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Krzysztof kuhy Rudnicki
bd24dd57ea
kuhy overruled the "do not fix coursework to be better than it was submitted" rule: git history is the archive, so the working tree can be clean. This lands the config and the mechanical half of the cleanup -- 9,281 ruff findings under the fleet config down to 3,686, with no per-file-ignores anywhere. What is in here: - pyproject.toml, the first one in this repo. select = ["ALL"], mypy strict, and the vendored list mirrored into ruff exclude and coverage omit. Five rules are off and each says why; CPY001 is the only judgement call, because stamping a copyright header onto 137 university assignments would assert a licence that was never claimed. No hook is wired up yet -- a gate lands only once its subject is clean. - ruff format + the safe fixes, then a second pass over the docstring rules ruff marks unsafe (they only edit docstring text). 108 files. - TRAK/sightpy is vendored, not coursework: it is rafael-fuente/Python-Raytracer (BSD 3-Clause). Verified against a fresh upstream clone with line endings normalised -- 30 of 32 files byte-identical, ray.py and scene.py carrying a 12- and 13-line local patch that counts rays for the report. 35 files that should never have been linted, now excluded and restored. - WDWR, SPD, MOM and ECRYPT are fully clean, by hand and verified by running them: WDWR's three plot scripts print the same statistics as before, MOM's PuLP model solves to the same cost. WDWR's graph.py had a 342-character CSV header embedded as a string literal, which cannot be wrapped -- the data moved to data/pareto_front.csv. Two 0-byte .py stubs are deleted; an empty file is not a deliverable and git still has it. Three harness bugs found by actually running things: - `run` now forces MPLBACKEND=Agg. TRAK finished its ray trace in 37 seconds and then sat for 45 minutes: plt.show() had opened an image viewer and was waiting for someone to close the window. - TRAK's main.py only pops that viewer with --show now, and its runner writes to a gitignored build/ via the new --entry-args. The default output path is outputs/output.png, which is TRACKED -- a plain run overwrote a submitted deliverable. - course_lib.sh was 357 lines, over this repo's own cap. Split into course_probe.sh and course_run.sh. Verified: ECOTE 26 tests pass, ECRYPT_PROJECT 99 pass, TRAK renders cornell_box in 37s and exits 0, every module compiles, and every import ruff removed was checked to be genuinely unused. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
166 lines
3.2 KiB
Python
Executable File
166 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Plot the inverse distribution of RISK for the three WDWR scenarios.
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The companion of graph2.py: same three scenarios, same empirical cumulative
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probability on the y axis, but the Gini risk measure on the x axis instead of
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the profit.
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"""
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import logging
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import matplotlib.pyplot as plt
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import numpy as np
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logger = logging.getLogger(__name__)
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logging.basicConfig(format="%(message)s", level=logging.INFO)
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# Sized for a report figure rather than for the screen.
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LABEL_FONT_SIZE = 16
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# Data for the three scenarios - Risk values
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scenario1_risks = [
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184.458,
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195.09,
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207.678,
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278.26,
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220.823,
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181.042,
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195.281,
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199.255,
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182.194,
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181.295,
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285.773,
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185.306,
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272.997,
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288.405,
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223.407,
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213.341,
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208.693,
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354.01,
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217.093,
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301.892,
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200.499,
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637.424,
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408.232,
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308.314,
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181.042,
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470.693,
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212.039,
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184.694,
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345.455,
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205.291,
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]
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scenario2_risks = [
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403.784,
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414.043,
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408.225,
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664.379,
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413.447,
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448.303,
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513.763,
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439.966,
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403.784,
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408.366,
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773.926,
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459.531,
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537.056,
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795.835,
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542.569,
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512.817,
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471.741,
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879.425,
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441.46,
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533.521,
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659.742,
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990.73,
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842.758,
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776.278,
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404.787,
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989.35,
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481.833,
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409.978,
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795.275,
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421.261,
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]
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scenario3_risks = [
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559.839,
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576.633,
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596.264,
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942.092,
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560.045,
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679.026,
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683.47,
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627.607,
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559.839,
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562.478,
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1245.41,
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638.37,
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675.366,
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1132.8,
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784.609,
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686.634,
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680.842,
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1115.92,
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572.793,
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682.114,
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914.647,
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1211.67,
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1051.95,
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1104.87,
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583.36,
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1360.71,
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743.425,
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577.734,
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1191.09,
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581.791,
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]
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# Create figure with single plot
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plt.figure(figsize=(12, 8))
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# Sort the data for each scenario (descending order)
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scenario1_sorted = sorted(scenario1_risks, reverse=True)
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scenario2_sorted = sorted(scenario2_risks, reverse=True)
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scenario3_sorted = sorted(scenario3_risks, reverse=True)
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# Create y-axis values (cumulative distribution from 0 to 1)
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num_points = len(scenario1_risks)
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y_values = [i / num_points for i in range(1, num_points + 1)]
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# Plot all three scenarios on the same graph
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plt.plot(
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scenario1_sorted, y_values, "b-o", label="Scenariusz 1", linewidth=2, markersize=4
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)
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plt.plot(
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scenario2_sorted, y_values, "g-s", label="Scenariusz 2", linewidth=2, markersize=4
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)
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plt.plot(
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scenario3_sorted, y_values, "r-^", label="Scenariusz 3", linewidth=2, markersize=4
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)
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# Add labels and title
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plt.xlabel("Ryzyko", fontsize=LABEL_FONT_SIZE)
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plt.ylabel("Odwrotna dystrybuanta", fontsize=LABEL_FONT_SIZE)
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plt.xticks(fontsize=LABEL_FONT_SIZE)
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plt.yticks(fontsize=LABEL_FONT_SIZE)
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plt.ylim(0, 1) # Set y-axis limits to cut off values outside 0-1 range
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plt.grid(visible=True, alpha=0.3)
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plt.legend(fontsize=LABEL_FONT_SIZE)
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# Format the plot
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plt.tight_layout()
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# Show the plot
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plt.show()
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# Print statistics for each scenario
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logger.info("Scenario Statistics:")
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for number, risks in enumerate(
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(scenario1_risks, scenario2_risks, scenario3_risks), start=1
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):
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logger.info(
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"Scenario %d - Mean: %.2f, Std: %.2f", number, np.mean(risks), np.std(risks)
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)
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