generate results

.github/workflows/results.yml

@@ -0,0 +1,25 @@
+name: generate results
+
+on:
+  push:
+    branches: [ main ]
+
+
+jobs:
+  generate-results:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+      - run: pip install jinja2 pandas pytz seaborn
+      - run: python generate-results.py
+      - uses: EndBug/add-and-commit@v9 # You can change this to use a specific version.
+        with:
+          add: 'Results.md *.png'
+          author_name: ilastik-bot
+          committer_name: ilastik bot
+          message: '(bot) Updated results'
+          new_branch: results
+          push: 'origin results --force'

Results.md.in

@@ -0,0 +1,24 @@
+# Results
+
+generated {{ now }}, from results of {{ n_hosts }} machines
+
+## Unique
+
+### Memory usage
+
+[![unique-memory-usage](unique-max-memory.png)]
+
+### Time
+
+[![unique-runningtime](unique-runningtime.png)]
+
+## Bincount
+
+### Memory usage
+
+[![unique-memory-usage](unique-max-memory.png)]
+
+### Time
+
+[![unique-runningtime](unique-runningtime.png)]
+

generate-results.py

@@ -0,0 +1,75 @@
+import datetime
+from datetime import timedelta, timezone
+from pathlib import Path
+
+import pandas
+import pytz
+import seaborn as sns
+from jinja2 import Template
+
+utctzinfo = timezone(timedelta(hours=0))
+timeformat = "%Y-%m-%dT%H:%M:%S"
+
+
+def gen_plots():
+    shape_index_mapping = {
+        "(512, 128, 1)": "0",  # numpy.prod((512, 128, 1)),
+        "(1024, 512, 1)": "1",  # numpy.prod((1024, 512, 1)),
+        "(2048, 1024, 1)": "2",  # numpy.prod((2048, 1024, 1)),
+        "(512, 512, 32)": "3",  # numpy.prod((512, 512, 32)),
+        "(1024, 1024, 256)": "4",  # numpy.prod((1024, 1024, 256))
+    }
+
+    results_unique = pandas.read_csv("results-unique.csv", sep="\t")
+
+    results_unique["shape_ind"] = results_unique["shape"].map(shape_index_mapping)
+
+    p = sns.lineplot(x="shape_ind", y="t", hue="method", data=results_unique)
+    p.set(yscale="log")
+    fig = p.get_figure()
+    fig.savefig("unique-runningtime.png")
+
+    p.clear()
+
+    p = sns.lineplot(x="shape_ind", y="mem_max", hue="method", data=results_unique)
+    p.set(yscale="log")
+    fig = p.get_figure()
+    fig.savefig("unique-max-memory.png")
+
+    p.clear()
+
+    results_bincount = pandas.read_csv("results-bincount.csv", sep="\t")
+
+    results_bincount["shape_ind"] = results_bincount["shape"].map(shape_index_mapping)
+
+    p = sns.lineplot(x="shape_ind", y="t", hue="method", data=results_bincount)
+    p.set(yscale="log")
+    fig = p.get_figure()
+    fig.savefig("bincount-runningtime.png")
+
+    p.clear()
+
+    p = sns.lineplot(x="shape_ind", y="mem_max", hue="method", data=results_bincount)
+    p.set(yscale="log")
+    fig = p.get_figure()
+    fig.savefig("bincount-max-memory.png")
+
+    p.clear()
+
+
+def main():
+    template = Template(Path("Results.md.in").read_text())
+    gen_plots()
+    n_hosts = len(pandas.read_csv("results-unique.csv", sep="\t")["host"].unique())
+
+    tz = pytz.timezone("Europe/Berlin")
+    now = datetime.datetime.now(tz)
+
+    # render
+    rendered = template.render(now=now.strftime(timeformat), n_hosts=n_hosts)
+
+    Path("Results.md").write_text(rendered)
+
+
+if __name__ == "__main__":
+    main()