Usage monitoring -- Some finishing touches

usage-monitoring/README.md

@@ -9,10 +9,16 @@ Create a conda environment with the necessary additional dependencies with:
 ## Usage
 
 For usage run `python usage_monitoring.py`. When running this script, you will
-need to `source` a valid `openrc.sh`.
+need to `source` a valid `openrc.sh` or have a valid `clouds.yaml` in `~/.config/openstack/`.
 
 See the [Jetstream2 docs](https://docs.jetstream-cloud.org/ui/cli/auth/) for
-information on how to acquire an `openrc.sh` file.
+information on how to acquire an `openrc.sh` or `clouds.yaml` file.
+
+### Installing
+
+Run the included `install.sh` script. This will create the appropriate directories if necessary and soft/symlink the appropriate files. To "uninstall" the script, simply remove the symlinks.
+
+Finally, ensure that `~/.local/bin` is in your `PATH`.
 
 ### Activating the Environment
 
@@ -26,6 +32,10 @@ environment, use `conda run`, as in `usage_monitoring.sh`:
 
 `conda run -n usage-monitoring python usage_monitoring.py [options]`
 
+Additionally, if you've configured your shell to include `~/.local/bin` in your `PATH`, you can run `usage_monitoring.py` as an executable, as the script includes the appropriate "hashbang" (a.k.a. "shebang" `#!`):
+
+`usage_monitoring.py [options]`
+
 # Cron
 
 To collect usage data on a daily basis, run the wrapper script (after setting

usage-monitoring/usage_monitoring.py

@@ -1,7 +1,8 @@
-#! /usr/bin/env conda run -n usage-monitoring python
+#! /usr/bin/env -S conda run -n usage-monitoring python
 
-from subprocess import run
+from subprocess import run, CalledProcessError
 from os.path import isfile, expanduser
+from sys import stderr
 
 import argparse
 
@@ -21,15 +22,25 @@ def load_config(config_path):
             c['test_csv_file'] = expanduser(c['test_csv_file'])
             return c
     except FileNotFoundError:
-        print(f'File {config_path} not found! Exiting ...')
+        print(f'File {config_path} not found! Exiting ...', file=stderr)
         exit(1)
 
 def create_os_token(token_file):
-    token = run(
-        ['openstack', 'token', 'issue', '-f', 'json'],
-        capture_output=True,
-        check=True
-    )
+    try:
+        token = run(
+            ['openstack', 'token', 'issue', '-f', 'json'],
+            capture_output=True,
+            check=True
+        )
+    except CalledProcessError as ex:
+        print(
+            f'{ex}\n',
+            f'STDOUT: {ex.stdout.decode()}\n',
+            f'STDERR: {ex.stderr.decode()}\n'
+            'It is possible that a valid openrc.sh was not sourced. Exiting ...',
+            file=stderr
+        )
+        exit(1)
     with open(token_file, 'w') as f:
         f.write(token.stdout.decode())
 
@@ -54,7 +65,7 @@ def query_accounting_api(token):
     try:
         response.raise_for_status()
     except Exception as ex:
-        print(ex)
+        print(ex, file=stderr)
         exit(1)
     query = json.loads(response.text)
     return query
@@ -265,7 +276,7 @@ def generate_usage_plot(resources, analyses, allocation_resources):
     for resource_type in allocation_resources:
         data = get_data_by_resource(resources, resource_type)
         if data.empty:
-            print(f'No available data for {resource_type}')
+            print(f'No available data for {resource_type}', file=stderr)
             continue
 
         timestamps = pd.array(data['timestamp'])
@@ -324,10 +335,10 @@ def main():
         for resource_type in c['allocation_resources']:
             data = get_data_by_resource(resources, resource_type)
             if data.empty:
-                print(f'No available data for {resource_type}. Skipping ...')
+                print(f'No available data for {resource_type}. Skipping ...', file=stderr)
                 continue
             if len(data) < 2:
-                print(f'Not enough data for {resource_type}: len(data) = {len(data)}. Skipping ...')
+                print(f'Not enough data for {resource_type}: len(data) = {len(data)}. Skipping ...', file=stderr)
             # Perform analysis (usage rates, "forecast", )
             analyses.append(usage_analysis(data,args['analysis_days']))