chore(deps): bump ruff to v0.16.3

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.10
+    rev: v0.16.3
     hooks:
       - id: ruff
         args: [--fix]

CHANGELOG.md

@@ -108,9 +108,10 @@ Be sure to review the breaking changes before upgrading.
 
   bench = MicroBench()
 
+
   @bench
-  def my_function():
-      ...
+  def my_function(): ...
+
 
   for _ in range(10):
       my_function()
@@ -150,6 +151,7 @@ Be sure to review the breaking changes before upgrading.
   async def fetch():
       await asyncio.sleep(0.01)
 
+
   asyncio.run(fetch())
 
   async with bench.arecord('load'):
@@ -288,10 +290,12 @@ Be sure to review the breaking changes before upgrading.
   ```python
   from microbench import MicroBench, FileOutput, RedisOutput
 
-  bench = MicroBench(outputs=[
-      FileOutput('/home/user/results.jsonl'),
-      RedisOutput('microbench:mykey', host='redis-host', port=6379),
-  ])
+  bench = MicroBench(
+      outputs=[
+          FileOutput('/home/user/results.jsonl'),
+          RedisOutput('microbench:mykey', host='redis-host', port=6379),
+      ]
+  )
   ```
 
   `get_results()` delegates to the first sink that supports reading back
@@ -395,19 +399,22 @@ Be sure to review the breaking changes before upgrading.
   ```python
   from microbench import MicroBenchRedis
 
+
   class RedisBench(MicroBenchRedis):
       redis_connection = {'host': 'localhost', 'port': 6379}
       redis_key = 'microbench:mykey'
 
+
   bench = RedisBench()
   ```
 
   After:
   ```python
   from microbench import MicroBench, RedisOutput
 
-  bench = MicroBench(outputs=[RedisOutput('microbench:mykey',
-                                           host='localhost', port=6379)])
+  bench = MicroBench(
+      outputs=[RedisOutput('microbench:mykey', host='localhost', port=6379)]
+  )
   ```
 
 - **`LiveStream` updated for v2 record schema**: field references updated from

README.md

@@ -146,15 +146,17 @@ from microbench import MicroBench
 
 bench = MicroBench(outfile='/home/user/results.jsonl', experiment='baseline')
 
+
 @bench
 def my_function(n):
     return sum(range(n))
 
+
 my_function(1_000_000)
 
-results = bench.get_results()              # list of dicts — no extra dependencies
+results = bench.get_results()  # list of dicts — no extra dependencies
 results = bench.get_results(format='df')  # pandas DataFrame
-bench.summary()                           # quick stats printout
+bench.summary()  # quick stats printout
 ```
 
 Each call produces one record. With `get_results(flat=True)` the record looks
@@ -183,17 +185,19 @@ like:
 from microbench import MicroBench, MBFunctionCall, MBHostInfo, MBSlurmInfo
 import numpy, pandas, time
 
+
 class MyBench(MicroBench, MBFunctionCall, MBHostInfo, MBSlurmInfo):
     outfile = '/home/user/my-benchmarks.jsonl'
-    capture_versions = (numpy, pandas)   # record live module versions
-    env_vars = ('CUDA_VISIBLE_DEVICES',) # capture env vars as env.<NAME>
+    capture_versions = (numpy, pandas)  # record live module versions
+    env_vars = ('CUDA_VISIBLE_DEVICES',)  # capture env vars as env.<NAME>
+
+
+benchmark = MyBench(experiment='run-1', iterations=3, duration_counter=time.monotonic)
 
-benchmark = MyBench(experiment='run-1', iterations=3,
-                    duration_counter=time.monotonic)
 
 @benchmark
-def myfunction(arg1, arg2):
-    ...
+def myfunction(arg1, arg2): ...
+
 
 myfunction(x, y)
 ```

docs/cli.md

@@ -279,6 +279,7 @@ Read the results:
 
 ```python
 from microbench import FileOutput
+
 df = FileOutput('/scratch/user/results.jsonl').get_results(flat=True, format='df')
 df['total_duration'] = df['call.durations'].apply(sum)
 df.groupby('slurm.job_id')['total_duration'].describe()
@@ -444,6 +445,7 @@ Read results back from Redis with Python:
 
 ```python
 import redis, json
+
 client = redis.StrictRedis(host='redis.example.com')
 records = [json.loads(r) for r in client.lrange('bench:results', 0, -1)]
 ```
@@ -452,6 +454,7 @@ Or via microbench's `RedisOutput.get_results()`:
 
 ```python
 from microbench import RedisOutput
+
 results = RedisOutput('bench:results', host='redis.example.com').get_results()
 ```
 
@@ -548,15 +551,19 @@ Analyse with `get_results()`:
 
 ```python
 from microbench import FileOutput
+
 results = FileOutput('results.jsonl').get_results()
 
 # Flatten all samples for the first iteration across all records
 import pandas
-samples = pandas.DataFrame([
-    s
-    for r in results
-    for s in r['call']['monitor'][0]   # [0] = first iteration
-])
+
+samples = pandas.DataFrame(
+    [
+        s
+        for r in results
+        for s in r['call']['monitor'][0]  # [0] = first iteration
+    ]
+)
 samples['rss_mb'] = samples['rss_bytes'] / 1024 / 1024
 print(samples[['timestamp', 'cpu_percent', 'rss_mb']])
 ```

docs/getting-started.md

@@ -46,9 +46,11 @@ from microbench import MicroBench
 
 bench = MicroBench()
 
+
 @bench
 def my_function(x):
-    return x ** 2
+    return x**2
+
 
 my_function(42)
 ```
@@ -57,7 +59,7 @@ By default results are captured into an in-memory buffer. Read them back as
 a list of dicts:
 
 ```python
-results = bench.get_results()          # list of dicts — no extra dependencies
+results = bench.get_results()  # list of dicts — no extra dependencies
 results = bench.get_results(format='df')  # pandas DataFrame
 ```
 
@@ -87,21 +89,22 @@ Every record contains these fields automatically (all nested under `mb` or `call
 Here's an extended example to give you an idea of real-world usage.
 
 ```python
-from microbench import MicroBench, MBFunctionCall, \
-    MBHostInfo, MBSlurmInfo
+from microbench import MicroBench, MBFunctionCall, MBHostInfo, MBSlurmInfo
 import numpy, pandas, time
 
+
 class MyBench(MicroBench, MBFunctionCall, MBHostInfo, MBSlurmInfo):
     outfile = '/home/user/my-benchmarks.jsonl'
     capture_versions = (numpy, pandas)
     env_vars = ('CUDA_VISIBLE_DEVICES',)
 
-benchmark = MyBench(experiment='run-1', iterations=3,
-                    duration_counter=time.monotonic)
+
+benchmark = MyBench(experiment='run-1', iterations=3, duration_counter=time.monotonic)
+
 
 @benchmark
-def myfunction(arg1, arg2):
-    ...
+def myfunction(arg1, arg2): ...
+
 
 myfunction(x, y)
 ```
@@ -153,13 +156,14 @@ object per line). Read them back with pandas:
 
 ```python
 import pandas
+
 results = pandas.read_json('/home/user/results.jsonl', lines=True)
 ```
 
 Or via `get_results()`, which works regardless of the output destination:
 
 ```python
-results = bench.get_results()              # list of dicts — no extra dependencies
+results = bench.get_results()  # list of dicts — no extra dependencies
 results = bench.get_results(format='df')  # pandas DataFrame
 ```
 
@@ -173,6 +177,7 @@ bench.summary()
 
 # or pass any list of result dicts:
 from microbench import summary
+
 summary(bench.get_results())
 ```
 
@@ -196,7 +201,7 @@ Use `flat=True` to flatten nested fields (e.g. `slurm`, `git`,
 into pandas or a spreadsheet:
 
 ```python
-results = bench.get_results(flat=True)          # list of flat dicts
+results = bench.get_results(flat=True)  # list of flat dicts
 results = bench.get_results(format='df', flat=True)  # flat DataFrame
 # 'call' dict becomes: call.name, call.durations, call.start_time, ...
 # 'slurm' dict becomes: slurm.job_id, slurm.cpus_on_node, ...
@@ -213,9 +218,11 @@ section of a script:
 ```python
 from microbench import MicroBench, MBHostInfo
 
+
 class MyBench(MicroBench, MBHostInfo):
     outfile = '/home/user/results.jsonl'
 
+
 bench = MyBench(experiment='run-1')
 
 with bench.record('data_loading'):
@@ -256,10 +263,12 @@ process exits — no restructuring of the script is required:
 ```python
 from microbench import MicroBench, MBHostInfo, MBSlurmInfo
 
+
 class MyBench(MicroBench, MBHostInfo, MBSlurmInfo):
     outfile = '/scratch/results.jsonl'
     capture_optional = True  # recommended: don't let a failed capture abort exit
 
+
 bench = MyBench(experiment='baseline')
 bench.record_on_exit('simulation')
 

docs/index.md

@@ -26,10 +26,12 @@ from microbench import MicroBench
 
 bench = MicroBench(outfile='results.jsonl')
 
+
 @bench
 def my_function(n):
     return sum(range(n))
 
+
 my_function(1_000_000)
 ```
 

docs/user-guide/advanced.md

@@ -120,11 +120,13 @@ contain `N` entries per named phase:
 ```python
 bench = MicroBench(iterations=3)
 
+
 @bench
 def pipeline():
     with bench.time('step'):
         ...
 
+
 pipeline()
 # call.timings → [{"name": "step", ...}, {"name": "step", ...}, {"name": "step", ...}]
 ```
@@ -159,6 +161,7 @@ analysis:
 
 ```python
 import pandas
+
 results = pandas.read_json('/home/user/results.jsonl', lines=True)
 
 # Records where the call raised
@@ -178,6 +181,7 @@ a class attribute to catch failures instead and record them in
 ```python
 from microbench import MicroBench, MBNvidiaSmi, MBCondaPackages
 
+
 class MyBench(MicroBench, MBNvidiaSmi, MBCondaPackages):
     capture_optional = True  # missing nvidia-smi or conda won't abort the run
 ```
@@ -217,21 +221,26 @@ To handle custom types, subclass `JSONEncoder`:
 import microbench as mb
 from igraph import Graph
 
+
 class MyEncoder(mb.JSONEncoder):
     def default(self, o):
         if isinstance(o, Graph):
             return str(o)
         return super().default(o)
 
+
 class MyBench(mb.MicroBench, mb.MBReturnValue):
     pass
 
+
 bench = MyBench(json_encoder=MyEncoder)
 
+
 @bench
 def make_graph():
     return Graph(2, ((0, 1), (0, 2)))
 
+
 make_graph()  # no warning
 ```
 
@@ -250,11 +259,13 @@ running in the same Python process:
 ```python
 from microbench.livestream import LiveStream
 
+
 class MyStream(LiveStream):
     def process_alert(self, data):
         if sum(data.get('call', {}).get('durations', [])) > 10.0:
             host = data.get('host', {}).get('hostname', 'unknown')
-            print(f"Slow call on {host}: {data['call']['durations']}")
+            print(f'Slow call on {host}: {data["call"]["durations"]}')
+
 
 stream = MyStream('/home/user/results.jsonl')
 # ... runs in background while your job continues ...
@@ -270,6 +281,7 @@ your benchmark job writes to the file:
 from microbench.livestream import LiveStream
 import time
 
+
 class Watcher(LiveStream):
     def filter(self, data):
         # Only show records from GPU nodes
@@ -279,7 +291,8 @@ class Watcher(LiveStream):
         name = data.get('call', {}).get('name', '?')
         host = data.get('host', {}).get('hostname', '?')
         durs = data.get('call', {}).get('durations', [])
-        print(f"{name} | {host} | {durs}")
+        print(f'{name} | {host} | {durs}')
+
 
 stream = Watcher('/home/user/results.jsonl')
 try:
@@ -314,7 +327,9 @@ import pandas
 results = pandas.read_json('/home/user/results.jsonl', lines=True)
 
 # Compare the environment of the slowest and fastest calls
-results_flat = pandas.DataFrame(FileOutput('/home/user/results.jsonl').get_results(flat=True))
+results_flat = pandas.DataFrame(
+    FileOutput('/home/user/results.jsonl').get_results(flat=True)
+)
 slowest = results_flat.loc[results_flat['call.durations'].apply(sum).idxmax()]
 fastest = results_flat.loc[results_flat['call.durations'].apply(sum).idxmin()]