updated instructions with example refactor

examples/pennant/README.md

@@ -1,14 +1,20 @@
 # PENNANT DSI example
 This is an example for creating and querying a DSI database from PENNANT output.
-Output from 10 runs is included in the directory. If you wish to generate your own output, you can download PENNANT: from: [https://github.com/lanl/PENNANT](https://github.com/lanl/PENNANT) and compile with make after editing the Makefile.
+Output from 10 runs is included in the directory. If you wish to generate your own output, you can download PENNANT: from: [https://github.com/lanl/PENNANT](https://github.com/lanl/PENNANT) and compile with make after editing the Makefile. Runs are provided in this folder ready to ingest.
+
+## Move into the PENNANT directory
+Assuming the DSI library is installed, move first into the root DSI directory, then:
+```
+cd examples/pennant/
+```
 
 ## Create a csv from the included outputs
 Create a csv file from the outputs:
 ```
-./parse_slurm_output.py --testname leblanc
+python3 parse_slurm_output.py --testname leblanc
 ```
 
 ## Create a DSI db and query it
 ```
-./create_and_query_dsi_db.py --testname leblanc
+python3 create_and_query_dsi_db.py --testname leblanc
 ```

examples/wildfire/readme.md

@@ -2,11 +2,12 @@
 
 This example highlights the use of the DSI Framework with QUIC-Fire fire-atmosphere simualation data and resulting images. The original file, wildfire.csv, lists 1889 runs of a wildfire simulation. Each row is a unique run with input and output values and associated image url. The columns list the various parameters of interest. The input columns are: wild_speed, wdir (wind direction), smois (surface moisture), fuels, ignition, safe_unsafe_ignition_pattern, safe_unsafe_fire_behavior, does_fire_meet_objectives, and rationale_if_unsafe. The output of the simulation (and post-processing steps) include the burned_area and the url to the wildfire images stored on the San Diego Super Computer.
 
-All paths in this example are defined as from the main dsi repository folder. Please ensure that your current directory is ..../dsi/.
+All paths in this example are defined as from the main dsi repository folder, then follow the instructions.
 
-To run this example, load dsi. Then run:
+To run this example, install dsi. Then run:
 
-    python3 examples/wildfire/wildfire.py
+    cd examples/wildfire/
+    python3 wildfire.py
 
 This will generate a wildfire.cdb folder with downloaded images from the server and a data.csv file of numerical properties of interest. This *cdb* folder is called a Cinema Database (https://github.com/cinemascience). A cinema database is comprised of a *csv* file where each row of the table is a data element (a run or ensemble member of a simulation or experiment, for example) and each column is a property of a data element. Any column name that starts with 'FILE' is a path to a file corresponding to the data element.