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charts.py
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charts.py
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import json
import os
import pandas as pd
import matplotlib.pyplot as plt
import sys
import requests
from pathlib import Path
from dotenv import load_dotenv
from alive_progress import alive_bar
from collections import Counter
from spreadsheets import component_stats, monthly_stats, overall_issue_stats
load_dotenv()
label_v9 = "Fluent UI react-components (v9)"
label_epic = "Type: Epic"
label_feature = "Type: Feature"
label_bug = "Type: Bug :bug:"
label_needs_backlog_grooming = "Needs: Backlog review"
label_a11y = "Area: Accessibility"
label_soft_close = "Resolution: Soft Close"
label_parter_ask = "Partner Ask"
label_needs_triage = "Needs: Triage :mag:"
def fetch_issues(repo, token):
issues = []
page = 1
with alive_bar(0, title="Fetching issues", unit=" pages") as bar:
while True:
url = f"https://api.github.com/repos/{repo}/issues?state=all&page={
page}&per_page=100&labels=Fluent UI react-components (v9)"
headers = {"Authorization": f"Bearer {token}"}
response = requests.get(url, headers=headers)
if response.status_code != 200:
raise Exception(f"Error fetching issues: {
response.status_code}")
page_issues = response.json()
if not page_issues:
break
issues.extend(page_issues)
page += 1
bar()
return issues
def get_charts_data(issues):
all_issues = [
issue for issue in issues if "pull_request" not in issue
]
issues_minimal = []
for issue in all_issues:
labels_set = set([label["name"] for label in issue["labels"]])
issues_minimal.append(
{
"id": issue["id"],
"title": issue["title"],
"labels": labels_set,
"created_at": issue["created_at"],
"state": issue["state"].lower(),
}
)
df_issues_all = pd.DataFrame(issues_minimal)
df_issues_all["created_at"] = pd.to_datetime(
df_issues_all["created_at"], yearfirst=True
)
df_issues = pd.DataFrame(
[issue for issue in issues_minimal if issue["state"] == "open"]
)
df_issues["created_at"] = pd.to_datetime(
df_issues["created_at"], yearfirst=True)
df_issues_closed = pd.DataFrame(
[issue for issue in issues_minimal if issue["state"] == "closed"]
)
df_issues_closed["created_at"] = pd.to_datetime(
df_issues_closed["created_at"], yearfirst=True
)
issue_labels = [
issue["labels"] for issue in issues_minimal if issue["state"] == "open"
]
all_labels = set()
for labels in issue_labels:
all_labels = all_labels.union(labels)
component_names = set([label.replace("Component: ", "") for label in all_labels if label.startswith("Component:")])
return all_issues, issues_minimal, df_issues, df_issues_closed, issue_labels, component_names
def plot_labels_pie(issue_labels):
labels_counter = Counter()
no_go_labels = set([label_v9])
for labels in issue_labels:
for label in labels:
if label in no_go_labels:
continue
labels_counter.update([label])
most_common_nr = 10
labels = [
f"{label} - {v}"
for (label, v) in labels_counter.most_common(most_common_nr)
]
values = [value for (_, value)
in labels_counter.most_common(most_common_nr)]
_, ax = plt.subplots(figsize=(16, 16))
ax.set_title(
f"Top {most_common_nr} labels out of {
len(issue_labels)} open v9 issues in microsoft/fluentui"
)
plt.pie(values, labels=labels, autopct="%1.0f%%")
return plt
def plot_components_issue_bar(issue_labels):
labels_counter = Counter()
for labels in issue_labels:
for label in labels:
labels_counter.update([label])
components_counters = {
k: v for k, v in labels_counter.items() if k.startswith("Component:")
}
sorted_components_counters = dict(
sorted(components_counters.items(), key=lambda item: item[0])
)
labels = [
key.replace("Component: ", "")
for key in list(sorted_components_counters.keys())
]
values = list(sorted_components_counters.values())
_, ax = plt.subplots(figsize=(16, 20))
ax.barh(labels, values)
# Show top values
ax.invert_yaxis()
# Add annotation to bars
for i in ax.patches:
plt.text(
i.get_width() + 0.2,
i.get_y() + 0.5,
str(round((i.get_width()), 2)),
fontsize=10,
fontweight="bold",
color="grey",
)
# Add Plot Title
ax.set_title("v9 Component issues")
return plt
def plot_issues_in_the_past_12_months_line(df_issues, df_issues_closed, label=None):
data_source = df_issues if label is None else df_issues[
df_issues["labels"].apply(lambda x: label in x)
]
data_source_closed = df_issues_closed if label is None else df_issues_closed[
df_issues_closed["labels"].apply(lambda x: label in x)
]
data = (
data_source.groupby(
[data_source["created_at"].dt.year,
data_source["created_at"].dt.month_name()],
sort=False,
)["labels"]
.count()
.head(12)
)
data_closed = (
data_source_closed.groupby(
[
data_source_closed["created_at"].dt.year,
data_source_closed["created_at"].dt.month_name(),
],
sort=False,
)["labels"]
.count()
.head(12)
)
values = list(data.values)
labels = [f"{k[0]} {k[1]}" for k in list(data.index)]
closed_values = list(data_closed.values)
closed_labels = [f"{k[0]} {k[1]}" for k in list(data_closed.index)]
_, ax = plt.subplots(figsize=(26, 9))
ax.set_title("Issues opened in the past 12 months")
ax.invert_xaxis()
plt.plot(labels, values, label="Opened issues", linestyle="-", marker="o")
plt.plot(
closed_labels, closed_values, label="Closed issues", linestyle="--",
marker="o"
)
plt.legend()
return plt
def plot_backlog_grooming_line(df_issues, df_issues_closed):
backlog_grooming_df = df_issues[
df_issues["labels"].apply(lambda x: label_needs_backlog_grooming in x)
].sort_values(by="created_at", ascending=False)
backlog_grooming_closed_df = df_issues_closed[
df_issues_closed["labels"].apply(
lambda x: label_needs_backlog_grooming in x)
].sort_values(by="created_at", ascending=False)
data = (
backlog_grooming_df.groupby(
[
backlog_grooming_df["created_at"].dt.year,
backlog_grooming_df["created_at"].dt.month_name(),
],
sort=False,
)["labels"]
.count()
.head(12)
)
data_closed = (
backlog_grooming_closed_df.groupby(
[
backlog_grooming_closed_df["created_at"].dt.year,
backlog_grooming_closed_df["created_at"].dt.month_name(),
],
sort=False,
)["labels"]
.count()
.head(12)
)
values = list(data.values)
labels = [f"{k[0]} {k[1]}" for k in list(data.index)]
closed_values = list(data_closed.values)
closed_labels = [f"{k[0]} {k[1]}" for k in list(data_closed.index)]
_, ax = plt.subplots(figsize=(26, 9))
ax.set_title("Issues that required backlog grooming in the past 12 months")
ax.invert_xaxis()
plt.plot(labels, values, label="Opened issues", linestyle="-", marker="o")
plt.plot(
closed_labels, closed_values, label="Closed issues", linestyle="--",
marker="o"
)
plt.legend()
return plt
def plot_closed_epics_line(df_issues, df_issues_closed, label_v9, label_epic):
released_components_df = df_issues_closed[
df_issues_closed["labels"].apply(
lambda x: label_v9 in x and label_epic in x)
].sort_values(by="created_at", ascending=False)
open_components_df = df_issues[
df_issues["labels"].apply(
lambda x: label_v9 in x and label_epic in x)
].sort_values(by="created_at", ascending=False)
data = (
released_components_df.groupby(
[
released_components_df["created_at"].dt.year,
released_components_df["created_at"].dt.month_name(),
],
sort=False,
)["labels"]
.count()
.head(12)
)
data_open = (
open_components_df.groupby(
[
open_components_df["created_at"].dt.year,
open_components_df["created_at"].dt.month_name(),
],
sort=False,
)["labels"]
.count()
.head(12)
)
values = list(data.values)
labels = [f"{k[0]} {k[1]}" for k in list(data.index)]
open_values = list(data_open.values)
open_labels = [f"{k[0]} {k[1]}" for k in list(data_open.index)]
_, ax = plt.subplots(figsize=(26, 9))
ax.set_title("Closed epics (components / large items)")
ax.invert_xaxis()
plt.plot(labels, values, linestyle="-", marker="o")
plt.plot(open_labels, open_values, linestyle="--", marker="o")
return plt
def plot_triage_issues_line(df_issues):
created_issues = df_issues.sort_values(by="created_at", ascending=False)
created_issues["created_at"] = pd.to_datetime(
created_issues["created_at"] - pd.to_timedelta(1, unit="w"))
triage_df = df_issues[
df_issues["labels"].apply(lambda x: label_needs_triage in x)
].sort_values(by="created_at", ascending=False)
triage_df["created_at"] = pd.to_datetime(
triage_df["created_at"] - pd.to_timedelta(1, unit="w"))
data_created = (
created_issues.groupby(
[
created_issues["created_at"].dt.year,
created_issues["created_at"].dt.month_name(),
pd.Grouper(key="created_at", freq="W-MON"),
],
)["labels"]
.count()
.sort_index(ascending=False)
.head(12)
)
data = (
triage_df.groupby(
[
triage_df["created_at"].dt.year,
triage_df["created_at"].dt.month_name(),
pd.Grouper(key="created_at", freq="W-MON"),
],
)["labels"]
.count()
.sort_index(ascending=False)
)
created_values = list(data_created.values)
created_labels = [k[2].strftime("%Y-%m-%d")
for k in list(data_created.index)]
values = list(data.values)
labels = [k[2].strftime("%Y-%m-%d") for k in list(data.index)]
_, ax = plt.subplots(figsize=(26, 9))
ax.set_title("Issues were created and issues that need triage")
ax.invert_xaxis()
for i, txt in enumerate(values):
ax.annotate(
txt,
(labels[i], values[i]),
textcoords="offset points",
xytext=(0, 7),
ha="center",
)
for i, txt in enumerate(created_values):
ax.annotate(
txt,
(created_labels[i], created_values[i]),
textcoords="offset points",
xytext=(0, 7),
ha="center",
)
plt.plot(labels, values, label="Issues needing triage",
linestyle="-", marker="o")
plt.plot(created_labels, created_values,
label="Created issues", linestyle="--", marker="o")
plt.xticks(rotation=45)
plt.legend()
return plt
def _generate_and_save_plots(issues):
_, issues_minimal, df_issues, df_issues_closed, issue_labels, component_names = get_charts_data(
issues)
with alive_bar(7, title="Generating and saving charts", unit=" charts") as bar:
plt = plot_labels_pie(issue_labels)
plt.tight_layout()
plt.savefig("images/stats-01.png")
bar()
plt = plot_components_issue_bar(issue_labels)
plt.tight_layout()
plt.savefig("images/stats-02.png")
bar()
plt = plot_issues_in_the_past_12_months_line(
df_issues, df_issues_closed)
plt.tight_layout()
plt.savefig("images/stats-03.png")
bar()
plt = plot_backlog_grooming_line(df_issues, df_issues_closed)
plt.tight_layout()
plt.savefig("images/stats-04.png")
bar()
plt = plot_closed_epics_line(df_issues, df_issues_closed, label_v9, label_epic)
plt.tight_layout()
plt.savefig("images/stats-05.png")
bar()
plt = plot_triage_issues_line(df_issues)
plt.tight_layout()
plt.savefig("images/stats-06.png")
bar()
plt = plot_issues_in_the_past_12_months_line(
df_issues, df_issues_closed, label_a11y)
plt.tight_layout()
plt.savefig("images/stats-07.png")
bar()
def _generate_and_save_sheets(issues):
_, _issues_minimal, df_issues, df_issues_closed, _issue_labels, component_names = get_charts_data(
issues)
with alive_bar(3, title="Generating and saving spreadsheets", unit=" sheets") as bar:
overall_issue_stats(df_issues, label_bug, label_feature, label_epic)
bar()
monthly_stats(df_issues, df_issues_closed)
bar()
component_stats(component_names, df_issues, label_bug, label_feature, label_epic)
bar()
def main():
repo = "microsoft/fluentui"
token = os.environ["GITHUB_TOKEN"]
data_folder = f"data/{sys.argv[1]}"
Path(data_folder).mkdir(parents=True, exist_ok=True)
issues = fetch_issues(repo, token)
with open(f"{data_folder}/issues.json", "w") as f:
json.dump(issues, f)
_generate_and_save_plots(issues)
_generate_and_save_sheets(issues)
if __name__ == "__main__":
main()