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e-marshall · Feb 4, 2024 · 202bd80 · 202bd80
2 parents 8db8d40 + 4b85e8a
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diff --git a/_config.yml b/_config.yml
@@ -5,7 +5,7 @@ author: emma marshall
 execute:
   execute_notebooks: 'off' # 
   allow_errors: true
-  timeout: 500
+  timeout: 1000
 
 # Add a bibtex file so that we can create citations
 bibtex_bibfiles:

diff --git a/appendix.md b/appendix.md
@@ -12,7 +12,7 @@ However, for this dataset, I found that the `xr.open_mfdataset()` function wasn'
 The stack I used contains multiple scenes that cover the same area of interest (multiple viewing geometries). If you wanted to select only scenes from a single viewing geometry at the expense of a denser time series, `xr.open_mfdataset()` might work a bit better (I didn't try this so cannot say for sure)
 ```
 
-Ultimately, I decided to use the approach of creating GDAL VRT objects, and reading those in with `rioxarray.open_rasterio()` to organize the data as xarray objects. This worked much better from a memory perspective but created much more work with organizing metadata and structuring the dataset in an analysis-ready format. The `xr.open_mfdataset()` function seems like a much more efficient approach if your dataset is well-aligned with its parameters (ie. a spatially uniform stack). While it did not end up being the best tool for this task, I decided to include the notebook with the `xr.open_mfdataset()` approach anyway in case it is useful to see a demosntration of this function. I learned a lot about how to structure a `preprocess` function and many other steps working on this example. 
+Ultimately, I decided to use the approach of creating GDAL VRT objects, and reading those in with `rioxarray.open_rasterio()` to organize the data as xarray objects. This worked much better from a memory perspective but created much more work with organizing metadata and structuring the dataset in an analysis-ready format. The `xr.open_mfdataset()` function seems like a much more efficient approach if your dataset is well-aligned with its parameters (ie. a spatially uniform stack). While it did not end up being the best tool for this task, I decided to include the notebook with the `xr.open_mfdataset()` approach anyway in case it is useful to see a demonstration of this function. I learned a lot about how to structure a `preprocess` function and many other steps working on this example. 
 
 Take a look at the notebook using `xr.open_mfdataset()` to read in stacks of ASF-processed Sentinel-1 RTC imagery files [here](asf_local_mf.ipynb)
 

diff --git a/asf_local_mf.ipynb b/asf_local_mf.ipynb
@@ -664,6 +664,14 @@
     "    return da"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "24c6e8db",
+   "metadata": {},
+   "source": [
+    "### An example of complicated chunking"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3c022c13-c49f-4b56-9463-49843294c934",
@@ -5503,7 +5511,7 @@
     },
     {
      "data": {
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+      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]

diff --git a/ch1_root.md b/ch1_root.md
@@ -4,10 +4,10 @@ The first chapter of this tutorial will demonstrate reading in and organizing Se
 
 ## Data processed and downloaded from Alaska Satellite Facility
 
-The first [notebook](asf_local_vrt.ipynb) demonstrates working with data that was processed by Alaska Satellite Facility through their [Hyp3 On-Demand service](https://hyp3-docs.asf.alaska.edu/v2-transition/) using HyP-3 SDK to submit jobs programmatically. The processed data is then downloaded locally. This notebook shows one approach for working with that data once its been downloaded locally.
+The first [notebook](asf_local_vrt.ipynb) (GDAL VRT approach) demonstrates working with data that was processed by Alaska Satellite Facility through their [Hyp3 On-Demand service](https://hyp3-docs.asf.alaska.edu/v2-transition/). The processed data is then downloaded locally. This notebook shows one approach for working with that data once downloaded locally.
 
-The second [notebook](asf_inspect.ipynb) shows preliminary dataset inspection of the ASF dataset once it has been read-in and organized.
+The second [notebook](asf_inspect.ipynb) (ASF-processed RTC data inspection) shows preliminary dataset inspection of the ASF dataset once it has been read in and organized.
 
 ## Data processed and accessed from Microsoft Planetary Computer
 
-This [notebook](PC_RTC.ipynb) demonstrates accessing data from Microsoft Planetary Computer's catalog. Microsoft Planetary Computer performs RTC processing of Sentinel-1 imagery similarly to ASF. It is then made available as cloud-optimized geotiffs and hosted on Microsoft Planetary Computer. This notebook demonstrates using STAC tools such as `pystac` and `stackstac` to access the cloud-hosted data locally. Microsoft Planetary Computer also hosts a jupyter hub server which you could access and use instead of doing so locally. Microsoft Planetary Computer requires a subscription (currently free). You can find out more about getting access [here](https://planetarycomputer.developer.azure-api.net/).
+This [notebook](PC_RTC.ipynb) (Microsoft Planetary Computer Sentinel-1 RTC Imagery) demonstrates accessing data from Microsoft Planetary Computer's catalog. Microsoft Planetary Computer performs RTC processing of Sentinel-1 imagery similarly to ASF. It is then made available as cloud-optimized GeoTIFFs and hosted on Microsoft Planetary Computer. This notebook demonstrates using STAC tools such as `pystac` and `stackstac` to access the cloud-hosted data locally. Microsoft Planetary Computer also hosts a jupyter hub server, which you could use instead of working with the data locally. Microsoft Planetary Computer requires a subscription (which is currently free). You can find out more about getting access [here](https://planetarycomputer.developer.azure-api.net/).