CLN: Standardize imports to only use 'as' when naming conflicts arise (…

…#147)
corteva · Jul 27, 2023 · d954dfe · d954dfe
1 parent 998ee3b
commit d954dfe
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Showing 9 changed files with 63 additions and 56 deletions.
diff --git a/docs/examples/categorical.ipynb b/docs/examples/categorical.ipynb
diff --git a/docs/examples/grid_to_vector_map.ipynb b/docs/examples/grid_to_vector_map.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "import json\n",
     "\n",
-    "import geopandas as gpd\n",
+    "import geopandas\n",
     "\n",
     "from geocube.api.core import make_geocube\n",
     "\n",
@@ -32,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ssurgo_data = gpd.read_file(\"../../test/test_data/input/soil_data_group.geojson\")"
+    "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")"
    ]
   },
   {
@@ -192,9 +192,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-     "## Convert data to grid\n",
-     "\n",
-     "See docs for [make_geocube](../geocube.rst#make-geocube)"
+    "## Convert data to grid\n",
+    "\n",
+    "See docs for [make_geocube](../geocube.rst#make-geocube)"
    ]
   },
   {
@@ -532,7 +532,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -546,7 +546,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   }
   }
  },
  "nbformat": 4,

diff --git a/docs/examples/rasterize_function.ipynb b/docs/examples/rasterize_function.ipynb
@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
+    "import geopandas\n",
     "\n",
     "from functools import partial\n",
     "from geocube.rasterize import rasterize_image\n",
@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
+    "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))"
    ]
   },
   {

diff --git a/docs/examples/rasterize_point_data.ipynb b/docs/examples/rasterize_point_data.ipynb
diff --git a/docs/examples/timestamp_missing_data.ipynb b/docs/examples/timestamp_missing_data.ipynb
diff --git a/docs/examples/zonal_statistics.ipynb b/docs/examples/zonal_statistics.ipynb
@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
+    "import geopandas\n",
     "import numpy\n",
     "import rioxarray\n",
     "import xarray\n",
@@ -43,7 +43,7 @@
     "# https://github.com/corteva/geocube/\n",
     "# You could also use the full path: \n",
     "# https://raw.githubusercontent.com/corteva/geocube/master/test/test_data/input/soil_data_group.geojson\n",
-    "ssurgo_data = gpd.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n",
+    "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n",
     "ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]\n",
     "# convert the key to group to the vector data to an integer as that is one of the\n",
     "# best data types for this type of mapping. If your data is not integer,\n",

diff --git a/geocube/xarray_extensions/vectorxarray.py b/geocube/xarray_extensions/vectorxarray.py
@@ -1,7 +1,7 @@
 """
 This module is an extension for xarray to provide support for vector datasets.
 """
-import geopandas as gpd
+import geopandas
 import numpy
 import rioxarray  # noqa: F401 pylint: disable=unused-import
 import xarray
@@ -68,7 +68,7 @@ def to_geodataframe(self):
         extra_coords = list(set(list(out_obj.coords)) - {"geometry"})
         if extra_coords:
             out_obj = out_obj.copy().reset_coords(extra_coords)
-        geodf = gpd.GeoDataFrame(out_obj.to_dataframe().reset_index())
+        geodf = geopandas.GeoDataFrame(out_obj.to_dataframe().reset_index())
         geodf.crs = self._obj.rio.crs
         return geodf
 

diff --git a/test/integration/api/test_core_integration.py b/test/integration/api/test_core_integration.py
@@ -2,7 +2,7 @@
 import json
 from functools import partial
 
-import geopandas as gpd
+import geopandas
 import pandas
 import pytest
 import xarray
@@ -41,8 +41,10 @@
     "input_geodata",
     [
         str(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
-        pandas.DataFrame(gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
+        pandas.DataFrame(
+            geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+        ),
     ],
 )
 def test_make_geocube(input_geodata, tmpdir):
@@ -79,7 +81,7 @@ def test_make_geocube(input_geodata, tmpdir):
 
 @pytest.mark.parametrize(
     "input_geodata",
-    [gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")],
+    [geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")],
 )
 def test_make_geocube__categorical(input_geodata, tmpdir):
     input_geodata["soil_type"] = [
@@ -119,7 +121,7 @@ def test_make_geocube__categorical(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_flat.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
     ],
 )
 def test_make_geocube__interpolate_na(input_geodata, tmpdir):
@@ -162,7 +164,7 @@ def test_make_geocube__interpolate_na(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_flat.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
     ],
 )
 def test_make_geocube__like(input_geodata, tmpdir):
@@ -198,7 +200,7 @@ def test_make_geocube__like(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_flat.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
     ],
 )
 def test_make_geocube__only_resolution(input_geodata, tmpdir):
@@ -238,9 +240,9 @@ def test_make_geocube__only_resolution(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "naive_time_vector_data.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "naive_time_vector_data.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "naive_time_vector_data.geojson"),
         TEST_INPUT_DATA_DIR / "time_vector_data.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
     ],
 )
 def test_make_geocube__convert_time(input_geodata, tmpdir):
@@ -307,7 +309,7 @@ def test_make_geocube__like_error_invalid_args(load_extra_kwargs):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_flat.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson"),
     ],
 )
 def test_make_geocube__no_measurements(input_geodata, tmpdir):
@@ -358,9 +360,9 @@ def test_make_geocube__no_geom(tmpdir):
 @pytest.mark.parametrize(
     "input_geodata",
     [
-        gpd.GeoDataFrame(columns=["test_col", "geometry"]),
-        gpd.GeoDataFrame(),
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson").drop(
+        geopandas.GeoDataFrame(columns=["test_col", "geometry"]),
+        geopandas.GeoDataFrame(),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson").drop(
             columns="geometry"
         ),
     ],
@@ -385,7 +387,7 @@ def test_make_geocube__no_resolution_error():
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_group.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
     ],
 )
 def test_make_geocube__group_by(input_geodata, tmpdir):
@@ -425,7 +427,7 @@ def test_make_geocube__group_by(input_geodata, tmpdir):
 
 @pytest.mark.parametrize(
     "input_geodata",
-    [gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson")],
+    [geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson")],
 )
 def test_make_geocube__group_by__categorical(input_geodata, tmpdir):
     input_geodata["soil_type"] = [
@@ -470,7 +472,7 @@ def test_make_geocube__group_by__categorical(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_group.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
     ],
 )
 def test_make_geocube__group_by_like(input_geodata, tmpdir):
@@ -510,7 +512,7 @@ def test_make_geocube__group_by_like(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_group.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
     ],
 )
 def test_make_geocube__group_by_only_resolution(input_geodata, tmpdir):
@@ -544,7 +546,7 @@ def test_make_geocube__group_by_only_resolution(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "time_vector_data.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
     ],
 )
 def test_make_geocube__group_by_time(input_geodata, tmpdir):
@@ -573,7 +575,7 @@ def test_make_geocube__group_by_time(input_geodata, tmpdir):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "time_vector_data.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "time_vector_data.geojson"),
     ],
 )
 def test_make_geocube__group_by_convert_with_time(input_geodata, tmpdir):
@@ -643,7 +645,7 @@ def test_make_geocube__group_by_like_error_invalid_args(load_extra_kwargs):
     "input_geodata",
     [
         TEST_INPUT_DATA_DIR / "soil_data_group.geojson",
-        gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
+        geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_group.geojson"),
     ],
 )
 def test_make_geocube__group_by_no_measurements(input_geodata, tmpdir):
@@ -819,7 +821,7 @@ def test_make_geocube__custom_rasterize_function__filter_null(
     ],
 )
 def test_make_geocube__minimize_dtype(dtype, fill, expected_type, tmpdir):
-    gdf = gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+    gdf = geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
     gdf["mask"] = 1
     gdf["mask"] = gdf["mask"].astype(dtype)
     out_grid = make_geocube(
@@ -849,7 +851,7 @@ def test_rasterize__like_1d():
     )
 
     geom_array = make_geocube(
-        gpd.GeoDataFrame({"in_geom": [1]}, geometry=[geom], crs="epsg:4326"),
+        geopandas.GeoDataFrame({"in_geom": [1]}, geometry=[geom], crs="epsg:4326"),
         like=like,
     )
     assert geom_array.rio.transform() == like.rio.transform()

diff --git a/test/integration/xarray_extensions/test_integration_xarray_extensions_vectorxarray.py b/test/integration/xarray_extensions/test_integration_xarray_extensions_vectorxarray.py
@@ -1,4 +1,4 @@
-import geopandas as gpd
+import geopandas
 import pytest
 import xarray
 from dateutil.parser import parse
@@ -16,7 +16,7 @@ def assert_test_dataframes_equal(gdf, gdf2):
 
 
 def test_from_geodataframe():
-    gdf = gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+    gdf = geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
     vxd = vectorxarray.from_geodataframe(gdf)
     assert all(gdf.geometry == vxd.geometry.values)
     assert sorted(gdf.columns.tolist() + ["spatial_ref"]) == sorted(vxd.variables)
@@ -26,13 +26,13 @@ def test_from_geodataframe():
 
 
 def test_to_geodataframe():
-    gdf = gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+    gdf = geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
     gdf2 = vectorxarray.from_geodataframe(gdf).vector.to_geodataframe()
     assert_test_dataframes_equal(gdf, gdf2)
 
 
 def test_to_netcdf(tmpdir):
-    gdf = gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+    gdf = geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
     vxd = vectorxarray.from_geodataframe(gdf)
     output_file = tmpdir / "test_vector.nc"
     vxd.vector.to_netcdf(output_file)
@@ -41,7 +41,7 @@ def test_to_netcdf(tmpdir):
 
 
 def test_multidimensional_error():
-    gdf = gpd.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
+    gdf = geopandas.read_file(TEST_INPUT_DATA_DIR / "soil_data_flat.geojson")
     vxd = vectorxarray.from_geodataframe(gdf)
     vxd2 = vxd.copy()
     vxd.coords["time"] = parse("20170516T000000")