Merge pull request #157 from djhoese/remove-test-warnings

Fix most warnings during tests

pyproject.toml

@@ -13,3 +13,19 @@ build-backend = "setuptools.build_meta"
 relative_files = true
 plugins = ["Cython.Coverage"]
 omit = ["trollimage/version.py", "versioneer.py"]
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
+xfail_strict = true
+filterwarnings = [
+    "error",
+    "ignore::rasterio.errors.NotGeoreferencedWarning",
+    # dateutil needs a new release
+    # https://github.com/dateutil/dateutil/issues/1314
+    'ignore:datetime.datetime.utcfromtimestamp\(\) is deprecated and scheduled for removal:DeprecationWarning:dateutil',
+]
+log_cli_level = "info"
+testpaths = [
+    "trollimage/tests",
+]

setup.cfg

@@ -1,10 +1,3 @@
-[bdist_rpm]
-requires=numpy python-pillow
-release=1
-
-[bdist_wheel]
-universal=1
-
 [flake8]
 max-line-length = 120
 exclude = 

trollimage/colormap.py

@@ -239,11 +239,11 @@ def __init__(self, *tuples, **kwargs):
                              f"{self.values.shape[0]} and {self.colors.shape[0]}.")
 
     def _validate_colors(self, colors):
-        colors = np.array(colors)
+        colors = np.asarray(colors)
         if colors.ndim != 2 or colors.shape[-1] not in (3, 4):
             raise ValueError("Colormap 'colors' must be RGB or RGBA. Got unexpected shape: {}".format(colors.shape))
         if not np.issubdtype(colors.dtype, np.floating):
-            warnings.warn("Colormap 'colors' should be flotaing point numbers between 0 and 1.", stacklevel=3)
+            warnings.warn("Colormap 'colors' should be floating point numbers between 0 and 1.", stacklevel=3)
             colors = colors.astype(np.float64)
         return colors
 

trollimage/tests/test_colormap.py

@@ -184,19 +184,20 @@ def test_diff_colors_values(self):
 
     def test_nonfloat_colors(self):
         """Pass integer colors to colormap."""
-        colormap.Colormap(
-            colors=np.arange(5 * 3, dtype=np.uint8).reshape((5, 3)),
-            values=np.linspace(0, 1, 5),
-        )
+        with pytest.warns(UserWarning, match="should be floating point"):
+            colormap.Colormap(
+                colors=np.arange(5 * 3, dtype=np.uint8).reshape((5, 3)),
+                values=np.linspace(0, 1, 5),
+            )
 
     def test_merge_nonmonotonic(self):
         """Test that merged colormaps must have monotonic values."""
         cmap1 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(2, 3, 5),
         )
         cmap2 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(0, 1, 5),
         )
         with pytest.raises(ValueError, match=r".*monotonic.*"):
@@ -274,11 +275,11 @@ def test_merge_rgb_rgba(self, colors1, colors2):
     def test_merge_equal_values(self):
         """Test that merged colormaps can have equal values at the merge point."""
         cmap1 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(0, 1, 5),
         )
         cmap2 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(1, 2, 5),
         )
         assert cmap1.values[-1] == cmap2.values[0]
@@ -288,11 +289,11 @@ def test_merge_equal_values(self):
     def test_merge_monotonic_decreasing(self):
         """Test that merged colormaps can be monotonically decreasing."""
         cmap1 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(2, 1, 5),
         )
         cmap2 = colormap.Colormap(
-            colors=np.arange(5 * 3).reshape((5, 3)),
+            colors=np.arange(5 * 3.0).reshape((5, 3)),
             values=np.linspace(1, 0, 5),
         )
         _assert_monotonic_values(cmap1, increasing=False)
@@ -557,7 +558,7 @@ def test_csv_roundtrip(self, tmp_path, csv_filename, new_range, color_scale):
         else:
             res = orig_cmap.to_csv(None, color_scale=color_scale)
             assert isinstance(res, str)
-            new_cmap = colormap.Colormap.from_file(res, color_scale=color_scale)
+            new_cmap = colormap.Colormap.from_string(res, color_scale=color_scale)
         np.testing.assert_allclose(orig_cmap.values, new_cmap.values)
         np.testing.assert_allclose(orig_cmap.colors, new_cmap.colors)
 
@@ -636,23 +637,27 @@ def test_cmap_from_file_bad_shape(self):
             with pytest.raises(ValueError):
                 colormap.Colormap.from_file(cmap_filename)
 
-    def test_cmap_from_np(self, tmp_path):
+    @pytest.mark.parametrize("color_scale", [None, 1.0])
+    def test_cmap_from_np(self, tmp_path, color_scale):
         """Test creating a colormap from a numpy file."""
-        cmap_data = _generate_cmap_test_data(None, "RGB")
+        cmap_data = _generate_cmap_test_data(color_scale, "RGB")
         fnp = tmp_path / "test.npy"
         np.save(fnp, cmap_data)
-        cmap = colormap.Colormap.from_np(fnp, color_scale=1)
+        cmap = colormap.Colormap.from_np(fnp, color_scale=color_scale or 255)
         np.testing.assert_allclose(cmap.values, [0, 0.33333333, 0.6666667, 1])
-        np.testing.assert_array_equal(cmap.colors, cmap_data)
+        exp_data = cmap_data if color_scale == 1.0 else cmap_data / 255.0
+        np.testing.assert_array_equal(cmap.colors, exp_data)
 
-    def test_cmap_from_csv(self, tmp_path, color_scale=1):
+    @pytest.mark.parametrize("color_scale", [None, 1.0])
+    def test_cmap_from_csv(self, tmp_path, color_scale):
         """Test creating a colormap from a CSV file."""
-        cmap_data = _generate_cmap_test_data(None, "RGB")
+        cmap_data = _generate_cmap_test_data(color_scale, "RGB")
         fnp = tmp_path / "test.csv"
         np.savetxt(fnp, cmap_data, delimiter=",")
-        cmap = colormap.Colormap.from_csv(fnp, color_scale=1)
+        cmap = colormap.Colormap.from_csv(fnp, color_scale=color_scale or 255)
         np.testing.assert_allclose(cmap.values, [0, 0.33333333, 0.66666667, 1])
-        np.testing.assert_array_equal(cmap.colors, cmap_data)
+        exp_data = cmap_data if color_scale == 1.0 else cmap_data / 255.0
+        np.testing.assert_array_equal(cmap.colors, exp_data)
 
 
 def test_cmap_from_string():
@@ -663,12 +668,14 @@ def test_cmap_from_string():
     np.testing.assert_array_equal(cmap.colors, [[0, 0, 0], [1, 1, 1], [2, 2, 2]])
 
 
-def test_cmap_from_ndarray():
+@pytest.mark.parametrize("color_scale", [None, 255, 1.0])
+def test_cmap_from_ndarray(color_scale):
     """Test creating a colormap from a numpy array."""
-    cmap_data = _generate_cmap_test_data(None, "RGB")
-    cmap = colormap.Colormap.from_ndarray(cmap_data, color_scale=1)
+    cmap_data = _generate_cmap_test_data(color_scale, "RGB")
+    cmap = colormap.Colormap.from_ndarray(cmap_data, color_scale=color_scale or 255)
     np.testing.assert_allclose(cmap.values, [0, 0.33333333, 0.66666667, 1])
-    np.testing.assert_array_equal(cmap.colors, cmap_data)
+    exp_data = cmap_data if color_scale == 1.0 else cmap_data / 255.0
+    np.testing.assert_array_equal(cmap.colors, exp_data)
 
 
 def test_cmap_from_name():

trollimage/tests/test_image.py

@@ -22,6 +22,7 @@
 import sys
 import tempfile
 import unittest
+from datetime import timezone, datetime
 import warnings
 from unittest import mock
 from collections import OrderedDict
@@ -951,7 +952,8 @@ def test_save_geotiff_float_dask_array_with_nans_and_fill_value(self, tmp_path):
         img = xrimage.XRImage(data)
         filename = tmp_path / "image.tif"
 
-        img.save(filename, fill_value=128)
+        with pytest.warns(UserWarning, match="fill value will overlap with valid data"):
+            img.save(filename, fill_value=128)
         with rio.open(filename) as f:
             file_data = f.read()
         assert file_data.shape == (3, 5, 5)  # no alpha band
@@ -1029,7 +1031,8 @@ def test_save_geotiff_float_dask_array_to_signed_int(self, tmp_path):
         img = xrimage.XRImage(data)
         filename = tmp_path / "image.tif"
 
-        img.save(filename, dtype=np.int16, fill_value=-128)
+        with pytest.warns(UserWarning, match="fill value will overlap with valid data"):
+            img.save(filename, dtype=np.int16, fill_value=-128)
         with rio.open(filename) as f:
             file_data = f.read()
         assert file_data.shape == (3, 5, 5)  # no alpha band
@@ -1088,8 +1091,6 @@ def test_save_geotiff_float_dask_array_with_alpha(self, tmp_path):
                         reason="'NamedTemporaryFile' not supported on Windows")
     def test_save_geotiff_datetime(self):
         """Test saving geotiffs when start_time is in the attributes."""
-        import datetime as dt
-
         data = xr.DataArray(np.arange(75).reshape(5, 5, 3), dims=[
             'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
 
@@ -1099,7 +1100,7 @@ def test_save_geotiff_datetime(self):
         assert "TIFFTAG_DATETIME" not in tags
 
         # Valid datetime
-        data.attrs['start_time'] = dt.datetime.utcnow()
+        data.attrs['start_time'] = datetime.now(timezone.utc)
         tags = _get_tags_after_writing_to_geotiff(data)
         assert "TIFFTAG_DATETIME" in tags
 
@@ -1819,42 +1820,39 @@ def test_weber_fechner_stretch(self, dtype):
         from trollimage import xrimage
 
         arr = np.arange(75., dtype=dtype).reshape(5, 5, 3) / 74.
-        data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
+        data = xr.DataArray(arr.copy() + 0.1, dims=['y', 'x', 'bands'],
                             coords={'bands': ['R', 'G', 'B']})
         img = xrimage.XRImage(data)
         img.stretch_weber_fechner(2.5, 0.2)
         enhs = img.data.attrs['enhancement_history'][0]
         assert enhs == {'weber_fechner': (2.5, 0.2)}
         assert img.data.dtype == dtype
-        res = np.array([[[-np.inf, -6.73656795, -5.0037],
-                         [-3.99003723, -3.27083205, -2.71297317],
-                         [-2.25716928, -1.87179258, -1.5379641],
-                         [-1.24350651, -0.98010522, -0.74182977],
-                         [-0.52430133, -0.32419456, -0.13892463]],
-
-                        [[0.03355755, 0.19490385, 0.34646541],
-                         [0.48936144, 0.6245295, 0.75276273],
-                         [0.87473814, 0.99103818, 1.10216759],
-                         [1.20856662, 1.31062161, 1.40867339],
-                         [1.50302421, 1.59394332, 1.68167162]],
-
-                        [[1.7664255, 1.84840006, 1.92777181],
-                         [2.00470095, 2.07933336, 2.1518022],
-                         [2.22222939, 2.29072683, 2.35739745],
-                         [2.42233616, 2.48563068, 2.54736221],
-                         [2.60760609, 2.66643234, 2.72390613]],
-
-                        [[2.78008827, 2.83503554, 2.88880105],
-                         [2.94143458, 2.99298279, 3.04348956],
-                         [3.09299613, 3.14154134, 3.18916183],
-                         [3.23589216, 3.28176501, 3.32681127],
-                         [3.37106022, 3.41453957, 3.45727566]],
-
-                        [[3.49929345, 3.54061671, 3.58126801],
-                         [3.62126886, 3.66063976, 3.69940022],
-                         [3.7375689, 3.7751636, 3.81220131],
-                         [3.84869831, 3.88467015, 3.92013174],
-                         [3.95509735, 3.98958065, 4.02359478]]], dtype=dtype)
+        res = np.array([
+            [[0., 0., 0.],
+             [0., 0., 0.],
+             [0., 0., 0.0993509],
+             [0.25663552, 0.40460747, 0.54430866],
+             [0.6766145, 0.8022693, 0.92190945]],
+            [[1.0360844, 1.1452721, 1.2498899],
+             [1.350305, 1.446842, 1.5397894],
+             [1.629405, 1.7159187, 1.7995386],
+             [1.8804514, 1.9588281, 2.0348217],
+             [2.1085734, 2.1802118, 2.2498538]],
+            [[2.3176088, 2.3835757, 2.4478464],
+             [2.5105066, 2.571634, 2.631303],
+             [2.689581, 2.7465308, 2.8022122],
+             [2.8566809, 2.9099874, 2.9621818],
+             [3.013308, 3.0634098, 3.1125278]],
+            [[3.1606987, 3.207959, 3.2543423],
+             [3.299881, 3.344605, 3.3885431],
+             [3.431722, 3.4741676, 3.515905],
+             [3.5569568, 3.597345, 3.6370919],
+             [3.6762161, 3.714738, 3.7526748]],
+            [[3.7900448, 3.826864, 3.863149],
+             [3.8989153, 3.934177, 3.968948],
+             [4.0032415, 4.037072, 4.07045],
+             [4.103389, 4.135899, 4.1679916],
+             [4.199678, 4.2309675, 4.2618704]]], dtype=dtype)
 
         np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
 
@@ -2334,7 +2332,7 @@ def test_colorize_geotiff_tag(self, tmp_path, colormap_tag):
                 assert "colormap" not in metadata
             else:
                 assert "colormap" in metadata
-                loaded_brbg = Colormap.from_file(metadata["colormap"])
+                loaded_brbg = Colormap.from_string(metadata["colormap"])
                 np.testing.assert_allclose(new_brbg.values, loaded_brbg.values)
                 np.testing.assert_allclose(new_brbg.colors, loaded_brbg.colors)
 
@@ -2523,7 +2521,7 @@ def test_palettize_geotiff_tag(self, tmp_path, colormap_tag, keep_palette):
                 assert "colormap" not in metadata
             else:
                 assert "colormap" in metadata
-                loaded_brbg = Colormap.from_file(metadata["colormap"])
+                loaded_brbg = Colormap.from_string(metadata["colormap"])
                 np.testing.assert_allclose(new_brbg.values, loaded_brbg.values)
                 np.testing.assert_allclose(new_brbg.colors, loaded_brbg.colors)
 

trollimage/xrimage.py

@@ -1242,11 +1242,12 @@ def stretch_weber_fechner(self, k, s0):
 
         p = k.ln(S/S0)
         p is perception, S is the stimulus, S0 is the stimulus threshold (the
-        highest unpercieved stimulus), and k is the factor.
+        highest unperceived stimulus), and k is the factor.
 
         """
         attrs = self.data.attrs
-        self.data = k * np.log(self.data / s0)
+        clipped_stimuli = np.clip(self.data, s0, None)
+        self.data = k * np.log(clipped_stimuli / s0)
         self.data.attrs = attrs
         self.data.attrs.setdefault('enhancement_history', []).append({'weber_fechner': (k, s0)})