Skip to content
Snippets Groups Projects
Commit a449183a authored by jlaura's avatar jlaura Committed by GitHub
Browse files

Merge pull request #8 from Kelvinrr/master 

Adding TES support
parents 2ae8fd50 c32f75af
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
.travis.yml
+ 1
− 0
View file @ a449183a
...@@ -43,6 +43,7 @@ install: ...@@ -43,6 +43,7 @@ install:
# Install not using env because build needs to be in root env # Install not using env because build needs to be in root env
- conda config --add channels conda-forge - conda config --add channels conda-forge
- conda config --add channels jlaura - conda config --add channels jlaura
- conda install python=$PYTHON_VERSION
- conda install -c conda-forge gdal h5py - conda install -c conda-forge gdal h5py
- conda install pandas sqlalchemy pyyaml networkx - conda install pandas sqlalchemy pyyaml networkx
- conda install -c jlaura pvl protobuf - conda install -c jlaura pvl protobuf
......
README.rst
+ 7
− 7
View file @ a449183a
...@@ -28,12 +28,12 @@ Installation is unfortunately complicated due to the difficulty in installing GD ...@@ -28,12 +28,12 @@ Installation is unfortunately complicated due to the difficulty in installing GD
1. Install Anaconda or Miniconda with Python 3.5 1. Install Anaconda or Miniconda with Python 3.5
2. In your ~/.condarc file add the following two lines in the channels section. 2. In your ~/.condarc file add the following two lines in the channels section.
`- conda-forge` `- conda-forge`
`- jlaura` `- jlaura`
3. Create a new environment to ensure that the installed packge is not going to collide with existing packages 3. Create a new environment to ensure that the installed package is not going to collide with existing packages
` conda create --name <somename> python=3` ``conda create --name <somename> python=3``
3. Install `plio` with `conda install plio`. 3. Install ``plio`` with ``conda install plio``.
appveyor.yml
+ 1
− 0
View file @ a449183a
...@@ -40,6 +40,7 @@ install: ...@@ -40,6 +40,7 @@ install:
- cmd: set PYTHONUNBUFFERED=1 - cmd: set PYTHONUNBUFFERED=1
- cmd: conda config --set show_channel_urls true - cmd: conda config --set show_channel_urls true
- cmd: conda install --yes python=3.5
- cmd: conda install -c pelson/channel/development --yes --quiet obvious-ci - cmd: conda install -c pelson/channel/development --yes --quiet obvious-ci
- cmd: conda config --add channels conda-forge - cmd: conda config --add channels conda-forge
- cmd: conda info - cmd: conda info
......
plio/examples/Tes/pos.fmt 0 → 100755
+ 87
− 0
View file @ a449183a
NAME = POS
COLUMNS = 6
ROW_BYTES = 54
INTERCHANGE_FORMAT = BINARY
PRIMARY_KEY = ( "SPACECRAFT_CLOCK_START_COUNT" )
DESCRIPTION = "
The POS table stores the positions of the spacecraft and sun
relative to the planet, the spacecraft's orientation quaternion,
and the Mars body quaternion, all relative to the J2000 system.
These data are initially derived from the project's SPICE kernels,
but may be corrected from various other sources. This table
may also include interpolated values where SPICE data were unavailable."
OBJECT = COLUMN
NAME = SPACECRAFT_CLOCK_START_COUNT
DATA_TYPE = MSB_UNSIGNED_INTEGER
START_BYTE = 1
BYTES = 4
ALIAS_NAME = sclk_time
DESCRIPTION = "The value of the spacecraft clock at the
beginning of the observation"
END_OBJECT = COLUMN
OBJECT = COLUMN
NAME = EPHEMERIS_TIME
DATA_TYPE = IEEE_REAL
START_BYTE = 5
BYTES = 8
ALIAS_NAME = et
DESCRIPTION = "Ephemeris time, seconds since 1/1/2000"
UNIT = "Seconds"
END_OBJECT = COLUMN
OBJECT = COLUMN
NAME = SPACECRAFT_POSITION
DATA_TYPE = IEEE_REAL
START_BYTE = 13
BYTES = 12
ITEMS = 3
ITEM_BYTES = 4
ALIAS_NAME = pos
DESCRIPTION = "Spacecraft position vector relative to Mars
in the J2000 reference frame"
UNIT = "KM"
END_OBJECT = COLUMN
OBJECT = COLUMN
NAME = SUN_POSITION
DATA_TYPE = IEEE_REAL
START_BYTE = 25
BYTES = 12
ITEMS = 3
ITEM_BYTES = 4
ALIAS_NAME = sun
DESCRIPTION = "Sun position vector relative to Mars in the J2000
reference frame"
UNIT = "KM"
END_OBJECT = COLUMN
OBJECT = COLUMN
NAME = SPACECRAFT_QUATERNION
DATA_TYPE = IEEE_REAL
START_BYTE = 37
BYTES = 16
ITEMS = 4
ITEM_BYTES = 4
ALIAS_NAME = quat
DESCRIPTION = "Spacecraft pointing quaternion in the J2000
reference frame"
END_OBJECT = COLUMN
OBJECT = COLUMN
NAME = POSITION_SOURCE_ID
DATA_TYPE = CHARACTER
START_BYTE = 53
BYTES = 2
ITEMS = 2
ITEM_BYTES = 1
ALIAS_NAME = id
DESCRIPTION = "2-character source ID.
First character is source of positions.
Second character is source of pointing.
See ancillary table for details."
END_OBJECT = COLUMN
plio/examples/Tes/pos10001.tab 0 → 100755
+ 0
− 0
View file @ a449183a
File added
plio/io/io_tes.py 0 → 100644
+ 246
− 0
View file @ a449183a
import numpy as np
import pandas as pd
import pvl
import sys
import functools
import json
from plio.io.io_json import read_json
from plio.utils._tes2numpy import tes_dtype_map
from plio.utils._tes2numpy import tes_columns
from plio.utils._tes2numpy import tes_scaling_factors
class Tes(object):
"""
Attributes
----------
spectra : panel
A pandas panel containing n individual spectra.
ancillary_data : dataframe
A pandas DataFrame of the parsed ancillary data (PVL label)
label : object
The raw PVL label object
"""
def __init__(self, input_data, var_file = None):
"""
Read the .spc file, parse the label, and extract the spectra
Parameters
----------
input_data : string
The PATH to the input .tab file
"""
def expand_column(df, expand_column, columns): # pragma: no cover
array = np.asarray([np.asarray(list(tup[0])) for tup in df[expand_column].as_matrix()], dtype=np.uint8)
new_df = pd.concat([df, pd.DataFrame(array, columns=columns)], axis=1)
del new_df[expand_column]
return new_df
def bolquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:3]), bit2bool(bitarr[3:4]))]
types = [('BOLOMETRIC_INERTIA_RATING', '>u1'), ('BOLOMETER_LAMP_ANOMALY', 'bool_')]
arr = np.array(lis, dtype=types)
return arr
def obsquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:2]), bitarr2int(bitarr[2:5]),
bitarr2int(bitarr[5:6]), bitarr2int(bitarr[6:7]),
bitarr2int(bitarr[7:8]), bitarr2int(bitarr[8:9]))]
types = [('HGA_MOTION', '>u1'), ('SOLAR_PANEL_MOTION', '>u1'), ('ALGOR_PATCH', '>u1'),
('IMC_PATCH', '>u1'), ('MOMENTUM_DESATURATION', '>u1'), ('EQUALIZATION_TABLE', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def obsclass2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:3]), bitarr2int(bitarr[3:7]),
bitarr2int(bitarr[7:11]), bitarr2int(bitarr[11:13]),
bitarr2int(bitarr[13:14]), bitarr2int(bitarr[14:16]),
bitarr2int(bitarr[16:]))]
types = [('MISSION_PHASE', '>u1'), ('INTENDED_TARGET', '>u1'), ('TES_SEQUENCE', '>u1'),
('NEON_LAMP_STATUS', '>u1'), ('TIMING_ACCURACY', '>u1'), ('SPARE', '>u1'), ('CLASSIFICATION_VALUE', '>u2')]
arr = np.array(lis, dtype=types)
return arr
def radquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:1]), bitarr2int(bitarr[1:2]),
bitarr2int(bitarr[2:3]), bitarr2int(bitarr[3:5]),
bitarr2int(bitarr[5:7]), bitarr2int(bitarr[5:8]),
bitarr2int(bitarr[8:9]))]
types = [('MAJOR_PHASE_INVERSION', '>u1'), ('ALGOR_RISK', '>u1'), ('CALIBRATION_FAILURE', '>u1'),
('CALIBRATION_QUALITY', '>u1'), ('SPECTROMETER_NOISE', '>u1'), ('SPECTRAL_INERTIA_RATING', '>u1'),
('DETECTOR_MASK_PROBLEM', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def atmquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:2]), bitarr2int(bitarr[2:4]))]
types = [('TEMPERATURE_PROFILE_RATING', '>u1'), ('ATMOSPHERIC_OPACITY_RATING', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def expand_column(df, expand_column, columns): # pragma: no cover
array = np.asarray([np.asarray(list(tup[0])) for tup in df[expand_column].as_matrix()], dtype=np.uint8)
new_df = pd.concat([df, pd.DataFrame(array, columns=columns)], axis=1)
del new_df[expand_column]
return new_df
def bolquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:3]), bit2bool(bitarr[3:4]))]
types = [('BOLOMETRIC_INERTIA_RATING', '>u1'), ('BOLOMETER_LAMP_ANOMALY', 'bool_')]
arr = np.array(lis, dtype=types)
return arr
def obsquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:2]), bitarr2int(bitarr[2:5]),
bitarr2int(bitarr[5:6]), bitarr2int(bitarr[6:7]),
bitarr2int(bitarr[7:8]), bitarr2int(bitarr[8:9]))]
types = [('HGA_MOTION', '>u1'), ('SOLAR_PANEL_MOTION', '>u1'), ('ALGOR_PATCH', '>u1'),
('IMC_PATCH', '>u1'), ('MOMENTUM_DESATURATION', '>u1'), ('EQUALIZATION_TABLE', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def obsclass2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:3]), bitarr2int(bitarr[3:7]),
bitarr2int(bitarr[7:11]), bitarr2int(bitarr[11:13]),
bitarr2int(bitarr[13:14]), bitarr2int(bitarr[14:16]),
bitarr2int(bitarr[16:]))]
types = [('MISSION_PHASE', '>u1'), ('INTENDED_TARGET', '>u1'), ('TES_SEQUENCE', '>u1'),
('NEON_LAMP_STATUS', '>u1'), ('TIMING_ACCURACY', '>u1'), ('SPARE', '>u1'), ('CLASSIFICATION_VALUE', '>u2')]
arr = np.array(lis, dtype=types)
return arr
def radquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:1]), bitarr2int(bitarr[1:2]),
bitarr2int(bitarr[2:3]), bitarr2int(bitarr[3:5]),
bitarr2int(bitarr[5:7]), bitarr2int(bitarr[5:8]),
bitarr2int(bitarr[8:9]))]
types = [('MAJOR_PHASE_INVERSION', '>u1'), ('ALGOR_RISK', '>u1'), ('CALIBRATION_FAILURE', '>u1'),
('CALIBRATION_QUALITY', '>u1'), ('SPECTROMETER_NOISE', '>u1'), ('SPECTRAL_INERTIA_RATING', '>u1'),
('DETECTOR_MASK_PROBLEM', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def atmquality2arr(arr): # pragma: no cover
bitarr = np.unpackbits(np.asarray(arr, dtype=np.uint8))
lis = [(bitarr2int(bitarr[0:2]), bitarr2int(bitarr[2:4]))]
types = [('TEMPERATURE_PROFILE_RATING', '>u1'), ('ATMOSPHERIC_OPACITY_RATING', '>u1')]
arr = np.array(lis, dtype=types)
return arr
def bitarr2int(arr): # pragma: no cover
arr = "".join(str(i) for i in arr)
return np.uint8(int(arr,2))
def bit2bool(bit): # pragma: no cover
return np.bool_(bit)
def expand_bitstrings(df, dataset): # pragma: no cover
if dataset == 'BOL':
quality_columns = ['ti_bol_rating', 'bol_ref_lamp']
df['quality'] = df['quality'].apply(bolquality2arr)
return expand_column(df, 'quality', quality_columns)
elif dataset == 'OBS':
quality_columns = ['hga_motion', 'pnl_motion', 'algor_patch', 'imc_patch',
'momentum', 'equal_tab']
class_columns = ['phase', 'type', 'sequence',
'lamp_status', 'timing', 'spare', 'class_value']
df['quality'] = df['quality'].apply(obsquality2arr)
df['class'] = df['class'].apply(obsclass2arr)
new_df = expand_column(df, 'quality', quality_columns)
new_df = expand_column(new_df, 'class', class_columns)
return new_df
elif dataset == 'RAD':
quality_columns = ['phase_inversion', 'algor_risk', 'calib_fail', 'calib_quality',
'spect_noise', 'ti_spc_rating', 'det_mask_problem']
df['quality'] = df['quality'].apply(radquality2arr)
return expand_column(df, 'quality', quality_columns)
elif dataset == 'ATM':
quality_columns = ['atm_pt_rating', 'atm_opacity_rating']
df['quality'] = df['quality'].apply(atmquality2arr)
return expand_column(df, 'quality', quality_columns)
else:
return df
self.label = pvl.load(input_data)
nrecords = self.label['TABLE']['ROWS']
nbytes_per_rec = self.label['RECORD_BYTES']
data_start = self.label['LABEL_RECORDS'] * self.label['RECORD_BYTES']
dataset = self.label['TABLE']['^STRUCTURE'].split('.')[0]
numpy_dtypes = tes_dtype_map
columns = tes_columns
scaling_factors = tes_scaling_factors
with open(input_data, 'rb') as file:
file.seek(data_start)
buffer = file.read(nrecords*nbytes_per_rec)
array = np.frombuffer(buffer, dtype=numpy_dtypes[dataset.upper()]).byteswap().newbyteorder()
df = pd.DataFrame(data=array, columns=columns[dataset.upper()])
# Read Radiance array if applicable
if dataset.upper() == 'RAD': # pragma: no cover
with open('{}.var'.format(path.splitext(f)[0]) , 'rb') as file:
buffer = file.read()
def process_rad(index):
if index is -1:
return None
length = np.frombuffer(buffer[index:index+2], dtype='>u2')[0]
exp = np.frombuffer(buffer[index+2:index+4], dtype='>i2')[0]
radarr = np.frombuffer(buffer[index+4:index+4+length-2], dtype='>i2') * (2**(exp-15))
if np.frombuffer(buffer[index+4+length-2:index+4+length], dtype='>u2')[0] != length:
warnings.warn("Last element did not match the length for file index {} in file {}".format(index, f))
return radarr
df["raw_rad"] = df["raw_rad"].apply(process_rad)
df["cal_rad"] = df["cal_rad"].apply(process_rad)
# Apply scaling factors
for column in scaling_factors[dataset]: # pragma: no cover
def scale(x):
return np.multiply(x, scaling_factors[dataset][column])
df[column] = df[column].apply(scale)
df = expand_bitstrings(df, dataset.upper())
self.data = df
plio/io/tests/test_io_spectral_profiler.py
+ 2
− 2
View file @ a449183a
...@@ -11,10 +11,10 @@ from plio.io import io_spectral_profiler ...@@ -11,10 +11,10 @@ from plio.io import io_spectral_profiler
from plio.io.io_gdal import GeoDataset from plio.io.io_gdal import GeoDataset
class Test_Spectral_Profiler_IO(unittest.TestCase): class Test_Spectral_Profiler_IO(unittest.TestCase):
def setUp(self): def setUp(self):
self.examplefile = get_path('SP_2C_02_02358_S138_E3586.spc') self.examplefile = get_path('SP_2C_02_02358_S138_E3586.spc')
def test_openspc(self): def test_openspc(self):
ds = io_spectral_profiler.Spectral_Profiler(self.examplefile) ds = io_spectral_profiler.Spectral_Profiler(self.examplefile)
self.assertEqual(ds.nspectra, 38) self.assertEqual(ds.nspectra, 38)
......
plio/io/tests/test_io_tes.py 0 → 100644
+ 34
− 0
View file @ a449183a
import os
import sys
import unittest
import pandas as pd
sys.path.insert(0, os.path.abspath('..'))
from plio.examples import get_path
from plio.io import io_tes
from plio.io.io_gdal import GeoDataset
from plio.utils._tes2numpy import tes2numpy
class Test_Tes_IO(unittest.TestCase):
def setUp(self):
self.examplefile = get_path('pos10001.tab')
def test_opentab(self):
ds = io_tes.Tes(self.examplefile)
self.assertEqual(ds.data.size, 119106)
self.assertIsInstance(ds.data, pd.DataFrame)
self.assertEqual(ds.data.columns.tolist(), ['sclk_time', 'et', 'pos', 'sun', 'quat', 'id'])
def test_tes2numpy(self):
self.assertEqual(tes2numpy('MSB_UNSIGNED_INTEGER', 4), '>u4')
self.assertEqual(tes2numpy('MSB_UNSIGNED_INTEGER', 4, 2), [('elem0', '>u4'), ('elem1', '>u4')])
with self.assertRaises(Exception):
tes2numpy('IEEE_REAL', 5)
if __name__ == '__main__':
unittest.main()
plio/utils/_tes2numpy.py 0 → 100644
+ 405
− 0
View file @ a449183a
def tes2numpy(msb_type, num_bytes, nelems=1):
"""
Converts a MSB data type to a numpy datatype
"""
valid_bytes = {
'MSB_UNSIGNED_INTEGER': [1,2,4,8,16,32,64],
'MSB_INTEGER': [1,2,4,8,16,32,64],
'IEEE_REAL': [1,2,4,8,16,32,64],
'CHARACTER': range(1,128),
'MSB_BIT_STRING': range(1,128)
}
msb_bit_string_type = [('byte{}'.format(i), '>u1') for i in range(num_bytes)]
dtype_map = {
'MSB_UNSIGNED_INTEGER': '>u{}'.format(num_bytes),
'MSB_INTEGER': '>i{}'.format(num_bytes),
'IEEE_REAL': '>f{}'.format(num_bytes),
'CHARACTER': 'a{}'.format(num_bytes),
'MSB_BIT_STRING': msb_bit_string_type
}
if num_bytes not in valid_bytes[msb_type] and nelems == 1:
raise Exception('invalid byte ({}) count for type ({})'.format(num_bytes, msb_type))
if nelems > 1:
# Must be an array
return [('elem{}'.format(i), dtype_map[msb_type]) for i in range(nelems)]
return dtype_map[msb_type]
tes_dtype_map = {'ATM': [('sclk_time', '>u4'),
('srf_pressure', '>u2'),
('nadir_pt',
[('elem0', '>u2'),
('elem1', '>u2'),
('elem2', '>u2'),
('elem3', '>u2'),
('elem4', '>u2'),
('elem5', '>u2'),
('elem6', '>u2'),
('elem7', '>u2'),
('elem8', '>u2'),
('elem9', '>u2'),
('elem10', '>u2'),
('elem11', '>u2'),
('elem12', '>u2'),
('elem13', '>u2'),
('elem14', '>u2'),
('elem15', '>u2'),
('elem16', '>u2'),
('elem17', '>u2'),
('elem18', '>u2'),
('elem19', '>u2'),
('elem20', '>u2'),
('elem21', '>u2'),
('elem22', '>u2'),
('elem23', '>u2'),
('elem24', '>u2'),
('elem25', '>u2'),
('elem26', '>u2'),
('elem27', '>u2'),
('elem28', '>u2'),
('elem29', '>u2'),
('elem30', '>u2'),
('elem31', '>u2'),
('elem32', '>u2'),
('elem33', '>u2'),
('elem34', '>u2'),
('elem35', '>u2'),
('elem36', '>u2'),
('elem37', '>u2')]),
('co2_cont_temp', '>u2'),
('srf_temp_est', '>u2'),
('rms_pt', '>f4'),
('best_fit_opacities',
[('elem0', '>i2'),
('elem1', '>i2'),
('elem2', '>i2'),
('elem3', '>i2'),
('elem4', '>i2'),
('elem5', '>i2'),
('elem6', '>i2'),
('elem7', '>i2'),
('elem8', '>i2')]),
('rms_opacities', '>f4'),
('co2_dw_flux', '>f4'),
('total_dw_flux', '>f4'),
('quality', [('byte0', '>u1'), ('byte1', '>u1')]),
('srf_radiance', '>i4'),
('version_id', 'a4')],
'BOL': [('sclk_time', '>u4'),
('detector', '>u1'),
('tic_count', '>u1'),
('vbol', '>i2'),
('tbol', '>i2'),
('cal_vbol', '>f4'),
('lambert_alb', '>f4'),
('ti_bol', '>f4'),
('brightness_temp_bol', '>u2'),
('vbol_version_id', 'a2'),
('tbol_version_id', 'a2'),
('quality', [('byte0', '>u1'), ('byte1', '>u1')])],
'GEO': [('sclk_time', '>u4'),
('detector', '>u1'),
('longitude', '>u2'),
('latitude', '>i2'),
('phase', '>u2'),
('emission', '>u2'),
('incidence', '>u2'),
('planetary_phase', '>u2'),
('heliocentric_lon', '>u2'),
('sub_sc_lon', '>u2'),
('sub_sc_lat', '>i2'),
('sub_solar_lon', '>u2'),
('sub_solar_lat', '>i2'),
('target_distance', '>u2'),
('height', '>u2'),
('altitude', '>u2'),
('local_time', '>u2'),
('solar_distance', '>u2'),
('angular_semidiameter', '>u2'),
('version_id', 'a4')],
'IFG': [('sclk_time', '>u4'), ('detector', '>u1'), ('ifgm', '>i4')],
'OBS': [('sclk_time', '>u4'),
('orbit', '>u2'),
('ock', '>u2'),
('ick', '>u4'),
('tic', '>u1'),
('pnt_angle', '>i2'),
('pnt_imc', '>u1'),
('pnt_view', 'a1'),
('scan_len', 'a1'),
('pckt_type', 'a1'),
('schedule_type', 'a1'),
('spc_gain', 'a1'),
('vbol_gain', 'a1'),
('tbol_gain', 'a1'),
('comp_pp', '>u1'),
('det_mask', '>u1'),
('class',
[('byte0', '>u1'), ('byte1', '>u1'), ('byte2', '>u1'), ('byte3', '>u1')]),
('quality',
[('byte0', '>u1'), ('byte1', '>u1'), ('byte2', '>u1'), ('byte3', '>u1')]),
('temps',
[('elem0', '>u2'), ('elem1', '>u2'), ('elem2', '>u2'), ('elem3', '>u2')]),
('ffti', '>u1')],
'POS': [('sclk_time', '>u4'),
('et', '>f8'),
('pos', [('elem0', '>f4'), ('elem1', '>f4'), ('elem2', '>f4')]),
('sun', [('elem0', '>f4'), ('elem1', '>f4'), ('elem2', '>f4')]),
('quat',
[('elem0', '>f4'), ('elem1', '>f4'), ('elem2', '>f4'), ('elem3', '>f4')]),
('id', [('elem0', 'a1'), ('elem1', 'a1')])],
'RAD': [('sclk_time', '>u4'),
('detector', '>u1'),
('spectral_mask', '>u1'),
('cmode', '>u2'),
('raw_rad', '>i4'),
('cal_rad', '>i4'),
('tdet', '>u2'),
('target_temp', '>u2'),
('ti_spc', '>f4'),
('version_id', 'a4'),
('quality',
[('byte0', '>u1'), ('byte1', '>u1'), ('byte2', '>u1'), ('byte3', '>u1')])],
'TLM': [('sclk_time', '>u4'),
('aux_temps',
[('elem0', '>u2'),
('elem1', '>u2'),
('elem2', '>u2'),
('elem3', '>u2'),
('elem4', '>u2'),
('elem5', '>u2'),
('elem6', '>u2'),
('elem7', '>u2'),
('elem8', '>u2'),
('elem9', '>u2'),
('elem10', '>u2'),
('elem11', '>u2')]),
('ifgm_max',
[('elem0', '>i2'),
('elem1', '>i2'),
('elem2', '>i2'),
('elem3', '>i2'),
('elem4', '>i2'),
('elem5', '>i2')]),
('ifgm_min',
[('elem0', '>i2'),
('elem1', '>i2'),
('elem2', '>i2'),
('elem3', '>i2'),
('elem4', '>i2'),
('elem5', '>i2')]),
('dsp_log',
[('elem0', '>u2'),
('elem1', '>u2'),
('elem2', '>u2'),
('elem3', '>u2'),
('elem4', '>u2'),
('elem5', '>u2')]),
('V1', '>i1'),
('V2', '>i1'),
('V3', '>i1'),
('V4', '>i1'),
('V5', '>i1'),
('V6', '>i1'),
('V7', '>i1'),
('V8', '>i1'),
('V9', '>i1'),
('V10', '>i1'),
('V11', '>i1'),
('V12', '>i1'),
('V13', '>i1'),
('V14', '>i1'),
('V15', '>i1'),
('V16', '>i1'),
('V17', '>i1'),
('V18', '>i1'),
('V19', '>i1'),
('V20', '>i1'),
('neon_lamp', '>u1'),
('neon_gain', 'a1'),
('neon_amp', '>i1'),
('neon_zpd', '>u2'),
('ifgm_zpd',
[('elem0', '>u2'),
('elem1', '>u2'),
('elem2', '>u2'),
('elem3', '>u2'),
('elem4', '>u2'),
('elem5', '>u2')]),
('ifgm_end',
[('elem0', '>u2'),
('elem1', '>u2'),
('elem2', '>u2'),
('elem3', '>u2'),
('elem4', '>u2'),
('elem5', '>u2')])]}
tes_columns = {'ATM': ['sclk_time',
'srf_pressure',
'nadir_pt',
'co2_cont_temp',
'srf_temp_est',
'rms_pt',
'best_fit_opacities',
'rms_opacities',
'co2_dw_flux',
'total_dw_flux',
'quality',
'srf_radiance',
'version_id'],
'BOL': ['sclk_time',
'detector',
'tic_count',
'vbol',
'tbol',
'cal_vbol',
'lambert_alb',
'ti_bol',
'brightness_temp_bol',
'vbol_version_id',
'tbol_version_id',
'quality'],
'GEO': ['sclk_time',
'detector',
'longitude',
'latitude',
'phase',
'emission',
'incidence',
'planetary_phase',
'heliocentric_lon',
'sub_sc_lon',
'sub_sc_lat',
'sub_solar_lon',
'sub_solar_lat',
'target_distance',
'height',
'altitude',
'local_time',
'solar_distance',
'angular_semidiameter',
'version_id'],
'IFG': ['sclk_time', 'detector', 'ifgm'],
'OBS': ['sclk_time',
'orbit',
'ock',
'ick',
'tic',
'pnt_angle',
'pnt_imc',
'pnt_view',
'scan_len',
'pckt_type',
'schedule_type',
'spc_gain',
'vbol_gain',
'tbol_gain',
'comp_pp',
'det_mask',
'class',
'quality',
'temps',
'ffti'],
'POS': ['sclk_time', 'et', 'pos', 'sun', 'quat', 'id'],
'RAD': ['sclk_time',
'detector',
'spectral_mask',
'cmode',
'raw_rad',
'cal_rad',
'tdet',
'target_temp',
'ti_spc',
'version_id',
'quality'],
'TLM': ['sclk_time',
'aux_temps',
'ifgm_max',
'ifgm_min',
'dsp_log',
'V1',
'V2',
'V3',
'V4',
'V5',
'V6',
'V7',
'V8',
'V9',
'V10',
'V11',
'V12',
'V13',
'V14',
'V15',
'V16',
'V17',
'V18',
'V19',
'V20',
'neon_lamp',
'neon_gain',
'neon_amp',
'neon_zpd',
'ifgm_zpd',
'ifgm_end']}
tes_scaling_factors = {'ATM': {'best_fit_opacities': 0.001,
'co2_cont_temp': 0.01,
'nadir_pt': 0.01,
'srf_pressure': 0.001,
'srf_temp_est': 0.01},
'BOL': {'brightness_temp_bol': 0.01,
'tbol': 0.000152587890625,
'vbol': 0.000152587890625},
'CMP': {},
'GEO': {'angular_semidiameter': 0.01,
'emission': 0.01,
'height': 0.01,
'heliocentric_lon': 0.01,
'incidence': 0.01,
'latitude': 0.01,
'local_time': 0.001,
'longitude': 0.01,
'phase': 0.01,
'planetary_phase': 0.01,
'solar_distance': 10000,
'sub_sc_lat': 0.01,
'sub_sc_lon': 0.01,
'sub_solar_lat': 0.01,
'sub_solar_lon': 0.01},
'IFG': {},
'OBS': {'pnt_angle': 0.046875, 'temps': 0.01},
'PCT': {},
'POS': {},
'RAD': {'target_temp': 0.01, 'tdet': 0.01},
'TLM': {'V1': 3.90625,
'V10': -0.15625,
'V11': 0.0976055,
'V12': -0.0985813,
'V13': 0.976562,
'V14': 0.0648437,
'V15': 0.045727,
'V16': 0.0480992,
'V17': 0.0478277,
'V18': 0.0488039,
'V19': 0.141966,
'V2': 1.95312,
'V20': -0.149688,
'V3': 0.278906,
'V4': 0.278906,
'V5': 4.45312,
'V6': 0.652344,
'V7': 0.119457,
'V8': -0.103067,
'V9': 0.15576,
'aux_temps': 0.01,
'ifgm_max': 0.000152587890625,
'ifgm_min': 0.000152587890625}}
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment