5892227966

Committed 17 Aug 2023 02:27PM UTC coverage: 33.736% (+7.0%) from 26.737%

Build # 5892227966

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Pull #565

github

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web-flow

Commit Message

Merge 9adf19bbe into 998fa208f

Pull Request Pull Request #565: Update report generator to use reportlab

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/src/pe_reports/charts.py

"""Class methods for report charts."""

# Standard Python Libraries
import os

# Third-Party Libraries
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

matplotlib.use("Agg")


# Factor to convert cm to inches
CM_CONVERSION_FACTOR = 2.54

# Get base directory to save images
BASE_DIR = os.path.abspath(os.path.dirname(__file__))


class Charts:
    """Build charts."""

    def __init__(self, df, width, height, name, title, x_label, y_label):
        """Initialize chart class."""
        self.df = df
        self.title = title
        self.x_label = x_label
        self.y_label = y_label
        self.width = width
        self.height = height
        self.name = name

    def pie(self):
        """Build pie chart."""
        df = self.df
        width = self.width
        height = self.height
        name = self.name
        plt.rcParams.update({"figure.max_open_warning": 0})
        category_name = df.columns[0]
        value_name = df.columns[1]
        df = df.sort_values(by=value_name, ascending=False)
        category_column = df[category_name]
        value_column = df[df.columns[1]]
        labels = category_column
        plt.gca().axis("equal")

        def autopct(pct):
            """Get percentages for the pie chart slices > 10%."""
            return ("%1.0f%%" % pct) if pct > 1 else ""

        pie = plt.pie(
            value_column,
            startangle=0,
            radius=1,
            autopct=autopct,
            textprops={"color": "w", "fontsize": 7},
        )
        plt.legend(
            pie[0],
            labels,
            bbox_to_anchor=(1, 0.5),
            loc="center right",
            fontsize=7,
            bbox_transform=plt.gcf().transFigure,
            frameon=False,
        )
        plt.subplots_adjust(left=0.2, wspace=0.2)
        plt.gcf().set_size_inches(
            width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
        )
        plt.savefig(
            BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
        )
        plt.clf()

    def stacked_bar(self):
        """Build stacked bar chart."""
        df = self.df
        title = self.title
        x_label = self.x_label
        y_label = self.y_label
        width = self.width
        height = self.height
        name = self.name
        color = ["#1357BE", "#D0342C"]
        df.plot(kind="bar", stacked=True, zorder=3, color=color)
        # Add title to chart
        plt.title(title, pad=15, fontsize=10)
        # Format chart's axis
        plt.xlabel(x_label, labelpad=10, fontdict={"size": 8})
        plt.ylabel(y_label, labelpad=10, fontdict={"size": 8})
        plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))
        plt.rc("axes", axisbelow=True)
        plt.grid(axis="y", zorder=0)
        plt.xticks(rotation=0)
        plt.ylim(ymin=0)
        # Set sizing for image
        plt.gcf().set_size_inches(
            width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
        )
        plt.tight_layout()
        # Save chart to assets directory
        plt.savefig(BASE_DIR + "/assets/" + name, transparent=True, dpi=500)
        plt.clf()

    def h_bar(self):
        """Build horizontal bar chart."""
        df = self.df
        x_label = self.x_label
        y_label = self.y_label
        width = self.width
        height = self.height
        name = self.name
        plt.rcParams.update({"figure.max_open_warning": 0})
        category_name = df.columns[0]
        value_name = df.columns[1]
        category_column = df[category_name].str.replace("Vulnerable Product - ", "")
        value_column = df[df.columns[1]]
        bar_width = 0.6
        fig, ax = plt.subplots()
        ax.spines.right.set_visible(False)
        ax.spines.top.set_visible(False)
        # Generate horizontal bar chart
        plt.barh(df.index, value_column, bar_width, align="center", color="#466fc6")
        # Specify axis atributes
        plt.xticks(fontsize=7)
        plt.yticks(fontsize=7)
        plt.xlim(xmin=0)
        plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))
        plt.gca().set_ylim(-1.0, len(category_column))
        plt.gca().set_yticks(df.index)
        plt.gca().set_yticklabels(category_column)
        plt.gca().set_xlabel(x_label, fontdict={"size": 8})
        plt.gca().set_ylabel(y_label)
        # Set sizing for image
        plt.gcf().set_size_inches(
            width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
        )
        plt.tight_layout()
        # Add data labels to each bar if greater than 0
        for i in range(len(df)):
            if df.loc[i, value_name] > 0:
                label = df.loc[i, value_name]
                plt.annotate(
                    label,  # this is the text
                    (df.loc[i, value_name], i),  # this is the point to label
                    textcoords="offset points",  # how to position the text
                    xytext=(7, -3),  # distance from text to points (x,y)
                    ha="center",  # horizontal alignment can be left, right or center
                    fontsize=8,
                )
        # Save chart to assets directory
        plt.savefig(
            BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
        )
        plt.clf()

    def line_chart(self):
        """Build line chart."""
        df = self.df
        x_label = self.x_label
        y_label = self.y_label
        width = self.width
        height = self.height
        name = self.name
        color = ["#7aa5c1", "#e08493"]
        fig, ax = plt.subplots()
        ax.spines["right"].set_visible(False)
        ax.spines["top"].set_visible(False)
        plt.set_loglevel("WARNING")
        # Plot first line on chart
        plt.plot(
            df.index,
            df[df.columns[0]],
            color=color[0],
            label=df.columns[0],
            linewidth=3,
            marker=".",
            markersize=10,
        )
        # If there is another column chart the second line
        if len(df.columns) == 2:
            plt.plot(
                df.index,
                df[df.columns[1]],
                color=color[1],
                label=df.columns[1],
                linewidth=3,
                linestyle="dashed",
                marker=".",
                markersize=10,
            )
        # Set the y-max to 110% of the max y value
        y_max = int(df[df.columns].max().max() * 1.1)
        plt.ylim(ymin=0, ymax=y_max * 1.10)
        # Place the legend in the upper right corner
        plt.legend(loc="upper right")
        # Set size of the chart
        plt.gcf().set_size_inches(
            width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
        )
        # Format tick marks and grid layout
        plt.xticks(fontsize=7)
        plt.yticks(fontsize=7)
        plt.gca().set_ylabel(y_label, labelpad=10, fontdict={"size": 8})
        plt.xlabel(x_label, labelpad=10, fontdict={"size": 8})
        plt.xticks(rotation=0)
        plt.grid(axis="y")
        plt.tight_layout()

        # Add data labels
        # Loop through the dataframe
        for row in df.itertuples():
            # Check if there is only one row of values
            if len(row) == 2:
                plt.annotate(
                    str(int(row[1])),
                    xy=(row[0], row[1]),
                    textcoords="offset points",  # Set the manner to position the text
                    xytext=(
                        0,
                        8,
                    ),  # Distance from text to points (x,y)
                    ha="center",  # Set horizontal alignment to center
                    color="#003e67",
                )
                # Check if there are two rows of data
            elif len(row) == 3:
                # Check if the two values are within 1/10th of the max y value
                value_diff = abs(row[1] - row[2])
                if value_diff < y_max / 10:
                    # If the values are on the bottom quarter of the graph don't label below values
                    if min(row[1], row[2]) < y_max / 4:
                        y1 = y2 = max(row[1], row[2])
                        if row[1] > row[2]:
                            y1_offset = 18
                            y2_offset = 8
                        else:
                            y1_offset = 8
                            y2_offset = 18
                    # If the values are not in the bottom quarter place the lower value below the point
                    else:
                        y1 = row[1]
                        y2 = row[2]
                        if row[1] > row[2]:
                            y1_offset = 8
                            y2_offset = -17
                        else:
                            y1_offset = -17
                            y2_offset = 8
                # If values are not close to each other put the labels directly above the value
                else:
                    y1 = row[1]
                    y2 = row[2]
                    y1_offset = 8
                    y2_offset = 8

                # Annotate the data points
                plt.annotate(
                    str(int(row[1])),
                    xy=(row[0], y1),
                    textcoords="offset points",  # Set how to position the text
                    xytext=(
                        0,
                        y1_offset,
                    ),  # Distance from text to points (x,y)
                    ha="center",  # Horizontal alignment can be left, right or center
                    color="#005288",
                )
                plt.annotate(
                    str(int(row[2])),
                    xy=(row[0], y2),
                    textcoords="offset points",  # Set how to position the text
                    xytext=(
                        0,
                        y2_offset,
                    ),  # Distance from text to points (x,y)
                    ha="center",  # Set horizontal alignment to center
                    # fontsize=2,
                    color="#c41230",
                )
        # Save chart to assets directory
        plt.savefig(
            BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
        )
        plt.clf()

1	"""Class methods for report charts."""
2
3	# Standard Python Libraries
4	import os	5✔
5
6	# Third-Party Libraries
7	import matplotlib	5✔
8	import matplotlib.pyplot as plt	5✔
9	from matplotlib.ticker import MaxNLocator	5✔
10
11	matplotlib.use("Agg")	5✔
12
13
14	# Factor to convert cm to inches
15	CM_CONVERSION_FACTOR = 2.54	5✔
16
17	# Get base directory to save images
18	BASE_DIR = os.path.abspath(os.path.dirname(__file__))	5✔
19
20
21	class Charts:	5✔
22	"""Build charts."""
23
24	def __init__(self, df, width, height, name, title, x_label, y_label):	5✔
25	"""Initialize chart class."""
26	self.df = df	×
27	self.title = title	×
28	self.x_label = x_label	×
29	self.y_label = y_label	×
30	self.width = width	×
31	self.height = height	×
32	self.name = name	×
33
34	def pie(self):	5✔
35	"""Build pie chart."""
36	df = self.df	×
37	width = self.width	×
38	height = self.height	×
39	name = self.name	×
40	plt.rcParams.update({"figure.max_open_warning": 0})	×
41	category_name = df.columns[0]	×
42	value_name = df.columns[1]	×
43	df = df.sort_values(by=value_name, ascending=False)	×
44	category_column = df[category_name]	×
45	value_column = df[df.columns[1]]	×
46	labels = category_column	×
47	plt.gca().axis("equal")	×
48
49	def autopct(pct):	×
50	"""Get percentages for the pie chart slices > 10%."""
51	return ("%1.0f%%" % pct) if pct > 1 else ""	×
52
53	pie = plt.pie(	×
54	value_column,
55	startangle=0,
56	radius=1,
57	autopct=autopct,
58	textprops={"color": "w", "fontsize": 7},
59	)
60	plt.legend(	×
61	pie[0],
62	labels,
63	bbox_to_anchor=(1, 0.5),
64	loc="center right",
65	fontsize=7,
66	bbox_transform=plt.gcf().transFigure,
67	frameon=False,
68	)
69	plt.subplots_adjust(left=0.2, wspace=0.2)	×
70	plt.gcf().set_size_inches(	×
71	width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
72	)
73	plt.savefig(	×
74	BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
75	)
76	plt.clf()	×
77
78	def stacked_bar(self):	5✔
79	"""Build stacked bar chart."""
80	df = self.df	×
81	title = self.title	×
82	x_label = self.x_label	×
83	y_label = self.y_label	×
84	width = self.width	×
85	height = self.height	×
86	name = self.name	×
87	color = ["#1357BE", "#D0342C"]	×
88	df.plot(kind="bar", stacked=True, zorder=3, color=color)	×
89	# Add title to chart
90	plt.title(title, pad=15, fontsize=10)	×
91	# Format chart's axis
92	plt.xlabel(x_label, labelpad=10, fontdict={"size": 8})	×
93	plt.ylabel(y_label, labelpad=10, fontdict={"size": 8})	×
94	plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))	×
95	plt.rc("axes", axisbelow=True)	×
96	plt.grid(axis="y", zorder=0)	×
97	plt.xticks(rotation=0)	×
98	plt.ylim(ymin=0)	×
99	# Set sizing for image
100	plt.gcf().set_size_inches(	×
101	width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
102	)
103	plt.tight_layout()	×
104	# Save chart to assets directory
105	plt.savefig(BASE_DIR + "/assets/" + name, transparent=True, dpi=500)	×
106	plt.clf()	×
107
108	def h_bar(self):	5✔
109	"""Build horizontal bar chart."""
110	df = self.df	×
111	x_label = self.x_label	×
112	y_label = self.y_label	×
113	width = self.width	×
114	height = self.height	×
115	name = self.name	×
116	plt.rcParams.update({"figure.max_open_warning": 0})	×
117	category_name = df.columns[0]	×
118	value_name = df.columns[1]	×
119	category_column = df[category_name].str.replace("Vulnerable Product - ", "")	×
120	value_column = df[df.columns[1]]	×
121	bar_width = 0.6	×
122	fig, ax = plt.subplots()	×
123	ax.spines.right.set_visible(False)	×
124	ax.spines.top.set_visible(False)	×
125	# Generate horizontal bar chart
126	plt.barh(df.index, value_column, bar_width, align="center", color="#466fc6")	×
127	# Specify axis atributes
128	plt.xticks(fontsize=7)	×
129	plt.yticks(fontsize=7)	×
130	plt.xlim(xmin=0)	×
131	plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))	×
132	plt.gca().set_ylim(-1.0, len(category_column))	×
133	plt.gca().set_yticks(df.index)	×
134	plt.gca().set_yticklabels(category_column)	×
135	plt.gca().set_xlabel(x_label, fontdict={"size": 8})	×
136	plt.gca().set_ylabel(y_label)	×
137	# Set sizing for image
138	plt.gcf().set_size_inches(	×
139	width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
140	)
141	plt.tight_layout()	×
142	# Add data labels to each bar if greater than 0
143	for i in range(len(df)):	×
144	if df.loc[i, value_name] > 0:	×
145	label = df.loc[i, value_name]	×
146	plt.annotate(	×
147	label, # this is the text
148	(df.loc[i, value_name], i), # this is the point to label
149	textcoords="offset points", # how to position the text
150	xytext=(7, -3), # distance from text to points (x,y)
151	ha="center", # horizontal alignment can be left, right or center
152	fontsize=8,
153	)
154	# Save chart to assets directory
155	plt.savefig(	×
156	BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
157	)
158	plt.clf()	×
159
160	def line_chart(self):	5✔
161	"""Build line chart."""
162	df = self.df	×
163	x_label = self.x_label	×
164	y_label = self.y_label	×
165	width = self.width	×
166	height = self.height	×
167	name = self.name	×
NEW 168	color = ["#7aa5c1", "#e08493"]	×
169	fig, ax = plt.subplots()	×
170	ax.spines["right"].set_visible(False)	×
171	ax.spines["top"].set_visible(False)	×
172	plt.set_loglevel("WARNING")	×
173	# Plot first line on chart
NEW 174	plt.plot(	×
175	df.index,
176	df[df.columns[0]],
177	color=color[0],
178	label=df.columns[0],
179	linewidth=3,
180	marker=".",
181	markersize=10,
182	)
183	# If there is another column chart the second line
NEW 184	if len(df.columns) == 2:	×
UNCOV 185	plt.plot(	×
186	df.index,
187	df[df.columns[1]],
188	color=color[1],
189	label=df.columns[1],
190	linewidth=3,
191	linestyle="dashed",
192	marker=".",
193	markersize=10,
194	)
195	# Set the y-max to 110% of the max y value
NEW 196	y_max = int(df[df.columns].max().max() * 1.1)	×
NEW 197	plt.ylim(ymin=0, ymax=y_max * 1.10)	×
198	# Place the legend in the upper right corner
NEW 199	plt.legend(loc="upper right")	×
200	# Set size of the chart
NEW 201	plt.gcf().set_size_inches(	×
202	width / CM_CONVERSION_FACTOR, height / CM_CONVERSION_FACTOR
203	)
204	# Format tick marks and grid layout
205	plt.xticks(fontsize=7)	×
206	plt.yticks(fontsize=7)	×
207	plt.gca().set_ylabel(y_label, labelpad=10, fontdict={"size": 8})	×
208	plt.xlabel(x_label, labelpad=10, fontdict={"size": 8})	×
209	plt.xticks(rotation=0)	×
210	plt.grid(axis="y")	×
UNCOV 211	plt.tight_layout()	×
212
213	# Add data labels
214	# Loop through the dataframe
NEW 215	for row in df.itertuples():	×
216	# Check if there is only one row of values
NEW 217	if len(row) == 2:	×
NEW 218	plt.annotate(	×
219	str(int(row[1])),
220	xy=(row[0], row[1]),
221	textcoords="offset points", # Set the manner to position the text
222	xytext=(
223	0,
224	8,
225	), # Distance from text to points (x,y)
226	ha="center", # Set horizontal alignment to center
227	color="#003e67",
228	)
229	# Check if there are two rows of data
NEW 230	elif len(row) == 3:	×
231	# Check if the two values are within 1/10th of the max y value
NEW 232	value_diff = abs(row[1] - row[2])	×
NEW 233	if value_diff < y_max / 10:	×
234	# If the values are on the bottom quarter of the graph don't label below values
NEW 235	if min(row[1], row[2]) < y_max / 4:	×
NEW 236	y1 = y2 = max(row[1], row[2])	×
NEW 237	if row[1] > row[2]:	×
NEW 238	y1_offset = 18	×
NEW 239	y2_offset = 8	×
240	else:
NEW 241	y1_offset = 8	×
NEW 242	y2_offset = 18	×
243	# If the values are not in the bottom quarter place the lower value below the point
244	else:
NEW 245	y1 = row[1]	×
NEW 246	y2 = row[2]	×
NEW 247	if row[1] > row[2]:	×
NEW 248	y1_offset = 8	×
NEW 249	y2_offset = -17	×
250	else:
NEW 251	y1_offset = -17	×
NEW 252	y2_offset = 8	×
253	# If values are not close to each other put the labels directly above the value
254	else:
NEW 255	y1 = row[1]	×
NEW 256	y2 = row[2]	×
NEW 257	y1_offset = 8	×
NEW 258	y2_offset = 8	×
259
260	# Annotate the data points
NEW 261	plt.annotate(	×
262	str(int(row[1])),
263	xy=(row[0], y1),
264	textcoords="offset points", # Set how to position the text
265	xytext=(
266	0,
267	y1_offset,
268	), # Distance from text to points (x,y)
269	ha="center", # Horizontal alignment can be left, right or center
270	color="#005288",
271	)
NEW 272	plt.annotate(	×
273	str(int(row[2])),
274	xy=(row[0], y2),
275	textcoords="offset points", # Set how to position the text
276	xytext=(
277	0,
278	y2_offset,
279	), # Distance from text to points (x,y)
280	ha="center", # Set horizontal alignment to center
281	# fontsize=2,
282	color="#c41230",
283	)
284	# Save chart to assets directory
285	plt.savefig(	×
286	BASE_DIR + "/assets/" + name, transparent=True, dpi=500, bbox_inches="tight"
287	)
288	plt.clf()	×

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