Writing custom Prepare Scripts for OpenDataCube?Assign Custom Band Combination To Image Catalogs In...
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Writing custom Prepare Scripts for OpenDataCube?
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How to write custom prepare scripts for Open Data Cube format ?
The docs say the following:
We expect that many new Data Cube instances will require custom prepare scripts to be written. It is generally a straightforward task of mapping metadata from one form to another and writing out a YAML document.
That's it.
Also the official training center is not helpful as it does not say anything about it.
Can someone explain this a bit more in detail or does someone have better sources or tutorial(s) other than analyzing the non-commented sample prepare scripts ?
raster imagery satellite file-formats opendatacube
edited 9 mins ago
PolyGeo♦
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
add a comment |
How to write custom prepare scripts for Open Data Cube format ?
The docs say the following:
We expect that many new Data Cube instances will require custom prepare scripts to be written. It is generally a straightforward task of mapping metadata from one form to another and writing out a YAML document.
That's it.
Also the official training center is not helpful as it does not say anything about it.
Can someone explain this a bit more in detail or does someone have better sources or tutorial(s) other than analyzing the non-commented sample prepare scripts ?
raster imagery satellite file-formats opendatacube
edited 9 mins ago
PolyGeo♦
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
add a comment |
How to write custom prepare scripts for Open Data Cube format ?
The docs say the following:
We expect that many new Data Cube instances will require custom prepare scripts to be written. It is generally a straightforward task of mapping metadata from one form to another and writing out a YAML document.
That's it.
Also the official training center is not helpful as it does not say anything about it.
Can someone explain this a bit more in detail or does someone have better sources or tutorial(s) other than analyzing the non-commented sample prepare scripts ?
raster imagery satellite file-formats opendatacube
edited 9 mins ago
PolyGeo♦
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
How to write custom prepare scripts for Open Data Cube format ?
The docs say the following:
We expect that many new Data Cube instances will require custom prepare scripts to be written. It is generally a straightforward task of mapping metadata from one form to another and writing out a YAML document.
That's it.
Also the official training center is not helpful as it does not say anything about it.
Can someone explain this a bit more in detail or does someone have better sources or tutorial(s) other than analyzing the non-commented sample prepare scripts ?
raster imagery satellite file-formats opendatacube
raster imagery satellite file-formats opendatacube
edited 9 mins ago
PolyGeo♦
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
edited 9 mins ago
PolyGeo♦
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
edited 9 mins ago
PolyGeo♦
53.6k1780240
edited 9 mins ago
PolyGeo♦
53.6k1780240
edited 9 mins ago
PolyGeo♦
53.6k1780240
53.6k1780240
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
asked Jul 19 '18 at 11:27
RoVoRoVo
4,814731
4,814731
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
While the official documentation of datacube-core is not yet very useful, the project owner CEOS has a much better documentation hidden in another github project named CEOS Data Cube UI.
- Ingestion Guide
The topic I was looking for can be found here.
I will not copy-paste the whole documentation. But the following is the information the prepare script should output to a yaml file:
creation_dt: '2014-03-01 00:00:00'
extent:
center_dt: '2014-03-01 11:59:59'
coord:
ll: {lat: -90.0000031, lon: -179.99999689999999}
lr: {lat: -90.0000031, lon: 180.0000031}
ul: {lat: 89.9999969, lon: -179.99999689999999}
ur: {lat: 89.9999969, lon: 180.0000031}
from_dt: '2014-03-01 00:00:00'
to_dt: '2014-03-01 23:59:59'
format: {name: GeoTiff}
grid_spatial:
projection:
geo_ref_points:
ll: {x: -179.9999969, y: -90.0000031}
lr: {x: 180.0000031, y: -90.0000031}
ul: {x: -179.9999969, y: 89.9999969}
ur: {x: 180.0000031, y: 89.9999969}
spatial_reference: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
id: e95ed0b5-ac2b-4c59-9aa8-2b3f0cd2ef40
image:
bands:
ice_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.ice.tif}
liquid_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquid.tif}
percent_liquid: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquidPercent.tif}
total_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.tif}
instrument: {name: GPM}
lineage:
source_datasets: {}
platform: {code: GPM}
processing_level: V04A
product_type: monthly
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
While the official documentation of datacube-core is not yet very useful, the project owner CEOS has a much better documentation hidden in another github project named CEOS Data Cube UI.
- Ingestion Guide
The topic I was looking for can be found here.
I will not copy-paste the whole documentation. But the following is the information the prepare script should output to a yaml file:
creation_dt: '2014-03-01 00:00:00'
extent:
center_dt: '2014-03-01 11:59:59'
coord:
ll: {lat: -90.0000031, lon: -179.99999689999999}
lr: {lat: -90.0000031, lon: 180.0000031}
ul: {lat: 89.9999969, lon: -179.99999689999999}
ur: {lat: 89.9999969, lon: 180.0000031}
from_dt: '2014-03-01 00:00:00'
to_dt: '2014-03-01 23:59:59'
format: {name: GeoTiff}
grid_spatial:
projection:
geo_ref_points:
ll: {x: -179.9999969, y: -90.0000031}
lr: {x: 180.0000031, y: -90.0000031}
ul: {x: -179.9999969, y: 89.9999969}
ur: {x: 180.0000031, y: 89.9999969}
spatial_reference: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
id: e95ed0b5-ac2b-4c59-9aa8-2b3f0cd2ef40
image:
bands:
ice_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.ice.tif}
liquid_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquid.tif}
percent_liquid: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquidPercent.tif}
total_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.tif}
instrument: {name: GPM}
lineage:
source_datasets: {}
platform: {code: GPM}
processing_level: V04A
product_type: monthly
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
add a comment |
While the official documentation of datacube-core is not yet very useful, the project owner CEOS has a much better documentation hidden in another github project named CEOS Data Cube UI.
- Ingestion Guide
The topic I was looking for can be found here.
I will not copy-paste the whole documentation. But the following is the information the prepare script should output to a yaml file:
creation_dt: '2014-03-01 00:00:00'
extent:
center_dt: '2014-03-01 11:59:59'
coord:
ll: {lat: -90.0000031, lon: -179.99999689999999}
lr: {lat: -90.0000031, lon: 180.0000031}
ul: {lat: 89.9999969, lon: -179.99999689999999}
ur: {lat: 89.9999969, lon: 180.0000031}
from_dt: '2014-03-01 00:00:00'
to_dt: '2014-03-01 23:59:59'
format: {name: GeoTiff}
grid_spatial:
projection:
geo_ref_points:
ll: {x: -179.9999969, y: -90.0000031}
lr: {x: 180.0000031, y: -90.0000031}
ul: {x: -179.9999969, y: 89.9999969}
ur: {x: 180.0000031, y: 89.9999969}
spatial_reference: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
id: e95ed0b5-ac2b-4c59-9aa8-2b3f0cd2ef40
image:
bands:
ice_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.ice.tif}
liquid_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquid.tif}
percent_liquid: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquidPercent.tif}
total_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.tif}
instrument: {name: GPM}
lineage:
source_datasets: {}
platform: {code: GPM}
processing_level: V04A
product_type: monthly
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
add a comment |
While the official documentation of datacube-core is not yet very useful, the project owner CEOS has a much better documentation hidden in another github project named CEOS Data Cube UI.
- Ingestion Guide
The topic I was looking for can be found here.
I will not copy-paste the whole documentation. But the following is the information the prepare script should output to a yaml file:
creation_dt: '2014-03-01 00:00:00'
extent:
center_dt: '2014-03-01 11:59:59'
coord:
ll: {lat: -90.0000031, lon: -179.99999689999999}
lr: {lat: -90.0000031, lon: 180.0000031}
ul: {lat: 89.9999969, lon: -179.99999689999999}
ur: {lat: 89.9999969, lon: 180.0000031}
from_dt: '2014-03-01 00:00:00'
to_dt: '2014-03-01 23:59:59'
format: {name: GeoTiff}
grid_spatial:
projection:
geo_ref_points:
ll: {x: -179.9999969, y: -90.0000031}
lr: {x: 180.0000031, y: -90.0000031}
ul: {x: -179.9999969, y: 89.9999969}
ur: {x: 180.0000031, y: 89.9999969}
spatial_reference: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
id: e95ed0b5-ac2b-4c59-9aa8-2b3f0cd2ef40
image:
bands:
ice_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.ice.tif}
liquid_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquid.tif}
percent_liquid: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquidPercent.tif}
total_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.tif}
instrument: {name: GPM}
lineage:
source_datasets: {}
platform: {code: GPM}
processing_level: V04A
product_type: monthly
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
While the official documentation of datacube-core is not yet very useful, the project owner CEOS has a much better documentation hidden in another github project named CEOS Data Cube UI.
- Ingestion Guide
The topic I was looking for can be found here.
I will not copy-paste the whole documentation. But the following is the information the prepare script should output to a yaml file:
creation_dt: '2014-03-01 00:00:00'
extent:
center_dt: '2014-03-01 11:59:59'
coord:
ll: {lat: -90.0000031, lon: -179.99999689999999}
lr: {lat: -90.0000031, lon: 180.0000031}
ul: {lat: 89.9999969, lon: -179.99999689999999}
ur: {lat: 89.9999969, lon: 180.0000031}
from_dt: '2014-03-01 00:00:00'
to_dt: '2014-03-01 23:59:59'
format: {name: GeoTiff}
grid_spatial:
projection:
geo_ref_points:
ll: {x: -179.9999969, y: -90.0000031}
lr: {x: 180.0000031, y: -90.0000031}
ul: {x: -179.9999969, y: 89.9999969}
ur: {x: 180.0000031, y: 89.9999969}
spatial_reference: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
id: e95ed0b5-ac2b-4c59-9aa8-2b3f0cd2ef40
image:
bands:
ice_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.ice.tif}
liquid_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquid.tif}
percent_liquid: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.liquidPercent.tif}
total_precipitation: {path: 3B-MO-GIS.MS.MRG.3IMERG.20140301-S000000-E235959.03.V04A.tif}
instrument: {name: GPM}
lineage:
source_datasets: {}
platform: {code: GPM}
processing_level: V04A
product_type: monthly
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
answered Jul 24 '18 at 9:28
RoVoRoVo
4,814731
4,814731
add a comment |
add a comment |
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