13743029525

Committed 09 Mar 2025 12:38AM UTC coverage: 94.444% (+0.02%) from 94.424%

Build # 13743029525

Build Type

Pull #13

github

Committed by

liamjdavis

Commit Message

Re-styled source code

Pull Request Pull Request #13: Added New RSTs for Docs

Run Details

2 of 2 new or added lines in 2 files covered. (100.0%)

1 existing line in 1 file now uncovered.

510 of 540 relevant lines covered (94.44%)

8.25 hits per line

Source File
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97.78

/MatPlus/BoxPlot.py

import matplotlib.pyplot as plt
import numpy as np


class BoxPlot:
    """
    A class to create box plots with optional notches, symbols for outliers, and vertical orientation.

    Attributes:
    ----------
    data : array-like
        The data to be plotted.
    notch : bool, optional
        Whether to draw a notch to indicate the confidence interval around the median. Default is False.
    sym : str, optional
        The symbol for outliers. Default is "b".
    vert : bool, optional
        Whether to draw the box plot vertically. Default is True.
    whis : float, optional
        The length of the whiskers as a multiple of the interquartile range (IQR). Default is 1.5.
    """
    def __init__(self, data, notch=False, sym="b", vert=True, whis=1.5):
        # Validate data type
        if not isinstance(data, (list, np.ndarray)):
            raise TypeError("Data must be a list or numpy array")

        # Validate numeric data
        try:
            numeric_data = [float(x) for x in data]
        except (ValueError, TypeError):
            raise TypeError("All elements must be numeric")

        # Validate data length
        if not data or len(data) == 0:
            raise ValueError("Data array cannot be empty")

        # Validate whis parameter
        if whis <= 0:
            raise ValueError("Whisker length must be positive")

        self.data = numeric_data
        self.notch = notch
        self.sym = sym
        self.vert = vert
        self.whis = whis

    # Rest of the class implementation remains the same
    def median(self):
        """Calculate median of the data"""
        return float(np.median(self.data))

    def quartiles(self):
        """Calculate first and third quartiles"""
        q1 = float(np.percentile(self.data, 25))
        q3 = float(np.percentile(self.data, 75))
        return (q1, q3)

    def outliers(self):
        """Identify outliers using whis*IQR rule"""
        q1, q3 = self.quartiles()
        iqr = q3 - q1
        lower_bound = q1 - (self.whis * iqr)
        upper_bound = q3 + (self.whis * iqr)
        return [x for x in self.data if x < lower_bound or x > upper_bound]

    def plot(self):
        """Generate box plot"""
        plt.style.use("_mpl-gallery")
        fig, ax = plt.subplots()

        # Set labels and ticks
        if self.vert:
            ax.set_xlabel("Data")
            ax.set_ylabel("Value")
            ax.set_xticks([1])
        else:
            ax.set_xlabel("Value")
            ax.set_ylabel("Data")
            ax.set_yticks([1])

        # Add grid for better readability
        ax.grid(True, linestyle="--", alpha=0.7)

        # Adjust layout to prevent label clipping
        plt.tight_layout()

        plt.show()

1	import matplotlib.pyplot as plt	6✔
2	import numpy as np	6✔
3
4
5	class BoxPlot:	6✔
6	"""	1✔
7	A class to create box plots with optional notches, symbols for outliers, and vertical orientation.
8
9	Attributes:
10	----------
11	data : array-like
12	The data to be plotted.
13	notch : bool, optional
14	Whether to draw a notch to indicate the confidence interval around the median. Default is False.
15	sym : str, optional
16	The symbol for outliers. Default is "b".
17	vert : bool, optional
18	Whether to draw the box plot vertically. Default is True.
19	whis : float, optional
20	The length of the whiskers as a multiple of the interquartile range (IQR). Default is 1.5.
21	"""
22	def __init__(self, data, notch=False, sym="b", vert=True, whis=1.5):	6✔
23	# Validate data type
24	if not isinstance(data, (list, np.ndarray)):	6✔
UNCOV 25	raise TypeError("Data must be a list or numpy array")	×
26
27	# Validate numeric data
28	try:	6✔
29	numeric_data = [float(x) for x in data]	6✔
30	except (ValueError, TypeError):	6✔
31	raise TypeError("All elements must be numeric")	6✔
32
33	# Validate data length
34	if not data or len(data) == 0:	6✔
35	raise ValueError("Data array cannot be empty")	6✔
36
37	# Validate whis parameter
38	if whis <= 0:	6✔
39	raise ValueError("Whisker length must be positive")	6✔
40
41	self.data = numeric_data	6✔
42	self.notch = notch	6✔
43	self.sym = sym	6✔
44	self.vert = vert	6✔
45	self.whis = whis	6✔
46
47	# Rest of the class implementation remains the same
48	def median(self):	6✔
49	"""Calculate median of the data"""
50	return float(np.median(self.data))	6✔
51
52	def quartiles(self):	6✔
53	"""Calculate first and third quartiles"""
54	q1 = float(np.percentile(self.data, 25))	6✔
55	q3 = float(np.percentile(self.data, 75))	6✔
56	return (q1, q3)	6✔
57
58	def outliers(self):	6✔
59	"""Identify outliers using whis*IQR rule"""
60	q1, q3 = self.quartiles()	6✔
61	iqr = q3 - q1	6✔
62	lower_bound = q1 - (self.whis * iqr)	6✔
63	upper_bound = q3 + (self.whis * iqr)	6✔
64	return [x for x in self.data if x < lower_bound or x > upper_bound]	6✔
65
66	def plot(self):	6✔
67	"""Generate box plot"""
68	plt.style.use("_mpl-gallery")	6✔
69	fig, ax = plt.subplots()	6✔
70
71	# Set labels and ticks
72	if self.vert:	6✔
73	ax.set_xlabel("Data")	6✔
74	ax.set_ylabel("Value")	6✔
75	ax.set_xticks([1])	6✔
76	else:
77	ax.set_xlabel("Value")	6✔
78	ax.set_ylabel("Data")	6✔
79	ax.set_yticks([1])	6✔
80
81	# Add grid for better readability
82	ax.grid(True, linestyle="--", alpha=0.7)	6✔
83
84	# Adjust layout to prevent label clipping
85	plt.tight_layout()	6✔
86
87	plt.show()	6✔

ac-i2i-engineering / MatPlus / 13743029525

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