def canopy_rt_optical(state, geom, resln=1.0):
"""A python wrapper to the SemiDiscrete optical
canopy RT model of Nadine Gobron. Runs the
model for the the whole of its valid spectra
range at a resolution set by resln.
:param state: Instance of the stateVector class.
:type state: instance
:param geom: Instance of the sensorGeomety class.
:type geom: instance
:param resln: the spectral resolution in nm [optional].
:type resln: float
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.wavl = np.arange(400, 2500 + resln, resln)
# generate a tmp file and write
# wavelengths and geometry into it
fd, temp_path = mkstemp(prefix='/tmp/senSyntmp__', text=True)
tmpFile = os.fdopen(fd, 'w')
print >> tmpFile, "1 %d" % len(spect.wavl),
for w in spect.wavl:
print >> tmpFile, " %f" % w,
print >> tmpFile, "\n%s %s %s %s" % (geom.vza, geom.vaa, geom.sza,
geom.saa)
tmpFile.close()
# set up nadim command line
cmd = "semiDiscrete"
if state.lai != None:
cmd = cmd + " -LAI %f -hc %f -rsl1 %f" % (
state.lai, state.can_height, 0.2 * (1. - 0.5 *
(state.soil_moisture / 100.)))
# Think about soil moisture implementation here
cmd = cmd + " < %s" % temp_path
# run process
p = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=sys.stderr,
shell=True)
out = p.stdout.readlines()
p.wait()
# clean up
os.remove(temp_path)
# process RT model output
rtOut = out[1].split()
for i in xrange(4, len(rtOut)):
reflTmp = np.append(spect.refl, float(rtOut[i]))
spect.refl = copy(reflTmp)
return spect
def passive_optical_rt(state,
geom,
mode='fast',
rsl1=0.2,
sm_coeff=0.5,
cab=75.0,
cw=0.01):
"""Function that simulates reflectances given surface biogeophysical variables and viewing geometries.
:param state: Instance of the StateVector class.
:type state: instance
:param geom: Instance of the SensorGeometry class.
:type geom: instance
:param mode: Run semiDiscrete in either fast ('fast') or slow ('slow') mode [optional].
:type resln: str
:param rsl1: weight of the first soil vector
:type rsl1: float
:param sm_coeff: weighting of soil moisture impact, bound between (0,1)
:type sm_coeff: float
:param cab: leaf chlorophyl concentration
:type cab: float
:param cw: equivelant leaf water thickness
:type cw: float
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.date_sat_ob = []
spect.date_land_ob = []
spect.soil_moisture = []
spect.lai = []
spect.can_height = []
spect.refl = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
reflectance = run_semidiscrete(state.soil_moisture[idx],
state.lai[idx],
state.can_height[idx],
geom.vza[date_utc[0]],
geom.vaa[date_utc[0]],
geom.sza[date_utc[0]],
geom.saa[date_utc[0]],
mode=mode,
rsl1=rsl1,
sm_coeff=sm_coeff,
cab=cab,
cw=cw)
spect.date_sat_ob.append(date_utc[1])
spect.date_land_ob.append(state.date_utc[idx])
spect.soil_moisture.append(state.soil_moisture[idx])
spect.lai.append(state.lai[idx])
spect.can_height.append(state.can_height[idx])
spect.refl.append(reflectance)
return spect
def canopy_rt_optical_fast(state, geom, resln=1.0, mode='fast'):
"""A python wrapper to the SemiDiscrete optical
canopy RT model of Nadine Gobron. Runs the
model for the the whole of its valid spectra
range at a resolution set by resln.
:param state: Instance of the StateVector class.
:type state: instance
:param geom: Instance of the SensorGeometry class.
:type geom: instance
:param mode: Run semiDiscrete in either fast ('fast') or slow ('slow') mode [optional].
:type resln: str
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.wavl = np.arange(400, 2500 + resln, resln)
# generate a tmp file and write geometry into it
fd, temp_path = mkstemp(prefix='/tmp/senSyntmp__', text=True)
tmpFile = os.fdopen(fd, 'w')
print >> tmpFile, "%s %s %s %s" % (geom.vza, geom.vaa, geom.sza, geom.saa)
# print "%s %s %s %s" %(geom.vza,geom.vaa,geom.sza,geom.saa)
tmpFile.close()
# set up nadim command line
if mode == 'fast':
cmd = "semiD -srf ../data/srf/s2a.srf -fast"
else:
cmd = "semiD -srf ../data/srf/s2a.srf"
if state.lai != None:
cmd = cmd + " -LAI %f -hc %f -rsl1 %f" % (
state.lai, state.can_height, 0.2 * (1. - 0.5 *
(state.soil_moisture / 100.)))
# Think about soil moisture implementation here
# include -cab (leaf chlorophyl concentration, default:75.0) and -cw (equivelant leaf water thickness, default: 0.01)??
# update 0.2 to tunable rsl1 (weight of the first soil vector, default: 0.2)
cmd = cmd + " < %s" % temp_path
# print cmd
# run process
p = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=sys.stderr,
shell=True)
out = p.stdout.readlines()
p.wait()
spect.refl = np.array([float(val) for val in out[0].split()])
return spect
def passive_optical_rt(state, geom):
"""A python wrapper to the SemiDiscrete optical
canopy RT model of Nadine Gobron. Runs the
model for the the whole of its valid spectra
range at a resolution set by resln.
:param state: Instance of the StateVector class.
:type state: instance
:param geom: Instance of the SensorGeometry class.
:type geom: instance
:param mode: Run semiDiscrete in either fast ('fast') or slow ('slow') mode [optional].
:type resln: str
:return: Instance of the spectra class.
:rtype: instance
"""
spect = sp.spectra()
spect.date_sat_ob = []
spect.date_land_ob = []
spect.soil_moisture = []
spect.lai = []
spect.can_height = []
spect.refl = []
for date_utc in enumerate(geom.date_utc):
idx, timedelt = sv.find_nearest_date_idx(state.date_utc, date_utc[1])
reflectance = run_semidiscrete(state.soil_moisture[idx],
state.lai[idx], state.can_height[idx],
geom.vza[date_utc[0]],
geom.vaa[date_utc[0]],
geom.sza[date_utc[0]],
geom.saa[date_utc[0]])
spect.date_sat_ob.append(date_utc[1])
spect.date_land_ob.append(state.date_utc[idx])
spect.soil_moisture.append(state.soil_moisture[idx])
spect.lai.append(state.lai[idx])
spect.can_height.append(state.can_height[idx])
spect.refl.append(reflectance)
return spect
def run_semidiscrete_fapar_albedo(soil_m, lai, can_height, sza, rsl1=0.2, sm_coeff=0.5, cab=75.0,\
cw=0.01, solar_spectrum_filename="data/srf/ASTMG173.csv"):
"""A python wrapper to the SemiDiscrete model which integrates over the wolar spectrum.
:param soil_m: soil moisture at specified time (m3 m-3)
:type soil_m: float
:param lai: Leaf area index at specified time (m2 m-2)
:type lai: float
:param can_height: canopy height at specified time (m)
:type can_height: float
:param sza: solar zenith angle (degrees)
:type sza: float
:param rsl1: weight of the first soil vector
:type rsl1: float
:param sm_coeff: weighting of soil moisture impact, bound between (0,1)
:type sm_coeff: float
:param cab: leaf chlorophyl concentration
:type cab: float
:param cw: equivelant leaf water thickness
:type cw: float
:return: fapar & albedo values for specified input.
:rtype: array
"""
spec = sp.spectra(fname=solar_spectrum_filename,
ftype="CSV",
wavlCol=0,
reflCol=3,
hdrLines=2)
spec.trim(400, 701)
fpr_w, alb_w = run_semidiscrete_fluxes(soil_m, lai, can_height, sza, rsl1,
sm_coeff, cab, cw)
fpr = sp.convolve(fpr_w, spec)
alb = sp.convolve(alb_w, spec)
return fpr, alb
def run_semidiscrete_fluxes(soil_m,
lai,
can_height,
sza,
rsl1=0.2,
sm_coeff=0.5,
cab=75.0,
cw=0.01):
"""A python wrapper to the SemiDiscrete optical canopy RT model of Nadine Gobron.
:param soil_m: soil moisture at specified time (m3 m-3)
:type soil_m: float
:param lai: Leaf area index at specified time (m2 m-2)
:type lai: float
:param can_height: canopy height at specified time (m)
:type can_height: float
:param sza: solar zenith angle (degrees)
:type sza: float
:param rsl1: weight of the first soil vector
:type rsl1: float
:param sm_coeff: weighting of soil moisture impact, bound between (0,1)
:type sm_coeff: float
:param cab: leaf chlorophyl concentration
:type cab: float
:param cw: equivelant leaf water thickness
:type cw: float
:return: fapar & albedo values for specified input.
:rtype: array
"""
# generate a tmp file and write geometry into it
dir_path = os.path.dirname(os.path.realpath(__file__))
exe = dir_path + "/semidiscrete_srf/semiD"
fd, temp_path = mkstemp(prefix='/tmp/senSyntmp__', text=True)
tmpFile = os.fdopen(fd, 'w')
print >> tmpFile, "0 0 %s 0" % sza
tmpFile.close()
# set up nadim command line
cmd = exe + " -fluxes -vis"
if lai != None:
cmd = cmd + " -LAI %f -hc %f -rsl1 %f -cab %f -cw %f" % (
lai, can_height, rsl1 * (1. - sm_coeff * soil_m), cab, cw)
cmd = cmd + " < %s" % temp_path
# run process
p = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=sys.stderr,
shell=True)
out = p.stdout.readlines()
p.wait()
fpr = sp.spectra()
alb = sp.spectra()
for o in out:
fpr.wavl = np.append(fpr.wavl, float(o.split()[0]))
alb.wavl = np.append(alb.wavl, float(o.split()[0]))
fpr.refl = np.append(fpr.refl, float(o.split()[1]))
alb.refl = np.append(alb.refl, float(o.split()[2]))
return fpr, alb
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from spectra import spectra
from spectra import spectra_dynamics
#%% test 1 - variance spectra
t1 = np.arange(1, 101)
sinal1 = np.sin(t1 / 4)
# creating example 1 instance
test1 = spectra(sinal1)
# calculating example 1 spectrum, with sliding windows
test1.calc_Var_Spectra('sliding')
# calculating example 1 spectrum, with independent windows
test1.calc_Var_Spectra('independent')
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
ax[0].plot(t1, sinal1)
test1.plot_Var_Spectra(ax=ax[1])
ax[0].set_xlabel('Time')
ax[0].set_title('Simulated periodic signal')