Modis data clarification and CI fixes

docs/earthpy-data-subsets.rst

@@ -49,21 +49,21 @@ natural earth, including global roads, political boundaries, and populated
 places. Finally, there are NEON Lidar data and insitu measurements for
 SOAP and SJER sites.
 
-cold-springs-modis-h5
+cold-springs-modis-h4
 ---------------------
 
 This dataset contains imagery from the Moderate Resolution Imaging Spectrometer
 `(MODIS)    <https://www.earthdatascience.org/courses/earth-analytics-python/multispectral-remote-sensing-modis/modis-remote-sensing-data-in-python/>`_
 maintained by NASA. `The satellite data <https://figshare.com/articles/Earth_Analytics_Cold_Springs_Fire_Remote_Sensing_Data/6083210>`_
-are for the 2016 Cold Springs fire near Nederland, CO. and are in h5 format.
+are for the 2016 Cold Springs fire near Nederland, CO. and are in h4 format.
 
 cold-springs-fire
 -----------------
 
 This dataset contains satellite and vector data for the
 `2016 Cold Springs fire near Nederland, CO <https://figshare.com/articles/Earth_Analytics_Cold_Springs_Fire_Remote_Sensing_Data/6083210>`_.
 The imagery used for this dataset comes from MODIS, National Agriculture
-Imagery Program `(NAIP) <https://www.earthdatascience.org/courses/earth-analytics-python/multispectral-remote-sensing-in-python/naip-imagery-raster-stacks-in-python/#work-with-naip-data-in-python>`_
+Imagery Program `(NAIP) <https://www.earthdatascience.org/courses/use-data-open-source-python/multispectral-remote-sensing/intro-naip/>`_
 (provided by the US Department of Agriculture), and
 Land-Use Satellite `(Landsat) <https://www.earthdatascience.org/courses/earth-analytics-python/multispectral-remote-sensing-in-python/landsat-bands-geotif-in-Python/>`_
 (provided by the USGS).

docs/get-started.rst

@@ -55,6 +55,6 @@ You can access these data subsets by using:
     >>> import earthpy as et
     >>> # View all available data keys
     >>> et.data.get_data()
-    Available Datasets: ['california-rim-fire', 'co-flood-extras', 'cold-springs-fire', 'cold-springs-modis-h5', 'colorado-flood', 'cs-test-landsat', 'cs-test-naip', 'ndvi-automation', 'spatial-vector-lidar', 'twitter-flood', 'vignette-elevation', 'vignette-landsat']
+    Available Datasets: ['california-rim-fire', 'co-flood-extras', 'cold-springs-fire', 'cold-springs-modis-h4', 'colorado-flood', 'cs-test-landsat', 'cs-test-naip', 'ndvi-automation', 'spatial-vector-lidar', 'twitter-flood', 'vignette-elevation', 'vignette-landsat']
     >>> # Download data subset to your `$HOME/earth-analytics/data` directory
     >>> data = et.data.get_data('cold-springs-fire', verbose=False)

earthpy/io.py

@@ -40,7 +40,7 @@
         ".",
         "zip",
     ),
-    "cold-springs-modis-h5": (
+    "cold-springs-modis-h4": (
         "https://ndownloader.figshare.com/files/10960112",
         ".",
         "zip",

earthpy/tests/conftest.py

@@ -179,9 +179,7 @@ def basic_geometry_gdf(basic_geometry):
     -------
     GeoDataFrame containing the basic_geometry polygon
     """
-    gdf = gpd.GeoDataFrame(
-        geometry=[basic_geometry], crs={"init": "epsg:4326"}
-    )
+    gdf = gpd.GeoDataFrame(geometry=[basic_geometry], crs="epsg:4326")
     return gdf
 
 

earthpy/tests/test_clip.py

@@ -13,9 +13,7 @@ def point_gdf():
     """ Create a point GeoDataFrame. """
     pts = np.array([[2, 2], [3, 4], [9, 8], [-12, -15]])
     gdf = gpd.GeoDataFrame(
-        [Point(xy) for xy in pts],
-        columns=["geometry"],
-        crs={"init": "epsg:4326"},
+        [Point(xy) for xy in pts], columns=["geometry"], crs="epsg:4326",
     )
     return gdf
 
@@ -24,9 +22,7 @@ def point_gdf():
 def single_rectangle_gdf():
     """Create a single rectangle for clipping. """
     poly_inters = Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)])
-    gdf = gpd.GeoDataFrame(
-        [1], geometry=[poly_inters], crs={"init": "epsg:4326"}
-    )
+    gdf = gpd.GeoDataFrame([1], geometry=[poly_inters], crs="epsg:4326")
     gdf["attr2"] = "site-boundary"
     return gdf
 
@@ -39,9 +35,7 @@ def larger_single_rectangle_gdf():
      are returned when you clip polygons. This fixture is larger which
      eliminates the slivers in the clip return."""
     poly_inters = Polygon([(-5, -5), (-5, 15), (15, 15), (15, -5), (-5, -5)])
-    gdf = gpd.GeoDataFrame(
-        [1], geometry=[poly_inters], crs={"init": "epsg:4326"}
-    )
+    gdf = gpd.GeoDataFrame([1], geometry=[poly_inters], crs="epsg:4326")
     gdf["attr2"] = ["study area"]
     return gdf
 
@@ -69,9 +63,7 @@ def two_line_gdf():
     """ Create Line Objects For Testing """
     linea = LineString([(1, 1), (2, 2), (3, 2), (5, 3)])
     lineb = LineString([(3, 4), (5, 7), (12, 2), (10, 5), (9, 7.5)])
-    gdf = gpd.GeoDataFrame(
-        [1, 2], geometry=[linea, lineb], crs={"init": "epsg:4326"}
-    )
+    gdf = gpd.GeoDataFrame([1, 2], geometry=[linea, lineb], crs="epsg:4326")
     return gdf
 
 
@@ -80,7 +72,7 @@ def multi_poly_gdf(donut_geometry):
     """ Create a multi-polygon GeoDataFrame. """
     multi_poly = donut_geometry.unary_union
     out_df = gpd.GeoDataFrame(
-        geometry=gpd.GeoSeries(multi_poly), crs={"init": "epsg:4326"}
+        geometry=gpd.GeoSeries(multi_poly), crs="epsg:4326"
     )
     out_df = out_df.rename(columns={0: "geometry"}).set_geometry("geometry")
     out_df["attr"] = ["pool"]
@@ -97,8 +89,7 @@ def multi_line(two_line_gdf):
     multiline_feat = two_line_gdf.unary_union
     linec = LineString([(2, 1), (3, 1), (4, 1), (5, 2)])
     out_df = gpd.GeoDataFrame(
-        geometry=gpd.GeoSeries([multiline_feat, linec]),
-        crs={"init": "epsg:4326"},
+        geometry=gpd.GeoSeries([multiline_feat, linec]), crs="epsg:4326",
     )
     out_df = out_df.rename(columns={0: "geometry"}).set_geometry("geometry")
     out_df["attr"] = ["road", "stream"]
@@ -113,7 +104,7 @@ def multi_point(point_gdf):
         gpd.GeoSeries(
             [multi_point, Point(2, 5), Point(-11, -14), Point(-10, -12)]
         ),
-        crs={"init": "epsg:4326"},
+        crs="epsg:4326",
     )
     out_df = out_df.rename(columns={0: "geometry"}).set_geometry("geometry")
     out_df["attr"] = ["tree", "another tree", "shrub", "berries"]
@@ -137,9 +128,7 @@ def test_returns_gdf(point_gdf, single_rectangle_gdf):
 def test_non_overlapping_geoms():
     """Test that a bounding box returns error if the extents don't overlap"""
     unit_box = Polygon([(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)])
-    unit_gdf = gpd.GeoDataFrame(
-        [1], geometry=[unit_box], crs={"init": "epsg:4326"}
-    )
+    unit_gdf = gpd.GeoDataFrame([1], geometry=[unit_box], crs="epsg:4326")
     non_overlapping_gdf = unit_gdf.copy()
     non_overlapping_gdf = non_overlapping_gdf.geometry.apply(
         lambda x: shapely.affinity.translate(x, xoff=20)

earthpy/tests/test_io.py

@@ -69,21 +69,21 @@ def test_colorado_counties():
     """ Check assumptions about county polygons. """
     counties = gpd.read_file(eio.path_to_example("colorado-counties.geojson"))
     assert counties.shape == (64, 13)
-    assert counties.crs == {"init": "epsg:4326"}
+    assert counties.crs == "epsg:4326"
 
 
 def test_colorado_glaciers():
     """ Check assumptions about glacier point locations. """
     glaciers = gpd.read_file(eio.path_to_example("colorado-glaciers.geojson"))
     assert glaciers.shape == (134, 2)
-    assert glaciers.crs == {"init": "epsg:4326"}
+    assert glaciers.crs == "epsg:4326"
 
 
 def test_continental_divide_trail():
     """ Check assumptions about Continental Divide Trail path. """
     cdt = gpd.read_file(eio.path_to_example("continental-div-trail.geojson"))
     assert cdt.shape == (1, 2)
-    assert cdt.crs == {"init": "epsg:4326"}
+    assert cdt.crs == "epsg:4326"
 
 
 """ Tests for the EarthlabData class. """