Merge pull request #29 from wpreimes/fix_cmdline

Add missing arg to cmd parser to fix reshuffling land points

src/ecmwf_models/era5/reshuffle.py

@@ -262,6 +262,7 @@ def main(args):
         variables=args.variables,
         bbox=args.bbox,
         h_steps=args.h_steps,
+        land_points=args.land_points,
         imgbuffer=args.imgbuffer,
     )
 

tests/test_grid.py

@@ -25,38 +25,49 @@
 Tests for grid generation
 """
 
-from ecmwf_models.grid import ERA_RegularImgGrid, get_grid_resolution, \
-    ERA_IrregularImgGrid, ERA5_RegularImgLandGrid
+from ecmwf_models.grid import (
+    ERA_RegularImgGrid,
+    get_grid_resolution,
+    ERA_IrregularImgGrid,
+    ERA5_RegularImgLandGrid,
+)
 import numpy as np
 
+
 def test_ERA_regular_grid():
     reg_grid = ERA_RegularImgGrid(0.3, 0.3)
     assert np.unique(reg_grid.activearrlat).size == 601
     assert np.unique(reg_grid.activearrlon).size == 1200
-    assert get_grid_resolution(reg_grid.activearrlat,
-                               reg_grid.activearrlon) == (0.3, 0.3)
+    assert get_grid_resolution(
+        reg_grid.activearrlat, reg_grid.activearrlon
+    ) == (
+        0.3,
+        0.3,
+    )
+
 
 def test_ERA_irregular_grid():
     # we test this with a regular grid, because it's easier
-    lon = np.arange(0, 360 - 1. / 2, 1.)
-    lat = np.arange(90, -1 * 90 - 1. / 2, -1 * 1.)
+    lon = np.arange(0, 360 - 1.0 / 2, 1.0)
+    lat = np.arange(90, -1 * 90 - 1.0 / 2, -1 * 1.0)
     lons, lats = np.meshgrid(lon, lat)
 
     grid = ERA_IrregularImgGrid(lons, lats)
 
-    assert grid == ERA_RegularImgGrid(1.,1.)
+    assert grid == ERA_RegularImgGrid(1.0, 1.0)
+
 
 def test_ERA5_landgrid_025():
-    grid = ERA5_RegularImgLandGrid(0.25, 0.25) # 0.25*0.25
+    grid = ERA5_RegularImgLandGrid(0.25, 0.25)  # 0.25*0.25
     assert grid.get_grid_points()[0].size == 244450
-    assert grid.find_nearest_gpi(16.375, 48.125)[0] == 240545
+    assert grid.find_nearest_gpi(16.25, 48.25) == (240545, 0.0)
     assert grid.gpi2cell(240545) == 1431
 
 
 def test_ERA5_landgrid_01():
-    grid = ERA5_RegularImgLandGrid(0.1, 0.1) # 0.1*0.1
+    grid = ERA5_RegularImgLandGrid(0.1, 0.1)  # 0.1*0.1
     assert grid.get_grid_points()[0].size == 1544191
-    assert grid.find_nearest_gpi(16.375, 48.125)[0] == 1508564
+    assert grid.find_nearest_gpi(16.4, 48.1) == (1508564, 0.0)
     np.testing.assert_almost_equal(grid.gpi2lonlat(1508564)[0], 16.4)
     np.testing.assert_almost_equal(grid.gpi2lonlat(1508564)[1], 48.1)
-    assert grid.gpi2cell(1508564) == 1431
+    assert grid.gpi2cell(1508564) == 1431

tests/tests_era5/test_era5_reshuffle.py

@@ -21,68 +21,146 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-'''
+"""
 Test module for image to time series conversion.
-'''
+"""
 
 import os
 import glob
 import tempfile
 import numpy as np
 import numpy.testing as nptest
+from datetime import datetime
+
 from ecmwf_models.era5.reshuffle import main
 from ecmwf_models import ERATs
+from ecmwf_models.era5.reshuffle import parse_args
+
+
+def test_parse_args():
+
+    args = parse_args(
+        [
+            "/in",
+            "/out",
+            "2000-01-01",
+            "2010-12-31",
+            "swvl1",
+            "swvl2",
+            "--land_points",
+            "True",
+            "--imgbuffer",
+            "1000",
+            "--bbox",
+            "12",
+            "46",
+            "17",
+            "50",
+        ]
+    )
+
+    assert isinstance(args.dataset_root, str) and args.dataset_root == "/in"
+    assert (
+        isinstance(args.timeseries_root, str)
+        and args.timeseries_root == "/out"
+    )
+    assert isinstance(args.start, datetime) and args.start == datetime(
+        2000, 1, 1
+    )
+    assert isinstance(args.end, datetime) and args.end == datetime(
+        2010, 12, 31
+    )
+    assert isinstance(args.variables, list) and len(args.variables) == 2
+    assert isinstance(args.land_points, bool) and args.land_points is True
+    assert isinstance(args.imgbuffer, int) and args.imgbuffer == 1000
+    assert (
+        isinstance(args.bbox, list)
+        and len(args.bbox) == 4
+        and all([isinstance(a, float) for a in args.bbox])
+    )
+
 
 def test_ERA5_reshuffle_nc():
     # test reshuffling era5 netcdf images to time series
 
-    inpath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..',
-                          "ecmwf_models-test-data", "ERA5", "netcdf")
+    inpath = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "..",
+        "ecmwf_models-test-data",
+        "ERA5",
+        "netcdf",
+    )
 
-    startdate = '2010-01-01'
-    enddate = '2010-01-01'
+    startdate = "2010-01-01"
+    enddate = "2010-01-01"
     parameters = ["swvl1", "swvl2"]
-    h_steps = ['--h_steps', '0', '12']
-    landpoints = ['--land_points', 'True']
-    bbox = ['--bbox', "12", '46', '17', '50']
+    h_steps = ["--h_steps", "0", "12"]
+    landpoints = ["--land_points", "True"]
+    bbox = ["--bbox", "12", "46", "17", "50"]
 
     with tempfile.TemporaryDirectory() as ts_path:
-        args = [inpath, ts_path, startdate, enddate] \
-               + parameters + h_steps + landpoints + bbox
+        args = (
+            [inpath, ts_path, startdate, enddate]
+            + parameters
+            + h_steps
+            + landpoints
+            + bbox
+        )
         main(args)
-        assert len(glob.glob(os.path.join(ts_path, "*.nc"))) == 5  # less files because only land points and bbox
-        ds = ERATs(ts_path, ioclass_kws={'read_bulk': True})
+        assert (
+            len(glob.glob(os.path.join(ts_path, "*.nc"))) == 5
+        )  # less files because only land points and bbox
+        ds = ERATs(ts_path, ioclass_kws={"read_bulk": True})
         ts = ds.read(15, 48)
         ds.close()
-        swvl1_values_should = np.array([0.402825,  0.390983], dtype=np.float32)
-        nptest.assert_allclose(ts['swvl1'].values, swvl1_values_should, rtol=1e-5)
-        swvl2_values_should = np.array([0.390512,  0.390981], dtype=np.float32)
-        nptest.assert_allclose(ts['swvl2'].values, swvl2_values_should, rtol=1e-5)
+        swvl1_values_should = np.array([0.402825, 0.390983], dtype=np.float32)
+        nptest.assert_allclose(
+            ts["swvl1"].values, swvl1_values_should, rtol=1e-5
+        )
+        swvl2_values_should = np.array([0.390512, 0.390981], dtype=np.float32)
+        nptest.assert_allclose(
+            ts["swvl2"].values, swvl2_values_should, rtol=1e-5
+        )
+
 
 def test_ERA5_reshuffle_grb():
     # test reshuffling era5 netcdf images to time series
 
-    inpath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..',
-                          "ecmwf_models-test-data", "ERA5", "netcdf")
-    startdate = '2010-01-01'
-    enddate = '2010-01-01'
+    inpath = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "..",
+        "ecmwf_models-test-data",
+        "ERA5",
+        "netcdf",
+    )
+    startdate = "2010-01-01"
+    enddate = "2010-01-01"
     parameters = ["swvl1", "swvl2"]
-    h_steps = ['--h_steps', '0', '12']
-    landpoints = ['--land_points', 'False']
-    bbox = ['--bbox', "12", '46', '17', '50']
+    h_steps = ["--h_steps", "0", "12"]
+    landpoints = ["--land_points", "False"]
+    bbox = ["--bbox", "12", "46", "17", "50"]
 
     with tempfile.TemporaryDirectory() as ts_path:
 
-        args = [inpath, ts_path, startdate, enddate] + parameters + \
-               h_steps + landpoints + bbox
+        args = (
+            [inpath, ts_path, startdate, enddate]
+            + parameters
+            + h_steps
+            + landpoints
+            + bbox
+        )
 
         main(args)
 
         assert len(glob.glob(os.path.join(ts_path, "*.nc"))) == 5
-        ds = ERATs(ts_path, ioclass_kws={'read_bulk': True})
+        ds = ERATs(ts_path, ioclass_kws={"read_bulk": True})
         ts = ds.read(15, 48)
         ds.close()
-        swvl1_values_should = np.array([0.402824,  0.390979], dtype=np.float32)
-        nptest.assert_allclose(ts['swvl1'].values, swvl1_values_should, rtol=1e-5)
-        swvl2_values_should = np.array([0.390514,  0.390980], dtype=np.float32)
-        nptest.assert_allclose(ts['swvl2'].values, swvl2_values_should, rtol=1e-5)
+        swvl1_values_should = np.array([0.402824, 0.390979], dtype=np.float32)
+        nptest.assert_allclose(
+            ts["swvl1"].values, swvl1_values_should, rtol=1e-5
+        )
+        swvl2_values_should = np.array([0.390514, 0.390980], dtype=np.float32)
+        nptest.assert_allclose(
+            ts["swvl2"].values, swvl2_values_should, rtol=1e-5
+        )