile,
self.minlat,
self.maxlat,
self.minlon,
self.maxlon,
self.dict,
verbose=verb)
elif self.grib in ('MERRA'):
assert False, 'Need to modify get_merra to fit with the new standards'
[lvls,latlist,lonlist,gph,tmp,vpr] = merra.get_merra(self.gfile,
self.minlat,
self.maxlat,
self.minlon,
self.maxlon,
self.dict,
verbose=verb)
lonlist[lonlist < 0.] += 360.0
# Make a height scale
hgt = np.linspace(self.dict['minAltP'], gph.max().round(), self.dict['nhgt'])
# Interpolate pressure, temperature and Humidity of hgt
[Pi,Ti,Vi] = processor.intP2H(lvls, hgt, gph, tmp, vpr, self.dict, verbose=verb)
# Calculate the delays
[DDry,DWet] = processor.PTV2del(Pi,Ti,Vi,hgt,self.dict,verbose=verb)
if Del.lower() == 'comb':
Delfn = DDry+DWet
elif Del.lower() == 'dry':
Delfn = DDry
elif Del.lower() == 'wet':
Delfn = DWet
else:
raise ValueError('Unrecognized delay type: {}'.format(Del))
if self.grib in ('NARR'):
assert False, 'Need to check narr.intdel'
[Delfn, latlist, lonlist] = narr.intdel(hgt, latlist, lonlist, Delfn)
#--------- Save things
self.Delfn = Delfn
self.latlist = latlist
self.lonlist = lonlist
self.lat = lat
self.lon = lon
self.hgt = hgt
self.Pi = Pi
self.Ti = Ti
self.Vi = Vi
self.verb = verb
# All done
return
def getdelay(self, dataobj, wvl=np.pi*4., writeStations=True):
'''Write delay to a matrix / HDF5 object or a file directly.
Bilinear Interpolation is used.
Args:
* dataobj : Final output. (str or HDF5 or np.array)
If str, output is written to file.
Kwargs:
* wvl : Wavelength in meters.
4*pi (by default) --> output results in delay in meters.
0.056 --> output results in delay in radians for C-band SAR.
.. note::
If dataobj is string, output is written to the file.
If np.array or HDF5 object, it should be of size (ny,nx).
'''
# To know the type of incidence (float, array)
if isinstance(self.inc, (int, float, np.float32, np.float64)):
cinc = np.cos(self.inc*np.pi/180.)
incFileFlag = 'number'
else:
incFileFlag = 'array'
# Get some info from the dictionary
minAltp = self.dict['minAltP']
# Check output and open file if necessary
outFile = isinstance(dataobj, str)
if outFile:
fout = open(dataobj,'wb')
dout = np.zeros((self.ny, self.nx))
else:
assert dataobj.shape == (self.ny, self.nx), 'PyAPS: Not a valid data object.'
dout = dataobj
#######################################################################################
# BILINEAR INTERPOLATION
# Create the 1d interpolator to interpolate delays in altitude direction
if self.verb:
print('PROGRESS: FINE INTERPOLATION OF HEIGHT LEVELS')
intp_1d = si.interp1d(self.hgt, self.Delfn, kind='cubic', axis=-1)
# Interpolate the delay function every meter, for each station
self.dem[np.isnan(self.dem)] = minAltp
self.dem[self.dem < minAltp] = minAltp
minH = np.max([np.nanmin(self.dem*self.mask), self.hgt.min()])
maxH = int(np.nanmax(self.dem*self.mask)) + 100.
kh = np.arange(minH,maxH)
self.Delfn_1m = intp_1d(kh)
self.alti = kh
# no reshape
Lonu = self.lonlist[0,:]
Latu = self.latlist[:,0]
# Create the cube interpolator for the bilinear method, to interpolate delays into a grid (x,y,z)
if self.verb:
print('PROGRESS: CREATE THE BILINEAR INTERPOLATION FUNCTION')
# Define a linear interpolating function on the 3D grid: ((x, y, z), data)
# We do the weird trick of [::-1,:,:] because Latu has to be in increasing order
# for the RegularGridInterpolator method of scipy.interpolate
linearint = si.RegularGridInterpolator((Latu[::-1], Lonu,kh),
self.Delfn_1m[::-1,:,:],
method='linear',
bounds_error=False,
fill_value = 0.0)
# Show progress bar
if self.verb:
toto = utils.ProgressBar(maxValue=self.ny)
print('PROGRESS: MAPPING THE DELAY')
# Loop on the lines
for m in range(self.ny):
# Update progress bar
if self.verb:
toto.update(m+1, every=5)
# Get latitude and longitude arrays
lati = self.lat[m,:]*self.mask[m,:]
loni = self.lon[m,:]*self.mask[m,:]
# Remove negative values
loni[loni<0.] += 360.
# Remove NaN values
ii = np.where(np.isnan(lati))
jj = np.where(np.isnan(loni))
xx = np.union1d(ii,jj)
lati[xx]=0.0
loni[xx]=0.0
# Get incidence if file provided
if incFileFlag == 'array':
cinc = np.cos(self.mask[m,:]*self.inc[m,:]*np.pi/180.)
# Make the bilinear interpolation
D = self.dem[m,:]
val = linearint(np.vstack((lati, loni, D)).T)*np.pi*4.0/(cinc*wvl)
val[xx] = np.nan
# Write outfile
if outFile:
resy = val.astype(np.float32)
resy.tofile(fout)
else:
dataobj[m,:] = val
if self.verb:
toto.close()
if outFile:
fout.close()
# All done
return
#######################################################################################
# WRITE STATIONS INFOS TO FILE
#if writeStations:
# stations_latfile = os.path.join(os.path.dirname(self.gfile),'latStations.txt')
# stations_lonfile = os.path.join(os.path.dirname(self.gfile),'lonStations.txt')
# if self.verb:
# print('SAVING STATIONS LATITUDES in: {}'.format(stations_latfile))
# print('SAVING STATIONS LONGITUDES in: {}'.format(stations_lonfile))
# np.savetxt(stations_latfile, self.latlist, fmt=['%1.2f'])
# np.savetxt(stations_lonfile, self.lonlist, fmt=['%1.2f'])
# for station in range(0,len(self.lonlist)):
# # Open file output
# sfile = 'station{}_{}.txt'.format(station,
# os.path.splitext(os.path.basename(self.gfile))[0])
# stationFile = os.path.join(os.path.dirname(self.gfile), sfile)
# myfile = open(stationFile, 'w')
# # Iterate over altitude level
# for i in range(self.Delfn_1m.shape[0]):
# altitudeValue = kh[i]
# phaseValue = self.Delfn_1m[i,:,:].flatten()[station]
# # Write file
# myfile.write("{} {}\n".format(altitudeValue, phaseValue))
# myfile.close()
#
# # Save kh into file
# khfile = os.path.join(os.path.dirname(self.gfile), 'kh.txt')
# np.savetxt(khfile, kh, newline='\n', fmt="%s")
########################################################################################
## CUBIC INTERPOLATION
## Create bicubic interpolator
#if self.verb:
# print('PROGRESS: CREATE THE CUBIC INTERPOLATION FUNCTION')
## Resize
#lonn, hgtn = np.meshgrid(self.lonlist, self.hgt)
#latn, hgtn = np.meshgrid(self.latlist, self.hgt)
## Define a cubic interpolating function on the 3D grid: ((x, y, z), data)
#cubicint = si.Rbf(lonn.flatten(), latn.flatten(), hgtn.flatten(), self.Delfn.flatten(),kind='cubic',fill_value = 0.0)
## Show progress bar
#if self.verb:
# toto = utils.ProgressBar(maxValue=self.ny)
# print('PROGRESS: MAPPING THE DELAY')
## Loop on the lines
#for m in range(self.ny):
# # Update progress bar
# if self.verb:
# toto.update(m+1, every=5)
# ###############################################
# ## Get values of the m line
# ## Longitude
# #print(self.lonlist.shape)
# #Lonu = np.unique(self.lonlist)
# #nLon = len(Lonu)
# #lonlisti = Lonu
# ## Latitude by iterating over self.latlist
# #if m == 0:
# # pos1 = 0
# # pos2 = nLon
# #else:
# # pos1 = pos1 + nLon
# # pos2 = pos2 + nLon
# #lonlisti = Lonu
# #latlisti = self.latlist[pos1:pos2]
# ## Height
# #hgtlisti = self.hgt
# #
# ## Delay by iterating over self.Delfn
# #print(self.Delfn.shape)
# #Delfni = (self.Delfn[pos1:pos2,:]).T
# ###############################################
# ## Define a cubic interpolating function on the 3D grid: ((x, y, z), data)
# #
# #lonn, hgtn = np.meshgrid(lonlisti, hgtlisti)
# #latn, hgtn = np.meshgrid(latlisti, hgtlisti)
# #cubicint = si.Rbf(lonn.flatten(), latn.flatten(), hgtn.flatten(), Delfni.flatten(), kind='cubic',fill_value = 0.0)
# #
# ###############################################
# # Get latitude and longitude arrays
# lati = self.lat[m,:]*self.mask[m,:]
# loni = self.lon[m,:]*self.mask[m,:]
# # Remove negative values
# loni[loni<0.] += 360.
#
# # Remove NaN values
# ii = np.where(np.isnan(lati))
# jj = np.where(np.isnan(loni))
# xx = np.union1d(ii,jj)
# lati[xx]=0.0
# loni[xx]=0.0
# # Get incidence if file provided
# if incFileFlag=='array':
# cinc = np.cos(self.mask[m,:]*self.inc[m,:]*np.pi/180.)
# # Make the interpolation
# hgti = self.dem[m,:]
# val = cubicint(loni.flatten(), lati.flatten(), hgti.flatten())
# val[xx] = np.nan
# # Write outfile
# if outFile:
# resy = val.astype(np.float32)
# resy.tofile(fout)
# else:
# dataobj[m,:] = val
#if self.verb:
# toto.close()
#if outFile:
# fout.close()
## All done
#return