obb.py

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import csv
import sys
from math import sqrt
 
import matplotlib.pyplot as plt
import numpy as np
 
filename = sys.argv[1]
 
with open(filename, "r") as file:
    reader = csv.reader(file, strict=True)
    fieldnames = None
    # fieldnames = reader.fieldnames
    for row in reader:
        if fieldnames is None:
            fieldnames = [n.strip() for n in row]
            values = [[] for _ in fieldnames]
            continue
 
        values[0].append(int(row[0]))
        for i, v in enumerate(row[1:]):
            values[i + 1].append(float(v))
 
# Compute mean and variance for each values, for each separating axis
means = [
    [
        0.0,
    ]
    * 12
    for _ in fieldnames[4:]
]
stddevs = [
    [
        0.0,
    ]
    * 12
    for _ in fieldnames[4:]
]
nb_occurence = [
    0,
] * 12
 
for i, id in enumerate(values[0]):
    nb_occurence[id] += 1
 
for i, id in enumerate(values[0]):
    for k, n in enumerate(fieldnames[4:]):
        v = values[k + 4][i]
        means[k][id] += v / nb_occurence[id]
        stddevs[k][id] += v * v / nb_occurence[id]
 
for k, n in enumerate(fieldnames[4:]):
    for id in range(12):
        # means  [k][id] /= nb_occurence[id]
        # stddevs[k][id] = sqrt (stddevs[k][id]) / nb_occurence[id] - means[k][id])
        stddevs[k][id] = sqrt(stddevs[k][id] - means[k][id] * means[k][id])
 
subplots = False
Nrows = 1
Ncols = 3
iplot = 1
time_vs_sep_axis = True
nb_occ_sep_axis = False
avg_time_vs_impl = True
 
if time_vs_sep_axis:
    if subplots:
        plt.subplot(Nrows, Ncols, iplot)
    else:
        plt.figure(iplot)
    plt.title("Time, with std dev, versus separating axis")
    for k, n in enumerate(fieldnames[4:]):
        # plt.errorbar(
        # [
        # np.linspace(0, 11, 12) + shift
        # for shift in np.linspace(
        # -0.2,
        # 0.2,
        # )
        # ],
        # means[k],
        # stddevs[k],
        # label=n,
        # )
        plt.errorbar(np.linspace(0, 11, 12), means[k], stddevs[k], label=n)
        # plt.errorbar(
        # np.linspace(0, 11, 12),
        # means[k],
        # [[0] * len(stddevs[k]), stddevs[k]],
        # label=n,
        # )
    plt.xlim([-0.5, 11.5])
    plt.ylabel("Time (ns)")
    plt.xlabel("Separating axis")
    plt.legend(loc="upper left")
 
    axx = plt.gca().twinx()
    axx.hist(
        values[0],
        bins=[i - 0.5 for i in range(13)],
        bottom=-0.5,
        cumulative=True,
        rwidth=0.5,
        fill=False,
        label="Cumulative occurence",
    )
    axx.set_ylabel("Nb occurence of a separating axis.")
    plt.legend(loc="lower right")
 
iplot += 1
if nb_occ_sep_axis:
    if subplots:
        plt.subplot(Nrows, Ncols, iplot)
    else:
        plt.figure(iplot)
    plt.title("Nb of occurence per separating axis")
    plt.hist(values[0], bins=[i - 0.5 for i in range(13)])
    plt.ylabel("Nb occurence")
    plt.xlabel("Separating axis")
    dlb_id = 1
    d_id = 2
    # plt.title ("Time, with std dev, versus distance")
    # for k, n in enumerate(fieldnames[4:]):
    # plt.plot (values[dlb_id], values[k+4], '.', label=n)
 
iplot += 1
if avg_time_vs_impl:
    if subplots:
        plt.subplot(Nrows, Ncols, iplot)
    else:
        plt.figure(iplot)
    plt.title("Average time versus the implementation")
    # plt.boxplot(values[4:], labels=fieldnames[4:], showmeans=True)
    _mins = np.min(values[4:], axis=1)
    _maxs = np.max(values[4:], axis=1)
    _means = np.mean(values[4:], axis=1)
    _stddev = np.std(values[4:], axis=1)
    _sorted = sorted(
        zip(fieldnames[4:], _means, _stddev, _mins, _maxs), key=lambda x: x[1]
    )
    plt.errorbar(
        [f for f, _, _, _, _ in _sorted],
        [m for _, m, _, _, _ in _sorted],
        [s for _, _, s, _, _ in _sorted],
        fmt="go",
        linestyle="",
        label="mean and std deviation",
    )
    plt.plot(
        [f for f, _, _, _, _ in _sorted],
        [v for _, _, _, v, _ in _sorted],
        "b+",
        ls="",
        label="min",
    )
    plt.plot(
        [f for f, _, _, _, _ in _sorted],
        [u for _, _, _, _, u in _sorted],
        "r+",
        ls="",
        label="max",
    )
    plt.ylabel("Time (ns)")
    plt.xticks(rotation=20)
    plt.legend()
 
plt.show()