Merge pull request nsidc#21 from nsidc/modify-outline

Modify cookbook outline to reflect comments and discussion

Author: andypbarrett

_quarto.yml

@@ -25,38 +25,47 @@ website:
     contents:
       - text: "Welcome to our Cookbook"
         href: index.qmd
-      - text: "Getting Started"
-        href: our-cookbook.qmd
-      - text: "Quick Start Guide"
-        # href: quickstart.qmd
-      - section: "Data Orientaion"
+      - section: "Getting Started"
+        contents:
+          - text: "How to use this cookbook"
+            href: our-cookbook.qmd
+          - text: "Tools we use"
+            href: tools-we-use.qmd
+#      - text: "Quick Start Guide"
+#        # href: quickstart.qmd
+      - section: "NSIDC Data"
         # href: reference-guides/data-orient-overview.qmd
         contents:
-           - text: "Types of data at NSIDC"
+           - text: "Types of data"
              href: reference-guides/data-structures.qmd
-           - text: "Common NSIDC file formats"
+           - text: "Common file formats"
              href: reference-guides/nsidc-file-formats.qmd
            - text: "NASA collections and granules"
-           - text: "Datasets"
+           - text: "NSIDC map projections and grids"
+             href: reference-guides/projections.qmd
+           - text: "Quirky Datasets"
              href: reference-guides/datasets.qmd
       - section: "Working with Data"
         # href: reference-guides/working-with-data-overview.qmd
         contents:
           - section: "Finding and Accessing Data"
             contents:
-              - text: "Dataset search and discovery"
+              - text: "Getting an Earthdata Login"
+              #  href: reference-guides/get-edl.qmd
+              - text: "Data search and discovery"
                 href: reference-guides/dataset-search-and-discovery.qmd
-              - text: "Dataset retrieval"
-              - text: "What is an API?"
+              - text: "Data access"
           - section: "Wrangling Data"
+            contents:
+              - text: "Subsetting"
+              - text: "Reformatting"
+              - text: "Reprojecting"
+              - text: "Resampling"
           - section: "Visualizing Data"
             contents:
-              - text: "Plotting NSIDC Data"
+              - text: "Plotting data - time series, scatter plots"
+              - text: "Plotting data on a map"
                 href: reference-guides/plotting-data.qmd
-              - text: "NSIDC map projections and grids"
-                href: reference-guides/projections.qmd
-          - text: "Tools we use"
-            href: tools-we-use.qmd
       - text: "Best Practices"
       - section: "How do I..."
         href: how-to-guides/overview.qmd
@@ -70,6 +79,9 @@ website:
               href: how-to-guides/netcdf_cf.qmd
             - text: "get the latitude and longitudes for grid cells"
               href: how-to-guides/get_latitude_and_longitude.qmd
+      - section: "Appendices"
+        contents:
+          - text: "What is an API?"
 
 format:
   html:

how-to-guides/get_latitude_and_longitude.qmd

@@ -8,7 +8,7 @@ date: last-modified
 My data are in a NetCDF file but there are no latitude and longitude coordinates.  How do I get the latitude and longitude for each grid cell center?
 
 ## Solution
-```
+```{python}
 import numpy as np
 
 import rioxarray
@@ -17,7 +17,7 @@ from pyproj import CRS, Transformer
 
 
 # Load dataset using decode_coords='all'
-ds = xr.open_dataset('example_data/NSIDC0081_SEAICE_PS_N25km_20230627_v2.0.nc',
+ds = xr.open_dataset('../example_data/NSIDC0081_SEAICE_PS_N25km_20230627_v2.0.nc',
                      decode_coords='all')
 
 # Instantiate source pyproj CRS from ds.rio.crs object

our-cookbook.qmd

@@ -1,10 +1,15 @@
 ---
-title: "Our Cookbook"
+title: "How to use this cookbook"
 ---
 
-## How to use this cookbook
+This Cookbook for NSIDC Data is more Julia Child's "Mastering the Art of French Cooking" than just a collection of recipes.  The aim is to not only provide easy to follow recipes for working with data but also to provide an understanding of the data managed by NSIDC and the tools available to work with the data.  It is not an in-depth guide to the many datasets housed by NSIDC but an introduction to the common types of data, file formats and data structures.  The hope is that the cookbook will provide a guide and foundataion to help you _master the art of working with cryospheric data_.
 
+The cookbook is divided into three sections: NSIDC DATA, Working with Data, and a recipes section, "How do I...".  We also include some appendices that include a best practices guide for Python scripting and some background details on computing.
 
-## Get Started
+The **NSIDC Data** section is an introduction to the types of data managed by NSIDC, the file formats used to store that data, coordinate reference systems and grids comon to NSIDC data, and a guide to NASA terminology for Earth science data.  There is also an introduction to some "quirky" datasets that do not fit more common simple data structures or are just a little more difficult to work with.
+
+The **Working with Data** section is a guide to the tools and applications for accomplishing common steps in scientific workflows: finding and accessing data, wrangling data, and visualizing data.  Wrangling data covers subsetting, reformatting, reprojecting and resampling data in preparation for analysis.
+
+The **How do I...** section is a list of recipes to acheive common tasks.  It is intended a list of solutions to common scientific programming tasks.
 
 

tools-we-use.qmd

@@ -1,25 +1,66 @@
 ---
 title: "Tools we use"
+#number-sections: true
 ---
 
-## Python libraries
-
-- earthaccess
-- xarray
-- Geopandas
-- rioxarray
-- rasterio
-- SlideRule
-- icepyx
-- cartopy
-- satpy
-- dask
-
-## Other tools
-- Earthdata Search
-- Open Altimetry
-- HDFView
-- Panoply
-- NCView
-- QGIS
-- ArcGIS
+Currently, this cookbook features Python Packages (@sec-pythonpkg) for working with data supported by NSIDC DAAC.  It also features some applications (@sec-applications) that are accessible through a web browser or as stand alone packages that need to be installed on your local machine.
+
+The focus on Python not only reflects the expertise of NSIDC DAAC but also reflects the popularity of Python within the Earth and atmospheric science communities.  However, we recognize that many of our users are more familiar with other programming langauges such as R and Matlab.  We hope that we will be able to include these langauges as the Cookbook develops.
+
+Using web or locally-installed applications is a good way to start to discover and learn about data.  We often use these applications to explore datasets, find what data is available, and quickly visualize data.  However, many investigations require large numbers of files to be accessed and processed.  It is often more efficient to write scripts in Python or some other language to do this.  In the cloud, scripts are often the only way to search for and access data.  Scripts are also a way to make workflows reproducible, something that is difficult to do with a GUI application.
+
+## Python Packages {#sec-pythonpkg}
+
+There are many Python packages available for working with Earth science data.  The packages we use in this Cookbook are an unashamedly opinionated selection; they are the tools we like to use.  We also think that these tools are the easiest to use for the types of data mananaged by NSIDC DAAC.  Most of the tools have been developed so that researchers do not have to worry about the low-level details of accessing and working with often complicated data used in Earth science.  This reduces the amount of code you have to write and also reduces the number of mistakes you will inevitably make.
+
+#### earthaccess
+[`earthaccess`](https://earthaccess.readthedocs.io/en/latest/) is a package to search for and access NASA Earth science data.
+
+#### xarray
+[`xarray`](https://xarray.dev/) is a package to work with N-dimensional data (e.g `(time,x,y,z)`).
+
+#### rioxarray
+[`rioxarray`](https://corteva.github.io/rioxarray/stable/index.html) is an extentsion to `xarray` that makes data "geospatially-aware".
+
+#### rasterio
+[`rasterio`](https://rasterio.readthedocs.io/en/stable/intro.html) is a Python geospatial library for working with raster data.
+
+#### Pandas
+[`pandas`](https://pandas.pydata.org/docs/index.html) is a package to work with tabular data (e.g. the kind of data stored in spreadsheets or databases).
+
+#### Geopandas
+[`geopandas`](https://geopandas.org/en/stable/) is an extension to `pandas` to work with geospatial data.
+
+#### cartopy
+TBD
+
+#### SlideRule
+TBD
+
+#### icepyx
+TBD
+
+#### satpy
+TBD
+
+#### dask
+TBD
+
+## Installing Python Packages
+
+
+## Applications {#sec-applications}
+
+#### Earthdata Search
+
+#### Open Altimetry
+
+#### HDFView
+
+#### Panoply
+
+#### NCView
+
+#### QGIS
+
+#### ArcGIS