def optical_depth_rayleigh(self):
if not self.__housekeeping:
txt = 'To claculate the optical depth you will have to set the housekeeping property with a timeseries containing Altitude (m) Temperature (C) and Pressure (hPa)'
raise AttributeError(txt)
if not self.__od_ray_orig:
hkt = self.housekeeping.copy()
# import pdb
# pdb.set_trace()
# to make sure there is no two values with the same altitude in a row (causes sims to fail) ...
clean = False
while not clean:
alt = hkt.data.Altitude.values
dist = alt[1:] - alt[:-1]
if (dist == 0).sum():
hkt.data.iloc[np.where(dist == 0)] = np.nan
else:
clean = True
# pdb.set_trace()
hkt.data.dropna(axis=1, inplace=True)
# pdb.set_trace()
# do the od calculations for each wavelength channel
c_list = np.array(sorted(self.wavelength_channels.items(), key=lambda x: x[1]))[:, 0]
for e, c in enumerate(c_list):
wl = self.wavelength_channels[c]
out = []
for i in range(hkt.data.shape[0]):
if i < 1:
out.append(0.)
continue
hktt = hkt.data.iloc[0:i, :]
alt = -1 * hktt.Altitude.values
press = hktt.Pressure_Pa.values
temp = hktt.Temperature.values
ray = _bray.rayleigh_optical_depth(alt, press, temp + 273.15, float(wl))
out.append(ray)
# pdb.set_trace()
hkt.data[wl] = np.array(out)
# pdb.set_trace()
wll = np.array(sorted(self.wavelength_channels.items(), key=lambda x: x[1]))[:, 1]#.astype(float)
# pdb.set_trace()
hkt = hkt._del_all_columns_but(wll)
# pdb.set_trace()
self.__od_ray_orig = hkt
if self.optical_depth_rayleigh_offsets != self.__od_ray_offset_last:
# pdb.set_trace()
self.__od_ray = self.__od_ray_orig.copy()
# pdb.set_trace()
# apply offset
wll = np.array(sorted(self.wavelength_channels.items(), key=lambda x: x[1]))[:, 1]#.astype(float)
for wl in wll:
self.__od_ray.data[wl] += self.optical_depth_rayleigh_offsets[wl]
self.__od_ray_offset_last = self.optical_depth_rayleigh_offsets
# pdb.set_trace()
return self.__od_ray
def simulate_from_rayleigh(
time_series,
layerbounderies,
# altitude,
# layerbounderies,
pressure,
temp,
wl,
no_angles,
rotations,
airmassfct,
sun_azimuth):
""" Fix this documentation!
Simulates miniSASP signal from a size distribution layer series
Arguments
---------
layerbounderies: array-like
altitude: float or array-like.
Altitude for which the mSASP signal is simulated for in meters
pressure: array, bool
Atmospheric pressure in mbar. If False, value is taken from international standard atmosphere.
temp: array, bool in K
wl: wavelength in nm
no_angles: int
total number of angles considered. This included the number in multiple roations. most likely this is
int(opt_prop.angular_scatt_func.shape[0] / 2) * rotations
rotations: int.
number of rotations the of the mSASP.
Returns
-------
pandas.DataFrame
containing the sky brightness as a function of mSASPS azimuth angle"""
layerbounderies = np.unique(layerbounderies.flatten())
altitude = (layerbounderies[1:] + layerbounderies[:-1]) / 2.
time_series = solar.get_sun_position_TS(time_series)
where = array_tools.find_closest(time_series.data.Altitude.values,
altitude)
solar_elev = time_series.data.Solar_position_elevation.values[where]
solar_az = time_series.data.Solar_position_azimuth.values[where]
alts = time_series.data.Altitude.values[
where] # thats over acurate, cal simply use the layerbounderies
if (type(pressure).__name__ == 'bool') or (type(temp).__name__ == 'bool'):
if pressure and temp:
# temp = time_series.data.Temperature
# pressure = time_series.data.Barometric_pressure_Pa
lb = pd.DataFrame(index=layerbounderies)
select_list = ["Temperature", "Altitude", "Pressure_Pa"]
bla = []
for i in ["Temperature", "Altitude", "Pressure_Pa"]:
if i not in time_series.data.columns:
bla.append(i)
if len(bla) != 0:
txt = 'The underlying housekeeping data has to have the following attributes for this operation to work: %s' % (
["Temperature", "Altitude", "Pressure_Pa"])
txt += '\nmissing:'
for i in bla:
txt += '\n \t' + i
# print(txt)
raise AttributeError(txt)
hkt = time_series.data.loc[:, select_list]
hkt.index = hkt.Altitude
hkt = hkt.sort_index()
hkt_lb = pd.concat([hkt, lb]).sort_index().interpolate()
hkt_lb = hkt_lb.groupby(hkt_lb.index).mean().reindex(lb.index)
temp = hkt_lb.Temperature.values + 273.15
pressure = hkt_lb.Pressure_Pa.values
else:
p, t = atmstd.standard_atmosphere(layerbounderies)
if type(pressure).__name__ == 'bool':
if pressure == False:
pressure = p
if type(temp).__name__ == 'bool':
if temp == False:
temp = t
# print(pressure, temp)
if (layerbounderies.shape != pressure.shape) or (layerbounderies.shape !=
temp.shape):
raise ValueError('altitude, pressure and tmp have to have same shape')
# time = time_series.data.index[where]
what_mSASP_sees_rayleigh = pd.DataFrame()
what_mSASP_sees_AOD_rayleigh = np.zeros(altitude.shape)
for alt in range(altitude.shape[0]):
# get the sun position at the time when the plane was at the particular altitude,
sol_el = solar_elev[alt]
sol_az = solar_az[alt]
# print(alts[alt:])
# return ray_scatt_fct
# angles between mSASP positions and sun. This is used to pick the angle in the phase functions
if sun_azimuth:
sun_azimuth = sol_az
else:
sun_azimuth = 0
mSASP2Sunangles = angle_MSASP_sun(
sol_el,
sun_azimuth=sun_azimuth,
no_angles=no_angles,
# pretty arbitrary number ... this is just to get a reasonal number of angles
no_rotations=rotations)
ray_scatt_fct = bray.rayleigh_angular_scattering_intensity(
layerbounderies[alt:], pressure[alt:], temp[alt:], wl,
mSASP2Sunangles.values.transpose())
ray_scatt_fct = pd.DataFrame(ray_scatt_fct,
index=mSASP2Sunangles.index)
# return layerbounderies[alt:], pressure[alt:],temp[alt:], wl, ray_scatt_fct
if airmassfct:
slant_adjust = 1. / np.sin(solar_elev[alt])
else:
slant_adjust = 1.
# closest_phase2sun_azi = array_tools.find_closest(ray_scatt_fct.index.values,
# mSASP2Sunangles.mSASP_sun_angle.values)
what_mSASP_sees_rayleigh[alts[alt]] = pd.Series(
ray_scatt_fct.values.transpose()[0] * slant_adjust)
# what_mSASP_sees_rayleigh.index = mSASP2Sunangles.index.values
what_mSASP_sees_AOD_rayleigh[alt] = bray.rayleigh_optical_depth(
layerbounderies[alt:], pressure[alt:], temp[alt:],
wl) * slant_adjust
# return layerbounderies[alt:],pressure[alt:],temp[alt:],wl, what_mSASP_sees_AOD_rayleigh[alt], slant_adjust
what_mSASP_sees_rayleigh.index = mSASP2Sunangles.index
what_mSASP_sees_AOD_rayleigh = pd.DataFrame(what_mSASP_sees_AOD_rayleigh,
index=altitude,
columns=['AOD_ray'])
return what_mSASP_sees_rayleigh, what_mSASP_sees_AOD_rayleigh
def simulate_from_rayleigh(time_series,
layerbounderies,
# altitude,
# layerbounderies,
pressure,
temp,
wl,
no_angles,
rotations,
airmassfct,
sun_azimuth):
""" Fix this documentation!
Simulates miniSASP signal from a size distribution layer series
Arguments
---------
layerbounderies: array-like
altitude: float or array-like.
Altitude for which the mSASP signal is simulated for in meters
pressure: array, bool
Atmospheric pressure in mbar. If False, value is taken from international standard atmosphere.
temp: array, bool in K
wl: wavelength in nm
no_angles: int
total number of angles considered. This included the number in multiple roations. most likely this is
int(opt_prop.angular_scatt_func.shape[0] / 2) * rotations
rotations: int.
number of rotations the of the mSASP.
Returns
-------
pandas.DataFrame
containing the sky brightness as a function of mSASPS azimuth angle"""
layerbounderies = np.unique(layerbounderies.flatten())
altitude = (layerbounderies[1:] + layerbounderies[:-1]) / 2.
time_series = solar.get_sun_position_TS(time_series)
where = array_tools.find_closest(time_series.data.Altitude.values, altitude)
solar_elev = time_series.data.Solar_position_elevation.values[where]
solar_az = time_series.data.Solar_position_azimuth.values[where]
alts = time_series.data.Altitude.values[where] # thats over acurate, cal simply use the layerbounderies
if (type(pressure).__name__ == 'bool') or (type(temp).__name__ == 'bool'):
if pressure and temp:
# temp = time_series.data.Temperature
# pressure = time_series.data.Barometric_pressure_Pa
lb = pd.DataFrame(index=layerbounderies)
select_list = ["Temperature", "Altitude", "Pressure_Pa"]
bla = []
for i in ["Temperature", "Altitude", "Pressure_Pa"]:
if i not in time_series.data.columns:
bla.append(i)
if len(bla) != 0:
txt='The underlying housekeeping data has to have the following attributes for this operation to work: %s'%(["Temperature", "Altitude", "Pressure_Pa"])
txt+='\nmissing:'
for i in bla:
txt += '\n \t' + i
# print(txt)
raise AttributeError(txt)
hkt = time_series.data.loc[:, select_list]
hkt.index = hkt.Altitude
hkt = hkt.sort_index()
hkt_lb = pd.concat([hkt, lb]).sort_index().interpolate()
hkt_lb = hkt_lb.groupby(hkt_lb.index).mean().reindex(lb.index)
temp = hkt_lb.Temperature.values + 273.15
pressure = hkt_lb.Pressure_Pa.values
else:
p, t = atmstd.standard_atmosphere(layerbounderies)
if type(pressure).__name__ == 'bool':
if pressure == False:
pressure = p
if type(temp).__name__ == 'bool':
if temp == False:
temp = t
# print(pressure, temp)
if (layerbounderies.shape != pressure.shape) or (layerbounderies.shape != temp.shape):
raise ValueError('altitude, pressure and tmp have to have same shape')
# time = time_series.data.index[where]
what_mSASP_sees_rayleigh = pd.DataFrame()
what_mSASP_sees_AOD_rayleigh = np.zeros(altitude.shape)
for alt in range(altitude.shape[0]):
# get the sun position at the time when the plane was at the particular altitude,
sol_el = solar_elev[alt]
sol_az = solar_az[alt]
# print(alts[alt:])
# return ray_scatt_fct
# angles between mSASP positions and sun. This is used to pick the angle in the phase functions
if sun_azimuth:
sun_azimuth = sol_az
else:
sun_azimuth = 0
mSASP2Sunangles = angle_MSASP_sun(sol_el,
sun_azimuth=sun_azimuth,
no_angles=no_angles,
# pretty arbitrary number ... this is just to get a reasonal number of angles
no_rotations=rotations)
ray_scatt_fct = bray.rayleigh_angular_scattering_intensity(layerbounderies[alt:], pressure[alt:], temp[alt:],
wl, mSASP2Sunangles.values.transpose())
ray_scatt_fct = pd.DataFrame(ray_scatt_fct, index=mSASP2Sunangles.index)
# return layerbounderies[alt:], pressure[alt:],temp[alt:], wl, ray_scatt_fct
if airmassfct:
slant_adjust = 1. / np.sin(solar_elev[alt])
else:
slant_adjust = 1.
# closest_phase2sun_azi = array_tools.find_closest(ray_scatt_fct.index.values,
# mSASP2Sunangles.mSASP_sun_angle.values)
what_mSASP_sees_rayleigh[alts[alt]] = pd.Series(ray_scatt_fct.values.transpose()[0] * slant_adjust)
# what_mSASP_sees_rayleigh.index = mSASP2Sunangles.index.values
what_mSASP_sees_AOD_rayleigh[alt] = bray.rayleigh_optical_depth(layerbounderies[alt:], pressure[alt:],
temp[alt:], wl) * slant_adjust
# return layerbounderies[alt:],pressure[alt:],temp[alt:],wl, what_mSASP_sees_AOD_rayleigh[alt], slant_adjust
what_mSASP_sees_rayleigh.index = mSASP2Sunangles.index
what_mSASP_sees_AOD_rayleigh = pd.DataFrame(what_mSASP_sees_AOD_rayleigh, index=altitude, columns=['AOD_ray'])
return what_mSASP_sees_rayleigh, what_mSASP_sees_AOD_rayleigh
def optical_depth_rayleigh(self):
if not self.__housekeeping:
txt = 'To claculate the optical depth you will have to set the housekeeping property with a timeseries containing Altitude (m) Temperature (C) and Pressure (hPa)'
raise AttributeError(txt)
if not self.__od_ray_orig:
hkt = self.housekeeping.copy()
# import pdb
# pdb.set_trace()
# to make sure there is no two values with the same altitude in a row (causes sims to fail) ...
clean = False
while not clean:
alt = hkt.data.Altitude.values
dist = alt[1:] - alt[:-1]
if (dist == 0).sum():
hkt.data.iloc[np.where(dist == 0)] = np.nan
else:
clean = True
# pdb.set_trace()
hkt.data.dropna(axis=1, inplace=True)
# pdb.set_trace()
# do the od calculations for each wavelength channel
c_list = np.array(
sorted(self.wavelength_channels.items(),
key=lambda x: x[1]))[:, 0]
for e, c in enumerate(c_list):
wl = self.wavelength_channels[c]
out = []
for i in range(hkt.data.shape[0]):
if i < 1:
out.append(0.)
continue
hktt = hkt.data.iloc[0:i, :]
alt = -1 * hktt.Altitude.values
press = hktt.Pressure_Pa.values
temp = hktt.Temperature.values
ray = _bray.rayleigh_optical_depth(alt,
press, temp + 273.15,
float(wl))
out.append(ray)
# pdb.set_trace()
hkt.data[wl] = np.array(out)
# pdb.set_trace()
wll = np.array(
sorted(self.wavelength_channels.items(),
key=lambda x: x[1]))[:, 1] #.astype(float)
# pdb.set_trace()
hkt = hkt._del_all_columns_but(wll)
# pdb.set_trace()
self.__od_ray_orig = hkt
if self.optical_depth_rayleigh_offsets != self.__od_ray_offset_last:
# pdb.set_trace()
self.__od_ray = self.__od_ray_orig.copy()
# pdb.set_trace()
# apply offset
wll = np.array(
sorted(self.wavelength_channels.items(),
key=lambda x: x[1]))[:, 1] #.astype(float)
for wl in wll:
self.__od_ray.data[wl] += self.optical_depth_rayleigh_offsets[
wl]
self.__od_ray_offset_last = self.optical_depth_rayleigh_offsets
# pdb.set_trace()
return self.__od_ray
