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Julien Veyssier authored
Signed-off-by:Julien Veyssier <eneiluj@posteo.net>
Unverified968c2408
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import os, sys
import pandas as pd
import numpy as np
import glob
import io
import matplotlib as mpl
import matplotlib.pyplot as plt
import plotly
import plotly.graph_objs as go
import cmocean
import utils.utils as u
from trios.process import *
from utils.sunposition import sunpos
dir = '/DATA/OBS2CO/data/sabine/data/raw'
odir = '/DATA/OBS2CO/data/sabine/data/awr'
file = os.path.join(dir, 'Ed_Ld_Lu_all_highroc_cruise_oslofjord.dat')
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
dff = pd.read_csv(file, sep="\t", date_parser=dateparse, index_col=2)
awr = awr_process()
awr.get_rho_values([30], [40], [135])
groupname = dff.columns.values[1] #'RT$station_area[n]'
def get_param(df, param='Ld', name='Lsky'):
ind = df.columns.str.contains(param)
df = df.loc[:, ind]
wl = df.columns.str.replace(param + '_', '').astype('float')
df.columns = pd.MultiIndex.from_product([[name], wl], names=['param', 'wl'])
return df
for group, df_ in dff.groupby(groupname):
print(group)
ofile = os.path.join(odir, 'awr_' + group + '.csv')
Rrs_df = pd.DataFrame()
Rrs_ = pd.DataFrame()
for time, df in df_.groupby(df_.index):
azi = [int(df.iloc[:, 0].unique()[0].split('_')[-1])]
lat = df.LAT.values.mean()
lon = df.LON.values.mean()
ws = [df.WIND_SPEED.values.mean()/2]
# TODO check altitude
alt = 0
Lsky = get_param(df, 'Ld', 'Lsky')
Lt = get_param(df, 'Lu', 'Lt')
Ed = get_param(df, 'Ed', 'Ed')
wl = Lsky.columns.levels[1].values
# compute solar angle (mean between first and last acquisition time
sza = sunpos(time, lat, lon, alt)[1]
Rrs, rho = awr.process(wl, Lt, Lsky.values, Ed.values, sza, ws=ws, azi=azi)
df.columns = pd.MultiIndex.from_product([df.columns.values, ['']], names=['param', 'wl'])
Rrs.columns = pd.MultiIndex.from_product([['Rrs(awr)'], wl], names=['param', 'wl'])
# ['Rrs(awr)'] * Rrs.shape[1]
Rrs = pd.concat([df.iloc[:, 0:10], Ed,Lsky,Lt,Rrs], axis=1)
Rrs['sza']=sza
Rrs['azi']=azi[0]
Rrs['rho']=rho.mean()
Rrs_ = pd.concat([Rrs_, Rrs], axis=0)
Rrs_df = pd.concat([Rrs_df, Rrs_], axis=0)
Rrs_df.to_csv(ofile)
if False:
dff = Rrs_df
# dff.columns = pd.MultiIndex.from_product([['Rrs(awr)'], dff.columns], names=['param', 'wl'])
# wl = dff.columns.droplevel().values
trace = []
i = 0
for station, df in dff.groupby('RT$station_area[n]'):
i = i + 1
for time, df_ in df.iterrows():
print(time)
trace.append(go.Scattergl(
x=wl, # spectrum,
y=df_.loc['Rrs(awr)'].values,
text='',
name=station,
legendgroup=station,
mode='lines',
line={
'color': 'rgba' + str(cmocean.cm.thermal(i * 25)[:-1])}
# 'line': go.scatter.Line(color='rgba({}, {}, {}, {})'.format(*s_m.to_rgba(parameters[i]).flatten()), width=2),
, ))
plotly.offline.plot(trace)