async def analyze(self, tup: NativeTreeMetricInput) -> Iterable[Metric]:
rate: Dict[str, Dict[bool, int]] = {
"blobs": {
True: 0,
False: 0
},
"trees": {
True: 0,
False: 0
},
"other": {
True: 0,
False: 0
},
}
q: List[Object] = [tup.tree]
while q:
obj = q.pop(0)
is_cached = obj.id in self.cache
if isinstance(obj, Tree):
q.extend(list(obj))
rate["trees"][is_cached] += 1
elif isinstance(obj, Blob):
rate["blobs"][is_cached] += 1
else:
rate["other"][is_cached] += 1
self.cache.add(obj.id)
return [Metric(self.name, rate, False)]
async def cache_hit(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
return [
Metric(
self.name,
None,
True,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
async def analyze(self, tup: NativeTreeMetricInput) -> Iterable[Metric]:
n = 0
q: List[Object] = [tup.tree]
while q:
obj = q.pop(0)
if isinstance(obj, Blob):
n += 1
elif isinstance(obj, Tree):
q.extend(list(obj))
return [Metric(self.name, n, False)]
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
result = [
Metric(
self.name,
NestingASTVisitor().visit(parse(tup.blob.obj.data)).result,
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
result = [
Metric(
self.name,
tup.blob.obj.data.count(b"\n") + 1,
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
result = [
Metric(
self.name,
len(line),
False,
ObjectIdentifier(tup.blob.id, tup.path),
f"L{i+1}",
) for i, line in enumerate(tup.blob.obj.data.split(b"\n"))
]
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
p = await create_subprocess_exec("wc", "-l", stdin=PIPE, stdout=PIPE)
stdout, _ = await p.communicate(tup.blob.data)
result = [
Metric(
self.name,
int(stdout),
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
return result
async def analyze(self, tup: NativeTreeMetricInput) -> Iterable[Metric]:
n = 0
q: List[Object] = [tup.tree]
while q:
obj = q.pop(0)
if isinstance(obj, Blob):
if obj.is_binary:
continue
n += obj.data.count(b"\n") + 1
elif isinstance(obj, Tree):
q.extend(list(obj))
return [Metric(self.name, n, False)]
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
try:
r_data = analyze(tup.blob.data.decode())
except (SyntaxError, UnicodeDecodeError):
return [] # TODO get an error output?
result = [
Metric(
self.name,
r_data._asdict(),
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
try:
mi_data = mi_visit(tup.blob.data.decode(), multi=True)
except (SyntaxError, UnicodeDecodeError):
return [] # TODO get an error output?
result = [
Metric(
self.name,
float(mi_data),
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
]
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
try:
h_data = h_visit(tup.blob.data.decode())
except (SyntaxError, UnicodeDecodeError):
return [] # TODO get an error output?
result = [
Metric(
self.name,
fn_data._asdict(),
False,
ObjectIdentifier(tup.blob.id, tup.path),
fn_name,
)
for fn_name, fn_data in h_data.functions
]
result.append(
Metric(
self.name,
h_data.total._asdict(),
False,
ObjectIdentifier(tup.blob.id, tup.path),
)
)
return result
async def analyze(self, tup: NativeBlobMetricInput) -> Iterable[Metric]:
try:
cc_data = cc_visit(tup.blob.data)
except (SyntaxError, UnicodeDecodeError):
return [] # TODO get an error output?
result = [
Metric(
self.name,
subobject.complexity,
False,
ObjectIdentifier(tup.blob.id, tup.path),
subobject.fullname,
) for subobject in cc_data
]
return result
async def analyze(self, tup: DirMetricInput) -> Iterable[Metric]:
p = await create_subprocess_exec("tokei",
"--output",
"json",
tup.path,
stdout=PIPE)
stdout, _ = await p.communicate()
data = loads(bytes(stdout).decode("utf-8"))
result = [
Metric(
self.name,
{
"category": category_name,
"blanks": report["stats"]["blanks"],
"code": report["stats"]["code"],
"comments": report["stats"]["comments"],
},
False,
ObjectIdentifier(
descend_tree(tup.tree, report["name"][len(tup.path) + 1:]),
report["name"][len(tup.path) + 1:],
),
) for category_name, reports in data["Total"]["children"].items()
for report in reports
]
result.append(
Metric(
self.name,
{
"blanks": data["Total"]["blanks"],
"code": data["Total"]["code"],
"comments": data["Total"]["comments"],
},
False,
))
return result
async def analyze(self, tup: NativeTreeMetricInput) -> Iterable[Metric]:
parent_trees = tuple(parent.tree.obj for parent in tup.commit.parents)
if not parent_trees: # orphan commit is diffed to empty tree
diffs = [tup.tree.obj.diff_to_tree(swap=True)]
else:
diffs = [
tup.tree.obj.diff_to_tree(parent_tree, swap=True)
for parent_tree in parent_trees
]
touched_lines = [
line.content for diff in diffs for patch in diff
for hunk in patch.hunks for line in hunk.lines
if line.content_offset > -1
]
return [Metric(self.name, touched_lines, False)]