13486272155

Committed 23 Feb 2025 07:37PM UTC coverage: 92.308% (+4.6%) from 87.727%

Build # 13486272155

Build Type

Pull #9

github

Committed by

BestLocation

Commit Message

add coverage

Pull Request Pull Request #9: Added boxplot and tests

Run Details

115 of 116 new or added lines in 2 files covered. (99.14%)

348 of 377 relevant lines covered (92.31%)

7.77 hits per line

Source File
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97.56

/MatPlus/BoxPlot.py

import matplotlib.pyplot as plt
import numpy as np


class BoxPlot:
    def __init__(self, data, notch=False, sym="b", vert=True, whis=1.5):
        """Initializes Boxplot object with the following parameters:
        data: data to be plotted
        notch: notch shape of the box
        sym: symbol for outliers
        vert: vertical orientation (deprecated, use orientation instead)
        whis: whisker length in number of IQR
        """
        # 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 orientation using new parameter name
        orientation = "vertical" if self.vert else "horizontal"

        # Set ticks properly
        if orientation == "vertical":
            ax.set_xticks([1])
            ax.set_xticklabels(["Data"])
        else:
            ax.set_yticks([1])
            ax.set_yticklabels(["Data"])

        plt.show()

1	import matplotlib.pyplot as plt	6✔
2	import numpy as np	6✔
3
4
5	class BoxPlot:	6✔
6	def __init__(self, data, notch=False, sym="b", vert=True, whis=1.5):	6✔
7	"""Initializes Boxplot object with the following parameters:
8	data: data to be plotted
9	notch: notch shape of the box
10	sym: symbol for outliers
11	vert: vertical orientation (deprecated, use orientation instead)
12	whis: whisker length in number of IQR
13	"""
14	# Validate data type
15	if not isinstance(data, (list, np.ndarray)):	6✔
NEW 16	raise TypeError("Data must be a list or numpy array")	×
17
18	# Validate numeric data
19	try:	6✔
20	numeric_data = [float(x) for x in data]	6✔
21	except (ValueError, TypeError):	6✔
22	raise TypeError("All elements must be numeric")	6✔
23
24	# Validate data length
25	if not data or len(data) == 0:	6✔
26	raise ValueError("Data array cannot be empty")	6✔
27
28	# Validate whis parameter
29	if whis <= 0:	6✔
30	raise ValueError("Whisker length must be positive")	6✔
31
32	self.data = numeric_data	6✔
33	self.notch = notch	6✔
34	self.sym = sym	6✔
35	self.vert = vert	6✔
36	self.whis = whis	6✔
37
38	# Rest of the class implementation remains the same
39	def median(self):	6✔
40	"""Calculate median of the data"""
41	return float(np.median(self.data))	6✔
42
43	def quartiles(self):	6✔
44	"""Calculate first and third quartiles"""
45	q1 = float(np.percentile(self.data, 25))	6✔
46	q3 = float(np.percentile(self.data, 75))	6✔
47	return (q1, q3)	6✔
48
49	def outliers(self):	6✔
50	"""Identify outliers using whis*IQR rule"""
51	q1, q3 = self.quartiles()	6✔
52	iqr = q3 - q1	6✔
53	lower_bound = q1 - (self.whis * iqr)	6✔
54	upper_bound = q3 + (self.whis * iqr)	6✔
55	return [x for x in self.data if x < lower_bound or x > upper_bound]	6✔
56
57	def plot(self):	6✔
58	"""Generate box plot"""
59	plt.style.use("_mpl-gallery")	6✔
60	fig, ax = plt.subplots()	6✔
61
62	# Set orientation using new parameter name
63	orientation = "vertical" if self.vert else "horizontal"	6✔
64
65	# Set ticks properly
66	if orientation == "vertical":	6✔
67	ax.set_xticks([1])	6✔
68	ax.set_xticklabels(["Data"])	6✔
69	else:
70	ax.set_yticks([1])	6✔
71	ax.set_yticklabels(["Data"])	6✔
72
73	plt.show()	6✔

ac-i2i-engineering / MatPlus / 13486272155

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