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Commit 5910f101 authored by Harmel Tristan's avatar Harmel Tristan
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package construction, executable 'trios_processing'

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README.md
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# Water color TriOS package
Package to help process TriOS radiometer data for various above-water or in-water setups.
Package to process TriOS-like radiometer data for various acquisition protocols:
- above-water radiometry
![figure](images/above_water_system.png)
- in-water radiometry
- surface-water radiometry
![figure](images/surface_water_system.png)
This package also contains tools for interactive visualization of the radiometric data:
![animated1](images/visu_trios_data.gif)
......@@ -27,7 +34,7 @@ python setup.py install
## Running the tests
...
## Authors
......
main.py deleted 100644 → 0
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import glob
import re
from trios.utils import utils as u
from trios.process import *
# import aeronet
# from config import *
# ------------------------------------------------
# above-water data files
dirfig = os.path.abspath('/DATA/OBS2CO/data/trios/fig')
dirout = os.path.abspath('/DATA/OBS2CO/data/trios/above_water')
awrfiles = glob.glob("/DATA/OBS2CO/data/trios/raw/aw*idpr*.csv")
iwrfiles = glob.glob("/DATA/OBS2CO/data/trios/raw/uw*idpr*.csv")
swrfiles = glob.glob("/DATA/OBS2CO/data/trios/raw/Lu0*idpr*.csv")
coordf = glob.glob("/DATA/OBS2CO/data/info/mesures_in_situ.csv")[0]
coords = pd.read_csv(coordf, sep=';')
coords
# get idpr numbers
idprs = np.unique([re.findall(r'idpr(\d+)', x)[0] for x in swrfiles])
# loop over idpr
for idpr in idprs:
# idpr=idprs[2]
print(idpr)
try:
c = coords[coords.ID_prel == int(idpr)] # .values[0]
lat = c['Lat'].values[0]
lon = c['Lon'].values[0]
alt = 0 #c['Altitude']
name = c['ID_lac'].values[0]
except:
continue
dff = pd.DataFrame()
# -----------------------------------------------
# AWR processing
# -----------------------------------------------
awr = u.awr_data(idpr, awrfiles)
if awr.file:
try:
df, wl = awr.reader(lat, lon, alt, name)
Rrs, rho = awr_process(df, wl).call_process(aot=0.05)
print(rho.mean)
# outputs saving
dff = pd.concat([df, Rrs], axis=1)
except:
pass
# -----------------------------------------------
# SWR processing
# -----------------------------------------------
swr = u.swr_data(idpr, swrfiles)
if swr.file:
df, wl = swr.reader(lat, lon, alt)
Rrs = swr_process(df, wl).call_process()
#Rrs.to_csv(os.path.join(dirout, 'trios_swr_' + name + '_idpr' + idpr + '.csv'))
dff = pd.concat([dff, Rrs], axis=1)
# -----------------------------------------------
# IWR processing
# -----------------------------------------------
# iwr = u.iwr_data(idpr, iwrfiles)
# if iwr.file:
# df, wl = iwr.reader(c[1], c[2], c[3])
# Rrs = iwr_process(df, wl).trios()
# dff = pd.concat([dff, Rrs], axis=1)
# writing output file
dff.to_csv(os.path.join(dirout, 'trios_awr_' + name + '_idpr' + idpr + '.csv'))
setup.py
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from setuptools import setup, find_packages
from grs import __version__
from trios import __version__
setup(
name='trios',
......@@ -13,7 +13,7 @@ setup(
},
include_package_data=True,
url='https://gitlab.irstea.fr/ETL-TELQUEL/etl/tree/dev/preprocessing/trios',
url='https://gitlab.irstea.fr/telquel-obs2co/insitu/trios',
license='MIT',
author='T. Harmel',
author_email='tristan.harmel@gmail.com',
......@@ -24,7 +24,7 @@ setup(
entry_points={
'console_scripts': [
'trios = main:main',
'trios_processing = trios.main:main',
'visu_trios = visu.data_visu:main'
]}
)
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2018-05-30 11:48:49;-NAN;-NAN;-NAN;-NAN;-NAN;-0.00399356882946705;-0.00391080888439925;-0.00379062126773989;-0.00368924040505404;-0.00357262529952962;-0.00350269808818495;-0.00344436529156562;-0.00335911686966419;-0.003280354736853;-0.00320101508761066;-0.00311440995112076;-0.00305475405193983;-0.00299403508794584;-0.00291233985032574;-0.00283430680386181;-0.00276925717601596;-0.00269673682527112;-0.00261664851360065;-0.00255498223371566;-0.00250077882920908;-0.00243333091035786;-0.00237192865540858;-0.00232802645728589;-0.00228671254909772;-0.00223538883218095;-0.00218105888676575;-0.00213372394112035;-0.00208803105152627;-0.00204211870837034;-0.00199756563013454;-0.00195443596561639;-0.00191240329985367;-0.00187386669934835;-0.00183856665574868;-0.00180261901710601;-0.00176539900442966;-0.00173099977375344;-0.00169764620669591;-0.00166475793735151;-0.00163484258179581;-0.00160585383041225;-0.00157566679112439;-0.00154652585021477;-0.00151981744129021;-0.00149240922208943;-0.00146657856812793;-0.00144157495837674;-0.0014146277729321;-0.00139041529793272;-0.00136987924614559;-0.00135299162229012;-0.00133362661075742;-0.00131198177753614;-0.00129062916616708;-0.00127372353612408;-0.00125617808710479;-0.00123541240281865;-0.0012145820900502;-0.00119817595281374;-0.00118453046098815;-0.00116734340601396;-0.00115132872424927;-0.00113625526391856;-0.00111985161637434;-0.00110374427560438;-0.00108965630473532;-0.00107772910281634;-0.00106608821128324;-0.00105396641464818;-0.00104070067248267;-0.00102788392611351;-0.00101549056275259;-0.00100327908623087;-0.00099194973606422;-0.000983121359006884;-0.000976379980578816;-0.000972169325525645;-0.000967834621833294;-0.000959567245747377;-0.000951661749573108;-0.000949631820193886;-0.000951277356678732;-0.000952648909762847;-0.000951022198165076;-0.000946051830175248;-0.000939545380571315;-0.000933168386959902;-0.000927941792486691;-0.000922866508522995;-0.000916591909471702;-0.000908978433559237;-0.000901834819045833;-0.000896173258205268;-0.000891460046237845;-0.00088615678606298;-0.000879358401273406;-0.000871984766015982;-0.000864452775923181;-0.000857621430916988;-0.000850662563393008;-0.000845640781382901;-0.000842350330048002;-0.000837178334938711;-0.000831156423565209;-0.00082488368767861;-0.000819311712118154;-0.00081334948734239;-0.000805026439868189;-0.000796290231828417;-0.000789984321123641;-0.000786917037857703;-0.000784845003479883;-0.000783415887086804;-0.000782777965089158;-0.000782841748768715;-0.0007797098598541;-0.000769661075199039;-0.000757088546791509;-0.000748423689482475;-0.000745479780007816;-0.000743506324133517;-0.000746422981129642;-0.000752748075903713;-0.000755613275798385;-0.000755922153234437;-0.000755968131714029;-0.000754736585259919;-0.000755617147972719;-0.000752754366285122;-0.000733275767514058;-0.00070258840793056;-0.000696784826254272;-0.000716613529905071;-0.000727409285700738;-0.000724924442161169;-0.000719200819204045;-0.000712517203395008;-0.000705875006539622;-0.000699686887894837;-0.000694812637000559;-0.000692494154481246;-0.000688050819808846;-0.000684614412013163;-0.000681370942421143;-0.000678603038755734;-0.000670413314205263;-0.000657335120368158;-0.000647900525460835;-0.000643348685853783;-0.000643182271764726;-0.000643865339947311;-0.000641864465171878;-0.000640226167532754;-0.000640848390124417;-0.000642742082574235;-0.000642598811518669;-0.000640462837577579;-0.000639010231747386;-0.00063649953717023;-0.000634051586979176;-0.000631324772890396;-0.000629510880275707;-0.00062875619288004;-0.000625374951531878;-0.000623397798670744;-0.000620189639785713;-0.000617135444249118;-0.000613896630349573;-0.000612217371644496;-0.000609988129480608;-0.000600422163686439;-0.000587367524371659;-0.00057470461977974;-0.000571786670367136;-0.000571800804370094;-0.000559627176576909;-0.00055821148609134;-0.000551024184631335;-0.000553074905387588;-0.000557485713258386;-0.00055720852483278;-0.00054477074566011;-0.000515628013819566;-0.000507255394928924;-0.000504164542263857;-0.000524923778923856;-0.000495396872759203;-0.00051708164313987;-0.000544362124488387;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN;-NAN
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OBJECTID,ID_lac,ID_station,ID_camp,Date_camp,IDGPS,ID_prel,Date_prel,h_debut,h_fin,Meteo_obs,Meteo_supp,Etat_surf,Carac_sta,Press_atmo,T_air,T_eau_surf,T_eau_fond,Profondeur,Secchi,Secchi_dis,Secchi_app,Filtr,Filtr_VolA,Filtr_VolB,Filtr_VolC,Filtr_obs,HPLC,HPLC_VolA,HPLC_VolB,HPLC_VolC,HPLC_obs,Cdom,Cdom_Vol,Cdom_run,Cdom_obs,Mes,Mes_obs,Cops,Cops_h_deb,Cops_h_fin,Cops_run_o,Cops_run_e,Cops_obs,Cage,Cage_run,Cage_h_deb,Cage_h_fin,Cage_obs,Comment,POINT_X,POINT_Y,Lat,Lon,TRIOS,TRIOS_type,TRIOS_obs,,
153,ALE2B,1,16,mai_2018,,150,30/05/2018,11:07,11:50,nuageux avec éclaircies,vent NE,vaguelettes 5 cm,tributaire,,31,22,19,7,oui,6.25,5.75,oui,400,400,400,,non,,,,,oui,,,,oui,,non,,,,,,oui,261,,,,,1233206.526,6155548.538,42.30351823,9.462897398,oui,Lu0+, above water, underwater,nombreuses feuilles en surface
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trios/__init__.py
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__package__ = 'trios'
__version__ = '1.0.0'
\ No newline at end of file
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''' Executable to process L1C images from Sentinel-2 and Landsat mission series
Usage:
trios_processing <input_dir> <IDpr> <measurement_type> --lat <lat> --lon <lon> \
[--altitude=alt] [--ofile <ofile>] [--odir <odir>] [--plot] [--figdir <figdir>]
trios_processing -h | --help
trios_processing -v | --version
Options:
-h --help Show this screen.
-v --version Show version.
<input_dir> Input directory where the trios.csv files are located
<IDpr> IDpr label of the measurement sequence to process
<measurement_type> awr, swr, iwr
--lat <lat> Latitude in decimal degrees
--lon <lon> Longitude in decimal degrees
--altitude=alt altitude of the scene to be processed in meters
[default: 0]
--odir odir Path to the output directory [default: ./]
--ofile ofile basename of the output file.
--plot Plot output data and save figure in <figdir>
--figdir figdir Directory where figures are saved [default: ./]
--no_clobber Do not process <input_dir> <IDpr> files if <output_file> already exists.
'''
from docopt import docopt
import glob
import re
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
from trios.utils.utils import plot as up
from trios.utils import utils as u
from trios.process import *
from trios import __package__, __version__
type_list = {'awr': 'aw', 'iwr': 'uw', 'swr': 'Lu0+'}
type_description = {'awr': 'Above-Water Radiometry', 'iwr': 'In-Water Radiometry',
'swr': 'Surface-Water Radiometry'}
def main():
args = docopt(__doc__, version=__package__ + ' ' + __version__)
print(args)
idir = os.path.abspath(args['<input_dir>'])
idpr = args['<IDpr>']
meas_type = args['<measurement_type>']
lat = float(args['--lat'])
lon = float(args['--lon'])
alt = float(args['--altitude'])
odir = os.path.abspath(args['--odir'])
ofile = args['--ofile']
name = ""
plot = args['--plot']
figdir = os.path.abspath(args['--figdir'])
try:
type_ = type_list[meas_type]
except:
raise SyntaxError('ERROR: bad request for <measurement_type>, should be either awr, iwr or swr')
files = glob.glob(os.path.join(idir, type_ + '*' + idpr + '*.csv'))
if not files:
raise IOError('No file available for IDpr ' + idpr + ' in ' + idir + ' for ' + type_description[meas_type])
if meas_type == 'swr':
# -----------------------------------------------
# SWR processing
# -----------------------------------------------
uswr = u.swr_data(idpr, files)
if uswr.file:
df, wl = uswr.reader(lat, lon, alt)
date = df.index[0].date().__str__()
if ofile:
ofile = os.path.join(odir, ofile)
else:
ofile = os.path.join(odir, 'Rrs_swr_' + date + '_idpr' + idpr + name + '.csv')
swr = swr_process(df, wl)
Rrs = swr.call_process(ofile)
if plot:
mpl.rcParams.update({'font.size': 18})
fig, ax = plt.subplots(figsize=(7, 6))
up.add_curve(ax, wl, Rrs.transpose().mean(axis=1), Rrs.transpose().std(axis=1), label='swr',
c='black')
ax.set_ylabel(r'$R_{rs}\ (sr^{-1})$')
ax.set_xlabel(r'Wavelength (nm)')
ax.set_title('ID: ' + idpr + ', ' + date + ', sza=' + str(round(df.sza.mean(), 2)))
fig.savefig(os.path.join(figdir, 'trios_swr_' + date + '_idpr' + idpr + '.png'), bbox_inches='tight')
plt.close()
elif meas_type == 'awr':
# -----------------------------------------------
# AWR processing
# -----------------------------------------------
azi = 135
vza = 40
ws = 2
method = 'M99'
uawr = u.awr_data(idpr, files)
if uawr.Edf:
df, wl = uawr.reader(lat, lon, alt)
date = df.index[0].date().__str__()
if ofile:
ofile = os.path.join(odir, ofile)
else:
ofile = os.path.join(odir, 'Rrs_awr_' + date + '_idpr' + idpr + name + '.csv')
awr = awr_process(df, wl, name, idpr)
if plot:
figfile = os.path.join(figdir, 'trios_awr_' + date + '_idpr' + idpr + '.png')
else:
figfile=""
Rrs = awr.call_process(method, ofile, vza=vza, azi=azi,plot_file=figfile)
elif meas_type == 'iwr':
pass
if __name__ == "__main__":
main()
trios/process.py
View file @ 5910f101
......@@ -4,6 +4,12 @@ import scipy.optimize as so
import plotly.graph_objs as go
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
from trios.utils.utils import plot as up
import trios.utils.utils as uu
from trios.utils.utils import reshape as r
import trios.utils.auxdata as ua
......@@ -11,11 +17,12 @@ from trios.config import *
class awr_process:
def __init__(self, df=None, wl=None):
def __init__(self, df=None, wl=None, name="", idpr=""):
self.df = df
self.aot = 0.1
self.ws = 2
self.wl = wl
self.name = name
self.idpr = idpr
self.rhosoaa_fine_file = rhosoaa_fine_file
self.rhosoaa_coarse_file = rhosoaa_coarse_file
self.M1999_file = M1999_file
......@@ -82,63 +89,119 @@ class awr_process:
rho_mobley = calc().spline_4d(grid, rho_, (ws, sza, vza, azi))
return rho_mobley
# def call_process(self, vza=[40], azi=[135], ws=2, aot=0.1):
# wl = self.wl
# Lt = self.df.loc[:, ("Lt")]
# Lsky = self.df.loc[:, ("Lsky")]
# Ed = self.df.loc[:, ("Ed")]
# sza = self.df.loc[:, ("sza")].values.mean()
# Rrs = self.process(wl, Lt, Lsky, Ed, sza, vza, azi, ws, aot)
# Rrs.columns = pd.MultiIndex.from_product([['Rrs(awr)'], self.Rrs.columns], names=['param', 'wl'])
# self.Rrs = Rrs
# return self.Rrs
#
# def process(self, wl, Lt, Lsky, Ed, sza, vza=[40], azi=[135], ws=[2], aot=[0.1]):
# '''
#
# :param wl:
# :param Lt:
# :param Lsky:
# :param Ed:
# :param sza:
# :param vza:
# :param azi:
# :param ws:
# :param aot:
# :return:
# '''
#
# # ------------------
# # filtering
# # ------------------
# ind_Ed, notused = calc.spectra_median_filter(Ed)
# ind_sky, notused = calc.spectra_median_filter(Lsky)
# ind = ind_Ed & ind_sky
# Lt, Lsky, Ed, sza = Lt[ind], Lsky[ind], Ed[ind], sza[ind]
#
# rho = self.get_rho_values(np.median(sza), vza, azi, wl=wl, ws=ws, aot=aot)
# self.Rrs = (Lt - rho * Lsky.values) / Ed.values
# self.Rrs.columns = pd.MultiIndex.from_product([['Rrs(awr)'], wl], names=['param', 'wl'])
# return self.Rrs, rho
def call_process(self, method='M99', ofile="", vza=40, azi=135, ws=2, aot=0.1, plot_file=""):
@staticmethod
def filtering(Lt, Lsky, Ed, **kargs):
'''
wl = self.wl
vza, azi, ws = [vza], [azi], [ws] # formatting for interpolation functions
:param Lt:
:param Lsky:
:param Ed:
:param kargs:
:return:
'''
# ------------------
# filtering
# ------------------
ind = self.filtering(self.df.Lt, self.df.Lsky, self.df.Ed)
clean = self.df[ind]
Lt, Lsky, Ed, sza = clean.Lt.values, clean.Lsky.values, clean.Ed.values, clean.sza.values
ind_Ed, notused = calc.spectra_median_filter(Ed, kargs)
ind_sky, notused = calc.spectra_median_filter(Lsky, kargs)
ind = ind_Ed & ind_sky
return ind
# -----------------------------
# data processing
# -----------------------------
if method == 'M99':
Rrs, rho = self.process_wrapper(wl, clean, clean.sza, vza=vza, azi=azi, ws=ws, aot=aot, method=method)
elif method == 'M15':
Rrs, rho = self.process_wrapper(wl, clean, clean.sza, vza=vza, azi=azi, ws=ws, aot=aot, method=method)
elif method == 'osoaa':
Rrs, rho = self.process_wrapper(wl, clean, clean.sza, vza=vza, azi=azi, ws=ws, aot=aot, method=method)
elif method == 'temp_opt':
Rrs, Rrs_opt_std = self.process_optimization(wl, Lt, Lsky, Ed, sza, vza=vza, azi=azi)
def process_wrapper(self, wl, df, sza, vza=[40], azi=[135], ws=[2], aot=[0.1], method='M99'):
self.Rrs = Rrs
if ofile:
Rrs_stat = Rrs.describe()
Rrs_stat.columns = Rrs_stat.columns.droplevel()
Rrs_stat = Rrs_stat.T
Rrs_stat.to_csv(ofile)
if plot_file:
# ------------------
# plotting
# ------------------
Ltm = Lt.mean(axis=0)
Edm = Ed.mean(axis=0)
mpl.rcParams.update({'font.size': 18})
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(20, 12))
fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.45)
# ---- Ed
ax = axs[0, 0]
up.add_curve(ax, wl, Ed.mean(axis=0),
label=r'$L_{sky}$', c='red') # just to put the two labels
up.add_curve(ax, wl, Ed.mean(axis=0), Ed.std(axis=0),
label=r'$E_s$', c='black')
ax.set_ylabel(r'$E_{d}(0^{+})$')
# ---- Lsky
ax2 = ax.twinx()
up.add_curve(ax2, wl, Lsky.mean(axis=0), Lsky.std(axis=0),
label=r'$L_{sky}$', c='red')
ax2.set_ylabel(r'$L_{sky}$', color='r')
ax2.tick_params('y', colors='r')
ax.set_xlabel(r'Wavelength (nm)')
ax.legend(loc='best', frameon=False)
# ---- Lt vs Lsurf
ax = axs[0, 1]
up.add_curve(ax, wl, Lt.mean(axis=0), Lt.std(axis=0),
label=r'$L_t$', c='black')
up.add_curve(ax, wl, Lsky.mean(axis=0) * rho, Lsky.std(axis=0) * rho,
label=method+' (' + str(round(rho, 4)) + ')', c='violet')
ax.set_ylabel(r'$L_t\ or L_{surf}$')
ax.set_xlabel(r'Wavelength (nm)')
# ---- Proportion o(Lt - Lsurf ) /Lt
ax = axs[0, 2]
up.add_curve(ax, wl, Lsky.mean(axis=0) * rho / Ltm, Lsky.std(axis=0) * rho,
label=method+' (' + str(round(rho, 4)) + ')', c='violet')
ax.set_ylabel(r'$L_{surf}/L_t$')
ax.set_xlabel(r'Wavelength (nm)')
# ---- Lw
ax = axs[1, 0]
up.add_curve(ax, wl, Rrs.mean(axis=0) * Edm, Rrs.std(axis=0) * Edm,
label=method+' (' + str(round(rho, 4)) + ')', c='violet')
ax.set_ylabel(r'$L_{w}\ (sr^{-1})$')
ax.set_xlabel(r'Wavelength (nm)')
# ---- Rrs
ax = axs[1, 1]
up.add_curve(ax, wl, Rrs.transpose().mean(axis=1), Rrs.transpose().std(axis=1),
label=method+' (' + str(round(rho, 4)) + ')', c='violet')
ax.set_ylabel(r'$R_{rs}\ (sr^{-1})$')
ax.set_xlabel(r'Wavelength (nm)')
ax.set_title('azi=' + str(azi) + ', vza=' + str(vza) + ', sza=' + str(round(sza.mean(), 2)))
fig.suptitle('trios_awr ' + self.name + ' idpr' + self.idpr, fontsize=16)
fig.savefig(plot_file)
plt.close()
return self.Rrs
def process_wrapper(self, wl, df, sza, vza=[40], azi=[135], ws=[2], aot=[0.1], method='M99'):
'''
Wrapper to call standard processing upon pandas multiindex dataframe format
:param wl:
:param df:
:param sza:
:param vza:
:param azi:
:param ws: