Beispiel #1
0
def example():
    """
    wl = 500
    alt = np.linspace(0,71000,20)
    P,T = ats.standard_atmosphere(alt)
    theta = np.linspace(0,np.pi, 50)
    out = rayleigh_angular_scattering_intensity(alt,P,T,wl,theta)
    plt.plot(theta,out)

    rod = rayleigh_optical_depth(alt,P,T,wl)
    print('rod: ', rod)
    rod_alt = integrate.simps(out * np.sin(theta) ,theta) * 2 * np.pi #this is exactly the AOD. the 2pi nesessary because we need to aditionally integrate over phi (from 0 to 2pi), since the function is independend from phi we simply need to multiply by 2pi
    print('rod, when integrating over all scattered light:', rod_alt)"""

    wl = 500
    alt = np.linspace(0, 71000, 20)
    P, T = ats.standard_atmosphere(alt)
    theta = np.linspace(0, np.pi, 50)
    out = rayleigh_angular_scattering_intensity(alt, P, T, wl, theta)
    plt.plot(theta, out)

    rod = rayleigh_optical_depth(alt, P, T, wl)
    print('rod: ', rod)
    rod_alt = integrate.simps(out * np.sin(theta),
                              theta) * 2 * np.pi  # this is exactly the AOD. the 2pi nesessary because we need to aditionally integrate over phi (from 0 to 2pi), since the function is independend from phi we simply need to multiply by 2pi
    print('rod, when integrating over all scattered light:', rod_alt)
def example():
    """
    wl = 500
    alt = np.linspace(0,71000,20)
    P,T = ats.standard_atmosphere(alt)
    theta = np.linspace(0,np.pi, 50)
    out = rayleigh_angular_scattering_intensity(alt,P,T,wl,theta)
    plt.plot(theta,out)

    rod = rayleigh_optical_depth(alt,P,T,wl)
    print('rod: ', rod)
    rod_alt = integrate.simps(out * np.sin(theta) ,theta) * 2 * np.pi #this is exactly the AOD. the 2pi nesessary because we need to aditionally integrate over phi (from 0 to 2pi), since the function is independend from phi we simply need to multiply by 2pi
    print('rod, when integrating over all scattered light:', rod_alt)"""

    wl = 500
    alt = np.linspace(0, 71000, 20)
    P, T = ats.standard_atmosphere(alt)
    theta = np.linspace(0, np.pi, 50)
    out = rayleigh_angular_scattering_intensity(alt, P, T, wl, theta)
    plt.plot(theta, out)

    rod = rayleigh_optical_depth(alt, P, T, wl)
    print('rod: ', rod)
    rod_alt = integrate.simps(out * np.sin(theta),
                              theta) * 2 * np.pi  # this is exactly the AOD. the 2pi nesessary because we need to aditionally integrate over phi (from 0 to 2pi), since the function is independend from phi we simply need to multiply by 2pi
    print('rod, when integrating over all scattered light:', rod_alt)
Beispiel #3
0
    def get_altitude(self, temperature=False):
        """Calculates the altitude from the measured barometric pressure
        Arguments
        ---------
        temperature: bool or array-like, optional
            False: temperature according to international standard is assumed.
            arraylike: actually measured temperature in Kelvin.

        Returns
        -------
        returns altitude and adds it to this instance
        """
        alt, tmp = atm_std.standard_atmosphere(self.data.Barometric_pressure, quantity='pressure')
        self.data['Altitude'] = alt
        return alt
Beispiel #4
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    def get_altitude(self, temperature=False):
        """Calculates the altitude from the measured barometric pressure
        Arguments
        ---------
        temperature: bool or array-like, optional
            False: temperature according to international standard is assumed.
            arraylike: actually measured temperature in Kelvin.

        Returns
        -------
        returns altitude and adds it to this instance
        """
        alt, tmp = atm_std.standard_atmosphere(self.data.Barometric_pressure,
                                               quantity='pressure')
        self.data['Altitude'] = alt
        return alt
Beispiel #5
0
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
Beispiel #6
0
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