Add first iteration of tests to repo

.github/workflows/dea-intertidal-image.yml

@@ -1,4 +1,4 @@
-name: DEA Intertidal Image Push
+name: Image build and test
 
 env:
   IMAGE_NAME: geoscienceaustralia/dea-intertidal
@@ -11,11 +11,13 @@ on:
     paths:
       - 'intertidal/**'
       - 'data/**'
+      - 'tests/**'
       - '.github/workflows/dea-intertidal-image.yml'
       - 'Dockerfile'
       - 'requirements.in'
       - 'setup.py'
       - 'docker-compose.yml'
+      - 'codecov.yaml'
 
   release:
     types: [created, edited, published]
@@ -31,13 +33,33 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
 
       - name: Build DEA Intertidal image
         timeout-minutes: 20
         shell: bash
         run: |
           docker-compose build
 
+      - name: Run tests
+        run: |
+          # Download tide modelling files and unzip
+          # TODO: Replace with S3 sync from dea-non-public-data
+          wget --no-verbose https://www.dropbox.com/s/uemd8ib2vfw5nad/tide_models.zip?dl=1 -O tide_models.zip
+          unzip -q tide_models.zip
+
+          # Run integration tests using Docker
+          docker compose run dea_intertidal pytest -v --cov=intertidal --cov-report=xml tests
+
+      - name: Upload coverage reports to Codecov
+        uses: codecov/codecov-action@v3
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+      # - name: Setup upterm session
+      #   uses: lhotari/action-upterm@v1
+
   push_ecr:
     needs: [test]
     runs-on: ubuntu-latest

README.md

@@ -4,6 +4,8 @@
 
 [![DOI](https://img.shields.io/badge/DOI-10.1016/j.ecss.2019.03.006-0e7fbf.svg)](https://doi.org/10.1016/j.ecss.2019.03.006)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![codecov](https://codecov.io/gh/GeoscienceAustralia/dea-intertidal/branch/main/graph/badge.svg?token=7HXSIPGT5I)](https://codecov.io/gh/GeoscienceAustralia/dea-intertidal)
+[![example workflow](https://github.com/GeoscienceAustralia/dea-intertidal/actions/workflows/dea-intertidal-image.yml/badge.svg)](https://github.com/GeoscienceAustralia/dea-intertidal/actions/workflows/dea-intertidal-image.yml)
 
 **License:** The code in this repository is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0 license](https://creativecommons.org/licenses/by/4.0/).
 

codecov.yaml

@@ -0,0 +1,8 @@
+ignore:
+  - "intertidal/validation.py"
+  - "intertidal/composites.py"
+
+coverage:
+  precision: 1
+  round: down
+  range: "50...90"

intertidal/.gitignore

@@ -6,4 +6,8 @@
 !*.ipynb
 !*.sh
 !*.yaml
-!*.gitignore
+!*.yml
+!*.in
+!*.gitignore
+!*.dockerignore
+!*Dockerfile

intertidal/elevation.py

@@ -820,7 +820,7 @@ def pixel_dem(interval_ds, ndwi_thresh=0.1, interp_intervals=200, smooth_radius=
         smoothed_ds = interval_ds.rolling(
             interval=smooth_radius,
             center=False,
-            min_periods=1,  #int(smooth_radius / 2.0),
+            min_periods=1,  # int(smooth_radius / 2.0),
         ).mean()
     else:
         smoothed_ds = interval_ds
@@ -844,6 +844,7 @@ def pixel_dem(interval_ds, ndwi_thresh=0.1, interp_intervals=200, smooth_radius=
     # Export as xr.Dataset
     return dem_flat.to_dataset(name="elevation")
 
+
 def pixel_dem_debug(
     x,
     y,
@@ -1113,7 +1114,7 @@ def clean_edge_pixels(ds):
     effects mean that modelled elevations are likely to be inaccurate.
 
     This function uses binary dilation to identify the edges of
-    intertidal elevation data with greater than 0 elevation. The 
+    intertidal elevation data with greater than 0 elevation. The
     resulting mask is applied to the elevation dataset to remove upper
     intertidal edge pixels from both elevation and uncertainty datasets.
 
@@ -1247,7 +1248,7 @@ def elevation(
             ancillary_points="data/raw/tide_correlations_2017-2019.geojson",
             # ancillary_points="data/raw/tide_correlation_points_spearmanndwi_nt.geojson",
             top_n=3,
-            reduce_method='mean',
+            reduce_method="mean",
             resolution=3000,
         )
 
@@ -1347,14 +1348,14 @@ def elevation(
 @click.option(
     "--start_date",
     type=str,
-    default="2020",
+    default="2019",
     help="The start date of satellite data to load from the "
     "datacube. This can be any date format accepted by datacube. ",
 )
 @click.option(
     "--end_date",
     type=str,
-    default="2022",
+    default="2021",
     help="The end date of satellite data to load from the "
     "datacube. This can be any date format accepted by datacube. ",
 )
@@ -1494,44 +1495,55 @@ def intertidal_cli(
     configure_s3_access(cloud_defaults=True, aws_unsigned=aws_unsigned)
 
     # Create output folder. If it doesn't exist, create it
-    output_dir = f"data/interim/{study_area}/{start_date}-{end_date}-spearman"
+    output_dir = f"data/interim/{study_area}/{start_date}-{end_date}"
     os.makedirs(output_dir, exist_ok=True)
 
     try:
         log.info(f"Study area {study_area}: Loading satellite data")
-
-        # Connect to datacube to load data
-        dc = datacube.Datacube(app="Intertidal_CLI")
-
+
         # Create local dask cluster to improve data load time
         client = create_local_dask_cluster(return_client=True)
 
-        satellite_ds = load_data(
-            dc=dc,
-            study_area=study_area,
-            time_range=(start_date, end_date),
-            resolution=resolution,
-            crs="EPSG:3577",
-            include_s2=True,
-            include_ls=True,
-            filter_gqa=True,
-            max_cloudcover=90,
-            skip_broken_datasets=True,
-        )
+        if study_area == "testing":
+            log.info(f"Running in testing mode")
+            import pickle
 
-        # Load data
-        satellite_ds.load()
+            with open("tests/data/satellite_ds.pickle", "rb") as handle:
+                satellite_ds = pickle.load(handle)
+            valid_mask = None
 
-        # Load data from GA's Australian Bathymetry and Topography Grid 2009
-        topobathy_ds = load_topobathy(
-            dc, satellite_ds, product="ga_multi_ausbath_0", resampling="bilinear"
-        )
+        else:
+
+            # Connect to datacube to load data
+            dc = datacube.Datacube(app="Intertidal_CLI")
+
+            satellite_ds = load_data(
+                dc=dc,
+                study_area=study_area,
+                time_range=(start_date, end_date),
+                resolution=resolution,
+                crs="EPSG:3577",
+                include_s2=True,
+                include_ls=True,
+                filter_gqa=True,
+                max_cloudcover=90,
+                skip_broken_datasets=True,
+            )
+
+            # Load data
+            satellite_ds.load()
+
+            # Load data from GA's Australian Bathymetry and Topography Grid 2009
+            topobathy_ds = load_topobathy(
+                dc, satellite_ds, product="ga_multi_ausbath_0", resampling="bilinear"
+            )
+            valid_mask = topobathy_ds.height_depth > -20
 
         # Calculate elevation
         log.info(f"Study area {study_area}: Calculating Intertidal Elevation")
         ds, ds_aux, tide_m = elevation(
             satellite_ds,
-            valid_mask=topobathy_ds.height_depth > -20,
+            valid_mask=valid_mask,
             ndwi_thresh=ndwi_thresh,
             min_freq=min_freq,
             max_freq=max_freq,

intertidal/exposure.py

@@ -14,8 +14,6 @@
 # from pyproj import Transformer
 # from scipy.signal import argrelmax, argrelmin
 
-from scipy.interpolate import interp1d 
-
 from dea_tools.coastal import pixel_tides, model_tides
 from intertidal.tide_modelling import pixel_tides_ensemble
 # from intertidal.elevation import pixel_tides_ensemble

intertidal/extents.py

@@ -77,7 +77,7 @@ def extents(
     freq,
     dem,
     corr,
-    land_use_mask="/gdata1/data/land_use/ABARES_CLUM/geotiff_clum_50m1220m/clum_50m1220m.tif",
+    land_use_mask="https://dea-public-data-dev.s3-ap-southeast-2.amazonaws.com/abares_clum_2020/clum_50m1220m.tiff",
 ):
     """
     Classify coastal ecosystems into broad classes based
@@ -147,9 +147,6 @@ def extents(
           crosses zero)
 
     """
-    ## Connect to datacube to load `ocean_da`
-    dc = datacube.Datacube(app="ocean_masking")
-
     # Load the land use dataset to mask out misclassified extents classes caused by urban land class
     landuse_da = load_reproject(
         path=land_use_mask,

notebooks/Intertidal_CLI.ipynb

@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "58c0e260-cd76-45d9-9dc3-5e4294be31c6",
    "metadata": {
     "tags": []
@@ -92,7 +92,7 @@
     "tide_model_dir = \"/gdata1/data/tide_models_clipped\"  # Directory containing tide model files\n",
     "\n",
     "# Intertidal Exposure params\n",
-    "modelled_freq = \"30min\"\n",
+    "modelled_freq = \"3h\"\n",
     "\n",
     "# Intertidal Composites params\n",
     "threshold_lowtide = 0.2\n",
@@ -102,7 +102,7 @@
     "# for testing elevation code, and outputting optional auxiliary outputs\n",
     "# for debugging (defaults are to calculate exposure/offsets, and not\n",
     "# output auxiliary files.\n",
-    "exposure_offsets = \"--no-exposure_offsets\"\n",
+    "exposure_offsets = \"\"  #\"--no-exposure_offsets\"\n",
     "output_auxiliaries = \"--output_auxiliaries\""
    ]
   },

requirements.in

@@ -14,7 +14,11 @@ pyTMD==2.0.5
 pandas==1.5.3
 pygeos==0.14
 pyproj==3.4.1
+pytest
+pytest-dependency
+pytest-cov
 pytz==2023.3
+rioxarray
 rasterio==1.3.4
 seaborn==0.13.0
 scikit_image==0.19.3

setup.py

@@ -25,6 +25,9 @@
     "pygeos",
     "pyproj",
     "pyTMD>=2.0.0",
+    "pytest",
+    "pytest-dependency",
+    "pytest-cov",
     "pytz",
     "rasterio",
     "setuptools-scm",
@@ -43,8 +46,8 @@
 NAME = "dea_intertidal"
 DESCRIPTION = "Tools for running Digital Earth Australia Intertidal"
 URL = "https://github.com/GeoscienceAustralia/dea-intertidal"
-EMAIL = "Robbi.BishopTaylor@ga.gov.au"
-AUTHOR = "Robbi Bishop-Taylor"
+EMAIL = "earth.observation@ga.gov.au"
+AUTHOR = "Geoscience Australia"
 REQUIRES_PYTHON = ">=3.8.0"
 
 # Setup kwargs

tests/test_intertidal.py

@@ -0,0 +1,53 @@
+import pytest
+from click.testing import CliRunner
+from intertidal.elevation import intertidal_cli, elevation
+import pickle
+
+
+@pytest.fixture()
+def satellite_ds():
+    with open("tests/data/satellite_ds.pickle", "rb") as handle:
+        return pickle.load(handle)
+
+
+def test_intertidal_cli():
+    runner = CliRunner()
+    result = runner.invoke(
+        intertidal_cli,
+        [
+            "--study_area",
+            "testing",
+            "--start_date",
+            "2020",
+            "--end_date",
+            "2022",
+            "--modelled_freq",
+            "3h",
+        ],
+    )
+    assert result.exit_code == 0
+
+
+def test_elevation(satellite_ds):
+    ds, ds_aux, tide_m = elevation(
+        satellite_ds,
+        valid_mask=None,
+        ndwi_thresh=0.1,
+        min_freq=0.01,
+        max_freq=0.99,
+        min_correlation=0.15,
+        windows_n=20,
+        window_prop_tide=0.15,
+        max_workers=None,
+        tide_model="FES2014",
+        tide_model_dir="/var/share/tide_models",
+        study_area=None,
+        log=None,
+    )
+
+    # Verify that ds contains correct variables
+    assert "elevation" in ds.data_vars
+    assert "elevation_uncertainty" in ds.data_vars
+
+    # Verify that ds is a single layer
+    assert "time" not in ds.dims