fix: remove kernel cwd workaround

2i2c-org · Dec 5, 2024 · 7499490 · 7499490
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diff --git a/book/01_Geospatial_Background/02_Working_with_Raster_Data.md b/book/01_Geospatial_Background/02_Working_with_Raster_Data.md
@@ -123,8 +123,7 @@ As an example, let's load data from a local GeoTIFF file using the Python `rioxa
 from pathlib import Path
 import rioxarray as rio
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 ```
 
 ```{code-cell} python

diff --git a/book/01_Geospatial_Background/03_Working_with_Vector_Data.md b/book/01_Geospatial_Background/03_Working_with_Vector_Data.md
@@ -78,7 +78,6 @@ Let's see how to parse and plot GeoJSON files using [GeoPandas](https://geopanda
 import geopandas as gpd
 from pathlib import Path
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 GEOJSON = Path(FILE_STEM, 'assets/cables.geojson')
 print(GEOJSON)
 ```

diff --git a/book/02_Software_Tools_Techniques/01_Loading_Raster_Data_from_GeoTIFF_Files.md b/book/02_Software_Tools_Techniques/01_Loading_Raster_Data_from_GeoTIFF_Files.md
@@ -24,7 +24,6 @@ import rasterio
 import rioxarray as rio
 from pathlib import Path
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 ```
 
 ***
@@ -48,7 +47,7 @@ Observe first that `open_rasterio` works on local file paths and remote URLs.
 
 ```{code-cell} python jupyter={"source_hidden": false}
 %%time
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 data = rio.open_rasterio(LOCAL_PATH)
 ```
 
@@ -90,7 +89,7 @@ From the [Rasterio documentation](https://rasterio.readthedocs.io/en/stable):
 
 ```{code-cell} python jupyter={"source_hidden": false}
 # Show rasterio.open works using context manager
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 print(LOCAL_PATH)
 ```
 

diff --git a/book/02_Software_Tools_Techniques/02_Array_Manipulation_with_Xarray.md b/book/02_Software_Tools_Techniques/02_Array_Manipulation_with_Xarray.md
@@ -25,7 +25,6 @@ from pathlib import Path
 import numpy as np, pandas as pd, xarray as xr
 import rioxarray as rio
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 ```
 
 ***
@@ -39,7 +38,7 @@ Let's load an example `xarray.DataArray` data structure from a file whose locati
 <!-- #endregion -->
 
 ```{code-cell} python jupyter={"source_hidden": false}
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 data = rio.open_rasterio(LOCAL_PATH)
 ```
 

diff --git a/book/02_Software_Tools_Techniques/03_Data_Visualization_with_GeoViews_HvPlot.md b/book/02_Software_Tools_Techniques/03_Data_Visualization_with_GeoViews_HvPlot.md
@@ -44,7 +44,6 @@ import geoviews as gv
 gv.extension('bokeh')
 from geoviews import opts
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 ```
 
 ***
@@ -200,7 +199,7 @@ The code below loads a Pandas DataFrame of temperature data.
 ```{code-cell} python jupyter={"source_hidden": false}
 import pandas as pd, numpy as np
 from pathlib import Path
-LOCAL_PATH = Path(FILE_STEM, 'assets/temperature.csv')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'temperature.csv'
 ```
 
 ```{code-cell} python jupyter={"source_hidden": false}
@@ -281,7 +280,7 @@ To start, load a local GeoTIFF file using `rioxarray` into an Zarray `DataArray`
 <!-- #endregion -->
 
 ```{code-cell} python jupyter={"source_hidden": false}
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 ```
 
 ```{code-cell} python jupyter={"source_hidden": false}

diff --git a/book/02_Software_Tools_Techniques/04_Constructing_Advanced_Visualizations.md b/book/02_Software_Tools_Techniques/04_Constructing_Advanced_Visualizations.md
@@ -44,7 +44,6 @@ gv.extension('bokeh')
 ```
 
 ```{code-cell} python jupyter={"source_hidden": false}
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 ASSET_PATH = Path(FILE_STEM, 'assets')
 SHAPE_FILE = ASSET_PATH / 'shapefiles' / 'mckinney' / 'McKinney_NIFC.shp'
 RASTER_FILES = list(ASSET_PATH.glob('OPERA*VEG*.tif'))

diff --git a/book/03_Using_NASA_EarthData/01_Using_OPERA_DIST_Products.md b/book/03_Using_NASA_EarthData/01_Using_OPERA_DIST_Products.md
@@ -74,15 +74,14 @@ gv.extension('bokeh')
 import hvplot.xarray
 from bokeh.models import FixedTicker
 
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
 ```
 
 <!-- #region jupyter={"source_hidden": false} -->
 We'll read the data from a local file `'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'`. Before loading the data, let's examine the metadata embedded in the filename.
 <!-- #endregion -->
 
 ```{code-cell} python jupyter={"source_hidden": false}
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-ANOM-MAX.tif'
 filename = LOCAL_PATH.name
 print(filename)
 ```
@@ -197,7 +196,7 @@ We'll load and relabel the `DataArray` as before.
 <!-- #endregion -->
 
 ```{code-cell} python jupyter={"source_hidden": false}
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-DIST-DATE.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-DIST-DATE.tif'
 data = rio.open_rasterio(LOCAL_PATH)
 data = data.rename({'x':'longitude', 'y':'latitude', 'band':'band'}).squeeze()
 ```
@@ -266,7 +265,7 @@ Notice the use of the `FixedTicker` in defining a colorbar better suited for a d
 <!-- #endregion -->
 
 ```{code-cell} python jupyter={"source_hidden": false}
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-DIST-STATUS.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DIST-ALERT-HLS_T10TEM_20220815T185931Z_20220817T153514Z_S2A_30_v0.1_VEG-DIST-STATUS.tif'
 data = rio.open_rasterio(LOCAL_PATH)
 data = data.rename({'x':'longitude', 'y':'latitude', 'band':'band'}).squeeze()
 ```

diff --git a/book/03_Using_NASA_EarthData/02_Using_OPERA_DSWx_Products.md b/book/03_Using_NASA_EarthData/02_Using_OPERA_DSWx_Products.md
@@ -78,8 +78,7 @@ from bokeh.models import FixedTicker
 ```
 
 ```{code-cell} python
-FILE_STEM = Path.cwd().parent if 'book' == Path.cwd().parent.stem else 'book'
-LOCAL_PATH = Path(FILE_STEM, 'assets/OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif')
+LOCAL_PATH = Path.cwd().parent / 'assets' / 'OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif'
 b01_wtr = rio.open_rasterio(LOCAL_PATH).rename({'x':'longitude', 'y':'latitude'}).squeeze()
 ```