Cryo 184

binder/environment.yml

@@ -16,7 +16,7 @@ dependencies:
 - netcdf4=1.6.2
 - geopy
 - pyresample
-- earthaccess>=0.5.2
+- earthaccess>=0.6.1
 - fiona
 - zarr
 - ipympl
@@ -35,9 +35,11 @@ dependencies:
 - pipreqsnb
 - conda-lock>=1.2.1
 - mamba>=1.0
+- coiled>=0.9.30
 - pip
 - pip:
   - git+https://github.com/icesat2py/icepyx.git
+  - git+https://github.com/ICESat2-SlideRule/h5coro.git@main
   - awscliv2
 platforms:
 - linux-64

notebooks/ICESat-2_Cloud_Access/h5cloud/read_atl10.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+
+#import coiled
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from rich import print as rprint
+from itertools import product
+from pqdm.threads import pqdm
+
+
+import earthaccess
+from h5coro import s3driver, webdriver
+import h5coro
+
+
+
+
+def get_strong_beams(f):
+    """Returns ground track for strong beams based on IS2 orientation"""
+    orient  = f['orbit_info/sc_orient'][0]
+
+    if orient == 0:
+        return [f"gt{i}l" for i in [1, 2, 3]]
+    elif orient == 1:
+        return [f"gt{i}r" for i in [1, 2, 3]]
+    else:
+        raise KeyError("Spacecraft orientation neither forward nor backward")
+
+
+
+
+
+
+def read_atl10(files, bounding_box=None, executors=4, environment="local", credentials=None):
+    """Returns a consolidated GeoPandas dataframe for a set of ATL10 file pointers.
+
+    Parameters:
+        files (list[S3FSFile]): list of authenticated fsspec file references to ATL10 on S3 (via earthaccess)
+        executors (int): number of threads
+
+    """
+    if environment == "local":
+        driver = webdriver.HTTPDriver
+    else:
+        driver = s3driver.S3Driver
+
+    GPS_EPOCH = pd.to_datetime('1980-01-06 00:00:00')
+
+    def read_h5coro(file):
+        """Reads datasets required for creating gridded freeboard from a single ATL10 file
+
+        file: an authenticated fsspec file reference on S3 (returned by earthaccess)
+
+        returns: a list of geopandas dataframes
+        """
+        # Open file object
+        h5 = h5coro.H5Coro(file, driver, credentials=credentials)
+
+        # Get strong beams based on orientation
+        ancillary_datasets = ["orbit_info/sc_orient", "ancillary_data/atlas_sdp_gps_epoch"]
+        f = h5.readDatasets(datasets=ancillary_datasets, block=True)
+        strong_beams = get_strong_beams(f)
+        atlas_sdp_gps_epoch = f["ancillary_data/atlas_sdp_gps_epoch"][:]
+
+        # Create list of datasets to load
+        datasets = ["freeboard_segment/latitude",
+                    "freeboard_segment/longitude",
+                    "freeboard_segment/delta_time",
+                    "freeboard_segment/seg_dist_x",
+                    "freeboard_segment/heights/height_segment_length_seg",
+                    "freeboard_segment/beam_fb_height",
+                    "freeboard_segment/heights/height_segment_type"]
+        ds_list = ["/".join(p) for p in list(product(strong_beams, datasets))]
+        # Load datasets
+        f = h5.readDatasets(datasets=ds_list, block=True)
+        # rprint(f["gt2l/freeboard_segment/latitude"], type(f["gt2l/freeboard_segment/latitude"]))
+
+        # Create a list of geopandas.DataFrames containing beams
+        tracks = []
+        for beam in strong_beams:
+            ds = {dataset.split("/")[-1]: f[dataset][:] for dataset in ds_list if dataset.startswith(beam)}
+
+            # Convert delta_time to datetime
+            ds["delta_time"] = GPS_EPOCH + pd.to_timedelta(ds["delta_time"]+atlas_sdp_gps_epoch, unit='s')
+            # we don't need nanoseconds to grid daily let alone weekly
+            ds["delta_time"] = ds["delta_time"].astype('datetime64[s]')
+
+            # Add beam identifier
+            ds["beam"] = beam
+
+            # Set fill values to NaN - assume 100 m as threshold
+            ds["beam_fb_height"] = np.where(ds["beam_fb_height"] > 100, np.nan, ds["beam_fb_height"])
+
+            geometry = gpd.points_from_xy(ds["longitude"], ds["latitude"])
+            del ds["longitude"]
+            del ds["latitude"]
+
+            gdf = gpd.GeoDataFrame(ds, geometry=geometry, crs="EPSG:4326")
+            gdf.dropna(axis=0, inplace=True)
+            if bounding_box is not None:
+                bbox = [float(coord) for coord in bounding_box.split(",")]
+                gdf = gdf.cx[bbox[0]:bbox[2],bbox[1]:bbox[3]]
+            tracks.append(gdf)
+
+        df = pd.concat(tracks)
+        return df
+
+    dfs = pqdm(files, read_h5coro, n_jobs=executors)
+    combined = pd.concat(dfs)
+
+    return combined
+
+

notebooks/ICESat-2_Cloud_Access/h5cloud/readme.md

@@ -0,0 +1,30 @@
+## Running and scaling Python with [Coiled serverless functions](https://docs.coiled.io/user_guide/usage/functions/index.html).
+
+This script contains the same code to read ATL10 data as the notebook, the one difference is that we are using a function decorator from Coiled that allows us to execute the function in the cloud with no modifications whatsoever. 
+
+The only requirement for this workflow is to have an active account with Coiled and execute this from our terminal:
+
+```bash
+coiled login
+```
+
+This will open a browser tab to authenticate ourselves with their APIs 
+
+> Note: If you would like to test his ask us to include you with Openscapes!
+
+
+Our functions can be paralleliza, scaling the computation to hundreds of nodes if needed in the same way we could use Amazon lambda functions. Once we install and activate [`nsidc-tutorials`](../../binder/environment.yml) We can run the script with the following python command:
+
+```bash
+python workflow.py --bbox="-180, -90, 180, -60" --year=2023 --out="test-2023-local" --env=local
+
+```
+
+This will run the code locally. If we want to run the code in the cloud we'll 
+
+```bash
+python workflow.py --bbox="-180, -90, 180, -60" --year=2023 --out="test-2023-local" --env=cloud
+
+```
+
+The first time we execute this function, the provisioning will take a couple minutes and will sync our current Python environment with the cloud instances executing our code.

notebooks/ICESat-2_Cloud_Access/h5cloud/workflow.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+
+import coiled
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from rich import print as rprint
+from itertools import product
+import argparse
+
+import earthaccess
+from h5coro import h5coro, s3driver
+
+from read_atl10 import read_atl10
+
+if __name__ == "__main__":
+
+    rprint(f"executing locally")
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--bbox', help='bbox')
+    parser.add_argument('--year', help='year to process')
+    parser.add_argument('--env', help='execute in the cloud or local, default:local')
+    parser.add_argument('--out', help='output file name')
+    args = parser.parse_args()
+
+
+    auth = earthaccess.login()
+
+    # ross_sea = (-180, -78, -160, -74)
+    # antarctic = (-180, -90, 180, -60)
+
+    year = int(args.year)
+    bbox = tuple([float(c) for c in args.bbox.split(",")])
+
+    print(f"Searching ATL10 data for year {year} ...")
+    granules = earthaccess.search_data(
+        short_name = 'ATL10',
+        version = '006',
+        cloud_hosted = True,
+        bounding_box = bbox,
+        temporal = (f'{args.year}-06-01',f'{args.year}-09-30'),
+        count=4,
+        debug=True
+    )
+
+
+    if args.env == "local":
+        files = [g.data_links(access="out_of_region")[0] for g in granules]
+        credentials = earthaccess.__auth__.token["access_token"]
+
+        df = read_atl10(files, bounding_box=args.bbox, environment="local", credentials=credentials)
+    else:
+        files = [g.data_links(access="direct")[0].replace("s3://", "") for g in granules]
+        aws_credentials = earthaccess.get_s3_credentials("NSIDC")
+        credentials = {
+          "aws_access_key_id": aws_credentials["accessKeyId"],
+          "aws_secret_access_key": aws_credentials["secretAccessKey"],
+          "aws_session_token": aws_credentials["sessionToken"]
+        }
+
+        @coiled.function(region= "us-west-2",
+                         memory= "4 GB",
+                         keepalive="1 HOUR")
+        def cloud_runnner(files, bounding_box, credentials):
+            df = read_atl10(files, bounding_box=bounding_box, environment="cloud", credentials=credentials)
+            return df
+
+        df = cloud_runnner(files, args.bbox, credentials=credentials)
+
+
+    df.to_parquet(f"{args.out}.parquet")
+    rprint(df)
+

notebooks/ICESat-2_Cloud_Access/ross_sea.json

@@ -0,0 +1,81 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "properties": {},
+      "geometry": {
+        "coordinates": [
+          [
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+              -73.84648052492354
+            ],
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+              -76.01486891873441
+            ],
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+              -83.76551904624706
+            ],
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+              -138.89031336546202,
+              -83.16126208208007
+            ],
+            [
+              -139.89760391715487,
+              -81.81509135152459
+            ],
+            [
+              -145.07462138020958,
+              -75.8454713912678
+            ],
+            [
+              -145.2859453568654,
+              -73.60545521193768
+            ],
+            [
+              -155.7529050321871,
+              -71.77435794070743
+            ],
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+              -173.60352774698885,
+              -71.50777786832501
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+              -187.08441940129651,
+              -71.32576778967325
+            ],
+            [
+              -191.09608556432593,
+              -73.84648052492354
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      },
+      "id": 0
+    }
+  ]
+}