def run_track(temp_dir,exp_full_name,out_conf_fil,date,rnx_rover):
dowstring = ''.join([str(e) for e in conv.dt2gpstime(date)])
bigcomand = ' '.join(("track -f" , out_conf_fil , '-d' , conv.dt2doy(date) ,'-w', dowstring))
print('INFO : command launched :')
print(bigcomand)
# START OF PROCESSING
os.chdir(temp_dir)
subprocess.call([bigcomand], executable='/bin/bash', shell=True)
outfiles = []
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*sum*'))
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*pos*'))
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*cmd*'))
Antobj_rov , Recobj_rov , Siteobj_rov , Locobj_rov = \
files_rw.read_rinex_2_dataobjts(rnx_rover)
[shutil.copy(e,out_dir) for e in outfiles]
[os.remove(e) for e in outfiles]
print("TRACK RUN FINISHED")
print('results available in ' , out_dir)
return None
def compar_orbit(Data_inp_1,
Data_inp_2,
step_data=900,
sats_used_list=['G'],
name1='',
name2='',
use_name_1_2_for_table_name=False,
RTNoutput=True,
convert_ECEF_ECI=True,
clean_null_values=True,
conv_coef=10**3,
return_satNull=False):
"""
Compares 2 GNSS orbits files (SP3), and gives a summary plot and a
statistics table
Parameters
----------
Data_inp_1 & Data_inp_2 : str or Pandas DataFrame
contains the orbits or path (string) to the sp3
step_data : int
per default data sampling
sats_used_list : list of str
used constellation or satellite : G E R C ... E01 , G02 ...
Individuals satellites are prioritary on whole constellations
e.g. ['G',"E04"]
RTNoutput : bool
select output, Radial Transverse Normal or XYZ
convert_ECEF_ECI : bool
convert sp3 ECEF => ECI, must be True in operational !
name1 & name2 : str (optionals)
optional custom names for the 2 orbits
use_name_1_2_for_table_name : bool
False : use name 1 and 2 for table name, use datafile instead
clean_null_values : bool or str
if True or "all" remove sat position in all X,Y,Z values
are null (0.000000)
if "any", remove sat position if X or Y or Z is null
if False, keep everything
conv_coef : int
conversion coefficient, km to m 10**3, km to mm 10**6
Returns
-------
Diff_sat_all : Pandas DataFrame
contains differences b/w Data_inp_1 & Data_inp_2
in Radial Transverse Normal OR XYZ frame
Attributes of Diff_sat_all :
Diff_sat_all.name : title of the table
Note
----
clean_null_values if useful (and necessary) only if
convert_ECEF_ECI = False
if convert_ECEF_ECI = True, the cleaning will be done by
a side effect trick : the convertion ECEF => ECI will generate NaN
for a zero-valued position
But, nevertheless, activating clean_null_values = True is better
This Note is in fact usefull if you want to see bad positions on a plot
=> Then convert_ECEF_ECI = False and clean_null_values = False
Source
------
"Coordinate Systems", ASEN 3200 1/24/06 George H. Born
"""
# selection of both used Constellations AND satellites
const_used_list = []
sv_used_list = []
for sat in sats_used_list:
if len(sat) == 1:
const_used_list.append(sat)
elif len(sat) == 3:
sv_used_list.append(sat)
if not sat[0] in const_used_list:
const_used_list.append(sat[0])
# Read the files or DataFrames
# metadata attributes are not copied
# Thus, manual copy ...
# (Dirty way, should be impoved without so many lines ...)
if type(Data_inp_1) is str:
D1orig = files_rw.read_sp3(Data_inp_1, epoch_as_pd_index=True)
else:
D1orig = Data_inp_1.copy(True)
try:
D1orig.name = Data_inp_1.name
except:
D1orig.name = "no_name"
try:
D1orig.path = Data_inp_1.path
except:
D1orig.path = "no_path"
try:
D1orig.filename = Data_inp_1.filename
except:
D1orig.filename = "no_filename"
if type(Data_inp_2) is str:
D2orig = files_rw.read_sp3(Data_inp_2, epoch_as_pd_index=True)
else:
D2orig = Data_inp_2.copy(True)
try:
D2orig.name = Data_inp_2.name
except:
D2orig.name = "no_name"
try:
D2orig.path = Data_inp_2.path
except:
D2orig.path = "no_path"
try:
D2orig.filename = Data_inp_2.filename
except:
D2orig.filename = "no_filename"
#### NB : It has been decided with GM that the index of a SP3 dataframe
#### will be integers, not epoch datetime anymore
#### BUT here, for legacy reasons, the index has to be datetime
if isinstance(D1orig.index[0], (int, np.integer)):
D1orig.set_index("epoch", inplace=True)
if isinstance(D2orig.index[0], (int, np.integer)):
D2orig.set_index("epoch", inplace=True)
Diff_sat_stk = []
# This block is for removing null values
if clean_null_values:
if clean_null_values == "all":
all_or_any = np.all
elif clean_null_values == "any":
all_or_any = np.any
else:
all_or_any = np.all
xyz_lst = ['x', 'y', 'z']
D1_null_bool = all_or_any(np.isclose(D1orig[xyz_lst], 0.), axis=1)
D2_null_bool = all_or_any(np.isclose(D2orig[xyz_lst], 0.), axis=1)
D1 = D1orig[np.logical_not(D1_null_bool)]
D2 = D2orig[np.logical_not(D2_null_bool)]
if np.any(D1_null_bool) or np.any(D2_null_bool):
sat_nul = utils.join_improved(
" ", *list(set(D1orig[D1_null_bool]["sat"])))
print("WARN : Null values contained in SP3 files : ")
print(
"f1:", np.sum(D1_null_bool),
utils.join_improved(" ",
*list(set(D1orig[D1_null_bool]["sat"]))))
print(
"f2:", np.sum(D2_null_bool),
utils.join_improved(" ",
*list(set(D2orig[D2_null_bool]["sat"]))))
else:
sat_nul = []
else:
D1 = D1orig.copy()
D2 = D2orig.copy()
for constuse in const_used_list:
D1const = D1[D1['const'] == constuse]
D2const = D2[D2['const'] == constuse]
# checking if the data correspond to the step
bool_step1 = np.mod((D1const.index - np.min(D1.index)).seconds,
step_data) == 0
bool_step2 = np.mod((D2const.index - np.min(D2.index)).seconds,
step_data) == 0
D1window = D1const[bool_step1]
D2window = D2const[bool_step2]
# find common sats and common epochs
sv_set = sorted(
list(set(D1window['sv']).intersection(set(D2window['sv']))))
epoc_set = sorted(
list(set(D1window.index).intersection(set(D2window.index))))
# if special selection of sats, then apply it
# (it is late and this selection is incredibely complicated ...)
if np.any([True if constuse in e else False for e in sv_used_list]):
# first find the selected sats for the good constellation
sv_used_select_list = [
int(e[1:]) for e in sv_used_list if constuse in e
]
#and apply it
sv_set = sorted(
list(set(sv_set).intersection(set(sv_used_select_list))))
for svv in sv_set:
# First research : find corresponding epoch for the SV
# this one is sufficent if there is no gaps (e.g. with 0.00000) i.e.
# same nb of obs in the 2 files
# NB : .reindex() is smart, it fills the DataFrame
# with NaN
try:
D1sv_orig = D1window[D1window['sv'] == svv].reindex(epoc_set)
D2sv_orig = D2window[D2window['sv'] == svv].reindex(epoc_set)
except Exception as exce:
print("ERR : Unable to re-index with an unique epoch")
print(
" are you sure there is no multiple-defined epochs for the same sat ?"
)
print(
" it happens e.g. when multiple ACs are in the same DataFrame "
)
print(
"TIP : Filter the input Dataframe before calling this fct with"
)
print(" DF = DF[DF['AC'] == 'gbm']")
raise exce
# Second research, it is a security in case of gap
# This step is useless, because .reindex() will fill the DataFrame
# with NaN
if len(D1sv_orig) != len(D2sv_orig):
print("INFO : different epochs nbr for SV", svv,
len(D1sv_orig), len(D2sv_orig))
epoc_sv_set = sorted(
list(
set(D1sv_orig.index).intersection(set(
D2sv_orig.index))))
D1sv = D1sv_orig.loc[epoc_sv_set]
D2sv = D2sv_orig.loc[epoc_sv_set]
else:
D1sv = D1sv_orig
D2sv = D2sv_orig
P1 = D1sv[['x', 'y', 'z']]
P2 = D2sv[['x', 'y', 'z']]
# Start ECEF => ECI
if convert_ECEF_ECI:
# Backup because the columns xyz will be reaffected
#D1sv_bkp = D1sv.copy()
#D2sv_bkp = D2sv.copy()
P1b = conv.ECEF2ECI(
np.array(P1),
conv.dt_gpstime2dt_utc(P1.index.to_pydatetime(),
out_array=True))
P2b = conv.ECEF2ECI(
np.array(P2),
conv.dt_gpstime2dt_utc(P2.index.to_pydatetime(),
out_array=True))
D1sv[['x', 'y', 'z']] = P1b
D2sv[['x', 'y', 'z']] = P2b
P1 = D1sv[['x', 'y', 'z']]
P2 = D2sv[['x', 'y', 'z']]
# End ECEF => ECI
if not RTNoutput:
# Compatible with the documentation +
# empirically tested with OV software
# it is P1 - P2 (and not P2 - P1)
Delta_P = P1 - P2
Diff_sat = Delta_P.copy()
Diff_sat.columns = ['dx', 'dy', 'dz']
else:
rnorm = np.linalg.norm(P1, axis=1)
Vx = utils.diff_pandas(D1sv, 'x')
Vy = utils.diff_pandas(D1sv, 'y')
Vz = utils.diff_pandas(D1sv, 'z')
V = pd.concat((Vx, Vy, Vz), axis=1)
V.columns = ['vx', 'vy', 'vz']
R = P1.divide(rnorm, axis=0)
R.columns = ['xnorm', 'ynorm', 'znorm']
H = pd.DataFrame(np.cross(R, V), columns=['hx', 'hy', 'hz'])
hnorm = np.linalg.norm(H, axis=1)
C = H.divide(hnorm, axis=0)
C.columns = ['hxnorm', 'hynorm', 'hznorm']
I = pd.DataFrame(np.cross(C, R), columns=['ix', 'iy', 'iz'])
R_ar = np.array(R)
I_ar = np.array(I)
C_ar = np.array(C)
#R_ar[1]
Beta = np.stack((R_ar, I_ar, C_ar), axis=1)
# Compatible with the documentation +
# empirically tested with OV software
# it is P1 - P2 (and not P2 - P1)
Delta_P = P1 - P2
# Final determination
Astk = []
for i in range(len(Delta_P)):
A = np.dot(Beta[i, :, :], np.array(Delta_P)[i])
Astk.append(A)
Diff_sat = pd.DataFrame(np.vstack(Astk),
index=P1.index,
columns=['dr', 'dt', 'dn'])
Diff_sat = Diff_sat * conv_coef # metrer conversion
Diff_sat['const'] = [constuse] * len(Diff_sat.index)
Diff_sat['sv'] = [svv] * len(Diff_sat.index)
Diff_sat['sat'] = [constuse + str(svv).zfill(2)] * len(
Diff_sat.index)
Diff_sat_stk.append(Diff_sat)
Diff_sat_all = pd.concat(Diff_sat_stk)
Date = Diff_sat.index[0]
# Attribute definition
if RTNoutput:
Diff_sat_all.frame_type = 'RTN'
# Pandas donesn't manage well iterable as attribute
# So, it is separated
Diff_sat_all.frame_col_name1 = 'dr'
Diff_sat_all.frame_col_name2 = 'dt'
Diff_sat_all.frame_col_name3 = 'dn'
else:
# Pandas donesn't manage well iterable as attribute
# So, it is separated
Diff_sat_all.frame_col_name1 = 'dx'
Diff_sat_all.frame_col_name2 = 'dy'
Diff_sat_all.frame_col_name3 = 'dz'
if convert_ECEF_ECI:
Diff_sat_all.frame_type = 'ECI'
else:
Diff_sat_all.frame_type = 'ECEF'
# Name definitions
if name1:
Diff_sat_all.name1 = name1
else:
Diff_sat_all.name1 = D1orig.name
if name2:
Diff_sat_all.name2 = name2
else:
Diff_sat_all.name2 = D2orig.name
Diff_sat_all.filename1 = D1orig.filename
Diff_sat_all.filename2 = D2orig.filename
Diff_sat_all.path1 = D1orig.path
Diff_sat_all.path2 = D2orig.path
Diff_sat_all.name = ' '.join(
('Orbits comparison (' + Diff_sat_all.frame_type + ') b/w',
Diff_sat_all.name1, '(ref.) and', Diff_sat_all.name2, ',',
Date.strftime("%Y-%m-%d"), ', doy', str(conv.dt2doy(Date))))
if return_satNull:
return Diff_sat_all, sat_nul
else:
return Diff_sat_all
def track_runner(rnx_rover,rnx_base,working_dir,experience_prefix,
XYZbase = [], XYZrover = [] , outtype = 'XYZ',mode = 'short',
interval=None,antmodfile = "~/gg/tables/antmod.dat",
calc_center='igs' , forced_sp3_path = '',
const="G",silent=False,rinex_full_path=False,
run_on_gfz_cluster=False,forced_iono_path=''):
# paths & files
working_dir = utils.create_dir(working_dir)
temp_dir = utils.create_dir(os.path.join(working_dir,'TEMP'))
out_dir = utils.create_dir(os.path.join(working_dir,'OUTPUT'))
if operational.check_if_compressed_rinex(rnx_rover):
rnx_rover = operational.crz2rnx(rnx_rover,temp_dir)
else:
shutil.copy(rnx_rover,temp_dir)
if operational.check_if_compressed_rinex(rnx_base):
rnx_base = operational.crz2rnx(rnx_base,temp_dir)
else:
shutil.copy(rnx_base,temp_dir)
# RINEX START & END
rov_srt, rov_end , rov_itv = operational.rinex_start_end(rnx_rover,1)
bas_srt, bas_end , bas_itv = operational.rinex_start_end(rnx_base,1)
# RINEX NAMES
rov_name = os.path.basename(rnx_rover)[0:4]
bas_name = os.path.basename(rnx_base)[0:4]
rov_name_uper = rov_name.upper()
bas_name_uper = bas_name.upper()
srt_str = rov_srt.strftime("%Y_%j")
exp_full_name = '_'.join((experience_prefix,rov_name,bas_name,srt_str))
out_conf_fil = os.path.join(out_dir,exp_full_name + '.cmd')
out_result_fil = os.path.join(out_dir,exp_full_name + '.out' )
print(out_conf_fil)
confobj = open(out_conf_fil,'w+')
# Obs Files
confobj.write(' obs_file' + '\n')
### just the basename, the caracter nb is limited (20210415)
if not rinex_full_path:
confobj.write(' '.join((' ',bas_name_uper,os.path.basename(rnx_base) ,'F'))+ '\n')
confobj.write(' '.join((' ',rov_name_uper,os.path.basename(rnx_rover),'K'))+ '\n')
else:
confobj.write(' '.join((' ',bas_name_uper,rnx_base ,'F'))+ '\n')
confobj.write(' '.join((' ',rov_name_uper,rnx_rover,'K'))+ '\n')
confobj.write('\n')
date = conv.rinexname2dt(os.path.basename(rnx_rover))
# Nav File
if forced_sp3_path == '':
strt_rnd = dt.datetime(*bas_srt.timetuple()[:3])
end_rnd = dt.datetime(*bas_end.timetuple()[:3])
orblis = operational.multi_downloader_orbs_clks( temp_dir ,
strt_rnd , end_rnd ,
archtype='/',
calc_center = calc_center)
#sp3Z = orblis[0]
sp3 = [utils.uncompress(sp3Z) for sp3Z in orblis]
sp3 = [e if ".sp3" in e[-5:] else e + ".sp3" for e in sp3]
else:
if utils.is_iterable(forced_sp3_path):
sp3 = forced_sp3_path
else:
sp3 = [forced_sp3_path]
for sp3_mono in sp3:
confobj.write(' '.join((' ','nav_file',sp3_mono ,' sp3'))+ '\n')
confobj.write('\n')
# Iono file
if forced_iono_path != '':
confobj.write(' ionex_file ' + forced_iono_path + '\n' )
# Mode
confobj.write(' mode ' + mode + '\n')
confobj.write('\n')
# Output
confobj.write(' pos_root ' + exp_full_name +'.pos' + '\n' )
confobj.write(' res_root ' + exp_full_name +'.res' + '\n' )
confobj.write(' sum_file ' + exp_full_name +'.sum' + '\n' )
confobj.write('\n')
# Outtype
confobj.write(' out_type ' + outtype + '\n')
confobj.write('\n')
# Interval
if not interval:
confobj.write(' interval ' + str(rov_itv) + '\n')
else:
confobj.write(' interval ' + str(interval) + '\n')
confobj.write('\n')
# Coords
bool_site_pos = False
if XYZbase != []:
if not bool_site_pos:
confobj.write(' site_pos \n')
bool_site_pos = True
XYZbase = [str(e) for e in XYZbase]
confobj.write(' '.join([' ', bas_name_uper] + XYZbase + ['\n']))
if XYZrover != []:
if not bool_site_pos:
confobj.write(' site_pos \n')
bool_site_pos = True
XYZrover = [str(e) for e in XYZrover]
confobj.write(' '.join([' ', rov_name_uper] + XYZrover + ['\n']))
if bool_site_pos:
confobj.write('\n')
# Offsets
confobj.write(' ante_off \n')
Antobj_rov , Recobj_rov , Siteobj_rov , Locobj_rov = \
files_rw.read_rinex_2_dataobjts(rnx_rover)
confobj.write(' '.join([' ', rov_name_uper ,
str(Antobj_rov.North_Ecc) ,
str(Antobj_rov.East_Ecc) ,
str(Antobj_rov.Up_Ecc) ,
Antobj_rov.Antenna_Type , '\n']))
Antobj_bas , Recobj_bas , Siteobj_bas , Locobj_bas = \
files_rw.read_rinex_2_dataobjts(rnx_base)
confobj.write(' '.join([' ', bas_name_uper ,
str(Antobj_bas.North_Ecc) ,
str(Antobj_bas.East_Ecc) ,
str(Antobj_bas.Up_Ecc) ,
Antobj_bas.Antenna_Type , '\n']))
confobj.write('\n')
# Site_stats
confobj.write(' site_stats \n')
confobj.write(' ' + bas_name_uper + " 0.1 0.1 0.1 0 0 0" + '\n')
confobj.write(' ' + rov_name_uper + " 20 20 20 0.5 0.5 0.5" + '\n')
confobj.write('\n')
# constellqtions
confobj.write(" TR_GNSS " + const + '\n')
# Misc
#confobj.write(" USE_GPTGMF" + '\n')
confobj.write(" ATM_MODELC GMF 0.5" + '\n')
confobj.write(" ANTMOD_FILE " + antmodfile + '\n')
confobj.write(" DCB_FILE " + "~/gg/incremental_updates/tables/dcb.dat.gnss" + '\n')
confobj.write(" atm_stats" + '\n')
confobj.write(' all 0.1 0.00030.00023' + '\n')
confobj.close()
#END OF FILE WRITING
dowstring = ''.join([str(e) for e in conv.dt2gpstime(date)])
bigcomand = ' '.join(("track -f" , out_conf_fil , '-d' , conv.dt2doy(date) ,'-w', dowstring))
if run_on_gfz_cluster:
bigcomand = "cjob -c '" + bigcomand + "'"
executable="/bin/csh"
else:
executable="/bin/bash"
print('INFO : command launched :')
print(bigcomand)
# START OF PROCESSING
if not silent:
os.chdir(temp_dir)
try:
subprocess.call([bigcomand], executable=executable, shell=True,timeout=60*20)
except subprocess.TimeoutExpired:
print("WARN: command timeout expired, skip")
pass
outfiles = []
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*sum*'))
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*pos*'))
outfiles = outfiles + glob.glob(os.path.join(temp_dir,exp_full_name + '*cmd*'))
Antobj_rov , Recobj_rov , Siteobj_rov , Locobj_rov = \
files_rw.read_rinex_2_dataobjts(rnx_rover)
[shutil.copy(e,out_dir) for e in outfiles]
[os.remove(e) for e in outfiles]
print("TRACK RUN FINISHED")
print('results available in ' , out_dir)
else:
print("Silent mode ON: nothing is launched")
return bigcomand
def gpt3(dtin, lat, lon, h_ell, C, it=0):
"""
This subroutine determines pressure, temperature, temperature lapse rate,
mean temperature of the water vapor, water vapour pressure, hydrostatic
and wet mapping function coefficients ah and aw, water vapour decrease
factor, geoid undulation and empirical tropospheric gradients for
specific sites near the earth's surface.
It is based on a 5 x 5 degree external grid file ('gpt3_5.grd') with mean
values as well as sine and cosine amplitudes for the annual and
semiannual variation of the coefficients.
Parameters:
----------
dtin :
datatime in Python datetime object
lat:
ellipsoidal latitude in radians [-pi/2:+pi/2]
lon:
longitude in radians [-pi:pi] or [0:2pi]
h_ell:
ellipsoidal height in m
it:
case 1 no time variation but static quantities, case 0 with time variation (annual and semiannual terms)
Returns:
----------
p:
pressure in hPa
T:
temperature in degrees Celsius
dT:
temperature lapse rate in degrees per km
Tm:
mean temperature weighted with the water vapor in degrees Kelvin
e:
water vapour pressure in hPa
ah:
hydrostatic mapping function coefficient at zero height (VMF3)
aw:
wet mapping function coefficient (VMF3)
la:
water vapour decrease factor
undu:
geoid undulation in m
Gn_h:
hydrostatic north gradient in m
Ge_h:
hydrostatic east gradient in m
Gn_w:
wet north gradient in m
Ge_w:
wet east gradient in m
Notes
----------
Modified for Python by Chaiyaporn Kitpracha
Source
----------
(c) Department of Geodesy and Geoinformation, Vienna University of
Technology, 2017
The copyright in this document is vested in the Department of Geodesy and
Geoinformation (GEO), Vienna University of Technology, Austria. This document
may only be reproduced in whole or in part, or stored in a retrieval
system, or transmitted in any form, or by any means electronic,
mechanical, photocopying or otherwise, either with the prior permission
of GEO or in accordance with the terms of ESTEC Contract No.
4000107329/12/NL/LvH.
D. Landskron, J. Böhm (2018), VMF3/GPT3: Refined Discrete and Empirical Troposphere Mapping Functions,
J Geod (2018) 92: 349., doi: 10.1007/s00190-017-1066-2.
Download at: https://link.springer.com/content/pdf/10.1007%2Fs00190-017-1066-2.pdf
"""
lat = np.array(lat)
lon = np.array(lon)
h_ell = np.array(h_ell)
# Extract data from grid
p_grid = C[:, 2:7] # pressure in Pascal
T_grid = C[:, 7:12] # temperature in Kelvin
Q_grid = C[:, 12:17] / 1000 # specific humidity in kg/kg
dT_grid = C[:, 17:22] / 1000 # temperature lapse rate in Kelvin/m
u_grid = C[:, 22] # geoid undulation in m
Hs_grid = C[:, 23] # orthometric grid height in m
ah_grid = C[:, 24:
29] / 1000 # hydrostatic mapping function coefficient, dimensionless
aw_grid = C[:, 29:
34] / 1000 # wet mapping function coefficient, dimensionless
la_grid = C[:, 34:39] # water vapor decrease factor, dimensionless
Tm_grid = C[:, 39:44] # mean temperature in Kelvin
Gn_h_grid = C[:, 44:49] / 100000 # hydrostatic north gradient in m
Ge_h_grid = C[:, 49:54] / 100000 # hydrostatic east gradient in m
Gn_w_grid = C[:, 54:59] / 100000 # wet north gradient in m
Ge_w_grid = C[:, 59:64] / 100000 # wet east gradient in m
# Convert from datetime to doy
doy = float(conv.dt2doy(dtin)) + conv.dt2fracday(dtin)
# determine the GPT3 coefficients
# mean gravity in m/s**2
gm = 9.80665
# molar mass of dry air in kg/mol
dMtr = 28.965e-3
# universal gas constant in J/K/mol
Rg = 8.3143
# factors for amplitudes
if it == 1: # then constant parameters
cosfy = 0
coshy = 0
sinfy = 0
sinhy = 0
else:
cosfy = np.cos(doy / 365.25 * 2 * np.pi) # coefficient for A1
coshy = np.cos(doy / 365.25 * 4 * np.pi) # coefficient for B1
sinfy = np.sin(doy / 365.25 * 2 * np.pi) # coefficient for A2
sinhy = np.sin(doy / 365.25 * 4 * np.pi) # coefficient for B2
nstat = lat.size
# initialization
p = np.zeros([nstat, 1])
T = np.zeros([nstat, 1])
dT = np.zeros([nstat, 1])
Tm = np.zeros([nstat, 1])
e = np.zeros([nstat, 1])
ah = np.zeros([nstat, 1])
aw = np.zeros([nstat, 1])
la = np.zeros([nstat, 1])
undu = np.zeros([nstat, 1])
Gn_h = np.zeros([nstat, 1])
Ge_h = np.zeros([nstat, 1])
Gn_w = np.zeros([nstat, 1])
Ge_w = np.zeros([nstat, 1])
if lon < 0:
plon = (lon + 2 * np.pi) * 180 / np.pi
else:
plon = lon * 180 / np.pi
ppod = (-lat + np.pi / 2) * 180 / np.pi
ipod = np.floor(ppod + 1)
ilon = np.floor(plon + 1)
# changed for the 1 degree grid
diffpod = (ppod - (ipod - 0.5))
difflon = (plon - (ilon - 0.5))
if ipod == 181:
ipod = 180
if ilon == 361:
ilon = 1
if ilon == 0:
ilon = 360
indx = np.zeros(4)
indx[0] = (ipod - 1) * 360 + ilon
# near the poles: nearest neighbour interpolation, otherwise: bilinear
# with the 1 degree grid the limits are lower and upper
bilinear = 0
if ppod > 0.5 and ppod < 179.5:
bilinear = 1
if bilinear == 0:
ix = int(indx[0]) - 1
# transforming ellipsoidal height to orthometric height
undu = u_grid[ix]
hgt = h_ell - undu
# pressure, temperature at the height of the grid
T0 = T_grid[ix, 0] + T_grid[ix, 1] * cosfy + T_grid[
ix, 2] * sinfy + T_grid[ix, 3] * coshy + T_grid[ix, 4] * sinhy
p0 = p_grid[ix, 0] + p_grid[ix, 1] * cosfy + p_grid[
ix, 2] * sinfy + p_grid[ix, 3] * coshy + p_grid[ix, 4] * sinhy
# specific humidity
Q = Q_grid[ix, 0] + Q_grid[ix, 1] * cosfy + Q_grid[
ix, 2] * sinfy + Q_grid[ix, 3] * coshy + Q_grid[ix, 4] * sinhy
# lapse rate of the temperature
dT = dT_grid[ix, 0] + dT_grid[ix, 1] * cosfy + dT_grid[
ix, 2] * sinfy + dT_grid[ix, 3] * coshy + dT_grid[ix, 4] * sinhy
# station height - grid height
redh = hgt - Hs_grid[ix]
# temperature at station height in Celsius
T = T0 + dT * redh - 273.15
# temperature lapse rate in degrees / km
dT = dT * 1000
# virtual temperature in Kelvin
Tv = T0 * [1 + 0.6077 * Q]
c = gm * dMtr / [Rg * Tv]
# pressure in hPa
p = [p0 * np.exp[-c * redh]] / 100
# hydrostatic and wet coefficients ah and aw
ah = ah_grid[ix, 0] + ah_grid[ix, 2] * cosfy + ah_grid[
ix, 3] * sinfy + ah_grid[ix, 4] * coshy + ah_grid[ix, 5] * sinhy
aw = aw_grid[ix, 0] + aw_grid[ix, 2] * cosfy + aw_grid[
ix, 3] * sinfy + aw_grid[ix, 4] * coshy + aw_grid[ix, 5] * sinhy
# water vapour decrease factor la
la = la_grid[ix,0] + \
la_grid[ix,1]*cosfy + la_grid[ix,2]*sinfy + \
la_grid[ix,3]*coshy + la_grid[ix,4]*sinhy
# mean temperature Tm
Tm = Tm_grid[ix,0] + \
Tm_grid[ix,1]*cosfy + Tm_grid[ix,2]*sinfy + \
Tm_grid[ix,3]*coshy + Tm_grid[ix,4]*sinhy
# north and east gradients [total, hydrostatic and wet]
Gn_h = Gn_h_grid[ix, 0] + Gn_h_grid[ix, 1] * cosfy + Gn_h_grid[
ix, 2] * sinfy + Gn_h_grid[ix, 3] * coshy + Gn_h_grid[ix,
4] * sinhy
Ge_h = Ge_h_grid[ix, 0] + Ge_h_grid[ix, 1] * cosfy + Ge_h_grid[
ix, 2] * sinfy + Ge_h_grid[ix, 3] * coshy + Ge_h_grid[ix,
4] * sinhy
Gn_w = Gn_w_grid[ix, 0] + Gn_w_grid[ix, 1] * cosfy + Gn_w_grid[
ix, 2] * sinfy + Gn_w_grid[ix, 3] * coshy + Gn_w_grid[ix,
4] * sinhy
Ge_w = Ge_w_grid[ix, 0] + Ge_w_grid[ix, 1] * cosfy + Ge_w_grid[
ix, 2] * sinfy + Ge_w_grid[ix, 3] * coshy + Ge_w_grid[ix,
4] * sinhy
# water vapor pressure in hPa
e0 = Q * p0 / [0.622 + 0.378 * Q] / 100 # on the grid
e = e0 * (100 * p / p0)**(
la + 1) # on the station height - (14] Askne and Nordius, 1987
else:
ipod1 = ipod + 1 * np.sign(diffpod)
ilon1 = ilon + 1 * np.sign(difflon)
# changed for the 1 degree grid
if ilon1 == 361:
ilon1 = 1
if ilon1 == 0:
ilon1 = 360
# get the number of the line
# changed for the 1 degree grid
indx[1] = (ipod1 - 1) * 360 + ilon # along same longitude
indx[2] = (ipod - 1) * 360 + ilon1 # along same polar distance
indx[3] = (ipod1 - 1) * 360 + ilon1 # diagonal
indx = indx.astype(int)
indx = indx - 1
# transforming ellipsoidal height to orthometric height: Hortho = -N + Hell
undul = u_grid[indx]
hgt = h_ell - undul
# pressure, temperature at the height of the grid
T0 = T_grid[indx, 0] + T_grid[indx, 1] * cosfy + T_grid[
indx, 2] * sinfy + T_grid[indx, 3] * coshy + T_grid[indx,
4] * sinhy
p0 = p_grid[indx, 0] + p_grid[indx, 1] * cosfy + p_grid[
indx, 2] * sinfy + p_grid[indx, 3] * coshy + p_grid[indx,
4] * sinhy
# humidity
Ql = Q_grid[indx, 0] + Q_grid[indx, 1] * cosfy + Q_grid[
indx, 2] * sinfy + Q_grid[indx, 3] * coshy + Q_grid[indx,
4] * sinhy
# reduction = stationheight - gridheight
Hs1 = Hs_grid[indx]
redh = hgt - Hs1
# lapse rate of the temperature in degree / m
dTl = dT_grid[indx, 0] + dT_grid[indx, 1] * cosfy + dT_grid[
indx, 2] * sinfy + dT_grid[indx, 3] * coshy + dT_grid[indx,
4] * sinhy
# temperature reduction to station height
Tl = T0 + dTl * redh - 273.15
# virtual temperature
Tv = T0 * (1 + 0.6077 * Ql)
c = gm * dMtr / (Rg * Tv)
# pressure in hPa
pl = (p0 * np.exp(-c * redh)) / 100
# hydrostatic and wet coefficients ah and aw
ahl = ah_grid[indx, 0] + ah_grid[indx, 1] * cosfy + ah_grid[
indx, 2] * sinfy + ah_grid[indx, 3] * coshy + ah_grid[indx,
4] * sinhy
awl = aw_grid[indx, 0] + aw_grid[indx, 1] * cosfy + aw_grid[
indx, 2] * sinfy + aw_grid[indx, 3] * coshy + aw_grid[indx,
4] * sinhy
# water vapour decrease factor la
lal = la_grid[indx, 0] + la_grid[indx, 1] * cosfy + la_grid[
indx, 2] * sinfy + la_grid[indx, 3] * coshy + la_grid[indx,
4] * sinhy
# mean temperature of the water vapor Tm
Tml = Tm_grid[indx, 0] + Tm_grid[indx, 1] * cosfy + Tm_grid[
indx, 2] * sinfy + Tm_grid[indx, 3] * coshy + Tm_grid[indx,
4] * sinhy
# north and east gradients [total, hydrostatic and wet]
Gn_hl = Gn_h_grid[indx, 0] + Gn_h_grid[indx, 1] * cosfy + Gn_h_grid[
indx, 2] * sinfy + Gn_h_grid[indx, 3] * coshy + Gn_h_grid[
indx, 4] * sinhy
Ge_hl = Ge_h_grid[indx, 0] + Ge_h_grid[indx, 1] * cosfy + Ge_h_grid[
indx, 2] * sinfy + Ge_h_grid[indx, 3] * coshy + Ge_h_grid[
indx, 4] * sinhy
Gn_wl = Gn_w_grid[indx, 0] + Gn_w_grid[indx, 1] * cosfy + Gn_w_grid[
indx, 2] * sinfy + Gn_w_grid[indx, 3] * coshy + Gn_w_grid[
indx, 4] * sinhy
Ge_wl = Ge_w_grid[indx, 0] + Ge_w_grid[indx, 1] * cosfy + Ge_w_grid[
indx, 2] * sinfy + Ge_w_grid[indx, 3] * coshy + Ge_w_grid[
indx, 4] * sinhy
# water vapor pressure in hPa
e0 = Ql * p0 / (0.622 + 0.378 * Ql) / 100 # on the grid
el = e0 * (100 * pl / p0)**(
lal + 1) # on the station height - [14] Askne and Nordius, 1987
dnpod1 = abs(diffpod) # distance nearer point
dnpod2 = 1 - dnpod1 # distance to distant point
dnlon1 = abs(difflon)
dnlon2 = 1 - dnlon1
# pressure
R1 = dnpod2 * pl[0] + dnpod1 * pl[1]
R2 = dnpod2 * pl[2] + dnpod1 * pl[3]
p = dnlon2 * R1 + dnlon1 * R2
# temperature
R1 = dnpod2 * Tl[0] + dnpod1 * Tl[1]
R2 = dnpod2 * Tl[2] + dnpod1 * Tl[3]
T = dnlon2 * R1 + dnlon1 * R2
# temperature in degree per km
R1 = dnpod2 * dTl[0] + dnpod1 * dTl[1]
R2 = dnpod2 * dTl[2] + dnpod1 * dTl[3]
dT = (dnlon2 * R1 + dnlon1 * R2) * 1000
# water vapor pressure in hPa
R1 = dnpod2 * el[0] + dnpod1 * el[1]
R2 = dnpod2 * el[2] + dnpod1 * el[3]
e = dnlon2 * R1 + dnlon1 * R2
# ah and aw
R1 = dnpod2 * ahl[0] + dnpod1 * ahl[1]
R2 = dnpod2 * ahl[2] + dnpod1 * ahl[3]
ah = dnlon2 * R1 + dnlon1 * R2
R1 = dnpod2 * awl[0] + dnpod1 * awl[1]
R2 = dnpod2 * awl[2] + dnpod1 * awl[3]
aw = dnlon2 * R1 + dnlon1 * R2
# undulation
R1 = dnpod2 * undul[0] + dnpod1 * undul[1]
R2 = dnpod2 * undul[2] + dnpod1 * undul[3]
undu = dnlon2 * R1 + dnlon1 * R2
# water vapor decrease factor la
R1 = dnpod2 * lal[0] + dnpod1 * lal[1]
R2 = dnpod2 * lal[2] + dnpod1 * lal[3]
la = dnlon2 * R1 + dnlon1 * R2
# gradients
R1 = dnpod2 * Gn_hl[0] + dnpod1 * Gn_hl[1]
R2 = dnpod2 * Gn_hl[2] + dnpod1 * Gn_hl[3]
Gn_h = (dnlon2 * R1 + dnlon1 * R2)
R1 = dnpod2 * Ge_hl[0] + dnpod1 * Ge_hl[1]
R2 = dnpod2 * Ge_hl[2] + dnpod1 * Ge_hl[3]
Ge_h = (dnlon2 * R1 + dnlon1 * R2)
R1 = dnpod2 * Gn_wl[0] + dnpod1 * Gn_wl[1]
R2 = dnpod2 * Gn_wl[2] + dnpod1 * Gn_wl[3]
Gn_w = (dnlon2 * R1 + dnlon1 * R2)
R1 = dnpod2 * Ge_wl[0] + dnpod1 * Ge_wl[1]
R2 = dnpod2 * Ge_wl[2] + dnpod1 * Ge_wl[3]
Ge_w = (dnlon2 * R1 + dnlon1 * R2)
# mean temperature of the water vapor Tm
R1 = dnpod2 * Tml[0] + dnpod1 * Tml[1]
R2 = dnpod2 * Tml[2] + dnpod1 * Tml[3]
Tm = dnlon2 * R1 + dnlon1 * R2
soln = [np.round(p,3),np.round(T,3),np.round(dT,3),np.round(Tm,3),np.round(e,3), \
np.round(ah,3),np.round(aw,3),np.round(la,3),np.round(undu,3),np.round(Gn_h,3),np.round(Ge_h,3), \
np.round(Gn_w,3),np.round(Ge_w,3)]
return soln