# Name of the data release, set paths
drelease = 'FLUXNET2015_a'
af_path = '/home/greg/sftp/eddyflux/Ameriflux_files/' + drelease + '/'
outpath = '/home/greg/current/NMEG_utils/processed_data/daily_aflx/' + drelease + '/'
# Years to load
startyr = 2007
endyr = 2016
# Sites to load
sites = ['Seg', 'Ses', 'Sen', 'Wjs', 'Mpj', 'Mpg', 'Vcp', 'Vcm']
# Fill a dict with multiyear dataframes for each site in sites
hourly = { x :
ld.get_multiyr_aflx( 'US-' + x, af_path, gapfilled=True,
startyear=startyr, endyear=endyr)
for x in sites }
# Resample to daily sums with integration
daily = { x :
tr.resample_30min_aflx( hourly[x], freq='1D',
c_fluxes=[ 'GPP', 'RECO', 'FC_F' ],
le_flux=[ 'LE_F' ],
avg_cols=[ 'TA_F', 'RH_F', 'SW_IN_F', 'RNET_F', 'VPD_F', 'PAR',
'LE_F', 'H_F' ],
int_cols=['LE_F', 'H_F' ],
sum_cols=['P_F'] , tair_col='TA_F' )
for x in hourly.keys() }
# Add ET, PET and put into daily dataframes
for i, site in enumerate(sites):
'Lass10 ECB'])
fig.add_subplot(2,1,2)
plt.plot(site_df_resamp.index, site_df_resamp.RECO_g_int, '-k')
plt.plot(site_df_resamp.index, site_df_resamp.Reco_MR2005_g_int, '--r')
plt.plot(site_df_resamp.index, site_df_resamp.Reco_GL2010_g_int, '--b')
plt.plot(site_df_resamp.index, site_df_resamp.Reco_MR2005_g_int_ecb, ':r')
plt.plot(site_df_resamp.index, site_df_resamp.Reco_GL2010_g_int_ecb, ':b')
plt.ylabel('Reco')
plt.show()
return fig
for i, site in enumerate( siteNames ):
# Get the multi-year ameriflux dataframe
site_df = ld.get_multiyr_aflx( site, datapath, gapfilled=True,
startyear=startyear, endyear=endyear )
# Create a daily dataframe these are pretty much the defaults
site_df_resamp = tr.resample_30min_aflx( site_df,
freq='1D', c_fluxes=[ 'GPP', 'RECO', 'FC_F' ],
le_flux=['LE_F'], avg_cols=['TA_F', 'RH_F', 'SW_IN_F', 'RNET_F'],
sum_cols=['P_F'] , tair_col='TA_F' )
# Get multiyear eddyproc file
site_df_MR2005 = ld.get_multiyr_eddyproc( site, datapath + 'eddyproc_out/',
GL2010=False, startyear=startyear, endyear=endyear )
# Ensure carbon balance steps (GPP = RE-NEE, add negative values to RE)
# Raw GPP (contains negative values)
diff = site_df_MR2005.Reco - site_df.FC_F
negGPPix = diff<0
site_df_MR2005['GPP_ecb'] = diff
import matplotlib.pyplot as plt
import pdb as pdb
import datetime as dt
# Years to load
start = 2007
end = 2015
# Sites to load
sites = ['Seg', 'Ses', 'Sen', 'Wjs', 'Mpj', 'Mpg', 'Vcp', 'Vcm']
#sites = ['Mpj','Wjs']
# Load hourly data into multiyear dataframes (1/site) within a dict
hourly = {
x: ld.get_multiyr_aflx('US-' + x,
af_path,
gapfilled=True,
startyear=start,
endyear=end)
for x in sites
}
# Now calculate the hours of C uptake
for site in sites:
# Add a column with 0.5 for each uptake period (to sum)
hourly[site]['hrs_C_uptake'] = 0
test = hourly[site].FC_F < 0
hourly[site].loc[test, 'hrs_C_uptake'] = 0.5
# Turn this into a daily dataset
daily = {
x: tr.resample_30min_aflx(
datapath = ('/home/greg/sftp/eddyflux/Ameriflux_files/provisional/')
fileList = os.listdir(datapath)
startyear = 2008
endyear = 2014
# List AF site names in same order
siteNames = ['US-Mpj']
for i, site in enumerate(siteNames):
# Get the multi-year ameriflux dataframe
site_df = ld.get_multiyr_aflx(site,
datapath,
gapfilled=True,
startyear=startyear,
endyear=endyear)
site_df = site_df[[
'FC_F', 'FC_F_FLAG', 'TA_F', 'TA_F_FLAG', 'VPD_F', 'VPD_F_FLAG'
]]
# Export
site_df.to_csv('../processed_data/' + site + '_Cleverly.csv',
na_rep='-9999',
date_format='%Y%m%d%H%M%S')
# Function for plotting fixed and original data
#def plotfixed(df, var1):
# # Plot filled series over original data