def test_i_vpd(self):
"""tests function which computes vpd index"""
frac = np.arange(0.0, 1.0, 0.2)
vpd = (1 - frac) * (gsi.VPD_MAX - gsi.VPD_MIN) + gsi.VPD_MIN
i_vpd = gsi.calc_i_vpd(vpd)
self.assertTrue(np.all(np.abs(i_vpd - frac) < 1e-5))
def test_i_vpd(self):
"""tests function which computes vpd index"""
frac = np.arange(0.0, 1.0, 0.2)
vpd = (1 - frac) * (gsi.VPD_MAX - gsi.VPD_MIN) + gsi.VPD_MIN
i_vpd = gsi.calc_i_vpd(vpd)
self.assertTrue(np.all(np.abs(i_vpd - frac) < 1e-5))
def indices_year(y, forcing_template, out_template) :
"""Processes one year.
Runs the calculation of indices for one year given an orchidee forcing
file for that year."""
# figure out filenames and open dataset.
forcing_file = forcing_template % (y,)
out_file = out_template % (y,)
ds = ForcingDataset(forcing_file, out_file)
# register required variables from NetCDF file so they are tracked
qair = ds.register_variable("Qair", u.dimensionless_unscaled)
tair = ds.register_variable("Tair", u.Kelvin)
ds.register_variable("PSurf", u.Pa)
rainf = ds.register_variable("Rainf", u.kg / (u.m**2) / u.s )
swdown = ds.register_variable("SWdown", u.W / (u.m**2))
print "NetCDF variables registered"
# setup netcdf dimensions in output file
ds.copyDimension("land")
ds.copyDimension("y")
ds.copyDimension("tstep")
ds.createDimension("days", 365)
# copy basic data
ds.copyVariable('nav_lat')
ds.copyVariable('nav_lon')
print "Variables copied from forcing file"
# create netcdf variables to hold indices in output file
daylength = ds.create_variable("daylength", ("days","y"), np.float32)
daylength.title = "Day Length"
daylength.units = "hours"
rh = ds.create_variable("rh", ("tstep","land"), np.float32)
rh.long_name = "Relative Humidity"
rh.units = "fraction"
rh_max = ds.create_variable("rh_max", ("days","land"), np.float32)
rh_max.long_name = "Maximum RH"
rh_max.units = "fraction"
rh_min = ds.create_variable("rh_min", ("days","land"), np.float32)
rh_min.long_name = "Minimum RH"
rh_min.units = "fraction"
rh_afternoon = ds.create_variable("rh_afternoon", ('days', 'land'),np.float32)
rh_afternoon.long_name = "RH in the afternoon"
rh_afternoon.units = "fraction"
t_min = ds.create_variable("t_min", ('days','land'), np.float32)
t_min.long_name = "minimum temperature for the 24 hours preceeding burning period"
t_min.units = "K"
t_max = ds.create_variable("t_max", ('days', 'land'), np.float32)
t_max.long_name = "maximum temperature for the 24 hours preceeding burning period"
t_max.units = "K"
t_afternoon = ds.create_variable("t_afternoon", ('days','land'), np.float32)
t_afternoon.long_name = "mid-burning-period temperature"
t_afternoon.units = "K"
i_tmin = ds.create_variable("i_tmin", ('days','land'), np.float32)
i_tmin.long_name = "GSI minimum temperature index component"
i_tmin.units = 'dimensionless'
i_photo = ds.create_variable('i_photo', ('days','land'), np.float32)
i_photo.long_name = 'GSI photoperiod index component'
i_photo.units = 'dimensionless'
i_vpd = ds.create_variable('i_vpd', ('days','land'), np.float32)
i_vpd.long_name = 'GSI vapor pressure deficit index component'
i_vpd.units = 'dimensionless'
i_gsi = ds.create_variable('gsi', ('days','land'), np.float32)
i_gsi.long_name = 'GSI Index'
i_gsi.units = 'dimensionless'
gsi_days = 10
i_gsi_avg = ds.create_variable('gsi_avg', ('days','land'), np.float32)
i_gsi_avg.long_name = 'GSI Index (%d day running average)' % gsi_days
i_gsi_avg.units = 'dimensionless'
precip = ds.create_variable('precip_duration', ('days','land'), np.float32)
precip.long_name = "Duration of precipitation"
precip.units = 'hours'
tot_precip = ds.create_variable('total_precip', ('days','land'), np.float32)
tot_precip.long_name = 'total precipitation over last 24 hours'
tot_precip.units = 'kg / m^2'
dd_days = 30
dd = ds.create_variable('dd', ('days','land'), np.int)
dd.long_name = 'dry day weighting (%d day window)' % dd_days
dd.units = 'none'
eqmc_bar = ds.create_variable('eqmc_bar', ('days','land'), np.float32)
eqmc_bar.long_name = 'Weighted daily average equilibrium moisture content'
eqmc_bar.units = 'percent'
fm1000 = ds.create_variable('fm1000', ('days','land'), np.float32)
fm1000.long_name = '1000 hour fuel moisture'
fm1000.units = 'percent'
fm100 = ds.create_variable('fm100', ('days','land'), np.float32)
fm100.long_name = '100 hour fuel moisture'
fm100.units = 'percent'
fm10 = ds.create_variable('fm10', ('days','land'), np.float32)
fm10.long_name = '10 hour fuel moisture'
fm10.units = 'percent'
fm1 = ds.create_variable('fm1', ('days','land'), np.float32)
fm1.long_name = '1 hour fuel moisture'
fm1.units = 'percent'
print "Output NetCDF variables created"
# moving window of GSI data
gsi_window = w.MovingWindow(i_gsi.shape, 0, gsi_days)
# moving window to compute dry day weights
dd_window = w.SequenceWindow(dd.shape, 0, dd_days)
# initialize fuel moisture calculators
fm1000_calc = fm.ThousandHourFM(fm1000.shape, 0)
fm100_calc = fm.HundredHourFM(fm100.shape, 0)
# base time
time_start = t.Time('%04d:001'%y, format='yday', scale='ut1')
if ( (y < 1960) or (y > 2013) ) :
time_start.delta_ut1_utc=0
# loop over days
# the cur_day property is the index of the next day to read in from the file.
# since the netcdf file is 0-based, indices are [0, 365)
while qair.cur_day < 365 :
i_day = qair.cur_day - 1
print qair.cur_day
# calculate daylengths, store as hours
day = time_start + (i_day*u.day)
if (hasattr(time_start,"delta_ut1_utc")) :
day.delta_ut1_utc = time_start.delta_ut1_utc
daylength_lut = ds.get_daylength_lookup(day)
daylength[i_day,:] = daylength_lut.values
# pull out/store minimum, maximum and afternoon temps
t_min[i_day,:] = tair.min(unitted=False)
t_max[i_day,:] = tair.max(unitted=False)
t_afternoon[i_day,:] = tair.ref_val(unitted=False)
# calculate RH & store
rh_dlts = ds.compute_rh()
rh_dlts.store_day(rh)
rh_max[i_day,:] = rh_dlts.max(unitted=False)
rh_min[i_day,:] = rh_dlts.min(unitted=False)
rh_afternoon[i_day,:] = rh_dlts.ref_val(unitted=False)
# calculate GSI indices and store
i_tmin[i_day,:] = gsi.calc_i_tmin(tair.min())
cell_daylengths = daylength_lut.get(ds.get_latitudes())
i_photo[i_day,:] = gsi.calc_i_photo(cell_daylengths)
i_vpd[i_day,:] = gsi.calc_i_vpd(ds.compute_afternoon_vpd())
gsi_vals = i_tmin[i_day,:] * i_photo[i_day,:] * i_vpd[i_day,:]
i_gsi[i_day,:] = gsi_vals
# calculate running GSI average and store
gsi_window.put(gsi_vals)
if gsi_window.ready() :
i_gsi_avg[i_day,:] = gsi_window.mean()
# calculate precipitation duration and store
precip_val = ds.compute_precip_duration()
precip[i_day,:] = precip_val
# calculate total precip over last 24 hours, and dry day flag
tot_precip_val = (rainf.sum() * ds.get_timestep()).to(u.kg/(u.m**2))
tot_precip[i_day,:] = tot_precip_val
dd_window.put( tot_precip_val < DRY_DAY_PRECIP )
dd[i_day,:] = dd_window.all_runs()
# calculate daily avg equilibrium moisture content
eqmc_bar_val = ds.compute_eqmc_bar(cell_daylengths)
eqmc_bar[i_day,:] = eqmc_bar_val
# calculate fuel moisture content
fm1000[i_day,:] = fm1000_calc.compute(eqmc_bar_val, precip_val).to(u.percent)
fm100_today = fm100_calc.compute(eqmc_bar_val, precip_val)
fm100[i_day,:] = fm100_today.to(u.percent)
fm1_today, fm10_today = fm.oneten_ofdm(
t_afternoon[i_day,:]*u.K,
rh_afternoon[i_day,:]*u.dimensionless_unscaled,
swdown.ref_val(), fm100_today)
fm10[i_day,:] = fm10_today.to(u.percent)
fm1[i_day,:] = fm1_today.to(u.percent)
# first time through, store the first day's data
if i_day == 1 :
rh_dlts.store_day(rh, current=False)
# load next day for all variables
ds.next()
ds.close()
def indices_year(y, forcing_template, out_template):
"""Processes one year.
Runs the calculation of indices for one year given an orchidee forcing
file for that year."""
# figure out filenames and open dataset.
forcing_file = forcing_template % (y, )
out_file = out_template % (y, )
ds = ForcingDataset(forcing_file, out_file)
# register required variables from NetCDF file so they are tracked
qair = ds.register_variable("Qair", u.dimensionless_unscaled)
tair = ds.register_variable("Tair", u.Kelvin)
ds.register_variable("PSurf", u.Pa)
rainf = ds.register_variable("Rainf", u.kg / (u.m**2) / u.s)
swdown = ds.register_variable("SWdown", u.W / (u.m**2))
print "NetCDF variables registered"
# setup netcdf dimensions in output file
ds.copyDimension("land")
ds.copyDimension("y")
ds.copyDimension("tstep")
ds.createDimension("days", 365)
# copy basic data
ds.copyVariable('nav_lat')
ds.copyVariable('nav_lon')
print "Variables copied from forcing file"
# create netcdf variables to hold indices in output file
daylength = ds.create_variable("daylength", ("days", "y"), np.float32)
daylength.title = "Day Length"
daylength.units = "hours"
rh = ds.create_variable("rh", ("tstep", "land"), np.float32)
rh.long_name = "Relative Humidity"
rh.units = "fraction"
rh_max = ds.create_variable("rh_max", ("days", "land"), np.float32)
rh_max.long_name = "Maximum RH"
rh_max.units = "fraction"
rh_min = ds.create_variable("rh_min", ("days", "land"), np.float32)
rh_min.long_name = "Minimum RH"
rh_min.units = "fraction"
rh_afternoon = ds.create_variable("rh_afternoon", ('days', 'land'),
np.float32)
rh_afternoon.long_name = "RH in the afternoon"
rh_afternoon.units = "fraction"
t_min = ds.create_variable("t_min", ('days', 'land'), np.float32)
t_min.long_name = "minimum temperature for the 24 hours preceeding burning period"
t_min.units = "K"
t_max = ds.create_variable("t_max", ('days', 'land'), np.float32)
t_max.long_name = "maximum temperature for the 24 hours preceeding burning period"
t_max.units = "K"
t_afternoon = ds.create_variable("t_afternoon", ('days', 'land'),
np.float32)
t_afternoon.long_name = "mid-burning-period temperature"
t_afternoon.units = "K"
i_tmin = ds.create_variable("i_tmin", ('days', 'land'), np.float32)
i_tmin.long_name = "GSI minimum temperature index component"
i_tmin.units = 'dimensionless'
i_photo = ds.create_variable('i_photo', ('days', 'land'), np.float32)
i_photo.long_name = 'GSI photoperiod index component'
i_photo.units = 'dimensionless'
i_vpd = ds.create_variable('i_vpd', ('days', 'land'), np.float32)
i_vpd.long_name = 'GSI vapor pressure deficit index component'
i_vpd.units = 'dimensionless'
i_gsi = ds.create_variable('gsi', ('days', 'land'), np.float32)
i_gsi.long_name = 'GSI Index'
i_gsi.units = 'dimensionless'
gsi_days = 10
i_gsi_avg = ds.create_variable('gsi_avg', ('days', 'land'), np.float32)
i_gsi_avg.long_name = 'GSI Index (%d day running average)' % gsi_days
i_gsi_avg.units = 'dimensionless'
precip = ds.create_variable('precip_duration', ('days', 'land'),
np.float32)
precip.long_name = "Duration of precipitation"
precip.units = 'hours'
tot_precip = ds.create_variable('total_precip', ('days', 'land'),
np.float32)
tot_precip.long_name = 'total precipitation over last 24 hours'
tot_precip.units = 'kg / m^2'
dd_days = 30
dd = ds.create_variable('dd', ('days', 'land'), np.int)
dd.long_name = 'dry day weighting (%d day window)' % dd_days
dd.units = 'none'
eqmc_bar = ds.create_variable('eqmc_bar', ('days', 'land'), np.float32)
eqmc_bar.long_name = 'Weighted daily average equilibrium moisture content'
eqmc_bar.units = 'percent'
fm1000 = ds.create_variable('fm1000', ('days', 'land'), np.float32)
fm1000.long_name = '1000 hour fuel moisture'
fm1000.units = 'percent'
fm100 = ds.create_variable('fm100', ('days', 'land'), np.float32)
fm100.long_name = '100 hour fuel moisture'
fm100.units = 'percent'
fm10 = ds.create_variable('fm10', ('days', 'land'), np.float32)
fm10.long_name = '10 hour fuel moisture'
fm10.units = 'percent'
fm1 = ds.create_variable('fm1', ('days', 'land'), np.float32)
fm1.long_name = '1 hour fuel moisture'
fm1.units = 'percent'
print "Output NetCDF variables created"
# moving window of GSI data
gsi_window = w.MovingWindow(i_gsi.shape, 0, gsi_days)
# moving window to compute dry day weights
dd_window = w.SequenceWindow(dd.shape, 0, dd_days)
# initialize fuel moisture calculators
fm1000_calc = fm.ThousandHourFM(fm1000.shape, 0)
fm100_calc = fm.HundredHourFM(fm100.shape, 0)
# base time
time_start = t.Time('%04d:001' % y, format='yday', scale='ut1')
if ((y < 1960) or (y > 2013)):
time_start.delta_ut1_utc = 0
# loop over days
# the cur_day property is the index of the next day to read in from the file.
# since the netcdf file is 0-based, indices are [0, 365)
while qair.cur_day < 365:
i_day = qair.cur_day - 1
print qair.cur_day
# calculate daylengths, store as hours
day = time_start + (i_day * u.day)
if (hasattr(time_start, "delta_ut1_utc")):
day.delta_ut1_utc = time_start.delta_ut1_utc
daylength_lut = ds.get_daylength_lookup(day)
daylength[i_day, :] = daylength_lut.values
# pull out/store minimum, maximum and afternoon temps
t_min[i_day, :] = tair.min(unitted=False)
t_max[i_day, :] = tair.max(unitted=False)
t_afternoon[i_day, :] = tair.ref_val(unitted=False)
# calculate RH & store
rh_dlts = ds.compute_rh()
rh_dlts.store_day(rh)
rh_max[i_day, :] = rh_dlts.max(unitted=False)
rh_min[i_day, :] = rh_dlts.min(unitted=False)
rh_afternoon[i_day, :] = rh_dlts.ref_val(unitted=False)
# calculate GSI indices and store
i_tmin[i_day, :] = gsi.calc_i_tmin(tair.min())
cell_daylengths = daylength_lut.get(ds.get_latitudes())
i_photo[i_day, :] = gsi.calc_i_photo(cell_daylengths)
i_vpd[i_day, :] = gsi.calc_i_vpd(ds.compute_afternoon_vpd())
gsi_vals = i_tmin[i_day, :] * i_photo[i_day, :] * i_vpd[i_day, :]
i_gsi[i_day, :] = gsi_vals
# calculate running GSI average and store
gsi_window.put(gsi_vals)
if gsi_window.ready():
i_gsi_avg[i_day, :] = gsi_window.mean()
# calculate precipitation duration and store
precip_val = ds.compute_precip_duration()
precip[i_day, :] = precip_val
# calculate total precip over last 24 hours, and dry day flag
tot_precip_val = (rainf.sum() * ds.get_timestep()).to(u.kg / (u.m**2))
tot_precip[i_day, :] = tot_precip_val
dd_window.put(tot_precip_val < DRY_DAY_PRECIP)
dd[i_day, :] = dd_window.all_runs()
# calculate daily avg equilibrium moisture content
eqmc_bar_val = ds.compute_eqmc_bar(cell_daylengths)
eqmc_bar[i_day, :] = eqmc_bar_val
# calculate fuel moisture content
fm1000[i_day, :] = fm1000_calc.compute(eqmc_bar_val,
precip_val).to(u.percent)
fm100_today = fm100_calc.compute(eqmc_bar_val, precip_val)
fm100[i_day, :] = fm100_today.to(u.percent)
fm1_today, fm10_today = fm.oneten_ofdm(
t_afternoon[i_day, :] * u.K,
rh_afternoon[i_day, :] * u.dimensionless_unscaled,
swdown.ref_val(), fm100_today)
fm10[i_day, :] = fm10_today.to(u.percent)
fm1[i_day, :] = fm1_today.to(u.percent)
# first time through, store the first day's data
if i_day == 1:
rh_dlts.store_day(rh, current=False)
# load next day for all variables
ds.next()
ds.close()