def compute_complexity(source):
result = []
blocks = cc_visit(source)
mix_path = mi_visit(source, True)
for func in blocks:
result.append(func.name + ": Rank:" + cc_rank(func.complexity))
return result
def mi(multi=True, exclude=None, show=False, *paths):
'''Analyze the given Python modules and compute the Maintainability Index.
The maintainability index (MI) is a compound metric, with the primary aim
of to determine how easy it will be to maintain a particular body of code.
:param multi: Whether or not to count multiline strings as comments. Most
of the time this is safe since multiline strings are used as functions
docstrings, but one should be aware that their use is not limited to
that and sometimes it would be wrong to count them as comment lines.
:param paths: The modules or packages to analyze.
'''
for name in iter_filenames(paths, exclude):
with open(name) as fobj:
try:
result = mi_visit(fobj.read(), multi)
except Exception as e:
log('{0}\n{1}ERROR: {2}', name, ' ' * 4, str(e))
continue
except KeyboardInterrupt:
log(name)
return
rank = mi_rank(result)
color = MI_RANKS[rank]
to_show = ''
if show:
to_show = ' ({0:.2f})'.format(result)
log('{0} - {1}{2}{3}{4}', name, color, rank, to_show, RESET)
def mi(multi=True, exclude=None, show=False, *paths):
'''Analyze the given Python modules and compute the Maintainability Index.
The maintainability index (MI) is a compound metric, with the primary aim
of to determine how easy it will be to maintain a particular body of code.
:param multi: Whether or not to count multiline strings as comments. Most
of the time this is safe since multiline strings are used as functions
docstrings, but one should be aware that their use is not limited to
that and sometimes it would be wrong to count them as comment lines.
:param paths: The modules or packages to analyze.
'''
for name in iter_filenames(paths, exclude):
with open(name) as fobj:
try:
result = mi_visit(fobj.read(), multi)
except Exception as e:
log('{0}\n{1}ERROR: {2}', name, ' ' * 4, str(e))
continue
except KeyboardInterrupt:
log(name)
return
rank = mi_rank(result)
color = MI_RANKS[rank]
to_show = ''
if show:
to_show = ' ({0:.2f})'.format(result)
log('{0} - {1}{2}{3}{4}', name, color, rank, to_show, RESET)
def mi(multi=True, exclude=None, ignore=None, show=False, *paths):
'''Analyze the given Python modules and compute the Maintainability Index.
The maintainability index (MI) is a compound metric, with the primary aim
being to determine how easy it will be to maintain a particular body of
code.
:param -e, --exclude <str>: Comma separated list of patterns to exclude.
:param -i, --ignore <str>: Comma separated list of patterns to ignore.
Radon won't even descend into those directories.
:param -m, --multi: If given, multiline strings are counted as comments.
:param -s, --show: If given, the actual MI value is shown in results.
:param paths: The modules or packages to analyze.
'''
for name in iter_filenames(paths, exclude, ignore):
with open(name) as fobj:
try:
result = mi_visit(fobj.read(), multi)
except Exception as e:
log(name, indent=1)
log_error(e, indent=1)
continue
except KeyboardInterrupt:
log(name)
return
rank = mi_rank(result)
color = MI_RANKS[rank]
to_show = '' if not show else ' ({0:.2f})'.format(result)
log('{0} - {1}{2}{3}{4}', name, color, rank, to_show, RESET)
def mi(multi=True, exclude=None, ignore=None, show=False, *paths):
'''Analyze the given Python modules and compute the Maintainability Index.
The maintainability index (MI) is a compound metric, with the primary aim
being to determine how easy it will be to maintain a particular body of
code.
:param -e, --exclude <str>: Comma separated list of patterns to exclude.
:param -i, --ignore <str>: Comma separated list of patterns to ignore.
Radon won't even descend into those directories.
:param -m, --multi: If given, multiline strings are counted as comments.
:param -s, --show: If given, the actual MI value is shown in results.
:param paths: The modules or packages to analyze.
'''
for name in iter_filenames(paths, exclude, ignore):
with open(name) as fobj:
try:
result = mi_visit(fobj.read(), multi)
except Exception as e:
log(name, indent=1)
log_error(e, indent=1)
continue
except KeyboardInterrupt:
log(name)
return
rank = mi_rank(result)
color = MI_RANKS[rank]
to_show = '' if not show else ' ({0:.2f})'.format(result)
log('{0} - {1}{2}{3}{4}', name, color, rank, to_show, RESET)
def maintainability(self):
logging.info("Maintainability analysis")
try:
self.maintain = mi_visit(self.__code,True)
return self.maintain
except SyntaxError:
logging.error("Syntax Error in analyzed code")
print("Syntax Error in analyzed code")
def maintainability(self):
logging.info("Maintainability analysis")
try:
self.maintain = mi_visit(self.__code, True)
return self.maintain
except SyntaxError:
logging.error("Syntax Error in analyzed code")
print("Syntax Error in analyzed code")
def compute_complexity(source):
result = []
# get complexity blocks
blocks = cc_visit(source)
# get MI score
mi = mi_visit(source, True)
for slave in blocks:
result.append(slave.name + "-Rank:" + cc_rank(slave.complexity))
return result
def compute_complexity(source):
result = []
# get cc blocks
blocks = cc_visit(source)
# get MI score
mi = mi_visit(source, True)
for func in blocks:
result.append(func.name + "- CC Rank:" + cc_rank(func.complexity))
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
def evaluate_code(kata_test, kata_implementation, kata_path):
"""
Evaluate the code resulting from executing the kata_implementation stared at the given path
:param kata_test: the test to pass
:param kata_implementation: the kata implementation
:param kata_path: the kata script
"""
time_consumption = timeit.timeit(lambda: kata_test(kata_implementation),
number=10000)
with open(kata_path, "r") as kata_file:
kata_code = kata_file.read()
mantainability_index = mi_visit(kata_code, False)
cyclomatic_complexity = cc_visit(kata_code)[0].complexity
return {
"time": time_consumption,
"mi": mantainability_index,
"cc": cyclomatic_complexity
}
def generate_radon_metrics(code_input):
radon_metrics = make_default_radon_metrics()
try:
# raw metrics
raw_metrics = analyze(code_input)
radon_metrics['loc'] = raw_metrics.loc
radon_metrics['lloc'] = raw_metrics.lloc
radon_metrics['sloc'] = raw_metrics.sloc
radon_metrics['comments'] = raw_metrics.comments
radon_metrics['multi'] = raw_metrics.multi
radon_metrics['single_comments'] = raw_metrics.single_comments
# cyclomatic complexity
cc = ComplexityVisitor.from_code(code_input)
radon_metrics['function_num'] = len(cc.functions)
total_function_complexity = 0.0
for fun in cc.functions:
total_function_complexity += fun.complexity
radon_metrics['total_function_complexity'] = total_function_complexity
radon_metrics['radon_functions_complexity'] = cc.functions_complexity
# calculate based on AST tree
v = h_visit_ast(h_visit(code_input))
radon_metrics['h1'] = v.h1
radon_metrics['h2'] = v.h2
radon_metrics['N1'] = v.N1
radon_metrics['N2'] = v.N2
radon_metrics['vocabulary'] = v.vocabulary
radon_metrics['length'] = v.length
radon_metrics['calculated_length'] = v.calculated_length
radon_metrics['volume'] = v.volume
radon_metrics['difficulty'] = v.difficulty
radon_metrics['effort'] = v.effort
radon_metrics['time'] = v.time
radon_metrics['bugs'] = v.bugs
# Maintainability Index (MI) based on
## the Halstead Volume, the Cyclomatic Complexity, the SLOC number and the number of comment lines
mi = mi_visit(code_input, multi=True)
radon_metrics['Maintainability_Index'] = mi
return radon_metrics
except:
return radon_metrics
def generate_radon_metrics(code_input):
radon_metrics = make_default_radon_metrics()
try:
# raw metrics
raw_metrics = analyze(code_input)
radon_metrics['loc'] = raw_metrics.loc
radon_metrics['lloc'] = raw_metrics.lloc
radon_metrics['sloc'] = raw_metrics.sloc
radon_metrics['comments'] = raw_metrics.comments
radon_metrics['multi'] = raw_metrics.multi
radon_metrics['single_comments'] = raw_metrics.single_comments
# cyclomatic complexity
cc = ComplexityVisitor.from_code(code_input)
radon_metrics['function_num'] = len(cc.functions)
total_function_complexity = 0.0
for fun in cc.functions:
total_function_complexity += fun.complexity
radon_metrics['total_function_complexity'] = total_function_complexity
radon_metrics['radon_functions_complexity'] = cc.functions_complexity
# calculate based on AST tree
v = h_visit_ast(h_visit(code_input))
radon_metrics['h1'] = v.h1
radon_metrics['h2'] = v.h2
radon_metrics['N1'] = v.N1
radon_metrics['N2'] = v.N2
radon_metrics['vocabulary'] = v.vocabulary
radon_metrics['length'] = v.length
radon_metrics['calculated_length'] = v.calculated_length
radon_metrics['volume'] = v.volume
radon_metrics['difficulty'] = v.difficulty
radon_metrics['effort'] = v.effort
radon_metrics['time'] = v.time
radon_metrics['bugs'] = v.bugs
# Maintainability Index (MI) based on
## the Halstead Volume, the Cyclomatic Complexity, the SLOC number and the number of comment lines
mi = mi_visit(code_input, multi=True)
radon_metrics['Maintainability_Index'] = mi
return radon_metrics
except:
return radon_metrics
def program_complexity(projno, funcno):
"""
Builds and returns two dictionaries mapping filename:cc and filename:mi.
"""
cc_dic = {}
mi_dic = {}
path = os.getcwd() + \
"/extracted_files/proj{0}_func{1}/".format(projno, funcno)
for fname in os.listdir(path):
if fname[-4:] == '.pyc': continue
try:
f = open(path + fname)
content = f.read()
blocks = cc_visit(content)
mi = mi_visit(content, True)
cc_dic[fname] = blocks[0].complexity
mi_dic[fname] = mi
f.close()
except:
print " ERROR: cannot find file " + path + fname
return cc_dic, mi_dic
param_dict = OrderedDict()
print('1. METHODS OF PROJECT FILES SORTED BY CYCLOMATIC COMPLEXITY')
for filename in iter_filenames(['.']):
file_methods = []
file_complexities = []
methods_dictionary = OrderedDict()
with open(filename) as fobj:
source = fobj.read()
# get cc blocks
blocks = cc_visit(source)
# get MI score
mi = mi_visit(source, True)
mi_dict[mi] = filename
# get raw metrics
raw = analyze(source)
hal_vol, complexity, logic_lines, com_lines = mi_parameters(source)
param_dict[complexity] = (complexity, filename, hal_vol, logic_lines,
com_lines)
# get metrics for each file
file_complexity = sorted_results(blocks)
try:
file_methods.append(re.findall('(?<=.Function\(name=\')[^\']'
'*(?=\')', str(file_complexity)))
file_complexities.append(re.findall('(?<=.complexity=)\d*',
def gobble(self, fobj):
'''Analyze the content of the file object.'''
mi = mi_visit(fobj.read(), self.config.multi)
rank = mi_rank(mi)
return {'mi': mi, 'rank': rank}
def gobble(self, fobj):
'''Analyze the content of the file object.'''
mi = mi_visit(fobj.read(), self.config.multi)
rank = mi_rank(mi)
return {'mi': mi, 'rank': rank}
from radon.complexity import cc_rank, cc_visit
from radon.metrics import mi_visit, mi_rank
from radon.raw import analyze
# Metrics that are usable: Maintainability Index
teststring = ('''
`
''')
def my_function():
print("Hello from a function")
def factorial(n):
if n < 2: return 1
return n * factorial(n - 1)
#print (analyze(teststring))
#print (cc_visit(teststring))
mivalue = (mi_visit(teststring, 0))
print(mivalue)
print(mi_rank(mivalue))
def gobble(self, fobj):
'''Analyze the content of the file object.'''
return {'mi': mi_visit(fobj.read(), self.config.multi)}
def maintainabilityIndex(code):
miScore = mi_visit(code, True)
miRank = mi_rank(miScore)
return [miScore, miRank]