def geom(index=-1):
data = rd.processing_orbits(1478, 8)
# read the orbit geometries for the pass
phase0 = data.geo.phase
emission0 = data.geo.emission
sza0 = data.geo.sza
lat0 = data.geo.Lat
lon0 = data.geo.Lon
#phase = phase0[-2:-1]
#emission = emission0[-2:-1]
#sza = sza0[-2:-1]
phase = phase0[np.array(index)]
emission = emission0[np.array(index)]
sza = sza0[np.array(index)]
lat = lat0[np.array(index)]
lon = lon0[np.array(index)]
geo = [
np.array([phase]),
np.array([emission]),
np.array([sza]),
np.array([lat]),
np.array([lon])
]
return geo
def getSPIRflux(lmin=None, lmax=None, idx=0, det=0):
"""
This function gets the NORMALIZED flux vectors from the SPICAV-IR routine
within a requested given range of wavelengths.
You can input an orbital epoch also, default is set at index=0.
lmin and lmax are in nanometers!!
"""
data = rd.processing_orbits(1478, 8)
if det == 0:
r0 = data.r0[:, idx]
w0 = data.w0[:, idx]
else:
r0 = data.r1[:, idx]
w0 = data.w1[:, idx]
# drop the "nan's" and normalize the radiance
w0 = w0[~np.isnan(r0)]
r0 = r0[~np.isnan(r0)] # remove nan's
if (lmin == None) & (lmax == None):
# get the SPICAV-IR radiance and wavelength data
r = r0
w = w0
else:
r = r0[(w0 > lmin) & (w0 < lmax)]
w = w0[(w0 > lmin) & (w0 < lmax)]
r = r / np.max(r)
return r, w
def getSPIRpol(lmin=None, lmax=None, idx=0, det=0, orbn=1478, orba=8):
"""
This function returns polarized flux data for a given orbit geometry =idx.
It does not do any processing as done by Loic Rossi to arrive at his selected
bands.
pol = (det1 - det0)/(det0 + det1)
Returns: pol,wavelength
Authr: G. Mahapatra
"""
data = rd.processing_orbits(orbn, orba)
r0 = data.r0[:, idx]
w0 = data.w0[:, idx]
r1 = data.r1[:, idx]
w1 = data.w1[:, idx]
if (lmin == None) & (lmax == None):
# get the SPICAV-IR radiance and wavelength data
r0 = r0
w0 = w0
r1 = r1
w1 = w1
else:
lidx = (w0 > lmin) & (w0 < lmax)
r0 = r0[lidx]
w0 = w0[lidx]
r1 = r1[lidx]
w1 = w1[lidx]
# calculate the polarization at all the available geometries
# polarization is calculated as (det1 - det0)/(det0 + det1)
pol = np.nan_to_num((r1 - r0) / (r0 + r1))
return pol, w1
This script creates errorbars for the degree of polarization of SPICAV-IR orbits. @author: gouravmahapatr, TU Delft """ import matplotlib.pyplot as plt import sys import os path = '/Users/gouravmahapatr/Dropbox/PhD/spicav_data' os.chdir(path) import read_spicav_ir as rd import linestyles as ls # load the data from a specific orbit data = rd.processing_orbits(1478, 8) # activate the get_bande() data.get_bande() # define the data step step = 20 # define the index idx = 0 # get the latitude lat = data.geo.Lat[idx] pol = data.band_pol[::step, idx] pol += 0.0
#localtime = data.geo.local_time
#lat = data.geo.Lat
#phase = data.geo.phase
#sza = data.geo.sza
#emission = data.geo.emission
localtime = []
lat = []
phase = []
sza = []
emi = []
azi = []
for i in range(len(orbit_list)):
orbit_n = orbit_list[i, 0]
orbit_a = orbit_list[i, 1]
data = rd.processing_orbits(orbit_n, orbit_a)
data.geo.calc_azimuth()
localtime.extend(list(data.geo.local_time))
lat.extend(list(data.geo.Lat))
phase.extend(list(data.geo.phase))
sza.extend(list(data.geo.sza))
emi.extend(list(data.geo.emission))
azi.extend(list(data.geo.azimuth))
cosbeta = pmd.get_cosbeta(np.array(phase), np.array(sza), np.array(emi),
np.array(azi)) # in radians
beta = np.degrees(np.arccos(cosbeta)) # Converted into degrees
''' Make the geometries file that will be used as an input to the geos_code'''
# first make sure the angles are all in array
# -*- coding: utf-8 -*- """ Created on Tue May 8 11:32:09 2018 Script to utilize the read_spicav_ir.py function to read and plot spicav IR data set. @author: Loic Rossi, gouravmahapatr """ import read_spicav_ir as rd import matplotlib.pyplot as plt """ load a specific orbit on the basis of its orbit number """ data = rd.processing_orbits(1478,8) plt.plot(data.geo.Alt_sc) # plt the spacecraft altitude plt.plot(data.geo.time) # plot the spacecraft time stamp associated with geometry data.geo.time.shape plt.plot(data.geo.phase, data.geo.Alt_sc) # plot the phase angle vs. SC alt plt.plot(data.geo.phase, data.geo.Lat) # plot phase angle vs. latitude plt.plot(data.geo.phase, data.geo.emission) plt.plot(data.geo.phase, data.geo.sza) data.w0 data.w0.shape data.w1.shape
# sum the gas opacities across all the layers
bmabs_lbl_tot = np.sum(bmabs_lbl,axis=1,keepdims=True)
# plot
plt.figure(figsize=[8,6])
plt.semilogy(wav,bmabs_lbl_tot,c='k',alpha=0.8)
plt.xlim([1.4,1.5])
plt.xlabel('Wavelength ($\mu m$)',fontsize='x-large')
plt.ylabel('Total gas opacity',fontsize='x-large')
plt.grid()
plt.tight_layout()
#%% get the SPICAV-IR data
# load the data from a specific orbit
if 'data' not in locals():
data = rd.processing_orbits(2269,8)
# activate the get_bande()
data.get_bande()
# get the latitude
lat = data.geo.Lat
# get the spicav wavelength
# Note: The actual wavelength differs b/w two flux channels!!
wav_spicav = data.band_wd0[:,0]
# get the values within 1400 and 1500 nm
idx = np.squeeze(np.array(np.where((wav_spicav<=wav.max()*1.e3)&(wav_spicav>=wav.min()*1.e3))))
wav_spicav_lim = wav_spicav[idx]