Quickstart#
Now that you installation went ok, let’s start with a small example. Let’s create our first Geodes object.
[1]:
from pygeodes import Geodes
geodes = Geodes()
Searching for collections#
Then we can start by searching for existing collections, for example with search term sentinel :
[2]:
collections, dataframe = geodes.search_collections("sentinel")
Indexing: 100%|██████████| 4/4 [00:00<00:00, 1954.25it/s]
Matched terms : {('title', b'sentinel'), ('description', b'sentinel')}
Let’s see what we found. As a result, we get a collections object, which is a list of Collection objects.
[3]:
collections
[3]:
[<Collection id=PEPS_S2_L1C>,
<Collection id=PEPS_S2_L2A>,
<Collection id=PEPS_S1_L2>,
<Collection id=PEPS_S1_L1>]
and a dataframe object, which is a geopandas.GeoDataFrame.
[4]:
dataframe
[4]:
| title | description | collection | |
|---|---|---|---|
| 0 | PEPS Sentinel-2 L1C tiles | Sentinel-2 L1C tiles acquisition and storage f... | <Collection id=PEPS_S2_L1C> |
| 1 | PEPS Sentinel-2 L2A tiles | Sentinel-2 L2A tiles acquisition and storage f... | <Collection id=PEPS_S2_L2A> |
| 2 | PEPS Sentinel-1 Level2 | Sentinel-1 Level-2 consists of geolocated geop... | <Collection id=PEPS_S1_L2> |
| 3 | PEPS Sentinel-1 Level1 | Sentinel-1 Level-1 products are the baseline p... | <Collection id=PEPS_S1_L1> |
The dataframe let’s see you quickly see what you found, with only a few columns (here description and title), but if you are more comfortable working with raw objects, it’s also possible.
Let’s see how many elements are in these collections.
[5]:
for collection in collections:
print(
f"collection {collection.title} has {collection.summaries.other.get('total_items')} elements"
)
collection PEPS Sentinel-2 L1C tiles has 23316151 elements
collection PEPS Sentinel-2 L2A tiles has 0 elements
collection PEPS Sentinel-1 Level2 has 1209303 elements
collection PEPS Sentinel-1 Level1 has 5412056 elements
We could want to add columns to our dataframe. To know which columns are available, use collection.list_available_keys() on a Collection object :
[6]:
collections[0].list_available_keys()
[6]:
{'assets.toar.description',
'assets.toar.href',
'assets.toar.roles',
'assets.toar.title',
'assets.toar.type',
'assets.wms_capabilities.description',
'assets.wms_capabilities.href',
'assets.wms_capabilities.roles',
'assets.wms_capabilities.title',
'assets.wms_capabilities.type',
'description',
'extent.spatial.bbox',
'extent.temporal.interval',
'id',
'keywords',
'license',
'links',
'providers',
'stac_extensions',
'stac_version',
'summaries.contactPoint:fn',
'summaries.contactPoint:hasEmail',
'summaries.dataType',
'summaries.dcs:dataGeometryType',
'summaries.dcs:itemType',
'summaries.dcs:platform',
'summaries.dcs:processingLevel',
'summaries.dcs:satellite',
'summaries.dcs:sensor',
'summaries.dcs:spatialResolutionInMeters',
'summaries.dcs:temporalResolutionInHours',
'summaries.distribution:accessURL',
'summaries.distribution:format',
'summaries.latest',
'summaries.thematiques:theme',
'summaries.thematiques:variables',
'summaries.total_items',
'summaries.versionInfo',
'title',
'type'}
Let’s add summaries.total_items to the dataframe :
[7]:
from pygeodes.utils.formatting import format_collections
new_dataframe = format_collections(
dataframe, columns_to_add={"summaries.total_items"}
)
[8]:
new_dataframe
[8]:
| title | description | collection | summaries.total_items | |
|---|---|---|---|---|
| 0 | PEPS Sentinel-2 L1C tiles | Sentinel-2 L1C tiles acquisition and storage f... | <Collection id=PEPS_S2_L1C> | 23316151 |
| 1 | PEPS Sentinel-2 L2A tiles | Sentinel-2 L2A tiles acquisition and storage f... | <Collection id=PEPS_S2_L2A> | 0 |
| 2 | PEPS Sentinel-1 Level2 | Sentinel-1 Level-2 consists of geolocated geop... | <Collection id=PEPS_S1_L2> | 1209303 |
| 3 | PEPS Sentinel-1 Level1 | Sentinel-1 Level-1 products are the baseline p... | <Collection id=PEPS_S1_L1> | 5412056 |
If you wish to produce a fresh new dataframe with your custom columns, use format_collections on a list of collections :
[9]:
new_dataframe = format_collections(
collections,
columns_to_add={"summaries.dcs:satellite", "summaries.dcs:sensor"},
)
[10]:
new_dataframe
[10]:
| summaries.dcs:sensor | title | description | summaries.dcs:satellite | collection | |
|---|---|---|---|---|---|
| 0 | [MSI] | PEPS Sentinel-2 L1C tiles | Sentinel-2 L1C tiles acquisition and storage f... | [sentinel-2] | <Collection id=PEPS_S2_L1C> |
| 1 | [MSI] | PEPS Sentinel-2 L2A tiles | Sentinel-2 L2A tiles acquisition and storage f... | [sentinel-2] | <Collection id=PEPS_S2_L2A> |
| 2 | [SAR-C] | PEPS Sentinel-1 Level2 | Sentinel-1 Level-2 consists of geolocated geop... | [sentinel-1] | <Collection id=PEPS_S1_L2> |
| 3 | [SAR-C] | PEPS Sentinel-1 Level1 | Sentinel-1 Level-1 products are the baseline p... | [sentinel-1] | <Collection id=PEPS_S1_L1> |
Note : title and description columns are always here by default.
Searching for items#
As for collections, we can search for items. To know which arguments to put in your query, please use :
[11]:
from pygeodes.utils.query import get_requestable_args
print(get_requestable_args())
{'version': 'v7.0', 'attributes': ['dataType (STRING)', 'date (DATE_ISO8601)', 'links (STRING)', 'productValidity (BOOLEAN)', 'doi (STRING_ARRAY)', 'withoutGeom (BOOLEAN)', 'temporal:startDate (DATE_ISO8601)', 'temporal:endDate (DATE_ISO8601)', 'temporal:processingDate (DATE_ISO8601)', 'processing:processingMode (STRING)', 'processing:processingContext (STRING)', 'processing:processingCorrection (STRING)', 'processing:processingVersion (STRING)', 'spatial:bbox (STRING)', 'spatial:nbCols (STRING)', 'spatial:nbRows (STRING)', 'spaceborne:satelliteSensor (STRING)', 'spaceborne:satellitePlatform (STRING)', 'spaceborne:sensorMode (STRING)', 'spaceborne:productLevel (STRING)', 'spaceborne:polarization (STRING)', 'spaceborne:cycleID (INTEGER)', 'spaceborne:missionTakeId (INTEGER)', 'spaceborne:s2TakeId (STRING)', 'spaceborne:orbitID (INTEGER)', 'spaceborne:absoluteOrbitID (LONG)', 'spaceborne:orbitDirection (STRING)', 'spaceborne:productType (STRING)', 'spaceborne:parameter (STRING)', 'spaceborne:pparameter (STRING)', 'spaceborne:product (STRING)', 'spaceborne:timeresolution (STRING)', 'spaceborne:classification (STRING)', 'spaceborne:swath (STRING)', 'spaceborne:bands (STRING)', 'spaceborne:tile (STRING)', 'spaceborne:cloudCover (DOUBLE)', 'spaceborne:waterCover (DOUBLE)', 'spaceborne:percentSaturatedPixelsMax (DOUBLE)', 'spaceborne:percentNoDataPixelsMax (DOUBLE)', 'spaceborne:nbColInterpolationErrorMax (DOUBLE)', 'spaceborne:percentGroundUsefulPixels (DOUBLE)', 'spaceborne:percentUsefulPixelsMin (DOUBLE)', 'spaceborne:angle (DOUBLE)', 'spaceborne:pitch (DOUBLE)', 'spaceborne:roll (DOUBLE)', 'spaceborne:continentsID (STRING_ARRAY)', 'spaceborne:area (DOUBLE)', 'spaceborne:subSwath (STRING)', 'spaceborne:subTile (STRING)', 'spaceborne:keywords (STRING_ARRAY)', 'spaceborne:political (JSON)']}
We see we can use spaceborne:absoluteOrbitID. Let’s search for example those whose orbit direction is 30972:
[12]:
query = {"spaceborne:absoluteOrbitID": {"eq": 30972}}
items, dataframe = geodes.search_items(query=query)
Found 852 items matching your query
852 item(s) found for query : {'spaceborne:absoluteOrbitID': {'eq': 30972}}
Again, we come out with an items object, and a dataframe object.
[13]:
len(items)
[13]:
852
[14]:
items[:10] # printing everything is useless
[14]:
[Item (URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-8bc0-633b0d4de8ce:V1),
Item (URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-8ff6-93477c65d994:V1),
Item (URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-8a90-fc54a92d51d4:V1),
Item (URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-9a1f-b9661283d745:V1),
Item (URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-8d95-a6e2d07711dc:V1),
Item (URN:FEATURE:DATA:gdh:a3fb0d87-6c9c-3223-8b38-7414ac309dda:V1),
Item (URN:FEATURE:DATA:gdh:524d8c1d-ae07-3bc1-acc6-6bf7b5b3581a:V1),
Item (URN:FEATURE:DATA:gdh:5b627fa7-0cb9-3f4b-859f-b07642e8dbd8:V1),
Item (URN:FEATURE:DATA:gdh:b9652250-b462-3b04-bdfe-004095fd4bfc:V1),
Item (URN:FEATURE:DATA:gdh:f630948f-900d-3743-a2c5-fd88b1e6cf9a:V1)]
[15]:
dataframe
[15]:
| id | spaceborne:absoluteOrbitID | collection | item | geometry | |
|---|---|---|---|---|---|
| 0 | URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-8bc0-6... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-... | POLYGON ((161.77151 -13.54330, 161.78322 -13.4... |
| 1 | URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-8ff6-9... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-... | POLYGON ((159.93635 -16.36716, 160.96413 -16.3... |
| 2 | URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-8a90-f... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-... | POLYGON ((159.95583 -19.98223, 160.31685 -19.9... |
| 3 | URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-9a1f-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-... | POLYGON ((159.96143 -20.88537, 160.10729 -20.8... |
| 4 | URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-8d95-a... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-... | POLYGON ((161.73641 -10.02803, 162.55115 -10.0... |
| ... | ... | ... | ... | ... | ... |
| 847 | URN:FEATURE:DATA:gdh:6089196a-2b30-396d-b9e3-1... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:6089196a-2b30-396d-... | POLYGON ((128.99917 -77.56443, 133.41004 -77.5... |
| 848 | URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-8436-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-... | POLYGON ((-172.46556 52.25345, -170.85700 52.2... |
| 849 | URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-8aa5-c... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-... | POLYGON ((175.19956 3.52883, 175.59973 3.52939... |
| 850 | URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-bfd4-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-... | POLYGON ((167.05275 -29.00034, 168.17876 -28.9... |
| 851 | URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-a774-1... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-... | POLYGON ((111.56813 -81.14807, 111.51180 -80.1... |
852 rows × 5 columns
Let’s have a look around our items.
A thing we could want to do is filter them by cloud cover, let’s say between 39 and 40. But this column doesn’t appear in the dataframe. To know which columns are available, use item.list_available_keys() on an Item object.
[16]:
items[0].list_available_keys()
[16]:
{'accessService:endpointDescription',
'accessService:endpointURL',
'dataType',
'datetime',
'id',
'latest',
'processing:processingVersion',
'spaceborne:absoluteOrbitID',
'spaceborne:cloudCover',
'spaceborne:continentsID',
'spaceborne:keywords',
'spaceborne:orbitDirection',
'spaceborne:orbitID',
'spaceborne:political.continents',
'spaceborne:productLevel',
'spaceborne:productTimeliness',
'spaceborne:productType',
'spaceborne:references',
'spaceborne:s2TakeId',
'spaceborne:satellitePlatform',
'spaceborne:satelliteSensor',
'spaceborne:sensorMode',
'spaceborne:tile',
'spatial:bbox',
'temporal:endDate',
'temporal:startDate',
'versionInfo'}
We see we can use spaceborne:cloudCover. We can add it using format_items.
[17]:
from pygeodes.utils.formatting import format_items
new_dataframe = format_items(
dataframe, columns_to_add={"spaceborne:cloudCover"}
)
[18]:
new_dataframe
[18]:
| id | spaceborne:absoluteOrbitID | collection | item | geometry | spaceborne:cloudCover | |
|---|---|---|---|---|---|---|
| 0 | URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-8bc0-6... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-... | POLYGON ((161.77151 -13.54330, 161.78322 -13.4... | 26.963872 |
| 1 | URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-8ff6-9... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-... | POLYGON ((159.93635 -16.36716, 160.96413 -16.3... | 30.640658 |
| 2 | URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-8a90-f... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-... | POLYGON ((159.95583 -19.98223, 160.31685 -19.9... | 99.388031 |
| 3 | URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-9a1f-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-... | POLYGON ((159.96143 -20.88537, 160.10729 -20.8... | 81.709039 |
| 4 | URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-8d95-a... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-... | POLYGON ((161.73641 -10.02803, 162.55115 -10.0... | 96.671742 |
| ... | ... | ... | ... | ... | ... | ... |
| 847 | URN:FEATURE:DATA:gdh:6089196a-2b30-396d-b9e3-1... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:6089196a-2b30-396d-... | POLYGON ((128.99917 -77.56443, 133.41004 -77.5... | 78.515004 |
| 848 | URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-8436-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-... | POLYGON ((-172.46556 52.25345, -170.85700 52.2... | 98.938786 |
| 849 | URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-8aa5-c... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-... | POLYGON ((175.19956 3.52883, 175.59973 3.52939... | 98.253387 |
| 850 | URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-bfd4-b... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-... | POLYGON ((167.05275 -29.00034, 168.17876 -28.9... | 100.000000 |
| 851 | URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-a774-1... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-... | POLYGON ((111.56813 -81.14807, 111.51180 -80.1... | 0.441100 |
852 rows × 6 columns
We’ve got our new dataframe. Let’s filter :
[19]:
filtered = new_dataframe[
(new_dataframe["spaceborne:cloudCover"] <= 40)
& (new_dataframe["spaceborne:cloudCover"] >= 39)
]
[20]:
filtered
[20]:
| id | spaceborne:absoluteOrbitID | collection | item | geometry | spaceborne:cloudCover | |
|---|---|---|---|---|---|---|
| 83 | URN:FEATURE:DATA:gdh:e4c321cd-3e90-3a97-a357-5... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:e4c321cd-3e90-3a97-... | POLYGON ((170.99981 -15.46502, 171.41679 -15.4... | 39.599036 |
| 461 | URN:FEATURE:DATA:gdh:c10655c6-e1ed-3173-a460-8... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:c10655c6-e1ed-3173-... | POLYGON ((144.49933 -66.76234, 145.76351 -66.7... | 39.071231 |
| 530 | URN:FEATURE:DATA:gdh:20afef16-f166-3e08-ba5e-c... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:20afef16-f166-3e08-... | POLYGON ((158.07383 -29.01282, 158.08245 -28.0... | 39.539162 |
| 721 | URN:FEATURE:DATA:gdh:9635442b-1548-39e7-9095-1... | 30972 | PEPS_S2_L1C | Item (URN:FEATURE:DATA:gdh:9635442b-1548-39e7-... | POLYGON ((161.75303 -11.83511, 162.15183 -11.8... | 39.921501 |
Let’s plot these items :
[21]:
m = filtered.explore()
[22]:
m
[22]:
Make this Notebook Trusted to load map: File -> Trust Notebook
If you want to have all available columns in your dataframe, just do :
[23]:
full_dataframe = format_items(
items, columns_to_add=items[0].list_available_keys()
)
[24]:
full_dataframe
[24]:
| datetime | spaceborne:absoluteOrbitID | spaceborne:keywords | collection | spaceborne:productType | dataType | spaceborne:orbitDirection | spaceborne:continentsID | spaceborne:productLevel | spaceborne:references | ... | processing:processingVersion | spaceborne:s2TakeId | accessService:endpointURL | spaceborne:political.continents | spatial:bbox | versionInfo | id | temporal:endDate | item | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-08-07T13:55:38.823000Z | 30972 | [location:tropical, location:southern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.00 | GS2A_20210527T233821_030972_N05.00 | https://s3.datalake.cnes.fr/sentinel2-l1c/57/L... | [] | 161.77151396695,-13.543297699414,161.816238781... | 00.00 | URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-8bc0-6... | 2021-05-27T23:38:21.024Z | Item (URN:FEATURE:DATA:gdh:c72b550c-52f5-3fa0-... | POLYGON ((161.77151 -13.54330, 161.78322 -13.4... |
| 1 | 2023-08-07T13:55:54.466000Z | 30972 | [location:tropical, location:southern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.00 | GS2A_20210527T233821_030972_N05.00 | https://s3.datalake.cnes.fr/sentinel2-l1c/57/L... | [] | 159.93634686752,-16.367164183361,160.954547796... | 00.00 | URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-8ff6-9... | 2021-05-27T23:38:21.024Z | Item (URN:FEATURE:DATA:gdh:2327b73a-4cda-3340-... | POLYGON ((159.93635 -16.36716, 160.96413 -16.3... |
| 2 | 2023-08-07T13:56:04.377000Z | 30972 | [location:tropical, location:southern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.00 | GS2A_20210527T233821_030972_N05.00 | https://s3.datalake.cnes.fr/sentinel2-l1c/57/K... | [] | 159.95583373941,-19.982230511911,160.342529421... | 00.00 | URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-8a90-f... | 2021-05-27T23:38:21.024Z | Item (URN:FEATURE:DATA:gdh:187a9026-dbc4-30fe-... | POLYGON ((159.95583 -19.98223, 160.31685 -19.9... |
| 3 | 2023-08-07T13:56:24.952000Z | 30972 | [location:tropical, location:southern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.00 | GS2A_20210527T233821_030972_N05.00 | https://s3.datalake.cnes.fr/sentinel2-l1c/57/K... | [] | 159.96143100666,-20.885366094496,160.136652542... | 00.00 | URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-9a1f-b... | 2021-05-27T23:38:21.024Z | Item (URN:FEATURE:DATA:gdh:ac4337b5-dca0-39a1-... | POLYGON ((159.96143 -20.88537, 160.10729 -20.8... |
| 4 | 2023-08-07T13:56:18.875000Z | 30972 | [location:tropical, location:southern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.00 | GS2A_20210527T233821_030972_N05.00 | https://s3.datalake.cnes.fr/sentinel2-l1c/57/L... | [] | 161.73640664108,-10.028033044382,162.571503370... | 00.00 | URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-8d95-a... | 2021-05-27T23:38:21.024Z | Item (URN:FEATURE:DATA:gdh:a96dc6c4-8550-339a-... | POLYGON ((161.73641 -10.02803, 162.55115 -10.0... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 847 | 2023-02-10T01:39:01.038000Z | 30972 | [location:southern, season:summer] | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [AN] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.09 | GS2B_20230209T231349_030972_N05.09 | https://s3.datalake.cnes.fr/sentinel2-l1c/52/C... | [{'id': 'continent:Antarctica:6255152', 'name'... | 128.99916801842,-77.564425417387,133.544499891... | 00.00 | URN:FEATURE:DATA:gdh:6089196a-2b30-396d-b9e3-1... | 2023-02-09T23:13:49.024Z | Item (URN:FEATURE:DATA:gdh:6089196a-2b30-396d-... | POLYGON ((128.99917 -77.56443, 133.41004 -77.5... |
| 848 | 2023-02-10T01:08:09.184000Z | 30972 | [location:northern, location:coastal, season:w... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [NA] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.09 | GS2B_20230209T223659_030972_N05.09 | https://s3.datalake.cnes.fr/sentinel2-l1c/02/U... | [{'id': 'continent:NorthAmerica:6255149', 'nam... | -172.46556,52.253449535173,-170.85373,53.24953... | 00.00 | URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-8436-b... | 2023-02-09T22:36:59.024Z | Item (URN:FEATURE:DATA:gdh:f2647d63-395a-3f41-... | POLYGON ((-172.46556 52.25345, -170.85700 52.2... |
| 849 | 2023-02-10T01:02:44.032000Z | 30972 | [location:tropical, location:northern, season:... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.09 | GS2B_20230209T225459_030972_N05.09 | https://s3.datalake.cnes.fr/sentinel2-l1c/60/N... | [] | 175.19955511589,3.5288285993601,175.6288934134... | 00.00 | URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-8aa5-c... | 2023-02-09T22:54:59.024Z | Item (URN:FEATURE:DATA:gdh:b0eb078f-3b5b-3200-... | POLYGON ((175.19956 3.52883, 175.59973 3.52939... |
| 850 | 2023-02-10T00:48:46.871000Z | 30972 | [location:southern, location:coastal, season:s... | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [OC] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.09 | GS2B_20230209T230409_030972_N05.09 | https://s3.datalake.cnes.fr/sentinel2-l1c/58/J... | [{'id': 'continent:Oceania:6255151', 'name': '... | 167.05275105063,-29.000336644821,168.175139345... | 00.00 | URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-bfd4-b... | 2023-02-09T23:04:09.024Z | Item (URN:FEATURE:DATA:gdh:7c4bc98f-9088-30eb-... | POLYGON ((167.05275 -29.00034, 168.17876 -28.9... |
| 851 | 2023-02-10T01:28:54.259000Z | 30972 | [location:southern, season:summer] | PEPS_S2_L1C | S2MSI1C | PEPS_S2_L1C | Descending | [AN] | L1C | [{'url': 'http://www.naturalearthdata.com/down... | ... | 05.09 | GS2B_20230209T231349_030972_N05.09 | https://s3.datalake.cnes.fr/sentinel2-l1c/49/C... | [{'id': 'continent:Antarctica:6255152', 'name'... | 111.56813158306,-81.148069515409,105.768297510... | 00.00 | URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-a774-1... | 2023-02-09T23:13:49.024Z | Item (URN:FEATURE:DATA:gdh:769975fa-4a98-3fd1-... | POLYGON ((111.56813 -81.14807, 111.51180 -80.1... |
852 rows × 30 columns
Providing an api key#
The next parts involve requests that require an api-key. You can register one using the following method. We will also set a default download directory, for later. We use a file config.json formed as follows :
{"api_key" : "MyApiKey","download_dir" : "/tmp"}
[25]:
from pygeodes import Config
conf = Config.from_file("config.json")
geodes.set_conf(conf)
Other ways to configure pygeodes are described in configuration.
Quicklook#
Now we can have a look at our items.
[26]:
for item in filtered["item"]:
print(f"Quicklook of {item}")
item.show_quicklook()
Quicklook of Item (URN:FEATURE:DATA:gdh:e4c321cd-3e90-3a97-a357-5d64ca3e8e49:V1)
Quicklook of Item (URN:FEATURE:DATA:gdh:c10655c6-e1ed-3173-a460-88573c359320:V1)
Quicklook of Item (URN:FEATURE:DATA:gdh:20afef16-f166-3e08-ba5e-c20e6cda60c1:V1)
Quicklook of Item (URN:FEATURE:DATA:gdh:9635442b-1548-39e7-9095-1d2f3bc89df4:V1)
Downloading items#
Now we could want to download these items for further use :
[27]:
for item in filtered["item"]:
item.download_archive()
Download completed at /tmp/S2B_MSIL1C_20230209T230019_N0509_R058_T59LND_20230210T001018.zip
Download completed at /tmp/S2B_MSIL1C_20230209T231349_N0509_R058_T54DXM_20230210T001003.zip
Download completed at /tmp/S2A_MSIL1C_20210527T234311_N0500_R087_T57JUJ_20230301T001059.zip
Download completed at /tmp/S2A_MSIL1C_20210527T233821_N0500_R087_T57LZH_20230316T103827.zip
As we provided /tmp as default download dir, the downloads are stored in this folder.