rename book lock file and other small nb changes (#51)

Author: e-marshall

book/background/5_software.md

@@ -10,15 +10,15 @@ There are two options for creating a software environment: [pixi](https://pixi.s
 1. Clone the book's GitHub repository:   
     ```git clone https://github.com/e-marshall/cloud-open-source-geospatial-datacube-workflows.git```
 
-2. Navigate into the repo environment:
+2. Navigate into the repo environment:  
 ```cd cloud-open-source-geospatial-datacube-workflows```
 
-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.
+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```
+4. Execute `pixi run` for each tutorial:  
+```pixi run itslive```  
+```pixi run sentinel1```  
 
 Note that the first `pixi run` will download specific versions of all required Python libraries to a hidden directory `./.pixi`. Subsequent runs activate that environment and execute code within it. You can also run `pixi shell` to "activate" the environment (set paths to executables and auxiliary files) and `exit` to deactivate it. 
 
@@ -27,16 +27,17 @@ Note that the first `pixi run` will download specific versions of all required P
 1. Clone this book's GitHub repository:  
 ```git clone https://github.com/e-marshall/cloud-open-source-geospatial-datacube-workflows.git```
 
-2. Navigate into the `book` sub-directory:  
+2. Navigate into the `book` sub-directory:    
 ```cd cloud-open-source-geospatial-datacube-workflows/book```
 
 3. Create and activate a conda environment from the `environment.yml` file located in the repo:  
-```conda env create -f environment.yml```
+```conda env create -f environment.yml```  
+```conda activate book```
 
-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.
+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`):  
+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/itslive/nbs/2_larger_than_memory_data.ipynb

@@ -547,9 +547,7 @@
    "source": [
     "{{conclusion}}\n",
     "\n",
-    "In this notebook, we identified a strategy for reading, chunking, and organizing this dataset that works within the memory constraints of my laptop and the size of the data. \n",
-    "\n",
-    "In the next notebook, we use vector data to narrow our focus in on a spatial area of interest and start examining ice velocity data."
+    "In this notebook, we identified a strategy for reading, chunking, and organizing this dataset that works within the memory constraints of my laptop and the size of the data. In the next notebook, we use vector data to narrow our focus in on a spatial area of interest and start examining ice velocity data."
    ]
   }
  ],

book/sentinel1/nbs/1_read_asf_data.ipynb

@@ -118,7 +118,7 @@
     "```\n",
     ".\n",
     "└── s1_asf_data\n",
-    "    ├── S1A_IW_20210502T121414_DVP_RTC30_G_gpuned_1424\n",
+    "    ├── S1A_IW_20220214T121353_DVP_RTC30_G_gpuned_E1E7\n",
     "    │   ├── S1A_IW_20220214T121353_DVP_RTC30_G_gpuned_51E7_VH.tif.xml\n",
     "    │   ├── S1A_IW_20220214T121353_DVP_RTC30_G_gpuned_51E7_rgb.kmz\n",
     "    │   ├── S1A_IW_20220214T121353_DVP_RTC30_G_gpuned_51E7_shape.prj\n",
@@ -505,9 +505,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "This notebook demonstrated reading large data into memory by creating a virtual dataset that references that full dataset without directly reading it. \n",
-    "\n",
-    "However, we also saw that reading the data in this way produces an object that lacks important metadata. The next notebook will go through the steps of locating and adding relevant metadata to the backscatter data cubes read in this notebook.\n"
+    "This notebook demonstrated reading large data into memory by creating a virtual dataset that references that full dataset without directly reading it. However, we also saw that reading the data in this way produces an object that lacks important metadata. The next notebook will go through the steps of locating and adding relevant metadata to the backscatter data cubes read in this notebook.\n"
    ]
   }
  ],