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https://github.com/kuhyx/WUT_Computer_Science.git
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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
168 lines
3.9 KiB
Python
Executable File
168 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Plot the inverse distribution of profit for the three WDWR scenarios.
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One curve per scenario: the sorted profits of its 100 sampled realisations
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against their empirical cumulative probability, which is how the report shows
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which scenario stochastically dominates the others.
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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
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scenario1_profits = [
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8283.101223531,
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8360.092861464,
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8023.436774628,
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7996.435353438,
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8108.734620296,
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8709.702668322,
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9236.429507678,
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8492.980846575,
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8912.962073143,
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8201.079841867,
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7947.08574225,
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8426.386242154,
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8633.510288097,
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7925.419317516,
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8216.653093455,
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8162.239336848,
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10606.276724793,
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7766.753696884,
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8902.966530729,
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8329.954853003,
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8615.534725088,
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9307.905933844,
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8711.836200338,
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7853.713101162,
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8160.850707138,
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8505.74747647,
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7807.599624626,
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8650.006339759,
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7571.333855925,
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9103.892851116,
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]
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scenario2_profits = [
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8779.880622914,
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8905.098118171,
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8457.038873118,
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8476.337700492,
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8611.662470696,
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9284.604268643,
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9787.525726881,
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9033.316521658,
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9469.444921654,
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8726.555858861,
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8437.173060618,
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8935.043125766,
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9172.334141304,
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8425.564554151,
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8725.52174345,
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8672.538442695,
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11321.501722433,
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8233.9854963,
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9465.167737138,
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8841.407418574,
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9162.816556878,
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9946.670992861,
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9281.411111594,
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8349.695093959,
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8671.731748701,
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9074.152941559,
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8296.712958538,
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9208.592940681,
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8041.287735389,
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9707.228511359,
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]
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scenario3_profits = [
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9268.899026395,
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9430.210649623,
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8919.631184021,
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8944.062758608,
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9095.775246456,
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9886.312580673,
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10387.393647889,
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9637.953603446,
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10098.221535588,
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9238.913391815,
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8980.977156104,
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9414.393022001,
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9722.356857713,
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8917.726783311,
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9178.474614142,
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9167.292074295,
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12011.512542804,
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8693.048121325,
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10039.47692674,
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9375.735645214,
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9730.36014247,
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10531.376234688,
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9750.101324443,
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8881.341453044,
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9186.289888284,
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9675.754350507,
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8773.329747496,
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9722.523537949,
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8489.277170975,
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10325.425491734,
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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_profits, reverse=True)
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scenario2_sorted = sorted(scenario2_profits, reverse=True)
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scenario3_sorted = sorted(scenario3_profits, reverse=True)
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# Create y-axis values (cumulative distribution from 0 to 1)
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num_points = len(scenario1_profits)
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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("Przeciętny Zysk", 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, profits in enumerate(
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(scenario1_profits, scenario2_profits, scenario3_profits), start=1
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):
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logger.info(
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"Scenario %d - Mean: %.2f, Std: %.2f",
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number,
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np.mean(profits),
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np.std(profits),
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)
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