Description:
planetarypy 0.32.0
PlanetaryPy
PlanetaryPy
Beta release: This will become (part of) the core package of the
PlanetaryPy organisation.
Potential logo:
Install
pip install planetarypy
This will pull in these other dependencies and their dependencies:
tomlkit pandas pvl numpy python-dateutil tqdm lxml yarl hirise-tools kalasiris
Suggested standard abbreviations:
Inside these docs the package will be called PLPY for brevity.
A standard Python import could be: plp or plpy
because the last p in plp can be pronounced out, we consider
these equivalent for human conversation and pronounce these
“plippy”.
General scope
First and foremost this package should provide support in working with
planetary science data.
With working we mean:
locating
retrieving
reading
further processing
of data.
Locating
This library manages, via its PDS tools, multiple PDS3 index files per
instrument that can be used for identifying data of interest. These
index files are automatically downloaded and converted to the very
performant (and cloud-ready)
parquet
file format. > Parquet is able to store advanced datatypes like
nan-capable integer and full datetime objects, as opposed to HDF5.
Retrieving
The interface to getting data is via a path-retrieving function based on
a PDS product-id. If that product-id is available locally, the path will
be returned. If it is not, it will previously be downloaded, stored in a
systematic fashion organized by mission and instrument, and then the
local path will be returned.
Reading
For now, the library only returns the path to the object and the user
needs to sort out the reading process. A recently funded NASA project
Planetary Data Reader will be integrated here, so that basic reading
into memory can be provided.
As such, we anticipate two classes of reading support: 1. basic reading
into numpy and/or xarray 1. added reader functionality like basic plots
and basic geospatial processing, as supported by interested parties
There will exist larger other packages that focus on working with a
given instrument’s data, in which case that package could become an
affiliated package with the planetarypy GitHub organization, if so
desired.
Further processing
In the future, additional frequently used procedures will be added to
this library, e.g. * frequently used GDAL/rasterio procedures *
frequently used SPICE operations * like surface illumination on a given
body
PDS tools
Look at the Apps docs to see what pds.apps exist for easily getting
PDS indexes. The find_index app is specifically useful when you don’t
know what index files exist.
So far, the following indexes are supported (but not necessarily all
tested within PLPY):
Cassini
ISS (all)
UVIS (all)
MRO
CTX EDR
HiRISE
EDR, RDR, DTM
EDR index has a bug (as delivered by the team, reported), where
I need to activate an existing fix for it.
LRO
Diviner (DLRE)
EDR, RDR
LOLA
EDR, RDR
More indexes
More indexes of other instruments can be easily added by following the
existing structure of what has been copied into your config at
~/.planetarypy_config.toml.
Please consider submitting a pull request for adding further PDS index
files into the config file at its source:
https://github.com/michaelaye/nbplanetary/blob/master/planetarypy/data/planetarypy_config.toml
Utils
Find something in Utils for working with NASA timestamps and a well
working URL download function
url_retrieve,
among other stuff.
Experiment/Instrument Specific
So far, planetarypy supports CTX EDR and HiRISE RGB.NOMAP data. Look
at the
CTX and
HiRISE pages for descriptions of classes for working with these data.
Bug reports
Please submit bug reports at
https://github.com/michaelaye/nbplanetary/issues
How to use
Indexes
See PDS apps for more details.
from planetarypy.pds.apps import get_index
ctrindex = get_index("mro.ctx", "edr", refresh=False)
ctrindex.sample(5, random_state=42) # setting random_state to always get same files for docs
<style scoped>
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vertical-align: middle;
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</style>
VOLUME_ID
FILE_SPECIFICATION_NAME
ORIGINAL_PRODUCT_ID
PRODUCT_ID
IMAGE_TIME
INSTRUMENT_ID
INSTRUMENT_MODE_ID
LINE_SAMPLES
LINES
SPATIAL_SUMMING
...
SUB_SOLAR_LATITUDE
SUB_SPACECRAFT_LONGITUDE
SUB_SPACECRAFT_LATITUDE
SOLAR_DISTANCE
SOLAR_LONGITUDE
LOCAL_TIME
IMAGE_SKEW_ANGLE
RATIONALE_DESC
DATA_QUALITY_DESC
ORBIT_NUMBER
86101
MROX_2757
DATA/J07_047671_1256_XN_54S338W.IMG
4A_04_10F0036A00
J07_047671_1256_XN_54S338W
2016-09-27 00:24:54.682
CTX
NIFL
2528
8192
1
...
-19.41
338.09
-54.43
207762612.4
230.58
15.48
90.1
Crater in Noachis Terra
OK
47671
54420
MROX_1819
DATA/D10_031170_1808_XN_00N221W.IMG
4A_04_109C017D00
D10_031170_1808_XN_00N221W
2013-03-21 06:58:09.892
CTX
NIFL
5056
7168
1
...
-24.39
221.52
0.88
209925149.5
286.15
14.49
90.1
Ride-along with HiRISE
OK
31170
71053
MROX_2331
DATA/F10_039530_1470_XI_33S203W.IMG
4A_04_10C9000F00
F10_039530_1470_XI_33S203W
2015-01-01 16:20:01.840
CTX
ITL
5056
52224
1
...
-25.3
203.96
-33.12
207070298.2
263.93
15.2
90.4
Terrain in Terra Cimmeria
OK
39530
51385
MROX_1742
DATA/D04_028919_2026_XN_22N327W.IMG
4A_04_109002A800
D04_028919_2026_XN_22N327W
2012-09-26 21:17:00.483
CTX
NIFL
5056
6144
1
...
0.67
327.06
22.78
219814412.3
178.44
15.53
90.1
Ride-along with HiRISE
OK
28919
51923
MROX_1757
DATA/D05_029283_1524_XN_27S180W.IMG
4A_04_1092020B00
D05_029283_1524_XN_27S180W
2012-10-25 05:44:21.786
CTX
NIFL
5056
9216
1
...
-6.28
178.36
-27.56
214830233.7
194.72
15.57
90.1
Ride-along with HiRISE
OK
29283
5 rows × 51 columns
hirise_rdr = get_index("mro.hirise", "rdr")
hirise_rdr.sample(5, random_state=42)
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
VOLUME_ID
FILE_NAME_SPECIFICATION
INSTRUMENT_HOST_ID
INSTRUMENT_ID
OBSERVATION_ID
PRODUCT_ID
PRODUCT_VERSION_ID
TARGET_NAME
ORBIT_NUMBER
MISSION_PHASE_NAME
...
LINE_PROJECTION_OFFSET
SAMPLE_PROJECTION_OFFSET
CORNER1_LATITUDE
CORNER1_LONGITUDE
CORNER2_LATITUDE
CORNER2_LONGITUDE
CORNER3_LATITUDE
CORNER3_LONGITUDE
CORNER4_LATITUDE
CORNER4_LONGITUDE
144822
MROHR_0001
RDR/ESP/ORB_074100_074199/ESP_074107_1410/ESP_...
MRO
HIRISE
ESP_074107_1410
ESP_074107_1410_COLOR
1
MARS
74107
Extended Science Phase
...
-4568562.5
2009279.5
-38.7446
159.311
-38.7465
159.287
-38.614
159.269
-38.6121
159.293
22518
MROHR_0001
RDR/ESP/ORB_014000_014099/ESP_014080_1040/ESP_...
MRO
HIRISE
ESP_014080_1040
ESP_014080_1040_RED
1
MARS
14080
Extended Science Phase
...
-3193960.0
-845180.0
-76.0185
165.215
-76.0315
164.858
-75.6958
164.658
-75.6831
165.005
86634
MROHR_0001
RDR/ESP/ORB_046300_046399/ESP_046395_1730/ESP_...
MRO
HIRISE
ESP_046395_1730
ESP_046395_1730_COLOR
1
MARS
46395
Extended Science Phase
...
-1584330.0
-26573400.0
-6.9957
292.569
-6.9979
292.551
-6.6838
292.51
-6.6815
292.529
138163
MROHR_0001
RDR/ESP/ORB_069100_069199/ESP_069178_2345/ESP_...
MRO
HIRISE
ESP_069178_2345
ESP_069178_2345_RED
1
MARS
69178
Extended Science Phase
...
12762196.0
15708938.0
53.8598
76.7591
53.8474
76.602
54.0013
76.5672
54.0137
76.7249
125490
MROHR_0001
RDR/ESP/ORB_063500_063599/ESP_063504_1995/ESP_...
MRO
HIRISE
ESP_063504_1995
ESP_063504_1995_COLOR
1
MARS
63504
Extended Science Phase
...
4638178.5
12794202.0
19.4523
124.149
19.45
124.128
19.5686
124.113
19.5709
124.133
5 rows × 54 columns
Instrument tools
CTX
from planetarypy.ctx import CTX
pid = ctrindex.sample(random_state=42).squeeze().PRODUCT_ID
pid
'J07_047671_1256_XN_54S338W'
ctx = CTX(pid)
ctx
PRODUCT_ID: J07_047671_1256_XN_54S338W
URL: https://pds-imaging.jpl.nasa.gov/data/mro/mars_reconnaissance_orbiter/ctx/mrox_2757/data/J07_047671_1256_XN_54S338W.IMG
source_path: /remote/trove/geo/planet/Mars/CTX/pds/mrox_2757/J07_047671_1256_XN_54S338W.IMG
Shape: (8192, 2528)
ctx.source_path
Path('/remote/trove/geo/planet/Mars/CTX/pds/mrox_2757/J07_047671_1256_XN_54S338W.IMG')
ctx.download()
File exists. Use `overwrite=True` to download fresh.
Acknowledgements
The creation of this library was in part supported by:
NASA Cassini UVIS mission
NASA SSW grant NNX15AH36G
NASA PDART grant 80NSSC20K0875
NASA SSW grant 80NSSC20K0748
German Space Agency (DLR Bonn), grant 50 OO 2204, on behalf of the
German Federal Ministry for Economic Affairs and Climate Action.
This research has made use of the USGS Integrated Software for Imagers
and Spectrometers (ISIS)(Laura et al. 2022).
Laura, Jason, Alex Acosta, Travis Addair, Lauren Adoram-Kershner, James
Alexander, Oleg Alexandrov, Stacey Alley, et al. 2022. Integrated
Software for Imagers and Spectrometers (version 7.2.0_RC1). Zenodo.
https://doi.org/10.5281/zenodo.7443567.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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