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Writing custom Prepare Scripts for OpenDataCube?


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2















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







share|improve this question





























    2















    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







    share|improve this question



























      2












      2








      2


      1






      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







      share|improve this question
















      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






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 9 mins ago









      PolyGeo♦

      53.6k1780240




      53.6k1780240










      asked Jul 19 '18 at 11:27









      RoVoRoVo

      4,814731




      4,814731






















          1 Answer
          1






          active

          oldest

          votes


















          1














          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







          share|improve this answer























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            1 Answer
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            1 Answer
            1






            active

            oldest

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            active

            oldest

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            active

            oldest

            votes









            1














            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







            share|improve this answer




























              1














              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







              share|improve this answer


























                1












                1








                1







                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







                share|improve this answer













                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








                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Jul 24 '18 at 9:28









                RoVoRoVo

                4,814731




                4,814731






























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                    Meter-Bus Содержание Параметры шины | Стандартизация |...

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                    RS-232IrDAHARTмодемтоковая петляИРПСMIDIIO-LinkRS-422RS-423RS-485LINM-BusKansas City StandardКомпакт-кассетагрампластинкаPPPSLIPIrDAHARTISO 7816MIDIIO-LinkModbusLINDMX-512P-NETProfibusIEC 608708250 UART16550 UART Сетевые протоколыПоследовательные шины физического уровняполевой шиныасинхронного интерфейсаOpen Metering SystemAESздесь У этого термина существуют и другие значения, см. M-bus (значения). M-Bus (Meter-Bus) — стандарт физического уровня для полевой шины на основе асинхронного интерфейса. Также под этим названием понимают коммуникационный протокол, используемый для связи устройств по этой шине. M-bus преимущественно применяется для приборов учета электрической энергии (электросчётчики), тепловой энергии (теплосчётчики), расходомеров воды и газа. Данные передаются на компьютерную станцию (сервер) напрямую или через концентраторы шины M-Bus, а также усилители-повторители сигнала. Содержание 1 Параметры шины 2 Стандартизация 2.1 Open Metering System ...
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