| Name | lasp-curryer JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | LASP SPICE extentions and geospatial data product generation tools. |
| upload_time | 2024-11-14 17:30:27 |
| maintainer | None |
| docs_url | None |
| author | Brandon Stone |
| requires_python | <4,>=3.9 |
| license | MIT |
| keywords |
lasp
sdp
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Curryer
A library for SPICE extensions and geospatial data product generation.
* Github: https://github.com/lasp/curryer
* PyPi: https://pypi.org/project/lasp-curryer/
## Core Features
* Extensions and wrappers for SPICE routines and common data patterns.
* Automation of SPICE kernel creation from JSON definition files and modern data
file formats and third-party data structures.
* Level-1 geospatial data processing routines (e.g., geolocation).
## Install
```shell
pip install lasp-curryer
```
### Data / Binary Files
_NOTE: Data files and precompiled binaries are not currently automated and thus
require manual downloading. This will be addressed in the next major release._
Download from the Curryer repo:
* `data/generic` - Generic spice kernels (e.g., leapsecond kernel)
* Download
* `data/<misssion>` - Mission specific kernels and/or kernel definitions.
* `data/gmted` - Digital Elevation Model (DEMs) with global coverage at
15-arc-second.
* Alternatively, use the script [download_dem.py](bin/download_dem.py) to
download different types and/or resolutions from the USGS.
Define the top-level directory using the environment variable `CURRYER_DATA_DIR`
or pass the path to routines which require data files.
Download Third-party Files:
* SPICE Utilities: https://naif.jpl.nasa.gov/naif/utilities.html
* At minimum: `mkspk`, `msopck`, `brief`, `ckbreif`
* SPICE Generic Kernels (large):
* [de430.bsp](https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de430.bsp),
place in `data/generic`.
* PyProj Data:
* Data directory: `import pyproj; print(pyproj.datadir.get_user_data_dir())`
* [EGM96 TIFF](https://cdn.proj.org/us_nga_egm96_15.tif)
## Examples
### SPICE Extensions
Time conversion:
```python
from curryer import spicetime
print(spicetime.adapt(0, from_='ugps', to='iso'))
# 1980-01-06 00:00:00.000000
print(spicetime.adapt('2024-11-13', 'iso'))
# 1415491218000000
print(spicetime.adapt(1415491218000000, to='et'))
# 784728069.1827033
import numpy as np
print(repr(spicetime.adapt(np.arange(4) * 60e6 + 1415491218000000, to='dt64')))
# array(['2024-11-13T00:00:00.000000', '2024-11-13T00:01:00.000000',
# '2024-11-13T00:02:00.000000', '2024-11-13T00:03:00.000000'],
# dtype='datetime64[us]')
```
Abstractions:
```python
from curryer import spicierpy
spicierpy.ext.infer_ids('ISS', 25544, from_norad=True)
# {'mission': 'ISS',
# 'spacecraft': -125544,
# 'clock': -125544,
# 'ephemeris': -125544,
# 'attitude': -125544000,
# 'instruments': {}}
earth = spicierpy.obj.Body('Earth')
print(earth, earth.id)
# Body(EARTH) 399
import curryer
mkrn = curryer.meta.MetaKernel.from_json(
'data/tsis1/tsis_v01.kernels.tm.json', sds_dir='data/generic', relative=True
)
print(mkrn)
# MetaKernel(Spacecraft(ISS_SC), Body(ISS_ELC3), Body(ISS_EXPA35), Body(TSIS_TADS),
# Body(TSIS_AZEL), Body(TSIS_TIM), Body(TSIS_TIM_GLINT))
with spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):
print(spicierpy.ext.instrument_boresight('TSIS_TIM'))
# [0. 0. 1.]
mkrn = curryer.meta.MetaKernel.from_json(
'tests/data/clarreo/cprs_v01.kernels.tm.json', sds_dir='data/generic', relative=True
)
print(mkrn)
# MetaKernel(Spacecraft(ISS_SC), Body(CPRS_BASE), Body(CPRS_PEDE),
# Body(CPRS_AZ), Body(CPRS_YOKE), Body(CPRS_EL), Body(CPRS_HYSICS))
with spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):
print(curryer.compute.spatial.pixel_vectors('CPRS_HYSICS'))
# (480,
# array([[ 0.00173869, -0.08715574, 0.99619318],
# [ 0.0017315 , -0.08679351, 0.99622482],
# [ 0.00172431, -0.08643127, 0.99625632],
# ...,
# [-0.00171712, 0.08606901, 0.9962877 ],
# [-0.00172431, 0.08643127, 0.99625632],
# [-0.0017315 , 0.08679351, 0.99622482]]))
```
### SPICE Kernel Creation
Create CLARREO Dynamic Kernels:
````python
import curryer
meta_kernel = 'tests/data/clarreo/cprs_v01.kernels.tm.json'
generic_dir = 'data/generic'
kernel_configs = [
'data/clarreo/iss_sc_v01.ephemeris.spk.json',
'data/clarreo/iss_sc_v01.attitude.ck.json',
'data/clarreo/cprs_az_v01.attitude.ck.json',
'data/clarreo/cprs_el_v01.attitude.ck.json',
]
output_dir = '/tmp'
input_file_or_obj = 'tests/data/demo/cprs_geolocation_tlm_20230101_20240430.nc'
# Load meta kernel details. Includes existing static kernels.
mkrn = curryer.meta.MetaKernel.from_json(meta_kernel, relative=True, sds_dir=generic_dir)
# Create the dynamic kernels from the JSONs alone. Note that they
# contain the reference to the input_data netcdf4 file to read.
generated_kernels = []
creator = curryer.kernels.create.KernelCreator(overwrite=False, append=False)
# Generate the kernels from the config and input data (file or object).
for kernel_config in kernel_configs:
generated_kernels.append(creator.write_from_json(
kernel_config, output_kernel=output_dir, input_data=input_file_or_obj,
))
````
### Level-1 Geospatial Processing
Geolocate CLARREO HYSICS Instrument:
```python
import pandas as pd
import curryer
meta_kernel = 'tests/data/clarreo/cprs_v01.kernels.tm.json'
generic_dir = 'data/generic'
time_range = ('2023-01-01', '2023-01-01T00:05:00')
ugps_times = curryer.spicetime.adapt(pd.date_range(*time_range, freq='67ms', inclusive='left'), 'iso')
# Load meta kernel details. Includes existing static kernels.
mkrn = curryer.meta.MetaKernel.from_json(meta_kernel, relative=True, sds_dir=generic_dir)
# Geolocate all the individual pixels and create the L1A data product!
with curryer.spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):
geoloc_inst = curryer.compute.spatial.Geolocate('CPRS_HYSICS')
l1a_dataset = geoloc_inst(ugps_times)
l1a_dataset.to_netcdf('cprs_geolocation_l1a_20230101.nc')
```
_Assumes dynamic kernels have been created and their file names defined within
the metakernel JSON file._
Raw data
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"description": "# Curryer\n\nA library for SPICE extensions and geospatial data product generation.\n\n* Github: https://github.com/lasp/curryer\n* PyPi: https://pypi.org/project/lasp-curryer/\n\n## Core Features\n* Extensions and wrappers for SPICE routines and common data patterns.\n* Automation of SPICE kernel creation from JSON definition files and modern data\nfile formats and third-party data structures.\n* Level-1 geospatial data processing routines (e.g., geolocation).\n\n\n## Install\n```shell\npip install lasp-curryer\n```\n\n### Data / Binary Files\n_NOTE: Data files and precompiled binaries are not currently automated and thus\nrequire manual downloading. This will be addressed in the next major release._\n\nDownload from the Curryer repo:\n* `data/generic` - Generic spice kernels (e.g., leapsecond kernel)\n * Download \n* `data/<misssion>` - Mission specific kernels and/or kernel definitions.\n* `data/gmted` - Digital Elevation Model (DEMs) with global coverage at\n15-arc-second.\n * Alternatively, use the script [download_dem.py](bin/download_dem.py) to\ndownload different types and/or resolutions from the USGS.\n\nDefine the top-level directory using the environment variable `CURRYER_DATA_DIR`\nor pass the path to routines which require data files.\n\nDownload Third-party Files:\n* SPICE Utilities: https://naif.jpl.nasa.gov/naif/utilities.html\n * At minimum: `mkspk`, `msopck`, `brief`, `ckbreif`\n* SPICE Generic Kernels (large):\n * [de430.bsp](https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de430.bsp),\nplace in `data/generic`.\n * PyProj Data:\n * Data directory: `import pyproj; print(pyproj.datadir.get_user_data_dir())`\n * [EGM96 TIFF](https://cdn.proj.org/us_nga_egm96_15.tif)\n\n\n## Examples\n\n### SPICE Extensions\nTime conversion:\n```python\nfrom curryer import spicetime\n\nprint(spicetime.adapt(0, from_='ugps', to='iso'))\n# 1980-01-06 00:00:00.000000\n\nprint(spicetime.adapt('2024-11-13', 'iso'))\n# 1415491218000000\n\nprint(spicetime.adapt(1415491218000000, to='et'))\n# 784728069.1827033\n\nimport numpy as np\n\nprint(repr(spicetime.adapt(np.arange(4) * 60e6 + 1415491218000000, to='dt64')))\n# array(['2024-11-13T00:00:00.000000', '2024-11-13T00:01:00.000000',\n# '2024-11-13T00:02:00.000000', '2024-11-13T00:03:00.000000'],\n# dtype='datetime64[us]')\n```\n\nAbstractions:\n```python\nfrom curryer import spicierpy\n\nspicierpy.ext.infer_ids('ISS', 25544, from_norad=True)\n# {'mission': 'ISS',\n# 'spacecraft': -125544,\n# 'clock': -125544,\n# 'ephemeris': -125544,\n# 'attitude': -125544000,\n# 'instruments': {}}\n\nearth = spicierpy.obj.Body('Earth')\nprint(earth, earth.id)\n# Body(EARTH) 399\n\nimport curryer\n\nmkrn = curryer.meta.MetaKernel.from_json(\n 'data/tsis1/tsis_v01.kernels.tm.json', sds_dir='data/generic', relative=True\n)\nprint(mkrn)\n# MetaKernel(Spacecraft(ISS_SC), Body(ISS_ELC3), Body(ISS_EXPA35), Body(TSIS_TADS),\n# Body(TSIS_AZEL), Body(TSIS_TIM), Body(TSIS_TIM_GLINT))\n\nwith spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):\n print(spicierpy.ext.instrument_boresight('TSIS_TIM'))\n# [0. 0. 1.]\n\nmkrn = curryer.meta.MetaKernel.from_json(\n 'tests/data/clarreo/cprs_v01.kernels.tm.json', sds_dir='data/generic', relative=True\n)\nprint(mkrn)\n# MetaKernel(Spacecraft(ISS_SC), Body(CPRS_BASE), Body(CPRS_PEDE),\n# Body(CPRS_AZ), Body(CPRS_YOKE), Body(CPRS_EL), Body(CPRS_HYSICS))\n\nwith spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):\n print(curryer.compute.spatial.pixel_vectors('CPRS_HYSICS'))\n# (480,\n# array([[ 0.00173869, -0.08715574, 0.99619318],\n# [ 0.0017315 , -0.08679351, 0.99622482],\n# [ 0.00172431, -0.08643127, 0.99625632],\n# ...,\n# [-0.00171712, 0.08606901, 0.9962877 ],\n# [-0.00172431, 0.08643127, 0.99625632],\n# [-0.0017315 , 0.08679351, 0.99622482]]))\n```\n\n\n### SPICE Kernel Creation\nCreate CLARREO Dynamic Kernels:\n````python\nimport curryer\n\nmeta_kernel = 'tests/data/clarreo/cprs_v01.kernels.tm.json'\ngeneric_dir = 'data/generic'\nkernel_configs = [\n 'data/clarreo/iss_sc_v01.ephemeris.spk.json',\n 'data/clarreo/iss_sc_v01.attitude.ck.json',\n 'data/clarreo/cprs_az_v01.attitude.ck.json',\n 'data/clarreo/cprs_el_v01.attitude.ck.json',\n]\noutput_dir = '/tmp'\ninput_file_or_obj = 'tests/data/demo/cprs_geolocation_tlm_20230101_20240430.nc'\n\n# Load meta kernel details. Includes existing static kernels.\nmkrn = curryer.meta.MetaKernel.from_json(meta_kernel, relative=True, sds_dir=generic_dir)\n\n# Create the dynamic kernels from the JSONs alone. Note that they\n# contain the reference to the input_data netcdf4 file to read.\ngenerated_kernels = []\ncreator = curryer.kernels.create.KernelCreator(overwrite=False, append=False)\n\n# Generate the kernels from the config and input data (file or object).\nfor kernel_config in kernel_configs:\n generated_kernels.append(creator.write_from_json(\n kernel_config, output_kernel=output_dir, input_data=input_file_or_obj,\n ))\n\n````\n\n\n### Level-1 Geospatial Processing\nGeolocate CLARREO HYSICS Instrument:\n```python\nimport pandas as pd\nimport curryer\n\nmeta_kernel = 'tests/data/clarreo/cprs_v01.kernels.tm.json'\ngeneric_dir = 'data/generic'\n\ntime_range = ('2023-01-01', '2023-01-01T00:05:00')\nugps_times = curryer.spicetime.adapt(pd.date_range(*time_range, freq='67ms', inclusive='left'), 'iso')\n\n# Load meta kernel details. Includes existing static kernels.\nmkrn = curryer.meta.MetaKernel.from_json(meta_kernel, relative=True, sds_dir=generic_dir)\n\n# Geolocate all the individual pixels and create the L1A data product!\nwith curryer.spicierpy.ext.load_kernel([mkrn.sds_kernels, mkrn.mission_kernels]):\n geoloc_inst = curryer.compute.spatial.Geolocate('CPRS_HYSICS')\n l1a_dataset = geoloc_inst(ugps_times)\n l1a_dataset.to_netcdf('cprs_geolocation_l1a_20230101.nc')\n\n```\n_Assumes dynamic kernels have been created and their file names defined within\nthe metakernel JSON file._\n\n",
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