def mpl_theme(width=12, height=8):
return [
pn.theme_matplotlib(),
pn.theme(figure_size=(width, height),
strip_background=pn.element_rect(color='w', fill='w'),
panel_grid=pn.element_line(color='k', alpha=.1))
]
def test_add_complete_complete():
theme1 = theme_gray()
theme2 = theme_matplotlib()
theme3 = theme1 + theme2
assert theme3 == theme2
def test_theme_matplotlib(self):
p = self.g + labs(title='Theme Matplotlib') + theme_matplotlib()
assert p + _theme == 'theme_matplotlib'
def cli():
parser = argparse.ArgumentParser(
description='GAP - Git Activity Predictor')
parser.add_argument('paths',
metavar='PATH',
type=str,
nargs='*',
default=['.'],
help='Paths to one or more git repositories')
parser.add_argument(
'--date',
type=lambda d: dateutil.parser.parse(d).date(),
required=False,
default=datetime.date.today(),
help='Date used for predictions (default to current date)')
parser.add_argument('--obs',
type=int,
required=False,
default=20,
help='Number of observations to consider')
parser.add_argument('--probs',
metavar='PROB',
type=float,
nargs='*',
required=False,
default=[0.5, 0.6, 0.7, 0.8, 0.9],
help='Probabilities to output, strictly in [0,1].')
parser.add_argument(
'--limit',
type=int,
required=False,
default=30,
help=
'Limit contributors to the one that were active at least once during the last x days (default 30)'
)
parser.add_argument(
'--mapping',
type=str,
nargs='?',
help=
'Mapping file to merge identities. This file must be a csv file where each line contains two values: the name to be merged, and the corresponding identity. Use "IGNORE" as identity to ignore specific names.'
)
parser.add_argument('--branches',
metavar='BRANCH',
type=str,
nargs='*',
default=list(),
help='Git branches to analyse (default to all).')
parser.add_argument(
'--as-dates',
dest='as_dates',
action='store_true',
help=
'Express predictions using dates instead of time differences in days')
group = parser.add_mutually_exclusive_group()
group.add_argument('--text',
action='store_true',
help='Print results as text.')
group.add_argument('--csv',
action='store_true',
help='Print results as csv.')
group.add_argument('--json',
action='store_true',
help='Print results as json.')
group.add_argument(
'--plot',
nargs='?',
const=True,
help='Export results to a plot. Filepath can be optionaly specified.')
args = parser.parse_args()
# Default plot location
if args.plot is True:
args.plot = str(args.date) + '.pdf'
# Default to text if not other option is provided
if not args.csv and not args.json and not args.plot:
args.text = True
# Identity mapping
if args.mapping:
d = pandas.read_csv(args.mapping, names=['source', 'target'])
mapping = {r.source: r.target for r in d.itertuples()}
else:
mapping = {}
raw_data = dict() # author -> dates of activity
# Get data from git
for path in args.paths:
try:
repo = git.Repo(path)
except Exception as e: # Must be refined
print('Unable to access repository {} ({}:{})'.format(
path, e.__class__.__name__, e))
sys.exit()
# Default branches
if len(args.branches) == 0:
commits = repo.iter_commits('--all')
else:
commits = repo.iter_commits(' '.join(args.branches))
for commit in commits:
try:
author = commit.author.name
identity = mapping.get(author, author)
if author.lower() != 'ignore' and identity.lower() == 'ignore':
continue
date = datetime.date.fromtimestamp(commit.authored_date)
raw_data.setdefault(identity, []).append(date)
except Exception as e:
print('Unable to read commit ({}: {}): {}'.format(
e.__class__.__name__, e, commit))
# Compute durations and apply model
data = [] # (author, past activities, predicted durations)
for author, commits in raw_data.items():
commits = sorted([e for e in commits if e <= args.date])
durations = dates_to_duration(commits, window_size=args.obs)
if len(durations) >= args.obs:
# Currently implemented with no censor
surv = SurvfuncRight(durations, [1] * len(durations))
predictions = [surv.quantile(p) for p in args.probs]
last_day = commits[-1]
if last_day >= args.date - datetime.timedelta(args.limit):
data.append((
author,
commits,
predictions,
))
# Prepare dataframe
df = pandas.DataFrame(index=set([a for a, c, p in data]),
columns=['last'] + args.probs)
if len(df) == 0:
print(
'No author has {} observations and was active at least once during the last {} days'
.format(args.obs, args.limit))
sys.exit()
df.index.name = 'author'
if not args.plot:
for author, commits, predictions in data:
last = commits[-1]
if args.as_dates:
df.at[author, 'last'] = last
else:
df.at[author, 'last'] = (last - args.date).days
for prob, p in zip(args.probs, predictions):
if args.as_dates:
df.at[author,
prob] = last + datetime.timedelta(days=int(p))
else:
df.at[author,
prob] = (last + datetime.timedelta(days=int(p)) -
args.date).days
df = df.sort_values(['last'] + args.probs,
ascending=[False] + [True] * len(args.probs))
df = df.astype(str)
if args.text:
pandas.set_option('expand_frame_repr', False)
pandas.set_option('display.max_columns', 999)
print(df)
elif args.csv:
print(df.to_csv())
elif args.json:
print(df.to_json(orient='index'))
else:
# Because of plotnine's way of initializing matplotlib
import warnings
warnings.filterwarnings("ignore")
VIEW_LIMIT = 28
activities = [
] # List of (author, day) where day is a delta w.r.t. given date
forecasts = [
] # List of (author, from_day, to_day, p) where probability p
# applies between from_day and to_day (delta w.r.t. given date)
for author, commits, predictions in data:
last = (commits[-1] - args.date).days
for e in commits:
activities.append((author, (e - args.date).days))
previous = previous_previous = 0
for d, p in zip(predictions, args.probs):
if d > previous:
forecasts.append((author, last + previous, last + d, p))
previous_previous = previous
previous = d
else:
forecasts.append(
(author, last + previous_previous, last + d, p))
activities = pandas.DataFrame(columns=['author', 'day'],
data=activities)
forecasts = pandas.DataFrame(columns=['author', 'fromd', 'tod', 'p'],
data=forecasts)
plot = (p9.ggplot(p9.aes(y='author')) + p9.geom_segment(
p9.aes('day - 0.5', 'author', xend='day + 0.5', yend='author'),
data=activities,
size=4,
color='orange',
) + p9.geom_segment(
p9.aes('fromd + 0.5',
'author',
xend='tod + 0.5',
yend='author',
alpha='factor(p)'),
data=forecasts.sort_values('p').drop_duplicates(
['author', 'fromd', 'tod'], keep='last'),
size=4,
color='steelblue',
) + p9.geom_vline(
xintercept=0,
color='r', alpha=0.5, linetype='dashed') + p9.scale_x_continuous(
name=' << past days {:^20} future days >>'.format(
str(args.date)),
breaks=range(-VIEW_LIMIT // 7 * 7,
(VIEW_LIMIT // 7 * 7) + 1, 7),
minor_breaks=6) + p9.scale_y_discrete(
name='',
limits=activities.sort_values(
'day', ascending=False)['author'].unique()) +
p9.scale_alpha_discrete(range=(0.2, 1), name=' ') +
p9.coord_cartesian(xlim=(-VIEW_LIMIT, VIEW_LIMIT)) +
p9.theme_matplotlib() + p9.theme(
figure_size=(6, 4 * activities['author'].nunique() / 15)))
fig = plot.draw()
fig.savefig(args.plot, bbox_inches='tight')
print('Plot exported to {}'.format(args.plot))
def test_theme_matplotlib(self):
p = self.g + labs(title='Theme Matplotlib') + theme_matplotlib()
assert p + _theme == 'theme_matplotlib'
def test_add_complete_complete():
theme1 = theme_gray()
theme2 = theme_matplotlib()
theme3 = theme1 + theme2
assert theme3 == theme2
