Infrared Sensor Data Example
[1]:
%matplotlib inline
import numpy as np
import xarray as xr
import warnings
from osirisl1services.readlevel1 import open_level1_ir
from osirisl1services.services import Level1Services
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from scipy.interpolate import interp1d
FIG_SIZE = (15, 6)
Load the IR data for orbit 6432, scan 12
[2]:
channel = 1
ir = open_level1_ir(orbit=6432, channel=channel)
The data and added variables are returned as xarray Datasets
[3]:
print(ir)
<xarray.Dataset>
Dimensions: (mjd: 2893, pixel: 128)
Coordinates:
* mjd (mjd) float64 5.239e+04 5.239e+04 ... 5.239e+04 5.239e+04
* pixel (pixel) int64 0 1 2 3 4 5 6 ... 121 122 123 124 125 126 127
Data variables: (12/17)
stw (mjd) int64 598896817 598896839 ... 598989644 598989676
exposureTime (mjd) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0
temperature (mjd) float64 -13.09 -12.96 -12.92 ... -13.09 -13.0 -13.09
tempavg (mjd) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
mode (mjd) int64 1294 1294 1294 1294 ... 1294 1294 1294 1294
scienceprog (mjd) int64 4092 4092 4092 4092 ... 4092 4092 4092 4092
... ...
processingflags (mjd) int64 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
data (mjd, pixel) float64 0.0 0.0 0.0 ... 8.121e+11 5.881e+11
error (mjd, pixel) float64 2.3e+10 2.3e+10 ... 2.3e+10 2.3e+10
flags (mjd, pixel) int64 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
orbit int64 6432
channel int64 1
The corresponding pointing, position, tangent point, and sza info can be accessed through the ‘l1’ xarray accessor
[4]:
print(ir.l1.look_ecef)
print(ir.l1.position_ecef)
print(ir.l1.altitude)
<xarray.DataArray (mjd: 2893, pixel: 128, xyz: 3)>
array([[[ 0.36142285, -0.15186206, 0.91995187],
[ 0.36165196, -0.1520894 , 0.91982426],
[ 0.36188102, -0.15231672, 0.91969655],
...,
[ 0.38971492, -0.18012921, 0.90314769],
[ 0.38993837, -0.18035406, 0.90300636],
[ 0.39016177, -0.18057887, 0.90286492]],
[[ 0.36211812, -0.15260177, 0.91955596],
[ 0.36234707, -0.15282908, 0.91942801],
[ 0.36257598, -0.15305637, 0.91929996],
...,
[ 0.39039019, -0.18086358, 0.90270918],
[ 0.39061347, -0.18108838, 0.90256751],
[ 0.39083671, -0.18131315, 0.90242574]],
[[ 0.3631572 , -0.15370826, 0.91896171],
[ 0.36338592, -0.15393551, 0.91883325],
[ 0.3636146 , -0.15416274, 0.91870467],
...,
...
...,
[ 0.27569692, -0.30767461, 0.91067422],
[ 0.27581061, -0.30797328, 0.91053883],
[ 0.27592426, -0.30827191, 0.91040333]],
[[ 0.26173752, -0.2715344 , 0.92615471],
[ 0.26185506, -0.27183728, 0.92603262],
[ 0.26197256, -0.27214013, 0.92591043],
...,
[ 0.27618046, -0.30912964, 0.91003474],
[ 0.27629394, -0.30942815, 0.90989883],
[ 0.27640739, -0.30972662, 0.90976281]],
[[ nan, nan, nan],
[ nan, nan, nan],
[ nan, nan, nan],
...,
[ nan, nan, nan],
[ nan, nan, nan],
[ nan, nan, nan]]])
Coordinates:
* mjd (mjd) float64 5.239e+04 5.239e+04 5.239e+04 ... 5.239e+04 5.239e+04
* pixel (pixel) int64 0 1 2 3 4 5 6 7 8 ... 120 121 122 123 124 125 126 127
* xyz (xyz) <U1 'x' 'y' 'z'
<xarray.DataArray (mjd: 2893, xyz: 3)>
array([[-5216570.81095428, 4643072.58023742, 11695.44940687],
[-5215134.71934416, 4644625.66671516, 21990.11973872],
[-5213024.95589154, 4646865.74762188, 36957.68438942],
...,
[-2864158.94169273, 6369399.27920743, -29287.7921628 ],
[-2861369.47845388, 6370675.56016623, -14320.19356146],
[ nan, nan, nan]])
Coordinates:
* mjd (mjd) float64 5.239e+04 5.239e+04 5.239e+04 ... 5.239e+04 5.239e+04
* xyz (xyz) <U1 'x' 'y' 'z'
<xarray.DataArray (mjd: 2893, pixel: 128)>
array([[ 1.14418815e+05, 1.13528091e+05, 1.12636588e+05, ...,
-2.91651930e+03, -3.90364143e+03, -4.89152762e+03],
[ 1.15484296e+05, 1.14594514e+05, 1.13703954e+05, ...,
-1.73428720e+03, -2.72048438e+03, -3.70744577e+03],
[ 1.16914400e+05, 1.16025886e+05, 1.15136593e+05, ...,
-1.47122374e+02, -1.13207528e+03, -2.11779258e+03],
...,
[ 1.47220320e+05, 1.46358637e+05, 1.45496171e+05, ...,
3.34841377e+04, 3.25255480e+04, 3.15661899e+04],
[ 1.48523291e+05, 1.47662813e+05, 1.46801553e+05, ...,
3.49364588e+04, 3.39790526e+04, 3.30208779e+04],
[ nan, nan, nan, ...,
nan, nan, nan]])
Coordinates:
* mjd (mjd) float64 5.239e+04 5.239e+04 5.239e+04 ... 5.239e+04 5.239e+04
* pixel (pixel) int64 0 1 2 3 4 5 6 7 8 ... 120 121 122 123 124 125 126 127
Plot the data vs pixel
[5]:
warnings.filterwarnings('ignore')
ir.data.plot(x='mjd', y='pixel', norm=LogNorm(), vmin=1e8, vmax=1e15, figsize=FIG_SIZE)
plt.ylim(127, 20)
[5]:
(127.0, 20.0)
Plot the data vs altitude
[6]:
alts = np.arange(10000., 90000., 250.)
ir_altitude = []
for (data, alt) in zip(ir.data, ir.l1.altitude):
f = interp1d(alt, data, bounds_error=False)
ir_altitude.append(f(alts))
ir_altitude = xr.DataArray(ir_altitude, coords=[ir.mjd, alts], dims=['mjd', 'altitude'])
ir_altitude.plot(x='mjd', y='altitude', norm=LogNorm(), vmin=1e10, vmax=1e12, figsize=FIG_SIZE)
[6]:
<matplotlib.collections.QuadMesh at 0x7faec6da2198>
Plot just night-time data vs altitude (be patient, calculating the sza’s takes a minute)
[7]:
ir_night = ir.where(ir.l1.sza > 90.)
ir_night_altitudes = ir.l1.altitude.where(ir.l1.sza > 90.)
alts = np.arange(50000., 90000., 250.)
ir_altitude = []
for (data, alt) in zip(ir_night.data, ir_night_altitudes):
f = interp1d(alt, data, bounds_error=False)
ir_altitude.append(f(alts))
ir_altitude = xr.DataArray(ir_altitude, coords=[ir.mjd, alts], dims=['mjd', 'altitude'])
ir_altitude.plot(x='mjd', y='altitude', norm=LogNorm(), vmin=1e10, vmax=1e12, figsize=FIG_SIZE)
[7]:
<matplotlib.collections.QuadMesh at 0x7faec226a710>
