[Git][debian-gis-team/trollimage][master] 4 commits: New upstream version 1.22.2
Antonio Valentino (@antonio.valentino)
gitlab at salsa.debian.org
Fri Dec 1 06:28:45 GMT 2023
Antonio Valentino pushed to branch master at Debian GIS Project / trollimage
Commits:
8bf18d9f by Antonio Valentino at 2023-12-01T06:24:47+00:00
New upstream version 1.22.2
- - - - -
cd19bfd3 by Antonio Valentino at 2023-12-01T06:24:53+00:00
Update upstream source from tag 'upstream/1.22.2'
Update to upstream version '1.22.2'
with Debian dir efc0ff0414e0d60a730c9719a27576124a22e9e4
- - - - -
1d23c59b by Antonio Valentino at 2023-12-01T06:25:33+00:00
New upstream release
- - - - -
8f6b94a9 by Antonio Valentino at 2023-12-01T06:27:52+00:00
Set distribution to unstable
- - - - -
7 changed files:
- CHANGELOG.md
- debian/changelog
- doc/examples/utilities-examples.py
- trollimage/tests/test_image.py
- trollimage/utilities.py
- trollimage/version.py
- trollimage/xrimage.py
Changes:
=====================================
CHANGELOG.md
=====================================
@@ -1,3 +1,14 @@
+## Version 1.22.2 (2023/11/26)
+
+### Pull Requests Merged
+
+#### Bugs fixed
+
+* [PR 154](https://github.com/pytroll/trollimage/pull/154) - Fix stretch when min/max value it outside input data type
+
+In this release 1 pull request was closed.
+
+
## Version 1.22.1 (2023/11/24)
### Issues Closed
=====================================
debian/changelog
=====================================
@@ -1,3 +1,9 @@
+trollimage (1.22.2-1) unstable; urgency=medium
+
+ * New upstream relese.
+
+ -- Antonio Valentino <antonio.valentino at tiscali.it> Fri, 01 Dec 2023 06:27:35 +0000
+
trollimage (1.22.1-1) unstable; urgency=medium
* New upstream release.
=====================================
doc/examples/utilities-examples.py
=====================================
@@ -1,22 +1,18 @@
+"""Example showing importing colormaps from on-disk files."""
from trollimage import utilities as tu
-
-#
-# Examples: importing colormaps
-#
-filename='setvak.rgb'
+# Examples: importing colormaps
+filename = 'setvak.rgb'
my_cmap = tu.cmap_from_text(filename)
print(my_cmap.colors)
my_cmap_norm = tu.cmap_from_text(filename, norm=True)
print(my_cmap_norm.colors)
-my_cmap_transp = tu.cmap_from_text(filename,norm=True, transparency=True)
+my_cmap_transp = tu.cmap_from_text(filename, norm=True, transparency=True)
print(my_cmap_transp.colors)
-filename='hrv.rgb'
-my_cmap_hex = tu.cmap_from_text(filename,hex=True)
+filename = 'hrv.rgb'
+my_cmap_hex = tu.cmap_from_text(filename, hex=True)
print(my_cmap_hex.colors)
-#
# Example: converting PIL to trollimage.Image
-#
image = "pifn.png"
timage = tu.pilimage2trollimage(image)
=====================================
trollimage/tests/test_image.py
=====================================
@@ -37,7 +37,6 @@ from trollimage.colormap import Colormap, brbg
from trollimage._xrimage_rasterio import RIODataset
from .utils import assert_maximum_dask_computes
-
EPSILON = 0.0001
@@ -1102,13 +1101,17 @@ class TestXRImage:
lons = xr.DataArray(da.from_array(np.arange(25).reshape(5, 5), chunks=5),
dims=['y', 'x'],
- attrs={'gcps': gcps,
- 'crs': crs})
+ attrs={
+ 'gcps': gcps,
+ 'crs': crs
+ })
lats = xr.DataArray(da.from_array(np.arange(25).reshape(5, 5), chunks=5),
dims=['y', 'x'],
- attrs={'gcps': gcps,
- 'crs': crs})
+ attrs={
+ 'gcps': gcps,
+ 'crs': crs
+ })
swath_def = SwathDefinition(lons, lats)
data = xr.DataArray(da.from_array(np.arange(75).reshape(5, 5, 3), chunks=5),
@@ -1377,7 +1380,7 @@ class TestXRImage:
# trigger COG driver to create 2 overview levels
# COG driver is only available in GDAL 3.1 or later
if rio.__gdal_version__ >= '3.1':
- data = xr.DataArray(np.arange(1200*1200*3).reshape(1200, 1200, 3), dims=[
+ data = xr.DataArray(np.arange(1200 * 1200 * 3).reshape(1200, 1200, 3), dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
assert np.issubdtype(img.data.dtype, np.integer)
@@ -1402,7 +1405,7 @@ class TestXRImage:
assert len(f.overviews(1)) == 2
# auto-levels
- data = np.zeros(25*25*3, dtype=np.uint8).reshape(25, 25, 3)
+ data = np.zeros(25 * 25 * 3, dtype=np.uint8).reshape(25, 25, 3)
data = xr.DataArray(data, dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
@@ -1413,7 +1416,7 @@ class TestXRImage:
assert len(f.overviews(1)) == 4
# auto-levels and resampling
- data = np.zeros(25*25*3, dtype=np.uint8).reshape(25, 25, 3)
+ data = np.zeros(25 * 25 * 3, dtype=np.uint8).reshape(25, 25, 3)
data = xr.DataArray(data, dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
@@ -1511,6 +1514,7 @@ class TestXRImage:
@pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
def test_crude_stretch_with_limits(self, dtype):
+ """Test crude stretch with different input dtypes."""
arr = np.arange(75, dtype=dtype).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
@@ -1519,14 +1523,18 @@ class TestXRImage:
assert img.data.dtype == dtype
np.testing.assert_allclose(img.data.values, arr / 74., rtol=1e-6)
- def test_crude_stretch_integer_data(self):
- arr = np.arange(75, dtype=int).reshape(5, 5, 3)
+ @pytest.mark.parametrize("dtype", [np.uint8, int])
+ # include a stretch within 8-bit uint and outside
+ @pytest.mark.parametrize("max_stretch", [74, 74 * 4])
+ def test_crude_stretch_integer_data(self, dtype, max_stretch):
+ """Test crude stretch with different input integer dtypes."""
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
- img.crude_stretch(0, 74)
+ img.crude_stretch(0, max_stretch)
assert img.data.dtype == np.float32
- np.testing.assert_allclose(img.data.values, arr.astype(np.float32) / 74., rtol=1e-6)
+ np.testing.assert_allclose(img.data.values, arr.astype(np.float32) / max_stretch, rtol=1e-6)
@pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
def test_invert(self, dtype):
@@ -2024,24 +2032,24 @@ class TestXRImage:
assert img3.data.dtype == dtype
np.testing.assert_allclose(
- (alpha1 + alpha2 * (1 - alpha1)).squeeze(),
- img3.data.sel(bands="A"))
+ (alpha1 + alpha2 * (1 - alpha1)).squeeze(),
+ img3.data.sel(bands="A"))
np.testing.assert_allclose(
img3.data.sel(bands="R").values,
np.array(
- [[1., 0.95833635, 0.9136842, 0.8666667, 0.8180645],
- [0.768815, 0.72, 0.6728228, 0.62857145, 0.5885714],
- [0.55412847, 0.5264665, 0.50666666, 0.495612, 0.49394494],
- [0.5020408, 0.52, 0.5476586, 0.5846154, 0.63027024],
- [0.683871, 0.7445614, 0.81142855, 0.8835443, 0.96]], dtype=dtype),
- rtol=2e-6)
+ [[1., 0.95833635, 0.9136842, 0.8666667, 0.8180645],
+ [0.768815, 0.72, 0.6728228, 0.62857145, 0.5885714],
+ [0.55412847, 0.5264665, 0.50666666, 0.495612, 0.49394494],
+ [0.5020408, 0.52, 0.5476586, 0.5846154, 0.63027024],
+ [0.683871, 0.7445614, 0.81142855, 0.8835443, 0.96]], dtype=dtype),
+ rtol=2e-6)
with pytest.raises(TypeError):
img1.blend("Salekhard")
wrongimg = xrimage.XRImage(
- xr.DataArray(np.zeros((0, 0)), dims=("y", "x")))
+ xr.DataArray(np.zeros((0, 0)), dims=("y", "x")))
with pytest.raises(ValueError):
img1.blend(wrongimg)
@@ -2072,7 +2080,7 @@ class TestXRImage:
data = xr.DataArray(np_data, dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
- dummy_args = [(OrderedDict(), ), {}]
+ dummy_args = [(OrderedDict(),), {}]
def dummy_fun(pil_obj, *args, **kwargs):
dummy_args[0] = args
@@ -2097,12 +2105,12 @@ class TestXRImage:
res = img.apply_pil(dummy_fun, 'RGB',
fun_args=('Hey', 'Jude'),
fun_kwargs={'chorus': "La lala lalalala"})
- assert dummy_args == [({}, ), {}]
+ assert dummy_args == [({},), {}]
res.data.data.compute()
assert dummy_args == [(OrderedDict(), 'Hey', 'Jude'), {'chorus': "La lala lalalala"}]
# Test HACK for _burn_overlay
- dummy_args = [(OrderedDict(), ), {}]
+ dummy_args = [(OrderedDict(),), {}]
def _burn_overlay(pil_obj, *args, **kwargs):
dummy_args[0] = args
@@ -2163,92 +2171,93 @@ class TestXRImageColorize:
9.24115112e-02, 6.24348956e-02, 2.53761173e-02, 4.08181032e-03,
4.27986478e-03, 4.17929690e-03, 3.78662146e-03, 3.12692318e-03,
2.24023940e-03, 1.17807264e-03, 3.21825881e-08]],
- [[1.88235327e-01, 2.05148705e-01, 2.22246533e-01, 2.39526068e-01,
- 2.56989487e-01, 2.74629823e-01, 2.92439994e-01, 3.10413422e-01,
- 3.32343814e-01, 3.57065419e-01, 3.82068278e-01, 4.07348961e-01,
- 4.32903760e-01, 4.58728817e-01, 4.84820203e-01],
- [5.12920806e-01, 5.47946930e-01, 5.82732545e-01, 6.17314757e-01,
- 6.51719365e-01, 6.85963748e-01, 7.20058900e-01, 7.54010901e-01,
- 7.76938576e-01, 7.97119666e-01, 8.17286145e-01, 8.37436050e-01,
- 8.57567245e-01, 8.77677444e-01, 8.97764221e-01],
- [9.14974807e-01, 9.26634684e-01, 9.36490370e-01, 9.44572058e-01,
- 9.50936890e-01, 9.55671974e-01, 9.58899694e-01, 9.60784377e-01,
- 9.54533524e-01, 9.48563492e-01, 9.42789228e-01, 9.37126769e-01,
- 9.31494725e-01, 9.25815456e-01, 9.20015949e-01],
- [9.08501736e-01, 8.93232137e-01, 8.77927046e-01, 8.62584949e-01,
- 8.47204357e-01, 8.31783811e-01, 8.16321878e-01, 7.98071154e-01,
- 7.68921238e-01, 7.39943765e-01, 7.11141597e-01, 6.82517728e-01,
- 6.54075286e-01, 6.25817541e-01, 5.97747906e-01],
- [5.70776450e-01, 5.44247780e-01, 5.17943011e-01, 4.91867999e-01,
- 4.66028940e-01, 4.40432405e-01, 4.15085375e-01, 3.90762603e-01,
- 3.67819656e-01, 3.45100714e-01, 3.22617398e-01, 3.00381887e-01,
- 2.78406993e-01, 2.56706267e-01, 2.35294109e-01]],
- [[1.96078164e-02, 2.42548791e-02, 2.74972980e-02, 2.96227786e-02,
- 3.17156285e-02, 3.38568546e-02, 3.60498743e-02, 3.82990372e-02,
- 5.17340107e-02, 7.13424499e-02, 9.00791380e-02, 1.08349520e-01,
- 1.26372958e-01, 1.44280386e-01, 1.62155431e-01],
- [1.84723724e-01, 2.25766583e-01, 2.66872651e-01, 3.08395883e-01,
- 3.50522786e-01, 3.93349758e-01, 4.36919863e-01, 4.81242202e-01,
- 5.19495725e-01, 5.56210021e-01, 5.93054317e-01, 6.30051817e-01,
- 6.67218407e-01, 7.04564489e-01, 7.42096284e-01],
- [7.75703258e-01, 8.04590533e-01, 8.34519408e-01, 8.64314044e-01,
- 8.92841230e-01, 9.19042335e-01, 9.41963593e-01, 9.60784303e-01,
- 9.45735072e-01, 9.32649658e-01, 9.21608884e-01, 9.12665692e-01,
- 9.05844317e-01, 9.01139812e-01, 8.98517976e-01],
- [8.86846758e-01, 8.68087757e-01, 8.49200397e-01, 8.30188000e-01,
- 8.11054008e-01, 7.91801982e-01, 7.72435606e-01, 7.51368872e-01,
- 7.24059052e-01, 6.97016433e-01, 6.70243011e-01, 6.43740898e-01,
- 6.17512331e-01, 5.91559687e-01, 5.65885492e-01],
- [5.39262087e-01, 5.12603461e-01, 4.86221750e-01, 4.60123396e-01,
- 4.34315297e-01, 4.08804859e-01, 3.83600057e-01, 3.58016749e-01,
- 3.31909003e-01, 3.06406088e-01, 2.81515756e-01, 2.57245695e-01,
- 2.33603632e-01, 2.10597439e-01, 1.88235281e-01]]], dtype=np.float64),
+ [[1.88235327e-01, 2.05148705e-01, 2.22246533e-01, 2.39526068e-01,
+ 2.56989487e-01, 2.74629823e-01, 2.92439994e-01, 3.10413422e-01,
+ 3.32343814e-01, 3.57065419e-01, 3.82068278e-01, 4.07348961e-01,
+ 4.32903760e-01, 4.58728817e-01, 4.84820203e-01],
+ [5.12920806e-01, 5.47946930e-01, 5.82732545e-01, 6.17314757e-01,
+ 6.51719365e-01, 6.85963748e-01, 7.20058900e-01, 7.54010901e-01,
+ 7.76938576e-01, 7.97119666e-01, 8.17286145e-01, 8.37436050e-01,
+ 8.57567245e-01, 8.77677444e-01, 8.97764221e-01],
+ [9.14974807e-01, 9.26634684e-01, 9.36490370e-01, 9.44572058e-01,
+ 9.50936890e-01, 9.55671974e-01, 9.58899694e-01, 9.60784377e-01,
+ 9.54533524e-01, 9.48563492e-01, 9.42789228e-01, 9.37126769e-01,
+ 9.31494725e-01, 9.25815456e-01, 9.20015949e-01],
+ [9.08501736e-01, 8.93232137e-01, 8.77927046e-01, 8.62584949e-01,
+ 8.47204357e-01, 8.31783811e-01, 8.16321878e-01, 7.98071154e-01,
+ 7.68921238e-01, 7.39943765e-01, 7.11141597e-01, 6.82517728e-01,
+ 6.54075286e-01, 6.25817541e-01, 5.97747906e-01],
+ [5.70776450e-01, 5.44247780e-01, 5.17943011e-01, 4.91867999e-01,
+ 4.66028940e-01, 4.40432405e-01, 4.15085375e-01, 3.90762603e-01,
+ 3.67819656e-01, 3.45100714e-01, 3.22617398e-01, 3.00381887e-01,
+ 2.78406993e-01, 2.56706267e-01, 2.35294109e-01]],
+ [[1.96078164e-02, 2.42548791e-02, 2.74972980e-02, 2.96227786e-02,
+ 3.17156285e-02, 3.38568546e-02, 3.60498743e-02, 3.82990372e-02,
+ 5.17340107e-02, 7.13424499e-02, 9.00791380e-02, 1.08349520e-01,
+ 1.26372958e-01, 1.44280386e-01, 1.62155431e-01],
+ [1.84723724e-01, 2.25766583e-01, 2.66872651e-01, 3.08395883e-01,
+ 3.50522786e-01, 3.93349758e-01, 4.36919863e-01, 4.81242202e-01,
+ 5.19495725e-01, 5.56210021e-01, 5.93054317e-01, 6.30051817e-01,
+ 6.67218407e-01, 7.04564489e-01, 7.42096284e-01],
+ [7.75703258e-01, 8.04590533e-01, 8.34519408e-01, 8.64314044e-01,
+ 8.92841230e-01, 9.19042335e-01, 9.41963593e-01, 9.60784303e-01,
+ 9.45735072e-01, 9.32649658e-01, 9.21608884e-01, 9.12665692e-01,
+ 9.05844317e-01, 9.01139812e-01, 8.98517976e-01],
+ [8.86846758e-01, 8.68087757e-01, 8.49200397e-01, 8.30188000e-01,
+ 8.11054008e-01, 7.91801982e-01, 7.72435606e-01, 7.51368872e-01,
+ 7.24059052e-01, 6.97016433e-01, 6.70243011e-01, 6.43740898e-01,
+ 6.17512331e-01, 5.91559687e-01, 5.65885492e-01],
+ [5.39262087e-01, 5.12603461e-01, 4.86221750e-01, 4.60123396e-01,
+ 4.34315297e-01, 4.08804859e-01, 3.83600057e-01, 3.58016749e-01,
+ 3.31909003e-01, 3.06406088e-01, 2.81515756e-01, 2.57245695e-01,
+ 2.33603632e-01, 2.10597439e-01, 1.88235281e-01]]], dtype=np.float64),
np.float32: np.array([[
- [0.32941175, 0.35765505, 0.38643414, 0.4156936 , 0.44535455,
- 0.47540084, 0.5058213 , 0.53660583, 0.56515497, 0.5920884 ,
- 0.61906797, 0.64608735, 0.67314035, 0.7002214 , 0.72732455],
- [0.7523298 , 0.76888514, 0.7854807 , 0.8021649 , 0.8189918 ,
- 0.8360192 , 0.8533116 , 0.8709379 , 0.88421535, 0.8963408 ,
- 0.90846986, 0.9206159 , 0.9327928 , 0.9450152 , 0.95729893],
- [0.9561593 , 0.9379867 , 0.925193 , 0.9186452 , 0.9189398 ,
- 0.9262958 , 0.9404795 , 0.96078414, 0.9400202 , 0.9179358 ,
- 0.89463943, 0.8702369 , 0.84483355, 0.8185319 , 0.79143286],
- [0.75844824, 0.7217417 , 0.6848227 , 0.64762676, 0.61007077,
- 0.57204896, 0.533422 , 0.49457097, 0.4574643 , 0.42000294,
+ [0.32941175, 0.35765505, 0.38643414, 0.4156936, 0.44535455,
+ 0.47540084, 0.5058213, 0.53660583, 0.56515497, 0.5920884,
+ 0.61906797, 0.64608735, 0.67314035, 0.7002214, 0.72732455],
+ [0.7523298, 0.76888514, 0.7854807, 0.8021649, 0.8189918,
+ 0.8360192, 0.8533116, 0.8709379, 0.88421535, 0.8963408,
+ 0.90846986, 0.9206159, 0.9327928, 0.9450152, 0.95729893],
+ [0.9561593, 0.9379867, 0.925193, 0.9186452, 0.9189398,
+ 0.9262958, 0.9404795, 0.96078414, 0.9400202, 0.9179358,
+ 0.89463943, 0.8702369, 0.84483355, 0.8185319, 0.79143286],
+ [0.75844824, 0.7217417, 0.6848227, 0.64762676, 0.61007077,
+ 0.57204896, 0.533422, 0.49457097, 0.4574643, 0.42000294,
0.38201877, 0.34326664, 0.30337277, 0.26172766, 0.21724297],
- [0.18990554, 0.1670632 , 0.14352395, 0.11888929, 0.09241185,
- 0.06243531, 0.02537645, 0.00408208, 0.0042801 , 0.00417955,
- 0.00378686, 0.00312716, 0.00224016, 0.00117794, 0. ]],
- [[0.18823533, 0.20514871, 0.22224654, 0.23952611, 0.25698954,
- 0.27462983, 0.2924401 , 0.31041354, 0.33234388, 0.35706556,
- 0.38206834, 0.40734893, 0.4329038 , 0.45872888, 0.4848204 ],
- [0.51292086, 0.54794705, 0.5827325 , 0.6173149 , 0.65171933,
- 0.6859637 , 0.720059 , 0.754011 , 0.7769386 , 0.7971198 ,
- 0.81728625, 0.8374362 , 0.8575672 , 0.87767744, 0.8977643 ],
- [0.9152645 , 0.92748123, 0.93765223, 0.9458176 , 0.9520587 ,
- 0.95650315, 0.9593312 , 0.96078444, 0.9547297 , 0.9488435 ,
- 0.9430687 , 0.9373506 , 0.93163645, 0.9258765 , 0.92002386],
- [0.9085018 , 0.89323217, 0.8779272 , 0.86258495, 0.84720427,
- 0.83178383, 0.81632185, 0.79807115, 0.7689212 , 0.73994386,
- 0.71114165, 0.68251765, 0.65407526, 0.62581754, 0.597748 ],
- [0.5707766 , 0.5442478 , 0.51794314, 0.49186793, 0.46602884,
- 0.4404323 , 0.4150853 , 0.39076254, 0.3678196 , 0.34510067,
- 0.32261738, 0.3003818 , 0.27840695, 0.25670624, 0.23529409]],
- [[0.01960781, 0.02425484, 0.02749731, 0.02962274, 0.03171561,
- 0.03385685, 0.03604981, 0.03829896, 0.05173399, 0.07134236,
- 0.0900792 , 0.10834952, 0.12637301, 0.14428039, 0.16215537],
- [0.18472369, 0.22576652, 0.26687256, 0.30839586, 0.35052276,
- 0.39334968, 0.43691984, 0.48124215, 0.51949567, 0.55621 ,
- 0.59305423, 0.6300518 , 0.6672183 , 0.7045645 , 0.7420964 ],
- [0.7757837 , 0.80522877, 0.83597785, 0.8665506 , 0.8955321 ,
- 0.9216227 , 0.9436848 , 0.96078426, 0.94695187, 0.93475634,
- 0.9242341 , 0.9154071 , 0.9082816 , 0.90284896, 0.8990856 ],
- [0.8868469 , 0.8680878 , 0.8492005 , 0.83018804, 0.8110541 ,
- 0.791802 , 0.7724357 , 0.7513689 , 0.7240591 , 0.69701654,
- 0.67024314, 0.64374095, 0.6175124 , 0.59155977, 0.5658856 ],
- [0.53926224, 0.5126035 , 0.48622182, 0.4601233 , 0.43431523,
- 0.40880483, 0.3836 , 0.35801673, 0.331909 , 0.30640608,
- 0.28151578, 0.25724563, 0.23360366, 0.21059746, 0.18823537]]], dtype=np.float32)}
+ [0.18990554, 0.1670632, 0.14352395, 0.11888929, 0.09241185,
+ 0.06243531, 0.02537645, 0.00408208, 0.0042801, 0.00417955,
+ 0.00378686, 0.00312716, 0.00224016, 0.00117794, 0.]],
+ [[0.18823533, 0.20514871, 0.22224654, 0.23952611, 0.25698954,
+ 0.27462983, 0.2924401, 0.31041354, 0.33234388, 0.35706556,
+ 0.38206834, 0.40734893, 0.4329038, 0.45872888, 0.4848204],
+ [0.51292086, 0.54794705, 0.5827325, 0.6173149, 0.65171933,
+ 0.6859637, 0.720059, 0.754011, 0.7769386, 0.7971198,
+ 0.81728625, 0.8374362, 0.8575672, 0.87767744, 0.8977643],
+ [0.9152645, 0.92748123, 0.93765223, 0.9458176, 0.9520587,
+ 0.95650315, 0.9593312, 0.96078444, 0.9547297, 0.9488435,
+ 0.9430687, 0.9373506, 0.93163645, 0.9258765, 0.92002386],
+ [0.9085018, 0.89323217, 0.8779272, 0.86258495, 0.84720427,
+ 0.83178383, 0.81632185, 0.79807115, 0.7689212, 0.73994386,
+ 0.71114165, 0.68251765, 0.65407526, 0.62581754, 0.597748],
+ [0.5707766, 0.5442478, 0.51794314, 0.49186793, 0.46602884,
+ 0.4404323, 0.4150853, 0.39076254, 0.3678196, 0.34510067,
+ 0.32261738, 0.3003818, 0.27840695, 0.25670624, 0.23529409]],
+ [[0.01960781, 0.02425484, 0.02749731, 0.02962274, 0.03171561,
+ 0.03385685, 0.03604981, 0.03829896, 0.05173399, 0.07134236,
+ 0.0900792, 0.10834952, 0.12637301, 0.14428039, 0.16215537],
+ [0.18472369, 0.22576652, 0.26687256, 0.30839586, 0.35052276,
+ 0.39334968, 0.43691984, 0.48124215, 0.51949567, 0.55621,
+ 0.59305423, 0.6300518, 0.6672183, 0.7045645, 0.7420964],
+ [0.7757837, 0.80522877, 0.83597785, 0.8665506, 0.8955321,
+ 0.9216227, 0.9436848, 0.96078426, 0.94695187, 0.93475634,
+ 0.9242341, 0.9154071, 0.9082816, 0.90284896, 0.8990856],
+ [0.8868469, 0.8680878, 0.8492005, 0.83018804, 0.8110541,
+ 0.791802, 0.7724357, 0.7513689, 0.7240591, 0.69701654,
+ 0.67024314, 0.64374095, 0.6175124, 0.59155977, 0.5658856],
+ [0.53926224, 0.5126035, 0.48622182, 0.4601233, 0.43431523,
+ 0.40880483, 0.3836, 0.35801673, 0.331909, 0.30640608,
+ 0.28151578, 0.25724563, 0.23360366, 0.21059746, 0.18823537]]], dtype=np.float32)
+ }
@pytest.mark.parametrize("colormap_tag", [None, "colormap"])
def test_colorize_geotiff_tag(self, tmp_path, colormap_tag):
@@ -2525,8 +2534,8 @@ class TestXRImageSaveScaleOffset:
self.img.crude_stretch([1], [24])
self._save_and_check_tags(
- expected_tags,
- scale_offset_tags=("scale", "offset"))
+ expected_tags,
+ scale_offset_tags=("scale", "offset"))
@pytest.mark.skipif(sys.platform.startswith('win'), reason="'NamedTemporaryFile' not supported on Windows")
def test_save_scale_offset_custom_labels(self):
@@ -2534,8 +2543,8 @@ class TestXRImageSaveScaleOffset:
expected_tags = {"gradient": 24.0 / 255, "axis_intercept": 0}
self.img.stretch()
self._save_and_check_tags(
- expected_tags,
- scale_offset_tags=("gradient", "axis_intercept"))
+ expected_tags,
+ scale_offset_tags=("gradient", "axis_intercept"))
def _get_tags_after_writing_to_geotiff(data):
=====================================
trollimage/utilities.py
=====================================
@@ -7,65 +7,65 @@ from trollimage.colormap import Colormap
from trollimage.image import Image
from PIL import Image as Pimage
+
def _hex_to_rgb(value):
- '''
- _hex_to_rgb converts a string of 3 hex color values
- into a tuple of decimal values
- '''
+ """Convert a string of 3 hex color values into a tuple of decimal values."""
value = value.lstrip('#')
dec = int(value, 16)
- return dec
+ return dec
+
+
+def _text_to_rgb(value, norm=False, cat=1, tot=1, offset=0.5, hex=False):
+ """Take text line and convert to RGB color.
-def _text_to_rgb(value,norm=False,cat=1, tot=1,offset=0.5,hex=False):
- '''
- _text_to_rgb takes as input a string composed by 3 values in the range [0,255]
+ This takes as input a string composed by 3 values in the range [0,255]
and returns a tuple of integers. If the parameters cat and tot are given,
the function generates a transparency value for this color and returns a tuple
of length 4.
tot is the total number of colors in the colormap
cat is the index of the current colour in the colormap
if norm is set to True, the input values are normalized between 0 and 1.
- '''
+ """
tokens = value.split()
if hex:
- for i in range(len(tokens)):
- tokens[i] = _hex_to_rgb(tokens[i])
- transparency = float(cat)/float(tot)+offset
+ for i in range(len(tokens)):
+ tokens[i] = _hex_to_rgb(tokens[i])
+ transparency = float(cat) / float(tot) + offset
if transparency > 1.0:
transparency = 1.0
if norm:
- return (float(tokens[0])/255.0, float(tokens[1])/255.0, float(tokens[2])/255.0, transparency)
- else:
+ return (float(tokens[0]) / 255.0, float(tokens[1]) / 255.0, float(tokens[2]) / 255.0, transparency)
+ else:
return (int(tokens[0]), int(tokens[1]), int(tokens[2]), int(round(transparency * 255.0)))
def _make_cmap(colors, position=None, bit=False):
- '''
- _make_cmap takes a list of tuples which contain RGB values. The RGB
+ """Convert list of tuples into Colormap object.
+
+ This takes a list of tuples which contain RGB values. The RGB
values may either be in 8-bit [0 to 255] (in which bit must be set to
True when called) or arithmetic [0 to 1] (default). _make_cmap returns
a cmap with equally spaced colors.
Arrange your tuples so that the first color is the lowest value for the
colorbar and the last is the highest.
position contains values from 0 to 1 to dictate the location of each color.
- '''
- bit_rgb = np.linspace(0,1,256)
- if position == None:
- position = np.linspace(0,1,len(colors))
- else:
- if len(position) != len(colors):
- sys.exit("position length must be the same as colors")
- elif position[0] != 0 or position[-1] != 1:
- sys.exit("position must start with 0 and end with 1")
+ """
+ if position is None:
+ position = np.linspace(0, 1, len(colors))
+ if len(position) != len(colors):
+ sys.exit("position length must be the same as colors")
+ elif position[0] != 0 or position[-1] != 1:
+ sys.exit("position must start with 0 and end with 1")
palette = [(i, (float(r), float(g), float(b), float(a))) for
- i, (r, g, b, a) in enumerate(colors)]
+ i, (r, g, b, a) in enumerate(colors)]
cmap = Colormap(*palette)
return cmap
def cmap_from_text(filename, norm=False, transparency=False, hex=False):
- '''
- cmap_from_text takes as input a file that contains a colormap in text format
+ """Convert text file colormap to Colormap object.
+
+ This takes as input a file that contains a colormap in text format
composed by lines with 3 values in the range [0,255] or [00,FF]
and returns a tuple of integers. If the parameters cat and tot are given,
the function generates a transparency value for this color and returns a tuple
@@ -73,36 +73,39 @@ def cmap_from_text(filename, norm=False, transparency=False, hex=False):
tot is the total number of colors in the colormap
cat is the index of the current colour in the colormap
if norm is set to True, the input values are normalized between 0 and 1.
- '''
- lines = [line.rstrip('\n') for line in open(filename)]
- _colors=[]
+ """
+ lines = [line.rstrip('\n') for line in open(filename)]
+ _colors = []
_tot = len(lines)
_index = 1
for i in lines:
if transparency:
- _colors.append(_text_to_rgb(i,norm=norm,cat=_index,tot=_tot,hex=hex))
+ _colors.append(_text_to_rgb(i, norm=norm, cat=_index, tot=_tot, hex=hex))
else:
- _colors.append(_text_to_rgb(i,norm=norm,hex=hex))
+ _colors.append(_text_to_rgb(i, norm=norm, hex=hex))
_index = _index + 1
return _make_cmap(_colors)
def _image2array(filepath):
- '''
+ """Extract individual R, G, and B channels from on-disk image file.
+
Utility function that converts an image file in 3 np arrays
that can be fed into geo_image.GeoImage in order to generate
a PyTROLL GeoImage object.
- '''
+ """
im = Pimage.open(filepath).convert('RGB')
(width, height) = im.size
- _r = np.array(list(im.getdata(0)))/255.0
- _g = np.array(list(im.getdata(1)))/255.0
- _b = np.array(list(im.getdata(2)))/255.0
+ _r = np.array(list(im.getdata(0))) / 255.0
+ _g = np.array(list(im.getdata(1))) / 255.0
+ _b = np.array(list(im.getdata(2))) / 255.0
_r = _r.reshape((height, width))
_g = _g.reshape((height, width))
_b = _b.reshape((height, width))
return _r, _g, _b
+
def pilimage2trollimage(pimage):
- (r,g,b) = _image2array(pimage)
- return Image((r,g,b), mode="RGB")
+ """Convert PIL Image to trollimage Image."""
+ (r, g, b) = _image2array(pimage)
+ return Image((r, g, b), mode="RGB")
=====================================
trollimage/version.py
=====================================
@@ -26,9 +26,9 @@ def get_keywords():
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
- git_refnames = " (HEAD -> main, tag: v1.22.1)"
- git_full = "a84299fffaee504540cb7ab69a70b5690fb23156"
- git_date = "2023-11-24 13:21:19 +0100"
+ git_refnames = " (tag: v1.22.2)"
+ git_full = "f77379d8554f074407e0bdb6cdec6cf551625b22"
+ git_date = "2023-11-26 10:35:12 -0600"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
=====================================
trollimage/xrimage.py
=====================================
@@ -1104,7 +1104,7 @@ class XRImage:
try:
val = val.astype(dtype)
except AttributeError:
- val = self.data.dtype.type(val)
+ val = dtype.type(val)
return val
View it on GitLab: https://salsa.debian.org/debian-gis-team/trollimage/-/compare/617f0619a71fdcb08c02535f62ecd21904594e65...8f6b94a93d7e06572c29f9852757a21d74a6f9fe
--
View it on GitLab: https://salsa.debian.org/debian-gis-team/trollimage/-/compare/617f0619a71fdcb08c02535f62ecd21904594e65...8f6b94a93d7e06572c29f9852757a21d74a6f9fe
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