updated docs to open_netcdf

usgs · Dec 19, 2023 · 6211baa · 6211baa
1 parent 65007b1
commit 6211baa
Show file tree

Hide file tree

Showing 4 changed files with 10 additions and 10 deletions.
diff --git a/documentation_source/source/examples/Creating_GS_Files/plot_aseg_tempest_to_netcdf.py b/documentation_source/source/examples/Creating_GS_Files/plot_aseg_tempest_to_netcdf.py
@@ -69,7 +69,7 @@
 
 #%%
 # Read back in the NetCDF file
-new_survey = Survey.read_netcdf(d_out)
+new_survey = Survey.open_netcdf(d_out)
 
 # Once the survey is read in, we can access variables like a standard xarray dataset.
 print(new_survey.raster.magnetic_tmi)

diff --git a/documentation_source/source/examples/Creating_GS_Files/plot_csv_resolve_to_netcdf.py b/documentation_source/source/examples/Creating_GS_Files/plot_csv_resolve_to_netcdf.py
@@ -57,7 +57,7 @@
 
 #%%
 # Reading back in the GS NetCDF file
-new_survey = Survey.read_netcdf(d_out)
+new_survey = Survey.open_netcdf(d_out)
 
 # Check the Survey information
 print(new_survey.xarray)

diff --git a/documentation_source/source/examples/Creating_GS_Files/plot_csv_skytem_to_netcdf.py b/documentation_source/source/examples/Creating_GS_Files/plot_csv_skytem_to_netcdf.py
@@ -32,7 +32,7 @@
 data_path = '..//..//supplemental//region//WI'
 
 # Survey metadata file
-metadata = join(data_path, "data//WI_SkyTEM_survey_md.json")
+metadata = join(data_path, "data//WI_SkyTEM_survey_md.yml")
 
 # Establish the Survey
 survey = Survey(metadata)
@@ -96,7 +96,7 @@
 
 #%%
 # Reading back in
-new_survey = Survey.read_netcdf(d_out)
+new_survey = Survey.open_netcdf(d_out)
 
 #%%
 # Plotting

diff --git a/documentation_source/source/examples/Creating_GS_Files/plot_tifs_to_netcdf.py b/documentation_source/source/examples/Creating_GS_Files/plot_tifs_to_netcdf.py
@@ -2,20 +2,20 @@
 GeoTIFFs to NetCDF
 ------------------
 
-In this example, we demonstrates the workflow for creating a GS file from the GeoTIFF (.tif/.tiff) file format. This includes adding individual TIF files as single 2-D variables, as well as how to create a 3-D variable by stacking multiple TIF files along a specified dimension. 
+In this example, we demonstrates the workflow for creating a GS file from the GeoTIFF (.tif/.tiff) file format. This includes adding individual TIF files as single 2-D variables, as well as how to create a 3-D variable by stacking multiple TIF files along a specified dimension.
 
-Additionally, this example shows how to handle Raster data that have differing x-y grids. Specifically, this example creates the following Raster datasets: 
+Additionally, this example shows how to handle Raster data that have differing x-y grids. Specifically, this example creates the following Raster datasets:
 
 1. Raster Dataset #1
     1a. 2-D magnetic grid, original x-y discretization (600 m cell size)
 2. Raster Dataset #2
     2a. 2-D magnetic grid, aligned to match the x-y dimensions of the resistivity layers (1000 m cell size)
-    
+
     2b. 3-D resistivity grid
 
 Dataset References:
 
-Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ. 
+Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ.
 
 James, S.R., and Minsley, B.J., 2021, Combined results and derivative products of hydrogeologic structure and properties from airborne electromagnetic surveys in the Mississippi Alluvial Plain: U.S. Geological Survey data release, https://doi.org/10.5066/P9382RCI.
 
@@ -69,12 +69,12 @@
 
 #%%
 # Reading back in the GS NetCDF file
-new_survey = Survey.read_netcdf(d_out)
+new_survey = Survey.open_netcdf(d_out)
 
 #%%
 # Plotting
 
-# Make a map-view plot of a specific data variable, using Xarray's plotter 
+# Make a map-view plot of a specific data variable, using Xarray's plotter
 # In this case, we slice the 3-D resistivity variable along the depth dimension
 new_survey.raster[1]['resistivity'].plot(col='z', vmax=3, cmap='jet')