Fix fixture_xr_image to open earth_day_01d_p directly with rioxarray (#…

…2963)

Rioxarray can now read the 3-bands from `@earth_day_01d_p` directly
without needing to parse the colorinterp information from the GeoTIFF file.

* Update baseline images for test_tilemap_* with GMT 6.5.0 and gs 10.02.1

* Trigger re-caching of earth_day_01d_p file

Modify pygmt/helpers/caching.py file slightly to create new cache.

* Remove elif block for manually creating RGB image

Downloading @earth_day_01d_p using GMT 6.4.0 actually works.
The new earth_day_01d_p.tif file processed on 2023-09-29 doesn't have
the quantized colormap, so no need for the extra handling. See
GenericMappingTools/gmtserver-admin#257 for more info.

pygmt/helpers/caching.py

@@ -1,12 +1,12 @@
 """
-Functions for download remote data files as cache.
+Functions for downloading remote data files to cache.
 """
 from pygmt.src import which
 
 
 def cache_data():
     """
-    Download GMT remote data files used in PyGMT tests and docs as cache files.
+    Download GMT remote data files used in PyGMT tests and docs to cache folder.
     """
     # List of datasets to download
     datasets = [
@@ -57,7 +57,7 @@ def cache_data():
         # Earth WDMAM grids
         "@earth_wdmam_01d_g",
         "@S90E000.earth_wdmam_03m_g.nc",  # Specific grid for 03m test
-        # Earth day/night grids
+        # Earth day/night images
         "@earth_day_01d_p",
         # Other cache files
         "@capitals.gmt",

pygmt/tests/baseline/test_tilemap_no_clip_False.png.dvc

@@ -1,4 +1,5 @@
 outs:
-- md5: e2a9a57f61bdca0c21ddec3e1ae5d819
-  size: 16595
+- md5: e55d9848135fc14c9cfc3d2c85bd82bb
+  size: 16472
   path: test_tilemap_no_clip_False.png
+  hash: md5

pygmt/tests/baseline/test_tilemap_no_clip_True.png.dvc

@@ -1,4 +1,5 @@
 outs:
-- md5: 86ce085faad17433dfabba675d0379ee
-  size: 38836
+- md5: 53949854f32b450e55f1be86102387e9
+  size: 38816
   path: test_tilemap_no_clip_True.png
+  hash: md5

pygmt/tests/baseline/test_tilemap_ogc_wgs84.png.dvc

@@ -1,4 +1,5 @@
 outs:
-- md5: 6da0601f305a6c3391e4a1aa0aee6173
-  size: 37139
+- md5: 5f225e4dd26f44e07bcbd8e713c67dbe
+  size: 37343
   path: test_tilemap_ogc_wgs84.png
+  hash: md5

pygmt/tests/baseline/test_tilemap_web_mercator.png.dvc

@@ -1,4 +1,5 @@
 outs:
-- md5: 2965f6711c5878014491c7e6f791048e
-  size: 62077
+- md5: 14f7afd5d48ed6aed88d495783f0ba42
+  size: 62398
   path: test_tilemap_web_mercator.png
+  hash: md5

pygmt/tests/test_grdimage_image.py

@@ -1,13 +1,9 @@
 """
 Test Figure.grdimage on 3-band RGB images.
 """
-import numpy as np
-import pandas as pd
 import pytest
-import xarray as xr
 from pygmt import Figure, which
 
-rasterio = pytest.importorskip("rasterio")
 rioxarray = pytest.importorskip("rioxarray")
 
 
@@ -19,26 +15,8 @@ def fixture_xr_image():
     """
     geotiff = which(fname="@earth_day_01d_p", download="c")
     with rioxarray.open_rasterio(filename=geotiff) as rda:
-        if len(rda.band) == 1:
-            with rasterio.open(fp=geotiff) as src:
-                df_colormap = pd.DataFrame.from_dict(
-                    data=src.colormap(1), orient="index"
-                )
-                array = src.read()
-
-                red = np.vectorize(df_colormap[0].get)(array)
-                green = np.vectorize(df_colormap[1].get)(array)
-                blue = np.vectorize(df_colormap[2].get)(array)
-                # alpha = np.vectorize(df_colormap[3].get)(array)
-
-            rda.data = red
-            da_red = rda.astype(dtype=np.uint8).copy()
-            rda.data = green
-            da_green = rda.astype(dtype=np.uint8).copy()
-            rda.data = blue
-            da_blue = rda.astype(dtype=np.uint8).copy()
-
-            xr_image = xr.concat(objs=[da_red, da_green, da_blue], dim="band")
+        if len(rda.band) == 3:
+            xr_image = rda.load()
         assert xr_image.sizes == {"band": 3, "y": 180, "x": 360}
         return xr_image