Include intermediate data in repo (#48)

* add vector data cube and update gitignore

* update instructions to ignore vector cube zarr data

* fix link ref in summary

* clear nbs + one small change

* Update .gitignore

remove old nb dir from gitignore

Author: e-marshall

.gitignore

@@ -10,10 +10,8 @@ package.json
 utils/get_links.py
 
 #Data 
-**/data/raster_data/*
-
-#Extra nbs
-sentinel1/subste_nbs
+sentinel1/data/raster_data/*
+itslive/data/raster_data/single_glacier_itslive.zarr/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

book/background/5_software.md

@@ -13,7 +13,10 @@ There are two options for creating a software environment: [pixi](https://pixi.s
 2. Navigate into the repo environment:
 ```cd cloud-open-source-geospatial-datacube-workflows```
 
-3. Execute `pixi run` for each tutorial:
+3. There is a small data cube included in the repo that is used in the tutorials. We don't want git to track this so we tell it to ignore this file path.
+```git update-index --assume-unchanged book/itslive/data/raster_data/regional_glacier_velocity_vector_cube.zarr/.```
+
+4. Execute `pixi run` for each tutorial:
 ```pixi run itslive```
 ```pixi run sentinel1```
 
@@ -30,7 +33,10 @@ Note that the first `pixi run` will download specific versions of all required P
 3. Create and activate a conda environment from the `environment.yml` file located in the repo:  
 ```conda env create -f environment.yml```
 
-4. Start Jupyterlab and navigate to the directories containing the Jupyter notebooks (`itslive/nbs` and `s1/nbs`):  
+4. There is a small data cube included in the repo that is used in the tutorials. We don't want git to track this so we tell it to ignore this file path.
+```git update-index --assume-unchanged book/itslive/data/raster_data/regional_glacier_velocity_vector_cube.zarr/.```
+
+5. Start Jupyterlab and navigate to the directories containing the Jupyter notebooks (`itslive/nbs` and `s1/nbs`):  
 ```jupyterlab```
 
 Both tutorials use functions that are stored in scripts associated with each dataset. You can find these scripts here: [`itslive_tools.py`](../itslive/nbs/itslive_tools.py) and [`s1_tools.py`](../sentinel1/nbs/s1_tools.py).

book/conclusion/summary.md

@@ -29,7 +29,7 @@ We saw many examples of situations where it would have been very difficult to ac
 ## Data visualization
 We used a number of visualization tools in these tutorials, each of which are appropriate for different situations and use-cases. These include interactive and static visualizations and plotting tools optimized for n-d array and vector datasets. 
 
-- [ITS_LIVE, nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/3_combining_raster_vector_data.html#crop-vector-data-to-spatial-extent-of-raster-data), [Adding basemaps to plots of raster and vector data, ITS_LIVE nb4](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/4_exploratory_data_analysis_single.html#load-raster-data-and-visualize-with-vector-data), [Interactive visualization of vector data cubes with Xvec and GeoPandas](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/5_exploratory_data_analysis_group.html#visualize-velocity-data), [Interactive visualization of 2-d data with Xarray and holoviz, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#interactive-visualization-of-layover-shadow-maps), [Using xr.FacetGrid for data inspection and cleaning](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#d-duplicate-time-steps), [Different ways of plotting 2-d data side-by-side, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#f-data-visualization), [Interactive visualization of time series data with Xarray and holoviz](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#backscatter-time-series), and [here](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/5_comparing_s1_rtc_datasets.html#d-visualize-comparisons).
+- [ITS_LIVE, nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/3_combining_raster_vector_data.html#crop-vector-data-to-spatial-extent-of-raster-data), [Adding basemaps to plots of raster and vector data, ITS_LIVE nb4](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/4_exploratory_data_analysis_single.html#load-raster-data-and-visualize-with-vector-data), [Interactive visualization of vector data cubes with Xvec and GeoPandas](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/5_exploratory_data_analysis_group.html#visualize-velocity-data), [Interactive visualization of 2-d data with Xarray and holoviz, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#interactive-visualization-of-layover-shadow-maps), [Using xr.FacetGrid for data inspection and cleaning](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#d-duplicate-time-steps), [Different ways of plotting 2-d data side-by-side, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#f-data-visualization), [Interactive visualization of time series data with Xarray and holoviz](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#backscatter-time-series), and [Visual dataset comparison](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/5_comparing_s1_rtc_datasets.html#d-visualize-comparisons).
 
 ## Making analysis-ready data cubes
 Several notebooks focused on the tasks of organizing data cubes so that they were appropriate representations of physical observables and attaching relevant metadata to relevant variables and along appropriate dimensions.

book/itslive/data/raster_data/regional_glacier_velocity_vector_cube.zarr/.zattrs

@@ -0,0 +1 @@
+{}

book/itslive/data/raster_data/regional_glacier_velocity_vector_cube.zarr/.zgroup

@@ -0,0 +1,3 @@
+{
+    "zarr_format": 2
+}