chore(release) : release 0.0.1

CHANGELOG.md

@@ -12,6 +12,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Auth datacube `earthdaily.earthdatastore.Auth().datacube(...)` function now manage multiple collections.
 - Uses `fields` to query only assets asked over Skyfox/EDS (enhance performance).
 
+### Changed
+
+- `asset_mapper` has now all assets from available collections.
+
 ## [0.0.1-rc9] - 2023-12-12
 
 ### Added

examples/field_evolution.py

@@ -35,18 +35,18 @@
     datetime=["2022-07-01", "2022-08-31"],
     assets=["red", "green", "blue"],
     mask_with="ag_cloud_mask",
-    # mask_statistics=True, # as you ask `clear_cover`it will force computing mask_statistics
     clear_cover=50,
-    resampling="cubic",
-    cross_calibration_collection="sentinel-2-l2a")
+)
 
 pivot_cube.clear_percent.plot.scatter(x="time")
 
 ##############################################################################
 # Plots cube with SCL with at least 50% of clear data
 # ----------------------------------------------------
 pivot_cube = pivot_cube.load()
-pivot_cube.to_array(dim="band").plot.imshow(vmin=0, vmax=0.4, col="time", col_wrap=3)
+pivot_cube.to_array(dim="band").plot.imshow(
+    vmin=0, vmax=0.4, col="time", col_wrap=3
+)
 
 plt.title("Clear cover percent with SCL")
 plt.title("Pivot evolution with SCL masks")

examples/first_steps_create_datacube.py

@@ -43,7 +43,6 @@
 s2_datacube.clear_percent.plot.scatter(x="time")
 plt.title("Percentage of clear pixels on the study site")
 plt.show()
-print(s2_datacube)
 
 s2_datacube[["red", "green", "blue"]].to_array(dim="band").plot.imshow(
     vmin=0, vmax=0.2, col="time", col_wrap=4
@@ -57,7 +56,9 @@
 # Request items
 
 items = eds.search(
-    "sentinel-2-l2a", intersects=geometry, datetime=["2022-08-01", "2022-08-09"]
+    "sentinel-2-l2a",
+    intersects=geometry,
+    datetime=["2022-08-01", "2022-08-09"],
 )
 
 ###########################################################

examples/multisensors_cube.py

@@ -19,10 +19,10 @@
 # -------------------------------------------
 
 eds = earthdatastore.Auth()
-collections = ['sentinel-2-l2a','landsat-c2l2-sr']
-datetime = ['2022-07-01','2022-09-01']
+collections = ["sentinel-2-l2a", "landsat-c2l2-sr"]
+datetime = ["2022-07-01", "2022-09-01"]
 intersects = datasets.load_pivot_corumba()
-assets = ["blue","green","red","nir"]
+assets = ["blue", "green", "red", "nir"]
 mask_with = "ag_cloud_mask"
 clear_cover = 50
 resampling = "cubic"
@@ -32,24 +32,40 @@
 # Create the multisensors datacube
 # -------------------------------------------
 
-datacube = eds.datacube(collections,assets=assets, datetime=datetime,intersects=intersects, mask_with=mask_with, clear_cover=clear_cover, cross_calibration_collection=cross_calibration_collection)
+datacube = eds.datacube(
+    collections,
+    assets=assets,
+    datetime=datetime,
+    intersects=intersects,
+    mask_with=mask_with,
+    clear_cover=clear_cover,
+    cross_calibration_collection=cross_calibration_collection,
+)
 
 # Add the NDVI
-datacube['ndvi'] = (datacube['nir']-datacube['red'])/(datacube['nir']+datacube["red"])
+datacube["ndvi"] = (datacube["nir"] - datacube["red"]) / (
+    datacube["nir"] + datacube["red"]
+)
 
-# Load in memory 
+# Load in memory
 datacube = datacube.load()
 
 ##############################################################################
 # See the evolution in RGB
 # -------------------------------------------
 
-datacube[['red','green','blue']].to_array(dim="band").plot.imshow(col='time',col_wrap=3,vmax=0.2)
+datacube[["red", "green", "blue"]].to_array(dim="band").plot.imshow(
+    col="time", col_wrap=3, vmax=0.2
+)
 plt.show()
 
 ##############################################################################
 # See the NDVI evolution
 # -------------------------------------------
 
-datacube['ndvi'].plot.imshow(col='time',col_wrap=3,vmin=0, vmax=0.8, cmap="RdYlGn");plt.show()
-datacube['ndvi'].groupby('time').mean(...).plot(x="time");plt.plot()
+datacube["ndvi"].plot.imshow(
+    col="time", col_wrap=3, vmin=0, vmax=0.8, cmap="RdYlGn"
+)
+datacube["ndvi"].groupby("time").mean(...).plot.line(x="time")
+plt.title("NDVI evolution")
+plt.show()