| Name | hapiutils JSON |
| Version |
0.1
JSON |
| download |
| home_page | None |
| Summary | Additional hapiclient tools including merge, etc |
| upload_time | 2024-08-12 15:21:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD-3-Clause |
| keywords |
hapi
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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|
# tools-python
Additional tools to support hapiclient, including merge, etc. Currently consists of 'merge_hapi()' and 'hapi_to_df()'. Cloud HAPI coming soon. Assuming you have fetched 2 HAPI datasets and their metadata using hapiclient ( dataA, metaA and dataB, metaB):
merged_data, merged_meta = hapiutils.merge_hapi(dataA, metaA, dataB, metaB, round_to_sec=True)
Useful to feed into hapi-nn (see https://github.com/hapi-server/application-neuralnetwork-python)
Also, you can cast HAPI data into a Pandas dataframe with:
hapidf = hapi_to_df(dataA)
Full example:
from hapiclient import hapi
from hapiplot import hapiplot
import hapiutils
opts = {'logging': False, 'usecache': True, 'cachedir': './hapicache' }
start = '2013-01-01T00:00:54Z'
stop = '2013-01-01T06:00:54.000Z'
serverA, datasetA, parametersA = 'https://cdaweb.gsfc.nasa.gov/hapi', 'OMNI2_H0_MRG1HR', 'DST1800'
serverB, datasetB, parametersB = "https://imag-data.bgs.ac.uk/GIN_V1/hapi", "cki/best-avail/PT1M/hdzf", "Field_Vector"
dataA, metaA = hapi(serverA, datasetA, parametersA, start, stop, **opts)
dataB, metaB = hapi(serverB, datasetB, parametersB, start, stop, **opts)
newAB, metaAB = hapiutils.merge_hapi(dataA, metaA, dataB, metaB, True)
hapiplot(newAB, metaAB)
Raw data
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"description": "# tools-python\nAdditional tools to support hapiclient, including merge, etc. Currently consists of 'merge_hapi()' and 'hapi_to_df()'. Cloud HAPI coming soon. Assuming you have fetched 2 HAPI datasets and their metadata using hapiclient ( dataA, metaA and dataB, metaB):\n\n merged_data, merged_meta = hapiutils.merge_hapi(dataA, metaA, dataB, metaB, round_to_sec=True)\n\nUseful to feed into hapi-nn (see https://github.com/hapi-server/application-neuralnetwork-python)\n\nAlso, you can cast HAPI data into a Pandas dataframe with:\n\n hapidf = hapi_to_df(dataA)\n\nFull example:\n\n from hapiclient import hapi\n from hapiplot import hapiplot\n import hapiutils\n\n opts = {'logging': False, 'usecache': True, 'cachedir': './hapicache' }\n start = '2013-01-01T00:00:54Z'\n stop = '2013-01-01T06:00:54.000Z'\n serverA, datasetA, parametersA = 'https://cdaweb.gsfc.nasa.gov/hapi', 'OMNI2_H0_MRG1HR', 'DST1800'\n serverB, datasetB, parametersB = \"https://imag-data.bgs.ac.uk/GIN_V1/hapi\", \"cki/best-avail/PT1M/hdzf\", \"Field_Vector\"\n\n dataA, metaA = hapi(serverA, datasetA, parametersA, start, stop, **opts)\n dataB, metaB = hapi(serverB, datasetB, parametersB, start, stop, **opts)\n newAB, metaAB = hapiutils.merge_hapi(dataA, metaA, dataB, metaB, True)\n hapiplot(newAB, metaAB)\n",
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